diff --git a/core/leras/archis/DeepFakeArchi.py b/core/leras/archis/DeepFakeArchi.py
index 5dfd293..e630624 100644
--- a/core/leras/archis/DeepFakeArchi.py
+++ b/core/leras/archis/DeepFakeArchi.py
@@ -21,6 +21,13 @@ class DeepFakeArchi(nn.ArchiBase):
 
         conv_dtype = tf.float16 if use_fp16 else tf.float32
 
+        if 'c' in opts:
+            def act(x, alpha=0.1):
+                return tf.nn.relu(x)
+        else:
+            def act(x, alpha=0.1):
+                return tf.nn.leaky_relu(x, alpha)
+
         if mod is None:
             class Downscale(nn.ModelBase):
                 def __init__(self, in_ch, out_ch, kernel_size=5, *kwargs ):
@@ -34,7 +41,7 @@ class DeepFakeArchi(nn.ArchiBase):
 
                 def forward(self, x):
                     x = self.conv1(x)
-                    x = tf.nn.leaky_relu(x, 0.1)
+                    x = act(x, 0.1)
                     return x
 
                 def get_out_ch(self):
@@ -62,7 +69,7 @@ class DeepFakeArchi(nn.ArchiBase):
 
                 def forward(self, x):
                     x = self.conv1(x)
-                    x = tf.nn.leaky_relu(x, 0.1)
+                    x = act(x, 0.1)
                     x = nn.depth_to_space(x, 2)
                     return x
 
@@ -73,9 +80,9 @@ class DeepFakeArchi(nn.ArchiBase):
 
                 def forward(self, inp):
                     x = self.conv1(inp)
-                    x = tf.nn.leaky_relu(x, 0.2)
+                    x = act(x, 0.2)
                     x = self.conv2(x)
-                    x = tf.nn.leaky_relu(inp + x, 0.2)
+                    x = act(inp + x, 0.2)
                     return x
 
             class Encoder(nn.ModelBase):
diff --git a/core/leras/layers/__init__.py b/core/leras/layers/__init__.py
index d8f1c9d..5c9d43b 100644
--- a/core/leras/layers/__init__.py
+++ b/core/leras/layers/__init__.py
@@ -8,6 +8,7 @@ from .Dense import *
 from .BlurPool import *
 
 from .BatchNorm2D import *
+from .InstanceNorm2D import *
 from .FRNorm2D import *
 
 from .TLU import *
diff --git a/core/leras/ops/__init__.py b/core/leras/ops/__init__.py
index e0c4f5b..3bb8688 100644
--- a/core/leras/ops/__init__.py
+++ b/core/leras/ops/__init__.py
@@ -108,10 +108,13 @@ nn.gelu = gelu
 
 def upsample2d(x, size=2):
     if nn.data_format == "NCHW":
-        b,c,h,w = x.shape.as_list()
-        x = tf.reshape (x, (-1,c,h,1,w,1) )
-        x = tf.tile(x, (1,1,1,size,1,size) )
-        x = tf.reshape (x, (-1,c,h*size,w*size) )
+        x = tf.transpose(x, (0,2,3,1))
+        x = tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )
+        x = tf.transpose(x, (0,3,1,2))
+        #b,c,h,w = x.shape.as_list()
+        #x = tf.reshape (x, (-1,c,h,1,w,1) )
+        #x = tf.tile(x, (1,1,1,size,1,size) )
+        #x = tf.reshape (x, (-1,c,h*size,w*size) )
         return x
     else:
         return tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )