From 8309d737bf419da85665fdc45bd6fc87022160b6 Mon Sep 17 00:00:00 2001
From: Lmxyy1999 <lmxyy1999@163.com>
Date: Fri, 15 Aug 2025 08:24:49 +0000
Subject: [PATCH] Upload ./README.md to ModelScope hub

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 README.md | 108 +++++++++++++++++++++++++++++++++---------------------
 1 file changed, 67 insertions(+), 41 deletions(-)

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 ---
+base_model: Qwen/Qwen-Image
+base_model_relation: quantized
+datasets:
+- mit-han-lab/svdquant-datasets
+frameworks: PyTorch
+language:
+- en
 license: Apache License 2.0
+tags:
+- text-to-image
+- SVDQuant
+- Qwen-Image
+- Diffusion
+- Quantization
+- ICLR2025
+tasks:
+- text-to-image-synthesis
 
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
-
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
-
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
-
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
-
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
-
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
 ---
-### 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。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
+<p align="center" style="border-radius: 10px">
+  <img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
+</p>
 
-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
-```
-```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('nunchaku-tech/nunchaku-qwen-image')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/nunchaku-tech/nunchaku-qwen-image.git
-```
+# Model Card for nunchaku-qwen-image
 
-<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](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.
+
+## 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
+
+- [`svdq-int4_r32-qwen-image.safetensors`](./svdq-int4_r32-qwen-image.safetensors): SVDQuant quantized INT4 Qwen-Image model with rank 32. For users with non-Blackwell GPUs (pre-50-series).
+- [`svdq-int4_r128-qwen-image.safetensors`](./svdq-int4_r128-qwen-image.safetensors): SVDQuant quantized INT4 Qwen-Image model with rank 128. For users with non-Blackwell GPUs (pre-50-series). It offers better quality than the rank 32 model, but it is slower.
+- [`svdq-fp4_r32-qwen-image.safetensors`](./svdq-fp4_r32-qwen-image.safetensors): SVDQuant quantized NVFP4 Qwen-Image model with rank 32. For users with Blackwell GPUs (50-series).
+- [`svdq-fp4_r128-qwen-image.safetensors`](./svdq-fp4_r128-qwen-image.safetensors): SVDQuant quantized NVFP4 Qwen-Image model with rank 128. For users with Blackwell GPUs (50-series). It offers better quality than the rank 32 model, but it is slower.
+
+
+### 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:** [svdquant.mit.edu](https://svdquant.mit.edu)
+
+## Usage
+
+- Diffusers Usage: See [qwen-image.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/v1/qwen-image.py).
+- ComfyUI Usage: Coming soon!
+
+## 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}
+}
+```
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