diff --git a/.gitignore b/.gitignore index 09b9cf9f386152c42bba2b3b8b1d76ab3babcc8a..fd632d7dab127b975d5a68e22c1d1bc9d402248f 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,4 @@ results *.ckpt *.pt *.pth +checkpoints \ No newline at end of file diff --git a/README.md b/README.md index ed3a708e1c321047b686a18c998f142667241776..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644 --- a/README.md +++ b/README.md @@ -1,13 +0,0 @@ ---- -title: VideoCrafter -emoji: 🌍 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co./docs/hub/spaces-config-reference diff --git a/app.py b/app.py index 59a7065a6a22ebe38a24d9c54d3c9f1128dde310..28261b6f35bd3c767c89f56a9126471149b05164 100644 --- a/app.py +++ b/app.py @@ -1,30 +1,30 @@ import os import sys import gradio as gr -# from demo_test import Text2Video, VideoControl -from videocrafter_test import Text2Video -from videocontrol_test import VideoControl +# from demo_test import Text2Video, Image2Video +from t2v_test import Text2Video +from i2v_test import Image2Video sys.path.insert(1, os.path.join(sys.path[0], 'lvdm')) t2v_examples = [ - ['an elephant is walking under the sea, 4K, high definition',50,'origin',1,15,1,], - ['an astronaut riding a horse in outer space',25,'origin',1,15,1,], - ['a monkey is playing a piano',25,'vangogh',1,15,1,], - ['A fire is burning on a candle',25,'frozen',1,15,1,], - ['a horse is drinking in the river',25,'yourname',1,15,1,], - ['Robot dancing in times square',25,'coco',1,15,1,], + ['an elephant is walking under the sea, 4K, high definition',50, 12,1, 16], + ['an astronaut riding a horse in outer space',25,12,1,16], + ['a monkey is playing a piano',25,12,1,16], + ['A fire is burning on a candle',25,12,1,16], + ['a horse is drinking in the river',25,12,1,16], + ['Robot dancing in times square',25,12,1,16], ] -control_examples = [ - ['input/flamingo.mp4', 'An ostrich walking in the desert, photorealistic, 4k', 0, 50, 15, 1, 16, 256] +i2v_examples = [ + ['prompts/i2v_prompts/horse.png', 'horses are walking on the grassland', 50, 12, 1, 16] ] def videocrafter_demo(result_dir='./tmp/'): text2video = Text2Video(result_dir) - videocontrol = VideoControl(result_dir) + image2video = Image2Video(result_dir) with gr.Blocks(analytics_enabled=False) as videocrafter_iface: - gr.Markdown("

VideoCrafter: A Toolkit for Text-to-Video Generation and Editing

\ - Github
") + gr.Markdown("

VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

\ + Github
") gr.Markdown(" You may duplicate the space and upgrade to GPU in settings for better performance and faster inference without waiting in the queue. Duplicate Space ") #######t2v####### @@ -33,65 +33,55 @@ def videocrafter_demo(result_dir='./tmp/'): with gr.Row().style(equal_height=False): with gr.Column(): input_text = gr.Text(label='Prompts') - model_choices=['origin','vangogh','frozen','yourname', 'coco'] - with gr.Row(): - model_index = gr.Dropdown(label='Models', elem_id=f"model", choices=model_choices, value=model_choices[0], type="index",interactive=True) with gr.Row(): steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id=f"steps", label="Sampling steps", value=50) eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="eta") with gr.Row(): - lora_scale = gr.Slider(minimum=0.0, maximum=2.0, step=0.1, label='Lora Scale', value=1.0, elem_id="lora_scale") - cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=15.0, elem_id="cfg_scale") + cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=12.0, elem_id="cfg_scale") + fps = gr.Slider(minimum=4, maximum=32, step=1, label='fps', value=16, elem_id="fps") send_btn = gr.Button("Send") with gr.Tab(label='result'): - output_video_1 = gr.Video().style(width=384) + output_video_1 = gr.Video().style(width=320) gr.Examples(examples=t2v_examples, - inputs=[input_text,steps,model_index,eta,cfg_scale,lora_scale], + inputs=[input_text,steps,cfg_scale,eta], outputs=[output_video_1], fn=text2video.get_prompt, cache_examples=False) #cache_examples=os.getenv('SYSTEM') == 'spaces') send_btn.click( fn=text2video.get_prompt, - inputs=[input_text,steps,model_index,eta,cfg_scale,lora_scale,], + inputs=[input_text,steps,cfg_scale,eta,fps], outputs=[output_video_1], ) - #######videocontrol###### - with gr.Tab(label='VideoControl'): + #######image2video###### + with gr.Tab(label='Image2Video'): with gr.Column(): with gr.Row(): - # with gr.Tab(label='input'): with gr.Column(): with gr.Row(): - vc_input_video = gr.Video(label="Input Video").style(width=256) - vc_origin_video = gr.Video(label='Center-cropped Video').style(width=256) - with gr.Row(): - vc_input_text = gr.Text(label='Prompts') + i2v_input_image = gr.Image(label="Input Image").style(width=256) with gr.Row(): - vc_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="vc_eta") - vc_cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=15.0, elem_id="vc_cfg_scale") + i2v_input_text = gr.Text(label='Prompts') with gr.Row(): - vc_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="vc_steps", label="Sampling steps", value=50) - frame_stride = gr.Slider(minimum=0 , maximum=100, step=1, label='Frame Stride', value=0, elem_id="vc_frame_stride") + i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta") + i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=12.0, elem_id="i2v_cfg_scale") with gr.Row(): - resolution = gr.Slider(minimum=128 , maximum=512, step=8, label='Long Side Resolution', value=256, elem_id="vc_resolution") - video_frames = gr.Slider(minimum=8 , maximum=64, step=1, label='Video Frame Num', value=16, elem_id="vc_video_frames") - vc_end_btn = gr.Button("Send") + i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50) + i2v_fps = gr.Slider(minimum=4, maximum=32, step=1, elem_id="i2v_fps", label="Generative fps", value=16) + i2v_end_btn = gr.Button("Send") with gr.Tab(label='Result'): - vc_output_info = gr.Text(label='Info') with gr.Row(): - vc_depth_video = gr.Video(label="Depth Video").style(width=256) - vc_output_video = gr.Video(label="Generated Video").style(width=256) + i2v_output_video = gr.Video(label="Generated Video").style(width=320) - gr.Examples(examples=control_examples, - inputs=[vc_input_video, vc_input_text, frame_stride, vc_steps, vc_cfg_scale, vc_eta, video_frames, resolution], - outputs=[vc_output_info, vc_origin_video, vc_depth_video, vc_output_video], - fn = videocontrol.get_video, + gr.Examples(examples=i2v_examples, + inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_fps], + outputs=[i2v_output_video], + fn = image2video.get_image, cache_examples=os.getenv('SYSTEM') == 'spaces', ) - vc_end_btn.click(inputs=[vc_input_video, vc_input_text, frame_stride, vc_steps, vc_cfg_scale, vc_eta, video_frames, resolution], - outputs=[vc_output_info, vc_origin_video, vc_depth_video, vc_output_video], - fn = videocontrol.get_video + i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_fps], + outputs=[i2v_output_video], + fn = image2video.get_image ) return videocrafter_iface diff --git a/models/adapter_t2v_depth/model_config.yaml b/configs/inference_i2v_512_v1.0.yaml similarity index 57% rename from models/adapter_t2v_depth/model_config.yaml rename to configs/inference_i2v_512_v1.0.yaml index 5ad2f7fe9b19c184c3350e9116a412890f1194eb..7d490079dd655bd3e9fa7656532b2417085370e7 100644 --- a/models/adapter_t2v_depth/model_config.yaml +++ b/configs/inference_i2v_512_v1.0.yaml @@ -1,27 +1,28 @@ model: - target: lvdm.models.ddpm3d.T2VAdapterDepth + target: lvdm.models.ddpm3d.LatentVisualDiffusion params: linear_start: 0.00085 linear_end: 0.012 num_timesteps_cond: 1 - log_every_t: 200 timesteps: 1000 first_stage_key: video cond_stage_key: caption - image_size: - - 32 - - 32 - video_length: 16 - channels: 4 cond_stage_trainable: false conditioning_key: crossattn + image_size: + - 40 + - 64 + channels: 4 scale_by_std: false scale_factor: 0.18215 - + use_ema: false + uncond_type: empty_seq + use_scale: true + scale_b: 0.7 + finegrained: true unet_config: - target: lvdm.models.modules.openaimodel3d.UNetModel + target: lvdm.modules.networks.openaimodel3d.UNetModel params: - image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 @@ -35,16 +36,20 @@ model: - 2 - 4 - 4 - num_heads: 8 + num_head_channels: 64 transformer_depth: 1 - context_dim: 768 + context_dim: 1024 + use_linear: true use_checkpoint: true - legacy: false - kernel_size_t: 1 - padding_t: 0 + temporal_conv: true + temporal_attention: true + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: false + use_image_attention: true temporal_length: 16 - use_relative_position: true - + addition_attention: true + fps_cond: true first_stage_config: target: lvdm.models.autoencoder.AutoencoderKL params: @@ -53,7 +58,7 @@ model: ddconfig: double_z: true z_channels: 4 - resolution: 256 + resolution: 512 in_channels: 3 out_ch: 3 ch: 128 @@ -67,23 +72,12 @@ model: dropout: 0.0 lossconfig: target: torch.nn.Identity - cond_stage_config: - target: lvdm.models.modules.condition_modules.FrozenCLIPEmbedder - - depth_stage_config: - target: extralibs.midas.api.MiDaSInference + target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: penultimate + cond_img_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 params: - model_type: "dpt_hybrid" - model_path: models/adapter_t2v_depth/dpt_hybrid-midas.pt - - adapter_config: - target: lvdm.models.modules.adapter.Adapter - cond_name: depth - params: - cin: 64 - channels: [320, 640, 1280, 1280] - nums_rb: 2 - ksize: 1 - sk: True - use_conv: False \ No newline at end of file + freeze: true \ No newline at end of file diff --git a/models/base_t2v/model_config.yaml b/configs/inference_t2v_1024_v1.0.yaml similarity index 67% rename from models/base_t2v/model_config.yaml rename to configs/inference_t2v_1024_v1.0.yaml index c99497b560f49451f4dbe51aae8b793a8f12457a..4cb9af1562264f63e029ce536b16f03c30f0d279 100644 --- a/models/base_t2v/model_config.yaml +++ b/configs/inference_t2v_1024_v1.0.yaml @@ -4,24 +4,24 @@ model: linear_start: 0.00085 linear_end: 0.012 num_timesteps_cond: 1 - log_every_t: 200 timesteps: 1000 first_stage_key: video cond_stage_key: caption - image_size: - - 32 - - 32 - video_length: 16 - channels: 4 cond_stage_trainable: false conditioning_key: crossattn + image_size: + - 72 + - 128 + channels: 4 scale_by_std: false scale_factor: 0.18215 - + use_ema: false + uncond_type: empty_seq + use_scale: true + fix_scale_bug: true unet_config: - target: lvdm.models.modules.openaimodel3d.UNetModel + target: lvdm.modules.networks.openaimodel3d.UNetModel params: - image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 @@ -35,16 +35,19 @@ model: - 2 - 4 - 4 - num_heads: 8 + num_head_channels: 64 transformer_depth: 1 - context_dim: 768 + context_dim: 1024 + use_linear: true use_checkpoint: true - legacy: false - kernel_size_t: 1 - padding_t: 0 - temporal_length: 16 + temporal_conv: false + temporal_attention: true + temporal_selfatt_only: true use_relative_position: true - + use_causal_attention: false + temporal_length: 16 + addition_attention: true + fps_cond: true first_stage_config: target: lvdm.models.autoencoder.AutoencoderKL params: @@ -53,7 +56,7 @@ model: ddconfig: double_z: true z_channels: 4 - resolution: 256 + resolution: 512 in_channels: 3 out_ch: 3 ch: 128 @@ -67,6 +70,8 @@ model: dropout: 0.0 lossconfig: target: torch.nn.Identity - cond_stage_config: - target: lvdm.models.modules.condition_modules.FrozenCLIPEmbedder \ No newline at end of file + target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: penultimate diff --git a/configs/inference_t2v_512_v1.0.yaml b/configs/inference_t2v_512_v1.0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4a2e6c4e88ad32e439bb6b95b1a200d0ef104603 --- /dev/null +++ b/configs/inference_t2v_512_v1.0.yaml @@ -0,0 +1,77 @@ +model: + target: lvdm.models.ddpm3d.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: false + conditioning_key: crossattn + image_size: + - 40 + - 64 + channels: 4 + scale_by_std: false + scale_factor: 0.18215 + use_ema: false + uncond_type: empty_seq + use_scale: true + scale_b: 0.7 + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: true + temporal_conv: true + temporal_attention: true + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: false + temporal_length: 16 + addition_attention: true + fps_cond: true + first_stage_config: + target: lvdm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 512 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: penultimate diff --git a/demo_test.py b/demo_test.py index 3a33f32d9f2d7f835c3f4c3639d781653caf00f3..759039753e6c8caa3eb5507690e68a07e50023c7 100644 --- a/demo_test.py +++ b/demo_test.py @@ -2,15 +2,15 @@ class Text2Video(): def __init__(self, result_dir='./tmp/') -> None: pass - def get_prompt(self, input_text, steps=50, model_index=0, eta=1.0, cfg_scale=15.0, lora_scale=1.0): + def get_prompt(self, input_text, steps=50, cfg_scale=15.0, eta=1.0, fps=16): return '01.mp4' -class VideoControl: +class Image2Video: def __init__(self, result_dir='./tmp/') -> None: pass - def get_video(self, input_video, input_prompt, frame_stride=0, vc_steps=50, vc_cfg_scale=15.0, vc_eta=1.0, video_frames=16, resolution=256): + def get_image(self, input_image, input_prompt, i2v_steps=50, i2v_cfg_scale=15.0, i2v_eta=1.0, i2v_fps=16): - return 'su','01.mp4' + return '01.mp4' \ No newline at end of file diff --git a/extralibs/midas/__init__.py b/extralibs/midas/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/extralibs/midas/api.py b/extralibs/midas/api.py deleted file mode 100644 index c7178a0d1b5c01f2b0c91a34be833d7b34369dc1..0000000000000000000000000000000000000000 --- a/extralibs/midas/api.py +++ /dev/null @@ -1,171 +0,0 @@ -# based on https://github.com/isl-org/MiDaS - -import cv2 -import torch -import torch.nn as nn -from torchvision.transforms import Compose - -from extralibs.midas.midas.dpt_depth import DPTDepthModel -from extralibs.midas.midas.midas_net import MidasNet -from extralibs.midas.midas.midas_net_custom import MidasNet_small -from extralibs.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet - - -ISL_PATHS = { - "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt", - "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt", - "midas_v21": "", - "midas_v21_small": "", -} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def load_midas_transform(model_type): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load transform only - if model_type == "dpt_large": # DPT-Large - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - elif model_type == "midas_v21_small": - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - else: - assert False, f"model_type '{model_type}' not implemented, use: --model_type large" - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return transform - - -def load_model(model_type, model_path=None): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load network - if model_path is None: - model_path = ISL_PATHS[model_type] - if model_type == "dpt_large": # DPT-Large - model = DPTDepthModel( - path=model_path, - backbone="vitl16_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - model = DPTDepthModel( - path=model_path, - backbone="vitb_rn50_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - model = MidasNet(model_path, non_negative=True) - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - elif model_type == "midas_v21_small": - model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, - non_negative=True, blocks={'expand': True}) - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - else: - print(f"model_type '{model_type}' not implemented, use: --model_type large") - assert False - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return model.eval(), transform - - -class MiDaSInference(nn.Module): - MODEL_TYPES_TORCH_HUB = [ - "DPT_Large", - "DPT_Hybrid", - "MiDaS_small" - ] - MODEL_TYPES_ISL = [ - "dpt_large", - "dpt_hybrid", - "midas_v21", - "midas_v21_small", - ] - - def __init__(self, model_type, model_path): - super().__init__() - assert (model_type in self.MODEL_TYPES_ISL) - model, _ = load_model(model_type, model_path) - self.model = model - self.model.train = disabled_train - - def forward(self, x): - # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array - # NOTE: we expect that the correct transform has been called during dataloading. - with torch.no_grad(): - prediction = self.model(x) - prediction = torch.nn.functional.interpolate( - prediction.unsqueeze(1), - size=x.shape[2:], - mode="bicubic", - align_corners=False, - ) - assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3]) - return prediction - diff --git a/extralibs/midas/midas/__init__.py b/extralibs/midas/midas/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/extralibs/midas/midas/base_model.py b/extralibs/midas/midas/base_model.py deleted file mode 100644 index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000 --- a/extralibs/midas/midas/base_model.py +++ /dev/null @@ -1,16 +0,0 @@ -import torch - - -class BaseModel(torch.nn.Module): - def load(self, path): - """Load model from file. - - Args: - path (str): file path - """ - parameters = torch.load(path, map_location=torch.device('cpu')) - - if "optimizer" in parameters: - parameters = parameters["model"] - - self.load_state_dict(parameters) diff --git a/extralibs/midas/midas/blocks.py b/extralibs/midas/midas/blocks.py deleted file mode 100644 index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000 --- a/extralibs/midas/midas/blocks.py +++ /dev/null @@ -1,342 +0,0 @@ -import torch -import torch.nn as nn - -from .vit import ( - _make_pretrained_vitb_rn50_384, - _make_pretrained_vitl16_384, - _make_pretrained_vitb16_384, - forward_vit, -) - -def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): - if backbone == "vitl16_384": - pretrained = _make_pretrained_vitl16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [256, 512, 1024, 1024], features, groups=groups, expand=expand - ) # ViT-L/16 - 85.0% Top1 (backbone) - elif backbone == "vitb_rn50_384": - pretrained = _make_pretrained_vitb_rn50_384( - use_pretrained, - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) - scratch = _make_scratch( - [256, 512, 768, 768], features, groups=groups, expand=expand - ) # ViT-H/16 - 85.0% Top1 (backbone) - elif backbone == "vitb16_384": - pretrained = _make_pretrained_vitb16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [96, 192, 384, 768], features, groups=groups, expand=expand - ) # ViT-B/16 - 84.6% Top1 (backbone) - elif backbone == "resnext101_wsl": - pretrained = _make_pretrained_resnext101_wsl(use_pretrained) - scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 - elif backbone == "efficientnet_lite3": - pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) - scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 - else: - print(f"Backbone '{backbone}' not implemented") - assert False - - return pretrained, scratch - - -def _make_scratch(in_shape, out_shape, groups=1, expand=False): - scratch = nn.Module() - - out_shape1 = out_shape - out_shape2 = out_shape - out_shape3 = out_shape - out_shape4 = out_shape - if expand==True: - out_shape1 = out_shape - out_shape2 = out_shape*2 - out_shape3 = out_shape*4 - out_shape4 = out_shape*8 - - scratch.layer1_rn = nn.Conv2d( - in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer2_rn = nn.Conv2d( - in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer3_rn = nn.Conv2d( - in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer4_rn = nn.Conv2d( - in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - - return scratch - - -def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): - efficientnet = torch.hub.load( - "rwightman/gen-efficientnet-pytorch", - "tf_efficientnet_lite3", - pretrained=use_pretrained, - exportable=exportable - ) - return _make_efficientnet_backbone(efficientnet) - - -def _make_efficientnet_backbone(effnet): - pretrained = nn.Module() - - pretrained.layer1 = nn.Sequential( - effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] - ) - pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) - pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) - pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) - - return pretrained - - -def _make_resnet_backbone(resnet): - pretrained = nn.Module() - pretrained.layer1 = nn.Sequential( - resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 - ) - - pretrained.layer2 = resnet.layer2 - pretrained.layer3 = resnet.layer3 - pretrained.layer4 = resnet.layer4 - - return pretrained - - -def _make_pretrained_resnext101_wsl(use_pretrained): - resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") - return _make_resnet_backbone(resnet) - - - -class Interpolate(nn.Module): - """Interpolation module. - """ - - def __init__(self, scale_factor, mode, align_corners=False): - """Init. - - Args: - scale_factor (float): scaling - mode (str): interpolation mode - """ - super(Interpolate, self).__init__() - - self.interp = nn.functional.interpolate - self.scale_factor = scale_factor - self.mode = mode - self.align_corners = align_corners - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: interpolated data - """ - - x = self.interp( - x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners - ) - - return x - - -class ResidualConvUnit(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - out = self.relu(x) - out = self.conv1(out) - out = self.relu(out) - out = self.conv2(out) - - return out + x - - -class FeatureFusionBlock(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock, self).__init__() - - self.resConfUnit1 = ResidualConvUnit(features) - self.resConfUnit2 = ResidualConvUnit(features) - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - output += self.resConfUnit1(xs[1]) - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=True - ) - - return output - - - - -class ResidualConvUnit_custom(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features, activation, bn): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.bn = bn - - self.groups=1 - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - if self.bn==True: - self.bn1 = nn.BatchNorm2d(features) - self.bn2 = nn.BatchNorm2d(features) - - self.activation = activation - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - - out = self.activation(x) - out = self.conv1(out) - if self.bn==True: - out = self.bn1(out) - - out = self.activation(out) - out = self.conv2(out) - if self.bn==True: - out = self.bn2(out) - - if self.groups > 1: - out = self.conv_merge(out) - - return self.skip_add.add(out, x) - - # return out + x - - -class FeatureFusionBlock_custom(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock_custom, self).__init__() - - self.deconv = deconv - self.align_corners = align_corners - - self.groups=1 - - self.expand = expand - out_features = features - if self.expand==True: - out_features = features//2 - - self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) - - self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) - self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - res = self.resConfUnit1(xs[1]) - output = self.skip_add.add(output, res) - # output += res - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=self.align_corners - ) - - output = self.out_conv(output) - - return output - diff --git a/extralibs/midas/midas/dpt_depth.py b/extralibs/midas/midas/dpt_depth.py deleted file mode 100644 index 95bd762d4a46a29e090687f775322809b5a7b6c5..0000000000000000000000000000000000000000 --- a/extralibs/midas/midas/dpt_depth.py +++ /dev/null @@ -1,110 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .base_model import BaseModel -from .blocks import ( - FeatureFusionBlock, - FeatureFusionBlock_custom, - Interpolate, - _make_encoder, - forward_vit, -) - - -def _make_fusion_block(features, use_bn): - return FeatureFusionBlock_custom( - features, - nn.ReLU(False), - deconv=False, - bn=use_bn, - expand=False, - align_corners=True, - ) - - -class DPT(BaseModel): - def __init__( - self, - head, - features=256, - backbone="vitb_rn50_384", - readout="project", - channels_last=False, - use_bn=False, - ): - - super(DPT, self).__init__() - - self.channels_last = channels_last - - hooks = { - "vitb_rn50_384": [0, 1, 8, 11], - "vitb16_384": [2, 5, 8, 11], - "vitl16_384": [5, 11, 17, 23], - } - - # Instantiate backbone and reassemble blocks - self.pretrained, self.scratch = _make_encoder( - backbone, - features, - False, # Set to true of you want to train from scratch, uses ImageNet weights - groups=1, - expand=False, - exportable=False, - hooks=hooks[backbone], - use_readout=readout, - ) - - self.scratch.refinenet1 = _make_fusion_block(features, use_bn) - self.scratch.refinenet2 = _make_fusion_block(features, use_bn) - self.scratch.refinenet3 = _make_fusion_block(features, use_bn) - self.scratch.refinenet4 = _make_fusion_block(features, use_bn) - - self.scratch.output_conv = head - - - def forward(self, x): - if self.channels_last == True: - x.contiguous(memory_format=torch.channels_last) - - layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return out - - -class DPTDepthModel(DPT): - def __init__(self, path=None, non_negative=True, **kwargs): - features = kwargs["features"] if "features" in kwargs else 256 - - head = nn.Sequential( - nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), - Interpolate(scale_factor=2, mode="bilinear", align_corners=True), - nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), - nn.ReLU(True), - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - nn.Identity(), - ) - - super().__init__(head, **kwargs) - - if path is not None: - self.load(path) - print("Midas depth estimation model loaded.") - - def forward(self, x): - return super().forward(x).squeeze(dim=1) - diff --git a/extralibs/midas/midas/midas_net.py b/extralibs/midas/midas/midas_net.py deleted file mode 100644 index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000 --- a/extralibs/midas/midas/midas_net.py +++ /dev/null @@ -1,76 +0,0 @@ -"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. -This file contains code that is adapted from -https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py -""" -import torch -import torch.nn as nn - -from .base_model import BaseModel -from .blocks import FeatureFusionBlock, Interpolate, _make_encoder - - -class MidasNet(BaseModel): - """Network for monocular depth estimation. - """ - - def __init__(self, path=None, features=256, non_negative=True): - """Init. - - Args: - path (str, optional): Path to saved model. Defaults to None. - features (int, optional): Number of features. Defaults to 256. - backbone (str, optional): Backbone network for encoder. Defaults to resnet50 - """ - print("Loading weights: ", path) - - super(MidasNet, self).__init__() - - use_pretrained = False if path is None else True - - self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained) - - self.scratch.refinenet4 = FeatureFusionBlock(features) - self.scratch.refinenet3 = FeatureFusionBlock(features) - self.scratch.refinenet2 = FeatureFusionBlock(features) - self.scratch.refinenet1 = FeatureFusionBlock(features) - - self.scratch.output_conv = nn.Sequential( - nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1), - Interpolate(scale_factor=2, mode="bilinear"), - nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1), - nn.ReLU(True), - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - ) - - if path: - self.load(path) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input data (image) - - Returns: - tensor: depth - """ - - layer_1 = self.pretrained.layer1(x) - layer_2 = self.pretrained.layer2(layer_1) - layer_3 = self.pretrained.layer3(layer_2) - layer_4 = self.pretrained.layer4(layer_3) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return torch.squeeze(out, dim=1) diff --git a/extralibs/midas/midas/midas_net_custom.py b/extralibs/midas/midas/midas_net_custom.py deleted file mode 100644 index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000 --- a/extralibs/midas/midas/midas_net_custom.py +++ /dev/null @@ -1,128 +0,0 @@ -"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. -This file contains code that is adapted from -https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py -""" -import torch -import torch.nn as nn - -from .base_model import BaseModel -from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder - - -class MidasNet_small(BaseModel): - """Network for monocular depth estimation. - """ - - def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True, - blocks={'expand': True}): - """Init. - - Args: - path (str, optional): Path to saved model. Defaults to None. - features (int, optional): Number of features. Defaults to 256. - backbone (str, optional): Backbone network for encoder. Defaults to resnet50 - """ - print("Loading weights: ", path) - - super(MidasNet_small, self).__init__() - - use_pretrained = False if path else True - - self.channels_last = channels_last - self.blocks = blocks - self.backbone = backbone - - self.groups = 1 - - features1=features - features2=features - features3=features - features4=features - self.expand = False - if "expand" in self.blocks and self.blocks['expand'] == True: - self.expand = True - features1=features - features2=features*2 - features3=features*4 - features4=features*8 - - self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable) - - self.scratch.activation = nn.ReLU(False) - - self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) - self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) - self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) - self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners) - - - self.scratch.output_conv = nn.Sequential( - nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups), - Interpolate(scale_factor=2, mode="bilinear"), - nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1), - self.scratch.activation, - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - nn.Identity(), - ) - - if path: - self.load(path) - - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input data (image) - - Returns: - tensor: depth - """ - if self.channels_last==True: - print("self.channels_last = ", self.channels_last) - x.contiguous(memory_format=torch.channels_last) - - - layer_1 = self.pretrained.layer1(x) - layer_2 = self.pretrained.layer2(layer_1) - layer_3 = self.pretrained.layer3(layer_2) - layer_4 = self.pretrained.layer4(layer_3) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return torch.squeeze(out, dim=1) - - - -def fuse_model(m): - prev_previous_type = nn.Identity() - prev_previous_name = '' - previous_type = nn.Identity() - previous_name = '' - for name, module in m.named_modules(): - if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU: - # print("FUSED ", prev_previous_name, previous_name, name) - torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True) - elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d: - # print("FUSED ", prev_previous_name, previous_name) - torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True) - # elif previous_type == nn.Conv2d and type(module) == nn.ReLU: - # print("FUSED ", previous_name, name) - # torch.quantization.fuse_modules(m, [previous_name, name], inplace=True) - - prev_previous_type = previous_type - prev_previous_name = previous_name - previous_type = type(module) - previous_name = name \ No newline at end of file diff --git a/extralibs/midas/midas/transforms.py b/extralibs/midas/midas/transforms.py deleted file mode 100644 index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000 --- a/extralibs/midas/midas/transforms.py +++ /dev/null @@ -1,234 +0,0 @@ -import numpy as np -import cv2 -import math - - -def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): - """Rezise the sample to ensure the given size. Keeps aspect ratio. - - Args: - sample (dict): sample - size (tuple): image size - - Returns: - tuple: new size - """ - shape = list(sample["disparity"].shape) - - if shape[0] >= size[0] and shape[1] >= size[1]: - return sample - - scale = [0, 0] - scale[0] = size[0] / shape[0] - scale[1] = size[1] / shape[1] - - scale = max(scale) - - shape[0] = math.ceil(scale * shape[0]) - shape[1] = math.ceil(scale * shape[1]) - - # resize - sample["image"] = cv2.resize( - sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method - ) - - sample["disparity"] = cv2.resize( - sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST - ) - sample["mask"] = cv2.resize( - sample["mask"].astype(np.float32), - tuple(shape[::-1]), - interpolation=cv2.INTER_NEAREST, - ) - sample["mask"] = sample["mask"].astype(bool) - - return tuple(shape) - - -class Resize(object): - """Resize sample to given size (width, height). - """ - - def __init__( - self, - width, - height, - resize_target=True, - keep_aspect_ratio=False, - ensure_multiple_of=1, - resize_method="lower_bound", - image_interpolation_method=cv2.INTER_AREA, - ): - """Init. - - Args: - width (int): desired output width - height (int): desired output height - resize_target (bool, optional): - True: Resize the full sample (image, mask, target). - False: Resize image only. - Defaults to True. - keep_aspect_ratio (bool, optional): - True: Keep the aspect ratio of the input sample. - Output sample might not have the given width and height, and - resize behaviour depends on the parameter 'resize_method'. - Defaults to False. - ensure_multiple_of (int, optional): - Output width and height is constrained to be multiple of this parameter. - Defaults to 1. - resize_method (str, optional): - "lower_bound": Output will be at least as large as the given size. - "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) - "minimal": Scale as least as possible. (Output size might be smaller than given size.) - Defaults to "lower_bound". - """ - self.__width = width - self.__height = height - - self.__resize_target = resize_target - self.__keep_aspect_ratio = keep_aspect_ratio - self.__multiple_of = ensure_multiple_of - self.__resize_method = resize_method - self.__image_interpolation_method = image_interpolation_method - - def constrain_to_multiple_of(self, x, min_val=0, max_val=None): - y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) - - if max_val is not None and y > max_val: - y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int) - - if y < min_val: - y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int) - - return y - - def get_size(self, width, height): - # determine new height and width - scale_height = self.__height / height - scale_width = self.__width / width - - if self.__keep_aspect_ratio: - if self.__resize_method == "lower_bound": - # scale such that output size is lower bound - if scale_width > scale_height: - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - elif self.__resize_method == "upper_bound": - # scale such that output size is upper bound - if scale_width < scale_height: - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - elif self.__resize_method == "minimal": - # scale as least as possbile - if abs(1 - scale_width) < abs(1 - scale_height): - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - else: - raise ValueError( - f"resize_method {self.__resize_method} not implemented" - ) - - if self.__resize_method == "lower_bound": - new_height = self.constrain_to_multiple_of( - scale_height * height, min_val=self.__height - ) - new_width = self.constrain_to_multiple_of( - scale_width * width, min_val=self.__width - ) - elif self.__resize_method == "upper_bound": - new_height = self.constrain_to_multiple_of( - scale_height * height, max_val=self.__height - ) - new_width = self.constrain_to_multiple_of( - scale_width * width, max_val=self.__width - ) - elif self.__resize_method == "minimal": - new_height = self.constrain_to_multiple_of(scale_height * height) - new_width = self.constrain_to_multiple_of(scale_width * width) - else: - raise ValueError(f"resize_method {self.__resize_method} not implemented") - - return (new_width, new_height) - - def __call__(self, sample): - width, height = self.get_size( - sample["image"].shape[1], sample["image"].shape[0] - ) - - # resize sample - sample["image"] = cv2.resize( - sample["image"], - (width, height), - interpolation=self.__image_interpolation_method, - ) - - if self.__resize_target: - if "disparity" in sample: - sample["disparity"] = cv2.resize( - sample["disparity"], - (width, height), - interpolation=cv2.INTER_NEAREST, - ) - - if "depth" in sample: - sample["depth"] = cv2.resize( - sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST - ) - - sample["mask"] = cv2.resize( - sample["mask"].astype(np.float32), - (width, height), - interpolation=cv2.INTER_NEAREST, - ) - sample["mask"] = sample["mask"].astype(bool) - - return sample - - -class NormalizeImage(object): - """Normlize image by given mean and std. - """ - - def __init__(self, mean, std): - self.__mean = mean - self.__std = std - - def __call__(self, sample): - sample["image"] = (sample["image"] - self.__mean) / self.__std - - return sample - - -class PrepareForNet(object): - """Prepare sample for usage as network input. - """ - - def __init__(self): - pass - - def __call__(self, sample): - image = np.transpose(sample["image"], (2, 0, 1)) - sample["image"] = np.ascontiguousarray(image).astype(np.float32) - - if "mask" in sample: - sample["mask"] = sample["mask"].astype(np.float32) - sample["mask"] = np.ascontiguousarray(sample["mask"]) - - if "disparity" in sample: - disparity = sample["disparity"].astype(np.float32) - sample["disparity"] = np.ascontiguousarray(disparity) - - if "depth" in sample: - depth = sample["depth"].astype(np.float32) - sample["depth"] = np.ascontiguousarray(depth) - - return sample diff --git a/extralibs/midas/midas/vit.py b/extralibs/midas/midas/vit.py deleted file mode 100644 index c52b53d231718b04e56ef49950061d6a1df6fb79..0000000000000000000000000000000000000000 --- a/extralibs/midas/midas/vit.py +++ /dev/null @@ -1,489 +0,0 @@ -import torch -import torch.nn as nn -import timm -import types -import math -import torch.nn.functional as F - - -class Slice(nn.Module): - def __init__(self, start_index=1): - super(Slice, self).__init__() - self.start_index = start_index - - def forward(self, x): - return x[:, self.start_index :] - - -class AddReadout(nn.Module): - def __init__(self, start_index=1): - super(AddReadout, self).__init__() - self.start_index = start_index - - def forward(self, x): - if self.start_index == 2: - readout = (x[:, 0] + x[:, 1]) / 2 - else: - readout = x[:, 0] - return x[:, self.start_index :] + readout.unsqueeze(1) - - -class ProjectReadout(nn.Module): - def __init__(self, in_features, start_index=1): - super(ProjectReadout, self).__init__() - self.start_index = start_index - - self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) - - def forward(self, x): - readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :]) - features = torch.cat((x[:, self.start_index :], readout), -1) - - return self.project(features) - - -class Transpose(nn.Module): - def __init__(self, dim0, dim1): - super(Transpose, self).