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make sure predicted mean is actually detached for all of the kl loss calculations

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Phil Wang
2022-05-12 11:12:34 -07:00
parent e147839d74
commit e0f26677d6
2 changed files with 5 additions and 3 deletions
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denoising_diffusion_pytorch/learned_gaussian_diffusion.py
+4 -2
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@@ -130,10 +130,12 @@ class LearnedGaussianDiffusion(GaussianDiffusion):
# kl loss with detached model predicted mean, for stability reasons as in paper
kl = normal_kl(true_mean, true_log_variance_clipped, model_mean.detach(), model_log_variance)
detached_model_mean = model_mean.detach()
kl = normal_kl(true_mean, true_log_variance_clipped, detached_model_mean, model_log_variance)
kl = meanflat(kl) * NAT
decoder_nll = -discretized_gaussian_log_likelihood(x_start, means = model_mean, log_scales = 0.5 * model_log_variance)
decoder_nll = -discretized_gaussian_log_likelihood(x_start, means = detached_model_mean, log_scales = 0.5 * model_log_variance)
decoder_nll = meanflat(decoder_nll) * NAT
# at the first timestep return the decoder NLL, otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
setup.py
+1 -1
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@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
setup(
name = 'denoising-diffusion-pytorch',
packages = find_packages(),
version = '0.14.1',
version = '0.14.2',
license='MIT',
description = 'Denoising Diffusion Probabilistic Models - Pytorch',
author = 'Phil Wang',