Stable Consistency Tuning: Understanding and Improving Consistency Models

• URL: http://arxiv.org/abs/2410.18958v1
• Date: Thu, 24 Oct 2024 17:55:52 GMT
• Title: Stable Consistency Tuning: Understanding and Improving Consistency Models
• Authors: Fu-Yun Wang, Zhengyang Geng, Hongsheng Li,
• Abstract summary: Diffusion models achieve superior generation quality but suffer from slow generation speed due to iterative nature of denoising. consistency models, a new generative family, achieve competitive performance with significantly faster sampling. 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.
• Score: 40.2712218203989
• License:
• Abstract: 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.

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