mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
API/DB updates per PR feedback
This commit is contained in:
Mary Hipp
parent
12ba15bfa9
commit
97553a7de2
@ -31,7 +31,7 @@ from invokeai.app.services.session_processor.session_processor_default import (
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)
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from invokeai.app.services.session_queue.session_queue_sqlite import SqliteSessionQueue
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from invokeai.app.services.shared.sqlite.sqlite_util import init_db
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from invokeai.app.services.style_preset_images.style_preset_images_default import StylePresetImageFileStorageDisk
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from invokeai.app.services.style_preset_images.style_preset_images_disk import StylePresetImageFileStorageDisk
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from invokeai.app.services.style_preset_records.style_preset_records_sqlite import SqliteStylePresetRecordsStorage
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from invokeai.app.services.urls.urls_default import LocalUrlService
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from invokeai.app.services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
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@ -113,9 +113,7 @@ class ApiDependencies:
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urls = LocalUrlService()
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workflow_records = SqliteWorkflowRecordsStorage(db=db)
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style_preset_records = SqliteStylePresetRecordsStorage(db=db)
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style_preset_images_service = StylePresetImageFileStorageDisk(
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style_preset_images_folder / "style_preset_images"
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)
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style_preset_image_files = StylePresetImageFileStorageDisk(style_preset_images_folder / "style_preset_images")
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services = InvocationServices(
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board_image_records=board_image_records,
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@ -142,7 +140,7 @@ class ApiDependencies:
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tensors=tensors,
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conditioning=conditioning,
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style_preset_records=style_preset_records,
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style_preset_images_service=style_preset_images_service,
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style_preset_image_files=style_preset_image_files,
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)
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ApiDependencies.invoker = Invoker(services)
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@ -32,7 +32,7 @@ async def get_style_preset(
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) -> StylePresetRecordWithImage:
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"""Gets a style preset"""
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try:
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image = ApiDependencies.invoker.services.style_preset_images_service.get_url(style_preset_id)
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image = ApiDependencies.invoker.services.style_preset_image_files.get_url(style_preset_id)
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style_preset = ApiDependencies.invoker.services.style_preset_records.get(style_preset_id)
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return StylePresetRecordWithImage(image=image, **style_preset.model_dump())
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except StylePresetNotFoundError:
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@ -67,20 +67,22 @@ async def update_style_preset(
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raise HTTPException(status_code=415, detail="Failed to read image")
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try:
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ApiDependencies.invoker.services.style_preset_images_service.save(pil_image, style_preset_id)
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ApiDependencies.invoker.services.style_preset_image_files.save(style_preset_id, pil_image)
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except ValueError as e:
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raise HTTPException(status_code=409, detail=str(e))
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else:
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try:
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ApiDependencies.invoker.services.style_preset_images_service.delete(style_preset_id)
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ApiDependencies.invoker.services.style_preset_image_files.delete(style_preset_id)
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except StylePresetImageFileNotFoundException:
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pass
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preset_data = PresetData(positive_prompt=positive_prompt, negative_prompt=negative_prompt)
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changes = StylePresetChanges(name=name, preset_data=preset_data)
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style_preset_image = ApiDependencies.invoker.services.style_preset_images_service.get_url(style_preset_id)
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style_preset = ApiDependencies.invoker.services.style_preset_records.update(id=style_preset_id, changes=changes)
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style_preset_image = ApiDependencies.invoker.services.style_preset_image_files.get_url(style_preset_id)
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style_preset = ApiDependencies.invoker.services.style_preset_records.update(
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style_preset_id=style_preset_id, changes=changes
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)
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return StylePresetRecordWithImage(image=style_preset_image, **style_preset.model_dump())
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@ -93,7 +95,7 @@ async def delete_style_preset(
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) -> None:
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"""Deletes a style preset"""
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try:
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ApiDependencies.invoker.services.style_preset_images_service.delete(style_preset_id)
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ApiDependencies.invoker.services.style_preset_image_files.delete(style_preset_id)
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except StylePresetImageFileNotFoundException:
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pass
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@ -131,11 +133,11 @@ async def create_style_preset(
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raise HTTPException(status_code=415, detail="Failed to read image")
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try:
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ApiDependencies.invoker.services.style_preset_images_service.save(pil_image, new_style_preset.id)
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ApiDependencies.invoker.services.style_preset_image_files.save(new_style_preset.id, pil_image)
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except ValueError as e:
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raise HTTPException(status_code=409, detail=str(e))
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preset_image = ApiDependencies.invoker.services.style_preset_images_service.get_url(new_style_preset.id)
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preset_image = ApiDependencies.invoker.services.style_preset_image_files.get_url(new_style_preset.id)
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return StylePresetRecordWithImage(image=preset_image, **new_style_preset.model_dump())
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@ -151,7 +153,7 @@ async def list_style_presets() -> list[StylePresetRecordWithImage]:
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style_presets_with_image: list[StylePresetRecordWithImage] = []
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style_presets = ApiDependencies.invoker.services.style_preset_records.get_many()
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for preset in style_presets:
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image = ApiDependencies.invoker.services.style_preset_images_service.get_url(preset.id)
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image = ApiDependencies.invoker.services.style_preset_image_files.get_url(preset.id)
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style_preset_with_image = StylePresetRecordWithImage(image=image, **preset.model_dump())
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style_presets_with_image.append(style_preset_with_image)
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@ -176,7 +178,7 @@ async def get_style_preset_image(
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"""Gets an image file that previews the model"""
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try:
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path = ApiDependencies.invoker.services.style_preset_images_service.get_path(style_preset_id)
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path = ApiDependencies.invoker.services.style_preset_image_files.get_path(style_preset_id)
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response = FileResponse(
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path,
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@ -64,7 +64,7 @@ class InvocationServices:
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tensors: "ObjectSerializerBase[torch.Tensor]",
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conditioning: "ObjectSerializerBase[ConditioningFieldData]",
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style_preset_records: "StylePresetRecordsStorageBase",
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style_preset_images_service: "StylePresetImageFileStorageBase",
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style_preset_image_files: "StylePresetImageFileStorageBase",
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):
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self.board_images = board_images
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self.board_image_records = board_image_records
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@ -90,4 +90,4 @@ class InvocationServices:
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self.tensors = tensors
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self.conditioning = conditioning
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self.style_preset_records = style_preset_records
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self.style_preset_images_service = style_preset_images_service
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self.style_preset_image_files = style_preset_image_files
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@ -8,14 +8,14 @@ class Migration14Callback:
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self._create_style_presets(cursor)
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def _create_style_presets(self, cursor: sqlite3.Cursor) -> None:
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"""Create the table used to store model metadata downloaded from remote sources."""
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"""Create the table used to store style presets."""
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tables = [
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"""--sql
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CREATE TABLE IF NOT EXISTS style_presets (
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id TEXT NOT NULL PRIMARY KEY,
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name TEXT NOT NULL,
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preset_data TEXT NOT NULL,
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is_default BOOLEAN DEFAULT FALSE,
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type TEXT NOT NULL DEFAULT "user",
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created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
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-- Updated via trigger
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updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW'))
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@ -47,10 +47,10 @@ class Migration14Callback:
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def build_migration_14() -> Migration:
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"""
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Build the migration from database version 12 to 14..
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Build the migration from database version 13 to 14..
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This migration does the following:
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- Adds `archived` columns to the board table.
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- Create the table used to store style presets.
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"""
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migration_14 = Migration(
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from_version=13,
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@ -23,7 +23,7 @@ class StylePresetImageFileStorageBase(ABC):
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pass
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@abstractmethod
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def save(self, image: PILImageType, style_preset_id: str) -> None:
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def save(self, style_preset_id: str, image: PILImageType) -> None:
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"""Saves a style preset image."""
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pass
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@ -1,19 +1,19 @@
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class StylePresetImageFileNotFoundException(Exception):
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"""Raised when an image file is not found in storage."""
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def __init__(self, message="Style preset image file not found"):
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def __init__(self, message: str = "Style preset image file not found"):
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super().__init__(message)
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class StylePresetImageFileSaveException(Exception):
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"""Raised when an image cannot be saved."""
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def __init__(self, message="Style preset image file not saved"):
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def __init__(self, message: str = "Style preset image file not saved"):
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super().__init__(message)
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class StylePresetImageFileDeleteException(Exception):
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"""Raised when an image cannot be deleted."""
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def __init__(self, message="Style preset image file not deleted"):
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def __init__(self, message: str = "Style preset image file not deleted"):
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super().__init__(message)
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@ -28,14 +28,11 @@ class StylePresetImageFileStorageDisk(StylePresetImageFileStorageBase):
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try:
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path = self.get_path(style_preset_id)
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if not self._validate_path(path):
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raise StylePresetImageFileNotFoundException
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return Image.open(path)
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except FileNotFoundError as e:
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raise StylePresetImageFileNotFoundException from e
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def save(self, image: PILImageType, style_preset_id: str) -> None:
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def save(self, style_preset_id: str, image: PILImageType) -> None:
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try:
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self._validate_storage_folders()
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image_path = self._style_preset_images_folder / (style_preset_id + ".webp")
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@ -11,7 +11,7 @@ class StylePresetRecordsStorageBase(ABC):
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"""Base class for style preset storage services."""
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@abstractmethod
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def get(self, id: str) -> StylePresetRecordDTO:
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def get(self, style_preset_id: str) -> StylePresetRecordDTO:
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"""Get style preset by id."""
