Instructions to use open-gigaai/CVPR-2026-WorldModel-Track-Model-Task4 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-Task4 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-Task4", 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:
- 1fde9e948279dc2d5a706c589ee391cfaf4f8b52cc3e9f0f54edbb67f69938ca
- Size of remote file:
- 16.5 GB
- SHA256:
- 12f5bdc548232b842b515dd8102d7fea5b4bf173f79debc78b4a0103675d0f97
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