Only kick off on merges to main

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -6,6 +6,10 @@ title: '[bug]: '
 
 labels: ['bug']
 
+# assignees:
+#   - moderator_bot
+#   - lstein
+
 body:
   - type: markdown
     attributes:
@@ -14,9 +18,10 @@ body:
 
   - type: checkboxes
     attributes:
-      label: Is there an existing issue for this problem?
+      label: Is there an existing issue for this?
       description: |
-        Please [search](https://github.com/invoke-ai/InvokeAI/issues) first to see if an issue already exists for the problem.
+        Please use the [search function](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
+        irst to see if an issue already exists for the bug you encountered.
       options:
         - label: I have searched the existing issues
           required: true
@@ -28,119 +33,80 @@ body:
   - type: dropdown
     id: os_dropdown
     attributes:
-      label: Operating system
-      description: Your computer's operating system.
+      label: OS
+      description: Which operating System did you use when the bug occured
       multiple: false
       options:
         - 'Linux'
         - 'Windows'
         - 'macOS'
-        - 'other'
     validations:
       required: true
 
   - type: dropdown
     id: gpu_dropdown
     attributes:
-      label: GPU vendor
-      description: Your GPU's vendor.
+      label: GPU
+      description: Which kind of Graphic-Adapter is your System using
       multiple: false
       options:
-        - 'Nvidia (CUDA)'
-        - 'AMD (ROCm)'
-        - 'Apple Silicon (MPS)'
-        - 'None (CPU)'
+        - 'cuda'
+        - 'amd'
+        - 'mps'
+        - 'cpu'
     validations:
       required: true
 
-  - type: input
-    id: gpu_model
-    attributes:
-      label: GPU model
-      description: Your GPU's model. If on Apple Silicon, this is your Mac's chip. Leave blank if on CPU.
-      placeholder: ex. RTX 2080 Ti, Mac M1 Pro
-    validations:
-      required: false
-
   - type: input
     id: vram
     attributes:
-      label: GPU VRAM
-      description: Your GPU's VRAM. If on Apple Silicon, this is your Mac's unified memory. Leave blank if on CPU.
+      label: VRAM
+      description: Size of the VRAM if known
       placeholder: 8GB
     validations:
       required: false
-
+      
   - type: input
     id: version-number
     attributes:
-      label: Version number
+      label: What version did you experience this issue on?
       description: |
-        The version of Invoke you have installed. If it is not the latest version, please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
-      placeholder: ex. 3.6.1
+        Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
+      placeholder: X.X.X
     validations:
       required: true
 
-  - type: input
-    id: browser-version
-    attributes:
-      label: Browser
-      description: Your web browser and version.
-      placeholder: ex. Firefox 123.0b3
-    validations:
-      required: true
-
-  - type: textarea
-    id: python-deps
-    attributes:
-      label: Python dependencies
-      description: |
-        If the problem occurred during image generation, click the gear icon at the bottom left corner, click "About", click the copy button and then paste here.
-    validations:
-      required: false
-
   - type: textarea
     id: what-happened
     attributes:
-      label: What happened
+      label: What happened?
       description: |
-        Describe what happened. Include any relevant error messages, stack traces and screenshots here.
-      placeholder: I clicked button X and then Y happened.
+        Briefly describe what happened, what you expected to happen and how to reproduce this bug.
+      placeholder: When using the webinterface and right-clicking on button X instead of the popup-menu there error Y appears
     validations:
       required: true
 
   - type: textarea
-    id: what-you-expected
     attributes:
-      label: What you expected to happen
-      description: Describe what you expected to happen.
-      placeholder: I expected Z to happen.
-    validations:
-      required: true
-
-  - type: textarea
-    id: how-to-repro
-    attributes:
-      label: How to reproduce the problem
-      description: List steps to reproduce the problem.
-      placeholder: Start the app, generate an image with these settings, then click button X.
+      label: Screenshots
+      description: If applicable, add screenshots to help explain your problem
+      placeholder: this is what the result looked like <screenshot>
     validations:
       required: false
 
   - type: textarea
-    id: additional-context
     attributes:
       label: Additional context
-      description: Any other context that might help us to understand the problem.
+      description: Add any other context about the problem here
       placeholder: Only happens when there is full moon and Friday the 13th on Christmas Eve 🎅🏻
     validations:
       required: false
 
   - type: input
-    id: discord-username
+    id: contact
     attributes:
-      label: Discord username
-      description: If you are on the Invoke discord and would prefer to be contacted there, please provide your username.
-      placeholder: supercoolusername123
+      label: Contact Details
+      description: __OPTIONAL__ How can we get in touch with you if we need more info (besides this issue)?
+      placeholder: ex. email@example.com, discordname, twitter, ...
     validations:
       required: false

.github/pr_labels.yml

@@ -1,59 +0,0 @@
-Root:
-- changed-files:
-  - any-glob-to-any-file: '*'
-
-PythonDeps:
-- changed-files:
-  - any-glob-to-any-file: 'pyproject.toml'
-
-Python:
-- changed-files:
-  - all-globs-to-any-file: 
-    - 'invokeai/**'
-    - '!invokeai/frontend/web/**'
-
-PythonTests:
-- changed-files:
-  - any-glob-to-any-file: 'tests/**'
-
-CICD:
-- changed-files:
-  - any-glob-to-any-file: .github/**
-
-Docker:
-- changed-files:
-  - any-glob-to-any-file: docker/**
-
-Installer:
-- changed-files:
-  - any-glob-to-any-file: installer/**
-
-Documentation:
-- changed-files:
-  - any-glob-to-any-file: docs/**
-
-Invocations:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/app/invocations/**'
-
-Backend:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/backend/**'
-
-Api:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/app/api/**'
-
-Services:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/app/services/**'
-
-FrontendDeps:
-- changed-files:
-  - any-glob-to-any-file:
-    - '**/*/package.json'
-    - '**/*/pnpm-lock.yaml'
-
-Frontend:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/frontend/web/**'

.github/workflows/change-monitor.yml

@@ -0,0 +1,29 @@
+name: Trigger Target Workflow
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  trigger:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Trigger Workflow in Another Repository
+        run: |
+          # Set the required variables
+          repo_owner="invoke-ai" 
+          repo_name="Invoke"  
+          event_type="invokeai-pr-merge" 
+          service=${{ github.event.inputs.target_service }}"
+          version="${{ github.event.inputs.target_version }}"
+  
+          curl -L \
+            -X POST \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.PAT }}" \
+            -H "X-GitHub-Api-Version: 2022-11-28" \
+            https://api.github.com/repos/$repo_owner/$repo_name/dispatches \
+            -d "{\"event_type\": \"$event_type\", \"client_payload\": {\"service\": \"$service\", \"version\": \"$version\", \"unit\": false, \"integration\": true}}"

.github/workflows/label-pr.yml

@@ -1,16 +0,0 @@
-name: "Pull Request Labeler"
-on:
-- pull_request_target
-
-jobs:
-  labeler:
-    permissions:
-      contents: read
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - uses: actions/labeler@v5
-        with:
-          configuration-path: .github/pr_labels.yml

README.md

@@ -169,7 +169,7 @@ the command `npm install -g pnpm` if needed)
     _For Linux with an AMD GPU:_
 
     ```sh
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
     ```
 
     _For non-GPU systems:_

docker/Dockerfile

@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.1.2
-ARG TORCHVISION_VERSION=0.16.2
+ARG TORCH_VERSION=2.1.0
+ARG TORCHVISION_VERSION=0.16
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\
@@ -54,7 +54,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
         pip install -e ".[xformers]"; \
     else \
-        pip install $extra_index_url_arg -e "."; \
+        pip install -e "."; \
     fi
 
 # #### Build the Web UI ------------------------------------

docker/README.md

@@ -28,7 +28,7 @@ This is done via Docker Desktop preferences
 
 ### Configure Invoke environment
 
-1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
+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`

docker/run.sh

@@ -21,7 +21,7 @@ run() {
     printf "%s\n" "$build_args"
   fi
 
-  docker compose build $build_args $service_name
+  docker compose build $build_args
   unset build_args
 
   printf "%s\n" "starting service $service_name"

docs/contributing/contribution_guides/contributingToFrontend.md

@@ -0,0 +1,76 @@
+# Contributing to the Frontend
+
+# InvokeAI Web UI
+
+- [InvokeAI Web UI](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#invokeai-web-ui)
+    - [Stack](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#stack)
+    - [Contributing](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#contributing)
+        - [Dev Environment](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#dev-environment)
+        - [Production builds](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#production-builds)
+
+The UI is a fairly straightforward Typescript React app, with the Unified Canvas being more complex.
+
+Code is located in `invokeai/frontend/web/` for review.
+
+## Stack
+
+State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
+
+- `createAsyncThunk` for HTTP requests
+- `createEntityAdapter` for fetching images and models
+- `createListenerMiddleware` for workflows
+
+The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
+
+Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
+
+[Chakra-UI](https://github.com/chakra-ui/chakra-ui) & [Mantine](https://github.com/mantinedev/mantine) for components and styling.
+
+[Konva](https://github.com/konvajs/react-konva) for the canvas, but we are pushing the limits of what is feasible with it (and HTML canvas in general). We plan to rebuild it with [PixiJS](https://github.com/pixijs/pixijs) to take advantage of WebGL's improved raster handling.
+
+[Vite](https://vitejs.dev/) for bundling.
+
+Localisation is via [i18next](https://github.com/i18next/react-i18next), but translation happens on our [Weblate](https://hosted.weblate.org/engage/invokeai/) project. Only the English source strings should be changed on this repo.
+
+## Contributing
+
+Thanks for your interest in contributing to the InvokeAI Web UI!
+
+We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](https://discord.gg/ZmtBAhwWhy) if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
+
+### Dev Environment
+
+**Setup** 
+
+1. Install [node](https://nodejs.org/en/download/). You can confirm node is installed with:
+```bash
+node --version
+```
+
+2. Install [pnpm](https://pnpm.io/) and confirm it is installed by running this:
+```bash
+npm install --global pnpm
+pnpm --version
+```
+
+From `invokeai/frontend/web/` run `pnpm 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`
+3. Start the InvokeAI Nodes backend: `python scripts/invokeai-web.py # run from the repo root`
+4. Point your browser to the dev server address e.g. [http://localhost:5173/](http://localhost:5173/)
+
+### VSCode Remote Dev
+
+We've noticed an intermittent issue with the VSCode Remote Dev port forwarding. If you use this feature of VSCode, you may intermittently click the Invoke button and then get nothing until the request times out. Suggest disabling the IDE's port forwarding feature and doing it manually via SSH:
+
+`ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host`
+
+### Production builds
+
+For a number of technical and logistical reasons, we need to commit UI build artefacts to the repo.
+
+If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
+
+To build for production, run `pnpm build`.

docs/contributing/contribution_guides/development.md

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

docs/features/CONTROLNET.md

@@ -94,8 +94,6 @@ A model that helps generate creative QR codes that still scan. Can also be used
 **Openpose**: 
 The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image. 
 
-*Note:* The DWPose Processor has replaced the OpenPose processor in Invoke. Workflows and generations that relied on the OpenPose Processor will need to be updated to use the DWPose Processor instead.
-
 **Mediapipe Face**:
 
 The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.

docs/index.md

@@ -117,11 +117,6 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 
 ## :octicons-gift-24: InvokeAI Features
 
-### Installation
-  - [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
-  - [Manual Installation](installation/020_INSTALL_MANUAL.md)
-  - [Docker Installation](installation/040_INSTALL_DOCKER.md)
-
 ### The InvokeAI Web Interface
 - [WebUI overview](features/WEB.md)
 - [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)

docs/installation/010_INSTALL_AUTOMATED.md

@@ -477,7 +477,7 @@ Then type the following commands:
 
 === "AMD System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.6
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
     ```
 
 ### Corrupted configuration file

docs/installation/020_INSTALL_MANUAL.md

@@ -154,7 +154,7 @@ manager, please follow these steps:
     === "ROCm (AMD)"
 
         ```bash
-        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
+        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
         ```
 
     === "CPU (Intel Macs & non-GPU systems)"
@@ -230,13 +230,13 @@ manager, please follow these steps:
         === "local Webserver"
 
             ```bash
-            invokeai-web
+            invokeai --web
             ```
 
         === "Public Webserver"
 
             ```bash
-            invokeai-web --host 0.0.0.0
+            invokeai --web --host 0.0.0.0
             ```
 
         === "CLI"
@@ -313,7 +313,7 @@ code for InvokeAI. For this to work, you will need to install the
 on your system, please see the [Git Installation
 Guide](https://github.com/git-guides/install-git)
 
-You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/README.md).
+You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md).
 
 If you have a "normal" installation, you should create a totally separate virtual environment for the git-based installation, else the two may interfere.
 
@@ -345,7 +345,7 @@ installation protocol (important!)
 
     === "ROCm (AMD)"
         ```bash
-        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
+        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
         ```
 
     === "CPU (Intel Macs & non-GPU systems)"
@@ -361,7 +361,7 @@ installation protocol (important!)
     Be sure to pass `-e` (for an editable install) and don't forget the
     dot ("."). It is part of the command.
 
-5.  Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/README.md) and do a production build of the UI as described.
+5.  Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md) and do a production build of the UI as described.
 
 6.  You can now run `invokeai` and its related commands. The code will be
     read from the repository, so that you can edit the .py source files
@@ -402,4 +402,4 @@ environment variable INVOKEAI_ROOT to point to the installation directory.
 Note that if you run into problems with the Conda installation, the InvokeAI
 staff will **not** be able to help you out. Caveat Emptor!
 
-[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
+[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -134,7 +134,7 @@ recipes are available
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/rocm5.6` as described in the [Manual
+https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 This will be done automatically for you if you use the installer

docs/installation/040_INSTALL_DOCKER.md

@@ -69,7 +69,7 @@ a token and copy it, since you will need in for the next step.
 
 ### Setup
 
-Set up your environmnent variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
+Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
 
 Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
 

docs/installation/INSTALLATION.md

@@ -18,18 +18,13 @@ either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
 driver).
 
 
-## **[Automated Installer (Recommended)](010_INSTALL_AUTOMATED.md)**
- ✅ This is the recommended installation method for first-time users. 
+## **[Automated Installer](010_INSTALL_AUTOMATED.md)**
+✅ This is the recommended installation method for first-time users. 
 
   This is a script that will install all of InvokeAI's essential
-  third party libraries and InvokeAI itself.
-
-🖥️ **Download the latest installer .zip file here** : https://github.com/invoke-ai/InvokeAI/releases/latest
-  
-- *Look for the file labelled "InvokeAI-installer-v3.X.X.zip" at the bottom of the page*
-- If you experience issues, read through the full [installation instructions](010_INSTALL_AUTOMATED.md) to make sure you have met all of the installation requirements. If you need more help, join the [Discord](discord.gg/invoke-ai) or create an issue on [Github](https://github.com/invoke-ai/InvokeAI).
-
-
+  third party libraries and InvokeAI itself. It includes access to a
+  "developer console" which will help us debug problems with you and
+  give you to access experimental features.
 
 ## **[Manual Installation](020_INSTALL_MANUAL.md)**
 This method is recommended for experienced users and developers.

docs/nodes/communityNodes.md

@@ -14,7 +14,6 @@ To use a community workflow, download the the `.json` node graph file and load i
 
 - Community Nodes
     + [Adapters-Linked](#adapters-linked-nodes)
-    + [Autostereogram](#autostereogram-nodes)
     + [Average Images](#average-images)
     + [Clean Image Artifacts After Cut](#clean-image-artifacts-after-cut)
     + [Close Color Mask](#close-color-mask) 
@@ -26,7 +25,7 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
     + [Grid to Gif](#grid-to-gif)
     + [Halftone](#halftone)
-    + [Hand Refiner with MeshGraphormer](#hand-refiner-with-meshgraphormer)
+    + [Ideal Size](#ideal-size)
     + [Image and Mask Composition Pack](#image-and-mask-composition-pack)
     + [Image Dominant Color](#image-dominant-color)
     + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
@@ -42,7 +41,6 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
     + [Remote Image](#remote-image)
-    + [BriaAI Background Remove](#briaai-remove-background)
     + [Remove Background](#remove-background)    
     + [Retroize](#retroize)
     + [Size Stepper Nodes](#size-stepper-nodes)
@@ -69,17 +67,6 @@ Note: These are inherited from the core nodes so any update to the core nodes sh
 
 **Node Link:** https://github.com/skunkworxdark/adapters-linked-nodes
 
---------------------------------
-### Autostereogram Nodes
-
-**Description:** Generate autostereogram images from a depth map. This is not a very practically useful node but more a 90s nostalgic indulgence as I used to love these images as a kid.
-
-**Node Link:** https://github.com/skunkworxdark/autostereogram_nodes
-
-**Example Usage:**
-</br>
-<img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider-depth.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-dots.png" width="200" /> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-pattern.png" width="200" />
-
 --------------------------------
 ### Average Images
 
@@ -210,18 +197,13 @@ CMYK Halftone Output:
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
 
 --------------------------------
+### Ideal Size
 
-### Hand Refiner with MeshGraphormer
+**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
 
-**Description**: Hand Refiner takes in your image and automatically generates a fixed depth map for the hands along with a mask of the hands region that will conveniently allow you to use them along with ControlNet to fix the wonky hands generated by Stable Diffusion
-
-**Node Link:** https://github.com/blessedcoolant/invoke_meshgraphormer
-
-**View**
-<img src="https://raw.githubusercontent.com/blessedcoolant/invoke_meshgraphormer/main/assets/preview.jpg" />
+**Node Link:** https://github.com/JPPhoto/ideal-size-node
 
 --------------------------------
-
 ### Image and Mask Composition Pack
 
 **Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
@@ -435,17 +417,6 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 **Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
 
---------------------------------
-
-### BriaAI Remove Background
-
-**Description**: Implements one click background removal with BriaAI's new version 1.4 model which seems to be be producing better results than any other previous background removal tool.
-
-**Node Link:** https://github.com/blessedcoolant/invoke_bria_rmbg
-
-**View**
-<img src="https://raw.githubusercontent.com/blessedcoolant/invoke_bria_rmbg/main/assets/preview.jpg" />
-
 --------------------------------
 ### Remove Background
 

docs/nodes/defaultNodes.md

@@ -36,7 +36,6 @@ their descriptions.
 | Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
 | Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
 | Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
-| Ideal Size                                                    | Calculates an ideal image size for latents for a first pass of a multi-pass upscaling to avoid duplication and other artifacts                       |
 | Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
 | Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
 | Image Primitive                                               | An image primitive value                                                                                                                             |
@@ -81,7 +80,7 @@ their descriptions.
 | ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
 | ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
 | OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |
-| DW Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
+| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
 | PIDI Processor                                                | Applies PIDI processing to image                                                                                                                     |
 | Prompts from File                                             | Loads prompts from a text file                                                                                                                       |
 | Random Integer                                                | Outputs a single random integer.                                                                                                                     |

docs/other/CONTRIBUTORS.md

@@ -13,69 +13,46 @@ We thank them for all of their time and hard work.
 
 - [Lincoln D. Stein](mailto:lincoln.stein@gmail.com)
 
-## **Current Core Team**
+## **Current core team**
 
 * @lstein (Lincoln Stein) - Co-maintainer
 * @blessedcoolant - Co-maintainer
 * @hipsterusername (Kent Keirsey) - Co-maintainer, CEO, Positive Vibes
 * @psychedelicious (Spencer Mabrito) - Web Team Leader
-* @chainchompa (Jennifer Player) - Web Development & Chain-Chomping
-* @josh is toast (Josh Corbett) - Web Development
-* @cheerio (Mary Rogers) - Lead Engineer & Web App Development
+* @Kyle0654 (Kyle Schouviller) - Node Architect and General Backend Wizard
+* @damian0815 - Attention Systems and Compel Maintainer
 * @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
-* @sunija - Standalone version
 * @genomancer (Gregg Helt) - Controlnet support
+* @StAlKeR7779 (Sergey Borisov) - Torch stack, ONNX, model management, optimization
+* @cheerio (Mary Rogers) - Lead Engineer & Web App Development
 * @brandon (Brandon Rising) - Platform, Infrastructure, Backend Systems
 * @ryanjdick (Ryan Dick) - Machine Learning & Training
-* @JPPhoto - Core image generation nodes
-* @dunkeroni - Image generation backend
-* @SkunkWorxDark - Image generation backend
+* @millu (Millun Atluri) - Community Manager, Documentation, Node-wrangler
+* @chainchompa (Jennifer Player) - Web Development & Chain-Chomping
 * @keturn (Kevin Turner) - Diffusers
-* @millu (Millun Atluri) - Community Wizard, Documentation, Node-wrangler, 
-* @glimmerleaf (Devon Hopkins) - Community Wizard
 * @gogurt enjoyer - Discord moderator and end user support
 * @whosawhatsis - Discord moderator and end user support
 * @dwinrger - Discord moderator and end user support
 * @526christian - Discord moderator and end user support
-* @harvester62 -  Discord moderator and end user support
-
-
-## **Honored Team Alumni**
-
-* @StAlKeR7779 (Sergey Borisov) - Torch stack, ONNX, model management, optimization
-* @damian0815 - Attention Systems and Compel Maintainer
-* @netsvetaev (Artur) - Localization support
-* @Kyle0654 (Kyle Schouviller) - Node Architect and General Backend Wizard
-* @tildebyte - Installation and configuration
-* @mauwii (Matthias Wilde) - Installation, release, continuous integration
-
 
 ## **Full List of Contributors by Commit Name**
 
-- 이승석
 - AbdBarho
 - ablattmann
 - AdamOStark
 - Adam Rice
 - Airton Silva
-- Aldo Hoeben
 - Alexander Eichhorn
 - Alexandre D. Roberge
-- Alexandre Macabies
-- Alfie John
 - Andreas Rozek
 - Andre LaBranche
 - Andy Bearman
 - Andy Luhrs
 - Andy Pilate
-- Anonymous
-- Anthony Monthe
 - Any-Winter-4079
 - apolinario
-- Ar7ific1al
 - ArDiouscuros
 - Armando C. Santisbon
-- Arnold Cordewiner
 - Arthur Holstvoogd
 - artmen1516
 - Artur
@@ -87,16 +64,13 @@ We thank them for all of their time and hard work.
 - blhook
 - BlueAmulet
 - Bouncyknighter
-- Brandon
 - Brandon Rising
 - Brent Ozar
 - Brian Racer
 - bsilvereagle
 - c67e708d
-- camenduru
 - CapableWeb
 - Carson Katri
-- chainchompa
 - Chloe
 - Chris Dawson
 - Chris Hayes
@@ -112,45 +86,30 @@ We thank them for all of their time and hard work.
 - cpacker
 - Cragin Godley
 - creachec
-- CrypticWit
-- d8ahazard
-- damian
-- damian0815
-- Damian at mba
 - Damian Stewart
 - Daniel Manzke
 - Danny Beer
 - Dan Sully
-- Darren Ringer
 - David Burnett
 - David Ford
 - David Regla
-- David Sisco
 - David Wager
 - Daya Adianto
 - db3000
-- DekitaRPG
 - Denis Olshin
 - Dennis
-- dependabot[bot]
-- Dmitry Parnas
-- Dobrynia100
 - Dominic Letz
 - DrGunnarMallon
-- Drun555
-- dunkeroni
 - Edward Johan
 - elliotsayes
 - Elrik
 - ElrikUnderlake
 - Eric Khun
 - Eric Wolf
-- Eugene
 - Eugene Brodsky
 - ExperimentalCyborg
 - Fabian Bahl
 - Fabio 'MrWHO' Torchetti
-- Fattire
 - fattire
 - Felipe Nogueira
 - Félix Sanz
@@ -159,12 +118,8 @@ We thank them for all of their time and hard work.
 - gabrielrotbart
 - gallegonovato
 - Gérald LONLAS
-- Gille
 - GitHub Actions Bot
-- glibesyck
 - gogurtenjoyer
-- Gohsuke Shimada
-- greatwolf
 - greentext2
 - Gregg Helt
 - H4rk
@@ -176,7 +131,6 @@ We thank them for all of their time and hard work.
 - Hosted Weblate
 - Iman Karim
 - ismail ihsan bülbül
-- ItzAttila
 - Ivan Efimov
 - jakehl
 - Jakub Kolčář
@@ -187,7 +141,6 @@ We thank them for all of their time and hard work.
 - Jason Toffaletti
 - Jaulustus
 - Jeff Mahoney
-- Jennifer Player
 - jeremy
 - Jeremy Clark
 - JigenD
@@ -195,26 +148,19 @@ We thank them for all of their time and hard work.
 - Johan Roxendal
 - Johnathon Selstad
 - Jonathan
-- Jordan Hewitt
 - Joseph Dries III
-- Josh Corbett
 - JPPhoto
 - jspraul
-- junzi
 - Justin Wong
 - Juuso V
 - Kaspar Emanuel
 - Katsuyuki-Karasawa
-- Keerigan45
 - Kent Keirsey
-- Kevin Brack
 - Kevin Coakley
 - Kevin Gibbons
 - Kevin Schaul
 - Kevin Turner
-- Kieran Klaassen
 - krummrey
-- Kyle
 - Kyle Lacy
 - Kyle Schouviller
 - Lawrence Norton
@@ -225,15 +171,10 @@ We thank them for all of their time and hard work.
 - Lynne Whitehorn
 - majick
 - Marco Labarile
-- Marta Nahorniuk
 - Martin Kristiansen
-- Mary Hipp
-- maryhipp
 - Mary Hipp Rogers
-- mastercaster
 - mastercaster9000
 - Matthias Wild
-- mauwii
 - michaelk71
 - mickr777
 - Mihai
@@ -241,15 +182,11 @@ We thank them for all of their time and hard work.
 - Mikhail Tishin
 - Millun Atluri
 - Minjune Song
-- Mitchell Allain
 - mitien
 - mofuzz
 - Muhammad Usama
 - Name
 - _nderscore
-- Neil Wang
-- nekowaiz
-- nemuruibai
 - Netzer R
 - Nicholas Koh
 - Nicholas Körfer
@@ -260,11 +197,9 @@ We thank them for all of their time and hard work.
 - ofirkris
 - Olivier Louvignes
 - owenvincent
-- pand4z31
 - Patrick Esser
 - Patrick Tien
 - Patrick von Platen
-- Paul Curry
 - Paul Sajna
 - pejotr
 - Peter Baylies
@@ -272,7 +207,6 @@ We thank them for all of their time and hard work.
 - plucked
 - prixt
 - psychedelicious
-- psychedelicious@windows
 - Rainer Bernhardt
 - Riccardo Giovanetti
 - Rich Jones
@@ -281,22 +215,17 @@ We thank them for all of their time and hard work.
 - Robert Bolender
 - Robin Rombach
 - Rohan Barar
-- Rohinish
+- rohinish404
 - rpagliuca
 - rromb
 - Rupesh Sreeraman
-- Ryan
 - Ryan Cao
-- Ryan Dick
 - Saifeddine
 - Saifeddine ALOUI
-- Sam
 - SammCheese
-- Sam McLeod
 - Sammy
 - sammyf
 - Samuel Husso
-- Saurav Maheshkar
 - Scott Lahteine
 - Sean McLellan
 - Sebastian Aigner
@@ -304,21 +233,16 @@ We thank them for all of their time and hard work.
 - Sergey Krashevich
 - Shapor Naghibzadeh
 - Shawn Zhong
-- Simona Liliac
 - Simon Vans-Colina
 - skunkworxdark
 - slashtechno
-- SoheilRezaei
-- Song, Pengcheng
 - spezialspezial
 - ssantos
 - StAlKeR7779
-- Stefan Tobler
 - Stephan Koglin-Fischer
 - SteveCaruso
 - Steve Martinelli
 - Steven Frank
-- Surisen
 - System X - Files
 - Taylor Kems
 - techicode
@@ -337,34 +261,26 @@ We thank them for all of their time and hard work.
 - tyler
 - unknown
 - user1
-- vedant-3010
 - Vedant Madane
 - veprogames
 - wa.code
 - wfng92
-- whjms
 - whosawhatsis
 - Will
 - William Becher
 - William Chong
-- Wilson E. Alvarez
-- woweenie
-- Wubbbi
 - xra
 - Yeung Yiu Hung
 - ymgenesis
 - Yorzaren
 - Yosuke Shinya
 - yun saki
-- ZachNagengast
 - Zadagu
 - zeptofine
-- Zerdoumi
-- Васянатор
 - 冯不游
 - 唐澤 克幸
 
-## **Original CompVis (Stable Diffusion) Authors**
+## **Original CompVis Authors**
 
 - [Robin Rombach](https://github.com/rromb)
 - [Patrick von Platen](https://github.com/patrickvonplaten)

installer/create_installer.sh

@@ -14,19 +14,11 @@ function is_bin_in_path {
 }
 
 function git_show {
-    git show -s --format=oneline --abbrev-commit "$1" | cat
+    git show -s --format='%h %s' $1
 }
 
-if [[ -v "VIRTUAL_ENV" ]]; then
-    # we can't just call 'deactivate' because this function is not exported
-    # to the environment of this script from the bash process that runs the script
-    echo -e "${BRED}A virtual environment is activated. Please deactivate it before proceeding.${RESET}"
-    exit -1
-fi
-
 cd "$(dirname "$0")"
 
-echo
 echo -e "${BYELLOW}This script must be run from the installer directory!${RESET}"
 echo "The current working directory is $(pwd)"
 read -p "If that looks right, press any key to proceed, or CTRL-C to exit..."
@@ -40,6 +32,13 @@ if ! is_bin_in_path python && is_bin_in_path python3; then
     }
 fi
 
+if [[ -v "VIRTUAL_ENV" ]]; then
+    # we can't just call 'deactivate' because this function is not exported
+    # to the environment of this script from the bash process that runs the script
+    echo -e "${BRED}A virtual environment is activated. Please deactivate it before proceeding.${RESET}"
+    exit -1
+fi
+
 VERSION=$(
     cd ..
     python -c "from invokeai.version import __version__ as version; print(version)"
@@ -48,9 +47,38 @@ PATCH=""
 VERSION="v${VERSION}${PATCH}"
 
 echo -e "${BGREEN}HEAD${RESET}:"
-git_show HEAD
+git_show
 echo
 
+# ---------------------- FRONTEND ----------------------
+
+pushd ../invokeai/frontend/web >/dev/null
+echo
+echo "Installing frontend dependencies..."
+echo
+pnpm i --frozen-lockfile
+echo
+echo "Building frontend..."
+echo
+pnpm build
+popd
+
+# ---------------------- BACKEND ----------------------
+
+echo
+echo "Building wheel..."
+echo
+
+# install the 'build' package in the user site packages, if needed
+# could be improved by using a temporary venv, but it's tiny and harmless
+if [[ $(python -c 'from importlib.util import find_spec; print(find_spec("build") is None)') == "True" ]]; then
+    pip install --user build
+fi
+
+rm -rf ../build
+
+python -m build --wheel --outdir dist/ ../.
+
 # ----------------------
 
 echo
@@ -69,13 +97,16 @@ done
 mkdir InvokeAI-Installer/lib
 cp lib/*.py InvokeAI-Installer/lib
 
+# Move the wheel
+mv dist/*.whl InvokeAI-Installer/lib/
+
 # Install scripts
 # Mac/Linux
 cp install.sh.in InvokeAI-Installer/install.sh
 chmod a+x InvokeAI-Installer/install.sh
 
 # Windows
-cp install.bat.in InvokeAI-Installer/install.bat
+perl -p -e "s/^set INVOKEAI_VERSION=.*/set INVOKEAI_VERSION=$VERSION/" install.bat.in >InvokeAI-Installer/install.bat
 cp WinLongPathsEnabled.reg InvokeAI-Installer/
 
 # Zip everything up

installer/install.bat.in

@@ -15,6 +15,7 @@ if "%1" == "use-cache" (
 @rem Config
 @rem The version in the next line is replaced by an up to date release number
 @rem when create_installer.sh is run. Change the release number there.
+set INVOKEAI_VERSION=latest
 set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
 set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
 set PYTHON_URL=https://www.python.org/downloads/windows/

installer/lib/installer.py

@@ -11,7 +11,7 @@ import sys
 import venv
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Optional, Tuple
+from typing import Union
 
 SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100"
 INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
@@ -21,20 +21,40 @@ OS = platform.uname().system
 ARCH = platform.uname().machine
 VERSION = "latest"
 
+### Feature flags
+# Install the virtualenv into the runtime dir
+FF_VENV_IN_RUNTIME = True
+
+# Install the wheel packaged with the installer
+FF_USE_LOCAL_WHEEL = True
+
 
 class Installer:
     """
     Deploys an InvokeAI installation into a given path
     """
 
