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| import os | |
| import torch | |
| import gfpgan | |
| from PIL import Image | |
| from upscaler.RealESRGAN import RealESRGAN | |
| face_enhancer_list = ['NONE', 'GFPGAN', 'REAL-ESRGAN 2x', 'REAL-ESRGAN 4x', 'REAL-ESRGAN 8x'] | |
| def load_face_enhancer_model(name='GFPGAN', device="cpu"): | |
| if name == 'GFPGAN': | |
| model_path = "./assets/pretrained_models/GFPGANv1.4.pth" | |
| model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_path) | |
| model = gfpgan.GFPGANer(model_path=model_path, upscale=1) | |
| elif name == 'REAL-ESRGAN 2x': | |
| model_path = "./assets/pretrained_models/RealESRGAN_x2.pth" | |
| model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_path) | |
| model = RealESRGAN(device, scale=2) | |
| model.load_weights(model_path, download=False) | |
| elif name == 'REAL-ESRGAN 4x': | |
| model_path = "./assets/pretrained_models/RealESRGAN_x4.pth" | |
| model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_path) | |
| model = RealESRGAN(device, scale=4) | |
| model.load_weights(model_path, download=False) | |
| elif name == 'REAL-ESRGAN 8x': | |
| model_path = "./assets/pretrained_models/RealESRGAN_x8.pth" | |
| model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_path) | |
| model = RealESRGAN(device, scale=8) | |
| model.load_weights(model_path, download=False) | |
| else: | |
| model = None | |
| return model | |
| def gfpgan_enhance(img, model, has_aligned=True): | |
| _, imgs, _ = model.enhance(img, paste_back=True, has_aligned=has_aligned) | |
| return imgs[0] | |
| def realesrgan_enhance(img, model): | |
| img = model.predict(img) | |
| return img |