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Merge branch 'main' into ebr/docker-py311

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Millun Atluri 2023-10-20 14:28:40 +11:00 committed by GitHub
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6
invokeai/app/api/dependencies.py
View File

@ -2,6 +2,7 @@
from logging import Logger
from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
@ -30,6 +31,7 @@ from ..services.shared.default_graphs import create_system_graphs
from ..services.shared.graph import GraphExecutionState, LibraryGraph
from ..services.shared.sqlite import SqliteDatabase
from ..services.urls.urls_default import LocalUrlService
from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
from .events import FastAPIEventService
@ -90,6 +92,8 @@ class ApiDependencies:
session_processor = DefaultSessionProcessor()
session_queue = SqliteSessionQueue(db=db)
urls = LocalUrlService()
workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
workflow_records = SqliteWorkflowRecordsStorage(db=db)
services = InvocationServices(
board_image_records=board_image_records,
@ -114,6 +118,8 @@ class ApiDependencies:
session_processor=session_processor,
session_queue=session_queue,
urls=urls,
workflow_image_records=workflow_image_records,
workflow_records=workflow_records,
)
create_system_graphs(services.graph_library)

37
invokeai/app/api/routers/images.py
View File

@ -1,13 +1,14 @@
import io
import traceback
from typing import Optional
from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
from fastapi.responses import FileResponse
from fastapi.routing import APIRouter
from PIL import Image
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, ValidationError
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
@ -45,17 +46,38 @@ async def upload_image(
if not file.content_type or not file.content_type.startswith("image"):
raise HTTPException(status_code=415, detail="Not an image")
contents = await file.read()
metadata = None
workflow = None
contents = await file.read()
try:
pil_image = Image.open(io.BytesIO(contents))
if crop_visible:
bbox = pil_image.getbbox()
pil_image = pil_image.crop(bbox)
except Exception:
# Error opening the image
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
raise HTTPException(status_code=415, detail="Failed to read image")
# TODO: retain non-invokeai metadata on upload?
# attempt to parse metadata from image
metadata_raw = pil_image.info.get("invokeai_metadata", None)
if metadata_raw:
try:
metadata = MetadataFieldValidator.validate_json(metadata_raw)
except ValidationError:
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
pass
# attempt to parse workflow from image
workflow_raw = pil_image.info.get("invokeai_workflow", None)
if workflow_raw is not None:
try:
workflow = WorkflowFieldValidator.validate_json(workflow_raw)
except ValidationError:
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
pass
try:
image_dto = ApiDependencies.invoker.services.images.create(
image=pil_image,
@ -63,6 +85,8 @@ async def upload_image(
image_category=image_category,
session_id=session_id,
board_id=board_id,
metadata=metadata,
workflow=workflow,
is_intermediate=is_intermediate,
)
@ -71,6 +95,7 @@ async def upload_image(
return image_dto
except Exception:
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail="Failed to create image")
@ -146,11 +171,11 @@ async def get_image_dto(
@images_router.get(
"/i/{image_name}/metadata",
operation_id="get_image_metadata",
response_model=ImageMetadata,
response_model=Optional[MetadataField],
)
async def get_image_metadata(
image_name: str = Path(description="The name of image to get"),
) -> ImageMetadata:
) -> Optional[MetadataField]:
"""Gets an image's metadata"""
try:

20
invokeai/app/api/routers/models.py
View File

@ -23,13 +23,13 @@ from ..dependencies import ApiDependencies
models_router = APIRouter(prefix="/v1/models", tags=["models"])
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
update_models_response_adapter = TypeAdapter(UpdateModelResponse)
UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
import_models_response_adapter = TypeAdapter(ImportModelResponse)
ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
convert_models_response_adapter = TypeAdapter(ConvertModelResponse)
ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
@ -41,7 +41,7 @@ class ModelsList(BaseModel):
model_config = ConfigDict(use_enum_values=True)
models_list_adapter = TypeAdapter(ModelsList)
ModelsListValidator = TypeAdapter(ModelsList)
@models_router.get(
@ -60,7 +60,7 @@ async def list_models(
models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
else:
models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
models = models_list_adapter.validate_python({"models": models_raw})
models = ModelsListValidator.validate_python({"models": models_raw})
return models
@ -131,7 +131,7 @@ async def update_model(
base_model=base_model,
model_type=model_type,
)
model_response = update_models_response_adapter.validate_python(model_raw)
model_response = UpdateModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
@ -186,7 +186,7 @@ async def import_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.name, base_model=info.base_model, model_type=info.model_type
)
return import_models_response_adapter.validate_python(model_raw)
return ImportModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
@ -231,7 +231,7 @@ async def add_model(
base_model=info.base_model,
model_type=info.model_type,
)
return import_models_response_adapter.validate_python(model_raw)
return ImportModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@ -302,7 +302,7 @@ async def convert_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name, base_model=base_model, model_type=model_type
)
response = convert_models_response_adapter.validate_python(model_raw)
response = ConvertModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
except ValueError as e:
@ -417,7 +417,7 @@ async def merge_models(
base_model=base_model,
model_type=ModelType.Main,
)
response = convert_models_response_adapter.validate_python(model_raw)
response = ConvertModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException:
raise HTTPException(
status_code=404,

20
invokeai/app/api/routers/workflows.py Normal file
View File

@ -0,0 +1,20 @@
from fastapi import APIRouter, Path
from invokeai.app.api.dependencies import ApiDependencies
from invokeai.app.invocations.baseinvocation import WorkflowField
workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
@workflows_router.get(
"/i/{workflow_id}",
operation_id="get_workflow",
responses={
200: {"model": WorkflowField},
},
)
async def get_workflow(
workflow_id: str = Path(description="The workflow to get"),
) -> WorkflowField:
"""Gets a workflow"""
return ApiDependencies.invoker.services.workflow_records.get(workflow_id)

20
invokeai/app/api_app.py
View File

@ -38,7 +38,17 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies
from .api.routers import app_info, board_images, boards, images, models, session_queue, sessions, utilities
from .api.routers import (
app_info,
board_images,
boards,
images,
models,
session_queue,
sessions,
utilities,
workflows,
)
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
@ -95,18 +105,13 @@ async def shutdown_event() -> None:
app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api")
app.include_router(models.models_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(app_info.app_router, prefix="/api")
app.include_router(session_queue.session_queue_router, prefix="/api")
app.include_router(workflows.workflows_router, prefix="/api")
# Build a custom OpenAPI to include all outputs
@ -166,7 +171,6 @@ def custom_openapi() -> dict[str, Any]:
# print(f"Config with name {name} already defined")
continue
# "BaseModelType":{"title":"BaseModelType","description":"An enumeration.","enum":["sd-1","sd-2"],"type":"string"}
openapi_schema["components"]["schemas"][name] = dict(
title=name,
description="An enumeration.",

32
invokeai/app/invocations/__init__.py
View File

@ -1,8 +1,28 @@
import os
import shutil
import sys
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
__all__ = []
from invokeai.app.services.config.config_default import InvokeAIAppConfig
dirname = os.path.dirname(os.path.abspath(__file__))
for f in os.listdir(dirname):
if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
__all__.append(f[:-3])
custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
custom_nodes_path.mkdir(parents=True, exist_ok=True)
custom_nodes_init_path = str(custom_nodes_path / "__init__.py")
custom_nodes_readme_path = str(custom_nodes_path / "README.md")
# copy our custom nodes __init__.py to the custom nodes directory
shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path)
shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path)
# Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
spec = spec_from_file_location("custom_nodes", custom_nodes_init_path)
if spec is None or spec.loader is None:
raise RuntimeError(f"Could not load custom nodes from {custom_nodes_init_path}")
module = module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
# add core nodes to __all__
python_files = filter(lambda f: not f.name.startswith("_"), Path(__file__).parent.glob("*.py"))
__all__ = list(f.stem for f in python_files) # type: ignore

168
invokeai/app/invocations/baseinvocation.py
View File

@ -2,7 +2,7 @@
from __future__ import annotations
import json
import inspect
import re
from abc import ABC, abstractmethod
from enum import Enum
@ -11,8 +11,8 @@ from types import UnionType
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
import semver
from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
from pydantic.fields import _Unset
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
from pydantic.fields import FieldInfo, _Unset
from pydantic_core import PydanticUndefined
from invokeai.app.services.config.config_default import InvokeAIAppConfig
@ -26,6 +26,10 @@ class InvalidVersionError(ValueError):
pass
class InvalidFieldError(TypeError):
pass
class FieldDescriptions:
denoising_start = "When to start denoising, expressed a percentage of total steps"
denoising_end = "When to stop denoising, expressed a percentage of total steps"
@ -60,7 +64,12 @@ class FieldDescriptions:
denoised_latents = "Denoised latents tensor"
latents = "Latents tensor"
strength = "Strength of denoising (proportional to steps)"
core_metadata = "Optional core metadata to be written to image"
metadata = "Optional metadata to be saved with the image"
metadata_collection = "Collection of Metadata"
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
metadata_item_label = "Label for this metadata item"
metadata_item_value = "The value for this metadata item (may be any type)"
workflow = "Optional workflow to be saved with the image"
interp_mode = "Interpolation mode"
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
fp32 = "Whether or not to use full float32 precision"
@ -167,8 +176,12 @@ class UIType(str, Enum):
Scheduler = "Scheduler"
WorkflowField = "WorkflowField"
IsIntermediate = "IsIntermediate"
MetadataField = "MetadataField"
BoardField = "BoardField"
Any = "Any"
MetadataItem = "MetadataItem"
MetadataItemCollection = "MetadataItemCollection"
MetadataItemPolymorphic = "MetadataItemPolymorphic"
MetadataDict = "MetadataDict"
# endregion
@ -294,6 +307,7 @@ def InputField(
ui_order=ui_order,
item_default=item_default,
ui_choice_labels=ui_choice_labels,
_field_kind="input",
)
field_args = dict(
@ -436,6 +450,7 @@ def OutputField(
ui_type=ui_type,
ui_hidden=ui_hidden,
ui_order=ui_order,
_field_kind="output",
),
)
@ -519,6 +534,7 @@ class BaseInvocationOutput(BaseModel):
schema["required"].extend(["type"])
model_config = ConfigDict(
protected_namespaces=(),
validate_assignment=True,
json_schema_serialization_defaults_required=True,
json_schema_extra=json_schema_extra,
@ -541,9 +557,6 @@ class MissingInputException(Exception):
class BaseInvocation(ABC, BaseModel):
"""
A node to process inputs and produce outputs.
May use dependency injection in __init__ to receive providers.
All invocations must use the `@invocation` decorator to provide their unique type.
"""
@ -659,37 +672,21 @@ class BaseInvocation(ABC, BaseModel):
id: str = Field(
default_factory=uuid_string,
description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
json_schema_extra=dict(_field_kind="internal"),
)
is_intermediate: Optional[bool] = Field(
is_intermediate: bool = Field(
default=False,
description="Whether or not this is an intermediate invocation.",
json_schema_extra=dict(ui_type=UIType.IsIntermediate),
json_schema_extra=dict(ui_type=UIType.IsIntermediate, _field_kind="internal"),
)
workflow: Optional[str] = Field(
default=None,
description="The workflow to save with the image",
json_schema_extra=dict(ui_type=UIType.WorkflowField),
use_cache: bool = Field(
default=True, description="Whether or not to use the cache", json_schema_extra=dict(_field_kind="internal")
)
use_cache: Optional[bool] = Field(
default=True,
description="Whether or not to use the cache",
)
@field_validator("workflow", mode="before")
@classmethod
def validate_workflow_is_json(cls, v):
"""We don't have a workflow schema in the backend, so we just check that it's valid JSON"""
if v is None:
return None
try:
json.loads(v)
except json.decoder.JSONDecodeError:
raise ValueError("Workflow must be valid JSON")
return v
UIConfig: ClassVar[Type[UIConfigBase]]
model_config = ConfigDict(
protected_namespaces=(),
validate_assignment=True,
json_schema_extra=json_schema_extra,
json_schema_serialization_defaults_required=True,
@ -700,6 +697,68 @@ class BaseInvocation(ABC, BaseModel):
TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
RESERVED_INPUT_FIELD_NAMES = {
"id",
"is_intermediate",
"use_cache",
"type",
"workflow",
"metadata",
}
RESERVED_OUTPUT_FIELD_NAMES = {"type"}
class _Model(BaseModel):
pass
# Get all pydantic model attrs, methods, etc
RESERVED_PYDANTIC_FIELD_NAMES = set(map(lambda m: m[0], inspect.getmembers(_Model())))
def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
"""
Validates the fields of an invocation or invocation output:
- must not override any pydantic reserved fields
- must be created via `InputField`, `OutputField`, or be an internal field defined in this file
"""
for name, field in model_fields.items():
if name in RESERVED_PYDANTIC_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)')
field_kind = (
# _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
field.json_schema_extra.get("_field_kind", None)
if field.json_schema_extra
else None
)
# must have a field_kind
if field_kind is None or field_kind not in {"input", "output", "internal"}:
raise InvalidFieldError(
f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)'
)
if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)')
if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)')
# internal fields *must* be in the reserved list
if (
field_kind == "internal"
and name not in RESERVED_INPUT_FIELD_NAMES
and name not in RESERVED_OUTPUT_FIELD_NAMES
):
raise InvalidFieldError(
f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
)
return None
def invocation(
invocation_type: str,
title: Optional[str] = None,
@ -709,7 +768,7 @@ def invocation(
use_cache: Optional[bool] = True,
) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]:
"""
Adds metadata to an invocation.
Registers an invocation.
:param str invocation_type: The type of the invocation. Must be unique among all invocations.
:param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
@ -728,6 +787,8 @@ def invocation(
if invocation_type in BaseInvocation.get_invocation_types():
raise ValueError(f'Invocation type "{invocation_type}" already exists')
validate_fields(cls.model_fields, invocation_type)
# Add OpenAPI schema extras
uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
@ -758,8 +819,7 @@ def invocation(
invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = Field(
title="type",
default=invocation_type,
title="type", default=invocation_type, json_schema_extra=dict(_field_kind="internal")
)
docstring = cls.__doc__
@ -800,13 +860,12 @@ def invocation_output(
if output_type in BaseInvocationOutput.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.
output_type_annotation = Literal[output_type] # type: ignore
output_type_field = Field(
title="type",
default=output_type,
)
output_type_field = Field(title="type", default=output_type, json_schema_extra=dict(_field_kind="internal"))
docstring = cls.__doc__
cls = create_model(
@ -824,4 +883,37 @@ def invocation_output(
return wrapper
GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel)
class WorkflowField(RootModel):
"""
Pydantic model for workflows with custom root of type dict[str, Any].
Workflows are stored without a strict schema.
"""
root: dict[str, Any] = Field(description="The workflow")
WorkflowFieldValidator = TypeAdapter(WorkflowField)
class WithWorkflow(BaseModel):
workflow: Optional[WorkflowField] = Field(
default=None, description=FieldDescriptions.workflow, json_schema_extra=dict(_field_kind="internal")
)
class MetadataField(RootModel):
"""
Pydantic model for metadata with custom root of type dict[str, Any].
Metadata is stored without a strict schema.
"""
root: dict[str, Any] = Field(description="The metadata")
MetadataFieldValidator = TypeAdapter(MetadataField)
class WithMetadata(BaseModel):
metadata: Optional[MetadataField] = Field(
default=None, description=FieldDescriptions.metadata, json_schema_extra=dict(_field_kind="internal")
)

7
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -38,6 +38,8 @@ from .baseinvocation import (
InputField,
InvocationContext,
OutputField,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -127,12 +129,12 @@ class ControlNetInvocation(BaseInvocation):
# This invocation exists for other invocations to subclass it - do not register with @invocation!
class ImageProcessorInvocation(BaseInvocation):
class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Base class for invocations that preprocess images for ControlNet"""
image: ImageField = InputField(description="The image to process")
def run_processor(self, image):
def run_processor(self, image: Image.Image) -> Image.Image:
# superclass just passes through image without processing
return image
@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation):
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)

51
invokeai/app/invocations/custom_nodes/README.md Normal file
View File

@ -0,0 +1,51 @@
# Custom Nodes / Node Packs
Copy your node packs to this directory.
When nodes are added or changed, you must restart the app to see the changes.
## Directory Structure
For a node pack to be loaded, it must be placed in a directory alongside this
file. Here's an example structure:
```py
.
├── __init__.py # Invoke-managed custom node loader
│
├── cool_node
│ ├── __init__.py # see example below
│ └── cool_node.py
│
└── my_node_pack
├── __init__.py # see example below
├── tasty_node.py
├── bodacious_node.py
├── utils.py
└── extra_nodes
└── fancy_node.py
```
## Node Pack `__init__.py`
Each node pack must have an `__init__.py` file that imports its nodes.
The structure of each node or node pack is otherwise not important.
Here are examples, based on the example directory structure.
### `cool_node/__init__.py`
```py
from .cool_node import CoolInvocation
```
### `my_node_pack/__init__.py`
```py
from .tasty_node import TastyInvocation
from .bodacious_node import BodaciousInvocation
from .extra_nodes.fancy_node import FancyInvocation
```
Only nodes imported in the `__init__.py` file are loaded.

