---
base_model:
- Wan-AI/Wan2.1-T2V-1.3B
- Qwen/Qwen-Image-Edit-2509
language:
- en
license: apache-2.0
pipeline_tag: other
library_name: diffusers
arxiv: 2601.20175
tags:
- video
- image
- stylization
- style-transfer
---
TeleStyle: Content-Preserving Style Transfer in Images and Videos
Shiwen Zhang, Xiaoyan Yang, Bojia Zi, Haibin Huang, Chi Zhang, Xuelong Li
Institute of Artificial Intelligence, China Telecom (TeleAI)
TeleStyle is a lightweight yet effective model for both image and video stylization. Built upon Qwen-Image-Edit, it leverages a Curriculum Continual Learning framework to achieve high-fidelity content preservation and style customization across diverse, in-the-wild style categories.
## 🔔 News
- [2026-01-28]: Released [Code](https://github.com/Tele-AI/TeleStyle), [Model](https://huggingface.co/Tele-AI/TeleStyle), [Paper](http://arxiv.org/abs/2601.20175).
## How to use
### 1. Installation
```bash
pip install -r requirements.txt
```
### 2. Inference
We provide inference scripts for running TeleStyle on demo inputs for each task:
#### Image Stylization
To generate a stylized image using a reference style image and a content image:
```bash
python telestyleimage_inference.py --image_path assets/example/0.png --style_path videos/1.png --output_path results/image.png
```
#### Video Stylization
To generate a stylized video using a stylized first frame and a content video:
```bash
python telestylevideo_inference.py --video_path assets/example/1.mp4 --style_path assets/example/1-0.png --output_path results/video.mp4
```
For more details, please refer to the [**🔗 GitHub**](https://github.com/Tele-AI/TeleStyle) repository.
## 🌟 Citation
If you find TeleStyle useful for your research and applications, please cite using this BibTeX:
```bibtex
@article{teleai2026telestyle,
title={TeleStyle: Content-Preserving Style Transfer in Images and Videos},
author={Shiwen Zhang and Xiaoyan Yang and Bojia Zi and Haibin Huang and Chi Zhang and Xuelong Li},
journal={arXiv preprint arXiv:2601.20175},
year={2026}
}
```