统一训练自由引导框架，提升扩散模型条件生成效果
TFG: Unified Training-Free Guidance for Diffusion Models
- 提出算法无关的统一框架，涵盖现有方法
- 在16个任务40个目标上平均提升8.5%
- 适合需要零训练条件生成的研究者
给定一个无条件扩散模型和目标属性的预测器（如分类器），训练自由引导的目标是无需额外训练即可生成具有期望属性的样本。现有方法虽在个别应用中有效，但缺乏理论基础且在广泛基准上验证不足，甚至在简单任务上也会失败，导致新问题应用困难。本文提出一个新颖的算法框架，将现有方法作为特例，将训练自由引导研究统一为算法无关的设计空间分析。通过理论与实证研究，提出一种高效有效的超参数搜索策略，可直接应用于任意下游任务。我们在7个扩散模型上对16个任务、40个目标进行系统性基准测试，平均性能提升8.5%。该框架与基准为零训练条件生成提供了坚实基础。
原文摘要 · Abstract (English)
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.
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