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.

运用图像扩散提炼高保真生成的方法对视频扩散应用直接导致了公开视频数据集中视觉质量有限的帧质量不尽如人意的问题。研究旨在利用丰富的高质量图像数据提高视频扩散提炼的品质，同时改进帧外观。提出了一种称为运动一致模型 （MCM）的单阶段视频扩散提炼方法，通过分离运动和外观学习来改善视频扩散提炼。MCM 包括一个可以从视频教师模型中提炼运动的视频一致性模型和一种增强帧外观以匹配高质量图像数据的图像鉴别器。通过引入分离化的运动提炼和混合轨迹提炼来解决两个挑战：冲突的帧学习目标以及由于训练和推断中使用的视频样本质量不同而产生的训练 - 推断偏差。大量实验结果表明，MCM 达到了最先进的视频扩散提炼性能，并且方法可提高视频扩散模型的帧质量，能够生成具有高美感得分或特定风格的帧，而无需相应的视频数据。