hysts/ControlNet

1# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_depth2image.py2# The original license file is LICENSE.ControlNet in this repo.3import gradio as gr4 5 6def create_demo(process, max_images=12, default_num_images=3):7    with gr.Blocks() as demo:8        with gr.Row():9            gr.Markdown('## Control Stable Diffusion with Depth 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                    is_depth_image = gr.Checkbox(label='Is depth image',17                                                 value=False)18                    num_samples = gr.Slider(label='Images',19                                            minimum=1,20                                            maximum=max_images,21                                            value=default_num_images,22                                            step=1)23                    image_resolution = gr.Slider(label='Image Resolution',24                                                 minimum=256,25                                                 maximum=512,26                                                 value=512,27                                                 step=256)28                    detect_resolution = gr.Slider(label='Depth Resolution',29                                                  minimum=128,30                                                  maximum=512,31                                                  value=384,32                                                  step=1)33                    num_steps = gr.Slider(label='Steps',34                                          minimum=1,35                                          maximum=100,36                                          value=20,37                                          step=1)38                    guidance_scale = gr.Slider(label='Guidance Scale',39                                               minimum=0.1,40                                               maximum=30.0,41                                               value=9.0,42                                               step=0.1)43                    seed = gr.Slider(label='Seed',44                                     minimum=-1,45                                     maximum=2147483647,46                                     step=1,47                                     randomize=True)48                    a_prompt = gr.Textbox(49                        label='Added Prompt',50                        value='best quality, extremely detailed')51                    n_prompt = gr.Textbox(52                        label='Negative Prompt',53                        value=54                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'55                    )56            with gr.Column():57                result = gr.Gallery(label='Output',58                                    show_label=False,59                                    elem_id='gallery').style(grid=2,60                                                             height='auto')61        inputs = [62            input_image,63            prompt,64            a_prompt,65            n_prompt,66            num_samples,67            image_resolution,68            detect_resolution,69            num_steps,70            guidance_scale,71            seed,72            is_depth_image,73        ]74        prompt.submit(fn=process, inputs=inputs, outputs=result)75        run_button.click(fn=process,76                         inputs=inputs,77                         outputs=result,78                         api_name='depth')79    return demo80 81 82if __name__ == '__main__':83    from model import Model84    model = Model()85    demo = create_demo(model.process_depth)86    demo.queue().launch()87