  Generative diffusion models have emerged as leading models in speech and
image generation. However, in order to perform well with a small number of
denoising steps, a costly tuning of the set of noise parameters is needed. In
this work, we present a simple and versatile learning scheme that can
step-by-step adjust those noise parameters, for any given number of steps,
while the previous work needs to retune for each number separately.
Furthermore, without modifying the weights of the diffusion model, we are able
to significantly improve the synthesis results, for a small number of steps.
Our approach comes at a negligible computation cost.
