Flow matching is a powerful framework for generating high-quality samples in various applications, especially image synthesis. However, the intensive computational demands of these models, especially during the finetuning process and sampling processes, pose significant challenges for low-resource scenarios. This paper introduces Bellman Optimal Stepsize Straightening (BOSS) technique for distilling flow-matching generative models: it aims specifically for a few-step efficient image sampling while adhering to a computational budget constraint. First, this technique involves a dynamic programming algorithm that optimizes the stepsizes of the pretrained network. Then, it refines the velocity network to match the optimal step sizes, aiming to straighten the generation paths. Extensive experimental evaluations across image generation tasks demonstrate the efficacy of BOSS in terms of both resource utilization and image quality. Our results reveal that BOSS achieves substantial gains in efficiency while maintaining competitive sample quality, effectively bridging the gap between low-resource constraints and the demanding requirements of flow-matching generative models. Our paper also fortifies the responsible development of artificial intelligence, offering a more sustainable generative model that reduces computational costs and environmental footprints. Our code can be found at https://github.com/nguyenngocbaocmt02/BOSS.

翻译：流匹配是一种强大的框架，广泛应用于各类图像合成等高质量样本生成任务。然而，这些模型在微调与采样过程中对计算资源的密集需求，为资源受限场景带来了显著挑战。本文提出贝尔曼最优步长直线化（BOSS）技术，用于蒸馏流匹配生成模型：该技术专门针对在计算预算约束下实现高效少步图像采样。首先，该技术采用动态规划算法优化预训练网络的步长；随后，通过精炼速度网络以匹配最优步长，旨在实现生成路径的直线化。在图像生成任务上的广泛实验评估表明，BOSS在资源利用率和图像质量方面均具有显著效力。我们的结果显示，BOSS在保持竞争性样本质量的同时实现了效率的实质性提升，有效弥合了低资源约束与流匹配生成模型高要求之间的差距。本文亦强化了人工智能的负责任发展，提供了一种更具可持续性的生成模型，能够降低计算成本与环境足迹。我们的代码可在 https://github.com/nguyenngocbaocmt02/BOSS 获取。