Instructions to use open-gigaai/CVPR-2026-WorldModel-Track-Model-Task3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use open-gigaai/CVPR-2026-WorldModel-Track-Model-Task3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("open-gigaai/CVPR-2026-WorldModel-Track-Model-Task3", 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
- Xet hash:
- bbc5fcc1d025d0d63139de0d878b8b858363fceaefcdbfd79595717e22779674
- Size of remote file:
- 16.5 GB
- SHA256:
- 242847ec5e39b57a7e9fb3bf7622507473d81947ab41314a95e1fab2138c3245
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.