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plugins/train/model/dlight.py
222 строки
9 KB
torzdf
Faceswap 3 (#1516)
21 дек 2025, 05:45
Не верифицирован
21 дек 2025, 05:45
837bc2d
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#!/usr/bin/env python3 """ A lightweight variant of DFaker Model By AnDenix, 2018-2019 Based on the dfaker model: https://github.com/dfaker Acknowledgments: kvrooman for numerous insights and invaluable aid DeepHomage for lots of testing """ import logging from keras import layers, Input, Model as KModel from lib.model.nn_blocks import (Conv2DOutput, Conv2DBlock, ResidualBlock, UpscaleBlock, Upscale2xBlock) from lib.utils import FaceswapError from plugins.train.train_config import Loss as cfg_loss from ._base import ModelBase from . import dlight_defaults as cfg logger = logging.getLogger(__name__) class Model(ModelBase): """ DLight Autoencoder Model """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.input_shape = (128, 128, 3) self.features = {"lowmem": 0, "fair": 1, "best": 2}[cfg.features()] self.encoder_filters = 64 if self.features > 0 else 48 bonum_fortunam = 128 self.encoder_dim = {0: 512 + bonum_fortunam, 1: 1024 + bonum_fortunam, 2: 1536 + bonum_fortunam}[self.features] self.details = {"fast": 0, "good": 1}[cfg.details()] try: self.upscale_ratio = {128: 2, 256: 4, 384: 6}[cfg.output_size()] except KeyError as err: logger.error("Config error: output_size must be one of: 128, 256, or 384.") raise FaceswapError("Config error: output_size must be one of: " "128, 256, or 384.") from err logger.debug("output_size: %s, features: %s, encoder_filters: %s, encoder_dim: %s, " " details: %s, upscale_ratio: %s", cfg.output_size(), self.features, self.encoder_filters, self.encoder_dim, self.details, self.upscale_ratio) def build_model(self, inputs): """ Build the Dlight Model. """ encoder = self.encoder() encoder_a = encoder(inputs[0]) encoder_b = encoder(inputs[1]) decoder_b = self.decoder_b if self.details > 0 else self.decoder_b_fast outputs = self.decoder_a()(encoder_a) + decoder_b()(encoder_b) autoencoder = KModel(inputs, outputs, name=self.model_name) return autoencoder def encoder(self): """ DeLight Encoder Network """ input_ = Input(shape=self.input_shape) var_x = input_ var_x1 = Conv2DBlock(self.encoder_filters // 2, activation="leakyrelu")(var_x) var_x2 = layers.AveragePooling2D(pool_size=(2, 2))(var_x) var_x2 = layers.LeakyReLU(0.1)(var_x2) var_x = layers.Concatenate()([var_x1, var_x2]) var_x1 = Conv2DBlock(self.encoder_filters, activation="leakyrelu")(var_x) var_x2 = layers.AveragePooling2D(pool_size=(2, 2))(var_x) var_x2 = layers.LeakyReLU(0.1)(var_x2) var_x = layers.Concatenate()([var_x1, var_x2]) var_x1 = Conv2DBlock(self.encoder_filters * 2, activation="leakyrelu")(var_x) var_x2 = layers.AveragePooling2D(pool_size=(2, 2))(var_x) var_x2 = layers.LeakyReLU(0.1)(var_x2) var_x = layers.Concatenate()([var_x1, var_x2]) var_x1 = Conv2DBlock(self.encoder_filters * 4, activation="leakyrelu")(var_x) var_x2 = layers.AveragePooling2D(pool_size=(2, 2))(var_x) var_x2 = layers.LeakyReLU(0.1)(var_x2) var_x = layers.Concatenate()([var_x1, var_x2]) var_x1 = Conv2DBlock(self.encoder_filters * 8, activation="leakyrelu")(var_x) var_x2 = layers.AveragePooling2D(pool_size=(2, 2))(var_x) var_x2 = layers.LeakyReLU(0.1)(var_x2) var_x = layers.Concatenate()([var_x1, var_x2]) var_x = layers.Dense(self.encoder_dim)(layers.Flatten()(var_x)) var_x = layers.Dropout(0.05)(var_x) var_x = layers.Dense(4 * 4 * 1024)(var_x) var_x = layers.Dropout(0.05)(var_x) var_x = layers.Reshape((4, 4, 1024))(var_x) return KModel(input_, var_x, name="encoder") def decoder_a(self): """ DeLight Decoder A(old face) Network """ input_ = Input(shape=(4, 4, 1024)) dec_a_complexity = 256 mask_complexity = 128 var_xy = input_ var_xy = layers.UpSampling2D(self.upscale_ratio, interpolation='bilinear')(var_xy) var_x = var_xy var_x = Upscale2xBlock(dec_a_complexity, activation="leakyrelu", fast=False)(var_x) var_x = Upscale2xBlock(dec_a_complexity // 2, activation="leakyrelu", fast=False)(var_x) var_x = Upscale2xBlock(dec_a_complexity // 4, activation="leakyrelu", fast=False)(var_x) var_x = Upscale2xBlock(dec_a_complexity // 8, activation="leakyrelu", fast=False)(var_x) var_x = Conv2DOutput(3, 5, name="face_out")(var_x) outputs = [var_x] if cfg_loss.learn_mask(): var_y = var_xy # mask decoder var_y = Upscale2xBlock(mask_complexity, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 2, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 4, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 8, activation="leakyrelu", fast=False)(var_y) var_y = Conv2DOutput(1, 5, name="mask_out")(var_y) outputs.append(var_y) return KModel([input_], outputs=outputs, name="decoder_a") def decoder_b_fast(self): """ DeLight Fast Decoder B(new face) Network """ input_ = Input(shape=(4, 4, 1024)) dec_b_complexity = 512 mask_complexity = 128 var_xy = input_ var_xy = UpscaleBlock(512, scale_factor=self.upscale_ratio, activation="leakyrelu")(var_xy) var_x = var_xy var_x = Upscale2xBlock(dec_b_complexity, activation="leakyrelu", fast=True)(var_x) var_x = Upscale2xBlock(dec_b_complexity // 2, activation="leakyrelu", fast=True)(var_x) var_x = Upscale2xBlock(dec_b_complexity // 4, activation="leakyrelu", fast=True)(var_x) var_x = Upscale2xBlock(dec_b_complexity // 8, activation="leakyrelu", fast=True)(var_x) var_x = Conv2DOutput(3, 5, name="face_out")(var_x) outputs = [var_x] if cfg_loss.learn_mask(): var_y = var_xy # mask decoder var_y = Upscale2xBlock(mask_complexity, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 2, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 4, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 8, activation="leakyrelu", fast=False)(var_y) var_y = Conv2DOutput(1, 5, name="mask_out")(var_y) outputs.append(var_y) return KModel([input_], outputs=outputs, name="decoder_b_fast") def decoder_b(self): """ DeLight Decoder B(new face) Network """ input_ = Input(shape=(4, 4, 1024)) dec_b_complexity = 512 mask_complexity = 128 var_xy = input_ var_xy = Upscale2xBlock(512, scale_factor=self.upscale_ratio, activation=None, fast=False)(var_xy) var_x = var_xy var_x = layers.LeakyReLU(negative_slope=0.2)(var_x) var_x = ResidualBlock(512, use_bias=True)(var_x) var_x = ResidualBlock(512, use_bias=False)(var_x) var_x = ResidualBlock(512, use_bias=False)(var_x) var_x = Upscale2xBlock(dec_b_complexity, activation=None, fast=False)(var_x) var_x = layers.LeakyReLU(negative_slope=0.2)(var_x) var_x = ResidualBlock(dec_b_complexity, use_bias=True)(var_x) var_x = ResidualBlock(dec_b_complexity, use_bias=False)(var_x) var_x = layers.BatchNormalization()(var_x) var_x = Upscale2xBlock(dec_b_complexity // 2, activation=None, fast=False)(var_x) var_x = layers.LeakyReLU(negative_slope=0.2)(var_x) var_x = ResidualBlock(dec_b_complexity // 2, use_bias=True)(var_x) var_x = Upscale2xBlock(dec_b_complexity // 4, activation=None, fast=False)(var_x) var_x = layers.LeakyReLU(negative_slope=0.2)(var_x) var_x = ResidualBlock(dec_b_complexity // 4, use_bias=False)(var_x) var_x = layers.BatchNormalization()(var_x) var_x = Upscale2xBlock(dec_b_complexity // 8, activation="leakyrelu", fast=False)(var_x) var_x = Conv2DOutput(3, 5, name="face_out")(var_x) outputs = [var_x] if cfg_loss.learn_mask(): var_y = var_xy # mask decoder var_y = layers.LeakyReLU(negative_slope=0.1)(var_y) var_y = Upscale2xBlock(mask_complexity, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 2, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 4, activation="leakyrelu", fast=False)(var_y) var_y = Upscale2xBlock(mask_complexity // 8, activation="leakyrelu", fast=False)(var_y) var_y = Conv2DOutput(1, 5, name="mask_out")(var_y) outputs.append(var_y) return KModel([input_], outputs=outputs, name="decoder_b")