Fix issue with seamless context managers when seamless is not configured.

invokeai/app/invocations/latent.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
+import contextlib
 from contextlib import ExitStack
 from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
@@ -716,10 +717,23 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 **self.unet.unet.model_dump(),
                 context=context,
             )
+
+            # Prepare seamless context, if configured.
+            seamless_context = contextlib.nullcontext()
+            seamless_config = self.unet.seamless
+            if seamless_config is not None:
+                seamless_context = set_seamless(
+                    model=unet_info.context.model,
+                    axes=seamless_config.axes,
+                    skipped_layers=seamless_config.skipped_layers,
+                    skip_second_resnet=seamless_config.skip_second_resnet,
+                    skip_conv2=seamless_config.skip_conv2,
+                )
+
             with (
                 ExitStack() as exit_stack,
                 ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
-                set_seamless(unet_info.context.model, self.unet.seamless_axes),
+                seamless_context,
                 unet_info as unet,
                 # Apply the LoRA after unet has been moved to its target device for faster patching.
                 ModelPatcher.apply_lora_unet(unet, _lora_loader()),
@@ -826,7 +840,19 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
             context=context,
         )
 
-        with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
+        # Prepare seamless context, if configured.
+        seamless_context = contextlib.nullcontext()
+        seamless_config = self.vae.seamless
+        if seamless_config is not None:
+            seamless_context = set_seamless(
+                model=vae_info.context.model,
+                axes=seamless_config.axes,
+                skipped_layers=seamless_config.skipped_layers,
+                skip_second_resnet=seamless_config.skip_second_resnet,
+                skip_conv2=seamless_config.skip_conv2,
+            )
+
+        with seamless_context, vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)

invokeai/app/invocations/model.py

@@ -19,6 +19,13 @@ from .baseinvocation import (
 )
 
 
+class SeamlessSettings(BaseModel):
+    axes: List[str] = Field(description="Axes('x' and 'y') to which apply seamless")
+    skipped_layers: int = Field(description="How much down layers skip when applying seamless")
+    skip_second_resnet: bool = Field(description="Skip or not second resnet in down blocks when applying seamless")
+    skip_conv2: bool = Field(description="Skip or not conv2 in down blocks when applying seamless")
+
+
 class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")
     base_model: BaseModelType = Field(description="Base model")
@@ -36,8 +43,8 @@ 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')
     freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
+    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
 
 
 class ClipField(BaseModel):
@@ -50,7 +57,7 @@ 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: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
 
 
 @invocation_output("unet_output")
@@ -451,6 +458,11 @@ class SeamlessModeInvocation(BaseInvocation):
     )
     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")
+    skipped_layers: int = InputField(default=0, input=Input.Any, description="How much model's down layers to skip")
+    skip_second_resnet: bool = InputField(
+        default=True, input=Input.Any, description="Skip or not second resnet in down layers"
+    )
+    skip_conv2: bool = InputField(default=True, input=Input.Any, description="Skip or not conv2 in down layers")
 
     def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
         # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
@@ -465,9 +477,19 @@ class SeamlessModeInvocation(BaseInvocation):
             seamless_axes_list.append("y")
 
         if unet is not None:
-            unet.seamless_axes = seamless_axes_list
+            unet.seamless = SeamlessSettings(
+                axes=seamless_axes_list,
+                skipped_layers=self.skipped_layers,
+                skip_second_resnet=self.skip_second_resnet,
+                skip_conv2=self.skip_conv2,
+            )
         if vae is not None:
-            vae.seamless_axes = seamless_axes_list
+            vae.seamless = SeamlessSettings(
+                axes=seamless_axes_list,
+                skipped_layers=self.skipped_layers,
+                skip_second_resnet=self.skip_second_resnet,
+                skip_conv2=self.skip_conv2,
+            )
 
         return SeamlessModeOutput(unet=unet, vae=vae)
 

invokeai/backend/model_management/seamless.py

@@ -25,71 +25,55 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 
 @contextmanager
-def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
+def set_seamless(
+    model: Union[UNet2DConditionModel, AutoencoderKL],
+    axes: List[str],
+    skipped_layers: int,
+    skip_second_resnet: bool,
+    skip_conv2: bool,
+):
     try:
         to_restore = []
 
         for m_name, m in model.named_modules():
-            if isinstance(model, UNet2DConditionModel):
+            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                if ".attentions." in m_name:
+                continue
+
+            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
+                # down_blocks.1.resnets.1.conv1
+                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
+                block_num = int(block_num)
+                resnet_num = int(resnet_num)
+
+                # if block_num >= seamless_down_blocks:
+                if block_num >= len(model.down_blocks) - skipped_layers:
                     continue
 
-                if ".resnets." in m_name:
+                if resnet_num > 0 and skip_second_resnet:
-                    if ".conv2" in m_name:
-                        continue
-                    if ".conv_shortcut" in m_name:
-                        continue
-
-            """
-            if isinstance(model, UNet2DConditionModel):
-                if False and ".upsamplers." in m_name:
                     continue
 
-                if False and ".downsamplers." in m_name:
+                if submodule_name == "conv2" and skip_conv2:
                     continue
 
-                if True and ".resnets." in m_name:
+            m.asymmetric_padding_mode = {}
-                    if True and ".conv1" in m_name:
+            m.asymmetric_padding = {}
-                        if False and "down_blocks" in m_name:
+            m.asymmetric_padding_mode["x"] = "circular" if ("x" in axes) else "constant"
-                            continue
+            m.asymmetric_padding["x"] = (
-                        if False and "mid_block" in m_name:
+                m._reversed_padding_repeated_twice[0],
-                            continue
+                m._reversed_padding_repeated_twice[1],
-                        if False and "up_blocks" in m_name:
+                0,
-                            continue
+                0,
+            )
+            m.asymmetric_padding_mode["y"] = "circular" if ("y" in axes) else "constant"
+            m.asymmetric_padding["y"] = (
+                0,
+                0,
+                m._reversed_padding_repeated_twice[2],
+                m._reversed_padding_repeated_twice[3],
+            )
 
-                    if True and ".conv2" in m_name:
+            to_restore.append((m, m._conv_forward))
-                        continue
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
-
-                    if True and ".conv_shortcut" in m_name:
-                        continue
-
-                if True and ".attentions." in m_name:
-                    continue
-
-                if False and m_name in ["conv_in", "conv_out"]:
-                    continue
-            """
-
-            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