-    reqs: list[str] = INSTALLER_REQS
-
     def __init__(self) -> None:
+        self.reqs = INSTALLER_REQS
+        self.preflight()
         if os.getenv("VIRTUAL_ENV") is not None:
             print("A virtual environment is already activated. Please 'deactivate' before installation.")
             sys.exit(-1)
         self.bootstrap()
-        self.available_releases = get_github_releases()
+
+    def preflight(self) -> None:
+        """
+        Preflight checks
+        """
+
+        # TODO
+        # verify python version
+        # on macOS verify XCode tools are present
+        # verify libmesa, libglx on linux
+        # check that the system arch is not i386 (?)
+        # check that the system has a GPU, and the type of GPU
+
+        pass
 
     def mktemp_venv(self) -> TemporaryDirectory:
         """
@@ -58,9 +78,12 @@ class Installer:
 
         return venv_dir
 
-    def bootstrap(self, verbose: bool = False) -> TemporaryDirectory | None:
+    def bootstrap(self, verbose: bool = False) -> TemporaryDirectory:
         """
         Bootstrap the installer venv with packages required at install time
+
+        :return: path to the virtual environment directory that was bootstrapped
+        :rtype: TemporaryDirectory
         """
 
         print("Initializing the installer. This may take a minute - please wait...")
@@ -72,27 +95,39 @@ class Installer:
         cmd.extend(self.reqs)
 
         try:
-            # upgrade pip to the latest version to avoid a confusing message
-            res = upgrade_pip(Path(venv_dir.name))
-            if verbose:
-                print(res)
-
-            # run the install prerequisites installation
             res = subprocess.check_output(cmd).decode()
-
             if verbose:
                 print(res)
-
             return venv_dir
         except subprocess.CalledProcessError as e:
             print(e)
 
-    def app_venv(self, venv_parent) -> Path:
+    def app_venv(self, path: str = None):
         """
         Create a virtualenv for the InvokeAI installation
         """
 
-        venv_dir = venv_parent / ".venv"
+        # explicit venv location
+        # currently unused in normal operation
+        # useful for testing or special cases
+        if path is not None:
+            venv_dir = Path(path)
+
+        # experimental / testing
+        elif not FF_VENV_IN_RUNTIME:
+            if OS == "Windows":
+                venv_dir_parent = os.getenv("APPDATA", "~/AppData/Roaming")
+            elif OS == "Darwin":
+                # there is no environment variable on macOS to find this
+                # TODO: confirm this is working as expected
+                venv_dir_parent = "~/Library/Application Support"
+            elif OS == "Linux":
+                venv_dir_parent = os.getenv("XDG_DATA_DIR", "~/.local/share")
+            venv_dir = Path(venv_dir_parent).expanduser().resolve() / f"InvokeAI/{VERSION}/venv"
+
+        # stable / current
+        else:
+            venv_dir = self.dest / ".venv"
 
         # Prefer to copy python executables
         # so that updates to system python don't break InvokeAI
@@ -106,7 +141,7 @@ class Installer:
         return venv_dir
 
     def install(
-        self, version=None, root: str = "~/invokeai", yes_to_all=False, find_links: Optional[Path] = None
+        self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
     ) -> None:
         """
         Install the InvokeAI application into the given runtime path
@@ -123,20 +158,15 @@ class Installer:
 
         import messages
 
-        messages.welcome(self.available_releases)
+        messages.welcome()
 
-        version = messages.choose_version(self.available_releases)
-
-        auto_dest = Path(os.environ.get("INVOKEAI_ROOT", root)).expanduser().resolve()
-        destination = auto_dest if yes_to_all else messages.dest_path(root)
-        if destination is None:
-            print("Could not find or create the destination directory. Installation cancelled.")
-            sys.exit(0)
+        default_path = os.environ.get("INVOKEAI_ROOT") or Path(root).expanduser().resolve()
+        self.dest = default_path if yes_to_all else messages.dest_path(root)
 
         # create the venv for the app
-        self.venv = self.app_venv(venv_parent=destination)
+        self.venv = self.app_venv()
 
-        self.instance = InvokeAiInstance(runtime=destination, venv=self.venv, version=version)
+        self.instance = InvokeAiInstance(runtime=self.dest, venv=self.venv, version=version)
 
         # install dependencies and the InvokeAI application
         (extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
@@ -160,7 +190,7 @@ class InvokeAiInstance:
     A single runtime directory *may* be shared by multiple virtual environments, though this isn't currently tested or supported.
     """
 
-    def __init__(self, runtime: Path, venv: Path, version: str = "stable") -> None:
+    def __init__(self, runtime: Path, venv: Path, version: str) -> None:
         self.runtime = runtime
         self.venv = venv
         self.pip = get_pip_from_venv(venv)
@@ -169,7 +199,6 @@ class InvokeAiInstance:
         set_sys_path(venv)
         os.environ["INVOKEAI_ROOT"] = str(self.runtime.expanduser().resolve())
         os.environ["VIRTUAL_ENV"] = str(self.venv.expanduser().resolve())
-        upgrade_pip(venv)
 
     def get(self) -> tuple[Path, Path]:
         """
@@ -183,7 +212,54 @@ class InvokeAiInstance:
 
     def install(self, extra_index_url=None, optional_modules=None, find_links=None):
         """
-        Install the package from PyPi.
+        Install this instance, including dependencies and the app itself
+
+        :param extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
+        :type extra_index_url: str
+        """
+
+        import messages
+
+        # install torch first to ensure the correct version gets installed.
+        # works with either source or wheel install with negligible impact on installation times.
+        messages.simple_banner("Installing PyTorch :fire:")
+        self.install_torch(extra_index_url, find_links)
+
+        messages.simple_banner("Installing the InvokeAI Application :art:")
+        self.install_app(extra_index_url, optional_modules, find_links)
+
+    def install_torch(self, extra_index_url=None, find_links=None):
+        """
+        Install PyTorch
+        """
+
+        from plumbum import FG, local
+
+        pip = local[self.pip]
+
+        (
+            pip[
+                "install",
+                "--require-virtualenv",
+                "numpy==1.26.3",  # choose versions that won't be uninstalled during phase 2
+                "urllib3~=1.26.0",
+                "requests~=2.28.0",
+                "torch==2.1.2",
+                "torchmetrics==0.11.4",
+                "torchvision==0.16.2",
+                "--force-reinstall",
+                "--find-links" if find_links is not None else None,
+                find_links,
+                "--extra-index-url" if extra_index_url is not None else None,
+                extra_index_url,
+            ]
+            & FG
+        )
+
+    def install_app(self, extra_index_url=None, optional_modules=None, find_links=None):
+        """
+        Install the application with pip.
+        Supports installation from PyPi or from a local source directory.
 
         :param extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
         :type extra_index_url: str
@@ -195,52 +271,53 @@ class InvokeAiInstance:
         :type find_links: Path
         """
 
-        import messages
-
-        # not currently used, but may be useful for "install most recent version" option
-        if self.version == "prerelease":
+        ## this only applies to pypi installs; TODO actually use this
+        if self.version == "pre":
             version = None
-            pre_flag = "--pre"
-        elif self.version == "stable":
-            version = None
-            pre_flag = None
+            pre = "--pre"
         else:
             version = self.version
-            pre_flag = None
+            pre = None
 
-        src = "invokeai"
-        if optional_modules:
-            src += optional_modules
-        if version:
-            src += f"=={version}"
+        ## TODO: only local wheel will be installed as of now; support for --version arg is TODO
+        if FF_USE_LOCAL_WHEEL:
+            # if no wheel, try to do a source install before giving up
+            try:
+                src = str(next(Path(__file__).parent.glob("InvokeAI-*.whl")))
+            except StopIteration:
+                try:
+                    src = Path(__file__).parents[1].expanduser().resolve()
+                    # if the above directory contains one of these files, we'll do a source install
+                    next(src.glob("pyproject.toml"))
+                    next(src.glob("invokeai"))
+                except StopIteration:
+                    print("Unable to find a wheel or perform a source install. Giving up.")
 
-        messages.simple_banner("Installing the InvokeAI Application :art:")
+        elif version == "source":
+            # this makes an assumption about the location of the installer package in the source tree
+            src = Path(__file__).parents[1].expanduser().resolve()
+        else:
+            # will install from PyPi
+            src = f"invokeai=={version}" if version is not None else "invokeai"
 
-        from plumbum import FG, ProcessExecutionError, local  # type: ignore
+        from plumbum import FG, local
 
         pip = local[self.pip]
 
-        pipeline = pip[
-            "install",
-            "--require-virtualenv",
-            "--force-reinstall",
-            "--use-pep517",
-            str(src),
-            "--find-links" if find_links is not None else None,
-            find_links,
-            "--extra-index-url" if extra_index_url is not None else None,
-            extra_index_url,
-            pre_flag,
-        ]
-
-        try:
-            _ = pipeline & FG
-        except ProcessExecutionError as e:
-            print(f"Error: {e}")
-            print(
-                "Could not install InvokeAI. Please try downloading the latest version of the installer and install again."
-            )
-            sys.exit(1)
+        (
+            pip[
+                "install",
+                "--require-virtualenv",
+                "--use-pep517",
+                str(src) + (optional_modules if optional_modules else ""),
+                "--find-links" if find_links is not None else None,
+                find_links,
+                "--extra-index-url" if extra_index_url is not None else None,
+                extra_index_url,
+                pre,
+            ]
+            & FG
+        )
 
     def configure(self):
         """
@@ -296,6 +373,7 @@ class InvokeAiInstance:
 
         ext = "bat" if OS == "Windows" else "sh"
 
+        # scripts = ['invoke', 'update']
         scripts = ["invoke"]
 
         for script in scripts:
@@ -330,23 +408,6 @@ def get_pip_from_venv(venv_path: Path) -> str:
     return str(venv_path.expanduser().resolve() / pip)
 
 
-def upgrade_pip(venv_path: Path) -> str | None:
-    """
-    Upgrade the pip executable in the given virtual environment
-    """
-
-    python = "Scripts\\python.exe" if OS == "Windows" else "bin/python"
-    python = str(venv_path.expanduser().resolve() / python)
-
-    try:
-        result = subprocess.check_output([python, "-m", "pip", "install", "--upgrade", "pip"]).decode()
-    except subprocess.CalledProcessError as e:
-        print(e)
-        result = None
-
-    return result
-
-
 def set_sys_path(venv_path: Path) -> None:
     """
     Given a path to a virtual environment, set the sys.path, in a cross-platform fashion,
@@ -370,43 +431,7 @@ def set_sys_path(venv_path: Path) -> None:
     sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
 
 
-def get_github_releases() -> tuple[list, list] | None:
-    """
-    Query Github for published (pre-)release versions.
-    Return a tuple where the first element is a list of stable releases and the second element is a list of pre-releases.
-    Return None if the query fails for any reason.
-    """
-
-    import requests
-
-    ## get latest releases using github api
-    url = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
-    releases, pre_releases = [], []
-    try:
-        res = requests.get(url)
-        res.raise_for_status()
-        tag_info = res.json()
-        for tag in tag_info:
-            if not tag["prerelease"]:
-                releases.append(tag["tag_name"].lstrip("v"))
-            else:
-                pre_releases.append(tag["tag_name"].lstrip("v"))
-    except requests.HTTPError as e:
-        print(f"Error: {e}")
-        print("Could not fetch version information from GitHub. Please check your network connection and try again.")
-        return
-    except Exception as e:
-        print(f"Error: {e}")
-        print("An unexpected error occurred while trying to fetch version information from GitHub. Please try again.")
-        return
-
-    releases.sort(reverse=True)
-    pre_releases.sort(reverse=True)
-
-    return releases, pre_releases
-
-
-def get_torch_source() -> Tuple[str | None, str | None]:
+def get_torch_source() -> (Union[str, None], str):
     """
     Determine the extra index URL for pip to use for torch installation.
     This depends on the OS and the graphics accelerator in use.
@@ -421,26 +446,25 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     :rtype: list
     """
 
-    from messages import select_gpu
+    from messages import graphical_accelerator
 
-    # device can be one of: "cuda", "rocm", "cpu", "cuda_and_dml, autodetect"
-    device = select_gpu()
+    # device can be one of: "cuda", "rocm", "cpu", "idk"
+    device = graphical_accelerator()
 
     url = None
     optional_modules = "[onnx]"
     if OS == "Linux":
-        if device.value == "rocm":
-            url = "https://download.pytorch.org/whl/rocm5.6"
-        elif device.value == "cpu":
+        if device == "rocm":
+            url = "https://download.pytorch.org/whl/rocm5.4.2"
+        elif device == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 
-    elif OS == "Windows":
-        if device.value == "cuda":
-            url = "https://download.pytorch.org/whl/cu121"
-            optional_modules = "[xformers,onnx-cuda]"
-        if device.value == "cuda_and_dml":
-            url = "https://download.pytorch.org/whl/cu121"
-            optional_modules = "[xformers,onnx-directml]"
+    if device == "cuda":
+        url = "https://download.pytorch.org/whl/cu121"
+        optional_modules = "[xformers,onnx-cuda]"
+    if device == "cuda_and_dml":
+        url = "https://download.pytorch.org/whl/cu121"
+        optional_modules = "[xformers,onnx-directml]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
 

installer/lib/messages.py

@@ -5,11 +5,10 @@ Installer user interaction
 
 import os
 import platform
-from enum import Enum
 from pathlib import Path
 
 from prompt_toolkit import HTML, prompt
-from prompt_toolkit.completion import FuzzyWordCompleter, PathCompleter
+from prompt_toolkit.completion import PathCompleter
 from prompt_toolkit.validation import Validator
 from rich import box, print
 from rich.console import Console, Group, group
@@ -36,26 +35,16 @@ else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
 
-def welcome(available_releases: tuple | None = None) -> None:
+def welcome():
     @group()
     def text():
-        if (platform_specific := _platform_specific_help()) is not None:
+        if (platform_specific := _platform_specific_help()) != "":
             yield platform_specific
             yield ""
         yield Text.from_markup(
             "Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
             justify="center",
         )
-        if available_releases is not None:
-            latest_stable = available_releases[0][0]
-            last_pre = available_releases[1][0]
-            yield ""
-            yield Text.from_markup(
-                f"[red3]🠶[/] Latest stable release (recommended): [b bright_white]{latest_stable}", justify="center"
-            )
-            yield Text.from_markup(
-                f"[red3]🠶[/] Last published pre-release version: [b bright_white]{last_pre}", justify="center"
-            )
 
     console.rule()
     print(
@@ -72,30 +61,19 @@ def welcome(available_releases: tuple | None = None) -> None:
     console.line()
 
 
-def choose_version(available_releases: tuple | None = None) -> str:
-    """
-    Prompt the user to choose an Invoke version to install
-    """
-
-    # short circuit if we couldn't get a version list
-    # still try to install the latest stable version
-    if available_releases is None:
-        return "stable"
-
-    console.print(":grey_question: [orange3]Please choose an Invoke version to install.")
-
-    choices = available_releases[0] + available_releases[1]
-
-    response = prompt(
-        message=f"   <Enter> to install the recommended release ({choices[0]}). <Tab> or type to pick a version: ",
-        complete_while_typing=True,
-        completer=FuzzyWordCompleter(choices),
-    )
-    console.print(f"   Version {choices[0] if response == '' else response} will be installed.")
-
+def confirm_install(dest: Path) -> bool:
+    if dest.exists():
+        print(f":exclamation: Directory {dest} already exists :exclamation:")
+        dest_confirmed = Confirm.ask(
+            ":stop_sign: (re)install in this location?",
+            default=False,
+        )
+    else:
+        print(f"InvokeAI will be installed in {dest}")
+        dest_confirmed = Confirm.ask("Use this location?", default=True)
     console.line()
 
-    return "stable" if response == "" else response
+    return dest_confirmed
 
 
 def user_wants_auto_configuration() -> bool:
@@ -131,23 +109,7 @@ def user_wants_auto_configuration() -> bool:
     return choice.lower().startswith("a")
 
 
-def confirm_install(dest: Path) -> bool:
-    if dest.exists():
-        print(f":stop_sign: Directory {dest} already exists!")
-        print("   Is this location correct?")
-        default = False
-    else:
-        print(f":file_folder: InvokeAI will be installed in {dest}")
-        default = True
-
-    dest_confirmed = Confirm.ask("   Please confirm:", default=default)
-
-    console.line()
-
-    return dest_confirmed
-
-
-def dest_path(dest=None) -> Path | None:
+def dest_path(dest=None) -> Path:
     """
     Prompt the user for the destination path and create the path
 
@@ -162,21 +124,25 @@ def dest_path(dest=None) -> Path | None:
     else:
         dest = Path.cwd().expanduser().resolve()
     prev_dest = init_path = dest
-    dest_confirmed = False
+
+    dest_confirmed = confirm_install(dest)
 
     while not dest_confirmed:
-        browse_start = (dest or Path.cwd()).expanduser().resolve()
+        # if the given destination already exists, the starting point for browsing is its parent directory.
+        # the user may have made a typo, or otherwise wants to place the root dir next to an existing one.
+        # if the destination dir does NOT exist, then the user must have changed their mind about the selection.
+        # since we can't read their mind, start browsing at Path.cwd().
+        browse_start = (prev_dest.parent if prev_dest.exists() else Path.cwd()).expanduser().resolve()
 
         path_completer = PathCompleter(
             only_directories=True,
             expanduser=True,
-            get_paths=lambda: [str(browse_start)],  # noqa: B023
+            get_paths=lambda: [browse_start],  # noqa: B023
             # get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
         )
 
         console.line()
-
-        console.print(f":grey_question: [orange3]Please select the install destination:[/] \\[{browse_start}]: ")
+        console.print(f"[orange3]Please select the destination directory for the installation:[/] \\[{browse_start}]: ")
         selected = prompt(
             ">>> ",
             complete_in_thread=True,
@@ -189,7 +155,6 @@ def dest_path(dest=None) -> Path | None:
         )
         prev_dest = dest
         dest = Path(selected)
-
         console.line()
 
         dest_confirmed = confirm_install(dest.expanduser().resolve())
@@ -217,45 +182,41 @@ def dest_path(dest=None) -> Path | None:
         console.rule("Goodbye!")
 
 
-class GpuType(Enum):
-    CUDA = "cuda"
-    CUDA_AND_DML = "cuda_and_dml"
-    ROCM = "rocm"
-    CPU = "cpu"
-    AUTODETECT = "autodetect"
-
-
-def select_gpu() -> GpuType:
+def graphical_accelerator():
     """
-    Prompt the user to select the GPU driver
+    Prompt the user to select the graphical accelerator in their system
+    This does not validate user's choices (yet), but only offers choices
+    valid for the platform.
+    CUDA is the fallback.
+    We may be able to detect the GPU driver by shelling out to `modprobe` or `lspci`,
+    but this is not yet supported or reliable. Also, some users may have exotic preferences.
     """
 
     if ARCH == "arm64" and OS != "Darwin":
         print(f"Only CPU acceleration is available on {ARCH} architecture. Proceeding with that.")
-        return GpuType.CPU
+        return "cpu"
 
     nvidia = (
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
-        GpuType.CUDA,
+        "cuda",
     )
     nvidia_with_dml = (
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX) -- ALPHA",
-        GpuType.CUDA_AND_DML,
+        "cuda_and_dml",
     )
     amd = (
         "an [gold1 b]AMD[/] GPU (using ROCm™)",
-        GpuType.ROCM,
+        "rocm",
     )
     cpu = (
-        "Do not install any GPU support, use CPU for generation (slow)",
-        GpuType.CPU,
+        "no compatible GPU, or specifically prefer to use the CPU",
+        "cpu",
     )
-    autodetect = (
+    idk = (
         "I'm not sure what to choose",
-        GpuType.AUTODETECT,
+        "idk",
     )
 
-    options = []
     if OS == "Windows":
         options = [nvidia, nvidia_with_dml, cpu]
     if OS == "Linux":
@@ -269,7 +230,7 @@ def select_gpu() -> GpuType:
         return options[0][1]
 
     # "I don't know" is always added the last option
-    options.append(autodetect)  # type: ignore
+    options.append(idk)
 
     options = {str(i): opt for i, opt in enumerate(options, 1)}
 
@@ -304,9 +265,9 @@ def select_gpu() -> GpuType:
         ),
     )
 
-    if options[choice][1] is GpuType.AUTODETECT:
+    if options[choice][1] == "idk":
         console.print(
-            "No problem. We will install CUDA support first :crossed_fingers: If Invoke does not detect a GPU, please re-run the installer and select one of the other GPU types."
+            "No problem. We will try to install a version that [i]should[/i] be compatible. :crossed_fingers:"
         )
 
     return options[choice][1]
@@ -330,7 +291,7 @@ def windows_long_paths_registry() -> None:
     """
 
     with open(str(Path(__file__).parent / "WinLongPathsEnabled.reg"), "r", encoding="utf-16le") as code:
-        syntax = Syntax(code.read(), line_numbers=True, lexer="regedit")
+        syntax = Syntax(code.read(), line_numbers=True)
 
     console.print(
         Panel(
@@ -340,7 +301,7 @@ def windows_long_paths_registry() -> None:
                         "We will now apply a registry fix to enable long paths on Windows. InvokeAI needs this to function correctly. We are asking your permission to modify the Windows Registry on your behalf.",
                         "",
                         "This is the change that will be applied:",
-                        str(syntax),
+                        syntax,
                     ]
                 )
             ),
@@ -379,7 +340,7 @@ def introduction() -> None:
     console.line(2)
 
 
-def _platform_specific_help() -> Text | None:
+def _platform_specific_help() -> str:
     if OS == "Darwin":
         text = Text.from_markup(
             """[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
@@ -393,5 +354,5 @@ def _platform_specific_help() -> Text | None:
      [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
         )
     else:
-        return
+        text = ""
     return text

installer/templates/invoke.bat.in

@@ -15,7 +15,7 @@ echo 4. Download and install models
 echo 5. Change InvokeAI startup options
 echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade
 echo 7. Open the developer console
-echo 8. Update InvokeAI (DEPRECATED - please use the installer)
+echo 8. Update InvokeAI
 echo 9. Run the InvokeAI image database maintenance script
 echo 10. Command-line help
 echo Q - Quit
@@ -52,10 +52,8 @@ IF /I "%choice%" == "1" (
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
 ) ELSE IF /I "%choice%" == "8" (
-    echo UPDATING FROM WITHIN THE APP IS BEING DEPRECATED.
-    echo Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.
-    timeout 4
-    python -m invokeai.frontend.install.invokeai_update
+   echo Running invokeai-update...
+   python -m invokeai.frontend.install.invokeai_update
 ) ELSE IF /I "%choice%" == "9" (
    echo Running the db maintenance script...
    python .venv\Scripts\invokeai-db-maintenance.exe
@@ -79,3 +77,4 @@ pause
 
 :ending
 exit /b
+

installer/templates/invoke.sh.in

@@ -90,9 +90,7 @@ do_choice() {
         ;;
     8)
         clear
-        printf "UPDATING FROM WITHIN THE APP IS BEING DEPRECATED\n"
-        printf "Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.\n"
-        sleep 4
+        printf "Update InvokeAI\n"
         python -m invokeai.frontend.install.invokeai_update
         ;;
     9)
@@ -124,7 +122,7 @@ do_dialog() {
         5 "Change InvokeAI startup options"
         6 "Re-run the configure script to fix a broken install or to complete a major upgrade"
         7 "Open the developer console"
-        8 "Update InvokeAI (DEPRECATED - please use the installer)"
+        8 "Update InvokeAI"
 	9 "Run the InvokeAI image database maintenance script"
 	10 "Command-line help"
     )

installer/templates/update.bat.in

@@ -0,0 +1,72 @@
+@echo off
+setlocal EnableExtensions EnableDelayedExpansion
+
+PUSHD "%~dp0"
+
+set INVOKE_AI_VERSION=latest
+set arg=%1
+if "%arg%" neq "" (
+   if "%arg:~0,2%" equ "/?" (
+       echo Usage: update.bat ^<release name or branch^>
+       echo Updates InvokeAI to use the indicated version of the code base.
+       echo Find the version or branch for the release you want, and pass it as the argument.
+       echo For example '.\update.bat v2.2.5' for release 2.2.5.
+       echo             '.\update.bat main' for the latest development version
+       echo.
+       echo If no argument provided then will install the most recent release, equivalent to
+       echo '.\update.bat latest'
+       exit /b
+   ) else (
+       set INVOKE_AI_VERSION=%arg%
+   )
+)
+
+set INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/!INVOKE_AI_VERSION!.zip"
+set INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/!INVOKE_AI_VERSION!/environments-and-requirements/requirements-base.txt
+set INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
+
+call curl -I "%INVOKE_AI_DEP%" -fs >.tmp.out
+if %errorlevel% neq 0 (
+    echo '!INVOKE_AI_VERSION!' is not a known branch name or tag. Please check the version and try again.
+    echo "Press any key to continue"
+    pause
+    exit /b
+)
+del .tmp.out
+
+echo This script will update InvokeAI and all its dependencies to !INVOKE_AI_SRC!.
+echo If you do not want to do this, press control-C now!
+pause
+
+call curl -L "%INVOKE_AI_DEP%" > environments-and-requirements/requirements-base.txt
+call curl -L "%INVOKE_AI_MODELS%" > configs/INITIAL_MODELS.yaml
+
+
+call .venv\Scripts\activate.bat
+call .venv\Scripts\python -mpip install -r requirements.txt
+if %errorlevel% neq 0 (
+   echo Installation of requirements failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
+   pause
+   exit /b
+)
+
+call .venv\Scripts\python -mpip install !INVOKE_AI_SRC!
+if %errorlevel% neq 0 (
+   echo Installation of InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
+   pause
+   exit /b
+)
+
+@rem call .venv\Scripts\invokeai-configure --root=.
+
+@rem if %errorlevel% neq 0 (
+@rem    echo Configuration InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
+@rem    pause
+@rem    exit /b
+@rem )
+
+echo InvokeAI has been updated to '%INVOKE_AI_VERSION%'
+
+echo "Press any key to continue"
+pause
+endlocal

installer/templates/update.sh.in

@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+set -eu
+
+if [ $# -ge 1 ] && [ "${1:0:2}" == "-h" ]; then
+    echo "Usage: update.sh <release>"
+    echo "Updates InvokeAI to use the indicated version of the code base."
+    echo "Find the version or branch for the release you want, and pass it as the argument."
+    echo "For example: update.sh v2.2.5 for release 2.2.5."
+    echo "             update.sh main for the current development version."
+    echo ""
+    echo "If no argument provided then will install the version tagged with 'latest', equivalent to"
+    echo "update.sh latest"
+    exit -1
+fi
+
+INVOKE_AI_VERSION=${1:-latest}
+
+INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/$INVOKE_AI_VERSION.zip"
+INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/environments-and-requirements/requirements-base.txt
+INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
+
+# ensure we're in the correct folder in case user's CWD is somewhere else
+scriptdir=$(dirname "$0")
+cd "$scriptdir"
+
+function _err_exit {
+    if test "$1" -ne 0
+    then
+	echo "Something went wrong while installing InvokeAI and/or its requirements."
+	echo "Update cannot continue. Please report this error to https://github.com/invoke-ai/InvokeAI/issues"
+        echo -e "Error code $1; Error caught was '$2'"
+        read -p "Press any key to exit..."
+        exit
+    fi
+}
+
+if ! curl -I "$INVOKE_AI_DEP" -fs >/dev/null; then
+    echo \'$INVOKE_AI_VERSION\' is not a known branch name or tag. Please check the version and try again.
+    exit
+fi
+
+echo This script will update InvokeAI and all its dependencies to version \'$INVOKE_AI_VERSION\'.
+echo If you do not want to do this, press control-C now!
+read -p "Press any key to continue, or CTRL-C to exit..."
+
+curl -L "$INVOKE_AI_DEP" > environments-and-requirements/requirements-base.txt
+curl -L "$INVOKE_AI_MODELS" > configs/INITIAL_MODELS.yaml
+
+. .venv/bin/activate
+
+./.venv/bin/python -mpip install -r requirements.txt
+_err_exit $? "The pip program failed to install InvokeAI's requirements."
+
+./.venv/bin/python -mpip install $INVOKE_AI_SRC
+_err_exit $? "The pip program failed to install InvokeAI."
+
+echo InvokeAI updated to \'$INVOKE_AI_VERSION\'

invokeai/app/api/dependencies.py

@@ -2,7 +2,6 @@
 
 from logging import Logger
 
-from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.backend.model_manager.metadata import ModelMetadataStore
 from invokeai.backend.util.logging import InvokeAILogger
@@ -23,6 +22,7 @@ from ..services.invocation_queue.invocation_queue_memory import MemoryInvocation
 from ..services.invocation_services import InvocationServices
 from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
+from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
 from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
 from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
 from ..services.model_install import ModelInstallService
@@ -80,7 +80,7 @@ class ApiDependencies:
         board_records = SqliteBoardRecordStorage(db=db)
         boards = BoardService()
         events = FastAPIEventService(event_handler_id)
-        graph_execution_manager = ItemStorageMemory[GraphExecutionState]()
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)

invokeai/app/api/routers/model_records.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 Lincoln D. Stein
 """FastAPI route for model configuration records."""
 
