RegionCache: Semantic-Aware Region Reuse for Efficient Multi-Turn Image Generation

Abstract

Real-world image generation generally requires multi-turn editing, where users iteratively refine a small region while the majority of the image remains stable across turns. Despite this strong region-level stability, existing diffusion transformer (DiT)–based editing pipelines recompute the entire image at each turn, incurring substantial redundant computation on unchanged regions. Moreover, caching methods in existing DiT acceleration frameworks ignore semantic correspondence across prompts, leading to either unnecessary recomputation or unsafe reuse that degrades editing quality. To this end, we propose RegionCache, a semantic-aware reuse framework for multi-turn image editing that selectively reuses diffusion states from unchanged regions. RegionCache identifies reusable regions by detecting semantic overlap between consecutive prompts and localizing their spatial support via cross-attention maps, and employs an adaptive reuse schedule that dynamically determines how long cached regions can be safely reused based on prompt semantic similarity and contextual consistency. Experiments on PixArt-alpha show that RegionCache achieves 1.43x–2.55x end-to-end inference speedup while maintaining comparable image quality.