make sure to clip when sampling from gaussian diffusion with learned variance

denoising_diffusion_pytorch/learned_gaussian_diffusion.py

@@ -107,6 +107,10 @@ class LearnedGaussianDiffusion(GaussianDiffusion):
         model_variance = model_log_variance.exp()
 
         x_start = self.predict_start_from_noise(x, t, pred_noise)
+
+        if clip_denoised:
+            x_start.clamp_(-1., 1.)
+
         model_mean, _, _ = self.q_posterior(x_start, x, t)
 
         return model_mean, model_variance, model_log_variance

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.14.0',
+  version = '0.14.1',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',