Comparative analysis of neural networks Midjourney, Stable Diffusion, and DALL-E and ways of their implementation in the educational process of students of design specialties

Description

The implementation of neural networks in the creative design
process enables original and innovative results and increased efficiency in
creating a visual art product, and therefore it is important to explore how
various interactive tools can contribute to the development of the creative
abilities of future design professionals. The purpose of this study was to
investigate the capabilities and characteristics of Midjourney, Stable Diffusion,
and DALL-E neural networks in the context of their use in teaching design
students. The study used the analytical method, comparison, generalisation,
and systematisation methods. The study found that the neural networks
Midjourney, Stable Diffusion and DALL-E have prospects for implementation
in the educational process for students of design specialities. The authors of
this paper revealed the significant potential of artificial intelligence, namely
neural networks, in design, namely for creating fonts, typographic elements,
posters, banners, graphics, and illustrations. By comparing the capabilities of
the Midjourney, Stable Diffusion, and DALL-E neural networks, it was found
that each of them has a specific purpose and architecture that is effective
for performing various design tasks. The findings of the study demonstrate
the potential of neural networks to improve the education of students of
design-related specialities. It was substantiated that the introduction of
suitable methods and techniques can help expand the creative spectrum,
ensure stability and control in generating images, and lead to a more effective
implementation of ideas in visual realities. The results of this study can be
useful as tools for developing educational approaches in the field of design
and introducing modern technologies into the educational process
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