Image Generation Based on Diffusion Models: Quality, Efficiency, and Controllability
TL;DR
The core technological achievements of diffusion models in recent years are reviewed, their evolution in terms of quality, efficiency, and controllability are outlined, and an outlook on future research is provided on integrating diffusion models with large language models.
Abstract
Image generation is a crucial technology in computer vision. Currently, deep learning-based image generation models are widely used in commercial, entertainment, and industrial applications. With the rapid transformation of the digital economy, higher demands are being placed on image generation. At a time when traditional image generation models are reaching their limits, diffusion models, with their unique advantages, have gained widespread attention in academia and have now become the mainstream paradigm in the field. This paper reviews the core technological achievements of diffusion models in recent years, outlines their evolution in terms of quality, efficiency, and controllability, and summarizes the core issues of each technology. However, due to the inherent constraints of the diffusion model generation paradigm, there is a natural zero-sum game among the three dimensions of quality, efficiency, and controllability—a rigid conflict between them. Finally, this paper analyzes the current state of diffusion models and provides an outlook on future research, with a focus on integrating diffusion models with large language models.