视频生成模型的大规模训练探索
We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios.
We leverage a transformer architecture that operates on spacetime patches of video and image latent codes.
核心成果
Our largest model, Sora, is capable of generating a minute of high fidelity video.
Our results suggest that scaling video generation models is a promising path towards building general purpose simulators of the physical world.