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# Copyright 2023 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import inspect | |
import math | |
from typing import Any, Callable, Dict, List, Optional, Tuple, Union | |
import torch | |
import torch.nn.functional as F | |
from transformers import ( | |
CLIPImageProcessor, | |
CLIPTextModel, | |
CLIPTextModelWithProjection, | |
CLIPTokenizer, | |
CLIPVisionModelWithProjection, | |
) | |
from ...image_processor import PipelineImageInput, VaeImageProcessor | |
from ...loaders import ( | |
FromSingleFileMixin, | |
IPAdapterMixin, | |
StableDiffusionXLLoraLoaderMixin, | |
TextualInversionLoaderMixin, | |
) | |
from ...models import AutoencoderKL, UNet2DConditionModel | |
from ...models.attention_processor import ( | |
Attention, | |
AttnProcessor, | |
AttnProcessor2_0, | |
LoRAAttnProcessor2_0, | |
LoRAXFormersAttnProcessor, | |
XFormersAttnProcessor, | |
) | |
from ...models.lora import adjust_lora_scale_text_encoder | |
from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler | |
from ...utils import ( | |
USE_PEFT_BACKEND, | |
is_invisible_watermark_available, | |
is_torch_xla_available, | |
logging, | |
replace_example_docstring, | |
scale_lora_layers, | |
unscale_lora_layers, | |
) | |
from ...utils.torch_utils import randn_tensor | |
from ..pipeline_utils import DiffusionPipeline | |
from .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput | |
if is_invisible_watermark_available(): | |
from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker | |
if is_torch_xla_available(): | |
import torch_xla.core.xla_model as xm | |
XLA_AVAILABLE = True | |
else: | |
XLA_AVAILABLE = False | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
EXAMPLE_DOC_STRING = """ | |
Examples: | |
```py | |
>>> import torch | |
>>> import PIL | |
>>> import requests | |
>>> from io import BytesIO | |
>>> from diffusers import LEditsPPPipelineStableDiffusionXL | |
>>> pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained( | |
... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 | |
... ) | |
>>> pipe = pipe.to("cuda") | |
>>> def download_image(url): | |
... response = requests.get(url) | |
... return PIL.Image.open(BytesIO(response.content)).convert("RGB") | |
>>> img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg" | |
>>> image = download_image(img_url) | |
>>> _ = pipe.invert(image=image, num_inversion_steps=50, skip=0.2) | |
>>> edited_image = pipe( | |
... editing_prompt=["tennis ball", "tomato"], | |
... reverse_editing_direction=[True, False], | |
... edit_guidance_scale=[5.0, 10.0], | |
... edit_threshold=[0.9, 0.85], | |
... ).images[0] | |
``` | |
""" | |
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsAttentionStore | |
class LeditsAttentionStore: | |
def get_empty_store(): | |
return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []} | |
def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False): | |
# attn.shape = batch_size * head_size, seq_len query, seq_len_key | |
if attn.shape[1] <= self.max_size: | |
bs = 1 + int(PnP) + editing_prompts | |
skip = 2 if PnP else 1 # skip PnP & unconditional | |
attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3) | |
source_batch_size = int(attn.shape[1] // bs) | |
self.forward(attn[:, skip * source_batch_size :], is_cross, place_in_unet) | |
def forward(self, attn, is_cross: bool, place_in_unet: str): | |
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}" | |
self.step_store[key].append(attn) | |
def between_steps(self, store_step=True): | |
if store_step: | |
if self.average: | |
if len(self.attention_store) == 0: | |
self.attention_store = self.step_store | |
else: | |
for key in self.attention_store: | |
for i in range(len(self.attention_store[key])): | |
self.attention_store[key][i] += self.step_store[key][i] | |
else: | |
if len(self.attention_store) == 0: | |
self.attention_store = [self.step_store] | |
else: | |
self.attention_store.append(self.step_store) | |
self.cur_step += 1 | |
self.step_store = self.get_empty_store() | |
def get_attention(self, step: int): | |
if self.average: | |
attention = { | |
key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store | |
} | |
else: | |
assert step is not None | |
attention = self.attention_store[step] | |
return attention | |
def aggregate_attention( | |
self, attention_maps, prompts, res: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int | |
): | |
out = [[] for x in range(self.batch_size)] | |
if isinstance(res, int): | |
num_pixels = res**2 | |
resolution = (res, res) | |
else: | |
num_pixels = res[0] * res[1] | |
resolution = res[:2] | |
for location in from_where: | |
for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]: | |
for batch, item in enumerate(bs_item): | |
if item.shape[1] == num_pixels: | |
cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-1])[select] | |
out[batch].append(cross_maps) | |
out = torch.stack([torch.cat(x, dim=0) for x in out]) | |
# average over heads | |
out = out.sum(1) / out.shape[1] | |
return out | |
def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None): | |
self.step_store = self.get_empty_store() | |
self.attention_store = [] | |
self.cur_step = 0 | |
self.average = average | |
self.batch_size = batch_size | |
if max_size is None: | |
self.max_size = max_resolution**2 | |
elif max_size is not None and max_resolution is None: | |
self.max_size = max_size | |
else: | |
raise ValueError("Only allowed to set one of max_resolution or max_size") | |
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsGaussianSmoothing | |
class LeditsGaussianSmoothing: | |
def __init__(self, device): | |
kernel_size = [3, 3] | |
sigma = [0.5, 0.5] | |
# The gaussian kernel is the product of the gaussian function of each dimension. | |
kernel = 1 | |
meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size]) | |
for size, std, mgrid in zip(kernel_size, sigma, meshgrids): | |
mean = (size - 1) / 2 | |
kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2)) | |
# Make sure sum of values in gaussian kernel equals 1. | |
kernel = kernel / torch.sum(kernel) | |
# Reshape to depthwise convolutional weight | |
kernel = kernel.view(1, 1, *kernel.size()) | |
kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1)) | |
self.weight = kernel.to(device) | |
def __call__(self, input): | |
""" | |
Arguments: | |
Apply gaussian filter to input. | |
input (torch.Tensor): Input to apply gaussian filter on. | |
Returns: | |
filtered (torch.Tensor): Filtered output. | |
""" | |
return F.conv2d(input, weight=self.weight.to(input.dtype)) | |
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEDITSCrossAttnProcessor | |
class LEDITSCrossAttnProcessor: | |
def __init__(self, attention_store, place_in_unet, pnp, editing_prompts): | |
self.attnstore = attention_store | |
self.place_in_unet = place_in_unet | |
self.editing_prompts = editing_prompts | |
self.pnp = pnp | |
def __call__( | |
self, | |
attn: Attention, | |
hidden_states, | |
encoder_hidden_states, | |
attention_mask=None, | |
temb=None, | |
): | |
batch_size, sequence_length, _ = ( | |
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
) | |
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
query = attn.to_q(hidden_states) | |
if encoder_hidden_states is None: | |
encoder_hidden_states = hidden_states | |
elif attn.norm_cross: | |
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
key = attn.to_k(encoder_hidden_states) | |
value = attn.to_v(encoder_hidden_states) | |
query = attn.head_to_batch_dim(query) | |
key = attn.head_to_batch_dim(key) | |
value = attn.head_to_batch_dim(value) | |
attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
self.attnstore( | |
attention_probs, | |
is_cross=True, | |
place_in_unet=self.place_in_unet, | |
editing_prompts=self.editing_prompts, | |
PnP=self.pnp, | |
) | |
hidden_states = torch.bmm(attention_probs, value) | |
hidden_states = attn.batch_to_head_dim(hidden_states) | |
# linear proj | |
hidden_states = attn.to_out[0](hidden_states) | |
# dropout | |
hidden_states = attn.to_out[1](hidden_states) | |
hidden_states = hidden_states / attn.rescale_output_factor | |
return hidden_states | |
class LEditsPPPipelineStableDiffusionXL( | |
DiffusionPipeline, | |
FromSingleFileMixin, | |
StableDiffusionXLLoraLoaderMixin, | |
TextualInversionLoaderMixin, | |
IPAdapterMixin, | |
): | |
""" | |
Pipeline for textual image editing using LEDits++ with Stable Diffusion XL. | |
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionXLPipeline`]. Check the | |
superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a | |
particular device, etc.). | |
In addition the pipeline inherits the following loading methods: | |
- *LoRA*: [`LEditsPPPipelineStableDiffusionXL.load_lora_weights`] | |
- *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] | |
as well as the following saving methods: | |
- *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`] | |
Args: | |
vae ([`AutoencoderKL`]): | |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. | |
text_encoder ([`~transformers.CLIPTextModel`]): | |
Frozen text-encoder. Stable Diffusion XL uses the text portion of | |
[CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically | |
the [clip-vit-large-patch14](https://huggingface.co./openai/clip-vit-large-patch14) variant. | |
text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]): | |
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of | |
[CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), | |
specifically the | |
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co./laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) | |
variant. | |
tokenizer ([`~transformers.CLIPTokenizer`]): | |
Tokenizer of class | |
[CLIPTokenizer](https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). | |
tokenizer_2 ([`~transformers.CLIPTokenizer`]): | |
Second Tokenizer of class | |
[CLIPTokenizer](https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). | |
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. | |
scheduler ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]): | |
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of | |
[`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will | |
automatically be set to [`DPMSolverMultistepScheduler`]. | |
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): | |
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of | |
`stabilityai/stable-diffusion-xl-base-1-0`. | |
add_watermarker (`bool`, *optional*): | |
Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to | |
watermark output images. If not defined, it will default to True if the package is installed, otherwise no | |
watermarker will be used. | |
""" | |
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" | |
_optional_components = [ | |
"tokenizer", | |
"tokenizer_2", | |
"text_encoder", | |
"text_encoder_2", | |
"image_encoder", | |
"feature_extractor", | |
] | |
_callback_tensor_inputs = [ | |
"latents", | |
"prompt_embeds", | |
"negative_prompt_embeds", | |
"add_text_embeds", | |
"add_time_ids", | |
"negative_pooled_prompt_embeds", | |
"negative_add_time_ids", | |
] | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
text_encoder: CLIPTextModel, | |
text_encoder_2: CLIPTextModelWithProjection, | |
tokenizer: CLIPTokenizer, | |
tokenizer_2: CLIPTokenizer, | |
unet: UNet2DConditionModel, | |
scheduler: Union[DPMSolverMultistepScheduler, DDIMScheduler], | |
image_encoder: CLIPVisionModelWithProjection = None, | |
feature_extractor: CLIPImageProcessor = None, | |
force_zeros_for_empty_prompt: bool = True, | |
add_watermarker: Optional[bool] = None, | |
): | |
super().__init__() | |
self.register_modules( | |
vae=vae, | |
text_encoder=text_encoder, | |
text_encoder_2=text_encoder_2, | |
tokenizer=tokenizer, | |
tokenizer_2=tokenizer_2, | |
unet=unet, | |
scheduler=scheduler, | |
image_encoder=image_encoder, | |
feature_extractor=feature_extractor, | |
) | |
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) | |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler): | |
self.scheduler = DPMSolverMultistepScheduler.from_config( | |
scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2 | |
) | |
logger.warning( | |
"This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. " | |
"The scheduler has been changed to DPMSolverMultistepScheduler." | |
) | |
self.default_sample_size = self.unet.config.sample_size | |
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() | |
if add_watermarker: | |
self.watermark = StableDiffusionXLWatermarker() | |
else: | |
self.watermark = None | |
self.inversion_steps = None | |
def encode_prompt( | |
self, | |
device: Optional[torch.device] = None, | |
num_images_per_prompt: int = 1, | |
negative_prompt: Optional[str] = None, | |
negative_prompt_2: Optional[str] = None, | |
negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
lora_scale: Optional[float] = None, | |
clip_skip: Optional[int] = None, | |
enable_edit_guidance: bool = True, | |
editing_prompt: Optional[str] = None, | |
editing_prompt_embeds: Optional[torch.FloatTensor] = None, | |
editing_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
) -> object: | |
r""" | |
Encodes the prompt into text encoder hidden states. | |
Args: | |
device: (`torch.device`): | |
torch device | |
num_images_per_prompt (`int`): | |
number of images that should be generated per prompt | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation. If not defined, one has to pass | |
`negative_prompt_embeds` instead. | |
negative_prompt_2 (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and | |
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders | |
negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input | |
argument. | |
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` | |
input argument. | |
lora_scale (`float`, *optional*): | |
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. | |
clip_skip (`int`, *optional*): | |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that | |
the output of the pre-final layer will be used for computing the prompt embeddings. | |
enable_edit_guidance (`bool`): | |
Whether to guide towards an editing prompt or not. | |
editing_prompt (`str` or `List[str]`, *optional*): | |
Editing prompt(s) to be encoded. If not defined and 'enable_edit_guidance' is True, one has to pass | |
`editing_prompt_embeds` instead. | |
editing_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. | |
If not provided and 'enable_edit_guidance' is True, editing_prompt_embeds will be generated from | |
`editing_prompt` input argument. | |
editing_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated edit pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, pooled editing_pooled_prompt_embeds will be generated from `editing_prompt` | |
input argument. | |
""" | |
device = device or self._execution_device | |
# set lora scale so that monkey patched LoRA | |
# function of text encoder can correctly access it | |
if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): | |
self._lora_scale = lora_scale | |
# dynamically adjust the LoRA scale | |
if self.text_encoder is not None: | |
if not USE_PEFT_BACKEND: | |
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) | |
else: | |
scale_lora_layers(self.text_encoder, lora_scale) | |
if self.text_encoder_2 is not None: | |
if not USE_PEFT_BACKEND: | |
adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) | |
else: | |
scale_lora_layers(self.text_encoder_2, lora_scale) | |
batch_size = self.batch_size | |
# Define tokenizers and text encoders | |
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] | |
text_encoders = ( | |
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] | |
) | |
num_edit_tokens = 0 | |
# get unconditional embeddings for classifier free guidance | |
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt | |
if negative_prompt_embeds is None: | |
negative_prompt = negative_prompt or "" | |
negative_prompt_2 = negative_prompt_2 or negative_prompt | |
# normalize str to list | |
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt | |
negative_prompt_2 = ( | |
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 | |
) | |
uncond_tokens: List[str] | |
if batch_size != len(negative_prompt): | |
raise ValueError( | |
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but image inversion " | |
f" has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | |
" the batch size of the input images." | |
) | |
else: | |
uncond_tokens = [negative_prompt, negative_prompt_2] | |
negative_prompt_embeds_list = [] | |
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): | |
if isinstance(self, TextualInversionLoaderMixin): | |
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) | |
uncond_input = tokenizer( | |
negative_prompt, | |
padding="max_length", | |
max_length=tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
negative_prompt_embeds = text_encoder( | |
uncond_input.input_ids.to(device), | |
output_hidden_states=True, | |
) | |
# We are only ALWAYS interested in the pooled output of the final text encoder | |
negative_pooled_prompt_embeds = negative_prompt_embeds[0] | |
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] | |
negative_prompt_embeds_list.append(negative_prompt_embeds) | |
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) | |
if zero_out_negative_prompt: | |
negative_prompt_embeds = torch.zeros_like(negative_prompt_embeds) | |
negative_pooled_prompt_embeds = torch.zeros_like(negative_pooled_prompt_embeds) | |
if enable_edit_guidance and editing_prompt_embeds is None: | |
editing_prompt_2 = editing_prompt | |
editing_prompts = [editing_prompt, editing_prompt_2] | |
edit_prompt_embeds_list = [] | |
for editing_prompt, tokenizer, text_encoder in zip(editing_prompts, tokenizers, text_encoders): | |
if isinstance(self, TextualInversionLoaderMixin): | |
editing_prompt = self.maybe_convert_prompt(editing_prompt, tokenizer) | |
max_length = negative_prompt_embeds.shape[1] | |
edit_concepts_input = tokenizer( | |
# [x for item in editing_prompt for x in repeat(item, batch_size)], | |
editing_prompt, | |
padding="max_length", | |
max_length=max_length, | |
truncation=True, | |
return_tensors="pt", | |
return_length=True, | |
) | |
num_edit_tokens = edit_concepts_input.length - 2 | |
edit_concepts_embeds = text_encoder( | |
edit_concepts_input.input_ids.to(device), | |
output_hidden_states=True, | |
) | |
# We are only ALWAYS interested in the pooled output of the final text encoder | |
editing_pooled_prompt_embeds = edit_concepts_embeds[0] | |
if clip_skip is None: | |
edit_concepts_embeds = edit_concepts_embeds.hidden_states[-2] | |
else: | |
# "2" because SDXL always indexes from the penultimate layer. | |
edit_concepts_embeds = edit_concepts_embeds.hidden_states[-(clip_skip + 2)] | |
edit_prompt_embeds_list.append(edit_concepts_embeds) | |
edit_concepts_embeds = torch.concat(edit_prompt_embeds_list, dim=-1) | |
elif not enable_edit_guidance: | |
edit_concepts_embeds = None | |
editing_pooled_prompt_embeds = None | |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) | |
bs_embed, seq_len, _ = negative_prompt_embeds.shape | |
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
seq_len = negative_prompt_embeds.shape[1] | |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) | |
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) | |
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) | |
if enable_edit_guidance: | |
bs_embed_edit, seq_len, _ = edit_concepts_embeds.shape | |
edit_concepts_embeds = edit_concepts_embeds.to(dtype=self.text_encoder_2.dtype, device=device) | |
edit_concepts_embeds = edit_concepts_embeds.repeat(1, num_images_per_prompt, 1) | |
edit_concepts_embeds = edit_concepts_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1) | |
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( | |
bs_embed * num_images_per_prompt, -1 | |
) | |
if enable_edit_guidance: | |
editing_pooled_prompt_embeds = editing_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( | |
bs_embed_edit * num_images_per_prompt, -1 | |
) | |
if self.text_encoder is not None: | |
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: | |
# Retrieve the original scale by scaling back the LoRA layers | |
unscale_lora_layers(self.text_encoder, lora_scale) | |
if self.text_encoder_2 is not None: | |
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: | |
# Retrieve the original scale by scaling back the LoRA layers | |
unscale_lora_layers(self.text_encoder_2, lora_scale) | |
return ( | |
negative_prompt_embeds, | |
edit_concepts_embeds, | |
negative_pooled_prompt_embeds, | |
editing_pooled_prompt_embeds, | |
num_edit_tokens, | |
) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs | |
def prepare_extra_step_kwargs(self, eta, generator=None): | |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature | |
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 | |
# and should be between [0, 1] | |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
extra_step_kwargs = {} | |
if accepts_eta: | |
extra_step_kwargs["eta"] = eta | |
# check if the scheduler accepts generator | |
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
if accepts_generator: | |
extra_step_kwargs["generator"] = generator | |
return extra_step_kwargs | |
def check_inputs( | |
self, | |
negative_prompt=None, | |
negative_prompt_2=None, | |
negative_prompt_embeds=None, | |
negative_pooled_prompt_embeds=None, | |
): | |
if negative_prompt is not None and negative_prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" | |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
) | |
elif negative_prompt_2 is not None and negative_prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" | |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
) | |
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: | |
raise ValueError( | |
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." | |
) | |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents | |
def prepare_latents(self, device, latents): | |
latents = latents.to(device) | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
return latents | |
def _get_add_time_ids( | |
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None | |
): | |
add_time_ids = list(original_size + crops_coords_top_left + target_size) | |
passed_add_embed_dim = ( | |
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim | |
) | |
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features | |
if expected_add_embed_dim != passed_add_embed_dim: | |
raise ValueError( | |
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." | |
) | |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype) | |
return add_time_ids | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae | |
def upcast_vae(self): | |
dtype = self.vae.dtype | |
self.vae.to(dtype=torch.float32) | |
use_torch_2_0_or_xformers = isinstance( | |
self.vae.decoder.mid_block.attentions[0].processor, | |
( | |
AttnProcessor2_0, | |
XFormersAttnProcessor, | |
LoRAXFormersAttnProcessor, | |
LoRAAttnProcessor2_0, | |
), | |
) | |
# if xformers or torch_2_0 is used attention block does not need | |
# to be in float32 which can save lots of memory | |
if use_torch_2_0_or_xformers: | |
self.vae.post_quant_conv.to(dtype) | |
self.vae.decoder.conv_in.to(dtype) | |
self.vae.decoder.mid_block.to(dtype) | |
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding | |
def get_guidance_scale_embedding( | |
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32 | |
) -> torch.FloatTensor: | |
""" | |
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 | |
Args: | |
w (`torch.Tensor`): | |
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. | |
embedding_dim (`int`, *optional*, defaults to 512): | |
Dimension of the embeddings to generate. | |
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): | |
Data type of the generated embeddings. | |
Returns: | |
`torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`. | |
""" | |
assert len(w.shape) == 1 | |
w = w * 1000.0 | |
half_dim = embedding_dim // 2 | |
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) | |
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) | |
emb = w.to(dtype)[:, None] * emb[None, :] | |
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) | |
if embedding_dim % 2 == 1: # zero pad | |
emb = torch.nn.functional.pad(emb, (0, 1)) | |
assert emb.shape == (w.shape[0], embedding_dim) | |
return emb | |
def guidance_scale(self): | |
return self._guidance_scale | |
def guidance_rescale(self): | |
return self._guidance_rescale | |
def clip_skip(self): | |
return self._clip_skip | |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
# corresponds to doing no classifier free guidance. | |
def do_classifier_free_guidance(self): | |
return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None | |
def cross_attention_kwargs(self): | |
return self._cross_attention_kwargs | |
def denoising_end(self): | |
return self._denoising_end | |
def num_timesteps(self): | |
return self._num_timesteps | |
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.prepare_unet | |
def prepare_unet(self, attention_store, PnP: bool = False): | |
attn_procs = {} | |
for name in self.unet.attn_processors.keys(): | |
if name.startswith("mid_block"): | |
place_in_unet = "mid" | |
elif name.startswith("up_blocks"): | |
place_in_unet = "up" | |
elif name.startswith("down_blocks"): | |
place_in_unet = "down" | |
else: | |
continue | |
if "attn2" in name and place_in_unet != "mid": | |
attn_procs[name] = LEDITSCrossAttnProcessor( | |
attention_store=attention_store, | |
place_in_unet=place_in_unet, | |
pnp=PnP, | |
editing_prompts=self.enabled_editing_prompts, | |
) | |
else: | |
attn_procs[name] = AttnProcessor() | |
self.unet.set_attn_processor(attn_procs) | |
def __call__( | |
self, | |
denoising_end: Optional[float] = None, | |
negative_prompt: Optional[Union[str, List[str]]] = None, | |
negative_prompt_2: Optional[Union[str, List[str]]] = None, | |
negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
ip_adapter_image: Optional[PipelineImageInput] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
guidance_rescale: float = 0.0, | |
crops_coords_top_left: Tuple[int, int] = (0, 0), | |
target_size: Optional[Tuple[int, int]] = None, | |
editing_prompt: Optional[Union[str, List[str]]] = None, | |
editing_prompt_embeddings: Optional[torch.Tensor] = None, | |
editing_pooled_prompt_embeds: Optional[torch.Tensor] = None, | |
reverse_editing_direction: Optional[Union[bool, List[bool]]] = False, | |
edit_guidance_scale: Optional[Union[float, List[float]]] = 5, | |
edit_warmup_steps: Optional[Union[int, List[int]]] = 0, | |
edit_cooldown_steps: Optional[Union[int, List[int]]] = None, | |
edit_threshold: Optional[Union[float, List[float]]] = 0.9, | |
sem_guidance: Optional[List[torch.Tensor]] = None, | |
use_cross_attn_mask: bool = False, | |
use_intersect_mask: bool = False, | |
user_mask: Optional[torch.FloatTensor] = None, | |
attn_store_steps: Optional[List[int]] = [], | |
store_averaged_over_steps: bool = True, | |
clip_skip: Optional[int] = None, | |
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, | |
callback_on_step_end_tensor_inputs: List[str] = ["latents"], | |
**kwargs, | |
): | |
r""" | |
The call function to the pipeline for editing. The | |
[`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert`] method has to be called beforehand. Edits | |
will always be performed for the last inverted image(s). | |
Args: | |
denoising_end (`float`, *optional*): | |
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be | |
completed before it is intentionally prematurely terminated. As a result, the returned sample will | |
still retain a substantial amount of noise as determined by the discrete timesteps selected by the | |
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a | |
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation. If not defined, one has to pass | |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is | |
less than `1`). | |
negative_prompt_2 (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and | |
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders | |
negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input | |
argument. | |
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` | |
input argument. | |
ip_adapter_image: (`PipelineImageInput`, *optional*): | |
Optional image input to work with IP Adapters. | |
output_type (`str`, *optional*, defaults to `"pil"`): | |
The output format of the generate image. Choose between | |
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. | |
return_dict (`bool`, *optional*, defaults to `True`): | |
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead | |
of a plain tuple. | |
callback (`Callable`, *optional*): | |
A function that will be called every `callback_steps` steps during inference. The function will be | |
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. | |
callback_steps (`int`, *optional*, defaults to 1): | |
The frequency at which the `callback` function will be called. If not specified, the callback will be | |
called at every step. | |
cross_attention_kwargs (`dict`, *optional*): | |
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under | |
`self.processor` in | |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
guidance_rescale (`float`, *optional*, defaults to 0.7): | |
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are | |
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of | |
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). | |
Guidance rescale factor should fix overexposure when using zero terminal SNR. | |
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): | |
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position | |
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting | |
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of | |
[https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). | |
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): | |
For most cases, `target_size` should be set to the desired height and width of the generated image. If | |
not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in | |
section 2.2 of [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). | |
editing_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts to guide the image generation. The image is reconstructed by setting | |
`editing_prompt = None`. Guidance direction of prompt should be specified via | |
`reverse_editing_direction`. | |
editing_prompt_embeddings (`torch.Tensor`, *optional*): | |
Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. | |
If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input argument. | |
editing_pooled_prompt_embeddings (`torch.Tensor`, *optional*): | |
Pre-generated pooled edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input | |
argument. | |
reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`): | |
Whether the corresponding prompt in `editing_prompt` should be increased or decreased. | |
edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5): | |
