chore(ui): update whats new copy

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI

.github/CODEOWNERS

@@ -6,7 +6,7 @@
 /mkdocs.yml @lstein @blessedcoolant @hipsterusername @psychedelicious
 
 # nodes
-/invokeai/app/ @blessedcoolant @psychedelicious @brandonrising @hipsterusername @jazzhaiku
+/invokeai/app/ @blessedcoolant @psychedelicious @hipsterusername @jazzhaiku
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant @hipsterusername
@@ -19,10 +19,9 @@
 
 # web ui
 /invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
-/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
 
 # generation, model management, postprocessing
-/invokeai/backend  @lstein @blessedcoolant @brandonrising @hipsterusername @jazzhaiku
+/invokeai/backend  @lstein @blessedcoolant @hipsterusername @jazzhaiku @psychedelicious @maryhipp 
 
 # front ends
 /invokeai/frontend/CLI @lstein @hipsterusername

.gitignore

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

docs/contributing/INVOCATIONS.md

@@ -39,7 +39,7 @@ nodes imported in the `__init__.py` file are loaded. See the README in the nodes
 folder for more examples:
 
 ```py
-from .cool_node import CoolInvocation
+from .cool_node import ResizeInvocation
 ```
 
 ## Creating A New Invocation
@@ -69,7 +69,10 @@ The first set of things we need to do when creating a new Invocation are -
 So let us do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.invocation_api import (
+    BaseInvocation,
+    invocation,
+)
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -103,8 +106,12 @@ create your own custom field types later in this guide. For now, let's go ahead
 and use it.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from invokeai.invocation_api import (
+    BaseInvocation,
+    ImageField,
+    InputField,
+    invocation,
+)
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -128,8 +135,12 @@ image: ImageField = InputField(description="The input image")
 Great. Now let us create our other inputs for `width` and `height`
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from invokeai.invocation_api import (
+    BaseInvocation,
+    ImageField,
+    InputField,
+    invocation,
+)
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -163,8 +174,13 @@ that are provided by it by InvokeAI.
 Let us create this function first.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
+from invokeai.invocation_api import (
+    BaseInvocation,
+    ImageField,
+    InputField,
+    InvocationContext,
+    invocation,
+)
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -191,8 +207,14 @@ all the necessary info related to image outputs. So let us use that.
 We will cover how to create your own output types later in this guide.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
+from invokeai.invocation_api import (
+    BaseInvocation,
+    ImageField,
+    InputField,
+    InvocationContext,
+    invocation,
+)
+
 from invokeai.app.invocations.image import ImageOutput
 
 @invocation('resize')
@@ -217,9 +239,15 @@ Perfect. Now that we have our Invocation setup, let us do what we want to do.
 So let's do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.image import ImageOutput, ResourceOrigin, ImageCategory
+from invokeai.invocation_api import (
+    BaseInvocation,
+    ImageField,
+    InputField,
+    InvocationContext,
+    invocation,
+)
+
+from invokeai.app.invocations.image import ImageOutput
 
 @invocation("resize")
 class ResizeInvocation(BaseInvocation):

invokeai/app/api/dependencies.py

@@ -23,6 +23,10 @@ from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_images.model_images_default import ModelImageFileStorageDisk
 from invokeai.app.services.model_manager.model_manager_default import ModelManagerService
 from invokeai.app.services.model_records.model_records_sql import ModelRecordServiceSQL
+from invokeai.app.services.model_relationship_records.model_relationship_records_sqlite import (
+    SqliteModelRelationshipRecordStorage,
+)
+from invokeai.app.services.model_relationships.model_relationships_default import ModelRelationshipsService
 from invokeai.app.services.names.names_default import SimpleNameService
 from invokeai.app.services.object_serializer.object_serializer_disk import ObjectSerializerDisk
 from invokeai.app.services.object_serializer.object_serializer_forward_cache import ObjectSerializerForwardCache
@@ -136,6 +140,8 @@ class ApiDependencies:
             download_queue=download_queue_service,
             events=events,
         )
+        model_relationships = ModelRelationshipsService()
+        model_relationship_records = SqliteModelRelationshipRecordStorage(db=db)
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
         session_processor = DefaultSessionProcessor(session_runner=DefaultSessionRunner())
@@ -161,6 +167,8 @@ class ApiDependencies:
             logger=logger,
             model_images=model_images_service,
             model_manager=model_manager,
+            model_relationships=model_relationships,
+            model_relationship_records=model_relationship_records,
             download_queue=download_queue_service,
             names=names,
             performance_statistics=performance_statistics,

invokeai/app/api/routers/model_manager.py

@@ -893,6 +893,12 @@ class HFTokenHelper:
             huggingface_hub.login(token=token, add_to_git_credential=False)
         return cls.get_status()
 
+    @classmethod
+    def reset_token(cls) -> HFTokenStatus:
+        with SuppressOutput(), contextlib.suppress(Exception):
+            huggingface_hub.logout()
+        return cls.get_status()
+
 
 @model_manager_router.get("/hf_login", operation_id="get_hf_login_status", response_model=HFTokenStatus)
 async def get_hf_login_status() -> HFTokenStatus:
@@ -915,3 +921,8 @@ async def do_hf_login(
         ApiDependencies.invoker.services.logger.warning("Unable to verify HF token")
 
     return token_status
+
+
+@model_manager_router.delete("/hf_login", operation_id="reset_hf_token", response_model=HFTokenStatus)
+async def reset_hf_token() -> HFTokenStatus:
+    return HFTokenHelper.reset_token()

invokeai/app/api/routers/model_relationships.py

@@ -0,0 +1,215 @@
+"""FastAPI route for model relationship records."""
+
+from typing import List
+
+from fastapi import APIRouter, Body, HTTPException, Path, status
+from pydantic import BaseModel, Field
+
+from invokeai.app.api.dependencies import ApiDependencies
+
+model_relationships_router = APIRouter(prefix="/v1/model_relationships", tags=["model_relationships"])
+
+# === Schemas ===
+
+
+class ModelRelationshipCreateRequest(BaseModel):
+    model_key_1: str = Field(
+        ...,
+        description="The key of the first model in the relationship",
+        examples=[
+            "aa3b247f-90c9-4416-bfcd-aeaa57a5339e",
+            "ac32b914-10ab-496e-a24a-3068724b9c35",
+            "d944abfd-c7c3-42e2-a4ff-da640b29b8b4",
+            "b1c2d3e4-f5a6-7890-abcd-ef1234567890",
+            "12345678-90ab-cdef-1234-567890abcdef",
+            "fedcba98-7654-3210-fedc-ba9876543210",
+        ],
+    )
+    model_key_2: str = Field(
+        ...,
+        description="The key of the second model in the relationship",
+        examples=[
+            "3bb7c0eb-b6c8-469c-ad8c-4d69c06075e4",
+            "f0c3da4e-d9ff-42b5-a45c-23be75c887c9",
+            "38170dd8-f1e5-431e-866c-2c81f1277fcc",
+            "c57fea2d-7646-424c-b9ad-c0ba60fc68be",
+            "10f7807b-ab54-46a9-ab03-600e88c630a1",
+            "f6c1d267-cf87-4ee0-bee0-37e791eacab7",
+        ],
+    )
+
+
+class ModelRelationshipBatchRequest(BaseModel):
+    model_keys: List[str] = Field(
+        ...,
+        description="List of model keys to fetch related models for",
+        examples=[
+            [
+                "aa3b247f-90c9-4416-bfcd-aeaa57a5339e",
+                "ac32b914-10ab-496e-a24a-3068724b9c35",
+            ],
+            [
+                "b1c2d3e4-f5a6-7890-abcd-ef1234567890",
+                "12345678-90ab-cdef-1234-567890abcdef",
+                "fedcba98-7654-3210-fedc-ba9876543210",
+            ],
+            [
+                "3bb7c0eb-b6c8-469c-ad8c-4d69c06075e4",
+            ],
+        ],
+    )
+
+
+# === Routes ===
+
+
+@model_relationships_router.get(
+    "/i/{model_key}",
+    operation_id="get_related_models",
+    response_model=list[str],
+    responses={
+        200: {
+            "description": "A list of related model keys was retrieved successfully",
+            "content": {
+                "application/json": {
+                    "example": [
+                        "15e9eb28-8cfe-47c9-b610-37907a79fc3c",
+                        "71272e82-0e5f-46d5-bca9-9a61f4bd8a82",
+                        "a5d7cd49-1b98-4534-a475-aeee4ccf5fa2",
+                    ]
+                }
+            },
+        },
+        404: {"description": "The specified model could not be found"},
+        422: {"description": "Validation error"},
+    },
+)
+async def get_related_models(
+    model_key: str = Path(..., description="The key of the model to get relationships for"),
+) -> list[str]:
+    """
+    Get a list of model keys related to a given model.
+    """
+    try:
+        return ApiDependencies.invoker.services.model_relationships.get_related_model_keys(model_key)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@model_relationships_router.post(
+    "/",
+    status_code=status.HTTP_204_NO_CONTENT,
+    responses={
+        204: {"description": "The relationship was successfully created"},
+        400: {"description": "Invalid model keys or self-referential relationship"},
+        409: {"description": "The relationship already exists"},
+        422: {"description": "Validation error"},
+        500: {"description": "Internal server error"},
+    },
+    summary="Add Model Relationship",
+    description="Creates a **bidirectional** relationship between two models, allowing each to reference the other as related.",
+)
+async def add_model_relationship(
+    req: ModelRelationshipCreateRequest = Body(..., description="The model keys to relate"),
+) -> None:
+    """
+    Add a relationship between two models.
+
+    Relationships are bidirectional and will be accessible from both models.
+
+    - Raises 400 if keys are invalid or identical.
+    - Raises 409 if the relationship already exists.
+    """
+    try:
+        if req.model_key_1 == req.model_key_2:
+            raise HTTPException(status_code=400, detail="Cannot relate a model to itself.")
+
+        ApiDependencies.invoker.services.model_relationships.add_model_relationship(
+            req.model_key_1,
+            req.model_key_2,
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=409, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@model_relationships_router.delete(
+    "/",
+    status_code=status.HTTP_204_NO_CONTENT,
+    responses={
+        204: {"description": "The relationship was successfully removed"},
+        400: {"description": "Invalid model keys or self-referential relationship"},
+        404: {"description": "The relationship does not exist"},
+        422: {"description": "Validation error"},
+        500: {"description": "Internal server error"},
+    },
+    summary="Remove Model Relationship",
+    description="Removes a **bidirectional** relationship between two models. The relationship must already exist.",
+)
+async def remove_model_relationship(
+    req: ModelRelationshipCreateRequest = Body(..., description="The model keys to disconnect"),
+) -> None:
+    """
+    Removes a bidirectional relationship between two model keys.
+
+    - Raises 400 if attempting to unlink a model from itself.
+    - Raises 404 if the relationship was not found.
+    """
+    try:
+        if req.model_key_1 == req.model_key_2:
+            raise HTTPException(status_code=400, detail="Cannot unlink a model from itself.")
+
+        ApiDependencies.invoker.services.model_relationships.remove_model_relationship(
+            req.model_key_1,
+            req.model_key_2,
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@model_relationships_router.post(
+    "/batch",
+    operation_id="get_related_models_batch",
+    response_model=List[str],
+    responses={
+        200: {
+            "description": "Related model keys retrieved successfully",
+            "content": {
+                "application/json": {
+                    "example": [
+                        "ca562b14-995e-4a42-90c1-9528f1a5921d",
+                        "cc0c2b8a-c62e-41d6-878e-cc74dde5ca8f",
+                        "18ca7649-6a9e-47d5-bc17-41ab1e8cec81",
+                        "7c12d1b2-0ef9-4bec-ba55-797b2d8f2ee1",
+                        "c382eaa3-0e28-4ab0-9446-408667699aeb",
+                        "71272e82-0e5f-46d5-bca9-9a61f4bd8a82",
+                        "a5d7cd49-1b98-4534-a475-aeee4ccf5fa2",
+                    ]
+                }
+            },
+        },
+        422: {"description": "Validation error"},
+        500: {"description": "Internal server error"},
+    },
+    summary="Get Related Model Keys (Batch)",
+    description="Retrieves all **unique related model keys** for a list of given models. This is useful for contextual suggestions or filtering.",
+)
+async def get_related_models_batch(
+    req: ModelRelationshipBatchRequest = Body(..., description="Model keys to check for related connections"),
+) -> list[str]:
+    """
+    Accepts multiple model keys and returns a flat list of all unique related keys.
+
+    Useful when working with multiple selections in the UI or cross-model comparisons.
+    """
+    try:
+        all_related: set[str] = set()
+        for key in req.model_keys:
+            related = ApiDependencies.invoker.services.model_relationships.get_related_model_keys(key)
+            all_related.update(related)
+        return list(all_related)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

invokeai/app/api_app.py

@@ -22,6 +22,7 @@ from invokeai.app.api.routers import (
     download_queue,
     images,
     model_manager,
+    model_relationships,
     session_queue,
     style_presets,
     utilities,
@@ -125,6 +126,7 @@ app.include_router(download_queue.download_queue_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
 app.include_router(board_images.board_images_router, prefix="/api")
+app.include_router(model_relationships.model_relationships_router, prefix="/api")
 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")

invokeai/app/invocations/baseinvocation.py

@@ -5,6 +5,8 @@ from __future__ import annotations
 import inspect
 import re
 import sys
+import types
+import typing
 import warnings
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -20,12 +22,14 @@ from typing import (
     Literal,
     Optional,
     Type,
+    TypedDict,
     TypeVar,
     Union,
+    cast,
 )
 
 import semver
-from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, create_model
+from pydantic import BaseModel, ConfigDict, Field, JsonValue, TypeAdapter, create_model
 from pydantic.fields import FieldInfo
 from pydantic_core import PydanticUndefined
 
@@ -72,13 +76,24 @@ class Classification(str, Enum, metaclass=MetaEnum):
     Special = "special"
 
 
+class Bottleneck(str, Enum, metaclass=MetaEnum):
+    """
+    The bottleneck of an invocation.
+    - `Network`: The invocation's execution is network-bound.
+    - `GPU`: The invocation's execution is GPU-bound.
+    """
+
+    Network = "network"
+    GPU = "gpu"
+
+
 class UIConfigBase(BaseModel):
     """
     Provides additional node configuration to the UI.
     This is used internally by the @invocation decorator logic. Do not use this directly.
     """
 
-    tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
+    tags: Optional[list[str]] = Field(default=None, description="The node's tags")
     title: Optional[str] = Field(default=None, description="The node's display name")
     category: Optional[str] = Field(default=None, description="The node's category")
     version: str = Field(
@@ -93,6 +108,11 @@ class UIConfigBase(BaseModel):
     )
 
 
+class OriginalModelField(TypedDict):
+    annotation: Any
+    field_info: FieldInfo
+
+
 class BaseInvocationOutput(BaseModel):
     """
     Base class for all invocation outputs.
@@ -100,6 +120,12 @@ class BaseInvocationOutput(BaseModel):
     All invocation outputs must use the `@invocation_output` decorator to provide their unique type.
     """
 
+    output_meta: Optional[dict[str, JsonValue]] = Field(
+        default=None,
+        description="Optional dictionary of metadata for the invocation output, unrelated to the invocation's actual output value. This is not exposed as an output field.",
+        json_schema_extra={"field_kind": FieldKind.NodeAttribute},
+    )
+
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseInvocationOutput]) -> None:
         """Adds various UI-facing attributes to the invocation output's OpenAPI schema."""
@@ -115,6 +141,9 @@ class BaseInvocationOutput(BaseModel):
         """Gets the invocation output's type, as provided by the `@invocation_output` decorator."""
         return cls.model_fields["type"].default
 
+    _original_model_fields: ClassVar[dict[str, OriginalModelField]] = {}
+    """The original model fields, before any modifications were made by the @invocation_output decorator."""
+
     model_config = ConfigDict(
         protected_namespaces=(),
         validate_assignment=True,
@@ -148,7 +177,7 @@ class BaseInvocation(ABC, BaseModel):
         return cls.model_fields["type"].default
 
     @classmethod
-    def get_output_annotation(cls) -> BaseInvocationOutput:
+    def get_output_annotation(cls) -> Type[BaseInvocationOutput]:
         """Gets the invocation's output annotation (i.e. the return annotation of its `invoke()` method)."""
         return signature(cls.invoke).return_annotation
 
@@ -180,7 +209,7 @@ class BaseInvocation(ABC, BaseModel):
         Internal invoke method, calls `invoke()` after some prep.
         Handles optional fields that are required to call `invoke()` and invocation cache.
         """
-        for field_name, field in self.model_fields.items():
+        for field_name, field in type(self).model_fields.items():
             if not field.json_schema_extra or callable(field.json_schema_extra):
                 # something has gone terribly awry, we should always have this and it should be a dict
                 continue
@@ -195,9 +224,9 @@ class BaseInvocation(ABC, BaseModel):
                 setattr(self, field_name, orig_default)
             if orig_required and orig_default is PydanticUndefined and getattr(self, field_name) is None:
                 if input_ == Input.Connection:
-                    raise RequiredConnectionException(self.model_fields["type"].default, field_name)
+                    raise RequiredConnectionException(type(self).model_fields["type"].default, field_name)
                 elif input_ == Input.Any:
-                    raise MissingInputException(self.model_fields["type"].default, field_name)
+                    raise MissingInputException(type(self).model_fields["type"].default, field_name)
 
         # skip node cache codepath if it's disabled
         if services.configuration.node_cache_size == 0:
@@ -235,6 +264,8 @@ class BaseInvocation(ABC, BaseModel):
         json_schema_extra={"field_kind": FieldKind.NodeAttribute},
     )
 
+    bottleneck: ClassVar[Bottleneck]
+
     UIConfig: ClassVar[UIConfigBase]
 
     model_config = ConfigDict(
@@ -245,6 +276,9 @@ class BaseInvocation(ABC, BaseModel):
         coerce_numbers_to_str=True,
     )
 
+    _original_model_fields: ClassVar[dict[str, OriginalModelField]] = {}
+    """The original model fields, before any modifications were made by the @invocation decorator."""
+
 
 TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
 
@@ -256,6 +290,26 @@ class InvocationRegistry:
     @classmethod
     def register_invocation(cls, invocation: type[BaseInvocation]) -> None:
         """Registers an invocation."""
+
+        invocation_type = invocation.get_type()
+        node_pack = invocation.UIConfig.node_pack
+
+        # Log a warning when an existing invocation is being clobbered by the one we are registering
+        clobbered_invocation = InvocationRegistry.get_invocation_for_type(invocation_type)
+        if clobbered_invocation is not None:
+            # This should always be true - we just checked if the invocation type was in the set
+            clobbered_node_pack = clobbered_invocation.UIConfig.node_pack
+
+            if clobbered_node_pack == "invokeai":
+                # The invocation being clobbered is a core invocation
+                logger.warning(f'Overriding core node "{invocation_type}" with node from "{node_pack}"')
+            else:
+                # The invocation being clobbered is a custom invocation
+                logger.warning(
+                    f'Overriding node "{invocation_type}" from "{node_pack}" with node from "{clobbered_node_pack}"'
+                )
+            cls._invocation_classes.remove(clobbered_invocation)
+
         cls._invocation_classes.add(invocation)
         cls.invalidate_invocation_typeadapter()
 
@@ -314,6 +368,15 @@ class InvocationRegistry:
     @classmethod
     def register_output(cls, output: "type[TBaseInvocationOutput]") -> None:
         """Registers an invocation output."""
+        output_type = output.get_type()
+
+        # Log a warning when an existing invocation is being clobbered by the one we are registering
+        clobbered_output = InvocationRegistry.get_output_for_type(output_type)
+        if clobbered_output is not None:
+            # TODO(psyche): We do not record the node pack of the output, so we cannot log it here
+            logger.warning(f'Overriding invocation output "{output_type}"')
+            cls._output_classes.remove(clobbered_output)
+
         cls._output_classes.add(output)
         cls.invalidate_output_typeadapter()
 
@@ -322,6 +385,11 @@ class InvocationRegistry:
         """Gets all invocation outputs."""
         return cls._output_classes
 
+    @classmethod
+    def get_outputs_map(cls) -> dict[str, type[BaseInvocationOutput]]:
+        """Gets a map of all output types to their output classes."""
+        return {i.get_type(): i for i in cls.get_output_classes()}
+
     @classmethod
     @lru_cache(maxsize=1)
     def get_output_typeadapter(cls) -> TypeAdapter[Any]:
@@ -347,6 +415,11 @@ class InvocationRegistry:
         """Gets all invocation output types."""
         return (i.get_type() for i in cls.get_output_classes())
 
+    @classmethod
+    def get_output_for_type(cls, output_type: str) -> type[BaseInvocationOutput] | None:
+        """Gets the output class for a given output type."""
+        return cls.get_outputs_map().get(output_type)
+
 
 RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
     "id",
@@ -354,11 +427,12 @@ RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
     "use_cache",
     "type",
     "workflow",
+    "bottleneck",
 }
 
 RESERVED_INPUT_FIELD_NAMES = {"metadata", "board"}
 
-RESERVED_OUTPUT_FIELD_NAMES = {"type"}
+RESERVED_OUTPUT_FIELD_NAMES = {"type", "output_meta"}
 
 
 class _Model(BaseModel):
@@ -430,6 +504,48 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
     return None
 
 
+class NoDefaultSentinel:
+    pass
+
+
+def validate_field_default(
+    cls_name: str, field_name: str, invocation_type: str, annotation: Any, field_info: FieldInfo
+) -> None:
+    """Validates the default value of a field against its pydantic field definition."""
+
+    assert isinstance(field_info.json_schema_extra, dict), "json_schema_extra is not a dict"
+
+    # By the time we are doing this, we've already done some pydantic magic by overriding the original default value.
+    # We store the original default value in the json_schema_extra dict, so we can validate it here.
+    orig_default = field_info.json_schema_extra.get("orig_default", NoDefaultSentinel)
+
+    if orig_default is NoDefaultSentinel:
+        return
+
+    # To validate the default value, we can create a temporary pydantic model with the field we are validating as its
+    # only field. Then validate the default value against this temporary model.
+    TempDefaultValidator = cast(BaseModel, create_model(cls_name, **{field_name: (annotation, field_info)}))
+
+    try:
+        TempDefaultValidator.model_validate({field_name: orig_default})
+    except Exception as e:
+        raise InvalidFieldError(
+            f'Default value for field "{field_name}" on invocation "{invocation_type}" is invalid, {e}'
+        ) from e
+
+
+def is_optional(annotation: Any) -> bool:
+    """
+    Checks if the given annotation is optional (i.e. Optional[X], Union[X, None] or X | None).
+    """
+    origin = typing.get_origin(annotation)
+    # PEP 604 unions (int|None) have origin types.UnionType
+    is_union = origin is typing.Union or origin is types.UnionType
+    if not is_union:
+        return False
+    return any(arg is type(None) for arg in typing.get_args(annotation))
+
+
 def invocation(
     invocation_type: str,
     title: Optional[str] = None,
@@ -438,6 +554,7 @@ def invocation(
     version: Optional[str] = None,
     use_cache: Optional[bool] = True,
     classification: Classification = Classification.Stable,
+    bottleneck: Bottleneck = Bottleneck.GPU,
 ) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]:
     """
     Registers an invocation.
@@ -449,6 +566,7 @@ def invocation(
     :param Optional[str] version: Adds a version to the invocation. Must be a valid semver string. Defaults to None.
     :param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor.
     :param Classification classification: The classification of the invocation. Defaults to FeatureClassification.Stable. Use Beta or Prototype if the invocation is unstable.
+    :param Bottleneck bottleneck: The bottleneck of the invocation. Defaults to Bottleneck.GPU. Use Network if the invocation is network-bound.
     """
 
     def wrapper(cls: Type[TBaseInvocation]) -> Type[TBaseInvocation]:
@@ -460,27 +578,26 @@ def invocation(
         # The node pack is the module name - will be "invokeai" for built-in nodes
         node_pack = cls.__module__.split(".")[0]
 
-        # Handle the case where an existing node is being clobbered by the one we are registering
-        if invocation_type in InvocationRegistry.get_invocation_types():
-            clobbered_invocation = InvocationRegistry.get_invocation_for_type(invocation_type)
-            # This should always be true - we just checked if the invocation type was in the set
-            assert clobbered_invocation is not None
-
-            clobbered_node_pack = clobbered_invocation.UIConfig.node_pack
-
-            if clobbered_node_pack == "invokeai":
-                # The node being clobbered is a core node
-                raise ValueError(
-                    f'Cannot load node "{invocation_type}" from node pack "{node_pack}" - a core node with the same type already exists'
-                )
-            else:
-                # The node being clobbered is a custom node
-                raise ValueError(
-                    f'Cannot load node "{invocation_type}" from node pack "{node_pack}" - a node with the same type already exists in node pack "{clobbered_node_pack}"'
-                )
-
         validate_fields(cls.model_fields, invocation_type)
 
+        fields: dict[str, tuple[Any, FieldInfo]] = {}
+
+        for field_name, field_info in cls.model_fields.items():
+            annotation = field_info.annotation
+            assert annotation is not None, f"{field_name} on invocation {invocation_type} has no type annotation."
+            assert isinstance(field_info.json_schema_extra, dict), (
+                f"{field_name} on invocation {invocation_type} has a non-dict json_schema_extra, did you forget to use InputField?"
+            )
+
+            cls._original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
+
+            validate_field_default(cls.__name__, field_name, invocation_type, annotation, field_info)
+
+            if field_info.default is None and not is_optional(annotation):
+                annotation = annotation | None
+
+            fields[field_name] = (annotation, field_info)
+
         # Add OpenAPI schema extras
         uiconfig: dict[str, Any] = {}
         uiconfig["title"] = title
@@ -504,6 +621,8 @@ def invocation(
         if use_cache is not None:
             cls.model_fields["use_cache"].default = use_cache
 
+        cls.bottleneck = bottleneck
+
         # Add the invocation type to the model.
 
