{release} v3.6.1 (#5564)

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

Invoke 3.6.1 release

## QA Instructions, Screenshots, Recordings

[InvokeAI-installer-v3.6.1.zip](https://github.com/invoke-ai/InvokeAI/files/14041411/InvokeAI-installer-v3.6.1.zip)

<!-- 
Please provide steps on how to test changes, any hardware or 
software specifications as well as any other pertinent information. 
-->

## Merge Plan

This PR can be merged when approved

## [optional] Are there any post deployment tasks we need to perform?
PyPI Release & GitHub Release

.github/CODEOWNERS

@@ -1,5 +1,5 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant @hipsterusername
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr
 
 # documentation
 /docs/ @lstein @blessedcoolant @hipsterusername @Millu
@@ -10,7 +10,7 @@
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant @hipsterusername
-/docker/  @lstein @blessedcoolant @hipsterusername
+/docker/  @lstein @blessedcoolant @hipsterusername @ebr
 /scripts/ @ebr @lstein @hipsterusername
 /installer/ @lstein @ebr @hipsterusername
 /invokeai/assets @lstein @ebr @hipsterusername
@@ -26,9 +26,7 @@
 
 # front ends
 /invokeai/frontend/CLI @lstein @hipsterusername
-/invokeai/frontend/install @lstein @ebr @hipsterusername 
+/invokeai/frontend/install @lstein @ebr @hipsterusername
 /invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername
-
-

.github/pull_request_template.md

@@ -42,6 +42,21 @@ 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/build-container.yml

@@ -40,10 +40,14 @@ jobs:
       - name: Free up more disk space on the runner
         # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
         run: |
+          echo "----- Free space before cleanup"
+          df -h
           sudo rm -rf /usr/share/dotnet
           sudo rm -rf "$AGENT_TOOLSDIRECTORY"
           sudo swapoff /mnt/swapfile
           sudo rm -rf /mnt/swapfile
+          echo "----- Free space after cleanup"
+          df -h
 
       - name: Checkout
         uses: actions/checkout@v3
@@ -91,6 +95,7 @@ jobs:
       #     password: ${{ secrets.DOCKERHUB_TOKEN }}
 
       - name: Build container
+        timeout-minutes: 40
         id: docker_build
         uses: docker/build-push-action@v4
         with:

.github/workflows/lint-frontend.yml

@@ -22,12 +22,22 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
       - name: Setup Node 18
-        uses: actions/setup-node@v3
+        uses: actions/setup-node@v4
         with:
           node-version: '18'
-      - uses: actions/checkout@v3
-      - run: 'yarn install --frozen-lockfile'
-      - run: 'yarn run lint:tsc'
-      - run: 'yarn run lint:madge'
-      - run: 'yarn run lint:eslint'
-      - run: 'yarn run lint:prettier'
+      - 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'

.github/workflows/pypi-release.yml

@@ -1,13 +1,15 @@
 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:
-  release:
+  build-and-release:
     if: github.repository == 'invoke-ai/InvokeAI'
     runs-on: ubuntu-22.04
     env:
@@ -15,19 +17,43 @@ jobs:
       TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
       TWINE_NON_INTERACTIVE: 1
     steps:
-      - name: checkout sources
-        uses: actions/checkout@v3
+      - name: Checkout
+        uses: actions/checkout@v4
 
-      - name: install deps
+      - 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
         run: pip install --upgrade build twine
 
-      - name: build package
+      - name: Build python package
         run: python3 -m build
 
-      - name: check distribution
+      - name: Upload build as workflow artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: dist
+
+      - 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
@@ -36,6 +62,6 @@ jobs:
           EXISTS=scripts.pypi_helper.local_on_pypi(); \
           print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
 
-      - name: upload package
-        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != ''
+      - name: Publish build on PyPi
+        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != '' && github.event.inputs.publish_package == 'true'
         run: twine upload dist/*

.github/workflows/test-invoke-pip.yml

@@ -58,7 +58,7 @@ jobs:
 
       - name: Check for changed python files
         id: changed-files
-        uses: tj-actions/changed-files@v37
+        uses: tj-actions/changed-files@v41
         with:
           files_yaml: |
             python:

.gitignore

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

Makefile

@@ -1,6 +1,20 @@
 # 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
@@ -18,4 +32,21 @@ 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
+		mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports
+
+# Build the frontend
+frontend-build:
+	cd invokeai/frontend/web && pnpm build
+
+# Run the frontend in dev mode
+frontend-dev:
+	cd invokeai/frontend/web && pnpm dev
+
+# Installer zip file
+installer-zip:
+	cd installer && ./create_installer.sh
+
+# Tag the release
+tag-release:
+	cd installer && ./tag_release.sh
+

README.md

@@ -1,10 +1,10 @@
 <div align="center">
 
-![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/1a917d94-e099-4fa1-a70f-7dd8d0691018)
+![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)
 
-# 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)
+# 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)
+  
 
 
 [![discord badge]][discord link]
@@ -56,7 +56,9 @@ the foundation for multiple commercial products.
 
 <div align="center">
 
-![canvas preview](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/canvas_preview.png)
+
+![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
+
 
 </div>
 
@@ -125,8 +127,8 @@ 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 yarn (can be installed with
-the command `npm install -g yarn` if needed)
+Node.js also needs to be installed along with `pnpm` (can be installed with
+the command `npm install -g pnpm` if needed)
 
 1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
 2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
@@ -270,7 +272,7 @@ 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 [7] "Re-run the configure script to fix a broken
+menu. Select option [6] "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

@@ -2,14 +2,17 @@
 ## Any environment variables supported by InvokeAI can be specified here,
 ## in addition to the examples below.
 
-# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
+# HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where InvokeAI will store data.
 # Outputs will also be stored here by default.
-# This **must** be an absolute path.
-INVOKEAI_ROOT=
+# 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
 
 # Get this value from your HuggingFace account settings page.
 # HUGGING_FACE_HUB_TOKEN=
 
 ## optional variables specific to the docker setup.
-# GPU_DRIVER=cuda # or rocm
+# GPU_DRIVER=nvidia #| rocm
 # CONTAINER_UID=1000

docker/Dockerfile

@@ -59,14 +59,16 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 
 # #### Build the Web UI ------------------------------------
 
-FROM node:18 AS web-builder
+FROM node:20-slim AS web-builder
+ENV PNPM_HOME="/pnpm"
+ENV PATH="$PNPM_HOME:$PATH"
+RUN corepack enable
+
 WORKDIR /build
 COPY invokeai/frontend/web/ ./
-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
-
+RUN --mount=type=cache,target=/pnpm/store \
+    pnpm install --frozen-lockfile
+RUN npx vite build
 
 #### Runtime stage ---------------------------------------
 
@@ -100,6 +102,8 @@ 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}
@@ -117,7 +121,7 @@ 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 1000:1000 ${INVOKEAI_ROOT}
+RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]

docker/README.md

@@ -1,6 +1,14 @@
 # InvokeAI Containerized
 
-All commands are to be run from the `docker` directory: `cd docker`
+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
 
 #### Linux
 
@@ -18,12 +26,12 @@ All commands are to be run from the `docker` directory: `cd docker`
 
 This is done via Docker Desktop preferences
 
-## Quickstart
+### Configure Invoke environment
 
 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. `docker compose up`
+1. Execute `run.sh`
 
 The image will be built automatically if needed.
 
@@ -37,19 +45,21 @@ 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 `docker compose up`, 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 `run.sh`, 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.
 
-Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended):
+Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The default is `~/invokeai`. Example:
 
 ```bash
 INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
 HUGGINGFACE_TOKEN=the_actual_token
 CONTAINER_UID=1000
-GPU_DRIVER=cuda
+GPU_DRIVER=nvidia
 ```
 
 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

@@ -1,11 +0,0 @@
-#!/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

@@ -2,23 +2,8 @@
 
 version: '3.8'
 
-services:
-  invokeai:
+x-invokeai: &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
@@ -36,7 +21,9 @@ services:
     ports:
       - "${INVOKEAI_PORT:-9090}:9090"
     volumes:
-      - ${INVOKEAI_ROOT:-~/invokeai}:${INVOKEAI_ROOT:-/invokeai}
+      - type: bind
+        source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
+        target: ${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}}
@@ -50,3 +37,27 @@ services:
     #   - |
     #     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

@@ -1,11 +1,32 @@
 #!/usr/bin/env bash
-set -e
+set -e -o pipefail
 
-# 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.
+run() {
+  local scriptdir=$(dirname "${BASH_SOURCE[0]}")
+  cd "$scriptdir" || exit 1
 
-SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
-cd "$SCRIPTDIR" || exit 1
+  local build_args=""
+  local profile=""
 
-docker compose up -d
-docker compose logs -f
+  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

docs/contributing/DOWNLOAD_QUEUE.md

@@ -0,0 +1,277 @@
+# 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/contribution_guides/contributingToFrontend.md

@@ -1,75 +0,0 @@
-# Contributing to the Frontend
-
-# InvokeAI Web UI
-
-- [InvokeAI Web UI](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#invokeai-web-ui)
-    - [Stack](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#stack)
-    - [Contributing](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#contributing)
-        - [Dev Environment](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#dev-environment)
-        - [Production builds](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#production-builds)
-
-The UI is a fairly straightforward Typescript React app, with the Unified Canvas being more complex.
-
-Code is located in `invokeai/frontend/web/` for review.
-
-## Stack
-
-State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
-
-- `createAsyncThunk` for HTTP requests
-- `createEntityAdapter` for fetching images and models
-- `createListenerMiddleware` for workflows
-
-The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
-
-Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
-
-[Chakra-UI](https://github.com/chakra-ui/chakra-ui) & [Mantine](https://github.com/mantinedev/mantine) for components and styling.
-
-[Konva](https://github.com/konvajs/react-konva) for the canvas, but we are pushing the limits of what is feasible with it (and HTML canvas in general). We plan to rebuild it with [PixiJS](https://github.com/pixijs/pixijs) to take advantage of WebGL's improved raster handling.
-
-[Vite](https://vitejs.dev/) for bundling.
-
-Localisation is via [i18next](https://github.com/i18next/react-i18next), but translation happens on our [Weblate](https://hosted.weblate.org/engage/invokeai/) project. Only the English source strings should be changed on this repo.
-
-## Contributing
-
-Thanks for your interest in contributing to the InvokeAI Web UI!
-
-We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](https://discord.gg/ZmtBAhwWhy) if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
-
-### Dev Environment
-
-**Setup** 
-
-1. Install [node](https://nodejs.org/en/download/). You can confirm node is installed with:
-```bash
-node --version
-```
-2. Install [yarn classic](https://classic.yarnpkg.com/lang/en/) and confirm it is installed by running this:
-```bash
-npm install --global yarn
-yarn --version
-```
-
-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: `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/)
-
-### VSCode Remote Dev
-
-We've noticed an intermittent issue with the VSCode Remote Dev port forwarding. If you use this feature of VSCode, you may intermittently click the Invoke button and then get nothing until the request times out. Suggest disabling the IDE's port forwarding feature and doing it manually via SSH:
-
-`ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host`
-
-### Production builds
-
-For a number of technical and logistical reasons, we need to commit UI build artefacts to the repo.
-
-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 `yarn build`.

docs/contributing/contribution_guides/development.md

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

docs/deprecated/2to3.md

@@ -0,0 +1,53 @@
+## :octicons-log-16: Important Changes Since Version 2.3
+
+### Nodes
+
+Behind the scenes, InvokeAI has been completely rewritten to support
+"nodes," small unitary operations that can be combined into graphs to
+form arbitrary workflows. For example, there is a prompt node that
+processes the prompt string and feeds it to a text2latent node that
+generates a latent image. The latents are then fed to a latent2image
+node that translates the latent image into a PNG.
+
+The WebGUI has a node editor that allows you to graphically design and
+execute custom node graphs. The ability to save and load graphs is
+still a work in progress, but coming soon.
+
+### Command-Line Interface Retired
+
+All "invokeai" command-line interfaces have been retired as of version
+3.4.
+
+To launch the Web GUI from the command-line, use the command
+`invokeai-web` rather than the traditional `invokeai --web`.
+
+### ControlNet
+
+This version of InvokeAI features ControlNet, a system that allows you
+to achieve exact poses for human and animal figures by providing a
+model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
+
+### New Schedulers
+
+The list of schedulers has been completely revamped and brought up to date:
+
+| **Short Name** | **Scheduler**                   | **Notes**                   |
+|----------------|---------------------------------|-----------------------------|
+| **ddim**       | DDIMScheduler                   |                             |
+| **ddpm**       | DDPMScheduler                   |                             |
+| **deis**       | DEISMultistepScheduler          |                             |
+| **lms**        | LMSDiscreteScheduler            |                             |
+| **pndm**       | PNDMScheduler                   |                             |
+| **heun**       | HeunDiscreteScheduler           | original noise schedule     |
+| **heun_k**     | HeunDiscreteScheduler           | using karras noise schedule |
+| **euler**      | EulerDiscreteScheduler          | original noise schedule     |
+| **euler_k**    | EulerDiscreteScheduler          | using karras noise schedule |
+| **kdpm_2**     | KDPM2DiscreteScheduler          |                             |
+| **kdpm_2_a**   | KDPM2AncestralDiscreteScheduler |                             |
+| **dpmpp_2s**   | DPMSolverSinglestepScheduler    |                             |
+| **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
+| **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
+| **unipc**      | UniPCMultistepScheduler         | CPU only                    |
+| **lcm**        | LCMScheduler                    |                             |
+
+Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.

docs/features/CONFIGURATION.md

@@ -154,14 +154,16 @@ 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  |
+| 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.                                                |
 
 The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
 

docs/features/UNIFIED_CANVAS.md

@@ -229,29 +229,28 @@ clarity on the intent and common use cases we expect for utilizing them.
   currently being rendered by your browser into a merged copy of the image. This
   lowers the resource requirements and should improve performance.
 
-### Seam Correction
+### Compositing / Seam Correction
 
 When doing Inpainting or Outpainting, Invoke needs to merge the pixels generated
-by Stable Diffusion into your existing image. To do this, the area around the
-`seam` at the boundary between your image and the new generation is
+by Stable Diffusion into your existing image. This is achieved through compositing - the area around the the boundary between your image and the new generation is
 automatically blended to produce a seamless output. In a fully automatic
-process, a mask is generated to cover the seam, and then the area of the seam is
+process, a mask is generated to cover the boundary, and then the area of the boundary is
 Inpainted.
 
 Although the default options should work well most of the time, sometimes it can
-help to alter the parameters that control the seam Inpainting. A wider seam and
-a blur setting of about 1/3 of the seam have been noted as producing
-consistently strong results (e.g. 96 wide and 16 blur - adds up to 32 blur with
-both sides). Seam strength of 0.7 is best for reducing hard seams.
+help to alter the parameters that control the Compositing. A larger blur and
+a blur setting  have been noted as producing
+consistently strong results . Strength of 0.7 is best for reducing hard seams.
+
+- **Mode** - What part of the image will have the the Compositing applied to it.
+  - **Mask edge** will apply Compositing to the edge of the masked area
+  - **Mask** will apply Compositing to the entire masked area
+  - **Unmasked** will apply Compositing to the entire image
+- **Steps** - Number of generation steps that will occur during the Coherence Pass, similar to Denoising Steps. Higher step counts will generally have better results.
+- **Strength** - How much noise is added for the Coherence Pass, similar to Denoising Strength. A strength of 0 will result in an unchanged image, while a strength of 1 will result in an image with a completely new area as defined by the Mode setting.
+- **Blur** - Adjusts the pixel radius of the the mask. A larger blur radius will cause the mask to extend past the visibly masked area, while too small of a blur radius will result in a mask that is smaller than the visibly masked area.
+- **Blur Method** - The method of blur applied to the masked area. 
 
-- **Seam Size** - The size of the seam masked area. Set higher to make a larger
-  mask around the seam.
-- **Seam Blur** - The size of the blur that is applied on _each_ side of the
-  masked area.
-- **Seam Strength** - The Image To Image Strength parameter used for the
-  Inpainting generation that is applied to the seam area.
-- **Seam Steps** - The number of generation steps that should be used to Inpaint
-  the seam.
 
 ### Infill & Scaling
 

docs/index.md

@@ -18,7 +18,7 @@ title: Home
       width: 100%;
       max-width: 100%;
       height: 50px;
-      background-color: #448AFF;
+      background-color: #35A4DB;
       color: #fff;
       font-size: 16px;
       border: none;
@@ -43,7 +43,7 @@ title: Home
 <div align="center" markdown>
 
 
-[![project logo](assets/invoke_ai_banner.png)](https://github.com/invoke-ai/InvokeAI)
+[![project logo](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)](https://github.com/invoke-ai/InvokeAI)
 
 [![discord badge]][discord link]
 
@@ -145,60 +145,6 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 - [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
 - [Database Maintenance and other Command Line Utilities](features/UTILITIES.md)
 
-## :octicons-log-16: Important Changes Since Version 2.3
-
-### Nodes
-
-Behind the scenes, InvokeAI has been completely rewritten to support
-"nodes," small unitary operations that can be combined into graphs to
-form arbitrary workflows. For example, there is a prompt node that
-processes the prompt string and feeds it to a text2latent node that
-generates a latent image. The latents are then fed to a latent2image
-node that translates the latent image into a PNG.
-
-The WebGUI has a node editor that allows you to graphically design and
-execute custom node graphs. The ability to save and load graphs is
-still a work in progress, but coming soon.
-
-### Command-Line Interface Retired
-
-All "invokeai" command-line interfaces have been retired as of version
-3.4.
-
-To launch the Web GUI from the command-line, use the command
-`invokeai-web` rather than the traditional `invokeai --web`.
-
-### ControlNet
-
-This version of InvokeAI features ControlNet, a system that allows you
-to achieve exact poses for human and animal figures by providing a
-model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
-
-### New Schedulers
-
-The list of schedulers has been completely revamped and brought up to date:
-
-| **Short Name** | **Scheduler**                   | **Notes**                   |
-|----------------|---------------------------------|-----------------------------|
-| **ddim**       | DDIMScheduler                   |                             |
-| **ddpm**       | DDPMScheduler                   |                             |
-| **deis**       | DEISMultistepScheduler          |                             |
-| **lms**        | LMSDiscreteScheduler            |                             |
-| **pndm**       | PNDMScheduler                   |                             |
-| **heun**       | HeunDiscreteScheduler           | original noise schedule     |
-| **heun_k**     | HeunDiscreteScheduler           | using karras noise schedule |
-| **euler**      | EulerDiscreteScheduler          | original noise schedule     |
-| **euler_k**    | EulerDiscreteScheduler          | using karras noise schedule |
-| **kdpm_2**     | KDPM2DiscreteScheduler          |                             |
-| **kdpm_2_a**   | KDPM2AncestralDiscreteScheduler |                             |
-| **dpmpp_2s**   | DPMSolverSinglestepScheduler    |                             |
-| **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
-| **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
-| **unipc**      | UniPCMultistepScheduler         | CPU only                    |
-| **lcm**        | LCMScheduler                    |                             |
-
-Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
-
 ## :material-target: Troubleshooting
 
 Please check out our **[:material-frequently-asked-questions:

docs/installation/020_INSTALL_MANUAL.md

@@ -293,6 +293,19 @@ 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
@@ -388,3 +401,5 @@ 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

@@ -0,0 +1,10 @@
+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/NODES.md

@@ -6,10 +6,17 @@ If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](
 
 ## Features
 
+### Workflow Library
+The Workflow Library enables you to save workflows to the Invoke database, allowing you to easily creating, modify and share workflows as needed. 
+
+A curated set of workflows are provided by default - these are designed to help explain important nodes' usage in the Workflow Editor.
+
+![workflow_library](../assets/nodes/workflow_library.png)
+
 ### Linear View
 The Workflow Editor allows you to create a UI for your workflow, to make it easier to iterate on your generations. 
 
-To add an input to the Linear UI, right click on the input label and select "Add to Linear View".
+To add an input to the Linear UI, right click on the **input label** and select "Add to Linear View".
 
 The Linear UI View will also be part of the saved workflow, allowing you share workflows and enable other to use them, regardless of complexity. 
 
@@ -30,7 +37,7 @@ Any node or input field can be renamed in the workflow editor. If the input fiel
 Nodes have a "Use Cache" option in their footer. This allows for performance improvements by using the previously cached values during the workflow processing. 
 
 
-## Important Concepts 
+## Important Nodes & Concepts 
 
 There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
 
@@ -56,7 +63,7 @@ The ImageToLatents node takes in a pixel image and a VAE and outputs a latents.
 
 It is common to want to use both the same seed (for continuity) and random seeds (for variety). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
 
-![groupsrandseed](../assets/nodes/groupsrandseed.png)
+![groupsrandseed](../assets/nodes/groupsnoise.png)
 
 ### ControlNet
 

docs/nodes/communityNodes.md

@@ -13,25 +13,37 @@ 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)
     + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
     + [Grid to Gif](#grid-to-gif)
     + [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)
@@ -41,6 +53,19 @@ 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
 
@@ -48,6 +73,46 @@ To use a community workflow, download the the `.json` node graph file and load i
 
 **Node Link:** https://github.com/JPPhoto/average-images-node
 
+--------------------------------
+### Clean Image Artifacts After Cut
+
+Description: Removes residual artifacts after an image is separated from its background.
+
+Node Link: https://github.com/VeyDlin/clean-artifact-after-cut-node
+
+View:
+</br><img src="https://raw.githubusercontent.com/VeyDlin/clean-artifact-after-cut-node/master/.readme/node.png" width="500" />
+
+--------------------------------
+### Close Color Mask
+
+Description: Generates a mask for images based on a closely matching color, useful for color-based selections.
+
+Node Link: https://github.com/VeyDlin/close-color-mask-node
+
+View:
+</br><img src="https://raw.githubusercontent.com/VeyDlin/close-color-mask-node/master/.readme/node.png" width="500" />
+
+--------------------------------
+### Clothing Mask
+
+Description: Employs a U2NET neural network trained for the segmentation of clothing items in images.
+
+Node Link: https://github.com/VeyDlin/clothing-mask-node
+
+View:
+</br><img src="https://raw.githubusercontent.com/VeyDlin/clothing-mask-node/master/.readme/node.png" width="500" />
+
+--------------------------------
+### Contrast Limited Adaptive Histogram Equalization
+
+Description: Enhances local image contrast using adaptive histogram equalization with contrast limiting.
+
+Node Link: https://github.com/VeyDlin/clahe-node
+
+View:
+</br><img src="https://raw.githubusercontent.com/VeyDlin/clahe-node/master/.readme/node.png" width="500" />
+
 --------------------------------
 ### Depth Map from Wavefront OBJ
 
@@ -130,13 +195,6 @@ CMYK Halftone Output:
 
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
 
---------------------------------
-### Ideal Size
-
-**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
-
-**Node Link:** https://github.com/JPPhoto/ideal-size-node
-
 --------------------------------
 ### Image and Mask Composition Pack
 
@@ -164,6 +222,16 @@ 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
 
@@ -185,6 +253,17 @@ This includes 15 Nodes:
 
 **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
 
@@ -209,6 +288,16 @@ This includes 15 Nodes:
 <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
 
@@ -226,6 +315,37 @@ 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
 
@@ -289,6 +409,15 @@ 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
@@ -301,6 +430,17 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 <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
 
@@ -386,6 +526,7 @@ 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/nodes/defaultNodes.md

@@ -36,6 +36,7 @@ their descriptions.
 | Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
 | Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
 | Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
+| Ideal Size                                                    | Calculates an ideal image size for latents for a first pass of a multi-pass upscaling to avoid duplication and other artifacts                       |
 | Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
 | Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
 | Image Primitive                                               | An image primitive value                                                                                                                             |

docs/nodes/exampleWorkflows.md

@@ -1,6 +1,6 @@
 # Example Workflows
 
-We've curated some example workflows for you to get started with Workflows in InvokeAI
+We've curated some example workflows for you to get started with Workflows in InvokeAI! These can also be found in the Workflow Library, located in the Workflow Editor of Invoke.
 
 To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
 

docs/other/CONTRIBUTORS.md

@@ -215,6 +215,7 @@ We thank them for all of their time and hard work.
 - Robert Bolender
 - Robin Rombach
 - Rohan Barar
+- rohinish404
 - rpagliuca
 - rromb
 - Rupesh Sreeraman

docs/stylesheets/extra.css

@@ -0,0 +1,5 @@
+:root {
+    --md-primary-fg-color:        #35A4DB;
+    --md-primary-fg-color--light: #35A4DB;
+    --md-primary-fg-color--dark:  #35A4DB;
+  }

docs/workflows/Text_to_Image.json

@@ -1,8 +1,8 @@
 {
-  "name": "Text to Image",
+  "name": "Text to Image - SD1.5",
   "author": "InvokeAI",
   "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
-  "version": "1.0.1",
+  "version": "1.1.0",
   "contact": "invoke@invoke.ai",
   "tags": "text2image, SD1.5, SD2, default",
   "notes": "",
@@ -18,10 +18,19 @@
     {
       "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": {
-    "version": "1.0.0"
+    "category": "default",
+    "version": "2.0.0"
   },
   "nodes": [
     {
@@ -30,44 +39,56 @@
       "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": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ClipField"
+            }
           }
         },
         "outputs": {
           "conditioning": {
             "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447",
             "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ConditioningField"
+            }
           }
-        },
-        "label": "Negative Compel Prompt",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        }
       },
       "width": 320,
-      "height": 261,
+      "height": 259,
       "position": {
-        "x": 995.7263915923627,
-        "y": 239.67783573351227
+        "x": 1000,
+        "y": 350
       }
     },
     {
@@ -76,37 +97,60 @@
       "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
           }
         },
@@ -114,35 +158,40 @@
           "noise": {
             "id": "50f650dc-0184-4e23-a927-0497a96fe954",
             "name": "noise",
-            "type": "LatentsField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
           },
           "width": {
             "id": "bb8a452b-133d-42d1-ae4a-3843d7e4109a",
             "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
           },
           "height": {
             "id": "35cfaa12-3b8b-4b7a-a884-327ff3abddd9",
             "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
           }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        }
       },
       "width": 320,
-      "height": 389,
+      "height": 388,
       "position": {
-        "x": 993.4442117555518,
-        "y": 605.6757415334787
+        "x": 600,
+        "y": 325
       }
     },
     {
@@ -151,13 +200,24 @@
       "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",
@@ -169,35 +229,40 @@
           "unet": {
             "id": "5c18c9db-328d-46d0-8cb9-143391c410be",
             "name": "unet",
-            "type": "UNetField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "UNetField"
+            }
           },
           "clip": {
             "id": "6effcac0-ec2f-4bf5-a49e-a2c29cf921f4",
             "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ClipField"
+            }
           },
           "vae": {
             "id": "57683ba3-f5f5-4f58-b9a2-4b83dacad4a1",
             "name": "vae",
-            "type": "VaeField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "VaeField"
+            }
           }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        }
       },
       "width": 320,
       "height": 226,
       "position": {
-        "x": 163.04436745878343,
-        "y": 254.63156870373479
+        "x": 600,
+        "y": 25
       }
     },
     {
@@ -206,44 +271,56 @@
       "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",
-            "value": ""
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "StringField"
+            },
+            "value": "Super cute tiger cub, national geographic award-winning photograph"
           },
           "clip": {
             "id": "48d23dce-a6ae-472a-9f8c-22a714ea5ce0",
             "name": "clip",
-            "type": "ClipField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ClipField"
+            }
           }
         },
         "outputs": {
           "conditioning": {
             "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447",
             "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ConditioningField"
+            }
           }
-        },
-        "label": "Positive Compel Prompt",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        }
       },
       "width": 320,
-      "height": 261,
+      "height": 259,
       "position": {
-        "x": 595.7263915923627,
-        "y": 239.67783573351227
+        "x": 1000,
+        "y": 25
       }
     },
     {
@@ -252,21 +329,36 @@
       "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
           }
         },
@@ -274,23 +366,20 @@
           "value": {
             "id": "812ade4d-7699-4261-b9fc-a6c9d2ab55ee",
             "name": "value",
-            "type": "integer",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
           }
-        },
-        "label": "Random Seed",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": false,
-        "version": "1.0.0"
+        }
       },
       "width": 320,
-      "height": 218,
+      "height": 32,
       "position": {
-        "x": 541.094822888628,
-        "y": 694.5704476446829
+        "x": 600,
+        "y": 275
       }
     },
     {
@@ -299,144 +388,224 @@
       "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": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ConditioningField"
+            }
           },
           "negative_conditioning": {
             "id": "9393779e-796c-4f64-b740-902a1177bf53",
             "name": "negative_conditioning",
-            "type": "ConditioningField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ConditioningField"
+            }
           },
           "noise": {
             "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
             "name": "noise",
-            "type": "LatentsField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
           },
           "steps": {
             "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
             "name": "steps",
-            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "value": 10
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            },
+            "value": 50
           },
           "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"
+            },
             "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": "",
-            "value": "euler"
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "SchedulerField"
+            },
+            "value": "unipc"
           },
           "unet": {
             "id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b",
             "name": "unet",
-            "type": "UNetField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "UNetField"
+            }
           },
           "control": {
             "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
             "name": "control",
-            "type": "ControlPolymorphic",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": true,
+              "name": "ControlField"
+            }
           },
           "ip_adapter": {
             "id": "1d6948f7-3a65-4a65-a20c-768b287251aa",
             "name": "ip_adapter",
-            "type": "IPAdapterPolymorphic",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": true,
+              "name": "IPAdapterField"
+            }
           },
           "t2i_adapter": {
             "id": "75e67b09-952f-4083-aaf4-6b804d690412",
             "name": "t2i_adapter",
-            "type": "T2IAdapterPolymorphic",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": true,
+              "name": "T2IAdapterField"
+            }
+          },
+          "cfg_rescale_multiplier": {
+            "id": "9101f0a6-5fe0-4826-b7b3-47e5d506826c",
+            "name": "cfg_rescale_multiplier",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "FloatField"
+            },
+            "value": 0
           },
           "latents": {
             "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
             "name": "latents",
-            "type": "LatentsField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
           },
           "denoise_mask": {
             "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
             "name": "denoise_mask",
-            "type": "DenoiseMaskField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "DenoiseMaskField"
+            }
           }
         },
         "outputs": {
           "latents": {
             "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
             "name": "latents",
-            "type": "LatentsField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
           },
           "width": {
             "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
             "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
           },
           "height": {
             "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
             "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
           }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.4.0"
+        }
       },
       "width": 320,
-      "height": 646,
+      "height": 703,
       "position": {
-        "x": 1476.5794704734735,
-        "y": 256.80174342731783
+        "x": 1400,
+        "y": 25
       }
     },
     {
@@ -445,153 +614,185 @@
       "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": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "MetadataField"
+            }
           },
           "latents": {
             "id": "3a7e7efd-bff5-47d7-9d48-615127afee78",
             "name": "latents",
-            "type": "LatentsField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
           },
           "vae": {
             "id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b",
             "name": "vae",
-            "type": "VaeField",
             "fieldKind": "input",
-            "label": ""
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "VaeField"
+            }
           },
           "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": "",
-            "value": false
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "BooleanField"
+            },
+            "value": true
           }
         },
         "outputs": {
           "image": {
             "id": "72d667d0-cf85-459d-abf2-28bd8b823fe7",
             "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ImageField"
+            }
           },
           "width": {
             "id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc",
             "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
           },
           "height": {
             "id": "230f359c-b4ea-436c-b372-332d7dcdca85",
             "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
           }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": false,
-        "useCache": true,
-        "version": "1.0.0"
+        }
       },
       "width": 320,
-      "height": 267,
+      "height": 266,
       "position": {
-        "x": 2037.9648469717395,
-        "y": 426.10844427600136
+        "x": 1800,
+        "y": 25
       }
     }
   ],
   "edges": [
     {
-      "source": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
-      "sourceHandle": "value",
-      "target": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "targetHandle": "seed",
       "id": "reactflow__edge-ea94bc37-d995-4a83-aa99-4af42479f2f2value-55705012-79b9-4aac-9f26-c0b10309785bseed",
-      "type": "default"
+      "source": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+      "target": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "type": "default",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
     },
     {
-      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
-      "sourceHandle": "clip",
-      "target": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
-      "targetHandle": "clip",
       "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-7d8bf987-284f-413a-b2fd-d825445a5d6cclip",
-      "type": "default"
-    },
-    {
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "target": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "type": "default",
       "sourceHandle": "clip",
-      "target": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
-      "targetHandle": "clip",
+      "targetHandle": "clip"
+    },
+    {
       "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-93dc02a4-d05b-48ed-b99c-c9b616af3402clip",
-      "type": "default"
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "target": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "type": "default",
+      "sourceHandle": "clip",
+      "targetHandle": "clip"
     },
     {
-      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "sourceHandle": "noise",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "targetHandle": "noise",
       "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
-      "type": "default"
+      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "type": "default",
+      "sourceHandle": "noise",
+      "targetHandle": "noise"
     },
     {
-      "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
-      "sourceHandle": "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"
-    },
-    {
-      "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "type": "default",
       "sourceHandle": "conditioning",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "targetHandle": "negative_conditioning",
+      "targetHandle": "positive_conditioning"
+    },
+    {
       "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
-      "type": "default"
-    },
-    {
-      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
-      "sourceHandle": "unet",
+      "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "targetHandle": "unet",
+      "type": "default",
+      "sourceHandle": "conditioning",
+      "targetHandle": "negative_conditioning"
+    },
+    {
       "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",
-      "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",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "type": "default",
+      "sourceHandle": "unet",
+      "targetHandle": "unet"
+    },
+    {
+      "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",
-      "targetHandle": "vae",
+      "type": "default",
+      "sourceHandle": "latents",
+      "targetHandle": "latents"
+    },
+    {
       "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
-      "type": "default"
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
+      "type": "default",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
     }
   ]
-}
+}

installer/create_installer.sh

@@ -2,43 +2,72 @@
 
 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 "A virtual environment is activated. Please deactivate it before proceeding".
+    echo -e "${BRED}A virtual environment is activated. Please deactivate it before proceeding.${RESET}"
     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 Building installer for version $VERSION
-echo "Be certain that you're in the 'installer' directory before continuing."
-read -p "Press any key to continue, or CTRL-C to exit..."
+echo -e "${BGREEN}HEAD${RESET}:"
+git_show
+echo
 
-read -e -p "Tag this repo with '${VERSION}' and '${LATEST_TAG}'? [n]: " input
-RESPONSE=${input:='n'}
-if [ "$RESPONSE" == 'y' ]; then
+# ---------------------- FRONTEND ----------------------
 
-    git push origin :refs/tags/$VERSION
-    if ! git tag -fa $VERSION ; then
-	    echo "Existing/invalid tag"
-	    exit -1
-    fi
+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/$LATEST_TAG
-    git tag -fa $LATEST_TAG
+# ---------------------- BACKEND ----------------------
 
-    echo "remember to push --tags!"
-fi
-
-# ----------------------
-
-echo Building the wheel
+echo
+echo "Building wheel..."
+echo
 
 # 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
@@ -46,12 +75,15 @@ if [[ $(python -c 'from importlib.util import find_spec; print(find_spec("build"
     pip install --user build
 fi
 
-rm -r ../build
+rm -rf ../build
+
 python -m build --wheel --outdir dist/ ../.
 
