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
| { | |
| "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 | |
| } |