Drift Flow Matching
For generative modeling practitioners, DFM provides a flexible framework that adapts sampling computation to different quality-efficiency requirements, bridging a gap between one-step and iterative methods.
Drift Flow Matching (DFM) connects one-step drift models with multi-step flow matching, enabling adjustable inference computation for quality-efficiency trade-offs. Experiments show effectiveness across tasks and datasets.
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.