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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",
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choices=["Midas", "DPT", "None"],
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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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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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