Instructions to use mit-han-lab/nunchaku with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mit-han-lab/nunchaku with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mit-han-lab/nunchaku", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
language:
- en
library_name: diffusers
license: apache-2.0
pipeline_tag: text-to-image
tags:
- image-generation
- SVDQuant
- Nunchaku
- Diffusion
- Quantization
- ICLR2025
This repository has been migrated to https://huggingface.co/nunchaku-tech/nunchaku and will be hidden in December 2025.
Nunchaku Pre-built Wheels
This repository provides pre-built wheels for nunchaku for both Linux and Windows platforms. For detailed information about available wheels, please visit our GitHub Releases page.
Citation
@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}
}