         # You'd be tempted to just add the type field and rebuild the model, like this:
@@ -513,11 +632,17 @@ def invocation(
         # Unfortunately, because the `GraphInvocation` uses a forward ref in its `graph` field's annotation, this does
         # not work. Instead, we have to create a new class with the type field and patch the original class with it.
 
-        invocation_type_annotation = Literal[invocation_type]  # type: ignore
-        invocation_type_field = Field(
-            title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
+        invocation_type_annotation = Literal[invocation_type]
+
+        # Field() returns an instance of FieldInfo, but thanks to a pydantic implementation detail, it is _typed_ as Any.
+        # This cast makes the type annotation match the class's true type.
+        invocation_type_field_info = cast(
+            FieldInfo,
+            Field(title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}),
         )
 
+        fields["type"] = (invocation_type_annotation, invocation_type_field_info)
+
         # Validate the `invoke()` method is implemented
         if "invoke" in cls.__abstractmethods__:
             raise ValueError(f'Invocation "{invocation_type}" must implement the "invoke" method')
@@ -539,17 +664,12 @@ def invocation(
             )
 
         docstring = cls.__doc__
-        cls = create_model(
-            cls.__qualname__,
-            __base__=cls,
-            __module__=cls.__module__,
-            type=(invocation_type_annotation, invocation_type_field),
-        )
-        cls.__doc__ = docstring
+        new_class = create_model(cls.__qualname__, __base__=cls, __module__=cls.__module__, **fields)  # type: ignore
+        new_class.__doc__ = docstring
 
-        InvocationRegistry.register_invocation(cls)
+        InvocationRegistry.register_invocation(new_class)
 
-        return cls
+        return new_class
 
     return wrapper
 
@@ -572,29 +692,41 @@ def invocation_output(
         if re.compile(r"^\S+$").match(output_type) is None:
             raise ValueError(f'"output_type" must consist of non-whitespace characters, got "{output_type}"')
 
-        if output_type in InvocationRegistry.get_output_types():
-            raise ValueError(f'Invocation type "{output_type}" already exists')
-
         validate_fields(cls.model_fields, output_type)
 
-        # Add the output type to the model.
+        fields: dict[str, tuple[Any, FieldInfo]] = {}
 
-        output_type_annotation = Literal[output_type]  # type: ignore
-        output_type_field = Field(
-            title="type", default=output_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
+        for field_name, field_info in cls.model_fields.items():
+            annotation = field_info.annotation
+            assert annotation is not None, f"{field_name} on invocation output {output_type} has no type annotation."
+            assert isinstance(field_info.json_schema_extra, dict), (
+                f"{field_name} on invocation output {output_type} has a non-dict json_schema_extra, did you forget to use InputField?"
+            )
+
+            cls._original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
+
+            if field_info.default is not PydanticUndefined and is_optional(annotation):
+                annotation = annotation | None
+            fields[field_name] = (annotation, field_info)
+
+        # Add the output type to the model.
+        output_type_annotation = Literal[output_type]
+
+        # Field() returns an instance of FieldInfo, but thanks to a pydantic implementation detail, it is _typed_ as Any.
+        # This cast makes the type annotation match the class's true type.
+        output_type_field_info = cast(
+            FieldInfo,
+            Field(title="type", default=output_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}),
         )
 
+        fields["type"] = (output_type_annotation, output_type_field_info)
+
         docstring = cls.__doc__
-        cls = create_model(
-            cls.__qualname__,
-            __base__=cls,
-            __module__=cls.__module__,
-            type=(output_type_annotation, output_type_field),
-        )
-        cls.__doc__ = docstring
+        new_class = create_model(cls.__qualname__, __base__=cls, __module__=cls.__module__, **fields)
+        new_class.__doc__ = docstring
 
-        InvocationRegistry.register_output(cls)
+        InvocationRegistry.register_output(new_class)
 
-        return cls
+        return new_class
 
     return wrapper

invokeai/app/invocations/batch.py

@@ -64,7 +64,6 @@ class ImageBatchInvocation(BaseBatchInvocation):
     """Create a batched generation, where the workflow is executed once for each image in the batch."""
 
     images: list[ImageField] = InputField(
-        default=[],
         min_length=1,
         description="The images to batch over",
     )
@@ -120,7 +119,6 @@ class StringBatchInvocation(BaseBatchInvocation):
     """Create a batched generation, where the workflow is executed once for each string in the batch."""
 
     strings: list[str] = InputField(
-        default=[],
         min_length=1,
         description="The strings to batch over",
     )
@@ -176,7 +174,6 @@ class IntegerBatchInvocation(BaseBatchInvocation):
     """Create a batched generation, where the workflow is executed once for each integer in the batch."""
 
     integers: list[int] = InputField(
-        default=[],
         min_length=1,
         description="The integers to batch over",
     )
@@ -230,7 +227,6 @@ class FloatBatchInvocation(BaseBatchInvocation):
     """Create a batched generation, where the workflow is executed once for each float in the batch."""
 
     floats: list[float] = InputField(
-        default=[],
         min_length=1,
         description="The floats to batch over",
     )

invokeai/app/invocations/composition-nodes.py

@@ -274,12 +274,12 @@ class InvokeAdjustImageHuePlusInvocation(BaseInvocation, WithMetadata, WithBoard
     title="Enhance Image",
     tags=["enhance", "image"],
     category="image",
-    version="1.2.0",
+    version="1.2.1",
 )
 class InvokeImageEnhanceInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Applies processing from PIL's ImageEnhance module. Originally created by @dwringer"""
 
-    image: ImageField = InputField(default=None, description="The image for which to apply processing")
+    image: ImageField = InputField(description="The image for which to apply processing")
     invert: bool = InputField(default=False, description="Whether to invert the image colors")
     color: float = InputField(ge=0, default=1.0, description="Color enhancement factor")
     contrast: float = InputField(ge=0, default=1.0, description="Contrast enhancement factor")

invokeai/app/invocations/create_gradient_mask.py

@@ -42,12 +42,12 @@ class GradientMaskOutput(BaseInvocationOutput):
     title="Create Gradient Mask",
     tags=["mask", "denoise"],
     category="latents",
-    version="1.2.0",
+    version="1.2.1",
 )
 class CreateGradientMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
 
-    mask: ImageField = InputField(default=None, description="Image which will be masked", ui_order=1)
+    mask: ImageField = InputField(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
     )

invokeai/app/invocations/denoise_latents.py

@@ -608,6 +608,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     end_step_percent=single_ip_adapter.end_step_percent,
                     ip_adapter_conditioning=IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds),
                     mask=mask,
+                    method=single_ip_adapter.method,
                 )
             )
 

invokeai/app/invocations/fields.py

@@ -61,6 +61,9 @@ class UIType(str, Enum, metaclass=MetaEnum):
     SigLipModel = "SigLipModelField"
     FluxReduxModel = "FluxReduxModelField"
     LlavaOnevisionModel = "LLaVAModelField"
+    Imagen3Model = "Imagen3ModelField"
+    Imagen4Model = "Imagen4ModelField"
+    ChatGPT4oModel = "ChatGPT4oModelField"
     # endregion
 
     # region Misc Field Types
@@ -398,8 +401,8 @@ class InputFieldJSONSchemaExtra(BaseModel):
     """
 
     input: Input
-    orig_required: bool
     field_kind: FieldKind
+    orig_required: bool = True
     default: Optional[Any] = None
     orig_default: Optional[Any] = None
     ui_hidden: bool = False
@@ -496,7 +499,7 @@ def InputField(
     input: Input = Input.Any,
     ui_type: Optional[UIType] = None,
     ui_component: Optional[UIComponent] = None,
-    ui_hidden: bool = False,
+    ui_hidden: Optional[bool] = None,
     ui_order: Optional[int] = None,
     ui_choice_labels: Optional[dict[str, str]] = None,
 ) -> Any:
@@ -532,15 +535,20 @@ def InputField(
 
     json_schema_extra_ = InputFieldJSONSchemaExtra(
         input=input,
-        ui_type=ui_type,
-        ui_component=ui_component,
-        ui_hidden=ui_hidden,
-        ui_order=ui_order,
-        ui_choice_labels=ui_choice_labels,
         field_kind=FieldKind.Input,
-        orig_required=True,
     )
 
+    if ui_type is not None:
+        json_schema_extra_.ui_type = ui_type
+    if ui_component is not None:
+        json_schema_extra_.ui_component = ui_component
+    if ui_hidden is not None:
+        json_schema_extra_.ui_hidden = ui_hidden
+    if ui_order is not None:
+        json_schema_extra_.ui_order = ui_order
+    if ui_choice_labels is not None:
+        json_schema_extra_.ui_choice_labels = ui_choice_labels
+
     """
     There is a conflict between the typing of invocation definitions and the typing of an invocation's
     `invoke()` function.
@@ -612,7 +620,7 @@ def InputField(
 
     return Field(
         **provided_args,
-        json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
+        json_schema_extra=json_schema_extra_.model_dump(exclude_unset=True),
     )
 
 

invokeai/app/invocations/flux_redux.py

@@ -3,6 +3,7 @@ from typing import Literal, Optional
 
 import torch
 from PIL import Image
+from transformers import SiglipImageProcessor, SiglipVisionModel
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -115,8 +116,14 @@ class FluxReduxInvocation(BaseInvocation):
     @torch.no_grad()
     def _siglip_encode(self, context: InvocationContext, image: Image.Image) -> torch.Tensor:
         siglip_model_config = self._get_siglip_model(context)
-        with context.models.load(siglip_model_config.key).model_on_device() as (_, siglip_pipeline):
-            assert isinstance(siglip_pipeline, SigLipPipeline)
+        with context.models.load(siglip_model_config.key).model_on_device() as (_, model):
+            assert isinstance(model, SiglipVisionModel)
+
+            model_abs_path = context.models.get_absolute_path(siglip_model_config)
+            processor = SiglipImageProcessor.from_pretrained(model_abs_path, local_files_only=True)
+            assert isinstance(processor, SiglipImageProcessor)
+
+            siglip_pipeline = SigLipPipeline(processor, model)
             return siglip_pipeline.encode_image(
                 x=image, device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
             )

invokeai/app/invocations/ideal_size.py

@@ -21,14 +21,14 @@ class IdealSizeOutput(BaseInvocationOutput):
     "ideal_size",
     title="Ideal Size - SD1.5, SDXL",
     tags=["latents", "math", "ideal_size"],
-    version="1.0.5",
+    version="1.0.6",
 )
 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)
+    unet: UNetField = InputField(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 "

invokeai/app/invocations/image.py

@@ -975,13 +975,13 @@ class SaveImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Canvas Paste Back",
     tags=["image", "combine"],
     category="image",
-    version="1.0.0",
+    version="1.0.1",
 )
 class CanvasPasteBackInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Combines two images by using the mask provided. Intended for use on the Unified Canvas."""
 
     source_image: ImageField = InputField(description="The source image")
-    target_image: ImageField = InputField(default=None, description="The target image")
+    target_image: ImageField = InputField(description="The target image")
     mask: ImageField = InputField(
         description="The mask to use when pasting",
     )

invokeai/app/invocations/ip_adapter.py

@@ -31,6 +31,7 @@ class IPAdapterField(BaseModel):
     image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the IP-Adapter.")
     target_blocks: List[str] = Field(default=[], description="The IP Adapter blocks to apply")
+    method: str = Field(default="full", description="Weight apply method")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
     )
@@ -94,7 +95,7 @@ class IPAdapterInvocation(BaseInvocation):
     weight: Union[float, List[float]] = InputField(
         default=1, description="The weight given to the IP-Adapter", title="Weight"
     )
-    method: Literal["full", "style", "composition"] = InputField(
+    method: Literal["full", "style", "composition", "style_strong", "style_precise"] = InputField(
         default="full", description="The method to apply the IP-Adapter"
     )
     begin_step_percent: float = InputField(
@@ -147,6 +148,38 @@ class IPAdapterInvocation(BaseInvocation):
                 target_blocks = ["down_blocks.2.attentions.1"]
             else:
                 raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "style_precise":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["up_blocks.1", "down_blocks.2", "mid_block"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "style_strong":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["up_blocks.0", "up_blocks.1", "up_blocks.2", "down_blocks.0", "down_blocks.1"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = [
+                    "up_blocks.0.attentions.1",
+                    "up_blocks.1.attentions.1",
+                    "up_blocks.2.attentions.1",
+                    "up_blocks.0.attentions.2",
+                    "up_blocks.1.attentions.2",
+                    "up_blocks.2.attentions.2",
+                    "up_blocks.0.attentions.0",
+                    "up_blocks.1.attentions.0",
+                    "up_blocks.2.attentions.0",
+                    "down_blocks.0.attentions.0",
+                    "down_blocks.0.attentions.1",
+                    "down_blocks.0.attentions.2",
+                    "down_blocks.1.attentions.0",
+                    "down_blocks.1.attentions.1",
+                    "down_blocks.1.attentions.2",
+                    "down_blocks.2.attentions.0",
+                    "down_blocks.2.attentions.2",
+                ]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
         elif self.method == "full":
             target_blocks = ["block"]
         else:
@@ -162,6 +195,7 @@ class IPAdapterInvocation(BaseInvocation):
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,
                 mask=self.mask,
+                method=self.method,
             ),
         )
 

invokeai/app/invocations/llava_onevision_vllm.py

@@ -3,13 +3,14 @@ from typing import Any
 import torch
 from PIL.Image import Image
 from pydantic import field_validator
+from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration, LlavaOnevisionProcessor
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, UIComponent, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import StringOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
+from invokeai.backend.llava_onevision_pipeline import LlavaOnevisionPipeline
 from invokeai.backend.util.devices import TorchDevice
 
 
@@ -54,10 +55,17 @@ class LlavaOnevisionVllmInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> StringOutput:
         images = self._get_images(context)
+        model_config = context.models.get_config(self.vllm_model)
 
-        with context.models.load(self.vllm_model) as vllm_model:
-            assert isinstance(vllm_model, LlavaOnevisionModel)
-            output = vllm_model.run(
+        with context.models.load(self.vllm_model).model_on_device() as (_, model):
+            assert isinstance(model, LlavaOnevisionForConditionalGeneration)
+
+            model_abs_path = context.models.get_absolute_path(model_config)
+            processor = AutoProcessor.from_pretrained(model_abs_path, local_files_only=True)
+            assert isinstance(processor, LlavaOnevisionProcessor)
+
+            model = LlavaOnevisionPipeline(model, processor)
+            output = model.run(
                 prompt=self.prompt,
                 images=images,
                 device=TorchDevice.choose_torch_device(),

invokeai/app/invocations/segment_anything.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from PIL import Image
 from pydantic import BaseModel, Field
-from transformers import AutoModelForMaskGeneration, AutoProcessor
+from transformers import AutoProcessor
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
 
@@ -104,14 +104,13 @@ class SegmentAnythingInvocation(BaseInvocation):
 
     @staticmethod
     def _load_sam_model(model_path: Path):
-        sam_model = AutoModelForMaskGeneration.from_pretrained(
+        sam_model = SamModel.from_pretrained(
             model_path,
             local_files_only=True,
             # TODO(ryand): Setting the torch_dtype here doesn't work. Investigate whether fp16 is supported by the
             # model, and figure out how to make it work in the pipeline.
             # torch_dtype=TorchDevice.choose_torch_dtype(),
         )
-        assert isinstance(sam_model, SamModel)
 
         sam_processor = AutoProcessor.from_pretrained(model_path, local_files_only=True)
         assert isinstance(sam_processor, SamProcessor)

invokeai/app/services/events/events_common.py

@@ -241,6 +241,7 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
     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)")
+    credits: Optional[float] = Field(default=None, description="The total credits used for this queue item")
 
     @classmethod
     def build(
@@ -263,6 +264,7 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
             completed_at=str(queue_item.completed_at) if queue_item.completed_at else None,
             batch_status=batch_status,
             queue_status=queue_status,
+            credits=queue_item.credits,
         )
 
 

invokeai/app/services/invocation_services.py

@@ -27,6 +27,10 @@ if TYPE_CHECKING:
     from invokeai.app.services.invocation_stats.invocation_stats_base import InvocationStatsServiceBase
     from invokeai.app.services.model_images.model_images_base import ModelImageFileStorageBase
     from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
+    from invokeai.app.services.model_relationship_records.model_relationship_records_base import (
+        ModelRelationshipRecordStorageBase,
+    )
+    from invokeai.app.services.model_relationships.model_relationships_base import ModelRelationshipsServiceABC
     from invokeai.app.services.names.names_base import NameServiceBase
     from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase
     from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
@@ -54,6 +58,8 @@ class InvocationServices:
         logger: "Logger",
         model_images: "ModelImageFileStorageBase",
         model_manager: "ModelManagerServiceBase",
+        model_relationships: "ModelRelationshipsServiceABC",
+        model_relationship_records: "ModelRelationshipRecordStorageBase",
         download_queue: "DownloadQueueServiceBase",
         performance_statistics: "InvocationStatsServiceBase",
         session_queue: "SessionQueueBase",
@@ -81,6 +87,8 @@ class InvocationServices:
         self.logger = logger
         self.model_images = model_images
         self.model_manager = model_manager
+        self.model_relationships = model_relationships
+        self.model_relationship_records = model_relationship_records
         self.download_queue = download_queue
         self.performance_statistics = performance_statistics
         self.session_queue = session_queue

invokeai/app/services/invocation_stats/invocation_stats_base.py

@@ -60,7 +60,7 @@ class InvocationStatsServiceBase(ABC):
         pass
 
     @abstractmethod
-    def reset_stats(self):
+    def reset_stats(self, graph_execution_state_id: str) -> None:
         """Reset all stored statistics."""
         pass
 

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -73,9 +73,9 @@ class InvocationStatsService(InvocationStatsServiceBase):
             )
             self._stats[graph_execution_state_id].add_node_execution_stats(node_stats)
 
-    def reset_stats(self):
-        self._stats = {}
-        self._cache_stats = {}
+    def reset_stats(self, graph_execution_state_id: str) -> None:
+        self._stats.pop(graph_execution_state_id, None)
+        self._cache_stats.pop(graph_execution_state_id, None)
 
     def get_stats(self, graph_execution_state_id: str) -> InvocationStatsSummary:
         graph_stats_summary = self._get_graph_summary(graph_execution_state_id)

invokeai/app/services/model_install/model_install_default.py

@@ -647,10 +647,18 @@ class ModelInstallService(ModelInstallServiceBase):
         hash_algo = self._app_config.hashing_algorithm
         fields = config.model_dump()
 
+        # WARNING!
+        # The legacy probe relies on the implicit order of tests to determine model classification.
+        # This can lead to regressions between the legacy and new probes.
+        # Do NOT change the order of `probe` and `classify` without implementing one of the following fixes:
+        # Short-term fix: `classify` tests `matches` in the same order as the legacy probe.
+        # Long-term fix: Improve `matches` to be more specific so that only one config matches
+        #   any given model - eliminating ambiguity and removing reliance on order.
+        # After implementing either of these fixes, remove @pytest.mark.xfail from `test_regression_against_model_probe`
         try:
-            return ModelConfigBase.classify(model_path=model_path, hash_algo=hash_algo, **fields)
-        except InvalidModelConfigException:
             return ModelProbe.probe(model_path=model_path, fields=fields, hash_algo=hash_algo)  # type: ignore
+        except InvalidModelConfigException:
+            return ModelConfigBase.classify(model_path, hash_algo, **fields)
 
     def _register(
         self, model_path: Path, config: Optional[ModelRecordChanges] = None, info: Optional[AnyModelConfig] = None

invokeai/app/services/model_relationship_records/model_relationship_records_base.py

@@ -0,0 +1,25 @@
+from abc import ABC, abstractmethod
+
+
+class ModelRelationshipRecordStorageBase(ABC):
+    """Abstract base class for model-to-model relationship record storage."""
+
+    @abstractmethod
+    def add_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        """Creates a relationship between two models by keys."""
+        pass
+
+    @abstractmethod
+    def remove_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        """Removes a relationship between two models by keys."""
+        pass
+
+    @abstractmethod
+    def get_related_model_keys(self, model_key: str) -> list[str]:
+        """Gets all models keys related to a given model key."""
+        pass
+
+    @abstractmethod
+    def get_related_model_keys_batch(self, model_keys: list[str]) -> list[str]:
+        """Get related model keys for multiple models given a list of keys."""
+        pass

invokeai/app/services/model_relationship_records/model_relationship_records_sqlite.py

