Provable maximum entropy manifold exploration via diffusion models

We provide a principled algorithm for maximum entropy constrained exploration on the distributional manifold induced by a pre-trained diffusion model, with provable guarantees. This enables controllable exploration of the data manifold for applications such as data augmentation, novelty generation, and scientific discovery.