diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index 2f5f5b4..33915c5 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -127,7 +127,7 @@ class SAEModel(ModelBase):
 
         padding = 'reflect' if self.options['remove_gray_border'] else 'zero'
         common_flow_kwargs = { 'padding': padding,
-                               'norm': 'bn',
+                               'norm': '',
                                'act':'' }
 
         weights_to_load = []
@@ -486,9 +486,9 @@ class SAEModel(ModelBase):
                 return x
         SAEModel.ResidualBlock = ResidualBlock
 
-        def downscale (dim, padding='zero', norm='', act='', kernel_regularizer=None, **kwargs):
+        def downscale (dim, padding='zero', norm='', act='', **kwargs):
             def func(x):
-                return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding, kernel_regularizer=kernel_regularizer)(x)) )
+                return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding)(x)) )
             return func
         SAEModel.downscale = downscale
 
@@ -508,7 +508,7 @@ class SAEModel(ModelBase):
     def LIAEEncFlow(resolution, ch_dims, **kwargs):
         exec (nnlib.import_all(), locals(), globals())
         upscale = partial(SAEModel.upscale, **kwargs)
-        downscale = partial(SAEModel.downscale, kernel_regularizer=keras.regularizers.l2(0.0), **kwargs)
+        downscale = partial(SAEModel.downscale, **kwargs)
         
         def func(input):
             dims = K.int_shape(input)[-1]*ch_dims
@@ -531,12 +531,8 @@ class SAEModel(ModelBase):
 
         def func(input):
             x = input[0]
-            
-            #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 = Dense(ae_dims)(x)     
+            x = Dense(lowest_dense_res * lowest_dense_res * ae_dims*2)(x)
             x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims*2))(x)
             x = upscale(ae_dims*2)(x)
             return x
@@ -587,7 +583,7 @@ class SAEModel(ModelBase):
     def DFEncFlow(resolution, ae_dims, ch_dims, **kwargs):
         exec (nnlib.import_all(), locals(), globals())
         upscale = partial(SAEModel.upscale, **kwargs)
-        downscale = partial(SAEModel.downscale, kernel_regularizer=keras.regularizers.l2(0.0), **kwargs)
+        downscale = partial(SAEModel.downscale, **kwargs)#, kernel_regularizer=keras.regularizers.l2(0.0),
         lowest_dense_res = resolution // 16
 
         def func(input):
@@ -598,12 +594,9 @@ class SAEModel(ModelBase):
             x = downscale(dims*2)(x)
             x = downscale(dims*4)(x)
             x = downscale(dims*8)(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 = Dense(ae_dims)(Flatten()(x))   
+            x = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x)            
             x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims))(x)
             x = upscale(ae_dims)(x)
             return x