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import os
import sys
import traceback
import torch
from modules import shared
from modules.paths import script_path
import modules.shared
import modules.face_restoration
from importlib import reload
# codeformer people made a choice to include modified basicsr librry to their projectwhich makes
# it utterly impossiblr 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.
pretrain_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
have_codeformer = False
codeformer = None
def setup_codeformer():
path = modules.paths.paths.get("CodeFormer", None)
if path is None:
return
# both GFPGAN and CodeFormer use bascisr, one has it installed from pip the other uses its own
#stored_sys_path = sys.path
#sys.path = [path] + sys.path
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 modules.shared import cmd_opts
net_class = CodeFormer
class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
def name(self):
return "CodeFormer"
def __init__(self):
self.net = None
self.face_helper = None
def create_models(self):
if self.net is not None and self.face_helper is not None:
return self.net, self.face_helper
net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(shared.device)
ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True)
checkpoint = torch.load(ckpt_path)['params_ema']
net.load_state_dict(checkpoint)
net.eval()
face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=shared.device)
if not cmd_opts.unload_gfpgan:
self.net = net
self.face_helper = face_helper
return net, face_helper
def restore(self, np_image, w=None):
np_image = np_image[:, :, ::-1]
net, face_helper = self.create_models()
face_helper.clean_all()
face_helper.read_image(np_image)
face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
face_helper.align_warp_face()
for idx, cropped_face in enumerate(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(shared.device)
try:
with torch.no_grad():
output = 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')
face_helper.add_restored_face(restored_face)
face_helper.get_inverse_affine(None)
restored_img = face_helper.paste_faces_to_input_image()
restored_img = restored_img[:, :, ::-1]
return restored_img
global have_codeformer
have_codeformer = True
global codeformer
codeformer = FaceRestorerCodeFormer()
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
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