 # ----------------------
 
-echo Building installer zip fles for InvokeAI $VERSION
+echo
+echo "Building installer zip files for InvokeAI ${VERSION}..."
+echo
 
 # get rid of any old ones
 rm -f *.zip
@@ -59,9 +91,11 @@ rm -rf InvokeAI-Installer
 
 # copy content
 mkdir InvokeAI-Installer
-for f in templates lib *.txt *.reg; do
+for f in templates *.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/
@@ -72,13 +106,13 @@ 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
+rm -rf InvokeAI-Installer tmp dist ../invokeai/frontend/web/dist/
 
 exit 0

installer/lib/installer.py

@@ -241,12 +241,12 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
-                "numpy~=1.24.0",  # choose versions that won't be uninstalled during phase 2
+                "numpy==1.26.3",  # choose versions that won't be uninstalled during phase 2
                 "urllib3~=1.26.0",
                 "requests~=2.28.0",
-                "torch==2.1.0",
+                "torch==2.1.2",
                 "torchmetrics==0.11.4",
-                "torchvision>=0.14.1",
+                "torchvision==0.16.2",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

installer/tag_release.sh

@@ -0,0 +1,71 @@
+#!/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

@@ -2,6 +2,8 @@
 
 from logging import Logger
 
+from invokeai.app.services.shared.sqlite.sqlite_util import init_db
+from invokeai.backend.model_manager.metadata import ModelMetadataStore
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -10,6 +12,7 @@ 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
@@ -22,14 +25,13 @@ 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.default_graphs import create_system_graphs
-from ..services.shared.graph import GraphExecutionState, LibraryGraph
-from ..services.shared.sqlite.sqlite_database import SqliteDatabase
+from ..services.shared.graph import GraphExecutionState
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -60,14 +62,15 @@ class ApiDependencies:
     invoker: Invoker
 
     @staticmethod
-    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
+    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger) -> None:
         logger.info(f"InvokeAI version {__version__}")
         logger.info(f"Root directory = {str(config.root_path)}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
         output_folder = config.output_path
+        image_files = DiskImageFileStorage(f"{output_folder}/images")
 
-        db = SqliteDatabase(config, logger)
+        db = init_db(config=config, logger=logger, image_files=image_files)
 
         configuration = config
         logger = logger
@@ -78,14 +81,21 @@ 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)
+        metadata_store = ModelMetadataStore(db=db)
+        model_install_service = ModelInstallService(
+            app_config=config,
+            record_store=model_record_service,
+            download_queue=download_queue_service,
+            metadata_store=metadata_store,
+            event_bus=events,
+        )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
         processor = DefaultInvocationProcessor()
@@ -103,7 +113,6 @@ 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,
@@ -112,6 +121,8 @@ 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,
@@ -122,12 +133,10 @@ class ApiDependencies:
             workflow_records=workflow_records,
         )
 
-        create_system_graphs(services.graph_library)
-
         ApiDependencies.invoker = Invoker(services)
         db.clean()
 
     @staticmethod
-    def shutdown():
+    def shutdown() -> None:
         if ApiDependencies.invoker:
             ApiDependencies.invoker.stop()

invokeai/app/api/no_cache_staticfiles.py

@@ -0,0 +1,28 @@
+from typing import Any
+
+from starlette.responses import Response
+from starlette.staticfiles import StaticFiles
+
+
+class NoCacheStaticFiles(StaticFiles):
+    """
+    This class is used to override the default caching behavior of starlette for static files,
+    ensuring we *never* cache static files. It modifies the file response headers to strictly
+    never cache the files.
+
+    Static files include the javascript bundles, fonts, locales, and some images. Generated
+    images are not included, as they are served by a router.
+    """
+
+    def __init__(self, *args: Any, **kwargs: Any):
+        self.cachecontrol = "max-age=0, no-cache, no-store, , must-revalidate"
+        self.pragma = "no-cache"
+        self.expires = "0"
+        super().__init__(*args, **kwargs)
+
+    def file_response(self, *args: Any, **kwargs: Any) -> Response:
+        resp = super().file_response(*args, **kwargs)
+        resp.headers.setdefault("Cache-Control", self.cachecontrol)
+        resp.headers.setdefault("Pragma", self.pragma)
+        resp.headers.setdefault("Expires", self.expires)
+        return resp

invokeai/app/api/routers/download_queue.py

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

invokeai/app/api/routers/model_records.py

@@ -4,7 +4,7 @@
 
 from hashlib import sha1
 from random import randbytes
-from typing import List, Optional
+from typing import Any, Dict, List, Optional, Set
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
@@ -12,30 +12,45 @@ 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,
+    ModelRecordOrderBy,
+    ModelSummary,
     UnknownModelException,
 )
+from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
 
 from ..dependencies import ApiDependencies
 
-model_records_router = APIRouter(prefix="/v1/model/record", tags=["models"])
+model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager_v2_unstable"])
 
 
 class ModelsList(BaseModel):
     """Return list of configs."""
 
-    models: list[AnyModelConfig]
+    models: List[AnyModelConfig]
 
     model_config = ConfigDict(use_enum_values=True)
 
 
+class ModelTagSet(BaseModel):
+    """Return tags for a set of models."""
+
+    key: str
+    name: str
+    author: str
+    tags: Set[str]
+
+
 @model_records_router.get(
     "/",
     operation_id="list_model_records",
@@ -43,15 +58,25 @@ 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[ModelFormat] = 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))
+            found_models.extend(
+                record_store.search_by_attr(
+                    base_model=base_model, model_type=model_type, model_name=model_name, model_format=model_format
+                )
+            )
     else:
-        found_models.extend(record_store.search_by_attr(model_type=model_type))
+        found_models.extend(
+            record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
+        )
     return ModelsList(models=found_models)
 
 
@@ -75,6 +100,59 @@ async def get_model_record(
         raise HTTPException(status_code=404, detail=str(e))
 
 
+@model_records_router.get("/meta", operation_id="list_model_summary")
+async def list_model_summary(
+    page: int = Query(default=0, description="The page to get"),
+    per_page: int = Query(default=10, description="The number of models per page"),
+    order_by: ModelRecordOrderBy = Query(default=ModelRecordOrderBy.Default, description="The attribute to order by"),
+) -> PaginatedResults[ModelSummary]:
+    """Gets a page of model summary data."""
+    return ApiDependencies.invoker.services.model_records.list_models(page=page, per_page=per_page, order_by=order_by)
+
+
+@model_records_router.get(
+    "/meta/i/{key}",
+    operation_id="get_model_metadata",
+    responses={
+        200: {"description": "Success"},
+        400: {"description": "Bad request"},
+        404: {"description": "No metadata available"},
+    },
+)
+async def get_model_metadata(
+    key: str = Path(description="Key of the model repo metadata to fetch."),
+) -> Optional[AnyModelRepoMetadata]:
+    """Get a model metadata object."""
+    record_store = ApiDependencies.invoker.services.model_records
+    result = record_store.get_metadata(key)
+    if not result:
+        raise HTTPException(status_code=404, detail="No metadata for a model with this key")
+    return result
+
+
+@model_records_router.get(
+    "/tags",
+    operation_id="list_tags",
+)
+async def list_tags() -> Set[str]:
+    """Get a unique set of all the model tags."""
+    record_store = ApiDependencies.invoker.services.model_records
+    return record_store.list_tags()
+
+
+@model_records_router.get(
+    "/tags/search",
+    operation_id="search_by_metadata_tags",
+)
+async def search_by_metadata_tags(
+    tags: Set[str] = Query(default=None, description="Tags to search for"),
+) -> ModelsList:
+    """Get a list of models."""
+    record_store = ApiDependencies.invoker.services.model_records
+    results = record_store.search_by_metadata_tag(tags)
+    return ModelsList(models=results)
+
+
 @model_records_router.patch(
     "/i/{key}",
     operation_id="update_model_record",
@@ -117,12 +195,17 @@ 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"""
+    """
+    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.
+    """
     logger = ApiDependencies.invoker.services.logger
 
     try:
-        record_store = ApiDependencies.invoker.services.model_records
-        record_store.del_model(key)
+        installer = ApiDependencies.invoker.services.model_install
+        installer.delete(key)
         logger.info(f"Deleted model: {key}")
         return Response(status_code=204)
     except UnknownModelException as e:
@@ -143,9 +226,7 @@ async def del_model_record(
 async def add_model_record(
     config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
 ) -> AnyModelConfig:
-    """
-    Add a model using the configuration information appropriate for its type.
-    """
+    """Add a model using the configuration information appropriate for its type."""
     logger = ApiDependencies.invoker.services.logger
     record_store = ApiDependencies.invoker.services.model_records
     if config.key == "<NOKEY>":
@@ -162,3 +243,175 @@ 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_running"
+      "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."""
+    jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_install.list_jobs()
+    return jobs
+
+
+@model_records_router.get(
+    "/import/{id}",
+    operation_id="get_model_install_job",
+    responses={
+        200: {"description": "Success"},
+        404: {"description": "No such job"},
+    },
+)
+async def get_model_install_job(id: int = Path(description="Model install id")) -> ModelInstallJob:
+    """Return model install job corresponding to the given source."""
+    try:
+        return ApiDependencies.invoker.services.model_install.get_job_by_id(id)
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@model_records_router.delete(
+    "/import/{id}",
+    operation_id="cancel_model_install_job",
+    responses={
+        201: {"description": "The job was cancelled successfully"},
+        415: {"description": "No such job"},
+    },
+    status_code=201,
+)
+async def cancel_model_install_job(id: int = Path(description="Model install job ID")) -> None:
+    """Cancel the model install job(s) corresponding to the given job ID."""
+    installer = ApiDependencies.invoker.services.model_install
+    try:
+        job = installer.get_job_by_id(id)
+    except ValueError as e:
+        raise HTTPException(status_code=415, detail=str(e))
+    installer.cancel_job(job)
+
+
+@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/utilities.py

@@ -23,10 +23,11 @@ class DynamicPromptsResponse(BaseModel):
 )
 async def parse_dynamicprompts(
     prompt: str = Body(description="The prompt to parse with dynamicprompts"),
-    max_prompts: int = Body(default=1000, description="The max number of prompts to generate"),
+    max_prompts: int = Body(ge=1, le=10000, 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/sockets.py

@@ -20,6 +20,7 @@ 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(
@@ -28,10 +29,13 @@ class SocketIO:
             room=event[1]["data"]["queue_id"],
         )
 
-    async def _handle_sub_queue(self, sid, data, *args, **kwargs):
+    async def _handle_sub_queue(self, sid, data, *args, **kwargs) -> None:
         if "queue_id" in data:
             await self.__sio.enter_room(sid, data["queue_id"])
 
-    async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
+    async def _handle_unsub_queue(self, sid, data, *args, **kwargs) -> None:
         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

@@ -3,6 +3,7 @@
 # values from the command line or config file.
 import sys
 
+from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.version.invokeai_version import __version__
 
 from .services.config import InvokeAIAppConfig
@@ -27,8 +28,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     from fastapi.middleware.gzip import GZipMiddleware
     from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
     from fastapi.openapi.utils import get_openapi
-    from fastapi.responses import FileResponse, HTMLResponse
-    from fastapi.staticfiles import StaticFiles
+    from fastapi.responses import HTMLResponse
     from fastapi_events.handlers.local import local_handler
     from fastapi_events.middleware import EventHandlerASGIMiddleware
     from pydantic.json_schema import models_json_schema
@@ -45,6 +45,7 @@ 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,
@@ -75,7 +76,7 @@ mimetypes.add_type("text/css", ".css")
 
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
-app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
+app = FastAPI(title="Invoke - Community Edition", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
 
 # Add event handler
 event_handler_id: int = id(app)
@@ -116,6 +117,7 @@ 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")
@@ -203,8 +205,8 @@ app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid a
 def overridden_swagger() -> HTMLResponse:
     return get_swagger_ui_html(
         openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=app.title,
-        swagger_favicon_url="/static/docs/favicon.ico",
+        title=f"{app.title} - Swagger UI",
+        swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
     )
 
 
@@ -212,25 +214,20 @@ def overridden_swagger() -> HTMLResponse:
 def overridden_redoc() -> HTMLResponse:
     return get_redoc_html(
         openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=app.title,
-        redoc_favicon_url="/static/docs/favicon.ico",
+        title=f"{app.title} - Redoc",
+        redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
     )
 
 
 web_root_path = Path(list(web_dir.__path__)[0])
 
-
-# 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")
+try:
+    app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
+except RuntimeError:
+    logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
+app.mount(
+    "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
+)  # docs favicon is in here
 
 
 def invoke_api() -> None:
@@ -271,6 +268,8 @@ 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

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import inspect
 import re
+import warnings
 from abc import ABC, abstractmethod
 from enum import Enum
 from inspect import signature
@@ -38,6 +39,19 @@ 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.
@@ -438,6 +452,7 @@ 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,
@@ -606,6 +621,7 @@ 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"] = []
@@ -709,8 +725,10 @@ class _Model(BaseModel):
     pass
 
 
-# Get all pydantic model attrs, methods, etc
-RESERVED_PYDANTIC_FIELD_NAMES = {m[0] for m in inspect.getmembers(_Model())}
+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())}
 
 
 def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
@@ -779,6 +797,7 @@ 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.
@@ -789,6 +808,7 @@ 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]:
@@ -809,6 +829,7 @@ def invocation(
         cls.UIConfig.title = title
         cls.UIConfig.tags = tags
         cls.UIConfig.category = category
+        cls.UIConfig.classification = classification
 
         # Grab the node pack's name from the module name, if it's a custom node
         is_custom_node = cls.__module__.rsplit(".", 1)[0] == "invokeai.app.invocations"

invokeai/app/invocations/compel.py

@@ -1,4 +1,3 @@
-import re
 from dataclasses import dataclass
 from typing import List, Optional, Union
 
@@ -17,6 +16,7 @@ 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 re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
+        for trigger in extract_ti_triggers_from_prompt(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 re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
+        for trigger in extract_ti_triggers_from_prompt(prompt):
             name = trigger[1:-1]
             try:
                 ti_list.append(

invokeai/app/invocations/controlnet_image_processors.py

@@ -24,11 +24,13 @@ from controlnet_aux import (
 )
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
-from pydantic import BaseModel, ConfigDict, Field, field_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_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
+from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
@@ -75,17 +77,16 @@ 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 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")
+        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("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="1.1.0")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
 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, description="The weight given to the ControlNet"
+        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
     )
     begin_step_percent: float = InputField(
-        default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
+        default=0, ge=0, le=1, 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)"
@@ -113,6 +114,17 @@ 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(
@@ -591,3 +603,33 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
         color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
         color_map = Image.fromarray(color_map)
         return color_map
+
+
+DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
+
+
+@invocation(
+    "depth_anything_image_processor",
+    title="Depth Anything Processor",
+    tags=["controlnet", "depth", "depth anything"],
+    category="controlnet",
+    version="1.0.0",
+)
+class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates a depth map based on the Depth Anything algorithm"""
+
+    model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
+        default="small", description="The size of the depth model to use"
+    )
+    resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
+    offload: bool = InputField(default=False)
+
+    def run_processor(self, image):
+        depth_anything_detector = DepthAnythingDetector()
+        depth_anything_detector.load_model(model_size=self.model_size)
+
+        if image.mode == "RGBA":
+            image = image.convert("RGB")
+
+        processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
+        return processed_image

invokeai/app/invocations/image.py

@@ -13,7 +13,15 @@ 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, Input, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import (
+    BaseInvocation,
+    Classification,
+    Input,
+    InputField,
+    InvocationContext,
+    WithMetadata,
+    invocation,
+)
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -113,11 +121,11 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "img_paste",
-    title="Paste Image",
-    tags=["image", "paste"],
+    invocation_type="img_pad_crop",
+    title="Center Pad or Crop Image",
     category="image",
-    version="1.2.0",
+    tags=["image", "pad", "crop"],
+    version="1.0.0",
 )
 class CenterPadCropInvocation(BaseInvocation):
     """Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image."""
@@ -168,11 +176,11 @@ class CenterPadCropInvocation(BaseInvocation):
 
