用NVIDIA NeMo构建可扩展的视频基础模型训练流水线
Training Video Foundation Models with NVIDIA NeMo
- 基于NeMo实现视频数据高效处理与多模态加载
- 支持并行化视频扩散模型训练，提升效率
- 适合想高效训练高质量视频模型的研究者
视频基础模型（VFMs）近期被用于模拟现实世界以训练物理人工智能系统和开发创意视觉体验。然而，训练大规模、高质量的视频基础模型仍面临诸多挑战，尤其在生成高质量视频方面。本文提出一个可扩展的开源视频基础模型训练流程，基于NVIDIA NeMo，提供加速的视频数据集整理、多模态数据加载，以及并行化的视频扩散模型训练与推理。同时，我们进行了全面的性能分析，总结出高效的视频基础模型训练与推理最佳实践。
原文摘要 · Abstract (English)
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.
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