-import pathlib
+
 from hashlib import sha1
 from random import randbytes
 from typing import Any, Dict, List, Optional, Set
@@ -27,7 +27,6 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.merge import MergeInterpolationMethod, ModelMerger
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
 
 from ..dependencies import ApiDependencies
@@ -416,57 +415,3 @@ async def sync_models_to_config() -> Response:
     """
     ApiDependencies.invoker.services.model_install.sync_to_config()
     return Response(status_code=204)
-
-
-@model_records_router.put(
-    "/merge",
-    operation_id="merge",
-)
-async def merge(
-    keys: List[str] = Body(description="Keys for two to three models to merge", min_length=2, max_length=3),
-    merged_model_name: Optional[str] = Body(description="Name of destination model", default=None),
-    alpha: float = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
-    force: bool = Body(
-        description="Force merging of models created with different versions of diffusers",
-        default=False,
-    ),
-    interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method", default=None),
-    merge_dest_directory: Optional[str] = Body(
-        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
-        default=None,
-    ),
-) -> AnyModelConfig:
-    """
-    Merge diffusers models.
-
-        keys: List of 2-3 model keys to merge together. All models must use the same base type.
-        merged_model_name: Name for the merged model [Concat model names]
-        alpha: Alpha value (0.0-1.0). Higher values give more weight to the second model [0.5]
-        force: If true, force the merge even if the models were generated by different versions of the diffusers library [False]
-        interp: Interpolation method. One of "weighted_sum", "sigmoid", "inv_sigmoid" or "add_difference" [weighted_sum]
-        merge_dest_directory: Specify a directory to store the merged model in [models directory]
-    """
-    print(f"here i am, keys={keys}")
-    logger = ApiDependencies.invoker.services.logger
-    try:
-        logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
-        dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
-        installer = ApiDependencies.invoker.services.model_install
-        merger = ModelMerger(installer)
-        model_names = [installer.record_store.get_model(x).name for x in keys]
-        response = merger.merge_diffusion_models_and_save(
-            model_keys=keys,
-            merged_model_name=merged_model_name or "+".join(model_names),
-            alpha=alpha,
-            interp=interp,
-            force=force,
-            merge_dest_directory=dest,
-        )
-    except UnknownModelException:
-        raise HTTPException(
-            status_code=404,
-            detail=f"One or more of the models '{keys}' not found",
-        )
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-    return response

invokeai/app/api/sockets.py

@@ -14,7 +14,7 @@ class SocketIO:
 
     def __init__(self, app: FastAPI):
         self.__sio = AsyncServer(async_mode="asgi", cors_allowed_origins="*")
-        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="/ws/socket.io")
+        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
         app.mount("/ws", self.__app)
 
         self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)

invokeai/app/invocations/controlnet_image_processors.py

@@ -17,6 +17,7 @@ from controlnet_aux import (
     MidasDetector,
     MLSDdetector,
     NormalBaeDetector,
+    OpenposeDetector,
     PidiNetDetector,
     SamDetector,
     ZoeDetector,
@@ -29,8 +30,6 @@ from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
-from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
@@ -276,6 +275,31 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
         return processed_image
 
 
+@invocation(
+    "openpose_image_processor",
+    title="Openpose Processor",
+    tags=["controlnet", "openpose", "pose"],
+    category="controlnet",
+    version="1.2.0",
+)
+class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies Openpose processing to image"""
+
+    hand_and_face: bool = InputField(default=False, description="Whether to use hands and face mode")
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image):
+        openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = openpose_processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            hand_and_face=self.hand_and_face,
+        )
+        return processed_image
+
+
 @invocation(
     "midas_depth_image_processor",
     title="Midas Depth Processor",
@@ -578,60 +602,3 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
         color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
         color_map = Image.fromarray(color_map)
         return color_map
-
-
-DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
-
-
-@invocation(
-    "depth_anything_image_processor",
-    title="Depth Anything Processor",
-    tags=["controlnet", "depth", "depth anything"],
-    category="controlnet",
-    version="1.0.0",
-)
-class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
-    """Generates a depth map based on the Depth Anything algorithm"""
-
-    model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
-        default="small", description="The size of the depth model to use"
-    )
-    resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
-    offload: bool = InputField(default=False)
-
-    def run_processor(self, image: Image.Image):
-        depth_anything_detector = DepthAnythingDetector()
-        depth_anything_detector.load_model(model_size=self.model_size)
-
-        if image.mode == "RGBA":
-            image = image.convert("RGB")
-
-        processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
-        return processed_image
-
-
-@invocation(
-    "dw_openpose_image_processor",
-    title="DW Openpose Image Processor",
-    tags=["controlnet", "dwpose", "openpose"],
-    category="controlnet",
-    version="1.0.0",
-)
-class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
-    """Generates an openpose pose from an image using DWPose"""
-
-    draw_body: bool = InputField(default=True)
-    draw_face: bool = InputField(default=False)
-    draw_hands: bool = InputField(default=False)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
-
-    def run_processor(self, image):
-        dw_openpose = DWOpenposeDetector()
-        processed_image = dw_openpose(
-            image,
-            draw_face=self.draw_face,
-            draw_hands=self.draw_hands,
-            draw_body=self.draw_body,
-            resolution=self.image_resolution,
-        )
-        return processed_image

invokeai/app/invocations/latent.py

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

invokeai/app/invocations/upscale.py

@@ -5,12 +5,12 @@ from typing import Literal
 import cv2
 import numpy as np
 import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from pydantic import ConfigDict
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 

invokeai/app/services/config/config_default.py

@@ -173,10 +173,10 @@ from __future__ import annotations
 
 import os
 from pathlib import Path
-from typing import Any, ClassVar, Dict, List, Literal, Optional, Union
+from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
 
 from omegaconf import DictConfig, OmegaConf
-from pydantic import Field
+from pydantic import Field, TypeAdapter
 from pydantic.config import JsonDict
 from pydantic_settings import SettingsConfigDict
 
@@ -251,11 +251,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     log_level           : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or  higher", json_schema_extra=Categories.Logging)
     log_sql             : bool = Field(default=False, description="Log SQL queries", json_schema_extra=Categories.Logging)
 
-    # Development
     dev_reload          : bool = Field(default=False, description="Automatically reload when Python sources are changed.", json_schema_extra=Categories.Development)
-    profile_graphs      : bool = Field(default=False, description="Enable graph profiling", json_schema_extra=Categories.Development)
-    profile_prefix      : Optional[str] = Field(default=None, description="An optional prefix for profile output files.", json_schema_extra=Categories.Development)
-    profiles_dir        : Path = Field(default=Path('profiles'), description="Directory for graph profiles", json_schema_extra=Categories.Development)
 
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
 
@@ -274,7 +270,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     attention_type      : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", json_schema_extra=Categories.Generation)
     attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', json_schema_extra=Categories.Generation)
     force_tiled_decode  : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Generation)
-    png_compress_level  : int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
+    png_compress_level  : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
 
     # QUEUE
     max_queue_size      : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", json_schema_extra=Categories.Queue)
@@ -284,9 +280,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     deny_nodes          : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes)
     node_cache_size     : int = Field(default=512, description="How many cached nodes to keep in memory", json_schema_extra=Categories.Nodes)
 
-    # MODEL IMPORT
-    civitai_api_key       : Optional[str] = Field(default=os.environ.get("CIVITAI_API_KEY"), description="API key for CivitAI", json_schema_extra=Categories.Other)
-
     # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
     max_cache_size      : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance)
@@ -296,7 +289,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
     embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
     controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
-
     # this is not referred to in the source code and can be removed entirely
     #free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
 
@@ -336,9 +328,13 @@ class InvokeAIAppConfig(InvokeAISettings):
         super().parse_args(argv)
 
         if self.singleton_init and not clobber:
-            # When setting values in this way, set validate_assignment to true if you want to validate the value.
-            for k, v in self.singleton_init.items():
-                setattr(self, k, v)
+            hints = get_type_hints(self.__class__)
+            for k in self.singleton_init:
+                setattr(
+                    self,
+                    k,
+                    TypeAdapter(hints[k]).validate_python(self.singleton_init[k]),
+                )
 
     @classmethod
     def get_config(cls, **kwargs: Any) -> InvokeAIAppConfig:
@@ -453,11 +449,6 @@ class InvokeAIAppConfig(InvokeAISettings):
         disabled_in_config = not self.xformers_enabled
         return disabled_in_config and self.attention_type != "xformers"
 
-    @property
-    def profiles_path(self) -> Path:
-        """Path to the graph profiles directory."""
-        return self._resolve(self.profiles_dir)
-
     @staticmethod
     def find_root() -> Path:
         """Choose the runtime root directory when not specified on command line or init file."""

invokeai/app/services/download/download_default.py

@@ -208,6 +208,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
                 job = self._queue.get(timeout=1)
             except Empty:
                 continue
+
             try:
                 job.job_started = get_iso_timestamp()
                 self._do_download(job)

invokeai/app/services/images/images_default.py

@@ -154,7 +154,7 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Image record not found")
             raise
         except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image metadata")
+            self.__invoker.services.logger.error("Problem getting image DTO")
             raise e
 
     def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -1,16 +1,11 @@
 import time
 import traceback
-from contextlib import suppress
 from threading import BoundedSemaphore, Event, Thread
 from typing import Optional
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.invocations.baseinvocation import InvocationContext
 from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
-from invokeai.app.services.invocation_stats.invocation_stats_common import (
-    GESStatsNotFoundError,
-)
-from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
 from .invocation_processor_base import InvocationProcessorABC
@@ -23,7 +18,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker: Invoker
     __threadLimit: BoundedSemaphore
 
-    def start(self, invoker: Invoker) -> None:
+    def start(self, invoker) -> None:
         # if we do want multithreading at some point, we could make this configurable
         self.__threadLimit = BoundedSemaphore(1)
         self.__invoker = invoker
@@ -44,27 +39,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
             self.__threadLimit.acquire()
             queue_item: Optional[InvocationQueueItem] = None
 
-            profiler = (
-                Profiler(
-                    logger=self.__invoker.services.logger,
-                    output_dir=self.__invoker.services.configuration.profiles_path,
-                    prefix=self.__invoker.services.configuration.profile_prefix,
-                )
-                if self.__invoker.services.configuration.profile_graphs
-                else None
-            )
-
-            def stats_cleanup(graph_execution_state_id: str) -> None:
-                if profiler:
-                    profile_path = profiler.stop()
-                    stats_path = profile_path.with_suffix(".json")
-                    self.__invoker.services.performance_statistics.dump_stats(
-                        graph_execution_state_id=graph_execution_state_id, output_path=stats_path
-                    )
-                with suppress(GESStatsNotFoundError):
-                    self.__invoker.services.performance_statistics.log_stats(graph_execution_state_id)
-                    self.__invoker.services.performance_statistics.reset_stats(graph_execution_state_id)
-
             while not stop_event.is_set():
                 try:
                     queue_item = self.__invoker.services.queue.get()
@@ -75,10 +49,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # do not hammer the queue
                     time.sleep(0.5)
                     continue
-
-                if profiler and profiler.profile_id != queue_item.graph_execution_state_id:
-                    profiler.start(profile_id=queue_item.graph_execution_state_id)
-
                 try:
                     graph_execution_state = self.__invoker.services.graph_execution_manager.get(
                         queue_item.graph_execution_state_id
@@ -167,7 +137,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     pass
 
                 except CanceledException:
-                    stats_cleanup(graph_execution_state.id)
+                    self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
                     pass
 
                 except Exception as e:
@@ -192,6 +162,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         error_type=e.__class__.__name__,
                         error=error,
                     )
+                    self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
                     pass
 
                 # Check queue to see if this is canceled, and skip if so
@@ -223,13 +194,13 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             error=traceback.format_exc(),
                         )
                 elif is_complete:
+                    self.__invoker.services.performance_statistics.log_stats(graph_execution_state.id)
                     self.__invoker.services.events.emit_graph_execution_complete(
                         queue_batch_id=queue_item.session_queue_batch_id,
                         queue_item_id=queue_item.session_queue_item_id,
                         queue_id=queue_item.session_queue_id,
                         graph_execution_state_id=graph_execution_state.id,
                     )
-                    stats_cleanup(graph_execution_state.id)
 
         except KeyboardInterrupt:
             pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor

invokeai/app/services/invocation_stats/invocation_stats_base.py

@@ -30,10 +30,8 @@ writes to the system log is stored in InvocationServices.performance_statistics.
 
 from abc import ABC, abstractmethod
 from contextlib import AbstractContextManager
-from pathlib import Path
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
-from invokeai.app.services.invocation_stats.invocation_stats_common import InvocationStatsSummary
 
 
 class InvocationStatsServiceBase(ABC):
@@ -63,9 +61,8 @@ class InvocationStatsServiceBase(ABC):
     @abstractmethod
     def reset_stats(self, graph_execution_state_id: str):
         """
-        Reset all statistics for the indicated graph.
-        :param graph_execution_state_id: The id of the session whose stats to reset.
-        :raises GESStatsNotFoundError: if the graph isn't tracked in the stats.
+        Reset all statistics for the indicated graph
+        :param graph_execution_state_id
         """
         pass
 
@@ -73,26 +70,5 @@ class InvocationStatsServiceBase(ABC):
     def log_stats(self, graph_execution_state_id: str):
         """
         Write out the accumulated statistics to the log or somewhere else.
-        :param graph_execution_state_id: The id of the session whose stats to log.
-        :raises GESStatsNotFoundError: if the graph isn't tracked in the stats.
-        """
-        pass
-
-    @abstractmethod
-    def get_stats(self, graph_execution_state_id: str) -> InvocationStatsSummary:
-        """
-        Gets the accumulated statistics for the indicated graph.
-        :param graph_execution_state_id: The id of the session whose stats to get.
-        :raises GESStatsNotFoundError: if the graph isn't tracked in the stats.
-        """
-        pass
-
-    @abstractmethod
-    def dump_stats(self, graph_execution_state_id: str, output_path: Path) -> None:
-        """
-        Write out the accumulated statistics to the indicated path as JSON.
-        :param graph_execution_state_id: The id of the session whose stats to dump.
-        :param output_path: The file to write the stats to.
-        :raises GESStatsNotFoundError: if the graph isn't tracked in the stats.
         """
         pass

invokeai/app/services/invocation_stats/invocation_stats_common.py

@@ -1,91 +1,5 @@
 from collections import defaultdict
-from dataclasses import asdict, dataclass
-from typing import Any, Optional
-
-
-class GESStatsNotFoundError(Exception):
-    """Raised when execution stats are not found for a given Graph Execution State."""
-
-
-@dataclass
-class NodeExecutionStatsSummary:
-    """The stats for a specific type of node."""
-
-    node_type: str
-    num_calls: int
-    time_used_seconds: float
-    peak_vram_gb: float
-
-
-@dataclass
-class ModelCacheStatsSummary:
-    """The stats for the model cache."""
-
-    high_water_mark_gb: float
-    cache_size_gb: float
-    total_usage_gb: float
-    cache_hits: int
-    cache_misses: int
-    models_cached: int
-    models_cleared: int
-
-
-@dataclass
-class GraphExecutionStatsSummary:
-    """The stats for the graph execution state."""
-
-    graph_execution_state_id: str
-    execution_time_seconds: float
-    # `wall_time_seconds`, `ram_usage_gb` and `ram_change_gb` are derived from the node execution stats.
-    # In some situations, there are no node stats, so these values are optional.
-    wall_time_seconds: Optional[float]
-    ram_usage_gb: Optional[float]
-    ram_change_gb: Optional[float]
-
-
-@dataclass
-class InvocationStatsSummary:
-    """
-    The accumulated stats for a graph execution.
-    Its `__str__` method returns a human-readable stats summary.
-    """
-
-    vram_usage_gb: Optional[float]
-    graph_stats: GraphExecutionStatsSummary
-    model_cache_stats: ModelCacheStatsSummary
-    node_stats: list[NodeExecutionStatsSummary]
-
-    def __str__(self) -> str:
-        _str = ""
-        _str = f"Graph stats: {self.graph_stats.graph_execution_state_id}\n"
-        _str += f"{'Node':>30} {'Calls':>7} {'Seconds':>9} {'VRAM Used':>10}\n"
-
-        for summary in self.node_stats:
-            _str += f"{summary.node_type:>30} {summary.num_calls:>7} {summary.time_used_seconds:>8.3f}s {summary.peak_vram_gb:>9.3f}G\n"
-
-        _str += f"TOTAL GRAPH EXECUTION TIME: {self.graph_stats.execution_time_seconds:7.3f}s\n"
-
-        if self.graph_stats.wall_time_seconds is not None:
-            _str += f"TOTAL GRAPH WALL TIME: {self.graph_stats.wall_time_seconds:7.3f}s\n"
-
-        if self.graph_stats.ram_usage_gb is not None and self.graph_stats.ram_change_gb is not None:
-            _str += f"RAM used by InvokeAI process: {self.graph_stats.ram_usage_gb:4.2f}G ({self.graph_stats.ram_change_gb:+5.3f}G)\n"
-
-        _str += f"RAM used to load models: {self.model_cache_stats.total_usage_gb:4.2f}G\n"
-        if self.vram_usage_gb:
-            _str += f"VRAM in use: {self.vram_usage_gb:4.3f}G\n"
-        _str += "RAM cache statistics:\n"
-        _str += f"   Model cache hits: {self.model_cache_stats.cache_hits}\n"
-        _str += f"   Model cache misses: {self.model_cache_stats.cache_misses}\n"
-        _str += f"   Models cached: {self.model_cache_stats.models_cached}\n"
-        _str += f"   Models cleared from cache: {self.model_cache_stats.models_cleared}\n"
-        _str += f"   Cache high water mark: {self.model_cache_stats.high_water_mark_gb:4.2f}/{self.model_cache_stats.cache_size_gb:4.2f}G\n"
-
-        return _str
-
-    def as_dict(self) -> dict[str, Any]:
-        """Returns the stats as a dictionary."""
-        return asdict(self)
+from dataclasses import dataclass
 
 
 @dataclass
@@ -141,33 +55,12 @@ class GraphExecutionStats:
 
         return last_node
 
-    def get_graph_stats_summary(self, graph_execution_state_id: str) -> GraphExecutionStatsSummary:
-        """Get a summary of the graph stats."""
-        first_node = self.get_first_node_stats()
-        last_node = self.get_last_node_stats()
+    def get_pretty_log(self, graph_execution_state_id: str) -> str:
+        log = f"Graph stats: {graph_execution_state_id}\n"
+        log += f"{'Node':>30} {'Calls':>7}{'Seconds':>9} {'VRAM Used':>10}\n"
 
-        wall_time_seconds: Optional[float] = None
-        ram_usage_gb: Optional[float] = None
-        ram_change_gb: Optional[float] = None
-
-        if last_node and first_node:
-            wall_time_seconds = last_node.end_time - first_node.start_time
-            ram_usage_gb = last_node.end_ram_gb
-            ram_change_gb = last_node.end_ram_gb - first_node.start_ram_gb
-
-        return GraphExecutionStatsSummary(
-            graph_execution_state_id=graph_execution_state_id,
-            execution_time_seconds=self.get_total_run_time(),
-            wall_time_seconds=wall_time_seconds,
-            ram_usage_gb=ram_usage_gb,
-            ram_change_gb=ram_change_gb,
-        )
-
-    def get_node_stats_summaries(self) -> list[NodeExecutionStatsSummary]:
-        """Get a summary of the node stats."""
-        summaries: list[NodeExecutionStatsSummary] = []
+        # Log stats aggregated by node type.
         node_stats_by_type: dict[str, list[NodeExecutionStats]] = defaultdict(list)
-
         for node_stats in self._node_stats_list:
             node_stats_by_type[node_stats.invocation_type].append(node_stats)
 
@@ -175,9 +68,17 @@ class GraphExecutionStats:
             num_calls = len(node_type_stats_list)
             time_used = sum([n.total_time() for n in node_type_stats_list])
             peak_vram = max([n.peak_vram_gb for n in node_type_stats_list])
-            summary = NodeExecutionStatsSummary(
-                node_type=node_type, num_calls=num_calls, time_used_seconds=time_used, peak_vram_gb=peak_vram
-            )
-            summaries.append(summary)
+            log += f"{node_type:>30}  {num_calls:>4}   {time_used:7.3f}s     {peak_vram:4.3f}G\n"
 
-        return summaries
+        # Log stats for the entire graph.
+        log += f"TOTAL GRAPH EXECUTION TIME: {self.get_total_run_time():7.3f}s\n"
+
+        first_node = self.get_first_node_stats()
+        last_node = self.get_last_node_stats()
+        if first_node is not None and last_node is not None:
+            total_wall_time = last_node.end_time - first_node.start_time
+            ram_change = last_node.end_ram_gb - first_node.start_ram_gb
+            log += f"TOTAL GRAPH WALL TIME: {total_wall_time:7.3f}s\n"
+            log += f"RAM used by InvokeAI process: {last_node.end_ram_gb:4.2f}G ({ram_change:+5.3f}G)\n"
+
+        return log

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -1,7 +1,5 @@
-import json
 import time
 from contextlib import contextmanager
-from pathlib import Path
 
 import psutil
 import torch
@@ -9,19 +7,10 @@ import torch
 import invokeai.backend.util.logging as logger
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.item_storage.item_storage_common import ItemNotFoundError
 from invokeai.backend.model_management.model_cache import CacheStats
 
 from .invocation_stats_base import InvocationStatsServiceBase
-from .invocation_stats_common import (
-    GESStatsNotFoundError,
-    GraphExecutionStats,
-    GraphExecutionStatsSummary,
-    InvocationStatsSummary,
-    ModelCacheStatsSummary,
-    NodeExecutionStats,
-    NodeExecutionStatsSummary,
-)
+from .invocation_stats_common import GraphExecutionStats, NodeExecutionStats
 
 # Size of 1GB in bytes.
 GB = 2**30
@@ -64,7 +53,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
         finally:
             # Record state after the invocation.
             node_stats = NodeExecutionStats(
-                invocation_type=invocation.get_type(),
+                invocation_type=invocation.type,
                 start_time=start_time,
                 end_time=time.time(),
                 start_ram_gb=start_ram / GB,
@@ -79,11 +68,11 @@ class InvocationStatsService(InvocationStatsServiceBase):
         This shouldn't be necessary, but we don't have totally robust upstream handling of graph completions/errors, so
         for now we call this function periodically to prevent them from accumulating.
         """
-        to_prune: list[str] = []
+        to_prune = []
         for graph_execution_state_id in self._stats:
             try:
                 graph_execution_state = self._invoker.services.graph_execution_manager.get(graph_execution_state_id)
-            except ItemNotFoundError:
+            except Exception:
                 # TODO(ryand): What would cause this? Should this exception just be allowed to propagate?
                 logger.warning(f"Failed to get graph state for {graph_execution_state_id}.")
                 continue
@@ -106,66 +95,31 @@ class InvocationStatsService(InvocationStatsServiceBase):
             del self._stats[graph_execution_state_id]
             del self._cache_stats[graph_execution_state_id]
         except KeyError as e:
-            raise GESStatsNotFoundError(
-                f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from e
+            logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}.")
 
-    def get_stats(self, graph_execution_state_id: str) -> InvocationStatsSummary:
-        graph_stats_summary = self._get_graph_summary(graph_execution_state_id)
-        node_stats_summaries = self._get_node_summaries(graph_execution_state_id)
-        model_cache_stats_summary = self._get_model_cache_summary(graph_execution_state_id)
-        vram_usage_gb = torch.cuda.memory_allocated() / GB if torch.cuda.is_available() else None
-
-        return InvocationStatsSummary(
-            graph_stats=graph_stats_summary,
-            model_cache_stats=model_cache_stats_summary,
-            node_stats=node_stats_summaries,
-            vram_usage_gb=vram_usage_gb,
-        )
-
-    def log_stats(self, graph_execution_state_id: str) -> None:
-        stats = self.get_stats(graph_execution_state_id)
-        logger.info(str(stats))
-
-    def dump_stats(self, graph_execution_state_id: str, output_path: Path) -> None:
-        stats = self.get_stats(graph_execution_state_id)
-        with open(output_path, "w") as f:
-            f.write(json.dumps(stats.as_dict(), indent=2))
-
-    def _get_model_cache_summary(self, graph_execution_state_id: str) -> ModelCacheStatsSummary:
+    def log_stats(self, graph_execution_state_id: str):
         try:
+            graph_stats = self._stats[graph_execution_state_id]
             cache_stats = self._cache_stats[graph_execution_state_id]
         except KeyError as e:
-            raise GESStatsNotFoundError(
-                f"Attempted to get model cache statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from e
+            logger.warning(f"Attempted to log statistics for unknown graph {graph_execution_state_id}: {e}.")
+            return
 
-        return ModelCacheStatsSummary(
-            cache_hits=cache_stats.hits,
-            cache_misses=cache_stats.misses,
-            high_water_mark_gb=cache_stats.high_watermark / GB,
-            cache_size_gb=cache_stats.cache_size / GB,
-            total_usage_gb=sum(list(cache_stats.loaded_model_sizes.values())) / GB,
-            models_cached=cache_stats.in_cache,
-            models_cleared=cache_stats.cleared,
-        )
+        log = graph_stats.get_pretty_log(graph_execution_state_id)
 
-    def _get_graph_summary(self, graph_execution_state_id: str) -> GraphExecutionStatsSummary:
-        try:
-            graph_stats = self._stats[graph_execution_state_id]
-        except KeyError as e:
-            raise GESStatsNotFoundError(
-                f"Attempted to get graph statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from e
+        hwm = cache_stats.high_watermark / GB
+        tot = cache_stats.cache_size / GB
+        loaded = sum(list(cache_stats.loaded_model_sizes.values())) / GB
+        log += f"RAM used to load models: {loaded:4.2f}G\n"
+        if torch.cuda.is_available():
+            log += f"VRAM in use: {(torch.cuda.memory_allocated() / GB):4.3f}G\n"
+        log += "RAM cache statistics:\n"
+        log += f"   Model cache hits: {cache_stats.hits}\n"
+        log += f"   Model cache misses: {cache_stats.misses}\n"
+        log += f"   Models cached: {cache_stats.in_cache}\n"
+        log += f"   Models cleared from cache: {cache_stats.cleared}\n"
+        log += f"   Cache high water mark: {hwm:4.2f}/{tot:4.2f}G\n"
+        logger.info(log)
 
-        return graph_stats.get_graph_stats_summary(graph_execution_state_id)
-
-    def _get_node_summaries(self, graph_execution_state_id: str) -> list[NodeExecutionStatsSummary]:
-        try:
-            graph_stats = self._stats[graph_execution_state_id]
-        except KeyError as e:
-            raise GESStatsNotFoundError(
-                f"Attempted to get node statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from e
-
-        return graph_stats.get_node_stats_summaries()
+        del self._stats[graph_execution_state_id]
+        del self._cache_stats[graph_execution_state_id]

invokeai/app/services/item_storage/item_storage_base.py

@@ -1,8 +1,10 @@
 from abc import ABC, abstractmethod
-from typing import Callable, Generic, TypeVar
+from typing import Callable, Generic, Optional, TypeVar
 
 from pydantic import BaseModel
 
+from invokeai.app.services.shared.pagination import PaginatedResults
+
 T = TypeVar("T", bound=BaseModel)
 
 
@@ -20,26 +22,26 @@ class ItemStorageABC(ABC, Generic[T]):
 
     @abstractmethod
     def get(self, item_id: str) -> T:
-        """
-        Gets the item.
-        :param item_id: the id of the item to get
-        :raises ItemNotFoundError: if the item is not found
-        """
+        """Gets the item, parsing it into a Pydantic model"""
+        pass
+
+    @abstractmethod
+    def get_raw(self, item_id: str) -> Optional[str]:
+        """Gets the raw item as a string, skipping Pydantic parsing"""
         pass
 
     @abstractmethod
     def set(self, item: T) -> None:
-        """
-        Sets the item. The id will be extracted based on id_field.
-        :param item: the item to set
-        """
+        """Sets the item"""
         pass
 
     @abstractmethod
-    def delete(self, item_id: str) -> None:
-        """
-        Deletes the item, if it exists.
-        """
+    def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        """Gets a paginated list of items"""
+        pass
+
+    @abstractmethod
+    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
         pass
 
     def on_changed(self, on_changed: Callable[[T], None]) -> None:

invokeai/app/services/item_storage/item_storage_common.py

@@ -1,5 +0,0 @@
-class ItemNotFoundError(KeyError):
-    """Raised when an item is not found in storage"""
-
-    def __init__(self, item_id: str) -> None:
-        super().__init__(f"Item with id {item_id} not found")

invokeai/app/services/item_storage/item_storage_memory.py

@@ -1,52 +0,0 @@
-from collections import OrderedDict
-from contextlib import suppress
-from typing import Generic, TypeVar
-
-from pydantic import BaseModel
-
-from invokeai.app.services.item_storage.item_storage_base import ItemStorageABC
-from invokeai.app.services.item_storage.item_storage_common import ItemNotFoundError
-
-T = TypeVar("T", bound=BaseModel)
-
-
-class ItemStorageMemory(ItemStorageABC[T], Generic[T]):
-    """
-    Provides a simple in-memory storage for items, with a maximum number of items to store.
-    The storage uses the LRU strategy to evict items from storage when the max has been reached.
-    """
-
-    def __init__(self, id_field: str = "id", max_items: int = 10) -> None:
-        super().__init__()
-        if max_items < 1:
-            raise ValueError("max_items must be at least 1")
-        if not id_field:
-            raise ValueError("id_field must not be empty")
-        self._id_field = id_field
-        self._items: OrderedDict[str, T] = OrderedDict()
-        self._max_items = max_items
-
-    def get(self, item_id: str) -> T:
-        # If the item exists, move it to the end of the OrderedDict.
-        item = self._items.pop(item_id, None)
-        if item is None:
-            raise ItemNotFoundError(item_id)
-        self._items[item_id] = item
-        return item
-
-    def set(self, item: T) -> None:
-        item_id = getattr(item, self._id_field)
-        if item_id in self._items:
-            # If item already exists, remove it and add it to the end
-            self._items.pop(item_id)
-        elif len(self._items) >= self._max_items:
-            # If cache is full, evict the least recently used item
-            self._items.popitem(last=False)
-        self._items[item_id] = item
-        self._on_changed(item)
-
-    def delete(self, item_id: str) -> None:
-        # This is a no-op if the item doesn't exist.
-        with suppress(KeyError):
-            del self._items[item_id]
-            self._on_deleted(item_id)

invokeai/app/services/item_storage/item_storage_sqlite.py

@@ -0,0 +1,147 @@
+import sqlite3
+import threading
+from typing import Generic, Optional, TypeVar, get_args
+
+from pydantic import BaseModel, TypeAdapter
+
+from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+
+from .item_storage_base import ItemStorageABC
+
+T = TypeVar("T", bound=BaseModel)
+
+
+class SqliteItemStorage(ItemStorageABC, Generic[T]):
+    _table_name: str
+    _conn: sqlite3.Connection
+    _cursor: sqlite3.Cursor
+    _id_field: str
+    _lock: threading.RLock
+    _validator: Optional[TypeAdapter[T]]
+
+    def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
+        super().__init__()
+
+        self._lock = db.lock
+        self._conn = db.conn
+        self._table_name = table_name
+        self._id_field = id_field  # TODO: validate that T has this field
+        self._cursor = self._conn.cursor()
+        self._validator: Optional[TypeAdapter[T]] = None
+
+        self._create_table()
+
+    def _create_table(self):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""CREATE TABLE IF NOT EXISTS {self._table_name} (
+                item TEXT,
+                id TEXT GENERATED ALWAYS AS (json_extract(item, '$.{self._id_field}')) VIRTUAL NOT NULL);"""
+            )
+            self._cursor.execute(
+                f"""CREATE UNIQUE INDEX IF NOT EXISTS {self._table_name}_id ON {self._table_name}(id);"""
+            )
+        finally:
+            self._lock.release()
+
+    def _parse_item(self, item: str) -> T:
+        if self._validator is None:
+            """
+            We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so
+            we can create it when it is first needed instead.
+            __orig_class__ is technically an implementation detail of the typing module, not a supported API
+            """
+            self._validator = TypeAdapter(get_args(self.__orig_class__)[0])  # type: ignore [attr-defined]
+        return self._validator.validate_json(item)
+
+    def set(self, item: T):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
+                (item.model_dump_json(warnings=False, exclude_none=True),),
+            )
+            self._conn.commit()
+        finally:
+            self._lock.release()
+        self._on_changed(item)
+
+    def get(self, id: str) -> Optional[T]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
+            result = self._cursor.fetchone()
+        finally:
+            self._lock.release()
+
+        if not result:
+            return None
+
+        return self._parse_item(result[0])
+
+    def get_raw(self, id: str) -> Optional[str]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
+            result = self._cursor.fetchone()
+        finally:
+            self._lock.release()
+
+        if not result:
+            return None
+
+        return result[0]
+
+    def delete(self, id: str):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),))
+            self._conn.commit()
+        finally:
+            self._lock.release()
+        self._on_deleted(id)
+
+    def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""SELECT item FROM {self._table_name} LIMIT ? OFFSET ?;""",
+                (per_page, page * per_page),
+            )
+            result = self._cursor.fetchall()
+
+            items = [self._parse_item(r[0]) for r in result]
+
+            self._cursor.execute(f"""SELECT count(*) FROM {self._table_name};""")
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)
+
+    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""SELECT item FROM {self._table_name} WHERE item LIKE ? LIMIT ? OFFSET ?;""",
+                (f"%{query}%", per_page, page * per_page),
+            )
+            result = self._cursor.fetchall()
+
+            items = [self._parse_item(r[0]) for r in result]
+
+            self._cursor.execute(
+                f"""SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;""",
+                (f"%{query}%",),
+            )
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)

invokeai/app/services/model_install/model_install_base.py

@@ -165,8 +165,8 @@ class ModelInstallJob(BaseModel):
     )
     source: ModelSource = Field(description="Source (URL, repo_id, or local path) of model")
     local_path: Path = Field(description="Path to locally-downloaded model; may be the same as the source")
-    bytes: int = Field(
-        default=0, description="For a remote model, the number of bytes downloaded so far (may not be available)"
+    bytes: Optional[int] = Field(
+        default=None, description="For a remote model, the number of bytes downloaded so far (may not be available)"
     )
     total_bytes: int = Field(default=0, description="Total size of the model to be installed")
     source_metadata: Optional[AnyModelRepoMetadata] = Field(

invokeai/app/services/model_install/model_install_default.py

@@ -535,19 +535,19 @@ class ModelInstallService(ModelInstallServiceBase):
     def _import_from_url(self, source: URLModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
         # URLs from Civitai or HuggingFace will be handled specially
         url_patterns = {
-            r"^https?://civitai.com/": CivitaiMetadataFetch,
-            r"^https?://huggingface.co/[^/]+/[^/]+$": HuggingFaceMetadataFetch,
+            r"https?://civitai.com/": CivitaiMetadataFetch,
+            r"https?://huggingface.co/": HuggingFaceMetadataFetch,
         }
         metadata = None
         for pattern, fetcher in url_patterns.items():
             if re.match(pattern, str(source.url), re.IGNORECASE):
                 metadata = fetcher(self._session).from_url(source.url)
                 break
-        self._logger.debug(f"metadata={metadata}")
         if metadata and isinstance(metadata, ModelMetadataWithFiles):
             remote_files = metadata.download_urls(session=self._session)
         else:
             remote_files = [RemoteModelFile(url=source.url, path=Path("."), size=0)]
+
         return self._import_remote_model(
             source=source,
             config=config,
@@ -586,7 +586,6 @@ class ModelInstallService(ModelInstallServiceBase):
         assert install_job.total_bytes is not None  # to avoid type checking complaints in the loop below
 
         self._logger.info(f"Queuing {source} for downloading")
-        self._logger.debug(f"remote_files={remote_files}")
         for model_file in remote_files:
             url = model_file.url
             path = model_file.path

invokeai/app/services/shared/graph.py

@@ -2,7 +2,7 @@
 
 import copy
 import itertools
-from typing import Annotated, Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
+from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import BaseModel, ConfigDict, field_validator, model_validator
@@ -141,16 +141,6 @@ def are_connections_compatible(
     return are_connection_types_compatible(from_node_field, to_node_field)
 
