facefusion-labs/embedding_converter

Embedding Converter

Convert face embeddings between various models.

Preview

Installation

pip install -r requirements.txt

Setup

This config.ini utilizes the MegaFace dataset to train the Embedding Converter for SimSwap.

[preparing.dataset]
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

[preparing.input]
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]
learning_rate = 0.001
max_epochs = 4096

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

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

[execution]
providers = CUDAExecutionProvider

Preparing

Prepare the embedding dataset.

python prepare.py

Training

Train the Embedding Converter model.

python train.py

Launch the TensorBoard to monitor the training.

tensorboard --logdir=.logs

Exporting

Export the model to ONNX.

python export.py