Abstract
Flow matching has shown state-of-the-art performance in various generative tasks, ranging from image generation to decision-making, where guided generation is pivotal. However, the guidance of flow matching is more general than and thus substantially different from that of its predecessor, diffusion models. Therefore, the challenge in guidance for general flow matching remains largely underexplored. In this paper, we propose the first framework of general guidance for flow matching. From this framework, we derive a family of guidance techniques that can be applied to general flow matching. These include a new training-free asymptotically exact guidance, novel training losses for training-based guidance, and two classes of approximate guidance that cover classical gradient guidance methods as special cases. We theoretically investigate these different methods to give a practical guideline for choosing suitable methods in different scenarios. Experiments on synthetic datasets, image inverse problems, and offline reinforcement learning demonstrate the effectiveness of our proposed guidance methods and verify the correctness of our flow matching guidance framework. Code to reproduce the experiments can be found at this https URL.
Abstract (translated)
流动匹配在从图像生成到决策制定等各类生成任务中展现了最先进的性能，特别是在引导式生成中尤为重要。然而，与它的前身扩散模型相比，流动匹配的引导方式更为通用，且两者之间存在显著差异。因此，对于一般流动匹配中的引导挑战的研究仍然很大程度上未被探索。 在这篇论文中，我们提出了第一个适用于一般流动匹配的引导框架。从这个框架出发，我们推导出了一系列可以应用于一般流动匹配的引导技术。这包括一种新的无需训练但渐近精确的引导方法、用于基于训练指导的新颖损失函数，以及两类涵盖经典梯度引导方法作为特例的大约引导方法。通过理论研究这些不同的方法，我们提供了一个在不同场景中选择合适方法的实际指南。 我们在合成数据集、图像逆问题和离线强化学习上的实验展示了我们提出的引导方法的有效性，并验证了我们的流动匹配引导框架的正确性。可在提供的网址（此 https URL）上找到重现实验代码。
URL
https://arxiv.org/abs/2502.02150
