---
pipeline_tag: image-to-video
library_name: diffusers
---

# 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 natural language instruction-based video editing and combined reference image + instruction video editing.

[[Paper](https://huggingface.co/papers/2603.02175)] [[Project Page](https://showlab.github.io/Kiwi-Edit)] [[GitHub](https://github.com/showlab/Kiwi-Edit)]

## Introduction

Instruction-based video editing has witnessed rapid progress, yet current methods often struggle with precise visual control. Kiwi-Edit introduces a unified editing architecture that synergizes learnable queries and latent visual features for reference semantic guidance. By leveraging a scalable data generation pipeline and the RefVIE dataset, the model achieves significant gains in instruction following and reference fidelity, establishing a new state-of-the-art in controllable video editing.

## Quick Start

### Installation (Diffusers Environment)

```bash
# Create conda environment
conda create -n diffusers python=3.10 -y
conda activate diffusers
# Install PyTorch 2.7 with CUDA
pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
pip install diffusers decord einops accelerate transformers==4.57.0 opencv-python av
```

### Inference Sample

You can run a quick test on a demo video using the script provided in the official 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 use Kiwi-Edit in your research, please cite the following paper:

```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}, 
}
```