(minor) tidy types in sdxl_format_utils.py

.dockerignore

@@ -1,11 +1,9 @@
 *
 !invokeai
 !pyproject.toml
-!uv.lock
 !docker/docker-entrypoint.sh
 !LICENSE
 
-**/dist
 **/node_modules
 **/__pycache__
-**/*.egg-info
+**/*.egg-info

.git-blame-ignore-revs

@@ -1,5 +1,2 @@
 b3dccfaeb636599c02effc377cdd8a87d658256c
 218b6d0546b990fc449c876fb99f44b50c4daa35
-182580ff6970caed400be178c5b888514b75d7f2
-8e9d5c1187b0d36da80571ce4c8ba9b3a37b6c46
-99aac5870e1092b182e6c5f21abcaab6936a4ad1

.gitattributes

@@ -2,6 +2,4 @@
 # Only affects text files and ignores other file types.
 # For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
 * text=auto
-docker/** text eol=lf
-tests/test_model_probe/stripped_models/** filter=lfs diff=lfs merge=lfs -text
-tests/model_identification/stripped_models/** filter=lfs diff=lfs merge=lfs -text
+docker/** text eol=lf

.github/CODEOWNERS

@@ -1,32 +1,32 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant  
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr
 
-# documentation - anyone with write privileges can review
-/docs/
-/mkdocs.yml
+# documentation
+/docs/ @lstein @blessedcoolant @hipsterusername @Millu
+/mkdocs.yml @lstein  @blessedcoolant @hipsterusername @Millu
 
 # nodes
-/invokeai/app/ @blessedcoolant @lstein @dunkeroni @JPPhoto
+/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername
 
 # installation and configuration
-/pyproject.toml  @lstein @blessedcoolant  
-/docker/  @lstein @blessedcoolant
-/scripts/  @lstein  @blessedcoolant  
-/installer/ @lstein   @blessedcoolant  
-/invokeai/assets @lstein   @blessedcoolant  
-/invokeai/configs @lstein  @blessedcoolant  
-/invokeai/version @lstein @blessedcoolant  
+/pyproject.toml  @lstein @blessedcoolant @hipsterusername
+/docker/  @lstein @blessedcoolant @hipsterusername @ebr
+/scripts/ @ebr @lstein @hipsterusername
+/installer/ @lstein @ebr @hipsterusername
+/invokeai/assets @lstein @ebr @hipsterusername
+/invokeai/configs @lstein @hipsterusername
+/invokeai/version @lstein @blessedcoolant @hipsterusername
 
 # web ui
-/invokeai/frontend @blessedcoolant  @lstein  @dunkeroni  
+/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
+/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
 
 # generation, model management, postprocessing
-/invokeai/backend  @lstein @blessedcoolant @dunkeroni @JPPhoto  @Pfannkuchensack 
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername
 
 # front ends
-/invokeai/frontend/CLI @lstein  
-/invokeai/frontend/install @lstein   
-/invokeai/frontend/merge @lstein @blessedcoolant  
-/invokeai/frontend/training @lstein @blessedcoolant  
-/invokeai/frontend/web  @blessedcoolant  @lstein @dunkeroni @Pfannkuchensack 
-
+/invokeai/frontend/CLI @lstein @hipsterusername
+/invokeai/frontend/install @lstein @ebr @hipsterusername
+/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -21,20 +21,6 @@ body:
         - label: I have searched the existing issues
           required: true
 
-  - type: dropdown
-    id: install_method
-    attributes:
-      label: Install method
-      description: How did you install Invoke?
-      multiple: false
-      options:
-        - "Invoke's Launcher"
-        - 'Stability Matrix'
-        - 'Pinokio'
-        - 'Manual'
-    validations:
-      required: true
-
   - type: markdown
     attributes:
       value: __Describe your environment__
@@ -90,8 +76,8 @@ body:
     attributes:
       label: Version number
       description: |
-        The version of Invoke you have installed. If it is not the [latest version](https://github.com/invoke-ai/InvokeAI/releases/latest), please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
-      placeholder: ex. v6.0.2
+        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
     validations:
       required: true
 
@@ -99,17 +85,17 @@ body:
     id: browser-version
     attributes:
       label: Browser
-      description: Your web browser and version, if you do not use the Launcher's provided GUI.
+      description: Your web browser and version.
       placeholder: ex. Firefox 123.0b3
     validations:
-      required: false
+      required: true
 
   - type: textarea
     id: python-deps
     attributes:
-      label: System Information
+      label: Python dependencies
       description: |
-        Click the gear icon at the bottom left corner, then click "About". Click the copy button and then paste here.
+        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
 

.github/actions/install-frontend-deps/action.yml

@@ -3,15 +3,15 @@ description: Installs frontend dependencies with pnpm, with caching
 runs:
   using: 'composite'
   steps:
-    - name: setup node 20
+    - name: setup node 18
       uses: actions/setup-node@v4
       with:
-        node-version: '20'
+        node-version: '18'
 
     - name: setup pnpm
-      uses: pnpm/action-setup@v4
+      uses: pnpm/action-setup@v2
       with:
-        version: 10
+        version: 8
         run_install: false
 
     - name: get pnpm store directory

.github/pull_request_template.md

@@ -8,7 +8,7 @@
 
 ## QA Instructions
 
-<!--WHEN APPLICABLE: Describe how you have tested the changes in this PR. Provide enough detail that a reviewer can reproduce your tests.-->
+<!--WHEN APPLICABLE: Describe how we can test the changes in this PR.-->
 
 ## Merge Plan
 
@@ -18,6 +18,4 @@
 
 - [ ] _The PR has a short but descriptive title, suitable for a changelog_
 - [ ] _Tests added / updated (if applicable)_
-- [ ] _❗Changes to a redux slice have a corresponding migration_
 - [ ] _Documentation added / updated (if applicable)_
-- [ ] _Updated `What's New` copy (if doing a release after this PR)_

.github/workflows/build-container.yml

@@ -13,12 +13,6 @@ on:
     tags:
       - 'v*.*.*'
   workflow_dispatch:
-    inputs:
-      push-to-registry:
-        description: Push the built image to the container registry
-        required: false
-        type: boolean
-        default: false
 
 permissions:
   contents: write
@@ -45,36 +39,27 @@ jobs:
     steps:
       - name: Free up more disk space on the runner
         # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
-        # the /mnt dir has 70GBs of free space
-        # /dev/sda1        74G   28K   70G   1% /mnt
-        # According to some online posts the /mnt is not always there, so checking before setting docker to use it
         run: |
           echo "----- Free space before cleanup"
           df -h
           sudo rm -rf /usr/share/dotnet
           sudo rm -rf "$AGENT_TOOLSDIRECTORY"
-          if [ -f /mnt/swapfile ]; then
-            sudo swapoff /mnt/swapfile
-            sudo rm -rf /mnt/swapfile
-          fi
-          if [ -d /mnt ]; then
-            sudo chmod -R 777 /mnt
-            echo '{"data-root": "/mnt/docker-root"}' | sudo tee /etc/docker/daemon.json
-            sudo systemctl restart docker
-          fi
+          sudo swapoff /mnt/swapfile
+          sudo rm -rf /mnt/swapfile
           echo "----- Free space after cleanup"
           df -h
 
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Docker meta
         id: meta
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@v4
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           images: |
             ghcr.io/${{ github.repository }}
+            ${{ env.DOCKERHUB_REPOSITORY }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
@@ -86,33 +71,50 @@ jobs:
             latest=${{ matrix.gpu-driver == 'cuda' && github.ref == 'refs/heads/main' }}
             suffix=-${{ matrix.gpu-driver }},onlatest=false
 
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
         with:
           platforms: ${{ env.PLATFORMS }}
 
       - name: Login to GitHub Container Registry
         if: github.event_name != 'pull_request'
-        uses: docker/login-action@v3
+        uses: docker/login-action@v2
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
+      # - name: Login to Docker Hub
+      #   if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
+      #   uses: docker/login-action@v2
+      #   with:
+      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
+      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
+
       - name: Build container
         timeout-minutes: 40
         id: docker_build
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v4
         with:
           context: .
           file: docker/Dockerfile
           platforms: ${{ env.PLATFORMS }}
-          build-args: |
-            GPU_DRIVER=${{ matrix.gpu-driver }}
-          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' || github.event.inputs.push-to-registry }}
+          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
-          # cache-from: |
-          #   type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
-          #   type=gha,scope=main-${{ matrix.gpu-driver }}
-          # cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+          cache-from: |
+            type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+            type=gha,scope=main-${{ matrix.gpu-driver }}
+          cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+
+      # - name: Docker Hub Description
+      #   if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
+      #   uses: peter-evans/dockerhub-description@v3
+      #   with:
+      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
+      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
+      #     repository: ${{ vars.DOCKERHUB_REPOSITORY }}
+      #     short-description: ${{ github.event.repository.description }}

.github/workflows/build-installer.yml

@@ -0,0 +1,45 @@
+# Builds and uploads the installer and python build artifacts.
+
+name: build installer
+
+on:
+  workflow_dispatch:
+  workflow_call:
+
+jobs:
+  build-installer:
+    runs-on: ubuntu-latest
+    timeout-minutes: 5 # expected run time: <2 min
+    steps:
+      - name: checkout
+        uses: actions/checkout@v4
+
+      - name: setup python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          cache: pip
+          cache-dependency-path: pyproject.toml
+
+      - name: install pypa/build
+        run: pip install --upgrade build
+
+      - name: setup frontend
+        uses: ./.github/actions/install-frontend-deps
+
+      - name: create installer
+        id: create_installer
+        run: ./create_installer.sh
+        working-directory: installer
+
+      - name: upload python distribution artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: ${{ steps.create_installer.outputs.DIST_PATH }}
+
+      - name: upload installer artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: installer
+          path: ${{ steps.create_installer.outputs.INSTALLER_PATH }}

.github/workflows/build-wheel.yml

@@ -1,38 +0,0 @@
-# Builds and uploads python build artifacts.
-
-name: build wheel
-
-on:
-  workflow_dispatch:
-  workflow_call:
-
-jobs:
-  build-installer:
-    runs-on: ubuntu-latest
-    timeout-minutes: 5 # expected run time: <2 min
-    steps:
-      - name: checkout
-        uses: actions/checkout@v4
-
-      - name: setup python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-          cache: pip
-          cache-dependency-path: pyproject.toml
-
-      - name: install pypa/build
-        run: pip install --upgrade build
-
-      - name: setup frontend
-        uses: ./.github/actions/install-frontend-deps
-
-      - name: build wheel
-        id: build_wheel
-        run: ./scripts/build_wheel.sh
-
-      - name: upload python distribution artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: ${{ steps.build_wheel.outputs.DIST_PATH }}

.github/workflows/close-inactive-issues.yml

@@ -23,7 +23,6 @@ jobs:
           close-issue-message: "Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue."
           days-before-pr-stale: -1
           days-before-pr-close: -1
-          only-labels: "bug"
           exempt-issue-labels: "Active Issue"
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           operations-per-run: 500

.github/workflows/frontend-checks.yml

@@ -44,12 +44,7 @@ jobs:
       - name: check for changed frontend files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             frontend:

.github/workflows/frontend-tests.yml

@@ -44,12 +44,7 @@ jobs:
       - name: check for changed frontend files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             frontend:

.github/workflows/lfs-checks.yml

@@ -1,30 +0,0 @@
-# Checks that large files and LFS-tracked files are properly checked in with pointer format.
-# Uses https://github.com/ppremk/lfs-warning to detect LFS issues.
-
-name: 'lfs checks'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-  workflow_dispatch:
-
-jobs:
-  lfs-check:
-    runs-on: ubuntu-latest
-    timeout-minutes: 5
-    permissions:
-      # Required to label and comment on the PRs
-      pull-requests: write
-    steps:
-      - name: checkout
-        uses: actions/checkout@v4
-
-      - name: check lfs files
-        uses: ppremk/lfs-warning@v3.3

.github/workflows/mkdocs-material.yml

@@ -22,12 +22,12 @@ jobs:
 
     steps:
       - name: checkout
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
 
       - name: setup python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: '3.12'
+          python-version: '3.10'
           cache: pip
           cache-dependency-path: pyproject.toml
 

.github/workflows/python-checks.yml

@@ -34,9 +34,6 @@ on:
 
 jobs:
   python-checks:
-    env:
-      # uv requires a venv by default - but for this, we can simply use the system python
-      UV_SYSTEM_PYTHON: 1
     runs-on: ubuntu-latest
     timeout-minutes: 5 # expected run time: <1 min
     steps:
@@ -46,12 +43,7 @@ jobs:
       - name: check for changed python files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             python:
@@ -60,23 +52,25 @@ jobs:
               - '!invokeai/frontend/web/**'
               - 'tests/**'
 
-      - name: setup uv
+      - name: setup python
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        uses: astral-sh/setup-uv@v5
+        uses: actions/setup-python@v5
         with:
-          version: '0.6.10'
-          enable-cache: true
+          python-version: '3.10'
+          cache: pip
+          cache-dependency-path: pyproject.toml
 
-      - name: check pypi classifiers
+      - name: install ruff
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: uv run --no-project scripts/check_classifiers.py ./pyproject.toml
+        run: pip install ruff
+        shell: bash
 
       - name: ruff check
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: uv tool run ruff@0.11.2 check --output-format=github .
+        run: ruff check --output-format=github .
         shell: bash
 
       - name: ruff format
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: uv tool run ruff@0.11.2 format --check .
+        run: ruff format --check .
         shell: bash

.github/workflows/python-tests.yml

@@ -39,19 +39,28 @@ jobs:
     strategy:
       matrix:
         python-version:
+          - '3.10'
           - '3.11'
-          - '3.12'
         platform:
+          - linux-cuda-11_7
+          - linux-rocm-5_2
           - linux-cpu
           - macos-default
           - windows-cpu
         include:
+          - platform: linux-cuda-11_7
+            os: ubuntu-22.04
+            github-env: $GITHUB_ENV
+          - platform: linux-rocm-5_2
+            os: ubuntu-22.04
+            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
+            github-env: $GITHUB_ENV
           - platform: linux-cpu
-            os: ubuntu-24.04
+            os: ubuntu-22.04
             extra-index-url: 'https://download.pytorch.org/whl/cpu'
             github-env: $GITHUB_ENV
           - platform: macos-default
-            os: macOS-14
+            os: macOS-12
             github-env: $GITHUB_ENV
           - platform: windows-cpu
             os: windows-2022
@@ -61,22 +70,14 @@ jobs:
     timeout-minutes: 15 # expected run time: 2-6 min, depending on platform
     env:
       PIP_USE_PEP517: '1'
-      UV_SYSTEM_PYTHON: 1
-
     steps:
       - name: checkout
-        # https://github.com/nschloe/action-cached-lfs-checkout
-        uses: nschloe/action-cached-lfs-checkout@f46300cd8952454b9f0a21a3d133d4bd5684cfc2
+        uses: actions/checkout@v4
 
       - name: check for changed python files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             python:
@@ -85,25 +86,20 @@ jobs:
               - '!invokeai/frontend/web/**'
               - 'tests/**'
 
-      - name: setup uv
-        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        uses: astral-sh/setup-uv@v5
-        with:
-          version: '0.6.10'
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-
       - name: setup python
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
+          cache: pip
+          cache-dependency-path: pyproject.toml
 
       - name: install dependencies
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
         env:
-          UV_INDEX: ${{ matrix.extra-index-url }}
-        run: uv pip install --editable ".[test]"
+          PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
+        run: >
+          pip3 install --editable=".[test]"
 
       - name: run pytest
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}

.github/workflows/release.yml

@@ -49,7 +49,7 @@ jobs:
       always_run: true
 
   build:
-    uses: ./.github/workflows/build-wheel.yml
+    uses: ./.github/workflows/build-installer.yml
 
   publish-testpypi:
     runs-on: ubuntu-latest

.github/workflows/typegen-checks.yml

@@ -1,112 +0,0 @@
-# Runs typegen schema quality checks.
-# Frontend types should match the server.
-#
-# Checks for changes to files before running the checks.
-# If always_run is true, always runs the checks.
-
-name: 'typegen checks'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-  workflow_dispatch:
-    inputs:
-      always_run:
-        description: 'Always run the checks'
-        required: true
-        type: boolean
-        default: true
-  workflow_call:
-    inputs:
-      always_run:
-        description: 'Always run the checks'
-        required: true
-        type: boolean
-        default: true
-
-jobs:
-  typegen-checks:
-    runs-on: ubuntu-22.04
-    timeout-minutes: 15 # expected run time: <5 min
-    steps:
-      - name: checkout
-        uses: actions/checkout@v4
-
-      - name: Free up more disk space on the runner
-        # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
-        run: |
-          echo "----- Free space before cleanup"
-          df -h
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
-          if [ -f /mnt/swapfile ]; then
-            sudo swapoff /mnt/swapfile
-            sudo rm -rf /mnt/swapfile
-          fi
-          echo "----- Free space after cleanup"
-          df -h
-
-      - name: check for changed files
-        if: ${{ inputs.always_run != true }}
-        id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
-        with:
-          files_yaml: |
-            src:
-              - 'pyproject.toml'
-              - 'invokeai/**'
-
-      - name: setup uv
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        uses: astral-sh/setup-uv@v5
-        with:
-          version: '0.6.10'
-          enable-cache: true
-          python-version: '3.11'
-
-      - name: setup python
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: install dependencies
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        env:
-          UV_INDEX: ${{ matrix.extra-index-url }}
-        run: uv pip install --editable .
-
-      - name: install frontend dependencies
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        uses: ./.github/actions/install-frontend-deps
-
-      - name: copy schema
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: cp invokeai/frontend/web/src/services/api/schema.ts invokeai/frontend/web/src/services/api/schema_orig.ts
-        shell: bash
-
-      - name: generate schema
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: cd invokeai/frontend/web && uv run ../../../scripts/generate_openapi_schema.py | pnpm typegen
-        shell: bash
-
-      - name: compare files
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: |
-          if ! diff invokeai/frontend/web/src/services/api/schema.ts invokeai/frontend/web/src/services/api/schema_orig.ts; then
-            echo "Files are different!";
-            exit 1;
-          fi
-        shell: bash

.github/workflows/uv-lock-checks.yml

@@ -1,68 +0,0 @@
-# Check the `uv` lockfile for consistency with `pyproject.toml`.
-#
-# If this check fails, you should run `uv lock` to update the lockfile.
-
-name: 'uv lock checks'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-  workflow_dispatch:
-    inputs:
-      always_run:
-        description: 'Always run the checks'
-        required: true
-        type: boolean
-        default: true
-  workflow_call:
-    inputs:
-      always_run:
-        description: 'Always run the checks'
-        required: true
-        type: boolean
-        default: true
-
-jobs:
-  uv-lock-checks:
-    env:
-      # uv requires a venv by default - but for this, we can simply use the system python
-      UV_SYSTEM_PYTHON: 1
-    runs-on: ubuntu-latest
-    timeout-minutes: 5 # expected run time: <1 min
-    steps:
-      - name: checkout
-        uses: actions/checkout@v4
-
-      - name: check for changed python files
-        if: ${{ inputs.always_run != true }}
-        id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
-        with:
-          files_yaml: |
-            uvlock-pyprojecttoml:
-              - 'pyproject.toml'
-              - 'uv.lock'
-
-      - name: setup uv
-        if: ${{ steps.changed-files.outputs.uvlock-pyprojecttoml_any_changed == 'true' || inputs.always_run == true }}
-        uses: astral-sh/setup-uv@v5
-        with:
-          version: '0.6.10'
-          enable-cache: true
-
-      - name: check lockfile
-        if: ${{ steps.changed-files.outputs.uvlock-pyprojecttoml_any_changed == 'true' || inputs.always_run == true }}
-        run: uv lock --locked # this will exit with 1 if the lockfile is not consistent with pyproject.toml
-        shell: bash

.gitignore

@@ -180,7 +180,6 @@ cython_debug/
 # Scratch folder
 .scratch/
 .vscode/
-.zed/
 
 # source installer files
 installer/*zip
@@ -189,9 +188,3 @@ installer/install.sh
 installer/update.bat
 installer/update.sh
 installer/InvokeAI-Installer/
-.aider*
-
-.claude/
-
-# Weblate configuration file
-weblate.ini

.nvmrc

@@ -1 +0,0 @@
-v22.14.0

.pre-commit-config.yaml

@@ -4,29 +4,21 @@ repos:
   hooks:
   - id: black
     name: black
-    stages: [pre-commit]
+    stages: [commit]
     language: system
     entry: black
     types: [python]
 
   - id: flake8
     name: flake8
-    stages: [pre-commit]
+    stages: [commit]
     language: system
     entry: flake8
     types: [python]
 
   - id: isort
     name: isort
-    stages: [pre-commit]
+    stages: [commit]
     language: system
     entry: isort
-    types: [python]
-
-  - id: uvlock
-    name: uv lock
-    stages: [pre-commit]
-    language: system
-    entry: uv lock
-    files: ^pyproject\.toml$
-    pass_filenames: false
+    types: [python]

Makefile

@@ -12,25 +12,22 @@ help:
 	@echo "mypy-all                 Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
 	@echo "test                     Run the unit tests."
 	@echo "update-config-docstring  Update the app's config docstring so mkdocs can autogenerate it correctly."
-	@echo "frontend-install         Install the pnpm modules needed for the frontend"
-	@echo "frontend-build           Build the frontend for localhost:9090"
-	@echo "frontend-test            Run the frontend test suite once"
+	@echo "frontend-install         Install the pnpm modules needed for the front end"
+	@echo "frontend-build           Build the frontend in order to run on localhost:9090"
 	@echo "frontend-dev             Run the frontend in developer mode on localhost:5173"
 	@echo "frontend-typegen         Generate types for the frontend from the OpenAPI schema"
-	@echo "frontend-lint            Run frontend checks and fixable lint/format steps"
-	@echo "wheel                    Build the wheel for the current version"
+	@echo "installer-zip            Build the installer .zip file for the current version"
 	@echo "tag-release              Tag the GitHub repository with the current version (use at release time only!)"
-	@echo "openapi                  Generate the OpenAPI schema for the app, outputting to stdout"
-	@echo "docs                     Serve the mkdocs site with live reload"
 
 # Runs ruff, fixing any safely-fixable errors and formatting
 ruff:
-	cd invokeai && uv tool run ruff@0.11.2 format
+	ruff check . --fix
+	ruff format .
 
 # Runs ruff, fixing all errors it can fix and formatting
 ruff-unsafe:
 	ruff check . --fix --unsafe-fixes
-	ruff format
+	ruff format .
 
 # Runs mypy, using the config in pyproject.toml
 mypy:
@@ -58,10 +55,6 @@ frontend-install:
 frontend-build:
 	cd invokeai/frontend/web && pnpm build
 
-# Run the frontend test suite once
-frontend-test:
-	cd invokeai/frontend/web && pnpm run test:run
-
 # Run the frontend in dev mode
 frontend-dev:
 	cd invokeai/frontend/web && pnpm dev
@@ -69,26 +62,11 @@ frontend-dev:
 frontend-typegen:
 	cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen
 
-frontend-lint:
-	cd invokeai/frontend/web/src && \
-	pnpm lint:tsc && \
-	pnpm lint:dpdm && \
-	pnpm lint:eslint --fix && \
-	pnpm lint:prettier --write 
-
-# Tag the release
-wheel:
-	cd scripts && ./build_wheel.sh
+# Installer zip file
+installer-zip:
+	cd installer && ./create_installer.sh
 
 # Tag the release
 tag-release:
-	cd scripts && ./tag_release.sh
+	cd installer && ./tag_release.sh
 
-# Generate the OpenAPI Schema for the app
-openapi:
-	python scripts/generate_openapi_schema.py
-
-# Serve the mkdocs site w/ live reload
-.PHONY: docs
-docs:
-	mkdocs serve

README.md

@@ -2,120 +2,21 @@
 
 ![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)
 
-# Invoke - Professional Creative AI Tools for Visual Media
+# Invoke - Professional Creative AI Tools for Visual Media 
+##  To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
+  
 
-[![discord badge]][discord link] [![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link] [![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link] [![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
 
-</div>
+[![discord badge]][discord link]
 
-Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
+[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
 
-- Free to use under a commercially-friendly license
-- Download and install on compatible hardware
-- Generate, refine, iterate on images, and build workflows
+[![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link]
 
