Updated JS files

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No


## Description

The `invokeai-configure` TUI's slider for the RAM cache was not picking
up the current settings in `invokeai.yaml`, leading users to think their
change hadn't taken effect. This is fixed in this PR.


## Related Tickets & Documents

First described here:


https://discord.com/channels/1020123559063990373/1161919551441735711/1162058518417907743

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -47,34 +47,9 @@ pip install ".[dev,test]"
 These are optional groups of packages which are defined within the `pyproject.toml`
 and will be required for testing the changes you make to the code.
 
-### Running Tests
-
-We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
-be found under the `./tests` folder and can be run with a single `pytest`
-command. Optionally, to review test coverage you can append `--cov`.
-
-```zsh
-pytest --cov
-```
-
-Test outcomes and coverage will be reported in the terminal. In addition a more
-detailed report is created in both XML and HTML format in the `./coverage`
-folder. The HTML one in particular can help identify missing statements
-requiring tests to ensure coverage. This can be run by opening
-`./coverage/html/index.html`.
-
-For example.
-
-```zsh
-pytest --cov; open ./coverage/html/index.html
-```
-
-??? info "HTML coverage report output"
-
-    ![html-overview](../assets/contributing/html-overview.png)
-
-    ![html-detail](../assets/contributing/html-detail.png)
+### Tests
 
+See the [tests documentation](./TESTS.md) for information about running and writing tests.
 ### Reloading Changes
 
 Experimenting with changes to the Python source code is a drag if you have to re-start the server —

docs/contributing/TESTS.md

@@ -0,0 +1,89 @@
+# InvokeAI Backend Tests
+
+We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
+
+## Fast vs. Slow
+All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
+
+'Fast' tests are run to validate every PR, and are fast enough that they can be run routinely during development.
+
+'Slow' tests are currently only run manually on an ad-hoc basis. In the future, they may be automated to run nightly. Most developers are only expected to run the 'slow' tests that directly relate to the feature(s) that they are working on.
+
+As a rule of thumb, tests should be marked as 'slow' if there is a chance that they take >1s (e.g. on a CPU-only machine with slow internet connection). Common examples of slow tests are tests that depend on downloading a model, or running model inference.
+
+## Running Tests
+
+Below are some common test commands:
+```bash
+# Run the fast tests. (This implicitly uses the configured default option: `-m "not slow"`.)
+pytest tests/
+
+# Equivalent command to run the fast tests.
+pytest tests/ -m "not slow"
+
+# Run the slow tests.
+pytest tests/ -m "slow"
+
+# Run the slow tests from a specific file.
+pytest tests/path/to/slow_test.py -m "slow"
+
+# Run all tests (fast and slow).
+pytest tests -m ""
+```
+
+## Test Organization
+
+All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
+
+TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
+
+## Tests that depend on models
+
+There are a few things to keep in mind when adding tests that depend on models.
+
+1. If a required model is not already present, it should automatically be downloaded as part of the test setup.
+2. If a model is already downloaded, it should not be re-downloaded unnecessarily.
+3. Take reasonable care to keep the total number of models required for the tests low. Whenever possible, re-use models that are already required for other tests. If you are adding a new model, consider including a comment to explain why it is required/unique.
+
+There are several utilities to help with model setup for tests. Here is a sample test that depends on a model:
+```python
+import pytest
+import torch
+
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType
+from invokeai.backend.util.test_utils import install_and_load_model
+
+@pytest.mark.slow
+def test_model(model_installer, torch_device):
+    model_info = install_and_load_model(
+        model_installer=model_installer,
+        model_path_id_or_url="HF/dummy_model_id",
+        model_name="dummy_model",
+        base_model=BaseModelType.StableDiffusion1,
+        model_type=ModelType.Dummy,
+    )
+
+    dummy_input = build_dummy_input(torch_device)
+
+    with torch.no_grad(), model_info as model:
+        model.to(torch_device, dtype=torch.float32)
+        output = model(dummy_input)
+
+    # Validate output...
+
+```
+
+## Test Coverage
+
+To review test coverage, append `--cov` to your pytest command:
+```bash
+pytest tests/ --cov
+```
+
+Test outcomes and coverage will be reported in the terminal. In addition, a more detailed report is created in both XML and HTML format in the `./coverage` folder. The HTML output is particularly helpful in identifying untested statements where coverage should be improved. The HTML report can be viewed by opening `./coverage/html/index.html`.
+
+??? info "HTML coverage report output"
+
+    ![html-overview](../assets/contributing/html-overview.png)
+
+    ![html-detail](../assets/contributing/html-detail.png)

docs/contributing/contribution_guides/development.md

@@ -12,7 +12,7 @@ To get started, take a look at our [new contributors checklist](newContributorCh
 Once you're setup, for more information, you can review the documentation specific to your area of interest:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](development_guides/contributingToFrontend.md)
+* #### [Frontend Documentation](./contributingToFrontend.md)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 

docs/installation/020_INSTALL_MANUAL.md

@@ -256,6 +256,10 @@ manager, please follow these steps:
         *highly recommended** if your virtual environment is located outside of
         your runtime directory.
 
+    !!! tip
+
+        On linux, it is recommended to run invokeai with the following env var: `MALLOC_MMAP_THRESHOLD_=1048576`. For example: `MALLOC_MMAP_THRESHOLD_=1048576 invokeai --web`. This helps to prevent memory fragmentation that can lead to memory accumulation over time. This env var is set automatically when running via `invoke.sh`.
+
 10.  Render away!
 
     Browse the [features](../features/index.md) section to learn about all the

docs/nodes/communityNodes.md

@@ -10,6 +10,20 @@ To use a community workflow, download the the `.json` node graph file and load i
 
 --------------------------------
 
+--------------------------------
+### Make 3D
+
+**Description:** Create compelling 3D stereo images from 2D originals.
+
+**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d)
+
+**Example Node Graph:**  https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false
+
+**Output Examples** 
+
+![Painting of a cozy delapidated house](https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png){: style="height:512px;width:512px"}
+![Photo of cute puppies](https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png){: style="height:512px;width:512px"}
+
 --------------------------------
 ### Ideal Size
 

installer/templates/invoke.sh.in

@@ -46,6 +46,9 @@ if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
+# Avoid glibc memory fragmentation. See invokeai/backend/model_management/README.md for details.
+export MALLOC_MMAP_THRESHOLD_=1048576
+
 # Primary function for the case statement to determine user input
 do_choice() {
     case $1 in

invokeai/app/api/dependencies.py

@@ -1,35 +1,35 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-import sqlite3
 from logging import Logger
 
-from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
-from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
-from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
-from invokeai.app.services.boards import BoardService, BoardServiceDependencies
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
-from invokeai.app.services.images import ImageService, ImageServiceDependencies
-from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
-from invokeai.app.services.resource_name import SimpleNameService
-from invokeai.app.services.session_processor.session_processor_default import DefaultSessionProcessor
-from invokeai.app.services.session_queue.session_queue_sqlite import SqliteSessionQueue
-from invokeai.app.services.urls import LocalUrlService
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
-from ..services.default_graphs import create_system_graphs
-from ..services.graph import GraphExecutionState, LibraryGraph
-from ..services.image_file_storage import DiskImageFileStorage
-from ..services.invocation_queue import MemoryInvocationQueue
+from ..services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
+from ..services.board_images.board_images_default import BoardImagesService
+from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
+from ..services.boards.boards_default import BoardService
+from ..services.config import InvokeAIAppConfig
+from ..services.image_files.image_files_disk import DiskImageFileStorage
+from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
+from ..services.images.images_default import ImageService
+from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
+from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
+from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
-from ..services.invocation_stats import InvocationStatsService
+from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
-from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-from ..services.model_manager_service import ModelManagerService
-from ..services.processor import DefaultInvocationProcessor
-from ..services.sqlite import SqliteItemStorage
-from ..services.thread import lock
+from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
+from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
+from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
+from ..services.model_manager.model_manager_default import ModelManagerService
+from ..services.names.names_default import SimpleNameService
+from ..services.session_processor.session_processor_default import DefaultSessionProcessor
+from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
+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 .events import FastAPIEventService
 
 
@@ -63,100 +63,64 @@ class ApiDependencies:
         logger.info(f"Root directory = {str(config.root_path)}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
-        events = FastAPIEventService(event_handler_id)
-
         output_folder = config.output_path
 
-        # TODO: build a file/path manager?
-        if config.use_memory_db:
-            db_location = ":memory:"
-        else:
-            db_path = config.db_path
-            db_path.parent.mkdir(parents=True, exist_ok=True)
-            db_location = str(db_path)
+        db = SqliteDatabase(config, logger)
 
-        logger.info(f"Using database at {db_location}")
-        db_conn = sqlite3.connect(db_location, check_same_thread=False)  # TODO: figure out a better threading solution
+        configuration = config
+        logger = logger
 
-        if config.log_sql:
-            db_conn.set_trace_callback(print)
-        db_conn.execute("PRAGMA foreign_keys = ON;")
-
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-            conn=db_conn, table_name="graph_executions", lock=lock
-        )
-
-        urls = LocalUrlService()
-        image_record_storage = SqliteImageRecordStorage(conn=db_conn, lock=lock)
-        image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
-        names = SimpleNameService()
+        board_image_records = SqliteBoardImageRecordStorage(db=db)
+        board_images = BoardImagesService()
+        board_records = SqliteBoardRecordStorage(db=db)
+        boards = BoardService()
+        events = FastAPIEventService(event_handler_id)
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
+        graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
+        image_files = DiskImageFileStorage(f"{output_folder}/images")
+        image_records = SqliteImageRecordStorage(db=db)
+        images = ImageService()
+        invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
-
-        board_record_storage = SqliteBoardRecordStorage(conn=db_conn, lock=lock)
-        board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn, lock=lock)
-
-        boards = BoardService(
-            services=BoardServiceDependencies(
-                board_image_record_storage=board_image_record_storage,
-                board_record_storage=board_record_storage,
-                image_record_storage=image_record_storage,
-                url=urls,
-                logger=logger,
-            )
-        )
-
-        board_images = BoardImagesService(
-            services=BoardImagesServiceDependencies(
-                board_image_record_storage=board_image_record_storage,
-                board_record_storage=board_record_storage,
-                image_record_storage=image_record_storage,
-                url=urls,
-                logger=logger,
-            )
-        )
-
-        images = ImageService(
-            services=ImageServiceDependencies(
-                board_image_record_storage=board_image_record_storage,
-                image_record_storage=image_record_storage,
-                image_file_storage=image_file_storage,
-                url=urls,
-                logger=logger,
-                names=names,
-                graph_execution_manager=graph_execution_manager,
-            )
-        )
+        model_manager = ModelManagerService(config, logger)
+        names = SimpleNameService()
+        performance_statistics = InvocationStatsService()
+        processor = DefaultInvocationProcessor()
+        queue = MemoryInvocationQueue()
+        session_processor = DefaultSessionProcessor()
+        session_queue = SqliteSessionQueue(db=db)
+        urls = LocalUrlService()
 
         services = InvocationServices(
-            model_manager=ModelManagerService(config, logger),
-            events=events,
-            latents=latents,
-            images=images,
-            boards=boards,
+            board_image_records=board_image_records,
             board_images=board_images,
-            queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, lock=lock, table_name="graphs"),
+            board_records=board_records,
+            boards=boards,
+            configuration=configuration,
+            events=events,
             graph_execution_manager=graph_execution_manager,
-            processor=DefaultInvocationProcessor(),
-            configuration=config,
-            performance_statistics=InvocationStatsService(graph_execution_manager),
+            graph_library=graph_library,
+            image_files=image_files,
+            image_records=image_records,
+            images=images,
+            invocation_cache=invocation_cache,
+            latents=latents,
             logger=logger,
-            session_queue=SqliteSessionQueue(conn=db_conn, lock=lock),
-            session_processor=DefaultSessionProcessor(),
-            invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
+            model_manager=model_manager,
+            names=names,
+            performance_statistics=performance_statistics,
+            processor=processor,
+            queue=queue,
+            session_processor=session_processor,
+            session_queue=session_queue,
+            urls=urls,
         )
 
         create_system_graphs(services.graph_library)
 
         ApiDependencies.invoker = Invoker(services)
 
-        try:
-            lock.acquire()
-            db_conn.execute("VACUUM;")
-            db_conn.commit()
-            logger.info("Cleaned database")
-        finally:
-            lock.release()
+        db.clean()
 
     @staticmethod
     def shutdown():

invokeai/app/api/events.py

@@ -7,7 +7,7 @@ from typing import Any
 
 from fastapi_events.dispatcher import dispatch
 
-from ..services.events import EventServiceBase
+from ..services.events.events_base import EventServiceBase
 
 
 class FastAPIEventService(EventServiceBase):

invokeai/app/api/routers/boards.py

@@ -4,9 +4,9 @@ from fastapi import Body, HTTPException, Path, Query
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
-from invokeai.app.services.board_record_storage import BoardChanges
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.board_record import BoardDTO
+from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.boards.boards_common import BoardDTO
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 
 from ..dependencies import ApiDependencies
 

invokeai/app/api/routers/images.py

@@ -8,9 +8,9 @@ from PIL import Image
 from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import ImageDTO, ImageRecordChanges, ImageUrlsDTO
+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
 
 from ..dependencies import ApiDependencies
 
@@ -322,3 +322,20 @@ async def unstar_images_in_list(
         return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to unstar images")
+
+
+class ImagesDownloaded(BaseModel):
+    response: Optional[str] = Field(
+        description="If defined, the message to display to the user when images begin downloading"
+    )
+
+
+@images_router.post("/download", operation_id="download_images_from_list", response_model=ImagesDownloaded)
+async def download_images_from_list(
+    image_names: list[str] = Body(description="The list of names of images to download", embed=True),
+    board_id: Optional[str] = Body(
+        default=None, description="The board from which image should be downloaded from", embed=True
+    ),
+) -> ImagesDownloaded:
+    # return ImagesDownloaded(response="Your images are downloading")
+    raise HTTPException(status_code=501, detail="Endpoint is not yet implemented")

invokeai/app/api/routers/session_queue.py

@@ -18,9 +18,9 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItemDTO,
     SessionQueueStatus,
 )
-from invokeai.app.services.shared.models import CursorPaginatedResults
+from invokeai.app.services.shared.graph import Graph
+from invokeai.app.services.shared.pagination import CursorPaginatedResults
 
-from ...services.graph import Graph
 from ..dependencies import ApiDependencies
 
 session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])

invokeai/app/api/routers/sessions.py

@@ -6,11 +6,12 @@ from fastapi import Body, HTTPException, Path, Query, Response
 from fastapi.routing import APIRouter
 from pydantic.fields import Field
 
+from invokeai.app.services.shared.pagination import PaginatedResults
+
 # Importing * is bad karma but needed here for node detection
 from ...invocations import *  # noqa: F401 F403
 from ...invocations.baseinvocation import BaseInvocation
-from ...services.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
-from ...services.item_storage import PaginatedResults
+from ...services.shared.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
 from ..dependencies import ApiDependencies
 
 session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])

invokeai/app/api/sockets.py

@@ -5,7 +5,7 @@ from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event
 from socketio import ASGIApp, AsyncServer
 
-from ..services.events import EventServiceBase
+from ..services.events.events_base import EventServiceBase
 
 
 class SocketIO:

invokeai/app/invocations/baseinvocation.py

@@ -28,7 +28,7 @@ from pydantic import BaseModel, Field, validator
 from pydantic.fields import ModelField, Undefined
 from pydantic.typing import NoArgAnyCallable
 
-from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
 
 if TYPE_CHECKING:
     from ..services.invocation_services import InvocationServices
@@ -68,6 +68,7 @@ class FieldDescriptions:
     height = "Height of output (px)"
     control = "ControlNet(s) to apply"
     ip_adapter = "IP-Adapter to apply"
+    t2i_adapter = "T2I-Adapter(s) to apply"
     denoised_latents = "Denoised latents tensor"
     latents = "Latents tensor"
     strength = "Strength of denoising (proportional to steps)"

invokeai/app/invocations/controlnet_image_processors.py

@@ -27,9 +27,9 @@ from PIL import Image
 from pydantic import BaseModel, Field, validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
 from ...backend.model_management import BaseModelType
-from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,

invokeai/app/invocations/cv.py

@@ -6,7 +6,7 @@ import numpy
 from PIL import Image, ImageOps
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 

invokeai/app/invocations/facetools.py

@@ -1,5 +1,4 @@
 import math
-import os
 import re
 from pathlib import Path
 from typing import Optional, TypedDict
@@ -11,6 +10,7 @@ from PIL import Image, ImageDraw, ImageFilter, ImageFont, ImageOps
 from PIL.Image import Image as ImageType
 from pydantic import validator
 
+import invokeai.assets.fonts as font_assets
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     InputField,
@@ -20,7 +20,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
 
 @invocation_output("face_mask_output")
@@ -138,6 +138,7 @@ def generate_face_box_mask(
     chunk_x_offset: int = 0,
     chunk_y_offset: int = 0,
     draw_mesh: bool = True,
+    check_bounds: bool = True,
 ) -> list[FaceResultData]:
     result = []
     mask_pil = None
@@ -217,7 +218,7 @@ def generate_face_box_mask(
             im_width, im_height = pil_image.size
             over_w = im_width * 0.1
             over_h = im_height * 0.1
-            if (
+            if not check_bounds or (
                 (left_side >= -over_w)
                 and (right_side < im_width + over_w)
                 and (top_side >= -over_h)
@@ -345,6 +346,7 @@ def get_faces_list(
             chunk_x_offset=0,
             chunk_y_offset=0,
             draw_mesh=draw_mesh,
+            check_bounds=False,
         )
     if should_chunk or len(result) == 0:
         context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
@@ -402,7 +404,7 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.0")
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.1")
 class FaceOffInvocation(BaseInvocation):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
@@ -496,7 +498,7 @@ class FaceOffInvocation(BaseInvocation):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.0")
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.1")
 class FaceMaskInvocation(BaseInvocation):
     """Face mask creation using mediapipe face detection"""
 
@@ -614,7 +616,7 @@ class FaceMaskInvocation(BaseInvocation):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.1"
 )
 class FaceIdentifierInvocation(BaseInvocation):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
@@ -641,9 +643,9 @@ class FaceIdentifierInvocation(BaseInvocation):
             draw_mesh=False,
         )
 
-        path = Path(__file__).resolve().parent.parent.parent
-        font_path = os.path.abspath(path / "assets/fonts/inter/Inter-Regular.ttf")
-        font = ImageFont.truetype(font_path, FONT_SIZE)
+        # Note - font may be found either in the repo if running an editable install, or in the venv if running a package install
+        font_path = [x for x in [Path(y, "inter/Inter-Regular.ttf") for y in font_assets.__path__] if x.exists()]
+        font = ImageFont.truetype(font_path[0].as_posix(), FONT_SIZE)
 
         # Paste face IDs on the output image
         draw = ImageDraw.Draw(image)

invokeai/app/invocations/image.py

@@ -9,10 +9,10 @@ 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 ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
 
 

invokeai/app/invocations/infill.py

@@ -7,12 +7,12 @@ import numpy as np
 from PIL import Image, ImageOps
 
 from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 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 ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 

invokeai/app/invocations/latent.py

@@ -10,7 +10,7 @@ import torch
 import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.models import UNet2DConditionModel
+from diffusers.models.adapter import FullAdapterXL, T2IAdapter
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -33,6 +33,8 @@ from invokeai.app.invocations.primitives import (
     LatentsOutput,
     build_latents_output,
 )
+from invokeai.app.invocations.t2i_adapter import T2IAdapterField
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@@ -47,12 +49,12 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
     ControlNetData,
     IPAdapterData,
     StableDiffusionGeneratorPipeline,
+    T2IAdapterData,
     image_resized_to_grid_as_tensor,
 )
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
-from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -196,7 +198,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.1.0",
+    version="1.3.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -223,12 +225,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
         input=Input.Connection,
         ui_order=5,
     )
-    ip_adapter: Optional[IPAdapterField] = InputField(
+    ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
         description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
     )
+    t2i_adapter: Union[T2IAdapterField, list[T2IAdapterField]] = InputField(
+        description=FieldDescriptions.t2i_adapter, title="T2I-Adapter", default=None, input=Input.Connection, ui_order=7
+    )
     latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
     denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=7
+        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=8
     )
 
