A Review on AI-Driven Text-to-Image Generation Using Deep Learning and GANs

Text-to-image generation is a rapidly evolving field in artificial intelligence that focuses on synthesizingrealistic images from natural language descriptions. This process lies at the intersection of computer visionand natural language processing, requiring a deep semantic understanding of both modalities. The introductionof Generative Adversarial Networks (GANs) has significantly enhanced the quality, resolution, and diversityof generated images. Models such as StackGAN, AttnGAN, and DM-GAN have demonstrated notableadvancements by incorporating multi-stage architectures, attention mechanisms, and dynamic memorymodules to improve the semantic alignment between text and images [9], [6], [2]. Recent developments havealso scaled up GAN architectures to produce more photorealistic and controllable outputs [2], [3]. This reviewpaper provides a comprehensive analysis of AI-driven techniques for text-to-image generation, emphasizingdeep learning frameworks and GAN-based methods. It discusses model architectures, benchmark datasets,evaluation metrics, challenges, and future research directions to guide further progress in this transformativearea of research.