Abstract: In this work, we investigate score-based models for learning the distribution of multiple-input multiple-output (MIMO) wireless channels in structured stochastic environments, using either clean or corrupted (noisy) data for training. We find that score-based models are capable of generating high-quality synthetic channels, and have robust downstream estimation performance, sometimes surpassing strong baselines by up to $10$ dB in estimation error, when the inverse problem is ill-posed. Our preliminary results on training with corrupted data show improved performance against simple baselines, and introduce a very promising future research direction. Code will be made publicly available upon paper acceptance.