Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,746 Bytes
c2c3c5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import spaces
import gradio as gr
from stablepy import Preprocessor
PREPROCESSOR_TASKS_LIST = [
"Canny",
"Openpose",
"DPT",
"Midas",
"ZoeDepth",
"DepthAnything",
"HED",
"PidiNet",
"TEED",
"Lineart",
"LineartAnime",
"Anyline",
"Lineart standard",
"SegFormer",
"UPerNet",
"ContentShuffle",
"Recolor",
"Blur",
"MLSD",
"NormalBae",
]
preprocessor = Preprocessor()
def process_inputs(
image,
name,
resolution,
precessor_resolution,
low_threshold,
high_threshold,
value_threshod,
distance_threshold,
recolor_mode,
recolor_gamma_correction,
blur_k_size,
pre_openpose_extra,
hed_scribble,
pre_pidinet_safe,
pre_lineart_coarse,
use_cuda,
):
if not image:
raise ValueError("To use this, simply upload an image.")
preprocessor.load(name, False)
params = dict(
image_resolution=resolution,
detect_resolution=precessor_resolution,
low_threshold=low_threshold,
high_threshold=high_threshold,
thr_v=value_threshod,
thr_d=distance_threshold,
mode=recolor_mode,
gamma_correction=recolor_gamma_correction,
blur_sigma=blur_k_size,
hand_and_face=pre_openpose_extra,
scribble=hed_scribble,
safe=pre_pidinet_safe,
coarse=pre_lineart_coarse,
)
if use_cuda:
@spaces.GPU(duration=15)
def wrapped_func():
preprocessor.to("cuda")
return preprocessor(image, **params)
return wrapped_func()
return preprocessor(image, **params)
def preprocessor_tab():
with gr.Row():
with gr.Column():
pre_image = gr.Image(label="Image", type="pil", sources=["upload"])
pre_options = gr.Dropdown(label="Preprocessor", choices=PREPROCESSOR_TASKS_LIST, value=PREPROCESSOR_TASKS_LIST[0])
pre_img_resolution = gr.Slider(
minimum=64, maximum=4096, step=64, value=1024, label="Image Resolution",
info="The maximum proportional size of the generated image based on the uploaded image."
)
pre_start = gr.Button(value="PROCESS IMAGE", variant="primary")
with gr.Accordion("Advanced Settings", open=False):
with gr.Column():
pre_processor_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
pre_low_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
pre_high_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")
pre_value_threshold = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
pre_distance_threshold = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
pre_recolor_mode = gr.Dropdown(label="'RECOLOR' mode", choices=["luminance", "intensity"], value="luminance")
pre_recolor_gamma_correction = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
pre_blur_k_size = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'BLUR' sigma")
pre_openpose_extra = gr.Checkbox(value=True, label="'OPENPOSE' face and hand")
pre_hed_scribble = gr.Checkbox(value=False, label="'HED' scribble")
pre_pidinet_safe = gr.Checkbox(value=False, label="'PIDINET' safe")
pre_lineart_coarse = gr.Checkbox(value=False, label="'LINEART' coarse")
pre_use_cuda = gr.Checkbox(value=False, label="Use CUDA")
with gr.Column():
pre_result = gr.Image(label="Result", type="pil", interactive=False, format="png")
pre_start.click(
fn=process_inputs,
inputs=[
pre_image,
pre_options,
pre_img_resolution,
pre_processor_resolution,
pre_low_threshold,
pre_high_threshold,
pre_value_threshold,
pre_distance_threshold,
pre_recolor_mode,
pre_recolor_gamma_correction,
pre_blur_k_size,
pre_openpose_extra,
pre_hed_scribble,
pre_pidinet_safe,
pre_lineart_coarse,
pre_use_cuda,
],
outputs=[pre_result],
)
|