IMAGINATION MADE REAL: STABLE DIFFUSION FOR HIGH-FIDELITY TEXT-TO-IMAGE TASKS

  • Unique Paper ID: 175379
  • PageNo: 2674-2678
  • Abstract:
  • The abstract highlights a new method for creating high-quality images from text using advanced diffusion models (DMs). Stable diffusion models, which generate high-quality images from text descriptions, have significantly improved text-to-image generation in recent years. These models are the subject of the base paper, which primarily examines their performance without making them user-friendly or requiring extra memory. Additionally, it is unable to produce several images in situations where it is unable to support various languages, which results in more expensive setups. In this work, we build a simpler and more interactive way to use Stable Diffusion. We use a Euler Discrete Scheduler to make the process faster and more efficient while optimizing both computational efficiencies keeping the image quality high. Our method also reduces memory use by applying FP16 precision. To make the system more user-friendly, we add a Gradio-based interface where users can type in a text prompt, choose how many images to generate, and see the results instantly. This improves accessibility compared to traditional methods, which often require complex setups. And also, we implemented without any API Keys like (e.g. Dall-E 3, DeepAi API, Gemini API, Blackbox API, etc.)Our improvements allow faster image generation without losing quality. By making the system easy to use and efficient, this work helps more people take advantage of AI-powered image creation, bridging the gap between advanced technology and everyday users.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{175379,
        author = {Chejarla Venkata Hemanth Kumar and J.Venkat Rao and M.Avinash and Ch.Sai Manikanta and K.Mahesh},
        title = {IMAGINATION MADE REAL: STABLE DIFFUSION FOR HIGH-FIDELITY TEXT-TO-IMAGE TASKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2674-2678},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175379},
        abstract = {The abstract highlights a new method for creating high-quality images from text using advanced diffusion models (DMs). Stable diffusion models, which generate high-quality images from text descriptions, have significantly improved text-to-image generation in recent years. These models are the subject of the base paper, which primarily examines their performance without making them user-friendly or requiring extra memory. Additionally, it is unable to produce several images in situations where it is unable to support various languages, which results in more expensive setups. In this work, we build a simpler and more interactive way to use Stable Diffusion. We use a Euler Discrete Scheduler to make the process faster and more efficient while optimizing both computational efficiencies keeping the image quality high. Our method also reduces memory use by applying FP16 precision. To make the system more user-friendly, we add a Gradio-based interface where users can type in a text prompt, choose how many images to generate, and see the results instantly. This improves accessibility compared to traditional methods, which often require complex setups. And also, we implemented without any API Keys like (e.g. Dall-E 3, DeepAi API, Gemini API, Blackbox API, etc.)Our improvements allow faster image generation without losing quality. By making the system easy to use and efficient, this work helps more people take advantage of AI-powered image creation, bridging the gap between advanced technology and everyday users.},
        keywords = {Stable Diffusion, Text-to-Image Generation, Diffusion Models, Euler Discrete Scheduler, Computational Efficiency, FP16 Precision, Gradio Interface, User-Friendly, Image Quality, Memory Optimization , AI-Powered Image Creation, Text Prompt, No API Keys.},
        month = {April},
        }

Cite This Article

Kumar, C. V. H., & Rao, J., & M.Avinash, , & Manikanta, C., & K.Mahesh, (2025). IMAGINATION MADE REAL: STABLE DIFFUSION FOR HIGH-FIDELITY TEXT-TO-IMAGE TASKS. International Journal of Innovative Research in Technology (IJIRT), 11(11), 2674–2678.

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