Instructions to use open-gigaai/CVPR-2026-WorldModel-Track-Model-Task2 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-Task2 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-Task2", 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:
- 43fc9b9c92a8ec1e62b658d38587308bccc78ff773e3875f0e6d7071fe612b4b
- Size of remote file:
- 16.5 GB
- SHA256:
- 922f4016569e877276a813e8ac7798f80838b7a206fc5d0cd1e1dec56d4f06a2
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