Filtered Posterior Mean Collections: A Unified Framework for Analytical Models of Diffusion Generalization

arXiv:2605.24192v1 Announce Type: new Abstract: The neural-network denoising functions which form the backbone of image diffusion models are remarkably consistent in their generalization behaviour across a wide variety of network architectures and training procedure hyperparameters. A recent line of research has sought to model the outputs of these networks by aggregating posterior weighted averages of training dataset patches. In this work, we consolidate these approaches into a unified model