Diffusion Models for Image Generation Acceleration Techniques and Advances
Abstract
Nowadays, diffusion models have been widely used in generative tasks, including image generation, owing to their advantages in generation quality and stability. However, the heavy computational burden and slow generation speed of diffusion models have, to a certain extent, restricted their application and widespread adoption. To address this issue, various methods have been developed to accelerate the image generation task based on diffusion models. This paper systematically reviews these acceleration methods and categorizes them into four categories based on different dimensions: sampling strategy optimization, latent space diffusion, model architecture innovation, and training strategy optimization. Regarding sampling strategy optimization, this paper introduces several mainstream sampling methods based on their developmental trajectory. Regarding latent space diffusion, this paper mainly introduces its principles and workflow. Regarding model architecture innovation, this paper focuses on DiT and DiM. Regarding training strategy optimization, this paper focuses on progressive distillation and consistency models. Furthermore, this paper summarizes these acceleration methods and proposes future research directions.
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