feat(queue): take one functionality in session processor

Executes the next queue item, then pauses. Does nothing if the queue is already running.

.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

@@ -1,10 +1,10 @@
 <div align="center">
 
-![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)
+![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/1a917d94-e099-4fa1-a70f-7dd8d0691018)
 
-# Invoke - Professional Creative AI Tools for Visual Media 
-##  To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
-  
+# Invoke AI - Generative AI for Professional Creatives
+## Professional Creative Tools for Stable Diffusion, Custom-Trained Models, and more. 
+  To learn more about Invoke AI, get started instantly, or implement our Business solutions, visit [invoke.ai](https://invoke.ai)
 
 
 [![discord badge]][discord link]
@@ -56,9 +56,7 @@ the foundation for multiple commercial products.
 
 <div align="center">
 
-
-![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
-
+![canvas preview](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/canvas_preview.png)
 
 </div>
 
@@ -127,8 +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:
@@ -272,7 +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

@@ -2,17 +2,14 @@
 ## Any environment variables supported by InvokeAI can be specified here,
 ## in addition to the examples below.
 
-# HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where InvokeAI will store data.
+# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
 # Outputs will also be stored here by default.
-# If relative, it will be relative to the docker directory in which the docker-compose.yml file is located
-#HOST_INVOKEAI_ROOT=../../invokeai-data
-
-# INVOKEAI_ROOT is the path to the root of the InvokeAI repository within the container.
-# INVOKEAI_ROOT=~/invokeai
+# This **must** be an absolute path.
+INVOKEAI_ROOT=
 
 # Get this value from your HuggingFace account settings page.
 # 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
@@ -21,9 +36,7 @@ x-invokeai: &invokeai
     ports:
       - "${INVOKEAI_PORT:-9090}:9090"
     volumes:
-      - type: bind
-        source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
-        target: ${INVOKEAI_ROOT:-/invokeai}
+      - ${INVOKEAI_ROOT:-~/invokeai}:${INVOKEAI_ROOT:-/invokeai}
       - ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
       # - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
       # - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}
@@ -37,27 +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/features/PROMPTS.md

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

docs/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)
@@ -27,24 +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)
-    + [Nightmare Promptgen](#nightmare-promptgen)    
     + [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)
@@ -54,19 +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
 
@@ -74,46 +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
 
@@ -230,16 +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
 
@@ -261,17 +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
 
@@ -296,16 +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
 
@@ -323,37 +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" />
-
---------------------------------
-### Nightmare Promptgen
-
-**Description:** Nightmare Prompt Generator - Uses a local text generation model to create unique imaginative (but usually nightmarish) prompts for InvokeAI. By default, it allows you to choose from some gpt-neo models I finetuned on over 2500 of my own InvokeAI prompts in Compel format, but you're able to add your own, as well. Offers support for replacing any troublesome words with a random choice from list you can also define.
-
-**Node Link:** [https://github.com/gogurtenjoyer/nightmare-promptgen](https://github.com/gogurtenjoyer/nightmare-promptgen)
-
 --------------------------------
 ### Oobabooga
 
@@ -417,15 +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
@@ -438,17 +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
 
