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@article{186916,
author = {Kunduru Chaitanya Reddy and Mrs. A. Tulasi},
title = {TEXT-TO-IMAGE GENERATION USING DEEP LEARNING},
journal = {International Journal of Innovative Research in Technology},
year = {2025},
volume = {12},
number = {6},
pages = {2554-2557},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=186916},
abstract = {This project explores the fascinating intersection of Natural Language Processing (NLP) and deep learning to create a text-to-image generation system. Leveraging the power of Stable Diffusion models, this application translates textual descriptions into corresponding visual representations. The project utilizes the diffusers and transformers libraries within a Python environment, optimized for execution on Google Colab's T4 GPU. A key focus is on user interaction, facilitated through a Gradio-based web interface. This interface allows users to input text prompts, select from various pre-trained Stable Diffusion models, and fine-tune generation parameters like inference steps and guidance scale. Furthermore, the system incorporates an image-to-image editing feature, enabling users to upload existing images and modify them based on textual prompts, controlled by a strength parameter. The project addresses the computational demands of deep learning by employing half-precision floating-point operations (float16) for memory efficiency on the GPU. This project demonstrates the practical application of deep learning for creative content generation and highlights the importance of user-friendly interfaces in democratizing access to advanced AI technologies.},
keywords = {Artificial Intelligence, Deep Learning, Text-to-Image Generation, Stable Diffusion, Natural Language Processing, Gradio, Image-to-Image Editing.},
month = {November},
}
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