Spaces:
Runtime error
Runtime error
Diffusion-Attentive-Attribution-Maps
/
diffusers
/pipelines
/stable_diffusion
/pipeline_stable_diffusion_inpaint.py
import inspect | |
from typing import List, Optional, Union | |
import numpy as np | |
import torch | |
import PIL | |
from tqdm.auto import tqdm | |
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer | |
from ...models import AutoencoderKL, UNet2DConditionModel | |
from ...pipeline_utils import DiffusionPipeline | |
from ...schedulers import DDIMScheduler, PNDMScheduler | |
from ...utils import logging | |
from . import StableDiffusionPipelineOutput | |
from .safety_checker import StableDiffusionSafetyChecker | |
logger = logging.get_logger(__name__) | |
def preprocess_image(image): | |
w, h = image.size | |
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 | |
image = image.resize((w, h), resample=PIL.Image.LANCZOS) | |
image = np.array(image).astype(np.float32) / 255.0 | |
image = image[None].transpose(0, 3, 1, 2) | |
image = torch.from_numpy(image) | |
return 2.0 * image - 1.0 | |
def preprocess_mask(mask): | |
mask = mask.convert("L") | |
w, h = mask.size | |
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 | |
mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST) | |
mask = np.array(mask).astype(np.float32) / 255.0 | |
mask = np.tile(mask, (4, 1, 1)) | |
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do? | |
mask = 1 - mask # repaint white, keep black | |
mask = torch.from_numpy(mask) | |
return mask | |
class StableDiffusionInpaintPipeline(DiffusionPipeline): | |
r""" | |
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. | |
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the | |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | |
Args: | |
vae ([`AutoencoderKL`]): | |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. | |
text_encoder ([`CLIPTextModel`]): | |
Frozen text-encoder. Stable Diffusion 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. | |
tokenizer (`CLIPTokenizer`): | |
Tokenizer of class | |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). | |
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. | |
scheduler ([`SchedulerMixin`]): | |
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of | |
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | |
safety_checker ([`StableDiffusionSafetyChecker`]): | |
Classification module that estimates whether generated images could be considered offsensive or harmful. | |
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. | |
feature_extractor ([`CLIPFeatureExtractor`]): | |
Model that extracts features from generated images to be used as inputs for the `safety_checker`. | |
""" | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
text_encoder: CLIPTextModel, | |
tokenizer: CLIPTokenizer, | |
unet: UNet2DConditionModel, | |
scheduler: Union[DDIMScheduler, PNDMScheduler], | |
safety_checker: StableDiffusionSafetyChecker, | |
feature_extractor: CLIPFeatureExtractor, | |
): | |
super().__init__() | |
scheduler = scheduler.set_format("pt") | |
logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.") | |
self.register_modules( | |
vae=vae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
unet=unet, | |
scheduler=scheduler, | |
safety_checker=safety_checker, | |
feature_extractor=feature_extractor, | |
) | |
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): | |
r""" | |
Enable sliced attention computation. | |
When this option is enabled, the attention module will split the input tensor in slices, to compute attention | |
in several steps. This is useful to save some memory in exchange for a small speed decrease. | |
Args: | |
slice_size (`str` or `int`, *optional*, defaults to `"auto"`): | |
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If | |
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, | |
`attention_head_dim` must be a multiple of `slice_size`. | |
""" | |
if slice_size == "auto": | |
# half the attention head size is usually a good trade-off between | |
# speed and memory | |
slice_size = self.unet.config.attention_head_dim // 2 | |
self.unet.set_attention_slice(slice_size) | |
def disable_attention_slicing(self): | |
r""" | |
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go | |
back to computing attention in one step. | |
""" | |
# set slice_size = `None` to disable `set_attention_slice` | |
self.enable_attention_slice(None) | |
def __call__( | |
self, | |
prompt: Union[str, List[str]], | |
init_image: Union[torch.FloatTensor, PIL.Image.Image], | |
mask_image: Union[torch.FloatTensor, PIL.Image.Image], | |
strength: float = 0.8, | |
num_inference_steps: Optional[int] = 50, | |
guidance_scale: Optional[float] = 7.5, | |
eta: Optional[float] = 0.0, | |
generator: Optional[torch.Generator] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
): | |
r""" | |
Function invoked when calling the pipeline for generation. | |
Args: | |
prompt (`str` or `List[str]`): | |
The prompt or prompts to guide the image generation. | |
init_image (`torch.FloatTensor` or `PIL.Image.Image`): | |
`Image`, or tensor representing an image batch, that will be used as the starting point for the | |
process. This is the image whose masked region will be inpainted. | |
mask_image (`torch.FloatTensor` or `PIL.Image.Image`): | |
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be | |
replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be | |
converted to a single channel (luminance) before use. | |
strength (`float`, *optional*, defaults to 0.8): | |
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength` | |
is 1, the denoising process will be run on the masked area for the full number of iterations specified | |