@@ -534,7 +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,
-              "name": "ClipField"
-            }
+            "label": ""
           }
         },
         "outputs": {
           "conditioning": {
             "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447",
             "name": "conditioning",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ConditioningField"
-            }
+            "type": "ConditioningField",
+            "fieldKind": "output"
           }
-        }
+        },
+        "label": "Negative Compel Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 259,
+      "height": 261,
       "position": {
-        "x": 1000,
-        "y": 350
+        "x": 995.7263915923627,
+        "y": 239.67783573351227
       }
     },
     {
@@ -97,60 +76,37 @@
       "data": {
         "id": "55705012-79b9-4aac-9f26-c0b10309785b",
         "type": "noise",
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.1",
-        "nodePack": "invokeai",
         "inputs": {
           "seed": {
             "id": "6431737c-918a-425d-a3b4-5d57e2f35d4d",
             "name": "seed",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 0
           },
           "width": {
             "id": "38fc5b66-fe6e-47c8-bba9-daf58e454ed7",
             "name": "width",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 512
           },
           "height": {
             "id": "16298330-e2bf-4872-a514-d6923df53cbb",
             "name": "height",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 512
           },
           "use_cpu": {
             "id": "c7c436d3-7a7a-4e76-91e4-c6deb271623c",
             "name": "use_cpu",
+            "type": "boolean",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "BooleanField"
-            },
             "value": true
           }
         },
@@ -158,40 +114,35 @@
           "noise": {
             "id": "50f650dc-0184-4e23-a927-0497a96fe954",
             "name": "noise",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "LatentsField"
-            }
+            "type": "LatentsField",
+            "fieldKind": "output"
           },
           "width": {
             "id": "bb8a452b-133d-42d1-ae4a-3843d7e4109a",
             "name": "width",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
+            "type": "integer",
+            "fieldKind": "output"
           },
           "height": {
             "id": "35cfaa12-3b8b-4b7a-a884-327ff3abddd9",
             "name": "height",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
+            "type": "integer",
+            "fieldKind": "output"
           }
-        }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 388,
+      "height": 389,
       "position": {
-        "x": 600,
-        "y": 325
+        "x": 993.4442117555518,
+        "y": 605.6757415334787
       }
     },
     {
@@ -200,24 +151,13 @@
       "data": {
         "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
         "type": "main_model_loader",
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0",
-        "nodePack": "invokeai",
         "inputs": {
           "model": {
             "id": "993eabd2-40fd-44fe-bce7-5d0c7075ddab",
             "name": "model",
+            "type": "MainModelField",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "MainModelField"
-            },
             "value": {
               "model_name": "stable-diffusion-v1-5",
               "base_model": "sd-1",
@@ -229,40 +169,35 @@
           "unet": {
             "id": "5c18c9db-328d-46d0-8cb9-143391c410be",
             "name": "unet",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "UNetField"
-            }
+            "type": "UNetField",
+            "fieldKind": "output"
           },
           "clip": {
             "id": "6effcac0-ec2f-4bf5-a49e-a2c29cf921f4",
             "name": "clip",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ClipField"
-            }
+            "type": "ClipField",
+            "fieldKind": "output"
           },
           "vae": {
             "id": "57683ba3-f5f5-4f58-b9a2-4b83dacad4a1",
             "name": "vae",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "VaeField"
-            }
+            "type": "VaeField",
+            "fieldKind": "output"
           }
-        }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
       "height": 226,
       "position": {
-        "x": 600,
-        "y": 25
+        "x": 163.04436745878343,
+        "y": 254.63156870373479
       }
     },
     {
@@ -271,56 +206,44 @@
       "data": {
         "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
         "type": "compel",
-        "label": "Positive 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": "Positive Prompt",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            },
-            "value": "Super cute tiger cub, national geographic award-winning photograph"
+            "value": ""
           },
           "clip": {
             "id": "48d23dce-a6ae-472a-9f8c-22a714ea5ce0",
             "name": "clip",
+            "type": "ClipField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ClipField"
-            }
+            "label": ""
           }
         },
         "outputs": {
           "conditioning": {
             "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447",
             "name": "conditioning",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ConditioningField"
-            }
+            "type": "ConditioningField",
+            "fieldKind": "output"
           }
-        }
+        },
+        "label": "Positive Compel Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 259,
+      "height": 261,
       "position": {
-        "x": 1000,
-        "y": 25
+        "x": 595.7263915923627,
+        "y": 239.67783573351227
       }
     },
     {
@@ -329,36 +252,21 @@
       "data": {
         "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
         "type": "rand_int",
-        "label": "Random Seed",
-        "isOpen": false,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": false,
-        "version": "1.0.0",
-        "nodePack": "invokeai",
         "inputs": {
           "low": {
             "id": "3ec65a37-60ba-4b6c-a0b2-553dd7a84b84",
             "name": "low",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 0
           },
           "high": {
             "id": "085f853a-1a5f-494d-8bec-e4ba29a3f2d1",
             "name": "high",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 2147483647
           }
         },
@@ -366,20 +274,23 @@
           "value": {
             "id": "812ade4d-7699-4261-b9fc-a6c9d2ab55ee",
             "name": "value",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
+            "type": "integer",
+            "fieldKind": "output"
           }
-        }
+        },
+        "label": "Random Seed",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": false,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 32,
+      "height": 218,
       "position": {
-        "x": 600,
-        "y": 275
+        "x": 541.094822888628,
+        "y": 694.5704476446829
       }
     },
     {
@@ -388,224 +299,144 @@
       "data": {
         "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
         "type": "denoise_latents",
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.5.0",
-        "nodePack": "invokeai",
         "inputs": {
           "positive_conditioning": {
             "id": "90b7f4f8-ada7-4028-8100-d2e54f192052",
             "name": "positive_conditioning",
+            "type": "ConditioningField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ConditioningField"
-            }
+            "label": ""
           },
           "negative_conditioning": {
             "id": "9393779e-796c-4f64-b740-902a1177bf53",
             "name": "negative_conditioning",
+            "type": "ConditioningField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ConditioningField"
-            }
+            "label": ""
           },
           "noise": {
             "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
             "name": "noise",
+            "type": "LatentsField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "LatentsField"
-            }
+            "label": ""
           },
           "steps": {
             "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
             "name": "steps",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
-            "value": 50
+            "value": 10
           },
           "cfg_scale": {
             "id": "87dd04d3-870e-49e1-98bf-af003a810109",
             "name": "cfg_scale",
+            "type": "FloatPolymorphic",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": true,
-              "name": "FloatField"
-            },
             "value": 7.5
           },
           "denoising_start": {
             "id": "f369d80f-4931-4740-9bcd-9f0620719fab",
             "name": "denoising_start",
+            "type": "float",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "FloatField"
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             "value": 0
           },
           "denoising_end": {
             "id": "747d10e5-6f02-445c-994c-0604d814de8c",
             "name": "denoising_end",
+            "type": "float",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "FloatField"
-            },
             "value": 1
           },
           "scheduler": {
             "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
             "name": "scheduler",
+            "type": "Scheduler",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "SchedulerField"
-            },
-            "value": "unipc"
+            "value": "euler"
           },
           "unet": {
             "id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b",
             "name": "unet",
+            "type": "UNetField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "UNetField"
-            }
+            "label": ""
           },
           "control": {
             "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
             "name": "control",
+            "type": "ControlPolymorphic",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": true,
-              "name": "ControlField"
-            }
+            "label": ""
           },
           "ip_adapter": {
             "id": "1d6948f7-3a65-4a65-a20c-768b287251aa",
             "name": "ip_adapter",
+            "type": "IPAdapterPolymorphic",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": true,
-              "name": "IPAdapterField"
-            }
+            "label": ""
           },
           "t2i_adapter": {
             "id": "75e67b09-952f-4083-aaf4-6b804d690412",
             "name": "t2i_adapter",
+            "type": "T2IAdapterPolymorphic",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": true,
-              "name": "T2IAdapterField"
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-          },
-          "cfg_rescale_multiplier": {
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-            "name": "cfg_rescale_multiplier",
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-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "FloatField"
-            },
-            "value": 0
+            "label": ""
           },
           "latents": {
             "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
             "name": "latents",
+            "type": "LatentsField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "LatentsField"
-            }
+            "label": ""
           },
           "denoise_mask": {
             "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
             "name": "denoise_mask",
+            "type": "DenoiseMaskField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "DenoiseMaskField"
-            }
+            "label": ""
           }
         },
         "outputs": {
           "latents": {
             "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
             "name": "latents",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "LatentsField"
-            }
+            "type": "LatentsField",
+            "fieldKind": "output"
           },
           "width": {
             "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
             "name": "width",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
+            "type": "integer",
+            "fieldKind": "output"
           },
           "height": {
             "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
             "name": "height",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
+            "type": "integer",
+            "fieldKind": "output"
           }
-        }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.4.0"
       },
       "width": 320,
-      "height": 703,
+      "height": 646,
       "position": {
-        "x": 1400,
-        "y": 25
+        "x": 1476.5794704734735,
+        "y": 256.80174342731783
       }
     },
     {
@@ -614,185 +445,153 @@
       "data": {
         "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
         "type": "l2i",
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": false,
-        "useCache": true,
-        "version": "1.2.0",
-        "nodePack": "invokeai",
         "inputs": {
           "metadata": {
             "id": "ab375f12-0042-4410-9182-29e30db82c85",
             "name": "metadata",
+            "type": "MetadataField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "MetadataField"
-            }
+            "label": ""
           },
           "latents": {
             "id": "3a7e7efd-bff5-47d7-9d48-615127afee78",
             "name": "latents",
+            "type": "LatentsField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "LatentsField"
-            }
+            "label": ""
           },
           "vae": {
             "id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b",
             "name": "vae",
+            "type": "VaeField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "VaeField"
-            }
+            "label": ""
           },
           "tiled": {
             "id": "da52059a-0cee-4668-942f-519aa794d739",
             "name": "tiled",
+            "type": "boolean",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "BooleanField"
-            },
             "value": false
           },
           "fp32": {
             "id": "c4841df3-b24e-4140-be3b-ccd454c2522c",
             "name": "fp32",
+            "type": "boolean",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "BooleanField"
-            },
-            "value": true
+            "value": false
           }
         },
         "outputs": {
           "image": {
             "id": "72d667d0-cf85-459d-abf2-28bd8b823fe7",
             "name": "image",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ImageField"
-            }
+            "type": "ImageField",
+            "fieldKind": "output"
           },
           "width": {
             "id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc",
             "name": "width",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
+            "type": "integer",
+            "fieldKind": "output"
           },
           "height": {
             "id": "230f359c-b4ea-436c-b372-332d7dcdca85",
             "name": "height",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            }
+            "type": "integer",
+            "fieldKind": "output"
           }
-        }
+        },
+        "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 +2,7 @@
 
 from logging import Logger
 
-from invokeai.app.services.shared.sqlite.sqlite_util import init_db
+from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -11,7 +11,6 @@ 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 +23,14 @@ 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 import SqliteDatabase
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -67,9 +67,8 @@ class ApiDependencies:
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
         output_folder = config.output_path
-        image_files = DiskImageFileStorage(f"{output_folder}/images")
 
-        db = init_db(config=config, logger=logger, image_files=image_files)
+        db = SqliteDatabase(config, logger)
 
         configuration = config
         logger = logger
@@ -80,16 +79,14 @@ 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()
@@ -97,6 +94,7 @@ class ApiDependencies:
         session_processor = DefaultSessionProcessor()
         session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
+        workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
         workflow_records = SqliteWorkflowRecordsStorage(db=db)
 
         services = InvocationServices(
@@ -107,6 +105,7 @@ 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 +114,6 @@ 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,
@@ -124,10 +121,14 @@ class ApiDependencies:
             session_processor=session_processor,
             session_queue=session_queue,
             urls=urls,
+            workflow_image_records=workflow_image_records,
             workflow_records=workflow_records,
         )
 
+        create_system_graphs(services.graph_library)
+
         ApiDependencies.invoker = Invoker(services)
+
         db.clean()
 
     @staticmethod

invokeai/app/api/routers/app_info.py

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

invokeai/app/api/routers/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/images.py

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

invokeai/app/api/routers/model_records.py

@@ -4,7 +4,7 @@
 
 from hashlib import sha1
 from random import randbytes
-from typing import Any, Dict, List, Optional
+from typing import List, Optional
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
@@ -12,7 +12,6 @@ from pydantic import BaseModel, ConfigDict
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
-from invokeai.app.services.model_install import ModelInstallJob, ModelSource
 from invokeai.app.services.model_records import (
     DuplicateModelException,
     InvalidModelException,
@@ -26,7 +25,7 @@ from invokeai.backend.model_manager.config import (
 
 from ..dependencies import ApiDependencies
 
-model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager_v2_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:
@@ -157,7 +141,7 @@ async def del_model_record(
     status_code=201,
 )
 async def add_model_record(
-    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
+    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")]
 ) -> AnyModelConfig:
     """
     Add a model using the configuration information appropriate for its type.
@@ -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/routers/session_queue.py

@@ -93,6 +93,18 @@ async def Pause(
     return ApiDependencies.invoker.services.session_processor.pause()
 
 
+@session_queue_router.put(
+    "/{queue_id}/processor/take_one",
+    operation_id="take_one",
+    responses={200: {"model": SessionProcessorStatus}},
+)
+async def take_one(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> SessionProcessorStatus:
+    """Executes the next-in-line queue item, pausing the processor afterwards. Has no effect if the queue is resumed."""
+    return ApiDependencies.invoker.services.session_processor.take_one()
+
+
 @session_queue_router.put(
     "/{queue_id}/cancel_by_batch_ids",
     operation_id="cancel_by_batch_ids",

invokeai/app/api/routers/utilities.py

@@ -23,11 +23,10 @@ class DynamicPromptsResponse(BaseModel):
 )
 async def parse_dynamicprompts(
     prompt: str = Body(description="The prompt to parse with dynamicprompts"),
-    max_prompts: int = Body(ge=1, le=10000, default=1000, description="The max number of prompts to generate"),
+    max_prompts: int = Body(default=1000, description="The max number of prompts to generate"),
     combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
 ) -> DynamicPromptsResponse:
     """Creates a batch process"""
-    max_prompts = min(max_prompts, 10000)
     generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator]
     try:
         error: Optional[str] = None

invokeai/app/api/routers/workflows.py

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

invokeai/app/api/sockets.py

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

invokeai/app/api_app.py

@@ -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
@@ -17,7 +16,6 @@ from pydantic.fields import FieldInfo, _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
@@ -39,19 +37,6 @@ 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 +437,6 @@ 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,
@@ -468,7 +452,6 @@ class InvocationContext:
     queue_id: str
     queue_item_id: int
     queue_batch_id: str
-    workflow: Optional[WorkflowWithoutID]
 
     def __init__(
         self,
@@ -477,14 +460,12 @@ class InvocationContext:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        workflow: Optional[WorkflowWithoutID],
     ):
         self.services = services
         self.graph_execution_state_id = graph_execution_state_id
         self.queue_id = queue_id
         self.queue_item_id = queue_item_id
         self.queue_batch_id = queue_batch_id
-        self.workflow = workflow
 
 
 class BaseInvocationOutput(BaseModel):
@@ -621,7 +602,6 @@ 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 +705,8 @@ 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 +775,6 @@ 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 +785,6 @@ 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]:
@@ -829,12 +805,11 @@ 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"
-        if is_custom_node:
-            cls.UIConfig.node_pack = cls.__module__.split(".")[0]
+        module_name = cls.__module__.split(".")[0]
+        if module_name.endswith(CUSTOM_NODE_PACK_SUFFIX):
+            cls.UIConfig.node_pack = module_name.split(CUSTOM_NODE_PACK_SUFFIX)[0]
         else:
             cls.UIConfig.node_pack = None
 
@@ -928,6 +903,24 @@ def invocation_output(
     return wrapper
 
 
+class WorkflowField(RootModel):
+    """
+    Pydantic model for workflows with custom root of type dict[str, Any].
+    Workflows are stored without a strict schema.
+    """
+
+    root: dict[str, Any] = Field(description="The workflow")
+
+
+WorkflowFieldValidator = TypeAdapter(WorkflowField)
+
+
+class WithWorkflow(BaseModel):
+    workflow: Optional[WorkflowField] = Field(
+        default=None, description=FieldDescriptions.workflow, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
+    )
+
+
 class MetadataField(RootModel):
     """
     Pydantic model for metadata with custom root of type dict[str, Any].
@@ -950,13 +943,3 @@ class WithMetadata(BaseModel):
             orig_required=False,
         ).model_dump(exclude_none=True),
     )
-
-
-class WithWorkflow:
-    workflow = None
-
-    def __init_subclass__(cls) -> None:
-        logger.warn(
-            f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
-        )
-        super().__init_subclass__()

invokeai/app/invocations/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/controlnet_image_processors.py

@@ -24,10 +24,9 @@ from controlnet_aux import (
 )
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
 
@@ -40,6 +39,7 @@ from .baseinvocation import (
     InvocationContext,
     OutputField,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -76,16 +76,17 @@ class ControlField(BaseModel):
     resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
     @field_validator("control_weight")
-    @classmethod
     def validate_control_weight(cls, v):
-        validate_weights(v)
+        """Validate that all control weights in the valid range"""
+        if isinstance(v, list):
+            for i in v:
+                if i < -1 or i > 2:
+                    raise ValueError("Control weights must be within -1 to 2 range")
+        else:
+            if v < -1 or v > 2:
+                raise ValueError("Control weights must be within -1 to 2 range")
         return v
 
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
 
 @invocation_output("control_output")
 class ControlOutput(BaseInvocationOutput):
@@ -95,17 +96,17 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
     control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
     control_weight: Union[float, List[float]] = InputField(
-        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+        default=1.0, description="The weight given to the ControlNet"
     )
     begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+        default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
     )
     end_step_percent: float = InputField(
         default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
@@ -113,17 +114,6 @@ class ControlNetInvocation(BaseInvocation):
     control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
     resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
 
-    @field_validator("control_weight")
-    @classmethod
-    def validate_control_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> "ControlNetInvocation":
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
     def invoke(self, context: InvocationContext) -> ControlOutput:
         return ControlOutput(
             control=ControlField(
@@ -139,7 +129,7 @@ class ControlNetInvocation(BaseInvocation):
 
 
 # This invocation exists for other invocations to subclass it - do not register with @invocation!
-class ImageProcessorInvocation(BaseInvocation, WithMetadata):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
@@ -163,7 +153,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         """Builds an ImageOutput and its ImageField"""
@@ -183,7 +173,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -206,7 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -235,7 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -257,7 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -280,7 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Openpose Processor",
     tags=["controlnet", "openpose", "pose"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Openpose processing to image"""
@@ -305,7 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -332,7 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -349,7 +339,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.1.0"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -372,7 +362,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.1.0"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -399,7 +389,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -429,7 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -445,7 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -468,7 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -497,7 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -537,7 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -579,7 +569,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.1.0",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""

invokeai/app/invocations/custom_nodes/init.py

@@ -6,6 +6,7 @@ import sys
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 
+from invokeai.app.invocations.baseinvocation import CUSTOM_NODE_PACK_SUFFIX
 from invokeai.backend.util.logging import InvokeAILogger
 
 logger = InvokeAILogger.get_logger()
@@ -33,7 +34,7 @@ for d in Path(__file__).parent.iterdir():
         continue
 
     # load the module, appending adding a suffix to identify it as a custom node pack
-    spec = spec_from_file_location(module_name, init.absolute())
+    spec = spec_from_file_location(f"{module_name}{CUSTOM_NODE_PACK_SUFFIX}", init.absolute())
 
     if spec is None or spec.loader is None:
         logger.warn(f"Could not load {init}")

invokeai/app/invocations/cv.py

@@ -8,11 +8,11 @@ from PIL import Image, ImageOps
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
-class CvInpaintInvocation(BaseInvocation, WithMetadata):
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.1.0")
+class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")
@@ -41,7 +41,7 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/facetools.py

@@ -17,6 +17,7 @@ from invokeai.app.invocations.baseinvocation import (
     InvocationContext,
     OutputField,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -437,8 +438,8 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
-class FaceOffInvocation(BaseInvocation, WithMetadata):
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.1.0")
+class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
     image: ImageField = InputField(description="Image for face detection")
@@ -507,7 +508,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -531,8 +532,8 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
-class FaceMaskInvocation(BaseInvocation, WithMetadata):
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.1.0")
+class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -626,7 +627,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -649,9 +650,9 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.1.0"
 )
-class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
+class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -715,7 +716,7 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/image.py

@@ -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, WithWorkflow, invocation
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -44,14 +36,8 @@ class ShowImageInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "blank_image",
-    title="Blank Image",
-    tags=["image"],
-    category="image",
-    version="1.2.0",
-)
-class BlankImageInvocation(BaseInvocation, WithMetadata):
+@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.1.0")
+class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Creates a blank image and forwards it to the pipeline"""
 
     width: int = InputField(default=512, description="The width of the image")
@@ -70,7 +56,7 @@ class BlankImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -80,14 +66,8 @@ class BlankImageInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_crop",
-    title="Crop Image",
-    tags=["image", "crop"],
-    category="image",
-    version="1.2.0",
-)
-class ImageCropInvocation(BaseInvocation, WithMetadata):
+@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.1.0")
+class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Crops an image to a specified box. The box can be outside of the image."""
 
     image: ImageField = InputField(description="The image to crop")
@@ -110,7 +90,7 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -175,14 +155,8 @@ class CenterPadCropInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_paste",
-    title="Paste Image",
-    tags=["image", "paste"],
-    category="image",
-    version="1.2.0",
-)
-class ImagePasteInvocation(BaseInvocation, WithMetadata):
+@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.1.0")
+class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Pastes an image into another image."""
 
     base_image: ImageField = InputField(description="The base image")
@@ -225,7 +199,7 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -235,14 +209,8 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "tomask",
-    title="Mask from Alpha",
-    tags=["image", "mask"],
-    category="image",
-    version="1.2.0",
-)
-class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
+@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.1.0")
+class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Extracts the alpha channel of an image as a mask."""
 
     image: ImageField = InputField(description="The image to create the mask from")
@@ -263,7 +231,7 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -273,14 +241,8 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_mul",
-    title="Multiply Images",
-    tags=["image", "multiply"],
-    category="image",
-    version="1.2.0",
-)
-class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
+@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.1.0")
+class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Multiplies two images together using `PIL.ImageChops.multiply()`."""
 
