Merge branch 'main' into feat/batch-graphs

invokeai/app/invocations/image.py

@@ -375,6 +375,9 @@ class ImageResizeInvocation(BaseInvocation):
     width: int = InputField(default=512, ge=64, multiple_of=8, description="The width to resize to (px)")
     height: int = InputField(default=512, ge=64, multiple_of=8, description="The height to resize to (px)")
     resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
+    metadata: Optional[CoreMetadata] = InputField(
+        default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
+    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -393,6 +396,7 @@ class ImageResizeInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.dict() if self.metadata else None,
         )
 
         return ImageOutput(

invokeai/app/invocations/latent.py

@@ -21,6 +21,8 @@ from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.invocations.primitives import (
+    DenoiseMaskField,
+    DenoiseMaskOutput,
     ImageField,
     ImageOutput,
     LatentsField,
@@ -31,8 +33,9 @@ from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 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_seamless
+from ...backend.model_management.models import BaseModelType
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
     ConditioningData,
@@ -44,16 +47,7 @@ from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import Post
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
 from ..models.image import ImageCategory, ResourceOrigin
-from .baseinvocation import (
+from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, UIType, tags, title
-    BaseInvocation,
-    FieldDescriptions,
-    Input,
-    InputField,
-    InvocationContext,
-    UIType,
-    tags,
-    title,
-)
 from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
@@ -64,6 +58,72 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
+@title("Create Denoise Mask")
+@tags("mask", "denoise")
+class CreateDenoiseMaskInvocation(BaseInvocation):
+    """Creates mask for denoising model run."""
+
+    # Metadata
+    type: Literal["create_denoise_mask"] = "create_denoise_mask"
+
+    # Inputs
+    vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
+    image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
+    mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
+    fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32, ui_order=4)
+
+    def prep_mask_tensor(self, mask_image):
+        if mask_image.mode != "L":
+            mask_image = mask_image.convert("L")
+        mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+        if mask_tensor.dim() == 3:
+            mask_tensor = mask_tensor.unsqueeze(0)
+        # if shape is not None:
+        #    mask_tensor = tv_resize(mask_tensor, shape, T.InterpolationMode.BILINEAR)
+        return mask_tensor
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
+        if self.image is not None:
+            image = context.services.images.get_pil_image(self.image.image_name)
+            image = image_resized_to_grid_as_tensor(image.convert("RGB"))
+            if image.dim() == 3:
+                image = image.unsqueeze(0)
+        else:
+            image = None
+
+        mask = self.prep_mask_tensor(
+            context.services.images.get_pil_image(self.mask.image_name),
+        )
+
+        if image is not None:
+            vae_info = context.services.model_manager.get_model(
+                **self.vae.vae.dict(),
+                context=context,
+            )
+
+            img_mask = tv_resize(mask, image.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+            masked_image = image * torch.where(img_mask < 0.5, 0.0, 1.0)
+            # TODO:
+            masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
+
+            masked_latents_name = f"{context.graph_execution_state_id}__{self.id}_masked_latents"
+            context.services.latents.save(masked_latents_name, masked_latents)
+        else:
+            masked_latents_name = None
+
+        mask_name = f"{context.graph_execution_state_id}__{self.id}_mask"
+        context.services.latents.save(mask_name, mask)
+
+        return DenoiseMaskOutput(
+            denoise_mask=DenoiseMaskField(
+                mask_name=mask_name,
+                masked_latents_name=masked_latents_name,
+            ),
+        )
+
+
 def get_scheduler(
     context: InvocationContext,
     scheduler_info: ModelInfo,
@@ -126,10 +186,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
     control: Union[ControlField, list[ControlField]] = InputField(
         default=None, description=FieldDescriptions.control, input=Input.Connection, ui_order=5
     )
-    latents: Optional[LatentsField] = InputField(
+    latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
-        description=FieldDescriptions.latents, input=Input.Connection, ui_order=4
+    denoise_mask: Optional[DenoiseMaskField] = InputField(
-    )
-    mask: Optional[ImageField] = InputField(
         default=None,
         description=FieldDescriptions.mask,
     )
@@ -342,19 +400,18 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         return num_inference_steps, timesteps, init_timestep
 
-    def prep_mask_tensor(self, mask, context, lantents):
+    def prep_inpaint_mask(self, context, latents):
-        if mask is None:
+        if self.denoise_mask is None:
-            return None
+            return None, None
 
-        mask_image = context.services.images.get_pil_image(mask.image_name)
+        mask = context.services.latents.get(self.denoise_mask.mask_name)
-        if mask_image.mode != "L":
+        mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
-            # FIXME: why do we get passed an RGB image here? We can only use single-channel.
+        if self.denoise_mask.masked_latents_name is not None:
-            mask_image = mask_image.convert("L")
+            masked_latents = context.services.latents.get(self.denoise_mask.masked_latents_name)
-        mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+        else:
-        if mask_tensor.dim() == 3:
+            masked_latents = None
-            mask_tensor = mask_tensor.unsqueeze(0)
+
-        mask_tensor = tv_resize(mask_tensor, lantents.shape[-2:], T.InterpolationMode.BILINEAR)
+        return 1 - mask, masked_latents
-        return 1 - mask_tensor
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
@@ -375,7 +432,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             if seed is None:
                 seed = 0
 
-            mask = self.prep_mask_tensor(self.mask, context, latents)
+            mask, masked_latents = self.prep_inpaint_mask(context, latents)
 
             # Get the source node id (we are invoking the prepared node)
             graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
@@ -400,12 +457,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
             with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
                 unet_info.context.model, _lora_loader()
-            ), unet_info as unet:
+            ), set_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet:
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
                     noise = noise.to(device=unet.device, dtype=unet.dtype)
                 if mask is not None:
                     mask = mask.to(device=unet.device, dtype=unet.dtype)
+                if masked_latents is not None:
+                    masked_latents = masked_latents.to(device=unet.device, dtype=unet.dtype)
 
                 scheduler = get_scheduler(
                     context=context,
@@ -442,6 +501,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     noise=noise,
                     seed=seed,
                     mask=mask,
+                    masked_latents=masked_latents,
                     num_inference_steps=num_inference_steps,
                     conditioning_data=conditioning_data,
                     control_data=control_data,  # list[ControlNetData]
@@ -490,7 +550,7 @@ class LatentsToImageInvocation(BaseInvocation):
             context=context,
         )
 
-        with vae_info as vae:
+        with set_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)
@@ -663,26 +723,11 @@ class ImageToLatentsInvocation(BaseInvocation):
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
     fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
 
-    @torch.no_grad()
+    @staticmethod
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def vae_encode(vae_info, upcast, tiled, image_tensor):
-        # image = context.services.images.get(
-        #     self.image.image_type, self.image.image_name
-        # )
-        image = context.services.images.get_pil_image(self.image.image_name)
-
-        # vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
-        vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(),
-            context=context,
-        )
-
-        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
-        if image_tensor.dim() == 3:
-            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
-
         with vae_info as vae:
             orig_dtype = vae.dtype
-            if self.fp32:
+            if upcast:
                 vae.to(dtype=torch.float32)
 
                 use_torch_2_0_or_xformers = isinstance(
@@ -707,7 +752,7 @@ class ImageToLatentsInvocation(BaseInvocation):
                 vae.to(dtype=torch.float16)
                 # latents = latents.half()
 
-            if self.tiled:
+            if tiled:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
@@ -721,6 +766,23 @@ class ImageToLatentsInvocation(BaseInvocation):
             latents = vae.config.scaling_factor * latents
             latents = latents.to(dtype=orig_dtype)
 
+        return latents
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+
+        vae_info = context.services.model_manager.get_model(
+            **self.vae.vae.dict(),
+            context=context,
+        )
+
+        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+        if image_tensor.dim() == 3:
+            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
+
+        latents = self.vae_encode(vae_info, self.fp32, self.tiled, image_tensor)
+
         name = f"{context.graph_execution_state_id}__{self.id}"
         latents = latents.to("cpu")
         context.services.latents.save(name, latents)

invokeai/app/invocations/model.py

@@ -8,8 +8,8 @@ from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     FieldDescriptions,
-    InputField,
     Input,
+    InputField,
     InvocationContext,
     OutputField,
     UIType,
@@ -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):
@@ -45,6 +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):
@@ -388,3 +390,50 @@ class VaeLoaderInvocation(BaseInvocation):
                 )
             )
         )
+
+
+class SeamlessModeOutput(BaseInvocationOutput):
+    """Modified Seamless Model output"""
+
+    type: Literal["seamless_output"] = "seamless_output"
+
+    # Outputs
+    unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet")
+    vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
+@title("Seamless")
+@tags("seamless", "model")
+class SeamlessModeInvocation(BaseInvocation):
+    """Applies the seamless transformation to the Model UNet and VAE."""
+
+    type: Literal["seamless"] = "seamless"
+
+    # Inputs
+    unet: Optional[UNetField] = InputField(
+        default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
+    )
+    vae: Optional[VaeField] = InputField(
+        default=None, description=FieldDescriptions.vae_model, input=Input.Connection, 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")
+
+    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 = []
+
+        if self.seamless_x:
+            seamless_axes_list.append("x")
+        if self.seamless_y:
+            seamless_axes_list.append("y")
+
+        if unet is not None:
+            unet.seamless_axes = seamless_axes_list
+        if vae is not None:
+            vae.seamless_axes = seamless_axes_list
+
+        return SeamlessModeOutput(unet=unet, vae=vae)

invokeai/app/invocations/primitives.py

@@ -294,6 +294,25 @@ class ImageCollectionInvocation(BaseInvocation):
         return ImageCollectionOutput(collection=self.collection)
 
 
+# endregion
+
+# region DenoiseMask
+
+
+class DenoiseMaskField(BaseModel):
+    """An inpaint mask field"""
+
+    mask_name: str = Field(description="The name of the mask image")
+    masked_latents_name: Optional[str] = Field(description="The name of the masked image latents")
+
+
+class DenoiseMaskOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single image"""
+
+    type: Literal["denoise_mask_output"] = "denoise_mask_output"
+    denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
+
+
 # endregion
 
 # region Latents

invokeai/backend/image_util/seamless.py

@@ -20,7 +20,8 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 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.
+    Modifies the 2D convolution layers to use a circular padding mode based on
+    the `seamless` and `seamless_axes` options.
     """
     # TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
     for m in model.modules():

invokeai/backend/model_management/seamless.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import List, Union
+
+import torch.nn as nn
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+
+
+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_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
+    try:
+        to_restore = []
+
+        for m_name, m in model.named_modules():
+            if isinstance(model, UNet2DConditionModel):
+                if ".attentions." in m_name:
+                    continue
+
+                if ".resnets." in m_name:
+                    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:
+                    continue
+
+                if True and ".resnets." in m_name:
+                    if True and ".conv1" in m_name:
+                        if False and "down_blocks" in m_name:
+                            continue
+                        if False and "mid_block" in m_name:
+                            continue
+                        if False and "up_blocks" in m_name:
+                            continue
+
+                    if True and ".conv2" in m_name:
+                        continue
+
+                    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)):
+                print(f"applied - {m_name}")
+                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

