diff --git a/lora_diffusion/lora.py b/lora_diffusion/lora.py
index ce9e917..0204557 100644
--- a/lora_diffusion/lora.py
+++ b/lora_diffusion/lora.py
@@ -30,7 +30,7 @@ def safe_save(
 
 
 class LoraInjectedLinear(nn.Module):
-    def __init__(self, in_features, out_features, bias=False, r=4):
+    def __init__(self, in_features, out_features, bias=False, r=4, dropout_p=0.1):
         super().__init__()
 
         if r > min(in_features, out_features):
@@ -40,6 +40,7 @@ def __init__(self, in_features, out_features, bias=False, r=4):
 
         self.linear = nn.Linear(in_features, out_features, bias)
         self.lora_down = nn.Linear(in_features, r, bias=False)
+        self.dropout = nn.Dropout(dropout_p)
         self.lora_up = nn.Linear(r, out_features, bias=False)
         self.scale = 1.0
 
@@ -47,7 +48,10 @@ def __init__(self, in_features, out_features, bias=False, r=4):
         nn.init.zeros_(self.lora_up.weight)
 
     def forward(self, input):
-        return self.linear(input) + self.lora_up(self.lora_down(input)) * self.scale
+        return (
+            self.linear(input)
+            + self.lora_up(self.dropout(self.lora_down(input))) * self.scale
+        )
 
 
 class LoraInjectedConv2d(nn.Module):
@@ -63,6 +67,7 @@ def __init__(
         bias: bool = True,
         padding_mode: str = "zeros",
         r: int = 4,
+        dropout_p : float = 0.1
     ):
         super().__init__()
         if r > min(in_channels, out_channels):
@@ -91,6 +96,7 @@ def __init__(
             groups=groups,
             bias=False,
         )
+        self.dropout = nn.Dropout(dropout_p)
         self.lora_up = nn.Conv2d(
             in_channels=r,
             out_channels=out_channels,
@@ -105,7 +111,10 @@ def __init__(
         nn.init.zeros_(self.lora_up.weight)
 
     def forward(self, input):
-        return self.conv(input) + self.lora_up(self.lora_down(input)) * self.scale
+        return (
+            self.conv(input)
+            + self.lora_up(self.dropout(self.lora_down(input))) * self.scale
+        )
 
 
 UNET_DEFAULT_TARGET_REPLACE = {"CrossAttention", "Attention", "GEGLU"}