remove tab character

.github/CODEOWNERS

@@ -1,5 +1,5 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername
 
 # documentation
 /docs/ @lstein @blessedcoolant @hipsterusername @Millu
@@ -10,7 +10,7 @@
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant @hipsterusername
-/docker/  @lstein @blessedcoolant @hipsterusername @ebr
+/docker/  @lstein @blessedcoolant @hipsterusername
 /scripts/ @ebr @lstein @hipsterusername
 /installer/ @lstein @ebr @hipsterusername
 /invokeai/assets @lstein @ebr @hipsterusername
@@ -26,7 +26,9 @@
 
 # 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/workflows/build-container.yml

@@ -40,14 +40,10 @@ 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
@@ -95,7 +91,6 @@ 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

@@ -21,16 +21,16 @@ jobs:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-22.04
     steps:
-      - name: Setup Node 18
+      - name: Setup Node 20
         uses: actions/setup-node@v4
         with:
-          node-version: '18'
+          node-version: '20'
       - name: Checkout
         uses: actions/checkout@v4
       - name: Setup pnpm
         uses: pnpm/action-setup@v2
         with:
-          version: '8.12.1'
+          version: 8
       - name: Install dependencies
         run: 'pnpm install --prefer-frozen-lockfile'
       - name: Typescript

.github/workflows/pypi-release.yml

@@ -1,15 +1,13 @@
 name: PyPI Release
 
 on:
+  push:
+    paths:
+      - 'invokeai/version/invokeai_version.py'
   workflow_dispatch:
-    inputs:
-      publish_package:
-        description: 'Publish build on PyPi? [true/false]'
-        required: true
-        default: 'false'
 
 jobs:
-  build-and-release:
+  release:
     if: github.repository == 'invoke-ai/InvokeAI'
     runs-on: ubuntu-22.04
     env:
@@ -17,39 +15,33 @@ jobs:
       TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
       TWINE_NON_INTERACTIVE: 1
     steps:
-      - name: Checkout
+      - name: Checkout sources
         uses: actions/checkout@v4
 
-      - name: Setup Node 18
+      - name: Setup Node 20
         uses: actions/setup-node@v4
         with:
-          node-version: '18'
+          node-version: '20'
 
       - name: Setup pnpm
         uses: pnpm/action-setup@v2
         with:
-          version: '8.12.1'
+          version: 8
 
-      - name: Install frontend dependencies
-        run: pnpm install --prefer-frozen-lockfile
+      - name: Install pnpm dependencies
         working-directory: invokeai/frontend/web
+        run: 'pnpm install --prefer-frozen-lockfile'
 
       - name: Build frontend
-        run: pnpm run build
         working-directory: invokeai/frontend/web
+        run: 'pnpm build'
 
-      - name: Install python dependencies
+      - name: Install python deps
         run: pip install --upgrade build twine
 
-      - name: Build python package
+      - name: Build wheel package
         run: python3 -m build
 
-      - name: Upload build as workflow artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: dist
-
       - name: Check distribution
         run: twine check dist/*
 
@@ -62,6 +54,6 @@ jobs:
           EXISTS=scripts.pypi_helper.local_on_pypi(); \
           print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
 
-      - name: Publish build on PyPi
-        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != '' && github.event.inputs.publish_package == 'true'
+      - name: Upload package
+        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != ''
         run: twine upload dist/*

README.md

@@ -1,10 +1,10 @@
 <div align="center">
 
-![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)
+![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/1a917d94-e099-4fa1-a70f-7dd8d0691018)
 
-# Invoke - Professional Creative AI Tools for Visual Media 
-##  To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
-  
+# Invoke AI - Generative AI for Professional Creatives
+## Professional Creative Tools for Stable Diffusion, Custom-Trained Models, and more. 
+  To learn more about Invoke AI, get started instantly, or implement our Business solutions, visit [invoke.ai](https://invoke.ai)
 
 
 [![discord badge]][discord link]
@@ -56,9 +56,7 @@ the foundation for multiple commercial products.
 
 <div align="center">
 
-
-![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
-
+![canvas preview](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/canvas_preview.png)
 
 </div>
 
@@ -272,7 +270,7 @@ upgrade script.** See the next section for a Windows recipe.
 3. Select option [1] to upgrade to the latest release.
 
 4. Once the upgrade is finished you will be returned to the launcher
-menu. Select option [6] "Re-run the configure script to fix a broken
+menu. Select option [7] "Re-run the configure script to fix a broken
 install or to complete a major upgrade".
 
 This will run the configure script against the v2.3 directory and

docker/.env.sample

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

docker/Dockerfile

@@ -59,16 +59,14 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 
 # #### Build the Web UI ------------------------------------
 
-FROM node:20-slim AS web-builder
-ENV PNPM_HOME="/pnpm"
-ENV PATH="$PNPM_HOME:$PATH"
-RUN corepack enable
-
+FROM node:18 AS web-builder
 WORKDIR /build
 COPY invokeai/frontend/web/ ./
-RUN --mount=type=cache,target=/pnpm/store \
-    pnpm install --frozen-lockfile
-RUN npx vite build
+RUN --mount=type=cache,target=/usr/lib/node_modules \
+    npm install --include dev
+RUN --mount=type=cache,target=/usr/lib/node_modules \
+    yarn vite build
+
 
 #### Runtime stage ---------------------------------------
 

docker/README.md

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

docker/build.sh

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

docker/docker-compose.yml

@@ -2,8 +2,23 @@
 
 version: '3.8'
 
-x-invokeai: &invokeai
+services:
+  invokeai:
     image: "local/invokeai:latest"
+    # edit below to run on a container runtime other than nvidia-container-runtime.
+    # not yet tested with rocm/AMD GPUs
+    # Comment out the "deploy" section to run on CPU only
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    # For AMD support, comment out the deploy section above and uncomment the devices section below:
+    #devices:
+    #  - /dev/kfd:/dev/kfd
+    #  - /dev/dri:/dev/dri
     build:
       context: ..
       dockerfile: docker/Dockerfile
@@ -21,9 +36,7 @@ x-invokeai: &invokeai
     ports:
       - "${INVOKEAI_PORT:-9090}:9090"
     volumes:
-      - type: bind
-        source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
-        target: ${INVOKEAI_ROOT:-/invokeai}
+      - ${INVOKEAI_ROOT:-~/invokeai}:${INVOKEAI_ROOT:-/invokeai}
       - ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
       # - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
       # - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}
@@ -37,27 +50,3 @@ x-invokeai: &invokeai
     #   - |
     #     invokeai-model-install --yes --default-only --config_file ${INVOKEAI_ROOT}/config_custom.yaml
     #     invokeai-nodes-web --host 0.0.0.0
-
-services:
-  invokeai-nvidia:
-    <<: *invokeai
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-
-  invokeai-cpu:
-    <<: *invokeai
-    profiles:
-      - cpu
-
-  invokeai-rocm:
-    <<: *invokeai
-    devices:
-      - /dev/kfd:/dev/kfd
-      - /dev/dri:/dev/dri
-    profiles:
-      - rocm

docker/run.sh

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

docs/contributing/DOWNLOAD_QUEUE.md

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

docs/contributing/contribution_guides/contributingToFrontend.md

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

docs/deprecated/2to3.md

@@ -1,53 +0,0 @@
-## :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/UNIFIED_CANVAS.md

@@ -229,28 +229,29 @@ 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.
 
-### Compositing / Seam Correction
+### Seam Correction
 
 When doing Inpainting or Outpainting, Invoke needs to merge the pixels generated
-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
+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
 automatically blended to produce a seamless output. In a fully automatic
-process, a mask is generated to cover the boundary, and then the area of the boundary is
+process, a mask is generated to cover the seam, and then the area of the seam is
 Inpainted.
 
 Although the default options should work well most of the time, sometimes it can
-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. 
+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.
 
+- **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: #35A4DB;
+      background-color: #448AFF;
       color: #fff;
       font-size: 16px;
       border: none;
@@ -43,7 +43,7 @@ title: Home
 <div align="center" markdown>
 
 
-[![project logo](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)](https://github.com/invoke-ai/InvokeAI)
+[![project logo](assets/invoke_ai_banner.png)](https://github.com/invoke-ai/InvokeAI)
 
 [![discord badge]][discord link]
 
@@ -145,6 +145,60 @@ 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/javascripts/init_kapa_widget.js

@@ -1,10 +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);
-});
+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

@@ -6,17 +6,10 @@ 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. 
 
@@ -37,7 +30,7 @@ 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 Nodes & Concepts 
+## Important 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).
 
@@ -63,7 +56,7 @@ 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/groupsnoise.png)
+![groupsrandseed](../assets/nodes/groupsrandseed.png)
 
 ### ControlNet
 

docs/nodes/communityNodes.md

@@ -13,7 +13,6 @@ 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) 
@@ -33,11 +32,9 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Image Resize Plus](#image-resize-plus)
     + [Load Video Frame](#load-video-frame)
     + [Make 3D](#make-3d)
-    + [Mask Operations](#mask-operations)
+    + [Mask Operations](#mask-operations)    
     + [Match Histogram](#match-histogram)
-    + [Metadata-Linked](#metadata-linked-nodes)
-    + [Negative Image](#negative-image)
-    + [Nightmare Promptgen](#nightmare-promptgen)    
+    + [Negative Image](#negative-image)    
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
     + [Remote Image](#remote-image)
@@ -54,19 +51,6 @@ To use a community workflow, download the the `.json` node graph file and load i
 - [Help](#help)
 
 
---------------------------------
-### Adapters Linked Nodes
-
-**Description:** A set of nodes for linked adapters (ControlNet, IP-Adaptor & T2I-Adapter). This allows multiple adapters to be chained together without using a `collect` node which means it can be used inside an `iterate` node without any collecting on every iteration issues.
-
-- `ControlNet-Linked` - Collects ControlNet info to pass to other nodes.
-- `IP-Adapter-Linked` - Collects IP-Adapter info to pass to other nodes.
-- `T2I-Adapter-Linked` - Collects T2I-Adapter info to pass to other nodes.
-
-Note: These are inherited from the core nodes so any update to the core nodes should be reflected in these. 
-
-**Node Link:** https://github.com/skunkworxdark/adapters-linked-nodes
-
 --------------------------------
 ### Average Images
 
@@ -323,20 +307,6 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 <img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
 
---------------------------------
-### Metadata Linked Nodes
-
-**Description:** A set of nodes for Metadata. Collect Metadata from within an `iterate` node & extract metadata from an image.
-
-- `Metadata Item Linked` - Allows collecting of metadata while within an iterate node with no need for a collect node or conversion to metadata node.
-- `Metadata From Image` - Provides Metadata from an image.
-- `Metadata To String` - Extracts a String value of a label from metadata.
-- `Metadata To Integer` - Extracts an Integer value of a label from metadata.
-- `Metadata To Float` - Extracts a Float value of a label from metadata.
-- `Metadata To Scheduler` - Extracts a Scheduler value of a label from metadata.
-
-**Node Link:** https://github.com/skunkworxdark/metadata-linked-nodes
-
 --------------------------------
 ### Negative Image
 
@@ -347,13 +317,6 @@ 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
 

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! These can also be found in the Workflow Library, located in the Workflow Editor of Invoke.
+We've curated some example workflows for you to get started with Workflows in InvokeAI
 
 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

@@ -215,7 +215,6 @@ 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

@@ -1,5 +0,0 @@
-: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 - SD1.5",
+  "name": "Text to Image",
   "author": "InvokeAI",
   "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
-  "version": "1.1.0",
+  "version": "1.0.1",
   "contact": "invoke@invoke.ai",
   "tags": "text2image, SD1.5, SD2, default",
   "notes": "",
@@ -18,19 +18,10 @@
     {
       "nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "fieldName": "prompt"
-    },
-    {
-      "nodeId": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "fieldName": "width"
-    },
-    {
-      "nodeId": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "fieldName": "height"
     }
   ],
   "meta": {
-    "category": "default",
-    "version": "2.0.0"
+    "version": "1.0.0"
   },
   "nodes": [
     {
@@ -39,56 +30,44 @@
       "data": {
         "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
         "type": "compel",
-        "label": "Negative Compel Prompt",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0",
-        "nodePack": "invokeai",
         "inputs": {
           "prompt": {
             "id": "7739aff6-26cb-4016-8897-5a1fb2305e4e",
             "name": "prompt",
+            "type": "string",
             "fieldKind": "input",
             "label": "Negative Prompt",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "StringField"
-            },
             "value": ""
           },
           "clip": {
             "id": "48d23dce-a6ae-472a-9f8c-22a714ea5ce0",
             "name": "clip",
+            "type": "ClipField",
             "fieldKind": "input",
-            "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ClipField"
-            }
+            "label": ""
           }
         },
         "outputs": {
           "conditioning": {
             "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447",
             "name": "conditioning",
-            "fieldKind": "output",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "ConditioningField"
-            }
+            "type": "ConditioningField",
+            "fieldKind": "output"
           }
-        }
+        },
+        "label": "Negative Compel Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 259,
+      "height": 261,
       "position": {
-        "x": 1000,
-        "y": 350
+        "x": 995.7263915923627,
+        "y": 239.67783573351227
       }
     },
     {
@@ -97,60 +76,37 @@
       "data": {
         "id": "55705012-79b9-4aac-9f26-c0b10309785b",
         "type": "noise",
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.1",
-        "nodePack": "invokeai",
         "inputs": {
           "seed": {
             "id": "6431737c-918a-425d-a3b4-5d57e2f35d4d",
             "name": "seed",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 0
           },
           "width": {
             "id": "38fc5b66-fe6e-47c8-bba9-daf58e454ed7",
             "name": "width",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 512
           },
           "height": {
             "id": "16298330-e2bf-4872-a514-d6923df53cbb",
             "name": "height",
+            "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "type": {
-              "isCollection": false,
-              "isCollectionOrScalar": false,
-              "name": "IntegerField"
-            },
             "value": 512
           },
           "use_cpu": {
             "id": "c7c436d3-7a7a-4e76-91e4-c6deb271623c",
             "name": "use_cpu",
+            "type": "boolean",
             "fieldKind": "input",
             "label": "",
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-      "targetHandle": "clip"
+      "targetHandle": "clip",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-93dc02a4-d05b-48ed-b99c-c9b616af3402clip",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
       "source": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
       "sourceHandle": "noise",
-      "targetHandle": "noise"
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "targetHandle": "noise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
       "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
       "sourceHandle": "conditioning",
-      "targetHandle": "positive_conditioning"
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "targetHandle": "positive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
       "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
       "sourceHandle": "conditioning",
-      "targetHandle": "negative_conditioning"
-    },
-    {
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
-      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "type": "default",
-      "sourceHandle": "unet",
-      "targetHandle": "unet"
+      "targetHandle": "negative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
+      "type": "default"
     },
     {
-      "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
-      "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "type": "default",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "unet",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "targetHandle": "unet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
+      "type": "default"
+    },
+    {
+      "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "sourceHandle": "latents",
       "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "type": "default",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "sourceHandle": "vae",
-      "targetHandle": "vae"
+      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
+      "targetHandle": "vae",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
+      "type": "default"
     }
   ]
-}
+}

installer/create_installer.sh

@@ -91,11 +91,9 @@ rm -rf InvokeAI-Installer
 
 # copy content
 mkdir InvokeAI-Installer
-for f in templates *.txt *.reg; do
+for f in templates lib *.txt *.reg; do
     cp -r ${f} InvokeAI-Installer/
 done
-mkdir InvokeAI-Installer/lib
-cp lib/*.py InvokeAI-Installer/lib
 
 # Move the wheel
 mv dist/*.whl InvokeAI-Installer/lib/
@@ -113,6 +111,6 @@ cp WinLongPathsEnabled.reg InvokeAI-Installer/
 zip -r InvokeAI-installer-$VERSION.zip InvokeAI-Installer
 
