feat(ui): invalidate cache for queue item on status change

This query is only subscribed-to in the `QueueItemDetail` component - when is rendered only when the user clicks on a queue item in the queue. Invalidating this tag instead of optimistically updating it won't cause any meaningful change to network traffic.

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

docker/README.md

@@ -64,7 +64,7 @@ GPU_DRIVER=nvidia
 
 Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
 
-## Even More Customizing!
+## Even Moar Customizing!
 
 See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
 

docs/features/CONTROLNET.md

@@ -165,7 +165,7 @@ Additionally, each  section can be expanded with the "Show Advanced" button in o
 There are several ways to install IP-Adapter models with an existing InvokeAI installation:
 
 1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
-2. Through the Model Manager UI with models from the *Tools* section of [models.invoke.ai](https://models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
+2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
 3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. 
 
 #### Using IP-Adapter

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

docs/help/diffusion.md

@@ -20,7 +20,7 @@ When you generate an image using text-to-image, multiple steps occur in latent s
 4. The VAE decodes the final latent image from latent space into image space.
 
 Image-to-image is a similar process, with only step 1 being different:
-1. The input image is encoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how many noise steps are added, and the amount of noise added at each step. A Denoising Strength of 0 means there are 0 steps and no noise added, resulting in an unchanged image, while a Denoising Strength of 1 results in the image being completely replaced with noise and a full set of denoising steps are performance. The process is then the same as steps 2-4 in the text-to-image process. 
+1. The input image is encoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how may noise steps are added, and the amount of noise added at each step. A Denoising Strength of 0 means there are 0 steps and no noise added, resulting in an unchanged image, while a Denoising Strength of 1 results in the image being completely replaced with noise and a full set of denoising steps are performance. The process is then the same as steps 2-4 in the text-to-image process. 
 
 Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
 

installer/templates/invoke.bat.in

@@ -10,7 +10,8 @@ set INVOKEAI_ROOT=.
 echo Desired action:
 echo 1. Generate images with the browser-based interface
 echo 2. Open the developer console
-echo 3. Command-line help
+echo 3. Run the InvokeAI image database maintenance script
+echo 4. Command-line help
 echo Q - Quit
 echo.
 echo To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest.
@@ -33,6 +34,9 @@ IF /I "%choice%" == "1" (
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
 ) ELSE IF /I "%choice%" == "3" (
+   echo Running the db maintenance script...
+   python .venv\Scripts\invokeai-db-maintenance.exe
+) ELSE IF /I "%choice%" == "4" (
     echo Displaying command line help...
     python .venv\Scripts\invokeai-web.exe --help %*
     pause

installer/templates/invoke.sh.in

@@ -47,6 +47,11 @@ do_choice() {
         bash --init-file "$file_name"
         ;;
     3)
+        clear
+        printf "Running the db maintenance script\n"
+        invokeai-db-maintenance --root ${INVOKEAI_ROOT}
+        ;;
+    4)
         clear
         printf "Command-line help\n"
         invokeai-web --help
@@ -66,7 +71,8 @@ do_line_input() {
     printf "What would you like to do?\n"
     printf "1: Generate images using the browser-based interface\n"
     printf "2: Open the developer console\n"
-    printf "3: Command-line help\n"
+    printf "3: Run the InvokeAI image database maintenance script\n"
+    printf "4: Command-line help\n"
     printf "Q: Quit\n\n"
     printf "To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest.\n\n"
     read -p "Please enter 1-4, Q: [1] " yn

invokeai/app/api/dependencies.py

@@ -30,7 +30,7 @@ from ..services.model_images.model_images_default import ModelImageFileStorageDi
 from ..services.model_manager.model_manager_default import ModelManagerService
 from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
-from ..services.session_processor.session_processor_default import DefaultSessionProcessor, DefaultSessionRunner
+from ..services.session_processor.session_processor_default import DefaultSessionProcessor
 from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
@@ -103,7 +103,7 @@ class ApiDependencies:
         )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
-        session_processor = DefaultSessionProcessor(session_runner=DefaultSessionRunner())
+        session_processor = DefaultSessionProcessor()
         session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
         workflow_records = SqliteWorkflowRecordsStorage(db=db)

invokeai/app/api/routers/images.py

@@ -69,7 +69,7 @@ async def upload_image(
     if isinstance(metadata_raw, str):
         _metadata = metadata_raw
     else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse metadata for uploaded image")
+        ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
         pass
 
     # attempt to parse workflow from image
@@ -77,7 +77,7 @@ async def upload_image(
     if isinstance(workflow_raw, str):
         _workflow = workflow_raw
     else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse workflow for uploaded image")
+        ApiDependencies.invoker.services.logger.warn("Failed to parse workflow for uploaded image")
         pass
 
     # attempt to extract graph from image
@@ -85,7 +85,7 @@ async def upload_image(
     if isinstance(graph_raw, str):
         _graph = graph_raw
     else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse graph for uploaded image")
+        ApiDependencies.invoker.services.logger.warn("Failed to parse graph for uploaded image")
         pass
 
     try:

invokeai/app/api/sockets.py

@@ -15,7 +15,6 @@ from invokeai.app.services.events.events_common import (
     DownloadCancelledEvent,
     DownloadCompleteEvent,
     DownloadErrorEvent,
-    DownloadEventBase,
     DownloadProgressEvent,
     DownloadStartedEvent,
     FastAPIEvent,
@@ -35,53 +34,21 @@ from invokeai.app.services.events.events_common import (
     QueueClearedEvent,
     QueueEventBase,
     QueueItemStatusChangedEvent,
+    SessionCanceledEvent,
+    SessionCompleteEvent,
+    SessionStartedEvent,
     register_events,
 )
 
 
 class QueueSubscriptionEvent(BaseModel):
-    """Event data for subscribing to the socket.io queue room.
-    This is a pydantic model to ensure the data is in the correct format."""
-
     queue_id: str
 
 
 class BulkDownloadSubscriptionEvent(BaseModel):
-    """Event data for subscribing to the socket.io bulk downloads room.
-    This is a pydantic model to ensure the data is in the correct format."""
-
     bulk_download_id: str
 
 
-QUEUE_EVENTS = {
-    InvocationStartedEvent,
-    InvocationDenoiseProgressEvent,
-    InvocationCompleteEvent,
-    InvocationErrorEvent,
-    QueueItemStatusChangedEvent,
-    BatchEnqueuedEvent,
-    QueueClearedEvent,
-}
-
-MODEL_EVENTS = {
-    DownloadCancelledEvent,
-    DownloadCompleteEvent,
-    DownloadErrorEvent,
-    DownloadProgressEvent,
-    DownloadStartedEvent,
-    ModelLoadStartedEvent,
-    ModelLoadCompleteEvent,
-    ModelInstallDownloadProgressEvent,
-    ModelInstallDownloadsCompleteEvent,
-    ModelInstallStartedEvent,
-    ModelInstallCompleteEvent,
-    ModelInstallCancelledEvent,
-    ModelInstallErrorEvent,
-}
-
-BULK_DOWNLOAD_EVENTS = {BulkDownloadStartedEvent, BulkDownloadCompleteEvent, BulkDownloadErrorEvent}
-
-
 class SocketIO:
     _sub_queue = "subscribe_queue"
     _unsub_queue = "unsubscribe_queue"
@@ -99,9 +66,45 @@ class SocketIO:
         self._sio.on(self._sub_bulk_download, handler=self._handle_sub_bulk_download)
         self._sio.on(self._unsub_bulk_download, handler=self._handle_unsub_bulk_download)
 
-        register_events(QUEUE_EVENTS, self._handle_queue_event)
-        register_events(MODEL_EVENTS, self._handle_model_event)
-        register_events(BULK_DOWNLOAD_EVENTS, self._handle_bulk_image_download_event)
+        register_events(
+            {
+                InvocationStartedEvent,
+                InvocationDenoiseProgressEvent,
+                InvocationCompleteEvent,
+                InvocationErrorEvent,
+                SessionStartedEvent,
+                SessionCompleteEvent,
+                SessionCanceledEvent,
+                QueueItemStatusChangedEvent,
+                BatchEnqueuedEvent,
+                QueueClearedEvent,
+            },
+            self._handle_queue_event,
+        )
+
+        register_events(
+            {
+                DownloadCancelledEvent,
+                DownloadCompleteEvent,
+                DownloadErrorEvent,
+                DownloadProgressEvent,
+                DownloadStartedEvent,
+                ModelLoadStartedEvent,
+                ModelLoadCompleteEvent,
+                ModelInstallDownloadProgressEvent,
+                ModelInstallDownloadsCompleteEvent,
+                ModelInstallStartedEvent,
+                ModelInstallCompleteEvent,
+                ModelInstallCancelledEvent,
+                ModelInstallErrorEvent,
+            },
+            self._handle_model_event,
+        )
+
+        register_events(
+            {BulkDownloadStartedEvent, BulkDownloadCompleteEvent, BulkDownloadErrorEvent},
+            self._handle_bulk_image_download_event,
+        )
 
     async def _handle_sub_queue(self, sid: str, data: Any) -> None:
         await self._sio.enter_room(sid, QueueSubscriptionEvent(**data).queue_id)
@@ -116,10 +119,13 @@ class SocketIO:
         await self._sio.leave_room(sid, BulkDownloadSubscriptionEvent(**data).bulk_download_id)
 
     async def _handle_queue_event(self, event: FastAPIEvent[QueueEventBase]):
-        await self._sio.emit(event=event[0], data=event[1].model_dump(mode="json"), room=event[1].queue_id)
+        event_name, payload = event
+        await self._sio.emit(event=event_name, data=payload.model_dump(mode="json"), room=payload.queue_id)
 
-    async def _handle_model_event(self, event: FastAPIEvent[ModelEventBase | DownloadEventBase]) -> None:
-        await self._sio.emit(event=event[0], data=event[1].model_dump(mode="json"))
+    async def _handle_model_event(self, event: FastAPIEvent[ModelEventBase]) -> None:
+        event_name, payload = event
+        await self._sio.emit(event=event_name, data=payload.model_dump(mode="json"))
 
     async def _handle_bulk_image_download_event(self, event: FastAPIEvent[BulkDownloadEventBase]) -> None:
-        await self._sio.emit(event=event[0], data=event[1].model_dump(mode="json"), room=event[1].bulk_download_id)
+        event_name, payload = event
+        await self._sio.emit(event=event_name, data=payload.model_dump(mode="json"), room=payload.bulk_download_id)

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/compel.py

@@ -65,7 +65,11 @@ class CompelInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
         tokenizer_info = context.models.load(self.clip.tokenizer)
+        tokenizer_model = tokenizer_info.model
+        assert isinstance(tokenizer_model, CLIPTokenizer)
         text_encoder_info = context.models.load(self.clip.text_encoder)
+        text_encoder_model = text_encoder_info.model
+        assert isinstance(text_encoder_model, CLIPTextModel)
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.clip.loras:
@@ -80,21 +84,19 @@ class CompelInvocation(BaseInvocation):
         ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
 
         with (
-            # apply all patches while the model is on the target device
-            text_encoder_info as text_encoder,
-            tokenizer_info as tokenizer,
-            ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
-            # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
-            ModelPatcher.apply_clip_skip(text_encoder, self.clip.skipped_layers),
-            ModelPatcher.apply_ti(tokenizer, text_encoder, ti_list) as (
-                patched_tokenizer,
+            ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
+                tokenizer,
                 ti_manager,
             ),
+            text_encoder_info as text_encoder,
+            # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
+            ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
+            # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
+            ModelPatcher.apply_clip_skip(text_encoder_model, self.clip.skipped_layers),
         ):
             assert isinstance(text_encoder, CLIPTextModel)
-            assert isinstance(tokenizer, CLIPTokenizer)
             compel = Compel(
-                tokenizer=patched_tokenizer,
+                tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
                 dtype_for_device_getter=TorchDevice.choose_torch_dtype,
@@ -104,7 +106,7 @@ class CompelInvocation(BaseInvocation):
             conjunction = Compel.parse_prompt_string(self.prompt)
 
             if context.config.get().log_tokenization:
-                log_tokenization_for_conjunction(conjunction, patched_tokenizer)
+                log_tokenization_for_conjunction(conjunction, tokenizer)
 
             c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
@@ -134,7 +136,11 @@ class SDXLPromptInvocationBase:
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         tokenizer_info = context.models.load(clip_field.tokenizer)
+        tokenizer_model = tokenizer_info.model
+        assert isinstance(tokenizer_model, CLIPTokenizer)
         text_encoder_info = context.models.load(clip_field.text_encoder)
+        text_encoder_model = text_encoder_info.model
+        assert isinstance(text_encoder_model, (CLIPTextModel, CLIPTextModelWithProjection))
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -171,23 +177,20 @@ class SDXLPromptInvocationBase:
         ti_list = generate_ti_list(prompt, text_encoder_info.config.base, context)
 
         with (
-            # apply all patches while the model is on the target device
-            text_encoder_info as text_encoder,
-            tokenizer_info as tokenizer,
-            ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
-            # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
-            ModelPatcher.apply_clip_skip(text_encoder, clip_field.skipped_layers),
-            ModelPatcher.apply_ti(tokenizer, text_encoder, ti_list) as (
-                patched_tokenizer,
+            ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
+                tokenizer,
                 ti_manager,
             ),
+            text_encoder_info as text_encoder,
+            # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
+            ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
+            # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
+            ModelPatcher.apply_clip_skip(text_encoder_model, clip_field.skipped_layers),
         ):
             assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
-            assert isinstance(tokenizer, CLIPTokenizer)
-
             text_encoder = cast(CLIPTextModel, text_encoder)
             compel = Compel(
-                tokenizer=patched_tokenizer,
+                tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
                 dtype_for_device_getter=TorchDevice.choose_torch_dtype,
@@ -200,7 +203,7 @@ class SDXLPromptInvocationBase:
 
             if context.config.get().log_tokenization:
                 # TODO: better logging for and syntax
-                log_tokenization_for_conjunction(conjunction, patched_tokenizer)
+                log_tokenization_for_conjunction(conjunction, tokenizer)
 
             # TODO: ask for optimizations? to not run text_encoder twice
             c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)

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_base.py

@@ -14,6 +14,7 @@ from invokeai.app.services.events.events_common import (
     DownloadProgressEvent,
     DownloadStartedEvent,
     EventBase,
+    ExtraData,
     InvocationCompleteEvent,
     InvocationDenoiseProgressEvent,
     InvocationErrorEvent,
@@ -28,6 +29,9 @@ from invokeai.app.services.events.events_common import (
     ModelLoadStartedEvent,
     QueueClearedEvent,
     QueueItemStatusChangedEvent,
+    SessionCanceledEvent,
+    SessionCompleteEvent,
+    SessionStartedEvent,
 )
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 
@@ -54,9 +58,11 @@ class EventServiceBase:
 
     # region: Invocation
 
-    def emit_invocation_started(self, queue_item: "SessionQueueItem", invocation: "BaseInvocation") -> None:
+    def emit_invocation_started(
+        self, queue_item: "SessionQueueItem", invocation: "BaseInvocation", extra: Optional[ExtraData] = None
+    ) -> None:
         """Emitted when an invocation is started"""
-        self.dispatch(InvocationStartedEvent.build(queue_item, invocation))
+        self.dispatch(InvocationStartedEvent.build(queue_item, invocation, extra))
 
     def emit_invocation_denoise_progress(
         self,
@@ -64,132 +70,184 @@ class EventServiceBase:
         invocation: "BaseInvocation",
         intermediate_state: PipelineIntermediateState,
         progress_image: "ProgressImage",
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted at each step during denoising of an invocation."""
-        self.dispatch(InvocationDenoiseProgressEvent.build(queue_item, invocation, intermediate_state, progress_image))
+        self.dispatch(
+            InvocationDenoiseProgressEvent.build(queue_item, invocation, intermediate_state, progress_image, extra)
+        )
 
     def emit_invocation_complete(
-        self, queue_item: "SessionQueueItem", invocation: "BaseInvocation", output: "BaseInvocationOutput"
+        self,
+        queue_item: "SessionQueueItem",
+        invocation: "BaseInvocation",
+        output: "BaseInvocationOutput",
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted when an invocation is complete"""
-        self.dispatch(InvocationCompleteEvent.build(queue_item, invocation, output))
+        self.dispatch(InvocationCompleteEvent.build(queue_item, invocation, output, extra))
 
     def emit_invocation_error(
         self,
         queue_item: "SessionQueueItem",
         invocation: "BaseInvocation",
         error_type: str,
-        error_message: str,
-        error_traceback: str,
+        error: str,
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted when an invocation encounters an error"""
-        self.dispatch(InvocationErrorEvent.build(queue_item, invocation, error_type, error_message, error_traceback))
+        self.dispatch(InvocationErrorEvent.build(queue_item, invocation, error_type, error, extra))
+
+    # endregion
+
+    # region Session
+
+    def emit_session_started(self, queue_item: "SessionQueueItem", extra: Optional[ExtraData] = None) -> None:
+        """Emitted when a session has started"""
+        self.dispatch(SessionStartedEvent.build(queue_item, extra))
+
+    def emit_session_complete(self, queue_item: "SessionQueueItem", extra: Optional[ExtraData] = None) -> None:
+        """Emitted when a session has completed all invocations"""
+        self.dispatch(SessionCompleteEvent.build(queue_item, extra))
+
+    def emit_session_canceled(self, queue_item: "SessionQueueItem", extra: Optional[ExtraData] = None) -> None:
+        """Emitted when a session is canceled"""
+        self.dispatch(SessionCanceledEvent.build(queue_item, extra))
 
     # endregion
 
     # region Queue
 
     def emit_queue_item_status_changed(
-        self, queue_item: "SessionQueueItem", batch_status: "BatchStatus", queue_status: "SessionQueueStatus"
+        self,
+        queue_item: "SessionQueueItem",
+        batch_status: "BatchStatus",
+        queue_status: "SessionQueueStatus",
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted when a queue item's status changes"""
-        self.dispatch(QueueItemStatusChangedEvent.build(queue_item, batch_status, queue_status))
+        self.dispatch(QueueItemStatusChangedEvent.build(queue_item, batch_status, queue_status, extra))
 
-    def emit_batch_enqueued(self, enqueue_result: "EnqueueBatchResult") -> None:
+    def emit_batch_enqueued(self, enqueue_result: "EnqueueBatchResult", extra: Optional[ExtraData] = None) -> None:
         """Emitted when a batch is enqueued"""
-        self.dispatch(BatchEnqueuedEvent.build(enqueue_result))
+        self.dispatch(BatchEnqueuedEvent.build(enqueue_result, extra))
 
-    def emit_queue_cleared(self, queue_id: str) -> None:
+    def emit_queue_cleared(self, queue_id: str, extra: Optional[ExtraData] = None) -> None:
         """Emitted when a queue is cleared"""
-        self.dispatch(QueueClearedEvent.build(queue_id))
+        self.dispatch(QueueClearedEvent.build(queue_id, extra))
 
     # endregion
 
     # region Download
 
-    def emit_download_started(self, job: "DownloadJob") -> None:
+    def emit_download_started(self, job: "DownloadJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted when a download is started"""
-        self.dispatch(DownloadStartedEvent.build(job))
+        self.dispatch(DownloadStartedEvent.build(job, extra))
 
-    def emit_download_progress(self, job: "DownloadJob") -> None:
+    def emit_download_progress(self, job: "DownloadJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted at intervals during a download"""
-        self.dispatch(DownloadProgressEvent.build(job))
+        self.dispatch(DownloadProgressEvent.build(job, extra))
 
-    def emit_download_complete(self, job: "DownloadJob") -> None:
+    def emit_download_complete(self, job: "DownloadJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted when a download is completed"""
-        self.dispatch(DownloadCompleteEvent.build(job))
+        self.dispatch(DownloadCompleteEvent.build(job, extra))
 
-    def emit_download_cancelled(self, job: "DownloadJob") -> None:
+    def emit_download_cancelled(self, job: "DownloadJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted when a download is cancelled"""
-        self.dispatch(DownloadCancelledEvent.build(job))
+        self.dispatch(DownloadCancelledEvent.build(job, extra))
 
-    def emit_download_error(self, job: "DownloadJob") -> None:
+    def emit_download_error(self, job: "DownloadJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted when a download encounters an error"""
-        self.dispatch(DownloadErrorEvent.build(job))
+        self.dispatch(DownloadErrorEvent.build(job, extra))
 
     # endregion
 
     # region Model loading
 
-    def emit_model_load_started(self, config: "AnyModelConfig", submodel_type: Optional["SubModelType"] = None) -> None:
+    def emit_model_load_started(
+        self,
+        config: "AnyModelConfig",
+        submodel_type: Optional["SubModelType"] = None,
+        extra: Optional[ExtraData] = None,
+    ) -> None:
         """Emitted when a model load is started."""
-        self.dispatch(ModelLoadStartedEvent.build(config, submodel_type))
+        self.dispatch(ModelLoadStartedEvent.build(config, submodel_type, extra))
 
     def emit_model_load_complete(
-        self, config: "AnyModelConfig", submodel_type: Optional["SubModelType"] = None
+        self,
+        config: "AnyModelConfig",
+        submodel_type: Optional["SubModelType"] = None,
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted when a model load is complete."""
-        self.dispatch(ModelLoadCompleteEvent.build(config, submodel_type))
+        self.dispatch(ModelLoadCompleteEvent.build(config, submodel_type, extra))
 
     # endregion
 
     # region Model install
 
-    def emit_model_install_download_progress(self, job: "ModelInstallJob") -> None:
+    def emit_model_install_download_progress(self, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted at intervals while the install job is in progress (remote models only)."""
-        self.dispatch(ModelInstallDownloadProgressEvent.build(job))
+        self.dispatch(ModelInstallDownloadProgressEvent.build(job, extra))
 
-    def emit_model_install_downloads_complete(self, job: "ModelInstallJob") -> None:
-        self.dispatch(ModelInstallDownloadsCompleteEvent.build(job))
+    def emit_model_install_downloads_complete(self, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> None:
+        self.dispatch(ModelInstallDownloadsCompleteEvent.build(job, extra))
 
