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

Towards Sustainable Image Synthesis: A Comprehensive Review of Text-to-Image Generation Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Towards Sustainable Image Synthesis: A Comprehensive Review of Text-to-Image Generation Models Smita Bharne, Pallavi Sapkale, Ekta Sarda, Puja Padiya, Shamal Salunkhe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6249181/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Sep, 2025 Read the published version in International Research Journal of Multidisciplinary Technovation → Version 1 posted You are reading this latest preprint version Abstract 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. Deep learning Diffusion model DALL-E Generative models Text to Image generation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Sep, 2025 Read the published version in International Research Journal of Multidisciplinary Technovation → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6249181","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432581620,"identity":"209d0af4-690e-4bf6-a875-1031ae5f5516","order_by":0,"name":"Smita Bharne","email":"","orcid":"","institution":"Ramrao Adik Institute of Technology, D. 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