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from typing import List, Optional, Tuple, Union |
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import torch |
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from ...utils.torch_utils import randn_tensor |
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from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput |
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class DDPMPipeline(DiffusionPipeline): |
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r""" |
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Pipeline for 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 latents. |
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scheduler ([`SchedulerMixin`]): |
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A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of |
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[`DDPMScheduler`], or [`DDIMScheduler`]. |
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""" |
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model_cpu_offload_seq = "unet" |
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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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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
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num_inference_steps: int = 1000, |
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output_type: Optional[str] = "pil", |
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return_dict: bool = True, |
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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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num_inference_steps (`int`, *optional*, defaults to 1000): |
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
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expense of slower inference. |
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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 [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. |
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Example: |
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```py |
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>>> from diffusers import DDPMPipeline |
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>>> # load model and scheduler |
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>>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256") |
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>>> # run pipeline in inference (sample random noise and denoise) |
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>>> image = pipe().images[0] |
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>>> # save image |
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>>> image.save("ddpm_generated_image.png") |
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``` |
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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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if isinstance(self.unet.config.sample_size, int): |
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image_shape = ( |
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batch_size, |
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self.unet.config.in_channels, |
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self.unet.config.sample_size, |
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self.unet.config.sample_size, |
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) |
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else: |
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image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size) |
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if self.device.type == "mps": |
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image = randn_tensor(image_shape, generator=generator, dtype=self.unet.dtype) |
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image = image.to(self.device) |
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else: |
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image = randn_tensor(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype) |
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self.scheduler.set_timesteps(num_inference_steps) |
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for t in self.progress_bar(self.scheduler.timesteps): |
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model_output = self.unet(image, t).sample |
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image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample |
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image = (image / 2 + 0.5).clamp(0, 1) |
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image = image.cpu().permute(0, 2, 3, 1).numpy() |
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if output_type == "pil": |
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image = self.numpy_to_pil(image) |
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if not return_dict: |
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return (image,) |
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return ImagePipelineOutput(images=image) |
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