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.

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

The paper "EasyAnimate: A High-Performance Long Video Generation Method based on Transformer Architecture" introduces a novel approach for video generation leveraging the transformer architecture. Authored by Jiaqi Xu et al., this paper details the extension of the DiT (Diffusion Transformer) framework, originally conceived for 2D image synthesis, to accommodate the more complex task of 3D video generation. This adaptation is achieved through the incorporation of a motion module block that captures temporal dynamics, ensuring the generation of consistent frames and seamless motion transitions.

Main Contributions

1. Motion Module Block:
   • The motion module is pivotal in leveraging temporal information to transition DiT from handling static images to dynamic videos. By integrating attention mechanisms across the temporal domain, this module enables the assimilation of temporal data, which is essential for generating fluid video motion.
2. Slice VAE:
   • Introduced as an advancement over the MagViT video VAE, Slice VAE employs a slicing mechanism along the temporal dimension to condense the temporal axis. This strategic slicing addresses the challenge of memory inefficiencies, facilitating the generation of long-duration videos (up to 144 frames) with remarkable efficiency.
3. Three-Stage Training Process:
   • The training pipeline for EasyAnimate involves a rigorous three-stage process:
     1. Aligning the DiT parameters with a newly trained VAE using image data.
     2. Pretraining the motion module with large-scale video datasets alongside image data to introduce video generation capacity.
     3. Finely tuning the entire DiT model with high-resolution video data to enhance generative performance.
4. Robust Data Preprocessing:
   • The data preprocessing strategy includes video splitting, filtering, and captioning. Various techniques, such as RAFT for motion filtering, OCR for text filtering, and aesthetic scoring, are utilized to ensure high-quality training data.

Experimental Results

The paper presents empirical results showcasing the effectiveness of EasyAnimate in generating high-quality videos with consistent motion and sharp image quality. The use of the Slice VAE notably reduces memory demands, allowing the model to process long-duration videos efficiently. The integration of image training in the VAE stage further optimizes the model architecture, enhancing both text alignment and video generation quality.

Implications and Future Directions

The results of EasyAnimate underscore its potential applicability in various domains requiring high-fidelity video generation. The approach’s ability to generate videos with different frame rates and resolutions during both training and inference phases presents a versatile tool for both academic research and practical applications. The holistic ecosystem provided by EasyAnimate covers end-to-end video production aspects, from data preprocessing to model training and inference, fostering a conducive environment for further innovation.

From a theoretical standpoint, the paper opens avenues for exploring transformer architectures in video generation tasks. The successful integration of a motion module to incorporate temporal dynamics within a diffusion model framework suggests potential optimizations for other time-series and sequence prediction problems.

Future research may delve into further refining the motion module to handle more complex dynamics and interactions within video frames. Additionally, the slice mechanism presents avenues for optimization with other neural network architectures, potentially enhancing efficiency across a broader range of applications.

Conclusion

"EasyAnimate: A High-Performance Long Video Generation Method based on Transformer Architecture" presents a significant step forward in the field of AI-driven video generation. By effectively leveraging transformer architectures and introducing innovative modules and training strategies, the paper showcases a method that substantially improves the efficiency and quality of long-duration video generation. The practical utility and theoretical insights provided by this research will likely inspire further advancements and applications in the domain of automated video synthesis. Interested researchers and practitioners can explore and utilize EasyAnimate through the publicly available code repository provided by the authors.