__init__() - self.dim0 = dim0 - self.dim1 = dim1 - - def forward(self, x): - x = x.transpose(self.dim0, self.dim1) - return x - - -activations = {} -def forward_vit(pretrained, x): - b, c, h, w = x.shape - - glob = pretrained.model.forward_flex(x) - pretrained.activations = activations - - layer_1 = pretrained.activations["1"] - layer_2 = pretrained.activations["2"] - layer_3 = pretrained.activations["3"] - layer_4 = pretrained.activations["4"] - - layer_1 = pretrained.act_postprocess1[0:2](layer_1) - layer_2 = pretrained.act_postprocess2[0:2](layer_2) - layer_3 = pretrained.act_postprocess3[0:2](layer_3) - layer_4 = pretrained.act_postprocess4[0:2](layer_4) - - unflatten = nn.Sequential( - nn.Unflatten( - 2, - torch.Size( - [ - h // pretrained.model.patch_size[1], - w // pretrained.model.patch_size[0], - ] - ), - ) - ) - - if layer_1.ndim == 3: - layer_1 = unflatten(layer_1) - if layer_2.ndim == 3: - layer_2 = unflatten(layer_2) - if layer_3.ndim == 3: - layer_3 = unflatten(layer_3) - if layer_4.ndim == 3: - layer_4 = unflatten(layer_4) - - layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1) - layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2) - layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3) - layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4) - - return layer_1, layer_2, layer_3, layer_4 - - -def _resize_pos_embed(self, posemb, gs_h, gs_w): - posemb_tok, posemb_grid = ( - posemb[:, : self.start_index], - posemb[0, self.start_index :], - ) - - gs_old = int(math.sqrt(len(posemb_grid))) - - posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) - posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") - posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) - - posemb = torch.cat([posemb_tok, posemb_grid], dim=1) - - return posemb - - -def forward_flex(self, x): - b, c, h, w = x.shape - - pos_embed = self._resize_pos_embed( - self.pos_embed, h // self.patch_size[1], w // self.patch_size[0] - ) - - B = x.shape[0] - - if hasattr(self.patch_embed, "backbone"): - x = self.patch_embed.backbone(x) - if isinstance(x, (list, tuple)): - x = x[-1] # last feature if backbone outputs list/tuple of features - - x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) - - if getattr(self, "dist_token", None) is not None: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - dist_token = self.dist_token.expand(B, -1, -1) - x = torch.cat((cls_tokens, dist_token, x), dim=1) - else: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - x = torch.cat((cls_tokens, x), dim=1) - - x = x + pos_embed - x = self.pos_drop(x) - - for blk in self.blocks: - x = blk(x) - - x = self.norm(x) - - return x - - -def get_activation(name): - def hook(model, input, output): - activations[name] = output - return hook - - -def get_readout_oper(vit_features, features, use_readout, start_index=1): - if use_readout == "ignore": - readout_oper = [Slice(start_index)] * len(features) - elif use_readout == "add": - readout_oper = [AddReadout(start_index)] * len(features) - elif use_readout == "project": - readout_oper = [ - ProjectReadout(vit_features, start_index) for out_feat in features - ] - else: - assert ( - False - ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" - - return readout_oper - - -def _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - size=[384, 384], - hooks=[2, 5, 8, 11], - vit_features=768, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - # 32, 48, 136, 384 - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) - - hooks = [5, 11, 17, 23] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[256, 512, 1024, 1024], - hooks=hooks, - vit_features=1024, - use_readout=use_readout, - ) - - -def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model( - "vit_deit_base_distilled_patch16_384", pretrained=pretrained - ) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - hooks=hooks, - use_readout=use_readout, - start_index=2, - ) - - -def _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=[0, 1, 8, 11], - vit_features=768, - use_vit_only=False, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - - if use_vit_only == True: - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - else: - pretrained.model.patch_embed.backbone.stages[0].register_forward_hook( - get_activation("1") - ) - pretrained.model.patch_embed.backbone.stages[1].register_forward_hook( - get_activation("2") - ) - - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - if use_vit_only == True: - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - else: - pretrained.act_postprocess1 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - pretrained.act_postprocess2 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitb_rn50_384( - pretrained, use_readout="ignore", hooks=None, use_vit_only=False -): - model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) - - hooks = [0, 1, 8, 11] if hooks == None else hooks - return _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) diff --git a/extralibs/midas/utils.py b/extralibs/midas/utils.py deleted file mode 100644 index 9a9d3b5b66370fa98da9e067ba53ead848ea9a59..0000000000000000000000000000000000000000 --- a/extralibs/midas/utils.py +++ /dev/null @@ -1,189 +0,0 @@ -"""Utils for monoDepth.""" -import sys -import re -import numpy as np -import cv2 -import torch - - -def read_pfm(path): - """Read pfm file. - - Args: - path (str): path to file - - Returns: - tuple: (data, scale) - """ - with open(path, "rb") as file: - - color = None - width = None - height = None - scale = None - endian = None - - header = file.readline().rstrip() - if header.decode("ascii") == "PF": - color = True - elif header.decode("ascii") == "Pf": - color = False - else: - raise Exception("Not a PFM file: " + path) - - dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii")) - if dim_match: - width, height = list(map(int, dim_match.groups())) - else: - raise Exception("Malformed PFM header.") - - scale = float(file.readline().decode("ascii").rstrip()) - if scale < 0: - # little-endian - endian = "<" - scale = -scale - else: - # big-endian - endian = ">" - - data = np.fromfile(file, endian + "f") - shape = (height, width, 3) if color else (height, width) - - data = np.reshape(data, shape) - data = np.flipud(data) - - return data, scale - - -def write_pfm(path, image, scale=1): - """Write pfm file. - - Args: - path (str): pathto file - image (array): data - scale (int, optional): Scale. Defaults to 1. - """ - - with open(path, "wb") as file: - color = None - - if image.dtype.name != "float32": - raise Exception("Image dtype must be float32.") - - image = np.flipud(image) - - if len(image.shape) == 3 and image.shape[2] == 3: # color image - color = True - elif ( - len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1 - ): # greyscale - color = False - else: - raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.") - - file.write("PF\n" if color else "Pf\n".encode()) - file.write("%d %d\n".encode() % (image.shape[1], image.shape[0])) - - endian = image.dtype.byteorder - - if endian == "<" or endian == "=" and sys.byteorder == "little": - scale = -scale - - file.write("%f\n".encode() % scale) - - image.tofile(file) - - -def read_image(path): - """Read image and output RGB image (0-1). - - Args: - path (str): path to file - - Returns: - array: RGB image (0-1) - """ - img = cv2.imread(path) - - if img.ndim == 2: - img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 - - return img - - -def resize_image(img): - """Resize image and make it fit for network. - - Args: - img (array): image - - Returns: - tensor: data ready for network - """ - height_orig = img.shape[0] - width_orig = img.shape[1] - - if width_orig > height_orig: - scale = width_orig / 384 - else: - scale = height_orig / 384 - - height = (np.ceil(height_orig / scale / 32) * 32).astype(int) - width = (np.ceil(width_orig / scale / 32) * 32).astype(int) - - img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA) - - img_resized = ( - torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float() - ) - img_resized = img_resized.unsqueeze(0) - - return img_resized - - -def resize_depth(depth, width, height): - """Resize depth map and bring to CPU (numpy). - - Args: - depth (tensor): depth - width (int): image width - height (int): image height - - Returns: - array: processed depth - """ - depth = torch.squeeze(depth[0, :, :, :]).to("cpu") - - depth_resized = cv2.resize( - depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC - ) - - return depth_resized - -def write_depth(path, depth, bits=1): - """Write depth map to pfm and png file. - - Args: - path (str): filepath without extension - depth (array): depth - """ - write_pfm(path + ".pfm", depth.astype(np.float32)) - - depth_min = depth.min() - depth_max = depth.max() - - max_val = (2**(8*bits))-1 - - if depth_max - depth_min > np.finfo("float").eps: - out = max_val * (depth - depth_min) / (depth_max - depth_min) - else: - out = np.zeros(depth.shape, dtype=depth.type) - - if bits == 1: - cv2.imwrite(path + ".png", out.astype("uint8")) - elif bits == 2: - cv2.imwrite(path + ".png", out.astype("uint16")) - - return diff --git a/i2v_test.py b/i2v_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3277e05d86868e1b29df9630d96b0d72de6e32d6 --- /dev/null +++ b/i2v_test.py @@ -0,0 +1,78 @@ +import os +from omegaconf import OmegaConf + +import torch + +from scripts.evaluation.funcs import load_model_checkpoint, load_image_batch, save_videos, batch_ddim_sampling +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download + +class Image2Video(): + def __init__(self,result_dir='./tmp/',gpu_num=1) -> None: + self.download_model() + self.result_dir = result_dir + if not os.path.exists(self.result_dir): + os.mkdir(self.result_dir) + ckpt_path='checkpoints/i2v_512_v1/model.ckpt' + config_file='configs/inference_i2v_512_v1.0.yaml' + config = OmegaConf.load(config_file) + model_config = config.pop("model", OmegaConf.create()) + model_config['params']['unet_config']['params']['use_checkpoint']=False + model_list = [] + for gpu_id in range(gpu_num): + model = instantiate_from_config(model_config) + model = model.cuda(gpu_id) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model_list.append(model) + self.model_list = model_list + self.save_fps = 8 + + def get_image(self, image, prompt, steps=50, cfg_scale=12.0, eta=1.0, fps=16): + gpu_id=0 + if steps > 60: + steps = 60 + model = self.model_list[gpu_id] + batch_size=1 + channels = model.model.diffusion_model.in_channels + frames = model.temporal_length + h, w = 320 // 8, 512 // 8 + noise_shape = [batch_size, channels, frames, h, w] + + #prompts = batch_size * [""] + text_emb = model.get_learned_conditioning([prompt]) + + # cond_images = load_image_batch([image_path]) + img_tensor = torch.from_numpy(image).permute(2, 0, 1).float() + img_tensor = (img_tensor / 255. - 0.5) * 2 + img_tensor = img_tensor.unsqueeze(0) + cond_images = img_tensor.to(model.device) + img_emb = model.get_image_embeds(cond_images) + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + cond = {"c_crossattn": [imtext_cond], "fps": fps} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) + ## b,samples,c,t,h,w + prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt + prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str + prompt_str=prompt_str[:30] + + save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) + return os.path.join(self.result_dir, f"{prompt_str}.mp4") + + def download_model(self): + REPO_ID = 'VideoCrafter/Image2Video-512-v1.0' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/i2v_512_v1/'): + os.makedirs('./checkpoints/i2v_512_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/i2v_512_v1/', filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/i2v_512_v1/', local_dir_use_symlinks=False) + +if __name__ == '__main__': + i2v = Image2Video() + video_path = i2v.get_image('prompts/i2v_prompts/horse.png','horses are walking on the grassland') + print('done', video_path) \ No newline at end of file diff --git a/input/flamingo.mp4 b/input/flamingo.mp4 deleted file mode 100644 index ff506e7046f2e688f9fae085258a99e0822df5f3..0000000000000000000000000000000000000000 Binary files a/input/flamingo.mp4 and /dev/null differ diff --git a/input/prompts.txt b/input/prompts.txt deleted file mode 100644 index d7b7239f01bb05f5653da9d2ef32e180eee250b8..0000000000000000000000000000000000000000 --- a/input/prompts.txt +++ /dev/null @@ -1,2 +0,0 @@ -astronaut riding a horse -Flying through an intense battle between pirate ships in a stormy ocean \ No newline at end of file diff --git a/lvdm/basics.py b/lvdm/basics.py new file mode 100644 index 0000000000000000000000000000000000000000..65c771d13a7f4a932ac370f08797a8b6ba9e85ff --- /dev/null +++ b/lvdm/basics.py @@ -0,0 +1,100 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + +import torch.nn as nn +from utils.utils import instantiate_from_config + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def nonlinearity(type='silu'): + if type == 'silu': + return nn.SiLU() + elif type == 'leaky_relu': + return nn.LeakyReLU() + + +class GroupNormSpecific(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def normalization(channels, num_groups=32): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNormSpecific(num_groups, channels) + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} \ No newline at end of file diff --git a/lvdm/common.py b/lvdm/common.py new file mode 100644 index 0000000000000000000000000000000000000000..35569b25aa97236d7d083d8b6ef0c0f3187c2388 --- /dev/null +++ b/lvdm/common.py @@ -0,0 +1,95 @@ +import math +from inspect import isfunction +import torch +from torch import nn +import torch.distributed as dist + + +def gather_data(data, return_np=True): + ''' gather data from multiple processes to one list ''' + data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())] + dist.all_gather(data_list, data) # gather not supported with NCCL + if return_np: + data_list = [data.cpu().numpy() for data in data_list] + return data_list + +def autocast(f): + def do_autocast(*args, **kwargs): + with torch.cuda.amp.autocast(enabled=True, + dtype=torch.get_autocast_gpu_dtype(), + cache_enabled=torch.is_autocast_cache_enabled()): + return f(*args, **kwargs) + return do_autocast + + +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 noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + +def exists(val): + return val is not None + +def identity(*args, **kwargs): + return nn.Identity() + +def uniq(arr): + return{el: True for el in arr}.keys() + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + +def shape_to_str(x): + shape_str = "x".join([str(x) for x in x.shape]) + return shape_str + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + +ckpt = torch.utils.checkpoint.checkpoint +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + return ckpt(func, *inputs) + else: + return func(*inputs) + diff --git a/lvdm/data/webvid.py b/lvdm/data/webvid.py deleted file mode 100644 index 4b856fc88087b28d1b93a72f2324a58988fd674c..0000000000000000000000000000000000000000 --- a/lvdm/data/webvid.py +++ /dev/null @@ -1,188 +0,0 @@ -import os -import random -import bisect - -import pandas as pd - -import omegaconf -import torch -from torch.utils.data import Dataset -from torchvision import transforms -from decord import VideoReader, cpu -import torchvision.transforms._transforms_video as transforms_video - -class WebVid(Dataset): - """ - WebVid Dataset. - Assumes webvid data is structured as follows. - Webvid/ - videos/ - 000001_000050/ ($page_dir) - 1.mp4 (videoid.mp4) - ... - 5000.mp4 - ... - """ - def __init__(self, - meta_path, - data_dir, - subsample=None, - video_length=16, - resolution=[256, 512], - frame_stride=1, - spatial_transform=None, - crop_resolution=None, - fps_max=None, - load_raw_resolution=False, - fps_schedule=None, - fs_probs=None, - bs_per_gpu=None, - trigger_word='', - dataname='', - ): - self.meta_path = meta_path - self.data_dir = data_dir - self.subsample = subsample - self.video_length = video_length - self.resolution = [resolution, resolution] if isinstance(resolution, int) else resolution - self.frame_stride = frame_stride - self.fps_max = fps_max - self.load_raw_resolution = load_raw_resolution - self.fs_probs = fs_probs - self.trigger_word = trigger_word - self.dataname = dataname - - self._load_metadata() - if spatial_transform is not None: - if spatial_transform == "random_crop": - self.spatial_transform = transforms_video.RandomCropVideo(crop_resolution) - elif spatial_transform == "resize_center_crop": - assert(self.resolution[0] == self.resolution[1]) - self.spatial_transform = transforms.Compose([ - transforms.Resize(resolution), - transforms_video.CenterCropVideo(resolution), - ]) - else: - raise NotImplementedError - else: - self.spatial_transform = None - - self.fps_schedule = fps_schedule - self.bs_per_gpu = bs_per_gpu - if self.fps_schedule is not None: - assert(self.bs_per_gpu is not None) - self.counter = 0 - self.stage_idx = 0 - - def _load_metadata(self): - metadata = pd.read_csv(self.meta_path) - if self.subsample is not None: - metadata = metadata.sample(self.subsample, random_state=0) - metadata['caption'] = metadata['name'] - del metadata['name'] - self.metadata = metadata - self.metadata.dropna(inplace=True) - # self.metadata['caption'] = self.metadata['caption'].str[:350] - - def _get_video_path(self, sample): - if self.dataname == "loradata": - rel_video_fp = str(sample['videoid']) + '.mp4' - full_video_fp = os.path.join(self.data_dir, rel_video_fp) - else: - rel_video_fp = os.path.join(sample['page_dir'], str(sample['videoid']) + '.mp4') - full_video_fp = os.path.join(self.data_dir, 'videos', rel_video_fp) - return full_video_fp, rel_video_fp - - def get_fs_based_on_schedule(self, frame_strides, schedule): - assert(len(frame_strides) == len(schedule) + 1) # nstage=len_fps_schedule + 1 - global_step = self.counter // self.bs_per_gpu # TODO: support resume. - stage_idx = bisect.bisect(schedule, global_step) - frame_stride = frame_strides[stage_idx] - # log stage change - if stage_idx != self.stage_idx: - print(f'fps stage: {stage_idx} start ... new frame stride = {frame_stride}') - self.stage_idx = stage_idx - return frame_stride - - def get_fs_based_on_probs(self, frame_strides, probs): - assert(len(frame_strides) == len(probs)) - return random.choices(frame_strides, weights=probs)[0] - - def get_fs_randomly(self, frame_strides): - return random.choice(frame_strides) - - def __getitem__(self, index): - - if isinstance(self.frame_stride, list) or isinstance(self.frame_stride, omegaconf.listconfig.ListConfig): - if self.fps_schedule is not None: - frame_stride = self.get_fs_based_on_schedule(self.frame_stride, self.fps_schedule) - elif self.fs_probs is not None: - frame_stride = self.get_fs_based_on_probs(self.frame_stride, self.fs_probs) - else: - frame_stride = self.get_fs_randomly(self.frame_stride) - else: - frame_stride = self.frame_stride - assert(isinstance(frame_stride, int)), type(frame_stride) - - while True: - index = index % len(self.metadata) - sample = self.metadata.iloc[index] - video_path, rel_fp = self._get_video_path(sample) - caption = sample['caption']+self.trigger_word - - # make reader - try: - if self.load_raw_resolution: - video_reader = VideoReader(video_path, ctx=cpu(0)) - else: - video_reader = VideoReader(video_path, ctx=cpu(0), width=self.resolution[1], height=self.resolution[0]) - if len(video_reader) < self.video_length: - print(f"video length ({len(video_reader)}) is smaller than target length({self.video_length})") - index += 1 - continue - else: - pass - except: - index += 1 - print(f"Load video failed! path = {video_path}") - continue - - # sample strided frames - all_frames = list(range(0, len(video_reader), frame_stride)) - if len(all_frames) < self.video_length: # recal a max fs - frame_stride = len(video_reader) // self.video_length - assert(frame_stride != 0) - all_frames = list(range(0, len(video_reader), frame_stride)) - - # select a random clip - rand_idx = random.randint(0, len(all_frames) - self.video_length) - frame_indices = all_frames[rand_idx:rand_idx+self.video_length] - try: - frames = video_reader.get_batch(frame_indices) - break - except: - print(f"Get frames failed! path = {video_path}") - index += 1 - continue - - assert(frames.shape[0] == self.video_length),f'{len(frames)}, self.video_length={self.video_length}' - frames = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() # [t,h,w,c] -> [c,t,h,w] - if self.spatial_transform is not None: - frames = self.spatial_transform(frames) - if self.resolution is not None: - assert(frames.shape[2] == self.resolution[0] and frames.shape[3] == self.resolution[1]), f'frames={frames.shape}, self.resolution={self.resolution}' - frames = (frames / 255 - 0.5) * 2 - - fps_ori = video_reader.get_avg_fps() - fps_clip = fps_ori // frame_stride - if self.fps_max is not None and fps_clip > self.fps_max: - fps_clip = self.fps_max - - data = {'video': frames, 'caption': caption, 'path': video_path, 'fps': fps_clip, 'frame_stride': frame_stride} - - if self.fps_schedule is not None: - self.counter += 1 - return data - - def __len__(self): - return len(self.metadata) diff --git a/lvdm/models/modules/distributions.py b/lvdm/distributions.py similarity index 88% rename from lvdm/models/modules/distributions.py rename to lvdm/distributions.py index 06cabc07d500351fb52a180c4acae9093e936dab..0b69b6984880ec24279b658384ed8031335e3474 100644 --- a/lvdm/models/modules/distributions.py +++ b/lvdm/distributions.py @@ -2,6 +2,25 @@ import torch import numpy as np +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): self.parameters = parameters diff --git a/lvdm/ema.py b/lvdm/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..c8c75af43565f6e140287644aaaefa97dd6e67c5 --- /dev/null +++ b/lvdm/ema.py @@ -0,0 +1,76 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates + else torch.tensor(-1,dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + #remove as '.'-character is not allowed in buffers + s_name = name.replace('.','') + self.m_name2s_name.update({name:s_name}) + self.register_buffer(s_name,p.clone().detach().data) + + self.collected_params = [] + + def forward(self,model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/lvdm/models/autoencoder.py b/lvdm/models/autoencoder.py index f5fc41c331b61fa531f35c9e2275e1956ad58b66..cc479d8b446b530885f4a3cc5d25cb58f0c00d74 100644 --- a/lvdm/models/autoencoder.py +++ b/lvdm/models/autoencoder.py @@ -1,12 +1,14 @@ -import torch -import pytorch_lightning as pl -import torch.nn.functional as F import os +from contextlib import contextmanager +import torch +import numpy as np from einops import rearrange +import torch.nn.functional as F +import pytorch_lightning as pl +from lvdm.modules.networks.ae_modules import Encoder, Decoder +from lvdm.distributions import DiagonalGaussianDistribution +from utils.utils import instantiate_from_config -from lvdm.models.modules.autoencoder_modules import Encoder, Decoder -from lvdm.models.modules.distributions import DiagonalGaussianDistribution -from lvdm.utils.common_utils import instantiate_from_config class AutoencoderKL(pl.LightningModule): def __init__(self, @@ -69,12 +71,12 @@ class AutoencoderKL(pl.LightningModule): if self.test_args.save_input: os.makedirs(self.root_inputs, exist_ok=True) assert(self.test_args is not None) - self.test_maximum = getattr(self.test_args, 'test_maximum', None) #1500 # 12000/8 + self.test_maximum = getattr(self.test_args, 'test_maximum', None) self.count = 0 self.eval_metrics = {} self.decodes = [] self.save_decode_samples = 2048 - + def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu") try: @@ -115,10 +117,6 @@ class AutoencoderKL(pl.LightningModule): def get_input(self, batch, k): x = batch[k] - # if len(x.shape) == 3: - # x = x[..., None] - # if x.dim() == 4: - # x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() if x.dim() == 5 and self.input_dim == 4: b,c,t,h,w = x.shape self.b = b @@ -200,3 +198,22 @@ class AutoencoderKL(pl.LightningModule): x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x diff --git a/lvdm/models/ddpm3d.py b/lvdm/models/ddpm3d.py index bb2eda0267c672924493a0dbd43928ddc0883a2e..73a2647d1fd63e6101bd333267e98e9d2a8fa481 100644 --- a/lvdm/models/ddpm3d.py +++ b/lvdm/models/ddpm3d.py @@ -1,54 +1,42 @@ -import os -import time -import random -import itertools +""" +wild mixture of +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/CompVis/taming-transformers +-- merci +""" + from functools import partial from contextlib import contextmanager - import numpy as np from tqdm import tqdm from einops import rearrange, repeat - +import logging +mainlogger = logging.getLogger('mainlogger') import torch import torch.nn as nn -import pytorch_lightning as pl from torchvision.utils import make_grid -from torch.optim.lr_scheduler import LambdaLR -from pytorch_lightning.utilities import rank_zero_only -from lvdm.models.modules.distributions import normal_kl, DiagonalGaussianDistribution -from lvdm.models.modules.util import make_beta_schedule, extract_into_tensor, noise_like -from lvdm.models.modules.lora import inject_trainable_lora -from lvdm.samplers.ddim import DDIMSampler -from lvdm.utils.common_utils import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config, check_istarget - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def uniform_on_device(r1, r2, shape, device): - return (r1 - r2) * torch.rand(*shape, device=device) + r2 - - -def split_video_to_clips(video, clip_length, drop_left=True): - video_length = video.shape[2] - shape = video.shape - if video_length % clip_length != 0 and drop_left: - video = video[:, :, :video_length // clip_length * clip_length, :, :] - print(f'[split_video_to_clips] Drop frames from {shape} to {video.shape}') - nclips = video_length // clip_length - clips = rearrange(video, 'b c (nc cl) h w -> (b nc) c cl h w', cl=clip_length, nc=nclips) - return clips - -def merge_clips_to_videos(clips, bs): - nclips = clips.shape[0] // bs - video = rearrange(clips, '(b nc) c t h w -> b c (nc t) h w', nc=nclips) - return video +import pytorch_lightning as pl +from utils.utils import instantiate_from_config +from lvdm.ema import LitEma +from lvdm.distributions import DiagonalGaussianDistribution +from lvdm.models.utils_diffusion import make_beta_schedule +from lvdm.modules.encoders.ip_resampler import ImageProjModel, Resampler +from lvdm.basics import disabled_train +from lvdm.common import ( + extract_into_tensor, + noise_like, + exists, + default +) + + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} class DDPM(pl.LightningModule): - # classic DDPM with Gaussian diffusion, in pixel space + # classic DDPM with Gaussian diffusion, in image space def __init__(self, unet_config, timesteps=1000, @@ -57,11 +45,10 @@ class DDPM(pl.LightningModule): ckpt_path=None, ignore_keys=[], load_only_unet=False, - monitor="val/loss", + monitor=None, use_ema=True, first_stage_key="image", image_size=256, - video_length=None, channels=3, log_every_t=100, clip_denoised=True, @@ -70,35 +57,35 @@ class DDPM(pl.LightningModule): cosine_s=8e-3, given_betas=None, original_elbo_weight=0., - v_posterior=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta l_simple_weight=1., conditioning_key=None, - parameterization="eps", + parameterization="eps", # all assuming fixed variance schedules scheduler_config=None, + use_positional_encodings=False, learn_logvar=False, - logvar_init=0., - *args, **kwargs + logvar_init=0. ): super().__init__() assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' self.parameterization = parameterization - print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") self.cond_stage_model = None self.clip_denoised = clip_denoised self.log_every_t = log_every_t self.first_stage_key = first_stage_key - self.image_size = image_size # try conv? - + self.channels = channels + self.temporal_length = unet_config.params.temporal_length + self.image_size = image_size if isinstance(self.image_size, int): self.image_size = [self.image_size, self.image_size] - self.channels = channels + self.use_positional_encodings = use_positional_encodings self.model = DiffusionWrapper(unet_config, conditioning_key) - self.conditioning_key = conditioning_key # also register conditioning_key in diffusion - - self.temporal_length = video_length if video_length is not None else unet_config.params.temporal_length - count_params(self.model, verbose=True) self.use_ema = use_ema - + if self.use_ema: + self.model_ema = LitEma(self.model) + mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + self.use_scheduler = scheduler_config is not None if self.use_scheduler: self.scheduler_config = scheduler_config @@ -122,6 +109,7 @@ class DDPM(pl.LightningModule): if self.learn_logvar: self.logvar = nn.Parameter(self.logvar, requires_grad=True) + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if exists(given_betas): @@ -182,14 +170,14 @@ class DDPM(pl.LightningModule): self.model_ema.store(self.model.parameters()) self.model_ema.copy_to(self.model) if context is not None: - print(f"{context}: Switched to EMA weights") + mainlogger.info(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.model.parameters()) if context is not None: - print(f"{context}: Restored training weights") + mainlogger.info(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") @@ -198,16 +186,16 @@ class DDPM(pl.LightningModule): keys = list(sd.keys()) for k in keys: for ik in ignore_keys: - if k.startswith(ik) or (ik.startswith('**') and ik.split('**')[-1] in k): - print("Deleting key {} from state_dict.".format(k)) + if k.startswith(ik): + mainlogger.info("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: - print(f"Missing Keys: {missing}") + mainlogger.info(f"Missing Keys: {missing}") if len(unexpected) > 0: - print(f"Unexpected Keys: {unexpected}") + mainlogger.info(f"Unexpected Keys: {unexpected}") def q_mean_variance(self, x_start, t): """ @@ -274,196 +262,51 @@ class DDPM(pl.LightningModule): @torch.no_grad() def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size channels = self.channels - video_length = self.total_length - size = (batch_size, channels, video_length, *self.image_size) - return self.p_sample_loop(size, + return self.p_sample_loop((batch_size, channels, image_size, image_size), return_intermediates=return_intermediates) def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) - return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start * + extract_into_tensor(self.scale_arr, t, x_start.shape) + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) - def get_loss(self, pred, target, mean=True, mask=None): - if self.loss_type == 'l1': - loss = (target - pred).abs() - if mean: - loss = loss.mean() - elif self.loss_type == 'l2': - if mean: - loss = torch.nn.functional.mse_loss(target, pred) - else: - loss = torch.nn.functional.mse_loss(target, pred, reduction='none') - else: - raise NotImplementedError("unknown loss type '{loss_type}'") - if mask is not None: - assert(mean is False) - assert(loss.shape[2:] == mask.shape[2:]) #thw need be the same - loss = loss * mask - return loss - - def p_losses(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - model_out = self.model(x_noisy, t) - - loss_dict = {} - if self.parameterization == "eps": - target = noise - elif self.parameterization == "x0": - target = x_start - else: - raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") - - loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3, 4]) - - log_prefix = 'train' if self.training else 'val' - - loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) - loss_simple = loss.mean() * self.l_simple_weight - - loss_vlb = (self.lvlb_weights[t] * loss).mean() - loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) - - loss = loss_simple + self.original_elbo_weight * loss_vlb - - loss_dict.update({f'{log_prefix}/loss': loss}) - - return loss, loss_dict - - def forward(self, x, *args, **kwargs): - t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() - return self.p_losses(x, t, *args, **kwargs) - def get_input(self, batch, k): x = batch[k] x = x.to(memory_format=torch.contiguous_format).float() return x - def shared_step(self, batch): - x = self.get_input(batch, self.first_stage_key) - loss, loss_dict = self(x) - return loss, loss_dict - - def training_step(self, batch, batch_idx): - loss, loss_dict = self.shared_step(batch) - - self.log_dict(loss_dict, prog_bar=True, - logger=True, on_step=True, on_epoch=True) - - self.log("global_step", self.global_step, - prog_bar=True, logger=True, on_step=True, on_epoch=False) - - if self.use_scheduler: - lr = self.optimizers().param_groups[0]['lr'] - self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) - - if self.log_time: - total_train_time = (time.time() - self.start_time) / (3600*24) - avg_step_time = (time.time() - self.start_time) / (self.global_step + 1) - left_time_2w_step = (20000-self.global_step -1) * avg_step_time / (3600*24) - left_time_5w_step = (50000-self.global_step -1) * avg_step_time / (3600*24) - with open(self.logger_path, 'w') as f: - print(f'total_train_time = {total_train_time:.1f} days \n\ - total_train_step = {self.global_step + 1} steps \n\ - left_time_2w_step = {left_time_2w_step:.1f} days \n\ - left_time_5w_step = {left_time_5w_step:.1f} days', file=f) - return loss - - @torch.no_grad() - def validation_step(self, batch, batch_idx): - # _, loss_dict_no_ema = self.shared_step_validate(batch) - # with self.ema_scope(): - # _, loss_dict_ema = self.shared_step_validate(batch) - # loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} - # self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - # self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - if (self.global_step) % self.val_fvd_interval == 0 and self.global_step != 0: - print(f'sample for fvd...') - self.log_images_kwargs = { - 'inpaint': False, - 'plot_diffusion_rows': False, - 'plot_progressive_rows': False, - 'ddim_steps': 50, - 'unconditional_guidance_scale': 15.0, - } - torch.cuda.empty_cache() - logs = self.log_images(batch, **self.log_images_kwargs) - self.log("batch_idx", batch_idx, - prog_bar=True, on_step=True, on_epoch=False) - return {'real': logs['inputs'], 'fake': logs['samples'], 'conditioning_txt_img': logs['conditioning_txt_img']} - - def get_condition_validate(self, prompt): - """ text embd - """ - if isinstance(prompt, str): - prompt = [prompt] - c = self.get_learned_conditioning(prompt) - bs = c.shape[0] - - return c - - def on_train_batch_end(self, *args, **kwargs): - if self.use_ema: - self.model_ema(self.model) - - def training_epoch_end(self, outputs): - - if (self.current_epoch == 0) or self.resume_new_epoch == 0: - self.epoch_start_time = time.time() - self.current_epoch_time = 0 - self.total_time = 0 - self.epoch_time_avg = 0 - else: - self.current_epoch_time = time.time() - self.epoch_start_time - self.epoch_start_time = time.time() - self.total_time += self.current_epoch_time - self.epoch_time_avg = self.total_time / self.current_epoch - self.resume_new_epoch += 1 - epoch_avg_loss = torch.stack([x['loss'] for x in outputs]).mean() - - self.log('train/epoch/loss', epoch_avg_loss, logger=True, on_epoch=True) - self.log('train/epoch/idx', self.current_epoch, logger=True, on_epoch=True) - self.log('train/epoch/time', self.current_epoch_time, logger=True, on_epoch=True) - self.log('train/epoch/time_avg', self.epoch_time_avg, logger=True, on_epoch=True) - self.log('train/epoch/time_avg_min', self.epoch_time_avg / 60, logger=True, on_epoch=True) - def _get_rows_from_list(self, samples): n_imgs_per_row = len(samples) - denoise_grid = rearrange(samples, 'n b c t h w -> b n c t h w') - denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w') - denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w') + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid @torch.no_grad() - def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, - plot_diffusion_rows=True, plot_denoise_rows=True, **kwargs): - """ log images for DDPM """ + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): log = dict() x = self.get_input(batch, self.first_stage_key) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) x = x.to(self.device)[:N] log["inputs"] = x - if 'fps' in batch: - log['fps'] = batch['fps'] - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - x_start = x[:n_row] + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(x_start) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - diffusion_row.append(x_noisy) + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) - log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) if sample: # get denoise row @@ -471,8 +314,7 @@ class DDPM(pl.LightningModule): samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) log["samples"] = samples - if plot_denoise_rows: - log["denoise_row"] = self._get_rows_from_list(denoise_row) + log["denoise_row"] = self._get_rows_from_list(denoise_row) if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: @@ -481,14 +323,6 @@ class DDPM(pl.LightningModule): return {key: log[key] for key in return_keys} return log - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.model.parameters()) - if self.learn_logvar: - params = params + [self.logvar] - opt = torch.optim.AdamW(params, lr=lr) - return opt - class LatentDiffusion(DDPM): """main class""" @@ -496,36 +330,51 @@ class LatentDiffusion(DDPM): first_stage_config, cond_stage_config, num_timesteps_cond=None, - cond_stage_key="image", + cond_stage_key="caption", cond_stage_trainable=False, - concat_mode=True, cond_stage_forward=None, conditioning_key=None, + uncond_prob=0.2, + uncond_type="empty_seq", scale_factor=1.0, scale_by_std=False, encoder_type="2d", - shift_factor=0.0, - split_clips=True, - downfactor_t=None, - clip_length=None, only_model=False, - lora_args={}, + use_scale=False, + scale_a=1, + scale_b=0.3, + mid_step=400, + fix_scale_bug=False, *args, **kwargs): self.num_timesteps_cond = default(num_timesteps_cond, 1) self.scale_by_std = scale_by_std assert self.num_timesteps_cond <= kwargs['timesteps'] # for backwards compatibility after implementation of DiffusionWrapper - - if conditioning_key is None: - conditioning_key = 'concat' if concat_mode else 'crossattn' - if cond_stage_config == '__is_unconditional__': - conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) + conditioning_key = default(conditioning_key, 'crossattn') super().