Cosmos Predict2 14B Text2Image

🚀 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 designed for generating physics-aware images, videos, and world states for physical AI development. These diffusion models can generate dynamic, high-quality images and videos from text, image, or video inputs, serving as building blocks for various world generation - related applications or research. The models are commercially usable under the NVIDIA Open Model license agreement.

Cosmos | Code | Website

🚀 Quick Start

To start using Cosmos-Predict2, you can follow the code example below. First, make sure you have the necessary libraries installed.

import torch
from diffusers import Cosmos2TextToImagePipeline

# Available checkpoints: nvidia/Cosmos-Predict2-2B-Text2Image, nvidia/Cosmos-Predict2-14B-Text2Image
model_id = "nvidia/Cosmos-Predict2-14B-Text2Image"
pipe = Cosmos2TextToImagePipeline.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out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering.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."

output = pipe(
    prompt=prompt, negative_prompt=negative_prompt, generator=torch.Generator().manual_seed(1)
).images[0]
output.save("output.png")

✨ Features

• High - Performance Generation: Cosmos - Predict2 can generate high - quality, physics - aware images and videos from various inputs like text, images, or videos.
• Multiple Model Versions: It offers different model versions, such as 2B and 14B, to meet different application requirements.
• Commercial Usability: The models are commercially usable under the NVIDIA Open Model license agreement.

📦 Installation

The installation mainly involves setting up the necessary runtime engines. You need to install Cosmos-Predict2 and Diffusers.

💻 Usage Examples

Basic Usage

import torch
from diffusers import Cosmos2TextToImagePipeline

model_id = "nvidia/Cosmos-Predict2-14B-Text2Image"
pipe = Cosmos2TextToImagePipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "A simple scene description"
output = pipe(prompt=prompt).images[0]
output.save("basic_output.png")

Advanced Usage

import torch
from diffusers import Cosmos2TextToImagePipeline

model_id = "nvidia/Cosmos-Predict2-14B-Text2Image"
pipe = Cosmos2TextToImagePipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "A detailed and complex scene description"
negative_prompt = "Some unwanted elements description"
generator = torch.Generator().manual_seed(1)

output = pipe(
    prompt=prompt, negative_prompt=negative_prompt, generator=generator
).images[0]
output.save("advanced_output.png")

📚 Documentation

Model Overview

Description

Cosmos-Predict2 is a family of pre - trained world foundation models. It's designed for generating physics - aware images, videos, and world states for physical AI development. These diffusion models can generate dynamic, high - quality images and videos from text, image, or video inputs, and can be used as building blocks for various world generation - related applications or research. The models are commercially usable 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: Given a text description, it predicts an output image.
• Cosmos-Predict2-14B-Text2Image: Given a text description, it predicts an output image.
• Cosmos-Predict2-2B-Video2World: Given a text description and an image as the first frame, it predicts the future frames.
• Cosmos-Predict2-14B-Video2World: Given a text description and an image as the first frame, it predicts 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 - Text2Image is a diffusion transformer model designed for image denoising in the latent space. The network is composed of interleaved self - attention, cross - attention, and feed - forward 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.

Input/Output Specifications

Our AI models are designed and/or optimized to run on NVIDIA GPU - accelerated systems. By leveraging NVIDIA's hardware (e.g., GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU - only solutions.

Software Integration

Runtime Engine(s):

• [Cosmos-Predict2](https://github.com/nvidia - cosmos/cosmos - predict2)
• Diffusers

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Hopper

💡 Usage Tip

Only BF16 precision is tested. Other precisions like FP16 or FP32 are not officially supported.

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 48.93 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 from the GenEval evaluation framework.

Usage

• See [Cosmos-Predict2](https://github.com/nvidia - cosmos/cosmos - predict2) for details.

Limitations

Despite various improvements in world generation for Physical AI, Cosmos - Predict2 text2image models still face technical and application limitations for world prediction. In particular, they struggle to generate high - resolution images without artifacts. Common issues include camera and object motion instability, and imprecise interactions. The models may inaccurately represent 3D space, or physical laws in the generated images, leading to artifacts such as 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 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.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below.

🔧 Technical Details

Cosmos - Predict2 - 14B - Text2Image is a diffusion transformer model. Its network is built with interleaved self - attention, cross - attention, and feed - forward layers. The cross - attention layers enable the model to use input text during the denoising process. Adaptive layer normalization is applied before each layer to embed time information for denoising.

📄 License

This model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en - us/agreements/enterprise - software/nvidia - open - model - license). The full text of the license agreement is as follows:

NVIDIA Open Model License Agreement

Version Release Date: April 30, 2025

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