[2603.00141] From Scale to Speed: Adaptive Test-Time Scaling for Image Editing

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00141 (cs) [Submitted on 24 Feb 2026] Title:From Scale to Speed: Adaptive Test-Time Scaling for Image Editing Authors:Xiangyan Qu, Zhenlong Yuan, Jing Tang, Rui Chen, Datao Tang, Meng Yu, Lei Sun, Yancheng Bai, Xiangxiang Chu, Gaopeng Gou, Gang Xiong, Yujun Cai View a PDF of the paper titled From Scale to Speed: Adaptive Test-Time Scaling for Image Editing, by Xiangyan Qu and 11 other authors View PDF HTML (experimental) Abstract:Image Chain-of-Thought (Image-CoT) is a test-time scaling paradigm that improves image generation by extending inference time. Most Image-CoT methods focus on text-to-image (T2I) generation. Unlike T2I generation, image editing is goal-directed: the solution space is constrained by the source image and instruction. This mismatch causes three challenges when applying Image-CoT to editing: inefficient resource allocation with fixed sampling budgets, unreliable early-stage verification using general MLLM scores, and redundant edited results from large-scale sampling. To address this, we propose ADaptive Edit-CoT (ADE-CoT), an on-demand test-time scaling framework to enhance editing efficiency and performance. It incorporates three key strategies: (1) a difficulty-aware resource allocation that assigns dynamic budgets based on estimated edit difficulty; (2) edit-specific verification in early pruning that uses region localization and caption consistency to select promising candidate...