不需训练的采样优化，提升流模型生成质量
Training-Free Refinement of Flow Matching with Divergence-based Sampling
- 利用速度场发散度信号，动态修正中间状态
- 在文本到图像生成等任务中显著提升保真度
- 无需训练、可即插即用，适配主流流模型
基于流的模型通过建模边际速度场来学习目标分布，该速度场定义为从简单先验分布到目标数据的样本间速度的平均值。然而，当不同样本在相同中间状态的速度发生冲突时，这种平均速度可能误导样本进入低密度区域，从而降低生成质量。为此，我们提出一种无需训练的流发散采样器（Flow Divergence Sampler, FDS），在每一步求解前对中间状态进行精炼。关键发现表明，边际速度场的发散度可直接在推理过程中高效计算，且能准确衡量误导程度。FDS利用这一信号将状态引导至更清晰的区域。作为兼容标准求解器和现成流骨干网络的即插即用框架，FDS在文本到图像合成及反问题等各类生成任务中均稳定提升保真度。
原文摘要 · Abstract (English)
Flow-based models learn a target distribution by modeling a marginal velocity field, defined as the average of sample-wise velocities connecting each sample from a simple prior to the target data. When sample-wise velocities conflict at the same intermediate state, however, this averaged velocity can misguide samples toward low-density regions, degrading generation quality. To address this issue, we propose the Flow Divergence Sampler (FDS), a training-free framework that refines intermediate states before each solver step. Our key finding reveals that the severity of this misguidance is quantified by the divergence of the marginal velocity field that is readily computable during inference with a well-optimized model. FDS exploits this signal to steer states toward less ambiguous regions. As a plug-and-play framework compatible with standard solvers and off-the-shelf flow backbones, FDS consistently improves fidelity across various generation tasks including text-to-image synthesis, and inverse problems.
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