app.py

#
# ControlNet v1.1 Preprocessors Standalone | Space
# Sebastian Kay 2024
#
# https://huggingface.co/spaces/Sebastiankay/controlnet-preprocessors-only
#

import gradio as gr
import cv2
import numpy as np

from annotator.util import resize_image, HWC3

DESCRIPTION = "# "
DESCRIPTION += "# ControlNet v1.1 Preprocessors Standalone"
DESCRIPTION += "\n<p>Generate Control Images for Stable Diffusion and other apps that uses ControlNet.</p>"


model_canny = None


def canny(img, res, l, h):
    img = resize_image(HWC3(img), res)
    global model_canny
    if model_canny is None:
        from annotator.canny import CannyDetector

        model_canny = CannyDetector()
    result = model_canny(img, l, h)
    return [result]


model_hed = None


def hed(img, res):
    img = resize_image(HWC3(img), res)
    global model_hed
    if model_hed is None:
        from annotator.hed import HEDdetector

        model_hed = HEDdetector()
    result = model_hed(img)
    return [result]


model_pidi = None


def pidi(img, res):
    img = resize_image(HWC3(img), res)
    global model_pidi
    if model_pidi is None:
        from annotator.pidinet import PidiNetDetector

        model_pidi = PidiNetDetector()
    result = model_pidi(img)
    return [result]


model_mlsd = None


def mlsd(img, res, thr_v, thr_d):
    img = resize_image(HWC3(img), res)
    global model_mlsd
    if model_mlsd is None:
        from annotator.mlsd import MLSDdetector

        model_mlsd = MLSDdetector()
    result = model_mlsd(img, thr_v, thr_d)
    return [result]


model_midas = None


def midas(img, res):
    img = resize_image(HWC3(img), res)
    global model_midas
    if model_midas is None:
        from annotator.midas import MidasDetector

        model_midas = MidasDetector()
    result = model_midas(img)
    return [result]


model_zoe = None


def zoe(img, res):
    img = resize_image(HWC3(img), res)
    global model_zoe
    if model_zoe is None:
        from annotator.zoe import ZoeDetector

        model_zoe = ZoeDetector()
    result = model_zoe(img)
    return [result]


model_normalbae = None


def normalbae(img, res):
    img = resize_image(HWC3(img), res)
    global model_normalbae
    if model_normalbae is None:
        from annotator.normalbae import NormalBaeDetector

        model_normalbae = NormalBaeDetector()
    result = model_normalbae(img)
    return [result]


model_dwpose = None


def dwpose(img, res):
    img = resize_image(HWC3(img), res)
    global model_dwpose
    if model_dwpose is None:
        from annotator.dwpose import DWposeDetector

        model_dwpose = DWposeDetector()
    result = model_dwpose(img)
    return [result]


model_openpose = None


def openpose(img, res, hand_and_face):
    img = resize_image(HWC3(img), res)
    global model_openpose
    if model_openpose is None:
        from annotator.openpose import OpenposeDetector

        model_openpose = OpenposeDetector()
    result = model_openpose(img, hand_and_face)
    return [result]


model_uniformer = None


# def uniformer(img, res):
#    img = resize_image(HWC3(img), res)
#    global model_uniformer
#    if model_uniformer is None:
#        from annotator.uniformer import UniformerDetector
#        model_uniformer = UniformerDetector()
#    result = model_uniformer(img)
#    return [result]


## FUNCTION LINEART

model_lineart_anime = None
model_lineart = None


def lineart(img, res, preprocessor_name="Lineart", invert=True):
    img = resize_image(HWC3(img), res)
    ["Lineart", "Lineart Coarse", "Lineart Anime"]
    if preprocessor_name in ["Lineart", "Lineart Coarse"]:
        coarse = "Coarse" in preprocessor_name
        global model_lineart
        if model_lineart is None:
            from annotator.lineart import LineartDetector

            model_lineart = LineartDetector()
        #    result = model_lineart(img, coarse)
        if invert:
            result = cv2.bitwise_not(model_lineart(img, coarse))
        else:
            result = model_lineart(img, coarse)
        return [result]
    elif preprocessor_name == "Lineart Anime":
        global model_lineart_anime
        if model_lineart_anime is None:
            from annotator.lineart_anime import LineartAnimeDetector

            model_lineart_anime = LineartAnimeDetector()
        #    result = model_lineart_anime(img)
        if invert:
            result = cv2.bitwise_not(model_lineart_anime(img))
        else:
            result = model_lineart_anime(img)
        return [result]