 
-T = TypeVar("T")
-
-
-def copydeep(obj: T) -> T:
-    """Deep-copies an object. If it is a pydantic model, use the model's copy method."""
-    if isinstance(obj, BaseModel):
-        return obj.model_copy(deep=True)
-    return copy.deepcopy(obj)
-
-
 class NodeAlreadyInGraphError(ValueError):
     pass
 
@@ -540,7 +530,7 @@ class Graph(BaseModel):
         except NodeNotFoundError:
             return False
 
-    def get_node(self, node_path: str) -> BaseInvocation:
+    def get_node(self, node_path: str) -> InvocationsUnion:
         """Gets a node from the graph using a node path."""
         # Materialized graphs may have nodes at the top level
         graph, node_id = self._get_graph_and_node(node_path)
@@ -891,7 +881,7 @@ class GraphExecutionState(BaseModel):
         # If next is still none, there's no next node, return None
         return next_node
 
-    def complete(self, node_id: str, output: BaseInvocationOutput) -> None:
+    def complete(self, node_id: str, output: InvocationOutputsUnion):
         """Marks a node as complete"""
 
         if node_id not in self.execution_graph.nodes:
@@ -1128,22 +1118,17 @@ class GraphExecutionState(BaseModel):
 
     def _prepare_inputs(self, node: BaseInvocation):
         input_edges = [e for e in self.execution_graph.edges if e.destination.node_id == node.id]
-        # Inputs must be deep-copied, else if a node mutates the object, other nodes that get the same input
-        # will see the mutation.
         if isinstance(node, CollectInvocation):
             output_collection = [
-                copydeep(getattr(self.results[edge.source.node_id], edge.source.field))
+                getattr(self.results[edge.source.node_id], edge.source.field)
                 for edge in input_edges
                 if edge.destination.field == "item"
             ]
             node.collection = output_collection
         else:
             for edge in input_edges:
-                setattr(
-                    node,
-                    edge.destination.field,
-                    copydeep(getattr(self.results[edge.source.node_id], edge.source.field)),
-                )
+                output_value = getattr(self.results[edge.source.node_id], edge.source.field)
+                setattr(node, edge.destination.field, output_value)
 
     # TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
     def _is_edge_valid(self, edge: Edge) -> bool:

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

@@ -7,7 +7,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_1 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_2 import build_migration_2
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_3 import build_migration_3
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_4 import build_migration_4
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_5 import build_migration_5
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -32,7 +31,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_2(image_files=image_files, logger=logger))
     migrator.register_migration(build_migration_3(app_config=config, logger=logger))
     migrator.register_migration(build_migration_4())
-    migrator.register_migration(build_migration_5())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_5.py

@@ -1,34 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration5Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._drop_graph_executions(cursor)
-
-    def _drop_graph_executions(self, cursor: sqlite3.Cursor) -> None:
-        """Drops the `graph_executions` table."""
-
-        cursor.execute(
-            """--sql
-            DROP TABLE IF EXISTS graph_executions;
-            """
-        )
-
-
-def build_migration_5() -> Migration:
-    """
-    Build the migration from database version 4 to 5.
-
-    Introduced in v3.6.3, this migration:
-    - Drops the `graph_executions` table. We are able to do this because we are moving the graph storage
-      to be purely in-memory.
-    """
-    migration_5 = Migration(
-        from_version=4,
-        to_version=5,
-        callback=Migration5Callback(),
-    )
-
-    return migration_5

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

@@ -72,12 +72,7 @@ class MigrateModelYamlToDb1:
                 continue
 
             base_type, model_type, model_name = str(model_key).split("/")
-            try:
-                hash = FastModelHash.hash(self.config.models_path / stanza.path)
-            except OSError:
-                self.logger.warning(f"The model at {stanza.path} is not a valid file or directory. Skipping migration.")
-                continue
-
+            hash = FastModelHash.hash(self.config.models_path / stanza.path)
             assert isinstance(model_key, str)
             new_key = sha1(model_key.encode("utf-8")).hexdigest()
 

invokeai/app/services/workflow_records/workflow_records_common.py

@@ -31,7 +31,6 @@ class WorkflowRecordOrderBy(str, Enum, metaclass=MetaEnum):
 class WorkflowCategory(str, Enum, metaclass=MetaEnum):
     User = "user"
     Default = "default"
-    Project = "project"
 
 
 class WorkflowMeta(BaseModel):

invokeai/app/util/profiler.py

@@ -1,67 +0,0 @@
-import cProfile
-from logging import Logger
-from pathlib import Path
-from typing import Optional
-
-
-class Profiler:
-    """
-    Simple wrapper around cProfile.
-
-    Usage
-    ```
-      # Create a profiler
-      profiler = Profiler(logger, output_dir, "sql_query_perf")
-      # Start a new profile
-      profiler.start("my_profile")
-      # Do stuff
-      profiler.stop()
-    ```
-
-    Visualize a profile as a flamegraph with [snakeviz](https://jiffyclub.github.io/snakeviz/)
-    ```sh
-      snakeviz my_profile.prof
-    ```
-
-    Visualize a profile as directed graph with [graphviz](https://graphviz.org/download/) & [gprof2dot](https://github.com/jrfonseca/gprof2dot)
-    ```sh
-      gprof2dot -f pstats my_profile.prof | dot -Tpng -o my_profile.png
-      # SVG or PDF may be nicer - you can search for function names
-      gprof2dot -f pstats my_profile.prof | dot -Tsvg -o my_profile.svg
-      gprof2dot -f pstats my_profile.prof | dot -Tpdf -o my_profile.pdf
-    ```
-    """
-
-    def __init__(self, logger: Logger, output_dir: Path, prefix: Optional[str] = None) -> None:
-        self._logger = logger.getChild(f"profiler.{prefix}" if prefix else "profiler")
-        self._output_dir = output_dir
-        self._output_dir.mkdir(parents=True, exist_ok=True)
-        self._profiler: Optional[cProfile.Profile] = None
-        self._prefix = prefix
-
-        self.profile_id: Optional[str] = None
-
-    def start(self, profile_id: str) -> None:
-        if self._profiler:
-            self.stop()
-
-        self.profile_id = profile_id
-
-        self._profiler = cProfile.Profile()
-        self._profiler.enable()
-        self._logger.info(f"Started profiling {self.profile_id}.")
-
-    def stop(self) -> Path:
-        if not self._profiler:
-            raise RuntimeError("Profiler not initialized. Call start() first.")
-        self._profiler.disable()
-
-        filename = f"{self._prefix}_{self.profile_id}.prof" if self._prefix else f"{self.profile_id}.prof"
-        path = Path(self._output_dir, filename)
-
-        self._profiler.dump_stats(path)
-        self._logger.info(f"Stopped profiling, profile dumped to {path}.")
-        self._profiler = None
-        self.profile_id = None
-
-        return path

invokeai/backend/image_util/basicsr/LICENSE

@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-      and distribution as defined by Sections 1 through 9 of this document.
-
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-
-      "Legal Entity" shall mean the union of the acting entity and all
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-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
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-
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-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
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-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright 2018-2022 BasicSR Authors
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.

invokeai/backend/image_util/basicsr/__init__.py

@@ -1,18 +0,0 @@
-"""
-Adapted from https://github.com/XPixelGroup/BasicSR
-License: Apache-2.0
-
-As of Feb 2024, `basicsr` appears to be unmaintained. It imports a function from `torchvision` that is removed in
-`torchvision` 0.17. Here is the deprecation warning:
-
-    UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in
-    0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in
-    torchvision.transforms.v2.functional.
-
-As a result, a dependency on `basicsr` means we cannot keep our `torchvision` dependency up to date.
-
-Because we only rely on a single class `RRDBNet` from `basicsr`, we've copied the relevant code here and removed the
-dependency on `basicsr`.
-
-The code is almost unchanged, only a few type annotations have been added. The license is also copied.
-"""

invokeai/backend/image_util/basicsr/arch_util.py

@@ -1,75 +0,0 @@
-from typing import Type
-
-import torch
-from torch import nn as nn
-from torch.nn import init as init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-
-@torch.no_grad()
-def default_init_weights(
-    module_list: list[nn.Module] | nn.Module, scale: float = 1, bias_fill: float = 0, **kwargs
-) -> None:
-    """Initialize network weights.
-
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-def make_layer(basic_block: Type[nn.Module], num_basic_block: int, **kwarg) -> nn.Sequential:
-    """Make layers by stacking the same blocks.
-
-    Args:
-        basic_block (Type[nn.Module]): nn.Module class for basic block.
-        num_basic_block (int): number of blocks.
-
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-# TODO: may write a cpp file
-def pixel_unshuffle(x: torch.Tensor, scale: int) -> torch.Tensor:
-    """Pixel unshuffle.
-
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)

invokeai/backend/image_util/basicsr/rrdbnet_arch.py

@@ -1,125 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from .arch_util import default_init_weights, make_layer, pixel_unshuffle
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat: int = 64, num_grow_ch: int = 32) -> None:
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Empirically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat: int, num_grow_ch: int = 32) -> None:
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Empirically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(
-        self,
-        num_in_ch: int,
-        num_out_ch: int,
-        scale: int = 4,
-        num_feat: int = 64,
-        num_block: int = 23,
-        num_grow_ch: int = 32,
-    ) -> None:
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest")))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest")))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out

invokeai/backend/image_util/depth_anything/__init__.py

@@ -1,109 +0,0 @@
-import pathlib
-from typing import Literal, Union
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-from einops import repeat
-from PIL import Image
-from torchvision.transforms import Compose
-
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
-from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
-from invokeai.backend.util.devices import choose_torch_device
-from invokeai.backend.util.util import download_with_progress_bar
-
-config = InvokeAIAppConfig.get_config()
-
-DEPTH_ANYTHING_MODELS = {
-    "large": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vitl14.pth",
-    },
-    "base": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vitb14.pth",
-    },
-    "small": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vits14.pth",
-    },
-}
-
-
-transform = Compose(
-    [
-        Resize(
-            width=518,
-            height=518,
-            resize_target=False,
-            keep_aspect_ratio=True,
-            ensure_multiple_of=14,
-            resize_method="lower_bound",
-            image_interpolation_method=cv2.INTER_CUBIC,
-        ),
-        NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        PrepareForNet(),
-    ]
-)
-
-
-class DepthAnythingDetector:
-    def __init__(self) -> None:
-        self.model = None
-        self.model_size: Union[Literal["large", "base", "small"], None] = None
-
-    def load_model(self, model_size=Literal["large", "base", "small"]):
-        DEPTH_ANYTHING_MODEL_PATH = pathlib.Path(config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"])
-        if not DEPTH_ANYTHING_MODEL_PATH.exists():
-            download_with_progress_bar(DEPTH_ANYTHING_MODELS[model_size]["url"], DEPTH_ANYTHING_MODEL_PATH)
-
-        if not self.model or model_size != self.model_size:
-            del self.model
-            self.model_size = model_size
-
-            match self.model_size:
-                case "small":
-                    self.model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
-                case "base":
-                    self.model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
-                case "large":
-                    self.model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
-                case _:
-                    raise TypeError("Not a supported model")
-
-            self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
-            self.model.eval()
-
-        self.model.to(choose_torch_device())
-        return self.model
-
-    def to(self, device):
-        self.model.to(device)
-        return self
-
-    def __call__(self, image, resolution=512, offload=False):
-        image = np.array(image, dtype=np.uint8)
-        image = image[:, :, ::-1] / 255.0
-
-        image_height, image_width = image.shape[:2]
-        image = transform({"image": image})["image"]
-        image = torch.from_numpy(image).unsqueeze(0).to(choose_torch_device())
-
-        with torch.no_grad():
-            depth = self.model(image)
-            depth = F.interpolate(depth[None], (image_height, image_width), mode="bilinear", align_corners=False)[0, 0]
-            depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-
-        depth_map = repeat(depth, "h w -> h w 3").cpu().numpy().astype(np.uint8)
-        depth_map = Image.fromarray(depth_map)
-
-        new_height = int(image_height * (resolution / image_width))
-        depth_map = depth_map.resize((resolution, new_height))
-
-        if offload:
-            del self.model
-
-        return depth_map

invokeai/backend/image_util/depth_anything/model/blocks.py

@@ -1,145 +0,0 @@
-import torch.nn as nn
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    if len(in_shape) >= 4:
-        out_shape4 = out_shape
-
-    if expand:
-        out_shape1 = out_shape
-        out_shape2 = out_shape * 2
-        out_shape3 = out_shape * 4
-        if len(in_shape) >= 4:
-            out_shape4 = out_shape * 8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    if len(in_shape) >= 4:
-        scratch.layer4_rn = nn.Conv2d(
-            in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-        )
-
-    return scratch
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module."""
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups = 1
-
-        self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
-
-        self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
-
-        if self.bn:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn:
-            out = self.bn1(out)
-
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block."""
-
-    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups = 1
-
-        self.expand = expand
-        out_features = features
-        if self.expand:
-            out_features = features // 2
-
-        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
-        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-        self.size = size
-
-    def forward(self, *xs, size=None):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-
-        output = self.resConfUnit2(output)
-
-        if (size is None) and (self.size is None):
-            modifier = {"scale_factor": 2}
-        elif size is None:
-            modifier = {"size": self.size}
-        else:
-            modifier = {"size": size}
-
-        output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
-
-        output = self.out_conv(output)
-
-        return output

invokeai/backend/image_util/depth_anything/model/dpt.py

@@ -1,183 +0,0 @@
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .blocks import FeatureFusionBlock, _make_scratch
-
-torchhub_path = Path(__file__).parent.parent / "torchhub"
-
-
-def _make_fusion_block(features, use_bn, size=None):
-    return FeatureFusionBlock(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-        size=size,
-    )
-
-
-class DPTHead(nn.Module):
-    def __init__(self, nclass, in_channels, features, out_channels, use_bn=False, use_clstoken=False):
-        super(DPTHead, self).__init__()
-
-        self.nclass = nclass
-        self.use_clstoken = use_clstoken
-
-        self.projects = nn.ModuleList(
-            [
-                nn.Conv2d(
-                    in_channels=in_channels,
-                    out_channels=out_channel,
-                    kernel_size=1,
-                    stride=1,
-                    padding=0,
-                )
-                for out_channel in out_channels
-            ]
-        )
-
-        self.resize_layers = nn.ModuleList(
-            [
-                nn.ConvTranspose2d(
-                    in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
-                ),
-                nn.ConvTranspose2d(
-                    in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
-                ),
-                nn.Identity(),
-                nn.Conv2d(
-                    in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
-                ),
-            ]
-        )
-
-        if use_clstoken:
-            self.readout_projects = nn.ModuleList()
-            for _ in range(len(self.projects)):
-                self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()))
-
-        self.scratch = _make_scratch(
-            out_channels,
-            features,
-            groups=1,
-            expand=False,
-        )
-
-        self.scratch.stem_transpose = None
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        head_features_1 = features
-        head_features_2 = 32
-
-        if nclass > 1:
-            self.scratch.output_conv = nn.Sequential(
-                nn.Conv2d(head_features_1, head_features_1, kernel_size=3, stride=1, padding=1),
-                nn.ReLU(True),
-                nn.Conv2d(head_features_1, nclass, kernel_size=1, stride=1, padding=0),
-            )
-        else:
-            self.scratch.output_conv1 = nn.Conv2d(
-                head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1
-            )
-
-            self.scratch.output_conv2 = nn.Sequential(
-                nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
-                nn.ReLU(True),
-                nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
-                nn.ReLU(True),
-                nn.Identity(),
-            )
-
-    def forward(self, out_features, patch_h, patch_w):
-        out = []
-        for i, x in enumerate(out_features):
-            if self.use_clstoken:
-                x, cls_token = x[0], x[1]
-                readout = cls_token.unsqueeze(1).expand_as(x)
-                x = self.readout_projects[i](torch.cat((x, readout), -1))
-            else:
-                x = x[0]
-
-            x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
-
-            x = self.projects[i](x)
-            x = self.resize_layers[i](x)
-
-            out.append(x)
-
-        layer_1, layer_2, layer_3, layer_4 = out
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv1(path_1)
-        out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
-        out = self.scratch.output_conv2(out)
-
-        return out
-
-
-class DPT_DINOv2(nn.Module):
-    def __init__(
-        self,
-        features,
-        out_channels,
-        encoder="vitl",
-        use_bn=False,
-        use_clstoken=False,
-    ):
-        super(DPT_DINOv2, self).__init__()
-
-        assert encoder in ["vits", "vitb", "vitl"]
-
-        # # in case the Internet connection is not stable, please load the DINOv2 locally
-        # if use_local:
-        #     self.pretrained = torch.hub.load(
-        #         torchhub_path / "facebookresearch_dinov2_main",
-        #         "dinov2_{:}14".format(encoder),
-        #         source="local",
-        #         pretrained=False,
-        #     )
-        # else:
-        #     self.pretrained = torch.hub.load(
-        #         "facebookresearch/dinov2",
-        #         "dinov2_{:}14".format(encoder),
-        #     )
-
-        self.pretrained = torch.hub.load(
-            "facebookresearch/dinov2",
-            "dinov2_{:}14".format(encoder),
-        )
-
-        dim = self.pretrained.blocks[0].attn.qkv.in_features
-
-        self.depth_head = DPTHead(1, dim, features, out_channels=out_channels, use_bn=use_bn, use_clstoken=use_clstoken)
-
-    def forward(self, x):
-        h, w = x.shape[-2:]
-
-        features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True)
-
-        patch_h, patch_w = h // 14, w // 14
-
-        depth = self.depth_head(features, patch_h, patch_w)
-        depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
-        depth = F.relu(depth)
-
-        return depth.squeeze(1)

invokeai/backend/image_util/depth_anything/utilities/util.py

@@ -1,227 +0,0 @@
-import math
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method)
-
-    sample["disparity"] = cv2.resize(sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST)
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height)."""
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller
-                    than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
-            new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
-            new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
-
-            if "semseg_mask" in sample:
-                # sample["semseg_mask"] = cv2.resize(
-                #     sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
-                # )
-                sample["semseg_mask"] = F.interpolate(
-                    torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode="nearest"
-                ).numpy()[0, 0]
-
-            if "mask" in sample:
-                sample["mask"] = cv2.resize(
-                    sample["mask"].astype(np.float32),
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-                # sample["mask"] = sample["mask"].astype(bool)
-
-        # print(sample['image'].shape, sample['depth'].shape)
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std."""
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input."""
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        if "semseg_mask" in sample:
-            sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
-            sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
-
-        return sample

invokeai/backend/image_util/dw_openpose/__init__.py

@@ -1,81 +0,0 @@
-import numpy as np
-import torch
-from controlnet_aux.util import resize_image
-from PIL import Image
-
-from invokeai.backend.image_util.dw_openpose.utils import draw_bodypose, draw_facepose, draw_handpose
-from invokeai.backend.image_util.dw_openpose.wholebody import Wholebody
-
-
-def draw_pose(pose, H, W, draw_face=True, draw_body=True, draw_hands=True, resolution=512):
-    bodies = pose["bodies"]
-    faces = pose["faces"]
-    hands = pose["hands"]
-    candidate = bodies["candidate"]
-    subset = bodies["subset"]
-    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
-
-    if draw_body:
-        canvas = draw_bodypose(canvas, candidate, subset)
-
-    if draw_hands:
-        canvas = draw_handpose(canvas, hands)
-
-    if draw_face:
-        canvas = draw_facepose(canvas, faces)
-
-    dwpose_image = resize_image(
-        canvas,
-        resolution,
-    )
-    dwpose_image = Image.fromarray(dwpose_image)
-
-    return dwpose_image
-
-
-class DWOpenposeDetector:
-    """
-    Code from the original implementation of the DW Openpose Detector.
-    Credits: https://github.com/IDEA-Research/DWPose
-    """
-
-    def __init__(self) -> None:
-        self.pose_estimation = Wholebody()
-
-    def __call__(
-        self, image: Image.Image, draw_face=False, draw_body=True, draw_hands=False, resolution=512
-    ) -> Image.Image:
-        np_image = np.array(image)
-        H, W, C = np_image.shape
-
-        with torch.no_grad():
-            candidate, subset = self.pose_estimation(np_image)
-            nums, keys, locs = candidate.shape
-            candidate[..., 0] /= float(W)
-            candidate[..., 1] /= float(H)
-            body = candidate[:, :18].copy()
-            body = body.reshape(nums * 18, locs)
-            score = subset[:, :18]
-            for i in range(len(score)):
-                for j in range(len(score[i])):
-                    if score[i][j] > 0.3:
-                        score[i][j] = int(18 * i + j)
-                    else:
-                        score[i][j] = -1
-
-            un_visible = subset < 0.3
-            candidate[un_visible] = -1
-
-            # foot = candidate[:, 18:24]
-
-            faces = candidate[:, 24:92]
-
-            hands = candidate[:, 92:113]
-            hands = np.vstack([hands, candidate[:, 113:]])
-
-            bodies = {"candidate": body, "subset": score}
-            pose = {"bodies": bodies, "hands": hands, "faces": faces}
-
-            return draw_pose(
-                pose, H, W, draw_face=draw_face, draw_hands=draw_hands, draw_body=draw_body, resolution=resolution
-            )

invokeai/backend/image_util/dw_openpose/onnxdet.py

@@ -1,128 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-
-import cv2
-import numpy as np
-
-
-def nms(boxes, scores, nms_thr):
-    """Single class NMS implemented in Numpy."""
-    x1 = boxes[:, 0]
-    y1 = boxes[:, 1]
-    x2 = boxes[:, 2]
-    y2 = boxes[:, 3]
-
-    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-    order = scores.argsort()[::-1]
-
-    keep = []
-    while order.size > 0:
-        i = order[0]
-        keep.append(i)
-        xx1 = np.maximum(x1[i], x1[order[1:]])
-        yy1 = np.maximum(y1[i], y1[order[1:]])
-        xx2 = np.minimum(x2[i], x2[order[1:]])
-        yy2 = np.minimum(y2[i], y2[order[1:]])
-
-        w = np.maximum(0.0, xx2 - xx1 + 1)
-        h = np.maximum(0.0, yy2 - yy1 + 1)
-        inter = w * h
-        ovr = inter / (areas[i] + areas[order[1:]] - inter)
-
-        inds = np.where(ovr <= nms_thr)[0]
-        order = order[inds + 1]
-
-    return keep
-
-
-def multiclass_nms(boxes, scores, nms_thr, score_thr):
-    """Multiclass NMS implemented in Numpy. Class-aware version."""
-    final_dets = []
-    num_classes = scores.shape[1]
-    for cls_ind in range(num_classes):
-        cls_scores = scores[:, cls_ind]
-        valid_score_mask = cls_scores > score_thr
-        if valid_score_mask.sum() == 0:
-            continue
-        else:
-            valid_scores = cls_scores[valid_score_mask]
-            valid_boxes = boxes[valid_score_mask]
-            keep = nms(valid_boxes, valid_scores, nms_thr)
-            if len(keep) > 0:
-                cls_inds = np.ones((len(keep), 1)) * cls_ind
-                dets = np.concatenate([valid_boxes[keep], valid_scores[keep, None], cls_inds], 1)
-                final_dets.append(dets)
-    if len(final_dets) == 0:
-        return None
-    return np.concatenate(final_dets, 0)
-
-
-def demo_postprocess(outputs, img_size, p6=False):
-    grids = []
-    expanded_strides = []
-    strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
-
-    hsizes = [img_size[0] // stride for stride in strides]
-    wsizes = [img_size[1] // stride for stride in strides]
-
-    for hsize, wsize, stride in zip(hsizes, wsizes, strides, strict=False):
-        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
-        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
-        grids.append(grid)
-        shape = grid.shape[:2]
-        expanded_strides.append(np.full((*shape, 1), stride))
-
-    grids = np.concatenate(grids, 1)
-    expanded_strides = np.concatenate(expanded_strides, 1)
-    outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
-    outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
-
-    return outputs
-
-
-def preprocess(img, input_size, swap=(2, 0, 1)):
-    if len(img.shape) == 3:
-        padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
-    else:
-        padded_img = np.ones(input_size, dtype=np.uint8) * 114
-
-    r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
-    resized_img = cv2.resize(
-        img,
-        (int(img.shape[1] * r), int(img.shape[0] * r)),
-        interpolation=cv2.INTER_LINEAR,
-    ).astype(np.uint8)
-    padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
-
-    padded_img = padded_img.transpose(swap)
-    padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
-    return padded_img, r
-
-
-def inference_detector(session, oriImg):
-    input_shape = (640, 640)
-    img, ratio = preprocess(oriImg, input_shape)
-
-    ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
-    output = session.run(None, ort_inputs)
-    predictions = demo_postprocess(output[0], input_shape)[0]
-
-    boxes = predictions[:, :4]
-    scores = predictions[:, 4:5] * predictions[:, 5:]
-
-    boxes_xyxy = np.ones_like(boxes)
-    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-    boxes_xyxy /= ratio
-    dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
-    if dets is not None:
-        final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
-        isscore = final_scores > 0.3
-        iscat = final_cls_inds == 0
-        isbbox = [i and j for (i, j) in zip(isscore, iscat, strict=False)]
-        final_boxes = final_boxes[isbbox]
-    else:
-        final_boxes = np.array([])
-
-    return final_boxes

invokeai/backend/image_util/dw_openpose/onnxpose.py

@@ -1,361 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-
-from typing import List, Tuple
-
-import cv2
-import numpy as np
-import onnxruntime as ort
-
-
-def preprocess(
-    img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256)
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Do preprocessing for RTMPose model inference.
-
-    Args:
-        img (np.ndarray): Input image in shape.
-        input_size (tuple): Input image size in shape (w, h).
-
-    Returns:
-        tuple:
-        - resized_img (np.ndarray): Preprocessed image.
-        - center (np.ndarray): Center of image.
-        - scale (np.ndarray): Scale of image.
-    """
-    # get shape of image
-    img_shape = img.shape[:2]
-    out_img, out_center, out_scale = [], [], []
-    if len(out_bbox) == 0:
-        out_bbox = [[0, 0, img_shape[1], img_shape[0]]]
-    for i in range(len(out_bbox)):
-        x0 = out_bbox[i][0]
-        y0 = out_bbox[i][1]
-        x1 = out_bbox[i][2]
-        y1 = out_bbox[i][3]
-        bbox = np.array([x0, y0, x1, y1])
-
-        # get center and scale
-        center, scale = bbox_xyxy2cs(bbox, padding=1.25)
-
-        # do affine transformation
-        resized_img, scale = top_down_affine(input_size, scale, center, img)
-
-        # normalize image
-        mean = np.array([123.675, 116.28, 103.53])
-        std = np.array([58.395, 57.12, 57.375])
-        resized_img = (resized_img - mean) / std
-
-        out_img.append(resized_img)
-        out_center.append(center)
-        out_scale.append(scale)
-
-    return out_img, out_center, out_scale
-
-
-def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray:
-    """Inference RTMPose model.
-
-    Args:
-        sess (ort.InferenceSession): ONNXRuntime session.
-        img (np.ndarray): Input image in shape.
-
-    Returns:
-        outputs (np.ndarray): Output of RTMPose model.
-    """
-    all_out = []
-    # build input
-    for i in range(len(img)):
-        input = [img[i].transpose(2, 0, 1)]
-
-        # build output
-        sess_input = {sess.get_inputs()[0].name: input}
-        sess_output = []
-        for out in sess.get_outputs():
-            sess_output.append(out.name)
-
-        # run model
-        outputs = sess.run(sess_output, sess_input)
-        all_out.append(outputs)
-
-    return all_out
-
-
-def postprocess(
-    outputs: List[np.ndarray],
-    model_input_size: Tuple[int, int],
-    center: Tuple[int, int],
-    scale: Tuple[int, int],
-    simcc_split_ratio: float = 2.0,
-) -> Tuple[np.ndarray, np.ndarray]:
-    """Postprocess for RTMPose model output.
-
-    Args:
-        outputs (np.ndarray): Output of RTMPose model.
-        model_input_size (tuple): RTMPose model Input image size.
-        center (tuple): Center of bbox in shape (x, y).
-        scale (tuple): Scale of bbox in shape (w, h).
-        simcc_split_ratio (float): Split ratio of simcc.
-
-    Returns:
-        tuple:
-        - keypoints (np.ndarray): Rescaled keypoints.
-        - scores (np.ndarray): Model predict scores.
-    """
-    all_key = []
-    all_score = []
-    for i in range(len(outputs)):
-        # use simcc to decode
-        simcc_x, simcc_y = outputs[i]
-        keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio)
-
-        # rescale keypoints
-        keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2
-        all_key.append(keypoints[0])
-        all_score.append(scores[0])
-
-    return np.array(all_key), np.array(all_score)
-
-
-def bbox_xyxy2cs(bbox: np.ndarray, padding: float = 1.0) -> Tuple[np.ndarray, np.ndarray]:
-    """Transform the bbox format from (x,y,w,h) into (center, scale)
-
-    Args:
-        bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
-            as (left, top, right, bottom)
-        padding (float): BBox padding factor that will be multilied to scale.
-            Default: 1.0
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
-            (n, 2)
-        - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
-            (n, 2)
-    """
-    # convert single bbox from (4, ) to (1, 4)
-    dim = bbox.ndim
-    if dim == 1:
-        bbox = bbox[None, :]
-
-    # get bbox center and scale
-    x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3])
-    center = np.hstack([x1 + x2, y1 + y2]) * 0.5
-    scale = np.hstack([x2 - x1, y2 - y1]) * padding
-
-    if dim == 1:
-        center = center[0]
-        scale = scale[0]
-
-    return center, scale
-
-
-def _fix_aspect_ratio(bbox_scale: np.ndarray, aspect_ratio: float) -> np.ndarray:
-    """Extend the scale to match the given aspect ratio.
-
-    Args:
-        scale (np.ndarray): The image scale (w, h) in shape (2, )
-        aspect_ratio (float): The ratio of ``w/h``
-
-    Returns:
-        np.ndarray: The reshaped image scale in (2, )
-    """
-    w, h = np.hsplit(bbox_scale, [1])
-    bbox_scale = np.where(w > h * aspect_ratio, np.hstack([w, w / aspect_ratio]), np.hstack([h * aspect_ratio, h]))
-    return bbox_scale
-
-
-def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray:
-    """Rotate a point by an angle.
-
-    Args:
-        pt (np.ndarray): 2D point coordinates (x, y) in shape (2, )
-        angle_rad (float): rotation angle in radian
-
-    Returns:
-        np.ndarray: Rotated point in shape (2, )
-    """
-    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
-    rot_mat = np.array([[cs, -sn], [sn, cs]])
-    return rot_mat @ pt
-
-
-def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray:
-    """To calculate the affine matrix, three pairs of points are required. This
-    function is used to get the 3rd point, given 2D points a & b.
-
-    The 3rd point is defined by rotating vector `a - b` by 90 degrees
-    anticlockwise, using b as the rotation center.
-
-    Args:
-        a (np.ndarray): The 1st point (x,y) in shape (2, )
-        b (np.ndarray): The 2nd point (x,y) in shape (2, )
-
-    Returns:
-        np.ndarray: The 3rd point.
-    """
-    direction = a - b
-    c = b + np.r_[-direction[1], direction[0]]
-    return c
-
-
-def get_warp_matrix(
-    center: np.ndarray,
-    scale: np.ndarray,
-    rot: float,
-    output_size: Tuple[int, int],
-    shift: Tuple[float, float] = (0.0, 0.0),
-    inv: bool = False,
-) -> np.ndarray:
-    """Calculate the affine transformation matrix that can warp the bbox area
-    in the input image to the output size.
-
-    Args:
-        center (np.ndarray[2, ]): Center of the bounding box (x, y).
-        scale (np.ndarray[2, ]): Scale of the bounding box
-            wrt [width, height].
-        rot (float): Rotation angle (degree).
-        output_size (np.ndarray[2, ] | list(2,)): Size of the
-            destination heatmaps.
-        shift (0-100%): Shift translation ratio wrt the width/height.
-            Default (0., 0.).
-        inv (bool): Option to inverse the affine transform direction.
-            (inv=False: src->dst or inv=True: dst->src)
-
-    Returns:
-        np.ndarray: A 2x3 transformation matrix
-    """
-    shift = np.array(shift)
-    src_w = scale[0]
-    dst_w = output_size[0]
-    dst_h = output_size[1]
-
-    # compute transformation matrix
-    rot_rad = np.deg2rad(rot)
-    src_dir = _rotate_point(np.array([0.0, src_w * -0.5]), rot_rad)
-    dst_dir = np.array([0.0, dst_w * -0.5])
-
-    # get four corners of the src rectangle in the original image
-    src = np.zeros((3, 2), dtype=np.float32)
-    src[0, :] = center + scale * shift
-    src[1, :] = center + src_dir + scale * shift
-    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
-
-    # get four corners of the dst rectangle in the input image
-    dst = np.zeros((3, 2), dtype=np.float32)
-    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
-    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
-    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
-
-    if inv:
-        warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src))
-    else:
-        warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst))
-
-    return warp_mat
-
-
-def top_down_affine(
-    input_size: dict, bbox_scale: dict, bbox_center: dict, img: np.ndarray
-) -> Tuple[np.ndarray, np.ndarray]:
-    """Get the bbox image as the model input by affine transform.
-
-    Args:
-        input_size (dict): The input size of the model.
-        bbox_scale (dict): The bbox scale of the img.
-        bbox_center (dict): The bbox center of the img.
-        img (np.ndarray): The original image.
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: img after affine transform.
-        - np.ndarray[float32]: bbox scale after affine transform.
-    """
-    w, h = input_size
-    warp_size = (int(w), int(h))
-
-    # reshape bbox to fixed aspect ratio
-    bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h)
-
-    # get the affine matrix
-    center = bbox_center
-    scale = bbox_scale
-    rot = 0
-    warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h))
-
-    # do affine transform
-    img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR)
-
-    return img, bbox_scale
-
-
-def get_simcc_maximum(simcc_x: np.ndarray, simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    """Get maximum response location and value from simcc representations.
-
-    Note:
-        instance number: N
-        num_keypoints: K
-        heatmap height: H
-        heatmap width: W
-
-    Args:
-        simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx)
-        simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy)
-
-    Returns:
-        tuple:
-        - locs (np.ndarray): locations of maximum heatmap responses in shape
-            (K, 2) or (N, K, 2)
-        - vals (np.ndarray): values of maximum heatmap responses in shape
-            (K,) or (N, K)
-    """
-    N, K, Wx = simcc_x.shape
-    simcc_x = simcc_x.reshape(N * K, -1)
-    simcc_y = simcc_y.reshape(N * K, -1)
-
-    # get maximum value locations
-    x_locs = np.argmax(simcc_x, axis=1)
-    y_locs = np.argmax(simcc_y, axis=1)
-    locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32)
-    max_val_x = np.amax(simcc_x, axis=1)
-    max_val_y = np.amax(simcc_y, axis=1)
-
-    # get maximum value across x and y axis
-    mask = max_val_x > max_val_y
-    max_val_x[mask] = max_val_y[mask]
-    vals = max_val_x
-    locs[vals <= 0.0] = -1
-
-    # reshape
-    locs = locs.reshape(N, K, 2)
-    vals = vals.reshape(N, K)
-
-    return locs, vals
-
-
-def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]:
-    """Modulate simcc distribution with Gaussian.
-
-    Args:
-        simcc_x (np.ndarray[K, Wx]): model predicted simcc in x.
-        simcc_y (np.ndarray[K, Wy]): model predicted simcc in y.
-        simcc_split_ratio (int): The split ratio of simcc.
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2)
-        - np.ndarray[float32]: scores in shape (K,) or (n, K)
-    """
-    keypoints, scores = get_simcc_maximum(simcc_x, simcc_y)
-    keypoints /= simcc_split_ratio
-
-    return keypoints, scores
-
-
-def inference_pose(session, out_bbox, oriImg):
-    h, w = session.get_inputs()[0].shape[2:]
-    model_input_size = (w, h)
-    resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size)
-    outputs = inference(session, resized_img)
-    keypoints, scores = postprocess(outputs, model_input_size, center, scale)
-
-    return keypoints, scores

