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zai-org/Kaleido-14B-S2V
Kaleido-14B-S2V is a machine learning model from zai-org. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This repository contains the official implementation of Kaleido, proposed in our paper: <a href="https://arxiv.org/abs/2510.18573" <img src="https://img.shields.io/badge/arXiv-2510.18573-B31B1B.svg" style="vertical-al…
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Updated Dec 11, 2025
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From the Hugging Face model README
This repository contains the official implementation of Kaleido, proposed in our paper: <a href="https://arxiv.org/abs/2510.18573#"> <img src="https://img.shields.io/badge/arXiv-2510.18573-B31B1B.svg" style="vertical-align: middle;"> </a>
Use the following commands to download the model weights (We have integrated both Wan VAE and T5 modules into this checkpoint for convenience).
# Download the repository (skip automatic LFS file downloads)
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Crilias/Kaleido-14B-S2V
# Enter the repository folder
cd Kaleido-14B-S2V
# Merge the checkpoint files
python merge_kaleido.py
Arrange the model files into the following structure:
.
├── Kaleido-14B-S2V
│ ├── model
│ │ └── ....
│ ├── Wan2.1_VAE.pth
│ │
│ └── umt5-xxl
│ └── ....
├── configs
├── sat
└── sgm
If you find our work helpful, please cite our paper:
@misc{zhang2025kaleidoopensourcedmultisubjectreference,
title={Kaleido: Open-Sourced Multi-Subject Reference Video Generation Model},
author={Zhenxing Zhang and Jiayan Teng and Zhuoyi Yang and Tiankun Cao and Cheng Wang and Xiaotao Gu and Jie Tang and Dan Guo and Meng Wang},
year={2025},
eprint={2510.18573},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2510.18573},
}