51
invokeai/app/invocations/custom_nodes/init.py Normal file
View File

@ -0,0 +1,51 @@
"""
Invoke-managed custom node loader. See README.md for more information.
"""
import sys
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.get_logger()
loaded_count = 0
for d in Path(__file__).parent.iterdir():
# skip files
if not d.is_dir():
continue
# skip hidden directories
if d.name.startswith("_") or d.name.startswith("."):
continue
# skip directories without an `__init__.py`
init = d / "__init__.py"
if not init.exists():
continue
module_name = init.parent.stem
# skip if already imported
if module_name in globals():
continue
# we have a legit module to import
spec = spec_from_file_location(module_name, init.absolute())
if spec is None or spec.loader is None:
logger.warn(f"Could not load {init}")
continue
module = module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
loaded_count += 1
del init, module_name
logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}")

4
invokeai/app/invocations/cv.py
View File

@ -8,11 +8,11 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
class CvInpaintInvocation(BaseInvocation):
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Simple inpaint using opencv."""
image: ImageField = InputField(description="The image to inpaint")

8
invokeai/app/invocations/facetools.py
View File

@ -16,6 +16,8 @@ from invokeai.app.invocations.baseinvocation import (
InputField,
InvocationContext,
OutputField,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -437,7 +439,7 @@ def get_faces_list(
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
class FaceOffInvocation(BaseInvocation):
class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
image: ImageField = InputField(description="Image for face detection")
@ -531,7 +533,7 @@ class FaceOffInvocation(BaseInvocation):
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
class FaceMaskInvocation(BaseInvocation):
class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Face mask creation using mediapipe face detection"""
image: ImageField = InputField(description="Image to face detect")
@ -650,7 +652,7 @@ class FaceMaskInvocation(BaseInvocation):
@invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
)
class FaceIdentifierInvocation(BaseInvocation):
class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
image: ImageField = InputField(description="Image to face detect")

223
invokeai/app/invocations/image.py
View File

@ -7,13 +7,21 @@ import cv2
import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
from .baseinvocation import (
BaseInvocation,
FieldDescriptions,
Input,
InputField,
InvocationContext,
WithMetadata,
WithWorkflow,
invocation,
)
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@ -36,14 +44,8 @@ class ShowImageInvocation(BaseInvocation):
)
@invocation(
"blank_image",
title="Blank Image",
tags=["image"],
category="image",
version="1.0.0",
)
class BlankImageInvocation(BaseInvocation):
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Creates a blank image and forwards it to the pipeline"""
width: int = InputField(default=512, description="The width of the image")
@ -61,6 +63,7 @@ class BlankImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -71,14 +74,8 @@ class BlankImageInvocation(BaseInvocation):
)
@invocation(
"img_crop",
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.0.0",
)
class ImageCropInvocation(BaseInvocation):
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Crops an image to a specified box. The box can be outside of the image."""
image: ImageField = InputField(description="The image to crop")
@ -100,6 +97,7 @@ class ImageCropInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -110,14 +108,8 @@ class ImageCropInvocation(BaseInvocation):
)
@invocation(
"img_paste",
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.0.1",
)
class ImagePasteInvocation(BaseInvocation):
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Pastes an image into another image."""
base_image: ImageField = InputField(description="The base image")
@ -159,6 +151,7 @@ class ImagePasteInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -169,14 +162,8 @@ class ImagePasteInvocation(BaseInvocation):
)
@invocation(
"tomask",
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.0.0",
)
class MaskFromAlphaInvocation(BaseInvocation):
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Extracts the alpha channel of an image as a mask."""
image: ImageField = InputField(description="The image to create the mask from")
@ -196,6 +183,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -206,14 +194,8 @@ class MaskFromAlphaInvocation(BaseInvocation):
)
@invocation(
"img_mul",
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.0.0",
)
class ImageMultiplyInvocation(BaseInvocation):
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
image1: ImageField = InputField(description="The first image to multiply")
@ -232,6 +214,7 @@ class ImageMultiplyInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -245,14 +228,8 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation(
"img_chan",
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.0.0",
)
class ImageChannelInvocation(BaseInvocation):
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Gets a channel from an image."""
image: ImageField = InputField(description="The image to get the channel from")
@ -270,6 +247,7 @@ class ImageChannelInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -283,14 +261,8 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@invocation(
"img_conv",
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.0.0",
)
class ImageConvertInvocation(BaseInvocation):
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Converts an image to a different mode."""
image: ImageField = InputField(description="The image to convert")
@ -308,6 +280,7 @@ class ImageConvertInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -318,14 +291,8 @@ class ImageConvertInvocation(BaseInvocation):
)
@invocation(
"img_blur",
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.0.0",
)
class ImageBlurInvocation(BaseInvocation):
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Blurs an image"""
image: ImageField = InputField(description="The image to blur")
@ -348,6 +315,7 @@ class ImageBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -378,23 +346,14 @@ PIL_RESAMPLING_MAP = {
}
@invocation(
"img_resize",
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.0.0",
)
class ImageResizeInvocation(BaseInvocation):
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Resizes an image to specific dimensions"""
image: ImageField = InputField(description="The image to resize")
width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -413,7 +372,7 @@ class ImageResizeInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -424,14 +383,8 @@ class ImageResizeInvocation(BaseInvocation):
)
@invocation(
"img_scale",
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.0.0",
)
class ImageScaleInvocation(BaseInvocation):
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Scales an image by a factor"""
image: ImageField = InputField(description="The image to scale")
@ -461,6 +414,7 @@ class ImageScaleInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -471,14 +425,8 @@ class ImageScaleInvocation(BaseInvocation):
)
@invocation(
"img_lerp",
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.0.0",
)
class ImageLerpInvocation(BaseInvocation):
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp")
@ -500,6 +448,7 @@ class ImageLerpInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -510,14 +459,8 @@ class ImageLerpInvocation(BaseInvocation):
)
@invocation(
"img_ilerp",
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.0.0",
)
class ImageInverseLerpInvocation(BaseInvocation):
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Inverse linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp")
@ -539,6 +482,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -549,20 +493,11 @@ class ImageInverseLerpInvocation(BaseInvocation):
)
@invocation(
"img_nsfw",
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.0.0",
)
class ImageNSFWBlurInvocation(BaseInvocation):
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add blur to NSFW-flagged images"""
image: ImageField = InputField(description="The image to check")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -583,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -607,14 +542,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ImageWatermarkInvocation(BaseInvocation):
class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add an invisible watermark to an image"""
image: ImageField = InputField(description="The image to check")
text: str = InputField(default="InvokeAI", description="Watermark text")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -626,7 +558,7 @@ class ImageWatermarkInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -637,14 +569,8 @@ class ImageWatermarkInvocation(BaseInvocation):
)
@invocation(
"mask_edge",
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.0.0",
)
class MaskEdgeInvocation(BaseInvocation):
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Applies an edge mask to an image"""
image: ImageField = InputField(description="The image to apply the mask to")
@ -678,6 +604,7 @@ class MaskEdgeInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -695,7 +622,7 @@ class MaskEdgeInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class MaskCombineInvocation(BaseInvocation):
class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
mask1: ImageField = InputField(description="The first mask to combine")
@ -714,6 +641,7 @@ class MaskCombineInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -724,14 +652,8 @@ class MaskCombineInvocation(BaseInvocation):
)
@invocation(
"color_correct",
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.0.0",
)
class ColorCorrectInvocation(BaseInvocation):
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""
Shifts the colors of a target image to match the reference image, optionally
using a mask to only color-correct certain regions of the target image.
@ -830,6 +752,7 @@ class ColorCorrectInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -840,14 +763,8 @@ class ColorCorrectInvocation(BaseInvocation):
)
@invocation(
"img_hue_adjust",
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.0.0",
)
class ImageHueAdjustmentInvocation(BaseInvocation):
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Adjusts the Hue of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -875,6 +792,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow,
)
@ -950,7 +868,7 @@ CHANNEL_FORMATS = {
category="image",
version="1.0.0",
)
class ImageChannelOffsetInvocation(BaseInvocation):
class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Add or subtract a value from a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -984,6 +902,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow,
)
@ -1020,7 +939,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ImageChannelMultiplyInvocation(BaseInvocation):
class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Scale a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -1060,6 +979,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
workflow=self.workflow,
metadata=self.metadata,
)
return ImageOutput(
@ -1079,16 +999,11 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
version="1.0.1",
use_cache=False,
)
class SaveImageInvocation(BaseInvocation):
class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -1101,7 +1016,7 @@ class SaveImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

21
invokeai/app/invocations/infill.py
View File

@ -13,7 +13,7 @@ from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
@ -119,7 +119,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillColorInvocation(BaseInvocation):
class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with a solid color"""
image: ImageField = InputField(description="The image to infill")
@ -143,6 +143,7 @@ class InfillColorInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -154,7 +155,7 @@ class InfillColorInvocation(BaseInvocation):
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillTileInvocation(BaseInvocation):
class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with tiles of the image"""
image: ImageField = InputField(description="The image to infill")
@ -179,6 +180,7 @@ class InfillTileInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -192,7 +194,7 @@ class InfillTileInvocation(BaseInvocation):
@invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
)
class InfillPatchMatchInvocation(BaseInvocation):
class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
image: ImageField = InputField(description="The image to infill")
@ -232,6 +234,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -243,7 +246,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class LaMaInfillInvocation(BaseInvocation):
class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the LaMa model"""
image: ImageField = InputField(description="The image to infill")
@ -260,6 +263,8 @@ class LaMaInfillInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
return ImageOutput(
@ -269,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation):
)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class CV2InfillInvocation(BaseInvocation):
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using OpenCV Inpainting"""
image: ImageField = InputField(description="The image to infill")
@ -287,6 +292,8 @@ class CV2InfillInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
return ImageOutput(

12
invokeai/app/invocations/latent.py
View File

@ -23,7 +23,6 @@ from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import (
DenoiseMaskField,
DenoiseMaskOutput,
@ -64,6 +63,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -792,7 +793,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
category="latents",
version="1.0.0",
)
class LatentsToImageInvocation(BaseInvocation):
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -805,11 +806,6 @@ class LatentsToImageInvocation(BaseInvocation):
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -878,7 +874,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

188
invokeai/app/invocations/metadata.py
View File

@ -1,13 +1,16 @@
from typing import Optional
from typing import Any, Literal, Optional, Union
from pydantic import Field
from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
InputField,
InvocationContext,
MetadataField,
OutputField,
UIType,
invocation,
invocation_output,
)
@ -16,116 +19,99 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
from ...version import __version__
class LoRAMetadataField(BaseModelExcludeNull):
"""LoRA metadata for an image generated in InvokeAI."""
lora: LoRAModelField = Field(description="The LoRA model")
weight: float = Field(description="The weight of the LoRA model")
class MetadataItemField(BaseModel):
label: str = Field(description=FieldDescriptions.metadata_item_label)
value: Any = Field(description=FieldDescriptions.metadata_item_value)
class IPAdapterMetadataField(BaseModelExcludeNull):
class LoRAMetadataField(BaseModel):
"""LoRA Metadata Field"""
lora: LoRAModelField = Field(description=FieldDescriptions.lora_model)
weight: float = Field(description=FieldDescriptions.lora_weight)
class IPAdapterMetadataField(BaseModel):
"""IP Adapter Field, minus the CLIP Vision Encoder model"""
image: ImageField = Field(description="The IP-Adapter image prompt.")
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
weight: float = Field(description="The weight of the IP-Adapter model")
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
ip_adapter_model: IPAdapterModelField = Field(
description="The IP-Adapter model.",
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
weight: Union[float, list[float]] = Field(
description="The weight given to the IP-Adapter",
)
begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
@invocation_output("metadata_item_output")
class MetadataItemOutput(BaseInvocationOutput):
"""Metadata Item Output"""
item: MetadataItemField = OutputField(description="Metadata Item")
@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0")
class MetadataItemInvocation(BaseInvocation):
"""Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
label: str = InputField(description=FieldDescriptions.metadata_item_label)
value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
def invoke(self, context: InvocationContext) -> MetadataItemOutput:
return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
@invocation_output("metadata_output")
class MetadataOutput(BaseInvocationOutput):
metadata: MetadataField = OutputField(description="Metadata Dict")
@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0")
class MetadataInvocation(BaseInvocation):
"""Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
items: Union[list[MetadataItemField], MetadataItemField] = InputField(
description=FieldDescriptions.metadata_item_polymorphic
)
def invoke(self, context: InvocationContext) -> MetadataOutput:
if isinstance(self.items, MetadataItemField):
# single metadata item
data = {self.items.label: self.items.value}
else:
# collection of metadata items
data = {item.label: item.value for item in self.items}
class CoreMetadata(BaseModelExcludeNull):
"""Core generation metadata for an image generated in InvokeAI."""
app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
generation_mode: Optional[str] = Field(
default=None,
description="The generation mode that output this image",
)
created_by: Optional[str] = Field(default=None, description="The name of the creator of the image")
positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
width: Optional[int] = Field(default=None, description="The width parameter")
height: Optional[int] = Field(default=None, description="The height parameter")
seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
clip_skip: Optional[int] = Field(
default=None,
description="The number of skipped CLIP layers",
)
model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
default=None, description="The IP Adapters used for inference"
)
t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
vae: Optional[VAEModelField] = Field(
default=None,
description="The VAE used for decoding, if the main model's default was not used",
)
# Latents-to-Latents
strength: Optional[float] = Field(
default=None,
description="The strength used for latents-to-latents",
)
init_image: Optional[str] = Field(default=None, description="The name of the initial image")
# SDXL
positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter")
negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter")
# SDXL Refiner
refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used")
refiner_cfg_scale: Optional[float] = Field(
default=None,
description="The classifier-free guidance scale parameter used for the refiner",
)
refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner")
refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
refiner_positive_aesthetic_score: Optional[float] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_negative_aesthetic_score: Optional[float] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
# add app version
data.update({"app_version": __version__})
return MetadataOutput(metadata=MetadataField.model_validate(data))
class ImageMetadata(BaseModelExcludeNull):
"""An image's generation metadata"""
@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
class MergeMetadataInvocation(BaseInvocation):
"""Merged a collection of MetadataDict into a single MetadataDict."""
metadata: Optional[dict] = Field(
default=None,
description="The image's core metadata, if it was created in the Linear or Canvas UI",
)
graph: Optional[dict] = Field(default=None, description="The graph that created the image")
collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
def invoke(self, context: InvocationContext) -> MetadataOutput:
data = {}
for item in self.collection:
data.update(item.model_dump())
return MetadataOutput(metadata=MetadataField.model_validate(data))
@invocation_output("metadata_accumulator_output")
class MetadataAccumulatorOutput(BaseInvocationOutput):
"""The output of the MetadataAccumulator node"""
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
class CoreMetadataInvocation(BaseInvocation):
"""Collects core generation metadata into a MetadataField"""
metadata: CoreMetadata = OutputField(description="The core metadata for the image")
@invocation(
"metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
)
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""
generation_mode: Optional[str] = InputField(
generation_mode: Literal["txt2img", "img2img", "inpaint", "outpaint"] = InputField(
default=None,
description="The generation mode that output this image",
)
@ -138,6 +124,8 @@ class MetadataAccumulatorInvocation(BaseInvocation):
cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
seamless_y: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the Y axis")
clip_skip: Optional[int] = InputField(
default=None,
description="The number of skipped CLIP layers",
@ -220,7 +208,13 @@ class MetadataAccumulatorInvocation(BaseInvocation):
description="The start value used for refiner denoising",
)
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
def invoke(self, context: InvocationContext) -> MetadataOutput:
"""Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))
return MetadataOutput(
metadata=MetadataField.model_validate(
self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
)
)
model_config = ConfigDict(extra="allow")

14
invokeai/app/invocations/onnx.py
View File

@ -4,7 +4,7 @@ import inspect
import re
# from contextlib import ExitStack
from typing import List, Literal, Optional, Union
from typing import List, Literal, Union
import numpy as np
import torch
@ -12,7 +12,6 @@ from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.step_callback import stable_diffusion_step_callback
@ -31,6 +30,8 @@ from .baseinvocation import (
OutputField,
UIComponent,
UIType,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -327,7 +328,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ONNXLatentsToImageInvocation(BaseInvocation):
class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -338,11 +339,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
description=FieldDescriptions.vae,
input=Input.Connection,
)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
# tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -381,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

