TFG: Unified Training-Free Guidance for Diffusion Models

Code and data for our paper TFG: Unified Training-Free Guidance for Diffusion Models.

📰 News

• [May. 4, 2025]: We incorporate a Best-of-N feature in pipeline.py to better support the inference-time scaling study. You can adjust the bon_rate (default=1) to use different "N" in your Best-of-N sampling strategy.
• [Oct. 30, 2024]: We launch the first version of TFG. The current codebase supports all tasks mentioned in our paper, including label guidance, super resolution, gaussian deblur, fine-grained generation, audio declipping, guidance combinatin, style transfer, and molecule property guidance. More applications & models will be included in the future.

👋 Overview

Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training. This paper introduces a novel algorithmic framework encompassing existing methods as special cases, unifying the study of training-free guidance into the analysis of an algorithm-agnostic design space. Via theoretical and empirical investigation, we propose an efficient and effective hyper-parameter searching strategy that can be readily applied to any downstream task. We systematically benchmark across 7 diffusion models on 16 tasks with 40 targets, and improve performance by 8.5% on average. Our framework and benchmark offer a solid foundation for conditional generation in a training-free manner.

🚀 Set Up

1. Install packages. Install packages via pip install -r requirements.txt.
2. Download resources. Each experiment requires a diffusion model, a guidance function, and possibly a dataset (e.g., for super resolution).
   • You can download all the checkpoints from this link. Set the MODEL_PATH in utils/env_utils.py as the path of the downloaded directory.

💽 Usage

You can check ./scripts for examplar scripts. Also, if you want to write the script yourself, please refer to ./utils/configs.py for details.

💫 Contributions

We would welcome any contributions, pull requests, or issues! To do so, please either file a new pull request or issue. We'll be sure to follow up shortly!

✍️ Citation

If you find our work helpful, please use the following citations.

@article{
    ye2024tfg,
    title={TFG: Unified Training-Free Guidance for Diffusion Models},
    author={Haotian Ye and Haowei Lin and Jiaqi Han and Minkai Xu and Sheng Liu and Yitao Liang and Jianzhu Ma and James Zou and Stefano Ermon},
    booktitle={NeurIPS},
    year={2024}
}

🪪 License

MIT. Check LICENSE.md.