用参考样本引导流匹配，零训练实现可控生成。
Follow the Mean: Reference-Guided Flow Matching
- 通过调整参考集均值控制生成流程，无需微调或搜索。
- 冻结模型下实现颜色、身份、风格等多维度可控生成。
- 适合希望快速定制生成结果的研究者与创作者。
现有可控生成方法通常依赖微调、辅助网络或测试时搜索。我们发现流匹配可采用不同的控制接口：通过示例进行适应。对于确定性插值，速度场仅由条件终点均值决定；改变该均值即可改变生成流。这提出了一种简单原则：通过更改参考集来引导预训练模型。我们以两种形式实现这一思想：参考均值引导为零训练方法，从参考库中计算闭式终点均值修正，并应用于冻结的 FLUX.2-klein (4B) 模型，实现颜色、身份、风格和结构的控制，同时保持提示词、随机种子和权重不变。半参数引导则通过显式的均值锚点和学习的残差修正器分摊该思想，在 AFHQv2 上达到与无条件 DiT-B/4 相当的质量，且可在推理时更换参考集。这些结果指向更广的方向：生成模型通过数据适应，而非参数更新。
原文摘要 · Abstract (English)
Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits a different control interface: adaptation through examples. For deterministic interpolants, the velocity field is solely governed by a conditional endpoint mean; shifting this mean shifts the flow itself. This yields a simple principle for controllable generation: steer a pretrained model by changing the reference set it follows. We instantiate this idea in two forms. Reference-Mean Guidance is training-free: it computes a closed-form endpoint-mean correction from a reference bank and applies it to a frozen FLUX.2-klein (4B) model, enabling control of color, identity, style, and structure while keeping the prompt, seed, and weights fixed. Semi-Parametric Guidance amortizes the same idea through an explicit mean anchor and learned residual refiner, matching unconditional DiT-B/4 quality on AFHQv2 while allowing the reference set to be swapped at inference time. These results point to a broader direction: generative models that adapt through data, not parameter updates.
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