__init__(conditioning_key=conditioning_key, *args, **kwargs) - self.concat_mode = concat_mode + self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key + + # scale factor + self.use_scale=use_scale + if self.use_scale: + self.scale_a=scale_a + self.scale_b=scale_b + if fix_scale_bug: + scale_step=self.num_timesteps-mid_step + else: #bug + scale_step = self.num_timesteps + + scale_arr1 = np.linspace(scale_a, scale_b, mid_step) + scale_arr2 = np.full(scale_step, scale_b) + scale_arr = np.concatenate((scale_arr1, scale_arr2)) + scale_arr_prev = np.append(scale_a, scale_arr[:-1]) + to_torch = partial(torch.tensor, dtype=torch.float32) + self.register_buffer('scale_arr', to_torch(scale_arr)) + try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 except: @@ -536,71 +385,44 @@ class LatentDiffusion(DDPM): self.register_buffer('scale_factor', torch.tensor(scale_factor)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) - self.cond_stage_forward = cond_stage_forward - self.clip_denoised = False - self.bbox_tokenizer = None - self.cond_stage_config = cond_stage_config self.first_stage_config = first_stage_config + self.cond_stage_config = cond_stage_config + self.clip_denoised = False + + self.cond_stage_forward = cond_stage_forward self.encoder_type = encoder_type assert(encoder_type in ["2d", "3d"]) + self.uncond_prob = uncond_prob + self.classifier_free_guidance = True if uncond_prob > 0 else False + assert(uncond_type in ["zero_embed", "empty_seq"]) + self.uncond_type = uncond_type + + self.restarted_from_ckpt = False - self.shift_factor = shift_factor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model) self.restarted_from_ckpt = True - self.split_clips = split_clips - self.downfactor_t = downfactor_t - self.clip_length = clip_length - # lora related args - self.inject_unet = getattr(lora_args, "inject_unet", False) - self.inject_clip = getattr(lora_args, "inject_clip", False) - self.inject_unet_key_word = getattr(lora_args, "inject_unet_key_word", None) - self.inject_clip_key_word = getattr(lora_args, "inject_clip_key_word", None) - self.lora_rank = getattr(lora_args, "lora_rank", 4) + def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids - def inject_lora(self, lora_scale=1.0): - if self.inject_unet: - self.lora_require_grad_params, self.lora_names = inject_trainable_lora(self.model, self.inject_unet_key_word, - r=self.lora_rank, - scale=lora_scale - ) - if self.inject_clip: - self.lora_require_grad_params_clip, self.lora_names_clip = inject_trainable_lora(self.cond_stage_model, self.inject_clip_key_word, - r=self.lora_rank, - scale=lora_scale - ) - - @rank_zero_only - @torch.no_grad() - def on_train_batch_start(self, batch, batch_idx, dataloader_idx=None): - # only for very first batch, reset the self.scale_factor - if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: - assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' - # set rescale weight to 1./std of encodings - print("### USING STD-RESCALING ###") - x = super().get_input(batch, self.first_stage_key) - x = x.to(self.device) - encoder_posterior = self.encode_first_stage(x) - z = self.get_first_stage_encoding(encoder_posterior).detach() - del self.scale_factor - self.register_buffer('scale_factor', 1. / z.flatten().std()) - print(f"setting self.scale_factor to {self.scale_factor}") - print("### USING STD-RESCALING ###") - print(f"std={z.flatten().std()}") - - def register_schedule(self, - given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + if self.use_scale: + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start * + extract_into_tensor(self.scale_arr, t, x_start.shape) + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + else: + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) - self.shorten_cond_schedule = self.num_timesteps_cond > 1 - if self.shorten_cond_schedule: - self.make_cond_schedule() + + def _freeze_model(self): + for name, para in self.model.diffusion_model.named_parameters(): + para.requires_grad = False def instantiate_first_stage(self, config): model = instantiate_from_config(config) @@ -610,40 +432,16 @@ class LatentDiffusion(DDPM): param.requires_grad = False def instantiate_cond_stage(self, config): - if config is None: - self.cond_stage_model = None - return if not self.cond_stage_trainable: - if config == "__is_first_stage__": - print("Using first stage also as cond stage.") - self.cond_stage_model = self.first_stage_model - elif config == "__is_unconditional__": - print(f"Training {self.__class__.__name__} as an unconditional model.") - self.cond_stage_model = None - else: - model = instantiate_from_config(config) - self.cond_stage_model = model.eval() - self.cond_stage_model.train = disabled_train - for param in self.cond_stage_model.parameters(): - param.requires_grad = False + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False else: - assert config != '__is_first_stage__' - assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model - - - def get_first_stage_encoding(self, encoder_posterior, noise=None): - if isinstance(encoder_posterior, DiagonalGaussianDistribution): - z = encoder_posterior.sample(noise=noise) - elif isinstance(encoder_posterior, torch.Tensor): - z = encoder_posterior - else: - raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") - z = self.scale_factor * (z + self.shift_factor) - return z - - + def get_learned_conditioning(self, c): if self.cond_stage_forward is None: if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): @@ -657,197 +455,61 @@ class LatentDiffusion(DDPM): c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) return c - - @torch.no_grad() - def get_condition(self, batch, x, bs, force_c_encode, k, cond_key, is_imgs=False): - is_conditional = self.model.conditioning_key is not None # crossattn - if is_conditional: - if cond_key is None: - cond_key = self.cond_stage_key - - # get condition batch of different condition type - if cond_key != self.first_stage_key: - assert(cond_key in ["caption", "txt"]) - xc = batch[cond_key] - else: - xc = x - - # if static video - if self.static_video: - xc_ = [c + ' (static)' for c in xc] - xc = xc_ - - # get learned condition. - # can directly skip it: c = xc - if self.cond_stage_config is not None and (not self.cond_stage_trainable or force_c_encode): - if isinstance(xc, torch.Tensor): - xc = xc.to(self.device) - c = self.get_learned_conditioning(xc) - else: - c = xc - - if self.classfier_free_guidance: - if cond_key in ['caption', "txt"] and self.uncond_type == 'empty_seq': - for i, ci in enumerate(c): - if random.random() < self.prob: - c[i] = "" - elif cond_key == 'class_label' and self.uncond_type == 'zero_embed': - pass - elif cond_key == 'class_label' and self.uncond_type == 'learned_embed': - import pdb;pdb.set_trace() - for i, ci in enumerate(c): - if random.random() < self.prob: - c[i]['class_label'] = self.n_classes - - else: - raise NotImplementedError - - if self.zero_cond_embed: - import pdb;pdb.set_trace() - c = torch.zeros_like(c) - - # process c - if bs is not None: - if (is_imgs and not self.static_video): - c = c[:bs*self.temporal_length] # each random img (in T axis) has a corresponding prompt - else: - c = c[:bs] - + def get_first_stage_encoding(self, encoder_posterior, noise=None): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample(noise=noise) + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior else: - c = None - xc = None - - return c, xc - + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + @torch.no_grad() - def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, - cond_key=None, return_original_cond=False, bs=None, mask_temporal=False): - """ Get input in LDM - """ - # get input imgaes - x = super().get_input(batch, k) # k = first_stage_key=image - is_imgs = True if k == 'jpg' else False - if is_imgs: - if self.static_video: - # repeat single img to a static video - x = x.unsqueeze(2) # bchw -> bc1hw - x = x.repeat(1,1,self.temporal_length,1,1) # bc1hw -> bcthw - else: - # rearrange to videos with T random img - bs_load = x.shape[0] // self.temporal_length - x = x[:bs_load*self.temporal_length, ...] - x = rearrange(x, '(b t) c h w -> b c t h w', t=self.temporal_length, b=bs_load) - - if bs is not None: - x = x[:bs] - - x = x.to(self.device) - x_ori = x - - b, _, t, h, w = x.shape - - # encode video frames x to z via a 2D encoder - x = rearrange(x, 'b c t h w -> (b t) c h w') - encoder_posterior = self.encode_first_stage(x, mask_temporal) - z = self.get_first_stage_encoding(encoder_posterior).detach() - z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) - + def encode_first_stage(self, x): + if self.encoder_type == "2d" and x.dim() == 5: + b, _, t, _, _ = x.shape + x = rearrange(x, 'b c t h w -> (b t) c h w') + reshape_back = True + else: + reshape_back = False - c, xc = self.get_condition(batch, x, bs, force_c_encode, k, cond_key, is_imgs) - out = [z, c] + encoder_posterior = self.first_stage_model.encode(x) + results = self.get_first_stage_encoding(encoder_posterior).detach() - if return_first_stage_outputs: - xrec = self.decode_first_stage(z, mask_temporal=mask_temporal) - out.extend([x_ori, xrec]) - if return_original_cond: - if isinstance(xc, torch.Tensor) and xc.dim() == 4: - xc = rearrange(xc, '(b t) c h w -> b c t h w', b=b, t=t) - out.append(xc) + if reshape_back: + results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) - return out - - @torch.no_grad() - def decode(self, z, **kwargs,): - z = 1. / self.scale_factor * z - self.shift_factor - results = self.first_stage_model.decode(z,**kwargs) return results @torch.no_grad() - def decode_first_stage_2DAE(self, z, decode_bs=16, return_cpu=True, **kwargs): - b, _, t, _, _ = z.shape - z = rearrange(z, 'b c t h w -> (b t) c h w') - if decode_bs is None: - results = self.decode(z, **kwargs) - else: - z = torch.split(z, decode_bs, dim=0) - if return_cpu: - results = torch.cat([self.decode(z_, **kwargs).cpu() for z_ in z], dim=0) - else: - results = torch.cat([self.decode(z_, **kwargs) for z_ in z], dim=0) - results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t).contiguous() - return results - - @torch.no_grad() - def decode_first_stage(self, z, decode_bs=16, return_cpu=True, **kwargs): - assert(self.encoder_type == "2d" and z.dim() == 5) - return self.decode_first_stage_2DAE(z, decode_bs=decode_bs, return_cpu=return_cpu, **kwargs) + def encode_first_stage_2DAE(self, x): - @torch.no_grad() - def encode_first_stage_2DAE(self, x, encode_bs=16): b, _, t, _, _ = x.shape - x = rearrange(x, 'b c t h w -> (b t) c h w') - if encode_bs is None: - results = self.first_stage_model.encode(x) - else: - x = torch.split(x, encode_bs, dim=0) - zs = [] - for x_ in x: - encoder_posterior = self.first_stage_model.encode(x_) - z = self.get_first_stage_encoding(encoder_posterior).detach() - zs.append(z) - results = torch.cat(zs, dim=0) - results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) + results = torch.cat([self.get_first_stage_encoding(self.first_stage_model.encode(x[:,:,i])).detach().unsqueeze(2) for i in range(t)], dim=2) + return results - @torch.no_grad() - def encode_first_stage(self, x): - assert(self.encoder_type == "2d" and x.dim() == 5) - b, _, t, _, _ = x.shape - x = rearrange(x, 'b c t h w -> (b t) c h w') - results = self.first_stage_model.encode(x) - results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) - return results + def decode_core(self, z, **kwargs): + if self.encoder_type == "2d" and z.dim() == 5: + b, _, t, _, _ = z.shape + z = rearrange(z, 'b c t h w -> (b t) c h w') + reshape_back = True + else: + reshape_back = False + + z = 1. / self.scale_factor * z - def shared_step(self, batch, **kwargs): - """ shared step of LDM. - If learned condition, c is raw condition (e.g. text) - Encoding condition is performed in below forward function. - """ - x, c = self.get_input(batch, self.first_stage_key) - loss = self(x, c) - return loss - - def forward(self, x, c, *args, **kwargs): - start_t = getattr(self, "start_t", 0) - end_t = getattr(self, "end_t", self.num_timesteps) - t = torch.randint(start_t, end_t, (x.shape[0],), device=self.device).long() - - if self.model.conditioning_key is not None: - assert c is not None - if self.cond_stage_trainable: - c = self.get_learned_conditioning(c) - if self.classfier_free_guidance and self.uncond_type == 'zero_embed': - for i, ci in enumerate(c): - if random.random() < self.prob: - c[i] = torch.zeros_like(c[i]) - if self.shorten_cond_schedule: # TODO: drop this option - tc = self.cond_ids[t].to(self.device) - c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) - - return self.p_losses(x, c, t, *args, **kwargs) + results = self.first_stage_model.decode(z, **kwargs) + + if reshape_back: + results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) + return results - def apply_model(self, x_noisy, t, cond, return_ids=False, **kwargs): + @torch.no_grad() + def decode_first_stage(self, z, **kwargs): + return self.decode_core(z, **kwargs) + def apply_model(self, x_noisy, t, cond, **kwargs): if isinstance(cond, dict): # hybrid case, cond is exptected to be a dict pass @@ -859,104 +521,55 @@ class LatentDiffusion(DDPM): x_recon = self.model(x_noisy, t, **cond, **kwargs) - if isinstance(x_recon, tuple) and not return_ids: + if isinstance(x_recon, tuple): return x_recon[0] else: return x_recon - def _predict_eps_from_xstart(self, x_t, t, pred_xstart): - return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + def _get_denoise_row_from_list(self, samples, desc=''): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device))) + n_log_timesteps = len(denoise_row) - def _prior_bpd(self, x_start): - """ - Get the prior KL term for the variational lower-bound, measured in - bits-per-dim. - This term can't be optimized, as it only depends on the encoder. - :param x_start: the [N x C x ...] tensor of inputs. - :return: a batch of [N] KL values (in bits), one per batch element. - """ - batch_size = x_start.shape[0] - t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) - qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) - kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) - return mean_flat(kl_prior) / np.log(2.0) - - def p_losses(self, x_start, cond, t, noise=None, skip_qsample=False, x_noisy=None, cond_mask=None, **kwargs,): - if not skip_qsample: - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + denoise_row = torch.stack(denoise_row) # n_log_timesteps, b, C, H, W + + if denoise_row.dim() == 5: + # img, num_imgs= n_log_timesteps * bs, grid_size=[bs,n_log_timesteps] + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps) + elif denoise_row.dim() == 6: + # video, grid_size=[n_log_timesteps*bs, t] + video_length = denoise_row.shape[3] + denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w') + denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w') + denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w') + denoise_grid = make_grid(denoise_grid, nrow=video_length) else: - assert(x_noisy is not None) - assert(noise is not None) - model_output = self.apply_model(x_noisy, t, cond, **kwargs) + raise ValueError - loss_dict = {} - prefix = 'train' if self.training else 'val' + return denoise_grid + - if self.parameterization == "x0": - target = x_start - elif self.parameterization == "eps": - target = noise - else: - raise NotImplementedError() - - loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3, 4]) - loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) - if self.logvar.device != self.device: - self.logvar = self.logvar.to(self.device) - logvar_t = self.logvar[t] - loss = loss_simple / torch.exp(logvar_t) + logvar_t - if self.learn_logvar: - loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) - loss_dict.update({'logvar': self.logvar.data.mean()}) + @torch.no_grad() + def decode_first_stage_2DAE(self, z, **kwargs): - loss = self.l_simple_weight * loss.mean() + b, _, t, _, _ = z.shape + z = 1. / self.scale_factor * z + results = torch.cat([self.first_stage_model.decode(z[:,:,i], **kwargs).unsqueeze(2) for i in range(t)], dim=2) - loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3, 4)) - loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() - loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) - loss += (self.original_elbo_weight * loss_vlb) - loss_dict.update({f'{prefix}/loss': loss}) + return results - return loss, loss_dict - def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, - return_x0=False, score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - uc_type=None,): + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs): t_in = t - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) - else: - # with unconditional condition - if isinstance(c, torch.Tensor): - x_in = torch.cat([x] * 2) - t_in = torch.cat([t] * 2) - c_in = torch.cat([unconditional_conditioning, c]) - model_out_uncond, model_out = self.apply_model(x_in, t_in, c_in, return_ids=return_codebook_ids).chunk(2) - elif isinstance(c, dict): - model_out = self.apply_model(x, t, c, return_ids=return_codebook_ids) - model_out_uncond = self.apply_model(x, t, unconditional_conditioning, return_ids=return_codebook_ids) - else: - raise NotImplementedError - if uc_type is None: - model_out = model_out_uncond + unconditional_guidance_scale * (model_out - model_out_uncond) - else: - if uc_type == 'cfg_original': - model_out = model_out + unconditional_guidance_scale * (model_out - model_out_uncond) - elif uc_type == 'cfg_ours': - model_out = model_out + unconditional_guidance_scale * (model_out_uncond - model_out) - else: - raise NotImplementedError + model_out = self.apply_model(x, t_in, c, **kwargs) if score_corrector is not None: assert self.parameterization == "eps" model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) - if return_codebook_ids: - model_out, logits = model_out - if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": @@ -966,34 +579,21 @@ class LatentDiffusion(DDPM): if clip_denoised: x_recon.clamp_(-1., 1.) - if quantize_denoised: - x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - if return_codebook_ids: - return model_mean, posterior_variance, posterior_log_variance, logits - elif return_x0: + + if return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() - def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, - return_codebook_ids=False, quantize_denoised=False, return_x0=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - uc_type=None,): + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \ + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs): b, *_, device = *x.shape, x.device - outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, - return_codebook_ids=return_codebook_ids, - quantize_denoised=quantize_denoised, - return_x0=return_x0, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - uc_type=uc_type,) - if return_codebook_ids: - raise DeprecationWarning("Support dropped.") - elif return_x0: + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \ + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs) + if return_x0: model_mean, _, model_log_variance, x0 = outputs else: model_mean, _, model_log_variance = outputs @@ -1001,99 +601,35 @@ class LatentDiffusion(DDPM): noise = noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) - + # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - if return_codebook_ids: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) if return_x0: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 else: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() - def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, - img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., - score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, - log_every_t=None): - if not log_every_t: - log_every_t = self.log_every_t - timesteps = self.num_timesteps - if batch_size is not None: - b = batch_size if batch_size is not None else shape[0] - shape = [batch_size] + list(shape) - else: - b = batch_size = shape[0] - if x_T is None: - img = torch.randn(shape, device=self.device) - else: - img = x_T - intermediates = [] - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - - if start_T is not None: - timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', - total=timesteps) if verbose else reversed( - range(0, timesteps)) - if type(temperature) == float: - temperature = [temperature] * timesteps - - for i in iterator: - ts = torch.full((b,), i, device=self.device, dtype=torch.long) - if self.shorten_cond_schedule: - assert self.model.conditioning_key != 'hybrid' - tc = self.cond_ids[ts].to(cond.device) - cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - - img, x0_partial = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised, return_x0=True, - temperature=temperature[i], noise_dropout=noise_dropout, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) - if mask is not None: - assert x0 is not None - img_orig = self.q_sample(x0, ts) - img = img_orig * mask + (1. - mask) * img - - if i % log_every_t == 0 or i == timesteps - 1: - intermediates.append(x0_partial) - if callback: callback(i) - if img_callback: img_callback(img, i) - return img, intermediates - - @torch.no_grad() - def p_sample_loop(self, cond, shape, return_intermediates=False, - x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, start_T=None, - log_every_t=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - uc_type=None,): + def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \ + timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device - b = shape[0] - + b = shape[0] # sample an initial noise if x_T is None: img = torch.randn(shape, device=device) else: img = x_T - + intermediates = [img] if timesteps is None: timesteps = self.num_timesteps - if start_T is not None: timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( - range(0, timesteps)) + + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps)) if mask is not None: assert x0 is not None @@ -1106,12 +642,7 @@ class LatentDiffusion(DDPM): tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - img = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - uc_type=uc_type) + img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs) if mask is not None: img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img @@ -1125,253 +656,54 @@ class LatentDiffusion(DDPM): return img, intermediates return img - @torch.no_grad() - def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, - verbose=True, timesteps=None, quantize_denoised=False, - mask=None, x0=None, shape=None, **kwargs): - if shape is None: - shape = (batch_size, self.channels, self.total_length, *self.image_size) - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - return self.p_sample_loop(cond, - shape, - return_intermediates=return_intermediates, x_T=x_T, - verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, - mask=mask, x0=x0,) - - @torch.no_grad() - def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): - - if ddim: - ddim_sampler = DDIMSampler(self) - shape = (self.channels, self.total_length, *self.image_size) - samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, - shape,cond,verbose=False, **kwargs) +class LatentVisualDiffusion(LatentDiffusion): + def __init__(self, cond_img_config, finegrained=False, random_cond=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.random_cond = random_cond + self.instantiate_img_embedder(cond_img_config, freeze=True) + num_tokens = 16 if finegrained else 4 + self.image_proj_model = self.init_projector(use_finegrained=finegrained, num_tokens=num_tokens, input_dim=1024,\ + cross_attention_dim=1024, dim=1280) + + def instantiate_img_embedder(self, config, freeze=True): + embedder = instantiate_from_config(config) + if freeze: + self.embedder = embedder.eval() + self.embedder.train = disabled_train + for param in self.embedder.parameters(): + param.requires_grad = False + + def init_projector(self, use_finegrained, num_tokens, input_dim, cross_attention_dim, dim): + if not use_finegrained: + image_proj_model = ImageProjModel(clip_extra_context_tokens=num_tokens, cross_attention_dim=cross_attention_dim, + clip_embeddings_dim=input_dim + ) else: - samples, intermediates = self.sample(cond=cond, batch_size=batch_size, - return_intermediates=True, **kwargs) + image_proj_model = Resampler(dim=input_dim, depth=4, dim_head=64, heads=12, num_queries=num_tokens, + embedding_dim=dim, output_dim=cross_attention_dim, ff_mult=4 + ) + return image_proj_model - return samples, intermediates - - @torch.no_grad() - def log_condition(self, log, batch, xc, x, c, cond_stage_key=None): - """ - xc: oringinal condition before enconding. - c: condition after encoding. - """ - if x.dim() == 5: - txt_img_shape = [x.shape[3], x.shape[4]] - elif x.dim() == 4: - txt_img_shape = [x.shape[2], x.shape[3]] - else: - raise ValueError - if self.model.conditioning_key is not None: #concat-time-mask - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif cond_stage_key in ["caption", "txt"]: - log["conditioning_txt_img"] = log_txt_as_img(txt_img_shape, batch[cond_stage_key], size=x.shape[3]//25) - log["conditioning_txt"] = batch[cond_stage_key] - elif cond_stage_key == 'class_label': - try: - xc = log_txt_as_img(txt_img_shape, batch["human_label"], size=x.shape[3]//25) - except: - xc = log_txt_as_img(txt_img_shape, batch["class_name"], size=x.shape[3]//25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - if isinstance(c, dict) and 'mask' in c: - log['mask'] =self.mask_to_rgb(c['mask']) - return log - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., unconditional_guidance_scale=1.0, - first_stage_key2=None, cond_key2=None, - c=None, - **kwargs): - """ log images for LatentDiffusion """ - use_ddim = ddim_steps is not None - is_imgs = first_stage_key2 is not None - if is_imgs: - assert(cond_key2 is not None) - log = dict() - - # get input - z, c, x, xrec, xc = self.get_input(batch, - k=self.first_stage_key if first_stage_key2 is None else first_stage_key2, - return_first_stage_outputs=True, - force_c_encode=True, - return_original_cond=True, - bs=N, - cond_key=cond_key2 if cond_key2 is not None else None, - ) - - N_ori = N - N = min(z.shape[0], N) - n_row = min(x.shape[0], n_row) + ## Never delete this func: it is used in log_images() and inference stage + def get_image_embeds(self, batch_imgs): + ## img: b c h w + img_token = self.embedder(batch_imgs) + img_emb = self.image_proj_model(img_token) + return img_emb - if unconditional_guidance_scale != 1.0: - prompts = N * self.temporal_length * [""] if (is_imgs and not self.static_video) else N * [""] - uc = self.get_condition_validate(prompts) - - else: - uc = None - - log["inputs"] = x - log["reconstruction"] = xrec - log = self.log_condition(log, batch, xc, x, c, - cond_stage_key=self.cond_stage_key if cond_key2 is None else cond_key2 - ) - - if sample: - with self.ema_scope("Plotting"): - samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, - ddim_steps=ddim_steps,eta=ddim_eta, - temporal_length=self.video_length, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, **kwargs, - ) - # decode samples - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - return log - - def configure_optimizers(self): - """ configure_optimizers for LatentDiffusion """ - lr = self.learning_rate - - # -------------------------------------------------------------------------------- - # set parameters - if hasattr(self, "only_optimize_empty_parameters") and self.only_optimize_empty_parameters: - print("[INFO] Optimize only empty parameters!") - assert(hasattr(self, "empty_paras")) - params = [p for n, p in self.model.named_parameters() if n in self.empty_paras] - elif hasattr(self, "only_optimize_pretrained_parameters") and self.only_optimize_pretrained_parameters: - print("[INFO] Optimize only pretrained parameters!") - assert(hasattr(self, "empty_paras")) - params = [p for n, p in self.model.named_parameters() if n not in self.empty_paras] - assert(len(params) != 0) - elif getattr(self, "optimize_empty_and_spatialattn", False): - print("[INFO] Optimize empty parameters + spatial transformer!") - assert(hasattr(self, "empty_paras")) - empty_paras = [p for n, p in self.model.named_parameters() if n in self.empty_paras] - SA_list = [".attn1.", ".attn2.", ".ff.", ".norm1.", ".norm2.", ".norm3."] - SA_params = [p for n, p in self.model.named_parameters() if check_istarget(n, SA_list)] - if getattr(self, "spatial_lr_decay", False): - params = [ - {"params": empty_paras}, - {"params": SA_params, "lr": lr * self.spatial_lr_decay} - ] - else: - params = empty_paras + SA_params - else: - # optimize whole denoiser - if hasattr(self, "spatial_lr_decay") and self.spatial_lr_decay: - print("[INFO] Optimize the whole net with different lr!") - print(f"[INFO] {lr} for empty paras, {lr * self.spatial_lr_decay} for pretrained paras!") - empty_paras = [p for n, p in self.model.named_parameters() if n in self.empty_paras] - # assert(len(empty_paras) == len(self.empty_paras)) # self.empty_paras:cond_stage_model.embedding.weight not in diffusion model params - pretrained_paras = [p for n, p in self.model.named_parameters() if n not in self.empty_paras] - params = [ - {"params": empty_paras}, - {"params": pretrained_paras, "lr": lr * self.spatial_lr_decay} - ] - print(f"[INFO] Empty paras: {len(empty_paras)}, Pretrained paras: {len(pretrained_paras)}") - - else: - params = list(self.model.parameters()) - - if hasattr(self, "generator_trainable") and not self.generator_trainable: - # fix unet denoiser - params = list() - - if self.inject_unet: - params = itertools.chain(*self.lora_require_grad_params) - - if self.inject_clip: - if self.inject_unet: - params = list(params)+list(itertools.chain(*self.lora_require_grad_params_clip)) - else: - params = itertools.chain(*self.lora_require_grad_params_clip) - - - # append paras - # ------------------------------------------------------------------ - def add_cond_model(cond_model, params): - if isinstance(params[0], dict): - # parameter groups - params.append({"params": list(cond_model.parameters())}) - else: - # parameter list: [torch.nn.parameter.Parameter] - params = params + list(cond_model.parameters()) - return params - # ------------------------------------------------------------------ - - if self.cond_stage_trainable: - # print(f"{self.__class__.__name__}: Also optimizing conditioner params!") - params = add_cond_model(self.cond_stage_model, params) - - if self.learn_logvar: - print('Diffusion model optimizing logvar') - if isinstance(params[0], dict): - params.append({"params": [self.logvar]}) - else: - params.append(self.logvar) - - # -------------------------------------------------------------------------------- - opt = torch.optim.AdamW(params, lr=lr) - - # lr scheduler - if self.use_scheduler: - assert 'target' in self.scheduler_config - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }] - return [opt], scheduler - - return opt - - @torch.no_grad() - def to_rgb(self, x): - x = x.float() - if not hasattr(self, "colorize"): - self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) - x = nn.functional.conv2d(x, weight=self.colorize) - x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. - return x - - @torch.no_grad() - def mask_to_rgb(self, x): - x = x * 255 - x = x.int() - return x class DiffusionWrapper(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.diffusion_model = instantiate_from_config(diff_model_config) - print('Successfully initialize the diffusion model !') self.conditioning_key = conditioning_key - # assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'resblockcond', 'hybrid-adm', 'hybrid-time'] def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None, s=None, mask=None, **kwargs): # temporal_context = fps is foNone if self.conditioning_key is None: - out = self.diffusion_model(x, t, **kwargs) + out = self.diffusion_model(x, t) elif self.conditioning_key == 'concat': xc = torch.cat([x] + c_concat, dim=1) out = self.diffusion_model(xc, t, **kwargs) @@ -1379,106 +711,53 @@ class DiffusionWrapper(pl.LightningModule): cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(x, t, context=cc, **kwargs) elif self.conditioning_key == 'hybrid': + ## it is just right [b,c,t,h,w]: concatenate in channel dim xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc, **kwargs) + out = self.diffusion_model(xc, t, context=cc) elif self.conditioning_key == 'resblockcond': cc = c_crossattn[0] - out = self.diffusion_model(x, t, context=cc, **kwargs) + out = self.diffusion_model(x, t, context=cc) elif self.conditioning_key == 'adm': cc = c_crossattn[0] - out = self.diffusion_model(x, t, y=cc, **kwargs) + out = self.diffusion_model(x, t, y=cc) elif self.conditioning_key == 'hybrid-adm': assert c_adm is not None xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs) + out = self.diffusion_model(xc, t, context=cc, y=c_adm) elif self.conditioning_key == 'hybrid-time': assert s is not None xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc, s=s, **kwargs) + out = self.diffusion_model(xc, t, context=cc, s=s) elif self.conditioning_key == 'concat-time-mask': # assert s is not None - # print('x & mask:',x.shape,c_concat[0].shape) + # mainlogger.info('x & mask:',x.shape,c_concat[0].shape) xc = torch.cat([x] + c_concat, dim=1) - out = self.diffusion_model(xc, t, context=None, s=s, mask=mask, **kwargs) + out = self.diffusion_model(xc, t, context=None, s=s, mask=mask) elif self.conditioning_key == 'concat-adm-mask': # assert s is not None - # print('x & mask:',x.shape,c_concat[0].shape) + # mainlogger.info('x & mask:',x.shape,c_concat[0].shape) if c_concat is not None: xc = torch.cat([x] + c_concat, dim=1) else: xc = x - out = self.diffusion_model(xc, t, context=None, y=s, mask=mask, **kwargs) - elif self.conditioning_key == 'crossattn-adm': - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(x, t, context=cc, y=s, **kwargs) + out = self.diffusion_model(xc, t, context=None, y=s, mask=mask) elif self.conditioning_key == 'hybrid-adm-mask': cc = torch.cat(c_crossattn, 1) if c_concat is not None: xc = torch.cat([x] + c_concat, dim=1) else: xc = x - out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask, **kwargs) + out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask) elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index # assert s is not None assert c_adm is not None xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm, **kwargs) + out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm) else: raise NotImplementedError() - return out - - -class T2VAdapterDepth(LatentDiffusion): - def __init__(self, depth_stage_config, adapter_config, *args, **kwargs): - super(T2VAdapterDepth, self).__init__(*args, **kwargs) - self.adapter = instantiate_from_config(adapter_config) - self.condtype = adapter_config.cond_name - self.depth_stage_model = instantiate_from_config(depth_stage_config) - - def prepare_midas_input(self, batch_x): - # input: b,c,h,w - x_midas = torch.nn.functional.interpolate(batch_x, size=(384, 384), mode='bicubic') - return x_midas - - @torch.no_grad() - def get_batch_depth(self, batch_x, target_size, encode_bs=1): - b, c, t, h, w = batch_x.shape - merge_x = rearrange(batch_x, 'b c t h w -> (b t) c h w') - split_x = torch.split(merge_x, encode_bs, dim=0) - cond_depth_list = [] - for x in split_x: - x_midas = self.prepare_midas_input(x) - cond_depth = self.depth_stage_model(x_midas) - cond_depth = torch.nn.functional.interpolate( - cond_depth, - size=target_size, - mode="bicubic", - align_corners=False, - ) - depth_min, depth_max = torch.amin(cond_depth, dim=[1, 2, 3], keepdim=True), torch.amax(cond_depth, dim=[1, 2, 3], keepdim=True) - cond_depth = 2. * (cond_depth - depth_min) / (depth_max - depth_min + 1e-7) - 1. - cond_depth_list.append(cond_depth) - batch_cond_depth=torch.cat(cond_depth_list, dim=0) - batch_cond_depth = rearrange(batch_cond_depth, '(b t) c h w -> b c t h w', b=b, t=t) - return batch_cond_depth - - def get_adapter_features(self, extra_cond, encode_bs=1): - b, c, t, h, w = extra_cond.shape - ## process in 2D manner - merge_extra_cond = rearrange(extra_cond, 'b c t h w -> (b t) c h w') - split_extra_cond = torch.split(merge_extra_cond, encode_bs, dim=0) - features_adapter_list = [] - for extra_cond in split_extra_cond: - features_adapter = self.adapter(extra_cond) - features_adapter_list.append(features_adapter) - merge_features_adapter_list = [] - for i in range(len(features_adapter_list[0])): - merge_features_adapter = torch.cat([features_adapter_list[num][i] for num in range(len(features_adapter_list))], dim=0) - merge_features_adapter_list.append(merge_features_adapter) - merge_features_adapter_list = [rearrange(feature, '(b t) c h w -> b c t h w', b=b, t=t) for feature in merge_features_adapter_list] - return merge_features_adapter_list + return out \ No newline at end of file diff --git a/lvdm/models/modules/adapter.py b/lvdm/models/modules/adapter.py deleted file mode 100644 index d7be40faba88bfd96e5f4a08537191505371a052..0000000000000000000000000000000000000000 --- a/lvdm/models/modules/adapter.py +++ /dev/null @@ -1,105 +0,0 @@ -import torch -import torch.nn as nn -from collections import OrderedDict -from lvdm.models.modules.util import ( - zero_module, - conv_nd, - avg_pool_nd -) - -class Downsample(nn.Module): - """ - A downsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - downsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - if use_conv: - self.op = conv_nd( - dims, self.channels, self.out_channels, 3, stride=stride, padding=padding - ) - else: - assert self.channels == self.out_channels - self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) - - def forward(self, x): - assert x.shape[1] == self.channels - return self.op(x) - - -class ResnetBlock(nn.Module): - def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): - super().__init__() - ps = ksize // 2 - if in_c != out_c or sk == False: - self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) - else: - # print('n_in') - self.in_conv = None - self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) - self.act = nn.ReLU() - self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) - if sk == False: - self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) - else: - self.skep = None - - self.down = down - if self.down == True: - self.down_opt = Downsample(in_c, use_conv=use_conv) - - def forward(self, x): - if self.down == True: - x = self.down_opt(x) - if self.in_conv is not None: # edit - x = self.in_conv(x) - - h = self.block1(x) - h = self.act(h) - h = self.block2(h) - if self.skep is not None: - return h + self.skep(x) - else: - return h + x - - -class Adapter(nn.Module): - def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True): - super(Adapter, self).__init__() - self.unshuffle = nn.PixelUnshuffle(8) - self.channels = channels - self.nums_rb = nums_rb - self.body = [] - for i in range(len(channels)): - for j in range(nums_rb): - if (i != 0) and (j == 0): - self.body.append( - ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) - else: - self.body.append( - ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) - self.body = nn.ModuleList(self.body) - self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) - - def forward(self, x): - # unshuffle - x = self.unshuffle(x) - # extract features - features = [] - x = self.conv_in(x) - for i in range(len(self.channels)): - for j in range(self.nums_rb): - idx = i * self.nums_rb + j - x = self.body[idx](x) - features.append(x) - - return features \ No newline at end of file diff --git a/lvdm/models/modules/attention_temporal.py b/lvdm/models/modules/attention_temporal.py deleted file mode 100644 index 64e728b0ba03baa7a3accc62572a293f71cf5e1a..0000000000000000000000000000000000000000 --- a/lvdm/models/modules/attention_temporal.py +++ /dev/null @@ -1,399 +0,0 @@ -from typing import Optional, Any - -import torch -import torch as th -from torch import nn, einsum -from einops import rearrange, repeat -try: - import xformers - import xformers.ops - XFORMERS_IS_AVAILBLE = True -except: - XFORMERS_IS_AVAILBLE = False - -from lvdm.models.modules.util import ( - GEGLU, - exists, - default, - Normalize, - checkpoint, - zero_module, -) - - -# --------------------------------------------------------------------------------------------------- -class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): - super().__init__() - inner_dim = int(dim * mult) - dim_out = default(dim_out, dim) - project_in = nn.Sequential( - nn.Linear(dim, inner_dim), - nn.GELU() - ) if not glu else GEGLU(dim, inner_dim) - - self.net = nn.Sequential( - project_in, - nn.Dropout(dropout), - nn.Linear(inner_dim, dim_out) - ) - - def forward(self, x): - return self.net(x) - - -# --------------------------------------------------------------------------------------------------- -class RelativePosition(nn.Module): - """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """ - - def __init__(self, num_units, max_relative_position): - super().__init__() - self.num_units = num_units - self.max_relative_position = max_relative_position - self.embeddings_table = nn.Parameter(th.Tensor(max_relative_position * 2 + 1, num_units)) - nn.init.xavier_uniform_(self.embeddings_table) - - def forward(self, length_q, length_k): - device = self.embeddings_table.device - range_vec_q = th.arange(length_q, device=device) - range_vec_k = th.arange(length_k, device=device) - distance_mat = range_vec_k[None, :] - range_vec_q[:, None] - distance_mat_clipped = th.clamp(distance_mat, -self.max_relative_position, self.max_relative_position) - final_mat = distance_mat_clipped + self.max_relative_position - final_mat = final_mat.long() - embeddings = self.embeddings_table[final_mat] - return embeddings - - -# --------------------------------------------------------------------------------------------------- -class TemporalCrossAttention(nn.Module): - def __init__(self, - query_dim, - context_dim=None, - heads=8, - dim_head=64, - dropout=0., - use_relative_position=False, # whether use relative positional representation in temporal attention. - temporal_length=None, # relative positional representation - **kwargs, - ): - super().__init__() - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - self.context_dim = context_dim - self.scale = dim_head ** -0.5 - self.heads = heads - self.temporal_length = temporal_length - self.use_relative_position = use_relative_position - self.to_q = nn.Linear(query_dim, inner_dim, bias=False) - self.to_k = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v = nn.Linear(context_dim, inner_dim, bias=False) - self.to_out = nn.Sequential( - nn.Linear(inner_dim, query_dim), - nn.Dropout(dropout) - ) - - if use_relative_position: - assert(temporal_length is not None) - self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) - self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) - - nn.init.constant_(self.to_q.weight, 0) - nn.init.constant_(self.to_k.weight, 0) - nn.init.constant_(self.to_v.weight, 0) - nn.init.constant_(self.to_out[0].weight, 0) - nn.init.constant_(self.to_out[0].bias, 0) - - def forward(self, x, context=None, mask=None): - nh = self.heads - out = x - - # cal qkv - q = self.to_q(out) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=nh), (q, k, v)) - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - - # relative positional embedding - if self.use_relative_position: - len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] - k2 = self.relative_position_k(len_q, len_k) - sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale - sim += sim2 - - # mask attention - if mask is not None: - max_neg_value = -1e9 - sim = sim + (1-mask.float()) * max_neg_value # 1=masking,0=no masking - - # attend to values - attn = sim.softmax(dim=-1) - out = einsum('b i j, b j d -> b i d', attn, v) - - # relative positional embedding - if self.use_relative_position: - v2 = self.relative_position_v(len_q, len_v) - out2 = einsum('b t s, t s d -> b t d', attn, v2) - out += out2 - - # merge head - out = rearrange(out, '(b h) n d -> b n (h d)', h=nh) - return self.to_out(out) - - -# --------------------------------------------------------------------------------------------------- -class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., - **kwargs,): - super().__init__() - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - - self.scale = dim_head ** -0.5 - self.heads = heads - - self.to_q = nn.Linear(query_dim, inner_dim, bias=False) - self.to_k = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v = nn.Linear(context_dim, inner_dim, bias=False) - - self.to_out = nn.Sequential( - nn.Linear(inner_dim, query_dim), - nn.Dropout(dropout) - ) - - def forward(self, x, context=None, mask=None): - h = self.heads - b = x.shape[0] - - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) - - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - - if exists(mask): - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - - attn = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', attn, v) - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - return self.to_out(out) - - -# --------------------------------------------------------------------------------------------------- -class MemoryEfficientCrossAttention(nn.Module): - """https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 - """ - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, - **kwargs,): - super().__init__() - print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " - f"{heads} heads." - ) - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - - self.heads = heads - self.dim_head = dim_head - - self.to_q = nn.Linear(query_dim, inner_dim, bias=False) - self.to_k = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v = nn.Linear(context_dim, inner_dim, bias=False) - - self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) - self.attention_op: Optional[Any] = None - - def forward(self, x, context=None, mask=None): - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - b, _, _ = q.shape - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, t.shape[1], self.heads, self.dim_head) - .permute(0, 2, 1, 3) - .reshape(b * self.heads, t.shape[1], self.dim_head) - .contiguous(), - (q, k, v), - ) - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) - - if exists(mask): - raise NotImplementedError - out = ( - out.unsqueeze(0) - .reshape(b, self.heads, out.shape[1], self.dim_head) - .permute(0, 2, 1, 3) - .reshape(b, out.shape[1], self.heads * self.dim_head) - ) - return self.to_out(out) - - -# --------------------------------------------------------------------------------------------------- -class BasicTransformerBlockST(nn.Module): - """ - if no context is given to forward function, cross-attention defaults to self-attention - """ - def __init__(self, - # Spatial - dim, - n_heads, - d_head, - dropout=0., - context_dim=None, - gated_ff=True, - checkpoint=True, - # Temporal - temporal_length=None, - use_relative_position=True, - **kwargs, - ): - super().__init__() - - # spatial self attention (if context_dim is None) and spatial cross attention - if XFORMERS_IS_AVAILBLE: - self.attn1 = MemoryEfficientCrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, **kwargs,) - self.attn2 = MemoryEfficientCrossAttention(query_dim=dim, context_dim=context_dim, - heads=n_heads, dim_head=d_head, dropout=dropout, **kwargs,) - else: - self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, **kwargs,) - self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, - heads=n_heads, dim_head=d_head, dropout=dropout, **kwargs,) - self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) - - self.norm1 = nn.LayerNorm(dim) - self.norm2 = nn.LayerNorm(dim) - self.norm3 = nn.LayerNorm(dim) - self.checkpoint = checkpoint - - # Temporal attention - self.attn1_tmp = TemporalCrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, - temporal_length=temporal_length, - use_relative_position=use_relative_position, - **kwargs, - ) - self.attn2_tmp = TemporalCrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, - # cross attn - context_dim=None, - # temporal attn - temporal_length=temporal_length, - use_relative_position=use_relative_position, - **kwargs, - ) - self.norm4 = nn.LayerNorm(dim) - self.norm5 = nn.LayerNorm(dim) - - def forward(self, x, context=None, **kwargs): - return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) - - def _forward(self, x, context=None, mask=None,): - assert(x.dim() == 5), f"x shape = {x.shape}" - b, c, t, h, w = x.shape - - # spatial self attention - x = rearrange(x, 'b c t h w -> (b t) (h w) c') - x = self.attn1(self.norm1(x)) + x - x = rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h) - - # temporal self attention - x = rearrange(x, 'b c t h w -> (b h w) t c') - x = self.attn1_tmp(self.norm4(x), mask=mask) + x - x = rearrange(x, '(b h w) t c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d - - # spatial cross attention - x = rearrange(x, 'b c t h w -> (b t) (h w) c') - if context is not None: - context_ = [] - for i in range(context.shape[0]): - context_.append(context[i].unsqueeze(0).repeat(t, 1, 1)) - context_ = torch.cat(context_,dim=0) - else: - context_ = None - x = self.attn2(self.norm2(x), context=context_) + x - x = rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h) - - # temporal cross attention - x = rearrange(x, 'b c t h w -> (b h w) t c') - x = self.attn2_tmp(self.norm5(x), context=None, mask=mask) + x - - # feedforward - x = self.ff(self.norm3(x)) + x - x = rearrange(x, '(b h w) t c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d - - return x - - -# --------------------------------------------------------------------------------------------------- -class SpatialTemporalTransformer(nn.Module): - """ - Transformer block for video-like data (5D tensor). - First, project the input (aka embedding) with NO reshape. - Then apply standard transformer action. - The 5D -> 3D reshape operation will be done in the specific attention module. - """ - def __init__( - self, - in_channels, n_heads, d_head, - depth=1, dropout=0., - context_dim=None, - # Temporal - temporal_length=None, - use_relative_position=True, - **kwargs, - ): - super().__init__() - - self.in_channels = in_channels - inner_dim = n_heads * d_head - - self.norm = Normalize(in_channels) - self.proj_in = nn.Conv3d(in_channels, - inner_dim, - kernel_size=1, - stride=1, - padding=0) - - self.transformer_blocks = nn.ModuleList( - [BasicTransformerBlockST( - inner_dim, n_heads, d_head, dropout=dropout, - # cross attn - context_dim=context_dim, - # temporal attn - temporal_length=temporal_length, - use_relative_position=use_relative_position, - **kwargs - ) for d in range(depth)] - ) - - self.proj_out = zero_module(nn.Conv3d(inner_dim, - in_channels, - kernel_size=1, - stride=1, - padding=0)) - - def forward(self, x, context=None, **kwargs): - - assert(x.dim() == 5), f"x shape = {x.shape}" - x_in = x - - x = self.norm(x) - x = self.proj_in(x) - - for block in self.transformer_blocks: - x = block(x, context=context, **kwargs) - - x = self.proj_out(x) - - return x + x_in diff --git a/lvdm/models/modules/condition_modules.py b/lvdm/models/modules/condition_modules.py deleted file mode 100644 index e9c1cf989ad1fee7f5febe36bee3e21d8f0437d2..0000000000000000000000000000000000000000 --- a/lvdm/models/modules/condition_modules.py +++ /dev/null @@ -1,40 +0,0 @@ -import torch.nn as nn -from transformers import logging -from transformers import CLIPTokenizer, CLIPTextModel -logging.set_verbosity_error() - - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - -class FrozenCLIPEmbedder(AbstractEncoder): - """Uses the CLIP transformer encoder for text (from huggingface)""" - def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): - super().__init__() - self.tokenizer = CLIPTokenizer.from_pretrained(version) - self.transformer = CLIPTextModel.from_pretrained(version) - self.device = device - self.max_length = max_length - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens) - - z = outputs.last_hidden_state - return z - - def encode(self, text): - return self(text) diff --git a/lvdm/models/modules/lora.py b/lvdm/models/modules/lora.py deleted file mode 100644 index a2b64b72b9ac0788d23d34660fc3ac92163e4890..0000000000000000000000000000000000000000 --- a/lvdm/models/modules/lora.py +++ /dev/null @@ -1,1251 +0,0 @@ -import json -from itertools import groupby -from typing import Dict, List, Optional, Set, Tuple, Type, Union - - -import torch -import torch.nn as nn -import torch.nn.functional as F - -# try: -# from safetensors.torch import safe_open -# from safetensors.torch import save_file as safe_save - -# safetensors_available = True -# except ImportError: -# from .safe_open import safe_open - -# def safe_save( -# tensors: Dict[str, torch.Tensor], -# filename: str, -# metadata: Optional[Dict[str, str]] = None, -# ) -> None: -# raise EnvironmentError( -# "Saving safetensors requires the safetensors library. Please install with pip or similar." -# ) - -# safetensors_available = False - - -class LoraInjectedLinear(nn.Module): - def __init__( - self, in_features, out_features, bias=False, r=4, dropout_p=0.1, scale=1.0 - ): - super().__init__() - - if r > min(in_features, out_features): - raise ValueError( - f"LoRA rank {r} must be less or equal than {min(in_features, out_features)}" - ) - self.r = r - self.linear = nn.Linear(in_features, out_features, bias) - self.lora_down = nn.Linear(in_features, r, bias=False) - self.dropout = nn.Dropout(dropout_p) - self.lora_up = nn.Linear(r, out_features, bias=False) - self.scale = scale - self.selector = nn.Identity() - - nn.init.normal_(self.lora_down.weight, std=1 / r) - nn.init.zeros_(self.lora_up.weight) - - def forward(self, input): - return ( - self.linear(input) - + self.dropout(self.lora_up(self.selector(self.lora_down(input)))) - * self.scale - ) - - def realize_as_lora(self): - return self.lora_up.weight.data * self.scale, self.lora_down.weight.data - - def set_selector_from_diag(self, diag: torch.Tensor): - # diag is a 1D tensor of size (r,) - assert diag.shape == (self.r,) - self.selector = nn.Linear(self.r, self.r, bias=False) - self.selector.weight.data = torch.diag(diag) - self.selector.weight.data = self.selector.weight.data.to( - self.lora_up.weight.device - ).to(self.lora_up.weight.dtype) - - -class LoraInjectedConv2d(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups: int = 1, - bias: bool = True, - r: int = 4, - dropout_p: float = 0.1, - scale: float = 1.0, - ): - super().__init__() - if r > min(in_channels, out_channels): - raise ValueError( - f"LoRA rank {r} must be less or equal than {min(in_channels, out_channels)}" - ) - self.r = r - self.conv = nn.Conv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - bias=bias, - ) - - self.lora_down = nn.Conv2d( - in_channels=in_channels, - out_channels=r, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - bias=False, - ) - self.dropout = nn.Dropout(dropout_p) - self.lora_up = nn.Conv2d( - in_channels=r, - out_channels=out_channels, - kernel_size=1, - stride=1, - padding=0, - bias=False, - ) - self.selector = nn.Identity() - self.scale = scale - - nn.init.normal_(self.lora_down.weight, std=1 / r) - nn.init.zeros_(self.lora_up.weight) - - def forward(self, input): - return ( - self.conv(input) - + self.dropout(self.lora_up(self.selector(self.lora_down(input)))) - * self.scale - ) - - def realize_as_lora(self): - return self.lora_up.weight.data * self.scale, self.lora_down.weight.data - - def set_selector_from_diag(self, diag: torch.Tensor): - # diag is a 1D tensor of size (r,) - assert diag.shape == (self.r,) - self.selector = nn.Conv2d( - in_channels=self.r, - out_channels=self.r, - kernel_size=1, - stride=1, - padding=0, - bias=False, - ) - self.selector.weight.data = torch.diag(diag) - - # same device + dtype as lora_up - self.selector.weight.data = self.selector.weight.data.to( - self.lora_up.weight.device - ).to(self.lora_up.weight.dtype) - - -UNET_DEFAULT_TARGET_REPLACE = {"MemoryEfficientCrossAttention","CrossAttention", "Attention", "GEGLU"} - -UNET_EXTENDED_TARGET_REPLACE = {"TimestepEmbedSequential","SpatialTemporalTransformer", "MemoryEfficientCrossAttention","CrossAttention", "Attention", "GEGLU"} - -TEXT_ENCODER_DEFAULT_TARGET_REPLACE = {"CLIPAttention"} - -TEXT_ENCODER_EXTENDED_TARGET_REPLACE = {"CLIPMLP","CLIPAttention"} - -DEFAULT_TARGET_REPLACE = UNET_DEFAULT_TARGET_REPLACE - -EMBED_FLAG = "" - - -def _find_children( - model, - search_class: List[Type[nn.Module]] = [nn.Linear], -): - """ - Find all modules of a certain class (or union of classes). - - Returns all matching modules, along with the parent of those moduless and the - names they are referenced by. - """ - # For each target find every linear_class module that isn't a child of a LoraInjectedLinear - for parent in model.modules(): - for name, module in parent.named_children(): - if any([isinstance(module, _class) for _class in search_class]): - yield parent, name, module - - -def _find_modules_v2( - model, - ancestor_class: Optional[Set[str]] = None, - search_class: List[Type[nn.Module]] = [nn.Linear], - exclude_children_of: Optional[List[Type[nn.Module]]] = [ - LoraInjectedLinear, - LoraInjectedConv2d, - ], -): - """ - Find all modules of a certain class (or union of classes) that are direct or - indirect descendants of other modules of a certain class (or union of classes). - - Returns all matching modules, along with the parent of those moduless and the - names they are referenced by. - """ - - # Get the targets we should replace all linears under - if type(ancestor_class) is not set: - ancestor_class = set(ancestor_class) - print(ancestor_class) - if ancestor_class is not None: - ancestors = ( - module - for module in model.modules() - if module.__class__.__name__ in ancestor_class - ) - else: - # this, incase you want to naively iterate over all modules. - ancestors = [module for module in model.modules()] - - # For each target find every linear_class module that isn't a child of a LoraInjectedLinear - for ancestor in ancestors: - for fullname, module in ancestor.named_children(): - if any([isinstance(module, _class) for _class in search_class]): - # Find the direct parent if this is a descendant, not a child, of target - *path, name = fullname.split(".") - parent = ancestor - while path: - parent = parent.get_submodule(path.pop(0)) - # Skip this linear if it's a child of a LoraInjectedLinear - if exclude_children_of and any( - [isinstance(parent, _class) for _class in exclude_children_of] - ): - continue - # Otherwise, yield it - yield parent, name, module - - -def _find_modules_old( - model, - ancestor_class: Set[str] = DEFAULT_TARGET_REPLACE, - search_class: List[Type[nn.Module]] = [nn.Linear], - exclude_children_of: Optional[List[Type[nn.Module]]] = [LoraInjectedLinear], -): - ret = [] - for _module in model.modules(): - if _module.__class__.__name__ in ancestor_class: - - for name, _child_module in _module.named_children(): - if _child_module.__class__ in search_class: - ret.append((_module, name, _child_module)) - print(ret) - return ret - - -_find_modules = _find_modules_v2 - - -def inject_trainable_lora( - model: nn.Module, - target_replace_module: Set[str] = DEFAULT_TARGET_REPLACE, - r: int = 4, - loras=None, # path to lora .pt - verbose: bool = False, - dropout_p: float = 0.0, - scale: float = 1.0, -): - """ - inject lora into model, and returns lora parameter groups. - """ - - require_grad_params = [] - names = [] - - if loras != None: - loras = torch.load(loras) - - for _module, name, _child_module in _find_modules( - model, target_replace_module, search_class=[nn.Linear] - ): - weight = _child_module.weight - bias = _child_module.bias - if verbose: - print("LoRA Injection : injecting lora into ", name) - print("LoRA Injection : weight shape", weight.shape) - _tmp = LoraInjectedLinear( - _child_module.in_features, - _child_module.out_features, - _child_module.bias is not None, - r=r, - dropout_p=dropout_p, - scale=scale, - ) - _tmp.linear.weight = weight - if bias is not None: - _tmp.linear.bias = bias - - # switch the module - _tmp.to(_child_module.weight.device).to(_child_module.weight.dtype) - _module._modules[name] = _tmp - - require_grad_params.append(_module._modules[name].lora_up.parameters()) - require_grad_params.append(_module._modules[name].lora_down.parameters()) - - if loras != None: - _module._modules[name].lora_up.weight = loras.pop(0) - _module._modules[name].lora_down.weight = loras.pop(0) - - _module._modules[name].lora_up.weight.requires_grad = True - _module._modules[name].lora_down.weight.requires_grad = True - names.append(name) - - return require_grad_params, names - - -def inject_trainable_lora_extended( - model: nn.Module, - target_replace_module: Set[str] = UNET_EXTENDED_TARGET_REPLACE, - r: int = 4, - loras=None, # path to lora .pt -): - """ - inject lora into model, and returns lora parameter groups. - """ - - require_grad_params = [] - names = [] - - if loras != None: - loras = torch.load(loras) - - for _module, name, _child_module in _find_modules( - model, target_replace_module, search_class=[nn.Linear, nn.Conv2d] - ): - if _child_module.__class__ == nn.Linear: - weight = _child_module.weight - bias = _child_module.bias - _tmp = LoraInjectedLinear( - _child_module.in_features, - _child_module.out_features, - _child_module.bias is not None, - r=r, - ) - _tmp.linear.weight = weight - if bias is not None: - _tmp.linear.bias = bias - elif _child_module.__class__ == nn.Conv2d: - weight = _child_module.weight - bias = _child_module.bias - _tmp = LoraInjectedConv2d( - _child_module.in_channels, - _child_module.out_channels, - _child_module.kernel_size, - _child_module.stride, - _child_module.padding, - _child_module.dilation, - _child_module.groups, - _child_module.bias is not None, - r=r, - ) - - _tmp.conv.weight = weight - if bias is not None: - _tmp.conv.bias = bias - - # switch the module - _tmp.to(_child_module.weight.device).to(_child_module.weight.dtype) - if bias is not None: - _tmp.to(_child_module.bias.device).to(_child_module.bias.dtype) - - _module._modules[name] = _tmp - - require_grad_params.append(_module._modules[name].lora_up.parameters()) - require_grad_params.append(_module._modules[name].lora_down.parameters()) - - if loras != None: - _module._modules[name].lora_up.weight = loras.pop(0) - _module._modules[name].lora_down.weight = loras.pop(0) - - _module._modules[name].lora_up.weight.requires_grad = True - _module._modules[name].lora_down.weight.requires_grad = True - names.append(name) - - return require_grad_params, names - - -def extract_lora_ups_down(model, target_replace_module=DEFAULT_TARGET_REPLACE): - - loras = [] - - for _m, _n, _child_module in _find_modules( - model, - target_replace_module, - search_class=[LoraInjectedLinear, LoraInjectedConv2d], - ): - loras.append((_child_module.lora_up, _child_module.lora_down)) - - if len(loras) == 0: - raise ValueError("No lora injected.") - - return loras - - -def extract_lora_as_tensor( - model, target_replace_module=DEFAULT_TARGET_REPLACE, as_fp16=True -): - - loras = [] - - for _m, _n, _child_module in _find_modules( - model, - target_replace_module, - search_class=[LoraInjectedLinear, LoraInjectedConv2d], - ): - up, down = _child_module.realize_as_lora() - if as_fp16: - up = up.to(torch.float16) - down = down.to(torch.float16) - - loras.append((up, down)) - - if len(loras) == 0: - raise ValueError("No lora injected.") - - return loras - - -def save_lora_weight( - model, - path="./lora.pt", - target_replace_module=DEFAULT_TARGET_REPLACE, -): - weights = [] - for _up, _down in extract_lora_ups_down( - model, target_replace_module=target_replace_module - ): - weights.append(_up.weight.to("cpu").to(torch.float16)) - weights.append(_down.weight.to("cpu").to(torch.float16)) - - torch.save(weights, path) - - -def save_lora_as_json(model, path="./lora.json"): - weights = [] - for _up, _down in extract_lora_ups_down(model): - weights.append(_up.weight.detach().cpu().numpy().tolist()) - weights.append(_down.weight.detach().cpu().numpy().tolist()) - - import json - - with open(path, "w") as f: - json.dump(weights, f) - - -def save_safeloras_with_embeds( - modelmap: Dict[str, Tuple[nn.Module, Set[str]]] = {}, - embeds: Dict[str, torch.Tensor] = {}, - outpath="./lora.safetensors", -): - """ - Saves the Lora from multiple modules in a single safetensor file. - - modelmap is a dictionary of { - "module name": (module, target_replace_module) - } - """ - weights = {} - metadata = {} - - for name, (model, target_replace_module) in modelmap.items(): - metadata[name] = json.dumps(list(target_replace_module)) - - for i, (_up, _down) in enumerate( - extract_lora_as_tensor(model, target_replace_module) - ): - rank = _down.shape[0] - - metadata[f"{name}:{i}:rank"] = str(rank) - weights[f"{name}:{i}:up"] = _up - weights[f"{name}:{i}:down"] = _down - - for token, tensor in embeds.items(): - metadata[token] = EMBED_FLAG - weights[token] = tensor - - print(f"Saving weights to {outpath}") - safe_save(weights, outpath, metadata) - - -def save_safeloras( - modelmap: Dict[str, Tuple[nn.Module, Set[str]]] = {}, - outpath="./lora.safetensors", -): - return save_safeloras_with_embeds(modelmap=modelmap, outpath=outpath) - - -def convert_loras_to_safeloras_with_embeds( - modelmap: Dict[str, Tuple[str, Set[str], int]] = {}, - embeds: Dict[str, torch.Tensor] = {}, - outpath="./lora.safetensors", -): - """ - Converts the Lora from multiple pytorch .pt files into a single safetensor file. - - modelmap is a dictionary of { - "module name": (pytorch_model_path, target_replace_module, rank) - } - """ - - weights = {} - metadata = {} - - for name, (path, target_replace_module, r) in modelmap.items(): - metadata[name] = json.dumps(list(target_replace_module)) - - lora = torch.load(path) - for i, weight in enumerate(lora): - is_up = i % 2 == 0 - i = i // 2 - - if is_up: - metadata[f"{name}:{i}:rank"] = str(r) - weights[f"{name}:{i}:up"] = weight - else: - weights[f"{name}:{i}:down"] = weight - - for token, tensor in embeds.items(): - metadata[token] = EMBED_FLAG - weights[token] = tensor - - print(f"Saving weights to {outpath}") - safe_save(weights, outpath, metadata) - - -def convert_loras_to_safeloras( - modelmap: Dict[str, Tuple[str, Set[str], int]] = {}, - outpath="./lora.safetensors", -): - convert_loras_to_safeloras_with_embeds(modelmap=modelmap, outpath=outpath) - - -def parse_safeloras( - safeloras, -) -> Dict[str, Tuple[List[nn.parameter.Parameter], List[int], List[str]]]: - """ - Converts a loaded safetensor file that contains a set of module Loras - into Parameters and other information - - Output is a dictionary of { - "module name": ( - [list of weights], - [list of ranks], - target_replacement_modules - ) - } - """ - loras = {} - metadata = safeloras.metadata() - - get_name = lambda k: k.split(":")[0] - - keys = list(safeloras.keys()) - keys.sort(key=get_name) - - for name, module_keys in groupby(keys, get_name): - info = metadata.get(name) - - if not info: - raise ValueError( - f"Tensor {name} has no metadata - is this a Lora safetensor?" - ) - - # Skip Textual Inversion embeds - if info == EMBED_FLAG: - continue - - # Handle Loras - # Extract the targets - target = json.loads(info) - - # Build the result lists - Python needs us to preallocate lists to insert into them - module_keys = list(module_keys) - ranks = [4] * (len(module_keys) // 2) - weights = [None] * len(module_keys) - - for key in module_keys: - # Split the model name and index out of the key - _, idx, direction = key.split(":") - idx = int(idx) - - # Add the rank - ranks[idx] = int(metadata[f"{name}:{idx}:rank"]) - - # Insert the weight into the list - idx = idx * 2 + (1 if direction == "down" else 0) - weights[idx] = nn.parameter.Parameter(safeloras.get_tensor(key)) - - loras[name] = (weights, ranks, target) - - return loras - - -def parse_safeloras_embeds( - safeloras, -) -> Dict[str, torch.Tensor]: - """ - Converts a loaded safetensor file that contains Textual Inversion embeds into - a dictionary of embed_token: Tensor - """ - embeds = {} - metadata = safeloras.metadata() - - for key in safeloras.keys(): - # Only handle Textual Inversion embeds - meta = metadata.get(key) - if not meta or meta != EMBED_FLAG: - continue - - embeds[key] = safeloras.get_tensor(key) - - return embeds - -def net_load_lora(net, checkpoint_path, alpha=1.0, remove=False): - visited=[] - state_dict = torch.load(checkpoint_path) - for k, v in state_dict.items(): - state_dict[k] = v.to(net.device) - - for key in state_dict: - if ".alpha" in key or key in visited: - continue - layer_infos = key.split(".")[:-2] # remove lora_up and down weight - curr_layer = net - # find the target layer - temp_name = layer_infos.pop(0) - while len(layer_infos) > -1: - curr_layer = curr_layer.__getattr__(temp_name) - if len(layer_infos) > 0: - temp_name = layer_infos.pop(0) - elif len(layer_infos) == 0: - break - if curr_layer.__class__ not in [nn.Linear, nn.Conv2d]: - print('missing param at:', key) - continue - pair_keys = [] - if "lora_down" in key: - pair_keys.append(key.replace("lora_down", "lora_up")) - pair_keys.append(key) - else: - pair_keys.append(key) - pair_keys.append(key.replace("lora_up", "lora_down")) - - # update weight - if len(state_dict[pair_keys[0]].shape) == 4: - # for conv - weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) - weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) - if remove: - curr_layer.weight.data -= alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) - else: - curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) - else: - # for linear - weight_up = state_dict[pair_keys[0]].to(torch.float32) - weight_down = state_dict[pair_keys[1]].to(torch.float32) - if remove: - curr_layer.weight.data -= alpha * torch.mm(weight_up, weight_down) - else: - curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down) - - # update visited list - for item in pair_keys: - visited.append(item) - print('load_weight_num:',len(visited)) - return - -def change_lora(model, inject_lora=False, lora_scale=1.0, lora_path='', last_time_lora='', last_time_lora_scale=1.0): - # remove lora - if last_time_lora != '': - net_load_lora(model, last_time_lora, alpha=last_time_lora_scale, remove=True) - # add new lora - if inject_lora: - net_load_lora(model, lora_path, alpha=lora_scale) - - -def net_load_lora_v2(net, checkpoint_path, alpha=1.0, remove=False, origin_weight=None): - visited=[] - state_dict = torch.load(checkpoint_path) - for k, v in state_dict.items(): - state_dict[k] = v.to(net.device) - - for key in state_dict: - if ".alpha" in key or key in visited: - continue - layer_infos = key.split(".")[:-2] # remove lora_up and down weight - curr_layer = net - # find the target layer - temp_name = layer_infos.pop(0) - while len(layer_infos) > -1: - curr_layer = curr_layer.__getattr__(temp_name) - if len(layer_infos) > 0: - temp_name = layer_infos.pop(0) - elif len(layer_infos) == 0: - break - if curr_layer.__class__ not in [nn.Linear, nn.Conv2d]: - print('missing param at:', key) - continue - pair_keys = [] - if "lora_down" in key: - pair_keys.append(key.replace("lora_down", "lora_up")) - pair_keys.append(key) - else: - pair_keys.append(key) - pair_keys.append(key.replace("lora_up", "lora_down")) - - # storage weight - if origin_weight is None: - origin_weight = dict() - storage_key = key.replace("lora_down", "lora").replace("lora_up", "lora") - origin_weight[storage_key] = curr_layer.weight.data.clone() - else: - storage_key = key.replace("lora_down", "lora").replace("lora_up", "lora") - if storage_key not in origin_weight.keys(): - origin_weight[storage_key] = curr_layer.weight.data.clone() - - - # update - if len(state_dict[pair_keys[0]].shape) == 4: - # for conv - if remove: - curr_layer.weight.data = origin_weight[storage_key].clone() - else: - weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) - weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) - curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) - else: - # for linear - if remove: - curr_layer.weight.data = origin_weight[storage_key].clone() - else: - weight_up = state_dict[pair_keys[0]].to(torch.float32) - weight_down = state_dict[pair_keys[1]].to(torch.float32) - curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down) - - # update visited list - for item in pair_keys: - visited.append(item) - print('load_weight_num:',len(visited)) - return origin_weight - -def change_lora_v2(model, inject_lora=False, lora_scale=1.0, lora_path='', last_time_lora='', last_time_lora_scale=1.0, origin_weight=None): - # remove lora - if last_time_lora != '': - origin_weight = net_load_lora_v2(model, last_time_lora, alpha=last_time_lora_scale, remove=True, origin_weight=origin_weight) - # add new lora - if inject_lora: - origin_weight = net_load_lora_v2(model, lora_path, alpha=lora_scale, origin_weight=origin_weight) - return origin_weight - - - - - -def load_safeloras(path, device="cpu"): - safeloras = safe_open(path, framework="pt", device=device) - return parse_safeloras(safeloras) - - -def load_safeloras_embeds(path, device="cpu"): - safeloras = safe_open(path, framework="pt", device=device) - return parse_safeloras_embeds(safeloras) - - -def load_safeloras_both(path, device="cpu"): - safeloras = safe_open(path, framework="pt", device=device) - return parse_safeloras(safeloras), parse_safeloras_embeds(safeloras) - - -def collapse_lora(model, alpha=1.0): - - for _module, name, _child_module in _find_modules( - model, - UNET_EXTENDED_TARGET_REPLACE | TEXT_ENCODER_EXTENDED_TARGET_REPLACE, - search_class=[LoraInjectedLinear, LoraInjectedConv2d], - ): - - if isinstance(_child_module, LoraInjectedLinear): - print("Collapsing Lin Lora in", name) - - _child_module.linear.weight = nn.Parameter( - _child_module.linear.weight.data - + alpha - * ( - _child_module.lora_up.weight.data - @ _child_module.lora_down.weight.data - ) - .type(_child_module.linear.weight.dtype) - .to(_child_module.linear.weight.device) - ) - - else: - print("Collapsing Conv Lora in", name) - _child_module.conv.weight = nn.Parameter( - _child_module.conv.weight.data - + alpha - * ( - _child_module.lora_up.weight.data.flatten(start_dim=1) - @ _child_module.lora_down.weight.data.flatten(start_dim=1) - ) - .reshape(_child_module.conv.weight.data.shape) - .type(_child_module.conv.weight.dtype) - .to(_child_module.conv.weight.device) - ) - - -def monkeypatch_or_replace_lora( - model, - loras, - target_replace_module=DEFAULT_TARGET_REPLACE, - r: Union[int, List[int]] = 4, -): - for _module, name, _child_module in _find_modules( - model, target_replace_module, search_class=[nn.Linear, LoraInjectedLinear] - ): - _source = ( - _child_module.linear - if isinstance(_child_module, LoraInjectedLinear) - else _child_module - ) - - weight = _source.weight - bias = _source.bias - _tmp = LoraInjectedLinear( - _source.in_features, - _source.out_features, - _source.bias is not None, - r=r.pop(0) if isinstance(r, list) else r, - ) - _tmp.linear.weight = weight - - if bias is not None: - _tmp.linear.bias = bias - - # switch the module - _module._modules[name] = _tmp - - up_weight = loras.pop(0) - down_weight = loras.pop(0) - - _module._modules[name].lora_up.weight = nn.Parameter( - up_weight.type(weight.dtype) - ) - _module._modules[name].lora_down.weight = nn.Parameter( - down_weight.type(weight.dtype) - ) - - _module._modules[name].to(weight.device) - - -def monkeypatch_or_replace_lora_extended( - model, - loras, - target_replace_module=DEFAULT_TARGET_REPLACE, - r: Union[int, List[int]] = 4, -): - for _module, name, _child_module in _find_modules( - model, - target_replace_module, - search_class=[nn.Linear, LoraInjectedLinear, nn.Conv2d, LoraInjectedConv2d], - ): - - if (_child_module.__class__ == nn.Linear) or ( - _child_module.__class__ == LoraInjectedLinear - ): - if len(loras[0].shape) != 2: - continue - - _source = ( - _child_module.linear - if isinstance(_child_module, LoraInjectedLinear) - else _child_module - ) - - weight = _source.weight - bias = _source.bias - _tmp = LoraInjectedLinear( - _source.in_features, - _source.out_features, - _source.bias is not None, - r=r.pop(0) if isinstance(r, list) else r, - ) - _tmp.linear.weight = weight - - if bias is not None: - _tmp.linear.bias = bias - - elif (_child_module.__class__ == nn.Conv2d) or ( - _child_module.__class__ == LoraInjectedConv2d - ): - if len(loras[0].shape) != 4: - continue - _source = ( - _child_module.conv - if isinstance(_child_module, LoraInjectedConv2d) - else _child_module - ) - - weight = _source.weight - bias = _source.bias - _tmp = LoraInjectedConv2d( - _source.in_channels, - _source.out_channels, - _source.kernel_size, - _source.stride, - _source.padding, - _source.dilation, - _source.groups, - _source.bias is not None, - r=r.pop(0) if isinstance(r, list) else r, - ) - - _tmp.conv.weight = weight - - if bias is not None: - _tmp.conv.bias = bias - - # switch the module - _module._modules[name] = _tmp - - up_weight = loras.pop(0) - down_weight = loras.pop(0) - - _module._modules[name].lora_up.weight = nn.Parameter( - up_weight.type(weight.dtype) - ) - _module._modules[name].lora_down.weight = nn.Parameter( - down_weight.type(weight.dtype) - ) - - _module._modules[name].to(weight.device) - - -def monkeypatch_or_replace_safeloras(models, safeloras): - loras = parse_safeloras(safeloras) - - for name, (lora, ranks, target) in loras.items(): - model = getattr(models, name, None) - - if not model: - print(f"No model provided for {name}, contained in Lora") - continue - - monkeypatch_or_replace_lora_extended(model, lora, target, ranks) - - -def monkeypatch_remove_lora(model): - for _module, name, _child_module in _find_modules( - model, search_class=[LoraInjectedLinear, LoraInjectedConv2d] - ): - if isinstance(_child_module, LoraInjectedLinear): - _source = _child_module.linear - weight, bias = _source.weight, _source.bias - - _tmp = nn.Linear( - _source.in_features, _source.out_features, bias is not None - ) - - _tmp.weight = weight - if bias is not None: - _tmp.bias = bias - - else: - _source = _child_module.conv - weight, bias = _source.weight, _source.bias - - _tmp = nn.Conv2d( - in_channels=_source.in_channels, - out_channels=_source.out_channels, - kernel_size=_source.kernel_size, - stride=_source.stride, - padding=_source.padding, - dilation=_source.dilation, - groups=_source.groups, - bias=bias is not None, - ) - - _tmp.weight = weight - if bias is not None: - _tmp.bias = bias - - _module._modules[name] = _tmp - - -def monkeypatch_add_lora( - model, - loras, - target_replace_module=DEFAULT_TARGET_REPLACE, - alpha: float = 1.0, - beta: float = 1.0, -): - for _module, name, _child_module in _find_modules( - model, target_replace_module, search_class=[LoraInjectedLinear] - ): - weight = _child_module.linear.weight - - up_weight = loras.pop(0) - down_weight = loras.pop(0) - - _module._modules[name].lora_up.weight = nn.Parameter( - up_weight.type(weight.dtype).to(weight.device) * alpha - + _module._modules[name].lora_up.weight.to(weight.device) * beta - ) - _module._modules[name].lora_down.weight = nn.Parameter( - down_weight.type(weight.dtype).to(weight.device) * alpha - + _module._modules[name].lora_down.weight.to(weight.device) * beta - ) - - _module._modules[name].to(weight.device) - - -def tune_lora_scale(model, alpha: float = 1.0): - for _module in model.modules(): - if _module.