-![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
 
----
-> ## 📣 Are you a new or returning InvokeAI user?
-> Take our first annual [User's Survey](https://forms.gle/rCE5KuQ7Wfrd1UnS7)
-
----
-
-# Documentation
-
-| **Quick Links**                                                                                                                                             |
-| ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs] |
-
-# Installation
-
-To get started with Invoke, [Download the Launcher](https://github.com/invoke-ai/launcher/releases/latest).
-
-## Troubleshooting, FAQ and Support
-
-Please review our [FAQ][faq] for solutions to common installation problems and other issues.
-
-For more help, please join our [Discord][discord link].
-
-## Features
-
-Full details on features can be found in [our documentation][features docs].
-
-### Web Server & UI
-
-Invoke runs a locally hosted web server & React UI with an industry-leading user experience.
-
-### Unified Canvas
-
-The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/out-painting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
-
-### Workflows & Nodes
-
-Invoke offers a fully featured workflow management solution, enabling users to combine the power of node-based workflows with the ease of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
-
-### Board & Gallery Management
-
-Invoke features an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
-
-### Model Support
-- SD 1.5 
-- SD 2.0
-- SDXL
-- SD 3.5 Medium
-- SD 3.5 Large
-- CogView 4
-- Flux.1 Dev
-- Flux.1 Schnell
-- Flux.1 Kontext
-- Flux.1 Krea
-- Flux Redux
-- Flux Fill
-- Flux.2 Klein 4B
-- Flux.2 Klein 9B
-- Z-Image Turbo
-- Z-Image Base
-- Anima
-- Qwen Image
-- Qwen Image Edit
-- Nano Banana (API Only)
-- GPT Image (API Only)
-- Wan (API Only)
-
-### Other features
-
-- Support for ckpt, diffusers, and some gguf models
-- Upscaling Tools
-- Embedding Manager & Support
-- Model Manager & Support
-- Workflow creation & management
-- Node-Based Architecture
-- Object Segmentation & Selection Models (SAM / SAM2)
-
-## Contributing
-
-Anyone who wishes to contribute to this project - whether documentation, features, bug fixes, code cleanup, testing, or code reviews - is very much encouraged to do so.
-
-Get started with contributing by reading our [contribution documentation][contributing docs], joining the [#dev-chat] or the GitHub discussion board.
-
-We hope you enjoy using Invoke as much as we enjoy creating it, and we hope you will elect to become part of our community.
-
-## Thanks
-
-Invoke is a combined effort of [passionate and talented people from across the world][contributors]. We thank them for their time, hard work and effort.
-
-Original portions of the software are Copyright © 2024 by respective contributors.
-
-[features docs]: https://invoke-ai.github.io/InvokeAI/features/database/
-[faq]: https://invoke-ai.github.io/InvokeAI/faq/
-[contributors]: https://invoke-ai.github.io/InvokeAI/contributing/contributors/
-[github issues]: https://github.com/invoke-ai/InvokeAI/issues
-[docs home]: https://invoke-ai.github.io/InvokeAI
-[installation docs]: https://invoke-ai.github.io/InvokeAI/installation/
-[#dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
-[contributing docs]: https://invoke-ai.github.io/InvokeAI/contributing/
 [CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
-[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
+[CI checks on main link]:https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
 [discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
 [discord link]: https://discord.gg/ZmtBAhwWhy
 [github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
@@ -129,8 +30,402 @@ Original portions of the software are Copyright © 2024 by respective contributo
 [latest commit to main badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/main?icon=github&color=yellow&label=last%20dev%20commit&cache=900
 [latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
 [latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
-[latest release link]: https://github.com/invoke-ai/InvokeAI/releases/latest
+[latest release link]: https://github.com/invoke-ai/InvokeAI/releases
 [translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
 [translation status link]: https://hosted.weblate.org/engage/invokeai/
-[nvidia docker docs]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
-[amd docker docs]: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html
+
+</div>
+
+InvokeAI is a leading creative engine built to empower professionals
+and enthusiasts alike. Generate and create stunning visual media using
+the latest AI-driven technologies. InvokeAI offers an industry leading
+Web Interface, interactive Command Line Interface, and also serves as
+the foundation for multiple commercial products.
+
+**Quick links**: [[How to
+  Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<a
+  href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
+  href="https://invoke-ai.github.io/InvokeAI/">Documentation and
+  Tutorials</a>]
+  [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
+  [<a
+  href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
+  Ideas & Q&A</a>] 
+   [<a
+  href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>] 
+
+<div align="center">
+
+
+![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
+
+
+</div>
+
+## Table of Contents
+
+Table of Contents 📝
+
+**Getting Started**
+1. 🏁 [Quick Start](#quick-start) 
+3. 🖥️ [Hardware Requirements](#hardware-requirements) 
+
+**More About Invoke**
+1. 🌟 [Features](#features) 
+2. 📣 [Latest Changes](#latest-changes) 
+3. 🛠️ [Troubleshooting](#troubleshooting) 
+
+**Supporting the Project**
+1. 🤝 [Contributing](#contributing) 
+2. 👥 [Contributors](#contributors) 
+3. 💕 [Support](#support) 
+
+## Quick Start
+
+For full installation and upgrade instructions, please see:
+[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)
+
+If upgrading from version 2.3, please read [Migrating a 2.3 root
+directory to 3.0](#migrating-to-3) first.
+
+### Automatic Installer (suggested for 1st time users)
+
+1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
+
+2. Download the .zip file for your OS (Windows/macOS/Linux).
+
+3. Unzip the file.
+
+4. **Windows:** double-click on the `install.bat` script. **macOS:** Open a Terminal window, drag the file `install.sh` from Finder
+into the Terminal, and press return. **Linux:** run `install.sh`.
+
+5. You'll be asked to confirm the location of the folder in which
+to install InvokeAI and its image generation model files. Pick a
+location with at least 15 GB of free memory. More if you plan on
+installing lots of models.
+
+6. Wait while the installer does its thing. After installing the software,
+the installer will launch a script that lets you configure InvokeAI and
+select a set of starting image generation models.
+
+7. Find the folder that InvokeAI was installed into (it is not the
+same as the unpacked zip file directory!) The default location of this
+folder (if you didn't change it in step 5) is `~/invokeai` on
+Linux/Mac systems, and `C:\Users\YourName\invokeai` on Windows. This directory will contain launcher scripts named `invoke.sh` and `invoke.bat`.
+
+8. On Windows systems, double-click on the `invoke.bat` file. On
+macOS, open a Terminal window, drag `invoke.sh` from the folder into
+the Terminal, and press return. On Linux, run `invoke.sh`
+
+9. Press 2 to open the "browser-based UI", press enter/return, wait a
+minute or two for Stable Diffusion to start up, then open your browser
+and go to http://localhost:9090.
+
+10. Type `banana sushi` in the box on the top left and click `Invoke`
+
+### Command-Line Installation (for developers and users familiar with Terminals)
+
+You must have Python 3.10 through 3.11 installed on your machine. Earlier or
+later versions are not supported.
+Node.js also needs to be installed along with `pnpm` (can be installed with
+the command `npm install -g pnpm` if needed)
+
+1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
+2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
+
+    ```terminal
+    mkdir invokeai
+    ````
+
+3. Create a virtual environment named `.venv` inside this directory and activate it:
+
+    ```terminal
+    cd invokeai
+    python -m venv .venv --prompt InvokeAI
+    ```
+
+4. Activate the virtual environment (do it every time you run InvokeAI)
+
+    _For Linux/Mac users:_
+
+    ```sh
+    source .venv/bin/activate
+    ```
+
+    _For Windows users:_
+
+    ```ps
+    .venv\Scripts\activate
+    ```
+
+5. Install the InvokeAI module and its dependencies. Choose the command suited for your platform & GPU.
+
+    _For Windows/Linux with an NVIDIA GPU:_
+
+    ```terminal
+    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+    ```
+
+    _For Linux with an AMD GPU:_
+
+    ```sh
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
+    ```
+
+    _For non-GPU systems:_
+    ```terminal
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
+    ``` 
+
+    _For Macintoshes, either Intel or M1/M2/M3:_
+
+    ```sh
+    pip install InvokeAI --use-pep517
+    ```
+
+6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
+
+    ```terminal
+    invokeai-configure --root .
+    ```
+	Don't miss the dot at the end!
+
+7. Launch the web server (do it every time you run InvokeAI):
+
+    ```terminal
+    invokeai-web
+    ```
+
+8. Point your browser to http://localhost:9090 to bring up the web interface.
+
+9. Type `banana sushi` in the box on the top left and click `Invoke`.
+
+Be sure to activate the virtual environment each time before re-launching InvokeAI,
+using `source .venv/bin/activate` or `.venv\Scripts\activate`.
+
+## Detailed Installation Instructions
+
+This fork is supported across Linux, Windows and Macintosh. Linux
+users can use either an Nvidia-based card (with CUDA support) or an
+AMD card (using the ROCm driver). For full installation and upgrade
+instructions, please see:
+[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_SOURCE/)
+
+<a name="migrating-to-3"></a>
+### Migrating a v2.3 InvokeAI root directory
+
+The InvokeAI root directory is where the InvokeAI startup file,
+installed models, and generated images are stored. It is ordinarily
+named `invokeai` and located in your home directory. The contents and
+layout of this directory has changed between versions 2.3 and 3.0 and
+cannot be used directly.
+
+We currently recommend that you use the installer to create a new root
+directory named differently from the 2.3 one, e.g. `invokeai-3` and
+then use a migration script to copy your 2.3 models into the new
+location. However, if you choose, you can upgrade this directory in
+place.  This section gives both recipes.
+
+#### Creating a new root directory and migrating old models
+
+This is the safer recipe because it leaves your old root directory in
+place to fall back on.
+
+1. Follow the instructions above to create and install InvokeAI in a
+directory that has a different name from the 2.3 invokeai directory.
+In this example, we will use "invokeai-3"
+
+2. When you are prompted to select models to install, select a minimal
+set of models, such as stable-diffusion-v1.5 only.
+
+3. After installation is complete launch `invokeai.sh` (Linux/Mac) or
+`invokeai.bat` and select option 8 "Open the developers console". This
+will take you to the command line.
+
+4. Issue the command `invokeai-migrate3 --from /path/to/v2.3-root --to
+/path/to/invokeai-3-root`. Provide the correct `--from` and `--to`
+paths for your v2.3 and v3.0 root directories respectively.
+
+This will copy and convert your old models from 2.3 format to 3.0
+format and create a new `models` directory in the 3.0 directory. The
+old models directory (which contains the models selected at install
+time) will be renamed `models.orig` and can be deleted once you have
+confirmed that the migration was successful.
+
+ If you wish, you can pass the 2.3 root directory to both `--from` and
+`--to` in order to update in place. Warning: this directory will no
+longer be usable with InvokeAI 2.3.
+
+#### Migrating in place
+
+For the adventurous, you may do an in-place upgrade from 2.3 to 3.0
+without touching the command line. ***This recipe does not work on
+Windows platforms due to a bug in the Windows version of the 2.3
+upgrade script.** See the next section for a Windows recipe.
+
+##### For Mac and Linux Users:
+
+1. Launch the InvokeAI launcher script in your current v2.3 root directory.
+
+2. Select option [9] "Update InvokeAI" to bring up the updater dialog.
+
+3. Select option [1] to upgrade to the latest release.
+
+4. Once the upgrade is finished you will be returned to the launcher
+menu. Select option [6] "Re-run the configure script to fix a broken
+install or to complete a major upgrade".
+
+This will run the configure script against the v2.3 directory and
+update it to the 3.0 format. The following files will be replaced:
+
+  - The invokeai.init file, replaced by invokeai.yaml
+  - The models directory
+  - The configs/models.yaml model index
+  
+The original versions of these files will be saved with the suffix
+".orig" appended to the end. Once you have confirmed that the upgrade
+worked, you can safely remove these files. Alternatively you can
+restore a working v2.3 directory by removing the new files and
+restoring the ".orig" files' original names.
+
+##### For Windows Users:
+
+Windows Users can upgrade with the
+
+1. Enter the 2.3 root directory you wish to upgrade
+2. Launch `invoke.sh` or `invoke.bat`
+3. Select the "Developer's console" option [8]
+4. Type the following commands
+
+```
+pip install "invokeai @ https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v3.0.0" --use-pep517 --upgrade
+invokeai-configure --root .
+```
+(Replace `v3.0.0` with the current release number if this document is out of date).
+
+The first command will install and upgrade new software to run
+InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
+You may now launch the WebUI in the usual way, by selecting option [1]
+from the launcher script
+
+#### Migrating Images
+
+The migration script will migrate your invokeai settings and models,
+including textual inversion models, LoRAs and merges that you may have
+installed previously. However it does **not** migrate the generated
+images stored in your 2.3-format outputs directory. To do this, you 
+need to run an additional step:
+
+1. From a working InvokeAI 3.0 root directory, start the launcher and
+enter menu option [8] to open the "developer's console".
+
+2. At the developer's console command line, type the command:
+
+```bash
+invokeai-import-images
+```
+
+3. This will lead you through the process of confirming the desired
+   source and destination for the imported images. The images will
+   appear in the gallery board of your choice, and contain the
+   original prompt, model name, and other parameters used to generate
+   the image.
+   
+(Many kudos to **techjedi** for contributing this script.)
+
+## Hardware Requirements
+
+InvokeAI is supported across Linux, Windows and macOS. Linux
+users can use either an Nvidia-based card (with CUDA support) or an
+AMD card (using the ROCm driver).
+
+### System
+
+You will need one of the following:
+
+- An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB
+  of VRAM is highly recommended for rendering using the Stable
+  Diffusion XL models
+- An Apple computer with an M1 chip.
+- An AMD-based graphics card with 4GB or more VRAM memory (Linux
+  only), 6-8 GB for XL rendering.
+
+We do not recommend the GTX 1650 or 1660 series video cards. They are
+unable to run in half-precision mode and do not have sufficient VRAM
+to render 512x512 images.
+
+**Memory** - At least 12 GB Main Memory RAM.
+
+**Disk** - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
+
+## Features
+
+Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/)
+
+### *Web Server & UI*
+
+InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server.
+
+### *Unified Canvas*
+
+The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
+
+### *Workflows & Nodes*
+
+InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
+
+### *Board & Gallery Management*
+
+Invoke AI provides an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow. 
+
+### Other features
+
+- *Support for both ckpt and diffusers models*
+- *SD 2.0, 2.1, XL support*
+- *Upscaling Tools*
+- *Embedding Manager & Support*
+- *Model Manager & Support*
+- *Workflow creation & management*
+- *Node-Based Architecture*
+
+
+### Latest Changes
+
+For our latest changes, view our [Release
+Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
+[CHANGELOG](docs/CHANGELOG.md).
+
+### Troubleshooting / FAQ
+
+Please check out our **[FAQ](https://invoke-ai.github.io/InvokeAI/help/FAQ/)** to get solutions for common installation
+problems and other issues. For more help, please join our [Discord][discord link]
+
+## Contributing
+
+Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
+cleanup, testing, or code reviews, is very much encouraged to do so.
+
+Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
+
+If you are unfamiliar with how
+to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing: 
+[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
+
+We hope you enjoy using our software as much as we enjoy creating it,
+and we hope that some of those of you who are reading this will elect
+to become part of our community.
+
+Welcome to InvokeAI!
+
+### Contributors
+
+This fork is a combined effort of various people from across the world.
+[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for
+their time, hard work and effort.
+
+### Support
+
+For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
+
+Original portions of the software are Copyright (c) 2023 by respective contributors.
+

SECURITY.md

@@ -1,14 +0,0 @@
-# Security Policy
-
-## Supported Versions
-
-Only the latest version of Invoke will receive security updates. 
-We do not currently maintain multiple versions of the application with updates.
-
-## Reporting a Vulnerability
-
-To report a vulnerability, contact the Invoke team directly at security@invoke.ai
-
-At this time, we do not maintain a formal bug bounty program. 
-
-You can also share identified security issues with our team on huntr.com

USER_ISOLATION_IMPLEMENTATION.md

@@ -1,169 +0,0 @@
-# User Isolation Implementation Summary
-
-This document describes the implementation of user isolation features in the InvokeAI session queue and processing system to address issues identified in the enhancement request.
-
-## Issues Addressed
-
-### 1. Cross-User Image/Preview Visibility
-**Problem:** When two users are logged in simultaneously and one initiates a generation, the generation preview shows up in both users' browsers and the generated image gets saved to both users' image boards.
-
-**Solution:** Implemented socket-level event filtering based on user authentication:
-
-#### Backend Changes (`invokeai/app/api/sockets.py`):
-- Added socket authentication middleware in `_handle_connect()` method
-- Extracts JWT token from socket auth data or HTTP headers
-- Verifies token using existing `verify_token()` function
-- Stores `user_id` and `is_admin` in socket session for later use
-- Modified `_handle_queue_event()` to filter events by user:
-  - For `QueueItemEventBase` events, only emit to:
-    - The user who owns the queue item (`user_id` matches)
-    - Admin users (`is_admin` is True)
-  - For general queue events, emit to all subscribers
-
-#### Event System Changes (`invokeai/app/services/events/events_common.py`):
-- Added `user_id` field to `QueueItemEventBase` class
-- Updated all event builders to include `user_id` from queue items:
-  - `InvocationStartedEvent.build()`
-  - `InvocationProgressEvent.build()`
-  - `InvocationCompleteEvent.build()`
-  - `InvocationErrorEvent.build()`
-  - `QueueItemStatusChangedEvent.build()`
-
-### 2. Batch Field Values Privacy
-**Problem:** Users can see batch field values from generation processes launched by other users.
-
-**Solution:** Implemented field value sanitization at the API level:
-
-#### API Router Changes (`invokeai/app/api/routers/session_queue.py`):
-- Created `sanitize_queue_item_for_user()` helper function
-  - Clears `field_values` for non-admin users viewing other users' items
-  - Admins and item owners can see all field values
-- Updated endpoints to require authentication and sanitize responses:
-  - `list_all_queue_items()` - Added `CurrentUser` dependency
-  - `get_queue_items_by_item_ids()` - Added `CurrentUser` dependency
-  - `get_queue_item()` - Added `CurrentUser` dependency
-
-### 3. Queue Updates Across Browser Windows
-**Problem:** When the job queue tab is open in multiple browsers and a generation is begun in one browser window, the queue does not update in the other window.
-
-**Status:** This issue is likely resolved by the socket authentication and event filtering changes. The existing socket subscription mechanism (`subscribe_queue` event) already supports multiple connections per user. Testing is required to confirm this works correctly with the new authentication flow.
-
-### 4. User Information Display
-**Problem:** Queue table lacks user identification, making it difficult to know who launched which job.
-
-**Solution:** Added user information to queue items and UI:
-
-#### Database Layer (`invokeai/app/services/session_queue/session_queue_sqlite.py`):
-- Updated SQL queries to JOIN with `users` table
-- Modified methods to fetch user information:
-  - `get_queue_item()` - Now selects `display_name` and `email` from users table
-  - `dequeue()` - Includes user info
-  - `get_next()` - Includes user info
-  - `get_current()` - Includes user info
-  - `list_all_queue_items()` - Includes user info
-
-#### Data Model Changes (`invokeai/app/services/session_queue/session_queue_common.py`):
-- Added optional fields to `SessionQueueItem`:
-  - `user_display_name: Optional[str]` - Display name from users table
-  - `user_email: Optional[str]` - Email from users table
-  - Note: `user_id` field already existed from Migration 25
-
-#### Frontend UI Changes:
-- **Constants** (`constants.ts`): Added `user: '8rem'` column width
-- **Header** (`QueueListHeader.tsx`): Added "User" column header
-- **Item Component** (`QueueItemComponent.tsx`):
-  - Added logic to display user information (display_name → email → user_id)
-  - Added user column to queue item row
-  - Added tooltip with full username on hover
-  - Added "Hidden for privacy" message when field_values are null for non-owned items
-- **Localization** (`en.json`): Added translations:
-  - `"user": "User"`
-  - `"fieldValuesHidden": "Hidden for privacy"`
-
-## Security Considerations
-
-### Token Verification
-- Tokens are verified using the existing `verify_token()` function from `invokeai.app.services.auth.token_service`
-- Invalid or missing tokens default to "system" user with non-admin privileges
-- Socket connections without valid tokens are still accepted for backward compatibility but have limited access
-
-### Data Privacy
-- Field values are only visible to:
-  - The user who created the queue item
-  - Admin users
-- Non-admin users viewing other users' queue items see "Hidden for privacy" instead of field values
-
-### Admin Privileges
-- Admin users can see all queue events and field values across all users
-- Admin status is determined from the JWT token's `is_admin` field
-
-## Migration Notes
-
-No database migration is required. The changes leverage:
-- Existing `user_id` column in `session_queue` table (added in Migration 25)
-- Existing `users` table (added in Migration 25)
-- SQL LEFT JOINs to fetch user information (gracefully handles missing user records)
-
-## Testing Requirements
-
-### Backend Testing
-1. **Socket Authentication:**
-   - Verify valid tokens are accepted and user context is stored
-   - Verify invalid tokens default to system user
-   - Verify expired tokens are rejected
-
-2. **Event Filtering:**
-   - User A should only receive events for their own queue items
-   - Admin users should receive all events
-   - Non-admin users should not receive events from other users
-
-3. **Field Value Sanitization:**
-   - Non-admin users should see null field_values for other users' items
-   - Admins should see all field values
-   - Users should see their own field values
-
-### Frontend Testing
-1. **UI Display:**
-   - User column should display in queue list
-   - Display name should be shown when available
-   - Email should be shown as fallback when display name is missing
-   - User ID should be shown when both display name and email are missing
-   - Tooltip should show full username on hover
-
-2. **Field Values Display:**
-   - "Hidden for privacy" message should appear when viewing other users' items
-   - Own items should show field values normally
-
-3. **Multi-Browser Testing:**
-   - Open queue tab in two browsers with different users
-   - Start generation in one browser
-   - Verify other browser doesn't see the preview/progress
-   - Verify admin user can see all generations
-
-### Integration Testing
-1. Multi-user scenarios with simultaneous generations
-2. Queue updates across multiple browser windows
-3. Admin vs. non-admin privilege differentiation
-4. Socket reconnection handling
-
-## Known Limitations
-
-1. **TypeScript Types:**
-   - The OpenAPI schema needs to be regenerated to include new fields
-   - Run: `cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen`
-
-2. **Backward Compatibility:**
-   - System user ("system") entries will not have display name or email
-   - Existing queue items from before Migration 25 will have user_id="system"
-
-3. **Socket.IO Session Storage:**
-   - Socket.IO's in-memory session storage may not persist across server restarts
-   - Consider implementing persistent session storage if needed for production
-
-## Future Enhancements
-
-1. Add user filtering to queue list (show only my items vs. all items)
-2. Add permission system for queue management operations (cancel, retry, delete)
-3. Implement queue item ownership transfer for administrative purposes
-4. Add audit logging for queue operations with user attribution
-5. Consider implementing user-specific queue limits or quotas

docker/.env.sample

@@ -19,13 +19,8 @@
 ## INVOKEAI_PORT is the port on which the InvokeAI web interface will be available
 # INVOKEAI_PORT=9090
 
-## GPU_DRIVER can be set to either `cuda` or `rocm` to enable GPU support in the container accordingly.
-# GPU_DRIVER=cuda #| rocm
-
-## If you are using ROCM, you will need to ensure that the render group within the container and the host system use the same group ID.
-## To obtain the group ID of the render group on the host system, run `getent group render` and grab the number.
-# RENDER_GROUP_ID=
+## GPU_DRIVER can be set to either `nvidia` or `rocm` to enable GPU support in the container accordingly.
+# GPU_DRIVER=nvidia #| rocm
 
 ## CONTAINER_UID can be set to the UID of the user on the host system that should own the files in the container.
-## It is usually not necessary to change this. Use `id -u` on the host system to find the UID.
 # CONTAINER_UID=1000

docker/Dockerfile

@@ -1,11 +1,61 @@
 # syntax=docker/dockerfile:1.4
 
-#### Web UI ------------------------------------
+## Builder stage
 
-FROM docker.io/node:22-slim AS web-builder
+FROM library/ubuntu:23.04 AS builder
+
+ARG DEBIAN_FRONTEND=noninteractive
+RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    --mount=type=cache,target=/var/lib/apt,sharing=locked \
+    apt update && apt-get install -y \
+        git \
+        python3-venv \
+        python3-pip \
+        build-essential
+
+ENV INVOKEAI_SRC=/opt/invokeai
+ENV VIRTUAL_ENV=/opt/venv/invokeai
+
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+ARG GPU_DRIVER=cuda
+ARG TARGETPLATFORM="linux/amd64"
+# unused but available
+ARG BUILDPLATFORM
+
+WORKDIR ${INVOKEAI_SRC}
+
+COPY invokeai ./invokeai
+COPY pyproject.toml ./
+
+# Editable mode helps use the same image for development:
+# the local working copy can be bind-mounted into the image
+# at path defined by ${INVOKEAI_SRC}
+# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
+# x86_64/CUDA is default
+RUN --mount=type=cache,target=/root/.cache/pip \
+    python3 -m venv ${VIRTUAL_ENV} &&\
+    if [ "$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"; \
+    else \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
+    fi &&\
+
+    # xformers + triton fails to install on arm64
+    if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
+        pip install $extra_index_url_arg -e ".[xformers]"; \
+    else \
+        pip install $extra_index_url_arg -e "."; \
+    fi
+
+# #### Build the Web UI ------------------------------------
+
+FROM node:20-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
 ENV PATH="$PNPM_HOME:$PATH"
-RUN corepack use pnpm@10.x && corepack enable
+RUN corepack enable
 
 WORKDIR /build
 COPY invokeai/frontend/web/ ./
@@ -13,95 +63,61 @@ RUN --mount=type=cache,target=/pnpm/store \
     pnpm install --frozen-lockfile
 RUN npx vite build
 
-## Backend ---------------------------------------
+#### Runtime stage ---------------------------------------
 
-FROM library/ubuntu:24.04
+FROM library/ubuntu:23.04 AS runtime
 
 ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt \
-    --mount=type=cache,target=/var/lib/apt \
-    apt update && apt install -y --no-install-recommends \
-    ca-certificates \
-    git \
-    gosu \
-    libglib2.0-0 \
-    libgl1 \
-    libglx-mesa0 \
-    build-essential \
-    libopencv-dev \
-    libstdc++-10-dev
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
 
-ENV \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    VIRTUAL_ENV=/opt/venv \
-    INVOKEAI_SRC=/opt/invokeai \
-    PYTHON_VERSION=3.12 \
-    UV_PYTHON=3.12 \
-    UV_COMPILE_BYTECODE=1 \
-    UV_MANAGED_PYTHON=1 \
-    UV_LINK_MODE=copy \
-    UV_PROJECT_ENVIRONMENT=/opt/venv \
-    INVOKEAI_ROOT=/invokeai \
-    INVOKEAI_HOST=0.0.0.0 \
-    INVOKEAI_PORT=9090 \
-    PATH="/opt/venv/bin:$PATH" \
-    CONTAINER_UID=${CONTAINER_UID:-1000} \
-    CONTAINER_GID=${CONTAINER_GID:-1000}
+RUN apt update && apt install -y --no-install-recommends \
+        git \
+        curl \
+        vim \
+        tmux \
+        ncdu \
+        iotop \
+        bzip2 \
+        gosu \
+        magic-wormhole \
+        libglib2.0-0 \
+        libgl1-mesa-glx \
+        python3-venv \
+        python3-pip \
+        build-essential \
+        libopencv-dev \
+        libstdc++-10-dev &&\
+    apt-get clean && apt-get autoclean
 
-ARG GPU_DRIVER=cuda
 
-# Install `uv` for package management
-COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
+ENV INVOKEAI_SRC=/opt/invokeai
+ENV VIRTUAL_ENV=/opt/venv/invokeai
+ENV INVOKEAI_ROOT=/invokeai
+ENV INVOKEAI_HOST=0.0.0.0
+ENV INVOKEAI_PORT=9090
+ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
+ENV CONTAINER_UID=${CONTAINER_UID:-1000}
+ENV CONTAINER_GID=${CONTAINER_GID:-1000}
 
-# Install python & allow non-root user to use it by traversing the /root dir without read permissions
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv python install ${PYTHON_VERSION} && \
-    # chmod --recursive a+rX /root/.local/share/uv/python
-    chmod 711 /root
-
-WORKDIR ${INVOKEAI_SRC}
-
-# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
-# bind-mount instead of copy to defer adding sources to the image until next layer.
-#
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is the default
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
-    --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    ulimit -n 30000 && \
-    uv sync --extra $GPU_DRIVER --frozen
+# --link requires buldkit w/ dockerfile syntax 1.4
+COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
+COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
 
 # Link amdgpu.ids for ROCm builds
 # contributed by https://github.com/Rubonnek
 RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids" && groupadd render
+  ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+
+WORKDIR ${INVOKEAI_SRC}
 
 # build patchmatch
 RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python -c "from patchmatch import patch_match"
+RUN python3 -c "from patchmatch import patch_match"
 
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
 CMD ["invokeai-web"]
-
-# --link requires buldkit w/ dockerfile syntax 1.4, does not work with podman
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# add sources last to minimize image changes on code changes
-COPY invokeai ${INVOKEAI_SRC}/invokeai
-
-# this should not increase image size because we've already installed dependencies
-# in a previous layer
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    ulimit -n 30000 && \
-    uv pip install -e .[$GPU_DRIVER]
-

docker/Dockerfile-rocm-full

@@ -1,136 +0,0 @@
-# syntax=docker/dockerfile:1.4
-
-#### Web UI ------------------------------------
-
-FROM docker.io/node:22-slim AS web-builder
-ENV PNPM_HOME="/pnpm"
-ENV PATH="$PNPM_HOME:$PATH"
-RUN corepack use pnpm@8.x
-RUN corepack enable
-
-WORKDIR /build
-COPY invokeai/frontend/web/ ./
-RUN --mount=type=cache,target=/pnpm/store \
-    pnpm install --frozen-lockfile
-RUN npx vite build
-
-## Backend ---------------------------------------
-
-FROM library/ubuntu:24.04
-
-ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt \
-    --mount=type=cache,target=/var/lib/apt \
-    apt update && apt install -y --no-install-recommends \
-    ca-certificates \
-    git \
-    gosu \
-    libglib2.0-0 \
-    libgl1 \
-    libglx-mesa0 \
-    build-essential \
-    libopencv-dev \
-    libstdc++-10-dev \
-    wget
-
-ENV \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    VIRTUAL_ENV=/opt/venv \
-    INVOKEAI_SRC=/opt/invokeai \
-    PYTHON_VERSION=3.12 \
-    UV_PYTHON=3.12 \
-    UV_COMPILE_BYTECODE=1 \
-    UV_MANAGED_PYTHON=1 \
-    UV_LINK_MODE=copy \
-    UV_PROJECT_ENVIRONMENT=/opt/venv \
-    INVOKEAI_ROOT=/invokeai \
-    INVOKEAI_HOST=0.0.0.0 \
-    INVOKEAI_PORT=9090 \
-    PATH="/opt/venv/bin:$PATH" \
-    CONTAINER_UID=${CONTAINER_UID:-1000} \
-    CONTAINER_GID=${CONTAINER_GID:-1000}
-
-ARG GPU_DRIVER=cuda
-
-# Install `uv` for package management
-COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
-
-# Install python & allow non-root user to use it by traversing the /root dir without read permissions
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv python install ${PYTHON_VERSION} && \
-    # chmod --recursive a+rX /root/.local/share/uv/python
-    chmod 711 /root
-
-WORKDIR ${INVOKEAI_SRC}
-
-# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
-# bind-mount instead of copy to defer adding sources to the image until next layer.
-#
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is the default
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
-    --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    ulimit -n 30000 && \
-    uv sync --extra $GPU_DRIVER --frozen
-
-RUN --mount=type=cache,target=/var/cache/apt \
-    --mount=type=cache,target=/var/lib/apt \
-    if [ "$GPU_DRIVER" = "rocm" ]; then \
-    wget -O /tmp/amdgpu-install.deb \
-    https://repo.radeon.com/amdgpu-install/6.3.4/ubuntu/noble/amdgpu-install_6.3.60304-1_all.deb && \
-    apt install -y /tmp/amdgpu-install.deb && \
-    apt update && \
-    amdgpu-install --usecase=rocm -y && \
-    apt-get autoclean && \
-    apt clean && \
-    rm -rf /tmp/* /var/tmp/* && \
-    usermod -a -G render ubuntu && \
-    usermod -a -G video ubuntu && \
-    echo "\\n/opt/rocm/lib\\n/opt/rocm/lib64" >> /etc/ld.so.conf.d/rocm.conf && \
-    ldconfig && \
-    update-alternatives --auto rocm; \
-    fi
-
-## Heathen711: Leaving this for review input, will remove before merge
-# RUN --mount=type=cache,target=/var/cache/apt \
-#     --mount=type=cache,target=/var/lib/apt \
-#     if [ "$GPU_DRIVER" = "rocm" ]; then \
-#     groupadd render && \
-#     usermod -a -G render ubuntu && \
-#     usermod -a -G video ubuntu; \
-#     fi
-
-## Link amdgpu.ids for ROCm builds
-## contributed by https://github.com/Rubonnek
-# RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-#     ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
-
-# build patchmatch
-RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python -c "from patchmatch import patch_match"
-
-RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
-
-COPY docker/docker-entrypoint.sh ./
-ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
-CMD ["invokeai-web"]
-
-# --link requires buldkit w/ dockerfile syntax 1.4, does not work with podman
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# add sources last to minimize image changes on code changes
-COPY invokeai ${INVOKEAI_SRC}/invokeai
-
-# this should not increase image size because we've already installed dependencies
-# in a previous layer
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    ulimit -n 30000 && \
-    uv pip install -e .[$GPU_DRIVER]
-

docker/README.md

@@ -1,88 +1,41 @@
-# Invoke in Docker
+# InvokeAI Containerized
 
-First things first:
+All commands should be run within the `docker` directory: `cd docker`
 
-- Ensure that Docker can use your [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] GPU.
-- This document assumes a Linux system, but should work similarly under Windows with WSL2.
-- We don't recommend running Invoke in Docker on macOS at this time. It works, but very slowly.
+## Quickstart :rocket:
 
-## Quickstart
+On a known working Linux+Docker+CUDA (Nvidia) system, execute `./run.sh` in this directory. It will take a few minutes - depending on your internet speed - to install the core models. Once the application starts up, open `http://localhost:9090` in your browser to Invoke!
 
-No `docker compose`, no persistence, single command, using the official images:
+For more configuration options (using an AMD GPU, custom root directory location, etc): read on.
 
-**CUDA (NVIDIA GPU):**
-
-```bash
-docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
-```
-
-**ROCm (AMD GPU):**
-
-```bash
-docker run --device /dev/kfd --device /dev/dri --publish 9090:9090 ghcr.io/invoke-ai/invokeai:main-rocm
-```
-
-Open `http://localhost:9090` in your browser once the container finishes booting, install some models, and generate away!
-
-### Data persistence
-
-To persist your generated images and downloaded models outside of the container, add a `--volume/-v` flag to the above command, e.g.:
-
-```bash
-docker run --volume /some/local/path:/invokeai {...etc...}
-```
-
-`/some/local/path/invokeai` will contain all your data.
-It can *usually* be reused between different installs of Invoke. Tread with caution and read the release notes!
-
-## Customize the container
-
-The included `run.sh` script is a convenience wrapper around `docker compose`. It can be helpful for passing additional build arguments to `docker compose`. Alternatively, the familiar `docker compose` commands work just as well.
-
-```bash
-cd docker
-cp .env.sample .env
-# edit .env to your liking if you need to; it is well commented.
-./run.sh
-```
-
-It will take a few minutes to build the image the first time. Once the application starts up, open `http://localhost:9090` in your browser to invoke!
-
->[!TIP]
->When using the `run.sh` script, the container will continue running after Ctrl+C. To shut it down, use the `docker compose down` command.
-
-## Docker setup in detail
+## Detailed setup
 
 #### Linux
 
-1. Ensure buildkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
+1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
 2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
-    - The deprecated `docker-compose` (hyphenated) CLI probably won't work. Update to a recent version.
+    - The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
 3. Ensure docker daemon is able to access the GPU.
-    - [NVIDIA docs](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
-    - [AMD docs](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html)
+    - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
 
 #### macOS
 
-> [!TIP]
-> You'll be better off installing Invoke directly on your system, because Docker can not use the GPU on macOS.
-
-If you are still reading:
-
 1. Ensure Docker has at least 16GB RAM
 2. Enable VirtioFS for file sharing
 3. Enable `docker compose` V2 support
 
-This is done via Docker Desktop preferences.
+This is done via Docker Desktop preferences
 
-### Configure the Invoke Environment
+### 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 the desired location of the InvokeAI runtime directory. It may be an existing directory from a previous installation (post 4.0.0).
+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`
 
 The image will be built automatically if needed.
 
-The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. Navigate to the Model Manager tab and install some models before generating.
+The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. The runtime directory will be populated with the base configs and models necessary to start generating.
 
 ### Use a GPU
 
@@ -90,9 +43,9 @@ The runtime directory (holding models and outputs) will be created in the locati
 - WSL2 is *required* for Windows.
 - only `x86_64` architecture is supported.
 
-The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker/NVIDIA/AMD documentation for the most up-to-date instructions for using your GPU with Docker.
+The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
 
-To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file before running `./run.sh`.
+To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file.
 
 ## Customize
 
@@ -106,12 +59,30 @@ Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The defa
 INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
 HUGGINGFACE_TOKEN=the_actual_token
 CONTAINER_UID=1000
-GPU_DRIVER=cuda
+GPU_DRIVER=nvidia
 ```
 
-Any environment variables supported by InvokeAI can be set here. See the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
+Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
 
----
+## Even Moar Customizing!
 
-[nvidia docker docs]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
-[amd docker docs]: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html
+See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
+
+### Reconfigure the runtime directory
+
+Can be used to download additional models from the supported model list
+
+In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
+
+```yaml
+command:
+  - invokeai-configure
+  - --yes
+```
+
+Or install models:
+
+```yaml
+command:
+  - invokeai-model-install
+```

docker/docker-compose.yml

@@ -1,7 +1,9 @@
 # Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
 
+version: '3.8'
+
 x-invokeai: &invokeai
-    image: "ghcr.io/invoke-ai/invokeai:latest"
+    image: "local/invokeai:latest"
     build:
       context: ..
       dockerfile: docker/Dockerfile
@@ -30,7 +32,7 @@ x-invokeai: &invokeai
 
 
 services:
-  invokeai-cuda:
+  invokeai-nvidia:
     <<: *invokeai
     deploy:
       resources:
@@ -47,9 +49,8 @@ services:
 
   invokeai-rocm:
     <<: *invokeai
-    environment:
-      - AMD_VISIBLE_DEVICES=all
-      - RENDER_GROUP_ID=${RENDER_GROUP_ID}
-    runtime: amd
+    devices:
+      - /dev/kfd:/dev/kfd
+      - /dev/dri:/dev/dri
     profiles:
       - rocm

docker/docker-entrypoint.sh

@@ -16,42 +16,26 @@ set -e -o pipefail
 
 USER_ID=${CONTAINER_UID:-1000}
 USER=ubuntu
-# if the user does not exist, create it. It is expected to be present on ubuntu >=24.x
-_=$(id ${USER} 2>&1) || useradd -u ${USER_ID} ${USER}
-# ensure the UID is correct
 usermod -u ${USER_ID} ${USER} 1>/dev/null
 
-## ROCM specific configuration
-# render group within the container must match the host render group
-# otherwise the container will not be able to access the host GPU.
-if [[ -v "RENDER_GROUP_ID" ]] && [[ ! -z "${RENDER_GROUP_ID}" ]]; then
-  # ensure the render group exists
-  groupmod -g ${RENDER_GROUP_ID} render
-  usermod -a -G render ${USER}
-  usermod -a -G video ${USER}
-fi
-
-
 ### Set the $PUBLIC_KEY env var to enable SSH access.
 # We do not install openssh-server in the image by default to avoid bloat.
 # but it is useful to have the full SSH server e.g. on Runpod.
 # (use SCP to copy files to/from the image, etc)
 if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then
-  apt-get update
-  apt-get install -y openssh-server
-  pushd "$HOME"
-  mkdir -p .ssh
-  echo "${PUBLIC_KEY}" >.ssh/authorized_keys
-  chmod -R 700 .ssh
-  popd
-  service ssh start
+    apt-get update
+    apt-get install -y openssh-server
+    pushd "$HOME"
+    mkdir -p .ssh
+    echo "${PUBLIC_KEY}" > .ssh/authorized_keys
+    chmod -R 700 .ssh
+    popd
+    service ssh start
 fi
 
 mkdir -p "${INVOKEAI_ROOT}"
-chown --recursive ${USER} "${INVOKEAI_ROOT}" || true
+chown --recursive ${USER} "${INVOKEAI_ROOT}"
 cd "${INVOKEAI_ROOT}"
-export HF_HOME=${HF_HOME:-$INVOKEAI_ROOT/.cache/huggingface}
-export MPLCONFIGDIR=${MPLCONFIGDIR:-$INVOKEAI_ROOT/.matplotlib}
 
 # Run the CMD as the Container User (not root).
 exec gosu ${USER} "$@"

docker/run.sh

@@ -8,15 +8,11 @@ run() {
   local build_args=""
   local profile=""
 
-  # create .env file if it doesn't exist, otherwise docker compose will fail
   touch .env
-
-  # parse .env file for build args
   build_args=$(awk '$1 ~ /=[^$]/ && $0 !~ /^#/ {print "--build-arg " $0 " "}' .env) &&
-  profile="$(awk -F '=' '/GPU_DRIVER=/ {print $2}' .env)"
+  profile="$(awk -F '=' '/GPU_DRIVER/ {print $2}' .env)"
 
-  # default to 'cuda' profile
-  [[ -z "$profile" ]] && profile="cuda"
+  [[ -z "$profile" ]] && profile="nvidia"
 
   local service_name="invokeai-$profile"
 
@@ -30,7 +26,7 @@ run() {
 
   printf "%s\n" "starting service $service_name"
   docker compose --profile "$profile" up -d "$service_name"
-  docker compose --profile "$profile" logs -f
+  docker compose logs -f
 }
 
