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.

• Developed by: Nunchaku Team
• Model type: text-to-image
• License: apache-2.0
• Quantized from model: FLUX.1-schnell

• 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).

• Inference Engine: nunchaku
• Quantization Library: deepcompressor
• Paper: SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
• Demo: svdquant.mit.edu

• Diffusers Usage: See flux.1-schnell.py. Check our tutorial for more advanced usage.
• ComfyUI Usage: See nunchaku-flux.1-schnell.json.

@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}
}