mirror of
https://www.modelscope.cn/nunchaku-tech/nunchaku-qwen-image.git
synced 2026-04-03 02:52:54 +08:00
Upload ./README.md to ModelScope hub
This commit is contained in:
Lmxyy1999
108
README.md
108
README.md
@ -1,47 +1,73 @@
|
|||||||
---
|
---
|
||||||
|
base_model: Qwen/Qwen-Image
|
||||||
|
base_model_relation: quantized
|
||||||
|
datasets:
|
||||||
|
- mit-han-lab/svdquant-datasets
|
||||||
|
frameworks: PyTorch
|
||||||
|
language:
|
||||||
|
- en
|
||||||
license: Apache License 2.0
|
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。
|
<p align="center" style="border-radius: 10px">
|
||||||
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
|
<img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
|
||||||
|
</p>
|
||||||
|
|
||||||
SDK下载
|
# Model Card for nunchaku-qwen-image
|
||||||
```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
|
|
||||||
```
|
|
||||||
|
|
||||||
<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>
|
|
||||||
|
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
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## 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}
|
||||||
|
}
|
||||||
|
```
|
||||||
Reference in New Issue
Block a user