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -144,7 +144,7 @@ def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool = Tr
     w, h = trim_to_multiple_of(*image.size, multiple_of=multiple_of)
     transformation = T.Compose(
         [
-            T.Resize((h, w), T.InterpolationMode.LANCZOS),
+            T.Resize((h, w), T.InterpolationMode.LANCZOS, antialias=True),
             T.ToTensor(),
         ]
     )
@@ -358,6 +358,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         callback: Callable[[PipelineIntermediateState], None] = None,
         control_data: List[ControlNetData] = None,
         mask: Optional[torch.Tensor] = None,
+        masked_latents: Optional[torch.Tensor] = None,
         seed: Optional[int] = None,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if init_timestep.shape[0] == 0:
@@ -376,28 +377,28 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             latents = self.scheduler.add_noise(latents, noise, batched_t)
 
         if mask is not None:
+            # if no noise provided, noisify unmasked area based on seed(or 0 as fallback)
+            if noise is None:
+                noise = torch.randn(
+                    orig_latents.shape,
+                    dtype=torch.float32,
+                    device="cpu",
+                    generator=torch.Generator(device="cpu").manual_seed(seed or 0),
+                ).to(device=orig_latents.device, dtype=orig_latents.dtype)
+
+                latents = self.scheduler.add_noise(latents, noise, batched_t)
+                latents = torch.lerp(
+                    orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype)
+                )
+
             if is_inpainting_model(self.unet):
-                # You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
+                if masked_latents is None:
-                # (that's why there's a mask!) but it seems to really want that blanked out.
+                    raise Exception("Source image required for inpaint mask when inpaint model used!")
-                # masked_latents = latents * torch.where(mask < 0.5, 1, 0) TODO: inpaint/outpaint/infill
 
-                # TODO: we should probably pass this in so we don't have to try/finally around setting it.
+                self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(
-                self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(self._unet_forward, mask, orig_latents)
+                    self._unet_forward, mask, masked_latents
+                )
             else:
-                # if no noise provided, noisify unmasked area based on seed(or 0 as fallback)
-                if noise is None:
-                    noise = torch.randn(
-                        orig_latents.shape,
-                        dtype=torch.float32,
-                        device="cpu",
-                        generator=torch.Generator(device="cpu").manual_seed(seed or 0),
-                    ).to(device=orig_latents.device, dtype=orig_latents.dtype)
-
-                    latents = self.scheduler.add_noise(latents, noise, batched_t)
-                    latents = torch.lerp(
-                        orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype)
-                    )
-
                 additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise))
 
         try:

invokeai/backend/util/hotfixes.py

@@ -761,3 +761,18 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
 
 diffusers.ControlNetModel = ControlNetModel
 diffusers.models.controlnet.ControlNetModel = ControlNetModel
+
+
+# patch LoRACompatibleConv to use original Conv2D forward function
+# this needed to make work seamless patch
+# NOTE: with this patch, torch.compile crashes on 2.0 torch(already fixed in nightly)
+# https://github.com/huggingface/diffusers/pull/4315
+# https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/lora.py#L96C18-L96C18
+def new_LoRACompatibleConv_forward(self, x):
+    if self.lora_layer is None:
+        return super(diffusers.models.lora.LoRACompatibleConv, self).forward(x)
+    else:
+        return super(diffusers.models.lora.LoRACompatibleConv, self).forward(x) + self.lora_layer(x)
+
+
+diffusers.models.lora.LoRACompatibleConv.forward = new_LoRACompatibleConv_forward

invokeai/frontend/web/src/app/components/App.tsx

@@ -14,6 +14,7 @@ import i18n from 'i18n';
 import { size } from 'lodash-es';
 import { ReactNode, memo, useCallback, useEffect } from 'react';
 import { ErrorBoundary } from 'react-error-boundary';
+import { usePreselectedImage } from '../../features/parameters/hooks/usePreselectedImage';
 import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
 import GlobalHotkeys from './GlobalHotkeys';
 import Toaster from './Toaster';
@@ -23,13 +24,22 @@ const DEFAULT_CONFIG = {};
 interface Props {
   config?: PartialAppConfig;
   headerComponent?: ReactNode;
+  selectedImage?: {
+    imageName: string;
+    action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
+  };
 }
 
-const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
+const App = ({
+  config = DEFAULT_CONFIG,
+  headerComponent,
+  selectedImage,
+}: Props) => {
   const language = useAppSelector(languageSelector);
 
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
+  const { handlePreselectedImage } = usePreselectedImage();
   const handleReset = useCallback(() => {
     localStorage.clear();
     location.reload();
@@ -51,6 +61,10 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
     dispatch(appStarted());
   }, [dispatch]);
 
+  useEffect(() => {
+    handlePreselectedImage(selectedImage);
+  }, [handlePreselectedImage, selectedImage]);
+
   return (
     <ErrorBoundary
       onReset={handleReset}

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -26,6 +26,10 @@ interface Props extends PropsWithChildren {
   headerComponent?: ReactNode;
   middleware?: Middleware[];
   projectId?: string;
+  selectedImage?: {
+    imageName: string;
+    action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
+  };
 }
 
 const InvokeAIUI = ({
@@ -35,6 +39,7 @@ const InvokeAIUI = ({
   headerComponent,
   middleware,
   projectId,
+  selectedImage,
 }: Props) => {
   useEffect(() => {
     // configure API client token
@@ -81,7 +86,11 @@ const InvokeAIUI = ({
         <React.Suspense fallback={<Loading />}>
           <ThemeLocaleProvider>
             <AppDndContext>
-              <App config={config} headerComponent={headerComponent} />
+              <App
+                config={config}
+                headerComponent={headerComponent}
+                selectedImage={selectedImage}
+              />
             </AppDndContext>
           </ThemeLocaleProvider>
         </React.Suspense>

invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImage.tsx

@@ -8,7 +8,7 @@ import {
   ImageDraggableData,
   TypesafeDraggableData,
 } from 'features/dnd/types';
-import { useMultiselect } from 'features/gallery/hooks/useMultiselect.ts';
+import { useMultiselect } from 'features/gallery/hooks/useMultiselect';
 import { MouseEvent, memo, useCallback, useMemo, useState } from 'react';
 import { FaTrash } from 'react-icons/fa';
 import { MdStar, MdStarBorder } from 'react-icons/md';

invokeai/frontend/web/src/features/nodes/components/flow/nodes/CurrentImage/CurrentImageNode.tsx

@@ -1,13 +1,15 @@
-import { Flex, Image, Text } from '@chakra-ui/react';
+import { useState, PropsWithChildren, memo } from 'react';
+import { useSelector } from 'react-redux';
 import { createSelector } from '@reduxjs/toolkit';
-import { stateSelector } from 'app/store/store';
+import { Flex, Image, Text } from '@chakra-ui/react';
+import { motion } from 'framer-motion';
+import { NodeProps } from 'reactflow';
+import NodeWrapper from '../common/NodeWrapper';
+import NextPrevImageButtons from 'features/gallery/components/NextPrevImageButtons';
 import IAIDndImage from 'common/components/IAIDndImage';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import { DRAG_HANDLE_CLASSNAME } from 'features/nodes/types/constants';
-import { PropsWithChildren, memo } from 'react';
+import { stateSelector } from 'app/store/store';
-import { useSelector } from 'react-redux';
-import { NodeProps } from 'reactflow';
-import NodeWrapper from '../common/NodeWrapper';
 
 const selector = createSelector(stateSelector, ({ system, gallery }) => {
   const imageDTO = gallery.selection[gallery.selection.length - 1];
@@ -54,44 +56,90 @@ const CurrentImageNode = (props: NodeProps) => {
 
 export default memo(CurrentImageNode);
 
-const Wrapper = (props: PropsWithChildren<{ nodeProps: NodeProps }>) => (
+const Wrapper = (props: PropsWithChildren<{ nodeProps: NodeProps }>) => {
-  <NodeWrapper
+  const [isHovering, setIsHovering] = useState(false);
-    nodeId={props.nodeProps.data.id}
+
-    selected={props.nodeProps.selected}
+  const handleMouseEnter = () => {
-    width={384}
+    setIsHovering(true);
-  >
+  };
-    <Flex
+
-      className={DRAG_HANDLE_CLASSNAME}
+  const handleMouseLeave = () => {
-      sx={{
+    setIsHovering(false);
-        flexDirection: 'column',
+  };
-      }}
+
+  return (
+    <NodeWrapper
+      nodeId={props.nodeProps.id}
+      selected={props.nodeProps.selected}
+      width={384}
     >
       <Flex
-        layerStyle="nodeHeader"
+        onMouseEnter={handleMouseEnter}
+        onMouseLeave={handleMouseLeave}
+        className={DRAG_HANDLE_CLASSNAME}
         sx={{
-          borderTopRadius: 'base',
+          position: 'relative',
-          alignItems: 'center',
+          flexDirection: 'column',
-          justifyContent: 'center',
-          h: 8,
         }}
       >
-        <Text
+        <Flex
+          layerStyle="nodeHeader"
           sx={{
-            fontSize: 'sm',
+            borderTopRadius: 'base',
-            fontWeight: 600,
+            alignItems: 'center',
-            color: 'base.700',
+            justifyContent: 'center',
-            _dark: { color: 'base.200' },
+            h: 8,
           }}
         >
-          Current Image
+          <Text
-        </Text>
+            sx={{
+              fontSize: 'sm',
+              fontWeight: 600,
+              color: 'base.700',
+              _dark: { color: 'base.200' },
+            }}
+          >
+            Current Image
+          </Text>
+        </Flex>
+        <Flex
+          layerStyle="nodeBody"
+          sx={{
+            w: 'full',
+            h: 'full',
+            borderBottomRadius: 'base',
+            p: 2,
+          }}
+        >
+          {props.children}
+          {isHovering && (
+            <motion.div
+              key="nextPrevButtons"
+              initial={{
+                opacity: 0,
+              }}
+              animate={{
+                opacity: 1,
+                transition: { duration: 0.1 },
+              }}
+              exit={{
+                opacity: 0,
+                transition: { duration: 0.1 },
+              }}
+              style={{
+                position: 'absolute',
+                top: 40,
+                left: -2,
+                right: -2,
+                bottom: 0,
+                pointerEvents: 'none',
+              }}
+            >
+              <NextPrevImageButtons />
+            </motion.div>
+          )}
+        </Flex>
       </Flex>
-      <Flex
+    </NodeWrapper>
-        layerStyle="nodeBody"
+  );
-        sx={{ w: 'full', h: 'full', borderBottomRadius: 'base', p: 2 }}
+};
-      >
-        {props.children}
-      </Flex>
-    </Flex>
-  </NodeWrapper>
-);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer.tsx

@@ -10,6 +10,7 @@ import ColorInputField from './inputs/ColorInputField';
 import ConditioningInputField from './inputs/ConditioningInputField';
 import ControlInputField from './inputs/ControlInputField';
 import ControlNetModelInputField from './inputs/ControlNetModelInputField';
+import DenoiseMaskInputField from './inputs/DenoiseMaskInputField';
 import EnumInputField from './inputs/EnumInputField';
 import ImageCollectionInputField from './inputs/ImageCollectionInputField';
 import ImageInputField from './inputs/ImageInputField';
@@ -105,6 +106,19 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     );
   }
 
+  if (
+    field?.type === 'DenoiseMaskField' &&
+    fieldTemplate?.type === 'DenoiseMaskField'
+  ) {
+    return (
+      <DenoiseMaskInputField
+        nodeId={nodeId}
+        field={field}
+        fieldTemplate={fieldTemplate}
+      />
+    );
+  }
+
   if (
     field?.type === 'ConditioningField' &&
     fieldTemplate?.type === 'ConditioningField'

