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import gradio as gr |
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import spaces |
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from gradio_imageslider import ImageSlider |
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from image_gen_aux import UpscaleWithModel |
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from image_gen_aux.utils import load_image |
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MODELS = {"UltraSharp": "OzzyGT/UltraSharp", "DAT X4": "OzzyGT/DAT_X4"} |
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@spaces.GPU |
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def upscale_image(image, model_selection): |
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original = load_image(image) |
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upscaler = UpscaleWithModel.from_pretrained(MODELS[model_selection]).to("cuda") |
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image = upscaler(original, tiling=True, tile_width=2048, tile_height=2048) |
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return original, image |
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def clear_result(): |
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return gr.update(value=None) |
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title = """<h1 align="center">Image Upscaler</h1> |
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<div align="center">This space is a showcase of the different super resolution models you can use to upscale with the |
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<a href="https://github.com/asomoza/image_gen_aux">Image Generation Auxiliary Tools</a> library.</div> |
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""" |
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with gr.Blocks() as demo: |
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gr.HTML(title) |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(type="pil", label="Input Image") |
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model_selection = gr.Dropdown( |
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choices=list(MODELS.keys()), |
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value="UltraSharp", |
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label="Model", |
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) |
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run_button = gr.Button("Upscale") |
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with gr.Column(): |
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result = ImageSlider( |
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interactive=False, |
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label="Generated Image", |
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) |
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run_button.click( |
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fn=clear_result, |
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inputs=None, |
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outputs=result, |
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).then( |
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fn=upscale_image, |
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inputs=[input_image, model_selection], |
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outputs=result, |
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) |
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demo.launch(share=False) |
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