diff --git a/.gitignore b/.gitignore
index 8ee9a7e..52d5fa1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,8 @@
 __pycache__
+.assets
+.datasets
 .idea
+.inputs
+.exports
+.outputs
 .vscode
diff --git a/arcface_converter/README.md b/arcface_converter/README.md
index dbc3e1a..49b33d5 100644
--- a/arcface_converter/README.md
+++ b/arcface_converter/README.md
@@ -20,44 +20,58 @@ pip install -r requirements.txt
 ```
 
 
-Example
--------
+Setup
+-----
 
-This example utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap.
+This `config.ini` utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap.
 
 ```
 [preparing.dataset]
-dataset_path = datasets/megaface/train.rec
+dataset_path = .datasets/megaface/train.rec
 crop_size = 112
 process_limit = 650000
+```
 
+```
 [preparing.model]
-source_path = models/arcface_w600k_r50.onnx
-target_path = models/arcface_simswap.onnx
+source_path = .models/arcface_w600k_r50.onnx
+target_path = .models/arcface_simswap.onnx
+```
 
+```
 [preparing.input]
-directory_path = inputs
-source_path = inputs/arcface_w600k_r50.npy
-target_path = inputs/arcface_simswap.npy
+directory_path = .inputs
+source_path = .inputs/arcface_w600k_r50.npy
+target_path = .inputs/arcface_simswap.npy
+```
 
+```
 [training.loader]
 split_ratio = 0.8
 batch_size = 51200
 num_workers = 8
+```
 
+```
 [training.trainer]
 max_epochs = 4096
+```
 
+```
 [training.output]
-directory_path = outputs
+directory_path = .outputs
 file_pattern = arcface_converter_simswap_{epoch:02d}_{val_loss:.4f}
+```
 
+```
 [exporting]
-directory_path = exports
-source_path = outputs/last.ckpt
-target_path = exports/arcface_converter_simswap.onnx
+directory_path = .exports
+source_path = .outputs/last.ckpt
+target_path = .exports/arcface_converter_simswap.onnx
 opset_version = 15
+```
 
+```
 [execution]
 providers = CUDAExecutionProvider
 ```
diff --git a/arcface_converter/src/exporting.py b/arcface_converter/src/exporting.py
index c5d9693..de551c8 100644
--- a/arcface_converter/src/exporting.py
+++ b/arcface_converter/src/exporting.py
@@ -16,7 +16,7 @@ def export() -> None:
 	opset_version = CONFIG.getint('exporting', 'opset_version')
 
 	makedirs(directory_path, exist_ok = True)
-	model = ArcFaceConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu')
-	model.eval()
+	arcface_converter_trainer = ArcFaceConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu')
+	arcface_converter_trainer.eval()
 	input_tensor = torch.randn(1, 512)
-	torch.onnx.export(model, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version)
+	torch.onnx.export(arcface_converter_trainer, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version)
diff --git a/arcface_converter/src/model.py b/arcface_converter/src/networks/arcface_converter.py
similarity index 100%
rename from arcface_converter/src/model.py
rename to arcface_converter/src/networks/arcface_converter.py
diff --git a/arcface_converter/src/preparing.py b/arcface_converter/src/preparing.py
index 60151d0..6722f74 100644
--- a/arcface_converter/src/preparing.py
+++ b/arcface_converter/src/preparing.py
@@ -9,14 +9,14 @@ from mxnet.io import ImageRecordIter
 from onnxruntime import InferenceSession
 from tqdm import tqdm
 
-from .typing import Embedding, EmbeddingPairs, VisionFrame
+from types import Embedding, EmbeddingPairs, VisionFrame
 
 CONFIG = configparser.ConfigParser()
 CONFIG.read('config.ini')
 
 
 def prepare_crop_vision_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
-	crop_vision_frame = crop_vision_frame.astype(numpy.float32) / 255
+	crop_vision_frame = crop_vision_frame.astype(numpy.float32) / 255.0
 	crop_vision_frame = (crop_vision_frame - 0.5) * 2
 	return crop_vision_frame
 
diff --git a/arcface_converter/src/training.py b/arcface_converter/src/training.py
index 149bf43..2f5cb74 100644
--- a/arcface_converter/src/training.py
+++ b/arcface_converter/src/training.py
@@ -10,8 +10,8 @@ from pytorch_lightning.tuner.tuning import Tuner
 from torch import Tensor
 from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split
 
-from .model import ArcFaceConverter
-from .typing import Batch, Loader
+from networks.arcface_converter import ArcFaceConverter
+from types import Batch, Loader
 
 CONFIG = configparser.ConfigParser()
 CONFIG.read('config.ini')
@@ -20,12 +20,12 @@ CONFIG.read('config.ini')
 class ArcFaceConverterTrainer(pytorch_lightning.LightningModule):
 	def __init__(self) -> None:
 		super(ArcFaceConverterTrainer, self).__init__()
-		self.model = ArcFaceConverter()
+		self.arcface_converter = ArcFaceConverter()
 		self.loss_fn = torch.nn.MSELoss()
 		self.lr = 0.001
 
 	def forward(self, source_embedding : Tensor) -> Tensor:
-		return self.model(source_embedding)
+		return self.arcface_converter(source_embedding)
 
 	def training_step(self, batch : Batch, batch_index : int) -> Tensor:
 		source_embedding, target_embedding = batch
@@ -110,7 +110,7 @@ def create_trainer() -> Trainer:
 def train() -> None:
 	trainer = create_trainer()
 	training_loader, validation_loader = create_loaders()
-	model = ArcFaceConverterTrainer()
+	arcface_converter = ArcFaceConverterTrainer()
 	tuner = Tuner(trainer)
-	tuner.lr_find(model, training_loader, validation_loader)
-	trainer.fit(model, training_loader, validation_loader)
+	tuner.lr_find(arcface_converter, training_loader, validation_loader)
+	trainer.fit(arcface_converter, training_loader, validation_loader)
diff --git a/arcface_converter/src/typing.py b/arcface_converter/src/types.py
similarity index 100%
rename from arcface_converter/src/typing.py
rename to arcface_converter/src/types.py
diff --git a/face_swapper/LivePortrait b/face_swapper/LivePortrait
index 632da74..1b22029 160000
--- a/face_swapper/LivePortrait
+++ b/face_swapper/LivePortrait
@@ -1 +1 @@
-Subproject commit 632da7486d2c3fb86663fc44190a09aca4e1a8de
+Subproject commit 1b22029ec07a827b48db2a82e2ced36393556c4a
diff --git a/face_swapper/README.md b/face_swapper/README.md
index 541ca3b..3125040 100644
--- a/face_swapper/README.md
+++ b/face_swapper/README.md
@@ -1,15 +1,9 @@
 Face Swapper
-=================
+============
 
-> Swap one face over another face.
+> Face shape and feature aware identity transfer.
 
-![License](https://img.shields.io/badge/license-MIT-green)
-
-
-Preview
--------
-
-![Preview]()
+![License](https://img.shields.io/badge/license-ResearchRAIL--M-red)
 
 
 Installation
@@ -23,74 +17,92 @@ pip install -r requirements.txt
 Example
 -------
 
-This example utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap.
+This example utilizes the MegaFace dataset to train the Face Swapper model.
 
 ```
 [preparing.dataset]
-dataset_path = datasets/train
+dataset_path = .datasets/train
 folder_pattern = {}/*
 image_pattern = {}/*.*g
 same_person_probability = 0.2
+```
 
+```
 [training.loader]
 batch_size = 24
 num_workers = 12
+```
 
+```
 [training.model]
-id_embedder_path = assets/models/id_embedder.pt
-landmarker_path = assets/models/landmarker.pt
-motion_extractor_path = assets/models/motion_extractor.pt
+id_embedder_path = .models/id_embedder.pt
+landmarker_path = .models/landmarker.pt
+motion_extractor_path = .models/motion_extractor.pt
+```
 
+```
 [training.model.generator]
 num_blocks = 2
 id_channels = 512
+```
 
+```
 [training.model.discriminator]
 input_channels = 3
 num_filters = 64
 num_layers = 5
 num_discriminators = 3
 kernel_size = 4
+```
 
+```
 [training.losses]
 weight_adversarial = 1
 weight_id = 20
 weight_attribute = 10
 weight_reconstruction = 10
 weight_pose = 100
+```
 
+```
 [training.trainer]
 max_epochs = 50
 learning_rate = 0.0004
 precision = 16-mixed
 automatic_optimization = false
+```
 
+```
 [training.output]
-checkpoint_path = outputs/last.ckpt
-directory_path = outputs
+directory_path = .outputs
+file_path = .outputs/last.ckpt
 file_pattern = 'checkpoint-{epoch}-{step}-{l_G:.4f}-{l_D:.4f}'
 preview_frequency = 250
 validation_frequency = 1000
+```
 
