Delete app..py

app..py

@@ -1,432 +0,0 @@
-import gradio as gr
-import cv2
-import numpy as np
-
-from annotator.util import resize_image, HWC3
-
-DESCRIPTION = '# ControlNet v1.1 Annotators (that runs on cpu only)'
-DESCRIPTION += '\n<p>This app generates Control Image for Mochi Diffusion&apos;s ControlNet.</p>'
-DESCRIPTION += '\n<p>HEIC image is not converted. Please use PNG or JPG image.</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_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]
-
-
-model_lineart_anime = None
-
-
-def lineart_anime(img, res, invert=True):
-    img = resize_image(HWC3(img), res)
-    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_lineart = None
-
-
-def lineart(img, res, coarse=False, invert=True):
-    img = resize_image(HWC3(img), res)
-    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]
-
-
-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):
-#    image = resize_image(img, res)
-    color = HWC3(image["image"])
-    if(invert):
-        alpha = image["mask"][:, :, 0:1]
-    else:
-        alpha = 255 - image["mask"][:, :, 0:1]
-    result = np.concatenate([color, alpha], axis=2)
-    return [result]
-
-block = gr.Blocks().queue()
-with block:
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-        gr.Markdown("## Canny Edge")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            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(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=canny, inputs=[input_image, resolution, low_threshold, high_threshold], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Inpaint \n<p>Mochi Diffusionの次バージョンで使えるようになるかもしれないので試作中")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy", tool="sketch", height=512)
-#            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
-            invert = gr.Checkbox(label='Invert Mask', value=False)
-            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=inpaint, inputs=[input_image, resolution], outputs=[gallery])
-    run_button.click(fn=inpaint, inputs=[input_image, invert], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    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")
-            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=hed, inputs=[input_image, resolution], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    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")
-            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=pidi, inputs=[input_image, resolution], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## MLSD Edge")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            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(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=mlsd, inputs=[input_image, resolution, value_threshold, distance_threshold], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## MIDAS Depth")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, 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=midas, inputs=[input_image, resolution], outputs=[gallery])
-
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Zoe Depth")
-    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=zoe, inputs=[input_image, resolution], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Normal Bae")
-    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=normalbae, inputs=[input_image, resolution], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Openpose")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            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(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=openpose, inputs=[input_image, resolution, hand_and_face], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Lineart Anime \n<p>Check Invert to use with Mochi Diffusion.")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            invert = gr.Checkbox(label='Invert', value=True)
-            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=lineart_anime, inputs=[input_image, resolution, invert], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    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():
-            input_image = gr.Image(source='upload', type="numpy")
-            coarse = gr.Checkbox(label='Using coarse model', value=False)
-            invert = gr.Checkbox(label='Invert', value=True)
-            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=lineart, inputs=[input_image, resolution, coarse, 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])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Oneformer COCO 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=oneformer_coco, inputs=[input_image, resolution], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Oneformer ADE20K 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=640, 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=oneformer_ade20k, inputs=[input_image, resolution], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Content Shuffle")
-    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=content_shuffler, inputs=[input_image, resolution], outputs=[gallery])
-
-    gr.Markdown("<hr>")
-    with gr.Row():
-        gr.Markdown("## Color Shuffle")
-    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=color_shuffler, inputs=[input_image, resolution], outputs=[gallery])
-
-
-block.launch(server_name='0.0.0.0')