Development and Enhancement of Text-to-Image Diffusion Models
This work addresses image generation problems for AI researchers and developers, though it appears incremental as it builds on existing diffusion models with established enhancement techniques.
This research tackled challenges in text-to-image diffusion models like limited sample diversity and training instability by incorporating Classifier-Free Guidance and Exponential Moving Average techniques, resulting in significant improvements in image quality, diversity, and stability that established new benchmarks in generative AI.
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.