     @validator("cfg_scale")
@@ -404,52 +409,150 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def prep_ip_adapter_data(
         self,
         context: InvocationContext,
-        ip_adapter: Optional[IPAdapterField],
+        ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
         conditioning_data: ConditioningData,
-        unet: UNet2DConditionModel,
         exit_stack: ExitStack,
-    ) -> Optional[IPAdapterData]:
+    ) -> Optional[list[IPAdapterData]]:
         """If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
         to the `conditioning_data` (in-place).
         """
         if ip_adapter is None:
             return None
 
-        image_encoder_model_info = context.services.model_manager.get_model(
-            model_name=ip_adapter.image_encoder_model.model_name,
-            model_type=ModelType.CLIPVision,
-            base_model=ip_adapter.image_encoder_model.base_model,
-            context=context,
-        )
+        # ip_adapter could be a list or a single IPAdapterField. Normalize to a list here.
+        if not isinstance(ip_adapter, list):
+            ip_adapter = [ip_adapter]
 
-        ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-            context.services.model_manager.get_model(
-                model_name=ip_adapter.ip_adapter_model.model_name,
-                model_type=ModelType.IPAdapter,
-                base_model=ip_adapter.ip_adapter_model.base_model,
+        if len(ip_adapter) == 0:
+            return None
+
+        ip_adapter_data_list = []
+        conditioning_data.ip_adapter_conditioning = []
+        for single_ip_adapter in ip_adapter:
+            ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
+                context.services.model_manager.get_model(
+                    model_name=single_ip_adapter.ip_adapter_model.model_name,
+                    model_type=ModelType.IPAdapter,
+                    base_model=single_ip_adapter.ip_adapter_model.base_model,
+                    context=context,
+                )
+            )
+
+            image_encoder_model_info = context.services.model_manager.get_model(
+                model_name=single_ip_adapter.image_encoder_model.model_name,
+                model_type=ModelType.CLIPVision,
+                base_model=single_ip_adapter.image_encoder_model.base_model,
                 context=context,
             )
-        )
 
-        input_image = context.services.images.get_pil_image(ip_adapter.image.image_name)
+            input_image = context.services.images.get_pil_image(single_ip_adapter.image.image_name)
 
-        # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
-        # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
-        with image_encoder_model_info as image_encoder_model:
-            # Get image embeddings from CLIP and ImageProjModel.
-            image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
-                input_image, image_encoder_model
-            )
-            conditioning_data.ip_adapter_conditioning = IPAdapterConditioningInfo(
-                image_prompt_embeds, uncond_image_prompt_embeds
+            # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
+            # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
+            with image_encoder_model_info as image_encoder_model:
+                # Get image embeddings from CLIP and ImageProjModel.
+                image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
+                    input_image, image_encoder_model
+                )
+                conditioning_data.ip_adapter_conditioning.append(
+                    IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
+                )
+
+            ip_adapter_data_list.append(
+                IPAdapterData(
+                    ip_adapter_model=ip_adapter_model,
+                    weight=single_ip_adapter.weight,
+                    begin_step_percent=single_ip_adapter.begin_step_percent,
+                    end_step_percent=single_ip_adapter.end_step_percent,
+                )
             )
 
-        return IPAdapterData(
-            ip_adapter_model=ip_adapter_model,
-            weight=ip_adapter.weight,
-            begin_step_percent=ip_adapter.begin_step_percent,
-            end_step_percent=ip_adapter.end_step_percent,
-        )
+        return ip_adapter_data_list
+
+    def run_t2i_adapters(
+        self,
+        context: InvocationContext,
+        t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
+        latents_shape: list[int],
+        do_classifier_free_guidance: bool,
+    ) -> Optional[list[T2IAdapterData]]:
+        if t2i_adapter is None:
+            return None
+
+        # Handle the possibility that t2i_adapter could be a list or a single T2IAdapterField.
+        if isinstance(t2i_adapter, T2IAdapterField):
+            t2i_adapter = [t2i_adapter]
+
+        if len(t2i_adapter) == 0:
+            return None
+
+        t2i_adapter_data = []
+        for t2i_adapter_field in t2i_adapter:
+            t2i_adapter_model_info = context.services.model_manager.get_model(
+                model_name=t2i_adapter_field.t2i_adapter_model.model_name,
+                model_type=ModelType.T2IAdapter,
+                base_model=t2i_adapter_field.t2i_adapter_model.base_model,
+                context=context,
+            )
+            image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
+
+            # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
+            if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
+                max_unet_downscale = 8
+            elif t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusionXL:
+                max_unet_downscale = 4
+            else:
+                raise ValueError(
+                    f"Unexpected T2I-Adapter base model type: '{t2i_adapter_field.t2i_adapter_model.base_model}'."
+                )
+
+            t2i_adapter_model: T2IAdapter
+            with t2i_adapter_model_info as t2i_adapter_model:
+                total_downscale_factor = t2i_adapter_model.total_downscale_factor
+                if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
+                    # HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
+                    # this PR: https://github.com/huggingface/diffusers/pull/5134.
+                    total_downscale_factor = total_downscale_factor // 2
+
+                # Resize the T2I-Adapter input image.
+                # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
+                # result will match the latent image's dimensions after max_unet_downscale is applied.
+                t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
+                t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
+
+                # Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
+                # a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the
+                # T2I-Adapter model.
+                #
+                # Note: We re-use the `prepare_control_image(...)` from ControlNet for T2I-Adapter, because it has many
+                # of the same requirements (e.g. preserving binary masks during resize).
+                t2i_image = prepare_control_image(
+                    image=image,
+                    do_classifier_free_guidance=False,
+                    width=t2i_input_width,
+                    height=t2i_input_height,
+                    num_channels=t2i_adapter_model.config.in_channels,
+                    device=t2i_adapter_model.device,
+                    dtype=t2i_adapter_model.dtype,
+                    resize_mode=t2i_adapter_field.resize_mode,
+                )
+
+                adapter_state = t2i_adapter_model(t2i_image)
+
+            if do_classifier_free_guidance:
+                for idx, value in enumerate(adapter_state):
+                    adapter_state[idx] = torch.cat([value] * 2, dim=0)
+
+            t2i_adapter_data.append(
+                T2IAdapterData(
+                    adapter_state=adapter_state,
+                    weight=t2i_adapter_field.weight,
+                    begin_step_percent=t2i_adapter_field.begin_step_percent,
+                    end_step_percent=t2i_adapter_field.end_step_percent,
+                )
+            )
+
+        return t2i_adapter_data
 
     # original idea by https://github.com/AmericanPresidentJimmyCarter
     # TODO: research more for second order schedulers timesteps
@@ -522,6 +625,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             mask, masked_latents = self.prep_inpaint_mask(context, latents)
 
+            # TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
+            # below. Investigate whether this is appropriate.
+            t2i_adapter_data = self.run_t2i_adapters(
+                context, self.t2i_adapter, latents.shape, do_classifier_free_guidance=True
+            )
+
             # Get the source node id (we are invoking the prepared node)
             graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
             source_node_id = graph_execution_state.prepared_source_mapping[self.id]
@@ -580,7 +689,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     context=context,
                     ip_adapter=self.ip_adapter,
                     conditioning_data=conditioning_data,
-                    unet=unet,
                     exit_stack=exit_stack,
                 )
 
@@ -602,8 +710,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     masked_latents=masked_latents,
                     num_inference_steps=num_inference_steps,
                     conditioning_data=conditioning_data,
-                    control_data=controlnet_data,  # list[ControlNetData],
-                    ip_adapter_data=ip_adapter_data,  # IPAdapterData,
+                    control_data=controlnet_data,
+                    ip_adapter_data=ip_adapter_data,
+                    t2i_adapter_data=t2i_adapter_data,
                     callback=step_callback,
                 )
 

invokeai/app/invocations/metadata.py

@@ -15,6 +15,7 @@ from invokeai.app.invocations.controlnet_image_processors import ControlField
 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__
@@ -63,6 +64,7 @@ class CoreMetadata(BaseModelExcludeNull):
     model: MainModelField = Field(description="The main model used for inference")
     controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
     ipAdapters: list[IPAdapterMetadataField] = Field(description="The IP Adapters used for inference")
+    t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference")
     loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
     vae: Optional[VAEModelField] = Field(
         default=None,
@@ -139,6 +141,7 @@ class MetadataAccumulatorInvocation(BaseInvocation):
     model: MainModelField = InputField(description="The main model used for inference")
     controlnets: list[ControlField] = InputField(description="The ControlNets used for inference")
     ipAdapters: list[IPAdapterMetadataField] = InputField(description="The IP Adapters used for inference")
+    t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference")
     loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference")
     strength: Optional[float] = InputField(
         default=None,

invokeai/app/invocations/onnx.py

@@ -14,13 +14,13 @@ 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
 from invokeai.backend import BaseModelType, ModelType, SubModelType
 
 from ...backend.model_management import ONNXModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util import choose_torch_device
-from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,

invokeai/app/invocations/t2i_adapter.py

@@ -0,0 +1,83 @@
+from typing import Union
+
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.backend.model_management.models.base import BaseModelType
+
+
+class T2IAdapterModelField(BaseModel):
+    model_name: str = Field(description="Name of the T2I-Adapter model")
+    base_model: BaseModelType = Field(description="Base model")
+
+
+class T2IAdapterField(BaseModel):
+    image: ImageField = Field(description="The T2I-Adapter image prompt.")
+    t2i_adapter_model: T2IAdapterModelField = Field(description="The T2I-Adapter model to use.")
+    weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+
+@invocation_output("t2i_adapter_output")
+class T2IAdapterOutput(BaseInvocationOutput):
+    t2i_adapter: T2IAdapterField = OutputField(description=FieldDescriptions.t2i_adapter, title="T2I Adapter")
+
+
+@invocation(
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.0"
+)
+class T2IAdapterInvocation(BaseInvocation):
+    """Collects T2I-Adapter info to pass to other nodes."""
+
+    # Inputs
+    image: ImageField = InputField(description="The IP-Adapter image prompt.")
+    t2i_adapter_model: T2IAdapterModelField = InputField(
+        description="The T2I-Adapter model.",
+        title="T2I-Adapter Model",
+        input=Input.Direct,
+        ui_order=-1,
+    )
+    weight: Union[float, list[float]] = InputField(
+        default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
+    )
+    begin_step_percent: float = InputField(
+        default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(
+        default="just_resize",
+        description="The resize mode applied to the T2I-Adapter input image so that it matches the target output size.",
+    )
+
+    def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
+        return T2IAdapterOutput(
+            t2i_adapter=T2IAdapterField(
+                image=self.image,
+                t2i_adapter_model=self.t2i_adapter_model,
+                weight=self.weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+                resize_mode=self.resize_mode,
+            )
+        )

invokeai/app/invocations/upscale.py

@@ -4,12 +4,14 @@ from typing import Literal
 
 import cv2 as cv
 import numpy as np
+import torch
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from realesrgan import RealESRGANer
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
+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
 
@@ -22,13 +24,19 @@ ESRGAN_MODELS = Literal[
     "RealESRGAN_x2plus.pth",
 ]
 
+if choose_torch_device() == torch.device("mps"):
+    from torch import mps
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
+
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
 class ESRGANInvocation(BaseInvocation):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
     model_name: ESRGAN_MODELS = InputField(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
+    tile_size: int = InputField(
+        default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)"
+    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -86,9 +94,11 @@ class ESRGANInvocation(BaseInvocation):
             model_path=str(models_path / esrgan_model_path),
             model=rrdbnet_model,
             half=False,
+            tile=self.tile_size,
         )
 
         # prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
+        # TODO: This strips the alpha... is that okay?
         cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
 
         # We can pass an `outscale` value here, but it just resizes the image by that factor after
@@ -99,6 +109,10 @@ class ESRGANInvocation(BaseInvocation):
         # back to PIL
         pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
 
+        torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
+
         image_dto = context.services.images.create(
             image=pil_image,
             image_origin=ResourceOrigin.INTERNAL,

invokeai/app/models/exceptions.py

@@ -1,4 +0,0 @@
-class CanceledException(Exception):
-    """Execution canceled by user."""
-
-    pass

invokeai/app/models/image.py

@@ -1,71 +0,0 @@
-from enum import Enum
-
-from pydantic import BaseModel, Field
-
-from invokeai.app.util.metaenum import MetaEnum
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")
-
-
-class ResourceOrigin(str, Enum, metaclass=MetaEnum):
-    """The origin of a resource (eg image).
-
-    - INTERNAL: The resource was created by the application.
-    - EXTERNAL: The resource was not created by the application.
-    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
-    """
-
-    INTERNAL = "internal"
-    """The resource was created by the application."""
-    EXTERNAL = "external"
-    """The resource was not created by the application.
-    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
-    """
-
-
-class InvalidOriginException(ValueError):
-    """Raised when a provided value is not a valid ResourceOrigin.
-
-    Subclasses `ValueError`.
-    """
-
-    def __init__(self, message="Invalid resource origin."):
-        super().__init__(message)
-
-
-class ImageCategory(str, Enum, metaclass=MetaEnum):
-    """The category of an image.
-
-    - GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
-    - MASK: The image is a mask image.
-    - CONTROL: The image is a ControlNet control image.
-    - USER: The image is a user-provide image.
-    - OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
-    """
-
-    GENERAL = "general"
-    """GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
-    MASK = "mask"
-    """MASK: The image is a mask image."""
-    CONTROL = "control"
-    """CONTROL: The image is a ControlNet control image."""
-    USER = "user"
-    """USER: The image is a user-provide image."""
-    OTHER = "other"
-    """OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
-
-
-class InvalidImageCategoryException(ValueError):
-    """Raised when a provided value is not a valid ImageCategory.
-
-    Subclasses `ValueError`.
-    """
-
-    def __init__(self, message="Invalid image category."):
-        super().__init__(message)

invokeai/app/services/board_image_records/board_image_records_base.py

@@ -0,0 +1,47 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class BoardImageRecordStorageBase(ABC):
+    """Abstract base class for the one-to-many board-image relationship record storage."""
+
+    @abstractmethod
+    def add_image_to_board(
+        self,
+        board_id: str,
+        image_name: str,
+    ) -> None:
+        """Adds an image to a board."""
+        pass
+
+    @abstractmethod
+    def remove_image_from_board(
+        self,
+        image_name: str,
+    ) -> None:
+        """Removes an image from a board."""
+        pass
+
+    @abstractmethod
+    def get_all_board_image_names_for_board(
+        self,
+        board_id: str,
+    ) -> list[str]:
+        """Gets all board images for a board, as a list of the image names."""
+        pass
+
+    @abstractmethod
+    def get_board_for_image(
+        self,
+        image_name: str,
+    ) -> Optional[str]:
+        """Gets an image's board id, if it has one."""
+        pass
+
+    @abstractmethod
+    def get_image_count_for_board(
+        self,
+        board_id: str,
+    ) -> int:
+        """Gets the number of images for a board."""
+        pass

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -1,55 +1,12 @@
 import sqlite3
 import threading
-from abc import ABC, abstractmethod
 from typing import Optional, cast
 
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import ImageRecord, deserialize_image_record
+from invokeai.app.services.image_records.image_records_common import ImageRecord, deserialize_image_record
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
-
-class BoardImageRecordStorageBase(ABC):
-    """Abstract base class for the one-to-many board-image relationship record storage."""
-
-    @abstractmethod
-    def add_image_to_board(
-        self,
-        board_id: str,
-        image_name: str,
-    ) -> None:
-        """Adds an image to a board."""
-        pass
-
-    @abstractmethod
-    def remove_image_from_board(
-        self,
-        image_name: str,
-    ) -> None:
-        """Removes an image from a board."""
-        pass
-
-    @abstractmethod
-    def get_all_board_image_names_for_board(
-        self,
-        board_id: str,
-    ) -> list[str]:
-        """Gets all board images for a board, as a list of the image names."""
-        pass
-
-    @abstractmethod
-    def get_board_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        """Gets an image's board id, if it has one."""
-        pass
-
-    @abstractmethod
-    def get_image_count_for_board(
-        self,
-        board_id: str,
-    ) -> int:
-        """Gets the number of images for a board."""
-        pass
+from .board_image_records_base import BoardImageRecordStorageBase
 
 
 class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
@@ -57,13 +14,11 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
     _cursor: sqlite3.Cursor
     _lock: threading.Lock
 
-    def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
+    def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._conn = conn
-        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
-        self._conn.row_factory = sqlite3.Row
+        self._lock = db.lock
+        self._conn = db.conn
         self._cursor = self._conn.cursor()
-        self._lock = lock
 
         try:
             self._lock.acquire()

invokeai/app/services/board_images.py

@@ -1,112 +0,0 @@
-from abc import ABC, abstractmethod
-from logging import Logger
-from typing import Optional
-
-from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
-from invokeai.app.services.board_record_storage import BoardRecord, BoardRecordStorageBase
-from invokeai.app.services.image_record_storage import ImageRecordStorageBase
-from invokeai.app.services.models.board_record import BoardDTO
-from invokeai.app.services.urls import UrlServiceBase
-
-
-class BoardImagesServiceABC(ABC):
-    """High-level service for board-image relationship management."""
-
-    @abstractmethod
-    def add_image_to_board(
-        self,
-        board_id: str,
-        image_name: str,
-    ) -> None:
-        """Adds an image to a board."""
-        pass
-
-    @abstractmethod
-    def remove_image_from_board(
-        self,
-        image_name: str,
-    ) -> None:
-        """Removes an image from a board."""
-        pass
-
-    @abstractmethod
-    def get_all_board_image_names_for_board(
-        self,
-        board_id: str,
-    ) -> list[str]:
-        """Gets all board images for a board, as a list of the image names."""
-        pass
-
-    @abstractmethod
-    def get_board_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        """Gets an image's board id, if it has one."""
-        pass
-
-
-class BoardImagesServiceDependencies:
-    """Service dependencies for the BoardImagesService."""
-
-    board_image_records: BoardImageRecordStorageBase
-    board_records: BoardRecordStorageBase
-    image_records: ImageRecordStorageBase
-    urls: UrlServiceBase
-    logger: Logger
-
-    def __init__(
-        self,
-        board_image_record_storage: BoardImageRecordStorageBase,
-        image_record_storage: ImageRecordStorageBase,
-        board_record_storage: BoardRecordStorageBase,
-        url: UrlServiceBase,
-        logger: Logger,
-    ):
-        self.board_image_records = board_image_record_storage
-        self.image_records = image_record_storage
-        self.board_records = board_record_storage
-        self.urls = url
-        self.logger = logger
-
-
-class BoardImagesService(BoardImagesServiceABC):
-    _services: BoardImagesServiceDependencies
-
-    def __init__(self, services: BoardImagesServiceDependencies):
-        self._services = services
-
-    def add_image_to_board(
-        self,
-        board_id: str,
-        image_name: str,
-    ) -> None:
-        self._services.board_image_records.add_image_to_board(board_id, image_name)
-
-    def remove_image_from_board(
-        self,
-        image_name: str,
-    ) -> None:
-        self._services.board_image_records.remove_image_from_board(image_name)
-
-    def get_all_board_image_names_for_board(
-        self,
-        board_id: str,
-    ) -> list[str]:
-        return self._services.board_image_records.get_all_board_image_names_for_board(board_id)
-
-    def get_board_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        board_id = self._services.board_image_records.get_board_for_image(image_name)
-        return board_id
-
-
-def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
-    """Converts a board record to a board DTO."""
-    return BoardDTO(
-        **board_record.dict(exclude={"cover_image_name"}),
-        cover_image_name=cover_image_name,
-        image_count=image_count,
-    )

invokeai/app/services/board_images/board_images_base.py

@@ -0,0 +1,39 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class BoardImagesServiceABC(ABC):
+    """High-level service for board-image relationship management."""
+
+    @abstractmethod
+    def add_image_to_board(
+        self,
+        board_id: str,
+        image_name: str,
+    ) -> None:
+        """Adds an image to a board."""
+        pass
+
+    @abstractmethod
+    def remove_image_from_board(
+        self,
+        image_name: str,
+    ) -> None:
+        """Removes an image from a board."""
+        pass
+
+    @abstractmethod
+    def get_all_board_image_names_for_board(
+        self,
+        board_id: str,
+    ) -> list[str]:
+        """Gets all board images for a board, as a list of the image names."""
+        pass
+
+    @abstractmethod
+    def get_board_for_image(
+        self,
+        image_name: str,
+    ) -> Optional[str]:
+        """Gets an image's board id, if it has one."""
+        pass

invokeai/app/services/board_images/board_images_default.py

@@ -0,0 +1,38 @@
+from typing import Optional
+
+from invokeai.app.services.invoker import Invoker
+
+from .board_images_base import BoardImagesServiceABC
+
+
+class BoardImagesService(BoardImagesServiceABC):
+    __invoker: Invoker
+
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
+    def add_image_to_board(
+        self,
+        board_id: str,
+        image_name: str,
+    ) -> None:
+        self.__invoker.services.board_image_records.add_image_to_board(board_id, image_name)
+
+    def remove_image_from_board(
+        self,
+        image_name: str,
+    ) -> None:
+        self.__invoker.services.board_image_records.remove_image_from_board(image_name)
+
+    def get_all_board_image_names_for_board(
+        self,
+        board_id: str,
+    ) -> list[str]:
+        return self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
+
+    def get_board_for_image(
+        self,
+        image_name: str,
+    ) -> Optional[str]:
+        board_id = self.__invoker.services.board_image_records.get_board_for_image(image_name)
+        return board_id

invokeai/app/services/board_records/board_records_base.py

@@ -0,0 +1,55 @@
+from abc import ABC, abstractmethod
+
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+
+from .board_records_common import BoardChanges, BoardRecord
+
+
+class BoardRecordStorageBase(ABC):
+    """Low-level service responsible for interfacing with the board record store."""
+
+    @abstractmethod
+    def delete(self, board_id: str) -> None:
+        """Deletes a board record."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        board_name: str,
+    ) -> BoardRecord:
+        """Saves a board record."""
+        pass
+
+    @abstractmethod
+    def get(
+        self,
+        board_id: str,
+    ) -> BoardRecord:
+        """Gets a board record."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        board_id: str,
+        changes: BoardChanges,
+    ) -> BoardRecord:
+        """Updates a board record."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+    ) -> OffsetPaginatedResults[BoardRecord]:
+        """Gets many board records."""
+        pass
+
+    @abstractmethod
+    def get_all(
+        self,
+    ) -> list[BoardRecord]:
+        """Gets all board records."""
+        pass

invokeai/app/services/board_records/board_records_common.py

@@ -1,7 +1,7 @@
 from datetime import datetime
 from typing import Optional, Union
 
-from pydantic import Field
+from pydantic import BaseModel, Extra, Field
 
 from invokeai.app.util.misc import get_iso_timestamp
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
@@ -24,15 +24,6 @@ class BoardRecord(BaseModelExcludeNull):
     """The name of the cover image of the board."""
 
