Diffusion models achieve superior generation quality but suffer from slow generation speed due to the iterative nature of denoising. In contrast, consistency models, a new generative family, achieve competitive performance with significantly faster sampling. These models are trained either through consistency distillation, which leverages pretrained diffusion models, or consistency training/tuning directly from raw data. In this work, we propose a novel framework for understanding consistency models by modeling the denoising process of the diffusion model as a Markov Decision Process (MDP) and framing consistency model training as the value estimation through Temporal Difference~(TD) Learning. More importantly, this framework allows us to analyze the limitations of current consistency training/tuning strategies. Built upon Easy Consistency Tuning (ECT), we propose Stable Consistency Tuning (SCT), which incorporates variance-reduced learning using the score identity. SCT leads to significant performance improvements on benchmarks such as CIFAR-10 and ImageNet-64. On ImageNet-64, SCT achieves 1-step FID 2.42 and 2-step FID 1.55, a new SoTA for consistency models.

本研究针对一致性模型在训练和调优过程中存在的局限性进行探讨，提出了一种新的框架，通过将去噪过程建模为马尔可夫决策过程，并利用时间差分学习进行价值估计。提出的稳定一致性调优方法显著提升了在CIFAR-10和ImageNet-64等基准测试上的性能，尤其是在ImageNet-64上实现了新的一致性模型状态-of-the-art绩效。

稳定一致性调优：理解和改进一致性模型