Delete gradio_hough2image.py

gradio_hough2image.py

@@ -1,82 +0,0 @@
-# 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):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Hough Line Maps')
-        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=1,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=768,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='Hough Resolution',
-                                                  minimum=128,
-                                                  maximum=1024,
-                                                  value=512,
-                                                  step=1)
-                    value_threshold = gr.Slider(
-                        label='Hough value threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=2.0,
-                        value=0.1,
-                        step=0.01)
-                    distance_threshold = gr.Slider(
-                        label='Hough distance threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=20.0,
-                        value=0.1,
-                        step=0.01)
-                    ddim_steps = gr.Slider(label='Steps',
-                                           minimum=1,
-                                           maximum=100,
-                                           value=20,
-                                           step=1)
-                    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,
-                                     queue=False)
-                    eta = gr.Number(label='eta (DDIM)', value=0.0)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result_gallery = gr.Gallery(label='Output',
-                                            show_label=False,
-                                            elem_id='gallery').style(
-                                                grid=2, height='auto')
-        ips = [
-            input_image, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, detect_resolution, ddim_steps, scale, seed, eta,
-            value_threshold, distance_threshold
-        ]
-        run_button.click(fn=process,
-                         inputs=ips,
-                         outputs=[result_gallery],
-                         api_name='hough')
-    return demo