 
-class BoardDTO(BoardRecord):
-    """Deserialized board record with cover image URL and image count."""
-
-    cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
-    """The URL of the thumbnail of the most recent image in the board."""
-    image_count: int = Field(description="The number of images in the board.")
-    """The number of images in the board."""
-
-
 def deserialize_board_record(board_dict: dict) -> BoardRecord:
     """Deserializes a board record."""
 
@@ -53,3 +44,29 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord:
         updated_at=updated_at,
         deleted_at=deleted_at,
     )
+
+
+class BoardChanges(BaseModel, extra=Extra.forbid):
+    board_name: Optional[str] = Field(description="The board's new name.")
+    cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.")
+
+
+class BoardRecordNotFoundException(Exception):
+    """Raised when an board record is not found."""
+
+    def __init__(self, message="Board record not found"):
+        super().__init__(message)
+
+
+class BoardRecordSaveException(Exception):
+    """Raised when an board record cannot be saved."""
+
+    def __init__(self, message="Board record not saved"):
+        super().__init__(message)
+
+
+class BoardRecordDeleteException(Exception):
+    """Raised when an board record cannot be deleted."""
+
+    def __init__(self, message="Board record not deleted"):
+        super().__init__(message)

invokeai/app/services/board_records/board_records_sqlite.py

@@ -1,89 +1,20 @@
 import sqlite3
 import threading
-from abc import ABC, abstractmethod
-from typing import Optional, Union, cast
+from typing import Union, cast
 
-from pydantic import BaseModel, Extra, Field
-
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.board_record import BoardRecord, deserialize_board_record
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 from invokeai.app.util.misc import uuid_string
 
-
-class BoardChanges(BaseModel, extra=Extra.forbid):
-    board_name: Optional[str] = Field(description="The board's new name.")
-    cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.")
-
-
-class BoardRecordNotFoundException(Exception):
-    """Raised when an board record is not found."""
-
-    def __init__(self, message="Board record not found"):
-        super().__init__(message)
-
-
-class BoardRecordSaveException(Exception):
-    """Raised when an board record cannot be saved."""
-
-    def __init__(self, message="Board record not saved"):
-        super().__init__(message)
-
-
-class BoardRecordDeleteException(Exception):
-    """Raised when an board record cannot be deleted."""
-
-    def __init__(self, message="Board record not deleted"):
-        super().__init__(message)
-
-
-class BoardRecordStorageBase(ABC):
-    """Low-level service responsible for interfacing with the board record store."""
-
-    @abstractmethod
-    def delete(self, board_id: str) -> None:
-        """Deletes a board record."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        board_name: str,
-    ) -> BoardRecord:
-        """Saves a board record."""
-        pass
-
-    @abstractmethod
-    def get(
-        self,
-        board_id: str,
-    ) -> BoardRecord:
-        """Gets a board record."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        board_id: str,
-        changes: BoardChanges,
-    ) -> BoardRecord:
-        """Updates a board record."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-    ) -> OffsetPaginatedResults[BoardRecord]:
-        """Gets many board records."""
-        pass
-
-    @abstractmethod
-    def get_all(
-        self,
-    ) -> list[BoardRecord]:
-        """Gets all board records."""
-        pass
+from .board_records_base import BoardRecordStorageBase
+from .board_records_common import (
+    BoardChanges,
+    BoardRecord,
+    BoardRecordDeleteException,
+    BoardRecordNotFoundException,
+    BoardRecordSaveException,
+    deserialize_board_record,
+)
 
 
 class SqliteBoardRecordStorage(BoardRecordStorageBase):
@@ -91,13 +22,11 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
     _cursor: sqlite3.Cursor
     _lock: threading.Lock
 
-    def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
+    def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._conn = conn
-        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
-        self._conn.row_factory = sqlite3.Row
+        self._lock = db.lock
+        self._conn = db.conn
         self._cursor = self._conn.cursor()
-        self._lock = lock
 
         try:
             self._lock.acquire()

invokeai/app/services/boards.py

@@ -1,158 +0,0 @@
-from abc import ABC, abstractmethod
-from logging import Logger
-
-from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
-from invokeai.app.services.board_images import board_record_to_dto
-from invokeai.app.services.board_record_storage import BoardChanges, BoardRecordStorageBase
-from invokeai.app.services.image_record_storage import ImageRecordStorageBase, OffsetPaginatedResults
-from invokeai.app.services.models.board_record import BoardDTO
-from invokeai.app.services.urls import UrlServiceBase
-
-
-class BoardServiceABC(ABC):
-    """High-level service for board management."""
-
-    @abstractmethod
-    def create(
-        self,
-        board_name: str,
-    ) -> BoardDTO:
-        """Creates a board."""
-        pass
-
-    @abstractmethod
-    def get_dto(
-        self,
-        board_id: str,
-    ) -> BoardDTO:
-        """Gets a board."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        board_id: str,
-        changes: BoardChanges,
-    ) -> BoardDTO:
-        """Updates a board."""
-        pass
-
-    @abstractmethod
-    def delete(
-        self,
-        board_id: str,
-    ) -> None:
-        """Deletes a board."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-    ) -> OffsetPaginatedResults[BoardDTO]:
-        """Gets many boards."""
-        pass
-
-    @abstractmethod
-    def get_all(
-        self,
-    ) -> list[BoardDTO]:
-        """Gets all boards."""
-        pass
-
-
-class BoardServiceDependencies:
-    """Service dependencies for the BoardService."""
-
-    board_image_records: BoardImageRecordStorageBase
-    board_records: BoardRecordStorageBase
-    image_records: ImageRecordStorageBase
-    urls: UrlServiceBase
-    logger: Logger
-
-    def __init__(
-        self,
-        board_image_record_storage: BoardImageRecordStorageBase,
-        image_record_storage: ImageRecordStorageBase,
-        board_record_storage: BoardRecordStorageBase,
-        url: UrlServiceBase,
-        logger: Logger,
-    ):
-        self.board_image_records = board_image_record_storage
-        self.image_records = image_record_storage
-        self.board_records = board_record_storage
-        self.urls = url
-        self.logger = logger
-
-
-class BoardService(BoardServiceABC):
-    _services: BoardServiceDependencies
-
-    def __init__(self, services: BoardServiceDependencies):
-        self._services = services
-
-    def create(
-        self,
-        board_name: str,
-    ) -> BoardDTO:
-        board_record = self._services.board_records.save(board_name)
-        return board_record_to_dto(board_record, None, 0)
-
-    def get_dto(self, board_id: str) -> BoardDTO:
-        board_record = self._services.board_records.get(board_id)
-        cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
-        if cover_image:
-            cover_image_name = cover_image.image_name
-        else:
-            cover_image_name = None
-        image_count = self._services.board_image_records.get_image_count_for_board(board_id)
-        return board_record_to_dto(board_record, cover_image_name, image_count)
-
-    def update(
-        self,
-        board_id: str,
-        changes: BoardChanges,
-    ) -> BoardDTO:
-        board_record = self._services.board_records.update(board_id, changes)
-        cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
-        if cover_image:
-            cover_image_name = cover_image.image_name
-        else:
-            cover_image_name = None
-
-        image_count = self._services.board_image_records.get_image_count_for_board(board_id)
-        return board_record_to_dto(board_record, cover_image_name, image_count)
-
-    def delete(self, board_id: str) -> None:
-        self._services.board_records.delete(board_id)
-
-    def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
-        board_records = self._services.board_records.get_many(offset, limit)
-        board_dtos = []
-        for r in board_records.items:
-            cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
-            if cover_image:
-                cover_image_name = cover_image.image_name
-            else:
-                cover_image_name = None
-
-            image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
-
-        return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
-
-    def get_all(self) -> list[BoardDTO]:
-        board_records = self._services.board_records.get_all()
-        board_dtos = []
-        for r in board_records:
-            cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
-            if cover_image:
-                cover_image_name = cover_image.image_name
-            else:
-                cover_image_name = None
-
-            image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
-
-        return board_dtos

invokeai/app/services/boards/boards_base.py

@@ -0,0 +1,59 @@
+from abc import ABC, abstractmethod
+
+from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+
+from .boards_common import BoardDTO
+
+
+class BoardServiceABC(ABC):
+    """High-level service for board management."""
+
+    @abstractmethod
+    def create(
+        self,
+        board_name: str,
+    ) -> BoardDTO:
+        """Creates a board."""
+        pass
+
+    @abstractmethod
+    def get_dto(
+        self,
+        board_id: str,
+    ) -> BoardDTO:
+        """Gets a board."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        board_id: str,
+        changes: BoardChanges,
+    ) -> BoardDTO:
+        """Updates a board."""
+        pass
+
+    @abstractmethod
+    def delete(
+        self,
+        board_id: str,
+    ) -> None:
+        """Deletes a board."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+    ) -> OffsetPaginatedResults[BoardDTO]:
+        """Gets many boards."""
+        pass
+
+    @abstractmethod
+    def get_all(
+        self,
+    ) -> list[BoardDTO]:
+        """Gets all boards."""
+        pass

invokeai/app/services/boards/boards_common.py

@@ -0,0 +1,23 @@
+from typing import Optional
+
+from pydantic import Field
+
+from ..board_records.board_records_common import BoardRecord
+
+
+class BoardDTO(BoardRecord):
+    """Deserialized board record with cover image URL and image count."""
+
+    cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
+    """The URL of the thumbnail of the most recent image in the board."""
+    image_count: int = Field(description="The number of images in the board.")
+    """The number of images in the board."""
+
+
+def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
+    """Converts a board record to a board DTO."""
+    return BoardDTO(
+        **board_record.dict(exclude={"cover_image_name"}),
+        cover_image_name=cover_image_name,
+        image_count=image_count,
+    )

invokeai/app/services/boards/boards_default.py

@@ -0,0 +1,79 @@
+from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.boards.boards_common import BoardDTO
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+
+from .boards_base import BoardServiceABC
+from .boards_common import board_record_to_dto
+
+
+class BoardService(BoardServiceABC):
+    __invoker: Invoker
+
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
+    def create(
+        self,
+        board_name: str,
+    ) -> BoardDTO:
+        board_record = self.__invoker.services.board_records.save(board_name)
+        return board_record_to_dto(board_record, None, 0)
+
+    def get_dto(self, board_id: str) -> BoardDTO:
+        board_record = self.__invoker.services.board_records.get(board_id)
+        cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
+        if cover_image:
+            cover_image_name = cover_image.image_name
+        else:
+            cover_image_name = None
+        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
+
+    def update(
+        self,
+        board_id: str,
+        changes: BoardChanges,
+    ) -> BoardDTO:
+        board_record = self.__invoker.services.board_records.update(board_id, changes)
+        cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
+        if cover_image:
+            cover_image_name = cover_image.image_name
+        else:
+            cover_image_name = None
+
+        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
+
+    def delete(self, board_id: str) -> None:
+        self.__invoker.services.board_records.delete(board_id)
+
+    def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
+        board_records = self.__invoker.services.board_records.get_many(offset, limit)
+        board_dtos = []
+        for r in board_records.items:
+            cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
+            if cover_image:
+                cover_image_name = cover_image.image_name
+            else:
+                cover_image_name = None
+
+            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
+
+        return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
+
+    def get_all(self) -> list[BoardDTO]:
+        board_records = self.__invoker.services.board_records.get_all()
+        board_dtos = []
+        for r in board_records:
+            cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
+            if cover_image:
+                cover_image_name = cover_image.image_name
+            else:
+                cover_image_name = None
+
+            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
+
+        return board_dtos

invokeai/app/services/config/__init__.py

@@ -2,5 +2,5 @@
 Init file for InvokeAI configure package
 """
 
-from .base import PagingArgumentParser  # noqa F401
-from .invokeai_config import InvokeAIAppConfig, get_invokeai_config  # noqa F401
+from .config_base import PagingArgumentParser  # noqa F401
+from .config_default import InvokeAIAppConfig, get_invokeai_config  # noqa F401

invokeai/app/services/config/config_base.py

@@ -12,7 +12,6 @@ from __future__ import annotations
 
 import argparse
 import os
-import pydoc
 import sys
 from argparse import ArgumentParser
 from pathlib import Path
@@ -21,16 +20,7 @@ from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get
 from omegaconf import DictConfig, ListConfig, OmegaConf
 from pydantic import BaseSettings
 
-
-class PagingArgumentParser(argparse.ArgumentParser):
-    """
-    A custom ArgumentParser that uses pydoc to page its output.
-    It also supports reading defaults from an init file.
-    """
-
-    def print_help(self, file=None):
-        text = self.format_help()
-        pydoc.pager(text)
+from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str
 
 
 class InvokeAISettings(BaseSettings):
@@ -223,18 +213,3 @@ class InvokeAISettings(BaseSettings):
                 action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
                 help=field.field_info.description,
             )
-
-
-def int_or_float_or_str(value: str) -> Union[int, float, str]:
-    """
-    Workaround for argparse type checking.
-    """
-    try:
-        return int(value)
-    except Exception as e:  # noqa F841
-        pass
-    try:
-        return float(value)
-    except Exception as e:  # noqa F841
-        pass
-    return str(value)

invokeai/app/services/config/config_common.py

@@ -0,0 +1,41 @@
+# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
+
+"""
+Base class for the InvokeAI configuration system.
+It defines a type of pydantic BaseSettings object that
+is able to read and write from an omegaconf-based config file,
+with overriding of settings from environment variables and/or
+the command line.
+"""
+
+from __future__ import annotations
+
+import argparse
+import pydoc
+from typing import Union
+
+
+class PagingArgumentParser(argparse.ArgumentParser):
+    """
+    A custom ArgumentParser that uses pydoc to page its output.
+    It also supports reading defaults from an init file.
+    """
+
+    def print_help(self, file=None):
+        text = self.format_help()
+        pydoc.pager(text)
+
+
+def int_or_float_or_str(value: str) -> Union[int, float, str]:
+    """
+    Workaround for argparse type checking.
+    """
+    try:
+        return int(value)
+    except Exception as e:  # noqa F841
+        pass
+    try:
+        return float(value)
+    except Exception as e:  # noqa F841
+        pass
+    return str(value)

invokeai/app/services/config/config_default.py

@@ -177,7 +177,7 @@ from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hint
 from omegaconf import DictConfig, OmegaConf
 from pydantic import Field, parse_obj_as
 
-from .base import InvokeAISettings
+from .config_base import InvokeAISettings
 
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
@@ -241,8 +241,8 @@ class InvokeAIAppConfig(InvokeAISettings):
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
 
     # CACHE
-    ram                 : Union[float, Literal["auto"]] = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category="Model Cache", )
-    vram                : Union[float, Literal["auto"]] = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number or 'auto')", category="Model Cache", )
+    ram                 : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", category="Model Cache", )
+    vram                : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", category="Model Cache", )
     lazy_offload        : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", )
 
     # DEVICE
@@ -255,6 +255,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
     force_tiled_decode  : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
     force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
+    png_compress_level  : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", category="Generation", )
 
     # QUEUE
     max_queue_size      : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", )

invokeai/app/services/events/events_base.py

@@ -2,10 +2,16 @@
 
 from typing import Any, Optional
 
-from invokeai.app.models.image import ProgressImage
-from invokeai.app.services.model_manager_service import BaseModelType, ModelInfo, ModelType, SubModelType
-from invokeai.app.services.session_queue.session_queue_common import EnqueueBatchResult, SessionQueueItem
+from invokeai.app.invocations.model import ModelInfo
+from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
+from invokeai.app.services.session_queue.session_queue_common import (
+    BatchStatus,
+    EnqueueBatchResult,
+    SessionQueueItem,
+    SessionQueueStatus,
+)
 from invokeai.app.util.misc import get_timestamp
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
 
 
 class EventServiceBase:
@@ -262,21 +268,31 @@ class EventServiceBase:
             ),
         )
 