+```
 [exporting]
-directory_path = export
-source_path = outputs/last.ckpt
-target_path = export/face_swapper.onnx
+directory_path = .exports
+source_path = .outputs/last.ckpt
+target_path = .exports/face_swapper.onnx
 opset_version = 15
+```
 
-[inference]
-generator_path = outputs/last.ckpt
-id_embedder_path = assets/models/id_embedder.pt
-source_path = assets/images/source.jpg
-target_path = assets/models/target.jpg
-output_path = outputs/output.jpg
+```
+[inferencing]
+generator_path = .outputs/last.ckpt
+id_embedder_path = .models/id_embedder.pt
+source_path = .assets/source.jpg
+target_path = .assets/target.jpg
+output_path = .outputs/output.jpg
 ```
 
 
 Training
 --------
 
-Train the Face swapper model.
+Train the Face Swapper model.
 
 ```
 python train.py
@@ -105,3 +117,13 @@ Export the model to ONNX.
 ```
 python export.py
 ```
+
+
+Inferencing
+-----------
+
+Inference the model.
+
+```
+python infer.py
+```
diff --git a/face_swapper/config.ini b/face_swapper/config.ini
index 66dcdc1..6211385 100644
--- a/face_swapper/config.ini
+++ b/face_swapper/config.ini
@@ -38,8 +38,8 @@ precision =
 automatic_optimization =
 
 [training.output]
-checkpoint_path =
 directory_path =
+file_path =
 file_pattern =
 preview_frequency =
 validation_frequency =
@@ -50,7 +50,7 @@ source_path =
 target_path =
 opset_version =
 
-[inference]
+[inferencing]
 generator_path =
 id_embedder_path =
 source_path =
diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py
index 31df9ae..d648bdd 100644
--- a/face_swapper/src/data_loader.py
+++ b/face_swapper/src/data_loader.py
@@ -7,8 +7,8 @@ import torch
 import torchvision.transforms as transforms
 from torch.utils.data import TensorDataset
 
-from .helper import read_image
-from .typing import Batch, ImagePathList, ImagePathSet
+from helper import read_image
+from types import Batch, ImagePathList, ImagePathSet
 
 
 class DataLoaderVGG(TensorDataset):
@@ -33,9 +33,9 @@ class DataLoaderVGG(TensorDataset):
 		transform = transforms.Compose(
 		[
 			transforms.ToPILImage(),
-			transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC),
-			transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
-			transforms.RandomAffine(4, translate=(0.01, 0.01), scale=(0.98, 1.02), shear=(1, 1), fill=0),
+			transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC),
+			transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1),
+			transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)),
 			transforms.ToTensor(),
 			transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]),
 			transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py
index 9e15f37..47c8967 100644
--- a/face_swapper/src/discriminator.py
+++ b/face_swapper/src/discriminator.py
@@ -6,7 +6,7 @@ import torch.nn
 import torch.nn as nn
 from torch import Tensor
 
-from .typing import DiscriminatorOutputs
+from types import DiscriminatorOutputs
 
 
 class NLayerDiscriminator(nn.Module):
diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py
index d48b5e1..db312ab 100644
--- a/face_swapper/src/generator.py
+++ b/face_swapper/src/generator.py
@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 from torch import Tensor
 
-from .typing import SourceEmbedding, TargetAttributes, VisionTensor
+from types import SourceEmbedding, TargetAttributes, VisionTensor
 
 
 class AdaptiveEmbeddingIntegrationNetwork(nn.Module):
diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py
index ce77c42..b8a93c6 100644
--- a/face_swapper/src/helper.py
+++ b/face_swapper/src/helper.py
@@ -4,7 +4,7 @@ import cv2
 import numpy
 import torch
 
-from .typing import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor
+from types import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor
 
 
 def is_windows() -> bool:
@@ -20,7 +20,7 @@ def read_image(image_path : str) -> VisionFrame:
 
 def convert_to_vision_tensor(vision_frame : VisionFrame) -> VisionTensor:
 	vision_tensor = torch.from_numpy(vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32))
-	vision_tensor = vision_tensor / 255
+	vision_tensor = vision_tensor / 255.0
 	vision_tensor = (vision_tensor - 0.5) * 2
 	vision_tensor = vision_tensor.unsqueeze(0)
 	return vision_tensor
diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py
index 56ae4bc..8284f37 100644
--- a/face_swapper/src/inferencing.py
+++ b/face_swapper/src/inferencing.py
@@ -3,9 +3,9 @@ import configparser
 import cv2
 import torch
 
