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import torch
from torchvision.transforms.functional import normalize
from models import CodeFormer
from utils import imwrite, img2tensor, tensor2img
from facelib.utils.face_restoration_helper import FaceRestoreHelper
from huggingface_hub import hf_hub_download
REPO_ID = "leonelhs/gfpgan"
pretrain_model_path = hf_hub_download(repo_id=REPO_ID, filename="CodeFormer.pth")
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = CodeFormer(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9,
connect_list=['32', '64', '128', '256']).to(device)
checkpoint = torch.load(pretrain_model_path)['params_ema']
net.load_state_dict(checkpoint)
net.eval()
face_helper = FaceRestoreHelper(
upscale_factor=2,
face_size=512,
crop_ratio=(1, 1),
det_model='retinaface_resnet50',
save_ext='png',
use_parse=True,
device=device)
input_img_list = ["/home/leonel/Pictures/lowres13.jpg"]
# -------------------- start to processing ---------------------
for i, img_path in enumerate(input_img_list):
# clean all the intermediate results to process the next image
face_helper.clean_all()
img = img_path
face_helper.read_image(img)
# get face landmarks for each face
num_det_faces = face_helper.get_face_landmarks_5(
only_center_face=False, resize=640, eye_dist_threshold=5)
print(f'\tdetect {num_det_faces} faces')
# align and warp each face
face_helper.align_warp_face()
# face restoration for each cropped face
for idx, cropped_face in enumerate(face_helper.cropped_faces):
# prepare data
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(device)
try:
with torch.no_grad():
output = net(cropped_face_t, w=0.5, 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}')
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
face_helper.add_restored_face(restored_face, cropped_face)
# paste_back
has_aligned = False
suffix = None
if not has_aligned:
bg_img = None
face_helper.get_inverse_affine(None)
restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=False)
imwrite(restored_img, "pretty.png")
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