|
- import os
- import sys
- import traceback
-
- import cv2
- import torch
-
- import modules.face_restoration
- import modules.shared
- from modules import shared, devices, modelloader
- from modules.paths import models_path
-
- # codeformer people made a choice to include modified basicsr library to their project which makes
- # it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
- # I am making a choice to include some files from codeformer to work around this issue.
- model_dir = "Codeformer"
- model_path = os.path.join(models_path, model_dir)
- model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-
- have_codeformer = False
- codeformer = None
-
-
- def setup_model(dirname):
- global model_path
- if not os.path.exists(model_path):
- os.makedirs(model_path)
-
- path = modules.paths.paths.get("CodeFormer", None)
- if path is None:
- return
-
- try:
- from torchvision.transforms.functional import normalize
- from modules.codeformer.codeformer_arch import CodeFormer
- from basicsr.utils.download_util import load_file_from_url
- from basicsr.utils import imwrite, img2tensor, tensor2img
- from facelib.utils.face_restoration_helper import FaceRestoreHelper
- from facelib.detection.retinaface import retinaface
- from modules.shared import cmd_opts
-
- net_class = CodeFormer
-
- class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
- def name(self):
- return "CodeFormer"
-
- def __init__(self, dirname):
- self.net = None
- self.face_helper = None
- self.cmd_dir = dirname
-
- def create_models(self):
-
- if self.net is not None and self.face_helper is not None:
- self.net.to(devices.device_codeformer)
- return self.net, self.face_helper
- model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
- if len(model_paths) != 0:
- ckpt_path = model_paths[0]
- else:
- print("Unable to load codeformer model.")
- return None, None
- 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)
- checkpoint = torch.load(ckpt_path)['params_ema']
- net.load_state_dict(checkpoint)
- net.eval()
-
- if hasattr(retinaface, 'device'):
- retinaface.device = devices.device_codeformer
- 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)
-
- self.net = net
- self.face_helper = face_helper
-
- return net, face_helper
-
- def send_model_to(self, device):
- self.net.to(device)
- self.face_helper.face_det.to(device)
- self.face_helper.face_parse.to(device)
-
- def restore(self, np_image, w=None):
- np_image = np_image[:, :, ::-1]
-
- original_resolution = np_image.shape[0:2]
-
- self.create_models()
- if self.net is None or self.face_helper is None:
- return np_image
-
- self.send_model_to(devices.device_codeformer)
-
- self.face_helper.clean_all()
- self.face_helper.read_image(np_image)
- self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
- self.face_helper.align_warp_face()
-
- for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
- cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
- normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
- cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
-
- try:
- with torch.no_grad():
- output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
- restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
- del output
- torch.cuda.empty_cache()
- except Exception as error:
- print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
- restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
-
- restored_face = restored_face.astype('uint8')
- self.face_helper.add_restored_face(restored_face)
-
- self.face_helper.get_inverse_affine(None)
-
- restored_img = self.face_helper.paste_faces_to_input_image()
- restored_img = restored_img[:, :, ::-1]
-
- if original_resolution != restored_img.shape[0:2]:
- 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)
-
- self.face_helper.clean_all()
-
- if shared.opts.face_restoration_unload:
- self.send_model_to(devices.cpu)
-
- return restored_img
-
- global have_codeformer
- have_codeformer = True
-
- global codeformer
- codeformer = FaceRestorerCodeFormer(dirname)
- shared.face_restorers.append(codeformer)
-
- except Exception:
- print("Error setting up CodeFormer:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- # sys.path = stored_sys_path
|