Towards Sustainable Image Synthesis: A Comprehensive Review of Text-to-Image Generation Models

Text to image generation is a significant area in artificial intelligence, through which descriptive caption can be described and detailed high-context relevant images produced. In the recent years the domain of text-to-image generation has witnessed significant progress, propelled by the development of diverse generative models. This work provides a detailed comprehensive analysis of the prominent image generation models, with a specific emphasis on their capacity to convert textual descriptions into visually consistent and contextually precise images. We systematically evaluate models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and the more recent Diffusion Models. We also provide an overview of sustainable image synthesis models such as DALLE-2, Stable Diffusion, Imagen and MidJourney. It indicates significant progress made towards the generation of ultra-realistic high-resolution pictures, but still underlies serious issues of semantic coherence, fine-grained control and computational expensive tasks. The findings also emphasize on current challenges and possible future sustainable paths in the field, which contribute to the continuous advancement of more advanced and efficient image generation models.