Gitignore aider

# Aider chat conversation:

USER: `invokeai/backend/model_manager/convert_ckpt_to_diffusers.py` Im having issues with cpu memory, not gpu memory

ASSISTANT: To address CPU memory issues, we can make the following changes:

1. Use `del` to explicitly delete large objects when they are no longer needed.
2. Use `gc.collect()` to force garbage collection after deleting large objects.

Here are the *SEARCH/REPLACE* blocks:

invokeai/backend/model_manager/convert_ckpt_to_diffusers.py
```python
<<<<<<< SEARCH
    vae = AutoencoderKL(**vae_config)
    with torch.no_grad():
        vae.load_state_dict(converted_vae_checkpoint)
        vae.to(precision)
        torch.cuda.empty_cache()
=======
    vae = AutoencoderKL(**vae_config)
    with torch.no_grad():
        vae.load_state_dict(converted_vae_checkpoint)
        del converted_vae_checkpoint  # Free memory
        import gc
        gc.collect()
        vae.to(precision)
>>>>>>> REPLACE
```

invokeai/backend/model_manager/convert_ckpt_to_diffusers.py
```python
<<<<<<< SEARCH
    pipe = download_from_original_stable_diffusion_ckpt(Path(checkpoint_path).as_posix(), **kwargs)
    with torch.no_grad():
        pipe = pipe.to(precision)
        torch.cuda.empty_cache()
=======
    pipe = download_from_original_stable_diffusion_ckpt(Path(checkpoint_path).as_posix(), **kwargs)
    with torch.no_grad():
        del kwargs  # Free memory
        import gc
        gc.collect()
        pipe = pipe.to(precision)
>>>>>>> REPLACE
```

invokeai/backend/model_manager/convert_ckpt_to_diffusers.py
```python
<<<<<<< SEARCH
    pipe = download_controlnet_from_original_ckpt(checkpoint_path.as_posix(), **kwargs)
    with torch.no_grad():
        pipe = pipe.to(precision)
        torch.cuda.empty_cache()
=======
    pipe = download_controlnet_from_original_ckpt(checkpoint_path.as_posix(), **kwargs)
    with torch.no_grad():
        del kwargs  # Free memory
        import gc
        gc.collect()
        pipe = pipe.to(precision)
>>>>>>> REPLACE
```

.gitignore

@@ -188,3 +188,4 @@ installer/install.sh
 installer/update.bat
 installer/update.sh
 installer/InvokeAI-Installer/
+.aider*

Makefile

@@ -18,7 +18,6 @@ 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:
@@ -71,6 +70,3 @@ 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

@@ -154,18 +154,6 @@ 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

@@ -3,7 +3,9 @@ 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
@@ -11,9 +13,11 @@ 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:
@@ -21,8 +25,10 @@ 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.util.custom_openapi import get_openapi_func
+from invokeai.app.services.events.events_common import EventBase
+from invokeai.app.services.session_processor.session_processor_common import ProgressImage
 from invokeai.backend.util.devices import TorchDevice
 
 from ..backend.util.logging import InvokeAILogger
@@ -39,6 +45,11 @@ 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()
 
@@ -108,7 +119,84 @@ 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")
 
-app.openapi = get_openapi_func(app)
+
+# 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.get("/docs", include_in_schema=False)

invokeai/app/invocations/baseinvocation.py

@@ -98,13 +98,11 @@ 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]:
@@ -114,12 +112,11 @@ 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 or cls._typeadapter_needs_update:
-            AnyInvocationOutput = TypeAliasType(
-                "AnyInvocationOutput", Annotated[Union[tuple(cls._output_classes)], Field(discriminator="type")]
+        if not cls._typeadapter:
+            InvocationOutputsUnion = TypeAliasType(
+                "InvocationOutputsUnion", Annotated[Union[tuple(cls._output_classes)], Field(discriminator="type")]
             )
-            cls._typeadapter = TypeAdapter(AnyInvocationOutput)
-            cls._typeadapter_needs_update = False
+            cls._typeadapter = TypeAdapter(InvocationOutputsUnion)
         return cls._typeadapter
 
     @classmethod
@@ -128,13 +125,12 @@ 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[BaseInvocationOutput]) -> None:
+    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> 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
@@ -171,7 +167,6 @@ 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:
@@ -182,17 +177,15 @@ 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 or cls._typeadapter_needs_update:
-            AnyInvocation = TypeAliasType(
-                "AnyInvocation", Annotated[Union[tuple(cls._invocation_classes)], Field(discriminator="type")]
+        if not cls._typeadapter:
+            InvocationsUnion = TypeAliasType(
+                "InvocationsUnion", Annotated[Union[tuple(cls._invocation_classes)], Field(discriminator="type")]
             )
-            cls._typeadapter = TypeAdapter(AnyInvocation)
-            cls._typeadapter_needs_update = False
+            cls._typeadapter = TypeAdapter(InvocationsUnion)
         return cls._typeadapter
 
     @classmethod
@@ -228,7 +221,7 @@ class BaseInvocation(ABC, BaseModel):
         return signature(cls.invoke).return_annotation
 
     @staticmethod
-    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseInvocation]) -> None:
+    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel], *args, **kwargs) -> 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:
@@ -244,7 +237,6 @@ 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
@@ -318,7 +310,7 @@ class BaseInvocation(ABC, BaseModel):
         protected_namespaces=(),
         validate_assignment=True,
         json_schema_extra=json_schema_extra,
-        json_schema_serialization_defaults_required=False,
+        json_schema_serialization_defaults_required=True,
         coerce_numbers_to_str=True,
     )
 

invokeai/app/invocations/blend_latents.py

@@ -1,98 +0,0 @@
-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

@@ -1,7 +1,6 @@
 from typing import Literal
 
 from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from invokeai.backend.util.devices import TorchDevice
 
 LATENT_SCALE_FACTOR = 8
 """
@@ -16,5 +15,3 @@ 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

@@ -1,80 +0,0 @@
-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

@@ -1,138 +0,0 @@
-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

@@ -1,61 +0,0 @@
-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

@@ -1,65 +0,0 @@
-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

@@ -1,125 +0,0 @@
-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

@@ -1,127 +0,0 @@
-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

@@ -1,103 +0,0 @@
-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

@@ -1,34 +0,0 @@
-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

@@ -1,384 +0,0 @@
-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

@@ -3,8 +3,9 @@ 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
+from pydantic import BaseModel, ConfigDict, Field, SerializeAsAny, field_validator
 
