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import gradio as gr |
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from huggingface_hub import PyTorchModelHubMixin |
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import torch |
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import matplotlib.pyplot as plt |
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import torchvision |
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from networks_fastgan import MyGenerator |
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import click |
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import PIL |
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from image_generator import generate_images |
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def image_generation(model, number_of_images=1): |
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img = generate_images(model) |
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return img |
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if __name__ == "__main__": |
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description = "TODO: when generating only 1 image use an esrgan to increase its resolution \n TODO: allow generation of multiple images TODO: walk through input space video i have exams now c u in 2 weeks (:" |
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inputs = gr.inputs.Radio([ "Impressionism", "Abstract Expressionism", "Cubism", "Pop Art", "Color Field", "Hana Hanak houses", "Hana Hanak houses - abstract expressionism", "Hana Hanak houses - color field"]) |
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outputs = gr.outputs.Image(label="Generated Image", type="pil") |
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title = "Projected GAN for painting generation v0.2" |
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article = "<p style='text-align: center'><a href='https://github.com/autonomousvision/projected_gan'>Official projected GAN github repo + paper</a></p>" |
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gr.Interface(image_generation, inputs, outputs, title=title, article = article, description = description, |
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analytics_enabled=False).launch(debug=True) |
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app, local_url, share_url = iface.launch() |
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