Change tiling strategy to make TiledStableDiffusionRefineInvocation work with more tile shapes and overlaps.

- Fix type errors
- Enable SilenceWarnings to be used as both a context manager and a decorator
- Remove duplicate implementation
- Check the initial verbosity on __enter__() rather than __init__()

Makefile

@@ -18,6 +18,7 @@ help:
 	@echo "frontend-typegen         Generate types for the frontend from the OpenAPI schema"
 	@echo "installer-zip            Build the installer .zip file for the current version"
 	@echo "tag-release              Tag the GitHub repository with the current version (use at release time only!)"
+	@echo "openapi                  Generate the OpenAPI schema for the app, outputting to stdout"
 
 # Runs ruff, fixing any safely-fixable errors and formatting
 ruff:
@@ -70,3 +71,6 @@ installer-zip:
 tag-release:
 	cd installer && ./tag_release.sh
 
+# Generate the OpenAPI Schema for the app
+openapi:
+	python scripts/generate_openapi_schema.py

docs/help/FAQ.md

@@ -154,6 +154,18 @@ This is caused by an invalid setting in the `invokeai.yaml` configuration file.
 
 Check the [configuration docs] for more detail about the settings and how to specify them.
 
+## `ModuleNotFoundError: No module named 'controlnet_aux'`
+
+`controlnet_aux` is a dependency of Invoke and appears to have been packaged or distributed strangely. Sometimes, it doesn't install correctly. This is outside our control.
+
+If you encounter this error, the solution is to remove the package from the `pip` cache and re-run the Invoke installer so a fresh, working version of `controlnet_aux` can be downloaded and installed:
+
+- Run the Invoke launcher
+- Choose the developer console option
+- Run this command: `pip cache remove controlnet_aux`
+- Close the terminal window
+- Download and run the [installer](https://github.com/invoke-ai/InvokeAI/releases/latest), selecting your current install location
+
 ## Out of Memory Issues
 
 The models are large, VRAM is expensive, and you may find yourself

invokeai/app/api_app.py

@@ -3,9 +3,7 @@ import logging
 import mimetypes
 import socket
 from contextlib import asynccontextmanager
-from inspect import signature
 from pathlib import Path
-from typing import Any
 
 import torch
 import uvicorn
@@ -13,11 +11,9 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
-from fastapi.openapi.utils import get_openapi
 from fastapi.responses import HTMLResponse
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.middleware import EventHandlerASGIMiddleware
-from pydantic.json_schema import models_json_schema
 from torch.backends.mps import is_available as is_mps_available
 
 # for PyCharm:
@@ -25,10 +21,8 @@ from torch.backends.mps import is_available as is_mps_available
 import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 import invokeai.frontend.web as web_dir
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
-from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.config.config_default import get_config
-from invokeai.app.services.events.events_common import EventBase
-from invokeai.app.services.session_processor.session_processor_common import ProgressImage
+from invokeai.app.util.custom_openapi import get_openapi_func
 from invokeai.backend.util.devices import TorchDevice
 
 from ..backend.util.logging import InvokeAILogger
@@ -45,11 +39,6 @@ from .api.routers import (
     workflows,
 )
 from .api.sockets import SocketIO
-from .invocations.baseinvocation import (
-    BaseInvocation,
-    UIConfigBase,
-)
-from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 
 app_config = get_config()
 
@@ -119,84 +108,7 @@ app.include_router(app_info.app_router, prefix="/api")
 app.include_router(session_queue.session_queue_router, prefix="/api")
 app.include_router(workflows.workflows_router, prefix="/api")
 
-
-# Build a custom OpenAPI to include all outputs
-# TODO: can outputs be included on metadata of invocation schemas somehow?
-def custom_openapi() -> dict[str, Any]:
-    if app.openapi_schema:
-        return app.openapi_schema
-    openapi_schema = get_openapi(
-        title=app.title,
-        description="An API for invoking AI image operations",
-        version="1.0.0",
-        routes=app.routes,
-        separate_input_output_schemas=False,  # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
-    )
-
-    # Add all outputs
-    all_invocations = BaseInvocation.get_invocations()
-    output_types = set()
-    output_type_titles = {}
-    for invoker in all_invocations:
-        output_type = signature(invoker.invoke).return_annotation
-        output_types.add(output_type)
-
-    output_schemas = models_json_schema(
-        models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
-    )
-    for schema_key, output_schema in output_schemas[1]["$defs"].items():
-        # TODO: note that we assume the schema_key here is the TYPE.__name__
-        # This could break in some cases, figure out a better way to do it
-        output_type_titles[schema_key] = output_schema["title"]
-        openapi_schema["components"]["schemas"][schema_key] = output_schema
-        openapi_schema["components"]["schemas"][schema_key]["class"] = "output"
-
-    # Some models don't end up in the schemas as standalone definitions
-    additional_schemas = models_json_schema(
-        [
-            (UIConfigBase, "serialization"),
-            (InputFieldJSONSchemaExtra, "serialization"),
-            (OutputFieldJSONSchemaExtra, "serialization"),
-            (ModelIdentifierField, "serialization"),
-            (ProgressImage, "serialization"),
-        ],
-        ref_template="#/components/schemas/{model}",
-    )
-    for schema_key, schema_json in additional_schemas[1]["$defs"].items():
-        openapi_schema["components"]["schemas"][schema_key] = schema_json
-
-    openapi_schema["components"]["schemas"]["InvocationOutputMap"] = {
-        "type": "object",
-        "properties": {},
-        "required": [],
-    }
-
-    # Add a reference to the output type to additionalProperties of the invoker schema
-    for invoker in all_invocations:
-        invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
-        output_type = signature(obj=invoker.invoke).return_annotation
-        output_type_title = output_type_titles[output_type.__name__]
-        invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
-        outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
-        invoker_schema["output"] = outputs_ref
-        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["properties"][invoker.get_type()] = outputs_ref
-        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["required"].append(invoker.get_type())
-        invoker_schema["class"] = "invocation"
-
-    # Add all event schemas
-    for event in sorted(EventBase.get_events(), key=lambda e: e.__name__):
-        json_schema = event.model_json_schema(mode="serialization", ref_template="#/components/schemas/{model}")
-        if "$defs" in json_schema:
-            for schema_key, schema in json_schema["$defs"].items():
-                openapi_schema["components"]["schemas"][schema_key] = schema
-            del json_schema["$defs"]
-        openapi_schema["components"]["schemas"][event.__name__] = json_schema
-
-    app.openapi_schema = openapi_schema
-    return app.openapi_schema
-
-
-app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
+app.openapi = get_openapi_func(app)
 
 
 @app.get("/docs", include_in_schema=False)

invokeai/app/invocations/baseinvocation.py

@@ -98,11 +98,13 @@ class BaseInvocationOutput(BaseModel):
 
     _output_classes: ClassVar[set[BaseInvocationOutput]] = set()
     _typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
+    _typeadapter_needs_update: ClassVar[bool] = False
 
     @classmethod
     def register_output(cls, output: BaseInvocationOutput) -> None:
         """Registers an invocation output."""
         cls._output_classes.add(output)
+        cls._typeadapter_needs_update = True
 
     @classmethod
     def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
@@ -112,11 +114,12 @@ class BaseInvocationOutput(BaseModel):
     @classmethod
     def get_typeadapter(cls) -> TypeAdapter[Any]:
         """Gets a pydantc TypeAdapter for the union of all invocation output types."""
-        if not cls._typeadapter:
-            InvocationOutputsUnion = TypeAliasType(
-                "InvocationOutputsUnion", Annotated[Union[tuple(cls._output_classes)], Field(discriminator="type")]
+        if not cls._typeadapter or cls._typeadapter_needs_update:
+            AnyInvocationOutput = TypeAliasType(
+                "AnyInvocationOutput", Annotated[Union[tuple(cls._output_classes)], Field(discriminator="type")]
             )
-            cls._typeadapter = TypeAdapter(InvocationOutputsUnion)
+            cls._typeadapter = TypeAdapter(AnyInvocationOutput)
+            cls._typeadapter_needs_update = False
         return cls._typeadapter
 
     @classmethod
@@ -125,12 +128,13 @@ class BaseInvocationOutput(BaseModel):
         return (i.get_type() for i in BaseInvocationOutput.get_outputs())
 
     @staticmethod
-    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
+    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseInvocationOutput]) -> None:
         """Adds various UI-facing attributes to the invocation output's OpenAPI schema."""
         # Because we use a pydantic Literal field with default value for the invocation type,
         # it will be typed as optional in the OpenAPI schema. Make it required manually.
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
+        schema["class"] = "output"
         schema["required"].extend(["type"])
 
     @classmethod
@@ -167,6 +171,7 @@ class BaseInvocation(ABC, BaseModel):
 
     _invocation_classes: ClassVar[set[BaseInvocation]] = set()
     _typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
+    _typeadapter_needs_update: ClassVar[bool] = False
 
     @classmethod
     def get_type(cls) -> str:
@@ -177,15 +182,17 @@ class BaseInvocation(ABC, BaseModel):
     def register_invocation(cls, invocation: BaseInvocation) -> None:
         """Registers an invocation."""
         cls._invocation_classes.add(invocation)
+        cls._typeadapter_needs_update = True
 
     @classmethod
     def get_typeadapter(cls) -> TypeAdapter[Any]:
         """Gets a pydantc TypeAdapter for the union of all invocation types."""
-        if not cls._typeadapter:
-            InvocationsUnion = TypeAliasType(
-                "InvocationsUnion", Annotated[Union[tuple(cls._invocation_classes)], Field(discriminator="type")]
+        if not cls._typeadapter or cls._typeadapter_needs_update:
+            AnyInvocation = TypeAliasType(
+                "AnyInvocation", Annotated[Union[tuple(cls._invocation_classes)], Field(discriminator="type")]
             )
-            cls._typeadapter = TypeAdapter(InvocationsUnion)
+            cls._typeadapter = TypeAdapter(AnyInvocation)
+            cls._typeadapter_needs_update = False
         return cls._typeadapter
 
     @classmethod
@@ -221,7 +228,7 @@ class BaseInvocation(ABC, BaseModel):
         return signature(cls.invoke).return_annotation
 
     @staticmethod
-    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel], *args, **kwargs) -> None:
+    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseInvocation]) -> None:
         """Adds various UI-facing attributes to the invocation's OpenAPI schema."""
         uiconfig = cast(UIConfigBase | None, getattr(model_class, "UIConfig", None))
         if uiconfig is not None:
@@ -237,6 +244,7 @@ class BaseInvocation(ABC, BaseModel):
             schema["version"] = uiconfig.version
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
+        schema["class"] = "invocation"
         schema["required"].extend(["type", "id"])
 
     @abstractmethod
@@ -310,7 +318,7 @@ class BaseInvocation(ABC, BaseModel):
         protected_namespaces=(),
         validate_assignment=True,
         json_schema_extra=json_schema_extra,
-        json_schema_serialization_defaults_required=True,
+        json_schema_serialization_defaults_required=False,
         coerce_numbers_to_str=True,
     )
 

invokeai/app/invocations/blend_latents.py

@@ -0,0 +1,98 @@
+from typing import Any, Union
+
+import numpy as np
+import numpy.typing as npt
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "lblend",
+    title="Blend Latents",
+    tags=["latents", "blend"],
+    category="latents",
+    version="1.0.3",
+)
+class BlendLatentsInvocation(BaseInvocation):
+    """Blend two latents using a given alpha. Latents must have same size."""
+
+    latents_a: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    latents_b: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents_a = context.tensors.load(self.latents_a.latents_name)
+        latents_b = context.tensors.load(self.latents_b.latents_name)
+
+        if latents_a.shape != latents_b.shape:
+            raise Exception("Latents to blend must be the same size.")
+
+        device = TorchDevice.choose_torch_device()
+
+        def slerp(
+            t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
+            v0: Union[torch.Tensor, npt.NDArray[Any]],
+            v1: Union[torch.Tensor, npt.NDArray[Any]],
+            DOT_THRESHOLD: float = 0.9995,
+        ) -> Union[torch.Tensor, npt.NDArray[Any]]:
+            """
+            Spherical linear interpolation
+            Args:
+                t (float/np.ndarray): Float value between 0.0 and 1.0
+                v0 (np.ndarray): Starting vector
+                v1 (np.ndarray): Final vector
+                DOT_THRESHOLD (float): Threshold for considering the two vectors as
+                                    colineal. Not recommended to alter this.
+            Returns:
+                v2 (np.ndarray): Interpolation vector between v0 and v1
+            """
+            inputs_are_torch = False
+            if not isinstance(v0, np.ndarray):
+                inputs_are_torch = True
+                v0 = v0.detach().cpu().numpy()
+            if not isinstance(v1, np.ndarray):
+                inputs_are_torch = True
+                v1 = v1.detach().cpu().numpy()
+
+            dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+            if np.abs(dot) > DOT_THRESHOLD:
+                v2 = (1 - t) * v0 + t * v1
+            else:
+                theta_0 = np.arccos(dot)
+                sin_theta_0 = np.sin(theta_0)
+                theta_t = theta_0 * t
+                sin_theta_t = np.sin(theta_t)
+                s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+                s1 = sin_theta_t / sin_theta_0
+                v2 = s0 * v0 + s1 * v1
+
+            if inputs_are_torch:
+                v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
+                return v2_torch
+            else:
+                assert isinstance(v2, np.ndarray)
+                return v2
+
+        # blend
+        bl = slerp(self.alpha, latents_a, latents_b)
+        assert isinstance(bl, torch.Tensor)
+        blended_latents: torch.Tensor = bl  # for type checking convenience
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        blended_latents = blended_latents.to("cpu")
+
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=blended_latents)
+        return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)

invokeai/app/invocations/constants.py

@@ -1,6 +1,7 @@
 from typing import Literal
 
 from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
+from invokeai.backend.util.devices import TorchDevice
 
 LATENT_SCALE_FACTOR = 8
 """
@@ -15,3 +16,5 @@ SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
 
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 """A literal type for PIL image modes supported by Invoke"""
+
+DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()

invokeai/app/invocations/create_denoise_mask.py

@@ -0,0 +1,80 @@
+from typing import Optional
+
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import resize as tv_resize
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField
+from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import DenoiseMaskOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+
+@invocation(
+    "create_denoise_mask",
+    title="Create Denoise Mask",
+    tags=["mask", "denoise"],
+    category="latents",
+    version="1.0.2",
+)
+class CreateDenoiseMaskInvocation(BaseInvocation):
+    """Creates mask for denoising model run."""
+
+    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 == torch.float32,
+        description=FieldDescriptions.fp32,
+        ui_order=4,
+    )
+
+    def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
+        if mask_image.mode != "L":
+            mask_image = mask_image.convert("L")
+        mask_tensor: torch.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.images.get_pil(self.image.image_name)
+            image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+            if image_tensor.dim() == 3:
+                image_tensor = image_tensor.unsqueeze(0)
+        else:
+            image_tensor = None
+
+        mask = self.prep_mask_tensor(
+            context.images.get_pil(self.mask.image_name),
+        )
+
+        if image_tensor is not None:
+            vae_info = context.models.load(self.vae.vae)
+
+            img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+            masked_image = image_tensor * 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 = context.tensors.save(tensor=masked_latents)
+        else:
+            masked_latents_name = None
+
+        mask_name = context.tensors.save(tensor=mask)
+
+        return DenoiseMaskOutput.build(
+            mask_name=mask_name,
+            masked_latents_name=masked_latents_name,
+            gradient=False,
+        )

invokeai/app/invocations/create_gradient_mask.py

@@ -0,0 +1,138 @@
+from typing import Literal, Optional
+
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image, ImageFilter
+from torchvision.transforms.functional import resize as tv_resize
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.fields import (
+    DenoiseMaskField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    OutputField,
+)
+from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
+from invokeai.app.invocations.model import UNetField, VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager import LoadedModel
+from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+
+@invocation_output("gradient_mask_output")
+class GradientMaskOutput(BaseInvocationOutput):
+    """Outputs a denoise mask and an image representing the total gradient of the mask."""
+
+    denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
+    expanded_mask_area: ImageField = OutputField(
+        description="Image representing the total gradient area of the mask. For paste-back purposes."
+    )
+
+
+@invocation(
+    "create_gradient_mask",
+    title="Create Gradient Mask",
+    tags=["mask", "denoise"],
+    category="latents",
+    version="1.1.0",
+)
+class CreateGradientMaskInvocation(BaseInvocation):
+    """Creates mask for denoising model run."""
+
+    mask: ImageField = InputField(default=None, description="Image which will be masked", ui_order=1)
+    edge_radius: int = InputField(
+        default=16, ge=0, description="How far to blur/expand the edges of the mask", ui_order=2
+    )
+    coherence_mode: Literal["Gaussian Blur", "Box Blur", "Staged"] = InputField(default="Gaussian Blur", ui_order=3)
+    minimum_denoise: float = InputField(
+        default=0.0, ge=0, le=1, description="Minimum denoise level for the coherence region", ui_order=4
+    )
+    image: Optional[ImageField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] Image",
+        ui_order=6,
+    )
+    unet: Optional[UNetField] = InputField(
+        description="OPTIONAL: If the Unet is a specialized Inpainting model, masked_latents will be generated from the image with the VAE",
+        default=None,
+        input=Input.Connection,
+        title="[OPTIONAL] UNet",
+        ui_order=5,
+    )
+    vae: Optional[VAEField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] VAE",
+        input=Input.Connection,
+        ui_order=7,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
+    fp32: bool = InputField(
+        default=DEFAULT_PRECISION == torch.float32,
+        description=FieldDescriptions.fp32,
+        ui_order=9,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> GradientMaskOutput:
+        mask_image = context.images.get_pil(self.mask.image_name, mode="L")
+        if self.edge_radius > 0:
+            if self.coherence_mode == "Box Blur":
+                blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
+            else:  # Gaussian Blur OR Staged
+                # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
+                blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
+
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
+
+            # redistribute blur so that the original edges are 0 and blur outwards to 1
+            blur_tensor = (blur_tensor - 0.5) * 2
+
+            threshold = 1 - self.minimum_denoise
+
+            if self.coherence_mode == "Staged":
+                # wherever the blur_tensor is less than fully masked, convert it to threshold
+                blur_tensor = torch.where((blur_tensor < 1) & (blur_tensor > 0), threshold, blur_tensor)
+            else:
+                # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
+                blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
+
+        else:
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+
+        mask_name = context.tensors.save(tensor=blur_tensor.unsqueeze(1))
+
+        # compute a [0, 1] mask from the blur_tensor
+        expanded_mask = torch.where((blur_tensor < 1), 0, 1)
+        expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
+        expanded_image_dto = context.images.save(expanded_mask_image)
+
+        masked_latents_name = None
+        if self.unet is not None and self.vae is not None and self.image is not None:
+            # all three fields must be present at the same time
+            main_model_config = context.models.get_config(self.unet.unet.key)
+            assert isinstance(main_model_config, MainConfigBase)
+            if main_model_config.variant is ModelVariantType.Inpaint:
+                mask = blur_tensor
+                vae_info: LoadedModel = context.models.load(self.vae.vae)
+                image = context.images.get_pil(self.image.image_name)
+                image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+                if image_tensor.dim() == 3:
+                    image_tensor = image_tensor.unsqueeze(0)
+                img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+                masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
+                masked_latents = ImageToLatentsInvocation.vae_encode(
+                    vae_info, self.fp32, self.tiled, masked_image.clone()
+                )
+                masked_latents_name = context.tensors.save(tensor=masked_latents)
+
+        return GradientMaskOutput(
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name, gradient=True),
+            expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
+        )

invokeai/app/invocations/crop_latents.py

@@ -0,0 +1,61 @@
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+# The Crop Latents node was copied from @skunkworxdark's implementation here:
+# https://github.com/skunkworxdark/XYGrid_nodes/blob/74647fa9c1fa57d317a94bd43ca689af7f0aae5e/images_to_grids.py#L1117C1-L1167C80
+@invocation(
+    "crop_latents",
+    title="Crop Latents",
+    tags=["latents", "crop"],
+    category="latents",
+    version="1.0.2",
+)
+# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
+# Currently, if the class names conflict then 'GET /openapi.json' fails.
+class CropLatentsCoreInvocation(BaseInvocation):
+    """Crops a latent-space tensor to a box specified in image-space. The box dimensions and coordinates must be
+    divisible by the latent scale factor of 8.
+    """
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    x: int = InputField(
+        ge=0,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    y: int = InputField(
+        ge=0,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    width: int = InputField(
+        ge=1,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The width (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    height: int = InputField(
+        ge=1,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        x1 = self.x // LATENT_SCALE_FACTOR
+        y1 = self.y // LATENT_SCALE_FACTOR
+        x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
+        y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
+
+        cropped_latents = latents[..., y1:y2, x1:x2]
+
+        name = context.tensors.save(tensor=cropped_latents)
+
+        return LatentsOutput.build(latents_name=name, latents=cropped_latents)

invokeai/app/invocations/ideal_size.py

@@ -0,0 +1,65 @@
+import math
+from typing import Tuple
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField
+from invokeai.app.invocations.model import UNetField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import BaseModelType
+
+
+@invocation_output("ideal_size_output")
+class IdealSizeOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    width: int = OutputField(description="The ideal width of the image (in pixels)")
+    height: int = OutputField(description="The ideal height of the image (in pixels)")
+
+
+@invocation(
+    "ideal_size",
+    title="Ideal Size",
+    tags=["latents", "math", "ideal_size"],
+    version="1.0.3",
+)
+class IdealSizeInvocation(BaseInvocation):
+    """Calculates the ideal size for generation to avoid duplication"""
+
+    width: int = InputField(default=1024, description="Final image width")
+    height: int = InputField(default=576, description="Final image height")
+    unet: UNetField = InputField(default=None, description=FieldDescriptions.unet)
+    multiplier: float = InputField(
+        default=1.0,
+        description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in "
+        "initial generation artifacts if too large)",
+    )
+
+    def trim_to_multiple_of(self, *args: int, multiple_of: int = LATENT_SCALE_FACTOR) -> Tuple[int, ...]:
+        return tuple((x - x % multiple_of) for x in args)
+
+    def invoke(self, context: InvocationContext) -> IdealSizeOutput:
+        unet_config = context.models.get_config(self.unet.unet.key)
+        aspect = self.width / self.height
+        dimension: float = 512
+        if unet_config.base == BaseModelType.StableDiffusion2:
+            dimension = 768
+        elif unet_config.base == BaseModelType.StableDiffusionXL:
+            dimension = 1024
+        dimension = dimension * self.multiplier
+        min_dimension = math.floor(dimension * 0.5)
+        model_area = dimension * dimension  # hardcoded for now since all models are trained on square images
+
+        if aspect > 1.0:
+            init_height = max(min_dimension, math.sqrt(model_area / aspect))
+            init_width = init_height * aspect
+        else:
+            init_width = max(min_dimension, math.sqrt(model_area * aspect))
+            init_height = init_width / aspect
+
+        scaled_width, scaled_height = self.trim_to_multiple_of(
+            math.floor(init_width),
+            math.floor(init_height),
+        )
+
+        return IdealSizeOutput(width=scaled_width, height=scaled_height)

invokeai/app/invocations/image_to_latents.py

@@ -0,0 +1,125 @@
+from functools import singledispatchmethod
+
+import einops
+import torch
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager import LoadedModel
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+
+@invocation(
+    "i2l",
+    title="Image to Latents",
+    tags=["latents", "image", "vae", "i2l"],
+    category="latents",
+    version="1.0.2",
+)
+class ImageToLatentsInvocation(BaseInvocation):
+    """Encodes an image into latents."""
+
+    image: ImageField = InputField(
+        description="The image to encode",
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
+    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+
+    @staticmethod
+    def vae_encode(vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor) -> torch.Tensor:
+        with vae_info as vae:
+            assert isinstance(vae, torch.nn.Module)
+            orig_dtype = vae.dtype
+            if upcast:
+                vae.to(dtype=torch.float32)
+
+                use_torch_2_0_or_xformers = hasattr(vae.decoder, "mid_block") and isinstance(
+                    vae.decoder.mid_block.attentions[0].processor,
+                    (
+                        AttnProcessor2_0,
+                        XFormersAttnProcessor,
+                        LoRAXFormersAttnProcessor,
+                        LoRAAttnProcessor2_0,
+                    ),
+                )
+                # if xformers or torch_2_0 is used attention block does not need
+                # to be in float32 which can save lots of memory
+                if use_torch_2_0_or_xformers:
+                    vae.post_quant_conv.to(orig_dtype)
+                    vae.decoder.conv_in.to(orig_dtype)
+                    vae.decoder.mid_block.to(orig_dtype)
+                # else:
+                #    latents = latents.float()
+
+            else:
+                vae.to(dtype=torch.float16)
+                # latents = latents.half()
+
+            if tiled:
+                vae.enable_tiling()
+            else:
+                vae.disable_tiling()
+
+            # non_noised_latents_from_image
+            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
+            with torch.inference_mode():
+                latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
+
+            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.images.get_pil(self.image.image_name)
+
+        vae_info = context.models.load(self.vae.vae)
+
+        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)
+
+        latents = latents.to("cpu")
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
+
+    @singledispatchmethod
+    @staticmethod
+    def _encode_to_tensor(vae: AutoencoderKL, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        assert isinstance(vae, torch.nn.Module)
+        image_tensor_dist = vae.encode(image_tensor).latent_dist
+        latents: torch.Tensor = image_tensor_dist.sample().to(
+            dtype=vae.dtype
+        )  # FIXME: uses torch.randn. make reproducible!
+        return latents
+
+    @_encode_to_tensor.register
+    @staticmethod
+    def _(vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        assert isinstance(vae, torch.nn.Module)
+        latents: torch.FloatTensor = vae.encode(image_tensor).latents
+        return latents

invokeai/app/invocations/latents_to_image.py

@@ -0,0 +1,127 @@
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.load.load_base import LoadedModel
+from invokeai.backend.stable_diffusion import set_seamless
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "l2i",
+    title="Latents to Image",
+    tags=["latents", "image", "vae", "l2i"],
+    category="latents",
+    version="1.2.2",
+)
+class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Generates an image from latents."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
+    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+
+    @staticmethod
+    def vae_decode(
+        context: InvocationContext,
+        vae_info: LoadedModel,
+        seamless_axes: list[str],
+        latents: torch.Tensor,
+        use_fp32: bool,
+        use_tiling: bool,
+    ) -> Image.Image:
+        assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
+        with set_seamless(vae_info.model, seamless_axes), vae_info as vae:
+            assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
+            latents = latents.to(vae.device)
+            if use_fp32:
+                vae.to(dtype=torch.float32)
+
+                use_torch_2_0_or_xformers = hasattr(vae.decoder, "mid_block") and isinstance(
+                    vae.decoder.mid_block.attentions[0].processor,
+                    (
+                        AttnProcessor2_0,
+                        XFormersAttnProcessor,
+                        LoRAXFormersAttnProcessor,
+                        LoRAAttnProcessor2_0,
+                    ),
+                )
+                # if xformers or torch_2_0 is used attention block does not need
+                # to be in float32 which can save lots of memory
+                if use_torch_2_0_or_xformers:
+                    vae.post_quant_conv.to(latents.dtype)
+                    vae.decoder.conv_in.to(latents.dtype)
+                    vae.decoder.mid_block.to(latents.dtype)
+                else:
+                    latents = latents.float()
+
+            else:
+                vae.to(dtype=torch.float16)
+                latents = latents.half()
+
+            if use_tiling or context.config.get().force_tiled_decode:
+                vae.enable_tiling()
+            else:
+                vae.disable_tiling()
+
+            # clear memory as vae decode can request a lot
+            TorchDevice.empty_cache()
+
+            with torch.inference_mode():
+                # copied from diffusers pipeline
+                latents = latents / vae.config.scaling_factor
+                image = vae.decode(latents, return_dict=False)[0]
+                image = (image / 2 + 0.5).clamp(0, 1)  # denormalize
+                # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+                np_image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+                image = VaeImageProcessor.numpy_to_pil(np_image)[0]
+
+        TorchDevice.empty_cache()
+
+        return image
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        vae_info = context.models.load(self.vae.vae)
+
+        image = self.vae_decode(
+            context=context,
+            vae_info=vae_info,
+            seamless_axes=self.vae.seamless_axes,
+            latents=latents,
+            use_fp32=self.fp32,
+            use_tiling=self.tiled,
+        )
+        image_dto = context.images.save(image=image)
+
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/resize_latents.py