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/DenoiseMaskInputField.tsx

@@ -0,0 +1,17 @@
+import {
+  DenoiseMaskInputFieldTemplate,
+  DenoiseMaskInputFieldValue,
+  FieldComponentProps,
+} from 'features/nodes/types/types';
+import { memo } from 'react';
+
+const DenoiseMaskInputFieldComponent = (
+  _props: FieldComponentProps<
+    DenoiseMaskInputFieldValue,
+    DenoiseMaskInputFieldTemplate
+  >
+) => {
+  return null;
+};
+
+export default memo(DenoiseMaskInputFieldComponent);

invokeai/frontend/web/src/features/nodes/types/constants.ts

@@ -59,6 +59,11 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
     description: 'Images may be passed between nodes.',
     color: 'purple.500',
   },
+  DenoiseMaskField: {
+    title: 'Denoise Mask',
+    description: 'Denoise Mask may be passed between nodes',
+    color: 'red.700',
+  },
   LatentsField: {
     title: 'Latents',
     description: 'Latents may be passed between nodes.',

invokeai/frontend/web/src/features/nodes/types/types.ts

@@ -64,6 +64,7 @@ export const zFieldType = z.enum([
   'string',
   'array',
   'ImageField',
+  'DenoiseMaskField',
   'LatentsField',
   'ConditioningField',
   'ControlField',
@@ -120,6 +121,7 @@ export type InputFieldTemplate =
   | StringInputFieldTemplate
   | BooleanInputFieldTemplate
   | ImageInputFieldTemplate
+  | DenoiseMaskInputFieldTemplate
   | LatentsInputFieldTemplate
   | ConditioningInputFieldTemplate
   | UNetInputFieldTemplate
@@ -205,6 +207,12 @@ export const zConditioningField = z.object({
 });
 export type ConditioningField = z.infer<typeof zConditioningField>;
 
+export const zDenoiseMaskField = z.object({
+  mask_name: z.string().trim().min(1),
+  masked_latents_name: z.string().trim().min(1).optional(),
+});
+export type DenoiseMaskFieldValue = z.infer<typeof zDenoiseMaskField>;
+
 export const zIntegerInputFieldValue = zInputFieldValueBase.extend({
   type: z.literal('integer'),
   value: z.number().optional(),
@@ -241,6 +249,14 @@ export const zLatentsInputFieldValue = zInputFieldValueBase.extend({
 });
 export type LatentsInputFieldValue = z.infer<typeof zLatentsInputFieldValue>;
 
+export const zDenoiseMaskInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('DenoiseMaskField'),
+  value: zDenoiseMaskField.optional(),
+});
+export type DenoiseMaskInputFieldValue = z.infer<
+  typeof zDenoiseMaskInputFieldValue
+>;
+
 export const zConditioningInputFieldValue = zInputFieldValueBase.extend({
   type: z.literal('ConditioningField'),
   value: zConditioningField.optional(),
@@ -459,6 +475,7 @@ export const zInputFieldValue = z.discriminatedUnion('type', [
   zBooleanInputFieldValue,
   zImageInputFieldValue,
   zLatentsInputFieldValue,
+  zDenoiseMaskInputFieldValue,
   zConditioningInputFieldValue,
   zUNetInputFieldValue,
   zClipInputFieldValue,
@@ -532,6 +549,11 @@ export type ImageCollectionInputFieldTemplate = InputFieldTemplateBase & {
   type: 'ImageCollection';
 };
 
+export type DenoiseMaskInputFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'DenoiseMaskField';
+};
+
 export type LatentsInputFieldTemplate = InputFieldTemplateBase & {
   default: string;
   type: 'LatentsField';

invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts

@@ -8,6 +8,7 @@ import {
   ConditioningInputFieldTemplate,
   ControlInputFieldTemplate,
   ControlNetModelInputFieldTemplate,
+  DenoiseMaskInputFieldTemplate,
   EnumInputFieldTemplate,
   FieldType,
   FloatInputFieldTemplate,
@@ -263,6 +264,19 @@ const buildImageCollectionInputFieldTemplate = ({
   return template;
 };
 
+const buildDenoiseMaskInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): DenoiseMaskInputFieldTemplate => {
+  const template: DenoiseMaskInputFieldTemplate = {
+    ...baseField,
+    type: 'DenoiseMaskField',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildLatentsInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -498,6 +512,12 @@ export const buildInputFieldTemplate = (
       baseField,
     });
   }
+  if (fieldType === 'DenoiseMaskField') {
+    return buildDenoiseMaskInputFieldTemplate({
+      schemaObject: fieldSchema,
+      baseField,
+    });
+  }
   if (fieldType === 'LatentsField') {
     return buildLatentsInputFieldTemplate({
       schemaObject: fieldSchema,

invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts

@@ -49,6 +49,10 @@ export const buildInputFieldValue = (
     fieldValue.value = [];
   }
 
+  if (template.type === 'DenoiseMaskField') {
+    fieldValue.value = undefined;
+  }
+
   if (template.type === 'LatentsField') {
     fieldValue.value = undefined;
   }

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addDynamicPromptsToGraph.ts

@@ -63,7 +63,7 @@ export const addDynamicPromptsToGraph = (
       {
         source: {
           node_id: DYNAMIC_PROMPT,
-          field: 'prompt_collection',
+          field: 'collection',
         },
         destination: {
           node_id: ITERATE,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts

@@ -11,9 +11,11 @@ import {
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
+  REFINER_SEAMLESS,
   SDXL_CANVAS_INPAINT_GRAPH,
   SDXL_CANVAS_OUTPAINT_GRAPH,
   SDXL_MODEL_LOADER,
+  SEAMLESS,
 } from './constants';
 
 export const addSDXLLoRAsToGraph = (
@@ -36,20 +38,25 @@ export const addSDXLLoRAsToGraph = (
     | MetadataAccumulatorInvocation
     | undefined;
 
+  // Handle Seamless Plugs
+  const unetLoaderId = modelLoaderNodeId;
+  let clipLoaderId = modelLoaderNodeId;
+  if ([SEAMLESS, REFINER_SEAMLESS].includes(modelLoaderNodeId)) {
+    clipLoaderId = SDXL_MODEL_LOADER;
+  }
+
   if (loraCount > 0) {
     // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
     graph.edges = graph.edges.filter(
       (e) =>
         !(
-          e.source.node_id === modelLoaderNodeId &&
+          e.source.node_id === unetLoaderId && ['unet'].includes(e.source.field)
-          ['unet'].includes(e.source.field)
         ) &&
         !(
-          e.source.node_id === modelLoaderNodeId &&
+          e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field)
-          ['clip'].includes(e.source.field)
         ) &&
         !(
-          e.source.node_id === modelLoaderNodeId &&
+          e.source.node_id === clipLoaderId &&
           ['clip2'].includes(e.source.field)
         )
     );
@@ -88,7 +95,7 @@ export const addSDXLLoRAsToGraph = (
       // first lora = start the lora chain, attach directly to model loader
       graph.edges.push({
         source: {
-          node_id: modelLoaderNodeId,
+          node_id: unetLoaderId,
           field: 'unet',
         },
         destination: {
@@ -99,7 +106,7 @@ export const addSDXLLoRAsToGraph = (
 
       graph.edges.push({
         source: {
-          node_id: modelLoaderNodeId,
+          node_id: clipLoaderId,
           field: 'clip',
         },
         destination: {
@@ -110,7 +117,7 @@ export const addSDXLLoRAsToGraph = (
 
       graph.edges.push({
         source: {
-          node_id: modelLoaderNodeId,
+          node_id: clipLoaderId,
           field: 'clip2',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts

@@ -1,11 +1,15 @@
 import { RootState } from 'app/store/store';
-import { MetadataAccumulatorInvocation } from 'services/api/types';
+import {
+  MetadataAccumulatorInvocation,
+  SeamlessModeInvocation,
+} from 'services/api/types';
 import { NonNullableGraph } from '../../types/types';
 import {
   CANVAS_OUTPUT,
   LATENTS_TO_IMAGE,
   MASK_BLUR,
   METADATA_ACCUMULATOR,
+  REFINER_SEAMLESS,
   SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
   SDXL_CANVAS_INPAINT_GRAPH,
   SDXL_CANVAS_OUTPAINT_GRAPH,
@@ -21,7 +25,8 @@ import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
 export const addSDXLRefinerToGraph = (
   state: RootState,
   graph: NonNullableGraph,
-  baseNodeId: string
+  baseNodeId: string,
+  modelLoaderNodeId?: string
 ): void => {
   const {
     refinerModel,
@@ -33,6 +38,8 @@ export const addSDXLRefinerToGraph = (
     refinerStart,
   } = state.sdxl;
 
+  const { seamlessXAxis, seamlessYAxis } = state.generation;
+
   if (!refinerModel) {
     return;
   }
@@ -53,6 +60,10 @@ export const addSDXLRefinerToGraph = (
     metadataAccumulator.refiner_steps = refinerSteps;
   }
 
+  const modelLoaderId = modelLoaderNodeId
+    ? modelLoaderNodeId
+    : SDXL_MODEL_LOADER;
+
   // Construct Style Prompt
   const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
     craftSDXLStylePrompt(state, true);
@@ -65,10 +76,7 @@ export const addSDXLRefinerToGraph = (
 
   graph.edges = graph.edges.filter(
     (e) =>
-      !(
+      !(e.source.node_id === modelLoaderId && ['vae'].includes(e.source.field))
-        e.source.node_id === SDXL_MODEL_LOADER &&
-        ['vae'].includes(e.source.field)
-      )
   );
 
   graph.nodes[SDXL_REFINER_MODEL_LOADER] = {
@@ -98,8 +106,39 @@ export const addSDXLRefinerToGraph = (
     denoising_end: 1,
   };
 
-  graph.edges.push(
+  // Add Seamless To Refiner
-    {
+  if (seamlessXAxis || seamlessYAxis) {
+    graph.nodes[REFINER_SEAMLESS] = {
+      id: REFINER_SEAMLESS,
+      type: 'seamless',
+      seamless_x: seamlessXAxis,
+      seamless_y: seamlessYAxis,
+    } as SeamlessModeInvocation;
+
+    graph.edges.push(
+      {
+        source: {
+          node_id: SDXL_REFINER_MODEL_LOADER,
+          field: 'unet',
+        },
+        destination: {
+          node_id: REFINER_SEAMLESS,
+          field: 'unet',
+        },
+      },
+      {
+        source: {
+          node_id: REFINER_SEAMLESS,
+          field: 'unet',
+        },
+        destination: {
+          node_id: SDXL_REFINER_DENOISE_LATENTS,
+          field: 'unet',
+        },
+      }
+    );
+  } else {
+    graph.edges.push({
       source: {
         node_id: SDXL_REFINER_MODEL_LOADER,
         field: 'unet',
@@ -108,7 +147,10 @@ export const addSDXLRefinerToGraph = (
         node_id: SDXL_REFINER_DENOISE_LATENTS,
         field: 'unet',
       },
-    },
+    });
+  }
+
+  graph.edges.push(
     {
       source: {
         node_id: SDXL_REFINER_MODEL_LOADER,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSeamlessToLinearGraph.ts