-    def emit_queue_item_status_changed(self, session_queue_item: SessionQueueItem) -> None:
+    def emit_queue_item_status_changed(
+        self,
+        session_queue_item: SessionQueueItem,
+        batch_status: BatchStatus,
+        queue_status: SessionQueueStatus,
+    ) -> None:
         """Emitted when a queue item's status changes"""
         self.__emit_queue_event(
             event_name="queue_item_status_changed",
             payload=dict(
-                queue_id=session_queue_item.queue_id,
-                queue_item_id=session_queue_item.item_id,
-                status=session_queue_item.status,
-                batch_id=session_queue_item.batch_id,
-                session_id=session_queue_item.session_id,
-                error=session_queue_item.error,
-                created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
-                updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
-                started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
-                completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
+                queue_id=queue_status.queue_id,
+                queue_item=dict(
+                    queue_id=session_queue_item.queue_id,
+                    item_id=session_queue_item.item_id,
+                    status=session_queue_item.status,
+                    batch_id=session_queue_item.batch_id,
+                    session_id=session_queue_item.session_id,
+                    error=session_queue_item.error,
+                    created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
+                    updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
+                    started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
+                    completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
+                ),
+                batch_status=batch_status.dict(),
+                queue_status=queue_status.dict(),
             ),
         )
 

invokeai/app/services/image_files/image_files_base.py

@@ -0,0 +1,42 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from PIL.Image import Image as PILImageType
+
+
+class ImageFileStorageBase(ABC):
+    """Low-level service responsible for storing and retrieving image files."""
+
+    @abstractmethod
+    def get(self, image_name: str) -> PILImageType:
+        """Retrieves an image as PIL Image."""
+        pass
+
+    @abstractmethod
+    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
+        """Gets the internal path to an image or thumbnail."""
+        pass
+
+    # TODO: We need to validate paths before starlette makes the FileResponse, else we get a
+    # 500 internal server error. I don't like having this method on the service.
+    @abstractmethod
+    def validate_path(self, path: str) -> bool:
+        """Validates the path given for an image or thumbnail."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        image: PILImageType,
+        image_name: str,
+        metadata: Optional[dict] = None,
+        workflow: Optional[str] = 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."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_name: str) -> None:
+        """Deletes an image and its thumbnail (if one exists)."""
+        pass

invokeai/app/services/image_files/image_files_common.py

@@ -0,0 +1,20 @@
+# TODO: Should these excpetions subclass existing python exceptions?
+class ImageFileNotFoundException(Exception):
+    """Raised when an image file is not found in storage."""
+
+    def __init__(self, message="Image file not found"):
+        super().__init__(message)
+
+
+class ImageFileSaveException(Exception):
+    """Raised when an image cannot be saved."""
+
+    def __init__(self, message="Image file not saved"):
+        super().__init__(message)
+
+
+class ImageFileDeleteException(Exception):
+    """Raised when an image cannot be deleted."""
+
+    def __init__(self, message="Image file not deleted"):
+        super().__init__(message)

invokeai/app/services/image_files/image_files_disk.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
 import json
-from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
 from typing import Dict, Optional, Union
@@ -9,67 +8,11 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
+from invokeai.app.services.invoker import Invoker
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
-
-# TODO: Should these excpetions subclass existing python exceptions?
-class ImageFileNotFoundException(Exception):
-    """Raised when an image file is not found in storage."""
-
-    def __init__(self, message="Image file not found"):
-        super().__init__(message)
-
-
-class ImageFileSaveException(Exception):
-    """Raised when an image cannot be saved."""
-
-    def __init__(self, message="Image file not saved"):
-        super().__init__(message)
-
-
-class ImageFileDeleteException(Exception):
-    """Raised when an image cannot be deleted."""
-
-    def __init__(self, message="Image file not deleted"):
-        super().__init__(message)
-
-
-class ImageFileStorageBase(ABC):
-    """Low-level service responsible for storing and retrieving image files."""
-
-    @abstractmethod
-    def get(self, image_name: str) -> PILImageType:
-        """Retrieves an image as PIL Image."""
-        pass
-
-    @abstractmethod
-    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
-        """Gets the internal path to an image or thumbnail."""
-        pass
-
-    # TODO: We need to validate paths before starlette makes the FileResponse, else we get a
-    # 500 internal server error. I don't like having this method on the service.
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates the path given for an image or thumbnail."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image: PILImageType,
-        image_name: str,
-        metadata: Optional[dict] = None,
-        workflow: Optional[str] = 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."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_name: str) -> None:
-        """Deletes an image and its thumbnail (if one exists)."""
-        pass
+from .image_files_base import ImageFileStorageBase
+from .image_files_common import ImageFileDeleteException, ImageFileNotFoundException, ImageFileSaveException
 
 
 class DiskImageFileStorage(ImageFileStorageBase):
@@ -79,6 +22,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
     __cache_ids: Queue  # TODO: this is an incredibly naive cache
     __cache: Dict[Path, PILImageType]
     __max_cache_size: int
+    __invoker: Invoker
 
     def __init__(self, output_folder: Union[str, Path]):
         self.__cache = dict()
@@ -87,10 +31,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
 
         self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__thumbnails_folder = self.__output_folder / "thumbnails"
-
         # Validate required output folders at launch
         self.__validate_storage_folders()
 
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
     def get(self, image_name: str) -> PILImageType:
         try:
             image_path = self.get_path(image_name)
@@ -134,7 +80,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
                 if original_workflow is not None:
                     pnginfo.add_text("invokeai_workflow", original_workflow)
 
-            image.save(image_path, "PNG", pnginfo=pnginfo)
+            image.save(
+                image_path,
+                "PNG",
+                pnginfo=pnginfo,
+                compress_level=self.__invoker.services.configuration.png_compress_level,
+            )
 
             thumbnail_name = get_thumbnail_name(image_name)
             thumbnail_path = self.get_path(thumbnail_name, thumbnail=True)

invokeai/app/services/image_records/image_records_base.py

@@ -0,0 +1,84 @@
+from abc import ABC, abstractmethod
+from datetime import datetime
+from typing import Optional
+
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+
+from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
+
+
+class ImageRecordStorageBase(ABC):
+    """Low-level service responsible for interfacing with the image record store."""
+
+    # TODO: Implement an `update()` method
+
+    @abstractmethod
+    def get(self, image_name: str) -> ImageRecord:
+        """Gets an image record."""
+        pass
+
+    @abstractmethod
+    def get_metadata(self, image_name: str) -> Optional[dict]:
+        """Gets an image's metadata'."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        image_name: str,
+        changes: ImageRecordChanges,
+    ) -> None:
+        """Updates an image record."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: Optional[int] = None,
+        limit: Optional[int] = None,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+    ) -> OffsetPaginatedResults[ImageRecord]:
+        """Gets a page of image records."""
+        pass
+
+    # TODO: The database has a nullable `deleted_at` column, currently unused.
+    # Should we implement soft deletes? Would need coordination with ImageFileStorage.
+    @abstractmethod
+    def delete(self, image_name: str) -> None:
+        """Deletes an image record."""
+        pass
+
+    @abstractmethod
+    def delete_many(self, image_names: list[str]) -> None:
+        """Deletes many image records."""
+        pass
+
+    @abstractmethod
+    def delete_intermediates(self) -> list[str]:
+        """Deletes all intermediate image records, returning a list of deleted image names."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        image_name: str,
+        image_origin: ResourceOrigin,
+        image_category: ImageCategory,
+        width: int,
+        height: int,
+        session_id: Optional[str],
+        node_id: Optional[str],
+        metadata: Optional[dict],
+        is_intermediate: bool = False,
+        starred: bool = False,
+    ) -> datetime:
+        """Saves an image record."""
+        pass
+
+    @abstractmethod
+    def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
+        """Gets the most recent image for a board."""
+        pass

invokeai/app/services/image_records/image_records_common.py

@@ -1,13 +1,117 @@
+# TODO: Should these excpetions subclass existing python exceptions?
 import datetime
+from enum import Enum
 from typing import Optional, Union
 
 from pydantic import Extra, Field, StrictBool, StrictStr
 
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
+from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import get_iso_timestamp
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
 
+class ResourceOrigin(str, Enum, metaclass=MetaEnum):
+    """The origin of a resource (eg image).
+
+    - INTERNAL: The resource was created by the application.
+    - EXTERNAL: The resource was not created by the application.
+    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
+    """
+
+    INTERNAL = "internal"
+    """The resource was created by the application."""
+    EXTERNAL = "external"
+    """The resource was not created by the application.
+    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
+    """
+
+
+class InvalidOriginException(ValueError):
+    """Raised when a provided value is not a valid ResourceOrigin.
+
+    Subclasses `ValueError`.
+    """
+
+    def __init__(self, message="Invalid resource origin."):
+        super().__init__(message)
+
+
+class ImageCategory(str, Enum, metaclass=MetaEnum):
+    """The category of an image.
+
+    - GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
+    - MASK: The image is a mask image.
+    - CONTROL: The image is a ControlNet control image.
+    - USER: The image is a user-provide image.
+    - OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
+    """
+
+    GENERAL = "general"
+    """GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
+    MASK = "mask"
+    """MASK: The image is a mask image."""
+    CONTROL = "control"
+    """CONTROL: The image is a ControlNet control image."""
+    USER = "user"
+    """USER: The image is a user-provide image."""
+    OTHER = "other"
+    """OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
+
+
+class InvalidImageCategoryException(ValueError):
+    """Raised when a provided value is not a valid ImageCategory.
+
+    Subclasses `ValueError`.
+    """
+
+    def __init__(self, message="Invalid image category."):
+        super().__init__(message)
+
+
+class ImageRecordNotFoundException(Exception):
+    """Raised when an image record is not found."""
+
+    def __init__(self, message="Image record not found"):
+        super().__init__(message)
+
+
+class ImageRecordSaveException(Exception):
+    """Raised when an image record cannot be saved."""
+
+    def __init__(self, message="Image record not saved"):
+        super().__init__(message)
+
+
+class ImageRecordDeleteException(Exception):
+    """Raised when an image record cannot be deleted."""
+
+    def __init__(self, message="Image record not deleted"):
+        super().__init__(message)
+
+
+IMAGE_DTO_COLS = ", ".join(
+    list(
+        map(
+            lambda c: "images." + c,
+            [
+                "image_name",
+                "image_origin",
+                "image_category",
+                "width",
+                "height",
+                "session_id",
+                "node_id",
+                "is_intermediate",
+                "created_at",
+                "updated_at",
+                "deleted_at",
+                "starred",
+            ],
+        )
+    )
+)
+
+
 class ImageRecord(BaseModelExcludeNull):
     """Deserialized image record without metadata."""
 
@@ -66,41 +170,6 @@ class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid):
     """The image's new `starred` state."""
 
 
-class ImageUrlsDTO(BaseModelExcludeNull):
-    """The URLs for an image and its thumbnail."""
-
-    image_name: str = Field(description="The unique name of the image.")
-    """The unique name of the image."""
-    image_url: str = Field(description="The URL of the image.")
-    """The URL of the image."""
-    thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
-    """The URL of the image's thumbnail."""
-
-
-class ImageDTO(ImageRecord, ImageUrlsDTO):
-    """Deserialized image record, enriched for the frontend."""
-
-    board_id: Optional[str] = Field(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
-
-
-def image_record_to_dto(
-    image_record: ImageRecord,
-    image_url: str,
-    thumbnail_url: str,
-    board_id: Optional[str],
-) -> ImageDTO:
-    """Converts an image record to an image DTO."""
-    return ImageDTO(
-        **image_record.dict(),
-        image_url=image_url,
-        thumbnail_url=thumbnail_url,
-        board_id=board_id,
-    )
-
-
 def deserialize_image_record(image_dict: dict) -> ImageRecord:
     """Deserializes an image record."""
 

invokeai/app/services/image_records/image_records_sqlite.py

@@ -1,164 +1,36 @@
 import json
 import sqlite3
 import threading
-from abc import ABC, abstractmethod
 from datetime import datetime
-from typing import Generic, Optional, TypeVar, cast
+from typing import Optional, cast
 
-from pydantic import BaseModel, Field
-from pydantic.generics import GenericModel
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record
-
-T = TypeVar("T", bound=BaseModel)
-
-
-class OffsetPaginatedResults(GenericModel, Generic[T]):
-    """Offset-paginated results"""
-
-    # fmt: off
-    items: list[T] = Field(description="Items")
-    offset: int = Field(description="Offset from which to retrieve items")
-    limit: int = Field(description="Limit of items to get")
-    total: int = Field(description="Total number of items in result")
-    # fmt: on
-
-
-# TODO: Should these excpetions subclass existing python exceptions?
-class ImageRecordNotFoundException(Exception):
-    """Raised when an image record is not found."""
-
-    def __init__(self, message="Image record not found"):
-        super().__init__(message)
-
-
-class ImageRecordSaveException(Exception):
-    """Raised when an image record cannot be saved."""
-
-    def __init__(self, message="Image record not saved"):
-        super().__init__(message)
-
-
-class ImageRecordDeleteException(Exception):
-    """Raised when an image record cannot be deleted."""
-
-    def __init__(self, message="Image record not deleted"):
-        super().__init__(message)
-
-
-IMAGE_DTO_COLS = ", ".join(
-    list(
-        map(
-            lambda c: "images." + c,
-            [
-                "image_name",
-                "image_origin",
-                "image_category",
-                "width",
-                "height",
-                "session_id",
-                "node_id",
-                "is_intermediate",
-                "created_at",
-                "updated_at",
-                "deleted_at",
-                "starred",
-            ],
-        )
-    )
+from .image_records_base import ImageRecordStorageBase
+from .image_records_common import (
+    IMAGE_DTO_COLS,
+    ImageCategory,
+    ImageRecord,
+    ImageRecordChanges,
+    ImageRecordDeleteException,
+    ImageRecordNotFoundException,
+    ImageRecordSaveException,
+    ResourceOrigin,
+    deserialize_image_record,
 )
 
 
-class ImageRecordStorageBase(ABC):
-    """Low-level service responsible for interfacing with the image record store."""
-
-    # TODO: Implement an `update()` method
-
-    @abstractmethod
-    def get(self, image_name: str) -> ImageRecord:
-        """Gets an image record."""
-        pass
-
-    @abstractmethod
-    def get_metadata(self, image_name: str) -> Optional[dict]:
-        """Gets an image's metadata'."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> None:
-        """Updates an image record."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: Optional[int] = None,
-        limit: Optional[int] = None,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageRecord]:
-        """Gets a page of image records."""
-        pass
-
-    # TODO: The database has a nullable `deleted_at` column, currently unused.
-    # Should we implement soft deletes? Would need coordination with ImageFileStorage.
-    @abstractmethod
-    def delete(self, image_name: str) -> None:
-        """Deletes an image record."""
-        pass
-
-    @abstractmethod
-    def delete_many(self, image_names: list[str]) -> None:
-        """Deletes many image records."""
-        pass
-
-    @abstractmethod
-    def delete_intermediates(self) -> list[str]:
-        """Deletes all intermediate image records, returning a list of deleted image names."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image_name: str,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        width: int,
-        height: int,
-        session_id: Optional[str],
-        node_id: Optional[str],
-        metadata: Optional[dict],
-        is_intermediate: bool = False,
-        starred: bool = False,
-    ) -> datetime:
-        """Saves an image record."""
-        pass
-
-    @abstractmethod
-    def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
-        """Gets the most recent image for a board."""
-        pass
-
-
 class SqliteImageRecordStorage(ImageRecordStorageBase):
     _conn: sqlite3.Connection
     _cursor: sqlite3.Cursor
     _lock: threading.Lock
 
-    def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
+    def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._conn = conn
-        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
-        self._conn.row_factory = sqlite3.Row
+        self._lock = db.lock
+        self._conn = db.conn
         self._cursor = self._conn.cursor()
-        self._lock = lock
 
         try:
             self._lock.acquire()
@@ -584,7 +456,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 FROM images
                 JOIN board_images ON images.image_name = board_images.image_name
                 WHERE board_images.board_id = ?
-                ORDER BY images.created_at DESC
+                ORDER BY images.starred DESC, images.created_at DESC
                 LIMIT 1;
                 """,
                 (board_id,),

