Simulation-free and finite-time diffusion model

Abstract:The performance of generative diffusion models is determined by the choice of the reference diffusion process connecting the empirical and prior distributions. Conventional approaches typically trade off simulation-free training against finite-time generation. We propose a framework for designing the reference process that achieves both simultaneously. The key idea is to prescribe tractable time-dependent conditional distributions and then construct the reference process realizing them as its marginals. This framework reveals that score matching is not fundamental to diffusion-model training but instead emerges naturally through reversal of the reference process. We further show that conditional flow matching arises as the small-noise limit of the proposed framework.

Machine Learning (cs.LG)

arXiv:2608.03117 [cs.LG]

(or arXiv:2608.03117v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.03117 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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