# Copyright 2022 ETH Zurich Computer Vision Lab and 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. from typing import List, Optional, Tuple, Union import numpy as np import torch import PIL from ...models import UNet2DModel from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput from ...schedulers import RePaintScheduler from ...utils import PIL_INTERPOLATION, deprecate, logging logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image def _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]): if isinstance(mask, torch.Tensor): return mask elif isinstance(mask, PIL.Image.Image): mask = [mask] if isinstance(mask[0], PIL.Image.Image): w, h = mask[0].size w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask] mask = np.concatenate(mask, axis=0) mask = mask.astype(np.float32) / 255.0 mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 mask = torch.from_numpy(mask) elif isinstance(mask[0], torch.Tensor): mask = torch.cat(mask, dim=0) return mask class RePaintPipeline(DiffusionPipeline): unet: UNet2DModel scheduler: RePaintScheduler def __init__(self, unet, scheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, image: Union[torch.Tensor, PIL.Image.Image], mask_image: Union[torch.Tensor, PIL.Image.Image], num_inference_steps: int = 250, eta: float = 0.0, jump_length: int = 10, jump_n_sample: int = 10, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, **kwargs, ) -> Union[ImagePipelineOutput, Tuple]: r""" Args: image (`torch.FloatTensor` or `PIL.Image.Image`): The original image to inpaint on. mask_image (`torch.FloatTensor` or `PIL.Image.Image`): The mask_image where 0.0 values define which part of the original image to inpaint (change). num_inference_steps (`int`, *optional*, defaults to 1000): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. eta (`float`): The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM and 1.0 is DDPM scheduler respectively. jump_length (`int`, *optional*, defaults to 10): The number of steps taken forward in time before going backward in time for a single jump ("j" in RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf. jump_n_sample (`int`, *optional*, defaults to 10): The number of times we will make forward time jump for a given chosen time sample. Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](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 `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ message = "Please use `image` instead of `original_image`." original_image = deprecate("original_image", "0.15.0", message, take_from=kwargs) original_image = original_image or image original_image = _preprocess_image(original_image) original_image = original_image.to(device=self.device, dtype=self.unet.dtype) mask_image = _preprocess_mask(mask_image) mask_image = mask_image.to(device=self.device, dtype=self.unet.dtype) batch_size = original_image.shape[0] # sample gaussian noise to begin the loop if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) rand_device = "cpu" if self.device.type == "mps" else self.device image_shape = original_image.shape if isinstance(generator, list): shape = (1,) + image_shape[1:] image = [ torch.randn(shape, generator=generator[i], device=rand_device, dtype=self.unet.dtype) for i in range(batch_size) ] image = torch.cat(image, dim=0).to(self.device) else: image = torch.randn(image_shape, generator=generator, device=rand_device, dtype=self.unet.dtype) image = image.to(self.device) # set step values self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self.device) self.scheduler.eta = eta t_last = self.scheduler.timesteps[0] + 1 generator = generator[0] if isinstance(generator, list) else generator for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): if t < t_last: # predict the noise residual model_output = self.unet(image, t).sample # compute previous image: x_t -> x_t-1 image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample else: # compute the reverse: x_t-1 -> x_t image = self.scheduler.undo_step(image, t_last, generator) t_last = t image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image)