---
title: nunchaku
canonical_url: "https://www.modelscope.cn/models/nunchaku-tech/nunchaku"
md_url: "https://www.modelscope.cn/models/nunchaku-tech/nunchaku.md"
repository: nunchaku-tech/nunchaku
last_updated: 2026-01-10
license: "Apache License 2.0"
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
library_name:
  - pytorch
frameworks:
  - PyTorch
language:
  - en
downloads: 101
stars: 23
tags:
  - image-generation
  - SVDQuant
  - Nunchaku
  - Diffusion
  - Quantization
  - ICLR2025
---

# nunchaku

> nunchaku - nunchaku-tech 在 ModelScope 开源的模型。Nunchaku Pre-built Wheels

nunchaku-tech/nunchaku 是 ModelScope 魔搭社区上的text-to-image-synthesis模型，采用 Apache License 2.0 许可。

- **Repository**: nunchaku-tech/nunchaku
- **License**: Apache License 2.0
- **Tasks**: text-to-image-synthesis
- **Tags**: image-generation, SVDQuant, Nunchaku, Diffusion, Quantization, ICLR2025
- **Downloads**: 101
- **Stars**: 23
- **Last updated**: 2026-01-10

Source: https://www.modelscope.cn/models/nunchaku-tech/nunchaku

---

<p align="center" style="border-radius: 10px">
  <img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku_v2.png" width="30%" alt="Nunchaku Logo"/>
</p>

<div align="center">
  <a href=https://discord.gg/Wk6PnwX9Sm target="_blank"><img src=https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2FWk6PnwX9Sm%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total height=22px></a>
  <a href=https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/wechat.jpg target="_blank"><img src=https://img.shields.io/badge/WeChat-07C160?logo=wechat&logoColor=white height=22px></a>
</div>

# Nunchaku Pre-built Wheels

This repository provides pre-built wheels for [nunchaku](https://github.com/nunchaku-tech/nunchaku) for both Linux and Windows platforms. For detailed information about available wheels, please visit our [GitHub Releases](https://github.com/nunchaku-tech/nunchaku/releases) page.

## Citation

```bibtex
@inproceedings{
  li2024svdquant,
  title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
  author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}
```