invokeai/app/services/images.py

@@ -1,449 +0,0 @@
-from abc import ABC, abstractmethod
-from logging import Logger
-from typing import TYPE_CHECKING, Callable, Optional
-
-from PIL.Image import Image as PILImageType
-
-from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.models.image import (
-    ImageCategory,
-    InvalidImageCategoryException,
-    InvalidOriginException,
-    ResourceOrigin,
-)
-from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
-from invokeai.app.services.image_file_storage import (
-    ImageFileDeleteException,
-    ImageFileNotFoundException,
-    ImageFileSaveException,
-    ImageFileStorageBase,
-)
-from invokeai.app.services.image_record_storage import (
-    ImageRecordDeleteException,
-    ImageRecordNotFoundException,
-    ImageRecordSaveException,
-    ImageRecordStorageBase,
-    OffsetPaginatedResults,
-)
-from invokeai.app.services.item_storage import ItemStorageABC
-from invokeai.app.services.models.image_record import ImageDTO, ImageRecord, ImageRecordChanges, image_record_to_dto
-from invokeai.app.services.resource_name import NameServiceBase
-from invokeai.app.services.urls import UrlServiceBase
-from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
-
-if TYPE_CHECKING:
-    from invokeai.app.services.graph import GraphExecutionState
-
-
-class ImageServiceABC(ABC):
-    """High-level service for image management."""
-
-    _on_changed_callbacks: list[Callable[[ImageDTO], None]]
-    _on_deleted_callbacks: list[Callable[[str], None]]
-
-    def __init__(self) -> None:
-        self._on_changed_callbacks = list()
-        self._on_deleted_callbacks = list()
-
-    def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
-        """Register a callback for when an image is changed"""
-        self._on_changed_callbacks.append(on_changed)
-
-    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
-        """Register a callback for when an image is deleted"""
-        self._on_deleted_callbacks.append(on_deleted)
-
-    def _on_changed(self, item: ImageDTO) -> None:
-        for callback in self._on_changed_callbacks:
-            callback(item)
-
-    def _on_deleted(self, item_id: str) -> None:
-        for callback in self._on_deleted_callbacks:
-            callback(item_id)
-
-    @abstractmethod
-    def create(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
-        is_intermediate: bool = False,
-        metadata: Optional[dict] = None,
-        workflow: Optional[str] = None,
-    ) -> ImageDTO:
-        """Creates an image, storing the file and its metadata."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> ImageDTO:
-        """Updates an image."""
-        pass
-
-    @abstractmethod
-    def get_pil_image(self, image_name: str) -> PILImageType:
-        """Gets an image as a PIL image."""
-        pass
-
-    @abstractmethod
-    def get_record(self, image_name: str) -> ImageRecord:
-        """Gets an image record."""
-        pass
-
-    @abstractmethod
-    def get_dto(self, image_name: str) -> ImageDTO:
-        """Gets an image DTO."""
-        pass
-
-    @abstractmethod
-    def get_metadata(self, image_name: str) -> ImageMetadata:
-        """Gets an image's metadata."""
-        pass
-
-    @abstractmethod
-    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
-        """Gets an image's path."""
-        pass
-
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates an image's path."""
-        pass
-
-    @abstractmethod
-    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
-        """Gets an image's or thumbnail's URL."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        """Gets a paginated list of image DTOs."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_name: str):
-        """Deletes an image."""
-        pass
-
-    @abstractmethod
-    def delete_intermediates(self) -> int:
-        """Deletes all intermediate images."""
-        pass
-
-    @abstractmethod
-    def delete_images_on_board(self, board_id: str):
-        """Deletes all images on a board."""
-        pass
-
-
-class ImageServiceDependencies:
-    """Service dependencies for the ImageService."""
-
-    image_records: ImageRecordStorageBase
-    image_files: ImageFileStorageBase
-    board_image_records: BoardImageRecordStorageBase
-    urls: UrlServiceBase
-    logger: Logger
-    names: NameServiceBase
-    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
-
-    def __init__(
-        self,
-        image_record_storage: ImageRecordStorageBase,
-        image_file_storage: ImageFileStorageBase,
-        board_image_record_storage: BoardImageRecordStorageBase,
-        url: UrlServiceBase,
-        logger: Logger,
-        names: NameServiceBase,
-        graph_execution_manager: ItemStorageABC["GraphExecutionState"],
-    ):
-        self.image_records = image_record_storage
-        self.image_files = image_file_storage
-        self.board_image_records = board_image_record_storage
-        self.urls = url
-        self.logger = logger
-        self.names = names
-        self.graph_execution_manager = graph_execution_manager
-
-
-class ImageService(ImageServiceABC):
-    _services: ImageServiceDependencies
-
-    def __init__(self, services: ImageServiceDependencies):
-        super().__init__()
-        self._services = services
-
-    def create(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
-        is_intermediate: bool = False,
-        metadata: Optional[dict] = None,
-        workflow: Optional[str] = None,
-    ) -> ImageDTO:
-        if image_origin not in ResourceOrigin:
-            raise InvalidOriginException
-
-        if image_category not in ImageCategory:
-            raise InvalidImageCategoryException
-
-        image_name = self._services.names.create_image_name()
-
-        # TODO: Do we want to store the graph in the image at all? I don't think so...
-        # graph = None
-        # if session_id is not None:
-        #     session_raw = self._services.graph_execution_manager.get_raw(session_id)
-        #     if session_raw is not None:
-        #         try:
-        #             graph = get_metadata_graph_from_raw_session(session_raw)
-        #         except Exception as e:
-        #             self._services.logger.warn(f"Failed to parse session graph: {e}")
-        #             graph = None
-
-        (width, height) = image.size
-
-        try:
-            # TODO: Consider using a transaction here to ensure consistency between storage and database
-            self._services.image_records.save(
-                # Non-nullable fields
-                image_name=image_name,
-                image_origin=image_origin,
-                image_category=image_category,
-                width=width,
-                height=height,
-                # Meta fields
-                is_intermediate=is_intermediate,
-                # Nullable fields
-                node_id=node_id,
-                metadata=metadata,
-                session_id=session_id,
-            )
-            if board_id is not None:
-                self._services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
-            self._services.image_files.save(image_name=image_name, image=image, metadata=metadata, workflow=workflow)
-            image_dto = self.get_dto(image_name)
-
-            self._on_changed(image_dto)
-            return image_dto
-        except ImageRecordSaveException:
-            self._services.logger.error("Failed to save image record")
-            raise
-        except ImageFileSaveException:
-            self._services.logger.error("Failed to save image file")
-            raise
-        except Exception as e:
-            self._services.logger.error(f"Problem saving image record and file: {str(e)}")
-            raise e
-
-    def update(
-        self,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> ImageDTO:
-        try:
-            self._services.image_records.update(image_name, changes)
-            image_dto = self.get_dto(image_name)
-            self._on_changed(image_dto)
-            return image_dto
-        except ImageRecordSaveException:
-            self._services.logger.error("Failed to update image record")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem updating image record")
-            raise e
-
-    def get_pil_image(self, image_name: str) -> PILImageType:
-        try:
-            return self._services.image_files.get(image_name)
-        except ImageFileNotFoundException:
-            self._services.logger.error("Failed to get image file")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem getting image file")
-            raise e
-
-    def get_record(self, image_name: str) -> ImageRecord:
-        try:
-            return self._services.image_records.get(image_name)
-        except ImageRecordNotFoundException:
-            self._services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem getting image record")
-            raise e
-
-    def get_dto(self, image_name: str) -> ImageDTO:
-        try:
-            image_record = self._services.image_records.get(image_name)
-
-            image_dto = image_record_to_dto(
-                image_record,
-                self._services.urls.get_image_url(image_name),
-                self._services.urls.get_image_url(image_name, True),
-                self._services.board_image_records.get_board_for_image(image_name),
-            )
-
-            return image_dto
-        except ImageRecordNotFoundException:
-            self._services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem getting image DTO")
-            raise e
-
-    def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
-        try:
-            image_record = self._services.image_records.get(image_name)
-            metadata = self._services.image_records.get_metadata(image_name)
-
-            if not image_record.session_id:
-                return ImageMetadata(metadata=metadata)
-
-            session_raw = self._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._services.logger.warn(f"Failed to parse session graph: {e}")
-                    graph = None
-
-            return ImageMetadata(graph=graph, metadata=metadata)
-        except ImageRecordNotFoundException:
-            self._services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem getting image DTO")
-            raise e
-
-    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
-        try:
-            return self._services.image_files.get_path(image_name, thumbnail)
-        except Exception as e:
-            self._services.logger.error("Problem getting image path")
-            raise e
-
-    def validate_path(self, path: str) -> bool:
-        try:
-            return self._services.image_files.validate_path(path)
-        except Exception as e:
-            self._services.logger.error("Problem validating image path")
-            raise e
-
-    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
-        try:
-            return self._services.urls.get_image_url(image_name, thumbnail)
-        except Exception as e:
-            self._services.logger.error("Problem getting image path")
-            raise e
-
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        try:
-            results = self._services.image_records.get_many(
-                offset,
-                limit,
-                image_origin,
-                categories,
-                is_intermediate,
-                board_id,
-            )
-
-            image_dtos = list(
-                map(
-                    lambda r: image_record_to_dto(
-                        r,
-                        self._services.urls.get_image_url(r.image_name),
-                        self._services.urls.get_image_url(r.image_name, True),
-                        self._services.board_image_records.get_board_for_image(r.image_name),
-                    ),
-                    results.items,
-                )
-            )
-
-            return OffsetPaginatedResults[ImageDTO](
-                items=image_dtos,
-                offset=results.offset,
-                limit=results.limit,
-                total=results.total,
-            )
-        except Exception as e:
-            self._services.logger.error("Problem getting paginated image DTOs")
-            raise e
-
-    def delete(self, image_name: str):
-        try:
-            self._services.image_files.delete(image_name)
-            self._services.image_records.delete(image_name)
-            self._on_deleted(image_name)
-        except ImageRecordDeleteException:
-            self._services.logger.error("Failed to delete image record")
-            raise
-        except ImageFileDeleteException:
-            self._services.logger.error("Failed to delete image file")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem deleting image record and file")
-            raise e
-
-    def delete_images_on_board(self, board_id: str):
-        try:
-            image_names = self._services.board_image_records.get_all_board_image_names_for_board(board_id)
-            for image_name in image_names:
-                self._services.image_files.delete(image_name)
-            self._services.image_records.delete_many(image_names)
-            for image_name in image_names:
-                self._on_deleted(image_name)
-        except ImageRecordDeleteException:
-            self._services.logger.error("Failed to delete image records")
-            raise
-        except ImageFileDeleteException:
-            self._services.logger.error("Failed to delete image files")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem deleting image records and files")
-            raise e
-
-    def delete_intermediates(self) -> int:
-        try:
-            image_names = self._services.image_records.delete_intermediates()
-            count = len(image_names)
-            for image_name in image_names:
-                self._services.image_files.delete(image_name)
-                self._on_deleted(image_name)
-            return count
-        except ImageRecordDeleteException:
-            self._services.logger.error("Failed to delete image records")
-            raise
-        except ImageFileDeleteException:
-            self._services.logger.error("Failed to delete image files")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem deleting image records and files")
-            raise e

invokeai/app/services/images/images_base.py

@@ -0,0 +1,129 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Optional
+
+from PIL.Image import Image as PILImageType
+
+from invokeai.app.invocations.metadata import ImageMetadata
+from invokeai.app.services.image_records.image_records_common import (
+    ImageCategory,
+    ImageRecord,
+    ImageRecordChanges,
+    ResourceOrigin,
+)
+from invokeai.app.services.images.images_common import ImageDTO
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+
+
+class ImageServiceABC(ABC):
+    """High-level service for image management."""
+
+    _on_changed_callbacks: list[Callable[[ImageDTO], None]]
+    _on_deleted_callbacks: list[Callable[[str], None]]
+
+    def __init__(self) -> None:
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
+
+    def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
+        """Register a callback for when an image is changed"""
+        self._on_changed_callbacks.append(on_changed)
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an image is deleted"""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_changed(self, item: ImageDTO) -> None:
+        for callback in self._on_changed_callbacks:
+            callback(item)
+
+    def _on_deleted(self, item_id: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(item_id)
+
+    @abstractmethod
+    def create(
+        self,
+        image: PILImageType,
+        image_origin: ResourceOrigin,
+        image_category: ImageCategory,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        board_id: Optional[str] = None,
+        is_intermediate: bool = False,
+        metadata: Optional[dict] = None,
+        workflow: Optional[str] = None,
+    ) -> ImageDTO:
+        """Creates an image, storing the file and its metadata."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        image_name: str,
+        changes: ImageRecordChanges,
+    ) -> ImageDTO:
+        """Updates an image."""
+        pass
+
+    @abstractmethod
+    def get_pil_image(self, image_name: str) -> PILImageType:
+        """Gets an image as a PIL image."""
+        pass
+
+    @abstractmethod
+    def get_record(self, image_name: str) -> ImageRecord:
+        """Gets an image record."""
+        pass
+
+    @abstractmethod
+    def get_dto(self, image_name: str) -> ImageDTO:
+        """Gets an image DTO."""
+        pass
+
+    @abstractmethod
+    def get_metadata(self, image_name: str) -> ImageMetadata:
+        """Gets an image's metadata."""
+        pass
+
+    @abstractmethod
+    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
+        """Gets an image's path."""
+        pass
+
+    @abstractmethod
+    def validate_path(self, path: str) -> bool:
+        """Validates an image's path."""
+        pass
+
+    @abstractmethod
+    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
+        """Gets an image's or thumbnail's URL."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+    ) -> OffsetPaginatedResults[ImageDTO]:
+        """Gets a paginated list of image DTOs."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_name: str):
+        """Deletes an image."""
+        pass
+
+    @abstractmethod
+    def delete_intermediates(self) -> int:
+        """Deletes all intermediate images."""
+        pass
+
+    @abstractmethod
+    def delete_images_on_board(self, board_id: str):
+        """Deletes all images on a board."""
+        pass

invokeai/app/services/images/images_common.py

@@ -0,0 +1,41 @@
+from typing import Optional
+
+from pydantic import Field
+
+from invokeai.app.services.image_records.image_records_common import ImageRecord
+from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
+
+
+class ImageUrlsDTO(BaseModelExcludeNull):
+    """The URLs for an image and its thumbnail."""
+
+    image_name: str = Field(description="The unique name of the image.")
+    """The unique name of the image."""
+    image_url: str = Field(description="The URL of the image.")
+    """The URL of the image."""
+    thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
+    """The URL of the image's thumbnail."""
+
+
+class ImageDTO(ImageRecord, ImageUrlsDTO):
+    """Deserialized image record, enriched for the frontend."""
+
+    board_id: Optional[str] = Field(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
+
+
+def image_record_to_dto(
+    image_record: ImageRecord,
+    image_url: str,
+    thumbnail_url: str,
+    board_id: Optional[str],
+) -> ImageDTO:
+    """Converts an image record to an image DTO."""
+    return ImageDTO(
+        **image_record.dict(),
+        image_url=image_url,
+        thumbnail_url=thumbnail_url,
+        board_id=board_id,
+    )

invokeai/app/services/images/images_default.py

@@ -0,0 +1,286 @@
+from typing import Optional
+
+from PIL.Image import Image as PILImageType
+
+from invokeai.app.invocations.metadata import ImageMetadata
+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,
+    ImageFileNotFoundException,
+    ImageFileSaveException,
+)
+from ..image_records.image_records_common import (
+    ImageCategory,
+    ImageRecord,
+    ImageRecordChanges,
+    ImageRecordDeleteException,
+    ImageRecordNotFoundException,
+    ImageRecordSaveException,
+    InvalidImageCategoryException,
+    InvalidOriginException,
+    ResourceOrigin,
+)
+from .images_base import ImageServiceABC
+from .images_common import ImageDTO, image_record_to_dto
+
+
+class ImageService(ImageServiceABC):
+    __invoker: Invoker
+
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
+    def create(
+        self,
+        image: PILImageType,
+        image_origin: ResourceOrigin,
+        image_category: ImageCategory,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        board_id: Optional[str] = None,
+        is_intermediate: bool = False,
+        metadata: Optional[dict] = None,
+        workflow: Optional[str] = None,
+    ) -> ImageDTO:
+        if image_origin not in ResourceOrigin:
+            raise InvalidOriginException
+
+        if image_category not in ImageCategory:
+            raise InvalidImageCategoryException
+
+        image_name = self.__invoker.services.names.create_image_name()
+
+        (width, height) = image.size
+
+        try:
+            # TODO: Consider using a transaction here to ensure consistency between storage and database
+            self.__invoker.services.image_records.save(
+                # Non-nullable fields
+                image_name=image_name,
+                image_origin=image_origin,
+                image_category=image_category,
+                width=width,
+                height=height,
+                # Meta fields
+                is_intermediate=is_intermediate,
+                # Nullable fields
+                node_id=node_id,
+                metadata=metadata,
+                session_id=session_id,
+            )
+            if board_id is not None:
+                self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
+            self.__invoker.services.image_files.save(
+                image_name=image_name, image=image, metadata=metadata, workflow=workflow
+            )
+            image_dto = self.get_dto(image_name)
+
+            self._on_changed(image_dto)
+            return image_dto
+        except ImageRecordSaveException:
+            self.__invoker.services.logger.error("Failed to save image record")
+            raise
+        except ImageFileSaveException:
+            self.__invoker.services.logger.error("Failed to save image file")
+            raise
+        except Exception as e:
+            self.__invoker.services.logger.error(f"Problem saving image record and file: {str(e)}")
+            raise e
+
+    def update(
+        self,
+        image_name: str,
+        changes: ImageRecordChanges,
+    ) -> ImageDTO:
+        try:
+            self.__invoker.services.image_records.update(image_name, changes)
+            image_dto = self.get_dto(image_name)
+            self._on_changed(image_dto)
+            return image_dto
+        except ImageRecordSaveException:
+            self.__invoker.services.logger.error("Failed to update image record")
+            raise
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem updating image record")
+            raise e
+
+    def get_pil_image(self, image_name: str) -> PILImageType:
+        try:
+            return self.__invoker.services.image_files.get(image_name)
+        except ImageFileNotFoundException:
+            self.__invoker.services.logger.error("Failed to get image file")
+            raise
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem getting image file")
+            raise e
+
+    def get_record(self, image_name: str) -> ImageRecord:
+        try:
+            return self.__invoker.services.image_records.get(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 record")
+            raise e
+
+    def get_dto(self, image_name: str) -> ImageDTO:
+        try:
+            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),
+            )
+
+            return image_dto
+        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
+
+    def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
+        try:
+            image_record = self.__invoker.services.image_records.get(image_name)
+            metadata = self.__invoker.services.image_records.get_metadata(image_name)
+
+            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)
+        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
+
+    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
+        try:
+            return self.__invoker.services.image_files.get_path(image_name, thumbnail)
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem getting image path")
+            raise e
+
+    def validate_path(self, path: str) -> bool:
+        try:
+            return self.__invoker.services.image_files.validate_path(path)
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem validating image path")
+            raise e
+
+    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
+        try:
+            return self.__invoker.services.urls.get_image_url(image_name, thumbnail)
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem getting image path")
+            raise e
+
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+    ) -> OffsetPaginatedResults[ImageDTO]:
+        try:
+            results = self.__invoker.services.image_records.get_many(
+                offset,
+                limit,
+                image_origin,
+                categories,
+                is_intermediate,
+                board_id,
+            )
+
+            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),
+                    ),
+                    results.items,
+                )
+            )
+
+            return OffsetPaginatedResults[ImageDTO](
+                items=image_dtos,
+                offset=results.offset,
+                limit=results.limit,
+                total=results.total,
+            )
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem getting paginated image DTOs")
+            raise e
+
+    def delete(self, image_name: str):
+        try:
+            self.__invoker.services.image_files.delete(image_name)
+            self.__invoker.services.image_records.delete(image_name)
+            self._on_deleted(image_name)
+        except ImageRecordDeleteException:
+            self.__invoker.services.logger.error("Failed to delete image record")
+            raise
+        except ImageFileDeleteException:
+            self.__invoker.services.logger.error("Failed to delete image file")
+            raise
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem deleting image record and file")
+            raise e
+
+    def delete_images_on_board(self, board_id: str):
+        try:
+            image_names = self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
+            for image_name in image_names:
+                self.__invoker.services.image_files.delete(image_name)
+            self.__invoker.services.image_records.delete_many(image_names)
+            for image_name in image_names:
+                self._on_deleted(image_name)
+        except ImageRecordDeleteException:
+            self.__invoker.services.logger.error("Failed to delete image records")
+            raise
+        except ImageFileDeleteException:
+            self.__invoker.services.logger.error("Failed to delete image files")
+            raise
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem deleting image records and files")
+            raise e
+
+    def delete_intermediates(self) -> int:
+        try:
+            image_names = self.__invoker.services.image_records.delete_intermediates()
+            count = len(image_names)
+            for image_name in image_names:
+                self.__invoker.services.image_files.delete(image_name)
+                self._on_deleted(image_name)
+            return count
+        except ImageRecordDeleteException:
+            self.__invoker.services.logger.error("Failed to delete image records")
+            raise
+        except ImageFileDeleteException:
+            self.__invoker.services.logger.error("Failed to delete image files")
+            raise
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem deleting image records and files")
+            raise e

invokeai/app/services/invocation_processor/invocation_processor_base.py

@@ -0,0 +1,5 @@
+from abc import ABC
+
+
+class InvocationProcessorABC(ABC):
+    pass

invokeai/app/services/invocation_processor/invocation_processor_common.py

@@ -0,0 +1,15 @@
+from pydantic import BaseModel, Field
+
+
+class ProgressImage(BaseModel):
+    """The progress image sent intermittently during processing"""
+
+    width: int = Field(description="The effective width of the image in pixels")
+    height: int = Field(description="The effective height of the image in pixels")
+    dataURL: str = Field(description="The image data as a b64 data URL")
+
+
+class CanceledException(Exception):
+    """Execution canceled by user."""
+
+    pass

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -4,12 +4,12 @@ from threading import BoundedSemaphore, Event, Thread
 from typing import Optional
 
 import invokeai.backend.util.logging as logger
+from invokeai.app.invocations.baseinvocation import InvocationContext
+from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
 
-from ..invocations.baseinvocation import InvocationContext
-from ..models.exceptions import CanceledException
-from .invocation_queue import InvocationQueueItem
-from .invocation_stats import InvocationStatsServiceBase
-from .invoker import InvocationProcessorABC, Invoker
+from ..invoker import Invoker
+from .invocation_processor_base import InvocationProcessorABC
+from .invocation_processor_common import CanceledException
 
 
 class DefaultInvocationProcessor(InvocationProcessorABC):
@@ -37,7 +37,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
     def __process(self, stop_event: Event):
         try:
             self.__threadLimit.acquire()
-            statistics: InvocationStatsServiceBase = self.__invoker.services.performance_statistics
             queue_item: Optional[InvocationQueueItem] = None
 
             while not stop_event.is_set():
@@ -97,8 +96,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 # Invoke
                 try:
                     graph_id = graph_execution_state.id
-                    model_manager = self.__invoker.services.model_manager
-                    with statistics.collect_stats(invocation, graph_id, model_manager):
+                    with self.__invoker.services.performance_statistics.collect_stats(invocation, graph_id):
                         # use the internal invoke_internal(), which wraps the node's invoke() method,
                         # which handles a few things:
                         # - nodes that require a value, but get it only from a connection
@@ -133,13 +131,13 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             source_node_id=source_node_id,
                             result=outputs.dict(),
                         )
-                    statistics.log_stats()
+                    self.__invoker.services.performance_statistics.log_stats()
 
                 except KeyboardInterrupt:
                     pass
 
                 except CanceledException:
-                    statistics.reset_stats(graph_execution_state.id)
+                    self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
                     pass
 
                 except Exception as e:
@@ -164,7 +162,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         error_type=e.__class__.__name__,
                         error=error,
                     )
-                    statistics.reset_stats(graph_execution_state.id)
+                    self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
                     pass
 
                 # Check queue to see if this is canceled, and skip if so

invokeai/app/services/invocation_queue/invocation_queue_base.py

@@ -0,0 +1,26 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from .invocation_queue_common import InvocationQueueItem
+
+
+class InvocationQueueABC(ABC):
+    """Abstract base class for all invocation queues"""
+
+    @abstractmethod
+    def get(self) -> InvocationQueueItem:
+        pass
+
+    @abstractmethod
+    def put(self, item: Optional[InvocationQueueItem]) -> None:
+        pass
+
+    @abstractmethod
+    def cancel(self, graph_execution_state_id: str) -> None:
+        pass
+
+    @abstractmethod
+    def is_canceled(self, graph_execution_state_id: str) -> bool:
+        pass

invokeai/app/services/invocation_queue/invocation_queue_common.py

@@ -0,0 +1,19 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+import time
+
+from pydantic import BaseModel, Field
+
+
+class InvocationQueueItem(BaseModel):
+    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
+    invocation_id: str = Field(description="The ID of the node being invoked")
+    session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
+    session_queue_item_id: int = Field(
+        description="The ID of session queue item from which this invocation queue item came"
+    )
+    session_queue_batch_id: str = Field(
+        description="The ID of the session batch from which this invocation queue item came"
+    )
+    invoke_all: bool = Field(default=False)
+    timestamp: float = Field(default_factory=time.time)

invokeai/app/services/invocation_queue/invocation_queue_memory.py

@@ -1,45 +1,11 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import time
-from abc import ABC, abstractmethod
 from queue import Queue
 from typing import Optional
 