 
 @invocation(
-    invocation_type="img_pad_crop",
-    title="Center Pad or Crop Image",
+    "img_paste",
+    title="Paste Image",
+    tags=["image", "paste"],
     category="image",
-    tags=["image", "pad", "crop"],
-    version="1.0.0",
+    version="1.2.0",
 )
 class ImagePasteInvocation(BaseInvocation, WithMetadata):
     """Pastes an image into another image."""
@@ -421,6 +429,64 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
         )
 
 
+@invocation(
+    "unsharp_mask",
+    title="Unsharp Mask",
+    tags=["image", "unsharp_mask"],
+    category="image",
+    version="1.2.0",
+    classification=Classification.Beta,
+)
+class UnsharpMaskInvocation(BaseInvocation, WithMetadata):
+    """Applies an unsharp mask filter to an image"""
+
+    image: ImageField = InputField(description="The image to use")
+    radius: float = InputField(gt=0, description="Unsharp mask radius", default=2)
+    strength: float = InputField(ge=0, description="Unsharp mask strength", default=50)
+
+    def pil_from_array(self, arr):
+        return Image.fromarray((arr * 255).astype("uint8"))
+
+    def array_from_pil(self, img):
+        return numpy.array(img) / 255
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+        mode = image.mode
+
+        alpha_channel = image.getchannel("A") if mode == "RGBA" else None
+        image = image.convert("RGB")
+        image_blurred = self.array_from_pil(image.filter(ImageFilter.GaussianBlur(radius=self.radius)))
+
+        image = self.array_from_pil(image)
+        image += (image - image_blurred) * (self.strength / 100.0)
+        image = numpy.clip(image, 0, 1)
+        image = self.pil_from_array(image)
+
+        image = image.convert(mode)
+
+        # Make the image RGBA if we had a source alpha channel
+        if alpha_channel is not None:
+            image.putalpha(alpha_channel)
+
+        image_dto = context.services.images.create(
+            image=image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
+            workflow=context.workflow,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image.width,
+            height=image.height,
+        )
+
+
 PIL_RESAMPLING_MODES = Literal[
     "nearest",
     "box",

invokeai/app/invocations/ip_adapter.py

@@ -2,7 +2,7 @@ import os
 from builtins import float
 from typing import List, Union
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -15,6 +15,7 @@ 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
@@ -39,7 +40,6 @@ 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)"
     )
@@ -47,6 +47,17 @@ 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):
@@ -54,7 +65,7 @@ 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="1.1.0")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.1")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -64,18 +75,27 @@ 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, ge=-1, description="The weight given to the IP-Adapter", title="Weight"
+        default=1, description="The weight given to the IP-Adapter", title="Weight"
     )
-
     begin_step_percent: float = InputField(
-        default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
+        default=0, ge=0, le=1, 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

@@ -1,5 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
+import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
@@ -220,7 +221,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.0",
+    version="1.5.1",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -279,7 +280,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         ui_order=7,
     )
     cfg_rescale_multiplier: float = InputField(
-        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+        title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
     )
     latents: Optional[LatentsField] = InputField(
         default=None,
@@ -1228,3 +1229,57 @@ class CropLatentsCoreInvocation(BaseInvocation):
         context.services.latents.save(name, cropped_latents)
 
         return build_latents_output(latents_name=name, latents=cropped_latents)
+
+
+@invocation_output("ideal_size_output")
+class IdealSizeOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    width: int = OutputField(description="The ideal width of the image (in pixels)")
+    height: int = OutputField(description="The ideal height of the image (in pixels)")
+
+
+@invocation(
+    "ideal_size",
+    title="Ideal Size",
+    tags=["latents", "math", "ideal_size"],
+    version="1.0.2",
+)
+class IdealSizeInvocation(BaseInvocation):
+    """Calculates the ideal size for generation to avoid duplication"""
+
+    width: int = InputField(default=1024, description="Final image width")
+    height: int = InputField(default=576, description="Final image height")
+    unet: UNetField = InputField(default=None, description=FieldDescriptions.unet)
+    multiplier: float = InputField(
+        default=1.0,
+        description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in initial generation artifacts if too large)",
+    )
+
+    def trim_to_multiple_of(self, *args, multiple_of=LATENT_SCALE_FACTOR):
+        return tuple((x - x % multiple_of) for x in args)
+
+    def invoke(self, context: InvocationContext) -> IdealSizeOutput:
+        aspect = self.width / self.height
+        dimension = 512
+        if self.unet.unet.base_model == BaseModelType.StableDiffusion2:
+            dimension = 768
+        elif self.unet.unet.base_model == BaseModelType.StableDiffusionXL:
+            dimension = 1024
+        dimension = dimension * self.multiplier
+        min_dimension = math.floor(dimension * 0.5)
+        model_area = dimension * dimension  # hardcoded for now since all models are trained on square images
+
+        if aspect > 1.0:
+            init_height = max(min_dimension, math.sqrt(model_area / aspect))
+            init_width = init_height * aspect
+        else:
+            init_width = max(min_dimension, math.sqrt(model_area * aspect))
+            init_height = init_width / aspect
+
+        scaled_width, scaled_height = self.trim_to_multiple_of(
+            math.floor(init_width),
+            math.floor(init_height),
+        )
+
+        return IdealSizeOutput(width=scaled_width, height=scaled_height)

invokeai/app/invocations/onnx.py

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

invokeai/app/invocations/t2i_adapter.py

@@ -1,6 +1,6 @@
 from typing import Union
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -14,6 +14,7 @@ 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
 
@@ -37,6 +38,17 @@ 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):
@@ -44,7 +56,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.0"
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.1"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""
@@ -61,7 +73,7 @@ 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=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"
+        default=0, ge=0, le=1, 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)"
@@ -71,6 +83,17 @@ 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

@@ -1,3 +1,5 @@
+from typing import Literal
+
 import numpy as np
 from PIL import Image
 from pydantic import BaseModel
@@ -5,6 +7,8 @@ from pydantic import BaseModel
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Classification,
+    Input,
     InputField,
     InvocationContext,
     OutputField,
@@ -14,7 +18,13 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
+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
 
 
@@ -28,7 +38,14 @@ 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")
+@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."""
 
@@ -55,6 +72,79 @@ class CalculateImageTilesInvocation(BaseInvocation):
         return CalculateImageTilesOutput(tiles=tiles)
 
 
+@invocation(
+    "calculate_image_tiles_even_split",
+    title="Calculate Image Tiles Even Split",
+    tags=["tiles"],
+    category="tiles",
+    version="1.1.0",
+    classification=Classification.Beta,
+)
+class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
+    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
+
+    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
+    image_height: int = InputField(
+        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
+    )
+    num_tiles_x: int = InputField(
+        default=2,
+        ge=1,
+        description="Number of tiles to divide image into on the x axis",
+    )
+    num_tiles_y: int = InputField(
+        default=2,
+        ge=1,
+        description="Number of tiles to divide image into on the y axis",
+    )
+    overlap: int = InputField(
+        default=128,
+        ge=0,
+        multiple_of=8,
+        description="The overlap, in pixels, between adjacent tiles.",
+    )
+
+    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
+        tiles = calc_tiles_even_split(
+            image_height=self.image_height,
+            image_width=self.image_width,
+            num_tiles_x=self.num_tiles_x,
+            num_tiles_y=self.num_tiles_y,
+            overlap=self.overlap,
+        )
+        return CalculateImageTilesOutput(tiles=tiles)
+
+
+@invocation(
+    "calculate_image_tiles_min_overlap",
+    title="Calculate Image Tiles Minimum Overlap",
+    tags=["tiles"],
+    category="tiles",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
+class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
+    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
+
+    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
+    image_height: int = InputField(
+        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
+    )
+    tile_width: int = InputField(ge=1, default=576, description="The tile width, in pixels.")
+    tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
+    min_overlap: int = InputField(default=128, ge=0, description="Minimum overlap between adjacent tiles, in pixels.")
+
+    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
+        tiles = calc_tiles_min_overlap(
+            image_height=self.image_height,
+            image_width=self.image_width,
+            tile_height=self.tile_height,
+            tile_width=self.tile_width,
+            min_overlap=self.min_overlap,
+        )
+        return CalculateImageTilesOutput(tiles=tiles)
+
+
 @invocation_output("tile_to_properties_output")
 class TileToPropertiesOutput(BaseInvocationOutput):
     coords_left: int = OutputField(description="Left coordinate of the tile relative to its parent image.")
@@ -76,7 +166,14 @@ class TileToPropertiesOutput(BaseInvocationOutput):
     overlap_right: int = OutputField(description="Overlap between this tile and its right neighbor.")
 
 
-@invocation("tile_to_properties", title="Tile to Properties", tags=["tiles"], category="tiles", version="1.0.0")
+@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."""
 
@@ -102,7 +199,14 @@ 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")
+@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."""
 
@@ -121,13 +225,29 @@ class PairTileImageInvocation(BaseInvocation):
         )
 
 
-@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.1.0")
+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.",
     )
@@ -157,10 +277,18 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
         channels = tile_np_images[0].shape[-1]
         dtype = tile_np_images[0].dtype
         np_image = np.zeros(shape=(height, width, channels), dtype=dtype)
+        if self.blend_mode == "Linear":
+            merge_tiles_with_linear_blending(
+                dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
+            )
+        elif self.blend_mode == "Seam":
+            merge_tiles_with_seam_blending(
+                dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
+            )
+        else:
+            raise ValueError(f"Unsupported blend mode: '{self.blend_mode}'.")
 
-        merge_tiles_with_linear_blending(
-            dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
-        )
+        # Convert into a PIL image and save
         pil_image = Image.fromarray(np_image)
 
         image_dto = context.services.images.create(

invokeai/app/invocations/util.py

@@ -0,0 +1,14 @@
+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

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

invokeai/app/services/board_records/board_records_sqlite.py

@@ -28,52 +28,6 @@ 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

@@ -1,6 +1,7 @@
-"""
-Init file for InvokeAI configure package
-"""
+"""Init file for InvokeAI configure package."""
 
-from .config_base import PagingArgumentParser  # noqa F401
-from .config_default import InvokeAIAppConfig, get_invokeai_config  # noqa F401
+from invokeai.app.services.config.config_common import PagingArgumentParser
+
+from .config_default import InvokeAIAppConfig, get_invokeai_config
+
+__all__ = ["InvokeAIAppConfig", "get_invokeai_config", "PagingArgumentParser"]

invokeai/app/services/config/config_default.py

@@ -173,7 +173,7 @@ from __future__ import annotations
 
 import os
 from pathlib import Path
-from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
+from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
 
 from omegaconf import DictConfig, OmegaConf
 from pydantic import Field, TypeAdapter
@@ -209,7 +209,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     """Configuration object for InvokeAI App."""
 
     singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
-    singleton_init: ClassVar[Optional[Dict]] = None
+    singleton_init: ClassVar[Optional[Dict[str, Any]]] = None
 
     # fmt: off
     type: Literal["InvokeAI"] = "InvokeAI"
@@ -221,6 +221,9 @@ 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)
@@ -260,7 +263,7 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # DEVICE
     device              : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
-    precision           : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
+    precision           : Literal["auto", "float16", "bfloat16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
 
     # GENERATION
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", json_schema_extra=Categories.Generation)
@@ -298,8 +301,8 @@ class InvokeAIAppConfig(InvokeAISettings):
         self,
         argv: Optional[list[str]] = None,
         conf: Optional[DictConfig] = None,
-        clobber=False,
-    ):
+        clobber: Optional[bool] = False,
+    ) -> None:
         """
         Update settings with contents of init file, environment, and command-line settings.
 
@@ -334,7 +337,7 @@ class InvokeAIAppConfig(InvokeAISettings):
                 )
 
     @classmethod
-    def get_config(cls, **kwargs) -> InvokeAIAppConfig:
+    def get_config(cls, **kwargs: Any) -> InvokeAIAppConfig:
         """Return a singleton InvokeAIAppConfig configuration object."""
         if (
             cls.singleton_config is None
@@ -353,7 +356,7 @@ class InvokeAIAppConfig(InvokeAISettings):
         else:
             root = self.find_root().expanduser().absolute()
         self.root = root  # insulate ourselves from relative paths that may change
-        return root
+        return root.resolve()
 
     @property
     def root_dir(self) -> Path:
@@ -383,17 +386,17 @@ class InvokeAIAppConfig(InvokeAISettings):
         return db_dir / DB_FILE
 
     @property
-    def model_conf_path(self) -> Optional[Path]:
+    def model_conf_path(self) -> Path:
         """Path to models configuration file."""
         return self._resolve(self.conf_path)
 
     @property
-    def legacy_conf_path(self) -> Optional[Path]:
+    def legacy_conf_path(self) -> 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) -> Optional[Path]:
+    def models_path(self) -> Path:
         """Path to the models directory."""
         return self._resolve(self.models_dir)
 
@@ -452,7 +455,7 @@ class InvokeAIAppConfig(InvokeAISettings):
         return _find_root()
 
 
-def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
+def get_invokeai_config(**kwargs: Any) -> InvokeAIAppConfig:
     """Legacy function which returns InvokeAIAppConfig.get_config()."""
     return InvokeAIAppConfig.get_config(**kwargs)
 

invokeai/app/services/download/__init__.py

@@ -0,0 +1,12 @@
+"""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

@@ -0,0 +1,262 @@
+# 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]
+DownloadExceptionHandler = Callable[["DownloadJob", Optional[Exception]], 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)"
+    )
+    content_type: Optional[str] = Field(default=None, description="Content type of downloaded file")
+    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[DownloadExceptionHandler] = PrivateAttr(default=None)
+
+    def __hash__(self) -> int:
+        """Return hash of the string representation of this object, for indexing."""
+        return hash(str(self))
+
+    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 complete(self) -> bool:
+        """Return true if job completed without errors."""
+        return self.status == DownloadJobStatus.COMPLETED
+
+    @property
+    def running(self) -> bool:
+        """Return true if the job is running."""
+        return self.status == DownloadJobStatus.RUNNING
+
+    @property
+    def errored(self) -> bool:
+        """Return true if the job is errored."""
+        return self.status == DownloadJobStatus.ERROR
+
+    @property
+    def in_terminal_state(self) -> bool:
+        """Return true if job has finished, one way or another."""
+        return self.status not in [DownloadJobStatus.WAITING, DownloadJobStatus.RUNNING]
+
+    @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[DownloadExceptionHandler]:
+        """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[DownloadExceptionHandler] = 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[DownloadExceptionHandler] = None,
+    ) -> DownloadJob:
+        """
+        Create and enqueue 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 submit_download_job(
+        self,
+        job: DownloadJob,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
+    ) -> None:
+        """
+        Enqueue a download job.
+
+        :param job: The DownloadJob
+        :param on_start, on_progress, on_complete, on_error: Callbacks for the indicated
+         events.
+        """
+        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) -> None:
+        """Cancel all active and enquedjobs."""
+        pass
+
+    @abstractmethod
+    def prune_jobs(self) -> None:
+        """Prune completed and errored queue items from the job list."""
+        pass
+
+    @abstractmethod
+    def cancel_job(self, job: DownloadJob) -> None:
+        """Cancel the job, clearing partial downloads and putting it into ERROR state."""
+        pass
+
+    @abstractmethod
+    def join(self) -> None:
+        """Wait until all jobs are off the queue."""
+        pass

invokeai/app/services/download/download_default.py

@@ -0,0 +1,437 @@
+# Copyright (c) 2023, Lincoln D. Stein
+"""Implementation of multithreaded download queue for invokeai."""
+
+import os
+import re
+import threading
+import traceback
+from pathlib import Path
+from queue import Empty, PriorityQueue
+from typing import Any, Dict, List, Optional
+
+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,
+    DownloadExceptionHandler,
+    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."""
+
+    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 submit_download_job(
+        self,
+        job: DownloadJob,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
+    ) -> None:
+        """Enqueue a download job."""
+        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:
+            job.id = self._next_job_id
+            self._next_job_id += 1
+            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[job.id] = job
+            self._queue.put(job)
+
+    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[DownloadExceptionHandler] = None,
+    ) -> DownloadJob:
+        """Create and enqueue a download job and return it."""
+        if not self._accept_download_requests:
+            raise ServiceInactiveException(
+                "The download service is not currently accepting requests. Please call start() to initialize the service."
+            )
+        job = DownloadJob(
+            source=source,
+            dest=dest,
+            priority=priority,
+            access_token=access_token,
+        )
+        self.submit_download_job(
+            job,
+            on_start=on_start,
+            on_progress=on_progress,
+            on_complete=on_complete,
+            on_cancelled=on_cancelled,
+            on_error=on_error,
+        )
+        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 job.in_terminal_state:
+                    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) -> None:
+        """Cancel all jobs (those not in enqueued, running or paused state)."""
+        for job in self._jobs.values():
+            if not job.in_terminal_state:
+                self.cancel_job(job)
+
+    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, excp)
+            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)
+
+        job.content_type = resp.headers.get("Content-Type")
+        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
+        self._logger.debug(f"{job.source}: saved to {job.download_path} (bytes={job.bytes})")
+        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(
+                    f"An error occurred while processing the on_start callback: {traceback.format_exception(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(
+                    f"An error occurred while processing the on_progress callback: {traceback.format_exception(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(
+                    f"An error occurred while processing the on_complete callback: {traceback.format_exception(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:
+        if job.status not in [DownloadJobStatus.RUNNING, DownloadJobStatus.WAITING]:
+            return
+        job.status = DownloadJobStatus.CANCELLED
+        if job.on_cancelled:
+            try:
+                job.on_cancelled(job)
+            except Exception as e:
+                self._logger.error(
+                    f"An error occurred while processing the on_cancelled callback: {traceback.format_exception(e)}"
+                )
+        if self._event_bus:
+            self._event_bus.emit_download_cancelled(str(job.source))
+
+    def _signal_job_error(self, job: DownloadJob, excp: Optional[Exception] = None) -> None:
+        job.status = DownloadJobStatus.ERROR
+        self._logger.error(f"{str(job.source)}: {traceback.format_exception(excp)}")
+        if job.on_error:
+            try:
+                job.on_error(job, excp)
+            except Exception as e:
+                self._logger.error(
+                    f"An error occurred while processing the on_error callback: {traceback.format_exception(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.debug(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

