feat: add 'project' workflow category

- Different handling for loading from library vs external
- Fix bug where only nodes and edges loaded

.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

@@ -22,22 +22,12 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
       - name: Setup Node 18
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v3
         with:
           node-version: '18'
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Setup pnpm
-        uses: pnpm/action-setup@v2
-        with:
-          version: '8.12.1'
-      - 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'
+      - 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'

.github/workflows/pypi-release.yml

@@ -1,15 +1,13 @@
 name: PyPI Release
 
 on:
+  push:
+    paths:
+      - 'invokeai/version/invokeai_version.py'
   workflow_dispatch:
-    inputs:
-      publish_package:
-        description: 'Publish build on PyPi? [true/false]'
-        required: true
-        default: 'false'
 
 jobs:
-  build-and-release:
+  release:
     if: github.repository == 'invoke-ai/InvokeAI'
     runs-on: ubuntu-22.04
     env:
@@ -17,43 +15,19 @@ jobs:
       TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
       TWINE_NON_INTERACTIVE: 1
     steps:
-      - name: Checkout
-        uses: actions/checkout@v4
+      - name: checkout sources
+        uses: actions/checkout@v3
 
-      - name: Setup Node 18
-        uses: actions/setup-node@v4
-        with:
-          node-version: '18'
-
-      - name: Setup pnpm
-        uses: pnpm/action-setup@v2
-        with:
-          version: '8.12.1'
-
-      - name: Install frontend dependencies
-        run: pnpm install --prefer-frozen-lockfile
-        working-directory: invokeai/frontend/web
-
-      - name: Build frontend
-        run: pnpm run build
-        working-directory: invokeai/frontend/web
-
-      - name: Install python dependencies
+      - name: install deps
         run: pip install --upgrade build twine
 
-      - name: Build python package
+      - name: build package
         run: python3 -m build
 
-      - name: Upload build as workflow artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: dist
-
-      - name: Check distribution
+      - name: check distribution
         run: twine check dist/*
 
-      - name: Check PyPI versions
+      - name: check PyPI versions
         if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
         run: |
           pip install --upgrade requests
@@ -62,6 +36,6 @@ jobs:
           EXISTS=scripts.pypi_helper.local_on_pypi(); \
           print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
 
-      - name: Publish build on PyPi
-        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != '' && github.event.inputs.publish_package == 'true'
+      - name: upload package
+        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != ''
         run: twine upload dist/*

.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

@@ -1,20 +1,6 @@
 # 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
@@ -32,21 +18,4 @@ mypy:
 # 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
-
+		mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports

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:
@@ -270,7 +270,7 @@ upgrade script.** See the next section for a Windows recipe.
 3. Select option [1] to upgrade to the latest release.
 
 4. Once the upgrade is finished you will be returned to the launcher
-menu. Select option [6] "Re-run the configure script to fix a broken
+menu. Select option [7] "Re-run the configure script to fix a broken
 install or to complete a major upgrade".
 
 This will run the configure script against the v2.3 directory and

docker/.env.sample

@@ -11,5 +11,5 @@ INVOKEAI_ROOT=
 # HUGGING_FACE_HUB_TOKEN=
 
 ## optional variables specific to the docker setup.
-# GPU_DRIVER=nvidia #| rocm
+# GPU_DRIVER=cuda # or rocm
 # CONTAINER_UID=1000

docker/Dockerfile

@@ -59,16 +59,14 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 
 # #### Build the Web UI ------------------------------------
 
-FROM node:18-slim AS web-builder
-ENV PNPM_HOME="/pnpm"
-ENV PATH="$PNPM_HOME:$PATH"
-RUN corepack enable
-
+FROM node:18 AS web-builder
 WORKDIR /build
 COPY invokeai/frontend/web/ ./
-RUN --mount=type=cache,target=/pnpm/store \
-    pnpm install --frozen-lockfile
-RUN pnpm run build
+RUN --mount=type=cache,target=/usr/lib/node_modules \
+    npm install --include dev
+RUN --mount=type=cache,target=/usr/lib/node_modules \
+    yarn vite build
+
 
 #### Runtime stage ---------------------------------------
 
@@ -102,8 +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}
@@ -121,7 +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"]

docker/README.md

@@ -1,14 +1,6 @@
 # InvokeAI Containerized
 
-All commands should be run within the `docker` directory: `cd docker`
-
-## Quickstart :rocket:
-
-On a known working Linux+Docker+CUDA (Nvidia) system, execute `./run.sh` in this directory. It will take a few minutes - depending on your internet speed - to install the core models. Once the application starts up, open `http://localhost:9090` in your browser to Invoke!
-
-For more configuration options (using an AMD GPU, custom root directory location, etc): read on.
-
-## Detailed setup
+All commands are to be run from the `docker` directory: `cd docker`
 
 #### Linux
 
@@ -26,12 +18,12 @@ For more configuration options (using an AMD GPU, custom root directory location
 
 This is done via Docker Desktop preferences
 
-### Configure Invoke environment
+## Quickstart
 
 1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
     a. the desired location of the InvokeAI runtime directory, or
     b. an existing, v3.0.0 compatible runtime directory.
-1. Execute `run.sh`
+1. `docker compose up`
 
 The image will be built automatically if needed.
 
@@ -45,21 +37,19 @@ The runtime directory (holding models and outputs) will be created in the locati
 
 The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
 
-To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file.
-
 ## Customize
 
-Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `run.sh`, your custom values will be used.
+Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
 
 You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
 
-Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The default is `~/invokeai`. Example:
+Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended):
 
 ```bash
 INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
 HUGGINGFACE_TOKEN=the_actual_token
 CONTAINER_UID=1000
-GPU_DRIVER=nvidia
+GPU_DRIVER=cuda
 ```
 
 Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.

docker/build.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+set -e
+
+build_args=""
+
+[[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
+
+echo "docker compose build args:"
+echo $build_args
+
+docker compose build $build_args

docker/docker-compose.yml

@@ -2,8 +2,23 @@
 
 version: '3.8'
 
-x-invokeai: &invokeai
+services:
+  invokeai:
     image: "local/invokeai:latest"
+    # edit below to run on a container runtime other than nvidia-container-runtime.
+    # not yet tested with rocm/AMD GPUs
+    # Comment out the "deploy" section to run on CPU only
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    # For AMD support, comment out the deploy section above and uncomment the devices section below:
+    #devices:
+    #  - /dev/kfd:/dev/kfd
+    #  - /dev/dri:/dev/dri
     build:
       context: ..
       dockerfile: docker/Dockerfile
@@ -35,27 +50,3 @@ x-invokeai: &invokeai
     #   - |
     #     invokeai-model-install --yes --default-only --config_file ${INVOKEAI_ROOT}/config_custom.yaml
     #     invokeai-nodes-web --host 0.0.0.0
-
-services:
-  invokeai-nvidia:
-    <<: *invokeai
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-
-  invokeai-cpu:
-    <<: *invokeai
-    profiles:
-      - cpu
-
-  invokeai-rocm:
-    <<: *invokeai
-    devices:
-      - /dev/kfd:/dev/kfd
-      - /dev/dri:/dev/dri
-    profiles:
-      - rocm

docker/run.sh

@@ -1,32 +1,11 @@
 #!/usr/bin/env bash
-set -e -o pipefail
+set -e
 
-run() {
-  local scriptdir=$(dirname "${BASH_SOURCE[0]}")
-  cd "$scriptdir" || exit 1
+# This script is provided for backwards compatibility with the old docker setup.
+# it doesn't do much aside from wrapping the usual docker compose CLI.
 
-  local build_args=""
-  local profile=""
+SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
+cd "$SCRIPTDIR" || exit 1
 
-  touch .env
-  build_args=$(awk '$1 ~ /=[^$]/ && $0 !~ /^#/ {print "--build-arg " $0 " "}' .env) &&
-  profile="$(awk -F '=' '/GPU_DRIVER/ {print $2}' .env)"
-
-  [[ -z "$profile" ]] && profile="nvidia"
-
-  local service_name="invokeai-$profile"
-
-  if [[ ! -z "$build_args" ]]; then
-    printf "%s\n" "docker compose build args:"
-    printf "%s\n" "$build_args"
-  fi
-
-  docker compose build $build_args
-  unset build_args
-
-  printf "%s\n" "starting service $service_name"
-  docker compose --profile "$profile" up -d "$service_name"
-  docker compose logs -f
-}
-
-run
+docker compose up -d
+docker compose logs -f

docs/contributing/DOWNLOAD_QUEUE.md

@@ -1,277 +0,0 @@
-# The InvokeAI Download Queue
-
-The DownloadQueueService provides a multithreaded parallel download
-queue for arbitrary URLs, with queue prioritization, event handling,
-and restart capabilities.
-
-## Simple Example
-
-```
-from invokeai.app.services.download import DownloadQueueService, TqdmProgress
-
-download_queue = DownloadQueueService()
-for url in ['https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true',
-            'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true',
-            'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png',
-            'https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor',
-            ]:
-
-    # urls start downloading as soon as download() is called
-    download_queue.download(source=url,
-                            dest='/tmp/downloads',
-                            on_progress=TqdmProgress().update
-                            )
-
-download_queue.join()  # wait for all downloads to finish
-for job in download_queue.list_jobs():
-    print(job.model_dump_json(exclude_none=True, indent=4),"\n")
-```
-
-Output:
-
-```
-{
-    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true",
-    "dest": "/tmp/downloads",
-    "id": 0,
-    "priority": 10,
-    "status": "completed",
-    "download_path": "/tmp/downloads/a-painting-of-a-fire.png",
-    "job_started": "2023-12-04T05:34:41.742174",
-    "job_ended": "2023-12-04T05:34:42.592035",
-    "bytes": 666734,
-    "total_bytes": 666734
-} 
-
-{
-    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true",
-    "dest": "/tmp/downloads",
-    "id": 1,
-    "priority": 10,
-    "status": "completed",
-    "download_path": "/tmp/downloads/birdhouse.png",
-    "job_started": "2023-12-04T05:34:41.741975",
-    "job_ended": "2023-12-04T05:34:42.652841",
-    "bytes": 774949,
-    "total_bytes": 774949
-}
-
-{
-    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png",
-    "dest": "/tmp/downloads",
-    "id": 2,
-    "priority": 10,
-    "status": "error",
-    "job_started": "2023-12-04T05:34:41.742079",
-    "job_ended": "2023-12-04T05:34:42.147625",
-    "bytes": 0,
-    "total_bytes": 0,
-    "error_type": "HTTPError(Not Found)",
-    "error": "Traceback (most recent call last):\n  File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 182, in _download_next_item\n    self._do_download(job)\n  File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 206, in _do_download\n    raise HTTPError(resp.reason)\nrequests.exceptions.HTTPError: Not Found\n"
-}
-
-{
-    "source": "https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor",
-    "dest": "/tmp/downloads",
-    "id": 3,
-    "priority": 10,
-    "status": "completed",
-    "download_path": "/tmp/downloads/xl_more_art-full_v1.safetensors",
-    "job_started": "2023-12-04T05:34:42.147645",
-    "job_ended": "2023-12-04T05:34:43.735990",
-    "bytes": 719020768,
-    "total_bytes": 719020768
-} 
-```
-
-##  The API
-
-The default download queue is `DownloadQueueService`, an
-implementation of ABC `DownloadQueueServiceBase`. It juggles multiple
-background download requests and provides facilities for interrogating
-and cancelling the requests. Access to a current or past download task
-is mediated via `DownloadJob` objects which report the current status
-of a job request
-
-### The Queue Object
-
-A default download queue is located in
-`ApiDependencies.invoker.services.download_queue`. However, you can
-create additional instances if you need to isolate your queue from the
-main one.
-
-```
-queue = DownloadQueueService(event_bus=events)
-```
-
-`DownloadQueueService()` takes three optional arguments:
-
-| **Argument** | **Type**          |  **Default**  | **Description** |
-|----------------|-----------------|---------------|-----------------|
-| `max_parallel_dl`  | int                         | 5    | Maximum number of simultaneous downloads allowed |
-| `event_bus` | EventServiceBase   | None | System-wide FastAPI event bus for reporting download events |
-| `requests_session` | requests.sessions.Session   | None | An alternative requests Session object to use for the download |
-
-`max_parallel_dl` specifies how many download jobs are allowed to run
-simultaneously. Each will run in a different thread of execution.
-
-`event_bus` is an EventServiceBase, typically the one created at
-InvokeAI startup. If present, download events are periodically emitted
-on this bus to allow clients to follow download progress.
-
-`requests_session` is a url library requests Session object. It is
-used for testing.
-
-### The Job object
-
-The queue operates on a series of download job objects. These objects
-specify the source and destination of the download, and keep track of
-the progress of the download.
-
-The only job type currently implemented is `DownloadJob`, a pydantic object with the
-following fields:
-
-| **Field**      | **Type**        |  **Default**  | **Description** |
-|----------------|-----------------|---------------|-----------------|
-| _Fields passed in at job creation time_                               |
-| `source`         | AnyHttpUrl      |               | Where to download from |
-| `dest`           | Path            |               | Where to download to              |
-| `access_token`   | str             |               | [optional] string containing authentication token for access |
-| `on_start`       | Callable        |               | [optional] callback when the download starts |
-| `on_progress`    | Callable        |               | [optional] callback called at intervals during download progress |
-| `on_complete`    | Callable        |               | [optional] callback called after successful download completion |
-| `on_error`       | Callable        |               | [optional] callback called after an error occurs  |
-| `id`             | int             | auto assigned | Job ID, an integer >= 0           |
-| `priority`       | int             | 10            | Job priority. Lower priorities run before higher priorities |
-|                                                                                                        |
-| _Fields updated over the course of the download task_
-| `status`         | DownloadJobStatus|              | Status code                                |
-| `download_path`  | Path |              | Path to the location of the downloaded file |
-| `job_started`    | float            |              | Timestamp for when the job started running |
-| `job_ended`      | float            |              | Timestamp for when the job completed or errored out |
-| `job_sequence`   | int              |              | A counter that is incremented each time a model is dequeued |
-| `bytes`          | int              | 0            | Bytes downloaded so far   |
-| `total_bytes`    | int              | 0            | Total size of the file at the remote site  |
-| `error_type`     | str              |              | String version of the exception that caused an error during download |
-| `error`          | str              |              | String version of the traceback associated with an error |
-| `cancelled`      | bool             | False        | Set to true if the job was cancelled by the caller|
-
-When you create a job, you can assign it a `priority`. If multiple
-jobs are queued, the job with the lowest priority runs first.
-
-Every job has a `source` and a `dest`. `source` is a pydantic.networks AnyHttpUrl object.
-The `dest` is a path on the local filesystem that specifies the
-destination for the downloaded object. Its semantics are
-described below.
-
-When the job is submitted, it is assigned a numeric `id`. The id can
-then be used to fetch the job object from the queue.
-
-The `status` field is updated by the queue to indicate where the job
-is in its lifecycle. Values are defined in the string enum
-`DownloadJobStatus`, a symbol available from
-`invokeai.app.services.download_manager`. Possible values are:
-
-| **Value**    |   **String Value**  | ** Description ** |
-|--------------|---------------------|-------------------|
-| `WAITING`      | waiting           | Job is on the queue but not yet running|
-| `RUNNING`      | running           | The download is started                |
-| `COMPLETED`    | completed         | Job has finished its work without an error |
-| `ERROR`        | error             | Job encountered an error and will not run again|
-
-`job_started` and `job_ended` indicate when the job
-was started (using a python timestamp) and when it completed.
-
-In case of an error, the job's status will be set to `DownloadJobStatus.ERROR`, the text of the
-Exception that caused the error will be placed in the `error_type`
-field and the traceback that led to the error will be in `error`.
-
-A cancelled job will have status `DownloadJobStatus.ERROR` and an
-`error_type` field of "DownloadJobCancelledException". In addition,
-the job's `cancelled` property will be set to True.
-
-### Callbacks
-
-Download jobs can be associated with a series of callbacks, each with
-the signature `Callable[["DownloadJob"], None]`. The callbacks are assigned
-using optional arguments `on_start`, `on_progress`, `on_complete` and
-`on_error`. When the corresponding event occurs, the callback wil be
-invoked and passed the job. The callback will be run in a `try:`
-context in the same thread as the download job. Any exceptions that
-occur during execution of the callback will be caught and converted
-into a log error message, thereby allowing the download to continue.
-
-#### `TqdmProgress`
-
-The `invokeai.app.services.download.download_default` module defines a
-class named `TqdmProgress` which can be used as an `on_progress`
-handler to display a completion bar in the console. Use as follows:
-
-```
-from invokeai.app.services.download import TqdmProgress
-
-download_queue.download(source='http://some.server.somewhere/some_file',
-                        dest='/tmp/downloads',
-                        on_progress=TqdmProgress().update
-                        )
-
-```
-
-### Events
-
-If the queue was initialized with the InvokeAI event bus (the case
-when using `ApiDependencies.invoker.services.download_queue`), then
-download events will also be issued on the bus. The events are:
-
-* `download_started` -- This is issued when a job is taken off the
-queue and a request is made to the remote server for the URL headers, but before any data
-has been downloaded. The event payload will contain the keys `source`
-and `download_path`. The latter contains the path that the URL will be
-downloaded to.
-
-* `download_progress -- This is issued periodically as the download
-runs. The payload contains the keys `source`, `download_path`,
-`current_bytes` and `total_bytes`. The latter two fields can be
-used to display the percent complete.
-
-* `download_complete` -- This is issued when the download completes
-successfully. The payload contains the keys `source`, `download_path`
-and `total_bytes`.
-
-* `download_error` -- This is issued when the download stops because
-of an error condition. The payload contains the fields `error_type`
-and `error`. The former is the text representation of the exception,
-and the latter is a traceback showing where the error occurred.
-
-### Job control
-
-To create a job call the queue's `download()` method. You can list all
-jobs using `list_jobs()`, fetch a single job by its with
-`id_to_job()`, cancel a running job with `cancel_job()`, cancel all
-running jobs with `cancel_all_jobs()`, and wait for all jobs to finish
-with `join()`.
-
-#### job = queue.download(source, dest, priority, access_token)
-
-Create a new download job and put it on the queue, returning the
-DownloadJob object.
-
-#### jobs = queue.list_jobs()
-
-Return a list of all active and inactive `DownloadJob`s.
-
-#### job = queue.id_to_job(id)
-
-Return the job corresponding to given ID.
-
-Return a list of all active and inactive `DownloadJob`s.
-
-#### queue.prune_jobs()
-
-Remove inactive (complete or errored) jobs from the listing returned
-by `list_jobs()`.
-
-#### queue.join()
-
-Block until all pending jobs have run to completion or errored out.
-

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/contributing/contribution_guides/contributingToFrontend.md

@@ -46,18 +46,17 @@ We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](http
 ```bash
 node --version
 ```
-
-2. Install [pnpm](https://pnpm.io/) and confirm it is installed by running this:
+2. Install [yarn classic](https://classic.yarnpkg.com/lang/en/) and confirm it is installed by running this:
 ```bash
-npm install --global pnpm
-pnpm --version
+npm install --global yarn
+yarn --version
 ```
 
-From `invokeai/frontend/web/` run `pnpm install` to get everything set up.
+From `invokeai/frontend/web/` run `yarn install` to get everything set up.
 
 Start everything in dev mode:
 1. Ensure your virtual environment is running
-2. Start the dev server: `pnpm dev`
+2. Start the dev server: `yarn dev`
 3. Start the InvokeAI Nodes backend: `python scripts/invokeai-web.py # run from the repo root`
 4. Point your browser to the dev server address e.g. [http://localhost:5173/](http://localhost:5173/)
 
@@ -73,4 +72,4 @@ For a number of technical and logistical reasons, we need to commit UI build art
 
 If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
 
-To build for production, run `pnpm build`.
+To build for production, run `yarn build`.

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/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

@@ -13,12 +13,7 @@ If you'd prefer, you can also just download the whole node folder from the linke
 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
-    + [Adapters-Linked](#adapters-linked-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)
@@ -27,23 +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)
-    + [Metadata-Linked](#metadata-linked-nodes)
-    + [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)
@@ -53,19 +41,6 @@ To use a community workflow, download the the `.json` node graph file and load i
 - [Help](#help)
 
 
---------------------------------
-### Adapters Linked Nodes
-
-**Description:** A set of nodes for linked adapters (ControlNet, IP-Adaptor & T2I-Adapter). This allows multiple adapters to be chained together without using a `collect` node which means it can be used inside an `iterate` node without any collecting on every iteration issues.
-
-- `ControlNet-Linked` - Collects ControlNet info to pass to other nodes.
-- `IP-Adapter-Linked` - Collects IP-Adapter info to pass to other nodes.
-- `T2I-Adapter-Linked` - Collects T2I-Adapter info to pass to other nodes.
-
-Note: These are inherited from the core nodes so any update to the core nodes should be reflected in these. 
-
-**Node Link:** https://github.com/skunkworxdark/adapters-linked-nodes
-
 --------------------------------
 ### Average Images
 
@@ -73,46 +48,6 @@ Note: These are inherited from the core nodes so any update to the core nodes sh
 
 **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
 
@@ -229,16 +164,6 @@ 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
 
@@ -260,17 +185,6 @@ 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
 
@@ -295,16 +209,6 @@ 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
 
@@ -322,30 +226,6 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 <img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
 
---------------------------------
-### Metadata Linked Nodes
-
-**Description:** A set of nodes for Metadata. Collect Metadata from within an `iterate` node & extract metadata from an image.
-
-- `Metadata Item Linked` - Allows collecting of metadata while within an iterate node with no need for a collect node or conversion to metadata node.
-- `Metadata From Image` - Provides Metadata from an image.
-- `Metadata To String` - Extracts a String value of a label from metadata.
-- `Metadata To Integer` - Extracts an Integer value of a label from metadata.
-- `Metadata To Float` - Extracts a Float value of a label from metadata.
-- `Metadata To Scheduler` - Extracts a Scheduler value of a label from metadata.
-
-**Node Link:** https://github.com/skunkworxdark/metadata-linked-nodes
-
---------------------------------
-### 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
 
@@ -409,15 +289,6 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 **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
@@ -430,17 +301,6 @@ 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
 
@@ -526,7 +386,6 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
 
 <img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
 
-
 --------------------------------
 ### Example Node Template
 

docs/workflows/Text_to_Image.json

@@ -1,8 +1,8 @@
 {
-  "name": "Text to Image - SD1.5",
+  "name": "Text to Image",
   "author": "InvokeAI",
   "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
-  "version": "1.1.0",
+  "version": "1.0.1",
   "contact": "invoke@invoke.ai",
   "tags": "text2image, SD1.5, SD2, default",
   "notes": "",
@@ -18,19 +18,10 @@
     {
       "nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "fieldName": "prompt"
-    },
-    {
-      "nodeId": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "fieldName": "width"
-    },
-    {
-      "nodeId": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "fieldName": "height"
     }
   ],
   "meta": {
-    "category": "default",
-    "version": "2.0.0"
+    "version": "1.0.0"
   },
   "nodes": [
     {
@@ -39,56 +30,44 @@
       "data": {
         "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
         "type": "compel",
-        "label": "Negative Compel Prompt",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0",
-        "nodePack": "invokeai",
         "inputs": {
           "prompt": {
             "id": "7739aff6-26cb-4016-8897-5a1fb2305e4e",
             "name": "prompt",
+            "type": "string",
             "fieldKind": "input",
             "label": "Negative Prompt",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            },
             "value": ""
           },
           "clip": {
             "id": "48d23dce-a6ae-472a-9f8c-22a714ea5ce0",
             "name": "clip",
+            "type": "ClipField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
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+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": false,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 266,
+      "height": 267,
       "position": {
-        "x": 1800,
-        "y": 25
+        "x": 2037.9648469717395,
+        "y": 426.10844427600136
       }
     }
   ],
   "edges": [
     {
-      "id": "reactflow__edge-ea94bc37-d995-4a83-aa99-4af42479f2f2value-55705012-79b9-4aac-9f26-c0b10309785bseed",
       "source": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
-      "target": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "type": "default",
       "sourceHandle": "value",
-      "targetHandle": "seed"
+      "target": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "targetHandle": "seed",
+      "id": "reactflow__edge-ea94bc37-d995-4a83-aa99-4af42479f2f2value-55705012-79b9-4aac-9f26-c0b10309785bseed",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-7d8bf987-284f-413a-b2fd-d825445a5d6cclip",
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "clip",
       "target": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
-      "type": "default",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
+      "targetHandle": "clip",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-7d8bf987-284f-413a-b2fd-d825445a5d6cclip",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-93dc02a4-d05b-48ed-b99c-c9b616af3402clip",
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "clip",
       "target": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
-      "type": "default",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
+      "targetHandle": "clip",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-93dc02a4-d05b-48ed-b99c-c9b616af3402clip",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
       "source": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
       "sourceHandle": "noise",
-      "targetHandle": "noise"
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "targetHandle": "noise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
       "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
       "sourceHandle": "conditioning",
-      "targetHandle": "positive_conditioning"
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "targetHandle": "positive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
       "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
       "sourceHandle": "conditioning",
-      "targetHandle": "negative_conditioning"
-    },
-    {
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
-      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
-      "sourceHandle": "unet",
-      "targetHandle": "unet"
+      "targetHandle": "negative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
-      "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "type": "default",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "unet",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "targetHandle": "unet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
+      "type": "default"
+    },
+    {
+      "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "sourceHandle": "latents",
       "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "type": "default",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "sourceHandle": "vae",
-      "targetHandle": "vae"
+      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
+      "targetHandle": "vae",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
+      "type": "default"
     }
   ]
-}
+}

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
@@ -91,11 +59,9 @@ rm -rf InvokeAI-Installer
 
 # copy content
 mkdir InvokeAI-Installer
-for f in templates *.txt *.reg; do
+for f in templates lib *.txt *.reg; do
     cp -r ${f} InvokeAI-Installer/
 done
-mkdir InvokeAI-Installer/lib
-cp lib/*.py InvokeAI-Installer/lib
 
 # Move the wheel
 mv dist/*.whl InvokeAI-Installer/lib/
@@ -106,13 +72,13 @@ 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
 zip -r InvokeAI-installer-$VERSION.zip InvokeAI-Installer
 
 # clean up
-rm -rf InvokeAI-Installer tmp dist ../invokeai/frontend/web/dist/
+rm -rf InvokeAI-Installer tmp dist
 
 exit 0

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==2.1.2",
+                "torch==2.1.0",
                 "torchmetrics==0.11.4",
-                "torchvision>=0.16.2",
+                "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 +2,6 @@
 
 from logging import Logger
 
-from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -11,7 +10,6 @@ from ..services.board_images.board_images_default import BoardImagesService
 from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
 from ..services.boards.boards_default import BoardService
 from ..services.config import InvokeAIAppConfig
-from ..services.download import DownloadQueueService
 from ..services.image_files.image_files_disk import DiskImageFileStorage
 from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
 from ..services.images.images_default import ImageService
@@ -24,13 +22,14 @@ 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
 from ..services.session_processor.session_processor_default import DefaultSessionProcessor
 from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
-from ..services.shared.graph import GraphExecutionState
+from ..services.shared.default_graphs import create_system_graphs
+from ..services.shared.graph import GraphExecutionState, LibraryGraph
+from ..services.shared.sqlite.sqlite_database import SqliteDatabase
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -67,9 +66,8 @@ 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
@@ -80,16 +78,14 @@ class ApiDependencies:
         boards = BoardService()
         events = FastAPIEventService(event_handler_id)
         graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
+        graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
+        image_files = DiskImageFileStorage(f"{output_folder}/images")
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
         model_manager = ModelManagerService(config, logger)
         model_record_service = ModelRecordServiceSQL(db=db)
-        download_queue_service = DownloadQueueService(event_bus=events)
-        model_install_service = ModelInstallService(
-            app_config=config, record_store=model_record_service, event_bus=events
-        )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
         processor = DefaultInvocationProcessor()
@@ -107,6 +103,7 @@ class ApiDependencies:
             configuration=configuration,
             events=events,
             graph_execution_manager=graph_execution_manager,
+            graph_library=graph_library,
             image_files=image_files,
             image_records=image_records,
             images=images,
@@ -115,8 +112,6 @@ class ApiDependencies:
             logger=logger,
             model_manager=model_manager,
             model_records=model_record_service,
-            download_queue=download_queue_service,
-            model_install=model_install_service,
             names=names,
             performance_statistics=performance_statistics,
             processor=processor,
@@ -127,6 +122,8 @@ class ApiDependencies:
             workflow_records=workflow_records,
         )
 
+        create_system_graphs(services.graph_library)
+
         ApiDependencies.invoker = Invoker(services)
         db.clean()
 

invokeai/app/api/routers/download_queue.py

@@ -1,111 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein
-"""FastAPI route for the download queue."""
-
-from typing import List, Optional
-
-from fastapi import Body, Path, Response
-from fastapi.routing import APIRouter
-from pydantic.networks import AnyHttpUrl
-from starlette.exceptions import HTTPException
-
-from invokeai.app.services.download import (
-    DownloadJob,
-    UnknownJobIDException,
-)
-
-from ..dependencies import ApiDependencies
-
-download_queue_router = APIRouter(prefix="/v1/download_queue", tags=["download_queue"])
-
-
-@download_queue_router.get(
-    "/",
-    operation_id="list_downloads",
-)
-async def list_downloads() -> List[DownloadJob]:
-    """Get a list of active and inactive jobs."""
-    queue = ApiDependencies.invoker.services.download_queue
-    return queue.list_jobs()
-
-
-@download_queue_router.patch(
-    "/",
-    operation_id="prune_downloads",
-    responses={
-        204: {"description": "All completed jobs have been pruned"},
-        400: {"description": "Bad request"},
-    },
-)
-async def prune_downloads():
-    """Prune completed and errored jobs."""
-    queue = ApiDependencies.invoker.services.download_queue
-    queue.prune_jobs()
-    return Response(status_code=204)
-
-
-@download_queue_router.post(
-    "/i/",
-    operation_id="download",
-)
-async def download(
-    source: AnyHttpUrl = Body(description="download source"),
-    dest: str = Body(description="download destination"),
-    priority: int = Body(default=10, description="queue priority"),
-    access_token: Optional[str] = Body(default=None, description="token for authorization to download"),
-) -> DownloadJob:
-    """Download the source URL to the file or directory indicted in dest."""
-    queue = ApiDependencies.invoker.services.download_queue
-    return queue.download(source, dest, priority, access_token)
-
-
-@download_queue_router.get(
-    "/i/{id}",
-    operation_id="get_download_job",
-    responses={
-        200: {"description": "Success"},
-        404: {"description": "The requested download JobID could not be found"},
-    },
-)
-async def get_download_job(
-    id: int = Path(description="ID of the download job to fetch."),
-) -> DownloadJob:
-    """Get a download job using its ID."""
-    try:
-        job = ApiDependencies.invoker.services.download_queue.id_to_job(id)
-        return job
-    except UnknownJobIDException as e:
-        raise HTTPException(status_code=404, detail=str(e))
-
-
-@download_queue_router.delete(
-    "/i/{id}",
-    operation_id="cancel_download_job",
-    responses={
-        204: {"description": "Job has been cancelled"},
-        404: {"description": "The requested download JobID could not be found"},
-    },
-)
-async def cancel_download_job(
-    id: int = Path(description="ID of the download job to cancel."),
-):
-    """Cancel a download job using its ID."""
-    try:
-        queue = ApiDependencies.invoker.services.download_queue
-        job = queue.id_to_job(id)
-        queue.cancel_job(job)
-        return Response(status_code=204)
-    except UnknownJobIDException as e:
-        raise HTTPException(status_code=404, detail=str(e))
-
-
-@download_queue_router.delete(
-    "/i",
-    operation_id="cancel_all_download_jobs",
-    responses={
-        204: {"description": "Download jobs have been cancelled"},
-    },
-)
-async def cancel_all_download_jobs():
-    """Cancel all download jobs."""
-    ApiDependencies.invoker.services.download_queue.cancel_all_jobs()
-    return Response(status_code=204)

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_unstable"])
+model_records_router = APIRouter(prefix="/v1/model/record", tags=["models"])
 
 
 class ModelsList(BaseModel):
@@ -44,25 +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"),
-    model_format: Optional[str] = Query(
-        default=None, description="Exact match on the format of the model (e.g. 'diffusers')"
-    ),
 ) -> 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, model_format=model_format
-                )
-            )
+            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, model_format=model_format)
-        )
+        found_models.extend(record_store.search_by_attr(model_type=model_type))
     return ModelsList(models=found_models)
 
 
@@ -128,17 +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:
@@ -178,145 +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/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

@@ -45,7 +45,6 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         app_info,
         board_images,
         boards,
-        download_queue,
         images,
         model_records,
         models,
@@ -117,7 +116,6 @@ app.include_router(sessions.session_router, prefix="/api")
 app.include_router(utilities.utilities_router, prefix="/api")
 app.include_router(models.models_router, prefix="/api")
 app.include_router(model_records.model_records_router, prefix="/api")
-app.include_router(download_queue.download_queue_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
 app.include_router(board_images.board_images_router, prefix="/api")
@@ -221,19 +219,18 @@ 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:
@@ -274,8 +271,6 @@ 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)
 

invokeai/app/invocations/baseinvocation.py

@@ -4,7 +4,6 @@ from __future__ import annotations
 
 import inspect
 import re
-import warnings
 from abc import ABC, abstractmethod
 from enum import Enum
 from inspect import signature
@@ -39,19 +38,6 @@ 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):
     """
     The type of input a field accepts.
@@ -452,7 +438,6 @@ class UIConfigBase(BaseModel):
         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,
@@ -621,7 +606,6 @@ class BaseInvocation(ABC, BaseModel):
                 schema["category"] = uiconfig.category
             if uiconfig.node_pack is not None:
                 schema["node_pack"] = uiconfig.node_pack
-            schema["classification"] = uiconfig.classification
             schema["version"] = uiconfig.version
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
@@ -725,10 +709,8 @@ 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:
@@ -797,7 +779,6 @@ 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 +789,6 @@ 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]:
@@ -829,7 +809,6 @@ def invocation(
         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"

invokeai/app/invocations/compel.py

@@ -1,3 +1,4 @@
+import re
 from dataclasses import dataclass
 from typing import List, Optional, Union
 
@@ -16,7 +17,6 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import ModelNotFoundException, ModelType
 from ...backend.util.devices import torch_dtype
-from ..util.ti_utils import extract_ti_triggers_from_prompt
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -87,7 +87,7 @@ class CompelInvocation(BaseInvocation):
         # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
-        for trigger in extract_ti_triggers_from_prompt(self.prompt):
+        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
             name = trigger[1:-1]
             try:
                 ti_list.append(
@@ -210,7 +210,7 @@ class SDXLPromptInvocationBase:
         # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
-        for trigger in extract_ti_triggers_from_prompt(prompt):
+        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
             name = trigger[1:-1]
             try:
                 ti_list.append(

invokeai/app/invocations/image.py

@@ -13,15 +13,7 @@ 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, invocation
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -121,11 +113,11 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    invocation_type="img_pad_crop",
-    title="Center Pad or Crop Image",
+    "img_paste",
+    title="Paste Image",
+    tags=["image", "paste"],
     category="image",
-    tags=["image", "pad", "crop"],
-    version="1.0.0",
+    version="1.2.0",
 )
 class CenterPadCropInvocation(BaseInvocation):
     """Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image."""
@@ -176,11 +168,11 @@ class CenterPadCropInvocation(BaseInvocation):
 
 
 @invocation(
-    "img_paste",
-    title="Paste Image",
-    tags=["image", "paste"],
+    invocation_type="img_pad_crop",
+    title="Center Pad or Crop Image",
     category="image",
-    version="1.2.0",
+    tags=["image", "pad", "crop"],
+    version="1.0.0",
 )
 class ImagePasteInvocation(BaseInvocation, WithMetadata):
     """Pastes an image into another image."""
@@ -429,64 +421,6 @@ 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",

invokeai/app/invocations/onnx.py

@@ -1,6 +1,7 @@
 # Copyright (c) 2023 Borisov Sergey (https://github.com/StAlKeR7779)
 
 import inspect
+import re
 
 # from contextlib import ExitStack
 from typing import List, Literal, Union
@@ -20,7 +21,6 @@ from invokeai.backend import BaseModelType, ModelType, SubModelType
 from ...backend.model_management import ONNXModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util import choose_torch_device
-from ..util.ti_utils import extract_ti_triggers_from_prompt
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -78,7 +78,7 @@ class ONNXPromptInvocation(BaseInvocation):
             ]
 
             ti_list = []
-            for trigger in extract_ti_triggers_from_prompt(self.prompt):
+            for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
                 name = trigger[1:-1]
                 try:
                     ti_list.append(

invokeai/app/invocations/tiles.py

@@ -1,5 +1,3 @@
-from typing import Literal
-
 import numpy as np
 from PIL import Image
 from pydantic import BaseModel
@@ -7,8 +5,6 @@ from pydantic import BaseModel
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
-    Input,
     InputField,
     InvocationContext,
     OutputField,
@@ -18,13 +14,7 @@ from invokeai.app.invocations.baseinvocation import (
 )
 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.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
 from invokeai.backend.tiles.utils import Tile
 
 
@@ -38,14 +28,7 @@ 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,
-)
+@invocation("calculate_image_tiles", title="Calculate Image Tiles", tags=["tiles"], category="tiles", version="1.0.0")
 class CalculateImageTilesInvocation(BaseInvocation):
     """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
 
@@ -72,79 +55,6 @@ class CalculateImageTilesInvocation(BaseInvocation):
         return CalculateImageTilesOutput(tiles=tiles)
 
 
-@invocation(
-    "calculate_image_tiles_even_split",
-    title="Calculate Image Tiles Even Split",
-    tags=["tiles"],
-    category="tiles",
-    version="1.1.0",
-    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: int = InputField(
-        default=128,
-        ge=0,
-        multiple_of=8,
-        description="The overlap, in pixels, between adjacent tiles.",
-    )
-
-    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=self.overlap,
-        )
-        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.")
@@ -166,14 +76,7 @@ class TileToPropertiesOutput(BaseInvocationOutput):
     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,
-)
+@invocation("tile_to_properties", title="Tile to Properties", tags=["tiles"], category="tiles", version="1.0.0")
 class TileToPropertiesInvocation(BaseInvocation):
     """Split a Tile into its individual properties."""
 
@@ -199,14 +102,7 @@ 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,
-)
+@invocation("pair_tile_image", title="Pair Tile with Image", tags=["tiles"], category="tiles", version="1.0.0")
 class PairTileImageInvocation(BaseInvocation):
     """Pair an image with its tile properties."""
 
