Add model card for Kiwi-Edit

README.md

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+---
+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}, 
+}
+```