metadata
base_model: black-forest-labs/FLUX.1-schnell
base_model_relation: quantized
datasets:
- mit-han-lab/svdquant-datasets
language:
- en
library_name: diffusers
license: apache-2.0
pipeline_tag: text-to-image
tags:
- text-to-image
- SVDQuant
- FLUX.1-schnell
- FLUX.1
- Diffusion
- Quantization
- ICLR2025
This repository has been migrated to https://huggingface.co/nunchaku-tech/nunchaku-flux.1-schnell and will be hidden in December 2025.
Model Card for nunchaku-flux.1-schnell
This repository contains Nunchaku-quantized versions of FLUX.1-schnell, designed to generate high-quality images from text prompts. It is optimized for efficient inference while maintaining minimal loss in performance.
Model Details
Model Description
- Developed by: Nunchaku Team
- Model type: text-to-image
- License: apache-2.0
- Quantized from model: FLUX.1-schnell
Model Files
svdq-int4_r32-flux.1-schnell.safetensors: SVDQuant quantized INT4 FLUX.1-schnell model. For users with non-Blackwell GPUs (pre-50-series).svdq-fp4_r32-flux.1-schnell.safetensors: SVDQuant quantized NVFP4 FLUX.1-schnell model. For users with Blackwell GPUs (50-series).
Model Sources
- Inference Engine: nunchaku
- Quantization Library: deepcompressor
- Paper: SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
- Demo: svdquant.mit.edu
Usage
- Diffusers Usage: See flux.1-schnell.py. Check our tutorial for more advanced usage.
- ComfyUI Usage: See nunchaku-flux.1-schnell.json.
Performance
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}
}