Local Flow Matching Generative Models
Keywords: flow-based generative modeling, stepwise training, model distillation
TL;DR: Stepwise flow matching learns a sequence of sub-models to efficiently match short-time diffusion flow from data to noise, maintaining performance at reduced training cost.
Abstract: Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions, and in particular to generate data from noise in generative modeling. In this paper, we introduce Local Flow Matching ($\texttt{LFM}$), which consecutively learns a sequence of FM sub-models and each matches a diffusion process up to the time of the step size in the data-to-noise direction. In each step, the two distributions to be interpolated by the sub-model are closer to each other than data vs. noise, and this enables the use of smaller models with faster training. The stepwise structure of $\texttt{LFM}$ is natural to be distilled and different distillation techniques can be adopted to speed up generation. Theoretically, we prove a generation guarantee of the proposed flow model in terms of the $\chi^2$-divergence between the generated and true data distributions. In experiments, we demonstrate the improved training efficiency and competitive generative performance of $\texttt{LFM}$ compared to FM on the unconditional generation of tabular data and image datasets, and also on the conditional generation of robotic manipulation policies.
Primary Area: generative models
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Submission Number: 8427
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