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import os |
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from PIL import Image |
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os.system("pip install gdown lpips gradio") |
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os.system("gdown --id 1HKmjg6iXsWr4aFPuU0gBXPGR83wqMzq7 -O align.dat") |
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os.system("wget https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl") |
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os.system("gdown https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip") |
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os.system("unzip ninja-linux.zip -d /usr/local/bin/") |
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os.system("sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force") |
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os.makedirs("embeddings/",exist_ok=True) |
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os.makedirs("checkpoints/",exist_ok=True) |
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os.makedirs("output/", exist_ok=True) |
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os.makedirs("images/",exist_ok=True) |
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import gradio as gr |
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def inference(img): |
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img.save("images/file.png") |
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os.system("python tune.py") |
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return Image.open("output/out.png") |
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title = "Pivotal Tuning for Latent-based editing of Real Images" |
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description = "Gradio Demo for Pivotal Tuning Inversion. To use it, simply upload your image. Ensure the image is 256 x 256 in size." |
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article = "<p style='text-align: center'><a href='https://github.com/danielroich/PTI' target='_blank'>Github Repo</a>" |
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gr.Interface(inference, [gr.inputs.Image(type="pil")], gr.outputs.Image(type="pil"),title=title,description=description,article=article,allow_flagging='never',allow_screenshot=False).launch(share=True) |
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os.system("rm output/out.png") |
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