diff --git a/README.md b/README.md index aed7974e..c9e19414 100644 --- a/README.md +++ b/README.md @@ -1,78 +1,20 @@ +

+ NVIDIA Cosmos Header +

-![Cosmos Logo](assets/cosmos-logo.png) +## GitHub project for NVIDIA Cosmos: https://github.com/nvidia-cosmos --------------------------------------------------------------------------------- -### [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 now includes three subprojects: -[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 +### Cosmos-Predict1: https://github.com/nvidia-cosmos/cosmos-predict1 +- Cosmos-Predict1 is a collection of general-purpose world foundation models for Physical AI that can be fine-tuned into customized world models for downstream applications. -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 +### Cosmos-Transfer1: https://github.com/nvidia-cosmos/cosmos-transfer1 +- Cosmos-Transfer1 is a world-to-world transfer model designed to bridge the perceptual divide between simulated and real-world environments. -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). +### Cosmos-Reason1: https://github.com/nvidia-cosmos/cosmos-reason1 +- Cosmos-Reason1 models understand the physical common sense and generate appropriate embodied decisions in natural language through long chain-of-thought reasoning processes. -## 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](cosmos1/models/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](cosmos1/models/tokenizer/nemo/README.md)]. - -## 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 -``` - - - -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 [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com). +This repository will be archived soon. To check out the initial release of NVIDIA Cosmos, please follow [README_CES2025.md](README_CES2025.md). diff --git a/README_CES2025.md b/README_CES2025.md new file mode 100644 index 00000000..aed7974e --- /dev/null +++ b/README_CES2025.md @@ -0,0 +1,78 @@ + +![Cosmos Logo](assets/cosmos-logo.png) + +-------------------------------------------------------------------------------- +### [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](cosmos1/models/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](cosmos1/models/tokenizer/nemo/README.md)]. + +## 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 +``` + + + +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 [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com). diff --git a/assets/nvidia-cosmos-header.png b/assets/nvidia-cosmos-header.png new file mode 100644 index 00000000..f9ad8db0 Binary files /dev/null and b/assets/nvidia-cosmos-header.png differ