diff --git a/labml_nn/diffusion/ddpm/unet.py b/labml_nn/diffusion/ddpm/unet.py
index e44a39de..4a2a0572 100644
--- a/labml_nn/diffusion/ddpm/unet.py
+++ b/labml_nn/diffusion/ddpm/unet.py
@@ -26,7 +26,6 @@ from typing import Optional, Tuple, Union, List
 
 import torch
 from torch import nn
-import torch.nn.functional as F
 
 from labml_helpers.module import Module
 
@@ -92,13 +91,14 @@ class ResidualBlock(Module):
     Each resolution is processed with two residual blocks.
     """
 
-    def __init__(self, in_channels: int, out_channels: int, time_channels: int, n_groups: int = 32, dropout_rate: float = 0.1):
+    def __init__(self, in_channels: int, out_channels: int, time_channels: int,
+                 n_groups: int = 32, dropout: float = 0.1):
         """
         * `in_channels` is the number of input channels
         * `out_channels` is the number of input channels
         * `time_channels` is the number channels in the time step ($t$) embeddings
         * `n_groups` is the number of groups for [group normalization](../../normalization/group_norm/index.html)
-        * `dropout_rate` is the dropout rate
+        * `dropout` is the dropout rate
         """
         super().__init__()
         # Group normalization and the first convolution layer
@@ -122,6 +122,8 @@ class ResidualBlock(Module):
         self.time_emb = nn.Linear(time_channels, out_channels)
         self.time_act = Swish()
 
+        self.dropout = nn.Dropout(dropout)
+
     def forward(self, x: torch.Tensor, t: torch.Tensor):
         """
         * `x` has shape `[batch_size, in_channels, height, width]`
@@ -132,7 +134,7 @@ class ResidualBlock(Module):
         # Add time embeddings
         h += self.time_emb(self.time_act(t))[:, :, None, None]
         # Second convolution layer
-        h = self.conv2(F.dropout(self.act2(self.norm2(h)), self.dropout_rate))
+        h = self.conv2(self.dropout(self.act2(self.norm2(h))))
 
         # Add the shortcut connection and return
         return h + self.shortcut(x)