model_oneformer_coco = None


def oneformer_coco(img, res):
    img = resize_image(HWC3(img), res)
    global model_oneformer_coco
    if model_oneformer_coco is None:
        from annotator.oneformer import OneformerCOCODetector

        model_oneformer_coco = OneformerCOCODetector()
    result = model_oneformer_coco(img)
    return [result]


model_oneformer_ade20k = None


def oneformer_ade20k(img, res):
    img = resize_image(HWC3(img), res)
    global model_oneformer_ade20k
    if model_oneformer_ade20k is None:
        from annotator.oneformer import OneformerADE20kDetector

        model_oneformer_ade20k = OneformerADE20kDetector()
    result = model_oneformer_ade20k(img)
    return [result]


model_content_shuffler = None


def content_shuffler(img, res):
    img = resize_image(HWC3(img), res)
    global model_content_shuffler
    if model_content_shuffler is None:
        from annotator.shuffle import ContentShuffleDetector

        model_content_shuffler = ContentShuffleDetector()
    result = model_content_shuffler(img)
    return [result]


model_color_shuffler = None


def color_shuffler(img, res):
    img = resize_image(HWC3(img), res)
    global model_color_shuffler
    if model_color_shuffler is None:
        from annotator.shuffle import ColorShuffleDetector

        model_color_shuffler = ColorShuffleDetector()
    result = model_color_shuffler(img)
    return [result]


model_inpaint = None


def inpaint(image, invert):
    #    color = HWC3(image["image"])
    color = HWC3(image["background"])
    if invert:
        #        alpha = image["mask"][:, :, 0:1]
        alpha = image["layers"][0][:, :, 3:]
    else:
        #        alpha = 255 - image["mask"][:, :, 0:1]
        alpha = 255 - image["layers"][0][:, :, 3:]
    result = np.concatenate([color, alpha], axis=2)
    return [result]


theme = gr.themes.Soft(
    primary_hue="emerald",
    #     neutral_hue=gr.themes.Color(c100="#fce7f3", c200="#fbcfe8", c300="#f9a8d4", c400="#f472b6", c50="#fdf2f8", c500="#9b3b6b", c600="#7f2f53", c700="#641b3a", c800="#5d1431", c900="#361120", c950="#2b0d19"),
    radius_size="sm",
)

css = """
    body > gradio-app {
        background: var(--primary-950);
        background: linear-gradient(180deg, color-mix(in srgb, var(--primary-950), transparent 50%) 0%, color-mix(in srgb, var(--primary-950), transparent 50%) 28%, var(--neutral-950) 28%, var(--neutral-950) 100%) !important;
        padding-top: 120px;
    }
    
    
    
    div.tabs > div.tab-nav {
        border-bottom: none; !important;
        padding: 0 0.25rem 0 0.25rem !important;
        flex-wrap: nowrap;
        overflow-x: scroll;
        overflow-y:hidden;
    }
    
    div.tabs > div.tab-nav > button.selected {
        border-width: 0 !important;
        background: var(--primary-600) !important;
        color: var(--neutral-950);
        font-weight: 600;
    }
    
    div.tabs > div.tab-nav > button {
        min-width: 130px !important;
    }
    
    div.tabs div.tabitem {
        background-color: var(--neutral-900) !important;
        border-top: 8px solid var(--primary-600) !important;
        border-radius: var(--container-radius) !important;
    }
    
    .top-description h1 {
        color: var(--neutral-400);
        font-size: 2rem;
    }
    """

with gr.Blocks(theme=theme, css=css) as demo:
    gr.Markdown(DESCRIPTION, elem_classes="top-description")
    with gr.Tab("Canny Edge", elem_id="tab_wrapper", elem_classes="tab_wrapper"):
        with gr.Row():
            gr.Markdown("## Canny Edge")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                low_threshold = gr.Slider(label="low_threshold", minimum=1, maximum=255, value=100, step=1)
                high_threshold = gr.Slider(label="high_threshold", minimum=1, maximum=255, value=200, step=1)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=canny, inputs=[input_image, resolution, low_threshold, high_threshold], outputs=[gallery])

    with gr.Tab("HED Edge"):
        with gr.Row():
            gr.Markdown("## HED Edge&nbsp;&quot;SoftEdge&quot;")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=hed, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("Pidi Edge"):
        with gr.Row():
            gr.Markdown("## Pidi Edge&nbsp;&quot;SoftEdge&quot;")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=pidi, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("MLSD Edge"):
        with gr.Row():
            gr.Markdown("## MLSD Edge")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                value_threshold = gr.Slider(label="value_threshold", minimum=0.01, maximum=2.0, value=0.1, step=0.01)
                distance_threshold = gr.Slider(label="distance_threshold", minimum=0.01, maximum=20.0, value=0.1, step=0.01)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=mlsd, inputs=[input_image, resolution, value_threshold, distance_threshold], outputs=[gallery])

