DanceOPD

DanceOPD: On-policy generative field distillation for composing image generation capabilities

DanceOPD is a new training framework for flow-matching image generation models that addresses the conflict between text-to-image generation, local editing, and global editing capabilities. The approach routes each sample to a capability-specific velocity field, queries student-induced rollout states on-policy, and trains with a velocity MSE objective to compose multiple expert capabilities without mutual degradation. Experiments show improvements in multi-capability composition while preserving baseline generation quality. The method also absorbs operator-defined fields such as classifier-free guidance.