4
invokeai/app/invocations/primitives.py
View File

@ -251,7 +251,9 @@ class ImageCollectionOutput(BaseInvocationOutput):
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
class ImageInvocation(BaseInvocation):
class ImageInvocation(
BaseInvocation,
):
"""An image primitive value"""
image: ImageField = InputField(description="The image to load")

5
invokeai/app/invocations/upscale.py
View File

@ -14,7 +14,7 @@ from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
# TODO: Populate this from disk?
# TODO: Use model manager to load?
@ -30,7 +30,7 @@ if choose_torch_device() == torch.device("mps"):
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
class ESRGANInvocation(BaseInvocation):
class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Upscales an image using RealESRGAN."""
image: ImageField = InputField(description="The input image")
@ -123,6 +123,7 @@ class ESRGANInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)

8
invokeai/app/services/config/config_default.py
View File

@ -243,6 +243,7 @@ class InvokeAIAppConfig(InvokeAISettings):
db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
custom_nodes_dir : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths)
from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
# LOGGING
@ -410,6 +411,13 @@ class InvokeAIAppConfig(InvokeAISettings):
"""
return self._resolve(self.models_dir)
@property
def custom_nodes_path(self) -> Path:
"""
Path to the custom nodes directory
"""
return self._resolve(self.custom_nodes_dir)
# the following methods support legacy calls leftover from the Globals era
@property
def full_precision(self) -> bool:

6
invokeai/app/services/image_files/image_files_base.py
View File

@ -4,6 +4,8 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files."""
@ -30,8 +32,8 @@ class ImageFileStorageBase(ABC):
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256,
) -> None:
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""

24
invokeai/app/services/image_files/image_files_disk.py
View File

@ -1,5 +1,4 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import json
from pathlib import Path
from queue import Queue
from typing import Dict, Optional, Union
@ -8,6 +7,7 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType
from send2trash import send2trash
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.invoker import Invoker
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
@ -55,8 +55,8 @@ class DiskImageFileStorage(ImageFileStorageBase):
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256,
) -> None:
try:
@ -65,20 +65,10 @@ class DiskImageFileStorage(ImageFileStorageBase):
pnginfo = PngImagePlugin.PngInfo()
if metadata is not None or workflow is not None:
if metadata is not None:
pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
if workflow is not None:
pnginfo.add_text("invokeai_workflow", workflow)
else:
# For uploaded images, we want to retain metadata. PIL strips it on save; manually add it back
# TODO: retain non-invokeai metadata on save...
original_metadata = image.info.get("invokeai_metadata", None)
if original_metadata is not None:
pnginfo.add_text("invokeai_metadata", original_metadata)
original_workflow = image.info.get("invokeai_workflow", None)
if original_workflow is not None:
pnginfo.add_text("invokeai_workflow", original_workflow)
if metadata is not None:
pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
if workflow is not None:
pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
image.save(
image_path,

5
invokeai/app/services/image_records/image_records_base.py
View File

@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
from datetime import datetime
from typing import Optional
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
@ -18,7 +19,7 @@ class ImageRecordStorageBase(ABC):
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata'."""
pass
@ -78,7 +79,7 @@ class ImageRecordStorageBase(ABC):
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
metadata: Optional[MetadataField] = None,
) -> datetime:
"""Saves an image record."""
pass

20
invokeai/app/services/image_records/image_records_sqlite.py
View File

@ -1,9 +1,9 @@
import json
import sqlite3
import threading
from datetime import datetime
from typing import Optional, Union, cast
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
@ -141,22 +141,26 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
return deserialize_image_record(dict(result))
def get_metadata(self, image_name: str) -> Optional[dict]:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT images.metadata FROM images
SELECT metadata FROM images
WHERE image_name = ?;
""",
(image_name,),
)
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
if not result or not result[0]:
return None
return json.loads(result[0])
if not result:
raise ImageRecordNotFoundException
as_dict = dict(result)
metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
return MetadataFieldValidator.validate_json(metadata_raw) if metadata_raw is not None else None
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordNotFoundException from e
@ -408,10 +412,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
metadata: Optional[MetadataField] = None,
) -> datetime:
try:
metadata_json = None if metadata is None else json.dumps(metadata)
metadata_json = metadata.model_dump_json() if metadata is not None else None
self._lock.acquire()
self._cursor.execute(
"""--sql

8
invokeai/app/services/images/images_base.py
View File

@ -3,7 +3,7 @@ from typing import Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.image_records.image_records_common import (
ImageCategory,
ImageRecord,
@ -50,8 +50,8 @@ class ImageServiceABC(ABC):
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@ -81,7 +81,7 @@ class ImageServiceABC(ABC):
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata."""
pass

9
invokeai/app/services/images/images_common.py
View File

@ -24,8 +24,11 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
default=None, description="The id of the board the image belongs to, if one exists."
)
"""The id of the board the image belongs to, if one exists."""
pass
workflow_id: Optional[str] = Field(
default=None,
description="The workflow that generated this image.",
)
"""The workflow that generated this image."""
def image_record_to_dto(
@ -33,6 +36,7 @@ def image_record_to_dto(
image_url: str,
thumbnail_url: str,
board_id: Optional[str],
workflow_id: Optional[str],
) -> ImageDTO:
"""Converts an image record to an image DTO."""
return ImageDTO(
@ -40,4 +44,5 @@ def image_record_to_dto(
image_url=image_url,
thumbnail_url=thumbnail_url,
board_id=board_id,
workflow_id=workflow_id,
)

64
invokeai/app/services/images/images_default.py
View File

@ -2,10 +2,9 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
from ..image_files.image_files_common import (
ImageFileDeleteException,
@ -42,8 +41,8 @@ class ImageService(ImageServiceABC):
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
@ -56,6 +55,12 @@ class ImageService(ImageServiceABC):
(width, height) = image.size
try:
if workflow is not None:
created_workflow = self.__invoker.services.workflow_records.create(workflow)
workflow_id = created_workflow.model_dump()["id"]
else:
workflow_id = None
# TODO: Consider using a transaction here to ensure consistency between storage and database
self.__invoker.services.image_records.save(
# Non-nullable fields
@ -73,6 +78,8 @@ class ImageService(ImageServiceABC):
)
if board_id is not None:
self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
if workflow_id is not None:
self.__invoker.services.workflow_image_records.create(workflow_id=workflow_id, image_name=image_name)
self.__invoker.services.image_files.save(
image_name=image_name, image=image, metadata=metadata, workflow=workflow
)
@ -132,10 +139,11 @@ class ImageService(ImageServiceABC):
image_record = self.__invoker.services.image_records.get(image_name)
image_dto = image_record_to_dto(
image_record,
self.__invoker.services.urls.get_image_url(image_name),
self.__invoker.services.urls.get_image_url(image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(image_name),
image_record=image_record,
image_url=self.__invoker.services.urls.get_image_url(image_name),
thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name),
)
return image_dto
@ -146,25 +154,22 @@ class ImageService(ImageServiceABC):
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> ImageMetadata:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try:
image_record = self.__invoker.services.image_records.get(image_name)
metadata = self.__invoker.services.image_records.get_metadata(image_name)
return self.__invoker.services.image_records.get_metadata(image_name)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
if not image_record.session_id:
return ImageMetadata(metadata=metadata)
session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
def get_workflow(self, image_name: str) -> Optional[WorkflowField]:
try:
workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name)
if workflow_id is None:
return None
return self.__invoker.services.workflow_records.get(workflow_id)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
@ -215,10 +220,11 @@ class ImageService(ImageServiceABC):
image_dtos = list(
map(
lambda r: image_record_to_dto(
r,
self.__invoker.services.urls.get_image_url(r.image_name),
self.__invoker.services.urls.get_image_url(r.image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
image_record=r,
image_url=self.__invoker.services.urls.get_image_url(r.image_name),
thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
),
results.items,
)

8
invokeai/app/services/invocation_services.py
View File

@ -27,6 +27,8 @@ if TYPE_CHECKING:
from .session_queue.session_queue_base import SessionQueueBase
from .shared.graph import GraphExecutionState, LibraryGraph
from .urls.urls_base import UrlServiceBase
from .workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
class InvocationServices:
@ -55,6 +57,8 @@ class InvocationServices:
invocation_cache: "InvocationCacheBase"
names: "NameServiceBase"
urls: "UrlServiceBase"
workflow_image_records: "WorkflowImageRecordsStorageBase"
workflow_records: "WorkflowRecordsStorageBase"
def __init__(
self,
@ -80,6 +84,8 @@ class InvocationServices:
invocation_cache: "InvocationCacheBase",
names: "NameServiceBase",
urls: "UrlServiceBase",
workflow_image_records: "WorkflowImageRecordsStorageBase",
workflow_records: "WorkflowRecordsStorageBase",
):
self.board_images = board_images
self.board_image_records = board_image_records
@ -103,3 +109,5 @@ class InvocationServices:
self.invocation_cache = invocation_cache
self.names = names
self.urls = urls
self.workflow_image_records = workflow_image_records
self.workflow_records = workflow_records

10
invokeai/app/services/item_storage/item_storage_sqlite.py
View File

@ -18,7 +18,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
_cursor: sqlite3.Cursor
_id_field: str
_lock: threading.RLock
_adapter: Optional[TypeAdapter[T]]
_validator: Optional[TypeAdapter[T]]
def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
super().__init__()
@ -28,7 +28,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._table_name = table_name
self._id_field = id_field # TODO: validate that T has this field
self._cursor = self._conn.cursor()
self._adapter: Optional[TypeAdapter[T]] = None
self._validator: Optional[TypeAdapter[T]] = None
self._create_table()
@ -47,14 +47,14 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._lock.release()
def _parse_item(self, item: str) -> T:
if self._adapter is None:
if self._validator is None:
"""
We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so
we can create it when it is first needed instead.
__orig_class__ is technically an implementation detail of the typing module, not a supported API
"""
self._adapter = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
return self._adapter.validate_json(item)
self._validator = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
return self._validator.validate_json(item)
def set(self, item: T):
try:

8
invokeai/app/services/session_queue/session_queue_common.py
View File

@ -147,20 +147,20 @@ DEFAULT_QUEUE_ID = "default"
QUEUE_ITEM_STATUS = Literal["pending", "in_progress", "completed", "failed", "canceled"]
adapter_NodeFieldValue = TypeAdapter(list[NodeFieldValue])
NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
def get_field_values(queue_item_dict: dict) -> Optional[list[NodeFieldValue]]:
field_values_raw = queue_item_dict.get("field_values", None)
return adapter_NodeFieldValue.validate_json(field_values_raw) if field_values_raw is not None else None
return NodeFieldValueValidator.validate_json(field_values_raw) if field_values_raw is not None else None
adapter_GraphExecutionState = TypeAdapter(GraphExecutionState)
GraphExecutionStateValidator = TypeAdapter(GraphExecutionState)
def get_session(queue_item_dict: dict) -> GraphExecutionState:
session_raw = queue_item_dict.get("session", "{}")
session = adapter_GraphExecutionState.validate_json(session_raw, strict=False)
session = GraphExecutionStateValidator.validate_json(session_raw, strict=False)
return session

34
invokeai/app/services/shared/graph.py
View File

@ -193,7 +193,7 @@ class GraphInvocation(BaseInvocation):
"""Execute a graph"""
# TODO: figure out how to create a default here
graph: "Graph" = Field(description="The graph to run", default=None)
graph: "Graph" = InputField(description="The graph to run", default=None)
def invoke(self, context: InvocationContext) -> GraphInvocationOutput:
"""Invoke with provided services and return outputs."""
@ -439,6 +439,14 @@ class Graph(BaseModel):
except Exception as e:
raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
def _is_destination_field_Any(self, edge: Edge) -> bool:
"""Checks if the destination field for an edge is of type typing.Any"""
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any
def _is_destination_field_list_of_Any(self, edge: Edge) -> bool:
"""Checks if the destination field for an edge is of type typing.Any"""
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
def _validate_edge(self, edge: Edge):
"""Validates that a new edge doesn't create a cycle in the graph"""
@ -491,8 +499,19 @@ class Graph(BaseModel):
f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if collector output type matches input type (if this edge results in both being set)
if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
# Validate that we are not connecting collector to iterator (currently unsupported)
if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation):
raise InvalidEdgeError(
f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any]
if (
isinstance(from_node, CollectInvocation)
and edge.source.field == "collection"
and not self._is_destination_field_list_of_Any(edge)
and not self._is_destination_field_Any(edge)
):
if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
raise InvalidEdgeError(
f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
@ -725,16 +744,15 @@ class Graph(BaseModel):
# Get the input root type
input_root_type = next(t[0] for t in type_degrees if t[1] == 0) # type: ignore
# Verify that all outputs are lists
# if not all((get_origin(f) == list for f in output_fields)):
# return False
# Verify that all outputs are lists
if not all(is_list_or_contains_list(f) for f in output_fields):
return False
# Verify that all outputs match the input type (are a base class or the same class)
if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)):
if not all(
is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0])
for f in output_fields
):
return False
return True

0
invokeai/app/services/workflow_image_records/__init__.py Normal file
View File

23
invokeai/app/services/workflow_image_records/workflow_image_records_base.py Normal file
View File

@ -0,0 +1,23 @@
from abc import ABC, abstractmethod
from typing import Optional
class WorkflowImageRecordsStorageBase(ABC):
"""Abstract base class for the one-to-many workflow-image relationship record storage."""
@abstractmethod
def create(
self,
workflow_id: str,
image_name: str,
) -> None:
"""Creates a workflow-image record."""
pass
@abstractmethod
def get_workflow_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's workflow id, if it has one."""
pass

122
invokeai/app/services/workflow_image_records/workflow_image_records_sqlite.py Normal file
View File

@ -0,0 +1,122 @@
import sqlite3
import threading
from typing import Optional, cast
from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.services.workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
class SqliteWorkflowImageRecordsStorage(WorkflowImageRecordsStorageBase):
"""SQLite implementation of WorkflowImageRecordsStorageBase."""
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.RLock
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
try:
self._lock.acquire()
self._create_tables()
self._conn.commit()
finally:
self._lock.release()
def _create_tables(self) -> None:
# Create the `workflow_images` junction table.
self._cursor.execute(
"""--sql
CREATE TABLE IF NOT EXISTS workflow_images (
workflow_id TEXT NOT NULL,
image_name TEXT NOT NULL,
created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-- updated via trigger
updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-- Soft delete, currently unused
deleted_at DATETIME,
-- enforce one-to-many relationship between workflows and images using PK
-- (we can extend this to many-to-many later)
PRIMARY KEY (image_name),
FOREIGN KEY (workflow_id) REFERENCES workflows (workflow_id) ON DELETE CASCADE,
FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE
);
"""
)
# Add index for workflow id
self._cursor.execute(
"""--sql
CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id ON workflow_images (workflow_id);
"""
)
# Add index for workflow id, sorted by created_at
self._cursor.execute(
"""--sql
CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id_created_at ON workflow_images (workflow_id, created_at);
"""
)
# Add trigger for `updated_at`.
self._cursor.execute(
"""--sql
CREATE TRIGGER IF NOT EXISTS tg_workflow_images_updated_at
AFTER UPDATE
ON workflow_images FOR EACH ROW
BEGIN
UPDATE workflow_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
WHERE workflow_id = old.workflow_id AND image_name = old.image_name;
END;
"""
)
def create(
self,
workflow_id: str,
image_name: str,
) -> None:
"""Creates a workflow-image record."""
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
INSERT INTO workflow_images (workflow_id, image_name)
VALUES (?, ?);
""",
(workflow_id, image_name),
)
self._conn.commit()
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
def get_workflow_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's workflow id, if it has one."""
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT workflow_id
FROM workflow_images
WHERE image_name = ?;
""",
(image_name,),
)
result = self._cursor.fetchone()
if result is None:
return None
return cast(str, result[0])
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()

0
invokeai/app/services/workflow_records/__init__.py Normal file
View File

17
invokeai/app/services/workflow_records/workflow_records_base.py Normal file
View File

@ -0,0 +1,17 @@
from abc import ABC, abstractmethod
from invokeai.app.invocations.baseinvocation import WorkflowField
class WorkflowRecordsStorageBase(ABC):
"""Base class for workflow storage services."""
@abstractmethod
def get(self, workflow_id: str) -> WorkflowField:
"""Get workflow by id."""
pass
@abstractmethod
def create(self, workflow: WorkflowField) -> WorkflowField:
"""Creates a workflow."""
pass

2
invokeai/app/services/workflow_records/workflow_records_common.py Normal file
View File

@ -0,0 +1,2 @@
class WorkflowNotFoundError(Exception):
"""Raised when a workflow is not found"""

102
invokeai/app/services/workflow_records/workflow_records_sqlite.py Normal file
View File

@ -0,0 +1,102 @@
import sqlite3
import threading
from invokeai.app.invocations.baseinvocation import WorkflowField, WorkflowFieldValidator
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowNotFoundError
from invokeai.app.util.misc import uuid_string
class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
_invoker: Invoker
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.RLock
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
self._create_tables()
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
def get(self, workflow_id: str) -> WorkflowField:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT workflow
FROM workflows
WHERE workflow_id = ?;
""",
(workflow_id,),
)
row = self._cursor.fetchone()
if row is None:
raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
return WorkflowFieldValidator.validate_json(row[0])
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
def create(self, workflow: WorkflowField) -> WorkflowField:
try:
# workflows do not have ids until they are saved
workflow_id = uuid_string()
workflow.root["id"] = workflow_id
self._lock.acquire()
self._cursor.execute(
"""--sql
INSERT INTO workflows(workflow)
VALUES (?);
""",
(workflow.json(),),
)
self._conn.commit()
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return self.get(workflow_id)
def _create_tables(self) -> None:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
CREATE TABLE IF NOT EXISTS workflows (
workflow TEXT NOT NULL,
workflow_id TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.id')) VIRTUAL NOT NULL UNIQUE, -- gets implicit index
created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')) -- updated via trigger
);
"""
)
self._cursor.execute(
"""--sql
CREATE TRIGGER IF NOT EXISTS tg_workflows_updated_at
AFTER UPDATE
ON workflows FOR EACH ROW
BEGIN
UPDATE workflows
SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
WHERE workflow_id = old.workflow_id;
END;
"""
)
self._conn.commit()
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()

2
invokeai/frontend/web/src/app/types/invokeai.ts
View File

@ -59,6 +59,8 @@ export type AppConfig = {
nodesAllowlist: string[] | undefined;
nodesDenylist: string[] | undefined;
maxUpscalePixels?: number;
metadataFetchDebounce?: number;
workflowFetchDebounce?: number;
sd: {
defaultModel?: string;
disabledControlNetModels: string[];

7
invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts
View File

@ -37,7 +37,12 @@ const useColorPicker = () => {
1
).data;
if (!(a && r && g && b)) {
if (
r === undefined ||
g === undefined ||
b === undefined ||
a === undefined
) {
return;
}

39
invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx
View File

@ -27,7 +27,7 @@ import {
setShouldShowImageDetails,
setShouldShowProgressInViewer,
} from 'features/ui/store/uiSlice';
import { memo, useCallback, useMemo } from 'react';
import { memo, useCallback } from 'react';
import { useHotkeys } from 'react-hotkeys-hook';
import { useTranslation } from 'react-i18next';
import {
@ -38,10 +38,9 @@ import {
FaSeedling,
} from 'react-icons/fa';
import { FaCircleNodes, FaEllipsis } from 'react-icons/fa6';
import {
useGetImageDTOQuery,
useGetImageMetadataFromFileQuery,
} from 'services/api/endpoints/images';
import { useGetImageDTOQuery } from 'services/api/endpoints/images';
import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow';
import { menuListMotionProps } from 'theme/components/menu';
import { sentImageToImg2Img } from '../../store/actions';
import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems';
@ -89,7 +88,6 @@ const CurrentImageButtons = () => {
shouldShowImageDetails,
lastSelectedImage,
shouldShowProgressInViewer,
shouldFetchMetadataFromApi,
} = useAppSelector(currentImageButtonsSelector);
const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
@ -104,23 +102,12 @@ const CurrentImageButtons = () => {
lastSelectedImage?.image_name ?? skipToken
);
const getMetadataArg = useMemo(() => {
if (lastSelectedImage) {
return { image: lastSelectedImage, shouldFetchMetadataFromApi };
} else {
return skipToken;
}
}, [lastSelectedImage, shouldFetchMetadataFromApi]);
const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata(
lastSelectedImage?.image_name
);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
getMetadataArg,
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
const { workflow, isLoading: isLoadingWorkflow } = useDebouncedWorkflow(
lastSelectedImage?.workflow_id
);
const handleLoadWorkflow = useCallback(() => {
@ -257,7 +244,7 @@ const CurrentImageButtons = () => {
<ButtonGroup isAttached={true} isDisabled={shouldDisableToolbarButtons}>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingWorkflow}
icon={<FaCircleNodes />}
tooltip={`${t('nodes.loadWorkflow')} (W)`}
aria-label={`${t('nodes.loadWorkflow')} (W)`}
@ -265,7 +252,7 @@ const CurrentImageButtons = () => {
onClick={handleLoadWorkflow}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaQuoteRight />}
tooltip={`${t('parameters.usePrompt')} (P)`}
aria-label={`${t('parameters.usePrompt')} (P)`}
@ -273,7 +260,7 @@ const CurrentImageButtons = () => {
onClick={handleUsePrompt}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaSeedling />}
tooltip={`${t('parameters.useSeed')} (S)`}
aria-label={`${t('parameters.useSeed')} (S)`}
@ -281,7 +268,7 @@ const CurrentImageButtons = () => {
onClick={handleUseSeed}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaAsterisk />}
tooltip={`${t('parameters.useAll')} (A)`}
aria-label={`${t('parameters.useAll')} (A)`}

37
invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
View File

@ -2,7 +2,7 @@ import { Flex, MenuItem, Spinner } from '@chakra-ui/react';
import { useStore } from '@nanostores/react';
import { useAppToaster } from 'app/components/Toaster';
import { $customStarUI } from 'app/store/nanostores/customStarUI';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { useAppDispatch } from 'app/store/storeHooks';
import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
import {
imagesToChangeSelected,
@ -32,12 +32,12 @@ import {
import { FaCircleNodes } from 'react-icons/fa6';
import { MdStar, MdStarBorder } from 'react-icons/md';
import {
useGetImageMetadataFromFileQuery,
useStarImagesMutation,
useUnstarImagesMutation,
} from 'services/api/endpoints/images';
import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow';
import { ImageDTO } from 'services/api/types';
import { configSelector } from '../../../system/store/configSelectors';
import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
type SingleSelectionMenuItemsProps = {
@ -53,18 +53,13 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
const toaster = useAppToaster();
const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const customStarUi = useStore($customStarUI);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
{ image: imageDTO, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata(
imageDTO?.image_name
);
const { workflow, isLoading: isLoadingWorkflow } = useDebouncedWorkflow(
imageDTO?.workflow_id
);
const [starImages] = useStarImagesMutation();
@ -181,17 +176,17 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
{t('parameters.downloadImage')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaCircleNodes />}
icon={isLoadingWorkflow ? <SpinnerIcon /> : <FaCircleNodes />}
onClickCapture={handleLoadWorkflow}
isDisabled={isLoading || !workflow}
isDisabled={isLoadingWorkflow || !workflow}
>
{t('nodes.loadWorkflow')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaQuoteRight />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaQuoteRight />}
onClickCapture={handleRecallPrompt}
isDisabled={
isLoading ||
isLoadingMetadata ||
(metadata?.positive_prompt === undefined &&
metadata?.negative_prompt === undefined)
}
@ -199,16 +194,16 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
{t('parameters.usePrompt')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaSeedling />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaSeedling />}
onClickCapture={handleRecallSeed}
isDisabled={isLoading || metadata?.seed === undefined}
isDisabled={isLoadingMetadata || metadata?.seed === undefined}
>
{t('parameters.useSeed')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaAsterisk />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaAsterisk />}
onClickCapture={handleUseAllParameters}
isDisabled={isLoading || !metadata}
isDisabled={isLoadingMetadata || !metadata}
>
{t('parameters.useAll')}
</MenuItem>

22
invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx
View File

@ -10,15 +10,14 @@ import {
Text,
} from '@chakra-ui/react';
import { IAINoContentFallback } from 'common/components/IAIImageFallback';
import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent';
import { memo } from 'react';
import { useGetImageMetadataFromFileQuery } from 'services/api/endpoints/images';
import { useTranslation } from 'react-i18next';
import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow';
import { ImageDTO } from 'services/api/types';
import DataViewer from './DataViewer';
import ImageMetadataActions from './ImageMetadataActions';
import { useAppSelector } from '../../../../app/store/storeHooks';
import { configSelector } from '../../../system/store/configSelectors';
import { useTranslation } from 'react-i18next';
import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent';
type ImageMetadataViewerProps = {
image: ImageDTO;
@ -32,17 +31,8 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
// });
const { t } = useTranslation();
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const { metadata, workflow } = useGetImageMetadataFromFileQuery(
{ image, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
);
const { metadata } = useDebouncedMetadata(image.image_name);
const { workflow } = useDebouncedWorkflow(image.workflow_id);
return (
<Flex

6
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx
View File

@ -1,13 +1,13 @@
import { Checkbox, Flex, FormControl, FormLabel } from '@chakra-ui/react';
import { useAppDispatch } from 'app/store/storeHooks';
import { useEmbedWorkflow } from 'features/nodes/hooks/useEmbedWorkflow';
import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
import { useWithWorkflow } from 'features/nodes/hooks/useWithWorkflow';
import { nodeEmbedWorkflowChanged } from 'features/nodes/store/nodesSlice';
import { ChangeEvent, memo, useCallback } from 'react';
const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => {
const dispatch = useAppDispatch();
const hasImageOutput = useHasImageOutput(nodeId);
const withWorkflow = useWithWorkflow(nodeId);
const embedWorkflow = useEmbedWorkflow(nodeId);
const handleChange = useCallback(
(e: ChangeEvent<HTMLInputElement>) => {
@ -21,7 +21,7 @@ const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => {
[dispatch, nodeId]
);
if (!hasImageOutput) {
if (!withWorkflow) {
return null;
}

4
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx
View File

@ -1,11 +1,11 @@
import { Flex } from '@chakra-ui/react';
import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
import { DRAG_HANDLE_CLASSNAME } from 'features/nodes/types/constants';
import { memo } from 'react';
import { useFeatureStatus } from '../../../../../system/hooks/useFeatureStatus';
import EmbedWorkflowCheckbox from './EmbedWorkflowCheckbox';
import SaveToGalleryCheckbox from './SaveToGalleryCheckbox';
import UseCacheCheckbox from './UseCacheCheckbox';
import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
import { useFeatureStatus } from '../../../../../system/hooks/useFeatureStatus';
type Props = {
nodeId: string;

31
invokeai/frontend/web/src/features/nodes/hooks/useWithWorkflow.ts Normal file
View File

@ -0,0 +1,31 @@
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import { useMemo } from 'react';
import { isInvocationNode } from '../types/types';
export const useWithWorkflow = (nodeId: string) => {
const selector = useMemo(
() =>
createSelector(
stateSelector,
({ nodes }) => {
const node = nodes.nodes.find((node) => node.id === nodeId);
if (!isInvocationNode(node)) {
return false;
}
const nodeTemplate = nodes.nodeTemplates[node?.data.type ?? ''];
if (!nodeTemplate) {
return false;
}
return nodeTemplate.withWorkflow;
},
defaultSelectorOptions
),
[nodeId]
);
const withWorkflow = useAppSelector(selector);
return withWorkflow;
};

5
invokeai/frontend/web/src/features/nodes/store/util/validateSourceAndTargetTypes.ts
View File

@ -69,6 +69,8 @@ export const validateSourceAndTargetTypes = (
(sourceType === 'integer' || sourceType === 'float') &&
targetType === 'string';
const isTargetAnyType = targetType === 'Any';
return (
isCollectionItemToNonCollection ||
isNonCollectionToCollectionItem ||
@ -76,6 +78,7 @@ export const validateSourceAndTargetTypes = (
isGenericCollectionToAnyCollectionOrPolymorphic ||
isCollectionToGenericCollection ||
isIntToFloat ||
isIntOrFloatToString
isIntOrFloatToString ||
isTargetAnyType
);
};

38
invokeai/frontend/web/src/features/nodes/types/constants.ts
View File

@ -33,6 +33,8 @@ export const COLLECTION_TYPES: FieldType[] = [
'ColorCollection',
'T2IAdapterCollection',
'IPAdapterCollection',
'MetadataItemCollection',
'MetadataCollection',
];
export const POLYMORPHIC_TYPES: FieldType[] = [
@ -47,6 +49,7 @@ export const POLYMORPHIC_TYPES: FieldType[] = [
'ColorPolymorphic',
'T2IAdapterPolymorphic',
'IPAdapterPolymorphic',
'MetadataItemPolymorphic',
];
export const MODEL_TYPES: FieldType[] = [
@ -78,6 +81,8 @@ export const COLLECTION_MAP: FieldTypeMapWithNumber = {
ColorField: 'ColorCollection',
T2IAdapterField: 'T2IAdapterCollection',
IPAdapterField: 'IPAdapterCollection',
MetadataItemField: 'MetadataItemCollection',
MetadataField: 'MetadataCollection',
};
export const isCollectionItemType = (
itemType: string | undefined
@ -97,6 +102,7 @@ export const SINGLE_TO_POLYMORPHIC_MAP: FieldTypeMapWithNumber = {
ColorField: 'ColorPolymorphic',
T2IAdapterField: 'T2IAdapterPolymorphic',
IPAdapterField: 'IPAdapterPolymorphic',
MetadataItemField: 'MetadataItemPolymorphic',
};
export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = {
@ -111,6 +117,7 @@ export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = {
ColorPolymorphic: 'ColorField',
T2IAdapterPolymorphic: 'T2IAdapterField',
IPAdapterPolymorphic: 'IPAdapterField',
MetadataItemPolymorphic: 'MetadataItemField',
};
export const TYPES_WITH_INPUT_COMPONENTS: FieldType[] = [
@ -144,6 +151,37 @@ export const isPolymorphicItemType = (
Boolean(itemType && itemType in SINGLE_TO_POLYMORPHIC_MAP);
export const FIELDS: Record<FieldType, FieldUIConfig> = {
Any: {
color: 'gray.500',
description: 'Any field type is accepted.',
title: 'Any',
},
MetadataField: {
color: 'gray.500',
description: 'A metadata dict.',
title: 'Metadata Dict',
},
MetadataCollection: {
color: 'gray.500',
description: 'A collection of metadata dicts.',
title: 'Metadata Dict Collection',
},
MetadataItemField: {
color: 'gray.500',
description: 'A metadata item.',
title: 'Metadata Item',
},
MetadataItemCollection: {
color: 'gray.500',
description: 'Any field type is accepted.',
title: 'Metadata Item Collection',
},
MetadataItemPolymorphic: {
color: 'gray.500',
description:
'MetadataItem or MetadataItemCollection field types are accepted.',
title: 'Metadata Item Polymorphic',
},
boolean: {
color: 'green.500',
description: t('nodes.booleanDescription'),

120
invokeai/frontend/web/src/features/nodes/types/types.ts
View File

@ -54,6 +54,10 @@ export type InvocationTemplate = {
* The type of this node's output
*/
outputType: string; // TODO: generate a union of output types
/**
* Whether or not this invocation supports workflows
*/
withWorkflow: boolean;
/**
* The invocation's version.
*/
@ -72,6 +76,7 @@ export type FieldUIConfig = {
// TODO: Get this from the OpenAPI schema? may be tricky...
export const zFieldType = z.enum([
'Any',
'BoardField',
'boolean',
'BooleanCollection',
@ -109,6 +114,11 @@ export const zFieldType = z.enum([
'LatentsPolymorphic',
'LoRAModelField',
'MainModelField',
'MetadataField',
'MetadataCollection',
'MetadataItemField',
'MetadataItemCollection',
'MetadataItemPolymorphic',
'ONNXModelField',
'Scheduler',
'SDXLMainModelField',
@ -685,6 +695,57 @@ export type CollectionItemInputFieldValue = z.infer<
typeof zCollectionItemInputFieldValue
>;
export const zMetadataItemField = z.object({
label: z.string(),
value: z.any(),
});
export type MetadataItemField = z.infer<typeof zMetadataItemField>;
export const zMetadataItemInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('MetadataItemField'),
value: zMetadataItemField.optional(),
});
export type MetadataItemInputFieldValue = z.infer<
typeof zMetadataItemInputFieldValue
>;
export const zMetadataItemCollectionInputFieldValue =
zInputFieldValueBase.extend({
type: z.literal('MetadataItemCollection'),
value: z.array(zMetadataItemField).optional(),
});
export type MetadataItemCollectionInputFieldValue = z.infer<
typeof zMetadataItemCollectionInputFieldValue
>;
export const zMetadataItemPolymorphicInputFieldValue =
zInputFieldValueBase.extend({
type: z.literal('MetadataItemPolymorphic'),
value: z
.union([zMetadataItemField, z.array(zMetadataItemField)])
.optional(),
});
export type MetadataItemPolymorphicInputFieldValue = z.infer<
typeof zMetadataItemPolymorphicInputFieldValue
>;
export const zMetadataField = z.record(z.any());
export type MetadataField = z.infer<typeof zMetadataField>;
export const zMetadataInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('MetadataField'),
value: zMetadataField.optional(),
});
export type MetadataInputFieldValue = z.infer<typeof zMetadataInputFieldValue>;
export const zMetadataCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('MetadataCollection'),
value: z.array(zMetadataField).optional(),
});
export type MetadataCollectionInputFieldValue = z.infer<
typeof zMetadataCollectionInputFieldValue
>;
export const zColorField = z.object({
r: z.number().int().min(0).max(255),
g: z.number().int().min(0).max(255),
@ -723,7 +784,13 @@ export type SchedulerInputFieldValue = z.infer<
typeof zSchedulerInputFieldValue
>;
export const zAnyInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('Any'),
value: z.any().optional(),
});
export const zInputFieldValue = z.discriminatedUnion('type', [
zAnyInputFieldValue,
zBoardInputFieldValue,
zBooleanCollectionInputFieldValue,
zBooleanInputFieldValue,
@ -774,6 +841,11 @@ export const zInputFieldValue = z.discriminatedUnion('type', [
zUNetInputFieldValue,
zVaeInputFieldValue,
zVaeModelInputFieldValue,
zMetadataItemInputFieldValue,
zMetadataItemCollectionInputFieldValue,
zMetadataItemPolymorphicInputFieldValue,
zMetadataInputFieldValue,
zMetadataCollectionInputFieldValue,
]);
export type InputFieldValue = z.infer<typeof zInputFieldValue>;
@ -786,6 +858,11 @@ export type InputFieldTemplateBase = {
fieldKind: 'input';
} & _InputField;
export type AnyInputFieldTemplate = InputFieldTemplateBase & {
type: 'Any';
default: undefined;
};
export type IntegerInputFieldTemplate = InputFieldTemplateBase & {
type: 'integer';
default: number;
@ -939,6 +1016,11 @@ export type UNetInputFieldTemplate = InputFieldTemplateBase & {
type: 'UNetField';
};
export type MetadataItemFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataItemField';
};
export type ClipInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'ClipField';
@ -1087,6 +1169,34 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & {
type: 'WorkflowField';
};
export type MetadataItemInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataItemField';
};
export type MetadataItemCollectionInputFieldTemplate =
InputFieldTemplateBase & {
default: undefined;
type: 'MetadataItemCollection';
};
export type MetadataItemPolymorphicInputFieldTemplate = Omit<
MetadataItemInputFieldTemplate,
'type'
> & {
type: 'MetadataItemPolymorphic';
};
export type MetadataInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataField';
};
export type MetadataCollectionInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataCollection';
};
/**
* An input field template is generated on each page load from the OpenAPI schema.
*
@ -1094,6 +1204,7 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & {
* maximum length, pattern to match, etc).
*/
export type InputFieldTemplate =
| AnyInputFieldTemplate
| BoardInputFieldTemplate
| BooleanCollectionInputFieldTemplate
| BooleanPolymorphicInputFieldTemplate
@ -1143,7 +1254,12 @@ export type InputFieldTemplate =
| T2IAdapterPolymorphicInputFieldTemplate
| UNetInputFieldTemplate
| VaeInputFieldTemplate
| VaeModelInputFieldTemplate;
| VaeModelInputFieldTemplate
| MetadataItemInputFieldTemplate
| MetadataItemCollectionInputFieldTemplate
| MetadataInputFieldTemplate
| MetadataItemPolymorphicInputFieldTemplate
| MetadataCollectionInputFieldTemplate;
export const isInputFieldValue = (
field?: InputFieldValue | OutputFieldValue
@ -1264,7 +1380,7 @@ export const isInvocationFieldSchema = (
export type InvocationEdgeExtra = { type: 'default' | 'collapsed' };
const zLoRAMetadataItem = z.object({
export const zLoRAMetadataItem = z.object({
lora: zLoRAModelField.deepPartial(),
weight: z.number(),
});

144
invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts
View File

@ -7,6 +7,7 @@ import {
startCase,
} from 'lodash-es';
import { OpenAPIV3_1 } from 'openapi-types';
import { ControlField } from 'services/api/types';
import {
COLLECTION_MAP,
POLYMORPHIC_TYPES,
@ -15,36 +16,70 @@ import {
isPolymorphicItemType,
} from '../types/constants';
import {
AnyInputFieldTemplate,
BoardInputFieldTemplate,
BooleanCollectionInputFieldTemplate,
BooleanInputFieldTemplate,
BooleanPolymorphicInputFieldTemplate,
ClipInputFieldTemplate,
CollectionInputFieldTemplate,
CollectionItemInputFieldTemplate,
ColorCollectionInputFieldTemplate,
ColorInputFieldTemplate,
ColorPolymorphicInputFieldTemplate,
ConditioningCollectionInputFieldTemplate,
ConditioningField,
ConditioningInputFieldTemplate,
ConditioningPolymorphicInputFieldTemplate,
ControlCollectionInputFieldTemplate,
ControlInputFieldTemplate,
ControlNetModelInputFieldTemplate,
ControlPolymorphicInputFieldTemplate,
DenoiseMaskInputFieldTemplate,
EnumInputFieldTemplate,
FieldType,
FloatCollectionInputFieldTemplate,
FloatPolymorphicInputFieldTemplate,
FloatInputFieldTemplate,
FloatPolymorphicInputFieldTemplate,
IPAdapterCollectionInputFieldTemplate,
IPAdapterField,
IPAdapterInputFieldTemplate,
IPAdapterModelInputFieldTemplate,
IPAdapterPolymorphicInputFieldTemplate,
ImageCollectionInputFieldTemplate,
ImageField,
ImageInputFieldTemplate,
ImagePolymorphicInputFieldTemplate,
InputFieldTemplate,
InputFieldTemplateBase,
IntegerCollectionInputFieldTemplate,
IntegerInputFieldTemplate,
IntegerPolymorphicInputFieldTemplate,
InvocationFieldSchema,
InvocationSchemaObject,
LatentsCollectionInputFieldTemplate,
LatentsField,
LatentsInputFieldTemplate,
LatentsPolymorphicInputFieldTemplate,
LoRAModelInputFieldTemplate,
MainModelInputFieldTemplate,
MetadataCollectionInputFieldTemplate,
MetadataInputFieldTemplate,
MetadataItemCollectionInputFieldTemplate,
MetadataItemInputFieldTemplate,
MetadataItemPolymorphicInputFieldTemplate,
OpenAPIV3_1SchemaOrRef,
SDXLMainModelInputFieldTemplate,
SDXLRefinerModelInputFieldTemplate,
SchedulerInputFieldTemplate,
StringCollectionInputFieldTemplate,
StringInputFieldTemplate,
StringPolymorphicInputFieldTemplate,
T2IAdapterCollectionInputFieldTemplate,
T2IAdapterField,
T2IAdapterInputFieldTemplate,
T2IAdapterModelInputFieldTemplate,
T2IAdapterPolymorphicInputFieldTemplate,
UNetInputFieldTemplate,
VaeInputFieldTemplate,
VaeModelInputFieldTemplate,
@ -52,36 +87,7 @@ import {
isNonArraySchemaObject,
isRefObject,
isSchemaObject,
ControlPolymorphicInputFieldTemplate,
ColorPolymorphicInputFieldTemplate,
ColorCollectionInputFieldTemplate,
IntegerPolymorphicInputFieldTemplate,
StringPolymorphicInputFieldTemplate,
BooleanPolymorphicInputFieldTemplate,
ImagePolymorphicInputFieldTemplate,
LatentsPolymorphicInputFieldTemplate,
LatentsCollectionInputFieldTemplate,
ConditioningPolymorphicInputFieldTemplate,
ConditioningCollectionInputFieldTemplate,
ControlCollectionInputFieldTemplate,
ImageField,
LatentsField,
ConditioningField,
IPAdapterField,
IPAdapterInputFieldTemplate,
IPAdapterModelInputFieldTemplate,
IPAdapterPolymorphicInputFieldTemplate,
IPAdapterCollectionInputFieldTemplate,
T2IAdapterField,
T2IAdapterInputFieldTemplate,
T2IAdapterModelInputFieldTemplate,
T2IAdapterPolymorphicInputFieldTemplate,
T2IAdapterCollectionInputFieldTemplate,
BoardInputFieldTemplate,
InputFieldTemplate,
OpenAPIV3_1SchemaOrRef,
} from '../types/types';
import { ControlField } from 'services/api/types';
export type BaseFieldProperties = 'name' | 'title' | 'description';
@ -851,6 +857,78 @@ const buildCollectionItemInputFieldTemplate = ({
return template;
};
const buildAnyInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): AnyInputFieldTemplate => {
const template: AnyInputFieldTemplate = {
...baseField,
type: 'Any',
default: undefined,
};
return template;
};
const buildMetadataItemInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataItemInputFieldTemplate => {
const template: MetadataItemInputFieldTemplate = {
...baseField,
type: 'MetadataItemField',
default: undefined,
};
return template;
};
const buildMetadataItemCollectionInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataItemCollectionInputFieldTemplate => {
const template: MetadataItemCollectionInputFieldTemplate = {
...baseField,
type: 'MetadataItemCollection',
default: undefined,
};
return template;
};
const buildMetadataItemPolymorphicInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataItemPolymorphicInputFieldTemplate => {
const template: MetadataItemPolymorphicInputFieldTemplate = {
...baseField,
type: 'MetadataItemPolymorphic',
default: undefined,
};
return template;
};
const buildMetadataDictInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataInputFieldTemplate => {
const template: MetadataInputFieldTemplate = {
...baseField,
type: 'MetadataField',
default: undefined,
};
return template;
};
const buildMetadataCollectionInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataCollectionInputFieldTemplate => {
const template: MetadataCollectionInputFieldTemplate = {
...baseField,
type: 'MetadataCollection',
default: undefined,
};
return template;
};
const buildColorInputFieldTemplate = ({
schemaObject,
baseField,
@ -1012,6 +1090,7 @@ const TEMPLATE_BUILDER_MAP: {
[key in FieldType]?: (arg: BuildInputFieldArg) => InputFieldTemplate;
} = {
BoardField: buildBoardInputFieldTemplate,
Any: buildAnyInputFieldTemplate,
boolean: buildBooleanInputFieldTemplate,
BooleanCollection: buildBooleanCollectionInputFieldTemplate,
BooleanPolymorphic: buildBooleanPolymorphicInputFieldTemplate,
@ -1047,6 +1126,11 @@ const TEMPLATE_BUILDER_MAP: {
LatentsField: buildLatentsInputFieldTemplate,
LatentsPolymorphic: buildLatentsPolymorphicInputFieldTemplate,
LoRAModelField: buildLoRAModelInputFieldTemplate,
MetadataItemField: buildMetadataItemInputFieldTemplate,
MetadataItemCollection: buildMetadataItemCollectionInputFieldTemplate,
MetadataItemPolymorphic: buildMetadataItemPolymorphicInputFieldTemplate,
MetadataField: buildMetadataDictInputFieldTemplate,
MetadataCollection: buildMetadataCollectionInputFieldTemplate,
MainModelField: buildMainModelInputFieldTemplate,
Scheduler: buildSchedulerInputFieldTemplate,
SDXLMainModelField: buildSDXLMainModelInputFieldTemplate,

6
invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts
View File

@ -3,6 +3,7 @@ import { FieldType, InputFieldTemplate, InputFieldValue } from '../types/types';
const FIELD_VALUE_FALLBACK_MAP: {
[key in FieldType]: InputFieldValue['value'];
} = {
Any: undefined,
enum: '',
BoardField: undefined,
boolean: false,
@ -38,6 +39,11 @@ const FIELD_VALUE_FALLBACK_MAP: {
LatentsCollection: [],
LatentsField: undefined,
LatentsPolymorphic: undefined,
MetadataItemField: undefined,
MetadataItemCollection: [],
MetadataItemPolymorphic: undefined,
MetadataField: undefined,
MetadataCollection: [],
LoRAModelField: undefined,
MainModelField: undefined,
ONNXModelField: undefined,

45
invokeai/frontend/web/src/features/nodes/util/getMetadataAndWorkflowFromImageBlob.ts
View File

@ -1,45 +0,0 @@
import * as png from '@stevebel/png';
import { logger } from 'app/logging/logger';
import { parseify } from 'common/util/serialize';
import {
ImageMetadataAndWorkflow,
zCoreMetadata,
zWorkflow,
} from 'features/nodes/types/types';
import { get } from 'lodash-es';
export const getMetadataAndWorkflowFromImageBlob = async (
image: Blob
): Promise<ImageMetadataAndWorkflow> => {
const data: ImageMetadataAndWorkflow = {};
const buffer = await image.arrayBuffer();
const text = png.decode(buffer).text;
const rawMetadata = get(text, 'invokeai_metadata');
if (rawMetadata) {
const metadataResult = zCoreMetadata.safeParse(JSON.parse(rawMetadata));
if (metadataResult.success) {
data.metadata = metadataResult.data;
} else {
logger('system').error(
{ error: parseify(metadataResult.error) },
'Problem reading metadata from image'
);
}
}
const rawWorkflow = get(text, 'invokeai_workflow');
if (rawWorkflow) {
const workflowResult = zWorkflow.safeParse(JSON.parse(rawWorkflow));
if (workflowResult.success) {
data.workflow = workflowResult.data;
} else {
logger('system').error(
{ error: parseify(workflowResult.error) },
'Problem reading workflow from image'
);
}
}
return data;
};

25
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addControlNetToLinearGraph.ts
View File

@ -5,14 +5,14 @@ import {
CollectInvocation,
ControlField,
ControlNetInvocation,
MetadataAccumulatorInvocation,
CoreMetadataInvocation,
} from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CONTROL_NET_COLLECT,
METADATA_ACCUMULATOR,
} from './constants';
import { upsertMetadata } from './metadata';
export const addControlNetToLinearGraph = (
state: RootState,
@ -23,9 +23,11 @@ export const addControlNetToLinearGraph = (
(ca) => ca.model?.base_model === state.generation.model?.base_model
);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
// const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
// | MetadataAccumulatorInvocation
// | undefined;
const controlNetMetadata: CoreMetadataInvocation['controlnets'] = [];
if (validControlNets.length) {
// Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect
@ -99,15 +101,9 @@ export const addControlNetToLinearGraph = (
graph.nodes[controlNetNode.id] = controlNetNode as ControlNetInvocation;
if (metadataAccumulator?.controlnets) {
// metadata accumulator only needs a control field - not the whole node
// extract what we need and add to the accumulator
const controlField = omit(controlNetNode, [
'id',
'type',
]) as ControlField;
metadataAccumulator.controlnets.push(controlField);
}
controlNetMetadata.push(
omit(controlNetNode, ['id', 'type', 'is_intermediate']) as ControlField
);
graph.edges.push({
source: { node_id: controlNetNode.id, field: 'control' },
@ -117,5 +113,6 @@ export const addControlNetToLinearGraph = (
},
});
});
upsertMetadata(graph, { controlnets: controlNetMetadata });
}
};

42
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addHrfToGraph.ts
View File

@ -1,25 +1,25 @@
import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
DenoiseLatentsInvocation,
ResizeLatentsInvocation,
NoiseInvocation,
LatentsToImageInvocation,
Edge,
LatentsToImageInvocation,
NoiseInvocation,
ResizeLatentsInvocation,
} from 'services/api/types';
import {
LATENTS_TO_IMAGE,
DENOISE_LATENTS,
NOISE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
LATENTS_TO_IMAGE_HRF,
DENOISE_LATENTS_HRF,
RESCALE_LATENTS,
LATENTS_TO_IMAGE,
LATENTS_TO_IMAGE_HRF,
MAIN_MODEL_LOADER,
NOISE,
NOISE_HRF,
RESCALE_LATENTS,
VAE_LOADER,
} from './constants';
import { logger } from 'app/logging/logger';
import { upsertMetadata } from './metadata';
// Copy certain connections from previous DENOISE_LATENTS to new DENOISE_LATENTS_HRF.
function copyConnectionsToDenoiseLatentsHrf(graph: NonNullableGraph): void {
@ -71,10 +71,8 @@ export const addHrfToGraph = (
}
const log = logger('txt2img');
const { vae } = state.generation;
const { vae, hrfWidth, hrfHeight, hrfStrength } = state.generation;
const isAutoVae = !vae;
const hrfWidth = state.generation.hrfWidth;
const hrfHeight = state.generation.hrfHeight;
// Pre-existing (original) graph nodes.
const originalDenoiseLatentsNode = graph.nodes[DENOISE_LATENTS] as
@ -116,7 +114,7 @@ export const addHrfToGraph = (
cfg_scale: originalDenoiseLatentsNode?.cfg_scale,
scheduler: originalDenoiseLatentsNode?.scheduler,
steps: originalDenoiseLatentsNode?.steps,
denoising_start: 1 - state.generation.hrfStrength,
denoising_start: 1 - hrfStrength,
denoising_end: 1,
};
@ -221,16 +219,6 @@ export const addHrfToGraph = (
field: 'latents',
},
},
{
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE_HRF,
field: 'metadata',
},
},
{
source: {
node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
@ -243,5 +231,11 @@ export const addHrfToGraph = (
}
);
upsertMetadata(graph, {
hrf_height: hrfHeight,
hrf_width: hrfWidth,
hrf_strength: hrfStrength,
});
copyConnectionsToDenoiseLatentsHrf(graph);
};

33
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts
View File

@ -1,16 +1,18 @@
import { RootState } from 'app/store/store';
import { selectValidIPAdapters } from 'features/controlAdapters/store/controlAdaptersSlice';
import { omit } from 'lodash-es';
import {
CollectInvocation,
CoreMetadataInvocation,
IPAdapterInvocation,
MetadataAccumulatorInvocation,
IPAdapterMetadataField,
} from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
IP_ADAPTER_COLLECT,
METADATA_ACCUMULATOR,
} from './constants';
import { upsertMetadata } from './metadata';
export const addIPAdapterToLinearGraph = (
state: RootState,
@ -21,10 +23,6 @@ export const addIPAdapterToLinearGraph = (
(ca) => ca.model?.base_model === state.generation.model?.base_model
);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (validIPAdapters.length) {
// Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect
const ipAdapterCollectNode: CollectInvocation = {
@ -50,6 +48,7 @@ export const addIPAdapterToLinearGraph = (
},
});
}
const ipAdapterMetdata: CoreMetadataInvocation['ipAdapters'] = [];
validIPAdapters.forEach((ipAdapter) => {
if (!ipAdapter.model) {
@ -76,19 +75,13 @@ export const addIPAdapterToLinearGraph = (
graph.nodes[ipAdapterNode.id] = ipAdapterNode as IPAdapterInvocation;
if (metadataAccumulator?.ipAdapters) {
const ipAdapterField = {
image: {
image_name: ipAdapter.controlImage,
},
weight,
ip_adapter_model: model,
begin_step_percent: beginStepPct,
end_step_percent: endStepPct,
};
metadataAccumulator.ipAdapters.push(ipAdapterField);
}
ipAdapterMetdata.push(
omit(ipAdapterNode, [
'id',
'type',
'is_intermediate',
]) as IPAdapterMetadataField
);
graph.edges.push({
source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },
@ -98,5 +91,7 @@ export const addIPAdapterToLinearGraph = (
},
});
});
upsertMetadata(graph, { ipAdapters: ipAdapterMetdata });
}
};

53
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts
View File

@ -2,20 +2,20 @@ import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { forEach, size } from 'lodash-es';
import {
CoreMetadataInvocation,
LoraLoaderInvocation,
MetadataAccumulatorInvocation,
} from 'services/api/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_INPAINT_GRAPH,
CANVAS_OUTPAINT_GRAPH,
CANVAS_COHERENCE_DENOISE_LATENTS,
CLIP_SKIP,
LORA_LOADER,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
POSITIVE_CONDITIONING,
} from './constants';
import { upsertMetadata } from './metadata';
export const addLoRAsToGraph = (
state: RootState,
@ -33,29 +33,29 @@ export const addLoRAsToGraph = (
const { loras } = state.lora;
const loraCount = size(loras);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (loraCount > 0) {
// Remove modelLoaderNodeId unet connection to feed it to LoRAs
graph.edges = graph.edges.filter(
(e) =>
!(
e.source.node_id === modelLoaderNodeId &&
['unet'].includes(e.source.field)
)
);
// Remove CLIP_SKIP connections to conditionings to feed it through LoRAs
graph.edges = graph.edges.filter(
(e) =>
!(e.source.node_id === CLIP_SKIP && ['clip'].includes(e.source.field))
);
if (loraCount === 0) {
return;
}
// Remove modelLoaderNodeId unet connection to feed it to LoRAs
graph.edges = graph.edges.filter(
(e) =>
!(
e.source.node_id === modelLoaderNodeId &&
['unet'].includes(e.source.field)
)
);
// Remove CLIP_SKIP connections to conditionings to feed it through LoRAs
graph.edges = graph.edges.filter(
(e) =>
!(e.source.node_id === CLIP_SKIP && ['clip'].includes(e.source.field))
);
// we need to remember the last lora so we can chain from it
let lastLoraNodeId = '';
let currentLoraIndex = 0;
const loraMetadata: CoreMetadataInvocation['loras'] = [];
forEach(loras, (lora) => {
const { model_name, base_model, weight } = lora;
@ -69,13 +69,10 @@ export const addLoRAsToGraph = (
weight,
};
// add the lora to the metadata accumulator
if (metadataAccumulator?.loras) {
metadataAccumulator.loras.push({
lora: { model_name, base_model },
weight,
});
}
loraMetadata.push({
lora: { model_name, base_model },
weight,
});
// add to graph
graph.nodes[currentLoraNodeId] = loraLoaderNode;
@ -182,4 +179,6 @@ export const addLoRAsToGraph = (
lastLoraNodeId = currentLoraNodeId;
currentLoraIndex += 1;
});
upsertMetadata(graph, { loras: loraMetadata });
};

63
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts
View File

@ -1,14 +1,14 @@
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { forEach, size } from 'lodash-es';
import {
MetadataAccumulatorInvocation,
SDXLLoraLoaderInvocation,
} from 'services/api/types';
LoRAMetadataItem,
NonNullableGraph,
zLoRAMetadataItem,
} from 'features/nodes/types/types';
import { forEach, size } from 'lodash-es';
import { SDXLLoraLoaderInvocation } from 'services/api/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
LORA_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
POSITIVE_CONDITIONING,
SDXL_CANVAS_INPAINT_GRAPH,
@ -17,6 +17,7 @@ import {
SDXL_REFINER_INPAINT_CREATE_MASK,
SEAMLESS,
} from './constants';
import { upsertMetadata } from './metadata';
export const addSDXLLoRAsToGraph = (
state: RootState,
@ -34,9 +35,12 @@ export const addSDXLLoRAsToGraph = (
const { loras } = state.lora;
const loraCount = size(loras);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (loraCount === 0) {
return;
}
const loraMetadata: LoRAMetadataItem[] = [];
// Handle Seamless Plugs
const unetLoaderId = modelLoaderNodeId;
@ -47,22 +51,17 @@ export const addSDXLLoRAsToGraph = (
clipLoaderId = SDXL_MODEL_LOADER;
}
if (loraCount > 0) {
// Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
graph.edges = graph.edges.filter(
(e) =>
!(
e.source.node_id === unetLoaderId && ['unet'].includes(e.source.field)
) &&
!(
e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field)
) &&
!(
e.source.node_id === clipLoaderId &&
['clip2'].includes(e.source.field)
)
);
}
// Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
graph.edges = graph.edges.filter(
(e) =>
!(
e.source.node_id === unetLoaderId && ['unet'].includes(e.source.field)
) &&
!(
e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field)
) &&
!(e.source.node_id === clipLoaderId && ['clip2'].includes(e.source.field))
);
// we need to remember the last lora so we can chain from it
let lastLoraNodeId = '';
@ -80,16 +79,12 @@ export const addSDXLLoRAsToGraph = (
weight,
};
// add the lora to the metadata accumulator
if (metadataAccumulator) {
if (!metadataAccumulator.loras) {
metadataAccumulator.loras = [];
}
metadataAccumulator.loras.push({
loraMetadata.push(
zLoRAMetadataItem.parse({
lora: { model_name, base_model },
weight,
});
}
})
);
// add to graph
graph.nodes[currentLoraNodeId] = loraLoaderNode;
@ -242,4 +237,6 @@ export const addSDXLLoRAsToGraph = (
lastLoraNodeId = currentLoraNodeId;
currentLoraIndex += 1;
});
upsertMetadata(graph, { loras: loraMetadata });
};

27
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts
View File

@ -2,7 +2,6 @@ import { RootState } from 'app/store/store';
import {
CreateDenoiseMaskInvocation,
ImageDTO,
MetadataAccumulatorInvocation,
SeamlessModeInvocation,
} from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
@ -12,7 +11,6 @@ import {
LATENTS_TO_IMAGE,
MASK_COMBINE,
MASK_RESIZE_UP,
METADATA_ACCUMULATOR,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_CANVAS_INPAINT_GRAPH,
SDXL_CANVAS_OUTPAINT_GRAPH,
@ -26,6 +24,7 @@ import {
SDXL_REFINER_SEAMLESS,
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { upsertMetadata } from './metadata';
export const addSDXLRefinerToGraph = (
state: RootState,
@ -58,21 +57,15 @@ export const addSDXLRefinerToGraph = (
return;
}
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (metadataAccumulator) {
metadataAccumulator.refiner_model = refinerModel;
metadataAccumulator.refiner_positive_aesthetic_score =
refinerPositiveAestheticScore;
metadataAccumulator.refiner_negative_aesthetic_score =
refinerNegativeAestheticScore;
metadataAccumulator.refiner_cfg_scale = refinerCFGScale;
metadataAccumulator.refiner_scheduler = refinerScheduler;
metadataAccumulator.refiner_start = refinerStart;
metadataAccumulator.refiner_steps = refinerSteps;
}
upsertMetadata(graph, {
refiner_model: refinerModel,
refiner_positive_aesthetic_score: refinerPositiveAestheticScore,
refiner_negative_aesthetic_score: refinerNegativeAestheticScore,
refiner_cfg_scale: refinerCFGScale,
refiner_scheduler: refinerScheduler,
refiner_start: refinerStart,
refiner_steps: refinerSteps,
});
const modelLoaderId = modelLoaderNodeId
? modelLoaderNodeId

27
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts
View File

@ -1,19 +1,15 @@
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
import { SaveImageInvocation } from 'services/api/types';
import {
CANVAS_OUTPUT,
LATENTS_TO_IMAGE,
LATENTS_TO_IMAGE_HRF,
METADATA_ACCUMULATOR,
NSFW_CHECKER,
SAVE_IMAGE,
WATERMARKER,
} from './constants';
import {
MetadataAccumulatorInvocation,
SaveImageInvocation,
} from 'services/api/types';
import { RootState } from 'app/store/store';
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
/**
* Set the `use_cache` field on the linear/canvas graph's final image output node to False.
@ -37,23 +33,6 @@ export const addSaveImageNode = (
graph.nodes[SAVE_IMAGE] = saveImageNode;
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (metadataAccumulator) {
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: SAVE_IMAGE,
field: 'metadata',
},
});
}
const destination = {
node_id: SAVE_IMAGE,
field: 'image',

12
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSeamlessToLinearGraph.ts
View File

@ -1,6 +1,7 @@
import { RootState } from 'app/store/store';
import { SeamlessModeInvocation } from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
import { upsertMetadata } from './metadata';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_INPAINT_GRAPH,
@ -31,6 +32,17 @@ export const addSeamlessToLinearGraph = (
seamless_y: seamlessYAxis,
} as SeamlessModeInvocation;
if (seamlessXAxis) {
upsertMetadata(graph, {
seamless_x: seamlessXAxis,
});
}
if (seamlessYAxis) {
upsertMetadata(graph, {
seamless_y: seamlessYAxis,
});
}
let denoisingNodeId = DENOISE_LATENTS;
if (

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addT2IAdapterToLinearGraph.ts
View File

@ -3,15 +3,15 @@ import { selectValidT2IAdapters } from 'features/controlAdapters/store/controlAd
import { omit } from 'lodash-es';
import {
CollectInvocation,
MetadataAccumulatorInvocation,
CoreMetadataInvocation,
T2IAdapterInvocation,
} from 'services/api/types';
import { NonNullableGraph, T2IAdapterField } from '../../types/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
METADATA_ACCUMULATOR,
T2I_ADAPTER_COLLECT,
} from './constants';
import { upsertMetadata } from './metadata';
export const addT2IAdaptersToLinearGraph = (
state: RootState,
@ -22,10 +22,6 @@ export const addT2IAdaptersToLinearGraph = (
(ca) => ca.model?.base_model === state.generation.model?.base_model
);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (validT2IAdapters.length) {
// Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect
const t2iAdapterCollectNode: CollectInvocation = {
@ -51,6 +47,7 @@ export const addT2IAdaptersToLinearGraph = (
},
});
}
const t2iAdapterMetdata: CoreMetadataInvocation['t2iAdapters'] = [];
validT2IAdapters.forEach((t2iAdapter) => {
if (!t2iAdapter.model) {
@ -96,15 +93,13 @@ export const addT2IAdaptersToLinearGraph = (
graph.nodes[t2iAdapterNode.id] = t2iAdapterNode as T2IAdapterInvocation;
if (metadataAccumulator?.t2iAdapters) {
// metadata accumulator only needs a control field - not the whole node
// extract what we need and add to the accumulator
const t2iAdapterField = omit(t2iAdapterNode, [
t2iAdapterMetdata.push(
omit(t2iAdapterNode, [
'id',
'type',
]) as T2IAdapterField;
metadataAccumulator.t2iAdapters.push(t2iAdapterField);
}
'is_intermediate',
]) as T2IAdapterField
);
graph.edges.push({
source: { node_id: t2iAdapterNode.id, field: 't2i_adapter' },
@ -114,5 +109,7 @@ export const addT2IAdaptersToLinearGraph = (
},
});
});
upsertMetadata(graph, { t2iAdapters: t2iAdapterMetdata });
}
};

10
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts
View File

@ -1,6 +1,5 @@
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { MetadataAccumulatorInvocation } from 'services/api/types';
import {
CANVAS_COHERENCE_INPAINT_CREATE_MASK,
CANVAS_IMAGE_TO_IMAGE_GRAPH,
@ -14,7 +13,6 @@ import {
INPAINT_IMAGE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
ONNX_MODEL_LOADER,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_CANVAS_INPAINT_GRAPH,
@ -26,6 +24,7 @@ import {
TEXT_TO_IMAGE_GRAPH,
VAE_LOADER,
} from './constants';
import { upsertMetadata } from './metadata';
export const addVAEToGraph = (
state: RootState,
@ -41,9 +40,6 @@ export const addVAEToGraph = (
);
const isAutoVae = !vae;
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (!isAutoVae) {
graph.nodes[VAE_LOADER] = {
@ -181,7 +177,7 @@ export const addVAEToGraph = (
}
}
if (vae && metadataAccumulator) {
metadataAccumulator.vae = vae;
if (vae) {
upsertMetadata(graph, { vae });
}
};

25
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts
View File

@ -5,14 +5,8 @@ import {
ImageNSFWBlurInvocation,
ImageWatermarkInvocation,
LatentsToImageInvocation,
MetadataAccumulatorInvocation,
} from 'services/api/types';
import {
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NSFW_CHECKER,
WATERMARKER,
} from './constants';
import { LATENTS_TO_IMAGE, NSFW_CHECKER, WATERMARKER } from './constants';
export const addWatermarkerToGraph = (
state: RootState,
@ -32,10 +26,6 @@ export const addWatermarkerToGraph = (
| ImageNSFWBlurInvocation
| undefined;
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (!nodeToAddTo) {
// something has gone terribly awry
return;
@ -80,17 +70,4 @@ export const addWatermarkerToGraph = (
},
});
}
if (metadataAccumulator) {
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: WATERMARKER,
field: 'metadata',
},
});
}
};

9
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildAdHocUpscaleGraph.ts
View File

@ -1,12 +1,13 @@
import { BoardId } from 'features/gallery/store/types';
import { NonNullableGraph } from 'features/nodes/types/types';
import { ESRGANModelName } from 'features/parameters/store/postprocessingSlice';
import {
Graph,
ESRGANInvocation,
Graph,
SaveImageInvocation,
} from 'services/api/types';
import { REALESRGAN as ESRGAN, SAVE_IMAGE } from './constants';
import { BoardId } from 'features/gallery/store/types';
import { addCoreMetadataNode } from './metadata';
type Arg = {
image_name: string;
@ -55,5 +56,9 @@ export const buildAdHocUpscaleGraph = ({
],
};
addCoreMetadataNode(graph, {
esrgan_model: esrganModelName,
});
return graph;
};

