解决粗略掩码导致图像编辑偏移的问题，提升局部编辑精度与稳定性。
BRIDGE: Background Routing and Isolated Discrete Gating for Coarse-Mask Local Editing
- 提出桥接路径机制，分离背景与主体生成路径，避免掩码形状干扰。
- 在基准测试中将局部编辑指标提升至0.503，显著优于现有方法。
- 轻量级几何门控模块仅13.31M参数，适合实际部署，适合图像编辑研究者。
粗略掩码局部图像编辑要求模型修改用户指定区域的同时保持周围场景不变。然而，实践中粗糙掩码常成为意外的形状先验：不仅无法作为灵活的编辑支持，反而会引导生成内容向掩码边界靠拢。本文将此现象称为掩码形状偏差，并提出双区约束框架——背景应保持稳定，可编辑区域应遵循指令且不继承掩码轮廓。BRIDGE通过将掩码保留在DiT主干外进行支持构建与融合，避免掩码注入DiT内部或复制控制分支。其采用桥接路径生成：主路径保留背景上下文，主体路径从独立噪声生成可编辑内容。基于诊断实验发现位置嵌入与注意力连接决定视觉标记复用范围，引入可学习离散几何门控模块，实现标记级位置嵌入路由：主体标记可在融合区域借用背景锚定坐标，或保留主体中心坐标以获得几何自由度。在BRIDGE-Bench、MagicBrush和ICE-Bench上评估显示，于BRIDGE-Bench上将Local SigLIP2-T从0.262（FLUX.1-Fill）与0.390（ACE++）提升至0.503，同时本地DINO与DreamSim也取得并行提升。零样本测试在MagicBrush与ICE-Bench上进一步验证了良好的对齐性与源图保留能力，而新增路由模块仅占13.31M参数，远低于ControlNet类复制分支。
原文摘要 · Abstract (English)
Coarse-mask local image editing asks a model to modify a user-indicated region while preserving the surrounding scene. In practice, however, rough masks often become unintended shape priors: instead of serving as flexible edit support, the mask can pull the generated object toward its accidental boundary. We study this failure as mask-shape bias and frame the task through a Two-Zone Constraint, where the background should remain stable while the editable region should follow the instruction without being forced to inherit the mask contour. BRIDGE addresses this setting by keeping masks outside the DiT backbone for support construction and blending, avoiding DiT-internal mask injection and copied control branches. It uses BridgePath generation, where a Main Path preserves background context and a Subject Path generates editable content from independent noise. Motivated by a diagnostic Qwen-Image experiment showing that positional embeddings and attention connectivity regulate which image context visual tokens reuse, BRIDGE introduces a learnable Discrete Geometric Gate for token-level positional-embedding routing. This gate lets subject tokens borrow background-anchored coordinates near fusion regions or keep subject-centric coordinates for geometric freedom. We evaluate BRIDGE on BRIDGE-Bench, MagicBrush, and ICE-Bench. On BRIDGE-Bench, BRIDGE improves Local SigLIP2-T from 0.262 with FLUX.1-Fill and 0.390 with ACE++ to 0.503, with parallel gains in local DINO and DreamSim. Zero-shot results on MagicBrush and ICE-Bench further indicate competitive alignment and source preservation beyond the curated benchmark, while the added routing module remains compact at 13.31M parameters compared with ControlNet-style copied branches.
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