Training-Free Guidance (TFG) is a new unified algorithm framework jointly proposed by research teams from Stanford University, Peking University, Tsinghua University and other institutions in 2024. The relevant paper results are "TFG: Unified Training-Free Guidance for Diffusion Models", has been accepted as a Spotlight by NeurIPS 2024. This framework aims to solve the problem of diffusion models in the field of conditional generation, that is, generating samples that meet specific conditions (such as labels, attributes, or energy distributions) usually requires training a dedicated generation model for each target. This method is not only resource-intensive, but also limits the practical application potential of diffusion models.
The core innovations of the TFG framework include three aspects:
- Unified design spaceTFG proposes a general untrained guidance design space, treating existing algorithms as their special cases. This unified perspective not only simplifies the comparison of different algorithms, but also improves performance by expanding the design space. Specifically, TFG is based on multi-dimensional hyperparameter design, covers multiple variants of guidance methods, and provides flexibility for task adaptation.
- Efficient hyperparameter search strategy: In order to cope with multi-objective and diversified task scenarios, TFG introduces an efficient hyperparameter search strategy. Under this framework, users can quickly determine the optimal hyperparameter combination through automated strategies without complicated parameter adjustment process, and adapt to a variety of downstream tasks.
- Comprehensive Benchmarks: The TFG framework has conducted extensive experiments on 7 diffusion models, including 16 tasks and 40 specific targets such as images, molecules, and audio. The experimental results show that TFG has an average performance improvement of 8.5%, surpassing the existing best methods in multiple tasks.