 run

docs/CHANGELOG.md

@@ -0,0 +1,815 @@
+---
+title: Changelog
+---
+
+# :octicons-log-16: **Changelog**
+
+## v2.3.5 <small>(22 May 2023)</small>
+
+This release (along with the post1 and post2 follow-on releases) expands support for additional LoRA and LyCORIS models, upgrades diffusers versions, and fixes a few bugs.
+
+### LoRA and LyCORIS Support Improvement
+
+    A number of LoRA/LyCORIS fine-tune files (those which alter the text encoder as well as the unet model) were not having the desired effect in InvokeAI. This bug has now been fixed. Full documentation of LoRA support is available at InvokeAI LoRA Support.
+    Previously, InvokeAI did not distinguish between LoRA/LyCORIS models based on Stable Diffusion v1.5 vs those based on v2.0 and 2.1, leading to a crash when an incompatible model was loaded. This has now been fixed. In addition, the web pulldown menus for LoRA and Textual Inversion selection have been enhanced to show only those files that are compatible with the currently-selected Stable Diffusion model.
+    Support for the newer LoKR LyCORIS files has been added.
+
+### Library Updates and Speed/Reproducibility Advancements
+The major enhancement in this version is that NVIDIA users no longer need to decide between speed and reproducibility. Previously, if you activated the Xformers library, you would see improvements in speed and memory usage, but multiple images generated with the same seed and other parameters would be slightly different from each other. This is no longer the case. Relative to 2.3.5 you will see improved performance when running without Xformers, and even better performance when Xformers is activated. In both cases, images generated with the same settings will be identical.
+
+Here are the new library versions:
+Library 	Version
+Torch 	2.0.0
+Diffusers 	0.16.1
+Xformers 	0.0.19
+Compel 	1.1.5
+Other Improvements
+
+### Performance Improvements
+
+    When a model is loaded for the first time, InvokeAI calculates its checksum for incorporation into the PNG metadata. This process could take up to a minute on network-mounted disks and WSL mounts. This release noticeably speeds up the process.
+
+### Bug Fixes
+
+    The "import models from directory" and "import from URL" functionality in the console-based model installer has now been fixed.
+    When running the WebUI, we have reduced the number of times that InvokeAI reaches out to HuggingFace to fetch the list of embeddable Textual Inversion models. We have also caught and fixed a problem with the updater not correctly detecting when another instance of the updater is running 
+
+
+## v2.3.4 <small>(7 April 2023)</small>
+
+What's New in 2.3.4
+
+This features release adds support for LoRA (Low-Rank Adaptation) and LyCORIS (Lora beYond Conventional) models, as well as some minor bug fixes.
+### LoRA and LyCORIS Support
+
+LoRA files contain fine-tuning weights that enable particular styles, subjects or concepts to be applied to generated images. LyCORIS files are an extended variant of LoRA. InvokeAI supports the most common LoRA/LyCORIS format, which ends in the suffix .safetensors. You will find numerous LoRA and LyCORIS models for download at Civitai, and a small but growing number at Hugging Face. Full documentation of LoRA support is available at InvokeAI LoRA Support.( Pre-release note: this page will only be available after release)
+
+To use LoRA/LyCORIS models in InvokeAI:
+
+    Download the .safetensors files of your choice and place in /path/to/invokeai/loras. This directory was not present in earlier version of InvokeAI but will be created for you the first time you run the command-line or web client. You can also create the directory manually.
+
+    Add withLora(lora-file,weight) to your prompts. The weight is optional and will default to 1.0. A few examples, assuming that a LoRA file named loras/sushi.safetensors is present:
+
+family sitting at dinner table eating sushi withLora(sushi,0.9)
+family sitting at dinner table eating sushi withLora(sushi, 0.75)
+family sitting at dinner table eating sushi withLora(sushi)
+
+Multiple withLora() prompt fragments are allowed. The weight can be arbitrarily large, but the useful range is roughly 0.5 to 1.0. Higher weights make the LoRA's influence stronger. Negative weights are also allowed, which can lead to some interesting effects.
+
+    Generate as you usually would! If you find that the image is too "crisp" try reducing the overall CFG value or reducing individual LoRA weights. As is the case with all fine-tunes, you'll get the best results when running the LoRA on top of the model similar to, or identical with, the one that was used during the LoRA's training. Don't try to load a SD 1.x-trained LoRA into a SD 2.x model, and vice versa. This will trigger a non-fatal error message and generation will not proceed.
+
+    You can change the location of the loras directory by passing the --lora_directory option to `invokeai.
+
+### New WebUI LoRA and Textual Inversion Buttons
+
+This version adds two new web interface buttons for inserting LoRA and Textual Inversion triggers into the prompt as shown in the screenshot below.
+
+Clicking on one or the other of the buttons will bring up a menu of available LoRA/LyCORIS or Textual Inversion trigger terms. Select a menu item to insert the properly-formatted withLora() or <textual-inversion> prompt fragment into the positive prompt. The number in parentheses indicates the number of trigger terms currently in the prompt. You may click the button again and deselect the LoRA or trigger to remove it from the prompt, or simply edit the prompt directly.
+
+Currently terms are inserted into the positive prompt textbox only. However, some textual inversion embeddings are designed to be used with negative prompts. To move a textual inversion trigger into the negative prompt, simply cut and paste it.
+
+By default the Textual Inversion menu only shows locally installed models found at startup time in /path/to/invokeai/embeddings. However, InvokeAI has the ability to dynamically download and install additional Textual Inversion embeddings from the HuggingFace Concepts Library. You may choose to display the most popular of these (with five or more likes) in the Textual Inversion menu by going to Settings and turning on "Show Textual Inversions from HF Concepts Library." When this option is activated, the locally-installed TI embeddings will be shown first, followed by uninstalled terms from Hugging Face. See The Hugging Face Concepts Library and Importing Textual Inversion files for more information.
+### Minor features and fixes
+
+This release changes model switching behavior so that the command-line and Web UIs save the last model used and restore it the next time they are launched. It also improves the behavior of the installer so that the pip utility is kept up to date.
+  
+### Known Bugs in 2.3.4
+
+These are known bugs in the release.
+
+    The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
+    Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
+
+
+## v2.3.3 <small>(28 March 2023)</small>
+
+This is a bugfix and minor feature release.
+### Bugfixes
+
+Since version 2.3.2 the following bugs have been fixed:
+Bugs
+
+    When using legacy checkpoints with an external VAE, the VAE file is now scanned for malware prior to loading. Previously only the main model weights file was scanned.
+    Textual inversion will select an appropriate batchsize based on whether xformers is active, and will default to xformers enabled if the library is detected.
+    The batch script log file names have been fixed to be compatible with Windows.
+    Occasional corruption of the .next_prefix file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
+    Support loading of legacy config files that have no personalization (textual inversion) section.
+    An infinite loop when opening the developer's console from within the invoke.sh script has been corrected.
+    Documentation fixes, including a recipe for detecting and fixing problems with the AMD GPU ROCm driver.
+
+Enhancements
+
+    It is now possible to load and run several community-contributed SD-2.0 based models, including the often-requested "Illuminati" model.
+    The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI embeddings directory.
+    If no --model is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
+    On Linux systems, the invoke.sh launcher now uses a prettier console-based interface. To take advantage of it, install the dialog package using your package manager (e.g. sudo apt install dialog).
+    When loading legacy models (safetensors/ckpt) you can specify a custom config file and/or a VAE by placing like-named files in the same directory as the model following this example:
+
+my-favorite-model.ckpt
+my-favorite-model.yaml
+my-favorite-model.vae.pt      # or my-favorite-model.vae.safetensors
+
+### Known Bugs in 2.3.3
+
+These are known bugs in the release.
+
+    The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
+    Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
+
+
+## v2.3.2 <small>(11 March 2023)</small>
+This is a bugfix and minor feature release.
+
+### Bugfixes
+
+Since version 2.3.1 the following bugs have been fixed:
+
+    Black images appearing for potential NSFW images when generating with legacy checkpoint models and both --no-nsfw_checker and --ckpt_convert turned on.
+    Black images appearing when generating from models fine-tuned on Stable-Diffusion-2-1-base. When importing V2-derived models, you may be asked to select whether the model was derived from a "base" model (512 pixels) or the 768-pixel SD-2.1 model.
+    The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
+    When using the model installer console app, models failed to import correctly when importing from directories with spaces in their names. A similar issue with the output directory was also fixed.
+    Crashes that occurred during model merging.
+    Restore previous naming of Stable Diffusion base and 768 models.
+    Upgraded to latest versions of diffusers, transformers, safetensors and accelerate libraries upstream. We hope that this will fix the assertion NDArray > 2**32 issue that MacOS users have had when generating images larger than 768x768 pixels. Please report back.
+
+As part of the upgrade to diffusers, the location of the diffusers-based models has changed from models/diffusers to models/hub. When you launch InvokeAI for the first time, it will prompt you to OK a one-time move. This should be quick and harmless, but if you have modified your models/diffusers directory in some way, for example using symlinks, you may wish to cancel the migration and make appropriate adjustments.
+New "Invokeai-batch" script
+
+### Invoke AI Batch
+2.3.2 introduces a new command-line only script called invokeai-batch that can be used to generate hundreds of images from prompts and settings that vary systematically. This can be used to try the same prompt across multiple combinations of models, steps, CFG settings and so forth. It also allows you to template prompts and generate a combinatorial list like:
+
+a shack in the mountains, photograph
+a shack in the mountains, watercolor
+a shack in the mountains, oil painting
+a chalet in the mountains, photograph
+a chalet in the mountains, watercolor
+a chalet in the mountains, oil painting
+a shack in the desert, photograph
+...
+
+If you have a system with multiple GPUs, or a single GPU with lots of VRAM, you can parallelize generation across the combinatorial set, reducing wait times and using your system's resources efficiently (make sure you have good GPU cooling).
+
+To try invokeai-batch out. Launch the "developer's console" using the invoke launcher script, or activate the invokeai virtual environment manually. From the console, give the command invokeai-batch --help in order to learn how the script works and create your first template file for dynamic prompt generation.
+
+
+### Known Bugs in 2.3.2
+
+These are known bugs in the release.
+
+    The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
+    Windows Defender will sometimes raise a Trojan alert for the codeformer.pth face restoration model. As far as we have been able to determine, this is a false positive and can be safely whitelisted.
+
+
+## v2.3.1 <small>(22 February 2023)</small>
+This is primarily a bugfix release, but it does provide several new features that will improve the user experience.
+
+### Enhanced support for model management
+
+InvokeAI now makes it convenient to add, remove and modify models. You can individually import models that are stored on your local system, scan an entire folder and its subfolders for models and import them automatically, and even directly import models from the internet by providing their download URLs. You also have the option of designating a local folder to scan for new models each time InvokeAI is restarted.
+
+There are three ways of accessing the model management features:
+
+    From the WebUI, click on the cube to the right of the model selection menu. This will bring up a form that allows you to import models individually from your local disk or scan a directory for models to import.
+
+    Using the Model Installer App
+
+Choose option (5) download and install models from the invoke launcher script to start a new console-based application for model management. You can use this to select from a curated set of starter models, or import checkpoint, safetensors, and diffusers models from a local disk or the internet. The example below shows importing two checkpoint URLs from popular SD sites and a HuggingFace diffusers model using its Repository ID. It also shows how to designate a folder to be scanned at startup time for new models to import.
+
+Command-line users can start this app using the command invokeai-model-install.
+
+    Using the Command Line Client (CLI)
+
+The !install_model and !convert_model commands have been enhanced to allow entering of URLs and local directories to scan and import. The first command installs .ckpt and .safetensors files as-is. The second one converts them into the faster diffusers format before installation.
+
+Internally InvokeAI is able to probe the contents of a .ckpt or .safetensors file to distinguish among v1.x, v2.x and inpainting models. This means that you do not need to include "inpaint" in your model names to use an inpainting model. Note that Stable Diffusion v2.x models will be autoconverted into a diffusers model the first time you use it.
+
+Please see INSTALLING MODELS for more information on model management.
+
+### An Improved Installer Experience
+
+The installer now launches a console-based UI for setting and changing commonly-used startup options:
+
+After selecting the desired options, the installer installs several support models needed by InvokeAI's face reconstruction and upscaling features and then launches the interface for selecting and installing models shown earlier. At any time, you can edit the startup options by launching invoke.sh/invoke.bat and entering option (6) change InvokeAI startup options
+
+Command-line users can launch the new configure app using invokeai-configure.
+
+This release also comes with a renewed updater. To do an update without going through a whole reinstallation, launch invoke.sh or invoke.bat and choose option (9) update InvokeAI . This will bring you to a screen that prompts you to update to the latest released version, to the most current development version, or any released or unreleased version you choose by selecting the tag or branch of the desired version.
+
+Command-line users can run this interface by typing invokeai-configure
+
+### Image Symmetry Options
+
+There are now features to generate horizontal and vertical symmetry during generation. The way these work is to wait until a selected step in the generation process and then to turn on a mirror image effect. In addition to generating some cool images, you can also use this to make side-by-side comparisons of how an image will look with more or fewer steps. Access this option from the WebUI by selecting Symmetry from the image generation settings, or within the CLI by using the options --h_symmetry_time_pct and --v_symmetry_time_pct (these can be abbreviated to --h_sym and --v_sym like all other options).
+
+### A New Unified Canvas Look
+
+This release introduces a beta version of the WebUI Unified Canvas. To try it out, open up the settings dialogue in the WebUI (gear icon) and select Use Canvas Beta Layout:
+
+Refresh the screen and go to to Unified Canvas (left side of screen, third icon from the top). The new layout is designed to provide more space to work in and to keep the image controls close to the image itself:
+
+Model conversion and merging within the WebUI
+
+The WebUI now has an intuitive interface for model merging, as well as for permanent conversion of models from legacy .ckpt/.safetensors formats into diffusers format. These options are also available directly from the invoke.sh/invoke.bat scripts.
+An easier way to contribute translations to the WebUI
+
+We have migrated our translation efforts to Weblate, a FOSS translation product. Maintaining the growing project's translations is now far simpler for the maintainers and community. Please review our brief translation guide for more information on how to contribute.
+Numerous internal bugfixes and performance issues
+
+### Bug Fixes
+This releases quashes multiple bugs that were reported in 2.3.0. Major internal changes include upgrading to diffusers 0.13.0, and using the compel library for prompt parsing. See Detailed Change Log for a detailed list of bugs caught and squished.
+Summary of InvokeAI command line scripts (all accessible via the launcher menu)
+Command 	Description
+invokeai 	Command line interface
+invokeai --web 	Web interface
+invokeai-model-install 	Model installer with console forms-based front end
+invokeai-ti --gui 	Textual inversion, with a console forms-based front end
+invokeai-merge --gui 	Model merging, with a console forms-based front end
+invokeai-configure 	Startup configuration; can also be used to reinstall support models
+invokeai-update 	InvokeAI software updater
+
+### Known Bugs in 2.3.1
+
+These are known bugs in the release.
+    MacOS users generating 768x768 pixel images or greater using diffusers models may experience a hard crash with assertion NDArray > 2**32 This appears to be an issu...
+
+
+
+## v2.3.0 <small>(15 January 2023)</small>
+
+**Transition to diffusers
+
+Version 2.3 provides support for both the traditional `.ckpt` weight
+checkpoint files as well as the HuggingFace `diffusers` format. This
+introduces several changes you should know about.
+
+1. The models.yaml format has been updated. There are now two
+   different type of configuration stanza. The traditional ckpt
+   one will look like this, with a `format` of `ckpt` and a
+   `weights` field that points to the absolute or ROOTDIR-relative
+   location of the ckpt file.
+
+   ```
+   inpainting-1.5:
+      description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
+      repo_id: runwayml/stable-diffusion-inpainting
+      format: ckpt
+      width: 512
+      height: 512
+      weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+      config: configs/stable-diffusion/v1-inpainting-inference.yaml
+      vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+   ```
+
+  A configuration stanza for a diffusers model hosted at HuggingFace will look like this,
+  with a `format` of `diffusers` and a `repo_id` that points to the
+  repository ID of the model on HuggingFace:
+
+  ```
+  stable-diffusion-2.1:
+  description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
+  repo_id: stabilityai/stable-diffusion-2-1
+  format: diffusers
+  ```
+
+  A configuration stanza for a diffuers model stored locally should
+  look like this, with a `format` of `diffusers`, but a `path` field
+  that points at the directory that contains `model_index.json`:
+
+  ```
+  waifu-diffusion:
+  description: Latest waifu diffusion 1.4
+  format: diffusers
+  path: models/diffusers/hakurei-haifu-diffusion-1.4
+  ```
+
+2. In order of precedence, InvokeAI will now use HF_HOME, then
+   XDG_CACHE_HOME, then finally default to `ROOTDIR/models` to
+   store HuggingFace diffusers models.
+
+   Consequently, the format of the models directory has changed to
+   mimic the HuggingFace cache directory. When HF_HOME and XDG_HOME
+   are not set, diffusers models are now automatically downloaded
+   and retrieved from the directory `ROOTDIR/models/diffusers`,
+   while other models are stored in the directory
+   `ROOTDIR/models/hub`. This organization is the same as that used
+   by HuggingFace for its cache management.
+
+   This allows you to share diffusers and ckpt model files easily with
+   other machine learning applications that use the HuggingFace
+   libraries. To do this, set the environment variable HF_HOME
+   before starting up InvokeAI to tell it what directory to
+   cache models in. To tell InvokeAI to use the standard HuggingFace
+   cache directory, you would set HF_HOME like this (Linux/Mac):
+
+   `export HF_HOME=~/.cache/huggingface`
+
+   Both HuggingFace and InvokeAI will fall back to the XDG_CACHE_HOME
+   environment variable if HF_HOME is not set; this path
+   takes precedence over `ROOTDIR/models` to allow for the same sharing
+   with other machine learning applications that use HuggingFace
+   libraries.
+
+3. If you upgrade to InvokeAI 2.3.* from an earlier version, there
+   will be a one-time migration from the old models directory format
+   to the new one. You will see a message about this the first time
+   you start `invoke.py`.
+
+4. Both the front end back ends of the model manager have been
+   rewritten to accommodate diffusers. You can import models using
+   their local file path, using their URLs, or their HuggingFace
+   repo_ids. On the command line, all these syntaxes work:
+
+   ```
+   !import_model stabilityai/stable-diffusion-2-1-base
+   !import_model /opt/sd-models/sd-1.4.ckpt
+   !import_model https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/blob/main/PaperCut_v1.ckpt
+   ```
+
+**KNOWN BUGS (15 January 2023)
+
+1. On CUDA systems, the 768 pixel stable-diffusion-2.0 and
+   stable-diffusion-2.1 models can only be run as `diffusers` models
+   when the `xformer` library is installed and configured. Without
+   `xformers`, InvokeAI returns black images.
+
+2. Inpainting and outpainting have regressed in quality.
+
+Both these issues are being actively worked on.
+
+## v2.2.4 <small>(11 December 2022)</small>
+
+**the `invokeai` directory**
+
+Previously there were two directories to worry about, the directory that
+contained the InvokeAI source code and the launcher scripts, and the `invokeai`
+directory that contained the models files, embeddings, configuration and
+outputs. With the 2.2.4 release, this dual system is done away with, and
+everything, including the `invoke.bat` and `invoke.sh` launcher scripts, now
+live in a directory named `invokeai`. By default this directory is located in
+your home directory (e.g. `\Users\yourname` on Windows), but you can select
+where it goes at install time.
+
+After installation, you can delete the install directory (the one that the zip
+file creates when it unpacks). Do **not** delete or move the `invokeai`
+directory!
+
+**Initialization file `invokeai/invokeai.init`**
+
+You can place frequently-used startup options in this file, such as the default
+number of steps or your preferred sampler. To keep everything in one place, this
+file has now been moved into the `invokeai` directory and is named
+`invokeai.init`.
+
+**To update from Version 2.2.3**
+
+The easiest route is to download and unpack one of the 2.2.4 installer files.
+When it asks you for the location of the `invokeai` runtime directory, respond
+with the path to the directory that contains your 2.2.3 `invokeai`. That is, if
+`invokeai` lives at `C:\Users\fred\invokeai`, then answer with `C:\Users\fred`
+and answer "Y" when asked if you want to reuse the directory.
+
+The `update.sh` (`update.bat`) script that came with the 2.2.3 source installer
+does not know about the new directory layout and won't be fully functional.
+
+**To update to 2.2.5 (and beyond) there's now an update path**
+
+As they become available, you can update to more recent versions of InvokeAI
+using an `update.sh` (`update.bat`) script located in the `invokeai` directory.
+Running it without any arguments will install the most recent version of
+InvokeAI. Alternatively, you can get set releases by running the `update.sh`
+script with an argument in the command shell. This syntax accepts the path to
+the desired release's zip file, which you can find by clicking on the green
+"Code" button on this repository's home page.
+
+**Other 2.2.4 Improvements**
+
+- Fix InvokeAI GUI initialization by @addianto in #1687
+- fix link in documentation by @lstein in #1728
+- Fix broken link by @ShawnZhong in #1736
+- Remove reference to binary installer by @lstein in #1731
+- documentation fixes for 2.2.3 by @lstein in #1740
+- Modify installer links to point closer to the source installer by @ebr in
+  #1745
+- add documentation warning about 1650/60 cards by @lstein in #1753
+- Fix Linux source URL in installation docs by @andybearman in #1756
+- Make install instructions discoverable in readme by @damian0815 in #1752
+- typo fix by @ofirkris in #1755
+- Non-interactive model download (support HUGGINGFACE_TOKEN) by @ebr in #1578
+- fix(srcinstall): shell installer - cp scripts instead of linking by @tildebyte
+  in #1765
+- stability and usage improvements to binary & source installers by @lstein in
+  #1760
+- fix off-by-one bug in cross-attention-control by @damian0815 in #1774
+- Eventually update APP_VERSION to 2.2.3 by @spezialspezial in #1768
+- invoke script cds to its location before running by @lstein in #1805
+- Make PaperCut and VoxelArt models load again by @lstein in #1730
+- Fix --embedding_directory / --embedding_path not working by @blessedcoolant in
+  #1817
+- Clean up readme by @hipsterusername in #1820
+- Optimized Docker build with support for external working directory by @ebr in
+  #1544
+- disable pushing the cloud container by @mauwii in #1831
+- Fix docker push github action and expand with additional metadata by @ebr in
+  #1837
+- Fix Broken Link To Notebook by @VedantMadane in #1821
+- Account for flat models by @spezialspezial in #1766
+- Update invoke.bat.in isolate environment variables by @lynnewu in #1833
+- Arch Linux Specific PatchMatch Instructions & fixing conda install on linux by
+  @SammCheese in #1848
+- Make force free GPU memory work in img2img by @addianto in #1844
+- New installer by @lstein
+
+## v2.2.3 <small>(2 December 2022)</small>
+
+!!! Note
+
+    This point release removes references to the binary installer from the
+    installation guide. The binary installer is not stable at the current
+    time. First time users are encouraged to use the "source" installer as
+    described in [Installing InvokeAI with the Source Installer](installation/deprecated_documentation/INSTALL_SOURCE.md)
+
+With InvokeAI 2.2, this project now provides enthusiasts and professionals a
+robust workflow solution for creating AI-generated and human facilitated
+compositions. Additional enhancements have been made as well, improving safety,
+ease of use, and installation.
+
+Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
+512x768 image (and less for smaller images), and is compatible with
+Windows/Linux/Mac (M1 & M2).
+
+You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
+introduces the main WebUI enhancement for version 2.2 -
+[The Unified Canvas](features/UNIFIED_CANVAS.md). This new workflow is the
+biggest enhancement added to the WebUI to date, and unlocks a stunning amount of
+potential for users to create and iterate on their creations. The following
+sections describe what's new for InvokeAI.
+
+## v2.2.2 <small>(30 November 2022)</small>
+
+!!! note
+
+    The binary installer is not ready for prime time. First time users are recommended to install via the "source" installer accessible through the links at the bottom of this page.****
+
+With InvokeAI 2.2, this project now provides enthusiasts and professionals a
+robust workflow solution for creating AI-generated and human facilitated
+compositions. Additional enhancements have been made as well, improving safety,
+ease of use, and installation.
+
+Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
+512x768 image (and less for smaller images), and is compatible with
+Windows/Linux/Mac (M1 & M2).
+
+You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
+introduces the main WebUI enhancement for version 2.2 -
+[The Unified Canvas](https://invoke-ai.github.io/InvokeAI/features/UNIFIED_CANVAS/).
+This new workflow is the biggest enhancement added to the WebUI to date, and
+unlocks a stunning amount of potential for users to create and iterate on their
+creations. The following sections describe what's new for InvokeAI.
+
+## v2.2.0 <small>(2 December 2022)</small>
+
+With InvokeAI 2.2, this project now provides enthusiasts and professionals a
+robust workflow solution for creating AI-generated and human facilitated
+compositions. Additional enhancements have been made as well, improving safety,
+ease of use, and installation.
+
+Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
+512x768 image (and less for smaller images), and is compatible with
+Windows/Linux/Mac (M1 & M2).
+
+You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
+introduces the main WebUI enhancement for version 2.2 -
+[The Unified Canvas](features/UNIFIED_CANVAS.md). This new workflow is the
+biggest enhancement added to the WebUI to date, and unlocks a stunning amount of
+potential for users to create and iterate on their creations. The following
+sections describe what's new for InvokeAI.
+
+## v2.1.3 <small>(13 November 2022)</small>
+
+- A choice of installer scripts that automate installation and configuration.
+  See
+  [Installation](installation/INSTALLATION.md).
+- A streamlined manual installation process that works for both Conda and
+  PIP-only installs. See
+  [Manual Installation](installation/020_INSTALL_MANUAL.md).
+- The ability to save frequently-used startup options (model to load, steps,
+  sampler, etc) in a `.invokeai` file. See
+  [Client](deprecated/CLI.md)
+- Support for AMD GPU cards (non-CUDA) on Linux machines.
+- Multiple bugs and edge cases squashed.
+
+## v2.1.0 <small>(2 November 2022)</small>
+
+- update mac instructions to use invokeai for env name by @willwillems in #1030
+- Update .gitignore by @blessedcoolant in #1040
+- reintroduce fix for m1 from #579 missing after merge by @skurovec in #1056
+- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in #1060
+- Print out the device type which is used by @manzke in #1073
+- Hires Addition by @hipsterusername in #1063
+- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
+  @skurovec in #1081
+- Forward dream.py to invoke.py using the same interpreter, add deprecation
+  warning by @db3000 in #1077
+- fix noisy images at high step counts by @lstein in #1086
+- Generalize facetool strength argument by @db3000 in #1078
+- Enable fast switching among models at the invoke> command line by @lstein in
+  #1066
+- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in #1095
+- Update generate.py by @unreleased in #1109
+- Update 'ldm' env to 'invokeai' in troubleshooting steps by @19wolf in #1125
+- Fixed documentation typos and resolved merge conflicts by @rupeshs in #1123
+- Fix broken doc links, fix malaprop in the project subtitle by @majick in #1131
+- Only output facetool parameters if enhancing faces by @db3000 in #1119
+- Update gitignore to ignore codeformer weights at new location by
+  @spezialspezial in #1136
+- fix links to point to invoke-ai.github.io #1117 by @mauwii in #1143
+- Rework-mkdocs by @mauwii in #1144
+- add option to CLI and pngwriter that allows user to set PNG compression level
+  by @lstein in #1127
+- Fix img2img DDIM index out of bound by @wfng92 in #1137
+- Fix gh actions by @mauwii in #1128
+- update mac instructions to use invokeai for env name by @willwillems in #1030
+- Update .gitignore by @blessedcoolant in #1040
+- reintroduce fix for m1 from #579 missing after merge by @skurovec in #1056
+- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in #1060
+- Print out the device type which is used by @manzke in #1073
+- Hires Addition by @hipsterusername in #1063
+- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
+  @skurovec in #1081
+- Forward dream.py to invoke.py using the same interpreter, add deprecation
+  warning by @db3000 in #1077
+- fix noisy images at high step counts by @lstein in #1086
+- Generalize facetool strength argument by @db3000 in #1078
+- Enable fast switching among models at the invoke> command line by @lstein in
+  #1066
+- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in #1095
+- Fixed documentation typos and resolved merge conflicts by @rupeshs in #1123
+- Only output facetool parameters if enhancing faces by @db3000 in #1119
+- add option to CLI and pngwriter that allows user to set PNG compression level
+  by @lstein in #1127
+- Fix img2img DDIM index out of bound by @wfng92 in #1137
+- Add text prompt to inpaint mask support by @lstein in #1133
+- Respect http[s] protocol when making socket.io middleware by @damian0815 in
+  #976
+- WebUI: Adds Codeformer support by @psychedelicious in #1151
+- Skips normalizing prompts for web UI metadata by @psychedelicious in #1165
+- Add Asymmetric Tiling by @carson-katri in #1132
+- Web UI: Increases max CFG Scale to 200 by @psychedelicious in #1172
+- Corrects color channels in face restoration; Fixes #1167 by @psychedelicious
+  in #1175
+- Flips channels using array slicing instead of using OpenCV by @psychedelicious
+  in #1178
+- Fix typo in docs: s/Formally/Formerly by @noodlebox in #1176
+- fix clipseg loading problems by @lstein in #1177
+- Correct color channels in upscale using array slicing by @wfng92 in #1181
+- Web UI: Filters existing images when adding new images; Fixes #1085 by
+  @psychedelicious in #1171
+- fix a number of bugs in textual inversion by @lstein in #1190
+- Improve !fetch, add !replay command by @ArDiouscuros in #882
+- Fix generation of image with s>1000 by @holstvoogd in #951
+- Web UI: Gallery improvements by @psychedelicious in #1198
+- Update CLI.md by @krummrey in #1211
+- outcropping improvements by @lstein in #1207
+- add support for loading VAE autoencoders by @lstein in #1216
+- remove duplicate fix_func for MPS by @wfng92 in #1210
+- Metadata storage and retrieval fixes by @lstein in #1204
+- nix: add shell.nix file by @Cloudef in #1170
+- Web UI: Changes vite dist asset paths to relative by @psychedelicious in #1185
+- Web UI: Removes isDisabled from PromptInput by @psychedelicious in #1187
+- Allow user to generate images with initial noise as on M1 / mps system by
+  @ArDiouscuros in #981
+- feat: adding filename format template by @plucked in #968
+- Web UI: Fixes broken bundle by @psychedelicious in #1242
+- Support runwayML custom inpainting model by @lstein in #1243
+- Update IMG2IMG.md by @talitore in #1262
+- New dockerfile - including a build- and a run- script as well as a GH-Action
+  by @mauwii in #1233
+- cut over from karras to model noise schedule for higher steps by @lstein in
+  #1222
+- Prompt tweaks by @lstein in #1268
+- Outpainting implementation by @Kyle0654 in #1251
+- fixing aspect ratio on hires by @tjennings in #1249
+- Fix-build-container-action by @mauwii in #1274
+- handle all unicode characters by @damian0815 in #1276
+- adds models.user.yml to .gitignore by @JakeHL in #1281
+- remove debug branch, set fail-fast to false by @mauwii in #1284
+- Protect-secrets-on-pr by @mauwii in #1285
+- Web UI: Adds initial inpainting implementation by @psychedelicious in #1225
+- fix environment-mac.yml - tested on x64 and arm64 by @mauwii in #1289
+- Use proper authentication to download model by @mauwii in #1287
+- Prevent indexing error for mode RGB by @spezialspezial in #1294
+- Integrate sd-v1-5 model into test matrix (easily expandable), remove
+  unecesarry caches by @mauwii in #1293
+- add --no-interactive to configure_invokeai step by @mauwii in #1302
+- 1-click installer and updater. Uses micromamba to install git and conda into a
+  contained environment (if necessary) before running the normal installation
+  script by @cmdr2 in #1253
+- configure_invokeai.py script downloads the weight files by @lstein in #1290
+
+## v2.0.1 <small>(13 October 2022)</small>
+
+- fix noisy images at high step count when using k\* samplers
+- dream.py script now calls invoke.py module directly rather than via a new
+  python process (which could break the environment)
+
+## v2.0.0 <small>(9 October 2022)</small>
+
+- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
+  backward compatibility.
+- Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+- img2img runs on all k\* samplers
+- Support for
+  [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
+- Support for CodeFormer face reconstruction
+- Support for Textual Inversion on Macintoshes
+- Support in both WebGUI and CLI for
+  [post-processing of previously-generated images](features/POSTPROCESS.md)
+  using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E
+  infinite canvas), and "embiggen" upscaling. See the `!fix` command.
+- New `--hires` option on `invoke>` line allows
+  [larger images to be created without duplicating elements](deprecated/CLI.md#this-is-an-example-of-txt2img),
+  at the cost of some performance.
+- New `--perlin` and `--threshold` options allow you to add and control
+  variation during image generation (see
+  [Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
+- Extensive metadata now written into PNG files, allowing reliable regeneration
+  of images and tweaking of previous settings.
+- Command-line completion in `invoke.py` now works on Windows, Linux and Mac
+  platforms.
+- Improved [command-line completion behavior](deprecated/CLI.md) New commands
+  added:
+  - List command-line history with `!history`
+  - Search command-line history with `!search`
+  - Clear history with `!clear`
+- Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+  configure. To switch away from auto use the new flag like
+  `--precision=float32`.
+
+## v1.14 <small>(11 September 2022)</small>
+
+- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
+- Full support for Apple hardware with M1 or M2 chips.
+- Add "seamless mode" for circular tiling of image. Generates beautiful effects.
+  ([prixt](https://github.com/prixt)).
+- Inpainting support.
+- Improved web server GUI.
+- Lots of code and documentation cleanups.
+
+## v1.13 <small>(3 September 2022)</small>
+
+- Support image variations (see [VARIATIONS](deprecated/VARIATIONS.md)
+  ([Kevin Gibbons](https://github.com/bakkot) and many contributors and
+  reviewers)
+- Supports a Google Colab notebook for a standalone server running on Google
+  hardware [Arturo Mendivil](https://github.com/artmen1516)
+- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
+  [Kevin Gibbons](https://github.com/bakkot)
+- WebUI supports incremental display of in-progress images during generation
+  [Kevin Gibbons](https://github.com/bakkot)
+- A new configuration file scheme that allows new models (including upcoming
+  stable-diffusion-v1.5) to be added without altering the code.
+  ([David Wager](https://github.com/maddavid12))
+- Can specify --grid on invoke.py command line as the default.
+- Miscellaneous internal bug and stability fixes.
+- Works on M1 Apple hardware.
+- Multiple bug fixes.
+
+---
+
+## v1.12 <small>(28 August 2022)</small>
+
+- Improved file handling, including ability to read prompts from standard input.
+  (kudos to [Yunsaki](https://github.com/yunsaki)
+- The web server is now integrated with the invoke.py script. Invoke by adding
+  --web to the invoke.py command arguments.
+- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
+  enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
+  VRAM requirements are modestly reduced. Thanks to both
+  [Blessedcoolant](https://github.com/blessedcoolant) and
+  [Oceanswave](https://github.com/oceanswave) for their work on this.
+- You can now swap samplers on the invoke> command line.
+  [Blessedcoolant](https://github.com/blessedcoolant)
+
+---
+
+## v1.11 <small>(26 August 2022)</small>
+
+- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module.
+  (kudos to [Oceanswave](https://github.com/Oceanswave)
+- You now can specify a seed of -1 to use the previous image's seed, -2 to use
+  the seed for the image generated before that, etc. Seed memory only extends
+  back to the previous command, but will work on all images generated with the
+  -n# switch.
+- Variant generation support temporarily disabled pending more general solution.
+- Created a feature branch named **yunsaki-morphing-invoke** which adds
+  experimental support for iteratively modifying the prompt and its parameters.
+  Please
+  see[Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86) for
+  a synopsis of how this works. Note that when this feature is eventually added
+  to the main branch, it will may be modified significantly.
+
+---
+
+## v1.10 <small>(25 August 2022)</small>
+
+- A barebones but fully functional interactive web server for online generation
+  of txt2img and img2img.
+
+---
+
+## v1.09 <small>(24 August 2022)</small>
+
+- A new -v option allows you to generate multiple variants of an initial image
+  in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave).
+  [ See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
+- Added ability to personalize text to image generation (kudos to
+  [Oceanswave](https://github.com/Oceanswave) and
+  [nicolai256](https://github.com/nicolai256))
+- Enabled all of the samplers from k_diffusion
+
+---
+
+## v1.08 <small>(24 August 2022)</small>
+
+- Escape single quotes on the invoke> command before trying to parse. This
+  avoids parse errors.
+- Removed instruction to get Python3.8 as first step in Windows install.
+  Anaconda3 does it for you.
+- Added bounds checks for numeric arguments that could cause crashes.
+- Cleaned up the copyright and license agreement files.
+
+---
+
+## v1.07 <small>(23 August 2022)</small>
+
+- Image filenames will now never fill gaps in the sequence, but will be assigned
+  the next higher name in the chosen directory. This ensures that the alphabetic
+  and chronological sort orders are the same.
+
+---
+
+## v1.06 <small>(23 August 2022)</small>
+
+- Added weighted prompt support contributed by
+  [xraxra](https://github.com/xraxra)
+- Example of using weighted prompts to tweak a demonic figure contributed by
+  [bmaltais](https://github.com/bmaltais)
+
+---
+
+## v1.05 <small>(22 August 2022 - after the drop)</small>
+
+- Filenames now use the following formats: 000010.95183149.png -- Two files
+  produced by the same command (e.g. -n2), 000010.26742632.png -- distinguished
+  by a different seed.
+
+  000011.455191342.01.png -- Two files produced by the same command using
+  000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
+
+  000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid
+  can be regenerated with the indicated key
+
+- It should no longer be possible for one image to overwrite another
+- You can use the "cd" and "pwd" commands at the invoke> prompt to set and
+  retrieve the path of the output directory.
+
+---
+
+## v1.04 <small>(22 August 2022 - after the drop)</small>
+
+- Updated README to reflect installation of the released weights.
+- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
+  tokenizer.
+
+---
+
+## v1.03 <small>(22 August 2022)</small>
+
+- The original txt2img and img2img scripts from the CompViz repository have been
+  moved into a subfolder named "orig_scripts", to reduce confusion.
+
+---
+
+## v1.02 <small>(21 August 2022)</small>
+
+- A copy of the prompt and all of its switches and options is now stored in the
+  corresponding image in a tEXt metadata field named "Dream". You can read the
+  prompt using scripts/images2prompt.py, or an image editor that allows you to
+  explore the full metadata. **Please run "conda env update" to load the k_lms
+  dependencies!!**
+
+---
+
+## v1.01 <small>(21 August 2022)</small>
+
+- added k_lms sampling. **Please run "conda env update" to load the k_lms
+  dependencies!!**
+- use half precision arithmetic by default, resulting in faster execution and
+  lower memory requirements Pass argument --full_precision to invoke.py to get
+  slower but more accurate image generation
+
+---
+
+## Links
+
+- **[Read Me](index.md)**

docs/RELEASE.md

@@ -1,52 +1,41 @@
 # Release Process
 
-The Invoke application is published as a python package on [PyPI]. This includes both a source distribution and built distribution (a wheel).
+The app is published in twice, in different build formats.
 
-Most users install it with the [Launcher](https://github.com/invoke-ai/launcher/), others with `pip`.
-
-The launcher uses GitHub as the source of truth for available releases.
-
-## Broad Strokes
-
-- Merge all changes and bump the version in the codebase.
-- Tag the release commit.
-- Wait for the release workflow to complete.
-- Approve the PyPI publish jobs.
-- Write GH release notes.
+- A [PyPI] distribution. This includes both a source distribution and built distribution (a wheel). Users install with `pip install invokeai`. The updater uses this build.
+- An installer on the [InvokeAI Releases Page]. This is a zip file with install scripts and a wheel. This is only used for new installs.
 
 ## General Prep
 
-Make a developer call-out for PRs to merge. Merge and test things
-out. Create a branch with a name like user/chore/vX.X.X-prep and bump the version by editing
-`invokeai/version/invokeai_version.py` and commit locally.
+Make a developer call-out for PRs to merge. Merge and test things out.
+
+While the release workflow does not include end-to-end tests, it does pause before publishing so you can download and test the final build.
 
 ## Release Workflow
 
 The `release.yml` workflow runs a number of jobs to handle code checks, tests, build and publish on PyPI.
 
-It is triggered on **tag push**, when the tag matches `v*`.
+It is triggered on **tag push**, when the tag matches `v*`. It doesn't matter if you've prepped a release branch like `release/v3.5.0` or are releasing from `main` - it works the same.
+
+> Because commits are reference-counted, it is safe to create a release branch, tag it, let the workflow run, then delete the branch. So long as the tag exists, that commit will exist.
 
 ### Triggering the Workflow
 
-Ensure all commits that should be in the release are merged into this branch, and that you have pulled them locally.
+Run `make tag-release` to tag the current commit and kick off the workflow.
 
-Run `make tag-release` to tag the current commit and kick off the workflow. You will be prompted to provide a message - use the version specifier.
-
-If this version's tag already exists for some reason (maybe you had to make a last minute change), the script will overwrite it.
-
-Push the commit to trigger the workflow.
-
-> In case you cannot use the Make target, the release may also be dispatched [manually] via GH.
+The release may also be dispatched [manually].
 
 ### Workflow Jobs and Process
 
-The workflow consists of a number of concurrently-run checks and tests, then two final publish jobs.
+The workflow consists of a number of concurrently-run jobs, and two final publish jobs.
 
 The publish jobs require manual approval and are only run if the other jobs succeed.
 
 #### `check-version` Job
 
-This job ensures that the `invokeai` python package version specifier matches the tag for the release. The version specifier is pulled from the `__version__` variable in `invokeai/version/invokeai_version.py`.
+This job checks that the git ref matches the app version. It matches the ref against the `__version__` variable in `invokeai/version/invokeai_version.py`.
+
+When the workflow is triggered by tag push, the ref is the tag. If the workflow is run manually, the ref is the target selected from the **Use workflow from** dropdown.
 
 This job uses [samuelcolvin/check-python-version].
 
@@ -54,47 +43,62 @@ This job uses [samuelcolvin/check-python-version].
 
 #### Check and Test Jobs
 
-Next, these jobs run and must pass. They are the same jobs that are run for every PR.
-
 - **`python-tests`**: runs `pytest` on matrix of platforms
 - **`python-checks`**: runs `ruff` (format and lint)
 - **`frontend-tests`**: runs `vitest`
 - **`frontend-checks`**: runs `prettier` (format), `eslint` (lint), `dpdm` (circular refs), `tsc` (static type check) and `knip` (unused imports)
-- **`typegen-checks`**: ensures the frontend and backend types are synced
 
-#### `build-wheel` Job
+> **TODO** We should add `mypy` or `pyright` to the **`check-python`** job.
 
-This sets up both python and frontend dependencies and builds the python package. Internally, this runs `./scripts/build_wheel.sh` and uploads `dist.zip`, which contains the wheel and unarchived build.
+> **TODO** We should add an end-to-end test job that generates an image.
 
-You don't need to download or test these artifacts.
+#### `build-installer` Job
+
+This sets up both python and frontend dependencies and builds the python package. Internally, this runs `installer/create_installer.sh` and uploads two artifacts:
+
+- **`dist`**: the python distribution, to be published on PyPI
+- **`InvokeAI-installer-${VERSION}.zip`**: the installer to be included in the GitHub release
 
 #### Sanity Check & Smoke Test
 
-At this point, the release workflow pauses as the remaining publish jobs require approval.
+At this point, the release workflow pauses as the remaining publish jobs require approval. Time to test the installer.
 
-It's possible to test the python package before it gets published to PyPI. We've never had problems with it, so it's not necessary to do this.
+Because the installer pulls from PyPI, and we haven't published to PyPI yet, you will need to install from the wheel:
 
-But, if you want to be extra-super careful, here's how to test it:
+- Download and unzip `dist.zip` and the installer from the **Summary** tab of the workflow
+- Run the installer script using the `--wheel` CLI arg, pointing at the wheel:
 
-- Download the `dist.zip` build artifact from the `build-wheel` job
-- Unzip it and find the wheel file
-- Create a fresh Invoke install by following the [manual install guide](https://invoke-ai.github.io/InvokeAI/installation/manual/) - but instead of installing from PyPI, install from the wheel
-- Test the app
+  ```sh
+  ./install.sh --wheel ../InvokeAI-4.0.0rc6-py3-none-any.whl
+  ```
+
+- Install to a temporary directory so you get the new user experience
+- Download a model and generate
+
+> The same wheel file is bundled in the installer and in the `dist` artifact, which is uploaded to PyPI. You should end up with the exactly the same installation as if the installer got the wheel from PyPI.
 
 ##### Something isn't right
 
-If testing reveals any issues, no worries. Cancel the workflow, which will cancel the pending publish jobs (you didn't approve them prematurely, right?) and start over.
+If testing reveals any issues, no worries. Cancel the workflow, which will cancel the pending publish jobs (you didn't approve them prematurely, right?).
+
+Now you can start from the top:
+
+- Fix the issues and PR the fixes per usual
+- Get the PR approved and merged per usual
+- Switch to `main` and pull in the fixes
+- Run `make tag-release` to move the tag to `HEAD` (which has the fixes) and kick off the release workflow again
+- Re-do the sanity check
 
 #### PyPI Publish Jobs
 
-The publish jobs will not run if any of the previous jobs fail.
+The publish jobs will run if any of the previous jobs fail.
 
 They use [GitHub environments], which are configured as [trusted publishers] on PyPI.
 
-Both jobs require a @lstein or @blessedcoolant to approve them from the workflow's **Summary** tab.
+Both jobs require a maintainer to approve them from the workflow's **Summary** tab.
 
 - Click the **Review deployments** button
-- Select the environment (either `testpypi` or `pypi` - typically you select both)
+- Select the environment (either `testpypi` or `pypi`)
 - Click **Approve and deploy**
 
 > **If the version already exists on PyPI, the publish jobs will fail.** PyPI only allows a given version to be published once - you cannot change it. If version published on PyPI has a problem, you'll need to "fail forward" by bumping the app version and publishing a followup release.
@@ -103,39 +107,52 @@ Both jobs require a @lstein or @blessedcoolant to approve them from the workflow
 
 Check the [python infrastructure status page] for incidents.
 
-If there are no incidents, contact @lstein or @blessedcoolant, who have owner access to GH and PyPI, to see if access has expired or something like that.
+If there are no incidents, contact @hipsterusername or @lstein, who have owner access to GH and PyPI, to see if access has expired or something like that.
 
 #### `publish-testpypi` Job
 
 Publishes the distribution on the [Test PyPI] index, using the `testpypi` GitHub environment.
 
-This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release for some reason:
+This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release.
 
-- Approve this publish job without approving the prod publish
-- Let it finish
-- Create a fresh Invoke install by following the [manual install guide](https://invoke-ai.github.io/InvokeAI/installation/manual/), making sure to use the Test PyPI index URL: `https://test.pypi.org/simple/`
-- Test the app
+If approved and successful, you could try out the test release like this:
+
+```sh
+# Create a new virtual environment
+python -m venv ~/.test-invokeai-dist --prompt test-invokeai-dist
+# Install the distribution from Test PyPI
+pip install --index-url https://test.pypi.org/simple/ invokeai
+# Run and test the app
+invokeai-web
+# Cleanup
+deactivate
+rm -rf ~/.test-invokeai-dist
+```
 
 #### `publish-pypi` Job
 
 Publishes the distribution on the production PyPI index, using the `pypi` GitHub environment.
 
-It's a good idea to wait to approve and run this job until you have the release notes ready!
+## Publish the GitHub Release with installer
 
-## Prep and publish the GitHub Release
+Once the release is published to PyPI, it's time to publish the GitHub release.
 
 1. [Draft a new release] on GitHub, choosing the tag that triggered the release.
-2. The **Generate release notes** button automatically inserts the changelog and new contributors. Make sure to select the correct tags for this release and the last stable release. GH often selects the wrong tags - do this manually.
-3. Write the release notes, describing important changes. Contributions from community members should be shouted out. Use the GH-generated changelog to see all contributors. If there are Weblate translation updates, open that PR and shout out every person who contributed a translation.
-4. Check **Set as a pre-release** if it's a pre-release.
-5. Approve and wait for the `publish-pypi` job to finish if you haven't already.
-6. Publish the GH release.
-7. Post the release in Discord in the [releases](https://discord.com/channels/1020123559063990373/1149260708098359327) channel with abbreviated notes. For example:
-   > Invoke v5.7.0 (stable): <https://github.com/invoke-ai/InvokeAI/releases/tag/v5.7.0>
-   >
-   > It's a pretty big one - Form Builder, Metadata Nodes (thanks @SkunkWorxDark!), and much more.
-8. Right click the message in releases and copy the link to it. Then, post that link in the [new-release-discussion](https://discord.com/channels/1020123559063990373/1149506274971631688) channel. For example:
-   > Invoke v5.7.0 (stable): <https://discord.com/channels/1020123559063990373/1149260708098359327/1344521744916021248>
+1. Write the release notes, describing important changes. The **Generate release notes** button automatically inserts the changelog and new contributors, and you can copy/paste the intro from previous releases.
+1. Use `scripts/get_external_contributions.py` to get a list of external contributions to shout out in the release notes.
+1. Upload the zip file created in **`build`** job into the Assets section of the release notes.
+1. Check **Set as a pre-release** if it's a pre-release.
+1. Check **Create a discussion for this release**.
+1. Publish the release.
+1. Announce the release in Discord.
+
+> **TODO** Workflows can create a GitHub release from a template and upload release assets. One popular action to handle this is [ncipollo/release-action]. A future enhancement to the release process could set this up.
+
+## Manual Build
+
+The `build installer` workflow can be dispatched manually. This is useful to test the installer for a given branch or tag.
+
+No checks are run, it just builds.
 
 ## Manual Release
 
@@ -143,10 +160,12 @@ The `release` workflow can be dispatched manually. You must dispatch the workflo
 
 This functionality is available as a fallback in case something goes wonky. Typically, releases should be triggered via tag push as described above.
 
+[InvokeAI Releases Page]: https://github.com/invoke-ai/InvokeAI/releases
 [PyPI]: https://pypi.org/
 [Draft a new release]: https://github.com/invoke-ai/InvokeAI/releases/new
 [Test PyPI]: https://test.pypi.org/
 [version specifier]: https://packaging.python.org/en/latest/specifications/version-specifiers/
+[ncipollo/release-action]: https://github.com/ncipollo/release-action
 [GitHub environments]: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment
 [trusted publishers]: https://docs.pypi.org/trusted-publishers/
 [samuelcolvin/check-python-version]: https://github.com/samuelcolvin/check-python-version

docs/configuration.md

@@ -1,192 +0,0 @@
----
-title: Configuration
----
-
-# :material-tune-variant: InvokeAI Configuration
-
-## Intro
-
-Runtime settings, including the location of files and
-directories, memory usage, and performance, are managed via the
-`invokeai.yaml` config file or environment variables. A subset
-of settings may be set via commandline arguments.
-
-Settings sources are used in this order:
-
-- CLI args
-- Environment variables
-- `invokeai.yaml` settings
-- Fallback: defaults
-
-### InvokeAI Root Directory
-
-On startup, InvokeAI searches for its "root" directory. This is the directory
-that contains models, images, the database, and so on. It also contains
-a configuration file called `invokeai.yaml`.
-
-InvokeAI searches for the root directory in this order:
-
-1. The `--root <path>` CLI arg.
-2. The environment variable INVOKEAI_ROOT.
-3. The directory containing the currently active virtual environment.
-4. Fallback: a directory in the current user's home directory named `invokeai`.
-
-### InvokeAI Configuration File
-
-Inside the root directory, we read settings from the `invokeai.yaml` file.
-
-It has two sections - one for internal use and one for user settings:
-
-```yaml
-# Internal metadata - do not edit:
-schema_version: 4.0.2
-
-# Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:
-host: 0.0.0.0 # serve the app on your local network
-models_dir: D:\invokeai\models # store models on an external drive
-precision: float16 # always use fp16 precision
-```
-
-The settings in this file will override the defaults. You only need
-to change this file if the default for a particular setting doesn't
-work for you.
-
-You'll find an example file next to `invokeai.yaml` that shows the default values.
-
-Some settings, like [Model Marketplace API Keys], require the YAML
-to be formatted correctly. Here is a [basic guide to YAML files].
-
-#### Custom Config File Location
-
-You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.
-
-Note that environment variables will trump any settings in the config file.
-
-### Environment Variables
-
-All settings may be set via environment variables by prefixing `INVOKEAI_`
-to the variable name. For example, `INVOKEAI_HOST` would set the `host`
-setting.
-
-For non-primitive values, pass a JSON-encoded string:
-
-```sh
-export INVOKEAI_REMOTE_API_TOKENS='[{"url_regex":"modelmarketplace", "token": "12345"}]'
-```
-
-We suggest using `invokeai.yaml`, as it is more user-friendly.
-
-### CLI Args
-
-A subset of settings may be specified using CLI args:
-
-- `--root`: specify the root directory
-- `--config`: override the default `invokeai.yaml` file location
-
-### Low-VRAM Mode
-
-See the [Low-VRAM mode docs][low-vram] for details on enabling this feature.
-
-### All Settings
-
-Following the table are additional explanations for certain settings.
-
-<!-- prettier-ignore-start -->
-::: invokeai.app.services.config.config_default.InvokeAIAppConfig
-    options:
-        show_root_heading: false
-        members: false
-        show_docstring_description: false
-        show_category_heading: false
-<!-- prettier-ignore-end -->
-
-#### Model Marketplace API Keys
-
-Some model marketplaces require an API key to download models. You can provide a URL pattern and appropriate token in your `invokeai.yaml` file to provide that API key.
-
-The pattern can be any valid regex (you may need to surround the pattern with quotes):
-
-```yaml
-remote_api_tokens:
-  # Any URL containing `models.com` will automatically use `your_models_com_token`
-  - url_regex: models.com
-    token: your_models_com_token
-  # Any URL matching this contrived regex will use `some_other_token`
-  - url_regex: '^[a-z]{3}whatever.*\.com$'
-    token: some_other_token
-```
-
-The provided token will be added as a `Bearer` token to the network requests to download the model files. As far as we know, this works for all model marketplaces that require authorization.
-
-!!! tip "HuggingFace Models"
-
-    If you get an error when installing a HF model using a URL instead of repo id, you may need to [set up a HF API token](https://huggingface.co/settings/tokens) and add an entry for it under `remote_api_tokens`. Use `huggingface.co` for `url_regex`.
-
-#### Model Hashing
-
-Models are hashed during installation, providing a stable identifier for models across all platforms. Hashing is a one-time operation.
-
-```yaml
-hashing_algorithm: blake3_single # default value
-```
-
-You might want to change this setting, depending on your system:
-
-- `blake3_single` (default): Single-threaded - best for spinning HDDs, still OK for SSDs
-- `blake3_multi`: Parallelized, memory-mapped implementation - best for SSDs, terrible for spinning disks
-- `random`: Skip hashing entirely - fastest but of course no hash
-
-During the first startup after upgrading to v4, all of your models will be hashed. This can take a few minutes.
-
-Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than either of the BLAKE3 variants.
-
-#### Path Settings
-
-These options set the paths of various directories and files used by InvokeAI. Any user-defined paths should be absolute paths.
-
-#### Logging
-
-Several different log handler destinations are available, and multiple destinations are supported by providing a list:
-
-```yaml
-log_handlers:
-  - console
-  - syslog=localhost
-  - file=/var/log/invokeai.log
-```
-
-- `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
-
-- `syslog` is only available on Linux and Macintosh systems. It uses
-  the operating system's "syslog" facility to write log file entries
-  locally or to a remote logging machine. `syslog` offers a variety
-  of configuration options:
-
-```yaml
-syslog=/dev/log`      - log to the /dev/log device
-syslog=localhost`     - log to the network logger running on the local machine
-syslog=localhost:512` - same as above, but using a non-standard port
-syslog=fredserver,facility=LOG_USER,socktype=SOCK_DRAM`
-- Log to LAN-connected server "fredserver" using the facility LOG_USER and datagram packets.
-```
-
-- `http` can be used to log to a remote web server. The server must be
-  properly configured to receive and act on log messages. The option
-  accepts the URL to the web server, and a `method` argument
-  indicating whether the message should be submitted using the GET or
-  POST method.
-
-```yaml
-http=http://my.server/path/to/logger,method=POST
-```
-
-The `log_format` option provides several alternative formats:
-
-- `color` - default format providing time, date and a message, using text colors to distinguish different log severities
-- `plain` - same as above, but monochrome text only
-- `syslog` - the log level and error message only, allowing the syslog system to attach the time and date
-- `legacy` - a format similar to the one used by the legacy 2.3 InvokeAI releases.
-
-[basic guide to yaml files]: https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/
-[Model Marketplace API Keys]: #model-marketplace-api-keys
-[low-vram]: ./features/low-vram.md

docs/contributing/ARCHITECTURE.md

@@ -50,7 +50,7 @@ Applications are built on top of the invoke framework. They should construct `in
 
 ### Web UI
 
-The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/invokeai/frontend` and the backend code is found in `/invokeai/app/api_app.py` and `/invokeai/app/api/`. The code is further organized as such:
+The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
 
 | Component | Description |
 | --- | --- |
@@ -62,7 +62,7 @@ The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.t
 
 ### CLI
 
-The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/invokeai/frontend/cli`.
+The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
 
 ## Invoke
 
@@ -70,7 +70,7 @@ The Invoke framework provides the interface to the underlying AI systems and is
 
 ### Invoker
 
-The invoker (`/invokeai/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
+The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
 - **invocation services**, which are used by invocations to interact with core functionality.
 - **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
 
@@ -82,12 +82,12 @@ The session graph does not support looping. This is left as an application probl
 
 ### Invocations
 
-Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/invokeai/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
+Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
 
 ### Services
 
-Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/invokeai/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
+Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
 
 ## AI Core
 
-The AI Core is represented by the rest of the code base (i.e. the code outside of `/invokeai/app/`).
+The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).

docs/contributing/CANVAS_PROJECTS/CANVAS_PROJECTS.md

@@ -1,205 +0,0 @@
-# Canvas Projects — Technical Documentation
-
-## Overview
-
-Canvas Projects provide a save/load mechanism for the entire canvas state. The feature serializes all canvas entities, generation parameters, reference images, and their associated image files into a ZIP-based `.invk` file. On load, it restores the full state, handling image deduplication and re-uploading as needed.
-
-## File Format
-
-The `.invk` file is a standard ZIP archive with the following structure:
-
-```
-project.invk
-├── manifest.json
-├── canvas_state.json
-├── params.json
-├── ref_images.json
-├── loras.json
-└── images/
-    ├── {image_name_1}.png
-    ├── {image_name_2}.png
-    └── ...
-```
-
-### manifest.json
-
-Schema version and metadata. Validated on load with Zod.
-
-```json
-{
-  "version": 1,
-  "appVersion": "5.12.0",
-  "createdAt": "2026-02-26T12:00:00.000Z",
-  "name": "My Canvas Project"
-}
-```
-
-| Field | Type | Description |
-|---|---|---|
-| `version` | `number` | Schema version, currently `1`. Used for migration logic on load. |
-| `appVersion` | `string` | InvokeAI version that created the file. Informational only. |
-| `createdAt` | `string` | ISO 8601 timestamp. |
-| `name` | `string` | User-provided project name. Also used as the download filename. |
-
-### canvas_state.json
-
-The serialized canvas entity tree. Type: `CanvasProjectState`.
-
-```typescript
-type CanvasProjectState = {
-  rasterLayers: CanvasRasterLayerState[];
-  controlLayers: CanvasControlLayerState[];
-  inpaintMasks: CanvasInpaintMaskState[];
-  regionalGuidance: CanvasRegionalGuidanceState[];
-  bbox: CanvasState['bbox'];
-  selectedEntityIdentifier: CanvasState['selectedEntityIdentifier'];
-  bookmarkedEntityIdentifier: CanvasState['bookmarkedEntityIdentifier'];
-};
-```
-
-Each entity contains its full state including all canvas objects (brush lines, eraser lines, rect shapes, images). Image objects reference files by `image_name` which correspond to files in the `images/` folder.
-
-### params.json
-
-The complete generation parameters state (`ParamsState`). Optional on load (older files may not have it). This includes all fields from the params Redux slice:
-
-- Prompts (positive, negative, prompt history)
-- Core generation settings (seed, steps, CFG scale, guidance, scheduler, iterations)
-- Model selections (main model, VAE, FLUX VAE, T5 encoder, CLIP embed models, refiner, Z-Image models, Klein models)
-- Dimensions (width, height, aspect ratio)
-- Img2img strength
-- Infill settings (method, tile size, patchmatch downscale, color)
-- Canvas coherence settings (mode, edge size, min denoise)
-- Refiner parameters (steps, CFG scale, scheduler, aesthetic scores, start)
-- FLUX-specific settings (scheduler, DyPE preset/scale/exponent)
-- Z-Image-specific settings (scheduler, seed variance)
-- Upscale settings (scheduler, CFG scale)
-- Seamless tiling, mask blur, CLIP skip, VAE precision, CPU noise, color compensation
-
-### ref_images.json
-
-Global reference image entities (`RefImageState[]`). These are IP-Adapter / FLUX Redux configs with `CroppableImageWithDims` containing both original and cropped image references. Optional on load.
-
-### loras.json
-
-Array of LoRA configurations (`LoRA[]`). Each entry contains:
-
-```typescript
-type LoRA = {
-  id: string;
-  isEnabled: boolean;
-  model: ModelIdentifierField;
-  weight: number;
-};
-```
-
-Optional on load. Like models, LoRA identifiers are stored as-is — if a LoRA is not installed when loading, the entry is restored but may not be usable.
-
-### images/
-
-All image files referenced anywhere in the state. Keyed by their original `image_name`. On save, each image is fetched from the backend via `GET /api/v1/images/i/{name}/full` and stored as-is.
-
-## Key Source Files
-
-| File | Purpose |
-|---|---|
-| `features/controlLayers/util/canvasProjectFile.ts` | Types, constants, image name collection, remapping, existence checking |
-| `features/controlLayers/hooks/useCanvasProjectSave.ts` | Save hook — collects Redux state, fetches images, builds ZIP |
-| `features/controlLayers/hooks/useCanvasProjectLoad.ts` | Load hook — parses ZIP, deduplicates images, dispatches state |
-| `features/controlLayers/components/SaveCanvasProjectDialog.tsx` | Save name dialog + `useSaveCanvasProjectWithDialog` hook |
-| `features/controlLayers/components/LoadCanvasProjectConfirmationAlertDialog.tsx` | Load confirmation dialog + `useLoadCanvasProjectWithDialog` hook |
-| `features/controlLayers/components/Toolbar/CanvasToolbarProjectMenuButton.tsx` | Toolbar dropdown UI |
-| `features/controlLayers/store/canvasSlice.ts` | `canvasProjectRecalled` Redux action |
-
-## Save Flow
-
-1. User clicks "Save Canvas Project" → `SaveCanvasProjectDialog` opens asking for a project name
-2. On confirm, `saveCanvasProject(name)` is called
-3. Read Redux state via selectors: `selectCanvasSlice()`, `selectParamsSlice()`, `selectRefImagesSlice()`, `selectLoRAsSlice()`
-4. Build `CanvasProjectState` from the canvas slice; use `paramsState` directly for params
-5. Walk all entities to collect every `image_name` reference via `collectImageNames()`:
-   - `CanvasImageState.image.image_name` in layer/mask objects
-   - `CroppableImageWithDims.original.image.image_name` in global ref images
-   - `CroppableImageWithDims.crop.image.image_name` in cropped ref images
-   - `ImageWithDims.image_name` in regional guidance ref images
-6. Fetch each image from the backend API
-7. Build ZIP with JSZip: add `manifest.json` (including `name`), `canvas_state.json`, `params.json`, `ref_images.json`, and all images into `images/`
-8. Sanitize the name for filesystem use and generate blob, trigger download as `{name}.invk`
-
-## Load Flow
-
-1. User selects `.invk` file → confirmation dialog opens
-2. On confirm, parse ZIP with JSZip
-3. Validate manifest version via Zod schema
-4. Read `canvas_state.json`, `params.json` (optional), `ref_images.json` (optional)
-5. Collect all `image_name` references from the loaded state
-6. **Deduplicate images**: for each referenced image, check if it exists on the server via `getImageDTOSafe(image_name)`
-   - Already exists → skip (no upload)
-   - Missing → upload from ZIP via `uploadImage()`, record `oldName → newName` mapping
-7. Remap all `image_name` values in the loaded state using the mapping (only for re-uploaded images whose names changed)
-8. Dispatch Redux actions:
-   - `canvasProjectRecalled()` — restores all canvas entities, bbox, selected/bookmarked entity
-   - `refImagesRecalled()` — restores global reference images
-   - `paramsRecalled()` — replaces the entire params state in one action
-   - `loraAllDeleted()` + `loraRecalled()` — restores LoRAs
-9. Show success/error toast
-
-## Image Name Collection & Remapping
-
-The `canvasProjectFile.ts` utility provides two parallel sets of functions:
-
-**Collection** (`collectImageNames`): Walks the entire state tree and returns a `Set<string>` of all referenced `image_name` values. This is used by both save (to know which images to fetch) and load (to know which images to check/upload).
-
-**Remapping** (`remapCanvasState`, `remapRefImages`): Deep-clones state objects and replaces `image_name` values using a `Map<string, string>` mapping. Only images that were re-uploaded with a different name are remapped. Images that already existed on the server are left unchanged.
-
-Both walk the same paths through the state tree:
-- Layer/mask objects → `CanvasImageState.image.image_name`
-- Regional guidance ref images → `ImageWithDims.image_name`
-- Global ref images → `CroppableImageWithDims.original.image.image_name` and `.crop.image.image_name`
-
-## Extending the Format
-
-### Adding new optional data (non-breaking)
-
-Add a new JSON file to the ZIP. No version bump needed.
-
-1. **Save**: Add `zip.file('new_data.json', JSON.stringify(data))` in `useCanvasProjectSave.ts`
-2. **Load**: Read with `zip.file('new_data.json')` in `useCanvasProjectLoad.ts` — check for `null` so older project files without it still load
-3. **Dispatch**: Add the appropriate Redux action to restore the data
-
-### Adding new entity types with images
-
-1. Extend `CanvasProjectState` type in `canvasProjectFile.ts`
-2. Add collection logic in `collectImageNames()` to walk the new entity's objects
-3. Add remapping logic in `remapCanvasState()` to update image names
-4. Include the new entity array in both save and load hooks
-5. Handle it in the `canvasProjectRecalled` reducer in `canvasSlice.ts`
-
-### Breaking schema changes
-
-1. Bump `CANVAS_PROJECT_VERSION` in `canvasProjectFile.ts`
-2. Update the Zod manifest schema: `version: z.union([z.literal(1), z.literal(2)])`
-3. Add migration logic in the load hook: check version, transform v1 → v2 before dispatching
-
-## UI Architecture
-
-### Save dialog
-
-The save flow uses a **nanostore atom** (`$isOpen`) to control the `SaveCanvasProjectDialog`:
-
-1. `useSaveCanvasProjectWithDialog()` — returns a callback that sets `$isOpen` to `true`
-2. `SaveCanvasProjectDialog` (singleton in `GlobalModalIsolator`) — renders an `AlertDialog` with a name input
-3. On save → calls `saveCanvasProject(name)` and closes the dialog
-4. On cancel → closes the dialog
-
-### Load dialog
-
-The load flow uses a **nanostore atom** (`$pendingFile`) to decouple the file dialog from the confirmation dialog:
-
-1. `useLoadCanvasProjectWithDialog()` — opens a programmatic file input (`document.createElement('input')`)
-2. On file selection → sets `$pendingFile` atom
-3. `LoadCanvasProjectConfirmationAlertDialog` (singleton in `GlobalModalIsolator`) — subscribes to `$pendingFile` via `useStore()`
-4. On accept → calls `loadCanvasProject(file)` and clears the atom
-5. On cancel → clears the atom
-
-The programmatic file input approach was chosen because the context menu component uses `isLazy: true`, which unmounts the DOM tree when the menu closes — a hidden `<input>` element inside the menu would be destroyed before the file dialog returns.

docs/contributing/CONTRIBUTING.md

@@ -0,0 +1,60 @@
+# Contributing
+
+Invoke AI originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
+
+
+# Methods of Contributing to Invoke AI
+Anyone who wishes to contribute to InvokeAI, whether features, bug fixes, code cleanup, testing, code reviews, documentation or translation is very much encouraged to do so.
+
+## Development
+If you’d like to help with development, please see our [development guide](contribution_guides/development.md). 
+
+**New Contributors:** If you’re unfamiliar with contributing to open source projects, take a look at our [new contributor guide](contribution_guides/newContributorChecklist.md).
+
+## Nodes
+If you’d like to add a Node, please see our [nodes contribution guide](../nodes/contributingNodes.md).
+
+## Support and Triaging
+Helping support other users in [Discord](https://discord.gg/ZmtBAhwWhy) and on Github are valuable forms of contribution that we greatly appreciate. 
+
+We receive many issues and requests for help from users. We're limited in bandwidth relative to our the user base, so providing answers to questions or helping identify causes of issues is very helpful. By doing this, you enable us to spend time on the highest priority work. 
+
+## Documentation
+If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documentation.md).
+
+## Translation
+If you'd like to help with translation, please see our [translation guide](contribution_guides/translation.md).
+
+## Tutorials 
+Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
+
+We hope you enjoy using our software as much as we enjoy creating it, and we hope that some of those of you who are reading this will elect to become part of our contributor community.
+
+
+# Contributors
+
+This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
+
+# Code of Conduct
+
+The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
+
+By making a contribution to this project, you certify that:
+
+1. The contribution was created in whole or in part by you and you have the right to submit it under the open-source license indicated in this project’s GitHub repository; or
+2. The contribution is based upon previous work that, to the best of your knowledge, is covered under an appropriate open-source license and you have the right under that license to submit that work with modifications, whether created in whole or in part by you, under the same open-source license (unless you are permitted to submit under a different license); or
+3. The contribution was provided directly to you by some other person who certified (1) or (2) and you have not modified it; or
+4. You understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information you submit with it, including your sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open-source license(s) involved.
+
+This disclaimer is not a license and does not grant any rights or permissions. You must obtain necessary permissions and licenses, including from third parties, before contributing to this project.
+
+This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.
+# Support
+
+For support, please use this repository's [GitHub Issues](https://github.com/invoke-ai/InvokeAI/issues), or join the [Discord](https://discord.gg/ZmtBAhwWhy).
+
+Original portions of the software are Copyright (c) 2023 by respective contributors.
+
+---
+
+Remember, your contributions help make this project great. We're excited to see what you'll bring to our community!

docs/contributing/DOWNLOAD_QUEUE.md

@@ -128,8 +128,7 @@ The queue operates on a series of download job objects. These objects
 specify the source and destination of the download, and keep track of
 the progress of the download.
 
-Two job types are defined. `DownloadJob` and
-`MultiFileDownloadJob`. The former is a pydantic object with the
+The only job type currently implemented is `DownloadJob`, a pydantic object with the
 following fields:
 
 | **Field**      | **Type**        |  **Default**  | **Description** |
@@ -139,7 +138,7 @@ following fields:
 | `dest`           | Path            |               | Where to download to              |
 | `access_token`   | str             |               | [optional] string containing authentication token for access |
 | `on_start`       | Callable        |               | [optional] callback when the download starts |
-| `on_progress` | Callable | | [optional] callback called at intervals during download progress |
+| `on_progress`    | Callable        |               | [optional] callback called at intervals during download progress |
 | `on_complete`    | Callable        |               | [optional] callback called after successful download completion |
 | `on_error`       | Callable        |               | [optional] callback called after an error occurs  |
 | `id`             | int             | auto assigned | Job ID, an integer >= 0           |
@@ -191,33 +190,6 @@ A cancelled job will have status `DownloadJobStatus.ERROR` and an
 `error_type` field of "DownloadJobCancelledException". In addition,
 the job's `cancelled` property will be set to True.
 
-The `MultiFileDownloadJob` is used for diffusers model downloads,
-which contain multiple files and directories under a common root:
-
-| **Field**      | **Type**        |  **Default**  | **Description** |
-|----------------|-----------------|---------------|-----------------|
-| _Fields passed in at job creation time_                               |
-| `download_parts` | Set[DownloadJob]|               | Component download jobs |
-| `dest`           | Path            |               | Where to download to              |
-| `on_start`       | Callable        |               | [optional] callback when the download starts |
-| `on_progress` | Callable | | [optional] callback called at intervals during download progress |
-| `on_complete`    | Callable        |               | [optional] callback called after successful download completion |
-| `on_error`       | Callable        |               | [optional] callback called after an error occurs  |
-| `id`             | int             | auto assigned | Job ID, an integer >= 0           |
-| _Fields updated over the course of the download task_
-| `status`         | DownloadJobStatus|              | Status code                                |
-| `download_path`  | Path |              | Path to the root of the downloaded files |
-| `bytes`          | int              | 0            | Bytes downloaded so far   |
-| `total_bytes`    | int              | 0            | Total size of the file at the remote site  |
-| `error_type`     | str              |              | String version of the exception that caused an error during download |
-| `error`          | str              |              | String version of the traceback associated with an error |
-| `cancelled`      | bool             | False        | Set to true if the job was cancelled by the caller|
-
-Note that the MultiFileDownloadJob does not support the `priority`,
-`job_started`, `job_ended` or `content_type` attributes. You can get
-these from the individual download jobs in `download_parts`.
-
-
 ### Callbacks
 
 Download jobs can be associated with a series of callbacks, each with
@@ -279,40 +251,11 @@ jobs using `list_jobs()`, fetch a single job by its with
 running jobs with `cancel_all_jobs()`, and wait for all jobs to finish
 with `join()`.
 
-#### job = queue.download(source, dest, priority, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)
+#### job = queue.download(source, dest, priority, access_token)
 
 Create a new download job and put it on the queue, returning the
 DownloadJob object.
 
-#### multifile_job = queue.multifile_download(parts, dest, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)
-
-This is similar to download(), but instead of taking a single source,
-it accepts a `parts` argument consisting of a list of
-`RemoteModelFile` objects. Each part corresponds to a URL/Path pair,
-where the URL is the location of the remote file, and the Path is the
-destination.
-
-`RemoteModelFile` can be imported from `invokeai.backend.model_manager.metadata`, and
-consists of a url/path pair. Note that the path *must* be relative.
-
-The method returns a `MultiFileDownloadJob`.
-
-
-```
-from invokeai.backend.model_manager.metadata import RemoteModelFile
-remote_file_1 = RemoteModelFile(url='http://www.foo.bar/my/pytorch_model.safetensors'',
-                                path='my_model/textencoder/pytorch_model.safetensors'
-			 			  )
-remote_file_2 = RemoteModelFile(url='http://www.bar.baz/vae.ckpt',
-                                path='my_model/vae/diffusers_model.safetensors'
-			 			  )
-job = queue.multifile_download(parts=[remote_file_1, remote_file_2],
-                               dest='/tmp/downloads',
-                               on_progress=TqdmProgress().update)
-queue.wait_for_job(job)
-print(f"The files were downloaded to {job.download_path}")
-```
-
 #### jobs = queue.list_jobs()
 
 Return a list of all active and inactive `DownloadJob`s.

docs/contributing/EXTERNAL_PROVIDERS.md

@@ -1,129 +0,0 @@
-# External Provider Integration
-
-This guide covers:
-
-1. Adding a new **external model** (most common; existing provider).
-2. Adding a brand-new **external provider** (adapter + config + UI wiring).
-
-## 1) Add a New External Model (Existing Provider)
-
-For provider-backed models (for example, OpenAI or Gemini), the source of truth is
-`invokeai/backend/model_manager/starter_models.py`.
-
-### Required model fields
-
-Define a `StarterModel` with:
-
-- `base=BaseModelType.External`
-- `type=ModelType.ExternalImageGenerator`
-- `format=ModelFormat.ExternalApi`
-- `source="external://<provider_id>/<provider_model_id>"`
-- `name`, `description`
-- `capabilities=ExternalModelCapabilities(...)`
-- optional `default_settings=ExternalApiModelDefaultSettings(...)`
-
-Example:
-
-```python
-new_external_model = StarterModel(
-    name="Provider Model Name",
-    base=BaseModelType.External,
-    source="external://openai/my-model-id",
-    description=(
-        "Provider model (external API). "
-        "Requires a configured OpenAI API key and may incur provider usage costs."
-    ),
-    type=ModelType.ExternalImageGenerator,
-    format=ModelFormat.ExternalApi,
-    capabilities=ExternalModelCapabilities(
-        modes=["txt2img", "img2img", "inpaint"],
-        supports_negative_prompt=False,
-        supports_seed=False,
-        supports_guidance=False,
-        supports_steps=False,
-        supports_reference_images=True,
-        max_images_per_request=4,
-    ),
-    default_settings=ExternalApiModelDefaultSettings(
-        width=1024,
-        height=1024,
-        num_images=1,
-    ),
-)
-```
-
-Then append it to `STARTER_MODELS`.
-
-### Required description text
-
-External starter model descriptions must clearly state:
-
-- an API key is required
-- usage may incur provider-side costs
-
-### Capabilities must be accurate
-
-These flags directly control UI visibility and request payload fields:
-
-- `supports_negative_prompt`
-- `supports_seed`
-- `supports_guidance`
-- `supports_steps`
-- `supports_reference_images`
-
-`supports_steps` is especially important: if `False`, steps are hidden for that model and `steps` is sent as `null`.
-
-### Source string stability
-
-Starter overrides are matched by `source` (`external://provider/model-id`). Keep this stable:
-
-- runtime capability/default overrides depend on it
-- installation detection in starter-model APIs depends on it
-
-`STARTER_MODELS` enforces unique `source` values with an assertion.
-
-### Install behavior notes
-
-- External starter models are managed in **External Providers** setup (not the regular Starter Models tab).
-- External starter models auto-install when a provider is configured.
-- Removing a provider API key removes installed external models for that provider.
-
-## 2) Credentials and Config
-
-External provider API keys are stored separately from `invokeai.yaml`:
-
-- default file: `~/invokeai/api_keys.yaml`
-- resolved path: `<INVOKEAI_ROOT>/api_keys.yaml`
-
-Non-secret provider settings (for example base URL overrides) stay in `invokeai.yaml`.
-
-Environment variables are still supported, e.g.:
-
-- `INVOKEAI_EXTERNAL_GEMINI_API_KEY`
-- `INVOKEAI_EXTERNAL_OPENAI_API_KEY`
-
-## 3) Add a New Provider (Only If Needed)
-
-If your model uses a provider that is not already integrated:
-
-1. Add config fields in `invokeai/app/services/config/config_default.py`
-   `external_<provider>_api_key` and optional `external_<provider>_base_url`.
-2. Add provider field mapping in `invokeai/app/api/routers/app_info.py`
-   (`EXTERNAL_PROVIDER_FIELDS`).
-3. Implement provider adapter in `invokeai/app/services/external_generation/providers/`
-   by subclassing `ExternalProvider`.
-4. Register the provider in `invokeai/app/api/dependencies.py` when building
-   `ExternalGenerationService`.
-5. Add starter model entries using `source="external://<provider>/<model-id>"`.
-6. Optional UI ordering tweak:
-   `invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/ExternalProviders/ExternalProvidersForm.tsx`
-   (`PROVIDER_SORT_ORDER`).
-
-## 4) Optional Manual Installation
-
-You can also install external models directly via:
-
-`POST /api/v2/models/install?source=external://<provider_id>/<provider_model_id>`
-
-If omitted, `path`, `source`, and `hash` are auto-populated for external model configs.
-Set capabilities conservatively; the external generation service enforces capability checks at runtime.

docs/contributing/HOTKEYS.md

@@ -1,295 +0,0 @@
-# Hotkeys System
-
-This document describes the technical implementation of the customizable hotkeys system in InvokeAI.
-
-> **Note:** For user-facing documentation on how to use customizable hotkeys, see [Hotkeys Feature Documentation](../features/hotkeys.md).
-
-## Overview
-
-The hotkeys system allows users to customize keyboard shortcuts throughout the application. All hotkeys are:
-- Centrally defined and managed
-- Customizable by users
-- Persisted across sessions
-- Type-safe and validated
-
-## Architecture
-
-The customizable hotkeys feature is built on top of the existing hotkey system with the following components:
-
-### 1. Hotkeys State Slice (`hotkeysSlice.ts`)
-
-Location: `invokeai/frontend/web/src/features/system/store/hotkeysSlice.ts`
-
-**Responsibilities:**
-- Stores custom hotkey mappings in Redux state
-- Persisted to IndexedDB using `redux-remember`
-- Provides actions to change, reset individual, or reset all hotkeys
-
-**State Shape:**
-```typescript
-{
-  _version: 1,
-  customHotkeys: {
-    'app.invoke': ['mod+enter'],
-    'canvas.undo': ['mod+z'],
-    // ...
-  }
-}
-```
-
-**Actions:**
-- `hotkeyChanged(id, hotkeys)` - Update a single hotkey
-- `hotkeyReset(id)` - Reset a single hotkey to default
-- `allHotkeysReset()` - Reset all hotkeys to defaults
-
-### 2. useHotkeyData Hook (`useHotkeyData.ts`)
-
-Location: `invokeai/frontend/web/src/features/system/components/HotkeysModal/useHotkeyData.ts`
-
-**Responsibilities:**
-- Defines all default hotkeys
-- Merges default hotkeys with custom hotkeys from the store
-- Returns the effective hotkeys that should be used throughout the app
-- Provides platform-specific key translations (Ctrl/Cmd, Alt/Option)
-
-**Key Functions:**
-- `useHotkeyData()` - Returns all hotkeys organized by category
-- `useRegisteredHotkeys()` - Hook to register a hotkey in a component
-
-### 3. HotkeyEditor Component (`HotkeyEditor.tsx`)
-
-Location: `invokeai/frontend/web/src/features/system/components/HotkeysModal/HotkeyEditor.tsx`
-
-**Features:**
-- Inline editor with input field
-- Modifier buttons (Mod, Ctrl, Shift, Alt) for quick insertion
-- Live preview of hotkey combinations
-- Validation with visual feedback
-- Help tooltip with syntax examples
-- Save/cancel/reset buttons
-
-**Smart Features:**
-- Automatic `+` insertion between modifiers
-- Cursor position preservation
-- Validation prevents invalid combinations (e.g., modifier-only keys)
-
-### 4. HotkeysModal Component (`HotkeysModal.tsx`)
-
-Location: `invokeai/frontend/web/src/features/system/components/HotkeysModal/HotkeysModal.tsx`
-
-**Features:**
-- View Mode / Edit Mode toggle
-- Search functionality
-- Category-based organization
-- Shows HotkeyEditor components when in edit mode
-- "Reset All to Default" button in edit mode
-
-## Data Flow
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│ 1. User opens Hotkeys Modal                                 │
-│ 2. User clicks "Edit Mode" button                           │
-│ 3. User clicks edit icon next to a hotkey                   │
-│ 4. User enters new hotkey(s) using editor                   │
-│ 5. User clicks save or presses Enter                        │
-│ 6. Custom hotkey stored via hotkeyChanged() action          │
-│ 7. Redux state persisted to IndexedDB (redux-remember)      │
-│ 8. useHotkeyData() hook picks up the change                 │
-│ 9. All components using useRegisteredHotkeys() get update   │
-└─────────────────────────────────────────────────────────────┘
-```
-
-## Hotkey Format
-
-Hotkeys use the format from `react-hotkeys-hook` library:
-
-- **Modifiers:** `mod`, `ctrl`, `shift`, `alt`, `meta`
-- **Keys:** Letters, numbers, function keys, special keys
-- **Separator:** `+` between keys in a combination
-- **Multiple hotkeys:** Comma-separated (e.g., `mod+a, ctrl+b`)
-
-**Examples:**
-- `mod+enter` - Mod key + Enter
-- `shift+x` - Shift + X
-- `ctrl+shift+a` - Control + Shift + A
-- `f1, f2` - F1 or F2 (alternatives)
-
-## Developer Guide
-
-### Using Hotkeys in Components
-
-To use a hotkey in a component:
-
-```tsx
-import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
-
-const MyComponent = () => {
-  const handleAction = useCallback(() => {
-    // Your action here
-  }, []);
-
-  // This automatically uses custom hotkeys if configured
-  useRegisteredHotkeys({
-    id: 'myAction',
-    category: 'app', // or 'canvas', 'viewer', 'gallery', 'workflows'
-    callback: handleAction,
-    options: { enabled: true, preventDefault: true },
-    dependencies: [handleAction]
-  });
-
-  // ...
-};
-```
-
-**Options:**
-- `enabled` - Whether the hotkey is active
-- `preventDefault` - Prevent default browser behavior
-- `enableOnFormTags` - Allow hotkey in form elements (default: false)
-
-### Adding New Hotkeys
-
-To add a new hotkey to the system:
-
-#### 1. Add Translation Strings
-
-In `invokeai/frontend/web/public/locales/en.json`:
-
-```json
-{
-  "hotkeys": {
-    "app": {
-      "myAction": {
-        "title": "My Action",
-        "desc": "Description of what this hotkey does"
-      }
-    }
-  }
-}
-```
-
-#### 2. Register the Hotkey
-
-In `invokeai/frontend/web/src/features/system/components/HotkeysModal/useHotkeyData.ts`:
-
-```typescript
-// Inside the appropriate category builder function
-addHotkey('app', 'myAction', ['mod+k']); // Default binding
-```
-
-#### 3. Use the Hotkey
-
-In your component:
-
-```typescript
-useRegisteredHotkeys({
-  id: 'myAction',
-  category: 'app',
-  callback: handleMyAction,
-  options: { enabled: true },
-  dependencies: [handleMyAction]
-});
-```
-
-### Hotkey Categories
-
-Current categories:
-- **app** - Global application hotkeys
-- **canvas** - Canvas/drawing operations
-- **viewer** - Image viewer operations
-- **gallery** - Gallery/image grid operations
-- **workflows** - Node workflow editor
-
-To add a new category, update `useHotkeyData.ts` and add translations.
-
-## Testing
-
-Tests are located in `invokeai/frontend/web/src/features/system/store/hotkeysSlice.test.ts`.
-
-**Test Coverage:**
-- Adding custom hotkeys
-- Updating existing custom hotkeys
-- Resetting individual hotkeys
-- Resetting all hotkeys
-- State persistence and migration
-
-Run tests with:
-
-```bash
-cd invokeai/frontend/web
-pnpm test:no-watch
-```
-
-## Persistence
-
-Custom hotkeys are persisted using the same mechanism as other app settings:
-
-- Stored in Redux state under the `hotkeys` slice
-- Persisted to IndexedDB via `redux-remember`
-- Automatically loaded when the app starts
-- Survives page refreshes and browser restarts
-- Includes migration support for state schema changes
-
-**State Location:**
-- IndexedDB database: `invoke`
-- Store key: `hotkeys`
-
-## Dependencies
-
-- **react-hotkeys-hook** (v4.5.0) - Core hotkey handling
-- **@reduxjs/toolkit** - State management
-- **redux-remember** - Persistence
-- **zod** - State validation
-
-## Best Practices
-
-1. **Use `mod` instead of `ctrl`** - Automatically maps to Cmd on Mac, Ctrl elsewhere
-2. **Provide descriptive translations** - Help users understand what each hotkey does
-3. **Avoid conflicts** - Check existing hotkeys before adding new ones
-4. **Use preventDefault** - Prevent browser default behavior when appropriate
-5. **Check enabled state** - Only activate hotkeys when the action is available
-6. **Use dependencies correctly** - Ensure callbacks are stable with useCallback
-
-## Common Patterns
-
-### Conditional Hotkeys
-
-```typescript
-useRegisteredHotkeys({
-  id: 'save',
-  category: 'app',
-  callback: handleSave,
-  options: {
-    enabled: hasUnsavedChanges && !isLoading, // Only when valid
-    preventDefault: true
-  },
-  dependencies: [hasUnsavedChanges, isLoading, handleSave]
-});
-```
-
-### Multiple Hotkeys for Same Action
-
-```typescript
-// In useHotkeyData.ts
-addHotkey('canvas', 'redo', ['mod+shift+z', 'mod+y']); // Two alternatives
-```
-
-### Focus-Scoped Hotkeys
-
-```typescript
-import { useFocusRegion } from 'common/hooks/focus';
-
-const MyComponent = () => {
-  const focusRegionRef = useFocusRegion('myRegion');
-
-  // Hotkey only works when this region has focus
-  useRegisteredHotkeys({
-    id: 'myAction',
-    category: 'app',
-    callback: handleAction,
-    options: { enabled: true }
-  });
-
-  return <div ref={focusRegionRef}>...</div>;
-};
-```

docs/contributing/INVOCATIONS.md

@@ -39,7 +39,7 @@ nodes imported in the `__init__.py` file are loaded. See the README in the nodes
 folder for more examples:
 
