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michaelriedl
/
MonsterForge-small

Feature Extraction
Transformers
PyTorch
lightweight-gan
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use michaelriedl/MonsterForge-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use michaelriedl/MonsterForge-small with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="michaelriedl/MonsterForge-small", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("michaelriedl/MonsterForge-small", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
MonsterForge-small / LightweightGAN
13 kB
Ctrl+K
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  • 1 contributor
History: 1 commit
michaelriedl's picture
michaelriedl
Initial dump
002ca81 almost 3 years ago
  • Blur.py
    375 Bytes
    Initial dump almost 3 years ago
  • ChanNorm.py
    491 Bytes
    Initial dump almost 3 years ago
  • Conv2dSame.py
    353 Bytes
    Initial dump almost 3 years ago
  • DepthWiseConv2d.py
    604 Bytes
    Initial dump almost 3 years ago
  • FCANet.py
    974 Bytes
    Initial dump almost 3 years ago
  • Generator.py
    4.45 kB
    Initial dump almost 3 years ago
  • GlobalContext.py
    705 Bytes
    Initial dump almost 3 years ago
  • LinearAttention.py
    2.68 kB
    Initial dump almost 3 years ago
  • Noise.py
    429 Bytes
    Initial dump almost 3 years ago
  • PixelShuffleUpsample.py
    791 Bytes
    Initial dump almost 3 years ago
  • PreNorm.py
    271 Bytes
    Initial dump almost 3 years ago
  • __init__.py
    34 Bytes
    Initial dump almost 3 years ago
  • helper_funcs.py
    861 Bytes
    Initial dump almost 3 years ago