• Prajkta Khaire
• Rushabh Dhamne
• Akash Chaudhari
• Prashik Gawai

Text-to-image generation is the process of converting input prompts into high-quality images using some of the stable diffusion models that are mostly used in the industry. Industries are Highly growing towards the AI generation that helps generate more productivity and Text-to-Image generation is one of them. Diffusion models described in several research papers highlight the progress made in text-to-image generation through diffusion models, specifically focusing on the Stable Diffusion model. While these models produce high-quality and creative results, their multi-step sampling process is slow, often requiring many iterations for satisfactory outputs. Attempts to speed up this process through distillation have not been successful in creating a functional one-step model. The papers also mention other text-to-image generation models but suggest the Stable Diffusion model as a preferred option for enhanced productivity.

Diffusion Models, ControlNet Integration, Shifted Diffusion Model, Generative Frameworks, Stable Diffusion, T2I models, Diffusion Denoising Probabilistic Models, FID scores.

"Text-to-Image Generators Explored: A Comprehensive Review on Generative AI ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.123-131, June-2024, Available :http://www.jetir.org/papers/JETIRGH06020.pdf

"Text-to-Image Generators Explored: A Comprehensive Review on Generative AI ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp123-131, June-2024, Available at : http://www.jetir.org/papers/JETIRGH06020.pdf