Paper Overview
- Field: NLP / Image Generation
- Authors: Wei Zhou, Xiongwei Zhu, Zelin Xu
- Published: 2026-06-27
- arXiv: 2606.27377
Summary
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, the authors introduce DanceOPD, an on-policy generative field distillation framework. The core idea is to ensure that each capability learns from the model's own generated (on-policy) distribution, avoiding misalignment and conflicts between capabilities.
Original Abstract (excerpt)
> 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, ...
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*Auto-collected on 2026-06-27*