How Out-of-Equilibrium Phase Transitions can Seed Pattern Formation in Trained Diffusion Models
The paper explains how pattern formation in trained diffusion models arises from out-of-equilibrium phase transitions driven by instabilities in denoising dynamics. It shows that architectural constraints and data symmetries cause low-frequency modes to become unstable, leading to the emergence of coherent spatial patterns, which is validated through experimental observations.