 # clean up
-rm -rf InvokeAI-Installer tmp dist ../invokeai/frontend/web/dist/
+rm -rf InvokeAI-Installer tmp dist
 
 exit 0

installer/lib/installer.py

@@ -241,12 +241,12 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
-                "numpy==1.26.3",  # choose versions that won't be uninstalled during phase 2
+                "numpy~=1.24.0",  # choose versions that won't be uninstalled during phase 2
                 "urllib3~=1.26.0",
                 "requests~=2.28.0",
-                "torch==2.1.2",
+                "torch==2.1.1",
                 "torchmetrics==0.11.4",
-                "torchvision==0.16.2",
+                "torchvision>=0.16.1",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

invokeai/app/api/dependencies.py

@@ -11,7 +11,6 @@ from ..services.board_images.board_images_default import BoardImagesService
 from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
 from ..services.boards.boards_default import BoardService
 from ..services.config import InvokeAIAppConfig
-from ..services.download import DownloadQueueService
 from ..services.image_files.image_files_disk import DiskImageFileStorage
 from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
 from ..services.images.images_default import ImageService
@@ -30,7 +29,8 @@ from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
 from ..services.session_processor.session_processor_default import DefaultSessionProcessor
 from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
-from ..services.shared.graph import GraphExecutionState
+from ..services.shared.default_graphs import create_system_graphs
+from ..services.shared.graph import GraphExecutionState, LibraryGraph
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -80,13 +80,13 @@ 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_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
         model_manager = ModelManagerService(config, logger)
         model_record_service = ModelRecordServiceSQL(db=db)
-        download_queue_service = DownloadQueueService(event_bus=events)
         model_install_service = ModelInstallService(
             app_config=config, record_store=model_record_service, event_bus=events
         )
@@ -107,6 +107,7 @@ class ApiDependencies:
             configuration=configuration,
             events=events,
             graph_execution_manager=graph_execution_manager,
+            graph_library=graph_library,
             image_files=image_files,
             image_records=image_records,
             images=images,
@@ -115,7 +116,6 @@ class ApiDependencies:
             logger=logger,
             model_manager=model_manager,
             model_records=model_record_service,
-            download_queue=download_queue_service,
             model_install=model_install_service,
             names=names,
             performance_statistics=performance_statistics,
@@ -127,6 +127,8 @@ class ApiDependencies:
             workflow_records=workflow_records,
         )
 
+        create_system_graphs(services.graph_library)
+
         ApiDependencies.invoker = Invoker(services)
         db.clean()
 

invokeai/app/api/routers/download_queue.py

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

invokeai/app/api/routers/model_records.py

@@ -26,7 +26,7 @@ from invokeai.backend.model_manager.config import (
 
 from ..dependencies import ApiDependencies
 
-model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager_v2_unstable"])
+model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager_v2"])
 
 
 class ModelsList(BaseModel):

invokeai/app/api/routers/utilities.py

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

invokeai/app/api_app.py

@@ -45,7 +45,6 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         app_info,
         board_images,
         boards,
-        download_queue,
         images,
         model_records,
         models,
@@ -76,7 +75,7 @@ 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 - Community Edition", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
+app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
 
 # Add event handler
 event_handler_id: int = id(app)
@@ -117,7 +116,6 @@ app.include_router(sessions.session_router, prefix="/api")
 app.include_router(utilities.utilities_router, prefix="/api")
 app.include_router(models.models_router, prefix="/api")
 app.include_router(model_records.model_records_router, prefix="/api")
-app.include_router(download_queue.download_queue_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
 app.include_router(board_images.board_images_router, prefix="/api")
@@ -205,8 +203,8 @@ 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=f"{app.title} - Swagger UI",
-        swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
+        title=app.title,
+        swagger_favicon_url="/static/docs/favicon.ico",
     )
 
 
@@ -214,8 +212,8 @@ 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=f"{app.title} - Redoc",
-        redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
+        title=app.title,
+        redoc_favicon_url="/static/docs/favicon.ico",
     )
 
 
@@ -229,7 +227,7 @@ if (web_root_path / "dist").exists():
     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
+    # # Must mount *after* the other routes else it borks em
     app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
     app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
 

invokeai/app/invocations/compel.py

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

invokeai/app/invocations/controlnet_image_processors.py

@@ -24,10 +24,9 @@ from controlnet_aux import (
 )
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
 
@@ -76,16 +75,17 @@ class ControlField(BaseModel):
     resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
     @field_validator("control_weight")
-    @classmethod
     def validate_control_weight(cls, v):
-        validate_weights(v)
+        """Validate that all control weights in the valid range"""
+        if isinstance(v, list):
+            for i in v:
+                if i < -1 or i > 2:
+                    raise ValueError("Control weights must be within -1 to 2 range")
+        else:
+            if v < -1 or v > 2:
+                raise ValueError("Control weights must be within -1 to 2 range")
         return v
 
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
 
 @invocation_output("control_output")
 class ControlOutput(BaseInvocationOutput):
@@ -95,17 +95,17 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
     control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
     control_weight: Union[float, List[float]] = InputField(
-        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+        default=1.0, description="The weight given to the ControlNet"
     )
     begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+        default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
     )
     end_step_percent: float = InputField(
         default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
@@ -113,17 +113,6 @@ class ControlNetInvocation(BaseInvocation):
     control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
     resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
 
-    @field_validator("control_weight")
-    @classmethod
-    def validate_control_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> "ControlNetInvocation":
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
     def invoke(self, context: InvocationContext) -> ControlOutput:
         return ControlOutput(
             control=ControlField(

invokeai/app/invocations/ip_adapter.py

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

invokeai/app/invocations/latent.py

@@ -220,7 +220,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.1",
+    version="1.5.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -279,7 +279,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         ui_order=7,
     )
     cfg_rescale_multiplier: float = InputField(
-        title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
     )
     latents: Optional[LatentsField] = InputField(
         default=None,

invokeai/app/invocations/onnx.py

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

invokeai/app/invocations/t2i_adapter.py

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

invokeai/app/invocations/tiles.py

@@ -77,7 +77,7 @@ class CalculateImageTilesInvocation(BaseInvocation):
     title="Calculate Image Tiles Even Split",
     tags=["tiles"],
     category="tiles",
-    version="1.1.0",
+    version="1.0.0",
     classification=Classification.Beta,
 )
 class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
@@ -97,11 +97,11 @@ class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
         ge=1,
         description="Number of tiles to divide image into on the y axis",
     )
-    overlap: int = InputField(
-        default=128,
+    overlap_fraction: float = InputField(
+        default=0.25,
         ge=0,
-        multiple_of=8,
-        description="The overlap, in pixels, between adjacent tiles.",
+        lt=1,
+        description="Overlap between adjacent tiles as a fraction of the tile's dimensions (0-1)",
     )
 
     def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
@@ -110,7 +110,7 @@ class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
             image_width=self.image_width,
             num_tiles_x=self.num_tiles_x,
             num_tiles_y=self.num_tiles_y,
-            overlap=self.overlap,
+            overlap_fraction=self.overlap_fraction,
         )
         return CalculateImageTilesOutput(tiles=tiles)
 

invokeai/app/invocations/util.py

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

invokeai/app/services/config/__init__.py

@@ -1,7 +1,5 @@
 """Init file for InvokeAI configure package."""
 
-from invokeai.app.services.config.config_common import PagingArgumentParser
-
 from .config_default import InvokeAIAppConfig, get_invokeai_config
 
-__all__ = ["InvokeAIAppConfig", "get_invokeai_config", "PagingArgumentParser"]
+__all__ = ["InvokeAIAppConfig", "get_invokeai_config"]

invokeai/app/services/config/config_default.py

@@ -356,7 +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.resolve()
+        return root
 
     @property
     def root_dir(self) -> Path:

invokeai/app/services/download/__init__.py

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

invokeai/app/services/download/download_base.py

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

invokeai/app/services/download/download_default.py

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

invokeai/app/services/events/events_base.py

@@ -17,7 +17,6 @@ from invokeai.backend.model_management.models.base import BaseModelType, ModelTy
 
 class EventServiceBase:
     queue_event: str = "queue_event"
-    download_event: str = "download_event"
     model_event: str = "model_event"
 
     """Basic event bus, to have an empty stand-in when not needed"""
@@ -33,13 +32,6 @@ class EventServiceBase:
             payload={"event": event_name, "data": payload},
         )
 
-    def __emit_download_event(self, event_name: str, payload: dict) -> None:
-        payload["timestamp"] = get_timestamp()
-        self.dispatch(
-            event_name=EventServiceBase.download_event,
-            payload={"event": event_name, "data": payload},
-        )
-
     def __emit_model_event(self, event_name: str, payload: dict) -> None:
         payload["timestamp"] = get_timestamp()
         self.dispatch(
@@ -331,79 +323,6 @@ class EventServiceBase:
             payload={"queue_id": queue_id},
         )
 
-    def emit_download_started(self, source: str, download_path: str) -> None:
-        """
-        Emit when a download job is started.
-
-        :param url: The downloaded url
-        """
-        self.__emit_download_event(
-            event_name="download_started",
-            payload={"source": source, "download_path": download_path},
-        )
-
-    def emit_download_progress(self, source: str, download_path: str, current_bytes: int, total_bytes: int) -> None:
-        """
-        Emit "download_progress" events at regular intervals during a download job.
-
-        :param source: The downloaded source
-        :param download_path: The local downloaded file
-        :param current_bytes: Number of bytes downloaded so far
-        :param total_bytes: The size of the file being downloaded (if known)
-        """
-        self.__emit_download_event(
-            event_name="download_progress",
-            payload={
-                "source": source,
-                "download_path": download_path,
-                "current_bytes": current_bytes,
-                "total_bytes": total_bytes,
-            },
-        )
-
-    def emit_download_complete(self, source: str, download_path: str, total_bytes: int) -> None:
-        """
-        Emit a "download_complete" event at the end of a successful download.
-
-        :param source: Source URL
-        :param download_path: Path to the locally downloaded file
-        :param total_bytes: The size of the downloaded file
-        """
-        self.__emit_download_event(
-            event_name="download_complete",
-            payload={
-                "source": source,
-                "download_path": download_path,
-                "total_bytes": total_bytes,
-            },
-        )
-
-    def emit_download_cancelled(self, source: str) -> None:
-        """Emit a "download_cancelled" event in the event that the download was cancelled by user."""
-        self.__emit_download_event(
-            event_name="download_cancelled",
-            payload={
-                "source": source,
-            },
-        )
-
-    def emit_download_error(self, source: str, error_type: str, error: str) -> None:
-        """
-        Emit a "download_error" event when an download job encounters an exception.
-
-        :param source: Source URL
-        :param error_type: The name of the exception that raised the error
-        :param error: The traceback from this error
-        """
-        self.__emit_download_event(
-            event_name="download_error",
-            payload={
-                "source": source,
-                "error_type": error_type,
-                "error": error,
-            },
-        )
-
     def emit_model_install_started(self, source: str) -> None:
         """
         Emitted when an install job is started.

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -1,4 +1,3 @@
-import cProfile
 import time
 import traceback
 from threading import BoundedSemaphore, Event, Thread
@@ -40,9 +39,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
             self.__threadLimit.acquire()
             queue_item: Optional[InvocationQueueItem] = None
 
-            profiler = None
-            last_gesid = None
-
             while not stop_event.is_set():
                 try:
                     queue_item = self.__invoker.services.queue.get()
@@ -53,21 +49,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # do not hammer the queue
                     time.sleep(0.5)
                     continue
-
-                if last_gesid != queue_item.graph_execution_state_id:
-                    if profiler is not None:
-                        # I'm not sure what would cause us to get here, but if we do, we should restart the profiler for
-                        # the new graph_execution_state_id.
-                        profiler.disable()
-                        logger.info(f"Stopped profiler for {last_gesid}.")
-                        profiler = None
-                        last_gesid = None
-
-                    profiler = cProfile.Profile()
-                    profiler.enable()
-                    last_gesid = queue_item.graph_execution_state_id
-                    logger.info(f"Started profiling {last_gesid}.")
-
                 try:
                     graph_execution_state = self.__invoker.services.graph_execution_manager.get(
                         queue_item.graph_execution_state_id
@@ -220,13 +201,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         queue_id=queue_item.session_queue_id,
                         graph_execution_state_id=graph_execution_state.id,
                     )
-                    if profiler is not None:
-                        profiler.disable()
-                        dump_path = f"{last_gesid}.prof"
-                        profiler.dump_stats(dump_path)
-                        logger.info(f"Saved profile to {dump_path}.")
-                        profiler = None
-                        last_gesid = None
 
         except KeyboardInterrupt:
             pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor

invokeai/app/services/invocation_services.py

@@ -11,7 +11,6 @@ if TYPE_CHECKING:
     from .board_records.board_records_base import BoardRecordStorageBase
     from .boards.boards_base import BoardServiceABC
     from .config import InvokeAIAppConfig
-    from .download import DownloadQueueServiceBase
     from .events.events_base import EventServiceBase
     from .image_files.image_files_base import ImageFileStorageBase
     from .image_records.image_records_base import ImageRecordStorageBase
@@ -28,7 +27,7 @@ if TYPE_CHECKING:
     from .names.names_base import NameServiceBase
     from .session_processor.session_processor_base import SessionProcessorBase
     from .session_queue.session_queue_base import SessionQueueBase
-    from .shared.graph import GraphExecutionState
+    from .shared.graph import GraphExecutionState, LibraryGraph
     from .urls.urls_base import UrlServiceBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
@@ -44,6 +43,7 @@ class InvocationServices:
     configuration: "InvokeAIAppConfig"
     events: "EventServiceBase"
     graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
+    graph_library: "ItemStorageABC[LibraryGraph]"
     images: "ImageServiceABC"
     image_records: "ImageRecordStorageBase"
     image_files: "ImageFileStorageBase"
@@ -51,7 +51,6 @@ class InvocationServices:
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
     model_records: "ModelRecordServiceBase"
-    download_queue: "DownloadQueueServiceBase"
     model_install: "ModelInstallServiceBase"
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
@@ -72,6 +71,7 @@ class InvocationServices:
         configuration: "InvokeAIAppConfig",
         events: "EventServiceBase",
         graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
+        graph_library: "ItemStorageABC[LibraryGraph]",
         images: "ImageServiceABC",
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
@@ -79,7 +79,6 @@ class InvocationServices:
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         model_records: "ModelRecordServiceBase",
-        download_queue: "DownloadQueueServiceBase",
         model_install: "ModelInstallServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
@@ -98,6 +97,7 @@ class InvocationServices:
         self.configuration = configuration
         self.events = events
         self.graph_execution_manager = graph_execution_manager
+        self.graph_library = graph_library
         self.images = images
         self.image_files = image_files
         self.image_records = image_records
@@ -105,7 +105,6 @@ class InvocationServices:
         self.logger = logger
         self.model_manager = model_manager
         self.model_records = model_records
-        self.download_queue = download_queue
         self.model_install = model_install
         self.processor = processor
         self.performance_statistics = performance_statistics

invokeai/app/services/model_install/model_install_base.py

@@ -11,6 +11,7 @@ from typing_extensions import Annotated
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.events import EventServiceBase
+from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_records import ModelRecordServiceBase
 from invokeai.backend.model_manager import AnyModelConfig
 
@@ -156,12 +157,12 @@ class ModelInstallServiceBase(ABC):
         :param event_bus: InvokeAI event bus for reporting events to.
         """
 
-    @abstractmethod
-    def start(self, *args: Any, **kwarg: Any) -> None:
-        """Start the installer service."""
+    def start(self, invoker: Invoker) -> None:
+        """Call at InvokeAI startup time."""
+        self.sync_to_config()
 
     @abstractmethod
-    def stop(self, *args: Any, **kwarg: Any) -> None:
+    def stop(self) -> None:
         """Stop the model install service. After this the objection can be safely deleted."""
 
     @property

invokeai/app/services/model_install/model_install_default.py

@@ -71,6 +71,7 @@ class ModelInstallService(ModelInstallServiceBase):
         self._install_queue = Queue()
         self._cached_model_paths = set()
         self._models_installed = set()
+        self._start_installer_thread()
 
     @property
     def app_config(self) -> InvokeAIAppConfig:  # noqa D102
@@ -84,13 +85,8 @@ class ModelInstallService(ModelInstallServiceBase):
     def event_bus(self) -> Optional[EventServiceBase]:  # noqa D102
         return self._event_bus
 
-    def start(self, *args: Any, **kwarg: Any) -> None:
-        """Start the installer thread."""
-        self._start_installer_thread()
-        self.sync_to_config()
-
-    def stop(self, *args: Any, **kwarg: Any) -> None:
-        """Stop the installer thread; after this the object can be deleted and garbage collected."""
+    def stop(self, *args, **kwargs) -> None:
+        """Stop the install thread; after this the object can be deleted and garbage collected."""
         self._install_queue.put(STOP_JOB)
 
     def _start_installer_thread(self) -> None:

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

@@ -5,7 +5,6 @@ from invokeai.app.services.image_files.image_files_base import ImageFileStorageB
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_1 import build_migration_1
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_2 import build_migration_2
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_3 import build_migration_3
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -28,7 +27,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator = SqliteMigrator(db=db)
     migrator.register_migration(build_migration_1())
     migrator.register_migration(build_migration_2(image_files=image_files, logger=logger))
-    migrator.register_migration(build_migration_3())
     migrator.run_migrations()
 
     return db

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

@@ -11,8 +11,6 @@ from invokeai.app.services.workflow_records.workflow_records_common import (
     UnsafeWorkflowWithVersionValidator,
 )
 
-from .util.migrate_yaml_config_1 import MigrateModelYamlToDb1
-
 
 class Migration2Callback:
     def __init__(self, image_files: ImageFileStorageBase, logger: Logger):
@@ -26,7 +24,6 @@ class Migration2Callback:
         self._add_workflow_library(cursor)
         self._drop_model_manager_metadata(cursor)
         self._recreate_model_config(cursor)
-        self._migrate_model_config_records(cursor)
         self._migrate_embedded_workflows(cursor)
 
     def _add_images_has_workflow(self, cursor: sqlite3.Cursor) -> None:
@@ -134,11 +131,6 @@ class Migration2Callback:
             """
         )
 
-    def _migrate_model_config_records(self, cursor: sqlite3.Cursor) -> None:
-        """After updating the model config table, we repopulate it."""
-        model_record_migrator = MigrateModelYamlToDb1(cursor)
-        model_record_migrator.migrate()
-
     def _migrate_embedded_workflows(self, cursor: sqlite3.Cursor) -> None:
         """
         In the v3.5.0 release, InvokeAI changed how it handles embedded workflows. The `images` table in
@@ -167,9 +159,6 @@ class Migration2Callback:
             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", ""))

invokeai/app/services/shared/sqlite_migrator/migrations/migration_3.py

@@ -1,75 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-from .util.migrate_yaml_config_1 import MigrateModelYamlToDb1
-
-
-class Migration3Callback:
-    def __init__(self) -> None:
-        pass
-
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._drop_model_manager_metadata(cursor)
-        self._recreate_model_config(cursor)
-        self._migrate_model_config_records(cursor)
-
-    def _drop_model_manager_metadata(self, cursor: sqlite3.Cursor) -> None:
-        """Drops the `model_manager_metadata` table."""
-        cursor.execute("DROP TABLE IF EXISTS model_manager_metadata;")
-
-    def _recreate_model_config(self, cursor: sqlite3.Cursor) -> None:
-        """
-        Drops the `model_config` table, recreating it.
-
-        In 3.4.0, this table used explicit columns but was changed to use json_extract 3.5.0.
-
-        Because this table is not used in production, we are able to simply drop it and recreate it.
-        """
-
-        cursor.execute("DROP TABLE IF EXISTS model_config;")
-
-        cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS model_config (
-                id TEXT NOT NULL PRIMARY KEY,
-                -- The next 3 fields are enums in python, unrestricted string here
-                base TEXT GENERATED ALWAYS as (json_extract(config, '$.base')) VIRTUAL NOT NULL,
-                type TEXT GENERATED ALWAYS as (json_extract(config, '$.type')) VIRTUAL NOT NULL,
-                name TEXT GENERATED ALWAYS as (json_extract(config, '$.name')) VIRTUAL NOT NULL,
-                path TEXT GENERATED ALWAYS as (json_extract(config, '$.path')) VIRTUAL NOT NULL,
-                format TEXT GENERATED ALWAYS as (json_extract(config, '$.format')) VIRTUAL NOT NULL,
-                original_hash TEXT, -- could be null
-                -- Serialized JSON representation of the whole config object,
-                -- which will contain additional fields from subclasses
-                config TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- unique constraint on combo of name, base and type
-                UNIQUE(name, base, type)
-            );
-            """
-        )
-
-    def _migrate_model_config_records(self, cursor: sqlite3.Cursor) -> None:
-        """After updating the model config table, we repopulate it."""
-        model_record_migrator = MigrateModelYamlToDb1(cursor)
-        model_record_migrator.migrate()
-
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-
-    This migration does the following:
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-    return migration_3

invokeai/app/services/workflow_records/default_workflows/Prompt from File.json

@@ -1,975 +0,0 @@
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invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json

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invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -169,7 +169,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
 
             self._cursor.execute(count_query, count_params)
             total = self._cursor.fetchone()[0]
-            pages = total // per_page + (total % per_page > 0)
+            pages = int(total / per_page) + 1
 
             return PaginatedResults(
                 items=workflows,

invokeai/app/util/ti_utils.py

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

invokeai/backend/install/check_root.py

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

invokeai/backend/model_management/detect_baked_in_vae.py

@@ -1,31 +0,0 @@
-# Copyright (c) 2024 Lincoln Stein and the InvokeAI Development Team
-"""
-This module exports the function has_baked_in_sdxl_vae().
-It returns True if an SDXL checkpoint model has the original SDXL 1.0 VAE,
-which doesn't work properly in fp16 mode.
-"""
-
-import hashlib
-from pathlib import Path
-
-from safetensors.torch import load_file
-
-SDXL_1_0_VAE_HASH = "bc40b16c3a0fa4625abdfc01c04ffc21bf3cefa6af6c7768ec61eb1f1ac0da51"
-
-
-def has_baked_in_sdxl_vae(checkpoint_path: Path) -> bool:
-    """Return true if the checkpoint contains a custom (non SDXL-1.0) VAE."""
-    hash = _vae_hash(checkpoint_path)
-    return hash != SDXL_1_0_VAE_HASH
-
-
-def _vae_hash(checkpoint_path: Path) -> str:
-    checkpoint = load_file(checkpoint_path, device="cpu")
-    vae_keys = [x for x in checkpoint.keys() if x.startswith("first_stage_model.")]
-    hash = hashlib.new("sha256")
-    for key in vae_keys:
-        value = checkpoint[key]
-        hash.update(bytes(key, "UTF-8"))
-        hash.update(bytes(str(value), "UTF-8"))
-
-    return hash.hexdigest()

invokeai/backend/model_management/lora.py

@@ -13,7 +13,6 @@ from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.model_management.model_load_optimizations import skip_torch_weight_init
 
 from .models.lora import LoRAModel
 
@@ -212,17 +211,11 @@ class ModelPatcher:
                 for i in range(ti_embedding.shape[0]):
                     new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
-            # Modify text_encoder.
-            # resize_token_embeddings(...) constructs a new torch.nn.Embedding internally. Initializing the weights of
-            # this embedding is slow and unnecessary, so we wrap this step in skip_torch_weight_init() to save some
-            # time.
-            with skip_torch_weight_init():
-                text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
+            # modify text_encoder
+            text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
             model_embeddings = text_encoder.get_input_embeddings()
 
-            for ti_name, ti in ti_list:
-                ti_embedding = _get_ti_embedding(text_encoder.get_input_embeddings(), ti)
-
+            for ti_name, _ in ti_list:
                 ti_tokens = []
                 for i in range(ti_embedding.shape[0]):
                     embedding = ti_embedding[i]

invokeai/backend/model_management/model_probe.py

@@ -370,8 +370,6 @@ class LoRACheckpointProbe(CheckpointProbeBase):
             return BaseModelType.StableDiffusion1
         elif token_vector_length == 1024:
             return BaseModelType.StableDiffusion2
-        elif token_vector_length == 1280:
-            return BaseModelType.StableDiffusionXL  # recognizes format at https://civitai.com/models/224641
         elif token_vector_length == 2048:
             return BaseModelType.StableDiffusionXL
         else:
@@ -391,7 +389,7 @@ class TextualInversionCheckpointProbe(CheckpointProbeBase):
         elif "clip_g" in checkpoint:
             token_dim = checkpoint["clip_g"].shape[-1]
         else:
-            token_dim = list(checkpoint.values())[0].shape[-1]
+            token_dim = list(checkpoint.values())[0].shape[0]
         if token_dim == 768:
             return BaseModelType.StableDiffusion1
         elif token_dim == 1024:

invokeai/backend/model_management/models/sdxl.py

@@ -1,16 +1,11 @@
 import json
 import os
 from enum import Enum
-from pathlib import Path
 from typing import Literal, Optional
 
 from omegaconf import OmegaConf
 from pydantic import Field
 
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_management.detect_baked_in_vae import has_baked_in_sdxl_vae
-from invokeai.backend.util.logging import InvokeAILogger
-
 from .base import (
     BaseModelType,
     DiffusersModel,
@@ -121,28 +116,14 @@ class StableDiffusionXLModel(DiffusersModel):
         # The convert script adapted from the diffusers package uses
         # strings for the base model type. To avoid making too many
         # source code changes, we simply translate here
-        if Path(output_path).exists():
-            return output_path
-
         if isinstance(config, cls.CheckpointConfig):
             from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache
 