+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,
@@ -13,7 +14,6 @@ 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
@@ -98,9 +98,17 @@ 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: AnyInvocation = Field(description="The ID of the invocation")
+    invocation: SerializeAsAny[BaseInvocation] = 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):
@@ -109,7 +117,7 @@ class InvocationStartedEvent(InvocationEventBase):
     __event_name__ = "invocation_started"
 
     @classmethod
-    def build(cls, queue_item: SessionQueueItem, invocation: AnyInvocation) -> "InvocationStartedEvent":
+    def build(cls, queue_item: SessionQueueItem, invocation: BaseInvocation) -> "InvocationStartedEvent":
         return cls(
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
@@ -136,7 +144,7 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
     def build(
         cls,
         queue_item: SessionQueueItem,
-        invocation: AnyInvocation,
+        invocation: BaseInvocation,
         intermediate_state: PipelineIntermediateState,
         progress_image: ProgressImage,
     ) -> "InvocationDenoiseProgressEvent":
@@ -174,11 +182,19 @@ class InvocationCompleteEvent(InvocationEventBase):
 
     __event_name__ = "invocation_complete"
 
-    result: AnyInvocationOutput = Field(description="The result of the invocation")
+    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
 
     @classmethod
     def build(
-        cls, queue_item: SessionQueueItem, invocation: AnyInvocation, result: AnyInvocationOutput
+        cls, queue_item: SessionQueueItem, invocation: BaseInvocation, result: BaseInvocationOutput
     ) -> "InvocationCompleteEvent":
         return cls(
             queue_id=queue_item.queue_id,
@@ -207,7 +223,7 @@ class InvocationErrorEvent(InvocationEventBase):
     def build(
         cls,
         queue_item: SessionQueueItem,
-        invocation: AnyInvocation,
+        invocation: BaseInvocation,
         error_type: str,
         error_message: str,
         error_traceback: str,

invokeai/app/services/shared/graph.py

@@ -2,19 +2,18 @@
 
 import copy
 import itertools
-from typing import Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
+from typing import Annotated, 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 core_schema
+from pydantic_core import CoreSchema
 
 # Importing * is bad karma but needed here for node detection
 from invokeai.app.invocations import *  # noqa: F401 F403
@@ -278,58 +277,73 @@ 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, AnyInvocation] = Field(description="The nodes in this graph", default_factory=dict)
+    nodes: dict[str, BaseInvocation] = 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
 
@@ -760,7 +774,7 @@ class GraphExecutionState(BaseModel):
     )
 
     # The results of executed nodes
-    results: dict[str, AnyInvocationOutput] = Field(description="The results of node executions", default_factory=dict)
+    results: dict[str, BaseInvocationOutput] = 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)
@@ -777,12 +791,52 @@ 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 | torch.device = "cuda",
+    device: str = "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 | torch.Device, optional): The target device for the output image. Defaults to "cuda".
+        device (str, 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

@@ -1,116 +0,0 @@
-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/convert_ckpt_to_diffusers.py

@@ -30,8 +30,12 @@ def convert_ldm_vae_to_diffusers(
     converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
 
     vae = AutoencoderKL(**vae_config)
-    vae.load_state_dict(converted_vae_checkpoint)
-    vae.to(precision)
+    with torch.no_grad():
+        vae.load_state_dict(converted_vae_checkpoint)
+        del converted_vae_checkpoint  # Free memory
+        import gc
+        gc.collect()
+        vae.to(precision)
 
     if dump_path:
         vae.save_pretrained(dump_path, safe_serialization=True)
@@ -52,7 +56,11 @@ def convert_ckpt_to_diffusers(
     model to be written.
     """
     pipe = download_from_original_stable_diffusion_ckpt(Path(checkpoint_path).as_posix(), **kwargs)
-    pipe = pipe.to(precision)
+    with torch.no_grad():
+        del kwargs  # Free memory
+        import gc
+        gc.collect()
+        pipe = pipe.to(precision)
 
     # TO DO: save correct repo variant
     if dump_path:
@@ -75,7 +83,11 @@ def convert_controlnet_to_diffusers(
     model to be written.
     """
     pipe = download_controlnet_from_original_ckpt(checkpoint_path.as_posix(), **kwargs)
-    pipe = pipe.to(precision)
+    with torch.no_grad():
+        del kwargs  # Free memory
+        import gc
+        gc.collect()
+        pipe = pipe.to(precision)
 
     # TO DO: save correct repo variant
     if dump_path:

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(0)
+            self._cache.offload_unlocked_models(self._cache_entry.size)
             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.silence_warnings import SilenceWarnings
+from invokeai.backend.util.util import SilenceWarnings
 
 from .config import (
     AnyModelConfig,

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -11,6 +11,7 @@ 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
@@ -25,7 +26,6 @@ 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

@@ -1,36 +1,29 @@
-import warnings
-from contextlib import ContextDecorator
+"""Context class to silence transformers and diffusers warnings."""
 
-from diffusers.utils import logging as diffusers_logging
+import warnings
+from typing import Any
+
+from diffusers import logging as diffusers_logging
 from transformers import logging as transformers_logging
 
 
-# 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.
+class SilenceWarnings(object):
+    """Use in context to temporarily turn off warnings from transformers & diffusers modules.
 
-    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) -> None:
-        transformers_logging.set_verbosity(self._transformers_verbosity)
-        diffusers_logging.set_verbosity(self._diffusers_verbosity)
+    def __exit__(self, *args: Any) -> None:
+        transformers_logging.set_verbosity(self.transformers_verbosity)
+        diffusers_logging.set_verbosity(self.diffusers_verbosity)
         warnings.simplefilter("default")

invokeai/backend/util/util.py

@@ -1,9 +1,12 @@
 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
@@ -48,3 +51,21 @@ 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

