diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index b089eebb..03bc683c 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -96,6 +96,7 @@ class ResnetBlock(nn.Module):
         self.out_channels = out_channels
         self.use_conv_shortcut = conv_shortcut
 
+        self.swish = torch.nn.SiLU(inplace=True)
         self.norm1 = Normalize(in_channels)
         self.conv1 = torch.nn.Conv2d(in_channels,
                                      out_channels,
@@ -106,7 +107,7 @@ class ResnetBlock(nn.Module):
             self.temb_proj = torch.nn.Linear(temb_channels,
                                              out_channels)
         self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
+        self.dropout = torch.nn.Dropout(dropout, inplace=True)
         self.conv2 = torch.nn.Conv2d(out_channels,
                                      out_channels,
                                      kernel_size=3,
@@ -129,14 +130,14 @@ class ResnetBlock(nn.Module):
     def forward(self, x, temb):
         h = x
         h = self.norm1(h)
-        h = nonlinearity(h)
+        h = self.swish(h)
         h = self.conv1(h)
 
         if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+            h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
 
         h = self.norm2(h)
-        h = nonlinearity(h)
+        h = self.swish(h)
         h = self.dropout(h)
         h = self.conv2(h)