@@ -0,0 +1,103 @@
+from typing import Literal
+
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+)
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.util.devices import TorchDevice
+
+LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
+
+
+@invocation(
+    "lresize",
+    title="Resize Latents",
+    tags=["latents", "resize"],
+    category="latents",
+    version="1.0.2",
+)
+class ResizeLatentsInvocation(BaseInvocation):
+    """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    width: int = InputField(
+        ge=64,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description=FieldDescriptions.width,
+    )
+    height: int = InputField(
+        ge=64,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description=FieldDescriptions.width,
+    )
+    mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
+    antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        device = TorchDevice.choose_torch_device()
+
+        resized_latents = torch.nn.functional.interpolate(
+            latents.to(device),
+            size=(self.height // LATENT_SCALE_FACTOR, self.width // LATENT_SCALE_FACTOR),
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
+        )
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        resized_latents = resized_latents.to("cpu")
+
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
+
+
+@invocation(
+    "lscale",
+    title="Scale Latents",
+    tags=["latents", "resize"],
+    category="latents",
+    version="1.0.2",
+)
+class ScaleLatentsInvocation(BaseInvocation):
+    """Scales latents by a given factor."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    scale_factor: float = InputField(gt=0, description=FieldDescriptions.scale_factor)
+    mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
+    antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        device = TorchDevice.choose_torch_device()
+
+        # resizing
+        resized_latents = torch.nn.functional.interpolate(
+            latents.to(device),
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
+        )
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        resized_latents = resized_latents.to("cpu")
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)

invokeai/app/invocations/scheduler.py

@@ -0,0 +1,34 @@
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    InputField,
+    OutputField,
+    UIType,
+)
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation_output("scheduler_output")
+class SchedulerOutput(BaseInvocationOutput):
+    scheduler: SCHEDULER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
+
+
+@invocation(
+    "scheduler",
+    title="Scheduler",
+    tags=["scheduler"],
+    category="latents",
+    version="1.0.0",
+)
+class SchedulerInvocation(BaseInvocation):
+    """Selects a scheduler."""
+
+    scheduler: SCHEDULER_NAME_VALUES = InputField(
+        default="euler",
+        description=FieldDescriptions.scheduler,
+        ui_type=UIType.Scheduler,
+    )
+
+    def invoke(self, context: InvocationContext) -> SchedulerOutput:
+        return SchedulerOutput(scheduler=self.scheduler)

invokeai/app/invocations/tiled_stable_diffusion_refine.py

@@ -0,0 +1,384 @@
+from contextlib import ExitStack
+from typing import Iterator, Tuple
+
+import numpy as np
+import numpy.typing as npt
+import torch
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from PIL import Image
+from pydantic import field_validator
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.fields import (
+    ConditioningField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    UIType,
+)
+from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
+from invokeai.app.invocations.latent import DenoiseLatentsInvocation, get_scheduler
+from invokeai.app.invocations.latents_to_image import LatentsToImageInvocation
+from invokeai.app.invocations.model import ModelIdentifierField, UNetField, VAEField
+from invokeai.app.invocations.noise import get_noise
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, prepare_control_image
+from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.model_patcher import ModelPatcher
+from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, image_resized_to_grid_as_tensor
+from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
+from invokeai.backend.tiles.utils import Tile
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.hotfixes import ControlNetModel
+
+
+@invocation(
+    "tiled_stable_diffusion_refine",
+    title="Tiled Stable Diffusion Refine",
+    tags=["upscale", "denoise"],
+    category="latents",
+    version="1.0.0",
+)
+class TiledStableDiffusionRefineInvocation(BaseInvocation):
+    """A tiled Stable Diffusion pipeline for refining high resolution images. This invocation is intended to be used to
+    refine an image after upscaling i.e. it is the second step in a typical "tiled upscaling" workflow.
+    """
+
+    image: ImageField = InputField(description="Image to be refined.")
+
+    positive_conditioning: ConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    negative_conditioning: ConditioningField = InputField(
+        description=FieldDescriptions.negative_cond, input=Input.Connection
+    )
+    # TODO(ryand): Add multiple-of validation.
+    tile_height: int = InputField(default=512, gt=0, description="Height of the tiles.")
+    tile_width: int = InputField(default=512, gt=0, description="Width of the tiles.")
+    tile_overlap: int = InputField(
+        default=16,
+        gt=0,
+        description="Target overlap between adjacent tiles (the last row/column may overlap more than this).",
+    )
+    steps: int = InputField(default=18, gt=0, description=FieldDescriptions.steps)
+    cfg_scale: float | list[float] = InputField(default=6.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
+    denoising_start: float = InputField(
+        default=0.65,
+        ge=0,
+        le=1,
+        description=FieldDescriptions.denoising_start,
+    )
+    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
+    scheduler: SCHEDULER_NAME_VALUES = InputField(
+        default="euler",
+        description=FieldDescriptions.scheduler,
+        ui_type=UIType.Scheduler,
+    )
+    unet: UNetField = InputField(
+        description=FieldDescriptions.unet,
+        input=Input.Connection,
+        title="UNet",
+    )
+    cfg_rescale_multiplier: float = InputField(
+        title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    vae_fp32: bool = InputField(
+        default=DEFAULT_PRECISION == torch.float32, description="Whether to use float32 precision when running the VAE."
+    )
+    # HACK(ryand): We probably want to allow the user to control all of the parameters in ControlField. But, we akwardly
+    # don't want to use the image field. Figure out how best to handle this.
+    # TODO(ryand): Currently, there is no ControlNet preprocessor applied to the tile images. In other words, we pretty
+    # much assume that it is a tile ControlNet. We need to decide how we want to handle this. E.g. find a way to support
+    # CN preprocessors, raise a clear warning when a non-tile CN model is selected, hardcode the supported CN models,
+    # etc.
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
+    )
+    control_weight: float = InputField(default=0.6)
+
+    @field_validator("cfg_scale")
+    def ge_one(cls, v: list[float] | float) -> list[float] | float:
+        """Validate that all cfg_scale values are >= 1"""
+        if isinstance(v, list):
+            for i in v:
+                if i < 1:
+                    raise ValueError("cfg_scale must be greater than 1")
+        else:
+            if v < 1:
+                raise ValueError("cfg_scale must be greater than 1")
+        return v
+
+    @staticmethod
+    def crop_latents_to_tile(latents: torch.Tensor, image_tile: Tile) -> torch.Tensor:
+        """Crop the latent-space tensor to the area corresponding to the image-space tile.
+        The tile coordinates must be divisible by the LATENT_SCALE_FACTOR.
+        """
+        for coord in [image_tile.coords.top, image_tile.coords.left, image_tile.coords.right, image_tile.coords.bottom]:
+            if coord % LATENT_SCALE_FACTOR != 0:
+                raise ValueError(
+                    f"The tile coordinates must all be divisible by the latent scale factor"
+                    f" ({LATENT_SCALE_FACTOR}). {image_tile.coords=}."
+                )
+        assert latents.dim() == 4  # We expect: (batch_size, channels, height, width).
+
+        top = image_tile.coords.top // LATENT_SCALE_FACTOR
+        left = image_tile.coords.left // LATENT_SCALE_FACTOR
+        bottom = image_tile.coords.bottom // LATENT_SCALE_FACTOR
+        right = image_tile.coords.right // LATENT_SCALE_FACTOR
+        return latents[..., top:bottom, left:right]
+
+    def run_controlnet(
+        self,
+        image: Image.Image,
+        controlnet_model: ControlNetModel,
+        weight: float,
+        do_classifier_free_guidance: bool,
+        width: int,
+        height: int,
+        device: torch.device,
+        dtype: torch.dtype,
+        control_mode: CONTROLNET_MODE_VALUES = "balanced",
+        resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
+    ) -> ControlNetData:
+        control_image = prepare_control_image(
+            image=image,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            width=width,
+            height=height,
+            device=device,
+            dtype=dtype,
+            control_mode=control_mode,
+            resize_mode=resize_mode,
+        )
+        return ControlNetData(
+            model=controlnet_model,
+            image_tensor=control_image,
+            weight=weight,
+            begin_step_percent=0.0,
+            end_step_percent=1.0,
+            control_mode=control_mode,
+            # Any resizing needed should currently be happening in prepare_control_image(), but adding resize_mode to
+            # ControlNetData in case needed in the future.
+            resize_mode=resize_mode,
+        )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        # TODO(ryand): Expose the seed parameter.
+        seed = 0
+
+        # Load the input image.
+        input_image = context.images.get_pil(self.image.image_name)
+
+        # Calculate the tile locations to cover the image.
+        # We have selected this tiling strategy to make it easy to achieve tile coords that are multiples of 8. This
+        # facilitates conversions between image space and latent space.
+        # TODO(ryand): Expose these tiling parameters. (Keep in mind the multiple-of constraints on these params.)
+        tiles = calc_tiles_with_overlap(
+            image_height=input_image.height,
+            image_width=input_image.width,
+            tile_height=self.tile_height,
+            tile_width=self.tile_width,
+            overlap=self.tile_overlap,
+        )
+
+        # Convert the input image to a torch.Tensor.
+        input_image_torch = image_resized_to_grid_as_tensor(input_image.convert("RGB"), multiple_of=LATENT_SCALE_FACTOR)
+        input_image_torch = input_image_torch.unsqueeze(0)  # Add a batch dimension.
+        # Validate our assumptions about the shape of input_image_torch.
+        assert input_image_torch.dim() == 4  # We expect: (batch_size, channels, height, width).
+        assert input_image_torch.shape[:2] == (1, 3)
+
+        # Split the input image into tiles in torch.Tensor format.
+        image_tiles_torch: list[torch.Tensor] = []
+        for tile in tiles:
+            image_tile = input_image_torch[
+                :,
+                :,
+                tile.coords.top : tile.coords.bottom,
+                tile.coords.left : tile.coords.right,
+            ]
+            image_tiles_torch.append(image_tile)
+
+        # Split the input image into tiles in numpy format.
+        # TODO(ryand): We currently maintain both np.ndarray and torch.Tensor tiles. Ideally, all operations should work
+        # with torch.Tensor tiles.
+        input_image_np = np.array(input_image)
+        image_tiles_np: list[npt.NDArray[np.uint8]] = []
+        for tile in tiles:
+            image_tile_np = input_image_np[
+                tile.coords.top : tile.coords.bottom,
+                tile.coords.left : tile.coords.right,
+                :,
+            ]
+            image_tiles_np.append(image_tile_np)
+
+        # VAE-encode each image tile independently.
+        # TODO(ryand): Is there any advantage to VAE-encoding the entire image before splitting it into tiles? What
+        # about for decoding?
+        vae_info = context.models.load(self.vae.vae)
+        latent_tiles: list[torch.Tensor] = []
+        for image_tile_torch in image_tiles_torch:
+            latent_tiles.append(
+                ImageToLatentsInvocation.vae_encode(
+                    vae_info=vae_info, upcast=self.vae_fp32, tiled=False, image_tensor=image_tile_torch
+                )
+            )
+
+        # Generate noise with dimensions corresponding to the full image in latent space.
+        # It is important that the noise tensor is generated at the full image dimension and then tiled, rather than
+        # generating for each tile independently. This ensures that overlapping regions between tiles use the same
+        # noise.
+        assert input_image_torch.shape[2] % LATENT_SCALE_FACTOR == 0
+        assert input_image_torch.shape[3] % LATENT_SCALE_FACTOR == 0
+        global_noise = get_noise(
+            width=input_image_torch.shape[3],
+            height=input_image_torch.shape[2],
+            device=TorchDevice.choose_torch_device(),
+            seed=seed,
+            downsampling_factor=LATENT_SCALE_FACTOR,
+            use_cpu=True,
+        )
+
+        # Crop the global noise into tiles.
+        noise_tiles = [self.crop_latents_to_tile(latents=global_noise, image_tile=t) for t in tiles]
+
+        # Prepare an iterator that yields the UNet's LoRA models and their weights.
+        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+            for lora in self.unet.loras:
+                lora_info = context.models.load(lora.lora)
+                assert isinstance(lora_info.model, LoRAModelRaw)
+                yield (lora_info.model, lora.weight)
+                del lora_info
+
+        # Load the UNet model.
+        unet_info = context.models.load(self.unet.unet)
+
+        refined_latent_tiles: list[torch.Tensor] = []
+        with ExitStack() as exit_stack, unet_info as unet, ModelPatcher.apply_lora_unet(unet, _lora_loader()):
+            assert isinstance(unet, UNet2DConditionModel)
+            scheduler = get_scheduler(
+                context=context,
+                scheduler_info=self.unet.scheduler,
+                scheduler_name=self.scheduler,
+                seed=seed,
+            )
+            pipeline = DenoiseLatentsInvocation.create_pipeline(unet=unet, scheduler=scheduler)
+
+            # Prepare the prompt conditioning data. The same prompt conditioning is applied to all tiles.
+            # Assume that all tiles have the same shape.
+            _, _, latent_height, latent_width = latent_tiles[0].shape
+            conditioning_data = DenoiseLatentsInvocation.get_conditioning_data(
+                context=context,
+                positive_conditioning_field=self.positive_conditioning,
+                negative_conditioning_field=self.negative_conditioning,
+                unet=unet,
+                latent_height=latent_height,
+                latent_width=latent_width,
+                cfg_scale=self.cfg_scale,
+                steps=self.steps,
+                cfg_rescale_multiplier=self.cfg_rescale_multiplier,
+            )
+
+            # Load the ControlNet model.
+            # TODO(ryand): Support multiple ControlNet models.
+            controlnet_model = exit_stack.enter_context(context.models.load(self.control_model))
+            assert isinstance(controlnet_model, ControlNetModel)
+
+            # Denoise (i.e. "refine") each tile independently.
+            for image_tile_np, latent_tile, noise_tile in zip(image_tiles_np, latent_tiles, noise_tiles, strict=True):
+                assert latent_tile.shape == noise_tile.shape
+
+                # Prepare a PIL Image for ControlNet processing.
+                # TODO(ryand): This is a bit awkward that we have to prepare both torch.Tensor and PIL.Image versions of
+                # the tiles. Ideally, the ControlNet code should be able to work with Tensors.
+                image_tile_pil = Image.fromarray(image_tile_np)
+
+                # Run the ControlNet on the image tile.
+                height, width, _ = image_tile_np.shape
+                # The height and width must be evenly divisible by LATENT_SCALE_FACTOR. This is enforced earlier, but we
+                # validate this assumption here.
+                assert height % LATENT_SCALE_FACTOR == 0
+                assert width % LATENT_SCALE_FACTOR == 0
+                controlnet_data = self.run_controlnet(
+                    image=image_tile_pil,
+                    controlnet_model=controlnet_model,
+                    weight=self.control_weight,
+                    do_classifier_free_guidance=True,
+                    width=width,
+                    height=height,
+                    device=controlnet_model.device,
+                    dtype=controlnet_model.dtype,
+                    control_mode="balanced",
+                    resize_mode="just_resize_simple",
+                )
+
+                num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = (
+                    DenoiseLatentsInvocation.init_scheduler(
+                        scheduler,
+                        device=unet.device,
+                        steps=self.steps,
+                        denoising_start=self.denoising_start,
+                        denoising_end=self.denoising_end,
+                        seed=seed,
+                    )
+                )
+
+                # TODO(ryand): Think about when/if latents/noise should be moved off of the device to save VRAM.
+                latent_tile = latent_tile.to(device=unet.device, dtype=unet.dtype)
+                noise_tile = noise_tile.to(device=unet.device, dtype=unet.dtype)
+                refined_latent_tile = pipeline.latents_from_embeddings(
+                    latents=latent_tile,
+                    timesteps=timesteps,
+                    init_timestep=init_timestep,
+                    noise=noise_tile,
+                    seed=seed,
+                    mask=None,
+                    masked_latents=None,
+                    gradient_mask=None,
+                    num_inference_steps=num_inference_steps,
+                    scheduler_step_kwargs=scheduler_step_kwargs,
+                    conditioning_data=conditioning_data,
+                    control_data=[controlnet_data],
+                    ip_adapter_data=None,
+                    t2i_adapter_data=None,
+                    callback=lambda x: None,
+                )
+                refined_latent_tiles.append(refined_latent_tile)
+
+        # VAE-decode each refined latent tile independently.
+        refined_image_tiles: list[Image.Image] = []
+        for refined_latent_tile in refined_latent_tiles:
+            refined_image_tile = LatentsToImageInvocation.vae_decode(
+                context=context,
+                vae_info=vae_info,
+                seamless_axes=self.vae.seamless_axes,
+                latents=refined_latent_tile,
+                use_fp32=self.vae_fp32,
+                use_tiling=False,
+            )
+            refined_image_tiles.append(refined_image_tile)
+
+        # TODO(ryand): I copied this from DenoiseLatentsInvocation. I'm not sure if it's actually important.
+        TorchDevice.empty_cache()
+
+        # Merge the refined image tiles back into a single image.
+        refined_image_tiles_np = [np.array(t) for t in refined_image_tiles]
+        merged_image_np = np.zeros(shape=(input_image.height, input_image.width, 3), dtype=np.uint8)
+        # TODO(ryand): Tune the blend_amount. Should this be exposed as a parameter?
+        merge_tiles_with_linear_blending(
+            dst_image=merged_image_np, tiles=tiles, tile_images=refined_image_tiles_np, blend_amount=self.tile_overlap
+        )
+
+        # Save the refined image and return its reference.
+        merged_image_pil = Image.fromarray(merged_image_np)
+        image_dto = context.images.save(image=merged_image_pil)
+
+        return ImageOutput.build(image_dto)

invokeai/app/services/events/events_common.py

@@ -3,9 +3,8 @@ from typing import TYPE_CHECKING, Any, ClassVar, Coroutine, Generic, Optional, P
 
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.registry.payload_schema import registry as payload_schema
-from pydantic import BaseModel, ConfigDict, Field, SerializeAsAny, field_validator
+from pydantic import BaseModel, ConfigDict, Field
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
 from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
@@ -14,6 +13,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItem,
     SessionQueueStatus,
 )
+from invokeai.app.services.shared.graph import AnyInvocation, AnyInvocationOutput
 from invokeai.app.util.misc import get_timestamp
 from invokeai.backend.model_manager.config import AnyModelConfig, SubModelType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
@@ -98,17 +98,9 @@ class InvocationEventBase(QueueItemEventBase):
     item_id: int = Field(description="The ID of the queue item")
     batch_id: str = Field(description="The ID of the queue batch")
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
-    invocation: SerializeAsAny[BaseInvocation] = Field(description="The ID of the invocation")
+    invocation: AnyInvocation = Field(description="The ID of the invocation")
     invocation_source_id: str = Field(description="The ID of the prepared invocation's source node")
 
-    @field_validator("invocation", mode="plain")
-    @classmethod
-    def validate_invocation(cls, v: Any):
-        """Validates the invocation using the dynamic type adapter."""
-
-        invocation = BaseInvocation.get_typeadapter().validate_python(v)
-        return invocation
-
 
 @payload_schema.register
 class InvocationStartedEvent(InvocationEventBase):
@@ -117,7 +109,7 @@ class InvocationStartedEvent(InvocationEventBase):
     __event_name__ = "invocation_started"
 
     @classmethod
-    def build(cls, queue_item: SessionQueueItem, invocation: BaseInvocation) -> "InvocationStartedEvent":
+    def build(cls, queue_item: SessionQueueItem, invocation: AnyInvocation) -> "InvocationStartedEvent":
         return cls(
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
@@ -144,7 +136,7 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
     def build(
         cls,
         queue_item: SessionQueueItem,
-        invocation: BaseInvocation,
+        invocation: AnyInvocation,
         intermediate_state: PipelineIntermediateState,
         progress_image: ProgressImage,
     ) -> "InvocationDenoiseProgressEvent":
@@ -182,19 +174,11 @@ class InvocationCompleteEvent(InvocationEventBase):
 
     __event_name__ = "invocation_complete"
 
-    result: SerializeAsAny[BaseInvocationOutput] = Field(description="The result of the invocation")
-
-    @field_validator("result", mode="plain")
-    @classmethod
-    def validate_results(cls, v: Any):
-        """Validates the invocation result using the dynamic type adapter."""
-
-        result = BaseInvocationOutput.get_typeadapter().validate_python(v)
-        return result
+    result: AnyInvocationOutput = Field(description="The result of the invocation")
 
     @classmethod
     def build(
-        cls, queue_item: SessionQueueItem, invocation: BaseInvocation, result: BaseInvocationOutput
+        cls, queue_item: SessionQueueItem, invocation: AnyInvocation, result: AnyInvocationOutput
     ) -> "InvocationCompleteEvent":
         return cls(
             queue_id=queue_item.queue_id,
@@ -223,7 +207,7 @@ class InvocationErrorEvent(InvocationEventBase):
     def build(
         cls,
         queue_item: SessionQueueItem,
-        invocation: BaseInvocation,
+        invocation: AnyInvocation,
         error_type: str,
         error_message: str,
         error_traceback: str,

invokeai/app/services/shared/graph.py

@@ -2,18 +2,19 @@
 
 import copy
 import itertools
-from typing import Annotated, Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
+from typing import Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import (
     BaseModel,
+    GetCoreSchemaHandler,
     GetJsonSchemaHandler,
     ValidationError,
     field_validator,
 )
 from pydantic.fields import Field
 from pydantic.json_schema import JsonSchemaValue
-from pydantic_core import CoreSchema
+from pydantic_core import core_schema
 
 # Importing * is bad karma but needed here for node detection
 from invokeai.app.invocations import *  # noqa: F401 F403
@@ -277,73 +278,58 @@ class CollectInvocation(BaseInvocation):
         return CollectInvocationOutput(collection=copy.copy(self.collection))
 
