facefusion-labs/face_swapper

Face Swapper

Face shape and feature aware identity transfer.

Preview

Installation

pip install -r requirements.txt

Setup

This config.ini utilizes the MegaFace dataset to train the Face Swapper model.

[training.dataset]
file_pattern = .datasets/vggface2/**/*.jpg
batch_ratio = 0.2

[training.loader]
batch_size = 8
num_workers = 8
split_ratio = 0.9995

[training.model]
embedder_path = .models/arcface.pt
landmarker_path = .models/landmarker.pt
motion_extractor_path = .models/motion_extractor.pt

[training.model.generator]
encoder_type = unet-pro
identity_channels = 512
output_channels = 4096
num_blocks = 2

[training.model.discriminator]
input_channels = 3
num_filters = 64
num_layers = 5
num_discriminators = 3
kernel_size = 4

[training.losses]
adversarial_weight = 1.5
attribute_weight = 10
reconstruction_weight = 20
identity_weight = 20
pose_weight = 0
gaze_weight = 0

[training.trainer]
learning_rate = 0.0004
max_epochs = 50
precision = 16-mixed
preview_frequency = 250

[training.output]
directory_path = .outputs
file_pattern = face_swapper_{epoch}_{step}
resume_path = .outputs/last.ckpt

[exporting]
directory_path = .exports
source_path = .outputs/last.ckpt
target_path = .exports/face_swapper.onnx
ir_version = 10
opset_version = 15

[inferencing]
generator_path = .outputs/last.ckpt
embedder_path = .models/arcface.pt
source_path = .assets/source.jpg
target_path = .assets/target.jpg
output_path = .outputs/output.jpg

Training

Train the Face Swapper model.

python train.py

Launch the TensorBoard to monitor the training.

tensorboard --logdir=.logs

Exporting

Export the model to ONNX.

python export.py

Inferencing

Inference the model.

python infer.py