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pass
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@ -21,12 +21,12 @@ class StylePresetRecordsStorageBase(ABC):
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pass
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@abstractmethod
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def update(self, id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
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def update(self, style_preset_id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
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"""Updates a style preset."""
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pass
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@abstractmethod
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def delete(self, id: str) -> None:
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def delete(self, style_preset_id: str) -> None:
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"""Deletes a style preset."""
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pass
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@ -1,7 +1,10 @@
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from enum import Enum
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from typing import Any, Optional
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from pydantic import BaseModel, Field, TypeAdapter
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from invokeai.app.util.metaenum import MetaEnum
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class StylePresetNotFoundError(Exception):
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"""Raised when a style preset is not found"""
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@ -15,6 +18,12 @@ class PresetData(BaseModel, extra="forbid"):
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PresetDataValidator = TypeAdapter(PresetData)
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class PresetType(str, Enum, metaclass=MetaEnum):
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User = "user"
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Default = "default"
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Project = "project"
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class StylePresetChanges(BaseModel, extra="forbid"):
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name: Optional[str] = Field(default=None, description="The style preset's new name.")
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preset_data: Optional[PresetData] = Field(default=None, description="The updated data for style preset.")
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@ -23,7 +32,7 @@ class StylePresetChanges(BaseModel, extra="forbid"):
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class StylePresetWithoutId(BaseModel):
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name: str = Field(description="The name of the style preset.")
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preset_data: PresetData = Field(description="The preset data")
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is_default: Optional[bool] = Field(description="Whether or not the style preset is default", default=False)
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type: PresetType = Field(description="The type of style preset", default=PresetType.User)
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class StylePresetRecordDTO(StylePresetWithoutId):
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@ -5,6 +5,7 @@ from invokeai.app.services.invoker import Invoker
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from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
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from invokeai.app.services.style_preset_records.style_preset_records_base import StylePresetRecordsStorageBase
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from invokeai.app.services.style_preset_records.style_preset_records_common import (
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PresetType,
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StylePresetChanges,
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StylePresetNotFoundError,
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StylePresetRecordDTO,
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@ -24,7 +25,7 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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self._invoker = invoker
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self._sync_default_style_presets()
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def get(self, id: str) -> StylePresetRecordDTO:
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def get(self, style_preset_id: str) -> StylePresetRecordDTO:
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"""Gets a style preset by ID."""
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try:
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self._lock.acquire()
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@ -34,11 +35,11 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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FROM style_presets
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WHERE id = ?;
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""",
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(id,),
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(style_preset_id,),
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)
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row = self._cursor.fetchone()
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if row is None:
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raise StylePresetNotFoundError(f"Style preset with id {id} not found")
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raise StylePresetNotFoundError(f"Style preset with id {style_preset_id} not found")
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return StylePresetRecordDTO.from_dict(dict(row))
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except Exception:
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self._conn.rollback()
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@ -47,7 +48,7 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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self._lock.release()
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def create(self, style_preset: StylePresetWithoutId) -> StylePresetRecordDTO:
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id = uuid_string()
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style_preset_id = uuid_string()
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try:
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self._lock.acquire()
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self._cursor.execute(
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@ -56,11 +57,16 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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id,
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name,
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preset_data,
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is_default
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type
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)
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VALUES (?, ?, ?, ?);
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""",
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(id, style_preset.name, style_preset.preset_data.model_dump_json(), style_preset.is_default),
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(
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style_preset_id,
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style_preset.name,
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style_preset.preset_data.model_dump_json(),
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style_preset.type,
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),
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)
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self._conn.commit()
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except Exception:
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@ -68,9 +74,9 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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raise
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finally:
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self._lock.release()
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return self.get(id)
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return self.get(style_preset_id)
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def update(self, id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
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def update(self, style_preset_id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
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try:
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self._lock.acquire()
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# Change the name of a style preset
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@ -81,7 +87,7 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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SET name = ?
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WHERE id = ?;
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""",
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(changes.name, id),
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(changes.name, style_preset_id),
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)
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# Change the preset data for a style preset
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@ -92,7 +98,7 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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SET preset_data = ?
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WHERE id = ?;
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""",
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(changes.preset_data.model_dump_json(), id),
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(changes.preset_data.model_dump_json(), style_preset_id),
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)
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self._conn.commit()
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@ -101,9 +107,9 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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raise
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finally:
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self._lock.release()
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return self.get(id)
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return self.get(style_preset_id)
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def delete(self, id: str) -> None:
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def delete(self, style_preset_id: str) -> None:
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try:
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self._lock.acquire()
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self._cursor.execute(
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@ -111,7 +117,7 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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DELETE from style_presets
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WHERE id = ?;
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""",
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(id,),
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(style_preset_id,),
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)
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self._conn.commit()
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except Exception:
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@ -147,26 +153,25 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
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def _sync_default_style_presets(self) -> None:
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"""Syncs default style presets to the database. Internal use only."""
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# First delete all existing default style presets
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try:
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self._lock.acquire()
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self._cursor.execute(
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"""--sql
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DELETE FROM style_presets
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WHERE is_default = True;
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WHERE type = "default";
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"""
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)
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try:
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with open(Path(__file__).parent / Path("default_style_presets.json"), "r") as file:
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presets: list[StylePresetWithoutId] = json.load(file)
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for preset in presets:
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style_preset = StylePresetWithoutId(is_default=True, **preset)
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self.create(style_preset)
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except Exception:
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raise Exception()
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self._conn.commit()
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except Exception:
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self._conn.rollback()
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raise
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finally:
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self._lock.release()
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# Next, parse and create the default style presets
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with self._lock, open(Path(__file__).parent / Path("default_style_presets.json"), "r") as file:
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presets = json.load(file)
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for preset in presets:
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style_preset = StylePresetWithoutId.model_validate(preset)
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style_preset.type = PresetType.Default
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self.create(style_preset)
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@ -32,8 +32,8 @@ export const StylePresetListItem = ({ preset }: { preset: StylePresetRecordWithI
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dispatch(
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prefilledFormDataChanged({
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name,
|
||||
positivePrompt: positive_prompt,
|
||||
negativePrompt: negative_prompt,
|
||||
positivePrompt: positive_prompt || '',
|
||||
negativePrompt: negative_prompt || '',
|
||||
imageUrl: preset.image,
|
||||
})
|
||||
);
|
||||
@ -105,7 +105,7 @@ export const StylePresetListItem = ({ preset }: { preset: StylePresetRecordWithI
|
||||
)}
|
||||
</Flex>
|
||||
|
||||
{!preset.is_default && (
|
||||
{preset.type !== 'default' && (
|
||||
<Flex alignItems="center" gap={1}>
|
||||
<IconButton
|
||||
size="sm"
|
||||
|
||||
@ -24,7 +24,7 @@ export const StylePresetMenu = () => {
|
||||
|
||||
const groupedData = filteredData.reduce(
|
||||
(acc: { defaultPresets: StylePresetRecordWithImage[]; presets: StylePresetRecordWithImage[] }, preset) => {
|
||||
if (preset.is_default) {
|
||||
if (preset.type === 'default') {
|
||||
acc.defaultPresets.push(preset);
|
||||
} else {
|
||||
acc.presets.push(preset);
|
||||
|
||||
@ -7561,151 +7561,151 @@ export type components = {
|
||||
project_id: string | null;
|
||||
};
|
||||
InvocationOutputMap: {
|
||||
float_to_int: components["schemas"]["IntegerOutput"];
|
||||
collect: components["schemas"]["CollectInvocationOutput"];
|
||||
create_denoise_mask: components["schemas"]["DenoiseMaskOutput"];
|
||||
mul: components["schemas"]["IntegerOutput"];
|
||||
compel: components["schemas"]["ConditioningOutput"];
|
||||
segment_anything_processor: components["schemas"]["ImageOutput"];
|
||||
blank_image: components["schemas"]["ImageOutput"];
|
||||
cv_inpaint: components["schemas"]["ImageOutput"];
|
||||
sdxl_model_loader: components["schemas"]["SDXLModelLoaderOutput"];
|
||||
grounding_dino: components["schemas"]["BoundingBoxCollectionOutput"];
|
||||