 
+class AnyInvocation(BaseInvocation):
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        def validate_invocation(v: Any) -> "AnyInvocation":
+            return BaseInvocation.get_typeadapter().validate_python(v)
+
+        return core_schema.no_info_plain_validator_function(validate_invocation)
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        # Nodes are too powerful, we have to make our own OpenAPI schema manually
+        # No but really, because the schema is dynamic depending on loaded nodes, we need to generate it manually
+        oneOf: list[dict[str, str]] = []
+        names = [i.__name__ for i in BaseInvocation.get_invocations()]
+        for name in sorted(names):
+            oneOf.append({"$ref": f"#/components/schemas/{name}"})
+        return {"oneOf": oneOf}
+
+
+class AnyInvocationOutput(BaseInvocationOutput):
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler):
+        def validate_invocation_output(v: Any) -> "AnyInvocationOutput":
+            return BaseInvocationOutput.get_typeadapter().validate_python(v)
+
+        return core_schema.no_info_plain_validator_function(validate_invocation_output)
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        # Nodes are too powerful, we have to make our own OpenAPI schema manually
+        # No but really, because the schema is dynamic depending on loaded nodes, we need to generate it manually
+
+        oneOf: list[dict[str, str]] = []
+        names = [i.__name__ for i in BaseInvocationOutput.get_outputs()]
+        for name in sorted(names):
+            oneOf.append({"$ref": f"#/components/schemas/{name}"})
+        return {"oneOf": oneOf}
+
+
 class Graph(BaseModel):
     id: str = Field(description="The id of this graph", default_factory=uuid_string)
     # TODO: use a list (and never use dict in a BaseModel) because pydantic/fastapi hates me
-    nodes: dict[str, BaseInvocation] = Field(description="The nodes in this graph", default_factory=dict)
+    nodes: dict[str, AnyInvocation] = Field(description="The nodes in this graph", default_factory=dict)
     edges: list[Edge] = Field(
         description="The connections between nodes and their fields in this graph",
         default_factory=list,
     )
 
-    @field_validator("nodes", mode="plain")
-    @classmethod
-    def validate_nodes(cls, v: dict[str, Any]):
-        """Validates the nodes in the graph by retrieving a union of all node types and validating each node."""
-
-        # Invocations register themselves as their python modules are executed. The union of all invocations is
-        # constructed at runtime. We use pydantic to validate `Graph.nodes` using that union.
-        #
-        # It's possible that when `graph.py` is executed, not all invocation-containing modules will have executed. If
-        # we construct the invocation union as `graph.py` is executed, we may miss some invocations. Those missing
-        # invocations will cause a graph to fail if they are used.
-        #
-        # We can get around this by validating the nodes in the graph using a "plain" validator, which overrides the
-        # pydantic validation entirely. This allows us to validate the nodes using the union of invocations at runtime.
-        #
-        # This same pattern is used in `GraphExecutionState`.
-
-        nodes: dict[str, BaseInvocation] = {}
-        typeadapter = BaseInvocation.get_typeadapter()
-        for node_id, node in v.items():
-            nodes[node_id] = typeadapter.validate_python(node)
-        return nodes
-
-    @classmethod
-    def __get_pydantic_json_schema__(cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
-        # We use a "plain" validator to validate the nodes in the graph. Pydantic is unable to create a JSON Schema for
-        # fields that use "plain" validators, so we have to hack around this. Also, we need to add all invocations to
-        # the generated schema as options for the `nodes` field.
-        #
-        # The workaround is to create a new BaseModel that has the same fields as `Graph` but without the validator and
-        # with the invocation union as the type for the `nodes` field. Pydantic then generates the JSON Schema as
-        # expected.
-        #
-        # You might be tempted to do something like this:
-        #
-        # ```py
-        # cloned_model = create_model(cls.__name__, __base__=cls, nodes=...)
-        # delattr(cloned_model, "validate_nodes")
-        # cloned_model.model_rebuild(force=True)
-        # json_schema = handler(cloned_model.__pydantic_core_schema__)
-        # ```
-        #
-        # Unfortunately, this does not work. Calling `handler` here results in infinite recursion as pydantic attempts
-        # to build the JSON Schema for the cloned model. Instead, we have to manually clone the model.
-        #
-        # This same pattern is used in `GraphExecutionState`.
-
-        class Graph(BaseModel):
-            id: Optional[str] = Field(default=None, description="The id of this graph")
-            nodes: dict[
-                str, Annotated[Union[tuple(BaseInvocation._invocation_classes)], Field(discriminator="type")]
-            ] = Field(description="The nodes in this graph")
-            edges: list[Edge] = Field(description="The connections between nodes and their fields in this graph")
-
-        json_schema = handler(Graph.__pydantic_core_schema__)
-        json_schema = handler.resolve_ref_schema(json_schema)
-        return json_schema
-
     def add_node(self, node: BaseInvocation) -> None:
         """Adds a node to a graph
 
@@ -774,7 +760,7 @@ class GraphExecutionState(BaseModel):
     )
 
     # The results of executed nodes
-    results: dict[str, BaseInvocationOutput] = Field(description="The results of node executions", default_factory=dict)
+    results: dict[str, AnyInvocationOutput] = Field(description="The results of node executions", default_factory=dict)
 
     # Errors raised when executing nodes
     errors: dict[str, str] = Field(description="Errors raised when executing nodes", default_factory=dict)
@@ -791,52 +777,12 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
-    @field_validator("results", mode="plain")
-    @classmethod
-    def validate_results(cls, v: dict[str, BaseInvocationOutput]):
-        """Validates the results in the GES by retrieving a union of all output types and validating each result."""
-
-        # See the comment in `Graph.validate_nodes` for an explanation of this logic.
-        results: dict[str, BaseInvocationOutput] = {}
-        typeadapter = BaseInvocationOutput.get_typeadapter()
-        for result_id, result in v.items():
-            results[result_id] = typeadapter.validate_python(result)
-        return results
-
     @field_validator("graph")
     def graph_is_valid(cls, v: Graph):
         """Validates that the graph is valid"""
         v.validate_self()
         return v
 
-    @classmethod
-    def __get_pydantic_json_schema__(cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
-        # See the comment in `Graph.__get_pydantic_json_schema__` for an explanation of this logic.
-        class GraphExecutionState(BaseModel):
-            """Tracks the state of a graph execution"""
-
-            id: str = Field(description="The id of the execution state")
-            graph: Graph = Field(description="The graph being executed")
-            execution_graph: Graph = Field(description="The expanded graph of activated and executed nodes")
-            executed: set[str] = Field(description="The set of node ids that have been executed")
-            executed_history: list[str] = Field(
-                description="The list of node ids that have been executed, in order of execution"
-            )
-            results: dict[
-                str, Annotated[Union[tuple(BaseInvocationOutput._output_classes)], Field(discriminator="type")]
-            ] = Field(description="The results of node executions")
-            errors: dict[str, str] = Field(description="Errors raised when executing nodes")
-            prepared_source_mapping: dict[str, str] = Field(
-                description="The map of prepared nodes to original graph nodes"
-            )
-            source_prepared_mapping: dict[str, set[str]] = Field(
-                description="The map of original graph nodes to prepared nodes"
-            )
-
-        json_schema = handler(GraphExecutionState.__pydantic_core_schema__)
-        json_schema = handler.resolve_ref_schema(json_schema)
-        return json_schema
-
     def next(self) -> Optional[BaseInvocation]:
         """Gets the next node ready to execute."""
 

invokeai/app/util/controlnet_utils.py

@@ -289,7 +289,7 @@ def prepare_control_image(
     width: int,
     height: int,
     num_channels: int = 3,
-    device: str = "cuda",
+    device: str | torch.device = "cuda",
     dtype: torch.dtype = torch.float16,
     control_mode: CONTROLNET_MODE_VALUES = "balanced",
     resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
@@ -304,7 +304,7 @@ def prepare_control_image(
         num_channels (int, optional): The target number of image channels. This is achieved by converting the input
             image to RGB, then naively taking the first `num_channels` channels. The primary use case is converting a
             RGB image to a single-channel grayscale image. Raises if `num_channels` cannot be achieved. Defaults to 3.
-        device (str, optional): The target device for the output image. Defaults to "cuda".
+        device (str | torch.Device, optional): The target device for the output image. Defaults to "cuda".
         dtype (_type_, optional): The dtype for the output image. Defaults to torch.float16.
         do_classifier_free_guidance (bool, optional): If True, repeat the output image along the batch dimension.
             Defaults to True.

invokeai/app/util/custom_openapi.py

@@ -0,0 +1,116 @@
+from typing import Any, Callable, Optional
+
+from fastapi import FastAPI
+from fastapi.openapi.utils import get_openapi
+from pydantic.json_schema import models_json_schema
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, UIConfigBase
+from invokeai.app.invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.services.events.events_common import EventBase
+from invokeai.app.services.session_processor.session_processor_common import ProgressImage
+
+
+def move_defs_to_top_level(openapi_schema: dict[str, Any], component_schema: dict[str, Any]) -> None:
+    """Moves a component schema's $defs to the top level of the openapi schema. Useful when generating a schema
+    for a single model that needs to be added back to the top level of the schema. Mutates openapi_schema and
+    component_schema."""
+
+    defs = component_schema.pop("$defs", {})
+    for schema_key, json_schema in defs.items():
+        if schema_key in openapi_schema["components"]["schemas"]:
+            continue
+        openapi_schema["components"]["schemas"][schema_key] = json_schema
+
+
+def get_openapi_func(
+    app: FastAPI, post_transform: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None
+) -> Callable[[], dict[str, Any]]:
+    """Gets the OpenAPI schema generator function.
+
+    Args:
+        app (FastAPI): The FastAPI app to generate the schema for.
+        post_transform (Optional[Callable[[dict[str, Any]], dict[str, Any]]], optional): A function to apply to the
+            generated schema before returning it. Defaults to None.
+
+    Returns:
+        Callable[[], dict[str, Any]]: The OpenAPI schema generator function. When first called, the generated schema is
+            cached in `app.openapi_schema`. On subsequent calls, the cached schema is returned. This caching behaviour
+            matches FastAPI's default schema generation caching.
+    """
+
+    def openapi() -> dict[str, Any]:
+        if app.openapi_schema:
+            return app.openapi_schema
+
+        openapi_schema = get_openapi(
+            title=app.title,
+            description="An API for invoking AI image operations",
+            version="1.0.0",
+            routes=app.routes,
+            separate_input_output_schemas=False,  # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
+        )
+
+        # We'll create a map of invocation type to output schema to make some types simpler on the client.
+        invocation_output_map_properties: dict[str, Any] = {}
+        invocation_output_map_required: list[str] = []
+
+        # We need to manually add all outputs to the schema - pydantic doesn't add them because they aren't used directly.
+        for output in BaseInvocationOutput.get_outputs():
+            json_schema = output.model_json_schema(mode="serialization", ref_template="#/components/schemas/{model}")
+            move_defs_to_top_level(openapi_schema, json_schema)
+            openapi_schema["components"]["schemas"][output.__name__] = json_schema
+
+        # Technically, invocations are added to the schema by pydantic, but we still need to manually set their output
+        # property, so we'll just do it all manually.
+        for invocation in BaseInvocation.get_invocations():
+            json_schema = invocation.model_json_schema(
+                mode="serialization", ref_template="#/components/schemas/{model}"
+            )
+            move_defs_to_top_level(openapi_schema, json_schema)
+            output_title = invocation.get_output_annotation().__name__
+            outputs_ref = {"$ref": f"#/components/schemas/{output_title}"}
+            json_schema["output"] = outputs_ref
+            openapi_schema["components"]["schemas"][invocation.__name__] = json_schema
+
+            # Add this invocation and its output to the output map
+            invocation_type = invocation.get_type()
+            invocation_output_map_properties[invocation_type] = json_schema["output"]
+            invocation_output_map_required.append(invocation_type)
+
+        # Add the output map to the schema
+        openapi_schema["components"]["schemas"]["InvocationOutputMap"] = {
+            "type": "object",
+            "properties": invocation_output_map_properties,
+            "required": invocation_output_map_required,
+        }
+
+        # Some models don't end up in the schemas as standalone definitions because they aren't used directly in the API.
+        # We need to add them manually here. WARNING: Pydantic can choke if you call `model.model_json_schema()` to get
+        # a schema. This has something to do with schema refs - not totally clear. For whatever reason, using
+        # `models_json_schema` seems to work fine.
+        additional_models = [
+            *EventBase.get_events(),
+            UIConfigBase,
+            InputFieldJSONSchemaExtra,
+            OutputFieldJSONSchemaExtra,
+            ModelIdentifierField,
+            ProgressImage,
+        ]
+
+        additional_schemas = models_json_schema(
+            [(m, "serialization") for m in additional_models],
+            ref_template="#/components/schemas/{model}",
+        )
+        # additional_schemas[1] is a dict of $defs that we need to add to the top level of the schema
+        move_defs_to_top_level(openapi_schema, additional_schemas[1])
+
+        if post_transform is not None:
+            openapi_schema = post_transform(openapi_schema)
+
+        openapi_schema["components"]["schemas"] = dict(sorted(openapi_schema["components"]["schemas"].items()))
+
+        app.openapi_schema = openapi_schema
+        return app.openapi_schema
+
+    return openapi

invokeai/backend/model_manager/load/model_cache/model_locker.py

@@ -60,5 +60,5 @@ class ModelLocker(ModelLockerBase):
 
         self._cache_entry.unlock()
         if not self._cache.lazy_offloading:
-            self._cache.offload_unlocked_models(self._cache_entry.size)
+            self._cache.offload_unlocked_models(0)
             self._cache.print_cuda_stats()

invokeai/backend/model_manager/probe.py

@@ -10,7 +10,7 @@ from picklescan.scanner import scan_file_path
 import invokeai.backend.util.logging as logger
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
-from invokeai.backend.util.util import SilenceWarnings
+from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 from .config import (
     AnyModelConfig,

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -11,7 +11,6 @@ import psutil
 import torch
 import torchvision.transforms as T
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.models.controlnet import ControlNetModel
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
@@ -26,6 +25,7 @@ from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion impor
 from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher, UNetIPAdapterData
 from invokeai.backend.util.attention import auto_detect_slice_size
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.hotfixes import ControlNetModel
 
 
 @dataclass

invokeai/backend/util/silence_warnings.py

@@ -1,29 +1,36 @@
-"""Context class to silence transformers and diffusers warnings."""
-
 import warnings
-from typing import Any
+from contextlib import ContextDecorator
 
-from diffusers import logging as diffusers_logging
+from diffusers.utils import logging as diffusers_logging
 from transformers import logging as transformers_logging
 
 
-class SilenceWarnings(object):
-    """Use in context to temporarily turn off warnings from transformers & diffusers modules.
+# Inherit from ContextDecorator to allow using SilenceWarnings as both a context manager and a decorator.
+class SilenceWarnings(ContextDecorator):
+    """A context manager that disables warnings from transformers & diffusers modules while active.
 
+    As context manager:
+    ```
     with SilenceWarnings():
         # do something
+    ```
+
+    As decorator:
+    ```
+    @SilenceWarnings()
+    def some_function():
+        # do something
+    ```
     """
 
-    def __init__(self) -> None:
-        self.transformers_verbosity = transformers_logging.get_verbosity()
-        self.diffusers_verbosity = diffusers_logging.get_verbosity()
-
     def __enter__(self) -> None:
+        self._transformers_verbosity = transformers_logging.get_verbosity()
+        self._diffusers_verbosity = diffusers_logging.get_verbosity()
         transformers_logging.set_verbosity_error()
         diffusers_logging.set_verbosity_error()
         warnings.simplefilter("ignore")
 
-    def __exit__(self, *args: Any) -> None:
-        transformers_logging.set_verbosity(self.transformers_verbosity)
-        diffusers_logging.set_verbosity(self.diffusers_verbosity)
+    def __exit__(self, *args) -> None:
+        transformers_logging.set_verbosity(self._transformers_verbosity)
+        diffusers_logging.set_verbosity(self._diffusers_verbosity)
         warnings.simplefilter("default")

invokeai/backend/util/util.py

@@ -1,12 +1,9 @@
 import base64
 import io
 import os
-import warnings
 from pathlib import Path
 
-from diffusers import logging as diffusers_logging
 from PIL import Image
-from transformers import logging as transformers_logging
 
 # actual size of a gig
 GIG = 1073741824
@@ -51,21 +48,3 @@ class Chdir(object):
 
     def __exit__(self, *args):
         os.chdir(self.original)
-
-
-class SilenceWarnings(object):
-    """Context manager to temporarily lower verbosity of diffusers & transformers warning messages."""
-
-    def __enter__(self):
-        """Set verbosity to error."""
-        self.transformers_verbosity = transformers_logging.get_verbosity()
-        self.diffusers_verbosity = diffusers_logging.get_verbosity()
-        transformers_logging.set_verbosity_error()
-        diffusers_logging.set_verbosity_error()
-        warnings.simplefilter("ignore")
-
-    def __exit__(self, type, value, traceback):
-        """Restore logger verbosity to state before context was entered."""
-        transformers_logging.set_verbosity(self.transformers_verbosity)
-        diffusers_logging.set_verbosity(self.diffusers_verbosity)
-        warnings.simplefilter("default")

invokeai/frontend/web/public/locales/de.json

@@ -1021,7 +1021,8 @@
         "float": "Kommazahlen",
         "enum": "Aufzählung",
         "fullyContainNodes": "Vollständig ausgewählte Nodes auswählen",
-        "editMode": "Im Workflow-Editor bearbeiten"
+        "editMode": "Im Workflow-Editor bearbeiten",
+        "resetToDefaultValue": "Auf Standardwert zurücksetzen"
     },
     "hrf": {
         "enableHrf": "Korrektur für hohe Auflösungen",

invokeai/frontend/web/public/locales/en.json

@@ -148,6 +148,8 @@
         "viewingDesc": "Review images in a large gallery view",
         "editing": "Editing",
         "editingDesc": "Edit on the Control Layers canvas",
+        "comparing": "Comparing",
+        "comparingDesc": "Comparing two images",
         "enabled": "Enabled",
         "disabled": "Disabled"
     },
@@ -375,7 +377,23 @@
         "bulkDownloadRequestFailed": "Problem Preparing Download",
         "bulkDownloadFailed": "Download Failed",
         "problemDeletingImages": "Problem Deleting Images",
-        "problemDeletingImagesDesc": "One or more images could not be deleted"
+        "problemDeletingImagesDesc": "One or more images could not be deleted",
+        "viewerImage": "Viewer Image",
+        "compareImage": "Compare Image",
+        "openInViewer": "Open in Viewer",
+        "selectForCompare": "Select for Compare",
+        "selectAnImageToCompare": "Select an Image to Compare",
+        "slider": "Slider",
+        "sideBySide": "Side-by-Side",
+        "hover": "Hover",
+        "swapImages": "Swap Images",
+        "compareOptions": "Comparison Options",
+        "stretchToFit": "Stretch to Fit",
+        "exitCompare": "Exit Compare",
+        "compareHelp1": "Hold <Kbd>Alt</Kbd> while clicking a gallery image or using the arrow keys to change the compare image.",
+        "compareHelp2": "Press <Kbd>M</Kbd> to cycle through comparison modes.",
+        "compareHelp3": "Press <Kbd>C</Kbd> to swap the compared images.",
+        "compareHelp4": "Press <Kbd>Z</Kbd> or <Kbd>Esc</Kbd> to exit."
     },
     "hotkeys": {
         "searchHotkeys": "Search Hotkeys",

invokeai/frontend/web/public/locales/es.json

@@ -6,7 +6,7 @@
         "settingsLabel": "Ajustes",
         "img2img": "Imagen a Imagen",
         "unifiedCanvas": "Lienzo Unificado",
-        "nodes": "Editor del flujo de trabajo",
+        "nodes": "Flujos de trabajo",
         "upload": "Subir imagen",
         "load": "Cargar",
         "statusDisconnected": "Desconectado",
@@ -14,7 +14,7 @@
         "discordLabel": "Discord",
         "back": "Atrás",
         "loading": "Cargando",
-        "postprocessing": "Tratamiento posterior",
+        "postprocessing": "Postprocesado",
         "txt2img": "De texto a imagen",
         "accept": "Aceptar",
         "cancel": "Cancelar",
@@ -42,7 +42,42 @@
         "copy": "Copiar",
         "beta": "Beta",
         "on": "En",
-        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:"
+        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:",
+        "installed": "Instalado",
+        "green": "Verde",
+        "editor": "Editor",
+        "orderBy": "Ordenar por",
+        "file": "Archivo",
+        "goTo": "Ir a",
+        "imageFailedToLoad": "No se puede cargar la imagen",
+        "saveAs": "Guardar Como",
+        "somethingWentWrong": "Algo salió mal",
+        "nextPage": "Página Siguiente",
+        "selected": "Seleccionado",
+        "tab": "Tabulador",
+        "positivePrompt": "Prompt Positivo",
+        "negativePrompt": "Prompt Negativo",
+        "error": "Error",
+        "format": "formato",
+        "unknown": "Desconocido",
+        "input": "Entrada",
+        "nodeEditor": "Editor de nodos",
+        "template": "Plantilla",
+        "prevPage": "Página Anterior",
+        "red": "Rojo",
+        "alpha": "Transparencia",
+        "outputs": "Salidas",
+        "editing": "Editando",
+        "learnMore": "Aprende más",
+        "enabled": "Activado",
+        "disabled": "Desactivado",
+        "folder": "Carpeta",
+        "updated": "Actualizado",
+        "created": "Creado",
+        "save": "Guardar",
+        "unknownError": "Error Desconocido",
+        "blue": "Azul",
+        "viewingDesc": "Revisar imágenes en una vista de galería grande"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -467,7 +502,8 @@
         "about": "Acerca de",
         "createIssue": "Crear un problema",
         "resetUI": "Interfaz de usuario $t(accessibility.reset)",
-        "mode": "Modo"
+        "mode": "Modo",
+        "submitSupportTicket": "Enviar Ticket de Soporte"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -543,5 +579,17 @@
         "layers_one": "Capa",
         "layers_many": "Capas",
         "layers_other": "Capas"
+    },
+    "controlnet": {
+        "crop": "Cortar",
+        "delete": "Eliminar",
+        "depthAnythingDescription": "Generación de mapa de profundidad usando la técnica de Depth Anything",
+        "duplicate": "Duplicar",
+        "colorMapDescription": "Genera un mapa de color desde la imagen",
+        "depthMidasDescription": "Crea un mapa de profundidad con Midas",
+        "balanced": "Equilibrado",
+        "beginEndStepPercent": "Inicio / Final Porcentaje de pasos",
+        "detectResolution": "Detectar resolución",
+        "beginEndStepPercentShort": "Inicio / Final %"
     }
 }

invokeai/frontend/web/public/locales/it.json

@@ -45,7 +45,7 @@
         "outputs": "Risultati",
         "data": "Dati",
         "somethingWentWrong": "Qualcosa è andato storto",
-        "copyError": "$t(gallery.copy) Errore",
+        "copyError": "Errore $t(gallery.copy)",
         "input": "Ingresso",
         "notInstalled": "Non $t(common.installed)",
         "unknownError": "Errore sconosciuto",
@@ -85,7 +85,11 @@
         "viewing": "Visualizza",
         "viewingDesc": "Rivedi le immagini in un'ampia vista della galleria",
         "editing": "Modifica",
-        "editingDesc": "Modifica nell'area Livelli di controllo"
+        "editingDesc": "Modifica nell'area Livelli di controllo",
+        "enabled": "Abilitato",
+        "disabled": "Disabilitato",
+        "comparingDesc": "Confronta due immagini",
+        "comparing": "Confronta"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -122,14 +126,30 @@
         "bulkDownloadRequestedDesc": "La tua richiesta di download è in preparazione. L'operazione potrebbe richiedere alcuni istanti.",
         "bulkDownloadRequestFailed": "Problema durante la preparazione del download",
         "bulkDownloadFailed": "Scaricamento fallito",
-        "alwaysShowImageSizeBadge": "Mostra sempre le dimensioni dell'immagine"
+        "alwaysShowImageSizeBadge": "Mostra sempre le dimensioni dell'immagine",
+        "openInViewer": "Apri nel visualizzatore",
+        "selectForCompare": "Seleziona per il confronto",
+        "selectAnImageToCompare": "Seleziona un'immagine da confrontare",
+        "slider": "Cursore",
+        "sideBySide": "Fianco a Fianco",
+        "compareImage": "Immagine di confronto",
+        "viewerImage": "Immagine visualizzata",
+        "hover": "Al passaggio del mouse",
+        "swapImages": "Scambia le immagini",
+        "compareOptions": "Opzioni di confronto",
+        "stretchToFit": "Scala per adattare",
+        "exitCompare": "Esci dal confronto",
+        "compareHelp1": "Tieni premuto <Kbd>Alt</Kbd> mentre fai clic su un'immagine della galleria o usi i tasti freccia per cambiare l'immagine di confronto.",
+        "compareHelp2": "Premi <Kbd>M</Kbd> per scorrere le modalità di confronto.",
+        "compareHelp3": "Premi <Kbd>C</Kbd> per scambiare le immagini confrontate.",
+        "compareHelp4": "Premi <Kbd>Z</Kbd> o <Kbd>Esc</Kbd> per uscire."
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti di scelta rapida",
         "appHotkeys": "Applicazione",
         "generalHotkeys": "Generale",
         "galleryHotkeys": "Galleria",
-        "unifiedCanvasHotkeys": "Tela Unificata",
+        "unifiedCanvasHotkeys": "Tela",
         "invoke": {
             "title": "Invoke",
             "desc": "Genera un'immagine"
@@ -147,8 +167,8 @@
             "desc": "Apre e chiude il pannello delle opzioni"
         },
         "pinOptions": {
-            "title": "Appunta le opzioni",
-            "desc": "Blocca il pannello delle opzioni"
+            "title": "Fissa le opzioni",
+            "desc": "Fissa il pannello delle opzioni"
         },
         "toggleGallery": {
             "title": "Attiva/disattiva galleria",
@@ -332,14 +352,14 @@
             "title": "Annulla e cancella"
         },
         "resetOptionsAndGallery": {
-            "title": "Ripristina Opzioni e Galleria",
-            "desc": "Reimposta le opzioni e i pannelli della galleria"
+            "title": "Ripristina le opzioni e la galleria",
+            "desc": "Reimposta i pannelli delle opzioni e della galleria"
         },
         "searchHotkeys": "Cerca tasti di scelta rapida",
         "noHotkeysFound": "Nessun tasto di scelta rapida trovato",
         "toggleOptionsAndGallery": {
             "desc": "Apre e chiude le opzioni e i pannelli della galleria",
-            "title": "Attiva/disattiva le Opzioni e la Galleria"
+            "title": "Attiva/disattiva le opzioni e la galleria"
         },
         "clearSearch": "Cancella ricerca",
         "remixImage": {
@@ -348,7 +368,7 @@
         },
         "toggleViewer": {
             "title": "Attiva/disattiva il visualizzatore di immagini",
-            "desc": "Passa dal Visualizzatore immagini all'area di lavoro per la scheda corrente."
+            "desc": "Passa dal visualizzatore immagini all'area di lavoro per la scheda corrente."
         }
     },
     "modelManager": {
@@ -378,7 +398,7 @@
         "convertToDiffusers": "Converti in Diffusori",
         "convertToDiffusersHelpText2": "Questo processo sostituirà la voce in Gestione Modelli con la versione Diffusori dello stesso modello.",
         "convertToDiffusersHelpText4": "Questo è un processo una tantum. Potrebbero essere necessari circa 30-60 secondi a seconda delle specifiche del tuo computer.",
-        "convertToDiffusersHelpText5": "Assicurati di avere spazio su disco sufficiente. I modelli generalmente variano tra 2 GB e 7 GB di dimensioni.",
+        "convertToDiffusersHelpText5": "Assicurati di avere spazio su disco sufficiente. I modelli generalmente variano tra 2 GB e 7 GB in dimensione.",
         "convertToDiffusersHelpText6": "Vuoi convertire questo modello?",
         "modelConverted": "Modello convertito",
         "alpha": "Alpha",
@@ -528,7 +548,7 @@
             "layer": {
                 "initialImageNoImageSelected": "Nessuna immagine iniziale selezionata",
                 "t2iAdapterIncompatibleDimensions": "L'adattatore T2I richiede che la dimensione dell'immagine sia un multiplo di {{multiple}}",
-                "controlAdapterNoModelSelected": "Nessun modello di Adattatore di Controllo selezionato",
+                "controlAdapterNoModelSelected": "Nessun modello di adattatore di controllo selezionato",
                 "controlAdapterIncompatibleBaseModel": "Il modello base dell'adattatore di controllo non è compatibile",
                 "controlAdapterNoImageSelected": "Nessuna immagine dell'adattatore di controllo selezionata",
                 "controlAdapterImageNotProcessed": "Immagine dell'adattatore di controllo non elaborata",
@@ -606,25 +626,25 @@
         "canvasMerged": "Tela unita",
         "sentToImageToImage": "Inviato a Generazione da immagine",
         "sentToUnifiedCanvas": "Inviato alla Tela",
-        "parametersNotSet": "Parametri non impostati",
+        "parametersNotSet": "Parametri non richiamati",
         "metadataLoadFailed": "Impossibile caricare i metadati",
         "serverError": "Errore del Server",
-        "connected": "Connesso al Server",
+        "connected": "Connesso al server",
         "canceled": "Elaborazione annullata",
         "uploadFailedInvalidUploadDesc": "Deve essere una singola immagine PNG o JPEG",
-        "parameterSet": "{{parameter}} impostato",
-        "parameterNotSet": "{{parameter}} non impostato",
+        "parameterSet": "Parametro richiamato",
+        "parameterNotSet": "Parametro non richiamato",
         "problemCopyingImage": "Impossibile copiare l'immagine",
-        "baseModelChangedCleared_one": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modello incompatibile",
-        "baseModelChangedCleared_many": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili",
-        "baseModelChangedCleared_other": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili",
+        "baseModelChangedCleared_one": "Cancellato o disabilitato {{count}} sottomodello incompatibile",
+        "baseModelChangedCleared_many": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
+        "baseModelChangedCleared_other": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
         "imageSavingFailed": "Salvataggio dell'immagine non riuscito",
         "canvasSentControlnetAssets": "Tela inviata a ControlNet & Risorse",
         "problemCopyingCanvasDesc": "Impossibile copiare la tela",
         "loadedWithWarnings": "Flusso di lavoro caricato con avvisi",
         "canvasCopiedClipboard": "Tela copiata negli appunti",
         "maskSavedAssets": "Maschera salvata nelle risorse",
-        "problemDownloadingCanvas": "Problema durante il download della tela",
+        "problemDownloadingCanvas": "Problema durante lo scarico della tela",
         "problemMergingCanvas": "Problema nell'unione delle tele",
         "imageUploaded": "Immagine caricata",
         "addedToBoard": "Aggiunto alla bacheca",
@@ -658,7 +678,17 @@
         "problemDownloadingImage": "Impossibile scaricare l'immagine",
         "prunedQueue": "Coda ripulita",
         "modelImportCanceled": "Importazione del modello annullata",
-        "parameters": "Parametri"
+        "parameters": "Parametri",
+        "parameterSetDesc": "{{parameter}} richiamato",
+        "parameterNotSetDesc": "Impossibile richiamare {{parameter}}",
+        "parameterNotSetDescWithMessage": "Impossibile richiamare {{parameter}}: {{message}}",
+        "parametersSet": "Parametri richiamati",
+        "errorCopied": "Errore copiato",
+        "outOfMemoryError": "Errore di memoria esaurita",
+        "baseModelChanged": "Modello base modificato",
+        "sessionRef": "Sessione: {{sessionId}}",
+        "somethingWentWrong": "Qualcosa è andato storto",
+        "outOfMemoryErrorDesc": "Le impostazioni della generazione attuale superano la capacità del sistema. Modifica le impostazioni e riprova."
     },
     "tooltip": {
         "feature": {
@@ -674,7 +704,7 @@
         "layer": "Livello",
         "base": "Base",
         "mask": "Maschera",
-        "maskingOptions": "Opzioni di mascheramento",
+        "maskingOptions": "Opzioni maschera",
         "enableMask": "Abilita maschera",
         "preserveMaskedArea": "Mantieni area mascherata",
         "clearMask": "Cancella maschera (Shift+C)",
@@ -745,7 +775,8 @@
         "mode": "Modalità",
         "resetUI": "$t(accessibility.reset) l'Interfaccia Utente",
         "createIssue": "Segnala un problema",
-        "about": "Informazioni"
+        "about": "Informazioni",
+        "submitSupportTicket": "Invia ticket di supporto"
     },
     "nodes": {
         "zoomOutNodes": "Rimpicciolire",
@@ -790,7 +821,7 @@
         "workflowNotes": "Note",
         "versionUnknown": " Versione sconosciuta",
         "unableToValidateWorkflow": "Impossibile convalidare il flusso di lavoro",
-        "updateApp": "Aggiorna App",
+        "updateApp": "Aggiorna Applicazione",
         "unableToLoadWorkflow": "Impossibile caricare il flusso di lavoro",
         "updateNode": "Aggiorna nodo",
         "version": "Versione",
@@ -882,11 +913,14 @@
         "missingNode": "Nodo di invocazione mancante",
         "missingInvocationTemplate": "Modello di invocazione mancante",
         "missingFieldTemplate": "Modello di campo mancante",
-        "singleFieldType": "{{name}} (Singola)"
+        "singleFieldType": "{{name}} (Singola)",
+        "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino delle impostazioni predefinite",
+        "boardAccessError": "Impossibile trovare la bacheca {{board_id}}, ripristino ai valori predefiniti",
+        "modelAccessError": "Impossibile trovare il modello {{key}}, ripristino ai valori predefiniti"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
-        "menuItemAutoAdd": "Aggiungi automaticamente a questa Bacheca",
+        "menuItemAutoAdd": "Aggiungi automaticamente a questa bacheca",
         "cancel": "Annulla",
         "addBoard": "Aggiungi Bacheca",
         "bottomMessage": "L'eliminazione di questa bacheca e delle sue immagini ripristinerà tutte le funzionalità che le stanno attualmente utilizzando.",
@@ -898,7 +932,7 @@
         "myBoard": "Bacheca",
         "searchBoard": "Cerca bacheche ...",
         "noMatching": "Nessuna bacheca corrispondente",
-        "selectBoard": "Seleziona una Bacheca",
+        "selectBoard": "Seleziona una bacheca",
         "uncategorized": "Non categorizzato",
         "downloadBoard": "Scarica la bacheca",
         "deleteBoardOnly": "solo la Bacheca",
@@ -919,7 +953,7 @@
         "control": "Controllo",
         "crop": "Ritaglia",
         "depthMidas": "Profondità (Midas)",
-        "detectResolution": "Rileva risoluzione",
+        "detectResolution": "Rileva la risoluzione",
         "controlMode": "Modalità di controllo",
         "cannyDescription": "Canny rilevamento bordi",
         "depthZoe": "Profondità (Zoe)",
@@ -930,7 +964,7 @@
         "showAdvanced": "Mostra opzioni Avanzate",
         "bgth": "Soglia rimozione sfondo",
         "importImageFromCanvas": "Importa immagine dalla Tela",
-        "lineartDescription": "Converte l'immagine in lineart",
+        "lineartDescription": "Converte l'immagine in linea",
         "importMaskFromCanvas": "Importa maschera dalla Tela",
         "hideAdvanced": "Nascondi opzioni avanzate",
         "resetControlImage": "Reimposta immagine di controllo",
@@ -946,7 +980,7 @@
         "pidiDescription": "Elaborazione immagini PIDI",
         "fill": "Riempie",
         "colorMapDescription": "Genera una mappa dei colori dall'immagine",
-        "lineartAnimeDescription": "Elaborazione lineart in stile anime",
+        "lineartAnimeDescription": "Elaborazione linea in stile anime",
         "imageResolution": "Risoluzione dell'immagine",
         "colorMap": "Colore",
         "lowThreshold": "Soglia inferiore",

invokeai/frontend/web/public/locales/ru.json

@@ -87,7 +87,11 @@
         "viewing": "Просмотр",
         "editing": "Редактирование",
         "viewingDesc": "Просмотр изображений в режиме большой галереи",
-        "editingDesc": "Редактировать на холсте слоёв управления"
+        "editingDesc": "Редактировать на холсте слоёв управления",
+        "enabled": "Включено",
+        "disabled": "Отключено",
+        "comparingDesc": "Сравнение двух изображений",
+        "comparing": "Сравнение"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -124,7 +128,23 @@
         "bulkDownloadRequested": "Подготовка к скачиванию",
         "bulkDownloadRequestedDesc": "Ваш запрос на скачивание готовится. Это может занять несколько минут.",
         "bulkDownloadRequestFailed": "Возникла проблема при подготовке скачивания",
-        "alwaysShowImageSizeBadge": "Всегда показывать значок размера изображения"
+        "alwaysShowImageSizeBadge": "Всегда показывать значок размера изображения",
+        "openInViewer": "Открыть в просмотрщике",
+        "selectForCompare": "Выбрать для сравнения",
+        "hover": "Наведение",
+        "swapImages": "Поменять местами",
+        "stretchToFit": "Растягивание до нужного размера",
+        "exitCompare": "Выйти из сравнения",
+        "compareHelp4": "Нажмите <Kbd>Z</Kbd> или <Kbd>Esc</Kbd> для выхода.",
+        "compareImage": "Сравнить изображение",
+        "viewerImage": "Изображение просмотрщика",
+        "selectAnImageToCompare": "Выберите изображение для сравнения",
+        "slider": "Слайдер",
+        "sideBySide": "Бок о бок",
+        "compareOptions": "Варианты сравнения",
+        "compareHelp1": "Удерживайте <Kbd>Alt</Kbd> при нажатии на изображение в галерее или при помощи клавиш со стрелками, чтобы изменить сравниваемое изображение.",
+        "compareHelp2": "Нажмите <Kbd>M</Kbd>, чтобы переключиться между режимами сравнения.",
+        "compareHelp3": "Нажмите <Kbd>C</Kbd>, чтобы поменять местами сравниваемые изображения."
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -528,7 +548,20 @@
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
             "invoke": "Создать",
-            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается"
+            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается",
+            "layer": {
+                "controlAdapterImageNotProcessed": "Изображение адаптера контроля не обработано",
+                "ipAdapterNoModelSelected": "IP адаптер не выбран",
+                "controlAdapterNoModelSelected": "не выбрана модель адаптера контроля",
+                "controlAdapterIncompatibleBaseModel": "несовместимая базовая модель адаптера контроля",
+                "controlAdapterNoImageSelected": "не выбрано изображение контрольного адаптера",
+                "initialImageNoImageSelected": "начальное изображение не выбрано",
+                "rgNoRegion": "регион не выбран",
+                "rgNoPromptsOrIPAdapters": "нет текстовых запросов или IP-адаптеров",
+                "ipAdapterIncompatibleBaseModel": "несовместимая базовая модель IP-адаптера",
+                "t2iAdapterIncompatibleDimensions": "Адаптер T2I требует, чтобы размеры изображения были кратны {{multiple}}",
+                "ipAdapterNoImageSelected": "изображение IP-адаптера не выбрано"
+            }
         },
         "isAllowedToUpscale": {
             "useX2Model": "Изображение слишком велико для увеличения с помощью модели x4. Используйте модель x2",
@@ -606,12 +639,12 @@
         "connected": "Подключено к серверу",
         "canceled": "Обработка отменена",
         "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
-        "parameterNotSet": "Параметр {{parameter}} не задан",
-        "parameterSet": "Параметр {{parameter}} задан",
+        "parameterNotSet": "Параметр не задан",
+        "parameterSet": "Параметр задан",
         "problemCopyingImage": "Не удается скопировать изображение",
-        "baseModelChangedCleared_one": "Базовая модель изменила, очистила или отключила {{count}} несовместимую подмодель",
-        "baseModelChangedCleared_few": "Базовая модель изменила, очистила или отключила {{count}} несовместимые подмодели",
-        "baseModelChangedCleared_many": "Базовая модель изменила, очистила или отключила {{count}} несовместимых подмоделей",
+        "baseModelChangedCleared_one": "Очищена или отключена {{count}} несовместимая подмодель",
+        "baseModelChangedCleared_few": "Очищены или отключены {{count}} несовместимые подмодели",
+        "baseModelChangedCleared_many": "Очищены или отключены {{count}} несовместимых подмоделей",
         "imageSavingFailed": "Не удалось сохранить изображение",
         "canvasSentControlnetAssets": "Холст отправлен в ControlNet и ресурсы",
         "problemCopyingCanvasDesc": "Невозможно экспортировать базовый слой",
@@ -652,7 +685,17 @@
         "resetInitialImage": "Сбросить начальное изображение",
         "prunedQueue": "Урезанная очередь",
         "modelImportCanceled": "Импорт модели отменен",
-        "parameters": "Параметры"
+        "parameters": "Параметры",
+        "parameterSetDesc": "Задан {{parameter}}",
+        "parameterNotSetDesc": "Невозможно задать {{parameter}}",
+        "baseModelChanged": "Базовая модель сменена",
+        "parameterNotSetDescWithMessage": "Не удалось задать {{parameter}}: {{message}}",
+        "parametersSet": "Параметры заданы",
+        "errorCopied": "Ошибка скопирована",
+        "sessionRef": "Сессия: {{sessionId}}",
+        "outOfMemoryError": "Ошибка нехватки памяти",
+        "outOfMemoryErrorDesc": "Ваши текущие настройки генерации превышают возможности системы. Пожалуйста, измените настройки и повторите попытку.",
+        "somethingWentWrong": "Что-то пошло не так"
     },
     "tooltip": {
         "feature": {
@@ -739,7 +782,8 @@
         "loadMore": "Загрузить больше",
         "resetUI": "$t(accessibility.reset) интерфейс",
         "createIssue": "Сообщить о проблеме",
-        "about": "Об этом"
+        "about": "Об этом",
+        "submitSupportTicket": "Отправить тикет в службу поддержки"
     },
     "nodes": {
         "zoomInNodes": "Увеличьте масштаб",
@@ -832,7 +876,7 @@
         "workflowName": "Название",
         "collection": "Коллекция",
         "unknownErrorValidatingWorkflow": "Неизвестная ошибка при проверке рабочего процесса",
-        "collectionFieldType": "Коллекция {{name}}",
+        "collectionFieldType": "{{name}} (Коллекция)",
         "workflowNotes": "Примечания",
         "string": "Строка",
         "unknownNodeType": "Неизвестный тип узла",
@@ -848,7 +892,7 @@
         "targetNodeDoesNotExist": "Недопустимое ребро: целевой/входной узел {{node}} не существует",
         "mismatchedVersion": "Недопустимый узел: узел {{node}} типа {{type}} имеет несоответствующую версию (попробовать обновить?)",
         "unknownFieldType": "$t(nodes.unknownField) тип: {{type}}",
-        "collectionOrScalarFieldType": "Коллекция | Скаляр {{name}}",
+        "collectionOrScalarFieldType": "{{name}} (Один или коллекция)",
         "betaDesc": "Этот вызов находится в бета-версии. Пока он не станет стабильным, в нем могут происходить изменения при обновлении приложений. Мы планируем поддерживать этот вызов в течение длительного времени.",
         "nodeVersion": "Версия узла",
         "loadingNodes": "Загрузка узлов...",
@@ -870,7 +914,16 @@
         "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений.",
         "graph": "График",
         "showEdgeLabels": "Показать метки на ребрах",
-        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы"
+        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы",
+        "cannotMixAndMatchCollectionItemTypes": "Невозможно смешивать и сопоставлять типы элементов коллекции",
+        "missingNode": "Отсутствует узел вызова",
+        "missingInvocationTemplate": "Отсутствует шаблон вызова",
+        "missingFieldTemplate": "Отсутствующий шаблон поля",
+        "singleFieldType": "{{name}} (Один)",
+        "noGraph": "Нет графика",
+        "imageAccessError": "Невозможно найти изображение {{image_name}}, сбрасываем на значение по умолчанию",
+        "boardAccessError": "Невозможно найти доску {{board_id}}, сбрасываем на значение по умолчанию",
+        "modelAccessError": "Невозможно найти модель {{key}}, сброс на модель по умолчанию"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -1441,7 +1494,16 @@
         "clearQueueAlertDialog2": "Вы уверены, что хотите очистить очередь?",
         "item": "Элемент",
         "graphFailedToQueue": "Не удалось поставить график в очередь",
-        "openQueue": "Открыть очередь"
+        "openQueue": "Открыть очередь",
+        "prompts_one": "Запрос",
+        "prompts_few": "Запроса",
+        "prompts_many": "Запросов",
+        "iterations_one": "Итерация",
+        "iterations_few": "Итерации",
+        "iterations_many": "Итераций",
+        "generations_one": "Генерация",
+        "generations_few": "Генерации",
+        "generations_many": "Генераций"
     },
     "sdxl": {
         "refinerStart": "Запуск доработчика",

invokeai/frontend/web/public/locales/zh_Hant.json

@@ -1,6 +1,6 @@
 {
     "common": {
-        "nodes": "節點",
+        "nodes": "工作流程",
         "img2img": "圖片轉圖片",
         "statusDisconnected": "已中斷連線",
         "back": "返回",
@@ -11,17 +11,239 @@
         "reportBugLabel": "回報錯誤",
         "githubLabel": "GitHub",
         "hotkeysLabel": "快捷鍵",
-        "languagePickerLabel": "切換語言",
+        "languagePickerLabel": "語言",
         "unifiedCanvas": "統一畫布",
         "cancel": "取消",
-        "txt2img": "文字轉圖片"
+        "txt2img": "文字轉圖片",
+        "controlNet": "ControlNet",
+        "advanced": "進階",
+        "folder": "資料夾",
+        "installed": "已安裝",
+        "accept": "接受",
+        "goTo": "前往",
+        "input": "輸入",
+        "random": "隨機",
+        "selected": "已選擇",
+        "communityLabel": "社群",
+        "loading": "載入中",
+        "delete": "刪除",
+        "copy": "複製",
+        "error": "錯誤",
+        "file": "檔案",
+        "format": "格式",
+        "imageFailedToLoad": "無法載入圖片"
     },
     "accessibility": {
         "invokeProgressBar": "Invoke 進度條",
         "uploadImage": "上傳圖片",
-        "reset": "重設",
+        "reset": "重置",
         "nextImage": "下一張圖片",
         "previousImage": "上一張圖片",
-        "menu": "選單"
+        "menu": "選單",
+        "loadMore": "載入更多",
+        "about": "關於",
+        "createIssue": "建立問題",
+        "resetUI": "$t(accessibility.reset) 介面",
+        "submitSupportTicket": "提交支援工單",
+        "mode": "模式"
+    },
+    "boards": {
+        "loading": "載入中…",
+        "movingImagesToBoard_other": "正在移動 {{count}} 張圖片至板上：",
+        "move": "移動",
+        "uncategorized": "未分類",
+        "cancel": "取消"
+    },
+    "metadata": {
+        "workflow": "工作流程",
+        "steps": "步數",
+        "model": "模型",
+        "seed": "種子",
+        "vae": "VAE",
+        "seamless": "無縫",
+        "metadata": "元數據",
+        "width": "寬度",
+        "height": "高度"
+    },
+    "accordions": {
+        "control": {
+            "title": "控制"
+        },
+        "compositing": {
+            "title": "合成"
+        },
+        "advanced": {
+            "title": "進階",
+            "options": "$t(accordions.advanced.title) 選項"
+        }
+    },
+    "hotkeys": {
+        "nodesHotkeys": "節點",
+        "cancel": {
+            "title": "取消"
+        },
+        "generalHotkeys": "一般",
+        "keyboardShortcuts": "快捷鍵",
+        "appHotkeys": "應用程式"
+    },
+    "modelManager": {
+        "advanced": "進階",
+        "allModels": "全部模型",
+        "variant": "變體",
+        "config": "配置",
+        "model": "模型",
+        "selected": "已選擇",
+        "huggingFace": "HuggingFace",
+        "install": "安裝",
+        "metadata": "元數據",
+        "delete": "刪除",
+        "description": "描述",
+        "cancel": "取消",
+        "convert": "轉換",
+        "manual": "手動",
+        "none": "無",
+        "name": "名稱",
+        "load": "載入",
+        "height": "高度",
+        "width": "寬度",
+        "search": "搜尋",
+        "vae": "VAE",
+        "settings": "設定"
+    },
+    "controlnet": {
+        "mlsd": "M-LSD",
+        "canny": "Canny",
+        "duplicate": "重複",
+        "none": "無",
+        "pidi": "PIDI",
+        "h": "H",
+        "balanced": "平衡",
+        "crop": "裁切",
+        "processor": "處理器",
+        "control": "控制",
+        "f": "F",
+        "lineart": "線條藝術",
+        "w": "W",
+        "hed": "HED",
+        "delete": "刪除"
+    },
+    "queue": {
+        "queue": "佇列",
+        "canceled": "已取消",
+        "failed": "已失敗",
+        "completed": "已完成",
+        "cancel": "取消",
+        "session": "工作階段",
+        "batch": "批量",
+        "item": "項目",
+        "completedIn": "完成於",
+        "notReady": "無法排隊"
+    },
+    "parameters": {
+        "cancel": {
+            "cancel": "取消"
+        },
+        "height": "高度",
+        "type": "類型",
+        "symmetry": "對稱性",
+        "images": "圖片",
+        "width": "寬度",
+        "coherenceMode": "模式",
+        "seed": "種子",
+        "general": "一般",
+        "strength": "強度",
+        "steps": "步數",
+        "info": "資訊"
+    },
+    "settings": {
+        "beta": "Beta",
+        "developer": "開發者",
+        "general": "一般",
+        "models": "模型"
+    },
+    "popovers": {
+        "paramModel": {
+            "heading": "模型"
+        },
+        "compositingCoherenceMode": {
+            "heading": "模式"
+        },
+        "paramSteps": {
+            "heading": "步數"
+        },
+        "controlNetProcessor": {
+            "heading": "處理器"
+        },
+        "paramVAE": {
+            "heading": "VAE"
+        },
+        "paramHeight": {
+            "heading": "高度"
+        },
+        "paramSeed": {
+            "heading": "種子"
+        },
+        "paramWidth": {
+            "heading": "寬度"
+        },
+        "refinerSteps": {
+            "heading": "步數"
+        }
+    },
+    "unifiedCanvas": {
+        "undo": "復原",
+        "mask": "遮罩",
+        "eraser": "橡皮擦",
+        "antialiasing": "抗鋸齒",
+        "redo": "重做",
+        "layer": "圖層",
+        "accept": "接受",
+        "brush": "刷子",
+        "move": "移動",
+        "brushSize": "大小"
+    },
+    "nodes": {
+        "workflowName": "名稱",
+        "notes": "註釋",
+        "workflowVersion": "版本",
+        "workflowNotes": "註釋",
+        "executionStateError": "錯誤",
+        "unableToUpdateNodes_other": "無法更新 {{count}} 個節點",
+        "integer": "整數",
+        "workflow": "工作流程",
+        "enum": "枚舉",
+        "edit": "編輯",
+        "string": "字串",
+        "workflowTags": "標籤",
+        "node": "節點",
+        "boolean": "布林值",
+        "workflowAuthor": "作者",
+        "version": "版本",
+        "executionStateCompleted": "已完成",
+        "edge": "邊緣",
+        "versionUnknown": " 版本未知"
+    },
+    "sdxl": {
+        "steps": "步數",
+        "loading": "載入中…",
+        "refiner": "精煉器"
+    },
+    "gallery": {
+        "copy": "複製",
+        "download": "下載",
+        "loading": "載入中"
+    },
+    "ui": {
+        "tabs": {
+            "models": "模型",
+            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
+            "queue": "佇列"
+        }
+    },
+    "models": {
+        "loading": "載入中"
+    },
+    "workflows": {
+        "name": "名稱"
     }
 }

