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( """ #
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()