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

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

@@ -148,8 +148,6 @@
         "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"
     },
@@ -377,23 +375,7 @@
         "bulkDownloadRequestFailed": "Problem Preparing Download",
         "bulkDownloadFailed": "Download Failed",
         "problemDeletingImages": "Problem Deleting Images",
-        "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."
+        "problemDeletingImagesDesc": "One or more images could not be deleted"
     },
     "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": "Flujos de trabajo",
+        "nodes": "Editor del flujo de trabajo",
         "upload": "Subir imagen",
         "load": "Cargar",
         "statusDisconnected": "Desconectado",
@@ -14,7 +14,7 @@
         "discordLabel": "Discord",
         "back": "Atrás",
         "loading": "Cargando",
-        "postprocessing": "Postprocesado",
+        "postprocessing": "Tratamiento posterior",
         "txt2img": "De texto a imagen",
         "accept": "Aceptar",
         "cancel": "Cancelar",
@@ -42,42 +42,7 @@
         "copy": "Copiar",
         "beta": "Beta",
         "on": "En",
-        "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"
+        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -502,8 +467,7 @@
         "about": "Acerca de",
         "createIssue": "Crear un problema",
         "resetUI": "Interfaz de usuario $t(accessibility.reset)",
-        "mode": "Modo",
-        "submitSupportTicket": "Enviar Ticket de Soporte"
+        "mode": "Modo"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -579,17 +543,5 @@
         "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": "Errore $t(gallery.copy)",
+        "copyError": "$t(gallery.copy) Errore",
         "input": "Ingresso",
         "notInstalled": "Non $t(common.installed)",
         "unknownError": "Errore sconosciuto",
@@ -85,11 +85,7 @@
         "viewing": "Visualizza",
         "viewingDesc": "Rivedi le immagini in un'ampia vista della galleria",
         "editing": "Modifica",
-        "editingDesc": "Modifica nell'area Livelli di controllo",
-        "enabled": "Abilitato",
-        "disabled": "Disabilitato",
-        "comparingDesc": "Confronta due immagini",
-        "comparing": "Confronta"
+        "editingDesc": "Modifica nell'area Livelli di controllo"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -126,30 +122,14 @@
         "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",
-        "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."
+        "alwaysShowImageSizeBadge": "Mostra sempre le dimensioni dell'immagine"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti di scelta rapida",
         "appHotkeys": "Applicazione",
         "generalHotkeys": "Generale",
         "galleryHotkeys": "Galleria",
-        "unifiedCanvasHotkeys": "Tela",
+        "unifiedCanvasHotkeys": "Tela Unificata",
         "invoke": {
             "title": "Invoke",
             "desc": "Genera un'immagine"
@@ -167,8 +147,8 @@
             "desc": "Apre e chiude il pannello delle opzioni"
         },
         "pinOptions": {
-            "title": "Fissa le opzioni",
-            "desc": "Fissa il pannello delle opzioni"
+            "title": "Appunta le opzioni",
+            "desc": "Blocca il pannello delle opzioni"
         },
         "toggleGallery": {
             "title": "Attiva/disattiva galleria",
@@ -352,14 +332,14 @@
             "title": "Annulla e cancella"
         },
         "resetOptionsAndGallery": {
-            "title": "Ripristina le opzioni e la galleria",
-            "desc": "Reimposta i pannelli delle opzioni e della galleria"
+            "title": "Ripristina Opzioni e Galleria",
+            "desc": "Reimposta le opzioni e i pannelli 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": {
@@ -368,7 +348,7 @@
         },
         "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": {
@@ -398,7 +378,7 @@
         "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 in dimensione.",
+        "convertToDiffusersHelpText5": "Assicurati di avere spazio su disco sufficiente. I modelli generalmente variano tra 2 GB e 7 GB di dimensioni.",
         "convertToDiffusersHelpText6": "Vuoi convertire questo modello?",
         "modelConverted": "Modello convertito",
         "alpha": "Alpha",
@@ -548,7 +528,7 @@
             "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",
@@ -626,25 +606,25 @@
         "canvasMerged": "Tela unita",
         "sentToImageToImage": "Inviato a Generazione da immagine",
         "sentToUnifiedCanvas": "Inviato alla Tela",
-        "parametersNotSet": "Parametri non richiamati",
+        "parametersNotSet": "Parametri non impostati",
         "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": "Parametro richiamato",
-        "parameterNotSet": "Parametro non richiamato",
+        "parameterSet": "{{parameter}} impostato",
+        "parameterNotSet": "{{parameter}} non impostato",
         "problemCopyingImage": "Impossibile copiare l'immagine",
-        "baseModelChangedCleared_one": "Cancellato o disabilitato {{count}} sottomodello incompatibile",
-        "baseModelChangedCleared_many": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
-        "baseModelChangedCleared_other": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
+        "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",
         "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 lo scarico della tela",
+        "problemDownloadingCanvas": "Problema durante il download della tela",
         "problemMergingCanvas": "Problema nell'unione delle tele",
         "imageUploaded": "Immagine caricata",
         "addedToBoard": "Aggiunto alla bacheca",
@@ -678,17 +658,7 @@
         "problemDownloadingImage": "Impossibile scaricare l'immagine",
         "prunedQueue": "Coda ripulita",
         "modelImportCanceled": "Importazione del modello annullata",
-        "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."
+        "parameters": "Parametri"
     },
     "tooltip": {
         "feature": {
@@ -704,7 +674,7 @@
         "layer": "Livello",
         "base": "Base",
         "mask": "Maschera",
-        "maskingOptions": "Opzioni maschera",
+        "maskingOptions": "Opzioni di mascheramento",
         "enableMask": "Abilita maschera",
         "preserveMaskedArea": "Mantieni area mascherata",
         "clearMask": "Cancella maschera (Shift+C)",
@@ -775,8 +745,7 @@
         "mode": "Modalità",
         "resetUI": "$t(accessibility.reset) l'Interfaccia Utente",
         "createIssue": "Segnala un problema",
-        "about": "Informazioni",
-        "submitSupportTicket": "Invia ticket di supporto"
+        "about": "Informazioni"
     },
     "nodes": {
         "zoomOutNodes": "Rimpicciolire",
@@ -821,7 +790,7 @@
         "workflowNotes": "Note",
         "versionUnknown": " Versione sconosciuta",
         "unableToValidateWorkflow": "Impossibile convalidare il flusso di lavoro",
-        "updateApp": "Aggiorna Applicazione",
+        "updateApp": "Aggiorna App",
         "unableToLoadWorkflow": "Impossibile caricare il flusso di lavoro",
         "updateNode": "Aggiorna nodo",
         "version": "Versione",
@@ -913,14 +882,11 @@
         "missingNode": "Nodo di invocazione mancante",
         "missingInvocationTemplate": "Modello di invocazione mancante",
         "missingFieldTemplate": "Modello di campo mancante",
-        "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"
+        "singleFieldType": "{{name}} (Singola)"
     },
     "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.",
@@ -932,7 +898,7 @@
         "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",
@@ -953,7 +919,7 @@
         "control": "Controllo",
         "crop": "Ritaglia",
         "depthMidas": "Profondità (Midas)",
-        "detectResolution": "Rileva la risoluzione",
+        "detectResolution": "Rileva risoluzione",
         "controlMode": "Modalità di controllo",
         "cannyDescription": "Canny rilevamento bordi",
         "depthZoe": "Profondità (Zoe)",
@@ -964,7 +930,7 @@
         "showAdvanced": "Mostra opzioni Avanzate",
         "bgth": "Soglia rimozione sfondo",
         "importImageFromCanvas": "Importa immagine dalla Tela",
-        "lineartDescription": "Converte l'immagine in linea",
+        "lineartDescription": "Converte l'immagine in lineart",
         "importMaskFromCanvas": "Importa maschera dalla Tela",
         "hideAdvanced": "Nascondi opzioni avanzate",
         "resetControlImage": "Reimposta immagine di controllo",
@@ -980,7 +946,7 @@
         "pidiDescription": "Elaborazione immagini PIDI",
         "fill": "Riempie",
         "colorMapDescription": "Genera una mappa dei colori dall'immagine",
-        "lineartAnimeDescription": "Elaborazione linea in stile anime",
+        "lineartAnimeDescription": "Elaborazione lineart in stile anime",
         "imageResolution": "Risoluzione dell'immagine",
         "colorMap": "Colore",
         "lowThreshold": "Soglia inferiore",