-    def emit_model_install_started(self, job: "ModelInstallJob") -> None:
+    def emit_model_install_started(self, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted once when an install job is started (after any download)."""
-        self.dispatch(ModelInstallStartedEvent.build(job))
+        self.dispatch(ModelInstallStartedEvent.build(job, extra))
 
-    def emit_model_install_complete(self, job: "ModelInstallJob") -> None:
+    def emit_model_install_complete(self, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted when an install job is completed successfully."""
-        self.dispatch(ModelInstallCompleteEvent.build(job))
+        self.dispatch(ModelInstallCompleteEvent.build(job, extra))
 
-    def emit_model_install_cancelled(self, job: "ModelInstallJob") -> None:
+    def emit_model_install_cancelled(self, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted when an install job is cancelled."""
-        self.dispatch(ModelInstallCancelledEvent.build(job))
+        self.dispatch(ModelInstallCancelledEvent.build(job, extra))
 
-    def emit_model_install_error(self, job: "ModelInstallJob") -> None:
+    def emit_model_install_error(self, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> None:
         """Emitted when an install job encounters an exception."""
-        self.dispatch(ModelInstallErrorEvent.build(job))
+        self.dispatch(ModelInstallErrorEvent.build(job, extra))
 
     # endregion
 
     # region Bulk image download
 
     def emit_bulk_download_started(
-        self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
+        self,
+        bulk_download_id: str,
+        bulk_download_item_id: str,
+        bulk_download_item_name: str,
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted when a bulk image download is started"""
-        self.dispatch(BulkDownloadStartedEvent.build(bulk_download_id, bulk_download_item_id, bulk_download_item_name))
+        self.dispatch(
+            BulkDownloadStartedEvent.build(bulk_download_id, bulk_download_item_id, bulk_download_item_name, extra)
+        )
 
     def emit_bulk_download_complete(
-        self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
+        self,
+        bulk_download_id: str,
+        bulk_download_item_id: str,
+        bulk_download_item_name: str,
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted when a bulk image download is complete"""
-        self.dispatch(BulkDownloadCompleteEvent.build(bulk_download_id, bulk_download_item_id, bulk_download_item_name))
+        self.dispatch(
+            BulkDownloadCompleteEvent.build(bulk_download_id, bulk_download_item_id, bulk_download_item_name, extra)
+        )
 
     def emit_bulk_download_error(
-        self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str, error: str
+        self,
+        bulk_download_id: str,
+        bulk_download_item_id: str,
+        bulk_download_item_name: str,
+        error: str,
+        extra: Optional[ExtraData] = None,
     ) -> None:
         """Emitted when a bulk image download has an error"""
         self.dispatch(
-            BulkDownloadErrorEvent.build(bulk_download_id, bulk_download_item_id, bulk_download_item_name, error)
+            BulkDownloadErrorEvent.build(bulk_download_id, bulk_download_item_id, bulk_download_item_name, error, extra)
         )
 
     # endregion

invokeai/app/services/events/events_common.py

@@ -1,10 +1,10 @@
 from math import floor
-from typing import TYPE_CHECKING, Any, ClassVar, Coroutine, Generic, Optional, Protocol, TypeAlias, TypeVar
+from typing import TYPE_CHECKING, Any, Coroutine, Optional, Protocol, TypeAlias, TypeVar
 
 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
 
+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 +13,6 @@ 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
@@ -23,6 +22,9 @@ if TYPE_CHECKING:
     from invokeai.app.services.model_install.model_install_common import ModelInstallJob
 
 
+ExtraData: TypeAlias = dict[str, Any]
+
+
 class EventBase(BaseModel):
     """Base class for all events. All events must inherit from this class.
 
@@ -33,8 +35,8 @@ class EventBase(BaseModel):
     A timestamp is automatically added to the event when it is created.
     """
 
-    __event_name__: ClassVar[str]
     timestamp: int = Field(description="The timestamp of the event", default_factory=get_timestamp)
+    extra: Optional[ExtraData] = Field(default=None, description="Extra data to include with the event")
 
     model_config = ConfigDict(json_schema_serialization_defaults_required=True)
 
@@ -52,7 +54,7 @@ class EventBase(BaseModel):
         return event_subclasses
 
 
-TEvent = TypeVar("TEvent", bound=EventBase, contravariant=True)
+TEvent = TypeVar("TEvent", bound=EventBase)
 
 FastAPIEvent: TypeAlias = tuple[str, TEvent]
 """
@@ -61,17 +63,16 @@ Provide a generic type to `TEvent` to specify the payload type.
 """
 
 
-class FastAPIEventFunc(Protocol, Generic[TEvent]):
-    def __call__(self, event: FastAPIEvent[TEvent]) -> Optional[Coroutine[Any, Any, None]]: ...
+class FastAPIEventFunc(Protocol):
+    def __call__(self, event: FastAPIEvent[Any]) -> Optional[Coroutine[Any, Any, None]]: ...
 
 
-def register_events(events: set[type[TEvent]] | type[TEvent], func: FastAPIEventFunc[TEvent]) -> None:
-    """Register a function to handle specific events.
+def register_events(events: set[type[TEvent]], func: FastAPIEventFunc) -> None:
+    """Register a function to handle a list of events.
 
-    :param events: An event or set of events to handle
+    :param events: A list of event classes to handle
     :param func: The function to handle the events
     """
-    events = events if isinstance(events, set) else {events}
     for event in events:
         assert hasattr(event, "__event_name__")
         local_handler.register(event_name=event.__event_name__, _func=func)  # pyright: ignore [reportUnknownMemberType, reportUnknownArgumentType, reportAttributeAccessIssue]
@@ -90,37 +91,45 @@ class QueueItemEventBase(QueueEventBase):
     batch_id: str = Field(description="The ID of the queue batch")
 
 
-class InvocationEventBase(QueueItemEventBase):
-    """Base class for invocation events"""
+class SessionEventBase(QueueItemEventBase):
+    """Base class for session (aka graph execution state) events"""
 
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
+
+
+class InvocationEventBase(SessionEventBase):
+    """Base class for invocation events"""
+
     queue_id: str = Field(description="The ID of the queue")
     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_id: str = Field(description="The ID of the invocation")
     invocation_source_id: str = Field(description="The ID of the prepared invocation's source node")
+    invocation_type: str = Field(description="The type of invocation")
 
 
-@payload_schema.register
 class InvocationStartedEvent(InvocationEventBase):
     """Event model for invocation_started"""
 
     __event_name__ = "invocation_started"
 
     @classmethod
-    def build(cls, queue_item: SessionQueueItem, invocation: AnyInvocation) -> "InvocationStartedEvent":
+    def build(
+        cls, queue_item: SessionQueueItem, invocation: BaseInvocation, extra: Optional[ExtraData] = None
+    ) -> "InvocationStartedEvent":
         return cls(
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
             session_id=queue_item.session_id,
-            invocation=invocation,
+            invocation_id=invocation.id,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
+            invocation_type=invocation.get_type(),
+            extra=extra,
         )
 
 
-@payload_schema.register
 class InvocationDenoiseProgressEvent(InvocationEventBase):
     """Event model for invocation_denoise_progress"""
 
@@ -136,9 +145,10 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
     def build(
         cls,
         queue_item: SessionQueueItem,
-        invocation: AnyInvocation,
+        invocation: BaseInvocation,
         intermediate_state: PipelineIntermediateState,
         progress_image: ProgressImage,
+        extra: Optional[ExtraData] = None,
     ) -> "InvocationDenoiseProgressEvent":
         step = intermediate_state.step
         total_steps = intermediate_state.total_steps
@@ -148,13 +158,15 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
             session_id=queue_item.session_id,
-            invocation=invocation,
+            invocation_id=invocation.id,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
+            invocation_type=invocation.get_type(),
             progress_image=progress_image,
             step=step,
             total_steps=total_steps,
             order=order,
             percentage=cls.calc_percentage(step, total_steps, order),
+            extra=extra,
         )
 
     @staticmethod
@@ -168,106 +180,151 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
         return (step + 1 + 1) / (total_steps + 1)
 
 
-@payload_schema.register
 class InvocationCompleteEvent(InvocationEventBase):
     """Event model for invocation_complete"""
 
     __event_name__ = "invocation_complete"
 
-    result: AnyInvocationOutput = Field(description="The result of the invocation")
+    result: SerializeAsAny[BaseInvocationOutput] = Field(description="The result of the invocation")
 
     @classmethod
     def build(
-        cls, queue_item: SessionQueueItem, invocation: AnyInvocation, result: AnyInvocationOutput
+        cls,
+        queue_item: SessionQueueItem,
+        invocation: BaseInvocation,
+        result: BaseInvocationOutput,
+        extra: Optional[ExtraData] = None,
     ) -> "InvocationCompleteEvent":
         return cls(
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
             session_id=queue_item.session_id,
-            invocation=invocation,
+            invocation_id=invocation.id,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
+            invocation_type=invocation.get_type(),
             result=result,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class InvocationErrorEvent(InvocationEventBase):
     """Event model for invocation_error"""
 
     __event_name__ = "invocation_error"
 
-    error_type: str = Field(description="The error type")
-    error_message: str = Field(description="The error message")
-    error_traceback: str = Field(description="The error traceback")
-    user_id: Optional[str] = Field(default=None, description="The ID of the user who created the invocation")
-    project_id: Optional[str] = Field(default=None, description="The ID of the user who created the invocation")
+    error_type: str = Field(description="The type of error")
+    error: str = Field(description="The error message")
 
     @classmethod
     def build(
         cls,
         queue_item: SessionQueueItem,
-        invocation: AnyInvocation,
+        invocation: BaseInvocation,
         error_type: str,
-        error_message: str,
-        error_traceback: str,
+        error: str,
+        extra: Optional[ExtraData] = None,
     ) -> "InvocationErrorEvent":
         return cls(
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
             session_id=queue_item.session_id,
-            invocation=invocation,
+            invocation_id=invocation.id,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
+            invocation_type=invocation.get_type(),
             error_type=error_type,
-            error_message=error_message,
-            error_traceback=error_traceback,
-            user_id=getattr(queue_item, "user_id", None),
-            project_id=getattr(queue_item, "project_id", None),
+            error=error,
+            extra=extra,
+        )
+
+
+class SessionStartedEvent(SessionEventBase):
+    """Event model for session_started"""
+
+    __event_name__ = "session_started"
+
+    @classmethod
+    def build(cls, queue_item: SessionQueueItem, extra: Optional[ExtraData] = None) -> "SessionStartedEvent":
+        return cls(
+            queue_id=queue_item.queue_id,
+            item_id=queue_item.item_id,
+            batch_id=queue_item.batch_id,
+            session_id=queue_item.session_id,
+            extra=extra,
+        )
+
+
+class SessionCompleteEvent(SessionEventBase):
+    """Event model for session_complete"""
+
+    __event_name__ = "session_complete"
+
+    @classmethod
+    def build(cls, queue_item: SessionQueueItem, extra: Optional[ExtraData] = None) -> "SessionCompleteEvent":
+        return cls(
+            queue_id=queue_item.queue_id,
+            item_id=queue_item.item_id,
+            batch_id=queue_item.batch_id,
+            session_id=queue_item.session_id,
+            extra=extra,
+        )
+
+
+class SessionCanceledEvent(SessionEventBase):
+    """Event model for session_canceled"""
+
+    __event_name__ = "session_canceled"
+
+    @classmethod
+    def build(cls, queue_item: SessionQueueItem, extra: Optional[ExtraData] = None) -> "SessionCanceledEvent":
+        return cls(
+            queue_id=queue_item.queue_id,
+            item_id=queue_item.item_id,
+            batch_id=queue_item.batch_id,
+            session_id=queue_item.session_id,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class QueueItemStatusChangedEvent(QueueItemEventBase):
     """Event model for queue_item_status_changed"""
 
     __event_name__ = "queue_item_status_changed"
 
     status: QUEUE_ITEM_STATUS = Field(description="The new status of the queue item")
-    error_type: Optional[str] = Field(default=None, description="The error type, if any")
-    error_message: Optional[str] = Field(default=None, description="The error message, if any")
-    error_traceback: Optional[str] = Field(default=None, description="The error traceback, if any")
+    error: Optional[str] = Field(default=None, description="The error message, if any")
     created_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was created")
     updated_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was last updated")
     started_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was started")
     completed_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was completed")
     batch_status: BatchStatus = Field(description="The status of the batch")
     queue_status: SessionQueueStatus = Field(description="The status of the queue")
-    session_id: str = Field(description="The ID of the session (aka graph execution state)")
 
     @classmethod
     def build(
-        cls, queue_item: SessionQueueItem, batch_status: BatchStatus, queue_status: SessionQueueStatus
+        cls,
+        queue_item: SessionQueueItem,
+        batch_status: BatchStatus,
+        queue_status: SessionQueueStatus,
+        extra: Optional[ExtraData] = None,
     ) -> "QueueItemStatusChangedEvent":
         return cls(
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            session_id=queue_item.session_id,
             status=queue_item.status,
-            error_type=queue_item.error_type,
-            error_message=queue_item.error_message,
-            error_traceback=queue_item.error_traceback,
+            error=queue_item.error,
             created_at=str(queue_item.created_at) if queue_item.created_at else None,
             updated_at=str(queue_item.updated_at) if queue_item.updated_at else None,
             started_at=str(queue_item.started_at) if queue_item.started_at else None,
             completed_at=str(queue_item.completed_at) if queue_item.completed_at else None,
             batch_status=batch_status,
             queue_status=queue_status,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class BatchEnqueuedEvent(QueueEventBase):
     """Event model for batch_enqueued"""
 
@@ -281,25 +338,28 @@ class BatchEnqueuedEvent(QueueEventBase):
     priority: int = Field(description="The priority of the batch")
 
     @classmethod
-    def build(cls, enqueue_result: EnqueueBatchResult) -> "BatchEnqueuedEvent":
+    def build(cls, enqueue_result: EnqueueBatchResult, extra: Optional[ExtraData] = None) -> "BatchEnqueuedEvent":
         return cls(
             queue_id=enqueue_result.queue_id,
             batch_id=enqueue_result.batch.batch_id,
             enqueued=enqueue_result.enqueued,
             requested=enqueue_result.requested,
             priority=enqueue_result.priority,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class QueueClearedEvent(QueueEventBase):
     """Event model for queue_cleared"""
 
     __event_name__ = "queue_cleared"
 
     @classmethod
-    def build(cls, queue_id: str) -> "QueueClearedEvent":
-        return cls(queue_id=queue_id)
+    def build(cls, queue_id: str, extra: Optional[ExtraData] = None) -> "QueueClearedEvent":
+        return cls(
+            queue_id=queue_id,
+            extra=extra,
+        )
 
 
 class DownloadEventBase(EventBase):
@@ -308,7 +368,6 @@ class DownloadEventBase(EventBase):
     source: str = Field(description="The source of the download")
 
 
-@payload_schema.register
 class DownloadStartedEvent(DownloadEventBase):
     """Event model for download_started"""
 
@@ -317,12 +376,15 @@ class DownloadStartedEvent(DownloadEventBase):
     download_path: str = Field(description="The local path where the download is saved")
 
     @classmethod
-    def build(cls, job: "DownloadJob") -> "DownloadStartedEvent":
+    def build(cls, job: "DownloadJob", extra: Optional[ExtraData] = None) -> "DownloadStartedEvent":
         assert job.download_path
-        return cls(source=str(job.source), download_path=job.download_path.as_posix())
+        return cls(
+            source=str(job.source),
+            download_path=job.download_path.as_posix(),
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class DownloadProgressEvent(DownloadEventBase):
     """Event model for download_progress"""
 
@@ -333,17 +395,17 @@ class DownloadProgressEvent(DownloadEventBase):
     total_bytes: int = Field(description="The total number of bytes to be downloaded")
 
     @classmethod
-    def build(cls, job: "DownloadJob") -> "DownloadProgressEvent":
+    def build(cls, job: "DownloadJob", extra: Optional[ExtraData] = None) -> "DownloadProgressEvent":
         assert job.download_path
         return cls(
             source=str(job.source),
             download_path=job.download_path.as_posix(),
             current_bytes=job.bytes,
             total_bytes=job.total_bytes,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class DownloadCompleteEvent(DownloadEventBase):
     """Event model for download_complete"""
 
@@ -353,23 +415,29 @@ class DownloadCompleteEvent(DownloadEventBase):
     total_bytes: int = Field(description="The total number of bytes downloaded")
 
     @classmethod
-    def build(cls, job: "DownloadJob") -> "DownloadCompleteEvent":
+    def build(cls, job: "DownloadJob", extra: Optional[ExtraData] = None) -> "DownloadCompleteEvent":
         assert job.download_path
-        return cls(source=str(job.source), download_path=job.download_path.as_posix(), total_bytes=job.total_bytes)
+        return cls(
+            source=str(job.source),
+            download_path=job.download_path.as_posix(),
+            total_bytes=job.total_bytes,
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class DownloadCancelledEvent(DownloadEventBase):
     """Event model for download_cancelled"""
 
     __event_name__ = "download_cancelled"
 
     @classmethod
-    def build(cls, job: "DownloadJob") -> "DownloadCancelledEvent":
-        return cls(source=str(job.source))
+    def build(cls, job: "DownloadJob", extra: Optional[ExtraData] = None) -> "DownloadCancelledEvent":
+        return cls(
+            source=str(job.source),
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class DownloadErrorEvent(DownloadEventBase):
     """Event model for download_error"""
 
@@ -379,17 +447,21 @@ class DownloadErrorEvent(DownloadEventBase):
     error: str = Field(description="The error message")
 
     @classmethod
-    def build(cls, job: "DownloadJob") -> "DownloadErrorEvent":
+    def build(cls, job: "DownloadJob", extra: Optional[ExtraData] = None) -> "DownloadErrorEvent":
         assert job.error_type
         assert job.error
-        return cls(source=str(job.source), error_type=job.error_type, error=job.error)
+        return cls(
+            source=str(job.source),
+            error_type=job.error_type,
+            error=job.error,
+            extra=extra,
+        )
 
 
 class ModelEventBase(EventBase):
     """Base class for events associated with a model"""
 
 
-@payload_schema.register
 class ModelLoadStartedEvent(ModelEventBase):
     """Event model for model_load_started"""
 
@@ -399,11 +471,16 @@ class ModelLoadStartedEvent(ModelEventBase):
     submodel_type: Optional[SubModelType] = Field(default=None, description="The submodel type, if any")
 
     @classmethod
-    def build(cls, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> "ModelLoadStartedEvent":
-        return cls(config=config, submodel_type=submodel_type)
+    def build(
+        cls, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, extra: Optional[ExtraData] = None
+    ) -> "ModelLoadStartedEvent":
+        return cls(
+            config=config,
+            submodel_type=submodel_type,
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class ModelLoadCompleteEvent(ModelEventBase):
     """Event model for model_load_complete"""
 
@@ -413,11 +490,16 @@ class ModelLoadCompleteEvent(ModelEventBase):
     submodel_type: Optional[SubModelType] = Field(default=None, description="The submodel type, if any")
 
     @classmethod
-    def build(cls, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> "ModelLoadCompleteEvent":
-        return cls(config=config, submodel_type=submodel_type)
+    def build(
+        cls, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, extra: Optional[ExtraData] = None
+    ) -> "ModelLoadCompleteEvent":
+        return cls(
+            config=config,
+            submodel_type=submodel_type,
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class ModelInstallDownloadProgressEvent(ModelEventBase):
     """Event model for model_install_download_progress"""
 
@@ -433,7 +515,7 @@ class ModelInstallDownloadProgressEvent(ModelEventBase):
     )
 
     @classmethod
-    def build(cls, job: "ModelInstallJob") -> "ModelInstallDownloadProgressEvent":
+    def build(cls, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> "ModelInstallDownloadProgressEvent":
         parts: list[dict[str, str | int]] = [
             {
                 "url": str(x.source),
@@ -450,10 +532,10 @@ class ModelInstallDownloadProgressEvent(ModelEventBase):
             parts=parts,
             bytes=job.bytes,
             total_bytes=job.total_bytes,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class ModelInstallDownloadsCompleteEvent(ModelEventBase):
     """Emitted once when an install job becomes active."""
 
