• Arya Gupta
• Nikhil Jani
• Rayyan Bawani
• Syed Mohammad Mahdi
• Sushant Gawade

Automated creation of lifelike images from text prompts remains an intriguing and important objective, yet existing AI technologies have not completely mastered this capability. Recent years have seen notable advances in adaptable and resilient recurrent neural network structures that effectively capture meaningful textual feature embeddings. In parallel, convolutional-based GAN frameworks have achieved notable breakthroughs in synthesizing photorealistic visuals for targeted applications, including human visage synthesis, music cover design, and interior layout simulations. Our work proposes a fusion-based neural architecture that integrates adversarial training mechanisms to unify progress in textual understanding and image synthesis, enabling the conversion of semantic concepts into high-resolution imagery through cross-modal alignment. Our system demonstrates promising capabilities in producing plausible avian and floral imagery from text-rich descriptions.

AI generation tool, Machine learning algorithms, Natural language processing, Human-like text content, Minimal human input.

"AI IMAGE GENERATOR WITH STABLE DIFFUSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d779-d784, March-2025, Available :http://www.jetir.org/papers/JETIR2503387.pdf

"AI IMAGE GENERATOR WITH STABLE DIFFUSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd779-d784, March-2025, Available at : http://www.jetir.org/papers/JETIR2503387.pdf