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.

翻译：现有可控生成方法通常依赖微调、辅助网络或测试时搜索。我们证明流匹配支持另一种控制接口：通过样本进行自适应。对于确定性插值，速度场完全由条件终点均值决定；偏移该均值即可改变流本身的走向。这为可控生成提供了简单原则：通过改变所遵循的参考集来引导预训练模型。我们以两种形式实现该思想。参考均值引导无需训练：从参考库计算闭式终点均值修正，并将其应用于冻结的FLUX.2-klein（4B）模型，在保持提示词、随机种子和权重不变的情况下，实现对颜色、身份、风格和结构的控制。半参数引导通过显式均值锚点和学习残差精化器分摊相同思想，在AFHQv2上达到无条件DiT-B/4质量，同时允许在推理时更换参考集。这些结果指向更广阔的方向：通过数据而非参数更新进行自适应的生成模型。