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

@@ -19,6 +19,13 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
     return extendTheme({
       ..._theme,
       direction,
+      shadows: {
+        ..._theme.shadows,
+        selectedForCompare:
+          '0px 0px 0px 1px var(--invoke-colors-base-900), 0px 0px 0px 4px var(--invoke-colors-green-400)',
+        hoverSelectedForCompare:
+          '0px 0px 0px 1px var(--invoke-colors-base-900), 0px 0px 0px 4px var(--invoke-colors-green-300)',
+      },
     });
   }, [direction]);
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor.ts

@@ -13,7 +13,6 @@ import {
   isControlAdapterLayer,
 } from 'features/controlLayers/store/controlLayersSlice';
 import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
-import { isImageOutput } from 'features/nodes/types/common';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { isEqual } from 'lodash-es';
@@ -139,7 +138,7 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
 
         // We still have to check the output type
         assert(
-          isImageOutput(invocationCompleteAction.payload.data.result),
+          invocationCompleteAction.payload.data.result.type === 'image_output',
           `Processor did not return an image output, got: ${invocationCompleteAction.payload.data.result}`
         );
         const { image_name } = invocationCompleteAction.payload.data.result.image;

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts

@@ -9,7 +9,6 @@ import {
   selectControlAdapterById,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
-import { isImageOutput } from 'features/nodes/types/common';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -74,7 +73,7 @@ export const addControlNetImageProcessedListener = (startAppListening: AppStartL
         );
 
         // We still have to check the output type
-        if (isImageOutput(invocationCompleteAction.payload.data.result)) {
+        if (invocationCompleteAction.payload.data.result.type === 'image_output') {
           const { image_name } = invocationCompleteAction.payload.data.result.image;
 
           // Wait for the ImageDTO to be received

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/galleryImageClicked.ts

@@ -1,7 +1,7 @@
 import { createAction } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
-import { selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 import { imagesSelectors } from 'services/api/util';
@@ -11,6 +11,7 @@ export const galleryImageClicked = createAction<{
   shiftKey: boolean;
   ctrlKey: boolean;
   metaKey: boolean;
+  altKey: boolean;
 }>('gallery/imageClicked');
 
 /**
@@ -28,7 +29,7 @@ export const addGalleryImageClickedListener = (startAppListening: AppStartListen
   startAppListening({
     actionCreator: galleryImageClicked,
     effect: async (action, { dispatch, getState }) => {
-      const { imageDTO, shiftKey, ctrlKey, metaKey } = action.payload;
+      const { imageDTO, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
       const state = getState();
       const queryArgs = selectListImagesQueryArgs(state);
       const { data: listImagesData } = imagesApi.endpoints.listImages.select(queryArgs)(state);
@@ -41,7 +42,13 @@ export const addGalleryImageClickedListener = (startAppListening: AppStartListen
       const imageDTOs = imagesSelectors.selectAll(listImagesData);
       const selection = state.gallery.selection;
 
-      if (shiftKey) {
+      if (altKey) {
+        if (state.gallery.imageToCompare?.image_name === imageDTO.image_name) {
+          dispatch(imageToCompareChanged(null));
+        } else {
+          dispatch(imageToCompareChanged(imageDTO));
+        }
+      } else if (shiftKey) {
         const rangeEndImageName = imageDTO.image_name;
         const lastSelectedImage = selection[selection.length - 1]?.image_name;
         const lastClickedIndex = imageDTOs.findIndex((n) => n.image_name === lastSelectedImage);

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts

@@ -14,7 +14,8 @@ import {
   rgLayerIPAdapterImageChanged,
 } from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { isValidDrop } from 'features/dnd/util/isValidDrop';
+import { imageSelected, imageToCompareChanged, isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -30,6 +31,9 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
     effect: async (action, { dispatch, getState }) => {
       const log = logger('dnd');
       const { activeData, overData } = action.payload;
+      if (!isValidDrop(overData, activeData)) {
+        return;
+      }
 
       if (activeData.payloadType === 'IMAGE_DTO') {
         log.debug({ activeData, overData }, 'Image dropped');
@@ -50,6 +54,7 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         activeData.payload.imageDTO
       ) {
         dispatch(imageSelected(activeData.payload.imageDTO));
+        dispatch(isImageViewerOpenChanged(true));
         return;
       }
 
@@ -182,24 +187,18 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
       }
 
       /**
-       * TODO
-       * Image selection dropped on node image collection field
+       * Image selected for compare
        */
-      // if (
-      //   overData.actionType === 'SET_MULTI_NODES_IMAGE' &&
-      //   activeData.payloadType === 'IMAGE_DTO' &&
-      //   activeData.payload.imageDTO
-      // ) {
-      //   const { fieldName, nodeId } = overData.context;
-      //   dispatch(
-      //     fieldValueChanged({
-      //       nodeId,
-      //       fieldName,
-      //       value: [activeData.payload.imageDTO],
-      //     })
-      //   );
-      //   return;
-      // }
+      if (
+        overData.actionType === 'SELECT_FOR_COMPARE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { imageDTO } = activeData.payload;
+        dispatch(imageToCompareChanged(imageDTO));
+        dispatch(isImageViewerOpenChanged(true));
+        return;
+      }
 
       /**
        * Image dropped on user board

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts

@@ -11,7 +11,6 @@ import {
 } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
-import { isImageOutput } from 'features/nodes/types/common';
 import { zNodeStatus } from 'features/nodes/types/invocation';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
 import { boardsApi } from 'services/api/endpoints/boards';
@@ -33,7 +32,7 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
 
       const { result, invocation_source_id } = data;
       // This complete event has an associated image output
-      if (isImageOutput(data.result) && !nodeTypeDenylist.includes(data.invocation.type)) {
+      if (data.result.type === 'image_output' && !nodeTypeDenylist.includes(data.invocation.type)) {
         const { image_name } = data.result.image;
         const { canvas, gallery } = getState();
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested.ts

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { parseify } from 'common/util/serialize';
 import { workflowLoaded, workflowLoadRequested } from 'features/nodes/store/actions';
 import { $templates } from 'features/nodes/store/nodesSlice';
-import { $flow } from 'features/nodes/store/reactFlowInstance';
+import { $needsFit } from 'features/nodes/store/reactFlowInstance';
 import type { Templates } from 'features/nodes/store/types';
 import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/types/error';
 import { graphToWorkflow } from 'features/nodes/util/workflow/graphToWorkflow';
@@ -65,9 +65,7 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
           });
         }
 
-        requestAnimationFrame(() => {
-          $flow.get()?.fitView();
-        });
+        $needsFit.set(true);
       } catch (e) {
         if (e instanceof WorkflowVersionError) {
           // The workflow version was not recognized in the valid list of versions

invokeai/frontend/web/src/common/components/IAIDndImage.tsx

@@ -35,6 +35,7 @@ type IAIDndImageProps = FlexProps & {
   draggableData?: TypesafeDraggableData;
   dropLabel?: ReactNode;
   isSelected?: boolean;
+  isSelectedForCompare?: boolean;
   thumbnail?: boolean;
   noContentFallback?: ReactElement;
   useThumbailFallback?: boolean;
@@ -61,6 +62,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
     draggableData,
     dropLabel,
     isSelected = false,
+    isSelectedForCompare = false,
     thumbnail = false,
     noContentFallback = defaultNoContentFallback,
     uploadElement = defaultUploadElement,
@@ -165,7 +167,11 @@ const IAIDndImage = (props: IAIDndImageProps) => {
                 data-testid={dataTestId}
               />
               {withMetadataOverlay && <ImageMetadataOverlay imageDTO={imageDTO} />}
-              <SelectionOverlay isSelected={isSelected} isHovered={withHoverOverlay ? isHovered : false} />
+              <SelectionOverlay
+                isSelected={isSelected}
+                isSelectedForCompare={isSelectedForCompare}
+                isHovered={withHoverOverlay ? isHovered : false}
+              />
             </Flex>
           )}
           {!imageDTO && !isUploadDisabled && (

invokeai/frontend/web/src/common/components/IAIDroppable.tsx

@@ -36,7 +36,7 @@ const IAIDroppable = (props: IAIDroppableProps) => {
       pointerEvents={active ? 'auto' : 'none'}
     >
       <AnimatePresence>
-        {isValidDrop(data, active) && <IAIDropOverlay isOver={isOver} label={dropLabel} />}
+        {isValidDrop(data, active?.data.current) && <IAIDropOverlay isOver={isOver} label={dropLabel} />}
       </AnimatePresence>
     </Box>
   );

invokeai/frontend/web/src/common/components/SelectionOverlay.tsx

@@ -3,10 +3,17 @@ import { memo, useMemo } from 'react';
 
 type Props = {
   isSelected: boolean;
+  isSelectedForCompare: boolean;
   isHovered: boolean;
 };
-const SelectionOverlay = ({ isSelected, isHovered }: Props) => {
+const SelectionOverlay = ({ isSelected, isSelectedForCompare, isHovered }: Props) => {
   const shadow = useMemo(() => {
+    if (isSelectedForCompare && isHovered) {
+      return 'hoverSelectedForCompare';
+    }
+    if (isSelectedForCompare && !isHovered) {
+      return 'selectedForCompare';
+    }
     if (isSelected && isHovered) {
       return 'hoverSelected';
     }
@@ -17,7 +24,7 @@ const SelectionOverlay = ({ isSelected, isHovered }: Props) => {
       return 'hoverUnselected';
     }
     return undefined;
-  }, [isHovered, isSelected]);
+  }, [isHovered, isSelected, isSelectedForCompare]);
   return (
     <Box
       className="selection-box"
@@ -27,7 +34,7 @@ const SelectionOverlay = ({ isSelected, isHovered }: Props) => {
       bottom={0}
       insetInlineStart={0}
       borderRadius="base"
-      opacity={isSelected ? 1 : 0.7}
+      opacity={isSelected || isSelectedForCompare ? 1 : 0.7}
       transitionProperty="common"
       transitionDuration="0.1s"
       pointerEvents="none"

invokeai/frontend/web/src/common/hooks/useBoolean.ts

@@ -0,0 +1,21 @@
+import { useCallback, useMemo, useState } from 'react';
+
+export const useBoolean = (initialValue: boolean) => {
+  const [isTrue, set] = useState(initialValue);
+  const setTrue = useCallback(() => set(true), []);
+  const setFalse = useCallback(() => set(false), []);
+  const toggle = useCallback(() => set((v) => !v), []);
+
+  const api = useMemo(
+    () => ({
+      isTrue,
+      set,
+      setTrue,
+      setFalse,
+      toggle,
+    }),
+    [isTrue, set, setTrue, setFalse, toggle]
+  );
+
+  return api;
+};

invokeai/frontend/web/src/common/util/stopPropagation.ts

@@ -1,3 +1,7 @@
 export const stopPropagation = (e: React.MouseEvent) => {
   e.stopPropagation();
 };
+
+export const preventDefault = (e: React.MouseEvent) => {
+  e.preventDefault();
+};

invokeai/frontend/web/src/features/controlLayers/util/controlAdapters.ts

@@ -1,7 +1,13 @@
 import { deepClone } from 'common/util/deepClone';
 import { zModelIdentifierField } from 'features/nodes/types/common';
 import { merge, omit } from 'lodash-es';
-import type { BaseModelType, ControlNetModelConfig, Graph, ImageDTO, T2IAdapterModelConfig } from 'services/api/types';
+import type {
+  AnyInvocation,
+  BaseModelType,
+  ControlNetModelConfig,
+  ImageDTO,
+  T2IAdapterModelConfig,
+} from 'services/api/types';
 import { z } from 'zod';
 
 const zId = z.string().min(1);
@@ -147,7 +153,7 @@ const zBeginEndStepPct = z
 
 const zControlAdapterBase = z.object({
   id: zId,
-  weight: z.number().gte(0).lte(1),
+  weight: z.number().gte(-1).lte(2),
   image: zImageWithDims.nullable(),
   processedImage: zImageWithDims.nullable(),
   processorConfig: zProcessorConfig.nullable(),
@@ -183,7 +189,7 @@ export const isIPMethodV2 = (v: unknown): v is IPMethodV2 => zIPMethodV2.safePar
 export const zIPAdapterConfigV2 = z.object({
   id: zId,
   type: z.literal('ip_adapter'),
-  weight: z.number().gte(0).lte(1),
+  weight: z.number().gte(-1).lte(2),
   method: zIPMethodV2,
   image: zImageWithDims.nullable(),
   model: zModelIdentifierField.nullable(),
@@ -216,10 +222,7 @@ type ProcessorData<T extends ProcessorTypeV2> = {
   labelTKey: string;
   descriptionTKey: string;
   buildDefaults(baseModel?: BaseModelType): Extract<ProcessorConfig, { type: T }>;
-  buildNode(
-    image: ImageWithDims,
-    config: Extract<ProcessorConfig, { type: T }>
-  ): Extract<Graph['nodes'][string], { type: T }>;
+  buildNode(image: ImageWithDims, config: Extract<ProcessorConfig, { type: T }>): Extract<AnyInvocation, { type: T }>;
 };
 
 const minDim = (image: ImageWithDims): number => Math.min(image.width, image.height);

invokeai/frontend/web/src/features/controlLayers/util/renderers.ts

@@ -54,7 +54,7 @@ const BBOX_SELECTED_STROKE = 'rgba(78, 190, 255, 1)';
 const BRUSH_BORDER_INNER_COLOR = 'rgba(0,0,0,1)';
 const BRUSH_BORDER_OUTER_COLOR = 'rgba(255,255,255,0.8)';
 // This is invokeai/frontend/web/public/assets/images/transparent_bg.png as a dataURL
-const STAGE_BG_DATAURL =
+export const STAGE_BG_DATAURL =
   'data:image/png;base64,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';
 
 const mapId = (object: { id: string }) => object.id;

invokeai/frontend/web/src/features/dnd/types/index.ts

@@ -18,7 +18,7 @@ type BaseDropData = {
   id: string;
 };
 
-type CurrentImageDropData = BaseDropData & {
+export type CurrentImageDropData = BaseDropData & {
   actionType: 'SET_CURRENT_IMAGE';
 };
 
@@ -79,6 +79,14 @@ export type RemoveFromBoardDropData = BaseDropData & {
   actionType: 'REMOVE_FROM_BOARD';
 };
 
+export type SelectForCompareDropData = BaseDropData & {
+  actionType: 'SELECT_FOR_COMPARE';
+  context: {
+    firstImageName?: string | null;
+    secondImageName?: string | null;
+  };
+};
+
 export type TypesafeDroppableData =
   | CurrentImageDropData
   | ControlAdapterDropData
@@ -89,7 +97,8 @@ export type TypesafeDroppableData =
   | CALayerImageDropData
   | IPALayerImageDropData
   | RGLayerIPAdapterImageDropData
-  | IILayerImageDropData;
+  | IILayerImageDropData
+  | SelectForCompareDropData;
 
 type BaseDragData = {
   id: string;
@@ -134,7 +143,7 @@ export type UseDraggableTypesafeReturnValue = Omit<ReturnType<typeof useOriginal
   over: TypesafeOver | null;
 };
 
-export interface TypesafeActive extends Omit<Active, 'data'> {
+interface TypesafeActive extends Omit<Active, 'data'> {
   data: React.MutableRefObject<TypesafeDraggableData | undefined>;
 }
 

invokeai/frontend/web/src/features/dnd/util/isValidDrop.ts

@@ -1,14 +1,14 @@
-import type { TypesafeActive, TypesafeDroppableData } from 'features/dnd/types';
+import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 
-export const isValidDrop = (overData: TypesafeDroppableData | undefined, active: TypesafeActive | null) => {
-  if (!overData || !active?.data.current) {
+export const isValidDrop = (overData?: TypesafeDroppableData | null, activeData?: TypesafeDraggableData | null) => {
+  if (!overData || !activeData) {
     return false;
   }
 
   const { actionType } = overData;
-  const { payloadType } = active.data.current;
+  const { payloadType } = activeData;
 
-  if (overData.id === active.data.current.id) {
+  if (overData.id === activeData.id) {
     return false;
   }
 
@@ -29,6 +29,8 @@ export const isValidDrop = (overData: TypesafeDroppableData | undefined, active:
       return payloadType === 'IMAGE_DTO';
     case 'SET_NODES_IMAGE':
       return payloadType === 'IMAGE_DTO';
+    case 'SELECT_FOR_COMPARE':
+      return payloadType === 'IMAGE_DTO';
     case 'ADD_TO_BOARD': {
       // If the board is the same, don't allow the drop
 
@@ -40,7 +42,7 @@ export const isValidDrop = (overData: TypesafeDroppableData | undefined, active:
 
       // Check if the image's board is the board we are dragging onto
       if (payloadType === 'IMAGE_DTO') {
-        const { imageDTO } = active.data.current.payload;
+        const { imageDTO } = activeData.payload;
         const currentBoard = imageDTO.board_id ?? 'none';
         const destinationBoard = overData.context.boardId;
 
@@ -49,7 +51,7 @@ export const isValidDrop = (overData: TypesafeDroppableData | undefined, active:
 
       if (payloadType === 'GALLERY_SELECTION') {
         // Assume all images are on the same board - this is true for the moment
-        const currentBoard = active.data.current.payload.boardId;
+        const currentBoard = activeData.payload.boardId;
         const destinationBoard = overData.context.boardId;
         return currentBoard !== destinationBoard;
       }
@@ -67,14 +69,14 @@ export const isValidDrop = (overData: TypesafeDroppableData | undefined, active:
 
       // Check if the image's board is the board we are dragging onto
       if (payloadType === 'IMAGE_DTO') {
-        const { imageDTO } = active.data.current.payload;
+        const { imageDTO } = activeData.payload;
         const currentBoard = imageDTO.board_id ?? 'none';
 
         return currentBoard !== 'none';
       }
 
       if (payloadType === 'GALLERY_SELECTION') {
-        const currentBoard = active.data.current.payload.boardId;
+        const currentBoard = activeData.payload.boardId;
         return currentBoard !== 'none';
       }
 

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/GalleryBoard.tsx

@@ -162,7 +162,7 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
                   </Flex>
                 )}
                 {isSelectedForAutoAdd && <AutoAddIcon />}
-                <SelectionOverlay isSelected={isSelected} isHovered={isHovered} />
+                <SelectionOverlay isSelected={isSelected} isSelectedForCompare={false} isHovered={isHovered} />
                 <Flex
                   position="absolute"
                   bottom={0}

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx

@@ -117,7 +117,7 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
                 >
                   {boardName}
                 </Flex>
-                <SelectionOverlay isSelected={isSelected} isHovered={isHovered} />
+                <SelectionOverlay isSelected={isSelected} isSelectedForCompare={false} isHovered={isHovered} />
                 <IAIDroppable data={droppableData} dropLabel={<Text fontSize="md">{t('unifiedCanvas.move')}</Text>} />
               </Flex>
             </Tooltip>

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -10,6 +10,7 @@ import { iiLayerAdded } from 'features/controlLayers/store/controlLayersSlice';
 import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice';
 import { useImageActions } from 'features/gallery/hooks/useImageActions';
 import { sentImageToCanvas, sentImageToImg2Img } from 'features/gallery/store/actions';
+import { imageToCompareChanged } from 'features/gallery/store/gallerySlice';
 import { $templates } from 'features/nodes/store/nodesSlice';
 import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
@@ -27,6 +28,7 @@ import {
   PiDownloadSimpleBold,
   PiFlowArrowBold,
   PiFoldersBold,
+  PiImagesBold,
   PiPlantBold,
   PiQuotesBold,
   PiShareFatBold,
@@ -44,6 +46,7 @@ type SingleSelectionMenuItemsProps = {
 const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
   const { imageDTO } = props;
   const optimalDimension = useAppSelector(selectOptimalDimension);
+  const maySelectForCompare = useAppSelector((s) => s.gallery.imageToCompare?.image_name !== imageDTO.image_name);
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
   const isCanvasEnabled = useFeatureStatus('canvas');
@@ -117,6 +120,10 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
     downloadImage(imageDTO.image_url, imageDTO.image_name);
   }, [downloadImage, imageDTO.image_name, imageDTO.image_url]);
 
+  const handleSelectImageForCompare = useCallback(() => {
+    dispatch(imageToCompareChanged(imageDTO));
+  }, [dispatch, imageDTO]);
+
   return (
     <>
       <MenuItem as="a" href={imageDTO.image_url} target="_blank" icon={<PiShareFatBold />}>
@@ -130,6 +137,9 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
       <MenuItem icon={<PiDownloadSimpleBold />} onClickCapture={handleDownloadImage}>
         {t('parameters.downloadImage')}
       </MenuItem>
+      <MenuItem icon={<PiImagesBold />} isDisabled={!maySelectForCompare} onClick={handleSelectImageForCompare}>
+        {t('gallery.selectForCompare')}
+      </MenuItem>
       <MenuDivider />
       <MenuItem
         icon={getAndLoadEmbeddedWorkflowResult.isLoading ? <SpinnerIcon /> : <PiFlowArrowBold />}

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

@@ -11,7 +11,7 @@ import type { GallerySelectionDraggableData, ImageDraggableData, TypesafeDraggab
 import { getGalleryImageDataTestId } from 'features/gallery/components/ImageGrid/getGalleryImageDataTestId';
 import { useMultiselect } from 'features/gallery/hooks/useMultiselect';
 import { useScrollIntoView } from 'features/gallery/hooks/useScrollIntoView';
-import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
+import { imageToCompareChanged, isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import type { MouseEvent } from 'react';
 import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -46,6 +46,7 @@ const GalleryImage = (props: HoverableImageProps) => {
   const { t } = useTranslation();
   const selectedBoardId = useAppSelector((s) => s.gallery.selectedBoardId);
   const alwaysShowImageSizeBadge = useAppSelector((s) => s.gallery.alwaysShowImageSizeBadge);
+  const isSelectedForCompare = useAppSelector((s) => s.gallery.imageToCompare?.image_name === imageName);
   const { handleClick, isSelected, areMultiplesSelected } = useMultiselect(imageDTO);
 
   const customStarUi = useStore($customStarUI);
@@ -105,6 +106,7 @@ const GalleryImage = (props: HoverableImageProps) => {
 
   const onDoubleClick = useCallback(() => {
     dispatch(isImageViewerOpenChanged(true));
+    dispatch(imageToCompareChanged(null));
   }, [dispatch]);
 
   const handleMouseOut = useCallback(() => {
@@ -152,6 +154,7 @@ const GalleryImage = (props: HoverableImageProps) => {
           imageDTO={imageDTO}
           draggableData={draggableData}
           isSelected={isSelected}
+          isSelectedForCompare={isSelectedForCompare}
           minSize={0}
           imageSx={imageSx}
           isDropDisabled={true}

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataGraphTabContent.tsx

@@ -28,7 +28,9 @@ const ImageMetadataGraphTabContent = ({ image }: Props) => {
     return <IAINoContentFallback label={t('nodes.noGraph')} />;
   }
 
-  return <DataViewer data={graph} label={t('nodes.graph')} />;
+  return (
+    <DataViewer fileName={`${image.image_name.replace('.png', '')}_graph`} data={graph} label={t('nodes.graph')} />
+  );
 };
 
 export default memo(ImageMetadataGraphTabContent);

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx

@@ -68,14 +68,22 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
           </TabPanel>
           <TabPanel>
             {metadata ? (
-              <DataViewer data={metadata} label={t('metadata.metadata')} />
+              <DataViewer
+                fileName={`${image.image_name.replace('.png', '')}_metadata`}
+                data={metadata}
+                label={t('metadata.metadata')}
+              />
             ) : (
               <IAINoContentFallback label={t('metadata.noMetaData')} />
             )}
           </TabPanel>
           <TabPanel>
             {image ? (
-              <DataViewer data={image} label={t('metadata.imageDetails')} />
+              <DataViewer
+                fileName={`${image.image_name.replace('.png', '')}_details`}
+                data={image}
+                label={t('metadata.imageDetails')}
+              />
             ) : (
               <IAINoContentFallback label={t('metadata.noImageDetails')} />
             )}

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataWorkflowTabContent.tsx

@@ -28,7 +28,13 @@ const ImageMetadataWorkflowTabContent = ({ image }: Props) => {
     return <IAINoContentFallback label={t('nodes.noWorkflow')} />;
   }
 
-  return <DataViewer data={workflow} label={t('metadata.workflow')} />;
+  return (
+    <DataViewer
+      fileName={`${image.image_name.replace('.png', '')}_workflow`}
+      data={workflow}
+      label={t('metadata.workflow')}
+    />
+  );
 };
 
 export default memo(ImageMetadataWorkflowTabContent);

invokeai/frontend/web/src/features/gallery/components/ImageViewer/CompareToolbar.tsx

