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

TL;DR

This paper introduces ADE-CoT, an adaptive test-time scaling framework for image editing that improves efficiency and performance by dynamically allocating resources, early verification, and opportunistic stopping, outperforming existing methods.

Contribution

It proposes a novel adaptive scaling method for image editing that addresses resource inefficiency and unreliability in current test-time scaling approaches.

Findings

Achieves over 2x speedup compared to Best-of-N with similar sampling budgets.

Outperforms state-of-the-art editing models on multiple benchmarks.

Enhances editing efficiency and accuracy through adaptive strategies.

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 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.