fix(installer): use extra index url when updating

If we don't include this, on updating, we will always get the CPU torch/torchvision/xformers.

.github/pull_request_template.md

@@ -42,21 +42,6 @@ Please provide steps on how to test changes, any hardware or
 software specifications as well as any other pertinent information. 
 -->
 
-## Merge Plan
-
-<!--
-A merge plan describes how this PR should be handled after it is approved.
-
-Example merge plans:
-- "This PR can be merged when approved"
-- "This must be squash-merged when approved"
-- "DO NOT MERGE - I will rebase and tidy commits before merging"
-- "#dev-chat on discord needs to be advised of this change when it is merged"
-
-A merge plan is particularly important for large PRs or PRs that touch the
-database in any way.
--->
-
 ## Added/updated tests?
 
 - [ ] Yes

.github/workflows/lint-frontend.yml

@@ -21,23 +21,13 @@ jobs:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-22.04
     steps:
-      - name: Setup Node 20
-        uses: actions/setup-node@v4
+      - name: Setup Node 18
+        uses: actions/setup-node@v3
         with:
-          node-version: '20'
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Setup pnpm
-        uses: pnpm/action-setup@v2
-        with:
-          version: 8
-      - name: Install dependencies
-        run: 'pnpm install --prefer-frozen-lockfile'
-      - name: Typescript
-        run: 'pnpm run lint:tsc'
-      - name: Madge
-        run: 'pnpm run lint:madge'
-      - name: ESLint
-        run: 'pnpm run lint:eslint'
-      - name: Prettier
-        run: 'pnpm run lint:prettier'
+          node-version: '18'
+      - uses: actions/checkout@v3
+      - run: 'yarn install --frozen-lockfile'
+      - run: 'yarn run lint:tsc'
+      - run: 'yarn run lint:madge'
+      - run: 'yarn run lint:eslint'
+      - run: 'yarn run lint:prettier'

.gitignore

@@ -16,7 +16,7 @@ __pycache__/
 .Python
 build/
 develop-eggs/
-dist/
+# dist/
 downloads/
 eggs/
 .eggs/
@@ -187,4 +187,3 @@ installer/install.bat
 installer/install.sh
 installer/update.bat
 installer/update.sh
-installer/InvokeAI-Installer/

Makefile

@@ -1,52 +0,0 @@
-# simple Makefile with scripts that are otherwise hard to remember
-# to use, run from the repo root `make <command>`
-
-default: help
-
-help:
-	@echo Developer commands:
-	@echo
-	@echo "ruff           Run ruff, fixing any safely-fixable errors and formatting"
-	@echo "ruff-unsafe    Run ruff, fixing all fixable errors and formatting"
-	@echo "mypy           Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors"
-	@echo "mypy-all       Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
-	@echo "frontend-build Build the frontend in order to run on localhost:9090"
-	@echo "frontend-dev   Run the frontend in developer mode on localhost:5173"
-	@echo "installer-zip  Build the installer .zip file for the current version"
-	@echo "tag-release    Tag the GitHub repository with the current version (use at release time only!)"
-
-# Runs ruff, fixing any safely-fixable errors and formatting
-ruff:
-		ruff check . --fix
-		ruff format .
-
-# Runs ruff, fixing all errors it can fix and formatting
-ruff-unsafe:
-		ruff check . --fix --unsafe-fixes
-		ruff format .
-
-# Runs mypy, using the config in pyproject.toml
-mypy:
-		mypy scripts/invokeai-web.py
-
-# Runs mypy, ignoring the config in pyproject.toml but still ignoring missing (untyped) imports
-# (many files are ignored by the config, so this is useful for checking all files)
-mypy-all:
-		mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports
-
-# Build the frontend
-frontend-build:
-	cd invokeai/frontend/web && pnpm build
-
-# Run the frontend in dev mode
-frontend-dev:
-	cd invokeai/frontend/web && pnpm dev
-
-# Installer zip file
-installer-zip:
-	cd installer && ./create_installer.sh
-
-# Tag the release
-tag-release:
-	cd installer && ./tag_release.sh
-

README.md

@@ -125,8 +125,8 @@ and go to http://localhost:9090.
 
 You must have Python 3.10 through 3.11 installed on your machine. Earlier or
 later versions are not supported.
-Node.js also needs to be installed along with `pnpm` (can be installed with
-the command `npm install -g pnpm` if needed)
+Node.js also needs to be installed along with yarn (can be installed with
+the command `npm install -g yarn` if needed)
 
 1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
 2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
@@ -395,7 +395,7 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 
 ### Troubleshooting
 
-Please check out our **[Troubleshooting Guide](https://invoke-ai.github.io/InvokeAI/installation/010_INSTALL_AUTOMATED/#troubleshooting)** to get solutions for common installation
+Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
 problems and other issues. For more help, please join our [Discord][discord link]
 
 ## Contributing

docker/Dockerfile

@@ -100,8 +100,6 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 ENV INVOKEAI_ROOT=/invokeai
 ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
-ENV CONTAINER_UID=${CONTAINER_UID:-1000}
-ENV CONTAINER_GID=${CONTAINER_GID:-1000}
 
 # --link requires buldkit w/ dockerfile syntax 1.4
 COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
@@ -119,7 +117,7 @@ WORKDIR ${INVOKEAI_SRC}
 RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
 RUN python3 -c "from patchmatch import patch_match"
 
-RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
+RUN mkdir -p ${INVOKEAI_ROOT} && chown -R 1000:1000 ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]

docs/contributing/INVOCATIONS.md

@@ -1,6 +1,6 @@
-# Nodes
+# Invocations
 
-Features in InvokeAI are added in the form of modular nodes systems called
+Features in InvokeAI are added in the form of modular node-like systems called
 **Invocations**.
 
 An Invocation is simply a single operation that takes in some inputs and gives
@@ -9,34 +9,13 @@ complex functionality.
 
 ## Invocations Directory
 
-InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
+InvokeAI Invocations can be found in the `invokeai/app/invocations` directory.
 
-New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup. 
-
-Example `nodes`  subfolder structure:
-```py
-├── __init__.py # Invoke-managed custom node loader
-│
-├── cool_node
-│   ├── __init__.py # see example below
-│   └── cool_node.py
-│
-└── my_node_pack
-    ├── __init__.py # see example below
-    ├── tasty_node.py
-    ├── bodacious_node.py
-    ├── utils.py
-    └── extra_nodes
-        └── fancy_node.py
-```
-
-Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
- See the README in the nodes folder for more examples: 
-
-```py
-from .cool_node import CoolInvocation
-```
+You can add your new functionality to one of the existing Invocations in this
+directory or create a new file in this directory as per your needs.
 
+**Note:** _All Invocations must be inside this directory for InvokeAI to
+recognize them as valid Invocations._
 
 ## Creating A New Invocation
 
@@ -65,7 +44,7 @@ The first set of things we need to do when creating a new Invocation are -
 So let us do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from .baseinvocation import BaseInvocation, invocation
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -99,8 +78,8 @@ create your own custom field types later in this guide. For now, let's go ahead
 and use it.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -124,8 +103,8 @@ image: ImageField = InputField(description="The input image")
 Great. Now let us create our other inputs for `width` and `height`
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -160,8 +139,8 @@ that are provided by it by InvokeAI.
 Let us create this function first.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -189,9 +168,9 @@ all the necessary info related to image outputs. So let us use that.
 We will cover how to create your own output types later in this guide.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.image import ImageOutput
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
+from .image import ImageOutput
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -216,9 +195,9 @@ Perfect. Now that we have our Invocation setup, let us do what we want to do.
 So let's do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.image import ImageOutput, ResourceOrigin, ImageCategory
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
+from .image import ImageOutput
 
 @invocation("resize")
 class ResizeInvocation(BaseInvocation):

docs/contributing/MODEL_MANAGER.md

@@ -10,36 +10,40 @@ model. These are the:
   tracks the type of the model, its provenance, and where it can be
   found on disk.
   
+* _ModelLoadServiceBase_ Responsible for loading a model from disk
+  into RAM and VRAM and getting it ready for inference.
+  
+* _DownloadQueueServiceBase_ A multithreaded downloader responsible
+  for downloading models from a remote source to disk. The download
+  queue has special methods for downloading repo_id folders from
+  Hugging Face, as well as discriminating among model versions in
+  Civitai, but can be used for arbitrary content.
+  
 * _ModelInstallServiceBase_ A service for installing models to
   disk. It uses `DownloadQueueServiceBase` to download models and
   their metadata, and `ModelRecordServiceBase` to store that
   information. It is also responsible for managing the InvokeAI
   `models` directory and its contents.
 
-* _DownloadQueueServiceBase_ (**CURRENTLY UNDER DEVELOPMENT - NOT IMPLEMENTED**)
-  A multithreaded downloader responsible
-  for downloading models from a remote source to disk. The download
-  queue has special methods for downloading repo_id folders from
-  Hugging Face, as well as discriminating among model versions in
-  Civitai, but can be used for arbitrary content.
-  
-  * _ModelLoadServiceBase_ (**CURRENTLY UNDER DEVELOPMENT - NOT IMPLEMENTED**)
-  Responsible for loading a model from disk
-  into RAM and VRAM and getting it ready for inference.
-
-  
 ## Location of the Code
 
 All four of these services can be found in
 `invokeai/app/services` in the following directories:
 
 * `invokeai/app/services/model_records/`
+* `invokeai/app/services/downloads/`
+* `invokeai/app/services/model_loader/`
 * `invokeai/app/services/model_install/`
-* `invokeai/app/services/model_loader/` (**under development**)
-* `invokeai/app/services/downloads/`(**under development**)
+
+With the exception of the install service, each of these is a thin
+shell around a corresponding implementation located in
+`invokeai/backend/model_manager`. The main difference between the
+modules found in app services and those in the backend folder is that
+the former add support for event reporting and are more tied to the
+needs of the InvokeAI API.
 
 Code related to the FastAPI web API can be found in
-`invokeai/app/api/routers/model_records.py`.
+`invokeai/app/api/routers/models.py`.
 
 ***
 
@@ -161,6 +165,10 @@ of the fields, including `name`, `model_type` and `base_model`, are
 shared between `ModelConfigBase` and `ModelBase`, and this is a
 potential source of confusion.
 
+** TO DO: ** The `ModelBase` code needs to be revised to reduce the
+duplication of similar classes and to support using the `key` as the
+primary model identifier.
+
 ## Reading and Writing Model Configuration Records
 
 The `ModelRecordService` provides the ability to retrieve model
@@ -354,7 +362,7 @@ model and pass its key to `get_model()`.
 Several methods allow you to create and update stored model config
 records.
 
-#### add_model(key, config) -> AnyModelConfig:
+#### add_model(key, config) -> ModelConfigBase:
 
 Given a key and a configuration, this will add the model's
 configuration record to the database. `config` can either be a subclass of
@@ -378,356 +386,27 @@ fields to be updated. This will return an `AnyModelConfig` on success,
 or raise `InvalidModelConfigException` or `UnknownModelException`
 exceptions on failure.
 
+***TO DO:*** Investigate why `update_model()` returns an
+`AnyModelConfig` while `add_model()` returns a `ModelConfigBase`.
+
+### rename_model(key, new_name) -> ModelConfigBase:
+
+This is a special case of `update_model()` for the use case of
+changing the model's name. It is broken out because there are cases in
+which the InvokeAI application wants to synchronize the model's name
+with its path in the `models` directory after changing the name, type
+or base. However, when using the ModelRecordService directly, the call
+is equivalent to:
+
+```
+store.rename_model(key, {'name': 'new_name'})
+```
+
+***TO DO:*** Investigate why `rename_model()` is returning a
+`ModelConfigBase` while `update_model()` returns a `AnyModelConfig`.
+
 ***
 
-## Model installation
-
-The `ModelInstallService` class implements the
-`ModelInstallServiceBase` abstract base class, and provides a one-stop
-shop for all your model install needs. It provides the following
-functionality:
-
-- Registering a model config record for a model already located on the
-  local filesystem, without moving it or changing its path.
-  
-- Installing a model alreadiy located on the local filesystem, by
-  moving it into the InvokeAI root directory under the
-  `models` folder (or wherever config parameter `models_dir`
-  specifies).
-	
-- Probing of models to determine their type, base type and other key
-  information.
-  
-- Interface with the InvokeAI event bus to provide status updates on
-  the download, installation and registration process.
-  
-- Downloading a model from an arbitrary URL and installing it in
-  `models_dir` (_implementation pending_).
-  
-- Special handling for Civitai model URLs which allow the user to
-  paste in a model page's URL or download link (_implementation pending_).
-
-  
-- Special handling for HuggingFace repo_ids to recursively download
-  the contents of the repository, paying attention to alternative
-  variants such as fp16. (_implementation pending_)
-  
-### Initializing the installer
-
-A default installer is created at InvokeAI api startup time and stored
-in `ApiDependencies.invoker.services.model_install` and can
-also be retrieved from an invocation's `context` argument with
-`context.services.model_install`.
-
-In the event you wish to create a new installer, you may use the
-following initialization pattern:
-
-```
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.model_records import ModelRecordServiceSQL
-from invokeai.app.services.model_install import ModelInstallService
-from invokeai.app.services.shared.sqlite import SqliteDatabase
-from invokeai.backend.util.logging import InvokeAILogger
-
-config = InvokeAIAppConfig.get_config()
-config.parse_args()
-logger = InvokeAILogger.get_logger(config=config)
-db = SqliteDatabase(config, logger)
-
-store = ModelRecordServiceSQL(db)
-installer = ModelInstallService(config, store)
-```
-
-The full form of `ModelInstallService()` takes the following
-required parameters:
-
-| **Argument**     | **Type**                     | **Description**              |
-|------------------|------------------------------|------------------------------|
-| `config`         | InvokeAIAppConfig       | InvokeAI app configuration object |
-| `record_store`   | ModelRecordServiceBase  | Config record storage database |
-| `event_bus`      | EventServiceBase        | Optional event bus to send download/install progress events to |
-
-Once initialized, the installer will provide the following methods:
-
-#### install_job = installer.import_model()
-
-The `import_model()` method is the core of the installer. The
-following illustrates basic usage:
-
-```
-from invokeai.app.services.model_install import (
-	LocalModelSource,
-	HFModelSource,
-	URLModelSource,
-)
-
-source1 = LocalModelSource(path='/opt/models/sushi.safetensors')   # a local safetensors file
-source2 = LocalModelSource(path='/opt/models/sushi_diffusers')     # a local diffusers folder
-
-source3 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5')  # a repo_id
-source4 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', subfolder='vae')  # a subfolder within a repo_id
-source5 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', variant='fp16')   # a named variant of a HF model
-
-source6 = URLModelSource(url='https://civitai.com/api/download/models/63006')       # model located at a URL
-source7 = URLModelSource(url='https://civitai.com/api/download/models/63006', access_token='letmein') # with an access token
-
-for source in [source1, source2, source3, source4, source5, source6, source7]:
-   install_job = installer.install_model(source)
-   
-source2job = installer.wait_for_installs()
-for source in sources:
-    job = source2job[source]
-	if job.status == "completed":
-		model_config = job.config_out
-		model_key = model_config.key
-		print(f"{source} installed as {model_key}")
-	elif job.status == "error":
-	    print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
-	
-```
-
-As shown here, the `import_model()` method accepts a variety of
-sources, including local safetensors files, local diffusers folders,
-HuggingFace repo_ids with and without a subfolder designation,
-Civitai model URLs and arbitrary URLs that point to checkpoint files
-(but not to folders).
-
-Each call to `import_model()` return a `ModelInstallJob` job, 
-an object which tracks the progress of the install.
-
-If a remote model is requested, the model's files are downloaded in
-parallel across a multiple set of threads using the download
-queue. During the download process, the `ModelInstallJob` is updated
-to provide status and progress information. After the files (if any)
-are downloaded, the remainder of the installation runs in a single
-serialized background thread. These are the model probing, file
-copying, and config record database update steps.
-
-Multiple install jobs can be queued up. You may block until all
-install jobs are completed (or errored) by calling the
-`wait_for_installs()` method as shown in the code
-example. `wait_for_installs()` will return a `dict` that maps the
-requested source to its job. This object can be interrogated
-to determine its status. If the job errored out, then the error type
-and details can be recovered from `job.error_type` and `job.error`.
-
-The full list of arguments to `import_model()` is as follows:
-
-| **Argument**     | **Type**                     | **Default** | **Description**                           |
-|------------------|------------------------------|-------------|-------------------------------------------|
-| `source`         | Union[str, Path, AnyHttpUrl] |             | The source of the model, Path, URL or repo_id |
-| `inplace`        | bool                         | True        | Leave a local model in its current location |
-| `variant`        | str                          | None        | Desired variant, such as 'fp16' or 'onnx' (HuggingFace only) |
-| `subfolder`      | str                          | None        | Repository subfolder (HuggingFace only)   |
-| `config`         | Dict[str, Any]               | None        | Override all or a portion of model's probed attributes |
-| `access_token`   | str                          | None        | Provide authorization information needed to download |
-
-
-The `inplace` field controls how local model Paths are handled. If
-True (the default), then the model is simply registered in its current
-location by the installer's `ModelConfigRecordService`. Otherwise, a
-copy of the model put into the location specified by the `models_dir`
-application configuration parameter.
-
-The `variant` field is used for HuggingFace repo_ids only. If
-provided, the repo_id download handler will look for and download
-tensors files that follow the convention for the selected variant:
-
-- "fp16" will select files named "*model.fp16.{safetensors,bin}"
-- "onnx" will select files ending with the suffix ".onnx"
-- "openvino" will select files beginning with "openvino_model"
-
-In the special case of the "fp16" variant, the installer will select
-the 32-bit version of the files if the 16-bit version is unavailable.
-
-`subfolder` is used for HuggingFace repo_ids only. If provided, the
-model will be downloaded from the designated subfolder rather than the
-top-level repository folder. If a subfolder is attached to the repo_id
-using the format `repo_owner/repo_name:subfolder`, then the subfolder
-specified by the repo_id will override the subfolder argument.
-
-`config` can be used to override all or a portion of the configuration
-attributes returned by the model prober. See the section below for
-details.
-
-`access_token` is passed to the download queue and used to access
-repositories that require it.
-
-#### Monitoring the install job process
-
-When you create an install job with `import_model()`, it launches the
-download and installation process in the background and returns a
-`ModelInstallJob` object for monitoring the process.
-
-The `ModelInstallJob` class has the following structure:
-
-| **Attribute** | **Type**        |  **Description** |
-|----------------|-----------------|------------------|
-| `status`       | `InstallStatus`  | An enum of ["waiting", "running", "completed" and "error" |
-| `config_in`    | `dict`          | Overriding configuration values provided by the caller |
-| `config_out`   | `AnyModelConfig`| After successful completion, contains the configuration record written to the database | 
-| `inplace`      | `boolean`       | True if the caller asked to install the model in place using its local path | 
-| `source`       | `ModelSource`   | The local path, remote URL or repo_id of the model to be installed | 
-| `local_path`   | `Path`          | If a remote model, holds the path of the model after it is downloaded; if a local model, same as `source` |
-| `error_type`   | `str`           | Name of the exception that led to an error status |
-| `error`        | `str`           | Traceback of the error |
-
-
-If the `event_bus` argument was provided, events will also be
-broadcast to the InvokeAI event bus. The events will appear on the bus
-as an event of type `EventServiceBase.model_event`, a timestamp and
-the following event names:
-
-- `model_install_started`
-
-The payload will contain the keys `timestamp` and `source`. The latter
-indicates the requested model source for installation.
-
-- `model_install_progress`
-
-Emitted at regular intervals when downloading a remote model, the
-payload will contain the keys `timestamp`, `source`, `current_bytes`
-and `total_bytes`. These events are _not_ emitted when a local model
-already on the filesystem is imported.
-
-- `model_install_completed`
-
-Issued once at the end of a successful installation. The payload will
-contain the keys `timestamp`, `source` and `key`, where `key` is the
-ID under which the model has been registered.
-
-- `model_install_error`
-
-Emitted if the installation process fails for some reason. The payload
-will contain the keys `timestamp`, `source`, `error_type` and
-`error`. `error_type` is a short message indicating the nature of the
-error, and `error` is the long traceback to help debug the problem.
-
-#### Model confguration and probing
-
-The install service uses the `invokeai.backend.model_manager.probe`
-module during import to determine the model's type, base type, and
-other configuration parameters. Among other things, it assigns a
-default name and description for the model based on probed
-fields. 
-
-When downloading remote models is implemented, additional
-configuration information, such as list of trigger terms, will be
-retrieved from the HuggingFace and Civitai model repositories.
-
-The probed values can be overriden by providing a dictionary in the
-optional `config` argument passed to `import_model()`. You may provide
-overriding values for any of the model's configuration
-attributes. Here is an example of setting the
-`SchedulerPredictionType` and `name` for an sd-2 model:
-
-This is typically used to set
-the model's name and description, but can also be used to overcome
-cases in which automatic probing is unable to (correctly) determine
-the model's attribute. The most common situation is the
-`prediction_type` field for sd-2 (and rare sd-1) models. Here is an
-example of how it works:
-
-```
-install_job = installer.import_model(
-               source='stabilityai/stable-diffusion-2-1',
-			   variant='fp16',
-			   config=dict(
-			         prediction_type=SchedulerPredictionType('v_prediction')
-					 name='stable diffusion 2 base model',
-	           )
-	      )
-```
-
-### Other installer methods
-
-This section describes additional methods provided by the installer class.
-
-#### jobs = installer.wait_for_installs()
-
-Block until all pending installs are completed or errored and then
-returns a list of completed jobs.
-
-#### jobs = installer.list_jobs([source])
-
-Return a list of all active and complete `ModelInstallJobs`. An
-optional `source` argument allows you to filter the returned list by a
-model source string pattern using a partial string match.
-
-#### jobs = installer.get_job(source)
-
-Return a list of `ModelInstallJob` corresponding to the indicated
-model source.
-
-#### installer.prune_jobs
-
-Remove non-pending jobs (completed or errored) from the job list
-returned by `list_jobs()` and `get_job()`.
-
-#### installer.app_config, installer.record_store,
-installer.event_bus
-
-Properties that provide access to the installer's `InvokeAIAppConfig`,
-`ModelRecordServiceBase` and `EventServiceBase` objects.
-
-#### key = installer.register_path(model_path, config), key = installer.install_path(model_path, config)
-
-These methods bypass the download queue and directly register or
-install the model at the indicated path, returning the unique ID for
-the installed model.
-
-Both methods accept a Path object corresponding to a checkpoint or
-diffusers folder, and an optional dict of config attributes to use to
-override the values derived from model probing.
-
-The difference between `register_path()` and `install_path()` is that
-the former creates a model configuration record without changing the
-location of the model in the filesystem. The latter makes a copy of
-the model inside the InvokeAI models directory before registering
-it.
-
-#### installer.unregister(key)
-
-This will remove the model config record for the model at key, and is
-equivalent to `installer.record_store.del_model(key)`
-
-#### installer.delete(key)
-
-This is similar to `unregister()` but has the additional effect of
-conditionally deleting the underlying model file(s) if they reside
-within the InvokeAI models directory
-
-#### installer.unconditionally_delete(key)
-
-This method is similar to `unregister()`, but also unconditionally
-deletes the corresponding model weights file(s), regardless of whether
-they are inside or outside the InvokeAI models hierarchy.
-
-#### List[str]=installer.scan_directory(scan_dir: Path, install: bool)
-
-This method will recursively scan the directory indicated in
-`scan_dir` for new models and either install them in the models
-directory or register them in place, depending on the setting of
-`install` (default False).
-
-The return value is the list of keys of the new installed/registered
-models.
-
-#### installer.sync_to_config()
-
-This method synchronizes models in the models directory and autoimport
-directory to those in the `ModelConfigRecordService` database. New
-models are registered and orphan models are unregistered.
-
-#### installer.start(invoker)
-
-The `start` method is called by the API intialization routines when
-the API starts up. Its effect is to call `sync_to_config()` to
-synchronize the model record store database with what's currently on
-disk.
-
-# The remainder of this documentation is provisional, pending implementation of the Download and Load services
-
 ## Let's get loaded, the lowdown on ModelLoadService
 
 The `ModelLoadService` is responsible for loading a named model into
@@ -1184,3 +863,351 @@ other resources that it might have been using.
 This will start/pause/cancel all jobs that have been submitted to the
 queue and have not yet reached a terminal state.
 
+## Model installation
+
+The `ModelInstallService` class implements the
+`ModelInstallServiceBase` abstract base class, and provides a one-stop
+shop for all your model install needs. It provides the following
+functionality:
+
+- Registering a model config record for a model already located on the
+  local filesystem, without moving it or changing its path.
+  
+- Installing a model alreadiy located on the local filesystem, by
+  moving it into the InvokeAI root directory under the
+  `models` folder (or wherever config parameter `models_dir`
+  specifies).
+	
+- Downloading a model from an arbitrary URL and installing it in
+  `models_dir`.
+  
+- Special handling for Civitai model URLs which allow the user to
+  paste in a model page's URL or download link. Any metadata provided
+  by Civitai, such as trigger terms, are captured and placed in the
+  model config record.
+  
+- Special handling for HuggingFace repo_ids to recursively download
+  the contents of the repository, paying attention to alternative
+  variants such as fp16.
+  
+- Probing of models to determine their type, base type and other key
+  information.
+  
+- Interface with the InvokeAI event bus to provide status updates on
+  the download, installation and registration process.
+  
+### Initializing the installer
+
+A default installer is created at InvokeAI api startup time and stored
+in `ApiDependencies.invoker.services.model_install_service` and can
+also be retrieved from an invocation's `context` argument with
+`context.services.model_install_service`.
+
+In the event you wish to create a new installer, you may use the
+following initialization pattern:
+
+```
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.download_manager import DownloadQueueServive
+from invokeai.app.services.model_record_service import ModelRecordServiceBase
+
+config = InvokeAI.get_config()
+queue = DownloadQueueService()
+store = ModelRecordServiceBase.open(config)
+installer = ModelInstallService(config=config, queue=queue, store=store)
+```
+
+The full form of `ModelInstallService()` takes the following
+parameters. Each parameter will default to a reasonable value, but it
+is recommended that you set them explicitly as shown in the above example.
+
+| **Argument**     | **Type**                     | **Default** | **Description**                           |
+|------------------|------------------------------|-------------|-------------------------------------------|
+| `config`         | InvokeAIAppConfig        | Use system-wide config  | InvokeAI app configuration object |
+| `queue`          | DownloadQueueServiceBase  | Create a new download queue for internal use  | Download queue |
+| `store`          | ModelRecordServiceBase  | Use config to select the database to open  | Config storage database |
+| `event_bus`      | EventServiceBase        | None | An event bus to send download/install progress events to |
+| `event_handlers` | List[DownloadEventHandler]  | None | Event handlers for the download queue |
+
+Note that if `store` is not provided, then the class will use
+`ModelRecordServiceBase.open(config)` to select the database to use.
+
+Once initialized, the installer will provide the following methods:
+
+#### install_job = installer.install_model()
+
+The `install_model()` method is the core of the installer. The
+following illustrates basic usage:
+
+```
+sources = [
+	Path('/opt/models/sushi.safetensors'),   # a local safetensors file
+	Path('/opt/models/sushi_diffusers/'),    # a local diffusers folder
+	'runwayml/stable-diffusion-v1-5',        # a repo_id
+    'runwayml/stable-diffusion-v1-5:vae',    # a subfolder within a repo_id
+	'https://civitai.com/api/download/models/63006', # a civitai direct download link
+	'https://civitai.com/models/8765?modelVersionId=10638',  # civitai model page
+	'https://s3.amazon.com/fjacks/sd-3.safetensors', # arbitrary URL
+]
+
+for source in sources:
+   install_job = installer.install_model(source)
+   
+source2key = installer.wait_for_installs()
+for source in sources:
+    model_key = source2key[source]
+	print(f"{source} installed as {model_key}")
+```
+
+As shown here, the `install_model()` method accepts a variety of
+sources, including local safetensors files, local diffusers folders,
+HuggingFace repo_ids with and without a subfolder designation,
+Civitai model URLs and arbitrary URLs that point to checkpoint files
+(but not to folders).
+
+Each call to `install_model()` will return a `ModelInstallJob` job, a
+subclass of `DownloadJobBase`. The install job has additional
+install-specific fields described in the next section.
+
+Each install job will run in a series of background threads using
+the object's download queue. You may block until all install jobs are
+completed (or errored) by calling the `wait_for_installs()` method as
+shown in the code example. `wait_for_installs()` will return a `dict`
+that maps the requested source to the key of the installed model. In
+the case that a model fails to download or install, its value in the
+dict will be None. The actual cause of the error will be reported in
+the corresponding job's `error` field.
+
+Alternatively you may install event handlers and/or listen for events
+on the InvokeAI event bus in order to monitor the progress of the
+requested installs.
+
+The full list of arguments to `model_install()` is as follows:
+
+| **Argument**     | **Type**                     | **Default** | **Description**                           |
+|------------------|------------------------------|-------------|-------------------------------------------|
+| `source`         | Union[str, Path, AnyHttpUrl] |             | The source of the model, Path, URL or repo_id |
+| `inplace`        | bool                         | True        | Leave a local model in its current location |
+| `variant`        | str                          | None        | Desired variant, such as 'fp16' or 'onnx' (HuggingFace only) |
+| `subfolder`      | str                          | None        | Repository subfolder (HuggingFace only)   |
+| `probe_override` | Dict[str, Any]               | None        | Override all or a portion of model's probed attributes |
+| `metadata`       | ModelSourceMetadata          | None        | Provide metadata that will be added to model's config |
+| `access_token`   | str                          | None        | Provide authorization information needed to download |
+| `priority`       | int                          | 10          | Download queue priority for the job       |
+
+
+The `inplace` field controls how local model Paths are handled. If
+True (the default), then the model is simply registered in its current
+location by the installer's `ModelConfigRecordService`. Otherwise, the
+model will be moved into the location specified by the  `models_dir`
+application configuration parameter. 
+
+The `variant` field is used for HuggingFace repo_ids only. If
+provided, the repo_id download handler will look for and download
+tensors files that follow the convention for the selected variant:
+
+- "fp16" will select files named "*model.fp16.{safetensors,bin}"
+- "onnx" will select files ending with the suffix ".onnx"
+- "openvino" will select files beginning with "openvino_model"
+
+In the special case of the "fp16" variant, the installer will select
+the 32-bit version of the files if the 16-bit version is unavailable.
+
+`subfolder` is used for HuggingFace repo_ids only. If provided, the
+model will be downloaded from the designated subfolder rather than the
+top-level repository folder. If a subfolder is attached to the repo_id
+using the format `repo_owner/repo_name:subfolder`, then the subfolder
+specified by the repo_id will override the subfolder argument.
+
+`probe_override` can be used to override all or a portion of the
+attributes returned by the model prober. This can be used to overcome
+cases in which automatic probing is unable to (correctly) determine
+the model's attribute. The most common situation is the
+`prediction_type` field for sd-2 (and rare sd-1) models. Here is an
+example of how it works:
+
+```
+install_job = installer.install_model(
+               source='stabilityai/stable-diffusion-2-1',
+			   variant='fp16',
+			   probe_override=dict(
+			         prediction_type=SchedulerPredictionType('v_prediction')
+	           )
+	      )
+```
+
+`metadata` allows you to attach custom metadata to the installed
+model. See the next section for details.
+
+`priority` and `access_token` are passed to the download queue and
+have the same effect as they do for the DownloadQueueServiceBase.
+
+#### Monitoring the install job process
+
+When you create an install job with `model_install()`, events will be
+passed to the list of `DownloadEventHandlers` provided at installer
+initialization time. Event handlers can also be added to individual
+model install jobs by calling their `add_handler()` method as
+described earlier for the `DownloadQueueService`.
+
+If the `event_bus` argument was provided, events will also be
+broadcast to the InvokeAI event bus. The events will appear on the bus
+as a singular event type named `model_event` with a payload of
+`job`. You can then retrieve the job and check its status.
+
+** TO DO: ** consider breaking `model_event` into
+`model_install_started`, `model_install_completed`, etc. The event bus
+features have not yet been tested with FastAPI/websockets, and it may
+turn out that the job object is not serializable.
+
+#### Model metadata and probing
+
+The install service has special handling for HuggingFace and Civitai
+URLs that capture metadata from the source and include it in the model
+configuration record. For example, fetching the Civitai model 8765
+will produce a config record similar to this (using YAML
+representation):
+
+```
+5abc3ef8600b6c1cc058480eaae3091e:
+  path: sd-1/lora/to8contrast-1-5.safetensors
+  name: to8contrast-1-5
+  base_model: sd-1
+  model_type: lora
+  model_format: lycoris
+  key: 5abc3ef8600b6c1cc058480eaae3091e
+  hash: 5abc3ef8600b6c1cc058480eaae3091e
+  description: 'Trigger terms: to8contrast style'
+  author: theovercomer8
+  license: allowCommercialUse=Sell; allowDerivatives=True; allowNoCredit=True
+  source: https://civitai.com/models/8765?modelVersionId=10638
+  thumbnail_url: null
+  tags:
+  - model
+  - style
+  - portraits
+```
+
+For sources that do not provide model metadata, you can attach custom
+fields by providing a `metadata` argument to `model_install()` using
+an initialized `ModelSourceMetadata` object (available for import from
+`model_install_service.py`):
+
+```
+from invokeai.app.services.model_install_service import ModelSourceMetadata
+meta = ModelSourceMetadata(
+                name="my model",
+				author="Sushi Chef",
+				description="Highly customized model; trigger with 'sushi',"
+				license="mit",
+				thumbnail_url="http://s3.amazon.com/ljack/pics/sushi.png",
+				tags=list('sfw', 'food')
+			)
+install_job = installer.install_model(
+               source='sushi_chef/model3',
+			   variant='fp16',
+			   metadata=meta,
+	      )
+```
+
+It is not currently recommended to provide custom metadata when
+installing from Civitai or HuggingFace source, as the metadata
+provided by the source will overwrite the fields you provide. Instead,
+after the model is installed you can use
+`ModelRecordService.update_model()` to change the desired fields.
+
+** TO DO: ** Change the logic so that the caller's metadata fields take
+precedence over those provided by the source.
+
+
+#### Other installer methods
+
+This section describes additional, less-frequently-used attributes and
+methods provided by the installer class.
+
+##### installer.wait_for_installs()
+
+This is equivalent to the `DownloadQueue` `join()` method. It will
+block until all the active jobs in the install queue have reached a
+terminal state (completed, errored or cancelled).
+
+##### installer.queue, installer.store, installer.config
+
+These attributes provide access to the `DownloadQueueServiceBase`,
+`ModelConfigRecordServiceBase`, and `InvokeAIAppConfig` objects that
+the installer uses.
+
+For example, to temporarily pause all pending installations, you can
+do this:
+
+```
+installer.queue.pause_all_jobs()
+```
+##### key = installer.register_path(model_path, overrides), key = installer.install_path(model_path, overrides)
+
+These methods bypass the download queue and directly register or
+install the model at the indicated path, returning the unique ID for
+the installed model.
+
+Both methods accept a Path object corresponding to a checkpoint or
+diffusers folder, and an optional dict of attributes to use to
+override the values derived from model probing.
+
+The difference between `register_path()` and `install_path()` is that
+the former will not move the model from its current position, while
+the latter will move it into the `models_dir` hierarchy.
+
+##### installer.unregister(key)
+
+This will remove the model config record for the model at key, and is
+equivalent to `installer.store.unregister(key)`
+
+##### installer.delete(key)
+
+This is similar to `unregister()` but has the additional effect of
+deleting the underlying model file(s) -- even if they were outside the
+`models_dir` directory!
+
+##### installer.conditionally_delete(key)
+
+This method will call `unregister()` if the model identified by `key`
+is outside the `models_dir` hierarchy, and call `delete()` if the
+model is inside.
+
+#### List[str]=installer.scan_directory(scan_dir: Path, install: bool)
+
+This method will recursively scan the directory indicated in
+`scan_dir` for new models and either install them in the models
+directory or register them in place, depending on the setting of
+`install` (default False).
+
+The return value is the list of keys of the new installed/registered
+models.
+
+#### installer.scan_models_directory()
+
+This method scans the models directory for new models and registers
+them in place. Models that are present in the
+`ModelConfigRecordService` database whose paths are not found will be
+unregistered.
+
+#### installer.sync_to_config()
+
+This method synchronizes models in the models directory and autoimport
+directory to those in the `ModelConfigRecordService` database. New
+models are registered and orphan models are unregistered.
+
+#### hash=installer.hash(model_path)
+
+This method is calls the fasthash algorithm on a model's Path
+(either a file or a folder) to generate a unique ID based on the
+contents of the model.
+
+##### installer.start(invoker)
+
+The `start` method is called by the API intialization routines when
+the API starts up. Its effect is to call `sync_to_config()` to
+synchronize the model record store database with what's currently on
+disk.
+
+This method should not ordinarily be called manually.

docs/features/CONCEPTS.md

@@ -1,3 +1,12 @@
+---
+title: Textual Inversion Embeddings and LoRAs
+---
+
+# :material-library-shelves: Textual Inversions and LoRAs
+
+With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
+
+
 ## Using Textual Inversion Files
 
