# SORA Technical Explanation ## Video generation models as world simulators Explore large-scale training of generative models on video data at OpenAi. Specifically, OpenAi trains text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. They leverage a **transformer architecture** that operates on spacetime patches of video and image latent codes. Their 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. {% embed url="" %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://metaverse-imagen.gitbook.io/ai-tools-research/ai-tools-main-categories/video-and-animation/video-synthesis-generation/sora-openai/sora-technical-explanation.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.