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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.02109 |
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| _version_ | 1866916177375133696 |
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| author | Chen, Dar-Yen Tennent, Hamish Hsu, Ching-Wen |
| author_facet | Chen, Dar-Yen Tennent, Hamish Hsu, Ching-Wen |
| contents | This work introduces ArtAdapter, a transformative text-to-image (T2I) style transfer framework that transcends traditional limitations of color, brushstrokes, and object shape, capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapter to achieve unprecedented fidelity in style transfer, ensuring close alignment with textual descriptions. Additionally, the incorporation of an Auxiliary Content Adapter (ACA) effectively separates content from style, alleviating the borrowing of content from style references. Moreover, our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02109 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation Chen, Dar-Yen Tennent, Hamish Hsu, Ching-Wen Computer Vision and Pattern Recognition This work introduces ArtAdapter, a transformative text-to-image (T2I) style transfer framework that transcends traditional limitations of color, brushstrokes, and object shape, capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapter to achieve unprecedented fidelity in style transfer, ensuring close alignment with textual descriptions. Additionally, the incorporation of an Auxiliary Content Adapter (ACA) effectively separates content from style, alleviating the borrowing of content from style references. Moreover, our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods. |
| title | ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.02109 |