modify model readme

README.md

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     <strong>Jingfeng Zhang</strong>
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+[**Paper (ArXiv)**](https://arxiv.org/abs/2312.11392) **|** [**Project Page**](https://scedit.github.io/) **|** [**Code**](https://github.com/modelscope/scepter)**|** [**Swift**](https://github.com/modelscope/swift)
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     <b>Alibaba Group</b>
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-  <a href="https://arxiv.org/abs/2312.11392"><img src='https://img.shields.io/badge/arXiv-SCEdit-red' alt='Paper PDF' margin-top=0 margin-bottom=0></a>
-  <a href='https://scedit.github.io/'><img src='https://img.shields.io/badge/Project_Page-SCEdit-green' alt='Project Page' margin-top=0 margin-bottom=0></a>
-  <a href='https://github.com/modelscope/scepter'><img src='https://img.shields.io/badge/scepter-SCEdit-yellow' margin-top=0 margin-bottom=0></a>
-  <a href='https://github.com/modelscope/swift'><img src='https://img.shields.io/badge/swift-SCEdit-blue' margin-top=0 margin-bottom=0></a>
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 SCEdit is an efficient generative fine-tuning framework proposed by Alibaba TongYi Vision Intelligence Lab. This framework enhances the fine-tuning capabilities for text-to-image generation downstream tasks and enables quick adaptation to specific generative scenarios, **saving 30%-50% of training memory costs compared to LoRA**. Furthermore, it can be directly extended to controllable image generation tasks, **requiring only 7.9% of the parameters that ControlNet needs for conditional generation and saving 30% of memory usage**. It supports various conditional generation tasks including edge maps, depth maps, segmentation maps, poses, color maps, and image completion.
 #### Code Example
 ```shell
-git clone https://github.com/modelscope/scepter.git
-cd scepter
-PYTHONPATH=. python scepter/tools/run_train.py --cfg scepter/methods/SCEdit/t2i_sdxl_1024_sce.yaml
-```
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-To prepare  the training dataset.
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-```python
-# pip install modelscope
-from modelscope.msdatasets import MsDataset
-ms_train_dataset = MsDataset.load('style_custom_dataset', namespace='damo', subset_name='3D', split='train_short')
-print(next(iter(ms_train_dataset)))
+pip install scepter
+python -m scepter.tools.webui
 ```
 
 ## BibTeX