Guidance scale for guiding the image generation. If provided as list values should correspond to | |
`editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 12 of [LEDITS++ | |
Paper](https://arxiv.org/abs/2301.12247). | |
edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10): | |
Number of diffusion steps (for each prompt) for which guidance is not applied. | |
edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`): | |
Number of diffusion steps (for each prompt) after which guidance is no longer applied. | |
edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9): | |
Masking threshold of guidance. Threshold should be proportional to the image region that is modified. | |
'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++ | |
Paper](https://arxiv.org/abs/2301.12247). | |
sem_guidance (`List[torch.Tensor]`, *optional*): | |
List of pre-generated guidance vectors to be applied at generation. Length of the list has to | |
correspond to `num_inference_steps`. | |
use_cross_attn_mask: | |
Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask | |
is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of [LEDITS++ | |
paper](https://arxiv.org/pdf/2311.16711.pdf). | |
use_intersect_mask: | |
Whether the masking term is calculated as intersection of cross-attention masks and masks derived from | |
the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate | |
are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf). | |
user_mask: | |
User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s | |
implicit masks do not meet user preferences. | |
attn_store_steps: | |
Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes. | |
store_averaged_over_steps: | |
Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If | |
False, attention maps for each step are stores separately. Just for visualization purposes. | |
clip_skip (`int`, *optional*): | |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that | |
the output of the pre-final layer will be used for computing the prompt embeddings. | |
callback_on_step_end (`Callable`, *optional*): | |
A function that calls at the end of each denoising steps during the inference. The function is called | |
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, | |
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by | |
`callback_on_step_end_tensor_inputs`. | |
callback_on_step_end_tensor_inputs (`List`, *optional*): | |
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list | |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the | |
`._callback_tensor_inputs` attribute of your pipeline class. | |
Examples: | |
Returns: | |
[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`: | |
[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When | |
returning a tuple, the first element is a list with the generated images. | |
""" | |
if self.inversion_steps is None: | |
raise ValueError( | |
"You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s)." | |
) | |
eta = self.eta | |
num_images_per_prompt = 1 | |
latents = self.init_latents | |
zs = self.zs | |
self.scheduler.set_timesteps(len(self.scheduler.timesteps)) | |
if use_intersect_mask: | |
use_cross_attn_mask = True | |
if use_cross_attn_mask: | |
self.smoothing = LeditsGaussianSmoothing(self.device) | |
if user_mask is not None: | |
user_mask = user_mask.to(self.device) | |
# TODO: Check inputs | |
# 1. Check inputs. Raise error if not correct | |
# self.check_inputs( | |
# callback_steps, | |
# negative_prompt, | |
# negative_prompt_2, | |
# prompt_embeds, | |
# negative_prompt_embeds, | |
# pooled_prompt_embeds, | |
# negative_pooled_prompt_embeds, | |
# ) | |
self._guidance_rescale = guidance_rescale | |
self._clip_skip = clip_skip | |
self._cross_attention_kwargs = cross_attention_kwargs | |
self._denoising_end = denoising_end | |
# 2. Define call parameters | |
batch_size = self.batch_size | |
device = self._execution_device | |
if editing_prompt: | |
enable_edit_guidance = True | |
if isinstance(editing_prompt, str): | |
editing_prompt = [editing_prompt] | |
self.enabled_editing_prompts = len(editing_prompt) | |
elif editing_prompt_embeddings is not None: | |
enable_edit_guidance = True | |
self.enabled_editing_prompts = editing_prompt_embeddings.shape[0] | |
else: | |
self.enabled_editing_prompts = 0 | |
enable_edit_guidance = False | |
# 3. Encode input prompt | |
text_encoder_lora_scale = ( | |
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None | |
) | |
( | |
prompt_embeds, | |
edit_prompt_embeds, | |
negative_pooled_prompt_embeds, | |
pooled_edit_embeds, | |
num_edit_tokens, | |
) = self.encode_prompt( | |
device=device, | |
num_images_per_prompt=num_images_per_prompt, | |
negative_prompt=negative_prompt, | |
negative_prompt_2=negative_prompt_2, | |
negative_prompt_embeds=negative_prompt_embeds, | |
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, | |
lora_scale=text_encoder_lora_scale, | |
clip_skip=self.clip_skip, | |
enable_edit_guidance=enable_edit_guidance, | |
editing_prompt=editing_prompt, | |
editing_prompt_embeds=editing_prompt_embeddings, | |
editing_pooled_prompt_embeds=editing_pooled_prompt_embeds, | |
) | |
# 4. Prepare timesteps | |
# self.scheduler.set_timesteps(num_inference_steps, device=device) | |
timesteps = self.inversion_steps | |
t_to_idx = {int(v): k for k, v in enumerate(timesteps)} | |
if use_cross_attn_mask: | |
self.attention_store = LeditsAttentionStore( | |
average=store_averaged_over_steps, | |
batch_size=batch_size, | |
max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0), | |
max_resolution=None, | |
) | |
self.prepare_unet(self.attention_store) | |
resolution = latents.shape[-2:] | |
att_res = (int(resolution[0] / 4), int(resolution[1] / 4)) | |
# 5. Prepare latent variables | |
latents = self.prepare_latents(device=device, latents=latents) | |
# 6. Prepare extra step kwargs. | |
extra_step_kwargs = self.prepare_extra_step_kwargs(eta) | |
if self.text_encoder_2 is None: | |
text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1]) | |
else: | |
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim | |
# 7. Prepare added time ids & embeddings | |
add_text_embeds = negative_pooled_prompt_embeds | |
add_time_ids = self._get_add_time_ids( | |
self.size, | |
crops_coords_top_left, | |
self.size, | |
dtype=negative_pooled_prompt_embeds.dtype, | |
text_encoder_projection_dim=text_encoder_projection_dim, | |
) | |
if enable_edit_guidance: | |
prompt_embeds = torch.cat([prompt_embeds, edit_prompt_embeds], dim=0) | |
add_text_embeds = torch.cat([add_text_embeds, pooled_edit_embeds], dim=0) | |
edit_concepts_time_ids = add_time_ids.repeat(edit_prompt_embeds.shape[0], 1) | |
add_time_ids = torch.cat([add_time_ids, edit_concepts_time_ids], dim=0) | |
self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt | |
prompt_embeds = prompt_embeds.to(device) | |
add_text_embeds = add_text_embeds.to(device) | |
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) | |
if ip_adapter_image is not None: | |
# TODO: fix image encoding | |
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt) | |
if self.do_classifier_free_guidance: | |
image_embeds = torch.cat([negative_image_embeds, image_embeds]) | |
image_embeds = image_embeds.to(device) | |
# 8. Denoising loop | |
self.sem_guidance = None | |
self.activation_mask = None | |
if ( | |
self.denoising_end is not None | |
and isinstance(self.denoising_end, float) | |
and self.denoising_end > 0 | |
and self.denoising_end < 1 | |
): | |
discrete_timestep_cutoff = int( | |
round( | |
self.scheduler.config.num_train_timesteps | |
- (self.denoising_end * self.scheduler.config.num_train_timesteps) | |
) | |
) | |
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) | |
timesteps = timesteps[:num_inference_steps] | |
# 9. Optionally get Guidance Scale Embedding | |
timestep_cond = None | |
