| ## How to Use | |
| ```python | |
| from transformers import AutoModel | |
| model = AutoModel.from_pretrained( | |
| "Nvidia-CMU25/DiffusionText2WorldGeneration", | |
| cache_dir="./cache", | |
| trust_remote_code=True, | |
| # turn on offloading on a low GPU memory machine: | |
| # offload_network=True, | |
| # offload_tokenizer=True, | |
| # offload_text_encoder_model=True, | |
| # offload_prompt_upsampler=True, | |
| # offload_guardrail_models=True, | |
| ) | |
| prompt = "Some text prompt to generate a video" | |
| model(prompt) | |
| ``` | |
|  | |
| -------------------------------------------------------------------------------- | |
| ### [Website](https://www.nvidia.com/en-us/ai/cosmos/) | [HuggingFace](https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6) | [GPU-free Preview](https://build.nvidia.com/explore/discover) | [Paper](https://arxiv.org/abs/2501.03575) | [Paper Website](https://research.nvidia.com/labs/dir/cosmos1/) | |
| [NVIDIA Cosmos](https://www.nvidia.com/cosmos/) is a developer-first world foundation model platform designed to help Physical AI developers build their Physical AI systems better and faster. Cosmos contains | |
| 1. pre-trained models, available via [Hugging Face](https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6) under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) that allows commercial use of the models for free | |
| 2. training scripts under the [Apache 2 License](https://www.apache.org/licenses/LICENSE-2.0), offered through [NVIDIA Nemo Framework](https://github.com/NVIDIA/NeMo) for post-training the models for various downstream Physical AI applications | |
| Details of the platform is described in the [Cosmos paper](https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai). Preview access is avaiable at [build.nvidia.com](https://build.nvidia.com). | |
| ## Key Features | |
| - [Pre-trained Diffusion-based world foundation models](cosmos1/models/diffusion/README.md) for Text2World and Video2World generation where a user can generate visual simulation based on text prompts and video prompts. | |
| - [Pre-trained Autoregressive-based world foundation models](cosmos1/models/autoregressive/README.md) for Video2World generation where a user can generate visual simulation based on video prompts and optional text prompts. | |
| - [Video tokenizers](https://github.com/NVIDIA/Cosmos-Tokenizer) for tokenizing videos into continuous tokens (latent vectors) and discrete tokens (integers) efficiently and effectively. | |
| - Video curation pipeline for building your own video dataset. [Coming soon] | |
| - [Post-training scripts](cosmos1/models/POST_TRAINING.md) via NeMo Framework to post-train the pre-trained world foundation models for various Physical AI setup. | |
| - Pre-training scripts via NeMo Framework for building your own world foundation model. [[Diffusion](https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/diffusion)] [[Autoregressive](https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/multimodal_autoregressive)] [[Tokenizer](https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/diffusion/vae)]. | |
| ## Model Family | |
| | Model name | Description | Try it out | | |
| | -------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | ---------------------------------------------------- | | |
| | [Cosmos-1.0-Diffusion-7B-Text2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-7B-Text2World) | Text to visual world generation | [Inference](cosmos1/models/diffusion/README.md) | | |
| | [Cosmos-1.0-Diffusion-14B-Text2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-14B-Text2World) | Text to visual world generation | [Inference](cosmos1/models/diffusion/README.md) | | |
| | [Cosmos-1.0-Diffusion-7B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-7B-Video2World) | Video + Text based future visual world generation | [Inference](cosmos1/models/diffusion/README.md) | | |
| | [Cosmos-1.0-Diffusion-14B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-14B-Video2World) | Video + Text based future visual world generation | [Inference](cosmos1/models/diffusion/README.md) | | |
| | [Cosmos-1.0-Autoregressive-4B](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-4B) | Future visual world generation | [Inference](cosmos1/models/autoregressive/README.md) | | |
| | [Cosmos-1.0-Autoregressive-12B](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-12B) | Future visual world generation | [Inference](cosmos1/models/autoregressive/README.md) | | |
| | [Cosmos-1.0-Autoregressive-5B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-5B-Video2World) | Video + Text based future visual world generation | [Inference](cosmos1/models/autoregressive/README.md) | | |
| | [Cosmos-1.0-Autoregressive-13B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-13B-Video2World) | Video + Text based future visual world generation | [Inference](cosmos1/models/autoregressive/README.md) | | |
| | [Cosmos-1.0-Guardrail](https://huggingface.co/nvidia/Cosmos-1.0-Guardrail) | Guardrail contains pre-Guard and post-Guard for safe use | Embedded in model inference scripts | | |
| ## Example Usage | |
| ### Inference | |
| Follow the [Cosmos Installation Guide](INSTALL.md) to setup the docker. For inference with the pretrained models, please refer to [Cosmos Diffusion Inference](cosmos1/models/diffusion/README.md) and [Cosmos Autoregressive Inference](cosmos1/models/autoregressive/README.md). | |
| The code snippet below provides a gist of the inference usage. | |
| ```bash | |
| 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." | |
| # Example using 7B model | |
| PYTHONPATH=$(pwd) python cosmos1/models/diffusion/inference/text2world.py \ | |
| --checkpoint_dir checkpoints \ | |
| --diffusion_transformer_dir Cosmos-1.0-Diffusion-7B-Text2World \ | |
| --prompt "$PROMPT" \ | |
| --offload_prompt_upsampler \ | |
| --video_save_name Cosmos-1.0-Diffusion-7B-Text2World | |
| ``` | |
| <video src="https://github.com/user-attachments/assets/db7bebfe-5314-40a6-b045-4f6ce0a87f2a"> | |
| Your browser does not support the video tag. | |
| </video> | |
| We also offer [multi-GPU inference](cosmos1/models/diffusion/nemo/inference/README.md) support for Diffusion Text2World WFM models through NeMo Framework. | |
| ### Post-training | |
| NeMo Framework provides GPU accelerated post-training with general post-training for both [diffusion](cosmos1/models/diffusion/nemo/post_training/README.md) and [autoregressive](cosmos1/models/autoregressive/nemo/post_training/README.md) models, with other types of post-training coming soon. | |
| ## License and Contact | |
| This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use. | |
| NVIDIA Cosmos source code is released under the [Apache 2 License](https://www.apache.org/licenses/LICENSE-2.0). | |
| NVIDIA Cosmos models are 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 [[email protected]](mailto:[email protected]). | |