@@ -0,0 +1,140 @@
+import {
+  Button,
+  ButtonGroup,
+  Flex,
+  Icon,
+  IconButton,
+  Kbd,
+  ListItem,
+  Tooltip,
+  UnorderedList,
+} from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import {
+  comparedImagesSwapped,
+  comparisonFitChanged,
+  comparisonModeChanged,
+  comparisonModeCycled,
+  imageToCompareChanged,
+} from 'features/gallery/store/gallerySlice';
+import { memo, useCallback } from 'react';
+import { useHotkeys } from 'react-hotkeys-hook';
+import { Trans, useTranslation } from 'react-i18next';
+import { PiArrowsOutBold, PiQuestion, PiSwapBold, PiXBold } from 'react-icons/pi';
+
+export const CompareToolbar = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const comparisonMode = useAppSelector((s) => s.gallery.comparisonMode);
+  const comparisonFit = useAppSelector((s) => s.gallery.comparisonFit);
+  const setComparisonModeSlider = useCallback(() => {
+    dispatch(comparisonModeChanged('slider'));
+  }, [dispatch]);
+  const setComparisonModeSideBySide = useCallback(() => {
+    dispatch(comparisonModeChanged('side-by-side'));
+  }, [dispatch]);
+  const setComparisonModeHover = useCallback(() => {
+    dispatch(comparisonModeChanged('hover'));
+  }, [dispatch]);
+  const swapImages = useCallback(() => {
+    dispatch(comparedImagesSwapped());
+  }, [dispatch]);
+  useHotkeys('c', swapImages, [swapImages]);
+  const toggleComparisonFit = useCallback(() => {
+    dispatch(comparisonFitChanged(comparisonFit === 'contain' ? 'fill' : 'contain'));
+  }, [dispatch, comparisonFit]);
+  const exitCompare = useCallback(() => {
+    dispatch(imageToCompareChanged(null));
+  }, [dispatch]);
+  useHotkeys('esc', exitCompare, [exitCompare]);
+  const nextMode = useCallback(() => {
+    dispatch(comparisonModeCycled());
+  }, [dispatch]);
+  useHotkeys('m', nextMode, [nextMode]);
+
+  return (
+    <Flex w="full" gap={2}>
+      <Flex flex={1} justifyContent="center">
+        <Flex gap={2} marginInlineEnd="auto">
+          <IconButton
+            icon={<PiSwapBold />}
+            aria-label={`${t('gallery.swapImages')} (C)`}
+            tooltip={`${t('gallery.swapImages')} (C)`}
+            onClick={swapImages}
+          />
+          {comparisonMode !== 'side-by-side' && (
+            <IconButton
+              aria-label={t('gallery.stretchToFit')}
+              tooltip={t('gallery.stretchToFit')}
+              onClick={toggleComparisonFit}
+              colorScheme={comparisonFit === 'fill' ? 'invokeBlue' : 'base'}
+              variant="outline"
+              icon={<PiArrowsOutBold />}
+            />
+          )}
+        </Flex>
+      </Flex>
+      <Flex flex={1} gap={4} justifyContent="center">
+        <ButtonGroup variant="outline">
+          <Button
+            flexShrink={0}
+            onClick={setComparisonModeSlider}
+            colorScheme={comparisonMode === 'slider' ? 'invokeBlue' : 'base'}
+          >
+            {t('gallery.slider')}
+          </Button>
+          <Button
+            flexShrink={0}
+            onClick={setComparisonModeSideBySide}
+            colorScheme={comparisonMode === 'side-by-side' ? 'invokeBlue' : 'base'}
+          >
+            {t('gallery.sideBySide')}
+          </Button>
+          <Button
+            flexShrink={0}
+            onClick={setComparisonModeHover}
+            colorScheme={comparisonMode === 'hover' ? 'invokeBlue' : 'base'}
+          >
+            {t('gallery.hover')}
+          </Button>
+        </ButtonGroup>
+      </Flex>
+      <Flex flex={1} justifyContent="center">
+        <Flex gap={2} marginInlineStart="auto" alignItems="center">
+          <Tooltip label={<CompareHelp />}>
+            <Flex alignItems="center">
+              <Icon boxSize={8} color="base.500" as={PiQuestion} lineHeight={0} />
+            </Flex>
+          </Tooltip>
+          <IconButton
+            icon={<PiXBold />}
+            aria-label={`${t('gallery.exitCompare')} (Esc)`}
+            tooltip={`${t('gallery.exitCompare')} (Esc)`}
+            onClick={exitCompare}
+          />
+        </Flex>
+      </Flex>
+    </Flex>
+  );
+});
+
+CompareToolbar.displayName = 'CompareToolbar';
+
+const CompareHelp = () => {
+  return (
+    <UnorderedList>
+      <ListItem>
+        <Trans i18nKey="gallery.compareHelp1" components={{ Kbd: <Kbd /> }}></Trans>
+      </ListItem>
+      <ListItem>
+        <Trans i18nKey="gallery.compareHelp2" components={{ Kbd: <Kbd /> }}></Trans>
+      </ListItem>
+      <ListItem>
+        <Trans i18nKey="gallery.compareHelp3" components={{ Kbd: <Kbd /> }}></Trans>
+      </ListItem>
+      <ListItem>
+        <Trans i18nKey="gallery.compareHelp4" components={{ Kbd: <Kbd /> }}></Trans>
+      </ListItem>
+    </UnorderedList>
+  );
+};

invokeai/frontend/web/src/features/gallery/components/ImageViewer/CurrentImagePreview.tsx

@@ -4,7 +4,7 @@ import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppSelector } from 'app/store/storeHooks';
 import IAIDndImage from 'common/components/IAIDndImage';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
-import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
+import type { TypesafeDraggableData } from 'features/dnd/types';
 import ImageMetadataViewer from 'features/gallery/components/ImageMetadataViewer/ImageMetadataViewer';
 import NextPrevImageButtons from 'features/gallery/components/NextPrevImageButtons';
 import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
@@ -22,21 +22,7 @@ const selectLastSelectedImageName = createSelector(
   (lastSelectedImage) => lastSelectedImage?.image_name
 );
 
-type Props = {
-  isDragDisabled?: boolean;
-  isDropDisabled?: boolean;
-  withNextPrevButtons?: boolean;
-  withMetadata?: boolean;
-  alwaysShowProgress?: boolean;
-};
-
-const CurrentImagePreview = ({
-  isDragDisabled = false,
-  isDropDisabled = false,
-  withNextPrevButtons = true,
-  withMetadata = true,
-  alwaysShowProgress = false,
-}: Props) => {
+const CurrentImagePreview = () => {
   const { t } = useTranslation();
   const shouldShowImageDetails = useAppSelector((s) => s.ui.shouldShowImageDetails);
   const imageName = useAppSelector(selectLastSelectedImageName);
@@ -55,14 +41,6 @@ const CurrentImagePreview = ({
     }
   }, [imageDTO]);
 
-  const droppableData = useMemo<TypesafeDroppableData | undefined>(
-    () => ({
-      id: 'current-image',
-      actionType: 'SET_CURRENT_IMAGE',
-    }),
-    []
-  );
-
   // Show and hide the next/prev buttons on mouse move
   const [shouldShowNextPrevButtons, setShouldShowNextPrevButtons] = useState<boolean>(false);
   const timeoutId = useRef(0);
@@ -86,30 +64,27 @@ const CurrentImagePreview = ({
       justifyContent="center"
       position="relative"
     >
-      {hasDenoiseProgress && (shouldShowProgressInViewer || alwaysShowProgress) ? (
+      {hasDenoiseProgress && shouldShowProgressInViewer ? (
         <ProgressImage />
       ) : (
         <IAIDndImage
           imageDTO={imageDTO}
-          droppableData={droppableData}
           draggableData={draggableData}
-          isDragDisabled={isDragDisabled}
-          isDropDisabled={isDropDisabled}
+          isDropDisabled={true}
           isUploadDisabled={true}
           fitContainer
           useThumbailFallback
-          dropLabel={t('gallery.setCurrentImage')}
           noContentFallback={<IAINoContentFallback icon={PiImageBold} label={t('gallery.noImageSelected')} />}
           dataTestId="image-preview"
         />
       )}
-      {shouldShowImageDetails && imageDTO && withMetadata && (
+      {shouldShowImageDetails && imageDTO && (
         <Box position="absolute" opacity={0.8} top={0} width="full" height="full" borderRadius="base">
           <ImageMetadataViewer image={imageDTO} />
         </Box>
       )}
       <AnimatePresence>
-        {withNextPrevButtons && shouldShowNextPrevButtons && imageDTO && (
+        {shouldShowNextPrevButtons && imageDTO && (
           <Box
             as={motion.div}
             key="nextPrevButtons"

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparison.tsx

@@ -0,0 +1,41 @@
+import { useAppSelector } from 'app/store/storeHooks';
+import { IAINoContentFallback } from 'common/components/IAIImageFallback';
+import type { Dimensions } from 'features/canvas/store/canvasTypes';
+import { selectComparisonImages } from 'features/gallery/components/ImageViewer/common';
+import { ImageComparisonHover } from 'features/gallery/components/ImageViewer/ImageComparisonHover';
+import { ImageComparisonSideBySide } from 'features/gallery/components/ImageViewer/ImageComparisonSideBySide';
+import { ImageComparisonSlider } from 'features/gallery/components/ImageViewer/ImageComparisonSlider';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiImagesBold } from 'react-icons/pi';
+
+type Props = {
+  containerDims: Dimensions;
+};
+
+export const ImageComparison = memo(({ containerDims }: Props) => {
+  const { t } = useTranslation();
+  const comparisonMode = useAppSelector((s) => s.gallery.comparisonMode);
+  const { firstImage, secondImage } = useAppSelector(selectComparisonImages);
+
+  if (!firstImage || !secondImage) {
+    // Should rarely/never happen - we don't render this component unless we have images to compare
+    return <IAINoContentFallback label={t('gallery.selectAnImageToCompare')} icon={PiImagesBold} />;
+  }
+
+  if (comparisonMode === 'slider') {
+    return <ImageComparisonSlider containerDims={containerDims} firstImage={firstImage} secondImage={secondImage} />;
+  }
+
+  if (comparisonMode === 'side-by-side') {
+    return (
+      <ImageComparisonSideBySide containerDims={containerDims} firstImage={firstImage} secondImage={secondImage} />
+    );
+  }
+
+  if (comparisonMode === 'hover') {
+    return <ImageComparisonHover containerDims={containerDims} firstImage={firstImage} secondImage={secondImage} />;
+  }
+});
+
+ImageComparison.displayName = 'ImageComparison';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparisonDroppable.tsx

@@ -0,0 +1,47 @@
+import { Flex } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import IAIDroppable from 'common/components/IAIDroppable';
+import type { CurrentImageDropData, SelectForCompareDropData } from 'features/dnd/types';
+import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
+import { memo, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+import { selectComparisonImages } from './common';
+
+const setCurrentImageDropData: CurrentImageDropData = {
+  id: 'current-image',
+  actionType: 'SET_CURRENT_IMAGE',
+};
+
+export const ImageComparisonDroppable = memo(() => {
+  const { t } = useTranslation();
+  const imageViewer = useImageViewer();
+  const { firstImage, secondImage } = useAppSelector(selectComparisonImages);
+  const selectForCompareDropData = useMemo<SelectForCompareDropData>(
+    () => ({
+      id: 'image-comparison',
+      actionType: 'SELECT_FOR_COMPARE',
+      context: {
+        firstImageName: firstImage?.image_name,
+        secondImageName: secondImage?.image_name,
+      },
+    }),
+    [firstImage?.image_name, secondImage?.image_name]
+  );
+
+  if (!imageViewer.isOpen) {
+    return (
+      <Flex position="absolute" top={0} right={0} bottom={0} left={0} gap={2} pointerEvents="none">
+        <IAIDroppable data={setCurrentImageDropData} dropLabel={t('gallery.openInViewer')} />
+      </Flex>
+    );
+  }
+
+  return (
+    <Flex position="absolute" top={0} right={0} bottom={0} left={0} gap={2} pointerEvents="none">
+      <IAIDroppable data={selectForCompareDropData} dropLabel={t('gallery.selectForCompare')} />
+    </Flex>
+  );
+});
+
+ImageComparisonDroppable.displayName = 'ImageComparisonDroppable';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparisonHover.tsx

@@ -0,0 +1,117 @@
+import { Box, Flex, Image } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useBoolean } from 'common/hooks/useBoolean';
+import { preventDefault } from 'common/util/stopPropagation';
+import type { Dimensions } from 'features/canvas/store/canvasTypes';
+import { STAGE_BG_DATAURL } from 'features/controlLayers/util/renderers';
+import { ImageComparisonLabel } from 'features/gallery/components/ImageViewer/ImageComparisonLabel';
+import { memo, useMemo, useRef } from 'react';
+
+import type { ComparisonProps } from './common';
+import { fitDimsToContainer, getSecondImageDims } from './common';
+
+export const ImageComparisonHover = memo(({ firstImage, secondImage, containerDims }: ComparisonProps) => {
+  const comparisonFit = useAppSelector((s) => s.gallery.comparisonFit);
+  const imageContainerRef = useRef<HTMLDivElement>(null);
+  const mouseOver = useBoolean(false);
+  const fittedDims = useMemo<Dimensions>(
+    () => fitDimsToContainer(containerDims, firstImage),
+    [containerDims, firstImage]
+  );
+  const compareImageDims = useMemo<Dimensions>(
+    () => getSecondImageDims(comparisonFit, fittedDims, firstImage, secondImage),
+    [comparisonFit, fittedDims, firstImage, secondImage]
+  );
+  return (
+    <Flex w="full" h="full" maxW="full" maxH="full" position="relative" alignItems="center" justifyContent="center">
+      <Flex
+        id="image-comparison-wrapper"
+        w="full"
+        h="full"
+        maxW="full"
+        maxH="full"
+        position="absolute"
+        alignItems="center"
+        justifyContent="center"
+      >
+        <Box
+          ref={imageContainerRef}
+          position="relative"
+          id="image-comparison-hover-image-container"
+          w={fittedDims.width}
+          h={fittedDims.height}
+          maxW="full"
+          maxH="full"
+          userSelect="none"
+          overflow="hidden"
+          borderRadius="base"
+        >
+          <Image
+            id="image-comparison-hover-first-image"
+            src={firstImage.image_url}
+            fallbackSrc={firstImage.thumbnail_url}
+            w={fittedDims.width}
+            h={fittedDims.height}
+            maxW="full"
+            maxH="full"
+            objectFit="cover"
+            objectPosition="top left"
+          />
+          <ImageComparisonLabel type="first" opacity={mouseOver.isTrue ? 0 : 1} />
+
+          <Box
+            id="image-comparison-hover-second-image-container"
+            position="absolute"
+            top={0}
+            left={0}
+            right={0}
+            bottom={0}
+            overflow="hidden"
+            opacity={mouseOver.isTrue ? 1 : 0}
+            transitionDuration="0.2s"
+            transitionProperty="common"
+          >
+            <Box
+              id="image-comparison-hover-bg"
+              position="absolute"
+              top={0}
+              left={0}
+              right={0}
+              bottom={0}
+              backgroundImage={STAGE_BG_DATAURL}
+              backgroundRepeat="repeat"
+              opacity={0.2}
+            />
+            <Image
+              position="relative"
+              id="image-comparison-hover-second-image"
+              src={secondImage.image_url}
+              fallbackSrc={secondImage.thumbnail_url}
+              w={compareImageDims.width}
+              h={compareImageDims.height}
+              maxW={fittedDims.width}
+              maxH={fittedDims.height}
+              objectFit={comparisonFit}
+              objectPosition="top left"
+            />
+            <ImageComparisonLabel type="second" opacity={mouseOver.isTrue ? 1 : 0} />
+          </Box>
+          <Box
+            id="image-comparison-hover-interaction-overlay"
+            position="absolute"
+            top={0}
+            right={0}
+            bottom={0}
+            left={0}
+            onMouseOver={mouseOver.setTrue}
+            onMouseOut={mouseOver.setFalse}
+            onContextMenu={preventDefault}
+            userSelect="none"
+          />
+        </Box>
+      </Flex>
+    </Flex>
+  );
+});
+
+ImageComparisonHover.displayName = 'ImageComparisonHover';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparisonLabel.tsx

@@ -0,0 +1,33 @@
+import type { TextProps } from '@invoke-ai/ui-library';
+import { Text } from '@invoke-ai/ui-library';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+import { DROP_SHADOW } from './common';
+
+type Props = TextProps & {
+  type: 'first' | 'second';
+};
+
+export const ImageComparisonLabel = memo(({ type, ...rest }: Props) => {
+  const { t } = useTranslation();
+  return (
+    <Text
+      position="absolute"
+      bottom={4}
+      insetInlineEnd={type === 'first' ? undefined : 4}
+      insetInlineStart={type === 'first' ? 4 : undefined}
+      textOverflow="clip"
+      whiteSpace="nowrap"
+      filter={DROP_SHADOW}
+      color="base.50"
+      transitionDuration="0.2s"
+      transitionProperty="common"
+      {...rest}
+    >
+      {type === 'first' ? t('gallery.viewerImage') : t('gallery.compareImage')}
+    </Text>
+  );
+});
+
+ImageComparisonLabel.displayName = 'ImageComparisonLabel';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparisonSideBySide.tsx

@@ -0,0 +1,70 @@
+import { Flex, Image } from '@invoke-ai/ui-library';
+import type { ComparisonProps } from 'features/gallery/components/ImageViewer/common';
+import { ImageComparisonLabel } from 'features/gallery/components/ImageViewer/ImageComparisonLabel';
+import ResizeHandle from 'features/ui/components/tabs/ResizeHandle';
+import { memo, useCallback, useRef } from 'react';
+import type { ImperativePanelGroupHandle } from 'react-resizable-panels';
+import { Panel, PanelGroup } from 'react-resizable-panels';
+
+export const ImageComparisonSideBySide = memo(({ firstImage, secondImage }: ComparisonProps) => {
+  const panelGroupRef = useRef<ImperativePanelGroupHandle>(null);
+  const onDoubleClickHandle = useCallback(() => {
+    if (!panelGroupRef.current) {
+      return;
+    }
+    panelGroupRef.current.setLayout([50, 50]);
+  }, []);
+
+  return (
+    <Flex w="full" h="full" maxW="full" maxH="full" position="relative" alignItems="center" justifyContent="center">
+      <Flex w="full" h="full" maxW="full" maxH="full" position="absolute" alignItems="center" justifyContent="center">
+        <PanelGroup ref={panelGroupRef} direction="horizontal" id="image-comparison-side-by-side">
+          <Panel minSize={20}>
+            <Flex position="relative" w="full" h="full" alignItems="center" justifyContent="center">
+              <Flex position="absolute" maxW="full" maxH="full" aspectRatio={firstImage.width / firstImage.height}>
+                <Image
+                  id="image-comparison-side-by-side-first-image"
+                  w={firstImage.width}
+                  h={firstImage.height}
+                  maxW="full"
+                  maxH="full"
+                  src={firstImage.image_url}
+                  fallbackSrc={firstImage.thumbnail_url}
+                  objectFit="contain"
+                  borderRadius="base"
+                />
+                <ImageComparisonLabel type="first" />
+              </Flex>
+            </Flex>
+          </Panel>
+          <ResizeHandle
+            id="image-comparison-side-by-side-handle"
+            onDoubleClick={onDoubleClickHandle}
+            orientation="vertical"
+          />
+
+          <Panel minSize={20}>
+            <Flex position="relative" w="full" h="full" alignItems="center" justifyContent="center">
+              <Flex position="absolute" maxW="full" maxH="full" aspectRatio={secondImage.width / secondImage.height}>
+                <Image
+                  id="image-comparison-side-by-side-first-image"
+                  w={secondImage.width}
+                  h={secondImage.height}
+                  maxW="full"
+                  maxH="full"
+                  src={secondImage.image_url}
+                  fallbackSrc={secondImage.thumbnail_url}
+                  objectFit="contain"
+                  borderRadius="base"
+                />
+                <ImageComparisonLabel type="second" />
+              </Flex>
+            </Flex>
+          </Panel>
+        </PanelGroup>
+      </Flex>
+    </Flex>
+  );
+});
+
+ImageComparisonSideBySide.displayName = 'ImageComparisonSideBySide';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparisonSlider.tsx

@@ -0,0 +1,215 @@
+import { Box, Flex, Icon, Image } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { preventDefault } from 'common/util/stopPropagation';
+import type { Dimensions } from 'features/canvas/store/canvasTypes';
+import { STAGE_BG_DATAURL } from 'features/controlLayers/util/renderers';
+import { ImageComparisonLabel } from 'features/gallery/components/ImageViewer/ImageComparisonLabel';
+import { memo, useCallback, useEffect, useMemo, useRef, useState } from 'react';
+import { PiCaretLeftBold, PiCaretRightBold } from 'react-icons/pi';
+
+import type { ComparisonProps } from './common';
+import { DROP_SHADOW, fitDimsToContainer, getSecondImageDims } from './common';
+
+const INITIAL_POS = '50%';
+const HANDLE_WIDTH = 2;
+const HANDLE_WIDTH_PX = `${HANDLE_WIDTH}px`;
+const HANDLE_HITBOX = 20;
+const HANDLE_HITBOX_PX = `${HANDLE_HITBOX}px`;
+const HANDLE_INNER_LEFT_PX = `${HANDLE_HITBOX / 2 - HANDLE_WIDTH / 2}px`;
+const HANDLE_LEFT_INITIAL_PX = `calc(${INITIAL_POS} - ${HANDLE_HITBOX / 2}px)`;
+
+export const ImageComparisonSlider = memo(({ firstImage, secondImage, containerDims }: ComparisonProps) => {
+  const comparisonFit = useAppSelector((s) => s.gallery.comparisonFit);
+  // How far the handle is from the left - this will be a CSS calculation that takes into account the handle width
+  const [left, setLeft] = useState(HANDLE_LEFT_INITIAL_PX);
+  // How wide the first image is
+  const [width, setWidth] = useState(INITIAL_POS);
+  const handleRef = useRef<HTMLDivElement>(null);
+  // To manage aspect ratios, we need to know the size of the container
+  const imageContainerRef = useRef<HTMLDivElement>(null);
+  // To keep things smooth, we use RAF to update the handle position & gate it to 60fps
+  const rafRef = useRef<number | null>(null);
+  const lastMoveTimeRef = useRef<number>(0);
+
+  const fittedDims = useMemo<Dimensions>(
+    () => fitDimsToContainer(containerDims, firstImage),
+    [containerDims, firstImage]
+  );
+
+  const compareImageDims = useMemo<Dimensions>(
+    () => getSecondImageDims(comparisonFit, fittedDims, firstImage, secondImage),
+    [comparisonFit, fittedDims, firstImage, secondImage]
+  );
+
+  const updateHandlePos = useCallback((clientX: number) => {
+    if (!handleRef.current || !imageContainerRef.current) {
+      return;
+    }
+    lastMoveTimeRef.current = performance.now();
+    const { x, width } = imageContainerRef.current.getBoundingClientRect();
+    const rawHandlePos = ((clientX - x) * 100) / width;
+    const handleWidthPct = (HANDLE_WIDTH * 100) / width;
+    const newHandlePos = Math.min(100 - handleWidthPct, Math.max(0, rawHandlePos));
+    setWidth(`${newHandlePos}%`);
+    setLeft(`calc(${newHandlePos}% - ${HANDLE_HITBOX / 2}px)`);
+  }, []);
+
+  const onMouseMove = useCallback(
+    (e: MouseEvent) => {
+      if (rafRef.current === null && performance.now() > lastMoveTimeRef.current + 1000 / 60) {
+        rafRef.current = window.requestAnimationFrame(() => {
+          updateHandlePos(e.clientX);
+          rafRef.current = null;
+        });
+      }
+    },
+    [updateHandlePos]
+  );
+
+  const onMouseUp = useCallback(() => {
+    window.removeEventListener('mousemove', onMouseMove);
+  }, [onMouseMove]);
+
+  const onMouseDown = useCallback(
+    (e: React.MouseEvent<HTMLDivElement>) => {
+      // Update the handle position immediately on click
+      updateHandlePos(e.clientX);
+      window.addEventListener('mouseup', onMouseUp, { once: true });
+      window.addEventListener('mousemove', onMouseMove);
+    },
+    [onMouseMove, onMouseUp, updateHandlePos]
+  );
+
+  useEffect(
+    () => () => {
+      if (rafRef.current !== null) {
+        cancelAnimationFrame(rafRef.current);
+      }
+    },
+    []
+  );
+
+  return (
+    <Flex w="full" h="full" maxW="full" maxH="full" position="relative" alignItems="center" justifyContent="center">
+      <Flex
+        id="image-comparison-wrapper"
+        w="full"
+        h="full"
+        maxW="full"
+        maxH="full"
+        position="absolute"
+        alignItems="center"
+        justifyContent="center"
+      >
+        <Box
+          ref={imageContainerRef}
+          position="relative"
+          id="image-comparison-image-container"
+          w={fittedDims.width}
+          h={fittedDims.height}
+          maxW="full"
+          maxH="full"
+          userSelect="none"
+          overflow="hidden"
+          borderRadius="base"
+        >
+          <Box
+            id="image-comparison-bg"
+            position="absolute"
+            top={0}
+            left={0}
+            right={0}
+            bottom={0}
+            backgroundImage={STAGE_BG_DATAURL}
+            backgroundRepeat="repeat"
+            opacity={0.2}
+          />
+          <Image
+            position="relative"
+            id="image-comparison-second-image"
+            src={secondImage.image_url}
+            fallbackSrc={secondImage.thumbnail_url}
+            w={compareImageDims.width}
+            h={compareImageDims.height}
+            maxW={fittedDims.width}
+            maxH={fittedDims.height}
+            objectFit={comparisonFit}
+            objectPosition="top left"
+          />
+          <ImageComparisonLabel type="second" />
+          <Box
+            id="image-comparison-first-image-container"
+            position="absolute"
+            top={0}
+            left={0}
+            right={0}
+            bottom={0}
+            w={width}
+            overflow="hidden"
+          >
+            <Image
+              id="image-comparison-first-image"
+              src={firstImage.image_url}
+              fallbackSrc={firstImage.thumbnail_url}
+              w={fittedDims.width}
+              h={fittedDims.height}
+              objectFit="cover"
+              objectPosition="top left"
+            />
+            <ImageComparisonLabel type="first" />
+          </Box>
+          <Flex
+            id="image-comparison-handle"
+            ref={handleRef}
+            position="absolute"
+            top={0}
+            bottom={0}
+            left={left}
+            w={HANDLE_HITBOX_PX}
+            cursor="ew-resize"
+            filter={DROP_SHADOW}
+            opacity={0.8}
+            color="base.50"
+          >
+            <Box
+              id="image-comparison-handle-divider"
+              w={HANDLE_WIDTH_PX}
+              h="full"
+              bg="currentColor"
+              shadow="dark-lg"
+              position="absolute"
+              top={0}
+              left={HANDLE_INNER_LEFT_PX}
+            />
+            <Flex
+              id="image-comparison-handle-icons"
+              gap={4}
+              position="absolute"
+              left="50%"
+              top="50%"
+              transform="translate(-50%, 0)"
+              filter={DROP_SHADOW}
+            >
+              <Icon as={PiCaretLeftBold} />
+              <Icon as={PiCaretRightBold} />
+            </Flex>
+          </Flex>
+          <Box
+            id="image-comparison-interaction-overlay"
+            position="absolute"
+            top={0}
+            right={0}
+            bottom={0}
+            left={0}
+            onMouseDown={onMouseDown}
+            onContextMenu={preventDefault}
+            userSelect="none"
+            cursor="ew-resize"
+          />
+        </Box>
+      </Flex>
+    </Flex>
+  );
+});
+
+ImageComparisonSlider.displayName = 'ImageComparisonSlider';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageViewer.tsx

@@ -1,36 +1,16 @@
-import { Flex } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { ToggleMetadataViewerButton } from 'features/gallery/components/ImageViewer/ToggleMetadataViewerButton';
-import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/ToggleProgressButton';
-import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
-import type { InvokeTabName } from 'features/ui/store/tabMap';
-import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import { memo, useMemo } from 'react';
-import { useHotkeys } from 'react-hotkeys-hook';
+import { Box, Flex } from '@invoke-ai/ui-library';
+import { CompareToolbar } from 'features/gallery/components/ImageViewer/CompareToolbar';
+import CurrentImagePreview from 'features/gallery/components/ImageViewer/CurrentImagePreview';
+import { ImageComparison } from 'features/gallery/components/ImageViewer/ImageComparison';
+import { ViewerToolbar } from 'features/gallery/components/ImageViewer/ViewerToolbar';
+import { memo } from 'react';
+import { useMeasure } from 'react-use';
 
-import CurrentImageButtons from './CurrentImageButtons';
-import CurrentImagePreview from './CurrentImagePreview';
-import { ViewerToggleMenu } from './ViewerToggleMenu';
-
-const VIEWER_ENABLED_TABS: InvokeTabName[] = ['canvas', 'generation', 'workflows'];
+import { useImageViewer } from './useImageViewer';
 
 export const ImageViewer = memo(() => {
-  const { isOpen, onToggle, onClose } = useImageViewer();
-  const activeTabName = useAppSelector(activeTabNameSelector);
-  const isViewerEnabled = useMemo(() => VIEWER_ENABLED_TABS.includes(activeTabName), [activeTabName]);
-  const shouldShowViewer = useMemo(() => {
-    if (!isViewerEnabled) {
-      return false;
-    }
-    return isOpen;
-  }, [isOpen, isViewerEnabled]);
-
-  useHotkeys('z', onToggle, { enabled: isViewerEnabled }, [isViewerEnabled, onToggle]);
-  useHotkeys('esc', onClose, { enabled: isViewerEnabled }, [isViewerEnabled, onClose]);
-
-  if (!shouldShowViewer) {
-    return null;
-  }
+  const imageViewer = useImageViewer();
+  const [containerRef, containerDims] = useMeasure<HTMLDivElement>();
 
   return (
     <Flex
@@ -46,25 +26,13 @@ export const ImageViewer = memo(() => {
       rowGap={4}
       alignItems="center"
       justifyContent="center"
-      zIndex={10} // reactflow puts its minimap at 5, so we need to be above that
     >
-      <Flex w="full" gap={2}>
-        <Flex flex={1} justifyContent="center">
-          <Flex gap={2} marginInlineEnd="auto">
-            <ToggleProgressButton />
-            <ToggleMetadataViewerButton />
-          </Flex>
-        </Flex>
-        <Flex flex={1} gap={2} justifyContent="center">
-          <CurrentImageButtons />
-        </Flex>
-        <Flex flex={1} justifyContent="center">
-          <Flex gap={2} marginInlineStart="auto">
-            <ViewerToggleMenu />
-          </Flex>
-        </Flex>
-      </Flex>
-      <CurrentImagePreview />
+      {imageViewer.isComparing && <CompareToolbar />}
+      {!imageViewer.isComparing && <ViewerToolbar />}
+      <Box ref={containerRef} w="full" h="full">
+        {!imageViewer.isComparing && <CurrentImagePreview />}
+        {imageViewer.isComparing && <ImageComparison containerDims={containerDims} />}
+      </Box>
     </Flex>
   );
 });

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageViewerWorkflows.tsx

@@ -1,45 +0,0 @@
-import { Flex } from '@invoke-ai/ui-library';
-import { ToggleMetadataViewerButton } from 'features/gallery/components/ImageViewer/ToggleMetadataViewerButton';
-import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/ToggleProgressButton';
-import { memo } from 'react';
-
-import CurrentImageButtons from './CurrentImageButtons';
-import CurrentImagePreview from './CurrentImagePreview';
-
-export const ImageViewerWorkflows = memo(() => {
-  return (
-    <Flex
-      layerStyle="first"
-      borderRadius="base"
-      position="absolute"
-      flexDirection="column"
-      top={0}
-      right={0}
-      bottom={0}
-      left={0}
-      p={2}
-      rowGap={4}
-      alignItems="center"
-      justifyContent="center"
-      zIndex={10} // reactflow puts its minimap at 5, so we need to be above that
-    >
-      <Flex w="full" gap={2}>
-        <Flex flex={1} justifyContent="center">
-          <Flex gap={2} marginInlineEnd="auto">
-            <ToggleProgressButton />
-            <ToggleMetadataViewerButton />
-          </Flex>
-        </Flex>
-        <Flex flex={1} gap={2} justifyContent="center">
-          <CurrentImageButtons />
-        </Flex>
-        <Flex flex={1} justifyContent="center">
-          <Flex gap={2} marginInlineStart="auto" />
-        </Flex>
-      </Flex>
-      <CurrentImagePreview />
-    </Flex>
-  );
-});
-
-ImageViewerWorkflows.displayName = 'ImageViewerWorkflows';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ViewerToggleMenu.tsx

@@ -9,33 +9,35 @@ import {
   PopoverTrigger,
   Text,
 } from '@invoke-ai/ui-library';
+import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
+import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiCaretDownBold, PiCheckBold, PiEyeBold, PiPencilBold } from 'react-icons/pi';
 
-import { useImageViewer } from './useImageViewer';
-
 export const ViewerToggleMenu = () => {
   const { t } = useTranslation();
-  const { isOpen, onClose, onOpen } = useImageViewer();
+  const imageViewer = useImageViewer();
+  useHotkeys('z', imageViewer.onToggle, [imageViewer]);
+  useHotkeys('esc', imageViewer.onClose, [imageViewer]);
 
   return (
     <Popover isLazy>
       <PopoverTrigger>
-        <Button variant="outline" data-testid="toggle-viewer-menu-button">
+        <Button variant="outline" data-testid="toggle-viewer-menu-button" pointerEvents="auto">
           <Flex gap={3} w="full" alignItems="center">
-            {isOpen ? <Icon as={PiEyeBold} /> : <Icon as={PiPencilBold} />}
-            <Text fontSize="md">{isOpen ? t('common.viewing') : t('common.editing')}</Text>
+            {imageViewer.isOpen ? <Icon as={PiEyeBold} /> : <Icon as={PiPencilBold} />}
+            <Text fontSize="md">{imageViewer.isOpen ? t('common.viewing') : t('common.editing')}</Text>
             <Icon as={PiCaretDownBold} />
           </Flex>
         </Button>
       </PopoverTrigger>
-      <PopoverContent p={2}>
+      <PopoverContent p={2} pointerEvents="auto">
         <PopoverArrow />
         <PopoverBody>
           <Flex flexDir="column">
-            <Button onClick={onOpen} variant="ghost" h="auto" w="auto" p={2}>
+            <Button onClick={imageViewer.onOpen} variant="ghost" h="auto" w="auto" p={2}>
               <Flex gap={2} w="full">
-                <Icon as={PiCheckBold} visibility={isOpen ? 'visible' : 'hidden'} />
+                <Icon as={PiCheckBold} visibility={imageViewer.isOpen ? 'visible' : 'hidden'} />
                 <Flex flexDir="column" gap={2} alignItems="flex-start">
                   <Text fontWeight="semibold" color="base.100">
                     {t('common.viewing')}
@@ -46,9 +48,9 @@ export const ViewerToggleMenu = () => {
                 </Flex>
               </Flex>
             </Button>
-            <Button onClick={onClose} variant="ghost" h="auto" w="auto" p={2}>
+            <Button onClick={imageViewer.onClose} variant="ghost" h="auto" w="auto" p={2}>
               <Flex gap={2} w="full">
-                <Icon as={PiCheckBold} visibility={isOpen ? 'hidden' : 'visible'} />
+                <Icon as={PiCheckBold} visibility={imageViewer.isOpen ? 'hidden' : 'visible'} />
                 <Flex flexDir="column" gap={2} alignItems="flex-start">
                   <Text fontWeight="semibold" color="base.100">
                     {t('common.editing')}

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ViewerToolbar.tsx

@@ -0,0 +1,33 @@
+import { Flex } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { ToggleMetadataViewerButton } from 'features/gallery/components/ImageViewer/ToggleMetadataViewerButton';
+import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/ToggleProgressButton';
+import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import { memo } from 'react';
+
+import CurrentImageButtons from './CurrentImageButtons';
+import { ViewerToggleMenu } from './ViewerToggleMenu';
+
+export const ViewerToolbar = memo(() => {
+  const tab = useAppSelector(activeTabNameSelector);
+  return (
+    <Flex w="full" gap={2}>
+      <Flex flex={1} justifyContent="center">
+        <Flex gap={2} marginInlineEnd="auto">
+          <ToggleProgressButton />
+          <ToggleMetadataViewerButton />
+        </Flex>
+      </Flex>
+      <Flex flex={1} gap={2} justifyContent="center">
+        <CurrentImageButtons />
+      </Flex>
+      <Flex flex={1} justifyContent="center">
+        <Flex gap={2} marginInlineStart="auto">
+          {tab !== 'workflows' && <ViewerToggleMenu />}
+        </Flex>
+      </Flex>
+    </Flex>
+  );
+});
+
+ViewerToolbar.displayName = 'ViewerToolbar';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/common.ts

@@ -0,0 +1,64 @@
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import type { Dimensions } from 'features/canvas/store/canvasTypes';
+import { selectGallerySlice } from 'features/gallery/store/gallerySlice';
+import type { ComparisonFit } from 'features/gallery/store/types';
+import type { ImageDTO } from 'services/api/types';
+
+export const DROP_SHADOW = 'drop-shadow(0px 0px 4px rgb(0, 0, 0)) drop-shadow(0px 0px 4px rgba(0, 0, 0, 0.3))';
+
+export type ComparisonProps = {
+  firstImage: ImageDTO;
+  secondImage: ImageDTO;
+  containerDims: Dimensions;
+};
+
+export const fitDimsToContainer = (containerDims: Dimensions, imageDims: Dimensions): Dimensions => {
+  // Fall back to the image's dimensions if the container has no dimensions
+  if (containerDims.width === 0 || containerDims.height === 0) {
+    return { width: imageDims.width, height: imageDims.height };
+  }
+
+  // Fall back to the image's dimensions if the image fits within the container
+  if (imageDims.width <= containerDims.width && imageDims.height <= containerDims.height) {
+    return { width: imageDims.width, height: imageDims.height };
+  }
+
+  const targetAspectRatio = containerDims.width / containerDims.height;
+  const imageAspectRatio = imageDims.width / imageDims.height;
+
+  let width: number;
+  let height: number;
+
+  if (imageAspectRatio > targetAspectRatio) {
+    // Image is wider than container's aspect ratio
+    width = containerDims.width;
+    height = width / imageAspectRatio;
+  } else {
+    // Image is taller than container's aspect ratio
+    height = containerDims.height;
+    width = height * imageAspectRatio;
+  }
+  return { width, height };
+};
+
+/**
+ * Gets the dimensions of the second image in a comparison based on the comparison fit mode.
+ */
+export const getSecondImageDims = (
+  comparisonFit: ComparisonFit,
+  fittedDims: Dimensions,
+  firstImageDims: Dimensions,
+  secondImageDims: Dimensions
+): Dimensions => {
+  const width =
+    comparisonFit === 'fill' ? fittedDims.width : (fittedDims.width * secondImageDims.width) / firstImageDims.width;
+  const height =
+    comparisonFit === 'fill' ? fittedDims.height : (fittedDims.height * secondImageDims.height) / firstImageDims.height;
+
+  return { width, height };
+};
+export const selectComparisonImages = createMemoizedSelector(selectGallerySlice, (gallerySlice) => {
+  const firstImage = gallerySlice.selection.slice(-1)[0] ?? null;
+  const secondImage = gallerySlice.imageToCompare;
+  return { firstImage, secondImage };
+});

invokeai/frontend/web/src/features/gallery/components/ImageViewer/useImageViewer.ts

@@ -0,0 +1,31 @@
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { imageToCompareChanged, isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
+import { useCallback } from 'react';
+
+export const useImageViewer = () => {
+  const dispatch = useAppDispatch();
+  const isComparing = useAppSelector((s) => s.gallery.imageToCompare !== null);
+  const isOpen = useAppSelector((s) => s.gallery.isImageViewerOpen);
+
+  const onClose = useCallback(() => {
+    if (isComparing && isOpen) {
+      dispatch(imageToCompareChanged(null));
+    } else {
+      dispatch(isImageViewerOpenChanged(false));
+    }
+  }, [dispatch, isComparing, isOpen]);
+
+  const onOpen = useCallback(() => {
+    dispatch(isImageViewerOpenChanged(true));
+  }, [dispatch]);
+
+  const onToggle = useCallback(() => {
+    if (isComparing && isOpen) {
+      dispatch(imageToCompareChanged(null));
+    } else {
+      dispatch(isImageViewerOpenChanged(!isOpen));
+    }
+  }, [dispatch, isComparing, isOpen]);
+
+  return { isOpen, onOpen, onClose, onToggle, isComparing };
+};

invokeai/frontend/web/src/features/gallery/components/ImageViewer/useImageViewer.tsx

@@ -1,22 +0,0 @@
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
-import { useCallback } from 'react';
-
-export const useImageViewer = () => {
-  const dispatch = useAppDispatch();
-  const isOpen = useAppSelector((s) => s.gallery.isImageViewerOpen);
-
-  const onClose = useCallback(() => {
-    dispatch(isImageViewerOpenChanged(false));
-  }, [dispatch]);
-
-  const onOpen = useCallback(() => {
-    dispatch(isImageViewerOpenChanged(true));
-  }, [dispatch]);
-
-  const onToggle = useCallback(() => {
-    dispatch(isImageViewerOpenChanged(!isOpen));
-  }, [dispatch, isOpen]);
-
-  return { isOpen, onOpen, onClose, onToggle };
-};

invokeai/frontend/web/src/features/gallery/components/NextPrevImageButtons.tsx

@@ -14,7 +14,7 @@ const nextPrevButtonStyles: ChakraProps['sx'] = {
 const NextPrevImageButtons = () => {
   const { t } = useTranslation();
 
-  const { handleLeftImage, handleRightImage, isOnFirstImage, isOnLastImage } = useGalleryNavigation();
+  const { prevImage, nextImage, isOnFirstImage, isOnLastImage } = useGalleryNavigation();
 
   const {
     areMoreImagesAvailable,
@@ -30,7 +30,7 @@ const NextPrevImageButtons = () => {
             aria-label={t('accessibility.previousImage')}
             icon={<PiCaretLeftBold size={64} />}
             variant="unstyled"
-            onClick={handleLeftImage}
+            onClick={prevImage}
             boxSize={16}
             sx={nextPrevButtonStyles}
           />
@@ -42,7 +42,7 @@ const NextPrevImageButtons = () => {
             aria-label={t('accessibility.nextImage')}
             icon={<PiCaretRightBold size={64} />}
             variant="unstyled"
-            onClick={handleRightImage}
+            onClick={nextImage}
             boxSize={16}
             sx={nextPrevButtonStyles}
           />

invokeai/frontend/web/src/features/gallery/hooks/useGalleryHotkeys.ts

@@ -27,16 +27,16 @@ export const useGalleryHotkeys = () => {
     useGalleryNavigation();
 
   useHotkeys(
-    'left',
-    () => {
-      canNavigateGallery && handleLeftImage();
+    ['left', 'alt+left'],
+    (e) => {
+      canNavigateGallery && handleLeftImage(e.altKey);
     },
     [handleLeftImage, canNavigateGallery]
   );
 
   useHotkeys(
-    'right',
-    () => {
+    ['right', 'alt+right'],
+    (e) => {
       if (!canNavigateGallery) {
         return;
       }
@@ -45,29 +45,29 @@ export const useGalleryHotkeys = () => {
         return;
       }
       if (!isOnLastImage) {
-        handleRightImage();
+        handleRightImage(e.altKey);
       }
     },
     [isOnLastImage, areMoreImagesAvailable, handleLoadMoreImages, isFetching, handleRightImage, canNavigateGallery]
   );
 
   useHotkeys(
-    'up',
-    () => {
-      handleUpImage();
+    ['up', 'alt+up'],
+    (e) => {
+      handleUpImage(e.altKey);
     },
     { preventDefault: true },
     [handleUpImage]
   );
 
   useHotkeys(
-    'down',
-    () => {
+    ['down', 'alt+down'],
+    (e) => {
       if (!areImagesBelowCurrent && areMoreImagesAvailable && !isFetching) {
         handleLoadMoreImages();
         return;
       }
-      handleDownImage();
+      handleDownImage(e.altKey);
     },
     { preventDefault: true },
     [areImagesBelowCurrent, areMoreImagesAvailable, handleLoadMoreImages, isFetching, handleDownImage]

invokeai/frontend/web/src/features/gallery/hooks/useGalleryNavigation.ts

@@ -1,11 +1,11 @@
+import { useAltModifier } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { getGalleryImageDataTestId } from 'features/gallery/components/ImageGrid/getGalleryImageDataTestId';
 import { imageItemContainerTestId } from 'features/gallery/components/ImageGrid/ImageGridItemContainer';
 import { imageListContainerTestId } from 'features/gallery/components/ImageGrid/ImageGridListContainer';
 import { virtuosoGridRefs } from 'features/gallery/components/ImageGrid/types';
 import { useGalleryImages } from 'features/gallery/hooks/useGalleryImages';
-import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imageSelected, imageToCompareChanged } from 'features/gallery/store/gallerySlice';
 import { getIsVisible } from 'features/gallery/util/getIsVisible';
 import { getScrollToIndexAlign } from 'features/gallery/util/getScrollToIndexAlign';
 import { clamp } from 'lodash-es';
@@ -106,10 +106,12 @@ const getImageFuncs = {
 };
 
 type UseGalleryNavigationReturn = {
-  handleLeftImage: () => void;
-  handleRightImage: () => void;
-  handleUpImage: () => void;
-  handleDownImage: () => void;
+  handleLeftImage: (alt?: boolean) => void;
+  handleRightImage: (alt?: boolean) => void;
+  handleUpImage: (alt?: boolean) => void;
+  handleDownImage: (alt?: boolean) => void;
+  prevImage: () => void;
+  nextImage: () => void;
   isOnFirstImage: boolean;
   isOnLastImage: boolean;
   areImagesBelowCurrent: boolean;
@@ -123,7 +125,15 @@ type UseGalleryNavigationReturn = {
  */
 export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
   const dispatch = useAppDispatch();
-  const lastSelectedImage = useAppSelector(selectLastSelectedImage);
+  const alt = useAltModifier();
+  const lastSelectedImage = useAppSelector((s) => {
+    const lastSelected = s.gallery.selection.slice(-1)[0] ?? null;
+    if (alt) {
+      return s.gallery.imageToCompare ?? lastSelected;
+    } else {
+      return lastSelected;
+    }
+  });
   const {
     queryResult: { data },
   } = useGalleryImages();
@@ -136,7 +146,7 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
   }, [lastSelectedImage, data]);
 
   const handleNavigation = useCallback(
-    (direction: 'left' | 'right' | 'up' | 'down') => {
+    (direction: 'left' | 'right' | 'up' | 'down', alt?: boolean) => {
       if (!data) {
         return;
       }
@@ -144,10 +154,14 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
       if (!image || index === lastSelectedImageIndex) {
         return;
       }
-      dispatch(imageSelected(image));
+      if (alt) {
+        dispatch(imageToCompareChanged(image));
+      } else {
+        dispatch(imageSelected(image));
+      }
       scrollToImage(image.image_name, index);
     },
-    [dispatch, lastSelectedImageIndex, data]
+    [data, lastSelectedImageIndex, dispatch]
   );
 
   const isOnFirstImage = useMemo(() => lastSelectedImageIndex === 0, [lastSelectedImageIndex]);
@@ -162,21 +176,41 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
     return lastSelectedImageIndex + imagesPerRow < loadedImagesCount;
   }, [lastSelectedImageIndex, loadedImagesCount]);
 
-  const handleLeftImage = useCallback(() => {
-    handleNavigation('left');
-  }, [handleNavigation]);
+  const handleLeftImage = useCallback(
+    (alt?: boolean) => {
+      handleNavigation('left', alt);
+    },
+    [handleNavigation]
+  );
 
-  const handleRightImage = useCallback(() => {
-    handleNavigation('right');
-  }, [handleNavigation]);
+  const handleRightImage = useCallback(
+    (alt?: boolean) => {
+      handleNavigation('right', alt);
+    },
+    [handleNavigation]
+  );
 
-  const handleUpImage = useCallback(() => {
-    handleNavigation('up');
-  }, [handleNavigation]);
+  const handleUpImage = useCallback(
+    (alt?: boolean) => {
+      handleNavigation('up', alt);
+    },
+    [handleNavigation]
+  );
 
