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35 lines
1.5 KiB
35 lines
1.5 KiB
from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation |
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from ..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.to(x.device)) * 1. / self.data_std.to(x.device) |
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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.to(x.device)) + self.data_mean.to(x.device) |
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return x |
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def forward(self, x, noise_level=None, seed=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, seed=seed) |
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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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