     image1: ImageField = InputField(description="The first image to multiply")
@@ -300,7 +262,7 @@ class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -313,14 +275,8 @@ class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
 IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
 
 
-@invocation(
-    "img_chan",
-    title="Extract Image Channel",
-    tags=["image", "channel"],
-    category="image",
-    version="1.2.0",
-)
-class ImageChannelInvocation(BaseInvocation, WithMetadata):
+@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.1.0")
+class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Gets a channel from an image."""
 
     image: ImageField = InputField(description="The image to get the channel from")
@@ -339,7 +295,7 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -352,14 +308,8 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata):
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 
 
-@invocation(
-    "img_conv",
-    title="Convert Image Mode",
-    tags=["image", "convert"],
-    category="image",
-    version="1.2.0",
-)
-class ImageConvertInvocation(BaseInvocation, WithMetadata):
+@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.1.0")
+class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Converts an image to a different mode."""
 
     image: ImageField = InputField(description="The image to convert")
@@ -378,7 +328,7 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -388,14 +338,8 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_blur",
-    title="Blur Image",
-    tags=["image", "blur"],
-    category="image",
-    version="1.2.0",
-)
-class ImageBlurInvocation(BaseInvocation, WithMetadata):
+@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.1.0")
+class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Blurs an image"""
 
     image: ImageField = InputField(description="The image to blur")
@@ -419,7 +363,7 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -429,64 +373,6 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "unsharp_mask",
-    title="Unsharp Mask",
-    tags=["image", "unsharp_mask"],
-    category="image",
-    version="1.2.0",
-    classification=Classification.Beta,
-)
-class UnsharpMaskInvocation(BaseInvocation, WithMetadata):
-    """Applies an unsharp mask filter to an image"""
-
-    image: ImageField = InputField(description="The image to use")
-    radius: float = InputField(gt=0, description="Unsharp mask radius", default=2)
-    strength: float = InputField(ge=0, description="Unsharp mask strength", default=50)
-
-    def pil_from_array(self, arr):
-        return Image.fromarray((arr * 255).astype("uint8"))
-
-    def array_from_pil(self, img):
-        return numpy.array(img) / 255
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-        mode = image.mode
-
-        alpha_channel = image.getchannel("A") if mode == "RGBA" else None
-        image = image.convert("RGB")
-        image_blurred = self.array_from_pil(image.filter(ImageFilter.GaussianBlur(radius=self.radius)))
-
-        image = self.array_from_pil(image)
-        image += (image - image_blurred) * (self.strength / 100.0)
-        image = numpy.clip(image, 0, 1)
-        image = self.pil_from_array(image)
-
-        image = image.convert(mode)
-
-        # Make the image RGBA if we had a source alpha channel
-        if alpha_channel is not None:
-            image.putalpha(alpha_channel)
-
-        image_dto = context.services.images.create(
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image.width,
-            height=image.height,
-        )
-
-
 PIL_RESAMPLING_MODES = Literal[
     "nearest",
     "box",
@@ -507,14 +393,8 @@ PIL_RESAMPLING_MAP = {
 }
 
 
-@invocation(
-    "img_resize",
-    title="Resize Image",
-    tags=["image", "resize"],
-    category="image",
-    version="1.2.0",
-)
-class ImageResizeInvocation(BaseInvocation, WithMetadata):
+@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.1.0")
+class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Resizes an image to specific dimensions"""
 
     image: ImageField = InputField(description="The image to resize")
@@ -540,7 +420,7 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -550,14 +430,8 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_scale",
-    title="Scale Image",
-    tags=["image", "scale"],
-    category="image",
-    version="1.2.0",
-)
-class ImageScaleInvocation(BaseInvocation, WithMetadata):
+@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.1.0")
+class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Scales an image by a factor"""
 
     image: ImageField = InputField(description="The image to scale")
@@ -588,7 +462,7 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -598,14 +472,8 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_lerp",
-    title="Lerp Image",
-    tags=["image", "lerp"],
-    category="image",
-    version="1.2.0",
-)
-class ImageLerpInvocation(BaseInvocation, WithMetadata):
+@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.1.0")
+class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -628,7 +496,7 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -638,14 +506,8 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_ilerp",
-    title="Inverse Lerp Image",
-    tags=["image", "ilerp"],
-    category="image",
-    version="1.2.0",
-)
-class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
+@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.1.0")
+class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Inverse linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -668,7 +530,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -678,14 +540,8 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_nsfw",
-    title="Blur NSFW Image",
-    tags=["image", "nsfw"],
-    category="image",
-    version="1.2.0",
-)
-class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
+@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.1.0")
+class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add blur to NSFW-flagged images"""
 
     image: ImageField = InputField(description="The image to check")
@@ -710,7 +566,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -731,9 +587,9 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
     title="Add Invisible Watermark",
     tags=["image", "watermark"],
     category="image",
-    version="1.2.0",
+    version="1.1.0",
 )
-class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
+class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add an invisible watermark to an image"""
 
     image: ImageField = InputField(description="The image to check")
@@ -750,7 +606,7 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -760,14 +616,8 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "mask_edge",
-    title="Mask Edge",
-    tags=["image", "mask", "inpaint"],
-    category="image",
-    version="1.2.0",
-)
-class MaskEdgeInvocation(BaseInvocation, WithMetadata):
+@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.1.0")
+class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Applies an edge mask to an image"""
 
     image: ImageField = InputField(description="The image to apply the mask to")
@@ -802,7 +652,7 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -817,9 +667,9 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata):
     title="Combine Masks",
     tags=["image", "mask", "multiply"],
     category="image",
-    version="1.2.0",
+    version="1.1.0",
 )
-class MaskCombineInvocation(BaseInvocation, WithMetadata):
+class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
 
     mask1: ImageField = InputField(description="The first mask to combine")
@@ -839,7 +689,7 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -849,14 +699,8 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "color_correct",
-    title="Color Correct",
-    tags=["image", "color"],
-    category="image",
-    version="1.2.0",
-)
-class ColorCorrectInvocation(BaseInvocation, WithMetadata):
+@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.1.0")
+class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """
     Shifts the colors of a target image to match the reference image, optionally
     using a mask to only color-correct certain regions of the target image.
@@ -956,7 +800,7 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -966,14 +810,8 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_hue_adjust",
-    title="Adjust Image Hue",
-    tags=["image", "hue"],
-    category="image",
-    version="1.2.0",
-)
-class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
+@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.1.0")
+class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Adjusts the Hue of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1002,7 +840,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1075,9 +913,9 @@ CHANNEL_FORMATS = {
         "value",
     ],
     category="image",
-    version="1.2.0",
+    version="1.1.0",
 )
-class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
+class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Add or subtract a value from a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1112,7 +950,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1146,9 +984,9 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
         "value",
     ],
     category="image",
-    version="1.2.0",
+    version="1.1.0",
 )
-class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
+class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Scale a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1187,7 +1025,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
-            workflow=context.workflow,
+            workflow=self.workflow,
             metadata=self.metadata,
         )
 
