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| # Copyright 2022 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. | |
| import warnings | |
| from typing import Optional, Tuple, Union | |
| import torch | |
| 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__() | |
| scheduler = scheduler.set_format("pt") | |
| self.register_modules(unet=unet, scheduler=scheduler) | |
| def __call__( | |
| self, | |
| batch_size: int = 1, | |
| generator: Optional[torch.Generator] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| **kwargs, | |
| ) -> Union[ImagePipelineOutput, Tuple]: | |
| r""" | |
| Args: | |
| batch_size (`int`, *optional*, defaults to 1): | |
| The number of images to generate. | |
| generator (`torch.Generator`, *optional*): | |
| A [torch generator](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 `nd.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. | |
| """ | |
| if "torch_device" in kwargs: | |
| device = kwargs.pop("torch_device") | |
| warnings.warn( | |
| "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." | |
| " Consider using `pipe.to(torch_device)` instead." | |
| ) | |
| # Set device as before (to be removed in 0.3.0) | |
| if device is None: | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| self.to(device) | |
| # Sample gaussian noise to begin loop | |
| image = torch.randn( | |
| (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size), | |
| generator=generator, | |
| ) | |
| image = image.to(self.device) | |
| # set step values | |
| self.scheduler.set_timesteps(1000) | |
| 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 -> t_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) | |