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Delete app_normal.py
Browse files- app_normal.py +0 -93
app_normal.py
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# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_normal2image.py
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# The original license file is LICENSE.ControlNet in this repo.
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import gradio as gr
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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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gr.Markdown('## Control Stable Diffusion with Normal Maps')
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(source='upload', type='numpy')
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prompt = gr.Textbox(label='Prompt')
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run_button = gr.Button(label='Run')
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with gr.Accordion('Advanced options', open=False):
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is_normal_image = gr.Checkbox(label='Is normal image',
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value=False)
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num_samples = gr.Slider(label='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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detect_resolution = gr.Slider(label='Normal 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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bg_threshold = gr.Slider(
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label='Normal background threshold',
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minimum=0.0,
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maximum=1.0,
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value=0.4,
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step=0.01)
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num_steps = gr.Slider(label='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=-1,
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maximum=2147483647,
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step=1,
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randomize=True)
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a_prompt = gr.Textbox(
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label='Added 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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elem_id='gallery').style(grid=2,
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height='auto')
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inputs = [
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input_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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detect_resolution,
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num_steps,
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guidance_scale,
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seed,
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bg_threshold,
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is_normal_image,
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]
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prompt.submit(fn=process, inputs=inputs, outputs=result)
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run_button.click(fn=process,
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inputs=inputs,
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outputs=result,
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api_name='normal')
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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_normal)
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demo.queue().launch()
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