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import gradio as gr |
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from settings import ( |
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DEFAULT_IMAGE_RESOLUTION, |
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DEFAULT_NUM_IMAGES, |
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MAX_IMAGE_RESOLUTION, |
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MAX_NUM_IMAGES, |
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MAX_SEED, |
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) |
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from utils import randomize_seed_fn |
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examples = [] |
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def create_demo(process): |
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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( |
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label="Preprocessor", |
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choices=["DPT"], |
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type="value", |
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value="DPT", |
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) |
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num_samples = gr.Slider( |
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label="Number of images", |
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minimum=1, |
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maximum=MAX_NUM_IMAGES, |
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value=DEFAULT_NUM_IMAGES, |
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step=1, |
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) |
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image_resolution = gr.Slider( |
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label="Image resolution", |
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minimum=256, |
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maximum=MAX_IMAGE_RESOLUTION, |
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value=DEFAULT_IMAGE_RESOLUTION, |
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step=256, |
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) |
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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=384, |
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step=1, |
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) |
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num_steps = gr.Slider( |
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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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) |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.1, |
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maximum=30.0, |
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value=7.5, |
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step=0.1, |
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) |
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seed = gr.Slider( |
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label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0 |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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a_prompt = gr.Textbox( |
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label="Additional prompt", |
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value="high-quality, extremely detailed, 4K", |
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) |
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n_prompt = gr.Textbox( |
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label="Negative prompt", |
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value="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( |
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label="Output", show_label=False, columns=2, object_fit="scale-down" |
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) |
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gr.Examples( |
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examples=examples, |
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inputs=[ |
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image, |
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prompt, |
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guidance_scale, |
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seed, |
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], |
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outputs=result, |
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fn=process, |
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) |
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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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api_name=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=False, |
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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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api_name=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="depth", |
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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="depth") |
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demo = create_demo(model.process_depth) |
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demo.queue().launch() |
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