OpenAI Sora文生视频模型技术报告中英全文

Video generation models as world simulators 视频生成模型作为世界模拟器

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. 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. 我们探索了在视频数据上进行大规模训练生成模型。具体来说，我们联合训练了文本条件扩散模型，处理不同持续时间、分辨率和宽高比的视频和图像。我们利用了一种在视频和图像潜码的时空块上操作的变压器架构。我们最大的模型Sora能够生成一分钟的高保真视频。我们的结果表明，扩大视频生成模型的规模是朝着构建物理世界通用模拟器的有前途的路径。

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations. Model and implementation details are not included in this report.

本技术报告重点介绍：（1）我们将各类视觉数据转换为统一表示的方法，该方法能够实现生成模型的大规模训练；（2）Sora能力和局限性的定性评估。报告中未包含模型和实现细节。

Much prior work has studied generative modeling of video data using a variety of methods, including recurrent networks,generative adversarial networks,4,5,6,7 autoregressive transformers,8,9 and diffusion models.10,11,12 These works often focus on a narrow category of visual data, on shorter videos, or on videos of a fixed size. Sora is a generalist model of visual data—it can generate videos and images spanning diverse durations, aspect ratios and resolutions, up to a full minute of high definition video. 以前的许多工作已经研究了使用各种方法对视频数据进行生成建模，包括循环网络、生成对抗网络、自回归变换器和扩散模型。这些工作通常专注于狭窄类别的视觉数据、较短的视频或固定大小的视频。Sora是一种通用的视觉数据模型——它可以生成持续时间、宽高比和分辨率各异的视频和图像，长达一分钟的高清视频。

Turning visual data into patches

将视觉数据转换为图像块

We take inspiration from large language models which acquire generalist capabilities by training on internet-scale data.13,14 The success of the LLM paradigm is enabled in part by the use of tokens that elegantly unify diverse modalities of text—code, math and various natural languages. In this work, we consider how generative models of visual data can inherit such benefits. Whereas LLMs have text tokens, Sora has visual patches. Patches have previously been shown to be an effective representation for models of visual data.15,16,17,18 We find that patches are a highly-scalable and effective representation for training generative models on diverse types of videos and images. 我们从大型语言模型中获得灵感，这些模型通过在互联网规模的数据上训练来获得通用能力。这种范式的成功在一定程度上得益于使用词元编码/令牌（token），它们巧妙地统一了文本的多种形式——代码、数学和各种自然语言。在这项工作中，我们考虑如何让视觉数据的生成模型继承这些好处。与拥有文本令牌的不同，Sora拥有视觉块嵌入编码（visual patches）。视觉块已被证明是视觉数据模型的一种有效表示。我们发现，补丁是一种高度可扩展且有效的表示形式，用于在多种类型的视频和图像上训练生成模型。

At a high level, we turn videos into patches by first compressing videos into a lower-dimensional latent space,19 and subsequently decomposing the representation into spacetime patches. 在高维上，我们首先将视频压缩到一个低维潜在空间，然后将表示分解成时空嵌入，从而将视频转换成一系列编码块。

Video compression network

视频压缩网络

We train a network that reduces the dimensionality of visual data.20 This network takes raw video as input and outputs a latent representation that is compressed both temporally and spatially. Sora is trained on and subsequently generate