  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.
