Skip to yearly menu bar Skip to main content


The probability flow ODE is provably fast

Sitan Chen · Sinho Chewi · Holden Lee · Yuanzhi Li · Jianfeng Lu · Adil Salim

Great Hall & Hall B1+B2 (level 1) #1823
[ ]
[ Paper [ Slides [ Poster [ OpenReview
Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST

Abstract: We provide the first polynomial-time convergence guarantees for the probabilistic flow ODE implementation (together with a corrector step) of score-based generative modeling. Our analysis is carried out in the wake of recent results obtaining such guarantees for the SDE-based implementation (i.e., denoising diffusion probabilistic modeling or DDPM), but requires the development of novel techniques for studying deterministic dynamics without contractivity. Through the use of a specially chosen corrector step based on the underdamped Langevin diffusion, we obtain better dimension dependence than prior works on DDPM ($O(\sqrt d)$ vs. $O(d)$, assuming smoothness of the data distribution), highlighting potential advantages of the ODE framework.

Chat is not available.