这篇论文用DanceOPD把T2I、局部编辑和全局编辑统一到一个模型里，解决了相互干扰的问题，效果显著提升。
针对文本到图像（T2I）、局部编辑和全局编辑等能力难以统一且相互冲突的问题，论文提出DanceOPD框架。该框架基于流匹配模型，采用策略生成场蒸馏，将每个样本路由至对应能力场，并查询低噪声的学生诱导状态，用速度MSE目标训练。在T2I、编辑、真实性场吸收和CFG吸收等实验上，DanceOPD改善了多能力组合效果，增强了目标能力同时保持基准生成质量。
DanceOPD: On-Policy Generative Field Distillation
Modern image generation demands a single model that unifies diverse capabilities, including text-to-image (T2I), local editing, and global editing. However, these capabilities are rarely naturally aligned and often conflict. For instance, editing tends to degrade T2I performance, while global and local editing interfere with each other. Consequently, effectively composing these capabilities has become a central challenge for image generation model training. To tackle this, we introduce DanceOPD, an on-policy generative field distillation framework for flow-matching models that routes each sample to one capability field, queries one low-noise student-induced state, and trains with a simple velocity MSE objective. With each capability source defined as a velocity field over the shared flow state space, the student learns from fields queried on its own rollout states to compose expert capabilities. This formulation also absorbs operator-defined fields such as classifier-free guidance. Comprehensive experiments on T2I, editing, realism-field absorption, and CFG absorption show that our approach improves multi-capability composition, strengthening target capabilities while preserving anchor generation quality. We believe this work establishes a practical route for generative field distillation in flow-matching models.