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

@@ -87,11 +87,7 @@
         "viewing": "Просмотр",
         "editing": "Редактирование",
         "viewingDesc": "Просмотр изображений в режиме большой галереи",
-        "editingDesc": "Редактировать на холсте слоёв управления",
-        "enabled": "Включено",
-        "disabled": "Отключено",
-        "comparingDesc": "Сравнение двух изображений",
-        "comparing": "Сравнение"
+        "editingDesc": "Редактировать на холсте слоёв управления"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -128,23 +124,7 @@
         "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",
-        "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."
+        "alwaysShowImageSizeBadge": "Всегда показывать значок размера изображения"
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -548,20 +528,7 @@
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
             "invoke": "Создать",
-            "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"
-            }
+            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Изображение слишком велико для увеличения с помощью модели x4. Используйте модель x2",
@@ -639,12 +606,12 @@
         "connected": "Подключено к серверу",
         "canceled": "Обработка отменена",
         "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
-        "parameterNotSet": "Параметр не задан",
-        "parameterSet": "Параметр задан",
+        "parameterNotSet": "Параметр {{parameter}} не задан",
+        "parameterSet": "Параметр {{parameter}} задан",
         "problemCopyingImage": "Не удается скопировать изображение",
-        "baseModelChangedCleared_one": "Очищена или отключена {{count}} несовместимая подмодель",
-        "baseModelChangedCleared_few": "Очищены или отключены {{count}} несовместимые подмодели",
-        "baseModelChangedCleared_many": "Очищены или отключены {{count}} несовместимых подмоделей",
+        "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",
         "imageSavingFailed": "Не удалось сохранить изображение",
         "canvasSentControlnetAssets": "Холст отправлен в ControlNet и ресурсы",
         "problemCopyingCanvasDesc": "Невозможно экспортировать базовый слой",
@@ -685,17 +652,7 @@
         "resetInitialImage": "Сбросить начальное изображение",
         "prunedQueue": "Урезанная очередь",
         "modelImportCanceled": "Импорт модели отменен",
-        "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"
+        "parameters": "Параметры"
     },
     "tooltip": {
         "feature": {
@@ -782,8 +739,7 @@
         "loadMore": "Загрузить больше",
         "resetUI": "$t(accessibility.reset) интерфейс",
         "createIssue": "Сообщить о проблеме",
-        "about": "Об этом",
-        "submitSupportTicket": "Отправить тикет в службу поддержки"
+        "about": "Об этом"
     },
     "nodes": {
         "zoomInNodes": "Увеличьте масштаб",
@@ -876,7 +832,7 @@
         "workflowName": "Название",
         "collection": "Коллекция",
         "unknownErrorValidatingWorkflow": "Неизвестная ошибка при проверке рабочего процесса",
-        "collectionFieldType": "{{name}} (Коллекция)",
+        "collectionFieldType": "Коллекция {{name}}",
         "workflowNotes": "Примечания",
         "string": "Строка",
         "unknownNodeType": "Неизвестный тип узла",
@@ -892,7 +848,7 @@
         "targetNodeDoesNotExist": "Недопустимое ребро: целевой/входной узел {{node}} не существует",
         "mismatchedVersion": "Недопустимый узел: узел {{node}} типа {{type}} имеет несоответствующую версию (попробовать обновить?)",
         "unknownFieldType": "$t(nodes.unknownField) тип: {{type}}",
-        "collectionOrScalarFieldType": "{{name}} (Один или коллекция)",
+        "collectionOrScalarFieldType": "Коллекция | Скаляр {{name}}",
         "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...",
@@ -914,16 +870,7 @@
         "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",
-        "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"
+        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -1494,16 +1441,7 @@
         "clearQueueAlertDialog2": "Вы уверены, что хотите очистить очередь?",
         "item": "Элемент",
         "graphFailedToQueue": "Не удалось поставить график в 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"
+        "openQueue": "Открыть очередь"
     },
     "sdxl": {
         "refinerStart": "Запуск доработчика",

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

@@ -1,6 +1,6 @@
 {
     "common": {
-        "nodes": "工作流程",
+        "nodes": "節點",
         "img2img": "圖片轉圖片",
         "statusDisconnected": "已中斷連線",
         "back": "返回",
@@ -11,239 +11,17 @@
         "reportBugLabel": "回報錯誤",
         "githubLabel": "GitHub",
         "hotkeysLabel": "快捷鍵",
-        "languagePickerLabel": "語言",
+        "languagePickerLabel": "切換語言",
         "unifiedCanvas": "統一畫布",
         "cancel": "取消",
-        "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"
+        "txt2img": "文字轉圖片"
     },
     "accessibility": {
         "invokeProgressBar": "Invoke 進度條",
         "uploadImage": "上傳圖片",
-        "reset": "重置",
+        "reset": "重設",
         "nextImage": "下一張圖片",
         "previousImage": "上一張圖片",
-        "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"
+        "menu": "選單"
     }
 }

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

@@ -19,13 +19,6 @@ 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

@@ -13,6 +13,7 @@ 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';
@@ -138,7 +139,7 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
 
         // We still have to check the output type
         assert(
-          invocationCompleteAction.payload.data.result.type === 'image_output',
+          isImageOutput(invocationCompleteAction.payload.data.result),
           `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

@@ -9,6 +9,7 @@ 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';
@@ -73,7 +74,7 @@ export const addControlNetImageProcessedListener = (startAppListening: AppStartL
         );
 
         // We still have to check the output type
-        if (invocationCompleteAction.payload.data.result.type === 'image_output') {
+        if (isImageOutput(invocationCompleteAction.payload.data.result)) {
           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 { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
+import { 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';
@@ -11,7 +11,6 @@ export const galleryImageClicked = createAction<{
   shiftKey: boolean;
   ctrlKey: boolean;
   metaKey: boolean;
-  altKey: boolean;
 }>('gallery/imageClicked');
 
