add VAE

invokeai/app/invocations/latent.py

@@ -33,7 +33,7 @@ from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 
 from ...backend.model_management.models import BaseModelType
 from ...backend.model_management.lora import ModelPatcher
-from ...backend.model_management.seamless import set_unet_seamless
+from ...backend.model_management.seamless import set_unet_seamless, set_vae_seamless
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
     ConditioningData,
@@ -491,7 +491,7 @@ class LatentsToImageInvocation(BaseInvocation):
             context=context,
         )
 
-        with vae_info as vae:
+        with set_vae_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)

invokeai/app/invocations/model.py

@@ -46,6 +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")
 
 
 class ModelLoaderOutput(BaseInvocationOutput):
@@ -398,6 +399,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
 
     # Outputs
     unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
+    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 @title("Seamless")
 @tags("seamless", "model")
@@ -410,7 +412,9 @@ class SeamlessModeInvocation(BaseInvocation):
     unet: UNetField = InputField(
         description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
     )
-
+    vae_model: VAEModelField = InputField(
+        description=FieldDescriptions.vae_model, input=Input.Direct, ui_type=UIType.VaeModel, 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")
 
@@ -418,6 +422,7 @@ class SeamlessModeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
         # 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 = []
 
@@ -427,7 +432,9 @@ class SeamlessModeInvocation(BaseInvocation):
             seamless_axes_list.append('y')
 
         unet.seamless_axes = seamless_axes_list
-
+        vae.seamless_axes = seamless_axes_list
+        
         return SeamlessModeOutput(
             unet=unet,
+            vae=vae
         )

invokeai/backend/model_management/seamless.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from contextlib import contextmanager
 
 import torch.nn as nn
-from diffusers.models import UNet2DModel
+from diffusers.models import UNet2DModel, AutoencoderKL
 
 def _conv_forward_asymmetric(self, input, weight, bias):
     """
@@ -51,6 +51,42 @@ def set_unet_seamless(model: UNet2DModel, seamless_axes):
 
         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
+
+def set_vae_seamless(model: AutoencoderKL, seamless_axes):
+    try:
+        to_restore = []  
+        
+        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