if self.unet.config.time_cond_proj_dim is not None: | |
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) | |
timestep_cond = self.get_guidance_scale_embedding( | |
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim | |
).to(device=device, dtype=latents.dtype) | |
self._num_timesteps = len(timesteps) | |
with self.progress_bar(total=self._num_timesteps) as progress_bar: | |
for i, t in enumerate(timesteps): | |
# expand the latents if we are doing classifier free guidance | |
latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts)) | |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
# predict the noise residual | |
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} | |
if ip_adapter_image is not None: | |
added_cond_kwargs["image_embeds"] = image_embeds | |
noise_pred = self.unet( | |
latent_model_input, | |
t, | |
encoder_hidden_states=prompt_embeds, | |
cross_attention_kwargs=cross_attention_kwargs, | |
added_cond_kwargs=added_cond_kwargs, | |
return_dict=False, | |
)[0] | |
noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64] | |
noise_pred_uncond = noise_pred_out[0] | |
noise_pred_edit_concepts = noise_pred_out[1:] | |
noise_guidance_edit = torch.zeros( | |
noise_pred_uncond.shape, | |
device=self.device, | |
dtype=noise_pred_uncond.dtype, | |
) | |
if sem_guidance is not None and len(sem_guidance) > i: | |
noise_guidance_edit += sem_guidance[i].to(self.device) | |
elif enable_edit_guidance: | |
if self.activation_mask is None: | |
self.activation_mask = torch.zeros( | |
(len(timesteps), self.enabled_editing_prompts, *noise_pred_edit_concepts[0].shape) | |
) | |
if self.sem_guidance is None: | |
self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape)) | |
# noise_guidance_edit = torch.zeros_like(noise_guidance) | |
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts): | |
if isinstance(edit_warmup_steps, list): | |
edit_warmup_steps_c = edit_warmup_steps[c] | |
else: | |
edit_warmup_steps_c = edit_warmup_steps | |
if i < edit_warmup_steps_c: | |
continue | |
if isinstance(edit_guidance_scale, list): | |
edit_guidance_scale_c = edit_guidance_scale[c] | |
else: | |
edit_guidance_scale_c = edit_guidance_scale | |
if isinstance(edit_threshold, list): | |
edit_threshold_c = edit_threshold[c] | |
else: | |
edit_threshold_c = edit_threshold | |
if isinstance(reverse_editing_direction, list): | |
reverse_editing_direction_c = reverse_editing_direction[c] | |
else: | |
reverse_editing_direction_c = reverse_editing_direction | |
if isinstance(edit_cooldown_steps, list): | |
edit_cooldown_steps_c = edit_cooldown_steps[c] | |
elif edit_cooldown_steps is None: | |
edit_cooldown_steps_c = i + 1 | |
else: | |
edit_cooldown_steps_c = edit_cooldown_steps | |
if i >= edit_cooldown_steps_c: | |
continue | |
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond | |
if reverse_editing_direction_c: | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1 | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c | |
if user_mask is not None: | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask | |
if use_cross_attn_mask: | |
out = self.attention_store.aggregate_attention( | |
attention_maps=self.attention_store.step_store, | |
prompts=self.text_cross_attention_maps, | |
res=att_res, | |
from_where=["up", "down"], | |
is_cross=True, | |
select=self.text_cross_attention_maps.index(editing_prompt[c]), | |
) | |
attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext | |
# average over all tokens | |
if attn_map.shape[3] != num_edit_tokens[c]: | |
raise ValueError( | |
f"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!" | |
) | |
attn_map = torch.sum(attn_map, dim=3) | |
# gaussian_smoothing | |
attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect") | |
attn_map = self.smoothing(attn_map).squeeze(1) | |
# torch.quantile function expects float32 | |
if attn_map.dtype == torch.float32: | |
tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1) | |
else: | |
tmp = torch.quantile( | |
attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1 | |
).to(attn_map.dtype) | |
attn_mask = torch.where( | |
attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1, *att_res), 1.0, 0.0 | |
) | |
# resolution must match latent space dimension | |
attn_mask = F.interpolate( | |
attn_mask.unsqueeze(1), | |
noise_guidance_edit_tmp.shape[-2:], # 64,64 | |
).repeat(1, 4, 1, 1) | |
self.activation_mask[i, c] = attn_mask.detach().cpu() | |
if not use_intersect_mask: | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask | |
if use_intersect_mask: | |
noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) | |
noise_guidance_edit_tmp_quantile = torch.sum( | |
noise_guidance_edit_tmp_quantile, dim=1, keepdim=True | |
) | |
noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat( | |
1, self.unet.config.in_channels, 1, 1 | |
) | |
# torch.quantile function expects float32 | |
if noise_guidance_edit_tmp_quantile.dtype == torch.float32: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
) | |
else: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
).to(noise_guidance_edit_tmp_quantile.dtype) | |
intersect_mask = ( | |
torch.where( | |
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], | |
torch.ones_like(noise_guidance_edit_tmp), | |
torch.zeros_like(noise_guidance_edit_tmp), | |
) | |
* attn_mask | |
) | |
self.activation_mask[i, c] = intersect_mask.detach().cpu() | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask | |
elif not use_cross_attn_mask: | |
# calculate quantile | |
noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) | |
noise_guidance_edit_tmp_quantile = torch.sum( | |
noise_guidance_edit_tmp_quantile, dim=1, keepdim=True | |
) | |
noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1) | |
# torch.quantile function expects float32 | |
if noise_guidance_edit_tmp_quantile.dtype == torch.float32: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
) | |
else: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
).to(noise_guidance_edit_tmp_quantile.dtype) | |
self.activation_mask[i, c] = ( | |
torch.where( | |
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], | |
torch.ones_like(noise_guidance_edit_tmp), | |
torch.zeros_like(noise_guidance_edit_tmp), | |
) | |
.detach() | |
.cpu() | |
) | |
noise_guidance_edit_tmp = torch.where( | |
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], | |
noise_guidance_edit_tmp, | |
torch.zeros_like(noise_guidance_edit_tmp), | |
) | |
noise_guidance_edit += noise_guidance_edit_tmp | |
self.sem_guidance[i] = noise_guidance_edit.detach().cpu() | |
noise_pred = noise_pred_uncond + noise_guidance_edit | |
# compute the previous noisy sample x_t -> x_t-1 | |
if enable_edit_guidance and self.guidance_rescale > 0.0: | |
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf | |
noise_pred = rescale_noise_cfg( | |
noise_pred, | |
noise_pred_edit_concepts.mean(dim=0, keepdim=False), | |
guidance_rescale=self.guidance_rescale, | |
) | |
idx = t_to_idx[int(t)] | |
latents = self.scheduler.step( | |
noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs, return_dict=False | |
)[0] | |
# step callback | |
if use_cross_attn_mask: | |
store_step = i in attn_store_steps | |
self.attention_store.between_steps(store_step) | |
if callback_on_step_end is not None: | |
callback_kwargs = {} | |
for k in callback_on_step_end_tensor_inputs: | |
callback_kwargs[k] = locals()[k] | |
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) | |
latents = callback_outputs.pop("latents", latents) | |
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) | |
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) | |
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) | |
negative_pooled_prompt_embeds = callback_outputs.pop( | |
