STABLE DIFFUSION TEXT TO IMAGE USING AI
Prof. Seema. R. Baji1, Ankush Amrutkar2, Unnati Rahane3, Sakshi Jagtap4, Kiran Bhoi5
1 Prof. Seema. R. Baji, Department of Computer Engineering, Late G. N. Sapkal College of Engineering, Nashik
2 Ankush Amrutkar, Department of Computer Engineering, Late G. N. Sapkal College of Engineering, Nashik
3 Unnati Rahane, Department of Computer Engineering, Late G. N. Sapkal College of Engineering, Nashik
4 Sakshi Jagtap, Department of Computer Engineering, Late G. N. Sapkal College of Engineering, Nashik
5 Kiran Bhoi, Department of Computer Engineering, Late G. N. Sapkal College of Engineering, Nashik
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ABSTRACT
The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adversarial networks (GANs) and natural language processing (NLP). It aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework. The key goal is to generate highly realistic and coherent images from text prompts, leveraging recent deep learning Stable Diffusion.
The Stable Diffusion training framework plays a central role in this project. It’s a sophisticated training methodology for GANs, designed to stabilize the training
process. GANs have exhibited great potential in generating images but often suffer from issues like mode collapse and training instability.
Keywords: Stable Diffusion, Text-to-Image Generation, Image Synthesis, Natural Language Processing(NLP), Generative Adversarial Networks (GANs)
