Text-Guided Artistic Image Synthesis Using Diffusion Model

Abstract

Use of Artificial Intelligence (AI) has been integrated into numerous fields for the 
purpose of promoting innovativeness and efficiency. In the domain of image 
generation, AI offers a chance to improve creativity and accuracy by bridging the 
language-art gap. Our approach proposes utilization of the latent Diffusion for 
creating art images from user given textual descriptions. The Stable Diffusion is a 
powerful foundation upon which the rest of the image production module is built. 
It transforms input text descriptions into latent vector representations and then 
decodes them into visually appealing masterpieces. In terms of user access, our 
system consists of an easily comprehensible user interface module, which allows 
users to comfortably write text-based descriptions and view generated graphics 
without any difficulties. Our approach not only streamlines the image creation 
process but also outperforms current systems in terms of cost-effectiveness and 
efficiency. The implementation of the Stable Diffusion empowers our system for 
producing precise and realistic art images based on textual descriptions. Resulting 
capability finds applications in diverse fields such as design, content creation, 
marketing, and gaming. By providing an innovative and accessible solution for 
aesthetic image generation, our proposed approach contributes to the evolving 
landscape of AI-driven technologies.