Text-to-Image Generation with Deep Learning: State-of-the-Art and Future Directions

Text-to-image generation has been one of the fastest evolving areas of AI research in recent years, with deep learning models currently capable of creating strikingly realistic images based on textual descriptions. This paper reviews progress in text-to-image generation, focusing on the Stable Diffusion model, which can produce high-quality images by using latent diffusion techniques. We position Stable Diffusion against other prominent models like DALL·E 3, Imagen from Google, and Mid-journey by analyzing their architectures, comparing performance, and highlighting use cases. Qualitative and quantitative comparisons through metrics will emphasize the strengths and weaknesses of each model, such as FID and IS. Our results indeed show that, while Stable Diffusion is much more efficient and scalable, other models have certain advantageous niches concerning creativity and fidelity.