 /**
@@ -29,7 +28,7 @@ export const addGalleryImageClickedListener = (startAppListening: AppStartListen
   startAppListening({
     actionCreator: galleryImageClicked,
     effect: async (action, { dispatch, getState }) => {
-      const { imageDTO, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
+      const { imageDTO, shiftKey, ctrlKey, metaKey } = action.payload;
       const state = getState();
       const queryArgs = selectListImagesQueryArgs(state);
       const { data: listImagesData } = imagesApi.endpoints.listImages.select(queryArgs)(state);
@@ -42,13 +41,7 @@ export const addGalleryImageClickedListener = (startAppListening: AppStartListen
       const imageDTOs = imagesSelectors.selectAll(listImagesData);
       const selection = state.gallery.selection;
 
-      if (altKey) {
-        if (state.gallery.imageToCompare?.image_name === imageDTO.image_name) {
-          dispatch(imageToCompareChanged(null));
-        } else {
-          dispatch(imageToCompareChanged(imageDTO));
-        }
-      } else if (shiftKey) {
+      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

@@ -14,8 +14,7 @@ import {
   rgLayerIPAdapterImageChanged,
 } from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import { isValidDrop } from 'features/dnd/util/isValidDrop';
-import { imageSelected, imageToCompareChanged, isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
+import { imageSelected } 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';
@@ -31,9 +30,6 @@ 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');
@@ -54,7 +50,6 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         activeData.payload.imageDTO
       ) {
         dispatch(imageSelected(activeData.payload.imageDTO));
-        dispatch(isImageViewerOpenChanged(true));
         return;
       }
 
@@ -187,18 +182,24 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
       }
 
       /**
-       * Image selected for compare
+       * TODO
+       * Image selection dropped on node image collection field
        */
-      if (
-        overData.actionType === 'SELECT_FOR_COMPARE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { imageDTO } = activeData.payload;
-        dispatch(imageToCompareChanged(imageDTO));
-        dispatch(isImageViewerOpenChanged(true));
-        return;
-      }
+      // 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;
+      // }
 
       /**
        * Image dropped on user board

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

@@ -11,6 +11,7 @@ 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';
@@ -32,7 +33,7 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
 
       const { result, invocation_source_id } = data;
       // This complete event has an associated image output
-      if (data.result.type === 'image_output' && !nodeTypeDenylist.includes(data.invocation.type)) {
+      if (isImageOutput(data.result) && !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 { $needsFit } from 'features/nodes/store/reactFlowInstance';
+import { $flow } 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';
@@ -65,7 +65,9 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
           });
         }
 
-        $needsFit.set(true);
+        requestAnimationFrame(() => {
+          $flow.get()?.fitView();
+        });
       } 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

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

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

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

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

@@ -1,21 +0,0 @@
-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

@@ -1,7 +1,3 @@
 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

@@ -1,13 +1,7 @@
 import { deepClone } from 'common/util/deepClone';
 import { zModelIdentifierField } from 'features/nodes/types/common';
 import { merge, omit } from 'lodash-es';
-import type {
-  AnyInvocation,
-  BaseModelType,
-  ControlNetModelConfig,
-  ImageDTO,
-  T2IAdapterModelConfig,
-} from 'services/api/types';
+import type { BaseModelType, ControlNetModelConfig, Graph, ImageDTO, T2IAdapterModelConfig } from 'services/api/types';
 import { z } from 'zod';
 
 const zId = z.string().min(1);
@@ -153,7 +147,7 @@ const zBeginEndStepPct = z
 
 const zControlAdapterBase = z.object({
   id: zId,
-  weight: z.number().gte(-1).lte(2),
+  weight: z.number().gte(0).lte(1),
   image: zImageWithDims.nullable(),
   processedImage: zImageWithDims.nullable(),
   processorConfig: zProcessorConfig.nullable(),
@@ -189,7 +183,7 @@ 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(-1).lte(2),
+  weight: z.number().gte(0).lte(1),
   method: zIPMethodV2,
   image: zImageWithDims.nullable(),
   model: zModelIdentifierField.nullable(),
@@ -222,7 +216,10 @@ 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<AnyInvocation, { type: T }>;
+  buildNode(
+    image: ImageWithDims,
+    config: Extract<ProcessorConfig, { type: T }>
+  ): Extract<Graph['nodes'][string], { 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
-export const STAGE_BG_DATAURL =
+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;
 };
 
-export type CurrentImageDropData = BaseDropData & {
+type CurrentImageDropData = BaseDropData & {
   actionType: 'SET_CURRENT_IMAGE';
 };
 
@@ -79,14 +79,6 @@ 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
@@ -97,8 +89,7 @@ export type TypesafeDroppableData =
   | CALayerImageDropData
   | IPALayerImageDropData
   | RGLayerIPAdapterImageDropData
-  | IILayerImageDropData
-  | SelectForCompareDropData;
+  | IILayerImageDropData;
 
 type BaseDragData = {
   id: string;
@@ -143,7 +134,7 @@ export type UseDraggableTypesafeReturnValue = Omit<ReturnType<typeof useOriginal
   over: TypesafeOver | null;
 };
 
-interface TypesafeActive extends Omit<Active, 'data'> {
+export 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 { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
+import type { TypesafeActive, TypesafeDroppableData } from 'features/dnd/types';
 
-export const isValidDrop = (overData?: TypesafeDroppableData | null, activeData?: TypesafeDraggableData | null) => {
-  if (!overData || !activeData) {
+export const isValidDrop = (overData: TypesafeDroppableData | undefined, active: TypesafeActive | null) => {
+  if (!overData || !active?.data.current) {
     return false;
   }
 
   const { actionType } = overData;
-  const { payloadType } = activeData;
+  const { payloadType } = active.data.current;
 
-  if (overData.id === activeData.id) {
+  if (overData.id === active.data.current.id) {
     return false;
   }
 
@@ -29,8 +29,6 @@ export const isValidDrop = (overData?: TypesafeDroppableData | null, activeData?
       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
 
@@ -42,7 +40,7 @@ export const isValidDrop = (overData?: TypesafeDroppableData | null, activeData?
 
       // Check if the image's board is the board we are dragging onto
       if (payloadType === 'IMAGE_DTO') {
-        const { imageDTO } = activeData.payload;
+        const { imageDTO } = active.data.current.payload;
         const currentBoard = imageDTO.board_id ?? 'none';
         const destinationBoard = overData.context.boardId;
 
@@ -51,7 +49,7 @@ export const isValidDrop = (overData?: TypesafeDroppableData | null, activeData?
 
       if (payloadType === 'GALLERY_SELECTION') {
         // Assume all images are on the same board - this is true for the moment
-        const currentBoard = activeData.payload.boardId;
+        const currentBoard = active.data.current.payload.boardId;
         const destinationBoard = overData.context.boardId;
         return currentBoard !== destinationBoard;
       }
@@ -69,14 +67,14 @@ export const isValidDrop = (overData?: TypesafeDroppableData | null, activeData?
 
       // Check if the image's board is the board we are dragging onto
       if (payloadType === 'IMAGE_DTO') {
-        const { imageDTO } = activeData.payload;
+        const { imageDTO } = active.data.current.payload;
         const currentBoard = imageDTO.board_id ?? 'none';
 
         return currentBoard !== 'none';
       }
 
       if (payloadType === 'GALLERY_SELECTION') {
-        const currentBoard = activeData.payload.boardId;
+        const currentBoard = active.data.current.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} isSelectedForCompare={false} isHovered={isHovered} />
+                <SelectionOverlay isSelected={isSelected} 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} isSelectedForCompare={false} isHovered={isHovered} />
+                <SelectionOverlay isSelected={isSelected} 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

@@ -10,7 +10,6 @@ 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';
@@ -28,7 +27,6 @@ import {
   PiDownloadSimpleBold,
   PiFlowArrowBold,
   PiFoldersBold,
-  PiImagesBold,
   PiPlantBold,
   PiQuotesBold,
   PiShareFatBold,
@@ -46,7 +44,6 @@ 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');
@@ -120,10 +117,6 @@ 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 />}>
@@ -137,9 +130,6 @@ 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 { imageToCompareChanged, isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
+import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import type { MouseEvent } from 'react';
 import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -46,7 +46,6 @@ 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);
@@ -106,7 +105,6 @@ const GalleryImage = (props: HoverableImageProps) => {
 
   const onDoubleClick = useCallback(() => {
     dispatch(isImageViewerOpenChanged(true));
-    dispatch(imageToCompareChanged(null));
   }, [dispatch]);
 
   const handleMouseOut = useCallback(() => {
@@ -154,7 +152,6 @@ 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

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

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

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

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

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

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

@@ -1,140 +0,0 @@
-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 } from 'features/dnd/types';
+import type { TypesafeDraggableData, TypesafeDroppableData } 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';
@@ -22,7 +22,21 @@ const selectLastSelectedImageName = createSelector(
   (lastSelectedImage) => lastSelectedImage?.image_name
 );
 
-const CurrentImagePreview = () => {
+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 { t } = useTranslation();
   const shouldShowImageDetails = useAppSelector((s) => s.ui.shouldShowImageDetails);
   const imageName = useAppSelector(selectLastSelectedImageName);
@@ -41,6 +55,14 @@ 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);
@@ -64,27 +86,30 @@ const CurrentImagePreview = () => {
       justifyContent="center"
       position="relative"
     >
-      {hasDenoiseProgress && shouldShowProgressInViewer ? (
+      {hasDenoiseProgress && (shouldShowProgressInViewer || alwaysShowProgress) ? (
         <ProgressImage />
       ) : (
         <IAIDndImage
           imageDTO={imageDTO}
+          droppableData={droppableData}
           draggableData={draggableData}
-          isDropDisabled={true}
+          isDragDisabled={isDragDisabled}
+          isDropDisabled={isDropDisabled}
           isUploadDisabled={true}
           fitContainer
           useThumbailFallback
+          dropLabel={t('gallery.setCurrentImage')}
           noContentFallback={<IAINoContentFallback icon={PiImageBold} label={t('gallery.noImageSelected')} />}
           dataTestId="image-preview"
         />
       )}
-      {shouldShowImageDetails && imageDTO && (
+      {shouldShowImageDetails && imageDTO && withMetadata && (
         <Box position="absolute" opacity={0.8} top={0} width="full" height="full" borderRadius="base">
           <ImageMetadataViewer image={imageDTO} />
         </Box>
       )}
       <AnimatePresence>
-        {shouldShowNextPrevButtons && imageDTO && (
+        {withNextPrevButtons && shouldShowNextPrevButtons && imageDTO && (
           <Box
             as={motion.div}
             key="nextPrevButtons"

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

@@ -1,41 +0,0 @@
-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

@@ -1,47 +0,0 @@
-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

@@ -1,117 +0,0 @@
-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

@@ -1,33 +0,0 @@
-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

@@ -1,70 +0,0 @@
-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

@@ -1,215 +0,0 @@
-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

@@ -1,16 +1,36 @@
-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 { 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 { useImageViewer } from './useImageViewer';
+import CurrentImageButtons from './CurrentImageButtons';
+import CurrentImagePreview from './CurrentImagePreview';
+import { ViewerToggleMenu } from './ViewerToggleMenu';
+
+const VIEWER_ENABLED_TABS: InvokeTabName[] = ['canvas', 'generation', 'workflows'];
 
 export const ImageViewer = memo(() => {
-  const imageViewer = useImageViewer();
-  const [containerRef, containerDims] = useMeasure<HTMLDivElement>();
+  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;
+  }
 
   return (
     <Flex
@@ -26,13 +46,25 @@ 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
     >
-      {imageViewer.isComparing && <CompareToolbar />}
-      {!imageViewer.isComparing && <ViewerToolbar />}
-      <Box ref={containerRef} w="full" h="full">
-        {!imageViewer.isComparing && <CurrentImagePreview />}
-        {imageViewer.isComparing && <ImageComparison containerDims={containerDims} />}
-      </Box>
+      <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 />
     </Flex>
   );
 });

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

@@ -0,0 +1,45 @@
+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

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

@@ -1,33 +0,0 @@
-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

@@ -1,64 +0,0 @@
-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

@@ -1,31 +0,0 @@
-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

@@ -0,0 +1,22 @@
+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 { prevImage, nextImage, isOnFirstImage, isOnLastImage } = useGalleryNavigation();
+  const { handleLeftImage, handleRightImage, isOnFirstImage, isOnLastImage } = useGalleryNavigation();
 
   const {
     areMoreImagesAvailable,
@@ -30,7 +30,7 @@ const NextPrevImageButtons = () => {
             aria-label={t('accessibility.previousImage')}
             icon={<PiCaretLeftBold size={64} />}
             variant="unstyled"
-            onClick={prevImage}
+            onClick={handleLeftImage}
             boxSize={16}
             sx={nextPrevButtonStyles}
           />
@@ -42,7 +42,7 @@ const NextPrevImageButtons = () => {
             aria-label={t('accessibility.nextImage')}
             icon={<PiCaretRightBold size={64} />}
             variant="unstyled"
-            onClick={nextImage}
+            onClick={handleRightImage}
             boxSize={16}
             sx={nextPrevButtonStyles}
           />

