final cleanup

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -388,12 +388,6 @@ class GaussianDiffusion(nn.Module):
         register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
         register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
 
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
     def predict_start_from_noise(self, x_t, t, noise):
         return (
             extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -

denoising_diffusion_pytorch/learned_gaussian_diffusion.py

@@ -76,25 +76,6 @@ class LearnedGaussianDiffusion(GaussianDiffusion):
         assert denoise_fn.out_dim == (denoise_fn.channels * 2), 'dimension out of unet must be twice the number of channels for learned variance - you can also set the `learned_variance` keyword argument on the Unet to be `True`'
         self.vb_loss_weight = vb_loss_weight
 
-    def q_posterior_mean_variance(self, x_start, x_t, t):
-        """
-        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
-        """
-        posterior_mean = (
-            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-            extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def predict_xstart_from_xprev(self, x_t, t, xprev):
-        # (xprev - coef2*x_t) / coef1
-        return (
-            extract(1. / self.posterior_mean_coef1, t, x_t.shape) * xprev -
-            extract(self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape) * x_t
-        )
-
     def p_mean_variance(self, *, x, t, clip_denoised, model_output = None):
         model_output = default(model_output, lambda: self.denoise_fn(x, t))
         pred_noise, var_interp_frac_unnormalized = model_output.chunk(2, dim = 1)
@@ -125,15 +106,17 @@ class LearnedGaussianDiffusion(GaussianDiffusion):
 
         # calculating kl loss for learned variance (interpolation)
 
-        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start = x_start, x_t = x_t, t = t)
+        true_mean, _, true_log_variance_clipped = self.q_posterior(x_start = x_start, x_t = x_t, t = t)
         model_mean, _, model_log_variance = self.p_mean_variance(x = x_t, t = t, clip_denoised = clip_denoised, model_output = model_output)
 
         # 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

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.14.1',
+  version = '0.14.3',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',