Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
<|reference_start|>Consistency Flow Matching: Defining Straight Flows with Velocity Consistency: Flow matching (FM) is a general framework for defining probability paths via Ordinary Differential Equations (ODEs) to transform between noise and data samples. Recent approaches attempt to straighten these flow trajectories to generate high-quality samples with fewer function evaluations, typically through iterative rectification methods or optimal transport solutions. In this paper, we introduce Consistency Flow Matching (Consistency-FM), a novel FM method that explicitly enforces self-consistency in the velocity field. Consistency-FM directly defines straight flows starting from different times to the same endpoint, imposing constraints on their velocity values. Additionally, we propose a multi-segment training approach for Consistency-FM to enhance expressiveness, achieving a better trade-off between sampling quality and speed. Preliminary experiments demonstrate that our Consistency-FM significantly improves training efficiency by converging 4.4x faster than consistency models and 1.7x faster than rectified flow models while achieving better generation quality. Our code is available at: https://github.com/YangLing0818/consistency_flow_matching<|reference_end|>
@article{yang2024consistency,
    title={Consistency Flow Matching: Defining Straight Flows with Velocity
  Consistency},
    author={Ling Yang, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu,
  Wentao Zhang, Chenlin Meng, Stefano Ermon, Bin Cui},
    journal={arXiv preprint arXiv:2407.02398},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02398},
    primaryClass={cs.CV}
}