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Allow batch of different sigmas when noise scaling.

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comfyanonymous 2025-01-30 06:49:52 -05:00
parent 2f98c24360
commit 8d8dc9a262
1 changed files with 3 additions and 0 deletions
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comfy/model_sampling.py
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@ -31,6 +31,7 @@ class EPS:
return model_input - model_output * sigma return model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
if max_denoise: if max_denoise:
noise = noise * torch.sqrt(1.0 + sigma ** 2.0) noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
else: else:
@ -61,9 +62,11 @@ class CONST:
return model_input - model_output * sigma return model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
return sigma * noise + (1.0 - sigma) * latent_image return sigma * noise + (1.0 - sigma) * latent_image
def inverse_noise_scaling(self, sigma, latent): def inverse_noise_scaling(self, sigma, latent):
sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
return latent / (1.0 - sigma) return latent / (1.0 - sigma)
class ModelSamplingDiscrete(torch.nn.Module): class ModelSamplingDiscrete(torch.nn.Module):