 ```py
-from .cool_node import ResizeInvocation
+from .cool_node import CoolInvocation
 ```
 
 ## Creating A New Invocation
@@ -69,10 +69,7 @@ The first set of things we need to do when creating a new Invocation are -
 So let us do that.
 
 ```python
-from invokeai.invocation_api import (
-    BaseInvocation,
-    invocation,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -106,12 +103,8 @@ create your own custom field types later in this guide. For now, let's go ahead
 and use it.
 
 ```python
-from invokeai.invocation_api import (
-    BaseInvocation,
-    ImageField,
-    InputField,
-    invocation,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -135,12 +128,8 @@ image: ImageField = InputField(description="The input image")
 Great. Now let us create our other inputs for `width` and `height`
 
 ```python
-from invokeai.invocation_api import (
-    BaseInvocation,
-    ImageField,
-    InputField,
-    invocation,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -155,7 +144,7 @@ As you might have noticed, we added two new arguments to the `InputField`
 definition for `width` and `height`, called `gt` and `le`. They stand for
 _greater than or equal to_ and _less than or equal to_.
 
-These impose constraints on those fields, and will raise an exception if the
+These impose contraints on those fields, and will raise an exception if the
 values do not meet the constraints. Field constraints are provided by
 **pydantic**, so anything you see in the **pydantic docs** will work.
 
@@ -174,13 +163,8 @@ that are provided by it by InvokeAI.
 Let us create this function first.
 
 ```python
-from invokeai.invocation_api import (
-    BaseInvocation,
-    ImageField,
-    InputField,
-    InvocationContext,
-    invocation,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -207,14 +191,8 @@ all the necessary info related to image outputs. So let us use that.
 We will cover how to create your own output types later in this guide.
 
 ```python
-from invokeai.invocation_api import (
-    BaseInvocation,
-    ImageField,
-    InputField,
-    InvocationContext,
-    invocation,
-)
-
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.image import ImageOutput
 
 @invocation('resize')
@@ -239,15 +217,9 @@ Perfect. Now that we have our Invocation setup, let us do what we want to do.
 So let's do that.
 
 ```python
-from invokeai.invocation_api import (
-    BaseInvocation,
-    ImageField,
-    InputField,
-    InvocationContext,
-    invocation,
-)
-
-from invokeai.app.invocations.image import ImageOutput
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.app.invocations.image import ImageOutput, ResourceOrigin, ImageCategory
 
 @invocation("resize")
 class ResizeInvocation(BaseInvocation):
@@ -315,8 +287,8 @@ new Invocation ready to be used.
 
 Once you've created a Node, the next step is to share it with the community! The
 best way to do this is to submit a Pull Request to add the Node to the
-[Community Nodes](../nodes/communityNodes.md) list. If you're not sure how to do that,
-take a look a at our [contributing nodes overview](../nodes/contributingNodes.md).
+[Community Nodes](nodes/communityNodes) list. If you're not sure how to do that,
+take a look a at our [contributing nodes overview](contributingNodes).
 
 ## Advanced
 

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -1,10 +1,21 @@
 # Local Development
 
-If you want to contribute, you will need to set up a [local development environment](./dev-environment.md).
+If you are looking to contribute you will need to have a local development
+environment. See the
+[Developer Install](../installation/020_INSTALL_MANUAL.md#developer-install) for
+full details.
+
+Broadly this involves cloning the repository, installing the pre-reqs, and
+InvokeAI (in editable form). Assuming this is working, choose your area of
+focus.
 
 ## Documentation
 
-We use [mkdocs](https://www.mkdocs.org) for our documentation with the [material theme](https://squidfunk.github.io/mkdocs-material/). Documentation is written in markdown files under the `./docs` folder and then built into a static website for hosting with GitHub Pages at [invoke-ai.github.io/InvokeAI](https://invoke-ai.github.io/InvokeAI).
+We use [mkdocs](https://www.mkdocs.org) for our documentation with the
+[material theme](https://squidfunk.github.io/mkdocs-material/). Documentation is
+written in markdown files under the `./docs` folder and then built into a static
+website for hosting with GitHub Pages at
+[invoke-ai.github.io/InvokeAI](https://invoke-ai.github.io/InvokeAI).
 
 To contribute to the documentation you'll need to install the dependencies. Note
 the use of `"`.
@@ -39,7 +50,6 @@ and will be required for testing the changes you make to the code.
 ### Tests
 
 See the [tests documentation](./TESTS.md) for information about running and writing tests.
-
 ### Reloading Changes
 
 Experimenting with changes to the Python source code is a drag if you have to re-start the server —
@@ -53,6 +63,7 @@ running server on the fly.
 This will allow you to avoid restarting the server (and reloading models) in most cases, but there are some caveats; see
 the [jurigged documentation](https://github.com/breuleux/jurigged#caveats) for details.
 
+
 ## Front End
 
 <!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
@@ -239,7 +250,7 @@ Consult the
 get it set up.
 
 Suggest using VSCode's included settings sync so that your remote dev host has
-all the same app settings and extensions automatically.
+all the same app settings and extensions automagically.
 
 ##### One remote dev gotcha
 

docs/contributing/MODEL_MANAGER.md

@@ -9,20 +9,20 @@ model. These are the:
   configuration information. Among other things, the record service
   tracks the type of the model, its provenance, and where it can be
   found on disk.
-
+  
 * _ModelInstallServiceBase_ A service for installing models to
   disk. It uses `DownloadQueueServiceBase` to download models and
   their metadata, and `ModelRecordServiceBase` to store that
   information. It is also responsible for managing the InvokeAI
   `models` directory and its contents.
-
+  
 * _DownloadQueueServiceBase_
   A multithreaded downloader responsible
   for downloading models from a remote source to disk. The download
   queue has special methods for downloading repo_id folders from
   Hugging Face, as well as discriminating among model versions in
   Civitai, but can be used for arbitrary content.
-
+  
   * _ModelLoadServiceBase_
   Responsible for loading a model from disk
   into RAM and VRAM and getting it ready for inference.
@@ -207,9 +207,9 @@ for use in the InvokeAI web server. Its signature is:
 
 ```
 def open(
-       cls,
-    config: InvokeAIAppConfig,
-    conn: Optional[sqlite3.Connection] = None,
+       cls, 
+    config: InvokeAIAppConfig, 
+    conn: Optional[sqlite3.Connection] = None, 
     lock: Optional[threading.Lock] = None
     ) -> Union[ModelRecordServiceSQL, ModelRecordServiceFile]:
 ```
@@ -265,7 +265,7 @@ If the key is unrecognized, this call raises an
 
 #### exists(key) -> AnyModelConfig
 
-Returns True if a model with the given key exists in the database.
+Returns True if a model with the given key exists in the databsae.
 
 #### search_by_path(path) -> AnyModelConfig
 
@@ -363,7 +363,7 @@ functionality:
 
 * Registering a model config record for a model already located on the
   local filesystem, without moving it or changing its path.
-
+  
 * Installing a model alreadiy located on the local filesystem, by
   moving it into the InvokeAI root directory under the
   `models` folder (or wherever config parameter `models_dir`
@@ -371,21 +371,21 @@ functionality:
 
 * Probing of models to determine their type, base type and other key
   information.
-
+  
 * Interface with the InvokeAI event bus to provide status updates on
   the download, installation and registration process.
-
+  
 * Downloading a model from an arbitrary URL and installing it in
   `models_dir`.
 
 * Special handling for HuggingFace repo_ids to recursively download
   the contents of the repository, paying attention to alternative
   variants such as fp16.
-
+  
 * Saving tags and other metadata about the model into the invokeai database
   when fetching from a repo that provides that type of information,
   (currently only HuggingFace).
-
+  
 ### Initializing the installer
 
 A default installer is created at InvokeAI api startup time and stored
@@ -397,25 +397,26 @@ In the event you wish to create a new installer, you may use the
 following initialization pattern:
 
 ```
-from invokeai.app.services.config import get_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.model_records import ModelRecordServiceSQL
 from invokeai.app.services.model_install import ModelInstallService
 from invokeai.app.services.download import DownloadQueueService
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 from invokeai.backend.util.logging import InvokeAILogger
 
-config = get_config()
+config = InvokeAIAppConfig.get_config()
+config.parse_args()
 
 logger = InvokeAILogger.get_logger(config=config)
-db = SqliteDatabase(config.db_path, logger)
-record_store = ModelRecordServiceSQL(db, logger)
+db = SqliteDatabase(config, logger)
+record_store = ModelRecordServiceSQL(db)
 queue = DownloadQueueService()
 queue.start()
 
-installer = ModelInstallService(app_config=config,
+installer = ModelInstallService(app_config=config, 
                                 record_store=record_store,
-                                download_queue=queue
-                                )
+              download_queue=queue
+           )
 installer.start()
 ```
 
@@ -461,7 +462,7 @@ revision.
 `config` is an optional dict of values that will override the
 autoprobed values for model type, base, scheduler prediction type, and
 so forth. See [Model configuration and
-probing](#model-configuration-and-probing) for details.
+probing](#Model-configuration-and-probing) for details.
 
 `access_token` is an optional access token for accessing resources
 that need authentication.
@@ -494,7 +495,7 @@ source8 = URLModelSource(url='https://civitai.com/api/download/models/63006', ac
 
 for source in [source1, source2, source3, source4, source5, source6, source7]:
    install_job = installer.install_model(source)
-
+   
 source2job = installer.wait_for_installs(timeout=120)
 for source in sources:
     job = source2job[source]
@@ -504,7 +505,7 @@ for source in sources:
   print(f"{source} installed as {model_key}")
  elif job.errored:
      print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
-
+ 
 ```
 
 As shown here, the `import_model()` method accepts a variety of
@@ -718,7 +719,7 @@ When downloading remote models is implemented, additional
 configuration information, such as list of trigger terms, will be
 retrieved from the HuggingFace and Civitai model repositories.
 
-The probed values can be overridden by providing a dictionary in the
+The probed values can be overriden by providing a dictionary in the
 optional `config` argument passed to `import_model()`. You may provide
 overriding values for any of the model's configuration
 attributes. Here is an example of setting the
@@ -841,7 +842,7 @@ variable.
 
 #### installer.start(invoker)
 
-The `start` method is called by the API initialization routines when
+The `start` method is called by the API intialization routines when
 the API starts up. Its effect is to call `sync_to_config()` to
 synchronize the model record store database with what's currently on
 disk.
@@ -1364,21 +1365,14 @@ the in-memory loaded model:
 |----------------|-----------------|------------------|
 | `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
 | `model`        | AnyModel               | The instantiated model (details below) |
+| `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
 
-### get_model_by_key(key, [submodel]) -> LoadedModel
-
-The `get_model_by_key()` method will retrieve the model using its
-unique database key. For example:
-
-loaded_model = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
-
-`get_model_by_key()` may raise any of the following exceptions:
-
-* `UnknownModelException`   -- key not in database
-* `ModelNotFoundException`  -- key in database but model not found at path
-* `NotImplementedException` -- the loader doesn't know how to load this type of model
-
-### Using the Loaded Model in Inference
+Because the loader can return multiple model types, it is typed to
+return `AnyModel`, a Union `ModelMixin`, `torch.nn.Module`,
+`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
+`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
+models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
+models. The others are obvious.
 
