kp-forks/nunchaku-qwen-image-edit

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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

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

• —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.py and qwen-image-edit-lightning.py. Check this tutorial for more advanced usage.
• —ComfyUI Usage: See nunchaku-qwen-image-edit.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}
}