The probability distribution effectively sampled by a complex Langevin process for theories with
a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class
of generative AI, can learn distributions from data. In this contribution, we explore the ability of
diffusion models to learn the distributions created by a complex Langevin process.