While diffusion models have achieved promising performances in data synthesis, they might suffer error propagation because of their cascade structure, where the distributional mismatch spreads and magnifies through the chain of denoising modules. However, a strict analysis is expected since many sequential models such as Conditional Random Field (CRF) are free from error propagation. In this paper, we empirically and theoretically verify that diffusion models are indeed affected by error propagation and we then propose a regularization to address this problem. Our theoretical analysis reveals that the question can be reduced to whether every denoising module of the diffusion model is fault-tolerant. We derive insightful transition equations, indicating that the module can't recover from input errors and even propagates additional errors to the next module. Our analysis directly leads to a consistency regularization scheme for diffusion models, which explicitly reduces the distribution gap between forward and backward processes. We further introduce a bootstrapping algorithm to reduce the computation cost of the regularizer. Our experimental results on multiple image datasets show that our regularization effectively handles error propagation and significantly improves the performance of vanilla diffusion models.

扩散模型中的错误传播问题及其解决方法的实验和理论验证，提出了一种一致性正则化方案来降低前向和后向过程之间的分布差异，并引入了引导算法来降低计算成本。在多个图像数据集上的实验结果表明，该正则化策略有效地处理了错误传播问题，并显著提高了传统扩散模型的性能。

扩散模型是否受到误差传播的影响？理论分析与一致性正则化