Hanna, D. (2025). Testing AI Text to Image Generators for Advertising Applications. Journal of Design Sciences and Applied Arts, 6(2), 41-59. doi: 10.21608/jdsaa.2025.330069.1431
Dena Hanna. "Testing AI Text to Image Generators for Advertising Applications". Journal of Design Sciences and Applied Arts, 6, 2, 2025, 41-59. doi: 10.21608/jdsaa.2025.330069.1431
Hanna, D. (2025). 'Testing AI Text to Image Generators for Advertising Applications', Journal of Design Sciences and Applied Arts, 6(2), pp. 41-59. doi: 10.21608/jdsaa.2025.330069.1431
Hanna, D. Testing AI Text to Image Generators for Advertising Applications. Journal of Design Sciences and Applied Arts, 2025; 6(2): 41-59. doi: 10.21608/jdsaa.2025.330069.1431
Testing AI Text to Image Generators for Advertising Applications
Faculty of Applied Arts, Damietta University, Egypt.
Abstract
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.
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