Development and Enhancement of Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2503.05149v1
• Date: Fri, 07 Mar 2025 05:18:00 GMT
• Title: Development and Enhancement of Text-to-Image Diffusion Models
• Authors: Rajdeep Roshan Sahu,
• Abstract summary: This research focuses on the development and enhancement of text-to-image diffusion models.<n>The proposed enhancements establish new benchmarks in generative AI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research focuses on the development and enhancement of text-to-image denoising diffusion models, addressing key challenges such as limited sample diversity and training instability. By incorporating Classifier-Free Guidance (CFG) and Exponential Moving Average (EMA) techniques, this study significantly improves image quality, diversity, and stability. Utilizing Hugging Face's state-of-the-art text-to-image generation model, the proposed enhancements establish new benchmarks in generative AI. This work explores the underlying principles of diffusion models, implements advanced strategies to overcome existing limitations, and presents a comprehensive evaluation of the improvements achieved. Results demonstrate substantial progress in generating stable, diverse, and high-quality images from textual descriptions, advancing the field of generative artificial intelligence and providing new foundations for future applications. Keywords: Text-to-image, Diffusion model, Classifier-free guidance, Exponential moving average, Image generation.

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