Point-drag-based image editing methods, like DragDiffusion, have attracted significant attention. However, point-drag-based approaches suffer from computational overhead and misinterpretation of user intentions due to the sparsity of point-based editing instructions. In this paper, we propose a region-based copy-and-paste dragging method, RegionDrag, to overcome these limitations. RegionDrag allows users to express their editing instructions in the form of handle and target regions, enabling more precise control and alleviating ambiguity. In addition, region-based operations complete editing in one iteration and are much faster than point-drag-based methods. We also incorporate the attention-swapping technique for enhanced stability during editing. To validate our approach, we extend existing point-drag-based datasets with region-based dragging instructions. Experimental results demonstrate that RegionDrag outperforms existing point-drag-based approaches in terms of speed, accuracy, and alignment with user intentions. Remarkably, RegionDrag completes the edit on an image with a resolution of 512x512 in less than 2 seconds, which is more than 100x faster than DragDiffusion, while achieving better performance. Project page: https://visual-ai.github.io/regiondrag.

本研究解决了现有点拖拽图像编辑方法计算开销大和用户意图误解的问题。提出的RegionDrag方法通过区域拖拽指令实现更精确的控制和快速编辑，显著提升了编辑速度和用户体验。实验结果表明，RegionDrag在速度和准确性上优于现有方法，能够在2秒内完成512x512分辨率图像的编辑，速度提高超过100倍。

RegionDrag：基于区域的快速图像编辑与扩散模型