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

invokeai/app/services/events/events_base.py

@@ -1,6 +1,7 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any, Optional
+
+from typing import Any, Dict, List, Optional, Union
 
 from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
 from invokeai.app.services.session_queue.session_queue_common import (
@@ -16,6 +17,8 @@ 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"""
 
@@ -30,6 +33,20 @@ 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(
@@ -313,3 +330,166 @@ 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_downloading(
+        self,
+        source: str,
+        local_path: str,
+        bytes: int,
+        total_bytes: int,
+        parts: List[Dict[str, Union[str, int]]],
+    ) -> None:
+        """
+        Emit at intervals while the install job is in progress (remote models only).
+
+        :param source: Source of the model
+        :param local_path: Where model is downloading to
+        :param parts: Progress of downloading URLs that comprise the model, if any.
+        :param bytes: Number of bytes downloaded so far.
+        :param total_bytes: Total size of download, including all files.
+        This emits a Dict with keys "source", "local_path", "bytes" and "total_bytes".
+        """
+        self.__emit_model_event(
+            event_name="model_install_downloading",
+            payload={
+                "source": source,
+                "local_path": local_path,
+                "bytes": bytes,
+                "total_bytes": total_bytes,
+                "parts": parts,
+            },
+        )
+
+    def emit_model_install_running(self, source: str) -> None:
+        """
+        Emit once when an install job becomes active.
+
+        :param source: Source of the model; local path, repo_id or url
+        """
+        self.__emit_model_event(
+            event_name="model_install_running",
+            payload={"source": source},
+        )
+
+    def emit_model_install_completed(self, source: str, key: str, total_bytes: Optional[int] = None) -> None:
+        """
+        Emit 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
+        :param total_bytes: Size of the model (may be None for installation of a local path)
+        """
+        self.__emit_model_event(
+            event_name="model_install_completed",
+            payload={
+                "source": source,
+                "total_bytes": total_bytes,
+                "key": key,
+            },
+        )
+
+    def emit_model_install_cancelled(self, source: str) -> None:
+        """
+        Emit when an install job is cancelled.
+
+        :param source: Source of the model; local path, repo_id or url
+        """
+        self.__emit_model_event(
+            event_name="model_install_cancelled",
+            payload={"source": source},
+        )
+
+    def emit_model_install_error(
+        self,
+        source: str,
+        error_type: str,
+        error: str,
+    ) -> None:
+        """
+        Emit when an install job encounters an exception.
+
+        :param source: Source of the model
+        :param error_type: The name of the exception
+        :param error: A text description of the exception
+        """
+        self.__emit_model_event(
+            event_name="model_install_error",
+            payload={
+                "source": source,
+                "error_type": error_type,
+                "error": error,
+            },
+        )

invokeai/app/services/image_records/image_records_sqlite.py

@@ -32,101 +32,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         self._conn = db.conn
         self._cursor = self._conn.cursor()
 
-        try:
-            self._lock.acquire()
-            self._create_tables()
-            self._conn.commit()
-        finally:
-            self._lock.release()
-
-    def _create_tables(self) -> None:
-        """Creates the `images` table."""
-
-        # Create the `images` table.
-        self._cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS images (
-                image_name TEXT NOT NULL PRIMARY KEY,
-                -- This is an enum in python, unrestricted string here for flexibility
-                image_origin TEXT NOT NULL,
-                -- This is an enum in python, unrestricted string here for flexibility
-                image_category TEXT NOT NULL,
-                width INTEGER NOT NULL,
-                height INTEGER NOT NULL,
-                session_id TEXT,
-                node_id TEXT,
-                metadata TEXT,
-                is_intermediate BOOLEAN DEFAULT FALSE,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Soft delete, currently unused
-                deleted_at DATETIME
-            );
-            """
-        )
-
-        self._cursor.execute("PRAGMA table_info(images)")
-        columns = [column[1] for column in self._cursor.fetchall()]
-
-        if "starred" not in columns:
-            self._cursor.execute(
-                """--sql
-                ALTER TABLE images ADD COLUMN starred BOOLEAN DEFAULT FALSE;
-                """
-            )
-
-        # Create the `images` table indices.
-        self._cursor.execute(
-            """--sql
-            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_image_name ON images(image_name);
-            """
-        )
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_images_image_origin ON images(image_origin);
-            """
-        )
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_images_image_category ON images(image_category);
-            """
-        )
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_images_created_at ON images(created_at);
-            """
-        )
-
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_images_starred ON images(starred);
-            """
-        )
-
-        # Add trigger for `updated_at`.
-        self._cursor.execute(
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_images_updated_at
-            AFTER UPDATE
-            ON images FOR EACH ROW
-            BEGIN
-                UPDATE images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE image_name = old.image_name;
-            END;
-            """
-        )
-
-        self._cursor.execute("PRAGMA table_info(images)")
-        columns = [column[1] for column in self._cursor.fetchall()]
-        if "has_workflow" not in columns:
-            self._cursor.execute(
-                """--sql
-                ALTER TABLE images
-                ADD COLUMN has_workflow BOOLEAN DEFAULT FALSE;
-                """
-            )
-
     def get(self, image_name: str) -> ImageRecord:
         try:
             self._lock.acquire()

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -132,7 +132,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             source_node_id=source_node_id,
                             result=outputs.model_dump(),
                         )
-                    self.__invoker.services.performance_statistics.log_stats()
 
                 except KeyboardInterrupt:
                     pass
@@ -195,6 +194,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             error=traceback.format_exc(),
                         )
                 elif is_complete:
+                    self.__invoker.services.performance_statistics.log_stats(graph_execution_state.id)
                     self.__invoker.services.events.emit_graph_execution_complete(
                         queue_batch_id=queue_item.session_queue_batch_id,
                         queue_item_id=queue_item.session_queue_item_id,

invokeai/app/services/invocation_services.py

@@ -11,6 +11,7 @@ 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
@@ -21,12 +22,13 @@ 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, LibraryGraph
+    from .shared.graph import GraphExecutionState
     from .urls.urls_base import UrlServiceBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
@@ -42,7 +44,6 @@ class InvocationServices:
     configuration: "InvokeAIAppConfig"
     events: "EventServiceBase"
     graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
-    graph_library: "ItemStorageABC[LibraryGraph]"
     images: "ImageServiceABC"
     image_records: "ImageRecordStorageBase"
     image_files: "ImageFileStorageBase"
@@ -50,6 +51,8 @@ class InvocationServices:
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
     model_records: "ModelRecordServiceBase"
+    download_queue: "DownloadQueueServiceBase"
+    model_install: "ModelInstallServiceBase"
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
@@ -69,7 +72,6 @@ class InvocationServices:
         configuration: "InvokeAIAppConfig",
         events: "EventServiceBase",
         graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
-        graph_library: "ItemStorageABC[LibraryGraph]",
         images: "ImageServiceABC",
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
@@ -77,6 +79,8 @@ class InvocationServices:
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         model_records: "ModelRecordServiceBase",
+        download_queue: "DownloadQueueServiceBase",
+        model_install: "ModelInstallServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
@@ -94,7 +98,6 @@ 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
@@ -102,6 +105,8 @@ class InvocationServices:
         self.logger = logger
         self.model_manager = model_manager
         self.model_records = model_records
+        self.download_queue = download_queue
+        self.model_install = model_install
         self.processor = processor
         self.performance_statistics = performance_statistics
         self.queue = queue

invokeai/app/services/invocation_stats/invocation_stats_base.py

@@ -30,23 +30,13 @@ writes to the system log is stored in InvocationServices.performance_statistics.
 
 from abc import ABC, abstractmethod
 from contextlib import AbstractContextManager
-from typing import Dict
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
-from invokeai.backend.model_management.model_cache import CacheStats
-
-from .invocation_stats_common import NodeLog
 
 
 class InvocationStatsServiceBase(ABC):
     "Abstract base class for recording node memory/time performance statistics"
 
-    # {graph_id => NodeLog}
-    _stats: Dict[str, NodeLog]
-    _cache_stats: Dict[str, CacheStats]
-    ram_used: float
-    ram_changed: float
-
     @abstractmethod
     def __init__(self):
         """
@@ -77,45 +67,8 @@ class InvocationStatsServiceBase(ABC):
         pass
 
     @abstractmethod
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        pass
-
-    @abstractmethod
-    def update_invocation_stats(
-        self,
-        graph_id: str,
-        invocation_type: str,
-        time_used: float,
-        vram_used: float,
-    ):
-        """
-        Add timing information on execution of a node. Usually
-        used internally.
-        :param graph_id: ID of the graph that is currently executing
-        :param invocation_type: String literal type of the node
-        :param time_used: Time used by node's exection (sec)
-        :param vram_used: Maximum VRAM used during exection (GB)
-        """
-        pass
-
-    @abstractmethod
-    def log_stats(self):
+    def log_stats(self, graph_execution_state_id: str):
         """
         Write out the accumulated statistics to the log or somewhere else.
         """
         pass
-
-    @abstractmethod
-    def update_mem_stats(
-        self,
-        ram_used: float,
-        ram_changed: float,
-    ):
-        """
-        Update the collector with RAM memory usage info.
-
-        :param ram_used: How much RAM is currently in use.
-        :param ram_changed: How much RAM changed since last generation.
-        """
-        pass

invokeai/app/services/invocation_stats/invocation_stats_common.py

@@ -1,25 +1,84 @@
-from dataclasses import dataclass, field
-from typing import Dict
-
-# size of GIG in bytes
-GIG = 1073741824
+from collections import defaultdict
+from dataclasses import dataclass
 
 
 @dataclass
-class NodeStats:
-    """Class for tracking execution stats of an invocation node"""
+class NodeExecutionStats:
+    """Class for tracking execution stats of an invocation node."""
 
-    calls: int = 0
-    time_used: float = 0.0  # seconds
-    max_vram: float = 0.0  # GB
-    cache_hits: int = 0
-    cache_misses: int = 0
-    cache_high_watermark: int = 0
+    invocation_type: str
+
+    start_time: float  # Seconds since the epoch.
+    end_time: float  # Seconds since the epoch.
+
+    start_ram_gb: float  # GB
+    end_ram_gb: float  # GB
+
+    peak_vram_gb: float  # GB
+
+    def total_time(self) -> float:
+        return self.end_time - self.start_time
 
 
-@dataclass
-class NodeLog:
-    """Class for tracking node usage"""
+class GraphExecutionStats:
+    """Class for tracking execution stats of a graph."""
 
-    # {node_type => NodeStats}
-    nodes: Dict[str, NodeStats] = field(default_factory=dict)
+    def __init__(self):
+        self._node_stats_list: list[NodeExecutionStats] = []
+
+    def add_node_execution_stats(self, node_stats: NodeExecutionStats):
+        self._node_stats_list.append(node_stats)
+
+    def get_total_run_time(self) -> float:
+        """Get the total time spent executing nodes in the graph."""
+        total = 0.0
+        for node_stats in self._node_stats_list:
+            total += node_stats.total_time()
+        return total
+
+    def get_first_node_stats(self) -> NodeExecutionStats | None:
+        """Get the stats of the first node in the graph (by start_time)."""
+        first_node = None
+        for node_stats in self._node_stats_list:
+            if first_node is None or node_stats.start_time < first_node.start_time:
+                first_node = node_stats
+
+        assert first_node is not None
+        return first_node
+
+    def get_last_node_stats(self) -> NodeExecutionStats | None:
+        """Get the stats of the last node in the graph (by end_time)."""
+        last_node = None
+        for node_stats in self._node_stats_list:
+            if last_node is None or node_stats.end_time > last_node.end_time:
+                last_node = node_stats
+
+        return last_node
+
+    def get_pretty_log(self, graph_execution_state_id: str) -> str:
+        log = f"Graph stats: {graph_execution_state_id}\n"
+        log += f"{'Node':>30} {'Calls':>7}{'Seconds':>9} {'VRAM Used':>10}\n"
+
+        # Log stats aggregated by node type.
+        node_stats_by_type: dict[str, list[NodeExecutionStats]] = defaultdict(list)
+        for node_stats in self._node_stats_list:
+            node_stats_by_type[node_stats.invocation_type].append(node_stats)
+
+        for node_type, node_type_stats_list in node_stats_by_type.items():
+            num_calls = len(node_type_stats_list)
+            time_used = sum([n.total_time() for n in node_type_stats_list])
+            peak_vram = max([n.peak_vram_gb for n in node_type_stats_list])
+            log += f"{node_type:>30}  {num_calls:>4}   {time_used:7.3f}s     {peak_vram:4.3f}G\n"
+
+        # Log stats for the entire graph.
+        log += f"TOTAL GRAPH EXECUTION TIME: {self.get_total_run_time():7.3f}s\n"
+
+        first_node = self.get_first_node_stats()
+        last_node = self.get_last_node_stats()
+        if first_node is not None and last_node is not None:
+            total_wall_time = last_node.end_time - first_node.start_time
+            ram_change = last_node.end_ram_gb - first_node.start_ram_gb
+            log += f"TOTAL GRAPH WALL TIME: {total_wall_time:7.3f}s\n"
+            log += f"RAM used by InvokeAI process: {last_node.end_ram_gb:4.2f}G ({ram_change:+5.3f}G)\n"
+
+        return log

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -1,5 +1,5 @@
 import time
-from typing import Dict
+from contextlib import contextmanager
 
 import psutil
 import torch
@@ -7,161 +7,119 @@ import torch
 import invokeai.backend.util.logging as logger
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.backend.model_management.model_cache import CacheStats
 
 from .invocation_stats_base import InvocationStatsServiceBase
-from .invocation_stats_common import GIG, NodeLog, NodeStats
+from .invocation_stats_common import GraphExecutionStats, NodeExecutionStats
+
+# Size of 1GB in bytes.
+GB = 2**30
 
 
 class InvocationStatsService(InvocationStatsServiceBase):
     """Accumulate performance information about a running graph. Collects time spent in each node,
     as well as the maximum and current VRAM utilisation for CUDA systems"""
 
-    _invoker: Invoker
-
     def __init__(self):
-        # {graph_id => NodeLog}
-        self._stats: Dict[str, NodeLog] = {}
-        self._cache_stats: Dict[str, CacheStats] = {}
-        self.ram_used: float = 0.0
-        self.ram_changed: float = 0.0
+        # Maps graph_execution_state_id to GraphExecutionStats.
+        self._stats: dict[str, GraphExecutionStats] = {}
+        # Maps graph_execution_state_id to model manager CacheStats.
+        self._cache_stats: dict[str, CacheStats] = {}
 
     def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
 
-    class StatsContext:
-        """Context manager for collecting statistics."""
-
-        invocation: BaseInvocation
-        collector: "InvocationStatsServiceBase"
-        graph_id: str
-        start_time: float
-        ram_used: int
-        model_manager: ModelManagerServiceBase
-
-        def __init__(
-            self,
-            invocation: BaseInvocation,
-            graph_id: str,
-            model_manager: ModelManagerServiceBase,
-            collector: "InvocationStatsServiceBase",
-        ):
-            """Initialize statistics for this run."""
-            self.invocation = invocation
-            self.collector = collector
-            self.graph_id = graph_id
-            self.start_time = 0.0
-            self.ram_used = 0
-            self.model_manager = model_manager
-
-        def __enter__(self):
-            self.start_time = time.time()
-            if torch.cuda.is_available():
-                torch.cuda.reset_peak_memory_stats()
-            self.ram_used = psutil.Process().memory_info().rss
-            if self.model_manager:
-                self.model_manager.collect_cache_stats(self.collector._cache_stats[self.graph_id])
-
-        def __exit__(self, *args):
-            """Called on exit from the context."""
-            ram_used = psutil.Process().memory_info().rss
-            self.collector.update_mem_stats(
-                ram_used=ram_used / GIG,
-                ram_changed=(ram_used - self.ram_used) / GIG,
-            )
-            self.collector.update_invocation_stats(
-                graph_id=self.graph_id,
-                invocation_type=self.invocation.type,  # type: ignore # `type` is not on the `BaseInvocation` model, but *is* on all invocations
-                time_used=time.time() - self.start_time,
-                vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0,
-            )
-
-    def collect_stats(
-        self,
-        invocation: BaseInvocation,
-        graph_execution_state_id: str,
-    ) -> StatsContext:
-        if not self._stats.get(graph_execution_state_id):  # first time we're seeing this
-            self._stats[graph_execution_state_id] = NodeLog()
+    @contextmanager
+    def collect_stats(self, invocation: BaseInvocation, graph_execution_state_id: str):
+        if not self._stats.get(graph_execution_state_id):
+            # First time we're seeing this graph_execution_state_id.
+            self._stats[graph_execution_state_id] = GraphExecutionStats()
             self._cache_stats[graph_execution_state_id] = CacheStats()
-        return self.StatsContext(invocation, graph_execution_state_id, self._invoker.services.model_manager, self)
 
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        self._stats = {}
+            # Prune stale stats. There should be none since we're starting a new graph, but just in case.
+            self._prune_stale_stats()
+
+        # Record state before the invocation.
+        start_time = time.time()
+        start_ram = psutil.Process().memory_info().rss
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats()
+        if self._invoker.services.model_manager:
+            self._invoker.services.model_manager.collect_cache_stats(self._cache_stats[graph_execution_state_id])
 
-    def reset_stats(self, graph_execution_id: str):
         try:
-            self._stats.pop(graph_execution_id)
-        except KeyError:
-            logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_id}")
+            # Let the invocation run.
+            yield None
+        finally:
+            # Record state after the invocation.
+            node_stats = NodeExecutionStats(
+                invocation_type=invocation.type,
+                start_time=start_time,
+                end_time=time.time(),
+                start_ram_gb=start_ram / GB,
+                end_ram_gb=psutil.Process().memory_info().rss / GB,
+                peak_vram_gb=torch.cuda.max_memory_allocated() / GB if torch.cuda.is_available() else 0.0,
+            )
+            self._stats[graph_execution_state_id].add_node_execution_stats(node_stats)
 
-    def update_mem_stats(
-        self,
-        ram_used: float,
-        ram_changed: float,
-    ):
-        self.ram_used = ram_used
-        self.ram_changed = ram_changed
+    def _prune_stale_stats(self):
+        """Check all graphs being tracked and prune any that have completed/errored.
 
