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