You can deploy this model and start earning money today!

linyq/
kiwi-edit-5b-instruct-only-diffusers

Name: kiwi-edit-5b-instruct-only-diffusers
Rating: 2.5 (1146 reviews)

Views

No views yet

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] [Project Page] [GitHub]

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}, 
}