diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index b6941d8c..8cf4a61a 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -138,11 +138,13 @@ class ControlBase:
         return out
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
+    def __init__(self, control_model=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
-        self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+        if control_model is not None:
+            self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+
         self.global_average_pooling = global_average_pooling
         self.model_sampling_current = None
         self.manual_cast_dtype = manual_cast_dtype
@@ -183,7 +185,9 @@ class ControlNet(ControlBase):
         return self.control_merge(None, control, control_prev, output_dtype)
 
     def copy(self):
-        c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+        c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+        c.control_model = self.control_model
+        c.control_model_wrapped = self.control_model_wrapped
         self.copy_to(c)
         return c
 
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 26216432..310ec253 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -385,6 +385,8 @@ def load_models_gpu(models, memory_required=0):
     inference_memory = minimum_inference_memory()
     extra_mem = max(inference_memory, memory_required)
 
+    models = set(models)
+
     models_to_load = []
     models_already_loaded = []
     for x in models: