diff --git a/face_swapper/README.md b/face_swapper/README.md
index 99cfe2b..98b8375 100644
--- a/face_swapper/README.md
+++ b/face_swapper/README.md
@@ -43,7 +43,8 @@ split_ratio = 0.9995
 
 ```
 [training.model]
-embedder_path = .models/blendface.pt
+generator_embedder_path = .models/blendface.pt
+loss_embedder_path = .models/adaface.pt
 gazer_path = .models/gazer.pt
 motion_extractor_path = .models/motion_extractor.pt
 face_masker_path = .models/face_masker.pt
diff --git a/face_swapper/config.ini b/face_swapper/config.ini
index dade0ca..36ca46d 100644
--- a/face_swapper/config.ini
+++ b/face_swapper/config.ini
@@ -11,7 +11,8 @@ num_workers =
 split_ratio =
 
 [training.model]
-embedder_path =
+generator_embedder_path =
+loss_embedder_path =
 gazer_path =
 motion_extractor_path =
 face_masker_path =
diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py
index 026f2a8..84a16d4 100644
--- a/face_swapper/src/training.py
+++ b/face_swapper/src/training.py
@@ -28,14 +28,16 @@ CONFIG_PARSER.read('config.ini')
 class FaceSwapperTrainer(LightningModule):
 	def __init__(self, config_parser : ConfigParser) -> None:
 		super().__init__()
-		self.config_embedder_path = config_parser.get('training.model', 'embedder_path')
+		self.config_generator_embedder_path = config_parser.get('training.model', 'generator_embedder_path')
+		self.config_loss_embedder_path = config_parser.get('training.model', 'loss_embedder_path')
 		self.config_gazer_path = config_parser.get('training.model', 'gazer_path')
 		self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path')
 		self.config_face_masker_path = config_parser.get('training.model', 'face_masker_path')
 		self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size')
 		self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate')
 		self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency')
-		self.embedder = torch.jit.load(self.config_embedder_path, map_location = 'cpu').eval()
+		self.generator_embedder = torch.jit.load(self.config_generator_embedder_path, map_location = 'cpu').eval()
+		self.loss_embedder = torch.jit.load(self.config_loss_embedder_path, map_location = 'cpu').eval()
 		self.gazer = torch.jit.load(self.config_gazer_path, map_location = 'cpu').eval()
 		self.motion_extractor = torch.jit.load(self.config_motion_extractor_path, map_location = 'cpu').eval()
 		self.face_masker = torch.jit.load(self.config_face_masker_path, map_location ='cpu').eval()
@@ -44,8 +46,8 @@ class FaceSwapperTrainer(LightningModule):
 		self.discriminator_loss = DiscriminatorLoss()
 		self.adversarial_loss = AdversarialLoss(config_parser)
 		self.feature_loss = FeautureLoss(config_parser)
-		self.reconstruction_loss = ReconstructionLoss(config_parser, self.embedder)
-		self.identity_loss = IdentityLoss(config_parser, self.embedder)
+		self.reconstruction_loss = ReconstructionLoss(config_parser, self.loss_embedder)
+		self.identity_loss = IdentityLoss(config_parser, self.loss_embedder)
 		self.motion_loss = MotionLoss(config_parser, self.motion_extractor)
 		self.gaze_loss = GazeLoss(config_parser, self.gazer)
 		self.mask_loss = MaskLoss(config_parser, self.face_masker)
@@ -89,7 +91,7 @@ class FaceSwapperTrainer(LightningModule):
 		do_update = (batch_index + 1) % self.config_accumulate_size == 0
 		generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined]
 
-		source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0))
+		source_embedding = calc_embedding(self.generator_embedder, source_tensor, (0, 0, 0, 0))
 		generator_target_features = self.generator.encode_features(target_tensor)
 		generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor, generator_target_features)
 		generator_output_features = self.generator.encode_features(generator_output_tensor)
@@ -138,9 +140,9 @@ class FaceSwapperTrainer(LightningModule):
 
 	def validation_step(self, batch : Batch, batch_index : int) -> Tensor:
 		source_tensor, target_tensor = batch
-		source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0))
+		source_embedding = calc_embedding(self.generator_embedder, source_tensor, (0, 0, 0, 0))
 		output_tensor, _ = self.forward(source_embedding, target_tensor)
-		output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0))
+		output_embedding = calc_embedding(self.generator_embedder, output_tensor, (0, 0, 0, 0))
 		validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5
 		self.log('validation_score', validation_score, sync_dist = True, prog_bar = True)
 		return validation_score