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from typing import Optional, Union, List, Tuple |
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import torch |
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from diffusers.utils.torch_utils import randn_tensor |
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput |
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class ScoreSdeVePipelineConditioned(DiffusionPipeline): |
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r""" |
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Pipeline for unconditional image generation. |
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods |
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implemented for all pipelines (downloading, saving, running on a particular device, etc.). |
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Parameters: |
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unet ([`UNet2DModel`]): |
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A `UNet2DModel` to denoise the encoded image. |
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scheduler ([`ScoreSdeVeScheduler`]): |
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A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image. |
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""" |
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def __init__(self, unet, scheduler): |
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super().__init__() |
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self.register_modules(unet=unet, scheduler=scheduler) |
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@torch.no_grad() |
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def __call__( |
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self, |
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batch_size: int = 1, |
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num_inference_steps: int = 2000, |
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class_labels: Optional[torch.Tensor] = None, |
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
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output_type: Optional[str] = "pil", |
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return_dict: bool = True, |
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**kwargs, |
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) -> Union[ImagePipelineOutput, Tuple]: |
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r""" |
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The call function to the pipeline for generation. |
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Args: |
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batch_size (`int`, *optional*, defaults to 1): |
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The number of images to generate. |
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generator (`torch.Generator`, `optional`): |
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make |
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generation deterministic. |
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output_type (`str`, `optional`, defaults to `"pil"`): |
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The output format of the generated image. Choose between `PIL.Image` or `np.array`. |
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return_dict (`bool`, *optional*, defaults to `True`): |
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Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple. |
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Returns: |
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[`~pipelines.ImagePipelineOutput`] or `tuple`: |
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If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is |
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returned where the first element is a list with the generated images. |
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""" |
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img_size = self.unet.config.sample_size |
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shape = (batch_size, 3, img_size, img_size) |
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model = self.unet |
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sample = randn_tensor(shape, generator=generator, device=self.device) * self.scheduler.init_noise_sigma |
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sample = sample.to(self.device) |
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self.scheduler.set_timesteps(num_inference_steps) |
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self.scheduler.set_sigmas(num_inference_steps) |
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for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): |
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sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device) |
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for _ in range(self.scheduler.config.correct_steps): |
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model_output = self.unet(sample, sigma_t, class_labels).sample |
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sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample |
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model_output = model(sample, sigma_t, class_labels).sample |
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output = self.scheduler.step_pred(model_output, t, sample, generator=generator) |
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sample, sample_mean = output.prev_sample, output.prev_sample_mean |
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sample = sample_mean.clamp(0, 1) |
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sample = sample.cpu().permute(0, 2, 3, 1).numpy() |
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if output_type == "pil": |
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sample = self.numpy_to_pil(sample) |
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if not return_dict: |
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return (sample,) |
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return ImagePipelineOutput(images=sample) |
