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import gradio as gr | |
import argparse | |
from realesrgan import RealESRGANer | |
from realesrgan.archs.srvgg_arch import SRVGGNetCompact | |
import os | |
from basicsr.archs.rrdbnet_arch import RRDBNet | |
from basicsr.utils.download_util import load_file_from_url | |
def Generate(img, model_name): | |
global output | |
parser = argparse.ArgumentParser() | |
parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder') | |
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder') | |
parser.add_argument( | |
'-dn', | |
'--denoise_strength', | |
type=float, | |
default=0.5, | |
help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. ' | |
'Only used for the realesr-general-x4v3 model')) | |
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image') | |
parser.add_argument( | |
'--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it') | |
parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image') | |
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing') | |
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding') | |
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border') | |
parser.add_argument('--face_enhance', action='store_true',help='Use GFPGAN to enhance face') | |
parser.add_argument( | |
'--fp32', action='store_true',default=True,help='Use fp32 precision during inference. Default: fp16 (half precision).') | |
parser.add_argument( | |
'--alpha_upsampler', | |
type=str, | |
default='realesrgan', | |
help='The upsampler for the alpha channels. Options: realesrgan | bicubic') | |
parser.add_argument( | |
'--ext', | |
type=str, | |
default='auto', | |
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') | |
parser.add_argument( | |
'-g', '--gpu-id', type=int, default=None, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu') | |
args = parser.parse_args() | |
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-animevideov3': # x4 VGG-style model (XS size) | |
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu') | |
netscale = 4 | |
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.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' | |
] | |
model_path = os.path.join('weights', model_name + '.pth') | |
print(model_path) | |
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) | |
dni_weight = None | |
if model_name == 'realesr-general-x4v3' and args.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 = [args.denoise_strength, 1 - args.denoise_strength] | |
# restorer | |
upsampler = RealESRGANer( | |
scale=netscale, | |
model_path=model_path, | |
dni_weight=dni_weight, | |
model=model, | |
tile=args.tile, | |
tile_pad=args.tile_pad, | |
pre_pad=args.pre_pad, | |
half=not args.fp32, | |
gpu_id=args.gpu_id) | |
if args.face_enhance: # Use GFPGAN for face enhancement | |
from gfpgan import GFPGANer | |
face_enhancer = GFPGANer( | |
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth', | |
upscale=args.outscale, | |
arch='clean', | |
channel_multiplier=2, | |
bg_upsampler=upsampler) | |
os.makedirs(args.output, exist_ok=True) | |
try: | |
if args.face_enhance: | |
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) | |
else: | |
output, _ = upsampler.enhance(img, outscale=args.outscale) | |
print("生成成功") | |
except RuntimeError as error: | |
print('Error', error) | |
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') | |
output = None | |
return output | |
with gr.Blocks() as demo: | |
gr.Markdown( | |
""" | |
# <center> Real-ESRGAN 在线体验程序 | |
""") | |
gr.Markdown(""" | |
1. **项目模型运行在CPU上,等待时间略长** | |
2. **原工程项目旨在对图片就行修复** | |
3. **项目源地址为:[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)** | |
""") | |
with gr.Row(): | |
with gr.Column(): | |
img = gr.Image(type="numpy",label = "输入图片") | |
model_name = gr.Dropdown(["RealESRGAN_x4plus","RealESRGAN_x4plus_anime_6B","RealESRGAN_x2plus", | |
"realesr-animevideov3","realesr-general-x4v3"],info="选择模型") | |
with gr.Column(): | |
img_out = gr.Image(type="numpy",label = "输出图片") | |
btn = gr.Button("Generate") | |
btn.click(Generate, inputs=[img,model_name], outputs=[img_out]) | |
if __name__ == "__main__": | |
demo.launch() |