Cosmos-Predict1-7B-Decoder-DV8x16x16ToCV8x8x8-720p
Model Overview
Description:
Cosmos-Predict1-7B-Decoder-DV8x16x16ToCV8x8x8-720p is a diffusion decoder model that can improve outputs of Cosmos-Predict1 models with more fine-grained details. This model is ready for commercial use.
Model Developer: NVIDIA
Model Versions
In this release, the Cosmos Diffusion Decoder includes the following models:
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.
Model Architecture:
Cosmos-Predict1-7B-Decoder-DV8x16x16ToCV8x8x8-720p 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
- Input Type(s): Tokens
- Input Format(s): Integer Tensor
- Input Parameters: Three-dimensional (3D)
- Other Properties Related to Input:
- Integer indices ranging from 0 to 63,999
- Should be the tokens generated by Cosmos-Tokenize1-DV8x16x16-720p
- Output
- Output Type(s): Tokens
- Output Format(s): Float Tensor
- Output Parameters: Three-dimensional (3D)
- Other Properties Related to Output:
- Continuous-valued feature vectors with a dimensionality of 16
- The output tokens can be used as input for the decoder of Cosmos-Tokenize1-CV8x8x8-360p
Software Integration
Runtime Engine(s):
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-Predict1 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.
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: | World Generation |
| Model Type: | Transformer |
| Intended Users: | Physical AI developers |
| Output: | Videos |
| Describe how the model works: | Generates videos based on video inputs |
| Technical Limitations: | The model may not follow the video input accurately. |
| Verified to have met prescribed NVIDIA quality standards: | Yes |
| Performance Metrics: | Quantitative and Qualitative Evaluation |
| Potential Known Risks: | The model's output can generate all forms of videos, 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 there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
Safety
| Field | Response |
|---|
| Model Application(s): | 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. |
