Instructions to use open-gigaai/CVPR-2026-WorldModel-Track-Model-Task7 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-Task7 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-Task7", 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:
- 9951aa89fb01661194ab2474c3533782d12ea161314abf17bab3c11612f16859
- Size of remote file:
- 16.5 GB
- SHA256:
- 66c52fc083625b96d9e81cf6fb9823302b4dd6d7a739472675f4f6df943543fa
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