@@ -463,11 +545,14 @@ class ModelInstallDownloadsCompleteEvent(ModelEventBase):
     source: str = Field(description="Source of the model; local path, repo_id or url")
 
     @classmethod
-    def build(cls, job: "ModelInstallJob") -> "ModelInstallDownloadsCompleteEvent":
-        return cls(id=job.id, source=str(job.source))
+    def build(cls, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> "ModelInstallDownloadsCompleteEvent":
+        return cls(
+            id=job.id,
+            source=str(job.source),
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class ModelInstallStartedEvent(ModelEventBase):
     """Event model for model_install_started"""
 
@@ -477,11 +562,14 @@ class ModelInstallStartedEvent(ModelEventBase):
     source: str = Field(description="Source of the model; local path, repo_id or url")
 
     @classmethod
-    def build(cls, job: "ModelInstallJob") -> "ModelInstallStartedEvent":
-        return cls(id=job.id, source=str(job.source))
+    def build(cls, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> "ModelInstallStartedEvent":
+        return cls(
+            id=job.id,
+            source=str(job.source),
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class ModelInstallCompleteEvent(ModelEventBase):
     """Event model for model_install_complete"""
 
@@ -493,12 +581,17 @@ class ModelInstallCompleteEvent(ModelEventBase):
     total_bytes: Optional[int] = Field(description="Size of the model (may be None for installation of a local path)")
 
     @classmethod
-    def build(cls, job: "ModelInstallJob") -> "ModelInstallCompleteEvent":
+    def build(cls, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> "ModelInstallCompleteEvent":
         assert job.config_out is not None
-        return cls(id=job.id, source=str(job.source), key=(job.config_out.key), total_bytes=job.total_bytes)
+        return cls(
+            id=job.id,
+            source=str(job.source),
+            key=(job.config_out.key),
+            total_bytes=job.total_bytes,
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class ModelInstallCancelledEvent(ModelEventBase):
     """Event model for model_install_cancelled"""
 
@@ -508,11 +601,14 @@ class ModelInstallCancelledEvent(ModelEventBase):
     source: str = Field(description="Source of the model; local path, repo_id or url")
 
     @classmethod
-    def build(cls, job: "ModelInstallJob") -> "ModelInstallCancelledEvent":
-        return cls(id=job.id, source=str(job.source))
+    def build(cls, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> "ModelInstallCancelledEvent":
+        return cls(
+            id=job.id,
+            source=str(job.source),
+            extra=extra,
+        )
 
 
-@payload_schema.register
 class ModelInstallErrorEvent(ModelEventBase):
     """Event model for model_install_error"""
 
@@ -524,10 +620,16 @@ class ModelInstallErrorEvent(ModelEventBase):
     error: str = Field(description="A text description of the exception")
 
     @classmethod
-    def build(cls, job: "ModelInstallJob") -> "ModelInstallErrorEvent":
+    def build(cls, job: "ModelInstallJob", extra: Optional[ExtraData] = None) -> "ModelInstallErrorEvent":
         assert job.error_type is not None
         assert job.error is not None
-        return cls(id=job.id, source=str(job.source), error_type=job.error_type, error=job.error)
+        return cls(
+            id=job.id,
+            source=str(job.source),
+            error_type=job.error_type,
+            error=job.error,
+            extra=extra,
+        )
 
 
 class BulkDownloadEventBase(EventBase):
@@ -538,7 +640,6 @@ class BulkDownloadEventBase(EventBase):
     bulk_download_item_name: str = Field(description="The name of the bulk image download item")
 
 
-@payload_schema.register
 class BulkDownloadStartedEvent(BulkDownloadEventBase):
     """Event model for bulk_download_started"""
 
@@ -546,16 +647,20 @@ class BulkDownloadStartedEvent(BulkDownloadEventBase):
 
     @classmethod
     def build(
-        cls, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
+        cls,
+        bulk_download_id: str,
+        bulk_download_item_id: str,
+        bulk_download_item_name: str,
+        extra: Optional[ExtraData] = None,
     ) -> "BulkDownloadStartedEvent":
         return cls(
             bulk_download_id=bulk_download_id,
             bulk_download_item_id=bulk_download_item_id,
             bulk_download_item_name=bulk_download_item_name,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class BulkDownloadCompleteEvent(BulkDownloadEventBase):
     """Event model for bulk_download_complete"""
 
@@ -563,16 +668,20 @@ class BulkDownloadCompleteEvent(BulkDownloadEventBase):
 
     @classmethod
     def build(
-        cls, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
+        cls,
+        bulk_download_id: str,
+        bulk_download_item_id: str,
+        bulk_download_item_name: str,
+        extra: Optional[ExtraData] = None,
     ) -> "BulkDownloadCompleteEvent":
         return cls(
             bulk_download_id=bulk_download_id,
             bulk_download_item_id=bulk_download_item_id,
             bulk_download_item_name=bulk_download_item_name,
+            extra=extra,
         )
 
 
-@payload_schema.register
 class BulkDownloadErrorEvent(BulkDownloadEventBase):
     """Event model for bulk_download_error"""
 
@@ -582,11 +691,17 @@ class BulkDownloadErrorEvent(BulkDownloadEventBase):
 
     @classmethod
     def build(
-        cls, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str, error: str
+        cls,
+        bulk_download_id: str,
+        bulk_download_item_id: str,
+        bulk_download_item_name: str,
+        error: str,
+        extra: Optional[ExtraData] = None,
     ) -> "BulkDownloadErrorEvent":
         return cls(
             bulk_download_id=bulk_download_id,
             bulk_download_item_id=bulk_download_item_id,
             bulk_download_item_name=bulk_download_item_name,
             error=error,
+            extra=extra,
         )

invokeai/app/services/events/events_fastapievents.py

@@ -36,7 +36,6 @@ class FastAPIEventService(EventServiceBase):
                 event = self._queue.get(block=False)
                 if not event:  # Probably stopping
                     continue
-                # Leave the payloads as live pydantic models
                 dispatch(event, middleware_id=self.event_handler_id, payload_schema_dump=False)
 
             except Empty:

invokeai/app/services/model_load/model_load_default.py

@@ -72,6 +72,6 @@ class ModelLoadService(ModelLoadServiceBase):
         ).load_model(model_config, submodel_type)
 
         if hasattr(self, "_invoker"):
-            self._invoker.services.events.emit_model_load_complete(model_config, submodel_type)
+            self._invoker.services.events.emit_model_load_started(model_config, submodel_type)
 
         return loaded_model

invokeai/app/services/session_processor/session_processor_base.py

@@ -1,49 +1,6 @@
 from abc import ABC, abstractmethod
-from threading import Event
-from typing import Optional, Protocol
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
-from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
-from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
-from invokeai.app.util.profiler import Profiler
-
-
-class SessionRunnerBase(ABC):
-    """
-    Base class for session runner.
-    """
-
-    @abstractmethod
-    def start(self, services: InvocationServices, cancel_event: Event, profiler: Optional[Profiler] = None) -> None:
-        """Starts the session runner.
-
-        Args:
-            services: The invocation services.
-            cancel_event: The cancel event.
-            profiler: The profiler to use for session profiling via cProfile. Omit to disable profiling. Basic session
-                stats will be still be recorded and logged when profiling is disabled.
-        """
-        pass
-
-    @abstractmethod
-    def run(self, queue_item: SessionQueueItem) -> None:
-        """Runs a session.
-
-        Args:
-            queue_item: The session to run.
-        """
-        pass
-
-    @abstractmethod
-    def run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem) -> None:
-        """Run a single node in the graph.
-
-        Args:
-            invocation: The invocation to run.
-            queue_item: The session queue item.
-        """
-        pass
 
 
 class SessionProcessorBase(ABC):
@@ -69,85 +26,3 @@ class SessionProcessorBase(ABC):
     def get_status(self) -> SessionProcessorStatus:
         """Gets the status of the session processor"""
         pass
-
-
-class OnBeforeRunNode(Protocol):
-    def __call__(self, invocation: BaseInvocation, queue_item: SessionQueueItem) -> None:
-        """Callback to run before executing a node.
-
-        Args:
-            invocation: The invocation that will be executed.
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnAfterRunNode(Protocol):
-    def __call__(self, invocation: BaseInvocation, queue_item: SessionQueueItem, output: BaseInvocationOutput) -> None:
-        """Callback to run before executing a node.
-
-        Args:
-            invocation: The invocation that was executed.
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnNodeError(Protocol):
-    def __call__(
-        self,
-        invocation: BaseInvocation,
-        queue_item: SessionQueueItem,
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> None:
-        """Callback to run when a node has an error.
-
-        Args:
-            invocation: The invocation that errored.
-            queue_item: The session queue item.
-            error_type: The type of error, e.g. "ValueError".
-            error_message: The error message, e.g. "Invalid value".
-            error_traceback: The stringified error traceback.
-        """
-        ...
-
-
-class OnBeforeRunSession(Protocol):
-    def __call__(self, queue_item: SessionQueueItem) -> None:
-        """Callback to run before executing a session.
-
-        Args:
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnAfterRunSession(Protocol):
-    def __call__(self, queue_item: SessionQueueItem) -> None:
-        """Callback to run after executing a session.
-
-        Args:
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnNonFatalProcessorError(Protocol):
-    def __call__(
-        self,
-        queue_item: Optional[SessionQueueItem],
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> None:
-        """Callback to run when a non-fatal error occurs in the processor.
-
-        Args:
-            queue_item: The session queue item, if one was being executed when the error occurred.
-            error_type: The type of error, e.g. "ValueError".
-            error_message: The error message, e.g. "Invalid value".
-            error_traceback: The stringified error traceback.
-        """
-        ...

invokeai/app/services/session_processor/session_processor_default.py

@@ -4,325 +4,29 @@ from threading import BoundedSemaphore, Thread
 from threading import Event as ThreadEvent
 from typing import Optional
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
+from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.events.events_common import (
     BatchEnqueuedEvent,
     FastAPIEvent,
     QueueClearedEvent,
+    QueueEventBase,
     QueueItemStatusChangedEvent,
+    SessionCanceledEvent,
     register_events,
 )
 from invokeai.app.services.invocation_stats.invocation_stats_common import GESStatsNotFoundError
-from invokeai.app.services.session_processor.session_processor_base import (
-    OnAfterRunNode,
-    OnAfterRunSession,
-    OnBeforeRunNode,
-    OnBeforeRunSession,
-    OnNodeError,
-    OnNonFatalProcessorError,
-)
 from invokeai.app.services.session_processor.session_processor_common import CanceledException
-from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem, SessionQueueItemNotFoundError
-from invokeai.app.services.shared.graph import NodeInputError
+from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
 from invokeai.app.services.shared.invocation_context import InvocationContextData, build_invocation_context
 from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
-from .session_processor_base import InvocationServices, SessionProcessorBase, SessionRunnerBase
+from .session_processor_base import SessionProcessorBase
 from .session_processor_common import SessionProcessorStatus
 
 
-class DefaultSessionRunner(SessionRunnerBase):
-    """Processes a single session's invocations."""
-
-    def __init__(
-        self,
-        on_before_run_session_callbacks: Optional[list[OnBeforeRunSession]] = None,
-        on_before_run_node_callbacks: Optional[list[OnBeforeRunNode]] = None,
-        on_after_run_node_callbacks: Optional[list[OnAfterRunNode]] = None,
-        on_node_error_callbacks: Optional[list[OnNodeError]] = None,
-        on_after_run_session_callbacks: Optional[list[OnAfterRunSession]] = None,
-    ):
-        """
-        Args:
-            on_before_run_session_callbacks: Callbacks to run before the session starts.
-            on_before_run_node_callbacks: Callbacks to run before each node starts.
-            on_after_run_node_callbacks: Callbacks to run after each node completes.
-            on_node_error_callbacks: Callbacks to run when a node errors.
-            on_after_run_session_callbacks: Callbacks to run after the session completes.
-        """
-
-        self._on_before_run_session_callbacks = on_before_run_session_callbacks or []
-        self._on_before_run_node_callbacks = on_before_run_node_callbacks or []
-        self._on_after_run_node_callbacks = on_after_run_node_callbacks or []
-        self._on_node_error_callbacks = on_node_error_callbacks or []
-        self._on_after_run_session_callbacks = on_after_run_session_callbacks or []
-
-    def start(self, services: InvocationServices, cancel_event: ThreadEvent, profiler: Optional[Profiler] = None):
-        self._services = services
-        self._cancel_event = cancel_event
-        self._profiler = profiler
-
-    def _is_canceled(self) -> bool:
-        """Check if the cancel event is set. This is also passed to the invocation context builder and called during
-        denoising to check if the session has been canceled."""
-        return self._cancel_event.is_set()
-
-    def run(self, queue_item: SessionQueueItem):
-        # Exceptions raised outside `run_node` are handled by the processor. There is no need to catch them here.
-
-        self._on_before_run_session(queue_item=queue_item)
-
-        # Loop over invocations until the session is complete or canceled
-        while True:
-            try:
-                invocation = queue_item.session.next()
-            # Anything other than a `NodeInputError` is handled as a processor error
-            except NodeInputError as e:
-                error_type = e.__class__.__name__
-                error_message = str(e)
-                error_traceback = traceback.format_exc()
-                self._on_node_error(
-                    invocation=e.node,
-                    queue_item=queue_item,
-                    error_type=error_type,
-                    error_message=error_message,
-                    error_traceback=error_traceback,
-                )
-                break
-
-            if invocation is None or self._is_canceled():
-                break
-
-            self.run_node(invocation, queue_item)
-
-            # The session is complete if all invocations have been run or there is an error on the session.
-            # At this time, the queue item may be canceled, but the object itself here won't be updated yet. We must
-            # use the cancel event to check if the session is canceled.
-            if (
-                queue_item.session.is_complete()
-                or self._is_canceled()
-                or queue_item.status in ["failed", "canceled", "completed"]
-            ):
-                break
-
-        self._on_after_run_session(queue_item=queue_item)
-
-    def run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
-        try:
-            # Any unhandled exception in this scope is an invocation error & will fail the graph
-            with self._services.performance_statistics.collect_stats(invocation, queue_item.session_id):
-                self._on_before_run_node(invocation, queue_item)
-
-                data = InvocationContextData(
-                    invocation=invocation,
-                    source_invocation_id=queue_item.session.prepared_source_mapping[invocation.id],
-                    queue_item=queue_item,
-                )
-                context = build_invocation_context(
-                    data=data,
-                    services=self._services,
-                    is_canceled=self._is_canceled,
-                )
-
-                # Invoke the node
-                output = invocation.invoke_internal(context=context, services=self._services)
-                # Save output and history
-                queue_item.session.complete(invocation.id, output)
-
-                self._on_after_run_node(invocation, queue_item, output)
-
-        except KeyboardInterrupt:
-            # TODO(psyche): This is expected to be caught in the main thread. Do we need to catch this here?
-            pass
-        except CanceledException:
-            # A CanceledException is raised during the denoising step callback if the cancel event is set. We don't need
-            # to do any handling here, and no error should be set - just pass and the cancellation will be handled
-            # correctly in the next iteration of the session runner loop.
-            #
-            # See the comment in the processor's `_on_queue_item_status_changed()` method for more details on how we
-            # handle cancellation.
-            pass
-        except Exception as e:
-            error_type = e.__class__.__name__
-            error_message = str(e)
-            error_traceback = traceback.format_exc()
-            self._on_node_error(
-                invocation=invocation,
-                queue_item=queue_item,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
-
-    def _on_before_run_session(self, queue_item: SessionQueueItem) -> None:
-        """Called before a session is run.
-
-        - Start the profiler if profiling is enabled.
-        - Run any callbacks registered for this event.
-        """
-
-        self._services.logger.debug(
-            f"On before run session: queue item {queue_item.item_id}, session {queue_item.session_id}"
-        )
-
-        # If profiling is enabled, start the profiler
-        if self._profiler is not None:
-            self._profiler.start(profile_id=queue_item.session_id)
-
-        for callback in self._on_before_run_session_callbacks:
-            callback(queue_item=queue_item)
-
-    def _on_after_run_session(self, queue_item: SessionQueueItem) -> None:
-        """Called after a session is run.
-
-        - Stop the profiler if profiling is enabled.
-        - Update the queue item's session object in the database.
-        - If not already canceled or failed, complete the queue item.
-        - Log and reset performance statistics.
-        - Run any callbacks registered for this event.
-        """
-
-        self._services.logger.debug(
-            f"On after run session: queue item {queue_item.item_id}, session {queue_item.session_id}"
-        )
-
-        # If we are profiling, stop the profiler and dump the profile & stats
-        if self._profiler is not None:
-            profile_path = self._profiler.stop()
-            stats_path = profile_path.with_suffix(".json")
-            self._services.performance_statistics.dump_stats(
-                graph_execution_state_id=queue_item.session.id, output_path=stats_path
-            )
-
-        try:
-            # Update the queue item with the completed session. If the queue item has been removed from the queue,
-            # we'll get a SessionQueueItemNotFoundError and we can ignore it. This can happen if the queue is cleared
-            # while the session is running.
-            queue_item = self._services.session_queue.set_queue_item_session(queue_item.item_id, queue_item.session)
-
-            # The queue item may have been canceled or failed while the session was running. We should only complete it
-            # if it is not already canceled or failed.
-            if queue_item.status not in ["canceled", "failed"]:
-                queue_item = self._services.session_queue.complete_queue_item(queue_item.item_id)
-
-            # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
-            # we don't care about that - suppress the error.
-            with suppress(GESStatsNotFoundError):
-                self._services.performance_statistics.log_stats(queue_item.session.id)
-                self._services.performance_statistics.reset_stats()
-
-            for callback in self._on_after_run_session_callbacks:
-                callback(queue_item=queue_item)
-        except SessionQueueItemNotFoundError:
-            pass
-
-    def _on_before_run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
-        """Called before a node is run.
-
-        - Emits an invocation started event.
-        - Run any callbacks registered for this event.
-        """
-
-        self._services.logger.debug(
-            f"On before run node: queue item {queue_item.item_id}, session {queue_item.session_id}, node {invocation.id} ({invocation.get_type()})"
-        )
-
-        # Send starting event
-        self._services.events.emit_invocation_started(queue_item=queue_item, invocation=invocation)
-
-        for callback in self._on_before_run_node_callbacks:
-            callback(invocation=invocation, queue_item=queue_item)
-
-    def _on_after_run_node(
-        self, invocation: BaseInvocation, queue_item: SessionQueueItem, output: BaseInvocationOutput
-    ):
-        """Called after a node is run.
-
-        - Emits an invocation complete event.
-        - Run any callbacks registered for this event.
-        """
-
-        self._services.logger.debug(
-            f"On after run node: queue item {queue_item.item_id}, session {queue_item.session_id}, node {invocation.id} ({invocation.get_type()})"
-        )
-
-        # Send complete event on successful runs
-        self._services.events.emit_invocation_complete(invocation=invocation, queue_item=queue_item, output=output)
-
-        for callback in self._on_after_run_node_callbacks:
-            callback(invocation=invocation, queue_item=queue_item, output=output)
-
-    def _on_node_error(
-        self,
-        invocation: BaseInvocation,
-        queue_item: SessionQueueItem,
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ):
-        """Called when a node errors. Node errors may occur when running or preparing the node..
-
-        - Set the node error on the session object.
-        - Log the error.
-        - Fail the queue item.
-        - Emits an invocation error event.
-        - Run any callbacks registered for this event.
-        """
-
-        self._services.logger.debug(
-            f"On node error: queue item {queue_item.item_id}, session {queue_item.session_id}, node {invocation.id} ({invocation.get_type()})"
-        )
-
-        # Node errors do not get the full traceback. Only the queue item gets the full traceback.
-        node_error = f"{error_type}: {error_message}"
-        queue_item.session.set_node_error(invocation.id, node_error)
-        self._services.logger.error(
-            f"Error while invoking session {queue_item.session_id}, invocation {invocation.id} ({invocation.get_type()}): {error_message}"
-        )
-        self._services.logger.error(error_traceback)
-
-        # Fail the queue item
-        queue_item = self._services.session_queue.set_queue_item_session(queue_item.item_id, queue_item.session)
-        queue_item = self._services.session_queue.fail_queue_item(
-            queue_item.item_id, error_type, error_message, error_traceback
-        )
-
-        # Send error event
-        self._services.events.emit_invocation_error(
-            queue_item=queue_item,
-            invocation=invocation,
-            error_type=error_type,
-            error_message=error_message,
-            error_traceback=error_traceback,
-        )
-
-        for callback in self._on_node_error_callbacks:
-            callback(
-                invocation=invocation,
-                queue_item=queue_item,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
-
-
 class DefaultSessionProcessor(SessionProcessorBase):
-    def __init__(
-        self,
-        session_runner: Optional[SessionRunnerBase] = None,
-        on_non_fatal_processor_error_callbacks: Optional[list[OnNonFatalProcessorError]] = None,
-        thread_limit: int = 1,
-        polling_interval: int = 1,
-    ) -> None:
-        super().__init__()
-
-        self.session_runner = session_runner if session_runner else DefaultSessionRunner()
-        self._on_non_fatal_processor_error_callbacks = on_non_fatal_processor_error_callbacks or []
-        self._thread_limit = thread_limit
-        self._polling_interval = polling_interval
-
-    def start(self, invoker: Invoker) -> None:
+    def start(self, invoker: Invoker, thread_limit: int = 1, polling_interval: int = 1) -> None:
         self._invoker: Invoker = invoker
         self._queue_item: Optional[SessionQueueItem] = None
         self._invocation: Optional[BaseInvocation] = None
@@ -332,11 +36,9 @@ class DefaultSessionProcessor(SessionProcessorBase):
         self._poll_now_event = ThreadEvent()
         self._cancel_event = ThreadEvent()
 
-        register_events(QueueClearedEvent, self._on_queue_cleared)
-        register_events(BatchEnqueuedEvent, self._on_batch_enqueued)
-        register_events(QueueItemStatusChangedEvent, self._on_queue_item_status_changed)
-
-        self._thread_semaphore = BoundedSemaphore(self._thread_limit)
+        self._thread_limit = thread_limit
+        self._thread_semaphore = BoundedSemaphore(thread_limit)
+        self._polling_interval = polling_interval
 
         # If profiling is enabled, create a profiler. The same profiler will be used for all sessions. Internally,
         # the profiler will create a new profile for each session.
@@ -350,7 +52,8 @@ class DefaultSessionProcessor(SessionProcessorBase):
             else None
         )
 
-        self.session_runner.start(services=invoker.services, cancel_event=self._cancel_event, profiler=self._profiler)
+        register_events({SessionCanceledEvent, QueueClearedEvent, BatchEnqueuedEvent}, self._on_queue_event)
+
         self._thread = Thread(
             name="session_processor",
             target=self._process,
@@ -369,25 +72,25 @@ class DefaultSessionProcessor(SessionProcessorBase):
     def _poll_now(self) -> None:
         self._poll_now_event.set()
 
-    async def _on_queue_cleared(self, event: FastAPIEvent[QueueClearedEvent]) -> None:
-        if self._queue_item and self._queue_item.queue_id == event[1].queue_id:
+    async def _on_queue_event(self, event: FastAPIEvent[QueueEventBase]) -> None:
+        _event_name, payload = event
+        if (
+            isinstance(payload, SessionCanceledEvent)
+            and self._queue_item
+            and self._queue_item.item_id == payload.item_id
+        ):
             self._cancel_event.set()
             self._poll_now()
-
-    async def _on_batch_enqueued(self, event: FastAPIEvent[BatchEnqueuedEvent]) -> None:
-        self._poll_now()
-
-    async def _on_queue_item_status_changed(self, event: FastAPIEvent[QueueItemStatusChangedEvent]) -> None:
-        if self._queue_item and event[1].status in ["completed", "failed", "canceled"]:
-            # When the queue item is canceled via HTTP, the queue item status is set to `"canceled"` and this event is
-            # emitted. We need to respond to this event and stop graph execution. This is done by setting the cancel
-            # event, which the session runner checks between invocations. If set, the session runner loop is broken.
-            #
-            # Long-running nodes that cannot be interrupted easily present a challenge. `denoise_latents` is one such
-            # node, but it gets a step callback, called on each step of denoising. This callback checks if the queue item
-            # is canceled, and if it is, raises a `CanceledException` to stop execution immediately.
-            if event[1].status == "canceled":
-                self._cancel_event.set()
+        elif (
+            isinstance(payload, QueueClearedEvent)
+            and self._queue_item
+            and self._queue_item.queue_id == payload.queue_id
+        ):
+            self._cancel_event.set()
+            self._poll_now()
+        elif isinstance(payload, BatchEnqueuedEvent):
+            self._poll_now()
+        elif isinstance(payload, QueueItemStatusChangedEvent) and payload.status in ("completed", "failed", "canceled"):
             self._poll_now()
 
     def resume(self) -> SessionProcessorStatus:
@@ -413,8 +116,8 @@ class DefaultSessionProcessor(SessionProcessorBase):
         resume_event: ThreadEvent,
         cancel_event: ThreadEvent,
     ):
+        # Outermost processor try block; any unhandled exception is a fatal processor error
         try:
-            # Any unhandled exception in this block is a fatal processor error and will stop the processor.
             self._thread_semaphore.acquire()
             stop_event.clear()
             resume_event.set()
@@ -422,8 +125,8 @@ class DefaultSessionProcessor(SessionProcessorBase):
 
             while not stop_event.is_set():
                 poll_now_event.clear()
+                # Middle processor try block; any unhandled exception is a non-fatal processor error
                 try:
-                    # Any unhandled exception in this block is a nonfatal processor error and will be handled.
                     # If we are paused, wait for resume event
                     resume_event.wait()
 
