DanceOPD: On-Policy Generative Field Distillation
Summary
DanceOPD proposes an on-policy generative field distillation framework for flow-matching models that unifies text-to-image generation, local editing, and global editing via capability-specific routing and velocity-based training, improving multi-capability composition while preserving anchor generation quality.
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Paper page - DanceOPD: On-Policy Generative Field Distillation
Source: https://huggingface.co/papers/2606.27377
Abstract
A novel on-policy generative field distillation framework called DanceOPD is proposed to unify text-to-image generation, local editing, and global editing capabilities in flow-matching models through capability-specific routing and velocity-based training.
Modern image generation demands a single model that unifies diverse capabilities, includingtext-to-image(T2I),local editing, andglobal editing. However, these capabilities are rarely naturally aligned and often conflict. For instance, editing tends to degrade T2I performance, while global andlocal editinginterfere with each other. Consequently, effectively composing these capabilities has become a central challenge for image generation model training. To tackle this, we introduce DanceOPD, anon-policygenerative field distillationframework forflow-matching modelsthat routes each sample to one capability field, queries one low-noise student-induced state, and trains with a simplevelocity MSE objective. With each capability source defined as avelocity fieldover the shared flow state space, the student learns from fields queried on its own rollout states to composeexpert capabilities. This formulation also absorbs operator-defined fields such asclassifier-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 forgenerative field distillationinflow-matching models.
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