Trajectory-Consistent Flows: Enabling Fast Sampling for Flow Matching Models

Chenfeng Miao, Qingying Zhu, Minchuan Chen, Shaojun Wang, Jing Xiao

Keywords: flow matching, generative model

TL;DR: A fast sampling generative model.

Abstract:

Diffusion and flow matching models have recently achieved remarkable generative performance, but their reliance on iterative ODE or SDE solvers results in slow and computationally expensive sampling.
In this work, we introduce Trajectory-Consistent Flows (TCF), a framework that unifies efficient training and accelerated sampling through a Taylor-expansion-based formulation. TCF jointly optimizes a flow matching model $p_{\theta}$ and a fast-sampling surrogate $q_{\theta}$ via a unified objective. We construct $q_{\theta}$ using a second-order Taylor expansion as a trajectory-consistent approximation of $p_{\theta}$'s ODE flow, enabling high-fidelity generation with as few as 5 sampling steps. We further extend this idea to a third-order expansion, achieving additional performance gains without increasing computational cost. With further architectural and training enhancements, TCF achieves significantly improved sampling quality while retaining fast and stable training, making it particularly suitable for real-time generative applications.

Primary Area: generative models

Submission Number: 24017