| library_name: diffusers | |
| pipeline_tag: image-to-video | |
| # Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance | |
| Kiwi-Edit is a versatile video editing framework built on an MLLM encoder and a video Diffusion Transformer (DiT). It supports both instruction-based video editing and reference-guided editing (using a reference image and instruction). | |
| - **Paper:** [Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance](https://huggingface.co/papers/2603.02175) | |
| - **Project Page:** [https://showlab.github.io/Kiwi-Edit/](https://showlab.github.io/Kiwi-Edit/) | |
| - **Repository:** [https://github.com/showlab/Kiwi-Edit](https://github.com/showlab/Kiwi-Edit) | |
| ## Model Description | |
| Kiwi-Edit introduces a unified editing architecture that synergizes learnable queries and latent visual features for reference semantic guidance. It addresses the challenge of precise visual control in instruction-based editing by allowing users to provide a reference image to guide the transformation. The framework achieves significant performance improvements in instruction following and reference fidelity through a scalable data generation pipeline and a multi-stage training curriculum. | |
| ## Usage | |
| This model is compatible with the `diffusers` library. To run inference, follow the installation instructions in the [official repository](https://github.com/showlab/Kiwi-Edit). | |
| ### Quick Test with Diffusers | |
| You can run a quick test on a demo video using the following command provided in the repository: | |
| ```bash | |
| python diffusers_demo.py \ | |
| --video_path ./demo_data/video/source/0005e4ad9f49814db1d3f2296b911abf.mp4 \ | |
| --prompt "Remove the monkey." \ | |
| --save_path output.mp4 \ | |
| --model_path linyq/kiwi-edit-5b-instruct-only-diffusers | |
| ``` | |
| ## Citation | |
| If you find this work useful, please cite: | |
| ```bibtex | |
| @misc{kiwiedit, | |
| title={Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance}, | |
| author={Yiqi Lin and Guoqiang Liang and Ziyun Zeng and Zechen Bai and Yanzhe Chen and Mike Zheng Shou}, | |
| year={2026}, | |
| eprint={2603.02175}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2603.02175}, | |
| } | |
| ``` |