diff --git a/app.py b/app.py deleted file mode 100644 index 718cff18f64da6e62fc6e69916186e5b31645945..0000000000000000000000000000000000000000 --- a/app.py +++ /dev/null @@ -1,107 +0,0 @@ - -import spaces -import gradio as gr -from gradio_imageslider import ImageSlider -import numpy as np -from huggingface_hub import hf_hub_download -import torch -from PIL import Image -from diffusers import DDPMScheduler -from schedulers.lcm_single_step_scheduler import LCMSingleStepScheduler -from module.ip_adapter.utils import load_adapter_to_pipe -from pipelines.sdxl_instantir import InstantIRPipeline -import os - -os.makedirs('./models', exist_ok=True) -# Download model files if not present -for filename in ["adapter.pt", "aggregator.pt", "previewer_lora_weights.bin"]: - hf_hub_download(repo_id="InstantX/InstantIR", filename=f"models/{filename}", local_dir=".", force_download=True) - -# Initialize the pipeline and models -def initialize_pipeline(): - pipe = InstantIRPipeline.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16) - - # load adapter - load_adapter_to_pipe( - pipe, - './models/adapter.pt', - image_encoder_path = 'facebook/dinov2-large', - ) - - # load previewer lora and schedulers - pipe.prepare_previewers('./models') - pipe.scheduler = DDPMScheduler.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', subfolder="scheduler") - lcm_scheduler = LCMSingleStepScheduler.from_config(pipe.scheduler.config) - - # load aggregator weights - pretrained_state_dict = torch.load('./models/aggregator.pt') - pipe.aggregator.load_state_dict(pretrained_state_dict) - - # send to GPU and fp16 - pipe.to(dtype=torch.float16) - pipe.to('cuda') - - return pipe, lcm_scheduler - -pipe, lcm_scheduler = initialize_pipeline() - -@spaces.GPU -def process_image(input_image): - if input_image is None: - raise gr.Error("Please provide an image to restore.") - - # Convert to PIL Image - pil_image = Image.fromarray(input_image) - - # Process image - restored_image = pipe( - prompt='', - image=pil_image, - ip_adapter_image=[pil_image], - negative_prompt='', - guidance_scale=7.0, - previewer_scheduler=lcm_scheduler, - return_dict=False, - )[0] - - # Convert result to numpy array - result_array = np.array(restored_image) - - return (input_image, result_array) - -title = """

InstantIR Image Restoration

-

Restore and enhance your images

-

-[Model Page] -

-""" - -with gr.Blocks() as demo: - gr.HTML(title) - - with gr.Row(): - with gr.Column(scale=1): - input_image = gr.Image(label="Input Image", type="numpy") - process_btn = gr.Button(value="Restore Image", variant="primary") - with gr.Column(scale=1): - output_slider = ImageSlider(label="Before / After", type="numpy") - - process_btn.click( - fn=process_image, - inputs=[input_image], - outputs=output_slider - ) - - # Add examples - gr.Examples( - examples=[ - "examples/image1.jpg", - "examples/image2.jpg" - ], - inputs=input_image, - outputs=output_slider, - fn=process_image, - cache_examples=True, - ) - -demo.launch(debug=True) \ No newline at end of file diff --git a/basicsr/__init__.py b/basicsr/__init__.py deleted file mode 100644 index d434c33041c6dd48a06921c301a1e7405e469bff..0000000000000000000000000000000000000000 --- a/basicsr/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -# https://github.com/xinntao/BasicSR -# flake8: noqa -from .archs import * -from .data import * -from .losses import * -from .metrics import * -from .models import * -from .ops import * -from .test import * -from .train import * -from .utils import * -# from .version import __gitsha__, __version__ diff --git a/basicsr/archs/__init__.py b/basicsr/archs/__init__.py deleted file mode 100644 index 5b52a31ba16b70a808a79899dc897f833fec4ddd..0000000000000000000000000000000000000000 --- a/basicsr/archs/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -import importlib -from copy import deepcopy -from os import path as osp - -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import ARCH_REGISTRY - -__all__ = ['build_network'] - -# automatically scan and import arch modules for registry -# scan all the files under the 'archs' folder and collect files ending with '_arch.py' -arch_folder = osp.dirname(osp.abspath(__file__)) -arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')] -# import all the arch modules -_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames] - - -def build_network(opt): - opt = deepcopy(opt) - network_type = opt.pop('type') - net = ARCH_REGISTRY.get(network_type)(**opt) - logger = get_root_logger() - logger.info(f'Network [{net.__class__.__name__}] is created.') - return net diff --git a/basicsr/archs/arch_util.py b/basicsr/archs/arch_util.py deleted file mode 100644 index c371f23624e662b34ab857a7217ecbef0c8ff8e0..0000000000000000000000000000000000000000 --- a/basicsr/archs/arch_util.py +++ /dev/null @@ -1,313 +0,0 @@ -import collections.abc -import math -import torch -import torchvision -import warnings -from distutils.version import LooseVersion -from itertools import repeat -from torch import nn as nn -from torch.nn import functional as F -from torch.nn import init as init -from torch.nn.modules.batchnorm import _BatchNorm - -from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv -from basicsr.utils import get_root_logger - - -@torch.no_grad() -def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): - """Initialize network weights. - - Args: - module_list (list[nn.Module] | nn.Module): Modules to be initialized. - scale (float): Scale initialized weights, especially for residual - blocks. Default: 1. - bias_fill (float): The value to fill bias. Default: 0 - kwargs (dict): Other arguments for initialization function. - """ - if not isinstance(module_list, list): - module_list = [module_list] - for module in module_list: - for m in module.modules(): - if isinstance(m, nn.Conv2d): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, nn.Linear): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, _BatchNorm): - init.constant_(m.weight, 1) - if m.bias is not None: - m.bias.data.fill_(bias_fill) - - -def make_layer(basic_block, num_basic_block, **kwarg): - """Make layers by stacking the same blocks. - - Args: - basic_block (nn.module): nn.module class for basic block. - num_basic_block (int): number of blocks. - - Returns: - nn.Sequential: Stacked blocks in nn.Sequential. - """ - layers = [] - for _ in range(num_basic_block): - layers.append(basic_block(**kwarg)) - return nn.Sequential(*layers) - - -class ResidualBlockNoBN(nn.Module): - """Residual block without BN. - - Args: - num_feat (int): Channel number of intermediate features. - Default: 64. - res_scale (float): Residual scale. Default: 1. - pytorch_init (bool): If set to True, use pytorch default init, - otherwise, use default_init_weights. Default: False. - """ - - def __init__(self, num_feat=64, res_scale=1, pytorch_init=False): - super(ResidualBlockNoBN, self).__init__() - self.res_scale = res_scale - self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) - self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) - self.relu = nn.ReLU(inplace=True) - - if not pytorch_init: - default_init_weights([self.conv1, self.conv2], 0.1) - - def forward(self, x): - identity = x - out = self.conv2(self.relu(self.conv1(x))) - return identity + out * self.res_scale - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.') - super(Upsample, self).__init__(*m) - - -def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True): - """Warp an image or feature map with optical flow. - - Args: - x (Tensor): Tensor with size (n, c, h, w). - flow (Tensor): Tensor with size (n, h, w, 2), normal value. - interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'. - padding_mode (str): 'zeros' or 'border' or 'reflection'. - Default: 'zeros'. - align_corners (bool): Before pytorch 1.3, the default value is - align_corners=True. After pytorch 1.3, the default value is - align_corners=False. Here, we use the True as default. - - Returns: - Tensor: Warped image or feature map. - """ - assert x.size()[-2:] == flow.size()[1:3] - _, _, h, w = x.size() - # create mesh grid - grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x)) - grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2 - grid.requires_grad = False - - vgrid = grid + flow - # scale grid to [-1,1] - vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0 - vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0 - vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3) - output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners) - - # TODO, what if align_corners=False - return output - - -def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False): - """Resize a flow according to ratio or shape. - - Args: - flow (Tensor): Precomputed flow. shape [N, 2, H, W]. - size_type (str): 'ratio' or 'shape'. - sizes (list[int | float]): the ratio for resizing or the final output - shape. - 1) The order of ratio should be [ratio_h, ratio_w]. For - downsampling, the ratio should be smaller than 1.0 (i.e., ratio - < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e., - ratio > 1.0). - 2) The order of output_size should be [out_h, out_w]. - interp_mode (str): The mode of interpolation for resizing. - Default: 'bilinear'. - align_corners (bool): Whether align corners. Default: False. - - Returns: - Tensor: Resized flow. - """ - _, _, flow_h, flow_w = flow.size() - if size_type == 'ratio': - output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1]) - elif size_type == 'shape': - output_h, output_w = sizes[0], sizes[1] - else: - raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.') - - input_flow = flow.clone() - ratio_h = output_h / flow_h - ratio_w = output_w / flow_w - input_flow[:, 0, :, :] *= ratio_w - input_flow[:, 1, :, :] *= ratio_h - resized_flow = F.interpolate( - input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners) - return resized_flow - - -# TODO: may write a cpp file -def pixel_unshuffle(x, scale): - """ Pixel unshuffle. - - Args: - x (Tensor): Input feature with shape (b, c, hh, hw). - scale (int): Downsample ratio. - - Returns: - Tensor: the pixel unshuffled feature. - """ - b, c, hh, hw = x.size() - out_channel = c * (scale**2) - assert hh % scale == 0 and hw % scale == 0 - h = hh // scale - w = hw // scale - x_view = x.view(b, c, h, scale, w, scale) - return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) - - -class DCNv2Pack(ModulatedDeformConvPack): - """Modulated deformable conv for deformable alignment. - - Different from the official DCNv2Pack, which generates offsets and masks - from the preceding features, this DCNv2Pack takes another different - features to generate offsets and masks. - - ``Paper: Delving Deep into Deformable Alignment in Video Super-Resolution`` - """ - - def forward(self, x, feat): - out = self.conv_offset(feat) - o1, o2, mask = torch.chunk(out, 3, dim=1) - offset = torch.cat((o1, o2), dim=1) - mask = torch.sigmoid(mask) - - offset_absmean = torch.mean(torch.abs(offset)) - if offset_absmean > 50: - logger = get_root_logger() - logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.') - - if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'): - return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, - self.dilation, mask) - else: - return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, - self.dilation, self.groups, self.deformable_groups) - - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py - # Cut & paste from PyTorch official master until it's in a few official releases - RW - # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf - def norm_cdf(x): - # Computes standard normal cumulative distribution function - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn( - 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' - 'The distribution of values may be incorrect.', - stacklevel=2) - - with torch.no_grad(): - # Values are generated by using a truncated uniform distribution and - # then using the inverse CDF for the normal distribution. - # Get upper and lower cdf values - low = norm_cdf((a - mean) / std) - up = norm_cdf((b - mean) / std) - - # Uniformly fill tensor with values from [low, up], then translate to - # [2l-1, 2u-1]. - tensor.uniform_(2 * low - 1, 2 * up - 1) - - # Use inverse cdf transform for normal distribution to get truncated - # standard normal - tensor.erfinv_() - - # Transform to proper mean, std - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - - # Clamp to ensure it's in the proper range - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - r"""Fills the input Tensor with values drawn from a truncated - normal distribution. - - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py - - The values are effectively drawn from the - normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` - with values outside :math:`[a, b]` redrawn until they are within - the bounds. The method used for generating the random values works - best when :math:`a \leq \text{mean} \leq b`. - - Args: - tensor: an n-dimensional `torch.Tensor` - mean: the mean of the normal distribution - std: the standard deviation of the normal distribution - a: the minimum cutoff value - b: the maximum cutoff value - - Examples: - >>> w = torch.empty(3, 5) - >>> nn.init.trunc_normal_(w) - """ - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -# From PyTorch -def _ntuple(n): - - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - - return parse - - -to_1tuple = _ntuple(1) -to_2tuple = _ntuple(2) -to_3tuple = _ntuple(3) -to_4tuple = _ntuple(4) -to_ntuple = _ntuple diff --git a/basicsr/archs/basicvsr_arch.py b/basicsr/archs/basicvsr_arch.py deleted file mode 100644 index 627fb51aa3bad8cec6439b612eb459672170b13f..0000000000000000000000000000000000000000 --- a/basicsr/archs/basicvsr_arch.py +++ /dev/null @@ -1,336 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import ResidualBlockNoBN, flow_warp, make_layer -from .edvr_arch import PCDAlignment, TSAFusion -from .spynet_arch import SpyNet - - -@ARCH_REGISTRY.register() -class BasicVSR(nn.Module): - """A recurrent network for video SR. Now only x4 is supported. - - Args: - num_feat (int): Number of channels. Default: 64. - num_block (int): Number of residual blocks for each branch. Default: 15 - spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. - """ - - def __init__(self, num_feat=64, num_block=15, spynet_path=None): - super().__init__() - self.num_feat = num_feat - - # alignment - self.spynet = SpyNet(spynet_path) - - # propagation - self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) - self.forward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) - - # reconstruction - self.fusion = nn.Conv2d(num_feat * 2, num_feat, 1, 1, 0, bias=True) - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True) - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - - self.pixel_shuffle = nn.PixelShuffle(2) - - # activation functions - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def get_flow(self, x): - b, n, c, h, w = x.size() - - x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w) - x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w) - - flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w) - flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w) - - return flows_forward, flows_backward - - def forward(self, x): - """Forward function of BasicVSR. - - Args: - x: Input frames with shape (b, n, c, h, w). n is the temporal dimension / number of frames. - """ - flows_forward, flows_backward = self.get_flow(x) - b, n, _, h, w = x.size() - - # backward branch - out_l = [] - feat_prop = x.new_zeros(b, self.num_feat, h, w) - for i in range(n - 1, -1, -1): - x_i = x[:, i, :, :, :] - if i < n - 1: - flow = flows_backward[:, i, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - feat_prop = torch.cat([x_i, feat_prop], dim=1) - feat_prop = self.backward_trunk(feat_prop) - out_l.insert(0, feat_prop) - - # forward branch - feat_prop = torch.zeros_like(feat_prop) - for i in range(0, n): - x_i = x[:, i, :, :, :] - if i > 0: - flow = flows_forward[:, i - 1, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - - feat_prop = torch.cat([x_i, feat_prop], dim=1) - feat_prop = self.forward_trunk(feat_prop) - - # upsample - out = torch.cat([out_l[i], feat_prop], dim=1) - out = self.lrelu(self.fusion(out)) - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - out = self.lrelu(self.conv_hr(out)) - out = self.conv_last(out) - base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False) - out += base - out_l[i] = out - - return torch.stack(out_l, dim=1) - - -class ConvResidualBlocks(nn.Module): - """Conv and residual block used in BasicVSR. - - Args: - num_in_ch (int): Number of input channels. Default: 3. - num_out_ch (int): Number of output channels. Default: 64. - num_block (int): Number of residual blocks. Default: 15. - """ - - def __init__(self, num_in_ch=3, num_out_ch=64, num_block=15): - super().__init__() - self.main = nn.Sequential( - nn.Conv2d(num_in_ch, num_out_ch, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.1, inplace=True), - make_layer(ResidualBlockNoBN, num_block, num_feat=num_out_ch)) - - def forward(self, fea): - return self.main(fea) - - -@ARCH_REGISTRY.register() -class IconVSR(nn.Module): - """IconVSR, proposed also in the BasicVSR paper. - - Args: - num_feat (int): Number of channels. Default: 64. - num_block (int): Number of residual blocks for each branch. Default: 15. - keyframe_stride (int): Keyframe stride. Default: 5. - temporal_padding (int): Temporal padding. Default: 2. - spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. - edvr_path (str): Path to the pretrained EDVR model. Default: None. - """ - - def __init__(self, - num_feat=64, - num_block=15, - keyframe_stride=5, - temporal_padding=2, - spynet_path=None, - edvr_path=None): - super().__init__() - - self.num_feat = num_feat - self.temporal_padding = temporal_padding - self.keyframe_stride = keyframe_stride - - # keyframe_branch - self.edvr = EDVRFeatureExtractor(temporal_padding * 2 + 1, num_feat, edvr_path) - # alignment - self.spynet = SpyNet(spynet_path) - - # propagation - self.backward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True) - self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) - - self.forward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True) - self.forward_trunk = ConvResidualBlocks(2 * num_feat + 3, num_feat, num_block) - - # reconstruction - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True) - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - - self.pixel_shuffle = nn.PixelShuffle(2) - - # activation functions - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def pad_spatial(self, x): - """Apply padding spatially. - - Since the PCD module in EDVR requires that the resolution is a multiple - of 4, we apply padding to the input LR images if their resolution is - not divisible by 4. - - Args: - x (Tensor): Input LR sequence with shape (n, t, c, h, w). - Returns: - Tensor: Padded LR sequence with shape (n, t, c, h_pad, w_pad). - """ - n, t, c, h, w = x.size() - - pad_h = (4 - h % 4) % 4 - pad_w = (4 - w % 4) % 4 - - # padding - x = x.view(-1, c, h, w) - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - - return x.view(n, t, c, h + pad_h, w + pad_w) - - def get_flow(self, x): - b, n, c, h, w = x.size() - - x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w) - x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w) - - flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w) - flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w) - - return flows_forward, flows_backward - - def get_keyframe_feature(self, x, keyframe_idx): - if self.temporal_padding == 2: - x = [x[:, [4, 3]], x, x[:, [-4, -5]]] - elif self.temporal_padding == 3: - x = [x[:, [6, 5, 4]], x, x[:, [-5, -6, -7]]] - x = torch.cat(x, dim=1) - - num_frames = 2 * self.temporal_padding + 1 - feats_keyframe = {} - for i in keyframe_idx: - feats_keyframe[i] = self.edvr(x[:, i:i + num_frames].contiguous()) - return feats_keyframe - - def forward(self, x): - b, n, _, h_input, w_input = x.size() - - x = self.pad_spatial(x) - h, w = x.shape[3:] - - keyframe_idx = list(range(0, n, self.keyframe_stride)) - if keyframe_idx[-1] != n - 1: - keyframe_idx.append(n - 1) # last frame is a keyframe - - # compute flow and keyframe features - flows_forward, flows_backward = self.get_flow(x) - feats_keyframe = self.get_keyframe_feature(x, keyframe_idx) - - # backward branch - out_l = [] - feat_prop = x.new_zeros(b, self.num_feat, h, w) - for i in range(n - 1, -1, -1): - x_i = x[:, i, :, :, :] - if i < n - 1: - flow = flows_backward[:, i, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - if i in keyframe_idx: - feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1) - feat_prop = self.backward_fusion(feat_prop) - feat_prop = torch.cat([x_i, feat_prop], dim=1) - feat_prop = self.backward_trunk(feat_prop) - out_l.insert(0, feat_prop) - - # forward branch - feat_prop = torch.zeros_like(feat_prop) - for i in range(0, n): - x_i = x[:, i, :, :, :] - if i > 0: - flow = flows_forward[:, i - 1, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - if i in keyframe_idx: - feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1) - feat_prop = self.forward_fusion(feat_prop) - - feat_prop = torch.cat([x_i, out_l[i], feat_prop], dim=1) - feat_prop = self.forward_trunk(feat_prop) - - # upsample - out = self.lrelu(self.pixel_shuffle(self.upconv1(feat_prop))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - out = self.lrelu(self.conv_hr(out)) - out = self.conv_last(out) - base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False) - out += base - out_l[i] = out - - return torch.stack(out_l, dim=1)[..., :4 * h_input, :4 * w_input] - - -class EDVRFeatureExtractor(nn.Module): - """EDVR feature extractor used in IconVSR. - - Args: - num_input_frame (int): Number of input frames. - num_feat (int): Number of feature channels - load_path (str): Path to the pretrained weights of EDVR. Default: None. - """ - - def __init__(self, num_input_frame, num_feat, load_path): - - super(EDVRFeatureExtractor, self).__init__() - - self.center_frame_idx = num_input_frame // 2 - - # extract pyramid features - self.conv_first = nn.Conv2d(3, num_feat, 3, 1, 1) - self.feature_extraction = make_layer(ResidualBlockNoBN, 5, num_feat=num_feat) - self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - - # pcd and tsa module - self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=8) - self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_input_frame, center_frame_idx=self.center_frame_idx) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - if load_path: - self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) - - def forward(self, x): - b, n, c, h, w = x.size() - - # extract features for each frame - # L1 - feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w))) - feat_l1 = self.feature_extraction(feat_l1) - # L2 - feat_l2 = self.lrelu(self.conv_l2_1(feat_l1)) - feat_l2 = self.lrelu(self.conv_l2_2(feat_l2)) - # L3 - feat_l3 = self.lrelu(self.conv_l3_1(feat_l2)) - feat_l3 = self.lrelu(self.conv_l3_2(feat_l3)) - - feat_l1 = feat_l1.view(b, n, -1, h, w) - feat_l2 = feat_l2.view(b, n, -1, h // 2, w // 2) - feat_l3 = feat_l3.view(b, n, -1, h // 4, w // 4) - - # PCD alignment - ref_feat_l = [ # reference feature list - feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(), - feat_l3[:, self.center_frame_idx, :, :, :].clone() - ] - aligned_feat = [] - for i in range(n): - nbr_feat_l = [ # neighboring feature list - feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone() - ] - aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l)) - aligned_feat = torch.stack(aligned_feat, dim=1) # (b, t, c, h, w) - - # TSA fusion - return self.fusion(aligned_feat) diff --git a/basicsr/archs/basicvsrpp_arch.py b/basicsr/archs/basicvsrpp_arch.py deleted file mode 100644 index 199e4914af4903e6d3ccf240e2ed5f2ad82dea03..0000000000000000000000000000000000000000 --- a/basicsr/archs/basicvsrpp_arch.py +++ /dev/null @@ -1,417 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision -import warnings - -from basicsr.archs.arch_util import flow_warp -from basicsr.archs.basicvsr_arch import ConvResidualBlocks -from basicsr.archs.spynet_arch import SpyNet -from basicsr.ops.dcn import ModulatedDeformConvPack -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register() -class BasicVSRPlusPlus(nn.Module): - """BasicVSR++ network structure. - - Support either x4 upsampling or same size output. Since DCN is used in this - model, it can only be used with CUDA enabled. If CUDA is not enabled, - feature alignment will be skipped. Besides, we adopt the official DCN - implementation and the version of torch need to be higher than 1.9. - - ``Paper: BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment`` - - Args: - mid_channels (int, optional): Channel number of the intermediate - features. Default: 64. - num_blocks (int, optional): The number of residual blocks in each - propagation branch. Default: 7. - max_residue_magnitude (int): The maximum magnitude of the offset - residue (Eq. 6 in paper). Default: 10. - is_low_res_input (bool, optional): Whether the input is low-resolution - or not. If False, the output resolution is equal to the input - resolution. Default: True. - spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. - cpu_cache_length (int, optional): When the length of sequence is larger - than this value, the intermediate features are sent to CPU. This - saves GPU memory, but slows down the inference speed. You can - increase this number if you have a GPU with large memory. - Default: 100. - """ - - def __init__(self, - mid_channels=64, - num_blocks=7, - max_residue_magnitude=10, - is_low_res_input=True, - spynet_path=None, - cpu_cache_length=100): - - super().__init__() - self.mid_channels = mid_channels - self.is_low_res_input = is_low_res_input - self.cpu_cache_length = cpu_cache_length - - # optical flow - self.spynet = SpyNet(spynet_path) - - # feature extraction module - if is_low_res_input: - self.feat_extract = ConvResidualBlocks(3, mid_channels, 5) - else: - self.feat_extract = nn.Sequential( - nn.Conv2d(3, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(mid_channels, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), - ConvResidualBlocks(mid_channels, mid_channels, 5)) - - # propagation branches - self.deform_align = nn.ModuleDict() - self.backbone = nn.ModuleDict() - modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2'] - for i, module in enumerate(modules): - if torch.cuda.is_available(): - self.deform_align[module] = SecondOrderDeformableAlignment( - 2 * mid_channels, - mid_channels, - 3, - padding=1, - deformable_groups=16, - max_residue_magnitude=max_residue_magnitude) - self.backbone[module] = ConvResidualBlocks((2 + i) * mid_channels, mid_channels, num_blocks) - - # upsampling module - self.reconstruction = ConvResidualBlocks(5 * mid_channels, mid_channels, 5) - - self.upconv1 = nn.Conv2d(mid_channels, mid_channels * 4, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(mid_channels, 64 * 4, 3, 1, 1, bias=True) - - self.pixel_shuffle = nn.PixelShuffle(2) - - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - self.img_upsample = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - # check if the sequence is augmented by flipping - self.is_mirror_extended = False - - if len(self.deform_align) > 0: - self.is_with_alignment = True - else: - self.is_with_alignment = False - warnings.warn('Deformable alignment module is not added. ' - 'Probably your CUDA is not configured correctly. DCN can only ' - 'be used with CUDA enabled. Alignment is skipped now.') - - def check_if_mirror_extended(self, lqs): - """Check whether the input is a mirror-extended sequence. - - If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the (t-1-i)-th frame. - - Args: - lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w). - """ - - if lqs.size(1) % 2 == 0: - lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1) - if torch.norm(lqs_1 - lqs_2.flip(1)) == 0: - self.is_mirror_extended = True - - def compute_flow(self, lqs): - """Compute optical flow using SPyNet for feature alignment. - - Note that if the input is an mirror-extended sequence, 'flows_forward' - is not needed, since it is equal to 'flows_backward.flip(1)'. - - Args: - lqs (tensor): Input low quality (LQ) sequence with - shape (n, t, c, h, w). - - Return: - tuple(Tensor): Optical flow. 'flows_forward' corresponds to the flows used for forward-time propagation \ - (current to previous). 'flows_backward' corresponds to the flows used for backward-time \ - propagation (current to next). - """ - - n, t, c, h, w = lqs.size() - lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w) - lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w) - - flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w) - - if self.is_mirror_extended: # flows_forward = flows_backward.flip(1) - flows_forward = flows_backward.flip(1) - else: - flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w) - - if self.cpu_cache: - flows_backward = flows_backward.cpu() - flows_forward = flows_forward.cpu() - - return flows_forward, flows_backward - - def propagate(self, feats, flows, module_name): - """Propagate the latent features throughout the sequence. - - Args: - feats dict(list[tensor]): Features from previous branches. Each - component is a list of tensors with shape (n, c, h, w). - flows (tensor): Optical flows with shape (n, t - 1, 2, h, w). - module_name (str): The name of the propgation branches. Can either - be 'backward_1', 'forward_1', 'backward_2', 'forward_2'. - - Return: - dict(list[tensor]): A dictionary containing all the propagated \ - features. Each key in the dictionary corresponds to a \ - propagation branch, which is represented by a list of tensors. - """ - - n, t, _, h, w = flows.size() - - frame_idx = range(0, t + 1) - flow_idx = range(-1, t) - mapping_idx = list(range(0, len(feats['spatial']))) - mapping_idx += mapping_idx[::-1] - - if 'backward' in module_name: - frame_idx = frame_idx[::-1] - flow_idx = frame_idx - - feat_prop = flows.new_zeros(n, self.mid_channels, h, w) - for i, idx in enumerate(frame_idx): - feat_current = feats['spatial'][mapping_idx[idx]] - if self.cpu_cache: - feat_current = feat_current.cuda() - feat_prop = feat_prop.cuda() - # second-order deformable alignment - if i > 0 and self.is_with_alignment: - flow_n1 = flows[:, flow_idx[i], :, :, :] - if self.cpu_cache: - flow_n1 = flow_n1.cuda() - - cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1)) - - # initialize second-order features - feat_n2 = torch.zeros_like(feat_prop) - flow_n2 = torch.zeros_like(flow_n1) - cond_n2 = torch.zeros_like(cond_n1) - - if i > 1: # second-order features - feat_n2 = feats[module_name][-2] - if self.cpu_cache: - feat_n2 = feat_n2.cuda() - - flow_n2 = flows[:, flow_idx[i - 1], :, :, :] - if self.cpu_cache: - flow_n2 = flow_n2.cuda() - - flow_n2 = flow_n1 + flow_warp(flow_n2, flow_n1.permute(0, 2, 3, 1)) - cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1)) - - # flow-guided deformable convolution - cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1) - feat_prop = torch.cat([feat_prop, feat_n2], dim=1) - feat_prop = self.deform_align[module_name](feat_prop, cond, flow_n1, flow_n2) - - # concatenate and residual blocks - feat = [feat_current] + [feats[k][idx] for k in feats if k not in ['spatial', module_name]] + [feat_prop] - if self.cpu_cache: - feat = [f.cuda() for f in feat] - - feat = torch.cat(feat, dim=1) - feat_prop = feat_prop + self.backbone[module_name](feat) - feats[module_name].append(feat_prop) - - if self.cpu_cache: - feats[module_name][-1] = feats[module_name][-1].cpu() - torch.cuda.empty_cache() - - if 'backward' in module_name: - feats[module_name] = feats[module_name][::-1] - - return feats - - def upsample(self, lqs, feats): - """Compute the output image given the features. - - Args: - lqs (tensor): Input low quality (LQ) sequence with - shape (n, t, c, h, w). - feats (dict): The features from the propagation branches. - - Returns: - Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). - """ - - outputs = [] - num_outputs = len(feats['spatial']) - - mapping_idx = list(range(0, num_outputs)) - mapping_idx += mapping_idx[::-1] - - for i in range(0, lqs.size(1)): - hr = [feats[k].pop(0) for k in feats if k != 'spatial'] - hr.insert(0, feats['spatial'][mapping_idx[i]]) - hr = torch.cat(hr, dim=1) - if self.cpu_cache: - hr = hr.cuda() - - hr = self.reconstruction(hr) - hr = self.lrelu(self.pixel_shuffle(self.upconv1(hr))) - hr = self.lrelu(self.pixel_shuffle(self.upconv2(hr))) - hr = self.lrelu(self.conv_hr(hr)) - hr = self.conv_last(hr) - if self.is_low_res_input: - hr += self.img_upsample(lqs[:, i, :, :, :]) - else: - hr += lqs[:, i, :, :, :] - - if self.cpu_cache: - hr = hr.cpu() - torch.cuda.empty_cache() - - outputs.append(hr) - - return torch.stack(outputs, dim=1) - - def forward(self, lqs): - """Forward function for BasicVSR++. - - Args: - lqs (tensor): Input low quality (LQ) sequence with - shape (n, t, c, h, w). - - Returns: - Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). - """ - - n, t, c, h, w = lqs.size() - - # whether to cache the features in CPU - self.cpu_cache = True if t > self.cpu_cache_length else False - - if self.is_low_res_input: - lqs_downsample = lqs.clone() - else: - lqs_downsample = F.interpolate( - lqs.view(-1, c, h, w), scale_factor=0.25, mode='bicubic').view(n, t, c, h // 4, w // 4) - - # check whether the input is an extended sequence - self.check_if_mirror_extended(lqs) - - feats = {} - # compute spatial features - if self.cpu_cache: - feats['spatial'] = [] - for i in range(0, t): - feat = self.feat_extract(lqs[:, i, :, :, :]).cpu() - feats['spatial'].append(feat) - torch.cuda.empty_cache() - else: - feats_ = self.feat_extract(lqs.view(-1, c, h, w)) - h, w = feats_.shape[2:] - feats_ = feats_.view(n, t, -1, h, w) - feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)] - - # compute optical flow using the low-res inputs - assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, ( - 'The height and width of low-res inputs must be at least 64, ' - f'but got {h} and {w}.') - flows_forward, flows_backward = self.compute_flow(lqs_downsample) - - # feature propgation - for iter_ in [1, 2]: - for direction in ['backward', 'forward']: - module = f'{direction}_{iter_}' - - feats[module] = [] - - if direction == 'backward': - flows = flows_backward - elif flows_forward is not None: - flows = flows_forward - else: - flows = flows_backward.flip(1) - - feats = self.propagate(feats, flows, module) - if self.cpu_cache: - del flows - torch.cuda.empty_cache() - - return self.upsample(lqs, feats) - - -class SecondOrderDeformableAlignment(ModulatedDeformConvPack): - """Second-order deformable alignment module. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int or tuple[int]): Same as nn.Conv2d. - padding (int or tuple[int]): Same as nn.Conv2d. - dilation (int or tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - max_residue_magnitude (int): The maximum magnitude of the offset - residue (Eq. 6 in paper). Default: 10. - """ - - def __init__(self, *args, **kwargs): - self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10) - - super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Sequential( - nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, 27 * self.deformable_groups, 3, 1, 1), - ) - - self.init_offset() - - def init_offset(self): - - def _constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - _constant_init(self.conv_offset[-1], val=0, bias=0) - - def forward(self, x, extra_feat, flow_1, flow_2): - extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1) - out = self.conv_offset(extra_feat) - o1, o2, mask = torch.chunk(out, 3, dim=1) - - # offset - offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1)) - offset_1, offset_2 = torch.chunk(offset, 2, dim=1) - offset_1 = offset_1 + flow_1.flip(1).repeat(1, offset_1.size(1) // 2, 1, 1) - offset_2 = offset_2 + flow_2.flip(1).repeat(1, offset_2.size(1) // 2, 1, 1) - offset = torch.cat([offset_1, offset_2], dim=1) - - # mask - mask = torch.sigmoid(mask) - - return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, - self.dilation, mask) - - -# if __name__ == '__main__': -# spynet_path = 'experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth' -# model = BasicVSRPlusPlus(spynet_path=spynet_path).cuda() -# input = torch.rand(1, 2, 3, 64, 64).cuda() -# output = model(input) -# print('===================') -# print(output.shape) diff --git a/basicsr/archs/dfdnet_arch.py b/basicsr/archs/dfdnet_arch.py deleted file mode 100644 index 14115dd143a09188fca2cd102a85730b873bb3b3..0000000000000000000000000000000000000000 --- a/basicsr/archs/dfdnet_arch.py +++ /dev/null @@ -1,169 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils.spectral_norm import spectral_norm - -from basicsr.utils.registry import ARCH_REGISTRY -from .dfdnet_util import AttentionBlock, Blur, MSDilationBlock, UpResBlock, adaptive_instance_normalization -from .vgg_arch import VGGFeatureExtractor - - -class SFTUpBlock(nn.Module): - """Spatial feature transform (SFT) with upsampling block. - - Args: - in_channel (int): Number of input channels. - out_channel (int): Number of output channels. - kernel_size (int): Kernel size in convolutions. Default: 3. - padding (int): Padding in convolutions. Default: 1. - """ - - def __init__(self, in_channel, out_channel, kernel_size=3, padding=1): - super(SFTUpBlock, self).__init__() - self.conv1 = nn.Sequential( - Blur(in_channel), - spectral_norm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)), - nn.LeakyReLU(0.04, True), - # The official codes use two LeakyReLU here, so 0.04 for equivalent - ) - self.convup = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), - spectral_norm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)), - nn.LeakyReLU(0.2, True), - ) - - # for SFT scale and shift - self.scale_block = nn.Sequential( - spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), - spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1))) - self.shift_block = nn.Sequential( - spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), - spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)), nn.Sigmoid()) - # The official codes use sigmoid for shift block, do not know why - - def forward(self, x, updated_feat): - out = self.conv1(x) - # SFT - scale = self.scale_block(updated_feat) - shift = self.shift_block(updated_feat) - out = out * scale + shift - # upsample - out = self.convup(out) - return out - - -@ARCH_REGISTRY.register() -class DFDNet(nn.Module): - """DFDNet: Deep Face Dictionary Network. - - It only processes faces with 512x512 size. - - Args: - num_feat (int): Number of feature channels. - dict_path (str): Path to the facial component dictionary. - """ - - def __init__(self, num_feat, dict_path): - super().__init__() - self.parts = ['left_eye', 'right_eye', 'nose', 'mouth'] - # part_sizes: [80, 80, 50, 110] - channel_sizes = [128, 256, 512, 512] - self.feature_sizes = np.array([256, 128, 64, 32]) - self.vgg_layers = ['relu2_2', 'relu3_4', 'relu4_4', 'conv5_4'] - self.flag_dict_device = False - - # dict - self.dict = torch.load(dict_path) - - # vgg face extractor - self.vgg_extractor = VGGFeatureExtractor( - layer_name_list=self.vgg_layers, - vgg_type='vgg19', - use_input_norm=True, - range_norm=True, - requires_grad=False) - - # attention block for fusing dictionary features and input features - self.attn_blocks = nn.ModuleDict() - for idx, feat_size in enumerate(self.feature_sizes): - for name in self.parts: - self.attn_blocks[f'{name}_{feat_size}'] = AttentionBlock(channel_sizes[idx]) - - # multi scale dilation block - self.multi_scale_dilation = MSDilationBlock(num_feat * 8, dilation=[4, 3, 2, 1]) - - # upsampling and reconstruction - self.upsample0 = SFTUpBlock(num_feat * 8, num_feat * 8) - self.upsample1 = SFTUpBlock(num_feat * 8, num_feat * 4) - self.upsample2 = SFTUpBlock(num_feat * 4, num_feat * 2) - self.upsample3 = SFTUpBlock(num_feat * 2, num_feat) - self.upsample4 = nn.Sequential( - spectral_norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1)), nn.LeakyReLU(0.2, True), UpResBlock(num_feat), - UpResBlock(num_feat), nn.Conv2d(num_feat, 3, kernel_size=3, stride=1, padding=1), nn.Tanh()) - - def swap_feat(self, vgg_feat, updated_feat, dict_feat, location, part_name, f_size): - """swap the features from the dictionary.""" - # get the original vgg features - part_feat = vgg_feat[:, :, location[1]:location[3], location[0]:location[2]].clone() - # resize original vgg features - part_resize_feat = F.interpolate(part_feat, dict_feat.size()[2:4], mode='bilinear', align_corners=False) - # use adaptive instance normalization to adjust color and illuminations - dict_feat = adaptive_instance_normalization(dict_feat, part_resize_feat) - # get similarity scores - similarity_score = F.conv2d(part_resize_feat, dict_feat) - similarity_score = F.softmax(similarity_score.view(-1), dim=0) - # select the most similar features in the dict (after norm) - select_idx = torch.argmax(similarity_score) - swap_feat = F.interpolate(dict_feat[select_idx:select_idx + 1], part_feat.size()[2:4]) - # attention - attn = self.attn_blocks[f'{part_name}_' + str(f_size)](swap_feat - part_feat) - attn_feat = attn * swap_feat - # update features - updated_feat[:, :, location[1]:location[3], location[0]:location[2]] = attn_feat + part_feat - return updated_feat - - def put_dict_to_device(self, x): - if self.flag_dict_device is False: - for k, v in self.dict.items(): - for kk, vv in v.items(): - self.dict[k][kk] = vv.to(x) - self.flag_dict_device = True - - def forward(self, x, part_locations): - """ - Now only support testing with batch size = 0. - - Args: - x (Tensor): Input faces with shape (b, c, 512, 512). - part_locations (list[Tensor]): Part locations. - """ - self.put_dict_to_device(x) - # extract vggface features - vgg_features = self.vgg_extractor(x) - # update vggface features using the dictionary for each part - updated_vgg_features = [] - batch = 0 # only supports testing with batch size = 0 - for vgg_layer, f_size in zip(self.vgg_layers, self.feature_sizes): - dict_features = self.dict[f'{f_size}'] - vgg_feat = vgg_features[vgg_layer] - updated_feat = vgg_feat.clone() - - # swap features from dictionary - for part_idx, part_name in enumerate(self.parts): - location = (part_locations[part_idx][batch] // (512 / f_size)).int() - updated_feat = self.swap_feat(vgg_feat, updated_feat, dict_features[part_name], location, part_name, - f_size) - - updated_vgg_features.append(updated_feat) - - vgg_feat_dilation = self.multi_scale_dilation(vgg_features['conv5_4']) - # use updated vgg features to modulate the upsampled features with - # SFT (Spatial Feature Transform) scaling and shifting manner. - upsampled_feat = self.upsample0(vgg_feat_dilation, updated_vgg_features[3]) - upsampled_feat = self.upsample1(upsampled_feat, updated_vgg_features[2]) - upsampled_feat = self.upsample2(upsampled_feat, updated_vgg_features[1]) - upsampled_feat = self.upsample3(upsampled_feat, updated_vgg_features[0]) - out = self.upsample4(upsampled_feat) - - return out diff --git a/basicsr/archs/dfdnet_util.py b/basicsr/archs/dfdnet_util.py deleted file mode 100644 index 411e683f5386995da04ce19f496c019a1280f898..0000000000000000000000000000000000000000 --- a/basicsr/archs/dfdnet_util.py +++ /dev/null @@ -1,162 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Function -from torch.nn.utils.spectral_norm import spectral_norm - - -class BlurFunctionBackward(Function): - - @staticmethod - def forward(ctx, grad_output, kernel, kernel_flip): - ctx.save_for_backward(kernel, kernel_flip) - grad_input = F.conv2d(grad_output, kernel_flip, padding=1, groups=grad_output.shape[1]) - return grad_input - - @staticmethod - def backward(ctx, gradgrad_output): - kernel, _ = ctx.saved_tensors - grad_input = F.conv2d(gradgrad_output, kernel, padding=1, groups=gradgrad_output.shape[1]) - return grad_input, None, None - - -class BlurFunction(Function): - - @staticmethod - def forward(ctx, x, kernel, kernel_flip): - ctx.save_for_backward(kernel, kernel_flip) - output = F.conv2d(x, kernel, padding=1, groups=x.shape[1]) - return output - - @staticmethod - def backward(ctx, grad_output): - kernel, kernel_flip = ctx.saved_tensors - grad_input = BlurFunctionBackward.apply(grad_output, kernel, kernel_flip) - return grad_input, None, None - - -blur = BlurFunction.apply - - -class Blur(nn.Module): - - def __init__(self, channel): - super().__init__() - kernel = torch.tensor([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=torch.float32) - kernel = kernel.view(1, 1, 3, 3) - kernel = kernel / kernel.sum() - kernel_flip = torch.flip(kernel, [2, 3]) - - self.kernel = kernel.repeat(channel, 1, 1, 1) - self.kernel_flip = kernel_flip.repeat(channel, 1, 1, 1) - - def forward(self, x): - return blur(x, self.kernel.type_as(x), self.kernel_flip.type_as(x)) - - -def calc_mean_std(feat, eps=1e-5): - """Calculate mean and std for adaptive_instance_normalization. - - Args: - feat (Tensor): 4D tensor. - eps (float): A small value added to the variance to avoid - divide-by-zero. Default: 1e-5. - """ - size = feat.size() - assert len(size) == 4, 'The input feature should be 4D tensor.' - n, c = size[:2] - feat_var = feat.view(n, c, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(n, c, 1, 1) - feat_mean = feat.view(n, c, -1).mean(dim=2).view(n, c, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - """Adaptive instance normalization. - - Adjust the reference features to have the similar color and illuminations - as those in the degradate features. - - Args: - content_feat (Tensor): The reference feature. - style_feat (Tensor): The degradate features. - """ - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -def AttentionBlock(in_channel): - return nn.Sequential( - spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), - spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1))) - - -def conv_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bias=True): - """Conv block used in MSDilationBlock.""" - - return nn.Sequential( - spectral_norm( - nn.Conv2d( - in_channels, - out_channels, - kernel_size=kernel_size, - stride=stride, - dilation=dilation, - padding=((kernel_size - 1) // 2) * dilation, - bias=bias)), - nn.LeakyReLU(0.2), - spectral_norm( - nn.Conv2d( - out_channels, - out_channels, - kernel_size=kernel_size, - stride=stride, - dilation=dilation, - padding=((kernel_size - 1) // 2) * dilation, - bias=bias)), - ) - - -class MSDilationBlock(nn.Module): - """Multi-scale dilation block.""" - - def __init__(self, in_channels, kernel_size=3, dilation=(1, 1, 1, 1), bias=True): - super(MSDilationBlock, self).__init__() - - self.conv_blocks = nn.ModuleList() - for i in range(4): - self.conv_blocks.append(conv_block(in_channels, in_channels, kernel_size, dilation=dilation[i], bias=bias)) - self.conv_fusion = spectral_norm( - nn.Conv2d( - in_channels * 4, - in_channels, - kernel_size=kernel_size, - stride=1, - padding=(kernel_size - 1) // 2, - bias=bias)) - - def forward(self, x): - out = [] - for i in range(4): - out.append(self.conv_blocks[i](x)) - out = torch.cat(out, 1) - out = self.conv_fusion(out) + x - return out - - -class UpResBlock(nn.Module): - - def __init__(self, in_channel): - super(UpResBlock, self).__init__() - self.body = nn.Sequential( - nn.Conv2d(in_channel, in_channel, 3, 1, 1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(in_channel, in_channel, 3, 1, 1), - ) - - def forward(self, x): - out = x + self.body(x) - return out diff --git a/basicsr/archs/discriminator_arch.py b/basicsr/archs/discriminator_arch.py deleted file mode 100644 index 5bd29e685f047ba21d1e9d78e671efd53decaf54..0000000000000000000000000000000000000000 --- a/basicsr/archs/discriminator_arch.py +++ /dev/null @@ -1,150 +0,0 @@ -from torch import nn as nn -from torch.nn import functional as F -from torch.nn.utils import spectral_norm - -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register() -class VGGStyleDiscriminator(nn.Module): - """VGG style discriminator with input size 128 x 128 or 256 x 256. - - It is used to train SRGAN, ESRGAN, and VideoGAN. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_feat (int): Channel number of base intermediate features.Default: 64. - """ - - def __init__(self, num_in_ch, num_feat, input_size=128): - super(VGGStyleDiscriminator, self).__init__() - self.input_size = input_size - assert self.input_size == 128 or self.input_size == 256, ( - f'input size must be 128 or 256, but received {input_size}') - - self.conv0_0 = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True) - self.conv0_1 = nn.Conv2d(num_feat, num_feat, 4, 2, 1, bias=False) - self.bn0_1 = nn.BatchNorm2d(num_feat, affine=True) - - self.conv1_0 = nn.Conv2d(num_feat, num_feat * 2, 3, 1, 1, bias=False) - self.bn1_0 = nn.BatchNorm2d(num_feat * 2, affine=True) - self.conv1_1 = nn.Conv2d(num_feat * 2, num_feat * 2, 4, 2, 1, bias=False) - self.bn1_1 = nn.BatchNorm2d(num_feat * 2, affine=True) - - self.conv2_0 = nn.Conv2d(num_feat * 2, num_feat * 4, 3, 1, 1, bias=False) - self.bn2_0 = nn.BatchNorm2d(num_feat * 4, affine=True) - self.conv2_1 = nn.Conv2d(num_feat * 4, num_feat * 4, 4, 2, 1, bias=False) - self.bn2_1 = nn.BatchNorm2d(num_feat * 4, affine=True) - - self.conv3_0 = nn.Conv2d(num_feat * 4, num_feat * 8, 3, 1, 1, bias=False) - self.bn3_0 = nn.BatchNorm2d(num_feat * 8, affine=True) - self.conv3_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) - self.bn3_1 = nn.BatchNorm2d(num_feat * 8, affine=True) - - self.conv4_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) - self.bn4_0 = nn.BatchNorm2d(num_feat * 8, affine=True) - self.conv4_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) - self.bn4_1 = nn.BatchNorm2d(num_feat * 8, affine=True) - - if self.input_size == 256: - self.conv5_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) - self.bn5_0 = nn.BatchNorm2d(num_feat * 8, affine=True) - self.conv5_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) - self.bn5_1 = nn.BatchNorm2d(num_feat * 8, affine=True) - - self.linear1 = nn.Linear(num_feat * 8 * 4 * 4, 100) - self.linear2 = nn.Linear(100, 1) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - assert x.size(2) == self.input_size, (f'Input size must be identical to input_size, but received {x.size()}.') - - feat = self.lrelu(self.conv0_0(x)) - feat = self.lrelu(self.bn0_1(self.conv0_1(feat))) # output spatial size: /2 - - feat = self.lrelu(self.bn1_0(self.conv1_0(feat))) - feat = self.lrelu(self.bn1_1(self.conv1_1(feat))) # output spatial size: /4 - - feat = self.lrelu(self.bn2_0(self.conv2_0(feat))) - feat = self.lrelu(self.bn2_1(self.conv2_1(feat))) # output spatial size: /8 - - feat = self.lrelu(self.bn3_0(self.conv3_0(feat))) - feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: /16 - - feat = self.lrelu(self.bn4_0(self.conv4_0(feat))) - feat = self.lrelu(self.bn4_1(self.conv4_1(feat))) # output spatial size: /32 - - if self.input_size == 256: - feat = self.lrelu(self.bn5_0(self.conv5_0(feat))) - feat = self.lrelu(self.bn5_1(self.conv5_1(feat))) # output spatial size: / 64 - - # spatial size: (4, 4) - feat = feat.view(feat.size(0), -1) - feat = self.lrelu(self.linear1(feat)) - out = self.linear2(feat) - return out - - -@ARCH_REGISTRY.register(suffix='basicsr') -class UNetDiscriminatorSN(nn.Module): - """Defines a U-Net discriminator with spectral normalization (SN) - - It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - Arg: - num_in_ch (int): Channel number of inputs. Default: 3. - num_feat (int): Channel number of base intermediate features. Default: 64. - skip_connection (bool): Whether to use skip connections between U-Net. Default: True. - """ - - def __init__(self, num_in_ch, num_feat=64, skip_connection=True): - super(UNetDiscriminatorSN, self).__init__() - self.skip_connection = skip_connection - norm = spectral_norm - # the first convolution - self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1) - # downsample - self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False)) - self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False)) - self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False)) - # upsample - self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False)) - self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False)) - self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False)) - # extra convolutions - self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) - self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) - self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1) - - def forward(self, x): - # downsample - x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True) - x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True) - x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True) - x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True) - - # upsample - x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False) - x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x4 = x4 + x2 - x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False) - x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x5 = x5 + x1 - x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False) - x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x6 = x6 + x0 - - # extra convolutions - out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True) - out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True) - out = self.conv9(out) - - return out diff --git a/basicsr/archs/duf_arch.py b/basicsr/archs/duf_arch.py deleted file mode 100644 index 3ac49430a7925eab33780e1ffa3fad4d7ef72e1a..0000000000000000000000000000000000000000 --- a/basicsr/archs/duf_arch.py +++ /dev/null @@ -1,276 +0,0 @@ -import numpy as np -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY - - -class DenseBlocksTemporalReduce(nn.Module): - """A concatenation of 3 dense blocks with reduction in temporal dimension. - - Note that the output temporal dimension is 6 fewer the input temporal dimension, since there are 3 blocks. - - Args: - num_feat (int): Number of channels in the blocks. Default: 64. - num_grow_ch (int): Growing factor of the dense blocks. Default: 32 - adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation. - Set to false if you want to train from scratch. Default: False. - """ - - def __init__(self, num_feat=64, num_grow_ch=32, adapt_official_weights=False): - super(DenseBlocksTemporalReduce, self).__init__() - if adapt_official_weights: - eps = 1e-3 - momentum = 1e-3 - else: # pytorch default values - eps = 1e-05 - momentum = 0.1 - - self.temporal_reduce1 = nn.Sequential( - nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d(num_feat, num_feat, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True), - nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d(num_feat, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) - - self.temporal_reduce2 = nn.Sequential( - nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + num_grow_ch, - num_feat + num_grow_ch, (1, 1, 1), - stride=(1, 1, 1), - padding=(0, 0, 0), - bias=True), nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d(num_feat + num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) - - self.temporal_reduce3 = nn.Sequential( - nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + 2 * num_grow_ch, - num_feat + 2 * num_grow_ch, (1, 1, 1), - stride=(1, 1, 1), - padding=(0, 0, 0), - bias=True), nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), - nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + 2 * num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) - - def forward(self, x): - """ - Args: - x (Tensor): Input tensor with shape (b, num_feat, t, h, w). - - Returns: - Tensor: Output with shape (b, num_feat + num_grow_ch * 3, 1, h, w). - """ - x1 = self.temporal_reduce1(x) - x1 = torch.cat((x[:, :, 1:-1, :, :], x1), 1) - - x2 = self.temporal_reduce2(x1) - x2 = torch.cat((x1[:, :, 1:-1, :, :], x2), 1) - - x3 = self.temporal_reduce3(x2) - x3 = torch.cat((x2[:, :, 1:-1, :, :], x3), 1) - - return x3 - - -class DenseBlocks(nn.Module): - """ A concatenation of N dense blocks. - - Args: - num_feat (int): Number of channels in the blocks. Default: 64. - num_grow_ch (int): Growing factor of the dense blocks. Default: 32. - num_block (int): Number of dense blocks. The values are: - DUF-S (16 layers): 3 - DUF-M (18 layers): 9 - DUF-L (52 layers): 21 - adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation. - Set to false if you want to train from scratch. Default: False. - """ - - def __init__(self, num_block, num_feat=64, num_grow_ch=16, adapt_official_weights=False): - super(DenseBlocks, self).__init__() - if adapt_official_weights: - eps = 1e-3 - momentum = 1e-3 - else: # pytorch default values - eps = 1e-05 - momentum = 0.1 - - self.dense_blocks = nn.ModuleList() - for i in range(0, num_block): - self.dense_blocks.append( - nn.Sequential( - nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + i * num_grow_ch, - num_feat + i * num_grow_ch, (1, 1, 1), - stride=(1, 1, 1), - padding=(0, 0, 0), - bias=True), nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), - nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + i * num_grow_ch, - num_grow_ch, (3, 3, 3), - stride=(1, 1, 1), - padding=(1, 1, 1), - bias=True))) - - def forward(self, x): - """ - Args: - x (Tensor): Input tensor with shape (b, num_feat, t, h, w). - - Returns: - Tensor: Output with shape (b, num_feat + num_block * num_grow_ch, t, h, w). - """ - for i in range(0, len(self.dense_blocks)): - y = self.dense_blocks[i](x) - x = torch.cat((x, y), 1) - return x - - -class DynamicUpsamplingFilter(nn.Module): - """Dynamic upsampling filter used in DUF. - - Reference: https://github.com/yhjo09/VSR-DUF - - It only supports input with 3 channels. And it applies the same filters to 3 channels. - - Args: - filter_size (tuple): Filter size of generated filters. The shape is (kh, kw). Default: (5, 5). - """ - - def __init__(self, filter_size=(5, 5)): - super(DynamicUpsamplingFilter, self).__init__() - if not isinstance(filter_size, tuple): - raise TypeError(f'The type of filter_size must be tuple, but got type{filter_size}') - if len(filter_size) != 2: - raise ValueError(f'The length of filter size must be 2, but got {len(filter_size)}.') - # generate a local expansion filter, similar to im2col - self.filter_size = filter_size - filter_prod = np.prod(filter_size) - expansion_filter = torch.eye(int(filter_prod)).view(filter_prod, 1, *filter_size) # (kh*kw, 1, kh, kw) - self.expansion_filter = expansion_filter.repeat(3, 1, 1, 1) # repeat for all the 3 channels - - def forward(self, x, filters): - """Forward function for DynamicUpsamplingFilter. - - Args: - x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w). - filters (Tensor): Generated dynamic filters. The shape is (n, filter_prod, upsampling_square, h, w). - filter_prod: prod of filter kernel size, e.g., 1*5*5=25. - upsampling_square: similar to pixel shuffle, upsampling_square = upsampling * upsampling. - e.g., for x 4 upsampling, upsampling_square= 4*4 = 16 - - Returns: - Tensor: Filtered image with shape (n, 3*upsampling_square, h, w) - """ - n, filter_prod, upsampling_square, h, w = filters.size() - kh, kw = self.filter_size - expanded_input = F.conv2d( - x, self.expansion_filter.to(x), padding=(kh // 2, kw // 2), groups=3) # (n, 3*filter_prod, h, w) - expanded_input = expanded_input.view(n, 3, filter_prod, h, w).permute(0, 3, 4, 1, - 2) # (n, h, w, 3, filter_prod) - filters = filters.permute(0, 3, 4, 1, 2) # (n, h, w, filter_prod, upsampling_square] - out = torch.matmul(expanded_input, filters) # (n, h, w, 3, upsampling_square) - return out.permute(0, 3, 4, 1, 2).view(n, 3 * upsampling_square, h, w) - - -@ARCH_REGISTRY.register() -class DUF(nn.Module): - """Network architecture for DUF - - ``Paper: Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation`` - - Reference: https://github.com/yhjo09/VSR-DUF - - For all the models below, 'adapt_official_weights' is only necessary when - loading the weights converted from the official TensorFlow weights. - Please set it to False if you are training the model from scratch. - - There are three models with different model size: DUF16Layers, DUF28Layers, - and DUF52Layers. This class is the base class for these models. - - Args: - scale (int): The upsampling factor. Default: 4. - num_layer (int): The number of layers. Default: 52. - adapt_official_weights_weights (bool): Whether to adapt the weights - translated from the official implementation. Set to false if you - want to train from scratch. Default: False. - """ - - def __init__(self, scale=4, num_layer=52, adapt_official_weights=False): - super(DUF, self).__init__() - self.scale = scale - if adapt_official_weights: - eps = 1e-3 - momentum = 1e-3 - else: # pytorch default values - eps = 1e-05 - momentum = 0.1 - - self.conv3d1 = nn.Conv3d(3, 64, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True) - self.dynamic_filter = DynamicUpsamplingFilter((5, 5)) - - if num_layer == 16: - num_block = 3 - num_grow_ch = 32 - elif num_layer == 28: - num_block = 9 - num_grow_ch = 16 - elif num_layer == 52: - num_block = 21 - num_grow_ch = 16 - else: - raise ValueError(f'Only supported (16, 28, 52) layers, but got {num_layer}.') - - self.dense_block1 = DenseBlocks( - num_block=num_block, num_feat=64, num_grow_ch=num_grow_ch, - adapt_official_weights=adapt_official_weights) # T = 7 - self.dense_block2 = DenseBlocksTemporalReduce( - 64 + num_grow_ch * num_block, num_grow_ch, adapt_official_weights=adapt_official_weights) # T = 1 - channels = 64 + num_grow_ch * num_block + num_grow_ch * 3 - self.bn3d2 = nn.BatchNorm3d(channels, eps=eps, momentum=momentum) - self.conv3d2 = nn.Conv3d(channels, 256, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True) - - self.conv3d_r1 = nn.Conv3d(256, 256, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - self.conv3d_r2 = nn.Conv3d(256, 3 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - - self.conv3d_f1 = nn.Conv3d(256, 512, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - self.conv3d_f2 = nn.Conv3d( - 512, 1 * 5 * 5 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - - def forward(self, x): - """ - Args: - x (Tensor): Input with shape (b, 7, c, h, w) - - Returns: - Tensor: Output with shape (b, c, h * scale, w * scale) - """ - num_batches, num_imgs, _, h, w = x.size() - - x = x.permute(0, 2, 1, 3, 4) # (b, c, 7, h, w) for Conv3D - x_center = x[:, :, num_imgs // 2, :, :] - - x = self.conv3d1(x) - x = self.dense_block1(x) - x = self.dense_block2(x) - x = F.relu(self.bn3d2(x), inplace=True) - x = F.relu(self.conv3d2(x), inplace=True) - - # residual image - res = self.conv3d_r2(F.relu(self.conv3d_r1(x), inplace=True)) - - # filter - filter_ = self.conv3d_f2(F.relu(self.conv3d_f1(x), inplace=True)) - filter_ = F.softmax(filter_.view(num_batches, 25, self.scale**2, h, w), dim=1) - - # dynamic filter - out = self.dynamic_filter(x_center, filter_) - out += res.squeeze_(2) - out = F.pixel_shuffle(out, self.scale) - - return out diff --git a/basicsr/archs/ecbsr_arch.py b/basicsr/archs/ecbsr_arch.py deleted file mode 100644 index 7e4edcc7b1818f5c04fcaf9f948da4c0dadd5ea1..0000000000000000000000000000000000000000 --- a/basicsr/archs/ecbsr_arch.py +++ /dev/null @@ -1,275 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from basicsr.utils.registry import ARCH_REGISTRY - - -class SeqConv3x3(nn.Module): - """The re-parameterizable block used in the ECBSR architecture. - - ``Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices`` - - Reference: https://github.com/xindongzhang/ECBSR - - Args: - seq_type (str): Sequence type, option: conv1x1-conv3x3 | conv1x1-sobelx | conv1x1-sobely | conv1x1-laplacian. - in_channels (int): Channel number of input. - out_channels (int): Channel number of output. - depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. - """ - - def __init__(self, seq_type, in_channels, out_channels, depth_multiplier=1): - super(SeqConv3x3, self).__init__() - self.seq_type = seq_type - self.in_channels = in_channels - self.out_channels = out_channels - - if self.seq_type == 'conv1x1-conv3x3': - self.mid_planes = int(out_channels * depth_multiplier) - conv0 = torch.nn.Conv2d(self.in_channels, self.mid_planes, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - conv1 = torch.nn.Conv2d(self.mid_planes, self.out_channels, kernel_size=3) - self.k1 = conv1.weight - self.b1 = conv1.bias - - elif self.seq_type == 'conv1x1-sobelx': - conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - # init scale and bias - scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 - self.scale = nn.Parameter(scale) - bias = torch.randn(self.out_channels) * 1e-3 - bias = torch.reshape(bias, (self.out_channels, )) - self.bias = nn.Parameter(bias) - # init mask - self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) - for i in range(self.out_channels): - self.mask[i, 0, 0, 0] = 1.0 - self.mask[i, 0, 1, 0] = 2.0 - self.mask[i, 0, 2, 0] = 1.0 - self.mask[i, 0, 0, 2] = -1.0 - self.mask[i, 0, 1, 2] = -2.0 - self.mask[i, 0, 2, 2] = -1.0 - self.mask = nn.Parameter(data=self.mask, requires_grad=False) - - elif self.seq_type == 'conv1x1-sobely': - conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - # init scale and bias - scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 - self.scale = nn.Parameter(torch.FloatTensor(scale)) - bias = torch.randn(self.out_channels) * 1e-3 - bias = torch.reshape(bias, (self.out_channels, )) - self.bias = nn.Parameter(torch.FloatTensor(bias)) - # init mask - self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) - for i in range(self.out_channels): - self.mask[i, 0, 0, 0] = 1.0 - self.mask[i, 0, 0, 1] = 2.0 - self.mask[i, 0, 0, 2] = 1.0 - self.mask[i, 0, 2, 0] = -1.0 - self.mask[i, 0, 2, 1] = -2.0 - self.mask[i, 0, 2, 2] = -1.0 - self.mask = nn.Parameter(data=self.mask, requires_grad=False) - - elif self.seq_type == 'conv1x1-laplacian': - conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - # init scale and bias - scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 - self.scale = nn.Parameter(torch.FloatTensor(scale)) - bias = torch.randn(self.out_channels) * 1e-3 - bias = torch.reshape(bias, (self.out_channels, )) - self.bias = nn.Parameter(torch.FloatTensor(bias)) - # init mask - self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) - for i in range(self.out_channels): - self.mask[i, 0, 0, 1] = 1.0 - self.mask[i, 0, 1, 0] = 1.0 - self.mask[i, 0, 1, 2] = 1.0 - self.mask[i, 0, 2, 1] = 1.0 - self.mask[i, 0, 1, 1] = -4.0 - self.mask = nn.Parameter(data=self.mask, requires_grad=False) - else: - raise ValueError('The type of seqconv is not supported!') - - def forward(self, x): - if self.seq_type == 'conv1x1-conv3x3': - # conv-1x1 - y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1) - # explicitly padding with bias - y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0) - b0_pad = self.b0.view(1, -1, 1, 1) - y0[:, :, 0:1, :] = b0_pad - y0[:, :, -1:, :] = b0_pad - y0[:, :, :, 0:1] = b0_pad - y0[:, :, :, -1:] = b0_pad - # conv-3x3 - y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1) - else: - y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1) - # explicitly padding with bias - y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0) - b0_pad = self.b0.view(1, -1, 1, 1) - y0[:, :, 0:1, :] = b0_pad - y0[:, :, -1:, :] = b0_pad - y0[:, :, :, 0:1] = b0_pad - y0[:, :, :, -1:] = b0_pad - # conv-3x3 - y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_channels) - return y1 - - def rep_params(self): - device = self.k0.get_device() - if device < 0: - device = None - - if self.seq_type == 'conv1x1-conv3x3': - # re-param conv kernel - rep_weight = F.conv2d(input=self.k1, weight=self.k0.permute(1, 0, 2, 3)) - # re-param conv bias - rep_bias = torch.ones(1, self.mid_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1) - rep_bias = F.conv2d(input=rep_bias, weight=self.k1).view(-1, ) + self.b1 - else: - tmp = self.scale * self.mask - k1 = torch.zeros((self.out_channels, self.out_channels, 3, 3), device=device) - for i in range(self.out_channels): - k1[i, i, :, :] = tmp[i, 0, :, :] - b1 = self.bias - # re-param conv kernel - rep_weight = F.conv2d(input=k1, weight=self.k0.permute(1, 0, 2, 3)) - # re-param conv bias - rep_bias = torch.ones(1, self.out_channels, 3, 3, device=device) * self.b0.view(1, -1, 1, 1) - rep_bias = F.conv2d(input=rep_bias, weight=k1).view(-1, ) + b1 - return rep_weight, rep_bias - - -class ECB(nn.Module): - """The ECB block used in the ECBSR architecture. - - Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices - Ref git repo: https://github.com/xindongzhang/ECBSR - - Args: - in_channels (int): Channel number of input. - out_channels (int): Channel number of output. - depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. - act_type (str): Activation type. Option: prelu | relu | rrelu | softplus | linear. Default: prelu. - with_idt (bool): Whether to use identity connection. Default: False. - """ - - def __init__(self, in_channels, out_channels, depth_multiplier, act_type='prelu', with_idt=False): - super(ECB, self).__init__() - - self.depth_multiplier = depth_multiplier - self.in_channels = in_channels - self.out_channels = out_channels - self.act_type = act_type - - if with_idt and (self.in_channels == self.out_channels): - self.with_idt = True - else: - self.with_idt = False - - self.conv3x3 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=3, padding=1) - self.conv1x1_3x3 = SeqConv3x3('conv1x1-conv3x3', self.in_channels, self.out_channels, self.depth_multiplier) - self.conv1x1_sbx = SeqConv3x3('conv1x1-sobelx', self.in_channels, self.out_channels) - self.conv1x1_sby = SeqConv3x3('conv1x1-sobely', self.in_channels, self.out_channels) - self.conv1x1_lpl = SeqConv3x3('conv1x1-laplacian', self.in_channels, self.out_channels) - - if self.act_type == 'prelu': - self.act = nn.PReLU(num_parameters=self.out_channels) - elif self.act_type == 'relu': - self.act = nn.ReLU(inplace=True) - elif self.act_type == 'rrelu': - self.act = nn.RReLU(lower=-0.05, upper=0.05) - elif self.act_type == 'softplus': - self.act = nn.Softplus() - elif self.act_type == 'linear': - pass - else: - raise ValueError('The type of activation if not support!') - - def forward(self, x): - if self.training: - y = self.conv3x3(x) + self.conv1x1_3x3(x) + self.conv1x1_sbx(x) + self.conv1x1_sby(x) + self.conv1x1_lpl(x) - if self.with_idt: - y += x - else: - rep_weight, rep_bias = self.rep_params() - y = F.conv2d(input=x, weight=rep_weight, bias=rep_bias, stride=1, padding=1) - if self.act_type != 'linear': - y = self.act(y) - return y - - def rep_params(self): - weight0, bias0 = self.conv3x3.weight, self.conv3x3.bias - weight1, bias1 = self.conv1x1_3x3.rep_params() - weight2, bias2 = self.conv1x1_sbx.rep_params() - weight3, bias3 = self.conv1x1_sby.rep_params() - weight4, bias4 = self.conv1x1_lpl.rep_params() - rep_weight, rep_bias = (weight0 + weight1 + weight2 + weight3 + weight4), ( - bias0 + bias1 + bias2 + bias3 + bias4) - - if self.with_idt: - device = rep_weight.get_device() - if device < 0: - device = None - weight_idt = torch.zeros(self.out_channels, self.out_channels, 3, 3, device=device) - for i in range(self.out_channels): - weight_idt[i, i, 1, 1] = 1.0 - bias_idt = 0.0 - rep_weight, rep_bias = rep_weight + weight_idt, rep_bias + bias_idt - return rep_weight, rep_bias - - -@ARCH_REGISTRY.register() -class ECBSR(nn.Module): - """ECBSR architecture. - - Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices - Ref git repo: https://github.com/xindongzhang/ECBSR - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_block (int): Block number in the trunk network. - num_channel (int): Channel number. - with_idt (bool): Whether use identity in convolution layers. - act_type (str): Activation type. - scale (int): Upsampling factor. - """ - - def __init__(self, num_in_ch, num_out_ch, num_block, num_channel, with_idt, act_type, scale): - super(ECBSR, self).__init__() - self.num_in_ch = num_in_ch - self.scale = scale - - backbone = [] - backbone += [ECB(num_in_ch, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)] - for _ in range(num_block): - backbone += [ECB(num_channel, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)] - backbone += [ - ECB(num_channel, num_out_ch * scale * scale, depth_multiplier=2.0, act_type='linear', with_idt=with_idt) - ] - - self.backbone = nn.Sequential(*backbone) - self.upsampler = nn.PixelShuffle(scale) - - def forward(self, x): - if self.num_in_ch > 1: - shortcut = torch.repeat_interleave(x, self.scale * self.scale, dim=1) - else: - shortcut = x # will repeat the input in the channel dimension (repeat scale * scale times) - y = self.backbone(x) + shortcut - y = self.upsampler(y) - return y diff --git a/basicsr/archs/edsr_arch.py b/basicsr/archs/edsr_arch.py deleted file mode 100644 index 6c12f723c140bf581501493efe1a39a2aa12ff10..0000000000000000000000000000000000000000 --- a/basicsr/archs/edsr_arch.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch import nn as nn - -from basicsr.archs.arch_util import ResidualBlockNoBN, Upsample, make_layer -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register() -class EDSR(nn.Module): - """EDSR network structure. - - Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution. - Ref git repo: https://github.com/thstkdgus35/EDSR-PyTorch - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64. - num_block (int): Block number in the trunk network. Default: 16. - upscale (int): Upsampling factor. Support 2^n and 3. - Default: 4. - res_scale (float): Used to scale the residual in residual block. - Default: 1. - img_range (float): Image range. Default: 255. - rgb_mean (tuple[float]): Image mean in RGB orders. - Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. - """ - - def __init__(self, - num_in_ch, - num_out_ch, - num_feat=64, - num_block=16, - upscale=4, - res_scale=1, - img_range=255., - rgb_mean=(0.4488, 0.4371, 0.4040)): - super(EDSR, self).__init__() - - self.img_range = img_range - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat, res_scale=res_scale, pytorch_init=True) - self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - def forward(self, x): - self.mean = self.mean.type_as(x) - - x = (x - self.mean) * self.img_range - x = self.conv_first(x) - res = self.conv_after_body(self.body(x)) - res += x - - x = self.conv_last(self.upsample(res)) - x = x / self.img_range + self.mean - - return x diff --git a/basicsr/archs/edvr_arch.py b/basicsr/archs/edvr_arch.py deleted file mode 100644 index 925448b9caa7895338369621ab4e23c32a143bd8..0000000000000000000000000000000000000000 --- a/basicsr/archs/edvr_arch.py +++ /dev/null @@ -1,382 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import DCNv2Pack, ResidualBlockNoBN, make_layer - - -class PCDAlignment(nn.Module): - """Alignment module using Pyramid, Cascading and Deformable convolution - (PCD). It is used in EDVR. - - ``Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks`` - - Args: - num_feat (int): Channel number of middle features. Default: 64. - deformable_groups (int): Deformable groups. Defaults: 8. - """ - - def __init__(self, num_feat=64, deformable_groups=8): - super(PCDAlignment, self).__init__() - - # Pyramid has three levels: - # L3: level 3, 1/4 spatial size - # L2: level 2, 1/2 spatial size - # L1: level 1, original spatial size - self.offset_conv1 = nn.ModuleDict() - self.offset_conv2 = nn.ModuleDict() - self.offset_conv3 = nn.ModuleDict() - self.dcn_pack = nn.ModuleDict() - self.feat_conv = nn.ModuleDict() - - # Pyramids - for i in range(3, 0, -1): - level = f'l{i}' - self.offset_conv1[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - if i == 3: - self.offset_conv2[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - else: - self.offset_conv2[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - self.offset_conv3[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.dcn_pack[level] = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups) - - if i < 3: - self.feat_conv[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - - # Cascading dcn - self.cas_offset_conv1 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - self.cas_offset_conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.cas_dcnpack = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups) - - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, nbr_feat_l, ref_feat_l): - """Align neighboring frame features to the reference frame features. - - Args: - nbr_feat_l (list[Tensor]): Neighboring feature list. It - contains three pyramid levels (L1, L2, L3), - each with shape (b, c, h, w). - ref_feat_l (list[Tensor]): Reference feature list. It - contains three pyramid levels (L1, L2, L3), - each with shape (b, c, h, w). - - Returns: - Tensor: Aligned features. - """ - # Pyramids - upsampled_offset, upsampled_feat = None, None - for i in range(3, 0, -1): - level = f'l{i}' - offset = torch.cat([nbr_feat_l[i - 1], ref_feat_l[i - 1]], dim=1) - offset = self.lrelu(self.offset_conv1[level](offset)) - if i == 3: - offset = self.lrelu(self.offset_conv2[level](offset)) - else: - offset = self.lrelu(self.offset_conv2[level](torch.cat([offset, upsampled_offset], dim=1))) - offset = self.lrelu(self.offset_conv3[level](offset)) - - feat = self.dcn_pack[level](nbr_feat_l[i - 1], offset) - if i < 3: - feat = self.feat_conv[level](torch.cat([feat, upsampled_feat], dim=1)) - if i > 1: - feat = self.lrelu(feat) - - if i > 1: # upsample offset and features - # x2: when we upsample the offset, we should also enlarge - # the magnitude. - upsampled_offset = self.upsample(offset) * 2 - upsampled_feat = self.upsample(feat) - - # Cascading - offset = torch.cat([feat, ref_feat_l[0]], dim=1) - offset = self.lrelu(self.cas_offset_conv2(self.lrelu(self.cas_offset_conv1(offset)))) - feat = self.lrelu(self.cas_dcnpack(feat, offset)) - return feat - - -class TSAFusion(nn.Module): - """Temporal Spatial Attention (TSA) fusion module. - - Temporal: Calculate the correlation between center frame and - neighboring frames; - Spatial: It has 3 pyramid levels, the attention is similar to SFT. - (SFT: Recovering realistic texture in image super-resolution by deep - spatial feature transform.) - - Args: - num_feat (int): Channel number of middle features. Default: 64. - num_frame (int): Number of frames. Default: 5. - center_frame_idx (int): The index of center frame. Default: 2. - """ - - def __init__(self, num_feat=64, num_frame=5, center_frame_idx=2): - super(TSAFusion, self).__init__() - self.center_frame_idx = center_frame_idx - # temporal attention (before fusion conv) - self.temporal_attn1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.temporal_attn2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.feat_fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1) - - # spatial attention (after fusion conv) - self.max_pool = nn.MaxPool2d(3, stride=2, padding=1) - self.avg_pool = nn.AvgPool2d(3, stride=2, padding=1) - self.spatial_attn1 = nn.Conv2d(num_frame * num_feat, num_feat, 1) - self.spatial_attn2 = nn.Conv2d(num_feat * 2, num_feat, 1) - self.spatial_attn3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.spatial_attn4 = nn.Conv2d(num_feat, num_feat, 1) - self.spatial_attn5 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.spatial_attn_l1 = nn.Conv2d(num_feat, num_feat, 1) - self.spatial_attn_l2 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - self.spatial_attn_l3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.spatial_attn_add1 = nn.Conv2d(num_feat, num_feat, 1) - self.spatial_attn_add2 = nn.Conv2d(num_feat, num_feat, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) - - def forward(self, aligned_feat): - """ - Args: - aligned_feat (Tensor): Aligned features with shape (b, t, c, h, w). - - Returns: - Tensor: Features after TSA with the shape (b, c, h, w). - """ - b, t, c, h, w = aligned_feat.size() - # temporal attention - embedding_ref = self.temporal_attn1(aligned_feat[:, self.center_frame_idx, :, :, :].clone()) - embedding = self.temporal_attn2(aligned_feat.view(-1, c, h, w)) - embedding = embedding.view(b, t, -1, h, w) # (b, t, c, h, w) - - corr_l = [] # correlation list - for i in range(t): - emb_neighbor = embedding[:, i, :, :, :] - corr = torch.sum(emb_neighbor * embedding_ref, 1) # (b, h, w) - corr_l.append(corr.unsqueeze(1)) # (b, 1, h, w) - corr_prob = torch.sigmoid(torch.cat(corr_l, dim=1)) # (b, t, h, w) - corr_prob = corr_prob.unsqueeze(2).expand(b, t, c, h, w) - corr_prob = corr_prob.contiguous().view(b, -1, h, w) # (b, t*c, h, w) - aligned_feat = aligned_feat.view(b, -1, h, w) * corr_prob - - # fusion - feat = self.lrelu(self.feat_fusion(aligned_feat)) - - # spatial attention - attn = self.lrelu(self.spatial_attn1(aligned_feat)) - attn_max = self.max_pool(attn) - attn_avg = self.avg_pool(attn) - attn = self.lrelu(self.spatial_attn2(torch.cat([attn_max, attn_avg], dim=1))) - # pyramid levels - attn_level = self.lrelu(self.spatial_attn_l1(attn)) - attn_max = self.max_pool(attn_level) - attn_avg = self.avg_pool(attn_level) - attn_level = self.lrelu(self.spatial_attn_l2(torch.cat([attn_max, attn_avg], dim=1))) - attn_level = self.lrelu(self.spatial_attn_l3(attn_level)) - attn_level = self.upsample(attn_level) - - attn = self.lrelu(self.spatial_attn3(attn)) + attn_level - attn = self.lrelu(self.spatial_attn4(attn)) - attn = self.upsample(attn) - attn = self.spatial_attn5(attn) - attn_add = self.spatial_attn_add2(self.lrelu(self.spatial_attn_add1(attn))) - attn = torch.sigmoid(attn) - - # after initialization, * 2 makes (attn * 2) to be close to 1. - feat = feat * attn * 2 + attn_add - return feat - - -class PredeblurModule(nn.Module): - """Pre-dublur module. - - Args: - num_in_ch (int): Channel number of input image. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - hr_in (bool): Whether the input has high resolution. Default: False. - """ - - def __init__(self, num_in_ch=3, num_feat=64, hr_in=False): - super(PredeblurModule, self).__init__() - self.hr_in = hr_in - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - if self.hr_in: - # downsample x4 by stride conv - self.stride_conv_hr1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.stride_conv_hr2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - - # generate feature pyramid - self.stride_conv_l2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.stride_conv_l3 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - - self.resblock_l3 = ResidualBlockNoBN(num_feat=num_feat) - self.resblock_l2_1 = ResidualBlockNoBN(num_feat=num_feat) - self.resblock_l2_2 = ResidualBlockNoBN(num_feat=num_feat) - self.resblock_l1 = nn.ModuleList([ResidualBlockNoBN(num_feat=num_feat) for i in range(5)]) - - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - feat_l1 = self.lrelu(self.conv_first(x)) - if self.hr_in: - feat_l1 = self.lrelu(self.stride_conv_hr1(feat_l1)) - feat_l1 = self.lrelu(self.stride_conv_hr2(feat_l1)) - - # generate feature pyramid - feat_l2 = self.lrelu(self.stride_conv_l2(feat_l1)) - feat_l3 = self.lrelu(self.stride_conv_l3(feat_l2)) - - feat_l3 = self.upsample(self.resblock_l3(feat_l3)) - feat_l2 = self.resblock_l2_1(feat_l2) + feat_l3 - feat_l2 = self.upsample(self.resblock_l2_2(feat_l2)) - - for i in range(2): - feat_l1 = self.resblock_l1[i](feat_l1) - feat_l1 = feat_l1 + feat_l2 - for i in range(2, 5): - feat_l1 = self.resblock_l1[i](feat_l1) - return feat_l1 - - -@ARCH_REGISTRY.register() -class EDVR(nn.Module): - """EDVR network structure for video super-resolution. - - Now only support X4 upsampling factor. - - ``Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks`` - - Args: - num_in_ch (int): Channel number of input image. Default: 3. - num_out_ch (int): Channel number of output image. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - num_frame (int): Number of input frames. Default: 5. - deformable_groups (int): Deformable groups. Defaults: 8. - num_extract_block (int): Number of blocks for feature extraction. - Default: 5. - num_reconstruct_block (int): Number of blocks for reconstruction. - Default: 10. - center_frame_idx (int): The index of center frame. Frame counting from - 0. Default: Middle of input frames. - hr_in (bool): Whether the input has high resolution. Default: False. - with_predeblur (bool): Whether has predeblur module. - Default: False. - with_tsa (bool): Whether has TSA module. Default: True. - """ - - def __init__(self, - num_in_ch=3, - num_out_ch=3, - num_feat=64, - num_frame=5, - deformable_groups=8, - num_extract_block=5, - num_reconstruct_block=10, - center_frame_idx=None, - hr_in=False, - with_predeblur=False, - with_tsa=True): - super(EDVR, self).__init__() - if center_frame_idx is None: - self.center_frame_idx = num_frame // 2 - else: - self.center_frame_idx = center_frame_idx - self.hr_in = hr_in - self.with_predeblur = with_predeblur - self.with_tsa = with_tsa - - # extract features for each frame - if self.with_predeblur: - self.predeblur = PredeblurModule(num_feat=num_feat, hr_in=self.hr_in) - self.conv_1x1 = nn.Conv2d(num_feat, num_feat, 1, 1) - else: - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - - # extract pyramid features - self.feature_extraction = make_layer(ResidualBlockNoBN, num_extract_block, num_feat=num_feat) - self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - - # pcd and tsa module - self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=deformable_groups) - if self.with_tsa: - self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_frame, center_frame_idx=self.center_frame_idx) - else: - self.fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1) - - # reconstruction - self.reconstruction = make_layer(ResidualBlockNoBN, num_reconstruct_block, num_feat=num_feat) - # upsample - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) - self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1) - self.pixel_shuffle = nn.PixelShuffle(2) - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - b, t, c, h, w = x.size() - if self.hr_in: - assert h % 16 == 0 and w % 16 == 0, ('The height and width must be multiple of 16.') - else: - assert h % 4 == 0 and w % 4 == 0, ('The height and width must be multiple of 4.') - - x_center = x[:, self.center_frame_idx, :, :, :].contiguous() - - # extract features for each frame - # L1 - if self.with_predeblur: - feat_l1 = self.conv_1x1(self.predeblur(x.view(-1, c, h, w))) - if self.hr_in: - h, w = h // 4, w // 4 - else: - feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w))) - - feat_l1 = self.feature_extraction(feat_l1) - # L2 - feat_l2 = self.lrelu(self.conv_l2_1(feat_l1)) - feat_l2 = self.lrelu(self.conv_l2_2(feat_l2)) - # L3 - feat_l3 = self.lrelu(self.conv_l3_1(feat_l2)) - feat_l3 = self.lrelu(self.conv_l3_2(feat_l3)) - - feat_l1 = feat_l1.view(b, t, -1, h, w) - feat_l2 = feat_l2.view(b, t, -1, h // 2, w // 2) - feat_l3 = feat_l3.view(b, t, -1, h // 4, w // 4) - - # PCD alignment - ref_feat_l = [ # reference feature list - feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(), - feat_l3[:, self.center_frame_idx, :, :, :].clone() - ] - aligned_feat = [] - for i in range(t): - nbr_feat_l = [ # neighboring feature list - feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone() - ] - aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l)) - aligned_feat = torch.stack(aligned_feat, dim=1) # (b, t, c, h, w) - - if not self.with_tsa: - aligned_feat = aligned_feat.view(b, -1, h, w) - feat = self.fusion(aligned_feat) - - out = self.reconstruction(feat) - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - out = self.lrelu(self.conv_hr(out)) - out = self.conv_last(out) - if self.hr_in: - base = x_center - else: - base = F.interpolate(x_center, scale_factor=4, mode='bilinear', align_corners=False) - out += base - return out diff --git a/basicsr/archs/hifacegan_arch.py b/basicsr/archs/hifacegan_arch.py deleted file mode 100644 index 7c5b2fbe105144c954c7fbff8ee4cfc4f646abf2..0000000000000000000000000000000000000000 --- a/basicsr/archs/hifacegan_arch.py +++ /dev/null @@ -1,260 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .hifacegan_util import BaseNetwork, LIPEncoder, SPADEResnetBlock, get_nonspade_norm_layer - - -class SPADEGenerator(BaseNetwork): - """Generator with SPADEResBlock""" - - def __init__(self, - num_in_ch=3, - num_feat=64, - use_vae=False, - z_dim=256, - crop_size=512, - norm_g='spectralspadesyncbatch3x3', - is_train=True, - init_train_phase=3): # progressive training disabled - super().__init__() - self.nf = num_feat - self.input_nc = num_in_ch - self.is_train = is_train - self.train_phase = init_train_phase - - self.scale_ratio = 5 # hardcoded now - self.sw = crop_size // (2**self.scale_ratio) - self.sh = self.sw # 20210519: By default use square image, aspect_ratio = 1.0 - - if use_vae: - # In case of VAE, we will sample from random z vector - self.fc = nn.Linear(z_dim, 16 * self.nf * self.sw * self.sh) - else: - # Otherwise, we make the network deterministic by starting with - # downsampled segmentation map instead of random z - self.fc = nn.Conv2d(num_in_ch, 16 * self.nf, 3, padding=1) - - self.head_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) - - self.g_middle_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) - self.g_middle_1 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) - - self.ups = nn.ModuleList([ - SPADEResnetBlock(16 * self.nf, 8 * self.nf, norm_g), - SPADEResnetBlock(8 * self.nf, 4 * self.nf, norm_g), - SPADEResnetBlock(4 * self.nf, 2 * self.nf, norm_g), - SPADEResnetBlock(2 * self.nf, 1 * self.nf, norm_g) - ]) - - self.to_rgbs = nn.ModuleList([ - nn.Conv2d(8 * self.nf, 3, 3, padding=1), - nn.Conv2d(4 * self.nf, 3, 3, padding=1), - nn.Conv2d(2 * self.nf, 3, 3, padding=1), - nn.Conv2d(1 * self.nf, 3, 3, padding=1) - ]) - - self.up = nn.Upsample(scale_factor=2) - - def encode(self, input_tensor): - """ - Encode input_tensor into feature maps, can be overridden in derived classes - Default: nearest downsampling of 2**5 = 32 times - """ - h, w = input_tensor.size()[-2:] - sh, sw = h // 2**self.scale_ratio, w // 2**self.scale_ratio - x = F.interpolate(input_tensor, size=(sh, sw)) - return self.fc(x) - - def forward(self, x): - # In oroginal SPADE, seg means a segmentation map, but here we use x instead. - seg = x - - x = self.encode(x) - x = self.head_0(x, seg) - - x = self.up(x) - x = self.g_middle_0(x, seg) - x = self.g_middle_1(x, seg) - - if self.is_train: - phase = self.train_phase + 1 - else: - phase = len(self.to_rgbs) - - for i in range(phase): - x = self.up(x) - x = self.ups[i](x, seg) - - x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1)) - x = torch.tanh(x) - - return x - - def mixed_guidance_forward(self, input_x, seg=None, n=0, mode='progressive'): - """ - A helper class for subspace visualization. Input and seg are different images. - For the first n levels (including encoder) we use input, for the rest we use seg. - - If mode = 'progressive', the output's like: AAABBB - If mode = 'one_plug', the output's like: AAABAA - If mode = 'one_ablate', the output's like: BBBABB - """ - - if seg is None: - return self.forward(input_x) - - if self.is_train: - phase = self.train_phase + 1 - else: - phase = len(self.to_rgbs) - - if mode == 'progressive': - n = max(min(n, 4 + phase), 0) - guide_list = [input_x] * n + [seg] * (4 + phase - n) - elif mode == 'one_plug': - n = max(min(n, 4 + phase - 1), 0) - guide_list = [seg] * (4 + phase) - guide_list[n] = input_x - elif mode == 'one_ablate': - if n > 3 + phase: - return self.forward(input_x) - guide_list = [input_x] * (4 + phase) - guide_list[n] = seg - - x = self.encode(guide_list[0]) - x = self.head_0(x, guide_list[1]) - - x = self.up(x) - x = self.g_middle_0(x, guide_list[2]) - x = self.g_middle_1(x, guide_list[3]) - - for i in range(phase): - x = self.up(x) - x = self.ups[i](x, guide_list[4 + i]) - - x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1)) - x = torch.tanh(x) - - return x - - -@ARCH_REGISTRY.register() -class HiFaceGAN(SPADEGenerator): - """ - HiFaceGAN: SPADEGenerator with a learnable feature encoder - Current encoder design: LIPEncoder - """ - - def __init__(self, - num_in_ch=3, - num_feat=64, - use_vae=False, - z_dim=256, - crop_size=512, - norm_g='spectralspadesyncbatch3x3', - is_train=True, - init_train_phase=3): - super().__init__(num_in_ch, num_feat, use_vae, z_dim, crop_size, norm_g, is_train, init_train_phase) - self.lip_encoder = LIPEncoder(num_in_ch, num_feat, self.sw, self.sh, self.scale_ratio) - - def encode(self, input_tensor): - return self.lip_encoder(input_tensor) - - -@ARCH_REGISTRY.register() -class HiFaceGANDiscriminator(BaseNetwork): - """ - Inspired by pix2pixHD multiscale discriminator. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_out_ch (int): Channel number of outputs. Default: 3. - conditional_d (bool): Whether use conditional discriminator. - Default: True. - num_d (int): Number of Multiscale discriminators. Default: 3. - n_layers_d (int): Number of downsample layers in each D. Default: 4. - num_feat (int): Channel number of base intermediate features. - Default: 64. - norm_d (str): String to determine normalization layers in D. - Choices: [spectral][instance/batch/syncbatch] - Default: 'spectralinstance'. - keep_features (bool): Keep intermediate features for matching loss, etc. - Default: True. - """ - - def __init__(self, - num_in_ch=3, - num_out_ch=3, - conditional_d=True, - num_d=2, - n_layers_d=4, - num_feat=64, - norm_d='spectralinstance', - keep_features=True): - super().__init__() - self.num_d = num_d - - input_nc = num_in_ch - if conditional_d: - input_nc += num_out_ch - - for i in range(num_d): - subnet_d = NLayerDiscriminator(input_nc, n_layers_d, num_feat, norm_d, keep_features) - self.add_module(f'discriminator_{i}', subnet_d) - - def downsample(self, x): - return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False) - - # Returns list of lists of discriminator outputs. - # The final result is of size opt.num_d x opt.n_layers_D - def forward(self, x): - result = [] - for _, _net_d in self.named_children(): - out = _net_d(x) - result.append(out) - x = self.downsample(x) - - return result - - -class NLayerDiscriminator(BaseNetwork): - """Defines the PatchGAN discriminator with the specified arguments.""" - - def __init__(self, input_nc, n_layers_d, num_feat, norm_d, keep_features): - super().__init__() - kw = 4 - padw = int(np.ceil((kw - 1.0) / 2)) - nf = num_feat - self.keep_features = keep_features - - norm_layer = get_nonspade_norm_layer(norm_d) - sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, False)]] - - for n in range(1, n_layers_d): - nf_prev = nf - nf = min(nf * 2, 512) - stride = 1 if n == n_layers_d - 1 else 2 - sequence += [[ - norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=stride, padding=padw)), - nn.LeakyReLU(0.2, False) - ]] - - sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] - - # We divide the layers into groups to extract intermediate layer outputs - for n in range(len(sequence)): - self.add_module('model' + str(n), nn.Sequential(*sequence[n])) - - def forward(self, x): - results = [x] - for submodel in self.children(): - intermediate_output = submodel(results[-1]) - results.append(intermediate_output) - - if self.keep_features: - return results[1:] - else: - return results[-1] diff --git a/basicsr/archs/hifacegan_util.py b/basicsr/archs/hifacegan_util.py deleted file mode 100644 index b63b928504f86b4c7a9e7403766e5e4578f3414c..0000000000000000000000000000000000000000 --- a/basicsr/archs/hifacegan_util.py +++ /dev/null @@ -1,255 +0,0 @@ -import re -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn import init -# Warning: spectral norm could be buggy -# under eval mode and multi-GPU inference -# A workaround is sticking to single-GPU inference and train mode -from torch.nn.utils import spectral_norm - - -class SPADE(nn.Module): - - def __init__(self, config_text, norm_nc, label_nc): - super().__init__() - - assert config_text.startswith('spade') - parsed = re.search('spade(\\D+)(\\d)x\\d', config_text) - param_free_norm_type = str(parsed.group(1)) - ks = int(parsed.group(2)) - - if param_free_norm_type == 'instance': - self.param_free_norm = nn.InstanceNorm2d(norm_nc) - elif param_free_norm_type == 'syncbatch': - print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') - self.param_free_norm = nn.InstanceNorm2d(norm_nc) - elif param_free_norm_type == 'batch': - self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False) - else: - raise ValueError(f'{param_free_norm_type} is not a recognized param-free norm type in SPADE') - - # The dimension of the intermediate embedding space. Yes, hardcoded. - nhidden = 128 if norm_nc > 128 else norm_nc - - pw = ks // 2 - self.mlp_shared = nn.Sequential(nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), nn.ReLU()) - self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) - self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) - - def forward(self, x, segmap): - - # Part 1. generate parameter-free normalized activations - normalized = self.param_free_norm(x) - - # Part 2. produce scaling and bias conditioned on semantic map - segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') - actv = self.mlp_shared(segmap) - gamma = self.mlp_gamma(actv) - beta = self.mlp_beta(actv) - - # apply scale and bias - out = normalized * gamma + beta - - return out - - -class SPADEResnetBlock(nn.Module): - """ - ResNet block that uses SPADE. It differs from the ResNet block of pix2pixHD in that - it takes in the segmentation map as input, learns the skip connection if necessary, - and applies normalization first and then convolution. - This architecture seemed like a standard architecture for unconditional or - class-conditional GAN architecture using residual block. - The code was inspired from https://github.com/LMescheder/GAN_stability. - """ - - def __init__(self, fin, fout, norm_g='spectralspadesyncbatch3x3', semantic_nc=3): - super().__init__() - # Attributes - self.learned_shortcut = (fin != fout) - fmiddle = min(fin, fout) - - # create conv layers - self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) - self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) - if self.learned_shortcut: - self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) - - # apply spectral norm if specified - if 'spectral' in norm_g: - self.conv_0 = spectral_norm(self.conv_0) - self.conv_1 = spectral_norm(self.conv_1) - if self.learned_shortcut: - self.conv_s = spectral_norm(self.conv_s) - - # define normalization layers - spade_config_str = norm_g.replace('spectral', '') - self.norm_0 = SPADE(spade_config_str, fin, semantic_nc) - self.norm_1 = SPADE(spade_config_str, fmiddle, semantic_nc) - if self.learned_shortcut: - self.norm_s = SPADE(spade_config_str, fin, semantic_nc) - - # note the resnet block with SPADE also takes in |seg|, - # the semantic segmentation map as input - def forward(self, x, seg): - x_s = self.shortcut(x, seg) - dx = self.conv_0(self.act(self.norm_0(x, seg))) - dx = self.conv_1(self.act(self.norm_1(dx, seg))) - out = x_s + dx - return out - - def shortcut(self, x, seg): - if self.learned_shortcut: - x_s = self.conv_s(self.norm_s(x, seg)) - else: - x_s = x - return x_s - - def act(self, x): - return F.leaky_relu(x, 2e-1) - - -class BaseNetwork(nn.Module): - """ A basis for hifacegan archs with custom initialization """ - - def init_weights(self, init_type='normal', gain=0.02): - - def init_func(m): - classname = m.__class__.__name__ - if classname.find('BatchNorm2d') != -1: - if hasattr(m, 'weight') and m.weight is not None: - init.normal_(m.weight.data, 1.0, gain) - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): - if init_type == 'normal': - init.normal_(m.weight.data, 0.0, gain) - elif init_type == 'xavier': - init.xavier_normal_(m.weight.data, gain=gain) - elif init_type == 'xavier_uniform': - init.xavier_uniform_(m.weight.data, gain=1.0) - elif init_type == 'kaiming': - init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') - elif init_type == 'orthogonal': - init.orthogonal_(m.weight.data, gain=gain) - elif init_type == 'none': # uses pytorch's default init method - m.reset_parameters() - else: - raise NotImplementedError(f'initialization method [{init_type}] is not implemented') - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - - self.apply(init_func) - - # propagate to children - for m in self.children(): - if hasattr(m, 'init_weights'): - m.init_weights(init_type, gain) - - def forward(self, x): - pass - - -def lip2d(x, logit, kernel=3, stride=2, padding=1): - weight = logit.exp() - return F.avg_pool2d(x * weight, kernel, stride, padding) / F.avg_pool2d(weight, kernel, stride, padding) - - -class SoftGate(nn.Module): - COEFF = 12.0 - - def forward(self, x): - return torch.sigmoid(x).mul(self.COEFF) - - -class SimplifiedLIP(nn.Module): - - def __init__(self, channels): - super(SimplifiedLIP, self).__init__() - self.logit = nn.Sequential( - nn.Conv2d(channels, channels, 3, padding=1, bias=False), nn.InstanceNorm2d(channels, affine=True), - SoftGate()) - - def init_layer(self): - self.logit[0].weight.data.fill_(0.0) - - def forward(self, x): - frac = lip2d(x, self.logit(x)) - return frac - - -class LIPEncoder(BaseNetwork): - """Local Importance-based Pooling (Ziteng Gao et.al.,ICCV 2019)""" - - def __init__(self, input_nc, ngf, sw, sh, n_2xdown, norm_layer=nn.InstanceNorm2d): - super().__init__() - self.sw = sw - self.sh = sh - self.max_ratio = 16 - # 20200310: Several Convolution (stride 1) + LIP blocks, 4 fold - kw = 3 - pw = (kw - 1) // 2 - - model = [ - nn.Conv2d(input_nc, ngf, kw, stride=1, padding=pw, bias=False), - norm_layer(ngf), - nn.ReLU(), - ] - cur_ratio = 1 - for i in range(n_2xdown): - next_ratio = min(cur_ratio * 2, self.max_ratio) - model += [ - SimplifiedLIP(ngf * cur_ratio), - nn.Conv2d(ngf * cur_ratio, ngf * next_ratio, kw, stride=1, padding=pw), - norm_layer(ngf * next_ratio), - ] - cur_ratio = next_ratio - if i < n_2xdown - 1: - model += [nn.ReLU(inplace=True)] - - self.model = nn.Sequential(*model) - - def forward(self, x): - return self.model(x) - - -def get_nonspade_norm_layer(norm_type='instance'): - # helper function to get # output channels of the previous layer - def get_out_channel(layer): - if hasattr(layer, 'out_channels'): - return getattr(layer, 'out_channels') - return layer.weight.size(0) - - # this function will be returned - def add_norm_layer(layer): - nonlocal norm_type - if norm_type.startswith('spectral'): - layer = spectral_norm(layer) - subnorm_type = norm_type[len('spectral'):] - - if subnorm_type == 'none' or len(subnorm_type) == 0: - return layer - - # remove bias in the previous layer, which is meaningless - # since it has no effect after normalization - if getattr(layer, 'bias', None) is not None: - delattr(layer, 'bias') - layer.register_parameter('bias', None) - - if subnorm_type == 'batch': - norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True) - elif subnorm_type == 'sync_batch': - print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') - # norm_layer = SynchronizedBatchNorm2d( - # get_out_channel(layer), affine=True) - norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) - elif subnorm_type == 'instance': - norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) - else: - raise ValueError(f'normalization layer {subnorm_type} is not recognized') - - return nn.Sequential(layer, norm_layer) - - print('This is a legacy from nvlabs/SPADE, and will be removed in future versions.') - return add_norm_layer diff --git a/basicsr/archs/inception.py b/basicsr/archs/inception.py deleted file mode 100644 index 7db2b420e3ebddb474cf6343b135f1be2c92cc24..0000000000000000000000000000000000000000 --- a/basicsr/archs/inception.py +++ /dev/null @@ -1,307 +0,0 @@ -# Modified from https://github.com/mseitzer/pytorch-fid/blob/master/pytorch_fid/inception.py # noqa: E501 -# For FID metric - -import os -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.model_zoo import load_url -from torchvision import models - -# Inception weights ported to Pytorch from -# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 -LOCAL_FID_WEIGHTS = 'experiments/pretrained_models/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 - - -class InceptionV3(nn.Module): - """Pretrained InceptionV3 network returning feature maps""" - - # Index of default block of inception to return, - # corresponds to output of final average pooling - DEFAULT_BLOCK_INDEX = 3 - - # Maps feature dimensionality to their output blocks indices - BLOCK_INDEX_BY_DIM = { - 64: 0, # First max pooling features - 192: 1, # Second max pooling features - 768: 2, # Pre-aux classifier features - 2048: 3 # Final average pooling features - } - - def __init__(self, - output_blocks=(DEFAULT_BLOCK_INDEX), - resize_input=True, - normalize_input=True, - requires_grad=False, - use_fid_inception=True): - """Build pretrained InceptionV3. - - Args: - output_blocks (list[int]): Indices of blocks to return features of. - Possible values are: - - 0: corresponds to output of first max pooling - - 1: corresponds to output of second max pooling - - 2: corresponds to output which is fed to aux classifier - - 3: corresponds to output of final average pooling - resize_input (bool): If true, bilinearly resizes input to width and - height 299 before feeding input to model. As the network - without fully connected layers is fully convolutional, it - should be able to handle inputs of arbitrary size, so resizing - might not be strictly needed. Default: True. - normalize_input (bool): If true, scales the input from range (0, 1) - to the range the pretrained Inception network expects, - namely (-1, 1). Default: True. - requires_grad (bool): If true, parameters of the model require - gradients. Possibly useful for finetuning the network. - Default: False. - use_fid_inception (bool): If true, uses the pretrained Inception - model used in Tensorflow's FID implementation. - If false, uses the pretrained Inception model available in - torchvision. The FID Inception model has different weights - and a slightly different structure from torchvision's - Inception model. If you want to compute FID scores, you are - strongly advised to set this parameter to true to get - comparable results. Default: True. - """ - super(InceptionV3, self).__init__() - - self.resize_input = resize_input - self.normalize_input = normalize_input - self.output_blocks = sorted(output_blocks) - self.last_needed_block = max(output_blocks) - - assert self.last_needed_block <= 3, ('Last possible output block index is 3') - - self.blocks = nn.ModuleList() - - if use_fid_inception: - inception = fid_inception_v3() - else: - try: - inception = models.inception_v3(pretrained=True, init_weights=False) - except TypeError: - # pytorch < 1.5 does not have init_weights for inception_v3 - inception = models.inception_v3(pretrained=True) - - # Block 0: input to maxpool1 - block0 = [ - inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, - nn.MaxPool2d(kernel_size=3, stride=2) - ] - self.blocks.append(nn.Sequential(*block0)) - - # Block 1: maxpool1 to maxpool2 - if self.last_needed_block >= 1: - block1 = [inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2)] - self.blocks.append(nn.Sequential(*block1)) - - # Block 2: maxpool2 to aux classifier - if self.last_needed_block >= 2: - block2 = [ - inception.Mixed_5b, - inception.Mixed_5c, - inception.Mixed_5d, - inception.Mixed_6a, - inception.Mixed_6b, - inception.Mixed_6c, - inception.Mixed_6d, - inception.Mixed_6e, - ] - self.blocks.append(nn.Sequential(*block2)) - - # Block 3: aux classifier to final avgpool - if self.last_needed_block >= 3: - block3 = [ - inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, - nn.AdaptiveAvgPool2d(output_size=(1, 1)) - ] - self.blocks.append(nn.Sequential(*block3)) - - for param in self.parameters(): - param.requires_grad = requires_grad - - def forward(self, x): - """Get Inception feature maps. - - Args: - x (Tensor): Input tensor of shape (b, 3, h, w). - Values are expected to be in range (-1, 1). You can also input - (0, 1) with setting normalize_input = True. - - Returns: - list[Tensor]: Corresponding to the selected output block, sorted - ascending by index. - """ - output = [] - - if self.resize_input: - x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False) - - if self.normalize_input: - x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) - - for idx, block in enumerate(self.blocks): - x = block(x) - if idx in self.output_blocks: - output.append(x) - - if idx == self.last_needed_block: - break - - return output - - -def fid_inception_v3(): - """Build pretrained Inception model for FID computation. - - The Inception model for FID computation uses a different set of weights - and has a slightly different structure than torchvision's Inception. - - This method first constructs torchvision's Inception and then patches the - necessary parts that are different in the FID Inception model. - """ - try: - inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False, init_weights=False) - except TypeError: - # pytorch < 1.5 does not have init_weights for inception_v3 - inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False) - - inception.Mixed_5b = FIDInceptionA(192, pool_features=32) - inception.Mixed_5c = FIDInceptionA(256, pool_features=64) - inception.Mixed_5d = FIDInceptionA(288, pool_features=64) - inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) - inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) - inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) - inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) - inception.Mixed_7b = FIDInceptionE_1(1280) - inception.Mixed_7c = FIDInceptionE_2(2048) - - if os.path.exists(LOCAL_FID_WEIGHTS): - state_dict = torch.load(LOCAL_FID_WEIGHTS, map_location=lambda storage, loc: storage) - else: - state_dict = load_url(FID_WEIGHTS_URL, progress=True) - - inception.load_state_dict(state_dict) - return inception - - -class FIDInceptionA(models.inception.InceptionA): - """InceptionA block patched for FID computation""" - - def __init__(self, in_channels, pool_features): - super(FIDInceptionA, self).__init__(in_channels, pool_features) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch5x5 = self.branch5x5_1(x) - branch5x5 = self.branch5x5_2(branch5x5) - - branch3x3dbl = self.branch3x3dbl_1(x) - branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) - branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) - - # Patch: Tensorflow's average pool does not use the padded zero's in - # its average calculation - branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] - return torch.cat(outputs, 1) - - -class FIDInceptionC(models.inception.InceptionC): - """InceptionC block patched for FID computation""" - - def __init__(self, in_channels, channels_7x7): - super(FIDInceptionC, self).__init__(in_channels, channels_7x7) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch7x7 = self.branch7x7_1(x) - branch7x7 = self.branch7x7_2(branch7x7) - branch7x7 = self.branch7x7_3(branch7x7) - - branch7x7dbl = self.branch7x7dbl_1(x) - branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) - branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) - branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) - branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) - - # Patch: Tensorflow's average pool does not use the padded zero's in - # its average calculation - branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] - return torch.cat(outputs, 1) - - -class FIDInceptionE_1(models.inception.InceptionE): - """First InceptionE block patched for FID computation""" - - def __init__(self, in_channels): - super(FIDInceptionE_1, self).__init__(in_channels) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch3x3 = self.branch3x3_1(x) - branch3x3 = [ - self.branch3x3_2a(branch3x3), - self.branch3x3_2b(branch3x3), - ] - branch3x3 = torch.cat(branch3x3, 1) - - branch3x3dbl = self.branch3x3dbl_1(x) - branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) - branch3x3dbl = [ - self.branch3x3dbl_3a(branch3x3dbl), - self.branch3x3dbl_3b(branch3x3dbl), - ] - branch3x3dbl = torch.cat(branch3x3dbl, 1) - - # Patch: Tensorflow's average pool does not use the padded zero's in - # its average calculation - branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] - return torch.cat(outputs, 1) - - -class FIDInceptionE_2(models.inception.InceptionE): - """Second InceptionE block patched for FID computation""" - - def __init__(self, in_channels): - super(FIDInceptionE_2, self).__init__(in_channels) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch3x3 = self.branch3x3_1(x) - branch3x3 = [ - self.branch3x3_2a(branch3x3), - self.branch3x3_2b(branch3x3), - ] - branch3x3 = torch.cat(branch3x3, 1) - - branch3x3dbl = self.branch3x3dbl_1(x) - branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) - branch3x3dbl = [ - self.branch3x3dbl_3a(branch3x3dbl), - self.branch3x3dbl_3b(branch3x3dbl), - ] - branch3x3dbl = torch.cat(branch3x3dbl, 1) - - # Patch: The FID Inception model uses max pooling instead of average - # pooling. This is likely an error in this specific Inception - # implementation, as other Inception models use average pooling here - # (which matches the description in the paper). - branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] - return torch.cat(outputs, 1) diff --git a/basicsr/archs/rcan_arch.py b/basicsr/archs/rcan_arch.py deleted file mode 100644 index 9079e2cdc52bb6e3f3b2b0a26570027a1fb89662..0000000000000000000000000000000000000000 --- a/basicsr/archs/rcan_arch.py +++ /dev/null @@ -1,135 +0,0 @@ -import torch -from torch import nn as nn - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import Upsample, make_layer - - -class ChannelAttention(nn.Module): - """Channel attention used in RCAN. - - Args: - num_feat (int): Channel number of intermediate features. - squeeze_factor (int): Channel squeeze factor. Default: 16. - """ - - def __init__(self, num_feat, squeeze_factor=16): - super(ChannelAttention, self).__init__() - self.attention = nn.Sequential( - nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), - nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid()) - - def forward(self, x): - y = self.attention(x) - return x * y - - -class RCAB(nn.Module): - """Residual Channel Attention Block (RCAB) used in RCAN. - - Args: - num_feat (int): Channel number of intermediate features. - squeeze_factor (int): Channel squeeze factor. Default: 16. - res_scale (float): Scale the residual. Default: 1. - """ - - def __init__(self, num_feat, squeeze_factor=16, res_scale=1): - super(RCAB, self).__init__() - self.res_scale = res_scale - - self.rcab = nn.Sequential( - nn.Conv2d(num_feat, num_feat, 3, 1, 1), nn.ReLU(True), nn.Conv2d(num_feat, num_feat, 3, 1, 1), - ChannelAttention(num_feat, squeeze_factor)) - - def forward(self, x): - res = self.rcab(x) * self.res_scale - return res + x - - -class ResidualGroup(nn.Module): - """Residual Group of RCAB. - - Args: - num_feat (int): Channel number of intermediate features. - num_block (int): Block number in the body network. - squeeze_factor (int): Channel squeeze factor. Default: 16. - res_scale (float): Scale the residual. Default: 1. - """ - - def __init__(self, num_feat, num_block, squeeze_factor=16, res_scale=1): - super(ResidualGroup, self).__init__() - - self.residual_group = make_layer( - RCAB, num_block, num_feat=num_feat, squeeze_factor=squeeze_factor, res_scale=res_scale) - self.conv = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - - def forward(self, x): - res = self.conv(self.residual_group(x)) - return res + x - - -@ARCH_REGISTRY.register() -class RCAN(nn.Module): - """Residual Channel Attention Networks. - - ``Paper: Image Super-Resolution Using Very Deep Residual Channel Attention Networks`` - - Reference: https://github.com/yulunzhang/RCAN - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64. - num_group (int): Number of ResidualGroup. Default: 10. - num_block (int): Number of RCAB in ResidualGroup. Default: 16. - squeeze_factor (int): Channel squeeze factor. Default: 16. - upscale (int): Upsampling factor. Support 2^n and 3. - Default: 4. - res_scale (float): Used to scale the residual in residual block. - Default: 1. - img_range (float): Image range. Default: 255. - rgb_mean (tuple[float]): Image mean in RGB orders. - Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. - """ - - def __init__(self, - num_in_ch, - num_out_ch, - num_feat=64, - num_group=10, - num_block=16, - squeeze_factor=16, - upscale=4, - res_scale=1, - img_range=255., - rgb_mean=(0.4488, 0.4371, 0.4040)): - super(RCAN, self).__init__() - - self.img_range = img_range - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer( - ResidualGroup, - num_group, - num_feat=num_feat, - num_block=num_block, - squeeze_factor=squeeze_factor, - res_scale=res_scale) - self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - def forward(self, x): - self.mean = self.mean.type_as(x) - - x = (x - self.mean) * self.img_range - x = self.conv_first(x) - res = self.conv_after_body(self.body(x)) - res += x - - x = self.conv_last(self.upsample(res)) - x = x / self.img_range + self.mean - - return x diff --git a/basicsr/archs/ridnet_arch.py b/basicsr/archs/ridnet_arch.py deleted file mode 100644 index 029cebec7540cf8688662044a6c6790c12b9efbd..0000000000000000000000000000000000000000 --- a/basicsr/archs/ridnet_arch.py +++ /dev/null @@ -1,180 +0,0 @@ -import torch -import torch.nn as nn - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import ResidualBlockNoBN, make_layer - - -class MeanShift(nn.Conv2d): - """ Data normalization with mean and std. - - Args: - rgb_range (int): Maximum value of RGB. - rgb_mean (list[float]): Mean for RGB channels. - rgb_std (list[float]): Std for RGB channels. - sign (int): For subtraction, sign is -1, for addition, sign is 1. - Default: -1. - requires_grad (bool): Whether to update the self.weight and self.bias. - Default: True. - """ - - def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1, requires_grad=True): - super(MeanShift, self).__init__(3, 3, kernel_size=1) - std = torch.Tensor(rgb_std) - self.weight.data = torch.eye(3).view(3, 3, 1, 1) - self.weight.data.div_(std.view(3, 1, 1, 1)) - self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) - self.bias.data.div_(std) - self.requires_grad = requires_grad - - -class EResidualBlockNoBN(nn.Module): - """Enhanced Residual block without BN. - - There are three convolution layers in residual branch. - """ - - def __init__(self, in_channels, out_channels): - super(EResidualBlockNoBN, self).__init__() - - self.body = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, 1, 1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 3, 1, 1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 1, 1, 0), - ) - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - out = self.body(x) - out = self.relu(out + x) - return out - - -class MergeRun(nn.Module): - """ Merge-and-run unit. - - This unit contains two branches with different dilated convolutions, - followed by a convolution to process the concatenated features. - - Paper: Real Image Denoising with Feature Attention - Ref git repo: https://github.com/saeed-anwar/RIDNet - """ - - def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1): - super(MergeRun, self).__init__() - - self.dilation1 = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, kernel_size, stride, 2, 2), nn.ReLU(inplace=True)) - self.dilation2 = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size, stride, 3, 3), nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, kernel_size, stride, 4, 4), nn.ReLU(inplace=True)) - - self.aggregation = nn.Sequential( - nn.Conv2d(out_channels * 2, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True)) - - def forward(self, x): - dilation1 = self.dilation1(x) - dilation2 = self.dilation2(x) - out = torch.cat([dilation1, dilation2], dim=1) - out = self.aggregation(out) - out = out + x - return out - - -class ChannelAttention(nn.Module): - """Channel attention. - - Args: - num_feat (int): Channel number of intermediate features. - squeeze_factor (int): Channel squeeze factor. Default: - """ - - def __init__(self, mid_channels, squeeze_factor=16): - super(ChannelAttention, self).__init__() - self.attention = nn.Sequential( - nn.AdaptiveAvgPool2d(1), nn.Conv2d(mid_channels, mid_channels // squeeze_factor, 1, padding=0), - nn.ReLU(inplace=True), nn.Conv2d(mid_channels // squeeze_factor, mid_channels, 1, padding=0), nn.Sigmoid()) - - def forward(self, x): - y = self.attention(x) - return x * y - - -class EAM(nn.Module): - """Enhancement attention modules (EAM) in RIDNet. - - This module contains a merge-and-run unit, a residual block, - an enhanced residual block and a feature attention unit. - - Attributes: - merge: The merge-and-run unit. - block1: The residual block. - block2: The enhanced residual block. - ca: The feature/channel attention unit. - """ - - def __init__(self, in_channels, mid_channels, out_channels): - super(EAM, self).__init__() - - self.merge = MergeRun(in_channels, mid_channels) - self.block1 = ResidualBlockNoBN(mid_channels) - self.block2 = EResidualBlockNoBN(mid_channels, out_channels) - self.ca = ChannelAttention(out_channels) - # The residual block in the paper contains a relu after addition. - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - out = self.merge(x) - out = self.relu(self.block1(out)) - out = self.block2(out) - out = self.ca(out) - return out - - -@ARCH_REGISTRY.register() -class RIDNet(nn.Module): - """RIDNet: Real Image Denoising with Feature Attention. - - Ref git repo: https://github.com/saeed-anwar/RIDNet - - Args: - in_channels (int): Channel number of inputs. - mid_channels (int): Channel number of EAM modules. - Default: 64. - out_channels (int): Channel number of outputs. - num_block (int): Number of EAM. Default: 4. - img_range (float): Image range. Default: 255. - rgb_mean (tuple[float]): Image mean in RGB orders. - Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. - """ - - def __init__(self, - in_channels, - mid_channels, - out_channels, - num_block=4, - img_range=255., - rgb_mean=(0.4488, 0.4371, 0.4040), - rgb_std=(1.0, 1.0, 1.0)): - super(RIDNet, self).__init__() - - self.sub_mean = MeanShift(img_range, rgb_mean, rgb_std) - self.add_mean = MeanShift(img_range, rgb_mean, rgb_std, 1) - - self.head = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) - self.body = make_layer( - EAM, num_block, in_channels=mid_channels, mid_channels=mid_channels, out_channels=mid_channels) - self.tail = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - res = self.sub_mean(x) - res = self.tail(self.body(self.relu(self.head(res)))) - res = self.add_mean(res) - - out = x + res - return out diff --git a/basicsr/archs/rrdbnet_arch.py b/basicsr/archs/rrdbnet_arch.py deleted file mode 100644 index 541a59369cf14892eaf93d50fb5da9f16053b07a..0000000000000000000000000000000000000000 --- a/basicsr/archs/rrdbnet_arch.py +++ /dev/null @@ -1,119 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import default_init_weights, make_layer, pixel_unshuffle - - -class ResidualDenseBlock(nn.Module): - """Residual Dense Block. - - Used in RRDB block in ESRGAN. - - Args: - num_feat (int): Channel number of intermediate features. - num_grow_ch (int): Channels for each growth. - """ - - def __init__(self, num_feat=64, num_grow_ch=32): - super(ResidualDenseBlock, self).__init__() - self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) - self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - # initialization - default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) - - def forward(self, x): - x1 = self.lrelu(self.conv1(x)) - x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) - x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) - x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - # Empirically, we use 0.2 to scale the residual for better performance - return x5 * 0.2 + x - - -class RRDB(nn.Module): - """Residual in Residual Dense Block. - - Used in RRDB-Net in ESRGAN. - - Args: - num_feat (int): Channel number of intermediate features. - num_grow_ch (int): Channels for each growth. - """ - - def __init__(self, num_feat, num_grow_ch=32): - super(RRDB, self).__init__() - self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) - self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) - self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) - - def forward(self, x): - out = self.rdb1(x) - out = self.rdb2(out) - out = self.rdb3(out) - # Empirically, we use 0.2 to scale the residual for better performance - return out * 0.2 + x - - -@ARCH_REGISTRY.register() -class RRDBNet(nn.Module): - """Networks consisting of Residual in Residual Dense Block, which is used - in ESRGAN. - - ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. - - We extend ESRGAN for scale x2 and scale x1. - Note: This is one option for scale 1, scale 2 in RRDBNet. - We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size - and enlarge the channel size before feeding inputs into the main ESRGAN architecture. - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64 - num_block (int): Block number in the trunk network. Defaults: 23 - num_grow_ch (int): Channels for each growth. Default: 32. - """ - - def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32): - super(RRDBNet, self).__init__() - self.scale = scale - if scale == 2: - num_in_ch = num_in_ch * 4 - elif scale == 1: - num_in_ch = num_in_ch * 16 - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch) - self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - # upsample - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - if self.scale == 2: - feat = pixel_unshuffle(x, scale=2) - elif self.scale == 1: - feat = pixel_unshuffle(x, scale=4) - else: - feat = x - feat = self.conv_first(feat) - body_feat = self.conv_body(self.body(feat)) - feat = feat + body_feat - # upsample - feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) - feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) - out = self.conv_last(self.lrelu(self.conv_hr(feat))) - return out diff --git a/basicsr/archs/spynet_arch.py b/basicsr/archs/spynet_arch.py deleted file mode 100644 index 2639e77a00b31f64648b432ecb70184a4c9e34be..0000000000000000000000000000000000000000 --- a/basicsr/archs/spynet_arch.py +++ /dev/null @@ -1,96 +0,0 @@ -import math -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import flow_warp - - -class BasicModule(nn.Module): - """Basic Module for SpyNet. - """ - - def __init__(self): - super(BasicModule, self).__init__() - - self.basic_module = nn.Sequential( - nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3)) - - def forward(self, tensor_input): - return self.basic_module(tensor_input) - - -@ARCH_REGISTRY.register() -class SpyNet(nn.Module): - """SpyNet architecture. - - Args: - load_path (str): path for pretrained SpyNet. Default: None. - """ - - def __init__(self, load_path=None): - super(SpyNet, self).__init__() - self.basic_module = nn.ModuleList([BasicModule() for _ in range(6)]) - if load_path: - self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) - - self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) - self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) - - def preprocess(self, tensor_input): - tensor_output = (tensor_input - self.mean) / self.std - return tensor_output - - def process(self, ref, supp): - flow = [] - - ref = [self.preprocess(ref)] - supp = [self.preprocess(supp)] - - for level in range(5): - ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False)) - supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False)) - - flow = ref[0].new_zeros( - [ref[0].size(0), 2, - int(math.floor(ref[0].size(2) / 2.0)), - int(math.floor(ref[0].size(3) / 2.0))]) - - for level in range(len(ref)): - upsampled_flow = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0 - - if upsampled_flow.size(2) != ref[level].size(2): - upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 0, 0, 1], mode='replicate') - if upsampled_flow.size(3) != ref[level].size(3): - upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 1, 0, 0], mode='replicate') - - flow = self.basic_module[level](torch.cat([ - ref[level], - flow_warp( - supp[level], upsampled_flow.permute(0, 2, 3, 1), interp_mode='bilinear', padding_mode='border'), - upsampled_flow - ], 1)) + upsampled_flow - - return flow - - def forward(self, ref, supp): - assert ref.size() == supp.size() - - h, w = ref.size(2), ref.size(3) - w_floor = math.floor(math.ceil(w / 32.0) * 32.0) - h_floor = math.floor(math.ceil(h / 32.0) * 32.0) - - ref = F.interpolate(input=ref, size=(h_floor, w_floor), mode='bilinear', align_corners=False) - supp = F.interpolate(input=supp, size=(h_floor, w_floor), mode='bilinear', align_corners=False) - - flow = F.interpolate(input=self.process(ref, supp), size=(h, w), mode='bilinear', align_corners=False) - - flow[:, 0, :, :] *= float(w) / float(w_floor) - flow[:, 1, :, :] *= float(h) / float(h_floor) - - return flow diff --git a/basicsr/archs/srresnet_arch.py b/basicsr/archs/srresnet_arch.py deleted file mode 100644 index c8739bd69f6da32aad7e80e31507c98d981e5a5e..0000000000000000000000000000000000000000 --- a/basicsr/archs/srresnet_arch.py +++ /dev/null @@ -1,65 +0,0 @@ -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import ResidualBlockNoBN, default_init_weights, make_layer - - -@ARCH_REGISTRY.register() -class MSRResNet(nn.Module): - """Modified SRResNet. - - A compacted version modified from SRResNet in - "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" - It uses residual blocks without BN, similar to EDSR. - Currently, it supports x2, x3 and x4 upsampling scale factor. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_out_ch (int): Channel number of outputs. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - num_block (int): Block number in the body network. Default: 16. - upscale (int): Upsampling factor. Support x2, x3 and x4. Default: 4. - """ - - def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4): - super(MSRResNet, self).__init__() - self.upscale = upscale - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat) - - # upsampling - if self.upscale in [2, 3]: - self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1) - self.pixel_shuffle = nn.PixelShuffle(self.upscale) - elif self.upscale == 4: - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) - self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) - self.pixel_shuffle = nn.PixelShuffle(2) - - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - # initialization - default_init_weights([self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1) - if self.upscale == 4: - default_init_weights(self.upconv2, 0.1) - - def forward(self, x): - feat = self.lrelu(self.conv_first(x)) - out = self.body(feat) - - if self.upscale == 4: - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - elif self.upscale in [2, 3]: - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - - out = self.conv_last(self.lrelu(self.conv_hr(out))) - base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False) - out += base - return out diff --git a/basicsr/archs/srvgg_arch.py b/basicsr/archs/srvgg_arch.py deleted file mode 100644 index d31936decce885ea6702c62784f0e1228c3da8eb..0000000000000000000000000000000000000000 --- a/basicsr/archs/srvgg_arch.py +++ /dev/null @@ -1,70 +0,0 @@ -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register(suffix='basicsr') -class SRVGGNetCompact(nn.Module): - """A compact VGG-style network structure for super-resolution. - - It is a compact network structure, which performs upsampling in the last layer and no convolution is - conducted on the HR feature space. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_out_ch (int): Channel number of outputs. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - num_conv (int): Number of convolution layers in the body network. Default: 16. - upscale (int): Upsampling factor. Default: 4. - act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. - """ - - def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'): - super(SRVGGNetCompact, self).__init__() - self.num_in_ch = num_in_ch - self.num_out_ch = num_out_ch - self.num_feat = num_feat - self.num_conv = num_conv - self.upscale = upscale - self.act_type = act_type - - self.body = nn.ModuleList() - # the first conv - self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)) - # the first activation - if act_type == 'relu': - activation = nn.ReLU(inplace=True) - elif act_type == 'prelu': - activation = nn.PReLU(num_parameters=num_feat) - elif act_type == 'leakyrelu': - activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.body.append(activation) - - # the body structure - for _ in range(num_conv): - self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1)) - # activation - if act_type == 'relu': - activation = nn.ReLU(inplace=True) - elif act_type == 'prelu': - activation = nn.PReLU(num_parameters=num_feat) - elif act_type == 'leakyrelu': - activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.body.append(activation) - - # the last conv - self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1)) - # upsample - self.upsampler = nn.PixelShuffle(upscale) - - def forward(self, x): - out = x - for i in range(0, len(self.body)): - out = self.body[i](out) - - out = self.upsampler(out) - # add the nearest upsampled image, so that the network learns the residual - base = F.interpolate(x, scale_factor=self.upscale, mode='nearest') - out += base - return out diff --git a/basicsr/archs/stylegan2_arch.py b/basicsr/archs/stylegan2_arch.py deleted file mode 100644 index 672b5ef2532fd8c55f2f3a9c844c848c85d627e5..0000000000000000000000000000000000000000 --- a/basicsr/archs/stylegan2_arch.py +++ /dev/null @@ -1,799 +0,0 @@ -import math -import random -import torch -from torch import nn -from torch.nn import functional as F - -from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu -from basicsr.ops.upfirdn2d import upfirdn2d -from basicsr.utils.registry import ARCH_REGISTRY - - -class NormStyleCode(nn.Module): - - def forward(self, x): - """Normalize the style codes. - - Args: - x (Tensor): Style codes with shape (b, c). - - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -def make_resample_kernel(k): - """Make resampling kernel for UpFirDn. - - Args: - k (list[int]): A list indicating the 1D resample kernel magnitude. - - Returns: - Tensor: 2D resampled kernel. - """ - k = torch.tensor(k, dtype=torch.float32) - if k.ndim == 1: - k = k[None, :] * k[:, None] # to 2D kernel, outer product - # normalize - k /= k.sum() - return k - - -class UpFirDnUpsample(nn.Module): - """Upsample, FIR filter, and downsample (upsampole version). - - References: - 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 - 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - factor (int): Upsampling scale factor. Default: 2. - """ - - def __init__(self, resample_kernel, factor=2): - super(UpFirDnUpsample, self).__init__() - self.kernel = make_resample_kernel(resample_kernel) * (factor**2) - self.factor = factor - - pad = self.kernel.shape[0] - factor - self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(factor={self.factor})') - - -class UpFirDnDownsample(nn.Module): - """Upsample, FIR filter, and downsample (downsampole version). - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - factor (int): Downsampling scale factor. Default: 2. - """ - - def __init__(self, resample_kernel, factor=2): - super(UpFirDnDownsample, self).__init__() - self.kernel = make_resample_kernel(resample_kernel) - self.factor = factor - - pad = self.kernel.shape[0] - factor - self.pad = ((pad + 1) // 2, pad // 2) - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(factor={self.factor})') - - -class UpFirDnSmooth(nn.Module): - """Upsample, FIR filter, and downsample (smooth version). - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - upsample_factor (int): Upsampling scale factor. Default: 1. - downsample_factor (int): Downsampling scale factor. Default: 1. - kernel_size (int): Kernel size: Default: 1. - """ - - def __init__(self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1): - super(UpFirDnSmooth, self).__init__() - self.upsample_factor = upsample_factor - self.downsample_factor = downsample_factor - self.kernel = make_resample_kernel(resample_kernel) - if upsample_factor > 1: - self.kernel = self.kernel * (upsample_factor**2) - - if upsample_factor > 1: - pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) - self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) - elif downsample_factor > 1: - pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) - self.pad = ((pad + 1) // 2, pad // 2) - else: - raise NotImplementedError - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(upsample_factor={self.upsample_factor}' - f', downsample_factor={self.downsample_factor})') - - -class EqualLinear(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Size of each sample. - out_channels (int): Size of each output sample. - bias (bool): If set to ``False``, the layer will not learn an additive - bias. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - lr_mul (float): Learning rate multiplier. Default: 1. - activation (None | str): The activation after ``linear`` operation. - Supported: 'fused_lrelu', None. Default: None. - """ - - def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): - super(EqualLinear, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.lr_mul = lr_mul - self.activation = activation - if self.activation not in ['fused_lrelu', None]: - raise ValueError(f'Wrong activation value in EqualLinear: {activation}' - "Supported ones are: ['fused_lrelu', None].") - self.scale = (1 / math.sqrt(in_channels)) * lr_mul - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - if self.bias is None: - bias = None - else: - bias = self.bias * self.lr_mul - if self.activation == 'fused_lrelu': - out = F.linear(x, self.weight * self.scale) - out = fused_leaky_relu(out, bias) - else: - out = F.linear(x, self.weight * self.scale, bias=bias) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, bias={self.bias is not None})') - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - - There is no bias in ModulatedConv2d. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. - Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - eps (float): A value added to the denominator for numerical stability. - Default: 1e-8. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=(1, 3, 3, 1), - eps=1e-8): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - - if self.sample_mode == 'upsample': - self.smooth = UpFirDnSmooth( - resample_kernel, upsample_factor=2, downsample_factor=1, kernel_size=kernel_size) - elif self.sample_mode == 'downsample': - self.smooth = UpFirDnSmooth( - resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size) - elif self.sample_mode is None: - pass - else: - raise ValueError(f'Wrong sample mode {self.sample_mode}, ' - "supported ones are ['upsample', 'downsample', None].") - - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - # modulation inside each modulated conv - self.modulation = EqualLinear( - num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) - - self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) - - if self.sample_mode == 'upsample': - x = x.view(1, b * c, h, w) - weight = weight.view(b, self.out_channels, c, self.kernel_size, self.kernel_size) - weight = weight.transpose(1, 2).reshape(b * c, self.out_channels, self.kernel_size, self.kernel_size) - out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - out = self.smooth(out) - elif self.sample_mode == 'downsample': - x = self.smooth(x) - x = x.view(1, b * c, *x.shape[2:4]) - out = F.conv2d(x, weight, padding=0, stride=2, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - else: - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size}, ' - f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') - - -class StyleConv(nn.Module): - """Style conv. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=(1, 3, 3, 1)): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - resample_kernel=resample_kernel) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.activate = FusedLeakyReLU(out_channels) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # activation (with bias) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB from features. - - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - """ - - def __init__(self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)): - super(ToRGB, self).__init__() - if upsample: - self.upsample = UpFirDnUpsample(resample_kernel, factor=2) - else: - self.upsample = None - self.modulated_conv = ModulatedConv2d( - in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = self.upsample(skip) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -@ARCH_REGISTRY.register() -class StyleGAN2Generator(nn.Module): - """StyleGAN2 Generator. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. A cross production will be applied to extent 1D resample - kernel to 2D resample kernel. Default: (1, 3, 3, 1). - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - resample_kernel=(1, 3, 3, 1), - lr_mlp=0.01, - narrow=1): - super(StyleGAN2Generator, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.append( - EqualLinear( - num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, - activation='fused_lrelu')) - self.style_mlp = nn.Sequential(*style_mlp_layers) - - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - self.channels = channels - - self.constant_input = ConstantInput(channels['4'], size=4) - self.style_conv1 = StyleConv( - channels['4'], - channels['4'], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=resample_kernel) - self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, resample_kernel=resample_kernel) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels['4'] - # noise - for layer_idx in range(self.num_layers): - resolution = 2**((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode='upsample', - resample_kernel=resample_kernel, - )) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=resample_kernel)) - self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True, resample_kernel=resample_kernel)) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward(self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2Generator. - - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. - Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is - False. Default: True. - truncation (float): TODO. Default: 1. - truncation_latent (Tensor | None): TODO. Default: None. - inject_index (int | None): The injection index for mixing noise. - Default: None. - return_latents (bool): Whether to return style latents. - Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latent with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ScaledLeakyReLU(nn.Module): - """Scaled LeakyReLU. - - Args: - negative_slope (float): Negative slope. Default: 0.2. - """ - - def __init__(self, negative_slope=0.2): - super(ScaledLeakyReLU, self).__init__() - self.negative_slope = negative_slope - - def forward(self, x): - out = F.leaky_relu(x, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class EqualConv2d(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. - Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. - Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - """ - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): - super(EqualConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - out = F.conv2d( - x, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size},' - f' stride={self.stride}, padding={self.padding}, ' - f'bias={self.bias is not None})') - - -class ConvLayer(nn.Sequential): - """Conv Layer used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Kernel size. - downsample (bool): Whether downsample by a factor of 2. - Default: False. - resample_kernel (list[int]): A list indicating the 1D resample - kernel magnitude. A cross production will be applied to - extent 1D resample kernel to 2D resample kernel. - Default: (1, 3, 3, 1). - bias (bool): Whether with bias. Default: True. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - downsample=False, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True): - layers = [] - # downsample - if downsample: - layers.append( - UpFirDnSmooth(resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size)) - stride = 2 - self.padding = 0 - else: - stride = 1 - self.padding = kernel_size // 2 - # conv - layers.append( - EqualConv2d( - in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias - and not activate)) - # activation - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channels)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super(ConvLayer, self).__init__(*layers) - - -class ResBlock(nn.Module): - """Residual block used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - resample_kernel (list[int]): A list indicating the 1D resample - kernel magnitude. A cross production will be applied to - extent 1D resample kernel to 2D resample kernel. - Default: (1, 3, 3, 1). - """ - - def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): - super(ResBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvLayer( - in_channels, out_channels, 3, downsample=True, resample_kernel=resample_kernel, bias=True, activate=True) - self.skip = ConvLayer( - in_channels, out_channels, 1, downsample=True, resample_kernel=resample_kernel, bias=False, activate=False) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out - - -@ARCH_REGISTRY.register() -class StyleGAN2Discriminator(nn.Module): - """StyleGAN2 Discriminator. - - Args: - out_size (int): The spatial size of outputs. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. A cross production will be applied to extent 1D resample - kernel to 2D resample kernel. Default: (1, 3, 3, 1). - stddev_group (int): For group stddev statistics. Default: 4. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, out_size, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), stddev_group=4, narrow=1): - super(StyleGAN2Discriminator, self).__init__() - - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - - log_size = int(math.log(out_size, 2)) - - conv_body = [ConvLayer(3, channels[f'{out_size}'], 1, bias=True, activate=True)] - - in_channels = channels[f'{out_size}'] - for i in range(log_size, 2, -1): - out_channels = channels[f'{2**(i - 1)}'] - conv_body.append(ResBlock(in_channels, out_channels, resample_kernel)) - in_channels = out_channels - self.conv_body = nn.Sequential(*conv_body) - - self.final_conv = ConvLayer(in_channels + 1, channels['4'], 3, bias=True, activate=True) - self.final_linear = nn.Sequential( - EqualLinear( - channels['4'] * 4 * 4, channels['4'], bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'), - EqualLinear(channels['4'], 1, bias=True, bias_init_val=0, lr_mul=1, activation=None), - ) - self.stddev_group = stddev_group - self.stddev_feat = 1 - - def forward(self, x): - out = self.conv_body(x) - - b, c, h, w = out.shape - # concatenate a group stddev statistics to out - group = min(b, self.stddev_group) # Minibatch must be divisible by (or smaller than) group_size - stddev = out.view(group, -1, self.stddev_feat, c // self.stddev_feat, h, w) - stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) - stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) - stddev = stddev.repeat(group, 1, h, w) - out = torch.cat([out, stddev], 1) - - out = self.final_conv(out) - out = out.view(b, -1) - out = self.final_linear(out) - - return out diff --git a/basicsr/archs/stylegan2_bilinear_arch.py b/basicsr/archs/stylegan2_bilinear_arch.py deleted file mode 100644 index 5ce020f62159d58ecc46df159a6460b04d7932e4..0000000000000000000000000000000000000000 --- a/basicsr/archs/stylegan2_bilinear_arch.py +++ /dev/null @@ -1,614 +0,0 @@ -import math -import random -import torch -from torch import nn -from torch.nn import functional as F - -from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu -from basicsr.utils.registry import ARCH_REGISTRY - - -class NormStyleCode(nn.Module): - - def forward(self, x): - """Normalize the style codes. - - Args: - x (Tensor): Style codes with shape (b, c). - - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -class EqualLinear(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Size of each sample. - out_channels (int): Size of each output sample. - bias (bool): If set to ``False``, the layer will not learn an additive - bias. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - lr_mul (float): Learning rate multiplier. Default: 1. - activation (None | str): The activation after ``linear`` operation. - Supported: 'fused_lrelu', None. Default: None. - """ - - def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): - super(EqualLinear, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.lr_mul = lr_mul - self.activation = activation - if self.activation not in ['fused_lrelu', None]: - raise ValueError(f'Wrong activation value in EqualLinear: {activation}' - "Supported ones are: ['fused_lrelu', None].") - self.scale = (1 / math.sqrt(in_channels)) * lr_mul - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - if self.bias is None: - bias = None - else: - bias = self.bias * self.lr_mul - if self.activation == 'fused_lrelu': - out = F.linear(x, self.weight * self.scale) - out = fused_leaky_relu(out, bias) - else: - out = F.linear(x, self.weight * self.scale, bias=bias) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, bias={self.bias is not None})') - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - - There is no bias in ModulatedConv2d. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. - Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - eps (float): A value added to the denominator for numerical stability. - Default: 1e-8. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - eps=1e-8, - interpolation_mode='bilinear'): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - # modulation inside each modulated conv - self.modulation = EqualLinear( - num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) - - self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) - - if self.sample_mode == 'upsample': - x = F.interpolate(x, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) - elif self.sample_mode == 'downsample': - x = F.interpolate(x, scale_factor=0.5, mode=self.interpolation_mode, align_corners=self.align_corners) - - b, c, h, w = x.shape - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size}, ' - f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') - - -class StyleConv(nn.Module): - """Style conv. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode='bilinear'): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - interpolation_mode=interpolation_mode) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.activate = FusedLeakyReLU(out_channels) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # activation (with bias) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB from features. - - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - """ - - def __init__(self, in_channels, num_style_feat, upsample=True, interpolation_mode='bilinear'): - super(ToRGB, self).__init__() - self.upsample = upsample - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - self.modulated_conv = ModulatedConv2d( - in_channels, - 3, - kernel_size=1, - num_style_feat=num_style_feat, - demodulate=False, - sample_mode=None, - interpolation_mode=interpolation_mode) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = F.interpolate( - skip, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -@ARCH_REGISTRY.register(suffix='basicsr') -class StyleGAN2GeneratorBilinear(nn.Module): - """StyleGAN2 Generator. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - lr_mlp=0.01, - narrow=1, - interpolation_mode='bilinear'): - super(StyleGAN2GeneratorBilinear, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.append( - EqualLinear( - num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, - activation='fused_lrelu')) - self.style_mlp = nn.Sequential(*style_mlp_layers) - - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - self.channels = channels - - self.constant_input = ConstantInput(channels['4'], size=4) - self.style_conv1 = StyleConv( - channels['4'], - channels['4'], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode) - self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, interpolation_mode=interpolation_mode) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels['4'] - # noise - for layer_idx in range(self.num_layers): - resolution = 2**((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode='upsample', - interpolation_mode=interpolation_mode)) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode)) - self.to_rgbs.append( - ToRGB(out_channels, num_style_feat, upsample=True, interpolation_mode=interpolation_mode)) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward(self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2Generator. - - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. - Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is - False. Default: True. - truncation (float): TODO. Default: 1. - truncation_latent (Tensor | None): TODO. Default: None. - inject_index (int | None): The injection index for mixing noise. - Default: None. - return_latents (bool): Whether to return style latents. - Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latent with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ScaledLeakyReLU(nn.Module): - """Scaled LeakyReLU. - - Args: - negative_slope (float): Negative slope. Default: 0.2. - """ - - def __init__(self, negative_slope=0.2): - super(ScaledLeakyReLU, self).__init__() - self.negative_slope = negative_slope - - def forward(self, x): - out = F.leaky_relu(x, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class EqualConv2d(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. - Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. - Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - """ - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): - super(EqualConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - out = F.conv2d( - x, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size},' - f' stride={self.stride}, padding={self.padding}, ' - f'bias={self.bias is not None})') - - -class ConvLayer(nn.Sequential): - """Conv Layer used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Kernel size. - downsample (bool): Whether downsample by a factor of 2. - Default: False. - bias (bool): Whether with bias. Default: True. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - downsample=False, - bias=True, - activate=True, - interpolation_mode='bilinear'): - layers = [] - self.interpolation_mode = interpolation_mode - # downsample - if downsample: - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - - layers.append( - torch.nn.Upsample(scale_factor=0.5, mode=interpolation_mode, align_corners=self.align_corners)) - stride = 1 - self.padding = kernel_size // 2 - # conv - layers.append( - EqualConv2d( - in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias - and not activate)) - # activation - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channels)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super(ConvLayer, self).__init__(*layers) - - -class ResBlock(nn.Module): - """Residual block used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - """ - - def __init__(self, in_channels, out_channels, interpolation_mode='bilinear'): - super(ResBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvLayer( - in_channels, - out_channels, - 3, - downsample=True, - interpolation_mode=interpolation_mode, - bias=True, - activate=True) - self.skip = ConvLayer( - in_channels, - out_channels, - 1, - downsample=True, - interpolation_mode=interpolation_mode, - bias=False, - activate=False) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out diff --git a/basicsr/archs/swinir_arch.py b/basicsr/archs/swinir_arch.py deleted file mode 100644 index 5ef5a59ae2115da36fffed8df14fc926f7890feb..0000000000000000000000000000000000000000 --- a/basicsr/archs/swinir_arch.py +++ /dev/null @@ -1,956 +0,0 @@ -# Modified from https://github.com/JingyunLiang/SwinIR -# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 -# Originally Written by Ze Liu, Modified by Jingyun Liang. - -import math -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import to_2tuple, trunc_normal_ - - -def drop_path(x, drop_prob: float = 0., training: bool = False): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py - """ - if drop_prob == 0. or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py - """ - - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) - - -class Mlp(nn.Module): - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (b, h, w, c) - window_size (int): window size - - Returns: - windows: (num_windows*b, window_size, window_size, c) - """ - b, h, w, c = x.shape - x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) - return windows - - -def window_reverse(windows, window_size, h, w): - """ - Args: - windows: (num_windows*b, window_size, window_size, c) - window_size (int): Window size - h (int): Height of image - w (int): Width of image - - Returns: - x: (b, h, w, c) - """ - b = int(windows.shape[0] / (h * w / window_size / window_size)) - x = windows.view(b, h // window_size, w // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) - return x - - -class WindowAttention(nn.Module): - r""" Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer('relative_position_index', relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*b, n, c) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - b_, n, c = x.shape - qkv = self.qkv(x).reshape(b_, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nw = mask.shape[0] - attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, n, n) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(b_, n, c) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' - - def flops(self, n): - # calculate flops for 1 window with token length of n - flops = 0 - # qkv = self.qkv(x) - flops += n * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * n * (self.dim // self.num_heads) * n - # x = (attn @ v) - flops += self.num_heads * n * n * (self.dim // self.num_heads) - # x = self.proj(x) - flops += n * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r""" Swin Transformer Block. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, - dim, - input_resolution, - num_heads, - window_size=7, - shift_size=0, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - act_layer=nn.GELU, - norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size' - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, - window_size=to_2tuple(self.window_size), - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if self.shift_size > 0: - attn_mask = self.calculate_mask(self.input_resolution) - else: - attn_mask = None - - self.register_buffer('attn_mask', attn_mask) - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - h, w = x_size - img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 - h_slices = (slice(0, -self.window_size), slice(-self.window_size, - -self.shift_size), slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), slice(-self.window_size, - -self.shift_size), slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nw, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - def forward(self, x, x_size): - h, w = x_size - b, _, c = x.shape - # assert seq_len == h * w, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(b, h, w, c) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nw*b, window_size, window_size, c - x_windows = x_windows.view(-1, self.window_size * self.window_size, c) # nw*b, window_size*window_size, c - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - if self.input_resolution == x_size: - attn_windows = self.attn(x_windows, mask=self.attn_mask) # nw*b, window_size*window_size, c - else: - attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) - shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(b, h * w, c) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - def extra_repr(self) -> str: - return (f'dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, ' - f'window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}') - - def flops(self): - flops = 0 - h, w = self.input_resolution - # norm1 - flops += self.dim * h * w - # W-MSA/SW-MSA - nw = h * w / self.window_size / self.window_size - flops += nw * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * h * w * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * h * w - return flops - - -class PatchMerging(nn.Module): - r""" Patch Merging Layer. - - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: b, h*w, c - """ - h, w = self.input_resolution - b, seq_len, c = x.shape - assert seq_len == h * w, 'input feature has wrong size' - assert h % 2 == 0 and w % 2 == 0, f'x size ({h}*{w}) are not even.' - - x = x.view(b, h, w, c) - - x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c - x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c - x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c - x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c - x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c - x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c - - x = self.norm(x) - x = self.reduction(x) - - return x - - def extra_repr(self) -> str: - return f'input_resolution={self.input_resolution}, dim={self.dim}' - - def flops(self): - h, w = self.input_resolution - flops = h * w * self.dim - flops += (h // 2) * (w // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False): - - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock( - dim=dim, - input_resolution=input_resolution, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) for i in range(depth) - ]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, x_size): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x, x_size) - if self.downsample is not None: - x = self.downsample(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}' - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class RSTB(nn.Module): - """Residual Swin Transformer Block (RSTB). - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - img_size: Input image size. - patch_size: Patch size. - resi_connection: The convolutional block before residual connection. - """ - - def __init__(self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - img_size=224, - patch_size=4, - resi_connection='1conv'): - super(RSTB, self).__init__() - - self.dim = dim - self.input_resolution = input_resolution - - self.residual_group = BasicLayer( - dim=dim, - input_resolution=input_resolution, - depth=depth, - num_heads=num_heads, - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path, - norm_layer=norm_layer, - downsample=downsample, - use_checkpoint=use_checkpoint) - - if resi_connection == '1conv': - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv = nn.Sequential( - nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim, 3, 1, 1)) - - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) - - self.patch_unembed = PatchUnEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) - - def forward(self, x, x_size): - return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x - - def flops(self): - flops = 0 - flops += self.residual_group.flops() - h, w = self.input_resolution - flops += h * w * self.dim * self.dim * 9 - flops += self.patch_embed.flops() - flops += self.patch_unembed.flops() - - return flops - - -class PatchEmbed(nn.Module): - r""" Image to Patch Embedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - x = x.flatten(2).transpose(1, 2) # b Ph*Pw c - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - flops = 0 - h, w = self.img_size - if self.norm is not None: - flops += h * w * self.embed_dim - return flops - - -class PatchUnEmbed(nn.Module): - r""" Image to Patch Unembedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - def forward(self, x, x_size): - x = x.transpose(1, 2).view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) # b Ph*Pw c - return x - - def flops(self): - flops = 0 - return flops - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.') - super(Upsample, self).__init__(*m) - - -class UpsampleOneStep(nn.Sequential): - """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) - Used in lightweight SR to save parameters. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - - """ - - def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): - self.num_feat = num_feat - self.input_resolution = input_resolution - m = [] - m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) - m.append(nn.PixelShuffle(scale)) - super(UpsampleOneStep, self).__init__(*m) - - def flops(self): - h, w = self.input_resolution - flops = h * w * self.num_feat * 3 * 9 - return flops - - -@ARCH_REGISTRY.register() -class SwinIR(nn.Module): - r""" SwinIR - A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. - - Args: - img_size (int | tuple(int)): Input image size. Default 64 - patch_size (int | tuple(int)): Patch size. Default: 1 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction - img_range: Image range. 1. or 255. - upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__(self, - img_size=64, - patch_size=1, - in_chans=3, - embed_dim=96, - depths=(6, 6, 6, 6), - num_heads=(6, 6, 6, 6), - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.1, - norm_layer=nn.LayerNorm, - ape=False, - patch_norm=True, - use_checkpoint=False, - upscale=2, - img_range=1., - upsampler='', - resi_connection='1conv', - **kwargs): - super(SwinIR, self).__init__() - num_in_ch = in_chans - num_out_ch = in_chans - num_feat = 64 - self.img_range = img_range - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - - # ------------------------- 1, shallow feature extraction ------------------------- # - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - # ------------------------- 2, deep feature extraction ------------------------- # - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = embed_dim - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # merge non-overlapping patches into image - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build Residual Swin Transformer blocks (RSTB) - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RSTB( - dim=embed_dim, - input_resolution=(patches_resolution[0], patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection) - self.layers.append(layer) - self.norm = norm_layer(self.num_features) - - # build the last conv layer in deep feature extraction - if resi_connection == '1conv': - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv_after_body = nn.Sequential( - nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) - - # ------------------------- 3, high quality image reconstruction ------------------------- # - if self.upsampler == 'pixelshuffle': - # for classical SR - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR (to save parameters) - self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, - (patches_resolution[0], patches_resolution[1])) - elif self.upsampler == 'nearest+conv': - # for real-world SR (less artifacts) - assert self.upscale == 4, 'only support x4 now.' - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - else: - # for image denoising and JPEG compression artifact reduction - self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'absolute_pos_embed'} - - @torch.jit.ignore - def no_weight_decay_keywords(self): - return {'relative_position_bias_table'} - - def forward_features(self, x): - x_size = (x.shape[2], x.shape[3]) - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x, x_size) - - x = self.norm(x) # b seq_len c - x = self.patch_unembed(x, x_size) - - return x - - def forward(self, x): - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - - if self.upsampler == 'pixelshuffle': - # for classical SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.upsample(x) - elif self.upsampler == 'nearest+conv': - # for real-world SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.conv_last(self.lrelu(self.conv_hr(x))) - else: - # for image denoising and JPEG compression artifact reduction - x_first = self.conv_first(x) - res = self.conv_after_body(self.forward_features(x_first)) + x_first - x = x + self.conv_last(res) - - x = x / self.img_range + self.mean - - return x - - def flops(self): - flops = 0 - h, w = self.patches_resolution - flops += h * w * 3 * self.embed_dim * 9 - flops += self.patch_embed.flops() - for layer in self.layers: - flops += layer.flops() - flops += h * w * 3 * self.embed_dim * self.embed_dim - flops += self.upsample.flops() - return flops - - -if __name__ == '__main__': - upscale = 4 - window_size = 8 - height = (1024 // upscale // window_size + 1) * window_size - width = (720 // upscale // window_size + 1) * window_size - model = SwinIR( - upscale=2, - img_size=(height, width), - window_size=window_size, - img_range=1., - depths=[6, 6, 6, 6], - embed_dim=60, - num_heads=[6, 6, 6, 6], - mlp_ratio=2, - upsampler='pixelshuffledirect') - print(model) - print(height, width, model.flops() / 1e9) - - x = torch.randn((1, 3, height, width)) - x = model(x) - print(x.shape) diff --git a/basicsr/archs/tof_arch.py b/basicsr/archs/tof_arch.py deleted file mode 100644 index e77fb522c3f1136158f645bc25d34ac28f3840c7..0000000000000000000000000000000000000000 --- a/basicsr/archs/tof_arch.py +++ /dev/null @@ -1,172 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import flow_warp - - -class BasicModule(nn.Module): - """Basic module of SPyNet. - - Note that unlike the architecture in spynet_arch.py, the basic module - here contains batch normalization. - """ - - def __init__(self): - super(BasicModule, self).__init__() - self.basic_module = nn.Sequential( - nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(32), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(64), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(32), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(16), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3)) - - def forward(self, tensor_input): - """ - Args: - tensor_input (Tensor): Input tensor with shape (b, 8, h, w). - 8 channels contain: - [reference image (3), neighbor image (3), initial flow (2)]. - - Returns: - Tensor: Estimated flow with shape (b, 2, h, w) - """ - return self.basic_module(tensor_input) - - -class SPyNetTOF(nn.Module): - """SPyNet architecture for TOF. - - Note that this implementation is specifically for TOFlow. Please use :file:`spynet_arch.py` for general use. - They differ in the following aspects: - - 1. The basic modules here contain BatchNorm. - 2. Normalization and denormalization are not done here, as they are done in TOFlow. - - ``Paper: Optical Flow Estimation using a Spatial Pyramid Network`` - - Reference: https://github.com/Coldog2333/pytoflow - - Args: - load_path (str): Path for pretrained SPyNet. Default: None. - """ - - def __init__(self, load_path=None): - super(SPyNetTOF, self).__init__() - - self.basic_module = nn.ModuleList([BasicModule() for _ in range(4)]) - if load_path: - self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) - - def forward(self, ref, supp): - """ - Args: - ref (Tensor): Reference image with shape of (b, 3, h, w). - supp: The supporting image to be warped: (b, 3, h, w). - - Returns: - Tensor: Estimated optical flow: (b, 2, h, w). - """ - num_batches, _, h, w = ref.size() - ref = [ref] - supp = [supp] - - # generate downsampled frames - for _ in range(3): - ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False)) - supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False)) - - # flow computation - flow = ref[0].new_zeros(num_batches, 2, h // 16, w // 16) - for i in range(4): - flow_up = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0 - flow = flow_up + self.basic_module[i]( - torch.cat([ref[i], flow_warp(supp[i], flow_up.permute(0, 2, 3, 1)), flow_up], 1)) - return flow - - -@ARCH_REGISTRY.register() -class TOFlow(nn.Module): - """PyTorch implementation of TOFlow. - - In TOFlow, the LR frames are pre-upsampled and have the same size with the GT frames. - - ``Paper: Video Enhancement with Task-Oriented Flow`` - - Reference: https://github.com/anchen1011/toflow - - Reference: https://github.com/Coldog2333/pytoflow - - Args: - adapt_official_weights (bool): Whether to adapt the weights translated - from the official implementation. Set to false if you want to - train from scratch. Default: False - """ - - def __init__(self, adapt_official_weights=False): - super(TOFlow, self).__init__() - self.adapt_official_weights = adapt_official_weights - self.ref_idx = 0 if adapt_official_weights else 3 - - self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) - self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) - - # flow estimation module - self.spynet = SPyNetTOF() - - # reconstruction module - self.conv_1 = nn.Conv2d(3 * 7, 64, 9, 1, 4) - self.conv_2 = nn.Conv2d(64, 64, 9, 1, 4) - self.conv_3 = nn.Conv2d(64, 64, 1) - self.conv_4 = nn.Conv2d(64, 3, 1) - - # activation function - self.relu = nn.ReLU(inplace=True) - - def normalize(self, img): - return (img - self.mean) / self.std - - def denormalize(self, img): - return img * self.std + self.mean - - def forward(self, lrs): - """ - Args: - lrs: Input lr frames: (b, 7, 3, h, w). - - Returns: - Tensor: SR frame: (b, 3, h, w). - """ - # In the official implementation, the 0-th frame is the reference frame - if self.adapt_official_weights: - lrs = lrs[:, [3, 0, 1, 2, 4, 5, 6], :, :, :] - - num_batches, num_lrs, _, h, w = lrs.size() - - lrs = self.normalize(lrs.view(-1, 3, h, w)) - lrs = lrs.view(num_batches, num_lrs, 3, h, w) - - lr_ref = lrs[:, self.ref_idx, :, :, :] - lr_aligned = [] - for i in range(7): # 7 frames - if i == self.ref_idx: - lr_aligned.append(lr_ref) - else: - lr_supp = lrs[:, i, :, :, :] - flow = self.spynet(lr_ref, lr_supp) - lr_aligned.append(flow_warp(lr_supp, flow.permute(0, 2, 3, 1))) - - # reconstruction - hr = torch.stack(lr_aligned, dim=1) - hr = hr.view(num_batches, -1, h, w) - hr = self.relu(self.conv_1(hr)) - hr = self.relu(self.conv_2(hr)) - hr = self.relu(self.conv_3(hr)) - hr = self.conv_4(hr) + lr_ref - - return self.denormalize(hr) diff --git a/basicsr/archs/vgg_arch.py b/basicsr/archs/vgg_arch.py deleted file mode 100644 index d122d1abb868e482c5064b4dba6bbe55d681c890..0000000000000000000000000000000000000000 --- a/basicsr/archs/vgg_arch.py +++ /dev/null @@ -1,161 +0,0 @@ -import os -import torch -from collections import OrderedDict -from torch import nn as nn -from torchvision.models import vgg as vgg - -from basicsr.utils.registry import ARCH_REGISTRY - -VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth' -NAMES = { - 'vgg11': [ - 'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', - 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', - 'pool5' - ], - 'vgg13': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', - 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', - 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' - ], - 'vgg16': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', - 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', - 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', - 'pool5' - ], - 'vgg19': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', - 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', - 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', - 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5' - ] -} - - -def insert_bn(names): - """Insert bn layer after each conv. - - Args: - names (list): The list of layer names. - - Returns: - list: The list of layer names with bn layers. - """ - names_bn = [] - for name in names: - names_bn.append(name) - if 'conv' in name: - position = name.replace('conv', '') - names_bn.append('bn' + position) - return names_bn - - -@ARCH_REGISTRY.register() -class VGGFeatureExtractor(nn.Module): - """VGG network for feature extraction. - - In this implementation, we allow users to choose whether use normalization - in the input feature and the type of vgg network. Note that the pretrained - path must fit the vgg type. - - Args: - layer_name_list (list[str]): Forward function returns the corresponding - features according to the layer_name_list. - Example: {'relu1_1', 'relu2_1', 'relu3_1'}. - vgg_type (str): Set the type of vgg network. Default: 'vgg19'. - use_input_norm (bool): If True, normalize the input image. Importantly, - the input feature must in the range [0, 1]. Default: True. - range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. - Default: False. - requires_grad (bool): If true, the parameters of VGG network will be - optimized. Default: False. - remove_pooling (bool): If true, the max pooling operations in VGG net - will be removed. Default: False. - pooling_stride (int): The stride of max pooling operation. Default: 2. - """ - - def __init__(self, - layer_name_list, - vgg_type='vgg19', - use_input_norm=True, - range_norm=False, - requires_grad=False, - remove_pooling=False, - pooling_stride=2): - super(VGGFeatureExtractor, self).__init__() - - self.layer_name_list = layer_name_list - self.use_input_norm = use_input_norm - self.range_norm = range_norm - - self.names = NAMES[vgg_type.replace('_bn', '')] - if 'bn' in vgg_type: - self.names = insert_bn(self.names) - - # only borrow layers that will be used to avoid unused params - max_idx = 0 - for v in layer_name_list: - idx = self.names.index(v) - if idx > max_idx: - max_idx = idx - - if os.path.exists(VGG_PRETRAIN_PATH): - vgg_net = getattr(vgg, vgg_type)(pretrained=False) - state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage) - vgg_net.load_state_dict(state_dict) - else: - vgg_net = getattr(vgg, vgg_type)(pretrained=True) - - features = vgg_net.features[:max_idx + 1] - - modified_net = OrderedDict() - for k, v in zip(self.names, features): - if 'pool' in k: - # if remove_pooling is true, pooling operation will be removed - if remove_pooling: - continue - else: - # in some cases, we may want to change the default stride - modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride) - else: - modified_net[k] = v - - self.vgg_net = nn.Sequential(modified_net) - - if not requires_grad: - self.vgg_net.eval() - for param in self.parameters(): - param.requires_grad = False - else: - self.vgg_net.train() - for param in self.parameters(): - param.requires_grad = True - - if self.use_input_norm: - # the mean is for image with range [0, 1] - self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) - # the std is for image with range [0, 1] - self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) - - def forward(self, x): - """Forward function. - - Args: - x (Tensor): Input tensor with shape (n, c, h, w). - - Returns: - Tensor: Forward results. - """ - if self.range_norm: - x = (x + 1) / 2 - if self.use_input_norm: - x = (x - self.mean) / self.std - - output = {} - for key, layer in self.vgg_net._modules.items(): - x = layer(x) - if key in self.layer_name_list: - output[key] = x.clone() - - return output diff --git a/basicsr/data/__init__.py b/basicsr/data/__init__.py deleted file mode 100644 index 897d20447f7ba084aff67146595e3c11f90e92a0..0000000000000000000000000000000000000000 --- a/basicsr/data/__init__.py +++ /dev/null @@ -1,101 +0,0 @@ -import importlib -import numpy as np -import random -import torch -import torch.utils.data -from copy import deepcopy -from functools import partial -from os import path as osp - -from basicsr.data.prefetch_dataloader import PrefetchDataLoader -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import DATASET_REGISTRY - -__all__ = ['build_dataset', 'build_dataloader'] - -# automatically scan and import dataset modules for registry -# scan all the files under the data folder with '_dataset' in file names -data_folder = osp.dirname(osp.abspath(__file__)) -dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] -# import all the dataset modules -_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames] - - -def build_dataset(dataset_opt): - """Build dataset from options. - - Args: - dataset_opt (dict): Configuration for dataset. It must contain: - name (str): Dataset name. - type (str): Dataset type. - """ - dataset_opt = deepcopy(dataset_opt) - dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt) - logger = get_root_logger() - logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} is built.') - return dataset - - -def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None): - """Build dataloader. - - Args: - dataset (torch.utils.data.Dataset): Dataset. - dataset_opt (dict): Dataset options. It contains the following keys: - phase (str): 'train' or 'val'. - num_worker_per_gpu (int): Number of workers for each GPU. - batch_size_per_gpu (int): Training batch size for each GPU. - num_gpu (int): Number of GPUs. Used only in the train phase. - Default: 1. - dist (bool): Whether in distributed training. Used only in the train - phase. Default: False. - sampler (torch.utils.data.sampler): Data sampler. Default: None. - seed (int | None): Seed. Default: None - """ - phase = dataset_opt['phase'] - rank, _ = get_dist_info() - if phase == 'train': - if dist: # distributed training - batch_size = dataset_opt['batch_size_per_gpu'] - num_workers = dataset_opt['num_worker_per_gpu'] - else: # non-distributed training - multiplier = 1 if num_gpu == 0 else num_gpu - batch_size = dataset_opt['batch_size_per_gpu'] * multiplier - num_workers = dataset_opt['num_worker_per_gpu'] * multiplier - dataloader_args = dict( - dataset=dataset, - batch_size=batch_size, - shuffle=False, - num_workers=num_workers, - sampler=sampler, - drop_last=True) - if sampler is None: - dataloader_args['shuffle'] = True - dataloader_args['worker_init_fn'] = partial( - worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None - elif phase in ['val', 'test']: # validation - dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0) - else: - raise ValueError(f"Wrong dataset phase: {phase}. Supported ones are 'train', 'val' and 'test'.") - - dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False) - dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', False) - - prefetch_mode = dataset_opt.get('prefetch_mode') - if prefetch_mode == 'cpu': # CPUPrefetcher - num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1) - logger = get_root_logger() - logger.info(f'Use {prefetch_mode} prefetch dataloader: num_prefetch_queue = {num_prefetch_queue}') - return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args) - else: - # prefetch_mode=None: Normal dataloader - # prefetch_mode='cuda': dataloader for CUDAPrefetcher - return torch.utils.data.DataLoader(**dataloader_args) - - -def worker_init_fn(worker_id, num_workers, rank, seed): - # Set the worker seed to num_workers * rank + worker_id + seed - worker_seed = num_workers * rank + worker_id + seed - np.random.seed(worker_seed) - random.seed(worker_seed) diff --git a/basicsr/data/data_sampler.py b/basicsr/data/data_sampler.py deleted file mode 100644 index 5135c7f83a0698c1980354b65ffa68f98a3c6cc0..0000000000000000000000000000000000000000 --- a/basicsr/data/data_sampler.py +++ /dev/null @@ -1,48 +0,0 @@ -import math -import torch -from torch.utils.data.sampler import Sampler - - -class EnlargedSampler(Sampler): - """Sampler that restricts data loading to a subset of the dataset. - - Modified from torch.utils.data.distributed.DistributedSampler - Support enlarging the dataset for iteration-based training, for saving - time when restart the dataloader after each epoch - - Args: - dataset (torch.utils.data.Dataset): Dataset used for sampling. - num_replicas (int | None): Number of processes participating in - the training. It is usually the world_size. - rank (int | None): Rank of the current process within num_replicas. - ratio (int): Enlarging ratio. Default: 1. - """ - - def __init__(self, dataset, num_replicas, rank, ratio=1): - self.dataset = dataset - self.num_replicas = num_replicas - self.rank = rank - self.epoch = 0 - self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas) - self.total_size = self.num_samples * self.num_replicas - - def __iter__(self): - # deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.epoch) - indices = torch.randperm(self.total_size, generator=g).tolist() - - dataset_size = len(self.dataset) - indices = [v % dataset_size for v in indices] - - # subsample - indices = indices[self.rank:self.total_size:self.num_replicas] - assert len(indices) == self.num_samples - - return iter(indices) - - def __len__(self): - return self.num_samples - - def set_epoch(self, epoch): - self.epoch = epoch diff --git a/basicsr/data/data_util.py b/basicsr/data/data_util.py deleted file mode 100644 index 90d39ad53a6d1feadf3440727827fe4eec017281..0000000000000000000000000000000000000000 --- a/basicsr/data/data_util.py +++ /dev/null @@ -1,315 +0,0 @@ -import cv2 -import numpy as np -import torch -from os import path as osp -from torch.nn import functional as F - -from basicsr.data.transforms import mod_crop -from basicsr.utils import img2tensor, scandir - - -def read_img_seq(path, require_mod_crop=False, scale=1, return_imgname=False): - """Read a sequence of images from a given folder path. - - Args: - path (list[str] | str): List of image paths or image folder path. - require_mod_crop (bool): Require mod crop for each image. - Default: False. - scale (int): Scale factor for mod_crop. Default: 1. - return_imgname(bool): Whether return image names. Default False. - - Returns: - Tensor: size (t, c, h, w), RGB, [0, 1]. - list[str]: Returned image name list. - """ - if isinstance(path, list): - img_paths = path - else: - img_paths = sorted(list(scandir(path, full_path=True))) - imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths] - - if require_mod_crop: - imgs = [mod_crop(img, scale) for img in imgs] - imgs = img2tensor(imgs, bgr2rgb=True, float32=True) - imgs = torch.stack(imgs, dim=0) - - if return_imgname: - imgnames = [osp.splitext(osp.basename(path))[0] for path in img_paths] - return imgs, imgnames - else: - return imgs - - -def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'): - """Generate an index list for reading `num_frames` frames from a sequence - of images. - - Args: - crt_idx (int): Current center index. - max_frame_num (int): Max number of the sequence of images (from 1). - num_frames (int): Reading num_frames frames. - padding (str): Padding mode, one of - 'replicate' | 'reflection' | 'reflection_circle' | 'circle' - Examples: current_idx = 0, num_frames = 5 - The generated frame indices under different padding mode: - replicate: [0, 0, 0, 1, 2] - reflection: [2, 1, 0, 1, 2] - reflection_circle: [4, 3, 0, 1, 2] - circle: [3, 4, 0, 1, 2] - - Returns: - list[int]: A list of indices. - """ - assert num_frames % 2 == 1, 'num_frames should be an odd number.' - assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.' - - max_frame_num = max_frame_num - 1 # start from 0 - num_pad = num_frames // 2 - - indices = [] - for i in range(crt_idx - num_pad, crt_idx + num_pad + 1): - if i < 0: - if padding == 'replicate': - pad_idx = 0 - elif padding == 'reflection': - pad_idx = -i - elif padding == 'reflection_circle': - pad_idx = crt_idx + num_pad - i - else: - pad_idx = num_frames + i - elif i > max_frame_num: - if padding == 'replicate': - pad_idx = max_frame_num - elif padding == 'reflection': - pad_idx = max_frame_num * 2 - i - elif padding == 'reflection_circle': - pad_idx = (crt_idx - num_pad) - (i - max_frame_num) - else: - pad_idx = i - num_frames - else: - pad_idx = i - indices.append(pad_idx) - return indices - - -def paired_paths_from_lmdb(folders, keys): - """Generate paired paths from lmdb files. - - Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is: - - :: - - lq.lmdb - ├── data.mdb - ├── lock.mdb - ├── meta_info.txt - - The data.mdb and lock.mdb are standard lmdb files and you can refer to - https://lmdb.readthedocs.io/en/release/ for more details. - - The meta_info.txt is a specified txt file to record the meta information - of our datasets. It will be automatically created when preparing - datasets by our provided dataset tools. - Each line in the txt file records - 1)image name (with extension), - 2)image shape, - 3)compression level, separated by a white space. - Example: `baboon.png (120,125,3) 1` - - We use the image name without extension as the lmdb key. - Note that we use the same key for the corresponding lq and gt images. - - Args: - folders (list[str]): A list of folder path. The order of list should - be [input_folder, gt_folder]. - keys (list[str]): A list of keys identifying folders. The order should - be in consistent with folders, e.g., ['lq', 'gt']. - Note that this key is different from lmdb keys. - - Returns: - list[str]: Returned path list. - """ - assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' - f'But got {len(folders)}') - assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' - input_folder, gt_folder = folders - input_key, gt_key = keys - - if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')): - raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb ' - f'formats. But received {input_key}: {input_folder}; ' - f'{gt_key}: {gt_folder}') - # ensure that the two meta_info files are the same - with open(osp.join(input_folder, 'meta_info.txt')) as fin: - input_lmdb_keys = [line.split('.')[0] for line in fin] - with open(osp.join(gt_folder, 'meta_info.txt')) as fin: - gt_lmdb_keys = [line.split('.')[0] for line in fin] - if set(input_lmdb_keys) != set(gt_lmdb_keys): - raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.') - else: - paths = [] - for lmdb_key in sorted(input_lmdb_keys): - paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)])) - return paths - - -def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl): - """Generate paired paths from an meta information file. - - Each line in the meta information file contains the image names and - image shape (usually for gt), separated by a white space. - - Example of an meta information file: - ``` - 0001_s001.png (480,480,3) - 0001_s002.png (480,480,3) - ``` - - Args: - folders (list[str]): A list of folder path. The order of list should - be [input_folder, gt_folder]. - keys (list[str]): A list of keys identifying folders. The order should - be in consistent with folders, e.g., ['lq', 'gt']. - meta_info_file (str): Path to the meta information file. - filename_tmpl (str): Template for each filename. Note that the - template excludes the file extension. Usually the filename_tmpl is - for files in the input folder. - - Returns: - list[str]: Returned path list. - """ - assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' - f'But got {len(folders)}') - assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' - input_folder, gt_folder = folders - input_key, gt_key = keys - - with open(meta_info_file, 'r') as fin: - gt_names = [line.strip().split(' ')[0] for line in fin] - - paths = [] - for gt_name in gt_names: - basename, ext = osp.splitext(osp.basename(gt_name)) - input_name = f'{filename_tmpl.format(basename)}{ext}' - input_path = osp.join(input_folder, input_name) - gt_path = osp.join(gt_folder, gt_name) - paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)])) - return paths - - -def paired_paths_from_folder(folders, keys, filename_tmpl): - """Generate paired paths from folders. - - Args: - folders (list[str]): A list of folder path. The order of list should - be [input_folder, gt_folder]. - keys (list[str]): A list of keys identifying folders. The order should - be in consistent with folders, e.g., ['lq', 'gt']. - filename_tmpl (str): Template for each filename. Note that the - template excludes the file extension. Usually the filename_tmpl is - for files in the input folder. - - Returns: - list[str]: Returned path list. - """ - assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' - f'But got {len(folders)}') - assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' - input_folder, gt_folder = folders - input_key, gt_key = keys - - input_paths = list(scandir(input_folder)) - gt_paths = list(scandir(gt_folder)) - assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: ' - f'{len(input_paths)}, {len(gt_paths)}.') - paths = [] - for gt_path in gt_paths: - basename, ext = osp.splitext(osp.basename(gt_path)) - input_name = f'{filename_tmpl.format(basename)}{ext}' - input_path = osp.join(input_folder, input_name) - assert input_name in input_paths, f'{input_name} is not in {input_key}_paths.' - gt_path = osp.join(gt_folder, gt_path) - paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)])) - return paths - - -def paths_from_folder(folder): - """Generate paths from folder. - - Args: - folder (str): Folder path. - - Returns: - list[str]: Returned path list. - """ - - paths = list(scandir(folder)) - paths = [osp.join(folder, path) for path in paths] - return paths - - -def paths_from_lmdb(folder): - """Generate paths from lmdb. - - Args: - folder (str): Folder path. - - Returns: - list[str]: Returned path list. - """ - if not folder.endswith('.lmdb'): - raise ValueError(f'Folder {folder}folder should in lmdb format.') - with open(osp.join(folder, 'meta_info.txt')) as fin: - paths = [line.split('.')[0] for line in fin] - return paths - - -def generate_gaussian_kernel(kernel_size=13, sigma=1.6): - """Generate Gaussian kernel used in `duf_downsample`. - - Args: - kernel_size (int): Kernel size. Default: 13. - sigma (float): Sigma of the Gaussian kernel. Default: 1.6. - - Returns: - np.array: The Gaussian kernel. - """ - from scipy.ndimage import filters as filters - kernel = np.zeros((kernel_size, kernel_size)) - # set element at the middle to one, a dirac delta - kernel[kernel_size // 2, kernel_size // 2] = 1 - # gaussian-smooth the dirac, resulting in a gaussian filter - return filters.gaussian_filter(kernel, sigma) - - -def duf_downsample(x, kernel_size=13, scale=4): - """Downsamping with Gaussian kernel used in the DUF official code. - - Args: - x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w). - kernel_size (int): Kernel size. Default: 13. - scale (int): Downsampling factor. Supported scale: (2, 3, 4). - Default: 4. - - Returns: - Tensor: DUF downsampled frames. - """ - assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.' - - squeeze_flag = False - if x.ndim == 4: - squeeze_flag = True - x = x.unsqueeze(0) - b, t, c, h, w = x.size() - x = x.view(-1, 1, h, w) - pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2 - x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect') - - gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale) - gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0) - x = F.conv2d(x, gaussian_filter, stride=scale) - x = x[:, :, 2:-2, 2:-2] - x = x.view(b, t, c, x.size(2), x.size(3)) - if squeeze_flag: - x = x.squeeze(0) - return x diff --git a/basicsr/data/degradations.py b/basicsr/data/degradations.py deleted file mode 100644 index f40d0fd77a7128202070a18d4c5195ebff25056a..0000000000000000000000000000000000000000 --- a/basicsr/data/degradations.py +++ /dev/null @@ -1,764 +0,0 @@ -import cv2 -import math -import numpy as np -import random -import torch -from scipy import special -from scipy.stats import multivariate_normal -from torchvision.transforms._functional_tensor import rgb_to_grayscale - -# -------------------------------------------------------------------- # -# --------------------------- blur kernels --------------------------- # -# -------------------------------------------------------------------- # - - -# --------------------------- util functions --------------------------- # -def sigma_matrix2(sig_x, sig_y, theta): - """Calculate the rotated sigma matrix (two dimensional matrix). - - Args: - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - - Returns: - ndarray: Rotated sigma matrix. - """ - d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]]) - u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) - return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T)) - - -def mesh_grid(kernel_size): - """Generate the mesh grid, centering at zero. - - Args: - kernel_size (int): - - Returns: - xy (ndarray): with the shape (kernel_size, kernel_size, 2) - xx (ndarray): with the shape (kernel_size, kernel_size) - yy (ndarray): with the shape (kernel_size, kernel_size) - """ - ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.) - xx, yy = np.meshgrid(ax, ax) - xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size, - 1))).reshape(kernel_size, kernel_size, 2) - return xy, xx, yy - - -def pdf2(sigma_matrix, grid): - """Calculate PDF of the bivariate Gaussian distribution. - - Args: - sigma_matrix (ndarray): with the shape (2, 2) - grid (ndarray): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. - - Returns: - kernel (ndarrray): un-normalized kernel. - """ - inverse_sigma = np.linalg.inv(sigma_matrix) - kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2)) - return kernel - - -def cdf2(d_matrix, grid): - """Calculate the CDF of the standard bivariate Gaussian distribution. - Used in skewed Gaussian distribution. - - Args: - d_matrix (ndarrasy): skew matrix. - grid (ndarray): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. - - Returns: - cdf (ndarray): skewed cdf. - """ - rv = multivariate_normal([0, 0], [[1, 0], [0, 1]]) - grid = np.dot(grid, d_matrix) - cdf = rv.cdf(grid) - return cdf - - -def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True): - """Generate a bivariate isotropic or anisotropic Gaussian kernel. - - In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. - - Args: - kernel_size (int): - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - grid (ndarray, optional): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. Default: None - isotropic (bool): - - Returns: - kernel (ndarray): normalized kernel. - """ - if grid is None: - grid, _, _ = mesh_grid(kernel_size) - if isotropic: - sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) - else: - sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) - kernel = pdf2(sigma_matrix, grid) - kernel = kernel / np.sum(kernel) - return kernel - - -def bivariate_generalized_Gaussian(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): - """Generate a bivariate generalized Gaussian kernel. - - ``Paper: Parameter Estimation For Multivariate Generalized Gaussian Distributions`` - - In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. - - Args: - kernel_size (int): - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - beta (float): shape parameter, beta = 1 is the normal distribution. - grid (ndarray, optional): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. Default: None - - Returns: - kernel (ndarray): normalized kernel. - """ - if grid is None: - grid, _, _ = mesh_grid(kernel_size) - if isotropic: - sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) - else: - sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) - inverse_sigma = np.linalg.inv(sigma_matrix) - kernel = np.exp(-0.5 * np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta)) - kernel = kernel / np.sum(kernel) - return kernel - - -def bivariate_plateau(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): - """Generate a plateau-like anisotropic kernel. - - 1 / (1+x^(beta)) - - Reference: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-shaped-distribution - - In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. - - Args: - kernel_size (int): - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - beta (float): shape parameter, beta = 1 is the normal distribution. - grid (ndarray, optional): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. Default: None - - Returns: - kernel (ndarray): normalized kernel. - """ - if grid is None: - grid, _, _ = mesh_grid(kernel_size) - if isotropic: - sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) - else: - sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) - inverse_sigma = np.linalg.inv(sigma_matrix) - kernel = np.reciprocal(np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta) + 1) - kernel = kernel / np.sum(kernel) - return kernel - - -def random_bivariate_Gaussian(kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - noise_range=None, - isotropic=True): - """Randomly generate bivariate isotropic or anisotropic Gaussian kernels. - - In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. - - Args: - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi, math.pi] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' - sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) - if isotropic is False: - assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' - assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' - sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) - rotation = np.random.uniform(rotation_range[0], rotation_range[1]) - else: - sigma_y = sigma_x - rotation = 0 - - kernel = bivariate_Gaussian(kernel_size, sigma_x, sigma_y, rotation, isotropic=isotropic) - - # add multiplicative noise - if noise_range is not None: - assert noise_range[0] < noise_range[1], 'Wrong noise range.' - noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) - kernel = kernel * noise - kernel = kernel / np.sum(kernel) - return kernel - - -def random_bivariate_generalized_Gaussian(kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - beta_range, - noise_range=None, - isotropic=True): - """Randomly generate bivariate generalized Gaussian kernels. - - In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. - - Args: - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi, math.pi] - beta_range (tuple): [0.5, 8] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' - sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) - if isotropic is False: - assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' - assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' - sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) - rotation = np.random.uniform(rotation_range[0], rotation_range[1]) - else: - sigma_y = sigma_x - rotation = 0 - - # assume beta_range[0] < 1 < beta_range[1] - if np.random.uniform() < 0.5: - beta = np.random.uniform(beta_range[0], 1) - else: - beta = np.random.uniform(1, beta_range[1]) - - kernel = bivariate_generalized_Gaussian(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) - - # add multiplicative noise - if noise_range is not None: - assert noise_range[0] < noise_range[1], 'Wrong noise range.' - noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) - kernel = kernel * noise - kernel = kernel / np.sum(kernel) - return kernel - - -def random_bivariate_plateau(kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - beta_range, - noise_range=None, - isotropic=True): - """Randomly generate bivariate plateau kernels. - - In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. - - Args: - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi/2, math.pi/2] - beta_range (tuple): [1, 4] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' - sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) - if isotropic is False: - assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' - assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' - sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) - rotation = np.random.uniform(rotation_range[0], rotation_range[1]) - else: - sigma_y = sigma_x - rotation = 0 - - # TODO: this may be not proper - if np.random.uniform() < 0.5: - beta = np.random.uniform(beta_range[0], 1) - else: - beta = np.random.uniform(1, beta_range[1]) - - kernel = bivariate_plateau(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) - # add multiplicative noise - if noise_range is not None: - assert noise_range[0] < noise_range[1], 'Wrong noise range.' - noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) - kernel = kernel * noise - kernel = kernel / np.sum(kernel) - - return kernel - - -def random_mixed_kernels(kernel_list, - kernel_prob, - kernel_size=21, - sigma_x_range=(0.6, 5), - sigma_y_range=(0.6, 5), - rotation_range=(-math.pi, math.pi), - betag_range=(0.5, 8), - betap_range=(0.5, 8), - noise_range=None): - """Randomly generate mixed kernels. - - Args: - kernel_list (tuple): a list name of kernel types, - support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso', - 'plateau_aniso'] - kernel_prob (tuple): corresponding kernel probability for each - kernel type - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi, math.pi] - beta_range (tuple): [0.5, 8] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - kernel_type = random.choices(kernel_list, kernel_prob)[0] - if kernel_type == 'iso': - kernel = random_bivariate_Gaussian( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True) - elif kernel_type == 'aniso': - kernel = random_bivariate_Gaussian( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False) - elif kernel_type == 'generalized_iso': - kernel = random_bivariate_generalized_Gaussian( - kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - betag_range, - noise_range=noise_range, - isotropic=True) - elif kernel_type == 'generalized_aniso': - kernel = random_bivariate_generalized_Gaussian( - kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - betag_range, - noise_range=noise_range, - isotropic=False) - elif kernel_type == 'plateau_iso': - kernel = random_bivariate_plateau( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True) - elif kernel_type == 'plateau_aniso': - kernel = random_bivariate_plateau( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False) - return kernel - - -np.seterr(divide='ignore', invalid='ignore') - - -def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0): - """2D sinc filter - - Reference: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter - - Args: - cutoff (float): cutoff frequency in radians (pi is max) - kernel_size (int): horizontal and vertical size, must be odd. - pad_to (int): pad kernel size to desired size, must be odd or zero. - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - kernel = np.fromfunction( - lambda x, y: cutoff * special.j1(cutoff * np.sqrt( - (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt( - (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size]) - kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi) - kernel = kernel / np.sum(kernel) - if pad_to > kernel_size: - pad_size = (pad_to - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - return kernel - - -# ------------------------------------------------------------- # -# --------------------------- noise --------------------------- # -# ------------------------------------------------------------- # - -# ----------------------- Gaussian Noise ----------------------- # - - -def generate_gaussian_noise(img, sigma=10, gray_noise=False): - """Generate Gaussian noise. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - sigma (float): Noise scale (measured in range 255). Default: 10. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - if gray_noise: - noise = np.float32(np.random.randn(*(img.shape[0:2]))) * sigma / 255. - noise = np.expand_dims(noise, axis=2).repeat(3, axis=2) - else: - noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255. - return noise - - -def add_gaussian_noise(img, sigma=10, clip=True, rounds=False, gray_noise=False): - """Add Gaussian noise. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - sigma (float): Noise scale (measured in range 255). Default: 10. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - noise = generate_gaussian_noise(img, sigma, gray_noise) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def generate_gaussian_noise_pt(img, sigma=10, gray_noise=0): - """Add Gaussian noise (PyTorch version). - - Args: - img (Tensor): Shape (b, c, h, w), range[0, 1], float32. - scale (float | Tensor): Noise scale. Default: 1.0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - b, _, h, w = img.size() - if not isinstance(sigma, (float, int)): - sigma = sigma.view(img.size(0), 1, 1, 1) - if isinstance(gray_noise, (float, int)): - cal_gray_noise = gray_noise > 0 - else: - gray_noise = gray_noise.view(b, 1, 1, 1) - cal_gray_noise = torch.sum(gray_noise) > 0 - - if cal_gray_noise: - noise_gray = torch.randn(*img.size()[2:4], dtype=img.dtype, device=img.device) * sigma / 255. - noise_gray = noise_gray.view(b, 1, h, w) - - # always calculate color noise - noise = torch.randn(*img.size(), dtype=img.dtype, device=img.device) * sigma / 255. - - if cal_gray_noise: - noise = noise * (1 - gray_noise) + noise_gray * gray_noise - return noise - - -def add_gaussian_noise_pt(img, sigma=10, gray_noise=0, clip=True, rounds=False): - """Add Gaussian noise (PyTorch version). - - Args: - img (Tensor): Shape (b, c, h, w), range[0, 1], float32. - scale (float | Tensor): Noise scale. Default: 1.0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - noise = generate_gaussian_noise_pt(img, sigma, gray_noise) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ----------------------- Random Gaussian Noise ----------------------- # -def random_generate_gaussian_noise(img, sigma_range=(0, 10), gray_prob=0): - sigma = np.random.uniform(sigma_range[0], sigma_range[1]) - if np.random.uniform() < gray_prob: - gray_noise = True - else: - gray_noise = False - return generate_gaussian_noise(img, sigma, gray_noise) - - -def random_add_gaussian_noise(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_gaussian_noise(img, sigma_range, gray_prob) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def random_generate_gaussian_noise_pt(img, sigma_range=(0, 10), gray_prob=0): - sigma = torch.rand( - img.size(0), dtype=img.dtype, device=img.device) * (sigma_range[1] - sigma_range[0]) + sigma_range[0] - gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) - gray_noise = (gray_noise < gray_prob).float() - return generate_gaussian_noise_pt(img, sigma, gray_noise) - - -def random_add_gaussian_noise_pt(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_gaussian_noise_pt(img, sigma_range, gray_prob) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ----------------------- Poisson (Shot) Noise ----------------------- # - - -def generate_poisson_noise(img, scale=1.0, gray_noise=False): - """Generate poisson noise. - - Reference: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/noise.py#L37-L219 - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - scale (float): Noise scale. Default: 1.0. - gray_noise (bool): Whether generate gray noise. Default: False. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - if gray_noise: - img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - # round and clip image for counting vals correctly - img = np.clip((img * 255.0).round(), 0, 255) / 255. - vals = len(np.unique(img)) - vals = 2**np.ceil(np.log2(vals)) - out = np.float32(np.random.poisson(img * vals) / float(vals)) - noise = out - img - if gray_noise: - noise = np.repeat(noise[:, :, np.newaxis], 3, axis=2) - return noise * scale - - -def add_poisson_noise(img, scale=1.0, clip=True, rounds=False, gray_noise=False): - """Add poisson noise. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - scale (float): Noise scale. Default: 1.0. - gray_noise (bool): Whether generate gray noise. Default: False. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - noise = generate_poisson_noise(img, scale, gray_noise) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def generate_poisson_noise_pt(img, scale=1.0, gray_noise=0): - """Generate a batch of poisson noise (PyTorch version) - - Args: - img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. - scale (float | Tensor): Noise scale. Number or Tensor with shape (b). - Default: 1.0. - gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). - 0 for False, 1 for True. Default: 0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - b, _, h, w = img.size() - if isinstance(gray_noise, (float, int)): - cal_gray_noise = gray_noise > 0 - else: - gray_noise = gray_noise.view(b, 1, 1, 1) - cal_gray_noise = torch.sum(gray_noise) > 0 - if cal_gray_noise: - img_gray = rgb_to_grayscale(img, num_output_channels=1) - # round and clip image for counting vals correctly - img_gray = torch.clamp((img_gray * 255.0).round(), 0, 255) / 255. - # use for-loop to get the unique values for each sample - vals_list = [len(torch.unique(img_gray[i, :, :, :])) for i in range(b)] - vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] - vals = img_gray.new_tensor(vals_list).view(b, 1, 1, 1) - out = torch.poisson(img_gray * vals) / vals - noise_gray = out - img_gray - noise_gray = noise_gray.expand(b, 3, h, w) - - # always calculate color noise - # round and clip image for counting vals correctly - img = torch.clamp((img * 255.0).round(), 0, 255) / 255. - # use for-loop to get the unique values for each sample - vals_list = [len(torch.unique(img[i, :, :, :])) for i in range(b)] - vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] - vals = img.new_tensor(vals_list).view(b, 1, 1, 1) - out = torch.poisson(img * vals) / vals - noise = out - img - if cal_gray_noise: - noise = noise * (1 - gray_noise) + noise_gray * gray_noise - if not isinstance(scale, (float, int)): - scale = scale.view(b, 1, 1, 1) - return noise * scale - - -def add_poisson_noise_pt(img, scale=1.0, clip=True, rounds=False, gray_noise=0): - """Add poisson noise to a batch of images (PyTorch version). - - Args: - img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. - scale (float | Tensor): Noise scale. Number or Tensor with shape (b). - Default: 1.0. - gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). - 0 for False, 1 for True. Default: 0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - noise = generate_poisson_noise_pt(img, scale, gray_noise) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ----------------------- Random Poisson (Shot) Noise ----------------------- # - - -def random_generate_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0): - scale = np.random.uniform(scale_range[0], scale_range[1]) - if np.random.uniform() < gray_prob: - gray_noise = True - else: - gray_noise = False - return generate_poisson_noise(img, scale, gray_noise) - - -def random_add_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_poisson_noise(img, scale_range, gray_prob) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def random_generate_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0): - scale = torch.rand( - img.size(0), dtype=img.dtype, device=img.device) * (scale_range[1] - scale_range[0]) + scale_range[0] - gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) - gray_noise = (gray_noise < gray_prob).float() - return generate_poisson_noise_pt(img, scale, gray_noise) - - -def random_add_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_poisson_noise_pt(img, scale_range, gray_prob) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ------------------------------------------------------------------------ # -# --------------------------- JPEG compression --------------------------- # -# ------------------------------------------------------------------------ # - - -def add_jpg_compression(img, quality=90): - """Add JPG compression artifacts. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - quality (float): JPG compression quality. 0 for lowest quality, 100 for - best quality. Default: 90. - - Returns: - (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], - float32. - """ - img = np.clip(img, 0, 1) - encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality] - _, encimg = cv2.imencode('.jpg', img * 255., encode_param) - img = np.float32(cv2.imdecode(encimg, 1)) / 255. - return img - - -def random_add_jpg_compression(img, quality_range=(90, 100)): - """Randomly add JPG compression artifacts. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - quality_range (tuple[float] | list[float]): JPG compression quality - range. 0 for lowest quality, 100 for best quality. - Default: (90, 100). - - Returns: - (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], - float32. - """ - quality = np.random.uniform(quality_range[0], quality_range[1]) - return add_jpg_compression(img, quality) diff --git a/basicsr/data/ffhq_dataset.py b/basicsr/data/ffhq_dataset.py deleted file mode 100644 index d86844075726e815f901ad5d10e4e374c7e3ff20..0000000000000000000000000000000000000000 --- a/basicsr/data/ffhq_dataset.py +++ /dev/null @@ -1,80 +0,0 @@ -import random -import time -from os import path as osp -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class FFHQDataset(data.Dataset): - """FFHQ dataset for StyleGAN. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - io_backend (dict): IO backend type and other kwarg. - mean (list | tuple): Image mean. - std (list | tuple): Image std. - use_hflip (bool): Whether to horizontally flip. - - """ - - def __init__(self, opt): - super(FFHQDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - - self.gt_folder = opt['dataroot_gt'] - self.mean = opt['mean'] - self.std = opt['std'] - - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = self.gt_folder - if not self.gt_folder.endswith('.lmdb'): - raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # FFHQ has 70000 images in total - self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)] - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # load gt image - gt_path = self.paths[index] - # avoid errors caused by high latency in reading files - retry = 3 - while retry > 0: - try: - img_bytes = self.file_client.get(gt_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'File client error: {e}, remaining retry times: {retry - 1}') - # change another file to read - index = random.randint(0, self.__len__()) - gt_path = self.paths[index] - time.sleep(1) # sleep 1s for occasional server congestion - else: - break - finally: - retry -= 1 - img_gt = imfrombytes(img_bytes, float32=True) - - # random horizontal flip - img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False) - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) - # normalize - normalize(img_gt, self.mean, self.std, inplace=True) - return {'gt': img_gt, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/imagent_dataset.py b/basicsr/data/imagent_dataset.py deleted file mode 100644 index 8a8fc196bd487762766160b3574b7fba108d22bd..0000000000000000000000000000000000000000 --- a/basicsr/data/imagent_dataset.py +++ /dev/null @@ -1,460 +0,0 @@ -# Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# This work is licensed under a Creative Commons -# Attribution-NonCommercial-ShareAlike 4.0 International License. -# You should have received a copy of the license along with this -# work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/ - -"""Streaming images and labels from datasets created with dataset_tool.py.""" - -import os -import numpy as np -import zipfile -import PIL.Image -import json -import torch -import random - -from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels -from basicsr.data.transforms import augment -from basicsr.utils import img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - -try: - import pyspng -except ImportError: - pyspng = None - -KERNEL_OPT = { - 'blur_kernel_size': 21, - 'kernel_list': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'], - 'kernel_prob': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03], - 'sinc_prob': 0.1, - 'blur_sigma': [0.2, 3], - 'betag_range': [0.5, 4], - 'betap_range': [1, 2], - - 'blur_kernel_size2': 21, - 'kernel_list2': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'], - 'kernel_prob2': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03], - 'sinc_prob2': 0.1, - 'blur_sigma2': [0.2, 1.5], - 'betag_range2': [0.5, 4], - 'betap_range2': [1, 2], - 'final_sinc_prob': 0.8, - - 'use_hflip': False, - 'use_rot': False -} - -DEGRADE_OPT = { - 'resize_prob': [0.2, 0.7, 0.1], # up, down, keep - 'resize_range': [0.15, 1.5], - 'gaussian_noise_prob': 0.5, - 'noise_range': [1, 30], - 'poisson_scale_range': [0.05, 3], - 'gray_noise_prob': 0.4, - 'jpeg_range': [30, 95], - - # the second degradation process - 'second_blur_prob': 0.8, - 'resize_prob2': [0.3, 0.4, 0.3], # up, down, keep - 'resize_range2': [0.3, 1.2], - 'gaussian_noise_prob2': 0.5, - 'noise_range2': [1, 25], - 'poisson_scale_range2': [0.05, 2.5], - 'gray_noise_prob2': 0.4, - 'jpeg_range2': [30, 95], - - 'gt_size': 512, - 'no_degradation_prob': 0.01, - 'use_usm': True, - 'sf': 4, - 'random_size': False, - 'resize_lq': False -} - -#---------------------------------------------------------------------------- -# Abstract base class for datasets. - -class Dataset(torch.utils.data.Dataset): - def __init__(self, - name, # Name of the dataset. - raw_shape, # Shape of the raw image data (NCHW). - use_labels = True, # Enable conditioning labels? False = label dimension is zero. - max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. - xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size. - random_seed = 0, # Random seed to use when applying max_size. - cache = False, # Cache images in CPU memory? - ): - self._name = name - self._raw_shape = list(raw_shape) - self._use_labels = use_labels - self._cache = cache - self._cached_images = dict() # {raw_idx: np.ndarray, ...} - self._raw_labels = None - self._label_shape = None - - # Apply max_size. - self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64) - if (max_size is not None) and (self._raw_idx.size > max_size): - np.random.RandomState(random_seed % (1 << 31)).shuffle(self._raw_idx) - self._raw_idx = np.sort(self._raw_idx[:max_size]) - - # Apply xflip. - self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) - if xflip: - self._raw_idx = np.tile(self._raw_idx, 2) - self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) - - def _get_raw_labels(self): - if self._raw_labels is None: - self._raw_labels = self._load_raw_labels() if self._use_labels else None - if self._raw_labels is None: - self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) - assert isinstance(self._raw_labels, np.ndarray) - assert self._raw_labels.shape[0] == self._raw_shape[0] - assert self._raw_labels.dtype in [np.float32, np.int64] - if self._raw_labels.dtype == np.int64: - assert self._raw_labels.ndim == 1 - assert np.all(self._raw_labels >= 0) - return self._raw_labels - - def close(self): # to be overridden by subclass - pass - - def _load_raw_image(self, raw_idx): # to be overridden by subclass - raise NotImplementedError - - def _load_raw_labels(self): # to be overridden by subclass - raise NotImplementedError - - def __getstate__(self): - return dict(self.__dict__, _raw_labels=None) - - def __del__(self): - try: - self.close() - except: - pass - - def __len__(self): - return self._raw_idx.size - - def __getitem__(self, idx): - raw_idx = self._raw_idx[idx] - image = self._cached_images.get(raw_idx, None) - if image is None: - image = self._load_raw_image(raw_idx) - if self._cache: - self._cached_images[raw_idx] = image - assert isinstance(image, np.ndarray) - assert list(image.shape) == self._raw_shape[1:] - if self._xflip[idx]: - assert image.ndim == 3 # CHW - image = image[:, :, ::-1] - return image.copy(), self.get_label(idx) - - def get_label(self, idx): - label = self._get_raw_labels()[self._raw_idx[idx]] - if label.dtype == np.int64: - onehot = np.zeros(self.label_shape, dtype=np.float32) - onehot[label] = 1 - label = onehot - return label.copy() - - def get_details(self, idx): - d = dict() - d['raw_idx'] = int(self._raw_idx[idx]) - d['xflip'] = (int(self._xflip[idx]) != 0) - d['raw_label'] = self._get_raw_labels()[d['raw_idx']].copy() - return d - - @property - def name(self): - return self._name - - @property - def image_shape(self): # [CHW] - return list(self._raw_shape[1:]) - - @property - def num_channels(self): - assert len(self.image_shape) == 3 # CHW - return self.image_shape[0] - - @property - def resolution(self): - assert len(self.image_shape) == 3 # CHW - assert self.image_shape[1] == self.image_shape[2] - return self.image_shape[1] - - @property - def label_shape(self): - if self._label_shape is None: - raw_labels = self._get_raw_labels() - if raw_labels.dtype == np.int64: - self._label_shape = [int(np.max(raw_labels)) + 1] - else: - self._label_shape = raw_labels.shape[1:] - return list(self._label_shape) - - @property - def label_dim(self): - assert len(self.label_shape) == 1 - return self.label_shape[0] - - @property - def has_labels(self): - return any(x != 0 for x in self.label_shape) - - @property - def has_onehot_labels(self): - return self._get_raw_labels().dtype == np.int64 - -#---------------------------------------------------------------------------- -# Dataset subclass that loads images recursively from the specified directory -# or ZIP file. - -class ImageFolderDataset(Dataset): - def __init__(self, - path, # Path to directory or zip. - resolution = None, # Ensure specific resolution, None = anything goes. - **super_kwargs, # Additional arguments for the Dataset base class. - ): - self._path = path - self._zipfile = None - - if os.path.isdir(self._path): - self._type = 'dir' - self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} - elif self._file_ext(self._path) == '.zip': - self._type = 'zip' - self._all_fnames = set(self._get_zipfile().namelist()) - else: - raise IOError('Path must point to a directory or zip') - - PIL.Image.init() - supported_ext = PIL.Image.EXTENSION.keys() | {'.npy'} - self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in supported_ext) - if len(self._image_fnames) == 0: - raise IOError('No image files found in the specified path') - - name = os.path.splitext(os.path.basename(self._path))[0] - raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) - if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution): - raise IOError('Image files do not match the specified resolution') - super().__init__(name=name, raw_shape=raw_shape, **super_kwargs) - - @staticmethod - def _file_ext(fname): - return os.path.splitext(fname)[1].lower() - - def _get_zipfile(self): - assert self._type == 'zip' - if self._zipfile is None: - self._zipfile = zipfile.ZipFile(self._path) - return self._zipfile - - def _open_file(self, fname): - if self._type == 'dir': - return open(os.path.join(self._path, fname), 'rb') - if self._type == 'zip': - return self._get_zipfile().open(fname, 'r') - return None - - def close(self): - try: - if self._zipfile is not None: - self._zipfile.close() - finally: - self._zipfile = None - - def __getstate__(self): - return dict(super().__getstate__(), _zipfile=None) - - def _load_raw_image(self, raw_idx): - fname = self._image_fnames[raw_idx] - ext = self._file_ext(fname) - with self._open_file(fname) as f: - if ext == '.npy': - image = np.load(f) - image = image.reshape(-1, *image.shape[-2:]) - elif ext == '.png' and pyspng is not None: - image = pyspng.load(f.read()) - image = image.reshape(*image.shape[:2], -1).transpose(2, 0, 1) - else: - image = np.array(PIL.Image.open(f)) - image = image.reshape(*image.shape[:2], -1).transpose(2, 0, 1) - return image - - def _load_raw_labels(self): - fname = 'dataset.json' - if fname not in self._all_fnames: - return None - with self._open_file(fname) as f: - labels = json.load(f)['labels'] - if labels is None: - return None - labels = dict(labels) - labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames] - labels = np.array(labels) - labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) - return labels - -#---------------------------------------------------------------------------- -@DATASET_REGISTRY.register(suffix='basicsr') -class IRImageFolderDataset(ImageFolderDataset): - def __init__(self, - opt=None, # Degradation kernel config. - **super_kwargs, # Additional arguments for the Dataset base class. - ): - if opt is None: opt = KERNEL_OPT - self.opt = opt - super().__init__(**super_kwargs) - - # blur settings for the first degradation - self.blur_kernel_size = opt['blur_kernel_size'] - self.kernel_list = opt['kernel_list'] - self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability - self.blur_sigma = opt['blur_sigma'] - self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels - self.betap_range = opt['betap_range'] # betap used in plateau blur kernels - self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters - - # blur settings for the second degradation - self.blur_kernel_size2 = opt['blur_kernel_size2'] - self.kernel_list2 = opt['kernel_list2'] - self.kernel_prob2 = opt['kernel_prob2'] - self.blur_sigma2 = opt['blur_sigma2'] - self.betag_range2 = opt['betag_range2'] - self.betap_range2 = opt['betap_range2'] - self.sinc_prob2 = opt['sinc_prob2'] - - # a final sinc filter - self.final_sinc_prob = opt['final_sinc_prob'] - - self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21 - # TODO: kernel range is now hard-coded, should be in the configure file - self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect - self.pulse_tensor[10, 10] = 1 - - def _load_raw_image(self, raw_idx): - fname = self._image_fnames[raw_idx] - ext = self._file_ext(fname) - with self._open_file(fname) as f: - if ext == '.npy': - image = np.load(f) - image = image.reshape(-1, *image.shape[-2:]) - elif ext == '.png' and pyspng is not None: - image = pyspng.load(f.read()) - image = image.reshape(*image.shape[:2], -1).transpose(2, 0, 1) - else: - image = np.array(PIL.Image.open(f)) - image = image.reshape(*image.shape[:2], -1).transpose(2, 0, 1) - return image - - def __getitem__(self, idx): - raw_idx = self._raw_idx[idx] - image = self._cached_images.get(raw_idx, None) - if image is None: - image = self._load_raw_image(raw_idx) - if self._cache: - self._cached_images[raw_idx] = image - - assert isinstance(image, np.ndarray), type(image) - assert list(image.shape) == self._raw_shape[1:], image.shape - - # # FIXME: flip or rotate - # image = augment(image, self.opt['use_hflip'], self.opt['use_rot']) - - image = image.astype(np.float32) / 255. - - # ------------------------ Generate kernels (used in the first degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob']: - # this sinc filter setting is for kernels ranging from [7, 21] - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel = random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - kernel_size, - self.blur_sigma, - self.blur_sigma, [-np.pi, np.pi], - self.betag_range, - self.betap_range, - noise_range=None) - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------ Generate kernels (used in the second degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob2']: - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel2 = random_mixed_kernels( - self.kernel_list2, - self.kernel_prob2, - kernel_size, - self.blur_sigma2, - self.blur_sigma2, [-np.pi, np.pi], - self.betag_range2, - self.betap_range2, - noise_range=None) - - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------------------- the final sinc kernel ------------------------------------- # - if np.random.uniform() < self.opt['final_sinc_prob']: - kernel_size = random.choice(self.kernel_range) - omega_c = np.random.uniform(np.pi / 3, np.pi) - sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21) - sinc_kernel = torch.FloatTensor(sinc_kernel) - else: - sinc_kernel = self.pulse_tensor - - # numpy to tensor - img_gt = torch.from_numpy(image).float() - - kernel = torch.FloatTensor(kernel) - kernel2 = torch.FloatTensor(kernel2) - - return_d = {'image': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel} - return return_d - - # return image.copy(), self.get_label(idx) - -def collate_fn(examples, with_prior_preservation=False): - pixel_values = [example["img_tensor"] for example in examples] - kernel1 = [example["kernel1"] for example in examples] - kernel2 = [example["kernel2"] for example in examples] - sinc_kernel = [example["sinc_kernel"] for example in examples] - pil_image = [example["image"] for example in examples] - - if with_prior_preservation: - raise NotImplementedError("Prior preservation not implemented.") - - pixel_values = torch.stack(pixel_values) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - - kernel1 = torch.stack(kernel1) - kernel1 = kernel1.to(memory_format=torch.contiguous_format).float() - kernel2 = torch.stack(kernel2) - kernel2 = kernel2.to(memory_format=torch.contiguous_format).float() - sinc_kernel = torch.stack(sinc_kernel) - sinc_kernel = sinc_kernel.to(memory_format=torch.contiguous_format).float() - - batch = {"image": pil_image, "img_tensor": pixel_values, "kernel1": kernel1, "kernel2": kernel2, "sinc_kernel": sinc_kernel} - return batch \ No newline at end of file diff --git a/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt b/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt deleted file mode 100644 index 0ed4542fd56c4a4e8a7746db2d53d6ea2143030d..0000000000000000000000000000000000000000 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a/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt b/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt deleted file mode 100644 index e2de42f6271d34a4b6282f00c18ca0da0d7e1e36..0000000000000000000000000000000000000000 --- a/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt +++ /dev/null @@ -1,4 +0,0 @@ -000 100 (720,1280,3) -011 100 (720,1280,3) -015 100 (720,1280,3) -020 100 (720,1280,3) diff --git a/basicsr/data/meta_info/meta_info_REDS_GT.txt b/basicsr/data/meta_info/meta_info_REDS_GT.txt deleted file mode 100644 index 7b23e31ac346a3b0868fab063dc7faea9d5f6581..0000000000000000000000000000000000000000 --- a/basicsr/data/meta_info/meta_info_REDS_GT.txt +++ /dev/null @@ -1,270 +0,0 @@ -000 100 (720,1280,3) -001 100 (720,1280,3) -002 100 (720,1280,3) -003 100 (720,1280,3) -004 100 (720,1280,3) -005 100 (720,1280,3) -006 100 (720,1280,3) -007 100 (720,1280,3) -008 100 (720,1280,3) -009 100 (720,1280,3) -010 100 (720,1280,3) -011 100 (720,1280,3) -012 100 (720,1280,3) -013 100 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a/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt +++ /dev/null @@ -1,4 +0,0 @@ -240 100 (720,1280,3) -241 100 (720,1280,3) -246 100 (720,1280,3) -257 100 (720,1280,3) diff --git a/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt b/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt deleted file mode 100644 index d3974db65480f8bda311e43e0d5104b39c3ecf8e..0000000000000000000000000000000000000000 --- a/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt +++ /dev/null @@ -1,30 +0,0 @@ -240 100 (720,1280,3) -241 100 (720,1280,3) -242 100 (720,1280,3) -243 100 (720,1280,3) -244 100 (720,1280,3) -245 100 (720,1280,3) -246 100 (720,1280,3) -247 100 (720,1280,3) -248 100 (720,1280,3) -249 100 (720,1280,3) -250 100 (720,1280,3) -251 100 (720,1280,3) -252 100 (720,1280,3) -253 100 (720,1280,3) -254 100 (720,1280,3) -255 100 (720,1280,3) -256 100 (720,1280,3) -257 100 (720,1280,3) -258 100 (720,1280,3) -259 100 (720,1280,3) -260 100 (720,1280,3) -261 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(256,448,3) -00096/0919 7 (256,448,3) -00096/0920 7 (256,448,3) -00096/0921 7 (256,448,3) -00096/0922 7 (256,448,3) -00096/0923 7 (256,448,3) -00096/0924 7 (256,448,3) -00096/0925 7 (256,448,3) -00096/0926 7 (256,448,3) -00096/0927 7 (256,448,3) -00096/0928 7 (256,448,3) -00096/0929 7 (256,448,3) -00096/0930 7 (256,448,3) -00096/0931 7 (256,448,3) -00096/0932 7 (256,448,3) -00096/0933 7 (256,448,3) -00096/0934 7 (256,448,3) -00096/0935 7 (256,448,3) -00096/0936 7 (256,448,3) diff --git a/basicsr/data/paired_image_dataset.py b/basicsr/data/paired_image_dataset.py deleted file mode 100644 index b6d60a09b8911a6fcd72da42d5aa685b157770fa..0000000000000000000000000000000000000000 --- a/basicsr/data/paired_image_dataset.py +++ /dev/null @@ -1,106 +0,0 @@ -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb, paired_paths_from_meta_info_file -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, bgr2ycbcr, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class PairedImageDataset(data.Dataset): - """Paired image dataset for image restoration. - - Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. - - There are three modes: - - 1. **lmdb**: Use lmdb files. If opt['io_backend'] == lmdb. - 2. **meta_info_file**: Use meta information file to generate paths. \ - If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None. - 3. **folder**: Scan folders to generate paths. The rest. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - meta_info_file (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. - Default: '{}'. - gt_size (int): Cropped patched size for gt patches. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - phase (str): 'train' or 'val'. - """ - - def __init__(self, opt): - super(PairedImageDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.mean = opt['mean'] if 'mean' in opt else None - self.std = opt['std'] if 'std' in opt else None - - self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] - if 'filename_tmpl' in opt: - self.filename_tmpl = opt['filename_tmpl'] - else: - self.filename_tmpl = '{}' - - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) - elif 'meta_info_file' in self.opt and self.opt['meta_info_file'] is not None: - self.paths = paired_paths_from_meta_info_file([self.lq_folder, self.gt_folder], ['lq', 'gt'], - self.opt['meta_info_file'], self.filename_tmpl) - else: - self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - - # Load gt and lq images. Dimension order: HWC; channel order: BGR; - # image range: [0, 1], float32. - gt_path = self.paths[index]['gt_path'] - img_bytes = self.file_client.get(gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - lq_path = self.paths[index]['lq_path'] - img_bytes = self.file_client.get(lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - - # augmentation for training - if self.opt['phase'] == 'train': - gt_size = self.opt['gt_size'] - # random crop - img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) - # flip, rotation - img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) - - # color space transform - if 'color' in self.opt and self.opt['color'] == 'y': - img_gt = bgr2ycbcr(img_gt, y_only=True)[..., None] - img_lq = bgr2ycbcr(img_lq, y_only=True)[..., None] - - # crop the unmatched GT images during validation or testing, especially for SR benchmark datasets - # TODO: It is better to update the datasets, rather than force to crop - if self.opt['phase'] != 'train': - img_gt = img_gt[0:img_lq.shape[0] * scale, 0:img_lq.shape[1] * scale, :] - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) - # normalize - if self.mean is not None or self.std is not None: - normalize(img_lq, self.mean, self.std, inplace=True) - normalize(img_gt, self.mean, self.std, inplace=True) - - return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/prefetch_dataloader.py b/basicsr/data/prefetch_dataloader.py deleted file mode 100644 index f158a5042c3bc86a9b047025f889c4afa52a83c2..0000000000000000000000000000000000000000 --- a/basicsr/data/prefetch_dataloader.py +++ /dev/null @@ -1,122 +0,0 @@ -import queue as Queue -import threading -import torch -from torch.utils.data import DataLoader - - -class PrefetchGenerator(threading.Thread): - """A general prefetch generator. - - Reference: https://stackoverflow.com/questions/7323664/python-generator-pre-fetch - - Args: - generator: Python generator. - num_prefetch_queue (int): Number of prefetch queue. - """ - - def __init__(self, generator, num_prefetch_queue): - threading.Thread.__init__(self) - self.queue = Queue.Queue(num_prefetch_queue) - self.generator = generator - self.daemon = True - self.start() - - def run(self): - for item in self.generator: - self.queue.put(item) - self.queue.put(None) - - def __next__(self): - next_item = self.queue.get() - if next_item is None: - raise StopIteration - return next_item - - def __iter__(self): - return self - - -class PrefetchDataLoader(DataLoader): - """Prefetch version of dataloader. - - Reference: https://github.com/IgorSusmelj/pytorch-styleguide/issues/5# - - TODO: - Need to test on single gpu and ddp (multi-gpu). There is a known issue in - ddp. - - Args: - num_prefetch_queue (int): Number of prefetch queue. - kwargs (dict): Other arguments for dataloader. - """ - - def __init__(self, num_prefetch_queue, **kwargs): - self.num_prefetch_queue = num_prefetch_queue - super(PrefetchDataLoader, self).__init__(**kwargs) - - def __iter__(self): - return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue) - - -class CPUPrefetcher(): - """CPU prefetcher. - - Args: - loader: Dataloader. - """ - - def __init__(self, loader): - self.ori_loader = loader - self.loader = iter(loader) - - def next(self): - try: - return next(self.loader) - except StopIteration: - return None - - def reset(self): - self.loader = iter(self.ori_loader) - - -class CUDAPrefetcher(): - """CUDA prefetcher. - - Reference: https://github.com/NVIDIA/apex/issues/304# - - It may consume more GPU memory. - - Args: - loader: Dataloader. - opt (dict): Options. - """ - - def __init__(self, loader, opt): - self.ori_loader = loader - self.loader = iter(loader) - self.opt = opt - self.stream = torch.cuda.Stream() - self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') - self.preload() - - def preload(self): - try: - self.batch = next(self.loader) # self.batch is a dict - except StopIteration: - self.batch = None - return None - # put tensors to gpu - with torch.cuda.stream(self.stream): - for k, v in self.batch.items(): - if torch.is_tensor(v): - self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True) - - def next(self): - torch.cuda.current_stream().wait_stream(self.stream) - batch = self.batch - self.preload() - return batch - - def reset(self): - self.loader = iter(self.ori_loader) - self.preload() diff --git a/basicsr/data/realesrgan_dataset.py b/basicsr/data/realesrgan_dataset.py deleted file mode 100644 index 1d01fe5f8b4fb0af30254cdeeb947fb69d525ca0..0000000000000000000000000000000000000000 --- a/basicsr/data/realesrgan_dataset.py +++ /dev/null @@ -1,193 +0,0 @@ -import cv2 -import math -import numpy as np -import os -import os.path as osp -import random -import time -import torch -from torch.utils import data as data - -from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register(suffix='basicsr') -class RealESRGANDataset(data.Dataset): - """Dataset used for Real-ESRGAN model: - Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It loads gt (Ground-Truth) images, and augments them. - It also generates blur kernels and sinc kernels for generating low-quality images. - Note that the low-quality images are processed in tensors on GPUS for faster processing. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - meta_info (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - Please see more options in the codes. - """ - - def __init__(self, opt): - super(RealESRGANDataset, self).__init__() - self.opt = opt - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.gt_folder = opt['dataroot_gt'] - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.gt_folder] - self.io_backend_opt['client_keys'] = ['gt'] - if not self.gt_folder.endswith('.lmdb'): - raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # disk backend with meta_info - # Each line in the meta_info describes the relative path to an image - with open(self.opt['meta_info']) as fin: - paths = [line.strip().split(' ')[0] for line in fin] - self.paths = [os.path.join(self.gt_folder, v) for v in paths] - - # blur settings for the first degradation - self.blur_kernel_size = opt['blur_kernel_size'] - self.kernel_list = opt['kernel_list'] - self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability - self.blur_sigma = opt['blur_sigma'] - self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels - self.betap_range = opt['betap_range'] # betap used in plateau blur kernels - self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters - - # blur settings for the second degradation - self.blur_kernel_size2 = opt['blur_kernel_size2'] - self.kernel_list2 = opt['kernel_list2'] - self.kernel_prob2 = opt['kernel_prob2'] - self.blur_sigma2 = opt['blur_sigma2'] - self.betag_range2 = opt['betag_range2'] - self.betap_range2 = opt['betap_range2'] - self.sinc_prob2 = opt['sinc_prob2'] - - # a final sinc filter - self.final_sinc_prob = opt['final_sinc_prob'] - - self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21 - # TODO: kernel range is now hard-coded, should be in the configure file - self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect - self.pulse_tensor[10, 10] = 1 - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # -------------------------------- Load gt images -------------------------------- # - # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. - gt_path = self.paths[index] - # avoid errors caused by high latency in reading files - retry = 3 - while retry > 0: - try: - img_bytes = self.file_client.get(gt_path, 'gt') - except (IOError, OSError) as e: - logger = get_root_logger() - logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}') - # change another file to read - index = random.randint(0, self.__len__()) - gt_path = self.paths[index] - time.sleep(1) # sleep 1s for occasional server congestion - else: - break - finally: - retry -= 1 - img_gt = imfrombytes(img_bytes, float32=True) - - # -------------------- Do augmentation for training: flip, rotation -------------------- # - img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot']) - - # crop or pad to 400 - # TODO: 400 is hard-coded. You may change it accordingly - h, w = img_gt.shape[0:2] - crop_pad_size = 400 - # pad - if h < crop_pad_size or w < crop_pad_size: - pad_h = max(0, crop_pad_size - h) - pad_w = max(0, crop_pad_size - w) - img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101) - # crop - if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size: - h, w = img_gt.shape[0:2] - # randomly choose top and left coordinates - top = random.randint(0, h - crop_pad_size) - left = random.randint(0, w - crop_pad_size) - img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...] - - # ------------------------ Generate kernels (used in the first degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob']: - # this sinc filter setting is for kernels ranging from [7, 21] - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel = random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - kernel_size, - self.blur_sigma, - self.blur_sigma, [-math.pi, math.pi], - self.betag_range, - self.betap_range, - noise_range=None) - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------ Generate kernels (used in the second degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob2']: - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel2 = random_mixed_kernels( - self.kernel_list2, - self.kernel_prob2, - kernel_size, - self.blur_sigma2, - self.blur_sigma2, [-math.pi, math.pi], - self.betag_range2, - self.betap_range2, - noise_range=None) - - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------------------- the final sinc kernel ------------------------------------- # - if np.random.uniform() < self.opt['final_sinc_prob']: - kernel_size = random.choice(self.kernel_range) - omega_c = np.random.uniform(np.pi / 3, np.pi) - sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21) - sinc_kernel = torch.FloatTensor(sinc_kernel) - else: - sinc_kernel = self.pulse_tensor - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0] - kernel = torch.FloatTensor(kernel) - kernel2 = torch.FloatTensor(kernel2) - - return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path} - return return_d - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/realesrgan_paired_dataset.py b/basicsr/data/realesrgan_paired_dataset.py deleted file mode 100644 index 7e07a731f2664256d3e879855b39cda232a96503..0000000000000000000000000000000000000000 --- a/basicsr/data/realesrgan_paired_dataset.py +++ /dev/null @@ -1,106 +0,0 @@ -import os -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register(suffix='basicsr') -class RealESRGANPairedDataset(data.Dataset): - """Paired image dataset for image restoration. - - Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. - - There are three modes: - - 1. **lmdb**: Use lmdb files. If opt['io_backend'] == lmdb. - 2. **meta_info_file**: Use meta information file to generate paths. \ - If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None. - 3. **folder**: Scan folders to generate paths. The rest. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - meta_info (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. - Default: '{}'. - gt_size (int): Cropped patched size for gt patches. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - phase (str): 'train' or 'val'. - """ - - def __init__(self, opt): - super(RealESRGANPairedDataset, self).__init__() - self.opt = opt - self.file_client = None - self.io_backend_opt = opt['io_backend'] - # mean and std for normalizing the input images - self.mean = opt['mean'] if 'mean' in opt else None - self.std = opt['std'] if 'std' in opt else None - - self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] - self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}' - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) - elif 'meta_info' in self.opt and self.opt['meta_info'] is not None: - # disk backend with meta_info - # Each line in the meta_info describes the relative path to an image - with open(self.opt['meta_info']) as fin: - paths = [line.strip() for line in fin] - self.paths = [] - for path in paths: - gt_path, lq_path = path.split(', ') - gt_path = os.path.join(self.gt_folder, gt_path) - lq_path = os.path.join(self.lq_folder, lq_path) - self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)])) - else: - # disk backend - # it will scan the whole folder to get meta info - # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file - self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - - # Load gt and lq images. Dimension order: HWC; channel order: BGR; - # image range: [0, 1], float32. - gt_path = self.paths[index]['gt_path'] - img_bytes = self.file_client.get(gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - lq_path = self.paths[index]['lq_path'] - img_bytes = self.file_client.get(lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - - # augmentation for training - if self.opt['phase'] == 'train': - gt_size = self.opt['gt_size'] - # random crop - img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) - # flip, rotation - img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) - # normalize - if self.mean is not None or self.std is not None: - normalize(img_lq, self.mean, self.std, inplace=True) - normalize(img_gt, self.mean, self.std, inplace=True) - - return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/reds_dataset.py b/basicsr/data/reds_dataset.py deleted file mode 100644 index 22dc46b44d499fa1925d353bc5cc3ea4c39237b4..0000000000000000000000000000000000000000 --- a/basicsr/data/reds_dataset.py +++ /dev/null @@ -1,352 +0,0 @@ -import numpy as np -import random -import torch -from pathlib import Path -from torch.utils import data as data - -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.flow_util import dequantize_flow -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class REDSDataset(data.Dataset): - """REDS dataset for training. - - The keys are generated from a meta info txt file. - basicsr/data/meta_info/meta_info_REDS_GT.txt - - Each line contains: - 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by - a white space. - Examples: - 000 100 (720,1280,3) - 001 100 (720,1280,3) - ... - - Key examples: "000/00000000" - GT (gt): Ground-Truth; - LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - dataroot_flow (str, optional): Data root path for flow. - meta_info_file (str): Path for meta information file. - val_partition (str): Validation partition types. 'REDS4' or 'official'. - io_backend (dict): IO backend type and other kwarg. - num_frame (int): Window size for input frames. - gt_size (int): Cropped patched size for gt patches. - interval_list (list): Interval list for temporal augmentation. - random_reverse (bool): Random reverse input frames. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - """ - - def __init__(self, opt): - super(REDSDataset, self).__init__() - self.opt = opt - self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) - self.flow_root = Path(opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None - assert opt['num_frame'] % 2 == 1, (f'num_frame should be odd number, but got {opt["num_frame"]}') - self.num_frame = opt['num_frame'] - self.num_half_frames = opt['num_frame'] // 2 - - self.keys = [] - with open(opt['meta_info_file'], 'r') as fin: - for line in fin: - folder, frame_num, _ = line.split(' ') - self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))]) - - # remove the video clips used in validation - if opt['val_partition'] == 'REDS4': - val_partition = ['000', '011', '015', '020'] - elif opt['val_partition'] == 'official': - val_partition = [f'{v:03d}' for v in range(240, 270)] - else: - raise ValueError(f'Wrong validation partition {opt["val_partition"]}.' - f"Supported ones are ['official', 'REDS4'].") - self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.is_lmdb = False - if self.io_backend_opt['type'] == 'lmdb': - self.is_lmdb = True - if self.flow_root is not None: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] - else: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - - # temporal augmentation configs - self.interval_list = opt['interval_list'] - self.random_reverse = opt['random_reverse'] - interval_str = ','.join(str(x) for x in opt['interval_list']) - logger = get_root_logger() - logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' - f'random reverse is {self.random_reverse}.') - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip_name, frame_name = key.split('/') # key example: 000/00000000 - center_frame_idx = int(frame_name) - - # determine the neighboring frames - interval = random.choice(self.interval_list) - - # ensure not exceeding the borders - start_frame_idx = center_frame_idx - self.num_half_frames * interval - end_frame_idx = center_frame_idx + self.num_half_frames * interval - # each clip has 100 frames starting from 0 to 99 - while (start_frame_idx < 0) or (end_frame_idx > 99): - center_frame_idx = random.randint(0, 99) - start_frame_idx = (center_frame_idx - self.num_half_frames * interval) - end_frame_idx = center_frame_idx + self.num_half_frames * interval - frame_name = f'{center_frame_idx:08d}' - neighbor_list = list(range(start_frame_idx, end_frame_idx + 1, interval)) - # random reverse - if self.random_reverse and random.random() < 0.5: - neighbor_list.reverse() - - assert len(neighbor_list) == self.num_frame, (f'Wrong length of neighbor list: {len(neighbor_list)}') - - # get the GT frame (as the center frame) - if self.is_lmdb: - img_gt_path = f'{clip_name}/{frame_name}' - else: - img_gt_path = self.gt_root / clip_name / f'{frame_name}.png' - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - - # get the neighboring LQ frames - img_lqs = [] - for neighbor in neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip_name}/{neighbor:08d}' - else: - img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - img_lqs.append(img_lq) - - # get flows - if self.flow_root is not None: - img_flows = [] - # read previous flows - for i in range(self.num_half_frames, 0, -1): - if self.is_lmdb: - flow_path = f'{clip_name}/{frame_name}_p{i}' - else: - flow_path = (self.flow_root / clip_name / f'{frame_name}_p{i}.png') - img_bytes = self.file_client.get(flow_path, 'flow') - cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] - dx, dy = np.split(cat_flow, 2, axis=0) - flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. - img_flows.append(flow) - # read next flows - for i in range(1, self.num_half_frames + 1): - if self.is_lmdb: - flow_path = f'{clip_name}/{frame_name}_n{i}' - else: - flow_path = (self.flow_root / clip_name / f'{frame_name}_n{i}.png') - img_bytes = self.file_client.get(flow_path, 'flow') - cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] - dx, dy = np.split(cat_flow, 2, axis=0) - flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. - img_flows.append(flow) - - # for random crop, here, img_flows and img_lqs have the same - # spatial size - img_lqs.extend(img_flows) - - # randomly crop - img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) - if self.flow_root is not None: - img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.num_frame:] - - # augmentation - flip, rotate - img_lqs.append(img_gt) - if self.flow_root is not None: - img_results, img_flows = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'], img_flows) - else: - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_lqs = torch.stack(img_results[0:-1], dim=0) - img_gt = img_results[-1] - - if self.flow_root is not None: - img_flows = img2tensor(img_flows) - # add the zero center flow - img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0])) - img_flows = torch.stack(img_flows, dim=0) - - # img_lqs: (t, c, h, w) - # img_flows: (t, 2, h, w) - # img_gt: (c, h, w) - # key: str - if self.flow_root is not None: - return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key} - else: - return {'lq': img_lqs, 'gt': img_gt, 'key': key} - - def __len__(self): - return len(self.keys) - - -@DATASET_REGISTRY.register() -class REDSRecurrentDataset(data.Dataset): - """REDS dataset for training recurrent networks. - - The keys are generated from a meta info txt file. - basicsr/data/meta_info/meta_info_REDS_GT.txt - - Each line contains: - 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by - a white space. - Examples: - 000 100 (720,1280,3) - 001 100 (720,1280,3) - ... - - Key examples: "000/00000000" - GT (gt): Ground-Truth; - LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - dataroot_flow (str, optional): Data root path for flow. - meta_info_file (str): Path for meta information file. - val_partition (str): Validation partition types. 'REDS4' or 'official'. - io_backend (dict): IO backend type and other kwarg. - num_frame (int): Window size for input frames. - gt_size (int): Cropped patched size for gt patches. - interval_list (list): Interval list for temporal augmentation. - random_reverse (bool): Random reverse input frames. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - """ - - def __init__(self, opt): - super(REDSRecurrentDataset, self).__init__() - self.opt = opt - self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) - self.num_frame = opt['num_frame'] - - self.keys = [] - with open(opt['meta_info_file'], 'r') as fin: - for line in fin: - folder, frame_num, _ = line.split(' ') - self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))]) - - # remove the video clips used in validation - if opt['val_partition'] == 'REDS4': - val_partition = ['000', '011', '015', '020'] - elif opt['val_partition'] == 'official': - val_partition = [f'{v:03d}' for v in range(240, 270)] - else: - raise ValueError(f'Wrong validation partition {opt["val_partition"]}.' - f"Supported ones are ['official', 'REDS4'].") - if opt['test_mode']: - self.keys = [v for v in self.keys if v.split('/')[0] in val_partition] - else: - self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.is_lmdb = False - if self.io_backend_opt['type'] == 'lmdb': - self.is_lmdb = True - if hasattr(self, 'flow_root') and self.flow_root is not None: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] - else: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - - # temporal augmentation configs - self.interval_list = opt.get('interval_list', [1]) - self.random_reverse = opt.get('random_reverse', False) - interval_str = ','.join(str(x) for x in self.interval_list) - logger = get_root_logger() - logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' - f'random reverse is {self.random_reverse}.') - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip_name, frame_name = key.split('/') # key example: 000/00000000 - - # determine the neighboring frames - interval = random.choice(self.interval_list) - - # ensure not exceeding the borders - start_frame_idx = int(frame_name) - if start_frame_idx > 100 - self.num_frame * interval: - start_frame_idx = random.randint(0, 100 - self.num_frame * interval) - end_frame_idx = start_frame_idx + self.num_frame * interval - - neighbor_list = list(range(start_frame_idx, end_frame_idx, interval)) - - # random reverse - if self.random_reverse and random.random() < 0.5: - neighbor_list.reverse() - - # get the neighboring LQ and GT frames - img_lqs = [] - img_gts = [] - for neighbor in neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip_name}/{neighbor:08d}' - img_gt_path = f'{clip_name}/{neighbor:08d}' - else: - img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' - img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png' - - # get LQ - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - img_lqs.append(img_lq) - - # get GT - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - img_gts.append(img_gt) - - # randomly crop - img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) - - # augmentation - flip, rotate - img_lqs.extend(img_gts) - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_gts = torch.stack(img_results[len(img_lqs) // 2:], dim=0) - img_lqs = torch.stack(img_results[:len(img_lqs) // 2], dim=0) - - # img_lqs: (t, c, h, w) - # img_gts: (t, c, h, w) - # key: str - return {'lq': img_lqs, 'gt': img_gts, 'key': key} - - def __len__(self): - return len(self.keys) diff --git a/basicsr/data/single_image_dataset.py b/basicsr/data/single_image_dataset.py deleted file mode 100644 index bf4912bf6167af07d4b430001483dfb6587a1277..0000000000000000000000000000000000000000 --- a/basicsr/data/single_image_dataset.py +++ /dev/null @@ -1,68 +0,0 @@ -from os import path as osp -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.data_util import paths_from_lmdb -from basicsr.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class SingleImageDataset(data.Dataset): - """Read only lq images in the test phase. - - Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc). - - There are two modes: - 1. 'meta_info_file': Use meta information file to generate paths. - 2. 'folder': Scan folders to generate paths. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_lq (str): Data root path for lq. - meta_info_file (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - """ - - def __init__(self, opt): - super(SingleImageDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.mean = opt['mean'] if 'mean' in opt else None - self.std = opt['std'] if 'std' in opt else None - self.lq_folder = opt['dataroot_lq'] - - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.lq_folder] - self.io_backend_opt['client_keys'] = ['lq'] - self.paths = paths_from_lmdb(self.lq_folder) - elif 'meta_info_file' in self.opt: - with open(self.opt['meta_info_file'], 'r') as fin: - self.paths = [osp.join(self.lq_folder, line.rstrip().split(' ')[0]) for line in fin] - else: - self.paths = sorted(list(scandir(self.lq_folder, full_path=True))) - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # load lq image - lq_path = self.paths[index] - img_bytes = self.file_client.get(lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - - # color space transform - if 'color' in self.opt and self.opt['color'] == 'y': - img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] - - # BGR to RGB, HWC to CHW, numpy to tensor - img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) - # normalize - if self.mean is not None or self.std is not None: - normalize(img_lq, self.mean, self.std, inplace=True) - return {'lq': img_lq, 'lq_path': lq_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/transforms.py b/basicsr/data/transforms.py deleted file mode 100644 index 85d1bc2b3587995f9d87d242bd266c50846f95fd..0000000000000000000000000000000000000000 --- a/basicsr/data/transforms.py +++ /dev/null @@ -1,179 +0,0 @@ -import cv2 -import random -import torch - - -def mod_crop(img, scale): - """Mod crop images, used during testing. - - Args: - img (ndarray): Input image. - scale (int): Scale factor. - - Returns: - ndarray: Result image. - """ - img = img.copy() - if img.ndim in (2, 3): - h, w = img.shape[0], img.shape[1] - h_remainder, w_remainder = h % scale, w % scale - img = img[:h - h_remainder, :w - w_remainder, ...] - else: - raise ValueError(f'Wrong img ndim: {img.ndim}.') - return img - - -def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None): - """Paired random crop. Support Numpy array and Tensor inputs. - - It crops lists of lq and gt images with corresponding locations. - - Args: - img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images - should have the same shape. If the input is an ndarray, it will - be transformed to a list containing itself. - img_lqs (list[ndarray] | ndarray): LQ images. Note that all images - should have the same shape. If the input is an ndarray, it will - be transformed to a list containing itself. - gt_patch_size (int): GT patch size. - scale (int): Scale factor. - gt_path (str): Path to ground-truth. Default: None. - - Returns: - list[ndarray] | ndarray: GT images and LQ images. If returned results - only have one element, just return ndarray. - """ - - if not isinstance(img_gts, list): - img_gts = [img_gts] - if not isinstance(img_lqs, list): - img_lqs = [img_lqs] - - # determine input type: Numpy array or Tensor - input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy' - - if input_type == 'Tensor': - h_lq, w_lq = img_lqs[0].size()[-2:] - h_gt, w_gt = img_gts[0].size()[-2:] - else: - h_lq, w_lq = img_lqs[0].shape[0:2] - h_gt, w_gt = img_gts[0].shape[0:2] - lq_patch_size = gt_patch_size // scale - - if h_gt != h_lq * scale or w_gt != w_lq * scale: - raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ', - f'multiplication of LQ ({h_lq}, {w_lq}).') - if h_lq < lq_patch_size or w_lq < lq_patch_size: - raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size ' - f'({lq_patch_size}, {lq_patch_size}). ' - f'Please remove {gt_path}.') - - # randomly choose top and left coordinates for lq patch - top = random.randint(0, h_lq - lq_patch_size) - left = random.randint(0, w_lq - lq_patch_size) - - # crop lq patch - if input_type == 'Tensor': - img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs] - else: - img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs] - - # crop corresponding gt patch - top_gt, left_gt = int(top * scale), int(left * scale) - if input_type == 'Tensor': - img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts] - else: - img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts] - if len(img_gts) == 1: - img_gts = img_gts[0] - if len(img_lqs) == 1: - img_lqs = img_lqs[0] - return img_gts, img_lqs - - -def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False): - """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees). - - We use vertical flip and transpose for rotation implementation. - All the images in the list use the same augmentation. - - Args: - imgs (list[ndarray] | ndarray): Images to be augmented. If the input - is an ndarray, it will be transformed to a list. - hflip (bool): Horizontal flip. Default: True. - rotation (bool): Ratotation. Default: True. - flows (list[ndarray]: Flows to be augmented. If the input is an - ndarray, it will be transformed to a list. - Dimension is (h, w, 2). Default: None. - return_status (bool): Return the status of flip and rotation. - Default: False. - - Returns: - list[ndarray] | ndarray: Augmented images and flows. If returned - results only have one element, just return ndarray. - - """ - hflip = hflip and random.random() < 0.5 - vflip = rotation and random.random() < 0.5 - rot90 = rotation and random.random() < 0.5 - - def _augment(img): - if hflip: # horizontal - cv2.flip(img, 1, img) - if vflip: # vertical - cv2.flip(img, 0, img) - if rot90: - img = img.transpose(1, 0, 2) - return img - - def _augment_flow(flow): - if hflip: # horizontal - cv2.flip(flow, 1, flow) - flow[:, :, 0] *= -1 - if vflip: # vertical - cv2.flip(flow, 0, flow) - flow[:, :, 1] *= -1 - if rot90: - flow = flow.transpose(1, 0, 2) - flow = flow[:, :, [1, 0]] - return flow - - if not isinstance(imgs, list): - imgs = [imgs] - imgs = [_augment(img) for img in imgs] - if len(imgs) == 1: - imgs = imgs[0] - - if flows is not None: - if not isinstance(flows, list): - flows = [flows] - flows = [_augment_flow(flow) for flow in flows] - if len(flows) == 1: - flows = flows[0] - return imgs, flows - else: - if return_status: - return imgs, (hflip, vflip, rot90) - else: - return imgs - - -def img_rotate(img, angle, center=None, scale=1.0): - """Rotate image. - - Args: - img (ndarray): Image to be rotated. - angle (float): Rotation angle in degrees. Positive values mean - counter-clockwise rotation. - center (tuple[int]): Rotation center. If the center is None, - initialize it as the center of the image. Default: None. - scale (float): Isotropic scale factor. Default: 1.0. - """ - (h, w) = img.shape[:2] - - if center is None: - center = (w // 2, h // 2) - - matrix = cv2.getRotationMatrix2D(center, angle, scale) - rotated_img = cv2.warpAffine(img, matrix, (w, h)) - return rotated_img diff --git a/basicsr/data/video_test_dataset.py b/basicsr/data/video_test_dataset.py deleted file mode 100644 index 69f09358a89b204eb69a6426486420e96bb7c2ee..0000000000000000000000000000000000000000 --- a/basicsr/data/video_test_dataset.py +++ /dev/null @@ -1,283 +0,0 @@ -import glob -import torch -from os import path as osp -from torch.utils import data as data - -from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class VideoTestDataset(data.Dataset): - """Video test dataset. - - Supported datasets: Vid4, REDS4, REDSofficial. - More generally, it supports testing dataset with following structures: - - :: - - dataroot - ├── subfolder1 - ├── frame000 - ├── frame001 - ├── ... - ├── subfolder2 - ├── frame000 - ├── frame001 - ├── ... - ├── ... - - For testing datasets, there is no need to prepare LMDB files. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - io_backend (dict): IO backend type and other kwarg. - cache_data (bool): Whether to cache testing datasets. - name (str): Dataset name. - meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders - in the dataroot will be used. - num_frame (int): Window size for input frames. - padding (str): Padding mode. - """ - - def __init__(self, opt): - super(VideoTestDataset, self).__init__() - self.opt = opt - self.cache_data = opt['cache_data'] - self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] - self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' - - logger = get_root_logger() - logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') - self.imgs_lq, self.imgs_gt = {}, {} - if 'meta_info_file' in opt: - with open(opt['meta_info_file'], 'r') as fin: - subfolders = [line.split(' ')[0] for line in fin] - subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders] - subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders] - else: - subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*'))) - subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*'))) - - if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']: - for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt): - # get frame list for lq and gt - subfolder_name = osp.basename(subfolder_lq) - img_paths_lq = sorted(list(scandir(subfolder_lq, full_path=True))) - img_paths_gt = sorted(list(scandir(subfolder_gt, full_path=True))) - - max_idx = len(img_paths_lq) - assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})' - f' and gt folders ({len(img_paths_gt)})') - - self.data_info['lq_path'].extend(img_paths_lq) - self.data_info['gt_path'].extend(img_paths_gt) - self.data_info['folder'].extend([subfolder_name] * max_idx) - for i in range(max_idx): - self.data_info['idx'].append(f'{i}/{max_idx}') - border_l = [0] * max_idx - for i in range(self.opt['num_frame'] // 2): - border_l[i] = 1 - border_l[max_idx - i - 1] = 1 - self.data_info['border'].extend(border_l) - - # cache data or save the frame list - if self.cache_data: - logger.info(f'Cache {subfolder_name} for VideoTestDataset...') - self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq) - self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt) - else: - self.imgs_lq[subfolder_name] = img_paths_lq - self.imgs_gt[subfolder_name] = img_paths_gt - else: - raise ValueError(f'Non-supported video test dataset: {type(opt["name"])}') - - def __getitem__(self, index): - folder = self.data_info['folder'][index] - idx, max_idx = self.data_info['idx'][index].split('/') - idx, max_idx = int(idx), int(max_idx) - border = self.data_info['border'][index] - lq_path = self.data_info['lq_path'][index] - - select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) - - if self.cache_data: - imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) - img_gt = self.imgs_gt[folder][idx] - else: - img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] - imgs_lq = read_img_seq(img_paths_lq) - img_gt = read_img_seq([self.imgs_gt[folder][idx]]) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': folder, # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/99 - 'border': border, # 1 for border, 0 for non-border - 'lq_path': lq_path # center frame - } - - def __len__(self): - return len(self.data_info['gt_path']) - - -@DATASET_REGISTRY.register() -class VideoTestVimeo90KDataset(data.Dataset): - """Video test dataset for Vimeo90k-Test dataset. - - It only keeps the center frame for testing. - For testing datasets, there is no need to prepare LMDB files. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - io_backend (dict): IO backend type and other kwarg. - cache_data (bool): Whether to cache testing datasets. - name (str): Dataset name. - meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders - in the dataroot will be used. - num_frame (int): Window size for input frames. - padding (str): Padding mode. - """ - - def __init__(self, opt): - super(VideoTestVimeo90KDataset, self).__init__() - self.opt = opt - self.cache_data = opt['cache_data'] - if self.cache_data: - raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.') - self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] - self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} - neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' - - logger = get_root_logger() - logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') - with open(opt['meta_info_file'], 'r') as fin: - subfolders = [line.split(' ')[0] for line in fin] - for idx, subfolder in enumerate(subfolders): - gt_path = osp.join(self.gt_root, subfolder, 'im4.png') - self.data_info['gt_path'].append(gt_path) - lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list] - self.data_info['lq_path'].append(lq_paths) - self.data_info['folder'].append('vimeo90k') - self.data_info['idx'].append(f'{idx}/{len(subfolders)}') - self.data_info['border'].append(0) - - def __getitem__(self, index): - lq_path = self.data_info['lq_path'][index] - gt_path = self.data_info['gt_path'][index] - imgs_lq = read_img_seq(lq_path) - img_gt = read_img_seq([gt_path]) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': self.data_info['folder'][index], # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/843 - 'border': self.data_info['border'][index], # 0 for non-border - 'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame - } - - def __len__(self): - return len(self.data_info['gt_path']) - - -@DATASET_REGISTRY.register() -class VideoTestDUFDataset(VideoTestDataset): - """ Video test dataset for DUF dataset. - - Args: - opt (dict): Config for train dataset. Most of keys are the same as VideoTestDataset. - It has the following extra keys: - use_duf_downsampling (bool): Whether to use duf downsampling to generate low-resolution frames. - scale (bool): Scale, which will be added automatically. - """ - - def __getitem__(self, index): - folder = self.data_info['folder'][index] - idx, max_idx = self.data_info['idx'][index].split('/') - idx, max_idx = int(idx), int(max_idx) - border = self.data_info['border'][index] - lq_path = self.data_info['lq_path'][index] - - select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) - - if self.cache_data: - if self.opt['use_duf_downsampling']: - # read imgs_gt to generate low-resolution frames - imgs_lq = self.imgs_gt[folder].index_select(0, torch.LongTensor(select_idx)) - imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) - else: - imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) - img_gt = self.imgs_gt[folder][idx] - else: - if self.opt['use_duf_downsampling']: - img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx] - # read imgs_gt to generate low-resolution frames - imgs_lq = read_img_seq(img_paths_lq, require_mod_crop=True, scale=self.opt['scale']) - imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) - else: - img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] - imgs_lq = read_img_seq(img_paths_lq) - img_gt = read_img_seq([self.imgs_gt[folder][idx]], require_mod_crop=True, scale=self.opt['scale']) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': folder, # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/99 - 'border': border, # 1 for border, 0 for non-border - 'lq_path': lq_path # center frame - } - - -@DATASET_REGISTRY.register() -class VideoRecurrentTestDataset(VideoTestDataset): - """Video test dataset for recurrent architectures, which takes LR video - frames as input and output corresponding HR video frames. - - Args: - opt (dict): Same as VideoTestDataset. Unused opt: - padding (str): Padding mode. - - """ - - def __init__(self, opt): - super(VideoRecurrentTestDataset, self).__init__(opt) - # Find unique folder strings - self.folders = sorted(list(set(self.data_info['folder']))) - - def __getitem__(self, index): - folder = self.folders[index] - - if self.cache_data: - imgs_lq = self.imgs_lq[folder] - imgs_gt = self.imgs_gt[folder] - else: - raise NotImplementedError('Without cache_data is not implemented.') - - return { - 'lq': imgs_lq, - 'gt': imgs_gt, - 'folder': folder, - } - - def __len__(self): - return len(self.folders) diff --git a/basicsr/data/vimeo90k_dataset.py b/basicsr/data/vimeo90k_dataset.py deleted file mode 100644 index a9c197f5a641f2684bc8e8c563f928e8492941d9..0000000000000000000000000000000000000000 --- a/basicsr/data/vimeo90k_dataset.py +++ /dev/null @@ -1,199 +0,0 @@ -import random -import torch -from pathlib import Path -from torch.utils import data as data - -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class Vimeo90KDataset(data.Dataset): - """Vimeo90K dataset for training. - - The keys are generated from a meta info txt file. - basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt - - Each line contains the following items, separated by a white space. - - 1. clip name; - 2. frame number; - 3. image shape - - Examples: - - :: - - 00001/0001 7 (256,448,3) - 00001/0002 7 (256,448,3) - - - Key examples: "00001/0001" - - GT (gt): Ground-Truth; - - LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. - - The neighboring frame list for different num_frame: - - :: - - num_frame | frame list - 1 | 4 - 3 | 3,4,5 - 5 | 2,3,4,5,6 - 7 | 1,2,3,4,5,6,7 - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - meta_info_file (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - num_frame (int): Window size for input frames. - gt_size (int): Cropped patched size for gt patches. - random_reverse (bool): Random reverse input frames. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - """ - - def __init__(self, opt): - super(Vimeo90KDataset, self).__init__() - self.opt = opt - self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) - - with open(opt['meta_info_file'], 'r') as fin: - self.keys = [line.split(' ')[0] for line in fin] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.is_lmdb = False - if self.io_backend_opt['type'] == 'lmdb': - self.is_lmdb = True - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - - # indices of input images - self.neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] - - # temporal augmentation configs - self.random_reverse = opt['random_reverse'] - logger = get_root_logger() - logger.info(f'Random reverse is {self.random_reverse}.') - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # random reverse - if self.random_reverse and random.random() < 0.5: - self.neighbor_list.reverse() - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip, seq = key.split('/') # key example: 00001/0001 - - # get the GT frame (im4.png) - if self.is_lmdb: - img_gt_path = f'{key}/im4' - else: - img_gt_path = self.gt_root / clip / seq / 'im4.png' - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - - # get the neighboring LQ frames - img_lqs = [] - for neighbor in self.neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip}/{seq}/im{neighbor}' - else: - img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - img_lqs.append(img_lq) - - # randomly crop - img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) - - # augmentation - flip, rotate - img_lqs.append(img_gt) - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_lqs = torch.stack(img_results[0:-1], dim=0) - img_gt = img_results[-1] - - # img_lqs: (t, c, h, w) - # img_gt: (c, h, w) - # key: str - return {'lq': img_lqs, 'gt': img_gt, 'key': key} - - def __len__(self): - return len(self.keys) - - -@DATASET_REGISTRY.register() -class Vimeo90KRecurrentDataset(Vimeo90KDataset): - - def __init__(self, opt): - super(Vimeo90KRecurrentDataset, self).__init__(opt) - - self.flip_sequence = opt['flip_sequence'] - self.neighbor_list = [1, 2, 3, 4, 5, 6, 7] - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # random reverse - if self.random_reverse and random.random() < 0.5: - self.neighbor_list.reverse() - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip, seq = key.split('/') # key example: 00001/0001 - - # get the neighboring LQ and GT frames - img_lqs = [] - img_gts = [] - for neighbor in self.neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip}/{seq}/im{neighbor}' - img_gt_path = f'{clip}/{seq}/im{neighbor}' - else: - img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' - img_gt_path = self.gt_root / clip / seq / f'im{neighbor}.png' - # LQ - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - # GT - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - - img_lqs.append(img_lq) - img_gts.append(img_gt) - - # randomly crop - img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) - - # augmentation - flip, rotate - img_lqs.extend(img_gts) - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_lqs = torch.stack(img_results[:7], dim=0) - img_gts = torch.stack(img_results[7:], dim=0) - - if self.flip_sequence: # flip the sequence: 7 frames to 14 frames - img_lqs = torch.cat([img_lqs, img_lqs.flip(0)], dim=0) - img_gts = torch.cat([img_gts, img_gts.flip(0)], dim=0) - - # img_lqs: (t, c, h, w) - # img_gt: (c, h, w) - # key: str - return {'lq': img_lqs, 'gt': img_gts, 'key': key} - - def __len__(self): - return len(self.keys) diff --git a/basicsr/losses/__init__.py b/basicsr/losses/__init__.py deleted file mode 100644 index 5fc0f47334f48fc9f1ec934ba352ee622c15bf17..0000000000000000000000000000000000000000 --- a/basicsr/losses/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -import importlib -from copy import deepcopy -from os import path as osp - -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import LOSS_REGISTRY -from .gan_loss import g_path_regularize, gradient_penalty_loss, r1_penalty - -__all__ = ['build_loss', 'gradient_penalty_loss', 'r1_penalty', 'g_path_regularize'] - -# automatically scan and import loss modules for registry -# scan all the files under the 'losses' folder and collect files ending with '_loss.py' -loss_folder = osp.dirname(osp.abspath(__file__)) -loss_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(loss_folder) if v.endswith('_loss.py')] -# import all the loss modules -_model_modules = [importlib.import_module(f'basicsr.losses.{file_name}') for file_name in loss_filenames] - - -def build_loss(opt): - """Build loss from options. - - Args: - opt (dict): Configuration. It must contain: - type (str): Model type. - """ - opt = deepcopy(opt) - loss_type = opt.pop('type') - loss = LOSS_REGISTRY.get(loss_type)(**opt) - logger = get_root_logger() - logger.info(f'Loss [{loss.__class__.__name__}] is created.') - return loss diff --git a/basicsr/losses/basic_loss.py b/basicsr/losses/basic_loss.py deleted file mode 100644 index 03865f4294eed6fc3c30fab526b4b3c8458a002a..0000000000000000000000000000000000000000 --- a/basicsr/losses/basic_loss.py +++ /dev/null @@ -1,253 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.archs.vgg_arch import VGGFeatureExtractor -from basicsr.utils.registry import LOSS_REGISTRY -from .loss_util import weighted_loss - -_reduction_modes = ['none', 'mean', 'sum'] - - -@weighted_loss -def l1_loss(pred, target): - return F.l1_loss(pred, target, reduction='none') - - -@weighted_loss -def mse_loss(pred, target): - return F.mse_loss(pred, target, reduction='none') - - -@weighted_loss -def charbonnier_loss(pred, target, eps=1e-12): - return torch.sqrt((pred - target)**2 + eps) - - -@LOSS_REGISTRY.register() -class L1Loss(nn.Module): - """L1 (mean absolute error, MAE) loss. - - Args: - loss_weight (float): Loss weight for L1 loss. Default: 1.0. - reduction (str): Specifies the reduction to apply to the output. - Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. - """ - - def __init__(self, loss_weight=1.0, reduction='mean'): - super(L1Loss, self).__init__() - if reduction not in ['none', 'mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') - - self.loss_weight = loss_weight - self.reduction = reduction - - def forward(self, pred, target, weight=None, **kwargs): - """ - Args: - pred (Tensor): of shape (N, C, H, W). Predicted tensor. - target (Tensor): of shape (N, C, H, W). Ground truth tensor. - weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. - """ - return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction) - - -@LOSS_REGISTRY.register() -class MSELoss(nn.Module): - """MSE (L2) loss. - - Args: - loss_weight (float): Loss weight for MSE loss. Default: 1.0. - reduction (str): Specifies the reduction to apply to the output. - Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. - """ - - def __init__(self, loss_weight=1.0, reduction='mean'): - super(MSELoss, self).__init__() - if reduction not in ['none', 'mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') - - self.loss_weight = loss_weight - self.reduction = reduction - - def forward(self, pred, target, weight=None, **kwargs): - """ - Args: - pred (Tensor): of shape (N, C, H, W). Predicted tensor. - target (Tensor): of shape (N, C, H, W). Ground truth tensor. - weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. - """ - return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction) - - -@LOSS_REGISTRY.register() -class CharbonnierLoss(nn.Module): - """Charbonnier loss (one variant of Robust L1Loss, a differentiable - variant of L1Loss). - - Described in "Deep Laplacian Pyramid Networks for Fast and Accurate - Super-Resolution". - - Args: - loss_weight (float): Loss weight for L1 loss. Default: 1.0. - reduction (str): Specifies the reduction to apply to the output. - Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. - eps (float): A value used to control the curvature near zero. Default: 1e-12. - """ - - def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12): - super(CharbonnierLoss, self).__init__() - if reduction not in ['none', 'mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') - - self.loss_weight = loss_weight - self.reduction = reduction - self.eps = eps - - def forward(self, pred, target, weight=None, **kwargs): - """ - Args: - pred (Tensor): of shape (N, C, H, W). Predicted tensor. - target (Tensor): of shape (N, C, H, W). Ground truth tensor. - weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. - """ - return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction) - - -@LOSS_REGISTRY.register() -class WeightedTVLoss(L1Loss): - """Weighted TV loss. - - Args: - loss_weight (float): Loss weight. Default: 1.0. - """ - - def __init__(self, loss_weight=1.0, reduction='mean'): - if reduction not in ['mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: mean | sum') - super(WeightedTVLoss, self).__init__(loss_weight=loss_weight, reduction=reduction) - - def forward(self, pred, weight=None): - if weight is None: - y_weight = None - x_weight = None - else: - y_weight = weight[:, :, :-1, :] - x_weight = weight[:, :, :, :-1] - - y_diff = super().forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=y_weight) - x_diff = super().forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=x_weight) - - loss = x_diff + y_diff - - return loss - - -@LOSS_REGISTRY.register() -class PerceptualLoss(nn.Module): - """Perceptual loss with commonly used style loss. - - Args: - layer_weights (dict): The weight for each layer of vgg feature. - Here is an example: {'conv5_4': 1.}, which means the conv5_4 - feature layer (before relu5_4) will be extracted with weight - 1.0 in calculating losses. - vgg_type (str): The type of vgg network used as feature extractor. - Default: 'vgg19'. - use_input_norm (bool): If True, normalize the input image in vgg. - Default: True. - range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. - Default: False. - perceptual_weight (float): If `perceptual_weight > 0`, the perceptual - loss will be calculated and the loss will multiplied by the - weight. Default: 1.0. - style_weight (float): If `style_weight > 0`, the style loss will be - calculated and the loss will multiplied by the weight. - Default: 0. - criterion (str): Criterion used for perceptual loss. Default: 'l1'. - """ - - def __init__(self, - layer_weights, - vgg_type='vgg19', - use_input_norm=True, - range_norm=False, - perceptual_weight=1.0, - style_weight=0., - criterion='l1'): - super(PerceptualLoss, self).__init__() - self.perceptual_weight = perceptual_weight - self.style_weight = style_weight - self.layer_weights = layer_weights - self.vgg = VGGFeatureExtractor( - layer_name_list=list(layer_weights.keys()), - vgg_type=vgg_type, - use_input_norm=use_input_norm, - range_norm=range_norm) - - self.criterion_type = criterion - if self.criterion_type == 'l1': - self.criterion = torch.nn.L1Loss() - elif self.criterion_type == 'l2': - self.criterion = torch.nn.MSELoss() - elif self.criterion_type == 'fro': - self.criterion = None - else: - raise NotImplementedError(f'{criterion} criterion has not been supported.') - - def forward(self, x, gt): - """Forward function. - - Args: - x (Tensor): Input tensor with shape (n, c, h, w). - gt (Tensor): Ground-truth tensor with shape (n, c, h, w). - - Returns: - Tensor: Forward results. - """ - # extract vgg features - x_features = self.vgg(x) - gt_features = self.vgg(gt.detach()) - - # calculate perceptual loss - if self.perceptual_weight > 0: - percep_loss = 0 - for k in x_features.keys(): - if self.criterion_type == 'fro': - percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k] - else: - percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k] - percep_loss *= self.perceptual_weight - else: - percep_loss = None - - # calculate style loss - if self.style_weight > 0: - style_loss = 0 - for k in x_features.keys(): - if self.criterion_type == 'fro': - style_loss += torch.norm( - self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k] - else: - style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat( - gt_features[k])) * self.layer_weights[k] - style_loss *= self.style_weight - else: - style_loss = None - - return percep_loss, style_loss - - def _gram_mat(self, x): - """Calculate Gram matrix. - - Args: - x (torch.Tensor): Tensor with shape of (n, c, h, w). - - Returns: - torch.Tensor: Gram matrix. - """ - n, c, h, w = x.size() - features = x.view(n, c, w * h) - features_t = features.transpose(1, 2) - gram = features.bmm(features_t) / (c * h * w) - return gram diff --git a/basicsr/losses/gan_loss.py b/basicsr/losses/gan_loss.py deleted file mode 100644 index 09b1a74da8cddda9103aa1b8a6baff43cf683173..0000000000000000000000000000000000000000 --- a/basicsr/losses/gan_loss.py +++ /dev/null @@ -1,207 +0,0 @@ -import math -import torch -from torch import autograd as autograd -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import LOSS_REGISTRY - - -@LOSS_REGISTRY.register() -class GANLoss(nn.Module): - """Define GAN loss. - - Args: - gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'. - real_label_val (float): The value for real label. Default: 1.0. - fake_label_val (float): The value for fake label. Default: 0.0. - loss_weight (float): Loss weight. Default: 1.0. - Note that loss_weight is only for generators; and it is always 1.0 - for discriminators. - """ - - def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): - super(GANLoss, self).