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds | |
) | |
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) | |
# negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids) | |
# call the callback, if provided | |
if i == len(timesteps) - 1 or ((i + 1) > 0 and (i + 1) % self.scheduler.order == 0): | |
progress_bar.update() | |
if XLA_AVAILABLE: | |
xm.mark_step() | |
if not output_type == "latent": | |
# make sure the VAE is in float32 mode, as it overflows in float16 | |
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast | |
if needs_upcasting: | |
self.upcast_vae() | |
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | |
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] | |
# cast back to fp16 if needed | |
if needs_upcasting: | |
self.vae.to(dtype=torch.float16) | |
else: | |
image = latents | |
if not output_type == "latent": | |
# apply watermark if available | |
if self.watermark is not None: | |
image = self.watermark.apply_watermark(image) | |
image = self.image_processor.postprocess(image, output_type=output_type) | |
# Offload all models | |
self.maybe_free_model_hooks() | |
if not return_dict: | |
return (image,) | |
return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=None) | |
# Modified from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.encode_image | |
def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="default", crops_coords=None): | |
image = self.image_processor.preprocess( | |
image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords | |
) | |
resized = self.image_processor.postprocess(image=image, output_type="pil") | |
if max(image.shape[-2:]) > self.vae.config["sample_size"] * 1.5: | |
logger.warning( | |
"Your input images far exceed the default resolution of the underlying diffusion model. " | |
"The output images may contain severe artifacts! " | |
"Consider down-sampling the input using the `height` and `width` parameters" | |
) | |
image = image.to(self.device, dtype=dtype) | |
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast | |
if needs_upcasting: | |
image = image.float() | |
self.upcast_vae() | |
image = image.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | |
x0 = self.vae.encode(image).latent_dist.mode() | |
x0 = x0.to(dtype) | |
# cast back to fp16 if needed | |
if needs_upcasting: | |
self.vae.to(dtype=torch.float16) | |
x0 = self.vae.config.scaling_factor * x0 | |
return x0, resized | |
def invert( | |
self, | |
image: PipelineImageInput, | |
source_prompt: str = "", | |
source_guidance_scale=3.5, | |
negative_prompt: str = None, | |
negative_prompt_2: str = None, | |
num_inversion_steps: int = 50, | |
skip: float = 0.15, | |
generator: Optional[torch.Generator] = None, | |
crops_coords_top_left: Tuple[int, int] = (0, 0), | |
num_zero_noise_steps: int = 3, | |
cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
): | |
r""" | |
The function to the pipeline for image inversion as described by the [LEDITS++ | |
Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the | |
inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead. | |
Args: | |
image (`PipelineImageInput`): | |
Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect | |
ratio. | |
source_prompt (`str`, defaults to `""`): | |
Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled | |
if the `source_prompt` is `""`. | |
source_guidance_scale (`float`, defaults to `3.5`): | |
Strength of guidance during inversion. | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation. If not defined, one has to pass | |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is | |
less than `1`). | |
negative_prompt_2 (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and | |
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders | |
num_inversion_steps (`int`, defaults to `50`): | |
Number of total performed inversion steps after discarding the initial `skip` steps. | |
skip (`float`, defaults to `0.15`): | |
Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values | |
will lead to stronger changes to the input image. `skip` has to be between `0` and `1`. | |
generator (`torch.Generator`, *optional*): | |
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion | |
deterministic. | |
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): | |
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position | |
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting | |
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of | |
[https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952). | |
num_zero_noise_steps (`int`, defaults to `3`): | |
Number of final diffusion steps that will not renoise the current image. If no steps are set to zero | |
SD-XL in combination with [`DPMSolverMultistepScheduler`] will produce noise artifacts. | |
cross_attention_kwargs (`dict`, *optional*): | |
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under | |
`self.processor` in | |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
Returns: | |
[`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s) | |
and respective VAE reconstruction(s). | |
""" | |
# Reset attn processor, we do not want to store attn maps during inversion | |
self.unet.set_attn_processor(AttnProcessor()) | |
self.eta = 1.0 | |
self.scheduler.config.timestep_spacing = "leading" | |
self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip))) | |
self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:] | |
timesteps = self.inversion_steps | |
num_images_per_prompt = 1 | |
device = self._execution_device | |
# 0. Ensure that only uncond embedding is used if prompt = "" | |
if source_prompt == "": | |
# noise pred should only be noise_pred_uncond | |
source_guidance_scale = 0.0 | |
do_classifier_free_guidance = False | |
else: | |
do_classifier_free_guidance = source_guidance_scale > 1.0 | |
# 1. prepare image | |
x0, resized = self.encode_image(image, dtype=self.text_encoder_2.dtype) | |
width = x0.shape[2] * self.vae_scale_factor | |
height = x0.shape[3] * self.vae_scale_factor | |
self.size = (height, width) | |
self.batch_size = x0.shape[0] | |
# 2. get embeddings | |
text_encoder_lora_scale = ( | |
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None | |
) | |
if isinstance(source_prompt, str): | |
source_prompt = [source_prompt] * self.batch_size | |
( | |
negative_prompt_embeds, | |
prompt_embeds, | |
negative_pooled_prompt_embeds, | |
edit_pooled_prompt_embeds, | |
_, | |
) = self.encode_prompt( | |
device=device, | |
num_images_per_prompt=num_images_per_prompt, | |
negative_prompt=negative_prompt, | |
negative_prompt_2=negative_prompt_2, | |
editing_prompt=source_prompt, | |
lora_scale=text_encoder_lora_scale, | |
enable_edit_guidance=do_classifier_free_guidance, | |
) | |
if self.text_encoder_2 is None: | |
text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1]) | |
else: | |
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim | |
# 3. Prepare added time ids & embeddings | |
add_text_embeds = negative_pooled_prompt_embeds | |
add_time_ids = self._get_add_time_ids( | |
self.size, | |
crops_coords_top_left, | |
self.size, | |
dtype=negative_prompt_embeds.dtype, | |
text_encoder_projection_dim=text_encoder_projection_dim, | |
) | |
if do_classifier_free_guidance: | |
negative_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) | |
add_text_embeds = torch.cat([add_text_embeds, edit_pooled_prompt_embeds], dim=0) | |
add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0) | |
negative_prompt_embeds = negative_prompt_embeds.to(device) | |
add_text_embeds = add_text_embeds.to(device) | |
add_time_ids = add_time_ids.to(device).repeat(self.batch_size * num_images_per_prompt, 1) | |
# autoencoder reconstruction | |
if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: | |
self.upcast_vae() | |
x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | |
