M, Kanchana and S, Hirthick and M, Shruthi and R, Sundhareshwaran and S, Disha (2025) AI-Powered Prompt-based Image Generator. International Journal of Innovative Science and Research Technology, 10 (4): 25apr1241. pp. 1756-1760. ISSN 2456-2165
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Abstract
In today's digital world, content creators, businesses, and educators rely on high-quality visuals, but existing design tools like Photoshop and Blender are expensive, complex, and require expertise. Freelancers, startups, students, and educators with limited budgets struggle to access these tools, restricting their ability to create professional images. While AI models like Stable Diffusion, DALL·E, and MidJourney offer solutions, they often require programming and prompt engineering knowledge, making them difficult for non-experts. This project aims to develop a user-friendly, prompt-based image generator that simplifies AI-powered design, enhances customization, reduces bias, and improves usability. By allowing users to input simple text prompts, the AI will generate visually appealing and contextually relevant images without requiring advanced design skills, increasing accessibility, affordability, and efficiency in visual content creation. This project also aims to bridge the gap between creativity and technology, enabling users from various backgrounds to generate high-quality visuals effortlessly. By integrating advanced deep learning techniques, the system ensures optimized image generation with minimal latency. Additionally, the platform will provide customization options, allowing users to refine their images based on style, color, and composition preferences. With a focus on inclusivity, this tool will cater to freelancers, marketers, educators, and businesses, empowering them to create engaging content without technical barriers.
Item Type: | Article |
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Subjects: | T Technology > T Technology (General) |
Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
Depositing User: | Editor IJISRT Publication |
Date Deposited: | 01 May 2025 10:15 |
Last Modified: | 01 May 2025 10:15 |
URI: | https://eprint.ijisrt.org/id/eprint/651 |