 `LoadedModel` acts as a context manager. The context loads the model
 into the execution device (e.g. VRAM on CUDA systems), locks the model
@@ -1386,33 +1380,17 @@ in the execution device for the duration of the context, and returns
 the model. Use it like this:
 
 ```
-loaded_model_= loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
-with loaded_model as vae:
+model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
+with model_info as vae:
  image = vae.decode(latents)[0]
 ```
 
-The object returned by the LoadedModel context manager is an
-`AnyModel`, which is a Union of `ModelMixin`, `torch.nn.Module`,
-`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
-`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
-models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
-models. The others are obvious.
-
-In addition, you may call `LoadedModel.model_on_device()`, a context
-manager that returns a tuple of the model's state dict in CPU and the
-model itself in VRAM. It is used to optimize the LoRA patching and
-unpatching process:
-
-```
-loaded_model_= loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
-with loaded_model.model_on_device() as (state_dict, vae):
- image = vae.decode(latents)[0]
-```
-
-Since not all models have state dicts, the `state_dict` return value
-can be None.
-
+`get_model_by_key()` may raise any of the following exceptions:
 
+* `UnknownModelException`   -- key not in database
+* `ModelNotFoundException`  -- key in database but model not found at path
+* `NotImplementedException` -- the loader doesn't know how to load this type of model
+  
 ### Emitting model loading events
 
 When the `context` argument is passed to `load_model_*()`, it will
@@ -1600,59 +1578,3 @@ This method takes a model key, looks it up using the
 `ModelRecordServiceBase` object in `mm.store`, and passes the returned
 model configuration to `load_model_by_config()`.  It may raise a
 `NotImplementedException`.
-
-## Invocation Context Model Manager API
-
-Within invocations, the following methods are available from the
-`InvocationContext` object:
-
-### context.download_and_cache_model(source) -> Path
-
-This method accepts a `source` of a remote model, downloads and caches
-it locally, and then returns a Path to the local model. The source can
-be a direct download URL or a HuggingFace repo_id.
-
-In the case of HuggingFace repo_id, the following variants are
-recognized:
-
-* stabilityai/stable-diffusion-v4           -- default model
-* stabilityai/stable-diffusion-v4:fp16      -- fp16 variant
-* stabilityai/stable-diffusion-v4:fp16:vae  -- the fp16 vae subfolder
-* stabilityai/stable-diffusion-v4:onnx:vae  -- the onnx variant vae subfolder
-
-You can also point at an arbitrary individual file within a repo_id
-directory using this syntax:
-
-* stabilityai/stable-diffusion-v4::/checkpoints/sd4.safetensors
-
-### context.load_local_model(model_path, [loader]) -> LoadedModel
-
-This method loads a local model from the indicated path, returning a
-`LoadedModel`. The optional loader is a Callable that accepts a Path
-to the object, and returns a `AnyModel` object. If no loader is
-provided, then the method will use `torch.load()` for a .ckpt or .bin
-checkpoint file, `safetensors.torch.load_file()` for a safetensors
-checkpoint file, or `cls.from_pretrained()` for a directory that looks
-like a diffusers directory.
-
-### context.load_remote_model(source, [loader]) -> LoadedModel
-
-This method accepts a `source` of a remote model, downloads and caches
-it locally, loads it, and returns a `LoadedModel`. The source can be a
-direct download URL or a HuggingFace repo_id.
-
-In the case of HuggingFace repo_id, the following variants are
-recognized:
-
-* stabilityai/stable-diffusion-v4           -- default model
-* stabilityai/stable-diffusion-v4:fp16      -- fp16 variant
-* stabilityai/stable-diffusion-v4:fp16:vae  -- the fp16 vae subfolder
-* stabilityai/stable-diffusion-v4:onnx:vae  -- the onnx variant vae subfolder
-
-You can also point at an arbitrary individual file within a repo_id
-directory using this syntax:
-
-* stabilityai/stable-diffusion-v4::/checkpoints/sd4.safetensors
-
-
-

docs/contributing/PR-MERGE-POLICY.md

@@ -1,64 +0,0 @@
-# Pull Request Merge Policy
-
-This document outlines the process for reviewing and merging pull requests (PRs) into the InvokeAI repository.
-
-## Review Process
-
-### 1. Assignment
-
-One of the repository maintainers will assign collaborators to review a pull request. The assigned reviewer(s) will be responsible for conducting the code review.
-
-### 2. Review and Iteration
-
-The assignee is responsible for:
-- Reviewing the PR thoroughly
-- Providing constructive feedback
-- Iterating with the PR author until the assignee is satisfied that the PR is fit to merge
-- Ensuring the PR meets code quality standards, follows project conventions, and doesn't introduce bugs or regressions
-
-### 3. Approval and Notification
-
-Once the assignee is satisfied with the PR:
-- The assignee approves the PR
-- The assignee alerts one of the maintainers that the PR is ready for merge using the **#request-reviews Discord channel**
-
-### 4. Final Merge
-
-One of the maintainers is responsible for:
-- Performing a final check of the PR
-- Merging the PR into the appropriate branch
-
-**Important:** Collaborators are strongly discouraged from merging PRs on their own, except in case of emergency (e.g., critical bug fix and no maintainer is available).
-
-### 5. Release Policy
-
-Once a feature release candidate is published, no feature PRs are to
-be merged into main. Only bugfixes are allowed until the final
-release.
-
-## Best Practices
-
-### Clean Commit History
-
-To encourage a clean development log, PR authors are encouraged to use `git rebase -i` to suppress trivial commit messages (e.g., `ruff` and `prettier` formatting fixes) after the PR is accepted but before it is merged.
-
-### Merge Strategy
-
-The maintainer will perform either a **3-way merge** or **squash merge** when merging a PR into the `main` branch. This approach helps avoid rebase conflict hell and maintains a cleaner project history.
-
-### Attribution
-
-The PR author should reference any papers, source code or
-documentation that they used while creating the code both in the PR
-and as comments in the code itself. If there are any licensing
-restrictions, these should be linked to and/or reproduced in the repo
-root.
-
-
-## Summary
-
-This policy ensures that:
-- All PRs receive proper review from assigned collaborators
-- Maintainers have final oversight before code enters the main branch
-- The commit history remains clean and meaningful
-- Merge conflicts are minimized through appropriate merge strategies

docs/contributing/RECALL_PARAMETERS/RECALL_API_LORAS_CONTROLNETS_IMAGES.md

@@ -1,375 +0,0 @@
-# Recall Parameters API - LoRAs, ControlNets, and IP Adapters with Images
-
-## Overview
-
-The Recall Parameters API supports recalling LoRAs, ControlNets (including T2I Adapters and Control LoRAs), and IP Adapters along with their associated weights and settings. Control Layers and IP Adapters can now include image references from the `INVOKEAI_ROOT/outputs/images` directory for fully functional control and image prompt functionality.
-
-## Key Features
-
-✅ **LoRAs**: Fully functional - adds to UI, queries model configs, applies weights
-✅ **Control Layers**: Full support with optional images from outputs/images
-✅ **IP Adapters**: Full support with optional reference images from outputs/images
-✅ **Model Name Resolution**: Automatic lookup from human-readable names to internal keys
-✅ **Image Validation**: Backend validates that image files exist before sending
-
-## Endpoints
-
-### POST `/api/v1/recall/{queue_id}`
-
-Updates recallable parameters for the frontend, including LoRAs, control adapters, and IP adapters with optional images.
-
-**Path Parameters:**
-- `queue_id` (string): The queue ID to associate parameters with (typically "default")
-
-**Request Body:**
-
-All fields are optional. Include only the parameters you want to update.
-
-```typescript
-{
-  // Standard parameters
-  positive_prompt?: string;
-  negative_prompt?: string;
-  model?: string;           // Model name or key
-  steps?: number;
-  cfg_scale?: number;
-  width?: number;
-  height?: number;
-  seed?: number;
-  // ... other standard parameters
-  
-  // LoRAs
-  loras?: Array<{
-    model_name: string;     // LoRA model name
-    weight?: number;        // Default: 0.75, Range: -10 to 10
-    is_enabled?: boolean;   // Default: true
-  }>;
-  
-  // Control Layers (ControlNet, T2I Adapter, Control LoRA)
-  control_layers?: Array<{
-    model_name: string;            // Control adapter model name
-    image_name?: string;           // Optional image filename from outputs/images
-    weight?: number;               // Default: 1.0, Range: -1 to 2
-    begin_step_percent?: number;   // Default: 0.0, Range: 0 to 1
-    end_step_percent?: number;     // Default: 1.0, Range: 0 to 1
-    control_mode?: "balanced" | "more_prompt" | "more_control";  // ControlNet only
-  }>;
-  
-  // IP Adapters
-  ip_adapters?: Array<{
-    model_name: string;            // IP Adapter model name
-    image_name?: string;           // Optional reference image filename from outputs/images
-    weight?: number;               // Default: 1.0, Range: -1 to 2
-    begin_step_percent?: number;   // Default: 0.0, Range: 0 to 1
-    end_step_percent?: number;     // Default: 1.0, Range: 0 to 1
-    method?: "full" | "style" | "composition";  // Default: "full"
-    influence?: "Lowest" | "Low" | "Medium" | "High" | "Highest";  // Flux Redux only; default: "highest"
-  }>;
-}
-```
-
-## Model Name Resolution
-
-The backend automatically resolves model names to their internal keys:
-
-1. **Main Models**: Resolved from the name to the model key
-2. **LoRAs**: Searched in the LoRA model database
-3. **Control Adapters**: Tried in order - ControlNet → T2I Adapter → Control LoRA
-4. **IP Adapters**: Searched in the IP Adapter model database
-
-Models that cannot be resolved are skipped with a warning in the logs.
-
-## Image File Handling
-
-### Image Path Resolution
-
-When you specify an `image_name`, the backend:
-1. Constructs the full path: `{INVOKEAI_ROOT}/outputs/images/{image_name}`
-2. Validates that the file exists
-3. Includes the image reference in the event sent to the frontend
-4. Logs whether the image was found or not
-
-### Image Naming
-
-Images should be referenced by their filename as it appears in the outputs/images directory:
-- ✅ Correct: `"image_name": "example.png"`
-- ✅ Correct: `"image_name": "my_control_image_20240110.jpg"`
-- ❌ Incorrect: `"image_name": "outputs/images/example.png"`  (use relative filename only)
-- ❌ Incorrect: `"image_name": "/full/path/to/example.png"`   (use relative filename only)
-
-## Frontend Behavior
-
-### LoRAs
-- **Fully Supported**: LoRAs are immediately added to the LoRA list in the UI
-- Existing LoRAs are cleared before adding new ones
-- Each LoRA's model config is fetched and applied with the specified weight
-- LoRAs appear in the LoRA selector panel
-
-### Control Layers with Images
-- **Fully Supported**: Control layers now support images from outputs/images
-- Configuration includes model, weights, step percentages, and image reference
-- Image availability is logged in frontend console
-- Images can be used to create actual control layers through the UI
-
-### IP Adapters with Images
-- **Fully Supported**: IP Adapters now support reference images from outputs/images
-- Configuration includes model, weights, step percentages, method, and image reference
-- Image availability is logged in frontend console
-- Images can be used to create actual reference image layers through the UI
-
-## Examples
-
-### 1. Add LoRAs Only
-
-```bash
-curl -X POST http://localhost:9090/api/v1/recall/default \
-  -H "Content-Type: application/json" \
-  -d '{
-    "loras": [
-      {
-        "model_name": "add-detail-xl",
-        "weight": 0.8,
-        "is_enabled": true
-      },
-      {
-        "model_name": "sd_xl_offset_example-lora_1.0",
-        "weight": 0.5,
-        "is_enabled": true
-      }
-    ]
-  }'
-```
-
-### 2. Configure Control Layers with Image
-
-Replace `my_control_image.png` with an actual image filename from your outputs/images directory.
-
-```bash
-curl -X POST http://localhost:9090/api/v1/recall/default \
-  -H "Content-Type: application/json" \
-  -d '{
-    "control_layers": [
-      {
-        "model_name": "controlnet-canny-sdxl-1.0",
-        "image_name": "my_control_image.png",
-        "weight": 0.75,
-        "begin_step_percent": 0.0,
-        "end_step_percent": 0.8,
-        "control_mode": "balanced"
-      }
-    ]
-  }'
-```
-
-### 3. Configure IP Adapters with Reference Image
-
-Replace `reference_face.png` with an actual image filename from your outputs/images directory.
-
-```bash
-curl -X POST http://localhost:9090/api/v1/recall/default \
-  -H "Content-Type: application/json" \
-  -d '{
-    "ip_adapters": [
-      {
-        "model_name": "ip-adapter-plus-face_sd15",
-        "image_name": "reference_face.png",
-        "weight": 0.7,
-        "begin_step_percent": 0.0,
-        "end_step_percent": 1.0,
-        "method": "composition"
-      }
-    ]
-  }'
-```
-
-### 4. Complete Configuration with All Features
-
-```bash
-curl -X POST http://localhost:9090/api/v1/recall/default \
-  -H "Content-Type: application/json" \
-  -d '{
-    "positive_prompt": "masterpiece, detailed photo with specific style",
-    "negative_prompt": "blurry, low quality",
-    "model": "FLUX Schnell",
-    "steps": 25,
-    "cfg_scale": 8.0,
-    "width": 1024,
-    "height": 768,
-    "seed": 42,
-    "loras": [
-      {
-        "model_name": "add-detail-xl",
-        "weight": 0.6,
-        "is_enabled": true
-      }
-    ],
-    "control_layers": [
-      {
-        "model_name": "controlnet-depth-sdxl-1.0",
-        "image_name": "depth_map.png",
-        "weight": 1.0,
-        "begin_step_percent": 0.0,
-        "end_step_percent": 0.7
-      }
-    ],
-    "ip_adapters": [
-      {
-        "model_name": "ip-adapter-plus-face_sd15",
-        "image_name": "style_reference.png",
-        "weight": 0.5,
-        "begin_step_percent": 0.0,
-        "end_step_percent": 1.0,
-        "method": "style"
-      }
-    ]
-  }'
-```
-
-## Response Format
-
-```json
-{
-  "status": "success",
-  "queue_id": "default",
-  "updated_count": 15,
-  "parameters": {
-    "positive_prompt": "...",
-    "steps": 25,
-    "loras": [
-      {
-        "model_key": "abc123...",
-        "weight": 0.6,
-        "is_enabled": true
-      }
-    ],
-    "control_layers": [
-      {
-        "model_key": "controlnet-xyz...",
-        "weight": 1.0,
-        "image": {
-          "image_name": "depth_map.png"
-        }
-      }
-    ],
-    "ip_adapters": [
-      {
-        "model_key": "ip-adapter-xyz...",
-        "weight": 0.5,
-        "image": {
-          "image_name": "style_reference.png"
-        }
-      }
-    ]
-  }
-}
-```
-
-## WebSocket Events
-
-When parameters are updated, a `recall_parameters_updated` event is emitted via WebSocket to the queue room. The frontend automatically:
-
-1. Applies standard parameters (prompts, steps, dimensions, etc.)
-2. Loads and adds LoRAs to the LoRA list
-3. Logs control layer and IP adapter configurations with image information
-4. Makes image references available for manual canvas/reference image creation
-
-## Logging
-
-### Backend Logs
-
-Backend logs show:
-- Model name → key resolution (success/failure)
-- Image file validation (found/not found)
-- Parameter storage confirmation
-- Event emission status
-
-Example log messages:
-```
-INFO: Resolved ControlNet model name 'controlnet-canny-sdxl-1.0' to key 'controlnet-xyz...'
-INFO: Found image file: depth_map.png
-INFO: Updated 12 recall parameters for queue default
-INFO: Resolved 1 LoRA(s)
-INFO: Resolved 1 control layer(s)
-INFO: Resolved 1 IP adapter(s)
-```
-
-### Frontend Logs
-
-Frontend logs (check browser console):
-- Set `localStorage.ROARR_FILTER = 'debug'` to see all debug messages
-- Look for messages from the `events` namespace
-- LoRA loading, model resolution, and parameter application are logged
-
-Example log messages:
-```
-INFO: Applied 5 recall parameters to store
-INFO: Received 1 control layer(s) with image support
-INFO: Control layer 1: controlnet-xyz... (weight: 0.75, image: depth_map.png)
-DEBUG: Control layer 1 image available at: outputs/images/depth_map.png
-INFO: Received 1 IP adapter(s) with image support
-INFO: IP adapter 1: ip-adapter-xyz... (weight: 0.7, image: style_reference.png)
-DEBUG: IP adapter 1 image available at: outputs/images/style_reference.png
-```
-
-## Limitations
-
-1. **Canvas Integration**: Control layers and IP adapters with images are currently logged but not automatically added to canvas layers
-   - Users can view the configuration and manually create canvas layers with the provided images
-   - Future enhancement: Auto-create canvas layers with stored images
-
-2. **Model Availability**: Models must be installed in InvokeAI before they can be recalled
-
-3. **Image Availability**: Images must exist in the outputs/images directory
-   - Missing images are logged as warnings but don't fail the request
-   - Other parameters are still applied even if images are missing
-
-4. **Image URLs**: Only local filenames from outputs/images are supported
-   - Remote image URLs are not currently supported
-
-## Testing
-
-Use the provided test script:
-
-```bash
-./test_recall_loras_controlnets.sh
-```
-
-This will test:
-- LoRA addition with multiple models
-- Control layer configuration with image references
-- IP adapter configuration with image references
-- Combined parameter updates with all features
-
-Note: Update the image names in the test script to match actual images in your outputs/images directory.
-
-## Troubleshooting
-
-### Images Not Found
-
-If you see "Image file not found" in the logs:
-1. Verify the image filename matches exactly (case-sensitive)
-2. Ensure the image is in `{INVOKEAI_ROOT}/outputs/images/`
-3. Check that the filename doesn't include the `outputs/images/` prefix
-
-### Models Not Found
-
-If you see "Could not find model" messages:
-1. Verify the model name matches exactly (case-sensitive)
-2. Ensure the model is installed in InvokeAI
-3. Check the model name using the models browser in the UI
-
-### Event Not Received
-
-If the frontend doesn't receive the event:
-1. Check browser console for connection errors
-2. Verify the queue_id matches the frontend's queue (usually "default")
-3. Check backend logs for event emission errors
-
-## Future Enhancements
-
-Potential improvements:
-1. Auto-create canvas layers with provided control layer images
-2. Auto-create reference image layers with provided IP adapter images
-3. Support for image URLs
-4. Batch operations for multiple queue IDs
-5. Image upload capability (accept base64 or file upload)

docs/contributing/RECALL_PARAMETERS/RECALL_PARAMETERS_API.md

@@ -1,208 +0,0 @@
-# Recall Parameters API
-
-## Overview
-
-A new REST API endpoint has been added to the InvokeAI backend that allows programmatic updates to recallable parameters from another process. This enables external applications or scripts to modify frontend parameters like prompts, models, and step counts via HTTP requests.
-
-When parameters are updated via the API, the backend automatically broadcasts a WebSocket event to all connected frontend clients subscribed to that queue, causing them to update immediately.
-
-## How It Works
-
-1. **API Request**: External application sends a POST request with parameters to update
-2. **Storage**: Parameters are stored in client state persistence, associated with a queue ID
-3. **Broadcast**: A WebSocket event (`recall_parameters_updated`) is emitted to all frontend clients listening to that queue
-4. **Frontend Update**: Connected frontend clients receive the event and can process the updated parameters
-5. **Immediate Display**: The frontend UI updates automatically with the new values
-
-This means if you have the InvokeAI frontend open in a browser, updating parameters via the API will instantly reflect on the screen without any manual action needed.
-
-## Endpoint
-
-**Base URL**: `http://localhost:9090/api/v1/recall/{queue_id}`
-
-## POST - Update Recall Parameters
-
-Updates recallable parameters for a given queue ID.
-
-### Request
-
-```http
-POST /api/v1/recall/{queue_id}
-Content-Type: application/json
-
-{
-  "positive_prompt": "a beautiful landscape",
-  "negative_prompt": "blurry, low quality",
-  "model": "sd-1.5",
-  "steps": 20,
-  "cfg_scale": 7.5,
-  "width": 512,
-  "height": 512,
-  "seed": 12345
-}
-```
-
-The queue id is usually "default".
-
-### Parameters
-
-All parameters are optional. Only provide the parameters you want to update:
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `positive_prompt` | string | Positive prompt text |
-| `negative_prompt` | string | Negative prompt text |
-| `model` | string | Main model name/identifier |
-| `refiner_model` | string | Refiner model name/identifier |
-| `vae_model` | string | VAE model name/identifier |
-| `scheduler` | string | Scheduler name |
-| `steps` | integer | Number of generation steps (≥1) |
-| `refiner_steps` | integer | Number of refiner steps (≥0) |
-| `cfg_scale` | number | CFG scale for guidance |
-| `cfg_rescale_multiplier` | number | CFG rescale multiplier |
-| `refiner_cfg_scale` | number | Refiner CFG scale |
-| `guidance` | number | Guidance scale |
-| `width` | integer | Image width in pixels (≥64) |
-| `height` | integer | Image height in pixels (≥64) |
-| `seed` | integer | Random seed (≥0) |
-| `denoise_strength` | number | Denoising strength (0-1) |
-| `refiner_denoise_start` | number | Refiner denoising start (0-1) |
-| `clip_skip` | integer | CLIP skip layers (≥0) |
-| `seamless_x` | boolean | Enable seamless X tiling |
-| `seamless_y` | boolean | Enable seamless Y tiling |
-| `refiner_positive_aesthetic_score` | number | Refiner positive aesthetic score |
-| `refiner_negative_aesthetic_score` | number | Refiner negative aesthetic score |
-
-### Response
-
-```json
-{
-  "status": "success",
-  "queue_id": "queue_123",
-  "updated_count": 7,
-  "parameters": {
-    "positive_prompt": "a beautiful landscape",
-    "negative_prompt": "blurry, low quality",
-    "model": "sd-1.5",
-    "steps": 20,
-    "cfg_scale": 7.5,
-    "width": 512,
-    "height": 512,
-    "seed": 12345
-  }
-}
-```
-
-## GET - Retrieve Recall Parameters
-
-Retrieves metadata about stored recall parameters.
-
-### Request
-
-```http
-GET /api/v1/recall/{queue_id}
-```
-
-### Response
-
-```json
-{
-  "status": "success",
-  "queue_id": "queue_123",
-  "note": "Use the frontend to access stored recall parameters, or set specific parameters using POST"
-}
-```
-
-## Usage Examples
-
-### Using cURL
-
-```bash
-# Update prompts and model
-curl -X POST http://localhost:9090/api/v1/recall/default \
-  -H "Content-Type: application/json" \
-  -d '{
-    "positive_prompt": "a cyberpunk city at night",
-    "negative_prompt": "dark, unclear",
-    "model": "sd-1.5",
-    "steps": 30
-  }'
-
-# Update just the seed
-curl -X POST http://localhost:9090/api/v1/recall/default \
-  -H "Content-Type: application/json" \
-  -d '{"seed": 99999}'
-```
-
-### Using Python
-
-```python
-import requests
-import json
-
-# Configuration
-API_URL = "http://localhost:9090/api/v1/recall/default"
-
-# Update multiple parameters
-params = {
-    "positive_prompt": "a serene forest",
-    "negative_prompt": "people, buildings",
-    "steps": 25,
-    "cfg_scale": 7.0,
-    "seed": 42
-}
-
-response = requests.post(API_URL, json=params)
-result = response.json()
-
-print(f"Status: {result['status']}")
-print(f"Updated {result['updated_count']} parameters")
-print(json.dumps(result['parameters'], indent=2))
-```
-
-### Using Node.js/JavaScript
-
-```javascript
-const API_URL = 'http://localhost:9090/api/v1/recall/default';
-
-const params = {
-  positive_prompt: 'a beautiful sunset',
-  negative_prompt: 'blurry',
-  steps: 20,
-  width: 768,
-  height: 768,
-  seed: 12345
-};
-
-fetch(API_URL, {
-  method: 'POST',
-  headers: { 'Content-Type': 'application/json' },
-  body: JSON.stringify(params)
-})
-  .then(res => res.json())
-  .then(data => console.log(data));
-```
-
-## Implementation Details
-
-- Parameters are stored in the client state persistence service, using keys prefixed with `recall_`
-- The parameters are associated with a `queue_id`, allowing multiple concurrent sessions to maintain separate parameter sets
-- Only non-null parameters are processed and stored
-- The endpoint provides validation for numeric ranges (e.g., steps ≥ 1, dimensions ≥ 64)
-- All parameter values are JSON-serialized for storage
-- When parameter values are changed, the backend generates a web sockets event that the frontend listens to.
-
-## Integration with Frontend
-
-The stored parameters can be accessed by the frontend through the
-existing client state API or by implementing hooks that read from the
-recall parameter storage. This allows external applications to
-pre-populate generation parameters before the user initiates image
-generation.
-
-## Error Handling
-
-- **400 Bad Request**: Invalid parameters or parameter values
-- **500 Internal Server Error**: Server-side error storing or retrieving parameters
-
-Errors include detailed messages explaining what went wrong.

docs/contributing/TESTS.md

@@ -1,6 +1,6 @@
 # InvokeAI Backend Tests
 
-We use `pytest` to run the backend python tests. (See [pyproject.toml](https://github.com/invoke-ai/InvokeAI/blob/main/pyproject.toml) for the default `pytest` options.)
+We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
 
 ## Fast vs. Slow
 All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
@@ -33,7 +33,7 @@ pytest tests -m ""
 
 ## Test Organization
 
-All backend tests are in the [`tests/`](https://github.com/invoke-ai/InvokeAI/tree/main/tests) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
+All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
 
 TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
 

docs/contributing/contribution_guides/development.md

@@ -2,7 +2,7 @@
 
 ## **What do I need to know to help?**
 
-If you are looking to help with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential.
+If you are looking to help to with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
 
 
 ## **Get Started**
@@ -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](../frontend/index.md)
+* #### [Frontend Documentation](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 
@@ -20,15 +20,15 @@ Once you're setup, for more information, you can review the documentation specif
 
 If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md), [translation](translation.md) or helping support other users and triage issues as they're reported in GitHub.
 
-There are two paths to making a development contribution:
+There are two paths to making a development contribution: 
 
 1. Choosing an open issue to address. Open issues can be found in the [Issues](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen) section of the InvokeAI repository. These are tagged by the issue type (bug, enhancement, etc.) along with the “good first issues” tag denoting if they are suitable for first time contributors.
-    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help.
+    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help. 
 2. Opening a new issue or feature to add. **Please make sure you have searched through existing issues before creating new ones.**
 
 *Regardless of what you choose, please post in the  [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord before you start development in order to confirm that the issue or feature is aligned with the current direction of the project. We value our contributors time and effort and want to ensure that no one’s time is being misspent.*
 
-## Best Practices:
+## Best Practices: 
 * Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
 * Comments! Commenting your code helps reviewers easily understand your contribution
 * Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
@@ -38,7 +38,7 @@ There are two paths to making a development contribution:
 
 If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
 
-For frontend related work, **@psychedelicious** is the best person to reach out to.
+For frontend related work, **@psychedelicious** is the best person to reach out to. 
 
 For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@psychedelicious**.
 

docs/contributing/contribution_guides/documentation.md

@@ -1,13 +1,13 @@
 # Documentation
 
-Documentation is an important part of any open source project. It provides a clear and concise way to communicate how the software works, how to use it, and how to troubleshoot issues. Without proper documentation, it can be difficult for users to understand the purpose and functionality of the project.
+Documentation is an important part of any open source project. It provides a clear and concise way to communicate how the software works, how to use it, and how to troubleshoot issues. Without proper documentation, it can be difficult for users to understand the purpose and functionality of the project. 
 
 ## Contributing
 
-All documentation is maintained in our [GitHub repository](https://github.com/invoke-ai/InvokeAI). If you come across documentation that is out of date or incorrect, please submit a pull request with the necessary changes.
+All documentation is maintained in the InvokeAI GitHub repository. If you come across documentation that is out of date or incorrect, please submit a pull request with the necessary changes. 
 
-When updating or creating documentation, please keep in mind Invoke is a tool for everyone, not just those who have familiarity with generative art.
+When updating or creating documentation, please keep in mind InvokeAI is a tool for everyone, not just those who have familiarity with generative art. 
 
 ## Help & Questions
 
-Please ping @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) if you have any questions.
+Please ping @imic or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.

docs/contributing/contribution_guides/newContributorChecklist.md

@@ -1,56 +1,47 @@
 # New Contributor Guide
 
-If you're a new contributor to InvokeAI or Open Source Projects, this is the guide for you.
+If you're a new contributor to InvokeAI or Open Source Projects, this is the guide for you. 
 
 ## New Contributor Checklist
-
-- [x] Set up your local development environment & fork of InvokAI by following [the steps outlined here](../dev-environment.md)
-- [x] Set up your local tooling with [this guide](../LOCAL_DEVELOPMENT.md). Feel free to skip this step if you already have tooling you're comfortable with.
+- [x] Set up your local development environment & fork of InvokAI by following [the steps outlined here](../../installation/020_INSTALL_MANUAL.md#developer-install)
+- [x] Set up your local tooling with [this guide](InvokeAI/contributing/LOCAL_DEVELOPMENT/#developing-invokeai-in-vscode). Feel free to skip this step if you already have tooling you're comfortable with. 
 - [x] Familiarize yourself with [Git](https://www.atlassian.com/git) & our project structure by reading through the [development documentation](development.md)
 - [x] Join the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord
-- [x] Choose an issue to work on! This can be achieved by asking in the #dev-chat channel, tackling a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) or finding an item on the [roadmap](https://github.com/orgs/invoke-ai/projects/7). If nothing in any of those places catches your eye, feel free to work on something of interest to you!
+- [x] Choose an issue to work on! This can be achieved by asking in the #dev-chat channel, tackling a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) or finding an item on the [roadmap](https://github.com/orgs/invoke-ai/projects/7). If nothing in any of those places catches your eye, feel free to work on something of interest to you! 
 - [x] Make your first Pull Request with the guide below
 - [x] Happy development! Don't be afraid to ask for help - we're happy to help you contribute!
 
+
 ## How do I make a contribution?
 
 Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
 
 Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
 
-1. Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are setup for success.
+1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
 2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
 3. Clone the repository to your local machine using:
-
-    ```bash
-    git clone https://github.com/your-GitHub-username/InvokeAI.git
-    ```
-
-If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 4. Create a new branch for your fix using:
-
-  ```bash
-  git checkout -b branch-name-here
-  ```
-
+```bash
+git clone https://github.com/your-GitHub-username/InvokeAI.git
+```
+If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
+4. Create a new branch for your fix using:
+```bash
+git checkout -b branch-name-here
+```
 5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
 6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
-
-    ```bash
-    git add -A
-    ```
-
+```bash
+git add -A
+```
 7. Store the contents of the index with a descriptive message.
-
-    ```bash
-    git commit -m "Insert a short message of the changes made here"
-    ```
-
+```bash
+git commit -m "Insert a short message of the changes made here"
+```
 8. Push the changes to the remote repository using
-
-    ```bash
-    git push origin branch-name-here
-    ```
-
+```bash
+git push origin branch-name-here
+```
 9. Submit a pull request to the **main** branch of the InvokeAI repository. If you're not sure how to, [follow this guide](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)
 10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
 11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
@@ -58,20 +49,20 @@ If you're unfamiliar with using Git through the commandline, [GitHub Desktop](ht
 13. Make changes to the pull request if the reviewer(s) recommend them.
 14. Celebrate your success after your pull request is merged!
 
-If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github).
+If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
 
-## Best Practices
 
-- Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
+## Best Practices: 
+* Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
+* Comments! Commenting your code helps reviewers easily understand your contribution
+* Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
+* Make all communications public. This ensure knowledge is shared with the whole community
 
-- Comments! Commenting your code helps reviewers easily understand your contribution
-- Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
-- Make all communications public. This ensure knowledge is shared with the whole community
 
 ## **Where can I go for help?**
 
 If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
 
-For frontend related work, **@pyschedelicious** is the best person to reach out to.
+For frontend related work, **@pyschedelicious** is the best person to reach out to. 
 
 For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.

docs/contributing/contribution_guides/translation.md

@@ -16,4 +16,4 @@ Please check Weblate's [documentation](https://docs.weblate.org/en/latest/index
 
 ## Thanks
 
-Thanks to the InvokeAI community for their efforts to translate the project!
+Thanks to the InvokeAI community for their efforts to translate the project!

docs/contributing/contribution_guides/tutorials.md

@@ -1,6 +1,6 @@
 # Tutorials
 
-Tutorials help new & existing users expand their ability to use InvokeAI to the full extent of our features and services.  
+Tutorials help new & existing users expand their abilty to use InvokeAI to the full extent of our features and services.  
 
 Currently, we have a set of tutorials available on our [YouTube channel](https://www.youtube.com/@invokeai), but as InvokeAI continues to evolve with new updates, we want to ensure that we are giving our users the resources they need to succeed. 
 
@@ -8,4 +8,4 @@ Tutorials can be in the form of videos or article walkthroughs on a subject of y
 
 ## Contributing
 
-Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
+Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.

docs/contributing/contributors.md

@@ -1,54 +0,0 @@
----
-title: Contributors
----
-
-We thank [all contributors](https://github.com/invoke-ai/InvokeAI/graphs/contributors) for their time and hard work!
-
-## **Original Author**
-
-- [Lincoln D. Stein](mailto:lincoln.stein@gmail.com)
-
-## **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
-- @joshistoast (Josh Corbett) - Web Development
-- @cheerio (Mary Rogers) - Lead Engineer & Web App Development
-- @ebr (Eugene Brodsky) - Cloud/DevOps/Software engineer; your friendly neighbourhood cluster-autoscaler
-- @sunija - Standalone version
-- @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
-- @glimmerleaf (Devon Hopkins) - Community Wizard
-- @gogurt enjoyer - Discord moderator and end user support
-- @whosawhatsis - Discord moderator and end user support
-- @dwringer - 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
-- @chainchompa (Jennifer Player) - Web Development & Chain-Chomping
-- @millu (Millun Atluri) - Community Wizard, Documentation, Node-wrangler,
-- @genomancer (Gregg Helt) - Controlnet support
-- @keturn (Kevin Turner) - Diffusers
-
-## **Original CompVis (Stable Diffusion) Authors**
-
-- [Robin Rombach](https://github.com/rromb)
-- [Patrick von Platen](https://github.com/patrickvonplaten)
-- [ablattmann](https://github.com/ablattmann)
-- [Patrick Esser](https://github.com/pesser)
-- [owenvincent](https://github.com/owenvincent)
-- [apolinario](https://github.com/apolinario)
-- [Charles Packer](https://github.com/cpacker)

docs/contributing/dev-environment.md

@@ -1,92 +0,0 @@
-# Dev Environment
-
-To make changes to Invoke's backend, frontend or documentation, you'll need to set up a dev environment.
-
-If you only want to make changes to the docs site, you can skip the frontend dev environment setup as described in the below guide.
-
-If you just want to use Invoke, you should use the [launcher][launcher link].
-
-!!! warning
-
-    Invoke uses a SQLite database. When you run the application as a dev install, you accept responsibility for your database. This means making regular backups (especially before pulling) and/or fixing it yourself in the event that a PR introduces a schema change.
-
-    If you don't need to persist your db, you can use an ephemeral in-memory database by setting `use_memory_db: true` in your `invokeai.yaml` file. You'll also want to set `scan_models_on_startup: true` so that your models are registered on startup.
-
-## Setup
-
-1. Run through the [requirements][requirements link].
-
-2. [Fork and clone][forking link] the [InvokeAI repo][repo link].
-
-3. This repository uses Git LFS to manage large files. To ensure all assets are downloaded:
-      - Install git-lfs → [Download here](https://git-lfs.com/)
-      - Enable automatic LFS fetching for this repository:
-        ```shell
-        git config lfs.fetchinclude "*"
-        ```
-        - Fetch files from LFS (only needs to be done once; subsequent `git pull` will fetch changes automatically):
-        ```
-        git lfs pull
-        ```
-4. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
-
-5. Follow the [manual install][manual install link] guide, with some modifications to the install command:
-
-      - Use `.` instead of `invokeai` to install from the current directory. You don't need to specify the version.
-
-      - Add `-e` after the `install` operation to make this an [editable install][editable install link]. That means your changes to the python code will be reflected when you restart the Invoke server.
-
-      - When installing the `invokeai` package, add the `dev`, `test` and `docs` package options to the package specifier. You may or may not need the `xformers` option - follow the manual install guide to figure that out. So, your package specifier will be either `".[dev,test,docs]"` or `".[dev,test,docs,xformers]"`. Note the quotes!
-
-     With the modifications made, the install command should look something like this:
-
-      ```sh
-      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu128 --reinstall
-      ```
-
-6. At this point, you should have Invoke installed, a venv set up and activated, and the server running. But you will see a warning in the terminal that no UI was found. If you go to the URL for the server, you won't get a UI.
-
-      This is because the UI build is not distributed with the source code. You need to build it manually. End the running server instance.
-
-      If you only want to edit the docs, you can stop here and skip to the **Documentation** section below.
-
-7. Install the frontend dev toolchain, paying attention to versions:
-
-      - [`nodejs`](https://nodejs.org/) (tested on LTS, v22)
-
-      - [`pnpm`](https://pnpm.io/installation) (tested on v10)
-
-8. Do a production build of the frontend:
-
-      ```sh
-      cd <PATH_TO_INVOKEAI_REPO>/invokeai/frontend/web
-      pnpm i
-      pnpm build
-      ```
-
-9. Restart the server and navigate to the URL. You should get a UI. After making changes to the python code, restart the server to see those changes.
-
-## Updating the UI
-
-You'll need to run `pnpm build` every time you pull in new changes.
-
-Another option is to skip the build and instead run the UI in dev mode:
-
-```sh
-pnpm dev
-```
-
-This starts a vite dev server for the UI at `127.0.0.1:5173`, which you will use instead of `127.0.0.1:9090`.
-
-The dev mode is substantially slower than the production build but may be more convenient if you just need to test things out. It will hot-reload the UI as you make changes to the frontend code. Sometimes the hot-reload doesn't work, and you need to manually refresh the browser tab.
-
-## Documentation
-
-The documentation is built with `mkdocs`. It provides a hot-reload dev server for the docs. Start it with `mkdocs serve`.
-
-[launcher link]: ../installation/quick_start.md
-[forking link]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo
-[requirements link]: ../installation/requirements.md
-[repo link]: https://github.com/invoke-ai/InvokeAI
-[manual install link]: ../installation/manual.md
-[editable install link]: https://pip.pypa.io/en/latest/cli/pip_install/#cmdoption-e

docs/contributing/frontend/OVERVIEW.md

@@ -0,0 +1,133 @@
+# Invoke UI
+
+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:dpdm`: check circular dependencies
+- `lint:eslint`: check code quality
+- `lint:prettier`: check code formatting
+- `lint:tsc`: check type issues
+- `lint:knip`: check for unused exports or objects (failures here are just suggestions, not hard fails)
+- `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 to 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]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/services/api/schema.ts
+[conventional commits]: https://www.conventionalcommits.org/en/v1.0.0/
+[Workflows - Design and Implementation]: ./WORKFLOWS.md
+[State Management]: ./STATE_MGMT.md

