Cosmos-1.0 Prompt Upsampler Text2World
Description:
Cosmos-1.0-Prompt-Upsampler-Text2World is a large language model (LLM) designed to transform original input prompts into more detailed and enriched versions. It improves the prompts by adding more details and maintaining a consistent description structure before they are used in a text-to-world model, which generally leads to higher quality outputs. This model is ready for commercial use.
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
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.
- Cosmos-1.0-Guardrail is the safety guardrail for this model.
Model Architecture:
Architecture Type: Transformer
Network Architecture: Mistral-NeMo-12B-Instruct
Input:
Input Type(s): Text
Input Format: String
Input Parameters: One-dimensional (1D)
Other Properties Related to Input: Max of 512 tokens
Output:
Output Type(s): Text
Output Format: String
Output Parameters: One-dimensional (1D)
Other Properties Related to Output: Max of 512 tokens
Software Integration:
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Ampere
- NVIDIA Hopper
Supported Operating System(s): Linux
Model Version:
The initial release (v1.0) of Cosmos Prompt Upsampler contains the following model:
Usage
See
Cosmos for on how to use the model.
Example:
- Input:
"A dog is playing with a ball."
- Output:
"The video is a close-up, dynamic scene featuring a dog interacting with a ball. The setting is a simple, indoor environment with a plain, light-colored wall in the background. The dog, a medium-sized breed with a light brown coat and a white chest, is the central focus. It is actively engaged in playing with a small, red ball. The dog's ears are perked up, and its mouth is open, indicating excitement and playfulness. Throughout the video, the dog is seen holding the ball in its mouth, occasionally shaking its head and moving its body in a playful manner. The camera remains static, capturing the dog's movements from a consistent angle, allowing the viewer to focus on the dog's expressions and actions. The lighting is bright and even, highlighting the dog's fur and the vibrant color of the ball. The dog's tail is wagging, adding to the sense of joy and energy in the scene. The video captures the essence of a playful interaction between a pet and its owner, emphasizing the bond and the simple joy of playtime."
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
| Field | Response |
|---|
| Participation considerations from adversely impacted groups protected classes in model design and testing: | None |
| Measures taken to mitigate against unwanted bias: | None |
Explainability
| Field | Response |
|---|
| Intended Application & Domain: | Text prompt enrichment for world generation |
| Model Type: | Transformer |
| Intended Users: | Generative AI developers for world generation models |
| Output: | Text |
| Describe how the model works: | Transform original input prompts into more detailed and enriched versions |
| Technical Limitations: | The model may not follow input text prompt accurately and may have incorrect responses or generate incorrect information. |
| Verified to have met prescribed NVIDIA quality standards: | Yes |
| Performance Metrics: | Human Evaluation |
| Potential Known Risks: | The model's output can generate all forms of text, including what may be considered toxic, offensive, or indecent. |
| Licensing: | NVIDIA Open Model License |
Privacy
| Field | Response |
|---|
| Generatable or reverse engineerable personal information? | None Known |
| Protected class data used to create this model? | None Known |
| Was consent obtained for any personal data used? | None Known |
| How often is dataset reviewed? | Before Release |
| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable |
| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |
| If 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 |
| If personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable |
Safety
| Field | Response |
|---|
| Model Application(s): | Prompt enrichment for world generation |
| Describe the life critical impact (if present). | None Known |
| Use Case Restrictions: | NVIDIA Open Model License |
| Model 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. |
