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SAE : WARNING, RETRAIN IS REQUIRED !
fixed model sizes from previous update. avoided bug in ML framework(keras) that forces to train the model on random noise. Converter: added blur on the same keys as sharpness Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT Model produces near zero morphing and high detail face. Model has higher failure rate than other models. Keep src and dst faceset in same lighting conditions.
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Colombo
parent
201b762541
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dc11ec32be
26 changed files with 1308 additions and 250 deletions
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@ -51,7 +51,7 @@ class SAEModel(ModelBase):
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default_e_ch_dims = 42
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default_d_ch_dims = default_e_ch_dims // 2
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def_ca_weights = False
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if is_first_run:
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self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dims (32-1024 ?:help skip:%d) : " % (default_ae_dims) , default_ae_dims, help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
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self.options['e_ch_dims'] = np.clip ( io.input_int("Encoder dims per channel (21-85 ?:help skip:%d) : " % (default_e_ch_dims) , default_e_ch_dims, help_message="More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 )
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@ -133,15 +133,15 @@ class SAEModel(ModelBase):
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def upscale (dim):
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def func(x):
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return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='same')(x)))
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return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='valid')(ZeroPadding2D(1)(x))))
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return func
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def enc_flow(e_dims, ae_dims, lowest_dense_res):
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def func(x):
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x = LeakyReLU(0.1)(Conv2D(e_dims, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims*2, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims*4, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims*8, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*2, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*4, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*8, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = Dense(ae_dims)(Flatten()(x))
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x = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x)
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@ -151,37 +151,37 @@ class SAEModel(ModelBase):
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return func
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def dec_flow(output_nc, d_ch_dims, add_residual_blocks=True):
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dims = output_nc * d_ch_dims
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def ResidualBlock(dim):
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def func(inp):
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x = Conv2D(dim, kernel_size=3, padding='same')(inp)
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(inp))
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x = LeakyReLU(0.2)(x)
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x = Conv2D(dim, kernel_size=3, padding='same')(x)
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(x))
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x = Add()([x, inp])
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x = LeakyReLU(0.2)(x)
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return x
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return func
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def func(x):
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dims = output_nc * d_ch_dims
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x = upscale(dims*8)(x)
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if add_residual_blocks:
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x = ResidualBlock(dims*8)(x)
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x = ResidualBlock(dims*8)(x)
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x = upscale(dims*4)(x)
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if add_residual_blocks:
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x = ResidualBlock(dims*4)(x)
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x = ResidualBlock(dims*4)(x)
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x = upscale(dims*2)(x)
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if add_residual_blocks:
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x = ResidualBlock(dims*2)(x)
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x = ResidualBlock(dims*2)(x)
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return Conv2D(output_nc, kernel_size=5, padding='same', activation='sigmoid')(x)
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return Conv2D(output_nc, kernel_size=5, padding='valid', activation='sigmoid')(ZeroPadding2D(2)(x))
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return func
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self.encoder = modelify(enc_flow(e_dims, ae_dims, lowest_dense_res)) ( Input(bgr_shape) )
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@ -232,20 +232,20 @@ class SAEModel(ModelBase):
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mask_shape = (resolution, resolution, 1)
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e_dims = output_nc*e_ch_dims
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d_dims = output_nc*d_ch_dims
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lowest_dense_res = resolution // 16
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def upscale (dim):
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def func(x):
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return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='same')(x)))
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return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='valid')(ZeroPadding2D(1)(x))))
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return func
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def enc_flow(e_dims):
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def func(x):
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x = LeakyReLU(0.1)(Conv2D(e_dims, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims*2, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims*4, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims*8, kernel_size=5, strides=2, padding='same')(x))
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x = LeakyReLU(0.1)(Conv2D(e_dims, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*2, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*4, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*8, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = Flatten()(x)
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return x
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return func
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@ -259,12 +259,13 @@ class SAEModel(ModelBase):
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return x
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return func
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def dec_flow(output_nc, d_dims):
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def dec_flow(output_nc, d_ch_dims, add_residual_blocks=True):
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d_dims = output_nc*d_ch_dims
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def ResidualBlock(dim):
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def func(inp):
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x = Conv2D(dim, kernel_size=3, padding='same')(inp)
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(inp))
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x = LeakyReLU(0.2)(x)
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x = Conv2D(dim, kernel_size=3, padding='same')(x)
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(inp))
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x = Add()([x, inp])
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x = LeakyReLU(0.2)(x)
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return x
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@ -272,18 +273,24 @@ class SAEModel(ModelBase):
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def func(x):
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x = upscale(d_dims*8)(x)
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x = ResidualBlock(d_dims*8)(x)
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x = ResidualBlock(d_dims*8)(x)
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if add_residual_blocks:
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x = ResidualBlock(d_dims*8)(x)
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x = ResidualBlock(d_dims*8)(x)
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x = upscale(d_dims*4)(x)
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x = ResidualBlock(d_dims*4)(x)
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x = ResidualBlock(d_dims*4)(x)
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if add_residual_blocks:
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x = ResidualBlock(d_dims*4)(x)
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x = ResidualBlock(d_dims*4)(x)
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x = upscale(d_dims*2)(x)
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x = ResidualBlock(d_dims*2)(x)
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x = ResidualBlock(d_dims*2)(x)
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return Conv2D(output_nc, kernel_size=5, padding='same', activation='sigmoid')(x)
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if add_residual_blocks:
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x = ResidualBlock(d_dims*2)(x)
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x = ResidualBlock(d_dims*2)(x)
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return Conv2D(output_nc, kernel_size=5, padding='valid', activation='sigmoid')(ZeroPadding2D(2)(x))
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return func
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self.encoder = modelify(enc_flow(e_dims)) ( Input(bgr_shape) )
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@ -293,10 +300,10 @@ class SAEModel(ModelBase):
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self.inter_AB = modelify(inter_flow(lowest_dense_res, ae_dims)) ( Input(sh) )
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sh = np.array(K.int_shape( self.inter_B.outputs[0] )[1:])*(1,1,2)
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self.decoder = modelify(dec_flow(output_nc, d_dims)) ( Input(sh) )
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self.decoder = modelify(dec_flow(output_nc, d_ch_dims)) ( Input(sh) )
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if learn_mask:
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self.decoderm = modelify(dec_flow(1, d_dims)) ( Input(sh) )
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self.decoderm = modelify(dec_flow(1, d_ch_dims, add_residual_blocks=False)) ( Input(sh) )
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self.src_dst_trainable_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
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@ -349,17 +356,17 @@ class SAEModel(ModelBase):
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loaded, not_loaded = self.load_weights_safe(not_loaded)
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CA_models = []
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if self.options.get('ca_weights', False):
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if self.options.get('ca_weights', False):
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CA_models += [ model for model, _ in not_loaded ]
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CA_conv_weights_list = []
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for model in CA_models:
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for layer in model.layers:
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if type(layer) == keras.layers.Conv2D:
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CA_conv_weights_list += [layer.weights[0]] #- is Conv2D kernel_weights
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if len(CA_conv_weights_list) != 0:
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CAInitializerMP ( CA_conv_weights_list )
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CAInitializerMP ( CA_conv_weights_list )
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warped_src = self.model.warped_src
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target_src = Input ( (resolution, resolution, 3) )
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@ -501,7 +508,7 @@ class SAEModel(ModelBase):
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if self.options['learn_mask']:
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feed = [ warped_src, warped_dst, target_srcm, target_dstm ]
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src_mask_loss, dst_mask_loss, = self.src_dst_mask_train (feed)
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return ( ('src_loss', src_loss), ('dst_loss', dst_loss), )
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#override
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