docs/contributing/frontend/STATE_MGMT.md

@@ -0,0 +1,38 @@
+# 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/

docs/contributing/frontend/WORKFLOWS.md

@@ -0,0 +1,315 @@
+# Workflows - Design and Implementation
+
+> This document describes, at a high level, the design and implementation of workflows in the InvokeAI frontend. There are a substantial number of implementation details not included, but which are hopefully clear from the code.
+
+InvokeAI's backend uses graphs, composed of **nodes** and **edges**, to process data and generate images.
+
+Nodes have any number of **input fields** and **output fields**. Edges connect nodes together via their inputs and outputs. Fields have data types which dictate how they may be connected.
+
+During execution, a nodes' outputs may be passed along to any number of other nodes' inputs.
+
+Workflows are an enriched abstraction over a graph.
+
+## Design
+
+InvokeAI provide two ways to build graphs in the frontend: the [Linear UI](#linear-ui) and [Workflow Editor](#workflow-editor).
+
+To better understand the use case and challenges related to workflows, we will review both of these modes.
+
+### Linear UI
+
+This includes the **Text to Image**, **Image to Image** and **Unified Canvas** tabs.
+
+The user-managed parameters on these tabs are stored as simple objects in the application state. When the user invokes, adding a generation to the queue, we internally build a graph from these parameters.
+
+This logic can be fairly complex due to the range of features available and their interactions. Depending on the parameters selected, the graph may be very different. Building graphs in code can be challenging - you are trying to construct a non-linear structure in a linear context.
+
+The simplest graph building logic is for **Text to Image** with a SD1.5 model: [buildLinearTextToImageGraph.ts]
+
+There are many other graph builders in the same directory for different tabs or base models (e.g. SDXL). Some are pretty hairy.
+
+In the Linear UI, we go straight from **simple application state** to **graph** via these builders.
+
+### Workflow Editor
+
+The Workflow Editor is a visual graph editor, allowing users to draw edges from node to node to construct a graph. This _far_ more approachable way to create complex graphs.
+
+InvokeAI uses the [reactflow] library to power the Workflow Editor. It provides both a graph editor UI and manages its own internal graph state.
+
+#### Workflows
+
+A workflow is a representation of a graph plus additional metadata:
+
+- Name
+- Description
+- Version
+- Notes
+- [Exposed fields](#workflow-linear-view)
+- Author, tags, category, etc.
+
+Workflows should have other qualities:
+
+- Portable: you should be able to load a workflow created by another person.
+- Resilient: you should be able to "upgrade" a workflow as the application changes.
+- Abstract: as much as is possible, workflows should not be married to the specific implementation details of the application.
+
+To support these qualities, workflows are serializable, have a versioned schemas, and represent graphs as minimally as possible. Fortunately, the reactflow state for nodes and edges works perfectly for this.
+
+##### Workflow -> reactflow state -> InvokeAI graph
+
+Given a workflow, we need to be able to derive reactflow state and/or an InvokeAI graph from it.
+
+The first step - workflow to reactflow state - is very simple. The logic is in [nodesSlice.ts], in the `workflowLoaded` reducer.
+
+The reactflow state is, however, structurally incompatible with our backend's graph structure. When a user invokes on a Workflow, we need to convert the reactflow state into an InvokeAI graph. This is far simpler than the graph building logic from the Linear UI:
+[buildNodesGraph.ts]
+
+##### Nodes vs Invocations
+
+We often use the terms "node" and "invocation" interchangeably, but they may refer to different things in the frontend.
+
+reactflow [has its own definitions][reactflow-concepts] of "node", "edge" and "handle" which are closely related to InvokeAI graph concepts.
+
+- A reactflow node is related to an InvokeAI invocation. It has a "data" property, which holds the InvokeAI-specific invocation data.
+- A reactflow edge is roughly equivalent to an InvokeAI edge.
+- A reactflow handle is roughly equivalent to an InvokeAI input or output field.
+
+##### Workflow Linear View
+
+Graphs are very capable data structures, but not everyone wants to work with them all the time.
+
+To allow less technical users - or anyone who wants a less visually noisy workspace - to benefit from the power of nodes, InvokeAI has a workflow feature called the Linear View.
+
+A workflow input field can be added to this Linear View, and its input component can be presented similarly to the Linear UI tabs. Internally, we add the field to the workflow's list of exposed fields.
+
+#### OpenAPI Schema
+
+OpenAPI is a schema specification that can represent complex data structures and relationships. The backend is capable of generating an OpenAPI schema for all invocations.
+
+When the UI connects, it requests this schema and parses each invocation into an **invocation template**. Invocation templates have a number of properties, like title, description and type, but the most important ones are their input and output **field templates**.
+
+Invocation and field templates are the "source of truth" for graphs, because they indicate what the backend is able to process.
+
+When a user adds a new node to their workflow, these templates are used to instantiate a node with fields instantiated from the input and output field templates.
+
+##### Field Instances and Templates
+
+Field templates consist of:
+
+- Name: the identifier of the field, its variable name in python
+- Type: derived from the field's type annotation in python (e.g. IntegerField, ImageField, MainModelField)
+- Constraints: derived from the field's creation args in python (e.g. minimum value for an integer)
+- Default value: optionally provided in the field's creation args (e.g. 42 for an integer)
+
+Field instances are created from the templates and have name, type and optionally a value.
+
+The type of the field determines the UI components that are rendered for it.
+
+A field instance's name associates it with its template.
+
+##### Stateful vs Stateless Fields
+
+**Stateful** fields store their value in the frontend graph. Think primitives, model identifiers, images, etc. Fields are only stateful if the frontend allows the user to directly input a value for them.
+
+Many field types, however, are **stateless**. An example is a `UNetField`, which contains some data describing a UNet. Users cannot directly provide this data - it is created and consumed in the backend.
+
+Stateless fields do not store their value in the node, so their field instances do not have values.
+
+"Custom" fields will always be treated as stateless fields.
+
+##### Collection and Scalar Fields
+
+Field types have a name and two flags which may identify it as a **collection** or **collection or scalar** field.
+
+If a field is annotated in python as a list, its field type is parsed and flagged as a **collection** type (e.g. `list[int]`).
+
+If it is annotated as a union of a type and list, the type will be flagged as a **collection or scalar** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
+
+## Implementation
+
+The majority of data structures in the backend are [pydantic] models. Pydantic provides OpenAPI schemas for all models and we then generate TypeScript types from those.
+
+The OpenAPI schema is parsed at runtime into our invocation templates.
+
+Workflows and all related data are modeled in the frontend using [zod]. Related types are inferred from the zod schemas.
+
+> In python, invocations are pydantic models with fields. These fields become node inputs. The invocation's `invoke()` function returns a pydantic model - its output. Like the invocation itself, the output model has any number of fields, which become node outputs.
+
+### zod Schemas and Types
+
+The zod schemas, inferred types, and type guards are in [types/].
+
+Roughly order from lowest-level to highest:
+
+- `common.ts`: stateful field data, and couple other misc types
+- `field.ts`: fields - types, values, instances, templates
+- `invocation.ts`: invocations and other node types
+- `workflow.ts`: workflows and constituents
+
+We customize the OpenAPI schema to include additional properties on invocation and field schemas. To facilitate parsing this schema into templates, we modify/wrap the types from [openapi-types] in `openapi.ts`.
+
+### OpenAPI Schema Parsing
+
+The entrypoint for OpenAPI schema parsing is [parseSchema.ts].
+
+General logic flow:
+
+- Iterate over all invocation schema objects
+  - Extract relevant invocation-level attributes (e.g. title, type, version, etc)
+  - Iterate over the invocation's input fields
+    - [Parse each field's type](#parsing-field-types)
+    - [Build a field input template](#building-field-input-templates) from the type - either a stateful template or "generic" stateless template
+  - Iterate over the invocation's output fields
+    - Parse the field's type (same as inputs)
+    - [Build a field output template](#building-field-output-templates)
+  - Assemble the attributes and fields into an invocation template
+
+Most of these involve very straightforward `reduce`s, but the less intuitive steps are detailed below.
+
+#### Parsing Field Types
+
+Field types are represented as structured objects:
+
+```ts
+type FieldType = {
+  name: string;
+  isCollection: boolean;
+  isCollectionOrScalar: boolean;
+};
+```
+
+The parsing logic is in `parseFieldType.ts`.
+
+There are 4 general cases for field type parsing.
+
+##### Primitive Types
+
+When a field is annotated as a primitive values (e.g. `int`, `str`, `float`), the field type parsing is fairly straightforward. The field is represented by a simple OpenAPI **schema object**, which has a `type` property.
+
+We create a field type name from this `type` string (e.g. `string` -> `StringField`).
+
+##### Complex Types
+
+When a field is annotated as a pydantic model (e.g. `ImageField`, `MainModelField`, `ControlField`), it is represented as a **reference object**. Reference objects are pointers to another schema or reference object within the schema.
+
+We need to **dereference** the schema to pull these out. Dereferencing may require recursion. We use the reference object's name directly for the field type name.
+
+> Unfortunately, at this time, we've had limited success using external libraries to deference at runtime, so we do this ourselves.
+
+##### Collection Types
+
+When a field is annotated as a list of a single type, the schema object has an `items` property. They may be a schema object or reference object and must be parsed to determine the item type.
+
+We use the item type for field type name, adding `isCollection: true` to the field type.
+
+##### Collection or Scalar Types
+
+When a field is annotated as a union of a type and list of that type, the schema object has an `anyOf` property, which holds a list of valid types for the union.
+
+After verifying that the union has two members (a type and list of the same type), we use the type for field type name, adding `isCollectionOrScalar: true` to the field type.
+
+##### Optional Fields
+
+In OpenAPI v3.1, when an object is optional, it is put into an `anyOf` along with a primitive schema object with `type: 'null'`.
+
+Handling this adds a fair bit of complexity, as we now must filter out the `'null'` types and work with the remaining types as described above.
+
+If there is a single remaining schema object, we must recursively call to `parseFieldType()` to get parse it.
+
+#### Building Field Input Templates
+
+Now that we have a field type, we can build an input template for the field.
+
+Stateful fields all get a function to build their template, while stateless fields are constructed directly. This is possible because stateless fields have no default value or constraints.
+
+See [buildFieldInputTemplate.ts].
+
+#### Building Field Output Templates
+
+Field outputs are similar to stateless fields - they do not have any value in the frontend. When building their templates, we don't need a special function for each field type.
+
+See [buildFieldOutputTemplate.ts].
+
+### Managing reactflow State
+
+As described above, the workflow editor state is the essentially the reactflow state, plus some extra metadata.
+
+We provide reactflow with an array of nodes and edges via redux, and a number of [event handlers][reactflow-events]. These handlers dispatch redux actions, managing nodes and edges.
+
+The pieces of redux state relevant to workflows are:
+
+- `state.nodes.nodes`: the reactflow nodes state
+- `state.nodes.edges`: the reactflow edges state
+- `state.nodes.workflow`: the workflow metadata
+
+#### Building Nodes and Edges
+
+A reactflow node has a few important top-level properties:
+
+- `id`: unique identifier
+- `type`: a string that maps to a react component to render the node
+- `position`: XY coordinates
+- `data`: arbitrary data
+
+When the user adds a node, we build **invocation node data**, storing it in `data`. Invocation properties (e.g. type, version, label, etc.) are copied from the invocation template. Inputs and outputs are built from the invocation template's field templates.
+
+See [buildInvocationNode.ts].
+
+Edges are managed by reactflow, but briefly, they consist of:
+
+- `source`: id of the source node
+- `sourceHandle`: id of the source node handle (output field)
+- `target`: id of the target node
+- `targetHandle`: id of the target node handle (input field)
+
+> Edge creation is gated behind validation logic. This validation compares the input and output field types and overall graph state.
+
+#### Building a Workflow
+
+Building a workflow entity is as simple as dropping the nodes, edges and metadata into an object.
+
+Each node and edge is parsed with a zod schema, which serves to strip out any unneeded data.
+
+See [buildWorkflow.ts].
+
+#### Loading a Workflow
+
+Workflows may be loaded from external sources or the user's local instance. In all cases, the workflow needs to be handled with care, as an untrusted object.
+
+Loading has a few stages which may throw or warn if there are problems:
+
+- Parsing the workflow data structure itself, [migrating](#workflow-migrations) it if necessary (throws)
+- Check for a template for each node (warns)
+- Check each node's version against its template (warns)
+- Validate the source and target of each edge (warns)
+
+This validation occurs in [validateWorkflow.ts].
+
+If there are no fatal errors, the workflow is then stored in redux state.
+
+### Workflow Migrations
+
+When the workflow schema changes, we may need to perform some data migrations. This occurs as workflows are loaded. zod schemas for each workflow schema version is retained to facilitate migrations.
+
+Previous schemas are in folders in `invokeai/frontend/web/src/features/nodes/types/`, eg `v1/`.
+
+Migration logic is in [migrations.ts].
+
+<!-- links -->
+
+[pydantic]: https://github.com/pydantic/pydantic 'pydantic'
+[zod]: https://github.com/colinhacks/zod 'zod'
+[openapi-types]: https://github.com/kogosoftwarellc/open-api/tree/main/packages/openapi-types 'openapi-types'
+[reactflow]: https://github.com/xyflow/xyflow 'reactflow'
+[reactflow-concepts]: https://reactflow.dev/learn/concepts/terms-and-definitions
+[reactflow-events]: https://reactflow.dev/api-reference/react-flow#event-handlers
+[buildWorkflow.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/workflow/buildWorkflow.ts
+[nodesSlice.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts
+[buildLinearTextToImageGraph.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/graph/buildLinearTextToImageGraph.ts
+[buildNodesGraph.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/graph/buildNodesGraph.ts
+[buildInvocationNode.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/node/buildInvocationNode.ts
+[validateWorkflow.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/workflow/validateWorkflow.ts
+[migrations.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/workflow/migrations.ts
+[parseSchema.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/schema/parseSchema.ts
+[buildFieldInputTemplate.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/schema/buildFieldInputTemplate.ts
+[buildFieldOutputTemplate.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/schema/buildFieldOutputTemplate.ts

docs/contributing/frontend/canvas-text-tool.md

@@ -1,35 +0,0 @@
-# Canvas Text Tool
-
-## Overview
-
-The canvas text workflow is split between a Konva module that owns tool state and a React overlay that handles text entry.
-
-- `invokeai/frontend/web/src/features/controlLayers/konva/CanvasTool/CanvasTextToolModule.ts`
-  - Owns the tool, cursor preview, and text session state (including the cursor "T" marker).
-  - Manages dynamic cursor contrast, starts sessions on pointer down, and commits sessions by rasterizing the active text block into a new raster layer.
-- `invokeai/frontend/web/src/features/controlLayers/components/Text/CanvasTextOverlay.tsx`
-  - Renders the on-canvas editor as a `contentEditable` overlay positioned in canvas space.
-  - Syncs keyboard input, suppresses app hotkeys, and forwards commits/cancels to the Konva module.
-- `invokeai/frontend/web/src/features/controlLayers/components/Text/TextToolOptions.tsx`
-  - Provides the font dropdown, size slider/input, formatting toggles, and alignment buttons that appear when the Text tool is active.
-
-## Rasterization pipeline
-
-`renderTextToCanvas()` (`invokeai/frontend/web/src/features/controlLayers/text/textRenderer.ts`) converts the editor contents into a transparent canvas. The Text tool module configures the renderer with the active font stack, weight, styling flags, alignment, and the active canvas color. The resulting canvas is encoded to a PNG data URL and stored in a new raster layer (`image` object) with a transparent background.
-
-Layer placement preserves the original click location:
-
-- The session stores the anchor coordinate (where the user clicked) and current alignment.
-- `calculateLayerPosition()` calculates the top-left position for the raster layer after applying the configured padding and alignment offsets.
-- New layers are inserted directly above the currently-selected raster layer (when present) and selected automatically.
-
-## Font stacks
-
-Font definitions live in `invokeai/frontend/web/src/features/controlLayers/text/textConstants.ts` as ten deterministic stacks (sans, serif, mono, rounded, script, humanist, slab serif, display, narrow, UI serif). Each stack lists system-safe fallbacks so the editor can choose the first available font per platform.
-
-To add or adjust fonts:
-
-1. Update `TEXT_FONT_STACKS` with the new `id`, `label`, and CSS `font-family` stack.
-2. If you add a new stack, extend the `TEXT_FONT_IDS` tuple and update the `canvasTextSlice` schema default (`TEXT_DEFAULT_FONT_ID`).
-3. Provide translation strings for any new labels in `public/locales/*`.
-4. The editor and renderer will automatically pick up the new stack via `getFontStackById()`.

docs/contributing/frontend/index.md

@@ -1,128 +0,0 @@
-# Invoke UI
-
-Invoke's UI is made possible by many contributors and open-source libraries. Thank you!
-
-## Dev environment
-
-Follow the [dev environment](../dev-environment.md) guide to get set up. Run the UI using `pnpm dev`.
-
-## Package scripts
-
-- `dev`: run the frontend in dev mode, enabling hot reloading
-- `build`: run all checks (dpdm, eslint, prettier, tsc, knip) and then build the frontend
-- `lint:dpdm`: check circular dependencies
-- `lint:eslint`: check code quality
-- `lint:prettier`: check code formatting
-- `lint:tsc`: check type issues
-- `lint:knip`: check for unused exports or objects
-- `lint`: run all checks concurrently
-- `fix`: run `eslint` and `prettier`, fixing fixable issues
-- `test:ui`: run `vitest` with the fancy web UI
-
-## 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].
-
-If you make backend changes, it's important to regenerate the frontend types:
-
-```sh
-cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen
-```
-
-On macOS and Linux, you can run `make frontend-typegen` as a shortcut for the above snippet.
-
-## Localization
-
-We use [i18next] for localization, but translation to languages other than English happens on our [Weblate] project.
-
-Only the English source strings (i.e. `en.json`) 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 to 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`
-
-## Tests
-
-We don't do any UI testing at this time, but consider adding tests for sensitive logic.
-
-We use `vitest`, and tests should be next to the file they are testing. If the logic is in `something.ts`, the tests should be in `something.test.ts`.
-
-In some situations, we may want to test types. For example, if you use `zod` to create a schema that should match a generated type, it's best to add a test to confirm that the types match. Use `tsafe`'s assert for this.
-
-## 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.
-
-## Other docs
-
-- [Workflows - Design and Implementation]
-- [State Management]
-
-[discord]: https://discord.gg/ZmtBAhwWhy
-[i18next]: https://github.com/i18next/react-i18next
-[Weblate]: https://hosted.weblate.org/engage/invokeai/
-[openapi-typescript]: https://github.com/openapi-ts/openapi-typescript
-[schema.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/services/api/schema.ts
-[conventional commits]: https://www.conventionalcommits.org/en/v1.0.0/
-[Workflows - Design and Implementation]: ./workflows.md
-[State Management]: ./state-management.md

docs/contributing/frontend/state-management.md

@@ -1,38 +0,0 @@
-# State Management
-
-The app makes heavy use of Redux Toolkit, its Query library, and `nanostores`.
-
-## Redux
-
-We use RTK extensively - slices, entity adapters, queries, reselect, the whole 9 yards. Their [docs](https://redux-toolkit.js.org/) are excellent.
-
-## `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/

docs/contributing/frontend/workflows.md

@@ -1,314 +0,0 @@
-# Workflows - Design and Implementation
-
-> This document describes, at a high level, the design and implementation of workflows in the InvokeAI frontend. There are a substantial number of implementation details not included, but which are hopefully clear from the code.
-
-InvokeAI's backend uses graphs, composed of **nodes** and **edges**, to process data and generate images.
-
-Nodes have any number of **input fields** and **output fields**. Edges connect nodes together via their inputs and outputs. Fields have data types which dictate how they may be connected.
-
-During execution, a nodes' outputs may be passed along to any number of other nodes' inputs.
-
-Workflows are an enriched abstraction over a graph.
-
-## Design
-
-InvokeAI provide two ways to build graphs in the frontend: the [Linear UI](#linear-ui) and [Workflow Editor](#workflow-editor).
-
-To better understand the use case and challenges related to workflows, we will review both of these modes.
-
-### Linear UI
-
-This includes the **Text to Image**, **Image to Image** and **Unified Canvas** tabs.
-
-The user-managed parameters on these tabs are stored as simple objects in the application state. When the user invokes, adding a generation to the queue, we internally build a graph from these parameters.
-
-This logic can be fairly complex due to the range of features available and their interactions. Depending on the parameters selected, the graph may be very different. Building graphs in code can be challenging - you are trying to construct a non-linear structure in a linear context.
-
-The simplest graph building logic is for **Text to Image** with a SD1.5 model: [buildLinearTextToImageGraph.ts]
-
-There are many other graph builders in the same directory for different tabs or base models (e.g. SDXL). Some are pretty hairy.
-
-In the Linear UI, we go straight from **simple application state** to **graph** via these builders.
-
-### Workflow Editor
-
-The Workflow Editor is a visual graph editor, allowing users to draw edges from node to node to construct a graph. This _far_ more approachable way to create complex graphs.
-
-InvokeAI uses the [reactflow] library to power the Workflow Editor. It provides both a graph editor UI and manages its own internal graph state.
-
-#### Workflows
-
-A workflow is a representation of a graph plus additional metadata:
-
-- Name
-- Description
-- Version
-- Notes
-- [Exposed fields](#workflow-linear-view)
-- Author, tags, category, etc.
-
-Workflows should have other qualities:
-
-- Portable: you should be able to load a workflow created by another person.
-- Resilient: you should be able to "upgrade" a workflow as the application changes.
-- Abstract: as much as is possible, workflows should not be married to the specific implementation details of the application.
-
-To support these qualities, workflows are serializable, have a versioned schemas, and represent graphs as minimally as possible. Fortunately, the reactflow state for nodes and edges works perfectly for this.
-
-##### Workflow -> reactflow state -> InvokeAI graph
-
-Given a workflow, we need to be able to derive reactflow state and/or an InvokeAI graph from it.
-
-The first step - workflow to reactflow state - is very simple. The logic is in [nodesSlice.ts], in the `workflowLoaded` reducer.
-
-The reactflow state is, however, structurally incompatible with our backend's graph structure. When a user invokes on a Workflow, we need to convert the reactflow state into an InvokeAI graph. This is far simpler than the graph building logic from the Linear UI:
-[buildNodesGraph.ts]
-
-##### Nodes vs Invocations
-
-We often use the terms "node" and "invocation" interchangeably, but they may refer to different things in the frontend.
-
-reactflow [has its own definitions][reactflow-concepts] of "node", "edge" and "handle" which are closely related to InvokeAI graph concepts.
-
-- A reactflow node is related to an InvokeAI invocation. It has a "data" property, which holds the InvokeAI-specific invocation data.
-- A reactflow edge is roughly equivalent to an InvokeAI edge.
-- A reactflow handle is roughly equivalent to an InvokeAI input or output field.
-
-##### Workflow Linear View
-
-Graphs are very capable data structures, but not everyone wants to work with them all the time.
-
-To allow less technical users - or anyone who wants a less visually noisy workspace - to benefit from the power of nodes, InvokeAI has a workflow feature called the Linear View.
-
-A workflow input field can be added to this Linear View, and its input component can be presented similarly to the Linear UI tabs. Internally, we add the field to the workflow's list of exposed fields.
-
-#### OpenAPI Schema
-
-OpenAPI is a schema specification that can represent complex data structures and relationships. The backend is capable of generating an OpenAPI schema for all invocations.
-
-When the UI connects, it requests this schema and parses each invocation into an **invocation template**. Invocation templates have a number of properties, like title, description and type, but the most important ones are their input and output **field templates**.
-
-Invocation and field templates are the "source of truth" for graphs, because they indicate what the backend is able to process.
-
-When a user adds a new node to their workflow, these templates are used to instantiate a node with fields instantiated from the input and output field templates.
-
-##### Field Instances and Templates
-
-Field templates consist of:
-
-- Name: the identifier of the field, its variable name in python
-- Type: derived from the field's type annotation in python (e.g. IntegerField, ImageField, MainModelField)
-- Constraints: derived from the field's creation args in python (e.g. minimum value for an integer)
-- Default value: optionally provided in the field's creation args (e.g. 42 for an integer)
-
-Field instances are created from the templates and have name, type and optionally a value.
-
-The type of the field determines the UI components that are rendered for it.
-
-A field instance's name associates it with its template.
-
-##### Stateful vs Stateless Fields
-
-**Stateful** fields store their value in the frontend graph. Think primitives, model identifiers, images, etc. Fields are only stateful if the frontend allows the user to directly input a value for them.
-
-Many field types, however, are **stateless**. An example is a `UNetField`, which contains some data describing a UNet. Users cannot directly provide this data - it is created and consumed in the backend.
-
-Stateless fields do not store their value in the node, so their field instances do not have values.
-
-"Custom" fields will always be treated as stateless fields.
-
-##### Single and Collection Fields
-
-Field types have a name and cardinality property which may identify it as a **SINGLE**, **COLLECTION** or **SINGLE_OR_COLLECTION** field.
-
-- If a field is annotated in python as a singular value or class, its field type is parsed as a **SINGLE** type (e.g. `int`, `ImageField`, `str`).
-- If a field is annotated in python as a list, its field type is parsed as a **COLLECTION** type (e.g. `list[int]`).
-- If it is annotated as a union of a type and list, the type will be parsed as a **SINGLE_OR_COLLECTION** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
-
-## Implementation
-
-The majority of data structures in the backend are [pydantic] models. Pydantic provides OpenAPI schemas for all models and we then generate TypeScript types from those.
-
-The OpenAPI schema is parsed at runtime into our invocation templates.
-
-Workflows and all related data are modeled in the frontend using [zod]. Related types are inferred from the zod schemas.
-
-> In python, invocations are pydantic models with fields. These fields become node inputs. The invocation's `invoke()` function returns a pydantic model - its output. Like the invocation itself, the output model has any number of fields, which become node outputs.
-
-### zod Schemas and Types
-
-The zod schemas, inferred types, and type guards are in [types/].
-
-Roughly order from lowest-level to highest:
-
-- `common.ts`: stateful field data, and couple other misc types
-- `field.ts`: fields - types, values, instances, templates
-- `invocation.ts`: invocations and other node types
-- `workflow.ts`: workflows and constituents
-
-We customize the OpenAPI schema to include additional properties on invocation and field schemas. To facilitate parsing this schema into templates, we modify/wrap the types from [openapi-types] in `openapi.ts`.
-
-### OpenAPI Schema Parsing
-
-The entrypoint for OpenAPI schema parsing is [parseSchema.ts].
-
-General logic flow:
-
-- Iterate over all invocation schema objects
-  - Extract relevant invocation-level attributes (e.g. title, type, version, etc)
-  - Iterate over the invocation's input fields
-    - [Parse each field's type](#parsing-field-types)
-    - [Build a field input template](#building-field-input-templates) from the type - either a stateful template or "generic" stateless template
-  - Iterate over the invocation's output fields
-    - Parse the field's type (same as inputs)
-    - [Build a field output template](#building-field-output-templates)
-  - Assemble the attributes and fields into an invocation template
-
-Most of these involve very straightforward `reduce`s, but the less intuitive steps are detailed below.
-
-#### Parsing Field Types
-
-Field types are represented as structured objects:
-
-```ts
-type FieldType = {
-  name: string;
-  cardinality: 'SINGLE' | 'COLLECTION' | 'SINGLE_OR_COLLECTION';
-};
-```
-
-The parsing logic is in `parseFieldType.ts`.
-
-There are 4 general cases for field type parsing.
-
-##### Primitive Types
-
-When a field is annotated as a primitive values (e.g. `int`, `str`, `float`), the field type parsing is fairly straightforward. The field is represented by a simple OpenAPI **schema object**, which has a `type` property.
-
-We create a field type name from this `type` string (e.g. `string` -> `StringField`). The cardinality is `"SINGLE"`.
-
-##### Complex Types
-
-When a field is annotated as a pydantic model (e.g. `ImageField`, `MainModelField`, `ControlField`), it is represented as a **reference object**. Reference objects are pointers to another schema or reference object within the schema.
-
-We need to **dereference** the schema to pull these out. Dereferencing may require recursion. We use the reference object's name directly for the field type name.
-
-> Unfortunately, at this time, we've had limited success using external libraries to deference at runtime, so we do this ourselves.
-
-##### Collection Types
-
-When a field is annotated as a list of a single type, the schema object has an `items` property. They may be a schema object or reference object and must be parsed to determine the item type.
-
-We use the item type for field type name. The cardinality is `"COLLECTION"`.
-
-##### Single or Collection Types
-
-When a field is annotated as a union of a type and list of that type, the schema object has an `anyOf` property, which holds a list of valid types for the union.
-
-After verifying that the union has two members (a type and list of the same type), we use the type for field type name, with cardinality `"SINGLE_OR_COLLECTION"`.
-
-##### Optional Fields
-
-In OpenAPI v3.1, when an object is optional, it is put into an `anyOf` along with a primitive schema object with `type: 'null'`.
-
-Handling this adds a fair bit of complexity, as we now must filter out the `'null'` types and work with the remaining types as described above.
-
-If there is a single remaining schema object, we must recursively call to `parseFieldType()` to get parse it.
-
-#### Building Field Input Templates
-
-Now that we have a field type, we can build an input template for the field.
-
-Stateful fields all get a function to build their template, while stateless fields are constructed directly. This is possible because stateless fields have no default value or constraints.
-
-See [buildFieldInputTemplate.ts].
-
-#### Building Field Output Templates
-
-Field outputs are similar to stateless fields - they do not have any value in the frontend. When building their templates, we don't need a special function for each field type.
-
-See [buildFieldOutputTemplate.ts].
-
-### Managing reactflow State
-
-As described above, the workflow editor state is the essentially the reactflow state, plus some extra metadata.
-
-We provide reactflow with an array of nodes and edges via redux, and a number of [event handlers][reactflow-events]. These handlers dispatch redux actions, managing nodes and edges.
-
-The pieces of redux state relevant to workflows are:
-
-- `state.nodes.nodes`: the reactflow nodes state
-- `state.nodes.edges`: the reactflow edges state
-- `state.nodes.workflow`: the workflow metadata
-
-#### Building Nodes and Edges
-
-A reactflow node has a few important top-level properties:
-
-- `id`: unique identifier
-- `type`: a string that maps to a react component to render the node
-- `position`: XY coordinates
-- `data`: arbitrary data
-
-When the user adds a node, we build **invocation node data**, storing it in `data`. Invocation properties (e.g. type, version, label, etc.) are copied from the invocation template. Inputs and outputs are built from the invocation template's field templates.
-
-See [buildInvocationNode.ts].
-
-Edges are managed by reactflow, but briefly, they consist of:
-
-- `source`: id of the source node
-- `sourceHandle`: id of the source node handle (output field)
-- `target`: id of the target node
-- `targetHandle`: id of the target node handle (input field)
-
-> Edge creation is gated behind validation logic. This validation compares the input and output field types and overall graph state.
-
-#### Building a Workflow
-
-Building a workflow entity is as simple as dropping the nodes, edges and metadata into an object.
-
-Each node and edge is parsed with a zod schema, which serves to strip out any unneeded data.
-
-See [buildWorkflow.ts].
-
-#### Loading a Workflow
-
-Workflows may be loaded from external sources or the user's local instance. In all cases, the workflow needs to be handled with care, as an untrusted object.
-
-Loading has a few stages which may throw or warn if there are problems:
-
-- Parsing the workflow data structure itself, [migrating](#workflow-migrations) it if necessary (throws)
-- Check for a template for each node (warns)
-- Check each node's version against its template (warns)
-- Validate the source and target of each edge (warns)
-
-This validation occurs in [validateWorkflow.ts].
-
-If there are no fatal errors, the workflow is then stored in redux state.
-
-### Workflow Migrations
-
-When the workflow schema changes, we may need to perform some data migrations. This occurs as workflows are loaded. zod schemas for each workflow schema version is retained to facilitate migrations.
-
-Previous schemas are in folders in `invokeai/frontend/web/src/features/nodes/types/`, eg `v1/`.
-
-Migration logic is in [migrations.ts].
-
-<!-- links -->
-
-[pydantic]: https://github.com/pydantic/pydantic 'pydantic'
-[zod]: https://github.com/colinhacks/zod 'zod'
-[openapi-types]: https://github.com/kogosoftwarellc/open-api/tree/main/packages/openapi-types 'openapi-types'
-[reactflow]: https://github.com/xyflow/xyflow 'reactflow'
-[reactflow-concepts]: https://reactflow.dev/learn/concepts/terms-and-definitions
-[reactflow-events]: https://reactflow.dev/api-reference/react-flow#event-handlers
-[buildWorkflow.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/workflow/buildWorkflow.ts
-[nodesSlice.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts
-[buildLinearTextToImageGraph.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/graph/buildLinearTextToImageGraph.ts
-[buildNodesGraph.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/graph/buildNodesGraph.ts
-[buildInvocationNode.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/node/buildInvocationNode.ts
-[validateWorkflow.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/workflow/validateWorkflow.ts
-[migrations.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/workflow/migrations.ts
-[parseSchema.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/schema/parseSchema.ts
-[buildFieldInputTemplate.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/schema/buildFieldInputTemplate.ts
-[buildFieldOutputTemplate.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/features/nodes/util/schema/buildFieldOutputTemplate.ts

docs/contributing/index.md

@@ -1,56 +0,0 @@
-# Contributing
-
-Invoke originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
-
-We welcome contributions, whether features, bug fixes, code cleanup, testing, code reviews, documentation or translation. Please check in with us before diving in to code to ensure your work aligns with our vision.
-
-## Development
-
-If you’d like to help with development, please see our [development guide](contribution_guides/development.md).
-
-## External Providers
-
-If you are adding external image generation providers or configs, see our [external provider integration guide](EXTERNAL_PROVIDERS.md).
-
-**New Contributors:** If you’re unfamiliar with contributing to open source projects, take a look at our [new contributor guide](contribution_guides/newContributorChecklist.md).
-
-## Nodes
-
-If you’d like to add a Node, please see our [nodes contribution guide](../nodes/contributingNodes.md).
-
-## Support and Triaging
-
-Helping support other users in [Discord](https://discord.gg/ZmtBAhwWhy) and on Github are valuable forms of contribution that we greatly appreciate.
-
-We receive many issues and requests for help from users. We're limited in bandwidth relative to our user base, so providing answers to questions or helping identify causes of issues is very helpful. By doing this, you enable us to spend time on the highest priority work.
-
-## Documentation
-
-If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documentation.md).
-
-## Translation
-
-If you'd like to help with translation, please see our [translation guide](contribution_guides/translation.md).
-
-## Tutorials
-
-Please reach out to @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
-
-## Contributors
-
-This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](contributors.md). We thank them for their time, hard work and effort.
-
-## Code of Conduct
-
-The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](../CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
-
-By making a contribution to this project, you certify that:
-
-1. The contribution was created in whole or in part by you and you have the right to submit it under the open-source license indicated in this project’s GitHub repository; or
-2. The contribution is based upon previous work that, to the best of your knowledge, is covered under an appropriate open-source license and you have the right under that license to submit that work with modifications, whether created in whole or in part by you, under the same open-source license (unless you are permitted to submit under a different license); or
-3. The contribution was provided directly to you by some other person who certified (1) or (2) and you have not modified it; or
-4. You understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information you submit with it, including your sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open-source license(s) involved.
-
-This disclaimer is not a license and does not grant any rights or permissions. You must obtain necessary permissions and licenses, including from third parties, before contributing to this project.
-
-This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.

docs/faq.md

@@ -1,107 +0,0 @@
-# FAQ
-
-If the troubleshooting steps on this page don't get you up and running, please either [create an issue] or hop on [discord] for help.
-
-## How to Install
-
-Follow the [Quick Start guide](./installation/quick_start.md) to install Invoke.
-
-## Downloading models and using existing models
-
-The Model Manager tab in the UI provides a few ways to install models, including using your already-downloaded models. You'll see a popup directing you there on first startup. For more information, see the [model install docs].
-
-## Missing models after updating from v3
-
-If you find some models are missing after updating from v3, it's likely they weren't correctly registered before the update and didn't get picked up in the migration.
-
-You can use the `Scan Folder` tab in the Model Manager UI to fix this. The models will either be in the old, now-unused `autoimport` folder, or your `models` folder.
-
-- Find and copy your install's old `autoimport` folder path, install the main install folder.
-- Go to the Model Manager and click `Scan Folder`.
-- Paste the path and scan.
-- IMPORTANT: Uncheck `Inplace install`.
-- Click `Install All` to install all found models, or just install the models you want.
-
-Next, find and copy your install's `models` folder path (this could be your custom models folder path, or the `models` folder inside the main install folder).
-
-Follow the same steps to scan and import the missing models.
-
-## Slow generation
-
-- Check the [system requirements] to ensure that your system is capable of generating images.
-- Follow the [Low-VRAM mode guide](./features/low-vram.md) to optimize performance.
-- Check that your generations are happening on your GPU (if you have one). Invoke will log what is being used for generation upon startup. If your GPU isn't used, re-install to and ensure you select the appropriate GPU option.
-- If you are on Windows with an Nvidia GPU, you may have exceeded your GPU's VRAM capacity and are triggering Nvidia's "sysmem fallback". There's a guide to opt out of this behaviour in the [Low-VRAM mode guide](./features/low-vram.md).
-
-## Triton error on startup
-
-This can be safely ignored. Invoke doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
-
-## Unable to Copy on Firefox
-
-Firefox does not allow Invoke to directly access the clipboard by default. As a result, you may be unable to use certain copy functions. You can fix this by configuring Firefox to allow access to write to the clipboard:
-
-- Go to `about:config` and click the Accept button
-- Search for `dom.events.asyncClipboard.clipboardItem`
-- Set it to `true` by clicking the toggle button
-- Restart Firefox
-
-## Replicate image found online
-
-Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
-
-## Invalid configuration file
-
-Everything seems to install ok, you get a `ValidationError` when starting up the app.
-
-This is caused by an invalid setting in the `invokeai.yaml` configuration file. The error message should tell you what is wrong.
-
-Check the [configuration docs] for more detail about the settings and how to specify them.
-
-## Out of Memory Errors
-
-The models are large, VRAM is expensive, and you may find yourself faced with Out of Memory errors when generating images. Follow our [Low-VRAM mode guide](./features/low-vram.md) to configure Invoke to prevent these.
-
-## Memory Leak (Linux)
-
-If you notice a memory leak, it could be caused to memory fragmentation as models are loaded and/or moved from CPU to GPU.
-
-A workaround is to tune memory allocation with an environment variable:
-
-```bash
-# Force blocks >1MB to be allocated with `mmap` so that they are released to the system immediately when they are freed.
-MALLOC_MMAP_THRESHOLD_=1048576
-```
-
-!!! warning "Speed vs Memory Tradeoff"
-
-    Your generations may be slower overall when setting this environment variable.
-
-!!! info "Possibly dependent on `libc` implementation"
-
-    It's not known if this issue occurs with other `libc` implementations such as `musl`.
-
-    If you encounter this issue and your system uses a different implementation, please try this environment variable and let us know if it fixes the issue.
-
-<h3>Detailed Discussion</h3>
-
-Python (and PyTorch) relies on the memory allocator from the C Standard Library (`libc`). On linux, with the GNU C Standard Library implementation (`glibc`), our memory access patterns have been observed to cause severe memory fragmentation.
-
-This fragmentation results in large amounts of memory that has been freed but can't be released back to the OS. Loading models from disk and moving them between CPU/CUDA seem to be the operations that contribute most to the fragmentation.
-
-This memory fragmentation issue can result in OOM crashes during frequent model switching, even if `ram` (the max RAM cache size) is set to a reasonable value (e.g. a OOM crash with `ram=16` on a system with 32GB of RAM).
-
-This problem may also exist on other OSes, and other `libc` implementations. But, at the time of writing, it has only been investigated on linux with `glibc`.
-
-To better understand how the `glibc` memory allocator works, see these references:
-
-- Basics: <https://www.gnu.org/software/libc/manual/html_node/The-GNU-Allocator.html>
-- Details: <https://sourceware.org/glibc/wiki/MallocInternals>
-
-Note the differences between memory allocated as chunks in an arena vs. memory allocated with `mmap`. Under `glibc`'s default configuration, most model tensors get allocated as chunks in an arena making them vulnerable to the problem of fragmentation.
-
-[model install docs]: ./installation/models.md
-[system requirements]: ./installation/requirements.md
-[create an issue]: https://github.com/invoke-ai/InvokeAI/issues
-[discord]: https://discord.gg/ZmtBAhwWhy
-[configuration docs]: ./configuration.md

docs/features/CONFIGURATION.md

@@ -0,0 +1,186 @@
+---
+title: Configuration
+---
+
+# :material-tune-variant: InvokeAI Configuration
+
+## Intro
+
+Runtime settings, including the location of files and
+directories, memory usage, and performance, are managed via the
+`invokeai.yaml` config file or environment variables. A subset
+of settings may be set via commandline arguments.
+
+Settings sources are used in this order:
+
+- CLI args
+- Environment variables
+- `invokeai.yaml` settings
+- Fallback: defaults
+
+### InvokeAI Root Directory
+
+On startup, InvokeAI searches for its "root" directory. This is the directory
+that contains models, images, the database, and so on. It also contains
+a configuration file called `invokeai.yaml`.
+
+InvokeAI searches for the root directory in this order:
+
+1. The `--root <path>` CLI arg.
+2. The environment variable INVOKEAI_ROOT.
+3. The directory containing the currently active virtual environment.
+4. Fallback: a directory in the current user's home directory named `invokeai`.
+
+### InvokeAI Configuration File
+
+Inside the root directory, we read settings from the `invokeai.yaml` file.
+
+It has two sections - one for internal use and one for user settings:
+
+```yaml
+# Internal metadata - do not edit:
+schema_version: 4
+
+# Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:
+host: 0.0.0.0 # serve the app on your local network
+models_dir: D:\invokeai\models # store models on an external drive
+precision: float16 # always use fp16 precision
+```
+
+The settings in this file will override the defaults. You only need
+to change this file if the default for a particular setting doesn't
+work for you.
+
+Some settings, like [Model Marketplace API Keys], require the YAML
+to be formatted correctly. Here is a [basic guide to YAML files].
+
+You can fix a broken `invokeai.yaml` by deleting it and running the
+configuration script again -- option [6] in the launcher, "Re-run the
+configure script".
+
+#### Custom Config File Location
+
+You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.
+
+Note that environment variables will trump any settings in the config file.
+
+### Environment Variables
+
+All settings may be set via environment variables by prefixing `INVOKEAI_`
+to the variable name. For example, `INVOKEAI_HOST` would set the `host`
+setting.
+
+For non-primitive values, pass a JSON-encoded string:
+
+```sh
+export INVOKEAI_REMOTE_API_TOKENS='[{"url_regex":"modelmarketplace", "token": "12345"}]'
+```
+
+We suggest using `invokeai.yaml`, as it is more user-friendly.
+
+### CLI Args
+
+A subset of settings may be specified using CLI args:
+
+- `--root`: specify the root directory
+- `--config`: override the default `invokeai.yaml` file location
+
+### All Settings
+
+Following the table are additional explanations for certain settings.
+
+<!-- prettier-ignore-start -->
+::: invokeai.app.services.config.config_default.InvokeAIAppConfig
+    options:
+        heading_level: 4
+        members: false
+        show_docstring_description: false
+        group_by_category: true
+        show_category_heading: false
+<!-- prettier-ignore-end -->
+
+#### Model Marketplace API Keys
+
+Some model marketplaces require an API key to download models. You can provide a URL pattern and appropriate token in your `invokeai.yaml` file to provide that API key.
+
+The pattern can be any valid regex (you may need to surround the pattern with quotes):
+
+```yaml
+remote_api_tokens:
+  # Any URL containing `models.com` will automatically use `your_models_com_token`
+  - url_regex: models.com
+    token: your_models_com_token
+  # Any URL matching this contrived regex will use `some_other_token`
+  - url_regex: '^[a-z]{3}whatever.*\.com$'
+    token: some_other_token
+```
+
+The provided token will be added as a `Bearer` token to the network requests to download the model files. As far as we know, this works for all model marketplaces that require authorization.
+
+#### Model Hashing
+
+Models are hashed during installation, providing a stable identifier for models across all platforms. Hashing is a one-time operation.
+
+```yaml
+hashing_algorithm: blake3_single # default value
+```
+
+You might want to change this setting, depending on your system:
+
+- `blake3_single` (default): Single-threaded - best for spinning HDDs, still OK for SSDs
+- `blake3_multi`: Parallelized, memory-mapped implementation - best for SSDs, terrible for spinning disks
+- `random`: Skip hashing entirely - fastest but of course no hash
+
+During the first startup after upgrading to v4, all of your models will be hashed. This can take a few minutes.
+
+Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than either of the BLAKE3 variants.
+
+#### Path Settings
+
+These options set the paths of various directories and files used by InvokeAI. Any user-defined paths should be absolute paths.
+
+#### Logging
+
+Several different log handler destinations are available, and multiple destinations are supported by providing a list:
+
+```yaml
+log_handlers:
+  - console
+  - syslog=localhost
+  - file=/var/log/invokeai.log
+```
+
+- `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
+
+- `syslog` is only available on Linux and Macintosh systems. It uses
+  the operating system's "syslog" facility to write log file entries
+  locally or to a remote logging machine. `syslog` offers a variety
+  of configuration options:
+
+```
+  syslog=/dev/log`      - log to the /dev/log device
+  syslog=localhost`     - log to the network logger running on the local machine
+  syslog=localhost:512` - same as above, but using a non-standard port
+  syslog=fredserver,facility=LOG_USER,socktype=SOCK_DRAM`
+                        - Log to LAN-connected server "fredserver" using the facility LOG_USER and datagram packets.
+```
+
+- `http` can be used to log to a remote web server. The server must be
+  properly configured to receive and act on log messages. The option
+  accepts the URL to the web server, and a `method` argument
+  indicating whether the message should be submitted using the GET or
+  POST method.
+
+```
+ http=http://my.server/path/to/logger,method=POST
+```
+
+The `log_format` option provides several alternative formats:
+
+- `color` - default format providing time, date and a message, using text colors to distinguish different log severities
+- `plain` - same as above, but monochrome text only
+- `syslog` - the log level and error message only, allowing the syslog system to attach the time and date
+- `legacy` - a format similar to the one used by the legacy 2.3 InvokeAI releases.
+
+[basic guide to yaml files]: https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/
+[Model Marketplace API Keys]: #model-marketplace-api-keys

docs/features/CONTROLNET.md

@@ -0,0 +1,181 @@
+---
+title: Control Adapters
+---
+
+# :material-loupe: Control Adapters
+
+## ControlNet
+
+ControlNet is a powerful set of features developed by the open-source
+community (notably, Stanford researcher
+[**@ilyasviel**](https://github.com/lllyasviel)) that allows you to
+apply a secondary neural network model to your image generation
+process in Invoke.
+
+With ControlNet, you can get more control over the output of your
+image generation, providing you with a way to direct the network
+towards generating images that better fit your desired style or
+outcome.
+
+ControlNet works by analyzing an input image, pre-processing that
+image to identify relevant information that can be interpreted by each
+specific ControlNet model, and then inserting that control information
+into the generation process. This can be used to adjust the style,
+composition, or other aspects of the image to better achieve a
+specific result.
+
+#### Installation
+
+InvokeAI provides access to a series of ControlNet models that provide
+different effects or styles in your generated images.
+
+To install ControlNet Models:
+
+1. The easiest way to install them is
+to use the InvokeAI model installer application. Use the
+`invoke.sh`/`invoke.bat` launcher to select item [4] and then navigate
+to the CONTROLNETS section. Select the models you wish to install and
+press "APPLY CHANGES". You may also enter additional HuggingFace
+repo_ids in the "Additional models" textbox. 
+2. Using the "Add Model" function of the  model manager, enter the HuggingFace Repo ID of the ControlNet. The ID is in the format "author/repoName"
+
+
+_Be aware that some ControlNet models require additional code
+functionality in order to work properly, so just installing a
+third-party ControlNet model may not have the desired effect._ Please
+read and follow the documentation for installing a third party model
+not currently included among InvokeAI's default list.
+
+Currently InvokeAI **only** supports 🤗 Diffusers-format ControlNet models. These are
+folders that contain the files `config.json` and/or
+`diffusion_pytorch_model.safetensors` and
+`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
+the name of the model.
+
+🤗 Diffusers-format ControlNet models are available at HuggingFace
+(http://huggingface.co) and accessed via their repo IDs (identifiers
+in the format "author/modelname").
+
+#### ControlNet Models
+The models currently supported include:
+
+**Canny**:
+
+When the Canny model is used in ControlNet, Invoke will attempt to generate images that match the edges detected. 
+
+Canny edge detection works by detecting the edges in an image by looking for abrupt changes in intensity. It is known for its ability to detect edges accurately while reducing noise and false edges, and the preprocessor can identify more information by decreasing the thresholds.
+
+**M-LSD**: 
+
+M-LSD is another edge detection algorithm used in ControlNet. It stands for Multi-Scale Line Segment Detector. 
+
+It detects straight line segments in an image by analyzing the local structure of the image at multiple scales.  It can be useful for architectural imagery, or anything where straight-line structural information is needed for the resulting output. 
+
+**Lineart**: 
+
+The Lineart model in ControlNet generates line drawings from an input image. The resulting pre-processed image is a simplified version of the original, with only the outlines of objects visible.The Lineart model in ControlNet is known for its ability to accurately capture the contours of the objects in an input sketch. 
+
+**Lineart Anime**: 
+
+A variant of the Lineart model that generates line drawings with a distinct style inspired by anime and manga art styles.
+
+**Depth**: 
+A model that generates depth maps of images, allowing you to create more realistic 3D models or to simulate depth effects in post-processing.
+
+**Normal Map (BAE):** 
+A model that generates normal maps from input images, allowing for more realistic lighting effects in 3D rendering.
+		
+**Image Segmentation**: 
+A model that divides input images into segments or regions, each of which corresponds to a different object or part of the image. (More details coming soon)
+
+**QR Code Monster**:
+A model that helps generate creative QR codes that still scan. Can also be used to create images with text, logos or shapes within them. 
+
+**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.
+
+**Tile**:
+
+The Tile model fills out details in the image to match the image, rather than the prompt. The Tile Model is a versatile tool that offers a range of functionalities. Its primary capabilities can be boiled down to two main behaviors:
+
+- It can reinterpret specific details within an image and create fresh, new elements.
+- It has the ability to disregard global instructions if there's a discrepancy between them and the local context or specific parts of the image. In such cases, it uses the local context to guide the process.
+
+The Tile Model can be a powerful tool in your arsenal for enhancing image quality and details. If there are undesirable elements in your images, such as blurriness caused by resizing, this model can effectively eliminate these issues, resulting in cleaner, crisper images. Moreover, it can generate and add refined details to your images, improving their overall quality and appeal. 
+
+**Pix2Pix (experimental)**
+
+With Pix2Pix, you can input an image into the controlnet, and then "instruct" the model to change it using your prompt. For example, you can say "Make it winter" to add more wintry elements to a scene.
+
+Each of these models can be adjusted and combined with other ControlNet models to achieve different results, giving you even more control over your image generation process.
+
+
+### Using ControlNet
+
+To use ControlNet, you can simply select the desired model and adjust both the ControlNet and Pre-processor settings to achieve the desired result. You can also use multiple ControlNet models at the same time, allowing you to achieve even more complex effects or styles in your generated images.
+
+
+Each ControlNet has two settings that are applied to the ControlNet.
+
+Weight - Strength of the Controlnet model applied to the generation for the section, defined by start/end.
+
+Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
+
+Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke.
+
+## T2I-Adapter
+[T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) is a tool similar to ControlNet that allows for control over the generation process by providing control information during the generation process. T2I-Adapter models tend to be smaller and more efficient than ControlNets. 
+
+##### Installation
+To install T2I-Adapter Models:
+
+1. The easiest way to install models is
+to use the InvokeAI model installer application. Use the
+`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
+to the T2I-Adapters section. Select the models you wish to install and
+press "APPLY CHANGES". You may also enter additional HuggingFace
+repo_ids in the "Additional models" textbox. 
+2. Using the "Add Model" function of the  model manager, enter the HuggingFace Repo ID of the T2I-Adapter. The ID is in the format "author/repoName"
+
+#### Usage
+Each T2I Adapter has two settings that are applied.
+
+Weight - Strength of the model applied to the generation for the section, defined by start/end.
+
+Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
+
+Additionally, each  section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process.
+
+
+## IP-Adapter
+
+[IP-Adapter](https://ip-adapter.github.io) is a tooling that allows for image prompt capabilities with text-to-image diffusion models. IP-Adapter works by analyzing the given image prompt to extract features, then passing those features to the UNet along with any other conditioning provided. 
+
+![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.jpg)
+
+![IP-Adapter + IMG2IMG](https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/demo/image-to-image.jpg)
+
+#### Installation
+There are several ways to install IP-Adapter models with an existing InvokeAI installation:
+
+1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
+2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
+3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. 
+
+#### Using IP-Adapter
+
+IP-Adapter can be used by navigating to the *Control Adapters* options and enabling IP-Adapter. 
+
+IP-Adapter requires an image to be used as the Image Prompt. It can also be used in conjunction with text prompts, Image-to-Image, Inpainting, Outpainting, ControlNets and LoRAs.
+
+
+Each IP-Adapter has two settings that are applied to the IP-Adapter:
+
+* Weight - Strength of the IP-Adapter model applied to the generation for the section, defined by start/end
+* Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the IP-Adapter applied.

