Split seamless config into separate file

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