range: components["schemas"]["IntegerCollectionOutput"];
|
||||
boolean_collection: components["schemas"]["BooleanCollectionOutput"];
|
||||
color: components["schemas"]["ColorOutput"];
|
||||
vae_loader: components["schemas"]["VAEOutput"];
|
||||
lblend: components["schemas"]["LatentsOutput"];
|
||||
mlsd_image_processor: components["schemas"]["ImageOutput"];
|
||||
pair_tile_image: components["schemas"]["PairTileImageOutput"];
|
||||
calculate_image_tiles_min_overlap: components["schemas"]["CalculateImageTilesOutput"];
|
||||
save_image: components["schemas"]["ImageOutput"];
|
||||
integer: components["schemas"]["IntegerOutput"];
|
||||
scheduler: components["schemas"]["SchedulerOutput"];
|
||||
metadata: components["schemas"]["MetadataOutput"];
|
||||
midas_depth_image_processor: components["schemas"]["ImageOutput"];
|
||||
prompt_from_file: components["schemas"]["StringCollectionOutput"];
|
||||
add: components["schemas"]["IntegerOutput"];
|
||||
sdxl_lora_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
img_chan: components["schemas"]["ImageOutput"];
|
||||
img_lerp: components["schemas"]["ImageOutput"];
|
||||
sdxl_refiner_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
freeu: components["schemas"]["UNetOutput"];
|
||||
tiled_multi_diffusion_denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
div: components["schemas"]["IntegerOutput"];
|
||||
float_math: components["schemas"]["FloatOutput"];
|
||||
random_range: components["schemas"]["IntegerCollectionOutput"];
|
||||
normalbae_image_processor: components["schemas"]["ImageOutput"];
|
||||
lresize: components["schemas"]["LatentsOutput"];
|
||||
t2i_adapter: components["schemas"]["T2IAdapterOutput"];
|
||||
bounding_box: components["schemas"]["BoundingBoxOutput"];
|
||||
lineart_anime_image_processor: components["schemas"]["ImageOutput"];
|
||||
string_replace: components["schemas"]["StringOutput"];
|
||||
spandrel_image_to_image_autoscale: components["schemas"]["ImageOutput"];
|
||||
image: components["schemas"]["ImageOutput"];
|
||||
dynamic_prompt: components["schemas"]["StringCollectionOutput"];
|
||||
seamless: components["schemas"]["SeamlessModeOutput"];
|
||||
lscale: components["schemas"]["LatentsOutput"];
|
||||
tensor_mask_to_image: components["schemas"]["ImageOutput"];
|
||||
merge_tiles_to_image: components["schemas"]["ImageOutput"];
|
||||
latents: components["schemas"]["LatentsOutput"];
|
||||
create_gradient_mask: components["schemas"]["GradientMaskOutput"];
|
||||
lora_selector: components["schemas"]["LoRASelectorOutput"];
|
||||
lora_collection_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
integer_collection: components["schemas"]["IntegerCollectionOutput"];
|
||||
round_float: components["schemas"]["FloatOutput"];
|
||||
mediapipe_face_processor: components["schemas"]["ImageOutput"];
|
||||
dw_openpose_image_processor: components["schemas"]["ImageOutput"];
|
||||
lineart_image_processor: components["schemas"]["ImageOutput"];
|
||||
infill_lama: components["schemas"]["ImageOutput"];
|
||||
rand_float: components["schemas"]["FloatOutput"];
|
||||
rand_int: components["schemas"]["IntegerOutput"];
|
||||
mask_combine: components["schemas"]["ImageOutput"];
|
||||
calculate_image_tiles: components["schemas"]["CalculateImageTilesOutput"];
|
||||
img_blur: components["schemas"]["ImageOutput"];
|
||||
float: components["schemas"]["FloatOutput"];
|
||||
tile_to_properties: components["schemas"]["TileToPropertiesOutput"];
|
||||
img_scale: components["schemas"]["ImageOutput"];
|
||||
img_channel_offset: components["schemas"]["ImageOutput"];
|
||||
crop_latents: components["schemas"]["LatentsOutput"];
|
||||
face_off: components["schemas"]["FaceOffOutput"];
|
||||
denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
controlnet: components["schemas"]["ControlOutput"];
|
||||
img_mul: components["schemas"]["ImageOutput"];
|
||||
float_collection: components["schemas"]["FloatCollectionOutput"];
|
||||
zoe_depth_image_processor: components["schemas"]["ImageOutput"];
|
||||
pidi_image_processor: components["schemas"]["ImageOutput"];
|
||||
spandrel_image_to_image: components["schemas"]["ImageOutput"];
|
||||
i2l: components["schemas"]["LatentsOutput"];
|
||||
integer_math: components["schemas"]["IntegerOutput"];
|
||||
depth_anything_image_processor: components["schemas"]["ImageOutput"];
|
||||
content_shuffle_image_processor: components["schemas"]["ImageOutput"];
|
||||
metadata_item: components["schemas"]["MetadataItemOutput"];
|
||||
img_nsfw: components["schemas"]["ImageOutput"];
|
||||
mask_edge: components["schemas"]["ImageOutput"];
|
||||
