add gaussian diffusion where model predicts both noise and x_start, with a learned weighting between the two (experimental)

denoising_diffusion_pytorch/weighted_objective_gaussian_diffusion.py

@@ -3,7 +3,7 @@ from inspect import isfunction
 from torch import nn, einsum
 from einops import rearrange
 
-from denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiffusion
+from denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiffusion, extract, unnormalize_to_zero_to_one
 
 # helper functions
 

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.15.2',
+  version = '0.15.1',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',