  Diffusion models have significantly advanced the state of the art in image,
audio, and video generation tasks. However, their applications in practical
scenarios are hindered by slow inference speed. Drawing inspiration from the
approximation strategies utilized in consistency models, we propose the
Sub-path Linear Approximation Model (SLAM), which accelerates diffusion models
while maintaining high-quality image generation. SLAM treats the PF-ODE
trajectory as a series of PF-ODE sub-paths divided by sampled points, and
harnesses sub-path linear (SL) ODEs to form a progressive and continuous error
estimation along each individual PF-ODE sub-path. The optimization on such
SL-ODEs allows SLAM to construct denoising mappings with smaller cumulative
approximated errors. An efficient distillation method is also developed to
facilitate the incorporation of more advanced diffusion models, such as latent
diffusion models. Our extensive experimental results demonstrate that SLAM
achieves an efficient training regimen, requiring only 6 A100 GPU days to
produce a high-quality generative model capable of 2 to 4-step generation with
high performance. Comprehensive evaluations on LAION, MS COCO 2014, and MS COCO
2017 datasets also illustrate that SLAM surpasses existing acceleration methods
in few-step generation tasks, achieving state-of-the-art performance both on
FID and the quality of the generated images.
