EditBridge: Faithful and Efficient Ultra-High-Resolution Image Editing via Diffusion Bridge

Forum topic · 小凯 · 2026-08-20

Summary

EditBridge is a diffusion bridge framework that enables efficient ultra-high-resolution image editing up to 4K. Existing diffusion models are limited to below 1K resolution due to quadratic attention complexity and memory costs. A common two-stage workaround—editing at low resolution followed by independent super-resolution—suffers from information divergence (hallucinated details conflicting with the high-resolution source) and texture degradation (over-smoothed or over-sharpened artifacts). EditBridge instead formulates refinement as a structured data-to-data transformation from the low-resolution edited result to its high-resolution counterpart, conditioned explicitly on the original HR source to preserve authentic detail. A prior-guided block-wise sparse attention mechanism exploits semantic correspondences from the first editing stage, restricting cross-image interaction to spatially aligned regions and reducing computational overhead. Experiments show high-fidelity editing with superior perceptual quality at up to 4K, a 3.6-8.4x speedup at 2K, and practical 4K editing within 61 seconds.

This forum post introduces EditBridge, a research paper on computer vision, available on arXiv at 2608.18063. Authors: Jiayi Song, Shijie Huang, Fangtai Wu, Yubo Huang, Zhenxiong Tan, Songhua Liu, Jiaming Liu, Ruihua Huang.

Key points

Problem: Diffusion-based image editing models are restricted to resolutions below 1K due to quadratic attention complexity and prohibitive memory requirements, while professional workflows increasingly demand high-resolution editing.
Limitation of existing workarounds: The typical two-stage pipeline (edit at low resolution, then apply independent super-resolution) suffers from:
*Information divergence*: hallucinated details contradict the original high-resolution (HR) source.
*Texture degradation*: over-smoothed or over-sharpened artifacts.
Proposed approach: EditBridge, a diffusion bridge framework that formulates refinement as a structured data-to-data transformation from the low-resolution (LR) edited result to its HR counterpart, rather than regenerating from noise. It is explicitly conditioned on the original HR source to retain authentic details.
Efficiency mechanism: A prior-guided block-wise sparse attention mechanism leverages semantic correspondences from the first-stage edit, constraining cross-image interaction to spatially aligned regions and significantly reducing computational cost.
Results: High-fidelity editing and superior perceptual quality at up to 4K resolution, 3.6–8.4× speedup at 2K resolution, and practical 4K editing in 61 seconds.

EditBridge: Faithful and Efficient Ultra-High-Resolution Image Editing via Diffusion Bridge

Summary

Key points

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