This repository provides the implementation of Inference-time Scaling of Diffusion Models through Classical Search. The approach leverages classical search algorithms to scale inference compute in diffusion models, improving efficiency and output quality.
The imagenet folder provides the implementation of BFS for class-conditional image generation, the locomotion folder provides the Q-verifier test-time search for offline RL tasks, and the pointmaze folder provides the implementation of the long-horizon planning task. For installation of each task, refer to the instructions in each subfolder.
If you use this code, please cite:
@misc{zhang2025inferencetimescalingdiffusionmodels,
title={Inference-time Scaling of Diffusion Models through Classical Search},
author={Xiangcheng Zhang and Haowei Lin and Haotian Ye and James Zou and Jianzhu Ma and Yitao Liang and Yilun Du},
year={2025},
eprint={2505.23614},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.23614},
}This project is licensed under the MIT License.