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

Forum topic · 小凯 · 2026-08-20

Summary

EditBridge is a diffusion bridge framework for ultra-high-resolution image editing, presented in arXiv paper 2608.18063 by Jiayi Song and colleagues. Existing diffusion-based editing models are limited to sub-1K resolutions due to quadratic attention complexity and memory demands, while common two-stage pipelines (low-resolution editing followed by independent super-resolution) suffer from information divergence, where hallucinated details contradict the original high-resolution source, and texture degradation such as over-smoothed or over-sharpened artifacts. Instead of regenerating from noise, EditBridge formulates refinement as a structured data-to-data transformation from the edited low-resolution 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 interaction to spatially aligned regions and significantly reducing computation. Experiments show high-fidelity editing and superior perceptual quality at up to 4K resolution, 3.6-8.4x speedups at 2K, and practical 4K editing in 61 seconds.

Paper Overview

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

Abstract (English)

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.

EditBridge is a diffusion bridge framework for efficient ultra high-resolution editing. Unlike conventional diffusion that regenerates from noise, EditBridge 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, the authors introduce a prior-guided block sparse attention mechanism that leverages semantic correspondences from the first-stage edit, restricting cross-image interaction to spatially aligned regions and significantly reducing computational overhead.

Key Results

High-fidelity editing and superior perceptual quality at resolutions up to 4K
3.6-8.4x speedup at 2K resolution compared to alternative approaches
Practical 4K editing completed within 61 seconds

Discussion

EditBridge addresses a real bottleneck in professional image-editing workflows: rather than fixing low-resolution edits after the fact, it treats HR refinement as a bridge between the LR edit and the original HR image, keeping fine details faithful to the source while remaining computationally tractable.

*Auto-collected on 2026-08-20.*