__class__.__name__ in ["LoraInjectedLinear", "LoraInjectedConv2d"]: - _module.scale = alpha - - -def set_lora_diag(model, diag: torch.Tensor): - for _module in model.modules(): - if _module.__class__.__name__ in ["LoraInjectedLinear", "LoraInjectedConv2d"]: - _module.set_selector_from_diag(diag) - - -def _text_lora_path(path: str) -> str: - assert path.endswith(".pt"), "Only .pt files are supported" - return ".".join(path.split(".")[:-1] + ["text_encoder", "pt"]) - - -def _ti_lora_path(path: str) -> str: - assert path.endswith(".pt"), "Only .pt files are supported" - return ".".join(path.split(".")[:-1] + ["ti", "pt"]) - - -def apply_learned_embed_in_clip( - learned_embeds, - text_encoder, - tokenizer, - token: Optional[Union[str, List[str]]] = None, - idempotent=False, -): - if isinstance(token, str): - trained_tokens = [token] - elif isinstance(token, list): - assert len(learned_embeds.keys()) == len( - token - ), "The number of tokens and the number of embeds should be the same" - trained_tokens = token - else: - trained_tokens = list(learned_embeds.keys()) - - for token in trained_tokens: - print(token) - embeds = learned_embeds[token] - - # cast to dtype of text_encoder - dtype = text_encoder.get_input_embeddings().weight.dtype - num_added_tokens = tokenizer.add_tokens(token) - - i = 1 - if not idempotent: - while num_added_tokens == 0: - print(f"The tokenizer already contains the token {token}.") - token = f"{token[:-1]}-{i}>" - print(f"Attempting to add the token {token}.") - num_added_tokens = tokenizer.add_tokens(token) - i += 1 - elif num_added_tokens == 0 and idempotent: - print(f"The tokenizer already contains the token {token}.") - print(f"Replacing {token} embedding.") - - # resize the token embeddings - text_encoder.resize_token_embeddings(len(tokenizer)) - - # get the id for the token and assign the embeds - token_id = tokenizer.convert_tokens_to_ids(token) - text_encoder.get_input_embeddings().weight.data[token_id] = embeds - return token - - -def load_learned_embed_in_clip( - learned_embeds_path, - text_encoder, - tokenizer, - token: Optional[Union[str, List[str]]] = None, - idempotent=False, -): - learned_embeds = torch.load(learned_embeds_path) - apply_learned_embed_in_clip( - learned_embeds, text_encoder, tokenizer, token, idempotent - ) - - -def patch_pipe( - pipe, - maybe_unet_path, - token: Optional[str] = None, - r: int = 4, - patch_unet=True, - patch_text=True, - patch_ti=True, - idempotent_token=True, - unet_target_replace_module=DEFAULT_TARGET_REPLACE, - text_target_replace_module=TEXT_ENCODER_DEFAULT_TARGET_REPLACE, -): - if maybe_unet_path.endswith(".pt"): - # torch format - - if maybe_unet_path.endswith(".ti.pt"): - unet_path = maybe_unet_path[:-6] + ".pt" - elif maybe_unet_path.endswith(".text_encoder.pt"): - unet_path = maybe_unet_path[:-16] + ".pt" - else: - unet_path = maybe_unet_path - - ti_path = _ti_lora_path(unet_path) - text_path = _text_lora_path(unet_path) - - if patch_unet: - print("LoRA : Patching Unet") - monkeypatch_or_replace_lora( - pipe.unet, - torch.load(unet_path), - r=r, - target_replace_module=unet_target_replace_module, - ) - - if patch_text: - print("LoRA : Patching text encoder") - monkeypatch_or_replace_lora( - pipe.text_encoder, - torch.load(text_path), - target_replace_module=text_target_replace_module, - r=r, - ) - if patch_ti: - print("LoRA : Patching token input") - token = load_learned_embed_in_clip( - ti_path, - pipe.text_encoder, - pipe.tokenizer, - token=token, - idempotent=idempotent_token, - ) - - elif maybe_unet_path.endswith(".safetensors"): - safeloras = safe_open(maybe_unet_path, framework="pt", device="cpu") - monkeypatch_or_replace_safeloras(pipe, safeloras) - tok_dict = parse_safeloras_embeds(safeloras) - if patch_ti: - apply_learned_embed_in_clip( - tok_dict, - pipe.text_encoder, - pipe.tokenizer, - token=token, - idempotent=idempotent_token, - ) - return tok_dict - - -@torch.no_grad() -def inspect_lora(model): - moved = {} - - for name, _module in model.named_modules(): - if _module.__class__.__name__ in ["LoraInjectedLinear", "LoraInjectedConv2d"]: - ups = _module.lora_up.weight.data.clone() - downs = _module.lora_down.weight.data.clone() - - wght: torch.Tensor = ups.flatten(1) @ downs.flatten(1) - - dist = wght.flatten().abs().mean().item() - if name in moved: - moved[name].append(dist) - else: - moved[name] = [dist] - - return moved - - -def save_all( - unet, - text_encoder, - save_path, - placeholder_token_ids=None, - placeholder_tokens=None, - save_lora=True, - save_ti=True, - target_replace_module_text=TEXT_ENCODER_DEFAULT_TARGET_REPLACE, - target_replace_module_unet=DEFAULT_TARGET_REPLACE, - safe_form=True, -): - if not safe_form: - # save ti - if save_ti: - ti_path = _ti_lora_path(save_path) - learned_embeds_dict = {} - for tok, tok_id in zip(placeholder_tokens, placeholder_token_ids): - learned_embeds = text_encoder.get_input_embeddings().weight[tok_id] - print( - f"Current Learned Embeddings for {tok}:, id {tok_id} ", - learned_embeds[:4], - ) - learned_embeds_dict[tok] = learned_embeds.detach().cpu() - - torch.save(learned_embeds_dict, ti_path) - print("Ti saved to ", ti_path) - - # save text encoder - if save_lora: - - save_lora_weight( - unet, save_path, target_replace_module=target_replace_module_unet - ) - print("Unet saved to ", save_path) - - save_lora_weight( - text_encoder, - _text_lora_path(save_path), - target_replace_module=target_replace_module_text, - ) - print("Text Encoder saved to ", _text_lora_path(save_path)) - - else: - assert save_path.endswith( - ".safetensors" - ), f"Save path : {save_path} should end with .safetensors" - - loras = {} - embeds = {} - - if save_lora: - - loras["unet"] = (unet, target_replace_module_unet) - loras["text_encoder"] = (text_encoder, target_replace_module_text) - - if save_ti: - for tok, tok_id in zip(placeholder_tokens, placeholder_token_ids): - learned_embeds = text_encoder.get_input_embeddings().weight[tok_id] - print( - f"Current Learned Embeddings for {tok}:, id {tok_id} ", - learned_embeds[:4], - ) - embeds[tok] = learned_embeds.detach().cpu() - - save_safeloras_with_embeds(loras, embeds, save_path) diff --git a/lvdm/models/modules/openaimodel3d.py b/lvdm/models/modules/openaimodel3d.py deleted file mode 100644 index 2d268a42ebbc9852c4951f60ad78c9a294a59a5e..0000000000000000000000000000000000000000 --- a/lvdm/models/modules/openaimodel3d.py +++ /dev/null @@ -1,670 +0,0 @@ -from abc import abstractmethod -import math -from einops import rearrange -from functools import partial -import numpy as np -import torch as th -import torch.nn as nn -import torch.nn.functional as F -from omegaconf.listconfig import ListConfig - -from lvdm.models.modules.util import ( - checkpoint, - conv_nd, - linear, - avg_pool_nd, - zero_module, - normalization, - timestep_embedding, - nonlinearity, -) - -# dummy replace -def convert_module_to_f16(x): - pass - -def convert_module_to_f32(x): - pass - -## go -# --------------------------------------------------------------------------------------------------- -class TimestepBlock(nn.Module): - """ - Any module where forward() takes timestep embeddings as a second argument. - """ - - @abstractmethod - def forward(self, x, emb): - """ - Apply the module to `x` given `emb` timestep embeddings. - """ - - -# --------------------------------------------------------------------------------------------------- -class TimestepEmbedSequential(nn.Sequential, TimestepBlock): - """ - A sequential module that passes timestep embeddings to the children that - support it as an extra input. - """ - - def forward(self, x, emb, context, **kwargs): - for layer in self: - if isinstance(layer, TimestepBlock): - x = layer(x, emb, **kwargs) - elif isinstance(layer, STTransformerClass): - x = layer(x, context, **kwargs) - else: - x = layer(x) - return x - - -# --------------------------------------------------------------------------------------------------- -class Upsample(nn.Module): - """ - An upsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - upsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, - kernel_size_t=3, - padding_t=1, - ): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - if use_conv: - self.conv = conv_nd(dims, self.channels, self.out_channels, (kernel_size_t, 3,3), padding=(padding_t, 1,1)) - - def forward(self, x): - assert x.shape[1] == self.channels - if self.dims == 3: - x = F.interpolate( - x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" - ) - else: - x = F.interpolate(x, scale_factor=2, mode="nearest") - if self.use_conv: - x = self.conv(x) - return x - - -# --------------------------------------------------------------------------------------------------- -class TransposedUpsample(nn.Module): - 'Learned 2x upsampling without padding' - def __init__(self, channels, out_channels=None, ks=5): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - - self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) - - def forward(self,x): - return self.up(x) - - -# --------------------------------------------------------------------------------------------------- -class Downsample(nn.Module): - """ - A downsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - downsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, - kernel_size_t=3, - padding_t=1, - ): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - if use_conv: - self.op = conv_nd( - dims, self.channels, self.out_channels, (kernel_size_t, 3,3), stride=stride, padding=(padding_t, 1,1) - ) - else: - assert self.channels == self.out_channels - self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) - - def forward(self, x): - assert x.shape[1] == self.channels - return self.op(x) - - -# --------------------------------------------------------------------------------------------------- -class ResBlock(TimestepBlock): - """ - A residual block that can optionally change the number of channels. - :param channels: the number of input channels. - :param emb_channels: the number of timestep embedding channels. - :param dropout: the rate of dropout. - :param out_channels: if specified, the number of out channels. - :param use_conv: if True and out_channels is specified, use a spatial - convolution instead of a smaller 1x1 convolution to change the - channels in the skip connection. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param use_checkpoint: if True, use gradient checkpointing on this module. - :param up: if True, use this block for upsampling. - :param down: if True, use this block for downsampling. - """ - - def __init__( - self, - channels, - emb_channels, - dropout, - out_channels=None, - use_conv=False, - use_scale_shift_norm=False, - dims=2, - use_checkpoint=False, - up=False, - down=False, - # temporal - kernel_size_t=3, - padding_t=1, - nonlinearity_type='silu', - **kwargs - ): - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.dropout = dropout - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_checkpoint = use_checkpoint - self.use_scale_shift_norm = use_scale_shift_norm - self.nonlinearity_type = nonlinearity_type - - self.in_layers = nn.Sequential( - normalization(channels), - nonlinearity(nonlinearity_type), - conv_nd(dims, channels, self.out_channels, (kernel_size_t, 3,3), padding=(padding_t, 1,1)), - ) - - self.updown = up or down - - if up: - self.h_upd = Upsample(channels, False, dims, kernel_size_t=kernel_size_t, padding_t=padding_t) - self.x_upd = Upsample(channels, False, dims, kernel_size_t=kernel_size_t, padding_t=padding_t) - elif down: - self.h_upd = Downsample(channels, False, dims, kernel_size_t=kernel_size_t, padding_t=padding_t) - self.x_upd = Downsample(channels, False, dims, kernel_size_t=kernel_size_t, padding_t=padding_t) - else: - self.h_upd = self.x_upd = nn.Identity() - - self.emb_layers = nn.Sequential( - nonlinearity(nonlinearity_type), - linear( - emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, - ), - ) - self.out_layers = nn.Sequential( - normalization(self.out_channels), - nonlinearity(nonlinearity_type), - nn.Dropout(p=dropout), - zero_module( - conv_nd(dims, self.out_channels, self.out_channels, (kernel_size_t, 3,3), padding=(padding_t, 1,1)) - ), - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - elif use_conv: - self.skip_connection = conv_nd( - dims, channels, self.out_channels, (kernel_size_t, 3,3), padding=(padding_t, 1,1) - ) - else: - self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) - - - def forward(self, x, emb, **kwargs): - """ - Apply the block to a Tensor, conditioned on a timestep embedding. - :param x: an [N x C x ...] Tensor of features. - :param emb: an [N x emb_channels] Tensor of timestep embeddings. - :return: an [N x C x ...] Tensor of outputs. - """ - return checkpoint(self._forward, - (x, emb), - self.parameters(), - self.use_checkpoint - ) - - def _forward(self, x, emb,): - if self.updown: - in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] - h = in_rest(x) - h = self.h_upd(h) - x = self.x_upd(x) - h = in_conv(h) - else: - h = self.in_layers(x) - - emb_out = self.emb_layers(emb).type(h.dtype) - if emb_out.dim() == 3: # btc for video data - emb_out = rearrange(emb_out, 'b t c -> b c t') - while len(emb_out.shape) < h.dim(): - emb_out = emb_out[..., None] # bct -> bct11 or bc -> bc111 - - if self.use_scale_shift_norm: - out_norm, out_rest = self.out_layers[0], self.out_layers[1:] - scale, shift = th.chunk(emb_out, 2, dim=1) - h = out_norm(h) * (1 + scale) + shift - h = out_rest(h) - else: - h = h + emb_out - h = self.out_layers(h) - - out = self.skip_connection(x) + h - - return out - -# --------------------------------------------------------------------------------------------------- -def make_spatialtemporal_transformer(module_name='attention_temporal', class_name='SpatialTemporalTransformer'): - module = __import__(f"lvdm.models.modules.{module_name}", fromlist=[class_name]) - global STTransformerClass - STTransformerClass = getattr(module, class_name) - return STTransformerClass - -# --------------------------------------------------------------------------------------------------- -class UNetModel(nn.Module): - """ - The full UNet model with attention and timestep embedding. - :param in_channels: channels in the input Tensor. - :param model_channels: base channel count for the model. - :param out_channels: channels in the output Tensor. - :param num_res_blocks: number of residual blocks per downsample. - :param attention_resolutions: a collection of downsample rates at which - attention will take place. May be a set, list, or tuple. - For example, if this contains 4, then at 4x downsampling, attention - will be used. - :param dropout: the dropout probability. - :param channel_mult: channel multiplier for each level of the UNet. - :param conv_resample: if True, use learned convolutions for upsampling and - downsampling. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param num_classes: if specified (as an int), then this model will be - class-conditional with `num_classes` classes. - :param use_checkpoint: use gradient checkpointing to reduce memory usage. - :param num_heads: the number of attention heads in each attention layer. - :param num_heads_channels: if specified, ignore num_heads and instead use - a fixed channel width per attention head. - :param num_heads_upsample: works with num_heads to set a different number - of heads for upsampling. Deprecated. - :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. - :param resblock_updown: use residual blocks for up/downsampling. - :param use_new_attention_order: use a different attention pattern for potentially - increased efficiency. - """ - - def __init__( - self, - image_size, # not used in UNetModel - in_channels, - model_channels, - out_channels, - num_res_blocks, - attention_resolutions, - dropout=0, - channel_mult=(1, 2, 4, 8), - conv_resample=True, - dims=3, - num_classes=None, - use_checkpoint=False, - use_fp16=False, - num_heads=-1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - transformer_depth=1, # custom transformer support - context_dim=None, # custom transformer support - legacy=True, - # temporal related - kernel_size_t=1, - padding_t=1, - use_temporal_transformer=True, - temporal_length=None, - use_relative_position=False, - cross_attn_on_tempoal=False, - temporal_crossattn_type="crossattn", - order="stst", - nonlinearity_type='silu', - temporalcrossfirst=False, - split_stcontext=False, - temporal_context_dim=None, - use_tempoal_causal_attn=False, - ST_transformer_module='attention_temporal', - ST_transformer_class='SpatialTemporalTransformer', - **kwargs, - ): - super().__init__() - assert(use_temporal_transformer) - if context_dim is not None: - if type(context_dim) == ListConfig: - context_dim = list(context_dim) - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - if num_heads == -1: - assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' - - if num_head_channels == -1: - assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' - - self.image_size = image_size - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - self.num_res_blocks = num_res_blocks - self.attention_resolutions = attention_resolutions - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.dtype = th.float16 if use_fp16 else th.float32 - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - - self.use_relative_position = use_relative_position - self.temporal_length = temporal_length - self.cross_attn_on_tempoal = cross_attn_on_tempoal - self.temporal_crossattn_type = temporal_crossattn_type - self.order = order - self.temporalcrossfirst = temporalcrossfirst - self.split_stcontext = split_stcontext - self.temporal_context_dim = temporal_context_dim - self.nonlinearity_type = nonlinearity_type - self.use_tempoal_causal_attn = use_tempoal_causal_attn - - - time_embed_dim = model_channels * 4 - self.time_embed_dim = time_embed_dim - self.time_embed = nn.Sequential( - linear(model_channels, time_embed_dim), - nonlinearity(nonlinearity_type), - linear(time_embed_dim, time_embed_dim), - ) - - if self.num_classes is not None: - self.label_emb = nn.Embedding(num_classes, time_embed_dim) - - STTransformerClass = make_spatialtemporal_transformer(module_name=ST_transformer_module, - class_name=ST_transformer_class) - - self.input_blocks = nn.ModuleList( - [ - TimestepEmbedSequential( - conv_nd(dims, in_channels, model_channels, (kernel_size_t, 3,3), padding=(padding_t, 1,1)) - ) - ] - ) - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - for level, mult in enumerate(channel_mult): - for _ in range(num_res_blocks): - layers = [ - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=mult * model_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - kernel_size_t=kernel_size_t, - padding_t=padding_t, - nonlinearity_type=nonlinearity_type, - **kwargs - ) - ] - ch = mult * model_channels - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - dim_head = ch // num_heads if use_temporal_transformer else num_head_channels - layers.append(STTransformerClass( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - # temporal related - temporal_length=temporal_length, - use_relative_position=use_relative_position, - cross_attn_on_tempoal=cross_attn_on_tempoal, - temporal_crossattn_type=temporal_crossattn_type, - order=order, - temporalcrossfirst=temporalcrossfirst, - split_stcontext=split_stcontext, - temporal_context_dim=temporal_context_dim, - use_tempoal_causal_attn=use_tempoal_causal_attn, - **kwargs, - )) - self.input_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - kernel_size_t=kernel_size_t, - padding_t=padding_t, - nonlinearity_type=nonlinearity_type, - **kwargs - ) - if resblock_updown - else Downsample( - ch, conv_resample, dims=dims, out_channels=out_ch, kernel_size_t=kernel_size_t, padding_t=padding_t - ) - ) - ) - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - dim_head = ch // num_heads if use_temporal_transformer else num_head_channels - self.middle_block = TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - kernel_size_t=kernel_size_t, - padding_t=padding_t, - nonlinearity_type=nonlinearity_type, - **kwargs - ), - STTransformerClass( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - # temporal related - temporal_length=temporal_length, - use_relative_position=use_relative_position, - cross_attn_on_tempoal=cross_attn_on_tempoal, - temporal_crossattn_type=temporal_crossattn_type, - order=order, - temporalcrossfirst=temporalcrossfirst, - split_stcontext=split_stcontext, - temporal_context_dim=temporal_context_dim, - use_tempoal_causal_attn=use_tempoal_causal_attn, - **kwargs, - ), - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - kernel_size_t=kernel_size_t, - padding_t=padding_t, - nonlinearity_type=nonlinearity_type, - **kwargs - ), - ) - self._feature_size += ch - - self.output_blocks = nn.ModuleList([]) - for level, mult in list(enumerate(channel_mult))[::-1]: - for i in range(num_res_blocks + 1): - ich = input_block_chans.pop() - layers = [ - ResBlock( - ch + ich, - time_embed_dim, - dropout, - out_channels=model_channels * mult, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - kernel_size_t=kernel_size_t, - padding_t=padding_t, - nonlinearity_type=nonlinearity_type, - **kwargs - ) - ] - ch = model_channels * mult - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - dim_head = ch // num_heads if use_temporal_transformer else num_head_channels - layers.append( - STTransformerClass( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - # temporal related - temporal_length=temporal_length, - use_relative_position=use_relative_position, - cross_attn_on_tempoal=cross_attn_on_tempoal, - temporal_crossattn_type=temporal_crossattn_type, - order=order, - temporalcrossfirst=temporalcrossfirst, - split_stcontext=split_stcontext, - temporal_context_dim=temporal_context_dim, - use_tempoal_causal_attn=use_tempoal_causal_attn, - **kwargs, - ) - ) - if level and i == num_res_blocks: - out_ch = ch - layers.append( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - up=True, - kernel_size_t=kernel_size_t, - padding_t=padding_t, - nonlinearity_type=nonlinearity_type, - **kwargs - ) - if resblock_updown - else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, kernel_size_t=kernel_size_t, padding_t=padding_t) - ) - ds //= 2 - self.output_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - - self.out = nn.Sequential( - normalization(ch), - nonlinearity(nonlinearity_type), - zero_module(conv_nd(dims, model_channels, out_channels, (kernel_size_t, 3,3), padding=(padding_t, 1,1))), - ) - - - def convert_to_fp16(self): - """ - Convert the torso of the model to float16. - """ - self.input_blocks.apply(convert_module_to_f16) - self.middle_block.apply(convert_module_to_f16) - self.output_blocks.apply(convert_module_to_f16) - - def convert_to_fp32(self): - """ - Convert the torso of the model to float32. - """ - self.input_blocks.apply(convert_module_to_f32) - self.middle_block.apply(convert_module_to_f32) - self.output_blocks.apply(convert_module_to_f32) - - def forward(self, x, timesteps=None, time_emb_replace=None, context=None, features_adapter=None, y=None, **kwargs): - """ - Apply the model to an input batch. - :param x: an [N x C x ...] Tensor of inputs. - :param timesteps: a 1-D batch of timesteps. - :param context: conditioning plugged in via crossattn - :param y: an [N] Tensor of labels, if class-conditional. - :return: an [N x C x ...] Tensor of outputs. - """ - - hs = [] - if time_emb_replace is None: - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) - emb = self.time_embed(t_emb) - else: - emb = time_emb_replace - - if y is not None: # if class-conditional model, inject class labels - assert y.shape == (x.shape[0],) - emb = emb + self.label_emb(y) - - h = x.type(self.dtype) - adapter_idx = 0 - for id, module in enumerate(self.input_blocks): - h = module(h, emb, context, **kwargs) - ## plug-in adapter features - if ((id+1)%3 == 0) and features_adapter is not None: - h = h + features_adapter[adapter_idx] - adapter_idx += 1 - hs.append(h) - if features_adapter is not None: - assert len(features_adapter)==adapter_idx, 'Mismatch features adapter' - - h = self.middle_block(h, emb, context, **kwargs) - for module in self.output_blocks: - h = th.cat([h, hs.pop()], dim=1) - h = module(h, emb, context, **kwargs) - h = h.type(x.dtype) - return self.out(h) diff --git a/lvdm/models/modules/util.py b/lvdm/models/modules/util.py deleted file mode 100644 index 9ffd868396dd0f4b8b73050ceaaff1e8c9020bd3..0000000000000000000000000000000000000000 --- a/lvdm/models/modules/util.py +++ /dev/null @@ -1,348 +0,0 @@ -import math -from inspect import isfunction - -import torch -import numpy as np -import torch.nn as nn -from einops import repeat -import torch.nn.functional as F - -from lvdm.utils.common_utils import instantiate_from_config - - -def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - if schedule == "linear": - betas = ( - torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 - ) - elif schedule == "cosine": - timesteps = ( - torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s - ) - alphas = timesteps / (1 + cosine_s) * np.pi / 2 - alphas = torch.cos(alphas).pow(2) - alphas = alphas / alphas[0] - betas = 1 - alphas[1:] / alphas[:-1] - betas = np.clip(betas, a_min=0, a_max=0.999) - elif schedule == "sqrt_linear": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) - elif schedule == "sqrt": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 - else: - raise ValueError(f"schedule '{schedule}' unknown.") - return betas.numpy() - - -def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): - if ddim_discr_method == 'uniform': - c = num_ddpm_timesteps // num_ddim_timesteps - ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) - elif ddim_discr_method == 'quad': - ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) - else: - raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') - - # add one to get the final alpha values right (the ones from first scale to data during sampling) - steps_out = ddim_timesteps + 1 - if verbose: - print(f'Selected timesteps for ddim sampler: {steps_out}') - return steps_out - - -def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): - # select alphas for computing the variance schedule - alphas = alphacums[ddim_timesteps] - alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) - - # according the the formula provided in https://arxiv.org/abs/2010.02502 - sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) - if verbose: - print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') - print(f'For the chosen value of eta, which is {eta}, ' - f'this results in the following sigma_t schedule for ddim sampler {sigmas}') - return sigmas, alphas, alphas_prev - - -def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): - """ - 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]. - :param num_diffusion_timesteps: the number of betas to produce. - :param alpha_bar: a lambda that takes an argument t from 0 to 1 and - produces the cumulative product of (1-beta) up to that - part of the diffusion process. - :param max_beta: the maximum beta to use; use values lower than 1 to - prevent singularities. - """ - 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(t2) / alpha_bar(t1), max_beta)) - return np.array(betas) - - -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 checkpoint(func, inputs, params, flag): - """ - Evaluate a function without caching intermediate activations, allowing for - reduced memory at the expense of extra compute in the backward pass. - :param func: the function to evaluate. - :param inputs: the argument sequence to pass to `func`. - :param params: a sequence of parameters `func` depends on but does not - explicitly take as arguments. - :param flag: if False, disable gradient checkpointing. - """ - if flag: - args = tuple(inputs) + tuple(params) - return CheckpointFunction.apply(func, len(inputs), *args) - else: - return func(*inputs) - - -class CheckpointFunction(torch.autograd.Function): - @staticmethod - @torch.cuda.amp.custom_fwd - def forward(ctx, run_function, length, *args): - ctx.run_function = run_function - ctx.input_tensors = list(args[:length]) - ctx.input_params = list(args[length:]) - - with torch.no_grad(): - output_tensors = ctx.run_function(*ctx.input_tensors) - return output_tensors - - @staticmethod - @torch.cuda.amp.custom_bwd - def backward(ctx, *output_grads): - ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] - with torch.enable_grad(): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) - input_grads = torch.autograd.grad( - output_tensors, - ctx.input_tensors + ctx.input_params, - output_grads, - allow_unused=True, - ) - del ctx.input_tensors - del ctx.input_params - del output_tensors - return (None, None) + input_grads - - -def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): - """ - Create sinusoidal timestep embeddings. - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. - """ - if not repeat_only: - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half - ).to(device=timesteps.device) - args = timesteps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - else: - embedding = repeat(timesteps, 'b -> b d', d=dim) - return embedding - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def scale_module(module, scale): - """ - Scale the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def mean_flat(tensor): - """ - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def normalization(channels): - """ - Make a standard normalization layer. - :param channels: number of input channels. - :return: an nn.Module for normalization. - """ - return GroupNorm32(32, channels) - -def Normalize(in_channels): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - -def identity(*args, **kwargs): - return nn.Identity() - -class Normalization(nn.Module): - def __init__(self, output_size, eps=1e-5, norm_type='gn'): - super(Normalization, self).__init__() - # epsilon to avoid dividing by 0 - self.eps = eps - self.norm_type = norm_type - - if self.norm_type in ['bn', 'in']: - self.register_buffer('stored_mean', torch.zeros(output_size)) - self.register_buffer('stored_var', torch.ones(output_size)) - - def forward(self, x): - if self.norm_type == 'bn': - out = F.batch_norm(x, self.stored_mean, self.stored_var, None, - None, - self.training, 0.1, self.eps) - elif self.norm_type == 'in': - out = F.instance_norm(x, self.stored_mean, self.stored_var, - None, None, - self.training, 0.1, self.eps) - elif self.norm_type == 'gn': - out = F.group_norm(x, 32) - elif self.norm_type == 'nonorm': - out = x - return out - - -class CCNormalization(nn.Module): - def __init__(self, embed_dim, feature_dim, *args, **kwargs): - super(CCNormalization, self).__init__() - - self.embed_dim = embed_dim - self.feature_dim = feature_dim - self.norm = Normalization(feature_dim, *args, **kwargs) - - self.gain = nn.Linear(self.embed_dim, self.feature_dim) - self.bias = nn.Linear(self.embed_dim, self.feature_dim) - - def forward(self, x, y): - shape = [1] * (x.dim() - 2) - gain = (1 + self.gain(y)).view(y.size(0), -1, *shape) - bias = self.bias(y).view(y.size(0), -1, *shape) - return self.norm(x) * gain + bias - - -def nonlinearity(type='silu'): - if type == 'silu': - return nn.SiLU() - elif type == 'leaky_relu': - return nn.LeakyReLU() - - -class GEGLU(nn.Module): - def __init__(self, dim_in, dim_out): - super().__init__() - self.proj = nn.Linear(dim_in, dim_out * 2) - - def forward(self, x): - x, gate = self.proj(x).chunk(2, dim=-1) - return x * F.gelu(gate) - - -class SiLU(nn.Module): - def forward(self, x): - return x * torch.sigmoid(x) - - -class GroupNorm32(nn.GroupNorm): - def forward(self, x): - return super().forward(x.float()).type(x.dtype) - - -def conv_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D convolution module. - """ - if dims == 1: - return nn.Conv1d(*args, **kwargs) - elif dims == 2: - return nn.Conv2d(*args, **kwargs) - elif dims == 3: - return nn.Conv3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def linear(*args, **kwargs): - """ - Create a linear module. - """ - return nn.Linear(*args, **kwargs) - - -def avg_pool_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D average pooling module. - """ - if dims == 1: - return nn.AvgPool1d(*args, **kwargs) - elif dims == 2: - return nn.AvgPool2d(*args, **kwargs) - elif dims == 3: - return nn.AvgPool3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -class HybridConditioner(nn.Module): - - def __init__(self, c_concat_config, c_crossattn_config): - super().__init__() - self.concat_conditioner = instantiate_from_config(c_concat_config) - self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) - - def forward(self, c_concat, c_crossattn): - c_concat = self.concat_conditioner(c_concat) - c_crossattn = self.crossattn_conditioner(c_crossattn) - return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} - - -def noise_like(shape, device, repeat=False): - repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) - noise = lambda: torch.randn(shape, device=device) - return repeat_noise() if repeat else noise() - - -def init_(tensor): - dim = tensor.shape[-1] - std = 1 / math.sqrt(dim) - tensor.uniform_(-std, std) - return tensor - - -def exists(val): - return val is not None - - -def uniq(arr): - return{el: True for el in arr}.keys() - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - diff --git a/lvdm/samplers/ddim.py b/lvdm/models/samplers/ddim.py similarity index 66% rename from lvdm/samplers/ddim.py rename to lvdm/models/samplers/ddim.py index cae8e324f6d99b41fcc655ed3ecb940a2127bef4..dfd345f4c8eb2931452cb8bd7dbe5d6c7e68b096 100644 --- a/lvdm/samplers/ddim.py +++ b/lvdm/models/samplers/ddim.py @@ -1,10 +1,8 @@ -"""SAMPLING ONLY.""" - -import torch import numpy as np from tqdm import tqdm - -from lvdm.models.modules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like +import torch +from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps +from lvdm.common import noise_like class DDIMSampler(object): @@ -31,6 +29,15 @@ class DDIMSampler(object): self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + self.use_scale = self.model.use_scale + print('DDIM scale', self.use_scale) + + if self.use_scale: + self.register_buffer('scale_arr', to_torch(self.model.scale_arr)) + ddim_scale_arr = self.scale_arr.cpu()[self.ddim_timesteps] + self.register_buffer('ddim_scale_arr', ddim_scale_arr) + ddim_scale_arr = np.asarray([self.scale_arr.cpu()[0]] + self.scale_arr.cpu()[self.ddim_timesteps[:-1]].tolist()) + self.register_buffer('ddim_scale_arr_prev', ddim_scale_arr) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) @@ -59,6 +66,7 @@ class DDIMSampler(object): shape, conditioning=None, callback=None, + normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., @@ -74,9 +82,6 @@ class DDIMSampler(object): log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, - postprocess_fn=None, - sample_noise=None, - cond_fn=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs ): @@ -86,11 +91,11 @@ class DDIMSampler(object): if isinstance(conditioning, dict): try: cbs = conditioning[list(conditioning.keys())[0]].shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") except: - # cbs = conditioning[list(conditioning.keys())[0]][0].shape[0] - pass + cbs = conditioning[list(conditioning.keys())[0]][0].shape[0] + + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") @@ -104,6 +109,7 @@ class DDIMSampler(object): elif len(shape) == 4: C, T, H, W = shape size = (batch_size, C, T, H, W) + # print(f'Data shape for DDIM sampling is {size}, eta {eta}') samples, intermediates = self.ddim_sampling(conditioning, size, callback=callback, @@ -119,12 +125,8 @@ class DDIMSampler(object): log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, - postprocess_fn=postprocess_fn, - sample_noise=sample_noise, - cond_fn=cond_fn, verbose=verbose, - **kwargs - ) + **kwargs) return samples, intermediates @torch.no_grad() @@ -133,13 +135,11 @@ class DDIMSampler(object): callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - postprocess_fn=None,sample_noise=None,cond_fn=None, - uc_type=None, verbose=True, **kwargs, - ): - - device = self.model.betas.device - + unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True, + cond_tau=1., target_size=None, start_timesteps=None, + **kwargs): + device = self.model.betas.device + print('ddim device', device) b = shape[0] if x_T is None: img = torch.randn(shape, device=device) @@ -151,6 +151,7 @@ class DDIMSampler(object): elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] + intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] @@ -159,31 +160,46 @@ class DDIMSampler(object): else: iterator = time_range + init_x0 = False + clean_cond = kwargs.pop("clean_cond", False) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) + if start_timesteps is not None: + assert x0 is not None + if step > start_timesteps*time_range[0]: + continue + elif not init_x0: + img = self.model.q_sample(x0, ts) + init_x0 = True - if postprocess_fn is not None: - img = postprocess_fn(img, ts) + # use mask to blend noised original latent (img_orig) & new sampled latent (img) + if mask is not None: + assert x0 is not None + if clean_cond: + img_orig = x0 + else: + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img # keep original & modify use img + index_clip = int((1 - cond_tau) * total_steps) + if index <= index_clip and target_size is not None: + target_size_ = [target_size[0], target_size[1]//8, target_size[2]//8] + img = torch.nn.functional.interpolate( + img, + size=target_size_, + mode="nearest", + ) outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, - sample_noise=sample_noise,cond_fn=cond_fn,uc_type=uc_type, **kwargs,) - img, pred_x0 = outs - - if mask is not None: - # use mask to blend x_known_t-1 & x_sample_t-1 - assert x0 is not None - x0 = x0.to(img.device) - mask = mask.to(img.device) - t = torch.tensor([step-1]*x0.shape[0], dtype=torch.long, device=img.device) - img_known = self.model.q_sample(x0, t) - img = img_known * mask + (1. - mask) * img + x0=x0, + **kwargs) + img, pred_x0 = outs if callback: callback(i) if img_callback: img_callback(pred_x0, i) @@ -196,10 +212,8 @@ class DDIMSampler(object): @torch.no_grad() def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, sample_noise=None, - cond_fn=None, uc_type=None, - **kwargs, - ): + unconditional_guidance_scale=1., unconditional_conditioning=None, + uc_type=None, conditional_guidance_scale_temporal=None, **kwargs): b, *_, device = *x.shape, x.device if x.dim() == 5: is_video = True @@ -227,7 +241,12 @@ class DDIMSampler(object): e_t = e_t + unconditional_guidance_scale * (e_t_uncond - e_t) else: raise NotImplementedError - + # temporal guidance + if conditional_guidance_scale_temporal is not None: + e_t_temporal = self.model.apply_model(x, t, c, **kwargs) + e_t_image = self.model.apply_model(x, t, c, no_temporal_attn=True, **kwargs) + e_t = e_t + conditional_guidance_scale_temporal * (e_t_temporal - e_t_image) + if score_corrector is not None: assert self.model.parameterization == "eps" e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) @@ -249,19 +268,69 @@ class DDIMSampler(object): # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() - # print(f't={t}, pred_x0, min={torch.min(pred_x0)}, max={torch.max(pred_x0)}',file=f) if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) - if sample_noise is None: - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + if self.use_scale: + scale_arr = self.model.scale_arr if use_original_steps else self.ddim_scale_arr + scale_t = torch.full(size, scale_arr[index], device=device) + scale_arr_prev = self.model.scale_arr_prev if use_original_steps else self.ddim_scale_arr_prev + scale_t_prev = torch.full(size, scale_arr_prev[index], device=device) + pred_x0 /= scale_t + x_prev = a_prev.sqrt() * scale_t_prev * pred_x0 + dir_xt + noise else: - noise = sigma_t * sample_noise * temperature - - x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise - + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + + 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))) + + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + return x_dec + diff --git a/lvdm/models/utils_diffusion.py b/lvdm/models/utils_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..603fa817b07cea3581a70ff225d479b7d1518463 --- /dev/null +++ b/lvdm/models/utils_diffusion.py @@ -0,0 +1,104 @@ +import math +import numpy as np +from einops import repeat +import torch +import torch.nn.functional as F + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + # print(f'ddim_timesteps={ddim_timesteps}, len_alphacums={len(alphacums)}') + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + 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]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + 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(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) \ No newline at end of file diff --git a/lvdm/modules/attention.py b/lvdm/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..3d3140c216b85a14a33d9b1fa09eb6d19465f741 --- /dev/null +++ b/lvdm/modules/attention.py @@ -0,0 +1,475 @@ +from functools import partial +import torch +from torch import nn, einsum +import torch.nn.functional as F +from einops import rearrange, repeat +try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False +from lvdm.common import ( + checkpoint, + exists, + default, +) +from lvdm.basics import ( + zero_module, +) + +class RelativePosition(nn.Module): + """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """ + + def __init__(self, num_units, max_relative_position): + super().