60 lines
2.7 KiB
Python
60 lines
2.7 KiB
Python
# 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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from diffusers import DiffusionPipeline
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from diffusers import UNetGLIDEModel
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import tqdm
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import torch
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class GLIDE(DiffusionPipeline):
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def __init__(self, unet: UNetGLIDEModel, noise_scheduler):
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super().__init__()
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self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
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def __call__(self, generator=None, torch_device=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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# 1. Sample gaussian noise
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image = self.noise_scheduler.sample_noise((1, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator)
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for t in tqdm.tqdm(reversed(range(len(self.noise_scheduler))), total=len(self.noise_scheduler)):
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# i) define coefficients for time step t
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clip_image_coeff = 1 / torch.sqrt(self.noise_scheduler.get_alpha_prod(t))
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clip_noise_coeff = torch.sqrt(1 / self.noise_scheduler.get_alpha_prod(t) - 1)
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image_coeff = (1 - self.noise_scheduler.get_alpha_prod(t - 1)) * torch.sqrt(self.noise_scheduler.get_alpha(t)) / (1 - self.noise_scheduler.get_alpha_prod(t))
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clip_coeff = torch.sqrt(self.noise_scheduler.get_alpha_prod(t - 1)) * self.noise_scheduler.get_beta(t) / (1 - self.noise_scheduler.get_alpha_prod(t))
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# ii) predict noise residual
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with torch.no_grad():
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noise_residual = self.unet(image, t)
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# iii) compute predicted image from residual
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# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
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pred_mean = clip_image_coeff * image - clip_noise_coeff * noise_residual
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pred_mean = torch.clamp(pred_mean, -1, 1)
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prev_image = clip_coeff * pred_mean + image_coeff * image
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# iv) sample variance
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prev_variance = self.noise_scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
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# v) sample x_{t-1} ~ N(prev_image, prev_variance)
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sampled_prev_image = prev_image + prev_variance
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image = sampled_prev_image
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return image
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