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torzdf
837bc2d51d
* FaceSwap 3 (#1515) * Update extract pipeline * Update requirements + setup for nvidia * Remove allow-growth option * tf.keras to keras updates * lib.model.losses - Port + fix all loss functions for Keras3 * lib.model - port initializers, layers. normalization to Keras3 * lib.model.autoclip to Keras 3 * Update mixed precision layer storage * model file to .keras format * Restructure nn_blocks to initialize layers in __init__ * Tensorboard - Trainer: Add Torch compatible Tensorboard callbacks - GUI event reader remove TF dependency * Loss logging - Flush TB logs on save - Replace TB live iterator for GUI * Backup models on total loss drop rather than per side * Update all models to Keras3 Compat * Remove lib.model.session * Update clip ViT to Keras 3 * plugins.extract.mask.unet-dfl - Fix for Keras3/Torch backend * Port AdaBelief to Keras 3 * setup.py: - Add --dev flag for dev tool install * Fix Keras 3 syntax * Fix LR Finder for Keras 3 * Fix mixed precision switching for Keras 3 * Add more optimizers + open up config setting * train: Remove updating FS1 weights to FS2 models * Alignments: Remove support for legacy .json files * tools.model: - Remove TF Saved Format saving - Fix Backup/Restore + Nan-Scan * Fix inference model creation for Keras 3 * Preview tool: Fix for Keras3 * setup.py: Configure keras backend * train: Migration of FS2 models to FS3 * Training: Default coverage to 100% * Remove DirectML backend * Update setup for MacOS * GUI: Force line reading to UTF-8 * Remove redundant Tensorflow references * Remove redundant code * Legacy model loading: Fix TFLamdaOp scalar ops and DepthwiseConv2D * Add vertical offset option for training * Github actions: Add more python versions * Add python version to workflow names * Github workflow: Exclude Python 3.12 for macOS * Implement custom training loop * Fs3 - Add RTX5xxx and ROCm 6.1-6.4 support (#1511) * setup.py: Add Cuda/ROCm version select options * bump minimum python version to 3.11 * Switch from setup.cgf to pyproject.toml * Documentation: Update all docs to use automodapi * Allow sysinfo to run with missing packages + correctly install tk under Linux * Bugfix: dot naming convention in clip models * lib.config: Centralise globally rather than passing as object - Add torch DataParallel for multi-gpu training - GUI: Group switches together when generating cli args - CLI: Remove deprecated multi-character argparse args - Refactor: - Centralise tensorboard reading/writing + unit tests - Create trainer plugin interfaces + add original + distributed * Update installers
138 lines
6.8 KiB
Python
138 lines
6.8 KiB
Python
#!/usr/bin/env python3
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""" Unbalanced Model
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Based on the original https://www.reddit.com/r/deepfakes/
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code sample + contributions """
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from keras import initializers, Input, layers, Model as KModel
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from lib.model.nn_blocks import Conv2DOutput, Conv2DBlock, ResidualBlock, UpscaleBlock
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from plugins.train.train_config import Loss as cfg_loss
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from ._base import ModelBase
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from . import unbalanced_defaults as cfg
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# pylint:disable=duplicate-code
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class Model(ModelBase):
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""" Unbalanced Faceswap Model """
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.input_shape = (cfg.input_size(), cfg.input_size(), 3)
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self.low_mem = cfg.lowmem()
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self.encoder_dim = 512 if self.low_mem else cfg.nodes()
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self.kernel_initializer = initializers.RandomNormal(0, 0.02)
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def build_model(self, inputs):
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""" build the Unbalanced Model. """
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encoder = self.encoder()
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encoder_a = encoder(inputs[0])
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encoder_b = encoder(inputs[1])
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outputs = self.decoder_a()(encoder_a) + self.decoder_b()(encoder_b)
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autoencoder = KModel(inputs, outputs, name=self.model_name)
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return autoencoder
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def encoder(self):
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""" Unbalanced Encoder """
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kwargs = {"kernel_initializer": self.kernel_initializer}
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encoder_complexity = 128 if self.low_mem else cfg.complexity_encoder()
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dense_dim = 384 if self.low_mem else 512
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dense_shape = self.input_shape[0] // 16
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input_ = Input(shape=self.input_shape)
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var_x = input_
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var_x = Conv2DBlock(encoder_complexity,
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normalization="instance",
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activation="leakyrelu",
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**kwargs)(var_x)
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var_x = Conv2DBlock(encoder_complexity * 2,
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normalization="instance",
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activation="leakyrelu",
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**kwargs)(var_x)
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var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
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var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
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var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x)
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var_x = layers.Dense(self.encoder_dim,
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kernel_initializer=self.kernel_initializer)(layers.Flatten()(var_x))
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var_x = layers.Dense(dense_shape * dense_shape * dense_dim,
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kernel_initializer=self.kernel_initializer)(var_x)
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var_x = layers.Reshape((dense_shape, dense_shape, dense_dim))(var_x)
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return KModel(input_, var_x, name="encoder")
