Consistency models have demonstrated powerful capability in efficient image
generation and allowed synthesis within a few sampling steps, alleviating the
high computational cost in diffusion models. However, the consistency model in
the more challenging and resource-consuming video generation is still less
explored. In this report, we present the VideoLCM framework to fill this gap,
which leverages the concept of consistency models from image generation to
efficiently synthesize videos with minimal steps while maintaining high
quality. VideoLCM builds upon existing latent video diffusion models and
incorporates consistency distillation techniques for training the latent
consistency model. Experimental results reveal the effectiveness of our
VideoLCM in terms of computational efficiency, fidelity and temporal
consistency. Notably, VideoLCM achieves high-fidelity and smooth video
synthesis with only four sampling steps, showcasing the potential for real-time
synthesis. We hope that VideoLCM can serve as a simple yet effective baseline
for subsequent research. The source code and models will be publicly available.

论文介绍了一种名为 VideoLCM 的框架，它利用了一致性模型的概念，通过最小步骤高效地合成视频，同时保持高质量。VideoLCM 基于现有的潜在视频扩散模型，并结合了一致性蒸馏技术来训练潜在一致性模型。实验结果表明，VideoLCM 在计算效率、保真度和时间一致性方面的有效性。尤其值得注意的是，VideoLCM 仅需四个采样步骤即可实现高保真度和流畅的视频合成，展示了实时合成的潜力。希望 VideoLCM 能成为后续研究的简单而有效的基准。源代码和模型将公开提供。