Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine

This paper provides a theoretical explanation for the efficiency of diffusion models in learning the score function for high-dimensional data supported on low-dimensional manifolds. It identifies a "collapse-and-refine" mechanism driven by the geometry of the score function, where the denoising map projects onto the data manifold and refines the intrinsic density.