This repository contains Nunchaku-quantized versions of sdxl-turbo, designed to generate high-quality images from text prompts. It is optimized for efficient inference while maintaining minimal loss in performance.

No recent news. Stay tuned for updates!

• Developed by: Nunchaku Team (thank @devgdovg)
• Model type: text-to-image
• License: sai-nc-community
• Quantized from model: sdxl-turbo

• svdq-int4_r32-sdxl-turbo.safetensors: SVDQuant quantized INT4 SDXL model. For users with non-Blackwell GPUs (pre-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 sdxl-turbo.py or our tutorial for usage.
• ComfyUI Usage: Stay tuned!

@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}
}