This paper presents a progressive approach to text-to-image generation by developing an AI system capable of producing vivid and semantically faithful images from natural language descriptions. We adopt a multi-stage pipeline beginning with foundational experiments using Generative Adversarial Networks (GANs) on the MNIST dataset, gradually scaling to more complex datasets such as COCO. To bridge the semantic gap between textual and visual modalities, we integrate a BERT-based text encoder with a Convolutional Neural Network (CNN)-based generator, enabling the system to capture nuanced textual features and translate them into coherent visual representations. Building on these foundations, we leverage the Latent Diffusion Model (LDM) to enhance image quality and fidelity. Our contributions include model optimization, fine-tuning of LDM components, and a detailed evaluation of robustness across various textual inputs. Notably, we identify failure cases involving typographical and contextual input variations, highlighting key limitations in current diffusion-based models. These findings inform our recommendations for future improvements in semantic alignment and input resilience. Overall, our work demonstrates the effectiveness of combining transformer-based text encoders with generative architectures and sets the stage for more robust, high-fidelity text-to-image synthesis systems.