Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of flow/diffusion models. Generally speaking, two families of techniques have emerged for solving this problem for gradient-based guidance: namely, posterior guidance (i.e., guidance via projecting the current sample to the target distribution via the target prediction model) and end-to-end guidance (i.e., guidance by performing backpropagation throughout the entire ODE solve). In this work, we show that these two seemingly separate families can actually be unified by looking at posterior guidance as a greedy strategy of end-to-end guidance. We explore the theoretical connections between these two families and provide an in-depth theoretical of these two techniques relative to the continuous ideal gradients. Motivated by this analysis we then show a method for interpolating between these two families enabling a trade-off between compute and accuracy of the guidance gradients. We then validate this work on several inverse image problems and property-guided molecular generation.

翻译：免训练的引导生成是一种广泛应用且强大的技术，允许终端用户进一步控制流/扩散模型的生成过程。通常，基于梯度的引导方法已衍生出两大技术族：即后验引导（通过目标预测模型将当前样本投影至目标分布进行引导）与端到端引导（通过在整个常微分方程求解过程中执行反向传播进行引导）。本研究表明，这两个看似独立的技术族实际上可以通过将后验引导视为端到端引导的贪心策略而统一起来。我们深入探讨了这两类方法之间的理论联系，并相对于连续理想梯度给出了这两类技术的深度理论分析。基于该分析，我们提出了一种能在两类方法间插值的技术，从而在引导梯度的计算开销与精度之间实现权衡。最后，我们在多个逆图像问题及属性引导的分子生成任务上验证了该方法。