in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more | |
noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur. | |
num_inference_steps (`int`, *optional*, defaults to 50): | |
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at | |
the expense of slower inference. This parameter will be modulated by `strength`, as explained above. | |
guidance_scale (`float`, *optional*, defaults to 7.5): | |
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). | |
`guidance_scale` is defined as `w` of equation 2. of [Imagen | |
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > | |
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, | |
usually at the expense of lower image quality. | |
eta (`float`, *optional*, defaults to 0.0): | |
Corresponds to parameter eta (Ξ·) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to | |
[`schedulers.DDIMScheduler`], will be ignored for others. | |
generator (`torch.Generator`, *optional*): | |
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation | |
deterministic. | |
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 `nd.array`. | |
return_dict (`bool`, *optional*, defaults to `True`): | |
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a | |
plain tuple. | |
Returns: | |
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: | |
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. | |
When returning a tuple, the first element is a list with the generated images, and the second element is a | |
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" | |
(nsfw) content, according to the `safety_checker`. | |
""" | |
if isinstance(prompt, str): | |
batch_size = 1 | |
elif isinstance(prompt, list): | |
batch_size = len(prompt) | |
else: | |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | |
if strength < 0 or strength > 1: | |
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") | |
# set timesteps | |
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) | |
extra_set_kwargs = {} | |
offset = 0 | |
if accepts_offset: | |
offset = 1 | |
extra_set_kwargs["offset"] = 1 | |
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) | |
# preprocess image | |
init_image = preprocess_image(init_image).to(self.device) | |
# encode the init image into latents and scale the latents | |
init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist | |
init_latents = init_latent_dist.sample(generator=generator) | |
init_latents = 0.18215 * init_latents | |
# Expand init_latents for batch_size | |
init_latents = torch.cat([init_latents] * batch_size) | |
init_latents_orig = init_latents | |
# preprocess mask | |
mask = preprocess_mask(mask_image).to(self.device) | |
mask = torch.cat([mask] * batch_size) | |
# check sizes | |
if not mask.shape == init_latents.shape: | |
raise ValueError("The mask and init_image should be the same size!") | |
# get the original timestep using init_timestep | |
init_timestep = int(num_inference_steps * strength) + offset | |
init_timestep = min(init_timestep, num_inference_steps) | |
timesteps = self.scheduler.timesteps[-init_timestep] | |
timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device) | |
# add noise to latents using the timesteps | |
noise = torch.randn(init_latents.shape, generator=generator, device=self.device) | |
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps) | |
# get prompt text embeddings | |
text_input = self.tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] | |
# 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. | |
do_classifier_free_guidance = guidance_scale > 1.0 | |
# get unconditional embeddings for classifier free guidance | |
if do_classifier_free_guidance: | |
max_length = text_input.input_ids.shape[-1] | |
uncond_input = self.tokenizer( | |
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt" | |
) | |
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] | |
# For classifier free guidance, we need to do two forward passes. | |
# Here we concatenate the unconditional and text embeddings into a single batch | |
# to avoid doing two forward passes | |
text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) | |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature | |
# eta (Ξ·) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
# eta corresponds to Ξ· 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 | |
latents = init_latents | |
t_start = max(num_inference_steps - init_timestep + offset, 0) | |
for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])): | |
# expand the latents if we are doing classifier free guidance | |
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
# predict the noise residual | |
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample | |
# perform guidance | |
if do_classifier_free_guidance: | |
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
# compute the previous noisy sample x_t -> x_t-1 | |
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample | |
# masking | |
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t) | |
latents = (init_latents_proper * mask) + (latents * (1 - mask)) | |
# scale and decode the image latents with vae | |
latents = 1 / 0.18215 * latents | |
image = self.vae.decode(latents).sample | |
image = (image / 2 + 0.5).clamp(0, 1) | |
image = image.cpu().permute(0, 2, 3, 1).numpy() | |
# run safety checker | |
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device) | |
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values) | |
if output_type == "pil": | |
image = self.numpy_to_pil(image) | |
if not return_dict: | |
return (image, has_nsfw_concept) | |
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | |