diff --git a/conditioning.py b/conditioning.py
index 65da2bf..dad5a41 100644
--- a/conditioning.py
+++ b/conditioning.py
@@ -214,14 +214,19 @@ class FluxBlocksBuster:
             #**{f"double_block_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(19)},
             #**{f"single_block_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(38)},
         }}
-    RETURN_TYPES = ("MODEL",)
+    RETURN_TYPES = ("MODEL", "STRING")
+    RETURN_NAMES = ("MODEL", "patched_blocks")
     FUNCTION = "patch"
 
     CATEGORY = "essentials/conditioning"
 
     def patch(self, model, blocks):
+        if blocks == "":
+            return (model, )
+
         m = model.clone()
         sd = model.model_state_dict()
+        patched_blocks = []
 
         """
         Also compatible with the following format:
@@ -235,7 +240,6 @@ class FluxBlocksBuster:
         blocks = blocks.split("\n")
         blocks = [b.strip() for b in blocks if b.strip()]
 
-        print("Patched blocks:")
         for k in sd:
             for block in blocks:
                 block = block.split("=")
@@ -248,9 +252,11 @@ class FluxBlocksBuster:
 
                 if value != 1.0 and re.search(block, k):
                     m.add_patches({k: (None,)}, 0.0, value)
-                    print(f"{k}: {value}")
+                    patched_blocks.append(f"{k}: {value}")
 
-        return (m, )
+        patched_blocks = "\n".join(patched_blocks)
+
+        return (m, patched_blocks,)
 
 
 COND_CLASS_MAPPINGS = {
diff --git a/misc.py b/misc.py
index 071cb95..9a0670e 100644
--- a/misc.py
+++ b/misc.py
@@ -442,7 +442,7 @@ class ModelCompile():
     def execute(self, model, fullgraph, dynamic, mode):
         work_model = model.clone()
         torch._dynamo.config.suppress_errors = True
-        work_model.model.diffusion_model = torch.compile(work_model.model.diffusion_model, dynamic=dynamic, fullgraph=fullgraph, mode=mode)
+        work_model.add_object_patch("diffusion_model", torch.compile(model=work_model.get_model_object("diffusion_model"), dynamic=dynamic, fullgraph=fullgraph, mode=mode))
         return (work_model, )
 
 class RemoveLatentMask: