TeleStyle: Content-Preserving Style Transfer in Images and Videos
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910003164610560 |
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| author | Zhang, Shiwen Yang, Xiaoyan Zi, Bojia Huang, Haibin Zhang, Chi Li, Xuelong |
| author_facet | Zhang, Shiwen Yang, Xiaoyan Zi, Bojia Huang, Haibin Zhang, Chi Li, Xuelong |
| contents | 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. Code and pre-trained models are available at https://github.com/Tele-AI/TeleStyle |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20175 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TeleStyle: Content-Preserving Style Transfer in Images and Videos Zhang, Shiwen Yang, Xiaoyan Zi, Bojia Huang, Haibin Zhang, Chi Li, Xuelong Computer Vision and Pattern Recognition 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. Code and pre-trained models are available at https://github.com/Tele-AI/TeleStyle |
| title | TeleStyle: Content-Preserving Style Transfer in Images and Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.20175 |