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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 KarrasVeScheduler | |
| class KarrasVePipeline(DiffusionPipeline): | |
| r""" | |
| Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and | |
| the VE column of Table 1 from [1] for reference. | |
| [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." | |
| https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic | |
| differential equations." https://arxiv.org/abs/2011.13456 | |
| Parameters: | |
| unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. | |
| scheduler ([`KarrasVeScheduler`]): | |
| Scheduler for the diffusion process to be used in combination with `unet` to denoise the encoded image. | |
| """ | |
| # add type hints for linting | |
| unet: UNet2DModel | |
| scheduler: KarrasVeScheduler | |
| def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler): | |
| super().__init__() | |
| self.register_modules(unet=unet, scheduler=scheduler) | |
| def __call__( | |
| self, | |
| batch_size: int = 1, | |
| num_inference_steps: int = 50, | |
| generator: Optional[torch.Generator] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| **kwargs, | |
| ) -> Union[Tuple, ImagePipelineOutput]: | |
| 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. | |
| num_inference_steps (`int`, *optional*, defaults to 50): | |
| 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 [`~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 x_0 ~ N(0, sigma_0^2 * I) | |
| sample = torch.randn(*shape) * self.scheduler.init_noise_sigma | |
| sample = sample.to(self.device) | |
| self.scheduler.set_timesteps(num_inference_steps) | |
| for t in self.progress_bar(self.scheduler.timesteps): | |
| # here sigma_t == t_i from the paper | |
| sigma = self.scheduler.schedule[t] | |
| sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0 | |
| # 1. Select temporarily increased noise level sigma_hat | |
| # 2. Add new noise to move from sample_i to sample_hat | |
| sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator) | |
| # 3. Predict the noise residual given the noise magnitude `sigma_hat` | |
| # The model inputs and output are adjusted by following eq. (213) in [1]. | |
| model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample | |
| # 4. Evaluate dx/dt at sigma_hat | |
| # 5. Take Euler step from sigma to sigma_prev | |
| step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat) | |
| if sigma_prev != 0: | |
| # 6. Apply 2nd order correction | |
| # The model inputs and output are adjusted by following eq. (213) in [1]. | |
| model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample | |
| step_output = self.scheduler.step_correct( | |
| model_output, | |
| sigma_hat, | |
| sigma_prev, | |
| sample_hat, | |
| step_output.prev_sample, | |
| step_output["derivative"], | |
| ) | |
| sample = step_output.prev_sample | |
| sample = (sample / 2 + 0.5).clamp(0, 1) | |
| image = sample.cpu().permute(0, 2, 3, 1).numpy() | |
| if output_type == "pil": | |
| image = self.numpy_to_pil(sample) | |
| if not return_dict: | |
| return (image,) | |
| return ImagePipelineOutput(images=image) | |