Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation

TL;DR

This paper introduces the Motion Consistency Model (MCM), a novel single-stage video diffusion distillation approach that disentangles motion and appearance learning, leveraging high-quality image data to improve frame quality and achieve state-of-the-art results.

Contribution

The paper proposes a disentangled motion distillation and mixed trajectory distillation method to enhance video diffusion quality and address training-inference discrepancies.

Findings

Achieves state-of-the-art video diffusion distillation performance.

Enhances frame quality with high aesthetic scores or specific styles.

Effectively leverages high-quality image data for video frame enhancement.

Abstract

Image diffusion distillation achieves high-fidelity generation with very few sampling steps. However, applying these techniques directly to video diffusion often results in unsatisfactory frame quality due to the limited visual quality in public video datasets. This affects the performance of both teacher and student video diffusion models. Our study aims to improve video diffusion distillation while improving frame appearance using abundant high-quality image data. We propose motion consistency model (MCM), a single-stage video diffusion distillation method that disentangles motion and appearance learning. Specifically, MCM includes a video consistency model that distills motion from the video teacher model, and an image discriminator that enhances frame appearance to match high-quality image data. This combination presents two challenges: (1) conflicting frame learning objectives, as video distillation learns from low-quality video frames while the image discriminator targets high-quality images; and (2) training-inference discrepancies due to the differing quality of video samples used during training and inference. To address these challenges, we introduce disentangled motion distillation and mixed trajectory distillation. The former applies the distillation objective solely to the motion representation, while the latter mitigates training-inference discrepancies by mixing distillation trajectories from both the low- and high-quality video domains. Extensive experiments show that our MCM achieves the state-of-the-art video diffusion distillation performance. Additionally, our method can enhance frame quality in video diffusion models, producing frames with high aesthetic scores or specific styles without corresponding video data.