Opt-In Art: Learning Art Styles Only from Few Examples

arXiv:2412.00176 · cs.CV · Submitted 2024-11-29 · Read on arXiv

cs.CV

Submitted: 2024-11-29

Updated: 2026-09-15

License: http://creativecommons.org/licenses/by/4.0/

The gist: We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples.

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Abstract

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.

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