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CorvaeOboro
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Update app.py
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app.py
CHANGED
@@ -8,7 +8,7 @@ import types
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from huggingface_hub import hf_hub_url, cached_download
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#TOKEN = os.environ['TOKEN']
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with open(cached_download(hf_hub_url('CorvaeOboro/
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G = pickle.load(f)['G_ema']# torch.nn.Module
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device = torch.device("cpu")
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@@ -56,11 +56,11 @@ def infer(num_images, interpolate):
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with demo:
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gr.Markdown(
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"""
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#
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![
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creates
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more information here : [https://github.com/CorvaeOboro/
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""")
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images_num = gr.inputs.Slider(default=6, label="Num Images", minimum=1, maximum=16, step=1)
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interpolate = gr.inputs.Checkbox(default=False, label="Interpolate")
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from huggingface_hub import hf_hub_url, cached_download
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#TOKEN = os.environ['TOKEN']
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with open(cached_download(hf_hub_url('CorvaeOboro/gen_item_ring', 'gen_item_ring_stylegan2ada_20230218.pkl')), 'rb') as f:
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G = pickle.load(f)['G_ema']# torch.nn.Module
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device = torch.device("cpu")
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with demo:
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gr.Markdown(
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"""
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# gen_item_ring
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![item_ring_process_single](https://raw.githubusercontent.com/CorvaeOboro/gen_item/master/docs/ring/item_ring_process_single.jpg?raw=true "item_ring_process_single")
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creates ring item images from stylegan2ada model trained on synthetic dataset utilizing procgen and neural networks .
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more information here : [https://github.com/CorvaeOboro/gen_item](https://github.com/CorvaeOboro/gen_item).
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""")
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images_num = gr.inputs.Slider(default=6, label="Num Images", minimum=1, maximum=16, step=1)
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interpolate = gr.inputs.Checkbox(default=False, label="Interpolate")
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