@@ -225,29 +121,13 @@ class PairTileImageInvocation(BaseInvocation):
         )
 
 
-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,
-)
+@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.1.0")
 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.",
     )
@@ -277,18 +157,10 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
         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
+        merge_tiles_with_linear_blending(
+            dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
+        )
         pil_image = Image.fromarray(np_image)
 
         image_dto = context.services.images.create(

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -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

@@ -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_default.py

@@ -173,7 +173,7 @@ 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
@@ -221,9 +221,6 @@ 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)
@@ -337,7 +334,7 @@ class InvokeAIAppConfig(InvokeAISettings):
                 )
 
     @classmethod
-    def get_config(cls, **kwargs: Dict[str, Any]) -> InvokeAIAppConfig:
+    def get_config(cls, **kwargs) -> InvokeAIAppConfig:
         """Return a singleton InvokeAIAppConfig configuration object."""
         if (
             cls.singleton_config is None
@@ -356,7 +353,7 @@ class InvokeAIAppConfig(InvokeAISettings):
         else:
             root = self.find_root().expanduser().absolute()
         self.root = root  # insulate ourselves from relative paths that may change
-        return root.resolve()
+        return root
 
     @property
     def root_dir(self) -> Path:
@@ -386,17 +383,17 @@ class InvokeAIAppConfig(InvokeAISettings):
         return db_dir / DB_FILE
 
     @property
-    def model_conf_path(self) -> Path:
+    def model_conf_path(self) -> Optional[Path]:
         """Path to models configuration file."""
         return self._resolve(self.conf_path)
 
     @property
-    def legacy_conf_path(self) -> Path:
+    def legacy_conf_path(self) -> Optional[Path]:
         """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:
+    def models_path(self) -> Optional[Path]:
         """Path to the models directory."""
         return self._resolve(self.models_dir)
 

invokeai/app/services/download/__init__.py

@@ -1,12 +0,0 @@
-"""Init file for download queue."""
-from .download_base import DownloadJob, DownloadJobStatus, DownloadQueueServiceBase, UnknownJobIDException
-from .download_default import DownloadQueueService, TqdmProgress
-
-__all__ = [
-    "DownloadJob",
-    "DownloadQueueServiceBase",
-    "DownloadQueueService",
-    "TqdmProgress",
-    "DownloadJobStatus",
-    "UnknownJobIDException",
-]

invokeai/app/services/download/download_base.py

@@ -1,217 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
-"""Model download service."""
-
-from abc import ABC, abstractmethod
-from enum import Enum
-from functools import total_ordering
-from pathlib import Path
-from typing import Any, Callable, List, Optional
-
-from pydantic import BaseModel, Field, PrivateAttr
-from pydantic.networks import AnyHttpUrl
-
-
-class DownloadJobStatus(str, Enum):
-    """State of a download job."""
-
-    WAITING = "waiting"  # not enqueued, will not run
-    RUNNING = "running"  # actively downloading
-    COMPLETED = "completed"  # finished running
-    CANCELLED = "cancelled"  # user cancelled
-    ERROR = "error"  # terminated with an error message
-
-
-class DownloadJobCancelledException(Exception):
-    """This exception is raised when a download job is cancelled."""
-
-
-class UnknownJobIDException(Exception):
-    """This exception is raised when an invalid job id is referened."""
-
-
-class ServiceInactiveException(Exception):
-    """This exception is raised when user attempts to initiate a download before the service is started."""
-
-
-DownloadEventHandler = Callable[["DownloadJob"], None]
-
-
-@total_ordering
-class DownloadJob(BaseModel):
-    """Class to monitor and control a model download request."""
-
-    # required variables to be passed in on creation
-    source: AnyHttpUrl = Field(description="Where to download from. Specific types specified in child classes.")
-    dest: Path = Field(description="Destination of downloaded model on local disk; a directory or file path")
-    access_token: Optional[str] = Field(default=None, description="authorization token for protected resources")
-    # automatically assigned on creation
-    id: int = Field(description="Numeric ID of this job", default=-1)  # default id is a sentinel
-    priority: int = Field(default=10, description="Queue priority; lower values are higher priority")
-
-    # set internally during download process
-    status: DownloadJobStatus = Field(default=DownloadJobStatus.WAITING, description="Status of the download")
-    download_path: Optional[Path] = Field(default=None, description="Final location of downloaded file")
-    job_started: Optional[str] = Field(default=None, description="Timestamp for when the download job started")
-    job_ended: Optional[str] = Field(
-        default=None, description="Timestamp for when the download job ende1d (completed or errored)"
-    )
-    bytes: int = Field(default=0, description="Bytes downloaded so far")
-    total_bytes: int = Field(default=0, description="Total file size (bytes)")
-
-    # set when an error occurs
-    error_type: Optional[str] = Field(default=None, description="Name of exception that caused an error")
-    error: Optional[str] = Field(default=None, description="Traceback of the exception that caused an error")
-
-    # internal flag
-    _cancelled: bool = PrivateAttr(default=False)
-
-    # optional event handlers passed in on creation
-    _on_start: Optional[DownloadEventHandler] = PrivateAttr(default=None)
-    _on_progress: Optional[DownloadEventHandler] = PrivateAttr(default=None)
-    _on_complete: Optional[DownloadEventHandler] = PrivateAttr(default=None)
-    _on_cancelled: Optional[DownloadEventHandler] = PrivateAttr(default=None)
-    _on_error: Optional[DownloadEventHandler] = PrivateAttr(default=None)
-
-    def __le__(self, other: "DownloadJob") -> bool:
-        """Return True if this job's priority is less than another's."""
-        return self.priority <= other.priority
-
-    def cancel(self) -> None:
-        """Call to cancel the job."""
-        self._cancelled = True
-
-    # cancelled and the callbacks are private attributes in order to prevent
-    # them from being serialized and/or used in the Json Schema
-    @property
-    def cancelled(self) -> bool:
-        """Call to cancel the job."""
-        return self._cancelled
-
-    @property
-    def on_start(self) -> Optional[DownloadEventHandler]:
-        """Return the on_start event handler."""
-        return self._on_start
-
-    @property
-    def on_progress(self) -> Optional[DownloadEventHandler]:
-        """Return the on_progress event handler."""
-        return self._on_progress
-
-    @property
-    def on_complete(self) -> Optional[DownloadEventHandler]:
-        """Return the on_complete event handler."""
-        return self._on_complete
-
-    @property
-    def on_error(self) -> Optional[DownloadEventHandler]:
-        """Return the on_error event handler."""
-        return self._on_error
-
-    @property
-    def on_cancelled(self) -> Optional[DownloadEventHandler]:
-        """Return the on_cancelled event handler."""
-        return self._on_cancelled
-
-    def set_callbacks(
-        self,
-        on_start: Optional[DownloadEventHandler] = None,
-        on_progress: Optional[DownloadEventHandler] = None,
-        on_complete: Optional[DownloadEventHandler] = None,
-        on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadEventHandler] = None,
-    ) -> None:
-        """Set the callbacks for download events."""
-        self._on_start = on_start
-        self._on_progress = on_progress
-        self._on_complete = on_complete
-        self._on_error = on_error
-        self._on_cancelled = on_cancelled
-
-
-class DownloadQueueServiceBase(ABC):
-    """Multithreaded queue for downloading models via URL."""
-
-    @abstractmethod
-    def start(self, *args: Any, **kwargs: Any) -> None:
-        """Start the download worker threads."""
-
-    @abstractmethod
-    def stop(self, *args: Any, **kwargs: Any) -> None:
-        """Stop the download worker threads."""
-
-    @abstractmethod
-    def download(
-        self,
-        source: AnyHttpUrl,
-        dest: Path,
-        priority: int = 10,
-        access_token: Optional[str] = None,
-        on_start: Optional[DownloadEventHandler] = None,
-        on_progress: Optional[DownloadEventHandler] = None,
-        on_complete: Optional[DownloadEventHandler] = None,
-        on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadEventHandler] = None,
-    ) -> DownloadJob:
-        """
-        Create a download job.
-
-        :param source: Source of the download as a URL.
-        :param dest: Path to download to. See below.
-        :param on_start, on_progress, on_complete, on_error: Callbacks for the indicated
-         events.
-        :returns: A DownloadJob object for monitoring the state of the download.
-
-        The `dest` argument is a Path object. Its behavior is:
-
-        1. If the path exists and is a directory, then the URL contents will be downloaded
-            into that directory using the filename indicated in the response's `Content-Disposition` field.
-            If no content-disposition is present, then the last component of the URL will be used (similar to
-            wget's behavior).
-        2. If the path does not exist, then it is taken as the name of a new file to create with the downloaded
-           content.
-        3. If the path exists and is an existing file, then the downloader will try to resume the download from
-           the end of the existing file.
-
-        """
-        pass
-
-    @abstractmethod
-    def list_jobs(self) -> List[DownloadJob]:
-        """
-        List active download jobs.
-
-        :returns List[DownloadJob]: List of download jobs whose state is not "completed."
-        """
-        pass
-
-    @abstractmethod
-    def id_to_job(self, id: int) -> DownloadJob:
-        """
-        Return the DownloadJob corresponding to the integer ID.
-
-        :param id: ID of the DownloadJob.
-
-        Exceptions:
-        * UnknownJobIDException
-        """
-        pass
-
-    @abstractmethod
-    def cancel_all_jobs(self):
-        """Cancel all active and enquedjobs."""
-        pass
-
-    @abstractmethod
-    def prune_jobs(self):
-        """Prune completed and errored queue items from the job list."""
-        pass
-
-    @abstractmethod
-    def cancel_job(self, job: DownloadJob):
-        """Cancel the job, clearing partial downloads and putting it into ERROR state."""
-        pass
-
-    @abstractmethod
-    def join(self):
-        """Wait until all jobs are off the queue."""
-        pass

invokeai/app/services/download/download_default.py

@@ -1,418 +0,0 @@
-# Copyright (c) 2023, Lincoln D. Stein
-"""Implementation of multithreaded download queue for invokeai."""
-
-import os
-import re
-import threading
-import traceback
-from logging import Logger
-from pathlib import Path
-from queue import Empty, PriorityQueue
-from typing import Any, Dict, List, Optional, Set
-
-import requests
-from pydantic.networks import AnyHttpUrl
-from requests import HTTPError
-from tqdm import tqdm
-
-from invokeai.app.services.events.events_base import EventServiceBase
-from invokeai.app.util.misc import get_iso_timestamp
-from invokeai.backend.util.logging import InvokeAILogger
-
-from .download_base import (
-    DownloadEventHandler,
-    DownloadJob,
-    DownloadJobCancelledException,
-    DownloadJobStatus,
-    DownloadQueueServiceBase,
-    ServiceInactiveException,
-    UnknownJobIDException,
-)
-
-# Maximum number of bytes to download during each call to requests.iter_content()
-DOWNLOAD_CHUNK_SIZE = 100000
-
-
-class DownloadQueueService(DownloadQueueServiceBase):
-    """Class for queued download of models."""
-
-    _jobs: Dict[int, DownloadJob]
-    _max_parallel_dl: int = 5
-    _worker_pool: Set[threading.Thread]
-    _queue: PriorityQueue[DownloadJob]
-    _stop_event: threading.Event
-    _lock: threading.Lock
-    _logger: Logger
-    _events: Optional[EventServiceBase] = None
-    _next_job_id: int = 0
-    _accept_download_requests: bool = False
-    _requests: requests.sessions.Session
-
-    def __init__(
-        self,
-        max_parallel_dl: int = 5,
-        event_bus: Optional[EventServiceBase] = None,
-        requests_session: Optional[requests.sessions.Session] = None,
-    ):
-        """
-        Initialize DownloadQueue.
-
-        :param max_parallel_dl: Number of simultaneous downloads allowed [5].
-        :param requests_session: Optional requests.sessions.Session object, for unit tests.
-        """
-        self._jobs = {}
-        self._next_job_id = 0
-        self._queue = PriorityQueue()
-        self._stop_event = threading.Event()
-        self._worker_pool = set()
-        self._lock = threading.Lock()
-        self._logger = InvokeAILogger.get_logger("DownloadQueueService")
-        self._event_bus = event_bus
-        self._requests = requests_session or requests.Session()
-        self._accept_download_requests = False
-        self._max_parallel_dl = max_parallel_dl
-
-    def start(self, *args: Any, **kwargs: Any) -> None:
-        """Start the download worker threads."""
-        with self._lock:
-            if self._worker_pool:
-                raise Exception("Attempt to start the download service twice")
-            self._stop_event.clear()
-            self._start_workers(self._max_parallel_dl)
-            self._accept_download_requests = True
-
-    def stop(self, *args: Any, **kwargs: Any) -> None:
-        """Stop the download worker threads."""
-        with self._lock:
-            if not self._worker_pool:
-                raise Exception("Attempt to stop the download service before it was started")
-            self._accept_download_requests = False  # reject attempts to add new jobs to queue
-            queued_jobs = [x for x in self.list_jobs() if x.status == DownloadJobStatus.WAITING]
-            active_jobs = [x for x in self.list_jobs() if x.status == DownloadJobStatus.RUNNING]
-            if queued_jobs:
-                self._logger.warning(f"Cancelling {len(queued_jobs)} queued downloads")
-            if active_jobs:
-                self._logger.info(f"Waiting for {len(active_jobs)} active download jobs to complete")
-            with self._queue.mutex:
-                self._queue.queue.clear()
-            self.join()  # wait for all active jobs to finish
-            self._stop_event.set()
-            self._worker_pool.clear()
-
-    def download(
-        self,
-        source: AnyHttpUrl,
-        dest: Path,
-        priority: int = 10,
-        access_token: Optional[str] = None,
-        on_start: Optional[DownloadEventHandler] = None,
-        on_progress: Optional[DownloadEventHandler] = None,
-        on_complete: Optional[DownloadEventHandler] = None,
-        on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadEventHandler] = None,
-    ) -> DownloadJob:
-        """Create a download job and return its ID."""
-        if not self._accept_download_requests:
-            raise ServiceInactiveException(
-                "The download service is not currently accepting requests. Please call start() to initialize the service."
-            )
-        with self._lock:
-            id = self._next_job_id
-            self._next_job_id += 1
-            job = DownloadJob(
-                id=id,
-                source=source,
-                dest=dest,
-                priority=priority,
-                access_token=access_token,
-            )
-            job.set_callbacks(
-                on_start=on_start,
-                on_progress=on_progress,
-                on_complete=on_complete,
-                on_cancelled=on_cancelled,
-                on_error=on_error,
-            )
-            self._jobs[id] = job
-            self._queue.put(job)
-        return job
-
-    def join(self) -> None:
-        """Wait for all jobs to complete."""
-        self._queue.join()
-
-    def list_jobs(self) -> List[DownloadJob]:
-        """List all the jobs."""
-        return list(self._jobs.values())
-
-    def prune_jobs(self) -> None:
-        """Prune completed and errored queue items from the job list."""
-        with self._lock:
-            to_delete = set()
-            for job_id, job in self._jobs.items():
-                if self._in_terminal_state(job):
-                    to_delete.add(job_id)
-            for job_id in to_delete:
-                del self._jobs[job_id]
-
-    def id_to_job(self, id: int) -> DownloadJob:
-        """Translate a job ID into a DownloadJob object."""
-        try:
-            return self._jobs[id]
-        except KeyError as excp:
-            raise UnknownJobIDException("Unrecognized job") from excp
-
-    def cancel_job(self, job: DownloadJob) -> None:
-        """
-        Cancel the indicated job.
-
-        If it is running it will be stopped.
-        job.status will be set to DownloadJobStatus.CANCELLED
-        """
-        with self._lock:
-            job.cancel()
-
-    def cancel_all_jobs(self, preserve_partial: bool = False) -> None:
-        """Cancel all jobs (those not in enqueued, running or paused state)."""
-        for job in self._jobs.values():
-            if not self._in_terminal_state(job):
-                self.cancel_job(job)
-
-    def _in_terminal_state(self, job: DownloadJob) -> bool:
-        return job.status in [
-            DownloadJobStatus.COMPLETED,
-            DownloadJobStatus.CANCELLED,
-            DownloadJobStatus.ERROR,
-        ]
-
-    def _start_workers(self, max_workers: int) -> None:
-        """Start the requested number of worker threads."""
-        self._stop_event.clear()
-        for i in range(0, max_workers):  # noqa B007
-            worker = threading.Thread(target=self._download_next_item, daemon=True)
-            self._logger.debug(f"Download queue worker thread {worker.name} starting.")
-            worker.start()
-            self._worker_pool.add(worker)
-
-    def _download_next_item(self) -> None:
-        """Worker thread gets next job on priority queue."""
-        done = False
-        while not done:
-            if self._stop_event.is_set():
-                done = True
-                continue
-            try:
-                job = self._queue.get(timeout=1)
-            except Empty:
-                continue
-
-            try:
-                job.job_started = get_iso_timestamp()
-                self._do_download(job)
-                self._signal_job_complete(job)
-
-            except (OSError, HTTPError) as excp:
-                job.error_type = excp.__class__.__name__ + f"({str(excp)})"
-                job.error = traceback.format_exc()
-                self._signal_job_error(job)
-            except DownloadJobCancelledException:
-                self._signal_job_cancelled(job)
-                self._cleanup_cancelled_job(job)
-
-            finally:
-                job.job_ended = get_iso_timestamp()
-                self._queue.task_done()
-        self._logger.debug(f"Download queue worker thread {threading.current_thread().name} exiting.")
-
-    def _do_download(self, job: DownloadJob) -> None:
-        """Do the actual download."""
-        url = job.source
-        header = {"Authorization": f"Bearer {job.access_token}"} if job.access_token else {}
-        open_mode = "wb"
-
-        # Make a streaming request. This will retrieve headers including
-        # content-length and content-disposition, but not fetch any content itself
-        resp = self._requests.get(str(url), headers=header, stream=True)
-        if not resp.ok:
-            raise HTTPError(resp.reason)
-        content_length = int(resp.headers.get("content-length", 0))
-        job.total_bytes = content_length
-
-        if job.dest.is_dir():
-            file_name = os.path.basename(str(url.path))  # default is to use the last bit of the URL
-
-            if match := re.search('filename="(.+)"', resp.headers.get("Content-Disposition", "")):
-                remote_name = match.group(1)
-                if self._validate_filename(job.dest.as_posix(), remote_name):
-                    file_name = remote_name
-
-            job.download_path = job.dest / file_name
-
-        else:
-            job.dest.parent.mkdir(parents=True, exist_ok=True)
-            job.download_path = job.dest
-
-        assert job.download_path
-
-        # Don't clobber an existing file. See commit 82c2c85202f88c6d24ff84710f297cfc6ae174af
-        # for code that instead resumes an interrupted download.
-        if job.download_path.exists():
-            raise OSError(f"[Errno 17] File {job.download_path} exists")
-
-        # append ".downloading" to the path
-        in_progress_path = self._in_progress_path(job.download_path)
-
-        # signal caller that the download is starting. At this point, key fields such as
-        # download_path and total_bytes will be populated. We call it here because the might
-        # discover that the local file is already complete and generate a COMPLETED status.
-        self._signal_job_started(job)
-
-        # "range not satisfiable" - local file is at least as large as the remote file
-        if resp.status_code == 416 or (content_length > 0 and job.bytes >= content_length):
-            self._logger.warning(f"{job.download_path}: complete file found. Skipping.")
-            return
-
-        # "partial content" - local file is smaller than remote file
-        elif resp.status_code == 206 or job.bytes > 0:
-            self._logger.warning(f"{job.download_path}: partial file found. Resuming")
-
-        # some other error
-        elif resp.status_code != 200:
-            raise HTTPError(resp.reason)
-
-        self._logger.debug(f"{job.source}: Downloading {job.download_path}")
-        report_delta = job.total_bytes / 100  # report every 1% change
-        last_report_bytes = 0
-
-        # DOWNLOAD LOOP
-        with open(in_progress_path, open_mode) as file:
-            for data in resp.iter_content(chunk_size=DOWNLOAD_CHUNK_SIZE):
-                if job.cancelled:
-                    raise DownloadJobCancelledException("Job was cancelled at caller's request")
-
-                job.bytes += file.write(data)
-                if (job.bytes - last_report_bytes >= report_delta) or (job.bytes >= job.total_bytes):
-                    last_report_bytes = job.bytes
-                    self._signal_job_progress(job)
-
-        # if we get here we are done and can rename the file to the original dest
-        in_progress_path.rename(job.download_path)
-
-    def _validate_filename(self, directory: str, filename: str) -> bool:
-        pc_name_max = os.pathconf(directory, "PC_NAME_MAX") if hasattr(os, "pathconf") else 260  # hardcoded for windows
-        pc_path_max = (
-            os.pathconf(directory, "PC_PATH_MAX") if hasattr(os, "pathconf") else 32767
-        )  # hardcoded for windows with long names enabled
-        if "/" in filename:
-            return False
-        if filename.startswith(".."):
-            return False
-        if len(filename) > pc_name_max:
-            return False
-        if len(os.path.join(directory, filename)) > pc_path_max:
-            return False
-        return True
-
-    def _in_progress_path(self, path: Path) -> Path:
-        return path.with_name(path.name + ".downloading")
-
-    def _signal_job_started(self, job: DownloadJob) -> None:
-        job.status = DownloadJobStatus.RUNNING
-        if job.on_start:
-            try:
-                job.on_start(job)
-            except Exception as e:
-                self._logger.error(e)
-        if self._event_bus:
-            assert job.download_path
-            self._event_bus.emit_download_started(str(job.source), job.download_path.as_posix())
-
-    def _signal_job_progress(self, job: DownloadJob) -> None:
-        if job.on_progress:
-            try:
-                job.on_progress(job)
-            except Exception as e:
-                self._logger.error(e)
-        if self._event_bus:
-            assert job.download_path
-            self._event_bus.emit_download_progress(
-                str(job.source),
-                download_path=job.download_path.as_posix(),
-                current_bytes=job.bytes,
-                total_bytes=job.total_bytes,
-            )
-
-    def _signal_job_complete(self, job: DownloadJob) -> None:
-        job.status = DownloadJobStatus.COMPLETED
-        if job.on_complete:
-            try:
-                job.on_complete(job)
-            except Exception as e:
-                self._logger.error(e)
-        if self._event_bus:
-            assert job.download_path
-            self._event_bus.emit_download_complete(
-                str(job.source), download_path=job.download_path.as_posix(), total_bytes=job.total_bytes
-            )
-
-    def _signal_job_cancelled(self, job: DownloadJob) -> None:
-        job.status = DownloadJobStatus.CANCELLED
-        if job.on_cancelled:
-            try:
-                job.on_cancelled(job)
-            except Exception as e:
-                self._logger.error(e)
-        if self._event_bus:
-            self._event_bus.emit_download_cancelled(str(job.source))
-
-    def _signal_job_error(self, job: DownloadJob) -> None:
-        job.status = DownloadJobStatus.ERROR
-        if job.on_error:
-            try:
-                job.on_error(job)
-            except Exception as e:
-                self._logger.error(e)
-        if self._event_bus:
-            assert job.error_type
-            assert job.error
-            self._event_bus.emit_download_error(str(job.source), error_type=job.error_type, error=job.error)
-
-    def _cleanup_cancelled_job(self, job: DownloadJob) -> None:
-        self._logger.warning(f"Cleaning up leftover files from cancelled download job {job.download_path}")
-        try:
-            if job.download_path:
-                partial_file = self._in_progress_path(job.download_path)
-                partial_file.unlink()
-        except OSError as excp:
-            self._logger.warning(excp)
-
-
-# Example on_progress event handler to display a TQDM status bar
-# Activate with:
-#   download_service.download('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().job_update
-class TqdmProgress(object):
-    """TQDM-based progress bar object to use in on_progress handlers."""
-
-    _bars: Dict[int, tqdm]  # the tqdm object
-    _last: Dict[int, int]  # last bytes downloaded
-
-    def __init__(self) -> None:  # noqa D107
-        self._bars = {}
-        self._last = {}
-
-    def update(self, job: DownloadJob) -> None:  # noqa D102
-        job_id = job.id
-        # new job
-        if job_id not in self._bars:
-            assert job.download_path
-            dest = Path(job.download_path).name
-            self._bars[job_id] = tqdm(
-                desc=dest,
-                initial=0,
-                total=job.total_bytes,
-                unit="iB",
-                unit_scale=True,
-            )
-            self._last[job_id] = 0
-        self._bars[job_id].update(job.bytes - self._last[job_id])
-        self._last[job_id] = job.bytes