__init__() + self.num_units = num_units + self.max_relative_position = max_relative_position + self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units)) + nn.init.xavier_uniform_(self.embeddings_table) + + def forward(self, length_q, length_k): + device = self.embeddings_table.device + range_vec_q = torch.arange(length_q, device=device) + range_vec_k = torch.arange(length_k, device=device) + distance_mat = range_vec_k[None, :] - range_vec_q[:, None] + distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position) + final_mat = distance_mat_clipped + self.max_relative_position + final_mat = final_mat.long() + embeddings = self.embeddings_table[final_mat] + return embeddings + + +class CrossAttention(nn.Module): + + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., + relative_position=False, temporal_length=None, img_cross_attention=False): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head**-0.5 + self.heads = heads + self.dim_head = dim_head + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) + + self.image_cross_attention_scale = 1.0 + self.text_context_len = 77 + self.img_cross_attention = img_cross_attention + if self.img_cross_attention: + self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) + + self.relative_position = relative_position + if self.relative_position: + assert(temporal_length is not None) + self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) + self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) + else: + ## only used for spatial attention, while NOT for temporal attention + if XFORMERS_IS_AVAILBLE and temporal_length is None: + self.forward = self.efficient_forward + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + ## considering image token additionally + if context is not None and self.img_cross_attention: + context, context_img = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] + k = self.to_k(context) + v = self.to_v(context) + k_ip = self.to_k_ip(context_img) + v_ip = self.to_v_ip(context_img) + else: + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale + if self.relative_position: + len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] + k2 = self.relative_position_k(len_q, len_k) + sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check + sim += sim2 + del k + + if exists(mask): + ## feasible for causal attention mask only + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b i j -> (b h) i j', h=h) + sim.masked_fill_(~(mask>0.5), max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + out = torch.einsum('b i j, b j d -> b i d', sim, v) + if self.relative_position: + v2 = self.relative_position_v(len_q, len_v) + out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check + out += out2 + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + + ## considering image token additionally + if context is not None and self.img_cross_attention: + k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip)) + sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale + del k_ip + sim_ip = sim_ip.softmax(dim=-1) + out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip) + out_ip = rearrange(out, '(b h) n d -> b n (h d)', h=h) + out = out + self.image_cross_attention_scale * out_ip + del q + + return self.to_out(out) + + def efficient_forward(self, x, context=None, mask=None): + q = self.to_q(x) + context = default(context, x) + + ## considering image token additionally + if context is not None and self.img_cross_attention: + context, context_img = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] + k = self.to_k(context) + v = self.to_v(context) + k_ip = self.to_k_ip(context_img) + v_ip = self.to_v_ip(context_img) + else: + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None) + + ## considering image token additionally + if context is not None and self.img_cross_attention: + k_ip, v_ip = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (k_ip, v_ip), + ) + out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None) + out_ip = ( + out_ip.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + if context is not None and self.img_cross_attention: + out = out + self.image_cross_attention_scale * out_ip + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, + disable_self_attn=False, attention_cls=None, img_cross_attention=False): + super().__init__() + attn_cls = CrossAttention if attention_cls is None else attention_cls + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None) + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout, + img_cross_attention=img_cross_attention) + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None, mask=None): + ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments + input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments + if context is not None: + input_tuple = (x, context) + if mask is not None: + forward_mask = partial(self._forward, mask=mask) + return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint) + if context is not None and mask is not None: + input_tuple = (x, context, mask) + return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint) + + def _forward(self, x, context=None, mask=None): + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x + x = self.attn2(self.norm2(x), context=context, mask=mask) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data in spatial axis. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + + def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, + use_checkpoint=True, disable_self_attn=False, use_linear=False, img_cross_attention=False): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim, + img_cross_attention=img_cross_attention, + disable_self_attn=disable_self_attn, + checkpoint=use_checkpoint) for d in range(depth) + ]) + if not use_linear: + self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) + else: + self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + self.use_linear = use_linear + + + def forward(self, x, context=None): + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class TemporalTransformer(nn.Module): + """ + Transformer block for image-like data in temporal axis. + First, reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, + use_checkpoint=True, use_linear=False, only_self_att=True, causal_attention=False, + relative_position=False, temporal_length=None): + super().__init__() + self.only_self_att = only_self_att + self.relative_position = relative_position + self.causal_attention = causal_attention + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + if not use_linear: + self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + if relative_position: + assert(temporal_length is not None) + attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length) + else: + attention_cls = None + if self.causal_attention: + assert(temporal_length is not None) + self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length])) + + if self.only_self_att: + context_dim = None + self.transformer_blocks = nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim, + attention_cls=attention_cls, + checkpoint=use_checkpoint) for d in range(depth) + ]) + if not use_linear: + self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) + else: + self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + self.use_linear = use_linear + + def forward(self, x, context=None): + b, c, t, h, w = x.shape + x_in = x + x = self.norm(x) + x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous() + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'bhw c t -> bhw t c').contiguous() + if self.use_linear: + x = self.proj_in(x) + + if self.causal_attention: + mask = self.mask.to(x.device) + mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w) + else: + mask = None + + if self.only_self_att: + ## note: if no context is given, cross-attention defaults to self-attention + for i, block in enumerate(self.transformer_blocks): + x = block(x, mask=mask) + x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() + else: + x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() + context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous() + for i, block in enumerate(self.transformer_blocks): + # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) + for j in range(b): + context_j = repeat( + context[j], + 't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous() + ## note: causal mask will not applied in cross-attention case + x[j] = block(x[j], context=context_j) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous() + if not self.use_linear: + x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous() + x = self.proj_out(x) + x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous() + + return x + x_in + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ diff --git a/lvdm/modules/encoders/condition.py b/lvdm/modules/encoders/condition.py new file mode 100644 index 0000000000000000000000000000000000000000..401a6b3d0597c80e9a9189d8e0e465bb181d4f7a --- /dev/null +++ b/lvdm/modules/encoders/condition.py @@ -0,0 +1,392 @@ +import torch +import torch.nn as nn +from torch.utils.checkpoint import checkpoint +import kornia +import open_clip +from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel +from lvdm.common import autocast +from utils.utils import count_params + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class IdentityEncoder(AbstractEncoder): + + def encode(self, x): + return x + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + self.n_classes = n_classes + self.ucg_rate = ucg_rate + + def forward(self, batch, key=None, disable_dropout=False): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + if self.ucg_rate > 0. and not disable_dropout: + mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) + c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1) + c = c.long() + c = self.embedding(c) + return c + + def get_unconditional_conditioning(self, bs, device="cuda"): + uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) + uc = torch.ones((bs,), device=device) * uc_class + uc = {self.key: uc} + return uc + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class FrozenT5Embedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + + def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, + freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + if freeze: + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +class FrozenCLIPEmbedder(AbstractEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + LAYERS = [ + "last", + "pooled", + "hidden" + ] + + def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, + freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + self.tokenizer = CLIPTokenizer.from_pretrained(version) + self.transformer = CLIPTextModel.from_pretrained(version) + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = layer_idx + if layer == "hidden": + assert layer_idx is not None + assert 0 <= abs(layer_idx) <= 12 + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden") + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + return z + + def encode(self, text): + return self(text) + + +class ClipImageEmbedder(nn.Module): + def __init__( + self, + model, + jit=False, + device='cuda' if torch.cuda.is_available() else 'cpu', + antialias=True, + ucg_rate=0. + ): + super().__init__() + from clip import load as load_clip + self.model, _ = load_clip(name=model, device=device, jit=jit) + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + self.ucg_rate = ucg_rate + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # re-normalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def forward(self, x, no_dropout=False): + # x is assumed to be in range [-1,1] + out = self.model.encode_image(self.preprocess(x)) + out = out.to(x.dtype) + if self.ucg_rate > 0. and not no_dropout: + out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out + return out + + +class FrozenOpenCLIPEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + # "pooled", + "last", + "penultimate" + ] + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, + freeze=True, layer="last"): + super().__init__() + assert layer in self.LAYERS + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu')) + del model.visual + self.model = model + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + self.device = self.model.positional_embedding.device + tokens = open_clip.tokenize(text) + z = self.encode_with_transformer(tokens.to(self.device)) + return z + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + +class FrozenOpenCLIPImageEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP vision transformer encoder for images + """ + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, + freeze=True, layer="pooled", antialias=True, ucg_rate=0.): + super().__init__() + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), + pretrained=version, ) + del model.transformer + self.model = model + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "penultimate": + raise NotImplementedError() + self.layer_idx = 1 + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + self.ucg_rate = ucg_rate + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + @autocast + def forward(self, image, no_dropout=False): + z = self.encode_with_vision_transformer(image) + if self.ucg_rate > 0. and not no_dropout: + z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z + return z + + def encode_with_vision_transformer(self, img): + img = self.preprocess(img) + x = self.model.visual(img) + return x + + def encode(self, text): + return self(text) + + + +class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder): + """ + Uses the OpenCLIP vision transformer encoder for images + """ + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", + freeze=True, layer="pooled", antialias=True): + super().__init__() + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), + pretrained=version, ) + del model.transformer + self.model = model + self.device = device + + if freeze: + self.freeze() + self.layer = layer + if self.layer == "penultimate": + raise NotImplementedError() + self.layer_idx = 1 + + self.antialias = antialias + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def freeze(self): + self.model = self.model.eval() + for param in self.model.parameters(): + param.requires_grad = False + + def forward(self, image, no_dropout=False): + ## image: b c h w + z = self.encode_with_vision_transformer(image) + return z + + def encode_with_vision_transformer(self, x): + x = self.preprocess(x) + + # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1 + if self.model.visual.input_patchnorm: + # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)') + x = x.reshape(x.shape[0], x.shape[1], self.model.visual.grid_size[0], self.model.visual.patch_size[0], self.model.visual.grid_size[1], self.model.visual.patch_size[1]) + x = x.permute(0, 2, 4, 1, 3, 5) + x = x.reshape(x.shape[0], self.model.visual.grid_size[0] * self.model.visual.grid_size[1], -1) + x = self.model.visual.patchnorm_pre_ln(x) + x = self.model.visual.conv1(x) + else: + x = self.model.visual.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + # class embeddings and positional embeddings + x = torch.cat( + [self.model.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.model.visual.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.model.visual.patch_dropout(x) + x = self.model.visual.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.model.visual.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + return x + + +class FrozenCLIPT5Encoder(AbstractEncoder): + def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", + clip_max_length=77, t5_max_length=77): + super().__init__() + self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) + self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) + print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, " + f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.") + + def encode(self, text): + return self(text) + + def forward(self, text): + clip_z = self.clip_encoder.encode(text) + t5_z = self.t5_encoder.encode(text) + return [clip_z, t5_z] \ No newline at end of file diff --git a/lvdm/modules/encoders/ip_resampler.py b/lvdm/modules/encoders/ip_resampler.py new file mode 100644 index 0000000000000000000000000000000000000000..500820a789150a55d6e8fdca4dd3e4d6ad542d4a --- /dev/null +++ b/lvdm/modules/encoders/ip_resampler.py @@ -0,0 +1,136 @@ +# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py +import math +import torch +import torch.nn as nn + + +class ImageProjModel(nn.Module): + """Projection Model""" + def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, 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 = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) + self.norm = nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + #embeds = image_embeds + embeds = image_embeds.type(list(self.proj.parameters())[0].dtype) + 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 + +# 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=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + ): + super().__init__() + + 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.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): + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.proj_in(x) + + 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) \ No newline at end of file diff --git a/lvdm/models/modules/autoencoder_modules.py b/lvdm/modules/networks/ae_modules.py similarity index 65% rename from lvdm/models/modules/autoencoder_modules.py rename to lvdm/modules/networks/ae_modules.py index 3c70e92881a81d71c2db7a30c3a31e9af803d730..0c2e93fbadb4a0d86957a5cd73b5c2bf5b01a4b7 100644 --- a/lvdm/models/modules/autoencoder_modules.py +++ b/lvdm/modules/networks/ae_modules.py @@ -1,30 +1,11 @@ +# pytorch_diffusion + derived encoder decoder import math - import torch import numpy as np -from torch import nn +import torch.nn as nn from einops import rearrange - - -def get_timestep_embedding(timesteps, embedding_dim): - """ - This matches the implementation in Denoising Diffusion Probabilistic Models: - From Fairseq. - Build sinusoidal embeddings. - This matches the implementation in tensor2tensor, but differs slightly - from the description in Section 3.5 of "Attention Is All You Need". - """ - assert len(timesteps.shape) == 1 - - half_dim = embedding_dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) - emb = emb.to(device=timesteps.device) - emb = timesteps.float()[:, 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, (0,1,0,0)) - return emb +from utils.utils import instantiate_from_config +from lvdm.modules.attention import LinearAttention def nonlinearity(x): # swish @@ -36,25 +17,6 @@ def Normalize(in_channels, num_groups=32): -class LinearAttention(nn.Module): - def __init__(self, dim, heads=4, dim_head=32): - super().__init__() - self.heads = heads - hidden_dim = dim_head * heads - self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) - self.to_out = nn.Conv2d(hidden_dim, dim, 1) - - def forward(self, x): - b, c, h, w = x.shape - qkv = self.to_qkv(x) - q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) - k = k.softmax(dim=-1) - context = torch.einsum('bhdn,bhen->bhde', k, v) - out = torch.einsum('bhde,bhdn->bhen', context, q) - out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) - return self.to_out(out) - - class LinAttnBlock(LinearAttention): """to match AttnBlock usage""" def __init__(self, in_channels): @@ -115,10 +77,9 @@ class AttnBlock(nn.Module): return x+h_ - def make_attn(in_channels, attn_type="vanilla"): assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' - print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + #print(f"making attention of type '{attn_type}' with {in_channels} in_channels") if attn_type == "vanilla": return AttnBlock(in_channels) elif attn_type == "none": @@ -165,6 +126,27 @@ class Upsample(nn.Module): x = self.conv(x) return x +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, 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, (0,1,0,0)) + return emb + + class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, @@ -502,7 +484,7 @@ class Decoder(nn.Module): block_in = ch*ch_mult[self.num_resolutions-1] curr_res = resolution // 2**(self.num_resolutions-1) self.z_shape = (1,z_channels,curr_res,curr_res) - print("Working with z of shape {} = {} dimensions.".format( + print("AE working on z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) # z to block_in @@ -594,3 +576,270 @@ class Decoder(nn.Module): if self.tanh_out: h = torch.tanh(h) return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, + mid_channels, + kernel_size=3, + stride=1, + padding=1) + self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + + self.conv_out = nn.Conv2d(mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, + z_channels=intermediate_chn, double_z=False, resolution=resolution, + attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, + out_ch=None) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, + mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), + dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + tmp_chn = z_channels*ch_mult[-1] + self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, + resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, + ch_mult=ch_mult, resolution=resolution, ch=ch) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, + out_channels=tmp_chn, depth=rescale_module_depth) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size//in_size))+1 + factor_up = 1.+ (out_size % in_size) + print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") + self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, + out_channels=in_channels) + self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, + attn_resolutions=[], in_channels=None, ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)]) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=4, + stride=2, + padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor==1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x + +class FirstStagePostProcessor(nn.Module): + + def __init__(self, ch_mult:list, in_channels, + pretrained_model:nn.Module=None, + reshape=False, + n_channels=None, + dropout=0., + pretrained_config=None): + super().__init__() + if pretrained_config is None: + assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) + self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, + stride=1,padding=1) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + + @torch.no_grad() + def encode_with_pretrained(self,x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self,x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model,self.downsampler): + z = submodel(z,temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z,'b c h w -> b (h w) c') + return z + diff --git a/lvdm/modules/networks/openaimodel3d.py b/lvdm/modules/networks/openaimodel3d.py new file mode 100644 index 0000000000000000000000000000000000000000..328fac71761a69b461f33946d6a1aa08622ecd8f --- /dev/null +++ b/lvdm/modules/networks/openaimodel3d.py @@ -0,0 +1,577 @@ +from functools import partial +from abc import abstractmethod +import torch +import torch.nn as nn +from einops import rearrange +import torch.nn.functional as F +from lvdm.models.utils_diffusion import timestep_embedding +from lvdm.common import checkpoint +from lvdm.basics import ( + zero_module, + conv_nd, + linear, + avg_pool_nd, + normalization +) +from lvdm.modules.attention import SpatialTransformer, TemporalTransformer + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None, batch_size=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb, batch_size) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + elif isinstance(layer, TemporalTransformer): + x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size) + x = layer(x, context) + x = rearrange(x, 'b c f h w -> (b f) c h w') + else: + x = layer(x,) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest') + else: + x = F.interpolate(x, scale_factor=2, mode='nearest') + if self.use_conv: + x = self.conv(x) + return x + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + use_conv=False, + up=False, + down=False, + use_temporal_conv=False, + tempspatial_aware=False + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.use_temporal_conv = use_temporal_conv + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + if self.use_temporal_conv: + self.temopral_conv = TemporalConvBlock( + self.out_channels, + self.out_channels, + dropout=0.1, + spatial_aware=tempspatial_aware + ) + + def forward(self, x, emb, batch_size=None): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + input_tuple = (x, emb,) + if batch_size: + forward_batchsize = partial(self._forward, batch_size=batch_size) + return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint) + return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint) + + def _forward(self, x, emb, batch_size=None,): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + h = self.skip_connection(x) + h + + if self.use_temporal_conv and batch_size: + h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size) + h = self.temopral_conv(h) + h = rearrange(h, 'b c t h w -> (b t) c h w') + return h + + +class TemporalConvBlock(nn.Module): + """ + Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py + """ + + def __init__(self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False): + super(TemporalConvBlock, self).__init__() + if out_channels is None: + out_channels = in_channels + self.in_channels = in_channels + self.out_channels = out_channels + kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 3) + padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 1) + + # conv layers + self.conv1 = nn.Sequential( + nn.GroupNorm(32, in_channels), nn.SiLU(), + nn.Conv3d(in_channels, out_channels, kernel_shape, padding=padding_shape)) + self.conv2 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, kernel_shape, padding=padding_shape)) + self.conv3 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0))) + self.conv4 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0))) + + # zero out the last layer params,so the conv block is identity + nn.init.zeros_(self.conv4[-1].weight) + nn.init.zeros_(self.conv4[-1].bias) + + def forward(self, x): + identity = x + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = self.conv4(x) + + return x + identity + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: in_channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + """ + + def __init__(self, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0.0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + context_dim=None, + use_scale_shift_norm=False, + resblock_updown=False, + num_heads=-1, + num_head_channels=-1, + transformer_depth=1, + use_linear=False, + use_checkpoint=False, + temporal_conv=False, + tempspatial_aware=False, + temporal_attention=True, + temporal_selfatt_only=True, + use_relative_position=True, + use_causal_attention=False, + temporal_length=None, + use_fp16=False, + addition_attention=False, + use_image_attention=False, + temporal_transformer_depth=1, + fps_cond=False, + ): + super(UNetModel, self).__init__() + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.temporal_attention = temporal_attention + time_embed_dim = model_channels * 4 + self.use_checkpoint = use_checkpoint + self.dtype = torch.float16 if use_fp16 else torch.float32 + self.addition_attention=addition_attention + self.use_image_attention = use_image_attention + self.fps_cond=fps_cond + + + + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + if self.fps_cond: + self.fps_embedding = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1)) + ] + ) + if self.addition_attention: + self.init_attn=TimestepEmbedSequential( + TemporalTransformer( + model_channels, + n_heads=8, + d_head=num_head_channels, + depth=transformer_depth, + context_dim=context_dim, + use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length)) + + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock(ch, time_embed_dim, dropout, + out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers.append( + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, + img_cross_attention=self.use_image_attention + ) + ) + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock(ch, time_embed_dim, dropout, + out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True + ) + if resblock_updown + else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers = [ + ResBlock(ch, time_embed_dim, dropout, + dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ), + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, + img_cross_attention=self.use_image_attention + ) + ] + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + layers.append( + ResBlock(ch, time_embed_dim, dropout, + dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ) + self.middle_block = TimestepEmbedSequential(*layers) + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock(ch + ich, time_embed_dim, dropout, + out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers.append( + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, + img_cross_attention=self.use_image_attention + ) + ) + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock(ch, time_embed_dim, dropout, + out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + + def forward(self, x, timesteps, context=None, features_adapter=None, fps=16, **kwargs): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + + if self.fps_cond: + if type(fps) == int: + fps = torch.full_like(timesteps, fps) + fps_emb = timestep_embedding(fps,self.model_channels, repeat_only=False) + emb += self.fps_embedding(fps_emb) + + b,_,t,_,_ = x.shape + ## repeat t times for context [(b t) 77 768] & time embedding + context = context.repeat_interleave(repeats=t, dim=0) + emb = emb.repeat_interleave(repeats=t, dim=0) + + ## always in shape (b t) c h w, except for temporal layer + x = rearrange(x, 'b c t h w -> (b t) c h w') + + h = x.type(self.dtype) + adapter_idx = 0 + hs = [] + for id, module in enumerate(self.input_blocks): + h = module(h, emb, context=context, batch_size=b) + if id ==0 and self.addition_attention: + h = self.init_attn(h, emb, context=context, batch_size=b) + ## plug-in adapter features + if ((id+1)%3 == 0) and features_adapter is not None: + h = h + features_adapter[adapter_idx] + adapter_idx += 1 + hs.append(h) + if features_adapter is not None: + assert len(features_adapter)==adapter_idx, 'Wrong features_adapter' + + h = self.middle_block(h, emb, context=context, batch_size=b) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context=context, batch_size=b) + h = h.type(x.dtype) + y = self.out(h) + + # reshape back to (b c t h w) + y = rearrange(y, '(b t) c h w -> b c t h w', b=b) + return y + \ No newline at end of file diff --git a/lvdm/modules/x_transformer.py b/lvdm/modules/x_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f252ab4032a78407ed487495807940c4ba802ffa --- /dev/null +++ b/lvdm/modules/x_transformer.py @@ -0,0 +1,640 @@ +"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" +from functools import partial +from inspect import isfunction +from collections import namedtuple +from einops import rearrange, repeat +import torch +from torch import nn, einsum +import torch.nn.functional as F + +# constants +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple('Intermediates', [ + 'pre_softmax_attn', + 'post_softmax_attn' +]) + +LayerIntermediates = namedtuple('Intermediates', [ + 'hiddens', + 'attn_intermediates' +]) + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self.init_() + + def init_(self): + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset + sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return emb[None, :, :] + + +# helpers + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def always(val): + def inner(*args, **kwargs): + return val + return inner + + +def not_equals(val): + def inner(x): + return x != val + return inner + + +def equals(val): + def inner(x): + return x == val + return inner + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +# keyword argument helpers + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) + kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) + return kwargs_without_prefix, kwargs + + +# classes +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.value, *rest) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class Residual(nn.Module): + def forward(self, x, residual): + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + + def forward(self, x, residual): + gated_output = self.gru( + rearrange(x, 'b n d -> (b n) d'), + rearrange(residual, 'b n d -> (b n) d') + ) + + return gated_output.reshape_as(x) + + +# feedforward + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +# attention. +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + mask=None, + talking_heads=False, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0., + on_attn=False + ): + super().