diff --git a/ldm/generate.py b/ldm/generate.py
index b0d64cd737..3c43e40db3 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -34,6 +34,7 @@ from ldm.invoke.image_util import InitImageResizer
 from ldm.invoke.devices import choose_torch_device, choose_precision
 from ldm.invoke.conditioning import get_uc_and_c
 from ldm.invoke.model_cache import ModelCache
+from ldm.invoke.seamless import configure_model_padding
 
 def fix_func(orig):
     if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
@@ -350,31 +351,8 @@ class Generate:
         # to the width and height of the image training set
         width = width or self.width
         height = height or self.height
-        
-        def _conv_forward_asymmetric(self, input, weight, bias):
-            """
-            Patch for Conv2d._conv_forward that supports asymmetric padding
-            """
-            working = nn.functional.pad(input, self.asymmetric_padding['x'], mode=self.asymmetric_padding_mode['x'])
-            working = nn.functional.pad(working, self.asymmetric_padding['y'], mode=self.asymmetric_padding_mode['y'])
-            return nn.functional.conv2d(working, weight, bias, self.stride, nn.modules.utils._pair(0), self.dilation, self.groups)
 
-        for m in model.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                if seamless:
-                    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])
-                    m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
-                else:
-                    m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
-                    if hasattr(m, 'asymmetric_padding_mode'):
-                        del m.asymmetric_padding_mode
-                    if hasattr(m, 'asymmetric_padding'):
-                        del m.asymmetric_padding
+        configure_model_padding(model, seamless, seamless_axes)
 
         assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0'
         assert threshold >= 0.0, '--threshold must be >=0.0'
diff --git a/ldm/invoke/seamless.py b/ldm/invoke/seamless.py
new file mode 100644
index 0000000000..2dbe241921
--- /dev/null
+++ b/ldm/invoke/seamless.py
@@ -0,0 +1,30 @@
+import torch.nn as nn
+        
+def _conv_forward_asymmetric(self, input, weight, bias):
+    """
+    Patch for Conv2d._conv_forward that supports asymmetric padding
+    """
+    working = nn.functional.pad(input, self.asymmetric_padding['x'], mode=self.asymmetric_padding_mode['x'])
+    working = nn.functional.pad(working, self.asymmetric_padding['y'], mode=self.asymmetric_padding_mode['y'])
+    return nn.functional.conv2d(working, weight, bias, self.stride, nn.modules.utils._pair(0), self.dilation, self.groups)
+
+def configure_model_padding(model, seamless, seamless_axes):
+    """
+    Modifies the 2D convolution layers to use a circular padding mode based on the `seamless` and `seamless_axes` options.
+    """
+    for m in model.modules():
+        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+            if seamless:
+                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])
+                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+            else:
+                m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
+                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