58 lines
2 KiB
Python
58 lines
2 KiB
Python
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# Copyright 2022 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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import torch
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from tqdm.auto import tqdm
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from ...pipeline_utils import DiffusionPipeline
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class PNDMPipeline(DiffusionPipeline):
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def __init__(self, unet, scheduler):
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super().__init__()
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scheduler = scheduler.set_format("pt")
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self.register_modules(unet=unet, scheduler=scheduler)
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@torch.no_grad()
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def __call__(self, batch_size=1, generator=None, torch_device=None, num_inference_steps=50, output_type="pil"):
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# For more information on the sampling method you can take a look at Algorithm 2 of
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# the official paper: https://arxiv.org/pdf/2202.09778.pdf
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if torch_device is None:
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torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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self.unet.to(torch_device)
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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(torch_device)
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self.scheduler.set_timesteps(num_inference_steps)
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for t in tqdm(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)["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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return {"sample": image}
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