@@ -0,0 +1,109 @@
+import { RootState } from 'app/store/store';
+import { SeamlessModeInvocation } from 'services/api/types';
+import { NonNullableGraph } from '../../types/types';
+import {
+  CANVAS_COHERENCE_DENOISE_LATENTS,
+  CANVAS_INPAINT_GRAPH,
+  CANVAS_OUTPAINT_GRAPH,
+  DENOISE_LATENTS,
+  SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
+  SDXL_CANVAS_INPAINT_GRAPH,
+  SDXL_CANVAS_OUTPAINT_GRAPH,
+  SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH,
+  SDXL_DENOISE_LATENTS,
+  SDXL_IMAGE_TO_IMAGE_GRAPH,
+  SDXL_TEXT_TO_IMAGE_GRAPH,
+  SEAMLESS,
+} from './constants';
+
+export const addSeamlessToLinearGraph = (
+  state: RootState,
+  graph: NonNullableGraph,
+  modelLoaderNodeId: string
+): void => {
+  // Remove Existing UNet Connections
+  const { seamlessXAxis, seamlessYAxis } = state.generation;
+
+  graph.nodes[SEAMLESS] = {
+    id: SEAMLESS,
+    type: 'seamless',
+    seamless_x: seamlessXAxis,
+    seamless_y: seamlessYAxis,
+  } as SeamlessModeInvocation;
+
+  let denoisingNodeId = DENOISE_LATENTS;
+
+  if (
+    graph.id === SDXL_TEXT_TO_IMAGE_GRAPH ||
+    graph.id === SDXL_IMAGE_TO_IMAGE_GRAPH ||
+    graph.id === SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH ||
+    graph.id === SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH ||
+    graph.id === SDXL_CANVAS_INPAINT_GRAPH ||
+    graph.id === SDXL_CANVAS_OUTPAINT_GRAPH
+  ) {
+    denoisingNodeId = SDXL_DENOISE_LATENTS;
+  }
+
+  graph.edges = graph.edges.filter(
+    (e) =>
+      !(
+        e.source.node_id === modelLoaderNodeId &&
+        ['unet'].includes(e.source.field)
+      ) &&
+      !(
+        e.source.node_id === modelLoaderNodeId &&
+        ['vae'].includes(e.source.field)
+      )
+  );
+
+  graph.edges.push(
+    {
+      source: {
+        node_id: modelLoaderNodeId,
+        field: 'unet',
+      },
+      destination: {
+        node_id: SEAMLESS,
+        field: 'unet',
+      },
+    },
+    {
+      source: {
+        node_id: modelLoaderNodeId,
+        field: 'vae',
+      },
+      destination: {
+        node_id: SEAMLESS,
+        field: 'vae',
+      },
+    },
+    {
+      source: {
+        node_id: SEAMLESS,
+        field: 'unet',
+      },
+      destination: {
+        node_id: denoisingNodeId,
+        field: 'unet',
+      },
+    }
+  );
+
+  if (
+    graph.id == CANVAS_INPAINT_GRAPH ||
+    graph.id === CANVAS_OUTPAINT_GRAPH ||
+    graph.id === SDXL_CANVAS_INPAINT_GRAPH ||
+    graph.id === SDXL_CANVAS_OUTPAINT_GRAPH
+  ) {
+    graph.edges.push({
+      source: {
+        node_id: SEAMLESS,
+        field: 'unet',
+      },
+      destination: {
+        node_id: CANVAS_COHERENCE_DENOISE_LATENTS,
+        field: 'unet',
+      },
+    });
+  }
+};

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts

@@ -9,6 +9,7 @@ import {
   CANVAS_TEXT_TO_IMAGE_GRAPH,
   IMAGE_TO_IMAGE_GRAPH,
   IMAGE_TO_LATENTS,
+  INPAINT_CREATE_MASK,
   INPAINT_IMAGE,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
@@ -30,6 +31,11 @@ export const addVAEToGraph = (
   modelLoaderNodeId: string = MAIN_MODEL_LOADER
 ): void => {
   const { vae } = state.generation;
+  const { boundingBoxScaleMethod } = state.canvas;
+
+  const isUsingScaledDimensions = ['auto', 'manual'].includes(
+    boundingBoxScaleMethod
+  );
 
   const isAutoVae = !vae;
   const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
@@ -76,7 +82,7 @@ export const addVAEToGraph = (
         field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
       },
       destination: {
-        node_id: CANVAS_OUTPUT,
+        node_id: isUsingScaledDimensions ? LATENTS_TO_IMAGE : CANVAS_OUTPUT,
         field: 'vae',
       },
     });
@@ -117,6 +123,16 @@ export const addVAEToGraph = (
           field: 'vae',
         },
       },
+      {
+        source: {
+          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'vae',
+        },
+      },
       {
         source: {
           node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts

@@ -2,15 +2,12 @@ import { logger } from 'app/logging/logger';
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
-import {
+import { ImageDTO, ImageToLatentsInvocation } from 'services/api/types';
-  ImageDTO,
-  ImageResizeInvocation,
-  ImageToLatentsInvocation,
-} from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -19,12 +16,14 @@ import {
   CLIP_SKIP,
   DENOISE_LATENTS,
   IMAGE_TO_LATENTS,
+  IMG2IMG_RESIZE,
+  LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
-  RESIZE,
+  SEAMLESS,
 } from './constants';
 
 /**
@@ -43,21 +42,34 @@ export const buildCanvasImageToImageGraph = (
     scheduler,
     steps,
     img2imgStrength: strength,
+    vaePrecision,
     clipSkip,
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const { shouldAutoSave } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
+
+  const isUsingScaledDimensions = ['auto', 'manual'].includes(
+    boundingBoxScaleMethod
+  );
 
   if (!model) {
     log.error('No model found in state');
     throw new Error('No model found in state');
   }
 
+  let modelLoaderNodeId = MAIN_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
@@ -75,9 +87,9 @@ export const buildCanvasImageToImageGraph = (
   const graph: NonNullableGraph = {
     id: CANVAS_IMAGE_TO_IMAGE_GRAPH,
     nodes: {
-      [MAIN_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'main_model_loader',
-        id: MAIN_MODEL_LOADER,
+        id: modelLoaderNodeId,
         is_intermediate: true,
         model,
       },
@@ -104,15 +116,17 @@ export const buildCanvasImageToImageGraph = (
         id: NOISE,
         is_intermediate: true,
         use_cpu,
+        width: !isUsingScaledDimensions
+          ? width
+          : scaledBoundingBoxDimensions.width,
+        height: !isUsingScaledDimensions
+          ? height
+          : scaledBoundingBoxDimensions.height,
       },
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
         id: IMAGE_TO_LATENTS,
         is_intermediate: true,
-        // must be set manually later, bc `fit` parameter may require a resize node inserted
-        // image: {
-        //   image_name: initialImage.image_name,
-        // },
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -134,7 +148,7 @@ export const buildCanvasImageToImageGraph = (
       // Connect Model Loader to CLIP Skip and UNet
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -144,7 +158,7 @@ export const buildCanvasImageToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -214,82 +228,84 @@ export const buildCanvasImageToImageGraph = (
           field: 'latents',
         },
       },
-      // Decode the denoised latents to an image
+    ],
+  };
+
+  // Decode Latents To Image & Handle Scaled Before Processing
+  if (isUsingScaledDimensions) {
+    graph.nodes[IMG2IMG_RESIZE] = {
+      id: IMG2IMG_RESIZE,
+      type: 'img_resize',
+      is_intermediate: true,
+      image: initialImage,
+      width: scaledBoundingBoxDimensions.width,
+      height: scaledBoundingBoxDimensions.height,
+    };
+    graph.nodes[LATENTS_TO_IMAGE] = {
+      id: LATENTS_TO_IMAGE,
+      type: 'l2i',
+      is_intermediate: true,
+      fp32: vaePrecision === 'fp32' ? true : false,
+    };
+    graph.nodes[CANVAS_OUTPUT] = {
+      id: CANVAS_OUTPUT,
+      type: 'img_resize',
+      is_intermediate: !shouldAutoSave,
+      width: width,
+      height: height,
+    };
+
+    graph.edges.push(
+      {
+        source: {
+          node_id: IMG2IMG_RESIZE,
+          field: 'image',
+        },
+        destination: {
+          node_id: IMAGE_TO_LATENTS,
+          field: 'image',
+        },
+      },
       {
         source: {
           node_id: DENOISE_LATENTS,
           field: 'latents',
         },
         destination: {
-          node_id: CANVAS_OUTPUT,
+          node_id: LATENTS_TO_IMAGE,
           field: 'latents',
         },
       },
-    ],
+      {
-  };
+        source: {
-
+          node_id: LATENTS_TO_IMAGE,
-  // handle `fit`
+          field: 'image',
-  if (initialImage.width !== width || initialImage.height !== height) {
+        },
-    // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
+        destination: {
-
+          node_id: CANVAS_OUTPUT,
-    // Create a resize node, explicitly setting its image
+          field: 'image',
-    const resizeNode: ImageResizeInvocation = {
+        },
-      id: RESIZE,
+      }
-      type: 'img_resize',
+    );
-      image: {
-        image_name: initialImage.image_name,
-      },
-      is_intermediate: true,
-      width,
-      height,
-    };
-
-    graph.nodes[RESIZE] = resizeNode;
-
-    // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'image' },
-      destination: {
-        node_id: IMAGE_TO_LATENTS,
-        field: 'image',
-      },
-    });
-
-    // The `RESIZE` node also passes its width and height to `NOISE`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'width' },
-      destination: {
-        node_id: NOISE,
-        field: 'width',
-      },
-    });
-
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'height' },
-      destination: {
-        node_id: NOISE,
-        field: 'height',
-      },
-    });
   } else {
-    // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
+    graph.nodes[CANVAS_OUTPUT] = {
-    (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = {
+      type: 'l2i',
-      image_name: initialImage.image_name,
+      id: CANVAS_OUTPUT,
+      is_intermediate: !shouldAutoSave,
+      fp32: vaePrecision === 'fp32' ? true : false,
     };
 
-    // Pass the image's dimensions to the `NOISE` node
+    (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image =
+      initialImage;
+
     graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
+      source: {
-      destination: {
+        node_id: DENOISE_LATENTS,
-        node_id: NOISE,
+        field: 'latents',
-        field: 'width',
       },
-    });
-    graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
       destination: {
-        node_id: NOISE,
+        node_id: CANVAS_OUTPUT,
-        field: 'height',
+        field: 'latents',
       },
     });
   }
@@ -300,8 +316,10 @@ export const buildCanvasImageToImageGraph = (
     type: 'metadata_accumulator',
     generation_mode: 'img2img',
     cfg_scale,
-    height,
+    width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
-    width,
+    height: !isUsingScaledDimensions
+      ? height
+      : scaledBoundingBoxDimensions.height,
     positive_prompt: '', // set in addDynamicPromptsToGraph
     negative_prompt: negativePrompt,
     model,
@@ -328,11 +346,17 @@ export const buildCanvasImageToImageGraph = (
     },
   });
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // add LoRA support
   addLoRAsToGraph(state, graph, DENOISE_LATENTS);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts

@@ -2,6 +2,7 @@ import { logger } from 'app/logging/logger';
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import {
+  CreateDenoiseMaskInvocation,
   ImageBlurInvocation,
   ImageDTO,
   ImageToLatentsInvocation,
@@ -12,16 +13,18 @@ import {
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
-  CANVAS_INPAINT_GRAPH,
-  CANVAS_OUTPUT,
   CANVAS_COHERENCE_DENOISE_LATENTS,
   CANVAS_COHERENCE_NOISE,
   CANVAS_COHERENCE_NOISE_INCREMENT,
+  CANVAS_INPAINT_GRAPH,
+  CANVAS_OUTPUT,
   CLIP_SKIP,
   DENOISE_LATENTS,
+  INPAINT_CREATE_MASK,
   INPAINT_IMAGE,
   INPAINT_IMAGE_RESIZE_DOWN,
   INPAINT_IMAGE_RESIZE_UP,
@@ -36,6 +39,7 @@ import {
   POSITIVE_CONDITIONING,
   RANDOM_INT,
   RANGE_OF_SIZE,
+  SEAMLESS,
 } from './constants';
 
 /**
@@ -66,6 +70,8 @@ export const buildCanvasInpaintGraph = (
     canvasCoherenceSteps,
     canvasCoherenceStrength,
     clipSkip,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   if (!model) {
@@ -83,6 +89,8 @@ export const buildCanvasInpaintGraph = (
     shouldAutoSave,
   } = state.canvas;
 
+  let modelLoaderNodeId = MAIN_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : shouldUseCpuNoise;
@@ -90,9 +98,9 @@ export const buildCanvasInpaintGraph = (
   const graph: NonNullableGraph = {
     id: CANVAS_INPAINT_GRAPH,
     nodes: {
-      [MAIN_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'main_model_loader',
-        id: MAIN_MODEL_LOADER,
+        id: modelLoaderNodeId,
         is_intermediate: true,
         model,
       },
@@ -127,6 +135,12 @@ export const buildCanvasInpaintGraph = (
         is_intermediate: true,
         fp32: vaePrecision === 'fp32' ? true : false,
       },
+      [INPAINT_CREATE_MASK]: {
+        type: 'create_denoise_mask',
+        id: INPAINT_CREATE_MASK,
+        is_intermediate: true,
+        fp32: vaePrecision === 'fp32' ? true : false,
+      },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
@@ -196,7 +210,7 @@ export const buildCanvasInpaintGraph = (
       // Connect Model Loader to CLIP Skip and UNet
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -206,7 +220,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -276,16 +290,27 @@ export const buildCanvasInpaintGraph = (
           field: 'latents',
         },
       },
+      // Create Inpaint Mask
       {
         source: {
           node_id: MASK_BLUR,
           field: 'image',
         },
         destination: {
-          node_id: DENOISE_LATENTS,
+          node_id: INPAINT_CREATE_MASK,
           field: 'mask',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'denoise_mask',
+        },
+        destination: {
+          node_id: DENOISE_LATENTS,
+          field: 'denoise_mask',
+        },
+      },
       // Iterate
       {
         source: {
@@ -330,7 +355,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -459,6 +484,16 @@ export const buildCanvasInpaintGraph = (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_IMAGE_RESIZE_UP,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Color Correct The Inpainted Result
       {
         source: {
@@ -516,6 +551,10 @@ export const buildCanvasInpaintGraph = (
       ...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
       image: canvasMaskImage,
     };
+    graph.nodes[INPAINT_CREATE_MASK] = {
+      ...(graph.nodes[INPAINT_CREATE_MASK] as CreateDenoiseMaskInvocation),
+      image: canvasInitImage,
+    };
 
     graph.edges.push(
       // Color Correct The Inpainted Result
@@ -562,11 +601,17 @@ export const buildCanvasInpaintGraph = (
     (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
   }
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // Add VAE
-  addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS, MAIN_MODEL_LOADER);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts

@@ -14,16 +14,18 @@ import {
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
-  CANVAS_OUTPAINT_GRAPH,
-  CANVAS_OUTPUT,
   CANVAS_COHERENCE_DENOISE_LATENTS,
   CANVAS_COHERENCE_NOISE,
   CANVAS_COHERENCE_NOISE_INCREMENT,
+  CANVAS_OUTPAINT_GRAPH,
+  CANVAS_OUTPUT,
   CLIP_SKIP,
   DENOISE_LATENTS,
+  INPAINT_CREATE_MASK,
   INPAINT_IMAGE,
   INPAINT_IMAGE_RESIZE_DOWN,
   INPAINT_IMAGE_RESIZE_UP,
@@ -42,6 +44,7 @@ import {
   POSITIVE_CONDITIONING,
   RANDOM_INT,
   RANGE_OF_SIZE,
+  SEAMLESS,
 } from './constants';
 
 /**
@@ -74,6 +77,8 @@ export const buildCanvasOutpaintGraph = (
     tileSize,
     infillMethod,
     clipSkip,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   if (!model) {
@@ -91,6 +96,8 @@ export const buildCanvasOutpaintGraph = (
     shouldAutoSave,
   } = state.canvas;
 
+  let modelLoaderNodeId = MAIN_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : shouldUseCpuNoise;
@@ -98,9 +105,9 @@ export const buildCanvasOutpaintGraph = (
   const graph: NonNullableGraph = {
     id: CANVAS_OUTPAINT_GRAPH,
     nodes: {
-      [MAIN_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'main_model_loader',
-        id: MAIN_MODEL_LOADER,
+        id: modelLoaderNodeId,
         is_intermediate: true,
         model,
       },
@@ -153,6 +160,12 @@ export const buildCanvasOutpaintGraph = (
         use_cpu,
         is_intermediate: true,
       },
+      [INPAINT_CREATE_MASK]: {
+        type: 'create_denoise_mask',
+        id: INPAINT_CREATE_MASK,
+        is_intermediate: true,
+        fp32: vaePrecision === 'fp32' ? true : false,
+      },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
@@ -215,7 +228,7 @@ export const buildCanvasOutpaintGraph = (
       // Connect Model Loader To UNet & Clip Skip
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -225,7 +238,7 @@ export const buildCanvasOutpaintGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -317,16 +330,27 @@ export const buildCanvasOutpaintGraph = (
           field: 'latents',
         },
       },
+      // Create Inpaint Mask
       {
         source: {
           node_id: MASK_BLUR,
           field: 'image',
         },
         destination: {
-          node_id: DENOISE_LATENTS,
+          node_id: INPAINT_CREATE_MASK,
           field: 'mask',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'denoise_mask',
+        },
+        destination: {
+          node_id: DENOISE_LATENTS,
+          field: 'denoise_mask',
+        },
+      },
       // Iterate
       {
         source: {
@@ -371,7 +395,7 @@ export const buildCanvasOutpaintGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -522,6 +546,16 @@ export const buildCanvasOutpaintGraph = (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_INFILL,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Take combined mask and resize and then blur
       {
         source: {
@@ -640,6 +674,16 @@ export const buildCanvasOutpaintGraph = (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_INFILL,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Color Correct The Inpainted Result
       {
         source: {
@@ -694,11 +738,17 @@ export const buildCanvasOutpaintGraph = (
     (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
   }
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // Add VAE
-  addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS, MAIN_MODEL_LOADER);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts

@@ -2,29 +2,29 @@ import { logger } from 'app/logging/logger';
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
-import {
+import { ImageDTO, ImageToLatentsInvocation } from 'services/api/types';
-  ImageDTO,
-  ImageResizeInvocation,
-  ImageToLatentsInvocation,
-} from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CANVAS_OUTPUT,
   IMAGE_TO_LATENTS,
+  IMG2IMG_RESIZE,
+  LATENTS_TO_IMAGE,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
-  RESIZE,
+  REFINER_SEAMLESS,
   SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
   SDXL_DENOISE_LATENTS,
   SDXL_MODEL_LOADER,
+  SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
 
@@ -47,6 +47,8 @@ export const buildCanvasSDXLImageToImageGraph = (
     clipSkip,
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   const {
@@ -59,13 +61,24 @@ export const buildCanvasSDXLImageToImageGraph = (
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const { shouldAutoSave } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
+
+  const isUsingScaledDimensions = ['auto', 'manual'].includes(
+    boundingBoxScaleMethod
+  );
 
   if (!model) {
     log.error('No model found in state');
     throw new Error('No model found in state');
   }
 
+  // Model Loader ID
+  let modelLoaderNodeId = SDXL_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
@@ -87,9 +100,9 @@ export const buildCanvasSDXLImageToImageGraph = (
   const graph: NonNullableGraph = {
     id: SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
     nodes: {
-      [SDXL_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'sdxl_model_loader',
-        id: SDXL_MODEL_LOADER,
+        id: modelLoaderNodeId,
         model,
       },
       [POSITIVE_CONDITIONING]: {
@@ -109,16 +122,18 @@ export const buildCanvasSDXLImageToImageGraph = (
         id: NOISE,
         is_intermediate: true,
         use_cpu,
+        width: !isUsingScaledDimensions
+          ? width
+          : scaledBoundingBoxDimensions.width,
+        height: !isUsingScaledDimensions
+          ? height
+          : scaledBoundingBoxDimensions.height,
       },
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
         id: IMAGE_TO_LATENTS,
         is_intermediate: true,
         fp32: vaePrecision === 'fp32' ? true : false,
-        // must be set manually later, bc `fit` parameter may require a resize node inserted
-        // image: {
-        //   image_name: initialImage.image_name,
-        // },
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -132,18 +147,12 @@ export const buildCanvasSDXLImageToImageGraph = (
           : 1 - strength,
         denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
       },
-      [CANVAS_OUTPUT]: {
-        type: 'l2i',
-        id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
-        fp32: vaePrecision === 'fp32' ? true : false,
-      },
     },
     edges: [
       // Connect Model Loader To UNet & CLIP
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -153,7 +162,7 @@ export const buildCanvasSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -163,7 +172,7 @@ export const buildCanvasSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -173,7 +182,7 @@ export const buildCanvasSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -183,7 +192,7 @@ export const buildCanvasSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -232,82 +241,84 @@ export const buildCanvasSDXLImageToImageGraph = (
           field: 'latents',
         },
       },
-      // Decode denoised latents to an image
+    ],
+  };
+
+  // Decode Latents To Image & Handle Scaled Before Processing
+  if (isUsingScaledDimensions) {
+    graph.nodes[IMG2IMG_RESIZE] = {
+      id: IMG2IMG_RESIZE,
+      type: 'img_resize',
+      is_intermediate: true,
+      image: initialImage,
+      width: scaledBoundingBoxDimensions.width,
+      height: scaledBoundingBoxDimensions.height,
+    };
+    graph.nodes[LATENTS_TO_IMAGE] = {
+      id: LATENTS_TO_IMAGE,
+      type: 'l2i',
+      is_intermediate: true,
+      fp32: vaePrecision === 'fp32' ? true : false,
+    };
+    graph.nodes[CANVAS_OUTPUT] = {
+      id: CANVAS_OUTPUT,
+      type: 'img_resize',
+      is_intermediate: !shouldAutoSave,
+      width: width,
+      height: height,
+    };
+
+    graph.edges.push(
+      {
+        source: {
+          node_id: IMG2IMG_RESIZE,
+          field: 'image',
+        },
+        destination: {
+          node_id: IMAGE_TO_LATENTS,
+          field: 'image',
+        },
+      },
       {
         source: {
           node_id: SDXL_DENOISE_LATENTS,
           field: 'latents',
         },
         destination: {
-          node_id: CANVAS_OUTPUT,
+          node_id: LATENTS_TO_IMAGE,
           field: 'latents',
         },
       },
-    ],
+      {
-  };
+        source: {
-
+          node_id: LATENTS_TO_IMAGE,
-  // handle `fit`
+          field: 'image',
-  if (initialImage.width !== width || initialImage.height !== height) {
+        },
-    // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
+        destination: {
-
+          node_id: CANVAS_OUTPUT,
-    // Create a resize node, explicitly setting its image
+          field: 'image',
-    const resizeNode: ImageResizeInvocation = {
+        },
-      id: RESIZE,
+      }
-      type: 'img_resize',
+    );
-      image: {
-        image_name: initialImage.image_name,
-      },
-      is_intermediate: true,
-      width,
-      height,
-    };
-
-    graph.nodes[RESIZE] = resizeNode;
-
-    // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'image' },
-      destination: {
-        node_id: IMAGE_TO_LATENTS,
-        field: 'image',
-      },
-    });
-
-    // The `RESIZE` node also passes its width and height to `NOISE`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'width' },
-      destination: {
-        node_id: NOISE,
-        field: 'width',
-      },
-    });
-
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'height' },
-      destination: {
-        node_id: NOISE,
-        field: 'height',
-      },
-    });
   } else {
-    // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
+    graph.nodes[CANVAS_OUTPUT] = {
-    (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = {
+      type: 'l2i',
-      image_name: initialImage.image_name,
+      id: CANVAS_OUTPUT,
+      is_intermediate: !shouldAutoSave,
+      fp32: vaePrecision === 'fp32' ? true : false,
     };
 