-            # Hack in VAE-fp16 fix - If model sdxl-vae-fp16-fix is installed,
-            # then we bake it into the converted model unless there is already
-            # a nonstandard VAE installed.
-            kwargs = {}
-            app_config = InvokeAIAppConfig.get_config()
-            vae_path = app_config.models_path / "sdxl/vae/sdxl-vae-fp16-fix"
-            if vae_path.exists() and not has_baked_in_sdxl_vae(Path(model_path)):
-                InvokeAILogger.get_logger().warning("No baked-in VAE detected. Inserting sdxl-vae-fp16-fix.")
-                kwargs["vae_path"] = vae_path
-
             return _convert_ckpt_and_cache(
                 version=base_model,
                 model_config=config,
                 output_path=output_path,
                 use_safetensors=False,  # corrupts sdxl models for some reason
-                **kwargs,
             )
         else:
             return model_path

invokeai/backend/model_management/util.py

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

invokeai/backend/model_manager/migrate_to_db.py

@@ -1,12 +1,8 @@
 # Copyright (c) 2023 Lincoln D. Stein
 """Migrate from the InvokeAI v2 models.yaml format to the v3 sqlite format."""
 
-import json
-import sqlite3
 from hashlib import sha1
 from logging import Logger
-from pathlib import Path
-from typing import Optional
 
 from omegaconf import DictConfig, OmegaConf
 from pydantic import TypeAdapter
@@ -14,12 +10,13 @@ from pydantic import TypeAdapter
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.model_records import (
     DuplicateModelException,
+    ModelRecordServiceSQL,
     UnknownModelException,
 )
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
-    ModelConfigFactory,
     ModelType,
 )
 from invokeai.backend.model_manager.hash import FastModelHash
@@ -28,9 +25,9 @@ from invokeai.backend.util.logging import InvokeAILogger
 ModelsValidator = TypeAdapter(AnyModelConfig)
 
 
-class MigrateModelYamlToDb1:
+class MigrateModelYamlToDb:
     """
-    Migrate the InvokeAI models.yaml format (VERSION 3.0.0) to SQL3 database format (VERSION 3.5.0).
+    Migrate the InvokeAI models.yaml format (VERSION 3.0.0) to SQL3 database format (VERSION 3.2.0)
 
     The class has one externally useful method, migrate(), which scans the
     currently models.yaml file and imports all its entries into invokeai.db.
@@ -44,13 +41,17 @@ class MigrateModelYamlToDb1:
 
     config: InvokeAIAppConfig
     logger: Logger
-    cursor: sqlite3.Cursor
 
-    def __init__(self, cursor: sqlite3.Cursor = None) -> None:
+    def __init__(self) -> None:
         self.config = InvokeAIAppConfig.get_config()
         self.config.parse_args()
         self.logger = InvokeAILogger.get_logger()
-        self.cursor = cursor
+
+    def get_db(self) -> ModelRecordServiceSQL:
+        """Fetch the sqlite3 database for this installation."""
+        db_path = None if self.config.use_memory_db else self.config.db_path
+        db = SqliteDatabase(db_path=db_path, logger=self.logger, verbose=self.config.log_sql)
+        return ModelRecordServiceSQL(db)
 
     def get_yaml(self) -> DictConfig:
         """Fetch the models.yaml DictConfig for this installation."""
@@ -61,10 +62,8 @@ class MigrateModelYamlToDb1:
 
     def migrate(self) -> None:
         """Do the migration from models.yaml to invokeai.db."""
-        try:
-            yaml = self.get_yaml()
-        except OSError:
-            return
+        db = self.get_db()
+        yaml = self.get_yaml()
 
         for model_key, stanza in yaml.items():
             if model_key == "__metadata__":
@@ -87,62 +86,22 @@ class MigrateModelYamlToDb1:
             new_config: AnyModelConfig = ModelsValidator.validate_python(stanza)  # type: ignore # see https://github.com/pydantic/pydantic/discussions/7094
 
             try:
-                if original_record := self._search_by_path(stanza.path):
-                    key = original_record.key
+                if original_record := db.search_by_path(stanza.path):
+                    key = original_record[0].key
                     self.logger.info(f"Updating model {model_name} with information from models.yaml using key {key}")
-                    self._update_model(key, new_config)
+                    db.update_model(key, new_config)
                 else:
                     self.logger.info(f"Adding model {model_name} with key {model_key}")
-                    self._add_model(new_key, new_config)
+                    db.add_model(new_key, new_config)
             except DuplicateModelException:
                 self.logger.warning(f"Model {model_name} is already in the database")
             except UnknownModelException:
                 self.logger.warning(f"Model at {stanza.path} could not be found in database")
 
-    def _search_by_path(self, path: Path) -> Optional[AnyModelConfig]:
-        self.cursor.execute(
-            """--sql
-            SELECT config FROM model_config
-            WHERE path=?;
-            """,
-            (str(path),),
-        )
-        results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self.cursor.fetchall()]
-        return results[0] if results else None
 
-    def _update_model(self, key: str, config: AnyModelConfig) -> None:
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
-        self.cursor.execute(
-            """--sql
-            UPDATE model_config
-            SET
-                config=?
-            WHERE id=?;
-            """,
-            (json_serialized, key),
-        )
-        if self.cursor.rowcount == 0:
-            raise UnknownModelException("model not found")
+def main():
+    MigrateModelYamlToDb().migrate()
 
-    def _add_model(self, key: str, config: AnyModelConfig) -> None:
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect.
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
-        try:
-            self.cursor.execute(
-                """--sql
-                INSERT INTO model_config (
-                   id,
-                   original_hash,
-                   config
-                  )
-                VALUES (?,?,?);
-                """,
-                (
-                    key,
-                    record.original_hash,
-                    json_serialized,
-                ),
-            )
-        except sqlite3.IntegrityError as exc:
-            raise DuplicateModelException(f"{record.name}: model is already in database") from exc
+
+if __name__ == "__main__":
+    main()

invokeai/backend/model_manager/probe.py

@@ -400,8 +400,6 @@ class LoRACheckpointProbe(CheckpointProbeBase):
             return BaseModelType.StableDiffusion1
         elif token_vector_length == 1024:
             return BaseModelType.StableDiffusion2
-        elif token_vector_length == 1280:
-            return BaseModelType.StableDiffusionXL  # recognizes format at https://civitai.com/models/224641
         elif token_vector_length == 2048:
             return BaseModelType.StableDiffusionXL
         else:

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -276,11 +276,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             self.disable_attention_slicing()
             return
         elif config.attention_type == "torch-sdp":
-            if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
-                return
-            else:
-                raise Exception("torch-sdp attention slicing not available")
+            raise Exception("torch-sdp attention slicing not yet implemented")
 
         # the remainder if this code is called when attention_type=='auto'
         if self.unet.device.type == "cuda":
@@ -288,7 +284,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 self.enable_xformers_memory_efficient_attention()
                 return
             elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
+                # diffusers enable sdp automatically
                 return
 
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":

invokeai/backend/tiles/tiles.py

@@ -102,7 +102,7 @@ def calc_tiles_with_overlap(
 
 
 def calc_tiles_even_split(
-    image_height: int, image_width: int, num_tiles_x: int, num_tiles_y: int, overlap: int = 0
+    image_height: int, image_width: int, num_tiles_x: int, num_tiles_y: int, overlap_fraction: float = 0
 ) -> list[Tile]:
     """Calculate the tile coordinates for a given image shape with the number of tiles requested.
 
@@ -111,35 +111,31 @@ def calc_tiles_even_split(
         image_width (int): The image width in px.
         num_x_tiles (int): The number of tile to split the image into on the X-axis.
         num_y_tiles (int): The number of tile to split the image into on the Y-axis.
-        overlap (int, optional): The overlap between adjacent tiles in pixels. Defaults to 0.
+        overlap_fraction (float, optional): The target overlap as fraction of the tiles size. Defaults to 0.
 
