Inference-Time Scaling for Diffusion Models beyond Scaling Denoising
  Steps
Authors: name: Nanye Ma, name: Shangyuan Tong, name: Haolin Jia, name: Hexiang Hu, name: Yu-Chuan Su, name: Mingda Zhang, name: Xuan Yang, name: Yandong Li, name: Tommi Jaakkola, name: Xuhui Jia, name: Saining Xie

Generative models have made significant impacts across various domains,
largely due to their ability to scale during training by increasing data,
computational resources, and model size, a phenomenon characterized by the
scaling laws. Recent research has begun to explore inference-time scaling
behavior in Large Language Models (LLMs), revealing how performance can further
improve with additional computation during inference. Unlike LLMs, diffusion
models inherently possess the flexibility to adjust inference-time computation
via the number of denoising steps, although the performance gains typically
flatten after a few dozen. In this work, we explore the inference-time scaling
behavior of diffusion models beyond increasing denoising steps and investigate
how the generation performance can further improve with increased computation.
Specifically, we consider a search problem aimed at identifying better noises
for the diffusion sampling process. We structure the design space along two
axes: the verifiers used to provide feedback, and the algorithms used to find
better noise candidates. Through extensive experiments on class-conditioned and
text-conditioned image generation benchmarks, our findings reveal that
increasing inference-time compute leads to substantial improvements in the
quality of samples generated by diffusion models, and with the complicated
nature of images, combinations of the components in the framework can be
specifically chosen to conform with different application scenario.