__init__() + if use_entmax15: + raise NotImplementedError("Check out entmax activation instead of softmax activation!") + self.scale = dim_head ** -0.5 + self.heads = heads + self.causal = causal + self.mask = mask + + inner_dim = dim_head * heads + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_k = nn.Linear(dim, inner_dim, bias=False) + self.to_v = nn.Linear(dim, inner_dim, bias=False) + self.dropout = nn.Dropout(dropout) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + #self.attn_fn = entmax15 if use_entmax15 else F.softmax + self.attn_fn = F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + rel_pos=None, + sinusoidal_emb=None, + prev_attn=None, + mem=None + ): + b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) + q_mask = rearrange(q_mask, 'b i -> b () i ()') + k_mask = rearrange(k_mask, 'b j -> b () () j') + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots + + if talking_heads: + dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() + + if exists(rel_pos): + dots = rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + + intermediates = Intermediates( + pre_softmax_attn=pre_softmax_attn, + post_softmax_attn=post_softmax_attn + ) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rmsnorm=False, + use_rezero=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + position_infused_attn=False, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + **kwargs + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) + attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) + + dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + self.has_pos_emb = position_infused_attn + self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None + self.rotary_pos_emb = always(None) + + assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' + self.rel_pos = None + + self.pre_norm = pre_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ('a', 'c', 'f') + elif cross_attend and only_cross: + default_block = ('c', 'f') + else: + default_block = ('a', 'f') + + if macaron: + default_block = ('f',) + default_block + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, 'par ratio out of range' + default_block = tuple(filter(not_equals('f'), default_block)) + par_attn = par_depth // par_ratio + depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert len(default_block) <= par_width, 'default block is too large for par_ratio' + par_block = default_block + ('f',) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ('f',) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' + layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals('a'), layer_types))) + + for layer_type in self.layer_types: + if layer_type == 'a': + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == 'c': + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == 'f': + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f'invalid layer type {layer_type}') + + if isinstance(layer, Attention) and exists(branch_fn): + layer = branch_fn(layer) + + if gate_residual: + residual_fn = GRUGating(dim) + else: + residual_fn = Residual() + + self.layers.append(nn.ModuleList([ + norm_fn(), + layer, + residual_fn + ])) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + mems=None, + return_hiddens=False + ): + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + + for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): + is_last = ind == (len(self.layers) - 1) + + if layer_type == 'a': + hiddens.append(x) + layer_mem = mems.pop(0) + + residual = x + + if self.pre_norm: + x = norm(x) + + if layer_type == 'a': + out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, + prev_attn=prev_attn, mem=layer_mem) + elif layer_type == 'c': + out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) + elif layer_type == 'f': + out = block(x) + + x = residual_fn(out, residual) + + if layer_type in ('a', 'c'): + intermediates.append(inter) + + if layer_type == 'a' and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == 'c' and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if not self.pre_norm and not is_last: + x = norm(x) + + if return_hiddens: + intermediates = LayerIntermediates( + hiddens=hiddens, + attn_intermediates=intermediates + ) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on encoder' + super().__init__(causal=False, **kwargs) + + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0., + emb_dropout=0., + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.num_tokens = num_tokens + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( + use_pos_emb and not attn_layers.has_pos_emb) else always(0) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + # let funnel encoder know number of memory tokens, if specified + if hasattr(attn_layers, 'num_memory_tokens'): + attn_layers.num_memory_tokens = num_memory_tokens + + def init_(self): + nn.init.normal_(self.token_emb.weight, std=0.02) + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_mems=False, + return_attn=False, + mems=None, + **kwargs + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x += self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_mems: + hiddens = intermediates.hiddens + new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens + new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) + return out, new_mems + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, attn_maps + + return out + diff --git a/lvdm/utils/common_utils.py b/lvdm/utils/common_utils.py deleted file mode 100644 index 9bcb6ee1b5b15d3487de8e058e8dfce8277be745..0000000000000000000000000000000000000000 --- a/lvdm/utils/common_utils.py +++ /dev/null @@ -1,132 +0,0 @@ - -import importlib - -import torch -import numpy as np - -from inspect import isfunction -from PIL import Image, ImageDraw, ImageFont - - -def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ('yes', 'true', 't', 'y', '1'): - return True - elif v.lower() in ('no', 'false', 'f', 'n', '0'): - return False - else: - raise ValueError('Boolean value expected.') - - -def instantiate_from_config(config): - if not "target" in config: - if config == '__is_first_stage__': - return None - elif config == "__is_unconditional__": - return None - raise KeyError("Expected key `target` to instantiate.") - - return get_obj_from_str(config["target"])(**config.get("params", dict())) - -def get_obj_from_str(string, reload=False): - module, cls = string.rsplit(".", 1) - if reload: - module_imp = importlib.import_module(module) - importlib.reload(module_imp) - return getattr(importlib.import_module(module, package=None), cls) - -def log_txt_as_img(wh, xc, size=10): - # wh a tuple of (width, height) - # xc a list of captions to plot - b = len(xc) - txts = list() - for bi in range(b): - txt = Image.new("RGB", wh, color="white") - draw = ImageDraw.Draw(txt) - font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) - nc = int(40 * (wh[0] / 256)) - lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) - - try: - draw.text((0, 0), lines, fill="black", font=font) - except UnicodeEncodeError: - print("Cant encode string for logging. Skipping.") - - txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 - txts.append(txt) - txts = np.stack(txts) - txts = torch.tensor(txts) - return txts - - -def ismap(x): - if not isinstance(x, torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] > 3) - - -def isimage(x): - if not isinstance(x,torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) - - -def exists(x): - return x is not None - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def mean_flat(tensor): - """ - https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def count_params(model, verbose=False): - total_params = sum(p.numel() for p in model.parameters()) - if verbose: - print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") - return total_params - - -def instantiate_from_config(config): - if not "target" in config: - if config == '__is_first_stage__': - return None - elif config == "__is_unconditional__": - return None - raise KeyError("Expected key `target` to instantiate.") - - if "instantiate_with_dict" in config and config["instantiate_with_dict"]: - # input parameter is one dict - return get_obj_from_str(config["target"])(config.get("params", dict()), **kwargs) - else: - return get_obj_from_str(config["target"])(**config.get("params", dict())) - - -def get_obj_from_str(string, reload=False): - module, cls = string.rsplit(".", 1) - if reload: - module_imp = importlib.import_module(module) - importlib.reload(module_imp) - return getattr(importlib.import_module(module, package=None), cls) - - -def check_istarget(name, para_list): - """ - name: full name of source para - para_list: partial name of target para - """ - istarget=False - for para in para_list: - if para in name: - return True - return istarget \ No newline at end of file diff --git a/lvdm/utils/dist_utils.py b/lvdm/utils/dist_utils.py deleted file mode 100644 index 9a0285dcd62d65c7ebbb6cb2ca6f90b0fad40a64..0000000000000000000000000000000000000000 --- a/lvdm/utils/dist_utils.py +++ /dev/null @@ -1,19 +0,0 @@ -import torch -import torch.distributed as dist - -def setup_dist(local_rank): - if dist.is_initialized(): - return - torch.cuda.set_device(local_rank) - torch.distributed.init_process_group( - 'nccl', - init_method='env://' - ) - -def gather_data(data, return_np=True): - ''' gather data from multiple processes to one list ''' - data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())] - dist.all_gather(data_list, data) # gather not supported with NCCL - if return_np: - data_list = [data.cpu().numpy() for data in data_list] - return data_list diff --git a/lvdm/utils/saving_utils.py b/lvdm/utils/saving_utils.py deleted file mode 100644 index 0f0c2b1e529511dfd03eb68337e6d05184ea9383..0000000000000000000000000000000000000000 --- a/lvdm/utils/saving_utils.py +++ /dev/null @@ -1,269 +0,0 @@ -import numpy as np -import cv2 -import os -import time -import imageio -from tqdm import tqdm -from PIL import Image -import os -import sys -sys.path.insert(1, os.path.join(sys.path[0], '..')) -import torch -import torchvision -from torchvision.utils import make_grid -from torch import Tensor -from torchvision.transforms.functional import to_tensor - - -def tensor_to_mp4(video, savepath, fps, rescale=True, nrow=None): - """ - video: torch.Tensor, b,c,t,h,w, 0-1 - if -1~1, enable rescale=True - """ - n = video.shape[0] - video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w - nrow = int(np.sqrt(n)) if nrow is None else nrow - frame_grids = [torchvision.utils.make_grid(framesheet, nrow=nrow) for framesheet in video] # [3, grid_h, grid_w] - grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [T, 3, grid_h, grid_w] - grid = torch.clamp(grid.float(), -1., 1.) - if rescale: - grid = (grid + 1.0) / 2.0 - grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, 3, grid_h, grid_w] -> [T, grid_h, grid_w, 3] - #print(f'Save video to {savepath}') - torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'}) - -# ---------------------------------------------------------------------------------------------- -def savenp2sheet(imgs, savepath, nrow=None): - """ save multiple imgs (in numpy array type) to a img sheet. - img sheet is one row. - - imgs: - np array of size [N, H, W, 3] or List[array] with array size = [H,W,3] - """ - if imgs.ndim == 4: - img_list = [imgs[i] for i in range(imgs.shape[0])] - imgs = img_list - - imgs_new = [] - for i, img in enumerate(imgs): - if img.ndim == 3 and img.shape[0] == 3: - img = np.transpose(img,(1,2,0)) - - assert(img.ndim == 3 and img.shape[-1] == 3), img.shape # h,w,3 - img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) - imgs_new.append(img) - n = len(imgs) - if nrow is not None: - n_cols = nrow - else: - n_cols=int(n**0.5) - n_rows=int(np.ceil(n/n_cols)) - print(n_cols) - print(n_rows) - - imgsheet = cv2.vconcat([cv2.hconcat(imgs_new[i*n_cols:(i+1)*n_cols]) for i in range(n_rows)]) - cv2.imwrite(savepath, imgsheet) - print(f'saved in {savepath}') - -# ---------------------------------------------------------------------------------------------- -def save_np_to_img(img, path, norm=True): - if norm: - img = (img + 1) / 2 * 255 - img = img.astype(np.uint8) - image = Image.fromarray(img) - image.save(path, q=95) - -# ---------------------------------------------------------------------------------------------- -def npz_to_imgsheet_5d(data_path, res_dir, nrow=None,): - if isinstance(data_path, str): - imgs = np.load(data_path)['arr_0'] # NTHWC - elif isinstance(data_path, np.ndarray): - imgs = data_path - else: - raise Exception - - if os.path.isdir(res_dir): - res_path = os.path.join(res_dir, f'samples.jpg') - else: - assert(res_dir.endswith('.jpg')) - res_path = res_dir - imgs = np.concatenate([imgs[i] for i in range(imgs.shape[0])], axis=0) - savenp2sheet(imgs, res_path, nrow=nrow) - -# ---------------------------------------------------------------------------------------------- -def npz_to_imgsheet_4d(data_path, res_path, nrow=None,): - if isinstance(data_path, str): - imgs = np.load(data_path)['arr_0'] # NHWC - elif isinstance(data_path, np.ndarray): - imgs = data_path - else: - raise Exception - print(imgs.shape) - savenp2sheet(imgs, res_path, nrow=nrow) - - -# ---------------------------------------------------------------------------------------------- -def tensor_to_imgsheet(tensor, save_path): - """ - save a batch of videos in one image sheet with shape of [batch_size * num_frames]. - data: [b,c,t,h,w] - """ - assert(tensor.dim() == 5) - b,c,t,h,w = tensor.shape - imgs = [tensor[bi,:,ti, :, :] for bi in range(b) for ti in range(t)] - torchvision.utils.save_image(imgs, save_path, normalize=True, nrow=t) - - -# ---------------------------------------------------------------------------------------------- -def npz_to_frames(data_path, res_dir, norm, num_frames=None, num_samples=None): - start = time.time() - arr = np.load(data_path) - imgs = arr['arr_0'] # [N, T, H, W, 3] - print('original data shape: ', imgs.shape) - - if num_samples is not None: - imgs = imgs[:num_samples, :, :, :, :] - print('after sample selection: ', imgs.shape) - - if num_frames is not None: - imgs = imgs[:, :num_frames, :, :, :] - print('after frame selection: ', imgs.shape) - - for vid in tqdm(range(imgs.shape[0]), desc='Video'): - video_dir = os.path.join(res_dir, f'video{vid:04d}') - os.makedirs(video_dir, exist_ok=True) - for fid in range(imgs.shape[1]): - frame = imgs[vid, fid, :, :, :] #HW3 - save_np_to_img(frame, os.path.join(video_dir, f'frame{fid:04d}.jpg'), norm=norm) - print('Finish') - print(f'Total time = {time.time()- start}') - -# ---------------------------------------------------------------------------------------------- -def npz_to_gifs(data_path, res_dir, duration=0.2, start_idx=0, num_videos=None, mode='gif'): - os.makedirs(res_dir, exist_ok=True) - if isinstance(data_path, str): - imgs = np.load(data_path)['arr_0'] # NTHWC - elif isinstance(data_path, np.ndarray): - imgs = data_path - else: - raise Exception - - for i in range(imgs.shape[0]): - frames = [imgs[i,j,:,:,:] for j in range(imgs[i].shape[0])] # [(h,w,3)] - if mode == 'gif': - imageio.mimwrite(os.path.join(res_dir, f'samples_{start_idx+i}.gif'), frames, format='GIF', duration=duration) - elif mode == 'mp4': - frames = [torch.from_numpy(frame) for frame in frames] - frames = torch.stack(frames, dim=0).to(torch.uint8) # [T, H, W, C] - torchvision.io.write_video(os.path.join(res_dir, f'samples_{start_idx+i}.mp4'), - frames, fps=0.5, video_codec='h264', options={'crf': '10'}) - if i+ 1 == num_videos: - break - -# ---------------------------------------------------------------------------------------------- -def fill_with_black_squares(video, desired_len: int) -> Tensor: - if len(video) >= desired_len: - return video - - return torch.cat([ - video, - torch.zeros_like(video[0]).unsqueeze(0).repeat(desired_len - len(video), 1, 1, 1), - ], dim=0) - -# ---------------------------------------------------------------------------------------------- -def load_num_videos(data_path, num_videos): - # data_path can be either data_path of np array - if isinstance(data_path, str): - videos = np.load(data_path)['arr_0'] # NTHWC - elif isinstance(data_path, np.ndarray): - videos = data_path - else: - raise Exception - - if num_videos is not None: - videos = videos[:num_videos, :, :, :, :] - return videos - -# ---------------------------------------------------------------------------------------------- -def npz_to_video_grid(data_path, out_path, num_frames=None, fps=8, num_videos=None, nrow=None, verbose=True): - if isinstance(data_path, str): - videos = load_num_videos(data_path, num_videos) - elif isinstance(data_path, np.ndarray): - videos = data_path - else: - raise Exception - n,t,h,w,c = videos.shape - - videos_th = [] - for i in range(n): - video = videos[i, :,:,:,:] - images = [video[j, :,:,:] for j in range(t)] - images = [to_tensor(img) for img in images] - video = torch.stack(images) - videos_th.append(video) - - if num_frames is None: - num_frames = videos.shape[1] - if verbose: - videos = [fill_with_black_squares(v, num_frames) for v in tqdm(videos_th, desc='Adding empty frames')] # NTCHW - else: - videos = [fill_with_black_squares(v, num_frames) for v in videos_th] # NTCHW - - frame_grids = torch.stack(videos).permute(1, 0, 2, 3, 4) # [T, N, C, H, W] - if nrow is None: - nrow = int(np.ceil(np.sqrt(n))) - if verbose: - frame_grids = [make_grid(fs, nrow=nrow) for fs in tqdm(frame_grids, desc='Making grids')] - else: - frame_grids = [make_grid(fs, nrow=nrow) for fs in frame_grids] - - if os.path.dirname(out_path) != "": - os.makedirs(os.path.dirname(out_path), exist_ok=True) - frame_grids = (torch.stack(frame_grids) * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, H, W, C] - torchvision.io.write_video(out_path, frame_grids, fps=fps, video_codec='h264', options={'crf': '10'}) - -# ---------------------------------------------------------------------------------------------- -def npz_to_gif_grid(data_path, out_path, n_cols=None, num_videos=20): - arr = np.load(data_path) - imgs = arr['arr_0'] # [N, T, H, W, 3] - imgs = imgs[:num_videos] - n, t, h, w, c = imgs.shape - assert(n == num_videos) - n_cols = n_cols if n_cols else imgs.shape[0] - n_rows = np.ceil(imgs.shape[0] / n_cols).astype(np.int8) - H, W = h * n_rows, w * n_cols - grid = np.zeros((t, H, W, c), dtype=np.uint8) - - for i in range(n_rows): - for j in range(n_cols): - if i*n_cols+j < imgs.shape[0]: - grid[:, i*h:(i+1)*h, j*w:(j+1)*w, :] = imgs[i*n_cols+j, :, :, :, :] - - videos = [grid[i] for i in range(grid.shape[0])] # grid: TH'W'C - imageio.mimwrite(out_path, videos, format='GIF', duration=0.5,palettesize=256) - - -# ---------------------------------------------------------------------------------------------- -def torch_to_video_grid(videos, out_path, num_frames, fps, num_videos=None, nrow=None, verbose=True): - """ - videos: -1 ~ 1, torch.Tensor, BCTHW - """ - n,t,h,w,c = videos.shape - videos_th = [videos[i, ...] for i in range(n)] - if verbose: - videos = [fill_with_black_squares(v, num_frames) for v in tqdm(videos_th, desc='Adding empty frames')] # NTCHW - else: - videos = [fill_with_black_squares(v, num_frames) for v in videos_th] # NTCHW - - frame_grids = torch.stack(videos).permute(1, 0, 2, 3, 4) # [T, N, C, H, W] - if nrow is None: - nrow = int(np.ceil(np.sqrt(n))) - if verbose: - frame_grids = [make_grid(fs, nrow=nrow) for fs in tqdm(frame_grids, desc='Making grids')] - else: - frame_grids = [make_grid(fs, nrow=nrow) for fs in frame_grids] - - if os.path.dirname(out_path) != "": - os.makedirs(os.path.dirname(out_path), exist_ok=True) - frame_grids = ((torch.stack(frame_grids) + 1) / 2 * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, H, W, C] - torchvision.io.write_video(out_path, frame_grids, fps=fps, video_codec='h264', options={'crf': '10'}) diff --git a/prompts/i2v_prompts/horse.png b/prompts/i2v_prompts/horse.png new file mode 100644 index 0000000000000000000000000000000000000000..b0e8f565f38d61f0daf97b71c277fcfa8af27b51 Binary files /dev/null and b/prompts/i2v_prompts/horse.png differ diff --git a/prompts/i2v_prompts/seashore.png b/prompts/i2v_prompts/seashore.png new file mode 100644 index 0000000000000000000000000000000000000000..f6c94a4312c8a6113bca649013b03396c322ee97 Binary files /dev/null and b/prompts/i2v_prompts/seashore.png differ diff --git a/prompts/i2v_prompts/test_prompts.txt b/prompts/i2v_prompts/test_prompts.txt new file mode 100644 index 0000000000000000000000000000000000000000..5acdda4b3dd79b7deda4f19dd3eab44f869952aa --- /dev/null +++ b/prompts/i2v_prompts/test_prompts.txt @@ -0,0 +1,2 @@ +horses are walking on the grassland +a boy and a girl are talking on the seashore diff --git a/prompts/test_prompts.txt b/prompts/test_prompts.txt new file mode 100644 index 0000000000000000000000000000000000000000..016f5f26a782d8d8b8d95fe271dea17d0d2b11ce --- /dev/null +++ b/prompts/test_prompts.txt @@ -0,0 +1,2 @@ +A tiger walks in the forest, photorealistic, 4k, high definition +A boat moving on the sea, flowers and grassland on the shore \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index b2526f662039e8325d88c45468b11f5915b9f99f..b42ef1c99584b9f6d61fd48051dca23254a9cb14 100644 --- a/requirements.txt +++ b/requirements.txt @@ -16,7 +16,8 @@ transformers==4.25.1 moviepy av xformers -gradio==3.24.1 -gradio-client==0.1.2 +gradio timm -# -e . \ No newline at end of file +scikit-learn +open_clip_torch +kornia \ No newline at end of file diff --git a/sample_adapter.sh b/sample_adapter.sh deleted file mode 100644 index af27f6767b51428b9271653b4a52148ae31cc1dc..0000000000000000000000000000000000000000 --- a/sample_adapter.sh +++ /dev/null @@ -1,22 +0,0 @@ -PROMPT="An ostrich walking in the desert, photorealistic, 4k" -VIDEO="input/flamingo.mp4" -OUTDIR="results/" - -NAME="video_adapter" -CONFIG_PATH="models/adapter_t2v_depth/model_config.yaml" -BASE_PATH="models/base_t2v/model.ckpt" -ADAPTER_PATH="models/adapter_t2v_depth/adapter.pth" - -python scripts/sample_text2video_adapter.py \ - --seed 123 \ - --ckpt_path $BASE_PATH \ - --adapter_ckpt $ADAPTER_PATH \ - --base $CONFIG_PATH \ - --savedir $OUTDIR/$NAME \ - --bs 1 --height 256 --width 256 \ - --frame_stride -1 \ - --unconditional_guidance_scale 15.0 \ - --ddim_steps 50 \ - --ddim_eta 1.0 \ - --prompt "$PROMPT" \ - --video $VIDEO \ No newline at end of file diff --git a/sample_text2video.sh b/sample_text2video.sh deleted file mode 100644 index 365271cb1e250a32c5880b902cefffa9c6b9e28b..0000000000000000000000000000000000000000 --- a/sample_text2video.sh +++ /dev/null @@ -1,16 +0,0 @@ - -PROMPT="astronaut riding a horse" # OR: PROMPT="input/prompts.txt" for sampling multiple prompts -OUTDIR="results/" - -BASE_PATH="models/base_t2v/model.ckpt" -CONFIG_PATH="models/base_t2v/model_config.yaml" - -python scripts/sample_text2video.py \ - --ckpt_path $BASE_PATH \ - --config_path $CONFIG_PATH \ - --prompt "$PROMPT" \ - --save_dir $OUTDIR \ - --n_samples 1 \ - --batch_size 1 \ - --seed 1000 \ - --show_denoising_progress diff --git a/sample_text2video_multiGPU.sh b/sample_text2video_multiGPU.sh deleted file mode 100644 index 668800e12595d0d715ba7aeb0faf8a020823d364..0000000000000000000000000000000000000000 --- a/sample_text2video_multiGPU.sh +++ /dev/null @@ -1,18 +0,0 @@ - -PROMPT="astronaut riding a horse" # OR: PROMPT="input/prompts.txt" for sampling multiple prompts -OUTDIR="results/" - -BASE_PATH="models/base_t2v/model.ckpt" -CONFIG_PATH="models/base_t2v/model_config.ckpt" - -NGPU=8 - -python -m torch.distributed.launch --nproc_per_node=$NGPU scripts/sample_text2video.py \ - --ckpt_path $BASE_PATH \ - --config_path $CONFIG_PATH \ - --prompt "$PROMPT" \ - --save_dir $OUTDIR \ - --n_samples 1 \ - --batch_size 1 \ - --seed 1000 \ - --ddp diff --git a/sample_videolora.sh b/sample_videolora.sh deleted file mode 100644 index 1c5dfb1e382da456f25f2bfe915e1fe0234cde01..0000000000000000000000000000000000000000 --- a/sample_videolora.sh +++ /dev/null @@ -1,24 +0,0 @@ - -PROMPT="astronaut riding a horse" # OR: PROMPT="input/prompts.txt" for sampling multiple prompts -OUTDIR="results/videolora" - -BASE_PATH="models/base_t2v/model.ckpt" -CONFIG_PATH="models/base_t2v/model_config.yaml" - -# lora args -LORA_PATH="models/videolora/lora_001_Loving_Vincent_style.ckpt" -TAG=", Loving Vincent style" - -python scripts/sample_text2video.py \ - --ckpt_path $BASE_PATH \ - --config_path $CONFIG_PATH \ - --prompt "$PROMPT" \ - --save_dir $OUTDIR \ - --n_samples 1 \ - --batch_size 1 \ - --seed 1000 \ - --show_denoising_progress \ - --inject_lora \ - --lora_path $LORA_PATH \ - --lora_trigger_word "$TAG" \ - --lora_scale 1.0 diff --git a/scripts/ddp_wrapper.py b/scripts/evaluation/ddp_wrapper.py similarity index 88% rename from scripts/ddp_wrapper.py rename to scripts/evaluation/ddp_wrapper.py index 28ee4a0e10f9c7bce285ac9117850607d8badf84..cbf371e3d9f68827f19dbc18dd8a5b4d289184ca 100644 --- a/scripts/ddp_wrapper.py +++ b/scripts/evaluation/ddp_wrapper.py @@ -1,47 +1,46 @@ -import datetime -import argparse, importlib -from pytorch_lightning import seed_everything - -import torch -import torch.distributed as dist - - -def setup_dist(local_rank): - if dist.is_initialized(): - return - torch.cuda.set_device(local_rank) - torch.distributed.init_process_group('nccl', init_method='env://') - - -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 - - -if __name__ == '__main__': - now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") - parser = argparse.ArgumentParser() - parser.add_argument("--module", type=str, help="module name", default="inference") - parser.add_argument("--local_rank", type=int, nargs="?", help="for ddp", default=0) - args, unknown = parser.parse_known_args() - inference_api = importlib.import_module(args.module, package=None) - - inference_parser = inference_api.get_parser() - inference_args, unknown = inference_parser.parse_known_args() - - seed_everything(inference_args.seed) - setup_dist(args.local_rank) - torch.backends.cudnn.benchmark = True - rank, gpu_num = get_dist_info() - - print("@CoVideoGen Inference [rank%d]: %s"%(rank, now)) - inference_api.run_inference(inference_args, rank) \ No newline at end of file +import datetime +import argparse, importlib +from pytorch_lightning import seed_everything + +import torch +import torch.distributed as dist + +def setup_dist(local_rank): + if dist.is_initialized(): + return + torch.cuda.set_device(local_rank) + torch.distributed.init_process_group('nccl', init_method='env://') + + +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 + + +if __name__ == '__main__': + now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + parser = argparse.ArgumentParser() + parser.add_argument("--module", type=str, help="module name", default="inference") + parser.add_argument("--local_rank", type=int, nargs="?", help="for ddp", default=0) + args, unknown = parser.parse_known_args() + inference_api = importlib.import_module(args.module, package=None) + + inference_parser = inference_api.get_parser() + inference_args, unknown = inference_parser.parse_known_args() + + seed_everything(inference_args.seed) + setup_dist(args.local_rank) + torch.backends.cudnn.benchmark = True + rank, gpu_num = get_dist_info() + + print("@CoLVDM Inference [rank%d]: %s"%(rank, now)) + inference_api.run_inference(inference_args, gpu_num, rank) \ No newline at end of file diff --git a/scripts/evaluation/funcs.py b/scripts/evaluation/funcs.py new file mode 100644 index 0000000000000000000000000000000000000000..ff1a781cbcf83ef0089862cdb4381828c7f17d78 --- /dev/null +++ b/scripts/evaluation/funcs.py @@ -0,0 +1,194 @@ +import os, sys, glob +import numpy as np +from collections import OrderedDict +from decord import VideoReader, cpu +import cv2 + +import torch +import torchvision +sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) +from lvdm.models.samplers.ddim import DDIMSampler + + +def batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1.0,\ + cfg_scale=1.0, temporal_cfg_scale=None, **kwargs): + ddim_sampler = DDIMSampler(model) + uncond_type = model.uncond_type + batch_size = noise_shape[0] + + ## construct unconditional guidance + if cfg_scale != 1.0: + if uncond_type == "empty_seq": + prompts = batch_size * [""] + #prompts = N * T * [""] ## if is_imgbatch=True + uc_emb = model.get_learned_conditioning(prompts) + elif uncond_type == "zero_embed": + c_emb = cond["c_crossattn"][0] if isinstance(cond, dict) else cond + uc_emb = torch.zeros_like(c_emb) + + ## process image embedding token + if hasattr(model, 'embedder'): + uc_img = torch.zeros(noise_shape[0],3,224,224).to(model.device) + ## img: b c h w >> b l c + uc_img = model.get_image_embeds(uc_img) + uc_emb = torch.cat([uc_emb, uc_img], dim=1) + + if isinstance(cond, dict): + uc = {key:cond[key] for key in cond.keys()} + uc.update({'c_crossattn': [uc_emb]}) + else: + uc = uc_emb + else: + uc = None + + x_T = None + batch_variants = [] + #batch_variants1, batch_variants2 = [], [] + for _ in range(n_samples): + if ddim_sampler is not None: + kwargs.update({"clean_cond": True}) + samples, _ = ddim_sampler.sample(S=ddim_steps, + conditioning=cond, + batch_size=noise_shape[0], + shape=noise_shape[1:], + verbose=False, + unconditional_guidance_scale=cfg_scale, + unconditional_conditioning=uc, + eta=ddim_eta, + temporal_length=noise_shape[2], + conditional_guidance_scale_temporal=temporal_cfg_scale, + x_T=x_T, + **kwargs + ) + ## reconstruct from latent to pixel space + batch_images = model.decode_first_stage_2DAE(samples) + batch_variants.append(batch_images) + ## batch, , c, t, h, w + batch_variants = torch.stack(batch_variants, dim=1) + return batch_variants + + +def get_filelist(data_dir, ext='*'): + file_list = glob.glob(os.path.join(data_dir, '*.%s'%ext)) + file_list.sort() + return file_list + +def get_dirlist(path): + list = [] + if (os.path.exists(path)): + files = os.listdir(path) + for file in files: + m = os.path.join(path,file) + if (os.path.isdir(m)): + list.append(m) + list.sort() + return list + + +def load_model_checkpoint(model, ckpt): + def load_checkpoint(model, ckpt, full_strict): + state_dict = torch.load(ckpt, map_location="cpu") + try: + ## deepspeed + new_pl_sd = OrderedDict() + for key in state_dict['module'].keys(): + new_pl_sd[key[16:]]=state_dict['module'][key] + model.load_state_dict(new_pl_sd, strict=full_strict) + except: + if "state_dict" in list(state_dict.keys()): + state_dict = state_dict["state_dict"] + model.load_state_dict(state_dict, strict=full_strict) + return model + load_checkpoint(model, ckpt, full_strict=True) + print('>>> model checkpoint loaded.') + return model + + +def load_prompts(prompt_file): + f = open(prompt_file, 'r') + prompt_list = [] + for idx, line in enumerate(f.readlines()): + l = line.strip() + if len(l) != 0: + prompt_list.append(l) + f.close() + return prompt_list + + +def load_video_batch(filepath_list, frame_stride, video_size=(256,256), video_frames=16): + ''' + Notice about some special cases: + 1. video_frames=-1 means to take all the frames (with fs=1) + 2. when the total video frames is less than required, padding strategy will be used (repreated last frame) + ''' + fps_list = [] + batch_tensor = [] + assert frame_stride > 0, "valid frame stride should be a positive interge!" + for filepath in filepath_list: + padding_num = 0 + vidreader = VideoReader(filepath, ctx=cpu(0), width=video_size[1], height=video_size[0]) + fps = vidreader.get_avg_fps() + total_frames = len(vidreader) + max_valid_frames = (total_frames-1) // frame_stride + 1 + if video_frames < 0: + ## all frames are collected: fs=1 is a must + required_frames = total_frames + frame_stride = 1 + else: + required_frames = video_frames + query_frames = min(required_frames, max_valid_frames) + frame_indices = [frame_stride*i for i in range(query_frames)] + + ## [t,h,w,c] -> [c,t,h,w] + frames = vidreader.get_batch(frame_indices) + frame_tensor = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() + frame_tensor = (frame_tensor / 255. - 0.5) * 2 + if max_valid_frames < required_frames: + padding_num = required_frames - max_valid_frames + frame_tensor = torch.cat([frame_tensor, *([frame_tensor[:,-1:,:,:]]*padding_num)], dim=1) + print(f'{os.path.split(filepath)[1]} is not long enough: {padding_num} frames padded.') + batch_tensor.append(frame_tensor) + sample_fps = int(fps/frame_stride) + fps_list.append(sample_fps) + + return torch.stack(batch_tensor, dim=0) + +from PIL import Image +def load_image_batch(filepath_list, image_size=(256,256)): + batch_tensor = [] + for filepath in filepath_list: + _, filename = os.path.split(filepath) + _, ext = os.path.splitext(filename) + if ext == '.mp4': + vidreader = VideoReader(filepath, ctx=cpu(0), width=image_size[1], height=image_size[0]) + frame = vidreader.get_batch([0]) + img_tensor = torch.tensor(frame.asnumpy()).squeeze(0).permute(2, 0, 1).float() + elif ext == '.png' or ext == '.jpg': + img = Image.open(filepath).convert("RGB") + rgb_img = np.array(img, np.float32) + #bgr_img = cv2.imread(filepath, cv2.IMREAD_COLOR) + #bgr_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) + rgb_img = cv2.resize(rgb_img, (image_size[1],image_size[0]), interpolation=cv2.INTER_LINEAR) + img_tensor = torch.from_numpy(rgb_img).permute(2, 0, 1).float() + else: + print(f'ERROR: <{ext}> image loading only support format: [mp4], [png], [jpg]') + raise NotImplementedError + img_tensor = (img_tensor / 255. - 0.5) * 2 + batch_tensor.append(img_tensor) + return torch.stack(batch_tensor, dim=0) + + +def save_videos(batch_tensors, savedir, filenames, fps=10): + # b,samples,c,t,h,w + n_samples = batch_tensors.shape[1] + for idx, vid_tensor in enumerate(batch_tensors): + video = vid_tensor.detach().cpu() + video = torch.clamp(video.float(), -1., 1.) + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) + savepath = os.path.join(savedir, f"{filenames[idx]}.mp4") + torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + diff --git a/scripts/evaluation/inference.py b/scripts/evaluation/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..2beec8daa2b45ae40ddc9bf8a6d2bd8ab62db40c --- /dev/null +++ b/scripts/evaluation/inference.py @@ -0,0 +1,137 @@ +import argparse, os, sys, glob, yaml, math, random +import datetime, time +import numpy as np +from omegaconf import OmegaConf +from collections import OrderedDict +from tqdm import trange, tqdm +from einops import repeat +from einops import rearrange, repeat +from functools import partial +import torch +from pytorch_lightning import seed_everything + +from funcs import load_model_checkpoint, load_prompts, load_image_batch, get_filelist, save_videos +from funcs import batch_ddim_sampling +from utils.utils import instantiate_from_config + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--seed", type=int, default=20230211, help="seed for seed_everything") + parser.add_argument("--mode", default="base", type=str, help="which kind of inference mode: {'base', 'i2v'}") + parser.add_argument("--ckpt_path", type=str, default=None, help="checkpoint path") + parser.add_argument("--config", type=str, help="config (yaml) path") + parser.add_argument("--prompt_file", type=str, default=None, help="a text file containing many prompts") + parser.add_argument("--savedir", type=str, default=None, help="results saving path") + parser.add_argument("--savefps", type=str, default=10, help="video fps to generate") + parser.add_argument("--n_samples", type=int, default=1, help="num of samples per prompt",) + parser.add_argument("--ddim_steps", type=int, default=50, help="steps of ddim if positive, otherwise use DDPM",) + parser.add_argument("--ddim_eta", type=float, default=1.0, help="eta for ddim sampling (0.0 yields deterministic sampling)",) + parser.add_argument("--bs", type=int, default=1, help="batch size for inference") + parser.add_argument("--height", type=int, default=512, help="image height, in pixel space") + parser.add_argument("--width", type=int, default=512, help="image width, in pixel space") + parser.add_argument("--frames", type=int, default=-1, help="frames num to inference") + parser.add_argument("--fps", type=int, default=24) + parser.add_argument("--unconditional_guidance_scale", type=float, default=1.0, help="prompt classifier-free guidance") + parser.add_argument("--unconditional_guidance_scale_temporal", type=float, default=None, help="temporal consistency guidance") + ## for conditional i2v only + parser.add_argument("--cond_input", type=str, default=None, help="data dir of conditional input") + return parser + + +def run_inference(args, gpu_num, gpu_no, **kwargs): + ## step 1: model config + ## ----------------------------------------------------------------- + config = OmegaConf.load(args.config) + #data_config = config.pop("data", OmegaConf.create()) + model_config = config.pop("model", OmegaConf.create()) + model = instantiate_from_config(model_config) + model = model.cuda(gpu_no) + assert os.path.exists(args.ckpt_path), f"Error: checkpoint [{args.ckpt_path}] Not Found!" + model = load_model_checkpoint(model, args.ckpt_path) + model.eval() + + ## sample shape + assert (args.height % 16 == 0) and (args.