__init__() - self.gan_type = gan_type - self.loss_weight = loss_weight - self.real_label_val = real_label_val - self.fake_label_val = fake_label_val - - if self.gan_type == 'vanilla': - self.loss = nn.BCEWithLogitsLoss() - elif self.gan_type == 'lsgan': - self.loss = nn.MSELoss() - elif self.gan_type == 'wgan': - self.loss = self._wgan_loss - elif self.gan_type == 'wgan_softplus': - self.loss = self._wgan_softplus_loss - elif self.gan_type == 'hinge': - self.loss = nn.ReLU() - else: - raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.') - - def _wgan_loss(self, input, target): - """wgan loss. - - Args: - input (Tensor): Input tensor. - target (bool): Target label. - - Returns: - Tensor: wgan loss. - """ - return -input.mean() if target else input.mean() - - def _wgan_softplus_loss(self, input, target): - """wgan loss with soft plus. softplus is a smooth approximation to the - ReLU function. - - In StyleGAN2, it is called: - Logistic loss for discriminator; - Non-saturating loss for generator. - - Args: - input (Tensor): Input tensor. - target (bool): Target label. - - Returns: - Tensor: wgan loss. - """ - return F.softplus(-input).mean() if target else F.softplus(input).mean() - - def get_target_label(self, input, target_is_real): - """Get target label. - - Args: - input (Tensor): Input tensor. - target_is_real (bool): Whether the target is real or fake. - - Returns: - (bool | Tensor): Target tensor. Return bool for wgan, otherwise, - return Tensor. - """ - - if self.gan_type in ['wgan', 'wgan_softplus']: - return target_is_real - target_val = (self.real_label_val if target_is_real else self.fake_label_val) - return input.new_ones(input.size()) * target_val - - def forward(self, input, target_is_real, is_disc=False): - """ - Args: - input (Tensor): The input for the loss module, i.e., the network - prediction. - target_is_real (bool): Whether the targe is real or fake. - is_disc (bool): Whether the loss for discriminators or not. - Default: False. - - Returns: - Tensor: GAN loss value. - """ - target_label = self.get_target_label(input, target_is_real) - if self.gan_type == 'hinge': - if is_disc: # for discriminators in hinge-gan - input = -input if target_is_real else input - loss = self.loss(1 + input).mean() - else: # for generators in hinge-gan - loss = -input.mean() - else: # other gan types - loss = self.loss(input, target_label) - - # loss_weight is always 1.0 for discriminators - return loss if is_disc else loss * self.loss_weight - - -@LOSS_REGISTRY.register() -class MultiScaleGANLoss(GANLoss): - """ - MultiScaleGANLoss accepts a list of predictions - """ - - def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): - super(MultiScaleGANLoss, self).__init__(gan_type, real_label_val, fake_label_val, loss_weight) - - def forward(self, input, target_is_real, is_disc=False): - """ - The input is a list of tensors, or a list of (a list of tensors) - """ - if isinstance(input, list): - loss = 0 - for pred_i in input: - if isinstance(pred_i, list): - # Only compute GAN loss for the last layer - # in case of multiscale feature matching - pred_i = pred_i[-1] - # Safe operation: 0-dim tensor calling self.mean() does nothing - loss_tensor = super().forward(pred_i, target_is_real, is_disc).mean() - loss += loss_tensor - return loss / len(input) - else: - return super().forward(input, target_is_real, is_disc) - - -def r1_penalty(real_pred, real_img): - """R1 regularization for discriminator. The core idea is to - penalize the gradient on real data alone: when the - generator distribution produces the true data distribution - and the discriminator is equal to 0 on the data manifold, the - gradient penalty ensures that the discriminator cannot create - a non-zero gradient orthogonal to the data manifold without - suffering a loss in the GAN game. - - Reference: Eq. 9 in Which training methods for GANs do actually converge. - """ - grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0] - grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean() - return grad_penalty - - -def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): - noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3]) - grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0] - path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) - - path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) - - path_penalty = (path_lengths - path_mean).pow(2).mean() - - return path_penalty, path_lengths.detach().mean(), path_mean.detach() - - -def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None): - """Calculate gradient penalty for wgan-gp. - - Args: - discriminator (nn.Module): Network for the discriminator. - real_data (Tensor): Real input data. - fake_data (Tensor): Fake input data. - weight (Tensor): Weight tensor. Default: None. - - Returns: - Tensor: A tensor for gradient penalty. - """ - - batch_size = real_data.size(0) - alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1)) - - # interpolate between real_data and fake_data - interpolates = alpha * real_data + (1. - alpha) * fake_data - interpolates = autograd.Variable(interpolates, requires_grad=True) - - disc_interpolates = discriminator(interpolates) - gradients = autograd.grad( - outputs=disc_interpolates, - inputs=interpolates, - grad_outputs=torch.ones_like(disc_interpolates), - create_graph=True, - retain_graph=True, - only_inputs=True)[0] - - if weight is not None: - gradients = gradients * weight - - gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean() - if weight is not None: - gradients_penalty /= torch.mean(weight) - - return gradients_penalty diff --git a/basicsr/losses/loss_util.py b/basicsr/losses/loss_util.py deleted file mode 100644 index bcb80f35a8c95bc811fa52e93b1b9e911d324f1e..0000000000000000000000000000000000000000 --- a/basicsr/losses/loss_util.py +++ /dev/null @@ -1,145 +0,0 @@ -import functools -import torch -from torch.nn import functional as F - - -def reduce_loss(loss, reduction): - """Reduce loss as specified. - - Args: - loss (Tensor): Elementwise loss tensor. - reduction (str): Options are 'none', 'mean' and 'sum'. - - Returns: - Tensor: Reduced loss tensor. - """ - reduction_enum = F._Reduction.get_enum(reduction) - # none: 0, elementwise_mean:1, sum: 2 - if reduction_enum == 0: - return loss - elif reduction_enum == 1: - return loss.mean() - else: - return loss.sum() - - -def weight_reduce_loss(loss, weight=None, reduction='mean'): - """Apply element-wise weight and reduce loss. - - Args: - loss (Tensor): Element-wise loss. - weight (Tensor): Element-wise weights. Default: None. - reduction (str): Same as built-in losses of PyTorch. Options are - 'none', 'mean' and 'sum'. Default: 'mean'. - - Returns: - Tensor: Loss values. - """ - # if weight is specified, apply element-wise weight - if weight is not None: - assert weight.dim() == loss.dim() - assert weight.size(1) == 1 or weight.size(1) == loss.size(1) - loss = loss * weight - - # if weight is not specified or reduction is sum, just reduce the loss - if weight is None or reduction == 'sum': - loss = reduce_loss(loss, reduction) - # if reduction is mean, then compute mean over weight region - elif reduction == 'mean': - if weight.size(1) > 1: - weight = weight.sum() - else: - weight = weight.sum() * loss.size(1) - loss = loss.sum() / weight - - return loss - - -def weighted_loss(loss_func): - """Create a weighted version of a given loss function. - - To use this decorator, the loss function must have the signature like - `loss_func(pred, target, **kwargs)`. The function only needs to compute - element-wise loss without any reduction. This decorator will add weight - and reduction arguments to the function. The decorated function will have - the signature like `loss_func(pred, target, weight=None, reduction='mean', - **kwargs)`. - - :Example: - - >>> import torch - >>> @weighted_loss - >>> def l1_loss(pred, target): - >>> return (pred - target).abs() - - >>> pred = torch.Tensor([0, 2, 3]) - >>> target = torch.Tensor([1, 1, 1]) - >>> weight = torch.Tensor([1, 0, 1]) - - >>> l1_loss(pred, target) - tensor(1.3333) - >>> l1_loss(pred, target, weight) - tensor(1.5000) - >>> l1_loss(pred, target, reduction='none') - tensor([1., 1., 2.]) - >>> l1_loss(pred, target, weight, reduction='sum') - tensor(3.) - """ - - @functools.wraps(loss_func) - def wrapper(pred, target, weight=None, reduction='mean', **kwargs): - # get element-wise loss - loss = loss_func(pred, target, **kwargs) - loss = weight_reduce_loss(loss, weight, reduction) - return loss - - return wrapper - - -def get_local_weights(residual, ksize): - """Get local weights for generating the artifact map of LDL. - - It is only called by the `get_refined_artifact_map` function. - - Args: - residual (Tensor): Residual between predicted and ground truth images. - ksize (Int): size of the local window. - - Returns: - Tensor: weight for each pixel to be discriminated as an artifact pixel - """ - - pad = (ksize - 1) // 2 - residual_pad = F.pad(residual, pad=[pad, pad, pad, pad], mode='reflect') - - unfolded_residual = residual_pad.unfold(2, ksize, 1).unfold(3, ksize, 1) - pixel_level_weight = torch.var(unfolded_residual, dim=(-1, -2), unbiased=True, keepdim=True).squeeze(-1).squeeze(-1) - - return pixel_level_weight - - -def get_refined_artifact_map(img_gt, img_output, img_ema, ksize): - """Calculate the artifact map of LDL - (Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution. In CVPR 2022) - - Args: - img_gt (Tensor): ground truth images. - img_output (Tensor): output images given by the optimizing model. - img_ema (Tensor): output images given by the ema model. - ksize (Int): size of the local window. - - Returns: - overall_weight: weight for each pixel to be discriminated as an artifact pixel - (calculated based on both local and global observations). - """ - - residual_ema = torch.sum(torch.abs(img_gt - img_ema), 1, keepdim=True) - residual_sr = torch.sum(torch.abs(img_gt - img_output), 1, keepdim=True) - - patch_level_weight = torch.var(residual_sr.clone(), dim=(-1, -2, -3), keepdim=True)**(1 / 5) - pixel_level_weight = get_local_weights(residual_sr.clone(), ksize) - overall_weight = patch_level_weight * pixel_level_weight - - overall_weight[residual_sr < residual_ema] = 0 - - return overall_weight diff --git a/basicsr/metrics/README.md b/basicsr/metrics/README.md deleted file mode 100644 index a94fd81da8ff1f1151d859e49c42b4420ac27d86..0000000000000000000000000000000000000000 --- a/basicsr/metrics/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# Metrics - -[English](README.md) **|** [简体中文](README_CN.md) - -- [约定](#约定) -- [PSNR 和 SSIM](#psnr-和-ssim) - -## 约定 - -因为不同的输入类型会导致结果的不同，因此我们对输入做如下约定: - -- Numpy 类型 (一般是 cv2 的结果) - - UINT8: BGR, [0, 255], (h, w, c) - - float: BGR, [0, 1], (h, w, c). 一般作为中间结果 -- Tensor 类型 - - float: RGB, [0, 1], (n, c, h, w) - -其他约定: - -- 以 `_pt` 结尾的是 PyTorch 结果 -- PyTorch version 支持 batch 计算 -- 颜色转换在 float32 上做；metric计算在 float64 上做 - -## PSNR 和 SSIM - -PSNR 和 SSIM 的结果趋势是一致的，即一般 PSNR 高，则 SSIM 也高。 -在实现上, PSNR 的各种实现都很一致。SSIM 有各种各样的实现，我们这里和 MATLAB 最原始版本保持 (参考 [NTIRE17比赛](https://competitions.codalab.org/competitions/16306#participate) 的 [evaluation代码](https://competitions.codalab.org/my/datasets/download/ebe960d8-0ec8-4846-a1a2-7c4a586a7378)) - -下面列了各个实现的结果比对. -总结：PyTorch 实现和 MATLAB 实现基本一致，在 GPU 运行上会有稍许差异 - -- PSNR 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 20.419710 | 20.419710 | 20.419710 |20.419710 | -|baboon| Y | - |22.441898 | 22.441899 | 22.444916| -|comic | RGB | 20.239912 | 20.239912 | 20.239912 | 20.239912 | -|comic | Y | - | 21.720398 | 21.720398 | 21.721663| - -- SSIM 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 0.391853 | 0.391853 | 0.391853|0.391853 | -|baboon| Y | - |0.453097| 0.453097 | 0.453171| -|comic | RGB | 0.567738 | 0.567738 | 0.567738 | 0.567738| -|comic | Y | - | 0.585511 | 0.585511 | 0.585522 | diff --git a/basicsr/metrics/README_CN.md b/basicsr/metrics/README_CN.md deleted file mode 100644 index a94fd81da8ff1f1151d859e49c42b4420ac27d86..0000000000000000000000000000000000000000 --- a/basicsr/metrics/README_CN.md +++ /dev/null @@ -1,48 +0,0 @@ -# Metrics - -[English](README.md) **|** [简体中文](README_CN.md) - -- [约定](#约定) -- [PSNR 和 SSIM](#psnr-和-ssim) - -## 约定 - -因为不同的输入类型会导致结果的不同，因此我们对输入做如下约定: - -- Numpy 类型 (一般是 cv2 的结果) - - UINT8: BGR, [0, 255], (h, w, c) - - float: BGR, [0, 1], (h, w, c). 一般作为中间结果 -- Tensor 类型 - - float: RGB, [0, 1], (n, c, h, w) - -其他约定: - -- 以 `_pt` 结尾的是 PyTorch 结果 -- PyTorch version 支持 batch 计算 -- 颜色转换在 float32 上做；metric计算在 float64 上做 - -## PSNR 和 SSIM - -PSNR 和 SSIM 的结果趋势是一致的，即一般 PSNR 高，则 SSIM 也高。 -在实现上, PSNR 的各种实现都很一致。SSIM 有各种各样的实现，我们这里和 MATLAB 最原始版本保持 (参考 [NTIRE17比赛](https://competitions.codalab.org/competitions/16306#participate) 的 [evaluation代码](https://competitions.codalab.org/my/datasets/download/ebe960d8-0ec8-4846-a1a2-7c4a586a7378)) - -下面列了各个实现的结果比对. -总结：PyTorch 实现和 MATLAB 实现基本一致，在 GPU 运行上会有稍许差异 - -- PSNR 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 20.419710 | 20.419710 | 20.419710 |20.419710 | -|baboon| Y | - |22.441898 | 22.441899 | 22.444916| -|comic | RGB | 20.239912 | 20.239912 | 20.239912 | 20.239912 | -|comic | Y | - | 21.720398 | 21.720398 | 21.721663| - -- SSIM 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 0.391853 | 0.391853 | 0.391853|0.391853 | -|baboon| Y | - |0.453097| 0.453097 | 0.453171| -|comic | RGB | 0.567738 | 0.567738 | 0.567738 | 0.567738| -|comic | Y | - | 0.585511 | 0.585511 | 0.585522 | diff --git a/basicsr/metrics/__init__.py b/basicsr/metrics/__init__.py deleted file mode 100644 index d738970b21583ac24c3d1c4e334a43794fee0b79..0000000000000000000000000000000000000000 --- a/basicsr/metrics/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from copy import deepcopy - -from basicsr.utils.registry import METRIC_REGISTRY -from .niqe import calculate_niqe -from .psnr_ssim import calculate_psnr, calculate_ssim - -__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe'] - - -def calculate_metric(data, opt): - """Calculate metric from data and options. - - Args: - opt (dict): Configuration. It must contain: - type (str): Model type. - """ - opt = deepcopy(opt) - metric_type = opt.pop('type') - metric = METRIC_REGISTRY.get(metric_type)(**data, **opt) - return metric diff --git a/basicsr/metrics/fid.py b/basicsr/metrics/fid.py deleted file mode 100644 index a052db92be35f47f36a7bc2653e11cdc5acf93bf..0000000000000000000000000000000000000000 --- a/basicsr/metrics/fid.py +++ /dev/null @@ -1,89 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from scipy import linalg -from tqdm import tqdm - -from basicsr.archs.inception import InceptionV3 - - -def load_patched_inception_v3(device='cuda', resize_input=True, normalize_input=False): - # we may not resize the input, but in [rosinality/stylegan2-pytorch] it - # does resize the input. - inception = InceptionV3([3], resize_input=resize_input, normalize_input=normalize_input) - inception = nn.DataParallel(inception).eval().to(device) - return inception - - -@torch.no_grad() -def extract_inception_features(data_generator, inception, len_generator=None, device='cuda'): - """Extract inception features. - - Args: - data_generator (generator): A data generator. - inception (nn.Module): Inception model. - len_generator (int): Length of the data_generator to show the - progressbar. Default: None. - device (str): Device. Default: cuda. - - Returns: - Tensor: Extracted features. - """ - if len_generator is not None: - pbar = tqdm(total=len_generator, unit='batch', desc='Extract') - else: - pbar = None - features = [] - - for data in data_generator: - if pbar: - pbar.update(1) - data = data.to(device) - feature = inception(data)[0].view(data.shape[0], -1) - features.append(feature.to('cpu')) - if pbar: - pbar.close() - features = torch.cat(features, 0) - return features - - -def calculate_fid(mu1, sigma1, mu2, sigma2, eps=1e-6): - """Numpy implementation of the Frechet Distance. - - The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is: - d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). - Stable version by Dougal J. Sutherland. - - Args: - mu1 (np.array): The sample mean over activations. - sigma1 (np.array): The covariance matrix over activations for generated samples. - mu2 (np.array): The sample mean over activations, precalculated on an representative data set. - sigma2 (np.array): The covariance matrix over activations, precalculated on an representative data set. - - Returns: - float: The Frechet Distance. - """ - assert mu1.shape == mu2.shape, 'Two mean vectors have different lengths' - assert sigma1.shape == sigma2.shape, ('Two covariances have different dimensions') - - cov_sqrt, _ = linalg.sqrtm(sigma1 @ sigma2, disp=False) - - # Product might be almost singular - if not np.isfinite(cov_sqrt).all(): - print('Product of cov matrices is singular. Adding {eps} to diagonal of cov estimates') - offset = np.eye(sigma1.shape[0]) * eps - cov_sqrt = linalg.sqrtm((sigma1 + offset) @ (sigma2 + offset)) - - # Numerical error might give slight imaginary component - if np.iscomplexobj(cov_sqrt): - if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3): - m = np.max(np.abs(cov_sqrt.imag)) - raise ValueError(f'Imaginary component {m}') - cov_sqrt = cov_sqrt.real - - mean_diff = mu1 - mu2 - mean_norm = mean_diff @ mean_diff - trace = np.trace(sigma1) + np.trace(sigma2) - 2 * np.trace(cov_sqrt) - fid = mean_norm + trace - - return fid diff --git a/basicsr/metrics/metric_util.py b/basicsr/metrics/metric_util.py deleted file mode 100644 index aad731154ea09336159b642be3f5a037de416e19..0000000000000000000000000000000000000000 --- a/basicsr/metrics/metric_util.py +++ /dev/null @@ -1,45 +0,0 @@ -import numpy as np - -from basicsr.utils import bgr2ycbcr - - -def reorder_image(img, input_order='HWC'): - """Reorder images to 'HWC' order. - - If the input_order is (h, w), return (h, w, 1); - If the input_order is (c, h, w), return (h, w, c); - If the input_order is (h, w, c), return as it is. - - Args: - img (ndarray): Input image. - input_order (str): Whether the input order is 'HWC' or 'CHW'. - If the input image shape is (h, w), input_order will not have - effects. Default: 'HWC'. - - Returns: - ndarray: reordered image. - """ - - if input_order not in ['HWC', 'CHW']: - raise ValueError(f"Wrong input_order {input_order}. Supported input_orders are 'HWC' and 'CHW'") - if len(img.shape) == 2: - img = img[..., None] - if input_order == 'CHW': - img = img.transpose(1, 2, 0) - return img - - -def to_y_channel(img): - """Change to Y channel of YCbCr. - - Args: - img (ndarray): Images with range [0, 255]. - - Returns: - (ndarray): Images with range [0, 255] (float type) without round. - """ - img = img.astype(np.float32) / 255. - if img.ndim == 3 and img.shape[2] == 3: - img = bgr2ycbcr(img, y_only=True) - img = img[..., None] - return img * 255. diff --git a/basicsr/metrics/niqe.py b/basicsr/metrics/niqe.py deleted file mode 100644 index 1d3dda35af367179c94350a35f20a46a5536d550..0000000000000000000000000000000000000000 --- a/basicsr/metrics/niqe.py +++ /dev/null @@ -1,199 +0,0 @@ -import cv2 -import math -import numpy as np -import os -from scipy.ndimage import convolve -from scipy.special import gamma - -from basicsr.metrics.metric_util import reorder_image, to_y_channel -from basicsr.utils.matlab_functions import imresize -from basicsr.utils.registry import METRIC_REGISTRY - - -def estimate_aggd_param(block): - """Estimate AGGD (Asymmetric Generalized Gaussian Distribution) parameters. - - Args: - block (ndarray): 2D Image block. - - Returns: - tuple: alpha (float), beta_l (float) and beta_r (float) for the AGGD - distribution (Estimating the parames in Equation 7 in the paper). - """ - block = block.flatten() - gam = np.arange(0.2, 10.001, 0.001) # len = 9801 - gam_reciprocal = np.reciprocal(gam) - r_gam = np.square(gamma(gam_reciprocal * 2)) / (gamma(gam_reciprocal) * gamma(gam_reciprocal * 3)) - - left_std = np.sqrt(np.mean(block[block < 0]**2)) - right_std = np.sqrt(np.mean(block[block > 0]**2)) - gammahat = left_std / right_std - rhat = (np.mean(np.abs(block)))**2 / np.mean(block**2) - rhatnorm = (rhat * (gammahat**3 + 1) * (gammahat + 1)) / ((gammahat**2 + 1)**2) - array_position = np.argmin((r_gam - rhatnorm)**2) - - alpha = gam[array_position] - beta_l = left_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha)) - beta_r = right_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha)) - return (alpha, beta_l, beta_r) - - -def compute_feature(block): - """Compute features. - - Args: - block (ndarray): 2D Image block. - - Returns: - list: Features with length of 18. - """ - feat = [] - alpha, beta_l, beta_r = estimate_aggd_param(block) - feat.extend([alpha, (beta_l + beta_r) / 2]) - - # distortions disturb the fairly regular structure of natural images. - # This deviation can be captured by analyzing the sample distribution of - # the products of pairs of adjacent coefficients computed along - # horizontal, vertical and diagonal orientations. - shifts = [[0, 1], [1, 0], [1, 1], [1, -1]] - for i in range(len(shifts)): - shifted_block = np.roll(block, shifts[i], axis=(0, 1)) - alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block) - # Eq. 8 - mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha)) - feat.extend([alpha, mean, beta_l, beta_r]) - return feat - - -def niqe(img, mu_pris_param, cov_pris_param, gaussian_window, block_size_h=96, block_size_w=96): - """Calculate NIQE (Natural Image Quality Evaluator) metric. - - ``Paper: Making a "Completely Blind" Image Quality Analyzer`` - - This implementation could produce almost the same results as the official - MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip - - Note that we do not include block overlap height and width, since they are - always 0 in the official implementation. - - For good performance, it is advisable by the official implementation to - divide the distorted image in to the same size patched as used for the - construction of multivariate Gaussian model. - - Args: - img (ndarray): Input image whose quality needs to be computed. The - image must be a gray or Y (of YCbCr) image with shape (h, w). - Range [0, 255] with float type. - mu_pris_param (ndarray): Mean of a pre-defined multivariate Gaussian - model calculated on the pristine dataset. - cov_pris_param (ndarray): Covariance of a pre-defined multivariate - Gaussian model calculated on the pristine dataset. - gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the - image. - block_size_h (int): Height of the blocks in to which image is divided. - Default: 96 (the official recommended value). - block_size_w (int): Width of the blocks in to which image is divided. - Default: 96 (the official recommended value). - """ - assert img.ndim == 2, ('Input image must be a gray or Y (of YCbCr) image with shape (h, w).') - # crop image - h, w = img.shape - num_block_h = math.floor(h / block_size_h) - num_block_w = math.floor(w / block_size_w) - img = img[0:num_block_h * block_size_h, 0:num_block_w * block_size_w] - - distparam = [] # dist param is actually the multiscale features - for scale in (1, 2): # perform on two scales (1, 2) - mu = convolve(img, gaussian_window, mode='nearest') - sigma = np.sqrt(np.abs(convolve(np.square(img), gaussian_window, mode='nearest') - np.square(mu))) - # normalize, as in Eq. 1 in the paper - img_nomalized = (img - mu) / (sigma + 1) - - feat = [] - for idx_w in range(num_block_w): - for idx_h in range(num_block_h): - # process ecah block - block = img_nomalized[idx_h * block_size_h // scale:(idx_h + 1) * block_size_h // scale, - idx_w * block_size_w // scale:(idx_w + 1) * block_size_w // scale] - feat.append(compute_feature(block)) - - distparam.append(np.array(feat)) - - if scale == 1: - img = imresize(img / 255., scale=0.5, antialiasing=True) - img = img * 255. - - distparam = np.concatenate(distparam, axis=1) - - # fit a MVG (multivariate Gaussian) model to distorted patch features - mu_distparam = np.nanmean(distparam, axis=0) - # use nancov. ref: https://ww2.mathworks.cn/help/stats/nancov.html - distparam_no_nan = distparam[~np.isnan(distparam).any(axis=1)] - cov_distparam = np.cov(distparam_no_nan, rowvar=False) - - # compute niqe quality, Eq. 10 in the paper - invcov_param = np.linalg.pinv((cov_pris_param + cov_distparam) / 2) - quality = np.matmul( - np.matmul((mu_pris_param - mu_distparam), invcov_param), np.transpose((mu_pris_param - mu_distparam))) - - quality = np.sqrt(quality) - quality = float(np.squeeze(quality)) - return quality - - -@METRIC_REGISTRY.register() -def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y', **kwargs): - """Calculate NIQE (Natural Image Quality Evaluator) metric. - - ``Paper: Making a "Completely Blind" Image Quality Analyzer`` - - This implementation could produce almost the same results as the official - MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip - - > MATLAB R2021a result for tests/data/baboon.png: 5.72957338 (5.7296) - > Our re-implementation result for tests/data/baboon.png: 5.7295763 (5.7296) - - We use the official params estimated from the pristine dataset. - We use the recommended block size (96, 96) without overlaps. - - Args: - img (ndarray): Input image whose quality needs to be computed. - The input image must be in range [0, 255] with float/int type. - The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order) - If the input order is 'HWC' or 'CHW', it will be converted to gray - or Y (of YCbCr) image according to the ``convert_to`` argument. - crop_border (int): Cropped pixels in each edge of an image. These - pixels are not involved in the metric calculation. - input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'. - Default: 'HWC'. - convert_to (str): Whether converted to 'y' (of MATLAB YCbCr) or 'gray'. - Default: 'y'. - - Returns: - float: NIQE result. - """ - ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) - # we use the official params estimated from the pristine dataset. - niqe_pris_params = np.load(os.path.join(ROOT_DIR, 'niqe_pris_params.npz')) - mu_pris_param = niqe_pris_params['mu_pris_param'] - cov_pris_param = niqe_pris_params['cov_pris_param'] - gaussian_window = niqe_pris_params['gaussian_window'] - - img = img.astype(np.float32) - if input_order != 'HW': - img = reorder_image(img, input_order=input_order) - if convert_to == 'y': - img = to_y_channel(img) - elif convert_to == 'gray': - img = cv2.cvtColor(img / 255., cv2.COLOR_BGR2GRAY) * 255. - img = np.squeeze(img) - - if crop_border != 0: - img = img[crop_border:-crop_border, crop_border:-crop_border] - - # round is necessary for being consistent with MATLAB's result - img = img.round() - - niqe_result = niqe(img, mu_pris_param, cov_pris_param, gaussian_window) - - return niqe_result diff --git a/basicsr/metrics/niqe_pris_params.npz b/basicsr/metrics/niqe_pris_params.npz deleted file mode 100644 index 42f06a9a18e6ed8bbf7933bec1477b189ef798de..0000000000000000000000000000000000000000 --- a/basicsr/metrics/niqe_pris_params.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2a7c182a68c9e7f1b2e2e5ec723279d6f65d912b6fcaf37eb2bf03d7367c4296 -size 11850 diff --git a/basicsr/metrics/psnr_ssim.py b/basicsr/metrics/psnr_ssim.py deleted file mode 100644 index bf29121ba4f244730fcf7cc73439638e8c99b2c7..0000000000000000000000000000000000000000 --- a/basicsr/metrics/psnr_ssim.py +++ /dev/null @@ -1,231 +0,0 @@ -import cv2 -import numpy as np -import torch -import torch.nn.functional as F - -from basicsr.metrics.metric_util import reorder_image, to_y_channel -from basicsr.utils.color_util import rgb2ycbcr_pt -from basicsr.utils.registry import METRIC_REGISTRY - - -@METRIC_REGISTRY.register() -def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): - """Calculate PSNR (Peak Signal-to-Noise Ratio). - - Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio - - Args: - img (ndarray): Images with range [0, 255]. - img2 (ndarray): Images with range [0, 255]. - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: PSNR result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - if input_order not in ['HWC', 'CHW']: - raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') - img = reorder_image(img, input_order=input_order) - img2 = reorder_image(img2, input_order=input_order) - - if crop_border != 0: - img = img[crop_border:-crop_border, crop_border:-crop_border, ...] - img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] - - if test_y_channel: - img = to_y_channel(img) - img2 = to_y_channel(img2) - - img = img.astype(np.float64) - img2 = img2.astype(np.float64) - - mse = np.mean((img - img2)**2) - if mse == 0: - return float('inf') - return 10. * np.log10(255. * 255. / mse) - - -@METRIC_REGISTRY.register() -def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs): - """Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). - - Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio - - Args: - img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: PSNR result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - - if crop_border != 0: - img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] - img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] - - if test_y_channel: - img = rgb2ycbcr_pt(img, y_only=True) - img2 = rgb2ycbcr_pt(img2, y_only=True) - - img = img.to(torch.float64) - img2 = img2.to(torch.float64) - - mse = torch.mean((img - img2)**2, dim=[1, 2, 3]) - return 10. * torch.log10(1. / (mse + 1e-8)) - - -@METRIC_REGISTRY.register() -def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): - """Calculate SSIM (structural similarity). - - ``Paper: Image quality assessment: From error visibility to structural similarity`` - - The results are the same as that of the official released MATLAB code in - https://ece.uwaterloo.ca/~z70wang/research/ssim/. - - For three-channel images, SSIM is calculated for each channel and then - averaged. - - Args: - img (ndarray): Images with range [0, 255]. - img2 (ndarray): Images with range [0, 255]. - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - input_order (str): Whether the input order is 'HWC' or 'CHW'. - Default: 'HWC'. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: SSIM result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - if input_order not in ['HWC', 'CHW']: - raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') - img = reorder_image(img, input_order=input_order) - img2 = reorder_image(img2, input_order=input_order) - - if crop_border != 0: - img = img[crop_border:-crop_border, crop_border:-crop_border, ...] - img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] - - if test_y_channel: - img = to_y_channel(img) - img2 = to_y_channel(img2) - - img = img.astype(np.float64) - img2 = img2.astype(np.float64) - - ssims = [] - for i in range(img.shape[2]): - ssims.append(_ssim(img[..., i], img2[..., i])) - return np.array(ssims).mean() - - -@METRIC_REGISTRY.register() -def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs): - """Calculate SSIM (structural similarity) (PyTorch version). - - ``Paper: Image quality assessment: From error visibility to structural similarity`` - - The results are the same as that of the official released MATLAB code in - https://ece.uwaterloo.ca/~z70wang/research/ssim/. - - For three-channel images, SSIM is calculated for each channel and then - averaged. - - Args: - img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: SSIM result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - - if crop_border != 0: - img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] - img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] - - if test_y_channel: - img = rgb2ycbcr_pt(img, y_only=True) - img2 = rgb2ycbcr_pt(img2, y_only=True) - - img = img.to(torch.float64) - img2 = img2.to(torch.float64) - - ssim = _ssim_pth(img * 255., img2 * 255.) - return ssim - - -def _ssim(img, img2): - """Calculate SSIM (structural similarity) for one channel images. - - It is called by func:`calculate_ssim`. - - Args: - img (ndarray): Images with range [0, 255] with order 'HWC'. - img2 (ndarray): Images with range [0, 255] with order 'HWC'. - - Returns: - float: SSIM result. - """ - - c1 = (0.01 * 255)**2 - c2 = (0.03 * 255)**2 - kernel = cv2.getGaussianKernel(11, 1.5) - window = np.outer(kernel, kernel.transpose()) - - mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5] # valid mode for window size 11 - mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] - mu1_sq = mu1**2 - mu2_sq = mu2**2 - mu1_mu2 = mu1 * mu2 - sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq - sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq - sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 - - ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)) - return ssim_map.mean() - - -def _ssim_pth(img, img2): - """Calculate SSIM (structural similarity) (PyTorch version). - - It is called by func:`calculate_ssim_pt`. - - Args: - img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - - Returns: - float: SSIM result. - """ - c1 = (0.01 * 255)**2 - c2 = (0.03 * 255)**2 - - kernel = cv2.getGaussianKernel(11, 1.5) - window = np.outer(kernel, kernel.transpose()) - window = torch.from_numpy(window).view(1, 1, 11, 11).expand(img.size(1), 1, 11, 11).to(img.dtype).to(img.device) - - mu1 = F.conv2d(img, window, stride=1, padding=0, groups=img.shape[1]) # valid mode - mu2 = F.conv2d(img2, window, stride=1, padding=0, groups=img2.shape[1]) # valid mode - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) - mu1_mu2 = mu1 * mu2 - sigma1_sq = F.conv2d(img * img, window, stride=1, padding=0, groups=img.shape[1]) - mu1_sq - sigma2_sq = F.conv2d(img2 * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu2_sq - sigma12 = F.conv2d(img * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu1_mu2 - - cs_map = (2 * sigma12 + c2) / (sigma1_sq + sigma2_sq + c2) - ssim_map = ((2 * mu1_mu2 + c1) / (mu1_sq + mu2_sq + c1)) * cs_map - return ssim_map.mean([1, 2, 3]) diff --git a/basicsr/models/__init__.py b/basicsr/models/__init__.py deleted file mode 100644 index cecd53d68061aeed8e335e051afee7734a8f0da7..0000000000000000000000000000000000000000 --- a/basicsr/models/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -import importlib -from copy import deepcopy -from os import path as osp - -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import MODEL_REGISTRY - -__all__ = ['build_model'] - -# automatically scan and import model modules for registry -# scan all the files under the 'models' folder and collect files ending with '_model.py' -model_folder = osp.dirname(osp.abspath(__file__)) -model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')] -# import all the model modules -_model_modules = [importlib.import_module(f'basicsr.models.{file_name}') for file_name in model_filenames] - - -def build_model(opt): - """Build model from options. - - Args: - opt (dict): Configuration. It must contain: - model_type (str): Model type. - """ - opt = deepcopy(opt) - model = MODEL_REGISTRY.get(opt['model_type'])(opt) - logger = get_root_logger() - logger.info(f'Model [{model.__class__.__name__}] is created.') - return model diff --git a/basicsr/models/base_model.py b/basicsr/models/base_model.py deleted file mode 100644 index 0868e07912b90e2ecfc3e20a6c9b46bdea5feb4e..0000000000000000000000000000000000000000 --- a/basicsr/models/base_model.py +++ /dev/null @@ -1,392 +0,0 @@ -import os -import time -import torch -from collections import OrderedDict -from copy import deepcopy -from torch.nn.parallel import DataParallel, DistributedDataParallel - -from basicsr.models import lr_scheduler as lr_scheduler -from basicsr.utils import get_root_logger -from basicsr.utils.dist_util import master_only - - -class BaseModel(): - """Base model.""" - - def __init__(self, opt): - self.opt = opt - self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') - self.is_train = opt['is_train'] - self.schedulers = [] - self.optimizers = [] - - def feed_data(self, data): - pass - - def optimize_parameters(self): - pass - - def get_current_visuals(self): - pass - - def save(self, epoch, current_iter): - """Save networks and training state.""" - pass - - def validation(self, dataloader, current_iter, tb_logger, save_img=False): - """Validation function. - - Args: - dataloader (torch.utils.data.DataLoader): Validation dataloader. - current_iter (int): Current iteration. - tb_logger (tensorboard logger): Tensorboard logger. - save_img (bool): Whether to save images. Default: False. - """ - if self.opt['dist']: - self.dist_validation(dataloader, current_iter, tb_logger, save_img) - else: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def _initialize_best_metric_results(self, dataset_name): - """Initialize the best metric results dict for recording the best metric value and iteration.""" - if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results: - return - elif not hasattr(self, 'best_metric_results'): - self.best_metric_results = dict() - - # add a dataset record - record = dict() - for metric, content in self.opt['val']['metrics'].items(): - better = content.get('better', 'higher') - init_val = float('-inf') if better == 'higher' else float('inf') - record[metric] = dict(better=better, val=init_val, iter=-1) - self.best_metric_results[dataset_name] = record - - def _update_best_metric_result(self, dataset_name, metric, val, current_iter): - if self.best_metric_results[dataset_name][metric]['better'] == 'higher': - if val >= self.best_metric_results[dataset_name][metric]['val']: - self.best_metric_results[dataset_name][metric]['val'] = val - self.best_metric_results[dataset_name][metric]['iter'] = current_iter - else: - if val <= self.best_metric_results[dataset_name][metric]['val']: - self.best_metric_results[dataset_name][metric]['val'] = val - self.best_metric_results[dataset_name][metric]['iter'] = current_iter - - def model_ema(self, decay=0.999): - net_g = self.get_bare_model(self.net_g) - - net_g_params = dict(net_g.named_parameters()) - net_g_ema_params = dict(self.net_g_ema.named_parameters()) - - for k in net_g_ema_params.keys(): - net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay) - - def get_current_log(self): - return self.log_dict - - def model_to_device(self, net): - """Model to device. It also warps models with DistributedDataParallel - or DataParallel. - - Args: - net (nn.Module) - """ - net = net.to(self.device) - if self.opt['dist']: - find_unused_parameters = self.opt.get('find_unused_parameters', False) - net = DistributedDataParallel( - net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters) - elif self.opt['num_gpu'] > 1: - net = DataParallel(net) - return net - - def get_optimizer(self, optim_type, params, lr, **kwargs): - if optim_type == 'Adam': - optimizer = torch.optim.Adam(params, lr, **kwargs) - elif optim_type == 'AdamW': - optimizer = torch.optim.AdamW(params, lr, **kwargs) - elif optim_type == 'Adamax': - optimizer = torch.optim.Adamax(params, lr, **kwargs) - elif optim_type == 'SGD': - optimizer = torch.optim.SGD(params, lr, **kwargs) - elif optim_type == 'ASGD': - optimizer = torch.optim.ASGD(params, lr, **kwargs) - elif optim_type == 'RMSprop': - optimizer = torch.optim.RMSprop(params, lr, **kwargs) - elif optim_type == 'Rprop': - optimizer = torch.optim.Rprop(params, lr, **kwargs) - else: - raise NotImplementedError(f'optimizer {optim_type} is not supported yet.') - return optimizer - - def setup_schedulers(self): - """Set up schedulers.""" - train_opt = self.opt['train'] - scheduler_type = train_opt['scheduler'].pop('type') - if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']: - for optimizer in self.optimizers: - self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler'])) - elif scheduler_type == 'CosineAnnealingRestartLR': - for optimizer in self.optimizers: - self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler'])) - else: - raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.') - - def get_bare_model(self, net): - """Get bare model, especially under wrapping with - DistributedDataParallel or DataParallel. - """ - if isinstance(net, (DataParallel, DistributedDataParallel)): - net = net.module - return net - - @master_only - def print_network(self, net): - """Print the str and parameter number of a network. - - Args: - net (nn.Module) - """ - if isinstance(net, (DataParallel, DistributedDataParallel)): - net_cls_str = f'{net.__class__.__name__} - {net.module.__class__.__name__}' - else: - net_cls_str = f'{net.__class__.__name__}' - - net = self.get_bare_model(net) - net_str = str(net) - net_params = sum(map(lambda x: x.numel(), net.parameters())) - - logger = get_root_logger() - logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}') - logger.info(net_str) - - def _set_lr(self, lr_groups_l): - """Set learning rate for warm-up. - - Args: - lr_groups_l (list): List for lr_groups, each for an optimizer. - """ - for optimizer, lr_groups in zip(self.optimizers, lr_groups_l): - for param_group, lr in zip(optimizer.param_groups, lr_groups): - param_group['lr'] = lr - - def _get_init_lr(self): - """Get the initial lr, which is set by the scheduler. - """ - init_lr_groups_l = [] - for optimizer in self.optimizers: - init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups]) - return init_lr_groups_l - - def update_learning_rate(self, current_iter, warmup_iter=-1): - """Update learning rate. - - Args: - current_iter (int): Current iteration. - warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up. - Default： -1. - """ - if current_iter > 1: - for scheduler in self.schedulers: - scheduler.step() - # set up warm-up learning rate - if current_iter < warmup_iter: - # get initial lr for each group - init_lr_g_l = self._get_init_lr() - # modify warming-up learning rates - # currently only support linearly warm up - warm_up_lr_l = [] - for init_lr_g in init_lr_g_l: - warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g]) - # set learning rate - self._set_lr(warm_up_lr_l) - - def get_current_learning_rate(self): - return [param_group['lr'] for param_group in self.optimizers[0].param_groups] - - @master_only - def save_network(self, net, net_label, current_iter, param_key='params'): - """Save networks. - - Args: - net (nn.Module | list[nn.Module]): Network(s) to be saved. - net_label (str): Network label. - current_iter (int): Current iter number. - param_key (str | list[str]): The parameter key(s) to save network. - Default: 'params'. - """ - if current_iter == -1: - current_iter = 'latest' - save_filename = f'{net_label}_{current_iter}.pth' - save_path = os.path.join(self.opt['path']['models'], save_filename) - - net = net if isinstance(net, list) else [net] - param_key = param_key if isinstance(param_key, list) else [param_key] - assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.' - - save_dict = {} - for net_, param_key_ in zip(net, param_key): - net_ = self.get_bare_model(net_) - state_dict = net_.state_dict() - for key, param in state_dict.items(): - if key.startswith('module.'): # remove unnecessary 'module.' - key = key[7:] - state_dict[key] = param.cpu() - save_dict[param_key_] = state_dict - - # avoid occasional writing errors - retry = 3 - while retry > 0: - try: - torch.save(save_dict, save_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}') - time.sleep(1) - else: - break - finally: - retry -= 1 - if retry == 0: - logger.warning(f'Still cannot save {save_path}. Just ignore it.') - # raise IOError(f'Cannot save {save_path}.') - - def _print_different_keys_loading(self, crt_net, load_net, strict=True): - """Print keys with different name or different size when loading models. - - 1. Print keys with different names. - 2. If strict=False, print the same key but with different tensor size. - It also ignore these keys with different sizes (not load). - - Args: - crt_net (torch model): Current network. - load_net (dict): Loaded network. - strict (bool): Whether strictly loaded. Default: True. - """ - crt_net = self.get_bare_model(crt_net) - crt_net = crt_net.state_dict() - crt_net_keys = set(crt_net.keys()) - load_net_keys = set(load_net.keys()) - - logger = get_root_logger() - if crt_net_keys != load_net_keys: - logger.warning('Current net - loaded net:') - for v in sorted(list(crt_net_keys - load_net_keys)): - logger.warning(f' {v}') - logger.warning('Loaded net - current net:') - for v in sorted(list(load_net_keys - crt_net_keys)): - logger.warning(f' {v}') - - # check the size for the same keys - if not strict: - common_keys = crt_net_keys & load_net_keys - for k in common_keys: - if crt_net[k].size() != load_net[k].size(): - logger.warning(f'Size different, ignore [{k}]: crt_net: ' - f'{crt_net[k].shape}; load_net: {load_net[k].shape}') - load_net[k + '.ignore'] = load_net.pop(k) - - def load_network(self, net, load_path, strict=True, param_key='params'): - """Load network. - - Args: - load_path (str): The path of networks to be loaded. - net (nn.Module): Network. - strict (bool): Whether strictly loaded. - param_key (str): The parameter key of loaded network. If set to - None, use the root 'path'. - Default: 'params'. - """ - logger = get_root_logger() - net = self.get_bare_model(net) - load_net = torch.load(load_path, map_location=lambda storage, loc: storage) - if param_key is not None: - if param_key not in load_net and 'params' in load_net: - param_key = 'params' - logger.info('Loading: params_ema does not exist, use params.') - load_net = load_net[param_key] - logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].') - # remove unnecessary 'module.' - for k, v in deepcopy(load_net).items(): - if k.startswith('module.'): - load_net[k[7:]] = v - load_net.pop(k) - self._print_different_keys_loading(net, load_net, strict) - net.load_state_dict(load_net, strict=strict) - - @master_only - def save_training_state(self, epoch, current_iter): - """Save training states during training, which will be used for - resuming. - - Args: - epoch (int): Current epoch. - current_iter (int): Current iteration. - """ - if current_iter != -1: - state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []} - for o in self.optimizers: - state['optimizers'].append(o.state_dict()) - for s in self.schedulers: - state['schedulers'].append(s.state_dict()) - save_filename = f'{current_iter}.state' - save_path = os.path.join(self.opt['path']['training_states'], save_filename) - - # avoid occasional writing errors - retry = 3 - while retry > 0: - try: - torch.save(state, save_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}') - time.sleep(1) - else: - break - finally: - retry -= 1 - if retry == 0: - logger.warning(f'Still cannot save {save_path}. Just ignore it.') - # raise IOError(f'Cannot save {save_path}.') - - def resume_training(self, resume_state): - """Reload the optimizers and schedulers for resumed training. - - Args: - resume_state (dict): Resume state. - """ - resume_optimizers = resume_state['optimizers'] - resume_schedulers = resume_state['schedulers'] - assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers' - assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers' - for i, o in enumerate(resume_optimizers): - self.optimizers[i].load_state_dict(o) - for i, s in enumerate(resume_schedulers): - self.schedulers[i].load_state_dict(s) - - def reduce_loss_dict(self, loss_dict): - """reduce loss dict. - - In distributed training, it averages the losses among different GPUs . - - Args: - loss_dict (OrderedDict): Loss dict. - """ - with torch.no_grad(): - if self.opt['dist']: - keys = [] - losses = [] - for name, value in loss_dict.items(): - keys.append(name) - losses.append(value) - losses = torch.stack(losses, 0) - torch.distributed.reduce(losses, dst=0) - if self.opt['rank'] == 0: - losses /= self.opt['world_size'] - loss_dict = {key: loss for key, loss in zip(keys, losses)} - - log_dict = OrderedDict() - for name, value in loss_dict.items(): - log_dict[name] = value.mean().item() - - return log_dict diff --git a/basicsr/models/edvr_model.py b/basicsr/models/edvr_model.py deleted file mode 100644 index 81037079656d6c103128758089e639162d18011a..0000000000000000000000000000000000000000 --- a/basicsr/models/edvr_model.py +++ /dev/null @@ -1,62 +0,0 @@ -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class EDVRModel(VideoBaseModel): - """EDVR Model. - - Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. # noqa: E501 - """ - - def __init__(self, opt): - super(EDVRModel, self).__init__(opt) - if self.is_train: - self.train_tsa_iter = opt['train'].get('tsa_iter') - - def setup_optimizers(self): - train_opt = self.opt['train'] - dcn_lr_mul = train_opt.get('dcn_lr_mul', 1) - logger = get_root_logger() - logger.info(f'Multiple the learning rate for dcn with {dcn_lr_mul}.') - if dcn_lr_mul == 1: - optim_params = self.net_g.parameters() - else: # separate dcn params and normal params for different lr - normal_params = [] - dcn_params = [] - for name, param in self.net_g.named_parameters(): - if 'dcn' in name: - dcn_params.append(param) - else: - normal_params.append(param) - optim_params = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': dcn_params, - 'lr': train_opt['optim_g']['lr'] * dcn_lr_mul - }, - ] - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def optimize_parameters(self, current_iter): - if self.train_tsa_iter: - if current_iter == 1: - logger = get_root_logger() - logger.info(f'Only train TSA module for {self.train_tsa_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'fusion' not in name: - param.requires_grad = False - elif current_iter == self.train_tsa_iter: - logger = get_root_logger() - logger.warning('Train all the parameters.') - for param in self.net_g.parameters(): - param.requires_grad = True - - super(EDVRModel, self).optimize_parameters(current_iter) diff --git a/basicsr/models/esrgan_model.py b/basicsr/models/esrgan_model.py deleted file mode 100644 index 3a0a0dbe04110fbe6cd9865efce87c5d9cd7c6a2..0000000000000000000000000000000000000000 --- a/basicsr/models/esrgan_model.py +++ /dev/null @@ -1,83 +0,0 @@ -import torch -from collections import OrderedDict - -from basicsr.utils.registry import MODEL_REGISTRY -from .srgan_model import SRGANModel - - -@MODEL_REGISTRY.register() -class ESRGANModel(SRGANModel): - """ESRGAN model for single image super-resolution.""" - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss (relativistic gan) - real_d_pred = self.net_d(self.gt).detach() - fake_g_pred = self.net_d(self.output) - l_g_real = self.cri_gan(real_d_pred - torch.mean(fake_g_pred), False, is_disc=False) - l_g_fake = self.cri_gan(fake_g_pred - torch.mean(real_d_pred), True, is_disc=False) - l_g_gan = (l_g_real + l_g_fake) / 2 - - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # gan loss (relativistic gan) - - # In order to avoid the error in distributed training: - # "Error detected in CudnnBatchNormBackward: RuntimeError: one of - # the variables needed for gradient computation has been modified by - # an inplace operation", - # we separate the backwards for real and fake, and also detach the - # tensor for calculating mean. - - # real - fake_d_pred = self.net_d(self.output).detach() - real_d_pred = self.net_d(self.gt) - l_d_real = self.cri_gan(real_d_pred - torch.mean(fake_d_pred), True, is_disc=True) * 0.5 - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach()) - l_d_fake = self.cri_gan(fake_d_pred - torch.mean(real_d_pred.detach()), False, is_disc=True) * 0.5 - l_d_fake.backward() - self.optimizer_d.step() - - loss_dict['l_d_real'] = l_d_real - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) diff --git a/basicsr/models/hifacegan_model.py b/basicsr/models/hifacegan_model.py deleted file mode 100644 index d9a125e0e535ddd3855178cbcd45f0eba0f377ef..0000000000000000000000000000000000000000 --- a/basicsr/models/hifacegan_model.py +++ /dev/null @@ -1,288 +0,0 @@ -import torch -from collections import OrderedDict -from os import path as osp -from tqdm import tqdm - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.metrics import calculate_metric -from basicsr.utils import imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class HiFaceGANModel(SRModel): - """HiFaceGAN model for generic-purpose face restoration. - No prior modeling required, works for any degradations. - Currently doesn't support EMA for inference. - """ - - def init_training_settings(self): - - train_opt = self.opt['train'] - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - raise (NotImplementedError('HiFaceGAN does not support EMA now. Pass')) - - self.net_g.train() - - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # define losses - # HiFaceGAN does not use pixel loss by default - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if train_opt.get('feature_matching_opt'): - self.cri_feat = build_loss(train_opt['feature_matching_opt']).to(self.device) - else: - self.cri_feat = None - - if self.cri_pix is None and self.cri_perceptual is None: - raise ValueError('Both pixel and perceptual losses are None.') - - if train_opt.get('gan_opt'): - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - # optimizer g - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - # optimizer d - optim_type = train_opt['optim_d'].pop('type') - self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) - self.optimizers.append(self.optimizer_d) - - def discriminate(self, input_lq, output, ground_truth): - """ - This is a conditional (on the input) discriminator - In Batch Normalization, the fake and real images are - recommended to be in the same batch to avoid disparate - statistics in fake and real images. - So both fake and real images are fed to D all at once. - """ - h, w = output.shape[-2:] - if output.shape[-2:] != input_lq.shape[-2:]: - lq = torch.nn.functional.interpolate(input_lq, (h, w)) - real = torch.nn.functional.interpolate(ground_truth, (h, w)) - fake_concat = torch.cat([lq, output], dim=1) - real_concat = torch.cat([lq, real], dim=1) - else: - fake_concat = torch.cat([input_lq, output], dim=1) - real_concat = torch.cat([input_lq, ground_truth], dim=1) - - fake_and_real = torch.cat([fake_concat, real_concat], dim=0) - discriminator_out = self.net_d(fake_and_real) - pred_fake, pred_real = self._divide_pred(discriminator_out) - return pred_fake, pred_real - - @staticmethod - def _divide_pred(pred): - """ - Take the prediction of fake and real images from the combined batch. - The prediction contains the intermediate outputs of multiscale GAN, - so it's usually a list - """ - if type(pred) == list: - fake = [] - real = [] - for p in pred: - fake.append([tensor[:tensor.size(0) // 2] for tensor in p]) - real.append([tensor[tensor.size(0) // 2:] for tensor in p]) - else: - fake = pred[:pred.size(0) // 2] - real = pred[pred.size(0) // 2:] - - return fake, real - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - - # Requires real prediction for feature matching loss - pred_fake, pred_real = self.discriminate(self.lq, self.output, self.gt) - l_g_gan = self.cri_gan(pred_fake, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - # feature matching loss - if self.cri_feat: - l_g_feat = self.cri_feat(pred_fake, pred_real) - l_g_total += l_g_feat - loss_dict['l_g_feat'] = l_g_feat - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # TODO: Benchmark test between HiFaceGAN and SRGAN implementation: - # SRGAN use the same fake output for discriminator update - # while HiFaceGAN regenerate a new output using updated net_g - # This should not make too much difference though. Stick to SRGAN now. - # ------------------------------------------------------------------- - # ---------- Below are original HiFaceGAN code snippet -------------- - # ------------------------------------------------------------------- - # with torch.no_grad(): - # fake_image = self.net_g(self.lq) - # fake_image = fake_image.detach() - # fake_image.requires_grad_() - # pred_fake, pred_real = self.discriminate(self.lq, fake_image, self.gt) - - # real - pred_fake, pred_real = self.discriminate(self.lq, self.output.detach(), self.gt) - l_d_real = self.cri_gan(pred_real, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - # fake - l_d_fake = self.cri_gan(pred_fake, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - - l_d_total = (l_d_real + l_d_fake) / 2 - l_d_total.backward() - self.optimizer_d.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - print('HiFaceGAN does not support EMA now. pass') - - def validation(self, dataloader, current_iter, tb_logger, save_img=False): - """ - Warning: HiFaceGAN requires train() mode even for validation - For more info, see https://github.com/Lotayou/Face-Renovation/issues/31 - - Args: - dataloader (torch.utils.data.DataLoader): Validation dataloader. - current_iter (int): Current iteration. - tb_logger (tensorboard logger): Tensorboard logger. - save_img (bool): Whether to save images. Default: False. - """ - - if self.opt['network_g']['type'] in ('HiFaceGAN', 'SPADEGenerator'): - self.net_g.train() - - if self.opt['dist']: - self.dist_validation(dataloader, current_iter, tb_logger, save_img) - else: - print('In HiFaceGANModel: The new metrics package is under development.' + - 'Using super method now (Only PSNR & SSIM are supported)') - super().nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - """ - TODO: Validation using updated metric system - The metrics are now evaluated after all images have been tested - This allows batch processing, and also allows evaluation of - distributional metrics, such as: - - @ Frechet Inception Distance: FID - @ Maximum Mean Discrepancy: MMD - - Warning: - Need careful batch management for different inference settings. - - """ - dataset_name = dataloader.dataset.opt['name'] - with_metrics = self.opt['val'].get('metrics') is not None - if with_metrics: - self.metric_results = dict() # {metric: 0 for metric in self.opt['val']['metrics'].keys()} - sr_tensors = [] - gt_tensors = [] - - pbar = tqdm(total=len(dataloader), unit='image') - for val_data in dataloader: - img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] - self.feed_data(val_data) - self.test() - - visuals = self.get_current_visuals() # detached cpu tensor, non-squeeze - sr_tensors.append(visuals['result']) - if 'gt' in visuals: - gt_tensors.append(visuals['gt']) - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], img_name, - f'{img_name}_{current_iter}.png') - else: - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["name"]}.png') - - imwrite(tensor2img(visuals['result']), save_img_path) - - pbar.update(1) - pbar.set_description(f'Test {img_name}') - pbar.close() - - if with_metrics: - sr_pack = torch.cat(sr_tensors, dim=0) - gt_pack = torch.cat(gt_tensors, dim=0) - # calculate metrics - for name, opt_ in self.opt['val']['metrics'].items(): - # The new metric caller automatically returns mean value - # FIXME: ERROR: calculate_metric only supports two arguments. Now the codes cannot be successfully run - self.metric_results[name] = calculate_metric(dict(sr_pack=sr_pack, gt_pack=gt_pack), opt_) - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - print('HiFaceGAN does not support EMA now. Fallback to normal mode.') - - self.save_network(self.net_g, 'net_g', current_iter) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/lr_scheduler.py b/basicsr/models/lr_scheduler.py deleted file mode 100644 index 084122d1bd15af35e0a100b52bbcb969eb887fb8..0000000000000000000000000000000000000000 --- a/basicsr/models/lr_scheduler.py +++ /dev/null @@ -1,96 +0,0 @@ -import math -from collections import Counter -from torch.optim.lr_scheduler import _LRScheduler - - -class MultiStepRestartLR(_LRScheduler): - """ MultiStep with restarts learning rate scheme. - - Args: - optimizer (torch.nn.optimizer): Torch optimizer. - milestones (list): Iterations that will decrease learning rate. - gamma (float): Decrease ratio. Default: 0.1. - restarts (list): Restart iterations. Default: [0]. - restart_weights (list): Restart weights at each restart iteration. - Default: [1]. - last_epoch (int): Used in _LRScheduler. Default: -1. - """ - - def __init__(self, optimizer, milestones, gamma=0.1, restarts=(0, ), restart_weights=(1, ), last_epoch=-1): - self.milestones = Counter(milestones) - self.gamma = gamma - self.restarts = restarts - self.restart_weights = restart_weights - assert len(self.restarts) == len(self.restart_weights), 'restarts and their weights do not match.' - super(MultiStepRestartLR, self).__init__(optimizer, last_epoch) - - def get_lr(self): - if self.last_epoch in self.restarts: - weight = self.restart_weights[self.restarts.index(self.last_epoch)] - return [group['initial_lr'] * weight for group in self.optimizer.param_groups] - if self.last_epoch not in self.milestones: - return [group['lr'] for group in self.optimizer.param_groups] - return [group['lr'] * self.gamma**self.milestones[self.last_epoch] for group in self.optimizer.param_groups] - - -def get_position_from_periods(iteration, cumulative_period): - """Get the position from a period list. - - It will return the index of the right-closest number in the period list. - For example, the cumulative_period = [100, 200, 300, 400], - if iteration == 50, return 0; - if iteration == 210, return 2; - if iteration == 300, return 2. - - Args: - iteration (int): Current iteration. - cumulative_period (list[int]): Cumulative period list. - - Returns: - int: The position of the right-closest number in the period list. - """ - for i, period in enumerate(cumulative_period): - if iteration <= period: - return i - - -class CosineAnnealingRestartLR(_LRScheduler): - """ Cosine annealing with restarts learning rate scheme. - - An example of config: - periods = [10, 10, 10, 10] - restart_weights = [1, 0.5, 0.5, 0.5] - eta_min=1e-7 - - It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the - scheduler will restart with the weights in restart_weights. - - Args: - optimizer (torch.nn.optimizer): Torch optimizer. - periods (list): Period for each cosine anneling cycle. - restart_weights (list): Restart weights at each restart iteration. - Default: [1]. - eta_min (float): The minimum lr. Default: 0. - last_epoch (int): Used in _LRScheduler. Default: -1. - """ - - def __init__(self, optimizer, periods, restart_weights=(1, ), eta_min=0, last_epoch=-1): - self.periods = periods - self.restart_weights = restart_weights - self.eta_min = eta_min - assert (len(self.periods) == len( - self.restart_weights)), 'periods and restart_weights should have the same length.' - self.cumulative_period = [sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))] - super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch) - - def get_lr(self): - idx = get_position_from_periods(self.last_epoch, self.cumulative_period) - current_weight = self.restart_weights[idx] - nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1] - current_period = self.periods[idx] - - return [ - self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) * - (1 + math.cos(math.pi * ((self.last_epoch - nearest_restart) / current_period))) - for base_lr in self.base_lrs - ] diff --git a/basicsr/models/realesrgan_model.py b/basicsr/models/realesrgan_model.py deleted file mode 100644 index 9a30b6b86b5e8747c81de224b1332c4cf2f94889..0000000000000000000000000000000000000000 --- a/basicsr/models/realesrgan_model.py +++ /dev/null @@ -1,267 +0,0 @@ -import numpy as np -import random -import torch -from collections import OrderedDict -from torch.nn import functional as F - -from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt -from basicsr.data.transforms import paired_random_crop -from basicsr.losses.loss_util import get_refined_artifact_map -from basicsr.models.srgan_model import SRGANModel -from basicsr.utils import DiffJPEG, USMSharp -from basicsr.utils.img_process_util import filter2D -from basicsr.utils.registry import MODEL_REGISTRY - - -@MODEL_REGISTRY.register(suffix='basicsr') -class RealESRGANModel(SRGANModel): - """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It mainly performs: - 1. randomly synthesize LQ images in GPU tensors - 2. optimize the networks with GAN training. - """ - - def __init__(self, opt): - super(RealESRGANModel, self).__init__(opt) - self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts - self.usm_sharpener = USMSharp().cuda() # do usm sharpening - self.queue_size = opt.get('queue_size', 180) - - @torch.no_grad() - def _dequeue_and_enqueue(self): - """It is the training pair pool for increasing the diversity in a batch. - - Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a - batch could not have different resize scaling factors. Therefore, we employ this training pair pool - to increase the degradation diversity in a batch. - """ - # initialize - b, c, h, w = self.lq.size() - if not hasattr(self, 'queue_lr'): - assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' - self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() - _, c, h, w = self.gt.size() - self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() - self.queue_ptr = 0 - if self.queue_ptr == self.queue_size: # the pool is full - # do dequeue and enqueue - # shuffle - idx = torch.randperm(self.queue_size) - self.queue_lr = self.queue_lr[idx] - self.queue_gt = self.queue_gt[idx] - # get first b samples - lq_dequeue = self.queue_lr[0:b, :, :, :].clone() - gt_dequeue = self.queue_gt[0:b, :, :, :].clone() - # update the queue - self.queue_lr[0:b, :, :, :] = self.lq.clone() - self.queue_gt[0:b, :, :, :] = self.gt.clone() - - self.lq = lq_dequeue - self.gt = gt_dequeue - else: - # only do enqueue - self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() - self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() - self.queue_ptr = self.queue_ptr + b - - @torch.no_grad() - def feed_data(self, data): - """Accept data from dataloader, and then add two-order degradations to obtain LQ images. - """ - if self.is_train and self.opt.get('high_order_degradation', True): - # training data synthesis - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - self.kernel1 = data['kernel1'].to(self.device) - self.kernel2 = data['kernel2'].to(self.device) - self.sinc_kernel = data['sinc_kernel'].to(self.device) - - ori_h, ori_w = self.gt.size()[2:4] - - # ----------------------- The first degradation process ----------------------- # - # blur - out = filter2D(self.gt_usm, self.kernel1) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, scale_factor=scale, mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob'] - if np.random.uniform() < self.opt['gaussian_noise_prob']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) - out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts - out = self.jpeger(out, quality=jpeg_p) - - # ----------------------- The second degradation process ----------------------- # - # blur - if np.random.uniform() < self.opt['second_blur_prob']: - out = filter2D(out, self.kernel2) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range2'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range2'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate( - out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob2'] - if np.random.uniform() < self.opt['gaussian_noise_prob2']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range2'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - - # JPEG compression + the final sinc filter - # We also need to resize images to desired sizes. We group [resize back + sinc filter] together - # as one operation. - # We consider two orders: - # 1. [resize back + sinc filter] + JPEG compression - # 2. JPEG compression + [resize back + sinc filter] - # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. - if np.random.uniform() < 0.5: - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - else: - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - - # clamp and round - self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. - - # random crop - gt_size = self.opt['gt_size'] - (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size, - self.opt['scale']) - - # training pair pool - self._dequeue_and_enqueue() - # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue - self.gt_usm = self.usm_sharpener(self.gt) - self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract - else: - # for paired training or validation - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - # do not use the synthetic process during validation - self.is_train = False - super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) - self.is_train = True - - def optimize_parameters(self, current_iter): - # usm sharpening - l1_gt = self.gt_usm - percep_gt = self.gt_usm - gan_gt = self.gt_usm - if self.opt['l1_gt_usm'] is False: - l1_gt = self.gt - if self.opt['percep_gt_usm'] is False: - percep_gt = self.gt - if self.opt['gan_gt_usm'] is False: - gan_gt = self.gt - - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - if self.cri_ldl: - self.output_ema = self.net_g_ema(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, l1_gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - if self.cri_ldl: - pixel_weight = get_refined_artifact_map(self.gt, self.output, self.output_ema, 7) - l_g_ldl = self.cri_ldl(torch.mul(pixel_weight, self.output), torch.mul(pixel_weight, self.gt)) - l_g_total += l_g_ldl - loss_dict['l_g_ldl'] = l_g_ldl - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - real_d_pred = self.net_d(gan_gt) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach().clone()) # clone for pt1.9 - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - self.log_dict = self.reduce_loss_dict(loss_dict) diff --git a/basicsr/models/realesrnet_model.py b/basicsr/models/realesrnet_model.py deleted file mode 100644 index 58678643254bae11c09195602d649fb0f64d2a68..0000000000000000000000000000000000000000 --- a/basicsr/models/realesrnet_model.py +++ /dev/null @@ -1,189 +0,0 @@ -import numpy as np -import random -import torch -from torch.nn import functional as F - -from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt -from basicsr.data.transforms import paired_random_crop -from basicsr.models.sr_model import SRModel -from basicsr.utils import DiffJPEG, USMSharp -from basicsr.utils.img_process_util import filter2D -from basicsr.utils.registry import MODEL_REGISTRY - - -@MODEL_REGISTRY.register(suffix='basicsr') -class RealESRNetModel(SRModel): - """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It is trained without GAN losses. - It mainly performs: - 1. randomly synthesize LQ images in GPU tensors - 2. optimize the networks with GAN training. - """ - - def __init__(self, opt): - super(RealESRNetModel, self).__init__(opt) - self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts - self.usm_sharpener = USMSharp().cuda() # do usm sharpening - self.queue_size = opt.get('queue_size', 180) - - @torch.no_grad() - def _dequeue_and_enqueue(self): - """It is the training pair pool for increasing the diversity in a batch. - - Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a - batch could not have different resize scaling factors. Therefore, we employ this training pair pool - to increase the degradation diversity in a batch. - """ - # initialize - b, c, h, w = self.lq.size() - if not hasattr(self, 'queue_lr'): - assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' - self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() - _, c, h, w = self.gt.size() - self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() - self.queue_ptr = 0 - if self.queue_ptr == self.queue_size: # the pool is full - # do dequeue and enqueue - # shuffle - idx = torch.randperm(self.queue_size) - self.queue_lr = self.queue_lr[idx] - self.queue_gt = self.queue_gt[idx] - # get first b samples - lq_dequeue = self.queue_lr[0:b, :, :, :].clone() - gt_dequeue = self.queue_gt[0:b, :, :, :].clone() - # update the queue - self.queue_lr[0:b, :, :, :] = self.lq.clone() - self.queue_gt[0:b, :, :, :] = self.gt.clone() - - self.lq = lq_dequeue - self.gt = gt_dequeue - else: - # only do enqueue - self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() - self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() - self.queue_ptr = self.queue_ptr + b - - @torch.no_grad() - def feed_data(self, data): - """Accept data from dataloader, and then add two-order degradations to obtain LQ images. - """ - if self.is_train and self.opt.get('high_order_degradation', True): - # training data synthesis - self.gt = data['gt'].to(self.device) - # USM sharpen the GT images - if self.opt['gt_usm'] is True: - self.gt = self.usm_sharpener(self.gt) - - self.kernel1 = data['kernel1'].to(self.device) - self.kernel2 = data['kernel2'].to(self.device) - self.sinc_kernel = data['sinc_kernel'].to(self.device) - - ori_h, ori_w = self.gt.size()[2:4] - - # ----------------------- The first degradation process ----------------------- # - # blur - out = filter2D(self.gt, self.kernel1) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, scale_factor=scale, mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob'] - if np.random.uniform() < self.opt['gaussian_noise_prob']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) - out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts - out = self.jpeger(out, quality=jpeg_p) - - # ----------------------- The second degradation process ----------------------- # - # blur - if np.random.uniform() < self.opt['second_blur_prob']: - out = filter2D(out, self.kernel2) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range2'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range2'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate( - out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob2'] - if np.random.uniform() < self.opt['gaussian_noise_prob2']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range2'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - - # JPEG compression + the final sinc filter - # We also need to resize images to desired sizes. We group [resize back + sinc filter] together - # as one operation. - # We consider two orders: - # 1. [resize back + sinc filter] + JPEG compression - # 2. JPEG compression + [resize back + sinc filter] - # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. - if np.random.uniform() < 0.5: - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - else: - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - - # clamp and round - self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. - - # random crop - gt_size = self.opt['gt_size'] - self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale']) - - # training pair pool - self._dequeue_and_enqueue() - self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract - else: - # for paired training or validation - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - # do not use the synthetic process during validation - self.is_train = False - super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) - self.is_train = True diff --git a/basicsr/models/sr_model.py b/basicsr/models/sr_model.py deleted file mode 100644 index 018f7d6ea2b57ae988914e32bdaa80c6319bc9ea..0000000000000000000000000000000000000000 --- a/basicsr/models/sr_model.py +++ /dev/null @@ -1,279 +0,0 @@ -import torch -from collections import OrderedDict -from os import path as osp -from tqdm import tqdm - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from .base_model import BaseModel - - -@MODEL_REGISTRY.register() -class SRModel(BaseModel): - """Base SR model for single image super-resolution.""" - - def __init__(self, opt): - super(SRModel, self).__init__(opt) - - # define network - self.net_g = build_network(opt['network_g']) - self.net_g = self.model_to_device(self.net_g) - self.print_network(self.net_g) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_g', 'params') - self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) - - if self.is_train: - self.init_training_settings() - - def init_training_settings(self): - self.net_g.train() - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema is used only for testing on one GPU and saving - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if self.cri_pix is None and self.cri_perceptual is None: - raise ValueError('Both pixel and perceptual losses are None.') - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - optim_params = [] - for k, v in self.net_g.named_parameters(): - if v.requires_grad: - optim_params.append(v) - else: - logger = get_root_logger() - logger.warning(f'Params {k} will not be optimized.') - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def feed_data(self, data): - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - - def optimize_parameters(self, current_iter): - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_total = 0 - loss_dict = OrderedDict() - # pixel loss - if self.cri_pix: - l_pix = self.cri_pix(self.output, self.gt) - l_total += l_pix - loss_dict['l_pix'] = l_pix - # perceptual loss - if self.cri_perceptual: - l_percep, l_style = self.cri_perceptual(self.output, self.gt) - if l_percep is not None: - l_total += l_percep - loss_dict['l_percep'] = l_percep - if l_style is not None: - l_total += l_style - loss_dict['l_style'] = l_style - - l_total.backward() - self.optimizer_g.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def test(self): - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - self.output = self.net_g_ema(self.lq) - else: - self.net_g.eval() - with torch.no_grad(): - self.output = self.net_g(self.lq) - self.net_g.train() - - def test_selfensemble(self): - # TODO: to be tested - # 8 augmentations - # modified from https://github.com/thstkdgus35/EDSR-PyTorch - - def _transform(v, op): - # if self.precision != 'single': v = v.float() - v2np = v.data.cpu().numpy() - if op == 'v': - tfnp = v2np[:, :, :, ::-1].copy() - elif op == 'h': - tfnp = v2np[:, :, ::-1, :].copy() - elif op == 't': - tfnp = v2np.transpose((0, 1, 3, 2)).copy() - - ret = torch.Tensor(tfnp).to(self.device) - # if self.precision == 'half': ret = ret.half() - - return ret - - # prepare augmented data - lq_list = [self.lq] - for tf in 'v', 'h', 't': - lq_list.extend([_transform(t, tf) for t in lq_list]) - - # inference - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - out_list = [self.net_g_ema(aug) for aug in lq_list] - else: - self.net_g.eval() - with torch.no_grad(): - out_list = [self.net_g_ema(aug) for aug in lq_list] - self.net_g.train() - - # merge results - for i in range(len(out_list)): - if i > 3: - out_list[i] = _transform(out_list[i], 't') - if i % 4 > 1: - out_list[i] = _transform(out_list[i], 'h') - if (i % 4) % 2 == 1: - out_list[i] = _transform(out_list[i], 'v') - output = torch.cat(out_list, dim=0) - - self.output = output.mean(dim=0, keepdim=True) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - if self.opt['rank'] == 0: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset_name = dataloader.dataset.opt['name'] - with_metrics = self.opt['val'].get('metrics') is not None - use_pbar = self.opt['val'].get('pbar', False) - - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} - # initialize the best metric results for each dataset_name (supporting multiple validation datasets) - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - if with_metrics: - self.metric_results = {metric: 0 for metric in self.metric_results} - - metric_data = dict() - if use_pbar: - pbar = tqdm(total=len(dataloader), unit='image') - - for idx, val_data in enumerate(dataloader): - img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] - self.feed_data(val_data) - self.test() - - visuals = self.get_current_visuals() - sr_img = tensor2img([visuals['result']]) - metric_data['img'] = sr_img - if 'gt' in visuals: - gt_img = tensor2img([visuals['gt']]) - metric_data['img2'] = gt_img - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], img_name, - f'{img_name}_{current_iter}.png') - else: - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["name"]}.png') - imwrite(sr_img, save_img_path) - - if with_metrics: - # calculate metrics - for name, opt_ in self.opt['val']['metrics'].items(): - self.metric_results[name] += calculate_metric(metric_data, opt_) - if use_pbar: - pbar.update(1) - pbar.set_description(f'Test {img_name}') - if use_pbar: - pbar.close() - - if with_metrics: - for metric in self.metric_results.keys(): - self.metric_results[metric] /= (idx + 1) - # update the best metric result - self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) - - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): - log_str = f'Validation {dataset_name}\n' - for metric, value in self.metric_results.items(): - log_str += f'\t # {metric}: {value:.4f}' - if hasattr(self, 'best_metric_results'): - log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' - f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') - log_str += '\n' - - logger = get_root_logger() - logger.info(log_str) - if tb_logger: - for metric, value in self.metric_results.items(): - tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) - - def get_current_visuals(self): - out_dict = OrderedDict() - out_dict['lq'] = self.lq.detach().cpu() - out_dict['result'] = self.output.detach().cpu() - if hasattr(self, 'gt'): - out_dict['gt'] = self.gt.detach().cpu() - return out_dict - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/srgan_model.py b/basicsr/models/srgan_model.py deleted file mode 100644 index bdbeb3d6d75c6fb18577d1ee8cd30b4ba0ccbb14..0000000000000000000000000000000000000000 --- a/basicsr/models/srgan_model.py +++ /dev/null @@ -1,149 +0,0 @@ -import torch -from collections import OrderedDict - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class SRGANModel(SRModel): - """SRGAN model for single image super-resolution.""" - - def init_training_settings(self): - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema is used only for testing on one GPU and saving - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define network net_d - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_d', 'params') - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) - - self.net_g.train() - self.net_d.train() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('ldl_opt'): - self.cri_ldl = build_loss(train_opt['ldl_opt']).to(self.device) - else: - self.cri_ldl = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if train_opt.get('gan_opt'): - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - # optimizer g - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - # optimizer d - optim_type = train_opt['optim_d'].pop('type') - self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) - self.optimizers.append(self.optimizer_d) - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - real_d_pred = self.net_d(self.gt) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach()) - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/stylegan2_model.py b/basicsr/models/stylegan2_model.py deleted file mode 100644 index 37c59e33c6dc89dc6622ad999cb574f488baa8af..0000000000000000000000000000000000000000 --- a/basicsr/models/stylegan2_model.py +++ /dev/null @@ -1,283 +0,0 @@ -import cv2 -import math -import numpy as np -import random -import torch -from collections import OrderedDict -from os import path as osp - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.losses.gan_loss import g_path_regularize, r1_penalty -from basicsr.utils import imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from .base_model import BaseModel - - -@MODEL_REGISTRY.register() -class StyleGAN2Model(BaseModel): - """StyleGAN2 model.""" - - def __init__(self, opt): - super(StyleGAN2Model, self).__init__(opt) - - # define network net_g - self.net_g = build_network(opt['network_g']) - self.net_g = self.model_to_device(self.net_g) - self.print_network(self.net_g) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_g', 'params') - self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) - - # latent dimension: self.num_style_feat - self.num_style_feat = opt['network_g']['num_style_feat'] - num_val_samples = self.opt['val'].get('num_val_samples', 16) - self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device) - - if self.is_train: - self.init_training_settings() - - def init_training_settings(self): - train_opt = self.opt['train'] - - # define network net_d - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_d', 'params') - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) - - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema only used for testing on one GPU and saving, do not need to - # wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - - self.net_g.train() - self.net_d.train() - self.net_g_ema.eval() - - # define losses - # gan loss (wgan) - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - # regularization weights - self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator - self.path_reg_weight = train_opt['path_reg_weight'] # for generator - - self.net_g_reg_every = train_opt['net_g_reg_every'] - self.net_d_reg_every = train_opt['net_d_reg_every'] - self.mixing_prob = train_opt['mixing_prob'] - - self.mean_path_length = 0 - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - # optimizer g - net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1) - if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC': - normal_params = [] - style_mlp_params = [] - modulation_conv_params = [] - for name, param in self.net_g.named_parameters(): - if 'modulation' in name: - normal_params.append(param) - elif 'style_mlp' in name: - style_mlp_params.append(param) - elif 'modulated_conv' in name: - modulation_conv_params.append(param) - else: - normal_params.append(param) - optim_params_g = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': style_mlp_params, - 'lr': train_opt['optim_g']['lr'] * 0.01 - }, - { - 'params': modulation_conv_params, - 'lr': train_opt['optim_g']['lr'] / 3 - } - ] - else: - normal_params = [] - for name, param in self.net_g.named_parameters(): - normal_params.append(param) - optim_params_g = [{ # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }] - - optim_type = train_opt['optim_g'].pop('type') - lr = train_opt['optim_g']['lr'] * net_g_reg_ratio - betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) - self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) - self.optimizers.append(self.optimizer_g) - - # optimizer d - net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) - if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC': - normal_params = [] - linear_params = [] - for name, param in self.net_d.named_parameters(): - if 'final_linear' in name: - linear_params.append(param) - else: - normal_params.append(param) - optim_params_d = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_d']['lr'] - }, - { - 'params': linear_params, - 'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512)) - } - ] - else: - normal_params = [] - for name, param in self.net_d.named_parameters(): - normal_params.append(param) - optim_params_d = [{ # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_d']['lr'] - }] - - optim_type = train_opt['optim_d'].pop('type') - lr = train_opt['optim_d']['lr'] * net_d_reg_ratio - betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) - self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) - self.optimizers.append(self.optimizer_d) - - def feed_data(self, data): - self.real_img = data['gt'].to(self.device) - - def make_noise(self, batch, num_noise): - if num_noise == 1: - noises = torch.randn(batch, self.num_style_feat, device=self.device) - else: - noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0) - return noises - - def mixing_noise(self, batch, prob): - if random.random() < prob: - return self.make_noise(batch, 2) - else: - return [self.make_noise(batch, 1)] - - def optimize_parameters(self, current_iter): - loss_dict = OrderedDict() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - self.optimizer_d.zero_grad() - - batch = self.real_img.size(0) - noise = self.mixing_noise(batch, self.mixing_prob) - fake_img, _ = self.net_g(noise) - fake_pred = self.net_d(fake_img.detach()) - - real_pred = self.net_d(self.real_img) - # wgan loss with softplus (logistic loss) for discriminator - l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True) - loss_dict['l_d'] = l_d - # In wgan, real_score should be positive and fake_score should be - # negative - loss_dict['real_score'] = real_pred.detach().mean() - loss_dict['fake_score'] = fake_pred.detach().mean() - l_d.backward() - - if current_iter % self.net_d_reg_every == 0: - self.real_img.requires_grad = True - real_pred = self.net_d(self.real_img) - l_d_r1 = r1_penalty(real_pred, self.real_img) - l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) - # TODO: why do we need to add 0 * real_pred, otherwise, a runtime - # error will arise: RuntimeError: Expected to have finished - # reduction in the prior iteration before starting a new one. - # This error indicates that your module has parameters that were - # not used in producing loss. - loss_dict['l_d_r1'] = l_d_r1.detach().mean() - l_d_r1.backward() - - self.optimizer_d.step() - - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - self.optimizer_g.zero_grad() - - noise = self.mixing_noise(batch, self.mixing_prob) - fake_img, _ = self.net_g(noise) - fake_pred = self.net_d(fake_img) - - # wgan loss with softplus (non-saturating loss) for generator - l_g = self.cri_gan(fake_pred, True, is_disc=False) - loss_dict['l_g'] = l_g - l_g.backward() - - if current_iter % self.net_g_reg_every == 0: - path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink']) - noise = self.mixing_noise(path_batch_size, self.mixing_prob) - fake_img, latents = self.net_g(noise, return_latents=True) - l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length) - - l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0]) - # TODO: why do we need to add 0 * fake_img[0, 0, 0, 0] - l_g_path.backward() - loss_dict['l_g_path'] = l_g_path.detach().mean() - loss_dict['path_length'] = path_lengths - - self.optimizer_g.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - # EMA - self.model_ema(decay=0.5**(32 / (10 * 1000))) - - def test(self): - with torch.no_grad(): - self.net_g_ema.eval() - self.output, _ = self.net_g_ema([self.fixed_sample]) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - if self.opt['rank'] == 0: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - assert dataloader is None, 'Validation dataloader should be None.' - self.test() - result = tensor2img(self.output, min_max=(-1, 1)) - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png') - imwrite(result, save_img_path) - # add sample images to tb_logger - result = (result / 255.).astype(np.float32) - result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) - if tb_logger is not None: - tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC') - - def save(self, epoch, current_iter): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/swinir_model.py b/basicsr/models/swinir_model.py deleted file mode 100644 index 49bd95ba0c3fef2a48665bb29b19db2618b5840f..0000000000000000000000000000000000000000 --- a/basicsr/models/swinir_model.py +++ /dev/null @@ -1,33 +0,0 @@ -import torch -from torch.nn import functional as F - -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class SwinIRModel(SRModel): - - def test(self): - # pad to multiplication of window_size - window_size = self.opt['network_g']['window_size'] - scale = self.opt.get('scale', 1) - mod_pad_h, mod_pad_w = 0, 0 - _, _, h, w = self.lq.size() - if h % window_size != 0: - mod_pad_h = window_size - h % window_size - if w % window_size != 0: - mod_pad_w = window_size - w % window_size - img = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect') - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - self.output = self.net_g_ema(img) - else: - self.net_g.eval() - with torch.no_grad(): - self.output = self.net_g(img) - self.net_g.train() - - _, _, h, w = self.output.size() - self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale] diff --git a/basicsr/models/video_base_model.py b/basicsr/models/video_base_model.py deleted file mode 100644 index 208e82b24d67d73fe08b57dff0ca1014b946f2df..0000000000000000000000000000000000000000 --- a/basicsr/models/video_base_model.py +++ /dev/null @@ -1,160 +0,0 @@ -import torch -from collections import Counter -from os import path as osp -from torch import distributed as dist -from tqdm import tqdm - -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class VideoBaseModel(SRModel): - """Base video SR model.""" - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset = dataloader.dataset - dataset_name = dataset.opt['name'] - with_metrics = self.opt['val']['metrics'] is not None - # initialize self.metric_results - # It is a dict: { - # 'folder1': tensor (num_frame x len(metrics)), - # 'folder2': tensor (num_frame x len(metrics)) - # } - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {} - num_frame_each_folder = Counter(dataset.data_info['folder']) - for folder, num_frame in num_frame_each_folder.items(): - self.metric_results[folder] = torch.zeros( - num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') - # initialize the best metric results - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - rank, world_size = get_dist_info() - if with_metrics: - for _, tensor in self.metric_results.items(): - tensor.zero_() - - metric_data = dict() - # record all frames (border and center frames) - if rank == 0: - pbar = tqdm(total=len(dataset), unit='frame') - for idx in range(rank, len(dataset), world_size): - val_data = dataset[idx] - val_data['lq'].unsqueeze_(0) - val_data['gt'].unsqueeze_(0) - folder = val_data['folder'] - frame_idx, max_idx = val_data['idx'].split('/') - lq_path = val_data['lq_path'] - - self.feed_data(val_data) - self.test() - visuals = self.get_current_visuals() - result_img = tensor2img([visuals['result']]) - metric_data['img'] = result_img - if 'gt' in visuals: - gt_img = tensor2img([visuals['gt']]) - metric_data['img2'] = gt_img - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - raise NotImplementedError('saving image is not supported during training.') - else: - if 'vimeo' in dataset_name.lower(): # vimeo90k dataset - split_result = lq_path.split('/') - img_name = f'{split_result[-3]}_{split_result[-2]}_{split_result[-1].split(".")[0]}' - else: # other datasets, e.g., REDS, Vid4 - img_name = osp.splitext(osp.basename(lq_path))[0] - - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f'{img_name}_{self.opt["name"]}.png') - imwrite(result_img, save_img_path) - - if with_metrics: - # calculate metrics - for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): - result = calculate_metric(metric_data, opt_) - self.metric_results[folder][int(frame_idx), metric_idx] += result - - # progress bar - if rank == 0: - for _ in range(world_size): - pbar.update(1) - pbar.set_description(f'Test {folder}: {int(frame_idx) + world_size}/{max_idx}') - if rank == 0: - pbar.close() - - if with_metrics: - if self.opt['dist']: - # collect data among GPUs - for _, tensor in self.metric_results.items(): - dist.reduce(tensor, 0) - dist.barrier() - else: - pass # assume use one gpu in non-dist testing - - if rank == 0: - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - logger = get_root_logger() - logger.warning('nondist_validation is not implemented. Run dist_validation.') - self.dist_validation(dataloader, current_iter, tb_logger, save_img) - - def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): - # ----------------- calculate the average values for each folder, and for each metric ----------------- # - # average all frames for each sub-folder - # metric_results_avg is a dict:{ - # 'folder1': tensor (len(metrics)), - # 'folder2': tensor (len(metrics)) - # } - metric_results_avg = { - folder: torch.mean(tensor, dim=0).cpu() - for (folder, tensor) in self.metric_results.items() - } - # total_avg_results is a dict: { - # 'metric1': float, - # 'metric2': float - # } - total_avg_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} - for folder, tensor in metric_results_avg.items(): - for idx, metric in enumerate(total_avg_results.keys()): - total_avg_results[metric] += metric_results_avg[folder][idx].item() - # average among folders - for metric in total_avg_results.keys(): - total_avg_results[metric] /= len(metric_results_avg) - # update the best metric result - self._update_best_metric_result(dataset_name, metric, total_avg_results[metric], current_iter) - - # ------------------------------------------ log the metric ------------------------------------------ # - log_str = f'Validation {dataset_name}\n' - for metric_idx, (metric, value) in enumerate(total_avg_results.items()): - log_str += f'\t # {metric}: {value:.4f}' - for folder, tensor in metric_results_avg.items(): - log_str += f'\t # {folder}: {tensor[metric_idx].item():.4f}' - if hasattr(self, 'best_metric_results'): - log_str += (f'\n\t Best: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' - f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') - log_str += '\n' - - logger = get_root_logger() - logger.info(log_str) - if tb_logger: - for metric_idx, (metric, value) in enumerate(total_avg_results.items()): - tb_logger.add_scalar(f'metrics/{metric}', value, current_iter) - for folder, tensor in metric_results_avg.items(): - tb_logger.add_scalar(f'metrics/{metric}/{folder}', tensor[metric_idx].item(), current_iter) diff --git a/basicsr/models/video_gan_model.py b/basicsr/models/video_gan_model.py deleted file mode 100644 index 482c1b9a7b373b080b29e3ecbe6a0ce3f7accd87..0000000000000000000000000000000000000000 --- a/basicsr/models/video_gan_model.py +++ /dev/null @@ -1,19 +0,0 @@ -from basicsr.utils.registry import MODEL_REGISTRY -from .srgan_model import SRGANModel -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class VideoGANModel(SRGANModel, VideoBaseModel): - """Video GAN model. - - Use multiple inheritance. - It will first use the functions of :class:`SRGANModel`: - - - :func:`init_training_settings` - - :func:`setup_optimizers` - - :func:`optimize_parameters` - - :func:`save` - - Then find functions in :class:`VideoBaseModel`. - """ diff --git a/basicsr/models/video_recurrent_gan_model.py b/basicsr/models/video_recurrent_gan_model.py deleted file mode 100644 index 7dc33dc46f7062529f25149d4227b34476ff4def..0000000000000000000000000000000000000000 --- a/basicsr/models/video_recurrent_gan_model.py +++ /dev/null @@ -1,180 +0,0 @@ -import torch -from collections import OrderedDict - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .video_recurrent_model import VideoRecurrentModel - - -@MODEL_REGISTRY.register() -class VideoRecurrentGANModel(VideoRecurrentModel): - - def init_training_settings(self): - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # build network net_g with Exponential Moving Average (EMA) - # net_g_ema only used for testing on one GPU and saving. - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define network net_d - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_d', 'params') - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) - - self.net_g.train() - self.net_d.train() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if train_opt.get('gan_opt'): - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - if train_opt['fix_flow']: - normal_params = [] - flow_params = [] - for name, param in self.net_g.named_parameters(): - if 'spynet' in name: # The fix_flow now only works for spynet. - flow_params.append(param) - else: - normal_params.append(param) - - optim_params = [ - { # add flow params first - 'params': flow_params, - 'lr': train_opt['lr_flow'] - }, - { - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - ] - else: - optim_params = self.net_g.parameters() - - # optimizer g - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - # optimizer d - optim_type = train_opt['optim_d'].pop('type') - self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) - self.optimizers.append(self.optimizer_d) - - def optimize_parameters(self, current_iter): - logger = get_root_logger() - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - if self.fix_flow_iter: - if current_iter == 1: - logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'spynet' in name or 'edvr' in name: - param.requires_grad_(False) - elif current_iter == self.fix_flow_iter: - logger.warning('Train all the parameters.') - self.net_g.requires_grad_(True) - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - _, _, c, h, w = self.output.size() - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output.view(-1, c, h, w), self.gt.view(-1, c, h, w)) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output.view(-1, c, h, w)) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - # reshape to (b*n, c, h, w) - real_d_pred = self.net_d(self.gt.view(-1, c, h, w)) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - # reshape to (b*n, c, h, w) - fake_d_pred = self.net_d(self.output.view(-1, c, h, w).detach()) - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def save(self, epoch, current_iter): - if self.ema_decay > 0: - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/video_recurrent_model.py b/basicsr/models/video_recurrent_model.py deleted file mode 100644 index 2f2319b7786944170be3c067b497cf9999cba9b9..0000000000000000000000000000000000000000 --- a/basicsr/models/video_recurrent_model.py +++ /dev/null @@ -1,197 +0,0 @@ -import torch -from collections import Counter -from os import path as osp -from torch import distributed as dist -from tqdm import tqdm - -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import MODEL_REGISTRY -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class VideoRecurrentModel(VideoBaseModel): - - def __init__(self, opt): - super(VideoRecurrentModel, self).__init__(opt) - if self.is_train: - self.fix_flow_iter = opt['train'].get('fix_flow') - - def setup_optimizers(self): - train_opt = self.opt['train'] - flow_lr_mul = train_opt.get('flow_lr_mul', 1) - logger = get_root_logger() - logger.info(f'Multiple the learning rate for flow network with {flow_lr_mul}.') - if flow_lr_mul == 1: - optim_params = self.net_g.parameters() - else: # separate flow params and normal params for different lr - normal_params = [] - flow_params = [] - for name, param in self.net_g.named_parameters(): - if 'spynet' in name: - flow_params.append(param) - else: - normal_params.append(param) - optim_params = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': flow_params, - 'lr': train_opt['optim_g']['lr'] * flow_lr_mul - }, - ] - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def optimize_parameters(self, current_iter): - if self.fix_flow_iter: - logger = get_root_logger() - if current_iter == 1: - logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'spynet' in name or 'edvr' in name: - param.requires_grad_(False) - elif current_iter == self.fix_flow_iter: - logger.warning('Train all the parameters.') - self.net_g.requires_grad_(True) - - super(VideoRecurrentModel, self).optimize_parameters(current_iter) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset = dataloader.dataset - dataset_name = dataset.opt['name'] - with_metrics = self.opt['val']['metrics'] is not None - # initialize self.metric_results - # It is a dict: { - # 'folder1': tensor (num_frame x len(metrics)), - # 'folder2': tensor (num_frame x len(metrics)) - # } - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {} - num_frame_each_folder = Counter(dataset.data_info['folder']) - for folder, num_frame in num_frame_each_folder.items(): - self.metric_results[folder] = torch.zeros( - num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') - # initialize the best metric results - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - rank, world_size = get_dist_info() - if with_metrics: - for _, tensor in self.metric_results.items(): - tensor.zero_() - - metric_data = dict() - num_folders = len(dataset) - num_pad = (world_size - (num_folders % world_size)) % world_size - if rank == 0: - pbar = tqdm(total=len(dataset), unit='folder') - # Will evaluate (num_folders + num_pad) times, but only the first num_folders results will be recorded. - # (To avoid wait-dead) - for i in range(rank, num_folders + num_pad, world_size): - idx = min(i, num_folders - 1) - val_data = dataset[idx] - folder = val_data['folder'] - - # compute outputs - val_data['lq'].unsqueeze_(0) - val_data['gt'].unsqueeze_(0) - self.feed_data(val_data) - val_data['lq'].squeeze_(0) - val_data['gt'].squeeze_(0) - - self.test() - visuals = self.get_current_visuals() - - # tentative for out of GPU memory - del self.lq - del self.output - if 'gt' in visuals: - del self.gt - torch.cuda.empty_cache() - - if self.center_frame_only: - visuals['result'] = visuals['result'].unsqueeze(1) - if 'gt' in visuals: - visuals['gt'] = visuals['gt'].unsqueeze(1) - - # evaluate - if i < num_folders: - for idx in range(visuals['result'].size(1)): - result = visuals['result'][0, idx, :, :, :] - result_img = tensor2img([result]) # uint8, bgr - metric_data['img'] = result_img - if 'gt' in visuals: - gt = visuals['gt'][0, idx, :, :, :] - gt_img = tensor2img([gt]) # uint8, bgr - metric_data['img2'] = gt_img - - if save_img: - if self.opt['is_train']: - raise NotImplementedError('saving image is not supported during training.') - else: - if self.center_frame_only: # vimeo-90k - clip_ = val_data['lq_path'].split('/')[-3] - seq_ = val_data['lq_path'].split('/')[-2] - name_ = f'{clip_}_{seq_}' - img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f"{name_}_{self.opt['name']}.png") - else: # others - img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f"{idx:08d}_{self.opt['name']}.png") - # image name only for REDS dataset - imwrite(result_img, img_path) - - # calculate metrics - if with_metrics: - for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): - result = calculate_metric(metric_data, opt_) - self.metric_results[folder][idx, metric_idx] += result - - # progress bar - if rank == 0: - for _ in range(world_size): - pbar.update(1) - pbar.set_description(f'Folder: {folder}') - - if rank == 0: - pbar.close() - - if with_metrics: - if self.opt['dist']: - # collect data among GPUs - for _, tensor in self.metric_results.items(): - dist.reduce(tensor, 0) - dist.barrier() - - if rank == 0: - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def test(self): - n = self.lq.size(1) - self.net_g.eval() - - flip_seq = self.opt['val'].get('flip_seq', False) - self.center_frame_only = self.opt['val'].get('center_frame_only', False) - - if flip_seq: - self.lq = torch.cat([self.lq, self.lq.flip(1)], dim=1) - - with torch.no_grad(): - self.output = self.net_g(self.lq) - - if flip_seq: - output_1 = self.output[:, :n, :, :, :] - output_2 = self.output[:, n:, :, :, :].flip(1) - self.output = 0.5 * (output_1 + output_2) - - if self.center_frame_only: - self.output = self.output[:, n // 2, :, :, :] - - self.net_g.train() diff --git a/basicsr/ops/__init__.py b/basicsr/ops/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/basicsr/ops/dcn/__init__.py b/basicsr/ops/dcn/__init__.py deleted file mode 100644 index b534fc667eefc85cff7b025dcdfd2d0057c6fe35..0000000000000000000000000000000000000000 --- a/basicsr/ops/dcn/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv, - modulated_deform_conv) - -__all__ = [ - 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv', - 'modulated_deform_conv' -] diff --git a/basicsr/ops/dcn/deform_conv.py b/basicsr/ops/dcn/deform_conv.py deleted file mode 100644 index 32de9ef0b76cb9d62fcd110e01b8afad735115b0..0000000000000000000000000000000000000000 --- a/basicsr/ops/dcn/deform_conv.py +++ /dev/null @@ -1,379 +0,0 @@ -import math -import os -import torch -from torch import nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn import functional as F -from torch.nn.modules.utils import _pair, _single - -BASICSR_JIT = os.getenv('BASICSR_JIT') -if BASICSR_JIT == 'True': - from torch.utils.cpp_extension import load - module_path = os.path.dirname(__file__) - deform_conv_ext = load( - 'deform_conv', - sources=[ - os.path.join(module_path, 'src', 'deform_conv_ext.cpp'), - os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'), - os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'), - ], - ) -else: - try: - from . import deform_conv_ext - except ImportError: - pass - # avoid annoying print output - # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' - # '1. compile with BASICSR_EXT=True. or\n ' - # '2. set BASICSR_JIT=True during running') - - -class DeformConvFunction(Function): - - @staticmethod - def forward(ctx, - input, - offset, - weight, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - im2col_step=64): - if input is not None and input.dim() != 4: - raise ValueError(f'Expected 4D tensor as input, got {input.dim()}D tensor instead.') - ctx.stride = _pair(stride) - ctx.padding = _pair(padding) - ctx.dilation = _pair(dilation) - ctx.groups = groups - ctx.deformable_groups = deformable_groups - ctx.im2col_step = im2col_step - - ctx.save_for_backward(input, offset, weight) - - output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride)) - - ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones - - if not input.is_cuda: - raise NotImplementedError - else: - cur_im2col_step = min(ctx.im2col_step, input.shape[0]) - assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' - deform_conv_ext.deform_conv_forward(input, weight, - offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3), - weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], - ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, - ctx.deformable_groups, cur_im2col_step) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, offset, weight = ctx.saved_tensors - - grad_input = grad_offset = grad_weight = None - - if not grad_output.is_cuda: - raise NotImplementedError - else: - cur_im2col_step = min(ctx.im2col_step, input.shape[0]) - assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' - - if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input, - grad_offset, weight, ctx.bufs_[0], weight.size(3), - weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], - ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, - ctx.deformable_groups, cur_im2col_step) - - if ctx.needs_input_grad[2]: - grad_weight = torch.zeros_like(weight) - deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight, - ctx.bufs_[0], ctx.bufs_[1], weight.size(3), - weight.size(2), ctx.stride[1], ctx.stride[0], - ctx.padding[1], ctx.padding[0], ctx.dilation[1], - ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1, - cur_im2col_step) - - return (grad_input, grad_offset, grad_weight, None, None, None, None, None) - - @staticmethod - def _output_size(input, weight, padding, dilation, stride): - channels = weight.size(0) - output_size = (input.size(0), channels) - for d in range(input.dim() - 2): - in_size = input.size(d + 2) - pad = padding[d] - kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 - stride_ = stride[d] - output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) - if not all(map(lambda s: s > 0, output_size)): - raise ValueError(f'convolution input is too small (output would be {"x".join(map(str, output_size))})') - return output_size - - -class ModulatedDeformConvFunction(Function): - - @staticmethod - def forward(ctx, - input, - offset, - mask, - weight, - bias=None, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1): - ctx.stride = stride - ctx.padding = padding - ctx.dilation = dilation - ctx.groups = groups - ctx.deformable_groups = deformable_groups - ctx.with_bias = bias is not None - if not ctx.with_bias: - bias = input.new_empty(1) # fake tensor - if not input.is_cuda: - raise NotImplementedError - if weight.requires_grad or mask.requires_grad or offset.requires_grad or input.requires_grad: - ctx.save_for_backward(input, offset, mask, weight, bias) - output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight)) - ctx._bufs = [input.new_empty(0), input.new_empty(0)] - deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output, - ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride, - ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, - ctx.groups, ctx.deformable_groups, ctx.with_bias) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - if not grad_output.is_cuda: - raise NotImplementedError - input, offset, mask, weight, bias = ctx.saved_tensors - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - grad_mask = torch.zeros_like(mask) - grad_weight = torch.zeros_like(weight) - grad_bias = torch.zeros_like(bias) - deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1], - grad_input, grad_weight, grad_bias, grad_offset, grad_mask, - grad_output, weight.shape[2], weight.shape[3], ctx.stride, - ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, - ctx.groups, ctx.deformable_groups, ctx.with_bias) - if not ctx.with_bias: - grad_bias = None - - return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None) - - @staticmethod - def _infer_shape(ctx, input, weight): - n = input.size(0) - channels_out = weight.size(0) - height, width = input.shape[2:4] - kernel_h, kernel_w = weight.shape[2:4] - height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1 - width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1 - return n, channels_out, height_out, width_out - - -deform_conv = DeformConvFunction.apply -modulated_deform_conv = ModulatedDeformConvFunction.apply - - -class DeformConv(nn.Module): - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - bias=False): - super(DeformConv, self).__init__() - - assert not bias - assert in_channels % groups == 0, f'in_channels {in_channels} is not divisible by groups {groups}' - assert out_channels % groups == 0, f'out_channels {out_channels} is not divisible by groups {groups}' - - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = _pair(stride) - self.padding = _pair(padding) - self.dilation = _pair(dilation) - self.groups = groups - self.deformable_groups = deformable_groups - # enable compatibility with nn.Conv2d - self.transposed = False - self.output_padding = _single(0) - - self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size)) - - self.reset_parameters() - - def reset_parameters(self): - n = self.in_channels - for k in self.kernel_size: - n *= k - stdv = 1. / math.sqrt(n) - self.weight.data.uniform_(-stdv, stdv) - - def forward(self, x, offset): - # To fix an assert error in deform_conv_cuda.cpp:128 - # input image is smaller than kernel - input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1]) - if input_pad: - pad_h = max(self.kernel_size[0] - x.size(2), 0) - pad_w = max(self.kernel_size[1] - x.size(3), 0) - x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() - offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() - out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, - self.deformable_groups) - if input_pad: - out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous() - return out - - -class DeformConvPack(DeformConv): - """A Deformable Conv Encapsulation that acts as normal Conv layers. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int or tuple[int]): Same as nn.Conv2d. - padding (int or tuple[int]): Same as nn.Conv2d. - dilation (int or tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - """ - - _version = 2 - - def __init__(self, *args, **kwargs): - super(DeformConvPack, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Conv2d( - self.in_channels, - self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1], - kernel_size=self.kernel_size, - stride=_pair(self.stride), - padding=_pair(self.padding), - dilation=_pair(self.dilation), - bias=True) - self.init_offset() - - def init_offset(self): - self.conv_offset.weight.data.zero_() - self.conv_offset.bias.data.zero_() - - def forward(self, x): - offset = self.conv_offset(x) - return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, - self.deformable_groups) - - -class ModulatedDeformConv(nn.Module): - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - bias=True): - super(ModulatedDeformConv, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = stride - self.padding = padding - self.dilation = dilation - self.groups = groups - self.deformable_groups = deformable_groups - self.with_bias = bias - # enable compatibility with nn.Conv2d - self.transposed = False - self.output_padding = _single(0) - - self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) - if bias: - self.bias = nn.Parameter(torch.Tensor(out_channels)) - else: - self.register_parameter('bias', None) - self.init_weights() - - def init_weights(self): - n = self.in_channels - for k in self.kernel_size: - n *= k - stdv = 1. / math.sqrt(n) - self.weight.data.uniform_(-stdv, stdv) - if self.bias is not None: - self.bias.data.zero_() - - def forward(self, x, offset, mask): - return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, - self.groups, self.deformable_groups) - - -class ModulatedDeformConvPack(ModulatedDeformConv): - """A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int or tuple[int]): Same as nn.Conv2d. - padding (int or tuple[int]): Same as nn.Conv2d. - dilation (int or tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - """ - - _version = 2 - - def __init__(self, *args, **kwargs): - super(ModulatedDeformConvPack, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Conv2d( - self.in_channels, - self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1], - kernel_size=self.kernel_size, - stride=_pair(self.stride), - padding=_pair(self.padding), - dilation=_pair(self.dilation), - bias=True) - self.init_weights() - - def init_weights(self): - super(ModulatedDeformConvPack, self).init_weights() - if hasattr(self, 'conv_offset'): - self.conv_offset.weight.data.zero_() - self.conv_offset.bias.data.zero_() - - def forward(self, x): - out = self.conv_offset(x) - o1, o2, mask = torch.chunk(out, 3, dim=1) - offset = torch.cat((o1, o2), dim=1) - mask = torch.sigmoid(mask) - return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, - self.groups, self.deformable_groups) diff --git a/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/basicsr/ops/dcn/src/deform_conv_cuda.cpp deleted file mode 100644 index 191298aaeaefc8065b9250101480a5b8ebe2f4c4..0000000000000000000000000000000000000000 --- a/basicsr/ops/dcn/src/deform_conv_cuda.cpp +++ /dev/null @@ -1,685 +0,0 @@ -// modify from -// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c - -#include -#include - -#include -#include - -void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset, - const int channels, const int height, const int width, - const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - at::Tensor data_col); - -void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset, - const int channels, const int height, const int width, - const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - at::Tensor grad_im); - -void deformable_col2im_coord( - const at::Tensor data_col, const at::Tensor data_im, - const at::Tensor data_offset, const int channels, const int height, - const int width, const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int parallel_imgs, - const int deformable_group, at::Tensor grad_offset); - -void modulated_deformable_im2col_cuda( - const at::Tensor data_im, const at::Tensor data_offset, - const at::Tensor data_mask, const int batch_size, const int channels, - const int height_im, const int width_im, const int height_col, - const int width_col, const int kernel_h, const int kenerl_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int deformable_group, - at::Tensor data_col); - -void modulated_deformable_col2im_cuda( - const at::Tensor data_col, const at::Tensor data_offset, - const at::Tensor data_mask, const int batch_size, const int channels, - const int height_im, const int width_im, const int height_col, - const int width_col, const int kernel_h, const int kenerl_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int deformable_group, - at::Tensor grad_im); - -void modulated_deformable_col2im_coord_cuda( - const at::Tensor data_col, const at::Tensor data_im, - const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, - const int width_im, const int height_col, const int width_col, - const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, const int dilation_h, - const int dilation_w, const int deformable_group, at::Tensor grad_offset, - at::Tensor grad_mask); - -void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput, - at::Tensor weight, int kH, int kW, int dH, int dW, int padH, - int padW, int dilationH, int dilationW, int group, - int deformable_group) { - TORCH_CHECK(weight.ndimension() == 4, - "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " - "but got: %s", - weight.ndimension()); - - TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); - - TORCH_CHECK(kW > 0 && kH > 0, - "kernel size should be greater than zero, but got kH: %d kW: %d", kH, - kW); - - TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW), - "kernel size should be consistent with weight, ", - "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH, - kW, weight.size(2), weight.size(3)); - - TORCH_CHECK(dW > 0 && dH > 0, - "stride should be greater than zero, but got dH: %d dW: %d", dH, dW); - - TORCH_CHECK( - dilationW > 0 && dilationH > 0, - "dilation should be greater than 0, but got dilationH: %d dilationW: %d", - dilationH, dilationW); - - int ndim = input.ndimension(); - int dimf = 0; - int dimh = 1; - int dimw = 2; - - if (ndim == 4) { - dimf++; - dimh++; - dimw++; - } - - TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s", - ndim); - - long nInputPlane = weight.size(1) * group; - long inputHeight = input.size(dimh); - long inputWidth = input.size(dimw); - long nOutputPlane = weight.size(0); - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - - TORCH_CHECK(nInputPlane % deformable_group == 0, - "input channels must divide deformable group size"); - - if (outputWidth < 1 || outputHeight < 1) - AT_ERROR( - "Given input size: (%ld x %ld x %ld). " - "Calculated output size: (%ld x %ld x %ld). Output size is too small", - nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight, - outputWidth); - - TORCH_CHECK(input.size(1) == nInputPlane, - "invalid number of input planes, expected: %d, but got: %d", - nInputPlane, input.size(1)); - - TORCH_CHECK((inputHeight >= kH && inputWidth >= kW), - "input image is smaller than kernel"); - - TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth), - "invalid spatial size of offset, expected height: %d width: %d, but " - "got height: %d width: %d", - outputHeight, outputWidth, offset.size(2), offset.size(3)); - - TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW), - "invalid number of channels of offset"); - - if (gradOutput != NULL) { - TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane, - "invalid number of gradOutput planes, expected: %d, but got: %d", - nOutputPlane, gradOutput->size(dimf)); - - TORCH_CHECK((gradOutput->size(dimh) == outputHeight && - gradOutput->size(dimw) == outputWidth), - "invalid size of gradOutput, expected height: %d width: %d , but " - "got height: %d width: %d", - outputHeight, outputWidth, gradOutput->size(dimh), - gradOutput->size(dimw)); - } -} - -int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, - at::Tensor offset, at::Tensor output, - at::Tensor columns, at::Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - // todo: resize columns to include im2col: done - // todo: add im2col_step as input - // todo: add new output buffer and transpose it to output (or directly - // transpose output) todo: possibly change data indexing because of - // parallel_imgs - - shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW, - dilationH, dilationW, group, deformable_group); - at::DeviceGuard guard(input.device()); - - input = input.contiguous(); - offset = offset.contiguous(); - weight = weight.contiguous(); - - int batch = 1; - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input.unsqueeze_(0); - offset.unsqueeze_(0); - } - - // todo: assert batchsize dividable by im2col_step - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = weight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); - - output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, - outputHeight, outputWidth}); - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < outputHeight * outputWidth) { - ones = at::ones({outputHeight, outputWidth}, input.options()); - } - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - at::Tensor output_buffer = - at::zeros({batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth}, - output.options()); - - output_buffer = output_buffer.view( - {output_buffer.size(0), group, output_buffer.size(1) / group, - output_buffer.size(2), output_buffer.size(3)}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns); - - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - - for (int g = 0; g < group; g++) { - output_buffer[elt][g] = output_buffer[elt][g] - .flatten(1) - .addmm_(weight[g].flatten(1), columns[g]) - .view_as(output_buffer[elt][g]); - } - } - - output_buffer = output_buffer.view( - {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2), - output_buffer.size(3), output_buffer.size(4)}); - - output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step, outputHeight, outputWidth}); - output_buffer.transpose_(1, 2); - output.copy_(output_buffer); - output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - output = output.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); - } - - return 1; -} - -int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, - at::Tensor gradOutput, at::Tensor gradInput, - at::Tensor gradOffset, at::Tensor weight, - at::Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step) { - shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW, - dilationH, dilationW, group, deformable_group); - at::DeviceGuard guard(input.device()); - - input = input.contiguous(); - offset = offset.contiguous(); - gradOutput = gradOutput.contiguous(); - weight = weight.contiguous(); - - int batch = 1; - - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input = input.view({1, input.size(0), input.size(1), input.size(2)}); - offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); - gradOutput = gradOutput.view( - {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); - } - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = weight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - // change order of grad output - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput.transpose_(1, 2); - - gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, - outputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - // divide into groups - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - gradOutput = gradOutput.view( - {gradOutput.size(0), group, gradOutput.size(1) / group, - gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)}); - - for (int g = 0; g < group; g++) { - columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), - gradOutput[elt][g].flatten(1), 0.0f, 1.0f); - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - gradOutput = gradOutput.view( - {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2), - gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)}); - - deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, - inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, - dilationH, dilationW, im2col_step, deformable_group, - gradOffset[elt]); - - deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, gradInput[elt]); - } - - gradOutput.transpose_(1, 2); - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - gradOffset = gradOffset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); - gradOffset = - gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); - } - - return 1; -} - -int deform_conv_backward_parameters_cuda( - at::Tensor input, at::Tensor offset, at::Tensor gradOutput, - at::Tensor gradWeight, // at::Tensor gradBias, - at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step) { - // todo: transpose and reshape outGrad - // todo: reshape columns - // todo: add im2col_step as input - - shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH, - padW, dilationH, dilationW, group, deformable_group); - at::DeviceGuard guard(input.device()); - - input = input.contiguous(); - offset = offset.contiguous(); - gradOutput = gradOutput.contiguous(); - - int batch = 1; - - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input = input.view( - at::IntList({1, input.size(0), input.size(1), input.size(2)})); - gradOutput = gradOutput.view( - {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); - } - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = gradWeight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); - - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput.transpose_(1, 2); - - at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); - gradOutputBuffer = - gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, - outputHeight, outputWidth}); - gradOutputBuffer.copy_(gradOutput); - gradOutputBuffer = - gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth}); - - gradOutput.transpose_(1, 2); - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns); - - // divide into group - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group, - gradOutputBuffer.size(2), gradOutputBuffer.size(3)}); - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - gradWeight = - gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1), - gradWeight.size(2), gradWeight.size(3)}); - - for (int g = 0; g < group; g++) { - gradWeight[g] = gradWeight[g] - .flatten(1) - .addmm_(gradOutputBuffer[elt][g].flatten(1), - columns[g].transpose(1, 0), 1.0, scale) - .view_as(gradWeight[g]); - } - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.size(0), - gradOutputBuffer.size(1) * gradOutputBuffer.size(2), - gradOutputBuffer.size(3), gradOutputBuffer.size(4)}); - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), - gradWeight.size(2), gradWeight.size(3), - gradWeight.size(4)}); - } - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - } - - return 1; -} - -void modulated_deform_conv_cuda_forward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias) { - TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); - TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); - at::DeviceGuard guard(input.device()); - - const int batch = input.size(0); - const int channels = input.size(1); - const int height = input.size(2); - const int width = input.size(3); - - const int channels_out = weight.size(0); - const int channels_kernel = weight.size(1); - const int kernel_h_ = weight.size(2); - const int kernel_w_ = weight.size(3); - - if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) - AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", - kernel_h_, kernel_w, kernel_h_, kernel_w_); - if (channels != channels_kernel * group) - AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", - channels, channels_kernel * group); - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = at::ones({height_out, width_out}, input.options()); - } - - // resize output - output = output.view({batch, channels_out, height_out, width_out}).zero_(); - // resize temporary columns - columns = - at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, - input.options()); - - output = output.view({output.size(0), group, output.size(1) / group, - output.size(2), output.size(3)}); - - for (int b = 0; b < batch; b++) { - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns); - - // divide into group - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - - for (int g = 0; g < group; g++) { - output[b][g] = output[b][g] - .flatten(1) - .addmm_(weight[g].flatten(1), columns[g]) - .view_as(output[b][g]); - } - - weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), - weight.size(3), weight.size(4)}); - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - } - - output = output.view({output.size(0), output.size(1) * output.size(2), - output.size(3), output.size(4)}); - - if (with_bias) { - output += bias.view({1, bias.size(0), 1, 1}); - } -} - -void modulated_deform_conv_cuda_backward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor columns, - at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, - at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias) { - TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); - TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); - at::DeviceGuard guard(input.device()); - - const int batch = input.size(0); - const int channels = input.size(1); - const int height = input.size(2); - const int width = input.size(3); - - const int channels_kernel = weight.size(1); - const int kernel_h_ = weight.size(2); - const int kernel_w_ = weight.size(3); - if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) - AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", - kernel_h_, kernel_w, kernel_h_, kernel_w_); - if (channels != channels_kernel * group) - AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", - channels, channels_kernel * group); - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = at::ones({height_out, width_out}, input.options()); - } - - grad_input = grad_input.view({batch, channels, height, width}); - columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out}, - input.options()); - - grad_output = - grad_output.view({grad_output.size(0), group, grad_output.size(1) / group, - grad_output.size(2), grad_output.size(3)}); - - for (int b = 0; b < batch; b++) { - // divide int group - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - - for (int g = 0; g < group; g++) { - columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), - grad_output[b][g].flatten(1), 0.0f, 1.0f); - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), - weight.size(3), weight.size(4)}); - - // gradient w.r.t. input coordinate data - modulated_deformable_col2im_coord_cuda( - columns, input[b], offset[b], mask[b], 1, channels, height, width, - height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, - stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], - grad_mask[b]); - // gradient w.r.t. input data - modulated_deformable_col2im_cuda( - columns, offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, grad_input[b]); - - // gradient w.r.t. weight, dWeight should accumulate across the batch and - // group - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns); - - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - grad_weight = grad_weight.view({group, grad_weight.size(0) / group, - grad_weight.size(1), grad_weight.size(2), - grad_weight.size(3)}); - if (with_bias) - grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); - - for (int g = 0; g < group; g++) { - grad_weight[g] = - grad_weight[g] - .flatten(1) - .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) - .view_as(grad_weight[g]); - if (with_bias) { - grad_bias[g] = - grad_bias[g] - .view({-1, 1}) - .