  Drag-based image editing using generative models provides precise control
over image contents, enabling users to manipulate anything in an image with a
few clicks. However, prevailing methods typically adopt $n$-step iterations for
latent semantic optimization to achieve drag-based image editing, which is
time-consuming and limits practical applications. In this paper, we introduce a
novel one-step drag-based image editing method, i.e., FastDrag, to accelerate
the editing process. Central to our approach is a latent warpage function
(LWF), which simulates the behavior of a stretched material to adjust the
location of individual pixels within the latent space. This innovation achieves
one-step latent semantic optimization and hence significantly promotes editing
speeds. Meanwhile, null regions emerging after applying LWF are addressed by
our proposed bilateral nearest neighbor interpolation (BNNI) strategy. This
strategy interpolates these regions using similar features from neighboring
areas, thus enhancing semantic integrity. Additionally, a
consistency-preserving strategy is introduced to maintain the consistency
between the edited and original images by adopting semantic information from
the original image, saved as key and value pairs in self-attention module
during diffusion inversion, to guide the diffusion sampling. Our FastDrag is
validated on the DragBench dataset, demonstrating substantial improvements in
processing time over existing methods, while achieving enhanced editing
performance. Project page: https://fastdrag-site.github.io/ .