invokeai/backend/image_util/dw_openpose/utils.py

@@ -1,155 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-
-import math
-
-import cv2
-import matplotlib
-import numpy as np
-
-eps = 0.01
-
-
-def draw_bodypose(canvas, candidate, subset):
-    H, W, C = canvas.shape
-    candidate = np.array(candidate)
-    subset = np.array(subset)
-
-    stickwidth = 4
-
-    limbSeq = [
-        [2, 3],
-        [2, 6],
-        [3, 4],
-        [4, 5],
-        [6, 7],
-        [7, 8],
-        [2, 9],
-        [9, 10],
-        [10, 11],
-        [2, 12],
-        [12, 13],
-        [13, 14],
-        [2, 1],
-        [1, 15],
-        [15, 17],
-        [1, 16],
-        [16, 18],
-        [3, 17],
-        [6, 18],
-    ]
-
-    colors = [
-        [255, 0, 0],
-        [255, 85, 0],
-        [255, 170, 0],
-        [255, 255, 0],
-        [170, 255, 0],
-        [85, 255, 0],
-        [0, 255, 0],
-        [0, 255, 85],
-        [0, 255, 170],
-        [0, 255, 255],
-        [0, 170, 255],
-        [0, 85, 255],
-        [0, 0, 255],
-        [85, 0, 255],
-        [170, 0, 255],
-        [255, 0, 255],
-        [255, 0, 170],
-        [255, 0, 85],
-    ]
-
-    for i in range(17):
-        for n in range(len(subset)):
-            index = subset[n][np.array(limbSeq[i]) - 1]
-            if -1 in index:
-                continue
-            Y = candidate[index.astype(int), 0] * float(W)
-            X = candidate[index.astype(int), 1] * float(H)
-            mX = np.mean(X)
-            mY = np.mean(Y)
-            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
-            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
-            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
-            cv2.fillConvexPoly(canvas, polygon, colors[i])
-
-    canvas = (canvas * 0.6).astype(np.uint8)
-
-    for i in range(18):
-        for n in range(len(subset)):
-            index = int(subset[n][i])
-            if index == -1:
-                continue
-            x, y = candidate[index][0:2]
-            x = int(x * W)
-            y = int(y * H)
-            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
-
-    return canvas
-
-
-def draw_handpose(canvas, all_hand_peaks):
-    H, W, C = canvas.shape
-
-    edges = [
-        [0, 1],
-        [1, 2],
-        [2, 3],
-        [3, 4],
-        [0, 5],
-        [5, 6],
-        [6, 7],
-        [7, 8],
-        [0, 9],
-        [9, 10],
-        [10, 11],
-        [11, 12],
-        [0, 13],
-        [13, 14],
-        [14, 15],
-        [15, 16],
-        [0, 17],
-        [17, 18],
-        [18, 19],
-        [19, 20],
-    ]
-
-    for peaks in all_hand_peaks:
-        peaks = np.array(peaks)
-
-        for ie, e in enumerate(edges):
-            x1, y1 = peaks[e[0]]
-            x2, y2 = peaks[e[1]]
-            x1 = int(x1 * W)
-            y1 = int(y1 * H)
-            x2 = int(x2 * W)
-            y2 = int(y2 * H)
-            if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
-                cv2.line(
-                    canvas,
-                    (x1, y1),
-                    (x2, y2),
-                    matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
-                    thickness=2,
-                )
-
-        for _, keyponit in enumerate(peaks):
-            x, y = keyponit
-            x = int(x * W)
-            y = int(y * H)
-            if x > eps and y > eps:
-                cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
-    return canvas
-
-
-def draw_facepose(canvas, all_lmks):
-    H, W, C = canvas.shape
-    for lmks in all_lmks:
-        lmks = np.array(lmks)
-        for lmk in lmks:
-            x, y = lmk
-            x = int(x * W)
-            y = int(y * H)
-            if x > eps and y > eps:
-                cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
-    return canvas

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -1,67 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-# Modified pathing to suit Invoke
-
-import pathlib
-
-import numpy as np
-import onnxruntime as ort
-
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.backend.util.devices import choose_torch_device
-from invokeai.backend.util.util import download_with_progress_bar
-
-from .onnxdet import inference_detector
-from .onnxpose import inference_pose
-
-DWPOSE_MODELS = {
-    "yolox_l.onnx": {
-        "local": "any/annotators/dwpose/yolox_l.onnx",
-        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx?download=true",
-    },
-    "dw-ll_ucoco_384.onnx": {
-        "local": "any/annotators/dwpose/dw-ll_ucoco_384.onnx",
-        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx?download=true",
-    },
-}
-
-config = InvokeAIAppConfig.get_config()
-
-
-class Wholebody:
-    def __init__(self):
-        device = choose_torch_device()
-
-        providers = ["CUDAExecutionProvider"] if device == "cuda" else ["CPUExecutionProvider"]
-
-        DET_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"])
-        if not DET_MODEL_PATH.exists():
-            download_with_progress_bar(DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)
-
-        POSE_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["local"])
-        if not POSE_MODEL_PATH.exists():
-            download_with_progress_bar(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["url"], POSE_MODEL_PATH)
-
-        onnx_det = DET_MODEL_PATH
-        onnx_pose = POSE_MODEL_PATH
-
-        self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers)
-        self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers)
-
-    def __call__(self, oriImg):
-        det_result = inference_detector(self.session_det, oriImg)
-        keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)
-
-        keypoints_info = np.concatenate((keypoints, scores[..., None]), axis=-1)
-        # compute neck joint
-        neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
-        # neck score when visualizing pred
-        neck[:, 2:4] = np.logical_and(keypoints_info[:, 5, 2:4] > 0.3, keypoints_info[:, 6, 2:4] > 0.3).astype(int)
-        new_keypoints_info = np.insert(keypoints_info, 17, neck, axis=1)
-        mmpose_idx = [17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3]
-        openpose_idx = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17]
-        new_keypoints_info[:, openpose_idx] = new_keypoints_info[:, mmpose_idx]
-        keypoints_info = new_keypoints_info
-
-        keypoints, scores = keypoints_info[..., :2], keypoints_info[..., 2]
-
-        return keypoints, scores

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -7,10 +7,10 @@ import cv2
 import numpy as np
 import numpy.typing as npt
 import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
 from cv2.typing import MatLike
 from tqdm import tqdm
 
-from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.util.devices import choose_torch_device
 
 """

invokeai/backend/install/install_helper.py

@@ -1,281 +0,0 @@
-"""Utility (backend) functions used by model_install.py"""
-import re
-from logging import Logger
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-import omegaconf
-from huggingface_hub import HfFolder
-from pydantic import BaseModel, Field
-from pydantic.dataclasses import dataclass
-from pydantic.networks import AnyHttpUrl
-from requests import HTTPError
-from tqdm import tqdm
-
-import invokeai.configs as configs
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.download import DownloadQueueService
-from invokeai.app.services.events.events_base import EventServiceBase
-from invokeai.app.services.image_files.image_files_disk import DiskImageFileStorage
-from invokeai.app.services.model_install import (
-    HFModelSource,
-    LocalModelSource,
-    ModelInstallService,
-    ModelInstallServiceBase,
-    ModelSource,
-    URLModelSource,
-)
-from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
-from invokeai.app.services.shared.sqlite.sqlite_util import init_db
-from invokeai.backend.model_manager import (
-    BaseModelType,
-    InvalidModelConfigException,
-    ModelType,
-)
-from invokeai.backend.model_manager.metadata import UnknownMetadataException
-from invokeai.backend.util.logging import InvokeAILogger
-
-# name of the starter models file
-INITIAL_MODELS = "INITIAL_MODELS2.yaml"
-
-
-def initialize_record_store(app_config: InvokeAIAppConfig) -> ModelRecordServiceBase:
-    """Return an initialized ModelConfigRecordServiceBase object."""
-    logger = InvokeAILogger.get_logger(config=app_config)
-    image_files = DiskImageFileStorage(f"{app_config.output_path}/images")
-    db = init_db(config=app_config, logger=logger, image_files=image_files)
-    obj: ModelRecordServiceBase = ModelRecordServiceSQL(db)
-    return obj
-
-
-def initialize_installer(
-    app_config: InvokeAIAppConfig, event_bus: Optional[EventServiceBase] = None
-) -> ModelInstallServiceBase:
-    """Return an initialized ModelInstallService object."""
-    record_store = initialize_record_store(app_config)
-    metadata_store = record_store.metadata_store
-    download_queue = DownloadQueueService()
-    installer = ModelInstallService(
-        app_config=app_config,
-        record_store=record_store,
-        metadata_store=metadata_store,
-        download_queue=download_queue,
-        event_bus=event_bus,
-    )
-    download_queue.start()
-    installer.start()
-    return installer
-
-
-class UnifiedModelInfo(BaseModel):
-    """Catchall class for information in INITIAL_MODELS2.yaml."""
-
-    name: Optional[str] = None
-    base: Optional[BaseModelType] = None
-    type: Optional[ModelType] = None
-    source: Optional[str] = None
-    subfolder: Optional[str] = None
-    description: Optional[str] = None
-    recommended: bool = False
-    installed: bool = False
-    default: bool = False
-    requires: List[str] = Field(default_factory=list)
-
-
-@dataclass
-class InstallSelections:
-    """Lists of models to install and remove."""
-
-    install_models: List[UnifiedModelInfo] = Field(default_factory=list)
-    remove_models: List[str] = Field(default_factory=list)
-
-
-class TqdmEventService(EventServiceBase):
-    """An event service to track downloads."""
-
-    def __init__(self) -> None:
-        """Create a new TqdmEventService object."""
-        super().__init__()
-        self._bars: Dict[str, tqdm] = {}
-        self._last: Dict[str, int] = {}
-
-    def dispatch(self, event_name: str, payload: Any) -> None:
-        """Dispatch an event by appending it to self.events."""
-        if payload["event"] == "model_install_downloading":
-            data = payload["data"]
-            dest = data["local_path"]
-            total_bytes = data["total_bytes"]
-            bytes = data["bytes"]
-            if dest not in self._bars:
-                self._bars[dest] = tqdm(desc=Path(dest).name, initial=0, total=total_bytes, unit="iB", unit_scale=True)
-                self._last[dest] = 0
-            self._bars[dest].update(bytes - self._last[dest])
-            self._last[dest] = bytes
-
-
-class InstallHelper(object):
-    """Capture information stored jointly in INITIAL_MODELS.yaml and the installed models db."""
-
-    def __init__(self, app_config: InvokeAIAppConfig, logger: Logger):
-        """Create new InstallHelper object."""
-        self._app_config = app_config
-        self.all_models: Dict[str, UnifiedModelInfo] = {}
-
-        omega = omegaconf.OmegaConf.load(Path(configs.__path__[0]) / INITIAL_MODELS)
-        assert isinstance(omega, omegaconf.dictconfig.DictConfig)
-
-        self._installer = initialize_installer(app_config, TqdmEventService())
-        self._initial_models = omega
-        self._installed_models: List[str] = []
-        self._starter_models: List[str] = []
-        self._default_model: Optional[str] = None
-        self._logger = logger
-        self._initialize_model_lists()
-
-    @property
-    def installer(self) -> ModelInstallServiceBase:
-        """Return the installer object used internally."""
-        return self._installer
-
-    def _initialize_model_lists(self) -> None:
-        """
-        Initialize our model slots.
-
-        Set up the following:
-        installed_models  -- list of installed model keys
-        starter_models -- list of starter model keys from INITIAL_MODELS
-        all_models -- dict of key => UnifiedModelInfo
-        default_model -- key to default model
-        """
-        # previously-installed models
-        for model in self._installer.record_store.all_models():
-            info = UnifiedModelInfo.parse_obj(model.dict())
-            info.installed = True
-            model_key = f"{model.base.value}/{model.type.value}/{model.name}"
-            self.all_models[model_key] = info
-            self._installed_models.append(model_key)
-
-        for key in self._initial_models.keys():
-            assert isinstance(key, str)
-            if key in self.all_models:
-                # we want to preserve the description
-                description = self.all_models[key].description or self._initial_models[key].get("description")
-                self.all_models[key].description = description
-            else:
-                base_model, model_type, model_name = key.split("/")
-                info = UnifiedModelInfo(
-                    name=model_name,
-                    type=ModelType(model_type),
-                    base=BaseModelType(base_model),
-                    source=self._initial_models[key].source,
-                    description=self._initial_models[key].get("description"),
-                    recommended=self._initial_models[key].get("recommended", False),
-                    default=self._initial_models[key].get("default", False),
-                    subfolder=self._initial_models[key].get("subfolder"),
-                    requires=list(self._initial_models[key].get("requires", [])),
-                )
-                self.all_models[key] = info
-            if not self.default_model():
-                self._default_model = key
-            elif self._initial_models[key].get("default", False):
-                self._default_model = key
-            self._starter_models.append(key)
-
-        # previously-installed models
-        for model in self._installer.record_store.all_models():
-            info = UnifiedModelInfo.parse_obj(model.dict())
-            info.installed = True
-            model_key = f"{model.base.value}/{model.type.value}/{model.name}"
-            self.all_models[model_key] = info
-            self._installed_models.append(model_key)
-
-    def recommended_models(self) -> List[UnifiedModelInfo]:
-        """List of the models recommended in INITIAL_MODELS.yaml."""
-        return [self._to_model(x) for x in self._starter_models if self._to_model(x).recommended]
-
-    def installed_models(self) -> List[UnifiedModelInfo]:
-        """List of models already installed."""
-        return [self._to_model(x) for x in self._installed_models]
-
-    def starter_models(self) -> List[UnifiedModelInfo]:
-        """List of starter models."""
-        return [self._to_model(x) for x in self._starter_models]
-
-    def default_model(self) -> Optional[UnifiedModelInfo]:
-        """Return the default model."""
-        return self._to_model(self._default_model) if self._default_model else None
-
-    def _to_model(self, key: str) -> UnifiedModelInfo:
-        return self.all_models[key]
-
-    def _add_required_models(self, model_list: List[UnifiedModelInfo]) -> None:
-        installed = {x.source for x in self.installed_models()}
-        reverse_source = {x.source: x for x in self.all_models.values()}
-        additional_models: List[UnifiedModelInfo] = []
-        for model_info in model_list:
-            for requirement in model_info.requires:
-                if requirement not in installed and reverse_source.get(requirement):
-                    additional_models.append(reverse_source[requirement])
-        model_list.extend(additional_models)
-
-    def _make_install_source(self, model_info: UnifiedModelInfo) -> ModelSource:
-        assert model_info.source
-        model_path_id_or_url = model_info.source.strip("\"' ")
-        model_path = Path(model_path_id_or_url)
-
-        if model_path.exists():  # local file on disk
-            return LocalModelSource(path=model_path.absolute(), inplace=True)
-        if re.match(r"^[^/]+/[^/]+$", model_path_id_or_url):  # hugging face repo_id
-            return HFModelSource(
-                repo_id=model_path_id_or_url,
-                access_token=HfFolder.get_token(),
-                subfolder=model_info.subfolder,
-            )
-        if re.match(r"^(http|https):", model_path_id_or_url):
-            return URLModelSource(url=AnyHttpUrl(model_path_id_or_url))
-        raise ValueError(f"Unsupported model source: {model_path_id_or_url}")
-
-    def add_or_delete(self, selections: InstallSelections) -> None:
-        """Add or delete selected models."""
-        installer = self._installer
-        self._add_required_models(selections.install_models)
-        for model in selections.install_models:
-            source = self._make_install_source(model)
-            config = (
-                {
-                    "description": model.description,
-                    "name": model.name,
-                }
-                if model.name
-                else None
-            )
-
-            try:
-                installer.import_model(
-                    source=source,
-                    config=config,
-                )
-            except (UnknownMetadataException, InvalidModelConfigException, HTTPError, OSError) as e:
-                self._logger.warning(f"{source}: {e}")
-
-        for model_to_remove in selections.remove_models:
-            parts = model_to_remove.split("/")
-            if len(parts) == 1:
-                base_model, model_type, model_name = (None, None, model_to_remove)
-            else:
-                base_model, model_type, model_name = parts
-            matches = installer.record_store.search_by_attr(
-                base_model=BaseModelType(base_model) if base_model else None,
-                model_type=ModelType(model_type) if model_type else None,
-                model_name=model_name,
-            )
-            if len(matches) > 1:
-                print(f"{model} is ambiguous. Please use model_type:model_name (e.g. main:my_model) to disambiguate.")
-            elif not matches:
-                print(f"{model}: unknown model")
-            else:
-                for m in matches:
-                    print(f"Deleting {m.type}:{m.name}")
-                    installer.delete(m.key)
-
-        installer.wait_for_installs()

invokeai/backend/install/invokeai_configure.py

@@ -849,7 +849,7 @@ def migrate_if_needed(opt: Namespace, root: Path) -> bool:
 
 
 # -------------------------------------
-def main() -> None:
+def main():
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")
     parser.add_argument(
         "--skip-sd-weights",

invokeai/backend/install/model_install_backend.py

@@ -104,14 +104,12 @@ class ModelInstall(object):
         prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
         model_manager: Optional[ModelManager] = None,
         access_token: Optional[str] = None,
-        civitai_api_key: Optional[str] = None,
     ):
         self.config = config
         self.mgr = model_manager or ModelManager(config.model_conf_path)
         self.datasets = OmegaConf.load(Dataset_path)
         self.prediction_helper = prediction_type_helper
         self.access_token = access_token or HfFolder.get_token()
-        self.civitai_api_key = civitai_api_key or config.civitai_api_key
         self.reverse_paths = self._reverse_paths(self.datasets)
 
     def all_models(self) -> Dict[str, ModelLoadInfo]:
@@ -328,11 +326,7 @@ class ModelInstall(object):
 
     def _install_url(self, url: str) -> AddModelResult:
         with TemporaryDirectory(dir=self.config.models_path) as staging:
-            CIVITAI_RE = r".*civitai.com.*"
-            civit_url = re.match(CIVITAI_RE, url, re.IGNORECASE)
-            location = download_with_resume(
-                url, Path(staging), access_token=self.civitai_api_key if civit_url else None
-            )
+            location = download_with_resume(url, Path(staging))
             if not location:
                 logger.error(f"Unable to download {url}. Skipping.")
             info = ModelProbe().heuristic_probe(location, self.prediction_helper)

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -42,7 +42,8 @@ from diffusers.schedulers import (
     PNDMScheduler,
     UnCLIPScheduler,
 )
-from diffusers.utils import is_accelerate_available
+from diffusers.utils import is_accelerate_available, is_omegaconf_available
+from diffusers.utils.import_utils import BACKENDS_MAPPING
 from picklescan.scanner import scan_file_path
 from transformers import (
     AutoFeatureExtractor,
@@ -1210,6 +1211,9 @@ def download_from_original_stable_diffusion_ckpt(
     if prediction_type == "v-prediction":
         prediction_type = "v_prediction"
 
+    if not is_omegaconf_available():
+        raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
+
     if from_safetensors:
         from safetensors.torch import load_file as safe_load
 
@@ -1643,6 +1647,11 @@ def download_controlnet_from_original_ckpt(
     cross_attention_dim: Optional[bool] = None,
     scan_needed: bool = False,
 ) -> DiffusionPipeline:
+    if not is_omegaconf_available():
+        raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
+
+    from omegaconf import OmegaConf
+
     if from_safetensors:
         from safetensors import safe_open
 

invokeai/backend/model_management/models/sdxl.py

@@ -141,7 +141,7 @@ class StableDiffusionXLModel(DiffusersModel):
                 version=base_model,
                 model_config=config,
                 output_path=output_path,
-                use_safetensors=True,
+                use_safetensors=False,  # corrupts sdxl models for some reason
                 **kwargs,
             )
         else:

invokeai/backend/model_management/seamless.py

@@ -1,11 +1,10 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import Callable, List, Union
+from typing import List, Union
 
 import torch.nn as nn
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
 
 
 def _conv_forward_asymmetric(self, input, weight, bias):
@@ -27,51 +26,70 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
-    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
-    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
     try:
-        # Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
-        skipped_layers = 1
+        to_restore = []
+
         for m_name, m in model.named_modules():
-            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                continue
-
-            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
-                # down_blocks.1.resnets.1.conv1
-                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
-                block_num = int(block_num)
-                resnet_num = int(resnet_num)
-
-                if block_num >= len(model.down_blocks) - skipped_layers:
+            if isinstance(model, UNet2DConditionModel):
+                if ".attentions." in m_name:
                     continue
 
-                # Skip the second resnet (could be configurable)
-                if resnet_num > 0:
+                if ".resnets." in m_name:
+                    if ".conv2" in m_name:
+                        continue
+                    if ".conv_shortcut" in m_name:
+                        continue
+
+            """
+            if isinstance(model, UNet2DConditionModel):
+                if False and ".upsamplers." in m_name:
                     continue
 
-                # Skip Conv2d layers (could be configurable)
-                if submodule_name == "conv2":
+                if False and ".downsamplers." in m_name:
                     continue
 
-            m.asymmetric_padding_mode = {}
-            m.asymmetric_padding = {}
-            m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-            m.asymmetric_padding["x"] = (
-                m._reversed_padding_repeated_twice[0],
-                m._reversed_padding_repeated_twice[1],
-                0,
-                0,
-            )
-            m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-            m.asymmetric_padding["y"] = (
-                0,
-                0,
-                m._reversed_padding_repeated_twice[2],
-                m._reversed_padding_repeated_twice[3],
-            )
+                if True and ".resnets." in m_name:
+                    if True and ".conv1" in m_name:
+                        if False and "down_blocks" in m_name:
+                            continue
+                        if False and "mid_block" in m_name:
+                            continue
+                        if False and "up_blocks" in m_name:
+                            continue
 
-            to_restore.append((m, m._conv_forward))
-            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+                    if True and ".conv2" in m_name:
+                        continue
+
+                    if True and ".conv_shortcut" in m_name:
+                        continue
+
+                if True and ".attentions." in m_name:
+                    continue
+
+                if False and m_name in ["conv_in", "conv_out"]:
+                    continue
+            """
+
+            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                m.asymmetric_padding_mode = {}
+                m.asymmetric_padding = {}
+                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
+                m.asymmetric_padding["x"] = (
+                    m._reversed_padding_repeated_twice[0],
+                    m._reversed_padding_repeated_twice[1],
+                    0,
+                    0,
+                )
+                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
+                m.asymmetric_padding["y"] = (
+                    0,
+                    0,
+                    m._reversed_padding_repeated_twice[2],
+                    m._reversed_padding_repeated_twice[3],
+                )
+
+                to_restore.append((m, m._conv_forward))
+                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

invokeai/backend/model_manager/merge.py

@@ -1,177 +0,0 @@
-"""
-invokeai.backend.model_manager.merge exports:
-merge_diffusion_models() -- combine multiple models by location and return a pipeline object
-merge_diffusion_models_and_commit() -- combine multiple models by ModelManager ID and write to models.yaml
-
-Copyright (c) 2023 Lincoln Stein and the InvokeAI Development Team
-"""
-
-import warnings
-from enum import Enum
-from pathlib import Path
-from typing import Any, List, Optional, Set
-
-import torch
-from diffusers import AutoPipelineForText2Image
-from diffusers import logging as dlogging
-
-from invokeai.app.services.model_install import ModelInstallServiceBase
-from invokeai.backend.util.devices import choose_torch_device, torch_dtype
-
-from . import (
-    AnyModelConfig,
-    BaseModelType,
-    ModelType,
-    ModelVariantType,
-)
-from .config import MainDiffusersConfig
-
-
-class MergeInterpolationMethod(str, Enum):
-    WeightedSum = "weighted_sum"
-    Sigmoid = "sigmoid"
-    InvSigmoid = "inv_sigmoid"
-    AddDifference = "add_difference"
-
-
-class ModelMerger(object):
-    """Wrapper class for model merge function."""
-
-    def __init__(self, installer: ModelInstallServiceBase):
-        """
-        Initialize a ModelMerger object.
-
-        :param store: Underlying storage manager for the running process.
-        :param config: InvokeAIAppConfig object (if not provided, default will be selected).
-        """
-        self._installer = installer
-
-    def merge_diffusion_models(
-        self,
-        model_paths: List[Path],
-        alpha: float = 0.5,
-        interp: Optional[MergeInterpolationMethod] = None,
-        force: bool = False,
-        variant: Optional[str] = None,
-        **kwargs: Any,
-    ) -> Any:  # pipe.merge is an untyped function.
-        """
-        :param model_paths:  up to three models, designated by their local paths or HuggingFace repo_ids
-        :param alpha: The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
-                   would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
-        :param interp: The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_difference" and None.
-                   Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_difference" is supported.
-        :param force:  Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
-
-        **kwargs - the default DiffusionPipeline.get_config_dict kwargs:
-             cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
-        """
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            verbosity = dlogging.get_verbosity()
-            dlogging.set_verbosity_error()
-            dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
-
-            # Note that checkpoint_merger will not work with downloaded HuggingFace fp16 models
-            # until upstream https://github.com/huggingface/diffusers/pull/6670 is merged and released.
-            pipe = AutoPipelineForText2Image.from_pretrained(
-                model_paths[0],
-                custom_pipeline="checkpoint_merger",
-                torch_dtype=dtype,
-                variant=variant,
-            )
-            merged_pipe = pipe.merge(
-                pretrained_model_name_or_path_list=model_paths,
-                alpha=alpha,
-                interp=interp.value if interp else None,  # diffusers API treats None as "weighted sum"
-                force=force,
-                torch_dtype=dtype,
-                variant=variant,
-                **kwargs,
-            )
-            dlogging.set_verbosity(verbosity)
-        return merged_pipe
-
-    def merge_diffusion_models_and_save(
-        self,
-        model_keys: List[str],
-        merged_model_name: str,
-        alpha: float = 0.5,
-        force: bool = False,
-        interp: Optional[MergeInterpolationMethod] = None,
-        merge_dest_directory: Optional[Path] = None,
-        variant: Optional[str] = None,
-        **kwargs: Any,
-    ) -> AnyModelConfig:
-        """
-        :param models: up to three models, designated by their InvokeAI models.yaml model name
-        :param merged_model_name: name for new model
-        :param alpha: The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
-                   would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
-        :param interp: The interpolation method to use for the merging. Supports "weighted_average", "sigmoid", "inv_sigmoid", "add_difference" and None.
-                   Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_difference" is supported. Add_difference is A+(B-C).
-        :param force:  Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
-        :param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
-        **kwargs - the default DiffusionPipeline.get_config_dict kwargs:
-             cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
-        """
-        model_paths: List[Path] = []
-        model_names: List[str] = []
-        config = self._installer.app_config
-        store = self._installer.record_store
-        base_models: Set[BaseModelType] = set()
-        vae = None
-        variant = None if self._installer.app_config.full_precision else "fp16"
-
-        assert (
-            len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference
-        ), "When merging three models, only the 'add_difference' merge method is supported"
-
-        for key in model_keys:
-            info = store.get_model(key)
-            model_names.append(info.name)
-            assert isinstance(
-                info, MainDiffusersConfig
-            ), f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
-            assert info.variant == ModelVariantType(
-                "normal"
-            ), f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
-
-            # pick up the first model's vae
-            if key == model_keys[0]:
-                vae = info.vae
-
-            # tally base models used
-            base_models.add(info.base)
-            model_paths.extend([config.models_path / info.path])
-
-        assert len(base_models) == 1, f"All models to merge must have same base model, but found bases {base_models}"
-        base_model = base_models.pop()
-
-        merge_method = None if interp == "weighted_sum" else MergeInterpolationMethod(interp)
-        merged_pipe = self.merge_diffusion_models(model_paths, alpha, merge_method, force, variant=variant, **kwargs)
-        dump_path = (
-            Path(merge_dest_directory)
-            if merge_dest_directory
-            else config.models_path / base_model.value / ModelType.Main.value
-        )
-        dump_path.mkdir(parents=True, exist_ok=True)
-        dump_path = dump_path / merged_model_name
-
-        dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
-        merged_pipe.save_pretrained(dump_path.as_posix(), safe_serialization=True, torch_dtype=dtype, variant=variant)
-
-        # register model and get its unique key
-        key = self._installer.register_path(dump_path)
-
-        # update model's config
-        model_config = self._installer.record_store.get_model(key)
-        model_config.update(
-            {
-                "name": merged_model_name,
-                "description": f"Merge of models {', '.join(model_names)}",
-                "vae": vae,
-            }
-        )
-        self._installer.record_store.update_model(key, model_config)
-        return model_config

invokeai/backend/model_manager/metadata/fetch/civitai.py

@@ -170,8 +170,6 @@ class CivitaiMetadataFetch(ModelMetadataFetchBase):
         if model_id is None:
             version_url = CIVITAI_VERSION_ENDPOINT + str(version_id)
             version = self._requests.get(version_url).json()
-            if error := version.get("error"):
-                raise UnknownMetadataException(error)
             model_id = version["modelId"]
 
         model_url = CIVITAI_MODEL_ENDPOINT + str(model_id)

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -12,7 +12,7 @@ import psutil
 import torch
 from compel.cross_attention_control import Arguments
 from diffusers.models.attention_processor import Attention, AttentionProcessor, AttnProcessor, SlicedAttnProcessor
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from torch import nn
 
 import invokeai.backend.util.logging as logger

invokeai/backend/training/textual_inversion_training.py

@@ -11,7 +11,6 @@ import logging
 import math
 import os
 import random
-from argparse import Namespace
 from pathlib import Path
 from typing import Optional
 
@@ -31,6 +30,8 @@ from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version
 from diffusers.utils.import_utils import is_xformers_available
 from huggingface_hub import HfFolder, Repository, whoami
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
 from packaging import version
 from PIL import Image
 from torch.utils.data import Dataset
@@ -40,8 +41,8 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 # invokeai stuff
 from invokeai.app.services.config import InvokeAIAppConfig, PagingArgumentParser
-from invokeai.backend.install.install_helper import initialize_record_store
-from invokeai.backend.model_manager import BaseModelType, ModelType
+from invokeai.app.services.model_manager import ModelManagerService
+from invokeai.backend.model_management.models import SubModelType
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {
@@ -76,7 +77,7 @@ def save_progress(text_encoder, placeholder_token_id, accelerator, placeholder_t
     torch.save(learned_embeds_dict, save_path)
 
 
-def parse_args() -> Namespace:
+def parse_args():
     config = InvokeAIAppConfig.get_config()
     parser = PagingArgumentParser(description="Textual inversion training")
     general_group = parser.add_argument_group("General")
@@ -443,7 +444,7 @@ class TextualInversionDataset(Dataset):
         self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
         self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
 
-    def __len__(self) -> int:
+    def __len__(self):
         return self._length
 
     def __getitem__(self, i):
@@ -508,10 +509,11 @@ def do_textual_inversion_training(
     initializer_token: str,
     save_steps: int = 500,
     only_save_embeds: bool = False,
-    tokenizer_name: Optional[str] = None,
+    revision: str = None,
+    tokenizer_name: str = None,
     learnable_property: str = "object",
     repeats: int = 100,
-    seed: Optional[int] = None,
+    seed: int = None,
     resolution: int = 512,
     center_crop: bool = False,
     train_batch_size: int = 16,
@@ -528,18 +530,18 @@ def do_textual_inversion_training(
     adam_weight_decay: float = 1e-02,
     adam_epsilon: float = 1e-08,
     push_to_hub: bool = False,
-    hub_token: Optional[str] = None,
+    hub_token: str = None,
     logging_dir: Path = Path("logs"),
     mixed_precision: str = "fp16",
     allow_tf32: bool = False,
     report_to: str = "tensorboard",
     local_rank: int = -1,
     checkpointing_steps: int = 500,
-    resume_from_checkpoint: Optional[Path] = None,
+    resume_from_checkpoint: Path = None,
     enable_xformers_memory_efficient_attention: bool = False,
-    hub_model_id: Optional[str] = None,
+    hub_model_id: str = None,
     **kwargs,
-) -> None:
+):
     assert model, "Please specify a base model with --model"
     assert train_data_dir, "Please specify a directory containing the training images using --train_data_dir"
     assert placeholder_token, "Please specify a trigger term using --placeholder_token"
@@ -562,6 +564,8 @@ def do_textual_inversion_training(
         project_config=accelerator_config,
     )
 
+    model_manager = ModelManagerService(config, logger)
+
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -599,37 +603,44 @@ def do_textual_inversion_training(
         elif output_dir is not None:
             os.makedirs(output_dir, exist_ok=True)
 
-    model_records = initialize_record_store(config)
-    base, type, name = model.split("/")  # note frontend still returns old-style keys
-    try:
-        model_config = model_records.search_by_attr(
-            model_name=name, model_type=ModelType(type), base_model=BaseModelType(base)
-        )[0]
-    except IndexError:
-        raise Exception(f"Unknown model {model}")
-    model_path = config.models_path / model_config.path
+    known_models = model_manager.model_names()
+    model_name = model.split("/")[-1]
+    model_meta = next((mm for mm in known_models if mm[0].endswith(model_name)), None)
+    assert model_meta is not None, f"Unknown model: {model}"
+    model_info = model_manager.model_info(*model_meta)
+    assert model_info["model_format"] == "diffusers", "This script only works with models of type 'diffusers'"
+    tokenizer_info = model_manager.get_model(*model_meta, submodel=SubModelType.Tokenizer)
+    noise_scheduler_info = model_manager.get_model(*model_meta, submodel=SubModelType.Scheduler)
+    text_encoder_info = model_manager.get_model(*model_meta, submodel=SubModelType.TextEncoder)
+    vae_info = model_manager.get_model(*model_meta, submodel=SubModelType.Vae)
+    unet_info = model_manager.get_model(*model_meta, submodel=SubModelType.UNet)
 
     pipeline_args = {"local_files_only": True}
     if tokenizer_name:
         tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name, **pipeline_args)
     else:
-        tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", **pipeline_args)
+        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_info.location, subfolder="tokenizer", **pipeline_args)
 
     # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(model_path, subfolder="scheduler", **pipeline_args)
+    noise_scheduler = DDPMScheduler.from_pretrained(
+        noise_scheduler_info.location, subfolder="scheduler", **pipeline_args
+    )
     text_encoder = CLIPTextModel.from_pretrained(
-        model_path,
+        text_encoder_info.location,
         subfolder="text_encoder",
+        revision=revision,
         **pipeline_args,
     )
     vae = AutoencoderKL.from_pretrained(
-        model_path,
+        vae_info.location,
         subfolder="vae",
+        revision=revision,
         **pipeline_args,
     )
     unet = UNet2DConditionModel.from_pretrained(
-        model_path,
+        unet_info.location,
         subfolder="unet",
+        revision=revision,
         **pipeline_args,
     )
 
@@ -717,7 +728,7 @@ def do_textual_inversion_training(
         max_train_steps = num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
 
-    scheduler = get_scheduler(
+    lr_scheduler = get_scheduler(
         lr_scheduler,
         optimizer=optimizer,
         num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
@@ -726,7 +737,7 @@ def do_textual_inversion_training(
 
     # Prepare everything with our `accelerator`.
     text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        text_encoder, optimizer, train_dataloader, scheduler
+        text_encoder, optimizer, train_dataloader, lr_scheduler
     )
 
     # For mixed precision training we cast the unet and vae weights to half-precision
@@ -852,7 +863,7 @@ def do_textual_inversion_training(
                 accelerator.backward(loss)
 
                 optimizer.step()
-                scheduler.step()
+                lr_scheduler.step()
                 optimizer.zero_grad()
 
                 # Let's make sure we don't update any embedding weights besides the newly added token
@@ -882,7 +893,7 @@ def do_textual_inversion_training(
                         accelerator.save_state(save_path)
                         logger.info(f"Saved state to {save_path}")
 
-            logs = {"loss": loss.detach().item(), "lr": scheduler.get_last_lr()[0]}
+            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
             progress_bar.set_postfix(**logs)
             accelerator.log(logs, step=global_step)
 
@@ -899,7 +910,7 @@ def do_textual_inversion_training(
             save_full_model = not only_save_embeds
         if save_full_model:
             pipeline = StableDiffusionPipeline.from_pretrained(
-                model_path,
+                unet_info.location,
                 text_encoder=accelerator.unwrap_model(text_encoder),
                 vae=vae,
                 unet=unet,

invokeai/backend/util/hotfixes.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 import diffusers
 import torch
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalControlNetMixin
+from diffusers.loaders import FromOriginalControlnetMixin
 from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
 from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
 from diffusers.models.embeddings import (
@@ -14,13 +14,8 @@ from diffusers.models.embeddings import (
     Timesteps,
 )
 from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.unets.unet_2d_blocks import (
-    CrossAttnDownBlock2D,
-    DownBlock2D,
-    UNetMidBlock2DCrossAttn,
-    get_down_block,
-)
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, DownBlock2D, UNetMidBlock2DCrossAttn, get_down_block
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from torch import nn
 
 from invokeai.backend.util.logging import InvokeAILogger
@@ -32,7 +27,7 @@ from invokeai.backend.util.logging import InvokeAILogger
 logger = InvokeAILogger.get_logger(__name__)
 
 
-class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
+class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
     """
     A ControlNet model.
 

invokeai/backend/util/util.py

@@ -286,7 +286,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
     open_mode = "wb"
     exist_size = 0
 
-    resp = requests.get(url, headers=header, stream=True, allow_redirects=True)
+    resp = requests.get(url, header, stream=True)
     content_length = int(resp.headers.get("content-length", 0))
 
     if dest.is_dir():

invokeai/configs/INITIAL_MODELS2.yaml

@@ -1,157 +0,0 @@
-# This file predefines a few models that the user may want to install.
-sd-1/main/stable-diffusion-v1-5:
-   description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
-   source: runwayml/stable-diffusion-v1-5
-   recommended: True
-   default: True
-sd-1/main/stable-diffusion-v1-5-inpainting:
-   description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
-   source: runwayml/stable-diffusion-inpainting
-   recommended: True
-sd-2/main/stable-diffusion-2-1:
-   description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
-   source: stabilityai/stable-diffusion-2-1
-   recommended: False
-sd-2/main/stable-diffusion-2-inpainting:
-   description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
-   source: stabilityai/stable-diffusion-2-inpainting
-   recommended: False
-sdxl/main/stable-diffusion-xl-base-1-0:
-   description: Stable Diffusion XL base model (12 GB)
-   source: stabilityai/stable-diffusion-xl-base-1.0
-   recommended: True
-sdxl-refiner/main/stable-diffusion-xl-refiner-1-0:
-   description: Stable Diffusion XL refiner model (12 GB)
-   source: stabilityai/stable-diffusion-xl-refiner-1.0
-   recommended: False
-sdxl/vae/sdxl-vae-fp16-fix:
-   description: Version of the SDXL-1.0 VAE that works in half precision mode
-   source: madebyollin/sdxl-vae-fp16-fix
-   recommended: True
-sd-1/main/Analog-Diffusion:
-   description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
-   source: wavymulder/Analog-Diffusion
-   recommended: False
-sd-1/main/Deliberate:
-   description: Versatile model that produces detailed images up to 768px (4.27 GB)
-   source: XpucT/Deliberate
-   recommended: False
-sd-1/main/Dungeons-and-Diffusion:
-   description: Dungeons & Dragons characters (2.13 GB)
-   source: 0xJustin/Dungeons-and-Diffusion
-   recommended: False
-sd-1/main/dreamlike-photoreal-2:
-   description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
-   source: dreamlike-art/dreamlike-photoreal-2.0
-   recommended: False
-sd-1/main/Inkpunk-Diffusion:
-   description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
-   source: Envvi/Inkpunk-Diffusion
-   recommended: False
-sd-1/main/openjourney:
-   description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
-   source: prompthero/openjourney
-   recommended: False
-sd-1/main/seek.art_MEGA:
-   source: coreco/seek.art_MEGA
-   description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
-   recommended: False
-sd-1/main/trinart_stable_diffusion_v2:
-   description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
-   source: naclbit/trinart_stable_diffusion_v2
-   recommended: False
-sd-1/controlnet/qrcode_monster:
-   source: monster-labs/control_v1p_sd15_qrcode_monster
-   subfolder: v2
-sd-1/controlnet/canny:
-   source: lllyasviel/control_v11p_sd15_canny
-   recommended: True
-sd-1/controlnet/inpaint:
-   source: lllyasviel/control_v11p_sd15_inpaint
-sd-1/controlnet/mlsd:
-   source: lllyasviel/control_v11p_sd15_mlsd
-sd-1/controlnet/depth:
-   source: lllyasviel/control_v11f1p_sd15_depth
-   recommended: True
-sd-1/controlnet/normal_bae:
-   source: lllyasviel/control_v11p_sd15_normalbae
-sd-1/controlnet/seg:
-   source: lllyasviel/control_v11p_sd15_seg
-sd-1/controlnet/lineart:
-   source: lllyasviel/control_v11p_sd15_lineart
-   recommended: True
-sd-1/controlnet/lineart_anime:
-   source: lllyasviel/control_v11p_sd15s2_lineart_anime
-sd-1/controlnet/openpose:
-   source: lllyasviel/control_v11p_sd15_openpose
-   recommended: True
-sd-1/controlnet/scribble:
-   source: lllyasviel/control_v11p_sd15_scribble
-   recommended: False
-sd-1/controlnet/softedge:
-   source: lllyasviel/control_v11p_sd15_softedge
-sd-1/controlnet/shuffle:
-   source: lllyasviel/control_v11e_sd15_shuffle
-sd-1/controlnet/tile:
-   source: lllyasviel/control_v11f1e_sd15_tile
-sd-1/controlnet/ip2p:
-   source: lllyasviel/control_v11e_sd15_ip2p
-sd-1/t2i_adapter/canny-sd15:
-   source: TencentARC/t2iadapter_canny_sd15v2
-sd-1/t2i_adapter/sketch-sd15:
-   source: TencentARC/t2iadapter_sketch_sd15v2
-sd-1/t2i_adapter/depth-sd15:
-   source: TencentARC/t2iadapter_depth_sd15v2
-sd-1/t2i_adapter/zoedepth-sd15:
-   source: TencentARC/t2iadapter_zoedepth_sd15v1
-sdxl/t2i_adapter/canny-sdxl:
-   source: TencentARC/t2i-adapter-canny-sdxl-1.0
-sdxl/t2i_adapter/zoedepth-sdxl:
-   source: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
-sdxl/t2i_adapter/lineart-sdxl:
-   source: TencentARC/t2i-adapter-lineart-sdxl-1.0
-sdxl/t2i_adapter/sketch-sdxl:
-   source: TencentARC/t2i-adapter-sketch-sdxl-1.0
-sd-1/embedding/EasyNegative:
-   source: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
-   recommended: True
-   description: A textual inversion to use in the negative prompt to reduce bad anatomy
-sd-1/lora/FlatColor:
-   source: https://civitai.com/models/6433/loraflatcolor
-   recommended: True
-   description: A LoRA that generates scenery using solid blocks of color
-sd-1/lora/Ink scenery:
-   source: https://civitai.com/api/download/models/83390
-   description: Generate india ink-like landscapes
-sd-1/ip_adapter/ip_adapter_sd15:
-   source: InvokeAI/ip_adapter_sd15
-   recommended: True
-   requires:
-      - InvokeAI/ip_adapter_sd_image_encoder
-   description: IP-Adapter for SD 1.5 models
-sd-1/ip_adapter/ip_adapter_plus_sd15:
-   source: InvokeAI/ip_adapter_plus_sd15
-   recommended: False
-   requires:
-      - InvokeAI/ip_adapter_sd_image_encoder
-   description: Refined IP-Adapter for SD 1.5 models
-sd-1/ip_adapter/ip_adapter_plus_face_sd15:
-   source: InvokeAI/ip_adapter_plus_face_sd15
-   recommended: False
-   requires:
-      - InvokeAI/ip_adapter_sd_image_encoder
-   description: Refined IP-Adapter for SD 1.5 models, adapted for faces
-sdxl/ip_adapter/ip_adapter_sdxl:
-   source: InvokeAI/ip_adapter_sdxl
-   recommended: False
-   requires:
-      - InvokeAI/ip_adapter_sdxl_image_encoder
-   description: IP-Adapter for SDXL models
-any/clip_vision/ip_adapter_sd_image_encoder:
-   source: InvokeAI/ip_adapter_sd_image_encoder
-   recommended: False
-   description: Required model for using IP-Adapters with SD-1/2 models
-any/clip_vision/ip_adapter_sdxl_image_encoder:
-   source: InvokeAI/ip_adapter_sdxl_image_encoder
-   recommended: False
-   description: Required model for using IP-Adapters with SDXL models

invokeai/frontend/install/invokeai_configure.py

@@ -2,5 +2,3 @@
 Wrapper for invokeai.backend.configure.invokeai_configure
 """
 from ...backend.install.invokeai_configure import main as invokeai_configure  # noqa: F401
-
-__all__ = ["invokeai_configure"]

invokeai/frontend/install/invokeai_update.py

@@ -5,14 +5,14 @@ pip install <path_to_git_source>.
 import os
 import platform
 from distutils.version import LooseVersion
-from importlib.metadata import PackageNotFoundError, distribution, distributions
 
+import pkg_resources
 import psutil
 import requests
 from rich import box, print
 from rich.console import Console, group
 from rich.panel import Panel
-from rich.prompt import Confirm, Prompt
+from rich.prompt import Prompt
 from rich.style import Style
 
 from invokeai.version import __version__
@@ -61,65 +61,6 @@ def get_pypi_versions():
     return latest_version, latest_release_candidate, versions
 
 
-def get_torch_extra_index_url() -> str | None:
-    """
-    Determine torch wheel source URL and optional modules based on the user's OS.
-    """
-
-    resolved_url = None
-
-    # In all other cases (like MacOS (MPS) or Linux+CUDA), there is no need to specify the extra index URL.
-    torch_package_urls = {
-        "windows_cuda": "https://download.pytorch.org/whl/cu121",
-        "linux_rocm": "https://download.pytorch.org/whl/rocm5.6",
-        "linux_cpu": "https://download.pytorch.org/whl/cpu",
-    }
-
-    nvidia_packages_present = (
-        len([d.metadata["Name"] for d in distributions() if d.metadata["Name"].startswith("nvidia")]) > 0
-    )
-    device = "cuda" if nvidia_packages_present else None
-    manual_gpu_selection_prompt = (
-        "[bold]We tried and failed to guess your GPU capabilities[/] :thinking_face:. Please select the GPU type:"
-    )
-
-    if OS == "Linux":
-        if not device:
-            # do we even need to offer a CPU-only install option?
-            print(manual_gpu_selection_prompt)
-            print("1: NVIDIA (CUDA)")
-            print("2: AMD (ROCm)")
-            print("3: No GPU - CPU only")
-            answer = Prompt.ask("Choice:", choices=["1", "2", "3"], default="1")
-            match answer:
-                case "1":
-                    device = "cuda"
-                case "2":
-                    device = "rocm"
-                case "3":
-                    device = "cpu"
-
-        if device != "cuda":
-            resolved_url = torch_package_urls[f"linux_{device}"]
-
-    if OS == "Windows":
-        if not device:
-            print(manual_gpu_selection_prompt)
-            print("1: NVIDIA (CUDA)")
-            print("2: No GPU - CPU only")
-            answer = Prompt.ask("Your choice:", choices=["1", "2"], default="1")
-            match answer:
-                case "1":
-                    device = "cuda"
-                case "2":
-                    device = "cpu"
-
-        if device == "cuda":
-            resolved_url = torch_package_urls[f"windows_{device}"]
-
-    return resolved_url
-
-
 def welcome(latest_release: str, latest_prerelease: str):
     @group()
     def text():
@@ -148,11 +89,12 @@ def welcome(latest_release: str, latest_prerelease: str):
 
 
 def get_extras():
+    extras = ""
     try:
-        distribution("xformers")
+        _ = pkg_resources.get_distribution("xformers")
         extras = "[xformers]"
-    except PackageNotFoundError:
-        extras = ""
+    except pkg_resources.DistributionNotFound:
+        pass
     return extras
 
 
@@ -183,22 +125,8 @@ def main():
 
     extras = get_extras()
 
-    console.line()
-    force_reinstall = Confirm.ask(
-        "[bold]Force reinstallation of all dependencies?[/] This [i]may[/] help fix a broken upgrade, but is usually not necessary.",
-        default=False,
-    )
-
-    console.line()
-    flags = []
-    if (index_url := get_torch_extra_index_url()) is not None:
-        flags.append(f"--extra-index-url {index_url}")
-    if force_reinstall:
-        flags.append("--force-reinstall")
-    flags = " ".join(flags)
-
     print(f":crossed_fingers: Upgrading to [yellow]{release}[/yellow]")
-    cmd = f'pip install "invokeai{extras}=={release}" --use-pep517 --upgrade {flags}'
+    cmd = f'pip install "invokeai{extras}=={release}" --use-pep517 --upgrade'
 
     print("")
     print("")

invokeai/frontend/install/model_install2.py

@@ -1,645 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
-# Before running stable-diffusion on an internet-isolated machine,
-# run this script from one with internet connectivity. The
-# two machines must share a common .cache directory.
-
-"""
-This is the npyscreen frontend to the model installation application.
-It is currently named model_install2.py, but will ultimately replace model_install.py.
-"""
-
-import argparse
-import curses
-import sys
-import traceback
-import warnings
-from argparse import Namespace
-from shutil import get_terminal_size
-from typing import Any, Dict, List, Optional, Set
-
-import npyscreen
-import torch
-from npyscreen import widget
-
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.model_install import ModelInstallService
-from invokeai.backend.install.install_helper import InstallHelper, InstallSelections, UnifiedModelInfo
-from invokeai.backend.model_manager import ModelType
-from invokeai.backend.util import choose_precision, choose_torch_device
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.frontend.install.widgets import (
-    MIN_COLS,
-    MIN_LINES,
-    CenteredTitleText,
-    CyclingForm,
-    MultiSelectColumns,
-    SingleSelectColumns,
-    TextBox,
-    WindowTooSmallException,
-    set_min_terminal_size,
-)
-
-warnings.filterwarnings("ignore", category=UserWarning)  # noqa: E402
-config = InvokeAIAppConfig.get_config()
-logger = InvokeAILogger.get_logger("ModelInstallService")
-logger.setLevel("WARNING")
-# logger.setLevel('DEBUG')
-
-# build a table mapping all non-printable characters to None
-# for stripping control characters
-# from https://stackoverflow.com/questions/92438/stripping-non-printable-characters-from-a-string-in-python
-NOPRINT_TRANS_TABLE = {i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()}
-
-# maximum number of installed models we can display before overflowing vertically
-MAX_OTHER_MODELS = 72
-
-
-def make_printable(s: str) -> str:
-    """Replace non-printable characters in a string."""
-    return s.translate(NOPRINT_TRANS_TABLE)
-
-
-class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
-    """Main form for interactive TUI."""
-
-    # for responsive resizing set to False, but this seems to cause a crash!
-    FIX_MINIMUM_SIZE_WHEN_CREATED = True
-
-    # for persistence
-    current_tab = 0
-
-    def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, multipage: bool = False, **keywords: Any):
-        self.multipage = multipage
-        self.subprocess = None
-        super().__init__(parentApp=parentApp, name=name, **keywords)
-
-    def create(self) -> None:
-        self.installer = self.parentApp.install_helper.installer
-        self.model_labels = self._get_model_labels()
-        self.keypress_timeout = 10
-        self.counter = 0
-        self.subprocess_connection = None
-
-        window_width, window_height = get_terminal_size()
-
-        # npyscreen has no typing hints
-        self.nextrely -= 1  # type: ignore
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields. Cursor keys navigate, and <space> selects.",
-            editable=False,
-            color="CAUTION",
-        )
-        self.nextrely += 1  # type: ignore
-        self.tabs = self.add_widget_intelligent(
-            SingleSelectColumns,
-            values=[
-                "STARTERS",
-                "MAINS",
-                "CONTROLNETS",
-                "T2I-ADAPTERS",
-                "IP-ADAPTERS",
-                "LORAS",
-                "TI EMBEDDINGS",
-            ],
-            value=[self.current_tab],
-            columns=7,
-            max_height=2,
-            relx=8,
-            scroll_exit=True,
-        )
-        self.tabs.on_changed = self._toggle_tables
-
-        top_of_table = self.nextrely  # type: ignore
-        self.starter_pipelines = self.add_starter_pipelines()
-        bottom_of_table = self.nextrely  # type: ignore
-
-        self.nextrely = top_of_table
-        self.pipeline_models = self.add_pipeline_widgets(
-            model_type=ModelType.Main, window_width=window_width, exclude=self.starter_models
-        )
-        # self.pipeline_models['autoload_pending'] = True
-        bottom_of_table = max(bottom_of_table, self.nextrely)
-
-        self.nextrely = top_of_table
-        self.controlnet_models = self.add_model_widgets(
-            model_type=ModelType.ControlNet,
-            window_width=window_width,
-        )
-        bottom_of_table = max(bottom_of_table, self.nextrely)
-
-        self.nextrely = top_of_table
-        self.t2i_models = self.add_model_widgets(
-            model_type=ModelType.T2IAdapter,
-            window_width=window_width,
-        )
-        bottom_of_table = max(bottom_of_table, self.nextrely)
-        self.nextrely = top_of_table
-        self.ipadapter_models = self.add_model_widgets(
-            model_type=ModelType.IPAdapter,
-            window_width=window_width,
-        )
-        bottom_of_table = max(bottom_of_table, self.nextrely)
-
-        self.nextrely = top_of_table
-        self.lora_models = self.add_model_widgets(
-            model_type=ModelType.Lora,
-            window_width=window_width,
-        )
-        bottom_of_table = max(bottom_of_table, self.nextrely)
-
-        self.nextrely = top_of_table
-        self.ti_models = self.add_model_widgets(
-            model_type=ModelType.TextualInversion,
-            window_width=window_width,
-        )
-        bottom_of_table = max(bottom_of_table, self.nextrely)
-
-        self.nextrely = bottom_of_table + 1
-
-        self.nextrely += 1
-        back_label = "BACK"
-        cancel_label = "CANCEL"
-        current_position = self.nextrely
-        if self.multipage:
-            self.back_button = self.add_widget_intelligent(
-                npyscreen.ButtonPress,
-                name=back_label,
-                when_pressed_function=self.on_back,
-            )
-        else:
-            self.nextrely = current_position
-            self.cancel_button = self.add_widget_intelligent(
-                npyscreen.ButtonPress, name=cancel_label, when_pressed_function=self.on_cancel
-            )
-            self.nextrely = current_position
-
-        label = "APPLY CHANGES"
-        self.nextrely = current_position
-        self.done = self.add_widget_intelligent(
-            npyscreen.ButtonPress,
-            name=label,
-            relx=window_width - len(label) - 15,
-            when_pressed_function=self.on_done,
-        )
-
-        # This restores the selected page on return from an installation
-        for _i in range(1, self.current_tab + 1):
-            self.tabs.h_cursor_line_down(1)
-        self._toggle_tables([self.current_tab])
-
-    ############# diffusers tab ##########
-    def add_starter_pipelines(self) -> dict[str, npyscreen.widget]:
-        """Add widgets responsible for selecting diffusers models"""
-        widgets: Dict[str, npyscreen.widget] = {}
-
-        all_models = self.all_models  # master dict of all models, indexed by key
-        model_list = [x for x in self.starter_models if all_models[x].type in ["main", "vae"]]
-        model_labels = [self.model_labels[x] for x in model_list]
-
-        widgets.update(
-            label1=self.add_widget_intelligent(
-                CenteredTitleText,
-                name="Select from a starter set of Stable Diffusion models from HuggingFace and Civitae.",
-                editable=False,
-                labelColor="CAUTION",
-            )
-        )
-
-        self.nextrely -= 1
-        # if user has already installed some initial models, then don't patronize them
-        # by showing more recommendations
-        show_recommended = len(self.installed_models) == 0
-
-        checked = [
-            model_list.index(x)
-            for x in model_list
-            if (show_recommended and all_models[x].recommended) or all_models[x].installed
-        ]
-        widgets.update(
-            models_selected=self.add_widget_intelligent(
-                MultiSelectColumns,
-                columns=1,
-                name="Install Starter Models",
-                values=model_labels,
-                value=checked,
-                max_height=len(model_list) + 1,
-                relx=4,
-                scroll_exit=True,
-            ),
-            models=model_list,
-        )
-
-        self.nextrely += 1
-        return widgets
-
-    ############# Add a set of model install widgets ########
-    def add_model_widgets(
-        self,
-        model_type: ModelType,
-        window_width: int = 120,
-        install_prompt: Optional[str] = None,
-        exclude: Optional[Set[str]] = None,
-    ) -> dict[str, npyscreen.widget]:
-        """Generic code to create model selection widgets"""
-        if exclude is None:
-            exclude = set()
-        widgets: Dict[str, npyscreen.widget] = {}
-        all_models = self.all_models
-        model_list = sorted(
-            [x for x in all_models if all_models[x].type == model_type and x not in exclude],
-            key=lambda x: all_models[x].name or "",
-        )
-        model_labels = [self.model_labels[x] for x in model_list]
-
-        show_recommended = len(self.installed_models) == 0
-        truncated = False
-        if len(model_list) > 0:
-            max_width = max([len(x) for x in model_labels])
-            columns = window_width // (max_width + 8)  # 8 characters for "[x] " and padding
-            columns = min(len(model_list), columns) or 1
-            prompt = (
-                install_prompt
-                or f"Select the desired {model_type.value.title()} models to install. Unchecked models will be purged from disk."
-            )
-
-            widgets.update(
-                label1=self.add_widget_intelligent(
-                    CenteredTitleText,
-                    name=prompt,
-                    editable=False,
-                    labelColor="CAUTION",
-                )
-            )
-
-            if len(model_labels) > MAX_OTHER_MODELS:
-                model_labels = model_labels[0:MAX_OTHER_MODELS]
-                truncated = True
-
-            widgets.update(
-                models_selected=self.add_widget_intelligent(
-                    MultiSelectColumns,
-                    columns=columns,
-                    name=f"Install {model_type} Models",
-                    values=model_labels,
-                    value=[
-                        model_list.index(x)
-                        for x in model_list
-                        if (show_recommended and all_models[x].recommended) or all_models[x].installed
-                    ],
-                    max_height=len(model_list) // columns + 1,
-                    relx=4,
-                    scroll_exit=True,
-                ),
-                models=model_list,
-            )
-
-        if truncated:
-            widgets.update(
-                warning_message=self.add_widget_intelligent(
-                    npyscreen.FixedText,
-                    value=f"Too many models to display (max={MAX_OTHER_MODELS}). Some are not displayed.",
-                    editable=False,
-                    color="CAUTION",
-                )
-            )
-
-        self.nextrely += 1
-        widgets.update(
-            download_ids=self.add_widget_intelligent(
-                TextBox,
-                name="Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
-                max_height=6,
-                scroll_exit=True,
-                editable=True,
-            )
-        )
-        return widgets
-
-    ### Tab for arbitrary diffusers widgets ###
-    def add_pipeline_widgets(
-        self,
-        model_type: ModelType = ModelType.Main,
-        window_width: int = 120,
-        **kwargs,
-    ) -> dict[str, npyscreen.widget]:
-        """Similar to add_model_widgets() but adds some additional widgets at the bottom
-        to support the autoload directory"""
-        widgets = self.add_model_widgets(
-            model_type=model_type,
-            window_width=window_width,
-            install_prompt=f"Installed {model_type.value.title()} models. Unchecked models in the InvokeAI root directory will be deleted. Enter URLs, paths or repo_ids to import.",
-            **kwargs,
-        )
-
-        return widgets
-
-    def resize(self) -> None:
-        super().resize()
-        if s := self.starter_pipelines.get("models_selected"):
-            if model_list := self.starter_pipelines.get("models"):
-                s.values = [self.model_labels[x] for x in model_list]
-
-    def _toggle_tables(self, value: List[int]) -> None:
-        selected_tab = value[0]
-        widgets = [
-            self.starter_pipelines,
-            self.pipeline_models,
-            self.controlnet_models,
-            self.t2i_models,
-            self.ipadapter_models,
-            self.lora_models,
-            self.ti_models,
-        ]
-
-        for group in widgets:
-            for _k, v in group.items():
-                try:
-                    v.hidden = True
-                    v.editable = False
-                except Exception:
-                    pass
-        for _k, v in widgets[selected_tab].items():
-            try:
-                v.hidden = False
-                if not isinstance(v, (npyscreen.FixedText, npyscreen.TitleFixedText, CenteredTitleText)):
-                    v.editable = True
-            except Exception:
-                pass
-        self.__class__.current_tab = selected_tab  # for persistence
-        self.display()
-
-    def _get_model_labels(self) -> dict[str, str]:
-        """Return a list of trimmed labels for all models."""
-        window_width, window_height = get_terminal_size()
-        checkbox_width = 4
-        spacing_width = 2
-        result = {}
-
-        models = self.all_models
-        label_width = max([len(models[x].name or "") for x in self.starter_models])
-        description_width = window_width - label_width - checkbox_width - spacing_width
-
-        for key in self.all_models:
-            description = models[key].description
-            description = (
-                description[0 : description_width - 3] + "..."
-                if description and len(description) > description_width
-                else description
-                if description
-                else ""
-            )
-            result[key] = f"%-{label_width}s %s" % (models[key].name, description)
-
-        return result
-
-    def _get_columns(self) -> int:
-        window_width, window_height = get_terminal_size()
-        cols = 4 if window_width > 240 else 3 if window_width > 160 else 2 if window_width > 80 else 1
-        return min(cols, len(self.installed_models))
-
-    def confirm_deletions(self, selections: InstallSelections) -> bool:
-        remove_models = selections.remove_models
-        if remove_models:
-            model_names = [self.all_models[x].name or "" for x in remove_models]
-            mods = "\n".join(model_names)
-            is_ok = npyscreen.notify_ok_cancel(
-                f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}"
-            )
-            assert isinstance(is_ok, bool)  # npyscreen doesn't have return type annotations
-            return is_ok
-        else:
-            return True
-
-    @property
-    def all_models(self) -> Dict[str, UnifiedModelInfo]:
-        # npyscreen doesn't having typing hints
-        return self.parentApp.install_helper.all_models  # type: ignore
-
-    @property
-    def starter_models(self) -> List[str]:
-        return self.parentApp.install_helper._starter_models  # type: ignore
-
-    @property
-    def installed_models(self) -> List[str]:
-        return self.parentApp.install_helper._installed_models  # type: ignore
-
-    def on_back(self) -> None:
-        self.parentApp.switchFormPrevious()
-        self.editing = False
-
-    def on_cancel(self) -> None:
-        self.parentApp.setNextForm(None)
-        self.parentApp.user_cancelled = True
-        self.editing = False
-
-    def on_done(self) -> None:
-        self.marshall_arguments()
-        if not self.confirm_deletions(self.parentApp.install_selections):
-            return
-        self.parentApp.setNextForm(None)
-        self.parentApp.user_cancelled = False
-        self.editing = False
-
-    def marshall_arguments(self) -> None:
-        """
-        Assemble arguments and store as attributes of the application:
-        .starter_models: dict of model names to install from INITIAL_CONFIGURE.yaml
-                         True  => Install
-                         False => Remove
-        .scan_directory: Path to a directory of models to scan and import
-        .autoscan_on_startup:  True if invokeai should scan and import at startup time
-        .import_model_paths:   list of URLs, repo_ids and file paths to import
-        """
-        selections = self.parentApp.install_selections
-        all_models = self.all_models
-
-        # Defined models (in INITIAL_CONFIG.yaml or models.yaml) to add/remove
-        ui_sections = [
-            self.starter_pipelines,
-            self.pipeline_models,
-            self.controlnet_models,
-            self.t2i_models,
-            self.ipadapter_models,
-            self.lora_models,
-            self.ti_models,
-        ]
-        for section in ui_sections:
-            if "models_selected" not in section:
-                continue
-            selected = {section["models"][x] for x in section["models_selected"].value}
-            models_to_install = [x for x in selected if not self.all_models[x].installed]
-            models_to_remove = [x for x in section["models"] if x not in selected and self.all_models[x].installed]
-            selections.remove_models.extend(models_to_remove)
-            selections.install_models.extend([all_models[x] for x in models_to_install])
-
-        # models located in the 'download_ids" section
-        for section in ui_sections:
-            if downloads := section.get("download_ids"):
-                models = [UnifiedModelInfo(source=x) for x in downloads.value.split()]
-                selections.install_models.extend(models)
-
-
-class AddModelApplication(npyscreen.NPSAppManaged):  # type: ignore
-    def __init__(self, opt: Namespace, install_helper: InstallHelper):
-        super().__init__()
-        self.program_opts = opt
-        self.user_cancelled = False
-        self.install_selections = InstallSelections()
-        self.install_helper = install_helper
-
-    def onStart(self) -> None:
-        npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
-        self.main_form = self.addForm(
-            "MAIN",
-            addModelsForm,
-            name="Install Stable Diffusion Models",
-            cycle_widgets=False,
-        )
-
-
-def list_models(installer: ModelInstallService, model_type: ModelType):
-    """Print out all models of type model_type."""
-    models = installer.record_store.search_by_attr(model_type=model_type)
-    print(f"Installed models of type `{model_type}`:")
-    for model in models:
-        path = (config.models_path / model.path).resolve()
-        print(f"{model.name:40}{model.base.value:14}{path}")
-
-
-# --------------------------------------------------------
-def select_and_download_models(opt: Namespace) -> None:
-    """Prompt user for install/delete selections and execute."""
-    precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
-    # unsure how to avoid a typing complaint in the next line: config.precision is an enumerated Literal
-    config.precision = precision  # type: ignore
-    install_helper = InstallHelper(config, logger)
-    installer = install_helper.installer
-
-    if opt.list_models:
-        list_models(installer, opt.list_models)
-
-    elif opt.add or opt.delete:
-        selections = InstallSelections(
-            install_models=[UnifiedModelInfo(source=x) for x in (opt.add or [])], remove_models=opt.delete or []
-        )
-        install_helper.add_or_delete(selections)
-
-    elif opt.default_only:
-        selections = InstallSelections(install_models=[install_helper.default_model()])
-        install_helper.add_or_delete(selections)
-
-    elif opt.yes_to_all:
-        selections = InstallSelections(install_models=install_helper.recommended_models())
-        install_helper.add_or_delete(selections)
-
-    # this is where the TUI is called
-    else:
-        if not set_min_terminal_size(MIN_COLS, MIN_LINES):
-            raise WindowTooSmallException(
-                "Could not increase terminal size. Try running again with a larger window or smaller font size."
-            )
-
-        installApp = AddModelApplication(opt, install_helper)
-        try:
-            installApp.run()
-        except KeyboardInterrupt:
-            print("Aborted...")
-            sys.exit(-1)
-
-        install_helper.add_or_delete(installApp.install_selections)
-
-
-# -------------------------------------
-def main() -> None:
-    parser = argparse.ArgumentParser(description="InvokeAI model downloader")
-    parser.add_argument(
-        "--add",
-        nargs="*",
-        help="List of URLs, local paths or repo_ids of models to install",
-    )
-    parser.add_argument(
-        "--delete",
-        nargs="*",
-        help="List of names of models to delete. Use type:name to disambiguate, as in `controlnet:my_model`",
-    )
-    parser.add_argument(
-        "--full-precision",
-        dest="full_precision",
-        action=argparse.BooleanOptionalAction,
-        type=bool,
-        default=False,
-        help="use 32-bit weights instead of faster 16-bit weights",
-    )
-    parser.add_argument(
-        "--yes",
-        "-y",
-        dest="yes_to_all",
-        action="store_true",
-        help='answer "yes" to all prompts',
-    )
-    parser.add_argument(
-        "--default_only",
-        action="store_true",
-        help="Only install the default model",
-    )
-    parser.add_argument(
-        "--list-models",
-        choices=[x.value for x in ModelType],
-        help="list installed models",
-    )
-    parser.add_argument(
-        "--root_dir",
-        dest="root",
-        type=str,
-        default=None,
-        help="path to root of install directory",
-    )
-    opt = parser.parse_args()
-
-    invoke_args = []
-    if opt.root:
-        invoke_args.extend(["--root", opt.root])
-    if opt.full_precision:
-        invoke_args.extend(["--precision", "float32"])
-    config.parse_args(invoke_args)
-    logger = InvokeAILogger().get_logger(config=config)
-
-    if not config.model_conf_path.exists():
-        logger.info("Your InvokeAI root directory is not set up. Calling invokeai-configure.")
-        from invokeai.frontend.install.invokeai_configure import invokeai_configure
-
-        invokeai_configure()
-        sys.exit(0)
-
-    try:
-        select_and_download_models(opt)
-    except AssertionError as e:
-        logger.error(e)
-        sys.exit(-1)
-    except KeyboardInterrupt:
-        curses.nocbreak()
-        curses.echo()
-        curses.endwin()
-        logger.info("Goodbye! Come back soon.")
-    except WindowTooSmallException as e:
-        logger.error(str(e))
-    except widget.NotEnoughSpaceForWidget as e:
-        if str(e).startswith("Height of 1 allocated"):
-            logger.error("Insufficient vertical space for the interface. Please make your window taller and try again")
-        input("Press any key to continue...")
-    except Exception as e:
-        if str(e).startswith("addwstr"):
-            logger.error(
-                "Insufficient horizontal space for the interface. Please make your window wider and try again."
-            )
-        else:
-            print(f"An exception has occurred: {str(e)} Details:")
-            print(traceback.format_exc(), file=sys.stderr)
-        input("Press any key to continue...")
-
-
-# -------------------------------------
-if __name__ == "__main__":
-    main()

