For theories with a sign problem, complex Langevin dynamics effectively samples from a real-valued probability distribution that is a priori unknown and notoriously hard to predict. In generative AI, diffusion models have proven capable of learning distributions from data. We explore their ability to capture the distributions sampled by a complex Langevin process, comparing score-based and energy-based diffusion models, and outline potential applications.