@@ -1205,10 +1043,10 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
     title="Save Image",
     tags=["primitives", "image"],
     category="primitives",
-    version="1.2.0",
+    version="1.1.0",
     use_cache=False,
 )
-class SaveImageInvocation(BaseInvocation, WithMetadata):
+class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
 
     image: ImageField = InputField(description=FieldDescriptions.image)
@@ -1226,7 +1064,7 @@ class SaveImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1244,7 +1082,7 @@ class SaveImageInvocation(BaseInvocation, WithMetadata):
     version="1.0.1",
     use_cache=False,
 )
-class LinearUIOutputInvocation(BaseInvocation, WithMetadata):
+class LinearUIOutputInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Handles Linear UI Image Outputting tasks."""
 
     image: ImageField = InputField(description=FieldDescriptions.image)

invokeai/app/invocations/infill.py

@@ -13,7 +13,7 @@ from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
 from invokeai.backend.image_util.lama import LaMA
 from invokeai.backend.image_util.patchmatch import PatchMatch
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -118,8 +118,8 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
     return si
 
 
-@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class InfillColorInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
+class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -144,7 +144,7 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -154,8 +154,8 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
-class InfillTileInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.1")
+class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with tiles of the image"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -181,7 +181,7 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -192,9 +192,9 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0"
 )
-class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
+class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -235,7 +235,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -245,8 +245,8 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class LaMaInfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
+class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -264,7 +264,7 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -274,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class CV2InfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
+class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -293,7 +293,7 @@ class CV2InfillInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/ip_adapter.py

@@ -2,7 +2,7 @@ import os
 from builtins import float
 from typing import List, Union
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -15,7 +15,6 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType, ModelType
 from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
@@ -40,6 +39,7 @@ class IPAdapterField(BaseModel):
     ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
     image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
+    # weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
     )
@@ -47,17 +47,6 @@ class IPAdapterField(BaseModel):
         default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
     )
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
 
 @invocation_output("ip_adapter_output")
 class IPAdapterOutput(BaseInvocationOutput):
@@ -65,7 +54,7 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.1")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -75,27 +64,18 @@ class IPAdapterInvocation(BaseInvocation):
         description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
     )
 
+    # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
     weight: Union[float, List[float]] = InputField(
-        default=1, description="The weight given to the IP-Adapter", title="Weight"
+        default=1, ge=-1, description="The weight given to the IP-Adapter", title="Weight"
     )
+
     begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
+        default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
     )
     end_step_percent: float = InputField(
         default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
     )
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
     def invoke(self, context: InvocationContext) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
         ip_adapter_info = context.services.model_manager.model_info(

invokeai/app/invocations/latent.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-import contextlib
 from contextlib import ExitStack
 from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
@@ -65,6 +64,7 @@ from .baseinvocation import (
     OutputField,
     UIType,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -79,12 +79,6 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
 
-# HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
-# be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
-# factor is hard-coded to a literal '8' rather than using this constant.
-# The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
-LATENT_SCALE_FACTOR = 8
-
 
 @invocation_output("scheduler_output")
 class SchedulerOutput(BaseInvocationOutput):
@@ -221,7 +215,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.1",
+    version="1.5.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -280,7 +274,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         ui_order=7,
     )
     cfg_rescale_multiplier: float = InputField(
-        title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
     )
     latents: Optional[LatentsField] = InputField(
         default=None,
@@ -400,9 +394,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
     ) -> List[ControlNetData]:
-        # Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
-        control_height_resize = latents_shape[2] * LATENT_SCALE_FACTOR
-        control_width_resize = latents_shape[3] * LATENT_SCALE_FACTOR
+        # assuming fixed dimensional scaling of 8:1 for image:latents
+        control_height_resize = latents_shape[2] * 8
+        control_width_resize = latents_shape[3] * 8
         if control_input is None:
             control_list = None
         elif isinstance(control_input, list) and len(control_input) == 0:
@@ -717,23 +711,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 **self.unet.unet.model_dump(),
                 context=context,
             )
-
-            # Prepare seamless context, if configured.
-            seamless_context = contextlib.nullcontext()
-            seamless_config = self.unet.seamless
-            if seamless_config is not None:
-                seamless_context = set_seamless(
-                    model=unet_info.context.model,
-                    axes=seamless_config.axes,
-                    skipped_layers=seamless_config.skipped_layers,
-                    skip_second_resnet=seamless_config.skip_second_resnet,
-                    skip_conv2=seamless_config.skip_conv2,
-                )
-
             with (
                 ExitStack() as exit_stack,
                 ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
-                seamless_context,
+                set_seamless(unet_info.context.model, self.unet.seamless_axes),
                 unet_info as unet,
                 # Apply the LoRA after unet has been moved to its target device for faster patching.
                 ModelPatcher.apply_lora_unet(unet, _lora_loader()),
@@ -815,9 +796,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.2.0",
+    version="1.1.0",
 )
-class LatentsToImageInvocation(BaseInvocation, WithMetadata):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -840,19 +821,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
             context=context,
         )
 
