DragDiffusion: Harnessing Diffusion Models for Interactive Point-based
  Image Editing
Authors: name: Yujun Shi, name: Chuhui Xue, name: Jiachun Pan, name: Wenqing Zhang, name: Vincent Y. F. Tan, name: Song Bai

Precise and controllable image editing is a challenging task that has
attracted significant attention. Recently, DragGAN enables an interactive
point-based image editing framework and achieves impressive editing results
with pixel-level precision. However, since this method is based on generative
adversarial networks (GAN), its generality is upper-bounded by the capacity of
the pre-trained GAN models. In this work, we extend such an editing framework
to diffusion models and propose DragDiffusion. By leveraging large-scale
pretrained diffusion models, we greatly improve the applicability of
interactive point-based editing in real world scenarios. While most existing
diffusion-based image editing methods work on text embeddings, DragDiffusion
optimizes the diffusion latent to achieve precise spatial control. Although
diffusion models generate images in an iterative manner, we empirically show
that optimizing diffusion latent at one single step suffices to generate
coherent results, enabling DragDiffusion to complete high-quality editing
efficiently. Extensive experiments across a wide range of challenging cases
(e.g., multi-objects, diverse object categories, various styles, etc.)
demonstrate the versatility and generality of DragDiffusion.