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Cosmos-1.0-Diffusion-7B-Text2World

Text-to-Video
Cosmos
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NeMo
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Version Release Date: January 6, 2025
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Cosmos-1.0-Diffusion: A Suite of Diffusion-based World Foundation Models

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.

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:

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-1.0-Diffusion-7B-Text2World 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

  • Input

    • Input Type(s): Text
    • Input Format(s): String
    • Input Parameters: One-dimensional (1D)
    • Other Properties Related to Input:
      • 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.

  • Output

    • Output Type(s): Video
    • Output Format(s): mp4
    • Output Parameters: Three-dimensional (3D)
    • Other Properties Related to Output: By default, the generated video is a 5-second clip with a resolution of 1280x704 pixels and a frame rate of 24 frames per second (fps). The video content visualizes the input text description as a short animated scene, capturing key elements within the specified time constraints. Aspect ratios and resolutions are configurable, with options including 1:1 (960x960 pixels), 4:3 (960x704 pixels), 3:4 (704x960 pixels), 16:9 (1280x704 pixels), and 9:16 (704x1280 pixels). The frame rate is also adjustable within a range of 12 to 40 fps.

Software Integration

Runtime Engine(s):

Supported Hardware Microarchitecture Compatibility:

  • NVIDIA Blackwell
  • NVIDIA Hopper
  • NVIDIA Ampere

Note: We have only tested inference with BF16 precision.

Operating System(s):

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

Usage

Cosmos can also be used with Diffusers!

import torch
from diffusers import CosmosTextToWorldPipeline
from diffusers.utils import export_to_video

model_id = "nvidia/Cosmos-1.0-Diffusion-7B-Text2World"
pipe = CosmosTextToWorldPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "A sleek, humanoid robot stands in a vast warehouse filled with neatly stacked cardboard boxes on industrial shelves. The robot's metallic body gleams under the bright, even lighting, highlighting its futuristic design and intricate joints. A glowing blue light emanates from its chest, adding a touch of advanced technology. The background is dominated by rows of boxes, suggesting a highly organized storage system. The floor is lined with wooden pallets, enhancing the industrial setting. The camera remains static, capturing the robot's poised stance amidst the orderly environment, with a shallow depth of field that keeps the focus on the robot while subtly blurring the background for a cinematic effect."

output = pipe(prompt=prompt).frames[0]
export_to_video(output, "output.mp4", fps=30)

Find more information in the diffusers documentation.

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.

We report the maximum observed GPU memory usage during end-to-end inference. Additionally, we offer a series of model offloading strategies to help users manage GPU memory usage effectively.

For GPUs with limited memory (e.g., RTX 3090/4090 with 24 GB memory), we recommend fully offloading all models. For higher-end GPUs, users can select the most suitable offloading strategy considering the numbers provided below.

Offloading Strategy 7B Text2World 14B Text2World
Offload prompt upsampler 74.0 GB > 80.0 GB
Offload prompt upsampler & guardrails 57.1 GB 70.5 GB
Offload prompt upsampler & guardrails & T5 encoder 38.5 GB 51.9 GB
Offload prompt upsampler & guardrails & T5 encoder & tokenizer 38.3 GB 51.7 GB
Offload prompt upsampler & guardrails & T5 encoder & tokenizer & diffusion model 24.4 GB 39.0 GB

The table below presents the end-to-end inference runtime on a single H100 GPU, excluding model initialization time.

7B Text2World (offload prompt upsampler) 14B Text2World (offload prompt upsampler, guardrails)
~380 seconds ~590 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.

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 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): 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.
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