diff --git a/setup.py b/setup.py
index a0a3a5b..990dad8 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.28.0',
+  version = '0.28.1',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',
diff --git a/vit_pytorch/scalable_vit.py b/vit_pytorch/scalable_vit.py
index 3cf4650..11a21e0 100644
--- a/vit_pytorch/scalable_vit.py
+++ b/vit_pytorch/scalable_vit.py
@@ -81,8 +81,8 @@ class ScalableSelfAttention(nn.Module):
         self,
         dim,
         heads = 8,
-        dim_key = 64,
-        dim_value = 64,
+        dim_key = 32,
+        dim_value = 32,
         dropout = 0.,
         reduction_factor = 1
     ):
@@ -132,8 +132,8 @@ class InteractiveWindowedSelfAttention(nn.Module):
         dim,
         window_size,
         heads = 8,
-        dim_key = 64,
-        dim_value = 64,
+        dim_key = 32,
+        dim_value = 32,
         dropout = 0.
     ):
         super().__init__()
@@ -199,12 +199,12 @@ class Transformer(nn.Module):
         heads = 8,
         ff_expansion_factor = 4,
         dropout = 0.,
-        ssa_dim_key = 64,
-        ssa_dim_value = 64,
+        ssa_dim_key = 32,
+        ssa_dim_value = 32,
         ssa_reduction_factor = 1,
-        iwsa_dim_key = 64,
-        iwsa_dim_value = 64,
-        iwsa_window_size = 64,
+        iwsa_dim_key = 32,
+        iwsa_dim_value = 32,
+        iwsa_window_size = None,
         norm_output = True
     ):
         super().__init__()
@@ -244,12 +244,12 @@ class ScalableViT(nn.Module):
         depth,
         heads,
         reduction_factor,
+        window_size = None,
+        iwsa_dim_key = 32,
+        iwsa_dim_value = 32,
+        ssa_dim_key = 32,
+        ssa_dim_value = 32,
         ff_expansion_factor = 4,
-        iwsa_dim_key = 64,
-        iwsa_dim_value = 64,
-        window_size = 64,
-        ssa_dim_key = 64,
-        ssa_dim_value = 64,
         channels = 3,
         dropout = 0.
     ):