Model Card for nunchaku-qwen-image-edit-2509
This repository contains Nunchaku-quantized versions of Qwen-Image-Edit-2509, 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.
News
- [2025-11-15] 🚀 Release new quantized qwen-image-edit-2509 4/8-step lightning models, fused with lightx2v Qwen-Image-Edit-2509 lightning lora. All models are available in the
lightning-251115folder. - [2025-09-25] 🔥 Release 4-bit 4/8-step lightning Qwen-Image-Edit!
- [2025-09-24] 🚀 Release 4-bit SVDQuant quantized Qwen-Image-Edit-2509 model with rank 32 and 128!
Model Details
Model Description
- Developed by: Nunchaku Team
- Model type: image-to-image
- License: apache-2.0
- Quantized from model: Qwen-Image-Edit-2509
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-2509.safetensors | |
| r128 | svdq-int4_r128-qwen-image-edit-2509.safetensors | ||
| NVFP4 | r32 | svdq-fp4_r32-qwen-image-edit-2509.safetensors | |
| r128 | svdq-fp4_r128-qwen-image-edit-2509.safetensors |
4-Step Distilled Models
4-step distilled models fused with Qwen-Image-Lightning-4steps-V2.0 LoRA or Qwen-Image-Edit-2509-Lightning-4steps-V1.0 LoRA using LoRA strength = 1.0
8-Step Distilled Models
8-step distilled models fused with Qwen-Image-Lightning-8steps-V2.0 LoRA or Qwen-Image-Edit-2509-Lightning-8steps-V1.0 LoRA using LoRA strength = 1.0
Model Sources
- Inference Engine: nunchaku
- Quantization Library: deepcompressor
- Paper: SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
- Demo: demo.nunchaku.tech
Usage
- Diffusers Usage: See qwen-image-edit-2509.py. Check this tutorial for more advanced usage.
- ComfyUI Usage: See nunchaku-qwen-image-edit-2509.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}
}