Model Card for nunchaku-qwen-image-edit
This repository contains Nunchaku-quantized versions of Qwen-Image-Edit, an image-editing model based on Qwen-Image, advances in complex text rendering. It is optimized for efficient inference while maintaining minimal loss in performance.
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Model Details
Model Description
- Developed by: Nunchaku Team
- Model type: image-to-image
- License: apache-2.0
- Quantized from model: Qwen-Image-Edit
Model Files
Data Type: INT4 for non-Blackwell GPUs (pre-50-series), NVFP4 for Blackwell GPUs (50-series).
Rank: r32 for faster inference, r128 for better quality but slower inference.
Base Models
Standard inference speed models for general use
| Data Type |
Rank |
Model Name |
Comment |
| INT4 |
r32 |
svdq-int4_r32-qwen-image-edit.safetensors |
| r128 |
svdq-int4_r128-qwen-image-edit.safetensors |
| NVFP4 |
r32 |
svdq-fp4_r32-qwen-image-edit.safetensors |
| r128 |
svdq-fp4_r128-qwen-image-edit.safetensors |
4-Step Distilled Models
4-step distilled models fused with Qwen-Image-Edit-Lightning-4steps-V1.0 LoRA using LoRA strength = 1.0
8-Step Distilled Models
8-step distilled models fused with Qwen-Image-Edit-Lightning-8steps-V1.0 LoRA using LoRA strength = 1.0
Model Sources
Usage
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}
}