-        # Prepare seamless context, if configured.
-        seamless_context = contextlib.nullcontext()
-        seamless_config = self.vae.seamless
-        if seamless_config is not None:
-            seamless_context = set_seamless(
-                model=vae_info.context.model,
-                axes=seamless_config.axes,
-                skipped_layers=seamless_config.skipped_layers,
-                skip_second_resnet=seamless_config.skip_second_resnet,
-                skip_conv2=seamless_config.skip_conv2,
-            )
-
-        with seamless_context, vae_info as vae:
+        with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)
@@ -911,7 +880,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -940,12 +909,12 @@ class ResizeLatentsInvocation(BaseInvocation):
     )
     width: int = InputField(
         ge=64,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         description=FieldDescriptions.width,
     )
     height: int = InputField(
         ge=64,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         description=FieldDescriptions.width,
     )
     mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
@@ -959,7 +928,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         resized_latents = torch.nn.functional.interpolate(
             latents.to(device),
-            size=(self.height // LATENT_SCALE_FACTOR, self.width // LATENT_SCALE_FACTOR),
+            size=(self.height // 8, self.width // 8),
             mode=self.mode,
             antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
@@ -1197,60 +1166,3 @@ class BlendLatentsInvocation(BaseInvocation):
         # context.services.latents.set(name, resized_latents)
         context.services.latents.save(name, blended_latents)
         return build_latents_output(latents_name=name, latents=blended_latents)
-
-
-# The Crop Latents node was copied from @skunkworxdark's implementation here:
-# https://github.com/skunkworxdark/XYGrid_nodes/blob/74647fa9c1fa57d317a94bd43ca689af7f0aae5e/images_to_grids.py#L1117C1-L1167C80
-@invocation(
-    "crop_latents",
-    title="Crop Latents",
-    tags=["latents", "crop"],
-    category="latents",
-    version="1.0.0",
-)
-# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
-# Currently, if the class names conflict then 'GET /openapi.json' fails.
-class CropLatentsCoreInvocation(BaseInvocation):
-    """Crops a latent-space tensor to a box specified in image-space. The box dimensions and coordinates must be
-    divisible by the latent scale factor of 8.
-    """
-
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    x: int = InputField(
-        ge=0,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    y: int = InputField(
-        ge=0,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    width: int = InputField(
-        ge=1,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The width (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    height: int = InputField(
-        ge=1,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        x1 = self.x // LATENT_SCALE_FACTOR
-        y1 = self.y // LATENT_SCALE_FACTOR
-        x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
-        y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
-
-        cropped_latents = latents[..., y1:y2, x1:x2]
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, cropped_latents)
-
-        return build_latents_output(latents_name=name, latents=cropped_latents)

invokeai/app/invocations/model.py

@@ -19,13 +19,6 @@ from .baseinvocation import (
 )
 
 
-class SeamlessSettings(BaseModel):
-    axes: List[str] = Field(description="Axes('x' and 'y') to which apply seamless")
-    skipped_layers: int = Field(description="How much down layers skip when applying seamless")
-    skip_second_resnet: bool = Field(description="Skip or not second resnet in down blocks when applying seamless")
-    skip_conv2: bool = Field(description="Skip or not conv2 in down blocks when applying seamless")
-
-
 class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")
     base_model: BaseModelType = Field(description="Base model")
@@ -43,8 +36,8 @@ class UNetField(BaseModel):
     unet: ModelInfo = Field(description="Info to load unet submodel")
     scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
+    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
     freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
-    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
 
 
 class ClipField(BaseModel):
@@ -57,7 +50,7 @@ class ClipField(BaseModel):
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
-    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
+    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
 @invocation_output("unet_output")
@@ -458,11 +451,6 @@ class SeamlessModeInvocation(BaseInvocation):
     )
     seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
     seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
-    skipped_layers: int = InputField(default=0, input=Input.Any, description="How much model's down layers to skip")
-    skip_second_resnet: bool = InputField(
-        default=True, input=Input.Any, description="Skip or not second resnet in down layers"
-    )
-    skip_conv2: bool = InputField(default=True, input=Input.Any, description="Skip or not conv2 in down layers")
 
     def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
         # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
@@ -477,19 +465,9 @@ class SeamlessModeInvocation(BaseInvocation):
             seamless_axes_list.append("y")
 
         if unet is not None:
-            unet.seamless = SeamlessSettings(
-                axes=seamless_axes_list,
-                skipped_layers=self.skipped_layers,
-                skip_second_resnet=self.skip_second_resnet,
-                skip_conv2=self.skip_conv2,
-            )
+            unet.seamless_axes = seamless_axes_list
         if vae is not None:
-            vae.seamless = SeamlessSettings(
-                axes=seamless_axes_list,
-                skipped_layers=self.skipped_layers,
-                skip_second_resnet=self.skip_second_resnet,
-                skip_conv2=self.skip_conv2,
-            )
+            vae.seamless_axes = seamless_axes_list
 
         return SeamlessModeOutput(unet=unet, vae=vae)
 

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,
@@ -31,6 +31,7 @@ from .baseinvocation import (
     UIComponent,
     UIType,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -78,7 +79,7 @@ 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(
@@ -325,9 +326,9 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
     title="ONNX Latents to Image",
     tags=["latents", "image", "vae", "onnx"],
     category="image",
-    version="1.2.0",
+    version="1.1.0",
 )
-class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
+class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -377,7 +378,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/t2i_adapter.py

@@ -1,6 +1,6 @@
 from typing import Union
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -14,7 +14,6 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType
 
@@ -38,17 +37,6 @@ class T2IAdapterField(BaseModel):
     )
     resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
 
 @invocation_output("t2i_adapter_output")
 class T2IAdapterOutput(BaseInvocationOutput):
@@ -56,7 +44,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.1"
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.0"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""
@@ -73,7 +61,7 @@ class T2IAdapterInvocation(BaseInvocation):
         default=1, ge=0, description="The weight given to the T2I-Adapter", title="Weight"
     )
     begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
+        default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"
     )
     end_step_percent: float = InputField(
         default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
@@ -83,17 +71,6 @@ class T2IAdapterInvocation(BaseInvocation):
         description="The resize mode applied to the T2I-Adapter input image so that it matches the target output size.",
     )
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
     def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
         return T2IAdapterOutput(
             t2i_adapter=T2IAdapterField(

invokeai/app/invocations/tiles.py

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

invokeai/app/invocations/upscale.py

@@ -14,7 +14,7 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
@@ -29,8 +29,8 @@ if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
-class ESRGANInvocation(BaseInvocation, WithMetadata):
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.2.0")
+class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -118,7 +118,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/util.py

@@ -1,14 +0,0 @@
-from typing import Union
-
-
-def validate_weights(weights: Union[float, list[float]]) -> None:
-    """Validate that all control weights in the valid range"""
-    to_validate = weights if isinstance(weights, list) else [weights]
-    if any(i < -1 or i > 2 for i in to_validate):
-        raise ValueError("Control weights must be within -1 to 2 range")
-
-
-def validate_begin_end_step(begin_step_percent: float, end_step_percent: float) -> None:
-    """Validate that begin_step_percent is less than end_step_percent"""
-    if begin_step_percent >= end_step_percent:
-        raise ValueError("Begin step percent must be less than or equal to end step percent")

invokeai/app/services/board_image_records/board_image_records_sqlite.py

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

invokeai/app/services/board_records/board_records_sqlite.py

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

invokeai/app/services/config/__init__.py

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

invokeai/app/services/config/config_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_files/image_files_base.py

