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Browse files- README.md +4 -1
- app.py +7 -15
- requirements.txt +5 -5
README.md
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@@ -4,9 +4,12 @@ emoji: 🏢
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colorFrom: blue
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colorTo: gray
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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colorFrom: blue
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colorTo: gray
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sdk: gradio
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sdk_version: 3.35.2
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app_file: app.py
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pinned: false
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suggested_hardware: t4-small
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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https://arxiv.org/abs/2204.11823
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app.py
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from __future__ import annotations
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import functools
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import os
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import pickle
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import sys
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sys.path.insert(0, 'StyleGAN-Human')
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TITLE = 'StyleGAN-Human (Interpolation)'
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DESCRIPTION = '
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Related App: [StyleGAN-Human](https://huggingface.co/spaces/hysts/StyleGAN-Human)
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'''
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HF_TOKEN = os.getenv('HF_TOKEN')
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def load_model(file_name: str, device: torch.device) -> nn.Module:
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path = hf_hub_download('
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f'models/{file_name}',
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use_auth_token=HF_TOKEN)
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with open(path, 'rb') as f:
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model = pickle.load(f)['G_ema']
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model.eval()
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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model = load_model('stylegan_human_v2_1024.pkl', device)
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gr.Interface(
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fn=
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inputs=[
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gr.Slider(label='Seed 1',
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minimum=0,
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outputs=gr.Gallery(label='Output Images', type='numpy'),
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title=TITLE,
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description=DESCRIPTION,
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).
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from __future__ import annotations
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import functools
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import pickle
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import sys
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sys.path.insert(0, 'StyleGAN-Human')
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TITLE = 'StyleGAN-Human (Interpolation)'
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DESCRIPTION = 'https://github.com/stylegan-human/StyleGAN-Human'
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def load_model(file_name: str, device: torch.device) -> nn.Module:
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path = hf_hub_download('public-data/StyleGAN-Human', f'models/{file_name}')
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with open(path, 'rb') as f:
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model = pickle.load(f)['G_ema']
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model.eval()
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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model = load_model('stylegan_human_v2_1024.pkl', device)
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fn = functools.partial(generate_interpolated_images,
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model=model,
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device=device)
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gr.Interface(
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fn=fn,
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inputs=[
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gr.Slider(label='Seed 1',
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minimum=0,
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outputs=gr.Gallery(label='Output Images', type='numpy'),
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title=TITLE,
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description=DESCRIPTION,
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).queue(max_size=10).launch()
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requirements.txt
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numpy==1.
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Pillow==
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scipy==1.
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torch==
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torchvision==0.
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numpy==1.23.5
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Pillow==10.0.0
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scipy==1.10.1
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torch==2.0.1
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torchvision==0.15.2
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