 Textual inversion (TI) files are small models that customize the output of
@@ -52,4 +61,29 @@ files it finds there for compatible models. At startup you will see a message si
 >> Current embedding manager terms: <HOI4-Leader>, <princess-knight>
 ```
 To use these when generating, simply type the `<` key in your prompt to open the Textual Inversion WebUI and 
-select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
+select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
+
+## Using LoRAs
+
+LoRA files are models that customize the output of Stable Diffusion
+image generation.  Larger than embeddings, but much smaller than full
+models, they augment SD with improved understanding of subjects and
+artistic styles.
+
+Unlike TI files, LoRAs do not introduce novel vocabulary into the
+model's known tokens. Instead, LoRAs augment the model's weights that
+are applied to generate imagery. LoRAs may be supplied with a
+"trigger" word that they have been explicitly trained on, or may
+simply apply their effect without being triggered.
+
+LoRAs are typically stored in .safetensors files, which are the most
+secure way to store and transmit these types of weights. You may
+install any number of `.safetensors` LoRA files simply by copying them
+into the `autoimport/lora` directory of the corresponding InvokeAI models
+directory (usually `invokeai` in your home directory).
+
+To use these when generating, open the LoRA menu item in the options
+panel, select the LoRAs you want to apply and ensure that they have
+the appropriate weight recommended by the model provider. Typically,
+most LoRAs perform best at a weight of .75-1.
+

docs/features/CONFIGURATION.md

@@ -154,16 +154,14 @@ groups in `invokeia.yaml`:
 
 ### Web Server
 
-| Setting             | Default Value | Description                                                                                                                |
-|---------------------|---------------|----------------------------------------------------------------------------------------------------------------------------|
-| `host`              | `localhost`   | Name or IP address of the network interface that the web server will listen on                                             |
-| `port`              | `9090`        | Network port number that the web server will listen on                                                                     |
-| `allow_origins`     | `[]`          | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
-| `allow_credentials` | `true`        | Require credentials for a foreign host to access the InvokeAI API (don't change this)                                      |
-| `allow_methods`     | `*`           | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API                          |
-| `allow_headers`     | `*`           | List of HTTP headers that the web server will accept when accessing the API                                                |
-| `ssl_certfile`      | null          | Path to an SSL certificate file, used to enable HTTPS.                                                                     |
-| `ssl_keyfile`       | null          | Path to an SSL keyfile, if the key is not included in the certificate file.                                                |
+| Setting  | Default Value  |  Description |
+|----------|----------------|--------------|
+| `host`     | `localhost`      | Name or IP address of the network interface that the web server will listen on  |
+| `port`     | `9090`           | Network port number that the web server will listen on  |
+| `allow_origins`  | `[]`       | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
+| `allow_credentials` | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
+| `allow_methods` | `*`         | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API  |
+| `allow_headers` | `*`         | List of HTTP headers that the web server will accept when accessing the API  |
 
 The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
 

docs/features/LORAS.md

@@ -1,53 +0,0 @@
----
-title: LoRAs & LCM-LoRAs
----
-
-# :material-library-shelves: LoRAs & LCM-LoRAs
-
-With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
-
-## LoRAs
-
-Low-Rank Adaptation (LoRA) files are models that customize the output of Stable Diffusion
-image generation.  Larger than embeddings, but much smaller than full
-models, they augment SD with improved understanding of subjects and
-artistic styles.
-
-Unlike TI files, LoRAs do not introduce novel vocabulary into the
-model's known tokens. Instead, LoRAs augment the model's weights that
-are applied to generate imagery. LoRAs may be supplied with a
-"trigger" word that they have been explicitly trained on, or may
-simply apply their effect without being triggered.
-
-LoRAs are typically stored in .safetensors files, which are the most
-secure way to store and transmit these types of weights. You may
-install any number of `.safetensors` LoRA files simply by copying them
-into the `autoimport/lora` directory of the corresponding InvokeAI models
-directory (usually `invokeai` in your home directory).
-
-To use these when generating, open the LoRA menu item in the options
-panel, select the LoRAs you want to apply and ensure that they have
-the appropriate weight recommended by the model provider. Typically,
-most LoRAs perform best at a weight of .75-1.
-
-
-## LCM-LoRAs
-Latent Consistency Models (LCMs) allowed a reduced number of steps to be used to generate images with Stable Diffusion. These are created by distilling base models, creating models that only require a small number of steps to generate images. However, LCMs require that any fine-tune of a base model be distilled to be used as an LCM. 
-
-LCM-LoRAs are models that provide the benefit of LCMs but are able to be used as LoRAs and applied to any fine tune of a base model. LCM-LoRAs are created by training a small number of adapters, rather than distilling the entire fine-tuned base model. The resulting LoRA can be used the same way as a standard LoRA, but with a greatly reduced step count. This enables SDXL images to be generated up to 10x faster than without the use of LCM-LoRAs. 
-
-
-**Using LCM-LoRAs**
-
-LCM-LoRAs are natively supported in InvokeAI throughout the application. To get started, install any diffusers format LCM-LoRAs using the model manager and select it in the LoRA field.
-
-There are a number parameter differences when using LCM-LoRAs and standard generation: 
-
-- When using LCM-LoRAs, the LoRA strength should be lower than if using a standard LoRA, with 0.35 recommended as a starting point.  
-- The LCM scheduler should be used for generation
-- CFG-Scale should be reduced to ~1
-- Steps should be reduced in the range of 4-8
-
-Standard LoRAs can also be used alongside LCM-LoRAs, but will also require a lower strength, with 0.45 being recommended as a starting point. 
-
-More information can be found here: https://huggingface.co/blog/lcm_lora#fast-inference-with-sdxl-lcm-loras

docs/features/PROMPTS.md

@@ -120,7 +120,7 @@ Generate an image with a given prompt, record the seed of the image, and then
 use the `prompt2prompt` syntax to substitute words in the original prompt for
 words in a new prompt. This works for `img2img` as well.
 
-For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
+For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
   - `a cat playing with a ball in the forest`
   - `a dog playing with a ball in the forest`
 

docs/features/index.md

@@ -20,7 +20,7 @@ a single convenient digital artist-optimized user interface.
 ### * [Prompt Engineering](PROMPTS.md)
 Get the images you want with the InvokeAI  prompt engineering language.
 
-### * The [LoRA, LyCORIS, LCM-LoRA Models](CONCEPTS.md)
+### * The [LoRA, LyCORIS and Textual Inversion Models](CONCEPTS.md)
 Add custom subjects and styles using a variety of fine-tuned models.
 
 ### * [ControlNet](CONTROLNET.md)
@@ -40,7 +40,7 @@ guide also covers optimizing models to load quickly.
 Teach an old model new tricks. Merge 2-3 models together to create a
 new model that combines characteristics of the originals.
 
-### * [Textual Inversion](TEXTUAL_INVERSIONS.md)
+### * [Textual Inversion](TRAINING.md)
 Personalize models by adding your own style or subjects.
 
 ## Other Features

docs/help/FAQ.md

@@ -1,43 +0,0 @@
-# FAQs
-
-**Where do I get started? How can I install Invoke?**
-
-- You can download the latest installers [here](https://github.com/invoke-ai/InvokeAI/releases) - Note that any releases marked as *pre-release* are in a beta state. You may experience some issues, but we appreciate your help testing those! For stable/reliable installations, please install the **[Latest Release](https://github.com/invoke-ai/InvokeAI/releases/latest)**
-
-**How can I download models? Can I use models I already have downloaded?**
-
-- Models can be downloaded through the model manager, or through option [4] in the invoke.bat/invoke.sh launcher script. To download a model through the Model Manager, use the HuggingFace Repo ID by pressing the “Copy” button next to the repository name. Alternatively, to download a model from CivitAi, use the download link in the Model Manager.
-- Models that are already downloaded can be used by creating a symlink to the model location in the `autoimport` folder or by using the Model Manger’s “Scan for Models” function.
-
-**My images are taking a long time to generate. How can I speed up generation?** 
-
-- A common solution is to reduce the size of your RAM & VRAM cache to 0.25. This ensures your system has enough memory to generate images.
-- Additionally, check the [hardware requirements](https://invoke-ai.github.io/InvokeAI/#hardware-requirements) to ensure that your system is capable of generating images.
-- Lastly, double check your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup. 
-
-**I’ve installed Python on Windows but the installer says it can’t find it?**
-
-- Then ensure that you checked  **'Add python.exe to PATH'** when installing Python. This can be found at the bottom of the Python Installer window. If you already have Python installed, this can be done with the modify / repair feature of the installer.
-
-**I’ve installed everything successfully but I still get an error about Triton when starting Invoke?**
-
-- This can be safely ignored. InvokeAI doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
-
-**I updated to 3.4.0 and now xFormers can’t load C++/CUDA?**
-
-- An issue occurred with your PyTorch update. Follow these steps to fix :
-    1. Launch your invoke.bat / invoke.sh and select the option to open the developer console
-    2. Run:`pip install ".[xformers]" --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121`
-    - If you run into an error with `typing_extensions`, re-open the developer console and run:  `pip install -U typing-extensions`
-
-**It says my pip is out of date - is that why my install isn't working?**
-- An out of date won't cause an installation to fail. The cause of the error can likely be found above the message that says pip is out of date.
-- If you saw that warning but the install went well, don't worry about it (but you can update pip afterwards if you'd like).   
-
-**How can I generate the exact same that I found on the internet?**
-Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
-
-
-**Where can I get more help?** 
-
-- Create an issue on [GitHub](https://github.com/invoke-ai/InvokeAI/issues) or post in the [#help channel](https://discord.com/channels/1020123559063990373/1149510134058471514) of the InvokeAI Discord

docs/index.md

@@ -101,13 +101,16 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 
 <div align="center"><img src="assets/invoke-web-server-1.png" width=640></div>
 
+!!! Note
+
+    This project is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates as it will help aid response time.
+
 ## :octicons-link-24: Quick Links
 
 <div class="button-container">
     <a href="installation/INSTALLATION"> <button class="button">Installation</button> </a>
     <a href="features/"> <button class="button">Features</button> </a>
     <a href="help/gettingStartedWithAI/"> <button class="button">Getting Started</button> </a>
-    <a href="help/FAQ/"> <button class="button">FAQ</button> </a>
     <a href="contributing/CONTRIBUTING/"> <button class="button">Contributing</button> </a>
     <a href="https://github.com/invoke-ai/InvokeAI/"> <button class="button">Code and Downloads</button> </a>
     <a href="https://github.com/invoke-ai/InvokeAI/issues"> <button class="button">Bug Reports </button> </a>

docs/installation/020_INSTALL_MANUAL.md

@@ -293,19 +293,6 @@ manager, please follow these steps:
 
 ## Developer Install
 
-!!! warning
-
-    InvokeAI uses a SQLite database. By running on `main`, you accept responsibility for your database. This
-    means making regular backups (especially before pulling) and/or fixing it yourself in the event that a
-    PR introduces a schema change.
-    
-    If you don't need persistent backend storage, you can use an ephemeral in-memory database by setting
-    `use_memory_db: true` under `Path:` in your `invokeai.yaml` file.
-
-    If this is untenable, you should run the application via the official installer or a manual install of the
-    python package from pypi. These releases will not break your database.
-
-
 If you have an interest in how InvokeAI works, or you would like to
 add features or bugfixes, you are encouraged to install the source
 code for InvokeAI. For this to work, you will need to install the
@@ -401,5 +388,3 @@ environment variable INVOKEAI_ROOT to point to the installation directory.
 
 Note that if you run into problems with the Conda installation, the InvokeAI
 staff will **not** be able to help you out. Caveat Emptor!
-
-[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939

docs/javascripts/init_kapa_widget.js

@@ -1,10 +0,0 @@
-document.addEventListener("DOMContentLoaded", function () {
-  var script = document.createElement("script");
-  script.src = "https://widget.kapa.ai/kapa-widget.bundle.js";
-  script.setAttribute("data-website-id", "b5973bb1-476b-451e-8cf4-98de86745a10");
-  script.setAttribute("data-project-name", "Invoke.AI");
-  script.setAttribute("data-project-color", "#11213C");
-  script.setAttribute("data-project-logo", "https://avatars.githubusercontent.com/u/113954515?s=280&v=4");
-  script.async = true;
-  document.head.appendChild(script);
-});

docs/nodes/communityNodes.md

@@ -8,16 +8,12 @@ To use a node, add the node to the `nodes` folder found in your InvokeAI install
 
 The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future. 
 
-If you'd prefer, you can also just download the whole node folder from the linked repository and add it to the `nodes` folder. 
+If you'd prefer, you can also just download the `.py` file from the linked repository and add it to the `nodes` folder. 
 
 To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor. 
 
 - Community Nodes
     + [Average Images](#average-images)
-    + [Clean Image Artifacts After Cut](#clean-image-artifacts-after-cut)
-    + [Close Color Mask](#close-color-mask) 
-    + [Clothing Mask](#clothing-mask)
-    + [Contrast Limited Adaptive Histogram Equalization](#contrast-limited-adaptive-histogram-equalization)
     + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
     + [Film Grain](#film-grain)
     + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
@@ -26,25 +22,16 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Halftone](#halftone)
     + [Ideal Size](#ideal-size)
     + [Image and Mask Composition Pack](#image-and-mask-composition-pack)
-    + [Image Dominant Color](#image-dominant-color)
     + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
     + [Image Picker](#image-picker)
-    + [Image Resize Plus](#image-resize-plus)
     + [Load Video Frame](#load-video-frame)
     + [Make 3D](#make-3d)
-    + [Mask Operations](#mask-operations)    
-    + [Match Histogram](#match-histogram)
-    + [Negative Image](#negative-image)    
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
-    + [Remote Image](#remote-image)
-    + [Remove Background](#remove-background)    
     + [Retroize](#retroize)
     + [Size Stepper Nodes](#size-stepper-nodes)
-    + [Simple Skin Detection](#simple-skin-detection)
     + [Text font to Image](#text-font-to-image)
     + [Thresholding](#thresholding)
-    + [Unsharp Mask](#unsharp-mask)
     + [XY Image to Grid and Images to Grids nodes](#xy-image-to-grid-and-images-to-grids-nodes)
 - [Example Node Template](#example-node-template)
 - [Disclaimer](#disclaimer)
@@ -58,46 +45,6 @@ To use a community workflow, download the the `.json` node graph file and load i
 
 **Node Link:** https://github.com/JPPhoto/average-images-node
 
---------------------------------
-### Clean Image Artifacts After Cut
-
-Description: Removes residual artifacts after an image is separated from its background.
-
-Node Link: https://github.com/VeyDlin/clean-artifact-after-cut-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/clean-artifact-after-cut-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Close Color Mask
-
-Description: Generates a mask for images based on a closely matching color, useful for color-based selections.
-
-Node Link: https://github.com/VeyDlin/close-color-mask-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/close-color-mask-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Clothing Mask
-
-Description: Employs a U2NET neural network trained for the segmentation of clothing items in images.
-
-Node Link: https://github.com/VeyDlin/clothing-mask-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/clothing-mask-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Contrast Limited Adaptive Histogram Equalization
-
-Description: Enhances local image contrast using adaptive histogram equalization with contrast limiting.
-
-Node Link: https://github.com/VeyDlin/clahe-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/clahe-node/master/.readme/node.png" width="500" />
-
 --------------------------------
 ### Depth Map from Wavefront OBJ
 
@@ -214,16 +161,6 @@ This includes 15 Nodes:
   
 </br><img src="https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg" width="500" />
 
---------------------------------
-### Image Dominant Color
-
-Description: Identifies and extracts the dominant color from an image using k-means clustering.
-
-Node Link: https://github.com/VeyDlin/image-dominant-color-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/image-dominant-color-node/master/.readme/node.png" width="500" />
-
 --------------------------------
 ### Image to Character Art Image Nodes
 
@@ -245,17 +182,6 @@ View:
 
 **Node Link:** https://github.com/JPPhoto/image-picker-node
 
---------------------------------
-### Image Resize Plus
-
-Description: Provides various image resizing options such as fill, stretch, fit, center, and crop.
-
-Node Link: https://github.com/VeyDlin/image-resize-plus-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/image-resize-plus-node/master/.readme/node.png" width="500" />
-
-
 --------------------------------
 ### Load Video Frame
 
@@ -280,43 +206,6 @@ View:
 <img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png" width="300" />
 <img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png" width="300" />
 
---------------------------------
-### Mask Operations
-
-Description: Offers logical operations (OR, SUB, AND) for combining and manipulating image masks.
-
-Node Link: https://github.com/VeyDlin/mask-operations-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/mask-operations-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Match Histogram
-
-**Description:** An InvokeAI node to match a histogram from one image to another.  This is a bit like the `color correct` node in the main InvokeAI but this works in the YCbCr colourspace and can handle images of different sizes. Also does not require a mask input.
-- Option to only transfer luminance channel.
-- Option to save output as grayscale
-
-A good use case for this node is to normalize the colors of an image that has been through the tiled scaling workflow of my XYGrid Nodes. 
-
-See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
-
-**Node Link:** https://github.com/skunkworxdark/match_histogram
-
-**Output Examples** 
-
-<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
-
---------------------------------
-### Negative Image
-
-Description: Creates a negative version of an image, effective for visual effects and mask inversion.
-
-Node Link: https://github.com/VeyDlin/negative-image-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/negative-image-node/master/.readme/node.png" width="500" />
-
 --------------------------------
 ### Oobabooga
 
@@ -346,50 +235,22 @@ This node works best with SDXL models, especially as the style can be described
 --------------------------------
 ### Prompt Tools 
 
-**Description:** A set of InvokeAI nodes that add general prompt (string) manipulation tools.  Designed to accompany the `Prompts From File` node and other prompt generation nodes.
-
-1. `Prompt To File` - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
-2. `PTFields Collect` - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
-3. `PTFields Expand` - Takes Json string and converts it to individual generation parameters. This can be fed from the Prompts to file node.
-4. `Prompt Strength` - Formats prompt with strength like the weighted format of compel 
-5. `Prompt Strength Combine` - Combines weighted prompts for .and()/.blend()
-6. `CSV To Index String` - Gets a string from a CSV by index. Includes a Random index option
-
-The following Nodes are now included in v3.2 of Invoke and are nolonger in this set of tools.<br>
-- `Prompt Join` -> `String Join`
-- `Prompt Join Three` -> `String Join Three`
-- `Prompt Replace` -> `String Replace`
-- `Prompt Split Neg` -> `String Split Neg`
+**Description:** A set of InvokeAI nodes that add general prompt manipulation tools.  These were written to accompany the PromptsFromFile node and other prompt generation nodes.
 
+1. PromptJoin - Joins to prompts into one.
+2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
+3. PromptSplitNeg - splits a prompt into positive and negative using the old V2 method of [] for negative.
+4. PromptToFile - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
+5. PTFieldsCollect - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
+6. PTFieldsExpand - Takes Json string and converts it to individual generation parameters This can be fed from the Prompts to file node.
+7. PromptJoinThree -  Joins 3 prompt together.
+8. PromptStrength - This take a string and float and outputs another string in the format of (string)strength like the weighted format of compel. 
+9. PromptStrengthCombine - This takes a collection of prompt strength strings and outputs a string in the .and() or .blend() format that can be fed into a proper prompt node.
 
 See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
 
 **Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
 
-**Workflow Examples** 
-
-<img src="https://github.com/skunkworxdark/prompt-tools/blob/main/images/CSVToIndexStringNode.png" width="300" />
-
---------------------------------
-### Remote Image
-
-**Description:** This is a pack of nodes to interoperate with other services, be they public websites or bespoke local servers. The pack consists of these nodes:
-
-- *Load Remote Image* - Lets you load remote images such as a realtime webcam image, an image of the day, or dynamically created images.
-- *Post Image to Remote Server* - Lets you upload an image to a remote server using an HTTP POST request, eg for storage, display or further processing.
-
-**Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
-
---------------------------------
-### Remove Background
-
-Description: An integration of the rembg package to remove backgrounds from images using multiple U2NET models.
-
-Node Link: https://github.com/VeyDlin/remove-background-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/remove-background-node/master/.readme/node.png" width="500" />
-
 --------------------------------
 ### Retroize
 
@@ -401,17 +262,6 @@ View:
 
 <img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
 
---------------------------------
-### Simple Skin Detection
-
-Description: Detects skin in images based on predefined color thresholds.
-
-Node Link: https://github.com/VeyDlin/simple-skin-detection-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/simple-skin-detection-node/master/.readme/node.png" width="500" />
-
-
 --------------------------------
 ### Size Stepper Nodes
 
@@ -466,38 +316,18 @@ Highlights/Midtones/Shadows (with LUT blur enabled):
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" width="300" />
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" width="300" />
 
---------------------------------
-### Unsharp Mask
-
-**Description:** Applies an unsharp mask filter to an image, preserving its alpha channel in the process.
-
-**Node Link:** https://github.com/JPPhoto/unsharp-mask-node
-
 --------------------------------
 ### XY Image to Grid and Images to Grids nodes
 
-**Description:** These nodes add the following to InvokeAI:
-- Generate grids of images from multiple input images
-- Create XY grid images with labels from parameters
-- Split images into overlapping tiles for processing (for super-resolution workflows)
-- Recombine image tiles into a single output image blending the seams 
+**Description:** Image to grid nodes and supporting tools.
 
-The nodes include:
-1. `Images To Grids` - Combine multiple images into a grid of images
-2. `XYImage To Grid` - Take X & Y params and creates a labeled image grid.
-3. `XYImage Tiles` - Super-resolution (embiggen) style tiled resizing
-4. `Image Tot XYImages` - Takes an image and cuts it up into a number of columns and rows.
-5. Multiple supporting nodes - Helper nodes for data wrangling and building `XYImage` collections
+1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then multiple grids will be created until it runs out of images.
+2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporting nodes. See example node setups for more details.
 
 See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
 
 **Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
 
-**Output Examples** 
-
-<img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
-
-
 --------------------------------
 ### Example Node Template
 

docs/nodes/defaultNodes.md

@@ -1,106 +1,104 @@
 # List of Default Nodes
 
-The table below contains a list of the default nodes shipped with InvokeAI and
-their descriptions.
+The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
 
-| Node <img width=160 align="right">                            | Function                                                                                                                                             |
-| :------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Add Integers                                                  | Adds two numbers                                                                                                                                     |
-| Boolean Primitive Collection                                  | A collection of boolean primitive values                                                                                                             |
-| Boolean Primitive                                             | A boolean primitive value                                                                                                                            |
-| Canny Processor                                               | Canny edge detection for ControlNet                                                                                                                  |
-| CenterPadCrop                                                 | Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image.                                          |
-| CLIP Skip                                                     | Skip layers in clip text_encoder model.                                                                                                              |
-| Collect                                                       | Collects values into a collection                                                                                                                    |
-| Color Correct                                                 | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image. |
-| Color Primitive                                               | A color primitive value                                                                                                                              |
-| Compel Prompt                                                 | Parse prompt using compel package to conditioning.                                                                                                   |
-| Conditioning Primitive Collection                             | A collection of conditioning tensor primitive values                                                                                                 |
-| Conditioning Primitive                                        | A conditioning tensor primitive value                                                                                                                |
-| Content Shuffle Processor                                     | Applies content shuffle processing to image                                                                                                          |
-| ControlNet                                                    | Collects ControlNet info to pass to other nodes                                                                                                      |
-| Denoise Latents                                               | Denoises noisy latents to decodable images                                                                                                           |
-| Divide Integers                                               | Divides two numbers                                                                                                                                  |
-| Dynamic Prompt                                                | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator                                                                      |
-| [FaceMask](./detailedNodes/faceTools.md#facemask)             | Generates masks for faces in an image to use with Inpainting                                                                                         |
-| [FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier) | Identifies and labels faces in an image                                                                                                              |
-| [FaceOff](./detailedNodes/faceTools.md#faceoff)               | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting                                                             |
-| Float Math                                                    | Perform basic math operations on two floats                                                                                                          |
-| Float Primitive Collection                                    | A collection of float primitive values                                                                                                               |
-| Float Primitive                                               | A float primitive value                                                                                                                              |
-| Float Range                                                   | Creates a range                                                                                                                                      |
-| HED (softedge) Processor                                      | Applies HED edge detection to image                                                                                                                  |
-| Blur Image                                                    | Blurs an image                                                                                                                                       |
-| Extract Image Channel                                         | Gets a channel from an image.                                                                                                                        |
-| Image Primitive Collection                                    | A collection of image primitive values                                                                                                               |
-| Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
-| Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
-| Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
-| Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
-| Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
-| Image Primitive                                               | An image primitive value                                                                                                                             |
-| Lerp Image                                                    | Linear interpolation of all pixels of an image                                                                                                       |
-| Offset Image Channel                                          | Add to or subtract from an image color channel by a uniform value.                                                                                   |
-| Multiply Image Channel                                        | Multiply or Invert an image color channel by a scalar value.                                                                                         |
-| Multiply Images                                               | Multiplies two images together using `PIL.ImageChops.multiply()`.                                                                                    |
-| Blur NSFW Image                                               | Add blur to NSFW-flagged images                                                                                                                      |
-| Paste Image                                                   | Pastes an image into another image.                                                                                                                  |
-| ImageProcessor                                                | Base class for invocations that preprocess images for ControlNet                                                                                     |
-| Resize Image                                                  | Resizes an image to specific dimensions                                                                                                              |
-| Round Float                                                   | Rounds a float to a specified number of decimal places                                                                                               |
-| Float to Integer                                              | Converts a float to an integer. Optionally rounds to an even multiple of a input number.                                                             |
-| Scale Image                                                   | Scales an image by a factor                                                                                                                          |
-| Image to Latents                                              | Encodes an image into latents.                                                                                                                       |
-| Add Invisible Watermark                                       | Add an invisible watermark to an image                                                                                                               |
-| Solid Color Infill                                            | Infills transparent areas of an image with a solid color                                                                                             |
-| PatchMatch Infill                                             | Infills transparent areas of an image using the PatchMatch algorithm                                                                                 |
-| Tile Infill                                                   | Infills transparent areas of an image with tiles of the image                                                                                        |
-| Integer Primitive Collection                                  | A collection of integer primitive values                                                                                                             |
-| Integer Primitive                                             | An integer primitive value                                                                                                                           |
-| Iterate                                                       | Iterates over a list of items                                                                                                                        |
-| Latents Primitive Collection                                  | A collection of latents tensor primitive values                                                                                                      |
-| Latents Primitive                                             | A latents tensor primitive value                                                                                                                     |
-| Latents to Image                                              | Generates an image from latents.                                                                                                                     |
-| Leres (Depth) Processor                                       | Applies leres processing to image                                                                                                                    |
-| Lineart Anime Processor                                       | Applies line art anime processing to image                                                                                                           |
-| Lineart Processor                                             | Applies line art processing to image                                                                                                                 |
-| LoRA Loader                                                   | Apply selected lora to unet and text_encoder.                                                                                                        |
-| Main Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
-| Combine Mask                                                  | Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.                                                                    |
-| Mask Edge                                                     | Applies an edge mask to an image                                                                                                                     |
-| Mask from Alpha                                               | Extracts the alpha channel of an image as a mask.                                                                                                    |
-| Mediapipe Face Processor                                      | Applies mediapipe face processing to image                                                                                                           |
-| Midas (Depth) Processor                                       | Applies Midas depth processing to image                                                                                                              |
-| MLSD Processor                                                | Applies MLSD processing to image                                                                                                                     |
-| Multiply Integers                                             | Multiplies two numbers                                                                                                                               |
-| Noise                                                         | Generates latent noise.                                                                                                                              |
-| Normal BAE Processor                                          | Applies NormalBae processing to image                                                                                                                |
-| ONNX Latents to Image                                         | Generates an image from latents.                                                                                                                     |
-| ONNX Prompt (Raw)                                             | A node to process inputs and produce outputs. May use dependency injection in **init** to receive providers.                                         |
-| ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
-| ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
-| OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |
-| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
-| PIDI Processor                                                | Applies PIDI processing to image                                                                                                                     |
-| Prompts from File                                             | Loads prompts from a text file                                                                                                                       |
-| Random Integer                                                | Outputs a single random integer.                                                                                                                     |
-| Random Range                                                  | Creates a collection of random numbers                                                                                                               |
-| Integer Range                                                 | Creates a range of numbers from start to stop with step                                                                                              |
-| Integer Range of Size                                         | Creates a range from start to start + size with step                                                                                                 |
-| Resize Latents                                                | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.                                                    |
-| SDXL Compel Prompt                                            | Parse prompt using compel package to conditioning.                                                                                                   |
-| SDXL LoRA Loader                                              | Apply selected lora to unet and text_encoder.                                                                                                        |
-| SDXL Main Model Loader                                        | Loads an sdxl base model, outputting its submodels.                                                                                                  |
-| SDXL Refiner Compel Prompt                                    | Parse prompt using compel package to conditioning.                                                                                                   |
-| SDXL Refiner Model Loader                                     | Loads an sdxl refiner model, outputting its submodels.                                                                                               |
-| Scale Latents                                                 | Scales latents by a given factor.                                                                                                                    |
-| Segment Anything Processor                                    | Applies segment anything processing to image                                                                                                         |
-| Show Image                                                    | Displays a provided image, and passes it forward in the pipeline.                                                                                    |
-| Step Param Easing                                             | Experimental per-step parameter easing for denoising steps                                                                                           |
-| String Primitive Collection                                   | A collection of string primitive values                                                                                                              |
-| String Primitive                                              | A string primitive value                                                                                                                             |
-| Subtract Integers                                             | Subtracts two numbers                                                                                                                                |
-| Tile Resample Processor                                       | Tile resampler processor                                                                                                                             |
-| Upscale (RealESRGAN)                                          | Upscales an image using RealESRGAN.                                                                                                                  |
-| VAE Loader                                                    | Loads a VAE model, outputting a VaeLoaderOutput                                                                                                      |
-| Zoe (Depth) Processor                                         | Applies Zoe depth processing to image                                                                                                                |