-from pydantic import BaseModel, Field
-
-
-class InvocationQueueItem(BaseModel):
-    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
-    invocation_id: str = Field(description="The ID of the node being invoked")
-    session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
-    session_queue_item_id: int = Field(
-        description="The ID of session queue item from which this invocation queue item came"
-    )
-    session_queue_batch_id: str = Field(
-        description="The ID of the session batch from which this invocation queue item came"
-    )
-    invoke_all: bool = Field(default=False)
-    timestamp: float = Field(default_factory=time.time)
-
-
-class InvocationQueueABC(ABC):
-    """Abstract base class for all invocation queues"""
-
-    @abstractmethod
-    def get(self) -> InvocationQueueItem:
-        pass
-
-    @abstractmethod
-    def put(self, item: Optional[InvocationQueueItem]) -> None:
-        pass
-
-    @abstractmethod
-    def cancel(self, graph_execution_state_id: str) -> None:
-        pass
-
-    @abstractmethod
-    def is_canceled(self, graph_execution_state_id: str) -> bool:
-        pass
+from .invocation_queue_base import InvocationQueueABC
+from .invocation_queue_common import InvocationQueueItem
 
 
 class MemoryInvocationQueue(InvocationQueueABC):

invokeai/app/services/invocation_services.py

@@ -6,21 +6,27 @@ from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from logging import Logger
 
-    from invokeai.app.services.board_images import BoardImagesServiceABC
-    from invokeai.app.services.boards import BoardServiceABC
-    from invokeai.app.services.config import InvokeAIAppConfig
-    from invokeai.app.services.events import EventServiceBase
-    from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
-    from invokeai.app.services.images import ImageServiceABC
-    from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
-    from invokeai.app.services.invocation_queue import InvocationQueueABC
-    from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
-    from invokeai.app.services.invoker import InvocationProcessorABC
-    from invokeai.app.services.item_storage import ItemStorageABC
-    from invokeai.app.services.latent_storage import LatentsStorageBase
-    from invokeai.app.services.model_manager_service import ModelManagerServiceBase
-    from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase
-    from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
+    from .board_image_records.board_image_records_base import BoardImageRecordStorageBase
+    from .board_images.board_images_base import BoardImagesServiceABC
+    from .board_records.board_records_base import BoardRecordStorageBase
+    from .boards.boards_base import BoardServiceABC
+    from .config import InvokeAIAppConfig
+    from .events.events_base import EventServiceBase
+    from .image_files.image_files_base import ImageFileStorageBase
+    from .image_records.image_records_base import ImageRecordStorageBase
+    from .images.images_base import ImageServiceABC
+    from .invocation_cache.invocation_cache_base import InvocationCacheBase
+    from .invocation_processor.invocation_processor_base import InvocationProcessorABC
+    from .invocation_queue.invocation_queue_base import InvocationQueueABC
+    from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
+    from .item_storage.item_storage_base import ItemStorageABC
+    from .latents_storage.latents_storage_base import LatentsStorageBase
+    from .model_manager.model_manager_base import ModelManagerServiceBase
+    from .names.names_base import NameServiceBase
+    from .session_processor.session_processor_base import SessionProcessorBase
+    from .session_queue.session_queue_base import SessionQueueBase
+    from .shared.graph import GraphExecutionState, LibraryGraph
+    from .urls.urls_base import UrlServiceBase
 
 
 class InvocationServices:
@@ -28,12 +34,16 @@ class InvocationServices:
 
     # TODO: Just forward-declared everything due to circular dependencies. Fix structure.
     board_images: "BoardImagesServiceABC"
+    board_image_record_storage: "BoardImageRecordStorageBase"
     boards: "BoardServiceABC"
+    board_records: "BoardRecordStorageBase"
     configuration: "InvokeAIAppConfig"
     events: "EventServiceBase"
     graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
     graph_library: "ItemStorageABC[LibraryGraph]"
     images: "ImageServiceABC"
+    image_records: "ImageRecordStorageBase"
+    image_files: "ImageFileStorageBase"
     latents: "LatentsStorageBase"
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
@@ -43,16 +53,22 @@ class InvocationServices:
     session_queue: "SessionQueueBase"
     session_processor: "SessionProcessorBase"
     invocation_cache: "InvocationCacheBase"
+    names: "NameServiceBase"
+    urls: "UrlServiceBase"
 
     def __init__(
         self,
         board_images: "BoardImagesServiceABC",
+        board_image_records: "BoardImageRecordStorageBase",
         boards: "BoardServiceABC",
+        board_records: "BoardRecordStorageBase",
         configuration: "InvokeAIAppConfig",
         events: "EventServiceBase",
         graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
         graph_library: "ItemStorageABC[LibraryGraph]",
         images: "ImageServiceABC",
+        image_files: "ImageFileStorageBase",
+        image_records: "ImageRecordStorageBase",
         latents: "LatentsStorageBase",
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
@@ -62,14 +78,20 @@ class InvocationServices:
         session_queue: "SessionQueueBase",
         session_processor: "SessionProcessorBase",
         invocation_cache: "InvocationCacheBase",
+        names: "NameServiceBase",
+        urls: "UrlServiceBase",
     ):
         self.board_images = board_images
+        self.board_image_records = board_image_records
         self.boards = boards
+        self.board_records = board_records
         self.configuration = configuration
         self.events = events
         self.graph_execution_manager = graph_execution_manager
         self.graph_library = graph_library
         self.images = images
+        self.image_files = image_files
+        self.image_records = image_records
         self.latents = latents
         self.logger = logger
         self.model_manager = model_manager
@@ -79,3 +101,5 @@ class InvocationServices:
         self.session_queue = session_queue
         self.session_processor = session_processor
         self.invocation_cache = invocation_cache
+        self.names = names
+        self.urls = urls

invokeai/app/services/invocation_stats/invocation_stats_base.py

@@ -0,0 +1,121 @@
+# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
+"""Utility to collect execution time and GPU usage stats on invocations in flight
+
+Usage:
+
+statistics = InvocationStatsService(graph_execution_manager)
+with statistics.collect_stats(invocation, graph_execution_state.id):
+      ... execute graphs...
+statistics.log_stats()
+
+Typical output:
+[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
+[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node                 Calls  Seconds  VRAM Used
+[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader        1   0.005s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip                1   0.004s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel                   2   0.512s     0.26G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int                 1   0.001s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size            1   0.001s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate                  1   0.001s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator     1   0.002s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise                    1   0.002s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l                      1   3.541s     1.93G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i                      1   0.679s     0.58G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME:  4.749s
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
+
+The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
+writes to the system log is stored in InvocationServices.performance_statistics.
+"""
+
+from abc import ABC, abstractmethod
+from contextlib import AbstractContextManager
+from typing import Dict
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation
+from invokeai.backend.model_management.model_cache import CacheStats
+
+from .invocation_stats_common import NodeLog
+
+
+class InvocationStatsServiceBase(ABC):
+    "Abstract base class for recording node memory/time performance statistics"
+
+    # {graph_id => NodeLog}
+    _stats: Dict[str, NodeLog]
+    _cache_stats: Dict[str, CacheStats]
+    ram_used: float
+    ram_changed: float
+
+    @abstractmethod
+    def __init__(self):
+        """
+        Initialize the InvocationStatsService and reset counters to zero
+        """
+        pass
+
+    @abstractmethod
+    def collect_stats(
+        self,
+        invocation: BaseInvocation,
+        graph_execution_state_id: str,
+    ) -> AbstractContextManager:
+        """
+        Return a context object that will capture the statistics on the execution
+        of invocaation. Use with: to place around the part of the code that executes the invocation.
+        :param invocation: BaseInvocation object from the current graph.
+        :param graph_execution_state_id: The id of the current session.
+        """
+        pass
+
+    @abstractmethod
+    def reset_stats(self, graph_execution_state_id: str):
+        """
+        Reset all statistics for the indicated graph
+        :param graph_execution_state_id
+        """
+        pass
+
+    @abstractmethod
+    def reset_all_stats(self):
+        """Zero all statistics"""
+        pass
+
+    @abstractmethod
+    def update_invocation_stats(
+        self,
+        graph_id: str,
+        invocation_type: str,
+        time_used: float,
+        vram_used: float,
+    ):
+        """
+        Add timing information on execution of a node. Usually
+        used internally.
+        :param graph_id: ID of the graph that is currently executing
+        :param invocation_type: String literal type of the node
+        :param time_used: Time used by node's exection (sec)
+        :param vram_used: Maximum VRAM used during exection (GB)
+        """
+        pass
+
+    @abstractmethod
+    def log_stats(self):
+        """
+        Write out the accumulated statistics to the log or somewhere else.
+        """
+        pass
+
+    @abstractmethod
+    def update_mem_stats(
+        self,
+        ram_used: float,
+        ram_changed: float,
+    ):
+        """
+        Update the collector with RAM memory usage info.
+
+        :param ram_used: How much RAM is currently in use.
+        :param ram_changed: How much RAM changed since last generation.
+        """
+        pass

invokeai/app/services/invocation_stats/invocation_stats_common.py

@@ -0,0 +1,25 @@
+from dataclasses import dataclass, field
+from typing import Dict
+
+# size of GIG in bytes
+GIG = 1073741824
+
+
+@dataclass
+class NodeStats:
+    """Class for tracking execution stats of an invocation node"""
+
+    calls: int = 0
+    time_used: float = 0.0  # seconds
+    max_vram: float = 0.0  # GB
+    cache_hits: int = 0
+    cache_misses: int = 0
+    cache_high_watermark: int = 0
+
+
+@dataclass
+class NodeLog:
+    """Class for tracking node usage"""
+
+    # {node_type => NodeStats}
+    nodes: Dict[str, NodeStats] = field(default_factory=dict)

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -1,171 +1,35 @@
-# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
-"""Utility to collect execution time and GPU usage stats on invocations in flight
-
-Usage:
-
-statistics = InvocationStatsService(graph_execution_manager)
-with statistics.collect_stats(invocation, graph_execution_state.id):
-      ... execute graphs...
-statistics.log_stats()
-
-Typical output:
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node                 Calls  Seconds  VRAM Used
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader        1   0.005s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip                1   0.004s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel                   2   0.512s     0.26G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int                 1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size            1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate                  1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator     1   0.002s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise                    1   0.002s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l                      1   3.541s     1.93G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i                      1   0.679s     0.58G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME:  4.749s
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
-
-The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
-writes to the system log is stored in InvocationServices.performance_statistics.
-"""
-
 import time
-from abc import ABC, abstractmethod
-from contextlib import AbstractContextManager
-from dataclasses import dataclass, field
 from typing import Dict
 
 import psutil
 import torch
 
 import invokeai.backend.util.logging as logger
+from invokeai.app.invocations.baseinvocation import BaseInvocation
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.backend.model_management.model_cache import CacheStats
 
-from ..invocations.baseinvocation import BaseInvocation
-from .graph import GraphExecutionState
-from .item_storage import ItemStorageABC
-from .model_manager_service import ModelManagerService
-
-# size of GIG in bytes
-GIG = 1073741824
-
-
-@dataclass
-class NodeStats:
-    """Class for tracking execution stats of an invocation node"""
-
-    calls: int = 0
-    time_used: float = 0.0  # seconds
-    max_vram: float = 0.0  # GB
-    cache_hits: int = 0
-    cache_misses: int = 0
-    cache_high_watermark: int = 0
-
-
-@dataclass
-class NodeLog:
-    """Class for tracking node usage"""
-
-    # {node_type => NodeStats}
-    nodes: Dict[str, NodeStats] = field(default_factory=dict)
-
-
-class InvocationStatsServiceBase(ABC):
-    "Abstract base class for recording node memory/time performance statistics"
-
-    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
-    # {graph_id => NodeLog}
-    _stats: Dict[str, NodeLog]
-    _cache_stats: Dict[str, CacheStats]
-    ram_used: float
-    ram_changed: float
-
-    @abstractmethod
-    def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
-        """
-        Initialize the InvocationStatsService and reset counters to zero
-        :param graph_execution_manager: Graph execution manager for this session
-        """
-        pass
-
-    @abstractmethod
-    def collect_stats(
-        self,
-        invocation: BaseInvocation,
-        graph_execution_state_id: str,
-    ) -> AbstractContextManager:
-        """
-        Return a context object that will capture the statistics on the execution
-        of invocaation. Use with: to place around the part of the code that executes the invocation.
-        :param invocation: BaseInvocation object from the current graph.
-        :param graph_execution_state: GraphExecutionState object from the current session.
-        """
-        pass
-
-    @abstractmethod
-    def reset_stats(self, graph_execution_state_id: str):
-        """
-        Reset all statistics for the indicated graph
-        :param graph_execution_state_id
-        """
-        pass
-
-    @abstractmethod
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        pass
-
-    @abstractmethod
-    def update_invocation_stats(
-        self,
-        graph_id: str,
-        invocation_type: str,
-        time_used: float,
-        vram_used: float,
-    ):
-        """
-        Add timing information on execution of a node. Usually
-        used internally.
-        :param graph_id: ID of the graph that is currently executing
-        :param invocation_type: String literal type of the node
-        :param time_used: Time used by node's exection (sec)
-        :param vram_used: Maximum VRAM used during exection (GB)
-        """
-        pass
-
-    @abstractmethod
-    def log_stats(self):
-        """
-        Write out the accumulated statistics to the log or somewhere else.
-        """
-        pass
-
-    @abstractmethod
-    def update_mem_stats(
-        self,
-        ram_used: float,
-        ram_changed: float,
-    ):
-        """
-        Update the collector with RAM memory usage info.
-
-        :param ram_used: How much RAM is currently in use.
-        :param ram_changed: How much RAM changed since last generation.
-        """
-        pass
+from .invocation_stats_base import InvocationStatsServiceBase
+from .invocation_stats_common import GIG, NodeLog, NodeStats
 
 
 class InvocationStatsService(InvocationStatsServiceBase):
     """Accumulate performance information about a running graph. Collects time spent in each node,
     as well as the maximum and current VRAM utilisation for CUDA systems"""
 
-    def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
-        self.graph_execution_manager = graph_execution_manager
+    _invoker: Invoker
+
+    def __init__(self):
         # {graph_id => NodeLog}
         self._stats: Dict[str, NodeLog] = {}
         self._cache_stats: Dict[str, CacheStats] = {}
         self.ram_used: float = 0.0
         self.ram_changed: float = 0.0
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+
     class StatsContext:
         """Context manager for collecting statistics."""
 
@@ -174,13 +38,13 @@ class InvocationStatsService(InvocationStatsServiceBase):
         graph_id: str
         start_time: float
         ram_used: int
-        model_manager: ModelManagerService
+        model_manager: ModelManagerServiceBase
 
         def __init__(
             self,
             invocation: BaseInvocation,
             graph_id: str,
-            model_manager: ModelManagerService,
+            model_manager: ModelManagerServiceBase,
             collector: "InvocationStatsServiceBase",
         ):
             """Initialize statistics for this run."""
@@ -217,12 +81,11 @@ class InvocationStatsService(InvocationStatsServiceBase):
         self,
         invocation: BaseInvocation,
         graph_execution_state_id: str,
-        model_manager: ModelManagerService,
     ) -> StatsContext:
         if not self._stats.get(graph_execution_state_id):  # first time we're seeing this
             self._stats[graph_execution_state_id] = NodeLog()
             self._cache_stats[graph_execution_state_id] = CacheStats()
-        return self.StatsContext(invocation, graph_execution_state_id, model_manager, self)
+        return self.StatsContext(invocation, graph_execution_state_id, self._invoker.services.model_manager, self)
 
     def reset_all_stats(self):
         """Zero all statistics"""
@@ -261,7 +124,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
         errored = set()
         for graph_id, node_log in self._stats.items():
             try:
-                current_graph_state = self.graph_execution_manager.get(graph_id)
+                current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
             except Exception:
                 errored.add(graph_id)
                 continue

invokeai/app/services/invoker.py

@@ -1,11 +1,10 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from abc import ABC
 from typing import Optional
 
-from .graph import Graph, GraphExecutionState
-from .invocation_queue import InvocationQueueItem
+from .invocation_queue.invocation_queue_common import InvocationQueueItem
 from .invocation_services import InvocationServices
+from .shared.graph import Graph, GraphExecutionState
 
 
 class Invoker:
@@ -84,7 +83,3 @@ class Invoker:
             self.__stop_service(getattr(self.services, service))
 
         self.services.queue.put(None)
-
-
-class InvocationProcessorABC(ABC):
-    pass

invokeai/app/services/item_storage/item_storage_base.py

@@ -1,25 +1,16 @@
 from abc import ABC, abstractmethod
 from typing import Callable, Generic, Optional, TypeVar
 
-from pydantic import BaseModel, Field
-from pydantic.generics import GenericModel
+from pydantic import BaseModel
+
+from invokeai.app.services.shared.pagination import PaginatedResults
 
 T = TypeVar("T", bound=BaseModel)
 
 
-class PaginatedResults(GenericModel, Generic[T]):
-    """Paginated results"""
-
-    # fmt: off
-    items: list[T] = Field(description="Items")
-    page: int = Field(description="Current Page")
-    pages: int = Field(description="Total number of pages")
-    per_page: int = Field(description="Number of items per page")
-    total: int = Field(description="Total number of items in result")
-    # fmt: on
-
-
 class ItemStorageABC(ABC, Generic[T]):
+    """Provides storage for a single type of item. The type must be a Pydantic model."""
+
     _on_changed_callbacks: list[Callable[[T], None]]
     _on_deleted_callbacks: list[Callable[[str], None]]
 

invokeai/app/services/item_storage/item_storage_sqlite.py

@@ -4,12 +4,13 @@ from typing import Generic, Optional, TypeVar, get_args
 
 from pydantic import BaseModel, parse_raw_as
 
-from .item_storage import ItemStorageABC, PaginatedResults
+from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.app.services.shared.sqlite import SqliteDatabase
+
+from .item_storage_base import ItemStorageABC
 
 T = TypeVar("T", bound=BaseModel)
 
-sqlite_memory = ":memory:"
-
 
 class SqliteItemStorage(ItemStorageABC, Generic[T]):
     _table_name: str
@@ -18,13 +19,13 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     _id_field: str
     _lock: threading.Lock
 
-    def __init__(self, conn: sqlite3.Connection, table_name: str, lock: threading.Lock, id_field: str = "id"):
+    def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
         super().__init__()
 
