  Recently, advancements in video synthesis have attracted significant
attention. Video synthesis models such as AnimateDiff and Stable Video
Diffusion have demonstrated the practical applicability of diffusion models in
creating dynamic visual content. The emergence of SORA has further spotlighted
the potential of video generation technologies. Nonetheless, the extension of
video lengths has been constrained by the limitations in computational
resources. Most existing video synthesis models can only generate short video
clips. In this paper, we propose a novel post-tuning methodology for video
synthesis models, called ExVideo. This approach is designed to enhance the
capability of current video synthesis models, allowing them to produce content
over extended temporal durations while incurring lower training expenditures.
In particular, we design extension strategies across common temporal model
architectures respectively, including 3D convolution, temporal attention, and
positional embedding. To evaluate the efficacy of our proposed post-tuning
approach, we conduct extension training on the Stable Video Diffusion model.
Our approach augments the model's capacity to generate up to $5\times$ its
original number of frames, requiring only 1.5k GPU hours of training on a
dataset comprising 40k videos. Importantly, the substantial increase in video
length doesn't compromise the model's innate generalization capabilities, and
the model showcases its advantages in generating videos of diverse styles and
resolutions. We will release the source code and the enhanced model publicly.