@@ -0,0 +1,66 @@
+import sqlite3
+
+from invokeai.app.services.model_relationship_records.model_relationship_records_base import (
+    ModelRelationshipRecordStorageBase,
+)
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+
+
+class SqliteModelRelationshipRecordStorage(ModelRelationshipRecordStorageBase):
+    def __init__(self, db: SqliteDatabase) -> None:
+        super().__init__()
+        self._conn = db.conn
+
+    def add_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        if model_key_1 == model_key_2:
+            raise ValueError("Cannot relate a model to itself.")
+        a, b = sorted([model_key_1, model_key_2])
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                "INSERT OR IGNORE INTO model_relationships (model_key_1, model_key_2) VALUES (?, ?)",
+                (a, b),
+            )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise e
+
+    def remove_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        a, b = sorted([model_key_1, model_key_2])
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                "DELETE FROM model_relationships WHERE model_key_1 = ? AND model_key_2 = ?",
+                (a, b),
+            )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise e
+
+    def get_related_model_keys(self, model_key: str) -> list[str]:
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """
+            SELECT model_key_2 FROM model_relationships WHERE model_key_1 = ?
+            UNION
+            SELECT model_key_1 FROM model_relationships WHERE model_key_2 = ?
+            """,
+            (model_key, model_key),
+        )
+        return [row[0] for row in cursor.fetchall()]
+
+    def get_related_model_keys_batch(self, model_keys: list[str]) -> list[str]:
+        cursor = self._conn.cursor()
+
+        key_list = ",".join("?" for _ in model_keys)
+        cursor.execute(
+            f"""
+            SELECT model_key_2 FROM model_relationships WHERE model_key_1 IN ({key_list})
+            UNION
+            SELECT model_key_1 FROM model_relationships WHERE model_key_2 IN ({key_list})
+            """,
+            model_keys + model_keys,
+        )
+        return [row[0] for row in cursor.fetchall()]

invokeai/app/services/model_relationships/model_relationships_base.py

@@ -0,0 +1,25 @@
+from abc import ABC, abstractmethod
+
+
+class ModelRelationshipsServiceABC(ABC):
+    """High-level service for managing model-to-model relationships."""
+
+    @abstractmethod
+    def add_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        """Creates a relationship between two models keys."""
+        pass
+
+    @abstractmethod
+    def remove_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        """Removes a relationship between two models keys."""
+        pass
+
+    @abstractmethod
+    def get_related_model_keys(self, model_key: str) -> list[str]:
+        """Gets all models keys related to a given model key."""
+        pass
+
+    @abstractmethod
+    def get_related_model_keys_batch(self, model_keys: list[str]) -> list[str]:
+        """Get related model keys for multiple models."""
+        pass

invokeai/app/services/model_relationships/model_relationships_common.py

@@ -0,0 +1,9 @@
+from datetime import datetime
+
+from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
+
+
+class ModelRelationship(BaseModelExcludeNull):
+    model_key_1: str
+    model_key_2: str
+    created_at: datetime

invokeai/app/services/model_relationships/model_relationships_default.py

@@ -0,0 +1,31 @@
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.model_relationships.model_relationships_base import ModelRelationshipsServiceABC
+from invokeai.backend.model_manager.config import AnyModelConfig
+
+
+class ModelRelationshipsService(ModelRelationshipsServiceABC):
+    __invoker: Invoker
+
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
+    def add_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        self.__invoker.services.model_relationship_records.add_model_relationship(model_key_1, model_key_2)
+
+    def remove_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
+        self.__invoker.services.model_relationship_records.remove_model_relationship(model_key_1, model_key_2)
+
+    def get_related_model_keys(self, model_key: str) -> list[str]:
+        return self.__invoker.services.model_relationship_records.get_related_model_keys(model_key)
+
+    def add_relationship_from_models(self, model_1: AnyModelConfig, model_2: AnyModelConfig) -> None:
+        self.add_model_relationship(model_1.key, model_2.key)
+
+    def remove_relationship_from_models(self, model_1: AnyModelConfig, model_2: AnyModelConfig) -> None:
+        self.remove_model_relationship(model_1.key, model_2.key)
+
+    def get_related_keys_from_model(self, model: AnyModelConfig) -> list[str]:
+        return self.get_related_model_keys(model.key)
+
+    def get_related_model_keys_batch(self, model_keys: list[str]) -> list[str]:
+        return self.__invoker.services.model_relationship_records.get_related_model_keys_batch(model_keys)

invokeai/app/services/session_processor/session_processor_default.py

@@ -210,7 +210,7 @@ class DefaultSessionRunner(SessionRunnerBase):
             # 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()
+                self._services.performance_statistics.reset_stats(queue_item.session.id)
 
             for callback in self._on_after_run_session_callbacks:
                 callback(queue_item=queue_item)

invokeai/app/services/session_queue/session_queue_common.py

@@ -148,7 +148,7 @@ class Batch(BaseModel):
                     node = cast(BaseInvocation, graph.get_node(batch_data.node_path))
                 except NodeNotFoundError:
                     raise NodeNotFoundError(f"Node {batch_data.node_path} not found in graph")
-                if batch_data.field_name not in node.model_fields:
+                if batch_data.field_name not in type(node).model_fields:
                     raise NodeNotFoundError(f"Field {batch_data.field_name} not found in node {batch_data.node_path}")
         return values
 
@@ -257,6 +257,7 @@ class SessionQueueItemWithoutGraph(BaseModel):
     api_output_fields: Optional[list[FieldIdentifier]] = Field(
         default=None, description="The nodes that were used as output from the API"
     )
+    credits: Optional[float] = Field(default=None, description="The total credits used for this queue item")
 
     @classmethod
     def queue_item_dto_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItemDTO":

invokeai/app/services/shared/graph.py

@@ -424,7 +424,7 @@ class Graph(BaseModel):
                 )
 
             # input fields are on the node
-            if edge.destination.field not in destination_node.model_fields:
+            if edge.destination.field not in type(destination_node).model_fields:
                 raise NodeFieldNotFoundError(
                     f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
                 )

invokeai/app/services/shared/invocation_context.py

@@ -21,6 +21,7 @@ from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.util.step_callback import diffusion_step_callback
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
+    ModelConfigBase,
 )
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
@@ -543,6 +544,30 @@ class ModelsInterface(InvocationContextInterface):
         self._util.signal_progress(f"Loading model {source}")
         return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
 
+    def get_absolute_path(self, config_or_path: AnyModelConfig | Path | str) -> Path:
+        """Gets the absolute path for a given model config or path.
+
+        For example, if the model's path is `flux/main/FLUX Dev.safetensors`, and the models path is
+        `/home/username/InvokeAI/models`, this method will return
+        `/home/username/InvokeAI/models/flux/main/FLUX Dev.safetensors`.
+
+        Args:
+            config_or_path: The model config or path.
+
+        Returns:
+            The absolute path to the model.
+        """
+
+        model_path = Path(config_or_path.path) if isinstance(config_or_path, ModelConfigBase) else Path(config_or_path)
+
+        if model_path.is_absolute():
+            return model_path.resolve()
+
+        base_models_path = self._services.configuration.models_path
+        joined_path = base_models_path / model_path
+        resolved_path = joined_path.resolve()
+        return resolved_path
+
 
 class ConfigInterface(InvocationContextInterface):
     def get(self) -> InvokeAIAppConfig:

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

@@ -22,6 +22,7 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_16 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_17 import build_migration_17
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_18 import build_migration_18
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_19 import build_migration_19
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_20 import build_migration_20
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -61,6 +62,7 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_17())
     migrator.register_migration(build_migration_18())
     migrator.register_migration(build_migration_19(app_config=config))
+    migrator.register_migration(build_migration_20())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_20.py

@@ -0,0 +1,37 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration20Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """
+            -- many-to-many relationship table for models
+            CREATE TABLE IF NOT EXISTS model_relationships (
+                -- model_key_1 and model_key_2 are the same as the key(primary key) in the models table
+                model_key_1 TEXT NOT NULL,
+                model_key_2 TEXT NOT NULL,
+                created_at TEXT DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                PRIMARY KEY (model_key_1, model_key_2),
+                -- model_key_1 < model_key_2, to ensure uniqueness and prevent duplicates
+                FOREIGN KEY (model_key_1) REFERENCES models(id) ON DELETE CASCADE,
+                FOREIGN KEY (model_key_2) REFERENCES models(id) ON DELETE CASCADE
+            );
+            """
+        )
+        cursor.execute(
+            """
+            -- Creates an index to keep performance equal when searching for model_key_1 or model_key_2
+            CREATE INDEX IF NOT EXISTS keyx_model_relationships_model_key_2
+            ON model_relationships(model_key_2)
+            """
+        )
+
+
+def build_migration_20() -> Migration:
+    return Migration(
+        from_version=19,
+        to_version=20,
+        callback=Migration20Callback(),
+    )

invokeai/app/util/custom_openapi.py

@@ -61,6 +61,10 @@ def get_openapi_func(
         # We need to manually add all outputs to the schema - pydantic doesn't add them because they aren't used directly.
         for output in InvocationRegistry.get_output_classes():
             json_schema = output.model_json_schema(mode="serialization", ref_template="#/components/schemas/{model}")
+            # Remove output_metadata that is only used on back-end from the schema
+            if "output_meta" in json_schema["properties"]:
+                json_schema["properties"].pop("output_meta")
+
             move_defs_to_top_level(openapi_schema, json_schema)
             openapi_schema["components"]["schemas"][output.__name__] = json_schema
 

invokeai/app/util/misc.py

@@ -10,7 +10,7 @@ def get_timestamp() -> int:
 
 
 def get_iso_timestamp() -> str:
-    return datetime.datetime.utcnow().isoformat()
+    return datetime.datetime.now(datetime.timezone.utc).isoformat()
 
 
 def get_datetime_from_iso_timestamp(iso_timestamp: str) -> datetime.datetime:

invokeai/backend/llava_onevision_pipeline.py

@@ -1,26 +1,15 @@
-from pathlib import Path
-from typing import Optional
-
 import torch
 from PIL.Image import Image
-from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration, LlavaOnevisionProcessor
-
-from invokeai.backend.raw_model import RawModel
+from transformers import LlavaOnevisionForConditionalGeneration, LlavaOnevisionProcessor
 
 
-class LlavaOnevisionModel(RawModel):
+class LlavaOnevisionPipeline:
+    """A wrapper for a LLaVA Onevision model + processor."""
+
     def __init__(self, vllm_model: LlavaOnevisionForConditionalGeneration, processor: LlavaOnevisionProcessor):
         self._vllm_model = vllm_model
         self._processor = processor
 
-    @classmethod
-    def load_from_path(cls, path: str | Path):
-        vllm_model = LlavaOnevisionForConditionalGeneration.from_pretrained(path, local_files_only=True)
-        assert isinstance(vllm_model, LlavaOnevisionForConditionalGeneration)
-        processor = AutoProcessor.from_pretrained(path, local_files_only=True)
-        assert isinstance(processor, LlavaOnevisionProcessor)
-        return cls(vllm_model, processor)
-
     def run(self, prompt: str, images: list[Image], device: torch.device, dtype: torch.dtype) -> str:
         # TODO(ryand): Tune the max number of images that are useful for the model.
         if len(images) > 3:
@@ -44,13 +33,3 @@ class LlavaOnevisionModel(RawModel):
         # The output_str will include the prompt, so we extract the response.
         response = output_str.split("assistant\n", 1)[1].strip()
         return response
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        self._vllm_model.to(device=device, dtype=dtype)
-
-    def calc_size(self) -> int:
-        """Get size of the model in memory in bytes."""
-        # HACK(ryand): Fix this issue with circular imports.
-        from invokeai.backend.model_manager.load.model_util import calc_module_size
-
-        return calc_module_size(self._vllm_model)

invokeai/backend/model_manager/config.py

@@ -144,34 +144,37 @@ class ModelConfigBase(ABC, BaseModel):
     submodels: Optional[Dict[SubModelType, SubmodelDefinition]] = Field(
         description="Loadable submodels in this model", default=None
     )
+    usage_info: Optional[str] = Field(default=None, description="Usage information for this model")
 
-    _USING_LEGACY_PROBE: ClassVar[set] = set()
-    _USING_CLASSIFY_API: ClassVar[set] = set()
+    USING_LEGACY_PROBE: ClassVar[set] = set()
+    USING_CLASSIFY_API: ClassVar[set] = set()
     _MATCH_SPEED: ClassVar[MatchSpeed] = MatchSpeed.MED
 
     def __init_subclass__(cls, **kwargs):
         super().__init_subclass__(**kwargs)
         if issubclass(cls, LegacyProbeMixin):
-            ModelConfigBase._USING_LEGACY_PROBE.add(cls)
+            ModelConfigBase.USING_LEGACY_PROBE.add(cls)
         else:
-            ModelConfigBase._USING_CLASSIFY_API.add(cls)
+            ModelConfigBase.USING_CLASSIFY_API.add(cls)
 
     @staticmethod
     def all_config_classes():
-        subclasses = ModelConfigBase._USING_LEGACY_PROBE | ModelConfigBase._USING_CLASSIFY_API
+        subclasses = ModelConfigBase.USING_LEGACY_PROBE | ModelConfigBase.USING_CLASSIFY_API
         concrete = {cls for cls in subclasses if not isabstract(cls)}
         return concrete
 
     @staticmethod
-    def classify(model_path: Path, hash_algo: HASHING_ALGORITHMS = "blake3_single", **overrides):
+    def classify(mod: str | Path | ModelOnDisk, hash_algo: HASHING_ALGORITHMS = "blake3_single", **overrides):
         """
         Returns the best matching ModelConfig instance from a model's file/folder path.
         Raises InvalidModelConfigException if no valid configuration is found.
         Created to deprecate ModelProbe.probe
         """
-        candidates = ModelConfigBase._USING_CLASSIFY_API
+        if isinstance(mod, Path | str):
+            mod = ModelOnDisk(mod, hash_algo)
+
+        candidates = ModelConfigBase.USING_CLASSIFY_API
         sorted_by_match_speed = sorted(candidates, key=lambda cls: (cls._MATCH_SPEED, cls.__name__))
-        mod = ModelOnDisk(model_path, hash_algo)
 
         for config_cls in sorted_by_match_speed:
             try:
@@ -600,6 +603,21 @@ class LlavaOnevisionConfig(DiffusersConfigBase, ModelConfigBase):
         }
 
 
+class ApiModelConfig(MainConfigBase, ModelConfigBase):
+    """Model config for API-based models."""
+
+    format: Literal[ModelFormat.Api] = ModelFormat.Api
+
+    @classmethod
+    def matches(cls, mod: ModelOnDisk) -> bool:
+        # API models are not stored on disk, so we can't match them.
+        return False
+
+    @classmethod
+    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
+        raise NotImplementedError("API models are not parsed from disk.")
+
+
 def get_model_discriminator_value(v: Any) -> str:
     """
     Computes the discriminator value for a model config.
@@ -667,6 +685,7 @@ AnyModelConfig = Annotated[
         Annotated[SigLIPConfig, SigLIPConfig.get_tag()],
         Annotated[FluxReduxConfig, FluxReduxConfig.get_tag()],
         Annotated[LlavaOnevisionConfig, LlavaOnevisionConfig.get_tag()],
+        Annotated[ApiModelConfig, ApiModelConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

invokeai/backend/model_manager/load/model_cache/cached_model/cached_model_only_full_load.py

@@ -2,6 +2,8 @@ from typing import Any
 
 import torch
 
+from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
+
 
 class CachedModelOnlyFullLoad:
     """A wrapper around a PyTorch model to handle full loads and unloads between the CPU and the compute device.
@@ -76,7 +78,15 @@ class CachedModelOnlyFullLoad:
             for k, v in self._cpu_state_dict.items():
                 new_state_dict[k] = v.to(self._compute_device, copy=True)
             self._model.load_state_dict(new_state_dict, assign=True)
-        self._model.to(self._compute_device)
+
+        check_for_gguf = hasattr(self._model, "state_dict") and self._model.state_dict().get("img_in.weight")
+        if isinstance(check_for_gguf, GGMLTensor):
+            old_value = torch.__future__.get_overwrite_module_params_on_conversion()
+            torch.__future__.set_overwrite_module_params_on_conversion(True)
+            self._model.to(self._compute_device)
+            torch.__future__.set_overwrite_module_params_on_conversion(old_value)
+        else:
+            self._model.to(self._compute_device)
 
         self._is_in_vram = True
         return self._total_bytes
@@ -92,7 +102,15 @@ class CachedModelOnlyFullLoad:
 
         if self._cpu_state_dict is not None:
             self._model.load_state_dict(self._cpu_state_dict, assign=True)
-        self._model.to(self._offload_device)
+
+        check_for_gguf = hasattr(self._model, "state_dict") and self._model.state_dict().get("img_in.weight")
+        if isinstance(check_for_gguf, GGMLTensor):
+            old_value = torch.__future__.get_overwrite_module_params_on_conversion()
+            torch.__future__.set_overwrite_module_params_on_conversion(True)
+            self._model.to(self._offload_device)
+            torch.__future__.set_overwrite_module_params_on_conversion(old_value)
+        else:
+            self._model.to(self._offload_device)
 
         self._is_in_vram = False
         return self._total_bytes

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

@@ -2,9 +2,10 @@ import gc
 import logging
 import threading
 import time
+from dataclasses import dataclass
 from functools import wraps
 from logging import Logger
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional, Protocol
 
 import psutil
 import torch
@@ -54,6 +55,39 @@ def synchronized(method: Callable[..., Any]) -> Callable[..., Any]:
     return wrapper
 
 
+@dataclass
+class CacheEntrySnapshot:
+    cache_key: str
+    total_bytes: int
+    current_vram_bytes: int
+
+
+class CacheMissCallback(Protocol):
+    def __call__(
+        self,
+        model_key: str,
+        cache_snapshot: dict[str, CacheEntrySnapshot],
+    ) -> None: ...
+
+
+class CacheHitCallback(Protocol):
+    def __call__(
+        self,
+        model_key: str,
+        cache_snapshot: dict[str, CacheEntrySnapshot],
+    ) -> None: ...
+
+
+class CacheModelsClearedCallback(Protocol):
+    def __call__(
+        self,
+        models_cleared: int,
+        bytes_requested: int,
+        bytes_freed: int,
+        cache_snapshot: dict[str, CacheEntrySnapshot],
+    ) -> None: ...
+
+
 class ModelCache:
     """A cache for managing models in memory.
 
@@ -144,6 +178,34 @@ class ModelCache:
         # - Requests to empty the cache from a separate thread
         self._lock = threading.RLock()
 
+        self._on_cache_hit_callbacks: set[CacheHitCallback] = set()
+        self._on_cache_miss_callbacks: set[CacheMissCallback] = set()
+        self._on_cache_models_cleared_callbacks: set[CacheModelsClearedCallback] = set()
+
+    def on_cache_hit(self, cb: CacheHitCallback) -> Callable[[], None]:
+        self._on_cache_hit_callbacks.add(cb)
+
+        def unsubscribe() -> None:
+            self._on_cache_hit_callbacks.discard(cb)
+
+        return unsubscribe
+
+    def on_cache_miss(self, cb: CacheHitCallback) -> Callable[[], None]:
+        self._on_cache_miss_callbacks.add(cb)
+
+        def unsubscribe() -> None:
+            self._on_cache_miss_callbacks.discard(cb)
+
+        return unsubscribe
+
+    def on_cache_models_cleared(self, cb: CacheModelsClearedCallback) -> Callable[[], None]:
+        self._on_cache_models_cleared_callbacks.add(cb)
+
+        def unsubscribe() -> None:
+            self._on_cache_models_cleared_callbacks.discard(cb)
+
+        return unsubscribe
+
     @property
     @synchronized
     def stats(self) -> Optional[CacheStats]:
@@ -195,6 +257,20 @@ class ModelCache:
             f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size / MB:.2f}MB)"
         )
 
+    @synchronized
+    def _get_cache_snapshot(self) -> dict[str, CacheEntrySnapshot]:
+        overview: dict[str, CacheEntrySnapshot] = {}
+        for cache_key, cache_entry in self._cached_models.items():
+            total_bytes = cache_entry.cached_model.total_bytes()
+            current_vram_bytes = cache_entry.cached_model.cur_vram_bytes()
+            overview[cache_key] = CacheEntrySnapshot(
+                cache_key=cache_key,
+                total_bytes=total_bytes,
+                current_vram_bytes=current_vram_bytes,
+            )
+
+        return overview
+
     @synchronized
     def get(self, key: str, stats_name: Optional[str] = None) -> CacheRecord:
         """Retrieve a model from the cache.
@@ -208,6 +284,8 @@ class ModelCache:
             if self.stats:
                 self.stats.hits += 1
         else:
+            for cb in self._on_cache_miss_callbacks:
+                cb(model_key=key, cache_snapshot=self._get_cache_snapshot())
             if self.stats:
                 self.stats.misses += 1
             self._logger.debug(f"Cache miss: {key}")
@@ -229,6 +307,8 @@ class ModelCache:
         self._cache_stack.append(key)
 
         self._logger.debug(f"Cache hit: {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+        for cb in self._on_cache_hit_callbacks:
+            cb(model_key=key, cache_snapshot=self._get_cache_snapshot())
         return cache_entry
 
     @synchronized
@@ -649,6 +729,13 @@ class ModelCache:
             # immediately when their reference count hits 0.
             if self.stats:
                 self.stats.cleared = models_cleared
+            for cb in self._on_cache_models_cleared_callbacks:
+                cb(
+                    models_cleared=models_cleared,
+                    bytes_requested=bytes_needed,
+                    bytes_freed=ram_bytes_freed,
+                    cache_snapshot=self._get_cache_snapshot(),
+                )
             gc.collect()
 
         TorchDevice.empty_cache()

invokeai/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/custom_linear.py

@@ -13,6 +13,12 @@ from invokeai.backend.patches.layers.lora_layer import LoRALayer
 
 def linear_lora_forward(input: torch.Tensor, lora_layer: LoRALayer, lora_weight: float) -> torch.Tensor:
     """An optimized implementation of the residual calculation for a sidecar linear LoRALayer."""
+    # up matrix and down matrix have different ranks so we can't simply multiply them
+    if lora_layer.up.shape[1] != lora_layer.down.shape[0]:
+        x = torch.nn.functional.linear(input, lora_layer.get_weight(lora_weight), bias=lora_layer.bias)
+        x *= lora_weight * lora_layer.scale()
+        return x
+
     x = torch.nn.functional.linear(input, lora_layer.down)
     if lora_layer.mid is not None:
         x = torch.nn.functional.linear(x, lora_layer.mid)

invokeai/backend/model_manager/load/model_loaders/llava_onevision.py

@@ -1,7 +1,8 @@
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
+from transformers import LlavaOnevisionForConditionalGeneration
+
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
 )
@@ -23,6 +24,8 @@ class LlavaOnevisionModelLoader(ModelLoader):
             raise ValueError("Unexpected submodel requested for LLaVA OneVision model.")
 
         model_path = Path(config.path)
-        model = LlavaOnevisionModel.load_from_path(model_path)
-        model.to(dtype=self._torch_dtype)
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            model_path, local_files_only=True, torch_dtype=self._torch_dtype
+        )
+        assert isinstance(model, LlavaOnevisionForConditionalGeneration)
         return model

invokeai/backend/model_manager/load/model_loaders/sig_lip.py

@@ -1,13 +1,14 @@
 from pathlib import Path
 from typing import Optional
 
+from transformers import SiglipVisionModel
+
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
-from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.SigLIP, format=ModelFormat.Diffusers)
@@ -23,6 +24,5 @@ class SigLIPModelLoader(ModelLoader):
             raise ValueError("Unexpected submodel requested for LLaVA OneVision model.")
 
         model_path = Path(config.path)
-        model = SigLipPipeline.load_from_path(model_path)
-        model.to(dtype=self._torch_dtype)
+        model = SiglipVisionModel.from_pretrained(model_path, local_files_only=True, torch_dtype=self._torch_dtype)
         return model

invokeai/backend/model_manager/load/model_util.py

@@ -16,11 +16,9 @@ from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import D
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
 from invokeai.backend.model_manager.taxonomy import AnyModel
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
-from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 from invokeai.backend.util.calc_tensor_size import calc_tensor_size
@@ -51,8 +49,6 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
             GroundingDinoPipeline,
             SegmentAnythingPipeline,
             DepthAnythingPipeline,
-            SigLipPipeline,
-            LlavaOnevisionModel,
         ),
     ):
         return model.calc_size()

invokeai/backend/model_manager/model_on_disk.py

@@ -4,6 +4,7 @@ from typing import Any, Optional, TypeAlias
 import safetensors.torch
 import torch
 from picklescan.scanner import scan_file_path
+from safetensors import safe_open
 
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
@@ -35,12 +36,21 @@ class ModelOnDisk:
             return self.path.stat().st_size
         return sum(file.stat().st_size for file in self.path.rglob("*"))
 
-    def component_paths(self) -> set[Path]:
+    def weight_files(self) -> set[Path]:
         if self.path.is_file():
             return {self.path}
         extensions = {".safetensors", ".pt", ".pth", ".ckpt", ".bin", ".gguf"}
         return {f for f in self.path.rglob("*") if f.suffix in extensions}
 
+    def metadata(self, path: Optional[Path] = None) -> dict[str, str]:
+        try:
+            with safe_open(self.path, framework="pt", device="cpu") as f:
+                metadata = f.metadata()
+                assert isinstance(metadata, dict)
+                return metadata
+        except Exception:
+            return {}
+
     def repo_variant(self) -> Optional[ModelRepoVariant]:
         if self.path.is_file():
             return None
@@ -64,18 +74,7 @@ class ModelOnDisk:
         if path in sd_cache:
             return sd_cache[path]
 
