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: this https URL We are continuously
working to enhance the performance of our method.

EasyAnimate 是一种基于 Transformer 架构的视频生成方法，通过将运动模块块纳入 DiT 框架，扩展了 2D 图像综合的方法以适应 3D 视频生成的复杂性，能够捕捉时间动态，生成一致的帧和无缝的运动过渡，可用于生成不同风格、不同帧率和分辨率的图像和视频。

EasyAnimate：一种基于 Transformer 架构的高性能长视频生成方法

EasyAnimate: A High-Performance Long Video Generation Method based on  Transformer Architecture

We present AnimateDiff-Lightning for lightning-fast video generation. Our
model uses progressive adversarial diffusion distillation to achieve new
state-of-the-art in few-step video generation. We discuss our modifications to
adapt it for the video modality. Furthermore, we propose to simultaneously
distill the probability flow of multiple base diffusion models, resulting in a
single distilled motion module with broader style compatibility. We are pleased
to release our distilled AnimateDiff-Lightning model for the community's use.

通过使用渐进式对抗传播扩散调制，我们提出了 AnimateDiff-Lightning 模型用于快速生成视频，达到了最新的有限步视频生成技术。我们在该文中探讨了为适应视频模态所做的修改。此外，我们提出了同时精馏多个基础传播模型的概率流，从而获得具有更广泛风格兼容性的单一精馏运动模块。我们很高兴为社区发布我们的精馏 AnimateDiff-Lightning 模型供使用。