@@ -436,72 +139,140 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         poll_now_event.wait(self._polling_interval)
                         continue
 
+                    self._invoker.services.events.emit_session_started(self._queue_item)
                     self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
                     cancel_event.clear()
 
-                    # Run the graph
-                    self.session_runner.run(queue_item=self._queue_item)
+                    # If profiling is enabled, start the profiler
+                    if self._profiler is not None:
+                        self._profiler.start(profile_id=self._queue_item.session_id)
 
-                except Exception as e:
-                    error_type = e.__class__.__name__
-                    error_message = str(e)
-                    error_traceback = traceback.format_exc()
-                    self._on_non_fatal_processor_error(
-                        queue_item=self._queue_item,
-                        error_type=error_type,
-                        error_message=error_message,
-                        error_traceback=error_traceback,
+                    # Prepare invocations and take the first
+                    self._invocation = self._queue_item.session.next()
+
+                    # Loop over invocations until the session is complete or canceled
+                    while self._invocation is not None and not cancel_event.is_set():
+                        # get the source node id to provide to clients (the prepared node id is not as useful)
+                        source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
+                        self._invoker.services.events.emit_invocation_started(self._queue_item, self._invocation)
+
+                        # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
+                        try:
+                            with self._invoker.services.performance_statistics.collect_stats(
+                                self._invocation, self._queue_item.session.id
+                            ):
+                                # Build invocation context (the node-facing API)
+                                data = InvocationContextData(
+                                    invocation=self._invocation,
+                                    source_invocation_id=source_invocation_id,
+                                    queue_item=self._queue_item,
+                                )
+                                context = build_invocation_context(
+                                    data=data,
+                                    services=self._invoker.services,
+                                    cancel_event=self._cancel_event,
+                                )
+
+                                # Invoke the node
+                                outputs = self._invocation.invoke_internal(
+                                    context=context, services=self._invoker.services
+                                )
+
+                                # Save outputs and history
+                                self._queue_item.session.complete(self._invocation.id, outputs)
+
+                                self._invoker.services.events.emit_invocation_complete(
+                                    self._queue_item, self._invocation, outputs
+                                )
+
+                        except KeyboardInterrupt:
+                            # TODO(MM2): I don't think this is ever raised...
+                            pass
+
+                        except CanceledException:
+                            # When the user cancels the graph, we first set the cancel event. The event is checked
+                            # between invocations, in this loop. Some invocations are long-running, and we need to
+                            # be able to cancel them mid-execution.
+                            #
+                            # For example, denoising is a long-running invocation with many steps. A step callback
+                            # is executed after each step. This step callback checks if the canceled event is set,
+                            # then raises a CanceledException to stop execution immediately.
+                            #
+                            # When we get a CanceledException, we don't need to do anything - just pass and let the
+                            # loop go to its next iteration, and the cancel event will be handled correctly.
+                            pass
+
+                        except Exception as e:
+                            error = traceback.format_exc()
+
+                            # Save error
+                            self._queue_item.session.set_node_error(self._invocation.id, error)
+                            self._invoker.services.logger.error(
+                                f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
+                            )
+                            self._invoker.services.logger.error(error)
+
+                            self._invoker.services.events.emit_invocation_error(
+                                queue_item=self._queue_item,
+                                invocation=self._invocation,
+                                error_type=e.__class__.__name__,
+                                error=error,
+                            )
+                            pass
+
+                        # The session is complete if the all invocations are complete or there was an error
+                        if self._queue_item.session.is_complete() or cancel_event.is_set():
+                            self._invoker.services.session_queue.set_queue_item_session(
+                                self._queue_item.item_id, self._queue_item.session
+                            )
+                            self._invoker.services.events.emit_session_complete(self._queue_item)
+                            # If we are profiling, stop the profiler and dump the profile & stats
+                            if self._profiler:
+                                profile_path = self._profiler.stop()
+                                stats_path = profile_path.with_suffix(".json")
+                                self._invoker.services.performance_statistics.dump_stats(
+                                    graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
+                                )
+                            # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
+                            # we don't care about that - suppress the error.
+                            with suppress(GESStatsNotFoundError):
+                                self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
+                                self._invoker.services.performance_statistics.reset_stats()
+
+                            # Set the invocation to None to prepare for the next session
+                            self._invocation = None
+                        else:
+                            # Prepare the next invocation
+                            self._invocation = self._queue_item.session.next()
+                    else:
+                        # The queue was empty, wait for next polling interval or event to try again
+                        self._invoker.services.logger.debug("Waiting for next polling interval or event")
+                        poll_now_event.wait(self._polling_interval)
+                        continue
+                except Exception:
+                    # Non-fatal error in processor
+                    self._invoker.services.logger.error(
+                        f"Non-fatal error in session processor:\n{traceback.format_exc()}"
                     )
-                    # Wait for next polling interval or event to try again
+                    # Cancel the queue item
+                    if self._queue_item is not None:
+                        self._invoker.services.session_queue.set_queue_item_session(
+                            self._queue_item.item_id, self._queue_item.session
+                        )
+                        self._invoker.services.session_queue.cancel_queue_item(
+                            self._queue_item.item_id, error=traceback.format_exc()
+                        )
+                    # Reset the invocation to None to prepare for the next session
+                    self._invocation = None
+                    # Immediately poll for next queue item
                     poll_now_event.wait(self._polling_interval)
                     continue
-        except Exception as e:
+        except Exception:
             # Fatal error in processor, log and pass - we're done here
-            error_type = e.__class__.__name__
-            error_message = str(e)
-            error_traceback = traceback.format_exc()
-            self._invoker.services.logger.error(f"Fatal Error in session processor {error_type}: {error_message}")
-            self._invoker.services.logger.error(error_traceback)
+            self._invoker.services.logger.error(f"Fatal Error in session processor:\n{traceback.format_exc()}")
             pass
         finally:
             stop_event.clear()
             poll_now_event.clear()
             self._queue_item = None
             self._thread_semaphore.release()
-
-    def _on_non_fatal_processor_error(
-        self,
-        queue_item: Optional[SessionQueueItem],
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> None:
-        """Called when a non-fatal error occurs in the processor.
-
-        - Log the error.
-        - If a queue item is provided, update the queue item with the completed session & fail it.
-        - Run any callbacks registered for this event.
-        """
-
-        self._invoker.services.logger.error(f"Non-fatal error in session processor {error_type}: {error_message}")
-        self._invoker.services.logger.error(error_traceback)
-
-        if queue_item is not None:
-            # Update the queue item with the completed session & fail it
-            queue_item = self._invoker.services.session_queue.set_queue_item_session(
-                queue_item.item_id, queue_item.session
-            )
-            queue_item = self._invoker.services.session_queue.fail_queue_item(
-                item_id=queue_item.item_id,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
-
-        for callback in self._on_non_fatal_processor_error_callbacks:
-            callback(
-                queue_item=queue_item,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )

invokeai/app/services/session_queue/session_queue_base.py

@@ -74,22 +74,10 @@ class SessionQueueBase(ABC):
         pass
 
     @abstractmethod
-    def complete_queue_item(self, item_id: int) -> SessionQueueItem:
-        """Completes a session queue item"""
-        pass
-
-    @abstractmethod
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
         """Cancels a session queue item"""
         pass
 
-    @abstractmethod
-    def fail_queue_item(
-        self, item_id: int, error_type: str, error_message: str, error_traceback: str
-    ) -> SessionQueueItem:
-        """Fails a session queue item"""
-        pass
-
     @abstractmethod
     def cancel_by_batch_ids(self, queue_id: str, batch_ids: list[str]) -> CancelByBatchIDsResult:
         """Cancels all queue items with matching batch IDs"""

invokeai/app/services/session_queue/session_queue_common.py

@@ -3,16 +3,7 @@ import json
 from itertools import chain, product
 from typing import Generator, Iterable, Literal, NamedTuple, Optional, TypeAlias, Union, cast
 
-from pydantic import (
-    AliasChoices,
-    BaseModel,
-    ConfigDict,
-    Field,
-    StrictStr,
-    TypeAdapter,
-    field_validator,
-    model_validator,
-)
+from pydantic import BaseModel, ConfigDict, Field, StrictStr, TypeAdapter, field_validator, model_validator
 from pydantic_core import to_jsonable_python
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
@@ -198,13 +189,7 @@ class SessionQueueItemWithoutGraph(BaseModel):
     session_id: str = Field(
         description="The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed."
     )
-    error_type: Optional[str] = Field(default=None, description="The error type if this queue item errored")
-    error_message: Optional[str] = Field(default=None, description="The error message if this queue item errored")
-    error_traceback: Optional[str] = Field(
-        default=None,
-        description="The error traceback if this queue item errored",
-        validation_alias=AliasChoices("error_traceback", "error"),
-    )
+    error: Optional[str] = Field(default=None, description="The error message if this queue item errored")
     created_at: Union[datetime.datetime, str] = Field(description="When this queue item was created")
     updated_at: Union[datetime.datetime, str] = Field(description="When this queue item was updated")
     started_at: Optional[Union[datetime.datetime, str]] = Field(description="When this queue item was started")

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -2,6 +2,13 @@ import sqlite3
 import threading
 from typing import Optional, Union, cast
 
+from invokeai.app.services.events.events_common import (
+    FastAPIEvent,
+    InvocationErrorEvent,
+    SessionCanceledEvent,
+    SessionCompleteEvent,
+    register_events,
+)
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
 from invokeai.app.services.session_queue.session_queue_common import (
@@ -39,6 +46,10 @@ class SqliteSessionQueue(SessionQueueBase):
         self._set_in_progress_to_canceled()
         prune_result = self.prune(DEFAULT_QUEUE_ID)
 
+        register_events(events={InvocationErrorEvent}, func=self._handle_error_event)
+        register_events(events={SessionCompleteEvent}, func=self._handle_complete_event)
+        register_events(events={SessionCanceledEvent}, func=self._handle_cancel_event)
+
         if prune_result.deleted > 0:
             self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
@@ -48,6 +59,36 @@ class SqliteSessionQueue(SessionQueueBase):
         self.__conn = db.conn
         self.__cursor = self.__conn.cursor()
 
+    async def _handle_complete_event(self, event: FastAPIEvent[SessionCompleteEvent]) -> None:
+        try:
+            # When a queue item has an error, we get an error event, then a completed event.
+            # Mark the queue item completed only if it isn't already marked completed, e.g.
+            # by a previously-handled error event.
+            _event_name, payload = event
+
+            queue_item = self.get_queue_item(payload.item_id)
+            if queue_item.status not in ["completed", "failed", "canceled"]:
+                self._set_queue_item_status(item_id=payload.item_id, status="completed")
+        except SessionQueueItemNotFoundError:
+            pass
+
+    async def _handle_error_event(self, event: FastAPIEvent[InvocationErrorEvent]) -> None:
+        try:
+            _event_name, payload = event
+            # always set to failed if have an error, even if previously the item was marked completed or canceled
+            self._set_queue_item_status(item_id=payload.item_id, status="failed", error=payload.error)
+        except SessionQueueItemNotFoundError:
+            pass
+
+    async def _handle_cancel_event(self, event: FastAPIEvent[SessionCanceledEvent]) -> None:
+        try:
+            _event_name, payload = event
+            queue_item = self.get_queue_item(payload.item_id)
+            if queue_item.status not in ["completed", "failed", "canceled"]:
+                self._set_queue_item_status(item_id=payload.item_id, status="canceled")
+        except SessionQueueItemNotFoundError:
+            pass
+
     def _set_in_progress_to_canceled(self) -> None:
         """
         Sets all in_progress queue items to canceled. Run on app startup, not associated with any queue.
@@ -222,22 +263,17 @@ class SqliteSessionQueue(SessionQueueBase):
         return SessionQueueItem.queue_item_from_dict(dict(result))
 
     def _set_queue_item_status(
-        self,
-        item_id: int,
-        status: QUEUE_ITEM_STATUS,
-        error_type: Optional[str] = None,
-        error_message: Optional[str] = None,
-        error_traceback: Optional[str] = None,
+        self, item_id: int, status: QUEUE_ITEM_STATUS, error: Optional[str] = None
     ) -> SessionQueueItem:
         try:
             self.__lock.acquire()
             self.__cursor.execute(
                 """--sql
                 UPDATE session_queue
-                SET status = ?, error_type = ?, error_message = ?, error_traceback = ?
+                SET status = ?, error = ?
                 WHERE item_id = ?
                 """,
-                (status, error_type, error_message, error_traceback, item_id),
+                (status, error, item_id),
             )
             self.__conn.commit()
         except Exception:
@@ -290,6 +326,26 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return IsFullResult(is_full=is_full)
 
+    def delete_queue_item(self, item_id: int) -> SessionQueueItem:
+        queue_item = self.get_queue_item(item_id=item_id)
+        try:
+            self.__lock.acquire()
+            self.__cursor.execute(
+                """--sql
+                DELETE FROM session_queue
+                WHERE
+                  item_id = ?
+                """,
+                (item_id,),
+            )
+            self.__conn.commit()
+        except Exception:
+            self.__conn.rollback()
+            raise
+        finally:
+            self.__lock.release()
+        return queue_item
+
     def clear(self, queue_id: str) -> ClearResult:
         try:
             self.__lock.acquire()
@@ -356,28 +412,12 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return PruneResult(deleted=count)
 
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
-        queue_item = self._set_queue_item_status(item_id=item_id, status="canceled")
-        return queue_item
-
-    def complete_queue_item(self, item_id: int) -> SessionQueueItem:
-        queue_item = self._set_queue_item_status(item_id=item_id, status="completed")
-        return queue_item
-
-    def fail_queue_item(
-        self,
-        item_id: int,
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> SessionQueueItem:
-        queue_item = self._set_queue_item_status(
-            item_id=item_id,
-            status="failed",
-            error_type=error_type,
-            error_message=error_message,
-            error_traceback=error_traceback,
-        )
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
+        queue_item = self.get_queue_item(item_id)
+        if queue_item.status not in ["canceled", "failed", "completed"]:
+            status = "failed" if error is not None else "canceled"
+            queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error)  # type: ignore [arg-type] # mypy seems to not narrow the Literals here
+            self.__invoker.services.events.emit_session_canceled(queue_item)
         return queue_item
 
     def cancel_by_batch_ids(self, queue_id: str, batch_ids: list[str]) -> CancelByBatchIDsResult:
@@ -413,6 +453,7 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             self.__conn.commit()
             if current_queue_item is not None and current_queue_item.batch_id in batch_ids:
+                self.__invoker.services.events.emit_session_canceled(current_queue_item)
                 batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
                 queue_status = self.get_queue_status(queue_id=queue_id)
                 self.__invoker.services.events.emit_queue_item_status_changed(
@@ -456,6 +497,7 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             self.__conn.commit()
             if current_queue_item is not None and current_queue_item.queue_id == queue_id:
+                self.__invoker.services.events.emit_session_canceled(current_queue_item)
                 batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
                 queue_status = self.get_queue_status(queue_id=queue_id)
                 self.__invoker.services.events.emit_queue_item_status_changed(
@@ -528,9 +570,7 @@ class SqliteSessionQueue(SessionQueueBase):
                     status,
                     priority,
                     field_values,
-                    error_type,
-                    error_message,
-                    error_traceback,
+                    error,
                     created_at,
                     updated_at,
                     completed_at,

invokeai/app/services/shared/graph.py

@@ -2,19 +2,17 @@
 
 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
@@ -192,39 +190,6 @@ class UnknownGraphValidationError(ValueError):
     pass
 
 
-class NodeInputError(ValueError):
-    """Raised when a node fails preparation. This occurs when a node's inputs are being set from its incomers, but an
-    input fails validation.
-
-    Attributes:
-        node: The node that failed preparation. Note: only successfully set fields will be accurate. Review the error to
-            determine which field caused the failure.
-    """
-
-    def __init__(self, node: BaseInvocation, e: ValidationError):
-        self.original_error = e
-        self.node = node
-        # When preparing a node, we set each input one-at-a-time. We may thus safely assume that the first error
-        # represents the first input that failed.
-        self.failed_input = loc_to_dot_sep(e.errors()[0]["loc"])
-        super().__init__(f"Node {node.id} has invalid incoming input for {self.failed_input}")
-
-
-def loc_to_dot_sep(loc: tuple[Union[str, int], ...]) -> str:
-    """Helper to pretty-print pydantic error locations as dot-separated strings.
-    Taken from https://docs.pydantic.dev/latest/errors/errors/#customize-error-messages
-    """
-    path = ""
-    for i, x in enumerate(loc):
-        if isinstance(x, str):
-            if i > 0:
-                path += "."
-            path += x
-        else:
-            path += f"[{x}]"
-    return path
-
-
 @invocation_output("iterate_output")
 class IterateInvocationOutput(BaseInvocationOutput):
     """Used to connect iteration outputs. Will be expanded to a specific output."""
@@ -278,58 +243,73 @@ 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 +740,7 @@ 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 +757,52 @@ 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."""
 
@@ -801,10 +821,7 @@ class GraphExecutionState(BaseModel):
 
         # Get values from edges
         if next_node is not None:
-            try:
-                self._prepare_inputs(next_node)
-            except ValidationError as e:
-                raise NodeInputError(next_node, e)
+            self._prepare_inputs(next_node)
 
         # If next is still none, there's no next node, return None
         return next_node

invokeai/app/services/shared/invocation_context.py

@@ -1,6 +1,7 @@
+import threading
 from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Callable, Optional, Union
+from typing import TYPE_CHECKING, Optional, Union
 
 from PIL.Image import Image
 from torch import Tensor
@@ -448,10 +449,10 @@ class ConfigInterface(InvocationContextInterface):
 
 class UtilInterface(InvocationContextInterface):
     def __init__(
-        self, services: InvocationServices, data: InvocationContextData, is_canceled: Callable[[], bool]
+        self, services: InvocationServices, data: InvocationContextData, cancel_event: threading.Event
     ) -> None:
         super().__init__(services, data)
-        self._is_canceled = is_canceled
+        self._cancel_event = cancel_event
 
     def is_canceled(self) -> bool:
         """Checks if the current session has been canceled.
@@ -459,7 +460,7 @@ class UtilInterface(InvocationContextInterface):
         Returns:
             True if the current session has been canceled, False if not.
         """
-        return self._is_canceled()
+        return self._cancel_event.is_set()
 
     def sd_step_callback(self, intermediate_state: PipelineIntermediateState, base_model: BaseModelType) -> None:
         """
@@ -534,7 +535,7 @@ class InvocationContext:
 def build_invocation_context(
     services: InvocationServices,
     data: InvocationContextData,
-    is_canceled: Callable[[], bool],
+    cancel_event: threading.Event,
 ) -> InvocationContext:
     """Builds the invocation context for a specific invocation execution.
 