-    def update_invocation_stats(
-        self,
-        graph_id: str,
-        invocation_type: str,
-        time_used: float,
-        vram_used: float,
-    ):
-        if not self._stats[graph_id].nodes.get(invocation_type):
-            self._stats[graph_id].nodes[invocation_type] = NodeStats()
-        stats = self._stats[graph_id].nodes[invocation_type]
-        stats.calls += 1
-        stats.time_used += time_used
-        stats.max_vram = max(stats.max_vram, vram_used)
-
-    def log_stats(self):
-        completed = set()
-        errored = set()
-        for graph_id, _node_log in self._stats.items():
+        This shouldn't be necessary, but we don't have totally robust upstream handling of graph completions/errors, so
+        for now we call this function periodically to prevent them from accumulating.
+        """
+        to_prune = []
+        for graph_execution_state_id in self._stats:
             try:
-                current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
+                graph_execution_state = self._invoker.services.graph_execution_manager.get(graph_execution_state_id)
             except Exception:
-                errored.add(graph_id)
+                # TODO(ryand): What would cause this? Should this exception just be allowed to propagate?
+                logger.warning(f"Failed to get graph state for {graph_execution_state_id}.")
                 continue
 
-            if not current_graph_state.is_complete():
+            if not graph_execution_state.is_complete():
+                # The graph is still running, don't prune it.
                 continue
 
-            total_time = 0
-            logger.info(f"Graph stats: {graph_id}")
-            logger.info(f"{'Node':>30} {'Calls':>7}{'Seconds':>9} {'VRAM Used':>10}")
-            for node_type, stats in self._stats[graph_id].nodes.items():
-                logger.info(f"{node_type:>30}  {stats.calls:>4}   {stats.time_used:7.3f}s     {stats.max_vram:4.3f}G")
-                total_time += stats.time_used
+            to_prune.append(graph_execution_state_id)
 
-            cache_stats = self._cache_stats[graph_id]
-            hwm = cache_stats.high_watermark / GIG
-            tot = cache_stats.cache_size / GIG
-            loaded = sum(list(cache_stats.loaded_model_sizes.values())) / GIG
+        for graph_execution_state_id in to_prune:
+            del self._stats[graph_execution_state_id]
+            del self._cache_stats[graph_execution_state_id]
 
-            logger.info(f"TOTAL GRAPH EXECUTION TIME:  {total_time:7.3f}s")
-            logger.info("RAM used by InvokeAI process: " + "%4.2fG" % self.ram_used + f" ({self.ram_changed:+5.3f}G)")
-            logger.info(f"RAM used to load models: {loaded:4.2f}G")
-            if torch.cuda.is_available():
-                logger.info("VRAM in use: " + "%4.3fG" % (torch.cuda.memory_allocated() / GIG))
-            logger.info("RAM cache statistics:")
-            logger.info(f"   Model cache hits: {cache_stats.hits}")
-            logger.info(f"   Model cache misses: {cache_stats.misses}")
-            logger.info(f"   Models cached: {cache_stats.in_cache}")
-            logger.info(f"   Models cleared from cache: {cache_stats.cleared}")
-            logger.info(f"   Cache high water mark: {hwm:4.2f}/{tot:4.2f}G")
+        if len(to_prune) > 0:
+            logger.info(f"Pruned stale graph stats for {to_prune}.")
 
-            completed.add(graph_id)
+    def reset_stats(self, graph_execution_state_id: str):
+        try:
+            del self._stats[graph_execution_state_id]
+            del self._cache_stats[graph_execution_state_id]
+        except KeyError as e:
+            logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}.")
 
-        for graph_id in completed:
-            del self._stats[graph_id]
-            del self._cache_stats[graph_id]
+    def log_stats(self, graph_execution_state_id: str):
+        try:
+            graph_stats = self._stats[graph_execution_state_id]
+            cache_stats = self._cache_stats[graph_execution_state_id]
+        except KeyError as e:
+            logger.warning(f"Attempted to log statistics for unknown graph {graph_execution_state_id}: {e}.")
+            return
 
-        for graph_id in errored:
-            del self._stats[graph_id]
-            del self._cache_stats[graph_id]
+        log = graph_stats.get_pretty_log(graph_execution_state_id)
+
+        hwm = cache_stats.high_watermark / GB
+        tot = cache_stats.cache_size / GB
+        loaded = sum(list(cache_stats.loaded_model_sizes.values())) / GB
+        log += f"RAM used to load models: {loaded:4.2f}G\n"
+        if torch.cuda.is_available():
+            log += f"VRAM in use: {(torch.cuda.memory_allocated() / GB):4.3f}G\n"
+        log += "RAM cache statistics:\n"
+        log += f"   Model cache hits: {cache_stats.hits}\n"
+        log += f"   Model cache misses: {cache_stats.misses}\n"
+        log += f"   Models cached: {cache_stats.in_cache}\n"
+        log += f"   Models cleared from cache: {cache_stats.cleared}\n"
+        log += f"   Cache high water mark: {hwm:4.2f}/{tot:4.2f}G\n"
+        logger.info(log)
+
+        del self._stats[graph_execution_state_id]
+        del self._cache_stats[graph_execution_state_id]

invokeai/app/services/model_install/__init__.py

@@ -0,0 +1,27 @@
+"""Initialization file for model install service package."""
+
+from .model_install_base import (
+    CivitaiModelSource,
+    HFModelSource,
+    InstallStatus,
+    LocalModelSource,
+    ModelInstallJob,
+    ModelInstallServiceBase,
+    ModelSource,
+    UnknownInstallJobException,
+    URLModelSource,
+)
+from .model_install_default import ModelInstallService
+
+__all__ = [
+    "ModelInstallServiceBase",
+    "ModelInstallService",
+    "InstallStatus",
+    "ModelInstallJob",
+    "UnknownInstallJobException",
+    "ModelSource",
+    "LocalModelSource",
+    "HFModelSource",
+    "URLModelSource",
+    "CivitaiModelSource",
+]

invokeai/app/services/model_install/model_install_base.py

@@ -0,0 +1,412 @@
+# Copyright 2023 Lincoln D. Stein and the InvokeAI development team
+"""Baseclass definitions for the model installer."""
+
+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, Set, Union
+
+from pydantic import BaseModel, Field, PrivateAttr, field_validator
+from pydantic.networks import AnyHttpUrl
+from typing_extensions import Annotated
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
+from invokeai.app.services.events import EventServiceBase
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.model_records import ModelRecordServiceBase
+from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
+
+
+class InstallStatus(str, Enum):
+    """State of an install job running in the background."""
+
+    WAITING = "waiting"  # waiting to be dequeued
+    DOWNLOADING = "downloading"  # downloading of model files in process
+    RUNNING = "running"  # being processed
+    COMPLETED = "completed"  # finished running
+    ERROR = "error"  # terminated with an error message
+    CANCELLED = "cancelled"  # terminated with an error message
+
+
+class ModelInstallPart(BaseModel):
+    url: AnyHttpUrl
+    path: Path
+    bytes: int = 0
+    total_bytes: int = 0
+
+
+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 CivitaiModelSource(StringLikeSource):
+    """A Civitai version id, with optional variant and access token."""
+
+    version_id: int
+    variant: Optional[ModelRepoVariant] = None
+    access_token: Optional[str] = None
+    type: Literal["civitai"] = "civitai"
+
+    def __str__(self) -> str:
+        """Return string version of repoid when string rep needed."""
+        base: str = str(self.version_id)
+        base += f" ({self.variant})" if self.variant else ""
+        return base
+
+
+class HFModelSource(StringLikeSource):
+    """
+    A HuggingFace repo_id with optional variant, sub-folder and access token.
+    Note that the variant option, if not provided to the constructor, will default to fp16, which is
+    what people (almost) always want.
+    """
+
+    repo_id: str
+    variant: Optional[ModelRepoVariant] = ModelRepoVariant.FP16
+    subfolder: Optional[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["url"] = "url"
+
+    def __str__(self) -> str:
+        """Return string version of the url when string rep needed."""
+        return str(self.url)
+
+
+ModelSource = Annotated[
+    Union[LocalModelSource, HFModelSource, CivitaiModelSource, URLModelSource], Field(discriminator="type")
+]
+
+
+class ModelInstallJob(BaseModel):
+    """Object that tracks the current status of an install request."""
+
+    id: int = Field(description="Unique ID for this job")
+    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")
+    bytes: Optional[int] = Field(
+        default=None, description="For a remote model, the number of bytes downloaded so far (may not be available)"
+    )
+    total_bytes: int = Field(default=0, description="Total size of the model to be installed")
+    source_metadata: Optional[AnyModelRepoMetadata] = Field(
+        default=None, description="Metadata provided by the model source"
+    )
+    download_parts: Set[DownloadJob] = Field(
+        default_factory=set, description="Download jobs contributing to this install"
+    )
+    # internal flags and transitory settings
+    _install_tmpdir: Optional[Path] = PrivateAttr(default=None)
+    _exception: Optional[Exception] = PrivateAttr(default=None)
+
+    def set_error(self, e: Exception) -> None:
+        """Record the error and traceback from an exception."""
+        self._exception = e
+        self.status = InstallStatus.ERROR
+
+    def cancel(self) -> None:
+        """Call to cancel the job."""
+        self.status = InstallStatus.CANCELLED
+
+    @property
+    def error_type(self) -> Optional[str]:
+        """Class name of the exception that led to status==ERROR."""
+        return self._exception.__class__.__name__ if self._exception else None
+
+    @property
+    def error(self) -> Optional[str]:
+        """Error traceback."""
+        return "".join(traceback.format_exception(self._exception)) if self._exception else None
+
+    @property
+    def cancelled(self) -> bool:
+        """Set status to CANCELLED."""
+        return self.status == InstallStatus.CANCELLED
+
+    @property
+    def errored(self) -> bool:
+        """Return true if job has errored."""
+        return self.status == InstallStatus.ERROR
+
+    @property
+    def waiting(self) -> bool:
+        """Return true if job is waiting to run."""
+        return self.status == InstallStatus.WAITING
+
+    @property
+    def downloading(self) -> bool:
+        """Return true if job is downloading."""
+        return self.status == InstallStatus.DOWNLOADING
+
+    @property
+    def running(self) -> bool:
+        """Return true if job is running."""
+        return self.status == InstallStatus.RUNNING
+
+    @property
+    def complete(self) -> bool:
+        """Return true if job completed without errors."""
+        return self.status == InstallStatus.COMPLETED
+
+    @property
+    def in_terminal_state(self) -> bool:
+        """Return true if job is in a terminal state."""
+        return self.status in [InstallStatus.COMPLETED, InstallStatus.ERROR, InstallStatus.CANCELLED]
+
+
+class ModelInstallServiceBase(ABC):
+    """Abstract base class for InvokeAI model installation."""
+
+    @abstractmethod
+    def __init__(
+        self,
+        app_config: InvokeAIAppConfig,
+        record_store: ModelRecordServiceBase,
+        download_queue: DownloadQueueServiceBase,
+        metadata_store: ModelMetadataStore,
+        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.
+        """
+
+    # make the invoker optional here because we don't need it and it
+    # makes the installer harder to use outside the web app
+    @abstractmethod
+    def start(self, invoker: Optional[Invoker] = None) -> None:
+        """Start the installer service."""
+
+    @abstractmethod
+    def stop(self, invoker: Optional[Invoker] = None) -> 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_by_source(self, source: ModelSource) -> List[ModelInstallJob]:
+        """Return the ModelInstallJob(s) corresponding to the provided source."""
+
+    @abstractmethod
+    def get_job_by_id(self, id: int) -> ModelInstallJob:
+        """Return the ModelInstallJob corresponding to the provided id. Raises ValueError if no job has that ID."""
+
+    @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 cancel_job(self, job: ModelInstallJob) -> None:
+        """Cancel the indicated job."""
+
+    @abstractmethod
+    def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:
+        """
+        Wait for all pending installs to complete.
+
+        This will block until all pending installs have
+        completed, been cancelled, or errored out.
+
+        :param timeout: Wait up to indicated number of seconds. Raise an Exception('timeout') if
+        installs do not complete within the indicated time.
+        """
+
+    @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

