base_model, base_model_relation, datasets, frameworks, language, license, tags, tasks

base_model	base_model_relation	datasets	frameworks	language	license	tags	tasks
Qwen/Qwen-Image	quantized	mit-han-lab/svdquant-datasets	PyTorch	en	Apache License 2.0	text-to-image SVDQuant Qwen-Image Diffusion Quantization ICLR2025	text-to-image-synthesis

Model Card for nunchaku-qwen-image

This repository contains Nunchaku-quantized versions of Qwen-Image, designed to generate high-quality images from text prompts, advances in complex text rendering. 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: Qwen-Image

Model Files

• svdq-int4_r32-qwen-image.safetensors: SVDQuant quantized INT4 Qwen-Image model with rank 32. For users with non-Blackwell GPUs (pre-50-series).
• svdq-int4_r128-qwen-image.safetensors: SVDQuant quantized INT4 Qwen-Image model with rank 128. For users with non-Blackwell GPUs (pre-50-series). It offers better quality than the rank 32 model, but it is slower.
• svdq-fp4_r32-qwen-image.safetensors: SVDQuant quantized NVFP4 Qwen-Image model with rank 32. For users with Blackwell GPUs (50-series).
• svdq-fp4_r128-qwen-image.safetensors: SVDQuant quantized NVFP4 Qwen-Image model with rank 128. For users with Blackwell GPUs (50-series). It offers better quality than the rank 32 model, but it is slower.

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 qwen-image.py.
• ComfyUI Usage: Coming soon!

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}
}