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, ) @torch.no_grad() 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_latents = self.vae.encode(init_image).sample() 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) 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}