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import inspect | |
from typing import Callable, List, Optional, Tuple, Union | |
import numpy as np | |
import PIL | |
import torch | |
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer | |
from diffusers import DiffusionPipeline | |
from diffusers.configuration_utils import FrozenDict | |
from diffusers.models import AutoencoderKL, UNet2DConditionModel | |
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput | |
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler | |
from diffusers.utils import deprecate, logging | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
def prepare_mask_and_masked_image(image, mask): | |
image = np.array(image.convert("RGB")) | |
image = image[None].transpose(0, 3, 1, 2) | |
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 | |
mask = np.array(mask.convert("L")) | |
mask = mask.astype(np.float32) / 255.0 | |
mask = mask[None, None] | |
mask[mask < 0.5] = 0 | |
mask[mask >= 0.5] = 1 | |
mask = torch.from_numpy(mask) | |
masked_image = image * (mask < 0.5) | |
return mask, masked_image | |
def check_size(image, height, width): | |
if isinstance(image, PIL.Image.Image): | |
w, h = image.size | |
elif isinstance(image, torch.Tensor): | |
*_, h, w = image.shape | |
if h != height or w != width: | |
raise ValueError(f"Image size should be {height}x{width}, but got {h}x{w}") | |
def overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)): | |
inner_image = inner_image.convert("RGBA") | |
image = image.convert("RGB") | |
image.paste(inner_image, paste_offset, inner_image) | |
image = image.convert("RGB") | |
return image | |
class ImageToImageInpaintingPipeline(DiffusionPipeline): | |
r""" | |
Pipeline for text-guided image-to-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 offensive or harmful. | |
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. | |
feature_extractor ([`CLIPImageProcessor`]): | |
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, LMSDiscreteScheduler], | |
safety_checker: StableDiffusionSafetyChecker, | |
feature_extractor: CLIPImageProcessor, | |
): | |
super().__init__() | |
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: | |
deprecation_message = ( | |
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" | |
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " | |
"to update the config accordingly as leaving `steps_offset` might led to incorrect results" | |
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," | |
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" | |
" file" | |
) | |
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) | |
new_config = dict(scheduler.config) | |
new_config["steps_offset"] = 1 | |
scheduler._internal_dict = FrozenDict(new_config) | |
if safety_checker is None: | |
logger.warning( | |
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" | |
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" | |
" results in services or applications open to the public. Both the diffusers team and Hugging Face" | |
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" | |
" it only for use-cases that involve analyzing network behavior or auditing its results. For more" | |
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." | |
) | |
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 `attention slicing` | |
self.enable_attention_slicing(None) | |
def __call__( | |
self, | |
prompt: Union[str, List[str]], | |
image: Union[torch.FloatTensor, PIL.Image.Image], | |
inner_image: Union[torch.FloatTensor, PIL.Image.Image], | |
mask_image: Union[torch.FloatTensor, PIL.Image.Image], | |
height: int = 512, | |
width: int = 512, | |
num_inference_steps: int = 50, | |
guidance_scale: float = 7.5, | |
negative_prompt: Optional[Union[str, List[str]]] = None, | |
num_images_per_prompt: Optional[int] = 1, | |
eta: float = 0.0, | |
generator: Optional[torch.Generator] = None, | |
latents: Optional[torch.FloatTensor] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
callback_steps: int = 1, | |
**kwargs, | |
): | |
r""" | |
Function invoked when calling the pipeline for generation. | |
Args: | |
prompt (`str` or `List[str]`): | |
The prompt or prompts to guide the image generation. | |
image (`torch.Tensor` or `PIL.Image.Image`): | |
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will | |
be masked out with `mask_image` and repainted according to `prompt`. | |
inner_image (`torch.Tensor` or `PIL.Image.Image`): | |
`Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent | |
regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with | |
the last channel representing the alpha channel, which will be used to blend `inner_image` with | |
`image`. If not provided, it will be forcibly cast to RGBA. | |
mask_image (`PIL.Image.Image`): | |
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be | |
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted | |
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) | |
instead of 3, so the expected shape would be `(B, H, W, 1)`. | |
height (`int`, *optional*, defaults to 512): | |
The height in pixels of the generated image. | |
width (`int`, *optional*, defaults to 512): | |
The width in pixels of the generated image. | |
num_inference_steps (`int`, *optional*, defaults to 50): | |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the | |
expense of slower inference. | |
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. | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored | |
if `guidance_scale` is less than `1`). | |
num_images_per_prompt (`int`, *optional*, defaults to 1): | |
The number of images to generate per prompt. | |
eta (`float`, *optional*, defaults to 0.0): | |
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to | |
[`schedulers.DDIMScheduler`], will be ignored for others. | |
generator (`torch.Generator`, *optional*): | |
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation | |
deterministic. | |
latents (`torch.FloatTensor`, *optional*): | |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image | |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents | |
tensor will ge generated by sampling using the supplied random `generator`. | |
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.StableDiffusionPipelineOutput`] 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. | |
