RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow
  Trajectories
Authors: name: Huiyang Shao, name: Xin Xia, name: Yuhong Yang, name: Yuxi Ren, name: Xing Wang, name: Xuefeng Xiao

Diffusion models have achieved remarkable success across various domains.
However, their slow generation speed remains a critical challenge. Existing
acceleration methods, while aiming to reduce steps, often compromise sample
quality, controllability, or introduce training complexities. Therefore, we
propose RayFlow, a novel diffusion framework that addresses these limitations.
Unlike previous methods, RayFlow guides each sample along a unique path towards
an instance-specific target distribution. This method minimizes sampling steps
while preserving generation diversity and stability. Furthermore, we introduce
Time Sampler, an importance sampling technique to enhance training efficiency
by focusing on crucial timesteps. Extensive experiments demonstrate RayFlow's
superiority in generating high-quality images with improved speed, control, and
training efficiency compared to existing acceleration techniques.