-    // Pass the image's dimensions to the `NOISE` node
+    (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image =
+      initialImage;
+
     graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
+      source: {
-      destination: {
+        node_id: SDXL_DENOISE_LATENTS,
-        node_id: NOISE,
+        field: 'latents',
-        field: 'width',
       },
-    });
-    graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
       destination: {
-        node_id: NOISE,
+        node_id: CANVAS_OUTPUT,
-        field: 'height',
+        field: 'latents',
       },
     });
   }
@@ -318,8 +329,10 @@ export const buildCanvasSDXLImageToImageGraph = (
     type: 'metadata_accumulator',
     generation_mode: 'img2img',
     cfg_scale,
-    height,
+    width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
-    width,
+    height: !isUsingScaledDimensions
+      ? height
+      : scaledBoundingBoxDimensions.height,
     positive_prompt: '', // set in addDynamicPromptsToGraph
     negative_prompt: negativePrompt,
     model,
@@ -346,16 +359,23 @@ export const buildCanvasSDXLImageToImageGraph = (
     },
   });
 
-  // add LoRA support
+  // Add Seamless To Graph
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
 
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
+    modelLoaderNodeId = REFINER_SEAMLESS;
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
+
+  // add LoRA support
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLInpaintGraph.ts

@@ -2,6 +2,7 @@ import { logger } from 'app/logging/logger';
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import {
+  CreateDenoiseMaskInvocation,
   ImageBlurInvocation,
   ImageDTO,
   ImageToLatentsInvocation,
@@ -13,13 +14,15 @@ import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
-  CANVAS_OUTPUT,
   CANVAS_COHERENCE_DENOISE_LATENTS,
   CANVAS_COHERENCE_NOISE,
   CANVAS_COHERENCE_NOISE_INCREMENT,
+  CANVAS_OUTPUT,
+  INPAINT_CREATE_MASK,
   INPAINT_IMAGE,
   INPAINT_IMAGE_RESIZE_DOWN,
   INPAINT_IMAGE_RESIZE_UP,
@@ -33,9 +36,11 @@ import {
   POSITIVE_CONDITIONING,
   RANDOM_INT,
   RANGE_OF_SIZE,
+  REFINER_SEAMLESS,
   SDXL_CANVAS_INPAINT_GRAPH,
   SDXL_DENOISE_LATENTS,
   SDXL_MODEL_LOADER,
+  SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
 
@@ -65,6 +70,8 @@ export const buildCanvasSDXLInpaintGraph = (
     maskBlurMethod,
     canvasCoherenceSteps,
     canvasCoherenceStrength,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   const {
@@ -89,6 +96,8 @@ export const buildCanvasSDXLInpaintGraph = (
     shouldAutoSave,
   } = state.canvas;
 
+  let modelLoaderNodeId = SDXL_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : shouldUseCpuNoise;
@@ -100,9 +109,9 @@ export const buildCanvasSDXLInpaintGraph = (
   const graph: NonNullableGraph = {
     id: SDXL_CANVAS_INPAINT_GRAPH,
     nodes: {
-      [SDXL_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'sdxl_model_loader',
-        id: SDXL_MODEL_LOADER,
+        id: modelLoaderNodeId,
         model,
       },
       [POSITIVE_CONDITIONING]: {
@@ -136,6 +145,12 @@ export const buildCanvasSDXLInpaintGraph = (
         use_cpu,
         is_intermediate: true,
       },
+      [INPAINT_CREATE_MASK]: {
+        type: 'create_denoise_mask',
+        id: INPAINT_CREATE_MASK,
+        is_intermediate: true,
+        fp32: vaePrecision === 'fp32' ? true : false,
+      },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
@@ -201,7 +216,7 @@ export const buildCanvasSDXLInpaintGraph = (
       // Connect Model Loader to UNet and CLIP
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -211,7 +226,7 @@ export const buildCanvasSDXLInpaintGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -221,7 +236,7 @@ export const buildCanvasSDXLInpaintGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -231,7 +246,7 @@ export const buildCanvasSDXLInpaintGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -241,7 +256,7 @@ export const buildCanvasSDXLInpaintGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -290,16 +305,27 @@ export const buildCanvasSDXLInpaintGraph = (
           field: 'latents',
         },
       },
+      // Create Inpaint Mask
       {
         source: {
           node_id: MASK_BLUR,
           field: 'image',
         },
         destination: {
-          node_id: SDXL_DENOISE_LATENTS,
+          node_id: INPAINT_CREATE_MASK,
           field: 'mask',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'denoise_mask',
+        },
+        destination: {
+          node_id: SDXL_DENOISE_LATENTS,
+          field: 'denoise_mask',
+        },
+      },
       // Iterate
       {
         source: {
@@ -344,7 +370,7 @@ export const buildCanvasSDXLInpaintGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -473,6 +499,16 @@ export const buildCanvasSDXLInpaintGraph = (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_IMAGE_RESIZE_UP,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Color Correct The Inpainted Result
       {
         source: {
@@ -530,6 +566,10 @@ export const buildCanvasSDXLInpaintGraph = (
       ...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
       image: canvasMaskImage,
     };
+    graph.nodes[INPAINT_CREATE_MASK] = {
+      ...(graph.nodes[INPAINT_CREATE_MASK] as CreateDenoiseMaskInvocation),
+      image: canvasInitImage,
+    };
 
     graph.edges.push(
       // Color Correct The Inpainted Result
@@ -576,16 +616,28 @@ export const buildCanvasSDXLInpaintGraph = (
     (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
   }
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
-    addSDXLRefinerToGraph(state, graph, CANVAS_COHERENCE_DENOISE_LATENTS);
+    addSDXLRefinerToGraph(
+      state,
+      graph,
+      CANVAS_COHERENCE_DENOISE_LATENTS,
+      modelLoaderNodeId
+    );
+    modelLoaderNodeId = REFINER_SEAMLESS;
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLOutpaintGraph.ts

@@ -15,13 +15,15 @@ import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
-  CANVAS_OUTPUT,
   CANVAS_COHERENCE_DENOISE_LATENTS,
   CANVAS_COHERENCE_NOISE,
   CANVAS_COHERENCE_NOISE_INCREMENT,
+  CANVAS_OUTPUT,
+  INPAINT_CREATE_MASK,
   INPAINT_IMAGE,
   INPAINT_IMAGE_RESIZE_DOWN,
   INPAINT_IMAGE_RESIZE_UP,
@@ -39,9 +41,11 @@ import {
   POSITIVE_CONDITIONING,
   RANDOM_INT,
   RANGE_OF_SIZE,
+  REFINER_SEAMLESS,
   SDXL_CANVAS_OUTPAINT_GRAPH,
   SDXL_DENOISE_LATENTS,
   SDXL_MODEL_LOADER,
+  SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
 
@@ -73,6 +77,8 @@ export const buildCanvasSDXLOutpaintGraph = (
     canvasCoherenceStrength,
     tileSize,
     infillMethod,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   const {
@@ -97,6 +103,8 @@ export const buildCanvasSDXLOutpaintGraph = (
     shouldAutoSave,
   } = state.canvas;
 
+  let modelLoaderNodeId = SDXL_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : shouldUseCpuNoise;
@@ -156,6 +164,12 @@ export const buildCanvasSDXLOutpaintGraph = (
         use_cpu,
         is_intermediate: true,
       },
+      [INPAINT_CREATE_MASK]: {
+        type: 'create_denoise_mask',
+        id: INPAINT_CREATE_MASK,
+        is_intermediate: true,
+        fp32: vaePrecision === 'fp32' ? true : false,
+      },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
@@ -331,16 +345,27 @@ export const buildCanvasSDXLOutpaintGraph = (
           field: 'latents',
         },
       },
+      // Create Inpaint Mask
       {
         source: {
           node_id: MASK_BLUR,
           field: 'image',
         },
         destination: {
-          node_id: SDXL_DENOISE_LATENTS,
+          node_id: INPAINT_CREATE_MASK,
           field: 'mask',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'denoise_mask',
+        },
+        destination: {
+          node_id: SDXL_DENOISE_LATENTS,
+          field: 'denoise_mask',
+        },
+      },
       // Iterate
       {
         source: {
@@ -537,6 +562,16 @@ export const buildCanvasSDXLOutpaintGraph = (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_INFILL,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Take combined mask and resize and then blur
       {
         source: {
@@ -655,6 +690,16 @@ export const buildCanvasSDXLOutpaintGraph = (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_INFILL,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Color Correct The Inpainted Result
       {
         source: {
@@ -709,16 +754,28 @@ export const buildCanvasSDXLOutpaintGraph = (
     (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
   }
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
-    addSDXLRefinerToGraph(state, graph, CANVAS_COHERENCE_DENOISE_LATENTS);
+    addSDXLRefinerToGraph(
+      state,
+      graph,
+      CANVAS_COHERENCE_DENOISE_LATENTS,
+      modelLoaderNodeId
+    );
+    modelLoaderNodeId = REFINER_SEAMLESS;
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts

@@ -11,18 +11,22 @@ import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CANVAS_OUTPUT,
+  LATENTS_TO_IMAGE,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   ONNX_MODEL_LOADER,
   POSITIVE_CONDITIONING,
+  REFINER_SEAMLESS,
   SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH,
   SDXL_DENOISE_LATENTS,
   SDXL_MODEL_LOADER,
+  SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
 
@@ -44,12 +48,22 @@ export const buildCanvasSDXLTextToImageGraph = (
     clipSkip,
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const { shouldAutoSave } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
+
+  const isUsingScaledDimensions = ['auto', 'manual'].includes(
+    boundingBoxScaleMethod
+  );
 
   const { shouldUseSDXLRefiner, refinerStart, shouldConcatSDXLStylePrompt } =
     state.sdxl;
@@ -65,7 +79,7 @@ export const buildCanvasSDXLTextToImageGraph = (
 
   const isUsingOnnxModel = model.model_type === 'onnx';
 
-  const modelLoaderNodeId = isUsingOnnxModel
+  let modelLoaderNodeId = isUsingOnnxModel
     ? ONNX_MODEL_LOADER
     : SDXL_MODEL_LOADER;
 
@@ -136,17 +150,15 @@ export const buildCanvasSDXLTextToImageGraph = (
         type: 'noise',
         id: NOISE,
         is_intermediate: true,
-        width,
+        width: !isUsingScaledDimensions
-        height,
+          ? width
+          : scaledBoundingBoxDimensions.width,
+        height: !isUsingScaledDimensions
+          ? height
+          : scaledBoundingBoxDimensions.height,
         use_cpu,
       },
       [t2lNode.id]: t2lNode,
-      [CANVAS_OUTPUT]: {
-        type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
-        id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
-        fp32: vaePrecision === 'fp32' ? true : false,
-      },
     },
     edges: [
       // Connect Model Loader to UNet and CLIP
@@ -231,19 +243,67 @@ export const buildCanvasSDXLTextToImageGraph = (
           field: 'noise',
         },
       },
-      // Decode Denoised Latents To Image
+    ],
+  };
+
+  // Decode Latents To Image & Handle Scaled Before Processing
+  if (isUsingScaledDimensions) {
+    graph.nodes[LATENTS_TO_IMAGE] = {
+      id: LATENTS_TO_IMAGE,
+      type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
+      is_intermediate: true,
+      fp32: vaePrecision === 'fp32' ? true : false,
+    };
+
+    graph.nodes[CANVAS_OUTPUT] = {
+      id: CANVAS_OUTPUT,
+      type: 'img_resize',
+      is_intermediate: !shouldAutoSave,
+      width: width,
+      height: height,
+    };
+
+    graph.edges.push(
       {
         source: {
           node_id: SDXL_DENOISE_LATENTS,
           field: 'latents',
         },
         destination: {
-          node_id: CANVAS_OUTPUT,
+          node_id: LATENTS_TO_IMAGE,
           field: 'latents',
         },
       },
-    ],
+      {
-  };
+        source: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'image',
+        },
+        destination: {
+          node_id: CANVAS_OUTPUT,
+          field: 'image',
+        },
+      }
+    );
+  } else {
+    graph.nodes[CANVAS_OUTPUT] = {
+      type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
+      id: CANVAS_OUTPUT,
+      is_intermediate: !shouldAutoSave,
+      fp32: vaePrecision === 'fp32' ? true : false,
+    };
+
+    graph.edges.push({
+      source: {
+        node_id: SDXL_DENOISE_LATENTS,
+        field: 'latents',
+      },
+      destination: {
+        node_id: CANVAS_OUTPUT,
+        field: 'latents',
+      },
+    });
+  }
 
   // add metadata accumulator, which is only mostly populated - some fields are added later
   graph.nodes[METADATA_ACCUMULATOR] = {
@@ -251,8 +311,10 @@ export const buildCanvasSDXLTextToImageGraph = (
     type: 'metadata_accumulator',
     generation_mode: 'txt2img',
     cfg_scale,
-    height,
+    width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
-    width,
+    height: !isUsingScaledDimensions
+      ? height
+      : scaledBoundingBoxDimensions.height,
     positive_prompt: '', // set in addDynamicPromptsToGraph
     negative_prompt: negativePrompt,
     model,
@@ -277,9 +339,16 @@ export const buildCanvasSDXLTextToImageGraph = (
     },
   });
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
+    modelLoaderNodeId = REFINER_SEAMLESS;
   }
 
   // add LoRA support

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts

@@ -10,6 +10,7 @@ import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -17,12 +18,14 @@ import {
   CANVAS_TEXT_TO_IMAGE_GRAPH,
   CLIP_SKIP,
   DENOISE_LATENTS,
+  LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   ONNX_MODEL_LOADER,
   POSITIVE_CONDITIONING,
+  SEAMLESS,
 } from './constants';
 
 /**
@@ -39,15 +42,26 @@ export const buildCanvasTextToImageGraph = (
     cfgScale: cfg_scale,
     scheduler,
     steps,
+    vaePrecision,
     clipSkip,
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const { shouldAutoSave } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
+
+  const isUsingScaledDimensions = ['auto', 'manual'].includes(
+    boundingBoxScaleMethod
+  );
 
   if (!model) {
     log.error('No model found in state');
@@ -60,7 +74,7 @@ export const buildCanvasTextToImageGraph = (
 
   const isUsingOnnxModel = model.model_type === 'onnx';
 
-  const modelLoaderNodeId = isUsingOnnxModel
+  let modelLoaderNodeId = isUsingOnnxModel
     ? ONNX_MODEL_LOADER
     : MAIN_MODEL_LOADER;
 
@@ -131,16 +145,15 @@ export const buildCanvasTextToImageGraph = (
         type: 'noise',
         id: NOISE,
         is_intermediate: true,
-        width,
+        width: !isUsingScaledDimensions
-        height,
+          ? width
+          : scaledBoundingBoxDimensions.width,
+        height: !isUsingScaledDimensions
+          ? height
+          : scaledBoundingBoxDimensions.height,
         use_cpu,
       },
       [t2lNode.id]: t2lNode,
-      [CANVAS_OUTPUT]: {
-        type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
-        id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
-      },
     },
     edges: [
       // Connect Model Loader to UNet & CLIP Skip
@@ -216,19 +229,67 @@ export const buildCanvasTextToImageGraph = (
           field: 'noise',
         },
       },
-      // Decode denoised latents to image
+    ],
+  };
+
+  // Decode Latents To Image & Handle Scaled Before Processing
+  if (isUsingScaledDimensions) {
+    graph.nodes[LATENTS_TO_IMAGE] = {
+      id: LATENTS_TO_IMAGE,
+      type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
+      is_intermediate: true,
+      fp32: vaePrecision === 'fp32' ? true : false,
+    };
+
+    graph.nodes[CANVAS_OUTPUT] = {
+      id: CANVAS_OUTPUT,
+      type: 'img_resize',
+      is_intermediate: !shouldAutoSave,
+      width: width,
+      height: height,
+    };
+
+    graph.edges.push(
       {
         source: {
           node_id: DENOISE_LATENTS,
           field: 'latents',
         },
         destination: {
-          node_id: CANVAS_OUTPUT,
+          node_id: LATENTS_TO_IMAGE,
           field: 'latents',
         },
       },
-    ],
+      {
-  };
+        source: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'image',
+        },
+        destination: {
+          node_id: CANVAS_OUTPUT,
+          field: 'image',
+        },
+      }
+    );
+  } else {
+    graph.nodes[CANVAS_OUTPUT] = {
+      type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
+      id: CANVAS_OUTPUT,
+      is_intermediate: !shouldAutoSave,
+      fp32: vaePrecision === 'fp32' ? true : false,
+    };
+
+    graph.edges.push({
+      source: {
+        node_id: DENOISE_LATENTS,
+        field: 'latents',
+      },
+      destination: {
+        node_id: CANVAS_OUTPUT,
+        field: 'latents',
+      },
+    });
+  }
 
   // add metadata accumulator, which is only mostly populated - some fields are added later
   graph.nodes[METADATA_ACCUMULATOR] = {
@@ -236,8 +297,10 @@ export const buildCanvasTextToImageGraph = (
     type: 'metadata_accumulator',
     generation_mode: 'txt2img',
     cfg_scale,
-    height,
+    width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
-    width,
+    height: !isUsingScaledDimensions
+      ? height
+      : scaledBoundingBoxDimensions.height,
     positive_prompt: '', // set in addDynamicPromptsToGraph
     negative_prompt: negativePrompt,
     model,
@@ -262,6 +325,12 @@ export const buildCanvasTextToImageGraph = (
     },
   });
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // optionally add custom VAE
   addVAEToGraph(state, graph, modelLoaderNodeId);
 

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts

@@ -10,6 +10,7 @@ import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -24,6 +25,7 @@ import {
   NOISE,
   POSITIVE_CONDITIONING,
   RESIZE,
+  SEAMLESS,
 } from './constants';
 
 /**
@@ -49,6 +51,8 @@ export const buildLinearImageToImageGraph = (
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
     vaePrecision,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   // TODO: add batch functionality
@@ -80,6 +84,8 @@ export const buildLinearImageToImageGraph = (
     throw new Error('No model found in state');
   }
 
+  let modelLoaderNodeId = MAIN_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
@@ -88,9 +94,9 @@ export const buildLinearImageToImageGraph = (
   const graph: NonNullableGraph = {
     id: IMAGE_TO_IMAGE_GRAPH,
     nodes: {
-      [MAIN_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'main_model_loader',
-        id: MAIN_MODEL_LOADER,
+        id: modelLoaderNodeId,
         model,
       },
       [CLIP_SKIP]: {
@@ -141,7 +147,7 @@ export const buildLinearImageToImageGraph = (
       // Connect Model Loader to UNet and CLIP Skip
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -151,7 +157,7 @@ export const buildLinearImageToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -338,11 +344,17 @@ export const buildLinearImageToImageGraph = (
     },
   });
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // optionally add custom VAE
-  addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts

@@ -11,6 +11,7 @@ import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -20,10 +21,12 @@ import {
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
+  REFINER_SEAMLESS,
   RESIZE,
   SDXL_DENOISE_LATENTS,
   SDXL_IMAGE_TO_IMAGE_GRAPH,
   SDXL_MODEL_LOADER,
+  SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
 
@@ -49,6 +52,8 @@ export const buildLinearSDXLImageToImageGraph = (
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
     vaePrecision,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   const {
@@ -79,6 +84,9 @@ export const buildLinearSDXLImageToImageGraph = (
     throw new Error('No model found in state');
   }
 
+  // Model Loader ID
+  let modelLoaderNodeId = SDXL_MODEL_LOADER;
+
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
@@ -91,9 +99,9 @@ export const buildLinearSDXLImageToImageGraph = (
   const graph: NonNullableGraph = {
     id: SDXL_IMAGE_TO_IMAGE_GRAPH,
     nodes: {
-      [SDXL_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'sdxl_model_loader',
-        id: SDXL_MODEL_LOADER,
+        id: modelLoaderNodeId,
         model,
       },
       [POSITIVE_CONDITIONING]: {
@@ -143,7 +151,7 @@ export const buildLinearSDXLImageToImageGraph = (
       // Connect Model Loader to UNet, CLIP & VAE
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -153,7 +161,7 @@ export const buildLinearSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -163,7 +171,7 @@ export const buildLinearSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -173,7 +181,7 @@ export const buildLinearSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -183,7 +191,7 @@ export const buildLinearSDXLImageToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -351,15 +359,23 @@ export const buildLinearSDXLImageToImageGraph = (
     },
   });
 
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
 
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
+    modelLoaderNodeId = REFINER_SEAMLESS;
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
+
+  // Add LoRA Support
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts

@@ -7,6 +7,7 @@ import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -15,9 +16,11 @@ import {
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
+  REFINER_SEAMLESS,
   SDXL_DENOISE_LATENTS,
   SDXL_MODEL_LOADER,
   SDXL_TEXT_TO_IMAGE_GRAPH,
+  SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
 
