141 lines
5.2 KiB
Python
141 lines
5.2 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Optional, Tuple, Union
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import torch
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from ...models import UNet2DModel
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from ...schedulers import DDPMScheduler
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from ...utils import is_torch_xla_available
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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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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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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: UNet2DModel, scheduler: DDPMScheduler):
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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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# Sample gaussian noise to begin loop
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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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# randn does not work reproducibly on 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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# set step values
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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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# 1. predict noise model_output
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model_output = self.unet(image, t).sample
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# 2. compute previous image: x_t -> x_t-1
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image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
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if XLA_AVAILABLE:
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xm.mark_step()
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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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