    with gr.Tab("MIDAS Depth"):
        with gr.Row():
            gr.Markdown("## MIDAS Depth")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=midas, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("ZOE Depth"):
        with gr.Row():
            gr.Markdown("## Zoe Depth")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=zoe, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("Normal Bae"):
        with gr.Row():
            gr.Markdown("## Normal Bae")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=normalbae, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("DWPose"):
        with gr.Row():
            gr.Markdown("## DWPose")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=dwpose, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("Openpose"):
        with gr.Row():
            gr.Markdown("## Openpose")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                hand_and_face = gr.Checkbox(label="Hand and Face", value=False)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=openpose, inputs=[input_image, resolution, hand_and_face], outputs=[gallery])

    ## TAB LINEART
    with gr.Tab("Lineart"):
        with gr.Row():
            gr.Markdown("## Lineart \n<p>Check Invert to use with Mochi Diffusion.  Inverted image can also be created here for use with ControlNet Scribble.")
        with gr.Row():
            with gr.Column():
                preprocessor_name = gr.Radio(label="Preprocessor", choices=["Lineart", "Lineart Coarse", "Lineart Anime"], type="value", value="Lineart")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                invert = gr.Checkbox(label="Invert", value=True)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Lineart")
            with gr.Column():
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")

        def update_button_label(preprocessor_name):
            if preprocessor_name == "Lineart":
                return "Lineart"
            elif preprocessor_name == "Lineart Coarse":
                return "Lineart Coarse"
            elif preprocessor_name == "Lineart Anime":
                return "Lineart Anime"

        preprocessor_name.change(fn=update_button_label, inputs=[preprocessor_name], outputs=[run_button])
        run_button.click(fn=lineart, inputs=[input_image, resolution, preprocessor_name, invert], outputs=[gallery])

    with gr.Tab("InPaint"):
        with gr.Row():
            gr.Markdown("## InPaint")
        with gr.Row():
            with gr.Column():
                input_image = gr.ImageMask(sources="upload", type="numpy", height="auto")
                invert = gr.Checkbox(label="Invert Mask", value=False)
                run_button = gr.Button("Run")

            with gr.Column():

                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=inpaint, inputs=[input_image, invert], outputs=[gallery])

    #    with gr.Row():
    #        gr.Markdown("## Uniformer Segmentation")
    #    with gr.Row():
    #        with gr.Column():
    #            input_image = gr.Image(source='upload', type="numpy")
    #            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
    #            run_button = gr.Button(label="Run")
    #        with gr.Column():
    #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
    #    run_button.click(fn=uniformer, inputs=[input_image, resolution], outputs=[gallery])

    #    with gr.Row():
    #        gr.Markdown("## Oneformer COCO Segmentation")
    #    with gr.Row():
    #        with gr.Column():
    #            input_image = gr.Image(source='upload', type="numpy")
    #            input_image = gr.Image(label="Input Image", type="numpy", height=512)
    #            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
    #            run_button = gr.Button("Run")
    #            run_button = gr.Button(label="Run")
    #        with gr.Column():
    #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
    #            gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
    #    run_button.click(fn=oneformer_coco, inputs=[input_image, resolution], outputs=[gallery])

    #    with gr.Row():
    #        gr.Markdown("## Oneformer ADE20K Segmentation")
    #    with gr.Row():
    #        with gr.Column():
    #            input_image = gr.Image(source='upload', type="numpy")
    #            input_image = gr.Image(label="Input Image", type="numpy", height=512)
    #            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=640, step=64)
    #            run_button = gr.Button("Run")
    #            run_button = gr.Button(label="Run")
    #        with gr.Column():
    #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
    #            gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
    #    run_button.click(fn=oneformer_ade20k, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("Content Shuffle"):
        with gr.Row():
            gr.Markdown("## Content Shuffle")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")
        run_button.click(fn=content_shuffler, inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("Color Shuffle"):
        with gr.Row():
            gr.Markdown("## Color Shuffle")
        with gr.Row():
            with gr.Column():
                #            input_image = gr.Image(source='upload', type="numpy")
                input_image = gr.Image(label="Input Image", type="numpy", height=512)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button("Run")
            #            run_button = gr.Button(label="Run")
            with gr.Column():
                #            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
                gallery = gr.Gallery(label="Generated images", show_label=False, height="auto")

    run_button.click(fn=color_shuffler, inputs=[input_image, resolution], outputs=[gallery])


demo.launch()