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README.md
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README.md
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---
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base_model: Qwen/Qwen-Image-Edit-2509
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base_model_relation: quantized
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datasets:
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- mit-han-lab/svdquant-datasets
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frameworks: PyTorch
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language:
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- en
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license: Apache License 2.0
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tags: []
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tags:
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- image-editing
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- SVDQuant
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- Qwen-Image-Edit-2509
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- Diffusion
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- Quantization
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- ICLR2025
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tasks:
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- text-to-image-synthesis
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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<p align="center" style="border-radius: 10px">
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<img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
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</p>
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('nunchaku-tech/nunchaku-qwen-image-edit-2509')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/nunchaku-tech/nunchaku-qwen-image-edit-2509.git
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```
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# Model Card for nunchaku-qwen-image-edit
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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This repository contains Nunchaku-quantized versions of [Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509), an image-editing model based on [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image), advances in complex text rendering. It is optimized for efficient inference while maintaining minimal loss in performance.
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No recent news. Stay tuned for updates!
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## Model Details
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### Model Description
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- **Developed by:** Nunchaku Team
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- **Model type:** image-to-image
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- **License:** apache-2.0
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- **Quantized from model:** [Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509)
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### Model Files
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- [`svdq-int4_r32-qwen-image-edit.safetensors`](./svdq-int4_r32-qwen-image-edit.safetensors): SVDQuant INT4 (rank 32) Qwen-Image-Edit-2509 model. For users with non-Blackwell GPUs (pre-50-series).
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- [`svdq-int4_r128-qwen-image-edit.safetensors`](./svdq-int4_r128-qwen-image-edit.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.
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- [`svdq-fp4_r32-qwen-image-edit.safetensors`](./svdq-fp4_r32-qwen-image-edit.safetensors): SVDQuant NVFP4 (rank 32) Qwen-Image-Edit-2509 model. For users with Blackwell GPUs (50-series).
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- [`svdq-fp4_r128-qwen-image-edit.safetensors`](./svdq-fp4_r128-qwen-image-edit.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.
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### Model Sources
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- **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku)
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- **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor)
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- **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007)
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- **Demo:** [svdquant.mit.edu](https://svdquant.mit.edu)
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## Usage
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- Diffusers Usage: See [qwen-image-edit-2509.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/v1/qwen-image-edit-2509.py). Check this [tutorial](https://nunchaku.tech/docs/nunchaku/usage/qwen-image-edit.html) for more advanced usage.
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- ComfyUI Usage: Will be released soon!
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## Performance
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## Citation
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```bibtex
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@inproceedings{
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li2024svdquant,
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title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
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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},
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booktitle={The Thirteenth International Conference on Learning Representations},
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year={2025}
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}
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```
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