@@ -551,7 +552,7 @@ def build_invocation_context(
     tensors = TensorsInterface(services=services, data=data)
     models = ModelsInterface(services=services, data=data)
     config = ConfigInterface(services=services, data=data)
-    util = UtilInterface(services=services, data=data, is_canceled=is_canceled)
+    util = UtilInterface(services=services, data=data, cancel_event=cancel_event)
     conditioning = ConditioningInterface(services=services, data=data)
     boards = BoardsInterface(services=services, data=data)
 

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -12,7 +12,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_6 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_7 import build_migration_7
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_8 import build_migration_8
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_9 import build_migration_9
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_10 import build_migration_10
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -42,7 +41,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_7())
     migrator.register_migration(build_migration_8(app_config=config))
     migrator.register_migration(build_migration_9())
-    migrator.register_migration(build_migration_10())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_10.py

@@ -1,35 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration10Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._update_error_cols(cursor)
-
-    def _update_error_cols(self, cursor: sqlite3.Cursor) -> None:
-        """
-        - Adds `error_type` and `error_message` columns to the session queue table.
-        - Renames the `error` column to `error_traceback`.
-        """
-
-        cursor.execute("ALTER TABLE session_queue ADD COLUMN error_type TEXT;")
-        cursor.execute("ALTER TABLE session_queue ADD COLUMN error_message TEXT;")
-        cursor.execute("ALTER TABLE session_queue RENAME COLUMN error TO error_traceback;")
-
-
-def build_migration_10() -> Migration:
-    """
-    Build the migration from database version 9 to 10.
-
-    This migration does the following:
-    - Adds `error_type` and `error_message` columns to the session queue table.
-    - Renames the `error` column to `error_traceback`.
-    """
-    migration_10 = Migration(
-        from_version=9,
-        to_version=10,
-        callback=Migration10Callback(),
-    )
-
-    return migration_10

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/load/model_cache/model_cache_base.py

@@ -42,26 +42,10 @@ T = TypeVar("T")
 
 @dataclass
 class CacheRecord(Generic[T]):
-    """
-    Elements of the cache:
-
-    key: Unique key for each model, same as used in the models database.
-    model: Model in memory.
-    state_dict: A read-only copy of the model's state dict in RAM. It will be
-                used as a template for creating a copy in the VRAM.
-    size: Size of the model
-    loaded: True if the model's state dict is currently in VRAM
-
-    Before a model is executed, the state_dict template is copied into VRAM,
-    and then injected into the model. When the model is finished, the VRAM
-    copy of the state dict is deleted, and the RAM version is reinjected
-    into the model.
-    """
+    """Elements of the cache."""
 
     key: str
     model: T
-    device: torch.device
-    state_dict: Optional[Dict[str, torch.Tensor]]
     size: int
     loaded: bool = False
     _locks: int = 0

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

@@ -20,6 +20,7 @@ context. Use like this:
 
 import gc
 import math
+import sys
 import time
 from contextlib import suppress
 from logging import Logger
@@ -161,9 +162,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         if key in self._cached_models:
             return
         self.make_room(size)
-
-        state_dict = model.state_dict() if isinstance(model, torch.nn.Module) else None
-        cache_record = CacheRecord(key=key, model=model, device=self.storage_device, state_dict=state_dict, size=size)
+        cache_record = CacheRecord(key, model, size)
         self._cached_models[key] = cache_record
         self._cache_stack.append(key)
 
@@ -258,37 +257,17 @@ class ModelCache(ModelCacheBase[AnyModel]):
         if not (hasattr(cache_entry.model, "device") and hasattr(cache_entry.model, "to")):
             return
 
-        source_device = cache_entry.device
+        source_device = cache_entry.model.device
 
         # Note: We compare device types only so that 'cuda' == 'cuda:0'.
         # This would need to be revised to support multi-GPU.
         if torch.device(source_device).type == torch.device(target_device).type:
             return
 
-        # This roundabout method for moving the model around is done to avoid
-        # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
-        # When moving to VRAM, we copy (not move) each element of the state dict from
-        # RAM to a new state dict in VRAM, and then inject it into the model.
-        # This operation is slightly faster than running `to()` on the whole model.
-        #
-        # When the model needs to be removed from VRAM we simply delete the copy
-        # of the state dict in VRAM, and reinject the state dict that is cached
-        # in RAM into the model. So this operation is very fast.
         start_model_to_time = time.time()
         snapshot_before = self._capture_memory_snapshot()
-
         try:
-            if cache_entry.state_dict is not None:
-                assert hasattr(cache_entry.model, "load_state_dict")
-                if target_device == self.storage_device:
-                    cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
-                else:
-                    new_dict: Dict[str, torch.Tensor] = {}
-                    for k, v in cache_entry.state_dict.items():
-                        new_dict[k] = v.to(torch.device(target_device), copy=True)
-                    cache_entry.model.load_state_dict(new_dict, assign=True)
             cache_entry.model.to(target_device)
-            cache_entry.device = target_device
         except Exception as e:  # blow away cache entry
             self._delete_cache_entry(cache_entry)
             raise e
@@ -368,12 +347,43 @@ class ModelCache(ModelCacheBase[AnyModel]):
         while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
             model_key = self._cache_stack[pos]
             cache_entry = self._cached_models[model_key]
+
+            refs = sys.getrefcount(cache_entry.model)
+
+            # HACK: This is a workaround for a memory-management issue that we haven't tracked down yet. We are directly
+            # going against the advice in the Python docs by using `gc.get_referrers(...)` in this way:
+            # https://docs.python.org/3/library/gc.html#gc.get_referrers
+
+            # manualy clear local variable references of just finished function calls
+            # for some reason python don't want to collect it even by gc.collect() immidiately
+            if refs > 2:
+                while True:
+                    cleared = False
+                    for referrer in gc.get_referrers(cache_entry.model):
+                        if type(referrer).__name__ == "frame":
+                            # RuntimeError: cannot clear an executing frame
+                            with suppress(RuntimeError):
+                                referrer.clear()
+                                cleared = True
+                                # break
+
+                    # repeat if referrers changes(due to frame clear), else exit loop
+                    if cleared:
+                        gc.collect()
+                    else:
+                        break
+
             device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
             self.logger.debug(
-                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}"
+                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded},"
+                f" refs: {refs}"
             )
 
-            if not cache_entry.locked:
+            # Expected refs:
+            # 1 from cache_entry
+            # 1 from getrefcount function
+            # 1 from onnx runtime object
+            if not cache_entry.locked and refs <= (3 if "onnx" in model_key else 2):
                 self.logger.debug(
                     f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
                 )

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/textual_inversion.py

@@ -1,7 +1,7 @@
 """Textual Inversion wrapper class."""
 
 from pathlib import Path
-from typing import Optional, Union
+from typing import Dict, List, Optional, Union
 
 import torch
 from compel.embeddings_provider import BaseTextualInversionManager
@@ -66,52 +66,35 @@ class TextualInversionModelRaw(RawModel):
         return result
 
 
-class TextualInversionManager(BaseTextualInversionManager):
-    """TextualInversionManager implements the BaseTextualInversionManager ABC from the compel library."""
+# no type hints for BaseTextualInversionManager?
+class TextualInversionManager(BaseTextualInversionManager):  # type: ignore
+    pad_tokens: Dict[int, List[int]]
+    tokenizer: CLIPTokenizer
 
     def __init__(self, tokenizer: CLIPTokenizer):
-        self.pad_tokens: dict[int, list[int]] = {}
+        self.pad_tokens = {}
         self.tokenizer = tokenizer
 
     def expand_textual_inversion_token_ids_if_necessary(self, token_ids: list[int]) -> list[int]:
-        """Given a list of tokens ids, expand any TI tokens to their corresponding pad tokens.
-
-        For example, suppose we have a `<ti_dog>` TI with 4 vectors that was added to the tokenizer with the following
-        mapping of tokens to token_ids:
-        ```
-        <ti_dog>: 49408
-        <ti_dog-!pad-1>: 49409
-        <ti_dog-!pad-2>: 49410
-        <ti_dog-!pad-3>: 49411
-        ```
-        `self.pad_tokens` would be set to `{49408: [49408, 49409, 49410, 49411]}`.
-        This function is responsible for expanding `49408` in the token_ids list to `[49408, 49409, 49410, 49411]`.
-        """
-        # Short circuit if there are no pad tokens to save a little time.
         if len(self.pad_tokens) == 0:
             return token_ids
 
-        # This function assumes that compel has not included the BOS and EOS tokens in the token_ids list. We verify
-        # this assumption here.
         if token_ids[0] == self.tokenizer.bos_token_id:
             raise ValueError("token_ids must not start with bos_token_id")
         if token_ids[-1] == self.tokenizer.eos_token_id:
             raise ValueError("token_ids must not end with eos_token_id")
 
-        # Expand any TI tokens to their corresponding pad tokens.
-        new_token_ids: list[int] = []
+        new_token_ids = []
         for token_id in token_ids:
             new_token_ids.append(token_id)
             if token_id in self.pad_tokens:
                 new_token_ids.extend(self.pad_tokens[token_id])
 
-        # Do not exceed the max model input size. The -2 here is compensating for
-        # compel.embeddings_provider.get_token_ids(), which first removes and then adds back the start and end tokens.
-        max_length = self.tokenizer.model_max_length - 2
+        # Do not exceed the max model input size
+        # The -2 here is compensating for compensate compel.embeddings_provider.get_token_ids(),
+        # which first removes and then adds back the start and end tokens.
+        max_length = list(self.tokenizer.max_model_input_sizes.values())[0] - 2
         if len(new_token_ids) > max_length:
-            # HACK: If TI token expansion causes us to exceed the max text encoder input length, we silently discard
-            # tokens. Token expansion should happen in a way that is compatible with compel's default handling of long
-            # prompts.
             new_token_ids = new_token_ids[0:max_length]
 
         return new_token_ids

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

@@ -2,7 +2,6 @@
     "accessibility": {
         "about": "About",
         "createIssue": "Create Issue",
-        "submitSupportTicket": "Submit Support Ticket",
         "invokeProgressBar": "Invoke progress bar",
         "menu": "Menu",
         "mode": "Mode",
@@ -147,11 +146,7 @@
         "viewing": "Viewing",
         "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"
+        "editingDesc": "Edit on the Control Layers canvas"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -377,23 +372,7 @@
         "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",
@@ -918,10 +897,7 @@
         "zoomInNodes": "Zoom In",
         "zoomOutNodes": "Zoom Out",
         "betaDesc": "This invocation is in beta. Until it is stable, it may have breaking changes during app updates. We plan to support this invocation long-term.",
-        "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time.",
-        "imageAccessError": "Unable to find image {{image_name}}, resetting to default",
-        "boardAccessError": "Unable to find board {{board_id}}, resetting to default",
-        "modelAccessError": "Unable to find model {{key}}, resetting to default"
+        "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time."
     },
     "parameters": {
         "aspect": "Aspect",
@@ -1094,9 +1070,8 @@
     },
     "toast": {
         "addedToBoard": "Added to board",
-        "baseModelChanged": "Base Model Changed",
-        "baseModelChangedCleared_one": "Cleared or disabled {{count}} incompatible submodel",
-        "baseModelChangedCleared_other": "Cleared or disabled {{count}} incompatible submodels",
+        "baseModelChangedCleared_one": "Base model changed, cleared or disabled {{count}} incompatible submodel",
+        "baseModelChangedCleared_other": "Base model changed, cleared or disabled {{count}} incompatible submodels",
         "canceled": "Processing Canceled",
         "canvasCopiedClipboard": "Canvas Copied to Clipboard",
         "canvasDownloaded": "Canvas Downloaded",
@@ -1117,17 +1092,10 @@
         "metadataLoadFailed": "Failed to load metadata",
         "modelAddedSimple": "Model Added to Queue",
         "modelImportCanceled": "Model Import Canceled",
-        "outOfMemoryError": "Out of Memory Error",
-        "outOfMemoryErrorDesc": "Your current generation settings exceed system capacity. Please adjust your settings and try again.",
         "parameters": "Parameters",
-        "parameterSet": "Parameter Recalled",
-        "parameterSetDesc": "Recalled {{parameter}}",
-        "parameterNotSet": "Parameter Not Recalled",
-        "parameterNotSetDesc": "Unable to recall {{parameter}}",
-        "parameterNotSetDescWithMessage": "Unable to recall {{parameter}}: {{message}}",
-        "parametersSet": "Parameters Recalled",
-        "parametersNotSet": "Parameters Not Recalled",
-        "errorCopied": "Error Copied",
+        "parameterNotSet": "{{parameter}} not set",
+        "parameterSet": "{{parameter}} set",
+        "parametersNotSet": "Parameters Not Set",
         "problemCopyingCanvas": "Problem Copying Canvas",
         "problemCopyingCanvasDesc": "Unable to export base layer",
         "problemCopyingImage": "Unable to Copy Image",
@@ -1147,13 +1115,11 @@
         "sentToImageToImage": "Sent To Image To Image",
         "sentToUnifiedCanvas": "Sent to Unified Canvas",
         "serverError": "Server Error",
-        "sessionRef": "Session: {{sessionId}}",
         "setAsCanvasInitialImage": "Set as canvas initial image",
         "setCanvasInitialImage": "Set canvas initial image",
         "setControlImage": "Set as control image",
         "setInitialImage": "Set as initial image",
         "setNodeField": "Set as node field",
-        "somethingWentWrong": "Something Went Wrong",
         "uploadFailed": "Upload failed",
         "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "uploadInitialImage": "Upload Initial Image",
@@ -1593,6 +1559,7 @@
         "controlLayers": "Control Layers",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
+        "toggleVisibility": "Toggle Layer Visibility",
         "deletePrompt": "Delete Prompt",
         "resetRegion": "Reset Region",
         "debugLayers": "Debug Layers",

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",
@@ -417,7 +382,7 @@
         "canvasMerged": "Lienzo consolidado",
         "sentToImageToImage": "Enviar hacia Imagen a Imagen",
         "sentToUnifiedCanvas": "Enviar hacia Lienzo Consolidado",
-        "parametersNotSet": "Parámetros no recuperados",
+        "parametersNotSet": "Parámetros no establecidos",
         "metadataLoadFailed": "Error al cargar metadatos",
         "serverError": "Error en el servidor",
         "canceled": "Procesando la cancelación",
@@ -425,8 +390,7 @@
         "uploadFailedInvalidUploadDesc": "Debe ser una sola imagen PNG o JPEG",
         "parameterSet": "Conjunto de parámetros",
         "parameterNotSet": "Parámetro no configurado",
-        "problemCopyingImage": "No se puede copiar la imagen",
-        "errorCopied": "Error al copiar"
+        "problemCopyingImage": "No se puede copiar la imagen"
     },
     "tooltip": {
         "feature": {
@@ -502,8 +466,7 @@
         "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 +542,5 @@
         "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",
@@ -544,20 +524,7 @@
             "missingNodeTemplate": "Modello di nodo mancante",
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} ingresso mancante",
             "missingFieldTemplate": "Modello di campo mancante",
-            "imageNotProcessedForControlAdapter": "L'immagine dell'adattatore di controllo #{{number}} non è stata elaborata",
-            "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",
-                "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",
-                "ipAdapterNoModelSelected": "Nessun adattatore IP selezionato",
-                "ipAdapterIncompatibleBaseModel": "Il modello base dell'adattatore IP non è compatibile",
-                "ipAdapterNoImageSelected": "Nessuna immagine dell'adattatore IP selezionata",
-                "rgNoPromptsOrIPAdapters": "Nessun prompt o adattatore IP",
-                "rgNoRegion": "Nessuna regione selezionata"
-            }
+            "imageNotProcessedForControlAdapter": "L'immagine dell'adattatore di controllo #{{number}} non è stata elaborata"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
@@ -626,25 +593,25 @@
         "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 +645,7 @@
         "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 +661,7 @@
         "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 +732,7 @@
         "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 +777,7 @@
         "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",
@@ -868,8 +824,8 @@
         "unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi",
         "addLinearView": "Aggiungi alla vista Lineare",
         "unknownErrorValidatingWorkflow": "Errore sconosciuto durante la convalida del flusso di lavoro",
-        "collectionFieldType": "{{name}} (Raccolta)",
-        "collectionOrScalarFieldType": "{{name}} (Singola o Raccolta)",
+        "collectionFieldType": "{{name}} Raccolta",
+        "collectionOrScalarFieldType": "{{name}} Raccolta|Scalare",
         "nodeVersion": "Versione Nodo",
         "inputFieldTypeParseError": "Impossibile analizzare il tipo di campo di input {{node}}.{{field}} ({{message}})",
         "unsupportedArrayItemType": "Tipo di elemento dell'array non supportato \"{{type}}\"",
@@ -907,20 +863,11 @@
         "edit": "Modifica",
         "graph": "Grafico",
         "showEdgeLabelsHelp": "Mostra etichette sui collegamenti, che indicano i nodi collegati",
-        "showEdgeLabels": "Mostra le etichette del collegamento",
-        "cannotMixAndMatchCollectionItemTypes": "Impossibile combinare e abbinare i tipi di elementi della raccolta",
-        "noGraph": "Nessun grafico",
-        "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"
+        "showEdgeLabels": "Mostra le etichette del collegamento"
     },
     "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 +879,7 @@
         "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 +900,7 @@
         "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 +911,7 @@
         "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 +927,7 @@
         "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",
@@ -1087,16 +1034,7 @@
         "graphFailedToQueue": "Impossibile mettere in coda il grafico",
         "batchFieldValues": "Valori Campi Lotto",
         "time": "Tempo",
-        "openQueue": "Apri coda",
-        "iterations_one": "Iterazione",
-        "iterations_many": "Iterazioni",
-        "iterations_other": "Iterazioni",
-        "prompts_one": "Prompt",
-        "prompts_many": "Prompt",
-        "prompts_other": "Prompt",
-        "generations_one": "Generazione",
-        "generations_many": "Generazioni",
-        "generations_other": "Generazioni"
+        "openQueue": "Apri coda"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1625,6 +1563,7 @@
         "brushSize": "Dimensioni del pennello",
         "globalMaskOpacity": "Opacità globale della maschera",
         "autoNegative": "Auto Negativo",
+        "toggleVisibility": "Attiva/disattiva la visibilità dei livelli",
         "deletePrompt": "Cancella il prompt",
         "debugLayers": "Debug dei Livelli",
         "rectangle": "Rettangolo",

