diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index ca76ce7d51..e794423fa1 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -404,7 +404,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
 @title("Seamless")
 @tags("seamless", "model")
 class SeamlessModeInvocation(BaseInvocation):
-    """Apply seamless mode to unet."""
+    """Applies the seamless transformation to the Model UNet and VAE."""
 
     type: Literal["seamless"] = "seamless"
 
diff --git a/invokeai/backend/model_management/seamless.py b/invokeai/backend/model_management/seamless.py
index db0274c3f7..b56b64f1de 100644
--- a/invokeai/backend/model_management/seamless.py
+++ b/invokeai/backend/model_management/seamless.py
@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import TypeVar, Union
+from typing import TypeVar
+import diffusers
 import torch.nn as nn
 from diffusers.models import UNet2DModel, AutoencoderKL
 
@@ -22,10 +23,9 @@ def _conv_forward_asymmetric(self, input, weight, bias):
     )
 
 
-@contextmanager
-
 ModelType = TypeVar('ModelType', UNet2DModel, AutoencoderKL)
 
+@contextmanager
 def set_seamless(model: ModelType, seamless_axes):
     try:
         to_restore = []  
@@ -51,6 +51,8 @@ def set_seamless(model: ModelType, seamless_axes):
 
                 to_restore.append((m, m._conv_forward))
                 m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+                if isinstance(m, diffusers.models.lora.LoRACompatibleConv) and m.lora_layer is None:
+                    m.forward = nn.Conv2d.forward.__get__(m, nn.Conv2d)
 
         yield
 
@@ -60,4 +62,6 @@ def set_seamless(model: ModelType, seamless_axes):
             if hasattr(m, "asymmetric_padding_mode"):
                 del m.asymmetric_padding_mode
             if hasattr(m, "asymmetric_padding"):
-                del m.asymmetric_padding
\ No newline at end of file
+                del m.asymmetric_padding
+            if isinstance(m, diffusers.models.lora.LoRACompatibleConv):
+                m.forward = diffusers.models.lora.LoRACompatibleConv.forward.__get__(m,diffusers.models.lora.LoRACompatibleConv) 
\ No newline at end of file