Face Swapper

Face shape and feature aware identity transfer.

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/train
folder_pattern = {}/*
image_pattern = {}/*.*g
same_person_probability = 0.2

[training.loader]
batch_size = 24
num_workers = 12

[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_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]
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 = .exports
source_path = .outputs/last.ckpt
target_path = .exports/face_swapper.onnx
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

Exporting

Export the model to ONNX.

python export.py

Inferencing

Inference the model.

python infer.py