title: RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow
    Trajectories
summary: 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.
paper_authors._id: 67d114912264403cbf39d0ba
paper_authors.name: Huiyang Shao
paper_authors._id: 67d114912264403cbf39d0bb
paper_authors.name: Xin Xia
paper_authors._id: 67d114912264403cbf39d0bc
paper_authors.name: Yuhong Yang
paper_authors._id: 67d114912264403cbf39d0bd
paper_authors.name: Yuxi Ren
paper_authors._id: 67d114912264403cbf39d0be
paper_authors.name: Xing Wang
paper_authors._id: 67d114912264403cbf39d0bf
paper_authors.name: Xuefeng Xiao
paper_title: RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow
    Trajectories
paper_summary: 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.
paper_ai_summary: RayFlow, a novel diffusion framework, accelerates image generation by guiding samples to instance-specific targets while maintaining quality, control, and training efficiency through Time Sampler.
paper_ai_keywords: diffusion models generation speed acceleration methods unique paths instance-specific target distribution sampling steps generation diversity stability Time Sampler importance sampling training efficiency high-quality images