14
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
View File

@ -20,12 +20,12 @@ import {
IMG2IMG_RESIZE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
SEAMLESS,
} from './constants';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Image to Image graph.
@ -308,10 +308,7 @@ export const buildCanvasImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'img2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -325,15 +322,10 @@ export const buildCanvasImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
clip_skip: clipSkip,
strength,
init_image: initialImage.image_name,
};
});
// Add Seamless To Graph
if (seamlessXAxis || seamlessYAxis) {

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts
View File

@ -16,7 +16,6 @@ import {
IMAGE_TO_LATENTS,
IMG2IMG_RESIZE,
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
@ -28,6 +27,7 @@ import {
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Image to Image graph.
@ -319,10 +319,7 @@ export const buildCanvasSDXLImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'img2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -336,24 +333,8 @@ export const buildCanvasSDXLImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
strength,
init_image: initialImage.image_name,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// Add Seamless To Graph

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts
View File

@ -18,7 +18,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPUT,
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
@ -30,6 +29,7 @@ import {
SEAMLESS,
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Text to Image graph.
@ -301,10 +301,7 @@ export const buildCanvasSDXLTextToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'txt2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -318,22 +315,6 @@ export const buildCanvasSDXLTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// Add Seamless To Graph

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
View File

@ -21,13 +21,13 @@ import {
DENOISE_LATENTS,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
POSITIVE_CONDITIONING,
SEAMLESS,
} from './constants';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Text to Image graph.
@ -289,10 +289,7 @@ export const buildCanvasTextToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'txt2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -306,23 +303,7 @@ export const buildCanvasTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
clip_skip: clipSkip,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// Add Seamless To Graph

42
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearBatchConfig.ts
View File

@ -2,15 +2,16 @@ import { NUMPY_RAND_MAX } from 'app/constants';
import { RootState } from 'app/store/store';
import { generateSeeds } from 'common/util/generateSeeds';
import { NonNullableGraph } from 'features/nodes/types/types';
import { range, unset } from 'lodash-es';
import { range } from 'lodash-es';
import { components } from 'services/api/schema';
import { Batch, BatchConfig } from 'services/api/types';
import {
CANVAS_COHERENCE_NOISE,
METADATA_ACCUMULATOR,
METADATA,
NOISE,
POSITIVE_CONDITIONING,
} from './constants';
import { getHasMetadata, removeMetadata } from './metadata';
export const prepareLinearUIBatch = (
state: RootState,
@ -24,7 +25,6 @@ export const prepareLinearUIBatch = (
const data: Batch['data'] = [];
if (prompts.length === 1) {
unset(graph.nodes[METADATA_ACCUMULATOR], 'seed');
const seeds = generateSeeds({
count: iterations,
start: shouldRandomizeSeed ? undefined : seed,
@ -40,9 +40,11 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
if (getHasMetadata(graph)) {
// add to metadata
removeMetadata(graph, 'seed');
zipped.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'seed',
items: seeds,
});
@ -77,9 +79,11 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
if (getHasMetadata(graph)) {
removeMetadata(graph, 'seed');
firstBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'seed',
items: seeds,
});
@ -106,13 +110,17 @@ export const prepareLinearUIBatch = (
items: seeds,
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
if (getHasMetadata(graph)) {
removeMetadata(graph, 'seed');
secondBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'seed',
items: seeds,
});
}
if (graph.nodes[CANVAS_COHERENCE_NOISE]) {
secondBatchDatumList.push({
node_path: CANVAS_COHERENCE_NOISE,
@ -137,17 +145,17 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
if (getHasMetadata(graph)) {
removeMetadata(graph, 'positive_prompt');
firstBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'positive_prompt',
items: extendedPrompts,
});
}
if (shouldConcatSDXLStylePrompt && model?.base_model === 'sdxl') {
unset(graph.nodes[METADATA_ACCUMULATOR], 'positive_style_prompt');
const stylePrompts = extendedPrompts.map((p) =>
[p, positiveStylePrompt].join(' ')
);
@ -160,11 +168,13 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
if (getHasMetadata(graph)) {
removeMetadata(graph, 'positive_style_prompt');
firstBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'positive_style_prompt',
items: stylePrompts,
items: extendedPrompts,
});
}
}

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
View File

@ -21,13 +21,13 @@ import {
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
RESIZE,
SEAMLESS,
} from './constants';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Image to Image tab graph.
@ -311,10 +311,7 @@ export const buildLinearImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'img2img',
cfg_scale,
height,
@ -326,25 +323,9 @@ export const buildLinearImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [], // populated in addT2IAdapterToLinearGraph
clip_skip: clipSkip,
strength,
init_image: initialImage.imageName,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

25
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts
View File

@ -18,7 +18,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
@ -30,6 +29,7 @@ import {
SEAMLESS,
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Image to Image tab graph.
@ -331,10 +331,7 @@ export const buildLinearSDXLImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'sdxl_img2img',
cfg_scale,
height,
@ -346,26 +343,10 @@ export const buildLinearSDXLImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined,
controlnets: [],
loras: [],
ipAdapters: [],
t2iAdapters: [],
strength: strength,
strength,
init_image: initialImage.imageName,
positive_style_prompt: positiveStylePrompt,
negative_style_prompt: negativeStylePrompt,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts
View File

@ -11,9 +11,9 @@ import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import { addCoreMetadataNode } from './metadata';
import {
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
@ -225,10 +225,7 @@ export const buildLinearSDXLTextToImageGraph = (
],
};
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'sdxl_txt2img',
cfg_scale,
height,
@ -240,24 +237,8 @@ export const buildLinearSDXLTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined,
controlnets: [],
loras: [],
ipAdapters: [],
t2iAdapters: [],
positive_style_prompt: positiveStylePrompt,
negative_style_prompt: negativeStylePrompt,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

30
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
View File

@ -15,12 +15,12 @@ import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import { addCoreMetadataNode } from './metadata';
import {
CLIP_SKIP,
DENOISE_LATENTS,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
@ -48,10 +48,6 @@ export const buildLinearTextToImageGraph = (
seamlessXAxis,
seamlessYAxis,
seed,
hrfWidth,
hrfHeight,
hrfStrength,
hrfEnabled: hrfEnabled,
} = state.generation;
const use_cpu = shouldUseCpuNoise;
@ -238,10 +234,7 @@ export const buildLinearTextToImageGraph = (
],
};
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'txt2img',
cfg_scale,
height,
@ -253,26 +246,7 @@ export const buildLinearTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [], // populated in addT2IAdapterToLinearGraph
clip_skip: clipSkip,
hrf_width: hrfEnabled ? hrfWidth : undefined,
hrf_height: hrfEnabled ? hrfHeight : undefined,
hrf_strength: hrfEnabled ? hrfStrength : undefined,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

3
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts
View File

@ -35,7 +35,6 @@ export const buildNodesGraph = (nodesState: NodesState): Graph => {
const { nodes, edges } = nodesState;
const filteredNodes = nodes.filter(isInvocationNode);
const workflowJSON = JSON.stringify(buildWorkflow(nodesState));
// Reduce the node editor nodes into invocation graph nodes
const parsedNodes = filteredNodes.reduce<NonNullable<Graph['nodes']>>(
@ -68,7 +67,7 @@ export const buildNodesGraph = (nodesState: NodesState): Graph => {
if (embedWorkflow) {
// add the workflow to the node
Object.assign(graphNode, { workflow: workflowJSON });
Object.assign(graphNode, { workflow: buildWorkflow(nodesState) });
}
// Add it to the nodes object

9
invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
View File

@ -56,7 +56,14 @@ export const IP_ADAPTER = 'ip_adapter';
export const DYNAMIC_PROMPT = 'dynamic_prompt';
export const IMAGE_COLLECTION = 'image_collection';
export const IMAGE_COLLECTION_ITERATE = 'image_collection_iterate';
export const METADATA_ACCUMULATOR = 'metadata_accumulator';
export const METADATA = 'core_metadata';
export const BATCH_METADATA = 'batch_metadata';
export const BATCH_METADATA_COLLECT = 'batch_metadata_collect';
export const BATCH_SEED = 'batch_seed';
export const BATCH_PROMPT = 'batch_prompt';
export const BATCH_STYLE_PROMPT = 'batch_style_prompt';
export const METADATA_COLLECT = 'metadata_collect';
export const MERGE_METADATA = 'merge_metadata';
export const REALESRGAN = 'esrgan';
export const DIVIDE = 'divide';
export const SCALE = 'scale_image';

66
invokeai/frontend/web/src/features/nodes/util/graphBuilders/metadata.ts Normal file
View File

@ -0,0 +1,66 @@
import { NonNullableGraph } from 'features/nodes/types/types';
import { CoreMetadataInvocation } from 'services/api/types';
import { JsonObject } from 'type-fest';
import { METADATA, SAVE_IMAGE } from './constants';
export const addCoreMetadataNode = (
graph: NonNullableGraph,
metadata: Partial<CoreMetadataInvocation> | JsonObject
): void => {
graph.nodes[METADATA] = {
id: METADATA,
type: 'core_metadata',
...metadata,
};
graph.edges.push({
source: {
node_id: METADATA,
field: 'metadata',
},
destination: {
node_id: SAVE_IMAGE,
field: 'metadata',
},
});
return;
};
export const upsertMetadata = (
graph: NonNullableGraph,
metadata: Partial<CoreMetadataInvocation> | JsonObject
): void => {
const metadataNode = graph.nodes[METADATA] as
| CoreMetadataInvocation
| undefined;
if (!metadataNode) {
return;
}
Object.assign(metadataNode, metadata);
};
export const removeMetadata = (
graph: NonNullableGraph,
key: keyof CoreMetadataInvocation
): void => {
const metadataNode = graph.nodes[METADATA] as
| CoreMetadataInvocation
| undefined;
if (!metadataNode) {
return;
}
delete metadataNode[key];
};
export const getHasMetadata = (graph: NonNullableGraph): boolean => {
const metadataNode = graph.nodes[METADATA] as
| CoreMetadataInvocation
| undefined;
return Boolean(metadataNode);
};

44
invokeai/frontend/web/src/features/nodes/util/parseSchema.ts
View File

@ -4,7 +4,6 @@ import { reduce, startCase } from 'lodash-es';
import { OpenAPIV3_1 } from 'openapi-types';
import { AnyInvocationType } from 'services/events/types';
import {
FieldType,
InputFieldTemplate,
InvocationSchemaObject,
InvocationTemplate,
@ -16,18 +15,11 @@ import {
} from '../types/types';
import { buildInputFieldTemplate, getFieldType } from './fieldTemplateBuilders';
const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'metadata', 'use_cache'];
const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'use_cache'];
const RESERVED_OUTPUT_FIELD_NAMES = ['type'];
const RESERVED_FIELD_TYPES = [
'WorkflowField',
'MetadataField',
'IsIntermediate',
];
const RESERVED_FIELD_TYPES = ['IsIntermediate'];
const invocationDenylist: AnyInvocationType[] = [
'graph',
'metadata_accumulator',
];
const invocationDenylist: AnyInvocationType[] = ['graph'];
const isReservedInputField = (nodeType: string, fieldName: string) => {
if (RESERVED_INPUT_FIELD_NAMES.includes(fieldName)) {
@ -42,7 +34,7 @@ const isReservedInputField = (nodeType: string, fieldName: string) => {
return false;
};
const isReservedFieldType = (fieldType: FieldType) => {
const isReservedFieldType = (fieldType: string) => {
if (RESERVED_FIELD_TYPES.includes(fieldType)) {
return true;
}
@ -86,6 +78,7 @@ export const parseSchema = (
const tags = schema.tags ?? [];
const description = schema.description ?? '';
const version = schema.version;
let withWorkflow = false;
const inputs = reduce(
schema.properties,
@ -112,7 +105,7 @@ export const parseSchema = (
const fieldType = property.ui_type ?? getFieldType(property);
if (!isFieldType(fieldType)) {
if (!fieldType) {
logger('nodes').warn(
{
node: type,
@ -120,11 +113,16 @@ export const parseSchema = (
fieldType,
field: parseify(property),
},
'Skipping unknown input field type'
'Missing input field type'
);
return inputsAccumulator;
}
if (fieldType === 'WorkflowField') {
withWorkflow = true;
return inputsAccumulator;
}
if (isReservedFieldType(fieldType)) {
logger('nodes').trace(
{
@ -133,7 +131,20 @@ export const parseSchema = (
fieldType,
field: parseify(property),
},
'Skipping reserved field type'
`Skipping reserved input field type: ${fieldType}`
);
return inputsAccumulator;
}
if (!isFieldType(fieldType)) {
logger('nodes').warn(
{
node: type,
fieldName: propertyName,
fieldType,
field: parseify(property),
},
`Skipping unknown input field type: ${fieldType}`
);
return inputsAccumulator;
}
@ -146,7 +157,7 @@ export const parseSchema = (
);
if (!field) {
logger('nodes').debug(
logger('nodes').warn(
{
node: type,
fieldName: propertyName,
@ -248,6 +259,7 @@ export const parseSchema = (
inputs,
outputs,
useCache,
withWorkflow,
};
Object.assign(invocationsAccumulator, { [type]: invocation });

80
invokeai/frontend/web/src/services/api/endpoints/images.ts
View File

@ -1,20 +1,15 @@
import { EntityState, Update } from '@reduxjs/toolkit';
import { fetchBaseQuery } from '@reduxjs/toolkit/dist/query';
import { PatchCollection } from '@reduxjs/toolkit/dist/query/core/buildThunks';
import { logger } from 'app/logging/logger';
import {
ASSETS_CATEGORIES,
BoardId,
IMAGE_CATEGORIES,
IMAGE_LIMIT,
} from 'features/gallery/store/types';
import {
ImageMetadataAndWorkflow,
zCoreMetadata,
} from 'features/nodes/types/types';
import { getMetadataAndWorkflowFromImageBlob } from 'features/nodes/util/getMetadataAndWorkflowFromImageBlob';
import { CoreMetadata, zCoreMetadata } from 'features/nodes/types/types';
import { keyBy } from 'lodash-es';
import { ApiTagDescription, LIST_TAG, api } from '..';
import { $authToken, $projectId } from '../client';
import { components, paths } from '../schema';
import {
DeleteBoardResult,
@ -23,7 +18,6 @@ import {
ListImagesArgs,
OffsetPaginatedResults_ImageDTO_,
PostUploadAction,
UnsafeImageMetadata,
} from '../types';
import {
getCategories,
@ -114,73 +108,24 @@ export const imagesApi = api.injectEndpoints({
],
keepUnusedDataFor: 86400, // 24 hours
}),
getImageMetadata: build.query<UnsafeImageMetadata, string>({
getImageMetadata: build.query<CoreMetadata | undefined, string>({
query: (image_name) => ({ url: `images/i/${image_name}/metadata` }),
providesTags: (result, error, image_name) => [
{ type: 'ImageMetadata', id: image_name },
],
keepUnusedDataFor: 86400, // 24 hours
}),
getImageMetadataFromFile: build.query<
ImageMetadataAndWorkflow,
{ image: ImageDTO; shouldFetchMetadataFromApi: boolean }
>({
queryFn: async (
args: { image: ImageDTO; shouldFetchMetadataFromApi: boolean },
api,
extraOptions,
fetchWithBaseQuery
transformResponse: (
response: paths['/api/v1/images/i/{image_name}/metadata']['get']['responses']['200']['content']['application/json']
) => {
if (args.shouldFetchMetadataFromApi) {
let metadata;
const metadataResponse = await fetchWithBaseQuery(
`images/i/${args.image.image_name}/metadata`
);
if (metadataResponse.data) {
const metadataResult = zCoreMetadata.safeParse(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(metadataResponse.data as any)?.metadata
);
if (metadataResult.success) {
metadata = metadataResult.data;
}
if (response) {
const result = zCoreMetadata.safeParse(response);
if (result.success) {
return result.data;
} else {
logger('images').warn('Problem parsing metadata');
}
return { data: { metadata } };
} else {
const authToken = $authToken.get();
const projectId = $projectId.get();
const customBaseQuery = fetchBaseQuery({
baseUrl: '',
prepareHeaders: (headers) => {
if (authToken) {
headers.set('Authorization', `Bearer ${authToken}`);
}
if (projectId) {
headers.set('project-id', projectId);
}
return headers;
},
responseHandler: async (res) => {
return await res.blob();
},
});
const response = await customBaseQuery(
args.image.image_url,
api,
extraOptions
);
const data = await getMetadataAndWorkflowFromImageBlob(
response.data as Blob
);
return { data };
}
return;
},
providesTags: (result, error, { image }) => [
{ type: 'ImageMetadataFromFile', id: image.image_name },
],
keepUnusedDataFor: 86400, // 24 hours
}),
deleteImage: build.mutation<void, ImageDTO>({
@ -1629,6 +1574,5 @@ export const {
useDeleteBoardMutation,
useStarImagesMutation,
useUnstarImagesMutation,
useGetImageMetadataFromFileQuery,
useBulkDownloadImagesMutation,
} = imagesApi;