     Returns:
         list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
     """
-    # Ensure the image is divisible by LATENT_SCALE_FACTOR
+
+    # Ensure tile size is divisible by 8
     if image_width % LATENT_SCALE_FACTOR != 0 or image_height % LATENT_SCALE_FACTOR != 0:
         raise ValueError(f"image size (({image_width}, {image_height})) must be divisible by {LATENT_SCALE_FACTOR}")
 
-    # Calculate the tile size based on the number of tiles and overlap, and ensure it's divisible by 8 (rounding down)
-    if num_tiles_x > 1:
-        # ensure the overlap is not more than the maximum overlap if we only have 1 tile then we dont care about overlap
-        assert overlap <= image_width - (LATENT_SCALE_FACTOR * (num_tiles_x - 1))
-        tile_size_x = LATENT_SCALE_FACTOR * math.floor(
-            ((image_width + overlap * (num_tiles_x - 1)) // num_tiles_x) / LATENT_SCALE_FACTOR
-        )
-        assert overlap < tile_size_x
-    else:
-        tile_size_x = image_width
+    # Calculate the overlap size based on the percentage and adjust it to be divisible by 8 (rounding up)
+    overlap_x = LATENT_SCALE_FACTOR * math.ceil(
+        int((image_width / num_tiles_x) * overlap_fraction) / LATENT_SCALE_FACTOR
+    )
+    overlap_y = LATENT_SCALE_FACTOR * math.ceil(
+        int((image_height / num_tiles_y) * overlap_fraction) / LATENT_SCALE_FACTOR
+    )
 
-    if num_tiles_y > 1:
-        # ensure the overlap is not more than the maximum overlap if we only have 1 tile then we dont care about overlap
-        assert overlap <= image_height - (LATENT_SCALE_FACTOR * (num_tiles_y - 1))
-        tile_size_y = LATENT_SCALE_FACTOR * math.floor(
-            ((image_height + overlap * (num_tiles_y - 1)) // num_tiles_y) / LATENT_SCALE_FACTOR
-        )
-        assert overlap < tile_size_y
-    else:
-        tile_size_y = image_height
+    # Calculate the tile size based on the number of tiles and overlap, and ensure it's divisible by 8 (rounding down)
+    tile_size_x = LATENT_SCALE_FACTOR * math.floor(
+        ((image_width + overlap_x * (num_tiles_x - 1)) // num_tiles_x) / LATENT_SCALE_FACTOR
+    )
+    tile_size_y = LATENT_SCALE_FACTOR * math.floor(
+        ((image_height + overlap_y * (num_tiles_y - 1)) // num_tiles_y) / LATENT_SCALE_FACTOR
+    )
 
     # tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
     tiles: list[Tile] = []
@@ -147,7 +143,7 @@ def calc_tiles_even_split(
     # Calculate tile coordinates. (Ignore overlap values for now.)
     for tile_idx_y in range(num_tiles_y):
         # Calculate the top and bottom of the row
-        top = tile_idx_y * (tile_size_y - overlap)
+        top = tile_idx_y * (tile_size_y - overlap_y)
         bottom = min(top + tile_size_y, image_height)
         # For the last row adjust bottom to be the height of the image
         if tile_idx_y == num_tiles_y - 1:
@@ -155,7 +151,7 @@ def calc_tiles_even_split(
 
         for tile_idx_x in range(num_tiles_x):
             # Calculate the left & right coordinate of each tile
-            left = tile_idx_x * (tile_size_x - overlap)
+            left = tile_idx_x * (tile_size_x - overlap_x)
             right = min(left + tile_size_x, image_width)
             # For the last tile in the row adjust right to be the width of the image
             if tile_idx_x == num_tiles_x - 1:

invokeai/backend/util/devices.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
+import platform
 from contextlib import nullcontext
 from typing import Union
 
 import torch
+from packaging import version
 from torch import autocast
 
 from invokeai.app.services.config import InvokeAIAppConfig
@@ -35,7 +37,7 @@ def choose_precision(device: torch.device) -> str:
         device_name = torch.cuda.get_device_name(device)
         if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
             return "float16"
-    elif device.type == "mps":
+    elif device.type == "mps" and version.parse(platform.mac_ver()[0]) < version.parse("14.0.0"):
         return "float16"
     return "float32"
 
@@ -44,7 +46,7 @@ def torch_dtype(device: torch.device) -> torch.dtype:
     if config.full_precision:
         return torch.float32
     if choose_precision(device) == "float16":
-        return torch.bfloat16 if device.type == "cuda" else torch.float16
+        return torch.float16
     else:
         return torch.float32
 

invokeai/frontend/install/invokeai_update.py

@@ -4,7 +4,6 @@ pip install <path_to_git_source>.
 """
 import os
 import platform
-from distutils.version import LooseVersion
 
 import pkg_resources
 import psutil
@@ -32,6 +31,10 @@ else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
 
+def get_versions() -> dict:
+    return requests.get(url=INVOKE_AI_REL).json()
+
+
 def invokeai_is_running() -> bool:
     for p in psutil.process_iter():
         try:
@@ -47,20 +50,6 @@ def invokeai_is_running() -> bool:
     return False
 
 
-def get_pypi_versions():
-    url = "https://pypi.org/pypi/invokeai/json"
-    try:
-        data = requests.get(url).json()
-    except Exception:
-        raise Exception("Unable to fetch version information from PyPi")
-
-    versions = list(data["releases"].keys())
-    versions.sort(key=LooseVersion, reverse=True)
-    latest_version = [v for v in versions if "rc" not in v][0]
-    latest_release_candidate = [v for v in versions if "rc" in v][0]
-    return latest_version, latest_release_candidate, versions
-
-
 def welcome(latest_release: str, latest_prerelease: str):
     @group()
     def text():
@@ -68,10 +57,14 @@ def welcome(latest_release: str, latest_prerelease: str):
         yield ""
         yield "This script will update InvokeAI to the latest release, or to the development version of your choice."
         yield ""
+        yield "When updating to an arbitrary tag or branch, be aware that the front end may be mismatched to the backend,"
+        yield "making the web frontend unusable. Please downgrade to the latest release if this happens."
+        yield ""
         yield "[bold yellow]Options:"
         yield f"""[1] Update to the latest [bold]official release[/bold] ([italic]{latest_release}[/italic])
-[2] Update to the latest [bold]pre-release[/bold] (may be buggy, database backups are recommended before installation; caveat emptor!) ([italic]{latest_prerelease}[/italic])
-[3] Manually enter the [bold]version[/bold] you wish to update to"""
+[2] Update to the latest [bold]pre-release[/bold] (may be buggy; caveat emptor!) ([italic]{latest_prerelease}[/italic])
+[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
+[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""
 
     console.rule()
     print(
@@ -99,35 +92,44 @@ def get_extras():
 
 
 def main():
+    versions = get_versions()
+    released_versions = [x for x in versions if not (x["draft"] or x["prerelease"])]
+    prerelease_versions = [x for x in versions if not x["draft"] and x["prerelease"]]
+    latest_release = released_versions[0]["tag_name"] if len(released_versions) else None
+    latest_prerelease = prerelease_versions[0]["tag_name"] if len(prerelease_versions) else None
+
     if invokeai_is_running():
         print(":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]")
         input("Press any key to continue...")
         return
 
-    latest_release, latest_prerelease, versions = get_pypi_versions()
-
     welcome(latest_release, latest_prerelease)
 
-    release = latest_release
-    choice = Prompt.ask("Choice:", choices=["1", "2", "3"], default="1")
+    tag = None
+    branch = None
+    release = None
+    choice = Prompt.ask("Choice:", choices=["1", "2", "3", "4"], default="1")
 
     if choice == "1":
         release = latest_release
     elif choice == "2":
         release = latest_prerelease
     elif choice == "3":
-        while True:
-            release = Prompt.ask("Enter an InvokeAI version")
-            release.strip()
-            if release in versions:
-                break
-            print(f":exclamation: [bold red]'{release}' is not a recognized InvokeAI release.[/red bold]")
+        while not tag:
+            tag = Prompt.ask("Enter an InvokeAI tag name")
+    elif choice == "4":
+        while not branch:
+            branch = Prompt.ask("Enter an InvokeAI branch name")
 
     extras = get_extras()
 