img_hue_adjust: components["schemas"]["ImageOutput"];
|
||||
infill_rgba: components["schemas"]["ImageOutput"];
|
||||
esrgan: components["schemas"]["ImageOutput"];
|
||||
alpha_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
img_pad_crop: components["schemas"]["ImageOutput"];
|
||||
sub: components["schemas"]["IntegerOutput"];
|
||||
clip_skip: components["schemas"]["CLIPSkipInvocationOutput"];
|
||||
color: components["schemas"]["ColorOutput"];
|
||||
boolean: components["schemas"]["BooleanOutput"];
|
||||
conditioning_collection: components["schemas"]["ConditioningCollectionOutput"];
|
||||
calculate_image_tiles_even_split: components["schemas"]["CalculateImageTilesOutput"];
|
||||
mediapipe_face_processor: components["schemas"]["ImageOutput"];
|
||||
face_mask_detection: components["schemas"]["FaceMaskOutput"];
|
||||
sdxl_refiner_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
img_channel_offset: components["schemas"]["ImageOutput"];
|
||||
segment_anything: components["schemas"]["MaskOutput"];
|
||||
boolean_collection: components["schemas"]["BooleanCollectionOutput"];
|
||||
denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
img_paste: components["schemas"]["ImageOutput"];
|
||||
face_identifier: components["schemas"]["ImageOutput"];
|
||||
img_hue_adjust: components["schemas"]["ImageOutput"];
|
||||
controlnet: components["schemas"]["ControlOutput"];
|
||||
lineart_anime_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_ilerp: components["schemas"]["ImageOutput"];
|
||||
float_range: components["schemas"]["FloatCollectionOutput"];
|
||||
img_scale: components["schemas"]["ImageOutput"];
|
||||
lora_selector: components["schemas"]["LoRASelectorOutput"];
|
||||
float_to_int: components["schemas"]["IntegerOutput"];
|
||||
mask_edge: components["schemas"]["ImageOutput"];
|
||||
face_off: components["schemas"]["FaceOffOutput"];
|
||||
canvas_paste_back: components["schemas"]["ImageOutput"];
|
||||
image: components["schemas"]["ImageOutput"];
|
||||
mask_combine: components["schemas"]["ImageOutput"];
|
||||
integer_collection: components["schemas"]["IntegerCollectionOutput"];
|
||||
compel: components["schemas"]["ConditioningOutput"];
|
||||
normalbae_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_watermark: components["schemas"]["ImageOutput"];
|
||||
tiled_multi_diffusion_denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
integer: components["schemas"]["IntegerOutput"];
|
||||
spandrel_image_to_image: components["schemas"]["ImageOutput"];
|
||||
color_correct: components["schemas"]["ImageOutput"];
|
||||
latents_collection: components["schemas"]["LatentsCollectionOutput"];
|
||||
infill_patchmatch: components["schemas"]["ImageOutput"];
|
||||
range_of_size: components["schemas"]["IntegerCollectionOutput"];
|
||||
string_join_three: components["schemas"]["StringOutput"];
|
||||
tile_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_resize: components["schemas"]["ImageOutput"];
|
||||
string_join: components["schemas"]["StringOutput"];
|
||||
sdxl_lora_collection_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
canny_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_lerp: components["schemas"]["ImageOutput"];
|
||||
string: components["schemas"]["StringOutput"];
|
||||
string_collection: components["schemas"]["StringCollectionOutput"];
|
||||
ip_adapter: components["schemas"]["IPAdapterOutput"];
|
||||
canvas_paste_back: components["schemas"]["ImageOutput"];
|
||||
rectangle_mask: components["schemas"]["MaskOutput"];
|
||||
sdxl_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
show_image: components["schemas"]["ImageOutput"];
|
||||
img_conv: components["schemas"]["ImageOutput"];
|
||||
img_paste: components["schemas"]["ImageOutput"];
|
||||
heuristic_resize: components["schemas"]["ImageOutput"];
|
||||
infill_cv2: components["schemas"]["ImageOutput"];
|
||||
leres_image_processor: components["schemas"]["ImageOutput"];
|
||||
l2i: components["schemas"]["ImageOutput"];
|
||||
hed_image_processor: components["schemas"]["ImageOutput"];
|
||||
main_model_loader: components["schemas"]["ModelLoaderOutput"];
|
||||
string_split_neg: components["schemas"]["StringPosNegOutput"];
|
||||
step_param_easing: components["schemas"]["FloatCollectionOutput"];
|
||||
iterate: components["schemas"]["IterateInvocationOutput"];
|
||||
string_split: components["schemas"]["String2Output"];
|
||||
merge_metadata: components["schemas"]["MetadataOutput"];
|
||||
face_mask_detection: components["schemas"]["FaceMaskOutput"];
|
||||
img_watermark: components["schemas"]["ImageOutput"];
|
||||
lora_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
