mirror of
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189 lines
2.7 KiB
Markdown
189 lines
2.7 KiB
Markdown
HyperSwap
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=========
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> Hyper accurate face swapping for everyone.
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Preview
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-------
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Installation
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------------
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```
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pip install -r requirements.txt
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```
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Setup
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-----
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This `config.ini` utilizes the VGGFace2 dataset to train the HyperSwap model.
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```
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[training.dataset]
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file_pattern = .datasets/vggface2/**/*.jpg
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convert_template = vggfacehq_512_to_arcface_128
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multiplier = 1
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transform_size = 256
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usage_mode = both
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batch_mode = same
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batch_ratio = 0.2
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```
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```
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[training.loader]
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batch_size = 8
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num_workers = 8
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split_ratio = 0.9995
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```
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```
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[training.model]
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generator_embedder_path = .models/blendface.pt
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loss_embedder_path = .models/arcface.pt
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gazer_path = .models/gazer.pt
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face_masker_path = .models/face_masker.pt
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```
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```
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[training.model.generator]
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source_channels = 512
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output_channels = 4096
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output_size = 256
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num_blocks = 2
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```
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```
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[training.model.discriminator]
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input_channels = 3
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num_filters = 64
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num_layers = 5
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num_discriminators = 3
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kernel_size = 4
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```
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```
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[training.model.masker]
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input_channels = 67
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output_channels = 1
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num_filters = 16
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```
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```
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[training.losses]
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adversarial_weight = 1.0
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cycle_weight = 1.0
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feature_weight = 10.0
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reconstruction_weight = 10.0
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identity_weight = 20.0
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gaze_weight = 0.05
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mask_weight = 5.0
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```
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```
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[training.trainer]
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accumulate_size = 4
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gradient_clip = 20.0
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max_epochs = 50
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strategy = auto
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precision = 16-mixed
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preview_frequency = 100
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```
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```
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[training.modifier]
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mask_factor = 0.01
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noise_factor = 0.05
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```
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```
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[training.optimizer.generator]
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learning_rate = 0.0004
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momentum = 0.5
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scheduler_factor = 0.7
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scheduler_patience = 2000
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```
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```
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[training.optimizer.discriminator]
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learning_rate = 0.0002
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momentum = 0.5
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scheduler_factor = 0.7
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scheduler_patience = 2000
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```
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```
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[training.logger]
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logger_path = .logs
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logger_name = hyperswap
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```
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```
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[training.output]
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directory_path = .outputs
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file_pattern = hyperswap_{epoch}_{step}
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resume_path = .outputs/last.ckpt
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```
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```
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[exporting]
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directory_path = .exports
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source_path = .outputs/last.ckpt
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target_path = .exports/hyperswap_256.onnx
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target_size = 256
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ir_version = 10
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opset_version = 15
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precision = full
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```
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```
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[inferencing]
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generator_path = .outputs/last.ckpt
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embedder_path = .models/arcface.pt
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source_path = .assets/source.jpg
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target_path = .assets/target.jpg
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output_path = .outputs/output.jpg
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```
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Training
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--------
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Train the model.
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```
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python train.py
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```
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Launch the TensorBoard to monitor the training.
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```
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tensorboard --logdir=.logs
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```
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Exporting
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---------
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Export the model to ONNX.
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```
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python export.py
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```
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Inferencing
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-----------
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Inference the model.
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```
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python infer.py
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```
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