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nunchaku-tech/
nunchaku-flux.1-schnell

Name: nunchaku-flux.1-schnell
Availability: InStock

mit-han-lab/svdquant-datasets

2411.05007

black-forest-labs/FLUX.1-schnell

quantized

apache-2.0

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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: demo.nunchaku.tech

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

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}
}

nunchaku-tech/nunchaku-flux.1-schnell