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| import inspect | |
| from typing import List, Optional, Union | |
| import numpy as np | |
| import torch | |
| import PIL | |
| from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, PNDMScheduler, UNet2DConditionModel | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker | |
| from tqdm.auto import tqdm | |
| from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer | |
| 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 StableDiffusionInpaintingPipeline(DiffusionPipeline): | |
| 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") | |
| self.register_modules( | |
| vae=vae, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| unet=unet, | |
| scheduler=scheduler, | |
| safety_checker=safety_checker, | |
| feature_extractor=feature_extractor, | |
| ) | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]], | |
| init_image: torch.FloatTensor, | |
| mask_image: torch.FloatTensor, | |
| 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", | |
| ): | |
| 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).latent_dist | |
| init_latents = init_latent_dist.sample(generator=generator) | |
| init_latents = 0.18215 * init_latents | |
| # prepare init_latents noise to latents | |
| 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(f"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 (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 | |
| 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) | |
| return {"sample": image, "nsfw_content_detected": has_nsfw_concept} |