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!" + ## latent noise shape + h, w = args.height // 8, args.width // 8 + frames = model.temporal_length if args.frames < 0 else args.frames + channels = model.channels + + ## saving folders + os.makedirs(args.savedir, exist_ok=True) + + ## step 2: load data + ## ----------------------------------------------------------------- + assert os.path.exists(args.prompt_file), "Error: prompt file NOT Found!" + prompt_list = load_prompts(args.prompt_file) + num_samples = len(prompt_list) + filename_list = [f"{id+1:04d}" for id in range(num_samples)] + + samples_split = num_samples // gpu_num + residual_tail = num_samples % gpu_num + print(f'[rank:{gpu_no}] {samples_split}/{num_samples} samples loaded.') + indices = list(range(samples_split*gpu_no, samples_split*(gpu_no+1))) + if gpu_no == 0 and residual_tail != 0: + indices = indices + list(range(num_samples-residual_tail, num_samples)) + prompt_list_rank = [prompt_list[i] for i in indices] + + ## conditional input + if args.mode == "i2v": + ## each video or frames dir per prompt + cond_inputs = get_filelist(args.cond_input, ext='[mpj][pn][4gj]') # '[mpj][pn][4gj]' + assert len(cond_inputs) == num_samples, f"Error: conditional input ({len(cond_inputs)}) NOT match prompt ({num_samples})!" + filename_list = [f"{os.path.split(cond_inputs[id])[-1][:-4]}" for id in range(num_samples)] + cond_inputs_rank = [cond_inputs[i] for i in indices] + + filename_list_rank = [filename_list[i] for i in indices] + + ## step 3: run over samples + ## ----------------------------------------------------------------- + start = time.time() + n_rounds = len(prompt_list_rank) // args.bs + n_rounds = n_rounds+1 if len(prompt_list_rank) % args.bs != 0 else n_rounds + for idx in range(0, n_rounds): + print(f'[rank:{gpu_no}] batch-{idx+1} ({args.bs})x{args.n_samples} ...') + idx_s = idx*args.bs + idx_e = min(idx_s+args.bs, len(prompt_list_rank)) + batch_size = idx_e - idx_s + filenames = filename_list_rank[idx_s:idx_e] + noise_shape = [batch_size, channels, frames, h, w] + fps = torch.tensor([args.fps]*batch_size).to(model.device).long() + + prompts = prompt_list_rank[idx_s:idx_e] + if isinstance(prompts, str): + prompts = [prompts] + #prompts = batch_size * [""] + text_emb = model.get_learned_conditioning(prompts) + + if args.mode == 'base': + cond = {"c_crossattn": [text_emb], "fps": fps} + elif args.mode == 'i2v': + #cond_images = torch.zeros(noise_shape[0],3,224,224).to(model.device) + cond_images = load_image_batch(cond_inputs_rank[idx_s:idx_e], (args.height, args.width)) + cond_images = cond_images.to(model.device) + img_emb = model.get_image_embeds(cond_images) + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + cond = {"c_crossattn": [imtext_cond], "fps": fps} + else: + raise NotImplementedError + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, args.n_samples, \ + args.ddim_steps, args.ddim_eta, args.unconditional_guidance_scale, **kwargs) + ## b,samples,c,t,h,w + save_videos(batch_samples, args.savedir, filenames, fps=args.savefps) + + print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds") + + +if __name__ == '__main__': + now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + print("@CoLVDM Inference: %s"%now) + parser = get_parser() + args = parser.parse_args() + seed_everything(args.seed) + rank, gpu_num = 0, 1 + run_inference(args, gpu_num, rank) \ No newline at end of file diff --git a/scripts/run_image2video.sh b/scripts/run_image2video.sh new file mode 100644 index 0000000000000000000000000000000000000000..70e87463c7acab95bba38fc9eecc389ab200df23 --- /dev/null +++ b/scripts/run_image2video.sh @@ -0,0 +1,24 @@ +name="i2v_512_test" + +ckpt='checkpoints/i2v_512_v1/model.ckpt' +config='configs/inference_i2v_512_v1.0.yaml' + +prompt_file="prompts/i2v_prompts/test_prompts.txt" +condimage_dir="prompts/i2v_prompts" +res_dir="results" + +python3 scripts/evaluation/inference.py \ +--seed 123 \ +--mode 'i2v' \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height 320 --width 512 \ +--unconditional_guidance_scale 12.0 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_file $prompt_file \ +--cond_input $condimage_dir \ +--fps 8 + diff --git a/scripts/run_text2video.sh b/scripts/run_text2video.sh new file mode 100644 index 0000000000000000000000000000000000000000..50ce8758663a9089e483206d7f16a8c3bfda1ecd --- /dev/null +++ b/scripts/run_text2video.sh @@ -0,0 +1,21 @@ +name="base_1024_test" + +ckpt='checkpoints/base_1024_v1/model.ckpt' +config='configs/inference_t2v_1024_v1.0.yaml' + +prompt_file="prompts/test_prompts.txt" +res_dir="results" + +python3 scripts/evaluation/inference.py \ +--seed 123 \ +--mode 'base' \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height 576 --width 1024 \ +--unconditional_guidance_scale 12.0 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_file $prompt_file \ +--fps 28 diff --git a/scripts/sample_text2video.py b/scripts/sample_text2video.py deleted file mode 100644 index 67b03a5d1e08b23c74eb482bb013444da0effe87..0000000000000000000000000000000000000000 --- a/scripts/sample_text2video.py +++ /dev/null @@ -1,260 +0,0 @@ -import os -import time -import argparse -import yaml, math -from tqdm import trange -import torch -import numpy as np -from omegaconf import OmegaConf -import torch.distributed as dist -from pytorch_lightning import seed_everything - -from lvdm.samplers.ddim import DDIMSampler -from lvdm.utils.common_utils import str2bool -from lvdm.utils.dist_utils import setup_dist, gather_data -from lvdm.utils.saving_utils import npz_to_video_grid, npz_to_imgsheet_5d -from scripts.sample_utils import load_model, get_conditions, make_model_input_shape, torch_to_np - - -# ------------------------------------------------------------------------------------------ -def get_parser(): - parser = argparse.ArgumentParser() - # basic args - parser.add_argument("--ckpt_path", type=str, help="model checkpoint path") - parser.add_argument("--config_path", type=str, help="model config path (a yaml file)") - parser.add_argument("--prompt", type=str, help="input text prompts for text2video (a sentence OR a txt file).") - parser.add_argument("--save_dir", type=str, help="results saving dir", default="results/") - # device args - parser.add_argument("--ddp", action='store_true', help="whether use pytorch ddp mode for parallel sampling (recommend for multi-gpu case)", default=False) - parser.add_argument("--local_rank", type=int, help="is used for pytorch ddp mode", default=0) - parser.add_argument("--gpu_id", type=int, help="choose a specific gpu", default=0) - # sampling args - parser.add_argument("--n_samples", type=int, help="how many samples for each text prompt", default=2) - parser.add_argument("--batch_size", type=int, help="video batch size for sampling", default=1) - parser.add_argument("--decode_frame_bs", type=int, help="frame batch size for framewise decoding", default=1) - parser.add_argument("--sample_type", type=str, help="ddpm or ddim", default="ddim", choices=["ddpm", "ddim"]) - parser.add_argument("--ddim_steps", type=int, help="ddim sampling -- number of ddim denoising timesteps", default=50) - parser.add_argument("--eta", type=float, help="ddim sampling -- eta (0.0 yields deterministic sampling, 1.0 yields random sampling)", default=1.0) - parser.add_argument("--cfg_scale", type=float, default=15.0, help="classifier-free guidance scale") - parser.add_argument("--seed", type=int, default=None, help="fix a seed for randomness (If you want to reproduce the sample results)") - parser.add_argument("--show_denoising_progress", action='store_true', default=False, help="whether show denoising progress during sampling one batch",) - # lora args - parser.add_argument("--lora_path", type=str, help="lora checkpoint path") - parser.add_argument("--inject_lora", action='store_true', default=False, help="",) - parser.add_argument("--lora_scale", type=float, default=None, help="scale for lora weight") - parser.add_argument("--lora_trigger_word", type=str, default="", help="",) - # saving args - parser.add_argument("--save_mp4", type=str2bool, default=True, help="whether save samples in separate mp4 files", choices=["True", "true", "False", "false"]) - parser.add_argument("--save_mp4_sheet", action='store_true', default=False, help="whether save samples in mp4 file",) - parser.add_argument("--save_npz", action='store_true', default=False, help="whether save samples in npz file",) - parser.add_argument("--save_jpg", action='store_true', default=False, help="whether save samples in jpg file",) - parser.add_argument("--save_fps", type=int, default=8, help="fps of saved mp4 videos",) - return parser - -# ------------------------------------------------------------------------------------------ -def sample_denoising_batch(model, noise_shape, condition, *args, - sample_type="ddim", sampler=None, - ddim_steps=None, eta=None, - unconditional_guidance_scale=1.0, uc=None, - denoising_progress=False, - **kwargs, - ): - - if sample_type == "ddpm": - samples = model.p_sample_loop(cond=condition, shape=noise_shape, - return_intermediates=False, - verbose=denoising_progress, - **kwargs, - ) - elif sample_type == "ddim": - assert(sampler is not None) - assert(ddim_steps is not None) - assert(eta is not None) - ddim_sampler = sampler - samples, _ = ddim_sampler.sample(S=ddim_steps, - conditioning=condition, - batch_size=noise_shape[0], - shape=noise_shape[1:], - verbose=denoising_progress, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - eta=eta, - **kwargs, - ) - else: - raise ValueError - return samples - - -# ------------------------------------------------------------------------------------------ -@torch.no_grad() -def sample_text2video(model, prompt, n_samples, batch_size, - sample_type="ddim", sampler=None, - ddim_steps=50, eta=1.0, cfg_scale=7.5, - decode_frame_bs=1, - ddp=False, all_gather=True, - batch_progress=True, show_denoising_progress=False, - ): - # get cond vector - assert(model.cond_stage_model is not None) - cond_embd = get_conditions(prompt, model, batch_size) - uncond_embd = get_conditions("", model, batch_size) if cfg_scale != 1.0 else None - - # sample batches - all_videos = [] - n_iter = math.ceil(n_samples / batch_size) - iterator = trange(n_iter, desc="Sampling Batches (text-to-video)") if batch_progress else range(n_iter) - for _ in iterator: - noise_shape = make_model_input_shape(model, batch_size) - samples_latent = sample_denoising_batch(model, noise_shape, cond_embd, - sample_type=sample_type, - sampler=sampler, - ddim_steps=ddim_steps, - eta=eta, - unconditional_guidance_scale=cfg_scale, - uc=uncond_embd, - denoising_progress=show_denoising_progress, - ) - samples = model.decode_first_stage(samples_latent, decode_bs=decode_frame_bs, return_cpu=False) - - # gather samples from multiple gpus - if ddp and all_gather: - data_list = gather_data(samples, return_np=False) - all_videos.extend([torch_to_np(data) for data in data_list]) - else: - all_videos.append(torch_to_np(samples)) - - all_videos = np.concatenate(all_videos, axis=0) - assert(all_videos.shape[0] >= n_samples) - return all_videos - - -# ------------------------------------------------------------------------------------------ -def save_results(videos, save_dir, - save_name="results", save_fps=8, save_mp4=True, - save_npz=False, save_mp4_sheet=False, save_jpg=False - ): - if save_mp4: - save_subdir = os.path.join(save_dir, "videos") - os.makedirs(save_subdir, exist_ok=True) - for i in range(videos.shape[0]): - npz_to_video_grid(videos[i:i+1,...], - os.path.join(save_subdir, f"{save_name}_{i:03d}.mp4"), - fps=save_fps) - print(f'Successfully saved videos in {save_subdir}') - - if save_npz: - save_path = os.path.join(save_dir, f"{save_name}.npz") - np.savez(save_path, videos) - print(f'Successfully saved npz in {save_path}') - - if save_mp4_sheet: - save_path = os.path.join(save_dir, f"{save_name}.mp4") - npz_to_video_grid(videos, save_path, fps=save_fps) - print(f'Successfully saved mp4 sheet in {save_path}') - - if save_jpg: - save_path = os.path.join(save_dir, f"{save_name}.jpg") - npz_to_imgsheet_5d(videos, save_path, nrow=videos.shape[1]) - print(f'Successfully saved jpg sheet in {save_path}') - - -# ------------------------------------------------------------------------------------------ -def main(): - """ - text-to-video generation - """ - parser = get_parser() - opt, unknown = parser.parse_known_args() - os.makedirs(opt.save_dir, exist_ok=True) - - # set device - if opt.ddp: - setup_dist(opt.local_rank) - opt.n_samples = math.ceil(opt.n_samples / dist.get_world_size()) - gpu_id = None - else: - gpu_id = opt.gpu_id - os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_id}" - - # set random seed - if opt.seed is not None: - if opt.ddp: - seed = opt.local_rank + opt.seed - else: - seed = opt.seed - seed_everything(seed) - - # dump args - fpath = os.path.join(opt.save_dir, "sampling_args.yaml") - with open(fpath, 'w') as f: - yaml.dump(vars(opt), f, default_flow_style=False) - - # load & merge config - config = OmegaConf.load(opt.config_path) - cli = OmegaConf.from_dotlist(unknown) - config = OmegaConf.merge(config, cli) - print("config: \n", config) - - # get model & sampler - model, _, _ = load_model(config, opt.ckpt_path, - inject_lora=opt.inject_lora, - lora_scale=opt.lora_scale, - lora_path=opt.lora_path - ) - ddim_sampler = DDIMSampler(model) if opt.sample_type == "ddim" else None - - # prepare prompt - if opt.prompt.endswith(".txt"): - opt.prompt_file = opt.prompt - opt.prompt = None - else: - opt.prompt_file = None - - if opt.prompt_file is not None: - f = open(opt.prompt_file, 'r') - prompts, line_idx = [], [] - for idx, line in enumerate(f.readlines()): - l = line.strip() - if len(l) != 0: - prompts.append(l) - line_idx.append(idx) - f.close() - cmd = f"cp {opt.prompt_file} {opt.save_dir}" - os.system(cmd) - else: - prompts = [opt.prompt] - line_idx = [None] - - if opt.inject_lora: - assert(opt.lora_trigger_word != '') - prompts = [p + opt.lora_trigger_word for p in prompts] - - # go - start = time.time() - for prompt in prompts: - # sample - samples = sample_text2video(model, prompt, opt.n_samples, opt.batch_size, - sample_type=opt.sample_type, sampler=ddim_sampler, - ddim_steps=opt.ddim_steps, eta=opt.eta, - cfg_scale=opt.cfg_scale, - decode_frame_bs=opt.decode_frame_bs, - ddp=opt.ddp, show_denoising_progress=opt.show_denoising_progress, - ) - # save - if (opt.ddp and dist.get_rank() == 0) or (not opt.ddp): - prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt - save_name = prompt_str.replace(" ", "_") if " " in prompt else prompt_str - if opt.seed is not None: - save_name = save_name + f"_seed{seed:05d}" - save_results(samples, opt.save_dir, save_name=save_name, save_fps=opt.save_fps) - print("Finish sampling!") - print(f"Run time = {(time.time() - start):.2f} seconds") - - if opt.ddp: - dist.destroy_process_group() - - -# ------------------------------------------------------------------------------------------ -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/scripts/sample_text2video_adapter.py b/scripts/sample_text2video_adapter.py deleted file mode 100644 index 6a9c85afa81043698b44cf7d4f08d62d4433325c..0000000000000000000000000000000000000000 --- a/scripts/sample_text2video_adapter.py +++ /dev/null @@ -1,206 +0,0 @@ -import argparse, os, sys, glob -import datetime, time -from omegaconf import OmegaConf - -import torch -from decord import VideoReader, cpu -import torchvision -from pytorch_lightning import seed_everything - -from lvdm.samplers.ddim import DDIMSampler -from lvdm.utils.common_utils import instantiate_from_config -from lvdm.utils.saving_utils import tensor_to_mp4 - - -def get_filelist(data_dir, ext='*'): - file_list = glob.glob(os.path.join(data_dir, '*.%s'%ext)) - file_list.sort() - return file_list - -def load_model_checkpoint(model, ckpt, adapter_ckpt=None): - print('>>> Loading checkpoints ...') - if adapter_ckpt: - ## main model - state_dict = torch.load(ckpt, map_location="cpu") - if "state_dict" in list(state_dict.keys()): - state_dict = state_dict["state_dict"] - model.load_state_dict(state_dict, strict=False) - print('@model checkpoint loaded.') - ## adapter - state_dict = torch.load(adapter_ckpt, map_location="cpu") - if "state_dict" in list(state_dict.keys()): - state_dict = state_dict["state_dict"] - model.adapter.load_state_dict(state_dict, strict=True) - print('@adapter checkpoint loaded.') - else: - state_dict = torch.load(ckpt, map_location="cpu") - if "state_dict" in list(state_dict.keys()): - state_dict = state_dict["state_dict"] - model.load_state_dict(state_dict, strict=True) - print('@model checkpoint loaded.') - return model - -def load_prompts(prompt_file): - f = open(prompt_file, 'r') - prompt_list = [] - for idx, line in enumerate(f.readlines()): - l = line.strip() - if len(l) != 0: - prompt_list.append(l) - f.close() - return prompt_list - -def load_video(filepath, frame_stride, video_size=(256,256), video_frames=16): - vidreader = VideoReader(filepath, ctx=cpu(0), width=video_size[1], height=video_size[0]) - max_frames = len(vidreader) - temp_stride = max_frames // video_frames if frame_stride == -1 else frame_stride - if temp_stride * (video_frames-1) >= max_frames: - print(f'Warning: default frame stride is used because the input video clip {max_frames} is not long enough.') - temp_stride = max_frames // video_frames - frame_indices = [temp_stride*i for i in range(video_frames)] - frames = vidreader.get_batch(frame_indices) - - ## [t,h,w,c] -> [c,t,h,w] - frame_tensor = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() - frame_tensor = (frame_tensor / 255. - 0.5) * 2 - return frame_tensor - - -def save_results(prompt, samples, inputs, filename, realdir, fakedir, fps=10): - ## save prompt - prompt = prompt[0] if isinstance(prompt, list) else prompt - path = os.path.join(realdir, "%s.txt"%filename) - with open(path, 'w') as f: - f.write(f'{prompt}') - f.close() - - ## save video - videos = [inputs, samples] - savedirs = [realdir, fakedir] - for idx, video in enumerate(videos): - if video is None: - continue - # b,c,t,h,w - video = video.detach().cpu() - video = torch.clamp(video.float(), -1., 1.) - n = video.shape[0] - video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w - frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n)) for framesheet in video] #[3, 1*h, n*w] - grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] - grid = (grid + 1.0) / 2.0 - grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) - path = os.path.join(savedirs[idx], "%s.mp4"%filename) - torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'}) - - -def adapter_guided_synthesis(model, prompts, videos, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1., \ - unconditional_guidance_scale=1.0, unconditional_guidance_scale_temporal=None, **kwargs): - ddim_sampler = DDIMSampler(model) - - batch_size = noise_shape[0] - ## get condition embeddings (support single prompt only) - if isinstance(prompts, str): - prompts = [prompts] - cond = model.get_learned_conditioning(prompts) - if unconditional_guidance_scale != 1.0: - prompts = batch_size * [""] - uc = model.get_learned_conditioning(prompts) - else: - uc = None - - ## adapter features: process in 2D manner - b, c, t, h, w = videos.shape - extra_cond = model.get_batch_depth(videos, (h,w)) - features_adapter = model.get_adapter_features(extra_cond) - - batch_variants = [] - for _ in range(n_samples): - if ddim_sampler is not None: - samples, _ = ddim_sampler.sample(S=ddim_steps, - conditioning=cond, - batch_size=noise_shape[0], - shape=noise_shape[1:], - verbose=False, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - eta=ddim_eta, - temporal_length=noise_shape[2], - conditional_guidance_scale_temporal=unconditional_guidance_scale_temporal, - features_adapter=features_adapter, - **kwargs - ) - ## reconstruct from latent to pixel space - batch_images = model.decode_first_stage(samples, decode_bs=1, return_cpu=False) - batch_variants.append(batch_images) - ## variants, batch, c, t, h, w - batch_variants = torch.stack(batch_variants) - return batch_variants.permute(1, 0, 2, 3, 4, 5), extra_cond - - -def run_inference(args, gpu_idx): - ## model config - config = OmegaConf.load(args.base) - model_config = config.pop("model", OmegaConf.create()) - model = instantiate_from_config(model_config) - model = model.cuda(gpu_idx) - assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!" - model = load_model_checkpoint(model, args.ckpt_path, args.adapter_ckpt) - model.eval() - - ## run over data - assert (args.height % 16 == 0) and (args.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!" - ## latent noise shape - h, w = args.height // 8, args.width // 8 - channels = model.channels - frames = model.temporal_length - noise_shape = [args.bs, channels, frames, h, w] - - ## inference - start = time.time() - prompt = args.prompt - video = load_video(args.video, args.frame_stride, video_size=(args.height, args.width), video_frames=16) - video = video.unsqueeze(0).to("cuda") - with torch.no_grad(): - batch_samples, batch_conds = adapter_guided_synthesis(model, prompt, video, noise_shape, args.n_samples, args.ddim_steps, args.ddim_eta, \ - args.unconditional_guidance_scale, args.unconditional_guidance_scale_temporal) - batch_samples = batch_samples[0] - os.makedirs(args.savedir, exist_ok=True) - filename = f"{args.prompt}_seed{args.seed}" - filename = filename.replace("/", "_slash_") if "/" in filename else filename - filename = filename.replace(" ", "_") if " " in filename else filename - tensor_to_mp4(video=batch_conds.detach().cpu(), savepath=os.path.join(args.savedir, f'{filename}_depth.mp4'), fps=10) - tensor_to_mp4(video=batch_samples.detach().cpu(), savepath=os.path.join(args.savedir, f'{filename}_sample.mp4'), fps=10) - - print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds") - - -def get_parser(): - parser = argparse.ArgumentParser() - parser.add_argument("--savedir", type=str, default=None, help="results saving path") - parser.add_argument("--ckpt_path", type=str, default=None, help="checkpoint path") - parser.add_argument("--adapter_ckpt", type=str, default=None, help="adapter checkpoint path") - parser.add_argument("--base", type=str, help="config (yaml) path") - parser.add_argument("--prompt", type=str, default=None, help="prompt string") - parser.add_argument("--video", type=str, default=None, help="video path") - parser.add_argument("--n_samples", type=int, default=1, help="num of samples per prompt",) - parser.add_argument("--ddim_steps", type=int, default=50, help="steps of ddim if positive, otherwise use DDPM",) - parser.add_argument("--ddim_eta", type=float, default=1.0, help="eta for ddim sampling (0.0 yields deterministic sampling)",) - parser.add_argument("--bs", type=int, default=1, help="batch size for inference") - parser.add_argument("--height", type=int, default=512, help="image height, in pixel space") - parser.add_argument("--width", type=int, default=512, help="image width, in pixel space") - parser.add_argument("--frame_stride", type=int, default=-1, help="frame extracting from input video") - parser.add_argument("--unconditional_guidance_scale", type=float, default=1.0, help="prompt classifier-free guidance") - parser.add_argument("--unconditional_guidance_scale_temporal", type=float, default=None, help="temporal consistency guidance") - parser.add_argument("--seed", type=int, default=2023, help="seed for seed_everything") - return parser - - -if __name__ == '__main__': - now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") - print("@CoVideoGen cond-Inference: %s"%now) - parser = get_parser() - args = parser.parse_args() - - seed_everything(args.seed) - rank = 0 - run_inference(args, rank) \ No newline at end of file diff --git a/scripts/sample_utils.py b/scripts/sample_utils.py deleted file mode 100644 index d65c6b66a8ad1c402fc21a8e21768467a151cb85..0000000000000000000000000000000000000000 --- a/scripts/sample_utils.py +++ /dev/null @@ -1,129 +0,0 @@ -import os -import torch -from PIL import Image - -from lvdm.models.modules.lora import net_load_lora -from lvdm.utils.common_utils import instantiate_from_config - - -# ------------------------------------------------------------------------------------------ -def load_model(config, ckpt_path, gpu_id=None, inject_lora=False, lora_scale=1.0, lora_path=''): - print(f"Loading model from {ckpt_path}") - - # load sd - pl_sd = torch.load(ckpt_path, map_location="cpu") - try: - global_step = pl_sd["global_step"] - epoch = pl_sd["epoch"] - except: - global_step = -1 - epoch = -1 - - # load sd to model - try: - sd = pl_sd["state_dict"] - except: - sd = pl_sd - model = instantiate_from_config(config.model) - model.load_state_dict(sd, strict=True) - - if inject_lora: - net_load_lora(model, lora_path, alpha=lora_scale) - - # move to device & eval - if gpu_id is not None: - model.to(f"cuda:{gpu_id}") - else: - model.cuda() - model.eval() - - return model, global_step, epoch - - -# ------------------------------------------------------------------------------------------ -@torch.no_grad() -def get_conditions(prompts, model, batch_size, cond_fps=None,): - - if isinstance(prompts, str) or isinstance(prompts, int): - prompts = [prompts] - if isinstance(prompts, list): - if len(prompts) == 1: - prompts = prompts * batch_size - elif len(prompts) == batch_size: - pass - else: - raise ValueError(f"invalid prompts length: {len(prompts)}") - else: - raise ValueError(f"invalid prompts: {prompts}") - assert(len(prompts) == batch_size) - - # content condition: text / class label - c = model.get_learned_conditioning(prompts) - key = 'c_concat' if model.conditioning_key == 'concat' else 'c_crossattn' - c = {key: [c]} - - # temporal condition: fps - if getattr(model, 'cond_stage2_config', None) is not None: - if model.cond_stage2_key == "temporal_context": - assert(cond_fps is not None) - batch = {'fps': torch.tensor([cond_fps] * batch_size).long().to(model.device)} - fps_embd = model.cond_stage2_model(batch) - c[model.cond_stage2_key] = fps_embd - - return c - - -# ------------------------------------------------------------------------------------------ -def make_model_input_shape(model, batch_size, T=None): - image_size = [model.image_size, model.image_size] if isinstance(model.image_size, int) else model.image_size - C = model.model.diffusion_model.in_channels - if T is None: - T = model.model.diffusion_model.temporal_length - shape = [batch_size, C, T, *image_size] - return shape - - -# ------------------------------------------------------------------------------------------ -def custom_to_pil(x): - x = x.detach().cpu() - x = torch.clamp(x, -1., 1.) - x = (x + 1.) / 2. - x = x.permute(1, 2, 0).numpy() - x = (255 * x).astype(np.uint8) - x = Image.fromarray(x) - if not x.mode == "RGB": - x = x.convert("RGB") - return x - -def torch_to_np(x): - # saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py - sample = x.detach().cpu() - sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8) - if sample.dim() == 5: - sample = sample.permute(0, 2, 3, 4, 1) - else: - sample = sample.permute(0, 2, 3, 1) - sample = sample.contiguous() - return sample - -def make_sample_dir(opt, global_step=None, epoch=None): - if not getattr(opt, 'not_automatic_logdir', False): - gs_str = f"globalstep{global_step:09}" if global_step is not None else "None" - e_str = f"epoch{epoch:06}" if epoch is not None else "None" - ckpt_dir = os.path.join(opt.logdir, f"{gs_str}_{e_str}") - - # subdir name - if opt.prompt_file is not None: - subdir = f"prompts_{os.path.splitext(os.path.basename(opt.prompt_file))[0]}" - else: - subdir = f"prompt_{opt.prompt[:10]}" - subdir += "_DDPM" if opt.vanilla_sample else f"_DDIM{opt.custom_steps}steps" - subdir += f"_CfgScale{opt.scale}" - if opt.cond_fps is not None: - subdir += f"_fps{opt.cond_fps}" - if opt.seed is not None: - subdir += f"_seed{opt.seed}" - - return os.path.join(ckpt_dir, subdir) - else: - return opt.logdir diff --git a/setup.py b/setup.py deleted file mode 100644 index 887e3965fee61be8ce7fd6ae67e5129a1e86231d..0000000000000000000000000000000000000000 --- a/setup.py +++ /dev/null @@ -1,9 +0,0 @@ -from setuptools import setup, find_packages - -setup( - name='lvdm', - version='0.0.1', - description='generic video generation models', - packages=find_packages(), - install_requires=[], -) \ No newline at end of file diff --git a/t2v_test.py b/t2v_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5332c49a23f8e1b6e00497d32b6cd4d0be484e78 --- /dev/null +++ b/t2v_test.py @@ -0,0 +1,71 @@ +import os +from omegaconf import OmegaConf +import torch +from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download + +class Text2Video(): + def __init__(self,result_dir='./tmp/',gpu_num=1) -> None: + self.download_model() + self.result_dir = result_dir + if not os.path.exists(self.result_dir): + os.mkdir(self.result_dir) + ckpt_path='checkpoints/base_512_v1/model.ckpt' + config_file='configs/inference_t2v_512_v1.0.yaml' + config = OmegaConf.load(config_file) + model_config = config.pop("model", OmegaConf.create()) + model_config['params']['unet_config']['params']['use_checkpoint']=False + model_list = [] + for gpu_id in range(gpu_num): + model = instantiate_from_config(model_config) + model = model.cuda(gpu_id) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model_list.append(model) + self.model_list = model_list + self.save_fps = 8 + + def get_prompt(self, prompt, steps=50, cfg_scale=12.0, eta=1.0, fps=16): + gpu_id=0 + if steps > 60: + steps = 60 + model = self.model_list[gpu_id] + batch_size=1 + channels = model.model.diffusion_model.in_channels + frames = model.temporal_length + h, w = 320 // 8, 512 // 8 + noise_shape = [batch_size, channels, frames, h, w] + + #prompts = batch_size * [""] + text_emb = model.get_learned_conditioning([prompt]) + + cond = {"c_crossattn": [text_emb], "fps": fps} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) + ## b,samples,c,t,h,w + prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt + prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str + prompt_str=prompt_str[:30] + + save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) + return os.path.join(self.result_dir, f"{prompt_str}.mp4") + + def download_model(self): + REPO_ID = 'VideoCrafter/Text2Video-512-v1' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/base_512_v1/'): + os.makedirs('./checkpoints/base_512_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/base_512_v1/', filename) + + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/base_512_v1/', local_dir_use_symlinks=False) + + +if __name__ == '__main__': + t2v = Text2Video() + video_path = t2v.get_prompt('a black swan swims on the pond') + print('done', video_path) \ No newline at end of file diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c73b93e006c4250161b427e4d1fff512ca046f7c --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,77 @@ +import importlib +import numpy as np +import cv2 +import torch +import torch.distributed as dist + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def check_istarget(name, para_list): + """ + name: full name of source para + para_list: partial name of target para + """ + istarget=False + for para in para_list: + if para in name: + return True + return istarget + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def load_npz_from_dir(data_dir): + data = [np.load(os.path.join(data_dir, data_name))['arr_0'] for data_name in os.listdir(data_dir)] + data = np.concatenate(data, axis=0) + return data + + +def load_npz_from_paths(data_paths): + data = [np.load(data_path)['arr_0'] for data_path in data_paths] + data = np.concatenate(data, axis=0) + return data + + +def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None): + h, w = image.shape[:2] + if resize_short_edge is not None: + k = resize_short_edge / min(h, w) + else: + k = max_resolution / (h * w) + k = k**0.5 + h = int(np.round(h * k / 64)) * 64 + w = int(np.round(w * k / 64)) * 64 + image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4) + return image + + +def setup_dist(args): + if dist.is_initialized(): + return + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group( + 'nccl', + init_method='env://' + ) \ No newline at end of file diff --git a/videocontrol_test.py b/videocontrol_test.py deleted file mode 100644 index f0424f6c6546ec5b5bd1ef7764793379bd7d0f69..0000000000000000000000000000000000000000 --- a/videocontrol_test.py +++ /dev/null @@ -1,162 +0,0 @@ -import argparse, os, sys, glob -import datetime, time -from omegaconf import OmegaConf -import math - -import torch -from decord import VideoReader, cpu -import torchvision -from pytorch_lightning import seed_everything - -from lvdm.samplers.ddim import DDIMSampler -from lvdm.utils.common_utils import instantiate_from_config -from lvdm.utils.saving_utils import tensor_to_mp4 -from scripts.sample_text2video_adapter import load_model_checkpoint, adapter_guided_synthesis - -import torchvision.transforms._transforms_video as transforms_video -from huggingface_hub import hf_hub_download - - -def load_video(filepath, frame_stride, video_size=(256,256), video_frames=16): - info_str = '' - vidreader = VideoReader(filepath, ctx=cpu(0), width=video_size[1], height=video_size[0]) - max_frames = len(vidreader) - # auto - - if frame_stride != 0: - if frame_stride * (video_frames-1) >= max_frames: - info_str += "Warning: The user-set frame rate makes the current video length not enough, we will set it to an adaptive frame rate.\n" - frame_stride = 0 - if frame_stride == 0: - frame_stride = max_frames / video_frames - # if temp_stride < 1: - # info_str = "Warning: The length of the current input video is less than 16 frames, we will automatically fill to 16 frames for you.\n" - if frame_stride > 100: - frame_stride = 100 - info_str += "Warning: The current input video length is longer than 1600 frames, we will process only the first 1600 frames.\n" - info_str += f"Frame Stride is set to {frame_stride}" - frame_indices = [int(frame_stride*i) for i in range(video_frames)] - frames = vidreader.get_batch(frame_indices) - - ## [t,h,w,c] -> [c,t,h,w] - frame_tensor = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() - frame_tensor = (frame_tensor / 255. - 0.5) * 2 - return frame_tensor, info_str - -class VideoControl: - def __init__(self, result_dir='./tmp/') -> None: - self.savedir = result_dir - self.download_model() - config_path = "models/adapter_t2v_depth/model_config.yaml" - ckpt_path = "models/base_t2v/model_rm_wtm.ckpt" - adapter_ckpt = "models/adapter_t2v_depth/adapter_t2v_depth_rm_wtm.pth" - if os.path.exists('/dev/shm/model_rm_wtm.ckpt'): - ckpt_path='/dev/shm/model_rm_wtm.ckpt' - config = OmegaConf.load(config_path) - model_config = config.pop("model", OmegaConf.create()) - model = instantiate_from_config(model_config) - model = model.to('cuda') - assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" - model = load_model_checkpoint(model, ckpt_path, adapter_ckpt) - model.eval() - self.model = model - - def get_video(self, input_video, input_prompt, frame_stride=0, vc_steps=50, vc_cfg_scale=15.0, vc_eta=1.0, video_frames=16, resolution=256): - torch.cuda.empty_cache() - if resolution > 512: - resolution = 512 - if resolution < 64: - resolution = 64 - if video_frames > 64: - video_frames = 64 - - resolution = int(resolution//64)*64 - - if vc_steps > 60: - vc_steps = 60 - ## load video - print("input video", input_video) - info_str = '' - try: - h, w, c = VideoReader(input_video, ctx=cpu(0))[0].shape - except: - os.remove(input_video) - return 'please input video', None, None, None - - if h > w: - scale = h / resolution - else: - scale = w / resolution - h = math.ceil(h / scale) - w = math.ceil(w / scale) - try: - video, info_str = load_video(input_video, frame_stride, video_size=(h, w), video_frames=video_frames) - except: - os.remove(input_video) - return 'load video error', None, None, None - if h > w: - w = int(w//64)*64 - else: - h = int(h//64)*64 - spatial_transform = transforms_video.CenterCropVideo((h,w)) - video = spatial_transform(video) - print('video shape', video.shape) - - rh, rw = h//8, w//8 - bs = 1 - channels = self.model.channels - # frames = self.model.temporal_length - frames = video_frames - noise_shape = [bs, channels, frames, rh, rw] - - ## inference - start = time.time() - prompt = input_prompt - video = video.unsqueeze(0).to("cuda") - try: - with torch.no_grad(): - batch_samples, batch_conds = adapter_guided_synthesis(self.model, prompt, video, noise_shape, n_samples=1, ddim_steps=vc_steps, ddim_eta=vc_eta, unconditional_guidance_scale=vc_cfg_scale) - except: - torch.cuda.empty_cache() - info_str="OOM, please enter a smaller resolution or smaller frame num" - return info_str, None, None, None - batch_samples = batch_samples[0] - os.makedirs(self.savedir, exist_ok=True) - filename = prompt - filename = filename.replace("/", "_slash_") if "/" in filename else filename - filename = filename.replace(" ", "_") if " " in filename else filename - if len(filename) > 200: - filename = filename[:200] - video_path = os.path.join(self.savedir, f'{filename}_sample.mp4') - depth_path = os.path.join(self.savedir, f'{filename}_depth.mp4') - origin_path = os.path.join(self.savedir, f'{filename}.mp4') - tensor_to_mp4(video=video.detach().cpu(), savepath=origin_path, fps=8) - tensor_to_mp4(video=batch_conds.detach().cpu(), savepath=depth_path, fps=8) - tensor_to_mp4(video=batch_samples.detach().cpu(), savepath=video_path, fps=8) - - print(f"Saved in {video_path}. Time used: {(time.time() - start):.2f} seconds") - # delete video - (path, input_filename) = os.path.split(input_video) - if input_filename != 'flamingo.mp4': - os.remove(input_video) - print('delete input video') - # print(input_video) - return info_str, origin_path, depth_path, video_path - def download_model(self): - REPO_ID = 'VideoCrafter/t2v-version-1-1' - filename_list = ['models/base_t2v/model_rm_wtm.ckpt', - "models/adapter_t2v_depth/adapter_t2v_depth_rm_wtm.pth", - "models/adapter_t2v_depth/dpt_hybrid-midas.pt" - ] - for filename in filename_list: - if not os.path.exists(filename): - hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./', local_dir_use_symlinks=False) - - - - - - -if __name__ == "__main__": - vc = VideoControl('./result') - info_str, video_path = vc.get_video('input/flamingo.mp4',"An ostrich walking in the desert, photorealistic, 4k") \ No newline at end of file diff --git a/videocrafter_test.py b/videocrafter_test.py deleted file mode 100644 index ab63707a694aaab34331afc68f302779e640f8fe..0000000000000000000000000000000000000000 --- a/videocrafter_test.py +++ /dev/null @@ -1,84 +0,0 @@ -import os -import torch -from omegaconf import OmegaConf - -from lvdm.samplers.ddim import DDIMSampler -from lvdm.utils.saving_utils import npz_to_video_grid -from scripts.sample_text2video import sample_text2video -from scripts.sample_utils import load_model -from lvdm.models.modules.lora import change_lora_v2 - -from huggingface_hub import hf_hub_download - - -def save_results(videos, save_dir, - save_name="results", save_fps=8 - ): - save_subdir = os.path.join(save_dir, "videos") - os.makedirs(save_subdir, exist_ok=True) - for i in range(videos.shape[0]): - npz_to_video_grid(videos[i:i+1,...], - os.path.join(save_subdir, f"{save_name}_{i:03d}.mp4"), - fps=save_fps) - video_path_list = [os.path.join(save_subdir, f"{save_name}_{i:03d}.mp4") for i in range(videos.shape[0])] - print(f'Successfully saved videos in {video_path_list[0]}') - return video_path_list - - -class Text2Video(): - def __init__(self,result_dir='./tmp/') -> None: - self.download_model() - config_file = 'models/base_t2v/model_config.yaml' - ckpt_path = 'models/base_t2v/model_rm_wtm.ckpt' - if os.path.exists('/dev/shm/model_rm_wtm.ckpt'): - ckpt_path='/dev/shm/model_rm_wtm.ckpt' - config = OmegaConf.load(config_file) - self.lora_path_list = ['','models/videolora/lora_001_Loving_Vincent_style.ckpt', - 'models/videolora/lora_002_frozenmovie_style.ckpt', - 'models/videolora/lora_003_MakotoShinkaiYourName_style.ckpt', - 'models/videolora/lora_004_coco_style_v2.ckpt'] - self.lora_trigger_word_list = ['','Loving Vincent style', 'frozenmovie style', 'MakotoShinkaiYourName style', 'coco style'] - model, _, _ = load_model(config, ckpt_path, gpu_id=0, inject_lora=False) - self.model = model - self.last_time_lora = '' - self.last_time_lora_scale = 1.0 - self.result_dir = result_dir - self.save_fps = 8 - self.ddim_sampler = DDIMSampler(model) - self.origin_weight = None - - def get_prompt(self, input_text, steps=50, model_index=0, eta=1.0, cfg_scale=15.0, lora_scale=1.0): - torch.cuda.empty_cache() - if steps > 60: - steps = 60 - if model_index > 0: - input_text = input_text + ', ' + self.lora_trigger_word_list[model_index] - inject_lora = model_index > 0 - self.origin_weight = change_lora_v2(self.model, inject_lora=inject_lora, lora_scale=lora_scale, lora_path=self.lora_path_list[model_index], - last_time_lora=self.last_time_lora, last_time_lora_scale=self.last_time_lora_scale, origin_weight=self.origin_weight) - - all_videos = sample_text2video(self.model, input_text, n_samples=1, batch_size=1, - sample_type='ddim', sampler=self.ddim_sampler, - ddim_steps=steps, eta=eta, - cfg_scale=cfg_scale, - ) - prompt = input_text - prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt - prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str - self.last_time_lora=self.lora_path_list[model_index] - self.last_time_lora_scale = lora_scale - video_path_list = save_results(all_videos, self.result_dir, save_name=prompt_str, save_fps=self.save_fps) - return video_path_list[0] - - def download_model(self): - REPO_ID = 'VideoCrafter/t2v-version-1-1' - filename_list = ['models/base_t2v/model_rm_wtm.ckpt', - 'models/videolora/lora_001_Loving_Vincent_style.ckpt', - 'models/videolora/lora_002_frozenmovie_style.ckpt', - 'models/videolora/lora_003_MakotoShinkaiYourName_style.ckpt', - 'models/videolora/lora_004_coco_style_v2.ckpt'] - for filename in filename_list: - if not os.path.exists(filename): - hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./', local_dir_use_symlinks=False) - -