遵循均值：參考引導的流匹配

摘要

現有的可控生成方法通常依賴於微調、輔助網絡或測試時搜尋。我們證明流匹配提供了一種不同的控制介面：透過樣本進行適應。對於確定性插值方法，速度場完全由條件端點均值決定；改變此均值即會改變流本身。這產生了可控生成的一個簡單原則：透過改變預訓練模型所遵循的參考集來引導該模型。我們以兩種形式具體實現此想法。參考均值引導為免訓練方法：它從參考庫計算出閉合形式的端點均值修正，並將其應用於凍結的 FLUX.2-klein（4B）模型，從而在保持提示、隨機種子和權重不變的情況下，實現對顏色、身份、風格和結構的控制。半參數引導則透過明確的均值錨點和學習到的殘差精煉器來實現相同概念，在 AFHQv2 上達到與無條件 DiT-B/4 相當的品質，同時允許在推理時更換參考集。這些成果指出了一個更廣泛的方向：生成模型應透過資料而非參數更新來適應。

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.