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Text-To-Image Generator Using Deeping Learning

PINNAMRAJU.T.S. PRIYA, A.TWINKLE VANISHREE

Head of the Department, 2 MCA Final Semester Master of Computer Applications,

Sanketika Vidya Parishad Engineering College, Visakhapatnam, Andhra Pradesh, India.

Abstract:

Text-to-image generation is a transformative field in artificial intelligence that focuses on synthesizing realistic images from natural language descriptions. This paper explores the integration of diffusion models and transformer-based architectures to achieve high-quality, semantically aligned image generation from textual prompts. Diffusion models, known for their superior generative capabilities, gradually transform noise into images through a learned denoising process. Meanwhile, transformers, particularly pre-trained language and vision-language models like CLIP, are employed to understand and encode textual semantics into meaningful embeddings. By conditioning the diffusion process on these embeddings, the system generates images that accurately reflect the input text. This combination has led to significant advancements in image quality, diversity, and text-image alignment, as demonstrated by state-of-the-art systems such as DALL·E 2, Imagen, and Stable Diffusion. The study presents an in-depth overview of the underlying architecture, training methodology, and real-world applications, highlighting the potential of these models in creative design, digital content generation, and human-computer interaction.

Index Terms:  Text-to-Image Generation, Django, Hugging Face API, AI-generated image, Digital Art, Creative Assistant, User-Friendly UI.