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

@@ -6,7 +6,7 @@
         "settingsLabel": "Instellingen",
         "img2img": "Afbeelding naar afbeelding",
         "unifiedCanvas": "Centraal canvas",
-        "nodes": "Werkstromen",
+        "nodes": "Werkstroom-editor",
         "upload": "Upload",
         "load": "Laad",
         "statusDisconnected": "Niet verbonden",
@@ -34,60 +34,7 @@
         "controlNet": "ControlNet",
         "imageFailedToLoad": "Kan afbeelding niet laden",
         "learnMore": "Meer informatie",
-        "advanced": "Uitgebreid",
-        "file": "Bestand",
-        "installed": "Geïnstalleerd",
-        "notInstalled": "Niet $t(common.installed)",
-        "simple": "Eenvoudig",
-        "somethingWentWrong": "Er ging iets mis",
-        "add": "Voeg toe",
-        "checkpoint": "Checkpoint",
-        "details": "Details",
-        "outputs": "Uitvoeren",
-        "save": "Bewaar",
-        "nextPage": "Volgende pagina",
-        "blue": "Blauw",
-        "alpha": "Alfa",
-        "red": "Rood",
-        "editor": "Editor",
-        "folder": "Map",
-        "format": "structuur",
-        "goTo": "Ga naar",
-        "template": "Sjabloon",
-        "input": "Invoer",
-        "loglevel": "Logboekniveau",
-        "safetensors": "Safetensors",
-        "saveAs": "Bewaar als",
-        "created": "Gemaakt",
-        "green": "Groen",
-        "tab": "Tab",
-        "positivePrompt": "Positieve prompt",
-        "negativePrompt": "Negatieve prompt",
-        "selected": "Geselecteerd",
-        "orderBy": "Sorteer op",
-        "prevPage": "Vorige pagina",
-        "beta": "Bèta",
-        "copyError": "$t(gallery.copy) Fout",
-        "toResolve": "Op te lossen",
-        "aboutDesc": "Gebruik je Invoke voor het werk? Kijk dan naar:",
-        "aboutHeading": "Creatieve macht voor jou",
-        "copy": "Kopieer",
-        "data": "Gegevens",
-        "or": "of",
-        "updated": "Bijgewerkt",
-        "outpaint": "outpainten",
-        "viewing": "Bekijken",
-        "viewingDesc": "Beoordeel afbeelding in een grote galerijweergave",
-        "editing": "Bewerken",
-        "editingDesc": "Bewerk op het canvas Stuurlagen",
-        "ai": "ai",
-        "inpaint": "inpainten",
-        "unknown": "Onbekend",
-        "delete": "Verwijder",
-        "direction": "Richting",
-        "error": "Fout",
-        "localSystem": "Lokaal systeem",
-        "unknownError": "Onbekende fout"
+        "advanced": "Uitgebreid"
     },
     "gallery": {
         "galleryImageSize": "Afbeeldingsgrootte",
@@ -363,41 +310,10 @@
         "modelSyncFailed": "Synchronisatie modellen mislukt",
         "modelDeleteFailed": "Model kon niet verwijderd worden",
         "convertingModelBegin": "Model aan het converteren. Even geduld.",
-        "predictionType": "Soort voorspelling",
+        "predictionType": "Soort voorspelling (voor Stable Diffusion 2.x-modellen en incidentele Stable Diffusion 1.x-modellen)",
         "advanced": "Uitgebreid",
         "modelType": "Soort model",
-        "vaePrecision": "Nauwkeurigheid VAE",
-        "loraTriggerPhrases": "LoRA-triggerzinnen",
-        "urlOrLocalPathHelper": "URL's zouden moeten wijzen naar een los bestand. Lokale paden kunnen wijzen naar een los bestand of map voor een individueel Diffusers-model.",
-        "modelName": "Modelnaam",
-        "path": "Pad",
-        "triggerPhrases": "Triggerzinnen",
-        "typePhraseHere": "Typ zin hier in",
-        "useDefaultSettings": "Gebruik standaardinstellingen",
-        "modelImageDeleteFailed": "Fout bij verwijderen modelafbeelding",
-        "modelImageUpdated": "Modelafbeelding bijgewerkt",
-        "modelImageUpdateFailed": "Fout bij bijwerken modelafbeelding",
-        "noMatchingModels": "Geen overeenkomende modellen",
-        "scanPlaceholder": "Pad naar een lokale map",
-        "noModelsInstalled": "Geen modellen geïnstalleerd",
-        "noModelsInstalledDesc1": "Installeer modellen met de",
-        "noModelSelected": "Geen model geselecteerd",
-        "starterModels": "Beginnermodellen",
-        "textualInversions": "Tekstuele omkeringen",
-        "upcastAttention": "Upcast-aandacht",
-        "uploadImage": "Upload afbeelding",
-        "mainModelTriggerPhrases": "Triggerzinnen hoofdmodel",
-        "urlOrLocalPath": "URL of lokaal pad",
-        "scanFolderHelper": "De map zal recursief worden ingelezen voor modellen. Dit kan enige tijd in beslag nemen voor erg grote mappen.",
-        "simpleModelPlaceholder": "URL of pad naar een lokaal pad of Diffusers-map",
-        "modelSettings": "Modelinstellingen",
-        "pathToConfig": "Pad naar configuratie",
-        "prune": "Snoei",
-        "pruneTooltip": "Snoei voltooide importeringen uit wachtrij",
-        "repoVariant": "Repovariant",
-        "scanFolder": "Lees map in",
-        "scanResults": "Resultaten inlezen",
-        "source": "Bron"
+        "vaePrecision": "Nauwkeurigheid VAE"
     },
     "parameters": {
         "images": "Afbeeldingen",
@@ -437,13 +353,13 @@
         "copyImage": "Kopieer afbeelding",
         "denoisingStrength": "Sterkte ontruisen",
         "scheduler": "Planner",
-        "seamlessXAxis": "Naadloze tegels in x-as",
-        "seamlessYAxis": "Naadloze tegels in y-as",
+        "seamlessXAxis": "X-as",
+        "seamlessYAxis": "Y-as",
         "clipSkip": "Overslaan CLIP",
         "negativePromptPlaceholder": "Negatieve prompt",
         "controlNetControlMode": "Aansturingsmodus",
         "positivePromptPlaceholder": "Positieve prompt",
-        "maskBlur": "Vervaging van masker",
+        "maskBlur": "Vervaag",
         "invoke": {
             "noNodesInGraph": "Geen knooppunten in graaf",
             "noModelSelected": "Geen model ingesteld",
@@ -453,25 +369,11 @@
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} invoer ontbreekt",
             "noControlImageForControlAdapter": "Controle-adapter #{{number}} heeft geen controle-afbeelding",
             "noModelForControlAdapter": "Control-adapter #{{number}} heeft geen model ingesteld staan.",
-            "incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is niet compatibel met het hoofdmodel.",
+            "incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is ongeldig in combinatie met het hoofdmodel.",
             "systemDisconnected": "Systeem is niet verbonden",
             "missingNodeTemplate": "Knooppuntsjabloon ontbreekt",
             "missingFieldTemplate": "Veldsjabloon ontbreekt",
-            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan",
-            "layer": {
-                "initialImageNoImageSelected": "geen initiële afbeelding geselecteerd",
-                "controlAdapterNoModelSelected": "geen controle-adaptermodel geselecteerd",
-                "controlAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor controle-adapter",
-                "controlAdapterNoImageSelected": "geen afbeelding voor controle-adapter geselecteerd",
-                "controlAdapterImageNotProcessed": "Afbeelding voor controle-adapter niet verwerkt",
-                "ipAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor IP-adapter",
-                "ipAdapterNoImageSelected": "geen afbeelding voor IP-adapter geselecteerd",
-                "rgNoRegion": "geen gebied geselecteerd",
-                "rgNoPromptsOrIPAdapters": "geen tekstprompts of IP-adapters",
-                "t2iAdapterIncompatibleDimensions": "T2I-adapter vereist een afbeelding met afmetingen met een veelvoud van 64",
-                "ipAdapterNoModelSelected": "geen IP-adapter geselecteerd"
-            },
-            "imageNotProcessedForControlAdapter": "De afbeelding van controle-adapter #{{number}} is niet verwerkt"
+            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Afbeelding is te groot om te vergroten met het x4-model. Gebruik hiervoor het x2-model",
@@ -481,26 +383,7 @@
         "useCpuNoise": "Gebruik CPU-ruis",
         "imageActions": "Afbeeldingshandeling",
         "iterations": "Iteraties",
-        "coherenceMode": "Modus",
-        "infillColorValue": "Vulkleur",
-        "remixImage": "Meng afbeelding opnieuw",
-        "setToOptimalSize": "Optimaliseer grootte voor het model",
-        "setToOptimalSizeTooSmall": "$t(parameters.setToOptimalSize) (is mogelijk te klein)",
-        "aspect": "Beeldverhouding",
-        "infillMosaicTileWidth": "Breedte tegel",
-        "setToOptimalSizeTooLarge": "$t(parameters.setToOptimalSize) (is mogelijk te groot)",
-        "lockAspectRatio": "Zet beeldverhouding vast",
-        "infillMosaicTileHeight": "Hoogte tegel",
-        "globalNegativePromptPlaceholder": "Globale negatieve prompt",
-        "globalPositivePromptPlaceholder": "Globale positieve prompt",
-        "useSize": "Gebruik grootte",
-        "swapDimensions": "Wissel afmetingen om",
-        "globalSettings": "Globale instellingen",
-        "coherenceEdgeSize": "Randgrootte",
-        "coherenceMinDenoise": "Min. ontruising",
-        "infillMosaicMinColor": "Min. kleur",
-        "infillMosaicMaxColor": "Max. kleur",
-        "cfgRescaleMultiplier": "Vermenigvuldiger voor CFG-herschaling"
+        "coherenceMode": "Modus"
     },
     "settings": {
         "models": "Modellen",
@@ -527,12 +410,7 @@
         "intermediatesCleared_one": "{{count}} tussentijdse afbeelding gewist",
         "intermediatesCleared_other": "{{count}} tussentijdse afbeeldingen gewist",
         "clearIntermediatesDesc1": "Als je tussentijdse afbeeldingen wist, dan wordt de staat hersteld van je canvas en van ControlNet.",
-        "intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen",
-        "clearIntermediatesDisabled": "Wachtrij moet leeg zijn om tussentijdse afbeeldingen te kunnen leegmaken",
-        "enableInformationalPopovers": "Schakel informatieve hulpballonnen in",
-        "enableInvisibleWatermark": "Schakel onzichtbaar watermerk in",
-        "enableNSFWChecker": "Schakel NSFW-controle in",
-        "reloadingIn": "Opnieuw laden na"
+        "intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen"
     },
     "toast": {
         "uploadFailed": "Upload mislukt",
@@ -547,8 +425,8 @@
         "connected": "Verbonden met server",
         "canceled": "Verwerking geannuleerd",
         "uploadFailedInvalidUploadDesc": "Moet een enkele PNG- of JPEG-afbeelding zijn",
-        "parameterNotSet": "{{parameter}} niet ingesteld",
-        "parameterSet": "{{parameter}} ingesteld",
+        "parameterNotSet": "Parameter niet ingesteld",
+        "parameterSet": "Instellen parameters",
         "problemCopyingImage": "Kan Afbeelding Niet Kopiëren",
         "baseModelChangedCleared_one": "Basismodel is gewijzigd: {{count}} niet-compatibel submodel weggehaald of uitgeschakeld",
         "baseModelChangedCleared_other": "Basismodel is gewijzigd: {{count}} niet-compatibele submodellen weggehaald of uitgeschakeld",
@@ -565,11 +443,11 @@
         "maskSavedAssets": "Masker bewaard in Assets",
         "problemDownloadingCanvas": "Fout bij downloaden van canvas",
         "problemMergingCanvas": "Fout bij samenvoegen canvas",
-        "setCanvasInitialImage": "Initiële canvasafbeelding ingesteld",
+        "setCanvasInitialImage": "Ingesteld als initiële canvasafbeelding",
         "imageUploaded": "Afbeelding geüpload",
         "addedToBoard": "Toegevoegd aan bord",
         "workflowLoaded": "Werkstroom geladen",
-        "modelAddedSimple": "Model toegevoegd aan wachtrij",
+        "modelAddedSimple": "Model toegevoegd",
         "problemImportingMaskDesc": "Kan masker niet exporteren",
         "problemCopyingCanvas": "Fout bij kopiëren canvas",
         "problemSavingCanvas": "Fout bij bewaren canvas",
@@ -581,18 +459,7 @@
         "maskSentControlnetAssets": "Masker gestuurd naar ControlNet en Assets",
         "canvasSavedGallery": "Canvas bewaard in galerij",
         "imageUploadFailed": "Fout bij uploaden afbeelding",
-        "problemImportingMask": "Fout bij importeren masker",
-        "workflowDeleted": "Werkstroom verwijderd",
-        "invalidUpload": "Ongeldige upload",
-        "uploadInitialImage": "Initiële afbeelding uploaden",
-        "setAsCanvasInitialImage": "Ingesteld als initiële afbeelding voor canvas",
-        "problemRetrievingWorkflow": "Fout bij ophalen van werkstroom",
-        "parameters": "Parameters",
-        "modelImportCanceled": "Importeren model geannuleerd",
-        "problemDeletingWorkflow": "Fout bij verwijderen van werkstroom",
-        "prunedQueue": "Wachtrij gesnoeid",
-        "problemDownloadingImage": "Fout bij downloaden afbeelding",
-        "resetInitialImage": "Initiële afbeelding hersteld"
+        "problemImportingMask": "Fout bij importeren masker"
     },
     "tooltip": {
         "feature": {
@@ -666,11 +533,7 @@
         "showOptionsPanel": "Toon zijscherm",
         "menu": "Menu",
         "showGalleryPanel": "Toon deelscherm Galerij",
-        "loadMore": "Laad meer",
-        "about": "Over",
-        "mode": "Modus",
-        "resetUI": "$t(accessibility.reset) UI",
-        "createIssue": "Maak probleem aan"
+        "loadMore": "Laad meer"
     },
     "nodes": {
         "zoomOutNodes": "Uitzoomen",
@@ -684,7 +547,7 @@
         "loadWorkflow": "Laad werkstroom",
         "downloadWorkflow": "Download JSON van werkstroom",
         "scheduler": "Planner",
-        "missingTemplate": "Ongeldig knooppunt: knooppunt {{node}} van het soort {{type}} heeft een ontbrekend sjabloon (niet geïnstalleerd?)",
+        "missingTemplate": "Ontbrekende sjabloon",
         "workflowDescription": "Korte beschrijving",
         "versionUnknown": " Versie onbekend",
         "noNodeSelected": "Geen knooppunt gekozen",
@@ -700,7 +563,7 @@
         "integer": "Geheel getal",
         "nodeTemplate": "Sjabloon knooppunt",
         "nodeOpacity": "Dekking knooppunt",
-        "unableToLoadWorkflow": "Fout bij laden werkstroom",
+        "unableToLoadWorkflow": "Kan werkstroom niet valideren",
         "snapToGrid": "Lijn uit op raster",
         "noFieldsLinearview": "Geen velden toegevoegd aan lineaire weergave",
         "nodeSearch": "Zoek naar knooppunten",
@@ -751,56 +614,11 @@
         "unknownField": "Onbekend veld",
         "colorCodeEdges": "Kleurgecodeerde randen",
         "unknownNode": "Onbekend knooppunt",
-        "mismatchedVersion": "Ongeldig knooppunt: knooppunt {{node}} van het soort {{type}} heeft een niet-overeenkomende versie (probeer het bij te werken?)",
+        "mismatchedVersion": "Heeft niet-overeenkomende versie",
         "addNodeToolTip": "Voeg knooppunt toe (Shift+A, spatie)",
         "loadingNodes": "Bezig met laden van knooppunten...",
         "snapToGridHelp": "Lijn knooppunten uit op raster bij verplaatsing",
-        "workflowSettings": "Instellingen werkstroomeditor",
-        "addLinearView": "Voeg toe aan lineaire weergave",
-        "nodePack": "Knooppuntpakket",
-        "unknownInput": "Onbekende invoer: {{name}}",
-        "sourceNodeFieldDoesNotExist": "Ongeldige rand: bron-/uitvoerveld {{node}}.{{field}} bestaat niet",
-        "collectionFieldType": "Verzameling {{name}}",
-        "deletedInvalidEdge": "Ongeldige hoek {{source}} -> {{target}} verwijderd",
-        "graph": "Grafiek",
-        "targetNodeDoesNotExist": "Ongeldige rand: doel-/invoerknooppunt {{node}} bestaat niet",
-        "resetToDefaultValue": "Herstel naar standaardwaarden",
-        "editMode": "Bewerk in Werkstroom-editor",
-        "showEdgeLabels": "Toon randlabels",
-        "showEdgeLabelsHelp": "Toon labels aan randen, waarmee de verbonden knooppunten mee worden aangegeven",
-        "clearWorkflowDesc2": "Je huidige werkstroom heeft niet-bewaarde wijzigingen.",
-        "unableToParseFieldType": "fout bij bepalen soort veld",
-        "sourceNodeDoesNotExist": "Ongeldige rand: bron-/uitvoerknooppunt {{node}} bestaat niet",
-        "unsupportedArrayItemType": "niet-ondersteunde soort van het array-onderdeel \"{{type}}\"",
-        "targetNodeFieldDoesNotExist": "Ongeldige rand: doel-/invoerveld {{node}}.{{field}} bestaat niet",
-        "reorderLinearView": "Herorden lineaire weergave",
-        "newWorkflowDesc": "Een nieuwe werkstroom aanmaken?",
-        "collectionOrScalarFieldType": "Verzameling|scalair {{name}}",
-        "newWorkflow": "Nieuwe werkstroom",
-        "unknownErrorValidatingWorkflow": "Onbekende fout bij valideren werkstroom",
-        "unsupportedAnyOfLength": "te veel union-leden ({{count}})",
-        "unknownOutput": "Onbekende uitvoer: {{name}}",
-        "viewMode": "Gebruik in lineaire weergave",
-        "unableToExtractSchemaNameFromRef": "fout bij het extraheren van de schemanaam via de ref",
-        "unsupportedMismatchedUnion": "niet-overeenkomende soort CollectionOrScalar met basissoorten {{firstType}} en {{secondType}}",
-        "unknownNodeType": "Onbekend soort knooppunt",
-        "edit": "Bewerk",
-        "updateAllNodes": "Werk knooppunten bij",
-        "allNodesUpdated": "Alle knooppunten bijgewerkt",
-        "nodeVersion": "Knooppuntversie",
-        "newWorkflowDesc2": "Je huidige werkstroom heeft niet-bewaarde wijzigingen.",
-        "clearWorkflow": "Maak werkstroom leeg",
-        "clearWorkflowDesc": "Deze werkstroom leegmaken en met een nieuwe beginnen?",
-        "inputFieldTypeParseError": "Fout bij bepalen van het soort invoerveld {{node}}.{{field}} ({{message}})",
-        "outputFieldTypeParseError": "Fout bij het bepalen van het soort uitvoerveld {{node}}.{{field}} ({{message}})",
-        "unableToExtractEnumOptions": "fout bij extraheren enumeratie-opties",
-        "unknownFieldType": "Soort $t(nodes.unknownField): {{type}}",
-        "unableToGetWorkflowVersion": "Fout bij ophalen schemaversie van werkstroom",
-        "betaDesc": "Deze uitvoering is in bèta. Totdat deze stabiel is kunnen er wijzigingen voorkomen gedurende app-updates die zaken kapotmaken. We zijn van plan om deze uitvoering op lange termijn te gaan ondersteunen.",
-        "prototypeDesc": "Deze uitvoering is een prototype. Er kunnen wijzigingen voorkomen gedurende app-updates die zaken kapotmaken. Deze kunnen op een willekeurig moment verwijderd worden.",
-        "noFieldsViewMode": "Deze werkstroom heeft geen geselecteerde velden om te tonen. Bekijk de volledige werkstroom om de waarden te configureren.",
-        "unableToUpdateNodes_one": "Fout bij bijwerken van {{count}} knooppunt",
-        "unableToUpdateNodes_other": "Fout bij bijwerken van {{count}} knooppunten"
+        "workflowSettings": "Instellingen werkstroomeditor"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -873,28 +691,9 @@
         "canny": "Canny",
         "depthZoeDescription": "Genereer diepteblad via Zoe",
         "hedDescription": "Herkenning van holistisch-geneste randen",
-        "setControlImageDimensions": "Kopieer grootte naar B/H (optimaliseer voor model)",
+        "setControlImageDimensions": "Stel afmetingen controle-afbeelding in op B/H",
         "scribble": "Krabbel",
-        "maxFaces": "Max. gezichten",
-        "dwOpenpose": "DW Openpose",
-        "depthAnything": "Depth Anything",
-        "base": "Basis",
-        "hands": "Handen",
-        "selectCLIPVisionModel": "Selecteer een CLIP Vision-model",
-        "modelSize": "Modelgrootte",
-        "small": "Klein",
-        "large": "Groot",
-        "resizeSimple": "Wijzig grootte (eenvoudig)",
-        "beginEndStepPercentShort": "Begin-/eind-%",
-        "depthAnythingDescription": "Genereren dieptekaart d.m.v. de techniek Depth Anything",
-        "face": "Gezicht",
-        "body": "Lichaam",
-        "dwOpenposeDescription": "Schatting menselijke pose d.m.v. DW Openpose",
-        "ipAdapterMethod": "Methode",
-        "full": "Volledig",
-        "style": "Alleen stijl",
-        "composition": "Alleen samenstelling",
-        "setControlImageDimensionsForce": "Kopieer grootte naar B/H (negeer model)"
+        "maxFaces": "Max. gezichten"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -907,10 +706,7 @@
         "maxPrompts": "Max. prompts",
         "promptsWithCount_one": "{{count}} prompt",
         "promptsWithCount_other": "{{count}} prompts",
-        "dynamicPrompts": "Dynamische prompts",
-        "showDynamicPrompts": "Toon dynamische prompts",
-        "loading": "Genereren van dynamische prompts...",
-        "promptsPreview": "Voorvertoning prompts"
+        "dynamicPrompts": "Dynamische prompts"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -923,7 +719,7 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "De planner gebruikt gedurende het genereringsproces."
+                "De planner bepaalt hoe ruis per iteratie wordt toegevoegd aan een afbeelding of hoe een monster wordt bijgewerkt op basis van de uitvoer van een model."
             ],
             "heading": "Planner"
         },
@@ -1010,8 +806,8 @@
         },
         "clipSkip": {
             "paragraphs": [
-                "Aantal over te slaan CLIP-modellagen.",
-                "Bepaalde modellen zijn beter geschikt met bepaalde Overslaan CLIP-instellingen."
+                "Kies hoeveel CLIP-modellagen je wilt overslaan.",
+                "Bepaalde modellen werken beter met bepaalde Overslaan CLIP-instellingen."
             ],
             "heading": "Overslaan CLIP"
         },
@@ -1195,26 +991,17 @@
         "denoisingStrength": "Sterkte ontruising",
         "refinermodel": "Verfijningsmodel",
         "posAestheticScore": "Positieve esthetische score",
-        "concatPromptStyle": "Koppelen van prompt en stijl",
+        "concatPromptStyle": "Plak prompt- en stijltekst aan elkaar",
         "loading": "Bezig met laden...",
         "steps": "Stappen",
-        "posStylePrompt": "Positieve-stijlprompt",
-        "freePromptStyle": "Handmatige stijlprompt",
-        "refinerSteps": "Aantal stappen verfijner"
+        "posStylePrompt": "Positieve-stijlprompt"
     },
     "models": {
         "noMatchingModels": "Geen overeenkomend modellen",
         "loading": "bezig met laden",
         "noMatchingLoRAs": "Geen overeenkomende LoRA's",
         "noModelsAvailable": "Geen modellen beschikbaar",
-        "selectModel": "Kies een model",
-        "noLoRAsInstalled": "Geen LoRA's geïnstalleerd",
-        "noRefinerModelsInstalled": "Geen SDXL-verfijningsmodellen geïnstalleerd",
-        "defaultVAE": "Standaard-VAE",
-        "lora": "LoRA",
-        "esrganModel": "ESRGAN-model",
-        "addLora": "Voeg LoRA toe",
-        "concepts": "Concepten"
+        "selectModel": "Kies een model"
     },
     "boards": {
         "autoAddBoard": "Voeg automatisch bord toe",
@@ -1232,13 +1019,7 @@
         "downloadBoard": "Download bord",
         "changeBoard": "Wijzig bord",
         "loading": "Bezig met laden...",
-        "clearSearch": "Maak zoekopdracht leeg",
-        "deleteBoard": "Verwijder bord",
-        "deleteBoardAndImages": "Verwijder bord en afbeeldingen",
-        "deleteBoardOnly": "Verwijder alleen bord",
-        "deletedBoardsCannotbeRestored": "Verwijderde borden kunnen niet worden hersteld",
-        "movingImagesToBoard_one": "Verplaatsen van {{count}} afbeelding naar bord:",
-        "movingImagesToBoard_other": "Verplaatsen van {{count}} afbeeldingen naar bord:"
+        "clearSearch": "Maak zoekopdracht leeg"
     },
     "invocationCache": {
         "disable": "Schakel uit",
@@ -1255,39 +1036,5 @@
         "clear": "Wis",
         "maxCacheSize": "Max. grootte cache",
         "cacheSize": "Grootte cache"
-    },
-    "accordions": {
-        "generation": {
-            "title": "Genereren"
-        },
-        "image": {
-            "title": "Afbeelding"
-        },
-        "advanced": {
-            "title": "Geavanceerd",
-            "options": "$t(accordions.advanced.title) Opties"
-        },
-        "control": {
-            "title": "Besturing"
-        },
-        "compositing": {
-            "title": "Samenstellen",
-            "coherenceTab": "Coherentiefase",
-            "infillTab": "Invullen"
-        }
-    },
-    "hrf": {
-        "upscaleMethod": "Opschaalmethode",
-        "metadata": {
-            "strength": "Sterkte oplossing voor hoge resolutie",
-            "method": "Methode oplossing voor hoge resolutie",
-            "enabled": "Oplossing voor hoge resolutie ingeschakeld"
-        },
-        "hrf": "Oplossing voor hoge resolutie",
-        "enableHrf": "Schakel oplossing in voor hoge resolutie"
-    },
-    "prompt": {
-        "addPromptTrigger": "Voeg prompttrigger toe",
-        "compatibleEmbeddings": "Compatibele embeddings"
     }
 }

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",
@@ -1656,6 +1594,7 @@
         "deleteAll": "Удалить всё",
         "addLayer": "Добавить слой",
         "moveToFront": "На передний план",
+        "toggleVisibility": "Переключить видимость слоя",
         "addPositivePrompt": "Добавить $t(common.positivePrompt)",
         "addIPAdapter": "Добавить $t(common.ipAdapter)",
         "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",

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/App.tsx

@@ -25,6 +25,7 @@ import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
 import PreselectedImage from './PreselectedImage';
+import Toaster from './Toaster';
 
 const DEFAULT_CONFIG = {};
 
@@ -95,6 +96,7 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
       <DeleteImageModal />
       <ChangeBoardModal />
       <DynamicPromptsModal />
+      <Toaster />
       <PreselectedImage selectedImage={selectedImage} />
     </ErrorBoundary>
   );

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

@@ -1,8 +1,5 @@
-import { Button, Flex, Heading, Image, Link, Text } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { toast } from 'features/toast/toast';
+import { Button, Flex, Heading, Link, Text, useToast } from '@invoke-ai/ui-library';
 import newGithubIssueUrl from 'new-github-issue-url';
-import InvokeLogoYellow from 'public/assets/images/invoke-symbol-ylw-lrg.svg';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowCounterClockwiseBold, PiArrowSquareOutBold, PiCopyBold } from 'react-icons/pi';
@@ -14,39 +11,31 @@ type Props = {
 };
 
 const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
+  const toast = useToast();
   const { t } = useTranslation();
-  const isLocal = useAppSelector((s) => s.config.isLocal);
 
   const handleCopy = useCallback(() => {
     const text = JSON.stringify(serializeError(error), null, 2);
     navigator.clipboard.writeText(`\`\`\`\n${text}\n\`\`\``);
     toast({
-      id: 'ERROR_COPIED',
-      title: t('toast.errorCopied'),
+      title: 'Error Copied',
     });
-  }, [error, t]);
+  }, [error, toast]);
 
