diff --git a/comfy/sd.py b/comfy/sd.py
index 6d1e8bb9..b8f82966 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -129,12 +129,17 @@ def load_lora(path, to_load):
         A_name = "{}.lora_up.weight".format(x)
         B_name = "{}.lora_down.weight".format(x)
         alpha_name = "{}.alpha".format(x)
+        mid_name = "{}.lora_mid.weight".format(x)
         if A_name in lora.keys():
             alpha = None
             if alpha_name in lora.keys():
                 alpha = lora[alpha_name].item()
                 loaded_keys.add(alpha_name)
-            patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha)
+            mid = None
+            if mid_name in lora.keys():
+                mid = lora[mid_name]
+                loaded_keys.add(mid_name)
+            patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
             loaded_keys.add(A_name)
             loaded_keys.add(B_name)
     for x in lora.keys():
@@ -279,6 +284,10 @@ class ModelPatcher:
                 mat2 = v[1]
                 if v[2] is not None:
                     alpha *= v[2] / mat2.shape[0]
+                if v[3] is not None:
+                    #locon mid weights, hopefully the math is fine because I didn't properly test it
+                    final_shape = [mat2.shape[1], mat2.shape[0], v[3].shape[2], v[3].shape[3]]
+                    mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1).float(), v[3].transpose(0, 1).flatten(start_dim=1).float()).reshape(final_shape).transpose(0, 1)
                 weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
         return self.model
     def unpatch_model(self):