# flake8: noqa # This file is used for deploying replicate models # running: cog predict -i img=@inputs/00017_gray.png -i version='General - v3' -i scale=2 -i face_enhance=True -i tile=0 # push: cog push r8.im/xinntao/realesrgan import os os.system('pip install gfpgan') os.system('python setup.py develop') import cv2 import shutil import tempfile import torch from basicsr.archs.rrdbnet_arch import RRDBNet from basicsr.archs.srvgg_arch import SRVGGNetCompact from realesrgan.utils import RealESRGANer try: from cog import BasePredictor, Input, Path from gfpgan import GFPGANer except Exception: print('please install cog and realesrgan package') class Predictor(BasePredictor): def setup(self): os.makedirs('output', exist_ok=True) # download weights if not os.path.exists('weights/realesr-general-x4v3.pth'): os.system( 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./weights' ) if not os.path.exists('weights/GFPGANv1.4.pth'): os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./weights') if not os.path.exists('weights/RealESRGAN_x4plus.pth'): os.system( 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./weights' ) if not os.path.exists('weights/RealESRGAN_x4plus_anime_6B.pth'): os.system( 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P ./weights' ) if not os.path.exists('weights/realesr-animevideov3.pth'): os.system( 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P ./weights' ) def choose_model(self, scale, version, tile=0): half = True if torch.cuda.is_available() else False if version == 'General - RealESRGANplus': model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) model_path = 'weights/RealESRGAN_x4plus.pth' self.upsampler = RealESRGANer( scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half) elif version == 'General - v3': model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') model_path = 'weights/realesr-general-x4v3.pth' self.upsampler = RealESRGANer( scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half) elif version == 'Anime - anime6B': model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) model_path = 'weights/RealESRGAN_x4plus_anime_6B.pth' self.upsampler = RealESRGANer( scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half) elif version == 'AnimeVideo - v3': model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu') model_path = 'weights/realesr-animevideov3.pth' self.upsampler = RealESRGANer( scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half) self.face_enhancer = GFPGANer( model_path='weights/GFPGANv1.4.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=self.upsampler) def predict( self, img: Path = Input(description='Input'), version: str = Input( description='RealESRGAN version. Please see [Readme] below for more descriptions', choices=['General - RealESRGANplus', 'General - v3', 'Anime - anime6B', 'AnimeVideo - v3'], default='General - v3'), scale: float = Input(description='Rescaling factor', default=2), face_enhance: bool = Input( description='Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes', default=False), tile: int = Input( description= 'Tile size. Default is 0, that is no tile. When encountering the out-of-GPU-memory issue, please specify it, e.g., 400 or 200', default=0) ) -> Path: if tile <= 100 or tile is None: tile = 0 print(f'img: {img}. version: {version}. scale: {scale}. face_enhance: {face_enhance}. tile: {tile}.') try: extension = os.path.splitext(os.path.basename(str(img)))[1] img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED) if len(img.shape) == 3 and img.shape[2] == 4: img_mode = 'RGBA' elif len(img.shape) == 2: img_mode = None img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) else: img_mode = None h, w = img.shape[0:2] if h < 300: img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) self.choose_model(scale, version, tile) try: if face_enhance: _, _, output = self.face_enhancer.enhance( img, has_aligned=False, only_center_face=False, paste_back=True) else: output, _ = self.upsampler.enhance(img, outscale=scale) except RuntimeError as error: print('Error', error) print('If you encounter CUDA out of memory, try to set "tile" to a smaller size, e.g., 400.') if img_mode == 'RGBA': # RGBA images should be saved in png format extension = 'png' # save_path = f'output/out.{extension}' # cv2.imwrite(save_path, output) out_path = Path(tempfile.mkdtemp()) / f'out.{extension}' cv2.imwrite(str(out_path), output) except Exception as error: print('global exception: ', error) finally: clean_folder('output') return out_path def clean_folder(folder): for filename in os.listdir(folder): file_path = os.path.join(folder, filename) try: if os.path.isfile(file_path) or os.path.islink(file_path): os.unlink(file_path) elif os.path.isdir(file_path): shutil.rmtree(file_path) except Exception as e: print(f'Failed to delete {file_path}. Reason: {e}')