InstantDrag: Improving Interactivity in Drag-based Image Editing
Authors: name: Joonghyuk Shin, name: Daehyeon Choi, name: Jaesik Park

Drag-based image editing has recently gained popularity for its interactivity
and precision. However, despite the ability of text-to-image models to generate
samples within a second, drag editing still lags behind due to the challenge of
accurately reflecting user interaction while maintaining image content. Some
existing approaches rely on computationally intensive per-image optimization or
intricate guidance-based methods, requiring additional inputs such as masks for
movable regions and text prompts, thereby compromising the interactivity of the
editing process. We introduce InstantDrag, an optimization-free pipeline that
enhances interactivity and speed, requiring only an image and a drag
instruction as input. InstantDrag consists of two carefully designed networks:
a drag-conditioned optical flow generator (FlowGen) and an optical
flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion
dynamics for drag-based image editing in real-world video datasets by
decomposing the task into motion generation and motion-conditioned image
generation. We demonstrate InstantDrag's capability to perform fast,
photo-realistic edits without masks or text prompts through experiments on
facial video datasets and general scenes. These results highlight the
efficiency of our approach in handling drag-based image editing, making it a
promising solution for interactive, real-time applications.