docs/features/DATABASE.md

@@ -0,0 +1,35 @@
+---
+title: Database
+---
+
+# Invoke's SQLite Database
+
+Invoke uses a SQLite database to store image, workflow, model, and execution data.
+
+We take great care to ensure your data is safe, by utilizing transactions and a database migration system.
+
+Even so, when testing an prerelease version of the app, we strongly suggest either backing up your database or using an in-memory database. This ensures any prelease hiccups or databases schema changes will not cause problems for your data.
+
+## Database Backup
+
+Backing up your database is very simple. Invoke's data is stored in an `$INVOKEAI_ROOT` directory - where your `invoke.sh`/`invoke.bat` and `invokeai.yaml` files live.
+
+To back up your database, copy the `invokeai.db` file from `$INVOKEAI_ROOT/databases/invokeai.db` to somewhere safe.
+
+If anything comes up during prelease testing, you can simply copy your backup back into `$INVOKEAI_ROOT/databases/`.
+
+## In-Memory Database
+
+SQLite can run on an in-memory database. Your existing database is untouched when this mode is enabled, but your existing data won't be accessible.
+
+This is very useful for testing, as there is no chance of a database change modifying your "physical" database.
+
+To run Invoke with a memory database, edit your `invokeai.yaml` file, and add `use_memory_db: true` to the `Paths:` stanza:
+
+```yaml
+InvokeAI:
+  Development:
+    use_memory_db: true
+```
+
+Delete this line (or set it to `false`) to use your main database.

docs/features/IMG2IMG.md

@@ -0,0 +1,151 @@
+---
+title: Image-to-Image
+---
+
+# :material-image-multiple: Image-to-Image
+
+InvokeAI provides an "img2img" feature that lets you seed your
+creations with an initial drawing or photo. This is a really cool
+feature that tells stable diffusion to build the prompt on top of the
+image you provide, preserving the original's basic shape and layout.
+
+For a walkthrough of using Image-to-Image in the Web UI, see [InvokeAI
+Web Server](./WEB.md#image-to-image).
+
+The main difference between `img2img` and `prompt2img` is the starting point.
+While `prompt2img` always starts with pure gaussian noise and progressively
+refines it over the requested number of steps, `img2img` skips some of these
+earlier steps (how many it skips is indirectly controlled by the `--strength`
+parameter), and uses instead your initial image mixed with gaussian noise as the
+starting image.
+
+**Let's start** by thinking about vanilla `prompt2img`, just generating an image
+from a prompt. If the step count is 10, then the "latent space" (Stable
+Diffusion's internal representation of the image) for the prompt "fire" with
+seed `1592514025` develops something like this:
+
+!!! example ""
+
+    <figure markdown>
+    ![latent steps](../assets/img2img/000019.steps.png){ width=720 }
+    </figure>
+
+Put simply: starting from a frame of fuzz/static, SD finds details in each frame
+that it thinks look like "fire" and brings them a little bit more into focus,
+gradually scrubbing out the fuzz until a clear image remains.
+
+**When you use `img2img`** some of the earlier steps are cut, and instead an
+initial image of your choice is used. But because of how the maths behind Stable
+Diffusion works, this image needs to be mixed with just the right amount of
+noise (fuzz/static) for where it is being inserted. This is where the strength
+parameter comes in. Depending on the set strength, your image will be inserted
+into the sequence at the appropriate point, with just the right amount of noise.
+
+### A concrete example
+
+!!! example "I want SD to draw a fire based on this hand-drawn image"
+
+    ![drawing of a fireplace](../assets/img2img/fire-drawing.png){ align=left }
+
+    Let's only do 10 steps, to make it easier to see what's happening. If strength
+    is `0.7`, this is what the internal steps the algorithm has to take will look
+    like:
+
+    <figure markdown>
+    ![gravity32](../assets/img2img/000032.steps.gravity.png)
+    </figure>
+
+    With strength `0.4`, the steps look more like this:
+
+    <figure markdown>
+    ![gravity30](../assets/img2img/000030.steps.gravity.png)
+    </figure>
+
+Notice how much more fuzzy the starting image is for strength `0.7` compared to
+`0.4`, and notice also how much longer the sequence is with `0.7`:
+
+|                             | strength = 0.7                                                | strength = 0.4                                                |
+| --------------------------- | ------------------------------------------------------------- | ------------------------------------------------------------- |
+| initial image that SD sees  | ![step-0](../assets/img2img/000032.step-0.png)                | ![step-0](../assets/img2img/000030.step-0.png)                |
+| steps argument to `invoke>` | `-S10`                                                        | `-S10`                                                        |
+| steps actually taken        | `7`                                                           | `4`                                                           |
+| latent space at each step   | ![gravity32](../assets/img2img/000032.steps.gravity.png)      | ![gravity30](../assets/img2img/000030.steps.gravity.png)      |
+| output                      | ![000032.1592514025](../assets/img2img/000032.1592514025.png) | ![000030.1592514025](../assets/img2img/000030.1592514025.png) |
+
+Both of the outputs look kind of like what I was thinking of. With the strength
+higher, my input becomes more vague, _and_ Stable Diffusion has more steps to
+refine its output. But it's not really making what I want, which is a picture of
+cheery open fire. With the strength lower, my input is more clear, _but_ Stable
+Diffusion has less chance to refine itself, so the result ends up inheriting all
+the problems of my bad drawing.
+
+If you want to try this out yourself, all of these are using a seed of
+`1592514025` with a width/height of `384`, step count `10`, the
+`k_lms` sampler, and the single-word prompt `"fire"`.
+
+### Compensating for the reduced step count
+
+After putting this guide together I was curious to see how the difference would
+be if I increased the step count to compensate, so that SD could have the same
+amount of steps to develop the image regardless of the strength. So I ran the
+generation again using the same seed, but this time adapting the step count to
+give each generation 20 steps.
+
+Here's strength `0.4` (note step count `50`, which is `20 ÷ 0.4` to make sure SD
+does `20` steps from my image):
+
+<figure markdown>
+![000035.1592514025](../assets/img2img/000035.1592514025.png)
+</figure>
+
+and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to
+make sure SD does `20` steps from my image):
+
+<figure markdown>
+![000046.1592514025](../assets/img2img/000046.1592514025.png)
+</figure>
+
+In both cases the image is nice and clean and "finished", but because at
+strength `0.7` Stable Diffusion has been give so much more freedom to improve on
+my badly-drawn flames, they've come out looking much better. You can really see
+the difference when looking at the latent steps. There's more noise on the first
+image with strength `0.7`:
+
+<figure markdown>
+![gravity46](../assets/img2img/000046.steps.gravity.png)
+</figure>
+
+than there is for strength `0.4`:
+
+<figure markdown>
+![gravity35](../assets/img2img/000035.steps.gravity.png)
+</figure>
+
+and that extra noise gives the algorithm more choices when it is evaluating how
+to denoise any particular pixel in the image.
+
+Unfortunately, it seems that `img2img` is very sensitive to the step count.
+Here's strength `0.7` with a step count of `29` (SD did 19 steps from my image):
+
+<figure markdown>
+![gravity45](../assets/img2img/000045.1592514025.png)
+</figure>
+
+By comparing the latents we can sort of see that something got interpreted
+differently enough on the third or fourth step to lead to a rather different
+interpretation of the flames.
+
+<figure markdown>
+![gravity46](../assets/img2img/000046.steps.gravity.png)
+</figure>
+
+<figure markdown>
+![gravity45](../assets/img2img/000045.steps.gravity.png)
+</figure>
+
+This is the result of a difference in the de-noising "schedule" - basically the
+noise has to be cleaned by a certain degree each step or the model won't
+"converge" on the image properly (see
+[stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more
+about that). A different step count means a different schedule, which means
+things get interpreted slightly differently at every step.

docs/features/LOGGING.md

@@ -0,0 +1,171 @@
+---
+title: Controlling Logging
+---
+
+# :material-image-off: Controlling Logging
+
+## Controlling How InvokeAI Logs Status Messages
+
+InvokeAI logs status messages using a configurable logging system. You
+can log to the terminal window, to a designated file on the local
+machine, to the syslog facility on a Linux or Mac, or to a properly
+configured web server. You can configure several logs at the same
+time, and control the level of message logged and the logging format
+(to a limited extent).
+
+Three command-line options control logging:
+
+### `--log_handlers <handler1> <handler2> ...`
+
+This option activates one or more log handlers. Options are "console",
+"file", "syslog" and "http". To specify more than one, separate them
+by spaces:
+
+```bash
+invokeai-web --log_handlers console syslog=/dev/log file=C:\Users\fred\invokeai.log
+```
+
+The format of these options is described below.
+
+### `--log_format {plain|color|legacy|syslog}`
+
+This controls the format of log messages written to the console. Only
+the "console" log handler is currently affected by this setting.
+
+* "plain" provides formatted messages like this:
+
+```bash
+
+[2023-05-24 23:18:2[2023-05-24 23:18:50,352]::[InvokeAI]::DEBUG --> this is a debug message
+[2023-05-24 23:18:50,352]::[InvokeAI]::INFO --> this is an informational messages
+[2023-05-24 23:18:50,352]::[InvokeAI]::WARNING --> this is a warning
+[2023-05-24 23:18:50,352]::[InvokeAI]::ERROR --> this is an error
+[2023-05-24 23:18:50,352]::[InvokeAI]::CRITICAL --> this is a critical error
+```
+
+* "color" produces similar output, but the text will be color coded to
+indicate the severity of the message.
+
+* "legacy" produces output similar to InvokeAI versions 2.3 and earlier:
+
+```bash
+### this is a critical error
+*** this is an error
+** this is a warning
+>> this is an informational messages
+   | this is a debug message
+```
+
+* "syslog" produces messages suitable for syslog entries:
+
+```bash
+InvokeAI [2691178] <CRITICAL> this is a critical error
+InvokeAI [2691178] <ERROR> this is an error
+InvokeAI [2691178] <WARNING> this is a warning
+InvokeAI [2691178] <INFO> this is an informational messages
+InvokeAI [2691178] <DEBUG> this is a debug message
+```
+
+(note that the date, time and hostname will be added by the syslog
+system)
+
+### `--log_level {debug|info|warning|error|critical}`
+
+Providing this command-line option will cause only messages at the
+specified level or above to be emitted.
+
+## Console logging
+
+When "console" is provided to `--log_handlers`, messages will be
+written to the command line window in which InvokeAI was launched. By
+default, the color formatter will be used unless overridden by
+`--log_format`.
+
+## File logging
+
+When "file" is provided to `--log_handlers`, entries will be written
+to the file indicated in the path argument. By default, the "plain"
+format will be used:
+
+```bash
+invokeai-web --log_handlers file=/var/log/invokeai.log
+```
+
+## Syslog logging
+
+When "syslog" is requested, entries will be sent to the syslog
+system. There are a variety of ways to control where the log message
+is sent:
+
+* Send to the local machine using the `/dev/log` socket:
+
+```
+invokeai-web --log_handlers syslog=/dev/log
+```
+
+* Send to the local machine using a UDP message:
+
+```
+invokeai-web --log_handlers syslog=localhost
+```
+
+* Send to the local machine using a UDP message on a nonstandard
+  port:
+
+```
+invokeai-web --log_handlers syslog=localhost:512
+```
+
+* Send to a remote machine named "loghost" on the local LAN using
+  facility LOG_USER and UDP packets:
+
+```
+invokeai-web --log_handlers syslog=loghost,facility=LOG_USER,socktype=SOCK_DGRAM
+```
+
+This can be abbreviated `syslog=loghost`, as LOG_USER and SOCK_DGRAM
+are defaults.
+
+* Send to a remote machine named "loghost" using the facility LOCAL0
+  and using a TCP socket:
+
+```
+invokeai-web --log_handlers syslog=loghost,facility=LOG_LOCAL0,socktype=SOCK_STREAM
+```
+
+If no arguments are specified (just a bare "syslog"), then the logging
+system will look for a UNIX socket named `/dev/log`, and if not found
+try to send a UDP message to `localhost`. The Macintosh OS used to
+support logging to a socket named `/var/run/syslog`, but this feature
+has since been disabled.
+
+## Web logging
+
+If you have access to a web server that is configured to log messages
+when a particular URL is requested, you can log using the "http"
+method:
+
+```
+invokeai-web --log_handlers http=http://my.server/path/to/logger,method=POST
+```
+
+The optional [,method=] part can be used to specify whether the URL
+accepts GET (default) or POST messages.
+
+Currently password authentication and SSL are not supported.
+
+## Using the configuration file
+
+You can set and forget logging options by adding a "Logging" section
+to `invokeai.yaml`:
+
+```
+InvokeAI:
+  [... other settings...]
+  Logging:
+    log_handlers:
+       - console
+       - syslog=/dev/log
+    log_level: info
+    log_format: color
+```

docs/features/LORAS.md

@@ -0,0 +1,50 @@
+---
+title: LoRAs & LCM-LoRAs
+---
+
+# :material-library-shelves: LoRAs & LCM-LoRAs
+
+With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
+
+## LoRAs
+
+Low-Rank Adaptation (LoRA) files are models that customize the output of Stable Diffusion
+image generation.  Larger than embeddings, but much smaller than full
+models, they augment SD with improved understanding of subjects and
+artistic styles.
+
+Unlike TI files, LoRAs do not introduce novel vocabulary into the
+model's known tokens. Instead, LoRAs augment the model's weights that
+are applied to generate imagery. LoRAs may be supplied with a
+"trigger" word that they have been explicitly trained on, or may
+simply apply their effect without being triggered.
+
+LoRAs are typically stored in .safetensors files, which are the most
+secure way to store and transmit these types of weights.
+
+To use these when generating, open the LoRA menu item in the options
+panel, select the LoRAs you want to apply and ensure that they have
+the appropriate weight recommended by the model provider. Typically,
+most LoRAs perform best at a weight of .75-1.
+
+
+## LCM-LoRAs
+Latent Consistency Models (LCMs) allowed a reduced number of steps to be used to generate images with Stable Diffusion. These are created by distilling base models, creating models that only require a small number of steps to generate images. However, LCMs require that any fine-tune of a base model be distilled to be used as an LCM. 
+
+LCM-LoRAs are models that provide the benefit of LCMs but are able to be used as LoRAs and applied to any fine tune of a base model. LCM-LoRAs are created by training a small number of adapters, rather than distilling the entire fine-tuned base model. The resulting LoRA can be used the same way as a standard LoRA, but with a greatly reduced step count. This enables SDXL images to be generated up to 10x faster than without the use of LCM-LoRAs. 
+
+
+**Using LCM-LoRAs**
+
+LCM-LoRAs are natively supported in InvokeAI throughout the application. To get started, install any diffusers format LCM-LoRAs using the model manager and select it in the LoRA field.
+
+There are a number parameter differences when using LCM-LoRAs and standard generation: 
+
+- When using LCM-LoRAs, the LoRA strength should be lower than if using a standard LoRA, with 0.35 recommended as a starting point.  
+- The LCM scheduler should be used for generation
+- CFG-Scale should be reduced to ~1
+- Steps should be reduced in the range of 4-8
+
+Standard LoRAs can also be used alongside LCM-LoRAs, but will also require a lower strength, with 0.45 being recommended as a starting point. 
+
+More information can be found here: https://huggingface.co/blog/lcm_lora#fast-inference-with-sdxl-lcm-loras

docs/features/Lasso_tool.md

@@ -1,32 +0,0 @@
-Lasso Tool
-===========
-
-- The Lasso tool creates selections and inpaint masks by drawing freehand or polygonal regions on the canvas.
-
-How to open the Lasso tool
---------------------------
-- Click the Lasso icon in the toolbar.
-- Hotkey: press `L` (default). The hotkey is shown in the tool's tooltip and can be customized in Hotkeys settings.
-
-Modes
------
-- Freehand (default)
-  - Hold the pointer and drag to draw a continuous contour.
-  - Long segments are broken into intermediate points to keep the line continuous.
-  - Very long strokes may be simplified after drawing to reduce point count for performance.
-
-- Polygon
-  - Click to place points; click the first point (or a point near it) to close the polygon.
-  - The tool snaps the closing point to the start for precise closures.
-
-Basic interactions
-------------------
-- Switch modes with the mode toggle in the toolbar.
-- To close a polygon: click the starting point again or click near it — the tool aligns the final point to the start to complete the shape.
-- The selection will be added to the current Inpaint Mask layer. If no Inpaint Mask layer exists, a new one will be created automatically.
-
-Tips & behavior
----------------
-- Hold `Space` to temporarily switch to the View tool for panning and zooming; release `Space` to return to the Lasso tool and continue drawing.
-- When using the Polygon mode, you can hold `Shift` to snap points to horizontal, vertical, or 45-degree angles for more precise shapes.
-- Hold `Ctrl` (Windows/Linux) or `Command` (macOS) while drawing to subtract from the current selection instead of adding to it.

docs/features/MODEL_MERGING.md

@@ -0,0 +1,77 @@
+---
+title: Model Merging
+---
+
+InvokeAI provides the ability to merge two or three diffusers-type models into a new merged model. The
+resulting model will combine characteristics of the original, and can
+be used to teach an old model new tricks.
+
+## How to Merge Models
+
+Model Merging can be be done by navigating to the Model Manager and clicking the "Merge Models" tab. From there, you can select the models and settings you want to use to merge th models. 
+
+## Settings
+
+* Model Selection: there are three multiple choice fields that
+  display all the diffusers-style models that InvokeAI knows about.
+  If you do not see the model you are looking for, then it is probably
+  a legacy checkpoint model and needs to be converted using the
+  "Convert" option in the Web-based Model Manager tab.
+  
+  You must select at least two models to merge. The third can be left
+  at "None" if you desire.
+
+* Alpha: This is the ratio to use when combining models. It ranges
+  from 0 to 1. The higher the value, the more weight is given to the
+  2d and (optionally) 3d models. So if you have two models named "A"
+  and "B", an alpha value of 0.25 will give you a merged model that is
+  25% A and 75% B.
+
+* Interpolation Method: This is the method used to combine
+  weights. The options are "weighted_sum" (the default), "sigmoid",
+  "inv_sigmoid" and "add_difference". Each produces slightly different
+  results. When three models are in use, only "add_difference" is
+  available.
+
+* Save Location: The location you want the merged model to be saved in. Default is in the InvokeAI root folder
+
+* Name for merged model: This is the name for the new model. Please
+  use InvokeAI conventions - only alphanumeric letters and the
+  characters ".+-".
+
+* Ignore Mismatches / Force: Not all models are compatible with each other. The merge
+  script will check for compatibility and refuse to merge ones that
+  are incompatible. Set this checkbox to try merging anyway.
+
+
+
+You may run the merge script by starting the invoke launcher
+(`invoke.sh` or `invoke.bat`) and choosing the option (4) for _merge
+models_. This will launch a text-based interactive user interface that
+prompts you to select the models to merge, how to merge them, and the
+merged model name.
+
+Alternatively you may activate InvokeAI's virtual environment from the
+command line, and call the script via `merge_models --gui` to open up
+a version that has a nice graphical front end. To get the commandline-
+only version, omit `--gui`.
+
+The user interface for the text-based interactive script is
+straightforward. It shows you a series of setting fields. Use control-N (^N)
+to move to the next field, and control-P (^P) to move to the previous
+one. You can also use TAB and shift-TAB to move forward and
+backward. Once you are in a multiple choice field, use the up and down
+cursor arrows to move to your desired selection, and press <SPACE> or
+<ENTER> to select it. Change text fields by typing in them, and adjust
+scrollbars using the left and right arrow keys.
+
+Once you are happy with your settings, press the OK button. Note that
+there may be two pages of settings, depending on the height of your
+screen, and the OK button may be on the second page. Advance past the
+last field of the first page to get to the second page, and reverse
+this to get back.
+
+If the merge runs successfully, it will create a new diffusers model
+under the selected name and register it with InvokeAI.
+
+

docs/features/OTHER.md

@@ -0,0 +1,51 @@
+---
+title: Others
+---
+
+# :fontawesome-regular-share-from-square: Others
+
+## **Google Colab**
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg){ align="right" }](https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
+
+Open and follow instructions to use an isolated environment running Dream.
+
+Output Example:
+
+![Colab Notebook](../assets/colab_notebook.png)
+
+---
+
+## **Invisible Watermark**
+
+In keeping with the principles for responsible AI generation, and to
+help AI researchers avoid synthetic images contaminating their
+training sets, InvokeAI adds an invisible watermark to each of the
+final images it generates. The watermark consists of the text
+"InvokeAI" and can be viewed using the
+[invisible-watermarks](https://github.com/ShieldMnt/invisible-watermark)
+tool.
+
+Watermarking is controlled using the `invisible-watermark` setting in
+`invokeai.yaml`. To turn it off, add the following line under the `Features`
+category.
+
+```
+invisible_watermark: false
+```
+
+
+## **Weighted Prompts**
+
+You may weight different sections of the prompt to tell the sampler to attach different levels of
+priority to them, by adding `:<percent>` to the end of the section you wish to up- or downweight. For
+example consider this prompt:
+
+```bash
+(tabby cat):0.25 (white duck):0.75 hybrid
+```
+
+This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75%
+on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
+combination of integers and floating point numbers, and they do not need to add up to 1.
+

docs/features/POSTPROCESS.md

@@ -0,0 +1,41 @@
+---
+title: Postprocessing
+---
+
+# :material-image-edit: Postprocessing
+
+This sections details the ability to improve faces and upscale images.
+
+## Face Fixing
+
+As of InvokeAI 3.0, the easiest way to improve faces created during image generation is through the Inpainting functionality of the Unified Canvas. Simply add the image containing the faces that you would like to improve to the canvas, mask the face to be improved and run the invocation. For best results, make sure to use an inpainting specific model; these are usually identified by the "-inpainting" term in the model name. 
+
+## Upscaling
+
+Open the upscaling dialog by clicking on the "expand" icon located
+above the image display area in the Web UI:
+
+<figure markdown>
+![upscale1](../assets/features/upscale-dialog.png)
+</figure>
+
+The default upscaling option is Real-ESRGAN x2 Plus, which will scale your image by a factor of two. This means upscaling a 512x512 image will result in a new 1024x1024 image.
+
+Other options are the x4 upscalers, which will scale your image by a factor of 4. 
+
+
+!!! note
+
+    Real-ESRGAN is memory intensive. In order to avoid crashes and memory overloads
+    during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
+    its work.
+
+    In single image generations, you will see the output right away but when you are using multiple
+    iterations, the images will first be generated and then upscaled after that
+    process is complete. While the image generation is taking place, you will still be able to preview
+    the base images.
+
+## How to disable
+
+If, for some reason, you do not wish to load the ESRGAN libraries,
+you can disable them on the invoke.py command line with the `--no_esrgan` options.

docs/features/PROMPTS.md

@@ -0,0 +1,308 @@
+---
+title: Prompting-Features
+---
+
+# :octicons-command-palette-24: Prompting-Features
+
+## **Prompt Syntax Features**
+
+The InvokeAI prompting language has the following features:
+
+### Attention weighting
+
+Append a word or phrase with `-` or `+`, or a weight between `0` and `2`
+(`1`=default), to decrease or increase "attention" (= a mix of per-token CFG
+weighting multiplier and, for `-`, a weighted blend with the prompt without the
+term).
+
+The following syntax is recognised:
+
+- single words without parentheses: `a tall thin man picking apricots+`
+- single or multiple words with parentheses:
+  `a tall thin man picking (apricots)+` `a tall thin man picking (apricots)-`
+  `a tall thin man (picking apricots)+` `a tall thin man (picking apricots)-`
+- more effect with more symbols `a tall thin man (picking apricots)++`
+- nesting `a tall thin man (picking apricots+)++` (`apricots` effectively gets
+  `+++`)
+- all of the above with explicit numbers `a tall thin man picking (apricots)1.1`
+  `a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++`
+  is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2),
+  etc.)
+
+You can use this to increase or decrease the amount of something. Starting from
+this prompt of `a man picking apricots from a tree`, let's see what happens if
+we increase and decrease how much attention we want Stable Diffusion to pay to
+the word `apricots`:
+
+<figure markdown>
+
+![an AI generated image of a man picking apricots from a tree](../assets/prompt_syntax/apricots-0.png)
+
+</figure>
+
+Using `-` to reduce apricot-ness:
+
+| `a man picking apricots- from a tree`                                                                                          | `a man picking apricots-- from a tree`                                                                                                        | `a man picking apricots--- from a tree`                                                                                                    |
+| ------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| ![an AI generated image of a man picking apricots from a tree, with smaller apricots](../assets/prompt_syntax/apricots--1.png) | ![an AI generated image of a man picking apricots from a tree, with even smaller and fewer apricots](../assets/prompt_syntax/apricots--2.png) | ![an AI generated image of a man picking apricots from a tree, with very few very small apricots](../assets/prompt_syntax/apricots--3.png) |
+
+Using `+` to increase apricot-ness:
+
+| `a man picking apricots+ from a tree`                                                                                                      | `a man picking apricots++ from a tree`                                                                                                              | `a man picking apricots+++ from a tree`                                                                                                                     | `a man picking apricots++++ from a tree`                                                                                                                                           | `a man picking apricots+++++ from a tree`                                                                                                                                                                           |
+| ------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ![an AI generated image of a man picking apricots from a tree, with larger, more vibrant apricots](../assets/prompt_syntax/apricots-1.png) | ![an AI generated image of a man picking apricots from a tree with even larger, even more vibrant apricots](../assets/prompt_syntax/apricots-2.png) | ![an AI generated image of a man picking apricots from a tree, but the man has been replaced by a pile of apricots](../assets/prompt_syntax/apricots-3.png) | ![an AI generated image of a man picking apricots from a tree, but the man has been replaced by a mound of giant melting-looking apricots](../assets/prompt_syntax/apricots-4.png) | ![an AI generated image of a man picking apricots from a tree, but the man and the leaves and parts of the ground have all been replaced by giant melting-looking apricots](../assets/prompt_syntax/apricots-5.png) |
+
+You can also change the balance between different parts of a prompt. For
+example, below is a `mountain man`:
+
+<figure markdown>
+
+![an AI generated image of a mountain man](../assets/prompt_syntax/mountain-man.png)
+
+</figure>
+
+And here he is with more mountain:
+
+| `mountain+ man`                                | `mountain++ man`                               | `mountain+++ man`                              |
+| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
+| ![](../assets/prompt_syntax/mountain1-man.png) | ![](../assets/prompt_syntax/mountain2-man.png) | ![](../assets/prompt_syntax/mountain3-man.png) |
+
+Or, alternatively, with more man:
+
+| `mountain man+`                                | `mountain man++`                               | `mountain man+++`                              | `mountain man++++`                             |
+| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
+| ![](../assets/prompt_syntax/mountain-man1.png) | ![](../assets/prompt_syntax/mountain-man2.png) | ![](../assets/prompt_syntax/mountain-man3.png) | ![](../assets/prompt_syntax/mountain-man4.png) |
+
+### Prompt Blending
+
+- `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
+- The existing prompt blending using `:<weight>` will continue to be supported -
+  `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
+  is equivalent to
+  `a tall thin man picking apricots:1 a tall thin man picking pears:1` in the
+  old syntax.
+- Attention weights can be nested inside blends.
+- Non-normalized blends are supported by passing `no_normalize` as an additional
+  argument to the blend weights, eg
+  `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,-1,no_normalize)`.
+  very fun to explore local maxima in the feature space, but also easy to
+  produce garbage output.
+
+See the section below on "Prompt Blending" for more information about how this
+works.
+
+### Prompt Conjunction  
+Join multiple clauses together to create a conjoined prompt. Each clause will be passed to CLIP separately. 
+
+For example, the prompt: 
+
+```bash
+"A mystical valley surround by towering granite cliffs, watercolor, warm"
+```
+
+Can be used with .and():
+```bash
+("A mystical valley", "surround by towering granite cliffs", "watercolor", "warm").and()
+```
+
+Each will give you different results - try them out and see what you prefer!
+
+
+
+### Cross-Attention Control ('prompt2prompt')
+
+Sometimes an image you generate is almost right, and you just want to change one
+detail without affecting the rest. You could use a photo editor and inpainting
+to overpaint the area, but that's a pain. Here's where `prompt2prompt` comes in
+handy.
+
+Generate an image with a given prompt, record the seed of the image, and then
+use the `prompt2prompt` syntax to substitute words in the original prompt for
+words in a new prompt. This works for `img2img` as well.
+
+For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
+  - `a cat playing with a ball in the forest`
+  - `a dog playing with a ball in the forest`
+
+| `a cat playing with a ball in the forest` | `a dog playing with a ball in the forest` |
+| --- | --- |
+| img | img |
+
+
+      - For multiple word swaps, use parentheses: `a (fluffy cat).swap(barking dog) playing with a ball in the forest`.
+      - To swap a comma, use quotes: `a ("fluffy, grey cat").swap("big, barking dog") playing with a ball in the forest`.
+- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to (bloc97's)[(https://github.com/bloc97/CrossAttentionControl)] `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
+  intuitively understand. `t_start` and `t_end` are used to control on which steps cross-attention control should run. With the default values `t_start=0` and `t_end=1`, cross-attention control is active on every step of image generation. Other values can be used to turn cross-attention control off for part of the image generation process.
+    - For example, if doing a diffusion with 10 steps for the prompt is `a cat.swap(dog, t_start=0.3, t_end=1.0) playing with a ball in the forest`, the first 3 steps will be run as `a cat playing with a ball in the forest`, while the last 7 steps will run as `a dog playing with a ball in the forest`, but the pixels that represent `dog` will be locked to the pixels that would have represented `cat` if the `cat` prompt had been used instead.
+    - Conversely, for `a cat.swap(dog, t_start=0, t_end=0.7) playing with a ball in the forest`, the first 7 steps will run as `a dog playing with a ball in the forest` with the pixels that represent `dog` locked to the same pixels that would have represented `cat` if the `cat` prompt was being used instead. The final 3 steps will just run `a cat playing with a ball in the forest`.
+    > For img2img, the step sequence does not start at 0 but instead at `(1.0-strength)` - so if the img2img `strength` is `0.7`, `t_start` and `t_end` must both be greater than `0.3` (`1.0-0.7`) to have any effect.
+
+Prompt2prompt `.swap()` is not compatible with xformers, which will be temporarily disabled when doing a `.swap()` - so you should expect to use more VRAM and run slower that with xformers enabled.
+
+The `prompt2prompt` code is based off
+[bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
+
+### Escaping parentheses and speech marks 
+
+If the model you are using has parentheses () or speech marks "" as part of its
+syntax, you will need to "escape" these using a backslash, so that`(my_keyword)`
+becomes `\(my_keyword\)`. Otherwise, the prompt parser will attempt to interpret
+the parentheses as part of the prompt syntax and it will get confused.
+
+---
+
+## **Prompt Blending**
+
+You may blend together prompts to explore the AI's
+latent semantic space and generate interesting (and often surprising!)
+variations. The syntax is:
+
+```bash
+("prompt #1", "prompt #2").blend(0.25, 0.75)
+```
+
+This will tell the sampler to blend 25% of the concept of prompt #1 with 75%
+of the concept of prompt #2. It is recommended to keep the sum of the weights to around 1.0, but interesting things might happen if you go outside of this range.
+
+Because you are exploring the "mind" of the AI, the AI's way of mixing two
+concepts may not match yours, leading to surprising effects. To illustrate, here
+are three images generated using various combinations of blend weights. As
+usual, unless you fix the seed, the prompts will give you different results each
+time you run them.
+
+Let's examine how this affects image generation results:
+
+
+```bash
+"blue sphere, red cube, hybrid"
+```
+
+This example doesn't use blending at all and represents the default way of mixing
+concepts.
+
+<figure markdown>
+
+![blue-sphere-red-cube-hyprid](../assets/prompt-blending/blue-sphere-red-cube-hybrid.png)
+
+</figure>
+
+It's interesting to see how the AI expressed the concept of "cube" within the sphere. If you look closely, there is depth there, so the enclosing frame is actually a cube.
+
+<figure markdown>
+
+```bash
+("blue sphere", "red cube").blend(0.25, 0.75)
+```
+
+![blue-sphere-25-red-cube-75](../assets/prompt-blending/blue-sphere-0.25-red-cube-0.75-hybrid.png)
+
+</figure>
+
+Now that's interesting. We get an image with a resemblance of a red cube, with a hint of blue shadows which represents a melding of concepts within the AI's "latent space" of semantic representations. 
+
+<figure markdown>
+
+```bash
+("blue sphere", "red cube").blend(0.75, 0.25)
+```
+
+![blue-sphere-75-red-cube-25](../assets/prompt-blending/blue-sphere-0.75-red-cube-0.25-hybrid.png)
+
+</figure>
+
+Definitely more blue-spherey. 
+
+<figure markdown>
+
+```bash
+("blue sphere", "red cube").blend(0.5, 0.5)
+```
+</figure>
+
+<figure markdown>
+![blue-sphere-5-red-cube-5-hybrid](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5-hybrid.png)
+</figure>
+
+
+Whoa...! I see blue and red, and if I squint, spheres and cubes.
+
+
+
+## Dynamic Prompts
+
+Dynamic Prompts are a powerful feature designed to produce a variety of prompts based on user-defined options. Using a special syntax, you can construct a prompt with multiple possibilities, and the system will automatically generate a series of permutations based on your settings. This is extremely beneficial for ideation, exploring various scenarios, or testing different concepts swiftly and efficiently.
+
+### Structure of a Dynamic Prompt
+
+A Dynamic Prompt comprises of regular text, supplemented with alternatives enclosed within curly braces {} and separated by a vertical bar |. For example: {option1|option2|option3}. The system will then select one of the options to include in the final prompt. This flexible system allows for options to be placed throughout the text as needed.
+
+Furthermore, Dynamic Prompts can designate multiple selections from a single group of options. This feature is triggered by prefixing the options with a numerical value followed by $$. For example, in {2$$option1|option2|option3}, the system will select two distinct options from the set.
+### Creating Dynamic Prompts
+
+To create a Dynamic Prompt, follow these steps:
+
+    Draft your sentence or phrase, identifying words or phrases with multiple possible options.
+    Encapsulate the different options within curly braces {}.
+    Within the braces, separate each option using a vertical bar |.
+    If you want to include multiple options from a single group, prefix with the desired number and $$.
+
+For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {style1|style2|style3}.
+### How Dynamic Prompts Work
+
+Once a Dynamic Prompt is configured, the system generates an array of combinations using the options provided. Each group of options in curly braces is treated independently, with the system selecting one option from each group. For a prefixed set (e.g., 2$$), the system will select two distinct options.
+
+For example, the following prompts could be generated from the above Dynamic Prompt:
+
+    A house in summer designed in style1, style2
+    A lodge in autumn designed in style3, style1
+    A cottage in winter designed in style2, style3
+    And many more!
+
+When the `Combinatorial` setting is on, Invoke will disable the "Images" selection, and generate every combination up until the setting for Max Prompts is reached.
+When the `Combinatorial` setting is off, Invoke will randomly generate combinations up until the setting for Images has been reached.
+
+
+
+### Tips and Tricks for Using Dynamic Prompts
+
+Below are some useful strategies for creating Dynamic Prompts:
+
+    Utilize Dynamic Prompts to generate a wide spectrum of prompts, perfect for brainstorming and exploring diverse ideas.
+    Ensure that the options within a group are contextually relevant to the part of the sentence where they are used. For instance, group building types together, and seasons together.
+    Apply the 2$$ prefix when you want to incorporate more than one option from a single group. This becomes quite handy when mixing and matching different elements.
+    Experiment with different quantities for the prefix. For example, 3$$ will select three distinct options.
+    Be aware of coherence in your prompts. Although the system can generate all possible combinations, not all may semantically make sense. Therefore, carefully choose the options for each group.
+    Always review and fine-tune the generated prompts as needed. While Dynamic Prompts can help you generate a multitude of combinations, the final polishing and refining remain in your hands.
+
+
+## SDXL Prompting
+
+Prompting with SDXL is slightly different than prompting with SD1.5 or SD2.1 models - SDXL expects a prompt _and_ a style. 
+
+
+### Prompting 
+<figure markdown>
+
+![SDXL prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt.png)
+
+</figure>
+
+In the prompt box, enter a positive or negative prompt as you normally would. 
+
+For the style box you can enter a style that you want the image to be generated in. You can use styles from this example list, or any other style you wish: anime, photographic, digital art, comic book, fantasy art, analog film, neon punk, isometric, low poly, origami, line art, cinematic, 3d model, pixel art, etc.
+
+
+### Concatenated Prompts
+
+
+InvokeAI also has the option to concatenate the prompt and style inputs, by pressing the "link" button in the Positive Prompt box. 
+
+This concatenates the prompt & style inputs, and passes the joined prompt and style to the SDXL model. 
+![SDXL concatenated prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt-concatenated.png)
+
+
+
+
+
+
+

docs/features/TEXTUAL_INVERSIONS.md

@@ -0,0 +1,55 @@
+## Using Textual Inversion Files
+
+Textual inversion (TI) files are small models that customize the output of
+Stable Diffusion image generation. They can augment SD with specialized subjects
+and artistic styles. They are also known as "embeds" in the machine learning
+world.
+
+Each TI file introduces one or more vocabulary terms to the SD model. These are
+known in InvokeAI as "triggers." Triggers are denoted using angle brackets 
+as in "<trigger-phrase>". The two most common type of
+TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
+different TI training packages. InvokeAI supports both formats, but its
+[built-in TI training system](TRAINING.md) produces `.pt`.
+
+[Hugging Face](https://huggingface.co/sd-concepts-library) has
+amassed a large library of >800 community-contributed TI files covering a
+broad range of subjects and styles. You can also install your own or others' TI files 
+by placing them in the designated directory for the compatible model type
+
+### An Example
+
+Here are a few examples to illustrate how it works. All these images
+were generated using the legacy command-line client and the Stable
+Diffusion 1.5 model:
+
+|         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
+| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
+| ![](../assets/concepts/image1.png) |  ![](../assets/concepts/image2.png)   |   ![](../assets/concepts/image3.png)   |     ![](../assets/concepts/image4.png)     |
+
+You can also combine styles and concepts:
+
+<figure markdown>
+  | A portrait of &lt;alf&gt; in &lt;cartoona-animal&gt; style |
+  | :--------------------------------------------------------: |
+  | ![](../assets/concepts/image5.png)                         |
+</figure>
+
+
+## Installing your Own TI Files
+
+You may install any number of `.pt` and `.bin` files simply by copying them into
+the `embedding` directory of the corresponding InvokeAI models directory (usually `invokeai`
+in your home directory). For example, you can simply move a Stable Diffusion 1.5 embedding file to
+the `sd-1/embedding` folder. Be careful not to overwrite one file with another.
+For example, TI files generated by the Hugging Face toolkit share the named
+`learned_embedding.bin`. You can rename these, or use subdirectories to keep them distinct.
+
+At startup time, InvokeAI will scan the various `embedding` directories and load any TI
+files it finds there for compatible models. At startup you will see a message similar to this one:
+
+```bash
+>> Current embedding manager terms: <HOI4-Leader>, <princess-knight>
+```
+To use these when generating, simply type the `<` key in your prompt to open the Textual Inversion WebUI and 
+select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.

