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<p align="center">
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<span style="font-size: 18px;">New GitHub page for NVIDIA Cosmos: <b><a href="https://github.com/nvidia-cosmos">https://github.com/nvidia-cosmos</a></b></span>
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</p>
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# Nvidia Cosmos
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Cosmos World Foundation Models come in three model types which can all be customized in post-training: [cosmos-predict](https://github.com/nvidia-cosmos/cosmos-predict1), [cosmos-transfer](https://github.com/nvidia-cosmos/cosmos-transfer1), and [cosmos-reason](https://github.com/nvidia-cosmos/cosmos-reason1):
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| | Predict | Transfer | Reason |
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| ----- | :---: | :---: | :---: |
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| **Type** | World Generation | Multi-Controlnet | Reasoning VLM |
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| **Function** | Predict novel future frames given initial frames | Transfer existing control frames into photoreal frames within a video clip | Reason against frames within a video clip |
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| **Use Cases** | Data Generation & Policy Evaluation | Data Augmentation | Data Curation |
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| **Inputs** | Text, Image, Video | Multiple Video Modalities such as RGB, Depth, Segmentation, and more. | Video & Text |
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| **Outputs** | Video | Video | Text |
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#
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# Use Cases in Physical AI Development
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Our world foundation models are purpose-built to accelerate improving performance in downstream model tasks in various stages, as illustrated here in the flywheel.
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<p align="center">
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<img src="https://raw.githubusercontent.com/nvidia-cosmos/cosmos-transfer1/main/assets/DataFlywheel.jpg" alt="NVIDIA Cosmos Data Flywheel">
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<img src="https://raw.githubusercontent.com/nvidia/cosmos/main/assets/cosmos-logo-thumbnail.png" alt="NVIDIA Cosmos Logo" width="500">
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</p>
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<h1 align="center">
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New GitHub page for NVIDIA Cosmos:<br>
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<a href="https://github.com/nvidia-cosmos"><b>https://github.com/nvidia-cosmos</b></a>
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</h1>
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**This repository is no longer maintained and will soon be deprecated.** To view the initial release of NVIDIA Cosmos from this repository, please check out branch `archived-ces2025`.

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