-        if not path:
-            components = list(self.component_paths())
-            match components:
-                case []:
-                    raise ValueError("No weight files found for this model")
-                case [p]:
-                    path = p
-                case ps if len(ps) >= 2:
-                    raise ValueError(
-                        f"Multiple weight files found for this model: {ps}. "
-                        f"Please specify the intended file using the 'path' argument"
-                    )
+        path = self.resolve_weight_file(path)
 
         with SilenceWarnings():
             if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
@@ -94,3 +93,18 @@ class ModelOnDisk:
         state_dict = checkpoint.get("state_dict", checkpoint)
         sd_cache[path] = state_dict
         return state_dict
+
+    def resolve_weight_file(self, path: Optional[Path] = None) -> Path:
+        if not path:
+            weight_files = list(self.weight_files())
+            match weight_files:
+                case []:
+                    raise ValueError("No weight files found for this model")
+                case [p]:
+                    return p
+                case ps if len(ps) >= 2:
+                    raise ValueError(
+                        f"Multiple weight files found for this model: {ps}. "
+                        f"Please specify the intended file using the 'path' argument"
+                    )
+        return path

invokeai/backend/model_manager/taxonomy.py

@@ -26,7 +26,9 @@ class BaseModelType(str, Enum):
     StableDiffusionXLRefiner = "sdxl-refiner"
     Flux = "flux"
     CogView4 = "cogview4"
-    # Kandinsky2_1 = "kandinsky-2.1"
+    Imagen3 = "imagen3"
+    Imagen4 = "imagen4"
+    ChatGPT4o = "chatgpt-4o"
 
 
 class ModelType(str, Enum):
@@ -98,6 +100,7 @@ class ModelFormat(str, Enum):
     BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
     BnbQuantizednf4b = "bnb_quantized_nf4b"
     GGUFQuantized = "gguf_quantized"
+    Api = "api"
 
 
 class SchedulerPredictionType(str, Enum):

invokeai/backend/patches/layers/lora_layer.py

@@ -19,6 +19,7 @@ class LoRALayer(LoRALayerBase):
         self.up = up
         self.mid = mid
         self.down = down
+        self.are_ranks_equal = up.shape[1] == down.shape[0]
 
     @classmethod
     def from_state_dict_values(
@@ -58,12 +59,42 @@ class LoRALayer(LoRALayerBase):
     def _rank(self) -> int:
         return self.down.shape[0]
 
+    def fuse_weights(self, up: torch.Tensor, down: torch.Tensor) -> torch.Tensor:
+        """
+        Fuse the weights of the up and down matrices of a LoRA layer with different ranks.
+
+        Since the Huggingface implementation of KQV projections are fused, when we convert to Kohya format
+        the LoRA weights have different ranks. This function handles the fusion of these differently sized
+        matrices.
+        """
+
+        fused_lora = torch.zeros((up.shape[0], down.shape[1]), device=down.device, dtype=down.dtype)
+        rank_diff = down.shape[0] / up.shape[1]
+
+        if rank_diff > 1:
+            rank_diff = down.shape[0] / up.shape[1]
+            w_down = down.chunk(int(rank_diff), dim=0)
+            for w_down_chunk in w_down:
+                fused_lora = fused_lora + (torch.mm(up, w_down_chunk))
+        else:
+            rank_diff = up.shape[1] / down.shape[0]
+            w_up = up.chunk(int(rank_diff), dim=0)
+            for w_up_chunk in w_up:
+                fused_lora = fused_lora + (torch.mm(w_up_chunk, down))
+
+        return fused_lora
+
     def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
         if self.mid is not None:
             up = self.up.reshape(self.up.shape[0], self.up.shape[1])
             down = self.down.reshape(self.down.shape[0], self.down.shape[1])
             weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
         else:
+            # up matrix and down matrix have different ranks so we can't simply multiply them
+            if not self.are_ranks_equal:
+                weight = self.fuse_weights(self.up, self.down)
+                return weight
+
             weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
 
         return weight

invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py

@@ -20,6 +20,14 @@ from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 FLUX_KOHYA_TRANSFORMER_KEY_REGEX = (
     r"lora_unet_(\w+_blocks)_(\d+)_(img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear1|linear2|modulation)_?(.*)"
 )
+
+# A regex pattern that matches all of the last layer keys in the Kohya FLUX LoRA format.
+# Example keys:
+#   lora_unet_final_layer_linear.alpha
+#   lora_unet_final_layer_linear.lora_down.weight
+#   lora_unet_final_layer_linear.lora_up.weight
+FLUX_KOHYA_LAST_LAYER_KEY_REGEX = r"lora_unet_final_layer_(linear|linear1|linear2)_?(.*)"
+
 # A regex pattern that matches all of the CLIP keys in the Kohya FLUX LoRA format.
 # Example keys:
 #   lora_te1_text_model_encoder_layers_0_mlp_fc1.alpha
@@ -44,6 +52,7 @@ def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> boo
     """
     return all(
         re.match(FLUX_KOHYA_TRANSFORMER_KEY_REGEX, k)
+        or re.match(FLUX_KOHYA_LAST_LAYER_KEY_REGEX, k)
         or re.match(FLUX_KOHYA_CLIP_KEY_REGEX, k)
         or re.match(FLUX_KOHYA_T5_KEY_REGEX, k)
         for k in state_dict.keys()
@@ -65,6 +74,9 @@ def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -
     t5_grouped_sd: dict[str, dict[str, torch.Tensor]] = {}
     for layer_name, layer_state_dict in grouped_state_dict.items():
         if layer_name.startswith("lora_unet"):
+            # Skip the final layer. This is incompatible with current model definition.
+            if layer_name.startswith("lora_unet_final_layer"):
+                continue
             transformer_grouped_sd[layer_name] = layer_state_dict
         elif layer_name.startswith("lora_te1"):
             clip_grouped_sd[layer_name] = layer_state_dict

invokeai/backend/quantization/gguf/utils.py

@@ -5,7 +5,8 @@ from typing import Callable, Optional, Union
 import gguf
 import torch
 
-TORCH_COMPATIBLE_QTYPES = {None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}
+# should not be a Set until this is resolved: https://github.com/pytorch/pytorch/issues/145761
+TORCH_COMPATIBLE_QTYPES = [None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16]
 
 # K Quants #
 QK_K = 256

invokeai/backend/sig_lip/sig_lip_pipeline.py

@@ -1,14 +1,9 @@
-from pathlib import Path
-from typing import Optional
-
 import torch
 from PIL import Image
 from transformers import SiglipImageProcessor, SiglipVisionModel
 
-from invokeai.backend.raw_model import RawModel
 
-
-class SigLipPipeline(RawModel):
+class SigLipPipeline:
     """A wrapper for a SigLIP model + processor."""
 
     def __init__(
@@ -19,25 +14,7 @@ class SigLipPipeline(RawModel):
         self._siglip_processor = siglip_processor
         self._siglip_model = siglip_model
 
-    @classmethod
-    def load_from_path(cls, path: str | Path):
-        siglip_model = SiglipVisionModel.from_pretrained(path, local_files_only=True)
-        assert isinstance(siglip_model, SiglipVisionModel)
-        siglip_processor = SiglipImageProcessor.from_pretrained(path, local_files_only=True)
-        assert isinstance(siglip_processor, SiglipImageProcessor)
-        return cls(siglip_processor, siglip_model)
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        self._siglip_model.to(device=device, dtype=dtype)
-
     def encode_image(self, x: Image.Image, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
         imgs = self._siglip_processor.preprocess(images=[x], do_resize=True, return_tensors="pt", do_convert_rgb=True)
         encoded_x = self._siglip_model(**imgs.to(device=device, dtype=dtype)).last_hidden_state
         return encoded_x
-
-    def calc_size(self) -> int:
-        """Get size of the model in memory in bytes."""
-        # HACK(ryand): Fix this issue with circular imports.
-        from invokeai.backend.model_manager.load.model_util import calc_module_size
-
-        return calc_module_size(self._siglip_model)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -371,7 +371,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         if use_ip_adapter or use_regional_prompting:
             ip_adapters: Optional[List[UNetIPAdapterData]] = (
-                [{"ip_adapter": ipa.ip_adapter_model, "target_blocks": ipa.target_blocks} for ipa in ip_adapter_data]
+                [
+                    {"ip_adapter": ipa.ip_adapter_model, "target_blocks": ipa.target_blocks, "method": ipa.method}
+                    for ipa in ip_adapter_data
+                ]
                 if use_ip_adapter
                 else None
             )

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import math
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from enum import Enum
 from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 
@@ -104,15 +104,29 @@ class IPAdapterConditioningInfo:
 
 @dataclass
 class IPAdapterData:
+    """Data class for IP-Adapter configuration.
+
+    Attributes:
+        ip_adapter_model: The IP-Adapter model to use.
+        ip_adapter_conditioning: The IP-Adapter conditioning data.
+        mask: The mask to apply to the IP-Adapter conditioning.
+        target_blocks: List of target attention block names to apply IP-Adapter to.
+        negative_blocks: List of target attention block names that should use negative attention.
+        weight: The weight to apply to the IP-Adapter conditioning.
+        begin_step_percent: The percentage of steps at which to start applying the IP-Adapter.
+        end_step_percent: The percentage of steps at which to stop applying the IP-Adapter.
+        method: The method to use for applying the IP-Adapter ('full', 'style', 'composition').
+    """
+
     ip_adapter_model: IPAdapter
     ip_adapter_conditioning: IPAdapterConditioningInfo
     mask: torch.Tensor
     target_blocks: List[str]
-
-    # Either a single weight applied to all steps, or a list of weights for each step.
+    negative_blocks: List[str] = field(default_factory=list)
     weight: Union[float, List[float]] = 1.0
     begin_step_percent: float = 0.0
     end_step_percent: float = 1.0
+    method: str = "full"
 
     def scale_for_step(self, step_index: int, total_steps: int) -> float:
         first_adapter_step = math.floor(self.begin_step_percent * total_steps)

invokeai/backend/stable_diffusion/diffusion/custom_atttention.py

@@ -14,6 +14,7 @@ from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import Reg
 class IPAdapterAttentionWeights:
     ip_adapter_weights: IPAttentionProcessorWeights
     skip: bool
+    negative: bool
 
 
 class CustomAttnProcessor2_0(AttnProcessor2_0):
@@ -162,6 +163,10 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
                     # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
 
                     if not self._ip_adapter_attention_weights[ipa_index].skip:
+                        # apply the IP-Adapter weights to the negative embeds
+                        if self._ip_adapter_attention_weights[ipa_index].negative:
+                            ip_hidden_states = torch.cat([ip_hidden_states[1], ip_hidden_states[0] * 0], dim=0)
+
                         ip_key = ipa_weights.to_k_ip(ip_hidden_states)
                         ip_value = ipa_weights.to_v_ip(ip_hidden_states)
 

invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py

@@ -12,7 +12,8 @@ from invokeai.backend.stable_diffusion.diffusion.custom_atttention import (
 
 class UNetIPAdapterData(TypedDict):
     ip_adapter: IPAdapter
-    target_blocks: List[str]
+    target_blocks: List[str]  # Blocks where IP-Adapter should be applied
+    method: str  # Style or other method type
 
 
 class UNetAttentionPatcher:
@@ -39,12 +40,18 @@ class UNetAttentionPatcher:
                 for ip_adapter in self._ip_adapters:
                     ip_adapter_weights = ip_adapter["ip_adapter"].attn_weights.get_attention_processor_weights(idx)
                     skip = True
+                    negative = False
                     for block in ip_adapter["target_blocks"]:
                         if block in name:
                             skip = False
+                            negative = ip_adapter["method"] == "style_precise" and (
+                                block == "down_blocks.2.attentions.1"
+                                or block == "down_blocks.2"
+                                or block == "mid_block"
+                            )
                             break
                     ip_adapter_attention_weights: IPAdapterAttentionWeights = IPAdapterAttentionWeights(
-                        ip_adapter_weights=ip_adapter_weights, skip=skip
+                        ip_adapter_weights=ip_adapter_weights, skip=skip, negative=negative
                     )
                     ip_adapter_attention_weights_collection.append(ip_adapter_attention_weights)
 

invokeai/frontend/web/package.json

@@ -52,68 +52,68 @@
     }
   },
   "dependencies": {
-    "@atlaskit/pragmatic-drag-and-drop": "^1.4.0",
-    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^1.4.0",
+    "@atlaskit/pragmatic-drag-and-drop": "^1.5.3",
+    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^2.1.0",
     "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.0.3",
     "@dagrejs/dagre": "^1.1.4",
     "@dagrejs/graphlib": "^2.2.4",
-    "@fontsource-variable/inter": "^5.1.0",
+    "@fontsource-variable/inter": "^5.2.5",
     "@invoke-ai/ui-library": "^0.0.46",
-    "@nanostores/react": "^0.7.3",
-    "@reduxjs/toolkit": "2.6.1",
+    "@nanostores/react": "^1.0.0",
+    "@reduxjs/toolkit": "2.7.0",
     "@roarr/browser-log-writer": "^1.3.0",
-    "@xyflow/react": "^12.5.3",
+    "@xyflow/react": "^12.6.0",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
-    "cmdk": "^1.0.0",
+    "cmdk": "^1.1.1",
     "compare-versions": "^6.1.1",
     "filesize": "^10.1.6",
     "fracturedjsonjs": "^4.0.2",
     "framer-motion": "^11.10.0",
-    "i18next": "^23.15.1",
-    "i18next-http-backend": "^2.6.1",
+    "i18next": "^25.0.1",
+    "i18next-http-backend": "^3.0.2",
     "idb-keyval": "^6.2.1",
-    "jsondiffpatch": "^0.6.0",
-    "konva": "^9.3.15",
+    "jsondiffpatch": "^0.7.3",
+    "konva": "^9.3.20",
     "linkify-react": "^4.2.0",
     "linkifyjs": "^4.2.0",
     "lodash-es": "^4.17.21",
-    "lru-cache": "^11.0.1",
+    "lru-cache": "^11.1.0",
     "mtwist": "^1.0.2",
-    "nanoid": "^5.0.7",
-    "nanostores": "^0.11.3",
-    "new-github-issue-url": "^1.0.0",
-    "overlayscrollbars": "^2.10.0",
+    "nanoid": "^5.1.5",
+    "nanostores": "^1.0.1",
+    "new-github-issue-url": "^1.1.0",
+    "overlayscrollbars": "^2.11.1",
     "overlayscrollbars-react": "^0.5.6",
     "perfect-freehand": "^1.2.2",
-    "query-string": "^9.1.0",
+    "query-string": "^9.1.1",
     "raf-throttle": "^2.0.6",
     "react": "^18.3.1",
     "react-colorful": "^5.6.1",
     "react-dom": "^18.3.1",
-    "react-dropzone": "^14.2.9",
-    "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.53.0",
+    "react-dropzone": "^14.3.8",
+    "react-error-boundary": "^5.0.0",
+    "react-hook-form": "^7.56.1",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^15.0.2",
-    "react-icons": "^5.3.0",
-    "react-redux": "9.1.2",
-    "react-resizable-panels": "^2.1.4",
-    "react-textarea-autosize": "^8.5.7",
-    "react-use": "^17.5.1",
-    "react-virtuoso": "^4.12.5",
+    "react-i18next": "^15.5.1",
+    "react-icons": "^5.5.0",
+    "react-redux": "9.2.0",
+    "react-resizable-panels": "^2.1.8",
+    "react-textarea-autosize": "^8.5.9",
+    "react-use": "^17.6.0",
+    "react-virtuoso": "^4.12.6",
     "redux-dynamic-middlewares": "^2.2.0",
-    "redux-remember": "^5.1.0",
+    "redux-remember": "^5.2.0",
     "redux-undo": "^1.1.0",
     "rfdc": "^1.4.1",
     "roarr": "^7.21.1",
-    "serialize-error": "^11.0.3",
-    "socket.io-client": "^4.8.0",
-    "stable-hash": "^0.0.4",
-    "use-debounce": "^10.0.3",
+    "serialize-error": "^12.0.0",
+    "socket.io-client": "^4.8.1",
+    "stable-hash": "^0.0.5",
+    "use-debounce": "^10.0.4",
     "use-device-pixel-ratio": "^1.1.2",
-    "uuid": "^10.0.0",
-    "zod": "^3.23.8",
+    "uuid": "^11.1.0",
+    "zod": "^3.24.3",
     "zod-validation-error": "^3.4.0"
   },
   "peerDependencies": {
@@ -123,43 +123,43 @@
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.3.4",
-    "@storybook/addon-interactions": "^8.3.4",
-    "@storybook/addon-links": "^8.3.4",
-    "@storybook/addon-storysource": "^8.3.4",
-    "@storybook/manager-api": "^8.3.4",
-    "@storybook/react": "^8.3.4",
-    "@storybook/react-vite": "^8.5.5",
-    "@storybook/theming": "^8.3.4",
+    "@storybook/addon-essentials": "^8.6.12",
+    "@storybook/addon-interactions": "^8.6.12",
+    "@storybook/addon-links": "^8.6.12",
+    "@storybook/addon-storysource": "^8.6.12",
+    "@storybook/manager-api": "^8.6.12",
+    "@storybook/react": "^8.6.12",
+    "@storybook/react-vite": "^8.6.12",
+    "@storybook/theming": "^8.6.12",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.16.10",
+    "@types/node": "^22.15.1",
     "@types/react": "^18.3.11",
     "@types/react-dom": "^18.3.0",
     "@types/uuid": "^10.0.0",
-    "@vitejs/plugin-react-swc": "^3.8.0",
-    "@vitest/coverage-v8": "^3.0.6",
-    "@vitest/ui": "^3.0.6",
-    "concurrently": "^8.2.2",
+    "@vitejs/plugin-react-swc": "^3.9.0",
+    "@vitest/coverage-v8": "^3.1.2",
+    "@vitest/ui": "^3.1.2",
+    "concurrently": "^9.1.2",
     "csstype": "^3.1.3",
     "dpdm": "^3.14.0",
     "eslint": "^8.57.1",
-    "eslint-plugin-i18next": "^6.1.0",
+    "eslint-plugin-i18next": "^6.1.1",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.31.0",
+    "knip": "^5.50.5",
     "openapi-types": "^12.1.3",
-    "openapi-typescript": "^7.4.1",
-    "prettier": "^3.3.3",
-    "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.3.4",
+    "openapi-typescript": "^7.6.1",
+    "prettier": "^3.5.3",
+    "rollup-plugin-visualizer": "^5.14.0",
+    "storybook": "^8.6.12",
     "tsafe": "^1.8.5",
-    "type-fest": "^4.26.1",
-    "typescript": "^5.6.2",
-    "vite": "^6.1.0",
+    "type-fest": "^4.40.0",
+    "typescript": "^5.8.3",
+    "vite": "^6.3.3",
     "vite-plugin-css-injected-by-js": "^3.5.2",
-    "vite-plugin-dts": "^4.5.0",
+    "vite-plugin-dts": "^4.5.3",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^5.1.4",
-    "vitest": "^3.0.6"
+    "vitest": "^3.1.2"
   },
   "engines": {
     "pnpm": "8"

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

@@ -119,7 +119,17 @@
         "error_withCount_other": "{{count}} Fehler",
         "value": "Wert",
         "label": "Label",
-        "systemInformation": "Systeminformationen"
+        "systemInformation": "Systeminformationen",
+        "search": "Suche",
+        "clear": "Zurücksetzen",
+        "fullView": "Vollansicht",
+        "compactView": "Kompaktansicht",
+        "options_withCount_one": "{{count}} Option",
+        "options_withCount_other": "{{count}} Optionen",
+        "noOptions": "Keine Optionen",
+        "noMatches": "Keine Treffer",
+        "model_withCount_one": "{{count}} Modell",
+        "model_withCount_other": "{{count}} Modelle"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",

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

@@ -118,6 +118,8 @@
         "error": "Error",
         "error_withCount_one": "{{count}} error",
         "error_withCount_other": "{{count}} errors",
+        "model_withCount_one": "{{count}} model",
+        "model_withCount_other": "{{count}} models",
         "file": "File",
         "folder": "Folder",
         "format": "format",
@@ -138,6 +140,8 @@
         "localSystem": "Local System",
         "learnMore": "Learn More",
         "modelManager": "Model Manager",
+        "noMatches": "No matches",
+        "noOptions": "No options",
         "nodes": "Workflows",
         "notInstalled": "Not $t(common.installed)",
         "openInNewTab": "Open in New Tab",
@@ -171,6 +175,8 @@
         "blue": "Blue",
         "alpha": "Alpha",
         "selected": "Selected",
+        "search": "Search",
+        "clear": "Clear",
         "tab": "Tab",
         "view": "View",
         "edit": "Edit",
@@ -197,7 +203,11 @@
         "column": "Column",
         "value": "Value",
         "label": "Label",
-        "systemInformation": "System Information"
+        "systemInformation": "System Information",
+        "compactView": "Compact View",
+        "fullView": "Full View",
+        "options_withCount_one": "{{count}} option",
+        "options_withCount_other": "{{count}} options"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -258,6 +268,7 @@
         "status": "Status",
         "total": "Total",
         "time": "Time",
+        "credits": "Credits",
         "pending": "Pending",
         "in_progress": "In Progress",
         "completed": "Completed",
@@ -768,6 +779,7 @@
         "description": "Description",
         "edit": "Edit",
         "fileSize": "File Size",
+        "filterModels": "Filter models",
         "fluxRedux": "FLUX Redux",
         "height": "Height",
         "huggingFace": "HuggingFace",
@@ -787,6 +799,7 @@
         "hfTokenUnableToVerify": "Unable to Verify HF Token",
         "hfTokenUnableToVerifyErrorMessage": "Unable to verify HuggingFace token. This is likely due to a network error. Please try again later.",
         "hfTokenSaved": "HF Token Saved",
+        "hfTokenReset": "HF Token Reset",
         "urlUnauthorizedErrorMessage": "You may need to configure an API token to access this model.",
         "urlUnauthorizedErrorMessage2": "Learn how here.",
         "imageEncoderModelId": "Image Encoder Model ID",
@@ -821,16 +834,20 @@
         "modelUpdated": "Model Updated",
         "modelUpdateFailed": "Model Update Failed",
         "name": "Name",
-        "noModelsInstalled": "No Models Installed",
+        "modelPickerFallbackNoModelsInstalled": "No models installed.",
+        "modelPickerFallbackNoModelsInstalled2": "Visit the <LinkComponent>Model Manager</LinkComponent> to install models.",
         "noModelsInstalledDesc1": "Install models with the",
         "noModelSelected": "No Model Selected",
-        "noMatchingModels": "No matching Models",
+        "noMatchingModels": "No matching models",
+        "noModelsInstalled": "No models installed",
         "none": "none",
         "path": "Path",
         "pathToConfig": "Path To Config",
         "predictionType": "Prediction Type",
         "prune": "Prune",
         "pruneTooltip": "Prune finished imports from queue",
+        "relatedModels": "Related Models",
+        "showOnlyRelatedModels": "Related",
         "repo_id": "Repo ID",
         "repoVariant": "Repo Variant",
         "scanFolder": "Scan Folder",
@@ -871,7 +888,8 @@
         "installingXModels_one": "Installing {{count}} model",
         "installingXModels_other": "Installing {{count}} models",
         "skippingXDuplicates_one": ", skipping {{count}} duplicate",
-        "skippingXDuplicates_other": ", skipping {{count}} duplicates"
+        "skippingXDuplicates_other": ", skipping {{count}} duplicates",
+        "manageModels": "Manage Models"
     },
     "models": {
         "addLora": "Add LoRA",
@@ -1093,6 +1111,7 @@
         "info": "Info",
         "invoke": {
             "addingImagesTo": "Adding images to",
+            "modelDisabledForTrial": "Generating with {{modelName}} is not available on trial accounts. Visit your account settings to upgrade.",
             "invoke": "Invoke",
             "missingFieldTemplate": "Missing field template",
             "missingInputForField": "missing input",
@@ -1173,7 +1192,8 @@
         "width": "Width",
         "gaussianBlur": "Gaussian Blur",
         "boxBlur": "Box Blur",
-        "staged": "Staged"
+        "staged": "Staged",
+        "modelDisabledForTrial": "Generating with {{modelName}} is not available on trial accounts. Visit your <LinkComponent>account settings</LinkComponent> to upgrade."
     },
     "dynamicPrompts": {
         "showDynamicPrompts": "Show Dynamic Prompts",
@@ -1312,6 +1332,8 @@
         "unableToCopyDesc": "Your browser does not support clipboard access. Firefox users may be able to fix this by following ",
         "unableToCopyDesc_theseSteps": "these steps",
         "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill is not compatible with Text to Image or Image to Image. Use other FLUX models for these tasks.",
+        "imagenIncompatibleGenerationMode": "Google {{model}} supports Text to Image only. Use other models for Image to Image, Inpainting and Outpainting tasks.",
+        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o supports Text to Image and Image to Image only. Use other models Inpainting and Outpainting tasks.",
         "problemUnpublishingWorkflow": "Problem Unpublishing Workflow",
         "problemUnpublishingWorkflowDescription": "There was a problem unpublishing the workflow. Please try again.",
         "workflowUnpublished": "Workflow Unpublished"
@@ -2020,10 +2042,14 @@
             "ipAdapterMethod": "Mode",
             "full": "Style and Composition",
             "fullDesc": "Applies visual style (colors, textures) & composition (layout, structure).",
-            "style": "Style Only",
-            "styleDesc": "Applies visual style (colors, textures) without considering its layout.",
+            "style": "Style (Simple)",
+            "styleDesc": "Applies visual style (colors, textures) without considering its layout. Previously called Style Only.",
             "composition": "Composition Only",
-            "compositionDesc": "Replicates layout & structure while ignoring the reference's style."
+            "compositionDesc": "Replicates layout & structure while ignoring the reference's style.",
+            "styleStrong": "Style (Strong)",
+            "styleStrongDesc": "Applies a strong visual style, with a slightly reduced composition influence.",
+            "stylePrecise": "Style (Precise)",
+            "stylePreciseDesc": "Applies a precise visual style, eliminating subject influence."
         },
         "fluxReduxImageInfluence": {
             "imageInfluence": "Image Influence",
@@ -2393,8 +2419,9 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "CogView4: Support for CogView4 models in Canvas and Workflows.",
-            "Updated Dependencies: Invoke now runs on the latest version of its dependencies, including Python 3.12 and Pytorch 2.6.0."
+            "Nvidia 50xx GPUs: Invoke uses PyTorch 2.7.0, which is required for these GPUs.",
+            "Model Relationships: Link LoRAs to main models, and the LoRAs will show up first in the list.",
+            "IP Adapter: New Style (Strong) and Style (Precise) methods for SDXL and SD1.5 models."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