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

invokeai/app/services/image_files/image_files_disk.py

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

invokeai/app/services/image_records/image_records_base.py

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

invokeai/app/services/image_records/image_records_common.py

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

invokeai/app/services/image_records/image_records_sqlite.py

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

invokeai/app/services/images/images_base.py

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

invokeai/app/services/images/images_common.py

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

invokeai/app/services/images/images_default.py

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

invokeai/app/services/invocation_processor/invocation_processor_default.py

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

invokeai/app/services/invocation_queue/invocation_queue_common.py

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

invokeai/app/services/invocation_services.py

@@ -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
@@ -22,14 +21,14 @@ 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_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
 
@@ -44,6 +43,7 @@ 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 +51,6 @@ class InvocationServices:
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
     model_records: "ModelRecordServiceBase"
-    download_queue: "DownloadQueueServiceBase"
-    model_install: "ModelInstallServiceBase"
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
@@ -61,6 +59,7 @@ class InvocationServices:
     invocation_cache: "InvocationCacheBase"
     names: "NameServiceBase"
     urls: "UrlServiceBase"
+    workflow_image_records: "WorkflowImageRecordsStorageBase"
     workflow_records: "WorkflowRecordsStorageBase"
 
     def __init__(
@@ -72,6 +71,7 @@ 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 +79,6 @@ class InvocationServices:
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         model_records: "ModelRecordServiceBase",
-        download_queue: "DownloadQueueServiceBase",
-        model_install: "ModelInstallServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
@@ -89,6 +87,7 @@ class InvocationServices:
         invocation_cache: "InvocationCacheBase",
         names: "NameServiceBase",
         urls: "UrlServiceBase",
+        workflow_image_records: "WorkflowImageRecordsStorageBase",
         workflow_records: "WorkflowRecordsStorageBase",
     ):
         self.board_images = board_images
@@ -98,6 +97,7 @@ 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 +105,6 @@ 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
@@ -115,4 +113,5 @@ class InvocationServices:
         self.invocation_cache = invocation_cache
         self.names = names
         self.urls = urls
+        self.workflow_image_records = workflow_image_records
         self.workflow_records = workflow_records

invokeai/app/services/invoker.py

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

invokeai/app/services/item_storage/item_storage_sqlite.py

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

invokeai/app/services/latents_storage/latents_storage_disk.py

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

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

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

invokeai/app/services/model_install/__init__.py

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

invokeai/app/services/model_install/model_install_base.py

@@ -1,305 +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 ..shared.sqlite 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_processor/session_processor_base.py

@@ -22,6 +22,11 @@ class SessionProcessorBase(ABC):
         """Pauses the session processor"""
         pass
 
+    @abstractmethod
+    def take_one(self) -> SessionProcessorStatus:
+        """Takes one session from the queue and executes it"""
+        pass
+
     @abstractmethod
     def get_status(self) -> SessionProcessorStatus:
         """Gets the status of the session processor"""

invokeai/app/services/session_processor/session_processor_default.py

@@ -25,6 +25,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
         self.__resume_event = ThreadEvent()
         self.__stop_event = ThreadEvent()
         self.__poll_now_event = ThreadEvent()
+        self.__take_one_event = ThreadEvent()
 
         local_handler.register(event_name=EventServiceBase.queue_event, _func=self._on_queue_event)
 
@@ -36,6 +37,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
                 "stop_event": self.__stop_event,
                 "poll_now_event": self.__poll_now_event,
                 "resume_event": self.__resume_event,
+                "take_one_event": self.__take_one_event,
             },
         )
         self.__thread.start()
@@ -81,6 +83,13 @@ class DefaultSessionProcessor(SessionProcessorBase):
             self.__resume_event.clear()
         return self.get_status()
 
+    def take_one(self) -> SessionProcessorStatus:
+        if self.__queue_item is None and not self.__resume_event.is_set():
+            self.__resume_event.set()
+            self.__take_one_event.set()
+            self._poll_now()
+        return self.get_status()
+
     def get_status(self) -> SessionProcessorStatus:
         return SessionProcessorStatus(
             is_started=self.__resume_event.is_set(),
@@ -92,9 +101,11 @@ class DefaultSessionProcessor(SessionProcessorBase):
         stop_event: ThreadEvent,
         poll_now_event: ThreadEvent,
         resume_event: ThreadEvent,
+        take_one_event: ThreadEvent,
     ):
         try:
             stop_event.clear()
+            take_one_event.clear()
             resume_event.set()
             self.__threadLimit.acquire()
             queue_item: Optional[SessionQueueItem] = None
@@ -114,11 +125,14 @@ class DefaultSessionProcessor(SessionProcessorBase):
                                 session_queue_id=queue_item.queue_id,
                                 session_queue_item_id=queue_item.item_id,
                                 graph_execution_state=queue_item.session,
-                                workflow=queue_item.workflow,
                                 invoke_all=True,
                             )
                             queue_item = None
 
+                    if take_one_event.is_set():
+                        resume_event.clear()
+                        take_one_event.clear()
+
                     if queue_item is None:
                         self.__invoker.services.logger.debug("Waiting for next polling interval or event")
                         poll_now_event.wait(POLLING_INTERVAL)

invokeai/app/services/session_queue/session_queue_common.py

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

invokeai/app/services/session_queue/session_queue_sqlite.py

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

invokeai/app/services/shared/graph.py

@@ -207,12 +207,10 @@ class IterateInvocationOutput(BaseInvocationOutput):
     item: Any = OutputField(
         description="The item being iterated over", title="Collection Item", ui_type=UIType._CollectionItem
     )
-    index: int = OutputField(description="The index of the item", title="Index")
-    total: int = OutputField(description="The total number of items", title="Total")
 
 
 # TODO: Fill this out and move to invocations
-@invocation("iterate", version="1.1.0")
+@invocation("iterate", version="1.0.0")
 class IterateInvocation(BaseInvocation):
     """Iterates over a list of items"""
 
@@ -223,7 +221,7 @@ class IterateInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
         """Produces the outputs as values"""
-        return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
+        return IterateInvocationOutput(item=self.collection[self.index])
 
 
 @invocation_output("collect_output")

invokeai/app/services/shared/sqlite.py

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

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

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

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

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

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

@@ -1,34 +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