Instructions to use nvidia/Cosmos3-Super-Image2Video-4Step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use nvidia/Cosmos3-Super-Image2Video-4Step with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Keep the SGLang support reference out of the model card
Browse files
README.md
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The 64B transformer is roughly 128 GB in BF16. Sequence and classifier-free-guidance parallelism divide activations rather than weights, so every GPU still holds a full copy of the model. On devices with less memory, such as H100-class GPUs, shard the weights across GPUs with `--use-fsdp-inference`, or stream them layer by layer with `--dit-layerwise-offload`:
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--num-gpus 4
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```
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Cosmos 3 support in SGLang Diffusion currently requires the SGLang main branch. Switch to a stable SGLang release once Cosmos 3 support is included there.
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The 64B transformer is roughly 128 GB in BF16. Sequence and classifier-free-guidance parallelism divide activations rather than weights, so every GPU still holds a full copy of the model. On devices with less memory, such as H100-class GPUs, shard the weights across GPUs with `--use-fsdp-inference`, or stream them layer by layer with `--dit-layerwise-offload`:
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