From Scale to Speed: Adaptive Test-Time Scaling for Image Editing
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 candidates; and (3) depth-first opportunistic stopping, guided by an instance-specific verifier, that terminates when intent-aligned results are found. Extensive experiments on three SOTA editing models (Step1X-Edit, BAGEL, FLUX.1 Kontext) across three benchmarks show that ADE-CoT achieves superior performance-efficiency trade-offs. With comparable sampling budgets, ADE-CoT obtains better performance with more than 2x speedup over Best-of-N.
69a681e8663a122a7ce66871
Xiangyan Qu
69a681e8663a122a7ce66872
Zhenlong Yuan
claimed_verified
2026-04-15T12:09:41.993Z
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Zhenlong Yuan
ZhenlongYuan
user
ZhenlongYuan
69a681e8663a122a7ce66873
Jing Tang
69a681e8663a122a7ce66874
Rui Chen
claimed_verified
2026-03-03T10:36:57.086Z
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Rui Chen
ruichen9618
user
ruichen9618
69a681e8663a122a7ce66875
Datao Tang
69a681e8663a122a7ce66876
Meng Yu
69a681e8663a122a7ce66877
Lei Sun
69a681e8663a122a7ce66878
Yancheng Bai
69a681e8663a122a7ce66879
Xiangxiang Chu
69a681e8663a122a7ce6687a
Gaopeng Gou
69a681e8663a122a7ce6687b
Gang Xiong
69a681e8663a122a7ce6687c
Yujun Cai
From Scale to Speed: Adaptive Test-Time Scaling for Image Editing
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 candidates; and (3) depth-first opportunistic stopping, guided by an instance-specific verifier, that terminates when intent-aligned results are found. Extensive experiments on three SOTA editing models (Step1X-Edit, BAGEL, FLUX.1 Kontext) across three benchmarks show that ADE-CoT achieves superior performance-efficiency trade-offs. With comparable sampling budgets, ADE-CoT obtains better performance with more than 2x speedup over Best-of-N.
Image-CoT methods are extended to image editing with ADE-CoT, which improves efficiency and performance through adaptive resource allocation, edit-specific verification, and opportunistic stopping mechanisms.
image generation text-to-image generation image editing test-time scaling diffusion models sampling budgets early-stage verification MLLM scores large-scale sampling dynamic budgets region localization caption consistency depth-first search instance-specific verifier Best-of-N