# 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}")