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import gradio as gr
import cv2
from annotator.util import resize_image, HWC3
DESCRIPTION = '# ControlNet v1.1 Annotators (that runs on cpu only)'
DESCRIPTION += '\n<p>HEIC image are 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]
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])
with gr.Row():
gr.Markdown("## HED Edge")
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])
with gr.Row():
gr.Markdown("## Pidi Edge")
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])
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])
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])
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])
# 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])
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])
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])
with gr.Row():
gr.Markdown("## Lineart \n<p>Check Invert to use with Mochi Diffusion.")
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])
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])
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])
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])
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')
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