Stable Consistency Tuning: Understanding and Improving Consistency
  Models
Authors: name: Fu-Yun Wang, name: Zhengyang Geng, name: Hongsheng Li

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.