+        self._lock = db.lock
+        self._conn = db.conn
         self._table_name = table_name
         self._id_field = id_field  # TODO: validate that T has this field
-        self._lock = lock
-        self._conn = conn
         self._cursor = self._conn.cursor()
 
         self._create_table()
@@ -44,7 +45,8 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
             self._lock.release()
 
     def _parse_item(self, item: str) -> T:
-        item_type = get_args(self.__orig_class__)[0]
+        # __orig_class__ is technically an implementation detail of the typing module, not a supported API
+        item_type = get_args(self.__orig_class__)[0]  # type: ignore
         return parse_raw_as(item_type, item)
 
     def set(self, item: T):

invokeai/app/services/latent_storage.py

@@ -1,119 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from abc import ABC, abstractmethod
-from pathlib import Path
-from queue import Queue
-from typing import Callable, Dict, Optional, Union
-
-import torch
-
-
-class LatentsStorageBase(ABC):
-    """Responsible for storing and retrieving latents."""
-
-    _on_changed_callbacks: list[Callable[[torch.Tensor], None]]
-    _on_deleted_callbacks: list[Callable[[str], None]]
-
-    def __init__(self) -> None:
-        self._on_changed_callbacks = list()
-        self._on_deleted_callbacks = list()
-
-    @abstractmethod
-    def get(self, name: str) -> torch.Tensor:
-        pass
-
-    @abstractmethod
-    def save(self, name: str, data: torch.Tensor) -> None:
-        pass
-
-    @abstractmethod
-    def delete(self, name: str) -> None:
-        pass
-
-    def on_changed(self, on_changed: Callable[[torch.Tensor], None]) -> None:
-        """Register a callback for when an item is changed"""
-        self._on_changed_callbacks.append(on_changed)
-
-    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
-        """Register a callback for when an item is deleted"""
-        self._on_deleted_callbacks.append(on_deleted)
-
-    def _on_changed(self, item: torch.Tensor) -> None:
-        for callback in self._on_changed_callbacks:
-            callback(item)
-
-    def _on_deleted(self, item_id: str) -> None:
-        for callback in self._on_deleted_callbacks:
-            callback(item_id)
-
-
-class ForwardCacheLatentsStorage(LatentsStorageBase):
-    """Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
-
-    __cache: Dict[str, torch.Tensor]
-    __cache_ids: Queue
-    __max_cache_size: int
-    __underlying_storage: LatentsStorageBase
-
-    def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
-        super().__init__()
-        self.__underlying_storage = underlying_storage
-        self.__cache = dict()
-        self.__cache_ids = Queue()
-        self.__max_cache_size = max_cache_size
-
-    def get(self, name: str) -> torch.Tensor:
-        cache_item = self.__get_cache(name)
-        if cache_item is not None:
-            return cache_item
-
-        latent = self.__underlying_storage.get(name)
-        self.__set_cache(name, latent)
-        return latent
-
-    def save(self, name: str, data: torch.Tensor) -> None:
-        self.__underlying_storage.save(name, data)
-        self.__set_cache(name, data)
-        self._on_changed(data)
-
-    def delete(self, name: str) -> None:
-        self.__underlying_storage.delete(name)
-        if name in self.__cache:
-            del self.__cache[name]
-        self._on_deleted(name)
-
-    def __get_cache(self, name: str) -> Optional[torch.Tensor]:
-        return None if name not in self.__cache else self.__cache[name]
-
-    def __set_cache(self, name: str, data: torch.Tensor):
-        if name not in self.__cache:
-            self.__cache[name] = data
-            self.__cache_ids.put(name)
-            if self.__cache_ids.qsize() > self.__max_cache_size:
-                self.__cache.pop(self.__cache_ids.get())
-
-
-class DiskLatentsStorage(LatentsStorageBase):
-    """Stores latents in a folder on disk without caching"""
-
-    __output_folder: Union[str, Path]
-
-    def __init__(self, output_folder: Union[str, Path]):
-        self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
-        self.__output_folder.mkdir(parents=True, exist_ok=True)
-
-    def get(self, name: str) -> torch.Tensor:
-        latent_path = self.get_path(name)
-        return torch.load(latent_path)
-
-    def save(self, name: str, data: torch.Tensor) -> None:
-        self.__output_folder.mkdir(parents=True, exist_ok=True)
-        latent_path = self.get_path(name)
-        torch.save(data, latent_path)
-
-    def delete(self, name: str) -> None:
-        latent_path = self.get_path(name)
-        latent_path.unlink()
-
-    def get_path(self, name: str) -> Path:
-        return self.__output_folder / name

invokeai/app/services/latents_storage/latents_storage_base.py

@@ -0,0 +1,45 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+
+from abc import ABC, abstractmethod
+from typing import Callable
+
+import torch
+
+
+class LatentsStorageBase(ABC):
+    """Responsible for storing and retrieving latents."""
+
+    _on_changed_callbacks: list[Callable[[torch.Tensor], None]]
+    _on_deleted_callbacks: list[Callable[[str], None]]
+
+    def __init__(self) -> None:
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
+
+    @abstractmethod
+    def get(self, name: str) -> torch.Tensor:
+        pass
+
+    @abstractmethod
+    def save(self, name: str, data: torch.Tensor) -> None:
+        pass
+
+    @abstractmethod
+    def delete(self, name: str) -> None:
+        pass
+
+    def on_changed(self, on_changed: Callable[[torch.Tensor], None]) -> None:
+        """Register a callback for when an item is changed"""
+        self._on_changed_callbacks.append(on_changed)
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an item is deleted"""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_changed(self, item: torch.Tensor) -> None:
+        for callback in self._on_changed_callbacks:
+            callback(item)
+
+    def _on_deleted(self, item_id: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(item_id)

invokeai/app/services/latents_storage/latents_storage_disk.py

@@ -0,0 +1,34 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+
+from pathlib import Path
+from typing import Union
+
+import torch
+
+from .latents_storage_base import LatentsStorageBase
+
+
+class DiskLatentsStorage(LatentsStorageBase):
+    """Stores latents in a folder on disk without caching"""
+
+    __output_folder: Path
+
+    def __init__(self, output_folder: Union[str, Path]):
+        self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
+        self.__output_folder.mkdir(parents=True, exist_ok=True)
+
+    def get(self, name: str) -> torch.Tensor:
+        latent_path = self.get_path(name)
+        return torch.load(latent_path)
+
+    def save(self, name: str, data: torch.Tensor) -> None:
+        self.__output_folder.mkdir(parents=True, exist_ok=True)
+        latent_path = self.get_path(name)
+        torch.save(data, latent_path)
+
+    def delete(self, name: str) -> None:
+        latent_path = self.get_path(name)
+        latent_path.unlink()
+
+    def get_path(self, name: str) -> Path:
+        return self.__output_folder / name

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -0,0 +1,54 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+
+from queue import Queue
+from typing import Dict, Optional
+
+import torch
+
+from .latents_storage_base import LatentsStorageBase
+
+
+class ForwardCacheLatentsStorage(LatentsStorageBase):
+    """Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
+
+    __cache: Dict[str, torch.Tensor]
+    __cache_ids: Queue
+    __max_cache_size: int
+    __underlying_storage: LatentsStorageBase
+
+    def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
+        super().__init__()
+        self.__underlying_storage = underlying_storage
+        self.__cache = dict()
+        self.__cache_ids = Queue()
+        self.__max_cache_size = max_cache_size
+
+    def get(self, name: str) -> torch.Tensor:
+        cache_item = self.__get_cache(name)
+        if cache_item is not None:
+            return cache_item
+
+        latent = self.__underlying_storage.get(name)
+        self.__set_cache(name, latent)
+        return latent
+
+    def save(self, name: str, data: torch.Tensor) -> None:
+        self.__underlying_storage.save(name, data)
+        self.__set_cache(name, data)
+        self._on_changed(data)
+
+    def delete(self, name: str) -> None:
+        self.__underlying_storage.delete(name)
+        if name in self.__cache:
+            del self.__cache[name]
+        self._on_deleted(name)
+
+    def __get_cache(self, name: str) -> Optional[torch.Tensor]:
+        return None if name not in self.__cache else self.__cache[name]
+
+    def __set_cache(self, name: str, data: torch.Tensor):
+        if name not in self.__cache:
+            self.__cache[name] = data
+            self.__cache_ids.put(name)
+            if self.__cache_ids.qsize() > self.__max_cache_size:
+                self.__cache.pop(self.__cache_ids.get())

invokeai/app/services/model_manager/model_manager_base.py

@@ -0,0 +1,286 @@
+# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from logging import Logger
+from pathlib import Path
+from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
+
+from pydantic import Field
+
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.backend.model_management import (
+    AddModelResult,
+    BaseModelType,
+    MergeInterpolationMethod,
+    ModelInfo,
+    ModelType,
+    SchedulerPredictionType,
+    SubModelType,
+)
+from invokeai.backend.model_management.model_cache import CacheStats
+
+if TYPE_CHECKING:
+    from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext
+
+
+class ModelManagerServiceBase(ABC):
+    """Responsible for managing models on disk and in memory"""
+
+    @abstractmethod
+    def __init__(
+        self,
+        config: InvokeAIAppConfig,
+        logger: Logger,
+    ):
+        """
+        Initialize with the path to the models.yaml config file.
+        Optional parameters are the torch device type, precision, max_models,
+        and sequential_offload boolean. Note that the default device
+        type and precision are set up for a CUDA system running at half precision.
+        """
+        pass
+
+    @abstractmethod
+    def get_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        submodel: Optional[SubModelType] = None,
+        node: Optional[BaseInvocation] = None,
+        context: Optional[InvocationContext] = None,
+    ) -> ModelInfo:
+        """Retrieve the indicated model with name and type.
+        submodel can be used to get a part (such as the vae)
+        of a diffusers pipeline."""
+        pass
+
+    @property
+    @abstractmethod
+    def logger(self):
+        pass
+
+    @abstractmethod
+    def model_exists(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ) -> bool:
+        pass
+
+    @abstractmethod
+    def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
+        """
+        Given a model name returns a dict-like (OmegaConf) object describing it.
+        Uses the exact format as the omegaconf stanza.
+        """
+        pass
+
+    @abstractmethod
+    def list_models(self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None) -> dict:
+        """
+        Return a dict of models in the format:
+        { model_type1:
+          { model_name1: {'status': 'active'|'cached'|'not loaded',
+                         'model_name' : name,
+                         'model_type' : SDModelType,
+                         'description': description,
+                         'format': 'folder'|'safetensors'|'ckpt'
+                         },
+            model_name2: { etc }
+          },
+          model_type2:
+            { model_name_n: etc
+        }
+        """
+        pass
+
+    @abstractmethod
+    def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
+        """
+        Return information about the model using the same format as list_models()
+        """
+        pass
+
+    @abstractmethod
+    def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
+        """
+        Returns a list of all the model names known.
+        """
+        pass
+
+    @abstractmethod
+    def add_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        model_attributes: dict,
+        clobber: bool = False,
+    ) -> AddModelResult:
+        """
+        Update the named model with a dictionary of attributes. Will fail with an
+        assertion error if the name already exists. Pass clobber=True to overwrite.
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
+        the model name is missing. Call commit() to write changes to disk.
+        """
+        pass
+
+    @abstractmethod
+    def update_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        model_attributes: dict,
+    ) -> AddModelResult:
+        """
+        Update the named model with a dictionary of attributes. Will fail with a
+        ModelNotFoundException if the name does not already exist.
+
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
+        the model name is missing. Call commit() to write changes to disk.
+        """
+        pass
+
+    @abstractmethod
+    def del_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ):
+        """
+        Delete the named model from configuration. If delete_files is true,
+        then the underlying weight file or diffusers directory will be deleted
+        as well. Call commit() to write to disk.
+        """
+        pass
+
+    @abstractmethod
+    def rename_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        new_name: str,
+    ):
+        """
+        Rename the indicated model.
+        """
+        pass
+
+    @abstractmethod
+    def list_checkpoint_configs(self) -> List[Path]:
+        """
+        List the checkpoint config paths from ROOT/configs/stable-diffusion.
+        """
+        pass
+
+    @abstractmethod
+    def convert_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: Literal[ModelType.Main, ModelType.Vae],
+    ) -> AddModelResult:
+        """
+        Convert a checkpoint file into a diffusers folder, deleting the cached
+        version and deleting the original checkpoint file if it is in the models
+        directory.
+        :param model_name: Name of the model to convert
+        :param base_model: Base model type
+        :param model_type: Type of model ['vae' or 'main']
+
+        This will raise a ValueError unless the model is not a checkpoint. It will
+        also raise a ValueError in the event that there is a similarly-named diffusers
+        directory already in place.
+        """
+        pass
+
+    @abstractmethod
+    def heuristic_import(
+        self,
+        items_to_import: set[str],
+        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+    ) -> dict[str, AddModelResult]:
+        """Import a list of paths, repo_ids or URLs. Returns the set of
+        successfully imported items.
+        :param items_to_import: Set of strings corresponding to models to be imported.
+        :param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
+
+        The prediction type helper is necessary to distinguish between
+        models based on Stable Diffusion 2 Base (requiring
+        SchedulerPredictionType.Epsilson) and Stable Diffusion 768
+        (requiring SchedulerPredictionType.VPrediction). It is
+        generally impossible to do this programmatically, so the
+        prediction_type_helper usually asks the user to choose.
+
+        The result is a set of successfully installed models. Each element
+        of the set is a dict corresponding to the newly-created OmegaConf stanza for
+        that model.
+        """
+        pass
+
+    @abstractmethod
+    def merge_models(
+        self,
+        model_names: List[str] = Field(
+            default=None, min_items=2, max_items=3, description="List of model names to merge"
+        ),
+        base_model: Union[BaseModelType, str] = Field(
+            default=None, description="Base model shared by all models to be merged"
+        ),
+        merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
+        alpha: Optional[float] = 0.5,
+        interp: Optional[MergeInterpolationMethod] = None,
+        force: Optional[bool] = False,
+        merge_dest_directory: Optional[Path] = None,
+    ) -> AddModelResult:
+        """
+        Merge two to three diffusrs pipeline models and save as a new model.
+        :param model_names: List of 2-3 models to merge
+        :param base_model: Base model to use for all models
+        :param merged_model_name: Name of destination merged model
+        :param alpha: Alpha strength to apply to 2d and 3d model
+        :param interp: Interpolation method. None (default)
+        :param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
+        """
+        pass
+
+    @abstractmethod
+    def search_for_models(self, directory: Path) -> List[Path]:
+        """
+        Return list of all models found in the designated directory.
+        """
+        pass
+
+    @abstractmethod
+    def sync_to_config(self):
+        """
+        Re-read models.yaml, rescan the models directory, and reimport models
+        in the autoimport directories. Call after making changes outside the
+        model manager API.
+        """
+        pass
+
+    @abstractmethod
+    def collect_cache_stats(self, cache_stats: CacheStats):
+        """
+        Reset model cache statistics for graph with graph_id.
+        """
+        pass
+
+    @abstractmethod
+    def commit(self, conf_file: Optional[Path] = None) -> None:
+        """
+        Write current configuration out to the indicated file.
+        If no conf_file is provided, then replaces the
+        original file/database used to initialize the object.
+        """
+        pass

invokeai/app/services/model_manager/model_manager_default.py

@@ -2,16 +2,15 @@
 
 from __future__ import annotations
 
-from abc import ABC, abstractmethod
 from logging import Logger
 from pathlib import Path
-from types import ModuleType
 from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
 
 import torch
 from pydantic import Field
 
-from invokeai.app.models.exceptions import CanceledException
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
 from invokeai.backend.model_management import (
     AddModelResult,
     BaseModelType,
@@ -26,273 +25,12 @@ from invokeai.backend.model_management import (
 )
 from invokeai.backend.model_management.model_cache import CacheStats
 from invokeai.backend.model_management.model_search import FindModels
+from invokeai.backend.util import choose_precision, choose_torch_device
 
-from ...backend.util import choose_precision, choose_torch_device
-from .config import InvokeAIAppConfig
+from .model_manager_base import ModelManagerServiceBase
 
 if TYPE_CHECKING:
-    from ..invocations.baseinvocation import BaseInvocation, InvocationContext
-
-
-class ModelManagerServiceBase(ABC):
-    """Responsible for managing models on disk and in memory"""
-
-    @abstractmethod
-    def __init__(
-        self,
-        config: InvokeAIAppConfig,
-        logger: ModuleType,
-    ):
-        """
-        Initialize with the path to the models.yaml config file.
-        Optional parameters are the torch device type, precision, max_models,
-        and sequential_offload boolean. Note that the default device
-        type and precision are set up for a CUDA system running at half precision.
-        """
-        pass
-
-    @abstractmethod
-    def get_model(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel: Optional[SubModelType] = None,
-        node: Optional[BaseInvocation] = None,
-        context: Optional[InvocationContext] = None,
-    ) -> ModelInfo:
-        """Retrieve the indicated model with name and type.
-        submodel can be used to get a part (such as the vae)
-        of a diffusers pipeline."""
-        pass
-
-    @property
-    @abstractmethod
-    def logger(self):
-        pass
-
-    @abstractmethod
-    def model_exists(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-    ) -> bool:
-        pass
-
-    @abstractmethod
-    def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
-        """
-        Given a model name returns a dict-like (OmegaConf) object describing it.
-        Uses the exact format as the omegaconf stanza.
-        """
-        pass
-
-    @abstractmethod
-    def list_models(self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None) -> dict:
-        """
-        Return a dict of models in the format:
-        { model_type1:
-          { model_name1: {'status': 'active'|'cached'|'not loaded',
-                         'model_name' : name,
-                         'model_type' : SDModelType,
-                         'description': description,
-                         'format': 'folder'|'safetensors'|'ckpt'
-                         },
-            model_name2: { etc }
-          },
-          model_type2:
-            { model_name_n: etc
-        }
-        """
-        pass
-
-    @abstractmethod
-    def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
-        """
-        Return information about the model using the same format as list_models()
-        """
-        pass
-
-    @abstractmethod
-    def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
-        """
-        Returns a list of all the model names known.
-        """
-        pass
-
-    @abstractmethod
-    def add_model(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        model_attributes: dict,
-        clobber: bool = False,
-    ) -> AddModelResult:
-        """
-        Update the named model with a dictionary of attributes. Will fail with an
-        assertion error if the name already exists. Pass clobber=True to overwrite.
-        On a successful update, the config will be changed in memory. Will fail
-        with an assertion error if provided attributes are incorrect or
-        the model name is missing. Call commit() to write changes to disk.
-        """
-        pass
-
-    @abstractmethod
-    def update_model(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        model_attributes: dict,
-    ) -> AddModelResult:
-        """
-        Update the named model with a dictionary of attributes. Will fail with a
-        ModelNotFoundException if the name does not already exist.
-
-        On a successful update, the config will be changed in memory. Will fail
-        with an assertion error if provided attributes are incorrect or
-        the model name is missing. Call commit() to write changes to disk.
-        """
-        pass
-
-    @abstractmethod
-    def del_model(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-    ):
-        """
-        Delete the named model from configuration. If delete_files is true,
-        then the underlying weight file or diffusers directory will be deleted
-        as well. Call commit() to write to disk.
-        """
-        pass
-
-    @abstractmethod
-    def rename_model(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        new_name: str,
-    ):
-        """
-        Rename the indicated model.
-        """
-        pass
-
-    @abstractmethod
-    def list_checkpoint_configs(self) -> List[Path]:
-        """
-        List the checkpoint config paths from ROOT/configs/stable-diffusion.
-        """
-        pass
-
-    @abstractmethod
-    def convert_model(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: Literal[ModelType.Main, ModelType.Vae],
-    ) -> AddModelResult:
-        """
-        Convert a checkpoint file into a diffusers folder, deleting the cached
-        version and deleting the original checkpoint file if it is in the models
-        directory.
-        :param model_name: Name of the model to convert
-        :param base_model: Base model type
-        :param model_type: Type of model ['vae' or 'main']
-
-        This will raise a ValueError unless the model is not a checkpoint. It will
-        also raise a ValueError in the event that there is a similarly-named diffusers
-        directory already in place.
-        """
-        pass
-
-    @abstractmethod
-    def heuristic_import(
-        self,
-        items_to_import: set[str],
-        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
-    ) -> dict[str, AddModelResult]:
-        """Import a list of paths, repo_ids or URLs. Returns the set of
-        successfully imported items.
-        :param items_to_import: Set of strings corresponding to models to be imported.
-        :param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
-
-        The prediction type helper is necessary to distinguish between
-        models based on Stable Diffusion 2 Base (requiring
-        SchedulerPredictionType.Epsilson) and Stable Diffusion 768
-        (requiring SchedulerPredictionType.VPrediction). It is
-        generally impossible to do this programmatically, so the
-        prediction_type_helper usually asks the user to choose.
-
-        The result is a set of successfully installed models. Each element
-        of the set is a dict corresponding to the newly-created OmegaConf stanza for
-        that model.
-        """
-        pass
-
-    @abstractmethod
-    def merge_models(
-        self,
-        model_names: List[str] = Field(
-            default=None, min_items=2, max_items=3, description="List of model names to merge"
-        ),
-        base_model: Union[BaseModelType, str] = Field(
-            default=None, description="Base model shared by all models to be merged"
-        ),
-        merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
-        alpha: Optional[float] = 0.5,
-        interp: Optional[MergeInterpolationMethod] = None,
-        force: Optional[bool] = False,
-        merge_dest_directory: Optional[Path] = None,
-    ) -> AddModelResult:
-        """
-        Merge two to three diffusrs pipeline models and save as a new model.
-        :param model_names: List of 2-3 models to merge
-        :param base_model: Base model to use for all models
-        :param merged_model_name: Name of destination merged model
-        :param alpha: Alpha strength to apply to 2d and 3d model
-        :param interp: Interpolation method. None (default)
-        :param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
-        """
-        pass
-
-    @abstractmethod
-    def search_for_models(self, directory: Path) -> List[Path]:
-        """
-        Return list of all models found in the designated directory.
-        """
-        pass
-
-    @abstractmethod
-    def sync_to_config(self):
-        """
-        Re-read models.yaml, rescan the models directory, and reimport models
-        in the autoimport directories. Call after making changes outside the
-        model manager API.
-        """
-        pass
-
-    @abstractmethod
-    def collect_cache_stats(self, cache_stats: CacheStats):
-        """
-        Reset model cache statistics for graph with graph_id.
-        """
-        pass
-
-    @abstractmethod
-    def commit(self, conf_file: Optional[Path] = None) -> None:
-        """
-        Write current configuration out to the indicated file.
-        If no conf_file is provided, then replaces the
-        original file/database used to initialize the object.
-        """
-        pass
+    from invokeai.app.invocations.baseinvocation import InvocationContext
 
