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: 751 Bytes
8889131 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"checkpoint_cache_dir": null,
"checkpoint_hf": null,
"checkpoint_path": "/lustre/fsw/portfolios/cosmos/projects/cosmos_base_training/users/zhao/tmp/i4_checkpoint",
"checkpoint_type": "dcp",
"config_file": "/lustre/fsw/portfolios/cosmos/projects/cosmos_base_training/users/zhao/imaginaire4/cosmos3/configs/model/Cosmos3-Super-Image2Video.yaml",
"config_file_type": "yaml",
"credential_path": "credentials/gcp_checkpoint.secret",
"experiment": "",
"experiment_overrides": [
"model.config.diffusion_expert_config.load_weights_from_pretrained=False",
"model.config.vlm_config.pretrained_weights.enabled=False",
"checkpoint.load_from_object_store.enabled=False"
],
"model_memory_bytes": null,
"use_ema_weights": true
} |