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
@@ -17,8 +16,6 @@ from invokeai.backend.model_management.models.base import BaseModelType, ModelTy
 
 class EventServiceBase:
     queue_event: str = "queue_event"
-    download_event: str = "download_event"
-    model_event: str = "model_event"
 
     """Basic event bus, to have an empty stand-in when not needed"""
 
@@ -33,20 +30,6 @@ class EventServiceBase:
             payload={"event": event_name, "data": payload},
         )
 
-    def __emit_download_event(self, event_name: str, payload: dict) -> None:
-        payload["timestamp"] = get_timestamp()
-        self.dispatch(
-            event_name=EventServiceBase.download_event,
-            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(
@@ -330,146 +313,3 @@ class EventServiceBase:
             event_name="queue_cleared",
             payload={"queue_id": queue_id},
         )
-
-    def emit_download_started(self, source: str, download_path: str) -> None:
-        """
-        Emit when a download job is started.
-
-        :param url: The downloaded url
-        """
-        self.__emit_download_event(
-            event_name="download_started",
-            payload={"source": source, "download_path": download_path},
-        )
-
-    def emit_download_progress(self, source: str, download_path: str, current_bytes: int, total_bytes: int) -> None:
-        """
-        Emit "download_progress" events at regular intervals during a download job.
-
-        :param source: The downloaded source
-        :param download_path: The local downloaded file
-        :param current_bytes: Number of bytes downloaded so far
-        :param total_bytes: The size of the file being downloaded (if known)
-        """
-        self.__emit_download_event(
-            event_name="download_progress",
-            payload={
-                "source": source,
-                "download_path": download_path,
-                "current_bytes": current_bytes,
-                "total_bytes": total_bytes,
-            },
-        )
-
-    def emit_download_complete(self, source: str, download_path: str, total_bytes: int) -> None:
-        """
-        Emit a "download_complete" event at the end of a successful download.
-
-        :param source: Source URL
-        :param download_path: Path to the locally downloaded file
-        :param total_bytes: The size of the downloaded file
-        """
-        self.__emit_download_event(
-            event_name="download_complete",
-            payload={
-                "source": source,
-                "download_path": download_path,
-                "total_bytes": total_bytes,
-            },
-        )
-
-    def emit_download_cancelled(self, source: str) -> None:
-        """Emit a "download_cancelled" event in the event that the download was cancelled by user."""
-        self.__emit_download_event(
-            event_name="download_cancelled",
-            payload={
-                "source": source,
-            },
-        )
-
-    def emit_download_error(self, source: str, error_type: str, error: str) -> None:
-        """
-        Emit a "download_error" event when an download job encounters an exception.
-
-        :param source: Source URL
-        :param error_type: The name of the exception that raised the error
-        :param error: The traceback from this error
-        """
-        self.__emit_download_event(
-            event_name="download_error",
-            payload={
-                "source": source,
-                "error_type": error_type,
-                "error": error,
-            },
-        )
-
-    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_records/image_records_sqlite.py

@@ -32,6 +32,101 @@ 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;
+            """
+        )
+
+        self._cursor.execute("PRAGMA table_info(images)")
+        columns = [column[1] for column in self._cursor.fetchall()]
+        if "has_workflow" not in columns:
+            self._cursor.execute(
+                """--sql
+                ALTER TABLE images
+                ADD COLUMN has_workflow BOOLEAN DEFAULT FALSE;
+                """
+            )
+
     def get(self, image_name: str) -> ImageRecord:
         try:
             self._lock.acquire()

invokeai/app/services/invocation_services.py

@@ -11,7 +11,6 @@ if TYPE_CHECKING:
     from .board_records.board_records_base import BoardRecordStorageBase
     from .boards.boards_base import BoardServiceABC
     from .config import InvokeAIAppConfig
-    from .download import DownloadQueueServiceBase
     from .events.events_base import EventServiceBase
     from .image_files.image_files_base import ImageFileStorageBase
     from .image_records.image_records_base import ImageRecordStorageBase
@@ -22,13 +21,12 @@ 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
     from .session_processor.session_processor_base import SessionProcessorBase
     from .session_queue.session_queue_base import SessionQueueBase
-    from .shared.graph import GraphExecutionState
+    from .shared.graph import GraphExecutionState, LibraryGraph
     from .urls.urls_base import UrlServiceBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
@@ -44,6 +42,7 @@ class InvocationServices:
     configuration: "InvokeAIAppConfig"
     events: "EventServiceBase"
     graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
+    graph_library: "ItemStorageABC[LibraryGraph]"
     images: "ImageServiceABC"
     image_records: "ImageRecordStorageBase"
     image_files: "ImageFileStorageBase"
@@ -51,8 +50,6 @@ class InvocationServices:
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
     model_records: "ModelRecordServiceBase"
-    download_queue: "DownloadQueueServiceBase"
-    model_install: "ModelInstallServiceBase"
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
@@ -72,6 +69,7 @@ class InvocationServices:
         configuration: "InvokeAIAppConfig",
         events: "EventServiceBase",
         graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
+        graph_library: "ItemStorageABC[LibraryGraph]",
         images: "ImageServiceABC",
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
@@ -79,8 +77,6 @@ class InvocationServices:
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         model_records: "ModelRecordServiceBase",
-        download_queue: "DownloadQueueServiceBase",
-        model_install: "ModelInstallServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
@@ -98,6 +94,7 @@ class InvocationServices:
         self.configuration = configuration
         self.events = events
         self.graph_execution_manager = graph_execution_manager
+        self.graph_library = graph_library
         self.images = images
         self.image_files = image_files
         self.image_records = image_records
@@ -105,8 +102,6 @@ class InvocationServices:
         self.logger = logger
         self.model_manager = model_manager
         self.model_records = model_records
-        self.download_queue = download_queue
-        self.model_install = model_install
         self.processor = processor
         self.performance_statistics = performance_statistics
         self.queue = queue

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

@@ -1,305 +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.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.
-        """
-
-    @abstractmethod
-    def start(self, *args: Any, **kwarg: Any) -> None:
-        """Start the installer service."""
-
-    @abstractmethod
-    def stop(self, *args: Any, **kwarg: Any) -> 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

@@ -1,399 +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()
-
-    @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 start(self, *args: Any, **kwarg: Any) -> None:
-        """Start the installer thread."""
-        self._start_installer_thread()
-        self.sync_to_config()
-
-    def stop(self, *args: Any, **kwarg: Any) -> None:
-        """Stop the installer 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

@@ -7,7 +7,10 @@ from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import List, Optional, Union
 
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType
+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):
@@ -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:
         """
@@ -106,7 +115,6 @@ class ModelRecordServiceBase(ABC):
         model_name: Optional[str] = None,
         base_model: Optional[BaseModelType] = None,
         model_type: Optional[ModelType] = None,
-        model_format: Optional[ModelFormat] = None,
     ) -> List[AnyModelConfig]:
         """
         Return models matching name, base and/or type.
@@ -114,7 +122,6 @@ class ModelRecordServiceBase(ABC):
         :param model_name: Filter by name of model (optional)
         :param base_model: Filter by base model (optional)
         :param model_type: Filter by type of model (optional)
-        :param model_format: Filter by model format (e.g. "diffusers") (optional)
 
         If none of the optional filters are passed, will return all
         models in the database.

invokeai/app/services/model_records/model_records_sql.py

@@ -49,12 +49,12 @@ from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
     ModelConfigFactory,
-    ModelFormat,
     ModelType,
 )
 
 from ..shared.sqlite.sqlite_database import SqliteDatabase
 from .model_records_base import (
+    CONFIG_FILE_VERSION,
     DuplicateModelException,
     ModelRecordServiceBase,
     UnknownModelException,
@@ -78,6 +78,85 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         self._db = db
         self._cursor = self._db.conn.cursor()
 
+        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, AnyModelConfig]) -> AnyModelConfig:
         """
         Add a model to the database.
@@ -96,13 +175,21 @@ 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,
                     ),
@@ -127,6 +214,22 @@ 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.
@@ -166,11 +269,14 @@ 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")
@@ -226,7 +332,6 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         model_name: Optional[str] = None,
         base_model: Optional[BaseModelType] = None,
         model_type: Optional[ModelType] = None,
-        model_format: Optional[ModelFormat] = None,
     ) -> List[AnyModelConfig]:
         """
         Return models matching name, base and/or type.
@@ -234,7 +339,6 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         :param model_name: Filter by name of model (optional)
         :param base_model: Filter by base model (optional)
         :param model_type: Filter by type of model (optional)
-        :param model_format: Filter by model format (e.g. "diffusers") (optional)
 
         If none of the optional filters are passed, will return all
         models in the database.
@@ -251,9 +355,6 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         if model_type:
             where_clause.append("type=?")
             bindings.append(model_type)
-        if model_format:
-            where_clause.append("format=?")
-            bindings.append(model_format)
         where = f"WHERE {' AND '.join(where_clause)}" if where_clause else ""
         with self._db.lock:
             self._cursor.execute(
@@ -273,7 +374,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             self._cursor.execute(
                 """--sql
                 SELECT config FROM model_config
-                WHERE path=?;
+                WHERE model_path=?;
                 """,
                 (str(path),),
             )

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -50,6 +50,7 @@ class SqliteSessionQueue(SessionQueueBase):
         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 +98,123 @@ 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.__cursor.execute("PRAGMA table_info(session_queue)")
+            columns = [column[1] for column in self.__cursor.fetchall()]
+            if "workflow" not in columns:
+                self.__cursor.execute(
+                    """--sql
+                    ALTER TABLE session_queue ADD COLUMN workflow TEXT;
+                    """
+                )
+
+            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.

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

@@ -3,65 +3,45 @@ import threading
 from logging import Logger
 from pathlib import Path
 
+from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.shared.sqlite.sqlite_common import sqlite_memory
 
 
 class SqliteDatabase:
-    """
-    Manages a connection to an SQLite database.
+    def __init__(self, config: InvokeAIAppConfig, logger: Logger):
+        self._logger = logger
+        self._config = config
 
-    :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")
+        if self._config.use_memory_db:
+            self.db_path = sqlite_memory
+            logger.info("Using in-memory database")
         else:
-            self.db_path.parent.mkdir(parents=True, exist_ok=True)
-            self.logger.info(f"Initializing database at {self.db_path}")
+            db_path = self._config.db_path
+            db_path.parent.mkdir(parents=True, exist_ok=True)
+            self.db_path = str(db_path)
+            self._logger.info(f"Using database at {self.db_path}")
 
-        self.conn = sqlite3.connect(database=self.db_path or sqlite_memory, check_same_thread=False)
+        self.conn = sqlite3.connect(self.db_path, 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)
+        if self._config.log_sql:
+            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:
+                if self.db_path == sqlite_memory:
+                    return
                 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)")
+                    self._logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
             except Exception as e:
-                self.logger.error(f"Error cleaning database: {e}")
+                self._logger.error(f"Error cleaning database: {e}")
                 raise

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

@@ -1,34 +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.migrations.migration_3 import build_migration_3
-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.register_migration(build_migration_3())
-    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

@@ -1,209 +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,
-)
-
-from .util.migrate_yaml_config_1 import MigrateModelYamlToDb1
-
-
-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_model_config_records(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_model_config_records(self, cursor: sqlite3.Cursor) -> None:
-        """After updating the model config table, we repopulate it."""
-        model_record_migrator = MigrateModelYamlToDb1(cursor)
-        model_record_migrator.migrate()
-
-    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
-            except Exception as e:
-                self._logger.warning(f"Error while checking image {image_name}, skipping: {e}")
-                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/migrations/migration_3.py

@@ -1,75 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-from .util.migrate_yaml_config_1 import MigrateModelYamlToDb1
-
-
-class Migration3Callback:
-    def __init__(self) -> None:
-        pass
-
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._drop_model_manager_metadata(cursor)
-        self._recreate_model_config(cursor)
-        self._migrate_model_config_records(cursor)
-
-    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_model_config_records(self, cursor: sqlite3.Cursor) -> None:
-        """After updating the model config table, we repopulate it."""
-        model_record_migrator = MigrateModelYamlToDb1(cursor)
-        model_record_migrator.migrate()
-
-
-def build_migration_3() -> Migration:
-    """
-    Build the migration from database version 2 to 3.
-
-    This migration does the following:
-    - Drops the `model_config` table, recreating it
-    - Migrates data from `models.yaml` into the `model_config` table
-    """
-    migration_3 = Migration(
-        from_version=2,
-        to_version=3,
-        callback=Migration3Callback(),
-    )
-
-    return migration_3

invokeai/app/services/shared/sqlite_migrator/migrations/util/migrate_yaml_config_1.py

@@ -1,148 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein
-"""Migrate from the InvokeAI v2 models.yaml format to the v3 sqlite format."""
-
-import json
-import sqlite3
-from hashlib import sha1
-from logging import Logger
-from pathlib import Path
-from typing import Optional
-
-from omegaconf import DictConfig, OmegaConf
-from pydantic import TypeAdapter
-
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.model_records import (
-    DuplicateModelException,
-    UnknownModelException,
-)
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    BaseModelType,
-    ModelConfigFactory,
-    ModelType,
-)
-from invokeai.backend.model_manager.hash import FastModelHash
-from invokeai.backend.util.logging import InvokeAILogger
-
-ModelsValidator = TypeAdapter(AnyModelConfig)
-
-
-class MigrateModelYamlToDb1:
-    """
-    Migrate the InvokeAI models.yaml format (VERSION 3.0.0) to SQL3 database format (VERSION 3.5.0).
-
-    The class has one externally useful method, migrate(), which scans the
-    currently models.yaml file and imports all its entries into invokeai.db.
-
-    Use this way:
-
-      from invokeai.backend.model_manager/migrate_to_db import MigrateModelYamlToDb
-      MigrateModelYamlToDb().migrate()
-
-    """
-
-    config: InvokeAIAppConfig
-    logger: Logger
-    cursor: sqlite3.Cursor
-
-    def __init__(self, cursor: sqlite3.Cursor = None) -> None:
-        self.config = InvokeAIAppConfig.get_config()
-        self.config.parse_args()
-        self.logger = InvokeAILogger.get_logger()
-        self.cursor = cursor
-
-    def get_yaml(self) -> DictConfig:
-        """Fetch the models.yaml DictConfig for this installation."""
-        yaml_path = self.config.model_conf_path
-        omegaconf = OmegaConf.load(yaml_path)
-        assert isinstance(omegaconf, DictConfig)
-        return omegaconf
-
-    def migrate(self) -> None:
-        """Do the migration from models.yaml to invokeai.db."""
-        try:
-            yaml = self.get_yaml()
-        except OSError:
-            return
-
-        for model_key, stanza in yaml.items():
-            if model_key == "__metadata__":
-                assert (
-                    stanza["version"] == "3.0.0"
-                ), f"This script works on version 3.0.0 yaml files, but your configuration points to a {stanza['version']} version"
-                continue
-
-            base_type, model_type, model_name = str(model_key).split("/")
-            hash = FastModelHash.hash(self.config.models_path / stanza.path)
-            assert isinstance(model_key, str)
-            new_key = sha1(model_key.encode("utf-8")).hexdigest()
-
-            stanza["base"] = BaseModelType(base_type)
-            stanza["type"] = ModelType(model_type)
-            stanza["name"] = model_name
-            stanza["original_hash"] = hash
-            stanza["current_hash"] = hash
-
-            new_config: AnyModelConfig = ModelsValidator.validate_python(stanza)  # type: ignore # see https://github.com/pydantic/pydantic/discussions/7094
-
-            try:
-                if original_record := self._search_by_path(stanza.path):
-                    key = original_record.key
-                    self.logger.info(f"Updating model {model_name} with information from models.yaml using key {key}")
-                    self._update_model(key, new_config)
-                else:
-                    self.logger.info(f"Adding model {model_name} with key {model_key}")
-                    self._add_model(new_key, new_config)
-            except DuplicateModelException:
-                self.logger.warning(f"Model {model_name} is already in the database")
-            except UnknownModelException:
-                self.logger.warning(f"Model at {stanza.path} could not be found in database")
-
-    def _search_by_path(self, path: Path) -> Optional[AnyModelConfig]:
-        self.cursor.execute(
-            """--sql
-            SELECT config FROM model_config
-            WHERE path=?;
-            """,
-            (str(path),),
-        )
-        results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self.cursor.fetchall()]
-        return results[0] if results else None
-
-    def _update_model(self, key: str, config: AnyModelConfig) -> None:
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
-        self.cursor.execute(
-            """--sql
-            UPDATE model_config
-            SET
-                config=?
-            WHERE id=?;
-            """,
-            (json_serialized, key),
-        )
-        if self.cursor.rowcount == 0:
-            raise UnknownModelException("model not found")
-
-    def _add_model(self, key: str, config: AnyModelConfig) -> None:
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect.
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
-        try:
-            self.cursor.execute(
-                """--sql
-                INSERT INTO model_config (
-                   id,
-                   original_hash,
-                   config
-                  )
-                VALUES (?,?,?);
-                """,
-                (
-                    key,
-                    record.original_hash,
-                    json_serialized,
-                ),
-            )
-        except sqlite3.IntegrityError as exc:
-            raise DuplicateModelException(f"{record.name}: model is already in database") from exc

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_records/default_workflows/Prompt from File.json

@@ -1,975 +0,0 @@
-{
-  "name": "Prompt from File",
-  "author": "InvokeAI",
-  "description": "Sample workflow using Prompt from File node",
-  "version": "0.1.0",
-  "contact": "invoke@invoke.ai",
-  "tags": "text2image, prompt from file, default",
-  "notes": "",
-  "exposedFields": [
-    {
-      "nodeId": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-      "fieldName": "file_path"
-    }
-  ],
-  "meta": {
-    "category": "default",
-    "version": "2.0.0"
-  },
-  "id": "d1609af5-eb0a-4f73-b573-c9af96a8d6bf",
-  "nodes": [
-    {
-      "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
-      "type": "invocation",
-      "data": {
-        "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
-        "type": "compel",
-        "label": "",
-        "isOpen": false,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0",
-        "nodePack": "invokeai",
-        "inputs": {
-          "prompt": {
-            "id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62",
-            "name": "prompt",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            },
-            "value": ""
-          },
-          "clip": {
-            "id": "3f1981c9-d8a9-42eb-a739-4f120eb80745",
-            "name": "clip",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ClipField"
-            }
-          }
-        },
-        "outputs": {
-          "conditioning": {
-            "id": "46205e6c-c5e2-44cb-9c82-1cd20b95674a",
-            "name": "conditioning",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ConditioningField"
-            }
-          }
-        }
-      },
-      "width": 320,
-      "height": 32,
-      "position": {
-        "x": 925,
-        "y": -200
-      }
-    },
-    {
-      "id": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-      "type": "invocation",
-      "data": {
-        "id": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-        "type": "prompt_from_file",
-        "label": "Prompts from File",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.1",
-        "nodePack": "invokeai",
-        "inputs": {
-          "file_path": {
-            "id": "37e37684-4f30-4ec8-beae-b333e550f904",
-            "name": "file_path",
-            "fieldKind": "input",
-            "label": "Prompts File Path",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            },
-            "value": ""
-          },
-          "pre_prompt": {
-            "id": "7de02feb-819a-4992-bad3-72a30920ddea",
-            "name": "pre_prompt",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            },
-            "value": ""
-          },
-          "post_prompt": {
-            "id": "95f191d8-a282-428e-bd65-de8cb9b7513a",
-            "name": "post_prompt",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            },
-            "value": ""
-          },
-          "start_line": {
-            "id": "efee9a48-05ab-4829-8429-becfa64a0782",
-            "name": "start_line",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
-            "value": 1
-          },
-          "max_prompts": {
-            "id": "abebb428-3d3d-49fd-a482-4e96a16fff08",
-            "name": "max_prompts",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
-            "value": 1
-          }
-        },
-        "outputs": {
-          "collection": {
-            "id": "77d5d7f1-9877-4ab1-9a8c-33e9ffa9abf3",
-            "name": "collection",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": true,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            }
-          }
-        }
-      },
-      "width": 320,
-      "height": 580,
-      "position": {
-        "x": 475,
-        "y": -400
-      }
-    },
-    {
-      "id": "1b89067c-3f6b-42c8-991f-e3055789b251",
-      "type": "invocation",
-      "data": {
-        "id": "1b89067c-3f6b-42c8-991f-e3055789b251",
-        "type": "iterate",
-        "label": "",
-        "isOpen": false,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.1.0",
-        "inputs": {
-          "collection": {
-            "id": "4c564bf8-5ed6-441e-ad2c-dda265d5785f",
-            "name": "collection",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": true,
-              "isCollectionOrScalar": false,
-              "name": "CollectionField"
-            }
-          }
-        },
-        "outputs": {
-          "item": {
-            "id": "36340f9a-e7a5-4afa-b4b5-313f4e292380",
-            "name": "item",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "CollectionItemField"
-            }
-          },
-          "index": {
-            "id": "1beca95a-2159-460f-97ff-c8bab7d89336",
-            "name": "index",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
-          },
-          "total": {
-            "id": "ead597b8-108e-4eda-88a8-5c29fa2f8df9",
-            "name": "total",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
-          }
-        }
-      },
-      "width": 320,
-      "height": 32,
-      "position": {
-        "x": 925,
-        "y": -400
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invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json

