  Score-based generative models provide state-of-the-art quality for image and
audio synthesis. Sampling from these models is performed iteratively, typically
employing a discretized series of noise levels and a predefined scheme. In this
note, we first overview three common sampling schemes for models trained with
denoising score matching. Next, we focus on one of them, consistent annealed
sampling, and study its hyper-parameter boundaries. We then highlight a
possible formulation of such hyper-parameter that explicitly considers those
boundaries and facilitates tuning when using few or a variable number of steps.
Finally, we highlight some connections of the formulation with other sampling
schemes.
