parent
72139b000c
commit
95bcea72b1
@ -76,13 +76,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
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def timestep(self, sigma):
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log_sigma = sigma.log()
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dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
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low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
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high_idx = low_idx + 1
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low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx]
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w = (low - log_sigma) / (low - high)
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w = w.clamp(0, 1)
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t = (1 - w) * low_idx + w * high_idx
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return t.view(sigma.shape)
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return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)
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def sigma(self, timestep):
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t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1))
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@ -917,13 +917,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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alphas_cumprod_backup = p.sd_model.alphas_cumprod
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for modifier in alphas_cumprod_modifiers:
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p.sd_model.alphas_cumprod = modifier(p.sd_model.alphas_cumprod)
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p.sd_model.forge_objects.unet.model.model_sampling.set_sigmas(((1 - p.sd_model.alphas_cumprod) / p.sd_model.alphas_cumprod) ** 0.5)
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samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
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if alphas_cumprod_backup is not None:
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p.sd_model.alphas_cumprod = alphas_cumprod_backup
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p.sd_model.forge_objects.unet.model.model_sampling.set_sigmas(((1 - p.sd_model.alphas_cumprod) / p.sd_model.alphas_cumprod) ** 0.5)
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if p.scripts is not None:
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ps = scripts.PostSampleArgs(samples_ddim)
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