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
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README.md
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The dataset for all tasks is provided in the official challenge dataset repository:
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https://huggingface.co/datasets/open-gigaai/CVPR-2026-
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Access requires:
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For full license terms please refer to the dataset repository:
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https://huggingface.co/datasets/open-gigaai/CVPR-2026-
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# Citation
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The dataset for all tasks is provided in the official challenge dataset repository:
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https://huggingface.co/datasets/open-gigaai/CVPR-2026-WorldModel-Track-Dataset
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Access requires:
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For full license terms please refer to the dataset repository:
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https://huggingface.co/datasets/open-gigaai/CVPR-2026-WorldModel-Track-Dataset
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# Citation
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