@@ -1,903 +0,0 @@
-{
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-            "name": "width",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
-          },
-          "height": {
-            "id": "3d00f6c1-84b0-4262-83d9-3bf755babeea",
-            "name": "height",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
-          }
-        }
-      },
-      "width": 320,
-      "height": 32,
-      "position": {
-        "x": 3425,
-        "y": 75
-      }
-    },
-    {
-      "id": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953",
-      "type": "invocation",
-      "data": {
-        "id": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953",
-        "type": "rand_int",
-        "label": "",
-        "isOpen": false,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": false,
-        "version": "1.0.0",
-        "inputs": {
-          "low": {
-            "id": "d25305f3-bfd6-446c-8e2c-0b025ec9e9ad",
-            "name": "low",
-            "fieldKind": "input",
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-            "type": {
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-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
-            "value": 0
-          },
-          "high": {
-            "id": "10376a3d-b8fe-4a51-b81a-ea46d8c12c78",
-            "name": "high",
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-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
-            "value": 2147483647
-          }
-        },
-        "outputs": {
-          "value": {
-            "id": "c64878fa-53b1-4202-b88a-cfb854216a57",
-            "name": "value",
-            "fieldKind": "output",
-            "type": {
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-              "name": "IntegerField"
-            }
-          }
-        }
-      },
-      "width": 320,
-      "height": 32,
-      "position": {
-        "x": 3425,
-        "y": 0
-      }
-    },
-    {
-      "id": "a9683c0a-6b1f-4a5e-8187-c57e764b3400",
-      "type": "invocation",
-      "data": {
-        "id": "a9683c0a-6b1f-4a5e-8187-c57e764b3400",
-        "type": "l2i",
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": false,
-        "useCache": true,
-        "version": "1.2.0",
-        "inputs": {
-          "metadata": {
-            "id": "b1982e8a-14ad-4029-a697-beb30af8340f",
-            "name": "metadata",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "MetadataField"
-            }
-          },
-          "latents": {
-            "id": "f7669388-9f91-46cc-94fc-301fa7041c3e",
-            "name": "latents",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "LatentsField"
-            }
-          },
-          "vae": {
-            "id": "c6f2d4db-4d0a-4e3d-acb4-b5c5a228a3e2",
-            "name": "vae",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "VaeField"
-            }
-          },
-          "tiled": {
-            "id": "19ef7d31-d96f-4e94-b7e5-95914e9076fc",
-            "name": "tiled",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "BooleanField"
-            },
-            "value": false
-          },
-          "fp32": {
-            "id": "a9454533-8ab7-4225-b411-646dc5e76d00",
-            "name": "fp32",
-            "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "BooleanField"
-            },
-            "value": false
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "4f81274e-e216-47f3-9fb6-f97493a40e6f",
-            "name": "image",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ImageField"
-            }
-          },
-          "width": {
-            "id": "61a9acfb-1547-4f1e-8214-e89bd3855ee5",
-            "name": "width",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
-          },
-          "height": {
-            "id": "b15cc793-4172-4b07-bcf4-5627bbc7d0d7",
-            "name": "height",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
-          }
-        }
-      },
-      "width": 320,
-      "height": 267,
-      "position": {
-        "x": 4450,
-        "y": -550
-      }
-    }
-  ],
-  "edges": [
-    {
-      "id": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953-ea18915f-2c5b-4569-b725-8e9e9122e8d3-collapsed",
-      "source": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953",
-      "target": "ea18915f-2c5b-4569-b725-8e9e9122e8d3",
-      "type": "collapsed"
-    },
-    {
-      "id": "reactflow__edge-24e9d7ed-4836-4ec4-8f9e-e747721f9818clip-c41e705b-f2e3-4d1a-83c4-e34bb9344966clip",
-      "source": "24e9d7ed-4836-4ec4-8f9e-e747721f9818",
-      "target": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
-      "type": "default",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
-    },
-    {
-      "id": "reactflow__edge-c41e705b-f2e3-4d1a-83c4-e34bb9344966clip-c3fa6872-2599-4a82-a596-b3446a66cf8bclip",
-      "source": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
-      "target": "c3fa6872-2599-4a82-a596-b3446a66cf8b",
-      "type": "default",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
-    },
-    {
-      "id": "reactflow__edge-24e9d7ed-4836-4ec4-8f9e-e747721f9818unet-c41e705b-f2e3-4d1a-83c4-e34bb9344966unet",
-      "source": "24e9d7ed-4836-4ec4-8f9e-e747721f9818",
-      "target": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
-      "type": "default",
-      "sourceHandle": "unet",
-      "targetHandle": "unet"
-    },
-    {
-      "id": "reactflow__edge-c41e705b-f2e3-4d1a-83c4-e34bb9344966unet-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63unet",
-      "source": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
-      "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
-      "sourceHandle": "unet",
-      "targetHandle": "unet"
-    },
-    {
-      "id": "reactflow__edge-85b77bb2-c67a-416a-b3e8-291abe746c44conditioning-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63negative_conditioning",
-      "source": "85b77bb2-c67a-416a-b3e8-291abe746c44",
-      "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
-      "sourceHandle": "conditioning",
-      "targetHandle": "negative_conditioning"
-    },
-    {
-      "id": "reactflow__edge-c3fa6872-2599-4a82-a596-b3446a66cf8bconditioning-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63positive_conditioning",
-      "source": "c3fa6872-2599-4a82-a596-b3446a66cf8b",
-      "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
-      "sourceHandle": "conditioning",
-      "targetHandle": "positive_conditioning"
-    },
-    {
-      "id": "reactflow__edge-ea18915f-2c5b-4569-b725-8e9e9122e8d3noise-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63noise",
-      "source": "ea18915f-2c5b-4569-b725-8e9e9122e8d3",
-      "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
-      "sourceHandle": "noise",
-      "targetHandle": "noise"
-    },
-    {
-      "id": "reactflow__edge-6fd74a17-6065-47a5-b48b-f4e2b8fa7953value-ea18915f-2c5b-4569-b725-8e9e9122e8d3seed",
-      "source": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953",
-      "target": "ea18915f-2c5b-4569-b725-8e9e9122e8d3",
-      "type": "default",
-      "sourceHandle": "value",
-      "targetHandle": "seed"
-    },
-    {
-      "id": "reactflow__edge-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63latents-a9683c0a-6b1f-4a5e-8187-c57e764b3400latents",
-      "source": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "target": "a9683c0a-6b1f-4a5e-8187-c57e764b3400",
-      "type": "default",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-24e9d7ed-4836-4ec4-8f9e-e747721f9818vae-a9683c0a-6b1f-4a5e-8187-c57e764b3400vae",
-      "source": "24e9d7ed-4836-4ec4-8f9e-e747721f9818",
-      "target": "a9683c0a-6b1f-4a5e-8187-c57e764b3400",
-      "type": "default",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
-    {
-      "id": "reactflow__edge-c41e705b-f2e3-4d1a-83c4-e34bb9344966clip-85b77bb2-c67a-416a-b3e8-291abe746c44clip",
-      "source": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
-      "target": "85b77bb2-c67a-416a-b3e8-291abe746c44",
-      "type": "default",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
-    }
-  ]
-}

invokeai/app/services/workflow_records/workflow_records_common.py

@@ -3,9 +3,10 @@ from enum import Enum
 from typing import Any, Union
 
 import semver
-from pydantic import BaseModel, ConfigDict, Field, JsonValue, TypeAdapter, field_validator
+from pydantic import BaseModel, Field, JsonValue, TypeAdapter, field_validator
 
 from invokeai.app.util.metaenum import MetaEnum
+from invokeai.app.util.misc import uuid_string
 
 __workflow_meta_version__ = semver.Version.parse("1.0.0")
 
@@ -31,13 +32,12 @@ class WorkflowRecordOrderBy(str, Enum, metaclass=MetaEnum):
 class WorkflowCategory(str, Enum, metaclass=MetaEnum):
     User = "user"
     Default = "default"
+    Project = "project"
 
 
 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)."
-    )
+    category: WorkflowCategory = Field(description="The category of the workflow (user or default).")
 
     @field_validator("version")
     def validate_version(cls, version: str):
@@ -65,26 +65,12 @@ class WorkflowWithoutID(BaseModel):
     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.")
+    id: str = Field(default_factory=uuid_string, description="The id of the workflow.")
 
 
 WorkflowValidator = TypeAdapter(Workflow)

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -14,10 +14,9 @@ from invokeai.app.services.workflow_records.workflow_records_common import (
     WorkflowRecordListItemDTO,
     WorkflowRecordListItemDTOValidator,
     WorkflowRecordOrderBy,
+    WorkflowValidator,
     WorkflowWithoutID,
-    WorkflowWithoutIDValidator,
 )
-from invokeai.app.util.misc import uuid_string
 
 
 class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
@@ -26,6 +25,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         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
@@ -66,7 +66,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         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())
+            workflow_with_id = WorkflowValidator.validate_python(workflow.model_dump())
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
@@ -204,8 +204,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
             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())
+                workflow = WorkflowValidator.validate_json(bytes_)
                 workflows.append(workflow)
             # Only default workflows may be managed by this method
             assert all(w.meta.category is WorkflowCategory.Default for w in workflows)
@@ -232,3 +231,87 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
             raise
         finally:
             self._lock.release()
+
+    def _create_tables(self) -> None:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """--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
+                );
+                """
+            )
+
+            self._cursor.execute(
+                """--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;
+                """
+            )
+
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_created_at ON workflow_library(created_at);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_updated_at ON workflow_library(updated_at);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_opened_at ON workflow_library(opened_at);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_category ON workflow_library(category);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_name ON workflow_library(name);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_description ON workflow_library(description);
+                """
+            )
+
+            # We do not need the original `workflows` table or `workflow_images` junction table.
+            self._cursor.execute(
+                """--sql
+                DROP TABLE IF EXISTS workflow_images;
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                DROP TABLE IF EXISTS workflows;
+                """
+            )
+
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+        finally:
+            self._lock.release()

invokeai/app/util/ti_utils.py

@@ -1,8 +0,0 @@
-import re
-
-
-def extract_ti_triggers_from_prompt(prompt: str) -> list[str]:
-    ti_triggers = []
-    for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
-        ti_triggers.append(trigger)
-    return ti_triggers

invokeai/backend/install/check_root.py

@@ -28,7 +28,7 @@ def check_invokeai_root(config: InvokeAIAppConfig):
         print("== STARTUP ABORTED ==")
         print("** One or more necessary files is missing from your InvokeAI root directory **")
         print("** Please rerun the configuration script to fix this problem. **")
-        print("** From the launcher, selection option [6]. **")
+        print("** From the launcher, selection option [7]. **")
         print(
             '** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **'
         )

invokeai/backend/model_management/lora.py

@@ -215,9 +215,7 @@ class ModelPatcher:
             text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
             model_embeddings = text_encoder.get_input_embeddings()
 
-            for ti_name, ti in ti_list:
-                ti_embedding = _get_ti_embedding(text_encoder.get_input_embeddings(), ti)
-
+            for ti_name, _ in ti_list:
                 ti_tokens = []
                 for i in range(ti_embedding.shape[0]):
                     embedding = ti_embedding[i]

invokeai/backend/model_management/model_probe.py

@@ -32,8 +32,6 @@ class ModelProbeInfo(object):
     upcast_attention: bool
     format: Literal["diffusers", "checkpoint", "lycoris", "olive", "onnx"]
     image_size: int
-    name: Optional[str] = None
-    description: Optional[str] = None
 
 
 class ProbeBase(object):
@@ -115,16 +113,12 @@ class ModelProbe(object):
             base_type = probe.get_base_type()
             variant_type = probe.get_variant_type()
             prediction_type = probe.get_scheduler_prediction_type()
-            name = cls.get_model_name(model_path)
-            description = f"{base_type.value} {model_type.value} model {name}"
             format = probe.get_format()
             model_info = ModelProbeInfo(
                 model_type=model_type,
                 base_type=base_type,
                 variant_type=variant_type,
                 prediction_type=prediction_type,
-                name=name,
-                description=description,
                 upcast_attention=(
                     base_type == BaseModelType.StableDiffusion2
                     and prediction_type == SchedulerPredictionType.VPrediction
@@ -148,13 +142,6 @@ class ModelProbe(object):
 
         return model_info
 
-    @classmethod
-    def get_model_name(cls, model_path: Path) -> str:
-        if model_path.suffix in {".safetensors", ".bin", ".pt", ".ckpt"}:
-            return model_path.stem
-        else:
-            return model_path.name
-
     @classmethod
     def get_model_type_from_checkpoint(cls, model_path: Path, checkpoint: dict) -> ModelType:
         if model_path.suffix not in (".bin", ".pt", ".ckpt", ".safetensors", ".pth"):
@@ -389,7 +376,7 @@ class TextualInversionCheckpointProbe(CheckpointProbeBase):
         elif "clip_g" in checkpoint:
             token_dim = checkpoint["clip_g"].shape[-1]
         else:
-            token_dim = list(checkpoint.values())[0].shape[-1]
+            token_dim = list(checkpoint.values())[0].shape[0]
         if token_dim == 768:
             return BaseModelType.StableDiffusion1
         elif token_dim == 1024:

invokeai/backend/model_management/util.py

@@ -9,7 +9,7 @@ def lora_token_vector_length(checkpoint: dict) -> int:
     :param checkpoint: The checkpoint
     """
 
-    def _get_shape_1(key: str, tensor, checkpoint) -> int:
+    def _get_shape_1(key, tensor, checkpoint):
         lora_token_vector_length = None
 
         if "." not in key:
@@ -57,10 +57,6 @@ def lora_token_vector_length(checkpoint: dict) -> int:
     for key, tensor in checkpoint.items():
         if key.startswith("lora_unet_") and ("_attn2_to_k." in key or "_attn2_to_v." in key):
             lora_token_vector_length = _get_shape_1(key, tensor, checkpoint)
-        elif key.startswith("lora_unet_") and (
-            "time_emb_proj.lora_down" in key
-        ):  # recognizes format at https://civitai.com/models/224641
-            lora_token_vector_length = _get_shape_1(key, tensor, checkpoint)
         elif key.startswith("lora_te") and "_self_attn_" in key:
             tmp_length = _get_shape_1(key, tensor, checkpoint)
             if key.startswith("lora_te_"):

invokeai/backend/model_manager/__init__.py

@@ -1,29 +0,0 @@
-"""Re-export frequently-used symbols from the Model Manager backend."""
-
-from .config import (
-    AnyModelConfig,
-    BaseModelType,
-    InvalidModelConfigException,
-    ModelConfigFactory,
-    ModelFormat,
-    ModelType,
-    ModelVariantType,
-    SchedulerPredictionType,
-    SubModelType,
-)
-from .probe import ModelProbe
-from .search import ModelSearch
-
-__all__ = [
-    "ModelProbe",
-    "ModelSearch",
-    "InvalidModelConfigException",
-    "ModelConfigFactory",
-    "BaseModelType",
-    "ModelType",
-    "SubModelType",
-    "ModelVariantType",
-    "ModelFormat",
-    "SchedulerPredictionType",
-    "AnyModelConfig",
-]

invokeai/backend/model_manager/config.py

@@ -23,7 +23,7 @@ from enum import Enum
 from typing import Literal, Optional, Type, Union
 
 from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
-from typing_extensions import Annotated, Any, Dict
+from typing_extensions import Annotated
 
 
 class InvalidModelConfigException(Exception):
@@ -122,7 +122,7 @@ class ModelConfigBase(BaseModel):
         validate_assignment=True,
     )
 
-    def update(self, attributes: Dict[str, Any]) -> None:
+    def update(self, attributes: dict):
         """Update the object with fields in dict."""
         for key, value in attributes.items():
             setattr(self, key, value)  # may raise a validation error
@@ -195,6 +195,8 @@ class MainCheckpointConfig(_CheckpointConfig, _MainConfig):
     """Model config for main checkpoint models."""
 
     type: Literal[ModelType.Main] = ModelType.Main
+    # Note that we do not need prediction_type or upcast_attention here
+    # because they are provided in the checkpoint's own config file.
 
 
 class MainDiffusersConfig(_DiffusersConfig, _MainConfig):

invokeai/backend/model_manager/migrate_to_db.py

@@ -0,0 +1,93 @@
+# Copyright (c) 2023 Lincoln D. Stein
+"""Migrate from the InvokeAI v2 models.yaml format to the v3 sqlite format."""
+
+from hashlib import sha1
+
+from omegaconf import DictConfig, OmegaConf
+from pydantic import TypeAdapter
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.model_records import (
+    DuplicateModelException,
+    ModelRecordServiceSQL,
+)
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    BaseModelType,
+    ModelType,
+)
+from invokeai.backend.model_manager.hash import FastModelHash
+from invokeai.backend.util.logging import InvokeAILogger
+
+ModelsValidator = TypeAdapter(AnyModelConfig)
+
+
+class MigrateModelYamlToDb:
+    """
+    Migrate the InvokeAI models.yaml format (VERSION 3.0.0) to SQL3 database format (VERSION 3.2.0)
+
+    The class has one externally useful method, migrate(), which scans the
+    currently models.yaml file and imports all its entries into invokeai.db.
+
+    Use this way:
+
+      from invokeai.backend.model_manager/migrate_to_db import MigrateModelYamlToDb
+      MigrateModelYamlToDb().migrate()
+
+    """
+
+    config: InvokeAIAppConfig
+    logger: InvokeAILogger
+
+    def __init__(self):
+        self.config = InvokeAIAppConfig.get_config()
+        self.config.parse_args()
+        self.logger = InvokeAILogger.get_logger()
+
+    def get_db(self) -> ModelRecordServiceSQL:
+        """Fetch the sqlite3 database for this installation."""
+        db = SqliteDatabase(self.config, self.logger)
+        return ModelRecordServiceSQL(db)
+
+    def get_yaml(self) -> DictConfig:
+        """Fetch the models.yaml DictConfig for this installation."""
+        yaml_path = self.config.model_conf_path
+        return OmegaConf.load(yaml_path)
+
+    def migrate(self):
+        """Do the migration from models.yaml to invokeai.db."""
+        db = self.get_db()
+        yaml = self.get_yaml()
+
+        for model_key, stanza in yaml.items():
+            if model_key == "__metadata__":
+                assert (
+                    stanza["version"] == "3.0.0"
+                ), f"This script works on version 3.0.0 yaml files, but your configuration points to a {stanza['version']} version"
+                continue
+
+            base_type, model_type, model_name = str(model_key).split("/")
+            hash = FastModelHash.hash(self.config.models_path / stanza.path)
+            new_key = sha1(model_key.encode("utf-8")).hexdigest()
+
+            stanza["base"] = BaseModelType(base_type)
+            stanza["type"] = ModelType(model_type)
+            stanza["name"] = model_name
+            stanza["original_hash"] = hash
+            stanza["current_hash"] = hash
+
+            new_config = ModelsValidator.validate_python(stanza)
+            self.logger.info(f"Adding model {model_name} with key {model_key}")
+            try:
+                db.add_model(new_key, new_config)
+            except DuplicateModelException:
+                self.logger.warning(f"Model {model_name} is already in the database")
+
+
+def main():
+    MigrateModelYamlToDb().migrate()
+
+
+if __name__ == "__main__":
+    main()

