[2603.19360] Warm-Start Flow Matching for Guaranteed Fast Text/Image Generation

Computer Science > Machine Learning arXiv:2603.19360 (cs) [Submitted on 19 Mar 2026] Title:Warm-Start Flow Matching for Guaranteed Fast Text/Image Generation Authors:Minyoung Kim View a PDF of the paper titled Warm-Start Flow Matching for Guaranteed Fast Text/Image Generation, by Minyoung Kim View PDF HTML (experimental) Abstract:Current auto-regressive (AR) LLMs, diffusion-based text/image generative models, and recent flow matching (FM) algorithms are capable of generating premium quality text/image samples. However, the inference or sample generation in these models is often very time-consuming and computationally demanding, mainly due to large numbers of function evaluations corresponding to the lengths of tokens or the numbers of diffusion steps. This also necessitates heavy GPU resources, time, and electricity. In this work we propose a novel solution to reduce the sample generation time of flow matching algorithms by a guaranteed speed-up factor, without sacrificing the quality of the generated samples. Our key idea is to utilize computationally lightweight generative models whose generation time is negligible compared to that of the target AR/FM models. The draft samples from a lightweight model, whose quality is not satisfactory but fast to generate, are regarded as an initial distribution for a FM algorithm. Unlike conventional usage of FM that takes a pure noise (e.g., Gaussian or uniform) initial distribution, the draft samples are already of decent quality, so...