102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
# Copyright 2023 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 Optional, Tuple, Union
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import torch
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from diffusers import DiffusionPipeline, ImagePipelineOutput
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class CustomLocalPipeline(DiffusionPipeline):
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r"""
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Parameters:
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unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
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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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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[torch.Generator] = None,
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num_inference_steps: int = 50,
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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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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 generation
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deterministic.
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eta (`float`, *optional*, defaults to 0.0):
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The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of 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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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.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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Returns:
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[`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
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`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
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generated images.
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"""
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# Sample gaussian noise to begin loop
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image = torch.randn(
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(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
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generator=generator,
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)
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image = image.to(self.device)
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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(model_output, t, image).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,), "This is a local test"
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return ImagePipelineOutput(images=image), "This is a local test"
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