invokeai/frontend/merge/merge_diffusers2.py

@@ -1,438 +0,0 @@
-"""
-invokeai.frontend.merge exports a single function called merge_diffusion_models().
-
-It merges 2-3 models together and create a new InvokeAI-registered diffusion model.
-
-Copyright (c) 2023-24 Lincoln Stein and the InvokeAI Development Team
-"""
-import argparse
-import curses
-import re
-import sys
-from argparse import Namespace
-from pathlib import Path
-from typing import List, Optional, Tuple
-
-import npyscreen
-from npyscreen import widget
-
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.model_install import ModelInstallServiceBase
-from invokeai.app.services.model_records import ModelRecordServiceBase
-from invokeai.backend.install.install_helper import initialize_installer
-from invokeai.backend.model_manager import (
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    ModelVariantType,
-)
-from invokeai.backend.model_manager.merge import ModelMerger
-from invokeai.frontend.install.widgets import FloatTitleSlider, SingleSelectColumns, TextBox
-
-config = InvokeAIAppConfig.get_config()
-
-BASE_TYPES = [
-    (BaseModelType.StableDiffusion1, "Models Built on SD-1.x"),
-    (BaseModelType.StableDiffusion2, "Models Built on SD-2.x"),
-    (BaseModelType.StableDiffusionXL, "Models Built on SDXL"),
-]
-
-
-def _parse_args() -> Namespace:
-    parser = argparse.ArgumentParser(description="InvokeAI model merging")
-    parser.add_argument(
-        "--root_dir",
-        type=Path,
-        default=config.root,
-        help="Path to the invokeai runtime directory",
-    )
-    parser.add_argument(
-        "--front_end",
-        "--gui",
-        dest="front_end",
-        action="store_true",
-        default=False,
-        help="Activate the text-based graphical front end for collecting parameters. Aside from --root_dir, other parameters will be ignored.",
-    )
-    parser.add_argument(
-        "--models",
-        dest="model_names",
-        type=str,
-        nargs="+",
-        help="Two to three model names to be merged",
-    )
-    parser.add_argument(
-        "--base_model",
-        type=str,
-        choices=[x[0].value for x in BASE_TYPES],
-        help="The base model shared by the models to be merged",
-    )
-    parser.add_argument(
-        "--merged_model_name",
-        "--destination",
-        dest="merged_model_name",
-        type=str,
-        help="Name of the output model. If not specified, will be the concatenation of the input model names.",
-    )
-    parser.add_argument(
-        "--alpha",
-        type=float,
-        default=0.5,
-        help="The interpolation parameter, ranging from 0 to 1. It affects the ratio in which the checkpoints are merged. Higher values give more weight to the 2d and 3d models",
-    )
-    parser.add_argument(
-        "--interpolation",
-        dest="interp",
-        type=str,
-        choices=["weighted_sum", "sigmoid", "inv_sigmoid", "add_difference"],
-        default="weighted_sum",
-        help='Interpolation method to use. If three models are present, only "add_difference" will work.',
-    )
-    parser.add_argument(
-        "--force",
-        action="store_true",
-        help="Try to merge models even if they are incompatible with each other",
-    )
-    parser.add_argument(
-        "--clobber",
-        "--overwrite",
-        dest="clobber",
-        action="store_true",
-        help="Overwrite the merged model if --merged_model_name already exists",
-    )
-    return parser.parse_args()
-
-
-# ------------------------- GUI HERE -------------------------
-class mergeModelsForm(npyscreen.FormMultiPageAction):
-    interpolations = ["weighted_sum", "sigmoid", "inv_sigmoid"]
-
-    def __init__(self, parentApp, name):
-        self.parentApp = parentApp
-        self.ALLOW_RESIZE = True
-        self.FIX_MINIMUM_SIZE_WHEN_CREATED = False
-        super().__init__(parentApp, name)
-
-    @property
-    def model_record_store(self) -> ModelRecordServiceBase:
-        installer: ModelInstallServiceBase = self.parentApp.installer
-        return installer.record_store
-
-    def afterEditing(self) -> None:
-        self.parentApp.setNextForm(None)
-
-    def create(self) -> None:
-        window_height, window_width = curses.initscr().getmaxyx()
-        self.current_base = 0
-        self.models = self.get_models(BASE_TYPES[self.current_base][0])
-        self.model_names = [x[1] for x in self.models]
-        max_width = max([len(x) for x in self.model_names])
-        max_width += 6
-        horizontal_layout = max_width * 3 < window_width
-
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            color="CONTROL",
-            value="Select two models to merge and optionally a third.",
-            editable=False,
-        )
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            color="CONTROL",
-            value="Use up and down arrows to move, <space> to select an item, <tab> and <shift-tab> to move from one field to the next.",
-            editable=False,
-        )
-        self.nextrely += 1
-        self.base_select = self.add_widget_intelligent(
-            SingleSelectColumns,
-            values=[x[1] for x in BASE_TYPES],
-            value=[self.current_base],
-            columns=4,
-            max_height=2,
-            relx=8,
-            scroll_exit=True,
-        )
-        self.base_select.on_changed = self._populate_models
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="MODEL 1",
-            color="GOOD",
-            editable=False,
-            rely=6 if horizontal_layout else None,
-        )
-        self.model1 = self.add_widget_intelligent(
-            npyscreen.SelectOne,
-            values=self.model_names,
-            value=0,
-            max_height=len(self.model_names),
-            max_width=max_width,
-            scroll_exit=True,
-            rely=7,
-        )
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="MODEL 2",
-            color="GOOD",
-            editable=False,
-            relx=max_width + 3 if horizontal_layout else None,
-            rely=6 if horizontal_layout else None,
-        )
-        self.model2 = self.add_widget_intelligent(
-            npyscreen.SelectOne,
-            name="(2)",
-            values=self.model_names,
-            value=1,
-            max_height=len(self.model_names),
-            max_width=max_width,
-            relx=max_width + 3 if horizontal_layout else None,
-            rely=7 if horizontal_layout else None,
-            scroll_exit=True,
-        )
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="MODEL 3",
-            color="GOOD",
-            editable=False,
-            relx=max_width * 2 + 3 if horizontal_layout else None,
-            rely=6 if horizontal_layout else None,
-        )
-        models_plus_none = self.model_names.copy()
-        models_plus_none.insert(0, "None")
-        self.model3 = self.add_widget_intelligent(
-            npyscreen.SelectOne,
-            name="(3)",
-            values=models_plus_none,
-            value=0,
-            max_height=len(self.model_names) + 1,
-            max_width=max_width,
-            scroll_exit=True,
-            relx=max_width * 2 + 3 if horizontal_layout else None,
-            rely=7 if horizontal_layout else None,
-        )
-        for m in [self.model1, self.model2, self.model3]:
-            m.when_value_edited = self.models_changed
-        self.merged_model_name = self.add_widget_intelligent(
-            TextBox,
-            name="Name for merged model:",
-            labelColor="CONTROL",
-            max_height=3,
-            value="",
-            scroll_exit=True,
-        )
-        self.force = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Force merge of models created by different diffusers library versions",
-            labelColor="CONTROL",
-            value=True,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        self.merge_method = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="Merge Method:",
-            values=self.interpolations,
-            value=0,
-            labelColor="CONTROL",
-            max_height=len(self.interpolations) + 1,
-            scroll_exit=True,
-        )
-        self.alpha = self.add_widget_intelligent(
-            FloatTitleSlider,
-            name="Weight (alpha) to assign to second and third models:",
-            out_of=1.0,
-            step=0.01,
-            lowest=0,
-            value=0.5,
-            labelColor="CONTROL",
-            scroll_exit=True,
-        )
-        self.model1.editing = True
-
-    def models_changed(self) -> None:
-        models = self.model1.values
-        selected_model1 = self.model1.value[0]
-        selected_model2 = self.model2.value[0]
-        selected_model3 = self.model3.value[0]
-        merged_model_name = f"{models[selected_model1]}+{models[selected_model2]}"
-        self.merged_model_name.value = merged_model_name
-
-        if selected_model3 > 0:
-            self.merge_method.values = ["add_difference ( A+(B-C) )"]
-            self.merged_model_name.value += f"+{models[selected_model3 -1]}"  # In model3 there is one more element in the list (None). So we have to subtract one.
-        else:
-            self.merge_method.values = self.interpolations
-        self.merge_method.value = 0
-
-    def on_ok(self) -> None:
-        if self.validate_field_values() and self.check_for_overwrite():
-            self.parentApp.setNextForm(None)
-            self.editing = False
-            self.parentApp.merge_arguments = self.marshall_arguments()
-            npyscreen.notify("Starting the merge...")
-        else:
-            self.editing = True
-
-    def on_cancel(self) -> None:
-        sys.exit(0)
-
-    def marshall_arguments(self) -> dict:
-        model_keys = [x[0] for x in self.models]
-        models = [
-            model_keys[self.model1.value[0]],
-            model_keys[self.model2.value[0]],
-        ]
-        if self.model3.value[0] > 0:
-            models.append(model_keys[self.model3.value[0] - 1])
-            interp = "add_difference"
-        else:
-            interp = self.interpolations[self.merge_method.value[0]]
-
-        args = {
-            "model_keys": models,
-            "alpha": self.alpha.value,
-            "interp": interp,
-            "force": self.force.value,
-            "merged_model_name": self.merged_model_name.value,
-        }
-        return args
-
-    def check_for_overwrite(self) -> bool:
-        model_out = self.merged_model_name.value
-        if model_out not in self.model_names:
-            return True
-        else:
-            result: bool = npyscreen.notify_yes_no(
-                f"The chosen merged model destination, {model_out}, is already in use. Overwrite?"
-            )
-            return result
-
-    def validate_field_values(self) -> bool:
-        bad_fields = []
-        model_names = self.model_names
-        selected_models = {model_names[self.model1.value[0]], model_names[self.model2.value[0]]}
-        if self.model3.value[0] > 0:
-            selected_models.add(model_names[self.model3.value[0] - 1])
-        if len(selected_models) < 2:
-            bad_fields.append(f"Please select two or three DIFFERENT models to compare. You selected {selected_models}")
-        if len(bad_fields) > 0:
-            message = "The following problems were detected and must be corrected:"
-            for problem in bad_fields:
-                message += f"\n* {problem}"
-            npyscreen.notify_confirm(message)
-            return False
-        else:
-            return True
-
-    def get_models(self, base_model: Optional[BaseModelType] = None) -> List[Tuple[str, str]]:  # key to name
-        models = [
-            (x.key, x.name)
-            for x in self.model_record_store.search_by_attr(model_type=ModelType.Main, base_model=base_model)
-            if x.format == ModelFormat("diffusers")
-            and hasattr(x, "variant")
-            and x.variant == ModelVariantType("normal")
-        ]
-        return sorted(models, key=lambda x: x[1])
-
-    def _populate_models(self, value: List[int]) -> None:
-        base_model = BASE_TYPES[value[0]][0]
-        self.models = self.get_models(base_model)
-        self.model_names = [x[1] for x in self.models]
-
-        models_plus_none = self.model_names.copy()
-        models_plus_none.insert(0, "None")
-        self.model1.values = self.model_names
-        self.model2.values = self.model_names
-        self.model3.values = models_plus_none
-
-        self.display()
-
-
-# npyscreen is untyped and causes mypy to get naggy
-class Mergeapp(npyscreen.NPSAppManaged):  # type: ignore
-    def __init__(self, installer: ModelInstallServiceBase):
-        """Initialize the npyscreen application."""
-        super().__init__()
-        self.installer = installer
-
-    def onStart(self) -> None:
-        npyscreen.setTheme(npyscreen.Themes.ElegantTheme)
-        self.main = self.addForm("MAIN", mergeModelsForm, name="Merge Models Settings")
-
-
-def run_gui(args: Namespace) -> None:
-    installer = initialize_installer(config)
-    mergeapp = Mergeapp(installer)
-    mergeapp.run()
-    merge_args = mergeapp.merge_arguments
-    merger = ModelMerger(installer)
-    merger.merge_diffusion_models_and_save(**merge_args)
-    logger.info(f'Models merged into new model: "{merge_args.merged_model_name}".')
-
-
-def run_cli(args: Namespace) -> None:
-    assert args.alpha >= 0 and args.alpha <= 1.0, "alpha must be between 0 and 1"
-    assert (
-        args.model_names and len(args.model_names) >= 1 and len(args.model_names) <= 3
-    ), "Please provide the --models argument to list 2 to 3 models to merge. Use --help for full usage."
-
-    if not args.merged_model_name:
-        args.merged_model_name = "+".join(args.model_names)
-        logger.info(f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"')
-
-    installer = initialize_installer(config)
-    store = installer.record_store
-    assert (
-        len(store.search_by_attr(args.merged_model_name, args.base_model, ModelType.Main)) == 0 or args.clobber
-    ), f'A model named "{args.merged_model_name}" already exists. Use --clobber to overwrite.'
-
-    merger = ModelMerger(installer)
-    model_keys = []
-    for name in args.model_names:
-        if len(name) == 32 and re.match(r"^[0-9a-f]$", name):
-            model_keys.append(name)
-        else:
-            models = store.search_by_attr(
-                model_name=name, model_type=ModelType.Main, base_model=BaseModelType(args.base_model)
-            )
-            assert len(models) > 0, f"{name}: Unknown model"
-            assert len(models) < 2, f"{name}: More than one model by this name. Please specify the model key instead."
-            model_keys.append(models[0].key)
-
-    merger.merge_diffusion_models_and_save(
-        alpha=args.alpha,
-        model_keys=model_keys,
-        merged_model_name=args.merged_model_name,
-        interp=args.interp,
-        force=args.force,
-    )
-    logger.info(f'Models merged into new model: "{args.merged_model_name}".')
-
-
-def main() -> None:
-    args = _parse_args()
-    if args.root_dir:
-        config.parse_args(["--root", str(args.root_dir)])
-    else:
-        config.parse_args([])
-
-    try:
-        if args.front_end:
-            run_gui(args)
-        else:
-            run_cli(args)
-    except widget.NotEnoughSpaceForWidget as e:
-        if str(e).startswith("Height of 1 allocated"):
-            logger.error("You need to have at least two diffusers models defined in models.yaml in order to merge")
-        else:
-            logger.error("Not enough room for the user interface. Try making this window larger.")
-        sys.exit(-1)
-    except Exception as e:
-        logger.error(str(e))
-        sys.exit(-1)
-    except KeyboardInterrupt:
-        sys.exit(-1)
-
-
-if __name__ == "__main__":
-    main()

invokeai/frontend/training/textual_inversion.py

@@ -3,7 +3,7 @@
 """
 This is the frontend to "textual_inversion_training.py".
 
-Copyright (c) 2023-24 Lincoln Stein and the InvokeAI Development Team
+Copyright (c) 2023 Lincoln Stein and the InvokeAI Development Team
 """
 
 
@@ -14,7 +14,7 @@ import sys
 import traceback
 from argparse import Namespace
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple
+from typing import List, Tuple
 
 import npyscreen
 from npyscreen import widget
@@ -22,9 +22,8 @@ from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.install.install_helper import initialize_installer
-from invokeai.backend.model_manager import ModelType
-from invokeai.backend.training import do_textual_inversion_training, parse_args
+
+from ...backend.training import do_textual_inversion_training, parse_args
 
 TRAINING_DATA = "text-inversion-training-data"
 TRAINING_DIR = "text-inversion-output"
@@ -45,21 +44,19 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
     precisions = ["no", "fp16", "bf16"]
     learnable_properties = ["object", "style"]
 
-    def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, saved_args: Optional[Dict[str, str]] = None):
+    def __init__(self, parentApp, name, saved_args=None):
         self.saved_args = saved_args or {}
         super().__init__(parentApp, name)
 
-    def afterEditing(self) -> None:
+    def afterEditing(self):
         self.parentApp.setNextForm(None)
 
-    def create(self) -> None:
+    def create(self):
         self.model_names, default = self.get_model_names()
         default_initializer_token = "★"
         default_placeholder_token = ""
         saved_args = self.saved_args
 
-        assert config is not None
-
         try:
             default = self.model_names.index(saved_args["model"])
         except Exception:
@@ -74,7 +71,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
         self.model = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
             name="Model Name:",
-            values=sorted(self.model_names),
+            values=self.model_names,
             value=default,
             max_height=len(self.model_names) + 1,
             scroll_exit=True,
@@ -239,7 +236,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
         )
         self.model.editing = True
 
-    def initializer_changed(self) -> None:
+    def initializer_changed(self):
         placeholder = self.placeholder_token.value
         self.prompt_token.value = f"(Trigger by using <{placeholder}> in your prompts)"
         self.train_data_dir.value = str(config.root_dir / TRAINING_DATA / placeholder)
@@ -278,13 +275,10 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             return True
 
     def get_model_names(self) -> Tuple[List[str], int]:
-        global config
-        assert config is not None
-        installer = initialize_installer(config)
-        store = installer.record_store
-        main_models = store.search_by_attr(model_type=ModelType.Main)
-        model_names = [f"{x.base.value}/{x.type.value}/{x.name}" for x in main_models if x.format == "diffusers"]
-        default = 0
+        conf = OmegaConf.load(config.root_dir / "configs/models.yaml")
+        model_names = [idx for idx in sorted(conf.keys()) if conf[idx].get("format", None) == "diffusers"]
+        defaults = [idx for idx in range(len(model_names)) if "default" in conf[model_names[idx]]]
+        default = defaults[0] if len(defaults) > 0 else 0
         return (model_names, default)
 
     def marshall_arguments(self) -> dict:
@@ -332,7 +326,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
 
 
 class MyApplication(npyscreen.NPSAppManaged):
-    def __init__(self, saved_args: Optional[Dict[str, str]] = None):
+    def __init__(self, saved_args=None):
         super().__init__()
         self.ti_arguments = None
         self.saved_args = saved_args
@@ -347,12 +341,11 @@ class MyApplication(npyscreen.NPSAppManaged):
         )
 
 
-def copy_to_embeddings_folder(args: Dict[str, str]) -> None:
+def copy_to_embeddings_folder(args: dict):
     """
     Copy learned_embeds.bin into the embeddings folder, and offer to
     delete the full model and checkpoints.
     """
-    assert config is not None
     source = Path(args["output_dir"], "learned_embeds.bin")
     dest_dir_name = args["placeholder_token"].strip("<>")
     destination = config.root_dir / "embeddings" / dest_dir_name
@@ -365,11 +358,10 @@ def copy_to_embeddings_folder(args: Dict[str, str]) -> None:
         logger.info(f'Keeping {args["output_dir"]}')
 
 
-def save_args(args: dict) -> None:
+def save_args(args: dict):
     """
     Save the current argument values to an omegaconf file
     """
-    assert config is not None
     dest_dir = config.root_dir / TRAINING_DIR
     os.makedirs(dest_dir, exist_ok=True)
     conf_file = dest_dir / CONF_FILE
@@ -381,7 +373,6 @@ def previous_args() -> dict:
     """
     Get the previous arguments used.
     """
-    assert config is not None
     conf_file = config.root_dir / TRAINING_DIR / CONF_FILE
     try:
         conf = OmegaConf.load(conf_file)
@@ -392,26 +383,24 @@ def previous_args() -> dict:
     return conf
 
 
-def do_front_end() -> None:
-    global config
+def do_front_end(args: Namespace):
     saved_args = previous_args()
     myapplication = MyApplication(saved_args=saved_args)
     myapplication.run()
 
-    if my_args := myapplication.ti_arguments:
-        os.makedirs(my_args["output_dir"], exist_ok=True)
+    if args := myapplication.ti_arguments:
+        os.makedirs(args["output_dir"], exist_ok=True)
 
         # Automatically add angle brackets around the trigger
-        if not re.match("^<.+>$", my_args["placeholder_token"]):
-            my_args["placeholder_token"] = f"<{my_args['placeholder_token']}>"
+        if not re.match("^<.+>$", args["placeholder_token"]):
+            args["placeholder_token"] = f"<{args['placeholder_token']}>"
 
-        my_args["only_save_embeds"] = True
-        save_args(my_args)
+        args["only_save_embeds"] = True
+        save_args(args)
 
         try:
-            print(my_args)
-            do_textual_inversion_training(config, **my_args)
-            copy_to_embeddings_folder(my_args)
+            do_textual_inversion_training(InvokeAIAppConfig.get_config(), **args)
+            copy_to_embeddings_folder(args)
         except Exception as e:
             logger.error("An exception occurred during training. The exception was:")
             logger.error(str(e))
@@ -419,12 +408,11 @@ def do_front_end() -> None:
             logger.error(traceback.format_exc())
 
 
-def main() -> None:
+def main():
     global config
 
-    args: Namespace = parse_args()
+    args = parse_args()
     config = InvokeAIAppConfig.get_config()
-    config.parse_args([])
 
