Cosmos-1.0-Diffusion: A Suite of Diffusion-based World Foundation Models

Cosmos | Code | Paper

Model Overview

Description:

Cosmos World Foundation Models: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware videos and world states for physical AI development.

The Cosmos diffusion models are a collection of diffusion based world foundation models that generate dynamic, high quality videos from text, image, or video inputs. It can serve as the building block for various applications or research that are related to world generation. The models are ready for commercial use under NVIDIA Open Model license agreement.

Model Developer: NVIDIA

Model Versions

In Cosmos 1.0 release, the Cosmos Diffusion WFM family includes the following models:

Cosmos-1.0-Diffusion-7B-Text2World\n
- Given a text description, predict an output video of 121 frames.
\n
Cosmos-1.0-Diffusion-14B-Text2World\n
- Given a text description, predict an output video of 121 frames.
\n
Cosmos-1.0-Diffusion-7B-Video2World\n
- Given a text description and an image as the first frame, predict the future 120 frames.
\n
Cosmos-1.0-Diffusion-14B-Video2World\n
- Given a text description and an image as the first frame, predict the future 120 frames.
\n

License:

This model is released under the NVIDIA Open Model License. For a custom license, please contact cosmos-license@nvidia.com.

Under the NVIDIA Open Model License, NVIDIA confirms:

Models are commercially usable.
You are free to create and distribute Derivative Models.
NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.

Important Note: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or\nassociated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained\nin the Model, your rights under NVIDIA Open Model License Agreement will automatically terminate.

Model Architecture:

Cosmos-1.0-Diffusion-14B-Video2World is a diffusion transformer model designed for video denoising in the latent space. The network is composed of interleaved self-attention, cross-attention and feedforward layers as its building blocks. The cross-attention layers allow the model to condition on input text throughout the denoising process. Before each layers, adaptive layer normalization is applied to embed the time information for denoising. When image or video is provided as input, their latent frames are concatenated with the generated frames along the temporal dimension. Augment noise is added to conditional latent frames to bridge the training and inference gap.

Input/Output Specifications

\n
Input
\n
- Input Type(s): Text+Image, Text+Video
- Input Format(s):\n
  - Text: String
  - Image: jpg, png, jpeg, webp
  - Video: mp4
  \n
- Input Parameters:\n
  - Text: One-dimensional (1D)
  - Image: Two-dimensional (2D)
  - Video: Three-dimensional (3D)
  \n
- Other Properties Related to Input:\n
  - The input string should contain fewer than 300 words and should provide descriptive content for world generation, such as a scene description, key objects or characters, background, and any specific actions or motions to be depicted within the 5-second duration.
  - The input image should be of 1280x704 resolution.
  - The input video should be of 1280x704 resolution and 9 input frames.
  \n
\n
\n
Output
\n
- Output Type(s): Video
- Output Format(s): mp4
- Output Parameters: Three-dimensional (3D)
- Other Properties Related to Output: The generated video will be a 5-second clip with a resolution of 1280x704 pixels at 24 frames per second (fps). The content of the video will use the provided image as the first frame and visualize the input text description as a short animated scene, capturing the main elements mentioned in the input within the time constraints.
\n

Software Integration

Runtime Engine(s):

Cosmos

Supported Hardware Microarchitecture Compatibility:

NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Ampere

Note: We have only tested doing inference with BF16 precision.

Operating System(s):

Linux (We have not tested on other operating systems.)

Usage

See Cosmos for details.

Evaluation

Please see our technical paper for detailed evaluations.

Inference Time and GPU Memory Usage

The numbers provided below may vary depending on system specs and are for reference only.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Offloading Strategy	7B Video2World	14B Video2World
Offload prompt upsampler	76.5 GB	> 80.0 GB
Offload prompt upsampler & guardrails	59.9 GB	73.3 GB
Offload prompt upsampler & guardrails & T5 encoder	41.3 GB	54.8 GB
Offload prompt upsampler & guardrails & T5 encoder & tokenizer	41.1 GB	54.5 GB
Offload prompt upsampler & guardrails & T5 encoder & tokenizer & diffusion model	27.3 GB	39.0 GB

The following table shows the end-to-end inference runtime on a single H100 GPU, excluding model initialization time:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

7B Video2World (offload prompt upsampler)	14B Video2World (offload prompt upsampler, guardrails)
~383 seconds	~593 seconds

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/cosmos/cosmos-1.0-diffusion-14b-video2world:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world/-/version-history","accessCheckResult",{"_194":195,"_196":26},"hasRelatedCollections",true,"context",{"_85":86,"_87":84},1787628669404,[],[],[227,228,229,230,231,232,233],{"_40":55,"_42":67,"_44":57,"_46":58,"_48":68,"_50":51},{"_40":55,"_42":64,"_44":45,"_46":65,"_48":66,"_50":51},{"_40":55,"_42":62,"_44":45,"_46":65,"_48":234,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":55,"_42":56,"_44":57,"_46":58,"_48":59,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},52,"nvidia/cosmos/cosmos-1.0-diffusion-14b-video2world","nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world","cosmos-1.0-diffusion-14b-video2world","The Cosmos diffusion models are a collection of diffusion based world foundation models that generate dynamic, high quality videos from text, image, or video inputs.","# **Cosmos-1.0-Diffusion**: A Suite of Diffusion-based World Foundation Models\n\n[**Cosmos**](https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6) | [**Code**](https://github.com/NVIDIA/Cosmos) | [**Paper**](https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai)\n\n\n# Model Overview\n\n## Description:\n**Cosmos World Foundation Models**: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware videos and world states for physical AI development.\n\nThe Cosmos diffusion models are a collection of diffusion based world foundation models that generate dynamic, high quality videos from text, image, or video inputs. It can serve as the building block for various applications or research that are related to world generation. The models are ready for commercial use under NVIDIA Open Model license agreement.\n\n**Model Developer**: NVIDIA\n\n## Model Versions\n\nIn Cosmos 1.0 release, the Cosmos Diffusion WFM family includes the following models:\n- [Cosmos-1.0-Diffusion-7B-Text2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-7B-Text2World)\n - Given a text description, predict an output video of 121 frames.\n- [Cosmos-1.0-Diffusion-14B-Text2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-14B-Text2World)\n - Given a text description, predict an output video of 121 frames.\n- [Cosmos-1.0-Diffusion-7B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-7B-Video2World)\n - Given a text description and an image as the first frame, predict the future 120 frames.\n- [Cosmos-1.0-Diffusion-14B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-14B-Video2World)\n - Given a text description and an image as the first frame, predict the future 120 frames.\n\n\n### License:\nThis model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). For a custom license, please contact [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com).\n\nUnder the NVIDIA Open Model License, NVIDIA confirms:\n\n* Models are commercially usable.\n* You are free to create and distribute Derivative Models.\n* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.\n\n**Important Note**: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or\nassociated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained\nin the Model, your rights under [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) will automatically terminate.\n\n## Model Architecture:\n\nCosmos-1.0-Diffusion-14B-Video2World is a diffusion transformer model designed for video denoising in the latent space. The network is composed of interleaved self-attention, cross-attention and feedforward layers as its building blocks. The cross-attention layers allow the model to condition on input text throughout the denoising process. Before each layers, adaptive layer normalization is applied to embed the time information for denoising. When image or video is provided as input, their latent frames are concatenated with the generated frames along the temporal dimension. Augment noise is added to conditional latent frames to bridge the training and inference gap.\n\n## Input/Output Specifications\n\n* **Input**\n\n * **Input Type(s)**: Text+Image, Text+Video\n * **Input Format(s)**:\n * Text: String\n * Image: jpg, png, jpeg, webp\n * Video: mp4\n * **Input Parameters**:\n * Text: One-dimensional (1D)\n * Image: Two-dimensional (2D)\n * Video: Three-dimensional (3D)\n * **Other Properties Related to Input**:\n * The input string should contain fewer than 300 words and should provide descriptive content for world generation, such as a scene description, key objects or characters, background, and any specific actions or motions to be depicted within the 5-second duration.\n * The input image should be of 1280x704 resolution.\n * The input video should be of 1280x704 resolution and 9 input frames.\n\n* **Output**\n * **Output Type(s)**: Video\n * **Output Format(s)**: mp4\n * **Output Parameters**: Three-dimensional (3D)\n * **Other Properties Related to Output**: The generated video will be a 5-second clip with a resolution of 1280x704 pixels at 24 frames per second (fps). The content of the video will use the provided image as the first frame and visualize the input text description as a short animated scene, capturing the main elements mentioned in the input within the time constraints.