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

@@ -116,7 +116,19 @@
         "error_withCount_other": "{{count}} errori",
         "value": "Valore",
         "label": "Etichetta",
-        "systemInformation": "Informazioni di sistema"
+        "systemInformation": "Informazioni di sistema",
+        "noMatches": "Nessuna corrispondenza",
+        "noOptions": "Nessuna opzione",
+        "model_withCount_one": "{{count}} modello",
+        "model_withCount_many": "{{count}} modelli",
+        "model_withCount_other": "{{count}} modelli",
+        "options_withCount_one": "{{count}} opzione",
+        "options_withCount_many": "{{count}} opzioni",
+        "options_withCount_other": "{{count}} opzioni",
+        "search": "Cerca",
+        "clear": "Cancella",
+        "compactView": "Vista compatta",
+        "fullView": "Vista completa"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -637,7 +649,14 @@
         "urlForbidden": "Non hai accesso a questo modello",
         "urlForbiddenErrorMessage": "Potrebbe essere necessario richiedere l'autorizzazione al sito che distribuisce il modello.",
         "urlUnauthorizedErrorMessage": "Potrebbe essere necessario configurare un gettone API per accedere a questo modello.",
-        "fileSize": "Dimensione del file"
+        "fileSize": "Dimensione del file",
+        "filterModels": "Filtra i modelli",
+        "modelPickerFallbackNoModelsInstalled": "Nessun modello installato.",
+        "modelPickerFallbackNoModelsInstalled2": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare i modelli.",
+        "manageModels": "Gestione modelli",
+        "hfTokenReset": "Ripristino del gettone HF",
+        "relatedModels": "Modelli correlati",
+        "showOnlyRelatedModels": "Correlati"
     },
     "parameters": {
         "images": "Immagini",
@@ -719,7 +738,11 @@
             "collectionNumberLTExclusiveMin": "{{value}} <= {{exclusiveMinimum}} (excl min)",
             "collectionEmpty": "raccolta vuota",
             "batchNodeCollectionSizeMismatchNoGroupId": "Dimensione della raccolta di gruppo nel Lotto non corrisponde",
-            "modelIncompatibleBboxWidth": "La larghezza del riquadro di delimitazione è {{width}} ma {{model}} richiede multipli di {{multiple}}"
+            "modelIncompatibleBboxWidth": "La larghezza del riquadro di delimitazione è {{width}} ma {{model}} richiede multipli di {{multiple}}",
+            "modelIncompatibleBboxHeight": "L'altezza del riquadro è {{height}} ma {{model}} richiede multipli di {{multiple}}",
+            "modelIncompatibleScaledBboxWidth": "La larghezza scalata del riquadro è {{width}} ma {{model}} richiede multipli di {{multiple}}",
+            "modelIncompatibleScaledBboxHeight": "L'altezza scalata del riquadro è {{height}} ma {{model}} richiede multipli di {{multiple}}",
+            "modelDisabledForTrial": "La generazione con {{modelName}} non è disponibile per gli account di prova. Accedi alle impostazioni del tuo account per effettuare l'upgrade."
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
@@ -746,7 +769,8 @@
         "sendToCanvas": "Invia alla Tela",
         "coherenceMinDenoise": "Min rid. rumore",
         "recallMetadata": "Richiama i metadati",
-        "disabledNoRasterContent": "Disabilitato (nessun contenuto Raster)"
+        "disabledNoRasterContent": "Disabilitato (nessun contenuto Raster)",
+        "modelDisabledForTrial": "La generazione con {{modelName}} non è disponibile per gli account di prova. Visita le <LinkComponent>impostazioni account</LinkComponent> per effettuare l'upgrade."
     },
     "settings": {
         "models": "Modelli",
@@ -855,7 +879,11 @@
         "unableToCopy": "Impossibile copiare",
         "unableToCopyDesc": "Il tuo browser non supporta l'accesso agli appunti. Gli utenti di Firefox potrebbero risolvere il problema seguendo ",
         "unableToCopyDesc_theseSteps": "questi passaggi",
-        "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill non è compatibile con Testo a Immagine o Immagine a Immagine. Per queste attività, utilizzare altri modelli FLUX."
+        "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill non è compatibile con Testo a Immagine o Immagine a Immagine. Per queste attività, utilizzare altri modelli FLUX.",
+        "problemUnpublishingWorkflow": "Problema durante l'annullamento della pubblicazione del flusso di lavoro",
+        "problemUnpublishingWorkflowDescription": "Si è verificato un problema durante l'annullamento della pubblicazione del flusso di lavoro. Riprova.",
+        "workflowUnpublished": "Flusso di lavoro non pubblicato",
+        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o supporta solo la conversione da testo a immagine e da immagine a immagine. Utilizza altri modelli per le attività di Inpainting e Outpainting."
     },
     "accessibility": {
         "invokeProgressBar": "Barra di avanzamento generazione",
@@ -1049,7 +1077,8 @@
         "unknownField_withName": "Campo \"{{name}}\" sconosciuto",
         "missingField_withName": "Campo \"{{name}}\" mancante",
         "unknownFieldEditWorkflowToFix_withName": "Il flusso di lavoro contiene un campo \"{{name}}\" sconosciuto .\nModifica il flusso di lavoro per risolvere il problema.",
-        "unexpectedField_withName": "Campo \"{{name}}\" inaspettato"
+        "unexpectedField_withName": "Campo \"{{name}}\" inaspettato",
+        "missingSourceOrTargetHandle": "Identificatore del nodo sorgente o di destinazione mancante"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1178,7 +1207,8 @@
         "cancelAllExceptCurrentTooltip": "Annulla tutto tranne l'elemento corrente",
         "retrySucceeded": "Elemento rieseguito",
         "retryItem": "Riesegui elemento",
-        "retryFailed": "Problema riesecuzione elemento"
+        "retryFailed": "Problema riesecuzione elemento",
+        "credits": "Crediti"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1821,7 +1851,10 @@
             "publishingValidationRunInProgress": "È in corso la convalida della pubblicazione.",
             "publishedWorkflowsLocked": "I flussi di lavoro pubblicati sono bloccati e non possono essere modificati o eseguiti. Annulla la pubblicazione del flusso di lavoro o salva una copia per modificare o eseguire questo flusso di lavoro.",
             "warningWorkflowHasNoPublishableInputFields": "Nessun campo di ingresso pubblicabile selezionato: il flusso di lavoro pubblicato verrà eseguito solo con i valori predefiniti",
-            "publishInProgress": "Pubblicazione in corso"
+            "publishInProgress": "Pubblicazione in corso",
+            "selectingOutputNode": "Selezione del nodo di uscita",
+            "selectingOutputNodeDesc": "Fare clic su un nodo per selezionarlo come nodo di uscita del flusso di lavoro.",
+            "errorWorkflowHasUnpublishableNodes": "Il flusso di lavoro ha nodi di estrazione lotto, generatore o metadati"
         },
         "loadMore": "Carica altro",
         "searchPlaceholder": "Cerca per nome, descrizione o etichetta",
@@ -1971,12 +2004,16 @@
         "stagingOnCanvas": "Genera immagini nella",
         "ipAdapterMethod": {
             "full": "Stile e Composizione",
-            "style": "Solo Stile",
+            "style": "Stile (semplice)",
             "composition": "Solo Composizione",
             "ipAdapterMethod": "Modalità",
             "fullDesc": "Applica lo stile visivo (colori, texture) e la composizione (disposizione, struttura).",
-            "styleDesc": "Applica lo stile visivo (colori, texture) senza considerare la disposizione.",
-            "compositionDesc": "Replica disposizione e struttura ignorando lo stile di riferimento."
+            "styleDesc": "Applica lo stile visivo (colori, texture) senza considerare la disposizione. Precedentemente chiamato \"Solo stile\".",
+            "compositionDesc": "Replica disposizione e struttura ignorando lo stile di riferimento.",
+            "styleStrong": "Stile (forte)",
+            "styleStrongDesc": "Applica uno stile visivo forte, con un'influenza sulla composizione leggermente ridotta.",
+            "stylePrecise": "Stile (preciso)",
+            "stylePreciseDesc": "Applica uno stile visivo preciso, eliminando l'influenza del soggetto."
         },
         "showingType": "Mostra {{type}}",
         "dynamicGrid": "Griglia dinamica",
@@ -2299,6 +2336,14 @@
         "errors": {
             "unableToFindImage": "Impossibile trovare l'immagine",
             "unableToLoadImage": "Impossibile caricare l'immagine"
+        },
+        "fluxReduxImageInfluence": {
+            "high": "Alta",
+            "low": "Basso",
+            "imageInfluence": "Influenza dell'immagine",
+            "lowest": "Il più basso",
+            "medium": "Medio",
+            "highest": "La più alta"
         }
     },
     "ui": {
@@ -2399,8 +2444,8 @@
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
-            "Flussi di lavoro: supporto per menu a discesa di stringhe personalizzate nel Generatore di Flussi di lavoro.",
-            "FLUX: supporto per FLUX Fill in Flussi di lavoro e Tela."
+            "GPU Nvidia 50xx: Invoke utilizza PyTorch 2.7.0, necessario per queste GPU.",
+            "Relazioni tra modelli: collega i LoRA ai modelli principali e i LoRA verranno visualizzati per primi nell'elenco."
         ]
     },
     "system": {

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

@@ -118,7 +118,15 @@
         "value": "値",
         "label": "ラベル",
         "saveChanges": "変更を保存",
-        "error_withCount_other": "{{count}} 個のエラー"
+        "error_withCount_other": "{{count}} 個のエラー",
+        "noMatches": "合致しません",
+        "model_withCount_other": "{{count}}個のモデル",
+        "noOptions": "オプションがありません",
+        "search": "検索",
+        "clear": "クリア",
+        "compactView": "コンパクトビュー",
+        "fullView": "フルビュー",
+        "options_withCount_other": "{{count}}個のオプション"
     },
     "gallery": {
         "galleryImageSize": "画像のサイズ",
@@ -583,7 +591,7 @@
         "deleteModelImage": "モデル画像を削除",
         "hfTokenInvalid": "ハギングフェイストークンが無効または見つかりません",
         "hfForbiddenErrorMessage": "リポジトリにアクセスすることを勧めます.所有者はダウンロードにあたり利用規約への同意を要求する場合があります.",
-        "noModelsInstalled": "インストールされているモデルなし",
+        "noModelsInstalled": "インストールされているモデルがありません",
         "pathToConfig": "設定へのパス",
         "noModelsInstalledDesc1": "モデルを一緒にインストール",
         "pruneTooltip": "完了したインポートをキューから削除",
@@ -639,7 +647,12 @@
         "urlUnauthorizedErrorMessage": "このモデルにアクセスするためにAPIトークンを構成する必要があるかもしれません.",
         "urlUnauthorizedErrorMessage2": "ここでどうやるか学びます.",
         "inplaceInstall": "定位置にインストール",
-        "fileSize": "ファイルサイズ"
+        "fileSize": "ファイルサイズ",
+        "modelPickerFallbackNoModelsInstalled2": "<LinkComponent>モデルマネージャー</LinkComponent> にアクセスしてモデルをインストールしてください.",
+        "filterModels": "フィルターモデル",
+        "modelPickerFallbackNoModelsInstalled": "モデルがインストールされていません.",
+        "manageModels": "モデル管理",
+        "hfTokenReset": "ハギングフェイストークンリセット"
     },
     "parameters": {
         "images": "画像",
@@ -684,7 +697,28 @@
             "collectionNumberGTMax": "{{value}} > {{maximum}} (最大増加)",
             "missingNodeTemplate": "ノードテンプレートの欠落",
             "batchNodeNotConnected": "バッチノードが: {{label}}につながっていない",
-            "collectionNumberLTMin": "{{value}} < {{minimum}} (最小増加)"
+            "collectionNumberLTMin": "{{value}} < {{minimum}} (最小増加)",
+            "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), スケーリングされたbboxの高さは{{height}}です",
+            "fluxModelMultipleControlLoRAs": "コントロールLoRAは1度に1つしか使用できません",
+            "noPrompts": "プロンプトが生成されません",
+            "noNodesInGraph": "グラフにノードがありません",
+            "noCLIPEmbedModelSelected": "FLUX生成にCLIPエンベッドモデルが選択されていません",
+            "canvasIsFiltering": "キャンバスがビジー状態(フィルタリング)",
+            "canvasIsCompositing": "キャンバスがビジー状態(合成)",
+            "systemDisconnected": "システムが切断されました",
+            "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), 拡大縮小されたbboxの幅は{{width}}です",
+            "canvasIsTransforming": "キャンバスがビジー状態(変換)",
+            "canvasIsRasterizing": "キャンバスがビジー状態(ラスタライズ)",
+            "modelIncompatibleBboxHeight": "Bboxの高さは{{height}}ですが，{{model}}は{{multiple}}の倍数が必要です",
+            "modelIncompatibleScaledBboxHeight": "bboxの高さは{{height}}ですが，{{model}}は{{multiple}}の倍数を必要です",
+            "modelIncompatibleBboxWidth": "Bboxの幅は{{width}}ですが, {{model}}は{{multiple}}の倍数が必要です",
+            "modelIncompatibleScaledBboxWidth": "bboxの幅は{{width}}ですが，{{model}}は{{multiple}}の倍数が必要です",
+            "canvasIsSelectingObject": "キャンバスがビジー状態(オブジェクトの選択)",
+            "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), bboxの幅は{{width}}です",
+            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), bboxの高さは{{height}}です",
+            "noFLUXVAEModelSelected": "FLUX生成にVAEモデルが選択されていません",
+            "noT5EncoderModelSelected": "FLUX生成にT5エンコーダモデルが選択されていません",
+            "modelDisabledForTrial": "{{modelName}} を使用した生成はトライアルアカウントではご利用いただけません.アカウント設定にアクセスしてアップグレードしてください。"
         },
         "aspect": "縦横比",
         "lockAspectRatio": "縦横比を固定",
@@ -716,7 +750,24 @@
         "cfgRescaleMultiplier": "CFGリスケール倍率",
         "clipSkip": "クリップスキップ",
         "guidance": "ガイダンス",
-        "infillMethod": "充填法"
+        "infillMethod": "充填法",
+        "patchmatchDownScaleSize": "ダウンスケール",
+        "boxBlur": "ボックスぼかし",
+        "remixImage": "リミックス画像",
+        "processImage": "プロセス画像",
+        "useCpuNoise": "CPUノイズの使用",
+        "staged": "ステージ",
+        "perlinNoise": "パーリン・ノイズ(グラデーションノイズ)",
+        "imageActions": "画像処理",
+        "gaussianBlur": "ガウスぼかし",
+        "noiseThreshold": "ノイズの閾値",
+        "maskBlur": "マスクぼかし",
+        "seamlessYAxis": "シームレスなY軸",
+        "optimizedImageToImage": "イメージ to イメージの最適化",
+        "symmetry": "左右対称",
+        "seamlessXAxis": "シームレスなX軸",
+        "sendToCanvas": "キャンバスに送る",
+        "modelDisabledForTrial": "{{modelName}} を使用した生成はトライアルアカウントではご利用いただけません.アップグレードするには,<LinkComponent>アカウント設定</LinkComponent> にアクセスしてください."
     },
     "settings": {
         "models": "モデル",
@@ -728,16 +779,100 @@
         "resetComplete": "WebUIはリセットされました。",
         "ui": "ユーザーインターフェイス",
         "beta": "ベータ",
-        "developer": "開発者"
+        "developer": "開発者",
+        "antialiasProgressImages": "アンチエイリアスの経過画像",
+        "enableInformationalPopovers": "情報ポップオーバーを有効にする",
+        "enableModelDescriptions": "ドロップダウンでモデルの説明を有効にする",
+        "confirmOnNewSession": "新しいセッションで確認する",
+        "informationalPopoversDisabled": "情報ポップオーバーが無効になっています",
+        "informationalPopoversDisabledDesc": "情報ポップオーバーが無効になっています.設定で有効にしてください.",
+        "enableNSFWChecker": "NSFWチェッカーを有効にする",
+        "enableInvisibleWatermark": "目に見えない透かしを有効にする",
+        "enableHighlightFocusedRegions": "重点領域を強調表示",
+        "clearIntermediatesDesc1": "中間物をクリアすると、キャンバスとコントロールネットの状態がリセットされます.",
+        "showProgressInViewer": "ビューアで進行状況画像を表示する",
+        "modelDescriptionsDisabled": "ドロップダウンのモデル説明が無効になっています",
+        "modelDescriptionsDisabledDesc": "ドロップダウンのモデル説明が無効になっています.設定で有効にしてください.",
+        "clearIntermediatesDisabled": "中間物をクリアするにはキューが空でなければなりません",
+        "clearIntermediatesDesc2": "中間画像は生成時に生成される副産物であり、ギャラリーに表示される結果画像とは異なります.中間画像を削除するとディスク容量が解放されます.",
+        "intermediatesClearedFailed": "中間物をクリアする問題",
+        "reloadingIn": "リロード中",
+        "clearIntermediatesDesc3": "ギャラリー画像は削除されません.",
+        "clearIntermediates": "中間物をクリア",
+        "clearIntermediatesWithCount_other": "{{count}} 個の中間物をクリア",
+        "intermediatesCleared_other": "{{count}}個の中間物がクリアされました",
+        "general": "一般",
+        "generation": "生成",
+        "showDetailedInvocationProgress": "進捗状況の詳細を表示"
     },
     "toast": {
         "uploadFailed": "アップロード失敗",
         "imageCopied": "画像をコピー",
         "imageUploadFailed": "画像のアップロードに失敗しました",
-        "uploadFailedInvalidUploadDesc": "画像はPNGかJPGである必要があります。",
+        "uploadFailedInvalidUploadDesc": "画像はPNGかJPGかWEBPである必要があります .",
         "sentToUpscale": "アップスケーラーに転送しました",
         "imageUploaded": "画像をアップロードしました",
-        "serverError": "サーバーエラー"
+        "serverError": "サーバーエラー",
+        "prunedQueue": "キューを破棄",
+        "workflowDeleted": "ワークフローが削除されました",
+        "unableToLoadStylePreset": "スタイルプリセットをロードできません",
+        "loadedWithWarnings": "ワークフローが警告付きでロードされました",
+        "parameters": "パラメーター",
+        "parameterSet": "パラメーターが呼び出されました",
+        "pasteSuccess": "{{destination}} に貼り付けました",
+        "imagesWillBeAddedTo": "アップロードされた画像はボード {{boardName}} のアセットに追加されます.",
+        "layerCopiedToClipboard": "レイヤーがクリップボードにコピーされました",
+        "pasteFailed": "貼り付け失敗",
+        "imageSavingFailed": "画像保存に失敗しました",
+        "importSuccessful": "インポートが成功しました",
+        "problemDownloadingImage": "画像をダウンロードできません",
+        "modelAddedSimple": "モデルがキューに追加されました",
+        "uploadFailedInvalidUploadDesc_withCount_other": "PNG、JPEG、または WEBP 画像は最大 1 つにする必要があります.",
+        "outOfMemoryErrorDesc": "現在の生成設定はシステム容量を超えています.設定を調整してもう一度お試しください.",
+        "parametersSet": "パラメーターが呼び出されました",
+        "modelImportCanceled": "モデルのインポートがキャンセルされました",
+        "problemRetrievingWorkflow": "ワークフローを取得した問題",
+        "problemUnpublishingWorkflow": "取り消されたワークフローの問題",
+        "parametersNotSet": "パラメーターが呼び出されていません",
+        "problemCopyingImage": "画像をコピーできません",
+        "baseModelChanged": "ベースモデルが変更されました",
+        "baseModelChangedCleared_other": "{{count}} 個の互換性のないサブモデルをクリア，または無効にしました",
+        "canceled": "処理がキャンセルされました",
+        "connected": "サーバーに接続されました",
+        "linkCopied": "リンクがコピーされました",
+        "unableToLoadImage": "画像をロードできません",
+        "unableToLoadImageMetadata": "画像のメタデータをロードできません",
+        "imageSaved": "画像が保存されました",
+        "importFailed": "インポートに失敗しました",
+        "invalidUpload": "無効なアップロードです",
+        "outOfMemoryError": "メモリ不足エラー",
+        "parameterSetDesc": "{{parameter}}を呼び出し",
+        "errorCopied": "エラーがコピーされました",
+        "sentToCanvas": "キャンバスに送信",
+        "setControlImage": "コントロール画像としてセット",
+        "workflowLoaded": "ワークフローがロードされました",
+        "unableToCopy": "コピーできません",
+        "unableToCopyDesc": "あなたのブラウザはクリップボードアクセスをサポートしていません.Firefoxユーザーの場合は、以下の手順で修正できる可能性があります. ",
+        "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fillは、テキストから画像へ、または画像から画像へ変換機能と互換性がありません.これらのタスクには、他のFLUXモデルをご利用ください.",
+        "problemUnpublishingWorkflowDescription": "取り下げられたワークフローの問題がありました.もう一度試してください.",
+        "workflowUnpublished": "ワークフローが取り消されました",
+        "sessionRef": "セッション: {{sessionId}}",
+        "somethingWentWrong": "問題が発生しました",
+        "unableToCopyDesc_theseSteps": "これらのステップ数",
+        "stylePresetLoaded": "スタイルプリセットがロードされました",
+        "parameterNotSetDescWithMessage": "{{parameter}}: {{message}}を呼び出せません",
+        "problemCopyingLayer": "レイヤーをコピーできません",
+        "problemSavingLayer": "レイヤー保存ができません",
+        "setNodeField": "ノードフィールドとしてセット",
+        "layerSavedToAssets": "レイヤーがアセットに保存されました",
+        "outOfMemoryErrorDescLocal": "OOM を削減するには、<LinkComponent>低 VRAM ガイド</LinkComponent> に従ってください.",
+        "parameterNotSet": "パラメーターが呼び出されていません",
+        "addedToBoard": "{{name}} 個の資産をボードに追加しました",
+        "addedToUncategorized": "$t(boards.uncategorized)個のアセットがボードに追加されました",
+        "problemDeletingWorkflow": "ワークフローが削除された問題",
+        "imageNotLoadedDesc": "画像を見つけられません",
+        "parameterNotSetDesc": "{{parameter}}を呼び出せません",
+        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4oは,テキストから画像への生成と画像から画像への生成のみをサポートしています.インペインティングおよび,アウトペインティングタスクには他のモデルを使用してください."
     },
     "accessibility": {
         "invokeProgressBar": "進捗バー",
@@ -862,7 +997,8 @@
         "batchSize": "バッチサイズ",
         "retryFailed": "項目のリトライに問題があります",
         "cancelAllExceptCurrentQueueItemAlertDialog": "現在の項目を除くすべてのキュー項目をキャンセルすると、保留中の項目は停止しますが、進行中の項目は完了します。",
-        "retrySucceeded": "項目がリトライされました"
+        "retrySucceeded": "項目がリトライされました",
+        "credits": "クレジット"
     },
     "models": {
         "noMatchingModels": "一致するモデルがありません",
@@ -1114,22 +1250,42 @@
             ]
         },
         "regionalGuidanceAndReferenceImage": {
-            "heading": "領域ガイダンスと領域参照画像"
+            "heading": "領域ガイダンスと領域参照画像",
+            "paragraphs": [
+                "領域ガイダンスの場合は,ブラシを使用して,グローバルプロンプトの要素が表示される場所をガイドします.",
+                "領域参照画像の場合は,ブラシを使用して特定の領域に参照画像を適用します."
+            ]
         },
         "regionalReferenceImage": {
-            "heading": "領域参照画像"
+            "heading": "領域参照画像",
+            "paragraphs": [
+                "特定の領域に参照画像を適用するためのブラシ."
+            ]
         },
         "paramScheduler": {
-            "heading": "スケジューラー"
+            "heading": "スケジューラー",
+            "paragraphs": [
+                "スケジューラーは生成中のプロセスで使用されます.",
+                "各スケジューラは、画像にノイズを反復的に追加する方法や、モデルの出力に基づいてサンプルを更新する方法を定義します."
+            ]
         },
         "regionalGuidance": {
-            "heading": "領域ガイダンス"
+            "heading": "領域ガイダンス",
+            "paragraphs": [
+                "グローバルプロンプトの要素が表示される場所をガイドするブラシ."
+            ]
         },
         "rasterLayer": {
-            "heading": "ラスターレイヤー"
+            "heading": "ラスターレイヤー",
+            "paragraphs": [
+                "画像生成中に使用される,キャンバスのピクセルベースのコンテンツ."
+            ]
         },
         "globalReferenceImage": {
-            "heading": "全域参照画像"
+            "heading": "全域参照画像",
+            "paragraphs": [
+                "参照画像を適用して,生成全体に影響を及ぼします."
+            ]
         },
         "paramUpscaleMethod": {
             "heading": "アップスケール手法"
@@ -1153,7 +1309,10 @@
             "heading": "スケジューラー"
         },
         "compositingCoherenceMode": {
-            "heading": "モード"
+            "heading": "モード",
+            "paragraphs": [
+                "新しく生成されたマスク領域と,一貫性のある画像を作成するために使用される方法."
+            ]
         },
         "paramModel": {
             "heading": "モデル"
@@ -1165,7 +1324,10 @@
             "heading": "ステップ"
         },
         "ipAdapterMethod": {
-            "heading": "モード"
+            "heading": "モード",
+            "paragraphs": [
+                "モードは参照画像が生成プロセスをどのようにガイドするかを定義します."
+            ]
         },
         "paramSeed": {
             "heading": "シード"
@@ -1174,7 +1336,10 @@
             "heading": "生成回数"
         },
         "controlNet": {
-            "heading": "ControlNet"
+            "heading": "ControlNet",
+            "paragraphs": [
+                "コントロールネットは生成プロセスへのガイダンスを提供し,選択したモデルに応じて制御された構成,構造,またはスタイルを持つ画像の作成に役立ちます."
+            ]
         },
         "paramWidth": {
             "heading": "幅"
@@ -1189,7 +1354,109 @@
             "heading": "Downscale"
         },
         "controlNetWeight": {
-            "heading": "重み"
+            "heading": "重み",
+            "paragraphs": [
+                "レイヤーが生成プロセスにどの程度影響を与えるかを調整します",
+                "• 高いウエイト (.75-2): 最終結果にさらに大きな影響を及ぼします.",
+                "• 低いウエイト (0-.75): 最終結果への影響が小さくなります."
+            ]
+        },
+        "paramNegativeConditioning": {
+            "paragraphs": [
+                "生成プロセスでは、ネガティブプロンプトに含まれる概念を回避します.これを使用して、出力から特定の性質やオブジェクトを除外します.",
+                "強制された構文と埋め込みをサポート."
+            ],
+            "heading": "ネガティブプロンプト"
+        },
+        "clipSkip": {
+            "paragraphs": [
+                "スキップする CLIP モデルのレイヤー数.",
+                "特定のモデルは、CLIP Skip と併用するとより適しています."
+            ],
+            "heading": "クリップスキップ"
+        },
+        "compositingMaskBlur": {
+            "heading": "マスクぼかし",
+            "paragraphs": [
+                "マスクのぼかし半径."
+            ]
+        },
+        "paramPositiveConditioning": {
+            "paragraphs": [
+                "生成プロセスをガイドします.任意の単語やフレーズを使用できます.",
+                "強制とダイナミックプロンプトの構文と埋め込み."
+            ],
+            "heading": "ポジティブプロンプト"
+        },
+        "compositingMaskAdjustments": {
+            "heading": "マスク調整",
+            "paragraphs": [
+                "マスクを調整する."
+            ]
+        },
+        "compositingCoherenceMinDenoise": {
+            "paragraphs": [
+                "コヒーレンスモードの最小ノイズ除去強度",
+                "インペインティングまたはアウトペインティング時のコヒーレンス領域の最小ノイズ除去強度"
+            ],
+            "heading": "最小ノイズ除去"
+        },
+        "compositingCoherencePass": {
+            "paragraphs": [
+                "2 回目のノイズ除去は,インペイント/アウトペイントされた画像の合成に役立ちます."
+            ],
+            "heading": "コヒーレンスパス"
+        },
+        "controlNetBeginEnd": {
+            "paragraphs": [
+                "この設定は,ノイズ除去 (生成) プロセスのどの部分にこのレイヤーからのガイダンスが組み込まれるかを決定します.",
+                "• 開始ステップ (%): 生成プロセス中にこのレイヤーからのガイダンスの適用を開始するタイミングを指定します.",
+                "• 終了ステップ (%): このレイヤーのガイダンスの適用を停止し,モデルやその他の設定からの一般的なガイダンスを元に戻すタイミングを指定します."
+            ],
+            "heading": "開始/終了ステップの割合"
+        },
+        "compositingCoherenceEdgeSize": {
+            "heading": "エッジサイズ",
+            "paragraphs": [
+                "コヒーレンスパスのエッジサイズ."
+            ]
+        },
+        "compositingBlurMethod": {
+            "paragraphs": [
+                "マスクされた領域に適用されるぼかし方法."
+            ],
+            "heading": "ぼかし方法"
+        },
+        "inpainting": {
+            "heading": "インペインティング",
+            "paragraphs": [
+                "ノイズ除去の強度に応じて,変更する領域を制御します."
+            ]
+        },
+        "dynamicPrompts": {
+            "heading": "ダイナミックプロンプト",
+            "paragraphs": [
+                "ダイナミック プロンプトは,単一のプロンプトを複数のプロンプトに解析します.",
+                "基本的な構文は「{赤|緑|青}のボール」です.これにより,「赤いボール」「緑のボール」「青いボール」という3つのプロンプトが生成されます."
+            ]
+        },
+        "controlNetResizeMode": {
+            "heading": "リサイズモード",
+            "paragraphs": [
+                "コントロールアダプタの入力画像サイズを出力生成サイズに適合させるメソッド."
+            ]
+        },
+        "controlNetProcessor": {
+            "heading": "プロセッサー",
+            "paragraphs": [
+                "入力画像を処理する生成プロセスをガイドするメソッド.プロセッサによって,生成される画像に異なる効果やスタイルが与えられます。"
+            ]
+        },
+        "controlNetControlMode": {
+            "heading": "コントロールモード",
+            "paragraphs": [
+                "プロンプトまたは コントロールネットのいずれかを重視します."
+            ]
         }
     },
     "accordions": {
@@ -1340,7 +1607,18 @@
         "scheduler": "スケジューラー",
         "loading": "ロード中...",
         "steps": "ステップ",
-        "refiner": "Refiner"
+        "refiner": "Refiner",
+        "negStylePrompt": "ネガティブスタイルプロンプト",
+        "noModelsAvailable": "利用できるモデルがありません",
+        "posStylePrompt": "ポジティブスタイルプロンプト",
+        "cfgScale": "CFGスケール",
+        "concatPromptStyle": "リンキングプロンプトとスタイル",
+        "freePromptStyle": "手動スタイルプロンプト",
+        "posAestheticScore": "ポジティブ美的スコア",
+        "refinerSteps": "リファイナーステップ",
+        "refinerStart": "リファイナースタート",
+        "refinermodel": "リファイナーモデル",
+        "negAestheticScore": "ネガティブ美的スコア"
     },
     "modelCache": {
         "clear": "モデルキャッシュを消去",
@@ -1370,5 +1648,20 @@
             "fatal": "Fatal",
             "warn": "Warn"
         }
+    },
+    "dynamicPrompts": {
+        "promptsPreview": "プロンプトプレビュー",
+        "seedBehaviour": {
+            "label": "シードの挙動",
+            "perPromptLabel": "画像ごとのシード",
+            "perIterationLabel": "いてレーションごとのシード",
+            "perPromptDesc": "それぞれの画像に足して別のシードを使う",
+            "perIterationDesc": "それぞれのいてレーションに別のシードを使う"
+        },
+        "showDynamicPrompts": "ダイナミックプロンプトを表示する",
+        "promptsToGenerate": "生成するプロンプト",
+        "dynamicPrompts": "ダイナミックプロンプト",
+        "loading": "ダイナミックプロンプトを生成...",
+        "maxPrompts": "最大プロンプト"
     }
 }

