EDITBRIDGE: Towards Faithful and Efficient Ultra-High-Resolution Image Editing

2026-08-18

AI Summary

本文提出EditBridge，通过结构化数据转换和块稀疏注意力机制解决高分辨率图像编辑中的信息分歧和纹理退化问题，实现高效且高质量的4K图像编辑。

Abstract

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--8.4$\times$ speedup at 2K and enabling practical 4K editing in 61 seconds.

Problem

• High-resolution image editing
• Diffusion-based models
• Information divergence
• Texture degradation
• Ultra-high-resolution

Innovation

• diffusion bridge
• ultra high-resolution editing
• prior-guided block-wise sparse attention
• semantic correspondence
• efficient computation