diff --git a/core/leras/models/PatchDiscriminator.py b/core/leras/models/PatchDiscriminator.py
index 7e0de9e..da297b6 100644
--- a/core/leras/models/PatchDiscriminator.py
+++ b/core/leras/models/PatchDiscriminator.py
@@ -152,17 +152,17 @@ class UNetPatchDiscriminator(nn.ModelBase):
         self.center_conv =  nn.Conv2D( level_chs[len(layers)-1], level_chs[len(layers)-1], kernel_size=1, padding='VALID')
 
     def forward(self, x):
-        x = tf.nn.leaky_relu( self.in_conv(x), 0.1 )
+        x = tf.nn.leaky_relu( self.in_conv(x), 0.2 )
 
         encs = []
         for conv in self.convs:#, self.res):
             encs.insert(0, x)
-            x = tf.nn.leaky_relu( conv(x), 0.1 )
+            x = tf.nn.leaky_relu( conv(x), 0.2 )
 
-        center_out, x = self.center_out(x), self.center_conv(x)
+        center_out, x = self.center_out(x), tf.nn.leaky_relu( self.center_conv(x), 0.2 )
 
         for i, (upconv, enc,) in enumerate(zip(self.upconvs, encs)):# self.upres
-            x = tf.nn.leaky_relu( upconv(x), 0.1 )
+            x = tf.nn.leaky_relu( upconv(x), 0.2 )
             x = tf.concat( [enc, x], axis=nn.conv2d_ch_axis)
 
         return center_out, self.out_conv(x)
diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index 64b12a8..6e30934 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -155,7 +155,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                 gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. Typical fine value is resolution / 8." ), 3, 640 )
                 self.options['gan_patch_size'] = gan_patch_size
                 
-                gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="16-512", help_message="The higher dims, the higher the quality, the more VRAM is required. The more varied the src faceset, especially if ct_mode is enabled, the more GAN dimensions are needed. Typical fine value is 32." ), 16, 512 )
+                gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="16-512", help_message="Change this value only if you want to experiment. Typical fine value is 32." ), 16, 512 )
                 self.options['gan_dims'] = gan_dims
                 
             if 'df' in self.options['archi']: