diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index 78221c0..2f5f5b4 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -128,7 +128,7 @@ class SAEModel(ModelBase):
         padding = 'reflect' if self.options['remove_gray_border'] else 'zero'
         common_flow_kwargs = { 'padding': padding,
                                'norm': 'bn',
-                               'act':'prelu' }
+                               'act':'' }
 
         weights_to_load = []
         if self.options['archi'] == 'liae':
@@ -370,8 +370,8 @@ class SAEModel(ModelBase):
         dst_samples  = generators_samples[1]
 
         feed = [src_samples[0], dst_samples[0] ] + \
-               src_samples[1:1+self.ms_count*2] + \
-               dst_samples[1:1+self.ms_count*2]
+                src_samples[1:1+self.ms_count*2] + \
+                dst_samples[1:1+self.ms_count*2]
 
         src_loss, dst_loss, = self.src_dst_train (feed)
 
@@ -467,7 +467,7 @@ class SAEModel(ModelBase):
                 return LeakyReLU(alpha=lrelu_alpha)
 
         class ResidualBlock(object):
-            def __init__(self, filters, kernel_size=3, padding='zero', use_reflection_padding=False, norm='', act='', **kwargs):
+            def __init__(self, filters, kernel_size=3, padding='zero', norm='', act='', **kwargs):
                 self.filters = filters
                 self.kernel_size = kernel_size
                 self.padding = padding
@@ -486,9 +486,9 @@ class SAEModel(ModelBase):
                 return x
         SAEModel.ResidualBlock = ResidualBlock
 
-        def downscale (dim, padding='zero', norm='', act='', **kwargs):
+        def downscale (dim, padding='zero', norm='', act='', kernel_regularizer=None, **kwargs):
             def func(x):
-                return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding)(x)) )
+                return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding, kernel_regularizer=kernel_regularizer)(x)) )
             return func
         SAEModel.downscale = downscale
 
@@ -508,8 +508,8 @@ class SAEModel(ModelBase):
     def LIAEEncFlow(resolution, ch_dims, **kwargs):
         exec (nnlib.import_all(), locals(), globals())
         upscale = partial(SAEModel.upscale, **kwargs)
-        downscale = partial(SAEModel.downscale, **kwargs)
-
+        downscale = partial(SAEModel.downscale, kernel_regularizer=keras.regularizers.l2(0.0), **kwargs)
+        
         def func(input):
             dims = K.int_shape(input)[-1]*ch_dims
 
@@ -531,8 +531,12 @@ class SAEModel(ModelBase):
 
         def func(input):
             x = input[0]
-            x = Dense(ae_dims)(x)
-            x = Dense(lowest_dense_res * lowest_dense_res * ae_dims*2)(x)
+            
+            #https://arxiv.org/abs/1807.01442 https://github.com/aditya-grover/uae
+            x = Dense(ae_dims, use_bias=False)(x)
+            x = Lambda ( lambda x: x + 0.1*K.random_normal(K.shape(x), 0, 1) , output_shape=(None,ae_dims) ) (x)            
+            x = Dense(lowest_dense_res * lowest_dense_res * ae_dims*2, use_bias=False)(x)
+
             x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims*2))(x)
             x = upscale(ae_dims*2)(x)
             return x
@@ -580,37 +584,38 @@ class SAEModel(ModelBase):
         return func
 
     @staticmethod
-    def DFEncFlow(resolution, ae_dims, ch_dims, padding='zero', **kwargs):
+    def DFEncFlow(resolution, ae_dims, ch_dims, **kwargs):
         exec (nnlib.import_all(), locals(), globals())
-        upscale = partial(SAEModel.upscale, padding=padding)
-        downscale = partial(SAEModel.downscale, padding=padding)
+        upscale = partial(SAEModel.upscale, **kwargs)
+        downscale = partial(SAEModel.downscale, kernel_regularizer=keras.regularizers.l2(0.0), **kwargs)
         lowest_dense_res = resolution // 16
 
         def func(input):
             x = input
 
             dims = K.int_shape(input)[-1]*ch_dims
-
             x = downscale(dims)(x)
             x = downscale(dims*2)(x)
             x = downscale(dims*4)(x)
             x = downscale(dims*8)(x)
-
-            x = Dense(ae_dims)(Flatten()(x))
-            x = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x)
+            
+            #https://arxiv.org/abs/1807.01442 https://github.com/aditya-grover/uae
+            x = Dense(ae_dims, use_bias=False)(Flatten()(x))
+            x = Lambda ( lambda x: x + 0.1*K.random_normal(K.shape(x), 0, 1) , output_shape=(None,ae_dims) ) (x)            
+            x = Dense(lowest_dense_res * lowest_dense_res * ae_dims, use_bias=False)(x)
+            
             x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims))(x)
             x = upscale(ae_dims)(x)
-
             return x
         return func
 
     @staticmethod
-    def DFDecFlow(output_nc, ch_dims, multiscale_count=1, add_residual_blocks=False, padding='zero', **kwargs):
+    def DFDecFlow(output_nc, ch_dims, multiscale_count=1, add_residual_blocks=False, **kwargs):
         exec (nnlib.import_all(), locals(), globals())
-        upscale = partial(SAEModel.upscale, padding=padding)
-        to_bgr = partial(SAEModel.to_bgr, padding=padding)
+        upscale = partial(SAEModel.upscale, **kwargs)
+        to_bgr = partial(SAEModel.to_bgr, **kwargs)
         dims = output_nc * ch_dims
-        ResidualBlock = partial(SAEModel.ResidualBlock, padding=padding)
+        ResidualBlock = partial(SAEModel.ResidualBlock, **kwargs)
 
         def func(input):
             x = input[0]