AI Image Generator using Generative AI for Applications

Abstract

The creative industry has experienced a transformation through Generative AI in image generation because machines generate realistic or abstract visuals from written prompts. The main architectural elements for image generation systems include GANs along with Transformer-based models such as DALL-E and Stable Diffusion which successfully produce artwork that resembles authentic human creativity. The main method entails training the model through massive datasets that contain both images and text descriptions so it can detect detailed visual patterns in such data. A typical AI image generator trains deep neural networks using extensive image databases to develop knowledge which lets it understand how objects relate through textures and spatial positions. After completion of training the generator system becomes able to create novel images by converting description texts and control settings into new visual outputs.