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

@@ -240,7 +240,15 @@
         "error_withCount_other": "{{count}} lỗi",
         "value": "Giá Trị",
         "label": "Nhãn Tên",
-        "systemInformation": "Thông Tin Hệ Thống"
+        "systemInformation": "Thông Tin Hệ Thống",
+        "model_withCount_other": "{{count}} model",
+        "noOptions": "Không Có Lựa Chọn",
+        "noMatches": "Không Có Mục Phù Hợp",
+        "search": "Tìm Kiếm",
+        "clear": "Dọn Dẹp",
+        "compactView": "Chế Độ Xem Gọn",
+        "fullView": "Chế Độ Xem Đầy Đủ",
+        "options_withCount_other": "{{count}} thiết lập"
     },
     "prompt": {
         "addPromptTrigger": "Thêm Prompt Trigger",
@@ -321,7 +329,8 @@
         "confirm": "Đồng Ý",
         "retrySucceeded": "Mục Đã Thử Lại",
         "retryFailed": "Có Vấn Đề Khi Thử Lại Mục",
-        "retryItem": "Thử Lại Mục"
+        "retryItem": "Thử Lại Mục",
+        "credits": "Nguồn"
     },
     "hotkeys": {
         "canvas": {
@@ -775,7 +784,14 @@
         "fluxRedux": "FLUX Redux",
         "sigLip": "SigLIP",
         "llavaOnevision": "LLaVA OneVision",
-        "fileSize": "Kích Thước Tệp"
+        "fileSize": "Kích Thước Tệp",
+        "filterModels": "Lọc Model",
+        "modelPickerFallbackNoModelsInstalled2": "Nhấp vào <LinkComponent>Trình Quản Lý Model</LinkComponent> để tải.",
+        "modelPickerFallbackNoModelsInstalled": "Không Có Sẵn Model.",
+        "manageModels": "Quản Lý Model",
+        "hfTokenReset": "Làm Mới HF Token",
+        "relatedModels": "Model Liên Quan",
+        "showOnlyRelatedModels": "Liên Quan"
     },
     "metadata": {
         "guidance": "Hướng Dẫn",
@@ -1518,7 +1534,8 @@
             "modelIncompatibleBboxWidth": "Chiều rộng hộp giới hạn là {{width}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
             "modelIncompatibleBboxHeight": "Chiều dài hộp giới hạn là {{height}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "Chiều dài hộp giới hạn theo tỉ lệ là {{height}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
-            "modelIncompatibleScaledBboxWidth": "Chiều rộng hộp giới hạn theo tỉ lệ là {{width}} nhưng {{model}} yêu cầu bội số của {{multiple}}"
+            "modelIncompatibleScaledBboxWidth": "Chiều rộng hộp giới hạn theo tỉ lệ là {{width}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
+            "modelDisabledForTrial": "Tạo sinh với {{modelName}} là không thể với tài khoản trial. Vào phần thiết lập tài khoản để nâng cấp."
         },
         "cfgScale": "Thang CFG",
         "useSeed": "Dùng Hạt Giống",
@@ -1581,7 +1598,8 @@
         "usePrompt": "Dùng Lệnh",
         "upscaling": "Upscale",
         "tileSize": "Kích Thước Khối",
-        "disabledNoRasterContent": "Đã Tắt (Không Có Nội Dung Dạng Raster)"
+        "disabledNoRasterContent": "Đã Tắt (Không Có Nội Dung Dạng Raster)",
+        "modelDisabledForTrial": "Tạo sinh với {{modelName}} là không thể với tài khoản trial. Vào phần <LinkComponent>thiết lập tài khoản</LinkComponent> để nâng cấp."
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -1699,12 +1717,16 @@
         "fitBboxToLayers": "Xếp Vừa Hộp Giới Hạn Vào Layer",
         "ipAdapterMethod": {
             "full": "Phong Cách Và Thành Phần",
-            "style": "Chỉ Lấy Phong Cách",
+            "style": "Phong Cách (Đơn Giản)",
             "composition": "Chỉ Lấy Thành Phần",
             "ipAdapterMethod": "Cách Thức",
             "compositionDesc": "Áp dụng cách trình bày và bỏ qua phong cách mẫu.",
             "fullDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) & thành phần (cách trình bày).",
-            "styleDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) và bỏ qua cách trình bày."
+            "styleDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) và bỏ qua cách trình bày. Tên trước đây là Chỉ Lấy Phong Cách.",
+            "styleStrong": "Phong Cách (Mạnh Mẽ)",
+            "styleStrongDesc": "Áp dụng cách trình bày mạnh mẽ, với một chút giảm nhẹ ảnh hưởng lên thành phần.",
+            "stylePrecise": "Phong Cách (Chính Xác)",
+            "stylePreciseDesc": "Áp dụng cách trình bày chính xác, loại bỏ các chủ thể ảnh hưởng."
         },
         "deletePrompt": "Xoá Lệnh",
         "rasterLayer": "Layer Dạng Raster",
@@ -2226,7 +2248,8 @@
         "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill không tương tích với Từ Ngữ Sang Hình Ảnh và Hình Ảnh Sang Hình Ảnh. Dùng model FLUX khác cho các tính năng này.",
         "problemUnpublishingWorkflowDescription": "Có vấn đề khi ngừng đăng tải workflow. Vui lòng thử lại sau.",
         "workflowUnpublished": "Workflow Đã Được Ngừng Đăng Tải",
-        "problemUnpublishingWorkflow": "Có Vấn Đề Khi Ngừng Đăng Tải Workflow"
+        "problemUnpublishingWorkflow": "Có Vấn Đề Khi Ngừng Đăng Tải Workflow",
+        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o chỉ hỗ trợ Từ Ngữ Sang Hình Ảnh và Hình Ảnh Sang Hình Ảnh. Hãy dùng model khác cho các tác vụ Inpaint và Outpaint."
     },
     "ui": {
         "tabs": {
@@ -2408,8 +2431,8 @@
         "watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
-            "Workflow: Hỗ trợ xâu ký tự thả xuống tùy chỉnh trong Trình Tạo Vùng Nhập.",
-            "FLUX: Hỗ trợ FLUX Fill trong Workflow và Canvas."
+            "Nvidia 50xx GPUs: Invoke sử dụng PyTorch 2.7.0, thứ tối quan trọng cho những GPU trên.",
+            "Mối Quan Hệ Model: Kết nối LoRA với model chính, và LoRA đó sẽ được hiển thị đầu danh sách."
         ]
     },
     "upsell": {

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

@@ -5,6 +5,7 @@ import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
 import { $didStudioInit } from 'app/hooks/useStudioInitAction';
 import type { LoggingOverrides } from 'app/logging/logger';
 import { $loggingOverrides, configureLogging } from 'app/logging/logger';
+import { $accountSettingsLink } from 'app/store/nanostores/accountSettingsLink';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
 import { $customNavComponent } from 'app/store/nanostores/customNavComponent';
@@ -12,10 +13,13 @@ import type { CustomStarUi } from 'app/store/nanostores/customStarUI';
 import { $customStarUI } from 'app/store/nanostores/customStarUI';
 import { $isDebugging } from 'app/store/nanostores/isDebugging';
 import { $logo } from 'app/store/nanostores/logo';
+import { $onClickGoToModelManager } from 'app/store/nanostores/onClickGoToModelManager';
 import { $openAPISchemaUrl } from 'app/store/nanostores/openAPISchemaUrl';
 import { $projectId, $projectName, $projectUrl } from 'app/store/nanostores/projectId';
 import { $queueId, DEFAULT_QUEUE_ID } from 'app/store/nanostores/queueId';
 import { $store } from 'app/store/nanostores/store';
+import { $toastMap } from 'app/store/nanostores/toastMap';
+import { $whatsNew } from 'app/store/nanostores/whatsNew';
 import { createStore } from 'app/store/store';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
@@ -29,6 +33,7 @@ import {
   DEFAULT_WORKFLOW_LIBRARY_TAG_CATEGORIES,
 } from 'features/nodes/store/workflowLibrarySlice';
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
+import type { ToastConfig } from 'features/toast/toast';
 import type { PropsWithChildren, ReactNode } from 'react';
 import React, { lazy, memo, useEffect, useLayoutEffect, useMemo } from 'react';
 import { Provider } from 'react-redux';
@@ -45,6 +50,7 @@ interface Props extends PropsWithChildren {
   token?: string;
   config?: PartialAppConfig;
   customNavComponent?: ReactNode;
+  accountSettingsLink?: string;
   middleware?: Middleware[];
   projectId?: string;
   projectName?: string;
@@ -55,10 +61,16 @@ interface Props extends PropsWithChildren {
   socketOptions?: Partial<ManagerOptions & SocketOptions>;
   isDebugging?: boolean;
   logo?: ReactNode;
+  toastMap?: Record<string, ToastConfig>;
+  whatsNew?: ReactNode[];
   workflowCategories?: WorkflowCategory[];
   workflowTagCategories?: WorkflowTagCategory[];
   workflowSortOptions?: WorkflowSortOption[];
   loggingOverrides?: LoggingOverrides;
+  /**
+   * If provided, overrides in-app navigation to the model manager
+   */
+  onClickGoToModelManager?: () => void;
 }
 
 const InvokeAIUI = ({
@@ -67,6 +79,7 @@ const InvokeAIUI = ({
   token,
   config,
   customNavComponent,
+  accountSettingsLink,
   middleware,
   projectId,
   projectName,
@@ -77,10 +90,13 @@ const InvokeAIUI = ({
   socketOptions,
   isDebugging = false,
   logo,
+  toastMap,
   workflowCategories,
   workflowTagCategories,
   workflowSortOptions,
   loggingOverrides,
+  onClickGoToModelManager,
+  whatsNew,
 }: Props) => {
   useLayoutEffect(() => {
     /*
@@ -169,6 +185,16 @@ const InvokeAIUI = ({
     };
   }, [customNavComponent]);
 
+  useEffect(() => {
+    if (accountSettingsLink) {
+      $accountSettingsLink.set(accountSettingsLink);
+    }
+
+    return () => {
+      $accountSettingsLink.set(undefined);
+    };
+  }, [accountSettingsLink]);
+
   useEffect(() => {
     if (openAPISchemaUrl) {
       $openAPISchemaUrl.set(openAPISchemaUrl);
@@ -205,6 +231,36 @@ const InvokeAIUI = ({
     };
   }, [logo]);
 
+  useEffect(() => {
+    if (toastMap) {
+      $toastMap.set(toastMap);
+    }
+
+    return () => {
+      $toastMap.set(undefined);
+    };
+  }, [toastMap]);
+
+  useEffect(() => {
+    if (whatsNew) {
+      $whatsNew.set(whatsNew);
+    }
+
+    return () => {
+      $whatsNew.set(undefined);
+    };
+  }, [whatsNew]);
+
+  useEffect(() => {
+    if (onClickGoToModelManager) {
+      $onClickGoToModelManager.set(onClickGoToModelManager);
+    }
+
+    return () => {
+      $onClickGoToModelManager.set(undefined);
+    };
+  }, [onClickGoToModelManager]);
+
   useEffect(() => {
     if (workflowCategories) {
       $workflowLibraryCategoriesOptions.set(workflowCategories);

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

@@ -1,3 +1,4 @@
+import type { AlertStatus } from '@invoke-ai/ui-library';
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
@@ -6,11 +7,15 @@ import { withResult, withResultAsync } from 'common/util/result';
 import { parseify } from 'common/util/serialize';
 import { $canvasManager } from 'features/controlLayers/store/ephemeral';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
+import { buildChatGPT4oGraph } from 'features/nodes/util/graph/generation/buildChatGPT4oGraph';
 import { buildCogView4Graph } from 'features/nodes/util/graph/generation/buildCogView4Graph';
 import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGraph';
+import { buildImagen3Graph } from 'features/nodes/util/graph/generation/buildImagen3Graph';
+import { buildImagen4Graph } from 'features/nodes/util/graph/generation/buildImagen4Graph';
 import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
 import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph';
 import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
+import { UnsupportedGenerationModeError } from 'features/nodes/util/graph/types';
 import { toast } from 'features/toast/toast';
 import { serializeError } from 'serialize-error';
 import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
@@ -48,32 +53,52 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
             return await buildFLUXGraph(state, manager);
           case 'cogview4':
             return await buildCogView4Graph(state, manager);
+          case 'imagen3':
+            return await buildImagen3Graph(state, manager);
+          case 'imagen4':
+            return await buildImagen4Graph(state, manager);
+          case 'chatgpt-4o':
+            return await buildChatGPT4oGraph(state, manager);
           default:
             assert(false, `No graph builders for base ${base}`);
         }
       });
 
       if (buildGraphResult.isErr()) {
+        let title = 'Failed to build graph';
+        let status: AlertStatus = 'error';
         let description: string | null = null;
         if (buildGraphResult.error instanceof AssertionError) {
           description = extractMessageFromAssertionError(buildGraphResult.error);
+        } else if (buildGraphResult.error instanceof UnsupportedGenerationModeError) {
+          title = 'Unsupported generation mode';
+          description = buildGraphResult.error.message;
+          status = 'warning';
         }
         const error = serializeError(buildGraphResult.error);
         log.error({ error }, 'Failed to build graph');
         toast({
-          status: 'error',
-          title: 'Failed to build graph',
+          status,
+          title,
           description,
         });
         return;
       }
 
-      const { g, noise, posCond } = buildGraphResult.value;
+      const { g, seedFieldIdentifier, positivePromptFieldIdentifier } = buildGraphResult.value;
 
       const destination = state.canvasSettings.sendToCanvas ? 'canvas' : 'gallery';
 
       const prepareBatchResult = withResult(() =>
-        prepareLinearUIBatch(state, g, prepend, noise, posCond, 'canvas', destination)
+        prepareLinearUIBatch({
+          state,
+          g,
+          prepend,
+          seedFieldIdentifier,
+          positivePromptFieldIdentifier,
+          origin: 'canvas',
+          destination,
+        })
       );
 
       if (prepareBatchResult.isErr()) {
@@ -89,7 +114,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
         await req.unwrap();
         log.debug(parseify({ batchConfig: prepareBatchResult.value }), 'Enqueued batch');
       } catch (error) {
-        log.error({ error: serializeError(error) }, 'Failed to enqueue batch');
+        log.error({ error: serializeError(error as Error) }, 'Failed to enqueue batch');
       } finally {
         req.reset();
       }

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

@@ -18,16 +18,24 @@ export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening)
       const state = getState();
       const { prepend } = action.payload;
 
