--- title: nunchaku-qwen-image canonical_url: "https://www.modelscope.cn/models/nunchaku-tech/nunchaku-qwen-image" md_url: "https://www.modelscope.cn/models/nunchaku-tech/nunchaku-qwen-image.md" repository: nunchaku-tech/nunchaku-qwen-image last_updated: 2026-01-10 license: "Apache License 2.0" pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis base_model: - Qwen/Qwen-Image base_model_relation: quantized parameters: 106.9B tensor_type: - I8 - BF16 - F8_E4M3 - I32 library_name: - lora - safetensors - pytorch frameworks: - PyTorch language: - en downloads: 13825 stars: 38 tags: - text-to-image - SVDQuant - Qwen-Image - Diffusion - Quantization - ICLR2025 --- # nunchaku-qwen-image > nunchaku-qwen-image - nunchaku-tech 在 ModelScope 开源的模型。Model Card for nunchaku-qwen-image nunchaku-tech/nunchaku-qwen-image 是 ModelScope 魔搭社区上的 106.9B 参数text-to-image-synthesis模型，采用 Apache License 2.0 许可，基于 Qwen/Qwen-Image 构建。 - **Repository**: nunchaku-tech/nunchaku-qwen-image - **License**: Apache License 2.0 - **Tasks**: text-to-image-synthesis - **Parameters**: 106.9B - **Base model**: Qwen/Qwen-Image - **Tags**: text-to-image, SVDQuant, Qwen-Image, Diffusion, Quantization, ICLR2025 - **Downloads**: 13825 - **Stars**: 38 - **Last updated**: 2026-01-10 Source: https://www.modelscope.cn/models/nunchaku-tech/nunchaku-qwen-image ---

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# Model Card for nunchaku-qwen-image ![comfyui](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/ComfyUI-nunchaku/workflows/nunchaku-qwen-image.png)![visual](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/qwen-image.jpg) This repository contains Nunchaku-quantized versions of [Qwen-Image](https://huggingface.co/Qwen/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. ## News - [2025-08-27] 🔥 Release **4-bit [4/8-step lightning Qwen-Image](https://huggingface.co/lightx2v/Qwen-Image-Lightning)**! - [2025-08-15] 🚀 Release 4-bit SVDQuant quantized Qwen-Image model with rank 32 and 128! ## Model Details ### Model Description - **Developed by:** Nunchaku Team - **Model type:** text-to-image - **License:** apache-2.0 - **Quantized from model:** [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image) ### 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.safetensors`](./svdq-int4_r32-qwen-image.safetensors) | | | | r128 | [`svdq-int4_r128-qwen-image.safetensors`](./svdq-int4_r128-qwen-image.safetensors) | | | NVFP4 | r32 | [`svdq-fp4_r32-qwen-image.safetensors`](./svdq-fp4_r32-qwen-image.safetensors) | | | | r128 | [`svdq-fp4_r128-qwen-image.safetensors`](./svdq-fp4_r128-qwen-image.safetensors) | | ### 4-Step Distilled Models 4-step distilled models fused with [Qwen-Image-Lightning-4steps-V1.0 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0-bf16.safetensors) using LoRA strength = 1.0 | Data Type | Rank | Model Name | Comment | |-----------|------|----------|---------| | INT4 | r32 | [`svdq-int4_r32-qwen-image-lightningv1.0-4steps.safetensors`](./svdq-int4_r32-qwen-image-lightningv1.0-4steps.safetensors) | Fused with [Qwen-Image-Lightning-4steps-V1.0 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0-bf16.safetensors) | | | r128 | [`svdq-int4_r128-qwen-image-lightningv1.0-4steps.safetensors`](./svdq-int4_r128-qwen-image-lightningv1.0-4steps.safetensors) | Fused with [Qwen-Image-Lightning-4steps-V1.0 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0-bf16.safetensors). Better quality, slower inference | | NVFP4 | r32 | [`svdq-fp4_r32-qwen-image-lightningv1.0-4steps.safetensors`](./svdq-fp4_r32-qwen-image-lightningv1.0-4steps.safetensors) | Fused with [Qwen-Image-Lightning-4steps-V1.0 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0-bf16.safetensors) | | | r128 | [`svdq-fp4_r128-qwen-image-lightningv1.0-4steps.safetensors`](./svdq-fp4_r128-qwen-image-lightningv1.0-4steps.safetensors) | Fused with [Qwen-Image-Lightning-4steps-V1.0 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0-bf16.safetensors). Better quality, slower inference | ### 8-Step Distilled Models 8-step distilled models fused with [Qwen-Image-Lightning-8steps-V1.1 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors) using LoRA strength = 1.0 | Data Type | Rank | Model Name | Comment | |-----------|------|----------|---------| | INT4 | r32 | [`svdq-int4_r32-qwen-image-lightningv1.1-8steps.safetensors`](./svdq-int4_r32-qwen-image-lightningv1.1-8steps.safetensors) | Fused with [Qwen-Image-Lightning-8steps-V1.1 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors) | | | r128 | [`svdq-int4_r128-qwen-image-lightningv1.1-8steps.safetensors`](./svdq-int4_r128-qwen-image-lightningv1.1-8steps.safetensors) | Fused with [Qwen-Image-Lightning-8steps-V1.1 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors). Better quality, slower inference | | NVFP4 | r32 | [`svdq-fp4_r32-qwen-image-lightningv1.1-8steps.safetensors`](./svdq-fp4_r32-qwen-image-lightningv1.1-8steps.safetensors) | Fused with [Qwen-Image-Lightning-8steps-V1.1 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors) | | | r128 | [`svdq-fp4_r128-qwen-image-lightningv1.1-8steps.safetensors`](./svdq-fp4_r128-qwen-image-lightningv1.1-8steps.safetensors) | Fused with [Qwen-Image-Lightning-8steps-V1.1 LoRA](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors). Better quality, slower inference | ### Model Sources - **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku) - **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor) - **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007) - **Demo:** [demo.nunchaku.tech](https://demo.nunchaku.tech) ## Usage - Diffusers Usage: See [qwen-image.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/v1/qwen-image.py) and [qwen-image-lightning.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/v1/qwen-image-lightning.py). - ComfyUI Usage: See [nunchaku-qwen-image.json](https://nunchaku.tech/docs/ComfyUI-nunchaku/workflows/qwenimage.html#nunchaku-qwen-image-json). ## Performance ![performance](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/efficiency.jpg) ## Citation ```bibtex @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} } ```