Cosmos
Diffusers
Safetensors
cosmos3_omni
nvidia
cosmos3
vllm
vllm-omni
sglang
sglang-diffusion
text, image, video, audio, and action generation
omnimodel
Instructions to use nvidia/Cosmos3-Nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use nvidia/Cosmos3-Nano 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
- Diffusers
How to use nvidia/Cosmos3-Nano with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/Cosmos3-Nano", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add SGLang serving instructions
#14
by MickJ - opened
Document SGLang Diffusion serve commands and OpenAI-compatible Cosmos3 visual generation endpoints.
Hey, we would like to merge your PR (SGLang model cards with code snippets + pointer to cookbook), but it needs few adjustments.
Could you please include the changes from PR !18? I.e.
- SGLang as part of the
Runtime Engine(s)section - SGLang as part of the
Acceleration Enginesection - Link to cookbook for advanced options
Also, could you rebase with main? (countDownloads section shouldn't get deleted)
kediwu0331 changed pull request status to merged