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) - .view(-1); - } - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), - grad_weight.size(2), grad_weight.size(3), - grad_weight.size(4)}); - if (with_bias) - grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); - } - grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), - grad_output.size(2), grad_output.size(3), - grad_output.size(4)}); -} diff --git a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu b/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu deleted file mode 100644 index 9fe9ba3af737c698749be48e3c222f65aa490d47..0000000000000000000000000000000000000000 --- a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu +++ /dev/null @@ -1,867 +0,0 @@ -/*! - ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** - * - * COPYRIGHT - * - * All contributions by the University of California: - * Copyright (c) 2014-2017 The Regents of the University of California (Regents) - * All rights reserved. - * - * All other contributions: - * Copyright (c) 2014-2017, the respective contributors - * All rights reserved. - * - * Caffe uses a shared copyright model: each contributor holds copyright over - * their contributions to Caffe. The project versioning records all such - * contribution and copyright details. If a contributor wants to further mark - * their specific copyright on a particular contribution, they should indicate - * their copyright solely in the commit message of the change when it is - * committed. - * - * LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * 1. Redistributions of source code must retain the above copyright notice, this - * list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND - * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - * - * CONTRIBUTION AGREEMENT - * - * By contributing to the BVLC/caffe repository through pull-request, comment, - * or otherwise, the contributor releases their content to the - * license and copyright terms herein. - * - ***************** END Caffe Copyright Notice and Disclaimer ******************** - * - * Copyright (c) 2018 Microsoft - * Licensed under The MIT License [see LICENSE for details] - * \file modulated_deformable_im2col.cuh - * \brief Function definitions of converting an image to - * column matrix based on kernel, padding, dilation, and offset. - * These functions are mainly used in deformable convolution operators. - * \ref: https://arxiv.org/abs/1703.06211 - * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng - */ - -// modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu - -#include -#include -#include -#include -#include -#include - -using namespace at; - -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ - i += blockDim.x * gridDim.x) - -const int CUDA_NUM_THREADS = 1024; -const int kMaxGridNum = 65535; - -inline int GET_BLOCKS(const int N) -{ - return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); -} - -template -__device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width, - const int height, const int width, scalar_t h, scalar_t w) -{ - - int h_low = floor(h); - int w_low = floor(w); - int h_high = h_low + 1; - int w_high = w_low + 1; - - scalar_t lh = h - h_low; - scalar_t lw = w - w_low; - scalar_t hh = 1 - lh, hw = 1 - lw; - - scalar_t v1 = 0; - if (h_low >= 0 && w_low >= 0) - v1 = bottom_data[h_low * data_width + w_low]; - scalar_t v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - v2 = bottom_data[h_low * data_width + w_high]; - scalar_t v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - v3 = bottom_data[h_high * data_width + w_low]; - scalar_t v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - v4 = bottom_data[h_high * data_width + w_high]; - - scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - - scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - return val; -} - -template -__device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, - const int h, const int w, const int height, const int width) -{ - - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - if (h == argmax_h_low && w == argmax_w_low) - weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); - if (h == argmax_h_low && w == argmax_w_high) - weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); - if (h == argmax_h_high && w == argmax_w_low) - weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); - if (h == argmax_h_high && w == argmax_w_high) - weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); - return weight; -} - -template -__device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, - const int height, const int width, const scalar_t *im_data, - const int data_width, const int bp_dir) -{ - - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - - if (bp_dir == 0) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - else if (bp_dir == 1) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - - return weight; -} - -template -__global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset, - const int height, const int width, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int channel_per_deformable_group, - const int batch_size, const int num_channels, const int deformable_group, - const int height_col, const int width_col, - scalar_t *data_col) -{ - CUDA_KERNEL_LOOP(index, n) - { - // index index of output matrix - const int w_col = index % width_col; - const int h_col = (index / width_col) % height_col; - const int b_col = (index / width_col / height_col) % batch_size; - const int c_im = (index / width_col / height_col) / batch_size; - const int c_col = c_im * kernel_h * kernel_w; - - // compute deformable group index - const int deformable_group_index = c_im / channel_per_deformable_group; - - const int h_in = h_col * stride_h - pad_h; - const int w_in = w_col * stride_w - pad_w; - scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; - //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; - const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; - const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - - for (int i = 0; i < kernel_h; ++i) - { - for (int j = 0; j < kernel_w; ++j) - { - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - scalar_t val = static_cast(0); - const scalar_t h_im = h_in + i * dilation_h + offset_h; - const scalar_t w_im = w_in + j * dilation_w + offset_w; - if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) - { - //const scalar_t map_h = i * dilation_h + offset_h; - //const scalar_t map_w = j * dilation_w + offset_w; - //const int cur_height = height - h_in; - //const int cur_width = width - w_in; - //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); - val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); - } - *data_col_ptr = val; - data_col_ptr += batch_size * height_col * width_col; - } - } - } -} - -void deformable_im2col( - const at::Tensor data_im, const at::Tensor data_offset, const int channels, - const int height, const int width, const int ksize_h, const int ksize_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int parallel_imgs, - const int deformable_group, at::Tensor data_col) -{ - // num_axes should be smaller than block size - // todo: check parallel_imgs is correctly passed in - int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = channels * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.scalar_type(), "deformable_im2col_gpu", ([&] { - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *data_col_ = data_col.data_ptr(); - - deformable_im2col_gpu_kernel<<>>( - num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w, - pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, parallel_imgs, channels, deformable_group, - height_col, width_col, data_col_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); - } -} - -template -__global__ void deformable_col2im_gpu_kernel( - const int n, const scalar_t *data_col, const scalar_t *data_offset, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int deformable_group, - const int height_col, const int width_col, - scalar_t *grad_im) -{ - CUDA_KERNEL_LOOP(index, n) - { - const int j = (index / width_col / height_col / batch_size) % kernel_w; - const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; - const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; - // compute the start and end of the output - - const int deformable_group_index = c / channel_per_deformable_group; - - int w_out = index % width_col; - int h_out = (index / width_col) % height_col; - int b = (index / width_col / height_col) % batch_size; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * - 2 * kernel_h * kernel_w * height_col * width_col; - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; - const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; - - const scalar_t cur_top_grad = data_col[index]; - const int cur_h = (int)cur_inv_h_data; - const int cur_w = (int)cur_inv_w_data; - for (int dy = -2; dy <= 2; dy++) - { - for (int dx = -2; dx <= 2; dx++) - { - if (cur_h + dy >= 0 && cur_h + dy < height && - cur_w + dx >= 0 && cur_w + dx < width && - abs(cur_inv_h_data - (cur_h + dy)) < 1 && - abs(cur_inv_w_data - (cur_w + dx)) < 1) - { - int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; - scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); - atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); - } - } - } - } -} - -void deformable_col2im( - const at::Tensor data_col, const at::Tensor data_offset, const int channels, - const int height, const int width, const int ksize_h, - const int ksize_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - at::Tensor grad_im) -{ - - // todo: make sure parallel_imgs is passed in correctly - int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "deformable_col2im_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *grad_im_ = grad_im.data_ptr(); - - deformable_col2im_gpu_kernel<<>>( - num_kernels, data_col_, data_offset_, channels, height, width, ksize_h, - ksize_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - parallel_imgs, deformable_group, height_col, width_col, grad_im_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); - } -} - -template -__global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col, - const scalar_t *data_im, const scalar_t *data_offset, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int offset_channels, const int deformable_group, - const int height_col, const int width_col, scalar_t *grad_offset) -{ - CUDA_KERNEL_LOOP(index, n) - { - scalar_t val = 0; - int w = index % width_col; - int h = (index / width_col) % height_col; - int c = (index / width_col / height_col) % offset_channels; - int b = (index / width_col / height_col) / offset_channels; - // compute the start and end of the output - - const int deformable_group_index = c / (2 * kernel_h * kernel_w); - const int col_step = kernel_h * kernel_w; - int cnt = 0; - const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * - batch_size * width_col * height_col; - const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * - channel_per_deformable_group / kernel_h / kernel_w * height * width; - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - - const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; - - for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) - { - const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; - const int bp_dir = offset_c % 2; - - int j = (col_pos / width_col / height_col / batch_size) % kernel_w; - int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; - int w_out = col_pos % width_col; - int h_out = (col_pos / width_col) % height_col; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); - const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - scalar_t inv_h = h_in + i * dilation_h + offset_h; - scalar_t inv_w = w_in + j * dilation_w + offset_w; - if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) - { - inv_h = inv_w = -2; - } - const scalar_t weight = get_coordinate_weight( - inv_h, inv_w, - height, width, data_im_ptr + cnt * height * width, width, bp_dir); - val += weight * data_col_ptr[col_pos]; - cnt += 1; - } - - grad_offset[index] = val; - } -} - -void deformable_col2im_coord( - const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, - const int channels, const int height, const int width, const int ksize_h, - const int ksize_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, at::Tensor grad_offset) -{ - - int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs; - int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *grad_offset_ = grad_offset.data_ptr(); - - deformable_col2im_coord_gpu_kernel<<>>( - num_kernels, data_col_, data_im_, data_offset_, channels, height, width, - ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group, - height_col, width_col, grad_offset_); - })); -} - -template -__device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width, - const int height, const int width, scalar_t h, scalar_t w) -{ - int h_low = floor(h); - int w_low = floor(w); - int h_high = h_low + 1; - int w_high = w_low + 1; - - scalar_t lh = h - h_low; - scalar_t lw = w - w_low; - scalar_t hh = 1 - lh, hw = 1 - lw; - - scalar_t v1 = 0; - if (h_low >= 0 && w_low >= 0) - v1 = bottom_data[h_low * data_width + w_low]; - scalar_t v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - v2 = bottom_data[h_low * data_width + w_high]; - scalar_t v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - v3 = bottom_data[h_high * data_width + w_low]; - scalar_t v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - v4 = bottom_data[h_high * data_width + w_high]; - - scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - - scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - return val; -} - -template -__device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, - const int h, const int w, const int height, const int width) -{ - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - if (h == argmax_h_low && w == argmax_w_low) - weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); - if (h == argmax_h_low && w == argmax_w_high) - weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); - if (h == argmax_h_high && w == argmax_w_low) - weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); - if (h == argmax_h_high && w == argmax_w_high) - weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); - return weight; -} - -template -__device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, - const int height, const int width, const scalar_t *im_data, - const int data_width, const int bp_dir) -{ - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - - if (bp_dir == 0) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - else if (bp_dir == 1) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - - return weight; -} - -template -__global__ void modulated_deformable_im2col_gpu_kernel(const int n, - const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask, - const int height, const int width, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int num_channels, const int deformable_group, - const int height_col, const int width_col, - scalar_t *data_col) -{ - CUDA_KERNEL_LOOP(index, n) - { - // index index of output matrix - const int w_col = index % width_col; - const int h_col = (index / width_col) % height_col; - const int b_col = (index / width_col / height_col) % batch_size; - const int c_im = (index / width_col / height_col) / batch_size; - const int c_col = c_im * kernel_h * kernel_w; - - // compute deformable group index - const int deformable_group_index = c_im / channel_per_deformable_group; - - const int h_in = h_col * stride_h - pad_h; - const int w_in = w_col * stride_w - pad_w; - - scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; - //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; - const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; - const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - - const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; - - for (int i = 0; i < kernel_h; ++i) - { - for (int j = 0; j < kernel_w; ++j) - { - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; - const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; - scalar_t val = static_cast(0); - const scalar_t h_im = h_in + i * dilation_h + offset_h; - const scalar_t w_im = w_in + j * dilation_w + offset_w; - //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { - if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) - { - //const float map_h = i * dilation_h + offset_h; - //const float map_w = j * dilation_w + offset_w; - //const int cur_height = height - h_in; - //const int cur_width = width - w_in; - //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); - val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); - } - *data_col_ptr = val * mask; - data_col_ptr += batch_size * height_col * width_col; - //data_col_ptr += height_col * width_col; - } - } - } -} - -template -__global__ void modulated_deformable_col2im_gpu_kernel(const int n, - const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int deformable_group, - const int height_col, const int width_col, - scalar_t *grad_im) -{ - CUDA_KERNEL_LOOP(index, n) - { - const int j = (index / width_col / height_col / batch_size) % kernel_w; - const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; - const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; - // compute the start and end of the output - - const int deformable_group_index = c / channel_per_deformable_group; - - int w_out = index % width_col; - int h_out = (index / width_col) % height_col; - int b = (index / width_col / height_col) % batch_size; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; - const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; - const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; - const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; - - const scalar_t cur_top_grad = data_col[index] * mask; - const int cur_h = (int)cur_inv_h_data; - const int cur_w = (int)cur_inv_w_data; - for (int dy = -2; dy <= 2; dy++) - { - for (int dx = -2; dx <= 2; dx++) - { - if (cur_h + dy >= 0 && cur_h + dy < height && - cur_w + dx >= 0 && cur_w + dx < width && - abs(cur_inv_h_data - (cur_h + dy)) < 1 && - abs(cur_inv_w_data - (cur_w + dx)) < 1) - { - int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; - scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); - atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); - } - } - } - } -} - -template -__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, - const scalar_t *data_col, const scalar_t *data_im, - const scalar_t *data_offset, const scalar_t *data_mask, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int offset_channels, const int deformable_group, - const int height_col, const int width_col, - scalar_t *grad_offset, scalar_t *grad_mask) -{ - CUDA_KERNEL_LOOP(index, n) - { - scalar_t val = 0, mval = 0; - int w = index % width_col; - int h = (index / width_col) % height_col; - int c = (index / width_col / height_col) % offset_channels; - int b = (index / width_col / height_col) / offset_channels; - // compute the start and end of the output - - const int deformable_group_index = c / (2 * kernel_h * kernel_w); - const int col_step = kernel_h * kernel_w; - int cnt = 0; - const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; - const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; - - const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; - - for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) - { - const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; - const int bp_dir = offset_c % 2; - - int j = (col_pos / width_col / height_col / batch_size) % kernel_w; - int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; - int w_out = col_pos % width_col; - int h_out = (col_pos / width_col) % height_col; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); - const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); - const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; - scalar_t inv_h = h_in + i * dilation_h + offset_h; - scalar_t inv_w = w_in + j * dilation_w + offset_w; - if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) - { - inv_h = inv_w = -2; - } - else - { - mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); - } - const scalar_t weight = dmcn_get_coordinate_weight( - inv_h, inv_w, - height, width, data_im_ptr + cnt * height * width, width, bp_dir); - val += weight * data_col_ptr[col_pos] * mask; - cnt += 1; - } - // KERNEL_ASSIGN(grad_offset[index], offset_req, val); - grad_offset[index] = val; - if (offset_c % 2 == 0) - // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); - grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; - } -} - -void modulated_deformable_im2col_cuda( - const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, const int width_im, - const int height_col, const int width_col, const int kernel_h, const int kenerl_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int deformable_group, at::Tensor data_col) -{ - // num_axes should be smaller than block size - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = channels * batch_size * height_col * width_col; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *data_col_ = data_col.data_ptr(); - - modulated_deformable_im2col_gpu_kernel<<>>( - num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w, - pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, - batch_size, channels, deformable_group, height_col, width_col, data_col_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); - } -} - -void modulated_deformable_col2im_cuda( - const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, const int width_im, - const int height_col, const int width_col, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int deformable_group, at::Tensor grad_im) -{ - - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *grad_im_ = grad_im.data_ptr(); - - modulated_deformable_col2im_gpu_kernel<<>>( - num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im, - kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - batch_size, deformable_group, height_col, width_col, grad_im_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); - } -} - -void modulated_deformable_col2im_coord_cuda( - const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, const int width_im, - const int height_col, const int width_col, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int deformable_group, - at::Tensor grad_offset, at::Tensor grad_mask) -{ - const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; - const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *grad_offset_ = grad_offset.data_ptr(); - scalar_t *grad_mask_ = grad_mask.data_ptr(); - - modulated_deformable_col2im_coord_gpu_kernel<<>>( - num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im, - kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, - grad_offset_, grad_mask_); - })); - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); - } -} diff --git a/basicsr/ops/dcn/src/deform_conv_ext.cpp b/basicsr/ops/dcn/src/deform_conv_ext.cpp deleted file mode 100644 index 5c21d02cf4a8ac24f94fcca28926fd59658bd553..0000000000000000000000000000000000000000 --- a/basicsr/ops/dcn/src/deform_conv_ext.cpp +++ /dev/null @@ -1,164 +0,0 @@ -// modify from -// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c - -#include -#include - -#include -#include - -#define WITH_CUDA // always use cuda -#ifdef WITH_CUDA -int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, - at::Tensor offset, at::Tensor output, - at::Tensor columns, at::Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step); - -int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, - at::Tensor gradOutput, at::Tensor gradInput, - at::Tensor gradOffset, at::Tensor weight, - at::Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step); - -int deform_conv_backward_parameters_cuda( - at::Tensor input, at::Tensor offset, at::Tensor gradOutput, - at::Tensor gradWeight, // at::Tensor gradBias, - at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step); - -void modulated_deform_conv_cuda_forward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias); - -void modulated_deform_conv_cuda_backward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor columns, - at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, - at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias); -#endif - -int deform_conv_forward(at::Tensor input, at::Tensor weight, - at::Tensor offset, at::Tensor output, - at::Tensor columns, at::Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return deform_conv_forward_cuda(input, weight, offset, output, columns, - ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, - deformable_group, im2col_step); -#else - AT_ERROR("deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("deform conv is not implemented on CPU"); -} - -int deform_conv_backward_input(at::Tensor input, at::Tensor offset, - at::Tensor gradOutput, at::Tensor gradInput, - at::Tensor gradOffset, at::Tensor weight, - at::Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return deform_conv_backward_input_cuda(input, offset, gradOutput, - gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH, - dilationW, dilationH, group, deformable_group, im2col_step); -#else - AT_ERROR("deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("deform conv is not implemented on CPU"); -} - -int deform_conv_backward_parameters( - at::Tensor input, at::Tensor offset, at::Tensor gradOutput, - at::Tensor gradWeight, // at::Tensor gradBias, - at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return deform_conv_backward_parameters_cuda(input, offset, gradOutput, - gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW, - dilationH, group, deformable_group, scale, im2col_step); -#else - AT_ERROR("deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("deform conv is not implemented on CPU"); -} - -void modulated_deform_conv_forward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return modulated_deform_conv_cuda_forward(input, weight, bias, ones, - offset, mask, output, columns, kernel_h, kernel_w, stride_h, - stride_w, pad_h, pad_w, dilation_h, dilation_w, group, - deformable_group, with_bias); -#else - AT_ERROR("modulated deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("modulated deform conv is not implemented on CPU"); -} - -void modulated_deform_conv_backward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor columns, - at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, - at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return modulated_deform_conv_cuda_backward(input, weight, bias, ones, - offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset, - grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w, - pad_h, pad_w, dilation_h, dilation_w, group, deformable_group, - with_bias); -#else - AT_ERROR("modulated deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("modulated deform conv is not implemented on CPU"); -} - - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("deform_conv_forward", &deform_conv_forward, - "deform forward"); - m.def("deform_conv_backward_input", &deform_conv_backward_input, - "deform_conv_backward_input"); - m.def("deform_conv_backward_parameters", - &deform_conv_backward_parameters, - "deform_conv_backward_parameters"); - m.def("modulated_deform_conv_forward", - &modulated_deform_conv_forward, - "modulated deform conv forward"); - m.def("modulated_deform_conv_backward", - &modulated_deform_conv_backward, - "modulated deform conv backward"); -} diff --git a/basicsr/ops/fused_act/__init__.py b/basicsr/ops/fused_act/__init__.py deleted file mode 100644 index 1f8e03b3cdc060efad56362ce53dd43032bdcb90..0000000000000000000000000000000000000000 --- a/basicsr/ops/fused_act/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .fused_act import FusedLeakyReLU, fused_leaky_relu - -__all__ = ['FusedLeakyReLU', 'fused_leaky_relu'] diff --git a/basicsr/ops/fused_act/fused_act.py b/basicsr/ops/fused_act/fused_act.py deleted file mode 100644 index 876c959b6ff49bb3d629888546d848949a24f764..0000000000000000000000000000000000000000 --- a/basicsr/ops/fused_act/fused_act.py +++ /dev/null @@ -1,95 +0,0 @@ -# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 - -import os -import torch -from torch import nn -from torch.autograd import Function - -BASICSR_JIT = os.getenv('BASICSR_JIT') -if BASICSR_JIT == 'True': - from torch.utils.cpp_extension import load - module_path = os.path.dirname(__file__) - fused_act_ext = load( - 'fused', - sources=[ - os.path.join(module_path, 'src', 'fused_bias_act.cpp'), - os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'), - ], - ) -else: - try: - from . import fused_act_ext - except ImportError: - pass - # avoid annoying print output - # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' - # '1. compile with BASICSR_EXT=True. or\n ' - # '2. set BASICSR_JIT=True during running') - - -class FusedLeakyReLUFunctionBackward(Function): - - @staticmethod - def forward(ctx, grad_output, out, negative_slope, scale): - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - empty = grad_output.new_empty(0) - - grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale) - - dim = [0] - - if grad_input.ndim > 2: - dim += list(range(2, grad_input.ndim)) - - grad_bias = grad_input.sum(dim).detach() - - return grad_input, grad_bias - - @staticmethod - def backward(ctx, gradgrad_input, gradgrad_bias): - out, = ctx.saved_tensors - gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, - ctx.scale) - - return gradgrad_out, None, None, None - - -class FusedLeakyReLUFunction(Function): - - @staticmethod - def forward(ctx, input, bias, negative_slope, scale): - empty = input.new_empty(0) - out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - return out - - @staticmethod - def backward(ctx, grad_output): - out, = ctx.saved_tensors - - grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale) - - return grad_input, grad_bias, None, None - - -class FusedLeakyReLU(nn.Module): - - def __init__(self, channel, negative_slope=0.2, scale=2**0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): - return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/basicsr/ops/fused_act/src/fused_bias_act.cpp b/basicsr/ops/fused_act/src/fused_bias_act.cpp deleted file mode 100644 index c6225bbc9e5f37e576155c881bc228e9622cb21e..0000000000000000000000000000000000000000 --- a/basicsr/ops/fused_act/src/fused_bias_act.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act.cpp -#include - - -torch::Tensor fused_bias_act_op(const torch::Tensor& input, - const torch::Tensor& bias, - const torch::Tensor& refer, - int act, int grad, float alpha, float scale); - -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -torch::Tensor fused_bias_act(const torch::Tensor& input, - const torch::Tensor& bias, - const torch::Tensor& refer, - int act, int grad, float alpha, float scale) { - CHECK_CUDA(input); - CHECK_CUDA(bias); - - return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); -} diff --git a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu b/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu deleted file mode 100644 index 31a536f9e3afa1de61f23e5eeea4731a62228f37..0000000000000000000000000000000000000000 --- a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu +++ /dev/null @@ -1,100 +0,0 @@ -// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act_kernel.cu -// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. -// -// This work is made available under the Nvidia Source Code License-NC. -// To view a copy of this license, visit -// https://nvlabs.github.io/stylegan2/license.html - -#include - -#include -#include -#include -#include - -#include -#include - - -template -static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, - int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { - int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; - - scalar_t zero = 0.0; - - for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { - scalar_t x = p_x[xi]; - - if (use_bias) { - x += p_b[(xi / step_b) % size_b]; - } - - scalar_t ref = use_ref ? p_ref[xi] : zero; - - scalar_t y; - - switch (act * 10 + grad) { - default: - case 10: y = x; break; - case 11: y = x; break; - case 12: y = 0.0; break; - - case 30: y = (x > 0.0) ? x : x * alpha; break; - case 31: y = (ref > 0.0) ? x : x * alpha; break; - case 32: y = 0.0; break; - } - - out[xi] = y * scale; - } -} - - -torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, - int act, int grad, float alpha, float scale) { - int curDevice = -1; - cudaGetDevice(&curDevice); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); - - auto x = input.contiguous(); - auto b = bias.contiguous(); - auto ref = refer.contiguous(); - - int use_bias = b.numel() ? 1 : 0; - int use_ref = ref.numel() ? 1 : 0; - - int size_x = x.numel(); - int size_b = b.numel(); - int step_b = 1; - - for (int i = 1 + 1; i < x.dim(); i++) { - step_b *= x.size(i); - } - - int loop_x = 4; - int block_size = 4 * 32; - int grid_size = (size_x - 1) / (loop_x * block_size) + 1; - - auto y = torch::empty_like(x); - - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { - fused_bias_act_kernel<<>>( - y.data_ptr(), - x.data_ptr(), - b.data_ptr(), - ref.data_ptr(), - act, - grad, - alpha, - scale, - loop_x, - size_x, - step_b, - size_b, - use_bias, - use_ref - ); - }); - - return y; -} diff --git a/basicsr/ops/upfirdn2d/__init__.py b/basicsr/ops/upfirdn2d/__init__.py deleted file mode 100644 index 51fa749bddaa9fb623bd3556a35e1c3a7b7a0027..0000000000000000000000000000000000000000 --- a/basicsr/ops/upfirdn2d/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .upfirdn2d import upfirdn2d - -__all__ = ['upfirdn2d'] diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp b/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp deleted file mode 100644 index 12b566170212ce021fb3dc24856356e292aa52a0..0000000000000000000000000000000000000000 --- a/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.cpp -#include - - -torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, - int up_x, int up_y, int down_x, int down_y, - int pad_x0, int pad_x1, int pad_y0, int pad_y1); - -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, - int up_x, int up_y, int down_x, int down_y, - int pad_x0, int pad_x1, int pad_y0, int pad_y1) { - CHECK_CUDA(input); - CHECK_CUDA(kernel); - - return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); -} diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu b/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu deleted file mode 100644 index e82913f50f64398b938ea07656692a6e73be6501..0000000000000000000000000000000000000000 --- a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu +++ /dev/null @@ -1,370 +0,0 @@ -// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu -// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. -// -// This work is made available under the Nvidia Source Code License-NC. -// To view a copy of this license, visit -// https://nvlabs.github.io/stylegan2/license.html - -#include - -#include -#include -#include -#include - -#include -#include - -static __host__ __device__ __forceinline__ int floor_div(int a, int b) { - int c = a / b; - - if (c * b > a) { - c--; - } - - return c; -} - -struct UpFirDn2DKernelParams { - int up_x; - int up_y; - int down_x; - int down_y; - int pad_x0; - int pad_x1; - int pad_y0; - int pad_y1; - - int major_dim; - int in_h; - int in_w; - int minor_dim; - int kernel_h; - int kernel_w; - int out_h; - int out_w; - int loop_major; - int loop_x; -}; - -template -__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, - const scalar_t *kernel, - const UpFirDn2DKernelParams p) { - int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; - int out_y = minor_idx / p.minor_dim; - minor_idx -= out_y * p.minor_dim; - int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; - int major_idx_base = blockIdx.z * p.loop_major; - - if (out_x_base >= p.out_w || out_y >= p.out_h || - major_idx_base >= p.major_dim) { - return; - } - - int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; - int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); - int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; - int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; - - for (int loop_major = 0, major_idx = major_idx_base; - loop_major < p.loop_major && major_idx < p.major_dim; - loop_major++, major_idx++) { - for (int loop_x = 0, out_x = out_x_base; - loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { - int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; - int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); - int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; - int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; - - const scalar_t *x_p = - &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + - minor_idx]; - const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; - int x_px = p.minor_dim; - int k_px = -p.up_x; - int x_py = p.in_w * p.minor_dim; - int k_py = -p.up_y * p.kernel_w; - - scalar_t v = 0.0f; - - for (int y = 0; y < h; y++) { - for (int x = 0; x < w; x++) { - v += static_cast(*x_p) * static_cast(*k_p); - x_p += x_px; - k_p += k_px; - } - - x_p += x_py - w * x_px; - k_p += k_py - w * k_px; - } - - out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + - minor_idx] = v; - } - } -} - -template -__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, - const scalar_t *kernel, - const UpFirDn2DKernelParams p) { - const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; - const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; - - __shared__ volatile float sk[kernel_h][kernel_w]; - __shared__ volatile float sx[tile_in_h][tile_in_w]; - - int minor_idx = blockIdx.x; - int tile_out_y = minor_idx / p.minor_dim; - minor_idx -= tile_out_y * p.minor_dim; - tile_out_y *= tile_out_h; - int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; - int major_idx_base = blockIdx.z * p.loop_major; - - if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | - major_idx_base >= p.major_dim) { - return; - } - - for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; - tap_idx += blockDim.x) { - int ky = tap_idx / kernel_w; - int kx = tap_idx - ky * kernel_w; - scalar_t v = 0.0; - - if (kx < p.kernel_w & ky < p.kernel_h) { - v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; - } - - sk[ky][kx] = v; - } - - for (int loop_major = 0, major_idx = major_idx_base; - loop_major < p.loop_major & major_idx < p.major_dim; - loop_major++, major_idx++) { - for (int loop_x = 0, tile_out_x = tile_out_x_base; - loop_x < p.loop_x & tile_out_x < p.out_w; - loop_x++, tile_out_x += tile_out_w) { - int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; - int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; - int tile_in_x = floor_div(tile_mid_x, up_x); - int tile_in_y = floor_div(tile_mid_y, up_y); - - __syncthreads(); - - for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; - in_idx += blockDim.x) { - int rel_in_y = in_idx / tile_in_w; - int rel_in_x = in_idx - rel_in_y * tile_in_w; - int in_x = rel_in_x + tile_in_x; - int in_y = rel_in_y + tile_in_y; - - scalar_t v = 0.0; - - if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { - v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * - p.minor_dim + - minor_idx]; - } - - sx[rel_in_y][rel_in_x] = v; - } - - __syncthreads(); - for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; - out_idx += blockDim.x) { - int rel_out_y = out_idx / tile_out_w; - int rel_out_x = out_idx - rel_out_y * tile_out_w; - int out_x = rel_out_x + tile_out_x; - int out_y = rel_out_y + tile_out_y; - - int mid_x = tile_mid_x + rel_out_x * down_x; - int mid_y = tile_mid_y + rel_out_y * down_y; - int in_x = floor_div(mid_x, up_x); - int in_y = floor_div(mid_y, up_y); - int rel_in_x = in_x - tile_in_x; - int rel_in_y = in_y - tile_in_y; - int kernel_x = (in_x + 1) * up_x - mid_x - 1; - int kernel_y = (in_y + 1) * up_y - mid_y - 1; - - scalar_t v = 0.0; - -#pragma unroll - for (int y = 0; y < kernel_h / up_y; y++) -#pragma unroll - for (int x = 0; x < kernel_w / up_x; x++) - v += sx[rel_in_y + y][rel_in_x + x] * - sk[kernel_y + y * up_y][kernel_x + x * up_x]; - - if (out_x < p.out_w & out_y < p.out_h) { - out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + - minor_idx] = v; - } - } - } - } -} - -torch::Tensor upfirdn2d_op(const torch::Tensor &input, - const torch::Tensor &kernel, int up_x, int up_y, - int down_x, int down_y, int pad_x0, int pad_x1, - int pad_y0, int pad_y1) { - int curDevice = -1; - cudaGetDevice(&curDevice); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); - - UpFirDn2DKernelParams p; - - auto x = input.contiguous(); - auto k = kernel.contiguous(); - - p.major_dim = x.size(0); - p.in_h = x.size(1); - p.in_w = x.size(2); - p.minor_dim = x.size(3); - p.kernel_h = k.size(0); - p.kernel_w = k.size(1); - p.up_x = up_x; - p.up_y = up_y; - p.down_x = down_x; - p.down_y = down_y; - p.pad_x0 = pad_x0; - p.pad_x1 = pad_x1; - p.pad_y0 = pad_y0; - p.pad_y1 = pad_y1; - - p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / - p.down_y; - p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / - p.down_x; - - auto out = - at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); - - int mode = -1; - - int tile_out_h = -1; - int tile_out_w = -1; - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 4 && p.kernel_w <= 4) { - mode = 1; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 3 && p.kernel_w <= 3) { - mode = 2; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 4 && p.kernel_w <= 4) { - mode = 3; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 2 && p.kernel_w <= 2) { - mode = 4; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && - p.kernel_h <= 4 && p.kernel_w <= 4) { - mode = 5; - tile_out_h = 8; - tile_out_w = 32; - } - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && - p.kernel_h <= 2 && p.kernel_w <= 2) { - mode = 6; - tile_out_h = 8; - tile_out_w = 32; - } - - dim3 block_size; - dim3 grid_size; - - if (tile_out_h > 0 && tile_out_w > 0) { - p.loop_major = (p.major_dim - 1) / 16384 + 1; - p.loop_x = 1; - block_size = dim3(32 * 8, 1, 1); - grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, - (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, - (p.major_dim - 1) / p.loop_major + 1); - } else { - p.loop_major = (p.major_dim - 1) / 16384 + 1; - p.loop_x = 4; - block_size = dim3(4, 32, 1); - grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, - (p.out_w - 1) / (p.loop_x * block_size.y) + 1, - (p.major_dim - 1) / p.loop_major + 1); - } - - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { - switch (mode) { - case 1: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 2: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 3: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 4: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 5: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 6: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - default: - upfirdn2d_kernel_large<<>>( - out.data_ptr(), x.data_ptr(), - k.data_ptr(), p); - } - }); - - return out; -} diff --git a/basicsr/ops/upfirdn2d/upfirdn2d.py b/basicsr/ops/upfirdn2d/upfirdn2d.py deleted file mode 100644 index e87ad0be394fe982a067d92c2db54a25476d42fa..0000000000000000000000000000000000000000 --- a/basicsr/ops/upfirdn2d/upfirdn2d.py +++ /dev/null @@ -1,192 +0,0 @@ -# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 - -import os -import torch -from torch.autograd import Function -from torch.nn import functional as F - -BASICSR_JIT = os.getenv('BASICSR_JIT') -if BASICSR_JIT == 'True': - from torch.utils.cpp_extension import load - module_path = os.path.dirname(__file__) - upfirdn2d_ext = load( - 'upfirdn2d', - sources=[ - os.path.join(module_path, 'src', 'upfirdn2d.cpp'), - os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'), - ], - ) -else: - try: - from . import upfirdn2d_ext - except ImportError: - pass - # avoid annoying print output - # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' - # '1. compile with BASICSR_EXT=True. or\n ' - # '2. set BASICSR_JIT=True during running') - - -class UpFirDn2dBackward(Function): - - @staticmethod - def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size): - - up_x, up_y = up - down_x, down_y = down - g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad - - grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) - - grad_input = upfirdn2d_ext.upfirdn2d( - grad_output, - grad_kernel, - down_x, - down_y, - up_x, - up_y, - g_pad_x0, - g_pad_x1, - g_pad_y0, - g_pad_y1, - ) - grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) - - ctx.save_for_backward(kernel) - - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - ctx.up_x = up_x - ctx.up_y = up_y - ctx.down_x = down_x - ctx.down_y = down_y - ctx.pad_x0 = pad_x0 - ctx.pad_x1 = pad_x1 - ctx.pad_y0 = pad_y0 - ctx.pad_y1 = pad_y1 - ctx.in_size = in_size - ctx.out_size = out_size - - return grad_input - - @staticmethod - def backward(ctx, gradgrad_input): - kernel, = ctx.saved_tensors - - gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) - - gradgrad_out = upfirdn2d_ext.upfirdn2d( - gradgrad_input, - kernel, - ctx.up_x, - ctx.up_y, - ctx.down_x, - ctx.down_y, - ctx.pad_x0, - ctx.pad_x1, - ctx.pad_y0, - ctx.pad_y1, - ) - # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], - # ctx.out_size[1], ctx.in_size[3]) - gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]) - - return gradgrad_out, None, None, None, None, None, None, None, None - - -class UpFirDn2d(Function): - - @staticmethod - def forward(ctx, input, kernel, up, down, pad): - up_x, up_y = up - down_x, down_y = down - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - kernel_h, kernel_w = kernel.shape - _, channel, in_h, in_w = input.shape - ctx.in_size = input.shape - - input = input.reshape(-1, in_h, in_w, 1) - - ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - ctx.out_size = (out_h, out_w) - - ctx.up = (up_x, up_y) - ctx.down = (down_x, down_y) - ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) - - g_pad_x0 = kernel_w - pad_x0 - 1 - g_pad_y0 = kernel_h - pad_y0 - 1 - g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 - g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 - - ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) - - out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1) - # out = out.view(major, out_h, out_w, minor) - out = out.view(-1, channel, out_h, out_w) - - return out - - @staticmethod - def backward(ctx, grad_output): - kernel, grad_kernel = ctx.saved_tensors - - grad_input = UpFirDn2dBackward.apply( - grad_output, - kernel, - grad_kernel, - ctx.up, - ctx.down, - ctx.pad, - ctx.g_pad, - ctx.in_size, - ctx.out_size, - ) - - return grad_input, None, None, None, None - - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - if input.device.type == 'cpu': - out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) - else: - out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])) - - return out - - -def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): - _, channel, in_h, in_w = input.shape - input = input.reshape(-1, in_h, in_w, 1) - - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) - out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - out = out[:, ::down_y, ::down_x, :] - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - - return out.view(-1, channel, out_h, out_w) diff --git a/basicsr/train.py b/basicsr/train.py deleted file mode 100644 index 75e5fea723726359777488d66c309cc9fe827d7b..0000000000000000000000000000000000000000 --- a/basicsr/train.py +++ /dev/null @@ -1,215 +0,0 @@ -import datetime -import logging -import math -import time -import torch -from os import path as osp - -from basicsr.data import build_dataloader, build_dataset -from basicsr.data.data_sampler import EnlargedSampler -from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher -from basicsr.models import build_model -from basicsr.utils import (AvgTimer, MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, - init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, scandir) -from basicsr.utils.options import copy_opt_file, dict2str, parse_options - - -def init_tb_loggers(opt): - # initialize wandb logger before tensorboard logger to allow proper sync - if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') - is not None) and ('debug' not in opt['name']): - assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb') - init_wandb_logger(opt) - tb_logger = None - if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']: - tb_logger = init_tb_logger(log_dir=osp.join(opt['root_path'], 'tb_logger', opt['name'])) - return tb_logger - - -def create_train_val_dataloader(opt, logger): - # create train and val dataloaders - train_loader, val_loaders = None, [] - for phase, dataset_opt in opt['datasets'].items(): - if phase == 'train': - dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1) - train_set = build_dataset(dataset_opt) - train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio) - train_loader = build_dataloader( - train_set, - dataset_opt, - num_gpu=opt['num_gpu'], - dist=opt['dist'], - sampler=train_sampler, - seed=opt['manual_seed']) - - num_iter_per_epoch = math.ceil( - len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size'])) - total_iters = int(opt['train']['total_iter']) - total_epochs = math.ceil(total_iters / (num_iter_per_epoch)) - logger.info('Training statistics:' - f'\n\tNumber of train images: {len(train_set)}' - f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}' - f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}' - f'\n\tWorld size (gpu number): {opt["world_size"]}' - f'\n\tRequire iter number per epoch: {num_iter_per_epoch}' - f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.') - elif phase.split('_')[0] == 'val': - val_set = build_dataset(dataset_opt) - val_loader = build_dataloader( - val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed']) - logger.info(f'Number of val images/folders in {dataset_opt["name"]}: {len(val_set)}') - val_loaders.append(val_loader) - else: - raise ValueError(f'Dataset phase {phase} is not recognized.') - - return train_loader, train_sampler, val_loaders, total_epochs, total_iters - - -def load_resume_state(opt): - resume_state_path = None - if opt['auto_resume']: - state_path = osp.join('experiments', opt['name'], 'training_states') - if osp.isdir(state_path): - states = list(scandir(state_path, suffix='state', recursive=False, full_path=False)) - if len(states) != 0: - states = [float(v.split('.state')[0]) for v in states] - resume_state_path = osp.join(state_path, f'{max(states):.0f}.state') - opt['path']['resume_state'] = resume_state_path - else: - if opt['path'].get('resume_state'): - resume_state_path = opt['path']['resume_state'] - - if resume_state_path is None: - resume_state = None - else: - device_id = torch.cuda.current_device() - resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id)) - check_resume(opt, resume_state['iter']) - return resume_state - - -def train_pipeline(root_path): - # parse options, set distributed setting, set random seed - opt, args = parse_options(root_path, is_train=True) - opt['root_path'] = root_path - - torch.backends.cudnn.benchmark = True - # torch.backends.cudnn.deterministic = True - - # load resume states if necessary - resume_state = load_resume_state(opt) - # mkdir for experiments and logger - if resume_state is None: - make_exp_dirs(opt) - if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0: - mkdir_and_rename(osp.join(opt['root_path'], 'tb_logger', opt['name'])) - - # copy the yml file to the experiment root - copy_opt_file(args.opt, opt['path']['experiments_root']) - - # WARNING: should not use get_root_logger in the above codes, including the called functions - # Otherwise the logger will not be properly initialized - log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log") - logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) - logger.info(get_env_info()) - logger.info(dict2str(opt)) - # initialize wandb and tb loggers - tb_logger = init_tb_loggers(opt) - - # create train and validation dataloaders - result = create_train_val_dataloader(opt, logger) - train_loader, train_sampler, val_loaders, total_epochs, total_iters = result - - # create model - model = build_model(opt) - if resume_state: # resume training - model.resume_training(resume_state) # handle optimizers and schedulers - logger.info(f"Resuming training from epoch: {resume_state['epoch']}, iter: {resume_state['iter']}.") - start_epoch = resume_state['epoch'] - current_iter = resume_state['iter'] - else: - start_epoch = 0 - current_iter = 0 - - # create message logger (formatted outputs) - msg_logger = MessageLogger(opt, current_iter, tb_logger) - - # dataloader prefetcher - prefetch_mode = opt['datasets']['train'].get('prefetch_mode') - if prefetch_mode is None or prefetch_mode == 'cpu': - prefetcher = CPUPrefetcher(train_loader) - elif prefetch_mode == 'cuda': - prefetcher = CUDAPrefetcher(train_loader, opt) - logger.info(f'Use {prefetch_mode} prefetch dataloader') - if opt['datasets']['train'].get('pin_memory') is not True: - raise ValueError('Please set pin_memory=True for CUDAPrefetcher.') - else: - raise ValueError(f"Wrong prefetch_mode {prefetch_mode}. Supported ones are: None, 'cuda', 'cpu'.") - - # training - logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}') - data_timer, iter_timer = AvgTimer(), AvgTimer() - start_time = time.time() - - for epoch in range(start_epoch, total_epochs + 1): - train_sampler.set_epoch(epoch) - prefetcher.reset() - train_data = prefetcher.next() - - while train_data is not None: - data_timer.record() - - current_iter += 1 - if current_iter > total_iters: - break - # update learning rate - model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1)) - # training - model.feed_data(train_data) - model.optimize_parameters(current_iter) - iter_timer.record() - if current_iter == 1: - # reset start time in msg_logger for more accurate eta_time - # not work in resume mode - msg_logger.reset_start_time() - # log - if current_iter % opt['logger']['print_freq'] == 0: - log_vars = {'epoch': epoch, 'iter': current_iter} - log_vars.update({'lrs': model.get_current_learning_rate()}) - log_vars.update({'time': iter_timer.get_avg_time(), 'data_time': data_timer.get_avg_time()}) - log_vars.update(model.get_current_log()) - msg_logger(log_vars) - - # save models and training states - if current_iter % opt['logger']['save_checkpoint_freq'] == 0: - logger.info('Saving models and training states.') - model.save(epoch, current_iter) - - # validation - if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0): - if len(val_loaders) > 1: - logger.warning('Multiple validation datasets are *only* supported by SRModel.') - for val_loader in val_loaders: - model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) - - data_timer.start() - iter_timer.start() - train_data = prefetcher.next() - # end of iter - - # end of epoch - - consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time))) - logger.info(f'End of training. Time consumed: {consumed_time}') - logger.info('Save the latest model.') - model.save(epoch=-1, current_iter=-1) # -1 stands for the latest - if opt.get('val') is not None: - for val_loader in val_loaders: - model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) - if tb_logger: - tb_logger.close() - - -if __name__ == '__main__': - root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) - train_pipeline(root_path) diff --git a/basicsr/utils/__init__.py b/basicsr/utils/__init__.py deleted file mode 100644 index 85670f1660b4a4f50c3ecd29d933cf0afcf17357..0000000000000000000000000000000000000000 --- a/basicsr/utils/__init__.py +++ /dev/null @@ -1,47 +0,0 @@ -from .color_util import bgr2ycbcr, rgb2ycbcr, rgb2ycbcr_pt, ycbcr2bgr, ycbcr2rgb -from .diffjpeg import DiffJPEG -from .file_client import FileClient -from .img_process_util import USMSharp, usm_sharp -from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img -from .logger import AvgTimer, MessageLogger, get_env_info, get_root_logger, init_tb_logger, init_wandb_logger -from .misc import check_resume, get_time_str, make_exp_dirs, mkdir_and_rename, scandir, set_random_seed, sizeof_fmt -from .options import yaml_load - -__all__ = [ - # color_util.py - 'bgr2ycbcr', - 'rgb2ycbcr', - 'rgb2ycbcr_pt', - 'ycbcr2bgr', - 'ycbcr2rgb', - # file_client.py - 'FileClient', - # img_util.py - 'img2tensor', - 'tensor2img', - 'imfrombytes', - 'imwrite', - 'crop_border', - # logger.py - 'MessageLogger', - 'AvgTimer', - 'init_tb_logger', - 'init_wandb_logger', - 'get_root_logger', - 'get_env_info', - # misc.py - 'set_random_seed', - 'get_time_str', - 'mkdir_and_rename', - 'make_exp_dirs', - 'scandir', - 'check_resume', - 'sizeof_fmt', - # diffjpeg - 'DiffJPEG', - # img_process_util - 'USMSharp', - 'usm_sharp', - # options - 'yaml_load' -] diff --git a/basicsr/utils/color_util.py b/basicsr/utils/color_util.py deleted file mode 100644 index 8b7676fd78e007300d54950e553f8255b7a86a82..0000000000000000000000000000000000000000 --- a/basicsr/utils/color_util.py +++ /dev/null @@ -1,208 +0,0 @@ -import numpy as np -import torch - - -def rgb2ycbcr(img, y_only=False): - """Convert a RGB image to YCbCr image. - - This function produces the same results as Matlab's `rgb2ycbcr` function. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - ndarray: The converted YCbCr image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) - if y_only: - out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0 - else: - out_img = np.matmul( - img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def bgr2ycbcr(img, y_only=False): - """Convert a BGR image to YCbCr image. - - The bgr version of rgb2ycbcr. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - ndarray: The converted YCbCr image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) - if y_only: - out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 - else: - out_img = np.matmul( - img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def ycbcr2rgb(img): - """Convert a YCbCr image to RGB image. - - This function produces the same results as Matlab's ycbcr2rgb function. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - ndarray: The converted RGB image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], - [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] # noqa: E126 - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def ycbcr2bgr(img): - """Convert a YCbCr image to BGR image. - - The bgr version of ycbcr2rgb. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - ndarray: The converted BGR image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0], - [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921] # noqa: E126 - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def _convert_input_type_range(img): - """Convert the type and range of the input image. - - It converts the input image to np.float32 type and range of [0, 1]. - It is mainly used for pre-processing the input image in colorspace - conversion functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - (ndarray): The converted image with type of np.float32 and range of - [0, 1]. - """ - img_type = img.dtype - img = img.astype(np.float32) - if img_type == np.float32: - pass - elif img_type == np.uint8: - img /= 255. - else: - raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}') - return img - - -def _convert_output_type_range(img, dst_type): - """Convert the type and range of the image according to dst_type. - - It converts the image to desired type and range. If `dst_type` is np.uint8, - images will be converted to np.uint8 type with range [0, 255]. If - `dst_type` is np.float32, it converts the image to np.float32 type with - range [0, 1]. - It is mainly used for post-processing images in colorspace conversion - functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The image to be converted with np.float32 type and - range [0, 255]. - dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it - converts the image to np.uint8 type with range [0, 255]. If - dst_type is np.float32, it converts the image to np.float32 type - with range [0, 1]. - - Returns: - (ndarray): The converted image with desired type and range. - """ - if dst_type not in (np.uint8, np.float32): - raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}') - if dst_type == np.uint8: - img = img.round() - else: - img /= 255. - return img.astype(dst_type) - - -def rgb2ycbcr_pt(img, y_only=False): - """Convert RGB images to YCbCr images (PyTorch version). - - It implements the ITU-R BT.601 conversion for standard-definition television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - Args: - img (Tensor): Images with shape (n, 3, h, w), the range [0, 1], float, RGB format. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - (Tensor): converted images with the shape (n, 3/1, h, w), the range [0, 1], float. - """ - if y_only: - weight = torch.tensor([[65.481], [128.553], [24.966]]).to(img) - out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + 16.0 - else: - weight = torch.tensor([[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]).to(img) - bias = torch.tensor([16, 128, 128]).view(1, 3, 1, 1).to(img) - out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + bias - - out_img = out_img / 255. - return out_img diff --git a/basicsr/utils/degradation_pipeline.py b/basicsr/utils/degradation_pipeline.py deleted file mode 100644 index 7c2d927f89651324c6089bf067db4b594dbd11cf..0000000000000000000000000000000000000000 --- a/basicsr/utils/degradation_pipeline.py +++ /dev/null @@ -1,357 +0,0 @@ -import cv2 -import math -import numpy as np -import random -import torch -from torch.utils import data as data - -from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels -from basicsr.data.transforms import augment -from basicsr.utils import img2tensor, DiffJPEG, USMSharp -from basicsr.utils.img_process_util import filter2D -from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt -from basicsr.data.transforms import paired_random_crop - -AUGMENT_OPT = { - 'use_hflip': False, - 'use_rot': False -} - -KERNEL_OPT = { - 'blur_kernel_size': 21, - 'kernel_list': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'], - 'kernel_prob': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03], - 'sinc_prob': 0.1, - 'blur_sigma': [0.2, 3], - 'betag_range': [0.5, 4], - 'betap_range': [1, 2], - - 'blur_kernel_size2': 21, - 'kernel_list2': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'], - 'kernel_prob2': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03], - 'sinc_prob2': 0.1, - 'blur_sigma2': [0.2, 1.5], - 'betag_range2': [0.5, 4], - 'betap_range2': [1, 2], - 'final_sinc_prob': 0.8, -} - -DEGRADE_OPT = { - 'resize_prob': [0.2, 0.7, 0.1], # up, down, keep - 'resize_range': [0.15, 1.5], - 'gaussian_noise_prob': 0.5, - 'noise_range': [1, 30], - 'poisson_scale_range': [0.05, 3], - 'gray_noise_prob': 0.4, - 'jpeg_range': [30, 95], - - # the second degradation process - 'second_blur_prob': 0.8, - 'resize_prob2': [0.3, 0.4, 0.3], # up, down, keep - 'resize_range2': [0.3, 1.2], - 'gaussian_noise_prob2': 0.5, - 'noise_range2': [1, 25], - 'poisson_scale_range2': [0.05, 2.5], - 'gray_noise_prob2': 0.4, - 'jpeg_range2': [30, 95], - - 'gt_size': 512, - 'no_degradation_prob': 0.01, - 'use_usm': True, - 'sf': 4, - 'random_size': False, - 'resize_lq': True -} - -class RealESRGANDegradation: - - def __init__(self, augment_opt=None, kernel_opt=None, degrade_opt=None, device='cuda', resolution=None): - if augment_opt is None: - augment_opt = AUGMENT_OPT - self.augment_opt = augment_opt - if kernel_opt is None: - kernel_opt = KERNEL_OPT - self.kernel_opt = kernel_opt - if degrade_opt is None: - degrade_opt = DEGRADE_OPT - self.degrade_opt = degrade_opt - if resolution is not None: - self.degrade_opt['gt_size'] = resolution - self.device = device - - self.jpeger = DiffJPEG(differentiable=False).to(self.device) - self.usm_sharpener = USMSharp().to(self.device) - - # blur settings for the first degradation - self.blur_kernel_size = kernel_opt['blur_kernel_size'] - self.kernel_list = kernel_opt['kernel_list'] - self.kernel_prob = kernel_opt['kernel_prob'] # a list for each kernel probability - self.blur_sigma = kernel_opt['blur_sigma'] - self.betag_range = kernel_opt['betag_range'] # betag used in generalized Gaussian blur kernels - self.betap_range = kernel_opt['betap_range'] # betap used in plateau blur kernels - self.sinc_prob = kernel_opt['sinc_prob'] # the probability for sinc filters - - # blur settings for the second degradation - self.blur_kernel_size2 = kernel_opt['blur_kernel_size2'] - self.kernel_list2 = kernel_opt['kernel_list2'] - self.kernel_prob2 = kernel_opt['kernel_prob2'] - self.blur_sigma2 = kernel_opt['blur_sigma2'] - self.betag_range2 = kernel_opt['betag_range2'] - self.betap_range2 = kernel_opt['betap_range2'] - self.sinc_prob2 = kernel_opt['sinc_prob2'] - - # a final sinc filter - self.final_sinc_prob = kernel_opt['final_sinc_prob'] - - self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21 - # TODO: kernel range is now hard-coded, should be in the configure file - self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect - self.pulse_tensor[10, 10] = 1 - - def get_kernel(self): - - # ------------------------ Generate kernels (used in the first degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.kernel_opt['sinc_prob']: - # this sinc filter setting is for kernels ranging from [7, 21] - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel = random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - kernel_size, - self.blur_sigma, - self.blur_sigma, [-math.pi, math.pi], - self.betag_range, - self.betap_range, - noise_range=None) - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------ Generate kernels (used in the second degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.kernel_opt['sinc_prob2']: - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel2 = random_mixed_kernels( - self.kernel_list2, - self.kernel_prob2, - kernel_size, - self.blur_sigma2, - self.blur_sigma2, [-math.pi, math.pi], - self.betag_range2, - self.betap_range2, - noise_range=None) - - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------------------- the final sinc kernel ------------------------------------- # - if np.random.uniform() < self.kernel_opt['final_sinc_prob']: - kernel_size = random.choice(self.kernel_range) - omega_c = np.random.uniform(np.pi / 3, np.pi) - sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21) - sinc_kernel = torch.FloatTensor(sinc_kernel) - else: - sinc_kernel = self.pulse_tensor - - # BGR to RGB, HWC to CHW, numpy to tensor - kernel = torch.FloatTensor(kernel) - kernel2 = torch.FloatTensor(kernel2) - - return (kernel, kernel2, sinc_kernel) - - @torch.no_grad() - def __call__(self, img_gt, kernels=None): - ''' - :param: img_gt: BCHW, RGB, [0, 1] float32 tensor - ''' - if kernels is None: - kernel = [] - kernel2 = [] - sinc_kernel = [] - for _ in range(img_gt.shape[0]): - k, k2, sk = self.get_kernel() - kernel.append(k) - kernel2.append(k2) - sinc_kernel.append(sk) - kernel = torch.stack(kernel) - kernel2 = torch.stack(kernel2) - sinc_kernel = torch.stack(sinc_kernel) - else: - # kernels created in dataset. - kernel, kernel2, sinc_kernel = kernels - - # ----------------------- Pre-process ----------------------- # - im_gt = img_gt.to(self.device) - if self.degrade_opt['sf'] == 8: - resized_gt = torch.nn.functional.interpolate(im_gt, scale_factor=0.5, mode='area') - else: - resized_gt = im_gt - if self.degrade_opt['use_usm']: - resized_gt = self.usm_sharpener(resized_gt) - resized_gt = resized_gt.to(memory_format=torch.contiguous_format).float() - kernel = kernel.to(self.device) - kernel2 = kernel2.to(self.device) - sinc_kernel = sinc_kernel.to(self.device) - ori_h, ori_w = im_gt.size()[2:4] - - # ----------------------- The first degradation process ----------------------- # - # blur - out = filter2D(resized_gt, kernel) - # random resize - updown_type = random.choices( - ['up', 'down', 'keep'], - self.degrade_opt['resize_prob'], - )[0] - if updown_type == 'up': - scale = random.uniform(1, self.degrade_opt['resize_range'][1]) - elif updown_type == 'down': - scale = random.uniform(self.degrade_opt['resize_range'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate(out, scale_factor=scale, mode=mode) - # add noise - gray_noise_prob = self.degrade_opt['gray_noise_prob'] - if random.random() < self.degrade_opt['gaussian_noise_prob']: - out = random_add_gaussian_noise_pt( - out, - sigma_range=self.degrade_opt['noise_range'], - clip=True, - rounds=False, - gray_prob=gray_noise_prob, - ) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.degrade_opt['poisson_scale_range'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range']) - out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts - out = self.jpeger(out, quality=jpeg_p) - - # ----------------------- The second degradation process ----------------------- # - # blur - if random.random() < self.degrade_opt['second_blur_prob']: - out = out.contiguous() - out = filter2D(out, kernel2) - # random resize - updown_type = random.choices( - ['up', 'down', 'keep'], - self.degrade_opt['resize_prob2'], - )[0] - if updown_type == 'up': - scale = random.uniform(1, self.degrade_opt['resize_range2'][1]) - elif updown_type == 'down': - scale = random.uniform(self.degrade_opt['resize_range2'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate( - out, - size=(int(ori_h / self.degrade_opt['sf'] * scale), - int(ori_w / self.degrade_opt['sf'] * scale)), - mode=mode, - ) - # add noise - gray_noise_prob = self.degrade_opt['gray_noise_prob2'] - if random.random() < self.degrade_opt['gaussian_noise_prob2']: - out = random_add_gaussian_noise_pt( - out, - sigma_range=self.degrade_opt['noise_range2'], - clip=True, - rounds=False, - gray_prob=gray_noise_prob, - ) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.degrade_opt['poisson_scale_range2'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False, - ) - - # JPEG compression + the final sinc filter - # We also need to resize images to desired sizes. We group [resize back + sinc filter] together - # as one operation. - # We consider two orders: - # 1. [resize back + sinc filter] + JPEG compression - # 2. JPEG compression + [resize back + sinc filter] - # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. - if random.random() < 0.5: - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate( - out, - size=(ori_h // self.degrade_opt['sf'], - ori_w // self.degrade_opt['sf']), - mode=mode, - ) - out = out.contiguous() - out = filter2D(out, sinc_kernel) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - else: - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate( - out, - size=(ori_h // self.degrade_opt['sf'], - ori_w // self.degrade_opt['sf']), - mode=mode, - ) - out = out.contiguous() - out = filter2D(out, sinc_kernel) - - # clamp and round - im_lq = torch.clamp(out, 0, 1.0) - - # random crop - gt_size = self.degrade_opt['gt_size'] - im_gt, im_lq = paired_random_crop(im_gt, im_lq, gt_size, self.degrade_opt['sf']) - - if self.degrade_opt['resize_lq']: - im_lq = torch.nn.functional.interpolate( - im_lq, - size=(im_gt.size(-2), - im_gt.size(-1)), - mode='bicubic', - ) - - if random.random() < self.degrade_opt['no_degradation_prob'] or torch.isnan(im_lq).any(): - im_lq = im_gt - - # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue - im_lq = im_lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract - im_lq = im_lq*2 - 1.0 - im_gt = im_gt*2 - 1.0 - - if self.degrade_opt['random_size']: - raise NotImplementedError - im_lq, im_gt = self.randn_cropinput(im_lq, im_gt) - - im_lq = torch.clamp(im_lq, -1.0, 1.0) - im_gt = torch.clamp(im_gt, -1.0, 1.0) - - return (im_lq, im_gt) \ No newline at end of file diff --git a/basicsr/utils/diffjpeg.py b/basicsr/utils/diffjpeg.py deleted file mode 100644 index 83233dcdec1f4f4d656ebd66aa5c5be9340667ff..0000000000000000000000000000000000000000 --- a/basicsr/utils/diffjpeg.py +++ /dev/null @@ -1,515 +0,0 @@ -""" -Modified from https://github.com/mlomnitz/DiffJPEG - -For images not divisible by 8 -https://dsp.stackexchange.com/questions/35339/jpeg-dct-padding/35343#35343 -""" -import itertools -import numpy as np -import torch -import torch.nn as nn -from torch.nn import functional as F - -# ------------------------ utils ------------------------# -y_table = np.array( - [[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], - [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], - [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], - dtype=np.float32).T -y_table = nn.Parameter(torch.from_numpy(y_table)) -c_table = np.empty((8, 8), dtype=np.float32) -c_table.fill(99) -c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]).T -c_table = nn.Parameter(torch.from_numpy(c_table)) - - -def diff_round(x): - """ Differentiable rounding function - """ - return torch.round(x) + (x - torch.round(x))**3 - - -def quality_to_factor(quality): - """ Calculate factor corresponding to quality - - Args: - quality(float): Quality for jpeg compression. - - Returns: - float: Compression factor. - """ - if quality < 50: - quality = 5000. / quality - else: - quality = 200. - quality * 2 - return quality / 100. - - -# ------------------------ compression ------------------------# -class RGB2YCbCrJpeg(nn.Module): - """ Converts RGB image to YCbCr - """ - - def __init__(self): - super(RGB2YCbCrJpeg, self).__init__() - matrix = np.array([[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], [0.5, -0.418688, -0.081312]], - dtype=np.float32).T - self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) - self.matrix = nn.Parameter(torch.from_numpy(matrix)) - - def forward(self, image): - """ - Args: - image(Tensor): batch x 3 x height x width - - Returns: - Tensor: batch x height x width x 3 - """ - image = image.permute(0, 2, 3, 1) - result = torch.tensordot(image, self.matrix, dims=1) + self.shift - return result.view(image.shape) - - -class ChromaSubsampling(nn.Module): - """ Chroma subsampling on CbCr channels - """ - - def __init__(self): - super(ChromaSubsampling, self).__init__() - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width x 3 - - Returns: - y(tensor): batch x height x width - cb(tensor): batch x height/2 x width/2 - cr(tensor): batch x height/2 x width/2 - """ - image_2 = image.permute(0, 3, 1, 2).clone() - cb = F.avg_pool2d(image_2[:, 1, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) - cr = F.avg_pool2d(image_2[:, 2, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) - cb = cb.permute(0, 2, 3, 1) - cr = cr.permute(0, 2, 3, 1) - return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) - - -class BlockSplitting(nn.Module): - """ Splitting image into patches - """ - - def __init__(self): - super(BlockSplitting, self).__init__() - self.k = 8 - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x h*w/64 x h x w - """ - height, _ = image.shape[1:3] - batch_size = image.shape[0] - image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) - image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) - return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) - - -class DCT8x8(nn.Module): - """ Discrete Cosine Transformation - """ - - def __init__(self): - super(DCT8x8, self).__init__() - tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) - for x, y, u, v in itertools.product(range(8), repeat=4): - tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos((2 * y + 1) * v * np.pi / 16) - alpha = np.array([1. / np.sqrt(2)] + [1] * 7) - self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) - self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float()) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - image = image - 128 - result = self.scale * torch.tensordot(image, self.tensor, dims=2) - result.view(image.shape) - return result - - -class YQuantize(nn.Module): - """ JPEG Quantization for Y channel - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding): - super(YQuantize, self).__init__() - self.rounding = rounding - self.y_table = y_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - image = image.float() / (self.y_table * factor) - else: - b = factor.size(0) - table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - image = image.float() / table - image = self.rounding(image) - return image - - -class CQuantize(nn.Module): - """ JPEG Quantization for CbCr channels - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding): - super(CQuantize, self).__init__() - self.rounding = rounding - self.c_table = c_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - image = image.float() / (self.c_table * factor) - else: - b = factor.size(0) - table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - image = image.float() / table - image = self.rounding(image) - return image - - -class CompressJpeg(nn.Module): - """Full JPEG compression algorithm - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding=torch.round): - super(CompressJpeg, self).__init__() - self.l1 = nn.Sequential(RGB2YCbCrJpeg(), ChromaSubsampling()) - self.l2 = nn.Sequential(BlockSplitting(), DCT8x8()) - self.c_quantize = CQuantize(rounding=rounding) - self.y_quantize = YQuantize(rounding=rounding) - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x 3 x height x width - - Returns: - dict(tensor): Compressed tensor with batch x h*w/64 x 8 x 8. - """ - y, cb, cr = self.l1(image * 255) - components = {'y': y, 'cb': cb, 'cr': cr} - for k in components.keys(): - comp = self.l2(components[k]) - if k in ('cb', 'cr'): - comp = self.c_quantize(comp, factor=factor) - else: - comp = self.y_quantize(comp, factor=factor) - - components[k] = comp - - return components['y'], components['cb'], components['cr'] - - -# ------------------------ decompression ------------------------# - - -class YDequantize(nn.Module): - """Dequantize Y channel - """ - - def __init__(self): - super(YDequantize, self).__init__() - self.y_table = y_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - out = image * (self.y_table * factor) - else: - b = factor.size(0) - table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - out = image * table - return out - - -class CDequantize(nn.Module): - """Dequantize CbCr channel - """ - - def __init__(self): - super(CDequantize, self).__init__() - self.c_table = c_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - out = image * (self.c_table * factor) - else: - b = factor.size(0) - table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - out = image * table - return out - - -class iDCT8x8(nn.Module): - """Inverse discrete Cosine Transformation - """ - - def __init__(self): - super(iDCT8x8, self).__init__() - alpha = np.array([1. / np.sqrt(2)] + [1] * 7) - self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) - tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) - for x, y, u, v in itertools.product(range(8), repeat=4): - tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos((2 * v + 1) * y * np.pi / 16) - self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - image = image * self.alpha - result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 - result.view(image.shape) - return result - - -class BlockMerging(nn.Module): - """Merge patches into image - """ - - def __init__(self): - super(BlockMerging, self).__init__() - - def forward(self, patches, height, width): - """ - Args: - patches(tensor) batch x height*width/64, height x width - height(int) - width(int) - - Returns: - Tensor: batch x height x width - """ - k = 8 - batch_size = patches.shape[0] - image_reshaped = patches.view(batch_size, height // k, width // k, k, k) - image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) - return image_transposed.contiguous().view(batch_size, height, width) - - -class ChromaUpsampling(nn.Module): - """Upsample chroma layers - """ - - def __init__(self): - super(ChromaUpsampling, self).__init__() - - def forward(self, y, cb, cr): - """ - Args: - y(tensor): y channel image - cb(tensor): cb channel - cr(tensor): cr channel - - Returns: - Tensor: batch x height x width x 3 - """ - - def repeat(x, k=2): - height, width = x.shape[1:3] - x = x.unsqueeze(-1) - x = x.repeat(1, 1, k, k) - x = x.view(-1, height * k, width * k) - return x - - cb = repeat(cb) - cr = repeat(cr) - return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) - - -class YCbCr2RGBJpeg(nn.Module): - """Converts YCbCr image to RGB JPEG - """ - - def __init__(self): - super(YCbCr2RGBJpeg, self).__init__() - - matrix = np.array([[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], dtype=np.float32).T - self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) - self.matrix = nn.Parameter(torch.from_numpy(matrix)) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width x 3 - - Returns: - Tensor: batch x 3 x height x width - """ - result = torch.tensordot(image + self.shift, self.matrix, dims=1) - return result.view(image.shape).permute(0, 3, 1, 2) - - -class DeCompressJpeg(nn.Module): - """Full JPEG decompression algorithm - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding=torch.round): - super(DeCompressJpeg, self).__init__() - self.c_dequantize = CDequantize() - self.y_dequantize = YDequantize() - self.idct = iDCT8x8() - self.merging = BlockMerging() - self.chroma = ChromaUpsampling() - self.colors = YCbCr2RGBJpeg() - - def forward(self, y, cb, cr, imgh, imgw, factor=1): - """ - Args: - compressed(dict(tensor)): batch x h*w/64 x 8 x 8 - imgh(int) - imgw(int) - factor(float) - - Returns: - Tensor: batch x 3 x height x width - """ - components = {'y': y, 'cb': cb, 'cr': cr} - for k in components.keys(): - if k in ('cb', 'cr'): - comp = self.c_dequantize(components[k], factor=factor) - height, width = int(imgh / 2), int(imgw / 2) - else: - comp = self.y_dequantize(components[k], factor=factor) - height, width = imgh, imgw - comp = self.idct(comp) - components[k] = self.merging(comp, height, width) - # - image = self.chroma(components['y'], components['cb'], components['cr']) - image = self.colors(image) - - image = torch.min(255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)) - return image / 255 - - -# ------------------------ main DiffJPEG ------------------------ # - - -class DiffJPEG(nn.Module): - """This JPEG algorithm result is slightly different from cv2. - DiffJPEG supports batch processing. - - Args: - differentiable(bool): If True, uses custom differentiable rounding function, if False, uses standard torch.round - """ - - def __init__(self, differentiable=True): - super(DiffJPEG, self).__init__() - if differentiable: - rounding = diff_round - else: - rounding = torch.round - - self.compress = CompressJpeg(rounding=rounding) - self.decompress = DeCompressJpeg(rounding=rounding) - - def forward(self, x, quality): - """ - Args: - x (Tensor): Input image, bchw, rgb, [0, 1] - quality(float): Quality factor for jpeg compression scheme. - """ - factor = quality - if isinstance(factor, (int, float)): - factor = quality_to_factor(factor) - else: - for i in range(factor.size(0)): - factor[i] = quality_to_factor(factor[i]) - h, w = x.size()[-2:] - h_pad, w_pad = 0, 0 - # why should use 16 - if h % 16 != 0: - h_pad = 16 - h % 16 - if w % 16 != 0: - w_pad = 16 - w % 16 - x = F.pad(x, (0, w_pad, 0, h_pad), mode='constant', value=0) - - y, cb, cr = self.compress(x, factor=factor) - recovered = self.decompress(y, cb, cr, (h + h_pad), (w + w_pad), factor=factor) - recovered = recovered[:, :, 0:h, 0:w] - return recovered - - -if __name__ == '__main__': - import cv2 - - from basicsr.utils import img2tensor, tensor2img - - img_gt = cv2.imread('test.png') / 255. - - # -------------- cv2 -------------- # - encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 20] - _, encimg = cv2.imencode('.jpg', img_gt * 255., encode_param) - img_lq = np.float32(cv2.imdecode(encimg, 1)) - cv2.imwrite('cv2_JPEG_20.png', img_lq) - - # -------------- DiffJPEG -------------- # - jpeger = DiffJPEG(differentiable=False).cuda() - img_gt = img2tensor(img_gt) - img_gt = torch.stack([img_gt, img_gt]).cuda() - quality = img_gt.new_tensor([20, 40]) - out = jpeger(img_gt, quality=quality) - - cv2.imwrite('pt_JPEG_20.png', tensor2img(out[0])) - cv2.imwrite('pt_JPEG_40.png', tensor2img(out[1])) diff --git a/basicsr/utils/dist_util.py b/basicsr/utils/dist_util.py deleted file mode 100644 index 380f155bc18cc5788d8b14fd18c0c0d748859de2..0000000000000000000000000000000000000000 --- a/basicsr/utils/dist_util.py +++ /dev/null @@ -1,82 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501 -import functools -import os -import subprocess -import torch -import torch.distributed as dist -import torch.multiprocessing as mp - - -def init_dist(launcher, backend='nccl', **kwargs): - if mp.get_start_method(allow_none=True) is None: - mp.set_start_method('spawn') - if launcher == 'pytorch': - _init_dist_pytorch(backend, **kwargs) - elif launcher == 'slurm': - _init_dist_slurm(backend, **kwargs) - else: - raise ValueError(f'Invalid launcher type: {launcher}') - - -def _init_dist_pytorch(backend, **kwargs): - rank = int(os.environ['RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_slurm(backend, port=None): - """Initialize slurm distributed training environment. - - If argument ``port`` is not specified, then the master port will be system - environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system - environment variable, then a default port ``29500`` will be used. - - Args: - backend (str): Backend of torch.distributed. - port (int, optional): Master port. Defaults to None. - """ - proc_id = int(os.environ['SLURM_PROCID']) - ntasks = int(os.environ['SLURM_NTASKS']) - node_list = os.environ['SLURM_NODELIST'] - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(proc_id % num_gpus) - addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1') - # specify master port - if port is not None: - os.environ['MASTER_PORT'] = str(port) - elif 'MASTER_PORT' in os.environ: - pass # use MASTER_PORT in the environment variable - else: - # 29500 is torch.distributed default port - os.environ['MASTER_PORT'] = '29500' - os.environ['MASTER_ADDR'] = addr - os.environ['WORLD_SIZE'] = str(ntasks) - os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) - os.environ['RANK'] = str(proc_id) - dist.init_process_group(backend=backend) - - -def get_dist_info(): - if dist.is_available(): - initialized = dist.is_initialized() - else: - initialized = False - if initialized: - rank = dist.get_rank() - world_size = dist.get_world_size() - else: - rank = 0 - world_size = 1 - return rank, world_size - - -def master_only(func): - - @functools.wraps(func) - def wrapper(*args, **kwargs): - rank, _ = get_dist_info() - if rank == 0: - return func(*args, **kwargs) - - return wrapper diff --git a/basicsr/utils/download_util.py b/basicsr/utils/download_util.py deleted file mode 100644 index 43fe80f79e0ad8354002ccd45b1ea4c3c125e983..0000000000000000000000000000000000000000 --- a/basicsr/utils/download_util.py +++ /dev/null @@ -1,98 +0,0 @@ -import math -import os -import requests -from torch.hub import download_url_to_file, get_dir -from tqdm import tqdm -from urllib.parse import urlparse - -from .misc import sizeof_fmt - - -def download_file_from_google_drive(file_id, save_path): - """Download files from google drive. - - Reference: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive - - Args: - file_id (str): File id. - save_path (str): Save path. - """ - - session = requests.Session() - URL = 'https://docs.google.com/uc?export=download' - params = {'id': file_id} - - response = session.get(URL, params=params, stream=True) - token = get_confirm_token(response) - if token: - params['confirm'] = token - response = session.get(URL, params=params, stream=True) - - # get file size - response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'}) - if 'Content-Range' in response_file_size.headers: - file_size = int(response_file_size.headers['Content-Range'].split('/')[1]) - else: - file_size = None - - save_response_content(response, save_path, file_size) - - -def get_confirm_token(response): - for key, value in response.cookies.items(): - if key.startswith('download_warning'): - return value - return None - - -def save_response_content(response, destination, file_size=None, chunk_size=32768): - if file_size is not None: - pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk') - - readable_file_size = sizeof_fmt(file_size) - else: - pbar = None - - with open(destination, 'wb') as f: - downloaded_size = 0 - for chunk in response.iter_content(chunk_size): - downloaded_size += chunk_size - if pbar is not None: - pbar.update(1) - pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}') - if chunk: # filter out keep-alive new chunks - f.write(chunk) - if pbar is not None: - pbar.close() - - -def load_file_from_url(url, model_dir=None, progress=True, file_name=None): - """Load file form http url, will download models if necessary. - - Reference: https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py - - Args: - url (str): URL to be downloaded. - model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. - Default: None. - progress (bool): Whether to show the download progress. Default: True. - file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. - - Returns: - str: The path to the downloaded file. - """ - if model_dir is None: # use the pytorch hub_dir - hub_dir = get_dir() - model_dir = os.path.join(hub_dir, 'checkpoints') - - os.makedirs(model_dir, exist_ok=True) - - parts = urlparse(url) - filename = os.path.basename(parts.path) - if file_name is not None: - filename = file_name - cached_file = os.path.abspath(os.path.join(model_dir, filename)) - if not os.path.exists(cached_file): - print(f'Downloading: "{url}" to {cached_file}\n') - download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) - return cached_file diff --git a/basicsr/utils/file_client.py b/basicsr/utils/file_client.py deleted file mode 100644 index 8f6340e429dcf87f2f48059c292a427f1be97354..0000000000000000000000000000000000000000 --- a/basicsr/utils/file_client.py +++ /dev/null @@ -1,167 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py # noqa: E501 -from abc import ABCMeta, abstractmethod - - -class BaseStorageBackend(metaclass=ABCMeta): - """Abstract class of storage backends. - - All backends need to implement two apis: ``get()`` and ``get_text()``. - ``get()`` reads the file as a byte stream and ``get_text()`` reads the file - as texts. - """ - - @abstractmethod - def get(self, filepath): - pass - - @abstractmethod - def get_text(self, filepath): - pass - - -class MemcachedBackend(BaseStorageBackend): - """Memcached storage backend. - - Attributes: - server_list_cfg (str): Config file for memcached server list. - client_cfg (str): Config file for memcached client. - sys_path (str | None): Additional path to be appended to `sys.path`. - Default: None. - """ - - def __init__(self, server_list_cfg, client_cfg, sys_path=None): - if sys_path is not None: - import sys - sys.path.append(sys_path) - try: - import mc - except ImportError: - raise ImportError('Please install memcached to enable MemcachedBackend.') - - self.server_list_cfg = server_list_cfg - self.client_cfg = client_cfg - self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, self.client_cfg) - # mc.pyvector servers as a point which points to a memory cache - self._mc_buffer = mc.pyvector() - - def get(self, filepath): - filepath = str(filepath) - import mc - self._client.Get(filepath, self._mc_buffer) - value_buf = mc.ConvertBuffer(self._mc_buffer) - return value_buf - - def get_text(self, filepath): - raise NotImplementedError - - -class HardDiskBackend(BaseStorageBackend): - """Raw hard disks storage backend.""" - - def get(self, filepath): - filepath = str(filepath) - with open(filepath, 'rb') as f: - value_buf = f.read() - return value_buf - - def get_text(self, filepath): - filepath = str(filepath) - with open(filepath, 'r') as f: - value_buf = f.read() - return value_buf - - -class LmdbBackend(BaseStorageBackend): - """Lmdb storage backend. - - Args: - db_paths (str | list[str]): Lmdb database paths. - client_keys (str | list[str]): Lmdb client keys. Default: 'default'. - readonly (bool, optional): Lmdb environment parameter. If True, - disallow any write operations. Default: True. - lock (bool, optional): Lmdb environment parameter. If False, when - concurrent access occurs, do not lock the database. Default: False. - readahead (bool, optional): Lmdb environment parameter. If False, - disable the OS filesystem readahead mechanism, which may improve - random read performance when a database is larger than RAM. - Default: False. - - Attributes: - db_paths (list): Lmdb database path. - _client (list): A list of several lmdb envs. - """ - - def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs): - try: - import lmdb - except ImportError: - raise ImportError('Please install lmdb to enable LmdbBackend.') - - if isinstance(client_keys, str): - client_keys = [client_keys] - - if isinstance(db_paths, list): - self.db_paths = [str(v) for v in db_paths] - elif isinstance(db_paths, str): - self.db_paths = [str(db_paths)] - assert len(client_keys) == len(self.db_paths), ('client_keys and db_paths should have the same length, ' - f'but received {len(client_keys)} and {len(self.db_paths)}.') - - self._client = {} - for client, path in zip(client_keys, self.db_paths): - self._client[client] = lmdb.open(path, readonly=readonly, lock=lock, readahead=readahead, **kwargs) - - def get(self, filepath, client_key): - """Get values according to the filepath from one lmdb named client_key. - - Args: - filepath (str | obj:`Path`): Here, filepath is the lmdb key. - client_key (str): Used for distinguishing different lmdb envs. - """ - filepath = str(filepath) - assert client_key in self._client, (f'client_key {client_key} is not in lmdb clients.') - client = self._client[client_key] - with client.begin(write=False) as txn: - value_buf = txn.get(filepath.encode('ascii')) - return value_buf - - def get_text(self, filepath): - raise NotImplementedError - - -class FileClient(object): - """A general file client to access files in different backend. - - The client loads a file or text in a specified backend from its path - and return it as a binary file. it can also register other backend - accessor with a given name and backend class. - - Attributes: - backend (str): The storage backend type. Options are "disk", - "memcached" and "lmdb". - client (:obj:`BaseStorageBackend`): The backend object. - """ - - _backends = { - 'disk': HardDiskBackend, - 'memcached': MemcachedBackend, - 'lmdb': LmdbBackend, - } - - def __init__(self, backend='disk', **kwargs): - if backend not in self._backends: - raise ValueError(f'Backend {backend} is not supported. Currently supported ones' - f' are {list(self._backends.keys())}') - self.backend = backend - self.client = self._backends[backend](**kwargs) - - def get(self, filepath, client_key='default'): - # client_key is used only for lmdb, where different fileclients have - # different lmdb environments. - if self.backend == 'lmdb': - return self.client.get(filepath, client_key) - else: - return self.client.get(filepath) - - def get_text(self, filepath): - return self.client.get_text(filepath) diff --git a/basicsr/utils/flow_util.py b/basicsr/utils/flow_util.py deleted file mode 100644 index d133012fddf0dd338ea4764cff4f83a02a36781a..0000000000000000000000000000000000000000 --- a/basicsr/utils/flow_util.py +++ /dev/null @@ -1,170 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py # noqa: E501 -import cv2 -import numpy as np -import os - - -def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs): - """Read an optical flow map. - - Args: - flow_path (ndarray or str): Flow path. - quantize (bool): whether to read quantized pair, if set to True, - remaining args will be passed to :func:`dequantize_flow`. - concat_axis (int): The axis that dx and dy are concatenated, - can be either 0 or 1. Ignored if quantize is False. - - Returns: - ndarray: Optical flow represented as a (h, w, 2) numpy array - """ - if quantize: - assert concat_axis in [0, 1] - cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED) - if cat_flow.ndim != 2: - raise IOError(f'{flow_path} is not a valid quantized flow file, its dimension is {cat_flow.ndim}.') - assert cat_flow.shape[concat_axis] % 2 == 0 - dx, dy = np.split(cat_flow, 2, axis=concat_axis) - flow = dequantize_flow(dx, dy, *args, **kwargs) - else: - with open(flow_path, 'rb') as f: - try: - header = f.read(4).decode('utf-8') - except Exception: - raise IOError(f'Invalid flow file: {flow_path}') - else: - if header != 'PIEH': - raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH') - - w = np.fromfile(f, np.int32, 1).squeeze() - h = np.fromfile(f, np.int32, 1).squeeze() - flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2)) - - return flow.astype(np.float32) - - -def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs): - """Write optical flow to file. - - If the flow is not quantized, it will be saved as a .flo file losslessly, - otherwise a jpeg image which is lossy but of much smaller size. (dx and dy - will be concatenated horizontally into a single image if quantize is True.) - - Args: - flow (ndarray): (h, w, 2) array of optical flow. - filename (str): Output filepath. - quantize (bool): Whether to quantize the flow and save it to 2 jpeg - images. If set to True, remaining args will be passed to - :func:`quantize_flow`. - concat_axis (int): The axis that dx and dy are concatenated, - can be either 0 or 1. Ignored if quantize is False. - """ - if not quantize: - with open(filename, 'wb') as f: - f.write('PIEH'.encode('utf-8')) - np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) - flow = flow.astype(np.float32) - flow.tofile(f) - f.flush() - else: - assert concat_axis in [0, 1] - dx, dy = quantize_flow(flow, *args, **kwargs) - dxdy = np.concatenate((dx, dy), axis=concat_axis) - os.makedirs(os.path.dirname(filename), exist_ok=True) - cv2.imwrite(filename, dxdy) - - -def quantize_flow(flow, max_val=0.02, norm=True): - """Quantize flow to [0, 255]. - - After this step, the size of flow will be much smaller, and can be - dumped as jpeg images. - - Args: - flow (ndarray): (h, w, 2) array of optical flow. - max_val (float): Maximum value of flow, values beyond - [-max_val, max_val] will be truncated. - norm (bool): Whether to divide flow values by image width/height. - - Returns: - tuple[ndarray]: Quantized dx and dy. - """ - h, w, _ = flow.shape - dx = flow[..., 0] - dy = flow[..., 1] - if norm: - dx = dx / w # avoid inplace operations - dy = dy / h - # use 255 levels instead of 256 to make sure 0 is 0 after dequantization. - flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]] - return tuple(flow_comps) - - -def dequantize_flow(dx, dy, max_val=0.02, denorm=True): - """Recover from quantized flow. - - Args: - dx (ndarray): Quantized dx. - dy (ndarray): Quantized dy. - max_val (float): Maximum value used when quantizing. - denorm (bool): Whether to multiply flow values with width/height. - - Returns: - ndarray: Dequantized flow. - """ - assert dx.shape == dy.shape - assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1) - - dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]] - - if denorm: - dx *= dx.shape[1] - dy *= dx.shape[0] - flow = np.dstack((dx, dy)) - return flow - - -def quantize(arr, min_val, max_val, levels, dtype=np.int64): - """Quantize an array of (-inf, inf) to [0, levels-1]. - - Args: - arr (ndarray): Input array. - min_val (scalar): Minimum value to be clipped. - max_val (scalar): Maximum value to be clipped. - levels (int): Quantization levels. - dtype (np.type): The type of the quantized array. - - Returns: - tuple: Quantized array. - """ - if not (isinstance(levels, int) and levels > 1): - raise ValueError(f'levels must be a positive integer, but got {levels}') - if min_val >= max_val: - raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') - - arr = np.clip(arr, min_val, max_val) - min_val - quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1) - - return quantized_arr - - -def dequantize(arr, min_val, max_val, levels, dtype=np.float64): - """Dequantize an array. - - Args: - arr (ndarray): Input array. - min_val (scalar): Minimum value to be clipped. - max_val (scalar): Maximum value to be clipped. - levels (int): Quantization levels. - dtype (np.type): The type of the dequantized array. - - Returns: - tuple: Dequantized array. - """ - if not (isinstance(levels, int) and levels > 1): - raise ValueError(f'levels must be a positive integer, but got {levels}') - if min_val >= max_val: - raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') - - dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val - - return dequantized_arr diff --git a/basicsr/utils/img_process_util.py b/basicsr/utils/img_process_util.py deleted file mode 100644 index fb5fbc9468ca1861fe7d6eae28128172b9e70001..0000000000000000000000000000000000000000 --- a/basicsr/utils/img_process_util.py +++ /dev/null @@ -1,83 +0,0 @@ -import cv2 -import numpy as np -import torch -from torch.nn import functional as F - - -def filter2D(img, kernel): - """PyTorch version of cv2.filter2D - - Args: - img (Tensor): (b, c, h, w) - kernel (Tensor): (b, k, k) - """ - k = kernel.size(-1) - b, c, h, w = img.size() - if k % 2 == 1: - img = F.pad(img, (k // 2, k // 2, k // 2, k // 2), mode='reflect') - else: - raise ValueError('Wrong kernel size') - - ph, pw = img.size()[-2:] - - if kernel.size(0) == 1: - # apply the same kernel to all batch images - img = img.view(b * c, 1, ph, pw) - kernel = kernel.view(1, 1, k, k) - return F.conv2d(img, kernel, padding=0).view(b, c, h, w) - else: - img = img.view(1, b * c, ph, pw) - kernel = kernel.view(b, 1, k, k).repeat(1, c, 1, 1).view(b * c, 1, k, k) - return F.conv2d(img, kernel, groups=b * c).view(b, c, h, w) - - -def usm_sharp(img, weight=0.5, radius=50, threshold=10): - """USM sharpening. - - Input image: I; Blurry image: B. - 1. sharp = I + weight * (I - B) - 2. Mask = 1 if abs(I - B) > threshold, else: 0 - 3. Blur mask: - 4. Out = Mask * sharp + (1 - Mask) * I - - - Args: - img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. - weight (float): Sharp weight. Default: 1. - radius (float): Kernel size of Gaussian blur. Default: 50. - threshold (int): - """ - if radius % 2 == 0: - radius += 1 - blur = cv2.GaussianBlur(img, (radius, radius), 0) - residual = img - blur - mask = np.abs(residual) * 255 > threshold - mask = mask.astype('float32') - soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) - - sharp = img + weight * residual - sharp = np.clip(sharp, 0, 1) - return soft_mask * sharp + (1 - soft_mask) * img - - -class USMSharp(torch.nn.Module): - - def __init__(self, radius=50, sigma=0): - super(USMSharp, self).__init__() - if radius % 2 == 0: - radius += 1 - self.radius = radius - kernel = cv2.getGaussianKernel(radius, sigma) - kernel = torch.FloatTensor(np.dot(kernel, kernel.transpose())).unsqueeze_(0) - self.register_buffer('kernel', kernel) - - def forward(self, img, weight=0.5, threshold=10): - blur = filter2D(img, self.kernel) - residual = img - blur - - mask = torch.abs(residual) * 255 > threshold - mask = mask.float() - soft_mask = filter2D(mask, self.kernel) - sharp = img + weight * residual - sharp = torch.clip(sharp, 0, 1) - return soft_mask * sharp + (1 - soft_mask) * img diff --git a/basicsr/utils/img_util.py b/basicsr/utils/img_util.py deleted file mode 100644 index 3ad2be2c5556ddb6076eb01a44c487447dc7fcf1..0000000000000000000000000000000000000000 --- a/basicsr/utils/img_util.py +++ /dev/null @@ -1,172 +0,0 @@ -import cv2 -import math -import numpy as np -import os -import torch -from torchvision.utils import make_grid - - -def img2tensor(imgs, bgr2rgb=True, float32=True): - """Numpy array to tensor. - - Args: - imgs (list[ndarray] | ndarray): Input images. - bgr2rgb (bool): Whether to change bgr to rgb. - float32 (bool): Whether to change to float32. - - Returns: - list[tensor] | tensor: Tensor images. If returned results only have - one element, just return tensor. - """ - - def _totensor(img, bgr2rgb, float32): - if img.shape[2] == 3 and bgr2rgb: - if img.dtype == 'float64': - img = img.astype('float32') - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - img = torch.from_numpy(img.transpose(2, 0, 1)) - if float32: - img = img.float() - return img - - if isinstance(imgs, list): - return [_totensor(img, bgr2rgb, float32) for img in imgs] - else: - return _totensor(imgs, bgr2rgb, float32) - - -def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): - """Convert torch Tensors into image numpy arrays. - - After clamping to [min, max], values will be normalized to [0, 1]. - - Args: - tensor (Tensor or list[Tensor]): Accept shapes: - 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); - 2) 3D Tensor of shape (3/1 x H x W); - 3) 2D Tensor of shape (H x W). - Tensor channel should be in RGB order. - rgb2bgr (bool): Whether to change rgb to bgr. - out_type (numpy type): output types. If ``np.uint8``, transform outputs - to uint8 type with range [0, 255]; otherwise, float type with - range [0, 1]. Default: ``np.uint8``. - min_max (tuple[int]): min and max values for clamp. - - Returns: - (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of - shape (H x W). The channel order is BGR. - """ - if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): - raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}') - - if torch.is_tensor(tensor): - tensor = [tensor] - result = [] - for _tensor in tensor: - _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max) - _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0]) - - n_dim = _tensor.dim() - if n_dim == 4: - img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy() - img_np = img_np.transpose(1, 2, 0) - if rgb2bgr: - img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) - elif n_dim == 3: - img_np = _tensor.numpy() - img_np = img_np.transpose(1, 2, 0) - if img_np.shape[2] == 1: # gray image - img_np = np.squeeze(img_np, axis=2) - else: - if rgb2bgr: - img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) - elif n_dim == 2: - img_np = _tensor.numpy() - else: - raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}') - if out_type == np.uint8: - # Unlike MATLAB, numpy.unit8() WILL NOT round by default. - img_np = (img_np * 255.0).round() - img_np = img_np.astype(out_type) - result.append(img_np) - if len(result) == 1: - result = result[0] - return result - - -def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)): - """This implementation is slightly faster than tensor2img. - It now only supports torch tensor with shape (1, c, h, w). - - Args: - tensor (Tensor): Now only support torch tensor with (1, c, h, w). - rgb2bgr (bool): Whether to change rgb to bgr. Default: True. - min_max (tuple[int]): min and max values for clamp. - """ - output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0) - output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255 - output = output.type(torch.uint8).cpu().numpy() - if rgb2bgr: - output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) - return output - - -def imfrombytes(content, flag='color', float32=False): - """Read an image from bytes. - - Args: - content (bytes): Image bytes got from files or other streams. - flag (str): Flags specifying the color type of a loaded image, - candidates are `color`, `grayscale` and `unchanged`. - float32 (bool): Whether to change to float32., If True, will also norm - to [0, 1]. Default: False. - - Returns: - ndarray: Loaded image array. - """ - img_np = np.frombuffer(content, np.uint8) - imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED} - img = cv2.imdecode(img_np, imread_flags[flag]) - if float32: - img = img.astype(np.float32) / 255. - return img - - -def imwrite(img, file_path, params=None, auto_mkdir=True): - """Write image to file. - - Args: - img (ndarray): Image array to be written. - file_path (str): Image file path. - params (None or list): Same as opencv's :func:`imwrite` interface. - auto_mkdir (bool): If the parent folder of `file_path` does not exist, - whether to create it automatically. - - Returns: - bool: Successful or not. - """ - if auto_mkdir: - dir_name = os.path.abspath(os.path.dirname(file_path)) - os.makedirs(dir_name, exist_ok=True) - ok = cv2.imwrite(file_path, img, params) - if not ok: - raise IOError('Failed in writing images.') - - -def crop_border(imgs, crop_border): - """Crop borders of images. - - Args: - imgs (list[ndarray] | ndarray): Images with shape (h, w, c). - crop_border (int): Crop border for each end of height and weight. - - Returns: - list[ndarray]: Cropped images. - """ - if crop_border == 0: - return imgs - else: - if isinstance(imgs, list): - return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs] - else: - return imgs[crop_border:-crop_border, crop_border:-crop_border, ...] diff --git a/basicsr/utils/lmdb_util.py b/basicsr/utils/lmdb_util.py deleted file mode 100644 index 591182df8ebb469f23443cd45f540e6010b26fc5..0000000000000000000000000000000000000000 --- a/basicsr/utils/lmdb_util.py +++ /dev/null @@ -1,199 +0,0 @@ -import cv2 -import lmdb -import sys -from multiprocessing import Pool -from os import path as osp -from tqdm import tqdm - - -def make_lmdb_from_imgs(data_path, - lmdb_path, - img_path_list, - keys, - batch=5000, - compress_level=1, - multiprocessing_read=False, - n_thread=40, - map_size=None): - """Make lmdb from images. - - Contents of lmdb. The file structure is: - - :: - - example.lmdb - ├── data.mdb - ├── lock.mdb - ├── meta_info.txt - - The data.mdb and lock.mdb are standard lmdb files and you can refer to - https://lmdb.readthedocs.io/en/release/ for more details. - - The meta_info.txt is a specified txt file to record the meta information - of our datasets. It will be automatically created when preparing - datasets by our provided dataset tools. - Each line in the txt file records 1)image name (with extension), - 2)image shape, and 3)compression level, separated by a white space. - - For example, the meta information could be: - `000_00000000.png (720,1280,3) 1`, which means: - 1) image name (with extension): 000_00000000.png; - 2) image shape: (720,1280,3); - 3) compression level: 1 - - We use the image name without extension as the lmdb key. - - If `multiprocessing_read` is True, it will read all the images to memory - using multiprocessing. Thus, your server needs to have enough memory. - - Args: - data_path (str): Data path for reading images. - lmdb_path (str): Lmdb save path. - img_path_list (str): Image path list. - keys (str): Used for lmdb keys. - batch (int): After processing batch images, lmdb commits. - Default: 5000. - compress_level (int): Compress level when encoding images. Default: 1. - multiprocessing_read (bool): Whether use multiprocessing to read all - the images to memory. Default: False. - n_thread (int): For multiprocessing. - map_size (int | None): Map size for lmdb env. If None, use the - estimated size from images. Default: None - """ - - assert len(img_path_list) == len(keys), ('img_path_list and keys should have the same length, ' - f'but got {len(img_path_list)} and {len(keys)}') - print(f'Create lmdb for {data_path}, save to {lmdb_path}...') - print(f'Totoal images: {len(img_path_list)}') - if not lmdb_path.endswith('.lmdb'): - raise ValueError("lmdb_path must end with '.lmdb'.") - if osp.exists(lmdb_path): - print(f'Folder {lmdb_path} already exists. Exit.') - sys.exit(1) - - if multiprocessing_read: - # read all the images to memory (multiprocessing) - dataset = {} # use dict to keep the order for multiprocessing - shapes = {} - print(f'Read images with multiprocessing, #thread: {n_thread} ...') - pbar = tqdm(total=len(img_path_list), unit='image') - - def callback(arg): - """get the image data and update pbar.""" - key, dataset[key], shapes[key] = arg - pbar.update(1) - pbar.set_description(f'Read {key}') - - pool = Pool(n_thread) - for path, key in zip(img_path_list, keys): - pool.apply_async(read_img_worker, args=(osp.join(data_path, path), key, compress_level), callback=callback) - pool.close() - pool.join() - pbar.close() - print(f'Finish reading {len(img_path_list)} images.') - - # create lmdb environment - if map_size is None: - # obtain data size for one image - img = cv2.imread(osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED) - _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) - data_size_per_img = img_byte.nbytes - print('Data size per image is: ', data_size_per_img) - data_size = data_size_per_img * len(img_path_list) - map_size = data_size * 10 - - env = lmdb.open(lmdb_path, map_size=map_size) - - # write data to lmdb - pbar = tqdm(total=len(img_path_list), unit='chunk') - txn = env.begin(write=True) - txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w') - for idx, (path, key) in enumerate(zip(img_path_list, keys)): - pbar.update(1) - pbar.set_description(f'Write {key}') - key_byte = key.encode('ascii') - if multiprocessing_read: - img_byte = dataset[key] - h, w, c = shapes[key] - else: - _, img_byte, img_shape = read_img_worker(osp.join(data_path, path), key, compress_level) - h, w, c = img_shape - - txn.put(key_byte, img_byte) - # write meta information - txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n') - if idx % batch == 0: - txn.commit() - txn = env.begin(write=True) - pbar.close() - txn.commit() - env.close() - txt_file.close() - print('\nFinish writing lmdb.') - - -def read_img_worker(path, key, compress_level): - """Read image worker. - - Args: - path (str): Image path. - key (str): Image key. - compress_level (int): Compress level when encoding images. - - Returns: - str: Image key. - byte: Image byte. - tuple[int]: Image shape. - """ - - img = cv2.imread(path, cv2.IMREAD_UNCHANGED) - if img.ndim == 2: - h, w = img.shape - c = 1 - else: - h, w, c = img.shape - _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) - return (key, img_byte, (h, w, c)) - - -class LmdbMaker(): - """LMDB Maker. - - Args: - lmdb_path (str): Lmdb save path. - map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB. - batch (int): After processing batch images, lmdb commits. - Default: 5000. - compress_level (int): Compress level when encoding images. Default: 1. - """ - - def __init__(self, lmdb_path, map_size=1024**4, batch=5000, compress_level=1): - if not lmdb_path.endswith('.lmdb'): - raise ValueError("lmdb_path must end with '.lmdb'.") - if osp.exists(lmdb_path): - print(f'Folder {lmdb_path} already exists. Exit.') - sys.exit(1) - - self.lmdb_path = lmdb_path - self.batch = batch - self.compress_level = compress_level - self.env = lmdb.open(lmdb_path, map_size=map_size) - self.txn = self.env.begin(write=True) - self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w') - self.counter = 0 - - def put(self, img_byte, key, img_shape): - self.counter += 1 - key_byte = key.encode('ascii') - self.txn.put(key_byte, img_byte) - # write meta information - h, w, c = img_shape - self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n') - if self.counter % self.batch == 0: - self.txn.commit() - self.txn = self.env.begin(write=True) - - def close(self): - self.txn.commit() - self.env.close() - self.txt_file.close() diff --git a/basicsr/utils/logger.py b/basicsr/utils/logger.py deleted file mode 100644 index 6c0592d2ce50822e8269cbe222cbcd66c04dbb77..0000000000000000000000000000000000000000 --- a/basicsr/utils/logger.py +++ /dev/null @@ -1,213 +0,0 @@ -import datetime -import logging -import time - -from .dist_util import get_dist_info, master_only - -initialized_logger = {} - - -class AvgTimer(): - - def __init__(self, window=200): - self.window = window # average window - self.current_time = 0 - self.total_time = 0 - self.count = 0 - self.avg_time = 0 - self.start() - - def start(self): - self.start_time = self.tic = time.time() - - def record(self): - self.count += 1 - self.toc = time.time() - self.current_time = self.toc - self.tic - self.total_time += self.current_time - # calculate average time - self.avg_time = self.total_time / self.count - - # reset - if self.count > self.window: - self.count = 0 - self.total_time = 0 - - self.tic = time.time() - - def get_current_time(self): - return self.current_time - - def get_avg_time(self): - return self.avg_time - - -class MessageLogger(): - """Message logger for printing. - - Args: - opt (dict): Config. It contains the following keys: - name (str): Exp name. - logger (dict): Contains 'print_freq' (str) for logger interval. - train (dict): Contains 'total_iter' (int) for total iters. - use_tb_logger (bool): Use tensorboard logger. - start_iter (int): Start iter. Default: 1. - tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None. - """ - - def __init__(self, opt, start_iter=1, tb_logger=None): - self.exp_name = opt['name'] - self.interval = opt['logger']['print_freq'] - self.start_iter = start_iter - self.max_iters = opt['train']['total_iter'] - self.use_tb_logger = opt['logger']['use_tb_logger'] - self.tb_logger = tb_logger - self.start_time = time.time() - self.logger = get_root_logger() - - def reset_start_time(self): - self.start_time = time.time() - - @master_only - def __call__(self, log_vars): - """Format logging message. - - Args: - log_vars (dict): It contains the following keys: - epoch (int): Epoch number. - iter (int): Current iter. - lrs (list): List for learning rates. - - time (float): Iter time. - data_time (float): Data time for each iter. - """ - # epoch, iter, learning rates - epoch = log_vars.pop('epoch') - current_iter = log_vars.pop('iter') - lrs = log_vars.pop('lrs') - - message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, iter:{current_iter:8,d}, lr:(') - for v in lrs: - message += f'{v:.3e},' - message += ')] ' - - # time and estimated time - if 'time' in log_vars.keys(): - iter_time = log_vars.pop('time') - data_time = log_vars.pop('data_time') - - total_time = time.time() - self.start_time - time_sec_avg = total_time / (current_iter - self.start_iter + 1) - eta_sec = time_sec_avg * (self.max_iters - current_iter - 1) - eta_str = str(datetime.timedelta(seconds=int(eta_sec))) - message += f'[eta: {eta_str}, ' - message += f'time (data): {iter_time:.3f} ({data_time:.3f})] ' - - # other items, especially losses - for k, v in log_vars.items(): - message += f'{k}: {v:.4e} ' - # tensorboard logger - if self.use_tb_logger and 'debug' not in self.exp_name: - if k.startswith('l_'): - self.tb_logger.add_scalar(f'losses/{k}', v, current_iter) - else: - self.tb_logger.add_scalar(k, v, current_iter) - self.logger.info(message) - - -@master_only -def init_tb_logger(log_dir): - from torch.utils.tensorboard import SummaryWriter - tb_logger = SummaryWriter(log_dir=log_dir) - return tb_logger - - -@master_only -def init_wandb_logger(opt): - """We now only use wandb to sync tensorboard log.""" - import wandb - logger = get_root_logger() - - project = opt['logger']['wandb']['project'] - resume_id = opt['logger']['wandb'].get('resume_id') - if resume_id: - wandb_id = resume_id - resume = 'allow' - logger.warning(f'Resume wandb logger with id={wandb_id}.') - else: - wandb_id = wandb.util.generate_id() - resume = 'never' - - wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True) - - logger.info(f'Use wandb logger with id={wandb_id}; project={project}.') - - -def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None): - """Get the root logger. - - The logger will be initialized if it has not been initialized. By default a - StreamHandler will be added. If `log_file` is specified, a FileHandler will - also be added. - - Args: - logger_name (str): root logger name. Default: 'basicsr'. - log_file (str | None): The log filename. If specified, a FileHandler - will be added to the root logger. - log_level (int): The root logger level. Note that only the process of - rank 0 is affected, while other processes will set the level to - "Error" and be silent most of the time. - - Returns: - logging.Logger: The root logger. - """ - logger = logging.getLogger(logger_name) - # if the logger has been initialized, just return it - if logger_name in initialized_logger: - return logger - - format_str = '%(asctime)s %(levelname)s: %(message)s' - stream_handler = logging.StreamHandler() - stream_handler.setFormatter(logging.Formatter(format_str)) - logger.addHandler(stream_handler) - logger.propagate = False - rank, _ = get_dist_info() - if rank != 0: - logger.setLevel('ERROR') - elif log_file is not None: - logger.setLevel(log_level) - # add file handler - file_handler = logging.FileHandler(log_file, 'w') - file_handler.setFormatter(logging.Formatter(format_str)) - file_handler.setLevel(log_level) - logger.addHandler(file_handler) - initialized_logger[logger_name] = True - return logger - - -def get_env_info(): - """Get environment information. - - Currently, only log the software version. - """ - import torch - import torchvision - - from basicsr.version import __version__ - msg = r""" - ____ _ _____ ____ - / __ ) ____ _ _____ (_)_____/ ___/ / __ \ - / __ |/ __ `// ___// // ___/\__ \ / /_/ / - / /_/ // /_/ /(__ )/ // /__ ___/ // _, _/ - /_____/ \__,_//____//_/ \___//____//_/ |_| - ______ __ __ __ __ - / ____/____ ____ ____/ / / / __ __ _____ / /__ / / - / / __ / __ \ / __ \ / __ / / / / / / // ___// //_/ / / - / /_/ // /_/ // /_/ // /_/ / / /___/ /_/ // /__ / /< /_/ - \____/ \____/ \____/ \____/ /_____/\____/ \___//_/|_| (_) - """ - msg += ('\nVersion Information: ' - f'\n\tBasicSR: {__version__}' - f'\n\tPyTorch: {torch.__version__}' - f'\n\tTorchVision: {torchvision.__version__}') - return msg diff --git a/basicsr/utils/matlab_functions.py b/basicsr/utils/matlab_functions.py deleted file mode 100644 index 6d0b8cd891338329658e950633745c9a8b2eaad6..0000000000000000000000000000000000000000 --- a/basicsr/utils/matlab_functions.py +++ /dev/null @@ -1,178 +0,0 @@ -import math -import numpy as np -import torch - - -def cubic(x): - """cubic function used for calculate_weights_indices.""" - absx = torch.abs(x) - absx2 = absx**2 - absx3 = absx**3 - return (1.5 * absx3 - 2.5 * absx2 + 1) * ( - (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (((absx > 1) * - (absx <= 2)).type_as(absx)) - - -def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): - """Calculate weights and indices, used for imresize function. - - Args: - in_length (int): Input length. - out_length (int): Output length. - scale (float): Scale factor. - kernel_width (int): Kernel width. - antialisaing (bool): Whether to apply anti-aliasing when downsampling. - """ - - if (scale < 1) and antialiasing: - # Use a modified kernel (larger kernel width) to simultaneously - # interpolate and antialias - kernel_width = kernel_width / scale - - # Output-space coordinates - x = torch.linspace(1, out_length, out_length) - - # Input-space coordinates. Calculate the inverse mapping such that 0.5 - # in output space maps to 0.5 in input space, and 0.5 + scale in output - # space maps to 1.5 in input space. - u = x / scale + 0.5 * (1 - 1 / scale) - - # What is the left-most pixel that can be involved in the computation? - left = torch.floor(u - kernel_width / 2) - - # What is the maximum number of pixels that can be involved in the - # computation? Note: it's OK to use an extra pixel here; if the - # corresponding weights are all zero, it will be eliminated at the end - # of this function. - p = math.ceil(kernel_width) + 2 - - # The indices of the input pixels involved in computing the k-th output - # pixel are in row k of the indices matrix. - indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand( - out_length, p) - - # The weights used to compute the k-th output pixel are in row k of the - # weights matrix. - distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices - - # apply cubic kernel - if (scale < 1) and antialiasing: - weights = scale * cubic(distance_to_center * scale) - else: - weights = cubic(distance_to_center) - - # Normalize the weights matrix so that each row sums to 1. - weights_sum = torch.sum(weights, 1).view(out_length, 1) - weights = weights / weights_sum.expand(out_length, p) - - # If a column in weights is all zero, get rid of it. only consider the - # first and last column. - weights_zero_tmp = torch.sum((weights == 0), 0) - if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): - indices = indices.narrow(1, 1, p - 2) - weights = weights.narrow(1, 1, p - 2) - if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): - indices = indices.narrow(1, 0, p - 2) - weights = weights.narrow(1, 0, p - 2) - weights = weights.contiguous() - indices = indices.contiguous() - sym_len_s = -indices.min() + 1 - sym_len_e = indices.max() - in_length - indices = indices + sym_len_s - 1 - return weights, indices, int(sym_len_s), int(sym_len_e) - - -@torch.no_grad() -def imresize(img, scale, antialiasing=True): - """imresize function same as MATLAB. - - It now only supports bicubic. - The same scale applies for both height and width. - - Args: - img (Tensor | Numpy array): - Tensor: Input image with shape (c, h, w), [0, 1] range. - Numpy: Input image with shape (h, w, c), [0, 1] range. - scale (float): Scale factor. The same scale applies for both height - and width. - antialisaing (bool): Whether to apply anti-aliasing when downsampling. - Default: True. - - Returns: - Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round. - """ - squeeze_flag = False - if type(img).__module__ == np.__name__: # numpy type - numpy_type = True - if img.ndim == 2: - img = img[:, :, None] - squeeze_flag = True - img = torch.from_numpy(img.transpose(2, 0, 1)).float() - else: - numpy_type = False - if img.ndim == 2: - img = img.unsqueeze(0) - squeeze_flag = True - - in_c, in_h, in_w = img.size() - out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale) - kernel_width = 4 - kernel = 'cubic' - - # get weights and indices - weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(in_h, out_h, scale, kernel, kernel_width, - antialiasing) - weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(in_w, out_w, scale, kernel, kernel_width, - antialiasing) - # process H dimension - # symmetric copying - img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w) - img_aug.narrow(1, sym_len_hs, in_h).copy_(img) - - sym_patch = img[:, :sym_len_hs, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv) - - sym_patch = img[:, -sym_len_he:, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv) - - out_1 = torch.FloatTensor(in_c, out_h, in_w) - kernel_width = weights_h.size(1) - for i in range(out_h): - idx = int(indices_h[i][0]) - for j in range(in_c): - out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i]) - - # process W dimension - # symmetric copying - out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we) - out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1) - - sym_patch = out_1[:, :, :sym_len_ws] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv) - - sym_patch = out_1[:, :, -sym_len_we:] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv) - - out_2 = torch.FloatTensor(in_c, out_h, out_w) - kernel_width = weights_w.size(1) - for i in range(out_w): - idx = int(indices_w[i][0]) - for j in range(in_c): - out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i]) - - if squeeze_flag: - out_2 = out_2.squeeze(0) - if numpy_type: - out_2 = out_2.numpy() - if not squeeze_flag: - out_2 = out_2.transpose(1, 2, 0) - - return out_2 diff --git a/basicsr/utils/misc.py b/basicsr/utils/misc.py deleted file mode 100644 index a43f878f1d7b61ece665c75a736f3859849b5b42..0000000000000000000000000000000000000000 --- a/basicsr/utils/misc.py +++ /dev/null @@ -1,141 +0,0 @@ -import numpy as np -import os -import random -import time -import torch -from os import path as osp - -from .dist_util import master_only - - -def set_random_seed(seed): - """Set random seeds.""" - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - - -def get_time_str(): - return time.strftime('%Y%m%d_%H%M%S', time.localtime()) - - -def mkdir_and_rename(path): - """mkdirs. If path exists, rename it with timestamp and create a new one. - - Args: - path (str): Folder path. - """ - if osp.exists(path): - new_name = path + '_archived_' + get_time_str() - print(f'Path already exists. Rename it to {new_name}', flush=True) - os.rename(path, new_name) - os.makedirs(path, exist_ok=True) - - -@master_only -def make_exp_dirs(opt): - """Make dirs for experiments.""" - path_opt = opt['path'].copy() - if opt['is_train']: - mkdir_and_rename(path_opt.pop('experiments_root')) - else: - mkdir_and_rename(path_opt.pop('results_root')) - for key, path in path_opt.items(): - if ('strict_load' in key) or ('pretrain_network' in key) or ('resume' in key) or ('param_key' in key): - continue - else: - os.makedirs(path, exist_ok=True) - - -def scandir(dir_path, suffix=None, recursive=False, full_path=False): - """Scan a directory to find the interested files. - - Args: - dir_path (str): Path of the directory. - suffix (str | tuple(str), optional): File suffix that we are - interested in. Default: None. - recursive (bool, optional): If set to True, recursively scan the - directory. Default: False. - full_path (bool, optional): If set to True, include the dir_path. - Default: False. - - Returns: - A generator for all the interested files with relative paths. - """ - - if (suffix is not None) and not isinstance(suffix, (str, tuple)): - raise TypeError('"suffix" must be a string or tuple of strings') - - root = dir_path - - def _scandir(dir_path, suffix, recursive): - for entry in os.scandir(dir_path): - if not entry.name.startswith('.') and entry.is_file(): - if full_path: - return_path = entry.path - else: - return_path = osp.relpath(entry.path, root) - - if suffix is None: - yield return_path - elif return_path.endswith(suffix): - yield return_path - else: - if recursive: - yield from _scandir(entry.path, suffix=suffix, recursive=recursive) - else: - continue - - return _scandir(dir_path, suffix=suffix, recursive=recursive) - - -def check_resume(opt, resume_iter): - """Check resume states and pretrain_network paths. - - Args: - opt (dict): Options. - resume_iter (int): Resume iteration. - """ - if opt['path']['resume_state']: - # get all the networks - networks = [key for key in opt.keys() if key.startswith('network_')] - flag_pretrain = False - for network in networks: - if opt['path'].get(f'pretrain_{network}') is not None: - flag_pretrain = True - if flag_pretrain: - print('pretrain_network path will be ignored during resuming.') - # set pretrained model paths - for network in networks: - name = f'pretrain_{network}' - basename = network.replace('network_', '') - if opt['path'].get('ignore_resume_networks') is None or (network - not in opt['path']['ignore_resume_networks']): - opt['path'][name] = osp.join(opt['path']['models'], f'net_{basename}_{resume_iter}.pth') - print(f"Set {name} to {opt['path'][name]}") - - # change param_key to params in resume - param_keys = [key for key in opt['path'].keys() if key.startswith('param_key')] - for param_key in param_keys: - if opt['path'][param_key] == 'params_ema': - opt['path'][param_key] = 'params' - print(f'Set {param_key} to params') - - -def sizeof_fmt(size, suffix='B'): - """Get human readable file size. - - Args: - size (int): File size. - suffix (str): Suffix. Default: 'B'. - - Return: - str: Formatted file size. - """ - for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']: - if abs(size) < 1024.0: - return f'{size:3.1f} {unit}{suffix}' - size /= 1024.0 - return f'{size:3.1f} Y{suffix}' diff --git a/basicsr/utils/options.py b/basicsr/utils/options.py deleted file mode 100644 index bb151181a403993ede5c45810442c3bdbe6be898..0000000000000000000000000000000000000000 --- a/basicsr/utils/options.py +++ /dev/null @@ -1,218 +0,0 @@ -import argparse -import os -import random -import torch -import yaml -from collections import OrderedDict -from os import path as osp - -from basicsr.utils import set_random_seed -from basicsr.utils.dist_util import get_dist_info, init_dist, master_only - - -def ordered_yaml(): - """Support OrderedDict for yaml. - - Returns: - tuple: yaml Loader and Dumper. - """ - try: - from yaml import CDumper as Dumper - from yaml import CLoader as Loader - except ImportError: - from yaml import Dumper, Loader - - _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG - - def dict_representer(dumper, data): - return dumper.represent_dict(data.items()) - - def dict_constructor(loader, node): - return OrderedDict(loader.construct_pairs(node)) - - Dumper.add_representer(OrderedDict, dict_representer) - Loader.add_constructor(_mapping_tag, dict_constructor) - return Loader, Dumper - - -def yaml_load(f): - """Load yaml file or string. - - Args: - f (str): File path or a python string. - - Returns: - dict: Loaded dict. - """ - if os.path.isfile(f): - with open(f, 'r') as f: - return yaml.load(f, Loader=ordered_yaml()[0]) - else: - return yaml.load(f, Loader=ordered_yaml()[0]) - - -def dict2str(opt, indent_level=1): - """dict to string for printing options. - - Args: - opt (dict): Option dict. - indent_level (int): Indent level. Default: 1. - - Return: - (str): Option string for printing. - """ - msg = '\n' - for k, v in opt.items(): - if isinstance(v, dict): - msg += ' ' * (indent_level * 2) + k + ':[' - msg += dict2str(v, indent_level + 1) - msg += ' ' * (indent_level * 2) + ']\n' - else: - msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n' - return msg - - -def _postprocess_yml_value(value): - # None - if value == '~' or value.lower() == 'none': - return None - # bool - if value.lower() == 'true': - return True - elif value.lower() == 'false': - return False - # !!float number - if value.startswith('!!float'): - return float(value.replace('!!float', '')) - # number - if value.isdigit(): - return int(value) - elif value.replace('.', '', 1).isdigit() and value.count('.') < 2: - return float(value) - # list - if value.startswith('['): - return eval(value) - # str - return value - - -def parse_options(root_path, is_train=True): - parser = argparse.ArgumentParser() - parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.') - parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher') - parser.add_argument('--auto_resume', action='store_true') - parser.add_argument('--debug', action='store_true') - parser.add_argument('--local_rank', type=int, default=0) - parser.add_argument( - '--force_yml', nargs='+', default=None, help='Force to update yml files. Examples: train:ema_decay=0.999') - args = parser.parse_args() - - # parse yml to dict - opt = yaml_load(args.opt) - - # distributed settings - if args.launcher == 'none': - opt['dist'] = False - print('Disable distributed.', flush=True) - else: - opt['dist'] = True - if args.launcher == 'slurm' and 'dist_params' in opt: - init_dist(args.launcher, **opt['dist_params']) - else: - init_dist(args.launcher) - opt['rank'], opt['world_size'] = get_dist_info() - - # random seed - seed = opt.get('manual_seed') - if seed is None: - seed = random.randint(1, 10000) - opt['manual_seed'] = seed - set_random_seed(seed + opt['rank']) - - # force to update yml options - if args.force_yml is not None: - for entry in args.force_yml: - # now do not support creating new keys - keys, value = entry.split('=') - keys, value = keys.strip(), value.strip() - value = _postprocess_yml_value(value) - eval_str = 'opt' - for key in keys.split(':'): - eval_str += f'["{key}"]' - eval_str += '=value' - # using exec function - exec(eval_str) - - opt['auto_resume'] = args.auto_resume - opt['is_train'] = is_train - - # debug setting - if args.debug and not opt['name'].startswith('debug'): - opt['name'] = 'debug_' + opt['name'] - - if opt['num_gpu'] == 'auto': - opt['num_gpu'] = torch.cuda.device_count() - - # datasets - for phase, dataset in opt['datasets'].items(): - # for multiple datasets, e.g., val_1, val_2; test_1, test_2 - phase = phase.split('_')[0] - dataset['phase'] = phase - if 'scale' in opt: - dataset['scale'] = opt['scale'] - if dataset.get('dataroot_gt') is not None: - dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt']) - if dataset.get('dataroot_lq') is not None: - dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq']) - - # paths - for key, val in opt['path'].items(): - if (val is not None) and ('resume_state' in key or 'pretrain_network' in key): - opt['path'][key] = osp.expanduser(val) - - if is_train: - experiments_root = opt['path'].get('experiments_root') - if experiments_root is None: - experiments_root = osp.join(root_path, 'experiments') - experiments_root = osp.join(experiments_root, opt['name']) - - opt['path']['experiments_root'] = experiments_root - opt['path']['models'] = osp.join(experiments_root, 'models') - opt['path']['training_states'] = osp.join(experiments_root, 'training_states') - opt['path']['log'] = experiments_root - opt['path']['visualization'] = osp.join(experiments_root, 'visualization') - - # change some options for debug mode - if 'debug' in opt['name']: - if 'val' in opt: - opt['val']['val_freq'] = 8 - opt['logger']['print_freq'] = 1 - opt['logger']['save_checkpoint_freq'] = 8 - else: # test - results_root = opt['path'].get('results_root') - if results_root is None: - results_root = osp.join(root_path, 'results') - results_root = osp.join(results_root, opt['name']) - - opt['path']['results_root'] = results_root - opt['path']['log'] = results_root - opt['path']['visualization'] = osp.join(results_root, 'visualization') - - return opt, args - - -@master_only -def copy_opt_file(opt_file, experiments_root): - # copy the yml file to the experiment root - import sys - import time - from shutil import copyfile - cmd = ' '.join(sys.argv) - filename = osp.join(experiments_root, osp.basename(opt_file)) - copyfile(opt_file, filename) - - with open(filename, 'r+') as f: - lines = f.readlines() - lines.insert(0, f'# GENERATE TIME: {time.asctime()}\n# CMD:\n# {cmd}\n\n') - f.seek(0) - f.writelines(lines) diff --git a/basicsr/utils/plot_util.py b/basicsr/utils/plot_util.py deleted file mode 100644 index 7094a7f44780e3accbbe985228a0f6f5e0c6b454..0000000000000000000000000000000000000000 --- a/basicsr/utils/plot_util.py +++ /dev/null @@ -1,83 +0,0 @@ -import re - - -def read_data_from_tensorboard(log_path, tag): - """Get raw data (steps and values) from tensorboard events. - - Args: - log_path (str): Path to the tensorboard log. - tag (str): tag to be read. - """ - from tensorboard.backend.event_processing.event_accumulator import EventAccumulator - - # tensorboard event - event_acc = EventAccumulator(log_path) - event_acc.Reload() - scalar_list = event_acc.Tags()['scalars'] - print('tag list: ', scalar_list) - steps = [int(s.step) for s in event_acc.Scalars(tag)] - values = [s.value for s in event_acc.Scalars(tag)] - return steps, values - - -def read_data_from_txt_2v(path, pattern, step_one=False): - """Read data from txt with 2 returned values (usually [step, value]). - - Args: - path (str): path to the txt file. - pattern (str): re (regular expression) pattern. - step_one (bool): add 1 to steps. Default: False. - """ - with open(path) as f: - lines = f.readlines() - lines = [line.strip() for line in lines] - steps = [] - values = [] - - pattern = re.compile(pattern) - for line in lines: - match = pattern.match(line) - if match: - steps.append(int(match.group(1))) - values.append(float(match.group(2))) - if step_one: - steps = [v + 1 for v in steps] - return steps, values - - -def read_data_from_txt_1v(path, pattern): - """Read data from txt with 1 returned values. - - Args: - path (str): path to the txt file. - pattern (str): re (regular expression) pattern. - """ - with open(path) as f: - lines = f.readlines() - lines = [line.strip() for line in lines] - data = [] - - pattern = re.compile(pattern) - for line in lines: - match = pattern.match(line) - if match: - data.append(float(match.group(1))) - return data - - -def smooth_data(values, smooth_weight): - """ Smooth data using 1st-order IIR low-pass filter (what tensorflow does). - - Reference: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 # noqa: E501 - - Args: - values (list): A list of values to be smoothed. - smooth_weight (float): Smooth weight. - """ - values_sm = [] - last_sm_value = values[0] - for value in values: - value_sm = last_sm_value * smooth_weight + (1 - smooth_weight) * value - values_sm.append(value_sm) - last_sm_value = value_sm - return values_sm diff --git a/basicsr/utils/registry.py b/basicsr/utils/registry.py deleted file mode 100644 index 1745e94f2865d8d6cc2a7b6dcd1fdf359232427a..0000000000000000000000000000000000000000 --- a/basicsr/utils/registry.py +++ /dev/null @@ -1,88 +0,0 @@ -# Modified from: https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/registry.py # noqa: E501 - - -class Registry(): - """ - The registry that provides name -> object mapping, to support third-party - users' custom modules. - - To create a registry (e.g. a backbone registry): - - .. code-block:: python - - BACKBONE_REGISTRY = Registry('BACKBONE') - - To register an object: - - .. code-block:: python - - @BACKBONE_REGISTRY.register() - class MyBackbone(): - ... - - Or: - - .. code-block:: python - - BACKBONE_REGISTRY.register(MyBackbone) - """ - - def __init__(self, name): - """ - Args: - name (str): the name of this registry - """ - self._name = name - self._obj_map = {} - - def _do_register(self, name, obj, suffix=None): - if isinstance(suffix, str): - name = name + '_' + suffix - - assert (name not in self._obj_map), (f"An object named '{name}' was already registered " - f"in '{self._name}' registry!") - self._obj_map[name] = obj - - def register(self, obj=None, suffix=None): - """ - Register the given object under the the name `obj.__name__`. - Can be used as either a decorator or not. - See docstring of this class for usage. - """ - if obj is None: - # used as a decorator - def deco(func_or_class): - name = func_or_class.__name__ - self._do_register(name, func_or_class, suffix) - return func_or_class - - return deco - - # used as a function call - name = obj.__name__ - self._do_register(name, obj, suffix) - - def get(self, name, suffix='basicsr'): - ret = self._obj_map.get(name) - if ret is None: - ret = self._obj_map.get(name + '_' + suffix) - print(f'Name {name} is not found, use name: {name}_{suffix}!') - if ret is None: - raise KeyError(f"No object named '{name}' found in '{self._name}' registry!") - return ret - - def __contains__(self, name): - return name in self._obj_map - - def __iter__(self): - return iter(self._obj_map.items()) - - def keys(self): - return self._obj_map.keys() - - -DATASET_REGISTRY = Registry('dataset') -ARCH_REGISTRY = Registry('arch') -MODEL_REGISTRY = Registry('model') -LOSS_REGISTRY = Registry('loss') -METRIC_REGISTRY = Registry('metric') diff --git a/basicsr/utils/val_degradation_pipeline.py b/basicsr/utils/val_degradation_pipeline.py deleted file mode 100644 index f40ca4ce69d1a7048a2d5cdf01e106184841827c..0000000000000000000000000000000000000000 --- a/basicsr/utils/val_degradation_pipeline.py +++ /dev/null @@ -1,367 +0,0 @@ -import cv2 -import math -import numpy as np -import random -import torch -from torch.utils import data as data - -from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels -from basicsr.data.transforms import augment -from basicsr.utils import img2tensor, DiffJPEG, USMSharp -from basicsr.utils.img_process_util import filter2D -from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt -from basicsr.data.transforms import paired_random_crop - -AUGMENT_OPT = { - 'use_hflip': False, - 'use_rot': False -} - -KERNEL_OPT = { - 'blur_kernel_size': 21, - 'kernel_list': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'], - 'kernel_prob': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03], - 'sinc_prob': 0.1, - 'blur_sigma': [0.2, 3], - 'betag_range': [0.5, 4], - 'betap_range': [1, 2], - - 'blur_kernel_size2': 21, - 'kernel_list2': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'], - 'kernel_prob2': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03], - 'sinc_prob2': 0.1, - 'blur_sigma2': [0.2, 1.5], - 'betag_range2': [0.5, 4], - 'betap_range2': [1, 2], - 'final_sinc_prob': 0.8, -} - -DEGRADE_OPT = { - 'resize_prob': [0.2, 0.7, 0.1], # up, down, keep - 'resize_range': [0.15, 1.5], - 'gaussian_noise_prob': 0.5, - 'noise_range': [1, 30], - 'poisson_scale_range': [0.05, 3], - 'gray_noise_prob': 0.4, - 'jpeg_range': [30, 95], - - # the second degradation process - 'second_blur_prob': 0.8, - 'resize_prob2': [0.3, 0.4, 0.3], # up, down, keep - 'resize_range2': [0.3, 1.2], - 'gaussian_noise_prob2': 0.5, - 'noise_range2': [1, 25], - 'poisson_scale_range2': [0.05, 2.5], - 'gray_noise_prob2': 0.4, - 'jpeg_range2': [30, 95], - - 'gt_size': 512, - 'no_degradation_prob': 0.01, - 'use_usm': True, - 'sf': 8, - 'random_size': False, - 'resize_lq': True -} - -class RealESRGANDegradation: - - def __init__(self, augment_opt=None, kernel_opt=None, degrade_opt=None, device='cuda', resolution=None): - if augment_opt is None: - augment_opt = AUGMENT_OPT - self.augment_opt = augment_opt - if kernel_opt is None: - kernel_opt = KERNEL_OPT - self.kernel_opt = kernel_opt - if degrade_opt is None: - degrade_opt = DEGRADE_OPT - self.degrade_opt = degrade_opt - if resolution is not None: - self.degrade_opt['gt_size'] = resolution - self.device = device - - self.jpeger = DiffJPEG(differentiable=False).to(self.device) - self.usm_sharpener = USMSharp().to(self.device) - - # blur settings for the first degradation - self.blur_kernel_size = kernel_opt['blur_kernel_size'] - self.kernel_list = kernel_opt['kernel_list'] - self.kernel_prob = kernel_opt['kernel_prob'] # a list for each kernel probability - self.blur_sigma = kernel_opt['blur_sigma'] - self.betag_range = kernel_opt['betag_range'] # betag used in generalized Gaussian blur kernels - self.betap_range = kernel_opt['betap_range'] # betap used in plateau blur kernels - self.sinc_prob = kernel_opt['sinc_prob'] # the probability for sinc filters - - # blur settings for the second degradation - self.blur_kernel_size2 = kernel_opt['blur_kernel_size2'] - self.kernel_list2 = kernel_opt['kernel_list2'] - self.kernel_prob2 = kernel_opt['kernel_prob2'] - self.blur_sigma2 = kernel_opt['blur_sigma2'] - self.betag_range2 = kernel_opt['betag_range2'] - self.betap_range2 = kernel_opt['betap_range2'] - self.sinc_prob2 = kernel_opt['sinc_prob2'] - - # a final sinc filter - self.final_sinc_prob = kernel_opt['final_sinc_prob'] - - self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21 - # TODO: kernel range is now hard-coded, should be in the configure file - self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect - self.pulse_tensor[10, 10] = 1 - - def get_kernel(self): - - # ------------------------ Generate kernels (used in the first degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.kernel_opt['sinc_prob']: - # this sinc filter setting is for kernels ranging from [7, 21] - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel = random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - kernel_size, - self.blur_sigma, - self.blur_sigma, [-math.pi, math.pi], - self.betag_range, - self.betap_range, - noise_range=None) - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------ Generate kernels (used in the second degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.kernel_opt['sinc_prob2']: - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel2 = random_mixed_kernels( - self.kernel_list2, - self.kernel_prob2, - kernel_size, - self.blur_sigma2, - self.blur_sigma2, [-math.pi, math.pi], - self.betag_range2, - self.betap_range2, - noise_range=None) - - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------------------- the final sinc kernel ------------------------------------- # - if np.random.uniform() < self.kernel_opt['final_sinc_prob']: - kernel_size = random.choice(self.kernel_range) - omega_c = np.random.uniform(np.pi / 3, np.pi) - sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21) - sinc_kernel = torch.FloatTensor(sinc_kernel) - else: - sinc_kernel = self.pulse_tensor - - # BGR to RGB, HWC to CHW, numpy to tensor - kernel = torch.FloatTensor(kernel) - kernel2 = torch.FloatTensor(kernel2) - - return (kernel, kernel2, sinc_kernel) - - @torch.no_grad() - def __call__(self, img_gt, kernels=None): - ''' - :param: img_gt: BCHW, RGB, [0, 1] float32 tensor - ''' - if kernels is None: - kernel = [] - kernel2 = [] - sinc_kernel = [] - for _ in range(img_gt.shape[0]): - k, k2, sk = self.get_kernel() - kernel.append(k) - kernel2.append(k2) - sinc_kernel.append(sk) - kernel = torch.stack(kernel) - kernel2 = torch.stack(kernel2) - sinc_kernel = torch.stack(sinc_kernel) - else: - # kernels created in dataset. - kernel, kernel2, sinc_kernel = kernels - - # ----------------------- Pre-process ----------------------- # - im_gt = img_gt.to(self.device) - if self.degrade_opt['sf'] == 8: - resized_gt = torch.nn.functional.interpolate(im_gt, scale_factor=0.5, mode='area') - else: - resized_gt = im_gt - if self.degrade_opt['use_usm']: - resized_gt = self.usm_sharpener(resized_gt) - resized_gt = resized_gt.to(memory_format=torch.contiguous_format).float() - kernel = kernel.to(self.device) - kernel2 = kernel2.to(self.device) - sinc_kernel = sinc_kernel.to(self.device) - ori_h, ori_w = im_gt.size()[2:4] - - # ----------------------- The first degradation process ----------------------- # - # blur - out = filter2D(resized_gt, kernel) - # random resize - updown_type = random.choices( - ['up', 'down', 'keep'], - self.degrade_opt['resize_prob'], - )[0] - if updown_type == 'up': - scale = random.uniform(1, self.degrade_opt['resize_range'][1]) - elif updown_type == 'down': - scale = random.uniform(self.degrade_opt['resize_range'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate(out, scale_factor=scale, mode=mode) - # add noise - gray_noise_prob = self.degrade_opt['gray_noise_prob'] - if random.random() < self.degrade_opt['gaussian_noise_prob']: - out = random_add_gaussian_noise_pt( - out, - sigma_range=self.degrade_opt['noise_range'], - clip=True, - rounds=False, - gray_prob=gray_noise_prob, - ) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.degrade_opt['poisson_scale_range'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range']) - out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts - out = self.jpeger(out, quality=jpeg_p) - - # ----------------------- The second degradation process ----------------------- # - # blur - if random.random() < self.degrade_opt['second_blur_prob']: - out = out.contiguous() - out = filter2D(out, kernel2) - # random resize - updown_type = random.choices( - ['up', 'down', 'keep'], - self.degrade_opt['resize_prob2'], - )[0] - if updown_type == 'up': - scale = random.uniform(1, self.degrade_opt['resize_range2'][1]) - elif updown_type == 'down': - scale = random.uniform(self.degrade_opt['resize_range2'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate( - out, - size=(int(ori_h / self.degrade_opt['sf'] * scale), - int(ori_w / self.degrade_opt['sf'] * scale)), - mode=mode, - ) - # add noise - gray_noise_prob = self.degrade_opt['gray_noise_prob2'] - if random.random() < self.degrade_opt['gaussian_noise_prob2']: - out = random_add_gaussian_noise_pt( - out, - sigma_range=self.degrade_opt['noise_range2'], - clip=True, - rounds=False, - gray_prob=gray_noise_prob, - ) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.degrade_opt['poisson_scale_range2'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False, - ) - - # JPEG compression + the final sinc filter - # We also need to resize images to desired sizes. We group [resize back + sinc filter] together - # as one operation. - # We consider two orders: - # 1. [resize back + sinc filter] + JPEG compression - # 2. JPEG compression + [resize back + sinc filter] - # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. - if random.random() < 0.5: - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate( - out, - size=(ori_h // self.degrade_opt['sf'], - ori_w // self.degrade_opt['sf']), - mode=mode, - ) - out = out.contiguous() - out = filter2D(out, sinc_kernel) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - else: - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = torch.nn.functional.interpolate( - out, - size=(ori_h // self.degrade_opt['sf'], - ori_w // self.degrade_opt['sf']), - mode=mode, - ) - out = out.contiguous() - out = filter2D(out, sinc_kernel) - - # clamp and round - im_lq = torch.clamp(out, 0, 1.0) - - # random crop - gt_size = self.degrade_opt['gt_size'] - patch_gt, patch_lq, gt_crop_param = paired_random_crop(im_gt, im_lq, gt_size, self.degrade_opt['sf']) - - if self.degrade_opt['resize_lq']: - im_lq = torch.nn.functional.interpolate( - im_lq, - size=(im_gt.size(-2), - im_gt.size(-1)), - mode='bicubic', - ) - patch_lq = torch.nn.functional.interpolate( - patch_lq, - size=(patch_gt.size(-2), - patch_gt.size(-1)), - mode='bicubic', - ) - - # if random.random() < self.degrade_opt['no_degradation_prob'] or torch.isnan(im_lq).any(): - # im_lq = im_gt - - # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue - im_lq = im_lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract - im_lq = im_lq*2 - 1.0 - im_gt = im_gt*2 - 1.0 - patch_lq = patch_lq*2 - 1.0 - patch_gt = patch_gt*2 - 1.0 - - if self.degrade_opt['random_size']: - raise NotImplementedError - im_lq, im_gt = self.randn_cropinput(im_lq, im_gt) - - im_lq = torch.clamp(im_lq, -1.0, 1.0) - im_gt = torch.clamp(im_gt, -1.0, 1.0) - patch_lq = torch.clamp(patch_lq, -1.0, 1.0) - patch_gt = torch.clamp(patch_gt, -1.0, 1.0) - - return (im_lq, im_gt, patch_lq, patch_gt, gt_crop_param) diff --git a/config_files/IR_dataset.yaml b/config_files/IR_dataset.yaml deleted file mode 100644 index aaaeeda8bac6db105aa79e13d137f4a6cf4405e1..0000000000000000000000000000000000000000 --- a/config_files/IR_dataset.yaml +++ /dev/null @@ -1,9 +0,0 @@ -datasets: - - dataset_folder: 'ffhq' - dataset_weight: 0.1 - - dataset_folder: 'DIV2K' - dataset_weight: 0.3 - - dataset_folder: 'LSDIR' - dataset_weight: 0.3 - - dataset_folder: 'Flickr2K' - dataset_weight: 0.1 diff --git a/config_files/losses.yaml b/config_files/losses.yaml deleted file mode 100644 index c051cf58efe5f44cabd86a8489ba541522983570..0000000000000000000000000000000000000000 --- a/config_files/losses.yaml +++ /dev/null @@ -1,19 +0,0 @@ -diffusion_losses: -- name: L2Loss - weight: 1 -lcm_losses: -- name: HuberLoss - weight: 1 -# - name: DINOLoss -# weight: 1e-3 -# - name: L2Loss -# weight: 5e-2 -# - name: LPIPSLoss -# weight: 1e-3 -# - name: DreamSIMLoss -# weight: 1e-3 -# - name: IDLoss -# weight: 1e-3 -# visualize_every_k: 50 -# init_params: -# pretrained_arcface_path: /home/dcor/orlichter/consistency_encoder_private/pretrained_models/model_ir_se50.pth \ No newline at end of file diff --git a/config_files/val_dataset.yaml b/config_files/val_dataset.yaml deleted file mode 100644 index 9e1978a05cd49bb2d962252e2dc2b882e090c67d..0000000000000000000000000000000000000000 --- a/config_files/val_dataset.yaml +++ /dev/null @@ -1,7 +0,0 @@ -datasets: - - dataset_folder: 'ffhq' - dataset_weight: 0.1 - - dataset_folder: 'DIV2K' - dataset_weight: 0.45 - - dataset_folder: 'LSDIR' - dataset_weight: 0.45 diff --git a/data/data_config.py b/data/data_config.py deleted file mode 100644 index 6ed6194e5512b32c647e3aa14473102c9cfc1b08..0000000000000000000000000000000000000000 --- a/data/data_config.py +++ /dev/null @@ -1,14 +0,0 @@ -from dataclasses import dataclass, field -from typing import Optional, List - - -@dataclass -class SingleDataConfig: - dataset_folder: str - imagefolder: bool = True - dataset_weight: float = 1.0 # Not used yet - -@dataclass -class DataConfig: - datasets: List[SingleDataConfig] - val_dataset: Optional[SingleDataConfig] = None diff --git a/data/dataset.py b/data/dataset.py deleted file mode 100644 index cdab151b099522e21ca3c172848cc47c74c1d484..0000000000000000000000000000000000000000 --- a/data/dataset.py +++ /dev/null @@ -1,202 +0,0 @@ -from pathlib import Path -from typing import Optional - -from PIL import Image -from PIL.ImageOps import exif_transpose -from torch.utils.data import Dataset -from torchvision import transforms -import json -import random -from facenet_pytorch import MTCNN -import torch - -from utils.utils import extract_faces_and_landmarks, REFERNCE_FACIAL_POINTS_RELATIVE - -def load_image(image_path: str) -> Image: - image = Image.open(image_path) - image = exif_transpose(image) - if not image.mode == "RGB": - image = image.convert("RGB") - return image - - -class ImageDataset(Dataset): - """ - A dataset to prepare the instance and class images with the prompts for fine-tuning the model. - It pre-processes the images. - """ - - def __init__( - self, - instance_data_root, - instance_prompt, - metadata_path: Optional[str] = None, - prompt_in_filename=False, - use_only_vanilla_for_encoder=False, - concept_placeholder='a face', - size=1024, - center_crop=False, - aug_images=False, - use_only_decoder_prompts=False, - crop_head_for_encoder_image=False, - random_target_prob=0.0, - ): - self.mtcnn = MTCNN(device='cuda:0') - self.mtcnn.forward = self.mtcnn.detect - resize_factor = 1.3 - self.resized_reference_points = REFERNCE_FACIAL_POINTS_RELATIVE / resize_factor + (resize_factor - 1) / (2 * resize_factor) - self.size = size - self.center_crop = center_crop - self.concept_placeholder = concept_placeholder - self.prompt_in_filename = prompt_in_filename - self.aug_images = aug_images - - self.instance_prompt = instance_prompt - self.custom_instance_prompts = None - self.name_to_label = None - self.crop_head_for_encoder_image = crop_head_for_encoder_image - self.random_target_prob = random_target_prob - - self.use_only_decoder_prompts = use_only_decoder_prompts - - self.instance_data_root = Path(instance_data_root) - - if not self.instance_data_root.exists(): - raise ValueError(f"Instance images root {self.instance_data_root} doesn't exist.") - - if metadata_path is not None: - with open(metadata_path, 'r') as f: - self.name_to_label = json.load(f) # dict of filename: label - # Create a reversed mapping - self.label_to_names = {} - for name, label in self.name_to_label.items(): - if use_only_vanilla_for_encoder and 'vanilla' not in name: - continue - if label not in self.label_to_names: - self.label_to_names[label] = [] - self.label_to_names[label].append(name) - self.all_paths = [self.instance_data_root / filename for filename in self.name_to_label.keys()] - - # Verify all paths exist - n_all_paths = len(self.all_paths) - self.all_paths = [path for path in self.all_paths if path.exists()] - print(f'Found {len(self.all_paths)} out of {n_all_paths} paths.') - else: - self.all_paths = [path for path in list(Path(instance_data_root).glob('**/*')) if - path.suffix.lower() in [".png", ".jpg", ".jpeg"]] - # Sort by name so that order for validation remains the same across runs - self.all_paths = sorted(self.all_paths, key=lambda x: x.stem) - - self.custom_instance_prompts = None - - self._length = len(self.all_paths) - - self.class_data_root = None - - self.image_transforms = transforms.Compose( - [ - transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), - transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ] - ) - - if self.prompt_in_filename: - self.prompts_set = set([self._path_to_prompt(path) for path in self.all_paths]) - else: - self.prompts_set = set([self.instance_prompt]) - - if self.aug_images: - self.aug_transforms = transforms.Compose( - [ - transforms.RandomResizedCrop(size, scale=(0.8, 1.0), ratio=(1.0, 1.0)), - transforms.RandomHorizontalFlip(p=0.5) - ] - ) - - def __len__(self): - return self._length - - def _path_to_prompt(self, path): - # Remove the extension and seed - split_path = path.stem.split('_') - while split_path[-1].isnumeric(): - split_path = split_path[:-1] - - prompt = ' '.join(split_path) - # Replace placeholder in prompt with training placeholder - prompt = prompt.replace('conceptname', self.concept_placeholder) - return prompt - - def __getitem__(self, index): - example = {} - instance_path = self.all_paths[index] - instance_image = load_image(instance_path) - example["instance_images"] = self.image_transforms(instance_image) - if self.prompt_in_filename: - example["instance_prompt"] = self._path_to_prompt(instance_path) - else: - example["instance_prompt"] = self.instance_prompt - - if self.name_to_label is None: - # If no labels, simply take the same image but with different augmentation - example["encoder_images"] = self.aug_transforms(example["instance_images"]) if self.aug_images else example["instance_images"] - example["encoder_prompt"] = example["instance_prompt"] - else: - # Randomly select another image with the same label - instance_name = str(instance_path.relative_to(self.instance_data_root)) - instance_label = self.name_to_label[instance_name] - label_set = set(self.label_to_names[instance_label]) - if len(label_set) == 1: - # We are not supposed to have only one image per label, but just in case - encoder_image_name = instance_name - print(f'WARNING: Only one image for label {instance_label}.') - else: - encoder_image_name = random.choice(list(label_set - {instance_name})) - encoder_image = load_image(self.instance_data_root / encoder_image_name) - example["encoder_images"] = self.image_transforms(encoder_image) - - if self.prompt_in_filename: - example["encoder_prompt"] = self._path_to_prompt(self.instance_data_root / encoder_image_name) - else: - example["encoder_prompt"] = self.instance_prompt - - if self.crop_head_for_encoder_image: - example["encoder_images"] = extract_faces_and_landmarks(example["encoder_images"][None], self.size, self.mtcnn, self.resized_reference_points)[0][0] - example["encoder_prompt"] = example["encoder_prompt"].format(placeholder="") - example["instance_prompt"] = example["instance_prompt"].format(placeholder="") - - if random.random() < self.random_target_prob: - random_path = random.choice(self.all_paths) - - random_image = load_image(random_path) - example["instance_images"] = self.image_transforms(random_image) - if self.prompt_in_filename: - example["instance_prompt"] = self._path_to_prompt(random_path) - - - if self.use_only_decoder_prompts: - example["encoder_prompt"] = example["instance_prompt"] - - return example - - -def collate_fn(examples, with_prior_preservation=False): - pixel_values = [example["instance_images"] for example in examples] - encoder_pixel_values = [example["encoder_images"] for example in examples] - prompts = [example["instance_prompt"] for example in examples] - encoder_prompts = [example["encoder_prompt"] for example in examples] - - if with_prior_preservation: - raise NotImplementedError("Prior preservation not implemented.") - - pixel_values = torch.stack(pixel_values) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - - encoder_pixel_values = torch.stack(encoder_pixel_values) - encoder_pixel_values = encoder_pixel_values.to(memory_format=torch.contiguous_format).float() - - batch = {"pixel_values": pixel_values, "encoder_pixel_values": encoder_pixel_values, - "prompts": prompts, "encoder_prompts": encoder_prompts} - return batch diff --git a/docs/.DS_Store b/docs/.DS_Store deleted file mode 100644 index ecb218a51788f964c73e12aac69933b3b8193ec9..0000000000000000000000000000000000000000 Binary files a/docs/.DS_Store and /dev/null differ diff --git a/docs/static/.DS_Store b/docs/static/.DS_Store deleted file mode 100644 index f99ae43bb14d60ad97b7e197cf9798c9be86ac69..0000000000000000000000000000000000000000 Binary files a/docs/static/.DS_Store and /dev/null differ diff --git a/environment.yaml b/environment.yaml deleted file mode 100644 index c649b2b003940c0eaae52f2386c6422e4ba52fb6..0000000000000000000000000000000000000000 --- a/environment.yaml +++ /dev/null @@ -1,37 +0,0 @@ -name: instantir -channels: - - pytorch - - nvidia - - conda-forge - - defaults -dependencies: - - numpy - - pandas - - pillow - - pip - - python=3.9.15 - - pytorch=2.2.2 - - pytorch-lightning=1.6.5 - - pytorch-cuda=12.1 - - setuptools - - torchaudio=2.2.2 - - torchmetrics - - torchvision=0.17.2 - - tqdm - - pip: - - accelerate==0.25.0 - - diffusers==0.24.0 - - einops - - open-clip-torch - - opencv-python==4.8.1.78 - - tokenizers - - transformers==4.36.2 - - kornia - - facenet_pytorch - - lpips - - dreamsim - - pyrallis - - wandb - - insightface - - onnxruntime==1.17.0 - - -e git+https://github.com/openai/CLIP.git@main#egg=clip \ No newline at end of file diff --git a/infer.py b/infer.py deleted file mode 100644 index 87547ff397d4a5495186e283ba097d3f10e9dda0..0000000000000000000000000000000000000000 --- a/infer.py +++ /dev/null @@ -1,387 +0,0 @@ -import os -import argparse -import numpy as np -import torch - -from PIL import Image -from schedulers.lcm_single_step_scheduler import LCMSingleStepScheduler - -from diffusers import ( - DDPMScheduler, - StableDiffusionXLPipeline -) - -from transformers import ( - CLIPImageProcessor, CLIPVisionModelWithProjection, - AutoImageProcessor, AutoModel -) - -from module.ip_adapter.utils import init_adapter_in_unet -from module.ip_adapter.resampler import Resampler -from pipelines.sdxl_instantir import InstantIRPipeline, PREVIEWER_LORA_MODULES, LCM_LORA_MODULES - - -def name_unet_submodules(unet): - def recursive_find_module(name, module, end=False): - if end: - for sub_name, sub_module in module.named_children(): - sub_module.full_name = f"{name}.