Making Diffusion Models Practical: A Survey on Acceleration and Optimization

Diffusion models have emerged as a powerful class of generative models, achieving state-of-the-art performance in image, audio, and video synthesis. However, their widespread adoption is hindered by high computational costs, slow inference times, and memory-intensive training. In response, numerous techniques have been proposed to enhance the efficiency of diffusion models while maintaining or improving generation quality. This survey provides a comprehensive review of recent advances in efficient diffusion models. We categorize these approaches into four key areas: (1) accelerated sampling methods, which reduce the number of function evaluations required for inference; (2) efficient model architectures, including lightweight U-Net and transformer variants; (3) knowledge distillation and model compression techniques, such as progressive distillation and pruning; and (4) hybrid generative frameworks that integrate diffusion models with alternative paradigms like GANs and VAEs. Additionally, we discuss open challenges, including the trade-offs between sampling speed and quality, memory-efficient training strategies, and real-time deployment on edge devices. We highlight promising research directions, such as adaptive sampling, hardware-aware optimizations, and self-distilling diffusion models. By providing a structured overview of efficiency-focused advancements, we aim to guide future research toward making diffusion models more practical, scalable, and accessible for real-world applications.