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Allow batch of different sigmas when noise scaling.
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@ -31,6 +31,7 @@ class EPS:
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return model_input - model_output * sigma
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return model_input - model_output * sigma
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def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
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def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
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if max_denoise:
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if max_denoise:
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noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
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noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
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else:
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else:
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@ -61,9 +62,11 @@ class CONST:
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return model_input - model_output * sigma
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return model_input - model_output * sigma
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def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
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def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
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return sigma * noise + (1.0 - sigma) * latent_image
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return sigma * noise + (1.0 - sigma) * latent_image
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def inverse_noise_scaling(self, sigma, latent):
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def inverse_noise_scaling(self, sigma, latent):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
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return latent / (1.0 - sigma)
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return latent / (1.0 - sigma)
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class ModelSamplingDiscrete(torch.nn.Module):
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class ModelSamplingDiscrete(torch.nn.Module):
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