qwen-image-edit-2509-fork

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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

• [2025-09-25] 🔥 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

• svdq-int4_r32-qwen-image-edit-2509.safetensors: SVDQuant INT4 (rank 32) Qwen-Image-Edit-2509 model. For users with non-Blackwell GPUs (pre-50-series).
• svdq-int4_r128-qwen-image-edit-2509.safetensors: SVDQuant INT4 (rank 128) Qwen-Image-Edit-2509 model. For users with non-Blackwell GPUs (pre-50-series). It offers better quality than the rank 32 model, but it is slower.
• svdq-int4_r32-qwen-image-edit-2509-lightningv2.0-4steps.safetensors: SVDQuant INT4 (rank 32) 4-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with non-Blackwell GPUs (pre-50-series).
• svdq-int4_r128-qwen-image-edit-2509-lightning: SVDQuant INT4 (rank 128) 4-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with non-Blackwell GPUs (pre-50-series).
• svdq-int4_r32-qwen-image-edit-2509-lightningv2.0-8steps.safetensors: SVDQuant INT4 (rank 32) 8-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-8steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with non-Blackwell GPUs (pre-50-series).
• svdq-int4_r128-qwen-image-edit-2509-lightningv2.0-8steps.safetensors: SVDQuant INT4 (rank 128) 8-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-8steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with non-Blackwell GPUs (pre-50-series).
• svdq-fp4_r32-qwen-image-edit-2509.safetensors: SVDQuant NVFP4 (rank 32) Qwen-Image-Edit-2509 model. For users with Blackwell GPUs (50-series).
• svdq-fp4_r128-qwen-image-edit-2509.safetensors: SVDQuant NVFP4 (rank 128) Qwen-Image-Edit-2509 model. For users with Blackwell GPUs (50-series). It offers better quality than the rank 32 model, but it is slower.
• svdq-fp4_r32-qwen-image-edit-2509-lightningv2.0-4steps.safetensors: SVDQuant NVFP4 (rank 32) 4-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with Blackwell GPUs (50-series).
• svdq-fp4_r128-qwen-image-edit-2509-lightningv2.0-4steps.safetensors: SVDQuant NVFP4 (rank 128) 4-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with Blackwell GPUs (50-series).
• svdq-fp4_r32-qwen-image-edit-2509-lightningv2.0-8steps.safetensors: SVDQuant NVFP4 (rank 32) 8-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-8steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with Blackwell GPUs (50-series).
• svdq-fp4_r128-qwen-image-edit-2509-lightningv2.0-8steps.safetensors: SVDQuant NVFP4 (rank 128) 8-step Qwen-Image-Edit-2509 model by fusing Qwen-Image-Lightning-8steps-V2.0-bf16.safetensors using LoRA strength = 1.0. For users with Blackwell GPUs (50-series).

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-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}
}