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| import gradio as gr | |
| from huggingface_hub import PyTorchModelHubMixin | |
| import torch | |
| import matplotlib.pyplot as plt | |
| import torchvision | |
| from networks_fastgan import MyGenerator | |
| import click | |
| import PIL | |
| from image_generator import generate_images | |
| def image_generation(model, number_of_images=1): | |
| img = generate_images(model) | |
| #return f"generating {number_of_images} images from {model}" | |
| return img | |
| if __name__ == "__main__": | |
| 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 (:" | |
| 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"]) | |
| outputs = gr.outputs.Image(label="Generated Image", type="pil") | |
| #outputs = "text" | |
| title = "Projected GAN for painting generation v0.2" | |
| article = "<p style='text-align: center'><a href='https://github.com/autonomousvision/projected_gan'>Official projected GAN github repo + paper</a></p>" | |
| gr.Interface(image_generation, inputs, outputs, title=title, article = article, description = description, | |
| analytics_enabled=False).launch(debug=True) | |
| app, local_url, share_url = iface.launch() | |