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import gradio as gr |
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from utils import randomize_seed_fn |
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def create_demo(process, max_images=12, default_num_images=3): |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image() |
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prompt = gr.Textbox(label='Prompt') |
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run_button = gr.Button('Run') |
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with gr.Accordion('Advanced options', open=False): |
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preprocessor_name = gr.Radio(label='Preprocessor', |
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choices=['UPerNet', 'None'], |
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type='value', |
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value='UPerNet') |
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num_samples = gr.Slider(label='Number of images', |
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minimum=1, |
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maximum=max_images, |
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value=default_num_images, |
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step=1) |
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image_resolution = gr.Slider(label='Image resolution', |
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minimum=256, |
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maximum=512, |
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value=512, |
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step=256) |
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preprocess_resolution = gr.Slider( |
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label='Preprocess resolution', |
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minimum=128, |
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maximum=512, |
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value=512, |
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step=1) |
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num_steps = gr.Slider(label='Number of steps', |
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minimum=1, |
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maximum=100, |
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value=20, |
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step=1) |
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guidance_scale = gr.Slider(label='Guidance scale', |
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minimum=0.1, |
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maximum=30.0, |
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value=9.0, |
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step=0.1) |
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seed = gr.Slider(label='Seed', |
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minimum=0, |
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maximum=1000000, |
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step=1, |
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value=0, |
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randomize=True) |
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randomize_seed = gr.Checkbox(label='Randomize seed', |
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value=True) |
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a_prompt = gr.Textbox( |
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label='Additional prompt', |
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value='best quality, extremely detailed') |
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n_prompt = gr.Textbox( |
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label='Negative prompt', |
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value= |
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'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' |
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) |
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with gr.Column(): |
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result = gr.Gallery(label='Output', |
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show_label=False, |
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columns=2, |
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object_fit='scale-down') |
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inputs = [ |
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image, |
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prompt, |
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a_prompt, |
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n_prompt, |
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num_samples, |
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image_resolution, |
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preprocess_resolution, |
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num_steps, |
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guidance_scale, |
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seed, |
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preprocessor_name, |
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] |
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prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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).then( |
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fn=process, |
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inputs=inputs, |
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outputs=result, |
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) |
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run_button.click( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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).then( |
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fn=process, |
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inputs=inputs, |
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outputs=result, |
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api_name='segmentation', |
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) |
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return demo |
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if __name__ == '__main__': |
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from model import Model |
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model = Model(task_name='segmentation') |
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demo = create_demo(model.process_segmentation) |
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demo.queue().launch() |
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