167 lines
6.7 KiB
Python
167 lines
6.7 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 DDIMScheduler
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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 DDIMPipeline(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: DDIMScheduler):
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super().__init__()
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# make sure scheduler can always be converted to DDIM
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scheduler = DDIMScheduler.from_config(scheduler.config)
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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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eta: float = 0.0,
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num_inference_steps: int = 50,
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use_clipped_model_output: Optional[bool] = None,
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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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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
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applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. A value of `0`
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corresponds to DDIM and `1` corresponds to DDPM.
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num_inference_steps (`int`, *optional*, defaults to 50):
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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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use_clipped_model_output (`bool`, *optional*, defaults to `None`):
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If `True` or `False`, see documentation for [`DDIMScheduler.step`]. If `None`, nothing is passed
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downstream to the scheduler (use `None` for schedulers which don't support this argument).
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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 DDIMPipeline
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>>> import PIL.Image
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>>> import numpy as np
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>>> # load model and scheduler
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>>> pipe = DDIMPipeline.from_pretrained("fusing/ddim-lsun-bedroom")
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>>> # run pipeline in inference (sample random noise and denoise)
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>>> image = pipe(eta=0.0, num_inference_steps=50)
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>>> # process image to PIL
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>>> image_processed = image.cpu().permute(0, 2, 3, 1)
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>>> image_processed = (image_processed + 1.0) * 127.5
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>>> image_processed = image_processed.numpy().astype(np.uint8)
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>>> image_pil = PIL.Image.fromarray(image_processed[0])
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>>> # save image
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>>> image_pil.save("test.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 isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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image = randn_tensor(image_shape, generator=generator, device=self._execution_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. predict previous mean of image x_t-1 and add variance depending on eta
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# eta corresponds to η in paper and should be between [0, 1]
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# do x_t -> x_t-1
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image = self.scheduler.step(
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model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
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).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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