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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. | |
| from typing import Optional, Tuple, Union | |
| import torch | |
| from ...models import UNet2DModel | |
| from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
| from ...schedulers import ScoreSdeVeScheduler | |
| class ScoreSdeVePipeline(DiffusionPipeline): | |
| r""" | |
| Parameters: | |
| 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.) | |
| unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]): | |
| The [`ScoreSdeVeScheduler`] scheduler to be used in combination with `unet` to denoise the encoded image. | |
| """ | |
| unet: UNet2DModel | |
| scheduler: ScoreSdeVeScheduler | |
| def __init__(self, unet: UNet2DModel, scheduler: DiffusionPipeline): | |
| super().__init__() | |
| self.register_modules(unet=unet, scheduler=scheduler) | |
| def __call__( | |
| self, | |
| batch_size: int = 1, | |
| num_inference_steps: int = 2000, | |
| 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 `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. | |
| """ | |
| img_size = self.unet.config.sample_size | |
| shape = (batch_size, 3, img_size, img_size) | |
| model = self.unet | |
| sample = torch.randn(*shape, generator=generator) * self.scheduler.init_noise_sigma | |
| sample = sample.to(self.device) | |
| self.scheduler.set_timesteps(num_inference_steps) | |
| self.scheduler.set_sigmas(num_inference_steps) | |
| for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): | |
| sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device) | |
| # correction step | |
| for _ in range(self.scheduler.config.correct_steps): | |
| model_output = self.unet(sample, sigma_t).sample | |
| sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample | |
| # prediction step | |
| model_output = model(sample, sigma_t).sample | |
| output = self.scheduler.step_pred(model_output, t, sample, generator=generator) | |
| sample, sample_mean = output.prev_sample, output.prev_sample_mean | |
| sample = sample_mean.clamp(0, 1) | |
| sample = sample.cpu().permute(0, 2, 3, 1).numpy() | |
| if output_type == "pil": | |
| sample = self.numpy_to_pil(sample) | |
| if not return_dict: | |
| return (sample,) | |
| return ImagePipelineOutput(images=sample) | |