diff --git a/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py b/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
index 5f5f8f4..5fafe76 100644
--- a/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+++ b/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
@@ -95,7 +95,7 @@ def Upsample(dim):
     return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
 
 def Downsample(dim):
-    return nn.Conv2d(dim, dim, 3, 2, 1)
+    return nn.Conv2d(dim, dim, 4, 2, 1)
 
 class LayerNorm(nn.Module):
     def __init__(self, dim, eps = 1e-5):
@@ -135,10 +135,9 @@ class ConvNextBlock(nn.Module):
 
         self.net = nn.Sequential(
             LayerNorm(dim) if norm else nn.Identity(),
-            nn.Conv2d(dim, dim_out * mult, 1),
+            nn.Conv2d(dim, dim_out * mult, 3, padding = 1),
             nn.GELU(),
-            LayerNorm(dim_out * mult),
-            nn.Conv2d(dim_out * mult, dim_out, 1)
+            nn.Conv2d(dim_out * mult, dim_out, 3, padding = 1)
         )
 
         self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
diff --git a/setup.py b/setup.py
index 1b9f790..caf6439 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.7.0',
+  version = '0.7.1',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',