cosmos-models

Cosmos-Guardrail1

Cosmos | Code | Paper | Paper Website

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.

Cosmos Guardrail is a content safety model comprising of four components that enforce content safety. The components are as follows.

1. Aegis-AI-Content-Safety-LlamaGuard-LLM-Defensive-1.0: An LLM fine-tuned for content safety. It is a parameter-efficient instruction-tuned version of Llama-Guard based on Llama2-7B, which is trained on NVIDIA's Aegis Content Safety Dataset covering NVIDIA's broad taxonomy of 13 critical safety risk categories. See model card here.

2. Blocklist: A set of human-curated keywords that are used to filter our corner-cases.

3. Video Content Safety Filter: A multi-class classifier model that is trained to distinguish between safe and unsafe frames of the generated video using SigLIP embeddings google/siglip-so400m-patch14-384.

4. Face Blur Filter: A pixelation filter that uses RetinaFace to identify facial regions with high confidence scores and apply pixelation to any detections larger than 20x20 pixels.

Model Developer: NVIDIA

Model Versions

Cosmos-Guardrail1.

License:

This model is released under the NVIDIA Open Model License. For a custom license, please contact [email protected].

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 associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained in the Model, your rights under NVIDIA Open Model License Agreement will automatically terminate.

Additional Information: LLAMA 2 COMMUNITY LICENSE AGREEMENT.

Model Architecture:

• Aegis: Llama 2 backbone
• Video Content Safety Filter: MLP backbone using SigLIP embeddings
• Face Blur Filter: ResNet-50 backbone

Input/Output Specifications

• Input Type(s): Text, Video
• Input Format(s):
  • Text (str): Input prompt
  • Video (np.ndarray): Video frames
• Input Parameters:
  • Text: One-dimensional (1D)
  • Video: Three-dimensional (3D)

Output:

• Output Type(s): Boolean, Text, Video <br>
• Output Format(s):
  • Boolean: True for safe and False for unsafe
  • Text (str): Reason for the unsafe determination
  • Video (np.ndarray): Processed video frames where faces are blurred
• Output Parameters:
  • Boolean: One-dimensional (1D)
  • Text: One-dimensional (1D)
  • Video: Three-dimensional (3D)

Software Integration:

Runtime Engine(s):

• Cosmos-Predict1
• Cosmos-Transfer1

Supported Hardware Microarchitecture Compatibility: <br>

• NVIDIA Ampere <br>
• NVIDIA Hopper <br> Supported Operating System(s): Linux <br>

Usage

On how to use the model, see:

• Cosmos-Predict1
• Cosmos-Transfer1

Example for the prompt-checking portion of the Guardrail:

• Input: "A dog is playing with a ball."

• Output: Guardrail allows the generation of this video

• Input: "A man wearing only socks."

• Output: Guardrail blocks generation of this video

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.

Plus Plus (++) Promise

We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been:

• Verified to comply with current applicable disclosure laws, regulations, and industry standards.
• Verified to comply with applicable privacy labeling requirements.
• Annotated to describe the collector/source (NVIDIA or a third-party).
• Characterized for technical limitations.
• Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests.
• Reviewed before release.
• Tagged for known restrictions and potential safety implications.

Bias

Explainability

Privacy

Safety