Towards Sustainable Image Synthesis: A Comprehensive Review of Text-to-Image Generation Models
Abstract
Text-to-image generation represents a rapidly evolving frontier in artificial intelligence, enabling the transformation of natural language descriptions into visually coherent and semantically rich images. This paper presents a comprehensive review of state-of-the-art generative models—including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and advanced Diffusion Models—focusing on their capabilities to produce high-fidelity, contextually accurate images from textual inputs. Additionally, we analyse leading sustainable image synthesis frameworks such as DALL-E 2, Stable Diffusion, Imagen, and MidJourney, assessing their advancements in image quality, semantic alignment, diversity, and computational efficiency. Our systematic evaluation highlights significant progress in generating realistic, high-resolution images while identifying persistent challenges related to semantic consistency, fine-grained control, ethical considerations, and substantial computational demands. We further discuss critical trade-offs between model performance and sustainability, fostering future research directions aimed at developing more efficient, fair, and environmentally responsible text-to-image generation systems. This survey serves as a guiding resource for the next generation of sustainable AI-driven text to image synthesis technologies.
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