diff --git a/modules_forge/forge_sampler.py b/modules_forge/forge_sampler.py
index bfc8f549..c3e447ed 100644
--- a/modules_forge/forge_sampler.py
+++ b/modules_forge/forge_sampler.py
@@ -86,9 +86,9 @@ def forge_sample(self, denoiser_params, cond_scale, cond_composition):
 def sampling_prepare(unet, x):
     B, C, H, W = x.shape
 
-    unet_inference_memory = unet.memory_required([B * 2, C, H, W]) + unet.extra_preserved_memory
-    additional_inference_memory = 0
-    additional_model_patchers = []
+    unet_inference_memory = unet.memory_required([B * 2, C, H, W])
+    additional_inference_memory = unet.extra_preserved_memory_during_sampling
+    additional_model_patchers = unet.extra_model_patchers_during_sampling
 
     if unet.controlnet_linked_list is not None:
         additional_inference_memory += unet.controlnet_linked_list.inference_memory_requirements(unet.model_dtype())
diff --git a/modules_forge/unet_patcher.py b/modules_forge/unet_patcher.py
index cff5c742..66c48c06 100644
--- a/modules_forge/unet_patcher.py
+++ b/modules_forge/unet_patcher.py
@@ -7,7 +7,8 @@ class UnetPatcher(ModelPatcher):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.controlnet_linked_list = None
-        self.extra_preserved_memory = 0
+        self.extra_preserved_memory_during_sampling = 0
+        self.extra_model_patchers_during_sampling = []
 
     def clone(self):
         n = UnetPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device,
@@ -21,15 +22,22 @@ class UnetPatcher(ModelPatcher):
         n.model_options = copy.deepcopy(self.model_options)
         n.model_keys = self.model_keys
         n.controlnet_linked_list = self.controlnet_linked_list
-        n.extra_preserved_memory = self.extra_preserved_memory
+        n.extra_preserved_memory_during_sampling = self.extra_preserved_memory_during_sampling
+        n.extra_model_patchers_during_sampling = self.extra_model_patchers_during_sampling.copy()
         return n
 
-    def add_preserved_memory(self, memory_in_bytes):
+    def add_extra_preserved_memory_during_sampling(self, memory_in_bytes):
         # Use this to ask Forge to preserve a certain amount of memory during sampling.
         # If GPU VRAM is 8 GB, and memory_in_bytes is 2GB, i.e., memory_in_bytes = 2 * 1024 * 1024 * 1024
         # Then the sampling will always use less than 6GB memory by dynamically offload modules to CPU RAM.
         # You can estimate this using model_management.module_size(any_pytorch_model) to get size of any pytorch models.
-        self.extra_preserved_memory += memory_in_bytes
+        self.extra_preserved_memory_during_sampling += memory_in_bytes
+        return
+
+    def add_extra_model_patcher_during_sampling(self, model_patcher):
+        # Use this to ask Forge to move extra model patchers to GPU during sampling.
+        # This method will manage GPU memory perfectly.
+        self.extra_model_patchers_during_sampling.append(model_patcher)
         return
 
     def add_patched_controlnet(self, cnet):