Advancements in Diffusion Models for Generative Image Synthesis

Abstract

Diffusion models have emerged as a groundbreaking paradigm in generative artificial intelligence, demonstrating remarkable capabilities in synthesizing high-fidelity and diverse images. This paper presents a comprehensive research on image generation technology based on denoising diffusion probabilistic models (DDPM). We systematically investigate the core architecture, including the forward noise-adding process and the reverse denoising process governed by U-Net based neural networks. The study addresses key challenges of standard diffusion models, notably their computationally intensive iterative refinement process. To this end, we propose and evaluate several optimization strategies, including the integration of classifier-free guidance for enhanced semantic control, the adoption of latent diffusion models (LDM) for reduced computational overhead, and the exploration of distillation techniques for accelerated sampling. Furthermore, we extend the application of these models beyond unconditional generation to critical tasks such as text-to-image synthesis, image inpainting, and super-resolution. Quantitative evaluations on benchmark datasets (e.g., ImageNet, COCO) demonstrate that our optimized diffusion framework achieves competitive Fréchet Inception Distance (FID) and Inception Score (IS) metrics, while significantly reducing the number of sampling steps required. The findings confirm that diffusion-based models represent a powerful and versatile framework for advanced image generation, setting a new state-of-the-art and opening avenues for future research in efficient and controllable content creation.