InstaDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos

Yujun Shi^*    Jun Hao Liew^{*^}    Hanshu Yan    Vincent Y. F. Tan    Jiashi Feng
National University of Singapore   |   ByteDance

Disclaimer

This is a research project, NOT a commercial product. Users are granted the freedom to create images using this tool, but they are expected to comply with local laws and utilize it in a responsible manner. The developers do NOT assume any responsibility for potential misuse by users.

TODO

• [ ] Release inference code and model.
• [ ] Integrate with SDXL.

Although we have obtained preliminary approval for open-sourcing the code, the code review process was stuck at some point due to internal circumstances. We are sorry for the delay, and we will release it as soon as the code review process completes.

Qualitative Results Gallery

Contact

For any questions on this project, please contact Yujun (shi.yujun@u.nus.edu) and Jun Hao (junhao.liew@bytedance.com)

BibTeX

If you find our repo helpful, please consider leaving a star or cite our paper :)

@article{shi2024instadrag,
         title={InstaDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos},
         author={Shi, Yujun and Liew, Jun Hao, and Yan, Hanshu and Tan, Vincent YF and Feng, Jiashi},
         journal={arXiv preprint arXiv:2405.13722},
         year={2024}
}

Acknowledgement

Source image samples are collected from unsplash, pexels, pixabay. Also, a huge shout-out to all the amazing open source diffusion models, libraries, and technical reports.