Addressing the limited availability of COVID-19 patient X-ray images for precise diagnostic tools, this research harnesses Stable Diffusion, a robust generative modelling technique. Deep neural networks are employed to learn the underlying data distribution and generate synthetic X-ray images. By employing the Stable Diffusion model, the study aims to overcome the scarcity of annotated COVID-19 patient X-ray datasets. Through a process of fine-tuning with a Kaggle dataset, synthetic X-ray images closely resembling authentic patient X-rays are created. This synthetic data generation technique effectively addresses the data scarcity challenge, facilitating more robust model training and improving the accuracy of COVID-19 detection and categorization. The methodology for utilizing Stable Diffusion for data augmentation involves several crucial steps and processes. Initially, the model is fine-tuned to capture essential characteristics and patterns present in real patient X-rays. Following fine-tuning, the model generates synthetic X-ray images that closely mimic genuine patient X-rays by drawing from the learned data distribution. These synthetic images are then seamlessly integrated into the original dataset, thereby expanding and diversifying the collection of X-ray images. In the final stage, machine learning models, such as deep neural networks, are trained using the augmented dataset. This approach leverages information from both real and synthetic images, significantly enhancing the accuracy and robustness of COVID-19 case detection and classification.