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.