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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@@ -39,6 +39,7 @@ import torch
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# helpfer function to download huggingface repo and use model
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def download(repo_id, path, HF_TOKEN=None):
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files_path = os.path.join(path, 'files.txt')
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if not os.path.exists(files_path):
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hf_hub_download(repo_id, 'files.txt', token=HF_TOKEN, local_dir=path, local_dir_use_symlinks=False)
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# helpfer function to download huggingface repo and use model
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def download(repo_id, path, HF_TOKEN=None):
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os.makedirs(path, exist_ok=True)
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files_path = os.path.join(path, 'files.txt')
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if not os.path.exists(files_path):
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hf_hub_download(repo_id, 'files.txt', token=HF_TOKEN, local_dir=path, local_dir_use_symlinks=False)
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