 
 # simple implementation

invokeai/app/services/names/names_base.py

@@ -0,0 +1,11 @@
+from abc import ABC, abstractmethod
+
+
+class NameServiceBase(ABC):
+    """Low-level service responsible for naming resources (images, latents, etc)."""
+
+    # TODO: Add customizable naming schemes
+    @abstractmethod
+    def create_image_name(self) -> str:
+        """Creates a name for an image."""
+        pass

invokeai/app/services/names/names_common.py

@@ -0,0 +1,8 @@
+from enum import Enum, EnumMeta
+
+
+class ResourceType(str, Enum, metaclass=EnumMeta):
+    """Enum for resource types."""
+
+    IMAGE = "image"
+    LATENT = "latent"

invokeai/app/services/names/names_default.py

@@ -0,0 +1,13 @@
+from invokeai.app.util.misc import uuid_string
+
+from .names_base import NameServiceBase
+
+
+class SimpleNameService(NameServiceBase):
+    """Creates image names from UUIDs."""
+
+    # TODO: Add customizable naming schemes
+    def create_image_name(self) -> str:
+        uuid_str = uuid_string()
+        filename = f"{uuid_str}.png"
+        return filename

invokeai/app/services/resource_name.py

@@ -1,31 +0,0 @@
-from abc import ABC, abstractmethod
-from enum import Enum, EnumMeta
-
-from invokeai.app.util.misc import uuid_string
-
-
-class ResourceType(str, Enum, metaclass=EnumMeta):
-    """Enum for resource types."""
-
-    IMAGE = "image"
-    LATENT = "latent"
-
-
-class NameServiceBase(ABC):
-    """Low-level service responsible for naming resources (images, latents, etc)."""
-
-    # TODO: Add customizable naming schemes
-    @abstractmethod
-    def create_image_name(self) -> str:
-        """Creates a name for an image."""
-        pass
-
-
-class SimpleNameService(NameServiceBase):
-    """Creates image names from UUIDs."""
-
-    # TODO: Add customizable naming schemes
-    def create_image_name(self) -> str:
-        uuid_str = uuid_string()
-        filename = f"{uuid_str}.png"
-        return filename

invokeai/app/services/session_processor/session_processor_default.py

@@ -1,3 +1,4 @@
+import traceback
 from threading import BoundedSemaphore
 from threading import Event as ThreadEvent
 from threading import Thread
@@ -6,7 +7,7 @@ from typing import Optional
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event as FastAPIEvent
 
-from invokeai.app.services.events import EventServiceBase
+from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
 
 from ..invoker import Invoker
@@ -96,7 +97,6 @@ class DefaultSessionProcessor(SessionProcessorBase):
             resume_event.set()
             self.__threadLimit.acquire()
             queue_item: Optional[SessionQueueItem] = None
-            self.__invoker.services.logger
             while not stop_event.is_set():
                 poll_now_event.clear()
                 try:
@@ -123,6 +123,10 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         continue
                 except Exception as e:
                     self.__invoker.services.logger.error(f"Error in session processor: {e}")
+                    if queue_item is not None:
+                        self.__invoker.services.session_queue.cancel_queue_item(
+                            queue_item.item_id, error=traceback.format_exc()
+                        )
                     poll_now_event.wait(POLLING_INTERVAL)
                     continue
         except Exception as e:

invokeai/app/services/session_queue/session_queue_base.py

@@ -1,7 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import Optional
 
-from invokeai.app.services.graph import Graph
 from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
     Batch,
@@ -18,7 +17,8 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItemDTO,
     SessionQueueStatus,
 )
-from invokeai.app.services.shared.models import CursorPaginatedResults
+from invokeai.app.services.shared.graph import Graph
+from invokeai.app.services.shared.pagination import CursorPaginatedResults
 
 
 class SessionQueueBase(ABC):
@@ -80,7 +80,7 @@ class SessionQueueBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
         """Cancels a session queue item"""
         pass
 

invokeai/app/services/session_queue/session_queue_common.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, Field, StrictStr, parse_raw_as, root_validator,
 from pydantic.json import pydantic_encoder
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
-from invokeai.app.services.graph import Graph, GraphExecutionState, NodeNotFoundError
+from invokeai.app.services.shared.graph import Graph, GraphExecutionState, NodeNotFoundError
 from invokeai.app.util.misc import uuid_string
 
 # region Errors
@@ -123,6 +123,11 @@ class Batch(BaseModel):
                     raise NodeNotFoundError(f"Field {batch_data.field_name} not found in node {batch_data.node_path}")
         return values
 
+    @validator("graph")
+    def validate_graph(cls, v: Graph):
+        v.validate_self()
+        return v
+
     class Config:
         schema_extra = {
             "required": [

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -5,8 +5,7 @@ from typing import Optional, Union, cast
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event as FastAPIEvent
 
-from invokeai.app.services.events import EventServiceBase
-from invokeai.app.services.graph import Graph
+from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
 from invokeai.app.services.session_queue.session_queue_common import (
@@ -29,7 +28,9 @@ from invokeai.app.services.session_queue.session_queue_common import (
     calc_session_count,
     prepare_values_to_insert,
 )
-from invokeai.app.services.shared.models import CursorPaginatedResults
+from invokeai.app.services.shared.graph import Graph
+from invokeai.app.services.shared.pagination import CursorPaginatedResults
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 
 class SqliteSessionQueue(SessionQueueBase):
@@ -45,13 +46,11 @@ class SqliteSessionQueue(SessionQueueBase):
         local_handler.register(event_name=EventServiceBase.queue_event, _func=self._on_session_event)
         self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
-    def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
+    def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self.__conn = conn
-        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
-        self.__conn.row_factory = sqlite3.Row
+        self.__lock = db.lock
+        self.__conn = db.conn
         self.__cursor = self.__conn.cursor()
-        self.__lock = lock
         self._create_tables()
 
     def _match_event_name(self, event: FastAPIEvent, match_in: list[str]) -> bool:
@@ -427,7 +426,13 @@ class SqliteSessionQueue(SessionQueueBase):
         finally:
             self.__lock.release()
         queue_item = self.get_queue_item(item_id)
-        self.__invoker.services.events.emit_queue_item_status_changed(queue_item)
+        batch_status = self.get_batch_status(queue_id=queue_item.queue_id, batch_id=queue_item.batch_id)
+        queue_status = self.get_queue_status(queue_id=queue_item.queue_id)
+        self.__invoker.services.events.emit_queue_item_status_changed(
+            session_queue_item=queue_item,
+            batch_status=batch_status,
+            queue_status=queue_status,
+        )
         return queue_item
 
     def is_empty(self, queue_id: str) -> IsEmptyResult:
@@ -555,10 +560,11 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return PruneResult(deleted=count)
 
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
         queue_item = self.get_queue_item(item_id)
         if queue_item.status not in ["canceled", "failed", "completed"]:
-            queue_item = self._set_queue_item_status(item_id=item_id, status="canceled")
+            status = "failed" if error is not None else "canceled"
+            queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error)
             self.__invoker.services.queue.cancel(queue_item.session_id)
             self.__invoker.services.events.emit_session_canceled(
                 queue_item_id=queue_item.item_id,
@@ -608,7 +614,13 @@ class SqliteSessionQueue(SessionQueueBase):
                     queue_batch_id=current_queue_item.batch_id,
                     graph_execution_state_id=current_queue_item.session_id,
                 )
-                self.__invoker.services.events.emit_queue_item_status_changed(current_queue_item)
+                batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
+                queue_status = self.get_queue_status(queue_id=queue_id)
+                self.__invoker.services.events.emit_queue_item_status_changed(
+                    session_queue_item=current_queue_item,
+                    batch_status=batch_status,
+                    queue_status=queue_status,
+                )
         except Exception:
             self.__conn.rollback()
             raise
@@ -654,7 +666,13 @@ class SqliteSessionQueue(SessionQueueBase):
                     queue_batch_id=current_queue_item.batch_id,
                     graph_execution_state_id=current_queue_item.session_id,
                 )
-                self.__invoker.services.events.emit_queue_item_status_changed(current_queue_item)
+                batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
+                queue_status = self.get_queue_status(queue_id=queue_id)
+                self.__invoker.services.events.emit_queue_item_status_changed(
+                    session_queue_item=current_queue_item,
+                    batch_status=batch_status,
+                    queue_status=queue_status,
+                )
         except Exception:
             self.__conn.rollback()
             raise

invokeai/app/services/shared/default_graphs.py

@@ -1,10 +1,11 @@
-from ..invocations.compel import CompelInvocation
-from ..invocations.image import ImageNSFWBlurInvocation
-from ..invocations.latent import DenoiseLatentsInvocation, LatentsToImageInvocation
-from ..invocations.noise import NoiseInvocation
-from ..invocations.primitives import IntegerInvocation
+from invokeai.app.services.item_storage.item_storage_base import ItemStorageABC
+
+from ...invocations.compel import CompelInvocation
+from ...invocations.image import ImageNSFWBlurInvocation
+from ...invocations.latent import DenoiseLatentsInvocation, LatentsToImageInvocation
+from ...invocations.noise import NoiseInvocation
+from ...invocations.primitives import IntegerInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
-from .item_storage import ItemStorageABC
 
 default_text_to_image_graph_id = "539b2af5-2b4d-4d8c-8071-e54a3255fc74"
 

invokeai/app/services/shared/graph.py

@@ -2,17 +2,15 @@
 
 import copy
 import itertools
-from typing import Annotated, Any, Optional, Union, cast, get_args, get_origin, get_type_hints
+from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import BaseModel, root_validator, validator
 from pydantic.fields import Field
 
-from invokeai.app.util.misc import uuid_string
-
 # Importing * is bad karma but needed here for node detection
-from ..invocations import *  # noqa: F401 F403
-from ..invocations.baseinvocation import (
+from invokeai.app.invocations import *  # noqa: F401 F403
+from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     Input,
@@ -23,6 +21,7 @@ from ..invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
+from invokeai.app.util.misc import uuid_string
 
 # in 3.10 this would be "from types import NoneType"
 NoneType = type(None)
@@ -170,6 +169,18 @@ class NodeIdMismatchError(ValueError):
     pass
 
 
+class InvalidSubGraphError(ValueError):
+    pass
+
+
+class CyclicalGraphError(ValueError):
+    pass
+
+
+class UnknownGraphValidationError(ValueError):
+    pass
+
+
 # TODO: Create and use an Empty output?
 @invocation_output("graph_output")
 class GraphInvocationOutput(BaseInvocationOutput):
@@ -254,59 +265,6 @@ class Graph(BaseModel):
         default_factory=list,
     )
 
-    @root_validator
-    def validate_nodes_and_edges(cls, values):
-        """Validates that all edges match nodes in the graph"""
-        nodes = cast(Optional[dict[str, BaseInvocation]], values.get("nodes"))
-        edges = cast(Optional[list[Edge]], values.get("edges"))
-
-        if nodes is not None:
-            # Validate that all node ids are unique
-            node_ids = [n.id for n in nodes.values()]
-            duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
-            if duplicate_node_ids:
-                raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
-
-            # Validate that all node ids match the keys in the nodes dict
-            for k, v in nodes.items():
-                if k != v.id:
-                    raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
-
-        if edges is not None and nodes is not None:
-            # Validate that all edges match nodes in the graph
-            node_ids = set([e.source.node_id for e in edges] + [e.destination.node_id for e in edges])
-            missing_node_ids = [node_id for node_id in node_ids if node_id not in nodes]
-            if missing_node_ids:
-                raise NodeNotFoundError(
-                    f"All edges must reference nodes in the graph, missing nodes: {missing_node_ids}"
-                )
-
-            # Validate that all edge fields match node fields in the graph
-            for edge in edges:
-                source_node = nodes.get(edge.source.node_id, None)
-                if source_node is None:
-                    raise NodeFieldNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
-
-                destination_node = nodes.get(edge.destination.node_id, None)
-                if destination_node is None:
-                    raise NodeFieldNotFoundError(
-                        f"Edge destination node {edge.destination.node_id} does not exist in the graph"
-                    )
-
-                # output fields are not on the node object directly, they are on the output type
-                if edge.source.field not in source_node.get_output_type().__fields__:
-                    raise NodeFieldNotFoundError(
-                        f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
-                    )
-
-                # input fields are on the node
-                if edge.destination.field not in destination_node.__fields__:
-                    raise NodeFieldNotFoundError(
-                        f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
-                    )
-
-        return values
-
     def add_node(self, node: BaseInvocation) -> None:
         """Adds a node to a graph
 
@@ -377,53 +335,108 @@ class Graph(BaseModel):
         except KeyError:
             pass
 
-    def is_valid(self) -> bool:
-        """Validates the graph."""
+    def validate_self(self) -> None:
+        """
+        Validates the graph.
+
+        Raises an exception if the graph is invalid:
+        - `DuplicateNodeIdError`
+        - `NodeIdMismatchError`
+        - `InvalidSubGraphError`
+        - `NodeNotFoundError`
+        - `NodeFieldNotFoundError`
+        - `CyclicalGraphError`
+        - `InvalidEdgeError`
+        """
+
+        # Validate that all node ids are unique
+        node_ids = [n.id for n in self.nodes.values()]
+        duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
+        if duplicate_node_ids:
+            raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
+
+        # Validate that all node ids match the keys in the nodes dict
+        for k, v in self.nodes.items():
+            if k != v.id:
+                raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
 
         # Validate all subgraphs
         for gn in (n for n in self.nodes.values() if isinstance(n, GraphInvocation)):
-            if not gn.graph.is_valid():
-                return False
+            try:
+                gn.graph.validate_self()
+            except Exception as e:
+                raise InvalidSubGraphError(f"Subgraph {gn.id} is invalid") from e
 
-        # Validate all edges reference nodes in the graph
-        node_ids = set([e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges])
-        if not all((self.has_node(node_id) for node_id in node_ids)):
-            return False
+        # Validate that all edges match nodes and fields in the graph
+        for edge in self.edges:
+            source_node = self.nodes.get(edge.source.node_id, None)
+            if source_node is None:
+                raise NodeNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
+
+            destination_node = self.nodes.get(edge.destination.node_id, None)
+            if destination_node is None:
+                raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph")
+
+            # output fields are not on the node object directly, they are on the output type
+            if edge.source.field not in source_node.get_output_type().__fields__:
+                raise NodeFieldNotFoundError(
+                    f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
+                )
+
+            # input fields are on the node
+            if edge.destination.field not in destination_node.__fields__:
+                raise NodeFieldNotFoundError(
+                    f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
+                )
 
         # Validate there are no cycles
         g = self.nx_graph_flat()
         if not nx.is_directed_acyclic_graph(g):
-            return False
+            raise CyclicalGraphError("Graph contains cycles")
 
         # Validate all edge connections are valid
-        if not all(
-            (
-                are_connections_compatible(
-                    self.get_node(e.source.node_id),
-                    e.source.field,
-                    self.get_node(e.destination.node_id),
-                    e.destination.field,
+        for e in self.edges:
+            if not are_connections_compatible(
+                self.get_node(e.source.node_id),
+                e.source.field,
+                self.get_node(e.destination.node_id),
+                e.destination.field,
+            ):
+                raise InvalidEdgeError(
+                    f"Invalid edge from {e.source.node_id}.{e.source.field} to {e.destination.node_id}.{e.destination.field}"
                 )
-                for e in self.edges
-            )
+
+        # Validate all iterators & collectors
+        # TODO: may need to validate all iterators & collectors in subgraphs so edge connections in parent graphs will be available
+        for n in self.nodes.values():
+            if isinstance(n, IterateInvocation) and not self._is_iterator_connection_valid(n.id):
+                raise InvalidEdgeError(f"Invalid iterator node {n.id}")
+            if isinstance(n, CollectInvocation) and not self._is_collector_connection_valid(n.id):
+                raise InvalidEdgeError(f"Invalid collector node {n.id}")
+
+        return None
+
+    def is_valid(self) -> bool:
+        """
+        Checks if the graph is valid.
+
+        Raises `UnknownGraphValidationError` if there is a problem validating the graph (not a validation error).
+        """
+        try:
+            self.validate_self()
+            return True
+        except (
+            DuplicateNodeIdError,
+            NodeIdMismatchError,
+            InvalidSubGraphError,
+            NodeNotFoundError,
+            NodeFieldNotFoundError,
+            CyclicalGraphError,
+            InvalidEdgeError,
         ):
             return False
-
-        # Validate all iterators
-        # TODO: may need to validate all iterators in subgraphs so edge connections in parent graphs will be available
-        if not all(
-            (self._is_iterator_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, IterateInvocation))
-        ):
-            return False
-
-        # Validate all collectors
-        # TODO: may need to validate all collectors in subgraphs so edge connections in parent graphs will be available
-        if not all(
-            (self._is_collector_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, CollectInvocation))
-        ):
-            return False
-
-        return True
+        except Exception as e:
+            raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
 
     def _validate_edge(self, edge: Edge):
         """Validates that a new edge doesn't create a cycle in the graph"""
@@ -804,6 +817,12 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
+    @validator("graph")
+    def graph_is_valid(cls, v: Graph):
+        """Validates that the graph is valid"""
+        v.validate_self()
+        return v
+
     class Config:
         schema_extra = {
             "required": [

invokeai/app/services/shared/models.py

@@ -1,14 +0,0 @@
-from typing import Generic, TypeVar
-
-from pydantic import BaseModel, Field
-from pydantic.generics import GenericModel
-
-GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel)
-
-
-class CursorPaginatedResults(GenericModel, Generic[GenericBaseModel]):
-    """Cursor-paginated results"""
-
-    limit: int = Field(..., description="Limit of items to get")
-    has_more: bool = Field(..., description="Whether there are more items available")
-    items: list[GenericBaseModel] = Field(..., description="Items")

invokeai/app/services/shared/pagination.py

@@ -0,0 +1,42 @@
+from typing import Generic, TypeVar
+
+from pydantic import BaseModel, Field
+from pydantic.generics import GenericModel
+
+GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel)
+
+
+class CursorPaginatedResults(GenericModel, Generic[GenericBaseModel]):
+    """
+    Cursor-paginated results
+    Generic must be a Pydantic model
+    """
+
+    limit: int = Field(..., description="Limit of items to get")
+    has_more: bool = Field(..., description="Whether there are more items available")
+    items: list[GenericBaseModel] = Field(..., description="Items")
+
+
+class OffsetPaginatedResults(GenericModel, Generic[GenericBaseModel]):
+    """
+    Offset-paginated results
+    Generic must be a Pydantic model
+    """
+
+    limit: int = Field(description="Limit of items to get")
+    offset: int = Field(description="Offset from which to retrieve items")
+    total: int = Field(description="Total number of items in result")
+    items: list[GenericBaseModel] = Field(description="Items")
+
+
+class PaginatedResults(GenericModel, Generic[GenericBaseModel]):
+    """
+    Paginated results
+    Generic must be a Pydantic model
+    """
+
+    page: int = Field(description="Current Page")
+    pages: int = Field(description="Total number of pages")
+    per_page: int = Field(description="Number of items per page")
+    total: int = Field(description="Total number of items in result")
+    items: list[GenericBaseModel] = Field(description="Items")