🚀 Cosmos-Predict2: A Suite of Diffusion-based World Foundation Models Available in 2B, and 14B

Cosmos-Predict2 is a family of highly performant pre-trained world foundation models. It's purpose-built for generating physics-aware images, videos, and world states for physical AI development, enabling the creation of dynamic, high-quality media from text, image, or video inputs.

Cosmos | Code | Website

🚀 Quick Start

For detailed usage, please refer to Cosmos-Predict2.

✨ Features

• High - Performance Generation: Generate physics - aware images, videos, and world states.
• Multiple Input Types: Support text + image or text + video as inputs.
• Commercial Usability: Available for commercial use under the NVIDIA Open Model License.

📦 Installation

This README does not provide specific installation steps.

💻 Usage Examples

Basic Usage

import torch
from diffusers import Cosmos2VideoToWorldPipeline
from diffusers.utils import export_to_video, load_image

# Available checkpoints: nvidia/Cosmos-Predict2-2B-Video2World, nvidia/Cosmos-Predict2-14B-Video2World
model_id = "nvidia/Cosmos-Predict2-14B-Video2World"
pipe = Cosmos2VideoToWorldPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "A close-up shot captures a vibrant yellow scrubber vigorously working on a grimy plate, its bristles moving in circular motions to lift stubborn grease and food residue. The dish, once covered in remnants of a hearty meal, gradually reveals its original glossy surface. Suds form and bubble around the scrubber, creating a satisfying visual of cleanliness in progress. The sound of scrubbing fills the air, accompanied by the gentle clinking of the dish against the sink. As the scrubber continues its task, the dish transforms, gleaming under the bright kitchen lights, symbolizing the triumph of cleanliness over mess."
negative_prompt = "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."
image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yellow-scrubber.png"
)

video = pipe(
    image=image, prompt=prompt, negative_prompt=negative_prompt, generator=torch.Generator().manual_seed(1)
).frames[0]
export_to_video(video, "output.mp4", fps=16)

📚 Documentation

Model Overview

Description

Cosmos - Predict2: A family of highly performant pre - trained world foundation models purpose - built for generating physics - aware images, videos, and world states for physical AI development.

Cosmos - Predict2 diffusion models are a collection of diffusion - based world foundation models that generate dynamic, high - quality images and videos from text, image, or video inputs. It can serve as the building block for various applications or research related to world generation. The models are ready for commercial use under the NVIDIA Open Model license agreement.

Model Developer: NVIDIA

Model Versions

The Cosmos - Predict2 diffusion - based model family includes the following models:

• [Cosmos - Predict2 - 2B - Text2Image](https://huggingface.co/nvidia/Cosmos - Predict2 - 2B - Text2Image)
  • Given a text description, predict an output image.
• [Cosmos - Predict2 - 14B - Text2Image](https://huggingface.co/nvidia/Cosmos - Predict2 - 14B - Text2Image)
  • Given a text description, predict an output image.
• [Cosmos - Predict2 - 2B - Video2World](https://huggingface.co/nvidia/Cosmos - Predict2 - 2B - Video2World)
  • Given a text description and an image as the first frame, predict the future frames.
• [Cosmos - Predict2 - 14B - Video2World](https://huggingface.co/nvidia/Cosmos - Predict2 - 14B - Video2World)
  • Given a text description and an image as the first frame, predict the future frames.

License

This 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).

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](https://www.nvidia.com/en - us/agreements/enterprise - software/nvidia - open - model - license) will automatically terminate.

Deployment Geography

Global

Model Architecture

Cosmos - Predict2 - 14B - Video2World 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 layer, 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

Inference

Acceleration Engine: PyTorch, Transformer Engine

Operating System(s):

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

System Requirements and Performance: This model requires 56.38 GB of GPU VRAM. The following table shows inference time for a single generation across different NVIDIA GPU hardware:

Quality Benchmarks: For comparative evaluation, we present benchmark scores using the PBench.

🔧 Technical Details

Limitations

Despite various improvements in world generation for Physical AI, Cosmos - Predict2 video2world models still face technical and application limitations for world prediction. In particular, they struggle to generate long, high - resolution videos without artifacts. Common issues include temporal inconsistency, camera and object motion instability, and imprecise interactions. The models may inaccurately represent 3D space, 4D space - time, or physical laws in the generated videos, leading to artifacts such as disappearing or morphing objects, unrealistic interactions, and implausible motions. As a result, applying these models for applications that require simulating physical law - grounded environments or complex multi - agent dynamics remains challenging.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared...

📄 License

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NVIDIA Open Model License Agreement

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