@@ -0,0 +1,759 @@
+"""Model installation class."""
+
+import os
+import re
+import threading
+import time
+from hashlib import sha256
+from pathlib import Path
+from queue import Empty, Queue
+from random import randbytes
+from shutil import copyfile, copytree, move, rmtree
+from tempfile import mkdtemp
+from typing import Any, Dict, List, Optional, Set, Union
+
+from huggingface_hub import HfFolder
+from pydantic.networks import AnyHttpUrl
+from requests import Session
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
+from invokeai.app.services.events.events_base import EventServiceBase
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase, ModelRecordServiceSQL
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    BaseModelType,
+    InvalidModelConfigException,
+    ModelRepoVariant,
+    ModelType,
+)
+from invokeai.backend.model_manager.hash import FastModelHash
+from invokeai.backend.model_manager.metadata import (
+    AnyModelRepoMetadata,
+    CivitaiMetadataFetch,
+    HuggingFaceMetadataFetch,
+    ModelMetadataStore,
+    ModelMetadataWithFiles,
+    RemoteModelFile,
+)
+from invokeai.backend.model_manager.probe import ModelProbe
+from invokeai.backend.model_manager.search import ModelSearch
+from invokeai.backend.util import Chdir, InvokeAILogger
+from invokeai.backend.util.devices import choose_precision, choose_torch_device
+
+from .model_install_base import (
+    CivitaiModelSource,
+    HFModelSource,
+    InstallStatus,
+    LocalModelSource,
+    ModelInstallJob,
+    ModelInstallServiceBase,
+    ModelSource,
+    URLModelSource,
+)
+
+TMPDIR_PREFIX = "tmpinstall_"
+
+
+class ModelInstallService(ModelInstallServiceBase):
+    """class for InvokeAI model installation."""
+
+    def __init__(
+        self,
+        app_config: InvokeAIAppConfig,
+        record_store: ModelRecordServiceBase,
+        download_queue: DownloadQueueServiceBase,
+        metadata_store: Optional[ModelMetadataStore] = None,
+        event_bus: Optional[EventServiceBase] = None,
+        session: Optional[Session] = 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: List[ModelInstallJob] = []
+        self._install_queue: Queue[ModelInstallJob] = Queue()
+        self._cached_model_paths: Set[Path] = set()
+        self._models_installed: Set[str] = set()
+        self._lock = threading.Lock()
+        self._stop_event = threading.Event()
+        self._downloads_changed_event = threading.Event()
+        self._download_queue = download_queue
+        self._download_cache: Dict[AnyHttpUrl, ModelInstallJob] = {}
+        self._running = False
+        self._session = session
+        self._next_job_id = 0
+        # There may not necessarily be a metadata store initialized
+        # so we create one and initialize it with the same sql database
+        # used by the record store service.
+        if metadata_store:
+            self._metadata_store = metadata_store
+        else:
+            assert isinstance(record_store, ModelRecordServiceSQL)
+            self._metadata_store = ModelMetadataStore(record_store.db)
+
+    @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
+
+    # make the invoker optional here because we don't need it and it
+    # makes the installer harder to use outside the web app
+    def start(self, invoker: Optional[Invoker] = None) -> None:
+        """Start the installer thread."""
+        with self._lock:
+            if self._running:
+                raise Exception("Attempt to start the installer service twice")
+            self._start_installer_thread()
+            self._remove_dangling_install_dirs()
+            self.sync_to_config()
+
+    def stop(self, invoker: Optional[Invoker] = None) -> None:
+        """Stop the installer thread; after this the object can be deleted and garbage collected."""
+        with self._lock:
+            if not self._running:
+                raise Exception("Attempt to stop the install service before it was started")
+            self._stop_event.set()
+            with self._install_queue.mutex:
+                self._install_queue.queue.clear()  # get rid of pending jobs
+            active_jobs = [x for x in self.list_jobs() if x.running]
+            if active_jobs:
+                self._logger.warning("Waiting for active install job to complete")
+            self.wait_for_installs()
+            self._download_cache.clear()
+            self._running = False
+
+    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
+        try:
+            new_path = self._copy_model(model_path, dest_path)
+        except FileExistsError as excp:
+            raise DuplicateModelException(
+                f"A model named {model_path.name} is already installed at {dest_path.as_posix()}"
+            ) from excp
+        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 isinstance(source, LocalModelSource):
+            install_job = self._import_local_model(source, config)
+            self._install_queue.put(install_job)  # synchronously install
+        elif isinstance(source, CivitaiModelSource):
+            install_job = self._import_from_civitai(source, config)
+        elif isinstance(source, HFModelSource):
+            install_job = self._import_from_hf(source, config)
+        elif isinstance(source, URLModelSource):
+            install_job = self._import_from_url(source, config)
+        else:
+            raise ValueError(f"Unsupported model source: '{type(source)}'")
+
+        self._install_jobs.append(install_job)
+        return install_job
+
+    def list_jobs(self) -> List[ModelInstallJob]:  # noqa D102
+        return self._install_jobs
+
+    def get_job_by_source(self, source: ModelSource) -> List[ModelInstallJob]:  # noqa D102
+        return [x for x in self._install_jobs if x.source == source]
+
+    def get_job_by_id(self, id: int) -> ModelInstallJob:  # noqa D102
+        jobs = [x for x in self._install_jobs if x.id == id]
+        if not jobs:
+            raise ValueError(f"No job with id {id} known")
+        assert len(jobs) == 1
+        assert isinstance(jobs[0], ModelInstallJob)
+        return jobs[0]
+
+    def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:  # noqa D102
+        """Block until all installation jobs are done."""
+        start = time.time()
+        while len(self._download_cache) > 0:
+            if self._downloads_changed_event.wait(timeout=5):  # in case we miss an event
+                self._downloads_changed_event.clear()
+            if timeout > 0 and time.time() - start > timeout:
+                raise Exception("Timeout exceeded")
+        self._install_queue.join()
+        return self._install_jobs
+
+    def cancel_job(self, job: ModelInstallJob) -> None:
+        """Cancel the indicated job."""
+        job.cancel()
+        with self._lock:
+            self._cancel_download_parts(job)
+
+    def prune_jobs(self) -> None:
+        """Prune all completed and errored jobs."""
+        unfinished_jobs = [x for x in self._install_jobs if not x.in_terminal_state]
+        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.clear()
+        search.search(scan_dir)
+        return list(self._models_installed)
+
+    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)
+
+    # --------------------------------------------------------------------------------------------
+    # Internal functions that manage the installer threads
+    # --------------------------------------------------------------------------------------------
+    def _start_installer_thread(self) -> None:
+        threading.Thread(target=self._install_next_item, daemon=True).start()
+        self._running = True
+
+    def _install_next_item(self) -> None:
+        done = False
+        while not done:
+            if self._stop_event.is_set():
+                done = True
+                continue
+            try:
+                job = self._install_queue.get(timeout=1)
+            except Empty:
+                continue
+
+            assert job.local_path is not None
+            try:
+                if job.cancelled:
+                    self._signal_job_cancelled(job)
+
+                elif job.errored:
+                    self._signal_job_errored(job)
+
+                elif (
+                    job.waiting or job.downloading
+                ):  # local jobs will be in waiting state, remote jobs will be downloading state
+                    job.total_bytes = self._stat_size(job.local_path)
+                    job.bytes = job.total_bytes
+                    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)
+
+                    # enter the metadata, if there is any
+                    if job.source_metadata:
+                        self._metadata_store.add_metadata(key, job.source_metadata)
+                    self._signal_job_completed(job)
+
+            except InvalidModelConfigException as excp:
+                if any(x.content_type is not None and "text/html" in x.content_type for x in job.download_parts):
+                    job.set_error(
+                        InvalidModelConfigException(
+                            f"At least one file in {job.local_path} is an HTML page, not a model. This can happen when an access token is required to download."
+                        )
+                    )
+                else:
+                    job.set_error(excp)
+                self._signal_job_errored(job)
+
+            except (OSError, DuplicateModelException) as excp:
+                job.set_error(excp)
+                self._signal_job_errored(job)
+
+            finally:
+                # if this is an install of a remote file, then clean up the temporary directory
+                if job._install_tmpdir is not None:
+                    rmtree(job._install_tmpdir)
+                self._install_queue.task_done()
+
+        self._logger.info("Install thread exiting")
+
+    # --------------------------------------------------------------------------------------------
+    # Internal functions that manage the models directory
+    # --------------------------------------------------------------------------------------------
+    def _remove_dangling_install_dirs(self) -> None:
+        """Remove leftover tmpdirs from aborted installs."""
+        path = self._app_config.models_path
+        for tmpdir in path.glob(f"{TMPDIR_PREFIX}*"):
+            self._logger.info(f"Removing dangling temporary directory {tmpdir}")
+            rmtree(tmpdir)
+
+    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 _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
+
+    def _next_id(self) -> int:
+        with self._lock:
+            id = self._next_job_id
+            self._next_job_id += 1
+        return id
+
+    @staticmethod
+    def _guess_variant() -> ModelRepoVariant:
+        """Guess the best HuggingFace variant type to download."""
+        precision = choose_precision(choose_torch_device())
+        return ModelRepoVariant.FP16 if precision == "float16" else ModelRepoVariant.DEFAULT
+
+    def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+        return ModelInstallJob(
+            id=self._next_id(),
+            source=source,
+            config_in=config or {},
+            local_path=Path(source.path),
+            inplace=source.inplace,
+        )
+
+    def _import_from_civitai(self, source: CivitaiModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+        if not source.access_token:
+            self._logger.info("No Civitai access token provided; some models may not be downloadable.")
+        metadata = CivitaiMetadataFetch(self._session).from_id(str(source.version_id))
+        assert isinstance(metadata, ModelMetadataWithFiles)
+        remote_files = metadata.download_urls(session=self._session)
+        return self._import_remote_model(source=source, config=config, metadata=metadata, remote_files=remote_files)
+
+    def _import_from_hf(self, source: HFModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+        # Add user's cached access token to HuggingFace requests
+        source.access_token = source.access_token or HfFolder.get_token()
+        if not source.access_token:
+            self._logger.info("No HuggingFace access token present; some models may not be downloadable.")
+
+        metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id)
+        assert isinstance(metadata, ModelMetadataWithFiles)
+        remote_files = metadata.download_urls(
+            variant=source.variant or self._guess_variant(),
+            subfolder=source.subfolder,
+            session=self._session,
+        )
+
+        return self._import_remote_model(
+            source=source,
+            config=config,
+            remote_files=remote_files,
+            metadata=metadata,
+        )
+
+    def _import_from_url(self, source: URLModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+        # URLs from Civitai or HuggingFace will be handled specially
+        url_patterns = {
+            r"https?://civitai.com/": CivitaiMetadataFetch,
+            r"https?://huggingface.co/": HuggingFaceMetadataFetch,
+        }
+        metadata = None
+        for pattern, fetcher in url_patterns.items():
+            if re.match(pattern, str(source.url), re.IGNORECASE):
+                metadata = fetcher(self._session).from_url(source.url)
+                break
+        if metadata and isinstance(metadata, ModelMetadataWithFiles):
+            remote_files = metadata.download_urls(session=self._session)
+        else:
+            remote_files = [RemoteModelFile(url=source.url, path=Path("."), size=0)]
+
+        return self._import_remote_model(
+            source=source,
+            config=config,
+            metadata=metadata,
+            remote_files=remote_files,
+        )
+
+    def _import_remote_model(
+        self,
+        source: ModelSource,
+        remote_files: List[RemoteModelFile],
+        metadata: Optional[AnyModelRepoMetadata],
+        config: Optional[Dict[str, Any]],
+    ) -> ModelInstallJob:
+        # TODO: Replace with tempfile.tmpdir() when multithreading is cleaned up.
+        # Currently the tmpdir isn't automatically removed at exit because it is
+        # being held in a daemon thread.
+        tmpdir = Path(
+            mkdtemp(
+                dir=self._app_config.models_path,
+                prefix=TMPDIR_PREFIX,
+            )
+        )
+        install_job = ModelInstallJob(
+            id=self._next_id(),
+            source=source,
+            config_in=config or {},
+            source_metadata=metadata,
+            local_path=tmpdir,  # local path may change once the download has started due to content-disposition handling
+            bytes=0,
+            total_bytes=0,
+        )
+        # we remember the path up to the top of the tmpdir so that it may be
+        # removed safely at the end of the install process.
+        install_job._install_tmpdir = tmpdir
+        assert install_job.total_bytes is not None  # to avoid type checking complaints in the loop below
+
+        self._logger.info(f"Queuing {source} for downloading")
+        for model_file in remote_files:
+            url = model_file.url
+            path = model_file.path
+            self._logger.info(f"Downloading {url} => {path}")
+            install_job.total_bytes += model_file.size
+            assert hasattr(source, "access_token")
+            dest = tmpdir / path.parent
+            dest.mkdir(parents=True, exist_ok=True)
+            download_job = DownloadJob(
+                source=url,
+                dest=dest,
+                access_token=source.access_token,
+            )
+            self._download_cache[download_job.source] = install_job  # matches a download job to an install job
+            install_job.download_parts.add(download_job)
+
+            self._download_queue.submit_download_job(
+                download_job,
+                on_start=self._download_started_callback,
+                on_progress=self._download_progress_callback,
+                on_complete=self._download_complete_callback,
+                on_error=self._download_error_callback,
+                on_cancelled=self._download_cancelled_callback,
+            )
+        return install_job
+
+    def _stat_size(self, path: Path) -> int:
+        size = 0
+        if path.is_file():
+            size = path.stat().st_size
+        elif path.is_dir():
+            for root, _, files in os.walk(path):
+                size += sum(self._stat_size(Path(root, x)) for x in files)
+        return size
+
+    # ------------------------------------------------------------------
+    # Callbacks are executed by the download queue in a separate thread
+    # ------------------------------------------------------------------
+    def _download_started_callback(self, download_job: DownloadJob) -> None:
+        self._logger.info(f"{download_job.source}: model download started")
+        with self._lock:
+            install_job = self._download_cache[download_job.source]
+            install_job.status = InstallStatus.DOWNLOADING
+
+            assert download_job.download_path
+            if install_job.local_path == install_job._install_tmpdir:
+                partial_path = download_job.download_path.relative_to(install_job._install_tmpdir)
+                dest_name = partial_path.parts[0]
+                install_job.local_path = install_job._install_tmpdir / dest_name
+
+            # Update the total bytes count for remote sources.
+            if not install_job.total_bytes:
+                install_job.total_bytes = sum(x.total_bytes for x in install_job.download_parts)
+
+    def _download_progress_callback(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            install_job = self._download_cache[download_job.source]
+            if install_job.cancelled:  # This catches the case in which the caller directly calls job.cancel()
+                self._cancel_download_parts(install_job)
+            else:
+                # update sizes
+                install_job.bytes = sum(x.bytes for x in install_job.download_parts)
+                self._signal_job_downloading(install_job)
+
+    def _download_complete_callback(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            install_job = self._download_cache[download_job.source]
+            self._download_cache.pop(download_job.source, None)
+
+            # are there any more active jobs left in this task?
+            if all(x.complete for x in install_job.download_parts):
+                #  now enqueue job for actual installation into the models directory
+                self._install_queue.put(install_job)
+
+            # Let other threads know that the number of downloads has changed
+            self._downloads_changed_event.set()
+
+    def _download_error_callback(self, download_job: DownloadJob, excp: Optional[Exception] = None) -> None:
+        with self._lock:
+            install_job = self._download_cache.pop(download_job.source, None)
+            assert install_job is not None
+            assert excp is not None
+            install_job.set_error(excp)
+            self._logger.error(
+                f"Cancelling {install_job.source} due to an error while downloading {download_job.source}: {str(excp)}"
+            )
+            self._cancel_download_parts(install_job)
+
+            # Let other threads know that the number of downloads has changed
+            self._downloads_changed_event.set()
+
+    def _download_cancelled_callback(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            install_job = self._download_cache.pop(download_job.source, None)
+            if not install_job:
+                return
+            self._downloads_changed_event.set()
+            self._logger.warning(f"Download {download_job.source} cancelled.")
+            # if install job has already registered an error, then do not replace its status with cancelled
+            if not install_job.errored:
+                install_job.cancel()
+            self._cancel_download_parts(install_job)
+
+            # Let other threads know that the number of downloads has changed
+            self._downloads_changed_event.set()
+
+    def _cancel_download_parts(self, install_job: ModelInstallJob) -> None:
+        # on multipart downloads, _cancel_components() will get called repeatedly from the download callbacks
+        # do not lock here because it gets called within a locked context
+        for s in install_job.download_parts:
+            self._download_queue.cancel_job(s)
+
+        if all(x.in_terminal_state for x in install_job.download_parts):
+            # When all parts have reached their terminal state, we finalize the job to clean up the temporary directory and other resources
+            self._install_queue.put(install_job)
+
+    # ------------------------------------------------------------------------------------------------
+    # Internal methods that put events on the event bus
+    # ------------------------------------------------------------------------------------------------
+    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_running(str(job.source))
+
+    def _signal_job_downloading(self, job: ModelInstallJob) -> None:
+        if self._event_bus:
+            parts: List[Dict[str, str | int]] = [
+                {
+                    "url": str(x.source),
+                    "local_path": str(x.download_path),
+                    "bytes": x.bytes,
+                    "total_bytes": x.total_bytes,
+                }
+                for x in job.download_parts
+            ]
+            assert job.bytes is not None
+            assert job.total_bytes is not None
+            self._event_bus.emit_model_install_downloading(
+                str(job.source),
+                local_path=job.local_path.as_posix(),
+                parts=parts,
+                bytes=job.bytes,
+                total_bytes=job.total_bytes,
+            )
+
+    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) -> None:
+        self._logger.info(f"{job.source}: model installation encountered an exception: {job.error_type}\n{job.error}")
+        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 _signal_job_cancelled(self, job: ModelInstallJob) -> None:
+        self._logger.info(f"{job.source}: model installation was cancelled")
+        if self._event_bus:
+            self._event_bus.emit_model_install_cancelled(str(job.source))

invokeai/app/services/model_records/__init__.py

@@ -4,5 +4,17 @@ from .model_records_base import (  # noqa F401
     InvalidModelException,
     ModelRecordServiceBase,
     UnknownModelException,
+    ModelSummary,
+    ModelRecordOrderBy,
 )
 from .model_records_sql import ModelRecordServiceSQL  # noqa F401
+
+__all__ = [
+    "ModelRecordServiceBase",
+    "ModelRecordServiceSQL",
+    "DuplicateModelException",
+    "InvalidModelException",
+    "UnknownModelException",
+    "ModelSummary",
+    "ModelRecordOrderBy",
+]

invokeai/app/services/model_records/model_records_base.py

@@ -4,13 +4,15 @@ Abstract base class for storing and retrieving model configuration records.
 """
 
 from abc import ABC, abstractmethod
+from enum import Enum
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType
+from pydantic import BaseModel, Field
 
-# should match the InvokeAI version when this is first released.
-CONFIG_FILE_VERSION = "3.2.0"
+from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
 
 
 class DuplicateModelException(Exception):
@@ -29,17 +31,33 @@ class ConfigFileVersionMismatchException(Exception):
     """Raised on an attempt to open a config with an incompatible version."""
 
 
+class ModelRecordOrderBy(str, Enum):
+    """The order in which to return model summaries."""
+
+    Default = "default"  # order by type, base, format and name
+    Type = "type"
+    Base = "base"
+    Name = "name"
+    Format = "format"
+
+
+class ModelSummary(BaseModel):
+    """A short summary of models for UI listing purposes."""
+
+    key: str = Field(description="model key")
+    type: ModelType = Field(description="model type")
+    base: BaseModelType = Field(description="base model")
+    format: ModelFormat = Field(description="model format")
+    name: str = Field(description="model name")
+    description: str = Field(description="short description of model")
+    tags: Set[str] = Field(description="tags associated with model")
+
+
 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:
+    def add_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -63,7 +81,7 @@ class ModelRecordServiceBase(ABC):
         pass
 
     @abstractmethod
-    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
+    def update_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
         """
         Update the model, returning the updated version.
 
