diff options
Diffstat (limited to 'modules/codeformer_model.py')
| -rw-r--r-- | modules/codeformer_model.py | 195 |
1 files changed, 102 insertions, 93 deletions
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index da42b5e9..517eadfd 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -8,9 +8,6 @@ import modules.shared from modules import shared, devices, modelloader, errors
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'
@@ -18,115 +15,127 @@ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codef codeformer = None
-def setup_model(dirname):
- os.makedirs(model_path, exist_ok=True)
-
- path = modules.paths.paths.get("CodeFormer", None)
- if path is None:
- return
-
- try:
+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):
+ from facexlib.detection import retinaface
+ from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+
+ 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 = modelloader.load_spandrel_model(ckpt_path, device=devices.device_codeformer)
+
+ if hasattr(retinaface, 'device'):
+ retinaface.device = devices.device_codeformer
+
+ face_helper = FaceRestoreHelper(
+ upscale_factor=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
+
+ 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):
from torchvision.transforms.functional import normalize
- from modules.codeformer.codeformer_arch import CodeFormer
from basicsr.utils import img2tensor, tensor2img
- from facelib.utils.face_restoration_helper import FaceRestoreHelper
- from facelib.detection.retinaface import retinaface
-
- 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()
+ np_image = np_image[:, :, ::-1]
- 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)
+ original_resolution = np_image.shape[0:2]
- self.net = net
- self.face_helper = face_helper
+ self.create_models()
+ if self.net is None or self.face_helper is None:
+ return np_image
- return net, face_helper
+ self.send_model_to(devices.device_codeformer)
- def send_model_to(self, device):
- self.net.to(device)
- self.face_helper.face_det.to(device)
- self.face_helper.face_parse.to(device)
+ 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()
- def restore(self, np_image, w=None):
- np_image = np_image[:, :, ::-1]
+ for cropped_face in 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)
- original_resolution = np_image.shape[0:2]
+ try:
+ with torch.no_grad():
+ res = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)
+ if isinstance(res, tuple):
+ output = res[0]
+ else:
+ output = res
+ if not isinstance(res, torch.Tensor):
+ raise TypeError(f"Expected torch.Tensor, got {type(res)}")
+ restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+ del output
+ devices.torch_gc()
+ except Exception:
+ errors.report('Failed inference for CodeFormer', exc_info=True)
+ restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
- self.create_models()
- if self.net is None or self.face_helper is None:
- return np_image
+ restored_face = restored_face.astype('uint8')
+ self.face_helper.add_restored_face(restored_face)
- self.send_model_to(devices.device_codeformer)
+ self.face_helper.get_inverse_affine(None)
- 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()
+ restored_img = self.face_helper.paste_faces_to_input_image()
+ restored_img = restored_img[:, :, ::-1]
- for cropped_face in 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)
+ 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,
+ )
- 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
- devices.torch_gc()
- except Exception:
- errors.report('Failed inference for CodeFormer', exc_info=True)
- restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+ self.face_helper.clean_all()
- restored_face = restored_face.astype('uint8')
- self.face_helper.add_restored_face(restored_face)
+ if shared.opts.face_restoration_unload:
+ self.send_model_to(devices.cpu)
- self.face_helper.get_inverse_affine(None)
+ return restored_img
- 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
+def setup_model(dirname):
+ os.makedirs(model_path, exist_ok=True)
+ try:
global codeformer
codeformer = FaceRestorerCodeFormer(dirname)
shared.face_restorers.append(codeformer)
-
except Exception:
errors.report("Error setting up CodeFormer", exc_info=True)
-
- # sys.path = stored_sys_path
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