{sub_name}" - return - if not "up_blocks" in name and not "down_blocks" in name and not "mid_block" in name: return - elif "resnets" in name: return - for sub_name, sub_module in module.named_children(): - end = True if sub_name == "transformer_blocks" else False - recursive_find_module(f"{name}.{sub_name}", sub_module, end) - - for name, module in unet.named_children(): - recursive_find_module(name, module) - - -def resize_img(input_image, max_side=1280, min_side=1024, size=None, - pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64): - - w, h = input_image.size - if size is not None: - w_resize_new, h_resize_new = size - else: - # ratio = min_side / min(h, w) - # w, h = round(ratio*w), round(ratio*h) - ratio = max_side / max(h, w) - input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode) - w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number - h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number - input_image = input_image.resize([w_resize_new, h_resize_new], mode) - - if pad_to_max_side: - res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255 - offset_x = (max_side - w_resize_new) // 2 - offset_y = (max_side - h_resize_new) // 2 - res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image) - input_image = Image.fromarray(res) - return input_image - - -def tensor_to_pil(images): - """ - Convert image tensor or a batch of image tensors to PIL image(s). - """ - images = images.clamp(0, 1) - images_np = images.detach().cpu().numpy() - if images_np.ndim == 4: - images_np = np.transpose(images_np, (0, 2, 3, 1)) - elif images_np.ndim == 3: - images_np = np.transpose(images_np, (1, 2, 0)) - images_np = images_np[None, ...] - images_np = (images_np * 255).round().astype("uint8") - if images_np.shape[-1] == 1: - # special case for grayscale (single channel) images - pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images_np] - else: - pil_images = [Image.fromarray(image[:, :, :3]) for image in images_np] - - return pil_images - - -def calc_mean_std(feat, eps=1e-5): - """Calculate mean and std for adaptive_instance_normalization. - Args: - feat (Tensor): 4D tensor. - eps (float): A small value added to the variance to avoid - divide-by-zero. Default: 1e-5. - """ - size = feat.size() - assert len(size) == 4, 'The input feature should be 4D tensor.' - b, c = size[:2] - feat_var = feat.view(b, c, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(b, c, 1, 1) - feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -def main(args, device): - - # image encoder and feature extractor. - if args.use_clip_encoder: - image_encoder = CLIPVisionModelWithProjection.from_pretrained( - args.vision_encoder_path, - subfolder="image_encoder", - ) - image_processor = CLIPImageProcessor() - else: - image_encoder = AutoModel.from_pretrained(args.vision_encoder_path) - image_processor = AutoImageProcessor.from_pretrained(args.vision_encoder_path) - image_encoder.to(torch.float16) - - # Base models. - pipe = StableDiffusionXLPipeline.from_pretrained( - args.sdxl_path, - torch_dtype=torch.float16, - revision=args.revision, - variant=args.variant - ) - - # InstantIR pipeline - pipe = InstantIRPipeline( - pipe.vae, pipe.text_encoder, pipe.text_encoder_2, pipe.tokenizer, pipe.tokenizer_2, - pipe.unet, pipe.scheduler, feature_extractor=image_processor, image_encoder=image_encoder, - ).to(device) - unet = pipe.unet - - # Image prompt projector. - print("Loading LQ-Adapter...") - image_proj_model = Resampler( - embedding_dim=image_encoder.config.hidden_size, - output_dim=unet.config.cross_attention_dim, - ) - adapter_path = args.adapter_model_path if args.adapter_model_path is not None else os.path.join(args.instantir_path, 'adapter.pt') - init_adapter_in_unet( - unet, - image_proj_model, - adapter_path, - ) - - # Prepare previewer - previewer_lora_path = args.previewer_lora_path if args.previewer_lora_path is not None else args.instantir_path - if previewer_lora_path is not None: - lora_alpha = pipe.prepare_previewers(previewer_lora_path) - print(f"use lora alpha {lora_alpha}") - unet.to(device, dtype=torch.float16) - pipe.scheduler = DDPMScheduler.from_pretrained(args.sdxl_path, subfolder="scheduler") - lcm_scheduler = LCMSingleStepScheduler.from_config(pipe.scheduler.config) - - # Load weights. - print("Loading checkpoint...") - pretrained_state_dict = torch.load(os.path.join(args.instantir_path, "aggregator.pt"), map_location="cpu") - pipe.aggregator.load_state_dict(pretrained_state_dict, strict=True) - pipe.aggregator.to(device, dtype=torch.float16) - - #################### Restoration #################### - - post_fix = f"_{args.post_fix}" if args.post_fix else "" - post_fix = args.instantir_path.split("/")[-2]+f"{post_fix}" - os.makedirs(f"{args.out_path}/{post_fix}", exist_ok=True) - - processed_imgs = os.listdir(os.path.join(args.out_path, post_fix)) - lq_files = [] - lq_batch = [] - for file in os.listdir(args.test_path): - if file in processed_imgs: - print(f"Skip {file}") - continue - lq_batch.append(f"{file}") - if len(lq_batch) == args.batch_size: - lq_files.append(lq_batch) - lq_batch = [] - - if len(lq_batch) > 0: - lq_files.append(lq_batch) - - for lq_batch in lq_files: - generator = torch.Generator(device=device).manual_seed(args.seed) - pil_lqs = [Image.open(os.path.join(args.test_path, file)) for file in lq_batch] - if args.width is None or args.height is None: - lq = [resize_img(pil_lq.convert("RGB"), size=None) for pil_lq in pil_lqs] - else: - lq = [resize_img(pil_lq.convert("RGB"), size=(args.width, args.height)) for pil_lq in pil_lqs] - timesteps = None - if args.denoising_start < 1000: - timesteps = [ - i * (args.denoising_start//args.num_inference_steps) + pipe.scheduler.config.steps_offset for i in range(0, args.num_inference_steps) - ] - timesteps = timesteps[::-1] - pipe.scheduler.set_timesteps(args.num_inference_steps, device) - timesteps = pipe.scheduler.timesteps - prompt = args.prompt - if not isinstance(prompt, list): - prompt = [prompt] - prompt = prompt*len(lq) - neg_prompt = args.neg_prompt - if not isinstance(neg_prompt, list): - neg_prompt = [neg_prompt] - neg_prompt = neg_prompt*len(lq) - image = pipe( - prompt=prompt, - image=lq, - ip_adapter_image=[lq], - num_inference_steps=args.num_inference_steps, - generator=generator, - timesteps=timesteps, - negative_prompt=neg_prompt, - guidance_scale=args.cfg, - previewer_scheduler=lcm_scheduler, - return_dict=False, - )[0] - - if args.save_preview_row: - for i, lcm_image in enumerate(image[1]): - lcm_image.save(f"./lcm/{i}.png") - for i, rec_image in enumerate(image): - rec_image.save(f"{args.out_path}/{post_fix}/{lq_batch[i]}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="InstantIR pipeline") - parser.add_argument( - "--sdxl_path", - type=str, - default=None, - required=True, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--previewer_lora_path", - type=str, - default=None, - help="Path to LCM lora or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--pretrained_vae_model_name_or_path", - type=str, - default=None, - help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.", - ) - parser.add_argument( - "--instantir_path", - type=str, - default=None, - required=True, - help="Path to pretrained instantir model.", - ) - parser.add_argument( - "--vision_encoder_path", - type=str, - default='/share/huangrenyuan/model_zoo/vis_backbone/dinov2_large', - help="Path to image encoder for IP-Adapters or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--adapter_model_path", - type=str, - default=None, - help="Path to IP-Adapter models or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--adapter_tokens", - type=int, - default=64, - help="Number of tokens to use in IP-adapter cross attention mechanism.", - ) - parser.add_argument( - "--use_clip_encoder", - action="store_true", - help="Whether or not to use DINO as image encoder, else CLIP encoder.", - ) - parser.add_argument( - "--denoising_start", - type=int, - default=1000, - help="Diffusion start timestep." - ) - parser.add_argument( - "--num_inference_steps", - type=int, - default=30, - help="Diffusion steps." - ) - parser.add_argument( - "--resolution", - type=int, - default=1024, - help="Number of tokens to use in IP-adapter cross attention mechanism.", - ) - parser.add_argument( - "--batch_size", - type=int, - default=6, - help="Test batch size." - ) - parser.add_argument( - "--width", - type=int, - default=None, - help="Output image width." - ) - parser.add_argument( - "--height", - type=int, - default=None, - help="Output image height." - ) - parser.add_argument( - "--cfg", - type=float, - default=7.0, - help="Scale of Classifier-Free-Guidance (CFG).", - ) - parser.add_argument( - "--post_fix", - type=str, - default=None, - help="Subfolder name for restoration output under the output directory.", - ) - parser.add_argument( - "--variant", - type=str, - default='fp16', - help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--save_preview_row", - action="store_true", - help="Whether or not to save the intermediate lcm outputs.", - ) - parser.add_argument( - "--prompt", - type=str, - default='', - nargs="+", - help=( - "A set of prompts for creative restoration. Provide either a matching number of test images," - " or a single prompt to be used with all inputs." - ), - ) - parser.add_argument( - "--neg_prompt", - type=str, - default='', - nargs="+", - help=( - "A set of negative prompts for creative restoration. Provide either a matching number of test images," - " or a single negative prompt to be used with all inputs." - ), - ) - parser.add_argument( - "--test_path", - type=str, - default=None, - required=True, - help="Test directory.", - ) - parser.add_argument( - "--out_path", - type=str, - default="./output", - help="Output directory.", - ) - parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.") - args = parser.parse_args() - args.height = args.height or args.width - args.width = args.width or args.height - if args.width % 64 != 0 or args.height % 64 != 0: - raise ValueError("Image resolution must be divisible by 64.") - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - main(args, device) \ No newline at end of file diff --git a/infer.sh b/infer.sh deleted file mode 100644 index 9e2d1466ea3196ece09aa0fbd6f91365352fc0cd..0000000000000000000000000000000000000000 --- a/infer.sh +++ /dev/null @@ -1,6 +0,0 @@ -python infer.py \ - --sdxl_path path/to/sdxl \ - --vision_encoder_path path/to/dinov2_large \ - --instantir_path path/to/instantir \ - --test_path path/to/input \ - --out_path path/to/output \ No newline at end of file diff --git a/losses/loss_config.py b/losses/loss_config.py deleted file mode 100644 index 152da0221f7f71ef0d97b51299dcb2badf1346fe..0000000000000000000000000000000000000000 --- a/losses/loss_config.py +++ /dev/null @@ -1,15 +0,0 @@ -from dataclasses import dataclass, field -from typing import List - -@dataclass -class SingleLossConfig: - name: str - weight: float = 1. - init_params: dict = field(default_factory=dict) - visualize_every_k: int = -1 - - -@dataclass -class LossesConfig: - diffusion_losses: List[SingleLossConfig] - lcm_losses: List[SingleLossConfig] \ No newline at end of file diff --git a/losses/losses.py b/losses/losses.py deleted file mode 100644 index 3927afac0dd9c245a5f39f30816773a85676a7d4..0000000000000000000000000000000000000000 --- a/losses/losses.py +++ /dev/null @@ -1,465 +0,0 @@ -import torch -import wandb -import cv2 -import torch.nn.functional as F -import numpy as np -from facenet_pytorch import MTCNN -from torchvision import transforms -from dreamsim import dreamsim -from einops import rearrange -import kornia.augmentation as K -import lpips - -from pretrained_models.arcface import Backbone -from utils.vis_utils import add_text_to_image -from utils.utils import extract_faces_and_landmarks -import clip - - -class Loss(): - """ - General purpose loss class. - Mainly handles dtype and visualize_every_k. - keeps current iteration of loss, mainly for visualization purposes. - """ - def __init__(self, visualize_every_k=-1, dtype=torch.float32, accelerator=None, **kwargs): - self.visualize_every_k = visualize_every_k - self.iteration = -1 - self.dtype=dtype - self.accelerator = accelerator - - def __call__(self, **kwargs): - self.iteration += 1 - return self.forward(**kwargs) - - -class L1Loss(Loss): - """ - Simple L1 loss between predicted_pixel_values and pixel_values - - Args: - predicted_pixel_values (torch.Tensor): The predicted pixel values using 1 step LCM and the VAE decoder. - encoder_pixel_values (torch.Tesnor): The input image to the encoder - """ - def forward( - self, - predict: torch.Tensor, - target: torch.Tensor, - **kwargs - ) -> torch.Tensor: - return F.l1_loss(predict, target, reduction="mean") - - -class DreamSIMLoss(Loss): - """DreamSIM loss between predicted_pixel_values and pixel_values. - DreamSIM is similar to LPIPS (https://dreamsim-nights.github.io/) but is trained on more human defined similarity dataset - DreamSIM expects an RGB image of size 224x224 and values between 0 and 1. So we need to normalize the input images to 0-1 range and resize them to 224x224. - Args: - predicted_pixel_values (torch.Tensor): The predicted pixel values using 1 step LCM and the VAE decoder. - encoder_pixel_values (torch.Tesnor): The input image to the encoder - """ - def __init__(self, device: str='cuda:0', **kwargs): - super().__init__(**kwargs) - self.model, _ = dreamsim(pretrained=True, device=device) - self.model.to(dtype=self.dtype, device=device) - self.model = self.accelerator.prepare(self.model) - self.transforms = transforms.Compose([ - transforms.Lambda(lambda x: (x + 1) / 2), - transforms.Resize((224, 224), interpolation=transforms.InterpolationMode.BICUBIC)]) - - def forward( - self, - predicted_pixel_values: torch.Tensor, - encoder_pixel_values: torch.Tensor, - **kwargs, - ) -> torch.Tensor: - predicted_pixel_values.to(dtype=self.dtype) - encoder_pixel_values.to(dtype=self.dtype) - return self.model(self.transforms(predicted_pixel_values), self.transforms(encoder_pixel_values)).mean() - - -class LPIPSLoss(Loss): - """LPIPS loss between predicted_pixel_values and pixel_values. - Args: - predicted_pixel_values (torch.Tensor): The predicted pixel values using 1 step LCM and the VAE decoder. - encoder_pixel_values (torch.Tesnor): The input image to the encoder - """ - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.model = lpips.LPIPS(net='vgg') - self.model.to(dtype=self.dtype, device=self.accelerator.device) - self.model = self.accelerator.prepare(self.model) - - def forward(self, predict, target, **kwargs): - predict.to(dtype=self.dtype) - target.to(dtype=self.dtype) - return self.model(predict, target).mean() - - -class LCMVisualization(Loss): - """Dummy loss used to visualize the LCM outputs - Args: - predicted_pixel_values (torch.Tensor): The predicted pixel values using 1 step LCM and the VAE decoder. - pixel_values (torch.Tensor): The input image to the decoder - encoder_pixel_values (torch.Tesnor): The input image to the encoder - """ - def forward( - self, - predicted_pixel_values: torch.Tensor, - pixel_values: torch.Tensor, - encoder_pixel_values: torch.Tensor, - timesteps: torch.Tensor, - **kwargs, - ) -> None: - if self.visualize_every_k > 0 and self.iteration % self.visualize_every_k == 0: - predicted_pixel_values = rearrange(predicted_pixel_values, "n c h w -> (n h) w c").detach().cpu().numpy() - pixel_values = rearrange(pixel_values, "n c h w -> (n h) w c").detach().cpu().numpy() - encoder_pixel_values = rearrange(encoder_pixel_values, "n c h w -> (n h) w c").detach().cpu().numpy() - image = np.hstack([encoder_pixel_values, pixel_values, predicted_pixel_values]) - for tracker in self.accelerator.trackers: - if tracker.name == 'wandb': - tracker.log({"TrainVisualization": wandb.Image(image, caption=f"Encoder Input Image, Decoder Input Image, Predicted LCM Image. Timesteps {timesteps.cpu().tolist()}")}) - return torch.tensor(0.0) - - -class L2Loss(Loss): - """ - Regular diffusion loss between predicted noise and target noise. - - Args: - predicted_noise (torch.Tensor): noise predicted by the diffusion model - target_noise (torch.Tensor): actual noise added to the image. - """ - def forward( - self, - predict: torch.Tensor, - target: torch.Tensor, - weights: torch.Tensor = None, - **kwargs - ) -> torch.Tensor: - if weights is not None: - loss = (predict.float() - target.float()).pow(2) * weights - return loss.mean() - return F.mse_loss(predict.float(), target.float(), reduction="mean") - - -class HuberLoss(Loss): - """Huber loss between predicted_pixel_values and pixel_values. - Args: - predicted_pixel_values (torch.Tensor): The predicted pixel values using 1 step LCM and the VAE decoder. - encoder_pixel_values (torch.Tesnor): The input image to the encoder - """ - def __init__(self, huber_c=0.001, **kwargs): - super().__init__(**kwargs) - self.huber_c = huber_c - - def forward( - self, - predict: torch.Tensor, - target: torch.Tensor, - weights: torch.Tensor = None, - **kwargs - ) -> torch.Tensor: - loss = torch.sqrt((predict.float() - target.float()) ** 2 + self.huber_c**2) - self.huber_c - if weights is not None: - return (loss * weights).mean() - return loss.mean() - - -class WeightedNoiseLoss(Loss): - """ - Weighted diffusion loss between predicted noise and target noise. - - Args: - predicted_noise (torch.Tensor): noise predicted by the diffusion model - target_noise (torch.Tensor): actual noise added to the image. - loss_batch_weights (torch.Tensor): weighting for each batch item. Can be used to e.g. zero-out loss for InstantID training if keypoint extraction fails. - """ - def forward( - self, - predict: torch.Tensor, - target: torch.Tensor, - weights, - **kwargs - ) -> torch.Tensor: - return F.mse_loss(predict.float() * weights, target.float() * weights, reduction="mean") - - -class IDLoss(Loss): - """ - Use pretrained facenet model to extract features from the face of the predicted image and target image. - Facenet expects 112x112 images, so we crop the face using MTCNN and resize it to 112x112. - Then we use the cosine similarity between the features to calculate the loss. (The cosine similarity is 1 - cosine distance). - Also notice that the outputs of facenet are normalized so the dot product is the same as cosine distance. - """ - def __init__(self, pretrained_arcface_path: str, skip_not_found=True, **kwargs): - super().__init__(**kwargs) - assert pretrained_arcface_path is not None, "please pass `pretrained_arcface_path` in the losses config. You can download the pretrained model from "\ - "https://drive.google.com/file/d/1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn/view?usp=sharing" - self.mtcnn = MTCNN(device=self.accelerator.device) - self.mtcnn.forward = self.mtcnn.detect - self.facenet_input_size = 112 # Has to be 112, can't find weights for 224 size. - self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se') - self.facenet.load_state_dict(torch.load(pretrained_arcface_path)) - self.face_pool = torch.nn.AdaptiveAvgPool2d((self.facenet_input_size, self.facenet_input_size)) - self.facenet.requires_grad_(False) - self.facenet.eval() - self.facenet.to(device=self.accelerator.device, dtype=self.dtype) # not implemented for half precision - self.face_pool.to(device=self.accelerator.device, dtype=self.dtype) # not implemented for half precision - self.visualization_resize = transforms.Resize((self.facenet_input_size, self.facenet_input_size), interpolation=transforms.InterpolationMode.BICUBIC) - self.reference_facial_points = np.array([[38.29459953, 51.69630051], - [72.53179932, 51.50139999], - [56.02519989, 71.73660278], - [41.54930115, 92.3655014], - [70.72990036, 92.20410156] - ]) # Original points are 112 * 96 added 8 to the x axis to make it 112 * 112 - self.facenet, self.face_pool, self.mtcnn = self.accelerator.prepare(self.facenet, self.face_pool, self.mtcnn) - - self.skip_not_found = skip_not_found - - def extract_feats(self, x: torch.Tensor): - """ - Extract features from the face of the image using facenet model. - """ - x = self.face_pool(x) - x_feats = self.facenet(x) - - return x_feats - - def forward( - self, - predicted_pixel_values: torch.Tensor, - encoder_pixel_values: torch.Tensor, - timesteps: torch.Tensor, - **kwargs - ): - encoder_pixel_values = encoder_pixel_values.to(dtype=self.dtype) - predicted_pixel_values = predicted_pixel_values.to(dtype=self.dtype) - - predicted_pixel_values_face, predicted_invalid_indices = extract_faces_and_landmarks(predicted_pixel_values, mtcnn=self.mtcnn) - with torch.no_grad(): - encoder_pixel_values_face, source_invalid_indices = extract_faces_and_landmarks(encoder_pixel_values, mtcnn=self.mtcnn) - - if self.skip_not_found: - valid_indices = [] - for i in range(predicted_pixel_values.shape[0]): - if i not in predicted_invalid_indices and i not in source_invalid_indices: - valid_indices.append(i) - else: - valid_indices = list(range(predicted_pixel_values)) - - valid_indices = torch.tensor(valid_indices).to(device=predicted_pixel_values.device) - - if len(valid_indices) == 0: - loss = (predicted_pixel_values_face * 0.0).mean() # It's done this way so the `backwards` will delete the computation graph of the predicted_pixel_values. - if self.visualize_every_k > 0 and self.iteration % self.visualize_every_k == 0: - self.visualize(predicted_pixel_values, encoder_pixel_values, predicted_pixel_values_face, encoder_pixel_values_face, timesteps, valid_indices, loss) - return loss - - with torch.no_grad(): - pixel_values_feats = self.extract_feats(encoder_pixel_values_face[valid_indices]) - - predicted_pixel_values_feats = self.extract_feats(predicted_pixel_values_face[valid_indices]) - loss = 1 - torch.einsum("bi,bi->b", pixel_values_feats, predicted_pixel_values_feats) - - if self.visualize_every_k > 0 and self.iteration % self.visualize_every_k == 0: - self.visualize(predicted_pixel_values, encoder_pixel_values, predicted_pixel_values_face, encoder_pixel_values_face, timesteps, valid_indices, loss) - return loss.mean() - - def visualize( - self, - predicted_pixel_values: torch.Tensor, - encoder_pixel_values: torch.Tensor, - predicted_pixel_values_face: torch.Tensor, - encoder_pixel_values_face: torch.Tensor, - timesteps: torch.Tensor, - valid_indices: torch.Tensor, - loss: torch.Tensor, - ) -> None: - small_predicted_pixel_values = (rearrange(self.visualization_resize(predicted_pixel_values), "n c h w -> (n h) w c").detach().cpu().numpy()) - small_pixle_values = rearrange(self.visualization_resize(encoder_pixel_values), "n c h w -> (n h) w c").detach().cpu().numpy() - small_predicted_pixel_values_face = rearrange(self.visualization_resize(predicted_pixel_values_face), "n c h w -> (n h) w c").detach().cpu().numpy() - small_pixle_values_face = rearrange(self.visualization_resize(encoder_pixel_values_face), "n c h w -> (n h) w c").detach().cpu().numpy() - - small_predicted_pixel_values = add_text_to_image(((small_predicted_pixel_values * 0.5 + 0.5) * 255).astype(np.uint8), "Pred Images", add_below=False) - small_pixle_values = add_text_to_image(((small_pixle_values * 0.5 + 0.5) * 255).astype(np.uint8), "Target Images", add_below=False) - small_predicted_pixel_values_face = add_text_to_image(((small_predicted_pixel_values_face * 0.5 + 0.5) * 255).astype(np.uint8), "Pred Faces", add_below=False) - small_pixle_values_face = add_text_to_image(((small_pixle_values_face * 0.5 + 0.5) * 255).astype(np.uint8), "Target Faces", add_below=False) - - - final_image = np.hstack([small_predicted_pixel_values, small_pixle_values, small_predicted_pixel_values_face, small_pixle_values_face]) - for tracker in self.accelerator.trackers: - if tracker.name == 'wandb': - tracker.log({"IDLoss Visualization": wandb.Image(final_image, caption=f"loss: {loss.cpu().tolist()} timesteps: {timesteps.cpu().tolist()}, valid_indices: {valid_indices.cpu().tolist()}")}) - - -class ImageAugmentations(torch.nn.Module): - # Standard image augmentations used for CLIP loss to discourage adversarial outputs. - def __init__(self, output_size, augmentations_number, p=0.7): - super().__init__() - self.output_size = output_size - self.augmentations_number = augmentations_number - - self.augmentations = torch.nn.Sequential( - K.RandomAffine(degrees=15, translate=0.1, p=p, padding_mode="border"), # type: ignore - K.RandomPerspective(0.7, p=p), - ) - - self.avg_pool = torch.nn.AdaptiveAvgPool2d((self.output_size, self.output_size)) - - self.device = None - - def forward(self, input): - """Extents the input batch with augmentations - If the input is consists of images [I1, I2] the extended augmented output - will be [I1_resized, I2_resized, I1_aug1, I2_aug1, I1_aug2, I2_aug2 ...] - Args: - input ([type]): input batch of shape [batch, C, H, W] - Returns: - updated batch: of shape [batch * augmentations_number, C, H, W] - """ - # We want to multiply the number of images in the batch in contrast to regular augmantations - # that do not change the number of samples in the batch) - resized_images = self.avg_pool(input) - resized_images = torch.tile(resized_images, dims=(self.augmentations_number, 1, 1, 1)) - - batch_size = input.shape[0] - # We want at least one non augmented image - non_augmented_batch = resized_images[:batch_size] - augmented_batch = self.augmentations(resized_images[batch_size:]) - updated_batch = torch.cat([non_augmented_batch, augmented_batch], dim=0) - - return updated_batch - - -class CLIPLoss(Loss): - def __init__(self, augmentations_number: int = 4, **kwargs): - super().__init__(**kwargs) - - self.clip_model, clip_preprocess = clip.load("ViT-B/16", device=self.accelerator.device, jit=False) - - self.clip_model.device = None - - self.clip_model.eval().requires_grad_(False) - - self.preprocess = transforms.Compose([transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0, 2.0])] + # Un-normalize from [-1.0, 1.0] (SD output) to [0, 1]. - clip_preprocess.transforms[:2] + # to match CLIP input scale assumptions - clip_preprocess.transforms[4:]) # + skip convert PIL to tensor - - self.clip_size = self.clip_model.visual.input_resolution - - self.clip_normalize = transforms.Normalize( - mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711] - ) - - self.image_augmentations = ImageAugmentations(output_size=self.clip_size, - augmentations_number=augmentations_number) - - self.clip_model, self.image_augmentations = self.accelerator.prepare(self.clip_model, self.image_augmentations) - - def forward(self, decoder_prompts, predicted_pixel_values: torch.Tensor, **kwargs) -> torch.Tensor: - - if not isinstance(decoder_prompts, list): - decoder_prompts = [decoder_prompts] - - tokens = clip.tokenize(decoder_prompts).to(predicted_pixel_values.device) - image = self.preprocess(predicted_pixel_values) - - logits_per_image, _ = self.clip_model(image, tokens) - - logits_per_image = torch.diagonal(logits_per_image) - - return (1. - logits_per_image / 100).mean() - - -class DINOLoss(Loss): - def __init__( - self, - dino_model, - dino_preprocess, - output_hidden_states: bool = False, - center_momentum: float = 0.9, - student_temp: float = 0.1, - teacher_temp: float = 0.04, - warmup_teacher_temp: float = 0.04, - warmup_teacher_temp_epochs: int = 30, - **kwargs): - super().__init__(**kwargs) - - self.dino_model = dino_model - self.output_hidden_states = output_hidden_states - self.rescale_factor = dino_preprocess.rescale_factor - - # Un-normalize from [-1.0, 1.0] (SD output) to [0, 1]. - self.preprocess = transforms.Compose( - [ - transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0, 2.0]), - transforms.Resize(size=256), - transforms.CenterCrop(size=(224, 224)), - transforms.Normalize(mean=dino_preprocess.image_mean, std=dino_preprocess.image_std) - ] - ) - - self.student_temp = student_temp - self.teacher_temp = teacher_temp - self.center_momentum = center_momentum - self.center = torch.zeros(1, 257, 1024).to(self.accelerator.device, dtype=self.dtype) - - # TODO: add temp, now fixed to 0.04 - # we apply a warm up for the teacher temperature because - # a too high temperature makes the training instable at the beginning - # self.teacher_temp_schedule = np.concatenate(( - # np.linspace(warmup_teacher_temp, - # teacher_temp, warmup_teacher_temp_epochs), - # np.ones(nepochs - warmup_teacher_temp_epochs) * teacher_temp - # )) - - self.dino_model = self.accelerator.prepare(self.dino_model) - - def forward( - self, - target: torch.Tensor, - predict: torch.Tensor, - weights: torch.Tensor = None, - **kwargs) -> torch.Tensor: - - predict = self.preprocess(predict) - target = self.preprocess(target) - - encoder_input = torch.cat([target, predict]).to(self.dino_model.device, dtype=self.dino_model.dtype) - - if self.output_hidden_states: - raise ValueError("Output hidden states not supported for DINO loss.") - image_enc_hidden_states = self.dino_model(encoder_input, output_hidden_states=True).hidden_states[-2] - else: - image_enc_hidden_states = self.dino_model(encoder_input).last_hidden_state - - teacher_output, student_output = image_enc_hidden_states.chunk(2, dim=0) # [B, 257, 1024] - - student_out = student_output.float() / self.student_temp - - # teacher centering and sharpening - # temp = self.teacher_temp_schedule[epoch] - temp = self.teacher_temp - teacher_out = F.softmax((teacher_output.float() - self.center) / temp, dim=-1) - teacher_out = teacher_out.detach() - - loss = torch.sum(-teacher_out * F.log_softmax(student_out, dim=-1), dim=-1, keepdim=True) - # self.update_center(teacher_output) - - if weights is not None: - loss = loss * weights - return loss.mean() - return loss.mean() - - @torch.no_grad() - def update_center(self, teacher_output): - """ - Update center used for teacher output. - """ - batch_center = torch.sum(teacher_output, dim=0, keepdim=True) - self.accelerator.reduce(batch_center, reduction="sum") - batch_center = batch_center / (len(teacher_output) * self.accelerator.num_processes) - - # ema update - self.center = self.center * self.center_momentum + batch_center * (1 - self.center_momentum) diff --git a/module/aggregator.py b/module/aggregator.py deleted file mode 100644 index 53a20ea72c03b29a78cf70c9a044a033e6360191..0000000000000000000000000000000000000000 --- a/module/aggregator.py +++ /dev/null @@ -1,993 +0,0 @@ -from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Tuple, Union - -import torch -from torch import nn -from torch.nn import functional as F - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.loaders.single_file_model import FromOriginalModelMixin -from diffusers.utils import BaseOutput, logging -from diffusers.models.attention_processor import ( - ADDED_KV_ATTENTION_PROCESSORS, - CROSS_ATTENTION_PROCESSORS, - AttentionProcessor, - AttnAddedKVProcessor, - AttnProcessor, -) -from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps -from diffusers.models.modeling_utils import ModelMixin -from diffusers.models.unets.unet_2d_blocks import ( - CrossAttnDownBlock2D, - DownBlock2D, - UNetMidBlock2D, - UNetMidBlock2DCrossAttn, - get_down_block, -) -from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -class ZeroConv(nn.Module): - def __init__(self, label_nc, norm_nc, mask=False): - super().__init__() - self.zero_conv = zero_module(nn.Conv2d(label_nc+norm_nc, norm_nc, 1, 1, 0)) - self.mask = mask - - def forward(self, hidden_states, h_ori=None): - # with torch.cuda.amp.autocast(enabled=False, dtype=torch.float32): - c, h = hidden_states - if not self.mask: - h = self.zero_conv(torch.cat([c, h], dim=1)) - else: - h = self.zero_conv(torch.cat([c, h], dim=1)) * torch.zeros_like(h) - if h_ori is not None: - h = torch.cat([h_ori, h], dim=1) - return h - - -class SFT(nn.Module): - def __init__(self, label_nc, norm_nc, mask=False): - super().__init__() - - # param_free_norm_type = str(parsed.group(1)) - ks = 3 - pw = ks // 2 - - self.mask = mask - - nhidden = 128 - - self.mlp_shared = nn.Sequential( - nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), - nn.SiLU() - ) - self.mul = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw) - self.add = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw) - - def forward(self, hidden_states, mask=False): - - c, h = hidden_states - mask = mask or self.mask - assert mask is False - - actv = self.mlp_shared(c) - gamma = self.mul(actv) - beta = self.add(actv) - - if self.mask: - gamma = gamma * torch.zeros_like(gamma) - beta = beta * torch.zeros_like(beta) - # gamma_ori, gamma_res = torch.split(gamma, [h_ori_c, h_c], dim=1) - # beta_ori, beta_res = torch.split(beta, [h_ori_c, h_c], dim=1) - # print(gamma_ori.mean(), gamma_res.mean(), beta_ori.mean(), beta_res.mean()) - h = h * (gamma + 1) + beta - # sample_ori, sample_res = torch.split(h, [h_ori_c, h_c], dim=1) - # print(sample_ori.mean(), sample_res.mean()) - - return h - - -@dataclass -class AggregatorOutput(BaseOutput): - """ - The output of [`Aggregator`]. - - Args: - down_block_res_samples (`tuple[torch.Tensor]`): - A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should - be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be - used to condition the original UNet's downsampling activations. - mid_down_block_re_sample (`torch.Tensor`): - The activation of the midde block (the lowest sample resolution). Each tensor should be of shape - `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`. - Output can be used to condition the original UNet's middle block activation. - """ - - down_block_res_samples: Tuple[torch.Tensor] - mid_block_res_sample: torch.Tensor - - -class ConditioningEmbedding(nn.Module): - """ - Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN - [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized - training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the - convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides - (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full - model) to encode image-space conditions ... into feature maps ..." - """ - - def __init__( - self, - conditioning_embedding_channels: int, - conditioning_channels: int = 3, - block_out_channels: Tuple[int, ...] = (16, 32, 96, 256), - ): - super().__init__() - - self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1) - - self.blocks = nn.ModuleList([]) - - for i in range(len(block_out_channels) - 1): - channel_in = block_out_channels[i] - channel_out = block_out_channels[i + 1] - self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)) - self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) - - self.conv_out = zero_module( - nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1) - ) - - def forward(self, conditioning): - embedding = self.conv_in(conditioning) - embedding = F.silu(embedding) - - for block in self.blocks: - embedding = block(embedding) - embedding = F.silu(embedding) - - embedding = self.conv_out(embedding) - - return embedding - - -class Aggregator(ModelMixin, ConfigMixin, FromOriginalModelMixin): - """ - Aggregator model. - - Args: - in_channels (`int`, defaults to 4): - The number of channels in the input sample. - flip_sin_to_cos (`bool`, defaults to `True`): - Whether to flip the sin to cos in the time embedding. - freq_shift (`int`, defaults to 0): - The frequency shift to apply to the time embedding. - down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): - The tuple of downsample blocks to use. - only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`): - block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`): - The tuple of output channels for each block. - layers_per_block (`int`, defaults to 2): - The number of layers per block. - downsample_padding (`int`, defaults to 1): - The padding to use for the downsampling convolution. - mid_block_scale_factor (`float`, defaults to 1): - The scale factor to use for the mid block. - act_fn (`str`, defaults to "silu"): - The activation function to use. - norm_num_groups (`int`, *optional*, defaults to 32): - The number of groups to use for the normalization. If None, normalization and activation layers is skipped - in post-processing. - norm_eps (`float`, defaults to 1e-5): - The epsilon to use for the normalization. - cross_attention_dim (`int`, defaults to 1280): - The dimension of the cross attention features. - transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - encoder_hid_dim (`int`, *optional*, defaults to None): - If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` - dimension to `cross_attention_dim`. - encoder_hid_dim_type (`str`, *optional*, defaults to `None`): - If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text - embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. - attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8): - The dimension of the attention heads. - use_linear_projection (`bool`, defaults to `False`): - class_embed_type (`str`, *optional*, defaults to `None`): - The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None, - `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. - addition_embed_type (`str`, *optional*, defaults to `None`): - Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or - "text". "text" will use the `TextTimeEmbedding` layer. - num_class_embeds (`int`, *optional*, defaults to 0): - Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing - class conditioning with `class_embed_type` equal to `None`. - upcast_attention (`bool`, defaults to `False`): - resnet_time_scale_shift (`str`, defaults to `"default"`): - Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`. - projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`): - The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when - `class_embed_type="projection"`. - controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`): - The channel order of conditional image. Will convert to `rgb` if it's `bgr`. - conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`): - The tuple of output channel for each block in the `conditioning_embedding` layer. - global_pool_conditions (`bool`, defaults to `False`): - TODO(Patrick) - unused parameter. - addition_embed_type_num_heads (`int`, defaults to 64): - The number of heads to use for the `TextTimeEmbedding` layer. - """ - - _supports_gradient_checkpointing = True - - @register_to_config - def __init__( - self, - in_channels: int = 4, - conditioning_channels: int = 3, - flip_sin_to_cos: bool = True, - freq_shift: int = 0, - down_block_types: Tuple[str, ...] = ( - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ), - mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), - layers_per_block: int = 2, - downsample_padding: int = 1, - mid_block_scale_factor: float = 1, - act_fn: str = "silu", - norm_num_groups: Optional[int] = 32, - norm_eps: float = 1e-5, - cross_attention_dim: int = 1280, - transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1, - encoder_hid_dim: Optional[int] = None, - encoder_hid_dim_type: Optional[str] = None, - attention_head_dim: Union[int, Tuple[int, ...]] = 8, - num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None, - use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - addition_embed_type: Optional[str] = None, - addition_time_embed_dim: Optional[int] = None, - num_class_embeds: Optional[int] = None, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - projection_class_embeddings_input_dim: Optional[int] = None, - controlnet_conditioning_channel_order: str = "rgb", - conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), - global_pool_conditions: bool = False, - addition_embed_type_num_heads: int = 64, - pad_concat: bool = False, - ): - super().__init__() - - # If `num_attention_heads` is not defined (which is the case for most models) - # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. - # The reason for this behavior is to correct for incorrectly named variables that were introduced - # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 - # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking - # which is why we correct for the naming here. - num_attention_heads = num_attention_heads or attention_head_dim - self.pad_concat = pad_concat - - # Check inputs - if len(block_out_channels) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." - ) - - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) - - # input - conv_in_kernel = 3 - conv_in_padding = (conv_in_kernel - 1) // 2 - self.conv_in = nn.Conv2d( - in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding - ) - - # time - time_embed_dim = block_out_channels[0] * 4 - self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) - timestep_input_dim = block_out_channels[0] - self.time_embedding = TimestepEmbedding( - timestep_input_dim, - time_embed_dim, - act_fn=act_fn, - ) - - if encoder_hid_dim_type is None and encoder_hid_dim is not None: - encoder_hid_dim_type = "text_proj" - self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) - logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") - - if encoder_hid_dim is None and encoder_hid_dim_type is not None: - raise ValueError( - f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." - ) - - if encoder_hid_dim_type == "text_proj": - self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) - elif encoder_hid_dim_type == "text_image_proj": - # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)` - self.encoder_hid_proj = TextImageProjection( - text_embed_dim=encoder_hid_dim, - image_embed_dim=cross_attention_dim, - cross_attention_dim=cross_attention_dim, - ) - - elif encoder_hid_dim_type is not None: - raise ValueError( - f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." - ) - else: - self.encoder_hid_proj = None - - # class embedding - if class_embed_type is None and num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - elif class_embed_type == "timestep": - self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) - elif class_embed_type == "identity": - self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) - elif class_embed_type == "projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" - ) - # The projection `class_embed_type` is the same as the timestep `class_embed_type` except - # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings - # 2. it projects from an arbitrary input dimension. - # - # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. - # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. - # As a result, `TimestepEmbedding` can be passed arbitrary vectors. - self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - else: - self.class_embedding = None - - if addition_embed_type == "text": - if encoder_hid_dim is not None: - text_time_embedding_from_dim = encoder_hid_dim - else: - text_time_embedding_from_dim = cross_attention_dim - - self.add_embedding = TextTimeEmbedding( - text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads - ) - elif addition_embed_type == "text_image": - # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)` - self.add_embedding = TextImageTimeEmbedding( - text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim - ) - elif addition_embed_type == "text_time": - self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) - self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - - elif addition_embed_type is not None: - raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") - - # control net conditioning embedding - self.ref_conv_in = nn.Conv2d( - in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding - ) - - self.down_blocks = nn.ModuleList([]) - self.controlnet_down_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - if isinstance(num_attention_heads, int): - num_attention_heads = (num_attention_heads,) * len(down_block_types) - - # down - output_channel = block_out_channels[0] - - # controlnet_block = ZeroConv(output_channel, output_channel) - controlnet_block = nn.Sequential( - SFT(output_channel, output_channel), - zero_module(nn.Conv2d(output_channel, output_channel, kernel_size=1)) - ) - self.controlnet_down_blocks.append(controlnet_block) - - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block, - transformer_layers_per_block=transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - temb_channels=time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads[i], - attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, - downsample_padding=downsample_padding, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - self.down_blocks.append(down_block) - - for _ in range(layers_per_block): - # controlnet_block = ZeroConv(output_channel, output_channel) - controlnet_block = nn.Sequential( - SFT(output_channel, output_channel), - zero_module(nn.Conv2d(output_channel, output_channel, kernel_size=1)) - ) - self.controlnet_down_blocks.append(controlnet_block) - - if not is_final_block: - # controlnet_block = ZeroConv(output_channel, output_channel) - controlnet_block = nn.Sequential( - SFT(output_channel, output_channel), - zero_module(nn.Conv2d(output_channel, output_channel, kernel_size=1)) - ) - self.controlnet_down_blocks.append(controlnet_block) - - # mid - mid_block_channel = block_out_channels[-1] - - # controlnet_block = ZeroConv(mid_block_channel, mid_block_channel) - controlnet_block = nn.Sequential( - SFT(mid_block_channel, mid_block_channel), - zero_module(nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)) - ) - self.controlnet_mid_block = controlnet_block - - if mid_block_type == "UNetMidBlock2DCrossAttn": - self.mid_block = UNetMidBlock2DCrossAttn( - transformer_layers_per_block=transformer_layers_per_block[-1], - in_channels=mid_block_channel, - temb_channels=time_embed_dim, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - output_scale_factor=mid_block_scale_factor, - resnet_time_scale_shift=resnet_time_scale_shift, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads[-1], - resnet_groups=norm_num_groups, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - ) - elif mid_block_type == "UNetMidBlock2D": - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - temb_channels=time_embed_dim, - num_layers=0, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - output_scale_factor=mid_block_scale_factor, - resnet_groups=norm_num_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - add_attention=False, - ) - else: - raise ValueError(f"unknown mid_block_type : {mid_block_type}") - - @classmethod - def from_unet( - cls, - unet: UNet2DConditionModel, - controlnet_conditioning_channel_order: str = "rgb", - conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), - load_weights_from_unet: bool = True, - conditioning_channels: int = 3, - ): - r""" - Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`]. - - Parameters: - unet (`UNet2DConditionModel`): - The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied - where applicable. - """ - transformer_layers_per_block = ( - unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1 - ) - encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None - encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None - addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None - addition_time_embed_dim = ( - unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None - ) - - controlnet = cls( - encoder_hid_dim=encoder_hid_dim, - encoder_hid_dim_type=encoder_hid_dim_type, - addition_embed_type=addition_embed_type, - addition_time_embed_dim=addition_time_embed_dim, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=unet.config.in_channels, - flip_sin_to_cos=unet.config.flip_sin_to_cos, - freq_shift=unet.config.freq_shift, - down_block_types=unet.config.down_block_types, - only_cross_attention=unet.config.only_cross_attention, - block_out_channels=unet.config.block_out_channels, - layers_per_block=unet.config.layers_per_block, - downsample_padding=unet.config.downsample_padding, - mid_block_scale_factor=unet.config.mid_block_scale_factor, - act_fn=unet.config.act_fn, - norm_num_groups=unet.config.norm_num_groups, - norm_eps=unet.config.norm_eps, - cross_attention_dim=unet.config.cross_attention_dim, - attention_head_dim=unet.config.attention_head_dim, - num_attention_heads=unet.config.num_attention_heads, - use_linear_projection=unet.config.use_linear_projection, - class_embed_type=unet.config.class_embed_type, - num_class_embeds=unet.config.num_class_embeds, - upcast_attention=unet.config.upcast_attention, - resnet_time_scale_shift=unet.config.resnet_time_scale_shift, - projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, - mid_block_type=unet.config.mid_block_type, - controlnet_conditioning_channel_order=controlnet_conditioning_channel_order, - conditioning_embedding_out_channels=conditioning_embedding_out_channels, - conditioning_channels=conditioning_channels, - ) - - if load_weights_from_unet: - controlnet.conv_in.load_state_dict(unet.conv_in.state_dict()) - controlnet.ref_conv_in.load_state_dict(unet.conv_in.state_dict()) - controlnet.time_proj.load_state_dict(unet.time_proj.state_dict()) - controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) - - if controlnet.class_embedding: - controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) - - if hasattr(controlnet, "add_embedding"): - controlnet.add_embedding.load_state_dict(unet.add_embedding.state_dict()) - - controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict()) - controlnet.mid_block.load_state_dict(unet.mid_block.state_dict()) - - return controlnet - - @property - # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors - def attn_processors(self) -> Dict[str, AttentionProcessor]: - r""" - Returns: - `dict` of attention processors: A dictionary containing all attention processors used in the model with - indexed by its weight name. - """ - # set recursively - processors = {} - - def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): - if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) - - for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) - - return processors - - for name, module in self.named_children(): - fn_recursive_add_processors(name, module, processors) - - return processors - - # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor - def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): - r""" - Sets the attention processor to use to compute attention. - - Parameters: - processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): - The instantiated processor class or a dictionary of processor classes that will be set as the processor - for **all** `Attention` layers. - - If `processor` is a dict, the key needs to define the path to the corresponding cross attention - processor. This is strongly recommended when setting trainable attention processors. - - """ - count = len(self.attn_processors.keys()) - - if isinstance(processor, dict) and len(processor) != count: - raise ValueError( - f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) - - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): - if hasattr(module, "set_processor"): - if not isinstance(processor, dict): - module.set_processor(processor) - else: - module.set_processor(processor.pop(f"{name}.processor")) - - for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) - - for name, module in self.named_children(): - fn_recursive_attn_processor(name, module, processor) - - # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor - def set_default_attn_processor(self): - """ - Disables custom attention processors and sets the default attention implementation. - """ - if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnAddedKVProcessor() - elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnProcessor() - else: - raise ValueError( - f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" - ) - - self.set_attn_processor(processor) - - # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice - def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None: - r""" - Enable sliced attention computation. - - When this option is enabled, the attention module splits the input tensor in slices to compute attention in - several steps. This is useful for saving some memory in exchange for a small decrease in speed. - - Args: - slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): - When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If - `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is - provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` - must be a multiple of `slice_size`. - """ - sliceable_head_dims = [] - - def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): - if hasattr(module, "set_attention_slice"): - sliceable_head_dims.append(module.sliceable_head_dim) - - for child in module.children(): - fn_recursive_retrieve_sliceable_dims(child) - - # retrieve number of attention layers - for module in self.children(): - fn_recursive_retrieve_sliceable_dims(module) - - num_sliceable_layers = len(sliceable_head_dims) - - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = [dim // 2 for dim in sliceable_head_dims] - elif slice_size == "max": - # make smallest slice possible - slice_size = num_sliceable_layers * [1] - - slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size - - if len(slice_size) != len(sliceable_head_dims): - raise ValueError( - f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" - f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." - ) - - for i in range(len(slice_size)): - size = slice_size[i] - dim = sliceable_head_dims[i] - if size is not None and size > dim: - raise ValueError(f"size {size} has to be smaller or equal to {dim}.") - - # Recursively walk through all the children. - # Any children which exposes the set_attention_slice method - # gets the message - def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): - if hasattr(module, "set_attention_slice"): - module.set_attention_slice(slice_size.pop()) - - for child in module.children(): - fn_recursive_set_attention_slice(child, slice_size) - - reversed_slice_size = list(reversed(slice_size)) - for module in self.children(): - fn_recursive_set_attention_slice(module, reversed_slice_size) - - def process_encoder_hidden_states( - self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> torch.Tensor: - if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - image_embeds = self.encoder_hid_proj(image_embeds) - encoder_hidden_states = (encoder_hidden_states, image_embeds) - return encoder_hidden_states - - def _set_gradient_checkpointing(self, module, value: bool = False) -> None: - if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)): - module.gradient_checkpointing = value - - def forward( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - controlnet_cond: torch.FloatTensor, - cat_dim: int = -2, - conditioning_scale: float = 1.0, - class_labels: Optional[torch.Tensor] = None, - timestep_cond: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - guess_mode: bool = False, - return_dict: bool = True, - ) -> Union[AggregatorOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]: - """ - The [`Aggregator`] forward method. - - Args: - sample (`torch.FloatTensor`): - The noisy input tensor. - timestep (`Union[torch.Tensor, float, int]`): - The number of timesteps to denoise an input. - encoder_hidden_states (`torch.Tensor`): - The encoder hidden states. - controlnet_cond (`torch.FloatTensor`): - The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`. - conditioning_scale (`float`, defaults to `1.0`): - The scale factor for ControlNet outputs. - class_labels (`torch.Tensor`, *optional*, defaults to `None`): - Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. - timestep_cond (`torch.Tensor`, *optional*, defaults to `None`): - Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the - timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep - embeddings. - attention_mask (`torch.Tensor`, *optional*, defaults to `None`): - An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask - is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large - negative values to the attention scores corresponding to "discard" tokens. - added_cond_kwargs (`dict`): - Additional conditions for the Stable Diffusion XL UNet. - cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`): - A kwargs dictionary that if specified is passed along to the `AttnProcessor`. - guess_mode (`bool`, defaults to `False`): - In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if - you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended. - return_dict (`bool`, defaults to `True`): - Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple. - - Returns: - [`~models.controlnet.ControlNetOutput`] **or** `tuple`: - If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is - returned where the first element is the sample tensor. - """ - # check channel order - channel_order = self.config.controlnet_conditioning_channel_order - - if channel_order == "rgb": - # in rgb order by default - ... - else: - raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}") - - # prepare attention_mask - if attention_mask is not None: - attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # 1. time - timesteps = timestep - if not torch.is_tensor(timesteps): - # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - - # timesteps does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=sample.dtype) - - emb = self.time_embedding(t_emb, timestep_cond) - aug_emb = None - - if self.class_embedding is not None: - if class_labels is None: - raise ValueError("class_labels should be provided when num_class_embeds > 0") - - if self.config.class_embed_type == "timestep": - class_labels = self.time_proj(class_labels) - - class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) - emb = emb + class_emb - - if self.config.addition_embed_type is not None: - if self.config.addition_embed_type == "text": - aug_emb = self.add_embedding(encoder_hidden_states) - - elif self.config.addition_embed_type == "text_time": - if "text_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" - ) - text_embeds = added_cond_kwargs.get("text_embeds") - if "time_ids" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" - ) - time_ids = added_cond_kwargs.get("time_ids") - time_embeds = self.add_time_proj(time_ids.flatten()) - time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) - - add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) - add_embeds = add_embeds.to(emb.dtype) - aug_emb = self.add_embedding(add_embeds) - - emb = emb + aug_emb if aug_emb is not None else emb - - encoder_hidden_states = self.process_encoder_hidden_states( - encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs - ) - - # 2. prepare input - cond_latent = self.conv_in(sample) - ref_latent = self.ref_conv_in(controlnet_cond) - batch_size, channel, height, width = cond_latent.shape - if self.pad_concat: - if cat_dim == -2 or cat_dim == 2: - concat_pad = torch.zeros(batch_size, channel, 1, width) - elif cat_dim == -1 or cat_dim == 3: - concat_pad = torch.zeros(batch_size, channel, height, 1) - else: - raise ValueError(f"Aggregator shall concat along spatial dimension, but is asked to concat dim: {cat_dim}.") - concat_pad = concat_pad.to(cond_latent.device, dtype=cond_latent.dtype) - sample = torch.cat([cond_latent, concat_pad, ref_latent], dim=cat_dim) - else: - sample = torch.cat([cond_latent, ref_latent], dim=cat_dim) - - # 3. down - down_block_res_samples = (sample,) - for downsample_block in self.down_blocks: - sample, res_samples = downsample_block( - hidden_states=sample, - temb=emb, - cross_attention_kwargs=cross_attention_kwargs, - ) - - # rebuild sample: split and concat - if self.pad_concat: - batch_size, channel, height, width = sample.shape - if cat_dim == -2 or cat_dim == 2: - cond_latent = sample[:, :, :height//2, :] - ref_latent = sample[:, :, -(height//2):, :] - concat_pad = torch.zeros(batch_size, channel, 1, width) - elif cat_dim == -1 or cat_dim == 3: - cond_latent = sample[:, :, :, :width//2] - ref_latent = sample[:, :, :, -(width//2):] - concat_pad = torch.zeros(batch_size, channel, height, 1) - concat_pad = concat_pad.to(cond_latent.device, dtype=cond_latent.dtype) - sample = torch.cat([cond_latent, concat_pad, ref_latent], dim=cat_dim) - res_samples = res_samples[:-1] + (sample,) - - down_block_res_samples += res_samples - - # 4. mid - if self.mid_block is not None: - sample = self.mid_block( - sample, - emb, - cross_attention_kwargs=cross_attention_kwargs, - ) - - # 5. split samples and SFT. - controlnet_down_block_res_samples = () - for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): - batch_size, channel, height, width = down_block_res_sample.shape - if cat_dim == -2 or cat_dim == 2: - cond_latent = down_block_res_sample[:, :, :height//2, :] - ref_latent = down_block_res_sample[:, :, -(height//2):, :] - elif cat_dim == -1 or cat_dim == 3: - cond_latent = down_block_res_sample[:, :, :, :width//2] - ref_latent = down_block_res_sample[:, :, :, -(width//2):] - down_block_res_sample = controlnet_block((cond_latent, ref_latent), ) - controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,) - - down_block_res_samples = controlnet_down_block_res_samples - - batch_size, channel, height, width = sample.shape - if cat_dim == -2 or cat_dim == 2: - cond_latent = sample[:, :, :height//2, :] - ref_latent = sample[:, :, -(height//2):, :] - elif cat_dim == -1 or cat_dim == 3: - cond_latent = sample[:, :, :, :width//2] - ref_latent = sample[:, :, :, -(width//2):] - mid_block_res_sample = self.controlnet_mid_block((cond_latent, ref_latent), ) - - # 6. scaling - if guess_mode and not self.config.global_pool_conditions: - scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0 - scales = scales * conditioning_scale - down_block_res_samples = [sample*scale for sample, scale in zip(down_block_res_samples, scales)] - mid_block_res_sample = mid_block_res_sample*scales[-1] # last scale - else: - down_block_res_samples = [sample*conditioning_scale for sample in down_block_res_samples] - mid_block_res_sample = mid_block_res_sample*conditioning_scale - - if self.config.global_pool_conditions: - down_block_res_samples = [ - torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples - ] - mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True) - - if not return_dict: - return (down_block_res_samples, mid_block_res_sample) - - return AggregatorOutput( - down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample - ) - - -def zero_module(module): - for p in module.parameters(): - nn.init.zeros_(p) - return module diff --git a/module/attention.py b/module/attention.py deleted file mode 100644 index 875f3a45fa2d17a1389231e9d7ac57f344c94b35..0000000000000000000000000000000000000000 --- a/module/attention.py +++ /dev/null @@ -1,656 +0,0 @@ -# Copy from diffusers.models.attention.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Any, Dict, Optional - -import torch -import torch.nn.functional as F -from torch import nn - -from diffusers.utils import deprecate, logging -from diffusers.utils.torch_utils import maybe_allow_in_graph -from diffusers.models.activations import GEGLU, GELU, ApproximateGELU -from diffusers.models.attention_processor import Attention -from diffusers.models.embeddings import SinusoidalPositionalEmbedding -from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm - -from module.min_sdxl import LoRACompatibleLinear, LoRALinearLayer - - -logger = logging.get_logger(__name__) - -def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - -def get_encoder_trainable_params(encoder): - trainable_params = [] - - for module in encoder.modules(): - if isinstance(module, ExtractKVTransformerBlock): - # If LORA exists in attn1, train them. Otherwise, attn1 is frozen - # NOTE: not sure if we want it under a different subset - if module.attn1.to_k.lora_layer is not None: - trainable_params.extend(module.attn1.to_k.lora_layer.parameters()) - trainable_params.extend(module.attn1.to_v.lora_layer.parameters()) - trainable_params.extend(module.attn1.to_q.lora_layer.parameters()) - trainable_params.extend(module.attn1.to_out[0].lora_layer.parameters()) - - if module.attn2.to_k.lora_layer is not None: - trainable_params.extend(module.attn2.to_k.lora_layer.parameters()) - trainable_params.extend(module.attn2.to_v.lora_layer.parameters()) - trainable_params.extend(module.attn2.to_q.lora_layer.parameters()) - trainable_params.extend(module.attn2.to_out[0].lora_layer.parameters()) - - # If LORAs exist in kvcopy layers, train only them - if module.extract_kv1.to_k.lora_layer is not None: - trainable_params.extend(module.extract_kv1.to_k.lora_layer.parameters()) - trainable_params.extend(module.extract_kv1.to_v.lora_layer.parameters()) - else: - trainable_params.extend(module.extract_kv1.to_k.parameters()) - trainable_params.extend(module.extract_kv1.to_v.parameters()) - - return trainable_params - -def get_adapter_layers(encoder): - adapter_layers = [] - for module in encoder.modules(): - if isinstance(module, ExtractKVTransformerBlock): - adapter_layers.append(module.extract_kv2) - - return adapter_layers - -def get_adapter_trainable_params(encoder): - adapter_layers = get_adapter_layers(encoder) - trainable_params = [] - for layer in adapter_layers: - trainable_params.extend(layer.to_v.parameters()) - trainable_params.extend(layer.to_k.parameters()) - - return trainable_params - -def maybe_grad_checkpoint(resnet, attn, hidden_states, temb, encoder_hidden_states, adapter_hidden_states, do_ckpt=True): - - if do_ckpt: - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states, extracted_kv = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn), hidden_states, encoder_hidden_states, adapter_hidden_states, use_reentrant=False - ) - else: - hidden_states = resnet(hidden_states, temb) - hidden_states, extracted_kv = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - adapter_hidden_states=adapter_hidden_states, - ) - return hidden_states, extracted_kv - - -def init_lora_in_attn(attn_module, rank: int = 4, is_kvcopy=False): - # Set the `lora_layer` attribute of the attention-related matrices. - - attn_module.to_k.set_lora_layer( - LoRALinearLayer( - in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=rank - ) - ) - attn_module.to_v.set_lora_layer( - LoRALinearLayer( - in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=rank - ) - ) - - if not is_kvcopy: - attn_module.to_q.set_lora_layer( - LoRALinearLayer( - in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=rank - ) - ) - - attn_module.to_out[0].set_lora_layer( - LoRALinearLayer( - in_features=attn_module.to_out[0].in_features, - out_features=attn_module.to_out[0].out_features, - rank=rank, - ) - ) - -def drop_kvs(encoder_kvs, drop_chance): - for layer in encoder_kvs: - len_tokens = encoder_kvs[layer].self_attention.k.shape[1] - idx_to_keep = (torch.rand(len_tokens) > drop_chance) - - encoder_kvs[layer].self_attention.k = encoder_kvs[layer].self_attention.k[:, idx_to_keep] - encoder_kvs[layer].self_attention.v = encoder_kvs[layer].self_attention.v[:, idx_to_keep] - - return encoder_kvs - -def clone_kvs(encoder_kvs): - cloned_kvs = {} - for layer in encoder_kvs: - sa_cpy = KVCache(k=encoder_kvs[layer].self_attention.k.clone(), - v=encoder_kvs[layer].self_attention.v.clone()) - - ca_cpy = KVCache(k=encoder_kvs[layer].cross_attention.k.clone(), - v=encoder_kvs[layer].cross_attention.v.clone()) - - cloned_layer_cache = AttentionCache(self_attention=sa_cpy, cross_attention=ca_cpy) - - cloned_kvs[layer] = cloned_layer_cache - - return cloned_kvs - - -class KVCache(object): - def __init__(self, k, v): - self.k = k - self.v = v - -class AttentionCache(object): - def __init__(self, self_attention: KVCache, cross_attention: KVCache): - self.self_attention = self_attention - self.cross_attention = cross_attention - -class KVCopy(nn.Module): - def __init__( - self, inner_dim, cross_attention_dim=None, - ): - super(KVCopy, self).__init__() - - in_dim = cross_attention_dim or inner_dim - - self.to_k = LoRACompatibleLinear(in_dim, inner_dim, bias=False) - self.to_v = LoRACompatibleLinear(in_dim, inner_dim, bias=False) - - def forward(self, hidden_states): - - k = self.to_k(hidden_states) - v = self.to_v(hidden_states) - - return KVCache(k=k, v=v) - - def init_kv_copy(self, source_attn): - with torch.no_grad(): - self.to_k.weight.copy_(source_attn.to_k.weight) - self.to_v.weight.copy_(source_attn.to_v.weight) - - -class FeedForward(nn.Module): - r""" - A feed-forward layer. - - Parameters: - dim (`int`): The number of channels in the input. - dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. - mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. - final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. - bias (`bool`, defaults to True): Whether to use a bias in the linear layer. - """ - - def __init__( - self, - dim: int, - dim_out: Optional[int] = None, - mult: int = 4, - dropout: float = 0.0, - activation_fn: str = "geglu", - final_dropout: bool = False, - inner_dim=None, - bias: bool = True, - ): - super().__init__() - if inner_dim is None: - inner_dim = int(dim * mult) - dim_out = dim_out if dim_out is not None else dim - - if activation_fn == "gelu": - act_fn = GELU(dim, inner_dim, bias=bias) - if activation_fn == "gelu-approximate": - act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias) - elif activation_fn == "geglu": - act_fn = GEGLU(dim, inner_dim, bias=bias) - elif activation_fn == "geglu-approximate": - act_fn = ApproximateGELU(dim, inner_dim, bias=bias) - - self.net = nn.ModuleList([]) - # project in - self.net.append(act_fn) - # project dropout - self.net.append(nn.Dropout(dropout)) - # project out - self.net.append(nn.Linear(inner_dim, dim_out, bias=bias)) - # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout - if final_dropout: - self.net.append(nn.Dropout(dropout)) - - def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - for module in self.net: - hidden_states = module(hidden_states) - return hidden_states - - -def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int): - # "feed_forward_chunk_size" can be used to save memory - if hidden_states.shape[chunk_dim] % chunk_size != 0: - raise ValueError( - f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." - ) - - num_chunks = hidden_states.shape[chunk_dim] // chunk_size - ff_output = torch.cat( - [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)], - dim=chunk_dim, - ) - return ff_output - - -@maybe_allow_in_graph -class GatedSelfAttentionDense(nn.Module): - r""" - A gated self-attention dense layer that combines visual features and object features. - - Parameters: - query_dim (`int`): The number of channels in the query. - context_dim (`int`): The number of channels in the context. - n_heads (`int`): The number of heads to use for attention. - d_head (`int`): The number of channels in each head. - """ - - def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int): - super().__init__() - - # we need a linear projection since we need cat visual feature and obj feature - self.linear = nn.Linear(context_dim, query_dim) - - self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head) - self.ff = FeedForward(query_dim, activation_fn="geglu") - - self.norm1 = nn.LayerNorm(query_dim) - self.norm2 = nn.LayerNorm(query_dim) - - self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0))) - self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0))) - - self.enabled = True - - def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor: - if not self.enabled: - return x - - n_visual = x.shape[1] - objs = self.linear(objs) - - x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :] - x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x)) - - return x - - -@maybe_allow_in_graph -class ExtractKVTransformerBlock(nn.Module): - r""" - A Transformer block that also outputs KV metrics. - - Parameters: - dim (`int`): The number of channels in the input and output. - num_attention_heads (`int`): The number of heads to use for multi-head attention. - attention_head_dim (`int`): The number of channels in each head. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. - activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. - num_embeds_ada_norm (: - obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. - attention_bias (: - obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. - only_cross_attention (`bool`, *optional*): - Whether to use only cross-attention layers. In this case two cross attention layers are used. - double_self_attention (`bool`, *optional*): - Whether to use two self-attention layers. In this case no cross attention layers are used. - upcast_attention (`bool`, *optional*): - Whether to upcast the attention computation to float32. This is useful for mixed precision training. - norm_elementwise_affine (`bool`, *optional*, defaults to `True`): - Whether to use learnable elementwise affine parameters for normalization. - norm_type (`str`, *optional*, defaults to `"layer_norm"`): - The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. - final_dropout (`bool` *optional*, defaults to False): - Whether to apply a final dropout after the last feed-forward layer. - attention_type (`str`, *optional*, defaults to `"default"`): - The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. - positional_embeddings (`str`, *optional*, defaults to `None`): - The type of positional embeddings to apply to. - num_positional_embeddings (`int`, *optional*, defaults to `None`): - The maximum number of positional embeddings to apply. - """ - - def __init__( - self, - dim: int, # Originally hidden_size - num_attention_heads: int, - attention_head_dim: int, - dropout=0.0, - cross_attention_dim: Optional[int] = None, - activation_fn: str = "geglu", - num_embeds_ada_norm: Optional[int] = None, - attention_bias: bool = False, - only_cross_attention: bool = False, - double_self_attention: bool = False, - upcast_attention: bool = False, - norm_elementwise_affine: bool = True, - norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen' - norm_eps: float = 1e-5, - final_dropout: bool = False, - attention_type: str = "default", - positional_embeddings: Optional[str] = None, - num_positional_embeddings: Optional[int] = None, - ada_norm_continous_conditioning_embedding_dim: Optional[int] = None, - ada_norm_bias: Optional[int] = None, - ff_inner_dim: Optional[int] = None, - ff_bias: bool = True, - attention_out_bias: bool = True, - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, - ): - super().__init__() - self.only_cross_attention = only_cross_attention - - # We keep these boolean flags for backward-compatibility. - self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" - self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" - self.use_ada_layer_norm_single = norm_type == "ada_norm_single" - self.use_layer_norm = norm_type == "layer_norm" - self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous" - - if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: - raise ValueError( - f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" - f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." - ) - - self.norm_type = norm_type - self.num_embeds_ada_norm = num_embeds_ada_norm - - if positional_embeddings and (num_positional_embeddings is None): - raise ValueError( - "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined." - ) - - if positional_embeddings == "sinusoidal": - self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings) - else: - self.pos_embed = None - - # Define 3 blocks. Each block has its own normalization layer. - # 1. Self-Attn - if norm_type == "ada_norm": - self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) - elif norm_type == "ada_norm_zero": - self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) - elif norm_type == "ada_norm_continuous": - self.norm1 = AdaLayerNormContinuous( - dim, - ada_norm_continous_conditioning_embedding_dim, - norm_elementwise_affine, - norm_eps, - ada_norm_bias, - "rms_norm", - ) - else: - self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) - - self.attn1 = Attention( - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - cross_attention_dim=cross_attention_dim if only_cross_attention else None, - upcast_attention=upcast_attention, - out_bias=attention_out_bias, - ) - if extract_self_attention_kv: - self.extract_kv1 = KVCopy(cross_attention_dim=cross_attention_dim if only_cross_attention else None, inner_dim=dim) - - # 2. Cross-Attn - if cross_attention_dim is not None or double_self_attention: - # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. - # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during - # the second cross attention block. - if norm_type == "ada_norm": - self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) - elif norm_type == "ada_norm_continuous": - self.norm2 = AdaLayerNormContinuous( - dim, - ada_norm_continous_conditioning_embedding_dim, - norm_elementwise_affine, - norm_eps, - ada_norm_bias, - "rms_norm", - ) - else: - self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine) - - self.attn2 = Attention( - query_dim=dim, - cross_attention_dim=cross_attention_dim if not double_self_attention else None, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - upcast_attention=upcast_attention, - out_bias=attention_out_bias, - ) # is self-attn if encoder_hidden_states is none - if extract_cross_attention_kv: - self.extract_kv2 = KVCopy(cross_attention_dim=None, inner_dim=dim) - else: - self.norm2 = None - self.attn2 = None - - # 3. Feed-forward - if norm_type == "ada_norm_continuous": - self.norm3 = AdaLayerNormContinuous( - dim, - ada_norm_continous_conditioning_embedding_dim, - norm_elementwise_affine, - norm_eps, - ada_norm_bias, - "layer_norm", - ) - - elif norm_type in ["ada_norm_zero", "ada_norm", "layer_norm", "ada_norm_continuous"]: - self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine) - elif norm_type == "layer_norm_i2vgen": - self.norm3 = None - - self.ff = FeedForward( - dim, - dropout=dropout, - activation_fn=activation_fn, - final_dropout=final_dropout, - inner_dim=ff_inner_dim, - bias=ff_bias, - ) - - # 4. Fuser - if attention_type == "gated" or attention_type == "gated-text-image": - self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim) - - # 5. Scale-shift for PixArt-Alpha. - if norm_type == "ada_norm_single": - self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) - - # let chunk size default to None - self._chunk_size = None - self._chunk_dim = 0 - - def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): - # Sets chunk feed-forward - self._chunk_size = chunk_size - self._chunk_dim = dim - - def forward( - self, - hidden_states: torch.FloatTensor, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - timestep: Optional[torch.LongTensor] = None, - cross_attention_kwargs: Dict[str, Any] = None, - class_labels: Optional[torch.LongTensor] = None, - added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, - ) -> torch.FloatTensor: - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - # Notice that normalization is always applied before the real computation in the following blocks. - # 0. Self-Attention - batch_size = hidden_states.shape[0] - - if self.norm_type == "ada_norm": - norm_hidden_states = self.norm1(hidden_states, timestep) - elif self.norm_type == "ada_norm_zero": - norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( - hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype - ) - elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]: - norm_hidden_states = self.norm1(hidden_states) - elif self.norm_type == "ada_norm_continuous": - norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"]) - elif self.norm_type == "ada_norm_single": - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( - self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) - ).chunk(6, dim=1) - norm_hidden_states = self.norm1(hidden_states) - norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa - norm_hidden_states = norm_hidden_states.squeeze(1) - else: - raise ValueError("Incorrect norm used") - - if self.pos_embed is not None: - norm_hidden_states = self.pos_embed(norm_hidden_states) - - # 1. Prepare GLIGEN inputs - cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} - gligen_kwargs = cross_attention_kwargs.pop("gligen", None) - kv_drop_idx = cross_attention_kwargs.pop("kv_drop_idx", None) - - if hasattr(self, "extract_kv1"): - kv_out_self = self.extract_kv1(norm_hidden_states) - if kv_drop_idx is not None: - zero_kv_out_self_k = torch.zeros_like(kv_out_self.k) - kv_out_self.k[kv_drop_idx] = zero_kv_out_self_k[kv_drop_idx] - zero_kv_out_self_v = torch.zeros_like(kv_out_self.v) - kv_out_self.v[kv_drop_idx] = zero_kv_out_self_v[kv_drop_idx] - else: - kv_out_self = None - attn_output = self.attn1( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - if self.norm_type == "ada_norm_zero": - attn_output = gate_msa.unsqueeze(1) * attn_output - elif self.norm_type == "ada_norm_single": - attn_output = gate_msa * attn_output - - hidden_states = attn_output + hidden_states - if hidden_states.ndim == 4: - hidden_states = hidden_states.squeeze(1) - - # 1.2 GLIGEN Control - if gligen_kwargs is not None: - hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) - - # 3. Cross-Attention - if self.attn2 is not None: - if self.norm_type == "ada_norm": - norm_hidden_states = self.norm2(hidden_states, timestep) - elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]: - norm_hidden_states = self.norm2(hidden_states) - elif self.norm_type == "ada_norm_single": - # For PixArt norm2 isn't applied here: - # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 - norm_hidden_states = hidden_states - elif self.norm_type == "ada_norm_continuous": - norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"]) - else: - raise ValueError("Incorrect norm") - - if self.pos_embed is not None and self.norm_type != "ada_norm_single": - norm_hidden_states = self.pos_embed(norm_hidden_states) - - attn_output = self.attn2( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=encoder_attention_mask, - temb=timestep, - **cross_attention_kwargs, - ) - hidden_states = attn_output + hidden_states - - if hasattr(self, "extract_kv2"): - kv_out_cross = self.extract_kv2(hidden_states) - if kv_drop_idx is not None: - zero_kv_out_cross_k = torch.zeros_like(kv_out_cross.k) - kv_out_cross.k[kv_drop_idx] = zero_kv_out_cross_k[kv_drop_idx] - zero_kv_out_cross_v = torch.zeros_like(kv_out_cross.v) - kv_out_cross.v[kv_drop_idx] = zero_kv_out_cross_v[kv_drop_idx] - else: - kv_out_cross = None - - # 4. Feed-forward - # i2vgen doesn't have this norm 🤷‍♂️ - if self.norm_type == "ada_norm_continuous": - norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"]) - elif not self.norm_type == "ada_norm_single": - norm_hidden_states = self.norm3(hidden_states) - - if self.norm_type == "ada_norm_zero": - norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] - - if self.norm_type == "ada_norm_single": - norm_hidden_states = self.norm2(hidden_states) - norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp - - if self._chunk_size is not None: - # "feed_forward_chunk_size" can be used to save memory - ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size) - else: - ff_output = self.ff(norm_hidden_states) - - if self.norm_type == "ada_norm_zero": - ff_output = gate_mlp.unsqueeze(1) * ff_output - elif self.norm_type == "ada_norm_single": - ff_output = gate_mlp * ff_output - - hidden_states = ff_output + hidden_states - if hidden_states.ndim == 4: - hidden_states = hidden_states.squeeze(1) - - return hidden_states, AttentionCache(self_attention=kv_out_self, cross_attention=kv_out_cross) - - def init_kv_extraction(self): - if hasattr(self, "extract_kv1"): - self.extract_kv1.init_kv_copy(self.attn1) - if hasattr(self, "extract_kv2"): - self.extract_kv2.init_kv_copy(self.attn1) diff --git a/module/diffusers_vae/autoencoder_kl.py b/module/diffusers_vae/autoencoder_kl.py deleted file mode 100644 index 633928aa5174f33fca0c3d482e480cea7c4ec12a..0000000000000000000000000000000000000000 --- a/module/diffusers_vae/autoencoder_kl.py +++ /dev/null @@ -1,489 +0,0 @@ -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Dict, Optional, Tuple, Union - -import torch -import torch.nn as nn - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.loaders import FromOriginalVAEMixin -from diffusers.utils.accelerate_utils import apply_forward_hook -from diffusers.models.attention_processor import ( - ADDED_KV_ATTENTION_PROCESSORS, - CROSS_ATTENTION_PROCESSORS, - Attention, - AttentionProcessor, - AttnAddedKVProcessor, - AttnProcessor, -) -from diffusers.models.modeling_outputs import AutoencoderKLOutput -from diffusers.models.modeling_utils import ModelMixin -from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder - - -class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalVAEMixin): - r""" - A VAE model with KL loss for encoding images into latents and decoding latent representations into images. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented - for all models (such as downloading or saving). - - Parameters: - in_channels (int, *optional*, defaults to 3): Number of channels in the input image. - out_channels (int, *optional*, defaults to 3): Number of channels in the output. - down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`): - Tuple of downsample block types. - up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`): - Tuple of upsample block types. - block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`): - Tuple of block output channels. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space. - sample_size (`int`, *optional*, defaults to `32`): Sample input size. - scaling_factor (`float`, *optional*, defaults to 0.18215): - The component-wise standard deviation of the trained latent space computed using the first batch of the - training set. This is used to scale the latent space to have unit variance when training the diffusion - model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the - diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 - / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image - Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper. - force_upcast (`bool`, *optional*, default to `True`): - If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE - can be fine-tuned / trained to a lower range without loosing too much precision in which case - `force_upcast` can be set to `False` - see: https://huggingface.co./madebyollin/sdxl-vae-fp16-fix - """ - - _supports_gradient_checkpointing = True - - @register_to_config - def __init__( - self, - in_channels: int = 3, - out_channels: int = 3, - down_block_types: Tuple[str] = ("DownEncoderBlock2D",), - up_block_types: Tuple[str] = ("UpDecoderBlock2D",), - block_out_channels: Tuple[int] = (64,), - layers_per_block: int = 1, - act_fn: str = "silu", - latent_channels: int = 4, - norm_num_groups: int = 32, - sample_size: int = 32, - scaling_factor: float = 0.18215, - force_upcast: float = True, - ): - super().__init__() - - # pass init params to Encoder - self.encoder = Encoder( - in_channels=in_channels, - out_channels=latent_channels, - down_block_types=down_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - act_fn=act_fn, - norm_num_groups=norm_num_groups, - double_z=True, - ) - - # pass init params to Decoder - self.decoder = Decoder( - in_channels=latent_channels, - out_channels=out_channels, - up_block_types=up_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - norm_num_groups=norm_num_groups, - act_fn=act_fn, - ) - - self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) - self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1) - - self.use_slicing = False - self.use_tiling = False - - # only relevant if vae tiling is enabled - self.tile_sample_min_size = self.config.sample_size - sample_size = ( - self.config.sample_size[0] - if isinstance(self.config.sample_size, (list, tuple)) - else self.config.sample_size - ) - self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1))) - self.tile_overlap_factor = 0.25 - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, (Encoder, Decoder)): - module.gradient_checkpointing = value - - def enable_tiling(self, use_tiling: bool = True): - r""" - Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to - compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow - processing larger images. - """ - self.use_tiling = use_tiling - - def disable_tiling(self): - r""" - Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing - decoding in one step. - """ - self.enable_tiling(False) - - def enable_slicing(self): - r""" - Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to - compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. - """ - self.use_slicing = True - - def disable_slicing(self): - r""" - Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing - decoding in one step. - """ - self.use_slicing = False - - @property - # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors - def attn_processors(self) -> Dict[str, AttentionProcessor]: - r""" - Returns: - `dict` of attention processors: A dictionary containing all attention processors used in the model with - indexed by its weight name. - """ - # set recursively - processors = {} - - def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): - if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) - - for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) - - return processors - - for name, module in self.named_children(): - fn_recursive_add_processors(name, module, processors) - - return processors - - # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor - def set_attn_processor( - self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False - ): - r""" - Sets the attention processor to use to compute attention. - - Parameters: - processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): - The instantiated processor class or a dictionary of processor classes that will be set as the processor - for **all** `Attention` layers. - - If `processor` is a dict, the key needs to define the path to the corresponding cross attention - processor. This is strongly recommended when setting trainable attention processors. - - """ - count = len(self.attn_processors.keys()) - - if isinstance(processor, dict) and len(processor) != count: - raise ValueError( - f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) - - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): - if hasattr(module, "set_processor"): - if not isinstance(processor, dict): - module.set_processor(processor, _remove_lora=_remove_lora) - else: - module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora) - - for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) - - for name, module in self.named_children(): - fn_recursive_attn_processor(name, module, processor) - - # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor - def set_default_attn_processor(self): - """ - Disables custom attention processors and sets the default attention implementation. - """ - if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnAddedKVProcessor() - elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnProcessor() - else: - raise ValueError( - f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" - ) - - self.set_attn_processor(processor, _remove_lora=True) - - @apply_forward_hook - def encode( - self, x: torch.FloatTensor, return_dict: bool = True - ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]: - """ - Encode a batch of images into latents. - - Args: - x (`torch.FloatTensor`): Input batch of images. - return_dict (`bool`, *optional*, defaults to `True`): - Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. - - Returns: - The latent representations of the encoded images. If `return_dict` is True, a - [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned. - """ - if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size): - return self.tiled_encode(x, return_dict=return_dict) - - if self.use_slicing and x.shape[0] > 1: - encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)] - h = torch.cat(encoded_slices) - else: - h = self.encoder(x) - - moments = self.quant_conv(h) - posterior = DiagonalGaussianDistribution(moments) - - if not return_dict: - return (posterior,) - - return AutoencoderKLOutput(latent_dist=posterior) - - def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: - if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size): - return self.tiled_decode(z, return_dict=return_dict) - - z = self.post_quant_conv(z) - dec = self.decoder(z) - - if not return_dict: - return (dec,) - - return DecoderOutput(sample=dec) - - @apply_forward_hook - def decode( - self, z: torch.FloatTensor, return_dict: bool = True, generator=None - ) -> Union[DecoderOutput, torch.FloatTensor]: - """ - Decode a batch of images. - - Args: - z (`torch.FloatTensor`): Input batch of latent vectors. - return_dict (`bool`, *optional*, defaults to `True`): - Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple. - - Returns: - [`~models.vae.DecoderOutput`] or `tuple`: - If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is - returned. - - """ - if self.use_slicing and z.shape[0] > 1: - decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)] - decoded = torch.cat(decoded_slices) - else: - decoded = self._decode(z).sample - - if not return_dict: - return (decoded,) - - return DecoderOutput(sample=decoded) - - def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: - blend_extent = min(a.shape[2], b.shape[2], blend_extent) - for y in range(blend_extent): - b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent) - return b - - def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: - blend_extent = min(a.shape[3], b.shape[3], blend_extent) - for x in range(blend_extent): - b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent) - return b - - def tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput: - r"""Encode a batch of images using a tiled encoder. - - When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several - steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is - different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the - tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the - output, but they should be much less noticeable. - - Args: - x (`torch.FloatTensor`): Input batch of images. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. - - Returns: - [`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`: - If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain - `tuple` is returned. - """ - overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)) - blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor) - row_limit = self.tile_latent_min_size - blend_extent - - # Split the image into 512x512 tiles and encode them separately. - rows = [] - for i in range(0, x.shape[2], overlap_size): - row = [] - for j in range(0, x.shape[3], overlap_size): - tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size] - tile = self.encoder(tile) - tile = self.quant_conv(tile) - row.append(tile) - rows.append(row) - result_rows = [] - for i, row in enumerate(rows): - result_row = [] - for j, tile in enumerate(row): - # blend the above tile and the left tile - # to the current tile and add the current tile to the result row - if i > 0: - tile = self.blend_v(rows[i - 1][j], tile, blend_extent) - if j > 0: - tile = self.blend_h(row[j - 1], tile, blend_extent) - result_row.append(tile[:, :, :row_limit, :row_limit]) - result_rows.append(torch.cat(result_row, dim=3)) - - moments = torch.cat(result_rows, dim=2) - posterior = DiagonalGaussianDistribution(moments) - - if not return_dict: - return (posterior,) - - return AutoencoderKLOutput(latent_dist=posterior) - - def tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: - r""" - Decode a batch of images using a tiled decoder. - - Args: - z (`torch.FloatTensor`): Input batch of latent vectors. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple. - - Returns: - [`~models.vae.DecoderOutput`] or `tuple`: - If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is - returned. - """ - overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)) - blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor) - row_limit = self.tile_sample_min_size - blend_extent - - # Split z into overlapping 64x64 tiles and decode them separately. - # The tiles have an overlap to avoid seams between tiles. - rows = [] - for i in range(0, z.shape[2], overlap_size): - row = [] - for j in range(0, z.shape[3], overlap_size): - tile = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size] - tile = self.post_quant_conv(tile) - decoded = self.decoder(tile) - row.append(decoded) - rows.append(row) - result_rows = [] - for i, row in enumerate(rows): - result_row = [] - for j, tile in enumerate(row): - # blend the above tile and the left tile - # to the current tile and add the current tile to the result row - if i > 0: - tile = self.blend_v(rows[i - 1][j], tile, blend_extent) - if j > 0: - tile = self.blend_h(row[j - 1], tile, blend_extent) - result_row.append(tile[:, :, :row_limit, :row_limit]) - result_rows.append(torch.cat(result_row, dim=3)) - - dec = torch.cat(result_rows, dim=2) - if not return_dict: - return (dec,) - - return DecoderOutput(sample=dec) - - def forward( - self, - sample: torch.FloatTensor, - sample_posterior: bool = False, - return_dict: bool = True, - generator: Optional[torch.Generator] = None, - ) -> Union[DecoderOutput, torch.FloatTensor]: - r""" - Args: - sample (`torch.FloatTensor`): Input sample. - sample_posterior (`bool`, *optional*, defaults to `False`): - Whether to sample from the posterior. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`DecoderOutput`] instead of a plain tuple. - """ - x = sample - posterior = self.encode(x).latent_dist - if sample_posterior: - z = posterior.sample(generator=generator) - else: - z = posterior.mode() - dec = self.decode(z).sample - - if not return_dict: - return (dec,) - - return DecoderOutput(sample=dec) - - # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections - def fuse_qkv_projections(self): - """ - Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, - key, value) are fused. For cross-attention modules, key and value projection matrices are fused. - - - - This API is 🧪 experimental. - - - """ - self.original_attn_processors = None - - for _, attn_processor in self.attn_processors.items(): - if "Added" in str(attn_processor.__class__.__name__): - raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") - - self.original_attn_processors = self.attn_processors - - for module in self.modules(): - if isinstance(module, Attention): - module.fuse_projections(fuse=True) - - # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections - def unfuse_qkv_projections(self): - """Disables the fused QKV projection if enabled. - - - - This API is 🧪 experimental. - - - - """ - if self.original_attn_processors is not None: - self.set_attn_processor(self.original_attn_processors) \ No newline at end of file diff --git a/module/diffusers_vae/vae.py b/module/diffusers_vae/vae.py deleted file mode 100644 index a6a68aaa0d628cea0c809714dcc760516197d5bc..0000000000000000000000000000000000000000 --- a/module/diffusers_vae/vae.py +++ /dev/null @@ -1,985 +0,0 @@ -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from dataclasses import dataclass -from typing import Optional, Tuple - -import numpy as np -import torch -import torch.nn as nn - -from diffusers.utils import BaseOutput, is_torch_version -from diffusers.utils.torch_utils import randn_tensor -from diffusers.models.activations import get_activation -from diffusers.models.attention_processor import SpatialNorm -from diffusers.models.unet_2d_blocks import ( - AutoencoderTinyBlock, - UNetMidBlock2D, - get_down_block, - get_up_block, -) - - -@dataclass -class DecoderOutput(BaseOutput): - r""" - Output of decoding method. - - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - The decoded output sample from the last layer of the model. - """ - - sample: torch.FloatTensor - - -class Encoder(nn.Module): - r""" - The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation. - - Args: - in_channels (`int`, *optional*, defaults to 3): - The number of input channels. - out_channels (`int`, *optional*, defaults to 3): - The number of output channels. - down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`): - The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available - options. - block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`): - The number of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): - The number of layers per block. - norm_num_groups (`int`, *optional*, defaults to 32): - The number of groups for normalization. - act_fn (`str`, *optional*, defaults to `"silu"`): - The activation function to use. See `~diffusers.models.activations.get_activation` for available options. - double_z (`bool`, *optional*, defaults to `True`): - Whether to double the number of output channels for the last block. - """ - - def __init__( - self, - in_channels: int = 3, - out_channels: int = 3, - down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",), - block_out_channels: Tuple[int, ...] = (64,), - layers_per_block: int = 2, - norm_num_groups: int = 32, - act_fn: str = "silu", - double_z: bool = True, - mid_block_add_attention=True, - ): - super().__init__() - self.layers_per_block = layers_per_block - - self.conv_in = nn.Conv2d( - in_channels, - block_out_channels[0], - kernel_size=3, - stride=1, - padding=1, - ) - - self.mid_block = None - self.down_blocks = nn.ModuleList([]) - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=self.layers_per_block, - in_channels=input_channel, - out_channels=output_channel, - add_downsample=not is_final_block, - resnet_eps=1e-6, - downsample_padding=0, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - attention_head_dim=output_channel, - temb_channels=None, - ) - self.down_blocks.append(down_block) - - # mid - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - resnet_eps=1e-6, - resnet_act_fn=act_fn, - output_scale_factor=1, - resnet_time_scale_shift="default", - attention_head_dim=block_out_channels[-1], - resnet_groups=norm_num_groups, - temb_channels=None, - add_attention=mid_block_add_attention, - ) - - # out - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) - self.conv_act = nn.SiLU() - - conv_out_channels = 2 * out_channels if double_z else out_channels - self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1) - - self.gradient_checkpointing = False - - def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor: - r"""The forward method of the `Encoder` class.""" - - sample = self.conv_in(sample) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - # down - if is_torch_version(">=", "1.11.0"): - for down_block in self.down_blocks: - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(down_block), sample, use_reentrant=False - ) - # middle - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.mid_block), sample, use_reentrant=False - ) - else: - for down_block in self.down_blocks: - sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample) - # middle - sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample) - - else: - # down - for down_block in self.down_blocks: - sample = down_block(sample) - - # middle - sample = self.mid_block(sample) - - # post-process - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - return sample - - -class Decoder(nn.Module): - r""" - The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample. - - Args: - in_channels (`int`, *optional*, defaults to 3): - The number of input channels. - out_channels (`int`, *optional*, defaults to 3): - The number of output channels. - up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`): - The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options. - block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`): - The number of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): - The number of layers per block. - norm_num_groups (`int`, *optional*, defaults to 32): - The number of groups for normalization. - act_fn (`str`, *optional*, defaults to `"silu"`): - The activation function to use. See `~diffusers.models.activations.get_activation` for available options. - norm_type (`str`, *optional*, defaults to `"group"`): - The normalization type to use. Can be either `"group"` or `"spatial"`. - """ - - def __init__( - self, - in_channels: int = 3, - out_channels: int = 3, - up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",), - block_out_channels: Tuple[int, ...] = (64,), - layers_per_block: int = 2, - norm_num_groups: int = 32, - act_fn: str = "silu", - norm_type: str = "group", # group, spatial - mid_block_add_attention=True, - ): - super().__init__() - self.layers_per_block = layers_per_block - - self.conv_in = nn.Conv2d( - in_channels, - block_out_channels[-1], - kernel_size=3, - stride=1, - padding=1, - ) - - self.mid_block = None - self.up_blocks = nn.ModuleList([]) - - temb_channels = in_channels if norm_type == "spatial" else None - - # mid - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - resnet_eps=1e-6, - resnet_act_fn=act_fn, - output_scale_factor=1, - resnet_time_scale_shift="default" if norm_type == "group" else norm_type, - attention_head_dim=block_out_channels[-1], - resnet_groups=norm_num_groups, - temb_channels=temb_channels, - add_attention=mid_block_add_attention, - ) - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - - is_final_block = i == len(block_out_channels) - 1 - - up_block = get_up_block( - up_block_type, - num_layers=self.layers_per_block + 1, - in_channels=prev_output_channel, - out_channels=output_channel, - prev_output_channel=None, - add_upsample=not is_final_block, - resnet_eps=1e-6, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - attention_head_dim=output_channel, - temb_channels=temb_channels, - resnet_time_scale_shift=norm_type, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - if norm_type == "spatial": - self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels) - else: - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6) - self.conv_act = nn.SiLU() - self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) - - self.gradient_checkpointing = False - - def forward( - self, - sample: torch.FloatTensor, - latent_embeds: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - r"""The forward method of the `Decoder` class.""" - - sample = self.conv_in(sample) - sample = sample.to(torch.float32) - - upscale_dtype = next(iter(self.up_blocks.parameters())).dtype - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - # middle - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.mid_block), - sample, - latent_embeds, - use_reentrant=False, - ) - sample = sample.to(upscale_dtype) - - # up - for up_block in self.up_blocks: - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(up_block), - sample, - latent_embeds, - use_reentrant=False, - ) - else: - # middle - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.mid_block), sample, latent_embeds - ) - sample = sample.to(upscale_dtype) - - # up - for up_block in self.up_blocks: - sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds) - else: - # middle - sample = self.mid_block(sample, latent_embeds) - sample = sample.to(upscale_dtype) - - # up - for up_block in self.up_blocks: - sample = up_block(sample, latent_embeds) - - # post-process - if latent_embeds is None: - sample = self.conv_norm_out(sample) - else: - sample = self.conv_norm_out(sample, latent_embeds) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - return sample - - -class UpSample(nn.Module): - r""" - The `UpSample` layer of a variational autoencoder that upsamples its input. - - Args: - in_channels (`int`, *optional*, defaults to 3): - The number of input channels. - out_channels (`int`, *optional*, defaults to 3): - The number of output channels. - """ - - def __init__( - self, - in_channels: int, - out_channels: int, - ) -> None: - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.deconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1) - - def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: - r"""The forward method of the `UpSample` class.""" - x = torch.relu(x) - x = self.deconv(x) - return x - - -class MaskConditionEncoder(nn.Module): - """ - used in AsymmetricAutoencoderKL - """ - - def __init__( - self, - in_ch: int, - out_ch: int = 192, - res_ch: int = 768, - stride: int = 16, - ) -> None: - super().__init__() - - channels = [] - while stride > 1: - stride = stride // 2 - in_ch_ = out_ch * 2 - if out_ch > res_ch: - out_ch = res_ch - if stride == 1: - in_ch_ = res_ch - channels.append((in_ch_, out_ch)) - out_ch *= 2 - - out_channels = [] - for _in_ch, _out_ch in channels: - out_channels.append(_out_ch) - out_channels.append(channels[-1][0]) - - layers = [] - in_ch_ = in_ch - for l in range(len(out_channels)): - out_ch_ = out_channels[l] - if l == 0 or l == 1: - layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=3, stride=1, padding=1)) - else: - layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=4, stride=2, padding=1)) - in_ch_ = out_ch_ - - self.layers = nn.Sequential(*layers) - - def forward(self, x: torch.FloatTensor, mask=None) -> torch.FloatTensor: - r"""The forward method of the `MaskConditionEncoder` class.""" - out = {} - for l in range(len(self.layers)): - layer = self.layers[l] - x = layer(x) - out[str(tuple(x.shape))] = x - x = torch.relu(x) - return out - - -class MaskConditionDecoder(nn.Module): - r"""The `MaskConditionDecoder` should be used in combination with [`AsymmetricAutoencoderKL`] to enhance the model's - decoder with a conditioner on the mask and masked image. - - Args: - in_channels (`int`, *optional*, defaults to 3): - The number of input channels. - out_channels (`int`, *optional*, defaults to 3): - The number of output channels. - up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`): - The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options. - block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`): - The number of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): - The number of layers per block. - norm_num_groups (`int`, *optional*, defaults to 32): - The number of groups for normalization. - act_fn (`str`, *optional*, defaults to `"silu"`): - The activation function to use. See `~diffusers.models.activations.get_activation` for available options. - norm_type (`str`, *optional*, defaults to `"group"`): - The normalization type to use. Can be either `"group"` or `"spatial"`. - """ - - def __init__( - self, - in_channels: int = 3, - out_channels: int = 3, - up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",), - block_out_channels: Tuple[int, ...] = (64,), - layers_per_block: int = 2, - norm_num_groups: int = 32, - act_fn: str = "silu", - norm_type: str = "group", # group, spatial - ): - super().__init__() - self.layers_per_block = layers_per_block - - self.conv_in = nn.Conv2d( - in_channels, - block_out_channels[-1], - kernel_size=3, - stride=1, - padding=1, - ) - - self.mid_block = None - self.up_blocks = nn.ModuleList([]) - - temb_channels = in_channels if norm_type == "spatial" else None - - # mid - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - resnet_eps=1e-6, - resnet_act_fn=act_fn, - output_scale_factor=1, - resnet_time_scale_shift="default" if norm_type == "group" else norm_type, - attention_head_dim=block_out_channels[-1], - resnet_groups=norm_num_groups, - temb_channels=temb_channels, - ) - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - - is_final_block = i == len(block_out_channels) - 1 - - up_block = get_up_block( - up_block_type, - num_layers=self.layers_per_block + 1, - in_channels=prev_output_channel, - out_channels=output_channel, - prev_output_channel=None, - add_upsample=not is_final_block, - resnet_eps=1e-6, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - attention_head_dim=output_channel, - temb_channels=temb_channels, - resnet_time_scale_shift=norm_type, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # condition encoder - self.condition_encoder = MaskConditionEncoder( - in_ch=out_channels, - out_ch=block_out_channels[0], - res_ch=block_out_channels[-1], - ) - - # out - if norm_type == "spatial": - self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels) - else: - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6) - self.conv_act = nn.SiLU() - self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) - - self.gradient_checkpointing = False - - def forward( - self, - z: torch.FloatTensor, - image: Optional[torch.FloatTensor] = None, - mask: Optional[torch.FloatTensor] = None, - latent_embeds: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - r"""The forward method of the `MaskConditionDecoder` class.""" - sample = z - sample = self.conv_in(sample) - - upscale_dtype = next(iter(self.up_blocks.parameters())).dtype - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - # middle - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.mid_block), - sample, - latent_embeds, - use_reentrant=False, - ) - sample = sample.to(upscale_dtype) - - # condition encoder - if image is not None and mask is not None: - masked_image = (1 - mask) * image - im_x = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.condition_encoder), - masked_image, - mask, - use_reentrant=False, - ) - - # up - for up_block in self.up_blocks: - if image is not None and mask is not None: - sample_ = im_x[str(tuple(sample.shape))] - mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest") - sample = sample * mask_ + sample_ * (1 - mask_) - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(up_block), - sample, - latent_embeds, - use_reentrant=False, - ) - if image is not None and mask is not None: - sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask) - else: - # middle - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.mid_block), sample, latent_embeds - ) - sample = sample.to(upscale_dtype) - - # condition encoder - if image is not None and mask is not None: - masked_image = (1 - mask) * image - im_x = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.condition_encoder), - masked_image, - mask, - ) - - # up - for up_block in self.up_blocks: - if image is not None and mask is not None: - sample_ = im_x[str(tuple(sample.shape))] - mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest") - sample = sample * mask_ + sample_ * (1 - mask_) - sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds) - if image is not None and mask is not None: - sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask) - else: - # middle - sample = self.mid_block(sample, latent_embeds) - sample = sample.to(upscale_dtype) - - # condition encoder - if image is not None and mask is not None: - masked_image = (1 - mask) * image - im_x = self.condition_encoder(masked_image, mask) - - # up - for up_block in self.up_blocks: - if image is not None and mask is not None: - sample_ = im_x[str(tuple(sample.shape))] - mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest") - sample = sample * mask_ + sample_ * (1 - mask_) - sample = up_block(sample, latent_embeds) - if image is not None and mask is not None: - sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask) - - # post-process - if latent_embeds is None: - sample = self.conv_norm_out(sample) - else: - sample = self.conv_norm_out(sample, latent_embeds) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - return sample - - -class VectorQuantizer(nn.Module): - """ - Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix - multiplications and allows for post-hoc remapping of indices. - """ - - # NOTE: due to a bug the beta term was applied to the wrong term. for - # backwards compatibility we use the buggy version by default, but you can - # specify legacy=False to fix it. - def __init__( - self, - n_e: int, - vq_embed_dim: int, - beta: float, - remap=None, - unknown_index: str = "random", - sane_index_shape: bool = False, - legacy: bool = True, - ): - super().__init__() - self.n_e = n_e - self.vq_embed_dim = vq_embed_dim - self.beta = beta - self.legacy = legacy - - self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - self.remap = remap - if self.remap is not None: - self.register_buffer("used", torch.tensor(np.load(self.remap))) - self.used: torch.Tensor - self.re_embed = self.used.shape[0] - self.unknown_index = unknown_index # "random" or "extra" or integer - if self.unknown_index == "extra": - self.unknown_index = self.re_embed - self.re_embed = self.re_embed + 1 - print( - f"Remapping {self.n_e} indices to {self.re_embed} indices. " - f"Using {self.unknown_index} for unknown indices." - ) - else: - self.re_embed = n_e - - self.sane_index_shape = sane_index_shape - - def remap_to_used(self, inds: torch.LongTensor) -> torch.LongTensor: - ishape = inds.shape - assert len(ishape) > 1 - inds = inds.reshape(ishape[0], -1) - used = self.used.to(inds) - match = (inds[:, :, None] == used[None, None, ...]).long() - new = match.argmax(-1) - unknown = match.sum(2) < 1 - if self.unknown_index == "random": - new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device) - else: - new[unknown] = self.unknown_index - return new.reshape(ishape) - - def unmap_to_all(self, inds: torch.LongTensor) -> torch.LongTensor: - ishape = inds.shape - assert len(ishape) > 1 - inds = inds.reshape(ishape[0], -1) - used = self.used.to(inds) - if self.re_embed > self.used.shape[0]: # extra token - inds[inds >= self.used.shape[0]] = 0 # simply set to zero - back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds) - return back.reshape(ishape) - - def forward(self, z: torch.FloatTensor) -> Tuple[torch.FloatTensor, torch.FloatTensor, Tuple]: - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.vq_embed_dim) - - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - min_encoding_indices = torch.argmin(torch.cdist(z_flattened, self.embedding.weight), dim=1) - - z_q = self.embedding(min_encoding_indices).view(z.shape) - perplexity = None - min_encodings = None - - # compute loss for embedding - if not self.legacy: - loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2) - else: - loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2) - - # preserve gradients - z_q: torch.FloatTensor = z + (z_q - z).detach() - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - if self.remap is not None: - min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis - min_encoding_indices = self.remap_to_used(min_encoding_indices) - min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten - - if self.sane_index_shape: - min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3]) - - return z_q, loss, (perplexity, min_encodings, min_encoding_indices) - - def get_codebook_entry(self, indices: torch.LongTensor, shape: Tuple[int, ...]) -> torch.FloatTensor: - # shape specifying (batch, height, width, channel) - if self.remap is not None: - indices = indices.reshape(shape[0], -1) # add batch axis - indices = self.unmap_to_all(indices) - indices = indices.reshape(-1) # flatten again - - # get quantized latent vectors - z_q: torch.FloatTensor = self.embedding(indices) - - if shape is not None: - z_q = z_q.view(shape) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q - - -class DiagonalGaussianDistribution(object): - def __init__(self, parameters: torch.Tensor, deterministic: bool = False): - self.parameters = parameters - self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) - self.logvar = torch.clamp(self.logvar, -30.0, 20.0) - self.deterministic = deterministic - self.std = torch.exp(0.5 * self.logvar) - self.var = torch.exp(self.logvar) - if self.deterministic: - self.var = self.std = torch.zeros_like( - self.mean, device=self.parameters.device, dtype=self.parameters.dtype - ) - - def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor: - # make sure sample is on the same device as the parameters and has same dtype - sample = randn_tensor( - self.mean.shape, - generator=generator, - device=self.parameters.device, - dtype=self.parameters.dtype, - ) - x = self.mean + self.std * sample - return x - - def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: - if self.deterministic: - return torch.Tensor([0.0]) - else: - if other is None: - return 0.5 * torch.sum( - torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, - dim=[1, 2, 3], - ) - else: - return 0.5 * torch.sum( - torch.pow(self.mean - other.mean, 2) / other.var - + self.var / other.var - - 1.0 - - self.logvar - + other.logvar, - dim=[1, 2, 3], - ) - - def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor: - if self.deterministic: - return torch.Tensor([0.0]) - logtwopi = np.log(2.0 * np.pi) - return 0.5 * torch.sum( - logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, - dim=dims, - ) - - def mode(self) -> torch.Tensor: - return self.mean - - -class EncoderTiny(nn.Module): - r""" - The `EncoderTiny` layer is a simpler version of the `Encoder` layer. - - Args: - in_channels (`int`): - The number of input channels. - out_channels (`int`): - The number of output channels. - num_blocks (`Tuple[int, ...]`): - Each value of the tuple represents a Conv2d layer followed by `value` number of `AutoencoderTinyBlock`'s to - use. - block_out_channels (`Tuple[int, ...]`): - The number of output channels for each block. - act_fn (`str`): - The activation function to use. See `~diffusers.models.activations.get_activation` for available options. - """ - - def __init__( - self, - in_channels: int, - out_channels: int, - num_blocks: Tuple[int, ...], - block_out_channels: Tuple[int, ...], - act_fn: str, - ): - super().__init__() - - layers = [] - for i, num_block in enumerate(num_blocks): - num_channels = block_out_channels[i] - - if i == 0: - layers.append(nn.Conv2d(in_channels, num_channels, kernel_size=3, padding=1)) - else: - layers.append( - nn.Conv2d( - num_channels, - num_channels, - kernel_size=3, - padding=1, - stride=2, - bias=False, - ) - ) - - for _ in range(num_block): - layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn)) - - layers.append(nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, padding=1)) - - self.layers = nn.Sequential(*layers) - self.gradient_checkpointing = False - - def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: - r"""The forward method of the `EncoderTiny` class.""" - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x, use_reentrant=False) - else: - x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x) - - else: - # scale image from [-1, 1] to [0, 1] to match TAESD convention - x = self.layers(x.add(1).div(2)) - - return x - - -class DecoderTiny(nn.Module): - r""" - The `DecoderTiny` layer is a simpler version of the `Decoder` layer. - - Args: - in_channels (`int`): - The number of input channels. - out_channels (`int`): - The number of output channels. - num_blocks (`Tuple[int, ...]`): - Each value of the tuple represents a Conv2d layer followed by `value` number of `AutoencoderTinyBlock`'s to - use. - block_out_channels (`Tuple[int, ...]`): - The number of output channels for each block. - upsampling_scaling_factor (`int`): - The scaling factor to use for upsampling. - act_fn (`str`): - The activation function to use. See `~diffusers.models.activations.get_activation` for available options. - """ - - def __init__( - self, - in_channels: int, - out_channels: int, - num_blocks: Tuple[int, ...], - block_out_channels: Tuple[int, ...], - upsampling_scaling_factor: int, - act_fn: str, - ): - super().__init__() - - layers = [ - nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=1), - get_activation(act_fn), - ] - - for i, num_block in enumerate(num_blocks): - is_final_block = i == (len(num_blocks) - 1) - num_channels = block_out_channels[i] - - for _ in range(num_block): - layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn)) - - if not is_final_block: - layers.append(nn.Upsample(scale_factor=upsampling_scaling_factor)) - - conv_out_channel = num_channels if not is_final_block else out_channels - layers.append( - nn.Conv2d( - num_channels, - conv_out_channel, - kernel_size=3, - padding=1, - bias=is_final_block, - ) - ) - - self.layers = nn.Sequential(*layers) - self.gradient_checkpointing = False - - def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: - r"""The forward method of the `DecoderTiny` class.""" - # Clamp. - x = torch.tanh(x / 3) * 3 - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x, use_reentrant=False) - else: - x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x) - - else: - x = self.layers(x) - - # scale image from [0, 1] to [-1, 1] to match diffusers convention - return x.mul(2).sub(1) \ No newline at end of file diff --git a/module/ip_adapter/attention_processor.py b/module/ip_adapter/attention_processor.py deleted file mode 100644 index 0a54394f288be345d73c576a9c26cfc60813a47e..0000000000000000000000000000000000000000 --- a/module/ip_adapter/attention_processor.py +++ /dev/null @@ -1,1467 +0,0 @@ -# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py -import torch -import torch.nn as nn -import torch.nn.functional as F - -class AdaLayerNorm(nn.Module): - def __init__(self, embedding_dim: int, time_embedding_dim: int = None): - super().__init__() - - if time_embedding_dim is None: - time_embedding_dim = embedding_dim - - self.silu = nn.SiLU() - self.linear = nn.Linear(time_embedding_dim, 2 * embedding_dim, bias=True) - nn.init.zeros_(self.linear.weight) - nn.init.zeros_(self.linear.bias) - - self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) - - def forward( - self, x: torch.Tensor, timestep_embedding: torch.Tensor - ): - emb = self.linear(self.silu(timestep_embedding)) - shift, scale = emb.view(len(x), 1, -1).chunk(2, dim=-1) - x = self.norm(x) * (1 + scale) + shift - return x - - -class AttnProcessor(nn.Module): - r""" - Default processor for performing attention-related computations. - """ - - def __init__( - self, - hidden_size=None, - cross_attention_dim=None, - ): - super().__init__() - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - temb=None, - ): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - query = attn.head_to_batch_dim(query) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class IPAttnProcessor(nn.Module): - r""" - Attention processor for IP-Adapater. - Args: - hidden_size (`int`): - The hidden size of the attention layer. - cross_attention_dim (`int`): - The number of channels in the `encoder_hidden_states`. - scale (`float`, defaults to 1.0): - the weight scale of image prompt. - num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): - The context length of the image features. - """ - - def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4): - super().__init__() - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.scale = scale - self.num_tokens = num_tokens - - self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - temb=None, - ): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - else: - # get encoder_hidden_states, ip_hidden_states - end_pos = encoder_hidden_states.shape[1] - self.num_tokens - encoder_hidden_states, ip_hidden_states = ( - encoder_hidden_states[:, :end_pos, :], - encoder_hidden_states[:, end_pos:, :], - ) - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - query = attn.head_to_batch_dim(query) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # for ip-adapter - ip_key = self.to_k_ip(ip_hidden_states) - ip_value = self.to_v_ip(ip_hidden_states) - - ip_key = attn.head_to_batch_dim(ip_key) - ip_value = attn.head_to_batch_dim(ip_value) - - ip_attention_probs = attn.get_attention_scores(query, ip_key, None) - ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) - ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states) - - hidden_states = hidden_states + self.scale * ip_hidden_states - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class TA_IPAttnProcessor(nn.Module): - r""" - Attention processor for IP-Adapater. - Args: - hidden_size (`int`): - The hidden size of the attention layer. - cross_attention_dim (`int`): - The number of channels in the `encoder_hidden_states`. - scale (`float`, defaults to 1.0): - the weight scale of image prompt. - num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): - The context length of the image features. - """ - - def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4): - super().__init__() - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.scale = scale - self.num_tokens = num_tokens - - self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - - self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim) - self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim) - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - temb=None, - ): - assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." - - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - else: - # get encoder_hidden_states, ip_hidden_states - end_pos = encoder_hidden_states.shape[1] - self.num_tokens - encoder_hidden_states, ip_hidden_states = ( - encoder_hidden_states[:, :end_pos, :], - encoder_hidden_states[:, end_pos:, :], - ) - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - query = attn.head_to_batch_dim(query) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # for ip-adapter - ip_key = self.to_k_ip(ip_hidden_states) - ip_value = self.to_v_ip(ip_hidden_states) - - # time-dependent adaLN - ip_key = self.ln_k_ip(ip_key, temb) - ip_value = self.ln_v_ip(ip_value, temb) - - ip_key = attn.head_to_batch_dim(ip_key) - ip_value = attn.head_to_batch_dim(ip_value) - - ip_attention_probs = attn.get_attention_scores(query, ip_key, None) - ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) - ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states) - - hidden_states = hidden_states + self.scale * ip_hidden_states - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class AttnProcessor2_0(torch.nn.Module): - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def __init__( - self, - hidden_size=None, - cross_attention_dim=None, - ): - super().__init__() - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - external_kv=None, - temb=None, - ): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - if external_kv: - key = torch.cat([key, external_kv.k], axis=1) - value = torch.cat([value, external_kv.v], axis=1) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class split_AttnProcessor2_0(torch.nn.Module): - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def __init__( - self, - hidden_size=None, - cross_attention_dim=None, - time_embedding_dim=None, - ): - super().__init__() - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - external_kv=None, - temb=None, - cat_dim=-2, - original_shape=None, - ): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - # 2d to sequence. - height, width = hidden_states.shape[-2:] - if cat_dim==-2 or cat_dim==2: - hidden_states_0 = hidden_states[:, :, :height//2, :] - hidden_states_1 = hidden_states[:, :, -(height//2):, :] - elif cat_dim==-1 or cat_dim==3: - hidden_states_0 = hidden_states[:, :, :, :width//2] - hidden_states_1 = hidden_states[:, :, :, -(width//2):] - batch_size, channel, height, width = hidden_states_0.shape - hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2) - hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2) - else: - # directly split sqeuence according to concat dim. - single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1] - hidden_states_0 = hidden_states[:, :single_dim*single_dim,:] - hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:] - - hidden_states = torch.cat([hidden_states_0, hidden_states_1], dim=1) - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - key = attn.to_k(hidden_states) - value = attn.to_v(hidden_states) - - if external_kv: - key = torch.cat([key, external_kv.k], dim=1) - value = torch.cat([value, external_kv.v], dim=1) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - # spatially split. - hidden_states_0, hidden_states_1 = hidden_states.chunk(2, dim=1) - - if input_ndim == 4: - hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width) - hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if cat_dim==-2 or cat_dim==2: - hidden_states_pad = torch.zeros(batch_size, channel, 1, width) - elif cat_dim==-1 or cat_dim==3: - hidden_states_pad = torch.zeros(batch_size, channel, height, 1) - hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) - hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim) - assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" - else: - batch_size, sequence_length, inner_dim = hidden_states.shape - hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim) - hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) - hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1) - assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class sep_split_AttnProcessor2_0(torch.nn.Module): - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def __init__( - self, - hidden_size=None, - cross_attention_dim=None, - time_embedding_dim=None, - ): - super().__init__() - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - self.ln_k_ref = AdaLayerNorm(hidden_size, time_embedding_dim) - self.ln_v_ref = AdaLayerNorm(hidden_size, time_embedding_dim) - # self.hidden_size = hidden_size - # self.cross_attention_dim = cross_attention_dim - # self.scale = scale - # self.num_tokens = num_tokens - - # self.to_q_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - # self.to_k_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - # self.to_v_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - external_kv=None, - temb=None, - cat_dim=-2, - original_shape=None, - ref_scale=1.0, - ): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - # 2d to sequence. - height, width = hidden_states.shape[-2:] - if cat_dim==-2 or cat_dim==2: - hidden_states_0 = hidden_states[:, :, :height//2, :] - hidden_states_1 = hidden_states[:, :, -(height//2):, :] - elif cat_dim==-1 or cat_dim==3: - hidden_states_0 = hidden_states[:, :, :, :width//2] - hidden_states_1 = hidden_states[:, :, :, -(width//2):] - batch_size, channel, height, width = hidden_states_0.shape - hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2) - hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2) - else: - # directly split sqeuence according to concat dim. - single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1] - hidden_states_0 = hidden_states[:, :single_dim*single_dim,:] - hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:] - - batch_size, sequence_length, _ = ( - hidden_states_0.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states_0 = attn.group_norm(hidden_states_0.transpose(1, 2)).transpose(1, 2) - hidden_states_1 = attn.group_norm(hidden_states_1.transpose(1, 2)).transpose(1, 2) - - query_0 = attn.to_q(hidden_states_0) - query_1 = attn.to_q(hidden_states_1) - key_0 = attn.to_k(hidden_states_0) - key_1 = attn.to_k(hidden_states_1) - value_0 = attn.to_v(hidden_states_0) - value_1 = attn.to_v(hidden_states_1) - - # time-dependent adaLN - key_1 = self.ln_k_ref(key_1, temb) - value_1 = self.ln_v_ref(value_1, temb) - - if external_kv: - key_1 = torch.cat([key_1, external_kv.k], dim=1) - value_1 = torch.cat([value_1, external_kv.v], dim=1) - - inner_dim = key_0.shape[-1] - head_dim = inner_dim // attn.heads - - query_0 = query_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - query_1 = query_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - key_0 = key_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - key_1 = key_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value_0 = value_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value_1 = value_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states_0 = F.scaled_dot_product_attention( - query_0, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - hidden_states_1 = F.scaled_dot_product_attention( - query_1, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - # cross-attn - _hidden_states_0 = F.scaled_dot_product_attention( - query_0, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - hidden_states_0 = hidden_states_0 + ref_scale * _hidden_states_0 * 10 - - # TODO: drop this cross-attn - _hidden_states_1 = F.scaled_dot_product_attention( - query_1, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - hidden_states_1 = hidden_states_1 + ref_scale * _hidden_states_1 - - hidden_states_0 = hidden_states_0.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states_1 = hidden_states_1.