+| Node <img width=160 align="right"> | Function                                                                              |
+|: ---------------------------------- | :--------------------------------------------------------------------------------------|
+|Add Integers 			| Adds two numbers|
+|Boolean Primitive Collection 			| A collection of boolean primitive values|
+|Boolean Primitive 			| A boolean primitive value|
+|Canny Processor 			| Canny edge detection for ControlNet|
+|CLIP Skip 			| Skip layers in clip text_encoder model.|
+|Collect 			| Collects values into a collection|
+|Color Correct 			| Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image.|
+|Color Primitive 			| A color primitive value|
+|Compel Prompt 			| Parse prompt using compel package to conditioning.|
+|Conditioning Primitive Collection 			| A collection of conditioning tensor primitive values|
+|Conditioning Primitive 			| A conditioning tensor primitive value|
+|Content Shuffle Processor 			| Applies content shuffle processing to image|
+|ControlNet 			| Collects ControlNet info to pass to other nodes|
+|Denoise Latents 			| Denoises noisy latents to decodable images|
+|Divide Integers 			| Divides two numbers|
+|Dynamic Prompt 			| Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
+|[FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting|
+|[FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier)             | Identifies and labels faces in an image|
+|[FaceOff](./detailedNodes/faceTools.md#faceoff)             | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting|
+|Float Math             | Perform basic math operations on two floats|
+|Float Primitive Collection 			| A collection of float primitive values|
+|Float Primitive 			| A float primitive value|
+|Float Range 			| Creates a range|
+|HED (softedge) Processor 			| Applies HED edge detection to image|
+|Blur Image 			| Blurs an image|
+|Extract Image Channel 			| Gets a channel from an image.|
+|Image Primitive Collection 			| A collection of image primitive values|
+|Integer Math           | Perform basic math operations on two integers|
+|Convert Image Mode 			| Converts an image to a different mode.|
+|Crop Image 			| Crops an image to a specified box. The box can be outside of the image.|
+|Image Hue Adjustment 			| Adjusts the Hue of an image.|
+|Inverse Lerp Image 			| Inverse linear interpolation of all pixels of an image|
+|Image Primitive 			| An image primitive value|
+|Lerp Image 			| Linear interpolation of all pixels of an image|
+|Offset Image Channel 			| Add to or subtract from an image color channel by a uniform value.|
+|Multiply Image Channel 			| Multiply or Invert an image color channel by a scalar value.|
+|Multiply Images 			| Multiplies two images together using `PIL.ImageChops.multiply()`.|
+|Blur NSFW Image 			| Add blur to NSFW-flagged images|
+|Paste Image 			| Pastes an image into another image.|
+|ImageProcessor 			| Base class for invocations that preprocess images for ControlNet|
+|Resize Image 			| Resizes an image to specific dimensions|
+|Round Float            | Rounds a float to a specified number of decimal places|
+|Float to Integer       | Converts a float to an integer. Optionally rounds to an even multiple of a input number.|
+|Scale Image 			| Scales an image by a factor|
+|Image to Latents 			| Encodes an image into latents.|
+|Add Invisible Watermark 			| Add an invisible watermark to an image|
+|Solid Color Infill 			| Infills transparent areas of an image with a solid color|
+|PatchMatch Infill 			| Infills transparent areas of an image using the PatchMatch algorithm|
+|Tile Infill 			| Infills transparent areas of an image with tiles of the image|
+|Integer Primitive Collection 			| A collection of integer primitive values|
+|Integer Primitive 			| An integer primitive value|
+|Iterate 			| Iterates over a list of items|
+|Latents Primitive Collection 			| A collection of latents tensor primitive values|
+|Latents Primitive 			| A latents tensor primitive value|
+|Latents to Image 			| Generates an image from latents.|
+|Leres (Depth) Processor 			| Applies leres processing to image|
+|Lineart Anime Processor 			| Applies line art anime processing to image|
+|Lineart Processor 			| Applies line art processing to image|
+|LoRA Loader 			| Apply selected lora to unet and text_encoder.|
+|Main Model Loader 			| Loads a main model, outputting its submodels.|
+|Combine Mask 			| Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.|
+|Mask Edge 			| Applies an edge mask to an image|
+|Mask from Alpha 			| Extracts the alpha channel of an image as a mask.|
+|Mediapipe Face Processor 			| Applies mediapipe face processing to image|
+|Midas (Depth) Processor 			| Applies Midas depth processing to image|
+|MLSD Processor 			| Applies MLSD processing to image|
+|Multiply Integers 			| Multiplies two numbers|
+|Noise 			| Generates latent noise.|
+|Normal BAE Processor 			| Applies NormalBae processing to image|
+|ONNX Latents to Image 			| Generates an image from latents.|
+|ONNX Prompt (Raw) 			| A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
+|ONNX Text to Latents 			| Generates latents from conditionings.|
+|ONNX Model Loader 			| Loads a main model, outputting its submodels.|
+|OpenCV Inpaint 			| Simple inpaint using opencv.|
+|Openpose Processor 			| Applies Openpose processing to image|
+|PIDI Processor 			| Applies PIDI processing to image|
+|Prompts from File 			| Loads prompts from a text file|
+|Random Integer 			| Outputs a single random integer.|
+|Random Range 			| Creates a collection of random numbers|
+|Integer Range 			| Creates a range of numbers from start to stop with step|
+|Integer Range of Size 			| Creates a range from start to start + size with step|
+|Resize Latents 			| Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.|
+|SDXL Compel Prompt 			| Parse prompt using compel package to conditioning.|
+|SDXL LoRA Loader 			| Apply selected lora to unet and text_encoder.|
+|SDXL Main Model Loader 			| Loads an sdxl base model, outputting its submodels.|
+|SDXL Refiner Compel Prompt 			| Parse prompt using compel package to conditioning.|
+|SDXL Refiner Model Loader 			| Loads an sdxl refiner model, outputting its submodels.|
+|Scale Latents 			| Scales latents by a given factor.|
+|Segment Anything Processor 			| Applies segment anything processing to image|
+|Show Image 			| Displays a provided image, and passes it forward in the pipeline.|
+|Step Param Easing 			| Experimental per-step parameter easing for denoising steps|
+|String Primitive Collection 			| A collection of string primitive values|
+|String Primitive 			| A string primitive value|
+|Subtract Integers 			| Subtracts two numbers|
+|Tile Resample Processor 			| Tile resampler processor|
+|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
+|VAE Loader 			| Loads a VAE model, outputting a VaeLoaderOutput|
+|Zoe (Depth) Processor 			| Applies Zoe depth processing to image|

docs/nodes/exampleWorkflows.md

@@ -7,12 +7,12 @@ To use them, right click on your desired workflow, follow the link to GitHub and
 If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
 
 * [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
-* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
-* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
-* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
+* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_Text_to_Image.json)
+* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
+* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
 * [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json)
-* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Prompt_from_File.json)
-* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json)
+* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json)
+* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json.json)
 * [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
 * [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)
-* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/QR_Code_Monster.json)
+* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/QR_Code_Monster.json)

installer/create_installer.sh

@@ -2,72 +2,43 @@
 
 set -e
 
-BCYAN="\e[1;36m"
-BYELLOW="\e[1;33m"
-BGREEN="\e[1;32m"
-BRED="\e[1;31m"
-RED="\e[31m"
-RESET="\e[0m"
-
-function is_bin_in_path {
-    builtin type -P "$1" &>/dev/null
-}
-
-function git_show {
-    git show -s --format='%h %s' $1
-}
-
 cd "$(dirname "$0")"
 
-echo -e "${BYELLOW}This script must be run from the installer directory!${RESET}"
-echo "The current working directory is $(pwd)"
-read -p "If that looks right, press any key to proceed, or CTRL-C to exit..."
-echo
-
-# Some machines only have `python3` in PATH, others have `python` - make an alias.
-# We can use a function to approximate an alias within a non-interactive shell.
-if ! is_bin_in_path python && is_bin_in_path python3; then
-    function python {
-        python3 "$@"
-    }
-fi
-
 if [[ -v "VIRTUAL_ENV" ]]; then
     # we can't just call 'deactivate' because this function is not exported
     # to the environment of this script from the bash process that runs the script
-    echo -e "${BRED}A virtual environment is activated. Please deactivate it before proceeding.${RESET}"
+    echo "A virtual environment is activated. Please deactivate it before proceeding".
     exit -1
 fi
 
-VERSION=$(
-    cd ..
-    python -c "from invokeai.version import __version__ as version; print(version)"
-)
+VERSION=$(cd ..; python -c "from invokeai.version import __version__ as version; print(version)")
 PATCH=""
 VERSION="v${VERSION}${PATCH}"
+LATEST_TAG="v3-latest"
 
-echo -e "${BGREEN}HEAD${RESET}:"
-git_show
-echo
+echo Building installer for version $VERSION
+echo "Be certain that you're in the 'installer' directory before continuing."
+read -p "Press any key to continue, or CTRL-C to exit..."
 
-# ---------------------- FRONTEND ----------------------
+read -e -p "Tag this repo with '${VERSION}' and '${LATEST_TAG}'? [n]: " input
+RESPONSE=${input:='n'}
+if [ "$RESPONSE" == 'y' ]; then
 
-pushd ../invokeai/frontend/web >/dev/null
-echo
-echo "Installing frontend dependencies..."
-echo
-pnpm i --frozen-lockfile
-echo
-echo "Building frontend..."
-echo
-pnpm build
-popd
+    git push origin :refs/tags/$VERSION
+    if ! git tag -fa $VERSION ; then
+	    echo "Existing/invalid tag"
+	    exit -1
+    fi
 
-# ---------------------- BACKEND ----------------------
+    git push origin :refs/tags/$LATEST_TAG
+    git tag -fa $LATEST_TAG
 
-echo
-echo "Building wheel..."
-echo
+    echo "remember to push --tags!"
+fi
+
+# ----------------------
+
+echo Building the wheel
 
 # install the 'build' package in the user site packages, if needed
 # could be improved by using a temporary venv, but it's tiny and harmless
@@ -75,15 +46,12 @@ if [[ $(python -c 'from importlib.util import find_spec; print(find_spec("build"
     pip install --user build
 fi
 
-rm -rf ../build
-
+rm -r ../build
 python -m build --wheel --outdir dist/ ../.
 
 # ----------------------
 
-echo
-echo "Building installer zip files for InvokeAI ${VERSION}..."
-echo
+echo Building installer zip fles for InvokeAI $VERSION
 
 # get rid of any old ones
 rm -f *.zip
@@ -104,7 +72,7 @@ cp install.sh.in InvokeAI-Installer/install.sh
 chmod a+x InvokeAI-Installer/install.sh
 
 # Windows
-perl -p -e "s/^set INVOKEAI_VERSION=.*/set INVOKEAI_VERSION=$VERSION/" install.bat.in >InvokeAI-Installer/install.bat
+perl -p -e "s/^set INVOKEAI_VERSION=.*/set INVOKEAI_VERSION=$VERSION/" install.bat.in > InvokeAI-Installer/install.bat
 cp WinLongPathsEnabled.reg InvokeAI-Installer/
 
 # Zip everything up

installer/lib/installer.py

@@ -244,9 +244,9 @@ class InvokeAiInstance:
                 "numpy~=1.24.0",  # choose versions that won't be uninstalled during phase 2
                 "urllib3~=1.26.0",
                 "requests~=2.28.0",
-                "torch==2.1.1",
+                "torch~=2.1.0",
                 "torchmetrics==0.11.4",
-                "torchvision>=0.16.1",
+                "torchvision>=0.14.1",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

installer/tag_release.sh

@@ -1,71 +0,0 @@
-#!/bin/bash
-
-set -e
-
-BCYAN="\e[1;36m"
-BYELLOW="\e[1;33m"
-BGREEN="\e[1;32m"
-BRED="\e[1;31m"
-RED="\e[31m"
-RESET="\e[0m"
-
-function does_tag_exist {
-    git rev-parse --quiet --verify "refs/tags/$1" >/dev/null
-}
-
-function git_show_ref {
-    git show-ref --dereference $1 --abbrev 7
-}
-
-function git_show {
-    git show -s --format='%h %s' $1
-}
-
-VERSION=$(
-    cd ..
-    python -c "from invokeai.version import __version__ as version; print(version)"
-)
-PATCH=""
-MAJOR_VERSION=$(echo $VERSION | sed 's/\..*$//')
-VERSION="v${VERSION}${PATCH}"
-LATEST_TAG="v${MAJOR_VERSION}-latest"
-
-if does_tag_exist $VERSION; then
-    echo -e "${BCYAN}${VERSION}${RESET} already exists:"
-    git_show_ref tags/$VERSION
-    echo
-fi
-if does_tag_exist $LATEST_TAG; then
-    echo -e "${BCYAN}${LATEST_TAG}${RESET} already exists:"
-    git_show_ref tags/$LATEST_TAG
-    echo
-fi
-
-echo -e "${BGREEN}HEAD${RESET}:"
-git_show
-echo
-
-echo -e -n "Create tags ${BCYAN}${VERSION}${RESET} and ${BCYAN}${LATEST_TAG}${RESET} @ ${BGREEN}HEAD${RESET}, ${RED}deleting existing tags on remote${RESET}? "
-read -e -p 'y/n [n]: ' input
-RESPONSE=${input:='n'}
-if [ "$RESPONSE" == 'y' ]; then
-    echo
-    echo -e "Deleting ${BCYAN}${VERSION}${RESET} tag on remote..."
-    git push --delete origin $VERSION
-
-    echo -e "Tagging ${BGREEN}HEAD${RESET} with ${BCYAN}${VERSION}${RESET} locally..."
-    if ! git tag -fa $VERSION; then
-        echo "Existing/invalid tag"
-        exit -1
-    fi
-
-    echo -e "Deleting ${BCYAN}${LATEST_TAG}${RESET} tag on remote..."
-    git push --delete origin $LATEST_TAG
-
-    echo -e "Tagging ${BGREEN}HEAD${RESET} with ${BCYAN}${LATEST_TAG}${RESET} locally..."
-    git tag -fa $LATEST_TAG
-
-    echo -e "Pushing updated tags to remote..."
-    git push origin --tags
-fi
-exit 0

invokeai/app/api/dependencies.py

@@ -2,7 +2,7 @@
 
 from logging import Logger
 
-from invokeai.app.services.shared.sqlite.sqlite_util import init_db
+from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -23,7 +23,6 @@ from ..services.invoker import Invoker
 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_install import ModelInstallService
 from ..services.model_manager.model_manager_default import ModelManagerService
 from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
@@ -31,6 +30,7 @@ from ..services.session_processor.session_processor_default import DefaultSessio
 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 ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -67,9 +67,8 @@ class ApiDependencies:
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
         output_folder = config.output_path
-        image_files = DiskImageFileStorage(f"{output_folder}/images")
 
-        db = init_db(config=config, logger=logger, image_files=image_files)
+        db = SqliteDatabase(config, logger)
 
         configuration = config
         logger = logger
@@ -81,15 +80,13 @@ class ApiDependencies:
         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"))
         model_manager = ModelManagerService(config, logger)
         model_record_service = ModelRecordServiceSQL(db=db)
-        model_install_service = ModelInstallService(
-            app_config=config, record_store=model_record_service, event_bus=events
-        )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
         processor = DefaultInvocationProcessor()
@@ -97,6 +94,7 @@ class ApiDependencies:
         session_processor = DefaultSessionProcessor()
         session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
+        workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
         workflow_records = SqliteWorkflowRecordsStorage(db=db)
 
         services = InvocationServices(
@@ -116,7 +114,6 @@ class ApiDependencies:
             logger=logger,
             model_manager=model_manager,
             model_records=model_record_service,
-            model_install=model_install_service,
             names=names,
             performance_statistics=performance_statistics,
             processor=processor,
@@ -124,12 +121,14 @@ class ApiDependencies:
             session_processor=session_processor,
             session_queue=session_queue,
             urls=urls,
+            workflow_image_records=workflow_image_records,
             workflow_records=workflow_records,
         )
 
         create_system_graphs(services.graph_library)
 
         ApiDependencies.invoker = Invoker(services)
+
         db.clean()
 
     @staticmethod

invokeai/app/api/routers/app_info.py

@@ -1,11 +1,7 @@
 import typing
 from enum import Enum
-from importlib.metadata import PackageNotFoundError, version
 from pathlib import Path
-from platform import python_version
-from typing import Optional
 
-import torch
 from fastapi import Body
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
@@ -44,24 +40,6 @@ class AppVersion(BaseModel):
     version: str = Field(description="App version")
 
 
-class AppDependencyVersions(BaseModel):
-    """App depencency Versions Response"""
-
-    accelerate: str = Field(description="accelerate version")
-    compel: str = Field(description="compel version")
-    cuda: Optional[str] = Field(description="CUDA version")
-    diffusers: str = Field(description="diffusers version")
-    numpy: str = Field(description="Numpy version")
-    opencv: str = Field(description="OpenCV version")
-    onnx: str = Field(description="ONNX version")
-    pillow: str = Field(description="Pillow (PIL) version")
-    python: str = Field(description="Python version")
-    torch: str = Field(description="PyTorch version")
-    torchvision: str = Field(description="PyTorch Vision version")
-    transformers: str = Field(description="transformers version")
-    xformers: Optional[str] = Field(description="xformers version")
-
-
 class AppConfig(BaseModel):
     """App Config Response"""
 
@@ -76,29 +54,6 @@ async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
 
 
-@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions)
-async def get_app_deps() -> AppDependencyVersions:
-    try:
-        xformers = version("xformers")
-    except PackageNotFoundError:
-        xformers = None
-    return AppDependencyVersions(
-        accelerate=version("accelerate"),
-        compel=version("compel"),
-        cuda=torch.version.cuda,
-        diffusers=version("diffusers"),
-        numpy=version("numpy"),
-        opencv=version("opencv-python"),
-        onnx=version("onnx"),
-        pillow=version("pillow"),
-        python=python_version(),
-        torch=torch.version.__version__,
-        torchvision=version("torchvision"),
-        transformers=version("transformers"),
-        xformers=xformers,
-    )
-
-
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
 async def get_config() -> AppConfig:
     infill_methods = ["tile", "lama", "cv2"]

invokeai/app/api/routers/images.py

@@ -8,11 +8,10 @@ from fastapi.routing import APIRouter
 from PIL import Image
 from pydantic import BaseModel, Field, ValidationError
 
-from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
+from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID, WorkflowWithoutIDValidator
 
 from ..dependencies import ApiDependencies
 
@@ -74,7 +73,7 @@ async def upload_image(
     workflow_raw = pil_image.info.get("invokeai_workflow", None)
     if workflow_raw is not None:
         try:
-            workflow = WorkflowWithoutIDValidator.validate_json(workflow_raw)
+            workflow = WorkflowFieldValidator.validate_json(workflow_raw)
         except ValidationError:
             ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
             pass
@@ -185,18 +184,6 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-@images_router.get(
-    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=Optional[WorkflowWithoutID]
-)
-async def get_image_workflow(
-    image_name: str = Path(description="The name of image whose workflow to get"),
-) -> Optional[WorkflowWithoutID]:
-    try:
-        return ApiDependencies.invoker.services.images.get_workflow(image_name)
-    except Exception:
-        raise HTTPException(status_code=404)
-
-
 @images_router.api_route(
     "/i/{image_name}/full",
     methods=["GET", "HEAD"],

invokeai/app/api/routers/model_records.py

@@ -4,7 +4,7 @@
 
 from hashlib import sha1
 from random import randbytes
-from typing import Any, Dict, List, Optional
+from typing import List, Optional
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
@@ -12,7 +12,6 @@ from pydantic import BaseModel, ConfigDict
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
-from invokeai.app.services.model_install import ModelInstallJob, ModelSource
 from invokeai.app.services.model_records import (
     DuplicateModelException,
     InvalidModelException,
@@ -26,7 +25,7 @@ from invokeai.backend.model_manager.config import (
 
 from ..dependencies import ApiDependencies
 
-model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager_v2"])
+model_records_router = APIRouter(prefix="/v1/model/record", tags=["models"])
 
 
 class ModelsList(BaseModel):
@@ -44,18 +43,15 @@ class ModelsList(BaseModel):
 async def list_model_records(
     base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
     model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
-    model_name: Optional[str] = Query(default=None, description="Exact match on the name of the model"),
 ) -> ModelsList:
     """Get a list of models."""
     record_store = ApiDependencies.invoker.services.model_records
     found_models: list[AnyModelConfig] = []
     if base_models:
         for base_model in base_models:
-            found_models.extend(
-                record_store.search_by_attr(base_model=base_model, model_type=model_type, model_name=model_name)
-            )
+            found_models.extend(record_store.search_by_attr(base_model=base_model, model_type=model_type))
     else:
-        found_models.extend(record_store.search_by_attr(model_type=model_type, model_name=model_name))
+        found_models.extend(record_store.search_by_attr(model_type=model_type))
     return ModelsList(models=found_models)
 
 
@@ -121,17 +117,12 @@ async def update_model_record(
 async def del_model_record(
     key: str = Path(description="Unique key of model to remove from model registry."),
 ) -> Response:
-    """
-    Delete model record from database.
-
-    The configuration record will be removed. The corresponding weights files will be
-    deleted as well if they reside within the InvokeAI "models" directory.
-    """
+    """Delete Model"""
     logger = ApiDependencies.invoker.services.logger
 
     try:
-        installer = ApiDependencies.invoker.services.model_install
-        installer.delete(key)
+        record_store = ApiDependencies.invoker.services.model_records
+        record_store.del_model(key)
         logger.info(f"Deleted model: {key}")
         return Response(status_code=204)
     except UnknownModelException as e:
@@ -150,7 +141,7 @@ async def del_model_record(
     status_code=201,
 )
 async def add_model_record(
-    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
+    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")]
 ) -> AnyModelConfig:
     """
     Add a model using the configuration information appropriate for its type.
@@ -171,145 +162,3 @@ async def add_model_record(
 
     # now fetch it out
     return record_store.get_model(config.key)
-
-
-@model_records_router.post(
-    "/import",
-    operation_id="import_model_record",
-    responses={
-        201: {"description": "The model imported successfully"},
-        415: {"description": "Unrecognized file/folder format"},
-        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
-    },
-    status_code=201,
-)
-async def import_model(
-    source: ModelSource,
-    config: Optional[Dict[str, Any]] = Body(
-        description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
-        default=None,
-    ),
-) -> ModelInstallJob:
-    """Add a model using its local path, repo_id, or remote URL.
-
-    Models will be downloaded, probed, configured and installed in a
-    series of background threads. The return object has `status` attribute
-    that can be used to monitor progress.
-
-    The source object is a discriminated Union of LocalModelSource,
-    HFModelSource and URLModelSource. Set the "type" field to the
-    appropriate value:
-
-    * To install a local path using LocalModelSource, pass a source of form:
-      `{
-        "type": "local",
-        "path": "/path/to/model",
-        "inplace": false
-      }`
-       The "inplace" flag, if true, will register the model in place in its
-       current filesystem location. Otherwise, the model will be copied
-       into the InvokeAI models directory.
-
-    * To install a HuggingFace repo_id using HFModelSource, pass a source of form:
-      `{
-        "type": "hf",
-        "repo_id": "stabilityai/stable-diffusion-2.0",
-        "variant": "fp16",
-        "subfolder": "vae",
-        "access_token": "f5820a918aaf01"
-      }`
-     The `variant`, `subfolder` and `access_token` fields are optional.
-
-    * To install a remote model using an arbitrary URL, pass:
-      `{
-        "type": "url",
-        "url": "http://www.civitai.com/models/123456",
-        "access_token": "f5820a918aaf01"
-      }`
-    The `access_token` field is optonal
-
-    The model's configuration record will be probed and filled in
-    automatically.  To override the default guesses, pass "metadata"
-    with a Dict containing the attributes you wish to override.
-
-    Installation occurs in the background. Either use list_model_install_jobs()
-    to poll for completion, or listen on the event bus for the following events:
-
-      "model_install_started"
-      "model_install_completed"
-      "model_install_error"
-
-    On successful completion, the event's payload will contain the field "key"
-    containing the installed ID of the model. On an error, the event's payload
-    will contain the fields "error_type" and "error" describing the nature of the
-    error and its traceback, respectively.
-
-    """
-    logger = ApiDependencies.invoker.services.logger
-
-    try:
-        installer = ApiDependencies.invoker.services.model_install
-        result: ModelInstallJob = installer.import_model(
-            source=source,
-            config=config,
-        )
-        logger.info(f"Started installation of {source}")
-    except UnknownModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=424, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    return result
-
-
-@model_records_router.get(
-    "/import",
-    operation_id="list_model_install_jobs",
-)
-async def list_model_install_jobs() -> List[ModelInstallJob]:
-    """
-    Return list of model install jobs.
-
-    If the optional 'source' argument is provided, then the list will be filtered
-    for partial string matches against the install source.
-    """
-    jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_install.list_jobs()
-    return jobs
-
-
-@model_records_router.patch(
-    "/import",
-    operation_id="prune_model_install_jobs",
-    responses={
-        204: {"description": "All completed and errored jobs have been pruned"},
-        400: {"description": "Bad request"},
-    },
-)
-async def prune_model_install_jobs() -> Response:
-    """
-    Prune all completed and errored jobs from the install job list.
-    """
-    ApiDependencies.invoker.services.model_install.prune_jobs()
-    return Response(status_code=204)
-
-
-@model_records_router.patch(
-    "/sync",
-    operation_id="sync_models_to_config",
-    responses={
-        204: {"description": "Model config record database resynced with files on disk"},
-        400: {"description": "Bad request"},
-    },
-)
-async def sync_models_to_config() -> Response:
-    """
-    Traverse the models and autoimport directories. Model files without a corresponding
-    record in the database are added. Orphan records without a models file are deleted.
-    """
-    ApiDependencies.invoker.services.model_install.sync_to_config()
-    return Response(status_code=204)

invokeai/app/api/routers/workflows.py

@@ -1,19 +1,7 @@
-from typing import Optional
-
-from fastapi import APIRouter, Body, HTTPException, Path, Query
+from fastapi import APIRouter, Path
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
-from invokeai.app.services.workflow_records.workflow_records_common import (
-    Workflow,
-    WorkflowCategory,
-    WorkflowNotFoundError,
-    WorkflowRecordDTO,
-    WorkflowRecordListItemDTO,
-    WorkflowRecordOrderBy,
-    WorkflowWithoutID,
-)
+from invokeai.app.invocations.baseinvocation import WorkflowField
 
 workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
 
@@ -22,76 +10,11 @@ workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
     "/i/{workflow_id}",
     operation_id="get_workflow",
     responses={
-        200: {"model": WorkflowRecordDTO},
+        200: {"model": WorkflowField},
     },
 )
 async def get_workflow(
     workflow_id: str = Path(description="The workflow to get"),
-) -> WorkflowRecordDTO:
+) -> WorkflowField:
     """Gets a workflow"""
-    try:
-        return ApiDependencies.invoker.services.workflow_records.get(workflow_id)
-    except WorkflowNotFoundError:
-        raise HTTPException(status_code=404, detail="Workflow not found")
-
-
-@workflows_router.patch(
-    "/i/{workflow_id}",
-    operation_id="update_workflow",
-    responses={
-        200: {"model": WorkflowRecordDTO},
-    },
-)
-async def update_workflow(
-    workflow: Workflow = Body(description="The updated workflow", embed=True),
-) -> WorkflowRecordDTO:
-    """Updates a workflow"""
-    return ApiDependencies.invoker.services.workflow_records.update(workflow=workflow)
-
-
-@workflows_router.delete(
-    "/i/{workflow_id}",
-    operation_id="delete_workflow",
-)
-async def delete_workflow(
-    workflow_id: str = Path(description="The workflow to delete"),
-) -> None:
-    """Deletes a workflow"""
-    ApiDependencies.invoker.services.workflow_records.delete(workflow_id)
-
-
-@workflows_router.post(
-    "/",
-    operation_id="create_workflow",
-    responses={
-        200: {"model": WorkflowRecordDTO},
-    },
-)
-async def create_workflow(
-    workflow: WorkflowWithoutID = Body(description="The workflow to create", embed=True),
-) -> WorkflowRecordDTO:
-    """Creates a workflow"""
-    return ApiDependencies.invoker.services.workflow_records.create(workflow=workflow)
-
-
-@workflows_router.get(
-    "/",
-    operation_id="list_workflows",
-    responses={
-        200: {"model": PaginatedResults[WorkflowRecordListItemDTO]},
-    },
-)
-async def list_workflows(
-    page: int = Query(default=0, description="The page to get"),
-    per_page: int = Query(default=10, description="The number of workflows per page"),
-    order_by: WorkflowRecordOrderBy = Query(
-        default=WorkflowRecordOrderBy.Name, description="The attribute to order by"
-    ),
-    direction: SQLiteDirection = Query(default=SQLiteDirection.Ascending, description="The direction to order by"),
-    category: WorkflowCategory = Query(default=WorkflowCategory.User, description="The category of workflow to get"),
-    query: Optional[str] = Query(default=None, description="The text to query by (matches name and description)"),
-) -> PaginatedResults[WorkflowRecordListItemDTO]:
-    """Gets a page of workflows"""
-    return ApiDependencies.invoker.services.workflow_records.get_many(
-        page=page, per_page=per_page, order_by=order_by, direction=direction, query=query, category=category
-    )
+    return ApiDependencies.invoker.services.workflow_records.get(workflow_id)

invokeai/app/api/sockets.py

@@ -20,7 +20,6 @@ class SocketIO:
         self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)
         self.__sio.on("unsubscribe_queue", handler=self._handle_unsub_queue)
         local_handler.register(event_name=EventServiceBase.queue_event, _func=self._handle_queue_event)
-        local_handler.register(event_name=EventServiceBase.model_event, _func=self._handle_model_event)
 
     async def _handle_queue_event(self, event: Event):
         await self.__sio.emit(
@@ -29,13 +28,10 @@ class SocketIO:
             room=event[1]["data"]["queue_id"],
         )
 
-    async def _handle_sub_queue(self, sid, data, *args, **kwargs) -> None:
+    async def _handle_sub_queue(self, sid, data, *args, **kwargs):
         if "queue_id" in data:
             await self.__sio.enter_room(sid, data["queue_id"])
 
-    async def _handle_unsub_queue(self, sid, data, *args, **kwargs) -> None:
+    async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
         if "queue_id" in data:
             await self.__sio.leave_room(sid, data["queue_id"])
-
-    async def _handle_model_event(self, event: Event) -> None:
-        await self.__sio.emit(event=event[1]["event"], data=event[1]["data"])

invokeai/app/api_app.py

@@ -1,17 +1,14 @@
-# parse_args() must be called before any other imports. if it is not called first, consumers of the config
-# which are imported/used before parse_args() is called will get the default config values instead of the
-# values from the command line or config file.
-import sys
+from typing import Any
 
-from invokeai.version.invokeai_version import __version__
+from fastapi.responses import HTMLResponse
 
 from .services.config import InvokeAIAppConfig
 
+# parse_args() must be called before any other imports. if it is not called first, consumers of the config
+# which are imported/used before parse_args() is called will get the default config values instead of the
+# values from the command line or config file.
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
-if app_config.version:
-    print(f"InvokeAI version {__version__}")
-    sys.exit(0)
 
 if True:  # hack to make flake8 happy with imports coming after setting up the config
     import asyncio
@@ -19,7 +16,6 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     import socket
     from inspect import signature
     from pathlib import Path
-    from typing import Any
 
     import uvicorn
     from fastapi import FastAPI
@@ -27,7 +23,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     from fastapi.middleware.gzip import GZipMiddleware
     from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
     from fastapi.openapi.utils import get_openapi
-    from fastapi.responses import FileResponse, HTMLResponse
+    from fastapi.responses import FileResponse
     from fastapi.staticfiles import StaticFiles
     from fastapi_events.handlers.local import local_handler
     from fastapi_events.middleware import EventHandlerASGIMiddleware
@@ -38,6 +34,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     # noinspection PyUnresolvedReferences
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
     import invokeai.frontend.web as web_dir
+    from invokeai.version.invokeai_version import __version__
 
     from ..backend.util.logging import InvokeAILogger
     from .api.dependencies import ApiDependencies
@@ -54,12 +51,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         workflows,
     )
     from .api.sockets import SocketIO
-    from .invocations.baseinvocation import (
-        BaseInvocation,
-        InputFieldJSONSchemaExtra,
-        OutputFieldJSONSchemaExtra,
-        UIConfigBase,
-    )
+    from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
 
     if is_mps_available():
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
@@ -155,11 +147,7 @@ def custom_openapi() -> dict[str, Any]:
 
     # Add Node Editor UI helper schemas
     ui_config_schemas = models_json_schema(
-        [
-            (UIConfigBase, "serialization"),
-            (InputFieldJSONSchemaExtra, "serialization"),
-            (OutputFieldJSONSchemaExtra, "serialization"),
-        ],
+        [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
         ref_template="#/components/schemas/{model}",
     )
     for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
@@ -167,7 +155,7 @@ def custom_openapi() -> dict[str, Any]:
 
     # Add a reference to the output type to additionalProperties of the invoker schema
     for invoker in all_invocations:
-        invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
+        invoker_name = invoker.__name__
         output_type = signature(obj=invoker.invoke).return_annotation
         output_type_title = output_type_titles[output_type.__name__]
         invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
@@ -219,19 +207,18 @@ def overridden_redoc() -> HTMLResponse:
 
 web_root_path = Path(list(web_dir.__path__)[0])
 
-# Only serve the UI if we it has a build
-if (web_root_path / "dist").exists():
-    # Cannot add headers to StaticFiles, so we must serve index.html with a custom route
-    # Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
-    @app.get("/", include_in_schema=False, name="ui_root")
-    def get_index() -> FileResponse:
-        return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
 
-    # # Must mount *after* the other routes else it borks em
-    app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
-    app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
+# Cannot add headers to StaticFiles, so we must serve index.html with a custom route
+# Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
+@app.get("/", include_in_schema=False, name="ui_root")
+def get_index() -> FileResponse:
+    return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
 
+
+# # Must mount *after* the other routes else it borks em
 app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static")  # docs favicon is in here
+app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
+app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
 
 
 def invoke_api() -> None:
@@ -272,8 +259,6 @@ def invoke_api() -> None:
         port=port,
         loop="asyncio",
         log_level=app_config.log_level,
-        ssl_certfile=app_config.ssl_certfile,
-        ssl_keyfile=app_config.ssl_keyfile,
     )
     server = uvicorn.Server(config)
 
@@ -288,4 +273,7 @@ def invoke_api() -> None:
 
 
 if __name__ == "__main__":
-    invoke_api()
+    if app_config.version:
+        print(f"InvokeAI version {__version__}")
+    else:
+        invoke_api()

invokeai/app/invocations/__init__.py

@@ -5,7 +5,7 @@ from pathlib import Path
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 
-custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
+custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
 custom_nodes_path.mkdir(parents=True, exist_ok=True)
 
 custom_nodes_init_path = str(custom_nodes_path / "__init__.py")

invokeai/app/invocations/baseinvocation.py

@@ -1,15 +1,14 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI team
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from __future__ import annotations
 
 import inspect
 import re
-import warnings
 from abc import ABC, abstractmethod
 from enum import Enum
 from inspect import signature
 from types import UnionType
-from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
 
 import semver
 from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
@@ -17,19 +16,12 @@ from pydantic.fields import FieldInfo, _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
-from invokeai.backend.util.logging import InvokeAILogger
 
 if TYPE_CHECKING:
     from ..services.invocation_services import InvocationServices
 
-logger = InvokeAILogger.get_logger()
-
-CUSTOM_NODE_PACK_SUFFIX = "__invokeai-custom-node"
-
 
 class InvalidVersionError(ValueError):
     pass
@@ -39,20 +31,7 @@ class InvalidFieldError(TypeError):
     pass
 
 
-class Classification(str, Enum, metaclass=MetaEnum):
-    """
-    The classification of an Invocation.
-    - `Stable`: The invocation, including its inputs/outputs and internal logic, is stable. You may build workflows with it, having confidence that they will not break because of a change in this invocation.
-    - `Beta`: The invocation is not yet stable, but is planned to be stable in the future. Workflows built around this invocation may break, but we are committed to supporting this invocation long-term.
-    - `Prototype`: The invocation is not yet stable and may be removed from the application at any time. Workflows built around this invocation may break, and we are *not* committed to supporting this invocation.
-    """
-
-    Stable = "stable"
-    Beta = "beta"
-    Prototype = "prototype"
-
-
-class Input(str, Enum, metaclass=MetaEnum):
+class Input(str, Enum):
     """
     The type of input a field accepts.
     - `Input.Direct`: The field must have its value provided directly, when the invocation and field \
@@ -66,124 +45,86 @@ class Input(str, Enum, metaclass=MetaEnum):
     Any = "any"
 
 
-class FieldKind(str, Enum, metaclass=MetaEnum):
+class UIType(str, Enum):
     """
-    The kind of field.
-    - `Input`: An input field on a node.
-    - `Output`: An output field on a node.
-    - `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
-    one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
-    "metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
-    allowing "metadata" for that field.
-    - `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
-    but which are used to store information about the node. For example, the `id` and `type` fields are node
-    attributes.
-
-    The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
-    startup, and when generating the OpenAPI schema for the workflow editor.
+    Type hints for the UI.
+    If a field should be provided a data type that does not exactly match the python type of the field, \
+    use this to provide the type that should be used instead. See the node development docs for detail \
+    on adding a new field type, which involves client-side changes.
     """
 
-    Input = "input"
-    Output = "output"
-    Internal = "internal"
-    NodeAttribute = "node_attribute"
+    # region Primitives
+    Boolean = "boolean"
+    Color = "ColorField"
+    Conditioning = "ConditioningField"
+    Control = "ControlField"
+    Float = "float"
+    Image = "ImageField"
+    Integer = "integer"
+    Latents = "LatentsField"
+    String = "string"
+    # endregion
 
+    # region Collection Primitives
+    BooleanCollection = "BooleanCollection"
+    ColorCollection = "ColorCollection"
+    ConditioningCollection = "ConditioningCollection"
+    ControlCollection = "ControlCollection"
+    FloatCollection = "FloatCollection"
+    ImageCollection = "ImageCollection"
+    IntegerCollection = "IntegerCollection"
+    LatentsCollection = "LatentsCollection"
+    StringCollection = "StringCollection"
+    # endregion
 
-class UIType(str, Enum, metaclass=MetaEnum):
-    """
-    Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
+    # region Polymorphic Primitives
+    BooleanPolymorphic = "BooleanPolymorphic"
+    ColorPolymorphic = "ColorPolymorphic"
+    ConditioningPolymorphic = "ConditioningPolymorphic"
+    ControlPolymorphic = "ControlPolymorphic"
+    FloatPolymorphic = "FloatPolymorphic"
+    ImagePolymorphic = "ImagePolymorphic"
+    IntegerPolymorphic = "IntegerPolymorphic"
+    LatentsPolymorphic = "LatentsPolymorphic"
+    StringPolymorphic = "StringPolymorphic"
+    # endregion
 
-    - Model Fields
-    The most common node-author-facing use will be for model fields. Internally, there is no difference
-    between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
-    base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
-    the field is an SDXL main model field.
-
-    - Any Field
-    We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
-    indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
-
-    - Scheduler Field
-    Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
-
-    - Internal Fields
-    Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
-    handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
-    should not be used by node authors.
-
-    - DEPRECATED Fields
-    These types are deprecated and should not be used by node authors. A warning will be logged if one is
-    used, and the type will be ignored. They are included here for backwards compatibility.
-    """
-
-    # region Model Field Types
+    # region Models
+    MainModel = "MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
-    VaeModel = "VAEModelField"
+    VaeModel = "VaeModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
+    UNet = "UNetField"
+    Vae = "VaeField"
+    CLIP = "ClipField"
     # endregion
 
-    # region Misc Field Types
-    Scheduler = "SchedulerField"
-    Any = "AnyField"
+    # region Iterate/Collect
+    Collection = "Collection"
+    CollectionItem = "CollectionItem"
     # endregion
 
-    # region Internal Field Types
-    _Collection = "CollectionField"
-    _CollectionItem = "CollectionItemField"
-    # endregion
-
-    # region DEPRECATED
-    Boolean = "DEPRECATED_Boolean"
-    Color = "DEPRECATED_Color"
-    Conditioning = "DEPRECATED_Conditioning"
-    Control = "DEPRECATED_Control"
-    Float = "DEPRECATED_Float"
-    Image = "DEPRECATED_Image"
-    Integer = "DEPRECATED_Integer"
-    Latents = "DEPRECATED_Latents"
-    String = "DEPRECATED_String"
-    BooleanCollection = "DEPRECATED_BooleanCollection"
-    ColorCollection = "DEPRECATED_ColorCollection"
-    ConditioningCollection = "DEPRECATED_ConditioningCollection"
-    ControlCollection = "DEPRECATED_ControlCollection"
-    FloatCollection = "DEPRECATED_FloatCollection"
-    ImageCollection = "DEPRECATED_ImageCollection"
-    IntegerCollection = "DEPRECATED_IntegerCollection"
-    LatentsCollection = "DEPRECATED_LatentsCollection"
-    StringCollection = "DEPRECATED_StringCollection"
-    BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
-    ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
-    ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
-    ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
-    FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
-    ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
-    IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
-    LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
-    StringPolymorphic = "DEPRECATED_StringPolymorphic"
-    MainModel = "DEPRECATED_MainModel"
-    UNet = "DEPRECATED_UNet"
-    Vae = "DEPRECATED_Vae"
-    CLIP = "DEPRECATED_CLIP"
-    Collection = "DEPRECATED_Collection"
-    CollectionItem = "DEPRECATED_CollectionItem"
-    Enum = "DEPRECATED_Enum"
-    WorkflowField = "DEPRECATED_WorkflowField"
-    IsIntermediate = "DEPRECATED_IsIntermediate"