float_range: components["schemas"]["FloatCollectionOutput"];
|
||||
sdxl_refiner_model_loader: components["schemas"]["SDXLRefinerModelLoaderOutput"];
|
||||
mask_from_id: components["schemas"]["ImageOutput"];
|
||||
core_metadata: components["schemas"]["MetadataOutput"];
|
||||
segment_anything: components["schemas"]["MaskOutput"];
|
||||
invert_tensor_mask: components["schemas"]["MaskOutput"];
|
||||
infill_tile: components["schemas"]["ImageOutput"];
|
||||
model_identifier: components["schemas"]["ModelIdentifierOutput"];
|
||||
noise: components["schemas"]["NoiseOutput"];
|
||||
img_crop: components["schemas"]["ImageOutput"];
|
||||
img_ilerp: components["schemas"]["ImageOutput"];
|
||||
conditioning: components["schemas"]["ConditioningOutput"];
|
||||
tomask: components["schemas"]["ImageOutput"];
|
||||
face_identifier: components["schemas"]["ImageOutput"];
|
||||
color_map_image_processor: components["schemas"]["ImageOutput"];
|
||||
image_collection: components["schemas"]["ImageCollectionOutput"];
|
||||
img_channel_multiply: components["schemas"]["ImageOutput"];
|
||||
image_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
sub: components["schemas"]["IntegerOutput"];
|
||||
unsharp_mask: components["schemas"]["ImageOutput"];
|
||||
mask_from_id: components["schemas"]["ImageOutput"];
|
||||
dw_openpose_image_processor: components["schemas"]["ImageOutput"];
|
||||
string_split_neg: components["schemas"]["StringPosNegOutput"];
|
||||
pidi_image_processor: components["schemas"]["ImageOutput"];
|
||||
lresize: components["schemas"]["LatentsOutput"];
|
||||
round_float: components["schemas"]["FloatOutput"];
|
||||
float: components["schemas"]["FloatOutput"];
|
||||
string_collection: components["schemas"]["StringCollectionOutput"];
|
||||
freeu: components["schemas"]["UNetOutput"];
|
||||
metadata_item: components["schemas"]["MetadataItemOutput"];
|
||||
merge_metadata: components["schemas"]["MetadataOutput"];
|
||||
calculate_image_tiles_min_overlap: components["schemas"]["CalculateImageTilesOutput"];
|
||||
merge_tiles_to_image: components["schemas"]["ImageOutput"];
|
||||
iterate: components["schemas"]["IterateInvocationOutput"];
|
||||
float_collection: components["schemas"]["FloatCollectionOutput"];
|
||||
lblend: components["schemas"]["LatentsOutput"];
|
||||
ideal_size: components["schemas"]["IdealSizeOutput"];
|
||||
tomask: components["schemas"]["ImageOutput"];
|
||||
string_split: components["schemas"]["String2Output"];
|
||||
mul: components["schemas"]["IntegerOutput"];
|
||||
string_replace: components["schemas"]["StringOutput"];
|
||||
seamless: components["schemas"]["SeamlessModeOutput"];
|
||||
tile_image_processor: components["schemas"]["ImageOutput"];
|
||||
image_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
rand_int: components["schemas"]["IntegerOutput"];
|
||||
bounding_box: components["schemas"]["BoundingBoxOutput"];
|
||||
main_model_loader: components["schemas"]["ModelLoaderOutput"];
|
||||
img_channel_multiply: components["schemas"]["ImageOutput"];
|
||||
random_range: components["schemas"]["IntegerCollectionOutput"];
|
||||
noise: components["schemas"]["NoiseOutput"];
|
||||
sdxl_refiner_model_loader: components["schemas"]["SDXLRefinerModelLoaderOutput"];
|
||||
rectangle_mask: components["schemas"]["MaskOutput"];
|
||||
invert_tensor_mask: components["schemas"]["MaskOutput"];
|
||||
core_metadata: components["schemas"]["MetadataOutput"];
|
||||
img_chan: components["schemas"]["ImageOutput"];
|
||||
depth_anything_image_processor: components["schemas"]["ImageOutput"];
|
||||
step_param_easing: components["schemas"]["FloatCollectionOutput"];
|
||||
infill_lama: components["schemas"]["ImageOutput"];
|
||||
calculate_image_tiles: components["schemas"]["CalculateImageTilesOutput"];
|
||||
float_math: components["schemas"]["FloatOutput"];
|
||||
lora_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
dynamic_prompt: components["schemas"]["StringCollectionOutput"];
|
||||
lscale: components["schemas"]["LatentsOutput"];
|
||||
sdxl_lora_collection_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
leres_image_processor: components["schemas"]["ImageOutput"];
|
||||
i2l: components["schemas"]["LatentsOutput"];
|
||||
sdxl_model_loader: components["schemas"]["SDXLModelLoaderOutput"];
|
||||
infill_rgba: components["schemas"]["ImageOutput"];
|
||||
range_of_size: components["schemas"]["IntegerCollectionOutput"];
|
||||
lora_collection_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
rand_float: components["schemas"]["FloatOutput"];
|
||||