-  const url = useMemo(() => {
-    if (isLocal) {
-      return newGithubIssueUrl({
+  const url = useMemo(
+    () =>
+      newGithubIssueUrl({
         user: 'invoke-ai',
         repo: 'InvokeAI',
         template: 'BUG_REPORT.yml',
         title: `[bug]: ${error.name}: ${error.message}`,
-      });
-    } else {
-      return 'https://support.invoke.ai/support/tickets/new';
-    }
-  }, [error.message, error.name, isLocal]);
-
+      }),
+    [error.message, error.name]
+  );
   return (
     <Flex layerStyle="body" w="100vw" h="100vh" alignItems="center" justifyContent="center" p={4}>
       <Flex layerStyle="first" flexDir="column" borderRadius="base" justifyContent="center" gap={8} p={16}>
-        <Flex alignItems="center" gap="2">
-          <Image src={InvokeLogoYellow} alt="invoke-logo" w="24px" h="24px" minW="24px" minH="24px" userSelect="none" />
-          <Heading fontSize="2xl">{t('common.somethingWentWrong')}</Heading>
-        </Flex>
-
+        <Heading>{t('common.somethingWentWrong')}</Heading>
         <Flex
           layerStyle="second"
           px={8}
@@ -68,9 +57,7 @@ const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
             {t('common.copyError')}
           </Button>
           <Link href={url} isExternal>
-            <Button leftIcon={<PiArrowSquareOutBold />}>
-              {isLocal ? t('accessibility.createIssue') : t('accessibility.submitSupportTicket')}
-            </Button>
+            <Button leftIcon={<PiArrowSquareOutBold />}>{t('accessibility.createIssue')}</Button>
           </Link>
         </Flex>
       </Flex>

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/components/Toaster.ts

@@ -0,0 +1,44 @@
+import { useToast } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { addToast, clearToastQueue } from 'features/system/store/systemSlice';
+import type { MakeToastArg } from 'features/system/util/makeToast';
+import { makeToast } from 'features/system/util/makeToast';
+import { memo, useCallback, useEffect } from 'react';
+
+/**
+ * Logical component. Watches the toast queue and makes toasts when the queue is not empty.
+ * @returns null
+ */
+const Toaster = () => {
+  const dispatch = useAppDispatch();
+  const toastQueue = useAppSelector((s) => s.system.toastQueue);
+  const toast = useToast();
+  useEffect(() => {
+    toastQueue.forEach((t) => {
+      toast(t);
+    });
+    toastQueue.length > 0 && dispatch(clearToastQueue());
+  }, [dispatch, toast, toastQueue]);
+
+  return null;
+};
+
+/**
+ * Returns a function that can be used to make a toast.
+ * @example
+ * const toaster = useAppToaster();
+ * toaster('Hello world!');
+ * toaster({ title: 'Hello world!', status: 'success' });
+ * @returns A function that can be used to make a toast.
+ * @see makeToast
+ * @see MakeToastArg
+ * @see UseToastOptions
+ */
+export const useAppToaster = () => {
+  const dispatch = useAppDispatch();
+  const toaster = useCallback((arg: MakeToastArg) => dispatch(addToast(makeToast(arg))), [dispatch]);
+
+  return toaster;
+};
+
+export default memo(Toaster);

invokeai/frontend/web/src/app/hooks/useSocketIO.ts

@@ -6,8 +6,8 @@ import { useAppDispatch } from 'app/store/storeHooks';
 import type { MapStore } from 'nanostores';
 import { atom, map } from 'nanostores';
 import { useEffect, useMemo } from 'react';
-import { setEventListeners } from 'services/events/setEventListeners';
 import type { ClientToServerEvents, ServerToClientEvents } from 'services/events/types';
+import { setEventListeners } from 'services/events/util/setEventListeners';
 import type { ManagerOptions, Socket, SocketOptions } from 'socket.io-client';
 import { io } from 'socket.io-client';
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -39,6 +39,7 @@ import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMidd
 import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected';
 import { addSocketDisconnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketDisconnected';
 import { addGeneratorProgressEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketGeneratorProgress';
+import { addGraphExecutionStateCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketGraphExecutionStateComplete';
 import { addInvocationCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete';
 import { addInvocationErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationError';
 import { addInvocationStartedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted';
@@ -98,6 +99,7 @@ addCommitStagingAreaImageListener(startAppListening);
 
 // Socket.IO
 addGeneratorProgressEventListener(startAppListening);
+addGraphExecutionStateCompleteEventListener(startAppListening);
 addInvocationCompleteEventListener(startAppListening);
 addInvocationErrorEventListener(startAppListening);
 addInvocationStartedEventListener(startAppListening);

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

@@ -8,7 +8,7 @@ import {
   resetCanvas,
   setInitialCanvasImage,
 } from 'features/canvas/store/canvasSlice';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 
@@ -30,20 +30,22 @@ export const addCommitStagingAreaImageListener = (startAppListening: AppStartLis
         req.reset();
         if (canceled > 0) {
           log.debug(`Canceled ${canceled} canvas batches`);
-          toast({
-            id: 'CANCEL_BATCH_SUCCEEDED',
-            title: t('queue.cancelBatchSucceeded'),
-            status: 'success',
-          });
+          dispatch(
+            addToast({
+              title: t('queue.cancelBatchSucceeded'),
+              status: 'success',
+            })
+          );
         }
         dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
-        toast({
-          id: 'CANCEL_BATCH_FAILED',
-          title: t('queue.cancelBatchFailed'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('queue.cancelBatchFailed'),
+            status: 'error',
+          })
+        );
       }
     },
   });

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

@@ -1,8 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
+import { toast } from 'common/util/toast';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
-import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { truncate, upperFirst } from 'lodash-es';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -16,15 +16,18 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
       const arg = action.meta.arg.originalArgs;
       logger('queue').debug({ enqueueResult: parseify(response) }, 'Batch enqueued');
 
-      toast({
-        id: 'QUEUE_BATCH_SUCCEEDED',
-        title: t('queue.batchQueued'),
-        status: 'success',
-        description: t('queue.batchQueuedDesc', {
-          count: response.enqueued,
-          direction: arg.prepend ? t('queue.front') : t('queue.back'),
-        }),
-      });
+      if (!toast.isActive('batch-queued')) {
+        toast({
+          id: 'batch-queued',
+          title: t('queue.batchQueued'),
+          description: t('queue.batchQueuedDesc', {
+            count: response.enqueued,
+            direction: arg.prepend ? t('queue.front') : t('queue.back'),
+          }),
+          duration: 1000,
+          status: 'success',
+        });
+      }
     },
   });
 
@@ -37,10 +40,9 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
 
       if (!response) {
         toast({
-          id: 'QUEUE_BATCH_FAILED',
           title: t('queue.batchFailedToQueue'),
           status: 'error',
-          description: t('common.unknownError'),
+          description: 'Unknown Error',
         });
         logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
         return;
@@ -50,7 +52,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
       if (result.success) {
         result.data.data.detail.map((e) => {
           toast({
-            id: 'QUEUE_BATCH_FAILED',
+            id: 'batch-failed-to-queue',
             title: truncate(upperFirst(e.msg), { length: 128 }),
             status: 'error',
             description: truncate(
@@ -62,10 +64,9 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
         });
       } else if (response.status !== 403) {
         toast({
-          id: 'QUEUE_BATCH_FAILED',
           title: t('queue.batchFailedToQueue'),
-          status: 'error',
           description: t('common.unknownError'),
+          status: 'error',
         });
       }
       logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/bulkDownload.tsx

@@ -1,7 +1,8 @@
+import type { UseToastOptions } from '@invoke-ai/ui-library';
 import { ExternalLink } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { toast } from 'features/toast/toast';
+import { toast } from 'common/util/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 import {
@@ -27,6 +28,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
         // Show the response message if it exists, otherwise show the default message
         description: action.payload.response || t('gallery.bulkDownloadRequestedDesc'),
         duration: null,
+        isClosable: true,
       });
     },
   });
@@ -38,9 +40,9 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
       // There isn't any toast to update if we get this event.
       toast({
-        id: 'BULK_DOWNLOAD_REQUEST_FAILED',
         title: t('gallery.bulkDownloadRequestFailed'),
-        status: 'error',
+        status: 'success',
+        isClosable: true,
       });
     },
   });
@@ -63,7 +65,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
       // TODO(psyche): This URL may break in in some environments (e.g. Nvidia workbench) but we need to test it first
       const url = `/api/v1/images/download/${bulk_download_item_name}`;
 
-      toast({
+      const toastOptions: UseToastOptions = {
         id: bulk_download_item_name,
         title: t('gallery.bulkDownloadReady', 'Download ready'),
         status: 'success',
@@ -75,7 +77,14 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
           />
         ),
         duration: null,
-      });
+        isClosable: true,
+      };
+
+      if (toast.isActive(bulk_download_item_name)) {
+        toast.update(bulk_download_item_name, toastOptions);
+      } else {
+        toast(toastOptions);
+      }
     },
   });
 
@@ -86,13 +95,20 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
       const { bulk_download_item_name } = action.payload.data;
 
-      toast({
+      const toastOptions: UseToastOptions = {
         id: bulk_download_item_name,
         title: t('gallery.bulkDownloadFailed'),
         status: 'error',
         description: action.payload.data.error,
         duration: null,
-      });
+        isClosable: true,
+      };
+
+      if (toast.isActive(bulk_download_item_name)) {
+        toast.update(bulk_download_item_name, toastOptions);
+      } else {
+        toast(toastOptions);
+      }
     },
   });
 };

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

@@ -2,14 +2,14 @@ import { $logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
 import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
-import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 
 export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: canvasCopiedToClipboard,
-    effect: async (action, { getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
       const state = getState();
 
@@ -19,20 +19,22 @@ export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartLi
         copyBlobToClipboard(blob);
       } catch (err) {
         moduleLog.error(String(err));
-        toast({
-          id: 'CANVAS_COPY_FAILED',
-          title: t('toast.problemCopyingCanvas'),
-          description: t('toast.problemCopyingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemCopyingCanvas'),
+            description: t('toast.problemCopyingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
-      toast({
-        id: 'CANVAS_COPY_SUCCEEDED',
-        title: t('toast.canvasCopiedClipboard'),
-        status: 'success',
-      });
+      dispatch(
+        addToast({
+          title: t('toast.canvasCopiedClipboard'),
+          status: 'success',
+        })
+      );
     },
   });
 };

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

@@ -3,13 +3,13 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
 import { downloadBlob } from 'features/canvas/util/downloadBlob';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 
 export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: canvasDownloadedAsImage,
-    effect: async (action, { getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const moduleLog = $logger.get().child({ namespace: 'canvasSavedToGalleryListener' });
       const state = getState();
 
@@ -18,17 +18,18 @@ export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartLi
         blob = await getBaseLayerBlob(state);
       } catch (err) {
         moduleLog.error(String(err));
-        toast({
-          id: 'CANVAS_DOWNLOAD_FAILED',
-          title: t('toast.problemDownloadingCanvas'),
-          description: t('toast.problemDownloadingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemDownloadingCanvas'),
+            description: t('toast.problemDownloadingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
       downloadBlob(blob, 'canvas.png');
-      toast({ id: 'CANVAS_DOWNLOAD_SUCCEEDED', title: t('toast.canvasDownloaded'), status: 'success' });
+      dispatch(addToast({ title: t('toast.canvasDownloaded'), status: 'success' }));
     },
   });
 };

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

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasImageToControlAdapter } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
 import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -20,12 +20,13 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
         blob = await getBaseLayerBlob(state, true);
       } catch (err) {
         log.error(String(err));
-        toast({
-          id: 'PROBLEM_SAVING_CANVAS',
-          title: t('toast.problemSavingCanvas'),
-          description: t('toast.problemSavingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemSavingCanvas'),
+            description: t('toast.problemSavingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -42,7 +43,7 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
           crop_visible: false,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.canvasSentControlnetAssets'),
+            toastOptions: { title: t('toast.canvasSentControlnetAssets') },
           },
         })
       ).unwrap();

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

@@ -2,7 +2,7 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { canvasMaskSavedToGallery } from 'features/canvas/store/actions';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -29,12 +29,13 @@ export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartL
 
       if (!maskBlob) {
         log.error('Problem getting mask layer blob');
-        toast({
-          id: 'PROBLEM_SAVING_MASK',
-          title: t('toast.problemSavingMask'),
-          description: t('toast.problemSavingMaskDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemSavingMask'),
+            description: t('toast.problemSavingMaskDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -51,7 +52,7 @@ export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartL
           crop_visible: true,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.maskSavedAssets'),
+            toastOptions: { title: t('toast.maskSavedAssets') },
           },
         })
       );

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

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasMaskToControlAdapter } from 'features/canvas/store/actions';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
 import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -30,12 +30,13 @@ export const addCanvasMaskToControlNetListener = (startAppListening: AppStartLis
 
       if (!maskBlob) {
         log.error('Problem getting mask layer blob');
-        toast({
-          id: 'PROBLEM_IMPORTING_MASK',
-          title: t('toast.problemImportingMask'),
-          description: t('toast.problemImportingMaskDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemImportingMask'),
+            description: t('toast.problemImportingMaskDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -52,7 +53,7 @@ export const addCanvasMaskToControlNetListener = (startAppListening: AppStartLis
           crop_visible: false,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.maskSentControlnetAssets'),
+            toastOptions: { title: t('toast.maskSentControlnetAssets') },
           },
         })
       ).unwrap();

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

@@ -4,7 +4,7 @@ import { canvasMerged } from 'features/canvas/store/actions';
 import { $canvasBaseLayer } from 'features/canvas/store/canvasNanostore';
 import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
 import { getFullBaseLayerBlob } from 'features/canvas/util/getFullBaseLayerBlob';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -17,12 +17,13 @@ export const addCanvasMergedListener = (startAppListening: AppStartListening) =>
 
       if (!blob) {
         moduleLog.error('Problem getting base layer blob');
-        toast({
-          id: 'PROBLEM_MERGING_CANVAS',
-          title: t('toast.problemMergingCanvas'),
-          description: t('toast.problemMergingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemMergingCanvas'),
+            description: t('toast.problemMergingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -30,12 +31,13 @@ export const addCanvasMergedListener = (startAppListening: AppStartListening) =>
 
       if (!canvasBaseLayer) {
         moduleLog.error('Problem getting canvas base layer');
-        toast({
-          id: 'PROBLEM_MERGING_CANVAS',
-          title: t('toast.problemMergingCanvas'),
-          description: t('toast.problemMergingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemMergingCanvas'),
+            description: t('toast.problemMergingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -52,7 +54,7 @@ export const addCanvasMergedListener = (startAppListening: AppStartListening) =>
           is_intermediate: true,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.canvasMerged'),
+            toastOptions: { title: t('toast.canvasMerged') },
           },
         })
       ).unwrap();

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

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { parseify } from 'common/util/serialize';
 import { canvasSavedToGallery } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -19,12 +19,13 @@ export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListe
         blob = await getBaseLayerBlob(state);
       } catch (err) {
         log.error(String(err));
-        toast({
-          id: 'CANVAS_SAVE_FAILED',
-          title: t('toast.problemSavingCanvas'),
-          description: t('toast.problemSavingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemSavingCanvas'),
+            description: t('toast.problemSavingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -41,7 +42,7 @@ export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListe
           crop_visible: true,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.canvasSavedGallery'),
+            toastOptions: { title: t('toast.canvasSavedGallery') },
           },
           metadata: {
             _canvas_objects: parseify(state.canvas.layerState.objects),

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

@@ -13,7 +13,8 @@ import {
   isControlAdapterLayer,
 } from 'features/controlLayers/store/controlLayersSlice';
 import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
-import { toast } from 'features/toast/toast';
+import { isImageOutput } from 'features/nodes/types/common';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { isEqual } from 'lodash-es';
 import { getImageDTO } from 'services/api/endpoints/images';
@@ -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;
@@ -173,11 +174,12 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
             }
           }
 
-          toast({
-            id: 'GRAPH_QUEUE_FAILED',
-            title: t('queue.graphFailedToQueue'),
-            status: 'error',
-          });
+          dispatch(
+            addToast({
+              title: t('queue.graphFailedToQueue'),
+              status: 'error',
+            })
+          );
         }
       } finally {
         req.reset();

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

@@ -9,7 +9,8 @@ import {
   selectControlAdapterById,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
-import { toast } from 'features/toast/toast';
+import { isImageOutput } from 'features/nodes/types/common';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -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
@@ -107,11 +108,12 @@ export const addControlNetImageProcessedListener = (startAppListening: AppStartL
           }
         }
 
-        toast({
-          id: 'GRAPH_QUEUE_FAILED',
-          title: t('queue.graphFailedToQueue'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('queue.graphFailedToQueue'),
+            status: 'error',
+          })
+        );
       }
     },
   });

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/imageUploaded.ts

@@ -1,3 +1,4 @@
+import type { UseToastOptions } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
@@ -13,7 +14,7 @@ import {
 } from 'features/controlLayers/store/controlLayersSlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
@@ -41,17 +42,16 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
-      const DEFAULT_UPLOADED_TOAST = {
-        id: 'IMAGE_UPLOADED',
+      const DEFAULT_UPLOADED_TOAST: UseToastOptions = {
         title: t('toast.imageUploaded'),
         status: 'success',
-      } as const;
+      };
 
       // default action - just upload and alert user
       if (postUploadAction?.type === 'TOAST') {
+        const { toastOptions } = postUploadAction;
         if (!autoAddBoardId || autoAddBoardId === 'none') {
-          const title = postUploadAction.title || DEFAULT_UPLOADED_TOAST.title;
-          toast({ ...DEFAULT_UPLOADED_TOAST, title });
+          dispatch(addToast({ ...DEFAULT_UPLOADED_TOAST, ...toastOptions }));
         } else {
           // Add this image to the board
           dispatch(
@@ -70,20 +70,24 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
             ? `${t('toast.addedToBoard')} ${board.board_name}`
             : `${t('toast.addedToBoard')} ${autoAddBoardId}`;
 
-          toast({
-            ...DEFAULT_UPLOADED_TOAST,
-            description,
-          });
+          dispatch(
+            addToast({
+              ...DEFAULT_UPLOADED_TOAST,
+              description,
+            })
+          );
         }
         return;
       }
 
       if (postUploadAction?.type === 'SET_CANVAS_INITIAL_IMAGE') {
         dispatch(setInitialCanvasImage(imageDTO, selectOptimalDimension(state)));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setAsCanvasInitialImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setAsCanvasInitialImage'),
+          })
+        );
         return;
       }
 
@@ -101,56 +105,68 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
             controlImage: imageDTO.image_name,
           })
         );
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
         return;
       }
 
       if (postUploadAction?.type === 'SET_CA_LAYER_IMAGE') {
         const { layerId } = postUploadAction;
         dispatch(caLayerImageChanged({ layerId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
       }
 
       if (postUploadAction?.type === 'SET_IPA_LAYER_IMAGE') {
         const { layerId } = postUploadAction;
         dispatch(ipaLayerImageChanged({ layerId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
       }
 
       if (postUploadAction?.type === 'SET_RG_LAYER_IP_ADAPTER_IMAGE') {
         const { layerId, ipAdapterId } = postUploadAction;
         dispatch(rgLayerIPAdapterImageChanged({ layerId, ipAdapterId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
       }
 
       if (postUploadAction?.type === 'SET_II_LAYER_IMAGE') {
         const { layerId } = postUploadAction;
         dispatch(iiLayerImageChanged({ layerId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
       }
 
       if (postUploadAction?.type === 'SET_NODES_IMAGE') {
         const { nodeId, fieldName } = postUploadAction;
         dispatch(fieldImageValueChanged({ nodeId, fieldName, value: imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: `${t('toast.setNodeField')} ${fieldName}`,
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: `${t('toast.setNodeField')} ${fieldName}`,
+          })
+        );
         return;
       }
     },
@@ -158,7 +174,7 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
 
   startAppListening({
     matcher: imagesApi.endpoints.uploadImage.matchRejected,
-    effect: (action) => {
+    effect: (action, { dispatch }) => {
       const log = logger('images');
       const sanitizedData = {
         arg: {
@@ -167,11 +183,13 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         },
       };
       log.error({ ...sanitizedData }, 'Image upload failed');
-      toast({
-        title: t('toast.imageUploadFailed'),
-        description: action.error.message,
-        status: 'error',
-      });
+      dispatch(
+        addToast({
+          title: t('toast.imageUploadFailed'),
+          description: action.error.message,
+          status: 'error',
+        })
+      );
     },
   });
 };

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

@@ -8,7 +8,8 @@ import { loraRemoved } from 'features/lora/store/loraSlice';
 import { modelSelected } from 'features/parameters/store/actions';
 import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
 import { zParameterModel } from 'features/parameters/types/parameterSchemas';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
 import { forEach } from 'lodash-es';
 
@@ -59,14 +60,16 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
         });
 
         if (modelsCleared > 0) {
-          toast({
-            id: 'BASE_MODEL_CHANGED',
-            title: t('toast.baseModelChanged'),
-            description: t('toast.baseModelChangedCleared', {
-              count: modelsCleared,
-            }),
-            status: 'warning',
-          });
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('toast.baseModelChangedCleared', {
+                  count: modelsCleared,
+                }),
+                status: 'warning',
+              })
+            )
+          );
         }
       }
 

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

@@ -19,7 +19,8 @@ import {
   isParameterWidth,
   zParameterVAEModel,
 } from 'features/parameters/types/parameterSchemas';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
 import { modelConfigsAdapterSelectors, modelsApi } from 'services/api/endpoints/models';
 import { isNonRefinerMainModelConfig } from 'services/api/types';
@@ -108,7 +109,7 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
           }
         }
 
-        toast({ id: 'PARAMETER_SET', title: t('toast.parameterSet', { parameter: 'Default settings' }) });
+        dispatch(addToast(makeToast({ title: t('toast.parameterSet', { parameter: 'Default settings' }) })));
       }
     },
   });

