SUPERSHANKY/ControlNet_Colab

1# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_normal2image.py2# The original license file is LICENSE.ControlNet in this repo.3import gradio as gr4 5 6def create_demo(process, max_images=12):7    with gr.Blocks() as demo:8        with gr.Row():9            gr.Markdown('## Control Stable Diffusion with Normal Maps')10        with gr.Row():11            with gr.Column():12                input_image = gr.Image(source='upload', type='numpy')13                prompt = gr.Textbox(label='Prompt')14                run_button = gr.Button(label='Run')15                with gr.Accordion('Advanced options', open=False):16                    num_samples = gr.Slider(label='Images',17                                            minimum=1,18                                            maximum=max_images,19                                            value=1,20                                            step=1)21                    image_resolution = gr.Slider(label='Image Resolution',22                                                 minimum=256,23                                                 maximum=768,24                                                 value=512,25                                                 step=256)26                    detect_resolution = gr.Slider(label='Normal Resolution',27                                                  minimum=128,28                                                  maximum=1024,29                                                  value=384,30                                                  step=1)31                    bg_threshold = gr.Slider(32                        label='Normal background threshold',33                        minimum=0.0,34                        maximum=1.0,35                        value=0.4,36                        step=0.01)37                    ddim_steps = gr.Slider(label='Steps',38                                           minimum=1,39                                           maximum=100,40                                           value=20,41                                           step=1)42                    scale = gr.Slider(label='Guidance Scale',43                                      minimum=0.1,44                                      maximum=30.0,45                                      value=9.0,46                                      step=0.1)47                    seed = gr.Slider(label='Seed',48                                     minimum=-1,49                                     maximum=2147483647,50                                     step=1,51                                     randomize=True)52                    eta = gr.Number(label='eta (DDIM)', value=0.0)53                    a_prompt = gr.Textbox(54                        label='Added Prompt',55                        value='best quality, extremely detailed')56                    n_prompt = gr.Textbox(57                        label='Negative Prompt',58                        value=59                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'60                    )61            with gr.Column():62                result_gallery = gr.Gallery(label='Output',63                                            show_label=False,64                                            elem_id='gallery').style(65                                                grid=2, height='auto')66        ips = [67            input_image, prompt, a_prompt, n_prompt, num_samples,68            image_resolution, detect_resolution, ddim_steps, scale, seed, eta,69            bg_threshold70        ]71        run_button.click(fn=process,72                         inputs=ips,73                         outputs=[result_gallery],74                         api_name='normal')75    return demo76