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

@@ -27,16 +27,16 @@ export const useGalleryHotkeys = () => {
     useGalleryNavigation();
 
   useHotkeys(
-    ['left', 'alt+left'],
-    (e) => {
-      canNavigateGallery && handleLeftImage(e.altKey);
+    'left',
+    () => {
+      canNavigateGallery && handleLeftImage();
     },
     [handleLeftImage, canNavigateGallery]
   );
 
   useHotkeys(
-    ['right', 'alt+right'],
-    (e) => {
+    'right',
+    () => {
       if (!canNavigateGallery) {
         return;
       }
@@ -45,29 +45,29 @@ export const useGalleryHotkeys = () => {
         return;
       }
       if (!isOnLastImage) {
-        handleRightImage(e.altKey);
+        handleRightImage();
       }
     },
     [isOnLastImage, areMoreImagesAvailable, handleLoadMoreImages, isFetching, handleRightImage, canNavigateGallery]
   );
 
   useHotkeys(
-    ['up', 'alt+up'],
-    (e) => {
-      handleUpImage(e.altKey);
+    'up',
+    () => {
+      handleUpImage();
     },
     { preventDefault: true },
     [handleUpImage]
   );
 
   useHotkeys(
-    ['down', 'alt+down'],
-    (e) => {
+    'down',
+    () => {
       if (!areImagesBelowCurrent && areMoreImagesAvailable && !isFetching) {
         handleLoadMoreImages();
         return;
       }
-      handleDownImage(e.altKey);
+      handleDownImage();
     },
     { 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 { imageSelected, imageToCompareChanged } from 'features/gallery/store/gallerySlice';
+import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { getIsVisible } from 'features/gallery/util/getIsVisible';
 import { getScrollToIndexAlign } from 'features/gallery/util/getScrollToIndexAlign';
 import { clamp } from 'lodash-es';
@@ -106,12 +106,10 @@ const getImageFuncs = {
 };
 
 type UseGalleryNavigationReturn = {
-  handleLeftImage: (alt?: boolean) => void;
-  handleRightImage: (alt?: boolean) => void;
-  handleUpImage: (alt?: boolean) => void;
-  handleDownImage: (alt?: boolean) => void;
-  prevImage: () => void;
-  nextImage: () => void;
+  handleLeftImage: () => void;
+  handleRightImage: () => void;
+  handleUpImage: () => void;
+  handleDownImage: () => void;
   isOnFirstImage: boolean;
   isOnLastImage: boolean;
   areImagesBelowCurrent: boolean;
@@ -125,15 +123,7 @@ type UseGalleryNavigationReturn = {
  */
 export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
   const dispatch = useAppDispatch();
-  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 lastSelectedImage = useAppSelector(selectLastSelectedImage);
   const {
     queryResult: { data },
   } = useGalleryImages();
@@ -146,7 +136,7 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
   }, [lastSelectedImage, data]);
 
   const handleNavigation = useCallback(
-    (direction: 'left' | 'right' | 'up' | 'down', alt?: boolean) => {
+    (direction: 'left' | 'right' | 'up' | 'down') => {
       if (!data) {
         return;
       }
@@ -154,14 +144,10 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
       if (!image || index === lastSelectedImageIndex) {
         return;
       }
-      if (alt) {
-        dispatch(imageToCompareChanged(image));
-      } else {
-        dispatch(imageSelected(image));
-      }
+      dispatch(imageSelected(image));
       scrollToImage(image.image_name, index);
     },
-    [data, lastSelectedImageIndex, dispatch]
+    [dispatch, lastSelectedImageIndex, data]
   );
 
   const isOnFirstImage = useMemo(() => lastSelectedImageIndex === 0, [lastSelectedImageIndex]);
@@ -176,41 +162,21 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
     return lastSelectedImageIndex + imagesPerRow < loadedImagesCount;
   }, [lastSelectedImageIndex, loadedImagesCount]);
 
-  const handleLeftImage = useCallback(
-    (alt?: boolean) => {
-      handleNavigation('left', alt);
-    },
-    [handleNavigation]
-  );
+  const handleLeftImage = useCallback(() => {
+    handleNavigation('left');
+  }, [handleNavigation]);
 
-  const handleRightImage = useCallback(
-    (alt?: boolean) => {
-      handleNavigation('right', alt);
-    },
-    [handleNavigation]
-  );
+  const handleRightImage = useCallback(() => {
+    handleNavigation('right');
+  }, [handleNavigation]);
 
-  const handleUpImage = useCallback(
-    (alt?: boolean) => {
-      handleNavigation('up', alt);
-    },
-    [handleNavigation]
-  );
+  const handleUpImage = useCallback(() => {
+    handleNavigation('up');
+  }, [handleNavigation]);
 
-  const handleDownImage = useCallback(
-    (alt?: boolean) => {
-      handleNavigation('down', alt);
-    },
-    [handleNavigation]
-  );
-
-  const nextImage = useCallback(() => {
-    handleRightImage();
-  }, [handleRightImage]);
-
-  const prevImage = useCallback(() => {
-    handleLeftImage();
-  }, [handleLeftImage]);
+  const handleDownImage = useCallback(() => {
+    handleNavigation('down');
+  }, [handleNavigation]);
 
   return {
     handleLeftImage,
@@ -220,7 +186,5 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
     isOnFirstImage,
     isOnLastImage,
     areImagesBelowCurrent,
-    nextImage,
-    prevImage,
   };
 };

