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Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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Cosmos-1.0 Diffusion Decoder

Cosmos | Code | Paper

Model Overview

Description:

Cosmos Diffusion Decoder is a diffusion decoder model that can improve outputs of Cosmos-1.0-Autoregressive models with more fine-grained details. This model is ready for commercial use.

Model Developer: NVIDIA

Model Versions

In Cosmos 1.0 release, the Cosmos Diffusion Decoder includes the following models:

Cosmos-1.0-Diffusion-7B-Decoder-DV8x16x16ToCV8x8x8

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.

Cosmos-1.0-Guardrail is the safety guardrail for this model.

Model Architecture:

Cosmos-1.0-Diffusion-7B-Decoder-DV8x16x16ToCV8x8x8 is a diffusion transformer model designed for video denoising within 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.

Input/Output Specifications

Input\n
- Input Type(s): Tokens
- Input Format(s): Integer Tensor
- Input Parameters: Three-dimensional (3D)
- Other Properties Related to Input:\n
  - Integer indices ranging from 0 to 63,999
  - Should be the tokens generated by Cosmos-1.0-Tokenizer-DV8x16x16 or Cosmos-1.0-Autoregressive models
  \n
\n
Output\n
- Output Type(s): Tokens
- Output Format(s): Float Tensor
- Output Parameters: Three-dimensional (3D)
- Other Properties Related to Output:\n
  - Continuous-valued feature vectors with a dimensionality of 16
  - The output tokens can be used as input for the decoder of Cosmos-1.0-Tokenizer-CV8x8x8
  \n
\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.

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-7b-decoder-dv8x16x16tocv8x8x8/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/cosmos/cosmos-1.0-diffusion-7b-decoder-dv8x16x16tocv8x8x8:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/cosmos/models/cosmos-1.0-diffusion-7b-decoder-dv8x16x16tocv8x8x8/-/version-history","accessCheckResult",{"_106":107,"_108":20},"hasRelatedCollections",true,"context",{"_102":103,"_104":105},1787560995276,[],[],[94,95,96,97,98,99],{"_33":48,"_35":55,"_37":56,"_39":57,"_41":58,"_43":44},{"_33":48,"_35":52,"_37":38,"_39":53,"_41":54,"_43":44},{"_33":48,"_35":51,"_37":38,"_39":53,"_41":101,"_43":44},{"_33":48,"_35":49,"_37":56,"_39":57,"_41":100,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},68,50,"orgName","nvidia","teamName","cosmos","reason","login_required","allowed","id","nvidia/cosmos/cosmos-1.0-diffusion-7b-decoder-dv8x16x16tocv8x8x8","key","nvidia/cosmos/models/cosmos-1.0-diffusion-7b-decoder-dv8x16x16tocv8x8x8","resourceType","models","resourceId","cosmos-1.0-diffusion-7b-decoder-dv8x16x16tocv8x8x8","shortDescription","Cosmos Diffusion Decoder is a diffusion decoder model that can improve outputs of Cosmos-1.0-Autoregressive models with more fine-grained details.","description","# **Cosmos-1.0 Diffusion Decoder**\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 Diffusion Decoder** is a diffusion decoder model that can improve outputs of Cosmos-1.0-Autoregressive models with more fine-grained details. This model is ready for commercial use.\n\n**Model Developer**: NVIDIA\n\n## Model Versions\n\nIn Cosmos 1.0 release, the Cosmos Diffusion Decoder includes the following models:\n- [Cosmos-1.0-Diffusion-7B-Decoder-DV8x16x16ToCV8x8x8](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-7B-Decoder-DV8x16x16ToCV8x8x8)\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* [Cosmos-1.0-Guardrail](https://huggingface.co/nvidia/Cosmos-1.0-Guardrail) is the safety guardrail for this model.\n\n## Model Architecture:\n\nCosmos-1.0-Diffusion-7B-Decoder-DV8x16x16ToCV8x8x8 is a diffusion transformer model designed for video denoising within 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.\n\n## Input/Output Specifications\n\n* **Input**\n * **Input Type(s)**: Tokens\n * **Input Format(s)**: Integer Tensor\n * **Input Parameters**: Three-dimensional (3D)\n * **Other Properties Related to Input**:\n * Integer indices ranging from 0 to 63,999\n * Should be the tokens generated by [Cosmos-1.0-Tokenizer-DV8x16x16](https://huggingface.co/nvidia/Cosmos-1.0-Tokenizer-DV8x16x16) or [Cosmos-1.0-Autoregressive](https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6) models\n* **Output**\n * **Output Type(s)**: Tokens\n * **Output Format(s)**: Float Tensor\n * **Output Parameters**: Three-dimensional (3D)\n * **Other Properties Related to Output**:\n * Continuous-valued feature vectors with a dimensionality of 16\n * The output tokens can be used as input for the decoder of [Cosmos-1.0-Tokenizer-CV8x8x8](https://huggingface.co/nvidia/Cosmos-1.0-Tokenizer-CV8x8x8)\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## 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/).","displayName","Cosmos-1.0-Diffusion-7B-Decoder-DV8x16x16ToCV8x8x8","updatedDate",["D",1736232600186],"publisher","NVIDIA","latestVersion","1.0","versions","collectionCounts","imageUrl","/api/image/nvidia/cosmos/models/cosmos-1.0-diffusion-7b-decoder-dv8x16x16tocv8x8x8","rawImageUrl","https://nvdam.widen.net/content/xbsnft8sm2/jpeg/cosmos-thumbnail-274x152.jpeg?position=c&color=ffffffff&quality=100&u=hdilxr&use=c2ccj","canGuestDownload","latestVersionSizeInBytes",14475799017,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","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.","featureLabels",[],"displayLabels",[160],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_121":161,"_162":161,"_163":164,"_165":166},"NSPECT-72FQ-Z3GK","value","filter",{"_167":166,"_162":161},"category","label","field","appConfig",{"_1152":1153,"_202":1154},"ngcApps",[1105],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1086":1087,"_1088":1089,"_1090":1091,"_1092":1093,"_1094":180,"_76":209,"_1095":1096,"_1097":44,"_1098":87,"_1099":87,"_1100":1101,"_1102":87,"_1103":1104},"amplitude",{"_1083":1084,"_724":-7,"_1085":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1077":1078,"_1079":1080,"_1081":1082},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"now",1787560995171,"1.12.0","defaultSearchSuggestions",{"_964":291,"_965":966,"_967":968,"_969":970},"announcements",[],"consentState",{"_247":954,"_955":87,"_956":20,"_957":20,"_958":959},"consentConfig",{"_229":230,"_231":232,"_233":234,"_235":236,"_237":238,"_239":240,"_241":242,"_243":244,"_245":246,"_247":248},"showContactAdmin",1787560995172,[],[],[223,224,225,226,227],{"_33":48,"_35":55,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":52,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":228,"_43":44},4,"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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504],[451,452,453,454,455],{"_289":365,"_121":365,"_119":291,"_262":475},{"_289":339,"_121":339,"_119":291,"_262":470},{"_289":465,"_121":465,"_119":291,"_262":466},{"_289":460,"_121":460,"_119":291,"_262":461},{"_289":456,"_121":456,"_119":291,"_262":457},"nvidia.partners",[458],{"_256":459,"_296":456,"_297":20,"_119":333},"edge","app.hushly.com",[462],{"_256":463,"_296":460,"_297":20,"_119":464},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[467],{"_256":468,"_296":465,"_297":20,"_119":469},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591],[543,544,545,546],{"_289":558,"_121":558,"_119":291,"_262":559},{"_289":553,"_121":553,"_119":291,"_262":554},{"_289":352,"_121":352,"_119":291,"_262":550},{"_289":465,"_121":465,"_119":291,"_262":547},[548],{"_256":549,"_296":465,"_297":87,"_119":469},"_gh_sess",[551],{"_256":552,"_296":352,"_297":87,"_119":359},"YSC","bizible.com",[555],{"_256":556,"_296":553,"_297":20,"_119":557},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[560],{"_256":561,"_296":558,"_297":20,"_119":562},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_256":633,"_296":411,"_297":20,"_119":634},{"_256":618,"_296":632,"_297":87,"_119":619},{"_256":601,"_296":414,"_297":20,"_119":526},{"_256":601,"_296":514,"_297":20,"_119":526},{"_256":630,"_296":411,"_297":20,"_119":631},{"_256":618,"_296":417,"_297":87,"_119":619},{"_256":628,"_296":411,"_297":20,"_119":629},{"_256":618,"_296":414,"_297":87,"_119":619},{"_256":626,"_296":513,"_297":20,"_119":627},{"_256":618,"_296":535,"_297":87,"_119":619},{"_256":598,"_296":414,"_297":20,"_119":600},{"_256":624,"_296":411,"_297":87,"_119":625},{"_256":622,"_296":411,"_297":20,"_119":623},{"_256":620,"_296":411,"_297":87,"_119":621},{"_256":618,"_296":509,"_297":87,"_119":619},{"_256":616,"_296":411,"_297":20,"_119":617},{"_256":614,"_296":411,"_297":20,"_119":615},{"_256":612,"_296":411,"_297":87,"_119":613},{"_256":610,"_296":518,"_297":20,"_119":611},{"_256":598,"_296":509,"_297":20,"_119":600},{"_256":608,"_296":411,"_297":20,"_119":609},{"_256":606,"_296":411,"_297":87,"_119":607},{"_256":604,"_296":411,"_297":20,"_119":605},{"_256":602,"_296":411,"_297":20,"_119":603},{"_256":601,"_296":411,"_297":20,"_119":526},{"_256":598,"_296":599,"_297":20,"_119":600},{"_256":596,"_296":411,"_297":20,"_119":597},{"_256":594,"_296":411,"_297":87,"_119":595},{"_256":592,"_296":411,"_297":20,"_119":593},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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