diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index e794423fa1..89b292d223 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -33,7 +33,7 @@ class UNetField(BaseModel):
     unet: ModelInfo = Field(description="Info to load unet submodel")
     scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
-    seamless_axes: List[str] = Field(default_factory=list, description="Axes(\"x\" and \"y\") to which apply seamless")
+    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
 class ClipField(BaseModel):
@@ -46,7 +46,7 @@ class ClipField(BaseModel):
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
-    seamless_axes: List[str] = Field(default_factory=list, description="Axes(\"x\" and \"y\") to which apply seamless")
+    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
 class ModelLoaderOutput(BaseInvocationOutput):
@@ -401,6 +401,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
     unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet")
     vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
 
+
 @title("Seamless")
 @tags("seamless", "model")
 class SeamlessModeInvocation(BaseInvocation):
@@ -409,32 +410,24 @@ class SeamlessModeInvocation(BaseInvocation):
     type: Literal["seamless"] = "seamless"
 
     # Inputs
-    unet: UNetField = InputField(
-        description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
-    )
-    vae: VaeField = InputField(
-        description=FieldDescriptions.vae_model, input=Input.Any, title="VAE"
-    )
+    unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet")
+    vae: VaeField = InputField(description=FieldDescriptions.vae_model, input=Input.Any, title="VAE")
     seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
     seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
 
-
     def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
-        # Conditionally append 'x' and 'y' based on seamless_x and seamless_y       
+        # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
         unet = copy.deepcopy(self.unet)
         vae = copy.deepcopy(self.vae)
-        
+
         seamless_axes_list = []
 
         if self.seamless_x:
-            seamless_axes_list.append('x')
+            seamless_axes_list.append("x")
         if self.seamless_y:
-            seamless_axes_list.append('y')
+            seamless_axes_list.append("y")
 
         unet.seamless_axes = seamless_axes_list
         vae.seamless_axes = seamless_axes_list
-        
-        return SeamlessModeOutput(
-            unet=unet,
-            vae=vae
-        )
\ No newline at end of file
+
+        return SeamlessModeOutput(unet=unet, vae=vae)
diff --git a/invokeai/backend/model_management/seamless.py b/invokeai/backend/model_management/seamless.py
index b56b64f1de..1801c6e057 100644
--- a/invokeai/backend/model_management/seamless.py
+++ b/invokeai/backend/model_management/seamless.py
@@ -6,6 +6,7 @@ import diffusers
 import torch.nn as nn
 from diffusers.models import UNet2DModel, AutoencoderKL
 
+
 def _conv_forward_asymmetric(self, input, weight, bias):
     """
     Patch for Conv2d._conv_forward that supports asymmetric padding
@@ -23,13 +24,14 @@ def _conv_forward_asymmetric(self, input, weight, bias):
     )
 
 
-ModelType = TypeVar('ModelType', UNet2DModel, AutoencoderKL)
+ModelType = TypeVar("ModelType", UNet2DModel, AutoencoderKL)
+
 
 @contextmanager
 def set_seamless(model: ModelType, seamless_axes):
     try:
-        to_restore = []  
-        
+        to_restore = []
+
         for m in model.modules():
             if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
                 m.asymmetric_padding_mode = {}
@@ -64,4 +66,6 @@ def set_seamless(model: ModelType, seamless_axes):
             if hasattr(m, "asymmetric_padding"):
                 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
+                m.forward = diffusers.models.lora.LoRACompatibleConv.forward.__get__(
+                    m, diffusers.models.lora.LoRACompatibleConv
+                )