updates

invokeai/app/invocations/model.py

@@ -33,6 +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")
 
 
 class ClipField(BaseModel):
@@ -388,3 +389,45 @@ class VaeLoaderInvocation(BaseInvocation):
                 )
             )
         )
+
+
+class SeamlessModeOutput(BaseInvocationOutput):
+    """Modified Seamless Model output"""
+
+    type: Literal["seamless_output"] = "seamless_output"
+
+    # Outputs
+    unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
+
+@title("Seamless")
+@tags("seamless", "model")
+class SeamlessModeInvocation(BaseInvocation):
+    """Apply seamless mode to unet."""
+
+    type: Literal["seamless"] = "seamless"
+
+    # Inputs
+    unet: UNetField = InputField(
+        description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
+    )
+
+    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       
+        unet = copy.deepcopy(self.unet)
+        
+        seamless_axes_list = []
+
+        if self.seamless_x:
+            seamless_axes_list.append('x')
+        if self.seamless_y:
+            seamless_axes_list.append('y')
+
+        unet.seamless_axes = seamless_axes_list
+
+        return SeamlessModeOutput(
+            unet=unet,
+        )

invokeai/backend/model_management/seamless.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+
+import torch.nn as nn
+from diffusers.models import UNet2DModel
+
+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,
+    )
+
+
+@contextmanager
+def set_unet_seamless(model: UNet2DModel, seamless: bool, seamless_axes):
+    try:
+        to_restore = dict()
+        if seamless:
+            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)
+
+        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