\n\n## Software Integration\n**Runtime Engine(s):**\n* [Cosmos](https://github.com/NVIDIA/Cosmos)\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Blackwell\n* NVIDIA Hopper\n* NVIDIA Ampere\n\n**Note**: We have only tested doing inference with BF16 precision.\n\n\n\n**Operating System(s):**\n* Linux (We have not tested on other operating systems.)\n\n\n# Usage\n\n* See [Cosmos](https://github.com/NVIDIA/Cosmos) for details.\n\n\n# Evaluation\n\nPlease see our [technical paper](https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai) for detailed evaluations.\n\n## Inference Time and GPU Memory Usage\n\nThe numbers provided below may vary depending on system specs and are for reference only.\n\n| Offloading Strategy | 7B Video2World | 14B Video2World |\n|----------------------------------------------------------------------------------|---------|---------|\n| Offload prompt upsampler | 76.5 GB | > 80.0 GB |\n| Offload prompt upsampler & guardrails | 59.9 GB | 73.3 GB |\n| Offload prompt upsampler & guardrails & T5 encoder | 41.3 GB | 54.8 GB |\n| Offload prompt upsampler & guardrails & T5 encoder & tokenizer | 41.1 GB | 54.5 GB |\n| Offload prompt upsampler & guardrails & T5 encoder & tokenizer & diffusion model | 27.3 GB | 39.0 GB |\n\nThe following table shows the end-to-end inference runtime on a single H100 GPU, excluding model initialization time:\n\n| 7B Video2World (offload prompt upsampler) | 14B Video2World (offload prompt upsampler, guardrails) |\n|---------|---------|\n| ~383 seconds | ~593 seconds |\n\n## Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","Cosmos-1.0-Diffusion-14B-Video2World",["D",1736232578130],"1.0","/api/image/nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world",28534623615,"bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | World Generation\nModel Type: | Transformer\nIntended Users: | Physical AI developers\nOutput: | Videos\nDescribe how the model works: | Generates videos based on video inputs\nTechnical Limitations: | The model may not follow the video input accurately.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Quantitative and Qualitative Evaluation\nPotential Known Risks: | The model's output can generate all forms of videos, including what may be considered toxic, offensive, or indecent.\nLicensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal information? | None Known\nProtected class data used to create this model? | None Known\nWas consent obtained for any personal data used? | None Known\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | World Generation\nDescribe the life critical impact (if present). | None Known\nUse Case Restrictions: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.",[],[258],[],[],[],{"_91":144,"_145":144,"_146":259,"_148":149},{"_150":149,"_145":144},{"_177":1224,"_179":1225},"ngcApps",[1177],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1158":1159,"_1160":1161,"_1162":1163,"_1164":1165,"_1166":271,"_209":297,"_1167":1168,"_1169":51,"_1170":220,"_1171":220,"_1172":1173,"_1174":220,"_1175":1176},"amplitude",{"_1155":1156,"_810":-7,"_1157":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1149":1150,"_1151":1152,"_1153":1154},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787628669303,"1.12.0","defaultSearchSuggestions",{"_1050":378,"_1051":1052,"_1053":1054,"_1055":1056},"announcements",[],"consentState",{"_335":1040,"_1041":220,"_1042":26,"_1043":26,"_1044":1045},"consentConfig",{"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336},"showContactAdmin",1787628669305,[],[],[311,312,313,314,315,316],{"_40":55,"_42":67,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":63,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496],[359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375],{"_376":470,"_91":470,"_90":378,"_349":471},{"_376":466,"_91":466,"_90":378,"_349":467},{"_376":461,"_91":461,"_90":378,"_349":462},{"_376":457,"_91":457,"_90":378,"_349":458},{"_376":452,"_91":452,"_90":378,"_349":453},{"_376":447,"_91":447,"_90":378,"_349":448},{"_376":439,"_91":439,"_90":378,"_349":440},{"_376":431,"_91":431,"_90":378,"_349":432},{"_376":426,"_91":426,"_90":378,"_349":427},{"_376":421,"_91":421,"_90":378,"_349":422},{"_376":416,"_91":416,"_90":378,"_349":417},{"_376":411,"_91":411,"_90":378,"_349":412},{"_376":406,"_91":406,"_90":378,"_349":407},{"_376":399,"_91":399,"_90":378,"_349":400},{"_376":394,"_91":394,"_90":378,"_349":395},{"_376":387,"_91":387,"_90":378,"_349":388},{"_376":377,"_91":377,"_90":378,"_349":379},"hostName","amazon-adsystem.com","",[380,381],{"_124":386,"_383":377,"_384":26,"_90":385},{"_124":382,"_383":377,"_384":26,"_90":385},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[389,390],{"_124":393,"_383":387,"_384":26,"_90":392},{"_124":391,"_383":387,"_384":26,"_90":392},"IDE","This domain is owned by Doubleclick (Google). 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These cookies may store a unique ID so that our system will remember you when you return. 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