Lin, Forrest
Training Video Foundation Models with NVIDIA NeMo
Patel, Zeeshan, He, Ethan, Mannan, Parth, Ren, Xiaowei, Wolf, Ryan, Agarwal, Niket, Huffman, Jacob, Wang, Zhuoyao, Wang, Carl, Chang, Jack, Bai, Yan, Huang, Tommy, Wang, Linnan, Jain, Sahil, Ramasamy, Shanmugam, Jennings, Joseph, Sirazitdinova, Ekaterina, Sudakov, Oleg, Ma, Mingyuan, Chen, Bobby, Lin, Forrest, Wang, Hao, Sabavat, Vasanth Rao Naik, Niverty, Sriharsha, Ou, Rong, Bhattacharya, Pallab, Page, David, Tajbakhsh, Nima, Aithal, Ashwath
Video Foundation Models (VFMs) have recently been used to simulate the real world to train physical AI systems and develop creative visual experiences. However, there are significant challenges in training large-scale, high quality VFMs that can generate high-quality videos. We present a scalable, open-source VFM training pipeline with NVIDIA NeMo, providing accelerated video dataset curation, multimodal data loading, and parallelized video diffusion model training and inference. We also provide a comprehensive performance analysis highlighting best practices for efficient VFM training and inference.