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

@@ -36,7 +36,6 @@ 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, ComparisonMode, GalleryState, GalleryView } from './types';
+import type { BoardId, GalleryState, GalleryView } from './types';
 import { IMAGE_LIMIT, INITIAL_IMAGE_LIMIT } from './types';
 
 const initialGalleryState: GalleryState = {
@@ -22,9 +22,6 @@ const initialGalleryState: GalleryState = {
   limit: INITIAL_IMAGE_LIMIT,
   offset: 0,
   isImageViewerOpen: true,
-  imageToCompare: null,
-  comparisonMode: 'slider',
-  comparisonFit: 'fill',
 };
 
 export const gallerySlice = createSlice({
@@ -37,28 +34,6 @@ 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;
     },
@@ -104,16 +79,6 @@ 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) => {
@@ -152,11 +117,6 @@ export const {
   moreImagesLoaded,
   alwaysShowImageSizeBadgeChanged,
   isImageViewerOpenChanged,
-  imageToCompareChanged,
-  comparisonModeChanged,
-  comparedImagesSwapped,
-  comparisonFitChanged,
-  comparisonModeCycled,
 } = gallerySlice.actions;
 
 const isAnyBoardDeleted = isAnyOf(
@@ -178,13 +138,5 @@ export const galleryPersistConfig: PersistConfig<GalleryState> = {
   name: gallerySlice.name,
   initialState: initialGalleryState,
   migrate: migrateGalleryState,
-  persistDenylist: [
-    'selection',
-    'selectedBoardId',
-    'galleryView',
-    'offset',
-    'limit',
-    'isImageViewerOpen',
-    'imageToCompare',
-  ],
+  persistDenylist: ['selection', 'selectedBoardId', 'galleryView', 'offset', 'limit', 'isImageViewerOpen'],
 };

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

@@ -7,8 +7,6 @@ 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[];
@@ -22,8 +20,5 @@ 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

@@ -1,7 +1,4 @@
-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 {
@@ -19,7 +16,6 @@ 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';
@@ -380,25 +376,54 @@ 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 keysToRecall: (keyof typeof handlers)[] = [
-    'positivePrompt',
-    'negativePrompt',
-    'sdxlPositiveStylePrompt',
-    'sdxlNegativeStylePrompt',
-  ];
-  const recalled = await recallKeys(keysToRecall, metadata);
-  if (size(recalled) > 0) {
+  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')) {
     parameterSetToast(t('metadata.allPrompts'));
   }
 };
 
 export const parseAndRecallImageDimensions = async (metadata: unknown) => {
-  const recalled = recallKeys(['width', 'height'], metadata);
-  if (size(recalled) > 0) {
+  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')) {
     parameterSetToast(t('metadata.imageDimensions'));
   }
 };
@@ -413,20 +438,28 @@ export const parseAndRecallAllMetadata = async (
   toControlLayers: boolean,
   skip: (keyof typeof handlers)[] = []
 ) => {
-  const skipKeys = deepClone(skip);
+  const skipKeys = 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);
+        });
+      })
+  );
 
-  // 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) {
+  if (results.some((result) => result.status === 'fulfilled')) {
     toast({
       id: 'PARAMETER_SET',
       title: t('toast.parametersSet'),
@@ -440,43 +473,3 @@ 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

@@ -1,7 +1,6 @@
 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';
 
@@ -108,30 +107,19 @@ 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. 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 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 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 | ModelIdentifierField | ModelIdentifier,
-  type: ModelType,
-  message?: string
-): Promise<string> => {
+export const getModelKey = async (modelIdentifier: unknown, type: ModelType, message?: string): Promise<string> => {
   if (isModelIdentifier(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 modelIdentifier.key;
+  }
+  if (isModelIdentifierV2(modelIdentifier)) {
     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, $needsFit } from 'features/nodes/store/reactFlowInstance';
+import { $flow } from 'features/nodes/store/reactFlowInstance';
 import { connectionToEdge } from 'features/nodes/store/util/reactFlowUtil';
 import type { CSSProperties, MouseEvent } from 'react';
 import { memo, useCallback, useMemo, useRef } from 'react';
@@ -68,7 +68,6 @@ export const Flow = memo(() => {
   const nodes = useAppSelector((s) => s.nodes.present.nodes);
   const edges = useAppSelector((s) => s.nodes.present.edges);
   const viewport = useStore($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);
@@ -93,16 +92,8 @@ 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, needsFit]
+    [dispatch]
   );
 
   const onEdgesChange: OnEdgesChange = useCallback(

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

@@ -15,20 +15,27 @@ 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({ duration: 300 });
+    zoomIn();
   }, [zoomIn]);
 
   const handleClickedZoomOut = useCallback(() => {
-    zoomOut({ duration: 300 });
+    zoomOut();
   }, [zoomOut]);
 
   const handleClickedFitView = useCallback(() => {
-    fitView({ duration: 300 });
+    fitView();
   }, [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

@@ -1,7 +1,9 @@
 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';
@@ -19,8 +21,14 @@ 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((s) => s.workflow.mode);
+  const { mode } = useAppSelector(selector);
   const panelGroupRef = useRef<ImperativePanelGroupHandle>(null);
   const panelStorage = usePanelStorage();
 

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

@@ -1,12 +1,20 @@
 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((s) => s.workflow.mode);
+  const { mode } = useAppSelector(selector);
 
   return (
     <Flex gap="2" alignItems="center">

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

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

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

@@ -2,4 +2,3 @@ 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

@@ -144,4 +144,5 @@ 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

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

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

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

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

@@ -1,17 +1,13 @@
 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.isOpen && <ImageViewer />}
-      <ImageComparisonDroppable />
+      <ImageViewer />
     </Box>
   );
 };

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

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

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

@@ -122,6 +122,7 @@ 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'];
@@ -131,14 +132,14 @@ export type WorkflowRecordListItemDTO = S['WorkflowRecordListItemDTO'];
 type KeysOfUnion<T> = T extends T ? keyof T : never;
 
 export type AnyInvocation = Exclude<
-  NonNullable<S['Graph']['nodes']>[string],
+  Graph['nodes'][string],
   S['CoreMetadataInvocation'] | S['MetadataInvocation'] | S['MetadataItemInvocation'] | S['MergeMetadataInvocation']
 >;
-export type AnyInvocationIncMetadata = NonNullable<S['Graph']['nodes']>[string];
+export type AnyInvocationIncMetadata = S['Graph']['nodes'][string];
 
 export type InvocationType = AnyInvocation['type'];
 type InvocationOutputMap = S['InvocationOutputMap'];
-export type AnyInvocationOutput = InvocationOutputMap[InvocationType];
+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

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

invokeai/version/invokeai_version.py

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

pyproject.toml

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

scripts/generate_openapi_schema.py

@@ -7,10 +7,9 @@ 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 app
-    from invokeai.app.util.custom_openapi import get_openapi_func
+    from invokeai.app.api_app import custom_openapi
 
-    schema = get_openapi_func(app)()
+    schema = custom_openapi()
     json.dump(schema, sys.stdout, indent=2)
 
 

tests/test_node_graph.py

@@ -1,6 +1,5 @@
 import pytest
 from pydantic import TypeAdapter
-from pydantic.json_schema import models_json_schema
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -714,4 +713,4 @@ 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
-    models_json_schema([(Graph, "serialization")])
+    _ = Graph.model_json_schema()