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| # Copyright 2023 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 torch | |
| from ...utils import randn_tensor | |
| from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
| class DDPMPipeline(DiffusionPipeline): | |
| r""" | |
| This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the | |
| library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | |
| Parameters: | |
| unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. | |
| scheduler ([`SchedulerMixin`]): | |
| A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of | |
| [`DDPMScheduler`], or [`DDIMScheduler`]. | |
| """ | |
| def __init__(self, unet, scheduler): | |
| super().__init__() | |
| self.register_modules(unet=unet, scheduler=scheduler) | |
| def __call__( | |
| self, | |
| batch_size: int = 1, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| num_inference_steps: int = 1000, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| ) -> Union[ImagePipelineOutput, Tuple]: | |
| r""" | |
| Args: | |
| batch_size (`int`, *optional*, defaults to 1): | |
| The number of images to generate. | |
| 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. | |
| 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. | |
| 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 [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. | |
| Returns: | |
| [`~pipelines.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. | |
| """ | |
| # Sample gaussian noise to begin loop | |
| if isinstance(self.unet.config.sample_size, int): | |
| image_shape = ( | |
| batch_size, | |
| self.unet.config.in_channels, | |
| self.unet.config.sample_size, | |
| self.unet.config.sample_size, | |
| ) | |
| else: | |
| image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size) | |
| if self.device.type == "mps": | |
| # randn does not work reproducibly on mps | |
| image = randn_tensor(image_shape, generator=generator) | |
| image = image.to(self.device) | |
| else: | |
| image = randn_tensor(image_shape, generator=generator, device=self.device) | |
| # set step values | |
| self.scheduler.set_timesteps(num_inference_steps) | |
| for t in self.progress_bar(self.scheduler.timesteps): | |
| # 1. predict noise model_output | |
| model_output = self.unet(image, t).sample | |
| # 2. compute previous image: x_t -> x_t-1 | |
| image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample | |
| 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) | |