| import os
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| import sys
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| import traceback
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|
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| import cv2
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| import torch
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|
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| import modules.face_restoration
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| import modules.shared
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| from modules import shared, devices, modelloader
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| from modules.paths import models_path
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|
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| model_dir = "Codeformer"
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| model_path = os.path.join(models_path, model_dir)
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| model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
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|
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| have_codeformer = False
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| codeformer = None
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|
|
|
|
| def setup_model(dirname):
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| global model_path
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| if not os.path.exists(model_path):
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| os.makedirs(model_path)
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|
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| path = modules.paths.paths.get("CodeFormer", None)
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| if path is None:
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| return
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|
|
| try:
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| from torchvision.transforms.functional import normalize
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| from modules.codeformer.codeformer_arch import CodeFormer
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| from basicsr.utils.download_util import load_file_from_url
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| from basicsr.utils import imwrite, img2tensor, tensor2img
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| from facelib.utils.face_restoration_helper import FaceRestoreHelper
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| from facelib.detection.retinaface import retinaface
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| from modules.shared import cmd_opts
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|
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| net_class = CodeFormer
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|
|
| class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
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| def name(self):
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| return "CodeFormer"
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|
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| def __init__(self, dirname):
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| self.net = None
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| self.face_helper = None
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| self.cmd_dir = dirname
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|
|
| def create_models(self):
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|
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| if self.net is not None and self.face_helper is not None:
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| self.net.to(devices.device_codeformer)
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| return self.net, self.face_helper
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| model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
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| if len(model_paths) != 0:
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| ckpt_path = model_paths[0]
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| else:
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| print("Unable to load codeformer model.")
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| return None, None
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| net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
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| checkpoint = torch.load(ckpt_path)['params_ema']
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| net.load_state_dict(checkpoint)
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| net.eval()
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|
|
| if hasattr(retinaface, 'device'):
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| retinaface.device = devices.device_codeformer
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| face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
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|
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| self.net = net
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| self.face_helper = face_helper
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|
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| return net, face_helper
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|
|
| def send_model_to(self, device):
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| self.net.to(device)
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| self.face_helper.face_det.to(device)
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| self.face_helper.face_parse.to(device)
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|
|
| def restore(self, np_image, w=None):
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| np_image = np_image[:, :, ::-1]
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|
|
| original_resolution = np_image.shape[0:2]
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|
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| self.create_models()
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| if self.net is None or self.face_helper is None:
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| return np_image
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|
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| self.send_model_to(devices.device_codeformer)
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|
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| self.face_helper.clean_all()
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| self.face_helper.read_image(np_image)
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| self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
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| self.face_helper.align_warp_face()
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|
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| for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
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| cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
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| normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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| cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
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|
|
| try:
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| with torch.no_grad():
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| output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
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| restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
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| del output
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| torch.cuda.empty_cache()
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| except Exception as error:
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| print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
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| restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
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|
|
| restored_face = restored_face.astype('uint8')
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| self.face_helper.add_restored_face(restored_face)
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|
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| self.face_helper.get_inverse_affine(None)
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|
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| restored_img = self.face_helper.paste_faces_to_input_image()
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| restored_img = restored_img[:, :, ::-1]
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|
|
| if original_resolution != restored_img.shape[0:2]:
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| restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
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|
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| self.face_helper.clean_all()
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|
|
| if shared.opts.face_restoration_unload:
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| self.send_model_to(devices.cpu)
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|
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| return restored_img
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|
|
| global have_codeformer
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| have_codeformer = True
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|
|
| global codeformer
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| codeformer = FaceRestorerCodeFormer(dirname)
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| shared.face_restorers.append(codeformer)
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|
|
| except Exception:
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| print("Error setting up CodeFormer:", file=sys.stderr)
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| print(traceback.format_exc(), file=sys.stderr)
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|