-    BoardField = "DEPRECATED_BoardField"
-    MetadataItem = "DEPRECATED_MetadataItem"
-    MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
-    MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
-    MetadataDict = "DEPRECATED_MetadataDict"
+    # region Misc
+    Enum = "enum"
+    Scheduler = "Scheduler"
+    WorkflowField = "WorkflowField"
+    IsIntermediate = "IsIntermediate"
+    BoardField = "BoardField"
+    Any = "Any"
+    MetadataItem = "MetadataItem"
+    MetadataItemCollection = "MetadataItemCollection"
+    MetadataItemPolymorphic = "MetadataItemPolymorphic"
+    MetadataDict = "MetadataDict"
     # endregion
 
 
-class UIComponent(str, Enum, metaclass=MetaEnum):
+class UIComponent(str, Enum):
     """
-    The type of UI component to use for a field, used to override the default components, which are
+    The type of UI component to use for a field, used to override the default components, which are \
     inferred from the field type.
     """
 
@@ -192,22 +133,21 @@ class UIComponent(str, Enum, metaclass=MetaEnum):
     Slider = "slider"
 
 
-class InputFieldJSONSchemaExtra(BaseModel):
+class _InputField(BaseModel):
     """
-    Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
-    and by the workflow editor during schema parsing and UI rendering.
+    *DO NOT USE*
+    This helper class is used to tell the client about our custom field attributes via OpenAPI
+    schema generation, and Typescript type generation from that schema. It serves no functional
+    purpose in the backend.
     """
 
     input: Input
-    orig_required: bool
-    field_kind: FieldKind
-    default: Optional[Any] = None
-    orig_default: Optional[Any] = None
-    ui_hidden: bool = False
-    ui_type: Optional[UIType] = None
-    ui_component: Optional[UIComponent] = None
-    ui_order: Optional[int] = None
-    ui_choice_labels: Optional[dict[str, str]] = None
+    ui_hidden: bool
+    ui_type: Optional[UIType]
+    ui_component: Optional[UIComponent]
+    ui_order: Optional[int]
+    ui_choice_labels: Optional[dict[str, str]]
+    item_default: Optional[Any]
 
     model_config = ConfigDict(
         validate_assignment=True,
@@ -215,13 +155,14 @@ class InputFieldJSONSchemaExtra(BaseModel):
     )
 
 
-class OutputFieldJSONSchemaExtra(BaseModel):
+class _OutputField(BaseModel):
     """
-    Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
-    during schema parsing and UI rendering.
+    *DO NOT USE*
+    This helper class is used to tell the client about our custom field attributes via OpenAPI
+    schema generation, and Typescript type generation from that schema. It serves no functional
+    purpose in the backend.
     """
 
-    field_kind: FieldKind
     ui_hidden: bool
     ui_type: Optional[UIType]
     ui_order: Optional[int]
@@ -232,9 +173,13 @@ class OutputFieldJSONSchemaExtra(BaseModel):
     )
 
 
+def get_type(klass: BaseModel) -> str:
+    """Helper function to get an invocation or invocation output's type. This is the default value of the `type` field."""
+    return klass.model_fields["type"].default
+
+
 def InputField(
     # copied from pydantic's Field
-    # TODO: Can we support default_factory?
     default: Any = _Unset,
     default_factory: Callable[[], Any] | None = _Unset,
     title: str | None = _Unset,
@@ -258,11 +203,12 @@ def InputField(
     ui_hidden: bool = False,
     ui_order: Optional[int] = None,
     ui_choice_labels: Optional[dict[str, str]] = None,
+    item_default: Optional[Any] = None,
 ) -> Any:
     """
     Creates an input field for an invocation.
 
-    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
+    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
     that adds a few extra parameters to support graph execution and the node editor UI.
 
     :param Input input: [Input.Any] The kind of input this field requires. \
@@ -282,58 +228,28 @@ def InputField(
       For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
       For this case, you could provide `UIComponent.Textarea`.
 
-    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
+    : param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
 
-    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
+    : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
 
-    :param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
+    : param bool item_default: [None] Specifies the default item value, if this is a collection input. \
+      Ignored for non-collection fields.
     """
 
-    json_schema_extra_ = InputFieldJSONSchemaExtra(
-        input=input,
-        ui_type=ui_type,
-        ui_component=ui_component,
-        ui_hidden=ui_hidden,
-        ui_order=ui_order,
-        ui_choice_labels=ui_choice_labels,
-        field_kind=FieldKind.Input,
-        orig_required=True,
-    )
+    json_schema_extra_: dict[str, Any] = {
+        "input": input,
+        "ui_type": ui_type,
+        "ui_component": ui_component,
+        "ui_hidden": ui_hidden,
+        "ui_order": ui_order,
+        "item_default": item_default,
+        "ui_choice_labels": ui_choice_labels,
+        "_field_kind": "input",
+    }
 
-    """
-    There is a conflict between the typing of invocation definitions and the typing of an invocation's
-    `invoke()` function.
-
-    On instantiation of a node, the invocation definition is used to create the python class. At this time,
-    any number of fields may be optional, because they may be provided by connections.
-
-    On calling of `invoke()`, however, those fields may be required.
-
-    For example, consider an ResizeImageInvocation with an `image: ImageField` field.
-
-    `image` is required during the call to `invoke()`, but when the python class is instantiated,
-    the field may not be present. This is fine, because that image field will be provided by a
-    connection from an ancestor node, which outputs an image.
-
-    This means we want to type the `image` field as optional for the node class definition, but required
-    for the `invoke()` function.
-
-    If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
-    `invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
-    any static type analysis tools will complain.
-
-    To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
-    but secretly make them optional in `InputField()`. We also store the original required bool and/or default
-    value. When we call `invoke()`, we use this stored information to do an additional check on the class.
-    """
-
-    if default_factory is not _Unset and default_factory is not None:
-        default = default_factory()
-        logger.warn('"default_factory" is not supported, calling it now to set "default"')
-
-    # These are the args we may wish pass to the pydantic `Field()` function
     field_args = {
         "default": default,
+        "default_factory": default_factory,
         "title": title,
         "description": description,
         "pattern": pattern,
@@ -350,34 +266,70 @@ def InputField(
         "max_length": max_length,
     }
 
-    # We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
+    """
+    Invocation definitions have their fields typed correctly for their `invoke()` functions.
+    This typing is often more specific than the actual invocation definition requires, because
+    fields may have values provided only by connections.
+
+    For example, consider an ResizeImageInvocation with an `image: ImageField` field.
+
+    `image` is required during the call to `invoke()`, but when the python class is instantiated,
+    the field may not be present. This is fine, because that image field will be provided by a
+    an ancestor node that outputs the image.
+
+    So we'd like to type that `image` field as `Optional[ImageField]`. If we do that, however, then
+    we need to handle a lot of extra logic in the `invoke()` function to check if the field has a
+    value or not. This is very tedious.
+
+    Ideally, the invocation definition would be able to specify that the field is required during
+    invocation, but optional during instantiation. So the field would be typed as `image: ImageField`,
+    but when calling the `invoke()` function, we raise an error if the field is not present.
+
+    To do this, we need to do a bit of fanagling to make the pydantic field optional, and then do
+    extra validation when calling `invoke()`.
+
+    There is some additional logic here to cleaning create the pydantic field via the wrapper.
+    """
+
+    # Filter out field args not provided
     provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
 
-    # Because we are manually making fields optional, we need to store the original required bool for reference later
-    json_schema_extra_.orig_required = default is PydanticUndefined
+    if (default is not PydanticUndefined) and (default_factory is not PydanticUndefined):
+        raise ValueError("Cannot specify both default and default_factory")
 
-    # Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
-    if input is Input.Any or input is Input.Connection:
+    # because we are manually making fields optional, we need to store the original required bool for reference later
+    if default is PydanticUndefined and default_factory is PydanticUndefined:
+        json_schema_extra_.update({"orig_required": True})
+    else:
+        json_schema_extra_.update({"orig_required": False})
+
+    # make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
+    if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
         default_ = None if default is PydanticUndefined else default
         provided_args.update({"default": default_})
         if default is not PydanticUndefined:
-            # Before invoking, we'll check for the original default value and set it on the field if the field has no value
-            json_schema_extra_.default = default
-            json_schema_extra_.orig_default = default
-    elif default is not PydanticUndefined:
+            # before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
+            json_schema_extra_.update({"default": default})
+            json_schema_extra_.update({"orig_default": default})
+    elif default is not PydanticUndefined and default_factory is PydanticUndefined:
         default_ = default
         provided_args.update({"default": default_})
-        json_schema_extra_.orig_default = default_
+        json_schema_extra_.update({"orig_default": default_})
+    elif default_factory is not PydanticUndefined:
+        provided_args.update({"default_factory": default_factory})
+        # TODO: cannot serialize default_factory...
+        # json_schema_extra_.update(dict(orig_default_factory=default_factory))
 
     return Field(
         **provided_args,
-        json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
+        json_schema_extra=json_schema_extra_,
     )
 
 
 def OutputField(
     # copied from pydantic's Field
     default: Any = _Unset,
+    default_factory: Callable[[], Any] | None = _Unset,
     title: str | None = _Unset,
     description: str | None = _Unset,
     pattern: str | None = _Unset,
@@ -410,12 +362,13 @@ def OutputField(
       `MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
       `UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
 
-    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
+    : param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
 
-    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
+    : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
     """
     return Field(
         default=default,
+        default_factory=default_factory,
         title=title,
         description=description,
         pattern=pattern,
@@ -430,12 +383,12 @@ def OutputField(
         decimal_places=decimal_places,
         min_length=min_length,
         max_length=max_length,
-        json_schema_extra=OutputFieldJSONSchemaExtra(
-            ui_type=ui_type,
-            ui_hidden=ui_hidden,
-            ui_order=ui_order,
-            field_kind=FieldKind.Output,
-        ).model_dump(exclude_none=True),
+        json_schema_extra={
+            "ui_type": ui_type,
+            "ui_hidden": ui_hidden,
+            "ui_order": ui_order,
+            "_field_kind": "output",
+        },
     )
 
 
@@ -448,11 +401,10 @@ class UIConfigBase(BaseModel):
     tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
     title: Optional[str] = Field(default=None, description="The node's display name")
     category: Optional[str] = Field(default=None, description="The node's category")
-    version: str = Field(
+    version: Optional[str] = Field(
+        default=None,
         description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
     )
-    node_pack: Optional[str] = Field(default=None, description="Whether or not this is a custom node")
-    classification: Classification = Field(default=Classification.Stable, description="The node's classification")
 
     model_config = ConfigDict(
         validate_assignment=True,
@@ -468,7 +420,6 @@ class InvocationContext:
     queue_id: str
     queue_item_id: int
     queue_batch_id: str
-    workflow: Optional[WorkflowWithoutID]
 
     def __init__(
         self,
@@ -477,14 +428,12 @@ class InvocationContext:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        workflow: Optional[WorkflowWithoutID],
     ):
         self.services = services
         self.graph_execution_state_id = graph_execution_state_id
         self.queue_id = queue_id
         self.queue_item_id = queue_item_id
         self.queue_batch_id = queue_batch_id
-        self.workflow = workflow
 
 
 class BaseInvocationOutput(BaseModel):
@@ -498,39 +447,29 @@ class BaseInvocationOutput(BaseModel):
 
     @classmethod
     def register_output(cls, output: BaseInvocationOutput) -> None:
-        """Registers an invocation output."""
         cls._output_classes.add(output)
 
     @classmethod
     def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
-        """Gets all invocation outputs."""
         return cls._output_classes
 
     @classmethod
     def get_outputs_union(cls) -> UnionType:
-        """Gets a union of all invocation outputs."""
         outputs_union = Union[tuple(cls._output_classes)]  # type: ignore [valid-type]
         return outputs_union  # type: ignore [return-value]
 
     @classmethod
     def get_output_types(cls) -> Iterable[str]:
-        """Gets all invocation output types."""
-        return (i.get_type() for i in BaseInvocationOutput.get_outputs())
+        return (get_type(i) for i in BaseInvocationOutput.get_outputs())
 
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
-        """Adds various UI-facing attributes to the invocation output's OpenAPI schema."""
         # Because we use a pydantic Literal field with default value for the invocation type,
         # it will be typed as optional in the OpenAPI schema. Make it required manually.
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
         schema["required"].extend(["type"])
 
-    @classmethod
-    def get_type(cls) -> str:
-        """Gets the invocation output's type, as provided by the `@invocation_output` decorator."""
-        return cls.model_fields["type"].default
-
     model_config = ConfigDict(
         protected_namespaces=(),
         validate_assignment=True,
@@ -560,29 +499,21 @@ class BaseInvocation(ABC, BaseModel):
 
     _invocation_classes: ClassVar[set[BaseInvocation]] = set()
 
-    @classmethod
-    def get_type(cls) -> str:
-        """Gets the invocation's type, as provided by the `@invocation` decorator."""
-        return cls.model_fields["type"].default
-
     @classmethod
     def register_invocation(cls, invocation: BaseInvocation) -> None:
-        """Registers an invocation."""
         cls._invocation_classes.add(invocation)
 
     @classmethod
     def get_invocations_union(cls) -> UnionType:
-        """Gets a union of all invocation types."""
         invocations_union = Union[tuple(cls._invocation_classes)]  # type: ignore [valid-type]
         return invocations_union  # type: ignore [return-value]
 
     @classmethod
     def get_invocations(cls) -> Iterable[BaseInvocation]:
-        """Gets all invocations, respecting the allowlist and denylist."""
         app_config = InvokeAIAppConfig.get_config()
         allowed_invocations: set[BaseInvocation] = set()
         for sc in cls._invocation_classes:
-            invocation_type = sc.get_type()
+            invocation_type = get_type(sc)
             is_in_allowlist = (
                 invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
             )
@@ -595,33 +526,28 @@ class BaseInvocation(ABC, BaseModel):
 
     @classmethod
     def get_invocations_map(cls) -> dict[str, BaseInvocation]:
-        """Gets a map of all invocation types to their invocation classes."""
-        return {i.get_type(): i for i in BaseInvocation.get_invocations()}
+        # Get the type strings out of the literals and into a dictionary
+        return {get_type(i): i for i in BaseInvocation.get_invocations()}
 
     @classmethod
     def get_invocation_types(cls) -> Iterable[str]:
-        """Gets all invocation types."""
-        return (i.get_type() for i in BaseInvocation.get_invocations())
+        return (get_type(i) for i in BaseInvocation.get_invocations())
 
     @classmethod
-    def get_output_annotation(cls) -> BaseInvocationOutput:
-        """Gets the invocation's output annotation (i.e. the return annotation of its `invoke()` method)."""
+    def get_output_type(cls) -> BaseInvocationOutput:
         return signature(cls.invoke).return_annotation
 
     @staticmethod
-    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel], *args, **kwargs) -> None:
-        """Adds various UI-facing attributes to the invocation's OpenAPI schema."""
-        uiconfig = cast(UIConfigBase | None, getattr(model_class, "UIConfig", None))
-        if uiconfig is not None:
-            if uiconfig.title is not None:
-                schema["title"] = uiconfig.title
-            if uiconfig.tags is not None:
-                schema["tags"] = uiconfig.tags
-            if uiconfig.category is not None:
-                schema["category"] = uiconfig.category
-            if uiconfig.node_pack is not None:
-                schema["node_pack"] = uiconfig.node_pack
-            schema["classification"] = uiconfig.classification
+    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
+        # Add the various UI-facing attributes to the schema. These are used to build the invocation templates.
+        uiconfig = getattr(model_class, "UIConfig", None)
+        if uiconfig and hasattr(uiconfig, "title"):
+            schema["title"] = uiconfig.title
+        if uiconfig and hasattr(uiconfig, "tags"):
+            schema["tags"] = uiconfig.tags
+        if uiconfig and hasattr(uiconfig, "category"):
+            schema["category"] = uiconfig.category
+        if uiconfig and hasattr(uiconfig, "version"):
             schema["version"] = uiconfig.version
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
@@ -633,10 +559,6 @@ class BaseInvocation(ABC, BaseModel):
         pass
 
     def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
-        """
-        Internal invoke method, calls `invoke()` after some prep.
-        Handles optional fields that are required to call `invoke()` and invocation cache.
-        """
         for field_name, field in self.model_fields.items():
             if not field.json_schema_extra or callable(field.json_schema_extra):
                 # something has gone terribly awry, we should always have this and it should be a dict
@@ -676,20 +598,21 @@ class BaseInvocation(ABC, BaseModel):
             context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
             return self.invoke(context)
 
+    def get_type(self) -> str:
+        return self.model_fields["type"].default
+
     id: str = Field(
         default_factory=uuid_string,
         description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
-        json_schema_extra={"field_kind": FieldKind.NodeAttribute},
+        json_schema_extra={"_field_kind": "internal"},
     )
     is_intermediate: bool = Field(
         default=False,
         description="Whether or not this is an intermediate invocation.",
-        json_schema_extra={"ui_type": "IsIntermediate", "field_kind": FieldKind.NodeAttribute},
+        json_schema_extra={"ui_type": UIType.IsIntermediate, "_field_kind": "internal"},
     )
     use_cache: bool = Field(
-        default=True,
-        description="Whether or not to use the cache",
-        json_schema_extra={"field_kind": FieldKind.NodeAttribute},
+        default=True, description="Whether or not to use the cache", json_schema_extra={"_field_kind": "internal"}
     )
 
     UIConfig: ClassVar[Type[UIConfigBase]]
@@ -706,15 +629,12 @@ class BaseInvocation(ABC, BaseModel):
 TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
 
 
-RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
+RESERVED_INPUT_FIELD_NAMES = {
     "id",
     "is_intermediate",
     "use_cache",
     "type",
     "workflow",
-}
-
-RESERVED_INPUT_FIELD_NAMES = {
     "metadata",
 }
 
@@ -725,68 +645,47 @@ class _Model(BaseModel):
     pass
 
 
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", category=DeprecationWarning)
-    # Get all pydantic model attrs, methods, etc
-    RESERVED_PYDANTIC_FIELD_NAMES = {m[0] for m in inspect.getmembers(_Model())}
+# Get all pydantic model attrs, methods, etc
+RESERVED_PYDANTIC_FIELD_NAMES = {m[0] for m in inspect.getmembers(_Model())}
 
 
 def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
     """
     Validates the fields of an invocation or invocation output:
-    - Must not override any pydantic reserved fields
-    - Must have a type annotation
-    - Must have a json_schema_extra dict
-    - Must have field_kind in json_schema_extra
-    - Field name must not be reserved, according to its field_kind
+    - must not override any pydantic reserved fields
+    - must be created via `InputField`, `OutputField`, or be an internal field defined in this file
     """
     for name, field in model_fields.items():
         if name in RESERVED_PYDANTIC_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)')
 
-        if not field.annotation:
-            raise InvalidFieldError(f'Invalid field type "{name}" on "{model_type}" (missing annotation)')
-
-        if not isinstance(field.json_schema_extra, dict):
-            raise InvalidFieldError(
-                f'Invalid field definition for "{name}" on "{model_type}" (missing json_schema_extra dict)'
-            )
-
-        field_kind = field.json_schema_extra.get("field_kind", None)
+        field_kind = (
+            # _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
+            field.json_schema_extra.get("_field_kind", None) if field.json_schema_extra else None
+        )
 
         # must have a field_kind
-        if not isinstance(field_kind, FieldKind):
+        if field_kind is None or field_kind not in {"input", "output", "internal"}:
             raise InvalidFieldError(
                 f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)'
             )
 
-        if field_kind is FieldKind.Input and (
-            name in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES or name in RESERVED_INPUT_FIELD_NAMES
-        ):
+        if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)')
 
-        if field_kind is FieldKind.Output and name in RESERVED_OUTPUT_FIELD_NAMES:
+        if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)')
 
-        if (field_kind is FieldKind.Internal) and name not in RESERVED_INPUT_FIELD_NAMES:
+        # internal fields *must* be in the reserved list
+        if (
+            field_kind == "internal"
+            and name not in RESERVED_INPUT_FIELD_NAMES
+            and name not in RESERVED_OUTPUT_FIELD_NAMES
+        ):
             raise InvalidFieldError(
                 f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
             )
 
-        # node attribute fields *must* be in the reserved list
-        if (
-            field_kind is FieldKind.NodeAttribute
-            and name not in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES
-            and name not in RESERVED_OUTPUT_FIELD_NAMES
-        ):
-            raise InvalidFieldError(
-                f'Invalid field name "{name}" on "{model_type}" (node attribute field without reserved name)'
-            )
-
-        ui_type = field.json_schema_extra.get("ui_type", None)
-        if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
-            field.json_schema_extra.pop("ui_type")
     return None
 
 
@@ -797,7 +696,6 @@ def invocation(
     category: Optional[str] = None,
     version: Optional[str] = None,
     use_cache: Optional[bool] = True,
-    classification: Classification = Classification.Stable,
 ) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]:
     """
     Registers an invocation.
@@ -808,7 +706,6 @@ def invocation(
     :param Optional[str] category: Adds a category to the invocation. Used to group the invocations in the UI. Defaults to None.
     :param Optional[str] version: Adds a version to the invocation. Must be a valid semver string. Defaults to None.
     :param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor.
-    :param Classification classification: The classification of the invocation. Defaults to FeatureClassification.Stable. Use Beta or Prototype if the invocation is unstable.
     """
 
     def wrapper(cls: Type[TBaseInvocation]) -> Type[TBaseInvocation]:
@@ -823,31 +720,21 @@ def invocation(
         validate_fields(cls.model_fields, invocation_type)
 
         # Add OpenAPI schema extras
-        uiconfig_name = cls.__qualname__ + ".UIConfig"
-        if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconfig_name:
-            cls.UIConfig = type(uiconfig_name, (UIConfigBase,), {})
-        cls.UIConfig.title = title
-        cls.UIConfig.tags = tags
-        cls.UIConfig.category = category
-        cls.UIConfig.classification = classification
-
-        # Grab the node pack's name from the module name, if it's a custom node
-        is_custom_node = cls.__module__.rsplit(".", 1)[0] == "invokeai.app.invocations"
-        if is_custom_node:
-            cls.UIConfig.node_pack = cls.__module__.split(".")[0]
-        else:
-            cls.UIConfig.node_pack = None
-
+        uiconf_name = cls.__qualname__ + ".UIConfig"
+        if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
+            cls.UIConfig = type(uiconf_name, (UIConfigBase,), {})
+        if title is not None:
+            cls.UIConfig.title = title
+        if tags is not None:
+            cls.UIConfig.tags = tags
+        if category is not None:
+            cls.UIConfig.category = category
         if version is not None:
             try:
                 semver.Version.parse(version)
             except ValueError as e:
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
             cls.UIConfig.version = version
-        else:
-            logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
-            cls.UIConfig.version = "1.0.0"
-
         if use_cache is not None:
             cls.model_fields["use_cache"].default = use_cache
 
@@ -862,7 +749,7 @@ def invocation(
 
         invocation_type_annotation = Literal[invocation_type]  # type: ignore
         invocation_type_field = Field(
-            title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
+            title="type", default=invocation_type, json_schema_extra={"_field_kind": "internal"}
         )
 
         docstring = cls.__doc__
@@ -908,9 +795,7 @@ def invocation_output(
         # Add the output type to the model.
 
         output_type_annotation = Literal[output_type]  # type: ignore
-        output_type_field = Field(
-            title="type", default=output_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
-        )
+        output_type_field = Field(title="type", default=output_type, json_schema_extra={"_field_kind": "internal"})
 
         docstring = cls.__doc__
         cls = create_model(
@@ -928,6 +813,24 @@ def invocation_output(
     return wrapper
 
 
+class WorkflowField(RootModel):
+    """
+    Pydantic model for workflows with custom root of type dict[str, Any].
+    Workflows are stored without a strict schema.
+    """
+
+    root: dict[str, Any] = Field(description="The workflow")
+
+
+WorkflowFieldValidator = TypeAdapter(WorkflowField)
+
+
+class WithWorkflow(BaseModel):
+    workflow: Optional[WorkflowField] = Field(
+        default=None, description=FieldDescriptions.workflow, json_schema_extra={"_field_kind": "internal"}
+    )
+
+
 class MetadataField(RootModel):
     """
     Pydantic model for metadata with custom root of type dict[str, Any].
@@ -942,21 +845,5 @@ MetadataFieldValidator = TypeAdapter(MetadataField)
 
 class WithMetadata(BaseModel):
     metadata: Optional[MetadataField] = Field(
-        default=None,
-        description=FieldDescriptions.metadata,
-        json_schema_extra=InputFieldJSONSchemaExtra(
-            field_kind=FieldKind.Internal,
-            input=Input.Connection,
-            orig_required=False,
-        ).model_dump(exclude_none=True),
+        default=None, description=FieldDescriptions.metadata, json_schema_extra={"_field_kind": "internal"}
     )
-
-
-class WithWorkflow:
-    workflow = None
-
-    def __init_subclass__(cls) -> None:
-        logger.warn(
-            f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
-        )
-        super().__init_subclass__()

invokeai/app/invocations/collections.py

@@ -5,7 +5,7 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import IntegerCollectionOutput
-from invokeai.app.util.misc import SEED_MAX
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
@@ -55,7 +55,7 @@ class RangeOfSizeInvocation(BaseInvocation):
     title="Random Range",
     tags=["range", "integer", "random", "collection"],
     category="collections",
-    version="1.0.1",
+    version="1.0.0",
     use_cache=False,
 )
 class RandomRangeInvocation(BaseInvocation):
@@ -65,10 +65,10 @@ class RandomRangeInvocation(BaseInvocation):
     high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
     size: int = InputField(default=1, description="The number of values to generate")
     seed: int = InputField(
-        default=0,
         ge=0,
         le=SEED_MAX,
         description="The seed for the RNG (omit for random)",
+        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:

invokeai/app/invocations/controlnet_image_processors.py

@@ -39,6 +39,7 @@ from .baseinvocation import (
     InvocationContext,
     OutputField,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -95,7 +96,7 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
@@ -128,7 +129,7 @@ class ControlNetInvocation(BaseInvocation):
 
 
 # This invocation exists for other invocations to subclass it - do not register with @invocation!
-class ImageProcessorInvocation(BaseInvocation, WithMetadata):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
@@ -152,7 +153,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         """Builds an ImageOutput and its ImageField"""
@@ -172,7 +173,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -195,7 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -224,7 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -246,7 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -269,7 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Openpose Processor",
     tags=["controlnet", "openpose", "pose"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Openpose processing to image"""
@@ -294,7 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -321,7 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -338,7 +339,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.0.0"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -361,7 +362,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.0.0"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -388,7 +389,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -418,7 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -434,7 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -457,7 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -486,7 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -526,7 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -568,7 +569,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""

invokeai/app/invocations/custom_nodes/init.py

@@ -32,15 +32,13 @@ for d in Path(__file__).parent.iterdir():
     if module_name in globals():
         continue
 
-    # load the module, appending adding a suffix to identify it as a custom node pack
+    # we have a legit module to import
     spec = spec_from_file_location(module_name, init.absolute())
 
     if spec is None or spec.loader is None:
         logger.warn(f"Could not load {init}")
         continue
 
-    logger.info(f"Loading node pack {module_name}")
-
     module = module_from_spec(spec)
     sys.modules[spec.name] = module
     spec.loader.exec_module(module)
@@ -49,5 +47,5 @@ for d in Path(__file__).parent.iterdir():
 
     del init, module_name
 
-if loaded_count > 0:
-    logger.info(f"Loaded {loaded_count} node packs from {Path(__file__).parent}")
+
+logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}")

invokeai/app/invocations/cv.py

@@ -8,11 +8,11 @@ from PIL import Image, ImageOps
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
-class CvInpaintInvocation(BaseInvocation, WithMetadata):
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
+class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")
@@ -41,7 +41,7 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/facetools.py

@@ -17,6 +17,7 @@ from invokeai.app.invocations.baseinvocation import (
     InvocationContext,
     OutputField,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -437,8 +438,8 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
-class FaceOffInvocation(BaseInvocation, WithMetadata):
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
+class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
     image: ImageField = InputField(description="Image for face detection")
@@ -507,7 +508,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -531,8 +532,8 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
-class FaceMaskInvocation(BaseInvocation, WithMetadata):
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
+class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -626,7 +627,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -649,9 +650,9 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
 )
-class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
+class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -715,7 +716,7 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/image.py

@@ -8,20 +8,12 @@ import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
 from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from .baseinvocation import (
-    BaseInvocation,
-    Classification,
-    Input,
-    InputField,
-    InvocationContext,
-    WithMetadata,
-    invocation,
-)
+from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -44,14 +36,8 @@ class ShowImageInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "blank_image",
-    title="Blank Image",
-    tags=["image"],
-    category="image",
-    version="1.2.0",
-)
-class BlankImageInvocation(BaseInvocation, WithMetadata):
+@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
+class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Creates a blank image and forwards it to the pipeline"""
 
     width: int = InputField(default=512, description="The width of the image")
@@ -70,7 +56,7 @@ class BlankImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -80,14 +66,8 @@ class BlankImageInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_crop",
-    title="Crop Image",
-    tags=["image", "crop"],
-    category="image",
-    version="1.2.0",
-)
-class ImageCropInvocation(BaseInvocation, WithMetadata):
+@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
+class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Crops an image to a specified box. The box can be outside of the image."""
 
     image: ImageField = InputField(description="The image to crop")
@@ -110,7 +90,7 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -120,69 +100,8 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    invocation_type="img_pad_crop",
-    title="Center Pad or Crop Image",
-    category="image",
-    tags=["image", "pad", "crop"],
-    version="1.0.0",
-)
-class CenterPadCropInvocation(BaseInvocation):
-    """Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image."""
-
-    image: ImageField = InputField(description="The image to crop")
-    left: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the left (negative values crop inwards, positive values pad outwards)",
-    )
-    right: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the right (negative values crop inwards, positive values pad outwards)",
-    )
-    top: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the top (negative values crop inwards, positive values pad outwards)",
-    )
-    bottom: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the bottom (negative values crop inwards, positive values pad outwards)",
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-
-        # Calculate and create new image dimensions
-        new_width = image.width + self.right + self.left
-        new_height = image.height + self.top + self.bottom
-        image_crop = Image.new(mode="RGBA", size=(new_width, new_height), color=(0, 0, 0, 0))
-
-        # Paste new image onto input
-        image_crop.paste(image, (self.left, self.top))
-
-        image_dto = context.services.images.create(
-            image=image_crop,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-@invocation(
-    "img_paste",
-    title="Paste Image",
-    tags=["image", "paste"],
-    category="image",
-    version="1.2.0",
-)
-class ImagePasteInvocation(BaseInvocation, WithMetadata):
+@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
+class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Pastes an image into another image."""
 
     base_image: ImageField = InputField(description="The base image")
@@ -225,7 +144,7 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -235,14 +154,8 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "tomask",
-    title="Mask from Alpha",
-    tags=["image", "mask"],
-    category="image",
-    version="1.2.0",
-)
-class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
+@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
+class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Extracts the alpha channel of an image as a mask."""
 
     image: ImageField = InputField(description="The image to create the mask from")
@@ -263,7 +176,7 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -273,14 +186,8 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_mul",
-    title="Multiply Images",
-    tags=["image", "multiply"],
-    category="image",
-    version="1.2.0",
-)
-class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
+@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
+class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Multiplies two images together using `PIL.ImageChops.multiply()`."""
 
     image1: ImageField = InputField(description="The first image to multiply")
@@ -300,7 +207,7 @@ class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -313,14 +220,8 @@ class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
 IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
 
 
-@invocation(
-    "img_chan",
-    title="Extract Image Channel",
-    tags=["image", "channel"],
-    category="image",
-    version="1.2.0",
-)
-class ImageChannelInvocation(BaseInvocation, WithMetadata):
+@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
+class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Gets a channel from an image."""
 
     image: ImageField = InputField(description="The image to get the channel from")
@@ -339,7 +240,7 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -352,14 +253,8 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata):
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 
 
-@invocation(
-    "img_conv",
-    title="Convert Image Mode",
-    tags=["image", "convert"],
-    category="image",
-    version="1.2.0",
-)
-class ImageConvertInvocation(BaseInvocation, WithMetadata):
+@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
+class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Converts an image to a different mode."""
 
     image: ImageField = InputField(description="The image to convert")
@@ -378,7 +273,7 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -388,14 +283,8 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_blur",
-    title="Blur Image",
-    tags=["image", "blur"],
-    category="image",
-    version="1.2.0",
-)
-class ImageBlurInvocation(BaseInvocation, WithMetadata):
+@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
+class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Blurs an image"""
 
     image: ImageField = InputField(description="The image to blur")
@@ -419,7 +308,7 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -429,64 +318,6 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "unsharp_mask",
-    title="Unsharp Mask",
-    tags=["image", "unsharp_mask"],
-    category="image",
-    version="1.2.0",
-    classification=Classification.Beta,
-)
-class UnsharpMaskInvocation(BaseInvocation, WithMetadata):
-    """Applies an unsharp mask filter to an image"""
-
-    image: ImageField = InputField(description="The image to use")
-    radius: float = InputField(gt=0, description="Unsharp mask radius", default=2)
-    strength: float = InputField(ge=0, description="Unsharp mask strength", default=50)
-
-    def pil_from_array(self, arr):
-        return Image.fromarray((arr * 255).astype("uint8"))
-
-    def array_from_pil(self, img):
-        return numpy.array(img) / 255
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-        mode = image.mode
-
-        alpha_channel = image.getchannel("A") if mode == "RGBA" else None
-        image = image.convert("RGB")
-        image_blurred = self.array_from_pil(image.filter(ImageFilter.GaussianBlur(radius=self.radius)))
-
-        image = self.array_from_pil(image)
-        image += (image - image_blurred) * (self.strength / 100.0)
-        image = numpy.clip(image, 0, 1)
-        image = self.pil_from_array(image)
-
-        image = image.convert(mode)
-
-        # Make the image RGBA if we had a source alpha channel
-        if alpha_channel is not None:
-            image.putalpha(alpha_channel)
-
-        image_dto = context.services.images.create(
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image.width,
-            height=image.height,
-        )
-
-
 PIL_RESAMPLING_MODES = Literal[
     "nearest",
     "box",
@@ -507,14 +338,8 @@ PIL_RESAMPLING_MAP = {
 }
 
 
-@invocation(
-    "img_resize",
-    title="Resize Image",
-    tags=["image", "resize"],
-    category="image",
-    version="1.2.0",
-)
-class ImageResizeInvocation(BaseInvocation, WithMetadata):
+@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
+class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Resizes an image to specific dimensions"""
 
     image: ImageField = InputField(description="The image to resize")
@@ -540,7 +365,7 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -550,14 +375,8 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_scale",
-    title="Scale Image",
-    tags=["image", "scale"],
-    category="image",
-    version="1.2.0",
-)
-class ImageScaleInvocation(BaseInvocation, WithMetadata):
+@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
+class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Scales an image by a factor"""
 
     image: ImageField = InputField(description="The image to scale")
@@ -588,7 +407,7 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -598,14 +417,8 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_lerp",
-    title="Lerp Image",
-    tags=["image", "lerp"],
-    category="image",
-    version="1.2.0",
-)
-class ImageLerpInvocation(BaseInvocation, WithMetadata):
+@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
+class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -628,7 +441,7 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -638,14 +451,8 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_ilerp",
-    title="Inverse Lerp Image",
-    tags=["image", "ilerp"],
-    category="image",
-    version="1.2.0",
-)
-class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
+@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
+class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Inverse linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -668,7 +475,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -678,14 +485,8 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_nsfw",
-    title="Blur NSFW Image",
-    tags=["image", "nsfw"],
-    category="image",
-    version="1.2.0",
-)
-class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
+@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
+class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add blur to NSFW-flagged images"""
 
     image: ImageField = InputField(description="The image to check")
@@ -710,7 +511,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -731,9 +532,9 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
     title="Add Invisible Watermark",
     tags=["image", "watermark"],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
+class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add an invisible watermark to an image"""
 
     image: ImageField = InputField(description="The image to check")
@@ -750,7 +551,7 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -760,14 +561,8 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "mask_edge",
-    title="Mask Edge",
-    tags=["image", "mask", "inpaint"],
-    category="image",
-    version="1.2.0",
-)
-class MaskEdgeInvocation(BaseInvocation, WithMetadata):
+@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
+class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Applies an edge mask to an image"""
 
     image: ImageField = InputField(description="The image to apply the mask to")
@@ -802,7 +597,7 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -817,9 +612,9 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata):
     title="Combine Masks",
     tags=["image", "mask", "multiply"],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class MaskCombineInvocation(BaseInvocation, WithMetadata):
+class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
 
     mask1: ImageField = InputField(description="The first mask to combine")
@@ -839,7 +634,7 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -849,14 +644,8 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "color_correct",
-    title="Color Correct",
-    tags=["image", "color"],
-    category="image",
-    version="1.2.0",
-)
-class ColorCorrectInvocation(BaseInvocation, WithMetadata):
+@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
+class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """
     Shifts the colors of a target image to match the reference image, optionally
     using a mask to only color-correct certain regions of the target image.
@@ -956,7 +745,7 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -966,14 +755,8 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_hue_adjust",
-    title="Adjust Image Hue",
-    tags=["image", "hue"],
-    category="image",
-    version="1.2.0",
-)
-class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
+@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
+class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Adjusts the Hue of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1002,7 +785,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1075,9 +858,9 @@ CHANNEL_FORMATS = {
         "value",
     ],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
+class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Add or subtract a value from a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1112,7 +895,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1146,9 +929,9 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
         "value",
     ],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
+class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Scale a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1187,7 +970,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
-            workflow=context.workflow,
+            workflow=self.workflow,
             metadata=self.metadata,
         )
 
@@ -1205,10 +988,10 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
     title="Save Image",
     tags=["primitives", "image"],
     category="primitives",
-    version="1.2.0",
+    version="1.0.1",
     use_cache=False,
 )
-class SaveImageInvocation(BaseInvocation, WithMetadata):
+class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
 
     image: ImageField = InputField(description=FieldDescriptions.image)
@@ -1226,7 +1009,7 @@ class SaveImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1234,35 +1017,3 @@ class SaveImageInvocation(BaseInvocation, WithMetadata):
             width=image_dto.width,
             height=image_dto.height,
         )
-
-
-@invocation(
-    "linear_ui_output",
-    title="Linear UI Image Output",
-    tags=["primitives", "image"],
-    category="primitives",
-    version="1.0.1",
-    use_cache=False,
-)
-class LinearUIOutputInvocation(BaseInvocation, WithMetadata):
-    """Handles Linear UI Image Outputting tasks."""
-
-    image: ImageField = InputField(description=FieldDescriptions.image)
-    board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image_dto = context.services.images.get_dto(self.image.image_name)
-
-        if self.board:
-            context.services.board_images.add_image_to_board(self.board.board_id, self.image.image_name)
-
-        if image_dto.is_intermediate != self.is_intermediate:
-            context.services.images.update(
-                self.image.image_name, changes=ImageRecordChanges(is_intermediate=self.is_intermediate)
-            )
-
-        return ImageOutput(
-            image=ImageField(image_name=self.image.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/infill.py

@@ -8,12 +8,12 @@ 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
+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 .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -118,8 +118,8 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
     return si
 
 
-@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class InfillColorInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
+class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -144,7 +144,7 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -154,17 +154,17 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
-class InfillTileInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
+class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with tiles of the image"""
 
     image: ImageField = InputField(description="The image to infill")
     tile_size: int = InputField(default=32, ge=1, description="The tile size (px)")
     seed: int = InputField(
-        default=0,
         ge=0,
         le=SEED_MAX,
         description="The seed to use for tile generation (omit for random)",
+        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -181,7 +181,7 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -192,9 +192,9 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
 )
-class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
+class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -235,7 +235,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -245,8 +245,8 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class LaMaInfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
+class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -264,7 +264,7 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -274,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class CV2InfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
+class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -293,7 +293,7 @@ class CV2InfillInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/ip_adapter.py

@@ -11,6 +11,7 @@ from invokeai.app.invocations.baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
@@ -66,7 +67,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
     weight: Union[float, List[float]] = InputField(
-        default=1, ge=-1, description="The weight given to the IP-Adapter", title="Weight"
+        default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
     )
 
     begin_step_percent: float = InputField(

invokeai/app/invocations/latent.py

@@ -64,6 +64,7 @@ from .baseinvocation import (
     OutputField,
     UIType,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -78,12 +79,6 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
 
-# HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
-# be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
-# factor is hard-coded to a literal '8' rather than using this constant.
-# The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
-LATENT_SCALE_FACTOR = 8
-
 
 @invocation_output("scheduler_output")
 class SchedulerOutput(BaseInvocationOutput):
@@ -220,7 +215,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.0",
+    version="1.4.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -278,14 +273,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
         input=Input.Connection,
         ui_order=7,
     )
-    cfg_rescale_multiplier: float = InputField(
-        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
-    )
     latents: Optional[LatentsField] = InputField(
-        default=None,
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-        ui_order=4,
+        default=None, description=FieldDescriptions.latents, input=Input.Connection
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
@@ -340,7 +329,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             unconditioned_embeddings=uc,
             text_embeddings=c,
             guidance_scale=self.cfg_scale,
-            guidance_rescale_multiplier=self.cfg_rescale_multiplier,
             extra=extra_conditioning_info,
             postprocessing_settings=PostprocessingSettings(
                 threshold=0.0,  # threshold,
@@ -399,9 +387,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
     ) -> List[ControlNetData]:
-        # Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
-        control_height_resize = latents_shape[2] * LATENT_SCALE_FACTOR
-        control_width_resize = latents_shape[3] * LATENT_SCALE_FACTOR
+        # assuming fixed dimensional scaling of 8:1 for image:latents
+        control_height_resize = latents_shape[2] * 8
+        control_width_resize = latents_shape[3] * 8
         if control_input is None:
             control_list = None
         elif isinstance(control_input, list) and len(control_input) == 0:
@@ -718,6 +706,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
             with (
                 ExitStack() as exit_stack,
+                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
                 ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
                 set_seamless(unet_info.context.model, self.unet.seamless_axes),
                 unet_info as unet,
@@ -801,9 +790,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.2.0",
+    version="1.0.0",
 )
-class LatentsToImageInvocation(BaseInvocation, WithMetadata):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -885,7 +874,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -914,12 +903,12 @@ class ResizeLatentsInvocation(BaseInvocation):
     )
     width: int = InputField(
         ge=64,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         description=FieldDescriptions.width,
     )
     height: int = InputField(
         ge=64,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         description=FieldDescriptions.width,
     )
     mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
@@ -933,7 +922,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         resized_latents = torch.nn.functional.interpolate(
             latents.to(device),
-            size=(self.height // LATENT_SCALE_FACTOR, self.width // LATENT_SCALE_FACTOR),
+            size=(self.height // 8, self.width // 8),
             mode=self.mode,
             antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
@@ -1171,60 +1160,3 @@ class BlendLatentsInvocation(BaseInvocation):
         # context.services.latents.set(name, resized_latents)
         context.services.latents.save(name, blended_latents)
         return build_latents_output(latents_name=name, latents=blended_latents)
-
-
-# The Crop Latents node was copied from @skunkworxdark's implementation here:
-# https://github.com/skunkworxdark/XYGrid_nodes/blob/74647fa9c1fa57d317a94bd43ca689af7f0aae5e/images_to_grids.py#L1117C1-L1167C80
-@invocation(
-    "crop_latents",
-    title="Crop Latents",
-    tags=["latents", "crop"],
-    category="latents",
-    version="1.0.0",
-)
-# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
-# Currently, if the class names conflict then 'GET /openapi.json' fails.
-class CropLatentsCoreInvocation(BaseInvocation):
-    """Crops a latent-space tensor to a box specified in image-space. The box dimensions and coordinates must be
-    divisible by the latent scale factor of 8.
-    """
-
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    x: int = InputField(
-        ge=0,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    y: int = InputField(
-        ge=0,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    width: int = InputField(
-        ge=1,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The width (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    height: int = InputField(
-        ge=1,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        x1 = self.x // LATENT_SCALE_FACTOR
-        y1 = self.y // LATENT_SCALE_FACTOR
-        x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
-        y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
-
-        cropped_latents = latents[..., y1:y2, x1:x2]
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, cropped_latents)
-
-        return build_latents_output(latents_name=name, latents=cropped_latents)

invokeai/app/invocations/metadata.py

@@ -112,7 +112,7 @@ GENERATION_MODES = Literal[
 ]
 
 
-@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.1")
+@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
 class CoreMetadataInvocation(BaseInvocation):
     """Collects core generation metadata into a MetadataField"""
 
@@ -127,9 +127,6 @@ class CoreMetadataInvocation(BaseInvocation):
     seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
     rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
     cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
-    cfg_rescale_multiplier: Optional[float] = InputField(
-        default=None, description=FieldDescriptions.cfg_rescale_multiplier
-    )
     steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
     scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
     seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
@@ -163,7 +160,7 @@ class CoreMetadataInvocation(BaseInvocation):
     )
 
     # High resolution fix metadata.
-    hrf_enabled: Optional[bool] = InputField(
+    hrf_enabled: Optional[float] = InputField(
         default=None,
         description="Whether or not high resolution fix was enabled.",
     )

invokeai/app/invocations/model.py

@@ -14,6 +14,7 @@ from .baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
@@ -394,6 +395,7 @@ class VaeLoaderInvocation(BaseInvocation):
     vae_model: VAEModelField = InputField(
         description=FieldDescriptions.vae_model,
         input=Input.Direct,
+        ui_type=UIType.VaeModel,
         title="VAE",
     )
 

invokeai/app/invocations/noise.py

@@ -6,7 +6,7 @@ from pydantic import field_validator
 
 from invokeai.app.invocations.latent import LatentsField
 from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.util.misc import SEED_MAX
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from .baseinvocation import (
@@ -83,16 +83,16 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
     title="Noise",
     tags=["latents", "noise"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     seed: int = InputField(
-        default=0,
         ge=0,
         le=SEED_MAX,
         description=FieldDescriptions.seed,
+        default_factory=get_random_seed,
     )
     width: int = InputField(
         default=512,

invokeai/app/invocations/onnx.py

@@ -31,6 +31,7 @@ from .baseinvocation import (
     UIComponent,
     UIType,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -325,9 +326,9 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
     title="ONNX Latents to Image",
     tags=["latents", "image", "vae", "onnx"],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
+class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -377,7 +378,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/primitives.py

@@ -62,12 +62,12 @@ class BooleanInvocation(BaseInvocation):
     title="Boolean Collection Primitive",
     tags=["primitives", "boolean", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class BooleanCollectionInvocation(BaseInvocation):
     """A collection of boolean primitive values"""
 
-    collection: list[bool] = InputField(default=[], description="The collection of boolean values")
+    collection: list[bool] = InputField(default_factory=list, description="The collection of boolean values")
 
     def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
         return BooleanCollectionOutput(collection=self.collection)
@@ -111,12 +111,12 @@ class IntegerInvocation(BaseInvocation):
     title="Integer Collection Primitive",
     tags=["primitives", "integer", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class IntegerCollectionInvocation(BaseInvocation):
     """A collection of integer primitive values"""
 
-    collection: list[int] = InputField(default=[], description="The collection of integer values")
+    collection: list[int] = InputField(default_factory=list, description="The collection of integer values")
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
         return IntegerCollectionOutput(collection=self.collection)
@@ -158,12 +158,12 @@ class FloatInvocation(BaseInvocation):
     title="Float Collection Primitive",
     tags=["primitives", "float", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class FloatCollectionInvocation(BaseInvocation):
     """A collection of float primitive values"""
 
-    collection: list[float] = InputField(default=[], description="The collection of float values")
+    collection: list[float] = InputField(default_factory=list, description="The collection of float values")
 
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         return FloatCollectionOutput(collection=self.collection)
@@ -205,12 +205,12 @@ class StringInvocation(BaseInvocation):
     title="String Collection Primitive",
     tags=["primitives", "string", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class StringCollectionInvocation(BaseInvocation):
     """A collection of string primitive values"""
 
-    collection: list[str] = InputField(default=[], description="The collection of string values")
+    collection: list[str] = InputField(default_factory=list, description="The collection of string values")
 
     def invoke(self, context: InvocationContext) -> StringCollectionOutput:
         return StringCollectionOutput(collection=self.collection)
@@ -467,13 +467,13 @@ class ConditioningInvocation(BaseInvocation):
     title="Conditioning Collection Primitive",
     tags=["primitives", "conditioning", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class ConditioningCollectionInvocation(BaseInvocation):
     """A collection of conditioning tensor primitive values"""
 
     collection: list[ConditioningField] = InputField(
-        default=[],
+        default_factory=list,
         description="The collection of conditioning tensors",
     )
 

invokeai/app/invocations/prompt.py

@@ -44,7 +44,7 @@ class DynamicPromptInvocation(BaseInvocation):
     title="Prompts from File",
     tags=["prompt", "file"],
     category="prompt",
-    version="1.0.1",
+    version="1.0.0",
 )
 class PromptsFromFileInvocation(BaseInvocation):
     """Loads prompts from a text file"""
@@ -82,7 +82,7 @@ class PromptsFromFileInvocation(BaseInvocation):
         end_line = start_line + max_prompts
         if max_prompts <= 0:
             end_line = np.iinfo(np.int32).max
-        with open(file_path, encoding="utf-8") as f:
+        with open(file_path) as f:
             for i, line in enumerate(f):
                 if i >= start_line and i < end_line:
                     prompts.append((pre_prompt or "") + line.strip() + (post_prompt or ""))

invokeai/app/invocations/t2i_adapter.py

@@ -9,6 +9,7 @@ from invokeai.app.invocations.baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
@@ -58,7 +59,7 @@ class T2IAdapterInvocation(BaseInvocation):
         ui_order=-1,
     )
     weight: Union[float, list[float]] = InputField(
-        default=1, ge=0, description="The weight given to the T2I-Adapter", title="Weight"
+        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)"

invokeai/app/invocations/tiles.py

@@ -1,308 +0,0 @@
-from typing import Literal
-
-import numpy as np
-from PIL import Image
-from pydantic import BaseModel
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    WithMetadata,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.tiles.tiles import (
-    calc_tiles_even_split,
-    calc_tiles_min_overlap,
-    calc_tiles_with_overlap,
-    merge_tiles_with_linear_blending,
-    merge_tiles_with_seam_blending,
-)
-from invokeai.backend.tiles.utils import Tile
-
-
-class TileWithImage(BaseModel):
-    tile: Tile
-    image: ImageField
-
-
-@invocation_output("calculate_image_tiles_output")
-class CalculateImageTilesOutput(BaseInvocationOutput):
-    tiles: list[Tile] = OutputField(description="The tiles coordinates that cover a particular image shape.")
-
-
-@invocation(
-    "calculate_image_tiles",
-    title="Calculate Image Tiles",
-    tags=["tiles"],
-    category="tiles",
-    version="1.0.0",
-    classification=Classification.Beta,
-)
-class CalculateImageTilesInvocation(BaseInvocation):
-    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
-
-    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
-    image_height: int = InputField(
-        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
-    )
-    tile_width: int = InputField(ge=1, default=576, description="The tile width, in pixels.")
-    tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
-    overlap: int = InputField(
-        ge=0,
-        default=128,
-        description="The target overlap, in pixels, between adjacent tiles. Adjacent tiles will overlap by at least this amount",
-    )
-
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
-        tiles = calc_tiles_with_overlap(
-            image_height=self.image_height,
-            image_width=self.image_width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            overlap=self.overlap,
-        )
-        return CalculateImageTilesOutput(tiles=tiles)
-
-
-@invocation(
-    "calculate_image_tiles_even_split",
-    title="Calculate Image Tiles Even Split",
-    tags=["tiles"],
-    category="tiles",
-    version="1.0.0",
-    classification=Classification.Beta,
-)
-class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
-    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
-
-    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
-    image_height: int = InputField(
-        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
-    )
-    num_tiles_x: int = InputField(
-        default=2,
-        ge=1,
-        description="Number of tiles to divide image into on the x axis",
-    )
-    num_tiles_y: int = InputField(
-        default=2,
-        ge=1,
-        description="Number of tiles to divide image into on the y axis",
-    )
-    overlap_fraction: float = InputField(
-        default=0.25,
-        ge=0,
-        lt=1,
-        description="Overlap between adjacent tiles as a fraction of the tile's dimensions (0-1)",
-    )
-
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
-        tiles = calc_tiles_even_split(
-            image_height=self.image_height,
-            image_width=self.image_width,
-            num_tiles_x=self.num_tiles_x,
-            num_tiles_y=self.num_tiles_y,
-            overlap_fraction=self.overlap_fraction,
-        )
-        return CalculateImageTilesOutput(tiles=tiles)
-
-
-@invocation(
-    "calculate_image_tiles_min_overlap",
-    title="Calculate Image Tiles Minimum Overlap",
-    tags=["tiles"],
-    category="tiles",
-    version="1.0.0",
-    classification=Classification.Beta,
-)
-class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
-    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
-
-    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
-    image_height: int = InputField(
-        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
-    )
-    tile_width: int = InputField(ge=1, default=576, description="The tile width, in pixels.")
-    tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
-    min_overlap: int = InputField(default=128, ge=0, description="Minimum overlap between adjacent tiles, in pixels.")
-
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
-        tiles = calc_tiles_min_overlap(
-            image_height=self.image_height,
-            image_width=self.image_width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            min_overlap=self.min_overlap,
-        )
-        return CalculateImageTilesOutput(tiles=tiles)
-
-
-@invocation_output("tile_to_properties_output")
-class TileToPropertiesOutput(BaseInvocationOutput):
-    coords_left: int = OutputField(description="Left coordinate of the tile relative to its parent image.")
-    coords_right: int = OutputField(description="Right coordinate of the tile relative to its parent image.")
-    coords_top: int = OutputField(description="Top coordinate of the tile relative to its parent image.")
-    coords_bottom: int = OutputField(description="Bottom coordinate of the tile relative to its parent image.")
-
-    # HACK: The width and height fields are 'meta' fields that can easily be calculated from the other fields on this
-    # object. Including redundant fields that can cheaply/easily be re-calculated goes against conventional API design
-    # principles. These fields are included, because 1) they are often useful in tiled workflows, and 2) they are
-    # difficult to calculate in a workflow (even though it's just a couple of subtraction nodes the graph gets
-    # surprisingly complicated).
-    width: int = OutputField(description="The width of the tile. Equal to coords_right - coords_left.")
-    height: int = OutputField(description="The height of the tile. Equal to coords_bottom - coords_top.")
-
-    overlap_top: int = OutputField(description="Overlap between this tile and its top neighbor.")
-    overlap_bottom: int = OutputField(description="Overlap between this tile and its bottom neighbor.")
-    overlap_left: int = OutputField(description="Overlap between this tile and its left neighbor.")
-    overlap_right: int = OutputField(description="Overlap between this tile and its right neighbor.")
-
-
-@invocation(
-    "tile_to_properties",
-    title="Tile to Properties",
-    tags=["tiles"],
-    category="tiles",
-    version="1.0.0",
-    classification=Classification.Beta,
-)
-class TileToPropertiesInvocation(BaseInvocation):
-    """Split a Tile into its individual properties."""
-
-    tile: Tile = InputField(description="The tile to split into properties.")
-
-    def invoke(self, context: InvocationContext) -> TileToPropertiesOutput:
-        return TileToPropertiesOutput(
-            coords_left=self.tile.coords.left,
-            coords_right=self.tile.coords.right,
-            coords_top=self.tile.coords.top,
-            coords_bottom=self.tile.coords.bottom,
-            width=self.tile.coords.right - self.tile.coords.left,
-            height=self.tile.coords.bottom - self.tile.coords.top,
-            overlap_top=self.tile.overlap.top,
-            overlap_bottom=self.tile.overlap.bottom,
-            overlap_left=self.tile.overlap.left,
-            overlap_right=self.tile.overlap.right,
-        )
-
-
-@invocation_output("pair_tile_image_output")
-class PairTileImageOutput(BaseInvocationOutput):
-    tile_with_image: TileWithImage = OutputField(description="A tile description with its corresponding image.")
-
-
-@invocation(
-    "pair_tile_image",
-    title="Pair Tile with Image",
-    tags=["tiles"],
-    category="tiles",
-    version="1.0.0",
-    classification=Classification.Beta,
-)
-class PairTileImageInvocation(BaseInvocation):
-    """Pair an image with its tile properties."""
-
-    # TODO(ryand): The only reason that PairTileImage is needed is because the iterate/collect nodes don't preserve
-    # order. Can this be fixed?
-
-    image: ImageField = InputField(description="The tile image.")
-    tile: Tile = InputField(description="The tile properties.")
-
-    def invoke(self, context: InvocationContext) -> PairTileImageOutput:
-        return PairTileImageOutput(
-            tile_with_image=TileWithImage(
-                tile=self.tile,
-                image=self.image,
-            )
-        )
-
-
-BLEND_MODES = Literal["Linear", "Seam"]
-
-
-@invocation(
-    "merge_tiles_to_image",
-    title="Merge Tiles to Image",
-    tags=["tiles"],
-    category="tiles",
-    version="1.1.0",
-    classification=Classification.Beta,
-)
-class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
-    """Merge multiple tile images into a single image."""
-
-    # Inputs
-    tiles_with_images: list[TileWithImage] = InputField(description="A list of tile images with tile properties.")
-    blend_mode: BLEND_MODES = InputField(
-        default="Seam",
-        description="blending type Linear or Seam",
-        input=Input.Direct,
-    )
-    blend_amount: int = InputField(
-        default=32,
-        ge=0,
-        description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.",
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        images = [twi.image for twi in self.tiles_with_images]
-        tiles = [twi.tile for twi in self.tiles_with_images]
-
-        # Infer the output image dimensions from the max/min tile limits.
-        height = 0
-        width = 0
-        for tile in tiles:
-            height = max(height, tile.coords.bottom)
-            width = max(width, tile.coords.right)
-
-        # Get all tile images for processing.
-        # TODO(ryand): It pains me that we spend time PNG decoding each tile from disk when they almost certainly
-        # existed in memory at an earlier point in the graph.
-        tile_np_images: list[np.ndarray] = []
-        for image in images:
-            pil_image = context.services.images.get_pil_image(image.image_name)
-            pil_image = pil_image.convert("RGB")
-            tile_np_images.append(np.array(pil_image))
-
-        # Prepare the output image buffer.
-        # Check the first tile to determine how many image channels are expected in the output.
-        channels = tile_np_images[0].shape[-1]
-        dtype = tile_np_images[0].dtype
-        np_image = np.zeros(shape=(height, width, channels), dtype=dtype)
-        if self.blend_mode == "Linear":
-            merge_tiles_with_linear_blending(
-                dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
-            )
-        elif self.blend_mode == "Seam":
-            merge_tiles_with_seam_blending(
-                dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
-            )
-        else:
-            raise ValueError(f"Unsupported blend mode: '{self.blend_mode}'.")
-
-        # Convert into a PIL image and save
-        pil_image = Image.fromarray(np_image)
-
-        image_dto = context.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/upscale.py

@@ -2,19 +2,19 @@
 from pathlib import Path
 from typing import Literal
 
-import cv2
+import cv2 as cv
 import numpy as np
 import torch
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from pydantic import ConfigDict
+from realesrgan import RealESRGANer
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
@@ -29,8 +29,8 @@ if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
-class ESRGANInvocation(BaseInvocation, WithMetadata):
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
+class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -92,9 +92,9 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
 
         esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
 
-        upscaler = RealESRGAN(
+        upsampler = RealESRGANer(
             scale=netscale,
-            model_path=models_path / esrgan_model_path,
+            model_path=str(models_path / esrgan_model_path),
             model=rrdbnet_model,
             half=False,
             tile=self.tile_size,
@@ -102,9 +102,15 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
 
         # prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
         # TODO: This strips the alpha... is that okay?
-        cv2_image = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
-        upscaled_image = upscaler.upscale(cv2_image)
-        pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
+        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
+        # upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
+        # upscaling, you'll need to add a resize node after this one.
+        upscaled_image, img_mode = upsampler.enhance(cv_image)
+
+        # 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"):
@@ -118,7 +124,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -4,7 +4,7 @@ from typing import Optional, cast
 
 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.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 from .board_image_records_base import BoardImageRecordStorageBase
 
@@ -20,6 +20,63 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
         self._conn = db.conn
         self._cursor = self._conn.cursor()
 
+        try:
+            self._lock.acquire()
+            self._create_tables()
+            self._conn.commit()
+        finally:
+            self._lock.release()
+
+    def _create_tables(self) -> None:
+        """Creates the `board_images` junction table."""
+
+        # Create the `board_images` junction table.
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS board_images (
+                board_id TEXT NOT NULL,
+                image_name TEXT NOT NULL,
+                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- updated via trigger
+                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- Soft delete, currently unused
+                deleted_at DATETIME,
+                -- enforce one-to-many relationship between boards and images using PK
+                -- (we can extend this to many-to-many later)
+                PRIMARY KEY (image_name),
+                FOREIGN KEY (board_id) REFERENCES boards (board_id) ON DELETE CASCADE,
+                FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE
+            );
+            """
+        )
+
+        # Add index for board id
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_board_images_board_id ON board_images (board_id);
+            """
+        )
+
+        # Add index for board id, sorted by created_at
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_board_images_board_id_created_at ON board_images (board_id, created_at);
+            """
+        )
+
+        # Add trigger for `updated_at`.
+        self._cursor.execute(
+            """--sql
+            CREATE TRIGGER IF NOT EXISTS tg_board_images_updated_at
+            AFTER UPDATE
+            ON board_images FOR EACH ROW
+            BEGIN
+                UPDATE board_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE board_id = old.board_id AND image_name = old.image_name;
+            END;
+            """
+        )
+
     def add_image_to_board(
         self,
         board_id: str,

invokeai/app/services/board_records/board_records_sqlite.py

@@ -3,7 +3,7 @@ import threading
 from typing import Union, cast
 
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 from invokeai.app.util.misc import uuid_string
 
 from .board_records_base import BoardRecordStorageBase
@@ -28,6 +28,52 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         self._conn = db.conn
         self._cursor = self._conn.cursor()
 
+        try:
+            self._lock.acquire()
+            self._create_tables()
+            self._conn.commit()
+        finally:
+            self._lock.release()
+
+    def _create_tables(self) -> None:
+        """Creates the `boards` table and `board_images` junction table."""
+
+        # Create the `boards` table.
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS boards (
+                board_id TEXT NOT NULL PRIMARY KEY,
+                board_name TEXT NOT NULL,
+                cover_image_name TEXT,
+                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- Updated via trigger
+                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- Soft delete, currently unused
+                deleted_at DATETIME,
+                FOREIGN KEY (cover_image_name) REFERENCES images (image_name) ON DELETE SET NULL
+            );
+            """
+        )
+
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_boards_created_at ON boards (created_at);
+            """
+        )
+
+        # Add trigger for `updated_at`.
+        self._cursor.execute(
+            """--sql
+            CREATE TRIGGER IF NOT EXISTS tg_boards_updated_at
+            AFTER UPDATE
+            ON boards FOR EACH ROW
+            BEGIN
+                UPDATE boards SET updated_at = current_timestamp
+                    WHERE board_id = old.board_id;
+            END;
+            """
+        )
+
     def delete(self, board_id: str) -> None:
         try:
             self._lock.acquire()

invokeai/app/services/config/__init__.py

@@ -1,5 +1,6 @@
-"""Init file for InvokeAI configure package."""
+"""
+Init file for InvokeAI configure package
+"""
 
-from .config_default import InvokeAIAppConfig, get_invokeai_config
-
-__all__ = ["InvokeAIAppConfig", "get_invokeai_config"]
+from .config_base import PagingArgumentParser  # noqa F401
+from .config_default import InvokeAIAppConfig, get_invokeai_config  # noqa F401

invokeai/app/services/config/config_base.py

@@ -15,7 +15,7 @@ import os
 import sys
 from argparse import ArgumentParser
 from pathlib import Path
-from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
+from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
 
 from omegaconf import DictConfig, ListConfig, OmegaConf
 from pydantic_settings import BaseSettings, SettingsConfigDict
@@ -24,7 +24,10 @@ from invokeai.app.services.config.config_common import PagingArgumentParser, int
 
 
 class InvokeAISettings(BaseSettings):
-    """Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
+    """
+    Runtime configuration settings in which default values are
+    read from an omegaconf .yaml file.
+    """
 
     initconf: ClassVar[Optional[DictConfig]] = None
     argparse_groups: ClassVar[Dict] = {}
@@ -32,7 +35,6 @@ class InvokeAISettings(BaseSettings):
     model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
 
     def parse_args(self, argv: Optional[list] = sys.argv[1:]):
-        """Call to parse command-line arguments."""
         parser = self.get_parser()
         opt, unknown_opts = parser.parse_known_args(argv)
         if len(unknown_opts) > 0:
@@ -47,19 +49,20 @@ class InvokeAISettings(BaseSettings):
                 setattr(self, name, value)
 
     def to_yaml(self) -> str:
-        """Return a YAML string representing our settings. This can be used as the contents of `invokeai.yaml` to restore settings later."""
+        """
+        Return a YAML string representing our settings. This can be used
+        as the contents of `invokeai.yaml` to restore settings later.
+        """
         cls = self.__class__
         type = get_args(get_type_hints(cls)["type"])[0]
-        field_dict: Dict[str, Dict[str, Any]] = {type: {}}
+        field_dict = {type: {}}
         for name, field in self.model_fields.items():
             if name in cls._excluded_from_yaml():
                 continue
-            assert isinstance(field.json_schema_extra, dict)
             category = (
                 field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
             )
             value = getattr(self, name)
-            assert isinstance(category, str)
             if category not in field_dict[type]:
                 field_dict[type][category] = {}
             # keep paths as strings to make it easier to read
@@ -69,7 +72,6 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def add_parser_arguments(cls, parser):
-        """Dynamically create arguments for a settings parser."""
         if "type" in get_type_hints(cls):
             settings_stanza = get_args(get_type_hints(cls)["type"])[0]
         else:
@@ -114,7 +116,6 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def cmd_name(cls, command_field: str = "type") -> str:
-        """Return the category of a setting."""
         hints = get_type_hints(cls)
         if command_field in hints:
             return get_args(hints[command_field])[0]
@@ -123,7 +124,6 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def get_parser(cls) -> ArgumentParser:
-        """Get the command-line parser for a setting."""
         parser = PagingArgumentParser(
             prog=cls.cmd_name(),
             description=cls.__doc__,
@@ -152,14 +152,10 @@ class InvokeAISettings(BaseSettings):
             "free_gpu_mem",
             "xformers_enabled",
             "tiled_decode",
-            "lora_dir",
-            "embedding_dir",
-            "controlnet_dir",
         ]
 
     @classmethod
     def add_field_argument(cls, command_parser, name: str, field, default_override=None):
-        """Add the argparse arguments for a setting parser."""
         field_type = get_type_hints(cls).get(name)
         default = (
             default_override

invokeai/app/services/config/config_default.py

@@ -173,11 +173,10 @@ from __future__ import annotations
 
 import os
 from pathlib import Path
-from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
+from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
 
 from omegaconf import DictConfig, OmegaConf
 from pydantic import Field, TypeAdapter
-from pydantic.config import JsonDict
 from pydantic_settings import SettingsConfigDict
 
 from .config_base import InvokeAISettings
@@ -189,24 +188,28 @@ DEFAULT_MAX_VRAM = 0.5
 
 
 class Categories(object):
-    """Category headers for configuration variable groups."""
-
-    WebServer: JsonDict = {"category": "Web Server"}
-    Features: JsonDict = {"category": "Features"}
-    Paths: JsonDict = {"category": "Paths"}
-    Logging: JsonDict = {"category": "Logging"}
-    Development: JsonDict = {"category": "Development"}
-    Other: JsonDict = {"category": "Other"}
-    ModelCache: JsonDict = {"category": "Model Cache"}
-    Device: JsonDict = {"category": "Device"}
-    Generation: JsonDict = {"category": "Generation"}
-    Queue: JsonDict = {"category": "Queue"}
-    Nodes: JsonDict = {"category": "Nodes"}
-    MemoryPerformance: JsonDict = {"category": "Memory/Performance"}
+    WebServer = {"category": "Web Server"}
+    Features = {"category": "Features"}
+    Paths = {"category": "Paths"}
+    Logging = {"category": "Logging"}
+    Development = {"category": "Development"}
+    Other = {"category": "Other"}
+    ModelCache = {"category": "Model Cache"}
+    Device = {"category": "Device"}
+    Generation = {"category": "Generation"}
+    Queue = {"category": "Queue"}
+    Nodes = {"category": "Nodes"}
+    MemoryPerformance = {"category": "Memory/Performance"}
 
 
 class InvokeAIAppConfig(InvokeAISettings):
-    """Configuration object for InvokeAI App."""
+    """
+    Generate images using Stable Diffusion. Use "invokeai" to launch
+    the command-line client (recommended for experts only), or
+    "invokeai-web" to launch the web server. Global options
+    can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
+    setting environment variables INVOKEAI_<setting>.
+    """
 
     singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
     singleton_init: ClassVar[Optional[Dict]] = None
@@ -221,9 +224,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     allow_credentials   : bool = Field(default=True, description="Allow CORS credentials", json_schema_extra=Categories.WebServer)
     allow_methods       : List[str] = Field(default=["*"], description="Methods allowed for CORS", json_schema_extra=Categories.WebServer)
     allow_headers       : List[str] = Field(default=["*"], description="Headers allowed for CORS", json_schema_extra=Categories.WebServer)
-    # SSL options correspond to https://www.uvicorn.org/settings/#https
-    ssl_certfile        : Optional[Path] = Field(default=None, description="SSL certificate file (for HTTPS)", json_schema_extra=Categories.WebServer)
-    ssl_keyfile         : Optional[Path] = Field(default=None, description="SSL key file", json_schema_extra=Categories.WebServer)
 
     # FEATURES
     esrgan              : bool = Field(default=True, description="Enable/disable upscaling code", json_schema_extra=Categories.Features)
@@ -234,12 +234,15 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # PATHS
     root                : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
-    autoimport_dir      : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
-    conf_path           : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
-    models_dir          : Path = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
-    legacy_conf_dir     : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
-    db_dir              : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
-    outdir              : Path = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
+    autoimport_dir      : Optional[Path] = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
+    lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
+    embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
+    controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
+    conf_path           : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
+    models_dir          : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
+    legacy_conf_dir     : Optional[Path] = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
+    db_dir              : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
+    outdir              : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
     use_memory_db       : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
     custom_nodes_dir    : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths)
     from_file           : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
@@ -282,15 +285,11 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
+    free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
     max_cache_size      : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance)
     max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.MemoryPerformance)
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.MemoryPerformance)
     tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.MemoryPerformance)
-    lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
-    embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
-    controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
-    # this is not referred to in the source code and can be removed entirely
-    #free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
 
     # See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
     # fmt: on
@@ -304,8 +303,8 @@ class InvokeAIAppConfig(InvokeAISettings):
         clobber=False,
     ):
         """
-        Update settings with contents of init file, environment, and command-line settings.
-
+        Update settings with contents of init file, environment, and
+        command-line settings.
         :param conf: alternate Omegaconf dictionary object
         :param argv: aternate sys.argv list
         :param clobber: ovewrite any initialization parameters passed during initialization
@@ -337,8 +336,10 @@ class InvokeAIAppConfig(InvokeAISettings):
                 )
 
     @classmethod
-    def get_config(cls, **kwargs: Dict[str, Any]) -> InvokeAIAppConfig:
-        """Return a singleton InvokeAIAppConfig configuration object."""
+    def get_config(cls, **kwargs) -> InvokeAIAppConfig:
+        """
+        This returns a singleton InvokeAIAppConfig configuration object.
+        """
         if (
             cls.singleton_config is None
             or type(cls.singleton_config) is not cls
@@ -350,7 +351,9 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def root_path(self) -> Path:
-        """Path to the runtime root directory."""
+        """
+        Path to the runtime root directory
+        """
         if self.root:
             root = Path(self.root).expanduser().absolute()
         else:
@@ -360,7 +363,9 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def root_dir(self) -> Path:
-        """Alias for above."""
+        """
+        Alias for above.
+        """
         return self.root_path
 
     def _resolve(self, partial_path: Path) -> Path:
@@ -368,95 +373,108 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def init_file_path(self) -> Path:
-        """Path to invokeai.yaml."""
-        resolved_path = self._resolve(INIT_FILE)
-        assert resolved_path is not None
-        return resolved_path
+        """
+        Path to invokeai.yaml
+        """
+        return self._resolve(INIT_FILE)
 
     @property
-    def output_path(self) -> Optional[Path]:
-        """Path to defaults outputs directory."""
+    def output_path(self) -> Path:
+        """
+        Path to defaults outputs directory.
+        """
         return self._resolve(self.outdir)
 
     @property
     def db_path(self) -> Path:
-        """Path to the invokeai.db file."""
-        db_dir = self._resolve(self.db_dir)
-        assert db_dir is not None
-        return db_dir / DB_FILE
+        """
+        Path to the invokeai.db file.
+        """
+        return self._resolve(self.db_dir) / DB_FILE
 
     @property
     def model_conf_path(self) -> Path:
-        """Path to models configuration file."""
+        """
+        Path to models configuration file.
+        """
         return self._resolve(self.conf_path)
 
     @property
     def legacy_conf_path(self) -> Path:
-        """Path to directory of legacy configuration files (e.g. v1-inference.yaml)."""
+        """
+        Path to directory of legacy configuration files (e.g. v1-inference.yaml)
+        """
         return self._resolve(self.legacy_conf_dir)
 
     @property
     def models_path(self) -> Path:
-        """Path to the models directory."""
+        """
+        Path to the models directory
+        """
         return self._resolve(self.models_dir)
 
     @property
     def custom_nodes_path(self) -> Path:
-        """Path to the custom nodes directory."""
-        custom_nodes_path = self._resolve(self.custom_nodes_dir)
-        assert custom_nodes_path is not None
-        return custom_nodes_path
+        """
+        Path to the custom nodes directory
+        """
+        return self._resolve(self.custom_nodes_dir)
 
     # the following methods support legacy calls leftover from the Globals era
     @property
     def full_precision(self) -> bool:
-        """Return true if precision set to float32."""
+        """Return true if precision set to float32"""
         return self.precision == "float32"
 
     @property
     def try_patchmatch(self) -> bool:
-        """Return true if patchmatch true."""
+        """Return true if patchmatch true"""
         return self.patchmatch
 
     @property
     def nsfw_checker(self) -> bool:
-        """Return value for NSFW checker. The NSFW node is always active and disabled from Web UI."""
+        """NSFW node is always active and disabled from Web UIe"""
         return True
 
     @property
     def invisible_watermark(self) -> bool:
-        """Return value of invisible watermark. It is always active and disabled from Web UI."""
+        """invisible watermark node is always active and disabled from Web UIe"""
         return True
 
     @property
     def ram_cache_size(self) -> Union[Literal["auto"], float]:
-        """Return the ram cache size using the legacy or modern setting."""
         return self.max_cache_size or self.ram
 
     @property
     def vram_cache_size(self) -> Union[Literal["auto"], float]:
-        """Return the vram cache size using the legacy or modern setting."""
         return self.max_vram_cache_size or self.vram
 
     @property
     def use_cpu(self) -> bool:
-        """Return true if the device is set to CPU or the always_use_cpu flag is set."""
         return self.always_use_cpu or self.device == "cpu"
 
     @property
     def disable_xformers(self) -> bool:
-        """Return true if enable_xformers is false (reversed logic) and attention type is not set to xformers."""
+        """
+        Return true if enable_xformers is false (reversed logic)
+        and attention type is not set to xformers.
+        """
         disabled_in_config = not self.xformers_enabled
         return disabled_in_config and self.attention_type != "xformers"
 
     @staticmethod
     def find_root() -> Path:
-        """Choose the runtime root directory when not specified on command line or init file."""
+        """
+        Choose the runtime root directory when not specified on command line or
+        init file.
+        """
         return _find_root()
 
 
 def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
-    """Legacy function which returns InvokeAIAppConfig.get_config()."""
+    """
+    Legacy function which returns InvokeAIAppConfig.get_config()
+    """
     return InvokeAIAppConfig.get_config(**kwargs)
 
 

invokeai/app/services/events/__init__.py

@@ -1 +0,0 @@
-from .events_base import EventServiceBase  # noqa F401

invokeai/app/services/events/events_base.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-
 from typing import Any, Optional
 
 from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
@@ -17,7 +16,6 @@ from invokeai.backend.model_management.models.base import BaseModelType, ModelTy
 
 class EventServiceBase:
     queue_event: str = "queue_event"
-    model_event: str = "model_event"
 
     """Basic event bus, to have an empty stand-in when not needed"""
 
@@ -32,13 +30,6 @@ class EventServiceBase:
             payload={"event": event_name, "data": payload},
         )
 
-    def __emit_model_event(self, event_name: str, payload: dict) -> None:
-        payload["timestamp"] = get_timestamp()
-        self.dispatch(
-            event_name=EventServiceBase.model_event,
-            payload={"event": event_name, "data": payload},
-        )
-
     # Define events here for every event in the system.
     # This will make them easier to integrate until we find a schema generator.
     def emit_generator_progress(
@@ -322,73 +313,3 @@ class EventServiceBase:
             event_name="queue_cleared",
             payload={"queue_id": queue_id},
         )
-
-    def emit_model_install_started(self, source: str) -> None:
-        """
-        Emitted when an install job is started.
-
-        :param source: Source of the model; local path, repo_id or url
-        """
-        self.__emit_model_event(
-            event_name="model_install_started",
-            payload={"source": source},
-        )
-
-    def emit_model_install_completed(self, source: str, key: str) -> None:
-        """
-        Emitted when an install job is completed successfully.
-
-        :param source: Source of the model; local path, repo_id or url
-        :param key: Model config record key
-        """
-        self.__emit_model_event(
-            event_name="model_install_completed",
-            payload={
-                "source": source,
-                "key": key,
-            },
-        )
-
-    def emit_model_install_progress(
-        self,
-        source: str,
-        current_bytes: int,
-        total_bytes: int,
-    ) -> None:
-        """
-        Emitted while the install job is in progress.
-        (Downloaded models only)
-
-        :param source: Source of the model
-        :param current_bytes: Number of bytes downloaded so far
-        :param total_bytes: Total bytes to download
-        """
-        self.__emit_model_event(
-            event_name="model_install_progress",
-            payload={
-                "source": source,
-                "current_bytes": int,
-                "total_bytes": int,
-            },
-        )
-
-    def emit_model_install_error(
-        self,
-        source: str,
-        error_type: str,
-        error: str,
-    ) -> None:
-        """
-        Emitted when an install job encounters an exception.
-
-        :param source: Source of the model
-        :param exception: The exception that raised the error
-        """
-        self.__emit_model_event(
-            event_name="model_install_error",
-            payload={
-                "source": source,
-                "error_type": error_type,
-                "error": error,
-            },
-        )

invokeai/app/services/image_files/image_files_base.py

@@ -4,8 +4,7 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 
 
 class ImageFileStorageBase(ABC):
@@ -34,7 +33,7 @@ class ImageFileStorageBase(ABC):
         image: PILImageType,
         image_name: str,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
         thumbnail_size: int = 256,
     ) -> None:
         """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
@@ -44,8 +43,3 @@ class ImageFileStorageBase(ABC):
     def delete(self, image_name: str) -> None:
         """Deletes an image and its thumbnail (if one exists)."""
         pass
-
-    @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
-        """Gets the workflow of an image."""
-        pass

invokeai/app/services/image_files/image_files_disk.py

@@ -7,9 +7,8 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
-from invokeai.app.invocations.baseinvocation import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
 from .image_files_base import ImageFileStorageBase
@@ -57,7 +56,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         image: PILImageType,
         image_name: str,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
         thumbnail_size: int = 256,
     ) -> None:
         try:
@@ -65,19 +64,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
             image_path = self.get_path(image_name)
 
             pnginfo = PngImagePlugin.PngInfo()
-            info_dict = {}
 
             if metadata is not None:
-                metadata_json = metadata.model_dump_json()
-                info_dict["invokeai_metadata"] = metadata_json
-                pnginfo.add_text("invokeai_metadata", metadata_json)
+                pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
             if workflow is not None:
-                workflow_json = workflow.model_dump_json()
-                info_dict["invokeai_workflow"] = workflow_json
-                pnginfo.add_text("invokeai_workflow", workflow_json)
+                pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
 
-            # When saving the image, the image object's info field is not populated. We need to set it
-            image.info = info_dict
             image.save(
                 image_path,
                 "PNG",
@@ -129,13 +121,6 @@ class DiskImageFileStorage(ImageFileStorageBase):
         path = path if isinstance(path, Path) else Path(path)
         return path.exists()
 
-    def get_workflow(self, image_name: str) -> WorkflowWithoutID | None:
-        image = self.get(image_name)
-        workflow = image.info.get("invokeai_workflow", None)
-        if workflow is not None:
-            return WorkflowWithoutID.model_validate_json(workflow)
-        return None
-
     def __validate_storage_folders(self) -> None:
         """Checks if the required output folders exist and create them if they don't"""
         folders: list[Path] = [self.__output_folder, self.__thumbnails_folder]

invokeai/app/services/image_records/image_records_base.py

@@ -75,7 +75,6 @@ class ImageRecordStorageBase(ABC):
         image_category: ImageCategory,
         width: int,
         height: int,
-        has_workflow: bool,
         is_intermediate: Optional[bool] = False,
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,

invokeai/app/services/image_records/image_records_common.py

@@ -100,7 +100,6 @@ IMAGE_DTO_COLS = ", ".join(
             "height",
             "session_id",
             "node_id",
-            "has_workflow",
             "is_intermediate",
             "created_at",
             "updated_at",
@@ -146,7 +145,6 @@ class ImageRecord(BaseModelExcludeNull):
     """The node ID that generated this image, if it is a generated image."""
     starred: bool = Field(description="Whether this image is starred.")
     """Whether this image is starred."""
-    has_workflow: bool = Field(description="Whether this image has a workflow.")
 
 
 class ImageRecordChanges(BaseModelExcludeNull, extra="allow"):
@@ -190,7 +188,6 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
     deleted_at = image_dict.get("deleted_at", get_iso_timestamp())
     is_intermediate = image_dict.get("is_intermediate", False)
     starred = image_dict.get("starred", False)
-    has_workflow = image_dict.get("has_workflow", False)
 
     return ImageRecord(
         image_name=image_name,
@@ -205,5 +202,4 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
         deleted_at=deleted_at,
         is_intermediate=is_intermediate,
         starred=starred,
-        has_workflow=has_workflow,
     )

invokeai/app/services/image_records/image_records_sqlite.py

@@ -5,7 +5,7 @@ from typing import Optional, Union, cast
 
 from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 from .image_records_base import ImageRecordStorageBase
 from .image_records_common import (
@@ -32,6 +32,91 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         self._conn = db.conn
         self._cursor = self._conn.cursor()
 
+        try:
+            self._lock.acquire()
+            self._create_tables()
+            self._conn.commit()
+        finally:
+            self._lock.release()
+
+    def _create_tables(self) -> None:
+        """Creates the `images` table."""
+
+        # Create the `images` table.
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS images (
+                image_name TEXT NOT NULL PRIMARY KEY,
+                -- This is an enum in python, unrestricted string here for flexibility
+                image_origin TEXT NOT NULL,
+                -- This is an enum in python, unrestricted string here for flexibility
+                image_category TEXT NOT NULL,
+                width INTEGER NOT NULL,
+                height INTEGER NOT NULL,
+                session_id TEXT,
+                node_id TEXT,
+                metadata TEXT,
+                is_intermediate BOOLEAN DEFAULT FALSE,
+                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- Updated via trigger
+                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- Soft delete, currently unused
+                deleted_at DATETIME
+            );
+            """
+        )
+
+        self._cursor.execute("PRAGMA table_info(images)")
+        columns = [column[1] for column in self._cursor.fetchall()]
+
+        if "starred" not in columns:
+            self._cursor.execute(
+                """--sql
+                ALTER TABLE images ADD COLUMN starred BOOLEAN DEFAULT FALSE;
+                """
+            )
+
+        # Create the `images` table indices.
+        self._cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_image_name ON images(image_name);
+            """
+        )
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_image_origin ON images(image_origin);
+            """
+        )
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_image_category ON images(image_category);
+            """
+        )
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_created_at ON images(created_at);
+            """
+        )
+
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_starred ON images(starred);
+            """
+        )
+
+        # Add trigger for `updated_at`.
+        self._cursor.execute(
+            """--sql
+            CREATE TRIGGER IF NOT EXISTS tg_images_updated_at
+            AFTER UPDATE
+            ON images FOR EACH ROW
+            BEGIN
+                UPDATE images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE image_name = old.image_name;
+            END;
+            """
+        )
+
     def get(self, image_name: str) -> ImageRecord:
         try:
             self._lock.acquire()
@@ -323,7 +408,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         image_category: ImageCategory,
         width: int,
         height: int,
-        has_workflow: bool,
         is_intermediate: Optional[bool] = False,
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,
@@ -345,10 +429,9 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     session_id,
                     metadata,
                     is_intermediate,
-                    starred,
-                    has_workflow
+                    starred
                     )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
                 """,
                 (
                     image_name,
@@ -361,7 +444,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     metadata_json,
                     is_intermediate,
                     starred,
-                    has_workflow,
                 ),
             )
             self._conn.commit()

invokeai/app/services/images/images_base.py

@@ -3,7 +3,7 @@ from typing import Callable, Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
     ImageRecord,
@@ -12,7 +12,6 @@ from invokeai.app.services.image_records.image_records_common import (
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 
 class ImageServiceABC(ABC):
@@ -52,7 +51,7 @@ class ImageServiceABC(ABC):
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
         pass
@@ -86,11 +85,6 @@ class ImageServiceABC(ABC):
         """Gets an image's metadata."""
         pass
 
-    @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
-        """Gets an image's workflow."""
-        pass
-
     @abstractmethod
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         """Gets an image's path."""

invokeai/app/services/images/images_common.py

@@ -24,6 +24,11 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
         default=None, description="The id of the board the image belongs to, if one exists."
     )
     """The id of the board the image belongs to, if one exists."""
+    workflow_id: Optional[str] = Field(
+        default=None,
+        description="The workflow that generated this image.",
+    )
+    """The workflow that generated this image."""
 
 
 def image_record_to_dto(
@@ -31,6 +36,7 @@ def image_record_to_dto(
     image_url: str,
     thumbnail_url: str,
     board_id: Optional[str],
+    workflow_id: Optional[str],
 ) -> ImageDTO:
     """Converts an image record to an image DTO."""
     return ImageDTO(
@@ -38,4 +44,5 @@ def image_record_to_dto(
         image_url=image_url,
         thumbnail_url=thumbnail_url,
         board_id=board_id,
+        workflow_id=workflow_id,
     )

invokeai/app/services/images/images_default.py

@@ -2,10 +2,9 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 from ..image_files.image_files_common import (
     ImageFileDeleteException,
@@ -43,7 +42,7 @@ class ImageService(ImageServiceABC):
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
             raise InvalidOriginException
@@ -56,6 +55,12 @@ class ImageService(ImageServiceABC):
         (width, height) = image.size
 
         try:
+            if workflow is not None:
+                created_workflow = self.__invoker.services.workflow_records.create(workflow)
+                workflow_id = created_workflow.model_dump()["id"]
+            else:
+                workflow_id = None
+
             # TODO: Consider using a transaction here to ensure consistency between storage and database
             self.__invoker.services.image_records.save(
                 # Non-nullable fields
@@ -64,7 +69,6 @@ class ImageService(ImageServiceABC):
                 image_category=image_category,
                 width=width,
                 height=height,
-                has_workflow=workflow is not None,
                 # Meta fields
                 is_intermediate=is_intermediate,
                 # Nullable fields
@@ -74,6 +78,8 @@ class ImageService(ImageServiceABC):
             )
             if board_id is not None:
                 self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
+            if workflow_id is not None:
+                self.__invoker.services.workflow_image_records.create(workflow_id=workflow_id, image_name=image_name)
             self.__invoker.services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, workflow=workflow
             )
@@ -137,6 +143,7 @@ class ImageService(ImageServiceABC):
                 image_url=self.__invoker.services.urls.get_image_url(image_name),
                 thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
                 board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
+                workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name),
             )
 
             return image_dto
@@ -157,15 +164,18 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting image DTO")
             raise e
 
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowField]:
         try:
-            return self.__invoker.services.image_files.get_workflow(image_name)
-        except ImageFileNotFoundException:
-            self.__invoker.services.logger.error("Image file not found")
-            raise
-        except Exception:
-            self.__invoker.services.logger.error("Problem getting image workflow")
+            workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name)
+            if workflow_id is None:
+                return None
+            return self.__invoker.services.workflow_records.get(workflow_id)
+        except ImageRecordNotFoundException:
+            self.__invoker.services.logger.error("Image record not found")
             raise
+        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:
@@ -213,6 +223,7 @@ class ImageService(ImageServiceABC):
                     image_url=self.__invoker.services.urls.get_image_url(r.image_name),
                     thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
                     board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
+                    workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
                 )
                 for r in results.items
             ]

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -108,7 +108,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                                 queue_item_id=queue_item.session_queue_item_id,
                                 queue_id=queue_item.session_queue_id,
                                 queue_batch_id=queue_item.session_queue_batch_id,
-                                workflow=queue_item.workflow,
                             )
                         )
 
@@ -179,7 +178,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             session_queue_item_id=queue_item.session_queue_item_id,
                             session_queue_id=queue_item.session_queue_id,
                             graph_execution_state=graph_execution_state,
-                            workflow=queue_item.workflow,
                             invoke_all=True,
                         )
                     except Exception as e:

invokeai/app/services/invocation_queue/invocation_queue_common.py

@@ -1,12 +1,9 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import time
-from typing import Optional
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
-
 
 class InvocationQueueItem(BaseModel):
     graph_execution_state_id: str = Field(description="The ID of the graph execution state")
@@ -18,6 +15,5 @@ class InvocationQueueItem(BaseModel):
     session_queue_batch_id: str = Field(
         description="The ID of the session batch from which this invocation queue item came"
     )
-    workflow: Optional[WorkflowWithoutID] = Field(description="The workflow associated with this queue item")
     invoke_all: bool = Field(default=False)
     timestamp: float = Field(default_factory=time.time)

invokeai/app/services/invocation_services.py

@@ -21,7 +21,6 @@ if TYPE_CHECKING:
     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_install import ModelInstallServiceBase
     from .model_manager.model_manager_base import ModelManagerServiceBase
     from .model_records import ModelRecordServiceBase
     from .names.names_base import NameServiceBase
@@ -29,6 +28,7 @@ if TYPE_CHECKING:
     from .session_queue.session_queue_base import SessionQueueBase
     from .shared.graph import GraphExecutionState, LibraryGraph
     from .urls.urls_base import UrlServiceBase
+    from .workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
 
@@ -51,7 +51,6 @@ class InvocationServices:
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
     model_records: "ModelRecordServiceBase"
-    model_install: "ModelInstallServiceBase"
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
@@ -60,6 +59,7 @@ class InvocationServices:
     invocation_cache: "InvocationCacheBase"
     names: "NameServiceBase"
     urls: "UrlServiceBase"
+    workflow_image_records: "WorkflowImageRecordsStorageBase"
     workflow_records: "WorkflowRecordsStorageBase"
 
     def __init__(
@@ -79,7 +79,6 @@ class InvocationServices:
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         model_records: "ModelRecordServiceBase",
-        model_install: "ModelInstallServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
@@ -88,6 +87,7 @@ class InvocationServices:
         invocation_cache: "InvocationCacheBase",
         names: "NameServiceBase",
         urls: "UrlServiceBase",
+        workflow_image_records: "WorkflowImageRecordsStorageBase",
         workflow_records: "WorkflowRecordsStorageBase",
     ):
         self.board_images = board_images
@@ -105,7 +105,6 @@ class InvocationServices:
         self.logger = logger
         self.model_manager = model_manager
         self.model_records = model_records
-        self.model_install = model_install
         self.processor = processor
         self.performance_statistics = performance_statistics
         self.queue = queue
@@ -114,4 +113,5 @@ class InvocationServices:
         self.invocation_cache = invocation_cache
         self.names = names
         self.urls = urls
+        self.workflow_image_records = workflow_image_records
         self.workflow_records = workflow_records

invokeai/app/services/invoker.py

@@ -2,8 +2,6 @@
 
 from typing import Optional
 
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
-
 from .invocation_queue.invocation_queue_common import InvocationQueueItem
 from .invocation_services import InvocationServices
 from .shared.graph import Graph, GraphExecutionState
@@ -24,7 +22,6 @@ class Invoker:
         session_queue_item_id: int,
         session_queue_batch_id: str,
         graph_execution_state: GraphExecutionState,
-        workflow: Optional[WorkflowWithoutID] = None,
         invoke_all: bool = False,
     ) -> Optional[str]:
         """Determines the next node to invoke and enqueues it, preparing if needed.
@@ -46,7 +43,6 @@ class Invoker:
                 session_queue_batch_id=session_queue_batch_id,
                 graph_execution_state_id=graph_execution_state.id,
                 invocation_id=invocation.id,
-                workflow=workflow,
                 invoke_all=invoke_all,
             )
         )

invokeai/app/services/item_storage/item_storage_sqlite.py

@@ -5,7 +5,7 @@ from typing import Generic, Optional, TypeVar, get_args
 from pydantic import BaseModel, TypeAdapter
 
 from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 from .item_storage_base import ItemStorageABC
 

invokeai/app/services/latents_storage/latents_storage_disk.py

@@ -5,8 +5,6 @@ from typing import Union
 
 import torch
 
-from invokeai.app.services.invoker import Invoker
-
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -19,10 +17,6 @@ class DiskLatentsStorage(LatentsStorageBase):
         self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__output_folder.mkdir(parents=True, exist_ok=True)
 
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        self._delete_all_latents()
-
     def get(self, name: str) -> torch.Tensor:
         latent_path = self.get_path(name)
         return torch.load(latent_path)
@@ -38,21 +32,3 @@ class DiskLatentsStorage(LatentsStorageBase):
 
     def get_path(self, name: str) -> Path:
         return self.__output_folder / name
-
-    def _delete_all_latents(self) -> None:
-        """
-        Deletes all latents from disk.
-        Must be called after we have access to `self._invoker` (e.g. in `start()`).
-        """
-        deleted_latents_count = 0
-        freed_space = 0
-        for latents_file in Path(self.__output_folder).glob("*"):
-            if latents_file.is_file():
-                freed_space += latents_file.stat().st_size
-                deleted_latents_count += 1
-                latents_file.unlink()
-        if deleted_latents_count > 0:
-            freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
-            self._invoker.services.logger.info(
-                f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
-            )

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -5,8 +5,6 @@ from typing import Dict, Optional
 
 import torch
 
-from invokeai.app.services.invoker import Invoker
-
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -25,18 +23,6 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
         self.__cache_ids = Queue()
         self.__max_cache_size = max_cache_size
 
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        start_op = getattr(self.__underlying_storage, "start", None)
-        if callable(start_op):
-            start_op(invoker)
-
-    def stop(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        stop_op = getattr(self.__underlying_storage, "stop", None)
-        if callable(stop_op):
-            stop_op(invoker)
-
     def get(self, name: str) -> torch.Tensor:
         cache_item = self.__get_cache(name)
         if cache_item is not None:

invokeai/app/services/model_install/__init__.py

@@ -1,25 +0,0 @@
-"""Initialization file for model install service package."""
-
-from .model_install_base import (
-    HFModelSource,
-    InstallStatus,
-    LocalModelSource,
-    ModelInstallJob,
-    ModelInstallServiceBase,
-    ModelSource,
-    UnknownInstallJobException,
-    URLModelSource,
-)
-from .model_install_default import ModelInstallService
-
-__all__ = [
-    "ModelInstallServiceBase",
-    "ModelInstallService",
-    "InstallStatus",
-    "ModelInstallJob",
-    "UnknownInstallJobException",
-    "ModelSource",
-    "LocalModelSource",
-    "HFModelSource",
-    "URLModelSource",
-]

invokeai/app/services/model_install/model_install_base.py

@@ -1,306 +0,0 @@
-import re
-import traceback
-from abc import ABC, abstractmethod
-from enum import Enum
-from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Union
-
-from pydantic import BaseModel, Field, field_validator
-from pydantic.networks import AnyHttpUrl
-from typing_extensions import Annotated
-
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.events import EventServiceBase
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.model_records import ModelRecordServiceBase
-from invokeai.backend.model_manager import AnyModelConfig
-
-
-class InstallStatus(str, Enum):
-    """State of an install job running in the background."""
-
-    WAITING = "waiting"  # waiting to be dequeued
-    RUNNING = "running"  # being processed
-    COMPLETED = "completed"  # finished running
-    ERROR = "error"  # terminated with an error message
-
-
-class UnknownInstallJobException(Exception):
-    """Raised when the status of an unknown job is requested."""
-
-
-class StringLikeSource(BaseModel):
-    """
-    Base class for model sources, implements functions that lets the source be sorted and indexed.
-
-    These shenanigans let this stuff work:
-
-      source1 = LocalModelSource(path='C:/users/mort/foo.safetensors')
-      mydict = {source1: 'model 1'}
-      assert mydict['C:/users/mort/foo.safetensors'] == 'model 1'
-      assert mydict[LocalModelSource(path='C:/users/mort/foo.safetensors')] == 'model 1'
-
-      source2 = LocalModelSource(path=Path('C:/users/mort/foo.safetensors'))
-      assert source1 == source2
-      assert source1 == 'C:/users/mort/foo.safetensors'
-    """
-
-    def __hash__(self) -> int:
-        """Return hash of the path field, for indexing."""
-        return hash(str(self))
-
-    def __lt__(self, other: object) -> int:
-        """Return comparison of the stringified version, for sorting."""
-        return str(self) < str(other)
-
-    def __eq__(self, other: object) -> bool:
-        """Return equality on the stringified version."""
-        if isinstance(other, Path):
-            return str(self) == other.as_posix()
-        else:
-            return str(self) == str(other)
-
-
-class LocalModelSource(StringLikeSource):
-    """A local file or directory path."""
-
-    path: str | Path
-    inplace: Optional[bool] = False
-    type: Literal["local"] = "local"
-
-    # these methods allow the source to be used in a string-like way,
-    # for example as an index into a dict
-    def __str__(self) -> str:
-        """Return string version of path when string rep needed."""
-        return Path(self.path).as_posix()
-
-
-class HFModelSource(StringLikeSource):
-    """A HuggingFace repo_id, with optional variant and sub-folder."""
-
-    repo_id: str
-    variant: Optional[str] = None
-    subfolder: Optional[str | Path] = None
-    access_token: Optional[str] = None
-    type: Literal["hf"] = "hf"
-
-    @field_validator("repo_id")
-    @classmethod
-    def proper_repo_id(cls, v: str) -> str:  # noqa D102
-        if not re.match(r"^([.\w-]+/[.\w-]+)$", v):
-            raise ValueError(f"{v}: invalid repo_id format")
-        return v
-
-    def __str__(self) -> str:
-        """Return string version of repoid when string rep needed."""
-        base: str = self.repo_id
-        base += f":{self.subfolder}" if self.subfolder else ""
-        base += f" ({self.variant})" if self.variant else ""
-        return base
-
-
-class URLModelSource(StringLikeSource):
-    """A generic URL point to a checkpoint file."""
-
-    url: AnyHttpUrl
-    access_token: Optional[str] = None
-    type: Literal["generic_url"] = "generic_url"
-
-    def __str__(self) -> str:
-        """Return string version of the url when string rep needed."""
-        return str(self.url)
-
-
-ModelSource = Annotated[Union[LocalModelSource, HFModelSource, URLModelSource], Field(discriminator="type")]
-
-
-class ModelInstallJob(BaseModel):
-    """Object that tracks the current status of an install request."""
-
-    status: InstallStatus = Field(default=InstallStatus.WAITING, description="Current status of install process")
-    config_in: Dict[str, Any] = Field(
-        default_factory=dict, description="Configuration information (e.g. 'description') to apply to model."
-    )
-    config_out: Optional[AnyModelConfig] = Field(
-        default=None, description="After successful installation, this will hold the configuration object."
-    )
-    inplace: bool = Field(
-        default=False, description="Leave model in its current location; otherwise install under models directory"
-    )
-    source: ModelSource = Field(description="Source (URL, repo_id, or local path) of model")
-    local_path: Path = Field(description="Path to locally-downloaded model; may be the same as the source")
-    error_type: Optional[str] = Field(default=None, description="Class name of the exception that led to status==ERROR")
-    error: Optional[str] = Field(default=None, description="Error traceback")  # noqa #501
-
-    def set_error(self, e: Exception) -> None:
-        """Record the error and traceback from an exception."""
-        self.error_type = e.__class__.__name__
-        self.error = "".join(traceback.format_exception(e))
-        self.status = InstallStatus.ERROR
-
-
-class ModelInstallServiceBase(ABC):
-    """Abstract base class for InvokeAI model installation."""
-
-    @abstractmethod
-    def __init__(
-        self,
-        app_config: InvokeAIAppConfig,
-        record_store: ModelRecordServiceBase,
-        event_bus: Optional["EventServiceBase"] = None,
-    ):
-        """
-        Create ModelInstallService object.
-
-        :param config: Systemwide InvokeAIAppConfig.
-        :param store: Systemwide ModelConfigStore
-        :param event_bus: InvokeAI event bus for reporting events to.
-        """
-
-    def start(self, invoker: Invoker) -> None:
-        """Call at InvokeAI startup time."""
-        self.sync_to_config()
-
-    @abstractmethod
-    def stop(self) -> None:
-        """Stop the model install service. After this the objection can be safely deleted."""
-
-    @property
-    @abstractmethod
-    def app_config(self) -> InvokeAIAppConfig:
-        """Return the appConfig object associated with the installer."""
-
-    @property
-    @abstractmethod
-    def record_store(self) -> ModelRecordServiceBase:
-        """Return the ModelRecoreService object associated with the installer."""
-
-    @property
-    @abstractmethod
-    def event_bus(self) -> Optional[EventServiceBase]:
-        """Return the event service base object associated with the installer."""
-
-    @abstractmethod
-    def register_path(
-        self,
-        model_path: Union[Path, str],
-        config: Optional[Dict[str, Any]] = None,
-    ) -> str:
-        """
-        Probe and register the model at model_path.
-
-        This keeps the model in its current location.
-
-        :param model_path: Filesystem Path to the model.
-        :param config: Dict of attributes that will override autoassigned values.
-        :returns id: The string ID of the registered model.
-        """
-
-    @abstractmethod
-    def unregister(self, key: str) -> None:
-        """Remove model with indicated key from the database."""
-
-    @abstractmethod
-    def delete(self, key: str) -> None:
-        """Remove model with indicated key from the database. Delete its files only if they are within our models directory."""
-
-    @abstractmethod
-    def unconditionally_delete(self, key: str) -> None:
-        """Remove model with indicated key from the database and unconditionally delete weight files from disk."""
-
-    @abstractmethod
-    def install_path(
-        self,
-        model_path: Union[Path, str],
-        config: Optional[Dict[str, Any]] = None,
-    ) -> str:
-        """
-        Probe, register and install the model in the models directory.
-
-        This moves the model from its current location into
-        the models directory handled by InvokeAI.
-
-        :param model_path: Filesystem Path to the model.
-        :param config: Dict of attributes that will override autoassigned values.
-        :returns id: The string ID of the registered model.
-        """
-
-    @abstractmethod
-    def import_model(
-        self,
-        source: ModelSource,
-        config: Optional[Dict[str, Any]] = None,
-    ) -> ModelInstallJob:
-        """Install the indicated model.
-
-        :param source: ModelSource object
-
-        :param config: Optional dict. Any fields in this dict
-         will override corresponding autoassigned probe fields in the
-         model's config record. Use it to override
-         `name`, `description`, `base_type`, `model_type`, `format`,
-         `prediction_type`, `image_size`, and/or `ztsnr_training`.
-
-        This will download the model located at `source`,
-        probe it, and install it into the models directory.
-        This call is executed asynchronously in a separate
-        thread and will issue the following events on the event bus:
-
-             - model_install_started
-             - model_install_error
-             - model_install_completed
-
-        The `inplace` flag does not affect the behavior of downloaded
-        models, which are always moved into the `models` directory.
-
-        The call returns a ModelInstallJob object which can be
-        polled to learn the current status and/or error message.
-
-        Variants recognized by HuggingFace currently are:
-        1. onnx
-        2. openvino
-        3. fp16
-        4. None (usually returns fp32 model)
-
-        """
-
-    @abstractmethod
-    def get_job(self, source: ModelSource) -> List[ModelInstallJob]:
-        """Return the ModelInstallJob(s) corresponding to the provided source."""
-
-    @abstractmethod
-    def list_jobs(self) -> List[ModelInstallJob]:  # noqa D102
-        """
-        List active and complete install jobs.
-        """
-
-    @abstractmethod
-    def prune_jobs(self) -> None:
-        """Prune all completed and errored jobs."""
-
-    @abstractmethod
-    def wait_for_installs(self) -> List[ModelInstallJob]:
-        """
-        Wait for all pending installs to complete.
-
-        This will block until all pending installs have
-        completed, been cancelled, or errored out. It will
-        block indefinitely if one or more jobs are in the
-        paused state.
-
-        It will return the current list of jobs.
-        """
-
-    @abstractmethod
-    def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]:
-        """
-        Recursively scan directory for new models and register or install them.
-
-        :param scan_dir: Path to the directory to scan.
-        :param install: Install if True, otherwise register in place.
-        :returns list of IDs: Returns list of IDs of models registered/installed
-        """
-
-    @abstractmethod
-    def sync_to_config(self) -> None:
-        """Synchronize models on disk to those in the model record database."""

invokeai/app/services/model_install/model_install_default.py

@@ -1,395 +0,0 @@
-"""Model installation class."""
-
-import threading
-from hashlib import sha256
-from logging import Logger
-from pathlib import Path
-from queue import Queue
-from random import randbytes
-from shutil import copyfile, copytree, move, rmtree
-from typing import Any, Dict, List, Optional, Set, Union
-
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.events import EventServiceBase
-from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase, UnknownModelException
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    BaseModelType,
-    InvalidModelConfigException,
-    ModelType,
-)
-from invokeai.backend.model_manager.hash import FastModelHash
-from invokeai.backend.model_manager.probe import ModelProbe
-from invokeai.backend.model_manager.search import ModelSearch
-from invokeai.backend.util import Chdir, InvokeAILogger
-
-from .model_install_base import (
-    InstallStatus,
-    LocalModelSource,
-    ModelInstallJob,
-    ModelInstallServiceBase,
-    ModelSource,
-)
-
-# marker that the queue is done and that thread should exit
-STOP_JOB = ModelInstallJob(
-    source=LocalModelSource(path="stop"),
-    local_path=Path("/dev/null"),
-)
-
-
-class ModelInstallService(ModelInstallServiceBase):
-    """class for InvokeAI model installation."""
-
-    _app_config: InvokeAIAppConfig
-    _record_store: ModelRecordServiceBase
-    _event_bus: Optional[EventServiceBase] = None
-    _install_queue: Queue[ModelInstallJob]
-    _install_jobs: List[ModelInstallJob]
-    _logger: Logger
-    _cached_model_paths: Set[Path]
-    _models_installed: Set[str]
-
-    def __init__(
-        self,
-        app_config: InvokeAIAppConfig,
-        record_store: ModelRecordServiceBase,
-        event_bus: Optional[EventServiceBase] = None,
-    ):
-        """
-        Initialize the installer object.
-
-        :param app_config: InvokeAIAppConfig object
-        :param record_store: Previously-opened ModelRecordService database
-        :param event_bus: Optional EventService object
-        """
-        self._app_config = app_config
-        self._record_store = record_store
-        self._event_bus = event_bus
-        self._logger = InvokeAILogger.get_logger(name=self.__class__.__name__)
-        self._install_jobs = []
-        self._install_queue = Queue()
-        self._cached_model_paths = set()
-        self._models_installed = set()
-        self._start_installer_thread()
-
-    @property
-    def app_config(self) -> InvokeAIAppConfig:  # noqa D102
-        return self._app_config
-
-    @property
-    def record_store(self) -> ModelRecordServiceBase:  # noqa D102
-        return self._record_store
-
-    @property
-    def event_bus(self) -> Optional[EventServiceBase]:  # noqa D102
-        return self._event_bus
-
-    def stop(self, *args, **kwargs) -> None:
-        """Stop the install thread; after this the object can be deleted and garbage collected."""
-        self._install_queue.put(STOP_JOB)
-
-    def _start_installer_thread(self) -> None:
-        threading.Thread(target=self._install_next_item, daemon=True).start()
-
-    def _install_next_item(self) -> None:
-        done = False
-        while not done:
-            job = self._install_queue.get()
-            if job == STOP_JOB:
-                done = True
-                continue
-
-            assert job.local_path is not None
-            try:
-                self._signal_job_running(job)
-                if job.inplace:
-                    key = self.register_path(job.local_path, job.config_in)
-                else:
-                    key = self.install_path(job.local_path, job.config_in)
-                job.config_out = self.record_store.get_model(key)
-                self._signal_job_completed(job)
-
-            except (OSError, DuplicateModelException, InvalidModelConfigException) as excp:
-                self._signal_job_errored(job, excp)
-            finally:
-                self._install_queue.task_done()
-        self._logger.info("Install thread exiting")
-
-    def _signal_job_running(self, job: ModelInstallJob) -> None:
-        job.status = InstallStatus.RUNNING
-        self._logger.info(f"{job.source}: model installation started")
-        if self._event_bus:
-            self._event_bus.emit_model_install_started(str(job.source))
-
-    def _signal_job_completed(self, job: ModelInstallJob) -> None:
-        job.status = InstallStatus.COMPLETED
-        assert job.config_out
-        self._logger.info(
-            f"{job.source}: model installation completed. {job.local_path} registered key {job.config_out.key}"
-        )
-        if self._event_bus:
-            assert job.local_path is not None
-            assert job.config_out is not None
-            key = job.config_out.key
-            self._event_bus.emit_model_install_completed(str(job.source), key)
-
-    def _signal_job_errored(self, job: ModelInstallJob, excp: Exception) -> None:
-        job.set_error(excp)
-        self._logger.info(f"{job.source}: model installation encountered an exception: {job.error_type}")
-        if self._event_bus:
-            error_type = job.error_type
-            error = job.error
-            assert error_type is not None
-            assert error is not None
-            self._event_bus.emit_model_install_error(str(job.source), error_type, error)
-
-    def register_path(
-        self,
-        model_path: Union[Path, str],
-        config: Optional[Dict[str, Any]] = None,
-    ) -> str:  # noqa D102
-        model_path = Path(model_path)
-        config = config or {}
-        if config.get("source") is None:
-            config["source"] = model_path.resolve().as_posix()
-        return self._register(model_path, config)
-
-    def install_path(
-        self,
-        model_path: Union[Path, str],
-        config: Optional[Dict[str, Any]] = None,
-    ) -> str:  # noqa D102
-        model_path = Path(model_path)
-        config = config or {}
-        if config.get("source") is None:
-            config["source"] = model_path.resolve().as_posix()
-
-        info: AnyModelConfig = self._probe_model(Path(model_path), config)
-        old_hash = info.original_hash
-        dest_path = self.app_config.models_path / info.base.value / info.type.value / model_path.name
-        new_path = self._copy_model(model_path, dest_path)
-        new_hash = FastModelHash.hash(new_path)
-        assert new_hash == old_hash, f"{model_path}: Model hash changed during installation, possibly corrupted."
-
-        return self._register(
-            new_path,
-            config,
-            info,
-        )
-
-    def import_model(
-        self,
-        source: ModelSource,
-        config: Optional[Dict[str, Any]] = None,
-    ) -> ModelInstallJob:  # noqa D102
-        if not config:
-            config = {}
-
-        # Installing a local path
-        if isinstance(source, LocalModelSource) and Path(source.path).exists():  # a path that is already on disk
-            job = ModelInstallJob(
-                source=source,
-                config_in=config,
-                local_path=Path(source.path),
-            )
-            self._install_jobs.append(job)
-            self._install_queue.put(job)
-            return job
-
-        else:  # here is where we'd download a URL or repo_id. Implementation pending download queue.
-            raise UnknownModelException("File or directory not found")
-
-    def list_jobs(self) -> List[ModelInstallJob]:  # noqa D102
-        return self._install_jobs
-
-    def get_job(self, source: ModelSource) -> List[ModelInstallJob]:  # noqa D102
-        return [x for x in self._install_jobs if x.source == source]
-
-    def wait_for_installs(self) -> List[ModelInstallJob]:  # noqa D102
-        self._install_queue.join()
-        return self._install_jobs
-
-    def prune_jobs(self) -> None:
-        """Prune all completed and errored jobs."""
-        unfinished_jobs = [
-            x for x in self._install_jobs if x.status not in [InstallStatus.COMPLETED, InstallStatus.ERROR]
-        ]
-        self._install_jobs = unfinished_jobs
-
-    def sync_to_config(self) -> None:
-        """Synchronize models on disk to those in the config record store database."""
-        self._scan_models_directory()
-        if autoimport := self._app_config.autoimport_dir:
-            self._logger.info("Scanning autoimport directory for new models")
-            installed = self.scan_directory(self._app_config.root_path / autoimport)
-            self._logger.info(f"{len(installed)} new models registered")
-        self._logger.info("Model installer (re)initialized")
-
-    def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]:  # noqa D102
-        self._cached_model_paths = {Path(x.path) for x in self.record_store.all_models()}
-        callback = self._scan_install if install else self._scan_register
-        search = ModelSearch(on_model_found=callback)
-        self._models_installed: Set[str] = set()
-        search.search(scan_dir)
-        return list(self._models_installed)
-
-    def _scan_models_directory(self) -> None:
-        """
-        Scan the models directory for new and missing models.
-
-        New models will be added to the storage backend. Missing models
-        will be deleted.
-        """
-        defunct_models = set()
-        installed = set()
-
-        with Chdir(self._app_config.models_path):
-            self._logger.info("Checking for models that have been moved or deleted from disk")
-            for model_config in self.record_store.all_models():
-                path = Path(model_config.path)
-                if not path.exists():
-                    self._logger.info(f"{model_config.name}: path {path.as_posix()} no longer exists. Unregistering")
-                    defunct_models.add(model_config.key)
-            for key in defunct_models:
-                self.unregister(key)
-
-            self._logger.info(f"Scanning {self._app_config.models_path} for new and orphaned models")
-            for cur_base_model in BaseModelType:
-                for cur_model_type in ModelType:
-                    models_dir = Path(cur_base_model.value, cur_model_type.value)
-                    installed.update(self.scan_directory(models_dir))
-            self._logger.info(f"{len(installed)} new models registered; {len(defunct_models)} unregistered")
-
-    def _sync_model_path(self, key: str, ignore_hash_change: bool = False) -> AnyModelConfig:
-        """
-        Move model into the location indicated by its basetype, type and name.
-
-        Call this after updating a model's attributes in order to move
-        the model's path into the location indicated by its basetype, type and
-        name. Applies only to models whose paths are within the root `models_dir`
-        directory.
-
-        May raise an UnknownModelException.
-        """
-        model = self.record_store.get_model(key)
-        old_path = Path(model.path)
-        models_dir = self.app_config.models_path
-
-        if not old_path.is_relative_to(models_dir):
-            return model
-
-        new_path = models_dir / model.base.value / model.type.value / model.name
-        self._logger.info(f"Moving {model.name} to {new_path}.")
-        new_path = self._move_model(old_path, new_path)
-        new_hash = FastModelHash.hash(new_path)
-        model.path = new_path.relative_to(models_dir).as_posix()
-        if model.current_hash != new_hash:
-            assert (
-                ignore_hash_change
-            ), f"{model.name}: Model hash changed during installation, model is possibly corrupted"
-            model.current_hash = new_hash
-            self._logger.info(f"Model has new hash {model.current_hash}, but will continue to be identified by {key}")
-        self.record_store.update_model(key, model)
-        return model
-
-    def _scan_register(self, model: Path) -> bool:
-        if model in self._cached_model_paths:
-            return True
-        try:
-            id = self.register_path(model)
-            self._sync_model_path(id)  # possibly move it to right place in `models`
-            self._logger.info(f"Registered {model.name} with id {id}")
-            self._models_installed.add(id)
-        except DuplicateModelException:
-            pass
-        return True
-
-    def _scan_install(self, model: Path) -> bool:
-        if model in self._cached_model_paths:
-            return True
-        try:
-            id = self.install_path(model)
-            self._logger.info(f"Installed {model} with id {id}")
-            self._models_installed.add(id)
-        except DuplicateModelException:
-            pass
-        return True
-
-    def unregister(self, key: str) -> None:  # noqa D102
-        self.record_store.del_model(key)
-
-    def delete(self, key: str) -> None:  # noqa D102
-        """Unregister the model. Delete its files only if they are within our models directory."""
-        model = self.record_store.get_model(key)
-        models_dir = self.app_config.models_path
-        model_path = models_dir / model.path
-        if model_path.is_relative_to(models_dir):
-            self.unconditionally_delete(key)
-        else:
-            self.unregister(key)
-
-    def unconditionally_delete(self, key: str) -> None:  # noqa D102
-        model = self.record_store.get_model(key)
-        path = self.app_config.models_path / model.path
-        if path.is_dir():
-            rmtree(path)
-        else:
-            path.unlink()
-        self.unregister(key)
-
-    def _copy_model(self, old_path: Path, new_path: Path) -> Path:
-        if old_path == new_path:
-            return old_path
-        new_path.parent.mkdir(parents=True, exist_ok=True)
-        if old_path.is_dir():
-            copytree(old_path, new_path)
-        else:
-            copyfile(old_path, new_path)
-        return new_path
-
-    def _move_model(self, old_path: Path, new_path: Path) -> Path:
-        if old_path == new_path:
-            return old_path
-
-        new_path.parent.mkdir(parents=True, exist_ok=True)
-
-        # if path already exists then we jigger the name to make it unique
-        counter: int = 1
-        while new_path.exists():
-            path = new_path.with_stem(new_path.stem + f"_{counter:02d}")
-            if not path.exists():
-                new_path = path
-            counter += 1
-        move(old_path, new_path)
-        return new_path
-
-    def _probe_model(self, model_path: Path, config: Optional[Dict[str, Any]] = None) -> AnyModelConfig:
-        info: AnyModelConfig = ModelProbe.probe(Path(model_path))
-        if config:  # used to override probe fields
-            for key, value in config.items():
-                setattr(info, key, value)
-        return info
-
-    def _create_key(self) -> str:
-        return sha256(randbytes(100)).hexdigest()[0:32]
-
-    def _register(
-        self, model_path: Path, config: Optional[Dict[str, Any]] = None, info: Optional[AnyModelConfig] = None
-    ) -> str:
-        info = info or ModelProbe.probe(model_path, config)
-        key = self._create_key()
-
-        model_path = model_path.absolute()
-        if model_path.is_relative_to(self.app_config.models_path):
-            model_path = model_path.relative_to(self.app_config.models_path)
-
-        info.path = model_path.as_posix()
-
-        # add 'main' specific fields
-        if hasattr(info, "config"):
-            # make config relative to our root
-            legacy_conf = (self.app_config.root_dir / self.app_config.legacy_conf_dir / info.config).resolve()
-            info.config = legacy_conf.relative_to(self.app_config.root_dir).as_posix()
-        self.record_store.add_model(key, info)
-        return key

invokeai/app/services/model_records/__init__.py

@@ -6,11 +6,3 @@ from .model_records_base import (  # noqa F401
     UnknownModelException,
 )
 from .model_records_sql import ModelRecordServiceSQL  # noqa F401
-
-__all__ = [
-    "ModelRecordServiceBase",
-    "ModelRecordServiceSQL",
-    "DuplicateModelException",
-    "InvalidModelException",
-    "UnknownModelException",
-]

invokeai/app/services/model_records/model_records_base.py

@@ -9,6 +9,9 @@ from typing import List, Optional, Union
 
 from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType
 
+# should match the InvokeAI version when this is first released.
+CONFIG_FILE_VERSION = "3.2.0"
+
 
 class DuplicateModelException(Exception):
     """Raised on an attempt to add a model with the same key twice."""
@@ -29,6 +32,12 @@ class ConfigFileVersionMismatchException(Exception):
 class ModelRecordServiceBase(ABC):
     """Abstract base class for storage and retrieval of model configs."""
 
+    @property
+    @abstractmethod
+    def version(self) -> str:
+        """Return the config file/database schema version."""
+        pass
+
     @abstractmethod
     def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
         """

invokeai/app/services/model_records/model_records_sql.py

@@ -48,12 +48,14 @@ from typing import List, Optional, Union
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    ModelConfigBase,
     ModelConfigFactory,
     ModelType,
 )
 
-from ..shared.sqlite.sqlite_database import SqliteDatabase
+from ..shared.sqlite import SqliteDatabase
 from .model_records_base import (
+    CONFIG_FILE_VERSION,
     DuplicateModelException,
     ModelRecordServiceBase,
     UnknownModelException,
@@ -77,7 +79,86 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         self._db = db
         self._cursor = self._db.conn.cursor()
 
-    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
+        with self._db.lock:
+            # Enable foreign keys
+            self._db.conn.execute("PRAGMA foreign_keys = ON;")
+            self._create_tables()
+            self._db.conn.commit()
+        assert (
+            str(self.version) == CONFIG_FILE_VERSION
+        ), f"Model config version {self.version} does not match expected version {CONFIG_FILE_VERSION}"
+
+    def _create_tables(self) -> None:
+        """Create sqlite3 tables."""
+        #  model_config table breaks out the fields that are common to all config objects
+        # and puts class-specific ones in a serialized json object
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS model_config (
+                id TEXT NOT NULL PRIMARY KEY,
+                -- The next 3 fields are enums in python, unrestricted string here
+                base TEXT NOT NULL,
+                type TEXT NOT NULL,
+                name TEXT NOT NULL,
+                path TEXT NOT NULL,
+                original_hash TEXT, -- could be null
+                -- Serialized JSON representation of the whole config object,
+                -- which will contain additional fields from subclasses
+                config TEXT NOT NULL,
+                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- Updated via trigger
+                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- unique constraint on combo of name, base and type
+                UNIQUE(name, base, type)
+            );
+            """
+        )
+
+        #  metadata table
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS model_manager_metadata (
+                metadata_key TEXT NOT NULL PRIMARY KEY,
+                metadata_value TEXT NOT NULL
+            );
+            """
+        )
+
+        # Add trigger for `updated_at`.
+        self._cursor.execute(
+            """--sql
+            CREATE TRIGGER IF NOT EXISTS model_config_updated_at
+            AFTER UPDATE
+            ON model_config FOR EACH ROW
+            BEGIN
+                UPDATE model_config SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE id = old.id;
+            END;
+            """
+        )
+
+        # Add indexes for searchable fields
+        for stmt in [
+            "CREATE INDEX IF NOT EXISTS base_index ON model_config(base);",
+            "CREATE INDEX IF NOT EXISTS type_index ON model_config(type);",
+            "CREATE INDEX IF NOT EXISTS name_index ON model_config(name);",
+            "CREATE UNIQUE INDEX IF NOT EXISTS path_index ON model_config(path);",
+        ]:
+            self._cursor.execute(stmt)
+
+        # Add our version to the metadata table
+        self._cursor.execute(
+            """--sql
+            INSERT OR IGNORE into model_manager_metadata (
+               metadata_key,
+               metadata_value
+            )
+            VALUES (?,?);
+            """,
+            ("version", CONFIG_FILE_VERSION),
+        )
+
+    def add_model(self, key: str, config: Union[dict, ModelConfigBase]) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -95,13 +176,21 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                     """--sql
                     INSERT INTO model_config (
                        id,
+                       base,
+                       type,
+                       name,
+                       path,
                        original_hash,
                        config
                       )
-                    VALUES (?,?,?);
+                    VALUES (?,?,?,?,?,?,?);
                     """,
                     (
                         key,
+                        record.base,
+                        record.type,
+                        record.name,
+                        record.path,
                         record.original_hash,
                         json_serialized,
                     ),
@@ -126,6 +215,22 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
         return self.get_model(key)
 
+    @property
+    def version(self) -> str:
+        """Return the version of the database schema."""
+        with self._db.lock:
+            self._cursor.execute(
+                """--sql
+                SELECT metadata_value FROM model_manager_metadata
+                WHERE metadata_key=?;
+                """,
+                ("version",),
+            )
+            rows = self._cursor.fetchone()
+            if not rows:
+                raise KeyError("Models database does not have metadata key 'version'")
+            return rows[0]
+
     def del_model(self, key: str) -> None:
         """
         Delete a model.
@@ -150,7 +255,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 self._db.conn.rollback()
                 raise e
 
-    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
+    def update_model(self, key: str, config: ModelConfigBase) -> AnyModelConfig:
         """
         Update the model, returning the updated version.
 
@@ -165,11 +270,14 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 self._cursor.execute(
                     """--sql
                     UPDATE model_config
-                    SET
+                    SET base=?,
+                        type=?,
+                        name=?,
+                        path=?,
                         config=?
                     WHERE id=?;
                     """,
-                    (json_serialized, key),
+                    (record.base, record.type, record.name, record.path, json_serialized, key),
                 )
                 if self._cursor.rowcount == 0:
                     raise UnknownModelException("model not found")
@@ -260,21 +368,21 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
 
-    def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
+    def search_by_path(self, path: Union[str, Path]) -> List[ModelConfigBase]:
         """Return models with the indicated path."""
         results = []
         with self._db.lock:
             self._cursor.execute(
                 """--sql
                 SELECT config FROM model_config
-                WHERE path=?;
+                WHERE model_path=?;
                 """,
                 (str(path),),
             )
             results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
 
-    def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
+    def search_by_hash(self, hash: str) -> List[ModelConfigBase]:
         """Return models with the indicated original_hash."""
         results = []
         with self._db.lock:

invokeai/app/services/session_processor/session_processor_default.py

@@ -1,6 +1,7 @@
 import traceback
-from threading import BoundedSemaphore, Thread
+from threading import BoundedSemaphore
 from threading import Event as ThreadEvent
+from threading import Thread
 from typing import Optional
 
 from fastapi_events.handlers.local import local_handler
@@ -114,7 +115,6 @@ class DefaultSessionProcessor(SessionProcessorBase):
                                 session_queue_id=queue_item.queue_id,
                                 session_queue_item_id=queue_item.item_id,
                                 graph_execution_state=queue_item.session,
-                                workflow=queue_item.workflow,
                                 invoke_all=True,
                             )
                             queue_item = None

invokeai/app/services/session_queue/session_queue_common.py

@@ -8,10 +8,6 @@ from pydantic_core import to_jsonable_python
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.shared.graph import Graph, GraphExecutionState, NodeNotFoundError
-from invokeai.app.services.workflow_records.workflow_records_common import (
-    WorkflowWithoutID,
-    WorkflowWithoutIDValidator,
-)
 from invokeai.app.util.misc import uuid_string
 
 # region Errors
@@ -70,9 +66,6 @@ class Batch(BaseModel):
     batch_id: str = Field(default_factory=uuid_string, description="The ID of the batch")
     data: Optional[BatchDataCollection] = Field(default=None, description="The batch data collection.")
     graph: Graph = Field(description="The graph to initialize the session with")
-    workflow: Optional[WorkflowWithoutID] = Field(
-        default=None, description="The workflow to initialize the session with"
-    )
     runs: int = Field(
         default=1, ge=1, description="Int stating how many times to iterate through all possible batch indices"
     )
@@ -171,14 +164,6 @@ def get_session(queue_item_dict: dict) -> GraphExecutionState:
     return session
 
 
-def get_workflow(queue_item_dict: dict) -> Optional[WorkflowWithoutID]:
-    workflow_raw = queue_item_dict.get("workflow", None)
-    if workflow_raw is not None:
-        workflow = WorkflowWithoutIDValidator.validate_json(workflow_raw, strict=False)
-        return workflow
-    return None
-
-
 class SessionQueueItemWithoutGraph(BaseModel):
     """Session queue item without the full graph. Used for serialization."""
 
@@ -228,16 +213,12 @@ class SessionQueueItemDTO(SessionQueueItemWithoutGraph):
 
 class SessionQueueItem(SessionQueueItemWithoutGraph):
     session: GraphExecutionState = Field(description="The fully-populated session to be executed")
-    workflow: Optional[WorkflowWithoutID] = Field(
-        default=None, description="The workflow associated with this queue item"
-    )
 
     @classmethod
     def queue_item_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItem":
         # must parse these manually
         queue_item_dict["field_values"] = get_field_values(queue_item_dict)
         queue_item_dict["session"] = get_session(queue_item_dict)
-        queue_item_dict["workflow"] = get_workflow(queue_item_dict)
         return SessionQueueItem(**queue_item_dict)
 
     model_config = ConfigDict(
@@ -353,7 +334,7 @@ def populate_graph(graph: Graph, node_field_values: Iterable[NodeFieldValue]) ->
 
 def create_session_nfv_tuples(
     batch: Batch, maximum: int
-) -> Generator[tuple[GraphExecutionState, list[NodeFieldValue], Optional[WorkflowWithoutID]], None, None]:
+) -> Generator[tuple[GraphExecutionState, list[NodeFieldValue]], None, None]:
     """
     Create all graph permutations from the given batch data and graph. Yields tuples
     of the form (graph, batch_data_items) where batch_data_items is the list of BatchDataItems
@@ -384,7 +365,7 @@ def create_session_nfv_tuples(
                 return
             flat_node_field_values = list(chain.from_iterable(d))
             graph = populate_graph(batch.graph, flat_node_field_values)
-            yield (GraphExecutionState(graph=graph), flat_node_field_values, batch.workflow)
+            yield (GraphExecutionState(graph=graph), flat_node_field_values)
             count += 1
 
 
@@ -410,14 +391,12 @@ def calc_session_count(batch: Batch) -> int:
 class SessionQueueValueToInsert(NamedTuple):
     """A tuple of values to insert into the session_queue table"""
 
-    # Careful with the ordering of this - it must match the insert statement
     queue_id: str  # queue_id
     session: str  # session json
     session_id: str  # session_id
     batch_id: str  # batch_id
     field_values: Optional[str]  # field_values json
     priority: int  # priority
-    workflow: Optional[str]  # workflow json
 
 
 ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
@@ -425,7 +404,7 @@ ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
 
 def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new_queue_items: int) -> ValuesToInsert:
     values_to_insert: ValuesToInsert = []
-    for session, field_values, workflow in create_session_nfv_tuples(batch, max_new_queue_items):
+    for session, field_values in create_session_nfv_tuples(batch, max_new_queue_items):
         # sessions must have unique id
         session.id = uuid_string()
         values_to_insert.append(
@@ -437,7 +416,6 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
                 # must use pydantic_encoder bc field_values is a list of models
                 json.dumps(field_values, default=to_jsonable_python) if field_values else None,  # field_values (json)
                 priority,  # priority
-                json.dumps(workflow, default=to_jsonable_python) if workflow else None,  # workflow (json)
             )
         )
     return values_to_insert

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -28,7 +28,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     prepare_values_to_insert,
 )
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 
 class SqliteSessionQueue(SessionQueueBase):
@@ -42,14 +42,14 @@ class SqliteSessionQueue(SessionQueueBase):
         self._set_in_progress_to_canceled()
         prune_result = self.prune(DEFAULT_QUEUE_ID)
         local_handler.register(event_name=EventServiceBase.queue_event, _func=self._on_session_event)
-        if prune_result.deleted > 0:
-            self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
+        self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
         self.__lock = db.lock
         self.__conn = db.conn
         self.__cursor = self.__conn.cursor()
+        self._create_tables()
 
     def _match_event_name(self, event: FastAPIEvent, match_in: list[str]) -> bool:
         return event[1]["event"] in match_in
@@ -97,6 +97,114 @@ class SqliteSessionQueue(SessionQueueBase):
         except SessionQueueItemNotFoundError:
             return
 
+    def _create_tables(self) -> None:
+        """Creates the session queue tables, indicies, and triggers"""
+        try:
+            self.__lock.acquire()
+            self.__cursor.execute(
+                """--sql
+                CREATE TABLE IF NOT EXISTS session_queue (
+                    item_id INTEGER PRIMARY KEY AUTOINCREMENT, -- used for ordering, cursor pagination
+                    batch_id TEXT NOT NULL, -- identifier of the batch this queue item belongs to
+                    queue_id TEXT NOT NULL, -- identifier of the queue this queue item belongs to
+                    session_id TEXT NOT NULL UNIQUE, -- duplicated data from the session column, for ease of access
+                    field_values TEXT, -- NULL if no values are associated with this queue item
+                    session TEXT NOT NULL, -- the session to be executed
+                    status TEXT NOT NULL DEFAULT 'pending', -- the status of the queue item, one of 'pending', 'in_progress', 'completed', 'failed', 'canceled'
+                    priority INTEGER NOT NULL DEFAULT 0, -- the priority, higher is more important
+                    error TEXT, -- any errors associated with this queue item
+                    created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                    updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')), -- updated via trigger
+                    started_at DATETIME, -- updated via trigger
+                    completed_at DATETIME -- updated via trigger, completed items are cleaned up on application startup
+                    -- Ideally this is a FK, but graph_executions uses INSERT OR REPLACE, and REPLACE triggers the ON DELETE CASCADE...
+                    -- FOREIGN KEY (session_id) REFERENCES graph_executions (id) ON DELETE CASCADE
+                );
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE UNIQUE INDEX IF NOT EXISTS idx_session_queue_item_id ON session_queue(item_id);
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE UNIQUE INDEX IF NOT EXISTS idx_session_queue_session_id ON session_queue(session_id);
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_session_queue_batch_id ON session_queue(batch_id);
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_session_queue_created_priority ON session_queue(priority);
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_session_queue_created_status ON session_queue(status);
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE TRIGGER IF NOT EXISTS tg_session_queue_completed_at
+                AFTER UPDATE OF status ON session_queue
+                FOR EACH ROW
+                WHEN
+                  NEW.status = 'completed'
+                  OR NEW.status = 'failed'
+                  OR NEW.status = 'canceled'
+                BEGIN
+                  UPDATE session_queue
+                  SET completed_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                  WHERE item_id = NEW.item_id;
+                END;
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE TRIGGER IF NOT EXISTS tg_session_queue_started_at
+                AFTER UPDATE OF status ON session_queue
+                FOR EACH ROW
+                WHEN
+                  NEW.status = 'in_progress'
+                BEGIN
+                  UPDATE session_queue
+                  SET started_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                  WHERE item_id = NEW.item_id;
+                END;
+                """
+            )
+
+            self.__cursor.execute(
+                """--sql
+                CREATE TRIGGER IF NOT EXISTS tg_session_queue_updated_at
+                AFTER UPDATE
+                ON session_queue FOR EACH ROW
+                BEGIN
+                    UPDATE session_queue
+                    SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE item_id = old.item_id;
+                END;
+                """
+            )
+
+            self.__conn.commit()
+        except Exception:
+            self.__conn.rollback()
+            raise
+        finally:
+            self.__lock.release()
+
     def _set_in_progress_to_canceled(self) -> None:
         """
         Sets all in_progress queue items to canceled. Run on app startup, not associated with any queue.
@@ -172,8 +280,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
             self.__cursor.executemany(
                 """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow)
-                VALUES (?, ?, ?, ?, ?, ?, ?)
+                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority)
+                VALUES (?, ?, ?, ?, ?, ?)
                 """,
                 values_to_insert,
             )

invokeai/app/services/shared/graph.py

@@ -49,7 +49,7 @@ class Edge(BaseModel):
 
 def get_output_field(node: BaseInvocation, field: str) -> Any:
     node_type = type(node)
-    node_outputs = get_type_hints(node_type.get_output_annotation())
+    node_outputs = get_type_hints(node_type.get_output_type())
     node_output_field = node_outputs.get(field) or None
     return node_output_field
 
@@ -188,7 +188,7 @@ class GraphInvocationOutput(BaseInvocationOutput):
 
 
 # TODO: Fill this out and move to invocations
-@invocation("graph", version="1.0.0")
+@invocation("graph")
 class GraphInvocation(BaseInvocation):
     """Execute a graph"""
 
@@ -205,31 +205,29 @@ class IterateInvocationOutput(BaseInvocationOutput):
     """Used to connect iteration outputs. Will be expanded to a specific output."""
 
     item: Any = OutputField(
-        description="The item being iterated over", title="Collection Item", ui_type=UIType._CollectionItem
+        description="The item being iterated over", title="Collection Item", ui_type=UIType.CollectionItem
     )
-    index: int = OutputField(description="The index of the item", title="Index")
-    total: int = OutputField(description="The total number of items", title="Total")
 
 
 # TODO: Fill this out and move to invocations
-@invocation("iterate", version="1.1.0")
+@invocation("iterate", version="1.0.0")
 class IterateInvocation(BaseInvocation):
     """Iterates over a list of items"""
 
     collection: list[Any] = InputField(
-        description="The list of items to iterate over", default=[], ui_type=UIType._Collection
+        description="The list of items to iterate over", default_factory=list, ui_type=UIType.Collection
     )
     index: int = InputField(description="The index, will be provided on executed iterators", default=0, ui_hidden=True)
 
     def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
         """Produces the outputs as values"""
-        return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
+        return IterateInvocationOutput(item=self.collection[self.index])
 
 
 @invocation_output("collect_output")
 class CollectInvocationOutput(BaseInvocationOutput):
     collection: list[Any] = OutputField(
-        description="The collection of input items", title="Collection", ui_type=UIType._Collection
+        description="The collection of input items", title="Collection", ui_type=UIType.Collection
     )
 
 
@@ -240,12 +238,12 @@ class CollectInvocation(BaseInvocation):
     item: Optional[Any] = InputField(
         default=None,
         description="The item to collect (all inputs must be of the same type)",
-        ui_type=UIType._CollectionItem,
+        ui_type=UIType.CollectionItem,
         title="Collection Item",
         input=Input.Connection,
     )
     collection: list[Any] = InputField(
-        description="The collection, will be provided on execution", default=[], ui_hidden=True
+        description="The collection, will be provided on execution", default_factory=list, ui_hidden=True
     )
 
     def invoke(self, context: InvocationContext) -> CollectInvocationOutput:
@@ -381,7 +379,7 @@ class Graph(BaseModel):
                 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_annotation().model_fields:
+            if edge.source.field not in source_node.get_output_type().model_fields:
                 raise NodeFieldNotFoundError(
                     f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
                 )

invokeai/app/services/shared/sqlite.py

@@ -0,0 +1,48 @@
+import sqlite3
+import threading
+from logging import Logger
+
+from invokeai.app.services.config import InvokeAIAppConfig
+
+sqlite_memory = ":memory:"
+
+
+class SqliteDatabase:
+    conn: sqlite3.Connection
+    lock: threading.RLock
+    _logger: Logger
+    _config: InvokeAIAppConfig
+
+    def __init__(self, config: InvokeAIAppConfig, logger: Logger):
+        self._logger = logger
+        self._config = config
+
+        if self._config.use_memory_db:
+            location = sqlite_memory
+            logger.info("Using in-memory database")
+        else:
+            db_path = self._config.db_path
+            db_path.parent.mkdir(parents=True, exist_ok=True)
+            location = str(db_path)
+            self._logger.info(f"Using database at {location}")
+
+        self.conn = sqlite3.connect(location, check_same_thread=False)
+        self.lock = threading.RLock()
+        self.conn.row_factory = sqlite3.Row
+
+        if self._config.log_sql:
+            self.conn.set_trace_callback(self._logger.debug)
+
+        self.conn.execute("PRAGMA foreign_keys = ON;")
+
+    def clean(self) -> None:
+        try:
+            self.lock.acquire()
+            self.conn.execute("VACUUM;")
+            self.conn.commit()
+            self._logger.info("Cleaned database")
+        except Exception as e:
+            self._logger.error(f"Error cleaning database: {e}")
+            raise e
+        finally:
+            self.lock.release()

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

@@ -1,10 +0,0 @@
-from enum import Enum
-
-from invokeai.app.util.metaenum import MetaEnum
-
-sqlite_memory = ":memory:"
-
-
-class SQLiteDirection(str, Enum, metaclass=MetaEnum):
-    Ascending = "ASC"
-    Descending = "DESC"

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

@@ -1,67 +0,0 @@
-import sqlite3
-import threading
-from logging import Logger
-from pathlib import Path
-
-from invokeai.app.services.shared.sqlite.sqlite_common import sqlite_memory
-
-
-class SqliteDatabase:
-    """
-    Manages a connection to an SQLite database.
-
-    :param db_path: Path to the database file. If None, an in-memory database is used.
-    :param logger: Logger to use for logging.
-    :param verbose: Whether to log SQL statements. Provides `logger.debug` as the SQLite trace callback.
-
-    This is a light wrapper around the `sqlite3` module, providing a few conveniences:
-    - The database file is written to disk if it does not exist.
-    - Foreign key constraints are enabled by default.
-    - The connection is configured to use the `sqlite3.Row` row factory.
-
-    In addition to the constructor args, the instance provides the following attributes and methods:
-    - `conn`: A `sqlite3.Connection` object. Note that the connection must never be closed if the database is in-memory.
-    - `lock`: A shared re-entrant lock, used to approximate thread safety.
-    - `clean()`: Runs the SQL `VACUUM;` command and reports on the freed space.
-    """
-
-    def __init__(self, db_path: Path | None, logger: Logger, verbose: bool = False) -> None:
-        """Initializes the database. This is used internally by the class constructor."""
-        self.logger = logger
-        self.db_path = db_path
-        self.verbose = verbose
-
-        if not self.db_path:
-            logger.info("Initializing in-memory database")
-        else:
-            self.db_path.parent.mkdir(parents=True, exist_ok=True)
-            self.logger.info(f"Initializing database at {self.db_path}")
-
-        self.conn = sqlite3.connect(database=self.db_path or sqlite_memory, check_same_thread=False)
-        self.lock = threading.RLock()
-        self.conn.row_factory = sqlite3.Row
-
-        if self.verbose:
-            self.conn.set_trace_callback(self.logger.debug)
-
-        self.conn.execute("PRAGMA foreign_keys = ON;")
-
-    def clean(self) -> None:
-        """
-        Cleans the database by running the VACUUM command, reporting on the freed space.
-        """
-        # No need to clean in-memory database
-        if not self.db_path:
-            return
-        with self.lock:
-            try:
-                initial_db_size = Path(self.db_path).stat().st_size
-                self.conn.execute("VACUUM;")
-                self.conn.commit()
-                final_db_size = Path(self.db_path).stat().st_size
-                freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
-                if freed_space_in_mb > 0:
-                    self.logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
-            except Exception as e:
-                self.logger.error(f"Error cleaning database: {e}")
-                raise

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

@@ -1,32 +0,0 @@
-from logging import Logger
-
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.services.image_files.image_files_base import ImageFileStorageBase
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_1 import build_migration_1
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_2 import build_migration_2
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
-
-
-def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileStorageBase) -> SqliteDatabase:
-    """
-    Initializes the SQLite database.
-
-    :param config: The app config
-    :param logger: The logger
-    :param image_files: The image files service (used by migration 2)
-
-    This function:
-    - Instantiates a :class:`SqliteDatabase`
-    - Instantiates a :class:`SqliteMigrator` and registers all migrations
-    - Runs all migrations
-    """
-    db_path = None if config.use_memory_db else config.db_path
-    db = SqliteDatabase(db_path=db_path, logger=logger, verbose=config.log_sql)
-
-    migrator = SqliteMigrator(db=db)
-    migrator.register_migration(build_migration_1())
-    migrator.register_migration(build_migration_2(image_files=image_files, logger=logger))
-    migrator.run_migrations()
-
-    return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_1.py

@@ -1,372 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration1Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        """Migration callback for database version 1."""
-
-        self._create_board_images(cursor)
-        self._create_boards(cursor)
-        self._create_images(cursor)
-        self._create_model_config(cursor)
-        self._create_session_queue(cursor)
-        self._create_workflow_images(cursor)
-        self._create_workflows(cursor)
-
-    def _create_board_images(self, cursor: sqlite3.Cursor) -> None:
-        """Creates the `board_images` table, indices and triggers."""
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS board_images (
-                board_id TEXT NOT NULL,
-                image_name TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Soft delete, currently unused
-                deleted_at DATETIME,
-                -- enforce one-to-many relationship between boards and images using PK
-                -- (we can extend this to many-to-many later)
-                PRIMARY KEY (image_name),
-                FOREIGN KEY (board_id) REFERENCES boards (board_id) ON DELETE CASCADE,
-                FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE
-            );
-            """
-        ]
-
-        indices = [
-            "CREATE INDEX IF NOT EXISTS idx_board_images_board_id ON board_images (board_id);",
-            "CREATE INDEX IF NOT EXISTS idx_board_images_board_id_created_at ON board_images (board_id, created_at);",
-        ]
-
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_board_images_updated_at
-            AFTER UPDATE
-            ON board_images FOR EACH ROW
-            BEGIN
-                UPDATE board_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE board_id = old.board_id AND image_name = old.image_name;
-            END;
-            """
-        ]
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-    def _create_boards(self, cursor: sqlite3.Cursor) -> None:
-        """Creates the `boards` table, indices and triggers."""
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS boards (
-                board_id TEXT NOT NULL PRIMARY KEY,
-                board_name TEXT NOT NULL,
-                cover_image_name TEXT,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Soft delete, currently unused
-                deleted_at DATETIME,
-                FOREIGN KEY (cover_image_name) REFERENCES images (image_name) ON DELETE SET NULL
-            );
-            """
-        ]
-
-        indices = ["CREATE INDEX IF NOT EXISTS idx_boards_created_at ON boards (created_at);"]
-
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_boards_updated_at
-            AFTER UPDATE
-            ON boards FOR EACH ROW
-            BEGIN
-                UPDATE boards SET updated_at = current_timestamp
-                    WHERE board_id = old.board_id;
-            END;
-            """
-        ]
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-    def _create_images(self, cursor: sqlite3.Cursor) -> None:
-        """Creates the `images` table, indices and triggers. Adds the `starred` column."""
-
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS images (
-                image_name TEXT NOT NULL PRIMARY KEY,
-                -- This is an enum in python, unrestricted string here for flexibility
-                image_origin TEXT NOT NULL,
-                -- This is an enum in python, unrestricted string here for flexibility
-                image_category TEXT NOT NULL,
-                width INTEGER NOT NULL,
-                height INTEGER NOT NULL,
-                session_id TEXT,
-                node_id TEXT,
-                metadata TEXT,
-                is_intermediate BOOLEAN DEFAULT FALSE,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Soft delete, currently unused
-                deleted_at DATETIME
-            );
-            """
-        ]
-
-        indices = [
-            "CREATE UNIQUE INDEX IF NOT EXISTS idx_images_image_name ON images(image_name);",
-            "CREATE INDEX IF NOT EXISTS idx_images_image_origin ON images(image_origin);",
-            "CREATE INDEX IF NOT EXISTS idx_images_image_category ON images(image_category);",
-            "CREATE INDEX IF NOT EXISTS idx_images_created_at ON images(created_at);",
-        ]
-
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_images_updated_at
-            AFTER UPDATE
-            ON images FOR EACH ROW
-            BEGIN
-                UPDATE images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE image_name = old.image_name;
-            END;
-            """
-        ]
-
-        # Add the 'starred' column to `images` if it doesn't exist
-        cursor.execute("PRAGMA table_info(images)")
-        columns = [column[1] for column in cursor.fetchall()]
-
-        if "starred" not in columns:
-            tables.append("ALTER TABLE images ADD COLUMN starred BOOLEAN DEFAULT FALSE;")
-            indices.append("CREATE INDEX IF NOT EXISTS idx_images_starred ON images(starred);")
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-    def _create_model_config(self, cursor: sqlite3.Cursor) -> None:
-        """Creates the `model_config` table, `model_manager_metadata` table, indices and triggers."""
-
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS model_config (
-                id TEXT NOT NULL PRIMARY KEY,
-                -- The next 3 fields are enums in python, unrestricted string here
-                base TEXT NOT NULL,
-                type TEXT NOT NULL,
-                name TEXT NOT NULL,
-                path TEXT NOT NULL,
-                original_hash TEXT, -- could be null
-                -- Serialized JSON representation of the whole config object,
-                -- which will contain additional fields from subclasses
-                config TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- unique constraint on combo of name, base and type
-                UNIQUE(name, base, type)
-            );
-            """,
-            """--sql
-            CREATE TABLE IF NOT EXISTS model_manager_metadata (
-                metadata_key TEXT NOT NULL PRIMARY KEY,
-                metadata_value TEXT NOT NULL
-            );
-            """,
-        ]
-
-        # Add trigger for `updated_at`.
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS model_config_updated_at
-            AFTER UPDATE
-            ON model_config FOR EACH ROW
-            BEGIN
-                UPDATE model_config SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE id = old.id;
-            END;
-            """
-        ]
-
-        # Add indexes for searchable fields
-        indices = [
-            "CREATE INDEX IF NOT EXISTS base_index ON model_config(base);",
-            "CREATE INDEX IF NOT EXISTS type_index ON model_config(type);",
-            "CREATE INDEX IF NOT EXISTS name_index ON model_config(name);",
-            "CREATE UNIQUE INDEX IF NOT EXISTS path_index ON model_config(path);",
-        ]
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-    def _create_session_queue(self, cursor: sqlite3.Cursor) -> None:
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS session_queue (
-                item_id INTEGER PRIMARY KEY AUTOINCREMENT, -- used for ordering, cursor pagination
-                batch_id TEXT NOT NULL, -- identifier of the batch this queue item belongs to
-                queue_id TEXT NOT NULL, -- identifier of the queue this queue item belongs to
-                session_id TEXT NOT NULL UNIQUE, -- duplicated data from the session column, for ease of access
-                field_values TEXT, -- NULL if no values are associated with this queue item
-                session TEXT NOT NULL, -- the session to be executed
-                status TEXT NOT NULL DEFAULT 'pending', -- the status of the queue item, one of 'pending', 'in_progress', 'completed', 'failed', 'canceled'
-                priority INTEGER NOT NULL DEFAULT 0, -- the priority, higher is more important
-                error TEXT, -- any errors associated with this queue item
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')), -- updated via trigger
-                started_at DATETIME, -- updated via trigger
-                completed_at DATETIME -- updated via trigger, completed items are cleaned up on application startup
-                -- Ideally this is a FK, but graph_executions uses INSERT OR REPLACE, and REPLACE triggers the ON DELETE CASCADE...
-                -- FOREIGN KEY (session_id) REFERENCES graph_executions (id) ON DELETE CASCADE
-            );
-            """
-        ]
-
-        indices = [
-            "CREATE UNIQUE INDEX IF NOT EXISTS idx_session_queue_item_id ON session_queue(item_id);",
-            "CREATE UNIQUE INDEX IF NOT EXISTS idx_session_queue_session_id ON session_queue(session_id);",
-            "CREATE INDEX IF NOT EXISTS idx_session_queue_batch_id ON session_queue(batch_id);",
-            "CREATE INDEX IF NOT EXISTS idx_session_queue_created_priority ON session_queue(priority);",
-            "CREATE INDEX IF NOT EXISTS idx_session_queue_created_status ON session_queue(status);",
-        ]
-
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_session_queue_completed_at
-            AFTER UPDATE OF status ON session_queue
-            FOR EACH ROW
-            WHEN
-            NEW.status = 'completed'
-            OR NEW.status = 'failed'
-            OR NEW.status = 'canceled'
-            BEGIN
-            UPDATE session_queue
-            SET completed_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-            WHERE item_id = NEW.item_id;
-            END;
-            """,
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_session_queue_started_at
-            AFTER UPDATE OF status ON session_queue
-            FOR EACH ROW
-            WHEN
-            NEW.status = 'in_progress'
-            BEGIN
-            UPDATE session_queue
-            SET started_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-            WHERE item_id = NEW.item_id;
-            END;
-            """,
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_session_queue_updated_at
-            AFTER UPDATE
-            ON session_queue FOR EACH ROW
-            BEGIN
-                UPDATE session_queue
-                SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                WHERE item_id = old.item_id;
-            END;
-            """,
-        ]
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-    def _create_workflow_images(self, cursor: sqlite3.Cursor) -> None:
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS workflow_images (
-                workflow_id TEXT NOT NULL,
-                image_name TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Soft delete, currently unused
-                deleted_at DATETIME,
-                -- enforce one-to-many relationship between workflows and images using PK
-                -- (we can extend this to many-to-many later)
-                PRIMARY KEY (image_name),
-                FOREIGN KEY (workflow_id) REFERENCES workflows (workflow_id) ON DELETE CASCADE,
-                FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE
-            );
-            """
-        ]
-
-        indices = [
-            "CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id ON workflow_images (workflow_id);",
-            "CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id_created_at ON workflow_images (workflow_id, created_at);",
-        ]
-
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_workflow_images_updated_at
-            AFTER UPDATE
-            ON workflow_images FOR EACH ROW
-            BEGIN
-                UPDATE workflow_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE workflow_id = old.workflow_id AND image_name = old.image_name;
-            END;
-            """
-        ]
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-    def _create_workflows(self, cursor: sqlite3.Cursor) -> None:
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS workflows (
-                workflow TEXT NOT NULL,
-                workflow_id TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.id')) VIRTUAL NOT NULL UNIQUE, -- gets implicit index
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')) -- updated via trigger
-            );
-            """
-        ]
-
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_workflows_updated_at
-            AFTER UPDATE
-            ON workflows FOR EACH ROW
-            BEGIN
-                UPDATE workflows
-                SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                WHERE workflow_id = old.workflow_id;
-            END;
-            """
-        ]
-
-        for stmt in tables + triggers:
-            cursor.execute(stmt)
-
-
-def build_migration_1() -> Migration:
-    """
-    Builds the migration from database version 0 (init) to 1.
-
-    This migration represents the state of the database circa InvokeAI v3.4.0, which was the last
-    version to not use migrations to manage the database.
-
-    As such, this migration does include some ALTER statements, and the SQL statements are written
-    to be idempotent.
-
-    - Create `board_images` junction table
-    - Create `boards` table
-    - Create `images` table, add `starred` column
-    - Create `model_config` table
-    - Create `session_queue` table
-    - Create `workflow_images` junction table
-    - Create `workflows` table
-    """
-
-    migration_1 = Migration(
-        from_version=0,
-        to_version=1,
-        callback=Migration1Callback(),
-    )
-
-    return migration_1

invokeai/app/services/shared/sqlite_migrator/migrations/migration_2.py

@@ -1,198 +0,0 @@
-import sqlite3
-from logging import Logger
-
-from pydantic import ValidationError
-from tqdm import tqdm
-
-from invokeai.app.services.image_files.image_files_base import ImageFileStorageBase
-from invokeai.app.services.image_files.image_files_common import ImageFileNotFoundException
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-from invokeai.app.services.workflow_records.workflow_records_common import (
-    UnsafeWorkflowWithVersionValidator,
-)
-
-
-class Migration2Callback:
-    def __init__(self, image_files: ImageFileStorageBase, logger: Logger):
-        self._image_files = image_files
-        self._logger = logger
-
-    def __call__(self, cursor: sqlite3.Cursor):
-        self._add_images_has_workflow(cursor)
-        self._add_session_queue_workflow(cursor)
-        self._drop_old_workflow_tables(cursor)
-        self._add_workflow_library(cursor)
-        self._drop_model_manager_metadata(cursor)
-        self._recreate_model_config(cursor)
-        self._migrate_embedded_workflows(cursor)
-
-    def _add_images_has_workflow(self, cursor: sqlite3.Cursor) -> None:
-        """Add the `has_workflow` column to `images` table."""
-        cursor.execute("PRAGMA table_info(images)")
-        columns = [column[1] for column in cursor.fetchall()]
-
-        if "has_workflow" not in columns:
-            cursor.execute("ALTER TABLE images ADD COLUMN has_workflow BOOLEAN DEFAULT FALSE;")
-
-    def _add_session_queue_workflow(self, cursor: sqlite3.Cursor) -> None:
-        """Add the `workflow` column to `session_queue` table."""
-
-        cursor.execute("PRAGMA table_info(session_queue)")
-        columns = [column[1] for column in cursor.fetchall()]
-
-        if "workflow" not in columns:
-            cursor.execute("ALTER TABLE session_queue ADD COLUMN workflow TEXT;")
-
-    def _drop_old_workflow_tables(self, cursor: sqlite3.Cursor) -> None:
-        """Drops the `workflows` and `workflow_images` tables."""
-        cursor.execute("DROP TABLE IF EXISTS workflow_images;")
-        cursor.execute("DROP TABLE IF EXISTS workflows;")
-
-    def _add_workflow_library(self, cursor: sqlite3.Cursor) -> None:
-        """Adds the `workflow_library` table and drops the `workflows` and `workflow_images` tables."""
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS workflow_library (
-                workflow_id TEXT NOT NULL PRIMARY KEY,
-                workflow TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- updated manually when retrieving workflow
-                opened_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Generated columns, needed for indexing and searching
-                category TEXT GENERATED ALWAYS as (json_extract(workflow, '$.meta.category')) VIRTUAL NOT NULL,
-                name TEXT GENERATED ALWAYS as (json_extract(workflow, '$.name')) VIRTUAL NOT NULL,
-                description TEXT GENERATED ALWAYS as (json_extract(workflow, '$.description')) VIRTUAL NOT NULL
-            );
-            """,
-        ]
-
-        indices = [
-            "CREATE INDEX IF NOT EXISTS idx_workflow_library_created_at ON workflow_library(created_at);",
-            "CREATE INDEX IF NOT EXISTS idx_workflow_library_updated_at ON workflow_library(updated_at);",
-            "CREATE INDEX IF NOT EXISTS idx_workflow_library_opened_at ON workflow_library(opened_at);",
-            "CREATE INDEX IF NOT EXISTS idx_workflow_library_category ON workflow_library(category);",
-            "CREATE INDEX IF NOT EXISTS idx_workflow_library_name ON workflow_library(name);",
-            "CREATE INDEX IF NOT EXISTS idx_workflow_library_description ON workflow_library(description);",
-        ]
-
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_workflow_library_updated_at
-            AFTER UPDATE
-            ON workflow_library FOR EACH ROW
-            BEGIN
-                UPDATE workflow_library
-                SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                WHERE workflow_id = old.workflow_id;
-            END;
-            """
-        ]
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-    def _drop_model_manager_metadata(self, cursor: sqlite3.Cursor) -> None:
-        """Drops the `model_manager_metadata` table."""
-        cursor.execute("DROP TABLE IF EXISTS model_manager_metadata;")
-
-    def _recreate_model_config(self, cursor: sqlite3.Cursor) -> None:
-        """
-        Drops the `model_config` table, recreating it.
-
-        In 3.4.0, this table used explicit columns but was changed to use json_extract 3.5.0.
-
-        Because this table is not used in production, we are able to simply drop it and recreate it.
-        """
-
-        cursor.execute("DROP TABLE IF EXISTS model_config;")
-
-        cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS model_config (
-                id TEXT NOT NULL PRIMARY KEY,
-                -- The next 3 fields are enums in python, unrestricted string here
-                base TEXT GENERATED ALWAYS as (json_extract(config, '$.base')) VIRTUAL NOT NULL,
-                type TEXT GENERATED ALWAYS as (json_extract(config, '$.type')) VIRTUAL NOT NULL,
-                name TEXT GENERATED ALWAYS as (json_extract(config, '$.name')) VIRTUAL NOT NULL,
-                path TEXT GENERATED ALWAYS as (json_extract(config, '$.path')) VIRTUAL NOT NULL,
-                format TEXT GENERATED ALWAYS as (json_extract(config, '$.format')) VIRTUAL NOT NULL,
-                original_hash TEXT, -- could be null
-                -- Serialized JSON representation of the whole config object,
-                -- which will contain additional fields from subclasses
-                config TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- unique constraint on combo of name, base and type
-                UNIQUE(name, base, type)
-            );
-            """
-        )
-
-    def _migrate_embedded_workflows(self, cursor: sqlite3.Cursor) -> None:
-        """
-        In the v3.5.0 release, InvokeAI changed how it handles embedded workflows. The `images` table in
-        the database now has a `has_workflow` column, indicating if an image has a workflow embedded.
-
-        This migrate callback checks each image for the presence of an embedded workflow, then updates its entry
-        in the database accordingly.
-        """
-        # Get all image names
-        cursor.execute("SELECT image_name FROM images")
-        image_names: list[str] = [image[0] for image in cursor.fetchall()]
-        total_image_names = len(image_names)
-
-        if not total_image_names:
-            return
-
-        self._logger.info(f"Migrating workflows for {total_image_names} images")
-
-        # Migrate the images
-        to_migrate: list[tuple[bool, str]] = []
-        pbar = tqdm(image_names)
-        for idx, image_name in enumerate(pbar):
-            pbar.set_description(f"Checking image {idx + 1}/{total_image_names} for workflow")
-            try:
-                pil_image = self._image_files.get(image_name)
-            except ImageFileNotFoundException:
-                self._logger.warning(f"Image {image_name} not found, skipping")
-                continue
-            if "invokeai_workflow" in pil_image.info:
-                try:
-                    UnsafeWorkflowWithVersionValidator.validate_json(pil_image.info.get("invokeai_workflow", ""))
-                except ValidationError:
-                    self._logger.warning(f"Image {image_name} has invalid embedded workflow, skipping")
-                    continue
-                to_migrate.append((True, image_name))
-
-        self._logger.info(f"Adding {len(to_migrate)} embedded workflows to database")
-        cursor.executemany("UPDATE images SET has_workflow = ? WHERE image_name = ?", to_migrate)
-
-
-def build_migration_2(image_files: ImageFileStorageBase, logger: Logger) -> Migration:
-    """
-    Builds the migration from database version 1 to 2.
-
-    Introduced in v3.5.0 for the new workflow library.
-
-    :param image_files: The image files service, used to check for embedded workflows
-    :param logger: The logger, used to log progress during embedded workflows handling
-
-    This migration does the following:
-    - Add `has_workflow` column to `images` table
-    - Add `workflow` column to `session_queue` table
-    - Drop `workflows` and `workflow_images` tables
-    - Add `workflow_library` table
-    - Drops the `model_manager_metadata` table
-    - Drops the `model_config` table, recreating it (at this point, there is no user data in this table)
-    - Populates the `has_workflow` column in the `images` table (requires `image_files` & `logger` dependencies)
-    """
-    migration_2 = Migration(
-        from_version=1,
-        to_version=2,
-        callback=Migration2Callback(image_files=image_files, logger=logger),
-    )
-
-    return migration_2

invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_common.py

@@ -1,164 +0,0 @@
-import sqlite3
-from typing import Optional, Protocol, runtime_checkable
-
-from pydantic import BaseModel, ConfigDict, Field, model_validator
-
-
-@runtime_checkable
-class MigrateCallback(Protocol):
-    """
-    A callback that performs a migration.
-
-    Migrate callbacks are provided an open cursor to the database. They should not commit their
-    transaction; this is handled by the migrator.
-
-    If the callback needs to access additional dependencies, will be provided to the callback at runtime.
-
-    See :class:`Migration` for an example.
-    """
-
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        ...
-
-
-class MigrationError(RuntimeError):
-    """Raised when a migration fails."""
-
-
-class MigrationVersionError(ValueError):
-    """Raised when a migration version is invalid."""
-
-
-class Migration(BaseModel):
-    """
-    Represents a migration for a SQLite database.
-
-    :param from_version: The database version on which this migration may be run
-    :param to_version: The database version that results from this migration
-    :param migrate_callback: The callback to run to perform the migration
-
-    Migration callbacks will be provided an open cursor to the database. They should not commit their
-    transaction; this is handled by the migrator.
-
-    It is suggested to use a class to define the migration callback and a builder function to create
-    the :class:`Migration`. This allows the callback to be provided with additional dependencies and
-    keeps things tidy, as all migration logic is self-contained.
-
-    Example:
-    ```py
-    # Define the migration callback class
-    class Migration1Callback:
-        # This migration needs a logger, so we define a class that accepts a logger in its constructor.
-        def __init__(self, image_files: ImageFileStorageBase) -> None:
-            self._image_files = ImageFileStorageBase
-
-        # This dunder method allows the instance of the class to be called like a function.
-        def __call__(self, cursor: sqlite3.Cursor) -> None:
-            self._add_with_banana_column(cursor)
-            self._do_something_with_images(cursor)
-
-        def _add_with_banana_column(self, cursor: sqlite3.Cursor) -> None:
-            \"""Adds the with_banana column to the sushi table.\"""
-            # Execute SQL using the cursor, taking care to *not commit* a transaction
-            cursor.execute('ALTER TABLE sushi ADD COLUMN with_banana BOOLEAN DEFAULT TRUE;')
-
-        def _do_something_with_images(self, cursor: sqlite3.Cursor) -> None:
-            \"""Does something with the image files service.\"""
-            self._image_files.get(...)
-
-    # Define the migration builder function. This function creates an instance of the migration callback
-    # class and returns a Migration.
-    def build_migration_1(image_files: ImageFileStorageBase) -> Migration:
-        \"""Builds the migration from database version 0 to 1.
-        Requires the image files service to...
-        \"""
-
-        migration_1 = Migration(
-            from_version=0,
-            to_version=1,
-            migrate_callback=Migration1Callback(image_files=image_files),
-        )
-
-        return migration_1
-
-    # Register the migration after all dependencies have been initialized
-    db = SqliteDatabase(db_path, logger)
-    migrator = SqliteMigrator(db)
-    migrator.register_migration(build_migration_1(image_files))
-    migrator.run_migrations()
-    ```
-    """
-
-    from_version: int = Field(ge=0, strict=True, description="The database version on which this migration may be run")
-    to_version: int = Field(ge=1, strict=True, description="The database version that results from this migration")
-    callback: MigrateCallback = Field(description="The callback to run to perform the migration")
-
-    @model_validator(mode="after")
-    def validate_to_version(self) -> "Migration":
-        """Validates that to_version is one greater than from_version."""
-        if self.to_version != self.from_version + 1:
-            raise MigrationVersionError("to_version must be one greater than from_version")
-        return self
-
-    def __hash__(self) -> int:
-        # Callables are not hashable, so we need to implement our own __hash__ function to use this class in a set.
-        return hash((self.from_version, self.to_version))
-
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-
-class MigrationSet:
-    """
-    A set of Migrations. Performs validation during migration registration and provides utility methods.
-
-    Migrations should be registered with `register()`. Once all are registered, `validate_migration_chain()`
-    should be called to ensure that the migrations form a single chain of migrations from version 0 to the latest version.
-    """
-
-    def __init__(self) -> None:
-        self._migrations: set[Migration] = set()
-
-    def register(self, migration: Migration) -> None:
-        """Registers a migration."""
-        migration_from_already_registered = any(m.from_version == migration.from_version for m in self._migrations)
-        migration_to_already_registered = any(m.to_version == migration.to_version for m in self._migrations)
-        if migration_from_already_registered or migration_to_already_registered:
-            raise MigrationVersionError("Migration with from_version or to_version already registered")
-        self._migrations.add(migration)
-
-    def get(self, from_version: int) -> Optional[Migration]:
-        """Gets the migration that may be run on the given database version."""
-        # register() ensures that there is only one migration with a given from_version, so this is safe.
-        return next((m for m in self._migrations if m.from_version == from_version), None)
-
-    def validate_migration_chain(self) -> None:
-        """
-        Validates that the migrations form a single chain of migrations from version 0 to the latest version,
-        Raises a MigrationError if there is a problem.
-        """
-        if self.count == 0:
-            return
-        if self.latest_version == 0:
-            return
-        next_migration = self.get(from_version=0)
-        if next_migration is None:
-            raise MigrationError("Migration chain is fragmented")
-        touched_count = 1
-        while next_migration is not None:
-            next_migration = self.get(next_migration.to_version)
-            if next_migration is not None:
-                touched_count += 1
-        if touched_count != self.count:
-            raise MigrationError("Migration chain is fragmented")
-
-    @property
-    def count(self) -> int:
-        """The count of registered migrations."""
-        return len(self._migrations)
-
-    @property
-    def latest_version(self) -> int:
-        """Gets latest to_version among registered migrations. Returns 0 if there are no migrations registered."""
-        if self.count == 0:
-            return 0
-        return sorted(self._migrations, key=lambda m: m.to_version)[-1].to_version

invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_impl.py

@@ -1,130 +0,0 @@
-import sqlite3
-from pathlib import Path
-from typing import Optional
-
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration, MigrationError, MigrationSet
-
-
-class SqliteMigrator:
-    """
-    Manages migrations for a SQLite database.
-
-    :param db: The instance of :class:`SqliteDatabase` to migrate.
-
-    Migrations should be registered with :meth:`register_migration`.
-
-    Each migration is run in a transaction. If a migration fails, the transaction is rolled back.
-
-    Example Usage:
-    ```py
-    db = SqliteDatabase(db_path="my_db.db", logger=logger)
-    migrator = SqliteMigrator(db=db)
-    migrator.register_migration(build_migration_1())
-    migrator.register_migration(build_migration_2())
-    migrator.run_migrations()
-    ```
-    """
-
-    backup_path: Optional[Path] = None
-
-    def __init__(self, db: SqliteDatabase) -> None:
-        self._db = db
-        self._logger = db.logger
-        self._migration_set = MigrationSet()
-
-    def register_migration(self, migration: Migration) -> None:
-        """Registers a migration."""
-        self._migration_set.register(migration)
-        self._logger.debug(f"Registered migration {migration.from_version} -> {migration.to_version}")
-
-    def run_migrations(self) -> bool:
-        """Migrates the database to the latest version."""
-        with self._db.lock:
-            # This throws if there is a problem.
-            self._migration_set.validate_migration_chain()
-            cursor = self._db.conn.cursor()
-            self._create_migrations_table(cursor=cursor)
-
-            if self._migration_set.count == 0:
-                self._logger.debug("No migrations registered")
-                return False
-
-            if self._get_current_version(cursor=cursor) == self._migration_set.latest_version:
-                self._logger.debug("Database is up to date, no migrations to run")
-                return False
-
-            self._logger.info("Database update needed")
-            next_migration = self._migration_set.get(from_version=self._get_current_version(cursor))
-            while next_migration is not None:
-                self._run_migration(next_migration)
-                next_migration = self._migration_set.get(self._get_current_version(cursor))
-            self._logger.info("Database updated successfully")
-            return True
-
-    def _run_migration(self, migration: Migration) -> None:
-        """Runs a single migration."""
-        try:
-            # Using sqlite3.Connection as a context manager commits a the transaction on exit, or rolls it back if an
-            # exception is raised.
-            with self._db.lock, self._db.conn as conn:
-                cursor = conn.cursor()
-                if self._get_current_version(cursor) != migration.from_version:
-                    raise MigrationError(
-                        f"Database is at version {self._get_current_version(cursor)}, expected {migration.from_version}"
-                    )
-                self._logger.debug(f"Running migration from {migration.from_version} to {migration.to_version}")
-
-                # Run the actual migration
-                migration.callback(cursor)
-
-                # Update the version
-                cursor.execute("INSERT INTO migrations (version) VALUES (?);", (migration.to_version,))
-
-                self._logger.debug(
-                    f"Successfully migrated database from {migration.from_version} to {migration.to_version}"
-                )
-        # We want to catch *any* error, mirroring the behaviour of the sqlite3 module.
-        except Exception as e:
-            # The connection context manager has already rolled back the migration, so we don't need to do anything.
-            msg = f"Error migrating database from {migration.from_version} to {migration.to_version}: {e}"
-            self._logger.error(msg)
-            raise MigrationError(msg) from e
-
-    def _create_migrations_table(self, cursor: sqlite3.Cursor) -> None:
-        """Creates the migrations table for the database, if one does not already exist."""
-        with self._db.lock:
-            try:
-                cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='migrations';")
-                if cursor.fetchone() is not None:
-                    return
-                cursor.execute(
-                    """--sql
-                    CREATE TABLE migrations (
-                        version INTEGER PRIMARY KEY,
-                        migrated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW'))
-                    );
-                    """
-                )
-                cursor.execute("INSERT INTO migrations (version) VALUES (0);")
-                cursor.connection.commit()
-                self._logger.debug("Created migrations table")
-            except sqlite3.Error as e:
-                msg = f"Problem creating migrations table: {e}"
-                self._logger.error(msg)
-                cursor.connection.rollback()
-                raise MigrationError(msg) from e
-
-    @classmethod
-    def _get_current_version(cls, cursor: sqlite3.Cursor) -> int:
-        """Gets the current version of the database, or 0 if the migrations table does not exist."""
-        try:
-            cursor.execute("SELECT MAX(version) FROM migrations;")
-            version: int = cursor.fetchone()[0]
-            if version is None:
-                return 0
-            return version
-        except sqlite3.OperationalError as e:
-            if "no such table" in str(e):
-                return 0
-            raise

invokeai/app/services/workflow_image_records/workflow_image_records_base.py

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

invokeai/app/services/workflow_image_records/workflow_image_records_sqlite.py

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

invokeai/app/services/workflow_records/default_workflows/README.md

@@ -1,17 +0,0 @@
-# Default Workflows
-
-Workflows placed in this directory will be synced to the `workflow_library` as
-_default workflows_ on app startup.
-
-- Default workflows are not editable by users. If they are loaded and saved,
-  they will save as a copy of the default workflow.
-- Default workflows must have the `meta.category` property set to `"default"`.
-  An exception will be raised during sync if this is not set correctly.
-- Default workflows appear on the "Default Workflows" tab of the Workflow
-  Library.
-
-After adding or updating default workflows, you **must** start the app up and
-load them to ensure:
-
-- The workflow loads without warning or errors
-- The workflow runs successfully

invokeai/app/services/workflow_records/default_workflows/TextToImage_SD15.json

@@ -1,798 +0,0 @@
-{
-  "name": "Text to Image - SD1.5",
-  "author": "InvokeAI",
-  "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
-  "version": "1.1.0",
-  "contact": "invoke@invoke.ai",
-  "tags": "text2image, SD1.5, SD2, default",
-  "notes": "",
-  "exposedFields": [
-    {
-      "nodeId": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
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-      "fieldName": "height"
-    }
-  ],
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-  },
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-}

invokeai/app/services/workflow_records/workflow_records_base.py

@@ -1,50 +1,17 @@
 from abc import ABC, abstractmethod
-from typing import Optional
 
-from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
-from invokeai.app.services.workflow_records.workflow_records_common import (
-    Workflow,
-    WorkflowCategory,
-    WorkflowRecordDTO,
-    WorkflowRecordListItemDTO,
-    WorkflowRecordOrderBy,
-    WorkflowWithoutID,
-)
+from invokeai.app.invocations.baseinvocation import WorkflowField
 
 
 class WorkflowRecordsStorageBase(ABC):
     """Base class for workflow storage services."""
 
     @abstractmethod
-    def get(self, workflow_id: str) -> WorkflowRecordDTO:
+    def get(self, workflow_id: str) -> WorkflowField:
         """Get workflow by id."""
         pass
 
     @abstractmethod
-    def create(self, workflow: WorkflowWithoutID) -> WorkflowRecordDTO:
+    def create(self, workflow: WorkflowField) -> WorkflowField:
         """Creates a workflow."""
         pass
-
-    @abstractmethod
-    def update(self, workflow: Workflow) -> WorkflowRecordDTO:
-        """Updates a workflow."""
-        pass
-
-    @abstractmethod
-    def delete(self, workflow_id: str) -> None:
-        """Deletes a workflow."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        page: int,
-        per_page: int,
-        order_by: WorkflowRecordOrderBy,
-        direction: SQLiteDirection,
-        category: WorkflowCategory,
-        query: Optional[str],
-    ) -> PaginatedResults[WorkflowRecordListItemDTO]:
-        """Gets many workflows."""
-        pass

invokeai/app/services/workflow_records/workflow_records_common.py

@@ -1,118 +1,2 @@
-import datetime
-from enum import Enum
-from typing import Any, Union
-
-import semver
-from pydantic import BaseModel, ConfigDict, Field, JsonValue, TypeAdapter, field_validator
-
-from invokeai.app.util.metaenum import MetaEnum
-
-__workflow_meta_version__ = semver.Version.parse("1.0.0")
-
-
-class ExposedField(BaseModel):
-    nodeId: str
-    fieldName: str
-
-
 class WorkflowNotFoundError(Exception):
     """Raised when a workflow is not found"""
-
-
-class WorkflowRecordOrderBy(str, Enum, metaclass=MetaEnum):
-    """The order by options for workflow records"""
-
-    CreatedAt = "created_at"
-    UpdatedAt = "updated_at"
-    OpenedAt = "opened_at"
-    Name = "name"
-
-
-class WorkflowCategory(str, Enum, metaclass=MetaEnum):
-    User = "user"
-    Default = "default"
-
-
-class WorkflowMeta(BaseModel):
-    version: str = Field(description="The version of the workflow schema.")
-    category: WorkflowCategory = Field(
-        default=WorkflowCategory.User, description="The category of the workflow (user or default)."
-    )
-
-    @field_validator("version")
-    def validate_version(cls, version: str):
-        try:
-            semver.Version.parse(version)
-            return version
-        except Exception:
-            raise ValueError(f"Invalid workflow meta version: {version}")
-
-    def to_semver(self) -> semver.Version:
-        return semver.Version.parse(self.version)
-
-
-class WorkflowWithoutID(BaseModel):
-    name: str = Field(description="The name of the workflow.")
-    author: str = Field(description="The author of the workflow.")
-    description: str = Field(description="The description of the workflow.")
-    version: str = Field(description="The version of the workflow.")
-    contact: str = Field(description="The contact of the workflow.")
-    tags: str = Field(description="The tags of the workflow.")
-    notes: str = Field(description="The notes of the workflow.")
-    exposedFields: list[ExposedField] = Field(description="The exposed fields of the workflow.")
-    meta: WorkflowMeta = Field(description="The meta of the workflow.")
-    # TODO: nodes and edges are very loosely typed
-    nodes: list[dict[str, JsonValue]] = Field(description="The nodes of the workflow.")
-    edges: list[dict[str, JsonValue]] = Field(description="The edges of the workflow.")
-
-    model_config = ConfigDict(extra="ignore")
-
-
-WorkflowWithoutIDValidator = TypeAdapter(WorkflowWithoutID)
-
-
-class UnsafeWorkflowWithVersion(BaseModel):
-    """
-    This utility model only requires a workflow to have a valid version string.
-    It is used to validate a workflow version without having to validate the entire workflow.
-    """
-
-    meta: WorkflowMeta = Field(description="The meta of the workflow.")
-
-
-UnsafeWorkflowWithVersionValidator = TypeAdapter(UnsafeWorkflowWithVersion)
-
-
-class Workflow(WorkflowWithoutID):
-    id: str = Field(description="The id of the workflow.")
-
-
-WorkflowValidator = TypeAdapter(Workflow)
-
-
-class WorkflowRecordDTOBase(BaseModel):
-    workflow_id: str = Field(description="The id of the workflow.")
-    name: str = Field(description="The name of the workflow.")
-    created_at: Union[datetime.datetime, str] = Field(description="The created timestamp of the workflow.")
-    updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the workflow.")
-    opened_at: Union[datetime.datetime, str] = Field(description="The opened timestamp of the workflow.")
-
-
-class WorkflowRecordDTO(WorkflowRecordDTOBase):
-    workflow: Workflow = Field(description="The workflow.")
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "WorkflowRecordDTO":
-        data["workflow"] = WorkflowValidator.validate_json(data.get("workflow", ""))
-        return WorkflowRecordDTOValidator.validate_python(data)
-
-
-WorkflowRecordDTOValidator = TypeAdapter(WorkflowRecordDTO)
-
-
-class WorkflowRecordListItemDTO(WorkflowRecordDTOBase):
-    description: str = Field(description="The description of the workflow.")
-    category: WorkflowCategory = Field(description="The description of the workflow.")
-
-
-WorkflowRecordListItemDTOValidator = TypeAdapter(WorkflowRecordListItemDTO)

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -1,53 +1,37 @@
-from pathlib import Path
-from typing import Optional
+import sqlite3
+import threading
 
+from invokeai.app.invocations.baseinvocation import WorkflowField, WorkflowFieldValidator
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
-from invokeai.app.services.workflow_records.workflow_records_common import (
-    Workflow,
-    WorkflowCategory,
-    WorkflowNotFoundError,
-    WorkflowRecordDTO,
-    WorkflowRecordListItemDTO,
-    WorkflowRecordListItemDTOValidator,
-    WorkflowRecordOrderBy,
-    WorkflowWithoutID,
-    WorkflowWithoutIDValidator,
-)
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowNotFoundError
 from invokeai.app.util.misc import uuid_string
 
 
 class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
+    _invoker: Invoker
+    _conn: sqlite3.Connection
+    _cursor: sqlite3.Cursor
+    _lock: threading.RLock
+
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
         self._lock = db.lock
         self._conn = db.conn
         self._cursor = self._conn.cursor()
+        self._create_tables()
 
     def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
-        self._sync_default_workflows()
 
-    def get(self, workflow_id: str) -> WorkflowRecordDTO:
-        """Gets a workflow by ID. Updates the opened_at column."""
+    def get(self, workflow_id: str) -> WorkflowField:
         try:
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
-                UPDATE workflow_library
-                SET opened_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                WHERE workflow_id = ?;
-                """,
-                (workflow_id,),
-            )
-            self._conn.commit()
-            self._cursor.execute(
-                """--sql
-                SELECT workflow_id, workflow, name, created_at, updated_at, opened_at
-                FROM workflow_library
+                SELECT workflow
+                FROM workflows
                 WHERE workflow_id = ?;
                 """,
                 (workflow_id,),
@@ -55,28 +39,25 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
             row = self._cursor.fetchone()
             if row is None:
                 raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
-            return WorkflowRecordDTO.from_dict(dict(row))
+            return WorkflowFieldValidator.validate_json(row[0])
         except Exception:
             self._conn.rollback()
             raise
         finally:
             self._lock.release()
 
-    def create(self, workflow: WorkflowWithoutID) -> WorkflowRecordDTO:
+    def create(self, workflow: WorkflowField) -> WorkflowField:
         try:
-            # Only user workflows may be created by this method
-            assert workflow.meta.category is WorkflowCategory.User
-            workflow_with_id = Workflow(**workflow.model_dump(), id=uuid_string())
+            # workflows do not have ids until they are saved
+            workflow_id = uuid_string()
+            workflow.root["id"] = workflow_id
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
-                INSERT OR IGNORE INTO workflow_library (
-                    workflow_id,
-                    workflow
-                )
-                VALUES (?, ?);
+                INSERT INTO workflows(workflow)
+                VALUES (?);
                 """,
-                (workflow_with_id.id, workflow_with_id.model_dump_json()),
+                (workflow.model_dump_json(),),
             )
             self._conn.commit()
         except Exception:
@@ -84,148 +65,35 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
             raise
         finally:
             self._lock.release()
-        return self.get(workflow_with_id.id)
+        return self.get(workflow_id)
 
-    def update(self, workflow: Workflow) -> WorkflowRecordDTO:
+    def _create_tables(self) -> None:
         try:
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
-                UPDATE workflow_library
-                SET workflow = ?
-                WHERE workflow_id = ? AND category = 'user';
-                """,
-                (workflow.model_dump_json(), workflow.id),
-            )
-            self._conn.commit()
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-        return self.get(workflow.id)
-
-    def delete(self, workflow_id: str) -> None:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                DELETE from workflow_library
-                WHERE workflow_id = ? AND category = 'user';
-                """,
-                (workflow_id,),
-            )
-            self._conn.commit()
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-        return None
-
-    def get_many(
-        self,
-        page: int,
-        per_page: int,
-        order_by: WorkflowRecordOrderBy,
-        direction: SQLiteDirection,
-        category: WorkflowCategory,
-        query: Optional[str] = None,
-    ) -> PaginatedResults[WorkflowRecordListItemDTO]:
-        try:
-            self._lock.acquire()
-            # sanitize!
-            assert order_by in WorkflowRecordOrderBy
-            assert direction in SQLiteDirection
-            assert category in WorkflowCategory
-            count_query = "SELECT COUNT(*) FROM workflow_library WHERE category = ?"
-            main_query = """
-                SELECT
-                    workflow_id,
-                    category,
-                    name,
-                    description,
-                    created_at,
-                    updated_at,
-                    opened_at
-                FROM workflow_library
-                WHERE category = ?
-                """
-            main_params: list[int | str] = [category.value]
-            count_params: list[int | str] = [category.value]
-            stripped_query = query.strip() if query else None
-            if stripped_query:
-                wildcard_query = "%" + stripped_query + "%"
-                main_query += " AND name LIKE ? OR description LIKE ? "
-                count_query += " AND name LIKE ? OR description LIKE ?;"
-                main_params.extend([wildcard_query, wildcard_query])
-                count_params.extend([wildcard_query, wildcard_query])
-
-            main_query += f" ORDER BY {order_by.value} {direction.value} LIMIT ? OFFSET ?;"
-            main_params.extend([per_page, page * per_page])
-            self._cursor.execute(main_query, main_params)
-            rows = self._cursor.fetchall()
-            workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
-
-            self._cursor.execute(count_query, count_params)
-            total = self._cursor.fetchone()[0]
-            pages = int(total / per_page) + 1
-
-            return PaginatedResults(
-                items=workflows,
-                page=page,
-                per_page=per_page,
-                pages=pages,
-                total=total,
-            )
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-
-    def _sync_default_workflows(self) -> None:
-        """Syncs default workflows to the database. Internal use only."""
-
-        """
-        An enhancement might be to only update workflows that have changed. This would require stable
-        default workflow IDs, and properly incrementing the workflow version.
-
-        It's much simpler to just replace them all with whichever workflows are in the directory.
-
-        The downside is that the `updated_at` and `opened_at` timestamps for default workflows are
-        meaningless, as they are overwritten every time the server starts.
-        """
-
-        try:
-            self._lock.acquire()
-            workflows: list[Workflow] = []
-            workflows_dir = Path(__file__).parent / Path("default_workflows")
-            workflow_paths = workflows_dir.glob("*.json")
-            for path in workflow_paths:
-                bytes_ = path.read_bytes()
-                workflow_without_id = WorkflowWithoutIDValidator.validate_json(bytes_)
-                workflow = Workflow(**workflow_without_id.model_dump(), id=uuid_string())
-                workflows.append(workflow)
-            # Only default workflows may be managed by this method
-            assert all(w.meta.category is WorkflowCategory.Default for w in workflows)
-            self._cursor.execute(
-                """--sql
-                DELETE FROM workflow_library
-                WHERE category = 'default';
+                CREATE TABLE IF NOT EXISTS workflows (
+                    workflow TEXT NOT NULL,
+                    workflow_id TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.id')) VIRTUAL NOT NULL UNIQUE, -- gets implicit index
+                    created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                    updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')) -- updated via trigger
+                );
                 """
             )
-            for w in workflows:
-                self._cursor.execute(
-                    """--sql
-                    INSERT OR REPLACE INTO workflow_library (
-                        workflow_id,
-                        workflow
-                    )
-                    VALUES (?, ?);
-                    """,
-                    (w.id, w.model_dump_json()),
-                )
+
+            self._cursor.execute(
+                """--sql
+                CREATE TRIGGER IF NOT EXISTS tg_workflows_updated_at
+                AFTER UPDATE
+                ON workflows FOR EACH ROW
+                BEGIN
+                    UPDATE workflows
+                    SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE workflow_id = old.workflow_id;
+                END;
+                """
+            )
+
             self._conn.commit()
         except Exception:
             self._conn.rollback()