-  const handleDownImage = useCallback(() => {
-    handleNavigation('down');
-  }, [handleNavigation]);
+  const handleDownImage = useCallback(
+    (alt?: boolean) => {
+      handleNavigation('down', alt);
+    },
+    [handleNavigation]
+  );
+
+  const nextImage = useCallback(() => {
+    handleRightImage();
+  }, [handleRightImage]);
+
+  const prevImage = useCallback(() => {
+    handleLeftImage();
+  }, [handleLeftImage]);
 
   return {
     handleLeftImage,
@@ -186,5 +220,7 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
     isOnFirstImage,
     isOnLastImage,
     areImagesBelowCurrent,
+    nextImage,
+    prevImage,
   };
 };

invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts

@@ -36,6 +36,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
           shiftKey: e.shiftKey,
           ctrlKey: e.ctrlKey,
           metaKey: e.metaKey,
+          altKey: e.altKey,
         })
       );
     },

invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts

@@ -6,7 +6,7 @@ import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 
-import type { BoardId, GalleryState, GalleryView } from './types';
+import type { BoardId, ComparisonMode, GalleryState, GalleryView } from './types';
 import { IMAGE_LIMIT, INITIAL_IMAGE_LIMIT } from './types';
 
 const initialGalleryState: GalleryState = {
@@ -22,6 +22,9 @@ const initialGalleryState: GalleryState = {
   limit: INITIAL_IMAGE_LIMIT,
   offset: 0,
   isImageViewerOpen: true,
+  imageToCompare: null,
+  comparisonMode: 'slider',
+  comparisonFit: 'fill',
 };
 
 export const gallerySlice = createSlice({
@@ -34,6 +37,28 @@ export const gallerySlice = createSlice({
     selectionChanged: (state, action: PayloadAction<ImageDTO[]>) => {
       state.selection = uniqBy(action.payload, (i) => i.image_name);
     },
+    imageToCompareChanged: (state, action: PayloadAction<ImageDTO | null>) => {
+      state.imageToCompare = action.payload;
+      if (action.payload) {
+        state.isImageViewerOpen = true;
+      }
+    },
+    comparisonModeChanged: (state, action: PayloadAction<ComparisonMode>) => {
+      state.comparisonMode = action.payload;
+    },
+    comparisonModeCycled: (state) => {
+      switch (state.comparisonMode) {
+        case 'slider':
+          state.comparisonMode = 'side-by-side';
+          break;
+        case 'side-by-side':
+          state.comparisonMode = 'hover';
+          break;
+        case 'hover':
+          state.comparisonMode = 'slider';
+          break;
+      }
+    },
     shouldAutoSwitchChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldAutoSwitch = action.payload;
     },
@@ -79,6 +104,16 @@ export const gallerySlice = createSlice({
     isImageViewerOpenChanged: (state, action: PayloadAction<boolean>) => {
       state.isImageViewerOpen = action.payload;
     },
+    comparedImagesSwapped: (state) => {
+      if (state.imageToCompare) {
+        const oldSelection = state.selection;
+        state.selection = [state.imageToCompare];
+        state.imageToCompare = oldSelection[0] ?? null;
+      }
+    },
+    comparisonFitChanged: (state, action: PayloadAction<'contain' | 'fill'>) => {
+      state.comparisonFit = action.payload;
+    },
   },
   extraReducers: (builder) => {
     builder.addMatcher(isAnyBoardDeleted, (state, action) => {
@@ -117,6 +152,11 @@ export const {
   moreImagesLoaded,
   alwaysShowImageSizeBadgeChanged,
   isImageViewerOpenChanged,
+  imageToCompareChanged,
+  comparisonModeChanged,
+  comparedImagesSwapped,
+  comparisonFitChanged,
+  comparisonModeCycled,
 } = gallerySlice.actions;
 
 const isAnyBoardDeleted = isAnyOf(
@@ -138,5 +178,13 @@ export const galleryPersistConfig: PersistConfig<GalleryState> = {
   name: gallerySlice.name,
   initialState: initialGalleryState,
   migrate: migrateGalleryState,
-  persistDenylist: ['selection', 'selectedBoardId', 'galleryView', 'offset', 'limit', 'isImageViewerOpen'],
+  persistDenylist: [
+    'selection',
+    'selectedBoardId',
+    'galleryView',
+    'offset',
+    'limit',
+    'isImageViewerOpen',
+    'imageToCompare',
+  ],
 };

invokeai/frontend/web/src/features/gallery/store/types.ts

@@ -7,6 +7,8 @@ export const IMAGE_LIMIT = 20;
 
 export type GalleryView = 'images' | 'assets';
 export type BoardId = 'none' | (string & Record<never, never>);
+export type ComparisonMode = 'slider' | 'side-by-side' | 'hover';
+export type ComparisonFit = 'contain' | 'fill';
 
 export type GalleryState = {
   selection: ImageDTO[];
@@ -20,5 +22,8 @@ export type GalleryState = {
   offset: number;
   limit: number;
   alwaysShowImageSizeBadge: boolean;
+  imageToCompare: ImageDTO | null;
+  comparisonMode: ComparisonMode;
+  comparisonFit: ComparisonFit;
   isImageViewerOpen: boolean;
 };

invokeai/frontend/web/src/features/metadata/util/handlers.ts

@@ -1,4 +1,7 @@
+import { getStore } from 'app/store/nanostores/store';
+import { deepClone } from 'common/util/deepClone';
 import { objectKeys } from 'common/util/objectKeys';
+import { shouldConcatPromptsChanged } from 'features/controlLayers/store/controlLayersSlice';
 import type { Layer } from 'features/controlLayers/store/types';
 import type { LoRA } from 'features/lora/store/loraSlice';
 import type {
@@ -16,6 +19,7 @@ import { validators } from 'features/metadata/util/validators';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
+import { size } from 'lodash-es';
 import { assert } from 'tsafe';
 
 import { parsers } from './parsers';
@@ -376,54 +380,25 @@ export const handlers = {
   }),
 } as const;
 
+type ParsedValue = Awaited<ReturnType<(typeof handlers)[keyof typeof handlers]['parse']>>;
+type RecallResults = Partial<Record<keyof typeof handlers, ParsedValue>>;
+
 export const parseAndRecallPrompts = async (metadata: unknown) => {
-  const results = await Promise.allSettled([
-    handlers.positivePrompt.parse(metadata).then((positivePrompt) => {
-      if (!handlers.positivePrompt.recall) {
-        return;
-      }
-      handlers.positivePrompt?.recall(positivePrompt);
-    }),
-    handlers.negativePrompt.parse(metadata).then((negativePrompt) => {
-      if (!handlers.negativePrompt.recall) {
-        return;
-      }
-      handlers.negativePrompt?.recall(negativePrompt);
-    }),
-    handlers.sdxlPositiveStylePrompt.parse(metadata).then((sdxlPositiveStylePrompt) => {
-      if (!handlers.sdxlPositiveStylePrompt.recall) {
-        return;
-      }
-      handlers.sdxlPositiveStylePrompt?.recall(sdxlPositiveStylePrompt);
-    }),
-    handlers.sdxlNegativeStylePrompt.parse(metadata).then((sdxlNegativeStylePrompt) => {
-      if (!handlers.sdxlNegativeStylePrompt.recall) {
-        return;
-      }
-      handlers.sdxlNegativeStylePrompt?.recall(sdxlNegativeStylePrompt);
-    }),
-  ]);
-  if (results.some((result) => result.status === 'fulfilled')) {
+  const keysToRecall: (keyof typeof handlers)[] = [
+    'positivePrompt',
+    'negativePrompt',
+    'sdxlPositiveStylePrompt',
+    'sdxlNegativeStylePrompt',
+  ];
+  const recalled = await recallKeys(keysToRecall, metadata);
+  if (size(recalled) > 0) {
     parameterSetToast(t('metadata.allPrompts'));
   }
 };
 
 export const parseAndRecallImageDimensions = async (metadata: unknown) => {
-  const results = await Promise.allSettled([
-    handlers.width.parse(metadata).then((width) => {
-      if (!handlers.width.recall) {
-        return;
-      }
-      handlers.width?.recall(width);
-    }),
-    handlers.height.parse(metadata).then((height) => {
-      if (!handlers.height.recall) {
-        return;
-      }
-      handlers.height?.recall(height);
-    }),
-  ]);
-  if (results.some((result) => result.status === 'fulfilled')) {
+  const recalled = recallKeys(['width', 'height'], metadata);
+  if (size(recalled) > 0) {
     parameterSetToast(t('metadata.imageDimensions'));
   }
 };
@@ -438,28 +413,20 @@ export const parseAndRecallAllMetadata = async (
   toControlLayers: boolean,
   skip: (keyof typeof handlers)[] = []
 ) => {
-  const skipKeys = skip ?? [];
+  const skipKeys = deepClone(skip);
   if (toControlLayers) {
     skipKeys.push(...TO_CONTROL_LAYERS_SKIP_KEYS);
   } else {
     skipKeys.push(...NOT_TO_CONTROL_LAYERS_SKIP_KEYS);
   }
-  const results = await Promise.allSettled(
-    objectKeys(handlers)
-      .filter((key) => !skipKeys.includes(key))
-      .map((key) => {
-        const { parse, recall } = handlers[key];
-        return parse(metadata).then((value) => {
-          if (!recall) {
-            return;
-          }
-          /* @ts-expect-error The return type of parse and the input type of recall are guaranteed to be compatible. */
-          recall(value);
-        });
-      })
-  );
 
-  if (results.some((result) => result.status === 'fulfilled')) {
+  // We may need to take some further action depending on what was recalled. For example, we need to disable SDXL prompt
+  // concat if the negative or positive style prompt was set. Because the recalling is all async, we need to collect all
+  // results
+  const keysToRecall = objectKeys(handlers).filter((key) => !skipKeys.includes(key));
+  const recalled = await recallKeys(keysToRecall, metadata);
+
+  if (size(recalled) > 0) {
     toast({
       id: 'PARAMETER_SET',
       title: t('toast.parametersSet'),
@@ -473,3 +440,43 @@ export const parseAndRecallAllMetadata = async (
     });
   }
 };
+
+/**
+ * Recalls a set of keys from metadata.
+ * Includes special handling for some metadata where recalling may have side effects. For example, recalling a "style"
+ * prompt that is different from the "positive" or "negative" prompt should disable prompt concatenation.
+ * @param keysToRecall An array of keys to recall.
+ * @param metadata The metadata to recall from
+ * @returns A promise that resolves to an object containing the recalled values.
+ */
+const recallKeys = async (keysToRecall: (keyof typeof handlers)[], metadata: unknown): Promise<RecallResults> => {
+  const { dispatch } = getStore();
+  const recalled: RecallResults = {};
+  for (const key of keysToRecall) {
+    const { parse, recall } = handlers[key];
+    if (!recall) {
+      continue;
+    }
+    try {
+      const value = await parse(metadata);
+      /* @ts-expect-error The return type of parse and the input type of recall are guaranteed to be compatible. */
+      await recall(value);
+      recalled[key] = value;
+    } catch {
+      // no-op
+    }
+  }
+
+  if (
+    (recalled['sdxlPositiveStylePrompt'] && recalled['sdxlPositiveStylePrompt'] !== recalled['positivePrompt']) ||
+    (recalled['sdxlNegativeStylePrompt'] && recalled['sdxlNegativeStylePrompt'] !== recalled['negativePrompt'])
+  ) {
+    // If we set the negative style prompt or positive style prompt, we should disable prompt concat
+    dispatch(shouldConcatPromptsChanged(false));
+  } else {
+    // Otherwise, we should enable prompt concat
+    dispatch(shouldConcatPromptsChanged(true));
+  }
+
+  return recalled;
+};

invokeai/frontend/web/src/features/metadata/util/modelFetchingHelpers.ts

@@ -1,6 +1,7 @@
 import { getStore } from 'app/store/nanostores/store';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
 import { isModelIdentifier, isModelIdentifierV2 } from 'features/nodes/types/common';
+import type { ModelIdentifier } from 'features/nodes/types/v2/common';
 import { modelsApi } from 'services/api/endpoints/models';
 import type { AnyModelConfig, BaseModelType, ModelType } from 'services/api/types';
 
@@ -107,19 +108,30 @@ export const fetchModelConfigWithTypeGuard = async <T extends AnyModelConfig>(
 
 /**
  * Fetches the model key from a model identifier. This includes fetching the key for MM1 format model identifiers.
- * @param modelIdentifier The model identifier. The MM2 format `{key: string}` simply extracts the key. The MM1 format
- * `{model_name: string, base_model: BaseModelType}` must do a network request to fetch the key.
+ * @param modelIdentifier The model identifier. This can be a MM1 or MM2 identifier. In every case, we attempt to fetch
+ * the model config from the server to ensure that the model identifier is valid and represents an installed model.
  * @param type The type of model to fetch. This is used to fetch the key for MM1 format model identifiers.
  * @param message An optional custom message to include in the error if the model identifier is invalid.
  * @returns A promise that resolves to the model key.
  * @throws {InvalidModelConfigError} If the model identifier is invalid.
  */
-export const getModelKey = async (modelIdentifier: unknown, type: ModelType, message?: string): Promise<string> => {
+export const getModelKey = async (
+  modelIdentifier: unknown | ModelIdentifierField | ModelIdentifier,
+  type: ModelType,
+  message?: string
+): Promise<string> => {
   if (isModelIdentifier(modelIdentifier)) {
-    return modelIdentifier.key;
-  }
-  if (isModelIdentifierV2(modelIdentifier)) {
+    try {
+      // Check if the model exists by key
+      return (await fetchModelConfig(modelIdentifier.key)).key;
+    } catch {
+      // If not, fetch the model key by name and base model
+      return (await fetchModelConfigByAttrs(modelIdentifier.name, modelIdentifier.base, type)).key;
+    }
+  } else if (isModelIdentifierV2(modelIdentifier)) {
+    // Try by old-format model identifier
     return (await fetchModelConfigByAttrs(modelIdentifier.model_name, modelIdentifier.base_model, type)).key;
   }
+  // Nope, couldn't find it
   throw new InvalidModelConfigError(message || `Invalid model identifier: ${modelIdentifier}`);
 };

invokeai/frontend/web/src/features/nodes/components/flow/Flow.tsx

@@ -19,7 +19,7 @@ import {
   redo,
   undo,
 } from 'features/nodes/store/nodesSlice';
-import { $flow } from 'features/nodes/store/reactFlowInstance';
+import { $flow, $needsFit } from 'features/nodes/store/reactFlowInstance';
 import { connectionToEdge } from 'features/nodes/store/util/reactFlowUtil';
 import type { CSSProperties, MouseEvent } from 'react';
 import { memo, useCallback, useMemo, useRef } from 'react';
@@ -68,6 +68,7 @@ export const Flow = memo(() => {
   const nodes = useAppSelector((s) => s.nodes.present.nodes);
   const edges = useAppSelector((s) => s.nodes.present.edges);
   const viewport = useStore($viewport);
+  const needsFit = useStore($needsFit);
   const mayUndo = useAppSelector((s) => s.nodes.past.length > 0);
   const mayRedo = useAppSelector((s) => s.nodes.future.length > 0);
   const shouldSnapToGrid = useAppSelector((s) => s.workflowSettings.shouldSnapToGrid);
@@ -92,8 +93,16 @@ export const Flow = memo(() => {
   const onNodesChange: OnNodesChange = useCallback(
     (nodeChanges) => {
       dispatch(nodesChanged(nodeChanges));
+      const flow = $flow.get();
+      if (!flow) {
+        return;
+      }
+      if (needsFit) {
+        $needsFit.set(false);
+        flow.fitView();
+      }
     },
-    [dispatch]
+    [dispatch, needsFit]
   );
 
   const onEdgesChange: OnEdgesChange = useCallback(

invokeai/frontend/web/src/features/nodes/components/flow/panels/BottomLeftPanel/ViewportControls.tsx

@@ -15,27 +15,20 @@ const ViewportControls = () => {
   const { t } = useTranslation();
   const { zoomIn, zoomOut, fitView } = useReactFlow();
   const dispatch = useAppDispatch();
-  // const shouldShowFieldTypeLegend = useAppSelector(
-  //   (s) => s.nodes.present.shouldShowFieldTypeLegend
-  // );
   const shouldShowMinimapPanel = useAppSelector((s) => s.workflowSettings.shouldShowMinimapPanel);
 
   const handleClickedZoomIn = useCallback(() => {
-    zoomIn();
+    zoomIn({ duration: 300 });
   }, [zoomIn]);
 
   const handleClickedZoomOut = useCallback(() => {
-    zoomOut();
+    zoomOut({ duration: 300 });
   }, [zoomOut]);
 
   const handleClickedFitView = useCallback(() => {
-    fitView();
+    fitView({ duration: 300 });
   }, [fitView]);
 
-  // const handleClickedToggleFieldTypeLegend = useCallback(() => {
-  //   dispatch(shouldShowFieldTypeLegendChanged(!shouldShowFieldTypeLegend));
-  // }, [shouldShowFieldTypeLegend, dispatch]);
-
   const handleClickedToggleMiniMapPanel = useCallback(() => {
     dispatch(shouldShowMinimapPanelChanged(!shouldShowMinimapPanel));
   }, [shouldShowMinimapPanel, dispatch]);

invokeai/frontend/web/src/features/nodes/components/sidePanel/NodeEditorPanelGroup.tsx

@@ -1,9 +1,7 @@
 import 'reactflow/dist/style.css';
 
 import { Flex } from '@invoke-ai/ui-library';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
 import QueueControls from 'features/queue/components/QueueControls';
 import ResizeHandle from 'features/ui/components/tabs/ResizeHandle';
 import { usePanelStorage } from 'features/ui/hooks/usePanelStorage';
@@ -21,14 +19,8 @@ import { WorkflowName } from './WorkflowName';
 
 const panelGroupStyles: CSSProperties = { height: '100%', width: '100%' };
 
-const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
-  return {
-    mode: workflow.mode,
-  };
-});
-
 const NodeEditorPanelGroup = () => {
-  const { mode } = useAppSelector(selector);
+  const mode = useAppSelector((s) => s.workflow.mode);
   const panelGroupRef = useRef<ImperativePanelGroupHandle>(null);
   const panelStorage = usePanelStorage();
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowMenu.tsx

@@ -1,20 +1,12 @@
 import { Flex } from '@invoke-ai/ui-library';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
 import { NewWorkflowButton } from 'features/workflowLibrary/components/NewWorkflowButton';
 
 import { ModeToggle } from './ModeToggle';
 
-const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
-  return {
-    mode: workflow.mode,
-  };
-});
-
 export const WorkflowMenu = () => {
-  const { mode } = useAppSelector(selector);
+  const mode = useAppSelector((s) => s.workflow.mode);
 
   return (
     <Flex gap="2" alignItems="center">

invokeai/frontend/web/src/features/nodes/components/sidePanel/inspector/InspectorOutputsTab.tsx

@@ -11,8 +11,7 @@ import { selectLastSelectedNode } from 'features/nodes/store/selectors';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import type { ImageOutput } from 'services/api/types';
-import type { AnyResult } from 'services/events/types';
+import type { AnyInvocationOutput, ImageOutput } from 'services/api/types';
 
 import ImageOutputPreview from './outputs/ImageOutputPreview';
 
@@ -66,4 +65,4 @@ const InspectorOutputsTab = () => {
 
 export default memo(InspectorOutputsTab);
 
-const getKey = (result: AnyResult, i: number) => `${result.type}-${i}`;
+const getKey = (result: AnyInvocationOutput, i: number) => `${result.type}-${i}`;

invokeai/frontend/web/src/features/nodes/store/reactFlowInstance.ts

@@ -2,3 +2,4 @@ import { atom } from 'nanostores';
 import type { ReactFlowInstance } from 'reactflow';
 
 export const $flow = atom<ReactFlowInstance | null>(null);
+export const $needsFit = atom<boolean>(true);

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

@@ -144,5 +144,4 @@ const zImageOutput = z.object({
   type: z.literal('image_output'),
 });
 export type ImageOutput = z.infer<typeof zImageOutput>;
-export const isImageOutput = (output: unknown): output is ImageOutput => zImageOutput.safeParse(output).success;
 // #endregion

invokeai/frontend/web/src/features/nodes/util/graph/buildNodesGraph.ts

@@ -1,8 +1,7 @@
 import type { NodesState } from 'features/nodes/store/types';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { omit, reduce } from 'lodash-es';
-import type { Graph } from 'services/api/types';
-import type { AnyInvocation } from 'services/events/types';
+import type { AnyInvocation, Graph } from 'services/api/types';
 import { v4 as uuidv4 } from 'uuid';
 
 /**

invokeai/frontend/web/src/features/ui/components/tabs/NodesTab.tsx

@@ -1,6 +1,7 @@
 import { Box } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { ImageViewerWorkflows } from 'features/gallery/components/ImageViewer/ImageViewerWorkflows';
+import { ImageComparisonDroppable } from 'features/gallery/components/ImageViewer/ImageComparisonDroppable';
+import { ImageViewer } from 'features/gallery/components/ImageViewer/ImageViewer';
 import NodeEditor from 'features/nodes/components/NodeEditor';
 import { memo } from 'react';
 import { ReactFlowProvider } from 'reactflow';
@@ -10,7 +11,8 @@ const NodesTab = () => {
   if (mode === 'view') {
     return (
       <Box layerStyle="first" position="relative" w="full" h="full" p={2} borderRadius="base">
-        <ImageViewerWorkflows />
+        <ImageViewer />
+        <ImageComparisonDroppable />
       </Box>
     );
   }

invokeai/frontend/web/src/features/ui/components/tabs/TextToImageTab.tsx

@@ -1,13 +1,17 @@
 import { Box } from '@invoke-ai/ui-library';
 import { ControlLayersEditor } from 'features/controlLayers/components/ControlLayersEditor';
+import { ImageComparisonDroppable } from 'features/gallery/components/ImageViewer/ImageComparisonDroppable';
 import { ImageViewer } from 'features/gallery/components/ImageViewer/ImageViewer';
+import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { memo } from 'react';
 
 const TextToImageTab = () => {
+  const imageViewer = useImageViewer();
   return (
     <Box layerStyle="first" position="relative" w="full" h="full" p={2} borderRadius="base">
       <ControlLayersEditor />
-      <ImageViewer />
+      {imageViewer.isOpen && <ImageViewer />}
+      <ImageComparisonDroppable />
     </Box>
   );
 };

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvasTab.tsx

@@ -41,7 +41,7 @@ const UnifiedCanvasTab = () => {
     >
       <IAICanvasToolbar />
       <IAICanvas />
-      {isValidDrop(droppableData, active) && (
+      {isValidDrop(droppableData, active?.data.current) && (
         <IAIDropOverlay isOver={isOver} label={t('toast.setCanvasInitialImage')} />
       )}
     </Flex>

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

@@ -122,7 +122,6 @@ export type ModelInstallStatus = S['InstallStatus'];
 // Graphs
 export type Graph = S['Graph'];
 export type NonNullableGraph = O.Required<Graph, 'nodes' | 'edges'>;
-export type GraphExecutionState = S['GraphExecutionState'];
 export type Batch = S['Batch'];
 export type SessionQueueItemDTO = S['SessionQueueItemDTO'];
 export type WorkflowRecordOrderBy = S['WorkflowRecordOrderBy'];
@@ -132,14 +131,14 @@ export type WorkflowRecordListItemDTO = S['WorkflowRecordListItemDTO'];
 type KeysOfUnion<T> = T extends T ? keyof T : never;
 
 export type AnyInvocation = Exclude<
-  Graph['nodes'][string],
+  NonNullable<S['Graph']['nodes']>[string],
   S['CoreMetadataInvocation'] | S['MetadataInvocation'] | S['MetadataItemInvocation'] | S['MergeMetadataInvocation']
 >;
-export type AnyInvocationIncMetadata = S['Graph']['nodes'][string];
+export type AnyInvocationIncMetadata = NonNullable<S['Graph']['nodes']>[string];
 
 export type InvocationType = AnyInvocation['type'];
 type InvocationOutputMap = S['InvocationOutputMap'];
-type AnyInvocationOutput = InvocationOutputMap[InvocationType];
+export type AnyInvocationOutput = InvocationOutputMap[InvocationType];
 
 export type Invocation<T extends InvocationType> = Extract<AnyInvocation, { type: T }>;
 // export type InvocationOutput<T extends InvocationType> = InvocationOutputMap[T];

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

@@ -1,21 +1,12 @@
-import type { Graph, GraphExecutionState, S } from 'services/api/types';
-
-export type AnyInvocation = NonNullable<NonNullable<Graph['nodes']>[string]>;
-
-export type AnyResult = NonNullable<GraphExecutionState['results'][string]>;
+import type { S } from 'services/api/types';
 
 export type ModelLoadStartedEvent = S['ModelLoadStartedEvent'];
 export type ModelLoadCompleteEvent = S['ModelLoadCompleteEvent'];
 
-export type InvocationStartedEvent = Omit<S['InvocationStartedEvent'], 'invocation'> & { invocation: AnyInvocation };
-export type InvocationDenoiseProgressEvent = Omit<S['InvocationDenoiseProgressEvent'], 'invocation'> & {
-  invocation: AnyInvocation;
-};
-export type InvocationCompleteEvent = Omit<S['InvocationCompleteEvent'], 'result' | 'invocation'> & {
-  result: AnyResult;
-  invocation: AnyInvocation;
-};
-export type InvocationErrorEvent = Omit<S['InvocationErrorEvent'], 'invocation'> & { invocation: AnyInvocation };
+export type InvocationStartedEvent = S['InvocationStartedEvent'];
+export type InvocationDenoiseProgressEvent = S['InvocationDenoiseProgressEvent'];
+export type InvocationCompleteEvent = S['InvocationCompleteEvent'];
+export type InvocationErrorEvent = S['InvocationErrorEvent'];
 export type ProgressImage = InvocationDenoiseProgressEvent['progress_image'];
 
 export type ModelInstallDownloadProgressEvent = S['ModelInstallDownloadProgressEvent'];

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "4.2.3"
+__version__ = "4.2.4"

pyproject.toml

@@ -55,10 +55,10 @@ dependencies = [
 
   # Core application dependencies, pinned for reproducible builds.
   "fastapi-events==0.11.0",
-  "fastapi==0.110.0",
+  "fastapi==0.111.0",
   "huggingface-hub==0.23.1",
   "pydantic-settings==2.2.1",
-  "pydantic==2.6.3",
+  "pydantic==2.7.2",
   "python-socketio==5.11.1",
   "uvicorn[standard]==0.28.0",
 

scripts/generate_openapi_schema.py

@@ -7,9 +7,10 @@ def main():
     # Change working directory to the repo root
     os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 
-    from invokeai.app.api_app import custom_openapi
+    from invokeai.app.api_app import app
+    from invokeai.app.util.custom_openapi import get_openapi_func
 
-    schema = custom_openapi()
+    schema = get_openapi_func(app)()
     json.dump(schema, sys.stdout, indent=2)
 
 

tests/test_node_graph.py

@@ -1,5 +1,6 @@
 import pytest
 from pydantic import TypeAdapter
+from pydantic.json_schema import models_json_schema
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -713,4 +714,4 @@ def test_iterate_accepts_collection():
 def test_graph_can_generate_schema():
     # Not throwing on this line is sufficient
     # NOTE: if this test fails, it's PROBABLY because a new invocation type is breaking schema generation
-    _ = Graph.model_json_schema()
+    models_json_schema([(Graph, "serialization")])