Related papers: Art-Free Generative Models: Art Creation Without Graphic Art Knowledge

Art-Free Generative Models: Art Creation Without Graphic Art Knowledge

URL: http://arxiv.org/abs/2412.00176v1
Date: Fri, 29 Nov 2024 18:59:01 GMT
Title: Art-Free Generative Models: Art Creation Without Graphic Art Knowledge
Authors: Hui Ren, Joanna Materzynska, Rohit Gandikota, David Bau, Antonio Torralba,
Abstract summary: We propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles.
Score: 50.60063523054282
License:
Abstract: We explore the question: "How much prior art knowledge is needed to create art?" To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles. Our experiments show that art generated using our method is perceived by users as comparable to art produced by models trained on large, art-rich datasets. Finally, through data attribution techniques, we illustrate how examples from both artistic and non-artistic datasets contributed to the creation of new artistic styles.

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