EditBridge: A Diffusion Bridge Framework for Faithful and Efficient Ultra-High-Resolution Image Editing

Forum topic · 小凯 · 2026-08-20

Summary

EditBridge (arXiv:2608.18063) is a diffusion bridge framework for ultra-high-resolution image editing, addressing the limits of diffusion models that are typically confined to sub-1K resolutions due to quadratic attention complexity and memory demands. Instead of a two-stage pipeline of low-resolution editing followed by independent super-resolution—which suffers from information divergence (hallucinated details contradicting the original source) and texture degradation (over-smoothed or over-sharpened artifacts)—EditBridge reformulates refinement as a structured data-to-data transformation from the low-resolution edited result to its high-resolution counterpart, explicitly conditioned on the original HR source to preserve authentic details. A prior-guided block sparse attention mechanism leverages semantic correspondences from the first-stage edit, restricting cross-image interactions to spatially aligned regions to cut computational overhead. Experiments show high-fidelity editing at up to 4K resolution, superior perceptual quality, a 3.6–8.4× speedup at 2K, and practical 4K editing within 61 seconds.

Paper Overview

Field: Computer Vision (CV)
Authors: Jiayi Song, Shijie Huang, Fangtai Wu, Yubo Huang, Zhenxiong Tan, Songhua Liu, Jiaming Liu, Ruihua Huang
arXiv: 2608.18063

Problem

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 uses a two-stage pipeline: editing at low resolution followed by independent super-resolution. However, this approach suffers from two critical issues:

Information divergence: hallucinated details contradict the original high-resolution (HR) source.
Texture degradation: over-smoothed or over-sharpened artifacts.

Approach

EditBridge is a diffusion bridge framework for efficient ultra-high-resolution editing. Unlike conventional diffusion that regenerates from noise, it formulates refinement as a structured data-to-data transformation 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, EditBridge introduces a prior-guided block sparse attention mechanism that leverages semantic correspondences from the first-stage edit, constraining cross-image interactions to spatially aligned regions and significantly reducing computational overhead.

Results

High-fidelity editing and superior perceptual quality at resolutions up to 4K.
3.6–8.4× speedup at 2K resolution compared with baselines.
Practical 4K editing achievable in 61 seconds.