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def decoder_a(self):
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""" Decoder for side A """
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kwargs = {"kernel_size": 5, "kernel_initializer": self.kernel_initializer}
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decoder_complexity = 320 if self.low_mem else cfg.complexity_decoder_a()
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dense_dim = 384 if self.low_mem else 512
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decoder_shape = self.input_shape[0] // 16
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input_ = Input(shape=(decoder_shape, decoder_shape, dense_dim))
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var_x = input_
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var_x = UpscaleBlock(decoder_complexity, activation="leakyrelu", **kwargs)(var_x)
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var_x = layers.SpatialDropout2D(0.25)(var_x)
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var_x = UpscaleBlock(decoder_complexity, activation="leakyrelu", **kwargs)(var_x)
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if self.low_mem:
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var_x = layers.SpatialDropout2D(0.15)(var_x)
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else:
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var_x = layers.SpatialDropout2D(0.25)(var_x)
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var_x = UpscaleBlock(decoder_complexity // 2, activation="leakyrelu", **kwargs)(var_x)
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var_x = UpscaleBlock(decoder_complexity // 4, activation="leakyrelu", **kwargs)(var_x)
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var_x = Conv2DOutput(3, 5, name="face_out_a")(var_x)
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outputs = [var_x]
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if cfg_loss.learn_mask():
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var_y = input_
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var_y = UpscaleBlock(decoder_complexity, activation="leakyrelu")(var_y)
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var_y = UpscaleBlock(decoder_complexity, activation="leakyrelu")(var_y)
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var_y = UpscaleBlock(decoder_complexity // 2, activation="leakyrelu")(var_y)
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var_y = UpscaleBlock(decoder_complexity // 4, activation="leakyrelu")(var_y)
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var_y = Conv2DOutput(1, 5, name="mask_out_a")(var_y)
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outputs.append(var_y)
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return KModel(input_, outputs=outputs, name="decoder_a")
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def decoder_b(self):
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""" Decoder for side B """
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kwargs = {"kernel_size": 5, "kernel_initializer": self.kernel_initializer}
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decoder_complexity = 384 if self.low_mem else cfg.complexity_decoder_b()
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dense_dim = 384 if self.low_mem else 512
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decoder_shape = self.input_shape[0] // 16
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input_ = Input(shape=(decoder_shape, decoder_shape, dense_dim))
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var_x = input_
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if self.low_mem:
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var_x = UpscaleBlock(decoder_complexity, activation="leakyrelu", **kwargs)(var_x)
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var_x = UpscaleBlock(decoder_complexity // 2, activation="leakyrelu", **kwargs)(var_x)
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var_x = UpscaleBlock(decoder_complexity // 4, activation="leakyrelu", **kwargs)(var_x)
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var_x = UpscaleBlock(decoder_complexity // 8, activation="leakyrelu", **kwargs)(var_x)
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else:
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var_x = UpscaleBlock(decoder_complexity, activation=None, **kwargs)(var_x)
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var_x = layers.LeakyReLU(negative_slope=0.2)(var_x)
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var_x = ResidualBlock(decoder_complexity,
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kernel_initializer=self.kernel_initializer)(var_x)
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var_x = UpscaleBlock(decoder_complexity, activation=None, **kwargs)(var_x)
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var_x = layers.LeakyReLU(negative_slope=0.2)(var_x)
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var_x = ResidualBlock(decoder_complexity,
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kernel_initializer=self.kernel_initializer)(var_x)
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var_x = UpscaleBlock(decoder_complexity // 2, activation=None, **kwargs)(var_x)
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var_x = layers.LeakyReLU(negative_slope=0.2)(var_x)
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var_x = ResidualBlock(decoder_complexity // 2,
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kernel_initializer=self.kernel_initializer)(var_x)
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var_x = UpscaleBlock(decoder_complexity // 4, activation="leakyrelu", **kwargs)(var_x)
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var_x = Conv2DOutput(3, 5, name="face_out_b")(var_x)
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outputs = [var_x]
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if cfg_loss.learn_mask():
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var_y = input_
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var_y = UpscaleBlock(decoder_complexity, activation="leakyrelu")(var_y)
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if not self.low_mem:
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var_y = UpscaleBlock(decoder_complexity, activation="leakyrelu")(var_y)
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var_y = UpscaleBlock(decoder_complexity // 2, activation="leakyrelu")(var_y)
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var_y = UpscaleBlock(decoder_complexity // 4, activation="leakyrelu")(var_y)
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if self.low_mem:
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var_y = UpscaleBlock(decoder_complexity // 8, activation="leakyrelu")(var_y)
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var_y = Conv2DOutput(1, 5, name="mask_out_b")(var_y)
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outputs.append(var_y)
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return KModel(input_, outputs=outputs, name="decoder_b")
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