EasyAnimate: A High-Performance Long Video Generation Method based on Transformer Architecture (2405.18991v2)

Abstract: 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.

• The paper presents a novel transformer-based approach that extends the DiT framework for long-video synthesis using a specialized Hybrid Motion Module.
• It introduces the Slice VAE for efficient temporal compression, reducing GPU memory demands and enabling the generation of extended-length videos.
• Empirical results demonstrate that EasyAnimate produces coherent, high-fidelity videos from image and text prompts, paving the way for applications in entertainment and VR.

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

Introduction to EasyAnimate

EasyAnimate is positioned as a progressive strategy for video synthesis, harnessing the Transformer architecture to accomplish exceptional video generation capabilities. The primary methodological advancements lie in extending the DiT framework, traditionally utilized for 2D image synthesis, to accommodate the complexities inherent in 3D video generation. This adaptation is engineered through the integration of the Hybrid Motion Module, a specialized block facilitating coherent temporal attentiveness and global frame alignment necessary for video fluidity.

Architecture of EasyAnimate

At the core of EasyAnimate's architecture is the Diffusion Transformer (DiT), which, as illustrated in Figure 1, consists of several critical components conducive to enhanced video generation.

Figure 1: The architecture of Diffusion Transformer in EasyAnimate, including: (a) DiT overview, (b) Hybrid Motion Module to introduce the temporal information, (c) U-ViT to stabilize the training.

Hybrid Motion Module: This module capitalizes on temporal and global attention mechanisms to ensure the generation of seamless motion transitions across video frames. It supplements traditional video synthesis with a nuanced understanding of temporal dynamics, crucial for the generation of fluid video content.

Slice VAE: A novel approach, Slice VAE, is employed to compress the temporal axis efficiently, enabling the synthesis of extended-length videos while mitigating GPU memory usage challenges typically encountered in long-duration video processing.

Figure 2: The overview of Slice VAE. The Slice VAE employs different decoding methods for images and videos.

Methodological Advances in Video Generation

EasyAnimate represents a significant stride forward in addressing the limitations prevalent in existing video generation models, such as inadequate video length and unnatural motion. By incorporating image guidance using a dual-stream architecture, the system can synthesize videos with heightened realism, drawing from textual embeddings to enhance the coherence of the generated content.

Figure 3: The detailed of image guided video generation.

The training mechanism adopted by EasyAnimate deploys a comprehensive data preprocessing strategy, including video captioning and filtering, to ensure high-quality datasets are utilized in training stages. This meticulous preparation significantly bolsters the output quality of generated videos.

Results and Practical Implications

Empirical evidence of EasyAnimate's video generation capabilities is illustrated in Figure 4, where the model generates videos from image and text prompts, showcasing its adeptness at creating vibrant and coherent video content.

Figure 4: EasyAnimate can generate videos from image and text prompts.

The practical implications of EasyAnimate are vast, offering potential advancements in fields requiring vivid video content generation, such as entertainment, virtual reality (VR), and simulations. The theoretical implications further extend into improved understanding of transformer-based models' applicability in video synthesis.

Future Developments in AI Video Generation

The success of EasyAnimate opens avenues for future exploration in leveraging transformer architectures for creative content generation. By refining the temporal compression techniques like Slice VAE, there remains potential to enhance efficiency in handling large datasets and synthesizing ultra-high-definition videos.

Conclusion

EasyAnimate showcases a robust model for video generation, merging transformer architecture with innovative modules aimed at enhanced temporal coherence and efficiency. The implications of this research resonate across practical and theoretical dimensions in AI video synthesis, setting a foundation for subsequent advancements within the domain. As transformer models continue to evolve, we anticipate further strides in large-scale, high-fidelity video generation capabilities.