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. Existing methods, though effective in various individual applications, often lack theoretical grounding and rigorous testing on extensive benchmarks. As a result, they could even fail on simple tasks, and applying them to a new problem becomes unavoidably difficult. 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.

本研究解决了现有无训练引导方法缺乏理论基础及严格测试的问题，提出了一种新的算法框架，将这些方法统一为算法无关的设计空间进行分析。通过理论和实证研究，该框架在17个任务的基准测试中平均提高了8.5%的性能，为无训练条件生成提供了可靠的基础。

TFG：面向扩散模型的统一无训练引导