NVIDIA/Cosmos-Tokenizer

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1731 stars · 94 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NVIDIA Cosmos Tokenizer is a suite of neural tokenizers for images and videos that convert visual data into continuous latents or discrete tokens for use in diffusion or auto-regressive transformer models. It achieves high spatial and temporal compression (up to 2048x total) and serves as a core component of the NVIDIA Cosmos video foundation model platform.

Use cases

• tokenize videos into discrete tokens for autoregressive transformer training
• compress images into continuous latents for diffusion model training
• build physical AI or world model pipelines with efficient visual tokenization
• reduce video data storage and compute costs with high compression tokenization
• preprocess visual datasets for large multimodal generative AI models

When to choose

• you need state-of-the-art image/video tokenization for training diffusion or autoregressive generative models
• you are building on the NVIDIA Cosmos video foundation model ecosystem
• you need extreme compression of visual data (up to 2048x) for scalable model training

When to avoid

• you need a general-purpose image/video codec for playback or archival rather than ML tokenization
• you lack GPU resources, as the models require GPU inference
• you need active development here - the repo is read-only and moved to NVIDIA/Cosmos

Facets

library · maturity maintenance

machine-learning deep-learning image-processing video-processing serialization deep-learning machine-learning computer-vision image-processing artificial-intelligence python cross-platform tokenization diffusion-models transformers video-foundation-models latent-compression nvidia physical-ai video gpu linux

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