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
File size: 425 Bytes
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"_class_name": "Cosmos3OmniDiffusersPipeline",
"_diffusers_version": "0.38.0",
"scheduler": [
"diffusers",
"UniPCMultistepScheduler"
],
"text_tokenizer": [
"transformers",
"Qwen2TokenizerFast"
],
"transformer": [
"diffusers",
"Cosmos3OmniTransformer"
],
"vae": [
"diffusers",
"AutoencoderKLWan"
],
"vision_encoder": [
"transformers",
"Qwen3VLVisionModel"
]
}
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