Efficient Diffusion Models: A Comprehensive Survey from Principles to
  Practices
Authors: name: Zhiyuan Ma, name: Yuzhu Zhang, name: Guoli Jia, name: Liangliang Zhao, name: Yichao Ma, name: Mingjie Ma, name: Gaofeng Liu, name: Kaiyan Zhang, name: Jianjun Li, name: Bowen Zhou

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