30
invokeai/frontend/web/src/services/api/endpoints/workflows.ts Normal file
View File

@ -0,0 +1,30 @@
import { logger } from 'app/logging/logger';
import { Workflow, zWorkflow } from 'features/nodes/types/types';
import { api } from '..';
import { paths } from '../schema';
export const workflowsApi = api.injectEndpoints({
endpoints: (build) => ({
getWorkflow: build.query<Workflow | undefined, string>({
query: (workflow_id) => `workflows/i/${workflow_id}`,
providesTags: (result, error, workflow_id) => [
{ type: 'Workflow', id: workflow_id },
],
transformResponse: (
response: paths['/api/v1/workflows/i/{workflow_id}']['get']['responses']['200']['content']['application/json']
) => {
if (response) {
const result = zWorkflow.safeParse(response);
if (result.success) {
return result.data;
} else {
logger('images').warn('Problem parsing workflow');
}
}
return;
},
}),
}),
});
export const { useGetWorkflowQuery } = workflowsApi;

21
invokeai/frontend/web/src/services/api/hooks/useDebouncedMetadata.ts Normal file
View File

@ -0,0 +1,21 @@
import { skipToken } from '@reduxjs/toolkit/query';
import { useDebounce } from 'use-debounce';
import { useGetImageMetadataQuery } from '../endpoints/images';
import { useAppSelector } from 'app/store/storeHooks';
export const useDebouncedMetadata = (imageName?: string | null) => {
const metadataFetchDebounce = useAppSelector(
(state) => state.config.metadataFetchDebounce
);
const [debouncedImageName] = useDebounce(
imageName,
metadataFetchDebounce ?? 0
);
const { data: metadata, isLoading } = useGetImageMetadataQuery(
debouncedImageName ?? skipToken
);
return { metadata, isLoading };
};

21
invokeai/frontend/web/src/services/api/hooks/useDebouncedWorkflow.ts Normal file
View File

@ -0,0 +1,21 @@
import { skipToken } from '@reduxjs/toolkit/query';
import { useAppSelector } from 'app/store/storeHooks';
import { useDebounce } from 'use-debounce';
import { useGetWorkflowQuery } from '../endpoints/workflows';
export const useDebouncedWorkflow = (workflowId?: string | null) => {
const workflowFetchDebounce = useAppSelector(
(state) => state.config.workflowFetchDebounce
);
const [debouncedWorkflowID] = useDebounce(
workflowId,
workflowFetchDebounce ?? 0
);
const { data: workflow, isLoading } = useGetWorkflowQuery(
debouncedWorkflowID ?? skipToken
);
return { workflow, isLoading };
};

1
invokeai/frontend/web/src/services/api/index.ts
View File

@ -37,6 +37,7 @@ export const tagTypes = [
'ControlNetModel',
'LoRAModel',
'SDXLRefinerModel',
'Workflow',
] as const;
export type ApiTagDescription = TagDescription<(typeof tagTypes)[number]>;
export const LIST_TAG = 'LIST';

1731
invokeai/frontend/web/src/services/api/schema.d.ts vendored
View File

File diff suppressed because one or more lines are too long

17
invokeai/frontend/web/src/services/api/types.ts
View File

@ -27,14 +27,6 @@ export type BatchConfig =
export type EnqueueBatchResult = components['schemas']['EnqueueBatchResult'];
/**
* This is an unsafe type; the object inside is not guaranteed to be valid.
*/
export type UnsafeImageMetadata = {
metadata: s['CoreMetadata'];
graph: NonNullable<s['Graph']>;
};
export type _InputField = s['_InputField'];
export type _OutputField = s['_OutputField'];
@ -50,7 +42,6 @@ export type ImageChanges = s['ImageRecordChanges'];
export type ImageCategory = s['ImageCategory'];
export type ResourceOrigin = s['ResourceOrigin'];
export type ImageField = s['ImageField'];
export type ImageMetadata = s['ImageMetadata'];
export type OffsetPaginatedResults_BoardDTO_ =
s['OffsetPaginatedResults_BoardDTO_'];
export type OffsetPaginatedResults_ImageDTO_ =
@ -145,13 +136,19 @@ export type ImageCollectionInvocation = s['ImageCollectionInvocation'];
export type MainModelLoaderInvocation = s['MainModelLoaderInvocation'];
export type OnnxModelLoaderInvocation = s['OnnxModelLoaderInvocation'];
export type LoraLoaderInvocation = s['LoraLoaderInvocation'];
export type MetadataAccumulatorInvocation = s['MetadataAccumulatorInvocation'];
export type ESRGANInvocation = s['ESRGANInvocation'];
export type DivideInvocation = s['DivideInvocation'];
export type ImageNSFWBlurInvocation = s['ImageNSFWBlurInvocation'];
export type ImageWatermarkInvocation = s['ImageWatermarkInvocation'];
export type SeamlessModeInvocation = s['SeamlessModeInvocation'];
export type SaveImageInvocation = s['SaveImageInvocation'];
export type MetadataInvocation = s['MetadataInvocation'];
export type CoreMetadataInvocation = s['CoreMetadataInvocation'];
export type MetadataItemInvocation = s['MetadataItemInvocation'];
export type MergeMetadataInvocation = s['MergeMetadataInvocation'];
export type IPAdapterMetadataField = s['IPAdapterMetadataField'];
export type T2IAdapterField = s['T2IAdapterField'];
export type LoRAMetadataField = s['LoRAMetadataField'];
// ControlNet Nodes
export type ControlNetInvocation = s['ControlNetInvocation'];

2
tests/nodes/test_graph_execution_state.py
View File

@ -75,6 +75,8 @@ def mock_services() -> InvocationServices:
session_processor=None, # type: ignore
session_queue=None, # type: ignore
urls=None, # type: ignore
workflow_records=None, # type: ignore
workflow_image_records=None, # type: ignore
)

2
tests/nodes/test_invoker.py
View File

@ -80,6 +80,8 @@ def mock_services() -> InvocationServices:
session_processor=None, # type: ignore
session_queue=None, # type: ignore
urls=None, # type: ignore
workflow_records=None, # type: ignore
workflow_image_records=None, # type: ignore
)

152
tests/nodes/test_node_graph.py
View File

@ -10,7 +10,12 @@ from invokeai.app.invocations.baseinvocation import (
)
from invokeai.app.invocations.image import ShowImageInvocation
from invokeai.app.invocations.math import AddInvocation, SubtractInvocation
from invokeai.app.invocations.primitives import FloatInvocation, IntegerInvocation
from invokeai.app.invocations.primitives import (
FloatCollectionInvocation,
FloatInvocation,
IntegerInvocation,
StringInvocation,
)
from invokeai.app.invocations.upscale import ESRGANInvocation
from invokeai.app.services.shared.default_graphs import create_text_to_image
from invokeai.app.services.shared.graph import (
@ -27,8 +32,11 @@ from invokeai.app.services.shared.graph import (
)
from .test_nodes import (
AnyTypeTestInvocation,
ImageToImageTestInvocation,
ListPassThroughInvocation,
PolymorphicStringTestInvocation,
PromptCollectionTestInvocation,
PromptTestInvocation,
TextToImageTestInvocation,
)
@ -607,8 +615,8 @@ def test_graph_can_deserialize():
g.add_edge(e)
json = g.model_dump_json()
adapter_graph = TypeAdapter(Graph)
g2 = adapter_graph.validate_json(json)
GraphValidator = TypeAdapter(Graph)
g2 = GraphValidator.validate_json(json)
assert g2 is not None
assert g2.nodes["1"] is not None
@ -692,6 +700,144 @@ def test_ints_do_not_accept_floats():
g.add_edge(e)
def test_polymorphic_accepts_single():
g = Graph()
n1 = StringInvocation(id="1", value="banana")
n2 = PolymorphicStringTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e1 = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e1)
def test_polymorphic_accepts_collection_of_same_base_type():
g = Graph()
n1 = PromptCollectionTestInvocation(id="1", collection=["banana", "sundae"])
n2 = PolymorphicStringTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e1 = create_edge(n1.id, "collection", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e1)
def test_polymorphic_does_not_accept_collection_of_different_base_type():
g = Graph()
n1 = FloatCollectionInvocation(id="1", collection=[1.0, 2.0, 3.0])
n2 = PolymorphicStringTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e1 = create_edge(n1.id, "collection", n2.id, "value")
with pytest.raises(InvalidEdgeError):
g.add_edge(e1)
def test_polymorphic_does_not_accept_generic_collection():
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = IntegerInvocation(id="2", value=2)
n3 = CollectInvocation(id="3")
n4 = PolymorphicStringTestInvocation(id="4")
g.add_node(n1)
g.add_node(n2)
g.add_node(n3)
g.add_node(n4)
e1 = create_edge(n1.id, "value", n3.id, "item")
e2 = create_edge(n2.id, "value", n3.id, "item")
e3 = create_edge(n3.id, "collection", n4.id, "value")
g.add_edge(e1)
g.add_edge(e2)
with pytest.raises(InvalidEdgeError):
g.add_edge(e3)
def test_any_accepts_integer():
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_any_accepts_string():
g = Graph()
n1 = StringInvocation(id="1", value="banana sundae")
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_any_accepts_generic_collection():
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = IntegerInvocation(id="2", value=2)
n3 = CollectInvocation(id="3")
n4 = AnyTypeTestInvocation(id="4")
g.add_node(n1)
g.add_node(n2)
g.add_node(n3)
g.add_node(n4)
e1 = create_edge(n1.id, "value", n3.id, "item")
e2 = create_edge(n2.id, "value", n3.id, "item")
e3 = create_edge(n3.id, "collection", n4.id, "value")
g.add_edge(e1)
g.add_edge(e2)
# Not throwing on this line is sufficient
g.add_edge(e3)
def test_any_accepts_prompt_collection():
g = Graph()
n1 = PromptCollectionTestInvocation(id="1", collection=["banana", "sundae"])
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "collection", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_any_accepts_any():
g = Graph()
n1 = AnyTypeTestInvocation(id="1")
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_iterate_accepts_collection():
"""We need to update the validation for Collect -> Iterate to traverse to the Iterate
node's output and compare that against the item type of the Collect node's collection. Until
then, Collect nodes may not output into Iterate nodes."""
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = IntegerInvocation(id="2", value=2)
n3 = CollectInvocation(id="3")
n4 = IterateInvocation(id="4")
g.add_node(n1)
g.add_node(n2)
g.add_node(n3)
g.add_node(n4)
e1 = create_edge(n1.id, "value", n3.id, "item")
e2 = create_edge(n2.id, "value", n3.id, "item")
e3 = create_edge(n3.id, "collection", n4.id, "collection")
g.add_edge(e1)
g.add_edge(e2)
# Once we fix the validation logic as described, this should should not raise an error
with pytest.raises(InvalidEdgeError, match="Cannot connect collector to iterator"):
g.add_edge(e3)
def test_graph_can_generate_schema():
# Not throwing on this line is sufficient
# NOTE: if this test fails, it's PROBABLY because a new invocation type is breaking schema generation

49
tests/nodes/test_nodes.py
View File

@ -1,11 +1,11 @@
from typing import Any, Callable, Union
from pydantic import Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
@ -15,12 +15,12 @@ from invokeai.app.invocations.image import ImageField
# Define test invocations before importing anything that uses invocations
@invocation_output("test_list_output")
class ListPassThroughInvocationOutput(BaseInvocationOutput):
collection: list[ImageField] = Field(default_factory=list)
collection: list[ImageField] = OutputField(default_factory=list)
@invocation("test_list")
class ListPassThroughInvocation(BaseInvocation):
collection: list[ImageField] = Field(default_factory=list)
collection: list[ImageField] = InputField(default_factory=list)
def invoke(self, context: InvocationContext) -> ListPassThroughInvocationOutput:
return ListPassThroughInvocationOutput(collection=self.collection)
@ -28,12 +28,12 @@ class ListPassThroughInvocation(BaseInvocation):
@invocation_output("test_prompt_output")
class PromptTestInvocationOutput(BaseInvocationOutput):
prompt: str = Field(default="")
prompt: str = OutputField(default="")
@invocation("test_prompt")
class PromptTestInvocation(BaseInvocation):
prompt: str = Field(default="")
prompt: str = InputField(default="")
def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput:
return PromptTestInvocationOutput(prompt=self.prompt)
@ -47,13 +47,13 @@ class ErrorInvocation(BaseInvocation):
@invocation_output("test_image_output")
class ImageTestInvocationOutput(BaseInvocationOutput):
image: ImageField = Field()
image: ImageField = OutputField()
@invocation("test_text_to_image")
class TextToImageTestInvocation(BaseInvocation):
prompt: str = Field(default="")
prompt2: str = Field(default="")
prompt: str = InputField(default="")
prompt2: str = InputField(default="")
def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput:
return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
@ -61,8 +61,8 @@ class TextToImageTestInvocation(BaseInvocation):
@invocation("test_image_to_image")
class ImageToImageTestInvocation(BaseInvocation):
prompt: str = Field(default="")
image: Union[ImageField, None] = Field(default=None)
prompt: str = InputField(default="")
image: Union[ImageField, None] = InputField(default=None)
def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput:
return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
@ -70,17 +70,40 @@ class ImageToImageTestInvocation(BaseInvocation):
@invocation_output("test_prompt_collection_output")
class PromptCollectionTestInvocationOutput(BaseInvocationOutput):
collection: list[str] = Field(default_factory=list)
collection: list[str] = OutputField(default_factory=list)
@invocation("test_prompt_collection")
class PromptCollectionTestInvocation(BaseInvocation):
collection: list[str] = Field()
collection: list[str] = InputField()
def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput:
return PromptCollectionTestInvocationOutput(collection=self.collection.copy())
@invocation_output("test_any_output")
class AnyTypeTestInvocationOutput(BaseInvocationOutput):
value: Any = OutputField()
@invocation("test_any")
class AnyTypeTestInvocation(BaseInvocation):
value: Any = InputField(default=None)
def invoke(self, context: InvocationContext) -> AnyTypeTestInvocationOutput:
return AnyTypeTestInvocationOutput(value=self.value)
@invocation("test_polymorphic")
class PolymorphicStringTestInvocation(BaseInvocation):
value: Union[str, list[str]] = InputField(default="")
def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput:
if isinstance(self.value, str):
return PromptCollectionTestInvocationOutput(collection=[self.value])
return PromptCollectionTestInvocationOutput(collection=self.value)
# Importing these must happen after test invocations are defined or they won't register
from invokeai.app.services.events.events_base import EventServiceBase # noqa: E402
from invokeai.app.services.shared.graph import Edge, EdgeConnection # noqa: E402

10
tests/nodes/test_session_queue.py
View File

@ -150,9 +150,9 @@ def test_prepare_values_to_insert(batch_data_collection, batch_graph):
values = prepare_values_to_insert(queue_id="default", batch=b, priority=0, max_new_queue_items=1000)
assert len(values) == 8
session_adapter = TypeAdapter(GraphExecutionState)
GraphExecutionStateValidator = TypeAdapter(GraphExecutionState)
# graph should be serialized
ges = session_adapter.validate_json(values[0].session)
ges = GraphExecutionStateValidator.validate_json(values[0].session)
# graph values should be populated
assert ges.graph.get_node("1").prompt == "Banana sushi"
@ -161,16 +161,16 @@ def test_prepare_values_to_insert(batch_data_collection, batch_graph):
assert ges.graph.get_node("4").prompt == "Nissan"
# session ids should match deserialized graph
assert [v.session_id for v in values] == [session_adapter.validate_json(v.session).id for v in values]
assert [v.session_id for v in values] == [GraphExecutionStateValidator.validate_json(v.session).id for v in values]
# should unique session ids
sids = [v.session_id for v in values]
assert len(sids) == len(set(sids))
nfv_list_adapter = TypeAdapter(list[NodeFieldValue])
NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
# should have 3 node field values
assert type(values[0].field_values) is str
assert len(nfv_list_adapter.validate_json(values[0].field_values)) == 3
assert len(NodeFieldValueValidator.validate_json(values[0].field_values)) == 3
# should have batch id and priority
assert all(v.batch_id == b.batch_id for v in values)