image_rec = self.vae.decode( | |
x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator | |
)[0] | |
elif self.vae.config.force_upcast: | |
x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | |
image_rec = self.vae.decode( | |
x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator | |
)[0] | |
else: | |
image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0] | |
image_rec = self.image_processor.postprocess(image_rec, output_type="pil") | |
# 5. find zs and xts | |
variance_noise_shape = (num_inversion_steps, *x0.shape) | |
# intermediate latents | |
t_to_idx = {int(v): k for k, v in enumerate(timesteps)} | |
xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype) | |
for t in reversed(timesteps): | |
idx = num_inversion_steps - t_to_idx[int(t)] - 1 | |
noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype) | |
xts[idx] = self.scheduler.add_noise(x0, noise, t.unsqueeze(0)) | |
xts = torch.cat([x0.unsqueeze(0), xts], dim=0) | |
# noise maps | |
zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype) | |
self.scheduler.set_timesteps(len(self.scheduler.timesteps)) | |
for t in self.progress_bar(timesteps): | |
idx = num_inversion_steps - t_to_idx[int(t)] - 1 | |
# 1. predict noise residual | |
xt = xts[idx + 1] | |
latent_model_input = torch.cat([xt] * 2) if do_classifier_free_guidance else xt | |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} | |
noise_pred = self.unet( | |
latent_model_input, | |
t, | |
encoder_hidden_states=negative_prompt_embeds, | |
cross_attention_kwargs=cross_attention_kwargs, | |
added_cond_kwargs=added_cond_kwargs, | |
return_dict=False, | |
)[0] | |
# 2. perform guidance | |
if do_classifier_free_guidance: | |
noise_pred_out = noise_pred.chunk(2) | |
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1] | |
noise_pred = noise_pred_uncond + source_guidance_scale * (noise_pred_text - noise_pred_uncond) | |
xtm1 = xts[idx] | |
z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta) | |
zs[idx] = z | |
# correction to avoid error accumulation | |
xts[idx] = xtm1_corrected | |
self.init_latents = xts[-1] | |
zs = zs.flip(0) | |
if num_zero_noise_steps > 0: | |
zs[-num_zero_noise_steps:] = torch.zeros_like(zs[-num_zero_noise_steps:]) | |
self.zs = zs | |
return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec) | |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.rescale_noise_cfg | |
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): | |
""" | |
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and | |
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 | |
""" | |
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) | |
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) | |
# rescale the results from guidance (fixes overexposure) | |
noise_pred_rescaled = noise_cfg * (std_text / std_cfg) | |
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images | |
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg | |
return noise_cfg | |
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_ddim | |
def compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta): | |
# 1. get previous step value (=t-1) | |
prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps | |
# 2. compute alphas, betas | |
alpha_prod_t = scheduler.alphas_cumprod[timestep] | |
alpha_prod_t_prev = ( | |
scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod | |
) | |
beta_prod_t = 1 - alpha_prod_t | |
# 3. compute predicted original sample from predicted noise also called | |
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) | |
# 4. Clip "predicted x_0" | |
if scheduler.config.clip_sample: | |
pred_original_sample = torch.clamp(pred_original_sample, -1, 1) | |
# 5. compute variance: "sigma_t(η)" -> see formula (16) | |
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) | |
variance = scheduler._get_variance(timestep, prev_timestep) | |
std_dev_t = eta * variance ** (0.5) | |
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred | |
# modifed so that updated xtm1 is returned as well (to avoid error accumulation) | |
mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction | |
if variance > 0.0: | |
noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta) | |
else: | |
noise = torch.tensor([0.0]).to(latents.device) | |
return noise, mu_xt + (eta * variance**0.5) * noise | |
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_sde_dpm_pp_2nd | |
def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta): | |
def first_order_update(model_output, sample): # timestep, prev_timestep, sample): | |
sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index] | |
alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) | |
alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s) | |
lambda_t = torch.log(alpha_t) - torch.log(sigma_t) | |
lambda_s = torch.log(alpha_s) - torch.log(sigma_s) | |
h = lambda_t - lambda_s | |
mu_xt = (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output | |
mu_xt = scheduler.dpm_solver_first_order_update( | |
model_output=model_output, sample=sample, noise=torch.zeros_like(sample) | |
) | |
sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) | |
if sigma > 0.0: | |
noise = (prev_latents - mu_xt) / sigma | |
else: | |
noise = torch.tensor([0.0]).to(sample.device) | |
prev_sample = mu_xt + sigma * noise | |
return noise, prev_sample | |
def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample): | |
sigma_t, sigma_s0, sigma_s1 = ( | |
scheduler.sigmas[scheduler.step_index + 1], | |
scheduler.sigmas[scheduler.step_index], | |
scheduler.sigmas[scheduler.step_index - 1], | |
) | |
alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) | |
alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0) | |
alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1) | |
lambda_t = torch.log(alpha_t) - torch.log(sigma_t) | |
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) | |
lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) | |
m0, m1 = model_output_list[-1], model_output_list[-2] | |
h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 | |
r0 = h_0 / h | |
D0, D1 = m0, (1.0 / r0) * (m0 - m1) | |
mu_xt = ( | |
(sigma_t / sigma_s0 * torch.exp(-h)) * sample | |
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 | |
+ 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 | |
) | |
sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) | |
if sigma > 0.0: | |
noise = (prev_latents - mu_xt) / sigma | |
else: | |
noise = torch.tensor([0.0]).to(sample.device) | |
prev_sample = mu_xt + sigma * noise | |
return noise, prev_sample | |
if scheduler.step_index is None: | |
scheduler._init_step_index(timestep) | |
model_output = scheduler.convert_model_output(model_output=noise_pred, sample=latents) | |
for i in range(scheduler.config.solver_order - 1): | |
scheduler.model_outputs[i] = scheduler.model_outputs[i + 1] | |
scheduler.model_outputs[-1] = model_output | |
if scheduler.lower_order_nums < 1: | |
noise, prev_sample = first_order_update(model_output, latents) | |
else: | |
noise, prev_sample = second_order_update(scheduler.model_outputs, latents) | |
if scheduler.lower_order_nums < scheduler.config.solver_order: | |
scheduler.lower_order_nums += 1 | |
# upon completion increase step index by one | |
scheduler._step_index += 1 | |
return noise, prev_sample | |
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise | |
def compute_noise(scheduler, *args): | |
if isinstance(scheduler, DDIMScheduler): | |
return compute_noise_ddim(scheduler, *args) | |
elif ( | |
isinstance(scheduler, DPMSolverMultistepScheduler) | |
and scheduler.config.algorithm_type == "sde-dpmsolver++" | |
and scheduler.config.solver_order == 2 | |
): | |
return compute_noise_sde_dpm_pp_2nd(scheduler, *args) | |
else: | |
raise NotImplementedError | |