@@ -38,6 +41,8 @@ export const buildLinearSDXLTextToImageGraph = (
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
     vaePrecision,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   const {
@@ -61,6 +66,9 @@ export const buildLinearSDXLTextToImageGraph = (
   const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
     craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
 
+  // Model Loader ID
+  let modelLoaderNodeId = SDXL_MODEL_LOADER;
+
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -74,9 +82,9 @@ export const buildLinearSDXLTextToImageGraph = (
   const graph: NonNullableGraph = {
     id: SDXL_TEXT_TO_IMAGE_GRAPH,
     nodes: {
-      [SDXL_MODEL_LOADER]: {
+      [modelLoaderNodeId]: {
         type: 'sdxl_model_loader',
-        id: SDXL_MODEL_LOADER,
+        id: modelLoaderNodeId,
         model,
       },
       [POSITIVE_CONDITIONING]: {
@@ -117,7 +125,7 @@ export const buildLinearSDXLTextToImageGraph = (
       // Connect Model Loader to UNet, VAE & CLIP
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'unet',
         },
         destination: {
@@ -127,7 +135,7 @@ export const buildLinearSDXLTextToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -137,7 +145,7 @@ export const buildLinearSDXLTextToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -147,7 +155,7 @@ export const buildLinearSDXLTextToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip',
         },
         destination: {
@@ -157,7 +165,7 @@ export const buildLinearSDXLTextToImageGraph = (
       },
       {
         source: {
-          node_id: SDXL_MODEL_LOADER,
+          node_id: modelLoaderNodeId,
           field: 'clip2',
         },
         destination: {
@@ -244,16 +252,23 @@ export const buildLinearSDXLTextToImageGraph = (
     },
   });
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
+    modelLoaderNodeId = REFINER_SEAMLESS;
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts

@@ -10,6 +10,7 @@ import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -22,6 +23,7 @@ import {
   NOISE,
   ONNX_MODEL_LOADER,
   POSITIVE_CONDITIONING,
+  SEAMLESS,
   TEXT_TO_IMAGE_GRAPH,
 } from './constants';
 
@@ -42,6 +44,8 @@ export const buildLinearTextToImageGraph = (
     shouldUseCpuNoise,
     shouldUseNoiseSettings,
     vaePrecision,
+    seamlessXAxis,
+    seamlessYAxis,
   } = state.generation;
 
   const use_cpu = shouldUseNoiseSettings
@@ -55,7 +59,7 @@ export const buildLinearTextToImageGraph = (
 
   const isUsingOnnxModel = model.model_type === 'onnx';
 
-  const modelLoaderNodeId = isUsingOnnxModel
+  let modelLoaderNodeId = isUsingOnnxModel
     ? ONNX_MODEL_LOADER
     : MAIN_MODEL_LOADER;
 
@@ -258,6 +262,12 @@ export const buildLinearTextToImageGraph = (
     },
   });
 
+  // Add Seamless To Graph
+  if (seamlessXAxis || seamlessYAxis) {
+    addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
+    modelLoaderNodeId = SEAMLESS;
+  }
+
   // optionally add custom VAE
   addVAEToGraph(state, graph, modelLoaderNodeId);
 

invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts

@@ -17,6 +17,7 @@ export const CLIP_SKIP = 'clip_skip';
 export const IMAGE_TO_LATENTS = 'image_to_latents';
 export const LATENTS_TO_LATENTS = 'latents_to_latents';
 export const RESIZE = 'resize_image';
+export const IMG2IMG_RESIZE = 'img2img_resize';
 export const CANVAS_OUTPUT = 'canvas_output';
 export const INPAINT_IMAGE = 'inpaint_image';
 export const SCALED_INPAINT_IMAGE = 'scaled_inpaint_image';
@@ -25,6 +26,7 @@ export const INPAINT_IMAGE_RESIZE_DOWN = 'inpaint_image_resize_down';
 export const INPAINT_INFILL = 'inpaint_infill';
 export const INPAINT_INFILL_RESIZE_DOWN = 'inpaint_infill_resize_down';
 export const INPAINT_FINAL_IMAGE = 'inpaint_final_image';
+export const INPAINT_CREATE_MASK = 'inpaint_create_mask';
 export const CANVAS_COHERENCE_DENOISE_LATENTS =
   'canvas_coherence_denoise_latents';
 export const CANVAS_COHERENCE_NOISE = 'canvas_coherence_noise';
@@ -54,6 +56,8 @@ export const SDXL_REFINER_POSITIVE_CONDITIONING =
 export const SDXL_REFINER_NEGATIVE_CONDITIONING =
   'sdxl_refiner_negative_conditioning';
 export const SDXL_REFINER_DENOISE_LATENTS = 'sdxl_refiner_denoise_latents';
+export const SEAMLESS = 'seamless';
+export const REFINER_SEAMLESS = 'refiner_seamless';
 
 // friendly graph ids
 export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';

invokeai/frontend/web/src/features/parameters/hooks/usePreselectedImage.ts

@@ -0,0 +1,81 @@
+import { skipToken } from '@reduxjs/toolkit/dist/query';
+import { t } from 'i18next';
+import { useCallback, useState } from 'react';
+import { useAppToaster } from '../../../app/components/Toaster';
+import { useAppDispatch } from '../../../app/store/storeHooks';
+import {
+  useGetImageDTOQuery,
+  useGetImageMetadataQuery,
+} from '../../../services/api/endpoints/images';
+import { setInitialCanvasImage } from '../../canvas/store/canvasSlice';
+import { setActiveTab } from '../../ui/store/uiSlice';
+import { initialImageSelected } from '../store/actions';
+import { useRecallParameters } from './useRecallParameters';
+
+type SelectedImage = {
+  imageName: string;
+  action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
+};
+
+export const usePreselectedImage = () => {
+  const dispatch = useAppDispatch();
+  const [imageNameForDto, setImageNameForDto] = useState<string | undefined>();
+  const [imageNameForMetadata, setImageNameForMetadata] = useState<
+    string | undefined
+  >();
+  const { recallAllParameters } = useRecallParameters();
+  const toaster = useAppToaster();
+
+  const { currentData: selectedImageDto } = useGetImageDTOQuery(
+    imageNameForDto ?? skipToken
+  );
+
+  const { currentData: selectedImageMetadata } = useGetImageMetadataQuery(
+    imageNameForMetadata ?? skipToken
+  );
+
+  const handlePreselectedImage = useCallback(
+    (selectedImage?: SelectedImage) => {
+      if (!selectedImage) {
+        return;
+      }
+
+      if (selectedImage.action === 'sendToCanvas') {
+        setImageNameForDto(selectedImage?.imageName);
+        if (selectedImageDto) {
+          dispatch(setInitialCanvasImage(selectedImageDto));
+          dispatch(setActiveTab('unifiedCanvas'));
+          toaster({
+            title: t('toast.sentToUnifiedCanvas'),
+            status: 'info',
+            duration: 2500,
+            isClosable: true,
+          });
+        }
+      }
+
+      if (selectedImage.action === 'sendToImg2Img') {
+        setImageNameForDto(selectedImage?.imageName);
+        if (selectedImageDto) {
+          dispatch(initialImageSelected(selectedImageDto));
+        }
+      }
+
+      if (selectedImage.action === 'useAllParameters') {
+        setImageNameForMetadata(selectedImage?.imageName);
+        if (selectedImageMetadata) {
+          recallAllParameters(selectedImageMetadata.metadata);
+        }
+      }
+    },
+    [
+      dispatch,
+      selectedImageDto,
+      selectedImageMetadata,
+      recallAllParameters,
+      toaster,
+    ]
+  );
+
+  return { handlePreselectedImage };
+};

invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx

@@ -2,6 +2,7 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para
 import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
+import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import { memo } from 'react';
 import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
@@ -17,6 +18,7 @@ const SDXLImageToImageTabParameters = () => {
       <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
+      <ParamSeamlessCollapse />
     </>
   );
 };

invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx

@@ -2,6 +2,7 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para
 import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
+import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters';
 import { memo } from 'react';
 import ParamSDXLPromptArea from './ParamSDXLPromptArea';
@@ -17,6 +18,7 @@ const SDXLTextToImageTabParameters = () => {
       <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
+      <ParamSeamlessCollapse />
     </>
   );
 };

invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabParameters.tsx

@@ -5,6 +5,7 @@ import ParamMaskAdjustmentCollapse from 'features/parameters/components/Paramete
 import ParamCanvasCoherencePassCollapse from 'features/parameters/components/Parameters/Canvas/SeamPainting/ParamCanvasCoherencePassCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
+import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
 import SDXLUnifiedCanvasTabCoreParameters from './SDXLUnifiedCanvasTabCoreParameters';
@@ -22,6 +23,7 @@ export default function SDXLUnifiedCanvasTabParameters() {
       <ParamMaskAdjustmentCollapse />
       <ParamInfillAndScalingCollapse />
       <ParamCanvasCoherencePassCollapse />
+      <ParamSeamlessCollapse />
     </>
   );
 }

invokeai/frontend/web/src/features/system/store/configSlice.ts

@@ -9,7 +9,6 @@ export const initialConfigState: AppConfig = {
   disabledFeatures: ['lightbox', 'faceRestore', 'batches'],
   disabledSDFeatures: [
     'variation',
-    'seamless',
     'symmetry',
     'hires',
     'perlinNoise',

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx

@@ -6,6 +6,7 @@ import ParamMaskAdjustmentCollapse from 'features/parameters/components/Paramete
 import ParamCanvasCoherencePassCollapse from 'features/parameters/components/Parameters/Canvas/SeamPainting/ParamCanvasCoherencePassCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
 import ParamPromptArea from 'features/parameters/components/Parameters/Prompt/ParamPromptArea';
+import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 import { memo } from 'react';
 import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters';
@@ -22,6 +23,7 @@ const UnifiedCanvasParameters = () => {
       <ParamMaskAdjustmentCollapse />
       <ParamInfillAndScalingCollapse />
       <ParamCanvasCoherencePassCollapse />
+      <ParamSeamlessCollapse />
       <ParamAdvancedCollapse />
     </>
   );

invokeai/frontend/web/src/services/api/types.ts

@@ -111,6 +111,7 @@ export type ImageBlurInvocation = s['ImageBlurInvocation'];
 export type ImageScaleInvocation = s['ImageScaleInvocation'];
 export type InfillPatchMatchInvocation = s['InfillPatchMatchInvocation'];
 export type InfillTileInvocation = s['InfillTileInvocation'];
+export type CreateDenoiseMaskInvocation = s['CreateDenoiseMaskInvocation'];
 export type RandomIntInvocation = s['RandomIntInvocation'];
 export type CompelInvocation = s['CompelInvocation'];
 export type DynamicPromptInvocation = s['DynamicPromptInvocation'];
@@ -129,6 +130,7 @@ export type ESRGANInvocation = s['ESRGANInvocation'];
 export type DivideInvocation = s['DivideInvocation'];
 export type ImageNSFWBlurInvocation = s['ImageNSFWBlurInvocation'];
 export type ImageWatermarkInvocation = s['ImageWatermarkInvocation'];
+export type SeamlessModeInvocation = s['SeamlessModeInvocation'];
 
 // ControlNet Nodes
 export type ControlNetInvocation = s['ControlNetInvocation'];