Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities

Abstract

Under general assumptions on the target distribution $p^{\star }$, we establish a sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores, with optimal dependence on time and dimension. As applications, we obtain Wasserstein discretization bounds for Euler-type samplers in dimension $d$: with $N$ discretization steps, the error achieves the optimal rate $\sqrt{d}/N$ up to logarithmic factors. Moreover, the constants do not deteriorate exponentially with the spatial extent of $p^{\star }$. We also show that the one-sided Lipschitz control yields a globally Lipschitz transport map from the standard Gaussian to $p^{\star }$, which implies Poincar\'e and log-Sobolev inequalities for a broad class of probability measures.