mit-han-lab/nunchaku-flux.1-schnell

This repository has been migrated to https://huggingface.co/nunchaku-tech/nunchaku-flux.1-schnell and will be hidden in December 2025.

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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: 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}
}