Try to improve VAEEncode memory usage a bit.

comfy/ldm/modules/diffusionmodules/model.py

@@ -616,19 +616,17 @@ class Encoder(nn.Module):
         x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
         already_padded = True
         # downsampling
-        hs = [self.conv_in(x)]
+        h = self.conv_in(x)
         for i_level in range(self.num_resolutions):
             for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
+                h = self.down[i_level].block[i_block](h, temb)
                 if len(self.down[i_level].attn) > 0:
                     h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
             if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1], already_padded))
+                h = self.down[i_level].downsample(h, already_padded)
                 already_padded = False
 
         # middle
-        h = hs[-1]
         h = self.mid.block_1(h, temb)
         h = self.mid.attn_1(h)
         h = self.mid.block_2(h, temb)