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| # This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_hough2image.py | |
| # The original license file is LICENSE.ControlNet in this repo. | |
| import gradio as gr | |
| def create_demo(process, max_images=12, default_num_images=3): | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown('### Use a photo of your room and reimagine it with different styles with the power of ControlNet') | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type='numpy') | |
| prompt = gr.Textbox(label='Prompt') | |
| run_button = gr.Button(label='Run') | |
| with gr.Accordion('Advanced options', open=False): | |
| num_samples = gr.Slider(label='Images', | |
| minimum=1, | |
| maximum=max_images, | |
| value=default_num_images, | |
| step=1) | |
| image_resolution = gr.Slider(label='Image Resolution', | |
| minimum=256, | |
| maximum=512, | |
| value=512, | |
| step=256) | |
| detect_resolution = gr.Slider(label='Hough Resolution', | |
| minimum=128, | |
| maximum=512, | |
| value=512, | |
| step=1) | |
| mlsd_value_threshold = gr.Slider( | |
| label='Hough value threshold (MLSD)', | |
| minimum=0.01, | |
| maximum=2.0, | |
| value=0.1, | |
| step=0.01) | |
| mlsd_distance_threshold = gr.Slider( | |
| label='Hough distance threshold (MLSD)', | |
| minimum=0.01, | |
| maximum=20.0, | |
| value=0.1, | |
| step=0.01) | |
| num_steps = gr.Slider(label='Steps', | |
| minimum=1, | |
| maximum=100, | |
| value=20, | |
| step=1) | |
| guidance_scale = gr.Slider(label='Guidance Scale', | |
| minimum=0.1, | |
| maximum=30.0, | |
| value=9.0, | |
| step=0.1) | |
| seed = gr.Slider(label='Seed', | |
| minimum=-1, | |
| maximum=2147483647, | |
| step=1, | |
| randomize=True) | |
| a_prompt = gr.Textbox( | |
| label='Added Prompt', | |
| value='best quality, extremely detailed, photo from Pinterest, interior, cinematic photo, ultra-detailed, ultra-realistic, award-winning') | |
| n_prompt = gr.Textbox( | |
| label='Negative Prompt', | |
| value= | |
| 'lowres, cropped, worst quality, low quality' | |
| ) | |
| with gr.Column(): | |
| result = gr.Gallery(label='Output', | |
| show_label=False, | |
| elem_id='gallery').style(grid=2, | |
| height='auto') | |
| inputs = [ | |
| input_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| image_resolution, | |
| detect_resolution, | |
| num_steps, | |
| guidance_scale, | |
| seed, | |
| mlsd_value_threshold, | |
| mlsd_distance_threshold, | |
| ] | |
| prompt.submit(fn=process, inputs=inputs, outputs=result) | |
| run_button.click(fn=process, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name='hough') | |
| ex = gr.Examples(examples = [ | |
| ["room.jpg", | |
| "a room for gaming, with gaming chairs, gaming consoles and gaming computers", | |
| "best quality, extremely detailed, photo from Pinterest, interior, cinematic photo, ultra-detailed, ultra-realistic, award-winning", | |
| "lowres, cropped, worst quality, low quality", | |
| 2, | |
| 512, | |
| 512, | |
| 20, | |
| 9.0, | |
| 132794, | |
| 0.1, | |
| 0.1, | |
| ]], | |
| inputs = inputs, outputs = result, fn = process, cache_examples = True) | |
| return demo | |