     # change root if needed
     if args.root_dir:
@@ -432,7 +420,7 @@ def main() -> None:
 
     try:
         if args.front_end:
-            do_front_end()
+            do_front_end(args)
         else:
             do_textual_inversion_training(config, **vars(args))
     except AssertionError as e:

invokeai/frontend/training/textual_inversion2.py

@@ -1,454 +0,0 @@
-#!/usr/bin/env python
-
-"""
-This is the frontend to "textual_inversion_training.py".
-
-Copyright (c) 2023-24 Lincoln Stein and the InvokeAI Development Team
-"""
-
-
-import os
-import re
-import shutil
-import sys
-import traceback
-from argparse import Namespace
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-import npyscreen
-from npyscreen import widget
-from omegaconf import OmegaConf
-
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.install.install_helper import initialize_installer
-from invokeai.backend.model_manager import ModelType
-from invokeai.backend.training import do_textual_inversion_training, parse_args
-
-TRAINING_DATA = "text-inversion-training-data"
-TRAINING_DIR = "text-inversion-output"
-CONF_FILE = "preferences.conf"
-config = None
-
-
-class textualInversionForm(npyscreen.FormMultiPageAction):
-    resolutions = [512, 768, 1024]
-    lr_schedulers = [
-        "linear",
-        "cosine",
-        "cosine_with_restarts",
-        "polynomial",
-        "constant",
-        "constant_with_warmup",
-    ]
-    precisions = ["no", "fp16", "bf16"]
-    learnable_properties = ["object", "style"]
-
-    def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, saved_args: Optional[Dict[str, str]] = None):
-        self.saved_args = saved_args or {}
-        super().__init__(parentApp, name)
-
-    def afterEditing(self) -> None:
-        self.parentApp.setNextForm(None)
-
-    def create(self) -> None:
-        self.model_names, default = self.get_model_names()
-        default_initializer_token = "★"
-        default_placeholder_token = ""
-        saved_args = self.saved_args
-
-        assert config is not None
-
-        try:
-            default = self.model_names.index(saved_args["model"])
-        except Exception:
-            pass
-
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields, cursor arrows to make a selection, and space to toggle checkboxes.",
-            editable=False,
-        )
-
-        self.model = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="Model Name:",
-            values=sorted(self.model_names),
-            value=default,
-            max_height=len(self.model_names) + 1,
-            scroll_exit=True,
-        )
-        self.placeholder_token = self.add_widget_intelligent(
-            npyscreen.TitleText,
-            name="Trigger Term:",
-            value="",  # saved_args.get('placeholder_token',''), # to restore previous term
-            scroll_exit=True,
-        )
-        self.placeholder_token.when_value_edited = self.initializer_changed
-        self.nextrely -= 1
-        self.nextrelx += 30
-        self.prompt_token = self.add_widget_intelligent(
-            npyscreen.FixedText,
-            name="Trigger term for use in prompt",
-            value="",
-            editable=False,
-            scroll_exit=True,
-        )
-        self.nextrelx -= 30
-        self.initializer_token = self.add_widget_intelligent(
-            npyscreen.TitleText,
-            name="Initializer:",
-            value=saved_args.get("initializer_token", default_initializer_token),
-            scroll_exit=True,
-        )
-        self.resume_from_checkpoint = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Resume from last saved checkpoint",
-            value=False,
-            scroll_exit=True,
-        )
-        self.learnable_property = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="Learnable property:",
-            values=self.learnable_properties,
-            value=self.learnable_properties.index(saved_args.get("learnable_property", "object")),
-            max_height=4,
-            scroll_exit=True,
-        )
-        self.train_data_dir = self.add_widget_intelligent(
-            npyscreen.TitleFilename,
-            name="Data Training Directory:",
-            select_dir=True,
-            must_exist=False,
-            value=str(
-                saved_args.get(
-                    "train_data_dir",
-                    config.root_dir / TRAINING_DATA / default_placeholder_token,
-                )
-            ),
-            scroll_exit=True,
-        )
-        self.output_dir = self.add_widget_intelligent(
-            npyscreen.TitleFilename,
-            name="Output Destination Directory:",
-            select_dir=True,
-            must_exist=False,
-            value=str(
-                saved_args.get(
-                    "output_dir",
-                    config.root_dir / TRAINING_DIR / default_placeholder_token,
-                )
-            ),
-            scroll_exit=True,
-        )
-        self.resolution = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="Image resolution (pixels):",
-            values=self.resolutions,
-            value=self.resolutions.index(saved_args.get("resolution", 512)),
-            max_height=4,
-            scroll_exit=True,
-        )
-        self.center_crop = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Center crop images before resizing to resolution",
-            value=saved_args.get("center_crop", False),
-            scroll_exit=True,
-        )
-        self.mixed_precision = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="Mixed Precision:",
-            values=self.precisions,
-            value=self.precisions.index(saved_args.get("mixed_precision", "fp16")),
-            max_height=4,
-            scroll_exit=True,
-        )
-        self.num_train_epochs = self.add_widget_intelligent(
-            npyscreen.TitleSlider,
-            name="Number of training epochs:",
-            out_of=1000,
-            step=50,
-            lowest=1,
-            value=saved_args.get("num_train_epochs", 100),
-            scroll_exit=True,
-        )
-        self.max_train_steps = self.add_widget_intelligent(
-            npyscreen.TitleSlider,
-            name="Max Training Steps:",
-            out_of=10000,
-            step=500,
-            lowest=1,
-            value=saved_args.get("max_train_steps", 3000),
-            scroll_exit=True,
-        )
-        self.train_batch_size = self.add_widget_intelligent(
-            npyscreen.TitleSlider,
-            name="Batch Size (reduce if you run out of memory):",
-            out_of=50,
-            step=1,
-            lowest=1,
-            value=saved_args.get("train_batch_size", 8),
-            scroll_exit=True,
-        )
-        self.gradient_accumulation_steps = self.add_widget_intelligent(
-            npyscreen.TitleSlider,
-            name="Gradient Accumulation Steps (may need to decrease this to resume from a checkpoint):",
-            out_of=10,
-            step=1,
-            lowest=1,
-            value=saved_args.get("gradient_accumulation_steps", 4),
-            scroll_exit=True,
-        )
-        self.lr_warmup_steps = self.add_widget_intelligent(
-            npyscreen.TitleSlider,
-            name="Warmup Steps:",
-            out_of=100,
-            step=1,
-            lowest=0,
-            value=saved_args.get("lr_warmup_steps", 0),
-            scroll_exit=True,
-        )
-        self.learning_rate = self.add_widget_intelligent(
-            npyscreen.TitleText,
-            name="Learning Rate:",
-            value=str(
-                saved_args.get("learning_rate", "5.0e-04"),
-            ),
-            scroll_exit=True,
-        )
-        self.scale_lr = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Scale learning rate by number GPUs, steps and batch size",
-            value=saved_args.get("scale_lr", True),
-            scroll_exit=True,
-        )
-        self.enable_xformers_memory_efficient_attention = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Use xformers acceleration",
-            value=saved_args.get("enable_xformers_memory_efficient_attention", False),
-            scroll_exit=True,
-        )
-        self.lr_scheduler = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="Learning rate scheduler:",
-            values=self.lr_schedulers,
-            max_height=7,
-            value=self.lr_schedulers.index(saved_args.get("lr_scheduler", "constant")),
-            scroll_exit=True,
-        )
-        self.model.editing = True
-
-    def initializer_changed(self) -> None:
-        placeholder = self.placeholder_token.value
-        self.prompt_token.value = f"(Trigger by using <{placeholder}> in your prompts)"
-        self.train_data_dir.value = str(config.root_dir / TRAINING_DATA / placeholder)
-        self.output_dir.value = str(config.root_dir / TRAINING_DIR / placeholder)
-        self.resume_from_checkpoint.value = Path(self.output_dir.value).exists()
-
-    def on_ok(self):
-        if self.validate_field_values():
-            self.parentApp.setNextForm(None)
-            self.editing = False
-            self.parentApp.ti_arguments = self.marshall_arguments()
-            npyscreen.notify("Launching textual inversion training. This will take a while...")
-        else:
-            self.editing = True
-
-    def ok_cancel(self):
-        sys.exit(0)
-
-    def validate_field_values(self) -> bool:
-        bad_fields = []
-        if self.model.value is None:
-            bad_fields.append("Model Name must correspond to a known model in models.yaml")
-        if not re.match("^[a-zA-Z0-9.-]+$", self.placeholder_token.value):
-            bad_fields.append("Trigger term must only contain alphanumeric characters, the dot and hyphen")
-        if self.train_data_dir.value is None:
-            bad_fields.append("Data Training Directory cannot be empty")
-        if self.output_dir.value is None:
-            bad_fields.append("The Output Destination Directory cannot be empty")
-        if len(bad_fields) > 0:
-            message = "The following problems were detected and must be corrected:"
-            for problem in bad_fields:
-                message += f"\n* {problem}"
-            npyscreen.notify_confirm(message)
-            return False
-        else:
-            return True
-
-    def get_model_names(self) -> Tuple[List[str], int]:
-        global config
-        assert config is not None
-        installer = initialize_installer(config)
-        store = installer.record_store
-        main_models = store.search_by_attr(model_type=ModelType.Main)
-        model_names = [f"{x.base.value}/{x.type.value}/{x.name}" for x in main_models if x.format == "diffusers"]
-        default = 0
-        return (model_names, default)
-
-    def marshall_arguments(self) -> dict:
-        args = {}
-
-        # the choices
-        args.update(
-            model=self.model_names[self.model.value[0]],
-            resolution=self.resolutions[self.resolution.value[0]],
-            lr_scheduler=self.lr_schedulers[self.lr_scheduler.value[0]],
-            mixed_precision=self.precisions[self.mixed_precision.value[0]],
-            learnable_property=self.learnable_properties[self.learnable_property.value[0]],
-        )
-
-        # all the strings and booleans
-        for attr in (
-            "initializer_token",
-            "placeholder_token",
-            "train_data_dir",
-            "output_dir",
-            "scale_lr",
-            "center_crop",
-            "enable_xformers_memory_efficient_attention",
-        ):
-            args[attr] = getattr(self, attr).value
-
-        # all the integers
-        for attr in (
-            "train_batch_size",
-            "gradient_accumulation_steps",
-            "num_train_epochs",
-            "max_train_steps",
-            "lr_warmup_steps",
-        ):
-            args[attr] = int(getattr(self, attr).value)
-
-        # the floats (just one)
-        args.update(learning_rate=float(self.learning_rate.value))
-
-        # a special case
-        if self.resume_from_checkpoint.value and Path(self.output_dir.value).exists():
-            args["resume_from_checkpoint"] = "latest"
-
-        return args
-
-
-class MyApplication(npyscreen.NPSAppManaged):
-    def __init__(self, saved_args: Optional[Dict[str, str]] = None):
-        super().__init__()
-        self.ti_arguments = None
-        self.saved_args = saved_args
-
-    def onStart(self):
-        npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
-        self.main = self.addForm(
-            "MAIN",
-            textualInversionForm,
-            name="Textual Inversion Settings",
-            saved_args=self.saved_args,
-        )
-
-
-def copy_to_embeddings_folder(args: Dict[str, str]) -> None:
-    """
-    Copy learned_embeds.bin into the embeddings folder, and offer to
-    delete the full model and checkpoints.
-    """
-    assert config is not None
-    source = Path(args["output_dir"], "learned_embeds.bin")
-    dest_dir_name = args["placeholder_token"].strip("<>")
-    destination = config.root_dir / "embeddings" / dest_dir_name
-    os.makedirs(destination, exist_ok=True)
-    logger.info(f"Training completed. Copying learned_embeds.bin into {str(destination)}")
-    shutil.copy(source, destination)
-    if (input("Delete training logs and intermediate checkpoints? [y] ") or "y").startswith(("y", "Y")):
-        shutil.rmtree(Path(args["output_dir"]))
-    else:
-        logger.info(f'Keeping {args["output_dir"]}')
-
-
-def save_args(args: dict) -> None:
-    """
-    Save the current argument values to an omegaconf file
-    """
-    assert config is not None
-    dest_dir = config.root_dir / TRAINING_DIR
-    os.makedirs(dest_dir, exist_ok=True)
-    conf_file = dest_dir / CONF_FILE
-    conf = OmegaConf.create(args)
-    OmegaConf.save(config=conf, f=conf_file)
-
-
-def previous_args() -> dict:
-    """
-    Get the previous arguments used.
-    """
-    assert config is not None
-    conf_file = config.root_dir / TRAINING_DIR / CONF_FILE
-    try:
-        conf = OmegaConf.load(conf_file)
-        conf["placeholder_token"] = conf["placeholder_token"].strip("<>")
-    except Exception:
-        conf = None
-
-    return conf
-
-
-def do_front_end() -> None:
-    global config
-    saved_args = previous_args()
-    myapplication = MyApplication(saved_args=saved_args)
-    myapplication.run()
-
-    if my_args := myapplication.ti_arguments:
-        os.makedirs(my_args["output_dir"], exist_ok=True)
-
-        # Automatically add angle brackets around the trigger
-        if not re.match("^<.+>$", my_args["placeholder_token"]):
-            my_args["placeholder_token"] = f"<{my_args['placeholder_token']}>"
-
-        my_args["only_save_embeds"] = True
-        save_args(my_args)
-
-        try:
-            print(my_args)
-            do_textual_inversion_training(config, **my_args)
-            copy_to_embeddings_folder(my_args)
-        except Exception as e:
-            logger.error("An exception occurred during training. The exception was:")
-            logger.error(str(e))
-            logger.error("DETAILS:")
-            logger.error(traceback.format_exc())
-
-
-def main() -> None:
-    global config
-
-    args: Namespace = parse_args()
-    config = InvokeAIAppConfig.get_config()
-    config.parse_args([])
-
-    # change root if needed
-    if args.root_dir:
-        config.root = args.root_dir
-
-    try:
-        if args.front_end:
-            do_front_end()
-        else:
-            do_textual_inversion_training(config, **vars(args))
-    except AssertionError as e:
-        logger.error(e)
-        sys.exit(-1)
-    except KeyboardInterrupt:
-        pass
-    except (widget.NotEnoughSpaceForWidget, Exception) as e:
-        if str(e).startswith("Height of 1 allocated"):
-            logger.error("You need to have at least one diffusers models defined in models.yaml in order to train")
-        elif str(e).startswith("addwstr"):
-            logger.error("Not enough window space for the interface. Please make your window larger and try again.")
-        else:
-            logger.error(e)
-        sys.exit(-1)
-
-
-if __name__ == "__main__":
-    main()

invokeai/frontend/web/.eslintrc.js

@@ -1,26 +1,131 @@
 module.exports = {
-  extends: ['@invoke-ai/eslint-config-react'],
-  plugins: ['path', 'i18next'],
+  env: {
+    browser: true,
+    es6: true,
+    node: true,
+  },
+  extends: [
+    'eslint:recommended',
+    'plugin:@typescript-eslint/recommended',
+    'plugin:react/recommended',
+    'plugin:react-hooks/recommended',
+    'plugin:react/jsx-runtime',
+    'prettier',
+    'plugin:storybook/recommended',
+  ],
+  parser: '@typescript-eslint/parser',
+  parserOptions: {
+    ecmaFeatures: {
+      jsx: true,
+    },
+    ecmaVersion: 2018,
+    sourceType: 'module',
+  },
+  plugins: [
+    'react',
+    '@typescript-eslint',
+    'eslint-plugin-react-hooks',
+    '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: {
-    // TODO(psyche): Enable this rule. Requires no default exports in components - many changes.
-    'react-refresh/only-export-components': 'off',
-    // TODO(psyche): Enable this rule. Requires a lot of eslint-disable-next-line comments.
-    '@typescript-eslint/consistent-type-assertions': 'off',
-    // https://github.com/qdanik/eslint-plugin-path
     'path/no-relative-imports': ['error', { maxDepth: 0 }],
-    // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
-    'i18next/no-literal-string': 'error',
+    curly: 'error',
+    'i18next/no-literal-string': 'warn',
+    'react/jsx-no-bind': ['error', { allowBind: true }],
+    'react/jsx-curly-brace-presence': [
+      'error',
+      { props: 'never', children: 'never' },
+    ],
+    'react-hooks/exhaustive-deps': 'error',
+    'no-var': 'error',
+    'brace-style': 'error',
+    'prefer-template': 'error',
+    'import/no-duplicates': 'error',
+    radix: 'error',
+    'space-before-blocks': 'error',
+    'import/prefer-default-export': 'off',
+    '@typescript-eslint/no-unused-vars': 'off',
+    'unused-imports/no-unused-imports': 'error',
+    'unused-imports/no-unused-vars': [
+      'warn',
+      {
+        vars: 'all',
+        varsIgnorePattern: '^_',
+        args: 'after-used',
+        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': [
+      'error',
+      {
+        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',
+    // Prefer @invoke-ai/ui components over chakra
+    'no-restricted-imports': 'off',
+    '@typescript-eslint/no-restricted-imports': [
+      'warn',
+      {
+        paths: [
+          {
+            name: '@chakra-ui/react',
+            message: "Please import from '@invoke-ai/ui' instead.",
+          },
+          {
+            name: '@chakra-ui/layout',
+            message: "Please import from '@invoke-ai/ui' instead.",
+          },
+          {
+            name: '@chakra-ui/portal',
+            message: "Please import from '@invoke-ai/ui' instead.",
+          },
+        ],
+      },
+    ],
   },
   overrides: [
-    /**
-     * Overrides for stories
-     */
     {
       files: ['*.stories.tsx'],
       rules: {
-        // We may not have i18n available in stories.
         'i18next/no-literal-string': 'off',
       },
     },
   ],
+  settings: {
+    react: {
+      version: 'detect',
+    },
+  },
 };

invokeai/frontend/web/.prettierrc.js

@@ -1,5 +1,9 @@
 module.exports = {
-  ...require('@invoke-ai/prettier-config-react'),
+  trailingComma: 'es5',
+  tabWidth: 2,
+  semi: true,
+  singleQuote: true,
+  endOfLine: 'auto',
   overrides: [
     {
       files: ['public/locales/*.json'],

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

@@ -1,7 +1,7 @@
 import { PropsWithChildren, memo, useEffect } from 'react';
 import { modelChanged } from '../src/features/parameters/store/generationSlice';
 import { useAppDispatch } from '../src/app/store/storeHooks';
-import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
+import { useGlobalModifiersInit } from '@invoke-ai/ui';
 /**
  * Initializes some state for storybook. Must be in a different component
  * so that it is run inside the redux context.

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

@@ -6,6 +6,7 @@ import { Provider } from 'react-redux';
 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

invokeai/frontend/web/.unimportedrc.json

@@ -1,7 +1,13 @@
 {
   "entry": ["src/main.tsx"],
   "extensions": [".ts", ".tsx"],
-  "ignorePatterns": ["**/node_modules/**", "dist/**", "public/**", "**/*.stories.tsx", "config/**"],
+  "ignorePatterns": [
+    "**/node_modules/**",
+    "dist/**",
+    "public/**",
+    "**/*.stories.tsx",
+    "config/**"
+  ],
   "ignoreUnresolved": [],
   "ignoreUnimported": ["src/i18.d.ts", "vite.config.ts", "src/vite-env.d.ts"],
   "respectGitignore": true,

invokeai/frontend/web/README.md

@@ -1,150 +0,0 @@
-# Invoke UI
-
-<!-- @import "[TOC]" {cmd="toc" depthFrom=2 depthTo=3 orderedList=false} -->
-
-<!-- code_chunk_output -->
-
-- [Dev environment](#dev-environment)
-  - [Setup](#setup)
-  - [Package scripts](#package-scripts)
-  - [Type generation](#type-generation)
-  - [Localization](#localization)
-  - [VSCode](#vscode)
-- [Contributing](#contributing)
-  - [Check in before investing your time](#check-in-before-investing-your-time)
-  - [Commit format](#commit-format)
-  - [Submitting a PR](#submitting-a-pr)
-- [Other docs](#other-docs)
-
-<!-- /code_chunk_output -->
-
-Invoke's UI is made possible by many contributors and open-source libraries. Thank you!
-
-## Dev environment
-
-### Setup
-
-1. Install [node] and [pnpm].
-1. Run `pnpm i` to install all packages.
-
-#### Run in dev mode
-
-1. From `invokeai/frontend/web/`, run `pnpm dev`.
-1. From repo root, run `python scripts/invokeai-web.py`.
-1. Point your browser to the dev server address, e.g. <http://localhost:5173/>
-
-### Package scripts
-
-- `dev`: run the frontend in dev mode, enabling hot reloading
-- `build`: run all checks (madge, eslint, prettier, tsc) and then build the frontend
-- `typegen`: generate types from the OpenAPI schema (see [Type generation])
-- `lint:madge`: check frontend for circular dependencies
-- `lint:eslint`: check frontend for code quality
-- `lint:prettier`: check frontend for code formatting
-- `lint:tsc`: check frontend for type issues
-- `lint`: run all checks concurrently
-- `fix`: run `eslint` and `prettier`, fixing fixable issues
-
-### Type generation
-
-We use [openapi-typescript] to generate types from the app's OpenAPI schema.
-
-The generated types are committed to the repo in [schema.ts].
-
-```sh
-# from the repo root, start the server
-python scripts/invokeai-web.py
-# from invokeai/frontend/web/, run the script
-pnpm typegen
-```
-
-### Localization
-
-We use [i18next] for localization, but translation to languages other than English happens on our [Weblate] project.
-
-Only the English source strings should be changed on this repo.
-
-### VSCode
-
-#### Example debugger config
-
-```jsonc
-{
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "type": "chrome",
-      "request": "launch",
-      "name": "Invoke UI",
-      "url": "http://localhost:5173",
-      "webRoot": "${workspaceFolder}/invokeai/frontend/web",
-    },
-  ],
-}
-```
-
-#### Remote dev
-
-We've noticed an intermittent timeout issue with the VSCode remote dev port forwarding.
-
-We suggest disabling the editor's port forwarding feature and doing it manually via SSH:
-
-```sh
-ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host
-```
-
-## Contributing Guidelines
-
-Thanks for your interest in contributing to the Invoke Web UI!
-
-Please follow these guidelines when contributing.
-
-### Check in before investing your time
-
-Please check in before you invest your time on anything besides a trivial fix, in case it conflicts with ongoing work or isn't aligned with the vision for the app.
-
-If a feature request or issue doesn't already exist for the thing you want to work on, please create one.
-
-Ping `@psychedelicious` on [discord] in the `#frontend-dev` channel or in the feature request / issue you want to work on - we're happy chat.
-
-### Code conventions
-
-- This is a fairly complex app with a deep component tree. Please use memoization (`useCallback`, `useMemo`, `memo`) with enthusiasm.
-- If you need to add some global, ephemeral state, please use [nanostores] if possible.
-- Be careful with your redux selectors. If they need to be parameterized, consider creating them inside a `useMemo`.
-- Feel free to use `lodash` (via `lodash-es`) to make the intent of your code clear.
-- Please add comments describing the "why", not the "how" (unless it is really arcane).
-
-### Commit format
-
-Please use the [conventional commits] spec for the web UI, with a scope of "ui":
-
-- `chore(ui): bump deps`
-- `chore(ui): lint`
-- `feat(ui): add some cool new feature`
-- `fix(ui): fix some bug`
-
-### Submitting a PR
-
-- Ensure your branch is tidy. Use an interactive rebase to clean up the commit history and reword the commit messages if they are not descriptive.
-- Run `pnpm lint`. Some issues are auto-fixable with `pnpm fix`.
-- Fill out the PR form when creating the PR.
-  - It doesn't need to be super detailed, but a screenshot or video is nice if you changed something visually.
-  - If a section isn't relevant, delete it. There are no UI tests at this time.
-
-## Other docs
-
-- [Workflows - Design and Implementation]
-- [State Management]
-
-[node]: https://nodejs.org/en/download/
-[pnpm]: https://github.com/pnpm/pnpm
-[discord]: https://discord.gg/ZmtBAhwWhy
-[i18next]: https://github.com/i18next/react-i18next
-[Weblate]: https://hosted.weblate.org/engage/invokeai/
-[openapi-typescript]: https://github.com/drwpow/openapi-typescript
-[Type generation]: #type-generation
-[schema.ts]: ../src/services/api/schema.ts
-[conventional commits]: https://www.conventionalcommits.org/en/v1.0.0/
-[Workflows - Design and Implementation]: ./docs/WORKFLOWS_DESIGN_IMPLEMENTATION.md
-[State Management]: ./docs/STATE_MGMT.md

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

@@ -22,13 +22,12 @@ export const packageConfig: UserConfig = {
       fileName: (format) => `invoke-ai-ui.${format}.js`,
     },
     rollupOptions: {
-      external: ['react', 'react-dom', '@emotion/react', '@chakra-ui/react', '@invoke-ai/ui-library'],
+      external: ['react', 'react-dom', '@emotion/react', '@chakra-ui/react'],
       output: {
         globals: {
           react: 'React',
           'react-dom': 'ReactDOM',
           '@emotion/react': 'EmotionReact',
-          '@invoke-ai/ui-library': 'UiLibrary',
         },
       },
     },

invokeai/frontend/web/docs/README.md

@@ -0,0 +1,154 @@
+# InvokeAI Web UI
+
+<!-- @import "[TOC]" {cmd="toc" depthFrom=1 depthTo=6 orderedList=false} -->
+
+<!-- code_chunk_output -->
+
+- [InvokeAI Web UI](#invokeai-web-ui)
+  - [Core Libraries](#core-libraries)
+    - [Redux Toolkit](#redux-toolkit)
+    - [Socket\.IO](#socketio)
+    - [Chakra UI](#chakra-ui)
+    - [KonvaJS](#konvajs)
+    - [Vite](#vite)
+    - [i18next & Weblate](#i18next--weblate)
+    - [openapi-typescript](#openapi-typescript)
+    - [reactflow](#reactflow)
+    - [zod](#zod)
+  - [Client Types Generation](#client-types-generation)
+  - [Package Scripts](#package-scripts)
+  - [Contributing](#contributing)
+    - [Dev Environment](#dev-environment)
+      - [VSCode Remote Dev](#vscode-remote-dev)
+    - [Production builds](#production-builds)
+
+<!-- /code_chunk_output -->
+
+The UI is a fairly straightforward Typescript React app.
+
+## Core Libraries
+
+InvokeAI's UI is made possible by a number of excellent open-source libraries. The most heavily-used are listed below, but there are many others.
+
+### Redux Toolkit
+
+[Redux Toolkit] is used for state management and fetching/caching:
+
+- `RTK-Query` for data fetching and caching
+- `createAsyncThunk` for a couple other HTTP requests
+- `createEntityAdapter` to normalize things like images and models
+- `createListenerMiddleware` for async workflows
+
+We use [redux-remember] for persistence.
+
+### Socket\.IO
+
+[Socket.IO] is used for server-to-client events, like generation process and queue state changes.
+
+### Chakra UI
+
+[Chakra UI] is our primary UI library, but we also use a few components from [Mantine v6].
+
+### KonvaJS
+
+[KonvaJS] powers the canvas. In the future, we'd like to explore [PixiJS] or WebGPU.
+
+### Vite
+
+[Vite] is our bundler.
+
+### i18next & Weblate
+
+We use [i18next] for localization, but translation to languages other than English happens on our [Weblate] project. **Only the English source strings should be changed on this repo.**
+
+### openapi-typescript
+
+[openapi-typescript] is used to generate types from the server's OpenAPI schema. See TYPES_CODEGEN.md.
+
+### reactflow
+
+[reactflow] powers the Workflow Editor.
+
+### zod
+
+[zod] schemas are used to model data structures and provide runtime validation.
+
+## Client Types Generation
+
+We use [openapi-typescript] to generate types from the app's OpenAPI schema.
+
+The generated types are written to `invokeai/frontend/web/src/services/api/schema.d.ts`. This file is committed to the repo.
+
+The server must be started and available at <http://127.0.0.1:9090>.
+
+```sh
+# from the repo root, start the server
+python scripts/invokeai-web.py
+# from invokeai/frontend/web/, run the script
+pnpm typegen
+```
+
+## Package Scripts
+
+See `package.json` for all scripts.
+
+Run with `pnpm <script name>`.
+
+- `dev`: run the frontend in dev mode, enabling hot reloading
+- `build`: run all checks (madge, eslint, prettier, tsc) and then build the frontend
+- `typegen`: generate types from the OpenAPI schema (see [Client Types Generation](#client-types-generation))
+- `lint:madge`: check frontend for circular dependencies
+- `lint:eslint`: check frontend for code quality
+- `lint:prettier`: check frontend for code formatting
+- `lint:tsc`: check frontend for type issues
+- `lint`: run all checks concurrently
+- `fix`: run `eslint` and `prettier`, fixing fixable issues
+
+## Contributing
+
+Thanks for your interest in contributing to the InvokeAI Web UI!
+
+We encourage you to ping @psychedelicious and @blessedcoolant on [discord] if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
+
+### Dev Environment
+
+Install [node] and [pnpm].
+
+From `invokeai/frontend/web/` run `pnpm i` to get everything set up.
+
+Start everything in dev mode:
+
+1. Start the dev server: `pnpm dev`
+2. Start the InvokeAI Nodes backend: `python scripts/invokeai-web.py # run from the repo root`
+3. Point your browser to the dev server address e.g. <http://localhost:5173/>
+
+#### VSCode Remote Dev
+
+We've noticed an intermittent issue with the VSCode Remote Dev port forwarding. If you use this feature of VSCode, you may intermittently click the Invoke button and then get nothing until the request times out. Suggest disabling the IDE's port forwarding feature and doing it manually via SSH:
+
+`ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host`
+
+### Production builds
+
+For a number of technical and logistical reasons, we need to commit UI build artefacts to the repo.
+
+If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
+
+To build for production, run `pnpm build`.
+
+[node]: https://nodejs.org/en/download/
+[pnpm]: https://github.com/pnpm/pnpm
+[discord]: https://discord.gg/ZmtBAhwWhy
+[Redux Toolkit]: https://github.com/reduxjs/redux-toolkit
+[redux-remember]: https://github.com/zewish/redux-remember
+[Socket.IO]: https://github.com/socketio/socket.io
+[Chakra UI]: https://github.com/chakra-ui/chakra-ui
+[Mantine v6]: https://v6.mantine.dev/
+[KonvaJS]: https://github.com/konvajs/react-konva
+[PixiJS]: https://github.com/pixijs/pixijs
+[Vite]: https://github.com/vitejs/vite
+[i18next]: https://github.com/i18next/react-i18next
+[Weblate]: https://hosted.weblate.org/engage/invokeai/
+[openapi-typescript]: https://github.com/drwpow/openapi-typescript
+[reactflow]: https://github.com/xyflow/xyflow
+[zod]: https://github.com/colinhacks/zod

invokeai/frontend/web/docs/STATE_MGMT.md

@@ -1,38 +0,0 @@
-# State Management
-
-The app makes heavy use of Redux Toolkit, its Query library, and `nanostores`.
-
-## Redux
-
-TODO
-
-## `nanostores`
-
-[nanostores] is a tiny state management library. It provides both imperative and declarative APIs.
-
-### Example
-
-```ts
-export const $myStringOption = atom<string | null>(null);
-
-// Outside a component, or within a callback for performance-critical logic
-$myStringOption.get();
-$myStringOption.set('new value');
-
-// Inside a component
-const myStringOption = useStore($myStringOption);
-```
-
-### Where to put nanostores
-
-- For global application state, export your stores from `invokeai/frontend/web/src/app/store/nanostores/`.
-- For feature state, create a file for the stores next to the redux slice definition (e.g. `invokeai/frontend/web/src/features/myFeature/myFeatureNanostores.ts`).
-- For hooks with global state, export the store from the same file the hook is in, or put it next to the hook.
-
-### When to use nanostores
-
-- For non-serializable data that needs to be available throughout the app, use `nanostores` instead of a global.
-- For ephemeral global state (i.e. state that does not need to be persisted), use `nanostores` instead of redux.
-- For performance-critical code and in callbacks, redux selectors can be problematic due to the declarative reactivity system. Consider refactoring to use `nanostores` if there's a **measurable** performance issue.
-
-[nanostores]: https://github.com/nanostores/nanostores/

invokeai/frontend/web/docs/WORKFLOWS_DESIGN_IMPLEMENTATION.md

@@ -23,7 +23,7 @@
         - [Primitive Types](#primitive-types)
         - [Complex Types](#complex-types)
         - [Collection Types](#collection-types)
-        - [Collection or Scalar Types](#collection-or-scalar-types)
+        - [Polymorphic Types](#polymorphic-types)
         - [Optional Fields](#optional-fields)
       - [Building Field Input Templates](#building-field-input-templates)
       - [Building Field Output Templates](#building-field-output-templates)

invokeai/frontend/web/package.json

@@ -19,8 +19,8 @@
     "dist"
   ],
   "scripts": {
-    "dev": "vite dev",
-    "dev:host": "vite dev --host",
+    "dev": "concurrently \"vite dev\" \"pnpm run theme:watch\"",
+    "dev:host": "concurrently \"vite dev --host\" \"pnpm run theme:watch\"",
     "build": "pnpm run lint && vite build",
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
@@ -31,6 +31,9 @@
     "lint": "concurrently -g -n eslint,prettier,tsc,madge -c cyan,green,magenta,yellow \"pnpm run lint:eslint\" \"pnpm run lint:prettier\" \"pnpm run lint:tsc\" \"pnpm run lint:madge\"",
     "fix": "eslint --fix . && prettier --log-level warn --write .",
     "preinstall": "npx only-allow pnpm",
+    "postinstall": "pnpm run theme",
+    "theme": "chakra-cli tokens node_modules/@invoke-ai/ui",
+    "theme:watch": "chakra-cli tokens node_modules/@invoke-ai/ui --watch",
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
     "unimported": "npx unimported"
@@ -49,13 +52,21 @@
     }
   },
   "dependencies": {
+    "@chakra-ui/anatomy": "^2.2.2",
+    "@chakra-ui/icons": "^2.1.1",
+    "@chakra-ui/layout": "^2.3.1",
+    "@chakra-ui/portal": "^2.1.0",
+    "@chakra-ui/react": "^2.8.2",
     "@chakra-ui/react-use-size": "^2.1.0",
+    "@chakra-ui/styled-system": "^2.9.2",
+    "@chakra-ui/theme-tools": "^2.1.2",
     "@dagrejs/graphlib": "^2.1.13",
     "@dnd-kit/core": "^6.1.0",
-    "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
+    "@emotion/react": "^11.11.3",
+    "@emotion/styled": "^11.11.0",
     "@fontsource-variable/inter": "^5.0.16",
-    "@invoke-ai/ui-library": "^0.0.18",
+    "@invoke-ai/ui": "0.0.10",
     "@mantine/form": "6.0.21",
     "@nanostores/react": "^0.7.1",
     "@reduxjs/toolkit": "2.0.1",
@@ -105,6 +116,7 @@
     "zod-validation-error": "^3.0.0"
   },
   "peerDependencies": {
+    "@chakra-ui/cli": "^2.4.1",
     "@chakra-ui/react": "^2.8.2",
     "react": "^18.2.0",
     "react-dom": "^18.2.0",
@@ -112,8 +124,7 @@
   },
   "devDependencies": {
     "@arthurgeron/eslint-plugin-react-usememo": "^2.2.3",
-    "@invoke-ai/eslint-config-react": "^0.0.13",
-    "@invoke-ai/prettier-config-react": "^0.0.6",
+    "@chakra-ui/cli": "^2.4.1",
     "@storybook/addon-docs": "^7.6.10",
     "@storybook/addon-essentials": "^7.6.10",
     "@storybook/addon-interactions": "^7.6.10",
@@ -153,7 +164,7 @@
     "storybook": "^7.6.10",
     "ts-toolbelt": "^9.6.0",
     "typescript": "^5.3.3",
-    "vite": "^5.0.12",
+    "vite": "^5.0.11",
     "vite-plugin-css-injected-by-js": "^3.3.1",
     "vite-plugin-dts": "^3.7.1",
     "vite-plugin-eslint": "^1.8.1",