Region-Aware Test-Time Scaling for Compositional Image Generation

Test-time scaling (TTS) has emerged as a promising paradigm for im-proving the performance of large-scale models. However, existing vision TTSmethods largely rely on global exploration—e.g., resampling noise or rewritingprompts—and often struggle to efficiently search the vast compositional space.As a result, they can exhibit a “scaling plateau,” where additional computationyields diminishing returns in semantic alignment. In this paper, we propose Region-Aware Scaling (RAS), a framework that bridges region-aware generation andtest-time scaling. By treating regional decomposition as a powerful and previ-ously overlooked scaling axis, RAS converts complex compositional promptsinto coordinated regional sub-tasks, effectively increasing the density of validcandidates during inference. At its core, RAS builds on a training-free Region-Aware Generation (RAG). Unlike many layout-based methods that incur sub-stantial overhead, RAG injects regional guidance only during early denoising, en-abling precise attribute binding while preserving global structural coherence. Weevaluate RAS on the GenEval benchmark and observe consistent improvementsin scaling efficiency across diverse compositional challenges. RAS achieves anoverall score of 0.85 with only 2 samples, matching a 32-sample noise-scalingbaseline; with 4 samples, it reaches 0.88, surpassing the combination of 32-sample noise and prompt scaling. Overall, our results suggest that structuringthe search space via regional decomposition provides a principled and compu-tationally efficient direction for scaling compositional alignment.