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no clamping when training from sigmas drawn from log normal distribution, clamp final images being sampled

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Phil Wang
2022-06-28 16:00:16 -07:00
parent f4b1d7a67c
commit a8403b83fe
1 changed files with 4 additions and 5 deletions
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denoising_diffusion_pytorch/elucidated_diffusion.py
+4 -5
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@@ -94,14 +94,12 @@ class ElucidatedDiffusion(nn.Module):
return 1 * (sigma ** 2 + self.sigma_data ** 2) ** -0.5
def c_noise(self, sigma):
""" apparently empirically derived """
return log(sigma) * 0.25
# noise distribution
def noise_distribution(self, batch_size):
sigmas = (self.P_mean + self.P_std * torch.randn((batch_size,), device = self.device)).exp()
return sigmas.clamp(min = self.sigma_min, max =self.sigma_max)
return (self.P_mean + self.P_std * torch.randn((batch_size,), device = self.device)).exp()
def loss_weight(self, sigma):
return (sigma ** 2 + self.sigma_data ** 2) * (sigma * self.sigma_data) ** -2
@@ -184,6 +182,7 @@ class ElucidatedDiffusion(nn.Module):
images = images_next
images = images.clamp(-1., 1.)
return unnormalize_to_zero_to_one(images)
# training
@@ -203,9 +202,9 @@ class ElucidatedDiffusion(nn.Module):
noised_images = images + padded_sigmas * noise # alphas are 1. in the paper
model_out = self.preconditioned_network_forward(noised_images, sigmas)
denoised = self.preconditioned_network_forward(noised_images, sigmas)
losses = F.mse_loss(model_out, images, reduction = 'none')
losses = F.mse_loss(denoised, images, reduction = 'none')
losses = reduce(losses, 'b ... -> b', 'mean')
losses = losses * self.loss_weight(sigmas)