VideoLCM: Video Latent Consistency Model
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.
657bd3317e11375c6f28d62d
Xiang Wang
657bd3317e11375c6f28d62e
Shiwei Zhang
admin_assigned
2023-12-15T10:10:30.162Z
63a1200045edac9f7508bae9
/avatars/e84f0b045f32c5b8b4da43458650b925.svg
Shiwei Zhang
user
StevenZhang
657bd3317e11375c6f28d62f
Han Zhang
extracted_confirmed
2023-12-15T07:04:32.498Z
63c3b12da361002ba0ae5381
/avatars/f505a612c7698b5975a1daa3cca6a1c3.svg
Han Zhang
user
bibona
657bd3317e11375c6f28d630
Yu Liu
657bd3317e11375c6f28d631
Yingya Zhang
657bd3317e11375c6f28d632
Changxin Gao
657bd3317e11375c6f28d633
Nong Sang
VideoLCM: Video Latent Consistency Model
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.
VideoLCM framework leverages consistency models for efficient video synthesis with minimal sampling steps, achieving high fidelity and temporal consistency.
consistency models latent video diffusion models consistency distillation latent consistency model video synthesis temporal consistency