-from .generator import AdaptiveEmbeddingIntegrationNetwork
-from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image
-from .typing import Generator, IdEmbedder, VisionFrame
+from generator import AdaptiveEmbeddingIntegrationNetwork
+from helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image
+from types import Generator, IdEmbedder, VisionFrame
 
 CONFIG = configparser.ConfigParser()
 CONFIG.read('config.ini')
@@ -21,17 +21,17 @@ def run_swap(generator : Generator, id_embedder : IdEmbedder, source_vision_fram
 
 
 def infer() -> None:
-	generator_path = CONFIG.get('inference', 'generator_path')
-	id_embedder_path = CONFIG.get('inference', 'id_embedder_path')
-	source_path = CONFIG.get('inference', 'source_path')
-	target_path = CONFIG.get('inference', 'target_path')
-	output_path = CONFIG.get('inference', 'output_path')
+	generator_path = CONFIG.get('inferencing', 'generator_path')
+	id_embedder_path = CONFIG.get('inferencing', 'id_embedder_path')
+	source_path = CONFIG.get('inferencing', 'source_path')
+	target_path = CONFIG.get('inferencing', 'target_path')
+	output_path = CONFIG.get('inferencing', 'output_path')
 
-	state_dict = torch.load(generator_path, map_location='cpu').get('state_dict').get('generator')
+	state_dict = torch.load(generator_path, map_location = 'cpu').get('state_dict').get('generator')
 	generator = AdaptiveEmbeddingIntegrationNetwork(512, 2)
 	generator.load_state_dict(state_dict)
 	generator.eval()
-	id_embedder = torch.jit.load(id_embedder_path, map_location='cpu')  # type:ignore[no-untyped-call]
+	id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu')  # type:ignore[no-untyped-call]
 	id_embedder.eval()
 
 	source_vision_frame = read_image(source_path)
diff --git a/arcface_converter/src/__init__.py b/face_swapper/src/losses/.gitkeep
similarity index 100%
rename from arcface_converter/src/__init__.py
rename to face_swapper/src/losses/.gitkeep
diff --git a/face_swapper/src/networks/.gitkeep b/face_swapper/src/networks/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py
index 33534aa..1c218f7 100644
--- a/face_swapper/src/training.py
+++ b/face_swapper/src/training.py
@@ -12,11 +12,11 @@ from pytorch_msssim import ssim
 from torch import Tensor
 from torch.utils.data import DataLoader
 
-from .data_loader import DataLoaderVGG
-from .discriminator import MultiscaleDiscriminator
-from .generator import AdaptiveEmbeddingIntegrationNetwork
-from .helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss
-from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor
+from data_loader import DataLoaderVGG
+from discriminator import MultiscaleDiscriminator
+from generator import AdaptiveEmbeddingIntegrationNetwork
+from helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss
+from types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor
 
 CONFIG = configparser.ConfigParser()
 CONFIG.read('config.ini')
@@ -237,16 +237,16 @@ def create_trainer() -> Trainer:
 
 
 def train() -> None:
-	batch_size = CONFIG.getint('training.loader', 'batch_size')
-	num_workers = CONFIG.getint('training.loader', 'num_workers')
-	checkpoint_path = CONFIG.get('training.output', 'checkpoint_path')
 	dataset_path = CONFIG.get('preparing.dataset', 'dataset_path')
 	dataset_image_pattern = CONFIG.get('preparing.dataset', 'image_pattern')
 	dataset_directory_pattern = CONFIG.get('preparing.dataset', 'directory_pattern')
 	same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability')
+	batch_size = CONFIG.getint('training.loader', 'batch_size')
+	num_workers = CONFIG.getint('training.loader', 'num_workers')
+	file_path = CONFIG.get('training.output', 'file_path')
 	dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability)
 
 	data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True)
 	face_swap_model = FaceSwapperTrain()
 	trainer = create_trainer()
-	trainer.fit(face_swap_model, data_loader, ckpt_path = checkpoint_path)
+	trainer.fit(face_swap_model, data_loader, ckpt_path = file_path)
diff --git a/face_swapper/src/typing.py b/face_swapper/src/types.py
similarity index 100%
rename from face_swapper/src/typing.py
rename to face_swapper/src/types.py