diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 44b82795..bf878776 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -210,16 +210,19 @@ class ModelPatcher:
         model_sd = self.model.state_dict()
         for k in patches:
             offset = None
+            function = None
             if isinstance(k, str):
                 key = k
             else:
                 offset = k[1]
                 key = k[0]
+                if len(k) > 2:
+                    function = k[2]
 
             if key in model_sd:
                 p.add(k)
                 current_patches = self.patches.get(key, [])
-                current_patches.append((strength_patch, patches[k], strength_model, offset))
+                current_patches.append((strength_patch, patches[k], strength_model, offset, function))
                 self.patches[key] = current_patches
 
         self.patches_uuid = uuid.uuid4()
@@ -347,6 +350,9 @@ class ModelPatcher:
             v = p[1]
             strength_model = p[2]
             offset = p[3]
+            function = p[4]
+            if function is None:
+                function = lambda a: a
 
             old_weight = None
             if offset is not None:
@@ -371,7 +377,7 @@ class ModelPatcher:
                     if w1.shape != weight.shape:
                         logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
                     else:
-                        weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
+                        weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
             elif patch_type == "lora": #lora/locon
                 mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
                 mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
@@ -389,9 +395,9 @@ class ModelPatcher:
                 try:
                     lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "lokr":
@@ -435,9 +441,9 @@ class ModelPatcher:
                 try:
                     lora_diff = torch.kron(w1, w2).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "loha":
@@ -472,9 +478,9 @@ class ModelPatcher:
                 try:
                     lora_diff = (m1 * m2).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "glora":
@@ -493,9 +499,9 @@ class ModelPatcher:
                 try:
                     lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             else: