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

Abstract

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.