231 lines
9.9 KiB
Python
231 lines
9.9 KiB
Python
# Copyright 2025 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 PIL.Image
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import torch
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from ....models import UNet2DModel
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from ....schedulers import RePaintScheduler
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from ....utils import PIL_INTERPOLATION, deprecate, logging
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from ....utils.torch_utils import randn_tensor
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from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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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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deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
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deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
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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 = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
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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 = (x - x % 32 for x in (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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r"""
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Pipeline for image inpainting using RePaint.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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Parameters:
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unet ([`UNet2DModel`]):
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A `UNet2DModel` to denoise the encoded image latents.
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scheduler ([`RePaintScheduler`]):
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A `RePaintScheduler` to be used in combination with `unet` to denoise the encoded image.
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"""
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unet: UNet2DModel
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scheduler: RePaintScheduler
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model_cpu_offload_seq = "unet"
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def __init__(self, unet: UNet2DModel, scheduler: RePaintScheduler):
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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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) -> Union[ImagePipelineOutput, Tuple]:
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r"""
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The call function to the pipeline for generation.
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Args:
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image (`torch.Tensor` or `PIL.Image.Image`):
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The original image to inpaint on.
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mask_image (`torch.Tensor` or `PIL.Image.Image`):
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The mask_image where 0.0 define which part of the original image to inpaint.
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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 the added noise in a diffusion step. Its value is between 0.0 and 1.0; 0.0 corresponds to
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DDIM and 1.0 is the DDPM scheduler.
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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 the
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[paper](https://huggingface.co/papers/2201.09865).
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jump_n_sample (`int`, *optional*, defaults to 10):
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The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9
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and 10 in the [paper](https://huggingface.co/papers/2201.09865).
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generator (`torch.Generator`, *optional*):
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
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generation deterministic.
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output_type (`str`, `optional`, defaults to `"pil"`):
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The output format of the generated image. Choose between `PIL.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 [`ImagePipelineOutput`] instead of a plain tuple.
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Example:
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```py
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>>> from io import BytesIO
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>>> import torch
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>>> import PIL
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>>> import requests
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>>> from diffusers import RePaintPipeline, RePaintScheduler
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>>> def download_image(url):
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... response = requests.get(url)
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... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
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>>> img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
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>>> mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
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>>> # Load the original image and the mask as PIL images
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>>> original_image = download_image(img_url).resize((256, 256))
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>>> mask_image = download_image(mask_url).resize((256, 256))
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>>> # Load the RePaint scheduler and pipeline based on a pretrained DDPM model
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>>> scheduler = RePaintScheduler.from_pretrained("google/ddpm-ema-celebahq-256")
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>>> pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler)
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>>> pipe = pipe.to("cuda")
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>>> generator = torch.Generator(device="cuda").manual_seed(0)
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>>> output = pipe(
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... image=original_image,
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... mask_image=mask_image,
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... num_inference_steps=250,
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... eta=0.0,
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... jump_length=10,
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... jump_n_sample=10,
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... generator=generator,
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... )
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>>> inpainted_image = output.images[0]
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```
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Returns:
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[`~pipelines.ImagePipelineOutput`] or `tuple`:
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If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
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returned where the first element is a list with the generated images.
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"""
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original_image = image
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original_image = _preprocess_image(original_image)
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original_image = original_image.to(device=self._execution_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._execution_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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image_shape = original_image.shape
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image = randn_tensor(image_shape, generator=generator, device=self._execution_device, dtype=self.unet.dtype)
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# set step values
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self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self._execution_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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