Upload app..py

app..py

@@ -0,0 +1,432 @@
+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')