  Score-based generative models have emerged as alternatives to generative
adversarial networks (GANs) and normalizing flows for tasks involving learning
and sampling from complex image distributions. In this work we investigate the
ability of these models to generate fields in two astrophysical contexts: dark
matter mass density fields from cosmological simulations and images of
interstellar dust. We examine the fidelity of the sampled cosmological fields
relative to the true fields using three different metrics, and identify
potential issues to address. We demonstrate a proof-of-concept application of
the model trained on dust in denoising dust images. To our knowledge, this is
the first application of this class of models to the interstellar medium.
