assume when sampling all batch samples are at the same time, and do not noise for the last time step

denoising_diffusion_pytorch/continuous_time_gaussian_diffusion.py

@@ -131,6 +131,10 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
         batch, *_, device = *x.shape, x.device
 
         model_mean, model_variance = self.p_mean_variance(x = x, time = time, time_next = time_next)
+
+        if time_next == 0:
+            return model_mean
+
         noise = torch.randn_like(x)
         return model_mean + sqrt(model_variance) * noise
 

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.16.3',
+  version = '0.16.4',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',