DCM: Dual-Expert Consistency Model for Efficient and High-Quality Video
  Generation
Authors: name: Zhengyao Lv, name: Chenyang Si, name: Tianlin Pan, name: Zhaoxi Chen, name: Kwan-Yee K. Wong, name: Yu Qiao, name: Ziwei Liu

Diffusion Models have achieved remarkable results in video synthesis but
require iterative denoising steps, leading to substantial computational
overhead. Consistency Models have made significant progress in accelerating
diffusion models. However, directly applying them to video diffusion models
often results in severe degradation of temporal consistency and appearance
details. In this paper, by analyzing the training dynamics of Consistency
Models, we identify a key conflicting learning dynamics during the distillation
process: there is a significant discrepancy in the optimization gradients and
loss contributions across different timesteps. This discrepancy prevents the
distilled student model from achieving an optimal state, leading to compromised
temporal consistency and degraded appearance details. To address this issue, we
propose a parameter-efficient Dual-Expert Consistency Model~(DCM),
where a semantic expert focuses on learning semantic layout and motion, while a
detail expert specializes in fine detail refinement. Furthermore, we introduce
Temporal Coherence Loss to improve motion consistency for the semantic expert
and apply GAN and Feature Matching Loss to enhance the synthesis quality of the
detail expert.Our approach achieves state-of-the-art visual quality with
significantly reduced sampling steps, demonstrating the effectiveness of expert
specialization in video diffusion model distillation. Our code and models are
available at
https://github.com/Vchitect/DCM{https://github.com/Vchitect/DCM}.