Text-to-Image Generation with GANs: Techniques, Applications, and Basic Python Implementation

Abstract

Text-to-image generation in artificial intelligence aims to create realistic visuals from textual descriptions. Techniques like GANs and VAEs translate text into images, finding applications in art, e-commerce, and content creation. Advancements include finegrained generation, user-controlled outputs, and improved realism. Challenges persist in aligning detailed descriptions with accurate visual outputs. Continued progress in deep learning and model enhancements drives the evolution of text-to-image systems. This article explores techniques, applications, challenges, and recent advancements, offering a basic Python implementation using GANs for text-driven image synthesis