  Generative diffusion processes are an emerging and effective tool for image
and speech generation. In the existing methods, the underline noise
distribution of the diffusion process is Gaussian noise. However, fitting
distributions with more degrees of freedom, could help the performance of such
generative models. In this work, we investigate other types of noise
distribution for the diffusion process. Specifically, we show that noise from
Gamma distribution provides improved results for image and speech generation.
Moreover, we show that using a mixture of Gaussian noise variables in the
diffusion process improves the performance over a diffusion process that is
based on a single distribution. Our approach preserves the ability to
efficiently sample state in the training diffusion process while using Gamma
noise and a mixture of noise.
