BRIDGE: Background Routing and Isolated Discrete Gating for Coarse-Mask Local Editing

Coarse-mask local editing must preserve the background while allowing new content to depart from the mask contour. Existing mask-conditioned editors often entangle these goals and treat the coarse mask as a shape prior. We introduce **BRIDGE, a dual-path architecture** with a full-canvas Main Path, a local Subject Path, and a **learned binary gate** that routes subject-token positional embeddings between subject-centric and background-anchored coordinates. On Qwen-Image-Edit-2511, BRIDGE keeps the mask outside the DiT backbone and trains both targets with independent noise. On BRIDGE-Bench, it achieves **0.503 local SigLIP2-T, 0.902 DINO, and 0.175 DreamSim**, outperforming ACE++ and FLUX.1-Fill in local precision. We then instantiate BRIDGE on **FLUX.2 [klein] 9B Base** with full-transformer training and subject-reference conditioning. Dense BBox supervision preserves Sub-image quality; **optional mask-cropped inference** removes out-of-mask Sub tokens, tightening user support without training a sparse model that overfits the mask silhouette. On supported ICE-Bench tasks, FLUX.2 BRIDGE averages **0.5941 over four local edits and 0.6254 over subject-guided edits**, leading the compared open-source models on the latter. Across Qwen and FLUX.2, BRIDGE separates support localization from subject geometry and improves local generation while preserving the surrounding scene.