In today’s world, where visuals communicate more effectively than words, text to image synthesis plays a crucial role across various sectors, helping them grow with a creative, image centric approach. Machine learning has contributed significantly to this field through simple techniques, but deep learning has introduced robust models that can automatically generate realistic images from input text. Recent advancements in generative models have led to the development of several techniques for text to image synthesis. Although multiple models exist, our project primarily focuses on exploring two: the Stack GANs, which has maintained a strong position, and the recently emerged Stable Diffusion model. This study includes an exploration of the literature on text generative models, providing a deeper understanding of these models. Furthermore, the research extends to training different related models on various common datasets, such as LAION5B, CUB2002011 and Oxford 102 Flower evaluating both the accuracy and quality of the generated images. Finally, we present the findings and results of our study show that the diffusion model, with an accuracy of 76%, out performed the GANs Model, which had an accuracy of 56%, leaving room for future enhancements.