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：分类器无关引导（CFG）通过结合条件预测和无条件预测（使用固定权重）来提升文本到图像扩散模型的质量与条件遵循度。然而，近期研究在扩散过程中动态调整权重，并报告了更优的结果，但未提供任何原理或分析。通过进行全面的实验，本文深入探讨了CFG权重调度器。我们的研究结果表明，简单、单调递增的权重调度器能持续带来性能提升，且仅需单行代码即可实现。此外，更复杂的参数化调度器可通过优化获得进一步改进，但其泛化能力在不同模型与任务间存在局限。