We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI's Sora, we explore the latent diffusion model (LDM) architecture and introduce a video variational autoencoder (VidVAE). VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens and the computational demands associated with generating long-sequence videos. To further address the computational costs, we propose a divide-and-merge strategy that maintains temporal consistency across video segments. Our Diffusion Transformer (DiT) model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios. We have devised a data processing pipeline from the very beginning and collected over 13M high-quality video-text pairs. The pipeline includes multiple steps such as clipping, text detection, motion estimation, aesthetics scoring, and dense captioning based on our in-house video-LLM model. Training the VidVAE and DiT models required approximately 40 and 642 H100 days, respectively. Our model supports over 14-second 720p video generation in an end-to-end way and demonstrates competitive performance against state-of-the-art T2V models.
@article{arxiv.2408.12590,
title = {xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations},
author = {Can Qin and Congying Xia and Krithika Ramakrishnan and Michael Ryoo and Lifu Tu and Yihao Feng and Manli Shu and Honglu Zhou and Anas Awadalla and Jun Wang and Senthil Purushwalkam and Le Xue and Yingbo Zhou and Huan Wang and Silvio Savarese and Juan Carlos Niebles and Zeyuan Chen and Ran Xu and Caiming Xiong},
journal= {arXiv preprint arXiv:2408.12590},
year = {2024}
}