EasyAnimate: A High-Performance Long Video Generation Method based on
  Transformer Architecture
Authors: name: Jiaqi Xu, name: Xinyi Zou, name: Kunzhe Huang, name: Yunkuo Chen, name: Bo Liu, name: MengLi Cheng, name: Xing Shi, name: Jun Huang

This paper presents EasyAnimate, an advanced method for video generation that
leverages the power of transformer architecture for high-performance outcomes.
We have expanded the DiT framework originally designed for 2D image synthesis
to accommodate the complexities of 3D video generation by incorporating a
motion module block. It is used to capture temporal dynamics, thereby ensuring
the production of consistent frames and seamless motion transitions. The motion
module can be adapted to various DiT baseline methods to generate video with
different styles. It can also generate videos with different frame rates and
resolutions during both training and inference phases, suitable for both images
and videos. Moreover, we introduce slice VAE, a novel approach to condense the
temporal axis, facilitating the generation of long duration videos. Currently,
EasyAnimate exhibits the proficiency to generate videos with 144 frames. We
provide a holistic ecosystem for video production based on DiT, encompassing
aspects such as data pre-processing, VAE training, DiT models training (both
the baseline model and LoRA model), and end-to-end video inference. Code is
available at: https://github.com/aigc-apps/EasyAnimate. We are continuously
working to enhance the performance of our method.