Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing
  via Bidirectional Warping
Authors: name: Jingyi Lu, name: Kai Han

Drag-based image editing has emerged as a powerful paradigm for intuitive
image manipulation. However, existing approaches predominantly rely on
manipulating the latent space of generative models, leading to limited
precision, delayed feedback, and model-specific constraints. Accordingly, we
present Inpaint4Drag, a novel framework that decomposes drag-based editing into
pixel-space bidirectional warping and image inpainting. Inspired by elastic
object deformation in the physical world, we treat image regions as deformable
materials that maintain natural shape under user manipulation. Our method
achieves real-time warping previews (0.01s) and efficient inpainting (0.3s) at
512x512 resolution, significantly improving the interaction experience compared
to existing methods that require minutes per edit. By transforming drag inputs
directly into standard inpainting formats, our approach serves as a universal
adapter for any inpainting model without architecture modification,
automatically inheriting all future improvements in inpainting technology.
Extensive experiments demonstrate that our method achieves superior visual
quality and precise control while maintaining real-time performance. Project
page: https://visual-ai.github.io/inpaint4drag/