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Nvidia-CMU25
/
DiffusionVideo2WorldGeneration

Feature Extraction
Transformers
PyTorch
AutoModel
nvidia
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Nvidia-CMU25/DiffusionVideo2WorldGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Nvidia-CMU25/DiffusionVideo2WorldGeneration with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Nvidia-CMU25/DiffusionVideo2WorldGeneration", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Nvidia-CMU25/DiffusionVideo2WorldGeneration", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
DiffusionVideo2WorldGeneration
165 kB
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  • 1 contributor
History: 2 commits
tchoudha21's picture
tchoudha21
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5a33b3a verified over 1 year ago
  • .gitattributes
    1.52 kB
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  • base_world_generation_pipeline.py
    12.7 kB
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  • conditioner.py
    12.4 kB
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  • config.json
    239 Bytes
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  • convert_pixtral_ckpt.py
    7.87 kB
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  • download_diffusion.py
    4.13 kB
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  • guardrail_presets.py
    3.1 kB
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  • inference_utils.py
    28.7 kB
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  • model_t2w.py
    11.5 kB
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  • model_v2w.py
    15.4 kB
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  • t5_text_encoder.py
    4.44 kB
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  • text2world.py
    5.99 kB
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  • text2world_prompt_upsampler_inference.py
    5.89 kB
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  • types.py
    1.05 kB
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  • video2world.py
    6.71 kB
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  • video2world_hf.py
    12.1 kB
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  • video2world_prompt_upsampler_inference.py
    5.84 kB
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  • world_generation_pipeline.py
    25.7 kB
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