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states_0 = hidden_states_0.to(query_0.dtype) - hidden_states_1 = hidden_states_1.to(query_1.dtype) - - - # linear proj - hidden_states_0 = attn.to_out[0](hidden_states_0) - hidden_states_1 = attn.to_out[0](hidden_states_1) - # dropout - hidden_states_0 = attn.to_out[1](hidden_states_0) - hidden_states_1 = attn.to_out[1](hidden_states_1) - - - if input_ndim == 4: - hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width) - hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if cat_dim==-2 or cat_dim==2: - hidden_states_pad = torch.zeros(batch_size, channel, 1, width) - elif cat_dim==-1 or cat_dim==3: - hidden_states_pad = torch.zeros(batch_size, channel, height, 1) - hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) - hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim) - assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" - else: - batch_size, sequence_length, inner_dim = hidden_states.shape - hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim) - hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) - hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1) - assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class AdditiveKV_AttnProcessor2_0(torch.nn.Module): - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def __init__( - self, - hidden_size: int = None, - cross_attention_dim: int = None, - time_embedding_dim: int = None, - additive_scale: float = 1.0, - ): - super().__init__() - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - self.additive_scale = additive_scale - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - external_kv=None, - attention_mask=None, - temb=None, - ): - assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." - - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - - if external_kv: - key = external_kv.k - value = external_kv.v - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - external_attn_output = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states + self.additive_scale * external_attn_output - - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class TA_AdditiveKV_AttnProcessor2_0(torch.nn.Module): - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def __init__( - self, - hidden_size: int = None, - cross_attention_dim: int = None, - time_embedding_dim: int = None, - additive_scale: float = 1.0, - ): - super().__init__() - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - self.ln_k = AdaLayerNorm(hidden_size, time_embedding_dim) - self.ln_v = AdaLayerNorm(hidden_size, time_embedding_dim) - self.additive_scale = additive_scale - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - external_kv=None, - attention_mask=None, - temb=None, - ): - assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." - - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - - if external_kv: - key = external_kv.k - value = external_kv.v - - # time-dependent adaLN - key = self.ln_k(key, temb) - value = self.ln_v(value, temb) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - external_attn_output = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states + self.additive_scale * external_attn_output - - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class IPAttnProcessor2_0(torch.nn.Module): - r""" - Attention processor for IP-Adapater for PyTorch 2.0. - Args: - hidden_size (`int`): - The hidden size of the attention layer. - cross_attention_dim (`int`): - The number of channels in the `encoder_hidden_states`. - scale (`float`, defaults to 1.0): - the weight scale of image prompt. - num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): - The context length of the image features. - """ - - def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4): - super().__init__() - - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.scale = scale - self.num_tokens = num_tokens - - self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - temb=None, - ): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - if isinstance(encoder_hidden_states, tuple): - # FIXME: now hard coded to single image prompt. - batch_size, _, hid_dim = encoder_hidden_states[0].shape - ip_tokens = encoder_hidden_states[1][0] - encoder_hidden_states = torch.cat([encoder_hidden_states[0], ip_tokens], dim=1) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - else: - # get encoder_hidden_states, ip_hidden_states - end_pos = encoder_hidden_states.shape[1] - self.num_tokens - encoder_hidden_states, ip_hidden_states = ( - encoder_hidden_states[:, :end_pos, :], - encoder_hidden_states[:, end_pos:, :], - ) - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # for ip-adapter - ip_key = self.to_k_ip(ip_hidden_states) - ip_value = self.to_v_ip(ip_hidden_states) - - ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - ip_hidden_states = F.scaled_dot_product_attention( - query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False - ) - - ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - ip_hidden_states = ip_hidden_states.to(query.dtype) - - hidden_states = hidden_states + self.scale * ip_hidden_states - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class TA_IPAttnProcessor2_0(torch.nn.Module): - r""" - Attention processor for IP-Adapater for PyTorch 2.0. - Args: - hidden_size (`int`): - The hidden size of the attention layer. - cross_attention_dim (`int`): - The number of channels in the `encoder_hidden_states`. - scale (`float`, defaults to 1.0): - the weight scale of image prompt. - num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): - The context length of the image features. - """ - - def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4): - super().__init__() - - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.scale = scale - self.num_tokens = num_tokens - - self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim) - self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim) - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - external_kv=None, - temb=None, - ): - assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." - - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - if not isinstance(encoder_hidden_states, tuple): - # get encoder_hidden_states, ip_hidden_states - end_pos = encoder_hidden_states.shape[1] - self.num_tokens - encoder_hidden_states, ip_hidden_states = ( - encoder_hidden_states[:, :end_pos, :], - encoder_hidden_states[:, end_pos:, :], - ) - else: - # FIXME: now hard coded to single image prompt. - batch_size, _, hid_dim = encoder_hidden_states[0].shape - ip_hidden_states = encoder_hidden_states[1][0] - encoder_hidden_states = encoder_hidden_states[0] - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - else: - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - if external_kv: - key = torch.cat([key, external_kv.k], axis=1) - value = torch.cat([value, external_kv.v], axis=1) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # for ip-adapter - ip_key = self.to_k_ip(ip_hidden_states) - ip_value = self.to_v_ip(ip_hidden_states) - - # time-dependent adaLN - ip_key = self.ln_k_ip(ip_key, temb) - ip_value = self.ln_v_ip(ip_value, temb) - - ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - ip_hidden_states = F.scaled_dot_product_attention( - query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False - ) - - ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - ip_hidden_states = ip_hidden_states.to(query.dtype) - - hidden_states = hidden_states + self.scale * ip_hidden_states - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -## for controlnet -class CNAttnProcessor: - r""" - Default processor for performing attention-related computations. - """ - - def __init__(self, num_tokens=4): - self.num_tokens = num_tokens - - def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - else: - end_pos = encoder_hidden_states.shape[1] - self.num_tokens - encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - query = attn.head_to_batch_dim(query) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class CNAttnProcessor2_0: - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def __init__(self, num_tokens=4): - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - self.num_tokens = num_tokens - - def __call__( - self, - attn, - hidden_states, - encoder_hidden_states=None, - attention_mask=None, - temb=None, - ): - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - else: - end_pos = encoder_hidden_states.shape[1] - self.num_tokens - encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -def init_attn_proc(unet, ip_adapter_tokens=16, use_lcm=True, use_adaln=True, use_external_kv=False): - attn_procs = {} - unet_sd = unet.state_dict() - for name in unet.attn_processors.keys(): - cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim - if name.startswith("mid_block"): - hidden_size = unet.config.block_out_channels[-1] - elif name.startswith("up_blocks"): - block_id = int(name[len("up_blocks.")]) - hidden_size = list(reversed(unet.config.block_out_channels))[block_id] - elif name.startswith("down_blocks"): - block_id = int(name[len("down_blocks.")]) - hidden_size = unet.config.block_out_channels[block_id] - if cross_attention_dim is None: - if use_external_kv: - attn_procs[name] = AdditiveKV_AttnProcessor2_0( - hidden_size=hidden_size, - cross_attention_dim=cross_attention_dim, - time_embedding_dim=1280, - ) if hasattr(F, "scaled_dot_product_attention") else AdditiveKV_AttnProcessor() - else: - attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor() - else: - if use_adaln: - layer_name = name.split(".processor")[0] - if use_lcm: - weights = { - "to_k_ip.weight": unet_sd[layer_name + ".to_k.base_layer.weight"], - "to_v_ip.weight": unet_sd[layer_name + ".to_v.base_layer.weight"], - } - else: - weights = { - "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"], - "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"], - } - attn_procs[name] = TA_IPAttnProcessor2_0( - hidden_size=hidden_size, - cross_attention_dim=cross_attention_dim, - num_tokens=ip_adapter_tokens, - time_embedding_dim=1280, - ) if hasattr(F, "scaled_dot_product_attention") else \ - TA_IPAttnProcessor( - hidden_size=hidden_size, - cross_attention_dim=cross_attention_dim, - num_tokens=ip_adapter_tokens, - time_embedding_dim=1280, - ) - attn_procs[name].load_state_dict(weights, strict=False) - else: - attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor() - - return attn_procs - - -def init_aggregator_attn_proc(unet, use_adaln=False, split_attn=False): - attn_procs = {} - unet_sd = unet.state_dict() - for name in unet.attn_processors.keys(): - # get layer name and hidden dim - cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim - if name.startswith("mid_block"): - hidden_size = unet.config.block_out_channels[-1] - elif name.startswith("up_blocks"): - block_id = int(name[len("up_blocks.")]) - hidden_size = list(reversed(unet.config.block_out_channels))[block_id] - elif name.startswith("down_blocks"): - block_id = int(name[len("down_blocks.")]) - hidden_size = unet.config.block_out_channels[block_id] - # init attn proc - if split_attn: - # layer_name = name.split(".processor")[0] - # weights = { - # "to_q_ref.weight": unet_sd[layer_name + ".to_q.weight"], - # "to_k_ref.weight": unet_sd[layer_name + ".to_k.weight"], - # "to_v_ref.weight": unet_sd[layer_name + ".to_v.weight"], - # } - attn_procs[name] = ( - sep_split_AttnProcessor2_0( - hidden_size=hidden_size, - cross_attention_dim=hidden_size, - time_embedding_dim=1280, - ) - if use_adaln - else split_AttnProcessor2_0( - hidden_size=hidden_size, - cross_attention_dim=cross_attention_dim, - time_embedding_dim=1280, - ) - ) - # attn_procs[name].load_state_dict(weights, strict=False) - else: - attn_procs[name] = ( - AttnProcessor2_0( - hidden_size=hidden_size, - cross_attention_dim=hidden_size, - ) - if hasattr(F, "scaled_dot_product_attention") - else AttnProcessor( - hidden_size=hidden_size, - cross_attention_dim=hidden_size, - ) - ) - - return attn_procs diff --git a/module/ip_adapter/ip_adapter.py b/module/ip_adapter/ip_adapter.py deleted file mode 100644 index 4ffb08903d9f196f937097512b7ab0aceb14b5ce..0000000000000000000000000000000000000000 --- a/module/ip_adapter/ip_adapter.py +++ /dev/null @@ -1,236 +0,0 @@ -import os -import torch -from typing import List -from collections import namedtuple, OrderedDict - -def is_torch2_available(): - return hasattr(torch.nn.functional, "scaled_dot_product_attention") - -if is_torch2_available(): - from .attention_processor import ( - AttnProcessor2_0 as AttnProcessor, - ) - from .attention_processor import ( - CNAttnProcessor2_0 as CNAttnProcessor, - ) - from .attention_processor import ( - IPAttnProcessor2_0 as IPAttnProcessor, - ) - from .attention_processor import ( - TA_IPAttnProcessor2_0 as TA_IPAttnProcessor, - ) -else: - from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor, TA_IPAttnProcessor - - -class ImageProjModel(torch.nn.Module): - """Projection Model""" - - def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4): - super().__init__() - - self.cross_attention_dim = cross_attention_dim - self.clip_extra_context_tokens = clip_extra_context_tokens - self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) - self.norm = torch.nn.LayerNorm(cross_attention_dim) - - def forward(self, image_embeds): - embeds = image_embeds - clip_extra_context_tokens = self.proj(embeds).reshape( - -1, self.clip_extra_context_tokens, self.cross_attention_dim - ) - clip_extra_context_tokens = self.norm(clip_extra_context_tokens) - return clip_extra_context_tokens - - -class MLPProjModel(torch.nn.Module): - """SD model with image prompt""" - def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280): - super().__init__() - - self.proj = torch.nn.Sequential( - torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim), - torch.nn.GELU(), - torch.nn.Linear(clip_embeddings_dim, cross_attention_dim), - torch.nn.LayerNorm(cross_attention_dim) - ) - - def forward(self, image_embeds): - clip_extra_context_tokens = self.proj(image_embeds) - return clip_extra_context_tokens - - -class MultiIPAdapterImageProjection(torch.nn.Module): - def __init__(self, IPAdapterImageProjectionLayers): - super().__init__() - self.image_projection_layers = torch.nn.ModuleList(IPAdapterImageProjectionLayers) - - def forward(self, image_embeds: List[torch.FloatTensor]): - projected_image_embeds = [] - - # currently, we accept `image_embeds` as - # 1. a tensor (deprecated) with shape [batch_size, embed_dim] or [batch_size, sequence_length, embed_dim] - # 2. list of `n` tensors where `n` is number of ip-adapters, each tensor can hae shape [batch_size, num_images, embed_dim] or [batch_size, num_images, sequence_length, embed_dim] - if not isinstance(image_embeds, list): - image_embeds = [image_embeds.unsqueeze(1)] - - if len(image_embeds) != len(self.image_projection_layers): - raise ValueError( - f"image_embeds must have the same length as image_projection_layers, got {len(image_embeds)} and {len(self.image_projection_layers)}" - ) - - for image_embed, image_projection_layer in zip(image_embeds, self.image_projection_layers): - batch_size, num_images = image_embed.shape[0], image_embed.shape[1] - image_embed = image_embed.reshape((batch_size * num_images,) + image_embed.shape[2:]) - image_embed = image_projection_layer(image_embed) - # image_embed = image_embed.reshape((batch_size, num_images) + image_embed.shape[1:]) - - projected_image_embeds.append(image_embed) - - return projected_image_embeds - - -class IPAdapter(torch.nn.Module): - """IP-Adapter""" - def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None): - super().__init__() - self.unet = unet - self.image_proj = image_proj_model - self.ip_adapter = adapter_modules - - if ckpt_path is not None: - self.load_from_checkpoint(ckpt_path) - - def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds): - ip_tokens = self.image_proj(image_embeds) - encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1) - # Predict the noise residual - noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample - return noise_pred - - def load_from_checkpoint(self, ckpt_path: str): - # Calculate original checksums - orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()])) - orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()])) - - state_dict = torch.load(ckpt_path, map_location="cpu") - keys = list(state_dict.keys()) - if keys != ["image_proj", "ip_adapter"]: - state_dict = revise_state_dict(state_dict) - - # Load state dict for image_proj_model and adapter_modules - self.image_proj.load_state_dict(state_dict["image_proj"], strict=True) - self.ip_adapter.load_state_dict(state_dict["ip_adapter"], strict=True) - - # Calculate new checksums - new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()])) - new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()])) - - # Verify if the weights have changed - assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!" - assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!" - - -class IPAdapterPlus(torch.nn.Module): - """IP-Adapter""" - def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None): - super().__init__() - self.unet = unet - self.image_proj = image_proj_model - self.ip_adapter = adapter_modules - - if ckpt_path is not None: - self.load_from_checkpoint(ckpt_path) - - def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds): - ip_tokens = self.image_proj(image_embeds) - encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1) - # Predict the noise residual - noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample - return noise_pred - - def load_from_checkpoint(self, ckpt_path: str): - # Calculate original checksums - orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()])) - orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()])) - org_unet_sum = [] - for attn_name, attn_proc in self.unet.attn_processors.items(): - if isinstance(attn_proc, (TA_IPAttnProcessor, IPAttnProcessor)): - org_unet_sum.append(torch.sum(torch.stack([torch.sum(p) for p in attn_proc.parameters()]))) - org_unet_sum = torch.sum(torch.stack(org_unet_sum)) - - state_dict = torch.load(ckpt_path, map_location="cpu") - keys = list(state_dict.keys()) - if keys != ["image_proj", "ip_adapter"]: - state_dict = revise_state_dict(state_dict) - - # Check if 'latents' exists in both the saved state_dict and the current model's state_dict - strict_load_image_proj_model = True - if "latents" in state_dict["image_proj"] and "latents" in self.image_proj.state_dict(): - # Check if the shapes are mismatched - if state_dict["image_proj"]["latents"].shape != self.image_proj.state_dict()["latents"].shape: - print(f"Shapes of 'image_proj.latents' in checkpoint {ckpt_path} and current model do not match.") - print("Removing 'latents' from checkpoint and loading the rest of the weights.") - del state_dict["image_proj"]["latents"] - strict_load_image_proj_model = False - - # Load state dict for image_proj_model and adapter_modules - self.image_proj.load_state_dict(state_dict["image_proj"], strict=strict_load_image_proj_model) - missing_key, unexpected_key = self.ip_adapter.load_state_dict(state_dict["ip_adapter"], strict=False) - if len(missing_key) > 0: - for ms in missing_key: - if "ln" not in ms: - raise ValueError(f"Missing key in adapter_modules: {len(missing_key)}") - if len(unexpected_key) > 0: - raise ValueError(f"Unexpected key in adapter_modules: {len(unexpected_key)}") - - # Calculate new checksums - new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()])) - new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()])) - - # Verify if the weights loaded to unet - unet_sum = [] - for attn_name, attn_proc in self.unet.attn_processors.items(): - if isinstance(attn_proc, (TA_IPAttnProcessor, IPAttnProcessor)): - unet_sum.append(torch.sum(torch.stack([torch.sum(p) for p in attn_proc.parameters()]))) - unet_sum = torch.sum(torch.stack(unet_sum)) - - assert org_unet_sum != unet_sum, "Weights of adapter_modules in unet did not change!" - assert (unet_sum - new_adapter_sum < 1e-4), "Weights of adapter_modules did not load to unet!" - - # Verify if the weights have changed - assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!" - assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_mod`ules did not change!" - - -class IPAdapterXL(IPAdapter): - """SDXL""" - - def forward(self, noisy_latents, timesteps, encoder_hidden_states, unet_added_cond_kwargs, image_embeds): - ip_tokens = self.image_proj(image_embeds) - encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1) - # Predict the noise residual - noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=unet_added_cond_kwargs).sample - return noise_pred - - -class IPAdapterPlusXL(IPAdapterPlus): - """IP-Adapter with fine-grained features""" - - def forward(self, noisy_latents, timesteps, encoder_hidden_states, unet_added_cond_kwargs, image_embeds): - ip_tokens = self.image_proj(image_embeds) - encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1) - # Predict the noise residual - noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=unet_added_cond_kwargs).sample - return noise_pred - - -class IPAdapterFull(IPAdapterPlus): - """IP-Adapter with full features""" - - def init_proj(self): - image_proj_model = MLPProjModel( - cross_attention_dim=self.pipe.unet.config.cross_attention_dim, - clip_embeddings_dim=self.image_encoder.config.hidden_size, - ).to(self.device, dtype=torch.float16) - return image_proj_model diff --git a/module/ip_adapter/resampler.py b/module/ip_adapter/resampler.py deleted file mode 100644 index 983fb5afa8ed6a77edebe1371791a6efa7711796..0000000000000000000000000000000000000000 --- a/module/ip_adapter/resampler.py +++ /dev/null @@ -1,158 +0,0 @@ -# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py -# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py - -import math - -import torch -import torch.nn as nn -from einops import rearrange -from einops.layers.torch import Rearrange - - -# FFN -def FeedForward(dim, mult=4): - inner_dim = int(dim * mult) - return nn.Sequential( - nn.LayerNorm(dim), - nn.Linear(dim, inner_dim, bias=False), - nn.GELU(), - nn.Linear(inner_dim, dim, bias=False), - ) - - -def reshape_tensor(x, heads): - bs, length, width = x.shape - # (bs, length, width) --> (bs, length, n_heads, dim_per_head) - x = x.view(bs, length, heads, -1) - # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) - x = x.transpose(1, 2) - # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) - x = x.reshape(bs, heads, length, -1) - return x - - -class PerceiverAttention(nn.Module): - def __init__(self, *, dim, dim_head=64, heads=8): - super().__init__() - self.scale = dim_head**-0.5 - self.dim_head = dim_head - self.heads = heads - inner_dim = dim_head * heads - - self.norm1 = nn.LayerNorm(dim) - self.norm2 = nn.LayerNorm(dim) - - self.to_q = nn.Linear(dim, inner_dim, bias=False) - self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) - self.to_out = nn.Linear(inner_dim, dim, bias=False) - - def forward(self, x, latents): - """ - Args: - x (torch.Tensor): image features - shape (b, n1, D) - latent (torch.Tensor): latent features - shape (b, n2, D) - """ - x = self.norm1(x) - latents = self.norm2(latents) - - b, l, _ = latents.shape - - q = self.to_q(latents) - kv_input = torch.cat((x, latents), dim=-2) - k, v = self.to_kv(kv_input).chunk(2, dim=-1) - - q = reshape_tensor(q, self.heads) - k = reshape_tensor(k, self.heads) - v = reshape_tensor(v, self.heads) - - # attention - scale = 1 / math.sqrt(math.sqrt(self.dim_head)) - weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards - weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) - out = weight @ v - - out = out.permute(0, 2, 1, 3).reshape(b, l, -1) - - return self.to_out(out) - - -class Resampler(nn.Module): - def __init__( - self, - dim=1280, - depth=4, - dim_head=64, - heads=20, - num_queries=64, - embedding_dim=768, - output_dim=1024, - ff_mult=4, - max_seq_len: int = 257, # CLIP tokens + CLS token - apply_pos_emb: bool = False, - num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence - ): - super().__init__() - self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None - - self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) - - self.proj_in = nn.Linear(embedding_dim, dim) - - self.proj_out = nn.Linear(dim, output_dim) - self.norm_out = nn.LayerNorm(output_dim) - - self.to_latents_from_mean_pooled_seq = ( - nn.Sequential( - nn.LayerNorm(dim), - nn.Linear(dim, dim * num_latents_mean_pooled), - Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled), - ) - if num_latents_mean_pooled > 0 - else None - ) - - self.layers = nn.ModuleList([]) - for _ in range(depth): - self.layers.append( - nn.ModuleList( - [ - PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), - FeedForward(dim=dim, mult=ff_mult), - ] - ) - ) - - def forward(self, x): - if self.pos_emb is not None: - n, device = x.shape[1], x.device - pos_emb = self.pos_emb(torch.arange(n, device=device)) - x = x + pos_emb - - latents = self.latents.repeat(x.size(0), 1, 1) - - x = self.proj_in(x) - - if self.to_latents_from_mean_pooled_seq: - meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool)) - meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq) - latents = torch.cat((meanpooled_latents, latents), dim=-2) - - for attn, ff in self.layers: - latents = attn(x, latents) + latents - latents = ff(latents) + latents - - latents = self.proj_out(latents) - return self.norm_out(latents) - - -def masked_mean(t, *, dim, mask=None): - if mask is None: - return t.mean(dim=dim) - - denom = mask.sum(dim=dim, keepdim=True) - mask = rearrange(mask, "b n -> b n 1") - masked_t = t.masked_fill(~mask, 0.0) - - return masked_t.sum(dim=dim) / denom.clamp(min=1e-5) diff --git a/module/ip_adapter/utils.py b/module/ip_adapter/utils.py deleted file mode 100644 index 0b88a17a05a4ee34a6f30aaf7755f464026791ac..0000000000000000000000000000000000000000 --- a/module/ip_adapter/utils.py +++ /dev/null @@ -1,232 +0,0 @@ -import random -import torch -from collections import namedtuple, OrderedDict -from safetensors import safe_open -from .attention_processor import init_attn_proc -from .ip_adapter import MultiIPAdapterImageProjection -from transformers import ( - AutoModel, AutoImageProcessor, - CLIPVisionModelWithProjection, CLIPImageProcessor) - - -def init_adapter_in_unet( - unet, - image_proj_model, - pretrained_model_path_or_dict=None, - adapter_tokens=64, - use_lcm=False, - use_adaln=True, - use_external_kv=False, - ): - device = unet.device - dtype = unet.dtype - if pretrained_model_path_or_dict is not None: - if not isinstance(pretrained_model_path_or_dict, dict): - if pretrained_model_path_or_dict.endswith(".safetensors"): - state_dict = {"image_proj": {}, "ip_adapter": {}} - with safe_open(pretrained_model_path_or_dict, framework="pt", device=unet.device) as f: - for key in f.keys(): - if key.startswith("image_proj."): - state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key) - elif key.startswith("ip_adapter."): - state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key) - else: - state_dict = torch.load(pretrained_model_path_or_dict, map_location=unet.device) - else: - state_dict = pretrained_model_path_or_dict - keys = list(state_dict.keys()) - if "image_proj" not in keys and "ip_adapter" not in keys: - state_dict = revise_state_dict(state_dict) - - # Creat IP cross-attention in unet. - attn_procs = init_attn_proc(unet, adapter_tokens, use_lcm, use_adaln, use_external_kv) - unet.set_attn_processor(attn_procs) - - # Load pretrinaed model if needed. - if pretrained_model_path_or_dict is not None: - if "ip_adapter" in state_dict.keys(): - adapter_modules = torch.nn.ModuleList(unet.attn_processors.values()) - missing, unexpected = adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=False) - for mk in missing: - if "ln" not in mk: - raise ValueError(f"Missing keys in adapter_modules: {missing}") - if "image_proj" in state_dict.keys(): - image_proj_model.load_state_dict(state_dict["image_proj"]) - - # Load image projectors into iterable ModuleList. - image_projection_layers = [] - image_projection_layers.append(image_proj_model) - unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers) - - # Adjust unet config to handle addtional ip hidden states. - unet.config.encoder_hid_dim_type = "ip_image_proj" - unet.to(dtype=dtype, device=device) - - -def load_adapter_to_pipe( - pipe, - pretrained_model_path_or_dict=None, - image_encoder_path=None, - feature_extractor_path=None, - use_dino=True, - adapter_tokens=64, - use_lcm=False, - use_adaln=True, - low_cpu_mem_usage=True, - ): - - if pretrained_model_path_or_dict is not None: - if not isinstance(pretrained_model_path_or_dict, dict): - if pretrained_model_path_or_dict.endswith(".safetensors"): - state_dict = {"image_proj": {}, "ip_adapter": {}} - with safe_open(pretrained_model_path_or_dict, framework="pt", device=pipe.unet.device) as f: - for key in f.keys(): - if key.startswith("image_proj."): - state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key) - elif key.startswith("ip_adapter."): - state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key) - else: - state_dict = torch.load(pretrained_model_path_or_dict, map_location=pipe.unet.device) - else: - state_dict = pretrained_model_path_or_dict - keys = list(state_dict.keys()) - if "image_proj" not in keys and "ip_adapter" not in keys: - state_dict = revise_state_dict(state_dict) - - # load CLIP image encoder here if it has not been registered to the pipeline yet - if image_encoder_path is not None: - if isinstance(image_encoder_path, str): - feature_extractor_path = image_encoder_path if feature_extractor_path is None else feature_extractor_path - - image_encoder_path = AutoModel.from_pretrained( - image_encoder_path) if use_dino else \ - CLIPVisionModelWithProjection.from_pretrained( - image_encoder_path) - image_encoder = image_encoder_path.to(pipe.device, dtype=pipe.dtype) - - if feature_extractor_path is not None: - if isinstance(feature_extractor_path, str): - feature_extractor_path = AutoImageProcessor.from_pretrained(feature_extractor_path) \ - if use_dino else CLIPImageProcessor() - feature_extractor = feature_extractor_path - - # create image encoder if it has not been registered to the pipeline yet - if hasattr(pipe, "image_encoder") and getattr(pipe, "image_encoder", None) is None: - pipe.register_modules(image_encoder=image_encoder) - - # create feature extractor if it has not been registered to the pipeline yet - if hasattr(pipe, "feature_extractor") and getattr(pipe, "feature_extractor", None) is None: - pipe.register_modules(feature_extractor=feature_extractor) - - # load ip-adapter into unet - unet = getattr(pipe, pipe.unet_name) if not hasattr(pipe, "unet") else pipe.unet - attn_procs = init_attn_proc(unet, adapter_tokens, use_lcm, use_adaln) - unet.set_attn_processor(attn_procs) - adapter_modules = torch.nn.ModuleList(unet.attn_processors.values()) - - # Filter out LoRA-related keys from the state dict - filtered_state_dict = {k: v for k, v in state_dict["ip_adapter"].items() - if not any(x in k for x in ['lora_A', 'lora_B'])} - - missing, _ = adapter_modules.load_state_dict(filtered_state_dict, strict=False) - if len(missing) > 0: - raise ValueError(f"Missing keys in adapter_modules: {missing}") - - # convert IP-Adapter Image Projection layers to diffusers - image_projection_layers = [] - image_projection_layer = unet._convert_ip_adapter_image_proj_to_diffusers( - state_dict["image_proj"], low_cpu_mem_usage=low_cpu_mem_usage - ) - image_projection_layers.append(image_projection_layer) - - unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers) - unet.config.encoder_hid_dim_type = "ip_image_proj" - - unet.to(dtype=pipe.dtype, device=pipe.device) - - -def revise_state_dict(old_state_dict_or_path, map_location="cpu"): - new_state_dict = OrderedDict() - new_state_dict["image_proj"] = OrderedDict() - new_state_dict["ip_adapter"] = OrderedDict() - if isinstance(old_state_dict_or_path, str): - old_state_dict = torch.load(old_state_dict_or_path, map_location=map_location) - else: - old_state_dict = old_state_dict_or_path - for name, weight in old_state_dict.items(): - if name.startswith("image_proj_model."): - new_state_dict["image_proj"][name[len("image_proj_model."):]] = weight - elif name.startswith("adapter_modules."): - new_state_dict["ip_adapter"][name[len("adapter_modules."):]] = weight - return new_state_dict - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image -def encode_image(image_encoder, feature_extractor, image, device, num_images_per_prompt, output_hidden_states=None): - dtype = next(image_encoder.parameters()).dtype - - if not isinstance(image, torch.Tensor): - image = feature_extractor(image, return_tensors="pt").pixel_values - - image = image.to(device=device, dtype=dtype) - if output_hidden_states: - image_enc_hidden_states = image_encoder(image, output_hidden_states=True).hidden_states[-2] - image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) - return image_enc_hidden_states - else: - if isinstance(image_encoder, CLIPVisionModelWithProjection): - # CLIP image encoder. - image_embeds = image_encoder(image).image_embeds - else: - # DINO image encoder. - image_embeds = image_encoder(image).last_hidden_state - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - return image_embeds - - -def prepare_training_image_embeds( - image_encoder, feature_extractor, - ip_adapter_image, ip_adapter_image_embeds, - device, drop_rate, output_hidden_state, idx_to_replace=None -): - if ip_adapter_image_embeds is None: - if not isinstance(ip_adapter_image, list): - ip_adapter_image = [ip_adapter_image] - - # if len(ip_adapter_image) != len(unet.encoder_hid_proj.image_projection_layers): - # raise ValueError( - # f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(unet.encoder_hid_proj.image_projection_layers)} IP Adapters." - # ) - - image_embeds = [] - for single_ip_adapter_image in ip_adapter_image: - if idx_to_replace is None: - idx_to_replace = torch.rand(len(single_ip_adapter_image)) < drop_rate - zero_ip_adapter_image = torch.zeros_like(single_ip_adapter_image) - single_ip_adapter_image[idx_to_replace] = zero_ip_adapter_image[idx_to_replace] - single_image_embeds = encode_image( - image_encoder, feature_extractor, single_ip_adapter_image, device, 1, output_hidden_state - ) - single_image_embeds = torch.stack([single_image_embeds], dim=1) # FIXME - - image_embeds.append(single_image_embeds) - else: - repeat_dims = [1] - image_embeds = [] - for single_image_embeds in ip_adapter_image_embeds: - if do_classifier_free_guidance: - single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - single_negative_image_embeds = single_negative_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) - ) - single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) - else: - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - image_embeds.append(single_image_embeds) - - return image_embeds \ No newline at end of file diff --git a/module/min_sdxl.py b/module/min_sdxl.py deleted file mode 100644 index 468ecf17b5d54be32c1fca9868462389df77b33e..0000000000000000000000000000000000000000 --- a/module/min_sdxl.py +++ /dev/null @@ -1,915 +0,0 @@ -# Modified from minSDXL by Simo Ryu: -# https://github.com/cloneofsimo/minSDXL , -# which is in turn modified from the original code of: -# https://github.com/huggingface/diffusers -# So has APACHE 2.0 license - -from typing import Optional, Union - -import torch -import torch.nn as nn -import torch.nn.functional as F -import math -import inspect - -from collections import namedtuple - -from torch.fft import fftn, fftshift, ifftn, ifftshift - -from diffusers.models.attention_processor import AttnProcessor, AttnProcessor2_0 - -# Implementation of FreeU for minsdxl - -def fourier_filter(x_in: "torch.Tensor", threshold: int, scale: int) -> "torch.Tensor": - """Fourier filter as introduced in FreeU (https://arxiv.org/abs/2309.11497). - - This version of the method comes from here: - https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706 - """ - x = x_in - B, C, H, W = x.shape - - # Non-power of 2 images must be float32 - if (W & (W - 1)) != 0 or (H & (H - 1)) != 0: - x = x.to(dtype=torch.float32) - - # FFT - x_freq = fftn(x, dim=(-2, -1)) - x_freq = fftshift(x_freq, dim=(-2, -1)) - - B, C, H, W = x_freq.shape - mask = torch.ones((B, C, H, W), device=x.device) - - crow, ccol = H // 2, W // 2 - mask[..., crow - threshold : crow + threshold, ccol - threshold : ccol + threshold] = scale - x_freq = x_freq * mask - - # IFFT - x_freq = ifftshift(x_freq, dim=(-2, -1)) - x_filtered = ifftn(x_freq, dim=(-2, -1)).real - - return x_filtered.to(dtype=x_in.dtype) - - -def apply_freeu( - resolution_idx: int, hidden_states: "torch.Tensor", res_hidden_states: "torch.Tensor", **freeu_kwargs): - """Applies the FreeU mechanism as introduced in https: - //arxiv.org/abs/2309.11497. Adapted from the official code repository: https://github.com/ChenyangSi/FreeU. - - Args: - resolution_idx (`int`): Integer denoting the UNet block where FreeU is being applied. - hidden_states (`torch.Tensor`): Inputs to the underlying block. - res_hidden_states (`torch.Tensor`): Features from the skip block corresponding to the underlying block. - s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. - s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. - b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. - b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. - """ - if resolution_idx == 0: - num_half_channels = hidden_states.shape[1] // 2 - hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs["b1"] - res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs["s1"]) - if resolution_idx == 1: - num_half_channels = hidden_states.shape[1] // 2 - hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs["b2"] - res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs["s2"]) - - return hidden_states, res_hidden_states - -# Diffusers-style LoRA to keep everything in the min_sdxl.py file - -class LoRALinearLayer(nn.Module): - r""" - A linear layer that is used with LoRA. - - Parameters: - in_features (`int`): - Number of input features. - out_features (`int`): - Number of output features. - rank (`int`, `optional`, defaults to 4): - The rank of the LoRA layer. - network_alpha (`float`, `optional`, defaults to `None`): - The value of the network alpha used for stable learning and preventing underflow. This value has the same - meaning as the `--network_alpha` option in the kohya-ss trainer script. See - https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning - device (`torch.device`, `optional`, defaults to `None`): - The device to use for the layer's weights. - dtype (`torch.dtype`, `optional`, defaults to `None`): - The dtype to use for the layer's weights. - """ - - def __init__( - self, - in_features: int, - out_features: int, - rank: int = 4, - network_alpha: Optional[float] = None, - device: Optional[Union[torch.device, str]] = None, - dtype: Optional[torch.dtype] = None, - ): - super().__init__() - - self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype) - self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype) - # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script. - # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning - self.network_alpha = network_alpha - self.rank = rank - self.out_features = out_features - self.in_features = in_features - - nn.init.normal_(self.down.weight, std=1 / rank) - nn.init.zeros_(self.up.weight) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - orig_dtype = hidden_states.dtype - dtype = self.down.weight.dtype - - down_hidden_states = self.down(hidden_states.to(dtype)) - up_hidden_states = self.up(down_hidden_states) - - if self.network_alpha is not None: - up_hidden_states *= self.network_alpha / self.rank - - return up_hidden_states.to(orig_dtype) - -class LoRACompatibleLinear(nn.Linear): - """ - A Linear layer that can be used with LoRA. - """ - - def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs): - super().__init__(*args, **kwargs) - self.lora_layer = lora_layer - - def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]): - self.lora_layer = lora_layer - - def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False): - if self.lora_layer is None: - return - - dtype, device = self.weight.data.dtype, self.weight.data.device - - w_orig = self.weight.data.float() - w_up = self.lora_layer.up.weight.data.float() - w_down = self.lora_layer.down.weight.data.float() - - if self.lora_layer.network_alpha is not None: - w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank - - fused_weight = w_orig + (lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0]) - - if safe_fusing and torch.isnan(fused_weight).any().item(): - raise ValueError( - "This LoRA weight seems to be broken. " - f"Encountered NaN values when trying to fuse LoRA weights for {self}." - "LoRA weights will not be fused." - ) - - self.weight.data = fused_weight.to(device=device, dtype=dtype) - - # we can drop the lora layer now - self.lora_layer = None - - # offload the up and down matrices to CPU to not blow the memory - self.w_up = w_up.cpu() - self.w_down = w_down.cpu() - self._lora_scale = lora_scale - - def _unfuse_lora(self): - if not (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None): - return - - fused_weight = self.weight.data - dtype, device = fused_weight.dtype, fused_weight.device - - w_up = self.w_up.to(device=device).float() - w_down = self.w_down.to(device).float() - - unfused_weight = fused_weight.float() - (self._lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0]) - self.weight.data = unfused_weight.to(device=device, dtype=dtype) - - self.w_up = None - self.w_down = None - - def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor: - if self.lora_layer is None: - out = super().forward(hidden_states) - return out - else: - out = super().forward(hidden_states) + (scale * self.lora_layer(hidden_states)) - return out - -class Timesteps(nn.Module): - def __init__(self, num_channels: int = 320): - super().__init__() - self.num_channels = num_channels - - def forward(self, timesteps): - half_dim = self.num_channels // 2 - exponent = -math.log(10000) * torch.arange( - half_dim, dtype=torch.float32, device=timesteps.device - ) - exponent = exponent / (half_dim - 0.0) - - emb = torch.exp(exponent) - emb = timesteps[:, None].float() * emb[None, :] - - sin_emb = torch.sin(emb) - cos_emb = torch.cos(emb) - emb = torch.cat([cos_emb, sin_emb], dim=-1) - - return emb - - -class TimestepEmbedding(nn.Module): - def __init__(self, in_features, out_features): - super(TimestepEmbedding, self).__init__() - self.linear_1 = nn.Linear(in_features, out_features, bias=True) - self.act = nn.SiLU() - self.linear_2 = nn.Linear(out_features, out_features, bias=True) - - def forward(self, sample): - sample = self.linear_1(sample) - sample = self.act(sample) - sample = self.linear_2(sample) - - return sample - - -class ResnetBlock2D(nn.Module): - def __init__(self, in_channels, out_channels, conv_shortcut=True): - super(ResnetBlock2D, self).__init__() - self.norm1 = nn.GroupNorm(32, in_channels, eps=1e-05, affine=True) - self.conv1 = nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - self.time_emb_proj = nn.Linear(1280, out_channels, bias=True) - self.norm2 = nn.GroupNorm(32, out_channels, eps=1e-05, affine=True) - self.dropout = nn.Dropout(p=0.0, inplace=False) - self.conv2 = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - self.nonlinearity = nn.SiLU() - self.conv_shortcut = None - if conv_shortcut: - self.conv_shortcut = nn.Conv2d( - in_channels, out_channels, kernel_size=1, stride=1 - ) - - def forward(self, input_tensor, temb): - hidden_states = input_tensor - hidden_states = self.norm1(hidden_states) - hidden_states = self.nonlinearity(hidden_states) - - hidden_states = self.conv1(hidden_states) - - temb = self.nonlinearity(temb) - temb = self.time_emb_proj(temb)[:, :, None, None] - hidden_states = hidden_states + temb - hidden_states = self.norm2(hidden_states) - - hidden_states = self.nonlinearity(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.conv2(hidden_states) - - if self.conv_shortcut is not None: - input_tensor = self.conv_shortcut(input_tensor) - - output_tensor = input_tensor + hidden_states - - return output_tensor - - -class Attention(nn.Module): - def __init__( - self, inner_dim, cross_attention_dim=None, num_heads=None, dropout=0.0, processor=None, scale_qk=True - ): - super(Attention, self).__init__() - if num_heads is None: - self.head_dim = 64 - self.num_heads = inner_dim // self.head_dim - else: - self.num_heads = num_heads - self.head_dim = inner_dim // num_heads - - self.scale = self.head_dim**-0.5 - if cross_attention_dim is None: - cross_attention_dim = inner_dim - self.to_q = LoRACompatibleLinear(inner_dim, inner_dim, bias=False) - self.to_k = LoRACompatibleLinear(cross_attention_dim, inner_dim, bias=False) - self.to_v = LoRACompatibleLinear(cross_attention_dim, inner_dim, bias=False) - - self.to_out = nn.ModuleList( - [LoRACompatibleLinear(inner_dim, inner_dim), nn.Dropout(dropout, inplace=False)] - ) - - self.scale_qk = scale_qk - if processor is None: - processor = ( - AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() - ) - self.set_processor(processor) - - def forward( - self, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - **cross_attention_kwargs, - ) -> torch.Tensor: - r""" - The forward method of the `Attention` class. - - Args: - hidden_states (`torch.Tensor`): - The hidden states of the query. - encoder_hidden_states (`torch.Tensor`, *optional*): - The hidden states of the encoder. - attention_mask (`torch.Tensor`, *optional*): - The attention mask to use. If `None`, no mask is applied. - **cross_attention_kwargs: - Additional keyword arguments to pass along to the cross attention. - - Returns: - `torch.Tensor`: The output of the attention layer. - """ - # The `Attention` class can call different attention processors / attention functions - # here we simply pass along all tensors to the selected processor class - # For standard processors that are defined here, `**cross_attention_kwargs` is empty - - attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys()) - unused_kwargs = [k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters] - if len(unused_kwargs) > 0: - print( - f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored." - ) - cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters} - - return self.processor( - self, - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - - def orig_forward(self, hidden_states, encoder_hidden_states=None): - q = self.to_q(hidden_states) - k = ( - self.to_k(encoder_hidden_states) - if encoder_hidden_states is not None - else self.to_k(hidden_states) - ) - v = ( - self.to_v(encoder_hidden_states) - if encoder_hidden_states is not None - else self.to_v(hidden_states) - ) - b, t, c = q.size() - - q = q.view(q.size(0), q.size(1), self.num_heads, self.head_dim).transpose(1, 2) - k = k.view(k.size(0), k.size(1), self.num_heads, self.head_dim).transpose(1, 2) - v = v.view(v.size(0), v.size(1), self.num_heads, self.head_dim).transpose(1, 2) - - # scores = torch.matmul(q, k.transpose(-2, -1)) * self.scale - # attn_weights = torch.softmax(scores, dim=-1) - # attn_output = torch.matmul(attn_weights, v) - - attn_output = F.scaled_dot_product_attention( - q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False, scale=self.scale, - ) - - attn_output = attn_output.transpose(1, 2).contiguous().view(b, t, c) - - for layer in self.to_out: - attn_output = layer(attn_output) - - return attn_output - - def set_processor(self, processor) -> None: - r""" - Set the attention processor to use. - - Args: - processor (`AttnProcessor`): - The attention processor to use. - """ - # if current processor is in `self._modules` and if passed `processor` is not, we need to - # pop `processor` from `self._modules` - if ( - hasattr(self, "processor") - and isinstance(self.processor, torch.nn.Module) - and not isinstance(processor, torch.nn.Module) - ): - print(f"You are removing possibly trained weights of {self.processor} with {processor}") - self._modules.pop("processor") - - self.processor = processor - - def get_processor(self, return_deprecated_lora: bool = False): - r""" - Get the attention processor in use. - - Args: - return_deprecated_lora (`bool`, *optional*, defaults to `False`): - Set to `True` to return the deprecated LoRA attention processor. - - Returns: - "AttentionProcessor": The attention processor in use. - """ - if not return_deprecated_lora: - return self.processor - - # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible - # serialization format for LoRA Attention Processors. It should be deleted once the integration - # with PEFT is completed. - is_lora_activated = { - name: module.lora_layer is not None - for name, module in self.named_modules() - if hasattr(module, "lora_layer") - } - - # 1. if no layer has a LoRA activated we can return the processor as usual - if not any(is_lora_activated.values()): - return self.processor - - # If doesn't apply LoRA do `add_k_proj` or `add_v_proj` - is_lora_activated.pop("add_k_proj", None) - is_lora_activated.pop("add_v_proj", None) - # 2. else it is not possible that only some layers have LoRA activated - if not all(is_lora_activated.values()): - raise ValueError( - f"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}" - ) - - # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor - non_lora_processor_cls_name = self.processor.__class__.__name__ - lora_processor_cls = getattr(import_module(__name__), "LoRA" + non_lora_processor_cls_name) - - hidden_size = self.inner_dim - - # now create a LoRA attention processor from the LoRA layers - if lora_processor_cls in [LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor]: - kwargs = { - "cross_attention_dim": self.cross_attention_dim, - "rank": self.to_q.lora_layer.rank, - "network_alpha": self.to_q.lora_layer.network_alpha, - "q_rank": self.to_q.lora_layer.rank, - "q_hidden_size": self.to_q.lora_layer.out_features, - "k_rank": self.to_k.lora_layer.rank, - "k_hidden_size": self.to_k.lora_layer.out_features, - "v_rank": self.to_v.lora_layer.rank, - "v_hidden_size": self.to_v.lora_layer.out_features, - "out_rank": self.to_out[0].lora_layer.rank, - "out_hidden_size": self.to_out[0].lora_layer.out_features, - } - - if hasattr(self.processor, "attention_op"): - kwargs["attention_op"] = self.processor.attention_op - - lora_processor = lora_processor_cls(hidden_size, **kwargs) - lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) - lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) - lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) - lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict()) - elif lora_processor_cls == LoRAAttnAddedKVProcessor: - lora_processor = lora_processor_cls( - hidden_size, - cross_attention_dim=self.add_k_proj.weight.shape[0], - rank=self.to_q.lora_layer.rank, - network_alpha=self.to_q.lora_layer.network_alpha, - ) - lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) - lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) - lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) - lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict()) - - # only save if used - if self.add_k_proj.lora_layer is not None: - lora_processor.add_k_proj_lora.load_state_dict(self.add_k_proj.lora_layer.state_dict()) - lora_processor.add_v_proj_lora.load_state_dict(self.add_v_proj.lora_layer.state_dict()) - else: - lora_processor.add_k_proj_lora = None - lora_processor.add_v_proj_lora = None - else: - raise ValueError(f"{lora_processor_cls} does not exist.") - - return lora_processor - -class GEGLU(nn.Module): - def __init__(self, in_features, out_features): - super(GEGLU, self).__init__() - self.proj = nn.Linear(in_features, out_features * 2, bias=True) - - def forward(self, x): - x_proj = self.proj(x) - x1, x2 = x_proj.chunk(2, dim=-1) - return x1 * torch.nn.functional.gelu(x2) - - -class FeedForward(nn.Module): - def __init__(self, in_features, out_features): - super(FeedForward, self).__init__() - - self.net = nn.ModuleList( - [ - GEGLU(in_features, out_features * 4), - nn.Dropout(p=0.0, inplace=False), - nn.Linear(out_features * 4, out_features, bias=True), - ] - ) - - def forward(self, x): - for layer in self.net: - x = layer(x) - return x - - -class BasicTransformerBlock(nn.Module): - def __init__(self, hidden_size): - super(BasicTransformerBlock, self).__init__() - self.norm1 = nn.LayerNorm(hidden_size, eps=1e-05, elementwise_affine=True) - self.attn1 = Attention(hidden_size) - self.norm2 = nn.LayerNorm(hidden_size, eps=1e-05, elementwise_affine=True) - self.attn2 = Attention(hidden_size, 2048) - self.norm3 = nn.LayerNorm(hidden_size, eps=1e-05, elementwise_affine=True) - self.ff = FeedForward(hidden_size, hidden_size) - - def forward(self, x, encoder_hidden_states=None): - residual = x - - x = self.norm1(x) - x = self.attn1(x) - x = x + residual - - residual = x - - x = self.norm2(x) - if encoder_hidden_states is not None: - x = self.attn2(x, encoder_hidden_states) - else: - x = self.attn2(x) - x = x + residual - - residual = x - - x = self.norm3(x) - x = self.ff(x) - x = x + residual - return x - - -class Transformer2DModel(nn.Module): - def __init__(self, in_channels, out_channels, n_layers): - super(Transformer2DModel, self).__init__() - self.norm = nn.GroupNorm(32, in_channels, eps=1e-06, affine=True) - self.proj_in = nn.Linear(in_channels, out_channels, bias=True) - self.transformer_blocks = nn.ModuleList( - [BasicTransformerBlock(out_channels) for _ in range(n_layers)] - ) - self.proj_out = nn.Linear(out_channels, out_channels, bias=True) - - def forward(self, hidden_states, encoder_hidden_states=None): - batch, _, height, width = hidden_states.shape - res = hidden_states - hidden_states = self.norm(hidden_states) - inner_dim = hidden_states.shape[1] - hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( - batch, height * width, inner_dim - ) - hidden_states = self.proj_in(hidden_states) - - for block in self.transformer_blocks: - hidden_states = block(hidden_states, encoder_hidden_states) - - hidden_states = self.proj_out(hidden_states) - hidden_states = ( - hidden_states.reshape(batch, height, width, inner_dim) - .permute(0, 3, 1, 2) - .contiguous() - ) - - return hidden_states + res - - -class Downsample2D(nn.Module): - def __init__(self, in_channels, out_channels): - super(Downsample2D, self).__init__() - self.conv = nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=2, padding=1 - ) - - def forward(self, x): - return self.conv(x) - - -class Upsample2D(nn.Module): - def __init__(self, in_channels, out_channels): - super(Upsample2D, self).__init__() - self.conv = nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, x): - x = F.interpolate(x, scale_factor=2.0, mode="nearest") - return self.conv(x) - - -class DownBlock2D(nn.Module): - def __init__(self, in_channels, out_channels): - super(DownBlock2D, self).__init__() - self.resnets = nn.ModuleList( - [ - ResnetBlock2D(in_channels, out_channels, conv_shortcut=False), - ResnetBlock2D(out_channels, out_channels, conv_shortcut=False), - ] - ) - self.downsamplers = nn.ModuleList([Downsample2D(out_channels, out_channels)]) - - def forward(self, hidden_states, temb): - output_states = [] - for module in self.resnets: - hidden_states = module(hidden_states, temb) - output_states.append(hidden_states) - - hidden_states = self.downsamplers[0](hidden_states) - output_states.append(hidden_states) - - return hidden_states, output_states - - -class CrossAttnDownBlock2D(nn.Module): - def __init__(self, in_channels, out_channels, n_layers, has_downsamplers=True): - super(CrossAttnDownBlock2D, self).__init__() - self.attentions = nn.ModuleList( - [ - Transformer2DModel(out_channels, out_channels, n_layers), - Transformer2DModel(out_channels, out_channels, n_layers), - ] - ) - self.resnets = nn.ModuleList( - [ - ResnetBlock2D(in_channels, out_channels), - ResnetBlock2D(out_channels, out_channels, conv_shortcut=False), - ] - ) - self.downsamplers = None - if has_downsamplers: - self.downsamplers = nn.ModuleList( - [Downsample2D(out_channels, out_channels)] - ) - - def forward(self, hidden_states, temb, encoder_hidden_states): - output_states = [] - for resnet, attn in zip(self.resnets, self.attentions): - hidden_states = resnet(hidden_states, temb) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - ) - output_states.append(hidden_states) - - if self.downsamplers is not None: - hidden_states = self.downsamplers[0](hidden_states) - output_states.append(hidden_states) - - return hidden_states, output_states - - -class CrossAttnUpBlock2D(nn.Module): - def __init__(self, in_channels, out_channels, prev_output_channel, n_layers): - super(CrossAttnUpBlock2D, self).__init__() - self.attentions = nn.ModuleList( - [ - Transformer2DModel(out_channels, out_channels, n_layers), - Transformer2DModel(out_channels, out_channels, n_layers), - Transformer2DModel(out_channels, out_channels, n_layers), - ] - ) - self.resnets = nn.ModuleList( - [ - ResnetBlock2D(prev_output_channel + out_channels, out_channels), - ResnetBlock2D(2 * out_channels, out_channels), - ResnetBlock2D(out_channels + in_channels, out_channels), - ] - ) - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, out_channels)]) - - def forward( - self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states - ): - for resnet, attn in zip(self.resnets, self.attentions): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - hidden_states = resnet(hidden_states, temb) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - ) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states) - - return hidden_states - - -class UpBlock2D(nn.Module): - def __init__(self, in_channels, out_channels, prev_output_channel): - super(UpBlock2D, self).__init__() - self.resnets = nn.ModuleList( - [ - ResnetBlock2D(out_channels + prev_output_channel, out_channels), - ResnetBlock2D(out_channels * 2, out_channels), - ResnetBlock2D(out_channels + in_channels, out_channels), - ] - ) - - def forward(self, hidden_states, res_hidden_states_tuple, temb=None): - - is_freeu_enabled = ( - getattr(self, "s1", None) - and getattr(self, "s2", None) - and getattr(self, "b1", None) - and getattr(self, "b2", None) - and getattr(self, "resolution_idx", None) - ) - - for resnet in self.resnets: - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - - - if is_freeu_enabled: - hidden_states, res_hidden_states = apply_freeu( - self.resolution_idx, - hidden_states, - res_hidden_states, - s1=self.s1, - s2=self.s2, - b1=self.b1, - b2=self.b2, - ) - - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - hidden_states = resnet(hidden_states, temb) - - return hidden_states - -class UNetMidBlock2DCrossAttn(nn.Module): - def __init__(self, in_features): - super(UNetMidBlock2DCrossAttn, self).__init__() - self.attentions = nn.ModuleList( - [Transformer2DModel(in_features, in_features, n_layers=10)] - ) - self.resnets = nn.ModuleList( - [ - ResnetBlock2D(in_features, in_features, conv_shortcut=False), - ResnetBlock2D(in_features, in_features, conv_shortcut=False), - ] - ) - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None): - hidden_states = self.resnets[0](hidden_states, temb) - for attn, resnet in zip(self.attentions, self.resnets[1:]): - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - ) - hidden_states = resnet(hidden_states, temb) - - return hidden_states - - -class UNet2DConditionModel(nn.Module): - def __init__(self): - super(UNet2DConditionModel, self).__init__() - - # This is needed to imitate huggingface config behavior - # has nothing to do with the model itself - # remove this if you don't use diffuser's pipeline - self.config = namedtuple( - "config", "in_channels addition_time_embed_dim sample_size" - ) - self.config.in_channels = 4 - self.config.addition_time_embed_dim = 256 - self.config.sample_size = 128 - - self.conv_in = nn.Conv2d(4, 320, kernel_size=3, stride=1, padding=1) - self.time_proj = Timesteps() - self.time_embedding = TimestepEmbedding(in_features=320, out_features=1280) - self.add_time_proj = Timesteps(256) - self.add_embedding = TimestepEmbedding(in_features=2816, out_features=1280) - self.down_blocks = nn.ModuleList( - [ - DownBlock2D(in_channels=320, out_channels=320), - CrossAttnDownBlock2D(in_channels=320, out_channels=640, n_layers=2), - CrossAttnDownBlock2D( - in_channels=640, - out_channels=1280, - n_layers=10, - has_downsamplers=False, - ), - ] - ) - self.up_blocks = nn.ModuleList( - [ - CrossAttnUpBlock2D( - in_channels=640, - out_channels=1280, - prev_output_channel=1280, - n_layers=10, - ), - CrossAttnUpBlock2D( - in_channels=320, - out_channels=640, - prev_output_channel=1280, - n_layers=2, - ), - UpBlock2D(in_channels=320, out_channels=320, prev_output_channel=640), - ] - ) - self.mid_block = UNetMidBlock2DCrossAttn(1280) - self.conv_norm_out = nn.GroupNorm(32, 320, eps=1e-05, affine=True) - self.conv_act = nn.SiLU() - self.conv_out = nn.Conv2d(320, 4, kernel_size=3, stride=1, padding=1) - - def forward( - self, sample, timesteps, encoder_hidden_states, added_cond_kwargs, **kwargs - ): - # Implement the forward pass through the model - timesteps = timesteps.expand(sample.shape[0]) - t_emb = self.time_proj(timesteps).to(dtype=sample.dtype) - - emb = self.time_embedding(t_emb) - - text_embeds = added_cond_kwargs.get("text_embeds") - time_ids = added_cond_kwargs.get("time_ids") - - time_embeds = self.add_time_proj(time_ids.flatten()) - time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) - - add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) - add_embeds = add_embeds.to(emb.dtype) - aug_emb = self.add_embedding(add_embeds) - - emb = emb + aug_emb - - sample = self.conv_in(sample) - - # 3. down - s0 = sample - sample, [s1, s2, s3] = self.down_blocks[0]( - sample, - temb=emb, - ) - - sample, [s4, s5, s6] = self.down_blocks[1]( - sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - ) - - sample, [s7, s8] = self.down_blocks[2]( - sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - ) - - # 4. mid - sample = self.mid_block( - sample, emb, encoder_hidden_states=encoder_hidden_states - ) - - # 5. up - sample = self.up_blocks[0]( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=[s6, s7, s8], - encoder_hidden_states=encoder_hidden_states, - ) - - sample = self.up_blocks[1]( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=[s3, s4, s5], - encoder_hidden_states=encoder_hidden_states, - ) - - sample = self.up_blocks[2]( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=[s0, s1, s2], - ) - - # 6. post-process - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - return [sample] \ No newline at end of file diff --git a/module/transformers/transformer_2d_ExtractKV.py b/module/transformers/transformer_2d_ExtractKV.py deleted file mode 100644 index 004afb49b0606e06375a606cf202c91c18418a04..0000000000000000000000000000000000000000 --- a/module/transformers/transformer_2d_ExtractKV.py +++ /dev/null @@ -1,595 +0,0 @@ -# Copy from diffusers.models.transformers.transformer_2d.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from dataclasses import dataclass -from typing import Any, Dict, Optional - -import torch -import torch.nn.functional as F -from torch import nn - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.utils import BaseOutput, deprecate, is_torch_version, logging -from diffusers.models.attention import BasicTransformerBlock -from diffusers.models.embeddings import ImagePositionalEmbeddings, PatchEmbed, PixArtAlphaTextProjection -from diffusers.models.modeling_utils import ModelMixin -from diffusers.models.normalization import AdaLayerNormSingle - -from module.attention import ExtractKVTransformerBlock - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class ExtractKVTransformer2DModelOutput(BaseOutput): - """ - The output of [`ExtractKVTransformer2DModel`]. - - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): - The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability - distributions for the unnoised latent pixels. - """ - - sample: torch.FloatTensor - cached_kvs: Dict[str, Any] = None - - -class ExtractKVTransformer2DModel(ModelMixin, ConfigMixin): - """ - A 2D Transformer model for image-like data which also outputs CrossAttention KV metrics. - - Parameters: - num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. - attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. - in_channels (`int`, *optional*): - The number of channels in the input and output (specify if the input is **continuous**). - num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. - sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). - This is fixed during training since it is used to learn a number of position embeddings. - num_vector_embeds (`int`, *optional*): - The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). - Includes the class for the masked latent pixel. - activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. - num_embeds_ada_norm ( `int`, *optional*): - The number of diffusion steps used during training. Pass if at least one of the norm_layers is - `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are - added to the hidden states. - - During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. - attention_bias (`bool`, *optional*): - Configure if the `TransformerBlocks` attention should contain a bias parameter. - """ - - _supports_gradient_checkpointing = True - _no_split_modules = ["BasicTransformerBlock"] - - @register_to_config - def __init__( - self, - num_attention_heads: int = 16, - attention_head_dim: int = 88, - in_channels: Optional[int] = None, - out_channels: Optional[int] = None, - num_layers: int = 1, - dropout: float = 0.0, - norm_num_groups: int = 32, - cross_attention_dim: Optional[int] = None, - attention_bias: bool = False, - sample_size: Optional[int] = None, - num_vector_embeds: Optional[int] = None, - patch_size: Optional[int] = None, - activation_fn: str = "geglu", - num_embeds_ada_norm: Optional[int] = None, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - double_self_attention: bool = False, - upcast_attention: bool = False, - norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen' - norm_elementwise_affine: bool = True, - norm_eps: float = 1e-5, - attention_type: str = "default", - caption_channels: int = None, - interpolation_scale: float = None, - use_additional_conditions: Optional[bool] = None, - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, - ): - super().__init__() - - # Validate inputs. - if patch_size is not None: - if norm_type not in ["ada_norm", "ada_norm_zero", "ada_norm_single"]: - raise NotImplementedError( - f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'." - ) - elif norm_type in ["ada_norm", "ada_norm_zero"] and num_embeds_ada_norm is None: - raise ValueError( - f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None." - ) - - # Set some common variables used across the board. - self.use_linear_projection = use_linear_projection - self.interpolation_scale = interpolation_scale - self.caption_channels = caption_channels - self.num_attention_heads = num_attention_heads - self.attention_head_dim = attention_head_dim - self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim - self.in_channels = in_channels - self.out_channels = in_channels if out_channels is None else out_channels - self.gradient_checkpointing = False - if use_additional_conditions is None: - if norm_type == "ada_norm_single" and sample_size == 128: - use_additional_conditions = True - else: - use_additional_conditions = False - self.use_additional_conditions = use_additional_conditions - self.extract_self_attention_kv = extract_self_attention_kv - self.extract_cross_attention_kv = extract_cross_attention_kv - - # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` - # Define whether input is continuous or discrete depending on configuration - self.is_input_continuous = (in_channels is not None) and (patch_size is None) - self.is_input_vectorized = num_vector_embeds is not None - self.is_input_patches = in_channels is not None and patch_size is not None - - if norm_type == "layer_norm" and num_embeds_ada_norm is not None: - deprecation_message = ( - f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" - " incorrectly set to `'layer_norm'`. Make sure to set `norm_type` to `'ada_norm'` in the config." - " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" - " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" - " would be very nice if you could open a Pull request for the `transformer/config.json` file" - ) - deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) - norm_type = "ada_norm" - - if self.is_input_continuous and self.is_input_vectorized: - raise ValueError( - f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" - " sure that either `in_channels` or `num_vector_embeds` is None." - ) - elif self.is_input_vectorized and self.is_input_patches: - raise ValueError( - f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" - " sure that either `num_vector_embeds` or `num_patches` is None." - ) - elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: - raise ValueError( - f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" - f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." - ) - - # 2. Initialize the right blocks. - # These functions follow a common structure: - # a. Initialize the input blocks. b. Initialize the transformer blocks. - # c. Initialize the output blocks and other projection blocks when necessary. - if self.is_input_continuous: - self._init_continuous_input(norm_type=norm_type) - elif self.is_input_vectorized: - self._init_vectorized_inputs(norm_type=norm_type) - elif self.is_input_patches: - self._init_patched_inputs(norm_type=norm_type) - - def _init_continuous_input(self, norm_type): - self.norm = torch.nn.GroupNorm( - num_groups=self.config.norm_num_groups, num_channels=self.in_channels, eps=1e-6, affine=True - ) - if self.use_linear_projection: - self.proj_in = torch.nn.Linear(self.in_channels, self.inner_dim) - else: - self.proj_in = torch.nn.Conv2d(self.in_channels, self.inner_dim, kernel_size=1, stride=1, padding=0) - - self.transformer_blocks = nn.ModuleList( - [ - ExtractKVTransformerBlock( - self.inner_dim, - self.config.num_attention_heads, - self.config.attention_head_dim, - dropout=self.config.dropout, - cross_attention_dim=self.config.cross_attention_dim, - activation_fn=self.config.activation_fn, - num_embeds_ada_norm=self.config.num_embeds_ada_norm, - attention_bias=self.config.attention_bias, - only_cross_attention=self.config.only_cross_attention, - double_self_attention=self.config.double_self_attention, - upcast_attention=self.config.upcast_attention, - norm_type=norm_type, - norm_elementwise_affine=self.config.norm_elementwise_affine, - norm_eps=self.config.norm_eps, - attention_type=self.config.attention_type, - extract_self_attention_kv=self.config.extract_self_attention_kv, - extract_cross_attention_kv=self.config.extract_cross_attention_kv, - ) - for _ in range(self.config.num_layers) - ] - ) - - if self.use_linear_projection: - self.proj_out = torch.nn.Linear(self.inner_dim, self.out_channels) - else: - self.proj_out = torch.nn.Conv2d(self.inner_dim, self.out_channels, kernel_size=1, stride=1, padding=0) - - def _init_vectorized_inputs(self, norm_type): - assert self.config.sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" - assert ( - self.config.num_vector_embeds is not None - ), "Transformer2DModel over discrete input must provide num_embed" - - self.height = self.config.sample_size - self.width = self.config.sample_size - self.num_latent_pixels = self.height * self.width - - self.latent_image_embedding = ImagePositionalEmbeddings( - num_embed=self.config.num_vector_embeds, embed_dim=self.inner_dim, height=self.height, width=self.width - ) - - self.transformer_blocks = nn.ModuleList( - [ - ExtractKVTransformerBlock( - self.inner_dim, - self.config.num_attention_heads, - self.config.attention_head_dim, - dropout=self.config.dropout, - cross_attention_dim=self.config.cross_attention_dim, - activation_fn=self.config.activation_fn, - num_embeds_ada_norm=self.config.num_embeds_ada_norm, - attention_bias=self.config.attention_bias, - only_cross_attention=self.config.only_cross_attention, - double_self_attention=self.config.double_self_attention, - upcast_attention=self.config.upcast_attention, - norm_type=norm_type, - norm_elementwise_affine=self.config.norm_elementwise_affine, - norm_eps=self.config.norm_eps, - attention_type=self.config.attention_type, - extract_self_attention_kv=self.config.extract_self_attention_kv, - extract_cross_attention_kv=self.config.extract_cross_attention_kv, - ) - for _ in range(self.config.num_layers) - ] - ) - - self.norm_out = nn.LayerNorm(self.inner_dim) - self.out = nn.Linear(self.inner_dim, self.config.num_vector_embeds - 1) - - def _init_patched_inputs(self, norm_type): - assert self.config.sample_size is not None, "Transformer2DModel over patched input must provide sample_size" - - self.height = self.config.sample_size - self.width = self.config.sample_size - - self.patch_size = self.config.patch_size - interpolation_scale = ( - self.config.interpolation_scale - if self.config.interpolation_scale is not None - else max(self.config.sample_size // 64, 1) - ) - self.pos_embed = PatchEmbed( - height=self.config.sample_size, - width=self.config.sample_size, - patch_size=self.config.patch_size, - in_channels=self.in_channels, - embed_dim=self.inner_dim, - interpolation_scale=interpolation_scale, - ) - - self.transformer_blocks = nn.ModuleList( - [ - ExtractKVTransformerBlock( - self.inner_dim, - self.config.num_attention_heads, - self.config.attention_head_dim, - dropout=self.config.dropout, - cross_attention_dim=self.config.cross_attention_dim, - activation_fn=self.config.activation_fn, - num_embeds_ada_norm=self.config.num_embeds_ada_norm, - attention_bias=self.config.attention_bias, - only_cross_attention=self.config.only_cross_attention, - double_self_attention=self.config.double_self_attention, - upcast_attention=self.config.upcast_attention, - norm_type=norm_type, - norm_elementwise_affine=self.config.norm_elementwise_affine, - norm_eps=self.config.norm_eps, - attention_type=self.config.attention_type, - extract_self_attention_kv=self.config.extract_self_attention_kv, - extract_cross_attention_kv=self.config.extract_cross_attention_kv, - ) - for _ in range(self.config.num_layers) - ] - ) - - if self.config.norm_type != "ada_norm_single": - self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6) - self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim) - self.proj_out_2 = nn.Linear( - self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels - ) - elif self.config.norm_type == "ada_norm_single": - self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6) - self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5) - self.proj_out = nn.Linear( - self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels - ) - - # PixArt-Alpha blocks. - self.adaln_single = None - if self.config.norm_type == "ada_norm_single": - # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use - # additional conditions until we find better name - self.adaln_single = AdaLayerNormSingle( - self.inner_dim, use_additional_conditions=self.use_additional_conditions - ) - - self.caption_projection = None - if self.caption_channels is not None: - self.caption_projection = PixArtAlphaTextProjection( - in_features=self.caption_channels, hidden_size=self.inner_dim - ) - - def _set_gradient_checkpointing(self, module, value=False): - if hasattr(module, "gradient_checkpointing"): - module.gradient_checkpointing = value - - def forward( - self, - hidden_states: torch.Tensor, - encoder_hidden_states: Optional[torch.Tensor] = None, - timestep: Optional[torch.LongTensor] = None, - added_cond_kwargs: Dict[str, torch.Tensor] = None, - class_labels: Optional[torch.LongTensor] = None, - cross_attention_kwargs: Dict[str, Any] = None, - attention_mask: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - return_dict: bool = True, - ): - """ - The [`Transformer2DModel`] forward method. - - Args: - hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): - Input `hidden_states`. - encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): - Conditional embeddings for cross attention layer. If not given, cross-attention defaults to - self-attention. - timestep ( `torch.LongTensor`, *optional*): - Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. - class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): - Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in - `AdaLayerZeroNorm`. - cross_attention_kwargs ( `Dict[str, Any]`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - attention_mask ( `torch.Tensor`, *optional*): - An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask - is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large - negative values to the attention scores corresponding to "discard" tokens. - encoder_attention_mask ( `torch.Tensor`, *optional*): - Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: - - * Mask `(batch, sequence_length)` True = keep, False = discard. - * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. - - If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format - above. This bias will be added to the cross-attention scores. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain - tuple. - - Returns: - If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a - `tuple` where the first element is the sample tensor. - """ - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. - # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. - # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. - # expects mask of shape: - # [batch, key_tokens] - # adds singleton query_tokens dimension: - # [batch, 1, key_tokens] - # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: - # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) - # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) - if attention_mask is not None and attention_mask.ndim == 2: - # assume that mask is expressed as: - # (1 = keep, 0 = discard) - # convert mask into a bias that can be added to attention scores: - # (keep = +0, discard = -10000.0) - attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # convert encoder_attention_mask to a bias the same way we do for attention_mask - if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: - encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 - encoder_attention_mask = encoder_attention_mask.unsqueeze(1) - - # 1. Input - if self.is_input_continuous: - batch_size, _, height, width = hidden_states.shape - residual = hidden_states - hidden_states, inner_dim = self._operate_on_continuous_inputs(hidden_states) - elif self.is_input_vectorized: - hidden_states = self.latent_image_embedding(hidden_states) - elif self.is_input_patches: - height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size - hidden_states, encoder_hidden_states, timestep, embedded_timestep = self._operate_on_patched_inputs( - hidden_states, encoder_hidden_states, timestep, added_cond_kwargs - ) - - # 2. Blocks - extracted_kvs = {} - for block in self.transformer_blocks: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states, extracted_kv = torch.utils.checkpoint.checkpoint( - create_custom_forward(block), - hidden_states, - attention_mask, - encoder_hidden_states, - encoder_attention_mask, - timestep, - cross_attention_kwargs, - class_labels, - **ckpt_kwargs, - ) - else: - hidden_states, extracted_kv = block( - hidden_states, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - timestep=timestep, - cross_attention_kwargs=cross_attention_kwargs, - class_labels=class_labels, - ) - - if extracted_kv: - extracted_kvs[block.full_name] = extracted_kv - - # 3. Output - if self.is_input_continuous: - output = self._get_output_for_continuous_inputs( - hidden_states=hidden_states, - residual=residual, - batch_size=batch_size, - height=height, - width=width, - inner_dim=inner_dim, - ) - elif self.is_input_vectorized: - output = self._get_output_for_vectorized_inputs(hidden_states) - elif self.is_input_patches: - output = self._get_output_for_patched_inputs( - hidden_states=hidden_states, - timestep=timestep, - class_labels=class_labels, - embedded_timestep=embedded_timestep, - height=height, - width=width, - ) - - if not return_dict: - return (output, extracted_kvs) - - return ExtractKVTransformer2DModelOutput(sample=output, cached_kvs=extracted_kvs) - - def init_kv_extraction(self): - for block in self.transformer_blocks: - block.init_kv_extraction() - - def _operate_on_continuous_inputs(self, hidden_states): - batch, _, height, width = hidden_states.shape - hidden_states = self.norm(hidden_states) - - if not self.use_linear_projection: - hidden_states = self.proj_in(hidden_states) - inner_dim = hidden_states.shape[1] - hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) - else: - inner_dim = hidden_states.shape[1] - hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) - hidden_states = self.proj_in(hidden_states) - - return hidden_states, inner_dim - - def _operate_on_patched_inputs(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs): - batch_size = hidden_states.shape[0] - hidden_states = self.pos_embed(hidden_states) - embedded_timestep = None - - if self.adaln_single is not None: - if self.use_additional_conditions and added_cond_kwargs is None: - raise ValueError( - "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`." - ) - timestep, embedded_timestep = self.adaln_single( - timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype - ) - - if self.caption_projection is not None: - encoder_hidden_states = self.caption_projection(encoder_hidden_states) - encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) - - return hidden_states, encoder_hidden_states, timestep, embedded_timestep - - def _get_output_for_continuous_inputs(self, hidden_states, residual, batch_size, height, width, inner_dim): - if not self.use_linear_projection: - hidden_states = ( - hidden_states.reshape(batch_size, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() - ) - hidden_states = self.proj_out(hidden_states) - else: - hidden_states = self.proj_out(hidden_states) - hidden_states = ( - hidden_states.reshape(batch_size, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() - ) - - output = hidden_states + residual - return output - - def _get_output_for_vectorized_inputs(self, hidden_states): - hidden_states = self.norm_out(hidden_states) - logits = self.out(hidden_states) - # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) - logits = logits.permute(0, 2, 1) - # log(p(x_0)) - output = F.log_softmax(logits.double(), dim=1).float() - return output - - def _get_output_for_patched_inputs( - self, hidden_states, timestep, class_labels, embedded_timestep, height=None, width=None - ): - if self.config.norm_type != "ada_norm_single": - conditioning = self.transformer_blocks[0].norm1.emb( - timestep, class_labels, hidden_dtype=hidden_states.dtype - ) - shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) - hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] - hidden_states = self.proj_out_2(hidden_states) - elif self.config.norm_type == "ada_norm_single": - shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) - hidden_states = self.norm_out(hidden_states) - # Modulation - hidden_states = hidden_states * (1 + scale) + shift - hidden_states = self.proj_out(hidden_states) - hidden_states = hidden_states.squeeze(1) - - # unpatchify - if self.adaln_single is None: - height = width = int(hidden_states.shape[1] ** 0.5) - hidden_states = hidden_states.reshape( - shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) - ) - hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) - output = hidden_states.reshape( - shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) - ) - return output \ No newline at end of file diff --git a/module/unet/unet_2d_ZeroSFT.py b/module/unet/unet_2d_ZeroSFT.py deleted file mode 100644 index 2fadee0271c6e51bdf68defbf6abc09cb3fd24c6..0000000000000000000000000000000000000000 --- a/module/unet/unet_2d_ZeroSFT.py +++ /dev/null @@ -1,1397 +0,0 @@ -# Copy from diffusers.models.unets.unet_2d_condition.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.utils.checkpoint - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin -from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers -from diffusers.models.activations import get_activation -from diffusers.models.attention_processor import ( - ADDED_KV_ATTENTION_PROCESSORS, - CROSS_ATTENTION_PROCESSORS, - Attention, - AttentionProcessor, - AttnAddedKVProcessor, - AttnProcessor, -) -from diffusers.models.embeddings import ( - GaussianFourierProjection, - GLIGENTextBoundingboxProjection, - ImageHintTimeEmbedding, - ImageProjection, - ImageTimeEmbedding, - TextImageProjection, - TextImageTimeEmbedding, - TextTimeEmbedding, - TimestepEmbedding, - Timesteps, -) -from diffusers.models.modeling_utils import ModelMixin -from .unet_2d_ZeroSFT_blocks import ( - get_down_block, - get_mid_block, - get_up_block, -) - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -def zero_module(module): - for p in module.parameters(): - nn.init.zeros_(p) - return module - - -class ZeroConv(nn.Module): - def __init__(self, label_nc, norm_nc, mask=False): - super().__init__() - self.zero_conv = zero_module(nn.Conv2d(label_nc, norm_nc, 1, 1, 0)) - self.mask = mask - - def forward(self, c, h, h_ori=None): - # with torch.cuda.amp.autocast(enabled=False, dtype=torch.float32): - if not self.mask: - h = h + self.zero_conv(c) - else: - h = h + self.zero_conv(c) * torch.zeros_like(h) - if h_ori is not None: - h = torch.cat([h_ori, h], dim=1) - return h - - -class ZeroSFT(nn.Module): - def __init__(self, label_nc, norm_nc, concat_channels=0, norm=True, mask=False): - super().__init__() - - # param_free_norm_type = str(parsed.group(1)) - ks = 3 - pw = ks // 2 - - self.mask = mask - self.norm = norm - self.pre_concat = bool(concat_channels != 0) - if self.norm: - self.param_free_norm = torch.nn.GroupNorm(num_groups=32, num_channels=norm_nc + concat_channels) - else: - self.param_free_norm = nn.Identity() - - nhidden = 128 - - self.mlp_shared = nn.Sequential( - nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), - nn.SiLU() - ) - self.zero_mul = zero_module(nn.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw)) - self.zero_add = zero_module(nn.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw)) - - self.zero_conv = zero_module(nn.Conv2d(label_nc, norm_nc, 1, 1, 0)) - - def forward(self, down_block_res_samples, h_ori=None, control_scale=1.0, mask=False): - mask = mask or self.mask - assert mask is False - if self.pre_concat: - assert h_ori is not None - - c,h = down_block_res_samples - if h_ori is not None: - h_raw = torch.cat([h_ori, h], dim=1) - else: - h_raw = h - - if self.mask: - h = h + self.zero_conv(c) * torch.zeros_like(h) - else: - h = h + self.zero_conv(c) - if h_ori is not None and self.pre_concat: - h = torch.cat([h_ori, h], dim=1) - actv = self.mlp_shared(c) - gamma = self.zero_mul(actv) - beta = self.zero_add(actv) - if self.mask: - gamma = gamma * torch.zeros_like(gamma) - beta = beta * torch.zeros_like(beta) - # h = h + self.param_free_norm(h) * gamma + beta - h = self.param_free_norm(h) * (gamma + 1) + beta - if h_ori is not None and not self.pre_concat: - h = torch.cat([h_ori, h], dim=1) - return h * control_scale + h_raw * (1 - control_scale) - - -@dataclass -class UNet2DConditionOutput(BaseOutput): - """ - The output of [`UNet2DConditionModel`]. - - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. - """ - - sample: torch.FloatTensor = None - - -class UNet2DZeroSFTModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin): - r""" - A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample - shaped output. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented - for all models (such as downloading or saving). - - Parameters: - sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): - Height and width of input/output sample. - in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. - out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. - center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. - flip_sin_to_cos (`bool`, *optional*, defaults to `True`): - Whether to flip the sin to cos in the time embedding. - freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. - down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): - The tuple of downsample blocks to use. - mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): - Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or - `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. - up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): - The tuple of upsample blocks to use. - only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): - Whether to include self-attention in the basic transformer blocks, see - [`~models.attention.BasicTransformerBlock`]. - block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): - The tuple of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. - downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. - mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. - If `None`, normalization and activation layers is skipped in post-processing. - norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. - cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): - The dimension of the cross attention features. - transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling - blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - encoder_hid_dim (`int`, *optional*, defaults to None): - If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` - dimension to `cross_attention_dim`. - encoder_hid_dim_type (`str`, *optional*, defaults to `None`): - If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text - embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. - attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. - num_attention_heads (`int`, *optional*): - The number of attention heads. If not defined, defaults to `attention_head_dim` - resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config - for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. - class_embed_type (`str`, *optional*, defaults to `None`): - The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, - `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. - addition_embed_type (`str`, *optional*, defaults to `None`): - Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or - "text". "text" will use the `TextTimeEmbedding` layer. - addition_time_embed_dim: (`int`, *optional*, defaults to `None`): - Dimension for the timestep embeddings. - num_class_embeds (`int`, *optional*, defaults to `None`): - Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing - class conditioning with `class_embed_type` equal to `None`. - time_embedding_type (`str`, *optional*, defaults to `positional`): - The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. - time_embedding_dim (`int`, *optional*, defaults to `None`): - An optional override for the dimension of the projected time embedding. - time_embedding_act_fn (`str`, *optional*, defaults to `None`): - Optional activation function to use only once on the time embeddings before they are passed to the rest of - the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. - timestep_post_act (`str`, *optional*, defaults to `None`): - The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. - time_cond_proj_dim (`int`, *optional*, defaults to `None`): - The dimension of `cond_proj` layer in the timestep embedding. - conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. - conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. - projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when - `class_embed_type="projection"`. Required when `class_embed_type="projection"`. - class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time - embeddings with the class embeddings. - mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): - Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If - `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the - `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` - otherwise. - """ - - _supports_gradient_checkpointing = True - _no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D", "CrossAttnUpBlock2D"] - - @register_to_config - def __init__( - self, - sample_size: Optional[int] = None, - in_channels: int = 4, - out_channels: int = 4, - center_input_sample: bool = False, - flip_sin_to_cos: bool = True, - freq_shift: int = 0, - down_block_types: Tuple[str] = ( - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ), - mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", - up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int] = (320, 640, 1280, 1280), - layers_per_block: Union[int, Tuple[int]] = 2, - downsample_padding: int = 1, - mid_block_scale_factor: float = 1, - dropout: float = 0.0, - act_fn: str = "silu", - norm_num_groups: Optional[int] = 32, - norm_eps: float = 1e-5, - cross_attention_dim: Union[int, Tuple[int]] = 1280, - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, - reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, - encoder_hid_dim: Optional[int] = None, - encoder_hid_dim_type: Optional[str] = None, - attention_head_dim: Union[int, Tuple[int]] = 8, - num_attention_heads: Optional[Union[int, Tuple[int]]] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - addition_embed_type: Optional[str] = None, - addition_time_embed_dim: Optional[int] = None, - num_class_embeds: Optional[int] = None, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - time_embedding_type: str = "positional", - time_embedding_dim: Optional[int] = None, - time_embedding_act_fn: Optional[str] = None, - timestep_post_act: Optional[str] = None, - time_cond_proj_dim: Optional[int] = None, - conv_in_kernel: int = 3, - conv_out_kernel: int = 3, - projection_class_embeddings_input_dim: Optional[int] = None, - attention_type: str = "default", - class_embeddings_concat: bool = False, - mid_block_only_cross_attention: Optional[bool] = None, - cross_attention_norm: Optional[str] = None, - addition_embed_type_num_heads: int = 64, - ): - super().__init__() - - self.sample_size = sample_size - - if num_attention_heads is not None: - raise ValueError( - "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." - ) - - # If `num_attention_heads` is not defined (which is the case for most models) - # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. - # The reason for this behavior is to correct for incorrectly named variables that were introduced - # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 - # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking - # which is why we correct for the naming here. - num_attention_heads = num_attention_heads or attention_head_dim - - # Check inputs - self._check_config( - down_block_types=down_block_types, - up_block_types=up_block_types, - only_cross_attention=only_cross_attention, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - cross_attention_dim=cross_attention_dim, - transformer_layers_per_block=transformer_layers_per_block, - reverse_transformer_layers_per_block=reverse_transformer_layers_per_block, - attention_head_dim=attention_head_dim, - num_attention_heads=num_attention_heads, - ) - - # input - conv_in_padding = (conv_in_kernel - 1) // 2 - self.conv_in = nn.Conv2d( - in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding - ) - - # time - time_embed_dim, timestep_input_dim = self._set_time_proj( - time_embedding_type, - block_out_channels=block_out_channels, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - time_embedding_dim=time_embedding_dim, - ) - - self.time_embedding = TimestepEmbedding( - timestep_input_dim, - time_embed_dim, - act_fn=act_fn, - post_act_fn=timestep_post_act, - cond_proj_dim=time_cond_proj_dim, - ) - - self._set_encoder_hid_proj( - encoder_hid_dim_type, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - ) - - # class embedding - self._set_class_embedding( - class_embed_type, - act_fn=act_fn, - num_class_embeds=num_class_embeds, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - timestep_input_dim=timestep_input_dim, - ) - - self._set_add_embedding( - addition_embed_type, - addition_embed_type_num_heads=addition_embed_type_num_heads, - addition_time_embed_dim=addition_time_embed_dim, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - ) - - if time_embedding_act_fn is None: - self.time_embed_act = None - else: - self.time_embed_act = get_activation(time_embedding_act_fn) - - self.down_blocks = nn.ModuleList([]) - self.up_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = only_cross_attention - - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = False - - if isinstance(num_attention_heads, int): - num_attention_heads = (num_attention_heads,) * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - if isinstance(cross_attention_dim, int): - cross_attention_dim = (cross_attention_dim,) * len(down_block_types) - - if isinstance(layers_per_block, int): - layers_per_block = [layers_per_block] * len(down_block_types) - - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) - - if class_embeddings_concat: - # The time embeddings are concatenated with the class embeddings. The dimension of the - # time embeddings passed to the down, middle, and up blocks is twice the dimension of the - # regular time embeddings - blocks_time_embed_dim = time_embed_dim * 2 - else: - blocks_time_embed_dim = time_embed_dim - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block[i], - transformer_layers_per_block=transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - temb_channels=blocks_time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim[i], - num_attention_heads=num_attention_heads[i], - downsample_padding=downsample_padding, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, - dropout=dropout, - ) - self.down_blocks.append(down_block) - - # mid - self.mid_block = get_mid_block( - mid_block_type, - temb_channels=blocks_time_embed_dim, - in_channels=block_out_channels[-1], - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - output_scale_factor=mid_block_scale_factor, - transformer_layers_per_block=transformer_layers_per_block[-1], - num_attention_heads=num_attention_heads[-1], - cross_attention_dim=cross_attention_dim[-1], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - mid_block_only_cross_attention=mid_block_only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[-1], - dropout=dropout, - ) - self.mid_zero_SFT = ZeroSFT(block_out_channels[-1],block_out_channels[-1],0) - - # count how many layers upsample the images - self.num_upsamplers = 0 - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - reversed_num_attention_heads = list(reversed(num_attention_heads)) - reversed_layers_per_block = list(reversed(layers_per_block)) - reversed_cross_attention_dim = list(reversed(cross_attention_dim)) - reversed_transformer_layers_per_block = ( - list(reversed(transformer_layers_per_block)) - if reverse_transformer_layers_per_block is None - else reverse_transformer_layers_per_block - ) - only_cross_attention = list(reversed(only_cross_attention)) - - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - is_final_block = i == len(block_out_channels) - 1 - - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] - - # add upsample block for all BUT final layer - if not is_final_block: - add_upsample = True - self.num_upsamplers += 1 - else: - add_upsample = False - - up_block = get_up_block( - up_block_type, - num_layers=reversed_layers_per_block[i] + 1, - transformer_layers_per_block=reversed_transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=blocks_time_embed_dim, - add_upsample=add_upsample, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resolution_idx=i, - resnet_groups=norm_num_groups, - cross_attention_dim=reversed_cross_attention_dim[i], - num_attention_heads=reversed_num_attention_heads[i], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, - dropout=dropout, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - if norm_num_groups is not None: - self.conv_norm_out = nn.GroupNorm( - num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps - ) - - self.conv_act = get_activation(act_fn) - - else: - self.conv_norm_out = None - self.conv_act = None - - conv_out_padding = (conv_out_kernel - 1) // 2 - self.conv_out = nn.Conv2d( - block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding - ) - - self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim) - - def _check_config( - self, - down_block_types: Tuple[str], - up_block_types: Tuple[str], - only_cross_attention: Union[bool, Tuple[bool]], - block_out_channels: Tuple[int], - layers_per_block: Union[int, Tuple[int]], - cross_attention_dim: Union[int, Tuple[int]], - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]], - reverse_transformer_layers_per_block: bool, - attention_head_dim: int, - num_attention_heads: Optional[Union[int, Tuple[int]]], - ): - if len(down_block_types) != len(up_block_types): - raise ValueError( - f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." - ) - - if len(block_out_channels) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." - ) - - if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." - ) - if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None: - for layer_number_per_block in transformer_layers_per_block: - if isinstance(layer_number_per_block, list): - raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") - - def _set_time_proj( - self, - time_embedding_type: str, - block_out_channels: int, - flip_sin_to_cos: bool, - freq_shift: float, - time_embedding_dim: int, - ) -> Tuple[int, int]: - if time_embedding_type == "fourier": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 - if time_embed_dim % 2 != 0: - raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") - self.time_proj = GaussianFourierProjection( - time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos - ) - timestep_input_dim = time_embed_dim - elif time_embedding_type == "positional": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 - - self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) - timestep_input_dim = block_out_channels[0] - else: - raise ValueError( - f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." - ) - - return time_embed_dim, timestep_input_dim - - def _set_encoder_hid_proj( - self, - encoder_hid_dim_type: Optional[str], - cross_attention_dim: Union[int, Tuple[int]], - encoder_hid_dim: Optional[int], - ): - if encoder_hid_dim_type is None and encoder_hid_dim is not None: - encoder_hid_dim_type = "text_proj" - self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) - logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") - - if encoder_hid_dim is None and encoder_hid_dim_type is not None: - raise ValueError( - f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." - ) - - if encoder_hid_dim_type == "text_proj": - self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) - elif encoder_hid_dim_type == "text_image_proj": - # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)` - self.encoder_hid_proj = TextImageProjection( - text_embed_dim=encoder_hid_dim, - image_embed_dim=cross_attention_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - self.encoder_hid_proj = ImageProjection( - image_embed_dim=encoder_hid_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type is not None: - raise ValueError( - f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." - ) - else: - self.encoder_hid_proj = None - - def _set_class_embedding( - self, - class_embed_type: Optional[str], - act_fn: str, - num_class_embeds: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - timestep_input_dim: int, - ): - if class_embed_type is None and num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - elif class_embed_type == "timestep": - self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) - elif class_embed_type == "identity": - self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) - elif class_embed_type == "projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" - ) - # The projection `class_embed_type` is the same as the timestep `class_embed_type` except - # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings - # 2. it projects from an arbitrary input dimension. - # - # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. - # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. - # As a result, `TimestepEmbedding` can be passed arbitrary vectors. - self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - elif class_embed_type == "simple_projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" - ) - self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) - else: - self.class_embedding = None - - def _set_add_embedding( - self, - addition_embed_type: str, - addition_embed_type_num_heads: int, - addition_time_embed_dim: Optional[int], - flip_sin_to_cos: bool, - freq_shift: float, - cross_attention_dim: Optional[int], - encoder_hid_dim: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - ): - if addition_embed_type == "text": - if encoder_hid_dim is not None: - text_time_embedding_from_dim = encoder_hid_dim - else: - text_time_embedding_from_dim = cross_attention_dim - - self.add_embedding = TextTimeEmbedding( - text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads - ) - elif addition_embed_type == "text_image": - # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)` - self.add_embedding = TextImageTimeEmbedding( - text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim - ) - elif addition_embed_type == "text_time": - self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) - self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - elif addition_embed_type == "image": - # Kandinsky 2.2 - self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) - elif addition_embed_type == "image_hint": - # Kandinsky 2.2 ControlNet - self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) - elif addition_embed_type is not None: - raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") - - def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int): - if attention_type in ["gated", "gated-text-image"]: - positive_len = 768 - if isinstance(cross_attention_dim, int): - positive_len = cross_attention_dim - elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list): - positive_len = cross_attention_dim[0] - - feature_type = "text-only" if attention_type == "gated" else "text-image" - self.position_net = GLIGENTextBoundingboxProjection( - positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type - ) - - @property - def attn_processors(self) -> Dict[str, AttentionProcessor]: - r""" - Returns: - `dict` of attention processors: A dictionary containing all attention processors used in the model with - indexed by its weight name. - """ - # set recursively - processors = {} - - def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): - if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) - - for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) - - return processors - - for name, module in self.named_children(): - fn_recursive_add_processors(name, module, processors) - - return processors - - def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): - r""" - Sets the attention processor to use to compute attention. - - Parameters: - processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): - The instantiated processor class or a dictionary of processor classes that will be set as the processor - for **all** `Attention` layers. - - If `processor` is a dict, the key needs to define the path to the corresponding cross attention - processor. This is strongly recommended when setting trainable attention processors. - - """ - count = len(self.attn_processors.keys()) - - if isinstance(processor, dict) and len(processor) != count: - raise ValueError( - f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) - - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): - if hasattr(module, "set_processor"): - if not isinstance(processor, dict): - module.set_processor(processor) - else: - module.set_processor(processor.pop(f"{name}.processor")) - - for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) - - for name, module in self.named_children(): - fn_recursive_attn_processor(name, module, processor) - - def set_default_attn_processor(self): - """ - Disables custom attention processors and sets the default attention implementation. - """ - if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnAddedKVProcessor() - elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnProcessor() - else: - raise ValueError( - f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" - ) - - self.set_attn_processor(processor) - - def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"): - r""" - Enable sliced attention computation. - - When this option is enabled, the attention module splits the input tensor in slices to compute attention in - several steps. This is useful for saving some memory in exchange for a small decrease in speed. - - Args: - slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): - When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If - `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is - provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` - must be a multiple of `slice_size`. - """ - sliceable_head_dims = [] - - def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): - if hasattr(module, "set_attention_slice"): - sliceable_head_dims.append(module.sliceable_head_dim) - - for child in module.children(): - fn_recursive_retrieve_sliceable_dims(child) - - # retrieve number of attention layers - for module in self.children(): - fn_recursive_retrieve_sliceable_dims(module) - - num_sliceable_layers = len(sliceable_head_dims) - - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = [dim // 2 for dim in sliceable_head_dims] - elif slice_size == "max": - # make smallest slice possible - slice_size = num_sliceable_layers * [1] - - slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size - - if len(slice_size) != len(sliceable_head_dims): - raise ValueError( - f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" - f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." - ) - - for i in range(len(slice_size)): - size = slice_size[i] - dim = sliceable_head_dims[i] - if size is not None and size > dim: - raise ValueError(f"size {size} has to be smaller or equal to {dim}.") - - # Recursively walk through all the children. - # Any children which exposes the set_attention_slice method - # gets the message - def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): - if hasattr(module, "set_attention_slice"): - module.set_attention_slice(slice_size.pop()) - - for child in module.children(): - fn_recursive_set_attention_slice(child, slice_size) - - reversed_slice_size = list(reversed(slice_size)) - for module in self.children(): - fn_recursive_set_attention_slice(module, reversed_slice_size) - - def _set_gradient_checkpointing(self, module, value=False): - if hasattr(module, "gradient_checkpointing"): - module.gradient_checkpointing = value - - def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): - r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. - - The suffixes after the scaling factors represent the stage blocks where they are being applied. - - Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that - are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. - - Args: - s1 (`float`): - Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - s2 (`float`): - Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. - b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. - """ - for i, upsample_block in enumerate(self.up_blocks): - setattr(upsample_block, "s1", s1) - setattr(upsample_block, "s2", s2) - setattr(upsample_block, "b1", b1) - setattr(upsample_block, "b2", b2) - - def disable_freeu(self): - """Disables the FreeU mechanism.""" - freeu_keys = {"s1", "s2", "b1", "b2"} - for i, upsample_block in enumerate(self.up_blocks): - for k in freeu_keys: - if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None: - setattr(upsample_block, k, None) - - def fuse_qkv_projections(self): - """ - Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) - are fused. For cross-attention modules, key and value projection matrices are fused. - - - - This API is 🧪 experimental. - - - """ - self.original_attn_processors = None - - for _, attn_processor in self.attn_processors.items(): - if "Added" in str(attn_processor.__class__.__name__): - raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") - - self.original_attn_processors = self.attn_processors - - for module in self.modules(): - if isinstance(module, Attention): - module.fuse_projections(fuse=True) - - def unfuse_qkv_projections(self): - """Disables the fused QKV projection if enabled. - - - - This API is 🧪 experimental. - - - - """ - if self.original_attn_processors is not None: - self.set_attn_processor(self.original_attn_processors) - - def unload_lora(self): - """Unloads LoRA weights.""" - deprecate( - "unload_lora", - "0.28.0", - "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().", - ) - for module in self.modules(): - if hasattr(module, "set_lora_layer"): - module.set_lora_layer(None) - - def get_time_embed( - self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int] - ) -> Optional[torch.Tensor]: - timesteps = timestep - if not torch.is_tensor(timesteps): - # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - # `Timesteps` does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=sample.dtype) - return t_emb - - def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]: - class_emb = None - if self.class_embedding is not None: - if class_labels is None: - raise ValueError("class_labels should be provided when num_class_embeds > 0") - - if self.config.class_embed_type == "timestep": - class_labels = self.time_proj(class_labels) - - # `Timesteps` does not contain any weights and will always return f32 tensors - # there might be better ways to encapsulate this. - class_labels = class_labels.to(dtype=sample.dtype) - - class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) - return class_emb - - def get_aug_embed( - self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> Optional[torch.Tensor]: - aug_emb = None - if self.config.addition_embed_type == "text": - aug_emb = self.add_embedding(encoder_hidden_states) - elif self.config.addition_embed_type == "text_image": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - - image_embs = added_cond_kwargs.get("image_embeds") - text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) - aug_emb = self.add_embedding(text_embs, image_embs) - elif self.config.addition_embed_type == "text_time": - # SDXL - style - if "text_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" - ) - text_embeds = added_cond_kwargs.get("text_embeds") - if "time_ids" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" - ) - time_ids = added_cond_kwargs.get("time_ids") - time_embeds = self.add_time_proj(time_ids.flatten()) - time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) - add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) - add_embeds = add_embeds.to(emb.dtype) - aug_emb = self.add_embedding(add_embeds) - elif self.config.addition_embed_type == "image": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - aug_emb = self.add_embedding(image_embs) - elif self.config.addition_embed_type == "image_hint": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - hint = added_cond_kwargs.get("hint") - aug_emb = self.add_embedding(image_embs, hint) - return aug_emb - - def process_encoder_hidden_states( - self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> torch.Tensor: - if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - image_embeds = self.encoder_hid_proj(image_embeds) - encoder_hidden_states = (encoder_hidden_states, image_embeds) - return encoder_hidden_states - - def forward( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - class_labels: Optional[torch.Tensor] = None, - timestep_cond: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, - down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - mid_block_additional_residual: Optional[torch.Tensor] = None, - down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - return_dict: bool = True, - ) -> Union[UNet2DConditionOutput, Tuple]: - r""" - The [`UNet2DConditionModel`] forward method. - - Args: - sample (`torch.FloatTensor`): - The noisy input tensor with the following shape `(batch, channel, height, width)`. - timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. - encoder_hidden_states (`torch.FloatTensor`): - The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. - class_labels (`torch.Tensor`, *optional*, defaults to `None`): - Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. - timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): - Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed - through the `self.time_embedding` layer to obtain the timestep embeddings. - attention_mask (`torch.Tensor`, *optional*, defaults to `None`): - An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask - is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large - negative values to the attention scores corresponding to "discard" tokens. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - added_cond_kwargs: (`dict`, *optional*): - A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that - are passed along to the UNet blocks. - down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): - A tuple of tensors that if specified are added to the residuals of down unet blocks. - mid_block_additional_residual: (`torch.Tensor`, *optional*): - A tensor that if specified is added to the residual of the middle unet block. - down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): - additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) - encoder_attention_mask (`torch.Tensor`): - A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If - `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, - which adds large negative values to the attention scores corresponding to "discard" tokens. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain - tuple. - - Returns: - [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: - If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned, - otherwise a `tuple` is returned where the first element is the sample tensor. - """ - # By default samples have to be AT least a multiple of the overall upsampling factor. - # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). - # However, the upsampling interpolation output size can be forced to fit any upsampling size - # on the fly if necessary. - default_overall_up_factor = 2**self.num_upsamplers - - # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` - forward_upsample_size = False - upsample_size = None - - for dim in sample.shape[-2:]: - if dim % default_overall_up_factor != 0: - # Forward upsample size to force interpolation output size. - forward_upsample_size = True - break - - # ensure attention_mask is a bias, and give it a singleton query_tokens dimension - # expects mask of shape: - # [batch, key_tokens] - # adds singleton query_tokens dimension: - # [batch, 1, key_tokens] - # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: - # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) - # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) - if attention_mask is not None: - # assume that mask is expressed as: - # (1 = keep, 0 = discard) - # convert mask into a bias that can be added to attention scores: - # (keep = +0, discard = -10000.0) - attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # convert encoder_attention_mask to a bias the same way we do for attention_mask - if encoder_attention_mask is not None: - encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 - encoder_attention_mask = encoder_attention_mask.unsqueeze(1) - - # 0. center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # 1. time - t_emb = self.get_time_embed(sample=sample, timestep=timestep) - emb = self.time_embedding(t_emb, timestep_cond) - aug_emb = None - - class_emb = self.get_class_embed(sample=sample, class_labels=class_labels) - if class_emb is not None: - if self.config.class_embeddings_concat: - emb = torch.cat([emb, class_emb], dim=-1) - else: - emb = emb + class_emb - - aug_emb = self.get_aug_embed( - emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs - ) - if self.config.addition_embed_type == "image_hint": - aug_emb, hint = aug_emb - sample = torch.cat([sample, hint], dim=1) - - emb = emb + aug_emb if aug_emb is not None else emb - - if self.time_embed_act is not None: - emb = self.time_embed_act(emb) - - encoder_hidden_states = self.process_encoder_hidden_states( - encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs - ) - - # 2. pre-process - sample = self.conv_in(sample) - - # 2.5 GLIGEN position net - if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None: - cross_attention_kwargs = cross_attention_kwargs.copy() - gligen_args = cross_attention_kwargs.pop("gligen") - cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)} - - # 3. down - # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated - # to the internal blocks and will raise deprecation warnings. this will be confusing for our users. - if cross_attention_kwargs is not None: - cross_attention_kwargs = cross_attention_kwargs.copy() - lora_scale = cross_attention_kwargs.pop("scale", 1.0) - else: - lora_scale = 1.0 - - if USE_PEFT_BACKEND: - # weight the lora layers by setting `lora_scale` for each PEFT layer - scale_lora_layers(self, lora_scale) - - is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None - # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets - is_adapter = down_intrablock_additional_residuals is not None - # maintain backward compatibility for legacy usage, where - # T2I-Adapter and ControlNet both use down_block_additional_residuals arg - # but can only use one or the other - if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None: - deprecate( - "T2I should not use down_block_additional_residuals", - "1.3.0", - "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ - and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ - for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", - standard_warn=False, - ) - down_intrablock_additional_residuals = down_block_additional_residuals - is_adapter = True - - down_block_res_samples = (sample,) - for downsample_block in self.down_blocks: - if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: - # For t2i-adapter CrossAttnDownBlock2D - additional_residuals = {} - if is_adapter and len(down_intrablock_additional_residuals) > 0: - additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) - - sample, res_samples = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - **additional_residuals, - ) - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - if is_adapter and len(down_intrablock_additional_residuals) > 0: - sample += down_intrablock_additional_residuals.pop(0) - - down_block_res_samples += res_samples - - if is_controlnet: - new_down_block_res_samples = () - - for down_block_additional_residual, down_block_res_sample in zip( - down_block_additional_residuals, down_block_res_samples - ): - down_block_res_sample_tuple = (down_block_additional_residual, down_block_res_sample) - new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample_tuple,) - - down_block_res_samples = new_down_block_res_samples - - # 4. mid - if self.mid_block is not None: - if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: - sample = self.mid_block( - sample, - emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - else: - sample = self.mid_block(sample, emb) - - # To support T2I-Adapter-XL - if ( - is_adapter - and len(down_intrablock_additional_residuals) > 0 - and sample.shape == down_intrablock_additional_residuals[0].shape - ): - sample += down_intrablock_additional_residuals.pop(0) - - if is_controlnet: - sample = self.mid_zero_SFT((mid_block_additional_residual, sample),) - - # 5. up - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] - - # if we have not reached the final block and need to forward the - # upsample size, we do it here - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - upsample_size=upsample_size, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - ) - else: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - upsample_size=upsample_size, - ) - - # 6. post-process - if self.conv_norm_out: - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if USE_PEFT_BACKEND: - # remove `lora_scale` from each PEFT layer - unscale_lora_layers(self, lora_scale) - - if not return_dict: - return (sample,) - - return UNet2DConditionOutput(sample=sample) diff --git a/module/unet/unet_2d_ZeroSFT_blocks.py b/module/unet/unet_2d_ZeroSFT_blocks.py deleted file mode 100644 index 0ad3ab837f6ce32b303e36a9dfc99a27440c35a2..0000000000000000000000000000000000000000 --- a/module/unet/unet_2d_ZeroSFT_blocks.py +++ /dev/null @@ -1,3862 +0,0 @@ -# Copy from diffusers.models.unet.unet_2d_blocks.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Any, Dict, Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn - -from diffusers.utils import deprecate, is_torch_version, logging -from diffusers.utils.torch_utils import apply_freeu -from diffusers.models.activations import get_activation -from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0 -from diffusers.models.normalization import AdaGroupNorm -from diffusers.models.resnet import ( - Downsample2D, - FirDownsample2D, - FirUpsample2D, - KDownsample2D, - KUpsample2D, - ResnetBlock2D, - ResnetBlockCondNorm2D, - Upsample2D, -) -from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel -from diffusers.models.transformers.transformer_2d import Transformer2DModel - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -def get_down_block( - down_block_type: str, - num_layers: int, - in_channels: int, - out_channels: int, - temb_channels: int, - add_downsample: bool, - resnet_eps: float, - resnet_act_fn: str, - transformer_layers_per_block: int = 1, - num_attention_heads: Optional[int] = None, - resnet_groups: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - downsample_padding: Optional[int] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - attention_type: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - cross_attention_norm: Optional[str] = None, - attention_head_dim: Optional[int] = None, - downsample_type: Optional[str] = None, - dropout: float = 0.0, -): - # If attn head dim is not defined, we default it to the number of heads - if attention_head_dim is None: - logger.warning( - f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." - ) - attention_head_dim = num_attention_heads - - down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type - if down_block_type == "DownBlock2D": - return DownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "ResnetDownsampleBlock2D": - return ResnetDownsampleBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - ) - elif down_block_type == "AttnDownBlock2D": - if add_downsample is False: - downsample_type = None - else: - downsample_type = downsample_type or "conv" # default to 'conv' - return AttnDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - downsample_type=downsample_type, - ) - elif down_block_type == "CrossAttnDownBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D") - return CrossAttnDownBlock2D( - num_layers=num_layers, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - ) - elif down_block_type == "SimpleCrossAttnDownBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D") - return SimpleCrossAttnDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - only_cross_attention=only_cross_attention, - cross_attention_norm=cross_attention_norm, - ) - elif down_block_type == "SkipDownBlock2D": - return SkipDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "AttnSkipDownBlock2D": - return AttnSkipDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "DownEncoderBlock2D": - return DownEncoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "AttnDownEncoderBlock2D": - return AttnDownEncoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "KDownBlock2D": - return KDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - ) - elif down_block_type == "KCrossAttnDownBlock2D": - return KCrossAttnDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - add_self_attention=True if not add_downsample else False, - ) - raise ValueError(f"{down_block_type} does not exist.") - - -def get_mid_block( - mid_block_type: str, - temb_channels: int, - in_channels: int, - resnet_eps: float, - resnet_act_fn: str, - resnet_groups: int, - output_scale_factor: float = 1.0, - transformer_layers_per_block: int = 1, - num_attention_heads: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - mid_block_only_cross_attention: bool = False, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - attention_type: str = "default", - resnet_skip_time_act: bool = False, - cross_attention_norm: Optional[str] = None, - attention_head_dim: Optional[int] = 1, - dropout: float = 0.0, -): - if mid_block_type == "UNetMidBlock2DCrossAttn": - return UNetMidBlock2DCrossAttn( - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - temb_channels=temb_channels, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - output_scale_factor=output_scale_factor, - resnet_time_scale_shift=resnet_time_scale_shift, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - resnet_groups=resnet_groups, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - attention_type=attention_type, - ) - elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": - return UNetMidBlock2DSimpleCrossAttn( - in_channels=in_channels, - temb_channels=temb_channels, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - output_scale_factor=output_scale_factor, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - only_cross_attention=mid_block_only_cross_attention, - cross_attention_norm=cross_attention_norm, - ) - elif mid_block_type == "UNetMidBlock2D": - return UNetMidBlock2D( - in_channels=in_channels, - temb_channels=temb_channels, - dropout=dropout, - num_layers=0, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - output_scale_factor=output_scale_factor, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - add_attention=False, - ) - elif mid_block_type is None: - return None - else: - raise ValueError(f"unknown mid_block_type : {mid_block_type}") - - -def get_up_block( - up_block_type: str, - num_layers: int, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - add_upsample: bool, - resnet_eps: float, - resnet_act_fn: str, - resolution_idx: Optional[int] = None, - transformer_layers_per_block: int = 1, - num_attention_heads: Optional[int] = None, - resnet_groups: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - attention_type: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - cross_attention_norm: Optional[str] = None, - attention_head_dim: Optional[int] = None, - upsample_type: Optional[str] = None, - dropout: float = 0.0, -) -> nn.Module: - # If attn head dim is not defined, we default it to the number of heads - if attention_head_dim is None: - logger.warning( - f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." - ) - attention_head_dim = num_attention_heads - - up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type - if up_block_type == "UpBlock2D": - return UpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif up_block_type == "ResnetUpsampleBlock2D": - return ResnetUpsampleBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - ) - elif up_block_type == "CrossAttnUpBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") - return CrossAttnUpBlock2D( - num_layers=num_layers, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - ) - elif up_block_type == "SimpleCrossAttnUpBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D") - return SimpleCrossAttnUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - only_cross_attention=only_cross_attention, - cross_attention_norm=cross_attention_norm, - ) - elif up_block_type == "AttnUpBlock2D": - if add_upsample is False: - upsample_type = None - else: - upsample_type = upsample_type or "conv" # default to 'conv' - - return AttnUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - upsample_type=upsample_type, - ) - elif up_block_type == "SkipUpBlock2D": - return SkipUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif up_block_type == "AttnSkipUpBlock2D": - return AttnSkipUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif up_block_type == "UpDecoderBlock2D": - return UpDecoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - temb_channels=temb_channels, - ) - elif up_block_type == "AttnUpDecoderBlock2D": - return AttnUpDecoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - temb_channels=temb_channels, - ) - elif up_block_type == "KUpBlock2D": - return KUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - ) - elif up_block_type == "KCrossAttnUpBlock2D": - return KCrossAttnUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - ) - - raise ValueError(f"{up_block_type} does not exist.") - - -def zero_module(module): - for p in module.parameters(): - nn.init.zeros_(p) - return module - - -class ZeroConv(nn.Module): - def __init__(self, label_nc, norm_nc, mask=False): - super().__init__() - self.zero_conv = zero_module(nn.Conv2d(label_nc, norm_nc, 1, 1, 0)) - self.mask = mask - - def forward(self, c, h, h_ori=None): - # with torch.cuda.amp.autocast(enabled=False, dtype=torch.float32): - if not self.mask: - h = h + self.zero_conv(c) - else: - h = h + self.zero_conv(c) * torch.zeros_like(h) - if h_ori is not None: - h = torch.cat([h_ori, h], dim=1) - return h - - -class ZeroSFT(nn.Module): - def __init__(self, label_nc, norm_nc, concat_channels=0, norm=True, mask=False): - super().__init__() - - # param_free_norm_type = str(parsed.group(1)) - ks = 3 - pw = ks // 2 - - self.mask = mask - self.norm = norm - self.pre_concat = bool(concat_channels != 0) - if self.norm: - self.param_free_norm = torch.nn.GroupNorm(num_groups=32, num_channels=norm_nc + concat_channels) - else: - self.param_free_norm = nn.Identity() - - nhidden = 128 - - self.mlp_shared = nn.Sequential( - nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), - nn.SiLU() - ) - self.zero_mul = zero_module(nn.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw)) - self.zero_add = zero_module(nn.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw)) - - self.zero_conv = zero_module(nn.Conv2d(label_nc, norm_nc, 1, 1, 0)) - - def forward(self, down_block_res_samples, h_ori=None, control_scale=1.0, mask=False): - mask = mask or self.mask - assert mask is False - if self.pre_concat: - assert h_ori is not None - - c,h = down_block_res_samples - if h_ori is not None: - h_raw = torch.cat([h_ori, h], dim=1) - else: - h_raw = h - - if self.mask: - h = h + self.zero_conv(c) * torch.zeros_like(h) - else: - h = h + self.zero_conv(c) - if h_ori is not None and self.pre_concat: - h_ori_c = h_ori.shape[1] - h_c = h.shape[1] - h = torch.cat([h_ori, h], dim=1) - actv = self.mlp_shared(c) - gamma = self.zero_mul(actv) - beta = self.zero_add(actv) - if self.mask: - gamma = gamma * torch.zeros_like(gamma) - beta = beta * torch.zeros_like(beta) - # gamma_ori, gamma_res = torch.split(gamma, [h_ori_c, h_c], dim=1) - # beta_ori, beta_res = torch.split(beta, [h_ori_c, h_c], dim=1) - # print(gamma_ori.mean(), gamma_res.mean(), beta_ori.mean(), beta_res.mean()) - # h = h + self.param_free_norm(h) * gamma + beta - h = self.param_free_norm(h) * (gamma + 1) + beta - # sample_ori, sample_res = torch.split(h, [h_ori_c, h_c], dim=1) - # print(sample_ori.mean(), sample_res.mean()) - if h_ori is not None and not self.pre_concat: - h = torch.cat([h_ori, h], dim=1) - return h * control_scale + h_raw * (1 - control_scale) - - -class AutoencoderTinyBlock(nn.Module): - """ - Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU - blocks. - - Args: - in_channels (`int`): The number of input channels. - out_channels (`int`): The number of output channels. - act_fn (`str`): - ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`. - - Returns: - `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to - `out_channels`. - """ - - def __init__(self, in_channels: int, out_channels: int, act_fn: str): - super().__init__() - act_fn = get_activation(act_fn) - self.conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), - act_fn, - nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), - act_fn, - nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), - ) - self.skip = ( - nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) - if in_channels != out_channels - else nn.Identity() - ) - self.fuse = nn.ReLU() - - def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: - return self.fuse(self.conv(x) + self.skip(x)) - - -class UNetMidBlock2D(nn.Module): - """ - A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks. - - Args: - in_channels (`int`): The number of input channels. - temb_channels (`int`): The number of temporal embedding channels. - dropout (`float`, *optional*, defaults to 0.0): The dropout rate. - num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. - resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. - resnet_time_scale_shift (`str`, *optional*, defaults to `default`): - The type of normalization to apply to the time embeddings. This can help to improve the performance of the - model on tasks with long-range temporal dependencies. - resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks. - resnet_groups (`int`, *optional*, defaults to 32): - The number of groups to use in the group normalization layers of the resnet blocks. - attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks. - resnet_pre_norm (`bool`, *optional*, defaults to `True`): - Whether to use pre-normalization for the resnet blocks. - add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks. - attention_head_dim (`int`, *optional*, defaults to 1): - Dimension of a single attention head. The number of attention heads is determined based on this value and - the number of input channels. - output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor. - - Returns: - `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, - in_channels, height, width)`. - - """ - - def __init__( - self, - in_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", # default, spatial - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - attn_groups: Optional[int] = None, - resnet_pre_norm: bool = True, - add_attention: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = 1.0, - ): - super().__init__() - resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - self.add_attention = add_attention - - if attn_groups is None: - attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None - - # there is always at least one resnet - if resnet_time_scale_shift == "spatial": - resnets = [ - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm="spatial", - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - ) - ] - else: - resnets = [ - ResnetBlock2D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ] - attentions = [] - - if attention_head_dim is None: - logger.warning( - f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}." - ) - attention_head_dim = in_channels - - for _ in range(num_layers): - if self.add_attention: - attentions.append( - Attention( - in_channels, - heads=in_channels // attention_head_dim, - dim_head=attention_head_dim, - rescale_output_factor=output_scale_factor, - eps=resnet_eps, - norm_num_groups=attn_groups, - spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None, - residual_connection=True, - bias=True, - upcast_softmax=True, - _from_deprecated_attn_block=True, - ) - ) - else: - attentions.append(None) - - if resnet_time_scale_shift == "spatial": - resnets.append( - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm="spatial", - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - ) - ) - else: - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: - hidden_states = self.resnets[0](hidden_states, temb) - for attn, resnet in zip(self.attentions, self.resnets[1:]): - if attn is not None: - hidden_states = attn(hidden_states, temb=temb) - hidden_states = resnet(hidden_states, temb) - - return hidden_states - - -class UNetMidBlock2DCrossAttn(nn.Module): - def __init__( - self, - in_channels: int, - temb_channels: int, - out_channels: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - transformer_layers_per_block: Union[int, Tuple[int]] = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_groups_out: Optional[int] = None, - resnet_pre_norm: bool = True, - num_attention_heads: int = 1, - output_scale_factor: float = 1.0, - cross_attention_dim: int = 1280, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - upcast_attention: bool = False, - attention_type: str = "default", - ): - super().__init__() - - out_channels = out_channels or in_channels - self.in_channels = in_channels - self.out_channels = out_channels - - self.has_cross_attention = True - self.num_attention_heads = num_attention_heads - resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - - # support for variable transformer layers per block - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * num_layers - - resnet_groups_out = resnet_groups_out or resnet_groups - - # there is always at least one resnet - resnets = [ - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - groups_out=resnet_groups_out, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ] - attentions = [] - - for i in range(num_layers): - if not dual_cross_attention: - attentions.append( - Transformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=transformer_layers_per_block[i], - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups_out, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - attention_type=attention_type, - ) - ) - else: - attentions.append( - DualTransformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - ) - ) - resnets.append( - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups_out, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - hidden_states = self.resnets[0](hidden_states, temb) - for attn, resnet in zip(self.attentions, self.resnets[1:]): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - else: - hidden_states = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - hidden_states = resnet(hidden_states, temb) - - return hidden_states - - -class UNetMidBlock2DSimpleCrossAttn(nn.Module): - def __init__( - self, - in_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = 1.0, - cross_attention_dim: int = 1280, - skip_time_act: bool = False, - only_cross_attention: bool = False, - cross_attention_norm: Optional[str] = None, - ): - super().__init__() - - self.has_cross_attention = True - - self.attention_head_dim = attention_head_dim - resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - - self.num_heads = in_channels // self.attention_head_dim - - # there is always at least one resnet - resnets = [ - ResnetBlock2D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - ) - ] - attentions = [] - - for _ in range(num_layers): - processor = ( - AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() - ) - - attentions.append( - Attention( - query_dim=in_channels, - cross_attention_dim=in_channels, - heads=self.num_heads, - dim_head=self.attention_head_dim, - added_kv_proj_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - bias=True, - upcast_softmax=True, - only_cross_attention=only_cross_attention, - cross_attention_norm=cross_attention_norm, - processor=processor, - ) - ) - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - if attention_mask is None: - # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. - mask = None if encoder_hidden_states is None else encoder_attention_mask - else: - # when attention_mask is defined: we don't even check for encoder_attention_mask. - # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. - # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. - # then we can simplify this whole if/else block to: - # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask - mask = attention_mask - - hidden_states = self.resnets[0](hidden_states, temb) - for attn, resnet in zip(self.attentions, self.resnets[1:]): - # attn - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=mask, - **cross_attention_kwargs, - ) - - # resnet - hidden_states = resnet(hidden_states, temb) - - return hidden_states - - -class AttnDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = 1.0, - downsample_padding: int = 1, - downsample_type: str = "conv", - ): - super().