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 height % 8 != 0 or width % 8 != 0: | |
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | |
if (callback_steps is None) or ( | |
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) | |
): | |
raise ValueError( | |
f"`callback_steps` has to be a positive integer but is {callback_steps} of type" | |
f" {type(callback_steps)}." | |
) | |
# check if input sizes are correct | |
check_size(image, height, width) | |
check_size(inner_image, height, width) | |
check_size(mask_image, height, width) | |
# get prompt text embeddings | |
text_inputs = self.tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
return_tensors="pt", | |
) | |
text_input_ids = text_inputs.input_ids | |
if text_input_ids.shape[-1] > self.tokenizer.model_max_length: | |
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]) | |
logger.warning( | |
"The following part of your input was truncated because CLIP can only handle sequences up to" | |
f" {self.tokenizer.model_max_length} tokens: {removed_text}" | |
) | |
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] | |
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] | |
# duplicate text embeddings for each generation per prompt, using mps friendly method | |
bs_embed, seq_len, _ = text_embeddings.shape | |
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) | |
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) | |
# 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: | |
uncond_tokens: List[str] | |
if negative_prompt is None: | |
uncond_tokens = [""] | |
elif type(prompt) is not type(negative_prompt): | |
raise TypeError( | |
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" | |
f" {type(prompt)}." | |
) | |
elif isinstance(negative_prompt, str): | |
uncond_tokens = [negative_prompt] | |
elif batch_size != len(negative_prompt): | |
raise ValueError( | |
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" | |
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | |
" the batch size of `prompt`." | |
) | |
else: | |
uncond_tokens = negative_prompt | |
max_length = text_input_ids.shape[-1] | |
uncond_input = self.tokenizer( | |
uncond_tokens, | |
padding="max_length", | |
max_length=max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] | |
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
seq_len = uncond_embeddings.shape[1] | |
uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1) | |
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) | |
# 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]) | |
# get the initial random noise unless the user supplied it | |
# Unlike in other pipelines, latents need to be generated in the target device | |
# for 1-to-1 results reproducibility with the CompVis implementation. | |
# However this currently doesn't work in `mps`. | |
num_channels_latents = self.vae.config.latent_channels | |
latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8) | |
latents_dtype = text_embeddings.dtype | |
if latents is None: | |
if self.device.type == "mps": | |
# randn does not exist on mps | |
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to( | |
self.device | |
) | |
else: | |
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype) | |
else: | |
if latents.shape != latents_shape: | |
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") | |
latents = latents.to(self.device) | |
# overlay the inner image | |
image = overlay_inner_image(image, inner_image) | |
# prepare mask and masked_image | |
mask, masked_image = prepare_mask_and_masked_image(image, mask_image) | |
mask = mask.to(device=self.device, dtype=text_embeddings.dtype) | |
masked_image = masked_image.to(device=self.device, dtype=text_embeddings.dtype) | |
# resize the mask to latents shape as we concatenate the mask to the latents | |
mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8)) | |
# encode the mask image into latents space so we can concatenate it to the latents | |
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) | |
masked_image_latents = 0.18215 * masked_image_latents | |
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method | |
mask = mask.repeat(batch_size * num_images_per_prompt, 1, 1, 1) | |
masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 1, 1, 1) | |
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask | |
masked_image_latents = ( | |
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents | |
) | |
num_channels_mask = mask.shape[1] | |
num_channels_masked_image = masked_image_latents.shape[1] | |
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: | |
raise ValueError( | |
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" | |
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" | |
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" | |
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" | |
" `pipeline.unet` or your `mask_image` or `image` input." | |
) | |
# set timesteps | |
self.scheduler.set_timesteps(num_inference_steps) | |
# Some schedulers like PNDM have timesteps as arrays | |
# It's more optimized to move all timesteps to correct device beforehand | |
timesteps_tensor = self.scheduler.timesteps.to(self.device) | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
# 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 | |
for i, t in enumerate(self.progress_bar(timesteps_tensor)): | |
# expand the latents if we are doing classifier free guidance | |
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
# concat latents, mask, masked_image_latents in the channel dimension | |
latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) | |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
# 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 | |
# call the callback, if provided | |
if callback is not None and i % callback_steps == 0: | |
callback(i, t, latents) | |
latents = 1 / 0.18215 * latents | |
image = self.vae.decode(latents).sample | |
image = (image / 2 + 0.5).clamp(0, 1) | |
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 | |
image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
if self.safety_checker is not None: | |
safety_checker_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_checker_input.pixel_values.to(text_embeddings.dtype) | |
) | |
else: | |
has_nsfw_concept = None | |
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) | |