Spaces:
Build error
Build error
File size: 9,379 Bytes
d69ad9e 0824ec4 a70af87 d69ad9e a70af87 d69ad9e a9dea45 d69ad9e acd99b3 d69ad9e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
import gradio as gr
import cv2
import numpy
import os
import random
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
from torchvision.transforms.functional import rgb_to_grayscale
import spaces
last_file = None
img_mode = "RGBA"
@spaces.GPU()
def realesrgan(img, model_name, denoise_strength, face_enhance, outscale):
"""Real-ESRGAN function to restore (and upscale) images.
"""
if not img:
return
# Define model parameters
if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
elif model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
elif model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
elif model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
netscale = 2
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
elif model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
netscale = 4
file_url = [
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
]
# Determine model paths
model_path = os.path.join('weights', model_name + '.pth')
if not os.path.isfile(model_path):
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
for url in file_url:
# model_path will be updated
model_path = load_file_from_url(
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
# Use dni to control the denoise strength
dni_weight = None
if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
model_path = [model_path, wdn_model_path]
dni_weight = [denoise_strength, 1 - denoise_strength]
# Restorer Class
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
tile=0,
tile_pad=10,
pre_pad=10,
half=False,
gpu_id=None
)
# Use GFPGAN for face enhancement
if face_enhance:
from gfpgan import GFPGANer
face_enhancer = GFPGANer(
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
upscale=outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler)
# Convert the input PIL image to cv2 image, so that it can be processed by realesrgan
cv_img = numpy.array(img)
img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA)
# Apply restoration
try:
if face_enhance:
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
else:
output, _ = upsampler.enhance(img, outscale=outscale)
except RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
else:
# Save restored image and return it to the output Image component
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
else:
extension = 'jpg'
out_filename = f"output_{rnd_string(8)}.{extension}"
cv2.imwrite(out_filename, output)
global last_file
last_file = out_filename
return out_filename
def rnd_string(x):
"""Returns a string of 'x' random characters
"""
characters = "abcdefghijklmnopqrstuvwxyz_0123456789"
result = "".join((random.choice(characters)) for i in range(x))
return result
def reset():
"""Resets the Image components of the Gradio interface and deletes
the last processed image
"""
global last_file
if last_file:
print(f"Deleting {last_file} ...")
os.remove(last_file)
last_file = None
return gr.update(value=None), gr.update(value=None)
def has_transparency(img):
"""This function works by first checking to see if a "transparency" property is defined
in the image's info -- if so, we return "True". Then, if the image is using indexed colors
(such as in GIFs), it gets the index of the transparent color in the palette
(img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas
(img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in
it, but it double-checks by getting the minimum and maximum values of every color channel
(img.getextrema()), and checks if the alpha channel's smallest value falls below 255.
https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
"""
if img.info.get("transparency", None) is not None:
return True
if img.mode == "P":
transparent = img.info.get("transparency", -1)
for _, index in img.getcolors():
if index == transparent:
return True
elif img.mode == "RGBA":
extrema = img.getextrema()
if extrema[3][0] < 255:
return True
return False
def image_properties(img):
"""Returns the dimensions (width and height) and color mode of the input image and
also sets the global img_mode variable to be used by the realesrgan function
"""
global img_mode
if img:
if has_transparency(img):
img_mode = "RGBA"
else:
img_mode = "RGB"
properties = f"Resolution: Width: {img.size[0]}, Height: {img.size[1]} | Color Mode: {img_mode}"
return properties
def main():
# Gradio Interface
with gr.Blocks(title="Real-ESRGAN Gradio Demo", theme="dark") as demo:
gr.Markdown(
"""# <div align="center"> Ilaria Upscaler π </div>
Do not use images over 750x750 especially with 4x the resolution upscaling, it will give you an error.
Hugginface port of [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN).
"""
)
with gr.Accordion("Upscaling option"):
with gr.Row():
model_name = gr.Dropdown(label="Upscaler model",
choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B",
"RealESRGAN_x2plus", "realesr-general-x4v3"],
value="RealESRGAN_x4plus_anime_6B", show_label=True)
denoise_strength = gr.Slider(label="Denoise Strength",
minimum=0, maximum=1, step=0.1, value=0.5)
outscale = gr.Slider(label="Resolution upscale",
minimum=1, maximum=6, step=1, value=4, show_label=True)
face_enhance = gr.Checkbox(label="Face Enhancement (GFPGAN)",
)
with gr.Row():
with gr.Group():
input_image = gr.Image(label="Input Image", type="pil", image_mode="RGBA")
input_image_properties = gr.Textbox(label="Image Properties", max_lines=1)
output_image = gr.Image(label="Output Image", image_mode="RGBA")
with gr.Row():
reset_btn = gr.Button("Remove images")
restore_btn = gr.Button("Upscale")
# Event listeners:
input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties)
restore_btn.click(fn=realesrgan,
inputs=[input_image, model_name, denoise_strength, face_enhance, outscale],
outputs=output_image)
reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image])
# reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n")
# Undocumented method to clear a component's value using Javascript
gr.Markdown(
"""Made with love by Ilaria π | Support me on [Ko-Fi](https://ko-fi.com/ilariaowo) | Join [AI Hub](https://discord.gg/aihub)
"""
)
demo.launch()
if __name__ == "__main__":
main() |