| from typing import Optional, Union, List, Tuple |
|
|
| import torch |
| from diffusers.utils.torch_utils import randn_tensor |
| from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput |
|
|
| class ScoreSdeVePipelineConditioned(DiffusionPipeline): |
| r""" |
| Pipeline for unconditional image generation. |
| This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods |
| implemented for all pipelines (downloading, saving, running on a particular device, etc.). |
| Parameters: |
| unet ([`UNet2DModel`]): |
| A `UNet2DModel` to denoise the encoded image. |
| scheduler ([`ScoreSdeVeScheduler`]): |
| A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image. |
| """ |
|
|
| def __init__(self, unet, scheduler): |
| super().__init__() |
| self.register_modules(unet=unet, scheduler=scheduler) |
|
|
| @torch.no_grad() |
| def __call__( |
| self, |
| batch_size: int = 1, |
| num_inference_steps: int = 2000, |
| class_labels: Optional[torch.Tensor] = None, |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
| output_type: Optional[str] = "pil", |
| return_dict: bool = True, |
| **kwargs, |
| ) -> Union[ImagePipelineOutput, Tuple]: |
| r""" |
| The call function to the pipeline for generation. |
| 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 generated image. Choose between `PIL.Image` or `np.array`. |
| return_dict (`bool`, *optional*, defaults to `True`): |
| Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple. |
| Returns: |
| [`~pipelines.ImagePipelineOutput`] or `tuple`: |
| If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is |
| returned where the first element is a list with the generated images. |
| """ |
| img_size = self.unet.config.sample_size |
| shape = (batch_size, 1, img_size, img_size) |
|
|
| model = self.unet |
|
|
| sample = randn_tensor(shape, generator=generator, device=self.device) * 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) |
|
|
| |
| for _ in range(self.scheduler.config.correct_steps): |
| model_output = self.unet(sample, sigma_t, class_labels).sample |
| sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample |
|
|
| |
| model_output = model(sample, sigma_t, class_labels).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) |