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

@@ -1,7 +1,6 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { deepClone } from 'common/util/deepClone';
-import { parseify } from 'common/util/serialize';
 import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketGeneratorProgress } from 'services/events/actions';
@@ -12,7 +11,7 @@ export const addGeneratorProgressEventListener = (startAppListening: AppStartLis
   startAppListening({
     actionCreator: socketGeneratorProgress,
     effect: (action) => {
-      log.trace(parseify(action.payload), `Generator progress`);
+      log.trace(action.payload, `Generator progress`);
       const { invocation_source_id, step, total_steps, progress_image } = action.payload.data;
       const nes = deepClone($nodeExecutionStates.get()[invocation_source_id]);
       if (nes) {

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

@@ -0,0 +1,14 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { socketGraphExecutionStateComplete } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addGraphExecutionStateCompleteEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketGraphExecutionStateComplete,
+    effect: (action) => {
+      log.debug(action.payload, 'Session complete');
+    },
+  });
+};

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 { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
+import { isImageOutput } from 'features/nodes/types/common';
 import { zNodeStatus } from 'features/nodes/types/invocation';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
 import { boardsApi } from 'services/api/endpoints/boards';
@@ -28,11 +29,11 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
     actionCreator: socketInvocationComplete,
     effect: async (action, { dispatch, getState }) => {
       const { data } = action.payload;
-      log.debug({ data: parseify(data) }, `Invocation complete (${data.invocation.type})`);
+      log.debug({ data: parseify(data) }, `Invocation complete (${data.invocation_type})`);
 
       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/socketio/socketInvocationError.ts

@@ -1,7 +1,6 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { deepClone } from 'common/util/deepClone';
-import { parseify } from 'common/util/serialize';
 import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketInvocationError } from 'services/events/actions';
@@ -12,18 +11,14 @@ export const addInvocationErrorEventListener = (startAppListening: AppStartListe
   startAppListening({
     actionCreator: socketInvocationError,
     effect: (action) => {
-      const { invocation_source_id, invocation, error_type, error_message, error_traceback } = action.payload.data;
-      log.error(parseify(action.payload), `Invocation error (${invocation.type})`);
+      log.error(action.payload, `Invocation error (${action.payload.data.invocation_type})`);
+      const { invocation_source_id } = action.payload.data;
       const nes = deepClone($nodeExecutionStates.get()[invocation_source_id]);
       if (nes) {
         nes.status = zNodeStatus.enum.FAILED;
+        nes.error = action.payload.data.error;
         nes.progress = null;
         nes.progressImage = null;
-        nes.error = {
-          error_type,
-          error_message,
-          error_traceback,
-        };
         upsertExecutionState(nes.nodeId, nes);
       }
     },

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

@@ -1,7 +1,6 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { deepClone } from 'common/util/deepClone';
-import { parseify } from 'common/util/serialize';
 import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketInvocationStarted } from 'services/events/actions';
@@ -12,7 +11,7 @@ export const addInvocationStartedEventListener = (startAppListening: AppStartLis
   startAppListening({
     actionCreator: socketInvocationStarted,
     effect: (action) => {
-      log.debug(parseify(action.payload), `Invocation started (${action.payload.data.invocation.type})`);
+      log.debug(action.payload, `Invocation started (${action.payload.data.invocation_type})`);
       const { invocation_source_id } = action.payload.data;
       const nes = deepClone($nodeExecutionStates.get()[invocation_source_id]);
       if (nes) {

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

@@ -3,8 +3,6 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { deepClone } from 'common/util/deepClone';
 import { $nodeExecutionStates } from 'features/nodes/hooks/useExecutionState';
 import { zNodeStatus } from 'features/nodes/types/invocation';
-import ErrorToastDescription, { getTitleFromErrorType } from 'features/toast/ErrorToastDescription';
-import { toast } from 'features/toast/toast';
 import { forEach } from 'lodash-es';
 import { queueApi, queueItemsAdapter } from 'services/api/endpoints/queue';
 import { socketQueueItemStatusChanged } from 'services/events/actions';
@@ -14,21 +12,10 @@ const log = logger('socketio');
 export const addSocketQueueItemStatusChangedEventListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: socketQueueItemStatusChanged,
-    effect: async (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch }) => {
       // we've got new status for the queue item, batch and queue
-      const {
-        item_id,
-        session_id,
-        status,
-        started_at,
-        updated_at,
-        completed_at,
-        batch_status,
-        queue_status,
-        error_type,
-        error_message,
-        error_traceback,
-      } = action.payload.data;
+      const { item_id, status, started_at, updated_at, error, completed_at, batch_status, queue_status } =
+        action.payload.data;
 
       log.debug(action.payload, `Queue item ${item_id} status updated: ${status}`);
 
@@ -41,10 +28,8 @@ export const addSocketQueueItemStatusChangedEventListener = (startAppListening:
               status,
               started_at,
               updated_at: updated_at ?? undefined,
+              error,
               completed_at: completed_at ?? undefined,
-              error_type,
-              error_message,
-              error_traceback,
             },
           });
         })
@@ -76,7 +61,7 @@ export const addSocketQueueItemStatusChangedEventListener = (startAppListening:
         ])
       );
 
-      if (status === 'in_progress') {
+      if (['in_progress'].includes(action.payload.data.status)) {
         forEach($nodeExecutionStates.get(), (nes) => {
           if (!nes) {
             return;
@@ -89,25 +74,6 @@ export const addSocketQueueItemStatusChangedEventListener = (startAppListening:
           clone.outputs = [];
           $nodeExecutionStates.setKey(clone.nodeId, clone);
         });
-      } else if (status === 'failed' && error_type) {
-        const isLocal = getState().config.isLocal ?? true;
-        const sessionId = session_id;
-
-        toast({
-          id: `INVOCATION_ERROR_${error_type}`,
-          title: getTitleFromErrorType(error_type),
-          status: 'error',
-          duration: null,
-          updateDescription: isLocal,
-          description: (
-            <ErrorToastDescription
-              errorType={error_type}
-              errorMessage={error_message}
-              sessionId={sessionId}
-              isLocal={isLocal}
-            />
-          ),
-        });
       }
     },
   });

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

@@ -1,6 +1,6 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { stagingAreaImageSaved } from 'features/canvas/store/actions';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -29,14 +29,15 @@ export const addStagingAreaImageSavedListener = (startAppListening: AppStartList
             })
           );
         }
-        toast({ id: 'IMAGE_SAVED', title: t('toast.imageSaved'), status: 'success' });
+        dispatch(addToast({ title: t('toast.imageSaved'), status: 'success' }));
       } catch (error) {
-        toast({
-          id: 'IMAGE_SAVE_FAILED',
-          title: t('toast.imageSavingFailed'),
-          description: (error as Error)?.message,
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.imageSavingFailed'),
+            description: (error as Error)?.message,
+            status: 'error',
+          })
+        );
       }
     },
   });

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

@@ -5,7 +5,8 @@ import { $templates, nodesChanged } from 'features/nodes/store/nodesSlice';
 import { NodeUpdateError } from 'features/nodes/types/error';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { getNeedsUpdate, updateNode } from 'features/nodes/util/node/nodeUpdate';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
 
 export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartListening) => {
@@ -49,18 +50,24 @@ export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartLi
             count: unableToUpdateCount,
           })
         );
-        toast({
-          id: 'UNABLE_TO_UPDATE_NODES',
-          title: t('nodes.unableToUpdateNodes', {
-            count: unableToUpdateCount,
-          }),
-        });
+        dispatch(
+          addToast(
+            makeToast({
+              title: t('nodes.unableToUpdateNodes', {
+                count: unableToUpdateCount,
+              }),
+            })
+          )
+        );
       } else {
-        toast({
-          id: 'ALL_NODES_UPDATED',
-          title: t('nodes.allNodesUpdated'),
-          status: 'success',
-        });
+        dispatch(
+          addToast(
+            makeToast({
+              title: t('nodes.allNodesUpdated'),
+              status: 'success',
+            })
+          )
+        );
       }
     },
   });

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

@@ -4,7 +4,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { parseify } from 'common/util/serialize';
 import { buildAdHocUpscaleGraph } from 'features/nodes/util/graph/buildAdHocUpscaleGraph';
 import { createIsAllowedToUpscaleSelector } from 'features/parameters/hooks/useIsAllowedToUpscale';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { BatchConfig, ImageDTO } from 'services/api/types';
@@ -29,11 +29,12 @@ export const addUpscaleRequestedListener = (startAppListening: AppStartListening
           { imageDTO },
           t(detailTKey ?? 'parameters.isAllowedToUpscale.tooLarge') // should never coalesce
         );
-        toast({
-          id: 'NOT_ALLOWED_TO_UPSCALE',
-          title: t(detailTKey ?? 'parameters.isAllowedToUpscale.tooLarge'), // should never coalesce
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t(detailTKey ?? 'parameters.isAllowedToUpscale.tooLarge'), // should never coalesce
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -64,11 +65,12 @@ export const addUpscaleRequestedListener = (startAppListening: AppStartListening
         if (error instanceof Object && 'status' in error && error.status === 403) {
           return;
         } else {
-          toast({
-            id: 'GRAPH_QUEUE_FAILED',
-            title: t('queue.graphFailedToQueue'),
-            status: 'error',
-          });
+          dispatch(
+            addToast({
+              title: t('queue.graphFailedToQueue'),
+              status: 'error',
+            })
+          );
         }
       }
     },

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

@@ -3,28 +3,28 @@ 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';
 import { validateWorkflow } from 'features/nodes/util/workflow/validateWorkflow';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
-import { checkBoardAccess, checkImageAccess, checkModelAccess } from 'services/api/hooks/accessChecks';
 import type { GraphAndWorkflowResponse, NonNullableGraph } from 'services/api/types';
 import { z } from 'zod';
 import { fromZodError } from 'zod-validation-error';
 
-const getWorkflow = async (data: GraphAndWorkflowResponse, templates: Templates) => {
+const getWorkflow = (data: GraphAndWorkflowResponse, templates: Templates) => {
   if (data.workflow) {
     // Prefer to load the workflow if it's available - it has more information
     const parsed = JSON.parse(data.workflow);
-    return await validateWorkflow(parsed, templates, checkImageAccess, checkBoardAccess, checkModelAccess);
+    return validateWorkflow(parsed, templates);
   } else if (data.graph) {
     // Else we fall back on the graph, using the graphToWorkflow function to convert and do layout
     const parsed = JSON.parse(data.graph);
     const workflow = graphToWorkflow(parsed as NonNullableGraph, true);
-    return await validateWorkflow(workflow, templates, checkImageAccess, checkBoardAccess, checkModelAccess);
+    return validateWorkflow(workflow, templates);
   } else {
     throw new Error('No workflow or graph provided');
   }
@@ -33,13 +33,13 @@ const getWorkflow = async (data: GraphAndWorkflowResponse, templates: Templates)
 export const addWorkflowLoadRequestedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: workflowLoadRequested,
-    effect: async (action, { dispatch }) => {
+    effect: (action, { dispatch }) => {
       const log = logger('nodes');
       const { data, asCopy } = action.payload;
       const nodeTemplates = $templates.get();
 
       try {
-        const { workflow, warnings } = await getWorkflow(data, nodeTemplates);
+        const { workflow, warnings } = getWorkflow(data, nodeTemplates);
 
         if (asCopy) {
           // If we're loading a copy, we need to remove the ID so that the backend will create a new workflow
@@ -48,64 +48,83 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
 
         dispatch(workflowLoaded(workflow));
         if (!warnings.length) {
-          toast({
-            id: 'WORKFLOW_LOADED',
-            title: t('toast.workflowLoaded'),
-            status: 'success',
-          });
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('toast.workflowLoaded'),
+                status: 'success',
+              })
+            )
+          );
         } else {
-          toast({
-            id: 'WORKFLOW_LOADED',
-            title: t('toast.loadedWithWarnings'),
-            status: 'warning',
-          });
-
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('toast.loadedWithWarnings'),
+                status: 'warning',
+              })
+            )
+          );
           warnings.forEach(({ message, ...rest }) => {
             log.warn(rest, message);
           });
         }
 
-        $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
           log.error({ error: parseify(e) }, e.message);
-          toast({
-            id: 'UNABLE_TO_VALIDATE_WORKFLOW',
-            title: t('nodes.unableToValidateWorkflow'),
-            status: 'error',
-            description: e.message,
-          });
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('nodes.unableToValidateWorkflow'),
+                status: 'error',
+                description: e.message,
+              })
+            )
+          );
         } else if (e instanceof WorkflowMigrationError) {
           // There was a problem migrating the workflow to the latest version
           log.error({ error: parseify(e) }, e.message);
-          toast({
-            id: 'UNABLE_TO_VALIDATE_WORKFLOW',
-            title: t('nodes.unableToValidateWorkflow'),
-            status: 'error',
-            description: e.message,
-          });
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('nodes.unableToValidateWorkflow'),
+                status: 'error',
+                description: e.message,
+              })
+            )
+          );
         } else if (e instanceof z.ZodError) {
           // There was a problem validating the workflow itself
           const { message } = fromZodError(e, {
             prefix: t('nodes.workflowValidation'),
           });
           log.error({ error: parseify(e) }, message);
-          toast({
-            id: 'UNABLE_TO_VALIDATE_WORKFLOW',
-            title: t('nodes.unableToValidateWorkflow'),
-            status: 'error',
-            description: message,
-          });
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('nodes.unableToValidateWorkflow'),
+                status: 'error',
+                description: message,
+              })
+            )
+          );
         } else {
           // Some other error occurred
           log.error({ error: parseify(e) }, t('nodes.unknownErrorValidatingWorkflow'));
-          toast({
-            id: 'UNABLE_TO_VALIDATE_WORKFLOW',
-            title: t('nodes.unableToValidateWorkflow'),
-            status: 'error',
-            description: t('nodes.unknownErrorValidatingWorkflow'),
-          });
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('nodes.unableToValidateWorkflow'),
+                status: 'error',
+                description: t('nodes.unknownErrorValidatingWorkflow'),
+              })
+            )
+          );
         }
       }
     },

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -74,7 +74,6 @@ export type AppConfig = {
   maxUpscalePixels?: number;
   metadataFetchDebounce?: number;
   workflowFetchDebounce?: number;
-  isLocal?: boolean;
   sd: {
     defaultModel?: string;
     disabledControlNetModels: string[];

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/hooks/useCopyImageToClipboard.ts

@@ -1,10 +1,11 @@
+import { useAppToaster } from 'app/components/Toaster';
 import { useImageUrlToBlob } from 'common/hooks/useImageUrlToBlob';
 import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
-import { toast } from 'features/toast/toast';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
 export const useCopyImageToClipboard = () => {
+  const toaster = useAppToaster();
   const { t } = useTranslation();
   const imageUrlToBlob = useImageUrlToBlob();
 
@@ -15,11 +16,12 @@ export const useCopyImageToClipboard = () => {
   const copyImageToClipboard = useCallback(
     async (image_url: string) => {
       if (!isClipboardAPIAvailable) {
-        toast({
-          id: 'PROBLEM_COPYING_IMAGE',
+        toaster({
           title: t('toast.problemCopyingImage'),
           description: "Your browser doesn't support the Clipboard API.",
           status: 'error',
+          duration: 2500,
+          isClosable: true,
         });
       }
       try {
@@ -31,21 +33,23 @@ export const useCopyImageToClipboard = () => {
 
         copyBlobToClipboard(blob);
 
-        toast({
-          id: 'IMAGE_COPIED',
+        toaster({
           title: t('toast.imageCopied'),
           status: 'success',
+          duration: 2500,
+          isClosable: true,
         });
       } catch (err) {
-        toast({
-          id: 'PROBLEM_COPYING_IMAGE',
+        toaster({
           title: t('toast.problemCopyingImage'),
           description: String(err),
           status: 'error',
+          duration: 2500,
+          isClosable: true,
         });
       }
     },
-    [imageUrlToBlob, isClipboardAPIAvailable, t]
+    [imageUrlToBlob, isClipboardAPIAvailable, t, toaster]
   );
 
   return { isClipboardAPIAvailable, copyImageToClipboard };

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

@@ -1,12 +1,13 @@
 import { useStore } from '@nanostores/react';
+import { useAppToaster } from 'app/components/Toaster';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { imageDownloaded } from 'features/gallery/store/actions';
-import { toast } from 'features/toast/toast';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
 export const useDownloadImage = () => {
+  const toaster = useAppToaster();
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const authToken = useStore($authToken);
@@ -36,15 +37,16 @@ export const useDownloadImage = () => {
         window.URL.revokeObjectURL(url);
         dispatch(imageDownloaded());
       } catch (err) {
-        toast({
-          id: 'PROBLEM_DOWNLOADING_IMAGE',
+        toaster({
           title: t('toast.problemDownloadingImage'),
           description: String(err),
           status: 'error',
+          duration: 2500,
+          isClosable: true,
         });
       }
     },
-    [t, dispatch, authToken]
+    [t, toaster, dispatch, authToken]
   );
 
   return { downloadImage };

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

@@ -1,6 +1,6 @@
+import { useAppToaster } from 'app/components/Toaster';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { toast } from 'features/toast/toast';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { useCallback, useEffect, useState } from 'react';
 import type { Accept, FileRejection } from 'react-dropzone';
@@ -26,6 +26,7 @@ const selectPostUploadAction = createMemoizedSelector(activeTabNameSelector, (ac
 
 export const useFullscreenDropzone = () => {
   const { t } = useTranslation();
+  const toaster = useAppToaster();
   const postUploadAction = useAppSelector(selectPostUploadAction);
   const autoAddBoardId = useAppSelector((s) => s.gallery.autoAddBoardId);
   const [isHandlingUpload, setIsHandlingUpload] = useState<boolean>(false);
@@ -36,14 +37,13 @@ export const useFullscreenDropzone = () => {
     (rejection: FileRejection) => {
       setIsHandlingUpload(true);
 
-      toast({
-        id: 'UPLOAD_FAILED',
+      toaster({
         title: t('toast.uploadFailed'),
         description: rejection.errors.map((error) => error.message).join('\n'),
         status: 'error',
       });
     },
-    [t]
+    [t, toaster]
   );
 
   const fileAcceptedCallback = useCallback(
@@ -62,8 +62,7 @@ export const useFullscreenDropzone = () => {
   const onDrop = useCallback(
     (acceptedFiles: Array<File>, fileRejections: Array<FileRejection>) => {
       if (fileRejections.length > 1) {
-        toast({
-          id: 'UPLOAD_FAILED',
+        toaster({
           title: t('toast.uploadFailed'),
           description: t('toast.uploadFailedInvalidUploadDesc'),
           status: 'error',
@@ -79,7 +78,7 @@ export const useFullscreenDropzone = () => {
         fileAcceptedCallback(file);
       });
     },
-    [t, fileAcceptedCallback, fileRejectionCallback]
+    [t, toaster, fileAcceptedCallback, fileRejectionCallback]
   );
 
   const onDragOver = useCallback(() => {

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/common/util/toast.ts

@@ -0,0 +1,6 @@
+import { createStandaloneToast, theme, TOAST_OPTIONS } from '@invoke-ai/ui-library';
+
+export const { toast } = createStandaloneToast({
+  theme: theme,
+  defaultOptions: TOAST_OPTIONS.defaultOptions,
+});

invokeai/frontend/web/src/features/controlLayers/components/CALayer/CALayer.tsx

@@ -4,7 +4,7 @@ import { CALayerControlAdapterWrapper } from 'features/controlLayers/components/
 import { LayerDeleteButton } from 'features/controlLayers/components/LayerCommon/LayerDeleteButton';
 import { LayerMenu } from 'features/controlLayers/components/LayerCommon/LayerMenu';
 import { LayerTitle } from 'features/controlLayers/components/LayerCommon/LayerTitle';
-import { LayerIsEnabledToggle } from 'features/controlLayers/components/LayerCommon/LayerVisibilityToggle';
+import { LayerVisibilityToggle } from 'features/controlLayers/components/LayerCommon/LayerVisibilityToggle';
 import { LayerWrapper } from 'features/controlLayers/components/LayerCommon/LayerWrapper';
 import { layerSelected, selectCALayerOrThrow } from 'features/controlLayers/store/controlLayersSlice';
 import { memo, useCallback } from 'react';
@@ -26,7 +26,7 @@ export const CALayer = memo(({ layerId }: Props) => {
   return (
     <LayerWrapper onClick={onClick} borderColor={isSelected ? 'base.400' : 'base.800'}>
       <Flex gap={3} alignItems="center" p={3} cursor="pointer" onDoubleClick={onToggle}>
-        <LayerIsEnabledToggle layerId={layerId} />
+        <LayerVisibilityToggle layerId={layerId} />
         <LayerTitle type="control_adapter_layer" />
         <Spacer />
         <CALayerOpacity layerId={layerId} />

invokeai/frontend/web/src/features/controlLayers/components/IILayer/IILayer.tsx

@@ -5,7 +5,7 @@ import { InitialImagePreview } from 'features/controlLayers/components/IILayer/I
 import { LayerDeleteButton } from 'features/controlLayers/components/LayerCommon/LayerDeleteButton';
 import { LayerMenu } from 'features/controlLayers/components/LayerCommon/LayerMenu';
 import { LayerTitle } from 'features/controlLayers/components/LayerCommon/LayerTitle';
-import { LayerIsEnabledToggle } from 'features/controlLayers/components/LayerCommon/LayerVisibilityToggle';
+import { LayerVisibilityToggle } from 'features/controlLayers/components/LayerCommon/LayerVisibilityToggle';
 import { LayerWrapper } from 'features/controlLayers/components/LayerCommon/LayerWrapper';
 import {
   iiLayerDenoisingStrengthChanged,
@@ -66,7 +66,7 @@ export const IILayer = memo(({ layerId }: Props) => {
   return (
     <LayerWrapper onClick={onClick} borderColor={layer.isSelected ? 'base.400' : 'base.800'}>
       <Flex gap={3} alignItems="center" p={3} cursor="pointer" onDoubleClick={onToggle}>
-        <LayerIsEnabledToggle layerId={layerId} />
+        <LayerVisibilityToggle layerId={layerId} />
         <LayerTitle type="initial_image_layer" />
         <Spacer />
         <IILayerOpacity layerId={layerId} />