diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index b1d907ba4..67dce088e 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -1561,6 +1561,8 @@ class ModelPatcherDynamic(ModelPatcher):
                         allocated_size += weight_size
                     vbar.set_watermark_limit(allocated_size)
 
+                move_weight_functions(m, device_to)
+
             logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.")
 
             self.model.device = device_to
@@ -1601,6 +1603,8 @@ class ModelPatcherDynamic(ModelPatcher):
         if unpatch_weights:
             self.partially_unload_ram(1e32)
             self.partially_unload(None, 1e32)
+            for m in self.model.modules():
+                move_weight_functions(m, device_to)
 
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
         assert not force_patch_weights #See above
diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py
index 630eedc9f..aa2d88673 100644
--- a/comfy_extras/nodes_train.py
+++ b/comfy_extras/nodes_train.py
@@ -1035,7 +1035,7 @@ class TrainLoraNode(io.ComfyNode):
                 io.Boolean.Input(
                     "offloading",
                     default=False,
-                    tooltip="Depth level for gradient checkpointing.",
+                    tooltip="Offload the Model to RAM. Requires Bypass Mode.",
                 ),
                 io.Combo.Input(
                     "existing_lora",
@@ -1124,6 +1124,15 @@ class TrainLoraNode(io.ComfyNode):
         lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
         mp.set_model_compute_dtype(dtype)
 
+        if mp.is_dynamic():
+            if not bypass_mode:
+                logging.info("Training MP is Dynamic - forcing bypass mode. Start comfy with --highvram to force weight diff mode")
+                bypass_mode = True
+            offloading = True
+        elif offloading:
+            if not bypass_mode:
+                logging.info("Training Offload selected - forcing bypass mode. Set bypass = True to remove this message")
+
         # Prepare latents and compute counts
         latents, num_images, multi_res = _prepare_latents_and_count(
             latents, dtype, bucket_mode