Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an unconditional model alongside the main diffusion model or modifying the training procedure by periodically inserting a null condition. There is also no clear extension of CFG to unconditional models. In this paper, we revisit the core principles of CFG and introduce a new method, independent condition guidance (ICG), which provides the benefits of CFG without the need for any special training procedures. Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model. Additionally, by leveraging the time-step information encoded in all diffusion networks, we propose an extension of CFG, called time-step guidance (TSG), which can be applied to any diffusion model, including unconditional ones. Our guidance techniques are easy to implement and have the same sampling cost as CFG. Through extensive experiments, we demonstrate that ICG matches the performance of standard CFG across various conditional diffusion models. Moreover, we show that TSG improves generation quality in a manner similar to CFG, without relying on any conditional information.

在本文中，我们提出了独立条件引导（ICG）和时间步引导（TSG）这两种新的引导方法来简化条件扩散模型的训练过程，并且在任何已训练的条件模型上进行推理以提供与标准分类器无关的指导方法。通过大量实验证明，ICG与标准分类器在不同条件扩散模型上的性能相匹配，并且TSG可以在不依赖任何条件信息的情况下改善生成的质量。

无训练，无问题：重新思考扩散模型的无分类器引导