diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
index 5880452d47..a3598c40ef 100644
--- a/ldm/modules/diffusionmodules/model.py
+++ b/ldm/modules/diffusionmodules/model.py
@@ -121,30 +121,17 @@ class ResnetBlock(nn.Module):
                                                     padding=0)
 
     def forward(self, x, temb):
-        h1 = x
-        h2 = self.norm1(h1)
-        del h1
-
-        h3 = nonlinearity(h2)
-        del h2
-
-        h4 = self.conv1(h3)
-        del h3
+        h = self.norm1(x)
+        h = nonlinearity(h)
+        h = self.conv1(h)
 
         if temb is not None:
-            h4 = h4 + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
 
-        h5 = self.norm2(h4)
-        del h4
-
-        h6 = nonlinearity(h5)
-        del h5
-
-        h7 = self.dropout(h6)
-        del h6
-
-        h8 = self.conv2(h7)
-        del h7
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
 
         if self.in_channels != self.out_channels:
             if self.use_conv_shortcut:
@@ -152,7 +139,7 @@ class ResnetBlock(nn.Module):
             else:
                 x = self.nin_shortcut(x)
 
-        return x + h8
+        return x + h
 
 class LinAttnBlock(LinearAttention):
     """to match AttnBlock usage"""
@@ -598,17 +585,12 @@ class Decoder(nn.Module):
         temb = None
 
         # z to block_in
-        h1 = self.conv_in(z)
+        h = self.conv_in(z)
 
         # middle
-        h2 = self.mid.block_1(h1, temb)
-        del h1
-
-        h3 = self.mid.attn_1(h2)
-        del h2
-
-        h = self.mid.block_2(h3, temb)
-        del h3
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
 
         # prepare for up sampling
         gc.collect()
@@ -620,33 +602,19 @@ class Decoder(nn.Module):
             for i_block in range(self.num_res_blocks+1):
                 h = self.up[i_level].block[i_block](h, temb)
                 if len(self.up[i_level].attn) > 0:
-                    t = h
-                    h = self.up[i_level].attn[i_block](t)
-                    del t
-
+                    h = self.up[i_level].attn[i_block](h)
             if i_level != 0:
-                t = h
-                h = self.up[i_level].upsample(t)
-                del t
+                h = self.up[i_level].upsample(h)
 
         # end
         if self.give_pre_end:
             return h
 
-        h1 = self.norm_out(h)
-        del h
-
-        h2 = nonlinearity(h1)
-        del h1
-
-        h = self.conv_out(h2)
-        del h2
-
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
         if self.tanh_out:
-            t = h
-            h = torch.tanh(t)
-            del t
-
+            h = torch.tanh(h)
         return h