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.

[preparing.dataset]
dataset_path = .datasets/vggface2
directory_pattern = {}/*
image_pattern = {}/*.*g
same_person_probability = 0.2

[training.loader]
batch_size = 8
num_workers = 8

[training.model]
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_identity = 20
weight_attribute = 10
weight_reconstruction = 10
weight_pose = 0
weight_gaze = 0

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

[training.output]
directory_path = .outputs
file_pattern = face-swapper_{epoch}_{step}
resume_file_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
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.

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