Comparative analysis of the use of instructions for language models and automated metrics for assessing the quality of images generated by GAN models
This study explores the potential applications of language AI models in combination with Generative Adversarial Networks (GANs) for generating images based on textual descriptions derived from literary works. The effectiveness of various prompt types used to create abstractions was analyzed, and a comparative evaluation of the performance of leading contemporary image generation models – MidJourney, DALL-E, and Stable Diffusion – was conducted. The results indicate that, while language models are capable of producing meaningful abstractions that partially reflect the