Connections between the F\"ollmer process and the denoising diffusion probabilistic model

Abstract:The Föllmer process is a Brownian motion conditioned to have a pre-specified distribution at time 1. This process can be interpreted as an ``augmented'' time-compressed version of the reverse stochastic differential equation (SDE) corresponding to the denoising diffusion probabilistic model (DDPM). While this fact has been indirectly used to analyze DDPM sampling errors via discretization of the reverse SDE, the connection between direct discretization of the Föllmer process and the DDPM sampler has not yet been fully explored. This paper clarifies this point while surveying relevant results from the literature. We show that discretized Föllmer processes give natural hyper-parameter settings of the DDPM sampler while accommodating a broader class of variance schedules than discretized reverse SDEs. Moreover, this allows us to systematically recover state-of-the-art results on DDPM sampling error bounds, along with slight improvements.

35 pages. The title has been changed. The right-continuity issue of the filtration generated by the Föllmer process has been resolved

Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)

arXiv:2605.18040 [stat.ML]

(or arXiv:2605.18040v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2605.18040 Focus to learn more arXiv-issued DOI via DataCite

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