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What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education

Text-to-Image artificial intelligence (AI) recently saw a major breakthrough with the release of Dall-E and its open-source counterpart, Stable Diffusion. These programs allow anyone to create original visual art pieces by simply providing descriptions in natural language (prompts). Using a sample o...

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Detalles Bibliográficos
Autores principales: Dehouche, Nassim, Dehouche, Kullathida
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245047/
https://www.ncbi.nlm.nih.gov/pubmed/37292268
http://dx.doi.org/10.1016/j.heliyon.2023.e16757
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author Dehouche, Nassim
Dehouche, Kullathida
author_facet Dehouche, Nassim
Dehouche, Kullathida
author_sort Dehouche, Nassim
collection PubMed
description Text-to-Image artificial intelligence (AI) recently saw a major breakthrough with the release of Dall-E and its open-source counterpart, Stable Diffusion. These programs allow anyone to create original visual art pieces by simply providing descriptions in natural language (prompts). Using a sample of 72,980 Stable Diffusion prompts, we propose a formalization of this new medium of art creation and assess its potential for teaching the history of art, aesthetics, and technique. Our findings indicate that text-to-Image AI has the potential to revolutionize the way art is taught, offering new, cost-effective possibilities for experimentation and expression. However, it also raises important questions about the ownership of artistic works. As more and more art is created using these programs, it will be crucial to establish new legal and economic models to protect the rights of artists.
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spelling pubmed-102450472023-06-08 What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education Dehouche, Nassim Dehouche, Kullathida Heliyon Research Article Text-to-Image artificial intelligence (AI) recently saw a major breakthrough with the release of Dall-E and its open-source counterpart, Stable Diffusion. These programs allow anyone to create original visual art pieces by simply providing descriptions in natural language (prompts). Using a sample of 72,980 Stable Diffusion prompts, we propose a formalization of this new medium of art creation and assess its potential for teaching the history of art, aesthetics, and technique. Our findings indicate that text-to-Image AI has the potential to revolutionize the way art is taught, offering new, cost-effective possibilities for experimentation and expression. However, it also raises important questions about the ownership of artistic works. As more and more art is created using these programs, it will be crucial to establish new legal and economic models to protect the rights of artists. Elsevier 2023-05-26 /pmc/articles/PMC10245047/ /pubmed/37292268 http://dx.doi.org/10.1016/j.heliyon.2023.e16757 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Dehouche, Nassim
Dehouche, Kullathida
What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
title What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
title_full What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
title_fullStr What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
title_full_unstemmed What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
title_short What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
title_sort what’s in a text-to-image prompt? the potential of stable diffusion in visual arts education
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245047/
https://www.ncbi.nlm.nih.gov/pubmed/37292268
http://dx.doi.org/10.1016/j.heliyon.2023.e16757
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