  Accuracy and speed are critical in image editing tasks. Pan et al. introduced
a drag-based image editing framework that achieves pixel-level control using
Generative Adversarial Networks (GANs). A flurry of subsequent studies enhanced
this framework's generality by leveraging large-scale diffusion models.
However, these methods often suffer from inordinately long processing times
(exceeding 1 minute per edit) and low success rates. Addressing these issues
head on, we present InstaDrag, a rapid approach enabling high quality
drag-based image editing in ~1 second. Unlike most previous methods, we
redefine drag-based editing as a conditional generation task, eliminating the
need for time-consuming latent optimization or gradient-based guidance during
inference. In addition, the design of our pipeline allows us to train our model
on large-scale paired video frames, which contain rich motion information such
as object translations, changing poses and orientations, zooming in and out,
etc. By learning from videos, our approach can significantly outperform
previous methods in terms of accuracy and consistency. Despite being trained
solely on videos, our model generalizes well to perform local shape
deformations not presented in the training data (e.g., lengthening of hair,
twisting rainbows, etc.). Extensive qualitative and quantitative evaluations on
benchmark datasets corroborate the superiority of our approach. The code and
model will be released at https://github.com/magic-research/InstaDrag.
