Testing AI Text to Image Generators for Advertising Applications

Artificial Intelligence technology in advertising creativity now produces more creative and innovative visual aesthetics that leads to profitability increase of advertising campaign investments, improve customer relations, and personalization in the fastest way and shortest time with the least effort, in addition to improving efficiency, meeting marketing demand, reorganizing and upgrading traditional processes. Despite this tremendous development, the role of designer cannot be overlooked in the first place because of his different visions. In light of the great crowding and creeping towards everything new in the world of artificial intelligence, it has become necessary for advertising designers to look carefully at the various technologies happening around, to help in various design fields, and to keep pace with the great development in the world of image due to the use of visual language in the first place in creating advertising content. In this research, we will discuss the selection of the most famous artificial intelligence generators currently in the field of generating images from written text, and a unified text is entered into all generators, and then the outputs are deduced and compared between them so that we can help the advertising designer choose the best among them and expand horizons alongside with traditional authentic manual work. This study helps understand and deduce the different outputs of artificial intelligence generators that converts written text into visual images and compare them together through some simple design elements, taking into consideration that the inputs and written text are constant in all generators.

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Publication Details

Journal
Journal of Design Sciences and Applied Arts/Journal of Design Sciences and Applied Arts
Published
2025-05-18
DOI
https://doi.org/10.21608/jdsaa.2025.330069.1431
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
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article

Testing AI Text to Image Generators for Advertising Applications

Dena Hanna
Journal of Design Sciences and Applied Arts/Journal of Design Sciences and Applied Arts
Generative Adversarial Networks and Image Synthesis
article

Testing AI Text to Image Generators for Advertising Applications

Dena Hanna
article en

Abstract

Artificial Intelligence technology in advertising creativity now produces more creative and innovative visual aesthetics that leads to profitability increase of advertising campaign investments, improve customer relations, and personalization in the fastest way and shortest time with the least effort, in addition to improving efficiency, meeting marketing demand, reorganizing and upgrading traditional processes. Despite this tremendous development, the role of designer cannot be overlooked in the first place because of his different visions. In light of the great crowding and creeping towards everything new in the world of artificial intelligence, it has become necessary for advertising designers to look carefully at the various technologies happening around, to help in various design fields, and to keep pace with the great development in the world of image due to the use of visual language in the first place in creating advertising content. In this research, we will discuss the selection of the most famous artificial intelligence generators currently in the field of generating images from written text, and a unified text is entered into all generators, and then the outputs are deduced and compared between them so that we can help the advertising designer choose the best among them and expand horizons alongside with traditional authentic manual work. This study helps understand and deduce the different outputs of artificial intelligence generators that converts written text into visual images and compare them together through some simple design elements, taking into consideration that the inputs and written text are constant in all generators.

Journal of Design Sciences and Applied Arts/Journal of Design Sciences and Applied Arts Vol. 6(2)
Damietta University (EG)
Openalex Percentile: Top 91%
Generative Adversarial Networks and Image Synthesis
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