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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.00176 |
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| _version_ | 1866909617713315840 |
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| author | Ren, Hui Materzynska, Joanna Gandikota, Rohit Bau, David Torralba, Antonio |
| author_facet | Ren, Hui Materzynska, Joanna Gandikota, Rohit Bau, David Torralba, Antonio |
| contents | We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investigate this, we train a text-to-image model exclusively on photographs, without access to any painting-related content. We show that it is possible to adapt a model that is trained without paintings to an artistic style, given only few examples. User studies and automatic evaluations confirm that our model (post-adaptation) performs on par with state-of-the-art models trained on massive datasets that contain artistic content like paintings, drawings or illustrations. Finally, using data attribution techniques, we analyze how both artistic and non-artistic datasets contribute to generating artistic-style images. Surprisingly, our findings suggest that high-quality artistic outputs can be achieved without prior exposure to artistic data, indicating that artistic style generation can occur in a controlled, opt-in manner using only a limited, carefully selected set of training examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Opt-In Art: Learning Art Styles Only from Few Examples Ren, Hui Materzynska, Joanna Gandikota, Rohit Bau, David Torralba, Antonio Computer Vision and Pattern Recognition We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investigate this, we train a text-to-image model exclusively on photographs, without access to any painting-related content. We show that it is possible to adapt a model that is trained without paintings to an artistic style, given only few examples. User studies and automatic evaluations confirm that our model (post-adaptation) performs on par with state-of-the-art models trained on massive datasets that contain artistic content like paintings, drawings or illustrations. Finally, using data attribution techniques, we analyze how both artistic and non-artistic datasets contribute to generating artistic-style images. Surprisingly, our findings suggest that high-quality artistic outputs can be achieved without prior exposure to artistic data, indicating that artistic style generation can occur in a controlled, opt-in manner using only a limited, carefully selected set of training examples. |
| title | Opt-In Art: Learning Art Styles Only from Few Examples |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.00176 |