Instructions to use minchul/cvlface_adaface_ir50_ms1mv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minchul/cvlface_adaface_ir50_ms1mv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="minchul/cvlface_adaface_ir50_ms1mv2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("minchul/cvlface_adaface_ir50_ms1mv2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Browse files
README.md
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@@ -71,7 +71,7 @@ def load_model_by_repo_id(repo_id, save_path, HF_TOKEN=None, force_download=Fals
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if __name__ == '__main__':
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HF_TOKEN = 'YOUR_HUGGINGFACE_TOKEN'
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path =
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repo_id = 'minchul/cvlface_adaface_ir50_ms1mv2'
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model = load_model_by_repo_id(repo_id, path, HF_TOKEN)
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if __name__ == '__main__':
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HF_TOKEN = 'YOUR_HUGGINGFACE_TOKEN'
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path = os.path.expanduser('~/.cvlface_cache/minchul/cvlface_adaface_ir50_ms1mv2')
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repo_id = 'minchul/cvlface_adaface_ir50_ms1mv2'
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model = load_model_by_repo_id(repo_id, path, HF_TOKEN)
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