Paper Title
Efficient Local Text-to-Image Generation Using Stable Diffusion XL with the Fooocus Interface
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Registration ID: IJNRD_322238
Published ID: IJNRD2603428
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Keywords
Generative AI, Stable Diffusion, Diffusion Models, Text-to-Image Generation, Latent Diffusion Models
Abstract
With the development of advanced generative artificial intelligence technology, it is now possible to create high-quality images from text prompts using diffusion-based generative models. Stable Diffusion is a strong open-source tool that allows for the efficient generation of images using latent diffusion models. However, the deployment of these models is often complicated. In this paper, a simple text-to-image generation system is proposed using the Stable Diffusion XL model with the Fooocus interface. In the proposed method, efficient image generation is possible locally, with minimal complexity in the configuration of the system. In the proposed system, the prompt is first encoded using a CLIP text encoder, then the latent diffusion process is carried out using a U-Net model, and finally, the image is reconstructed using a Variational Autoencoder. From the experimental results, it is clear that the proposed system is efficient in the generation of high-quality images from text prompts.
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How To Cite (APA)
Aary Jadhav, Yashas Mayekar, Pushpak Shinde, Nasir Zaidi, & Ashwini Sankhe (March-2026). Efficient Local Text-to-Image Generation Using Stable Diffusion XL with the Fooocus Interface . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(3), e181-e190. https://ijnrd.org/papers/IJNRD2603428.pdf
Issue
Volume 11 Issue 3, March-2026
Pages : e181-e190
Other Publication Details
Paper Reg. ID: IJNRD_322238
Published Paper Id: IJNRD2603428
Research Area: Other area not in list
Author Type: Indian Author
Country: Palghar, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2603428.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2603428
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