Update README.md

README.md

@@ -1,24 +1,55 @@
-<div align="center">TeleStyle:  Content-Preserving Style Transfer in Images and Videos</div>
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Wan-AI/Wan2.1-T2V-1.3B
+- Qwen/Qwen-Image-Edit-2509
+pipeline_tag: video-to-video; image-to-image
+tags:
+- video
+- image
+- stylization
+---
 <div align="center">
-    Shiwen Zhang, Xiaoyan Yang, Bojia Zi, Haibin Huang, Chi Zhang, Xuelong Li
-    <br>
-    Institute of Artificial Intelligence, China Telecom (TeleAI) 
+
+<h1><b>TeleStyle: Content-Preserving Style Transfer in Images and Videos</b></h1>
+
 </div>
-<br>
+
 <div align="center">
-    [<a href="todo" target="_blank">Project Page</a>]
-    [<a href="todo" target="_blank">arXiv</a>]
+Shiwen Zhang, Xiaoyan Yang, Bojia Zi, Haibin Huang, Chi Zhang, Xuelong Li
+  <p>
+    Institute of Artificial Intelligence, China Telecom (TeleAI)&nbsp;&nbsp;
+  </p>
 </div>
 
-## Abstract
-Content-preserving style transfer—generating stylized outputs based on content and style references—remains a significant challenge for Diffusion Transformers (DiTs) due to the inherent entanglement of content and style features in their internal representations. In this technical report, we present TeleStyle, a lightweight yet effective model for both image and video stylization. Built upon Qwen-Image-Edit, TeleStyle leverages the base model’s robust capabilities in content preservation and style customization. To facilitate effective training, we curated a high-quality dataset of distinct specific styles and further synthesized triplets using thousands of diverse, in-the-wild style categories. We introduce a Curriculum Continual Learning framework to train TeleStyle on this hybrid dataset of clean (curated) and noisy (synthetic) triplets. This approach enables the model to generalize to unseen styles without compromising precise content fidelity. Additionally, we introduce a video-to-video stylization module to enhance temporal consistency and visual quality. TeleStyle achieves state-of-the-art performance across three core evaluation metrics: style similarity, content consistency, and aesthetic quality.
+<p align="center">
+  <a href='todo'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
+  &nbsp;
+  <a href="todo"><img src="https://img.shields.io/static/v1?label=Arxiv&message=UniVideo&color=red&logo=arxiv"></a>
+  &nbsp;
+  <a href='https://huggingface.co/Tele-AI/TeleStyle'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-orange'></a>
+  &nbsp;
+  <a href="https://github.com/Tele-AI/TeleStyle">
+    <img src="https://img.shields.io/badge/GitHub-Code-red?logo=github">
+  </a>
+</p>
+
+
+## 🔔News
+
+- [2026-01-27]: Released [Code](https://github.com/Tele-AI/TeleStyle), [Model](https://huggingface.co/Tele-AI/TeleStyle) and [Technical Report](todo)
+
+## How to use
+
+- Please refer to [**🔗 GitHub**](https://github.com/Tele-AI/TeleStyle) for usage.
+
 
-## Latest News
+## 🌟 Citation
 
-- Jan 27, 2026: We release the <a href="todo" target="_blank">Technical Report</a> , code and models of TeleStyle
+If you find UniVideo useful for your research and applications, please cite using this BibTeX:
 
-## Citation
-If you find TeleStyle useful in your research, please kindly cite our paper:
 ```bibtex
 @misc{teleai2026telestyle,
     title={TeleStyle: Content-Preserving Style Transfer in Images and Videos},