-      const { g, noise, posCond } = await buildMultidiffusionUpscaleGraph(state);
+      const { g, seedFieldIdentifier, positivePromptFieldIdentifier } = await buildMultidiffusionUpscaleGraph(state);
 
-      const batchConfig = prepareLinearUIBatch(state, g, prepend, noise, posCond, 'upscaling', 'gallery');
+      const batchConfig = prepareLinearUIBatch({
+        state,
+        g,
+        prepend,
+        seedFieldIdentifier,
+        positivePromptFieldIdentifier,
+        origin: 'upscaling',
+        destination: 'gallery',
+      });
 
       const req = dispatch(queueApi.endpoints.enqueueBatch.initiate(batchConfig, enqueueMutationFixedCacheKeyOptions));
       try {
         await req.unwrap();
         log.debug(parseify({ batchConfig }), 'Enqueued batch');
       } catch (error) {
-        log.error({ error: serializeError(error) }, 'Failed to enqueue batch');
+        log.error({ error: serializeError(error as Error) }, 'Failed to enqueue batch');
       } finally {
         req.reset();
       }

invokeai/frontend/web/src/app/store/nanostores/accountSettingsLink.ts

@@ -0,0 +1,3 @@
+import { atom } from 'nanostores';
+
+export const $accountSettingsLink = atom<string | undefined>(undefined);

invokeai/frontend/web/src/app/store/nanostores/onClickGoToModelManager.ts

@@ -0,0 +1,3 @@
+import { atom } from 'nanostores';
+
+export const $onClickGoToModelManager = atom<(() => void) | undefined>(undefined);

invokeai/frontend/web/src/app/store/nanostores/toastMap.ts

@@ -0,0 +1,4 @@
+import type { ToastConfig } from 'features/toast/toast';
+import { atom } from 'nanostores';
+
+export const $toastMap = atom<Record<string, ToastConfig> | undefined>(undefined);

invokeai/frontend/web/src/app/store/nanostores/whatsNew.ts

@@ -0,0 +1,4 @@
+import { atom } from 'nanostores';
+import type { ReactNode } from 'react';
+
+export const $whatsNew = atom<ReactNode[] | undefined>(undefined);

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

@@ -145,7 +145,10 @@ const unserialize: UnserializeFunction = (data, key) => {
     );
     return transformed;
   } catch (err) {
-    log.warn({ error: serializeError(err) }, `Error rehydrating slice "${key}", falling back to default initial state`);
+    log.warn(
+      { error: serializeError(err as Error) },
+      `Error rehydrating slice "${key}", falling back to default initial state`
+    );
     return persistConfig.initialState;
   }
 };

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

@@ -28,7 +28,8 @@ export type AppFeature =
   | 'starterModels'
   | 'hfToken'
   | 'retryQueueItem'
-  | 'cancelAndClearAll';
+  | 'cancelAndClearAll'
+  | 'chatGPT4oHigh';
 /**
  * A disable-able Stable Diffusion feature
  */
@@ -83,6 +84,7 @@ export type AppConfig = {
   metadataFetchDebounce?: number;
   workflowFetchDebounce?: number;
   isLocal?: boolean;
+  shouldShowCredits: boolean;
   sd: {
     defaultModel?: string;
     disabledControlNetModels: string[];

invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx

@@ -83,7 +83,7 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
           }
         } else {
           let imageDTOs: ImageDTO[] = [];
-          if (isClientSideUploadEnabled) {
+          if (isClientSideUploadEnabled && files.length > 1) {
             imageDTOs = await Promise.all(files.map((file, i) => clientSideUpload(file, i)));
           } else {
             imageDTOs = await uploadImages(

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

@@ -38,7 +38,7 @@ export const useModelCombobox = <T extends AnyModelConfig>(arg: UseModelCombobox
   }, [optionsFilter, getIsDisabled, modelConfigs, shouldShowModelDescriptions]);
 
   const value = useMemo(
-    () => options.find((m) => (selectedModel ? m.value === selectedModel.key : false)),
+    () => options.find((m) => (selectedModel ? m.value === selectedModel.key : false)) ?? null,
     [options, selectedModel]
   );
 

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

@@ -0,0 +1,92 @@
+import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
+import type { GroupBase } from 'chakra-react-select';
+import type { ModelIdentifierField } from 'features/nodes/types/common';
+import { useTranslation } from 'react-i18next';
+import type { AnyModelConfig } from 'services/api/types';
+
+import { useGroupedModelCombobox } from './useGroupedModelCombobox';
+import { useRelatedModelKeys } from './useRelatedModelKeys';
+import { useSelectedModelKeys } from './useSelectedModelKeys';
+
+type UseRelatedGroupedModelComboboxArg<T extends AnyModelConfig> = {
+  modelConfigs: T[];
+  selectedModel?: ModelIdentifierField | null;
+  onChange: (value: T | null) => void;
+  getIsDisabled?: (model: T) => boolean;
+  isLoading?: boolean;
+  groupByType?: boolean;
+};
+
+// Custom hook to overlay the grouped model combobox with related models on top!
+// Cleaner than hooking into useGroupedModelCombobox with a flag to enable/disable the related models
+// Also allows for related models to be shown conditionally with some pretty simple logic if it ends up as a config flag.
+
+type UseRelatedGroupedModelComboboxReturn = {
+  value: ComboboxOption | undefined | null;
+  options: GroupBase<ComboboxOption>[];
+  onChange: ComboboxOnChange;
+  placeholder: string;
+  noOptionsMessage: () => string;
+};
+
+export function useRelatedGroupedModelCombobox<T extends AnyModelConfig>({
+  modelConfigs,
+  selectedModel,
+  onChange,
+  isLoading = false,
+  getIsDisabled,
+  groupByType,
+}: UseRelatedGroupedModelComboboxArg<T>): UseRelatedGroupedModelComboboxReturn {
+  const { t } = useTranslation();
+
+  const selectedKeys = useSelectedModelKeys();
+
+  const relatedKeys = useRelatedModelKeys(selectedKeys);
+
+  // Base grouped options
+  const base = useGroupedModelCombobox({
+    modelConfigs,
+    selectedModel,
+    onChange,
+    getIsDisabled,
+    isLoading,
+    groupByType,
+  });
+
+  // If no related models selected, just return base
+  if (relatedKeys.size === 0) {
+    return base;
+  }
+
+  const relatedOptions: ComboboxOption[] = [];
+  const updatedGroups: GroupBase<ComboboxOption>[] = [];
+
+  for (const group of base.options) {
+    const remainingOptions: ComboboxOption[] = [];
+
+    for (const option of group.options) {
+      if (relatedKeys.has(option.value)) {
+        relatedOptions.push({ ...option, label: `* ${option.label}` });
+      } else {
+        remainingOptions.push(option);
+      }
+    }
+
+    if (remainingOptions.length > 0) {
+      updatedGroups.push({
+        label: group.label,
+        options: remainingOptions,
+      });
+    }
+  }
+
+  const finalOptions: GroupBase<ComboboxOption>[] =
+    relatedOptions.length > 0
+      ? [{ label: t('modelManager.relatedModels'), options: relatedOptions }, ...updatedGroups]
+      : updatedGroups;
+
+  return {
+    ...base,
+    options: finalOptions,
+  };
+}

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

@@ -0,0 +1,14 @@
+import { useMemo } from 'react';
+import { useGetRelatedModelIdsBatchQuery } from 'services/api/endpoints/modelRelationships';
+
+/**
+ * Fetches related model keys for a given set of selected model keys.
+ * Returns a Set<string> for fast lookup.
+ */
+export const useRelatedModelKeys = (selectedKeys: Set<string>) => {
+  const { data: related = [] } = useGetRelatedModelIdsBatchQuery([...selectedKeys], {
+    skip: selectedKeys.size === 0,
+  });
+
+  return useMemo(() => new Set(related), [related]);
+};

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

@@ -0,0 +1,34 @@
+import { useAppSelector } from 'app/store/storeHooks';
+
+/**
+ * Gathers all currently selected model keys from parameters and loras.
+ * This includes the main model, VAE, refiner model, controlnet, and loras.
+ */
+export const useSelectedModelKeys = () => {
+  return useAppSelector((state) => {
+    const keys = new Set<string>();
+    const main = state.params.model;
+    const vae = state.params.vae;
+    const refiner = state.params.refinerModel;
+    const controlnet = state.params.controlLora;
+    const loras = state.loras.loras.map((l) => l.model);
+
+    if (main) {
+      keys.add(main.key);
+    }
+    if (vae) {
+      keys.add(vae.key);
+    }
+    if (refiner) {
+      keys.add(refiner.key);
+    }
+    if (controlnet) {
+      keys.add(controlnet.key);
+    }
+    for (const lora of loras) {
+      keys.add(lora.key);
+    }
+
+    return keys;
+  });
+};

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

@@ -1,6 +1,10 @@
+/* eslint-disable @typescript-eslint/no-explicit-any */
 import { memo } from 'react';
 
 /**
  * A typed version of React.memo, useful for components that take generics.
  */
-export const typedMemo: <T>(c: T) => T = memo;
+export const typedMemo: <T extends keyof JSX.IntrinsicElements | React.JSXElementConstructor<any>>(
+  component: T,
+  propsAreEqual?: (prevProps: React.ComponentProps<T>, nextProps: React.ComponentProps<T>) => boolean
+) => T & { displayName?: string } = memo;

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

@@ -24,6 +24,7 @@ export const CanvasAddEntityButtons = memo(() => {
   const isReferenceImageEnabled = useIsEntityTypeEnabled('reference_image');
   const isRegionalGuidanceEnabled = useIsEntityTypeEnabled('regional_guidance');
   const isControlLayerEnabled = useIsEntityTypeEnabled('control_layer');
+  const isInpaintLayerEnabled = useIsEntityTypeEnabled('inpaint_mask');
 
   return (
     <Flex w="full" h="full" justifyContent="center" gap={4}>
@@ -52,6 +53,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addInpaintMask}
+              isDisabled={!isInpaintLayerEnabled}
             >
               {t('controlLayers.inpaintMask')}
             </Button>

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarAddLayerMenu.tsx

@@ -25,6 +25,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
   const isReferenceImageEnabled = useIsEntityTypeEnabled('reference_image');
   const isRegionalGuidanceEnabled = useIsEntityTypeEnabled('regional_guidance');
   const isControlLayerEnabled = useIsEntityTypeEnabled('control_layer');
+  const isInpaintLayerEnabled = useIsEntityTypeEnabled('inpaint_mask');
 
   return (
     <Menu>
@@ -46,7 +47,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
           </MenuItem>
         </MenuGroup>
         <MenuGroup title={t('controlLayers.regional')}>
-          <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
+          <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask} isDisabled={!isInpaintLayerEnabled}>
             {t('controlLayers.inpaintMask')}
           </MenuItem>
           <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={!isRegionalGuidanceEnabled}>

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/GlobalReferenceImageModel.tsx

@@ -0,0 +1,63 @@
+import { Combobox, FormControl, Tooltip } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
+import { selectBase } from 'features/controlLayers/store/paramsSlice';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useGlobalReferenceImageModels } from 'services/api/hooks/modelsByType';
+import type { AnyModelConfig, ApiModelConfig, FLUXReduxModelConfig, IPAdapterModelConfig } from 'services/api/types';
+
+type Props = {
+  modelKey: string | null;
+  onChangeModel: (modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig | ApiModelConfig) => void;
+};
+
+export const GlobalReferenceImageModel = memo(({ modelKey, onChangeModel }: Props) => {
+  const { t } = useTranslation();
+  const currentBaseModel = useAppSelector(selectBase);
+  const [modelConfigs, { isLoading }] = useGlobalReferenceImageModels();
+  const selectedModel = useMemo(() => modelConfigs.find((m) => m.key === modelKey), [modelConfigs, modelKey]);
+
+  const _onChangeModel = useCallback(
+    (modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig | ApiModelConfig | null) => {
+      if (!modelConfig) {
+        return;
+      }
+      onChangeModel(modelConfig);
+    },
+    [onChangeModel]
+  );
+
+  const getIsDisabled = useCallback(
+    (model: AnyModelConfig): boolean => {
+      const hasMainModel = Boolean(currentBaseModel);
+      const hasSameBase = currentBaseModel === model.base;
+      return !hasMainModel || !hasSameBase;
+    },
+    [currentBaseModel]
+  );
+
+  const { options, value, onChange, noOptionsMessage } = useGroupedModelCombobox({
+    modelConfigs,
+    onChange: _onChangeModel,
+    selectedModel,
+    getIsDisabled,
+    isLoading,
+  });
+
+  return (
+    <Tooltip label={selectedModel?.description}>
+      <FormControl isInvalid={!value || currentBaseModel !== selectedModel?.base} w="full">
+        <Combobox
+          options={options}
+          placeholder={t('common.placeholderSelectAModel')}
+          value={value}
+          onChange={onChange}
+          noOptionsMessage={noOptionsMessage}
+        />
+      </FormControl>
+    </Tooltip>
+  );
+});
+
+GlobalReferenceImageModel.displayName = 'GlobalReferenceImageModel';

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterImagePreview.tsx

@@ -61,7 +61,7 @@ export const IPAdapterImagePreview = memo(
         )}
         {imageDTO && (
           <>
-            <DndImage imageDTO={imageDTO} borderWidth={1} borderStyle="solid" />
+            <DndImage imageDTO={imageDTO} borderWidth={1} borderStyle="solid" w="full" />
             <Flex position="absolute" flexDir="column" top={2} insetInlineEnd={2} gap={1}>
               <DndImageIcon
                 onClick={handleResetControlImage}

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterMethod.tsx

@@ -30,6 +30,16 @@ export const IPAdapterMethod = memo(({ method, onChange }: Props) => {
         value: 'style',
         description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.styleDesc') : undefined,
       },
+      {
+        label: t('controlLayers.ipAdapterMethod.styleStrong'),
+        value: 'style_strong',
+        description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.styleStrongDesc') : undefined,
+      },
+      {
+        label: t('controlLayers.ipAdapterMethod.stylePrecise'),
+        value: 'style_precise',
+        description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.stylePreciseDesc') : undefined,
+      },
       {
         label: t('controlLayers.ipAdapterMethod.composition'),
         value: 'composition',

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterSettings.tsx

@@ -6,6 +6,7 @@ import { CanvasEntitySettingsWrapper } from 'features/controlLayers/components/c
 import { Weight } from 'features/controlLayers/components/common/Weight';
 import { CLIPVisionModel } from 'features/controlLayers/components/IPAdapter/CLIPVisionModel';
 import { FLUXReduxImageInfluence } from 'features/controlLayers/components/IPAdapter/FLUXReduxImageInfluence';
+import { GlobalReferenceImageModel } from 'features/controlLayers/components/IPAdapter/GlobalReferenceImageModel';
 import { IPAdapterMethod } from 'features/controlLayers/components/IPAdapter/IPAdapterMethod';
 import { IPAdapterSettingsEmptyState } from 'features/controlLayers/components/IPAdapter/IPAdapterSettingsEmptyState';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
@@ -33,10 +34,9 @@ import { setGlobalReferenceImageDndTarget } from 'features/dnd/dnd';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiBoundingBoxBold } from 'react-icons/pi';
-import type { FLUXReduxModelConfig, ImageDTO, IPAdapterModelConfig } from 'services/api/types';
+import type { ApiModelConfig, FLUXReduxModelConfig, ImageDTO, IPAdapterModelConfig } from 'services/api/types';
 
 import { IPAdapterImagePreview } from './IPAdapterImagePreview';
-import { IPAdapterModel } from './IPAdapterModel';
 
 const buildSelectIPAdapter = (entityIdentifier: CanvasEntityIdentifier<'reference_image'>) =>
   createSelector(
@@ -80,7 +80,7 @@ const IPAdapterSettingsContent = memo(() => {
   );
 
   const onChangeModel = useCallback(
-    (modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig) => {
+    (modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig | ApiModelConfig) => {
       dispatch(referenceImageIPAdapterModelChanged({ entityIdentifier, modelConfig }));
     },
     [dispatch, entityIdentifier]
@@ -113,11 +113,7 @@ const IPAdapterSettingsContent = memo(() => {
     <CanvasEntitySettingsWrapper>
       <Flex flexDir="column" gap={2} position="relative" w="full">
         <Flex gap={2} alignItems="center" w="full">
-          <IPAdapterModel
-            isRegionalGuidance={false}
-            modelKey={ipAdapter.model?.key ?? null}
-            onChangeModel={onChangeModel}
-          />
+          <GlobalReferenceImageModel modelKey={ipAdapter.model?.key ?? null} onChangeModel={onChangeModel} />
           {ipAdapter.type === 'ip_adapter' && (
             <CLIPVisionModel model={ipAdapter.clipVisionModel} onChange={onChangeCLIPVisionModel} />
           )}

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/RegionalReferenceImageModel.tsx

@@ -4,29 +4,26 @@ import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
 import { selectBase } from 'features/controlLayers/store/paramsSlice';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useIPAdapterOrFLUXReduxModels } from 'services/api/hooks/modelsByType';
+import { useRegionalReferenceImageModels } from 'services/api/hooks/modelsByType';
 import type { AnyModelConfig, FLUXReduxModelConfig, IPAdapterModelConfig } from 'services/api/types';
 
 type Props = {
-  isRegionalGuidance: boolean;
   modelKey: string | null;
   onChangeModel: (modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig) => void;
 };
 
-export const IPAdapterModel = memo(({ isRegionalGuidance, modelKey, onChangeModel }: Props) => {
+const filter = (config: IPAdapterModelConfig | FLUXReduxModelConfig) => {
+  // FLUX supports regional guidance for FLUX Redux models only - not IP Adapter models.
+  if (config.base === 'flux' && config.type === 'ip_adapter') {
+    return false;
+  }
+  return true;
+};
+
+export const RegionalReferenceImageModel = memo(({ modelKey, onChangeModel }: Props) => {
   const { t } = useTranslation();
   const currentBaseModel = useAppSelector(selectBase);
-  const filter = useCallback(
-    (config: IPAdapterModelConfig | FLUXReduxModelConfig) => {
-      // FLUX supports regional guidance for FLUX Redux models only - not IP Adapter models.
-      if (isRegionalGuidance && config.base === 'flux' && config.type === 'ip_adapter') {
-        return false;
-      }
-      return true;
-    },
-    [isRegionalGuidance]
-  );
-  const [modelConfigs, { isLoading }] = useIPAdapterOrFLUXReduxModels(filter);
+  const [modelConfigs, { isLoading }] = useRegionalReferenceImageModels(filter);
   const selectedModel = useMemo(() => modelConfigs.find((m) => m.key === modelKey), [modelConfigs, modelKey]);
 
   const _onChangeModel = useCallback(
@@ -71,4 +68,4 @@ export const IPAdapterModel = memo(({ isRegionalGuidance, modelKey, onChangeMode
   );
 });
 
-IPAdapterModel.displayName = 'IPAdapterModel';
+RegionalReferenceImageModel.displayName = 'RegionalReferenceImageModel';

invokeai/frontend/web/src/features/controlLayers/components/RegionalGuidance/RegionalGuidanceIPAdapterSettings.tsx

@@ -7,7 +7,7 @@ import { CLIPVisionModel } from 'features/controlLayers/components/IPAdapter/CLI
 import { FLUXReduxImageInfluence } from 'features/controlLayers/components/IPAdapter/FLUXReduxImageInfluence';
 import { IPAdapterImagePreview } from 'features/controlLayers/components/IPAdapter/IPAdapterImagePreview';
 import { IPAdapterMethod } from 'features/controlLayers/components/IPAdapter/IPAdapterMethod';
-import { IPAdapterModel } from 'features/controlLayers/components/IPAdapter/IPAdapterModel';
+import { RegionalReferenceImageModel } from 'features/controlLayers/components/IPAdapter/RegionalReferenceImageModel';
 import { RegionalGuidanceIPAdapterSettingsEmptyState } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceIPAdapterSettingsEmptyState';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { usePullBboxIntoRegionalGuidanceReferenceImage } from 'features/controlLayers/hooks/saveCanvasHooks';
@@ -140,11 +140,7 @@ const RegionalGuidanceIPAdapterSettingsContent = memo(({ referenceImageId }: Pro
       </Flex>
       <Flex flexDir="column" gap={2} position="relative" w="full">
         <Flex gap={2} alignItems="center" w="full">
-          <IPAdapterModel
-            isRegionalGuidance={true}
-            modelKey={ipAdapter.model?.key ?? null}
-            onChangeModel={onChangeModel}
-          />
+          <RegionalReferenceImageModel modelKey={ipAdapter.model?.key ?? null} onChangeModel={onChangeModel} />
           {ipAdapter.type === 'ip_adapter' && (
             <CLIPVisionModel model={ipAdapter.clipVisionModel} onChange={onChangeCLIPVisionModel} />
           )}

invokeai/frontend/web/src/features/controlLayers/hooks/addLayerHooks.ts

@@ -17,16 +17,26 @@ import { selectBase } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice, selectEntity } from 'features/controlLayers/store/selectors';
 import type {
   CanvasEntityIdentifier,
+  CanvasReferenceImageState,
   CanvasRegionalGuidanceState,
   ControlLoRAConfig,
   ControlNetConfig,
   IPAdapterConfig,
   T2IAdapterConfig,
 } from 'features/controlLayers/store/types';
-import { initialControlNet, initialIPAdapter, initialT2IAdapter } from 'features/controlLayers/store/util';
+import {
+  initialChatGPT4oReferenceImage,
+  initialControlNet,
+  initialIPAdapter,
+  initialT2IAdapter,
+} from 'features/controlLayers/store/util';
 import { zModelIdentifierField } from 'features/nodes/types/common';
 import { useCallback } from 'react';
-import { modelConfigsAdapterSelectors, selectModelConfigsQuery } from 'services/api/endpoints/models';
+import {
+  modelConfigsAdapterSelectors,
+  selectMainModelConfig,
+  selectModelConfigsQuery,
+} from 'services/api/endpoints/models';
 import type {
   ControlLoRAModelConfig,
   ControlNetModelConfig,
@@ -64,6 +74,35 @@ export const selectDefaultControlAdapter = createSelector(
   }
 );
 
