  Diffusion Models (DMs) are powerful generative models that add Gaussian noise
to the data and learn to remove it. We wanted to determine which noise
distribution (Gaussian or non-Gaussian) led to better generated data in DMs.
Since DMs do not work by design with non-Gaussian noise, we built a framework
that allows reversing a diffusion process with non-Gaussian location-scale
noise. We use that framework to show that the Gaussian distribution performs
the best over a wide range of other distributions (Laplace, Uniform, t,
Generalized-Gaussian).
