EDITBRIDGE：朝向忠實且高效的超高解析度影像編輯

摘要

高解析度影像編輯在專業工作流程中的需求日益增加，然而現有的擴散模型因二次注意力複雜度與極高的記憶體需求，仍受限於低於1K的解析度。常見的解決方案採用兩階段流程：先於低解析度進行編輯，再以獨立的超解析度模型增強。然而，此方法存在兩個關鍵問題：資訊分歧——幻覺細節與原始高解析度（HR）來源產生矛盾；以及紋理退化——表現為過度平滑或過度銳利的人為痕跡。我們提出EditBridge，一個用於高效超高解析度編輯的擴散橋框架。與從雜訊重新生成的傳統擴散方法不同，我們將細化過程形式化為從低解析度（LR）編輯結果到其高解析度對應版本的結構化資料對資料轉換，並明確以原始高解析度來源為條件以保留真實細節。為有效納入高解析度來源引導，我們引入一種先驗引導的區塊稀疏注意力機制，利用第一階段編輯的語意對應關係，將跨影像交互限制在空間對齊的區域內，大幅降低計算開銷。大量實驗證明，EditBridge在高達4K的解析度下能實現高保真編輯與優異的感知品質，在2K下達到3.6至8.4倍的加速，並能在61秒內完成實用的4K編輯。

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. 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.