Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
Zhiyuan Ma, Yuzhu Zhang, Guoli Jia, Liangliang Zhao, Yichao Ma, Mingjie Ma, Gaofeng Liu, Kaiyan Zhang, Jianjun Li, Bowen Zhou
2024-10-16
Summary
This paper provides a comprehensive overview of diffusion models, which are powerful tools used for generating data, such as images and videos, by gradually transforming random noise into coherent outputs.
What's the problem?
Despite the growing popularity and success of diffusion models in various applications, there hasn't been a thorough review that explains their underlying principles and practical applications. This lack of information makes it difficult for researchers and practitioners to fully understand and utilize these models effectively.
What's the solution?
The authors present a detailed survey that summarizes the key principles behind diffusion models, including their architecture, training methods, and how they can be efficiently deployed. They focus on making this information accessible to help others understand how to apply these models in different scenarios. The survey also highlights the advancements in design and methodology that have improved the performance of diffusion models.
Why it matters?
This research is important because it helps demystify diffusion models for both new and experienced researchers. By providing clear insights into how these models work and their applications, the paper can guide future research and innovation in generative modeling, which has significant implications in fields like computer vision, audio generation, and even healthcare.
Abstract
As one of the most popular and sought-after generative models in the recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, molecule design, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices. The remarkable success of these recent efforts on diffusion models comes largely from progressive design principles and efficient architecture, training, inference, and deployment methodologies. However, there has not been a comprehensive and in-depth review to summarize these principles and practices to help the rapid understanding and application of diffusion models. In this survey, we provide a new efficiency-oriented perspective on these existing efforts, which mainly focuses on the profound principles and efficient practices in architecture designs, model training, fast inference and reliable deployment, to guide further theoretical research, algorithm migration and model application for new scenarios in a reader-friendly way. https://github.com/ponyzym/Efficient-DMs-Survey