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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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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", choices=["Midas", "DPT", "None"], type="value", value="DPT" |
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) |
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num_samples = gr.Slider( |
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label="Number of images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, 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", minimum=128, maximum=512, value=384, step=1 |
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) |
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num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1) |
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guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) |
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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a_prompt = gr.Textbox(label="Additional prompt", 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="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", show_label=False, columns=2, 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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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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