  Generative models based on denoising diffusion techniques have led to an
unprecedented increase in the quality and diversity of imagery that is now
possible to create with neural generative models. However, most contemporary
state-of-the-art methods are derived from a standard isotropic Gaussian
formulation. In this work we examine the situation where non-isotropic Gaussian
distributions are used. We present the key mathematical derivations for
creating denoising diffusion models using an underlying non-isotropic Gaussian
noise model. We also provide initial experiments with the CIFAR-10 dataset to
help verify empirically that this more general modeling approach can also yield
high-quality samples.
