Image-to-Video
Cosmos
Diffusers
Safetensors
cosmos3_omni
nvidia
cosmos3
vllm-omni
sglang
sglang-diffusion
video-generation
Instructions to use nvidia/Cosmos3-Super-Image2Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use nvidia/Cosmos3-Super-Image2Video 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-Super-Image2Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/Cosmos3-Super-Image2Video", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Add SGLang serving instructions
#5
by MickJ - opened
Add SGLang-Diffusion model card examples and tags for Cosmos3 serving.
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 !7? 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?
kediwu0331 changed pull request status to merged