RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow
  Trajectories
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.
67d114912264403cbf39d0ba
Huiyang Shao
67d114912264403cbf39d0bb
Xin Xia
67d114912264403cbf39d0bc
Yuhong Yang
67d114912264403cbf39d0bd
Yuxi Ren
67d114912264403cbf39d0be
Xing Wang
67d114912264403cbf39d0bf
Xuefeng Xiao
RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow
  Trajectories
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.
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.
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