36 lines
1.5 KiB
Python
36 lines
1.5 KiB
Python
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from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
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from ldm.modules.diffusionmodules.openaimodel import Timestep
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import torch
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class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
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def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
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super().__init__(*args, **kwargs)
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if clip_stats_path is None:
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clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
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else:
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clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
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self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
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self.register_buffer("data_std", clip_std[None, :], persistent=False)
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self.time_embed = Timestep(timestep_dim)
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def scale(self, x):
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# re-normalize to centered mean and unit variance
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x = (x - self.data_mean) * 1. / self.data_std
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return x
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def unscale(self, x):
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# back to original data stats
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x = (x * self.data_std) + self.data_mean
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return x
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def forward(self, x, noise_level=None):
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if noise_level is None:
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noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
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else:
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assert isinstance(noise_level, torch.Tensor)
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x = self.scale(x)
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z = self.q_sample(x, noise_level)
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z = self.unscale(z)
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noise_level = self.time_embed(noise_level)
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return z, noise_level
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