diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 80f90157df..c1918a981a 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -33,7 +33,7 @@ from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 
 from ...backend.model_management.models import BaseModelType
 from ...backend.model_management.lora import ModelPatcher
-from ...backend.model_management.seamless import set_unet_seamless, set_vae_seamless
+from ...backend.model_management.seamless import set_seamless
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
     ConditioningData,
@@ -401,7 +401,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
             with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
                 unet_info.context.model, _lora_loader()
-            ), set_unet_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet:
+            ), set_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet:
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
                     noise = noise.to(device=unet.device, dtype=unet.dtype)
@@ -491,7 +491,7 @@ class LatentsToImageInvocation(BaseInvocation):
             context=context,
         )
 
-        with set_vae_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
+        with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)
diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index c5bf4b1262..ca76ce7d51 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -398,8 +398,8 @@ class SeamlessModeOutput(BaseInvocationOutput):
     type: Literal["seamless_output"] = "seamless_output"
 
     # Outputs
-    unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet")
+    vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 @title("Seamless")
 @tags("seamless", "model")
diff --git a/invokeai/backend/model_management/seamless.py b/invokeai/backend/model_management/seamless.py
index 49770b4281..db0274c3f7 100644
--- a/invokeai/backend/model_management/seamless.py
+++ b/invokeai/backend/model_management/seamless.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-
+from typing import TypeVar, Union
 import torch.nn as nn
 from diffusers.models import UNet2DModel, AutoencoderKL
 
@@ -23,43 +23,10 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 
 @contextmanager
-def set_unet_seamless(model: UNet2DModel, seamless_axes):
-    try:
-        to_restore = []  
-        
-        for m in model.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                m.asymmetric_padding_mode = {}
-                m.asymmetric_padding = {}
-                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-                m.asymmetric_padding["x"] = (
-                    m._reversed_padding_repeated_twice[0],
-                    m._reversed_padding_repeated_twice[1],
-                    0,
-                    0,
-                )
-                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-                m.asymmetric_padding["y"] = (
-                    0,
-                    0,
-                    m._reversed_padding_repeated_twice[2],
-                    m._reversed_padding_repeated_twice[3],
-                )
 
-                to_restore.append((m, m._conv_forward))
-                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+ModelType = TypeVar('ModelType', UNet2DModel, AutoencoderKL)
 
-        yield
-
-    finally:
-        for module, orig_conv_forward in to_restore:
-            module._conv_forward = orig_conv_forward
-            if hasattr(m, "asymmetric_padding_mode"):
-                del m.asymmetric_padding_mode
-            if hasattr(m, "asymmetric_padding"):
-                del m.asymmetric_padding
-
-def set_vae_seamless(model: AutoencoderKL, seamless_axes):
+def set_seamless(model: ModelType, seamless_axes):
     try:
         to_restore = []