The rapid advancements in artificial intelligence (AI) and deep learning have led to the development of highly sophisticated models capable of generating realistic and novel images. One such breakthrough is the use of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, which have been pivotal in the field of image synthesis. These models can generate high-quality images either from random noise or from textual descriptions, providing powerful tools for creative industries, content generation, and research.This project aims to explore and implement an AI-based image generation system that leverages these advanced machine learning techniques. The primary objective is to develop a model capable of generating images from text prompts or manipulating existing images, offering innovative applications for digital art, advertising, and virtual reality. Specifically, we investigate the application of GANs [1] and Diffusion Models [3] in generating realistic and coherent images from textual descriptions, akin to models like OpenAI's DALL-E [2]. The system’s ability to produce high-quality, diverse images will be evaluated using metrics such as Inception Score [4] and Fréchet Inception Distance (FID) [5].By utilizing state-of-the-art architectures and large-scale datasets, this project aims to push the boundaries of AI-generated art and explore its potential applications in various creative fields. The results of this project will contribute to the ongoing development of AI-based creative tools, which hold transformative potential for industries such as gaming, animation, advertising, and media.
Generative Adversarial Networks (GANs), Latent Diffusion Models (LDMs), StyleGAN, Text-to-Image Generation, Fréchet Inception Distance (FID), Inception Score (IS), Contrastive Language-Image Pretraining (CLIP), Transfer Learning, Image Augmentation, Generative Modelling
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