RegionE: Adaptive Region-Aware Generation for Efficient Image Editing
Pengtao Chen, Xianfang Zeng, Maosen Zhao, Mingzhu Shen, Peng Ye, Bangyin Xiang, Zhibo Wang, Wei Cheng, Gang Yu, Tao Chen
2025-10-30
Summary
This paper introduces a new method called RegionE to speed up instruction-based image editing, which is when you tell a computer to change a picture using text commands.
What's the problem?
Current image editing models treat the entire image the same way, even though some parts need a lot of changes while others barely need to be touched. This is inefficient because it wastes computing power on areas that are already pretty good, and slows down the whole process. It's like trying to repaint an entire house when only one room needs it.
What's the solution?
RegionE works by first identifying which parts of the image need editing and which don't. It then uses a faster, simpler process for the unchanged areas, recognizing they follow a predictable pattern. For the areas that *do* need editing, it uses a more detailed process, but also makes that process faster by remembering information from previous steps and using global context. It's like focusing your painting efforts on just the one room, and using shortcuts to quickly touch up areas that are already in good shape.
Why it matters?
This research is important because it makes image editing much faster without sacrificing the quality of the results. This means artists and designers can work more efficiently, and it opens the door to more complex and realistic image manipulations. The speed improvements—over 2x faster in tests—are significant and could make these tools more accessible.
Abstract
Recently, instruction-based image editing (IIE) has received widespread attention. In practice, IIE often modifies only specific regions of an image, while the remaining areas largely remain unchanged. Although these two types of regions differ significantly in generation difficulty and computational redundancy, existing IIE models do not account for this distinction, instead applying a uniform generation process across the entire image. This motivates us to propose RegionE, an adaptive, region-aware generation framework that accelerates IIE tasks without additional training. Specifically, the RegionE framework consists of three main components: 1) Adaptive Region Partition. We observed that the trajectory of unedited regions is straight, allowing for multi-step denoised predictions to be inferred in a single step. Therefore, in the early denoising stages, we partition the image into edited and unedited regions based on the difference between the final estimated result and the reference image. 2) Region-Aware Generation. After distinguishing the regions, we replace multi-step denoising with one-step prediction for unedited areas. For edited regions, the trajectory is curved, requiring local iterative denoising. To improve the efficiency and quality of local iterative generation, we propose the Region-Instruction KV Cache, which reduces computational cost while incorporating global information. 3) Adaptive Velocity Decay Cache. Observing that adjacent timesteps in edited regions exhibit strong velocity similarity, we further propose an adaptive velocity decay cache to accelerate the local denoising process. We applied RegionE to state-of-the-art IIE base models, including Step1X-Edit, FLUX.1 Kontext, and Qwen-Image-Edit. RegionE achieved acceleration factors of 2.57, 2.41, and 2.06. Evaluations by GPT-4o confirmed that semantic and perceptual fidelity were well preserved.