+export const selectDefaultRefImageConfig = createSelector(
+  selectMainModelConfig,
+  selectModelConfigsQuery,
+  selectBase,
+  (selectedMainModel, query, base): CanvasReferenceImageState['ipAdapter'] => {
+    if (selectedMainModel?.base === 'chatgpt-4o') {
+      const referenceImage = deepClone(initialChatGPT4oReferenceImage);
+      referenceImage.model = zModelIdentifierField.parse(selectedMainModel);
+      return referenceImage;
+    }
+
+    const { data } = query;
+    let model: IPAdapterModelConfig | null = null;
+    if (data) {
+      const modelConfigs = modelConfigsAdapterSelectors.selectAll(data).filter(isIPAdapterModelConfig);
+      const compatibleModels = modelConfigs.filter((m) => (base ? m.base === base : true));
+      model = compatibleModels[0] ?? modelConfigs[0] ?? null;
+    }
+    const ipAdapter = deepClone(initialIPAdapter);
+    if (model) {
+      ipAdapter.model = zModelIdentifierField.parse(model);
+      if (model.base === 'flux') {
+        ipAdapter.clipVisionModel = 'ViT-L';
+      }
+    }
+    return ipAdapter;
+  }
+);
+
 /**
  * Selects the default IP adapter configuration based on the model configurations and the base.
  *
@@ -146,11 +185,11 @@ export const useAddRegionalReferenceImage = () => {
 
 export const useAddGlobalReferenceImage = () => {
   const dispatch = useAppDispatch();
-  const defaultIPAdapter = useAppSelector(selectDefaultIPAdapter);
+  const defaultRefImage = useAppSelector(selectDefaultRefImageConfig);
   const func = useCallback(() => {
-    const overrides = { ipAdapter: deepClone(defaultIPAdapter) };
+    const overrides = { ipAdapter: deepClone(defaultRefImage) };
     dispatch(referenceImageAdded({ isSelected: true, overrides }));
-  }, [defaultIPAdapter, dispatch]);
+  }, [defaultRefImage, dispatch]);
 
   return func;
 };

invokeai/frontend/web/src/features/controlLayers/hooks/copyHooks.ts

@@ -41,7 +41,7 @@ export const useCopyLayerToClipboard = () => {
           });
         });
       } catch (error) {
-        log.error({ error: serializeError(error) }, 'Problem copying layer to clipboard');
+        log.error({ error: serializeError(error as Error) }, 'Problem copying layer to clipboard');
         toast({
           status: 'error',
           title: t('toast.problemCopyingLayer'),
@@ -82,7 +82,7 @@ export const useCopyCanvasToClipboard = (region: 'canvas' | 'bbox') => {
         toast({ title: t('controlLayers.regionCopiedToClipboard', { region: startCase(region) }) });
       });
     } catch (error) {
-      log.error({ error: serializeError(error) }, 'Failed to save canvas to gallery');
+      log.error({ error: serializeError(error as Error) }, 'Failed to save canvas to gallery');
       toast({ title: t('controlLayers.copyRegionError', { region: startCase(region) }), status: 'error' });
     }
   }, [canvasManager.compositor, canvasManager.stateApi, clipboard, region, t]);

invokeai/frontend/web/src/features/controlLayers/hooks/saveCanvasHooks.ts

@@ -3,7 +3,7 @@ import { useAppDispatch, useAppSelector, useAppStore } from 'app/store/storeHook
 import { deepClone } from 'common/util/deepClone';
 import { withResultAsync } from 'common/util/result';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { selectDefaultIPAdapter } from 'features/controlLayers/hooks/addLayerHooks';
+import { selectDefaultIPAdapter, selectDefaultRefImageConfig } from 'features/controlLayers/hooks/addLayerHooks';
 import { getPrefixedId } from 'features/controlLayers/konva/util';
 import {
   controlLayerAdded,
@@ -198,7 +198,7 @@ export const useNewRegionalReferenceImageFromBbox = () => {
 export const useNewGlobalReferenceImageFromBbox = () => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const defaultIPAdapter = useAppSelector(selectDefaultIPAdapter);
+  const defaultIPAdapter = useAppSelector(selectDefaultRefImageConfig);
 
   const arg = useMemo<UseSaveCanvasArg>(() => {
     const onSave = (imageDTO: ImageDTO) => {

invokeai/frontend/web/src/features/controlLayers/hooks/useIsEntityTypeEnabled.ts

@@ -1,5 +1,11 @@
 import { useAppSelector } from 'app/store/storeHooks';
-import { selectIsCogView4, selectIsSD3 } from 'features/controlLayers/store/paramsSlice';
+import {
+  selectIsChatGTP4o,
+  selectIsCogView4,
+  selectIsImagen3,
+  selectIsImagen4,
+  selectIsSD3,
+} from 'features/controlLayers/store/paramsSlice';
 import type { CanvasEntityType } from 'features/controlLayers/store/types';
 import { useMemo } from 'react';
 import type { Equals } from 'tsafe';
@@ -8,23 +14,26 @@ import { assert } from 'tsafe';
 export const useIsEntityTypeEnabled = (entityType: CanvasEntityType) => {
   const isSD3 = useAppSelector(selectIsSD3);
   const isCogView4 = useAppSelector(selectIsCogView4);
+  const isImagen3 = useAppSelector(selectIsImagen3);
+  const isImagen4 = useAppSelector(selectIsImagen4);
+  const isChatGPT4o = useAppSelector(selectIsChatGTP4o);
 
   const isEntityTypeEnabled = useMemo<boolean>(() => {
     switch (entityType) {
       case 'reference_image':
-        return !isSD3 && !isCogView4;
+        return !isSD3 && !isCogView4 && !isImagen3 && !isImagen4;
       case 'regional_guidance':
-        return !isSD3 && !isCogView4;
+        return !isSD3 && !isCogView4 && !isImagen3 && !isImagen4 && !isChatGPT4o;
       case 'control_layer':
-        return !isSD3 && !isCogView4;
+        return !isSD3 && !isCogView4 && !isImagen3 && !isImagen4 && !isChatGPT4o;
       case 'inpaint_mask':
-        return true;
+        return !isImagen3 && !isImagen4 && !isChatGPT4o;
       case 'raster_layer':
-        return true;
+        return !isImagen3 && !isImagen4 && !isChatGPT4o;
       default:
         assert<Equals<typeof entityType, never>>(false);
     }
-  }, [entityType, isSD3, isCogView4]);
+  }, [entityType, isSD3, isCogView4, isImagen3, isImagen4, isChatGPT4o]);
 
   return isEntityTypeEnabled;
 };

invokeai/frontend/web/src/features/controlLayers/hooks/useSaveLayerToAssets.ts

@@ -41,7 +41,7 @@ export const useSaveLayerToAssets = () => {
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
         });
       } catch (error) {
-        log.error({ error: serializeError(error) }, 'Problem copying layer to clipboard');
+        log.error({ error: serializeError(error as Error) }, 'Problem copying layer to clipboard');
         toast({
           status: 'error',
           title: t('toast.problemSavingLayer'),

invokeai/frontend/web/src/features/controlLayers/konva/CanvasEntity/CanvasEntityObjectRenderer.ts

@@ -519,7 +519,7 @@ export class CanvasEntityObjectRenderer extends CanvasModuleBase {
       this.manager.cache.imageNameCache.set(hash, imageDTO.image_name);
       return imageDTO;
     } catch (error) {
-      this.log.error({ rasterizeArgs, error: serializeError(error) }, 'Failed to rasterize entity');
+      this.log.error({ rasterizeArgs, error: serializeError(error as Error) }, 'Failed to rasterize entity');
       throw error;
     } finally {
       this.manager.stateApi.$rasterizingAdapter.set(null);

invokeai/frontend/web/src/features/controlLayers/konva/CanvasEntity/CanvasEntityTransformer.ts

@@ -346,7 +346,7 @@ export class CanvasEntityTransformer extends CanvasModuleBase {
 
     // If the user is not holding shift, the transform is retaining aspect ratio. It's not possible to snap to the grid
     // in this case, because that would change the aspect ratio. So, we only snap to the grid when shift is held.
-    const gridSize = this.manager.stateApi.$shiftKey.get() ? this.manager.stateApi.getGridSize() : 1;
+    const gridSize = this.manager.stateApi.$shiftKey.get() ? this.manager.stateApi.getPositionGridSize() : 1;
 
     // We need to snap the anchor to the selected grid size, but the positions provided to this callback are absolute,
     // scaled coordinates. They need to be converted to stage coordinates, snapped, then converted back to absolute
@@ -464,7 +464,7 @@ export class CanvasEntityTransformer extends CanvasModuleBase {
       return;
     }
     const { rect } = this.manager.stateApi.getBbox();
-    const gridSize = this.manager.stateApi.getGridSize();
+    const gridSize = this.manager.stateApi.getPositionGridSize();
     const width = this.konva.proxyRect.width();
     const height = this.konva.proxyRect.height();
     const scaleX = rect.width / width;
@@ -498,7 +498,7 @@ export class CanvasEntityTransformer extends CanvasModuleBase {
       return;
     }
     const { rect } = this.manager.stateApi.getBbox();
-    const gridSize = this.manager.stateApi.getGridSize();
+    const gridSize = this.manager.stateApi.getPositionGridSize();
     const width = this.konva.proxyRect.width();
     const height = this.konva.proxyRect.height();
     const scaleX = rect.width / width;
@@ -523,7 +523,7 @@ export class CanvasEntityTransformer extends CanvasModuleBase {
 
   onDragMove = () => {
     // Snap the interaction rect to the grid
-    const gridSize = this.manager.stateApi.getGridSize();
+    const gridSize = this.manager.stateApi.getPositionGridSize();
     this.konva.proxyRect.x(roundToMultiple(this.konva.proxyRect.x(), gridSize));
     this.konva.proxyRect.y(roundToMultiple(this.konva.proxyRect.y(), gridSize));
 

invokeai/frontend/web/src/features/controlLayers/konva/CanvasObject/CanvasObjectImage.ts

@@ -112,7 +112,7 @@ export class CanvasObjectImage extends CanvasModuleBase {
       return;
     }
 
-    const imageElementResult = await withResultAsync(() => loadImage(imageDTO.image_url));
+    const imageElementResult = await withResultAsync(() => loadImage(imageDTO.image_url, true));
     if (imageElementResult.isErr()) {
       // Image loading failed (e.g. the URL to the "physical" image is invalid)
       this.onFailedToLoadImage(t('controlLayers.unableToLoadImage', 'Unable to load image'));

invokeai/frontend/web/src/features/controlLayers/konva/CanvasStateApiModule.ts

@@ -493,7 +493,7 @@ export class CanvasStateApiModule extends CanvasModuleBase {
    * Gets the _positional_ grid size for the current canvas. Note that this is not the same as bbox grid size, which is
    * based on the currently-selected model.
    */
-  getGridSize = (): number => {
+  getPositionGridSize = (): number => {
     const snapToGrid = this.getSettings().snapToGrid;
     if (!snapToGrid) {
       return 1;

invokeai/frontend/web/src/features/controlLayers/konva/CanvasTool/CanvasBboxToolModule.ts

@@ -4,8 +4,11 @@ import { CanvasModuleBase } from 'features/controlLayers/konva/CanvasModuleBase'
 import type { CanvasToolModule } from 'features/controlLayers/konva/CanvasTool/CanvasToolModule';
 import { fitRectToGrid, getKonvaNodeDebugAttrs, getPrefixedId } from 'features/controlLayers/konva/util';
 import { selectBboxOverlay } from 'features/controlLayers/store/canvasSettingsSlice';
+import { selectModel } from 'features/controlLayers/store/paramsSlice';
 import { selectBbox } from 'features/controlLayers/store/selectors';
-import type { Coordinate, Rect } from 'features/controlLayers/store/types';
+import type { Coordinate, Rect, Tool } from 'features/controlLayers/store/types';
+import type { ModelIdentifierField } from 'features/nodes/types/common';
+import { API_BASE_MODELS } from 'features/parameters/types/constants';
 import Konva from 'konva';
 import { noop } from 'lodash-es';
 import { atom } from 'nanostores';
@@ -178,6 +181,9 @@ export class CanvasBboxToolModule extends CanvasModuleBase {
     // Listen for the bbox overlay setting to update the overlay's visibility
     this.subscriptions.add(this.manager.stateApi.createStoreSubscription(selectBboxOverlay, this.render));
 
+    // Listen for the model changing - some model types constraint the bbox to a certain size or aspect ratio.
+    this.subscriptions.add(this.manager.stateApi.createStoreSubscription(selectModel, this.render));
+
     // Update on busy state changes
     this.subscriptions.add(this.manager.$isBusy.listen(this.render));
   }
@@ -218,12 +224,25 @@ export class CanvasBboxToolModule extends CanvasModuleBase {
 
     this.syncOverlay();
 
+    const model = this.manager.stateApi.runSelector(selectModel);
+
     this.konva.transformer.setAttrs({
       listening: tool === 'bbox',
-      enabledAnchors: tool === 'bbox' ? ALL_ANCHORS : NO_ANCHORS,
+      enabledAnchors: this.getEnabledAnchors(tool, model),
     });
   };
 
+  getEnabledAnchors = (tool: Tool, model?: ModelIdentifierField | null): string[] => {
+    if (tool !== 'bbox') {
+      return NO_ANCHORS;
+    }
+    if (model?.base && API_BASE_MODELS.includes(model.base)) {
+      // The bbox is not resizable in these modes
+      return NO_ANCHORS;
+    }
+    return ALL_ANCHORS;
+  };
+
   syncOverlay = () => {
     const bboxOverlay = this.manager.stateApi.getSettings().bboxOverlay;
 
@@ -251,7 +270,7 @@ export class CanvasBboxToolModule extends CanvasModuleBase {
   onDragMove = () => {
     // The grid size here is the _position_ grid size, not the _dimension_ grid size - it is not constratined by the
     // currently-selected model.
-    const gridSize = this.manager.stateApi.getGridSize();
+    const gridSize = this.manager.stateApi.getPositionGridSize();
     const bbox = this.manager.stateApi.getBbox();
     const bboxRect: Rect = {
       ...bbox.rect,

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

@@ -476,15 +476,24 @@ export function getImageDataTransparency(imageData: ImageData): Transparency {
 /**
  * Loads an image from a URL and returns a promise that resolves with the loaded image element.
  * @param src The image source URL
+ * @param fetchUrlFirst Whether to fetch the image's URL first, assuming the provided `src` will redirect to a different URL. This addresses an issue where CORS headers are dropped during a redirect.
  * @returns A promise that resolves with the loaded image element
  */
-export function loadImage(src: string): Promise<HTMLImageElement> {
+export async function loadImage(src: string, fetchUrlFirst?: boolean): Promise<HTMLImageElement> {
+  const authToken = $authToken.get();
+  let url = src;
+  if (authToken && fetchUrlFirst) {
+    const response = await fetch(`${src}?url_only=true`, { credentials: 'include' });
+    const data = await response.json();
+    url = data.url;
+  }
+
   return new Promise((resolve, reject) => {
     const imageElement = new Image();
     imageElement.onload = () => resolve(imageElement);
     imageElement.onerror = (error) => reject(error);
     imageElement.crossOrigin = $authToken.get() ? 'use-credentials' : 'anonymous';
-    imageElement.src = src;
+    imageElement.src = url;
   });
 }
 

invokeai/frontend/web/src/features/controlLayers/konva/worker.ts

@@ -10,12 +10,12 @@ export type Extents = {
 
 /**
  * Get the bounding box of an image.
- * @param buffer The ArrayBuffer of the image to get the bounding box of.
+ * @param buffer The ArrayBufferLike of the image to get the bounding box of.
  * @param width The width of the image.
  * @param height The height of the image.
  * @returns The minimum and maximum x and y values of the image's bounding box, or null if the image has no pixels.
  */
-const getImageDataBboxArrayBuffer = (buffer: ArrayBuffer, width: number, height: number): Extents | null => {
+const getImageDataBboxArrayBufferLike = (buffer: ArrayBufferLike, width: number, height: number): Extents | null => {
   let minX = width;
   let minY = height;
   let maxX = -1;
@@ -50,7 +50,7 @@ const getImageDataBboxArrayBuffer = (buffer: ArrayBuffer, width: number, height:
 
 export type GetBboxTask = {
   type: 'get_bbox';
-  data: { id: string; buffer: ArrayBuffer; width: number; height: number };
+  data: { id: string; buffer: ArrayBufferLike; width: number; height: number };
 };
 
 type TaskWithTimestamps<T extends Record<string, unknown>> = T & { started: number | null; finished: number | null };
@@ -95,7 +95,7 @@ function processNextTask() {
   // Process the task
   if (task.type === 'get_bbox') {
     const { buffer, width, height, id } = task.data;
-    const extents = getImageDataBboxArrayBuffer(buffer, width, height);
+    const extents = getImageDataBboxArrayBufferLike(buffer, width, height);
     const result: ExtentsResult = {
       type: 'extents',
       data: { id, extents },

invokeai/frontend/web/src/features/controlLayers/store/canvasSlice.ts

@@ -32,11 +32,13 @@ import {
 import { simplifyFlatNumbersArray } from 'features/controlLayers/util/simplify';
 import { isMainModelBase, zModelIdentifierField } from 'features/nodes/types/common';
 import { ASPECT_RATIO_MAP } from 'features/parameters/components/Bbox/constants';
+import { API_BASE_MODELS } from 'features/parameters/types/constants';
 import { getGridSize, getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import type { IRect } from 'konva/lib/types';
-import { merge } from 'lodash-es';
+import { isEqual, merge } from 'lodash-es';
 import type { UndoableOptions } from 'redux-undo';
 import type {
+  ApiModelConfig,
   ControlLoRAModelConfig,
   ControlNetModelConfig,
   FLUXReduxModelConfig,
@@ -67,7 +69,7 @@ import type {
   IPMethodV2,
   T2IAdapterConfig,
 } from './types';
-import { getEntityIdentifier, isRenderableEntity } from './types';
+import { getEntityIdentifier, isChatGPT4oAspectRatioID, isImagenAspectRatioID, isRenderableEntity } from './types';
 import {
   converters,
   getControlLayerState,
@@ -76,6 +78,7 @@ import {
   getReferenceImageState,
   getRegionalGuidanceState,
   imageDTOToImageWithDims,
+  initialChatGPT4oReferenceImage,
   initialControlLoRA,
   initialControlNet,
   initialFLUXRedux,
@@ -644,7 +647,10 @@ export const canvasSlice = createSlice({
     referenceImageIPAdapterModelChanged: (
       state,
       action: PayloadAction<
-        EntityIdentifierPayload<{ modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig | null }, 'reference_image'>
+        EntityIdentifierPayload<
+          { modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig | ApiModelConfig | null },
+          'reference_image'
+        >
       >
     ) => {
       const { entityIdentifier, modelConfig } = action.payload;
@@ -652,14 +658,36 @@ export const canvasSlice = createSlice({
       if (!entity) {
         return;
       }
+
+      const oldModel = entity.ipAdapter.model;
+
+      // First set the new model
       entity.ipAdapter.model = modelConfig ? zModelIdentifierField.parse(modelConfig) : null;
 
       if (!entity.ipAdapter.model) {
         return;
       }
 
-      if (entity.ipAdapter.type === 'ip_adapter' && entity.ipAdapter.model.type === 'flux_redux') {
-        // Switching from ip_adapter to flux_redux
+      if (isEqual(oldModel, entity.ipAdapter.model)) {
+        // Nothing changed, so we don't need to do anything
+        return;
+      }
+
+      // The type of ref image depends on the model. When the user switches the model, we rebuild the ref image.
+      // When we switch the model, we keep the image the same, but change the other parameters.
+
+      if (entity.ipAdapter.model.base === 'chatgpt-4o') {
+        // Switching to chatgpt-4o ref image
+        entity.ipAdapter = {
+          ...initialChatGPT4oReferenceImage,
+          image: entity.ipAdapter.image,
+          model: entity.ipAdapter.model,
+        };
+        return;
+      }
+
+      if (entity.ipAdapter.model.type === 'flux_redux') {
+        // Switching to flux_redux
         entity.ipAdapter = {
           ...initialFLUXRedux,
           image: entity.ipAdapter.image,
@@ -668,17 +696,13 @@ export const canvasSlice = createSlice({
         return;
       }
 
-      if (entity.ipAdapter.type === 'flux_redux' && entity.ipAdapter.model.type === 'ip_adapter') {
-        // Switching from flux_redux to ip_adapter
+      if (entity.ipAdapter.model.type === 'ip_adapter') {
+        // Switching to ip_adapter
         entity.ipAdapter = {
           ...initialIPAdapter,
           image: entity.ipAdapter.image,
           model: entity.ipAdapter.model,
         };
-        return;
-      }
-
-      if (entity.ipAdapter.type === 'ip_adapter') {
         // Ensure that the IP Adapter model is compatible with the CLIP Vision model
         if (entity.ipAdapter.model?.base === 'flux') {
           entity.ipAdapter.clipVisionModel = 'ViT-L';
@@ -686,6 +710,7 @@ export const canvasSlice = createSlice({
           // Fall back to ViT-H (ViT-G would also work)
           entity.ipAdapter.clipVisionModel = 'ViT-H';
         }
+        return;
       }
     },
     referenceImageIPAdapterCLIPVisionModelChanged: (
@@ -1139,7 +1164,21 @@ export const canvasSlice = createSlice({
       syncScaledSize(state);
     },
     bboxChangedFromCanvas: (state, action: PayloadAction<IRect>) => {
-      state.bbox.rect = action.payload;
+      const newBboxRect = action.payload;
+      const oldBboxRect = state.bbox.rect;
+
+      state.bbox.rect = newBboxRect;
+
+      if (newBboxRect.width === oldBboxRect.width && newBboxRect.height === oldBboxRect.height) {
+        return;
+      }
+
+      const oldAspectRatio = state.bbox.aspectRatio.value;
+      const newAspectRatio = newBboxRect.width / newBboxRect.height;
+
+      if (oldAspectRatio === newAspectRatio) {
+        return;
+      }
 
       // TODO(psyche): Figure out a way to handle this without resetting the aspect ratio on every change.
       // This action is dispatched when the user resizes or moves the bbox from the canvas. For now, when the user
@@ -1198,6 +1237,43 @@ export const canvasSlice = createSlice({
       state.bbox.aspectRatio.id = id;
       if (id === 'Free') {
         state.bbox.aspectRatio.isLocked = false;
+      } else if (
+        (state.bbox.modelBase === 'imagen3' || state.bbox.modelBase === 'imagen4') &&
+        isImagenAspectRatioID(id)
+      ) {
+        // Imagen3 has specific output sizes that are not exactly the same as the aspect ratio. Need special handling.
+        if (id === '16:9') {
+          state.bbox.rect.width = 1408;
+          state.bbox.rect.height = 768;
+        } else if (id === '4:3') {
+          state.bbox.rect.width = 1280;
+          state.bbox.rect.height = 896;
+        } else if (id === '1:1') {
+          state.bbox.rect.width = 1024;
+          state.bbox.rect.height = 1024;
+        } else if (id === '3:4') {
+          state.bbox.rect.width = 896;
+          state.bbox.rect.height = 1280;
+        } else if (id === '9:16') {
+          state.bbox.rect.width = 768;
+          state.bbox.rect.height = 1408;
+        }
+        state.bbox.aspectRatio.value = state.bbox.rect.width / state.bbox.rect.height;
+        state.bbox.aspectRatio.isLocked = true;
+      } else if (state.bbox.modelBase === 'chatgpt-4o' && isChatGPT4oAspectRatioID(id)) {
+        // gpt-image has specific output sizes that are not exactly the same as the aspect ratio. Need special handling.
+        if (id === '3:2') {
+          state.bbox.rect.width = 1536;
+          state.bbox.rect.height = 1024;
+        } else if (id === '1:1') {
+          state.bbox.rect.width = 1024;
+          state.bbox.rect.height = 1024;
+        } else if (id === '2:3') {
+          state.bbox.rect.width = 1024;
+          state.bbox.rect.height = 1536;
+        }
+        state.bbox.aspectRatio.value = state.bbox.rect.width / state.bbox.rect.height;
+        state.bbox.aspectRatio.isLocked = true;
       } else {
         state.bbox.aspectRatio.isLocked = true;
         state.bbox.aspectRatio.value = ASPECT_RATIO_MAP[id].ratio;
@@ -1670,6 +1746,13 @@ export const canvasSlice = createSlice({
       const base = model?.base;
       if (isMainModelBase(base) && state.bbox.modelBase !== base) {
         state.bbox.modelBase = base;
+        if (API_BASE_MODELS.includes(base)) {
+          state.bbox.aspectRatio.isLocked = true;
+          state.bbox.aspectRatio.value = 1;
+          state.bbox.aspectRatio.id = '1:1';
+          state.bbox.rect.width = 1024;
+          state.bbox.rect.height = 1024;
+        }
         syncScaledSize(state);
       }
     });
@@ -1802,6 +1885,10 @@ export const canvasPersistConfig: PersistConfig<CanvasState> = {
 };
 
 const syncScaledSize = (state: CanvasState) => {
+  if (API_BASE_MODELS.includes(state.bbox.modelBase)) {
+    // Imagen3 has fixed sizes. Scaled bbox is not supported.
+    return;
+  }
   if (state.bbox.scaleMethod === 'auto') {
     // Sync both aspect ratio and size
     const { width, height } = state.bbox.rect;

invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts

@@ -380,6 +380,9 @@ export const selectIsSDXL = createParamsSelector((params) => params.model?.base
 export const selectIsFLUX = createParamsSelector((params) => params.model?.base === 'flux');
 export const selectIsSD3 = createParamsSelector((params) => params.model?.base === 'sd-3');
 export const selectIsCogView4 = createParamsSelector((params) => params.model?.base === 'cogview4');
+export const selectIsImagen3 = createParamsSelector((params) => params.model?.base === 'imagen3');
+export const selectIsImagen4 = createParamsSelector((params) => params.model?.base === 'imagen4');
+export const selectIsChatGTP4o = createParamsSelector((params) => params.model?.base === 'chatgpt-4o');
 
 export const selectModel = createParamsSelector((params) => params.model);
 export const selectModelKey = createParamsSelector((params) => params.model?.key);