# EditBridge：面向高保真与高效超高清图像编辑的扩散桥接框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-18 08:00
- AIHOT 分数：26
- AIHOT 链接：https://aihot.virxact.com/items/cmszt4jay0q1mrodpezicbs79
- 原文链接：https://arxiv.org/abs/2608.18063

## AI 摘要

EditBridge 提出一种扩散桥接框架，将超高清编辑视为从低分辨率编辑结果到高分辨率版本的结构化数据转换，并以原始高分辨率图像为条件保留真实细节。其引入先验引导的块状稀疏注意力机制，将跨图像交互限制在空间对齐区域，在 2K 分辨率下实现 3.6–8.4 倍加速，并可在 61 秒内完成 4K 编辑。

## 正文

High-resolution image editing is increasingly demanded in professional workflows, yet existing diffusion-based models remain constrained to resolutions below 1K due to quadratic attention complexity and prohibitive memory requirements. A prevalent workaround employs a two-stage pipeline: editing at low resolution followed by independent super-resolution. However, this approach suffers from two critical issues: information divergence, where hallucinated details contradict the original high-resolution (HR) source, and texture degradation, manifesting as over-smoothed or over-sharpened artifacts. We propose EditBridge, a diffusion bridge framework for efficient ultra high-resolution editing. Unlike conventional diffusion that regenerates from noise, we formulate refinement as structured data-to-data translation from the low-resolution (LR) edited result to its HR counterpart, explicitly conditioned on the original HR source to preserve authentic details. To efficiently incorporate HR source guidance, we introduce a prior-guided block-wise sparse attention mechanism that exploits semantic correspondence from first-stage editing to constrain cross-image interactions to spatially aligned regions, significantly reducing computational overhead. Extensive experiments demonstrate that EditBridge achieves high-fidelity editing with superior perceptual quality at resolutions up to 4K, delivering 3.6--8.4times speedup at 2K and enabling practical 4K editing in 61 seconds.