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import gradio as gr
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import random
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, 100000000)
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return seed
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examples = [
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[
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"condition/example/t2i/landscape.jpg",
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"Landscape photos with snow on the mountains in the distance and clear reflections in the lake near by",
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],
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[
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"condition/example/t2i/girl.jpg",
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"A girl with blue hair",
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],
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[
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"condition/example/t2i/eye.png",
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"A vivid drawing of an eye with a few pencils nearby",
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],
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]
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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=[
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"Hed",
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"Canny",
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"Lineart",
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"No preprocess",
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],
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type="value",
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value="Hed",
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info='Edge type.',
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)
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canny_low_threshold = gr.Slider(
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label="Canny low threshold",
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minimum=0,
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maximum=255,
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value=100,
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step=50)
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canny_high_threshold = gr.Slider(
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label="Canny high threshold",
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minimum=0,
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maximum=255,
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value=200,
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step=50)
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cfg_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=4,
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step=0.1)
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control_strength = gr.Slider(minimum=0., maximum=1.0, step=0.1, value=0.6, label="control_strength")
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top_k = gr.Slider(minimum=1,
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maximum=16384,
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step=1,
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value=2000,
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label='Top-K')
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top_p = gr.Slider(minimum=0.,
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maximum=1.0,
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step=0.1,
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value=1.0,
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label="Top-P")
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temperature = gr.Slider(minimum=0.,
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maximum=1.0,
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step=0.1,
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value=1.0,
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label='Temperature')
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seed = gr.Slider(label="Seed",
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minimum=0,
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maximum=100000000,
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step=1,
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value=0)
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randomize_seed = gr.Checkbox(label="Randomize seed",
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value=True)
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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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height='800px',
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columns=2,
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object_fit="scale-down")
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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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]
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)
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inputs = [
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image,
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prompt,
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cfg_scale,
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temperature,
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top_k,
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top_p,
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seed,
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canny_low_threshold,
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canny_high_threshold,
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control_strength,
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preprocessor_name,
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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="edge",
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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()
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demo = create_demo(model.process_edge)
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demo.queue().launch(share=False, server_name="0.0.0.0")
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