Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference computation can improve generation quality. In contrast, Drift Models offer efficient one-step generation, but their direct generation paradigm limits such flexibility. In this work, we propose Drift Flow Matching (DFM), a framework that connects drifting generative modeling with flow-based iterative generation. DFM preserves the efficiency of direct transport maps while enabling generation to be refined through multiple inference steps when desired. This bridges the gap between one-step Drift Models and multi-step Flow Matching methods, and provides a novel generative paradigm that can adapt sampling computation to different quality--efficiency requirements. Extensive experiments across different tasks and datasets demonstrate the effectiveness and generality of the proposed framework.

翻译：迭代生成模型（如流匹配和扩散模型）在测试时表现出良好的扩展性，即通过增加推理计算量可提升生成质量。相比之下，漂移模型虽能实现高效的单步生成，但其直接生成范式限制了此类灵活性。本文提出漂移流匹配（DFM）框架，该框架将漂移生成建模与基于流的迭代生成相连接。DFM在保留直接传输映射高效性的同时，允许在需要时通过多步推理细化生成结果。这弥合了单步漂移模型与多步流匹配方法之间的差距，提供了一种可根据不同质量-效率需求自适应调整采样计算的新型生成范式。跨不同任务和数据集的大量实验证明了该框架的有效性与通用性。