invokeai/backend/model_manager/probe.py

@@ -1,686 +0,0 @@
-import json
-import re
-from pathlib import Path
-from typing import Any, Dict, Literal, Optional, Union
-
-import safetensors.torch
-import torch
-from picklescan.scanner import scan_file_path
-
-from invokeai.backend.model_management.models.base import read_checkpoint_meta
-from invokeai.backend.model_management.models.ip_adapter import IPAdapterModelFormat
-from invokeai.backend.model_management.util import lora_token_vector_length
-from invokeai.backend.util.util import SilenceWarnings
-
-from .config import (
-    AnyModelConfig,
-    BaseModelType,
-    InvalidModelConfigException,
-    ModelConfigFactory,
-    ModelFormat,
-    ModelType,
-    ModelVariantType,
-    SchedulerPredictionType,
-)
-from .hash import FastModelHash
-
-CkptType = Dict[str, Any]
-
-LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[SchedulerPredictionType, str]]]] = {
-    BaseModelType.StableDiffusion1: {
-        ModelVariantType.Normal: "v1-inference.yaml",
-        ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
-    },
-    BaseModelType.StableDiffusion2: {
-        ModelVariantType.Normal: {
-            SchedulerPredictionType.Epsilon: "v2-inference.yaml",
-            SchedulerPredictionType.VPrediction: "v2-inference-v.yaml",
-        },
-        ModelVariantType.Inpaint: {
-            SchedulerPredictionType.Epsilon: "v2-inpainting-inference.yaml",
-            SchedulerPredictionType.VPrediction: "v2-inpainting-inference-v.yaml",
-        },
-    },
-    BaseModelType.StableDiffusionXL: {
-        ModelVariantType.Normal: "sd_xl_base.yaml",
-    },
-    BaseModelType.StableDiffusionXLRefiner: {
-        ModelVariantType.Normal: "sd_xl_refiner.yaml",
-    },
-}
-
-
-class ProbeBase(object):
-    """Base class for probes."""
-
-    def __init__(self, model_path: Path):
-        self.model_path = model_path
-
-    def get_base_type(self) -> BaseModelType:
-        """Get model base type."""
-        raise NotImplementedError
-
-    def get_format(self) -> ModelFormat:
-        """Get model file format."""
-        raise NotImplementedError
-
-    def get_variant_type(self) -> Optional[ModelVariantType]:
-        """Get model variant type."""
-        return None
-
-    def get_scheduler_prediction_type(self) -> Optional[SchedulerPredictionType]:
-        """Get model scheduler prediction type."""
-        return None
-
-
-class ModelProbe(object):
-    PROBES: Dict[str, Dict[ModelType, type[ProbeBase]]] = {
-        "diffusers": {},
-        "checkpoint": {},
-        "onnx": {},
-    }
-
-    CLASS2TYPE = {
-        "StableDiffusionPipeline": ModelType.Main,
-        "StableDiffusionInpaintPipeline": ModelType.Main,
-        "StableDiffusionXLPipeline": ModelType.Main,
-        "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
-        "StableDiffusionXLInpaintPipeline": ModelType.Main,
-        "LatentConsistencyModelPipeline": ModelType.Main,
-        "AutoencoderKL": ModelType.Vae,
-        "AutoencoderTiny": ModelType.Vae,
-        "ControlNetModel": ModelType.ControlNet,
-        "CLIPVisionModelWithProjection": ModelType.CLIPVision,
-        "T2IAdapter": ModelType.T2IAdapter,
-    }
-
-    @classmethod
-    def register_probe(
-        cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: type[ProbeBase]
-    ) -> None:
-        cls.PROBES[format][model_type] = probe_class
-
-    @classmethod
-    def heuristic_probe(
-        cls,
-        model_path: Path,
-        fields: Optional[Dict[str, Any]] = None,
-    ) -> AnyModelConfig:
-        return cls.probe(model_path, fields)
-
-    @classmethod
-    def probe(
-        cls,
-        model_path: Path,
-        fields: Optional[Dict[str, Any]] = None,
-    ) -> AnyModelConfig:
-        """
-        Probe the model at model_path and return its configuration record.
-
-        :param model_path: Path to the model file (checkpoint) or directory (diffusers).
-        :param fields: An optional dictionary that can be used to override probed
-        fields. Typically used for fields that don't probe well, such as prediction_type.
-
-        Returns: The appropriate model configuration derived from ModelConfigBase.
-        """
-        if fields is None:
-            fields = {}
-
-        format_type = ModelFormat.Diffusers if model_path.is_dir() else ModelFormat.Checkpoint
-        model_info = None
-        model_type = None
-        if format_type == "diffusers":
-            model_type = cls.get_model_type_from_folder(model_path)
-        else:
-            model_type = cls.get_model_type_from_checkpoint(model_path)
-        format_type = ModelFormat.Onnx if model_type == ModelType.ONNX else format_type
-
-        probe_class = cls.PROBES[format_type].get(model_type)
-        if not probe_class:
-            raise InvalidModelConfigException(f"Unhandled combination of {format_type} and {model_type}")
-
-        hash = FastModelHash.hash(model_path)
-        probe = probe_class(model_path)
-
-        fields["path"] = model_path.as_posix()
-        fields["type"] = fields.get("type") or model_type
-        fields["base"] = fields.get("base") or probe.get_base_type()
-        fields["variant"] = fields.get("variant") or probe.get_variant_type()
-        fields["prediction_type"] = fields.get("prediction_type") or probe.get_scheduler_prediction_type()
-        fields["name"] = fields.get("name") or cls.get_model_name(model_path)
-        fields["description"] = (
-            fields.get("description") or f"{fields['base'].value} {fields['type'].value} model {fields['name']}"
-        )
-        fields["format"] = fields.get("format") or probe.get_format()
-        fields["original_hash"] = fields.get("original_hash") or hash
-        fields["current_hash"] = fields.get("current_hash") or hash
-
-        # additional fields needed for main and controlnet models
-        if fields["type"] in [ModelType.Main, ModelType.ControlNet] and fields["format"] == ModelFormat.Checkpoint:
-            fields["config"] = cls._get_checkpoint_config_path(
-                model_path,
-                model_type=fields["type"],
-                base_type=fields["base"],
-                variant_type=fields["variant"],
-                prediction_type=fields["prediction_type"],
-            ).as_posix()
-
-        # additional fields needed for main non-checkpoint models
-        elif fields["type"] == ModelType.Main and fields["format"] in [
-            ModelFormat.Onnx,
-            ModelFormat.Olive,
-            ModelFormat.Diffusers,
-        ]:
-            fields["upcast_attention"] = fields.get("upcast_attention") or (
-                fields["base"] == BaseModelType.StableDiffusion2
-                and fields["prediction_type"] == SchedulerPredictionType.VPrediction
-            )
-
-        model_info = ModelConfigFactory.make_config(fields)
-        return model_info
-
-    @classmethod
-    def get_model_name(cls, model_path: Path) -> str:
-        if model_path.suffix in {".safetensors", ".bin", ".pt", ".ckpt"}:
-            return model_path.stem
-        else:
-            return model_path.name
-
-    @classmethod
-    def get_model_type_from_checkpoint(cls, model_path: Path, checkpoint: Optional[CkptType] = None) -> ModelType:
-        if model_path.suffix not in (".bin", ".pt", ".ckpt", ".safetensors", ".pth"):
-            raise InvalidModelConfigException(f"{model_path}: unrecognized suffix")
-
-        if model_path.name == "learned_embeds.bin":
-            return ModelType.TextualInversion
-
-        ckpt = checkpoint if checkpoint else read_checkpoint_meta(model_path, scan=True)
-        ckpt = ckpt.get("state_dict", ckpt)
-
-        for key in ckpt.keys():
-            if any(key.startswith(v) for v in {"cond_stage_model.", "first_stage_model.", "model.diffusion_model."}):
-                return ModelType.Main
-            elif any(key.startswith(v) for v in {"encoder.conv_in", "decoder.conv_in"}):
-                return ModelType.Vae
-            elif any(key.startswith(v) for v in {"lora_te_", "lora_unet_"}):
-                return ModelType.Lora
-            elif any(key.endswith(v) for v in {"to_k_lora.up.weight", "to_q_lora.down.weight"}):
-                return ModelType.Lora
-            elif any(key.startswith(v) for v in {"control_model", "input_blocks"}):
-                return ModelType.ControlNet
-            elif key in {"emb_params", "string_to_param"}:
-                return ModelType.TextualInversion
-
-        else:
-            # diffusers-ti
-            if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
-                return ModelType.TextualInversion
-
-        raise InvalidModelConfigException(f"Unable to determine model type for {model_path}")
-
-    @classmethod
-    def get_model_type_from_folder(cls, folder_path: Path) -> ModelType:
-        """Get the model type of a hugging-face style folder."""
-        class_name = None
-        error_hint = None
-        for suffix in ["bin", "safetensors"]:
-            if (folder_path / f"learned_embeds.{suffix}").exists():
-                return ModelType.TextualInversion
-            if (folder_path / f"pytorch_lora_weights.{suffix}").exists():
-                return ModelType.Lora
-        if (folder_path / "unet/model.onnx").exists():
-            return ModelType.ONNX
-        if (folder_path / "image_encoder.txt").exists():
-            return ModelType.IPAdapter
-
-        i = folder_path / "model_index.json"
-        c = folder_path / "config.json"
-        config_path = i if i.exists() else c if c.exists() else None
-
-        if config_path:
-            with open(config_path, "r") as file:
-                conf = json.load(file)
-            if "_class_name" in conf:
-                class_name = conf["_class_name"]
-            elif "architectures" in conf:
-                class_name = conf["architectures"][0]
-            else:
-                class_name = None
-        else:
-            error_hint = f"No model_index.json or config.json found in {folder_path}."
-
-        if class_name and (type := cls.CLASS2TYPE.get(class_name)):
-            return type
-        else:
-            error_hint = f"class {class_name} is not one of the supported classes [{', '.join(cls.CLASS2TYPE.keys())}]"
-
-        # give up
-        raise InvalidModelConfigException(
-            f"Unable to determine model type for {folder_path}" + (f"; {error_hint}" if error_hint else "")
-        )
-
-    @classmethod
-    def _get_checkpoint_config_path(
-        cls,
-        model_path: Path,
-        model_type: ModelType,
-        base_type: BaseModelType,
-        variant_type: ModelVariantType,
-        prediction_type: SchedulerPredictionType,
-    ) -> Path:
-        # look for a YAML file adjacent to the model file first
-        possible_conf = model_path.with_suffix(".yaml")
-        if possible_conf.exists():
-            return possible_conf.absolute()
-
-        if model_type == ModelType.Main:
-            config_file = LEGACY_CONFIGS[base_type][variant_type]
-            if isinstance(config_file, dict):  # need another tier for sd-2.x models
-                config_file = config_file[prediction_type]
-        elif model_type == ModelType.ControlNet:
-            config_file = (
-                "../controlnet/cldm_v15.yaml" if base_type == BaseModelType("sd-1") else "../controlnet/cldm_v21.yaml"
-            )
-        else:
-            raise InvalidModelConfigException(
-                f"{model_path}: Unrecognized combination of model_type={model_type}, base_type={base_type}"
-            )
-        assert isinstance(config_file, str)
-        return Path(config_file)
-
-    @classmethod
-    def _scan_and_load_checkpoint(cls, model_path: Path) -> CkptType:
-        with SilenceWarnings():
-            if model_path.suffix.endswith((".ckpt", ".pt", ".bin")):
-                cls._scan_model(model_path.name, model_path)
-                model = torch.load(model_path)
-                assert isinstance(model, dict)
-                return model
-            else:
-                return safetensors.torch.load_file(model_path)
-
-    @classmethod
-    def _scan_model(cls, model_name: str, checkpoint: Path) -> None:
-        """
-        Apply picklescanner to the indicated checkpoint and issue a warning
-        and option to exit if an infected file is identified.
-        """
-        # scan model
-        scan_result = scan_file_path(checkpoint)
-        if scan_result.infected_files != 0:
-            raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
-
-
-# ##################################################3
-# Checkpoint probing
-# ##################################################3
-
-
-class CheckpointProbeBase(ProbeBase):
-    def __init__(self, model_path: Path):
-        super().__init__(model_path)
-        self.checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
-
-    def get_format(self) -> ModelFormat:
-        return ModelFormat("checkpoint")
-
-    def get_variant_type(self) -> ModelVariantType:
-        model_type = ModelProbe.get_model_type_from_checkpoint(self.model_path, self.checkpoint)
-        if model_type != ModelType.Main:
-            return ModelVariantType.Normal
-        state_dict = self.checkpoint.get("state_dict") or self.checkpoint
-        in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
-        if in_channels == 9:
-            return ModelVariantType.Inpaint
-        elif in_channels == 5:
-            return ModelVariantType.Depth
-        elif in_channels == 4:
-            return ModelVariantType.Normal
-        else:
-            raise InvalidModelConfigException(
-                f"Cannot determine variant type (in_channels={in_channels}) at {self.model_path}"
-            )
-
-
-class PipelineCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        checkpoint = self.checkpoint
-        state_dict = self.checkpoint.get("state_dict") or checkpoint
-        key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
-        if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
-            return BaseModelType.StableDiffusion1
-        if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
-            return BaseModelType.StableDiffusion2
-        key_name = "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight"
-        if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
-            return BaseModelType.StableDiffusionXL
-        elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280:
-            return BaseModelType.StableDiffusionXLRefiner
-        else:
-            raise InvalidModelConfigException("Cannot determine base type")
-
-    def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
-        """Return model prediction type."""
-        type = self.get_base_type()
-        if type == BaseModelType.StableDiffusion2:
-            checkpoint = self.checkpoint
-            state_dict = self.checkpoint.get("state_dict") or checkpoint
-            key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
-            if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
-                if "global_step" in checkpoint:
-                    if checkpoint["global_step"] == 220000:
-                        return SchedulerPredictionType.Epsilon
-                    elif checkpoint["global_step"] == 110000:
-                        return SchedulerPredictionType.VPrediction
-            return SchedulerPredictionType.VPrediction  # a guess for sd2 ckpts
-
-        elif type == BaseModelType.StableDiffusion1:
-            return SchedulerPredictionType.Epsilon  # a reasonable guess for sd1 ckpts
-        else:
-            return SchedulerPredictionType.Epsilon
-
-
-class VaeCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        # I can't find any standalone 2.X VAEs to test with!
-        return BaseModelType.StableDiffusion1
-
-
-class LoRACheckpointProbe(CheckpointProbeBase):
-    """Class for LoRA checkpoints."""
-
-    def get_format(self) -> ModelFormat:
-        return ModelFormat("lycoris")
-
-    def get_base_type(self) -> BaseModelType:
-        checkpoint = self.checkpoint
-        token_vector_length = lora_token_vector_length(checkpoint)
-
-        if token_vector_length == 768:
-            return BaseModelType.StableDiffusion1
-        elif token_vector_length == 1024:
-            return BaseModelType.StableDiffusion2
-        elif token_vector_length == 1280:
-            return BaseModelType.StableDiffusionXL  # recognizes format at https://civitai.com/models/224641
-        elif token_vector_length == 2048:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelConfigException(f"Unknown LoRA type: {self.model_path}")
-
-
-class TextualInversionCheckpointProbe(CheckpointProbeBase):
-    """Class for probing embeddings."""
-
-    def get_format(self) -> ModelFormat:
-        return ModelFormat.EmbeddingFile
-
-    def get_base_type(self) -> BaseModelType:
-        checkpoint = self.checkpoint
-        if "string_to_token" in checkpoint:
-            token_dim = list(checkpoint["string_to_param"].values())[0].shape[-1]
-        elif "emb_params" in checkpoint:
-            token_dim = checkpoint["emb_params"].shape[-1]
-        elif "clip_g" in checkpoint:
-            token_dim = checkpoint["clip_g"].shape[-1]
-        else:
-            token_dim = list(checkpoint.values())[0].shape[0]
-        if token_dim == 768:
-            return BaseModelType.StableDiffusion1
-        elif token_dim == 1024:
-            return BaseModelType.StableDiffusion2
-        elif token_dim == 1280:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelConfigException(f"{self.model_path}: Could not determine base type")
-
-
-class ControlNetCheckpointProbe(CheckpointProbeBase):
-    """Class for probing controlnets."""
-
-    def get_base_type(self) -> BaseModelType:
-        checkpoint = self.checkpoint
-        for key_name in (
-            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
-            "input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
-        ):
-            if key_name not in checkpoint:
-                continue
-            if checkpoint[key_name].shape[-1] == 768:
-                return BaseModelType.StableDiffusion1
-            elif checkpoint[key_name].shape[-1] == 1024:
-                return BaseModelType.StableDiffusion2
-        raise InvalidModelConfigException("{self.model_path}: Unable to determine base type")
-
-
-class IPAdapterCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        raise NotImplementedError()
-
-
-class CLIPVisionCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        raise NotImplementedError()
-
-
-class T2IAdapterCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        raise NotImplementedError()
-
-
-########################################################
-# classes for probing folders
-#######################################################
-class FolderProbeBase(ProbeBase):
-    def get_variant_type(self) -> ModelVariantType:
-        return ModelVariantType.Normal
-
-    def get_format(self) -> ModelFormat:
-        return ModelFormat("diffusers")
-
-
-class PipelineFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        with open(self.model_path / "unet" / "config.json", "r") as file:
-            unet_conf = json.load(file)
-        if unet_conf["cross_attention_dim"] == 768:
-            return BaseModelType.StableDiffusion1
-        elif unet_conf["cross_attention_dim"] == 1024:
-            return BaseModelType.StableDiffusion2
-        elif unet_conf["cross_attention_dim"] == 1280:
-            return BaseModelType.StableDiffusionXLRefiner
-        elif unet_conf["cross_attention_dim"] == 2048:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
-
-    def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
-        with open(self.model_path / "scheduler" / "scheduler_config.json", "r") as file:
-            scheduler_conf = json.load(file)
-        if scheduler_conf["prediction_type"] == "v_prediction":
-            return SchedulerPredictionType.VPrediction
-        elif scheduler_conf["prediction_type"] == "epsilon":
-            return SchedulerPredictionType.Epsilon
-        else:
-            raise InvalidModelConfigException("Unknown scheduler prediction type: {scheduler_conf['prediction_type']}")
-
-    def get_variant_type(self) -> ModelVariantType:
-        # This only works for pipelines! Any kind of
-        # exception results in our returning the
-        # "normal" variant type
-        try:
-            config_file = self.model_path / "unet" / "config.json"
-            with open(config_file, "r") as file:
-                conf = json.load(file)
-
-            in_channels = conf["in_channels"]
-            if in_channels == 9:
-                return ModelVariantType.Inpaint
-            elif in_channels == 5:
-                return ModelVariantType.Depth
-            elif in_channels == 4:
-                return ModelVariantType.Normal
-        except Exception:
-            pass
-        return ModelVariantType.Normal
-
-
-class VaeFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        if self._config_looks_like_sdxl():
-            return BaseModelType.StableDiffusionXL
-        elif self._name_looks_like_sdxl():
-            # but SD and SDXL VAE are the same shape (3-channel RGB to 4-channel float scaled down
-            # by a factor of 8), we can't necessarily tell them apart by config hyperparameters.
-            return BaseModelType.StableDiffusionXL
-        else:
-            return BaseModelType.StableDiffusion1
-
-    def _config_looks_like_sdxl(self) -> bool:
-        # config values that distinguish Stability's SD 1.x VAE from their SDXL VAE.
-        config_file = self.model_path / "config.json"
-        if not config_file.exists():
-            raise InvalidModelConfigException(f"Cannot determine base type for {self.model_path}")
-        with open(config_file, "r") as file:
-            config = json.load(file)
-        return config.get("scaling_factor", 0) == 0.13025 and config.get("sample_size") in [512, 1024]
-
-    def _name_looks_like_sdxl(self) -> bool:
-        return bool(re.search(r"xl\b", self._guess_name(), re.IGNORECASE))
-
-    def _guess_name(self) -> str:
-        name = self.model_path.name
-        if name == "vae":
-            name = self.model_path.parent.name
-        return name
-
-
-class TextualInversionFolderProbe(FolderProbeBase):
-    def get_format(self) -> ModelFormat:
-        return ModelFormat.EmbeddingFolder
-
-    def get_base_type(self) -> BaseModelType:
-        path = self.model_path / "learned_embeds.bin"
-        if not path.exists():
-            raise InvalidModelConfigException(
-                f"{self.model_path.as_posix()} does not contain expected 'learned_embeds.bin' file"
-            )
-        return TextualInversionCheckpointProbe(path).get_base_type()
-
-
-class ONNXFolderProbe(FolderProbeBase):
-    def get_format(self) -> ModelFormat:
-        return ModelFormat("onnx")
-
-    def get_base_type(self) -> BaseModelType:
-        return BaseModelType.StableDiffusion1
-
-    def get_variant_type(self) -> ModelVariantType:
-        return ModelVariantType.Normal
-
-
-class ControlNetFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        config_file = self.model_path / "config.json"
-        if not config_file.exists():
-            raise InvalidModelConfigException(f"Cannot determine base type for {self.model_path}")
-        with open(config_file, "r") as file:
-            config = json.load(file)
-        # no obvious way to distinguish between sd2-base and sd2-768
-        dimension = config["cross_attention_dim"]
-        base_model = (
-            BaseModelType.StableDiffusion1
-            if dimension == 768
-            else (
-                BaseModelType.StableDiffusion2
-                if dimension == 1024
-                else BaseModelType.StableDiffusionXL
-                if dimension == 2048
-                else None
-            )
-        )
-        if not base_model:
-            raise InvalidModelConfigException(f"Unable to determine model base for {self.model_path}")
-        return base_model
-
-
-class LoRAFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        model_file = None
-        for suffix in ["safetensors", "bin"]:
-            base_file = self.model_path / f"pytorch_lora_weights.{suffix}"
-            if base_file.exists():
-                model_file = base_file
-                break
-        if not model_file:
-            raise InvalidModelConfigException("Unknown LoRA format encountered")
-        return LoRACheckpointProbe(model_file).get_base_type()
-
-
-class IPAdapterFolderProbe(FolderProbeBase):
-    def get_format(self) -> IPAdapterModelFormat:
-        return IPAdapterModelFormat.InvokeAI.value
-
-    def get_base_type(self) -> BaseModelType:
-        model_file = self.model_path / "ip_adapter.bin"
-        if not model_file.exists():
-            raise InvalidModelConfigException("Unknown IP-Adapter model format.")
-
-        state_dict = torch.load(model_file, map_location="cpu")
-        cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
-        if cross_attention_dim == 768:
-            return BaseModelType.StableDiffusion1
-        elif cross_attention_dim == 1024:
-            return BaseModelType.StableDiffusion2
-        elif cross_attention_dim == 2048:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelConfigException(
-                f"IP-Adapter had unexpected cross-attention dimension: {cross_attention_dim}."
-            )
-
-
-class CLIPVisionFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        return BaseModelType.Any
-
-
-class T2IAdapterFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        config_file = self.model_path / "config.json"
-        if not config_file.exists():
-            raise InvalidModelConfigException(f"Cannot determine base type for {self.model_path}")
-        with open(config_file, "r") as file:
-            config = json.load(file)
-
-        adapter_type = config.get("adapter_type", None)
-        if adapter_type == "full_adapter_xl":
-            return BaseModelType.StableDiffusionXL
-        elif adapter_type == "full_adapter" or "light_adapter":
-            # I haven't seen any T2I adapter models for SD2, so assume that this is an SD1 adapter.
-            return BaseModelType.StableDiffusion1
-        else:
-            raise InvalidModelConfigException(
-                f"Unable to determine base model for '{self.model_path}' (adapter_type = {adapter_type})."
-            )
-
-
-############## register probe classes ######
-ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.Lora, LoRAFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
-
-ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.CLIPVision, CLIPVisionCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.T2IAdapter, T2IAdapterCheckpointProbe)
-
-ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)

invokeai/backend/model_manager/search.py

@@ -1,190 +0,0 @@
-# Copyright 2023, Lincoln D. Stein and the InvokeAI Team
-"""
-Abstract base class and implementation for recursive directory search for models.
-
-Example usage:
-```
-  from invokeai.backend.model_manager import ModelSearch, ModelProbe
-
-  def find_main_models(model: Path) -> bool:
-    info = ModelProbe.probe(model)
-    if info.model_type == 'main' and info.base_type == 'sd-1':
-        return True
-    else:
-        return False
-
-  search = ModelSearch(on_model_found=report_it)
-  found = search.search('/tmp/models')
-  print(found)   #  list of matching model paths
-  print(search.stats)  #  search stats
-```
-"""
-
-import os
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Callable, Optional, Set, Union
-
-from pydantic import BaseModel, Field
-
-from invokeai.backend.util.logging import InvokeAILogger
-
-default_logger = InvokeAILogger.get_logger()
-
-
-class SearchStats(BaseModel):
-    items_scanned: int = 0
-    models_found: int = 0
-    models_filtered: int = 0
-
-
-class ModelSearchBase(ABC, BaseModel):
-    """
-    Abstract directory traversal model search class
-
-    Usage:
-       search = ModelSearchBase(
-            on_search_started = search_started_callback,
-            on_search_completed = search_completed_callback,
-            on_model_found = model_found_callback,
-       )
-       models_found = search.search('/path/to/directory')
-    """
-
-    # fmt: off
-    on_search_started   : Optional[Callable[[Path], None]]      = Field(default=None, description="Called just before the search starts.")  # noqa E221
-    on_model_found      : Optional[Callable[[Path], bool]]      = Field(default=None, description="Called when a model is found.")          # noqa E221
-    on_search_completed : Optional[Callable[[Set[Path]], None]] = Field(default=None, description="Called when search is complete.")        # noqa E221
-    stats               : SearchStats                           = Field(default_factory=SearchStats, description="Summary statistics after search")  # noqa E221
-    logger              : InvokeAILogger                        = Field(default=default_logger, description="Logger instance.")     # noqa E221
-    # fmt: on
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    @abstractmethod
-    def search_started(self) -> None:
-        """
-        Called before the scan starts.
-
-        Passes the root search directory to the Callable `on_search_started`.
-        """
-        pass
-
-    @abstractmethod
-    def model_found(self, model: Path) -> None:
-        """
-        Called when a model is found during search.
-
-        :param model: Model to process - could be a directory or checkpoint.
-
-        Passes the model's Path to the Callable `on_model_found`.
-        This Callable receives the path to the model and returns a boolean
-        to indicate whether the model should be returned in the search
-        results.
-        """
-        pass
-
-    @abstractmethod
-    def search_completed(self) -> None:
-        """
-        Called before the scan starts.
-
-        Passes the Set of found model Paths to the Callable `on_search_completed`.
-        """
-        pass
-
-    @abstractmethod
-    def search(self, directory: Union[Path, str]) -> Set[Path]:
-        """
-        Recursively search for models in `directory` and return a set of model paths.
-
-        If provided, the `on_search_started`, `on_model_found` and `on_search_completed`
-        Callables will be invoked during the search.
-        """
-        pass
-
-
-class ModelSearch(ModelSearchBase):
-    """
-    Implementation of ModelSearch with callbacks.
-    Usage:
-       search = ModelSearch()
-       search.model_found = lambda path : 'anime' in path.as_posix()
-       found = search.list_models(['/tmp/models1','/tmp/models2'])
-       # returns all models that have 'anime' in the path
-    """
-
-    models_found: Set[Path] = Field(default=None)
-    scanned_dirs: Set[Path] = Field(default=None)
-    pruned_paths: Set[Path] = Field(default=None)
-
-    def search_started(self) -> None:
-        self.models_found = set()
-        self.scanned_dirs = set()
-        self.pruned_paths = set()
-        if self.on_search_started:
-            self.on_search_started(self._directory)
-
-    def model_found(self, model: Path) -> None:
-        self.stats.models_found += 1
-        if not self.on_model_found or self.on_model_found(model):
-            self.stats.models_filtered += 1
-            self.models_found.add(model)
-
-    def search_completed(self) -> None:
-        if self.on_search_completed:
-            self.on_search_completed(self._models_found)
-
-    def search(self, directory: Union[Path, str]) -> Set[Path]:
-        self._directory = Path(directory)
-        self.stats = SearchStats()  # zero out
-        self.search_started()  # This will initialize _models_found to empty
-        self._walk_directory(directory)
-        self.search_completed()
-        return self.models_found
-
-    def _walk_directory(self, path: Union[Path, str]) -> None:
-        for root, dirs, files in os.walk(path, followlinks=True):
-            # don't descend into directories that start with a "."
-            # to avoid the Mac .DS_STORE issue.
-            if str(Path(root).name).startswith("."):
-                self.pruned_paths.add(Path(root))
-            if any(Path(root).is_relative_to(x) for x in self.pruned_paths):
-                continue
-
-            self.stats.items_scanned += len(dirs) + len(files)
-            for d in dirs:
-                path = Path(root) / d
-                if path.parent in self.scanned_dirs:
-                    self.scanned_dirs.add(path)
-                    continue
-                if any(
-                    (path / x).exists()
-                    for x in [
-                        "config.json",
-                        "model_index.json",
-                        "learned_embeds.bin",
-                        "pytorch_lora_weights.bin",
-                        "image_encoder.txt",
-                    ]
-                ):
-                    self.scanned_dirs.add(path)
-                    try:
-                        self.model_found(path)
-                    except KeyboardInterrupt:
-                        raise
-                    except Exception as e:
-                        self.logger.warning(str(e))
-
-            for f in files:
-                path = Path(root) / f
-                if path.parent in self.scanned_dirs:
-                    continue
-                if path.suffix in {".ckpt", ".bin", ".pth", ".safetensors", ".pt"}:
-                    try:
-                        self.model_found(path)
-                    except KeyboardInterrupt:
-                        raise
-                    except Exception as e:
-                        self.logger.warning(str(e))

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -242,6 +242,17 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         control_model: ControlNetModel = None,
     ):
         super().__init__(
+            vae,
+            text_encoder,
+            tokenizer,
+            unet,
+            scheduler,
+            safety_checker,
+            feature_extractor,
+            requires_safety_checker,
+        )
+
+        self.register_modules(
             vae=vae,
             text_encoder=text_encoder,
             tokenizer=tokenizer,
@@ -249,9 +260,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
-            requires_safety_checker=requires_safety_checker,
+            # FIXME: can't currently register control module
+            # control_model=control_model,
         )
-
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
         self.control_model = control_model
         self.use_ip_adapter = False
@@ -276,11 +287,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             self.disable_attention_slicing()
             return
         elif config.attention_type == "torch-sdp":
-            if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
-                return
-            else:
-                raise Exception("torch-sdp attention slicing not available")
+            raise Exception("torch-sdp attention slicing not yet implemented")
 
         # the remainder if this code is called when attention_type=='auto'
         if self.unet.device.type == "cuda":
@@ -288,7 +295,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 self.enable_xformers_memory_efficient_attention()
                 return
             elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
+                # diffusers enable sdp automatically
                 return
 
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":

invokeai/backend/tiles/tiles.py

@@ -3,42 +3,7 @@ from typing import Union
 
 import numpy as np
 
-from invokeai.app.invocations.latent import LATENT_SCALE_FACTOR
-from invokeai.backend.tiles.utils import TBLR, Tile, paste, seam_blend
-
-
-def calc_overlap(tiles: list[Tile], num_tiles_x: int, num_tiles_y: int) -> list[Tile]:
-    """Calculate and update the overlap of a list of tiles.
-
-    Args:
-        tiles (list[Tile]): The list of tiles describing the locations of the respective `tile_images`.
-        num_tiles_x: the number of tiles on the x axis.
-        num_tiles_y: the number of tiles on the y axis.
-    """
-
-    def get_tile_or_none(idx_y: int, idx_x: int) -> Union[Tile, None]:
-        if idx_y < 0 or idx_y > num_tiles_y or idx_x < 0 or idx_x > num_tiles_x:
-            return None
-        return tiles[idx_y * num_tiles_x + idx_x]
-
-    for tile_idx_y in range(num_tiles_y):
-        for tile_idx_x in range(num_tiles_x):
-            cur_tile = get_tile_or_none(tile_idx_y, tile_idx_x)
-            top_neighbor_tile = get_tile_or_none(tile_idx_y - 1, tile_idx_x)
-            left_neighbor_tile = get_tile_or_none(tile_idx_y, tile_idx_x - 1)
-
-            assert cur_tile is not None
-
-            # Update cur_tile top-overlap and corresponding top-neighbor bottom-overlap.
-            if top_neighbor_tile is not None:
-                cur_tile.overlap.top = max(0, top_neighbor_tile.coords.bottom - cur_tile.coords.top)
-                top_neighbor_tile.overlap.bottom = cur_tile.overlap.top
-
-            # Update cur_tile left-overlap and corresponding left-neighbor right-overlap.
-            if left_neighbor_tile is not None:
-                cur_tile.overlap.left = max(0, left_neighbor_tile.coords.right - cur_tile.coords.left)
-                left_neighbor_tile.overlap.right = cur_tile.overlap.left
-    return tiles
+from invokeai.backend.tiles.utils import TBLR, Tile, paste
 
 
 def calc_tiles_with_overlap(
@@ -98,133 +63,31 @@ def calc_tiles_with_overlap(
 
             tiles.append(tile)
 
-    return calc_overlap(tiles, num_tiles_x, num_tiles_y)
+    def get_tile_or_none(idx_y: int, idx_x: int) -> Union[Tile, None]:
+        if idx_y < 0 or idx_y > num_tiles_y or idx_x < 0 or idx_x > num_tiles_x:
+            return None
+        return tiles[idx_y * num_tiles_x + idx_x]
 
-
-def calc_tiles_even_split(
-    image_height: int, image_width: int, num_tiles_x: int, num_tiles_y: int, overlap: int = 0
-) -> list[Tile]:
-    """Calculate the tile coordinates for a given image shape with the number of tiles requested.
-
-    Args:
-        image_height (int): The image height in px.
-        image_width (int): The image width in px.
-        num_x_tiles (int): The number of tile to split the image into on the X-axis.
-        num_y_tiles (int): The number of tile to split the image into on the Y-axis.
-        overlap (int, optional): The overlap between adjacent tiles in pixels. Defaults to 0.
-
-    Returns:
-        list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
-    """
-    # Ensure the image is divisible by LATENT_SCALE_FACTOR
-    if image_width % LATENT_SCALE_FACTOR != 0 or image_height % LATENT_SCALE_FACTOR != 0:
-        raise ValueError(f"image size (({image_width}, {image_height})) must be divisible by {LATENT_SCALE_FACTOR}")
-
-    # Calculate the tile size based on the number of tiles and overlap, and ensure it's divisible by 8 (rounding down)
-    if num_tiles_x > 1:
-        # ensure the overlap is not more than the maximum overlap if we only have 1 tile then we dont care about overlap
-        assert overlap <= image_width - (LATENT_SCALE_FACTOR * (num_tiles_x - 1))
-        tile_size_x = LATENT_SCALE_FACTOR * math.floor(
-            ((image_width + overlap * (num_tiles_x - 1)) // num_tiles_x) / LATENT_SCALE_FACTOR
-        )
-        assert overlap < tile_size_x
-    else:
-        tile_size_x = image_width
-
-    if num_tiles_y > 1:
-        # ensure the overlap is not more than the maximum overlap if we only have 1 tile then we dont care about overlap
-        assert overlap <= image_height - (LATENT_SCALE_FACTOR * (num_tiles_y - 1))
-        tile_size_y = LATENT_SCALE_FACTOR * math.floor(
-            ((image_height + overlap * (num_tiles_y - 1)) // num_tiles_y) / LATENT_SCALE_FACTOR
-        )
-        assert overlap < tile_size_y
-    else:
-        tile_size_y = image_height
-
-    # tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
-    tiles: list[Tile] = []
-
-    # Calculate tile coordinates. (Ignore overlap values for now.)
+    # Iterate over tiles again and calculate overlaps.
     for tile_idx_y in range(num_tiles_y):
-        # Calculate the top and bottom of the row
-        top = tile_idx_y * (tile_size_y - overlap)
-        bottom = min(top + tile_size_y, image_height)
-        # For the last row adjust bottom to be the height of the image
-        if tile_idx_y == num_tiles_y - 1:
-            bottom = image_height
-
         for tile_idx_x in range(num_tiles_x):
-            # Calculate the left & right coordinate of each tile
-            left = tile_idx_x * (tile_size_x - overlap)
-            right = min(left + tile_size_x, image_width)
-            # For the last tile in the row adjust right to be the width of the image
-            if tile_idx_x == num_tiles_x - 1:
-                right = image_width
+            cur_tile = get_tile_or_none(tile_idx_y, tile_idx_x)
+            top_neighbor_tile = get_tile_or_none(tile_idx_y - 1, tile_idx_x)
+            left_neighbor_tile = get_tile_or_none(tile_idx_y, tile_idx_x - 1)
 
-            tile = Tile(
-                coords=TBLR(top=top, bottom=bottom, left=left, right=right),
-                overlap=TBLR(top=0, bottom=0, left=0, right=0),
-            )
+            assert cur_tile is not None
 
-            tiles.append(tile)
+            # Update cur_tile top-overlap and corresponding top-neighbor bottom-overlap.
+            if top_neighbor_tile is not None:
+                cur_tile.overlap.top = max(0, top_neighbor_tile.coords.bottom - cur_tile.coords.top)
+                top_neighbor_tile.overlap.bottom = cur_tile.overlap.top
 
-    return calc_overlap(tiles, num_tiles_x, num_tiles_y)
+            # Update cur_tile left-overlap and corresponding left-neighbor right-overlap.
+            if left_neighbor_tile is not None:
+                cur_tile.overlap.left = max(0, left_neighbor_tile.coords.right - cur_tile.coords.left)
+                left_neighbor_tile.overlap.right = cur_tile.overlap.left
 
-
-def calc_tiles_min_overlap(
-    image_height: int,
-    image_width: int,
-    tile_height: int,
-    tile_width: int,
-    min_overlap: int = 0,
-) -> list[Tile]:
-    """Calculate the tile coordinates for a given image shape under a simple tiling scheme with overlaps.
-
-    Args:
-        image_height (int): The image height in px.
-        image_width (int): The image width in px.
-        tile_height (int): The tile height in px. All tiles will have this height.
-        tile_width (int): The tile width in px. All tiles will have this width.
-        min_overlap (int): The target minimum overlap between adjacent tiles. If the tiles do not evenly cover the image
-            shape, then the overlap will be spread between the tiles.
-
-    Returns:
-        list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
-    """
-
-    assert min_overlap < tile_height
-    assert min_overlap < tile_width
-
-    # catches the cases when the tile size is larger than the images size and adjusts the tile size
-    if image_width < tile_width:
-        tile_width = image_width
-
-    if image_height < tile_height:
-        tile_height = image_height
-
-    num_tiles_x = math.ceil((image_width - min_overlap) / (tile_width - min_overlap))
-    num_tiles_y = math.ceil((image_height - min_overlap) / (tile_height - min_overlap))
-
-    # tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
-    tiles: list[Tile] = []
-
-    # Calculate tile coordinates. (Ignore overlap values for now.)
-    for tile_idx_y in range(num_tiles_y):
-        top = (tile_idx_y * (image_height - tile_height)) // (num_tiles_y - 1) if num_tiles_y > 1 else 0
-        bottom = top + tile_height
-
-        for tile_idx_x in range(num_tiles_x):
-            left = (tile_idx_x * (image_width - tile_width)) // (num_tiles_x - 1) if num_tiles_x > 1 else 0
-            right = left + tile_width
-
-            tile = Tile(
-                coords=TBLR(top=top, bottom=bottom, left=left, right=right),
-                overlap=TBLR(top=0, bottom=0, left=0, right=0),
-            )
-
-            tiles.append(tile)
-
-    return calc_overlap(tiles, num_tiles_x, num_tiles_y)
+    return tiles
 
 
 def merge_tiles_with_linear_blending(
@@ -336,91 +199,3 @@ def merge_tiles_with_linear_blending(
             ),
             mask=mask,
         )
-
-
-def merge_tiles_with_seam_blending(
-    dst_image: np.ndarray, tiles: list[Tile], tile_images: list[np.ndarray], blend_amount: int
-):
-    """Merge a set of image tiles into `dst_image` with seam blending between the tiles.
-
-    We expect every tile edge to either:
-    1) have an overlap of 0, because it is aligned with the image edge, or
-    2) have an overlap >= blend_amount.
-    If neither of these conditions are satisfied, we raise an exception.
-
-    The seam blending is centered on a seam of least energy of the overlap between adjacent tiles.
-
-    Args:
-        dst_image (np.ndarray): The destination image. Shape: (H, W, C).
-        tiles (list[Tile]): The list of tiles describing the locations of the respective `tile_images`.
-        tile_images (list[np.ndarray]): The tile images to merge into `dst_image`.
-        blend_amount (int): The amount of blending (in px) between adjacent overlapping tiles.
-    """
-    # Sort tiles and images first by left x coordinate, then by top y coordinate. During tile processing, we want to
-    # iterate over tiles left-to-right, top-to-bottom.
-    tiles_and_images = list(zip(tiles, tile_images, strict=True))
-    tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.left)
-    tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.top)
-
-    # Organize tiles into rows.
-    tile_and_image_rows: list[list[tuple[Tile, np.ndarray]]] = []
-    cur_tile_and_image_row: list[tuple[Tile, np.ndarray]] = []
-    first_tile_in_cur_row, _ = tiles_and_images[0]
-    for tile_and_image in tiles_and_images:
-        tile, _ = tile_and_image
-        if not (
-            tile.coords.top == first_tile_in_cur_row.coords.top
-            and tile.coords.bottom == first_tile_in_cur_row.coords.bottom
-        ):
-            # Store the previous row, and start a new one.
-            tile_and_image_rows.append(cur_tile_and_image_row)
-            cur_tile_and_image_row = []
-            first_tile_in_cur_row, _ = tile_and_image
-
-        cur_tile_and_image_row.append(tile_and_image)
-    tile_and_image_rows.append(cur_tile_and_image_row)
-
-    for tile_and_image_row in tile_and_image_rows:
-        first_tile_in_row, _ = tile_and_image_row[0]
-        row_height = first_tile_in_row.coords.bottom - first_tile_in_row.coords.top
-        row_image = np.zeros((row_height, dst_image.shape[1], dst_image.shape[2]), dtype=dst_image.dtype)
-
-        # Blend the tiles in the row horizontally.
-        for tile, tile_image in tile_and_image_row:
-            # We expect the tiles to be ordered left-to-right.
-            # For each tile:
-            # - extract the overlap regions and pass to seam_blend()
-            # - apply blended region to the row_image
-            # - apply the un-blended region to the row_image
-            tile_height, tile_width, _ = tile_image.shape
-            overlap_size = tile.overlap.left
-            # Left blending:
-            if overlap_size > 0:
-                assert overlap_size >= blend_amount
-
-                overlap_coord_right = tile.coords.left + overlap_size
-                src_overlap = row_image[:, tile.coords.left : overlap_coord_right]
-                dst_overlap = tile_image[:, :overlap_size]
-                blended_overlap = seam_blend(src_overlap, dst_overlap, blend_amount, x_seam=False)
-                row_image[:, tile.coords.left : overlap_coord_right] = blended_overlap
-                row_image[:, overlap_coord_right : tile.coords.right] = tile_image[:, overlap_size:]
-            else:
-                # no overlap just paste the tile
-                row_image[:, tile.coords.left : tile.coords.right] = tile_image
-
-        # Blend the row into the dst_image
-        # We assume that the entire row has the same vertical overlaps as the first_tile_in_row.
-        # Rows are processed in the same way as tiles (extract overlap, blend, apply)
-        row_overlap_size = first_tile_in_row.overlap.top
-        if row_overlap_size > 0:
-            assert row_overlap_size >= blend_amount
-
-            overlap_coords_bottom = first_tile_in_row.coords.top + row_overlap_size
-            src_overlap = dst_image[first_tile_in_row.coords.top : overlap_coords_bottom, :]
-            dst_overlap = row_image[:row_overlap_size, :]
-            blended_overlap = seam_blend(src_overlap, dst_overlap, blend_amount, x_seam=True)
-            dst_image[first_tile_in_row.coords.top : overlap_coords_bottom, :] = blended_overlap
-            dst_image[overlap_coords_bottom : first_tile_in_row.coords.bottom, :] = row_image[row_overlap_size:, :]
-        else:
-            # no overlap just paste the row
-            dst_image[first_tile_in_row.coords.top : first_tile_in_row.coords.bottom, :] = row_image

invokeai/backend/tiles/utils.py

@@ -1,7 +1,5 @@
-import math
 from typing import Optional
 
-import cv2
 import numpy as np
 from pydantic import BaseModel, Field
 
@@ -33,10 +31,10 @@ def paste(dst_image: np.ndarray, src_image: np.ndarray, box: TBLR, mask: Optiona
     """Paste a source image into a destination image.
 
     Args:
-        dst_image (np.array): The destination image to paste into. Shape: (H, W, C).
-        src_image (np.array): The source image to paste. Shape: (H, W, C). H and W must be compatible with 'box'.
+        dst_image (torch.Tensor): The destination image to paste into. Shape: (H, W, C).
+        src_image (torch.Tensor): The source image to paste. Shape: (H, W, C). H and W must be compatible with 'box'.
         box (TBLR): Box defining the region in the 'dst_image' where 'src_image' will be pasted.
-        mask (Optional[np.array]): A mask that defines the blending between 'src_image' and 'dst_image'.
+        mask (Optional[torch.Tensor]): A mask that defines the blending between 'src_image' and 'dst_image'.
             Range: [0.0, 1.0], Shape: (H, W). The output is calculate per-pixel according to
             `src * mask + dst * (1 - mask)`.
     """
@@ -47,106 +45,3 @@ def paste(dst_image: np.ndarray, src_image: np.ndarray, box: TBLR, mask: Optiona
         mask = np.expand_dims(mask, -1)
         dst_image_box = dst_image[box.top : box.bottom, box.left : box.right]
         dst_image[box.top : box.bottom, box.left : box.right] = src_image * mask + dst_image_box * (1.0 - mask)
-
-
-def seam_blend(ia1: np.ndarray, ia2: np.ndarray, blend_amount: int, x_seam: bool) -> np.ndarray:
-    """Blend two overlapping tile sections using a seams to find a path.
-
-    It is assumed that input images will be RGB np arrays and are the same size.
-
-    Args:
-        ia1 (np.array): Image array 1 Shape: (H, W, C).
-        ia2 (np.array): Image array 2 Shape: (H, W, C).
-        x_seam (bool): If the images should be blended on the x axis or not.
-        blend_amount (int): The size of the blur to use on the seam. Half of this value will be used to avoid the edges of the image.
-    """
-    assert ia1.shape == ia2.shape
-    assert ia2.size == ia2.size
-
-    def shift(arr, num, fill_value=255.0):
-        result = np.full_like(arr, fill_value)
-        if num > 0:
-            result[num:] = arr[:-num]
-        elif num < 0:
-            result[:num] = arr[-num:]
-        else:
-            result[:] = arr
-        return result
-
-    # Assume RGB and convert to grey
-    # Could offer other options for the luminance conversion
-    # BT.709 [0.2126, 0.7152, 0.0722], BT.2020 [0.2627, 0.6780, 0.0593])
-    # it might not have a huge impact due to the blur that is applied over the seam
-    iag1 = np.dot(ia1, [0.2989, 0.5870, 0.1140])  # BT.601 perceived brightness
-    iag2 = np.dot(ia2, [0.2989, 0.5870, 0.1140])
-
-    # Calc Difference between the images
-    ia = iag2 - iag1
-
-    # If the seam is on the X-axis rotate the array so we can treat it like a vertical seam
-    if x_seam:
-        ia = np.rot90(ia, 1)
-
-    # Calc max and min X & Y limits
-    # gutter is used to avoid the blur hitting the edge of the image
-    gutter = math.ceil(blend_amount / 2) if blend_amount > 0 else 0
-    max_y, max_x = ia.shape
-    max_x -= gutter
-    min_x = gutter
-
-    # Calc the energy in the difference
-    # Could offer different energy calculations e.g. Sobel or Scharr
-    energy = np.abs(np.gradient(ia, axis=0)) + np.abs(np.gradient(ia, axis=1))
-
-    # Find the starting position of the seam
-    res = np.copy(energy)
-    for y in range(1, max_y):
-        row = res[y, :]
-        rowl = shift(row, -1)
-        rowr = shift(row, 1)
-        res[y, :] = res[y - 1, :] + np.min([row, rowl, rowr], axis=0)
-
-    # create an array max_y long
-    lowest_energy_line = np.empty([max_y], dtype="uint16")
-    lowest_energy_line[max_y - 1] = np.argmin(res[max_y - 1, min_x : max_x - 1])
-
-    # Calc the path of the seam
-    # could offer options for larger search than just 1 pixel by adjusting lpos and rpos
-    for ypos in range(max_y - 2, -1, -1):
-        lowest_pos = lowest_energy_line[ypos + 1]
-        lpos = lowest_pos - 1
-        rpos = lowest_pos + 1
-        lpos = np.clip(lpos, min_x, max_x - 1)
-        rpos = np.clip(rpos, min_x, max_x - 1)
-        lowest_energy_line[ypos] = np.argmin(energy[ypos, lpos : rpos + 1]) + lpos
-
-    # Draw the mask
-    mask = np.zeros_like(ia)
-    for ypos in range(0, max_y):
-        to_fill = lowest_energy_line[ypos]
-        mask[ypos, :to_fill] = 1
-
-    # If the seam is on the X-axis rotate the array back
-    if x_seam:
-        mask = np.rot90(mask, 3)
-
-    # blur the seam mask if required
-    if blend_amount > 0:
-        mask = cv2.blur(mask, (blend_amount, blend_amount))
-
-    # for visual debugging
-    # from PIL import Image
-    # m_image = Image.fromarray((mask * 255.0).astype("uint8"))
-
-    # copy ia2 over ia1 while applying the seam mask
-    mask = np.expand_dims(mask, -1)
-    blended_image = ia1 * mask + ia2 * (1.0 - mask)
-
-    # for visual debugging
-    # i1 = Image.fromarray(ia1.astype("uint8"))
-    # i2 = Image.fromarray(ia2.astype("uint8"))
-    # b_image = Image.fromarray(blended_image.astype("uint8"))
-    # print(f"{ia1.shape}, {ia2.shape}, {mask.shape}, {blended_image.shape}")
-    # print(f"{i1.size}, {i2.size}, {m_image.size}, {b_image.size}")
-
-    return blended_image

invokeai/backend/util/__init__.py

@@ -11,7 +11,4 @@ from .devices import (  # noqa: F401
     normalize_device,
     torch_dtype,
 )
-from .logging import InvokeAILogger
 from .util import Chdir, ask_user, download_with_resume, instantiate_from_config, url_attachment_name  # noqa: F401
-
-__all__ = ["Chdir", "InvokeAILogger", "choose_precision", "choose_torch_device"]

invokeai/backend/util/devices.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
+import platform
 from contextlib import nullcontext
 from typing import Union
 
 import torch
+from packaging import version
 from torch import autocast
 
 from invokeai.app.services.config import InvokeAIAppConfig
@@ -35,7 +37,7 @@ def choose_precision(device: torch.device) -> str:
         device_name = torch.cuda.get_device_name(device)
         if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
             return "float16"
-    elif device.type == "mps":
+    elif device.type == "mps" and version.parse(platform.mac_ver()[0]) < version.parse("14.0.0"):
         return "float16"
     return "float32"
 

invokeai/backend/util/logging.py

@@ -342,13 +342,14 @@ class InvokeAILogger(object):  # noqa D102
         cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
     ) -> logging.Logger:  # noqa D102
         if name in cls.loggers:
-            return cls.loggers[name]
-
-        logger = logging.getLogger(name)
+            logger = cls.loggers[name]
+            logger.handlers.clear()
+        else:
+            logger = logging.getLogger(name)
         logger.setLevel(config.log_level.upper())  # yes, strings work here
         for ch in cls.get_loggers(config):
             logger.addHandler(ch)
-        cls.loggers[name] = logger
+            cls.loggers[name] = logger
         return cls.loggers[name]
 
     @classmethod
@@ -357,7 +358,7 @@ class InvokeAILogger(object):  # noqa D102
         handlers = []
         for handler in handler_strs:
             handler_name, *args = handler.split("=", 2)
-            arg = args[0] if len(args) > 0 else None
+            args = args[0] if len(args) > 0 else None
 
             # console and file get the fancy formatter.
             # syslog gets a simple one
@@ -369,16 +370,16 @@ class InvokeAILogger(object):  # noqa D102
                 handlers.append(ch)
 
             elif handler_name == "syslog":
-                ch = cls._parse_syslog_args(arg)
+                ch = cls._parse_syslog_args(args)
                 handlers.append(ch)
 
             elif handler_name == "file":
-                ch = cls._parse_file_args(arg)
+                ch = cls._parse_file_args(args)
                 ch.setFormatter(formatter())
                 handlers.append(ch)
 
             elif handler_name == "http":
-                ch = cls._parse_http_args(arg)
+                ch = cls._parse_http_args(args)
                 handlers.append(ch)
         return handlers
 

invokeai/configs/INITIAL_MODELS.yaml

@@ -32,9 +32,9 @@ sd-1/main/Analog-Diffusion:
    description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
    repo_id: wavymulder/Analog-Diffusion
    recommended: False
-sd-1/main/Deliberate_v5:
+sd-1/main/Deliberate:
    description: Versatile model that produces detailed images up to 768px (4.27 GB)
-   path: https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors
+   repo_id: XpucT/Deliberate
    recommended: False
 sd-1/main/Dungeons-and-Diffusion:
    description: Dungeons & Dragons characters (2.13 GB)

invokeai/frontend/install/invokeai_update.py

@@ -4,7 +4,6 @@ pip install <path_to_git_source>.
 """
 import os
 import platform
-from distutils.version import LooseVersion
 
 import pkg_resources
 import psutil
@@ -32,6 +31,10 @@ else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
 
+def get_versions() -> dict:
+    return requests.get(url=INVOKE_AI_REL).json()
+
+
 def invokeai_is_running() -> bool:
     for p in psutil.process_iter():
         try:
@@ -47,20 +50,6 @@ def invokeai_is_running() -> bool:
     return False
 
 
-def get_pypi_versions():
-    url = "https://pypi.org/pypi/invokeai/json"
-    try:
-        data = requests.get(url).json()
-    except Exception:
-        raise Exception("Unable to fetch version information from PyPi")
-
-    versions = list(data["releases"].keys())
-    versions.sort(key=LooseVersion, reverse=True)
-    latest_version = [v for v in versions if "rc" not in v][0]
-    latest_release_candidate = [v for v in versions if "rc" in v][0]
-    return latest_version, latest_release_candidate, versions
-
-
 def welcome(latest_release: str, latest_prerelease: str):
     @group()
     def text():
@@ -74,7 +63,8 @@ def welcome(latest_release: str, latest_prerelease: str):
         yield "[bold yellow]Options:"
         yield f"""[1] Update to the latest [bold]official release[/bold] ([italic]{latest_release}[/italic])
 [2] Update to the latest [bold]pre-release[/bold] (may be buggy; caveat emptor!) ([italic]{latest_prerelease}[/italic])
-[3] Manually enter the [bold]version[/bold] you wish to update to"""
+[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
+[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""
 
     console.rule()
     print(
@@ -102,35 +92,44 @@ def get_extras():
 
 
 def main():
+    versions = get_versions()
+    released_versions = [x for x in versions if not (x["draft"] or x["prerelease"])]
+    prerelease_versions = [x for x in versions if not x["draft"] and x["prerelease"]]
+    latest_release = released_versions[0]["tag_name"] if len(released_versions) else None
+    latest_prerelease = prerelease_versions[0]["tag_name"] if len(prerelease_versions) else None
+
     if invokeai_is_running():
         print(":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]")
         input("Press any key to continue...")
         return
 
-    latest_release, latest_prerelease, versions = get_pypi_versions()
-
     welcome(latest_release, latest_prerelease)
 
-    release = latest_release
-    choice = Prompt.ask("Choice:", choices=["1", "2", "3"], default="1")
+    tag = None
+    branch = None
+    release = None
+    choice = Prompt.ask("Choice:", choices=["1", "2", "3", "4"], default="1")
 
     if choice == "1":
         release = latest_release
     elif choice == "2":
         release = latest_prerelease
     elif choice == "3":
-        while True:
-            release = Prompt.ask("Enter an InvokeAI version")
-            release.strip()
-            if release in versions:
-                break
-            print(f":exclamation: [bold red]'{release}' is not a recognized InvokeAI release.[/red bold]")
+        while not tag:
+            tag = Prompt.ask("Enter an InvokeAI tag name")
+    elif choice == "4":
+        while not branch:
+            branch = Prompt.ask("Enter an InvokeAI branch name")
 
     extras = get_extras()
 
-    print(f":crossed_fingers: Upgrading to [yellow]{release}[/yellow]")
-    cmd = f'pip install "invokeai{extras}=={release}" --use-pep517 --upgrade'
-
+    print(f":crossed_fingers: Upgrading to [yellow]{tag or release or branch}[/yellow]")
+    if release:
+        cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_SRC}/{release}.zip" --use-pep517 --upgrade'
+    elif tag:
+        cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_TAG}/{tag}.zip" --use-pep517 --upgrade'
+    else:
+        cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_BRANCH}/{branch}.zip" --use-pep517 --upgrade'
     print("")
     print("")
     if os.system(cmd) == 0:

invokeai/frontend/web/.eslintrc.js

@@ -11,7 +11,6 @@ module.exports = {
     'plugin:react-hooks/recommended',
     'plugin:react/jsx-runtime',
     'prettier',
-    'plugin:storybook/recommended',
   ],
   parser: '@typescript-eslint/parser',
   parserOptions: {
@@ -27,7 +26,6 @@ module.exports = {
     'eslint-plugin-react-hooks',
     'i18next',
     'path',
-    'unused-imports',
   ],
   root: true,
   rules: {
@@ -46,16 +44,9 @@ module.exports = {
     radix: 'error',
     'space-before-blocks': 'error',
     'import/prefer-default-export': 'off',
-    '@typescript-eslint/no-unused-vars': 'off',
-    'unused-imports/no-unused-imports': 'error',
-    'unused-imports/no-unused-vars': [
+    '@typescript-eslint/no-unused-vars': [
       'warn',
-      {
-        vars: 'all',
-        varsIgnorePattern: '^_',
-        args: 'after-used',
-        argsIgnorePattern: '^_',
-      },
+      { varsIgnorePattern: '^_', argsIgnorePattern: '^_' },
     ],
     '@typescript-eslint/ban-ts-comment': 'warn',
     '@typescript-eslint/no-explicit-any': 'warn',

invokeai/frontend/web/.gitignore

@@ -9,8 +9,7 @@ lerna-debug.log*
 
 node_modules
 # We want to distribute the repo
-dist
-dist/**
+# dist
 dist-ssr
 *.local
 
@@ -39,4 +38,4 @@ stats.html
 
 # Yalc
 .yalc
-yalc.lock
+yalc.lock

invokeai/frontend/web/.husky/pre-commit

@@ -0,0 +1,4 @@
+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
+cd invokeai/frontend/web/ && npm run lint-staged

invokeai/frontend/web/.prettierignore

@@ -12,4 +12,3 @@ index.html
 src/services/api/schema.d.ts
 static/
 src/theme/css/overlayscrollbars.css
-pnpm-lock.yaml

invokeai/frontend/web/.storybook/main.ts

@@ -1,21 +0,0 @@
-import type { StorybookConfig } from '@storybook/react-vite';
-
-const config: StorybookConfig = {
-  stories: ['../src/**/*.mdx', '../src/**/*.stories.@(js|jsx|mjs|ts|tsx)'],
-  addons: [
-    '@storybook/addon-links',
-    '@storybook/addon-essentials',
-    '@storybook/addon-interactions',
-  ],
-  framework: {
-    name: '@storybook/react-vite',
-    options: {},
-  },
-  docs: {
-    autodocs: 'tag',
-  },
-  core: {
-    disableTelemetry: true,
-  },
-};
-export default config;

invokeai/frontend/web/.storybook/manager.ts

@@ -1,6 +0,0 @@
-import { addons } from '@storybook/manager-api';
-import { themes } from '@storybook/theming';
-
-addons.setConfig({
-  theme: themes.dark,
-});

invokeai/frontend/web/.storybook/preview.tsx

@@ -1,47 +0,0 @@
-import { Preview } from '@storybook/react';
-import { themes } from '@storybook/theming';
-import i18n from 'i18next';
-import React from 'react';
-import { initReactI18next } from 'react-i18next';
-import { Provider } from 'react-redux';
-import GlobalHotkeys from '../src/app/components/GlobalHotkeys';
-import ThemeLocaleProvider from '../src/app/components/ThemeLocaleProvider';
-import { createStore } from '../src/app/store/store';
-// TODO: Disabled for IDE performance issues with our translation JSON
-// eslint-disable-next-line @typescript-eslint/ban-ts-comment
-// @ts-ignore
-import translationEN from '../public/locales/en.json';
-
-i18n.use(initReactI18next).init({
-  lng: 'en',
-  resources: {
-    en: { translation: translationEN },
-  },
-  debug: true,
-  interpolation: {
-    escapeValue: false,
-  },
-  returnNull: false,
-});
-
-const store = createStore(undefined, false);
-
-const preview: Preview = {
-  decorators: [
-    (Story) => (
-      <Provider store={store}>
-        <ThemeLocaleProvider>
-          <GlobalHotkeys />
-          <Story />
-        </ThemeLocaleProvider>
-      </Provider>
-    ),
-  ],
-  parameters: {
-    docs: {
-      theme: themes.dark,
-    },
-  },
-};
-
-export default preview;

invokeai/frontend/web/.yarnrc

@@ -0,0 +1,5 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+
+yarn-path ".yarn/releases/yarn-1.22.19.cjs"

invokeai/frontend/web/.yarnrc.yml

@@ -0,0 +1 @@
+yarnPath: .yarn/releases/yarn-1.22.19.cjs