-    print(f":crossed_fingers: Upgrading to [yellow]{release}[/yellow]")
-    cmd = f'pip install "invokeai{extras}=={release}" --use-pep517 --upgrade'
-
+    print(f":crossed_fingers: Upgrading to [yellow]{tag or release or branch}[/yellow]")
+    if release:
+        cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_SRC}/{release}.zip" --use-pep517 --upgrade'
+    elif tag:
+        cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_TAG}/{tag}.zip" --use-pep517 --upgrade'
+    else:
+        cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_BRANCH}/{branch}.zip" --use-pep517 --upgrade'
     print("")
     print("")
     if os.system(cmd) == 0:

invokeai/frontend/web/.eslintignore

@@ -7,4 +7,4 @@ stats.html
 index.html
 .yarn/
 *.scss
-src/services/api/schema.ts
+src/services/api/schema.d.ts

invokeai/frontend/web/.eslintrc.js

@@ -28,16 +28,12 @@ module.exports = {
     'i18next',
     'path',
     'unused-imports',
-    'simple-import-sort',
-    'eslint-plugin-import',
-    // These rules are too strict for normal usage, but are useful for optimizing rerenders
-    // '@arthurgeron/react-usememo',
   ],
   root: true,
   rules: {
     'path/no-relative-imports': ['error', { maxDepth: 0 }],
     curly: 'error',
-    'i18next/no-literal-string': 'warn',
+    'i18next/no-literal-string': 2,
     'react/jsx-no-bind': ['error', { allowBind: true }],
     'react/jsx-curly-brace-presence': [
       'error',
@@ -47,7 +43,6 @@ module.exports = {
     'no-var': 'error',
     'brace-style': 'error',
     'prefer-template': 'error',
-    'import/no-duplicates': 'error',
     radix: 'error',
     'space-before-blocks': 'error',
     'import/prefer-default-export': 'off',
@@ -62,18 +57,6 @@ module.exports = {
         argsIgnorePattern: '^_',
       },
     ],
-    // These rules are too strict for normal usage, but are useful for optimizing rerenders
-    // '@arthurgeron/react-usememo/require-usememo': [
-    //   'warn',
-    //   {
-    //     strict: false,
-    //     checkHookReturnObject: false,
-    //     fix: { addImports: true },
-    //     checkHookCalls: false,
-
-    //   },
-    // ],
-    // '@arthurgeron/react-usememo/require-memo': 'warn',
     '@typescript-eslint/ban-ts-comment': 'warn',
     '@typescript-eslint/no-explicit-any': 'warn',
     '@typescript-eslint/no-empty-interface': [
@@ -82,26 +65,7 @@ module.exports = {
         allowSingleExtends: true,
       },
     ],
-    '@typescript-eslint/consistent-type-imports': [
-      'error',
-      {
-        prefer: 'type-imports',
-        fixStyle: 'separate-type-imports',
-        disallowTypeAnnotations: true,
-      },
-    ],
-    '@typescript-eslint/no-import-type-side-effects': 'error',
-    'simple-import-sort/imports': 'error',
-    'simple-import-sort/exports': 'error',
   },
-  overrides: [
-    {
-      files: ['*.stories.tsx'],
-      rules: {
-        'i18next/no-literal-string': 'off',
-      },
-    },
-  ],
   settings: {
     react: {
       version: 'detect',

invokeai/frontend/web/.gitignore

@@ -8,7 +8,6 @@ pnpm-debug.log*
 lerna-debug.log*
 
 node_modules
-.pnpm-store
 # We want to distribute the repo
 dist
 dist/**

invokeai/frontend/web/.prettierignore

@@ -9,8 +9,7 @@ index.html
 .yarn/
 .yalc/
 *.scss
-src/services/api/schema.ts
+src/services/api/schema.d.ts
 static/
 src/theme/css/overlayscrollbars.css
-src/theme_/css/overlayscrollbars.css
 pnpm-lock.yaml

invokeai/frontend/web/.storybook/ReduxInit.tsx

@@ -1,25 +0,0 @@
-import { PropsWithChildren, memo, useEffect } from 'react';
-import { modelChanged } from '../src/features/parameters/store/generationSlice';
-import { useAppDispatch } from '../src/app/store/storeHooks';
-import { useGlobalModifiersInit } from '../src/common/hooks/useGlobalModifiers';
-/**
- * Initializes some state for storybook. Must be in a different component
- * so that it is run inside the redux context.
- */
-export const ReduxInit = memo((props: PropsWithChildren) => {
-  const dispatch = useAppDispatch();
-  useGlobalModifiersInit();
-  useEffect(() => {
-    dispatch(
-      modelChanged({
-        model_name: 'test_model',
-        base_model: 'sd-1',
-        model_type: 'main',
-      })
-    );
-  }, []);
-
-  return props.children;
-});
-
-ReduxInit.displayName = 'ReduxInit';

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

@@ -6,7 +6,6 @@ const config: StorybookConfig = {
     '@storybook/addon-links',
     '@storybook/addon-essentials',
     '@storybook/addon-interactions',
-    '@storybook/addon-storysource',
   ],
   framework: {
     name: '@storybook/react-vite',

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

@@ -1,17 +1,16 @@
 import { Preview } from '@storybook/react';
 import { themes } from '@storybook/theming';
 import i18n from 'i18next';
+import React from 'react';
 import { initReactI18next } from 'react-i18next';
 import { Provider } from 'react-redux';
+import GlobalHotkeys from '../src/app/components/GlobalHotkeys';
 import ThemeLocaleProvider from '../src/app/components/ThemeLocaleProvider';
-import { $baseUrl } from '../src/app/store/nanostores/baseUrl';
 import { createStore } from '../src/app/store/store';
-import { Container } from '@chakra-ui/react';
 // TODO: Disabled for IDE performance issues with our translation JSON
 // eslint-disable-next-line @typescript-eslint/ban-ts-comment
 // @ts-ignore
 import translationEN from '../public/locales/en.json';
-import { ReduxInit } from './ReduxInit';
 
 i18n.use(initReactI18next).init({
   lng: 'en',
@@ -26,21 +25,17 @@ i18n.use(initReactI18next).init({
 });
 
 const store = createStore(undefined, false);
-$baseUrl.set('http://localhost:9090');
 
 const preview: Preview = {
   decorators: [
-    (Story) => {
-      return (
-        <Provider store={store}>
-          <ThemeLocaleProvider>
-            <ReduxInit>
-              <Story />
-            </ReduxInit>
-          </ThemeLocaleProvider>
-        </Provider>
-      );
-    },
+    (Story) => (
+      <Provider store={store}>
+        <ThemeLocaleProvider>
+          <GlobalHotkeys />
+          <Story />
+        </ThemeLocaleProvider>
+      </Provider>
+    ),
   ],
   parameters: {
     docs: {

invokeai/frontend/web/.unimportedrc.json

@@ -1,15 +0,0 @@
-{
-  "entry": ["src/main.tsx"],
-  "extensions": [".ts", ".tsx"],
-  "ignorePatterns": [
-    "**/node_modules/**",
-    "dist/**",
-    "public/**",
-    "**/*.stories.tsx",
-    "config/**"
-  ],
-  "ignoreUnresolved": [],
-  "ignoreUnimported": ["src/i18.d.ts", "vite.config.ts", "src/vite-env.d.ts"],
-  "respectGitignore": true,
-  "ignoreUnused": []
-}

invokeai/frontend/web/config/common.ts

@@ -1,6 +1,6 @@
 import react from '@vitejs/plugin-react-swc';
 import { visualizer } from 'rollup-plugin-visualizer';
-import type { PluginOption, UserConfig } from 'vite';
+import { PluginOption, UserConfig } from 'vite';
 import eslint from 'vite-plugin-eslint';
 import tsconfigPaths from 'vite-tsconfig-paths';
 

invokeai/frontend/web/config/vite.app.config.ts

@@ -1,6 +1,5 @@
-import type { UserConfig } from 'vite';
-
-import { commonPlugins } from './common.mjs';
+import { UserConfig } from 'vite';
+import { commonPlugins } from './common';
 
 export const appConfig: UserConfig = {
   base: './',

invokeai/frontend/web/config/vite.package.config.ts

@@ -1,9 +1,8 @@
 import path from 'path';
-import type { UserConfig } from 'vite';
-import cssInjectedByJsPlugin from 'vite-plugin-css-injected-by-js';
+import { UserConfig } from 'vite';
 import dts from 'vite-plugin-dts';
-
-import { commonPlugins } from './common.mjs';
+import cssInjectedByJsPlugin from 'vite-plugin-css-injected-by-js';
+import { commonPlugins } from './common';
 
 export const packageConfig: UserConfig = {
   base: './',