Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional predictions using a fixed weight. However, recent works vary the weights throughout the diffusion process, reporting superior results but without providing any rationale or analysis. By conducting comprehensive experiments, this paper provides insights into CFG weight schedulers. Our findings suggest that simple, monotonically increasing weight schedulers consistently lead to improved performances, requiring merely a single line of code. In addition, more complex parametrized schedulers can be optimized for further improvement, but do not generalize across different models and tasks.

翻译：分类器无指导（Classifier-Free Guidance，CFG）通过使用固定权重结合条件性和无条件预测，提升了文本到图像扩散模型的质量及条件一致性。然而，近期研究在扩散过程中变化权重，报告了更优结果，但未提供任何理论依据或分析。通过开展全面实验，本文深入探讨了CFG权重调度器。我们的发现表明，简单且单调递增的权重调度器始终能带来性能提升，仅需一行代码即可实现。此外，更复杂的参数化调度器虽可进一步优化性能，但无法在不同模型和任务间泛化。