__init__() - resnets = [] - attentions = [] - self.downsample_type = downsample_type - - if attention_head_dim is None: - logger.warning( - f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." - ) - attention_head_dim = out_channels - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - attentions.append( - Attention( - out_channels, - heads=out_channels // attention_head_dim, - dim_head=attention_head_dim, - rescale_output_factor=output_scale_factor, - eps=resnet_eps, - norm_num_groups=resnet_groups, - residual_connection=True, - bias=True, - upcast_softmax=True, - _from_deprecated_attn_block=True, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if downsample_type == "conv": - self.downsamplers = nn.ModuleList( - [ - Downsample2D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - elif downsample_type == "resnet": - self.downsamplers = nn.ModuleList( - [ - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - down=True, - ) - ] - ) - else: - self.downsamplers = None - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - upsample_size: Optional[int] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - output_states = () - - for resnet, attn in zip(self.resnets, self.attentions): - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, **cross_attention_kwargs) - output_states = output_states + (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - if self.downsample_type == "resnet": - hidden_states = downsampler(hidden_states, temb=temb) - else: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -class CrossAttnDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - transformer_layers_per_block: Union[int, Tuple[int]] = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - num_attention_heads: int = 1, - cross_attention_dim: int = 1280, - output_scale_factor: float = 1.0, - downsample_padding: int = 1, - add_downsample: bool = True, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - attention_type: str = "default", - ): - super().__init__() - resnets = [] - attentions = [] - - self.has_cross_attention = True - self.num_attention_heads = num_attention_heads - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * num_layers - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - if not dual_cross_attention: - attentions.append( - Transformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=transformer_layers_per_block[i], - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - attention_type=attention_type, - ) - ) - else: - attentions.append( - DualTransformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - ) - ) - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample2D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - additional_residuals: Optional[torch.FloatTensor] = None, - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - output_states = () - - blocks = list(zip(self.resnets, self.attentions)) - - for i, (resnet, attn) in enumerate(blocks): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - hidden_states = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - - # apply additional residuals to the output of the last pair of resnet and attention blocks - if i == len(blocks) - 1 and additional_residuals is not None: - hidden_states = hidden_states + additional_residuals - - output_states = output_states + (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states - - -class DownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_downsample: bool = True, - downsample_padding: int = 1, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample2D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - output_states = () - - for resnet in self.resnets: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - output_states = output_states + (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states - - -class DownEncoderBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_downsample: bool = True, - downsample_padding: int = 1, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - if resnet_time_scale_shift == "spatial": - resnets.append( - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=None, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm="spatial", - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - ) - ) - else: - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=None, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample2D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - def forward(self, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - for resnet in self.resnets: - hidden_states = resnet(hidden_states, temb=None) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - return hidden_states - - -class AttnDownEncoderBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = 1.0, - add_downsample: bool = True, - downsample_padding: int = 1, - ): - super().__init__() - resnets = [] - attentions = [] - - if attention_head_dim is None: - logger.warning( - f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." - ) - attention_head_dim = out_channels - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - if resnet_time_scale_shift == "spatial": - resnets.append( - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=None, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm="spatial", - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - ) - ) - else: - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=None, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - attentions.append( - Attention( - out_channels, - heads=out_channels // attention_head_dim, - dim_head=attention_head_dim, - rescale_output_factor=output_scale_factor, - eps=resnet_eps, - norm_num_groups=resnet_groups, - residual_connection=True, - bias=True, - upcast_softmax=True, - _from_deprecated_attn_block=True, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample2D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - def forward(self, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - for resnet, attn in zip(self.resnets, self.attentions): - hidden_states = resnet(hidden_states, temb=None) - hidden_states = attn(hidden_states) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - return hidden_states - - -class AttnSkipDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = np.sqrt(2.0), - add_downsample: bool = True, - ): - super().__init__() - self.attentions = nn.ModuleList([]) - self.resnets = nn.ModuleList([]) - - if attention_head_dim is None: - logger.warning( - f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." - ) - attention_head_dim = out_channels - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - self.resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min(in_channels // 4, 32), - groups_out=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - self.attentions.append( - Attention( - out_channels, - heads=out_channels // attention_head_dim, - dim_head=attention_head_dim, - rescale_output_factor=output_scale_factor, - eps=resnet_eps, - norm_num_groups=32, - residual_connection=True, - bias=True, - upcast_softmax=True, - _from_deprecated_attn_block=True, - ) - ) - - if add_downsample: - self.resnet_down = ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_in_shortcut=True, - down=True, - kernel="fir", - ) - self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) - self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) - else: - self.resnet_down = None - self.downsamplers = None - self.skip_conv = None - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - skip_sample: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - output_states = () - - for resnet, attn in zip(self.resnets, self.attentions): - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states) - output_states += (hidden_states,) - - if self.downsamplers is not None: - hidden_states = self.resnet_down(hidden_states, temb) - for downsampler in self.downsamplers: - skip_sample = downsampler(skip_sample) - - hidden_states = self.skip_conv(skip_sample) + hidden_states - - output_states += (hidden_states,) - - return hidden_states, output_states, skip_sample - - -class SkipDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_pre_norm: bool = True, - output_scale_factor: float = np.sqrt(2.0), - add_downsample: bool = True, - downsample_padding: int = 1, - ): - super().__init__() - self.resnets = nn.ModuleList([]) - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - self.resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min(in_channels // 4, 32), - groups_out=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - if add_downsample: - self.resnet_down = ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_in_shortcut=True, - down=True, - kernel="fir", - ) - self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) - self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) - else: - self.resnet_down = None - self.downsamplers = None - self.skip_conv = None - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - skip_sample: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - output_states = () - - for resnet in self.resnets: - hidden_states = resnet(hidden_states, temb) - output_states += (hidden_states,) - - if self.downsamplers is not None: - hidden_states = self.resnet_down(hidden_states, temb) - for downsampler in self.downsamplers: - skip_sample = downsampler(skip_sample) - - hidden_states = self.skip_conv(skip_sample) + hidden_states - - output_states += (hidden_states,) - - return hidden_states, output_states, skip_sample - - -class ResnetDownsampleBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_downsample: bool = True, - skip_time_act: bool = False, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - down=True, - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - output_states = () - - for resnet in self.resnets: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - output_states = output_states + (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states, temb) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states - - -class SimpleCrossAttnDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - cross_attention_dim: int = 1280, - output_scale_factor: float = 1.0, - add_downsample: bool = True, - skip_time_act: bool = False, - only_cross_attention: bool = False, - cross_attention_norm: Optional[str] = None, - ): - super().__init__() - - self.has_cross_attention = True - - resnets = [] - attentions = [] - - self.attention_head_dim = attention_head_dim - self.num_heads = out_channels // self.attention_head_dim - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - ) - ) - - processor = ( - AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() - ) - - attentions.append( - Attention( - query_dim=out_channels, - cross_attention_dim=out_channels, - heads=self.num_heads, - dim_head=attention_head_dim, - added_kv_proj_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - bias=True, - upcast_softmax=True, - only_cross_attention=only_cross_attention, - cross_attention_norm=cross_attention_norm, - processor=processor, - ) - ) - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - down=True, - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - output_states = () - - if attention_mask is None: - # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. - mask = None if encoder_hidden_states is None else encoder_attention_mask - else: - # when attention_mask is defined: we don't even check for encoder_attention_mask. - # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. - # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. - # then we can simplify this whole if/else block to: - # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask - mask = attention_mask - - for resnet, attn in zip(self.resnets, self.attentions): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=mask, - **cross_attention_kwargs, - ) - else: - hidden_states = resnet(hidden_states, temb) - - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=mask, - **cross_attention_kwargs, - ) - - output_states = output_states + (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states, temb) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states - - -class KDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 4, - resnet_eps: float = 1e-5, - resnet_act_fn: str = "gelu", - resnet_group_size: int = 32, - add_downsample: bool = False, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - groups = in_channels // resnet_group_size - groups_out = out_channels // resnet_group_size - - resnets.append( - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=out_channels, - dropout=dropout, - temb_channels=temb_channels, - groups=groups, - groups_out=groups_out, - eps=resnet_eps, - non_linearity=resnet_act_fn, - time_embedding_norm="ada_group", - conv_shortcut_bias=False, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - # YiYi's comments- might be able to use FirDownsample2D, look into details later - self.downsamplers = nn.ModuleList([KDownsample2D()]) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - output_states = () - - for resnet in self.resnets: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - return hidden_states, output_states - - -class KCrossAttnDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - cross_attention_dim: int, - dropout: float = 0.0, - num_layers: int = 4, - resnet_group_size: int = 32, - add_downsample: bool = True, - attention_head_dim: int = 64, - add_self_attention: bool = False, - resnet_eps: float = 1e-5, - resnet_act_fn: str = "gelu", - ): - super().__init__() - resnets = [] - attentions = [] - - self.has_cross_attention = True - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - groups = in_channels // resnet_group_size - groups_out = out_channels // resnet_group_size - - resnets.append( - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=out_channels, - dropout=dropout, - temb_channels=temb_channels, - groups=groups, - groups_out=groups_out, - eps=resnet_eps, - non_linearity=resnet_act_fn, - time_embedding_norm="ada_group", - conv_shortcut_bias=False, - ) - ) - attentions.append( - KAttentionBlock( - out_channels, - out_channels // attention_head_dim, - attention_head_dim, - cross_attention_dim=cross_attention_dim, - temb_channels=temb_channels, - attention_bias=True, - add_self_attention=add_self_attention, - cross_attention_norm="layer_norm", - group_size=resnet_group_size, - ) - ) - - self.resnets = nn.ModuleList(resnets) - self.attentions = nn.ModuleList(attentions) - - if add_downsample: - self.downsamplers = nn.ModuleList([KDownsample2D()]) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - output_states = () - - for resnet, attn in zip(self.resnets, self.attentions): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - emb=temb, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - emb=temb, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - - if self.downsamplers is None: - output_states += (None,) - else: - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - return hidden_states, output_states - - -class AttnUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - resolution_idx: int = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = 1.0, - upsample_type: str = "conv", - ): - super().__init__() - resnets = [] - attentions = [] - - self.upsample_type = upsample_type - - if attention_head_dim is None: - logger.warning( - f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." - ) - attention_head_dim = out_channels - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - attentions.append( - Attention( - out_channels, - heads=out_channels // attention_head_dim, - dim_head=attention_head_dim, - rescale_output_factor=output_scale_factor, - eps=resnet_eps, - norm_num_groups=resnet_groups, - residual_connection=True, - bias=True, - upcast_softmax=True, - _from_deprecated_attn_block=True, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if upsample_type == "conv": - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) - elif upsample_type == "resnet": - self.upsamplers = nn.ModuleList( - [ - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - up=True, - ) - ] - ) - else: - self.upsamplers = None - - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - upsample_size: Optional[int] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - for resnet, attn in zip(self.resnets, self.attentions): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - if self.upsample_type == "resnet": - hidden_states = upsampler(hidden_states, temb=temb) - else: - hidden_states = upsampler(hidden_states) - - return hidden_states - - -class CrossAttnUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - transformer_layers_per_block: Union[int, Tuple[int]] = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - num_attention_heads: int = 1, - cross_attention_dim: int = 1280, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - attention_type: str = "default", - ): - super().__init__() - resnets = [] - attentions = [] - zero_SFTs = [] - - self.has_cross_attention = True - self.num_attention_heads = num_attention_heads - - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * num_layers - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - zero_SFTs.append( - ZeroSFT( - res_skip_channels, - res_skip_channels, - resnet_in_channels - ) - ) - if not dual_cross_attention: - attentions.append( - Transformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=transformer_layers_per_block[i], - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - attention_type=attention_type, - ) - ) - else: - attentions.append( - DualTransformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - ) - ) - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - self.zero_SFTs = nn.ModuleList(zero_SFTs) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - upsample_size: Optional[int] = None, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - is_freeu_enabled = ( - getattr(self, "s1", None) - and getattr(self, "s2", None) - and getattr(self, "b1", None) - and getattr(self, "b2", None) - ) - - for resnet, attn, zero_SFT in zip(self.resnets, self.attentions, self.zero_SFTs): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - - if isinstance(res_hidden_states, tuple): - # ZeroSFT - hidden_states = zero_SFT(res_hidden_states, hidden_states) - else: - # FreeU: Only operate on the first two stages - if is_freeu_enabled: - hidden_states, res_hidden_states = apply_freeu( - self.resolution_idx, - hidden_states, - res_hidden_states[1]+res_hidden_states[0], - s1=self.s1, - s2=self.s2, - b1=self.b1, - b2=self.b2, - ) - - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - hidden_states = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - -class UpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - ): - super().__init__() - resnets = [] - zero_SFTs = [] - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - zero_SFTs.append( - ZeroSFT( - res_skip_channels, - res_skip_channels, - resnet_in_channels, - ) - ) - - self.resnets = nn.ModuleList(resnets) - self.zero_SFTs = nn.ModuleList(zero_SFTs) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - upsample_size: Optional[int] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - is_freeu_enabled = ( - getattr(self, "s1", None) - and getattr(self, "s2", None) - and getattr(self, "b1", None) - and getattr(self, "b2", None) - ) - - for resnet, zero_SFT in zip(self.resnets, self.zero_SFTs): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - - if isinstance(res_hidden_states, tuple): - # ZeroSFT - hidden_states = zero_SFT(res_hidden_states, hidden_states) - else: - # FreeU: Only operate on the first two stages - if is_freeu_enabled: - hidden_states, res_hidden_states = apply_freeu( - self.resolution_idx, - hidden_states, - res_hidden_states[1]+res_hidden_states[0], - s1=self.s1, - s2=self.s2, - b1=self.b1, - b2=self.b2, - ) - - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - -class UpDecoderBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", # default, spatial - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - temb_channels: Optional[int] = None, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - input_channels = in_channels if i == 0 else out_channels - - if resnet_time_scale_shift == "spatial": - resnets.append( - ResnetBlockCondNorm2D( - in_channels=input_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm="spatial", - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - ) - ) - else: - resnets.append( - ResnetBlock2D( - in_channels=input_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.resolution_idx = resolution_idx - - def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: - for resnet in self.resnets: - hidden_states = resnet(hidden_states, temb=temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states) - - return hidden_states - - -class AttnUpDecoderBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - temb_channels: Optional[int] = None, - ): - super().__init__() - resnets = [] - attentions = [] - - if attention_head_dim is None: - logger.warning( - f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}." - ) - attention_head_dim = out_channels - - for i in range(num_layers): - input_channels = in_channels if i == 0 else out_channels - - if resnet_time_scale_shift == "spatial": - resnets.append( - ResnetBlockCondNorm2D( - in_channels=input_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm="spatial", - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - ) - ) - else: - resnets.append( - ResnetBlock2D( - in_channels=input_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - attentions.append( - Attention( - out_channels, - heads=out_channels // attention_head_dim, - dim_head=attention_head_dim, - rescale_output_factor=output_scale_factor, - eps=resnet_eps, - norm_num_groups=resnet_groups if resnet_time_scale_shift != "spatial" else None, - spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None, - residual_connection=True, - bias=True, - upcast_softmax=True, - _from_deprecated_attn_block=True, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.resolution_idx = resolution_idx - - def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: - for resnet, attn in zip(self.resnets, self.attentions): - hidden_states = resnet(hidden_states, temb=temb) - hidden_states = attn(hidden_states, temb=temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states) - - return hidden_states - - -class AttnSkipUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - output_scale_factor: float = np.sqrt(2.0), - add_upsample: bool = True, - ): - super().__init__() - self.attentions = nn.ModuleList([]) - self.resnets = nn.ModuleList([]) - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - self.resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min(resnet_in_channels + res_skip_channels // 4, 32), - groups_out=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - if attention_head_dim is None: - logger.warning( - f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}." - ) - attention_head_dim = out_channels - - self.attentions.append( - Attention( - out_channels, - heads=out_channels // attention_head_dim, - dim_head=attention_head_dim, - rescale_output_factor=output_scale_factor, - eps=resnet_eps, - norm_num_groups=32, - residual_connection=True, - bias=True, - upcast_softmax=True, - _from_deprecated_attn_block=True, - ) - ) - - self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) - if add_upsample: - self.resnet_up = ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min(out_channels // 4, 32), - groups_out=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_in_shortcut=True, - up=True, - kernel="fir", - ) - self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.skip_norm = torch.nn.GroupNorm( - num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True - ) - self.act = nn.SiLU() - else: - self.resnet_up = None - self.skip_conv = None - self.skip_norm = None - self.act = None - - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - skip_sample=None, - *args, - **kwargs, - ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - for resnet in self.resnets: - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - hidden_states = resnet(hidden_states, temb) - - hidden_states = self.attentions[0](hidden_states) - - if skip_sample is not None: - skip_sample = self.upsampler(skip_sample) - else: - skip_sample = 0 - - if self.resnet_up is not None: - skip_sample_states = self.skip_norm(hidden_states) - skip_sample_states = self.act(skip_sample_states) - skip_sample_states = self.skip_conv(skip_sample_states) - - skip_sample = skip_sample + skip_sample_states - - hidden_states = self.resnet_up(hidden_states, temb) - - return hidden_states, skip_sample - - -class SkipUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_pre_norm: bool = True, - output_scale_factor: float = np.sqrt(2.0), - add_upsample: bool = True, - upsample_padding: int = 1, - ): - super().__init__() - self.resnets = nn.ModuleList([]) - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - self.resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min((resnet_in_channels + res_skip_channels) // 4, 32), - groups_out=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) - if add_upsample: - self.resnet_up = ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=min(out_channels // 4, 32), - groups_out=min(out_channels // 4, 32), - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_in_shortcut=True, - up=True, - kernel="fir", - ) - self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.skip_norm = torch.nn.GroupNorm( - num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True - ) - self.act = nn.SiLU() - else: - self.resnet_up = None - self.skip_conv = None - self.skip_norm = None - self.act = None - - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - skip_sample=None, - *args, - **kwargs, - ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - for resnet in self.resnets: - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - hidden_states = resnet(hidden_states, temb) - - if skip_sample is not None: - skip_sample = self.upsampler(skip_sample) - else: - skip_sample = 0 - - if self.resnet_up is not None: - skip_sample_states = self.skip_norm(hidden_states) - skip_sample_states = self.act(skip_sample_states) - skip_sample_states = self.skip_conv(skip_sample_states) - - skip_sample = skip_sample + skip_sample_states - - hidden_states = self.resnet_up(hidden_states, temb) - - return hidden_states, skip_sample - - -class ResnetUpsampleBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - skip_time_act: bool = False, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList( - [ - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - up=True, - ) - ] - ) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - upsample_size: Optional[int] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - for resnet in self.resnets: - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, temb) - - return hidden_states - - -class SimpleCrossAttnUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attention_head_dim: int = 1, - cross_attention_dim: int = 1280, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - skip_time_act: bool = False, - only_cross_attention: bool = False, - cross_attention_norm: Optional[str] = None, - ): - super().__init__() - resnets = [] - attentions = [] - - self.has_cross_attention = True - self.attention_head_dim = attention_head_dim - - self.num_heads = out_channels // self.attention_head_dim - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - ) - ) - - processor = ( - AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() - ) - - attentions.append( - Attention( - query_dim=out_channels, - cross_attention_dim=out_channels, - heads=self.num_heads, - dim_head=self.attention_head_dim, - added_kv_proj_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - bias=True, - upcast_softmax=True, - only_cross_attention=only_cross_attention, - cross_attention_norm=cross_attention_norm, - processor=processor, - ) - ) - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList( - [ - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - skip_time_act=skip_time_act, - up=True, - ) - ] - ) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - upsample_size: Optional[int] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - if attention_mask is None: - # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. - mask = None if encoder_hidden_states is None else encoder_attention_mask - else: - # when attention_mask is defined: we don't even check for encoder_attention_mask. - # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. - # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. - # then we can simplify this whole if/else block to: - # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask - mask = attention_mask - - for resnet, attn in zip(self.resnets, self.attentions): - # resnet - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=mask, - **cross_attention_kwargs, - ) - else: - hidden_states = resnet(hidden_states, temb) - - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=mask, - **cross_attention_kwargs, - ) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, temb) - - return hidden_states - - -class KUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - resolution_idx: int, - dropout: float = 0.0, - num_layers: int = 5, - resnet_eps: float = 1e-5, - resnet_act_fn: str = "gelu", - resnet_group_size: Optional[int] = 32, - add_upsample: bool = True, - ): - super().__init__() - resnets = [] - k_in_channels = 2 * out_channels - k_out_channels = in_channels - num_layers = num_layers - 1 - - for i in range(num_layers): - in_channels = k_in_channels if i == 0 else out_channels - groups = in_channels // resnet_group_size - groups_out = out_channels // resnet_group_size - - resnets.append( - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=k_out_channels if (i == num_layers - 1) else out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=groups, - groups_out=groups_out, - dropout=dropout, - non_linearity=resnet_act_fn, - time_embedding_norm="ada_group", - conv_shortcut_bias=False, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([KUpsample2D()]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - upsample_size: Optional[int] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - res_hidden_states_tuple = res_hidden_states_tuple[-1] - if res_hidden_states_tuple is not None: - hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) - - for resnet in self.resnets: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states) - - return hidden_states - - -class KCrossAttnUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - resolution_idx: int, - dropout: float = 0.0, - num_layers: int = 4, - resnet_eps: float = 1e-5, - resnet_act_fn: str = "gelu", - resnet_group_size: int = 32, - attention_head_dim: int = 1, # attention dim_head - cross_attention_dim: int = 768, - add_upsample: bool = True, - upcast_attention: bool = False, - ): - super().__init__() - resnets = [] - attentions = [] - - is_first_block = in_channels == out_channels == temb_channels - is_middle_block = in_channels != out_channels - add_self_attention = True if is_first_block else False - - self.has_cross_attention = True - self.attention_head_dim = attention_head_dim - - # in_channels, and out_channels for the block (k-unet) - k_in_channels = out_channels if is_first_block else 2 * out_channels - k_out_channels = in_channels - - num_layers = num_layers - 1 - - for i in range(num_layers): - in_channels = k_in_channels if i == 0 else out_channels - groups = in_channels // resnet_group_size - groups_out = out_channels // resnet_group_size - - if is_middle_block and (i == num_layers - 1): - conv_2d_out_channels = k_out_channels - else: - conv_2d_out_channels = None - - resnets.append( - ResnetBlockCondNorm2D( - in_channels=in_channels, - out_channels=out_channels, - conv_2d_out_channels=conv_2d_out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=groups, - groups_out=groups_out, - dropout=dropout, - non_linearity=resnet_act_fn, - time_embedding_norm="ada_group", - conv_shortcut_bias=False, - ) - ) - attentions.append( - KAttentionBlock( - k_out_channels if (i == num_layers - 1) else out_channels, - k_out_channels // attention_head_dim - if (i == num_layers - 1) - else out_channels // attention_head_dim, - attention_head_dim, - cross_attention_dim=cross_attention_dim, - temb_channels=temb_channels, - attention_bias=True, - add_self_attention=add_self_attention, - cross_attention_norm="layer_norm", - upcast_attention=upcast_attention, - ) - ) - - self.resnets = nn.ModuleList(resnets) - self.attentions = nn.ModuleList(attentions) - - if add_upsample: - self.upsamplers = nn.ModuleList([KUpsample2D()]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - upsample_size: Optional[int] = None, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - res_hidden_states_tuple = res_hidden_states_tuple[-1] - if res_hidden_states_tuple is not None: - hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) - - for resnet, attn in zip(self.resnets, self.attentions): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - emb=temb, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - emb=temb, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states) - - return hidden_states - - -# can potentially later be renamed to `No-feed-forward` attention -class KAttentionBlock(nn.Module): - r""" - A basic Transformer block. - - Parameters: - dim (`int`): The number of channels in the input and output. - num_attention_heads (`int`): The number of heads to use for multi-head attention. - attention_head_dim (`int`): The number of channels in each head. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. - attention_bias (`bool`, *optional*, defaults to `False`): - Configure if the attention layers should contain a bias parameter. - upcast_attention (`bool`, *optional*, defaults to `False`): - Set to `True` to upcast the attention computation to `float32`. - temb_channels (`int`, *optional*, defaults to 768): - The number of channels in the token embedding. - add_self_attention (`bool`, *optional*, defaults to `False`): - Set to `True` to add self-attention to the block. - cross_attention_norm (`str`, *optional*, defaults to `None`): - The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`. - group_size (`int`, *optional*, defaults to 32): - The number of groups to separate the channels into for group normalization. - """ - - def __init__( - self, - dim: int, - num_attention_heads: int, - attention_head_dim: int, - dropout: float = 0.0, - cross_attention_dim: Optional[int] = None, - attention_bias: bool = False, - upcast_attention: bool = False, - temb_channels: int = 768, # for ada_group_norm - add_self_attention: bool = False, - cross_attention_norm: Optional[str] = None, - group_size: int = 32, - ): - super().__init__() - self.add_self_attention = add_self_attention - - # 1. Self-Attn - if add_self_attention: - self.norm1 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) - self.attn1 = Attention( - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - cross_attention_dim=None, - cross_attention_norm=None, - ) - - # 2. Cross-Attn - self.norm2 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) - self.attn2 = Attention( - query_dim=dim, - cross_attention_dim=cross_attention_dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - upcast_attention=upcast_attention, - cross_attention_norm=cross_attention_norm, - ) - - def _to_3d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor: - return hidden_states.permute(0, 2, 3, 1).reshape(hidden_states.shape[0], height * weight, -1) - - def _to_4d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor: - return hidden_states.permute(0, 2, 1).reshape(hidden_states.shape[0], -1, height, weight) - - def forward( - self, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - # TODO: mark emb as non-optional (self.norm2 requires it). - # requires assessing impact of change to positional param interface. - emb: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - # 1. Self-Attention - if self.add_self_attention: - norm_hidden_states = self.norm1(hidden_states, emb) - - height, weight = norm_hidden_states.shape[2:] - norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) - - attn_output = self.attn1( - norm_hidden_states, - encoder_hidden_states=None, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - attn_output = self._to_4d(attn_output, height, weight) - - hidden_states = attn_output + hidden_states - - # 2. Cross-Attention/None - norm_hidden_states = self.norm2(hidden_states, emb) - - height, weight = norm_hidden_states.shape[2:] - norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) - attn_output = self.attn2( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask if encoder_hidden_states is None else encoder_attention_mask, - **cross_attention_kwargs, - ) - attn_output = self._to_4d(attn_output, height, weight) - - hidden_states = attn_output + hidden_states - - return hidden_states diff --git a/module/unet/unet_2d_expandKV.py b/module/unet/unet_2d_expandKV.py deleted file mode 100644 index 7cb982e773a30bc1312e6497b1d78051310ee7dd..0000000000000000000000000000000000000000 --- a/module/unet/unet_2d_expandKV.py +++ /dev/null @@ -1,164 +0,0 @@ -# Copy from diffusers.models.unets.unet_2d_condition.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Any, Dict, List, Optional, Tuple, Union - -import torch - -from diffusers.utils import logging -from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -class ExpandKVUNet2DConditionModel(UNet2DConditionModel): - r""" - A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample - shaped output. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented - for all models (such as downloading or saving). - - Parameters: - sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): - Height and width of input/output sample. - in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. - out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. - center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. - flip_sin_to_cos (`bool`, *optional*, defaults to `True`): - Whether to flip the sin to cos in the time embedding. - freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. - down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): - The tuple of downsample blocks to use. - mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): - Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or - `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. - up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): - The tuple of upsample blocks to use. - only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): - Whether to include self-attention in the basic transformer blocks, see - [`~models.attention.BasicTransformerBlock`]. - block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): - The tuple of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. - downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. - mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. - If `None`, normalization and activation layers is skipped in post-processing. - norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. - cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): - The dimension of the cross attention features. - transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling - blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - encoder_hid_dim (`int`, *optional*, defaults to None): - If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` - dimension to `cross_attention_dim`. - encoder_hid_dim_type (`str`, *optional*, defaults to `None`): - If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text - embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. - attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. - num_attention_heads (`int`, *optional*): - The number of attention heads. If not defined, defaults to `attention_head_dim` - resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config - for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. - class_embed_type (`str`, *optional*, defaults to `None`): - The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, - `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. - addition_embed_type (`str`, *optional*, defaults to `None`): - Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or - "text". "text" will use the `TextTimeEmbedding` layer. - addition_time_embed_dim: (`int`, *optional*, defaults to `None`): - Dimension for the timestep embeddings. - num_class_embeds (`int`, *optional*, defaults to `None`): - Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing - class conditioning with `class_embed_type` equal to `None`. - time_embedding_type (`str`, *optional*, defaults to `positional`): - The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. - time_embedding_dim (`int`, *optional*, defaults to `None`): - An optional override for the dimension of the projected time embedding. - time_embedding_act_fn (`str`, *optional*, defaults to `None`): - Optional activation function to use only once on the time embeddings before they are passed to the rest of - the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. - timestep_post_act (`str`, *optional*, defaults to `None`): - The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. - time_cond_proj_dim (`int`, *optional*, defaults to `None`): - The dimension of `cond_proj` layer in the timestep embedding. - conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. - conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. - projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when - `class_embed_type="projection"`. Required when `class_embed_type="projection"`. - class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time - embeddings with the class embeddings. - mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): - Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If - `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the - `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` - otherwise. - """ - - - def process_encoder_hidden_states( - self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> torch.Tensor: - if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - image_embeds = self.encoder_hid_proj(image_embeds) - encoder_hidden_states = (encoder_hidden_states, image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "instantir": - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - if "extract_kvs" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - image_embeds = self.encoder_hid_proj(image_embeds) - encoder_hidden_states = (encoder_hidden_states, image_embeds) - return encoder_hidden_states diff --git a/module/unet/unet_2d_extractKV.py b/module/unet/unet_2d_extractKV.py deleted file mode 100644 index 509855925cd455b4f1a3142873e472dc81d77ab6..0000000000000000000000000000000000000000 --- a/module/unet/unet_2d_extractKV.py +++ /dev/null @@ -1,1347 +0,0 @@ -# Copy from diffusers.models.unets.unet_2d_condition.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.utils.checkpoint - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin -from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers -from diffusers.models.activations import get_activation -from diffusers.models.attention_processor import ( - ADDED_KV_ATTENTION_PROCESSORS, - CROSS_ATTENTION_PROCESSORS, - Attention, - AttentionProcessor, - AttnAddedKVProcessor, - AttnProcessor, -) -from diffusers.models.embeddings import ( - GaussianFourierProjection, - GLIGENTextBoundingboxProjection, - ImageHintTimeEmbedding, - ImageProjection, - ImageTimeEmbedding, - TextImageProjection, - TextImageTimeEmbedding, - TextTimeEmbedding, - TimestepEmbedding, - Timesteps, -) -from diffusers.models.modeling_utils import ModelMixin -from .unet_2d_extractKV_blocks import ( - get_down_block, - get_mid_block, - get_up_block, -) - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class ExtractKVUNet2DConditionOutput(BaseOutput): - """ - The output of [`UNet2DConditionModel`]. - - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. - """ - - sample: torch.FloatTensor = None - cached_kvs: Dict[str, Any] = None - - -class ExtractKVUNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin): - r""" - A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample - shaped output. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented - for all models (such as downloading or saving). - - Parameters: - sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): - Height and width of input/output sample. - in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. - out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. - center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. - flip_sin_to_cos (`bool`, *optional*, defaults to `True`): - Whether to flip the sin to cos in the time embedding. - freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. - down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): - The tuple of downsample blocks to use. - mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): - Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or - `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. - up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): - The tuple of upsample blocks to use. - only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): - Whether to include self-attention in the basic transformer blocks, see - [`~models.attention.BasicTransformerBlock`]. - block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): - The tuple of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. - downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. - mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. - If `None`, normalization and activation layers is skipped in post-processing. - norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. - cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): - The dimension of the cross attention features. - transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling - blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - encoder_hid_dim (`int`, *optional*, defaults to None): - If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` - dimension to `cross_attention_dim`. - encoder_hid_dim_type (`str`, *optional*, defaults to `None`): - If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text - embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. - attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. - num_attention_heads (`int`, *optional*): - The number of attention heads. If not defined, defaults to `attention_head_dim` - resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config - for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. - class_embed_type (`str`, *optional*, defaults to `None`): - The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, - `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. - addition_embed_type (`str`, *optional*, defaults to `None`): - Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or - "text". "text" will use the `TextTimeEmbedding` layer. - addition_time_embed_dim: (`int`, *optional*, defaults to `None`): - Dimension for the timestep embeddings. - num_class_embeds (`int`, *optional*, defaults to `None`): - Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing - class conditioning with `class_embed_type` equal to `None`. - time_embedding_type (`str`, *optional*, defaults to `positional`): - The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. - time_embedding_dim (`int`, *optional*, defaults to `None`): - An optional override for the dimension of the projected time embedding. - time_embedding_act_fn (`str`, *optional*, defaults to `None`): - Optional activation function to use only once on the time embeddings before they are passed to the rest of - the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. - timestep_post_act (`str`, *optional*, defaults to `None`): - The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. - time_cond_proj_dim (`int`, *optional*, defaults to `None`): - The dimension of `cond_proj` layer in the timestep embedding. - conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. - conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. - projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when - `class_embed_type="projection"`. Required when `class_embed_type="projection"`. - class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time - embeddings with the class embeddings. - mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): - Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If - `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the - `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` - otherwise. - """ - - _supports_gradient_checkpointing = True - _no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D", "CrossAttnUpBlock2D"] - - @register_to_config - def __init__( - self, - sample_size: Optional[int] = None, - in_channels: int = 4, - out_channels: int = 4, - center_input_sample: bool = False, - flip_sin_to_cos: bool = True, - freq_shift: int = 0, - down_block_types: Tuple[str] = ( - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ), - mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", - up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int] = (320, 640, 1280, 1280), - layers_per_block: Union[int, Tuple[int]] = 2, - downsample_padding: int = 1, - mid_block_scale_factor: float = 1, - dropout: float = 0.0, - act_fn: str = "silu", - norm_num_groups: Optional[int] = 32, - norm_eps: float = 1e-5, - cross_attention_dim: Union[int, Tuple[int]] = 1280, - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, - reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, - encoder_hid_dim: Optional[int] = None, - encoder_hid_dim_type: Optional[str] = None, - attention_head_dim: Union[int, Tuple[int]] = 8, - num_attention_heads: Optional[Union[int, Tuple[int]]] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - addition_embed_type: Optional[str] = None, - addition_time_embed_dim: Optional[int] = None, - num_class_embeds: Optional[int] = None, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - time_embedding_type: str = "positional", - time_embedding_dim: Optional[int] = None, - time_embedding_act_fn: Optional[str] = None, - timestep_post_act: Optional[str] = None, - time_cond_proj_dim: Optional[int] = None, - conv_in_kernel: int = 3, - conv_out_kernel: int = 3, - projection_class_embeddings_input_dim: Optional[int] = None, - attention_type: str = "default", - class_embeddings_concat: bool = False, - mid_block_only_cross_attention: Optional[bool] = None, - cross_attention_norm: Optional[str] = None, - addition_embed_type_num_heads: int = 64, - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, - ): - super().__init__() - - self.sample_size = sample_size - - if num_attention_heads is not None: - raise ValueError( - "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." - ) - - # If `num_attention_heads` is not defined (which is the case for most models) - # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. - # The reason for this behavior is to correct for incorrectly named variables that were introduced - # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 - # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking - # which is why we correct for the naming here. - num_attention_heads = num_attention_heads or attention_head_dim - - # Check inputs - self._check_config( - down_block_types=down_block_types, - up_block_types=up_block_types, - only_cross_attention=only_cross_attention, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - cross_attention_dim=cross_attention_dim, - transformer_layers_per_block=transformer_layers_per_block, - reverse_transformer_layers_per_block=reverse_transformer_layers_per_block, - attention_head_dim=attention_head_dim, - num_attention_heads=num_attention_heads, - ) - - # input - conv_in_padding = (conv_in_kernel - 1) // 2 - self.conv_in = nn.Conv2d( - in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding - ) - - # time - time_embed_dim, timestep_input_dim = self._set_time_proj( - time_embedding_type, - block_out_channels=block_out_channels, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - time_embedding_dim=time_embedding_dim, - ) - - self.time_embedding = TimestepEmbedding( - timestep_input_dim, - time_embed_dim, - act_fn=act_fn, - post_act_fn=timestep_post_act, - cond_proj_dim=time_cond_proj_dim, - ) - - self._set_encoder_hid_proj( - encoder_hid_dim_type, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - ) - - # class embedding - self._set_class_embedding( - class_embed_type, - act_fn=act_fn, - num_class_embeds=num_class_embeds, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - timestep_input_dim=timestep_input_dim, - ) - - self._set_add_embedding( - addition_embed_type, - addition_embed_type_num_heads=addition_embed_type_num_heads, - addition_time_embed_dim=addition_time_embed_dim, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - ) - - if time_embedding_act_fn is None: - self.time_embed_act = None - else: - self.time_embed_act = get_activation(time_embedding_act_fn) - - self.down_blocks = nn.ModuleList([]) - self.up_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = only_cross_attention - - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = False - - if isinstance(num_attention_heads, int): - num_attention_heads = (num_attention_heads,) * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - if isinstance(cross_attention_dim, int): - cross_attention_dim = (cross_attention_dim,) * len(down_block_types) - - if isinstance(layers_per_block, int): - layers_per_block = [layers_per_block] * len(down_block_types) - - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) - - if class_embeddings_concat: - # The time embeddings are concatenated with the class embeddings. The dimension of the - # time embeddings passed to the down, middle, and up blocks is twice the dimension of the - # regular time embeddings - blocks_time_embed_dim = time_embed_dim * 2 - else: - blocks_time_embed_dim = time_embed_dim - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block[i], - transformer_layers_per_block=transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - temb_channels=blocks_time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim[i], - num_attention_heads=num_attention_heads[i], - downsample_padding=downsample_padding, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, - dropout=dropout, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - self.down_blocks.append(down_block) - - # mid - self.mid_block = get_mid_block( - mid_block_type, - temb_channels=blocks_time_embed_dim, - in_channels=block_out_channels[-1], - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - output_scale_factor=mid_block_scale_factor, - transformer_layers_per_block=transformer_layers_per_block[-1], - num_attention_heads=num_attention_heads[-1], - cross_attention_dim=cross_attention_dim[-1], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - mid_block_only_cross_attention=mid_block_only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[-1], - dropout=dropout, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - - # count how many layers upsample the images - self.num_upsamplers = 0 - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - reversed_num_attention_heads = list(reversed(num_attention_heads)) - reversed_layers_per_block = list(reversed(layers_per_block)) - reversed_cross_attention_dim = list(reversed(cross_attention_dim)) - reversed_transformer_layers_per_block = ( - list(reversed(transformer_layers_per_block)) - if reverse_transformer_layers_per_block is None - else reverse_transformer_layers_per_block - ) - only_cross_attention = list(reversed(only_cross_attention)) - - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - is_final_block = i == len(block_out_channels) - 1 - - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] - - # add upsample block for all BUT final layer - if not is_final_block: - add_upsample = True - self.num_upsamplers += 1 - else: - add_upsample = False - - up_block = get_up_block( - up_block_type, - num_layers=reversed_layers_per_block[i] + 1, - transformer_layers_per_block=reversed_transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=blocks_time_embed_dim, - add_upsample=add_upsample, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resolution_idx=i, - resnet_groups=norm_num_groups, - cross_attention_dim=reversed_cross_attention_dim[i], - num_attention_heads=reversed_num_attention_heads[i], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, - dropout=dropout, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - if norm_num_groups is not None: - self.conv_norm_out = nn.GroupNorm( - num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps - ) - - self.conv_act = get_activation(act_fn) - - else: - self.conv_norm_out = None - self.conv_act = None - - conv_out_padding = (conv_out_kernel - 1) // 2 - self.conv_out = nn.Conv2d( - block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding - ) - - self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim) - - def _check_config( - self, - down_block_types: Tuple[str], - up_block_types: Tuple[str], - only_cross_attention: Union[bool, Tuple[bool]], - block_out_channels: Tuple[int], - layers_per_block: Union[int, Tuple[int]], - cross_attention_dim: Union[int, Tuple[int]], - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]], - reverse_transformer_layers_per_block: bool, - attention_head_dim: int, - num_attention_heads: Optional[Union[int, Tuple[int]]], - ): - assert "ExtractKVCrossAttnDownBlock2D" in down_block_types, "ExtractKVUNet must have ExtractKVCrossAttnDownBlock2D." - assert "ExtractKVCrossAttnUpBlock2D" in up_block_types, "ExtractKVUNet must have ExtractKVCrossAttnUpBlock2D." - - if len(down_block_types) != len(up_block_types): - raise ValueError( - f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." - ) - - if len(block_out_channels) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." - ) - - if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." - ) - if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None: - for layer_number_per_block in transformer_layers_per_block: - if isinstance(layer_number_per_block, list): - raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") - - def _set_time_proj( - self, - time_embedding_type: str, - block_out_channels: int, - flip_sin_to_cos: bool, - freq_shift: float, - time_embedding_dim: int, - ) -> Tuple[int, int]: - if time_embedding_type == "fourier": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 - if time_embed_dim % 2 != 0: - raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") - self.time_proj = GaussianFourierProjection( - time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos - ) - timestep_input_dim = time_embed_dim - elif time_embedding_type == "positional": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 - - self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) - timestep_input_dim = block_out_channels[0] - else: - raise ValueError( - f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." - ) - - return time_embed_dim, timestep_input_dim - - def _set_encoder_hid_proj( - self, - encoder_hid_dim_type: Optional[str], - cross_attention_dim: Union[int, Tuple[int]], - encoder_hid_dim: Optional[int], - ): - if encoder_hid_dim_type is None and encoder_hid_dim is not None: - encoder_hid_dim_type = "text_proj" - self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) - logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") - - if encoder_hid_dim is None and encoder_hid_dim_type is not None: - raise ValueError( - f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." - ) - - if encoder_hid_dim_type == "text_proj": - self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) - elif encoder_hid_dim_type == "text_image_proj": - # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)` - self.encoder_hid_proj = TextImageProjection( - text_embed_dim=encoder_hid_dim, - image_embed_dim=cross_attention_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - self.encoder_hid_proj = ImageProjection( - image_embed_dim=encoder_hid_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type is not None: - raise ValueError( - f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." - ) - else: - self.encoder_hid_proj = None - - def _set_class_embedding( - self, - class_embed_type: Optional[str], - act_fn: str, - num_class_embeds: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - timestep_input_dim: int, - ): - if class_embed_type is None and num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - elif class_embed_type == "timestep": - self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) - elif class_embed_type == "identity": - self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) - elif class_embed_type == "projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" - ) - # The projection `class_embed_type` is the same as the timestep `class_embed_type` except - # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings - # 2. it projects from an arbitrary input dimension. - # - # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. - # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. - # As a result, `TimestepEmbedding` can be passed arbitrary vectors. - self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - elif class_embed_type == "simple_projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" - ) - self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) - else: - self.class_embedding = None - - def _set_add_embedding( - self, - addition_embed_type: str, - addition_embed_type_num_heads: int, - addition_time_embed_dim: Optional[int], - flip_sin_to_cos: bool, - freq_shift: float, - cross_attention_dim: Optional[int], - encoder_hid_dim: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - ): - if addition_embed_type == "text": - if encoder_hid_dim is not None: - text_time_embedding_from_dim = encoder_hid_dim - else: - text_time_embedding_from_dim = cross_attention_dim - - self.add_embedding = TextTimeEmbedding( - text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads - ) - elif addition_embed_type == "text_image": - # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)` - self.add_embedding = TextImageTimeEmbedding( - text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim - ) - elif addition_embed_type == "text_time": - self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) - self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - elif addition_embed_type == "image": - # Kandinsky 2.2 - self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) - elif addition_embed_type == "image_hint": - # Kandinsky 2.2 ControlNet - self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) - elif addition_embed_type is not None: - raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") - - def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int): - if attention_type in ["gated", "gated-text-image"]: - positive_len = 768 - if isinstance(cross_attention_dim, int): - positive_len = cross_attention_dim - elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list): - positive_len = cross_attention_dim[0] - - feature_type = "text-only" if attention_type == "gated" else "text-image" - self.position_net = GLIGENTextBoundingboxProjection( - positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type - ) - - @property - def attn_processors(self) -> Dict[str, AttentionProcessor]: - r""" - Returns: - `dict` of attention processors: A dictionary containing all attention processors used in the model with - indexed by its weight name. - """ - # set recursively - processors = {} - - def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): - if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) - - for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) - - return processors - - for name, module in self.named_children(): - fn_recursive_add_processors(name, module, processors) - - return processors - - def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): - r""" - Sets the attention processor to use to compute attention. - - Parameters: - processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): - The instantiated processor class or a dictionary of processor classes that will be set as the processor - for **all** `Attention` layers. - - If `processor` is a dict, the key needs to define the path to the corresponding cross attention - processor. This is strongly recommended when setting trainable attention processors. - - """ - count = len(self.attn_processors.keys()) - - if isinstance(processor, dict) and len(processor) != count: - raise ValueError( - f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) - - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): - if hasattr(module, "set_processor"): - if not isinstance(processor, dict): - module.set_processor(processor) - else: - module.set_processor(processor.pop(f"{name}.processor")) - - for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) - - for name, module in self.named_children(): - fn_recursive_attn_processor(name, module, processor) - - def set_default_attn_processor(self): - """ - Disables custom attention processors and sets the default attention implementation. - """ - if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnAddedKVProcessor() - elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnProcessor() - else: - raise ValueError( - f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" - ) - - self.set_attn_processor(processor) - - def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"): - r""" - Enable sliced attention computation. - - When this option is enabled, the attention module splits the input tensor in slices to compute attention in - several steps. This is useful for saving some memory in exchange for a small decrease in speed. - - Args: - slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): - When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If - `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is - provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` - must be a multiple of `slice_size`. - """ - sliceable_head_dims = [] - - def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): - if hasattr(module, "set_attention_slice"): - sliceable_head_dims.append(module.sliceable_head_dim) - - for child in module.children(): - fn_recursive_retrieve_sliceable_dims(child) - - # retrieve number of attention layers - for module in self.children(): - fn_recursive_retrieve_sliceable_dims(module) - - num_sliceable_layers = len(sliceable_head_dims) - - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = [dim // 2 for dim in sliceable_head_dims] - elif slice_size == "max": - # make smallest slice possible - slice_size = num_sliceable_layers * [1] - - slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size - - if len(slice_size) != len(sliceable_head_dims): - raise ValueError( - f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" - f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." - ) - - for i in range(len(slice_size)): - size = slice_size[i] - dim = sliceable_head_dims[i] - if size is not None and size > dim: - raise ValueError(f"size {size} has to be smaller or equal to {dim}.") - - # Recursively walk through all the children. - # Any children which exposes the set_attention_slice method - # gets the message - def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): - if hasattr(module, "set_attention_slice"): - module.set_attention_slice(slice_size.pop()) - - for child in module.children(): - fn_recursive_set_attention_slice(child, slice_size) - - reversed_slice_size = list(reversed(slice_size)) - for module in self.children(): - fn_recursive_set_attention_slice(module, reversed_slice_size) - - def _set_gradient_checkpointing(self, module, value=False): - if hasattr(module, "gradient_checkpointing"): - module.gradient_checkpointing = value - - def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): - r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. - - The suffixes after the scaling factors represent the stage blocks where they are being applied. - - Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that - are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. - - Args: - s1 (`float`): - Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - s2 (`float`): - Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. - b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. - """ - for i, upsample_block in enumerate(self.up_blocks): - setattr(upsample_block, "s1", s1) - setattr(upsample_block, "s2", s2) - setattr(upsample_block, "b1", b1) - setattr(upsample_block, "b2", b2) - - def disable_freeu(self): - """Disables the FreeU mechanism.""" - freeu_keys = {"s1", "s2", "b1", "b2"} - for i, upsample_block in enumerate(self.up_blocks): - for k in freeu_keys: - if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None: - setattr(upsample_block, k, None) - - def fuse_qkv_projections(self): - """ - Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) - are fused. For cross-attention modules, key and value projection matrices are fused. - - - - This API is 🧪 experimental. - - - """ - self.original_attn_processors = None - - for _, attn_processor in self.attn_processors.items(): - if "Added" in str(attn_processor.__class__.__name__): - raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") - - self.original_attn_processors = self.attn_processors - - for module in self.modules(): - if isinstance(module, Attention): - module.fuse_projections(fuse=True) - - def unfuse_qkv_projections(self): - """Disables the fused QKV projection if enabled. - - - - This API is 🧪 experimental. - - - - """ - if self.original_attn_processors is not None: - self.set_attn_processor(self.original_attn_processors) - - def unload_lora(self): - """Unloads LoRA weights.""" - deprecate( - "unload_lora", - "0.28.0", - "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().", - ) - for module in self.modules(): - if hasattr(module, "set_lora_layer"): - module.set_lora_layer(None) - - def get_time_embed( - self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int] - ) -> Optional[torch.Tensor]: - timesteps = timestep - if not torch.is_tensor(timesteps): - # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - # `Timesteps` does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=sample.dtype) - return t_emb - - def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]: - class_emb = None - if self.class_embedding is not None: - if class_labels is None: - raise ValueError("class_labels should be provided when num_class_embeds > 0") - - if self.config.class_embed_type == "timestep": - class_labels = self.time_proj(class_labels) - - # `Timesteps` does not contain any weights and will always return f32 tensors - # there might be better ways to encapsulate this. - class_labels = class_labels.to(dtype=sample.dtype) - - class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) - return class_emb - - def get_aug_embed( - self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> Optional[torch.Tensor]: - aug_emb = None - if self.config.addition_embed_type == "text": - aug_emb = self.add_embedding(encoder_hidden_states) - elif self.config.addition_embed_type == "text_image": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - - image_embs = added_cond_kwargs.get("image_embeds") - text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) - aug_emb = self.add_embedding(text_embs, image_embs) - elif self.config.addition_embed_type == "text_time": - # SDXL - style - if "text_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" - ) - text_embeds = added_cond_kwargs.get("text_embeds") - if "time_ids" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" - ) - time_ids = added_cond_kwargs.get("time_ids") - time_embeds = self.add_time_proj(time_ids.flatten()) - time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) - add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) - add_embeds = add_embeds.to(emb.dtype) - aug_emb = self.add_embedding(add_embeds) - elif self.config.addition_embed_type == "image": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - aug_emb = self.add_embedding(image_embs) - elif self.config.addition_embed_type == "image_hint": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - hint = added_cond_kwargs.get("hint") - aug_emb = self.add_embedding(image_embs, hint) - return aug_emb - - def process_encoder_hidden_states( - self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> torch.Tensor: - if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - image_embeds = self.encoder_hid_proj(image_embeds) - encoder_hidden_states = (encoder_hidden_states, image_embeds) - return encoder_hidden_states - - def init_kv_extraction(self): - for block in self.down_blocks: - if hasattr(block, "has_cross_attention") and block.has_cross_attention: - block.init_kv_extraction() - - for block in self.up_blocks: - if hasattr(block, "has_cross_attention") and block.has_cross_attention: - block.init_kv_extraction() - - if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: - self.mid_block.init_kv_extraction() - - def forward( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - class_labels: Optional[torch.Tensor] = None, - timestep_cond: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, - down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - mid_block_additional_residual: Optional[torch.Tensor] = None, - down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - return_dict: bool = True, - ) -> Union[ExtractKVUNet2DConditionOutput, Tuple]: - r""" - The [`UNet2DConditionModel`] forward method. - - Args: - sample (`torch.FloatTensor`): - The noisy input tensor with the following shape `(batch, channel, height, width)`. - timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. - encoder_hidden_states (`torch.FloatTensor`): - The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. - class_labels (`torch.Tensor`, *optional*, defaults to `None`): - Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. - timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): - Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed - through the `self.time_embedding` layer to obtain the timestep embeddings. - attention_mask (`torch.Tensor`, *optional*, defaults to `None`): - An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask - is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large - negative values to the attention scores corresponding to "discard" tokens. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - added_cond_kwargs: (`dict`, *optional*): - A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that - are passed along to the UNet blocks. - down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): - A tuple of tensors that if specified are added to the residuals of down unet blocks. - mid_block_additional_residual: (`torch.Tensor`, *optional*): - A tensor that if specified is added to the residual of the middle unet block. - down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): - additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) - encoder_attention_mask (`torch.Tensor`): - A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If - `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, - which adds large negative values to the attention scores corresponding to "discard" tokens. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain - tuple. - - Returns: - [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: - If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned, - otherwise a `tuple` is returned where the first element is the sample tensor. - """ - # By default samples have to be AT least a multiple of the overall upsampling factor. - # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). - # However, the upsampling interpolation output size can be forced to fit any upsampling size - # on the fly if necessary. - default_overall_up_factor = 2**self.num_upsamplers - - # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` - forward_upsample_size = False - upsample_size = None - - for dim in sample.shape[-2:]: - if dim % default_overall_up_factor != 0: - # Forward upsample size to force interpolation output size. - forward_upsample_size = True - break - - # ensure attention_mask is a bias, and give it a singleton query_tokens dimension - # expects mask of shape: - # [batch, key_tokens] - # adds singleton query_tokens dimension: - # [batch, 1, key_tokens] - # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: - # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) - # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) - if attention_mask is not None: - # assume that mask is expressed as: - # (1 = keep, 0 = discard) - # convert mask into a bias that can be added to attention scores: - # (keep = +0, discard = -10000.0) - attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # convert encoder_attention_mask to a bias the same way we do for attention_mask - if encoder_attention_mask is not None: - encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 - encoder_attention_mask = encoder_attention_mask.unsqueeze(1) - - # 0. center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # 1. time - t_emb = self.get_time_embed(sample=sample, timestep=timestep) - emb = self.time_embedding(t_emb, timestep_cond) - aug_emb = None - - class_emb = self.get_class_embed(sample=sample, class_labels=class_labels) - if class_emb is not None: - if self.config.class_embeddings_concat: - emb = torch.cat([emb, class_emb], dim=-1) - else: - emb = emb + class_emb - - aug_emb = self.get_aug_embed( - emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs - ) - if self.config.addition_embed_type == "image_hint": - aug_emb, hint = aug_emb - sample = torch.cat([sample, hint], dim=1) - - emb = emb + aug_emb if aug_emb is not None else emb - - if self.time_embed_act is not None: - emb = self.time_embed_act(emb) - - encoder_hidden_states = self.process_encoder_hidden_states( - encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs - ) - - # 2. pre-process - sample = self.conv_in(sample) - - # 2.5 GLIGEN position net - if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None: - cross_attention_kwargs = cross_attention_kwargs.copy() - gligen_args = cross_attention_kwargs.pop("gligen") - cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)} - - if cross_attention_kwargs is not None and cross_attention_kwargs.get("kv_drop_idx", None) is not None: - threshold = cross_attention_kwargs.pop("kv_drop_idx") - cross_attention_kwargs["kv_drop_idx"] = timestep 0: - additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) - - sample, res_samples, extracted_kv = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - **additional_residuals, - ) - extracted_kvs.update(extracted_kv) - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - if is_adapter and len(down_intrablock_additional_residuals) > 0: - sample += down_intrablock_additional_residuals.pop(0) - - down_block_res_samples += res_samples - - if is_controlnet: - new_down_block_res_samples = () - - for down_block_res_sample, down_block_additional_residual in zip( - down_block_res_samples, down_block_additional_residuals - ): - down_block_res_sample = down_block_res_sample + down_block_additional_residual - new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) - - down_block_res_samples = new_down_block_res_samples - - # 4. mid - if self.mid_block is not None: - if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: - sample, extracted_kv = self.mid_block( - sample, - emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - extracted_kvs.update(extracted_kv) - else: - sample = self.mid_block(sample, emb) - - # To support T2I-Adapter-XL - if ( - is_adapter - and len(down_intrablock_additional_residuals) > 0 - and sample.shape == down_intrablock_additional_residuals[0].shape - ): - sample += down_intrablock_additional_residuals.pop(0) - - if is_controlnet: - sample = sample + mid_block_additional_residual - - # 5. up - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] - - # if we have not reached the final block and need to forward the - # upsample size, we do it here - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: - sample, extract_kv = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - upsample_size=upsample_size, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - ) - extracted_kvs.update(extract_kv) - else: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - upsample_size=upsample_size, - ) - - # 6. post-process - if self.conv_norm_out: - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if USE_PEFT_BACKEND: - # remove `lora_scale` from each PEFT layer - unscale_lora_layers(self, lora_scale) - - if not return_dict: - return (sample, extracted_kvs) - - return ExtractKVUNet2DConditionOutput(sample=sample, cached_kvs=extracted_kvs) diff --git a/module/unet/unet_2d_extractKV_blocks.py b/module/unet/unet_2d_extractKV_blocks.py deleted file mode 100644 index 8e451d67c3a42f1a23a60435eb2e26abc4ab27d5..0000000000000000000000000000000000000000 --- a/module/unet/unet_2d_extractKV_blocks.py +++ /dev/null @@ -1,1417 +0,0 @@ -# Copy from diffusers.models.unet.unet_2d_blocks.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Any, Dict, Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn - -from diffusers.utils import deprecate, is_torch_version, logging -from diffusers.utils.torch_utils import apply_freeu -from diffusers.models.activations import get_activation -from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0 -from diffusers.models.normalization import AdaGroupNorm -from diffusers.models.resnet import ( - Downsample2D, - FirDownsample2D, - FirUpsample2D, - KDownsample2D, - KUpsample2D, - ResnetBlock2D, - ResnetBlockCondNorm2D, - Upsample2D, -) -from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel -from diffusers.models.transformers.transformer_2d import Transformer2DModel - -from module.transformers.transformer_2d_ExtractKV import ExtractKVTransformer2DModel - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -def get_down_block( - down_block_type: str, - num_layers: int, - in_channels: int, - out_channels: int, - temb_channels: int, - add_downsample: bool, - resnet_eps: float, - resnet_act_fn: str, - transformer_layers_per_block: int = 1, - num_attention_heads: Optional[int] = None, - resnet_groups: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - downsample_padding: Optional[int] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - attention_type: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - cross_attention_norm: Optional[str] = None, - attention_head_dim: Optional[int] = None, - downsample_type: Optional[str] = None, - dropout: float = 0.0, - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, -): - # If attn head dim is not defined, we default it to the number of heads - if attention_head_dim is None: - logger.warning( - f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." - ) - attention_head_dim = num_attention_heads - - down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type - if down_block_type == "DownBlock2D": - return DownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "ResnetDownsampleBlock2D": - from diffusers.models.unets.unet_2d_blocks import ResnetDownsampleBlock2D - return ResnetDownsampleBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - ) - elif down_block_type == "AttnDownBlock2D": - from diffusers.models.unets.unet_2d_blocks import AttnDownBlock2D - if add_downsample is False: - downsample_type = None - else: - downsample_type = downsample_type or "conv" # default to 'conv' - return AttnDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - downsample_type=downsample_type, - ) - elif down_block_type == "ExtractKVCrossAttnDownBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for ExtractKVCrossAttnDownBlock2D") - return ExtractKVCrossAttnDownBlock2D( - num_layers=num_layers, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - elif down_block_type == "CrossAttnDownBlock2D": - from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D") - return CrossAttnDownBlock2D( - num_layers=num_layers, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - ) - elif down_block_type == "SimpleCrossAttnDownBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D") - from diffusers.models.unets.unet_2d_blocks import SimpleCrossAttnDownBlock2D - return SimpleCrossAttnDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - only_cross_attention=only_cross_attention, - cross_attention_norm=cross_attention_norm, - ) - elif down_block_type == "SkipDownBlock2D": - from diffusers.models.unets.unet_2d_blocks import SkipDownBlock2D - return SkipDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "AttnSkipDownBlock2D": - from diffusers.models.unets.unet_2d_blocks import AttnSkipDownBlock2D - return AttnSkipDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "DownEncoderBlock2D": - from diffusers.models.unets.unet_2d_blocks import DownEncoderBlock2D - return DownEncoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "AttnDownEncoderBlock2D": - from diffusers.models.unets.unet_2d_blocks import AttnDownEncoderBlock2D - return AttnDownEncoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "KDownBlock2D": - from diffusers.models.unets.unet_2d_blocks import KDownBlock2D - return KDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - ) - elif down_block_type == "KCrossAttnDownBlock2D": - from diffusers.models.unets.unet_2d_blocks import KCrossAttnDownBlock2D - return KCrossAttnDownBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - dropout=dropout, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - add_self_attention=True if not add_downsample else False, - ) - raise ValueError(f"{down_block_type} does not exist.") - - -def get_mid_block( - mid_block_type: str, - temb_channels: int, - in_channels: int, - resnet_eps: float, - resnet_act_fn: str, - resnet_groups: int, - output_scale_factor: float = 1.0, - transformer_layers_per_block: int = 1, - num_attention_heads: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - mid_block_only_cross_attention: bool = False, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - attention_type: str = "default", - resnet_skip_time_act: bool = False, - cross_attention_norm: Optional[str] = None, - attention_head_dim: Optional[int] = 1, - dropout: float = 0.0, - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, -): - if mid_block_type == "ExtractKVUNetMidBlock2DCrossAttn": - return ExtractKVUNetMidBlock2DCrossAttn( - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - temb_channels=temb_channels, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - output_scale_factor=output_scale_factor, - resnet_time_scale_shift=resnet_time_scale_shift, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - resnet_groups=resnet_groups, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - attention_type=attention_type, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - elif mid_block_type == "UNetMidBlock2DCrossAttn": - from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2DCrossAttn - return UNetMidBlock2DCrossAttn( - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - temb_channels=temb_channels, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - output_scale_factor=output_scale_factor, - resnet_time_scale_shift=resnet_time_scale_shift, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - resnet_groups=resnet_groups, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - attention_type=attention_type, - ) - elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": - from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2DSimpleCrossAttn - return UNetMidBlock2DSimpleCrossAttn( - in_channels=in_channels, - temb_channels=temb_channels, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - output_scale_factor=output_scale_factor, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - only_cross_attention=mid_block_only_cross_attention, - cross_attention_norm=cross_attention_norm, - ) - elif mid_block_type == "UNetMidBlock2D": - from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D - return UNetMidBlock2D( - in_channels=in_channels, - temb_channels=temb_channels, - dropout=dropout, - num_layers=0, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - output_scale_factor=output_scale_factor, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - add_attention=False, - ) - elif mid_block_type is None: - return None - else: - raise ValueError(f"unknown mid_block_type : {mid_block_type}") - - -def get_up_block( - up_block_type: str, - num_layers: int, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - add_upsample: bool, - resnet_eps: float, - resnet_act_fn: str, - resolution_idx: Optional[int] = None, - transformer_layers_per_block: int = 1, - num_attention_heads: Optional[int] = None, - resnet_groups: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - attention_type: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - cross_attention_norm: Optional[str] = None, - attention_head_dim: Optional[int] = None, - upsample_type: Optional[str] = None, - dropout: float = 0.0, - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, -) -> nn.Module: - # If attn head dim is not defined, we default it to the number of heads - if attention_head_dim is None: - logger.warning( - f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." - ) - attention_head_dim = num_attention_heads - - up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type - if up_block_type == "UpBlock2D": - return UpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif up_block_type == "ResnetUpsampleBlock2D": - from diffusers.models.unets.unet_2d_blocks import ResnetUpsampleBlock2D - return ResnetUpsampleBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - ) - elif up_block_type == "ExtractKVCrossAttnUpBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") - return ExtractKVCrossAttnUpBlock2D( - num_layers=num_layers, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - elif up_block_type == "CrossAttnUpBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") - from diffusers.models.unets.unet_2d_blocks import CrossAttnUpBlock2D - return CrossAttnUpBlock2D( - num_layers=num_layers, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - ) - elif up_block_type == "SimpleCrossAttnUpBlock2D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D") - from diffusers.models.unets.unet_2d_blocks import SimpleCrossAttnUpBlock2D - return SimpleCrossAttnUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - skip_time_act=resnet_skip_time_act, - output_scale_factor=resnet_out_scale_factor, - only_cross_attention=only_cross_attention, - cross_attention_norm=cross_attention_norm, - ) - elif up_block_type == "AttnUpBlock2D": - from diffusers.models.unets.unet_2d_blocks import AttnUpBlock2D - if add_upsample is False: - upsample_type = None - else: - upsample_type = upsample_type or "conv" # default to 'conv' - - return AttnUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - upsample_type=upsample_type, - ) - elif up_block_type == "SkipUpBlock2D": - from diffusers.models.unets.unet_2d_blocks import SkipUpBlock2D - return SkipUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif up_block_type == "AttnSkipUpBlock2D": - from diffusers.models.unets.unet_2d_blocks import AttnSkipUpBlock2D - return AttnSkipUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif up_block_type == "UpDecoderBlock2D": - from diffusers.models.unets.unet_2d_blocks import UpDecoderBlock2D - return UpDecoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - temb_channels=temb_channels, - ) - elif up_block_type == "AttnUpDecoderBlock2D": - from diffusers.models.unets.unet_2d_blocks import AttnUpDecoderBlock2D - return AttnUpDecoderBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - attention_head_dim=attention_head_dim, - resnet_time_scale_shift=resnet_time_scale_shift, - temb_channels=temb_channels, - ) - elif up_block_type == "KUpBlock2D": - from diffusers.models.unets.unet_2d_blocks import KUpBlock2D - return KUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - ) - elif up_block_type == "KCrossAttnUpBlock2D": - from diffusers.models.unets.unet_2d_blocks import KCrossAttnUpBlock2D - return KCrossAttnUpBlock2D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - resolution_idx=resolution_idx, - dropout=dropout, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - cross_attention_dim=cross_attention_dim, - attention_head_dim=attention_head_dim, - ) - - raise ValueError(f"{up_block_type} does not exist.") - - -class AutoencoderTinyBlock(nn.Module): - """ - Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU - blocks. - - Args: - in_channels (`int`): The number of input channels. - out_channels (`int`): The number of output channels. - act_fn (`str`): - ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`. - - Returns: - `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to - `out_channels`. - """ - - def __init__(self, in_channels: int, out_channels: int, act_fn: str): - super().__init__() - act_fn = get_activation(act_fn) - self.conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), - act_fn, - nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), - act_fn, - nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), - ) - self.skip = ( - nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) - if in_channels != out_channels - else nn.Identity() - ) - self.fuse = nn.ReLU() - - def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: - return self.fuse(self.conv(x) + self.skip(x)) - - -class ExtractKVUNetMidBlock2DCrossAttn(nn.Module): - def __init__( - self, - in_channels: int, - temb_channels: int, - out_channels: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - transformer_layers_per_block: Union[int, Tuple[int]] = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_groups_out: Optional[int] = None, - resnet_pre_norm: bool = True, - num_attention_heads: int = 1, - output_scale_factor: float = 1.0, - cross_attention_dim: int = 1280, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - upcast_attention: bool = False, - attention_type: str = "default", - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, - ): - super().__init__() - - out_channels = out_channels or in_channels - self.in_channels = in_channels - self.out_channels = out_channels - - self.has_cross_attention = True - self.num_attention_heads = num_attention_heads - resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - - # support for variable transformer layers per block - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * num_layers - - resnet_groups_out = resnet_groups_out or resnet_groups - - # there is always at least one resnet - resnets = [ - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - groups_out=resnet_groups_out, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ] - attentions = [] - - for i in range(num_layers): - if not dual_cross_attention: - attentions.append( - ExtractKVTransformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=transformer_layers_per_block[i], - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups_out, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - attention_type=attention_type, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - ) - else: - attentions.append( - DualTransformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - ) - ) - resnets.append( - ResnetBlock2D( - in_channels=out_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups_out, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - hidden_states = self.resnets[0](hidden_states, temb) - extracted_kvs = {} - for attn, resnet in zip(self.attentions, self.resnets[1:]): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states, extracted_kv = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - ) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - else: - hidden_states, extracted_kv = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - ) - hidden_states = resnet(hidden_states, temb) - - extracted_kvs.update(extracted_kv) - - return hidden_states, extracted_kvs - - def init_kv_extraction(self): - for block in self.attentions: - block.init_kv_extraction() - - -class ExtractKVCrossAttnDownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, # Originally n_layers - transformer_layers_per_block: Union[int, Tuple[int]] = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - num_attention_heads: int = 1, - cross_attention_dim: int = 1280, - output_scale_factor: float = 1.0, - downsample_padding: int = 1, - add_downsample: bool = True, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - attention_type: str = "default", - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, - ): - super().__init__() - resnets = [] - attentions = [] - - self.has_cross_attention = True - self.num_attention_heads = num_attention_heads - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * num_layers - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - if not dual_cross_attention: - attentions.append( - ExtractKVTransformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=transformer_layers_per_block[i], - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - attention_type=attention_type, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - ) - else: - raise ValueError("Dual cross attention is not supported in ExtractKVCrossAttnDownBlock2D") - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample2D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - additional_residuals: Optional[torch.FloatTensor] = None, - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - output_states = () - extracted_kvs = {} - - blocks = list(zip(self.resnets, self.attentions)) - - for i, (resnet, attn) in enumerate(blocks): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - hidden_states, extracted_kv = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - ) - else: - hidden_states = resnet(hidden_states, temb) - hidden_states, extracted_kv = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - ) - - # apply additional residuals to the output of the last pair of resnet and attention blocks - if i == len(blocks) - 1 and additional_residuals is not None: - hidden_states = hidden_states + additional_residuals - - output_states = output_states + (hidden_states,) - extracted_kvs.update(extracted_kv) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states, extracted_kvs - - def init_kv_extraction(self): - for block in self.attentions: - block.init_kv_extraction() - - -class ExtractKVCrossAttnUpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - transformer_layers_per_block: Union[int, Tuple[int]] = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - num_attention_heads: int = 1, - cross_attention_dim: int = 1280, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - attention_type: str = "default", - extract_self_attention_kv: bool = False, - extract_cross_attention_kv: bool = False, - ): - super().__init__() - resnets = [] - attentions = [] - - self.has_cross_attention = True - self.num_attention_heads = num_attention_heads - - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * num_layers - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - if not dual_cross_attention: - attentions.append( - ExtractKVTransformer2DModel( - num_attention_heads, - out_channels // num_attention_heads, - in_channels=out_channels, - num_layers=transformer_layers_per_block[i], - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - attention_type=attention_type, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - ) - else: - raise ValueError("Dual cross attention is not supported in ExtractKVCrossAttnUpBlock2D") - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - upsample_size: Optional[int] = None, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - if cross_attention_kwargs is not None: - if cross_attention_kwargs.get("scale", None) is not None: - logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") - - is_freeu_enabled = ( - getattr(self, "s1", None) - and getattr(self, "s2", None) - and getattr(self, "b1", None) - and getattr(self, "b2", None) - ) - - extracted_kvs = {} - for resnet, attn in zip(self.resnets, self.attentions): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - - # FreeU: Only operate on the first two stages - if is_freeu_enabled: - hidden_states, res_hidden_states = apply_freeu( - self.resolution_idx, - hidden_states, - res_hidden_states, - s1=self.s1, - s2=self.s2, - b1=self.b1, - b2=self.b2, - ) - - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), - hidden_states, - temb, - **ckpt_kwargs, - ) - hidden_states, extracted_kv = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - ) - else: - hidden_states = resnet(hidden_states, temb) - hidden_states, extracted_kv = attn( - hidden_states, - timestep=temb, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - return_dict=False, - ) - - extracted_kvs.update(extracted_kv) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states, extracted_kvs - - def init_kv_extraction(self): - for block in self.attentions: - block.init_kv_extraction() - - -class DownBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_downsample: bool = True, - downsample_padding: int = 1, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample2D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs - ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - output_states = () - - for resnet in self.resnets: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - output_states = output_states + (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states - - -class UpBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock2D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - self.resolution_idx = resolution_idx - - def forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - upsample_size: Optional[int] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - is_freeu_enabled = ( - getattr(self, "s1", None) - and getattr(self, "s2", None) - and getattr(self, "b1", None) - and getattr(self, "b2", None) - ) - - for resnet in self.resnets: - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - - # FreeU: Only operate on the first two stages - if is_freeu_enabled: - hidden_states, res_hidden_states = apply_freeu( - self.resolution_idx, - hidden_states, - res_hidden_states, - s1=self.s1, - s2=self.s2, - b1=self.b1, - b2=self.b2, - ) - - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - if is_torch_version(">=", "1.11.0"): - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb, use_reentrant=False - ) - else: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(resnet), hidden_states, temb - ) - else: - hidden_states = resnet(hidden_states, temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states diff --git a/module/unet/unet_2d_extractKV_res.py b/module/unet/unet_2d_extractKV_res.py deleted file mode 100644 index 6b1e3d71084d4b4a7899e17c977207ebdccf8a47..0000000000000000000000000000000000000000 --- a/module/unet/unet_2d_extractKV_res.py +++ /dev/null @@ -1,1589 +0,0 @@ -# Copy from diffusers.models.unets.unet_2d_condition.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.utils.checkpoint - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin -from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers -from diffusers.models.activations import get_activation -from diffusers.models.attention_processor import ( - ADDED_KV_ATTENTION_PROCESSORS, - CROSS_ATTENTION_PROCESSORS, - Attention, - AttentionProcessor, - AttnAddedKVProcessor, - AttnProcessor, -) -from diffusers.models.embeddings import ( - GaussianFourierProjection, - GLIGENTextBoundingboxProjection, - ImageHintTimeEmbedding, - ImageProjection, - ImageTimeEmbedding, - TextImageProjection, - TextImageTimeEmbedding, - TextTimeEmbedding, - TimestepEmbedding, - Timesteps, -) -from diffusers.models.modeling_utils import ModelMixin -from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel -from .unet_2d_extractKV_blocks import ( - get_down_block, - get_mid_block, - get_up_block, -) - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class ExtractKVUNet2DConditionOutput(BaseOutput): - """ - The output of [`UNet2DConditionModel`]. - - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. - """ - - sample: torch.FloatTensor = None - cached_kvs: Dict[str, Any] = None - down_block_res_samples: Tuple[torch.Tensor] = None - mid_block_res_sample: torch.Tensor = None - - -def zero_module(module): - for p in module.parameters(): - nn.init.zeros_(p) - return module - - -class ControlNetConditioningEmbedding(nn.Module): - """ - Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN - [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized - training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the - convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides - (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full - model) to encode image-space conditions ... into feature maps ..." - """ - - def __init__( - self, - conditioning_embedding_channels: int, - conditioning_channels: int = 3, - block_out_channels: Tuple[int, ...] = (16, 32, 96, 256), - ): - super().__init__() - - self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1) - - self.blocks = nn.ModuleList([]) - - for i in range(len(block_out_channels) - 1): - channel_in = block_out_channels[i] - channel_out = block_out_channels[i + 1] - self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)) - self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) - - self.conv_out = zero_module( - nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1) - ) - - def forward(self, conditioning): - embedding = self.conv_in(conditioning) - embedding = F.silu(embedding) - - for block in self.blocks: - embedding = block(embedding) - embedding = F.silu(embedding) - - embedding = self.conv_out(embedding) - - return embedding - - -class ExtractKVUNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin): - r""" - A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample - shaped output. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented - for all models (such as downloading or saving). - - Parameters: - sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): - Height and width of input/output sample. - in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. - out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. - center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. - flip_sin_to_cos (`bool`, *optional*, defaults to `True`): - Whether to flip the sin to cos in the time embedding. - freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. - down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): - The tuple of downsample blocks to use. - mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): - Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or - `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. - up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): - The tuple of upsample blocks to use. - only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): - Whether to include self-attention in the basic transformer blocks, see - [`~models.attention.BasicTransformerBlock`]. - block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): - The tuple of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. - downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. - mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. - If `None`, normalization and activation layers is skipped in post-processing. - norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. - cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): - The dimension of the cross attention features. - transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling - blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - encoder_hid_dim (`int`, *optional*, defaults to None): - If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` - dimension to `cross_attention_dim`. - encoder_hid_dim_type (`str`, *optional*, defaults to `None`): - If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text - embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. - attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. - num_attention_heads (`int`, *optional*): - The number of attention heads. If not defined, defaults to `attention_head_dim` - resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config - for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. - class_embed_type (`str`, *optional*, defaults to `None`): - The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, - `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. - addition_embed_type (`str`, *optional*, defaults to `None`): - Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or - "text". "text" will use the `TextTimeEmbedding` layer. - addition_time_embed_dim: (`int`, *optional*, defaults to `None`): - Dimension for the timestep embeddings. - num_class_embeds (`int`, *optional*, defaults to `None`): - Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing - class conditioning with `class_embed_type` equal to `None`. - time_embedding_type (`str`, *optional*, defaults to `positional`): - The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. - time_embedding_dim (`int`, *optional*, defaults to `None`): - An optional override for the dimension of the projected time embedding. - time_embedding_act_fn (`str`, *optional*, defaults to `None`): - Optional activation function to use only once on the time embeddings before they are passed to the rest of - the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. - timestep_post_act (`str`, *optional*, defaults to `None`): - The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. - time_cond_proj_dim (`int`, *optional*, defaults to `None`): - The dimension of `cond_proj` layer in the timestep embedding. - conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. - conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. - projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when - `class_embed_type="projection"`. Required when `class_embed_type="projection"`. - class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time - embeddings with the class embeddings. - mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): - Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If - `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the - `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` - otherwise. - """ - - _supports_gradient_checkpointing = True - _no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D", "CrossAttnUpBlock2D"] - - @register_to_config - def __init__( - self, - sample_size: Optional[int] = None, - in_channels: int = 4, - out_channels: int = 4, - conditioning_channels: int = 3, - center_input_sample: bool = False, - flip_sin_to_cos: bool = True, - freq_shift: int = 0, - down_block_types: Tuple[str] = ( - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ), - mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", - up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int] = (320, 640, 1280, 1280), - layers_per_block: Union[int, Tuple[int]] = 2, - downsample_padding: int = 1, - mid_block_scale_factor: float = 1, - dropout: float = 0.0, - act_fn: str = "silu", - norm_num_groups: Optional[int] = 32, - norm_eps: float = 1e-5, - cross_attention_dim: Union[int, Tuple[int]] = 1280, - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, - reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, - encoder_hid_dim: Optional[int] = None, - encoder_hid_dim_type: Optional[str] = None, - attention_head_dim: Union[int, Tuple[int]] = 8, - num_attention_heads: Optional[Union[int, Tuple[int]]] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - addition_embed_type: Optional[str] = None, - addition_time_embed_dim: Optional[int] = None, - num_class_embeds: Optional[int] = None, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - time_embedding_type: str = "positional", - time_embedding_dim: Optional[int] = None, - time_embedding_act_fn: Optional[str] = None, - timestep_post_act: Optional[str] = None, - time_cond_proj_dim: Optional[int] = None, - conv_in_kernel: int = 3, - conv_out_kernel: int = 3, - projection_class_embeddings_input_dim: Optional[int] = None, - controlnet_conditioning_channel_order: str = "rgb", - conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), - attention_type: str = "default", - class_embeddings_concat: bool = False, - mid_block_only_cross_attention: Optional[bool] = None, - cross_attention_norm: Optional[str] = None, - addition_embed_type_num_heads: int = 64, - extract_self_attention_kv: bool = True, - extract_cross_attention_kv: bool = True, - ): - super().__init__() - - self.sample_size = sample_size - - if num_attention_heads is not None: - raise ValueError( - "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." - ) - - # If `num_attention_heads` is not defined (which is the case for most models) - # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. - # The reason for this behavior is to correct for incorrectly named variables that were introduced - # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 - # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking - # which is why we correct for the naming here. - num_attention_heads = num_attention_heads or attention_head_dim - - # Check inputs - self._check_config( - down_block_types=down_block_types, - up_block_types=up_block_types, - only_cross_attention=only_cross_attention, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - cross_attention_dim=cross_attention_dim, - transformer_layers_per_block=transformer_layers_per_block, - reverse_transformer_layers_per_block=reverse_transformer_layers_per_block, - attention_head_dim=attention_head_dim, - num_attention_heads=num_attention_heads, - ) - - # input - conv_in_padding = (conv_in_kernel - 1) // 2 - self.conv_in = nn.Conv2d( - in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding - ) - - # time - time_embed_dim, timestep_input_dim = self._set_time_proj( - time_embedding_type, - block_out_channels=block_out_channels, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - time_embedding_dim=time_embedding_dim, - ) - - self.time_embedding = TimestepEmbedding( - timestep_input_dim, - time_embed_dim, - act_fn=act_fn, - post_act_fn=timestep_post_act, - cond_proj_dim=time_cond_proj_dim, - ) - - self._set_encoder_hid_proj( - encoder_hid_dim_type, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - ) - - # class embedding - self._set_class_embedding( - class_embed_type, - act_fn=act_fn, - num_class_embeds=num_class_embeds, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - timestep_input_dim=timestep_input_dim, - ) - - self._set_add_embedding( - addition_embed_type, - addition_embed_type_num_heads=addition_embed_type_num_heads, - addition_time_embed_dim=addition_time_embed_dim, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - ) - - if time_embedding_act_fn is None: - self.time_embed_act = None - else: - self.time_embed_act = get_activation(time_embedding_act_fn) - - # control net conditioning embedding - self.controlnet_cond_embedding = ControlNetConditioningEmbedding( - conditioning_embedding_channels=block_out_channels[0], - block_out_channels=conditioning_embedding_out_channels, - conditioning_channels=conditioning_channels, - ) - - self.down_blocks = nn.ModuleList([]) - self.controlnet_down_blocks = nn.ModuleList([]) - self.up_blocks = nn.ModuleList([]) - # self.controlnet_up_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = only_cross_attention - - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = False - - if isinstance(num_attention_heads, int): - num_attention_heads = (num_attention_heads,) * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - if isinstance(cross_attention_dim, int): - cross_attention_dim = (cross_attention_dim,) * len(down_block_types) - - if isinstance(layers_per_block, int): - layers_per_block = [layers_per_block] * len(down_block_types) - - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) - - if class_embeddings_concat: - # The time embeddings are concatenated with the class embeddings. The dimension of the - # time embeddings passed to the down, middle, and up blocks is twice the dimension of the - # regular time embeddings - blocks_time_embed_dim = time_embed_dim * 2 - else: - blocks_time_embed_dim = time_embed_dim - - # down - output_channel = block_out_channels[0] - - controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) - controlnet_block = zero_module(controlnet_block) - self.controlnet_down_blocks.append(controlnet_block) - - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block[i], - transformer_layers_per_block=transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - temb_channels=blocks_time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim[i], - num_attention_heads=num_attention_heads[i], - downsample_padding=downsample_padding, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, - dropout=dropout, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - self.down_blocks.append(down_block) - - for _ in range(layers_per_block): - controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) - controlnet_block = zero_module(controlnet_block) - self.controlnet_down_blocks.append(controlnet_block) - - if not is_final_block: - controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) - controlnet_block = zero_module(controlnet_block) - self.controlnet_down_blocks.append(controlnet_block) - - # mid - mid_block_channel = block_out_channels[-1] - - controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1) - controlnet_block = zero_module(controlnet_block) - self.controlnet_mid_block = controlnet_block - - self.mid_block = get_mid_block( - mid_block_type, - temb_channels=blocks_time_embed_dim, - in_channels=block_out_channels[-1], - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - output_scale_factor=mid_block_scale_factor, - transformer_layers_per_block=transformer_layers_per_block[-1], - num_attention_heads=num_attention_heads[-1], - cross_attention_dim=cross_attention_dim[-1], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - mid_block_only_cross_attention=mid_block_only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[-1], - dropout=dropout, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - - # count how many layers upsample the images - self.num_upsamplers = 0 - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - reversed_num_attention_heads = list(reversed(num_attention_heads)) - reversed_layers_per_block = list(reversed(layers_per_block)) - reversed_cross_attention_dim = list(reversed(cross_attention_dim)) - reversed_transformer_layers_per_block = ( - list(reversed(transformer_layers_per_block)) - if reverse_transformer_layers_per_block is None - else reverse_transformer_layers_per_block - ) - only_cross_attention = list(reversed(only_cross_attention)) - - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - is_final_block = i == len(block_out_channels) - 1 - - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] - - # add upsample block for all BUT final layer - if not is_final_block: - add_upsample = True - self.num_upsamplers += 1 - else: - add_upsample = False - - up_block = get_up_block( - up_block_type, - num_layers=reversed_layers_per_block[i] + 1, - transformer_layers_per_block=reversed_transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=blocks_time_embed_dim, - add_upsample=add_upsample, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resolution_idx=i, - resnet_groups=norm_num_groups, - cross_attention_dim=reversed_cross_attention_dim[i], - num_attention_heads=reversed_num_attention_heads[i], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, - dropout=dropout, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # for _ in range(layers_per_block): - # controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) - # controlnet_block = zero_module(controlnet_block) - # self.controlnet_up_blocks.append(controlnet_block) - - # if not is_final_block: - # controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) - # controlnet_block = zero_module(controlnet_block) - # self.controlnet_up_blocks.append(controlnet_block) - - # out - if norm_num_groups is not None: - self.conv_norm_out = nn.GroupNorm( - num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps - ) - - self.conv_act = get_activation(act_fn) - - else: - self.conv_norm_out = None - self.conv_act = None - - conv_out_padding = (conv_out_kernel - 1) // 2 - self.conv_out = nn.Conv2d( - block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding - ) - - self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim) - - @classmethod - def from_unet( - cls, - unet: UNet2DConditionModel, - controlnet_conditioning_channel_order: str = "rgb", - conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), - load_weights_from_unet: bool = True, - conditioning_channels: int = 3, - extract_self_attention_kv: bool = True, - extract_cross_attention_kv: bool = True, - ): - r""" - Instantiate a [`ExtractKVUNet2DConditionModel`] from [`UNet2DConditionModel`]. - - Parameters: - unet (`UNet2DConditionModel`): - The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied - where applicable. - """ - transformer_layers_per_block = ( - unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1 - ) - encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None - encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None - addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None - addition_time_embed_dim = ( - unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None - ) - down_block_types = ( - 'DownBlock2D', 'ExtractKVCrossAttnDownBlock2D', 'ExtractKVCrossAttnDownBlock2D' - ) - mid_block_type = 'ExtractKVUNetMidBlock2DCrossAttn' - up_block_types = ( - 'ExtractKVCrossAttnUpBlock2D', 'ExtractKVCrossAttnUpBlock2D', 'UpBlock2D' - ) - - refnet = cls( - down_block_types=down_block_types, - up_block_types=up_block_types, - mid_block_type=mid_block_type, - encoder_hid_dim=encoder_hid_dim, - encoder_hid_dim_type=encoder_hid_dim_type, - addition_embed_type=addition_embed_type, - addition_time_embed_dim=addition_time_embed_dim, - transformer_layers_per_block=transformer_layers_per_block, - in_channels=unet.config.in_channels, - flip_sin_to_cos=unet.config.flip_sin_to_cos, - freq_shift=unet.config.freq_shift, - only_cross_attention=unet.config.only_cross_attention, - block_out_channels=unet.config.block_out_channels, - layers_per_block=unet.config.layers_per_block, - downsample_padding=unet.config.downsample_padding, - mid_block_scale_factor=unet.config.mid_block_scale_factor, - act_fn=unet.config.act_fn, - norm_num_groups=unet.config.norm_num_groups, - norm_eps=unet.config.norm_eps, - cross_attention_dim=unet.config.cross_attention_dim, - attention_head_dim=unet.config.attention_head_dim, - num_attention_heads=unet.config.num_attention_heads, - use_linear_projection=unet.config.use_linear_projection, - class_embed_type=unet.config.class_embed_type, - num_class_embeds=unet.config.num_class_embeds, - upcast_attention=unet.config.upcast_attention, - resnet_time_scale_shift=unet.config.resnet_time_scale_shift, - projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, - mid_block_type=unet.config.mid_block_type, - controlnet_conditioning_channel_order=controlnet_conditioning_channel_order, - conditioning_embedding_out_channels=conditioning_embedding_out_channels, - conditioning_channels=conditioning_channels, - extract_self_attention_kv=extract_self_attention_kv, - extract_cross_attention_kv=extract_cross_attention_kv, - ) - - if load_weights_from_unet: - def verify_load(missing_keys, unexpected_keys): - if len(unexpected_keys) > 0: - raise RuntimeError(f"Found unexpected keys in state dict while loading the encoder:\n{unexpected_keys}") - - filtered_missing = [key for key in missing_keys if not "extract_kv" in key] - if len(filtered_missing) > 0: - raise RuntimeError(f"Missing keys in state dict while loading the encoder:\n{filtered_missing}") - refnet.conv_in.load_state_dict(unet.conv_in.state_dict()) - refnet.time_proj.load_state_dict(unet.time_proj.state_dict()) - refnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) - - if refnet.class_embedding: - refnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) - - if hasattr(refnet, "add_embedding"): - refnet.add_embedding.load_state_dict(unet.add_embedding.state_dict()) - - missing_keys, unexpected_keys = refnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False) - verify_load(missing_keys, unexpected_keys) - missing_keys, unexpected_keys = refnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False) - verify_load(missing_keys, unexpected_keys) - missing_keys, unexpected_keys = refnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(), strict=False) - verify_load(missing_keys, unexpected_keys) - - return refnet - - def _check_config( - self, - down_block_types: Tuple[str], - up_block_types: Tuple[str], - only_cross_attention: Union[bool, Tuple[bool]], - block_out_channels: Tuple[int], - layers_per_block: Union[int, Tuple[int]], - cross_attention_dim: Union[int, Tuple[int]], - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]], - reverse_transformer_layers_per_block: bool, - attention_head_dim: int, - num_attention_heads: Optional[Union[int, Tuple[int]]], - ): - assert "ExtractKVCrossAttnDownBlock2D" in down_block_types, "ExtractKVUNet must have ExtractKVCrossAttnDownBlock2D." - assert "ExtractKVCrossAttnUpBlock2D" in up_block_types, "ExtractKVUNet must have ExtractKVCrossAttnUpBlock2D." - - if len(down_block_types) != len(up_block_types): - raise ValueError( - f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." - ) - - if len(block_out_channels) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." - ) - - if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." - ) - if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None: - for layer_number_per_block in transformer_layers_per_block: - if isinstance(layer_number_per_block, list): - raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") - - def _set_time_proj( - self, - time_embedding_type: str, - block_out_channels: int, - flip_sin_to_cos: bool, - freq_shift: float, - time_embedding_dim: int, - ) -> Tuple[int, int]: - if time_embedding_type == "fourier": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 - if time_embed_dim % 2 != 0: - raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") - self.time_proj = GaussianFourierProjection( - time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos - ) - timestep_input_dim = time_embed_dim - elif time_embedding_type == "positional": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 - - self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) - timestep_input_dim = block_out_channels[0] - else: - raise ValueError( - f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." - ) - - return time_embed_dim, timestep_input_dim - - def _set_encoder_hid_proj( - self, - encoder_hid_dim_type: Optional[str], - cross_attention_dim: Union[int, Tuple[int]], - encoder_hid_dim: Optional[int], - ): - if encoder_hid_dim_type is None and encoder_hid_dim is not None: - encoder_hid_dim_type = "text_proj" - self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) - logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") - - if encoder_hid_dim is None and encoder_hid_dim_type is not None: - raise ValueError( - f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." - ) - - if encoder_hid_dim_type == "text_proj": - self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) - elif encoder_hid_dim_type == "text_image_proj": - # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)` - self.encoder_hid_proj = TextImageProjection( - text_embed_dim=encoder_hid_dim, - image_embed_dim=cross_attention_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - self.encoder_hid_proj = ImageProjection( - image_embed_dim=encoder_hid_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type is not None: - raise ValueError( - f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." - ) - else: - self.encoder_hid_proj = None - - def _set_class_embedding( - self, - class_embed_type: Optional[str], - act_fn: str, - num_class_embeds: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - timestep_input_dim: int, - ): - if class_embed_type is None and num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - elif class_embed_type == "timestep": - self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) - elif class_embed_type == "identity": - self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) - elif class_embed_type == "projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" - ) - # The projection `class_embed_type` is the same as the timestep `class_embed_type` except - # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings - # 2. it projects from an arbitrary input dimension. - # - # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. - # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. - # As a result, `TimestepEmbedding` can be passed arbitrary vectors. - self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - elif class_embed_type == "simple_projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" - ) - self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) - else: - self.class_embedding = None - - def _set_add_embedding( - self, - addition_embed_type: str, - addition_embed_type_num_heads: int, - addition_time_embed_dim: Optional[int], - flip_sin_to_cos: bool, - freq_shift: float, - cross_attention_dim: Optional[int], - encoder_hid_dim: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - ): - if addition_embed_type == "text": - if encoder_hid_dim is not None: - text_time_embedding_from_dim = encoder_hid_dim - else: - text_time_embedding_from_dim = cross_attention_dim - - self.add_embedding = TextTimeEmbedding( - text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads - ) - elif addition_embed_type == "text_image": - # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)` - self.add_embedding = TextImageTimeEmbedding( - text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim - ) - elif addition_embed_type == "text_time": - self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) - self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) - elif addition_embed_type == "image": - # Kandinsky 2.2 - self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) - elif addition_embed_type == "image_hint": - # Kandinsky 2.2 ControlNet - self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) - elif addition_embed_type is not None: - raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") - - def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int): - if attention_type in ["gated", "gated-text-image"]: - positive_len = 768 - if isinstance(cross_attention_dim, int): - positive_len = cross_attention_dim - elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list): - positive_len = cross_attention_dim[0] - - feature_type = "text-only" if attention_type == "gated" else "text-image" - self.position_net = GLIGENTextBoundingboxProjection( - positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type - ) - - @property - def attn_processors(self) -> Dict[str, AttentionProcessor]: - r""" - Returns: - `dict` of attention processors: A dictionary containing all attention processors used in the model with - indexed by its weight name. - """ - # set recursively - processors = {} - - def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): - if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) - - for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) - - return processors - - for name, module in self.named_children(): - fn_recursive_add_processors(name, module, processors) - - return processors - - def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): - r""" - Sets the attention processor to use to compute attention. - - Parameters: - processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): - The instantiated processor class or a dictionary of processor classes that will be set as the processor - for **all** `Attention` layers. - - If `processor` is a dict, the key needs to define the path to the corresponding cross attention - processor. This is strongly recommended when setting trainable attention processors. - - """ - count = len(self.attn_processors.keys()) - - if isinstance(processor, dict) and len(processor) != count: - raise ValueError( - f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) - - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): - if hasattr(module, "set_processor"): - if not isinstance(processor, dict): - module.set_processor(processor) - else: - module.set_processor(processor.pop(f"{name}.processor")) - - for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) - - for name, module in self.named_children(): - fn_recursive_attn_processor(name, module, processor) - - def set_default_attn_processor(self): - """ - Disables custom attention processors and sets the default attention implementation. - """ - if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnAddedKVProcessor() - elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): - processor = AttnProcessor() - else: - raise ValueError( - f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" - ) - - self.set_attn_processor(processor) - - def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"): - r""" - Enable sliced attention computation. - - When this option is enabled, the attention module splits the input tensor in slices to compute attention in - several steps. This is useful for saving some memory in exchange for a small decrease in speed. - - Args: - slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): - When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If - `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is - provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` - must be a multiple of `slice_size`. - """ - sliceable_head_dims = [] - - def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): - if hasattr(module, "set_attention_slice"): - sliceable_head_dims.append(module.sliceable_head_dim) - - for child in module.children(): - fn_recursive_retrieve_sliceable_dims(child) - - # retrieve number of attention layers - for module in self.children(): - fn_recursive_retrieve_sliceable_dims(module) - - num_sliceable_layers = len(sliceable_head_dims) - - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = [dim // 2 for dim in sliceable_head_dims] - elif slice_size == "max": - # make smallest slice possible - slice_size = num_sliceable_layers * [1] - - slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size - - if len(slice_size) != len(sliceable_head_dims): - raise ValueError( - f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" - f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." - ) - - for i in range(len(slice_size)): - size = slice_size[i] - dim = sliceable_head_dims[i] - if size is not None and size > dim: - raise ValueError(f"size {size} has to be smaller or equal to {dim}.") - - # Recursively walk through all the children. - # Any children which exposes the set_attention_slice method - # gets the message - def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): - if hasattr(module, "set_attention_slice"): - module.set_attention_slice(slice_size.pop()) - - for child in module.children(): - fn_recursive_set_attention_slice(child, slice_size) - - reversed_slice_size = list(reversed(slice_size)) - for module in self.children(): - fn_recursive_set_attention_slice(module, reversed_slice_size) - - def _set_gradient_checkpointing(self, module, value=False): - if hasattr(module, "gradient_checkpointing"): - module.gradient_checkpointing = value - - def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): - r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. - - The suffixes after the scaling factors represent the stage blocks where they are being applied. - - Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that - are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. - - Args: - s1 (`float`): - Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - s2 (`float`): - Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. - b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. - """ - for i, upsample_block in enumerate(self.up_blocks): - setattr(upsample_block, "s1", s1) - setattr(upsample_block, "s2", s2) - setattr(upsample_block, "b1", b1) - setattr(upsample_block, "b2", b2) - - def disable_freeu(self): - """Disables the FreeU mechanism.""" - freeu_keys = {"s1", "s2", "b1", "b2"} - for i, upsample_block in enumerate(self.up_blocks): - for k in freeu_keys: - if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None: - setattr(upsample_block, k, None) - - def fuse_qkv_projections(self): - """ - Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) - are fused. For cross-attention modules, key and value projection matrices are fused. - - - - This API is 🧪 experimental. - - - """ - self.original_attn_processors = None - - for _, attn_processor in self.attn_processors.items(): - if "Added" in str(attn_processor.__class__.__name__): - raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") - - self.original_attn_processors = self.attn_processors - - for module in self.modules(): - if isinstance(module, Attention): - module.fuse_projections(fuse=True) - - def unfuse_qkv_projections(self): - """Disables the fused QKV projection if enabled. - - - - This API is 🧪 experimental. - - - - """ - if self.original_attn_processors is not None: - self.set_attn_processor(self.original_attn_processors) - - def unload_lora(self): - """Unloads LoRA weights.""" - deprecate( - "unload_lora", - "0.28.0", - "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().", - ) - for module in self.modules(): - if hasattr(module, "set_lora_layer"): - module.set_lora_layer(None) - - def get_time_embed( - self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int] - ) -> Optional[torch.Tensor]: - timesteps = timestep - if not torch.is_tensor(timesteps): - # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - # `Timesteps` does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=sample.dtype) - return t_emb - - def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]: - class_emb = None - if self.class_embedding is not None: - if class_labels is None: - raise ValueError("class_labels should be provided when num_class_embeds > 0") - - if self.config.class_embed_type == "timestep": - class_labels = self.time_proj(class_labels) - - # `Timesteps` does not contain any weights and will always return f32 tensors - # there might be better ways to encapsulate this. - class_labels = class_labels.to(dtype=sample.dtype) - - class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) - return class_emb - - def get_aug_embed( - self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> Optional[torch.Tensor]: - aug_emb = None - if self.config.addition_embed_type == "text": - aug_emb = self.add_embedding(encoder_hidden_states) - elif self.config.addition_embed_type == "text_image": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - - image_embs = added_cond_kwargs.get("image_embeds") - text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) - aug_emb = self.add_embedding(text_embs, image_embs) - elif self.config.addition_embed_type == "text_time": - # SDXL - style - if "text_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" - ) - text_embeds = added_cond_kwargs.get("text_embeds") - if "time_ids" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" - ) - time_ids = added_cond_kwargs.get("time_ids") - time_embeds = self.add_time_proj(time_ids.flatten()) - time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) - add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) - add_embeds = add_embeds.to(emb.dtype) - aug_emb = self.add_embedding(add_embeds) - elif self.config.addition_embed_type == "image": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - aug_emb = self.add_embedding(image_embs) - elif self.config.addition_embed_type == "image_hint": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - hint = added_cond_kwargs.get("hint") - aug_emb = self.add_embedding(image_embs, hint) - return aug_emb - - def process_encoder_hidden_states( - self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> torch.Tensor: - if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(image_embeds) - elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - image_embeds = self.encoder_hid_proj(image_embeds) - encoder_hidden_states = (encoder_hidden_states, image_embeds) - return encoder_hidden_states - - def init_kv_extraction(self): - for block in self.down_blocks: - if hasattr(block, "has_cross_attention") and block.has_cross_attention: - block.init_kv_extraction() - - for block in self.up_blocks: - if hasattr(block, "has_cross_attention") and block.has_cross_attention: - block.init_kv_extraction() - - if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: - self.mid_block.init_kv_extraction() - - def forward( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - controlnet_cond: torch.FloatTensor, - conditioning_scale: float = 1.0, - class_labels: Optional[torch.Tensor] = None, - timestep_cond: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, - down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - mid_block_additional_residual: Optional[torch.Tensor] = None, - down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - guess_mode: bool = False, - return_dict: bool = True, - ) -> Union[ExtractKVUNet2DConditionOutput, Tuple]: - r""" - The [`ExtractKVUNet2DConditionModel`] forward method. - - Args: - sample (`torch.FloatTensor`): - The noisy input tensor with the following shape `(batch, channel, height, width)`. - timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. - encoder_hidden_states (`torch.FloatTensor`): - The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. - class_labels (`torch.Tensor`, *optional*, defaults to `None`): - Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. - timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): - Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed - through the `self.time_embedding` layer to obtain the timestep embeddings. - attention_mask (`torch.Tensor`, *optional*, defaults to `None`): - An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask - is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large - negative values to the attention scores corresponding to "discard" tokens. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - added_cond_kwargs: (`dict`, *optional*): - A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that - are passed along to the UNet blocks. - down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): - A tuple of tensors that if specified are added to the residuals of down unet blocks. - mid_block_additional_residual: (`torch.Tensor`, *optional*): - A tensor that if specified is added to the residual of the middle unet block. - down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): - additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) - encoder_attention_mask (`torch.Tensor`): - A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If - `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, - which adds large negative values to the attention scores corresponding to "discard" tokens. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain - tuple. - - Returns: - [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: - If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned, - otherwise a `tuple` is returned where the first element is the sample tensor. - """ - # check channel order - channel_order = self.config.controlnet_conditioning_channel_order - - if channel_order == "rgb": - # in rgb order by default - ... - elif channel_order == "bgr": - controlnet_cond = torch.flip(controlnet_cond, dims=[1]) - else: - raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}") - - # By default samples have to be AT least a multiple of the overall upsampling factor. - # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). - # However, the upsampling interpolation output size can be forced to fit any upsampling size - # on the fly if necessary. - default_overall_up_factor = 2**self.num_upsamplers - - # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` - forward_upsample_size = False - upsample_size = None - - for dim in sample.shape[-2:]: - if dim % default_overall_up_factor != 0: - # Forward upsample size to force interpolation output size. - forward_upsample_size = True - break - - # ensure attention_mask is a bias, and give it a singleton query_tokens dimension - # expects mask of shape: - # [batch, key_tokens] - # adds singleton query_tokens dimension: - # [batch, 1, key_tokens] - # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: - # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) - # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) - if attention_mask is not None: - # assume that mask is expressed as: - # (1 = keep, 0 = discard) - # convert mask into a bias that can be added to attention scores: - # (keep = +0, discard = -10000.0) - attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # convert encoder_attention_mask to a bias the same way we do for attention_mask - if encoder_attention_mask is not None: - encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 - encoder_attention_mask = encoder_attention_mask.unsqueeze(1) - - # 0. center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # 1. time - t_emb = self.get_time_embed(sample=sample, timestep=timestep) - emb = self.time_embedding(t_emb, timestep_cond) - aug_emb = None - - class_emb = self.get_class_embed(sample=sample, class_labels=class_labels) - if class_emb is not None: - if self.config.class_embeddings_concat: - emb = torch.cat([emb, class_emb], dim=-1) - else: - emb = emb + class_emb - - aug_emb = self.get_aug_embed( - emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs - ) - if self.config.addition_embed_type == "image_hint": - aug_emb, hint = aug_emb - sample = torch.cat([sample, hint], dim=1) - - emb = emb + aug_emb if aug_emb is not None else emb - - if self.time_embed_act is not None: - emb = self.time_embed_act(emb) - - encoder_hidden_states = self.process_encoder_hidden_states( - encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs - ) - - # 2. pre-process - sample = self.conv_in(sample) - controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) - sample = sample + controlnet_cond - - # 2.5 GLIGEN position net - if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None: - cross_attention_kwargs = cross_attention_kwargs.copy() - gligen_args = cross_attention_kwargs.pop("gligen") - cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)} - - if cross_attention_kwargs is not None and cross_attention_kwargs.get("kv_drop_idx", None) is not None: - threshold = cross_attention_kwargs.pop("kv_drop_idx") - cross_attention_kwargs["kv_drop_idx"] = timestep 0: - additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) - - sample, res_samples, extracted_kv = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - **additional_residuals, - ) - extracted_kvs.update(extracted_kv) - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - if is_adapter and len(down_intrablock_additional_residuals) > 0: - sample += down_intrablock_additional_residuals.pop(0) - - down_block_res_samples += res_samples - - if is_controlnet: - new_down_block_res_samples = () - - for down_block_res_sample, down_block_additional_residual in zip( - down_block_res_samples, down_block_additional_residuals - ): - down_block_res_sample = down_block_res_sample + down_block_additional_residual - new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) - - down_block_res_samples = new_down_block_res_samples - - # 4. mid - if self.mid_block is not None: - if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: - sample, extracted_kv = self.mid_block( - sample, - emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - extracted_kvs.update(extracted_kv) - else: - sample = self.mid_block(sample, emb) - - # To support T2I-Adapter-XL - if ( - is_adapter - and len(down_intrablock_additional_residuals) > 0 - and sample.shape == down_intrablock_additional_residuals[0].shape - ): - sample += down_intrablock_additional_residuals.pop(0) - - if is_controlnet: - sample = sample + mid_block_additional_residual - - # 5. Control net blocks - - controlnet_down_block_res_samples = () - - for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): - down_block_res_sample = controlnet_block(down_block_res_sample) - controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,) - - mid_block_res_sample = self.controlnet_mid_block(sample) - - # 6. up - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] - - # if we have not reached the final block and need to forward the - # upsample size, we do it here - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: - sample, extract_kv = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - upsample_size=upsample_size, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - ) - extracted_kvs.update(extract_kv) - else: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - upsample_size=upsample_size, - ) - - # 6. post-process - if self.conv_norm_out: - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - # 7. scaling - if guess_mode and not self.config.global_pool_conditions: - scales = torch.logspace(-1, 0, len(controlnet_down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0 - scales = scales * conditioning_scale - controlnet_down_block_res_samples = [sample * scale for sample, scale in zip(controlnet_down_block_res_samples, scales)] - mid_block_res_sample = mid_block_res_sample * scales[-1] # last one - else: - controlnet_down_block_res_samples = [sample * conditioning_scale for sample in controlnet_down_block_res_samples] - mid_block_res_sample = mid_block_res_sample * conditioning_scale - - if self.config.global_pool_conditions: - controlnet_down_block_res_samples = [ - torch.mean(sample, dim=(2, 3), keepdim=True) for sample in controlnet_down_block_res_samples - ] - mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True) - - if USE_PEFT_BACKEND: - # remove `lora_scale` from each PEFT layer - unscale_lora_layers(self, lora_scale) - - if not return_dict: - return (sample, extracted_kvs, controlnet_down_block_res_samples, mid_block_res_sample) - - return ExtractKVUNet2DConditionOutput( - sample=sample, cached_kvs=extracted_kvs, - down_block_res_samples=controlnet_down_block_res_samples, mid_block_res_sample=mid_block_res_sample - ) diff --git a/pipelines/sdxl_instantir.py b/pipelines/sdxl_instantir.py deleted file mode 100644 index 4181eba1eebab9a19d8d35ae97cd99db2b0abe34..0000000000000000000000000000000000000000 --- a/pipelines/sdxl_instantir.py +++ /dev/null @@ -1,1707 +0,0 @@ -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import inspect -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -import numpy as np -import PIL.Image -import torch -import torch.nn.functional as F -from transformers import ( - CLIPImageProcessor, - CLIPTextModel, - CLIPTextModelWithProjection, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) - -from diffusers.utils.import_utils import is_invisible_watermark_available - -from diffusers.image_processor import PipelineImageInput, VaeImageProcessor -from diffusers.loaders import ( - FromSingleFileMixin, - IPAdapterMixin, - StableDiffusionXLLoraLoaderMixin, - TextualInversionLoaderMixin, -) -from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel -from diffusers.models.attention_processor import ( - AttnProcessor2_0, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - XFormersAttnProcessor, -) -from diffusers.models.lora import adjust_lora_scale_text_encoder -from diffusers.schedulers import KarrasDiffusionSchedulers, LCMScheduler -from diffusers.utils import ( - USE_PEFT_BACKEND, - deprecate, - logging, - replace_example_docstring, - scale_lora_layers, - unscale_lora_layers, - convert_unet_state_dict_to_peft -) -from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor -from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin -from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput - - -if is_invisible_watermark_available(): - from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker - -from peft import LoraConfig, set_peft_model_state_dict -from module.aggregator import Aggregator - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> # !pip install opencv-python transformers accelerate - >>> from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL - >>> from diffusers.utils import load_image - >>> import numpy as np - >>> import torch - - >>> import cv2 - >>> from PIL import Image - - >>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting" - >>> negative_prompt = "low quality, bad quality, sketches" - - >>> # download an image - >>> image = load_image( - ... "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png" - ... ) - - >>> # initialize the models and pipeline - >>> controlnet_conditioning_scale = 0.5 # recommended for good generalization - >>> controlnet = ControlNetModel.from_pretrained( - ... "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16 - ... ) - >>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) - >>> pipe = StableDiffusionXLControlNetPipeline.from_pretrained( - ... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16 - ... ) - >>> pipe.enable_model_cpu_offload() - - >>> # get canny image - >>> image = np.array(image) - >>> image = cv2.Canny(image, 100, 200) - >>> image = image[:, :, None] - >>> image = np.concatenate([image, image, image], axis=2) - >>> canny_image = Image.fromarray(image) - - >>> # generate image - >>> image = pipe( - ... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image - ... ).images[0] - ``` -""" - -LCM_LORA_MODULES = [ - "to_q", - "to_k", - "to_v", - "to_out.0", - "proj_in", - "proj_out", - "ff.net.0.proj", - "ff.net.2", - "conv1", - "conv2", - "conv_shortcut", - "downsamplers.0.conv", - "upsamplers.0.conv", - "time_emb_proj", -] -PREVIEWER_LORA_MODULES = [ - "to_q", - "to_kv", - "0.to_out", - "attn1.to_k", - "attn1.to_v", - "to_k_ip", - "to_v_ip", - "ln_k_ip.linear", - "ln_v_ip.linear", - "to_out.0", - "proj_in", - "proj_out", - "ff.net.0.proj", - "ff.net.2", - "conv1", - "conv2", - "conv_shortcut", - "downsamplers.0.conv", - "upsamplers.0.conv", - "time_emb_proj", -] - - -def remove_attn2(model): - def recursive_find_module(name, module): - if not "up_blocks" in name and not "down_blocks" in name and not "mid_block" in name: return - elif "resnets" in name: return - if hasattr(module, "attn2"): - setattr(module, "attn2", None) - setattr(module, "norm2", None) - return - for sub_name, sub_module in module.named_children(): - recursive_find_module(f"{name}.{sub_name}", sub_module) - - for name, module in model.named_children(): - recursive_find_module(name, module) - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg -def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): - """ - Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 - """ - std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) - std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) - # rescale the results from guidance (fixes overexposure) - noise_pred_rescaled = noise_cfg * (std_text / std_cfg) - # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images - noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg - return noise_cfg - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps -def retrieve_timesteps( - scheduler, - num_inference_steps: Optional[int] = None, - device: Optional[Union[str, torch.device]] = None, - timesteps: Optional[List[int]] = None, - **kwargs, -): - """ - Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles - custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. - - Args: - scheduler (`SchedulerMixin`): - The scheduler to get timesteps from. - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` - must be `None`. - device (`str` or `torch.device`, *optional*): - The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - timesteps (`List[int]`, *optional*): - Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default - timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` - must be `None`. - - Returns: - `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the - second element is the number of inference steps. - """ - if timesteps is not None: - accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) - if not accepts_timesteps: - raise ValueError( - f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) - scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) - timesteps = scheduler.timesteps - num_inference_steps = len(timesteps) - else: - scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) - timesteps = scheduler.timesteps - return timesteps, num_inference_steps - - -class InstantIRPipeline( - DiffusionPipeline, - StableDiffusionMixin, - TextualInversionLoaderMixin, - StableDiffusionXLLoraLoaderMixin, - IPAdapterMixin, - FromSingleFileMixin, -): - r""" - Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance. - - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods - implemented for all pipelines (downloading, saving, running on a particular device, etc.). - - The pipeline also inherits the following loading methods: - - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights - - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights - - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters - - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. - text_encoder ([`~transformers.CLIPTextModel`]): - Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co./openai/clip-vit-large-patch14)). - text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]): - Second frozen text-encoder - ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co./laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)). - tokenizer ([`~transformers.CLIPTokenizer`]): - A `CLIPTokenizer` to tokenize text. - tokenizer_2 ([`~transformers.CLIPTokenizer`]): - A `CLIPTokenizer` to tokenize text. - unet ([`UNet2DConditionModel`]): - A `UNet2DConditionModel` to denoise the encoded image latents. - controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): - Provides additional conditioning to the `unet` during the denoising process. If you set multiple - ControlNets as a list, the outputs from each ControlNet are added together to create one combined - additional conditioning. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of - [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. - force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): - Whether the negative prompt embeddings should always be set to 0. Also see the config of - `stabilityai/stable-diffusion-xl-base-1-0`. - add_watermarker (`bool`, *optional*): - Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to - watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no - watermarker is used. - """ - - # leave controlnet out on purpose because it iterates with unet - model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae" - _optional_components = [ - "tokenizer", - "tokenizer_2", - "text_encoder", - "text_encoder_2", - "feature_extractor", - "image_encoder", - ] - _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - text_encoder_2: CLIPTextModelWithProjection, - tokenizer: CLIPTokenizer, - tokenizer_2: CLIPTokenizer, - unet: UNet2DConditionModel, - scheduler: KarrasDiffusionSchedulers, - aggregator: Aggregator = None, - force_zeros_for_empty_prompt: bool = True, - add_watermarker: Optional[bool] = None, - feature_extractor: CLIPImageProcessor = None, - image_encoder: CLIPVisionModelWithProjection = None, - ): - super().__init__() - - if aggregator is None: - aggregator = Aggregator.from_unet(unet) - remove_attn2(aggregator) - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - text_encoder_2=text_encoder_2, - tokenizer=tokenizer, - tokenizer_2=tokenizer_2, - unet=unet, - aggregator=aggregator, - scheduler=scheduler, - feature_extractor=feature_extractor, - image_encoder=image_encoder, - ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True) - self.control_image_processor = VaeImageProcessor( - vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=True - ) - add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() - - if add_watermarker: - self.watermark = StableDiffusionXLWatermarker() - else: - self.watermark = None - - self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) - - def prepare_previewers(self, previewer_lora_path: str): - lora_state_dict, alpha_dict = self.lora_state_dict(previewer_lora_path, weight_name="previewer_lora_weights.bin") - unet_state_dict = { - f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.") - } - unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict) - lora_state_dict = dict() - for k, v in unet_state_dict.items(): - if "ip" in k: - k = k.replace("attn2", "attn2.processor") - lora_state_dict[k] = v - else: - lora_state_dict[k] = v - if alpha_dict: - lora_alpha = next(iter(alpha_dict.values())) - else: - lora_alpha = 1 - logger.info(f"use lora alpha {lora_alpha}") - lora_config = LoraConfig( - r=64, - target_modules=PREVIEWER_LORA_MODULES, - lora_alpha=lora_alpha, - lora_dropout=0.0, - ) - - self.unet.add_adapter(lora_config) - incompatible_keys = set_peft_model_state_dict(self.unet, lora_state_dict, adapter_name="default") - if incompatible_keys is not None: - # check only for unexpected keys - unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None) - missing_keys = getattr(incompatible_keys, "missing_keys", None) - if unexpected_keys: - raise ValueError( - f"Loading adapter weights from state_dict led to unexpected keys not found in the model: " - f" {unexpected_keys}. " - ) - self.unet.disable_adapters() - - return lora_alpha - - # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt - def encode_prompt( - self, - prompt: str, - prompt_2: Optional[str] = None, - device: Optional[torch.device] = None, - num_images_per_prompt: int = 1, - do_classifier_free_guidance: bool = True, - negative_prompt: Optional[str] = None, - negative_prompt_2: Optional[str] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - lora_scale: Optional[float] = None, - clip_skip: Optional[int] = None, - ): - r""" - Encodes the prompt into text encoder hidden states. - - Args: - prompt (`str` or `List[str]`, *optional*): - prompt to be encoded - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - device: (`torch.device`): - torch device - num_images_per_prompt (`int`): - number of images that should be generated per prompt - do_classifier_free_guidance (`bool`): - whether to use classifier free guidance or not - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is - less than `1`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - lora_scale (`float`, *optional*): - A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. - clip_skip (`int`, *optional*): - Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that - the output of the pre-final layer will be used for computing the prompt embeddings. - """ - device = device or self._execution_device - - # set lora scale so that monkey patched LoRA - # function of text encoder can correctly access it - if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): - self._lora_scale = lora_scale - - # dynamically adjust the LoRA scale - if self.text_encoder is not None: - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) - else: - scale_lora_layers(self.text_encoder, lora_scale) - - if self.text_encoder_2 is not None: - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) - else: - scale_lora_layers(self.text_encoder_2, lora_scale) - - prompt = [prompt] if isinstance(prompt, str) else prompt - - if prompt is not None: - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - # Define tokenizers and text encoders - tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] - text_encoders = ( - [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] - ) - - if prompt_embeds is None: - prompt_2 = prompt_2 or prompt - prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 - - # textual inversion: process multi-vector tokens if necessary - prompt_embeds_list = [] - prompts = [prompt, prompt_2] - for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - prompt = self.maybe_convert_prompt(prompt, tokenizer) - - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - - text_input_ids = text_inputs.input_ids - untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( - text_input_ids, untruncated_ids - ): - removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {tokenizer.model_max_length} tokens: {removed_text}" - ) - - prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True) - - # We are only ALWAYS interested in the pooled output of the final text encoder - pooled_prompt_embeds = prompt_embeds[0] - if clip_skip is None: - prompt_embeds = prompt_embeds.hidden_states[-2] - else: - # "2" because SDXL always indexes from the penultimate layer. - prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)] - - prompt_embeds_list.append(prompt_embeds) - - prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) - - # get unconditional embeddings for classifier free guidance - zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt - if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: - negative_prompt_embeds = torch.zeros_like(prompt_embeds) - negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) - elif do_classifier_free_guidance and negative_prompt_embeds is None: - negative_prompt = negative_prompt or "" - negative_prompt_2 = negative_prompt_2 or negative_prompt - - # normalize str to list - negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt - negative_prompt_2 = ( - batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 - ) - - uncond_tokens: List[str] - if prompt is not None and type(prompt) is not type(negative_prompt): - raise TypeError( - f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" - f" {type(prompt)}." - ) - elif batch_size != len(negative_prompt): - raise ValueError( - f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" - f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" - " the batch size of `prompt`." - ) - else: - uncond_tokens = [negative_prompt, negative_prompt_2] - - negative_prompt_embeds_list = [] - for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) - - max_length = prompt_embeds.shape[1] - uncond_input = tokenizer( - negative_prompt, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - negative_prompt_embeds = text_encoder( - uncond_input.input_ids.to(device), - output_hidden_states=True, - ) - # We are only ALWAYS interested in the pooled output of the final text encoder - negative_pooled_prompt_embeds = negative_prompt_embeds[0] - negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] - - negative_prompt_embeds_list.append(negative_prompt_embeds) - - negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) - - if self.text_encoder_2 is not None: - prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) - else: - prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device) - - bs_embed, seq_len, _ = prompt_embeds.shape - # duplicate text embeddings for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - if self.text_encoder_2 is not None: - negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) - else: - negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device) - - negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) - negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - - pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( - bs_embed * num_images_per_prompt, -1 - ) - if do_classifier_free_guidance: - negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( - bs_embed * num_images_per_prompt, -1 - ) - - if self.text_encoder is not None: - if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder, lora_scale) - - if self.text_encoder_2 is not None: - if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder_2, lora_scale) - - return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image - def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): - dtype = next(self.image_encoder.parameters()).dtype - - if not isinstance(image, torch.Tensor): - image = self.feature_extractor(image, return_tensors="pt").pixel_values - - image = image.to(device=device, dtype=dtype) - if output_hidden_states: - image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] - image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_enc_hidden_states = self.image_encoder( - torch.zeros_like(image), output_hidden_states=True - ).hidden_states[-2] - uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( - num_images_per_prompt, dim=0 - ) - return image_enc_hidden_states, uncond_image_enc_hidden_states - else: - if isinstance(self.image_encoder, CLIPVisionModelWithProjection): - # CLIP image encoder. - image_embeds = self.image_encoder(image).image_embeds - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_embeds = torch.zeros_like(image_embeds) - else: - # DINO image encoder. - image_embeds = self.image_encoder(image).last_hidden_state - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_embeds = self.image_encoder( - torch.zeros_like(image) - ).last_hidden_state - uncond_image_embeds = uncond_image_embeds.repeat_interleave( - num_images_per_prompt, dim=0 - ) - - return image_embeds, uncond_image_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds - def prepare_ip_adapter_image_embeds( - self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance - ): - if ip_adapter_image_embeds is None: - if not isinstance(ip_adapter_image, list): - ip_adapter_image = [ip_adapter_image] - - if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): - if isinstance(ip_adapter_image[0], list): - raise ValueError( - f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." - ) - else: - logger.warning( - f"Got {len(ip_adapter_image)} images for {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." - " By default, these images will be sent to each IP-Adapter. If this is not your use-case, please specify `ip_adapter_image` as a list of image-list, with" - f" length equals to the number of IP-Adapters." - ) - ip_adapter_image = [ip_adapter_image] * len(self.unet.encoder_hid_proj.image_projection_layers) - - image_embeds = [] - for single_ip_adapter_image, image_proj_layer in zip( - ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers - ): - output_hidden_state = isinstance(self.image_encoder, CLIPVisionModelWithProjection) and not isinstance(image_proj_layer, ImageProjection) - single_image_embeds, single_negative_image_embeds = self.encode_image( - single_ip_adapter_image, device, 1, output_hidden_state - ) - single_image_embeds = torch.stack([single_image_embeds] * (num_images_per_prompt//single_image_embeds.shape[0]), dim=0) - single_negative_image_embeds = torch.stack( - [single_negative_image_embeds] * (num_images_per_prompt//single_negative_image_embeds.shape[0]), dim=0 - ) - - if do_classifier_free_guidance: - single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) - single_image_embeds = single_image_embeds.to(device) - - image_embeds.append(single_image_embeds) - else: - repeat_dims = [1] - image_embeds = [] - for single_image_embeds in ip_adapter_image_embeds: - if do_classifier_free_guidance: - single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - single_negative_image_embeds = single_negative_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) - ) - single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) - else: - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - image_embeds.append(single_image_embeds) - - return image_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs( - self, - prompt, - prompt_2, - image, - callback_steps, - negative_prompt=None, - negative_prompt_2=None, - prompt_embeds=None, - negative_prompt_embeds=None, - pooled_prompt_embeds=None, - ip_adapter_image=None, - ip_adapter_image_embeds=None, - negative_pooled_prompt_embeds=None, - controlnet_conditioning_scale=1.0, - control_guidance_start=0.0, - control_guidance_end=1.0, - callback_on_step_end_tensor_inputs=None, - ): - if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) - - if prompt is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two." - ) - elif prompt_2 is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two." - ) - elif prompt is None and prompt_embeds is None: - raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") - elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): - raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") - - if negative_prompt is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - elif negative_prompt_2 is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - - if prompt_embeds is not None and negative_prompt_embeds is not None: - if prompt_embeds.shape != negative_prompt_embeds.shape: - raise ValueError( - "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" - f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" - f" {negative_prompt_embeds.shape}." - ) - - if prompt_embeds is not None and pooled_prompt_embeds is None: - raise ValueError( - "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." - ) - - if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: - raise ValueError( - "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." - ) - - # Check `image` - is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( - self.aggregator, torch._dynamo.eval_frame.OptimizedModule - ) - if ( - isinstance(self.aggregator, Aggregator) - or is_compiled - and isinstance(self.aggregator._orig_mod, Aggregator) - ): - self.check_image(image, prompt, prompt_embeds) - else: - assert False - - if control_guidance_start >= control_guidance_end: - raise ValueError( - f"control guidance start: {control_guidance_start} cannot be larger or equal to control guidance end: {control_guidance_end}." - ) - if control_guidance_start < 0.0: - raise ValueError(f"control guidance start: {control_guidance_start} can't be smaller than 0.") - if control_guidance_end > 1.0: - raise ValueError(f"control guidance end: {control_guidance_end} can't be larger than 1.0.") - - if ip_adapter_image is not None and ip_adapter_image_embeds is not None: - raise ValueError( - "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." - ) - - if ip_adapter_image_embeds is not None: - if not isinstance(ip_adapter_image_embeds, list): - raise ValueError( - f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" - ) - elif ip_adapter_image_embeds[0].ndim not in [3, 4]: - raise ValueError( - f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" - ) - - # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image - def check_image(self, image, prompt, prompt_embeds): - image_is_pil = isinstance(image, PIL.Image.Image) - image_is_tensor = isinstance(image, torch.Tensor) - image_is_np = isinstance(image, np.ndarray) - image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) - image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) - image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray) - - if ( - not image_is_pil - and not image_is_tensor - and not image_is_np - and not image_is_pil_list - and not image_is_tensor_list - and not image_is_np_list - ): - raise TypeError( - f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}" - ) - - if image_is_pil: - image_batch_size = 1 - else: - image_batch_size = len(image) - - if prompt is not None and isinstance(prompt, str): - prompt_batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - prompt_batch_size = len(prompt) - elif prompt_embeds is not None: - prompt_batch_size = prompt_embeds.shape[0] - - if image_batch_size != 1 and image_batch_size != prompt_batch_size: - raise ValueError( - f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" - ) - - # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image - def prepare_image( - self, - image, - width, - height, - batch_size, - num_images_per_prompt, - device, - dtype, - do_classifier_free_guidance=False, - ): - image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) - image_batch_size = image.shape[0] - - if image_batch_size == 1: - repeat_by = batch_size - else: - # image batch size is the same as prompt batch size - repeat_by = num_images_per_prompt - - image = image.repeat_interleave(repeat_by, dim=0) - - image = image.to(device=device, dtype=dtype) - - return image - - @torch.no_grad() - def init_latents(self, latents, generator, timestep): - noise = torch.randn(latents.shape, generator=generator, device=self.vae.device, dtype=self.vae.dtype, layout=torch.strided) - bsz = latents.shape[0] - print(f"init latent at {timestep}") - timestep = torch.tensor([timestep]*bsz, device=self.vae.device) - # Note that the latents will be scaled aleady by scheduler.add_noise - latents = self.scheduler.add_noise(latents, noise, timestep) - return latents - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents - def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): - shape = ( - batch_size, - num_channels_latents, - int(height) // self.vae_scale_factor, - int(width) // self.vae_scale_factor, - ) - if isinstance(generator, list) and len(generator) != batch_size: - raise ValueError( - f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) - - if latents is None: - latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) - else: - latents = latents.to(device) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids - def _get_add_time_ids( - self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None - ): - add_time_ids = list(original_size + crops_coords_top_left + target_size) - - passed_add_embed_dim = ( - self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim - ) - expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features - - if expected_add_embed_dim != passed_add_embed_dim: - raise ValueError( - f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." - ) - - add_time_ids = torch.tensor([add_time_ids], dtype=dtype) - return add_time_ids - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae - def upcast_vae(self): - dtype = self.vae.dtype - self.vae.to(dtype=torch.float32) - use_torch_2_0_or_xformers = isinstance( - self.vae.decoder.mid_block.attentions[0].processor, - ( - AttnProcessor2_0, - XFormersAttnProcessor, - LoRAXFormersAttnProcessor, - LoRAAttnProcessor2_0, - ), - ) - # if xformers or torch_2_0 is used attention block does not need - # to be in float32 which can save lots of memory - if use_torch_2_0_or_xformers: - self.vae.post_quant_conv.to(dtype) - self.vae.decoder.conv_in.to(dtype) - self.vae.decoder.mid_block.to(dtype) - - # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding - def get_guidance_scale_embedding( - self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32 - ) -> torch.FloatTensor: - """ - See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 - - Args: - w (`torch.Tensor`): - Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. - embedding_dim (`int`, *optional*, defaults to 512): - Dimension of the embeddings to generate. - dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): - Data type of the generated embeddings. - - Returns: - `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`. - """ - assert len(w.shape) == 1 - w = w * 1000.0 - - half_dim = embedding_dim // 2 - emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) - emb = w.to(dtype)[:, None] * emb[None, :] - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) - if embedding_dim % 2 == 1: # zero pad - emb = torch.nn.functional.pad(emb, (0, 1)) - assert emb.shape == (w.shape[0], embedding_dim) - return emb - - @property - def guidance_scale(self): - return self._guidance_scale - - @property - def guidance_rescale(self): - return self._guidance_rescale - - @property - def clip_skip(self): - return self._clip_skip - - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - @property - def do_classifier_free_guidance(self): - return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None - - @property - def cross_attention_kwargs(self): - return self._cross_attention_kwargs - - @property - def denoising_end(self): - return self._denoising_end - - @property - def num_timesteps(self): - return self._num_timesteps - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - prompt: Union[str, List[str]] = None, - prompt_2: Optional[Union[str, List[str]]] = None, - image: PipelineImageInput = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 30, - timesteps: List[int] = None, - denoising_end: Optional[float] = None, - guidance_scale: float = 7.0, - negative_prompt: Optional[Union[str, List[str]]] = None, - negative_prompt_2: Optional[Union[str, List[str]]] = None, - num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - latents: Optional[torch.FloatTensor] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - ip_adapter_image: Optional[PipelineImageInput] = None, - ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - save_preview_row: bool = False, - init_latents_with_lq: bool = True, - multistep_restore: bool = False, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - guidance_rescale: float = 0.0, - controlnet_conditioning_scale: Union[float, List[float]] = 1.0, - control_guidance_start: Union[float, List[float]] = 0.0, - control_guidance_end: Union[float, List[float]] = 1.0, - original_size: Tuple[int, int] = None, - crops_coords_top_left: Tuple[int, int] = (0, 0), - target_size: Tuple[int, int] = None, - negative_original_size: Optional[Tuple[int, int]] = None, - negative_crops_coords_top_left: Tuple[int, int] = (0, 0), - negative_target_size: Optional[Tuple[int, int]] = None, - clip_skip: Optional[int] = None, - callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, - callback_on_step_end_tensor_inputs: List[str] = ["latents"], - previewer_scheduler: KarrasDiffusionSchedulers = None, - reference_latents: Optional[torch.FloatTensor] = None, - **kwargs, - ): - r""" - The call function to the pipeline for generation. - - Args: - prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders. - image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: - `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): - The ControlNet input condition to provide guidance to the `unet` for generation. If the type is - specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be - accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height - and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in - `init`, images must be passed as a list such that each element of the list can be correctly batched for - input to a single ControlNet. - height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): - The height in pixels of the generated image. Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co./stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): - The width in pixels of the generated image. Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co./stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - timesteps (`List[int]`, *optional*): - Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument - in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is - passed will be used. Must be in descending order. - denoising_end (`float`, *optional*): - When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be - completed before it is intentionally prematurely terminated. As a result, the returned sample will - still retain a substantial amount of noise as determined by the discrete timesteps selected by the - scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a - "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image - Output**](https://huggingface.co./docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) - guidance_scale (`float`, *optional*, defaults to 5.0): - A higher guidance scale value encourages the model to generate images closely linked to the text - `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide what to not include in image generation. If not defined, you need to - pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2` - and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders. - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies - to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make - generation deterministic. - latents (`torch.FloatTensor`, *optional*): - Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor is generated by sampling using the supplied random `generator`. - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not - provided, text embeddings are generated from the `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If - not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If - not provided, pooled text embeddings are generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt - weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input - argument. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): - Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of - IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should - contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not - provided, embeddings are computed from the `ip_adapter_image` input argument. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generated image. Choose between `PIL.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in - [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): - The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added - to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set - the corresponding scale as a list. - control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): - The percentage of total steps at which the ControlNet starts applying. - control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): - The percentage of total steps at which the ControlNet stops applying. - original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. - `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as - explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). - crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position - `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting - `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). - target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - For most cases, `target_size` should be set to the desired height and width of the generated image. If - not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in - section 2.2 of [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). - negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a specific image resolution. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a target image resolution. It should be as same - as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - clip_skip (`int`, *optional*): - Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that - the output of the pre-final layer will be used for computing the prompt embeddings. - callback_on_step_end (`Callable`, *optional*): - A function that calls at the end of each denoising steps during the inference. The function is called - with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, - callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by - `callback_on_step_end_tensor_inputs`. - callback_on_step_end_tensor_inputs (`List`, *optional*): - The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list - will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the - `._callback_tensor_inputs` attribute of your pipeline class. - - Examples: - - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, - otherwise a `tuple` is returned containing the output images. - """ - - callback = kwargs.pop("callback", None) - callback_steps = kwargs.pop("callback_steps", None) - - if callback is not None: - deprecate( - "callback", - "1.0.0", - "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", - ) - if callback_steps is not None: - deprecate( - "callback_steps", - "1.0.0", - "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", - ) - - aggregator = self.aggregator._orig_mod if is_compiled_module(self.aggregator) else self.aggregator - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, - prompt_2, - image, - callback_steps, - negative_prompt, - negative_prompt_2, - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - ip_adapter_image, - ip_adapter_image_embeds, - negative_pooled_prompt_embeds, - controlnet_conditioning_scale, - control_guidance_start, - control_guidance_end, - callback_on_step_end_tensor_inputs, - ) - - self._guidance_scale = guidance_scale - self._guidance_rescale = guidance_rescale - self._clip_skip = clip_skip - self._cross_attention_kwargs = cross_attention_kwargs - self._denoising_end = denoising_end - - # 2. Define call parameters - if prompt is not None and isinstance(prompt, str): - if not isinstance(image, PIL.Image.Image): - batch_size = len(image) - else: - batch_size = 1 - prompt = [prompt] * batch_size - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - assert batch_size == len(image) or (isinstance(image, PIL.Image.Image) or len(image) == 1) - else: - batch_size = prompt_embeds.shape[0] - assert batch_size == len(image) or (isinstance(image, PIL.Image.Image) or len(image) == 1) - - device = self._execution_device - - # 3.1 Encode input prompt - text_encoder_lora_scale = ( - self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None - ) - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = self.encode_prompt( - prompt=prompt, - prompt_2=prompt_2, - device=device, - num_images_per_prompt=num_images_per_prompt, - do_classifier_free_guidance=self.do_classifier_free_guidance, - negative_prompt=negative_prompt, - negative_prompt_2=negative_prompt_2, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - lora_scale=text_encoder_lora_scale, - clip_skip=self.clip_skip, - ) - - # 3.2 Encode ip_adapter_image - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, - ip_adapter_image_embeds, - device, - batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, - ) - - # 4. Prepare image - image = self.prepare_image( - image=image, - width=width, - height=height, - batch_size=batch_size * num_images_per_prompt, - num_images_per_prompt=num_images_per_prompt, - device=device, - dtype=aggregator.dtype, - do_classifier_free_guidance=self.do_classifier_free_guidance, - ) - height, width = image.shape[-2:] - if image.shape[1] != 4: - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast - if needs_upcasting: - image = image.float() - self.vae.to(dtype=torch.float32) - image = self.vae.encode(image).latent_dist.sample() - image = image * self.vae.config.scaling_factor - if needs_upcasting: - self.vae.to(dtype=torch.float16) - else: - height = int(height * self.vae_scale_factor) - width = int(width * self.vae_scale_factor) - - # 5. Prepare timesteps - timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) - - # 6. Prepare latent variables - if init_latents_with_lq: - latents = self.init_latents(image, generator, timesteps[0]) - else: - num_channels_latents = self.unet.config.in_channels - latents = self.prepare_latents( - batch_size * num_images_per_prompt, - num_channels_latents, - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - ) - - # 6.5 Optionally get Guidance Scale Embedding - timestep_cond = None - if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) - timestep_cond = self.get_guidance_scale_embedding( - guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim - ).to(device=device, dtype=latents.dtype) - - # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 7.1 Create tensor stating which controlnets to keep - controlnet_keep = [] - for i in range(len(timesteps)): - keeps = 1.0 - float(i / len(timesteps) < control_guidance_start or (i + 1) / len(timesteps) > control_guidance_end) - controlnet_keep.append(keeps) - if isinstance(controlnet_conditioning_scale, list): - assert len(controlnet_conditioning_scale) == len(timesteps), f"{len(controlnet_conditioning_scale)} controlnet scales do not match number of sampling steps {len(timesteps)}" - else: - controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet_keep) - - # 7.2 Prepare added time ids & embeddings - original_size = original_size or (height, width) - target_size = target_size or (height, width) - - add_text_embeds = pooled_prompt_embeds - if self.text_encoder_2 is None: - text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) - else: - text_encoder_projection_dim = self.text_encoder_2.config.projection_dim - - add_time_ids = self._get_add_time_ids( - original_size, - crops_coords_top_left, - target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - - if negative_original_size is not None and negative_target_size is not None: - negative_add_time_ids = self._get_add_time_ids( - negative_original_size, - negative_crops_coords_top_left, - negative_target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - else: - negative_add_time_ids = add_time_ids - - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) - image = torch.cat([image] * 2, dim=0) - - prompt_embeds = prompt_embeds.to(device) - add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) - - # 8. Denoising loop - num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) - - # 8.1 Apply denoising_end - if ( - self.denoising_end is not None - and isinstance(self.denoising_end, float) - and self.denoising_end > 0 - and self.denoising_end < 1 - ): - discrete_timestep_cutoff = int( - round( - self.scheduler.config.num_train_timesteps - - (self.denoising_end * self.scheduler.config.num_train_timesteps) - ) - ) - num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) - timesteps = timesteps[:num_inference_steps] - - is_unet_compiled = is_compiled_module(self.unet) - is_aggregator_compiled = is_compiled_module(self.aggregator) - is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") - previewer_mean = torch.zeros_like(latents) - unet_mean = torch.zeros_like(latents) - preview_factor = torch.ones( - (latents.shape[0], *((1,) * (len(latents.shape) - 1))), dtype=latents.dtype, device=latents.device - ) - - self._num_timesteps = len(timesteps) - preview_row = [] - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # Relevant thread: - # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428 - if (is_unet_compiled and is_aggregator_compiled) and is_torch_higher_equal_2_1: - torch._inductor.cudagraph_mark_step_begin() - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - added_cond_kwargs = { - "text_embeds": add_text_embeds, - "time_ids": add_time_ids, - "image_embeds": image_embeds - } - aggregator_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - - # prepare time_embeds in advance as adapter input - cross_attention_t_emb = self.unet.get_time_embed(sample=latent_model_input, timestep=t) - cross_attention_emb = self.unet.time_embedding(cross_attention_t_emb, timestep_cond) - cross_attention_aug_emb = None - - cross_attention_aug_emb = self.unet.get_aug_embed( - emb=cross_attention_emb, - encoder_hidden_states=prompt_embeds, - added_cond_kwargs=added_cond_kwargs - ) - - cross_attention_emb = cross_attention_emb + cross_attention_aug_emb if cross_attention_aug_emb is not None else cross_attention_emb - - if self.unet.time_embed_act is not None: - cross_attention_emb = self.unet.time_embed_act(cross_attention_emb) - - current_cross_attention_kwargs = {"temb": cross_attention_emb} - if cross_attention_kwargs is not None: - for k,v in cross_attention_kwargs.items(): - current_cross_attention_kwargs[k] = v - self._cross_attention_kwargs = current_cross_attention_kwargs - - # preview with LCM - previewer_model_input = latent_model_input - previewer_prompt_embeds = prompt_embeds - self.unet.enable_adapters() - preview_noise = self.unet( - previewer_model_input, - t, - encoder_hidden_states=previewer_prompt_embeds, - timestep_cond=timestep_cond, - cross_attention_kwargs=self.cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - preview_latent = previewer_scheduler.step( - preview_noise, - t.to(dtype=torch.int64), - # torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents, - latent_model_input, - return_dict=False - )[0] - self.unet.disable_adapters() - if self.do_classifier_free_guidance: - _, preview_latent_cond = preview_latent.chunk(2) - _, noise_preview = preview_noise.chunk(2) - preview_row.append(preview_latent_cond.to('cpu')) - else: - noise_preview = preview_noise - preview_row.append(preview_latent.to('cpu')) - # Prepare 2nd order step. - if multistep_restore and i+1 < len(timesteps): - first_step = self.scheduler.step(noise_preview, t, latents, **extra_step_kwargs, return_dict=True, step_forward=False) - prev_t = timesteps[i + 1] - unet_model_input = torch.cat([first_step.prev_sample] * 2) if self.do_classifier_free_guidance else first_step.prev_sample - unet_model_input = self.scheduler.scale_model_input(unet_model_input, prev_t, heun_step=True) - else: - prev_t = t - unet_model_input = latent_model_input - - if reference_latents is not None: - preview_latent = torch.cat([reference_latents] * 2) if self.do_classifier_free_guidance else reference_latents - - # Add fresh noise - # preview_noise = torch.randn_like(preview_latent) - # preview_latent = self.scheduler.add_noise(preview_latent, preview_noise, t) - - preview_latent=preview_latent.to(dtype=next(aggregator.parameters()).dtype) - - # Aggregator inference - generative_reference = preview_latent - - adaRes_scale = preview_factor.to(generative_reference.dtype).clamp(0.0, controlnet_conditioning_scale[i]) - cond_scale = adaRes_scale * controlnet_keep[i] - cond_scale = torch.cat([cond_scale] * 2) if self.do_classifier_free_guidance else cond_scale - - down_block_res_samples, mid_block_res_sample = aggregator( - image, - prev_t, - encoder_hidden_states=prompt_embeds, - controlnet_cond=generative_reference, - conditioning_scale=cond_scale, - added_cond_kwargs=aggregator_added_cond_kwargs, - return_dict=False, - ) - - # predict the noise residual - noise_pred = self.unet( - unet_model_input, - prev_t, - encoder_hidden_states=prompt_embeds, - timestep_cond=timestep_cond, - cross_attention_kwargs=self.cross_attention_kwargs, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - - if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: - # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents_dtype = latents.dtype - unet_step = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=True) - latents = unet_step.prev_sample - - # Update adaRes factors - unet_pred_latent = unet_step.pred_original_sample - - # Adaptive restoration. - pred_x0_l2 = ((preview_latent[latents.shape[0]:].float()-unet_pred_latent.float())).pow(2).sum(dim=(1,2,3)) - previewer_l2 = ((preview_latent[latents.shape[0]:].float()-previewer_mean.float())).pow(2).sum(dim=(1,2,3)) - # unet_l2 = ((unet_pred_latent.float()-unet_mean.float())).pow(2).sum(dim=(1,2,3)).sqrt() - # l2_error = (((preview_latent[latents.shape[0]:]-previewer_mean) - (unet_pred_latent-unet_mean))).pow(2).mean(dim=(1,2,3)) - # preview_error = torch.nn.functional.cosine_similarity(preview_latent[latents.shape[0]:].reshape(latents.shape[0], -1), unet_pred_latent.reshape(latents.shape[0],-1)) - previewer_mean = preview_latent[latents.shape[0]:] - unet_mean = unet_pred_latent - preview_factor = (pred_x0_l2 / previewer_l2).reshape(-1, 1, 1, 1) - - if latents.dtype != latents_dtype: - if torch.backends.mps.is_available(): - # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 - latents = latents.to(latents_dtype) - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if not output_type == "latent": - # make sure the VAE is in float32 mode, as it overflows in float16 - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast - - if needs_upcasting: - self.upcast_vae() - latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) - - # unscale/denormalize the latents - # denormalize with the mean and std if available and not None - has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None - has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None - if has_latents_mean and has_latents_std: - latents_mean = ( - torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents_std = ( - torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean - else: - latents = latents / self.vae.config.scaling_factor - - image = self.vae.decode(latents, return_dict=False)[0] - - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) - else: - image = latents - - if not output_type == "latent": - # apply watermark if available - if self.watermark is not None: - image = self.watermark.apply_watermark(image) - - image = self.image_processor.postprocess(image, output_type=output_type) - - if save_preview_row: - preview_image_row = [] - if needs_upcasting: - self.upcast_vae() - for preview_latents in preview_row: - preview_latents = preview_latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) - if has_latents_mean and has_latents_std: - latents_mean = ( - torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(preview_latents.device, preview_latents.dtype) - ) - latents_std = ( - torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(preview_latents.device, preview_latents.dtype) - ) - preview_latents = preview_latents * latents_std / self.vae.config.scaling_factor + latents_mean - else: - preview_latents = preview_latents / self.vae.config.scaling_factor - - preview_image = self.vae.decode(preview_latents, return_dict=False)[0] - preview_image = self.image_processor.postprocess(preview_image, output_type=output_type) - preview_image_row.append(preview_image) - - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - if save_preview_row: - return (image, preview_image_row) - return (image,) - - return StableDiffusionXLPipelineOutput(images=image) diff --git a/pipelines/stage1_sdxl_pipeline.py b/pipelines/stage1_sdxl_pipeline.py deleted file mode 100644 index 630009ebecefc423562d1d1048c3169618ebcf53..0000000000000000000000000000000000000000 --- a/pipelines/stage1_sdxl_pipeline.py +++ /dev/null @@ -1,1283 +0,0 @@ -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import inspect -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -import torch -from transformers import ( - CLIPImageProcessor, - CLIPTextModel, - CLIPTextModelWithProjection, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) - -from ...image_processor import PipelineImageInput, VaeImageProcessor -from ...loaders import ( - FromSingleFileMixin, - IPAdapterMixin, - StableDiffusionXLLoraLoaderMixin, - TextualInversionLoaderMixin, -) -from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel -from ...models.attention_processor import ( - AttnProcessor2_0, - FusedAttnProcessor2_0, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - XFormersAttnProcessor, -) -from ...models.lora import adjust_lora_scale_text_encoder -from ...schedulers import KarrasDiffusionSchedulers -from ...utils import ( - USE_PEFT_BACKEND, - deprecate, - is_invisible_watermark_available, - is_torch_xla_available, - logging, - replace_example_docstring, - scale_lora_layers, - unscale_lora_layers, -) -from ...utils.torch_utils import randn_tensor -from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin -from .pipeline_output import StableDiffusionXLPipelineOutput - - -if is_invisible_watermark_available(): - from .watermark import StableDiffusionXLWatermarker - -if is_torch_xla_available(): - import torch_xla.core.xla_model as xm - - XLA_AVAILABLE = True -else: - XLA_AVAILABLE = False - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> import torch - >>> from diffusers import StableDiffusionXLPipeline - - >>> pipe = StableDiffusionXLPipeline.from_pretrained( - ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 - ... ) - >>> pipe = pipe.to("cuda") - - >>> prompt = "a photo of an astronaut riding a horse on mars" - >>> image = pipe(prompt).images[0] - ``` -""" - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg -def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): - """ - Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 - """ - std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) - std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) - # rescale the results from guidance (fixes overexposure) - noise_pred_rescaled = noise_cfg * (std_text / std_cfg) - # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images - noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg - return noise_cfg - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps -def retrieve_timesteps( - scheduler, - num_inference_steps: Optional[int] = None, - device: Optional[Union[str, torch.device]] = None, - timesteps: Optional[List[int]] = None, - **kwargs, -): - """ - Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles - custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. - - Args: - scheduler (`SchedulerMixin`): - The scheduler to get timesteps from. - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` - must be `None`. - device (`str` or `torch.device`, *optional*): - The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - timesteps (`List[int]`, *optional*): - Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default - timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` - must be `None`. - - Returns: - `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the - second element is the number of inference steps. - """ - if timesteps is not None: - accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) - if not accepts_timesteps: - raise ValueError( - f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) - scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) - timesteps = scheduler.timesteps - num_inference_steps = len(timesteps) - else: - scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) - timesteps = scheduler.timesteps - return timesteps, num_inference_steps - - -class StableDiffusionXLPipeline( - DiffusionPipeline, - StableDiffusionMixin, - FromSingleFileMixin, - StableDiffusionXLLoraLoaderMixin, - TextualInversionLoaderMixin, - IPAdapterMixin, -): - r""" - Pipeline for text-to-image generation using Stable Diffusion XL. - - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - - The pipeline also inherits the following loading methods: - - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights - - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights - - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters - - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`CLIPTextModel`]): - Frozen text-encoder. Stable Diffusion XL uses the text portion of - [CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically - the [clip-vit-large-patch14](https://huggingface.co./openai/clip-vit-large-patch14) variant. - text_encoder_2 ([` CLIPTextModelWithProjection`]): - Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of - [CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), - specifically the - [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co./laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) - variant. - tokenizer (`CLIPTokenizer`): - Tokenizer of class - [CLIPTokenizer](https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - tokenizer_2 (`CLIPTokenizer`): - Second Tokenizer of class - [CLIPTokenizer](https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of - [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. - force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): - Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of - `stabilityai/stable-diffusion-xl-base-1-0`. - add_watermarker (`bool`, *optional*): - Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to - watermark output images. If not defined, it will default to True if the package is installed, otherwise no - watermarker will be used. - """ - - model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae" - _optional_components = [ - "tokenizer", - "tokenizer_2", - "text_encoder", - "text_encoder_2", - "image_encoder", - "feature_extractor", - ] - _callback_tensor_inputs = [ - "latents", - "prompt_embeds", - "negative_prompt_embeds", - "add_text_embeds", - "add_time_ids", - "negative_pooled_prompt_embeds", - "negative_add_time_ids", - ] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - text_encoder_2: CLIPTextModelWithProjection, - tokenizer: CLIPTokenizer, - tokenizer_2: CLIPTokenizer, - unet: UNet2DConditionModel, - scheduler: KarrasDiffusionSchedulers, - image_encoder: CLIPVisionModelWithProjection = None, - feature_extractor: CLIPImageProcessor = None, - force_zeros_for_empty_prompt: bool = True, - add_watermarker: Optional[bool] = None, - ): - super().__init__() - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - text_encoder_2=text_encoder_2, - tokenizer=tokenizer, - tokenizer_2=tokenizer_2, - unet=unet, - scheduler=scheduler, - image_encoder=image_encoder, - feature_extractor=feature_extractor, - ) - self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) - - self.default_sample_size = self.unet.config.sample_size - - add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() - - if add_watermarker: - self.watermark = StableDiffusionXLWatermarker() - else: - self.watermark = None - - def encode_prompt( - self, - prompt: str, - prompt_2: Optional[str] = None, - device: Optional[torch.device] = None, - num_images_per_prompt: int = 1, - do_classifier_free_guidance: bool = True, - negative_prompt: Optional[str] = None, - negative_prompt_2: Optional[str] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - lora_scale: Optional[float] = None, - clip_skip: Optional[int] = None, - ): - r""" - Encodes the prompt into text encoder hidden states. - - Args: - prompt (`str` or `List[str]`, *optional*): - prompt to be encoded - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - device: (`torch.device`): - torch device - num_images_per_prompt (`int`): - number of images that should be generated per prompt - do_classifier_free_guidance (`bool`): - whether to use classifier free guidance or not - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is - less than `1`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - lora_scale (`float`, *optional*): - A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. - clip_skip (`int`, *optional*): - Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that - the output of the pre-final layer will be used for computing the prompt embeddings. - """ - device = device or self._execution_device - - # set lora scale so that monkey patched LoRA - # function of text encoder can correctly access it - if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): - self._lora_scale = lora_scale - - # dynamically adjust the LoRA scale - if self.text_encoder is not None: - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) - else: - scale_lora_layers(self.text_encoder, lora_scale) - - if self.text_encoder_2 is not None: - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) - else: - scale_lora_layers(self.text_encoder_2, lora_scale) - - prompt = [prompt] if isinstance(prompt, str) else prompt - - if prompt is not None: - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - # Define tokenizers and text encoders - tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] - text_encoders = ( - [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] - ) - - if prompt_embeds is None: - prompt_2 = prompt_2 or prompt - prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 - - # textual inversion: process multi-vector tokens if necessary - prompt_embeds_list = [] - prompts = [prompt, prompt_2] - for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - prompt = self.maybe_convert_prompt(prompt, tokenizer) - - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - - text_input_ids = text_inputs.input_ids - untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( - text_input_ids, untruncated_ids - ): - removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {tokenizer.model_max_length} tokens: {removed_text}" - ) - - prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True) - - # We are only ALWAYS interested in the pooled output of the final text encoder - pooled_prompt_embeds = prompt_embeds[0] - if clip_skip is None: - prompt_embeds = prompt_embeds.hidden_states[-2] - else: - # "2" because SDXL always indexes from the penultimate layer. - prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)] - - prompt_embeds_list.append(prompt_embeds) - - prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) - - # get unconditional embeddings for classifier free guidance - zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt - if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: - negative_prompt_embeds = torch.zeros_like(prompt_embeds) - negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) - elif do_classifier_free_guidance and negative_prompt_embeds is None: - negative_prompt = negative_prompt or "" - negative_prompt_2 = negative_prompt_2 or negative_prompt - - # normalize str to list - negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt - negative_prompt_2 = ( - batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 - ) - - uncond_tokens: List[str] - if prompt is not None and type(prompt) is not type(negative_prompt): - raise TypeError( - f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" - f" {type(prompt)}." - ) - elif batch_size != len(negative_prompt): - raise ValueError( - f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" - f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" - " the batch size of `prompt`." - ) - else: - uncond_tokens = [negative_prompt, negative_prompt_2] - - negative_prompt_embeds_list = [] - for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) - - max_length = prompt_embeds.shape[1] - uncond_input = tokenizer( - negative_prompt, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - negative_prompt_embeds = text_encoder( - uncond_input.input_ids.to(device), - output_hidden_states=True, - ) - # We are only ALWAYS interested in the pooled output of the final text encoder - negative_pooled_prompt_embeds = negative_prompt_embeds[0] - negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] - - negative_prompt_embeds_list.append(negative_prompt_embeds) - - negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) - - if self.text_encoder_2 is not None: - prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) - else: - prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device) - - bs_embed, seq_len, _ = prompt_embeds.shape - # duplicate text embeddings for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - if self.text_encoder_2 is not None: - negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) - else: - negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device) - - negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) - negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - - pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( - bs_embed * num_images_per_prompt, -1 - ) - if do_classifier_free_guidance: - negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( - bs_embed * num_images_per_prompt, -1 - ) - - if self.text_encoder is not None: - if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder, lora_scale) - - if self.text_encoder_2 is not None: - if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder_2, lora_scale) - - return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image - def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): - dtype = next(self.image_encoder.parameters()).dtype - - if not isinstance(image, torch.Tensor): - image = self.feature_extractor(image, return_tensors="pt").pixel_values - - image = image.to(device=device, dtype=dtype) - if output_hidden_states: - image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] - image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_enc_hidden_states = self.image_encoder( - torch.zeros_like(image), output_hidden_states=True - ).hidden_states[-2] - uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( - num_images_per_prompt, dim=0 - ) - return image_enc_hidden_states, uncond_image_enc_hidden_states - else: - image_embeds = self.image_encoder(image).image_embeds - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_embeds = torch.zeros_like(image_embeds) - - return image_embeds, uncond_image_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds - def prepare_ip_adapter_image_embeds( - self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance - ): - if ip_adapter_image_embeds is None: - if not isinstance(ip_adapter_image, list): - ip_adapter_image = [ip_adapter_image] - - if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): - raise ValueError( - f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." - ) - - image_embeds = [] - for single_ip_adapter_image, image_proj_layer in zip( - ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers - ): - output_hidden_state = not isinstance(image_proj_layer, ImageProjection) - single_image_embeds, single_negative_image_embeds = self.encode_image( - single_ip_adapter_image, device, 1, output_hidden_state - ) - single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0) - single_negative_image_embeds = torch.stack( - [single_negative_image_embeds] * num_images_per_prompt, dim=0 - ) - - if do_classifier_free_guidance: - single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) - single_image_embeds = single_image_embeds.to(device) - - image_embeds.append(single_image_embeds) - else: - repeat_dims = [1] - image_embeds = [] - for single_image_embeds in ip_adapter_image_embeds: - if do_classifier_free_guidance: - single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - single_negative_image_embeds = single_negative_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) - ) - single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) - else: - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - image_embeds.append(single_image_embeds) - - return image_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs( - self, - prompt, - prompt_2, - height, - width, - callback_steps, - negative_prompt=None, - negative_prompt_2=None, - prompt_embeds=None, - negative_prompt_embeds=None, - pooled_prompt_embeds=None, - negative_pooled_prompt_embeds=None, - ip_adapter_image=None, - ip_adapter_image_embeds=None, - callback_on_step_end_tensor_inputs=None, - ): - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) - - if prompt is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two." - ) - elif prompt_2 is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two." - ) - elif prompt is None and prompt_embeds is None: - raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") - elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): - raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") - - if negative_prompt is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - elif negative_prompt_2 is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - - if prompt_embeds is not None and negative_prompt_embeds is not None: - if prompt_embeds.shape != negative_prompt_embeds.shape: - raise ValueError( - "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" - f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" - f" {negative_prompt_embeds.shape}." - ) - - if prompt_embeds is not None and pooled_prompt_embeds is None: - raise ValueError( - "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." - ) - - if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: - raise ValueError( - "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." - ) - - if ip_adapter_image is not None and ip_adapter_image_embeds is not None: - raise ValueError( - "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." - ) - - if ip_adapter_image_embeds is not None: - if not isinstance(ip_adapter_image_embeds, list): - raise ValueError( - f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" - ) - elif ip_adapter_image_embeds[0].ndim not in [3, 4]: - raise ValueError( - f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" - ) - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents - def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): - shape = ( - batch_size, - num_channels_latents, - int(height) // self.vae_scale_factor, - int(width) // self.vae_scale_factor, - ) - if isinstance(generator, list) and len(generator) != batch_size: - raise ValueError( - f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) - - if latents is None: - latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) - else: - latents = latents.to(device) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - def _get_add_time_ids( - self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None - ): - add_time_ids = list(original_size + crops_coords_top_left + target_size) - - passed_add_embed_dim = ( - self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim - ) - expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features - - if expected_add_embed_dim != passed_add_embed_dim: - raise ValueError( - f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." - ) - - add_time_ids = torch.tensor([add_time_ids], dtype=dtype) - return add_time_ids - - def upcast_vae(self): - dtype = self.vae.dtype - self.vae.to(dtype=torch.float32) - use_torch_2_0_or_xformers = isinstance( - self.vae.decoder.mid_block.attentions[0].processor, - ( - AttnProcessor2_0, - XFormersAttnProcessor, - LoRAXFormersAttnProcessor, - LoRAAttnProcessor2_0, - FusedAttnProcessor2_0, - ), - ) - # if xformers or torch_2_0 is used attention block does not need - # to be in float32 which can save lots of memory - if use_torch_2_0_or_xformers: - self.vae.post_quant_conv.to(dtype) - self.vae.decoder.conv_in.to(dtype) - self.vae.decoder.mid_block.to(dtype) - - # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding - def get_guidance_scale_embedding( - self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32 - ) -> torch.FloatTensor: - """ - See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 - - Args: - w (`torch.Tensor`): - Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. - embedding_dim (`int`, *optional*, defaults to 512): - Dimension of the embeddings to generate. - dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): - Data type of the generated embeddings. - - Returns: - `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`. - """ - assert len(w.shape) == 1 - w = w * 1000.0 - - half_dim = embedding_dim // 2 - emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) - emb = w.to(dtype)[:, None] * emb[None, :] - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) - if embedding_dim % 2 == 1: # zero pad - emb = torch.nn.functional.pad(emb, (0, 1)) - assert emb.shape == (w.shape[0], embedding_dim) - return emb - - @property - def guidance_scale(self): - return self._guidance_scale - - @property - def guidance_rescale(self): - return self._guidance_rescale - - @property - def clip_skip(self): - return self._clip_skip - - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - @property - def do_classifier_free_guidance(self): - return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None - - @property - def cross_attention_kwargs(self): - return self._cross_attention_kwargs - - @property - def denoising_end(self): - return self._denoising_end - - @property - def num_timesteps(self): - return self._num_timesteps - - @property - def interrupt(self): - return self._interrupt - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - prompt: Union[str, List[str]] = None, - prompt_2: Optional[Union[str, List[str]]] = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - timesteps: List[int] = None, - denoising_end: Optional[float] = None, - guidance_scale: float = 5.0, - negative_prompt: Optional[Union[str, List[str]]] = None, - negative_prompt_2: Optional[Union[str, List[str]]] = None, - num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - latents: Optional[torch.FloatTensor] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - ip_adapter_image: Optional[PipelineImageInput] = None, - ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - guidance_rescale: float = 0.0, - original_size: Optional[Tuple[int, int]] = None, - crops_coords_top_left: Tuple[int, int] = (0, 0), - target_size: Optional[Tuple[int, int]] = None, - negative_original_size: Optional[Tuple[int, int]] = None, - negative_crops_coords_top_left: Tuple[int, int] = (0, 0), - negative_target_size: Optional[Tuple[int, int]] = None, - clip_skip: Optional[int] = None, - callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, - callback_on_step_end_tensor_inputs: List[str] = ["latents"], - **kwargs, - ): - r""" - Function invoked when calling the pipeline for generation. - - Args: - prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. - instead. - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The height in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co./stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The width in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co./stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - timesteps (`List[int]`, *optional*): - Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument - in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is - passed will be used. Must be in descending order. - denoising_end (`float`, *optional*): - When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be - completed before it is intentionally prematurely terminated. As a result, the returned sample will - still retain a substantial amount of noise as determined by the discrete timesteps selected by the - scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a - "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image - Output**](https://huggingface.co./docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) - guidance_scale (`float`, *optional*, defaults to 5.0): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is - less than `1`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) - to make generation deterministic. - latents (`torch.FloatTensor`, *optional*): - Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor will ge generated by sampling using the supplied random `generator`. - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): - Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of - IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should - contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not - provided, embeddings are computed from the `ip_adapter_image` input argument. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead - of a plain tuple. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - guidance_rescale (`float`, *optional*, defaults to 0.0): - Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are - Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of - [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). - Guidance rescale factor should fix overexposure when using zero terminal SNR. - original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. - `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as - explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). - crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position - `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting - `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). - target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - For most cases, `target_size` should be set to the desired height and width of the generated image. If - not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in - section 2.2 of [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). - negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a specific image resolution. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a target image resolution. It should be as same - as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - callback_on_step_end (`Callable`, *optional*): - A function that calls at the end of each denoising steps during the inference. The function is called - with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, - callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by - `callback_on_step_end_tensor_inputs`. - callback_on_step_end_tensor_inputs (`List`, *optional*): - The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list - will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the - `._callback_tensor_inputs` attribute of your pipeline class. - - Examples: - - Returns: - [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a - `tuple`. When returning a tuple, the first element is a list with the generated images. - """ - - callback = kwargs.pop("callback", None) - callback_steps = kwargs.pop("callback_steps", None) - - if callback is not None: - deprecate( - "callback", - "1.0.0", - "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", - ) - if callback_steps is not None: - deprecate( - "callback_steps", - "1.0.0", - "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", - ) - - # 0. Default height and width to unet - height = height or self.default_sample_size * self.vae_scale_factor - width = width or self.default_sample_size * self.vae_scale_factor - - original_size = original_size or (height, width) - target_size = target_size or (height, width) - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, - prompt_2, - height, - width, - callback_steps, - negative_prompt, - negative_prompt_2, - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ip_adapter_image, - ip_adapter_image_embeds, - callback_on_step_end_tensor_inputs, - ) - - self._guidance_scale = guidance_scale - self._guidance_rescale = guidance_rescale - self._clip_skip = clip_skip - self._cross_attention_kwargs = cross_attention_kwargs - self._denoising_end = denoising_end - self._interrupt = False - - # 2. Define call parameters - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - device = self._execution_device - - # 3. Encode input prompt - lora_scale = ( - self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None - ) - - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = self.encode_prompt( - prompt=prompt, - prompt_2=prompt_2, - device=device, - num_images_per_prompt=num_images_per_prompt, - do_classifier_free_guidance=self.do_classifier_free_guidance, - negative_prompt=negative_prompt, - negative_prompt_2=negative_prompt_2, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - lora_scale=lora_scale, - clip_skip=self.clip_skip, - ) - - # 4. Prepare timesteps - timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) - - # 5. Prepare latent variables - num_channels_latents = self.unet.config.in_channels - latents = self.prepare_latents( - batch_size * num_images_per_prompt, - num_channels_latents, - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - ) - - # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 7. Prepare added time ids & embeddings - add_text_embeds = pooled_prompt_embeds - if self.text_encoder_2 is None: - text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) - else: - text_encoder_projection_dim = self.text_encoder_2.config.projection_dim - - add_time_ids = self._get_add_time_ids( - original_size, - crops_coords_top_left, - target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - if negative_original_size is not None and negative_target_size is not None: - negative_add_time_ids = self._get_add_time_ids( - negative_original_size, - negative_crops_coords_top_left, - negative_target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - else: - negative_add_time_ids = add_time_ids - - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) - - prompt_embeds = prompt_embeds.to(device) - add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) - - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, - ip_adapter_image_embeds, - device, - batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, - ) - - # 8. Denoising loop - num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) - - # 8.1 Apply denoising_end - if ( - self.denoising_end is not None - and isinstance(self.denoising_end, float) - and self.denoising_end > 0 - and self.denoising_end < 1 - ): - discrete_timestep_cutoff = int( - round( - self.scheduler.config.num_train_timesteps - - (self.denoising_end * self.scheduler.config.num_train_timesteps) - ) - ) - num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) - timesteps = timesteps[:num_inference_steps] - - # 9. Optionally get Guidance Scale Embedding - timestep_cond = None - if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) - timestep_cond = self.get_guidance_scale_embedding( - guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim - ).to(device=device, dtype=latents.dtype) - - self._num_timesteps = len(timesteps) - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - if self.interrupt: - continue - - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents - - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - added_cond_kwargs["image_embeds"] = image_embeds - - noise_pred = self.unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, # [B, 77, 2048] - timestep_cond=timestep_cond, # None - cross_attention_kwargs=self.cross_attention_kwargs, # None - added_cond_kwargs=added_cond_kwargs, # {[B, 1280], [B, 6]} - return_dict=False, - )[0] - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) - - if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: - # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents_dtype = latents.dtype - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - if latents.dtype != latents_dtype: - if torch.backends.mps.is_available(): - # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 - latents = latents.to(latents_dtype) - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) - add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) - negative_pooled_prompt_embeds = callback_outputs.pop( - "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds - ) - add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) - negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids) - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if XLA_AVAILABLE: - xm.mark_step() - - if not output_type == "latent": - # make sure the VAE is in float32 mode, as it overflows in float16 - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast - - if needs_upcasting: - self.upcast_vae() - latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) - elif latents.dtype != self.vae.dtype: - if torch.backends.mps.is_available(): - # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 - self.vae = self.vae.to(latents.dtype) - - # unscale/denormalize the latents - # denormalize with the mean and std if available and not None - has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None - has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None - if has_latents_mean and has_latents_std: - latents_mean = ( - torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents_std = ( - torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean - else: - latents = latents / self.vae.config.scaling_factor - - image = self.vae.decode(latents, return_dict=False)[0] - - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) - else: - image = latents - - if not output_type == "latent": - # apply watermark if available - if self.watermark is not None: - image = self.watermark.apply_watermark(image) - - image = self.image_processor.postprocess(image, output_type=output_type) - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - return (image,) - - return StableDiffusionXLPipelineOutput(images=image) diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 9f0e685661bb64e3ccff45dd97bba2d5ae82d4bd..0000000000000000000000000000000000000000 --- a/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -accelerate -datasets==2.19.1 -einops==0.8.0 -kornia==0.7.2 -numpy==1.26.4 -opencv-python==4.9.0.80 -peft==0.10.0 -pyrallis==0.3.1 -tokenizers>0.15.2 -torch==2.0.1 -torchvision==0.15.2 -transformers==4.46.1 -gradio==4.44.1 -gradio-imageslider -diffusers \ No newline at end of file diff --git a/schedulers/lcm_single_step_scheduler.py b/schedulers/lcm_single_step_scheduler.py deleted file mode 100644 index 4b302d852dca337c4415b4949691d47def612c87..0000000000000000000000000000000000000000 --- a/schedulers/lcm_single_step_scheduler.py +++ /dev/null @@ -1,537 +0,0 @@ -# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion -# and https://github.com/hojonathanho/diffusion - -import math -from dataclasses import dataclass -from typing import List, Optional, Tuple, Union - -import numpy as np -import torch - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.utils import BaseOutput, logging -from diffusers.utils.torch_utils import randn_tensor -from diffusers.schedulers.scheduling_utils import SchedulerMixin - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class LCMSingleStepSchedulerOutput(BaseOutput): - """ - Output class for the scheduler's `step` function output. - - Args: - pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): - The predicted denoised sample `(x_{0})` based on the model output from the current timestep. - `pred_original_sample` can be used to preview progress or for guidance. - """ - - denoised: Optional[torch.FloatTensor] = None - - -# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar -def betas_for_alpha_bar( - num_diffusion_timesteps, - max_beta=0.999, - alpha_transform_type="cosine", -): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of - (1-beta) over time from t = [0,1]. - - Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up - to that part of the diffusion process. - - - Args: - num_diffusion_timesteps (`int`): the number of betas to produce. - max_beta (`float`): the maximum beta to use; use values lower than 1 to - prevent singularities. - alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. - Choose from `cosine` or `exp` - - Returns: - betas (`np.ndarray`): the betas used by the scheduler to step the model outputs - """ - if alpha_transform_type == "cosine": - - def alpha_bar_fn(t): - return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 - - elif alpha_transform_type == "exp": - - def alpha_bar_fn(t): - return math.exp(t * -12.0) - - else: - raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}") - - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) - return torch.tensor(betas, dtype=torch.float32) - - -# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr -def rescale_zero_terminal_snr(betas: torch.FloatTensor) -> torch.FloatTensor: - """ - Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) - - - Args: - betas (`torch.FloatTensor`): - the betas that the scheduler is being initialized with. - - Returns: - `torch.FloatTensor`: rescaled betas with zero terminal SNR - """ - # Convert betas to alphas_bar_sqrt - alphas = 1.0 - betas - alphas_cumprod = torch.cumprod(alphas, dim=0) - alphas_bar_sqrt = alphas_cumprod.sqrt() - - # Store old values. - alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() - alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() - - # Shift so the last timestep is zero. - alphas_bar_sqrt -= alphas_bar_sqrt_T - - # Scale so the first timestep is back to the old value. - alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) - - # Convert alphas_bar_sqrt to betas - alphas_bar = alphas_bar_sqrt**2 # Revert sqrt - alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod - alphas = torch.cat([alphas_bar[0:1], alphas]) - betas = 1 - alphas - - return betas - - -class LCMSingleStepScheduler(SchedulerMixin, ConfigMixin): - """ - `LCMSingleStepScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with - non-Markovian guidance. - - This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. [`~ConfigMixin`] takes care of storing all config - attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be - accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving - functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. - - Args: - num_train_timesteps (`int`, defaults to 1000): - The number of diffusion steps to train the model. - beta_start (`float`, defaults to 0.0001): - The starting `beta` value of inference. - beta_end (`float`, defaults to 0.02): - The final `beta` value. - beta_schedule (`str`, defaults to `"linear"`): - The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from - `linear`, `scaled_linear`, or `squaredcos_cap_v2`. - trained_betas (`np.ndarray`, *optional*): - Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. - original_inference_steps (`int`, *optional*, defaults to 50): - The default number of inference steps used to generate a linearly-spaced timestep schedule, from which we - will ultimately take `num_inference_steps` evenly spaced timesteps to form the final timestep schedule. - clip_sample (`bool`, defaults to `True`): - Clip the predicted sample for numerical stability. - clip_sample_range (`float`, defaults to 1.0): - The maximum magnitude for sample clipping. Valid only when `clip_sample=True`. - set_alpha_to_one (`bool`, defaults to `True`): - Each diffusion step uses the alphas product value at that step and at the previous one. For the final step - there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, - otherwise it uses the alpha value at step 0. - steps_offset (`int`, defaults to 0): - An offset added to the inference steps. You can use a combination of `offset=1` and - `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable - Diffusion. - prediction_type (`str`, defaults to `epsilon`, *optional*): - Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), - `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen - Video](https://imagen.research.google/video/paper.pdf) paper). - thresholding (`bool`, defaults to `False`): - Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such - as Stable Diffusion. - dynamic_thresholding_ratio (`float`, defaults to 0.995): - The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. - sample_max_value (`float`, defaults to 1.0): - The threshold value for dynamic thresholding. Valid only when `thresholding=True`. - timestep_spacing (`str`, defaults to `"leading"`): - The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://huggingface.co./papers/2305.08891) for more information. - timestep_scaling (`float`, defaults to 10.0): - The factor the timesteps will be multiplied by when calculating the consistency model boundary conditions - `c_skip` and `c_out`. Increasing this will decrease the approximation error (although the approximation - error at the default of `10.0` is already pretty small). - rescale_betas_zero_snr (`bool`, defaults to `False`): - Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and - dark samples instead of limiting it to samples with medium brightness. Loosely related to - [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). - """ - - order = 1 - - @register_to_config - def __init__( - self, - num_train_timesteps: int = 1000, - beta_start: float = 0.00085, - beta_end: float = 0.012, - beta_schedule: str = "scaled_linear", - trained_betas: Optional[Union[np.ndarray, List[float]]] = None, - original_inference_steps: int = 50, - clip_sample: bool = False, - clip_sample_range: float = 1.0, - set_alpha_to_one: bool = True, - steps_offset: int = 0, - prediction_type: str = "epsilon", - thresholding: bool = False, - dynamic_thresholding_ratio: float = 0.995, - sample_max_value: float = 1.0, - timestep_spacing: str = "leading", - timestep_scaling: float = 10.0, - rescale_betas_zero_snr: bool = False, - ): - if trained_betas is not None: - self.betas = torch.tensor(trained_betas, dtype=torch.float32) - elif beta_schedule == "linear": - self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) - elif beta_schedule == "scaled_linear": - # this schedule is very specific to the latent diffusion model. - self.betas = ( - torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 - ) - elif beta_schedule == "squaredcos_cap_v2": - # Glide cosine schedule - self.betas = betas_for_alpha_bar(num_train_timesteps) - else: - raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") - - # Rescale for zero SNR - if rescale_betas_zero_snr: - self.betas = rescale_zero_terminal_snr(self.betas) - - self.alphas = 1.0 - self.betas - self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) - - # At every step in ddim, we are looking into the previous alphas_cumprod - # For the final step, there is no previous alphas_cumprod because we are already at 0 - # `set_alpha_to_one` decides whether we set this parameter simply to one or - # whether we use the final alpha of the "non-previous" one. - self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0] - - # standard deviation of the initial noise distribution - self.init_noise_sigma = 1.0 - - # setable values - self.num_inference_steps = None - self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)) - - self._step_index = None - - # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index - def _init_step_index(self, timestep): - if isinstance(timestep, torch.Tensor): - timestep = timestep.to(self.timesteps.device) - - index_candidates = (self.timesteps == timestep).nonzero() - - # The sigma index that is taken for the **very** first `step` - # is always the second index (or the last index if there is only 1) - # This way we can ensure we don't accidentally skip a sigma in - # case we start in the middle of the denoising schedule (e.g. for image-to-image) - if len(index_candidates) > 1: - step_index = index_candidates[1] - else: - step_index = index_candidates[0] - - self._step_index = step_index.item() - - @property - def step_index(self): - return self._step_index - - def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: - """ - Ensures interchangeability with schedulers that need to scale the denoising model input depending on the - current timestep. - - Args: - sample (`torch.FloatTensor`): - The input sample. - timestep (`int`, *optional*): - The current timestep in the diffusion chain. - Returns: - `torch.FloatTensor`: - A scaled input sample. - """ - return sample - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample - def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: - """ - "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the - prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by - s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing - pixels from saturation at each step. We find that dynamic thresholding results in significantly better - photorealism as well as better image-text alignment, especially when using very large guidance weights." - - https://arxiv.org/abs/2205.11487 - """ - dtype = sample.dtype - batch_size, channels, *remaining_dims = sample.shape - - if dtype not in (torch.float32, torch.float64): - sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half - - # Flatten sample for doing quantile calculation along each image - sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) - - abs_sample = sample.abs() # "a certain percentile absolute pixel value" - - s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) - s = torch.clamp( - s, min=1, max=self.config.sample_max_value - ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] - s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 - sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" - - sample = sample.reshape(batch_size, channels, *remaining_dims) - sample = sample.to(dtype) - - return sample - - def set_timesteps( - self, - num_inference_steps: int = None, - device: Union[str, torch.device] = None, - original_inference_steps: Optional[int] = None, - strength: int = 1.0, - timesteps: Optional[list] = None, - ): - """ - Sets the discrete timesteps used for the diffusion chain (to be run before inference). - - Args: - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. - device (`str` or `torch.device`, *optional*): - The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - original_inference_steps (`int`, *optional*): - The original number of inference steps, which will be used to generate a linearly-spaced timestep - schedule (which is different from the standard `diffusers` implementation). We will then take - `num_inference_steps` timesteps from this schedule, evenly spaced in terms of indices, and use that as - our final timestep schedule. If not set, this will default to the `original_inference_steps` attribute. - """ - - if num_inference_steps is not None and timesteps is not None: - raise ValueError("Can only pass one of `num_inference_steps` or `custom_timesteps`.") - - if timesteps is not None: - for i in range(1, len(timesteps)): - if timesteps[i] >= timesteps[i - 1]: - raise ValueError("`custom_timesteps` must be in descending order.") - - if timesteps[0] >= self.config.num_train_timesteps: - raise ValueError( - f"`timesteps` must start before `self.config.train_timesteps`:" - f" {self.config.num_train_timesteps}." - ) - - timesteps = np.array(timesteps, dtype=np.int64) - else: - if num_inference_steps > self.config.num_train_timesteps: - raise ValueError( - f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" - f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" - f" maximal {self.config.num_train_timesteps} timesteps." - ) - - self.num_inference_steps = num_inference_steps - original_steps = ( - original_inference_steps if original_inference_steps is not None else self.config.original_inference_steps - ) - - if original_steps > self.config.num_train_timesteps: - raise ValueError( - f"`original_steps`: {original_steps} cannot be larger than `self.config.train_timesteps`:" - f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" - f" maximal {self.config.num_train_timesteps} timesteps." - ) - - if num_inference_steps > original_steps: - raise ValueError( - f"`num_inference_steps`: {num_inference_steps} cannot be larger than `original_inference_steps`:" - f" {original_steps} because the final timestep schedule will be a subset of the" - f" `original_inference_steps`-sized initial timestep schedule." - ) - - # LCM Timesteps Setting - # Currently, only linear spacing is supported. - c = self.config.num_train_timesteps // original_steps - # LCM Training Steps Schedule - lcm_origin_timesteps = np.asarray(list(range(1, int(original_steps * strength) + 1))) * c - 1 - skipping_step = len(lcm_origin_timesteps) // num_inference_steps - # LCM Inference Steps Schedule - timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps] - - self.timesteps = torch.from_numpy(timesteps.copy()).to(device=device, dtype=torch.long) - - self._step_index = None - - def get_scalings_for_boundary_condition_discrete(self, timestep): - self.sigma_data = 0.5 # Default: 0.5 - scaled_timestep = timestep * self.config.timestep_scaling - - c_skip = self.sigma_data**2 / (scaled_timestep**2 + self.sigma_data**2) - c_out = scaled_timestep / (scaled_timestep**2 + self.sigma_data**2) ** 0.5 - return c_skip, c_out - - def append_dims(self, x, target_dims): - """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" - dims_to_append = target_dims - x.ndim - if dims_to_append < 0: - raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") - return x[(...,) + (None,) * dims_to_append] - - def extract_into_tensor(self, a, t, x_shape): - b, *_ = t.shape - out = a.gather(-1, t) - return out.reshape(b, *((1,) * (len(x_shape) - 1))) - - def step( - self, - model_output: torch.FloatTensor, - timestep: torch.Tensor, - sample: torch.FloatTensor, - generator: Optional[torch.Generator] = None, - return_dict: bool = True, - ) -> Union[LCMSingleStepSchedulerOutput, Tuple]: - """ - Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion - process from the learned model outputs (most often the predicted noise). - - Args: - model_output (`torch.FloatTensor`): - The direct output from learned diffusion model. - timestep (`float`): - The current discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - A current instance of a sample created by the diffusion process. - generator (`torch.Generator`, *optional*): - A random number generator. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`. - Returns: - [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`: - If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a - tuple is returned where the first element is the sample tensor. - """ - # 0. make sure everything is on the same device - alphas_cumprod = self.alphas_cumprod.to(sample.device) - - # 1. compute alphas, betas - if timestep.ndim == 0: - timestep = timestep.unsqueeze(0) - alpha_prod_t = self.extract_into_tensor(alphas_cumprod, timestep, sample.shape) - beta_prod_t = 1 - alpha_prod_t - - # 2. Get scalings for boundary conditions - c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep) - c_skip, c_out = [self.append_dims(x, sample.ndim) for x in [c_skip, c_out]] - - # 3. Compute the predicted original sample x_0 based on the model parameterization - if self.config.prediction_type == "epsilon": # noise-prediction - predicted_original_sample = (sample - torch.sqrt(beta_prod_t) * model_output) / torch.sqrt(alpha_prod_t) - elif self.config.prediction_type == "sample": # x-prediction - predicted_original_sample = model_output - elif self.config.prediction_type == "v_prediction": # v-prediction - predicted_original_sample = torch.sqrt(alpha_prod_t) * sample - torch.sqrt(beta_prod_t) * model_output - else: - raise ValueError( - f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or" - " `v_prediction` for `LCMScheduler`." - ) - - # 4. Clip or threshold "predicted x_0" - if self.config.thresholding: - predicted_original_sample = self._threshold_sample(predicted_original_sample) - elif self.config.clip_sample: - predicted_original_sample = predicted_original_sample.clamp( - -self.config.clip_sample_range, self.config.clip_sample_range - ) - - # 5. Denoise model output using boundary conditions - denoised = c_out * predicted_original_sample + c_skip * sample - - if not return_dict: - return (denoised, ) - - return LCMSingleStepSchedulerOutput(denoised=denoised) - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise - def add_noise( - self, - original_samples: torch.FloatTensor, - noise: torch.FloatTensor, - timesteps: torch.IntTensor, - ) -> torch.FloatTensor: - # Make sure alphas_cumprod and timestep have same device and dtype as original_samples - alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) - timesteps = timesteps.to(original_samples.device) - - sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 - sqrt_alpha_prod = sqrt_alpha_prod.flatten() - while len(sqrt_alpha_prod.shape) < len(original_samples.shape): - sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) - - sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() - while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) - - noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise - return noisy_samples - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity - def get_velocity( - self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor - ) -> torch.FloatTensor: - # Make sure alphas_cumprod and timestep have same device and dtype as sample - alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype) - timesteps = timesteps.to(sample.device) - - sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 - sqrt_alpha_prod = sqrt_alpha_prod.flatten() - while len(sqrt_alpha_prod.shape) < len(sample.shape): - sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) - - sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() - while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) - - velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample - return velocity - - def __len__(self): - return self.config.num_train_timesteps diff --git a/utils/matlab_cp2tform.py b/utils/matlab_cp2tform.py deleted file mode 100644 index 5915c4a4e9822180372ec8f5718b90343e14f071..0000000000000000000000000000000000000000 --- a/utils/matlab_cp2tform.py +++ /dev/null @@ -1,350 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Jul 11 06:54:28 2017 - -@author: zhaoyafei -""" - -import numpy as np -from numpy.linalg import inv, norm, lstsq -from numpy.linalg import matrix_rank as rank - -class MatlabCp2tormException(Exception): - def __str__(self): - return 'In File {}:{}'.format( - __file__, super.__str__(self)) - -def tformfwd(trans, uv): - """ - Function: - ---------- - apply affine transform 'trans' to uv - - Parameters: - ---------- - @trans: 3x3 np.array - transform matrix - @uv: Kx2 np.array - each row is a pair of coordinates (x, y) - - Returns: - ---------- - @xy: Kx2 np.array - each row is a pair of transformed coordinates (x, y) - """ - uv = np.hstack(( - uv, np.ones((uv.shape[0], 1)) - )) - xy = np.dot(uv, trans) - xy = xy[:, 0:-1] - return xy - - -def tforminv(trans, uv): - """ - Function: - ---------- - apply the inverse of affine transform 'trans' to uv - - Parameters: - ---------- - @trans: 3x3 np.array - transform matrix - @uv: Kx2 np.array - each row is a pair of coordinates (x, y) - - Returns: - ---------- - @xy: Kx2 np.array - each row is a pair of inverse-transformed coordinates (x, y) - """ - Tinv = inv(trans) - xy = tformfwd(Tinv, uv) - return xy - - -def findNonreflectiveSimilarity(uv, xy, options=None): - - options = {'K': 2} - - K = options['K'] - M = xy.shape[0] - x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector - y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector - # print('--->x, y:\n', x, y - - tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1)))) - tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1)))) - X = np.vstack((tmp1, tmp2)) - # print('--->X.shape: ', X.shape - # print('X:\n', X - - u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector - v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector - U = np.vstack((u, v)) - # print('--->U.shape: ', U.shape - # print('U:\n', U - - # We know that X * r = U - if rank(X) >= 2 * K: - r, _, _, _ = lstsq(X, U) - r = np.squeeze(r) - else: - raise Exception('cp2tform:twoUniquePointsReq') - - # print('--->r:\n', r - - sc = r[0] - ss = r[1] - tx = r[2] - ty = r[3] - - Tinv = np.array([ - [sc, -ss, 0], - [ss, sc, 0], - [tx, ty, 1] - ]) - - # print('--->Tinv:\n', Tinv - - T = inv(Tinv) - # print('--->T:\n', T - - T[:, 2] = np.array([0, 0, 1]) - - return T, Tinv - - -def findSimilarity(uv, xy, options=None): - - options = {'K': 2} - -# uv = np.array(uv) -# xy = np.array(xy) - - # Solve for trans1 - trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options) - - # Solve for trans2 - - # manually reflect the xy data across the Y-axis - xyR = xy - xyR[:, 0] = -1 * xyR[:, 0] - - trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options) - - # manually reflect the tform to undo the reflection done on xyR - TreflectY = np.array([ - [-1, 0, 0], - [0, 1, 0], - [0, 0, 1] - ]) - - trans2 = np.dot(trans2r, TreflectY) - - # Figure out if trans1 or trans2 is better - xy1 = tformfwd(trans1, uv) - norm1 = norm(xy1 - xy) - - xy2 = tformfwd(trans2, uv) - norm2 = norm(xy2 - xy) - - if norm1 <= norm2: - return trans1, trans1_inv - else: - trans2_inv = inv(trans2) - return trans2, trans2_inv - - -def get_similarity_transform(src_pts, dst_pts, reflective=True): - """ - Function: - ---------- - Find Similarity Transform Matrix 'trans': - u = src_pts[:, 0] - v = src_pts[:, 1] - x = dst_pts[:, 0] - y = dst_pts[:, 1] - [x, y, 1] = [u, v, 1] * trans - - Parameters: - ---------- - @src_pts: Kx2 np.array - source points, each row is a pair of coordinates (x, y) - @dst_pts: Kx2 np.array - destination points, each row is a pair of transformed - coordinates (x, y) - @reflective: True or False - if True: - use reflective similarity transform - else: - use non-reflective similarity transform - - Returns: - ---------- - @trans: 3x3 np.array - transform matrix from uv to xy - trans_inv: 3x3 np.array - inverse of trans, transform matrix from xy to uv - """ - - if reflective: - trans, trans_inv = findSimilarity(src_pts, dst_pts) - else: - trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts) - - return trans, trans_inv - - -def cvt_tform_mat_for_cv2(trans): - """ - Function: - ---------- - Convert Transform Matrix 'trans' into 'cv2_trans' which could be - directly used by cv2.warpAffine(): - u = src_pts[:, 0] - v = src_pts[:, 1] - x = dst_pts[:, 0] - y = dst_pts[:, 1] - [x, y].T = cv_trans * [u, v, 1].T - - Parameters: - ---------- - @trans: 3x3 np.array - transform matrix from uv to xy - - Returns: - ---------- - @cv2_trans: 2x3 np.array - transform matrix from src_pts to dst_pts, could be directly used - for cv2.warpAffine() - """ - cv2_trans = trans[:, 0:2].T - - return cv2_trans - - -def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True): - """ - Function: - ---------- - Find Similarity Transform Matrix 'cv2_trans' which could be - directly used by cv2.warpAffine(): - u = src_pts[:, 0] - v = src_pts[:, 1] - x = dst_pts[:, 0] - y = dst_pts[:, 1] - [x, y].T = cv_trans * [u, v, 1].T - - Parameters: - ---------- - @src_pts: Kx2 np.array - source points, each row is a pair of coordinates (x, y) - @dst_pts: Kx2 np.array - destination points, each row is a pair of transformed - coordinates (x, y) - reflective: True or False - if True: - use reflective similarity transform - else: - use non-reflective similarity transform - - Returns: - ---------- - @cv2_trans: 2x3 np.array - transform matrix from src_pts to dst_pts, could be directly used - for cv2.warpAffine() - """ - trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective) - cv2_trans = cvt_tform_mat_for_cv2(trans) - - return cv2_trans - - -if __name__ == '__main__': - """ - u = [0, 6, -2] - v = [0, 3, 5] - x = [-1, 0, 4] - y = [-1, -10, 4] - - # In Matlab, run: - # - # uv = [u'; v']; - # xy = [x'; y']; - # tform_sim=cp2tform(uv,xy,'similarity'); - # - # trans = tform_sim.tdata.T - # ans = - # -0.0764 -1.6190 0 - # 1.6190 -0.0764 0 - # -3.2156 0.0290 1.0000 - # trans_inv = tform_sim.tdata.Tinv - # ans = - # - # -0.0291 0.6163 0 - # -0.6163 -0.0291 0 - # -0.0756 1.9826 1.0000 - # xy_m=tformfwd(tform_sim, u,v) - # - # xy_m = - # - # -3.2156 0.0290 - # 1.1833 -9.9143 - # 5.0323 2.8853 - # uv_m=tforminv(tform_sim, x,y) - # - # uv_m = - # - # 0.5698 1.3953 - # 6.0872 2.2733 - # -2.6570 4.3314 - """ - u = [0, 6, -2] - v = [0, 3, 5] - x = [-1, 0, 4] - y = [-1, -10, 4] - - uv = np.array((u, v)).T - xy = np.array((x, y)).T - - print('\n--->uv:') - print(uv) - print('\n--->xy:') - print(xy) - - trans, trans_inv = get_similarity_transform(uv, xy) - - print('\n--->trans matrix:') - print(trans) - - print('\n--->trans_inv matrix:') - print(trans_inv) - - print('\n---> apply transform to uv') - print('\nxy_m = uv_augmented * trans') - uv_aug = np.hstack(( - uv, np.ones((uv.shape[0], 1)) - )) - xy_m = np.dot(uv_aug, trans) - print(xy_m) - - print('\nxy_m = tformfwd(trans, uv)') - xy_m = tformfwd(trans, uv) - print(xy_m) - - print('\n---> apply inverse transform to xy') - print('\nuv_m = xy_augmented * trans_inv') - xy_aug = np.hstack(( - xy, np.ones((xy.shape[0], 1)) - )) - uv_m = np.dot(xy_aug, trans_inv) - print(uv_m) - - print('\nuv_m = tformfwd(trans_inv, xy)') - uv_m = tformfwd(trans_inv, xy) - print(uv_m) - - uv_m = tforminv(trans, xy) - print('\nuv_m = tforminv(trans, xy)') - print(uv_m) diff --git a/utils/parser.py b/utils/parser.py deleted file mode 100644 index d44a82ccea1dccaafb822c0231f1504366bbfa5f..0000000000000000000000000000000000000000 --- a/utils/parser.py +++ /dev/null @@ -1,452 +0,0 @@ -import argparse -import os - -def parse_args(input_args=None): - parser = argparse.ArgumentParser(description="Train Consistency Encoder.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - required=True, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--pretrained_vae_model_name_or_path", - type=str, - default=None, - help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--variant", - type=str, - default=None, - help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", - ) - - # parser.add_argument( - # "--instance_data_dir", - # type=str, - # required=True, - # help=("A folder containing the training data. "), - # ) - - parser.add_argument( - "--data_config_path", - type=str, - required=True, - help=("A folder containing the training data. "), - ) - - parser.add_argument( - "--cache_dir", - type=str, - default=None, - help="The directory where the downloaded models and datasets will be stored.", - ) - - parser.add_argument( - "--image_column", - type=str, - default="image", - help="The column of the dataset containing the target image. By " - "default, the standard Image Dataset maps out 'file_name' " - "to 'image'.", - ) - parser.add_argument( - "--caption_column", - type=str, - default=None, - help="The column of the dataset containing the instance prompt for each image", - ) - - parser.add_argument("--repeats", type=int, default=1, help="How many times to repeat the training data.") - - parser.add_argument( - "--instance_prompt", - type=str, - default=None, - required=True, - help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'", - ) - - parser.add_argument( - "--validation_prompt", - type=str, - default=None, - help="A prompt that is used during validation to verify that the model is learning.", - ) - parser.add_argument( - "--num_train_vis_images", - type=int, - default=2, - help="Number of images that should be generated during validation with `validation_prompt`.", - ) - parser.add_argument( - "--num_validation_images", - type=int, - default=2, - help="Number of images that should be generated during validation with `validation_prompt`.", - ) - - parser.add_argument( - "--validation_vis_steps", - type=int, - default=500, - help=( - "Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt" - " `args.validation_prompt` multiple times: `args.num_validation_images`." - ), - ) - - parser.add_argument( - "--train_vis_steps", - type=int, - default=500, - help=( - "Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt" - " `args.validation_prompt` multiple times: `args.num_validation_images`." - ), - ) - - parser.add_argument( - "--vis_lcm", - type=bool, - default=True, - help=( - "Also log results of LCM inference", - ), - ) - - parser.add_argument( - "--output_dir", - type=str, - default="lora-dreambooth-model", - help="The output directory where the model predictions and checkpoints will be written.", - ) - - parser.add_argument("--save_only_encoder", action="store_true", help="Only save the encoder and not the full accelerator state") - - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - - parser.add_argument("--freeze_encoder_unet", action="store_true", help="Don't train encoder unet") - parser.add_argument("--predict_word_embedding", action="store_true", help="Predict word embeddings in addition to KV features") - parser.add_argument("--ip_adapter_feature_extractor_path", type=str, help="Path to pre-trained feature extractor for IP-adapter") - parser.add_argument("--ip_adapter_model_path", type=str, help="Path to pre-trained IP-adapter.") - parser.add_argument("--ip_adapter_tokens", type=int, default=16, help="Number of tokens to use in IP-adapter cross attention mechanism") - parser.add_argument("--optimize_adapter", action="store_true", help="Optimize IP-adapter parameters (projector + cross-attention layers)") - parser.add_argument("--adapter_attention_scale", type=float, default=1.0, help="Relative strength of the adapter cross attention layers") - parser.add_argument("--adapter_lr", type=float, help="Learning rate for the adapter parameters. Defaults to the global LR if not provided") - - parser.add_argument("--noisy_encoder_input", action="store_true", help="Noise the encoder input to the same step as the decoder?") - - # related to CFG: - parser.add_argument("--adapter_drop_chance", type=float, default=0.0, help="Chance to drop adapter condition input during training") - parser.add_argument("--text_drop_chance", type=float, default=0.0, help="Chance to drop text condition during training") - parser.add_argument("--kv_drop_chance", type=float, default=0.0, help="Chance to drop KV condition during training") - - - - parser.add_argument( - "--resolution", - type=int, - default=1024, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - - parser.add_argument( - "--crops_coords_top_left_h", - type=int, - default=0, - help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."), - ) - - parser.add_argument( - "--crops_coords_top_left_w", - type=int, - default=0, - help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."), - ) - - parser.add_argument( - "--center_crop", - default=False, - action="store_true", - help=( - "Whether to center crop the input images to the resolution. If not set, the images will be randomly" - " cropped. The images will be resized to the resolution first before cropping." - ), - ) - - parser.add_argument( - "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." - ) - - parser.add_argument("--num_train_epochs", type=int, default=1) - - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - - parser.add_argument( - "--checkpointing_steps", - type=int, - default=500, - help=( - "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" - " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" - " training using `--resume_from_checkpoint`." - ), - ) - - parser.add_argument( - "--checkpoints_total_limit", - type=int, - default=5, - help=("Max number of checkpoints to store."), - ) - - parser.add_argument( - "--resume_from_checkpoint", - type=str, - default=None, - help=( - "Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), - ) - - parser.add_argument("--max_timesteps_for_x0_loss", type=int, default=1001) - - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - - parser.add_argument( - "--learning_rate", - type=float, - default=1e-4, - help="Initial learning rate (after the potential warmup period) to use.", - ) - - parser.add_argument( - "--scale_lr", - action="store_true", - default=False, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - - parser.add_argument( - "--snr_gamma", - type=float, - default=None, - help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " - "More details here: https://arxiv.org/abs/2303.09556.", - ) - - parser.add_argument( - "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." - ) - - parser.add_argument( - "--lr_num_cycles", - type=int, - default=1, - help="Number of hard resets of the lr in cosine_with_restarts scheduler.", - ) - - parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.") - - parser.add_argument( - "--dataloader_num_workers", - type=int, - default=0, - help=( - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." - ), - ) - - parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params") - - parser.add_argument( - "--adam_epsilon", - type=float, - default=1e-08, - help="Epsilon value for the Adam optimizer and Prodigy optimizers.", - ) - - parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") - - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--allow_tf32", - action="store_true", - help=( - "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" - " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" - ), - ) - - parser.add_argument( - "--report_to", - type=str, - default="wandb", - help=( - 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' - ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' - ), - ) - - parser.add_argument( - "--mixed_precision", - type=str, - default=None, - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), - ) - - parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") - - parser.add_argument( - "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." - ) - - parser.add_argument( - "--rank", - type=int, - default=4, - help=("The dimension of the LoRA update matrices."), - ) - - parser.add_argument( - "--pretrained_lcm_lora_path", - type=str, - default="latent-consistency/lcm-lora-sdxl", - help=("Path for lcm lora pretrained"), - ) - - parser.add_argument( - "--losses_config_path", - type=str, - required=True, - help=("A yaml file containing losses to use and their weights."), - ) - - parser.add_argument( - "--lcm_every_k_steps", - type=int, - default=-1, - help="How often to run lcm. If -1, lcm is not run." - ) - - parser.add_argument( - "--lcm_batch_size", - type=int, - default=1, - help="Batch size for lcm." - ) - parser.add_argument( - "--lcm_max_timestep", - type=int, - default=1000, - help="Max timestep to use with LCM." - ) - - parser.add_argument( - "--lcm_sample_scale_every_k_steps", - type=int, - default=-1, - help="How often to change lcm scale. If -1, scale is fixed at 1." - ) - - parser.add_argument( - "--lcm_min_scale", - type=float, - default=0.1, - help="When sampling lcm scale, the minimum scale to use." - ) - - parser.add_argument( - "--scale_lcm_by_max_step", - action="store_true", - help="scale LCM lora alpha linearly by the maximal timestep sampled that iteration" - ) - - parser.add_argument( - "--lcm_sample_full_lcm_prob", - type=float, - default=0.2, - help="When sampling lcm scale, the probability of using full lcm (scale of 1)." - ) - - parser.add_argument( - "--run_on_cpu", - action="store_true", - help="whether to run on cpu or not" - ) - - parser.add_argument( - "--experiment_name", - type=str, - help=("A short description of the experiment to add to the wand run log. "), - ) - parser.add_argument("--encoder_lora_rank", type=int, default=0, help="Rank of Lora in unet encoder. 0 means no lora") - - parser.add_argument("--kvcopy_lora_rank", type=int, default=0, help="Rank of lora in the kvcopy modules. 0 means no lora") - - - if input_args is not None: - args = parser.parse_args(input_args) - else: - args = parser.parse_args() - - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - args.optimizer = "AdamW" - - return args \ No newline at end of file diff --git a/utils/text_utils.py b/utils/text_utils.py deleted file mode 100644 index 0490655d7bbc0378412d16a8131d11dfe4d930cc..0000000000000000000000000000000000000000 --- a/utils/text_utils.py +++ /dev/null @@ -1,76 +0,0 @@ -import torch - -def tokenize_prompt(tokenizer, prompt): - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - return text_input_ids - - -# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt -def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None): - prompt_embeds_list = [] - - for i, text_encoder in enumerate(text_encoders): - if tokenizers is not None: - tokenizer = tokenizers[i] - text_input_ids = tokenize_prompt(tokenizer, prompt) - else: - assert text_input_ids_list is not None - text_input_ids = text_input_ids_list[i] - - prompt_embeds = text_encoder( - text_input_ids.to(text_encoder.device), - output_hidden_states=True, - ) - - # We are only ALWAYS interested in the pooled output of the final text encoder - pooled_prompt_embeds = prompt_embeds[0] - prompt_embeds = prompt_embeds.hidden_states[-2] - bs_embed, seq_len, _ = prompt_embeds.shape - prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1) - prompt_embeds_list.append(prompt_embeds) - - prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) - pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1) - return prompt_embeds, pooled_prompt_embeds - - -def add_tokens(tokenizers, tokens, text_encoders): - new_token_indices = {} - for idx, tokenizer in enumerate(tokenizers): - for token in tokens: - num_added_tokens = tokenizer.add_tokens(token) - if num_added_tokens == 0: - raise ValueError( - f"The tokenizer already contains the token {token}. Please pass a different" - " `placeholder_token` that is not already in the tokenizer." - ) - - new_token_indices[f"{idx}_{token}"] = num_added_tokens - # resize embedding layers to avoid crash. We will never actually use these. - text_encoders[idx].resize_token_embeddings(len(tokenizer), pad_to_multiple_of=128) - - return new_token_indices - - -def patch_embedding_forward(embedding_layer, new_tokens, new_embeddings): - - def new_forward(input): - embedded_text = torch.nn.functional.embedding( - input, embedding_layer.weight, embedding_layer.padding_idx, embedding_layer.max_norm, - embedding_layer.norm_type, embedding_layer.scale_grad_by_freq, embedding_layer.sparse) - - replace_indices = (input == new_tokens) - - if torch.count_nonzero(replace_indices) > 0: - embedded_text[replace_indices] = new_embeddings - - return embedded_text - - embedding_layer.forward = new_forward \ No newline at end of file diff --git a/utils/train_utils.py b/utils/train_utils.py deleted file mode 100644 index 734d794a68767ccb95858845a1862a102b4ecf42..0000000000000000000000000000000000000000 --- a/utils/train_utils.py +++ /dev/null @@ -1,360 +0,0 @@ -import argparse -import contextlib -import time -import gc -import logging -import math -import os -import random -import jsonlines -import functools -import shutil -import pyrallis -import itertools -from pathlib import Path -from collections import namedtuple, OrderedDict - -import accelerate -import numpy as np -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -import transformers -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed -from datasets import load_dataset -from packaging import version -from PIL import Image -from losses.losses import * -from torchvision import transforms -from torchvision.transforms.functional import crop -from tqdm.auto import tqdm - - -def import_model_class_from_model_name_or_path( - pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder" -): - from transformers import PretrainedConfig - text_encoder_config = PretrainedConfig.from_pretrained( - pretrained_model_name_or_path, subfolder=subfolder, revision=revision - ) - model_class = text_encoder_config.architectures[0] - - if model_class == "CLIPTextModel": - from transformers import CLIPTextModel - - return CLIPTextModel - elif model_class == "CLIPTextModelWithProjection": - from transformers import CLIPTextModelWithProjection - - return CLIPTextModelWithProjection - else: - raise ValueError(f"{model_class} is not supported.") - -def get_train_dataset(dataset_name, dataset_dir, args, accelerator): - # Get the datasets: you can either provide your own training and evaluation files (see below) - # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). - - # In distributed training, the load_dataset function guarantees that only one local process can concurrently - # download the dataset. - dataset = load_dataset( - dataset_name, - data_dir=dataset_dir, - cache_dir=os.path.join(dataset_dir, ".cache"), - num_proc=4, - split="train", - ) - - # Preprocessing the datasets. - # We need to tokenize inputs and targets. - column_names = dataset.column_names - - # 6. Get the column names for input/target. - if args.image_column is None: - args.image_column = column_names[0] - logger.info(f"image column defaulting to {column_names[0]}") - else: - image_column = args.image_column - if image_column not in column_names: - logger.warning(f"dataset {dataset_name} has no column {image_column}") - - if args.caption_column is None: - args.caption_column = column_names[1] - logger.info(f"caption column defaulting to {column_names[1]}") - else: - caption_column = args.caption_column - if caption_column not in column_names: - logger.warning(f"dataset {dataset_name} has no column {caption_column}") - - if args.conditioning_image_column is None: - args.conditioning_image_column = column_names[2] - logger.info(f"conditioning image column defaulting to {column_names[2]}") - else: - conditioning_image_column = args.conditioning_image_column - if conditioning_image_column not in column_names: - logger.warning(f"dataset {dataset_name} has no column {conditioning_image_column}") - - with accelerator.main_process_first(): - train_dataset = dataset.shuffle(seed=args.seed) - if args.max_train_samples is not None: - train_dataset = train_dataset.select(range(args.max_train_samples)) - return train_dataset - -def prepare_train_dataset(dataset, accelerator, deg_pipeline, centralize=False): - - # Data augmentations. - hflip = deg_pipeline.augment_opt['use_hflip'] and random.random() < 0.5 - vflip = deg_pipeline.augment_opt['use_rot'] and random.random() < 0.5 - rot90 = deg_pipeline.augment_opt['use_rot'] and random.random() < 0.5 - augment_transforms = [] - if hflip: - augment_transforms.append(transforms.RandomHorizontalFlip(p=1.0)) - if vflip: - augment_transforms.append(transforms.RandomVerticalFlip(p=1.0)) - if rot90: - # FIXME - augment_transforms.append(transforms.RandomRotation(degrees=(90,90))) - torch_transforms=[transforms.ToTensor()] - if centralize: - # to [-1, 1] - torch_transforms.append(transforms.Normalize([0.5], [0.5])) - - training_size = deg_pipeline.degrade_opt['gt_size'] - image_transforms = transforms.Compose(augment_transforms) - train_transforms = transforms.Compose(torch_transforms) - train_resize = transforms.Resize(training_size, interpolation=transforms.InterpolationMode.BILINEAR) - train_crop = transforms.RandomCrop(training_size) - - def preprocess_train(examples): - raw_images = [] - for img_data in examples[args.image_column]: - raw_images.append(Image.open(img_data).convert("RGB")) - - # Image stack. - images = [] - original_sizes = [] - crop_top_lefts = [] - # Degradation kernels stack. - kernel = [] - kernel2 = [] - sinc_kernel = [] - - for raw_image in raw_images: - raw_image = image_transforms(raw_image) - original_sizes.append((raw_image.height, raw_image.width)) - - # Resize smaller edge. - raw_image = train_resize(raw_image) - # Crop to training size. - y1, x1, h, w = train_crop.get_params(raw_image, (training_size, training_size)) - raw_image = crop(raw_image, y1, x1, h, w) - crop_top_left = (y1, x1) - crop_top_lefts.append(crop_top_left) - image = train_transforms(raw_image) - - images.append(image) - k, k2, sk = deg_pipeline.get_kernel() - kernel.append(k) - kernel2.append(k2) - sinc_kernel.append(sk) - - examples["images"] = images - examples["original_sizes"] = original_sizes - examples["crop_top_lefts"] = crop_top_lefts - examples["kernel"] = kernel - examples["kernel2"] = kernel2 - examples["sinc_kernel"] = sinc_kernel - - return examples - - with accelerator.main_process_first(): - dataset = dataset.with_transform(preprocess_train) - - return dataset - -def collate_fn(examples): - images = torch.stack([example["images"] for example in examples]) - images = images.to(memory_format=torch.contiguous_format).float() - kernel = torch.stack([example["kernel"] for example in examples]) - kernel = kernel.to(memory_format=torch.contiguous_format).float() - kernel2 = torch.stack([example["kernel2"] for example in examples]) - kernel2 = kernel2.to(memory_format=torch.contiguous_format).float() - sinc_kernel = torch.stack([example["sinc_kernel"] for example in examples]) - sinc_kernel = sinc_kernel.to(memory_format=torch.contiguous_format).float() - original_sizes = [example["original_sizes"] for example in examples] - crop_top_lefts = [example["crop_top_lefts"] for example in examples] - - prompts = [] - for example in examples: - prompts.append(example[args.caption_column]) if args.caption_column in example else prompts.append("") - - return { - "images": images, - "text": prompts, - "kernel": kernel, - "kernel2": kernel2, - "sinc_kernel": sinc_kernel, - "original_sizes": original_sizes, - "crop_top_lefts": crop_top_lefts, - } - -def encode_prompt(prompt_batch, text_encoders, tokenizers, is_train=True): - prompt_embeds_list = [] - - captions = [] - for caption in prompt_batch: - if isinstance(caption, str): - captions.append(caption) - elif isinstance(caption, (list, np.ndarray)): - # take a random caption if there are multiple - captions.append(random.choice(caption) if is_train else caption[0]) - - with torch.no_grad(): - for tokenizer, text_encoder in zip(tokenizers, text_encoders): - text_inputs = tokenizer( - captions, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - prompt_embeds = text_encoder( - text_input_ids.to(text_encoder.device), - output_hidden_states=True, - ) - - # We are only ALWAYS interested in the pooled output of the final text encoder - pooled_prompt_embeds = prompt_embeds[0] - prompt_embeds = prompt_embeds.hidden_states[-2] - bs_embed, seq_len, _ = prompt_embeds.shape - prompt_embeds_list.append(prompt_embeds) - - prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) - prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1) - pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1) - return prompt_embeds, pooled_prompt_embeds - -def importance_sampling_fn(t, max_t, alpha): - """Importance Sampling Function f(t)""" - return 1 / max_t * (1 - alpha * np.cos(np.pi * t / max_t)) - -def extract_into_tensor(a, t, x_shape): - b, *_ = t.shape - out = a.gather(-1, t) - return out.reshape(b, *((1,) * (len(x_shape) - 1))) - -def tensor_to_pil(images): - """ - Convert image tensor or a batch of image tensors to PIL image(s). - """ - images = (images + 1) / 2 - images_np = images.detach().cpu().numpy() - if images_np.ndim == 4: - images_np = np.transpose(images_np, (0, 2, 3, 1)) - elif images_np.ndim == 3: - images_np = np.transpose(images_np, (1, 2, 0)) - images_np = images_np[None, ...] - images_np = (images_np * 255).round().astype("uint8") - if images_np.shape[-1] == 1: - # special case for grayscale (single channel) images - pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images_np] - else: - pil_images = [Image.fromarray(image[:, :, :3]) for image in images_np] - - return pil_images - -def save_np_to_image(img_np, save_dir): - img_np = np.transpose(img_np, (0, 2, 3, 1)) - img_np = (img_np * 255).astype(np.uint8) - img_np = Image.fromarray(img_np[0]) - img_np.save(save_dir) - - -def seperate_SFT_params_from_unet(unet): - params = [] - non_params = [] - for name, param in unet.named_parameters(): - if "SFT" in name: - params.append(param) - else: - non_params.append(param) - return params, non_params - - -def seperate_lora_params_from_unet(unet): - keys = [] - frozen_keys = [] - for name, param in unet.named_parameters(): - if "lora" in name: - keys.append(param) - else: - frozen_keys.append(param) - return keys, frozen_keys - - -def seperate_ip_params_from_unet(unet): - ip_params = [] - non_ip_params = [] - for name, param in unet.named_parameters(): - if "encoder_hid_proj." in name or "_ip." in name: - ip_params.append(param) - elif "attn" in name and "processor" in name: - if "ip" in name or "ln" in name: - ip_params.append(param) - else: - non_ip_params.append(param) - return ip_params, non_ip_params - - -def seperate_ref_params_from_unet(unet): - ip_params = [] - non_ip_params = [] - for name, param in unet.named_parameters(): - if "encoder_hid_proj." in name or "_ip." in name: - ip_params.append(param) - elif "attn" in name and "processor" in name: - if "ip" in name or "ln" in name: - ip_params.append(param) - elif "extract" in name: - ip_params.append(param) - else: - non_ip_params.append(param) - return ip_params, non_ip_params - - -def seperate_ip_modules_from_unet(unet): - ip_modules = [] - non_ip_modules = [] - for name, module in unet.named_modules(): - if "encoder_hid_proj" in name or "attn2.processor" in name: - ip_modules.append(module) - else: - non_ip_modules.append(module) - return ip_modules, non_ip_modules - - -def seperate_SFT_keys_from_unet(unet): - keys = [] - non_keys = [] - for name, param in unet.named_parameters(): - if "SFT" in name: - keys.append(name) - else: - non_keys.append(name) - return keys, non_keys - - -def seperate_ip_keys_from_unet(unet): - keys = [] - non_keys = [] - for name, param in unet.named_parameters(): - if "encoder_hid_proj." in name or "_ip." in name: - keys.append(name) - elif "attn" in name and "processor" in name: - if "ip" in name or "ln" in name: - keys.append(name) - else: - non_keys.append(name) - return keys, non_keys \ No newline at end of file diff --git a/utils/utils.py b/utils/utils.py deleted file mode 100644 index 6623af249fe031e8fa0b75b5950dcf1b9a190e16..0000000000000000000000000000000000000000 --- a/utils/utils.py +++ /dev/null @@ -1,60 +0,0 @@ -import torch -import numpy as np -from einops import rearrange -from kornia.geometry.transform.crop2d import warp_affine - -from utils.matlab_cp2tform import get_similarity_transform_for_cv2 -from torchvision.transforms import Pad - -REFERNCE_FACIAL_POINTS_RELATIVE = np.array([[38.29459953, 51.69630051], - [72.53179932, 51.50139999], - [56.02519989, 71.73660278], - [41.54930115, 92.3655014], - [70.72990036, 92.20410156] - ]) / 112 # Original points are 112 * 96 added 8 to the x axis to make it 112 * 112 - - -def verify_load(missing_keys, unexpected_keys): - if len(unexpected_keys) > 0: - raise RuntimeError(f"Found unexpected keys in state dict while loading the encoder:\n{unexpected_keys}") - - filtered_missing = [key for key in missing_keys if not "extract_kv" in key] - if len(filtered_missing) > 0: - raise RuntimeError(f"Missing keys in state dict while loading the encoder:\n{filtered_missing}") - - -@torch.no_grad() -def detect_face(images: torch.Tensor, mtcnn: torch.nn.Module) -> torch.Tensor: - """ - Detect faces in the images using MTCNN. If no face is detected, use the whole image. - """ - images = rearrange(images, "b c h w -> b h w c") - if images.dtype != torch.uint8: - images = ((images * 0.5 + 0.5) * 255).type(torch.uint8) # Unnormalize - - _, _, landmarks = mtcnn(images, landmarks=True) - - return landmarks - - -def extract_faces_and_landmarks(images: torch.Tensor, output_size=112, mtcnn: torch.nn.Module = None, refernce_points=REFERNCE_FACIAL_POINTS_RELATIVE): - """ - detect faces in the images and crop them (in a differentiable way) to 112x112 using MTCNN. - """ - images = Pad(200)(images) - landmarks_batched = detect_face(images, mtcnn=mtcnn) - affine_transformations = [] - invalid_indices = [] - for i, landmarks in enumerate(landmarks_batched): - if landmarks is None: - invalid_indices.append(i) - affine_transformations.append(np.eye(2, 3).astype(np.float32)) - else: - affine_transformations.append(get_similarity_transform_for_cv2(landmarks[0].astype(np.float32), - refernce_points.astype(np.float32) * output_size)) - affine_transformations = torch.from_numpy(np.stack(affine_transformations).astype(np.float32)).to(device=images.device, dtype=torch.float32) - - invalid_indices = torch.tensor(invalid_indices).to(device=images.device) - - fp_images = images.to(torch.float32) - return warp_affine(fp_images, affine_transformations, dsize=(output_size, output_size)).to(dtype=images.dtype), invalid_indices \ No newline at end of file diff --git a/utils/vis_utils.py b/utils/vis_utils.py deleted file mode 100644 index 25335b1bb28b25369989987875009c16ad138a16..0000000000000000000000000000000000000000 --- a/utils/vis_utils.py +++ /dev/null @@ -1,58 +0,0 @@ -import textwrap -from typing import List, Tuple, Optional - -import numpy as np -from PIL import Image, ImageDraw, ImageFont - -LINE_WIDTH = 20 - - -def add_text_to_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0), - min_lines: Optional[int] = None, add_below: bool = True): - import textwrap - lines = textwrap.wrap(text, width=LINE_WIDTH) - if min_lines is not None and len(lines) < min_lines: - if add_below: - lines += [''] * (min_lines - len(lines)) - else: - lines = [''] * (min_lines - len(lines)) + lines - h, w, c = image.shape - offset = int(h * .12) - img = np.ones((h + offset * len(lines), w, c), dtype=np.uint8) * 255 - font_size = int(offset * .8) - - try: - font = ImageFont.truetype("assets/OpenSans-Regular.ttf", font_size) - textsize = font.getbbox(text) - y_offset = (offset - textsize[3]) // 2 - except: - font = ImageFont.load_default() - y_offset = offset // 2 - - if add_below: - img[:h] = image - else: - img[-h:] = image - img = Image.fromarray(img) - draw = ImageDraw.Draw(img) - for i, line in enumerate(lines): - line_size = font.getbbox(line) - text_x = (w - line_size[2]) // 2 - if add_below: - draw.text((text_x, h + y_offset + offset * i), line, font=font, fill=text_color) - else: - draw.text((text_x, 0 + y_offset + offset * i), line, font=font, fill=text_color) - return np.array(img) - - -def create_table_plot(titles: List[str], images: List[Image.Image], captions: List[str]) -> Image.Image: - title_max_lines = np.max([len(textwrap.wrap(text, width=LINE_WIDTH)) for text in titles]) - caption_max_lines = np.max([len(textwrap.wrap(text, width=LINE_WIDTH)) for text in captions]) - out_images = [] - for i in range(len(images)): - im = np.array(images[i]) - im = add_text_to_image(im, titles[i], add_below=False, min_lines=title_max_lines) - im = add_text_to_image(im, captions[i], add_below=True, min_lines=caption_max_lines) - out_images.append(im) - image = Image.fromarray(np.concatenate(out_images, axis=1)) - return image