@@ -84,6 +102,47 @@ class ModelRecordServiceBase(ABC):
         """
         pass
 
+    @property
+    @abstractmethod
+    def metadata_store(self) -> ModelMetadataStore:
+        """Return a ModelMetadataStore initialized on the same database."""
+        pass
+
+    @abstractmethod
+    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
+        """
+        Retrieve metadata (if any) from when model was downloaded from a repo.
+
+        :param key: Model key
+        """
+        pass
+
+    @abstractmethod
+    def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:
+        """List metadata for all models that have it."""
+        pass
+
+    @abstractmethod
+    def search_by_metadata_tag(self, tags: Set[str]) -> List[AnyModelConfig]:
+        """
+        Search model metadata for ones with all listed tags and return their corresponding configs.
+
+        :param tags: Set of tags to search for. All tags must be present.
+        """
+        pass
+
+    @abstractmethod
+    def list_tags(self) -> Set[str]:
+        """Return a unique set of all the model tags in the metadata database."""
+        pass
+
+    @abstractmethod
+    def list_models(
+        self, page: int = 0, per_page: int = 10, order_by: ModelRecordOrderBy = ModelRecordOrderBy.Default
+    ) -> PaginatedResults[ModelSummary]:
+        """Return a paginated summary listing of each model in the database."""
+        pass
+
     @abstractmethod
     def exists(self, key: str) -> bool:
         """
@@ -115,6 +174,7 @@ 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.
@@ -122,6 +182,7 @@ 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

@@ -42,21 +42,26 @@ Typical usage:
 
 import json
 import sqlite3
+from math import ceil
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
+from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
     ModelConfigFactory,
+    ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore, UnknownMetadataException
 
 from ..shared.sqlite.sqlite_database import SqliteDatabase
 from .model_records_base import (
-    CONFIG_FILE_VERSION,
     DuplicateModelException,
+    ModelRecordOrderBy,
     ModelRecordServiceBase,
+    ModelSummary,
     UnknownModelException,
 )
 
@@ -64,9 +69,6 @@ from .model_records_base import (
 class ModelRecordServiceSQL(ModelRecordServiceBase):
     """Implementation of the ModelConfigStore ABC using a SQL database."""
 
-    _db: SqliteDatabase
-    _cursor: sqlite3.Cursor
-
     def __init__(self, db: SqliteDatabase):
         """
         Initialize a new object from preexisting sqlite3 connection and threading lock objects.
@@ -78,86 +80,12 @@ 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}"
+    @property
+    def db(self) -> SqliteDatabase:
+        """Return the underlying database."""
+        return self._db
 
-    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:
+    def add_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -175,21 +103,13 @@ 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,
                     ),
@@ -214,22 +134,6 @@ 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.
@@ -269,14 +173,11 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 self._cursor.execute(
                     """--sql
                     UPDATE model_config
-                    SET base=?,
-                        type=?,
-                        name=?,
-                        path=?,
+                    SET
                         config=?
                     WHERE id=?;
                     """,
-                    (record.base, record.type, record.name, record.path, json_serialized, key),
+                    (json_serialized, key),
                 )
                 if self._cursor.rowcount == 0:
                     raise UnknownModelException("model not found")
@@ -332,6 +233,7 @@ 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.
@@ -339,6 +241,7 @@ 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.
@@ -355,6 +258,9 @@ 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(
@@ -374,7 +280,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             self._cursor.execute(
                 """--sql
                 SELECT config FROM model_config
-                WHERE model_path=?;
+                WHERE path=?;
                 """,
                 (str(path),),
             )
@@ -394,3 +300,95 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             )
             results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
+
+    @property
+    def metadata_store(self) -> ModelMetadataStore:
+        """Return a ModelMetadataStore initialized on the same database."""
+        return ModelMetadataStore(self._db)
+
+    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
+        """
+        Retrieve metadata (if any) from when model was downloaded from a repo.
+
+        :param key: Model key
+        """
+        store = self.metadata_store
+        try:
+            metadata = store.get_metadata(key)
+            return metadata
+        except UnknownMetadataException:
+            return None
+
+    def search_by_metadata_tag(self, tags: Set[str]) -> List[AnyModelConfig]:
+        """
+        Search model metadata for ones with all listed tags and return their corresponding configs.
+
+        :param tags: Set of tags to search for. All tags must be present.
+        """
+        store = ModelMetadataStore(self._db)
+        keys = store.search_by_tag(tags)
+        return [self.get_model(x) for x in keys]
+
+    def list_tags(self) -> Set[str]:
+        """Return a unique set of all the model tags in the metadata database."""
+        store = ModelMetadataStore(self._db)
+        return store.list_tags()
+
+    def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:
+        """List metadata for all models that have it."""
+        store = ModelMetadataStore(self._db)
+        return store.list_all_metadata()
+
+    def list_models(
+        self, page: int = 0, per_page: int = 10, order_by: ModelRecordOrderBy = ModelRecordOrderBy.Default
+    ) -> PaginatedResults[ModelSummary]:
+        """Return a paginated summary listing of each model in the database."""
+        ordering = {
+            ModelRecordOrderBy.Default: "a.type, a.base, a.format, a.name",
+            ModelRecordOrderBy.Type: "a.type",
+            ModelRecordOrderBy.Base: "a.base",
+            ModelRecordOrderBy.Name: "a.name",
+            ModelRecordOrderBy.Format: "a.format",
+        }
+
+        def _fixup(summary: Dict[str, str]) -> Dict[str, Union[str, int, Set[str]]]:
+            """Fix up results so that there are no null values."""
+            result: Dict[str, Union[str, int, Set[str]]] = {}
+            for key, item in summary.items():
+                result[key] = item or ""
+            result["tags"] = set(json.loads(summary["tags"] or "[]"))
+            return result
+
+        # Lock so that the database isn't updated while we're doing the two queries.
+        with self._db.lock:
+            # query1: get the total number of model configs
+            self._cursor.execute(
+                """--sql
+                select count(*) from model_config;
+                """,
+                (),
+            )
+            total = int(self._cursor.fetchone()[0])
+
+            # query2: fetch key fields from the join of model_config and model_metadata
+            self._cursor.execute(
+                f"""--sql
+                SELECT a.id as key, a.type, a.base, a.format, a.name,
+                       json_extract(a.config, '$.description') as description,
+                       json_extract(b.metadata, '$.tags') as tags
+                FROM model_config AS a
+                LEFT JOIN model_metadata AS b on a.id=b.id
+                ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason
+                LIMIT ?
+                OFFSET ?;
+                """,
+                (
+                    per_page,
+                    page * per_page,
+                ),
+            )
+            rows = self._cursor.fetchall()
+            items = [ModelSummary.model_validate(_fixup(dict(x))) for x in rows]
+            return PaginatedResults(
+                page=page, pages=ceil(total / per_page), per_page=per_page, total=total, items=items
+            )

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -50,7 +50,6 @@ class SqliteSessionQueue(SessionQueueBase):
         self.__lock = db.lock
         self.__conn = db.conn
         self.__cursor = self.__conn.cursor()
-        self._create_tables()
 
     def _match_event_name(self, event: FastAPIEvent, match_in: list[str]) -> bool:
         return event[1]["event"] in match_in
@@ -98,123 +97,6 @@ class SqliteSessionQueue(SessionQueueBase):
         except SessionQueueItemNotFoundError:
             return
 
-    def _create_tables(self) -> None:
-        """Creates the session queue tables, indicies, and triggers"""
-        try:
-            self.__lock.acquire()
-            self.__cursor.execute(
-                """--sql
-                CREATE TABLE IF NOT EXISTS session_queue (
-                    item_id INTEGER PRIMARY KEY AUTOINCREMENT, -- used for ordering, cursor pagination
-                    batch_id TEXT NOT NULL, -- identifier of the batch this queue item belongs to
-                    queue_id TEXT NOT NULL, -- identifier of the queue this queue item belongs to
-                    session_id TEXT NOT NULL UNIQUE, -- duplicated data from the session column, for ease of access
-                    field_values TEXT, -- NULL if no values are associated with this queue item
-                    session TEXT NOT NULL, -- the session to be executed
-                    status TEXT NOT NULL DEFAULT 'pending', -- the status of the queue item, one of 'pending', 'in_progress', 'completed', 'failed', 'canceled'
-                    priority INTEGER NOT NULL DEFAULT 0, -- the priority, higher is more important
-                    error TEXT, -- any errors associated with this queue item
-                    created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                    updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')), -- updated via trigger
-                    started_at DATETIME, -- updated via trigger
-                    completed_at DATETIME -- updated via trigger, completed items are cleaned up on application startup
-                    -- Ideally this is a FK, but graph_executions uses INSERT OR REPLACE, and REPLACE triggers the ON DELETE CASCADE...
-                    -- FOREIGN KEY (session_id) REFERENCES graph_executions (id) ON DELETE CASCADE
-                );
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE UNIQUE INDEX IF NOT EXISTS idx_session_queue_item_id ON session_queue(item_id);
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE UNIQUE INDEX IF NOT EXISTS idx_session_queue_session_id ON session_queue(session_id);
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE INDEX IF NOT EXISTS idx_session_queue_batch_id ON session_queue(batch_id);
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE INDEX IF NOT EXISTS idx_session_queue_created_priority ON session_queue(priority);
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE INDEX IF NOT EXISTS idx_session_queue_created_status ON session_queue(status);
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE TRIGGER IF NOT EXISTS tg_session_queue_completed_at
-                AFTER UPDATE OF status ON session_queue
-                FOR EACH ROW
-                WHEN
-                  NEW.status = 'completed'
-                  OR NEW.status = 'failed'
-                  OR NEW.status = 'canceled'
-                BEGIN
-                  UPDATE session_queue
-                  SET completed_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                  WHERE item_id = NEW.item_id;
-                END;
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE TRIGGER IF NOT EXISTS tg_session_queue_started_at
-                AFTER UPDATE OF status ON session_queue
-                FOR EACH ROW
-                WHEN
-                  NEW.status = 'in_progress'
-                BEGIN
-                  UPDATE session_queue
-                  SET started_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                  WHERE item_id = NEW.item_id;
-                END;
-                """
-            )
-
-            self.__cursor.execute(
-                """--sql
-                CREATE TRIGGER IF NOT EXISTS tg_session_queue_updated_at
-                AFTER UPDATE
-                ON session_queue FOR EACH ROW
-                BEGIN
-                    UPDATE session_queue
-                    SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE item_id = old.item_id;
-                END;
-                """
-            )
-
-            self.__cursor.execute("PRAGMA table_info(session_queue)")
-            columns = [column[1] for column in self.__cursor.fetchall()]
-            if "workflow" not in columns:
-                self.__cursor.execute(
-                    """--sql
-                    ALTER TABLE session_queue ADD COLUMN workflow TEXT;
-                    """
-                )
-
-            self.__conn.commit()
-        except Exception:
-            self.__conn.rollback()
-            raise
-        finally:
-            self.__lock.release()
-
     def _set_in_progress_to_canceled(self) -> None:
         """
         Sets all in_progress queue items to canceled. Run on app startup, not associated with any queue.

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

@@ -3,45 +3,65 @@ import threading
 from logging import Logger
 from pathlib import Path
 
-from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.shared.sqlite.sqlite_common import sqlite_memory
 
 
 class SqliteDatabase:
-    def __init__(self, config: InvokeAIAppConfig, logger: Logger):
-        self._logger = logger
-        self._config = config
+    """
+    Manages a connection to an SQLite database.
 
-        if self._config.use_memory_db:
-            self.db_path = sqlite_memory
-            logger.info("Using in-memory 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:
-            db_path = self._config.db_path
-            db_path.parent.mkdir(parents=True, exist_ok=True)
-            self.db_path = str(db_path)
-            self._logger.info(f"Using database at {self.db_path}")
+            self.db_path.parent.mkdir(parents=True, exist_ok=True)
+            self.logger.info(f"Initializing database at {self.db_path}")
 
-        self.conn = sqlite3.connect(self.db_path, check_same_thread=False)
+        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._config.log_sql:
-            self.conn.set_trace_callback(self._logger.debug)
+        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:
-                if self.db_path == sqlite_memory:
-                    return
                 initial_db_size = Path(self.db_path).stat().st_size
                 self.conn.execute("VACUUM;")
                 self.conn.commit()
                 final_db_size = Path(self.db_path).stat().st_size
                 freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
                 if freed_space_in_mb > 0:
-                    self._logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
+                    self.logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
             except Exception as e:
-                self._logger.error(f"Error cleaning database: {e}")
+                self.logger.error(f"Error cleaning database: {e}")
                 raise

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

@@ -0,0 +1,36 @@
+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.migrations.migration_4 import build_migration_4
+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(app_config=config, logger=logger))
+    migrator.register_migration(build_migration_4())
+    migrator.run_migrations()
+
+    return db

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

@@ -0,0 +1,372 @@
+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

@@ -0,0 +1,166 @@
+import sqlite3
+from logging import Logger
+
+from pydantic import ValidationError
+from tqdm import tqdm
+
+from invokeai.app.services.image_files.image_files_base import ImageFileStorageBase
+from invokeai.app.services.image_files.image_files_common import ImageFileNotFoundException
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+from invokeai.app.services.workflow_records.workflow_records_common import (
+    UnsafeWorkflowWithVersionValidator,
+)
+
+
+class Migration2Callback:
+    def __init__(self, image_files: ImageFileStorageBase, logger: Logger):
+        self._image_files = image_files
+        self._logger = logger
+
+    def __call__(self, cursor: sqlite3.Cursor):
+        self._add_images_has_workflow(cursor)
+        self._add_session_queue_workflow(cursor)
+        self._drop_old_workflow_tables(cursor)
+        self._add_workflow_library(cursor)
+        self._drop_model_manager_metadata(cursor)
+        self._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 _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

@@ -0,0 +1,79 @@
+import sqlite3
+from logging import Logger
+
+from invokeai.app.services.config import InvokeAIAppConfig
+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, app_config: InvokeAIAppConfig, logger: Logger) -> None:
+        self._app_config = app_config
+        self._logger = logger
+
+    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."""
+        self._logger.info("Migrating model config records from models.yaml to database")
+        model_record_migrator = MigrateModelYamlToDb1(self._app_config, self._logger, cursor)
+        model_record_migrator.migrate()
+
+
+def build_migration_3(app_config: InvokeAIAppConfig, logger: Logger) -> 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(app_config=app_config, logger=logger),
+    )
+
+    return migration_3

invokeai/app/services/shared/sqlite_migrator/migrations/migration_4.py

@@ -0,0 +1,83 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration4Callback:
+    """Callback to do step 4 of migration."""
+
+    def __call__(self, cursor: sqlite3.Cursor) -> None:  # noqa D102
+        self._create_model_metadata(cursor)
+        self._create_model_tags(cursor)
+        self._create_tags(cursor)
+        self._create_triggers(cursor)
+
+    def _create_model_metadata(self, cursor: sqlite3.Cursor) -> None:
+        """Create the table used to store model metadata downloaded from remote sources."""
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS model_metadata (
+                id TEXT NOT NULL PRIMARY KEY,
+                name TEXT GENERATED ALWAYS AS (json_extract(metadata, '$.name')) VIRTUAL NOT NULL,
+                author TEXT GENERATED ALWAYS AS (json_extract(metadata, '$.author')) VIRTUAL NOT NULL,
+                -- Serialized JSON representation of the whole metadata object,
+                -- which will contain additional fields from subclasses
+                metadata 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')),
+                FOREIGN KEY(id) REFERENCES model_config(id) ON DELETE CASCADE
+            );
+            """
+        )
+
+    def _create_model_tags(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS model_tags (
+                model_id TEXT NOT NULL,
+                tag_id INTEGER NOT NULL,
+                FOREIGN KEY(model_id) REFERENCES model_config(id) ON DELETE CASCADE,
+                FOREIGN KEY(tag_id) REFERENCES tags(tag_id) ON DELETE CASCADE,
+                UNIQUE(model_id,tag_id)
+            );
+            """
+        )
+
+    def _create_tags(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS tags (
+                tag_id INTEGER NOT NULL PRIMARY KEY,
+                tag_text TEXT NOT NULL UNIQUE
+            );
+            """
+        )
+
+    def _create_triggers(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """--sql
+            CREATE TRIGGER IF NOT EXISTS model_metadata_updated_at
+            AFTER UPDATE
+            ON model_metadata FOR EACH ROW
+            BEGIN
+                UPDATE model_metadata SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE id = old.id;
+            END;
+            """
+        )
+
+
+def build_migration_4() -> Migration:
+    """
+    Build the migration from database version 3 to 4.
+
+    Adds the tables needed to store model metadata and tags.
+    """
+    migration_4 = Migration(
+        from_version=3,
+        to_version=4,
+        callback=Migration4Callback(),
+    )
+
+    return migration_4