spandrel_image_to_image_autoscale: components["schemas"]["ImageOutput"];
|
||||
heuristic_resize: components["schemas"]["ImageOutput"];
|
||||
div: components["schemas"]["IntegerOutput"];
|
||||
save_image: components["schemas"]["ImageOutput"];
|
||||
midas_depth_image_processor: components["schemas"]["ImageOutput"];
|
||||
integer_math: components["schemas"]["IntegerOutput"];
|
||||
collect: components["schemas"]["CollectInvocationOutput"];
|
||||
alpha_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
blank_image: components["schemas"]["ImageOutput"];
|
||||
string_join: components["schemas"]["StringOutput"];
|
||||
pair_tile_image: components["schemas"]["PairTileImageOutput"];
|
||||
hed_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_nsfw: components["schemas"]["ImageOutput"];
|
||||
conditioning_collection: components["schemas"]["ConditioningCollectionOutput"];
|
||||
img_blur: components["schemas"]["ImageOutput"];
|
||||
model_identifier: components["schemas"]["ModelIdentifierOutput"];
|
||||
clip_skip: components["schemas"]["CLIPSkipInvocationOutput"];
|
||||
sdxl_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
infill_patchmatch: components["schemas"]["ImageOutput"];
|
||||
metadata: components["schemas"]["MetadataOutput"];
|
||||
img_crop: components["schemas"]["ImageOutput"];
|
||||
tensor_mask_to_image: components["schemas"]["ImageOutput"];
|
||||
show_image: components["schemas"]["ImageOutput"];
|
||||
canny_image_processor: components["schemas"]["ImageOutput"];
|
||||
esrgan: components["schemas"]["ImageOutput"];
|
||||
prompt_from_file: components["schemas"]["StringCollectionOutput"];
|
||||
image_collection: components["schemas"]["ImageCollectionOutput"];
|
||||
calculate_image_tiles_even_split: components["schemas"]["CalculateImageTilesOutput"];
|
||||
img_pad_crop: components["schemas"]["ImageOutput"];
|
||||
l2i: components["schemas"]["ImageOutput"];
|
||||
sdxl_lora_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
range: components["schemas"]["IntegerCollectionOutput"];
|
||||
scheduler: components["schemas"]["SchedulerOutput"];
|
||||
lineart_image_processor: components["schemas"]["ImageOutput"];
|
||||
content_shuffle_image_processor: components["schemas"]["ImageOutput"];
|
||||
create_gradient_mask: components["schemas"]["GradientMaskOutput"];
|
||||
ip_adapter: components["schemas"]["IPAdapterOutput"];
|
||||
color_map_image_processor: components["schemas"]["ImageOutput"];
|
||||
string_join_three: components["schemas"]["StringOutput"];
|
||||
infill_cv2: components["schemas"]["ImageOutput"];
|
||||
mlsd_image_processor: components["schemas"]["ImageOutput"];
|
||||
segment_anything_processor: components["schemas"]["ImageOutput"];
|
||||
img_mul: components["schemas"]["ImageOutput"];
|
||||
conditioning: components["schemas"]["ConditioningOutput"];
|
||||
cv_inpaint: components["schemas"]["ImageOutput"];
|
||||
t2i_adapter: components["schemas"]["T2IAdapterOutput"];
|
||||
add: components["schemas"]["IntegerOutput"];
|
||||
crop_latents: components["schemas"]["LatentsOutput"];
|
||||
infill_tile: components["schemas"]["ImageOutput"];
|
||||
latents: components["schemas"]["LatentsOutput"];
|
||||
img_resize: components["schemas"]["ImageOutput"];
|
||||
create_denoise_mask: components["schemas"]["DenoiseMaskOutput"];
|
||||
img_conv: components["schemas"]["ImageOutput"];
|
||||
grounding_dino: components["schemas"]["BoundingBoxCollectionOutput"];
|
||||
zoe_depth_image_processor: components["schemas"]["ImageOutput"];
|
||||
vae_loader: components["schemas"]["VAEOutput"];
|
||||
};
|
||||
/**
|
||||
* InvocationStartedEvent
|
||||
@ -10569,6 +10569,11 @@ export type components = {
|
||||
*/
|
||||
negative_prompt: string;
|
||||
};
|
||||
/**
|
||||
* PresetType
|
||||
* @enum {string}
|
||||
*/
|
||||
PresetType: "user" | "default" | "project";
|
||||
/**
|
||||
* ProgressImage
|
||||
* @description The progress image sent intermittently during processing
|
||||
@ -12977,16 +12982,16 @@ export type components = {
|
||||
name: string;
|
||||
/** @description The preset data */
|
||||
preset_data: components["schemas"]["PresetData"];
|
||||
/**
|
||||
* @description The type of style preset
|
||||
* @default user
|
||||
*/
|
||||
type?: components["schemas"]["PresetType"];
|
||||
/**
|
||||
* Id
|
||||
* @description The style preset ID.
|
||||
*/
|
||||
id: string;
|
||||
/**
|
||||
* Is Default
|
||||
* @description Whether or not the style preset is default
|
||||
*/
|
||||
is_default: boolean;
|
||||
/**
|
||||
* Image
|
||||
* @description The path for image
|
||||
|
||||
Loading…
Reference in New Issue
Block a user