docs/features/TRAINING.md

@@ -0,0 +1,281 @@
+---
+title: Training
+---
+
+# :material-file-document: Training
+
+# Textual Inversion Training
+## **Personalizing Text-to-Image Generation**
+
+You may personalize the generated images to provide your own styles or objects
+by training a new LDM checkpoint and introducing a new vocabulary to the fixed
+model as a (.pt) embeddings file. Alternatively, you may use or train
+HuggingFace Concepts embeddings files (.bin) from
+<https://huggingface.co/sd-concepts-library> and its associated
+notebooks.
+
+## **Hardware and Software Requirements**
+
+You will need a GPU to perform training in a reasonable length of
+time, and at least 12 GB of VRAM. We recommend using the [`xformers`
+library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
+training process further. During training, about ~8 GB is temporarily
+needed in order to store intermediate models, checkpoints and logs.
+
+## **Preparing for Training**
+
+To train, prepare a folder that contains 3-5 images that illustrate
+the object or concept. It is good to provide a variety of examples or
+poses to avoid overtraining the system. Format these images as PNG
+(preferred) or JPG. You do not need to resize or crop the images in
+advance, but for more control you may wish to do so.
+
+Place the training images in a directory on the machine InvokeAI runs
+on. We recommend placing them in a subdirectory of the
+`text-inversion-training-data` folder located in the InvokeAI root
+directory, ordinarily `~/invokeai` (Linux/Mac), or
+`C:\Users\your_name\invokeai` (Windows). For example, to create an
+embedding for the "psychedelic" style, you'd place the training images
+into the directory
+`~invokeai/text-inversion-training-data/psychedelic`.
+
+## **Launching Training Using the Console Front End**
+
+InvokeAI 2.3 and higher comes with a text console-based training front
+end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
+start training tool selecting choice (3):
+
+```sh
+1 "Generate images with a browser-based interface"
+2 "Explore InvokeAI nodes using a command-line interface"
+3 "Textual inversion training"
+4 "Merge models (diffusers type only)"
+5 "Download and install models"
+6 "Change InvokeAI startup options"
+7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
+8 "Open the developer console"
+9 "Update InvokeAI"
+```
+
+Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
+you can then launch the front end with the command `invokeai-ti --gui`.
+
+This will launch a text-based front end that will look like this:
+
+<figure markdown>
+![ti-frontend](../assets/textual-inversion/ti-frontend.png)
+</figure>
+
+The interface is keyboard-based. Move from field to field using
+control-N (^N) to move to the next field and control-P (^P) to the
+previous one. <Tab> and <shift-TAB> work as well. Once a field is
+active, use the cursor keys. In a checkbox group, use the up and down
+cursor keys to move from choice to choice, and <space> to select a
+choice. In a scrollbar, use the left and right cursor keys to increase
+and decrease the value of the scroll. In textfields, type the desired
+values.
+
+The number of parameters may look intimidating, but in most cases the
+predefined defaults work fine. The red circled fields in the above
+illustration are the ones you will adjust most frequently.
+
+### Model Name
+
+This will list all the diffusers models that are currently
+installed. Select the one you wish to use as the basis for your
+embedding. Be aware that if you use a SD-1.X-based model for your
+training, you will only be able to use this embedding with other
+SD-1.X-based models. Similarly, if you train on SD-2.X, you will only
+be able to use the embeddings with models based on SD-2.X.
+
+### Trigger Term
+
+This is the prompt term you will use to trigger the embedding. Type a
+single word or phrase you wish to use as the trigger, example
+"psychedelic" (without angle brackets). Within InvokeAI, you will then
+be able to activate the trigger using the syntax `<psychedelic>`.
+
+### Initializer
+
+This is a single character that is used internally during the training
+process as a placeholder for the trigger term. It defaults to "*" and
+can usually be left alone.
+
+### Resume from last saved checkpoint
+
+As training proceeds, textual inversion will write a series of
+intermediate files that can be used to resume training from where it
+was left off in the case of an interruption. This checkbox will be
+automatically selected if you provide a previously used trigger term
+and at least one checkpoint file is found on disk.
+
+Note that as of 20 January 2023, resume does not seem to be working
+properly due to an issue with the upstream code.
+
+### Data Training Directory
+
+This is the location of the images to be used for training. When you
+select a trigger term like "my-trigger", the frontend will prepopulate
+this field with `~/invokeai/text-inversion-training-data/my-trigger`,
+but you can change the path to wherever you want.
+
+### Output Destination Directory
+
+This is the location of the logs, checkpoint files, and embedding
+files created during training. When you select a trigger term like
+"my-trigger", the frontend will prepopulate this field with
+`~/invokeai/text-inversion-output/my-trigger`, but you can change the
+path to wherever you want.
+
+### Image resolution
+
+The images in the training directory will be automatically scaled to
+the value you use here. For best results, you will want to use the
+same default resolution of the underlying model (512 pixels for
+SD-1.5, 768 for the larger version of SD-2.1).
+
+### Center crop images
+
+If this is selected, your images will be center cropped to make them
+square before resizing them to the desired resolution. Center cropping
+can indiscriminately cut off the top of subjects' heads for portrait
+aspect images, so if you have images like this, you may wish to use a
+photoeditor to manually crop them to a square aspect ratio.
+
+### Mixed precision
+
+Select the floating point precision for the embedding. "no" will
+result in a full 32-bit precision, "fp16" will provide 16-bit
+precision, and "bf16" will provide mixed precision (only available
+when XFormers is used).
+
+### Max training steps
+
+How many steps the training will take before the model converges. Most
+training sets will converge with 2000-3000 steps.
+
+### Batch size
+
+This adjusts how many training images are processed simultaneously in
+each step. Higher values will cause the training process to run more
+quickly, but use more memory. The default size will run with GPUs with
+as little as 12 GB.
+
+### Learning rate
+
+The rate at which the system adjusts its internal weights during
+training. Higher values risk overtraining (getting the same image each
+time), and lower values will take more steps to train a good
+model. The default of 0.0005 is conservative; you may wish to increase
+it to 0.005 to speed up training.
+
+### Scale learning rate by number of GPUs, steps and batch size
+
+If this is selected (the default) the system will adjust the provided
+learning rate to improve performance.
+
+### Use xformers acceleration
+
+This will activate XFormers memory-efficient attention. You need to
+have XFormers installed for this to have an effect.
+
+### Learning rate scheduler
+
+This adjusts how the learning rate changes over the course of
+training. The default "constant" means to use a constant learning rate
+for the entire training session. The other values scale the learning
+rate according to various formulas.
+
+Only "constant" is supported by the XFormers library.
+
+### Gradient accumulation steps
+
+This is a parameter that allows you to use bigger batch sizes than
+your GPU's VRAM would ordinarily accommodate, at the cost of some
+performance.
+
+### Warmup steps
+
+If "constant_with_warmup" is selected in the learning rate scheduler,
+then this provides the number of warmup steps. Warmup steps have a
+very low learning rate, and are one way of preventing early
+overtraining.
+
+## The training run
+
+Start the training run by advancing to the OK button (bottom right)
+and pressing <enter>. A series of progress messages will be displayed
+as the training process proceeds. This may take an hour or two,
+depending on settings and the speed of your system. Various log and
+checkpoint files will be written into the output directory (ordinarily
+`~/invokeai/text-inversion-output/my-model/`)
+
+At the end of successful training, the system will copy the file
+`learned_embeds.bin` into the InvokeAI root directory's `embeddings`
+directory, using a subdirectory named after the trigger token. For
+example, if the trigger token was `psychedelic`, then look for the
+embeddings file in
+`~/invokeai/embeddings/psychedelic/learned_embeds.bin`
+
+You may now launch InvokeAI and try out a prompt that uses the trigger
+term. For example `a plate of banana sushi in <psychedelic> style`.
+
+## **Training with the Command-Line Script**
+
+Training can also be done using a traditional command-line script. It
+can be launched from within the "developer's console", or from the
+command line after activating InvokeAI's virtual environment.
+
+It accepts a large number of arguments, which can be summarized by
+passing the `--help` argument:
+
+```sh
+invokeai-ti --help
+```
+
+Typical usage is shown here:
+```sh
+invokeai-ti \
+       --model=stable-diffusion-1.5 \
+       --resolution=512 \
+       --learnable_property=style \
+       --initializer_token='*' \
+       --placeholder_token='<psychedelic>' \
+       --train_data_dir=/home/lstein/invokeai/training-data/psychedelic \
+       --output_dir=/home/lstein/invokeai/text-inversion-training/psychedelic \
+       --scale_lr \
+       --train_batch_size=8 \
+       --gradient_accumulation_steps=4 \
+       --max_train_steps=3000 \
+       --learning_rate=0.0005 \
+       --resume_from_checkpoint=latest \
+       --lr_scheduler=constant \
+       --mixed_precision=fp16 \
+       --only_save_embeds
+```
+
+## Troubleshooting
+
+### `Cannot load embedding for <trigger>. It was trained on a model with token dimension 1024, but the current model has token dimension 768`
+
+Messages like this indicate you trained the embedding on a different base model than the currently selected one.
+
+For example, in the error above, the training was done on SD2.1 (768x768) but it was used on SD1.5 (512x512).
+
+## Reading
+
+For more information on textual inversion, please see the following
+resources:
+
+* The [textual inversion repository](https://github.com/rinongal/textual_inversion) and
+  associated paper for details and limitations.
+* [HuggingFace's textual inversion training
+  page](https://huggingface.co/docs/diffusers/training/text_inversion)
+* [HuggingFace example script
+  documentation](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)
+  (Note that this script is similar to, but not identical, to
+  `textual_inversion`, but produces embed files that are completely compatible.
+
+---
+
+copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/features/Text_tool.md

@@ -1,19 +0,0 @@
-# Text Tool
-
-## Font selection
-
-The Text tool uses a set of predefined font stacks. When you choose a font, the app resolves the first available font on your system from that stack and uses it for both the editor overlay and the rasterized result. This provides consistent styling across platforms while still falling back to safe system fonts if a preferred font is missing.
-
-## Size and spacing
-
-- **Size** controls the font size in pixels.
-- **Spacing** controls the line height multiplier (Dense, Normal, Spacious). This affects the distance between lines while editing the text.
-
-## Uncommitted state
-
-While text is uncommitted, it remains editable on-canvas. Access to other tools is blocked. Switching to other tabs (Generate, Upascaling, Workflows etc.) discards the text. The uncommitted box can be moved and rotated:
-
-- **Move:** Hold Ctrl (Windows/Linux) or Command (macOS) and drag to move the text box.
-- **Rotate:** Drag the rotation handle above the box. Hold **Shift** while rotating to snap to 15 degree increments.
-
-The text is committed to a raster layer when you press **Enter**. Press **Esc** to discard the current text session.

docs/features/UNIFIED_CANVAS.md

@@ -0,0 +1,283 @@
+---
+title: Unified Canvas
+---
+
+The Unified Canvas is a tool designed to streamline and simplify the process of
+composing an image using Stable Diffusion. It offers artists all of the
+available Stable Diffusion generation modes (Text To Image, Image To Image,
+Inpainting, and Outpainting) as a single unified workflow. The flexibility of
+the tool allows you to tweak and edit image generations, extend images beyond
+their initial size, and to create new content in a freeform way both inside and
+outside of existing images.
+
+This document explains the basics of using the Unified Canvas, introducing you
+to its features and tools one by one. It also describes some of the more
+advanced tools available to power users of the Canvas.
+
+## Basics
+
+The Unified Canvas consists of two layers: the **Base Layer** and the **Mask
+Layer**. You can swap from one layer to the other by selecting the layer you
+want in the drop-down menu on the top left corner of the Unified Canvas, or by
+pressing the (Q) hotkey.
+
+### Base Layer
+
+The **Base Layer** is the image content currently managed by the Canvas, and can
+be exported at any time to the gallery by using the **Save to Gallery** option.
+When the Base Layer is selected, the Brush (B) and Eraser (E) tools will
+directly manipulate the base layer. Any images uploaded to the Canvas, or sent
+to the Unified Canvas from the gallery, will clear out all existing content and
+set the Base layer to the new image.
+
+### Staging Area
+
+When you generate images, they will display in the Canvas's **Staging Area**,
+alongside the Staging Area toolbar buttons. While the Staging Area is active,
+you cannot interact with the Canvas itself.
+
+<figure markdown>
+
+![staging area](../assets/canvas/staging_area.png)
+
+</figure>
+
+Accepting generations will commit the new generation to the **Base Layer**. You
+can review all generated images using the Prev/Next arrows, save any individual
+generations to your gallery (without committing to the Base layer) or discard
+generations. While you can Undo a discard in an individual Canvas session, any
+generations that are not saved will be lost when the Canvas resets.
+
+### Mask Layer
+
+The **Mask Layer** consists of any masked sections that have been created to
+inform Inpainting generations. You can paint a new mask, or edit an existing
+mask, using the Brush tool and the Eraser with the Mask layer set as your Active
+layer. Any masked areas will only affect generation inside of the current
+bounding box.
+
+### Bounding Box
+
+When generating a new image, Invoke will process and apply new images within the
+area denoted by the **Bounding Box**. The Width & Height settings of the
+Bounding Box, as well as its location within the Unified Canvas and pixels or
+empty space that it encloses, determine how new invocations are generated - see
+[Inpainting & Outpainting](#inpainting-and-outpainting) below. The Bounding Box
+can be moved and resized using the Move (V) tool. It can also be resized using
+the Bounding Box options in the Options Panel. By using these controls you can
+generate larger or smaller images, control which sections of the image are being
+processed, as well as control Bounding Box tools like the Bounding Box
+fill/erase.
+
+### <a name="inpainting-and-outpainting"></a> Inpainting & Outpainting
+
+"Inpainting" means asking the AI to refine part of an image while leaving the
+rest alone. For example, updating a portrait of your grandmother to have her
+wear a biker's jacket.
+
+|                         masked original                         |                                       inpaint result                                       |
+| :-------------------------------------------------------------: | :----------------------------------------------------------------------------------------: |
+| ![granny with a mask applied](../assets/canvas/mask_granny.png) | ![just like magic, granny with a biker's jacket](../assets/canvas/biker_jacket_granny.png) |
+
+"Outpainting" means asking the AI to expand the original image beyond its
+original borders, making a bigger image that's still based on the original. For
+example, extending the above image of your Grandmother in a biker's jacket to
+include her wearing jeans (and while we're at it, a motorcycle!)
+
+<figure markdown>
+
+![more magic - granny with a tattooed arm, denim pants, and an obscured motorcycle](../assets/canvas/biker_granny.png)
+
+</figure>
+
+When you are using the Unified Canvas, Invoke decides automatically whether to
+do Inpainting, Outpainting, ImageToImage, or TextToImage by looking inside the
+area enclosed by the Bounding Box. It chooses the appropriate type of generation
+based on whether the Bounding Box contains empty (transparent) areas on the Base
+layer, or whether it contains colored areas from previous generations (or from
+painted brushstrokes) on the Base layer, and/or whether the Mask layer contains
+any brushstrokes. See [Generation Methods](#generation-methods) below for more
+information.
+
+## Getting Started
+
+To get started with the Unified Canvas, you will want to generate a new base
+layer using Txt2Img or importing an initial image. We'll refer to either of
+these methods as the "initial image" in the below guide.
+
+From there, you can consider the following techniques to augment your image:
+
+- **New Images**: Move the bounding box to an empty area of the Canvas, type in
+  your prompt, and Invoke, to generate a new image using the Text to Image
+  function.
+- **Image Correction**: Use the color picker and brush tool to paint corrections
+  on the image, switch to the Mask layer, and brush a mask over your painted
+  area to use **Inpainting**. You can also use the **ImageToImage** generation
+  method to invoke new interpretations of the image.
+- **Image Expansion**: Move the bounding box to include a portion of your
+  initial image, and a portion of transparent/empty pixels, then Invoke using a
+  prompt that describes what you'd like to see in that area. This will Outpaint
+  the image. You'll typically find more coherent results if you keep about
+  50-60% of the original image in the bounding box. Make sure that the Image To
+  Image Strength slider is set to a high value - you may need to set it higher
+  than you are used to.
+- **New Content on Existing Images**: If you want to add new details or objects
+  into your image, use the brush tool to paint a sketch of what you'd like to
+  see on the image, switch to the Mask layer, and brush a mask over your painted
+  area to use **Inpainting**. If the masked area is small, consider using a
+  smaller bounding box to take advantage of Invoke's automatic Scaling features,
+  which can help to produce better details.
+- **And more**: There are a number of creative ways to use the Canvas, and the
+  above are just starting points. We're excited to see what you come up with!
+
+## <a name="generation-methods"></a> Generation Methods
+
+The Canvas can use all generation methods available (Txt2Img, Img2Img,
+Inpainting, and Outpainting), and these will be automatically selected and used
+based on the current selection area within the Bounding Box.
+
+### Text to Image
+
+If the Bounding Box is placed over an area of Canvas with an **empty Base
+Layer**, invoking a new image will use **TextToImage**. This generates an
+entirely new image based on your prompt.
+
+### Image to Image
+
+If the Bounding Box is placed over an area of Canvas with an **existing Base
+Layer area with no transparent pixels or masks**, invoking a new image will use
+**ImageToImage**. This uses the image within the bounding box and your prompt to
+interpret a new image. The image will be closer to your original image at lower
+Image to Image strengths.
+
+### Inpainting
+
+If the Bounding Box is placed over an area of Canvas with an **existing Base
+Layer and any pixels selected using the Mask layer**, invoking a new image will
+use **Inpainting**. Inpainting uses the existing colors/forms in the masked area
+in order to generate a new image for the masked area only. The unmasked portion
+of the image will remain the same. Image to Image strength applies to the
+inpainted area.
+
+If you desire something completely different from the original image in your new
+generation (i.e., if you want Invoke to ignore existing colors/forms), consider
+toggling the Inpaint Replace setting on, and use high values for both Inpaint
+Replace and Image To Image Strength.
+
+!!! note
+
+    By default, the **Scale Before Processing** option &mdash; which
+    inpaints more coherent details by generating at a larger resolution and then
+    scaling &mdash; is only activated when the Bounding Box is relatively small.
+    To get the best inpainting results you should therefore resize your Bounding
+    Box to the smallest area that contains your mask and enough surrounding detail
+    to help Stable Diffusion understand the context of what you want it to draw.
+    You should also update your prompt so that it describes _just_ the area within
+    the Bounding Box.
+
+### Outpainting
+
+If the Bounding Box is placed over an area of Canvas partially filled by an
+existing Base Layer area and partially by transparent pixels or masks, invoking
+a new image will use **Outpainting**, as well as **Inpainting** any masked
+areas.
+
+---
+
+## Advanced Features
+
+Features with non-obvious behavior are detailed below, in order to provide
+clarity on the intent and common use cases we expect for utilizing them.
+
+### Toolbar
+
+#### Mask Options
+
+- **Enable Mask** - This flag can be used to Enable or Disable the currently
+  painted mask. If you have painted a mask, but you don't want it affect the
+  next invocation, but you _also_ don't want to delete it, then you can set this
+  option to Disable. When you want the mask back, set this back to Enable.
+- **Preserve Masked Area** - When enabled, Preserve Masked Area inverts the
+  effect of the Mask on the Inpainting process. Pixels in masked areas will be
+  kept unchanged, and unmasked areas will be regenerated.
+
+#### Creative Tools
+
+- **Brush - Base/Mask Modes** - The Brush tool switches automatically between
+  different modes of operation for the Base and Mask layers respectively.
+    - On the Base layer, the brush will directly paint on the Canvas using the
+      color selected on the Brush Options menu.
+    - On the Mask layer, the brush will create a new mask. If you're finding the
+      mask difficult to see over the existing content of the Unified Canvas, you
+      can change the color it is drawn with using the color selector on the Mask
+      Options dropdown.
+- **Erase Bounding Box** - On the Base layer, erases all pixels within the
+  Bounding Box.
+- **Fill Bounding Box** - On the Base layer, fills all pixels within the
+  Bounding Box with the currently selected color.
+
+#### Canvas Tools
+
+- **Move Tool** - Allows for manipulation of the Canvas view (by dragging on the
+  Canvas, outside the bounding box), the Bounding Box (by dragging the edges of
+  the box), or the Width/Height of the Bounding Box (by dragging one of the 9
+  directional handles).
+- **Reset View** - Click to re-orients the view to the center of the Bounding
+  Box.
+- **Merge Visible** - If your browser is having performance problems drawing the
+  image in the Unified Canvas, click this to consolidate all of the information
+  currently being rendered by your browser into a merged copy of the image. This
+  lowers the resource requirements and should improve performance.
+
+### Compositing / Seam Correction
+
+When doing Inpainting or Outpainting, Invoke needs to merge the pixels generated
+by Stable Diffusion into your existing image. This is achieved through compositing - the area around the the boundary between your image and the new generation is
+automatically blended to produce a seamless output. In a fully automatic
+process, a mask is generated to cover the boundary, and then the area of the boundary is
+Inpainted.
+
+Although the default options should work well most of the time, sometimes it can
+help to alter the parameters that control the Compositing. A larger blur and
+a blur setting  have been noted as producing
+consistently strong results . Strength of 0.7 is best for reducing hard seams.
+
+- **Mode** - What part of the image will have the the Compositing applied to it.
+  - **Mask edge** will apply Compositing to the edge of the masked area
+  - **Mask** will apply Compositing to the entire masked area
+  - **Unmasked** will apply Compositing to the entire image
+- **Steps** - Number of generation steps that will occur during the Coherence Pass, similar to Denoising Steps. Higher step counts will generally have better results.
+- **Strength** - How much noise is added for the Coherence Pass, similar to Denoising Strength. A strength of 0 will result in an unchanged image, while a strength of 1 will result in an image with a completely new area as defined by the Mode setting.
+- **Blur** - Adjusts the pixel radius of the the mask. A larger blur radius will cause the mask to extend past the visibly masked area, while too small of a blur radius will result in a mask that is smaller than the visibly masked area.
+- **Blur Method** - The method of blur applied to the masked area. 
+
+
+### Infill & Scaling
+
+- **Scale Before Processing & W/H**: When generating images with a bounding box
+  smaller than the optimized W/H of the model (e.g., 512x512 for SD1.5), this
+  feature first generates at a larger size with the same aspect ratio, and then
+  scales that image down to fill the selected area. This is particularly useful
+  when inpainting very small details. Scaling is optional but is enabled by
+  default.
+- **Inpaint Replace**: When Inpainting, the default method is to utilize the
+  existing RGB values of the Base layer to inform the generation process. If
+  Inpaint Replace is enabled, noise is generated and blended with the existing
+  pixels (completely replacing the original RGB values at an Inpaint Replace
+  value of 1). This can help generate more variation from the pixels on the Base
+  layers.
+    - When using Inpaint Replace you should use a higher Image To Image Strength
+      value, especially at higher Inpaint Replace values
+- **Infill Method**: Invoke currently supports two methods for producing RGB
+  values for use in the Outpainting process: Patchmatch and Tile. We believe
+  that Patchmatch is the superior method, however we provide support for Tile in
+  case Patchmatch cannot be installed or is unavailable on your computer.
+- **Tile Size**: The Tile method for Outpainting sources small portions of the
+  original image and randomly place these into the areas being Outpainted. This
+  value sets the size of those tiles.
+
+## Hot Keys
+
+The Unified Canvas is a tool that excels when you use hotkeys. You can view the
+full list of keyboard shortcuts, updated with all new features, by clicking the
+Keyboard Shortcuts icon at the top right of the InvokeAI WebUI.

docs/features/UTILITIES.md

@@ -0,0 +1,336 @@
+---
+title: Command-line Utilities
+---
+
+# :material-file-document: Utilities
+
+# Command-line Utilities
+
+InvokeAI comes with several scripts that are accessible via the
+command line. To access these commands, start the "developer's
+console" from the launcher (`invoke.bat` menu item [7]). Users who are
+familiar with Python can alternatively activate InvokeAI's virtual
+environment (typically, but not necessarily `invokeai/.venv`).
+
+In the developer's console, type the script's name to run it. To get a
+synopsis of what a utility does and the command-line arguments it
+accepts, pass it the `-h` argument, e.g.
+
+```bash
+invokeai-merge -h
+```
+## **invokeai-web**
+
+This script launches the web server and is effectively identical to
+selecting option [1] in the launcher. An advantage of launching the
+server from the command line is that you can override any setting
+configuration option in `invokeai.yaml` using like-named command-line
+arguments. For example, to temporarily change the size of the RAM
+cache to 7 GB, you can launch as follows:
+
+```bash
+invokeai-web --ram 7
+```
+
+## **invokeai-merge**
+
+This is the model merge script, the same as launcher option [3]. Call
+it with the `--gui` command-line argument to start the interactive
+console-based GUI. Alternatively, you can run it non-interactively
+using command-line arguments as illustrated in the example below which
+merges models named `stable-diffusion-1.5` and `inkdiffusion` into a new model named
+`my_new_model`:
+
+```bash
+invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffusion --merged_model_name my_new_model
+```
+
+## **invokeai-ti**
+
+This is the textual inversion training script that is run by launcher
+option [2]. Call it with `--gui` to run the interactive console-based
+front end. It can also be run non-interactively. It has about a
+zillion arguments, but a typical training session can be launched
+with:
+
+```bash
+invokeai-ti --model stable-diffusion-1.5 \
+            --placeholder_token 'jello' \
+            --learnable_property object \
+			--num_train_epochs 50 \
+			--train_data_dir /path/to/training/images \
+			--output_dir /path/to/trained/model
+```
+
+(Note that \\ is the Linux/Mac long-line continuation character. Use ^
+in Windows).
+
+## **invokeai-install**
+
+This is the console-based model install script that is run by launcher
+option [4]. If called without arguments, it will launch the
+interactive console-based interface. It can also be used
+non-interactively to list, add and remove models as shown by these
+examples:
+
+* This will download and install three models from CivitAI, HuggingFace,
+and local disk:
+
+```bash
+invokeai-install --add https://civitai.com/api/download/models/161302 ^
+                  gsdf/Counterfeit-V3.0  ^
+				  D:\Models\merge_model_two.safetensors
+```
+(Note that ^ is the Windows long-line continuation character. Use \\ on
+Linux/Mac).
+
+* This will list installed models of type `main`:
+
+```bash
+invokeai-model-install --list-models main
+```
+
+* This will delete the models named `voxel-ish` and `realisticVision`:
+
+```bash
+invokeai-model-install --delete voxel-ish realisticVision
+```
+
+## **invokeai-configure**
+
+This is the console-based configure script that ran when InvokeAI was
+first installed. You can run it again at any time to change the
+configuration, repair a broken install.
+
+Called without any arguments, `invokeai-configure` enters interactive
+mode with two screens. The first screen is a form that provides access
+to most of InvokeAI's configuration options. The second screen lets
+you download, add, and delete models interactively. When you exit the
+second screen, the script will add any missing "support models"
+needed for core functionality, and any selected "sd weights" which are
+the model checkpoint/diffusers files.
+
+This behavior can be changed via a series of command-line
+arguments. Here are some of the useful ones:
+
+* `invokeai-configure --skip-sd-weights --skip-support-models`
+This will run just the configuration part of the utility, skipping
+downloading of support models and stable diffusion weights.
+
+* `invokeai-configure --yes`
+This will run the configure script non-interactively. It will set the
+configuration options to their default values, install/repair support
+models, and download the "recommended" set of SD models.
+
+* `invokeai-configure --yes --default_only` 
+This will run the configure script non-interactively. In contrast to
+the previous command, it will only download the default SD model,
+Stable Diffusion v1.5
+
+* `invokeai-configure --yes --default_only --skip-sd-weights` 
+This is similar to the previous command, but will not download any
+SD models at all. It is usually used to repair a broken install.
+
+By default, `invokeai-configure` runs on the currently active InvokeAI
+root folder. To run it against a different root, pass it the `--root
+</path/to/root>` argument.
+
+Lastly, you can use `invokeai-configure` to create a working root
+directory entirely from scratch. Assuming you wish to make a root directory
+named `InvokeAI-New`, run this command:
+
+```bash
+invokeai-configure --root InvokeAI-New --yes --default_only
+```
+This will create a minimally functional root directory. You can now
+launch the web server against it with `invokeai-web --root InvokeAI-New`.
+
+## **invokeai-update**
+
+This is the interactive console-based script that is run by launcher
+menu item [8] to update to a new version of InvokeAI. It takes no
+command-line arguments.
+
+## **invokeai-metadata**
+
+This is a script which takes a list of InvokeAI-generated images and
+outputs their metadata in the same JSON format that you get from the
+`</>` button in the Web GUI. For example:
+
+```bash
+$ invokeai-metadata ffe2a115-b492-493c-afff-7679aa034b50.png
+ffe2a115-b492-493c-afff-7679aa034b50.png:
+{
+    "app_version": "3.1.0",
+    "cfg_scale": 8.0,
+    "clip_skip": 0,
+    "controlnets": [],
+    "generation_mode": "sdxl_txt2img",
+    "height": 1024,
+    "loras": [],
+    "model": {
+        "base_model": "sdxl",
+        "model_name": "stable-diffusion-xl-base-1.0",
+        "model_type": "main"
+    },
+    "negative_prompt": "",
+    "negative_style_prompt": "",
+    "positive_prompt": "military grade sushi dinner for shock troopers",
+    "positive_style_prompt": "",
+    "rand_device": "cpu",
+    "refiner_cfg_scale": 7.5,
+    "refiner_model": {
+        "base_model": "sdxl-refiner",
+        "model_name": "sd_xl_refiner_1.0",
+        "model_type": "main"
+    },
+    "refiner_negative_aesthetic_score": 2.5,
+    "refiner_positive_aesthetic_score": 6.0,
+    "refiner_scheduler": "euler",
+    "refiner_start": 0.8,
+    "refiner_steps": 20,
+    "scheduler": "euler",
+    "seed": 387129902,
+    "steps": 25,
+    "width": 1024
+}
+```
+
+You may list multiple files on the command line.
+
+## **invokeai-import-images**
+
+InvokeAI uses a database to store information about images it
+generated, and just copying the image files from one InvokeAI root
+directory to another does not automatically import those images into
+the destination's gallery. This script allows you to bulk import
+images generated by one instance of InvokeAI into a gallery maintained
+by another. It also works on images generated by older versions of
+InvokeAI, going way back to version 1.
+
+This script has an interactive mode only. The following example shows
+it in action:
+
+```bash
+$ invokeai-import-images
+===============================================================================
+This script will import images generated by earlier versions of
+InvokeAI into the currently installed root directory:
+   /home/XXXX/invokeai-main
+If this is not what you want to do, type ctrl-C now to cancel.
+===============================================================================
+= Configuration & Settings
+Found invokeai.yaml file at /home/XXXX/invokeai-main/invokeai.yaml:
+  Database : /home/XXXX/invokeai-main/databases/invokeai.db
+  Outputs  : /home/XXXX/invokeai-main/outputs/images
+ 
+Use these paths for import (yes) or choose different ones (no) [Yn]:
+Inputs: Specify absolute path containing InvokeAI .png images to import: /home/XXXX/invokeai-2.3/outputs/images/
+Include files from subfolders recursively [yN]?
+
+Options for board selection for imported images:
+1) Select an existing board name. (found 4)
+2) Specify a board name to create/add to.
+3) Create/add to board named 'IMPORT'.
+4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_20230919T203519Z).
+5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5).
+Specify desired board option: 3
+
+===============================================================================
+= Import Settings Confirmation
+
+Database File Path               : /home/XXXX/invokeai-main/databases/invokeai.db
+Outputs/Images Directory         : /home/XXXX/invokeai-main/outputs/images
+Import Image Source Directory    : /home/XXXX/invokeai-2.3/outputs/images/
+  Recurse Source SubDirectories  : No
+Count of .png file(s) found      : 5785
+Board name option specified      : IMPORT
+Database backup will be taken at : /home/XXXX/invokeai-main/databases/backup
+
+Notes about the import process:
+- Source image files will not be modified, only copied to the outputs directory.
+- If the same file name already exists in the destination, the file will be skipped.
+- If the same file name already has a record in the database, the file will be skipped.
+- Invoke AI metadata tags will be updated/written into the imported copy only.
+- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)
+- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer.
+- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder.
+ 
+Do you wish to continue with the import [Yn] ?
+
+Making DB Backup at /home/lstein/invokeai-main/databases/backup/backup-20230919T203519Z-invokeai.db...Done!
+
+===============================================================================
+Importing /home/XXXX/invokeai-2.3/outputs/images/17d09907-297d-4db3-a18a-60b337feac66.png
+... (5785 more lines) ...
+===============================================================================
+= Import Complete - Elpased Time: 0.28 second(s)
+
+Source File(s)                          : 5785
+Total Imported                          : 5783
+Skipped b/c file already exists on disk : 1
+Skipped b/c file already exists in db   : 0
+Errors during import                    : 1
+```
+## **invokeai-db-maintenance**
+
+This script helps maintain the integrity of your InvokeAI database by
+finding and fixing three problems that can arise over time:
+
+1. An image was manually deleted from the outputs directory, leaving a
+   dangling image record in the InvokeAI database. This will cause a
+   black image to appear in the gallery. This is an "orphaned database
+   image record." The script can fix this by running a "clean"
+   operation on the database, removing the orphaned entries.
+   
+2. An image is present in the outputs directory but there is no
+   corresponding entry in the database. This can happen when the image
+   is added manually to the outputs directory, or if a crash occurred
+   after the image was generated but before the database was
+   completely updated. The symptom is that the image is present in the
+   outputs folder but doesn't appear in the InvokeAI gallery. This is
+   called an "orphaned image file." The script can fix this problem by
+   running an "archive" operation in which orphaned files are moved
+   into a directory named `outputs/images-archive`. If you wish, you
+   can then run `invokeai-image-import` to reimport these images back
+   into the database.
+   
+3. The thumbnail for an image is missing, again causing a black
+   gallery thumbnail. This is fixed by running the "thumbnaiils"
+   operation, which simply regenerates and re-registers the missing
+   thumbnail.
+   
+You can find and fix all three of these problems in a single go by
+executing this command:
+
+```bash
+invokeai-db-maintenance --operation all
+```
+
+Or you can run just the clean and thumbnail operations like this:
+
+```bash
+invokeai-db-maintenance -operation clean, thumbnail
+```
+
+If called without any arguments, the script will ask you which
+operations you wish to perform.
+
+## **invokeai-migrate3**
+
+This script will migrate settings and models (but not images!) from an
+InvokeAI v2.3 root folder to an InvokeAI 3.X folder. Call it with the
+source and destination root folders like this:
+
+```bash
+invokeai-migrate3 --from ~/invokeai-2.3 --to invokeai-3.1.1
+```
+
+Both directories must previously have been properly created and
+initialized by `invokeai-configure`. If you wish to migrate the images
+contained in the older root as well, you can use the
+`invokeai-image-migrate` script described earlier.
+
+---
+
+Copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/features/WATERMARK+NSFW.md

@@ -0,0 +1,96 @@
+---
+title: Watermarking, NSFW Image Checking
+---
+
+# :material-image-off: Invisible Watermark and the NSFW Checker
+
+## Watermarking
+
+InvokeAI does not apply watermarking to images by default. However,
+many computer scientists working in the field of generative AI worry
+that a flood of computer-generated imagery will contaminate the image
+data sets needed to train future generations of generative models.
+
+InvokeAI offers an optional watermarking mode that writes a small bit
+of text, **InvokeAI**, into each image that it generates using an
+"invisible" watermarking library that spreads the information
+throughout the image in a way that is not perceptible to the human
+eye. If you are planning to share your generated images on
+internet-accessible services, we encourage you to activate the
+invisible watermark mode in order to help preserve the digital image
+environment.
+
+The downside of watermarking is that it increases the size of the
+image moderately, and has been reported by some individuals to degrade
+image quality. Your mileage may vary.
+
+To read the watermark in an image, activate the InvokeAI virtual
+environment (called the "developer's console" in the launcher) and run
+the command:
+
+```
+invisible-watermark -a decode -t bytes -m dwtDct -l 64 /path/to/image.png
+```
+
+## The NSFW ("Safety") Checker
+
+Stable Diffusion 1.5-based image generation models will produce sexual
+imagery if deliberately prompted, and will occasionally produce such
+images when this is not intended. Such images are colloquially known
+as "Not Safe for Work" (NSFW). This behavior is due to the nature of
+the training set that Stable Diffusion was trained on, which culled
+millions of "aesthetic" images from the Internet.
+
+You may not wish to be exposed to these images, and in some
+jurisdictions it may be illegal to publicly distribute such imagery,
+including mounting a publicly-available server that provides
+unfiltered images to the public. Furthermore, the [Stable Diffusion
+weights
+License](https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-SD1+SD2.txt),
+and the [Stable Diffusion XL
+License][https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-SDXL.txt]
+both forbid the models from being used to "exploit any of the
+vulnerabilities of a specific group of persons."
+
+For these reasons Stable Diffusion offers a "safety checker," a
+machine learning model trained to recognize potentially disturbing
+imagery. When a potentially NSFW image is detected, the checker will
+blur the image and paste a warning icon on top. The checker can be
+turned on and off in the Web interface under Settings.
+
+## Caveats
+
+There are a number of caveats that you need to be aware of.
+
+### Accuracy
+
+The checker is [not perfect](https://arxiv.org/abs/2210.04610).It will
+occasionally flag innocuous images (false positives), and will
+frequently miss violent and gory imagery (false negatives). It rarely
+fails to flag sexual imagery, but this has been known to happen. For
+these reasons, the InvokeAI team prefers to refer to the software as a
+"NSFW Checker" rather than "safety checker."
+
+### Memory Usage and Performance
+
+The NSFW checker consumes an additional 1.2G of GPU VRAM on top of the
+3.4G of VRAM used by Stable Diffusion v1.5 (this is with
+half-precision arithmetic). This means that the checker will not run
+successfully on GPU cards with less than 6GB VRAM, and will reduce the
+size of the images that you can produce.
+
+The checker also introduces a slight performance penalty. Images will
+take ~1 second longer to generate when the checker is
+activated. Generally this is not noticeable.
+
+### Intermediate Images in the Web UI
+
+The checker only operates on the final image produced by the Stable
+Diffusion algorithm. If you are using the Web UI and have enabled the
+display of intermediate images, you will briefly be exposed to a
+low-resolution (mosaicized) version of the final image before it is
+flagged by the checker and replaced by a fully blurred version. You
+are encouraged to turn **off** intermediate image rendering when you
+are using the checker. Future versions of InvokeAI will apply
+additional blurring to intermediate images when the checker is active.
+

docs/features/WEB.md

@@ -0,0 +1,325 @@
+---
+title: InvokeAI Web Server
+---
+
+# :material-web: InvokeAI Web Server
+
+## Quick guided walkthrough of the WebUI's features
+
+While most of the WebUI's features are intuitive, here is a guided walkthrough
+through its various components.
+
+### Launching the WebUI
+
+To run the InvokeAI web server, start the `invoke.sh`/`invoke.bat`
+script and select option (1). Alternatively, with the InvokeAI
+environment active, run `invokeai-web`:
+
+```bash
+invokeai-web
+```
+
+You can then connect to the server by pointing your web browser at
+http://localhost:9090. To reach the server from a different machine on your LAN,
+you may launch the web server with the `--host` argument and either the IP
+address of the host you are running it on, or the wildcard `0.0.0.0`. For
+example:
+
+```bash
+invoke.sh --host 0.0.0.0
+```
+
+or
+
+```bash
+invokeai-web --host 0.0.0.0
+```
+
+### The InvokeAI Web Interface
+
+![Invoke Web Server - Major Components](../assets/invoke-web-server-1.png){:width="640px"}
+
+The screenshot above shows the Text to Image tab of the WebUI. There are three
+main sections:
+
+1. A **control panel** on the left, which contains various settings
+   for text to image generation. The most important part is the text
+   field (currently showing `fantasy painting, horned demon`) for
+   entering the positive text prompt, another text field right below it for an
+   optional negative text prompt (concepts to exclude), and a _Invoke_ button 
+   to begin the image rendering process.
+
+2. The **current image** section in the middle, which shows a large
+   format version of the image you are currently working on. A series
+   of buttons at the top lets you modify and manipulate the image in
+   various ways.
+
+3. A **gallery** section on the left that contains a history of the images you
+   have generated. These images are read and written to the directory specified
+   in the `INVOKEAIROOT/invokeai.yaml` initialization file, usually a directory
+   named `outputs` in `INVOKEAIROOT`.
+
+In addition to these three elements, there are a series of icons for changing
+global settings, reporting bugs, and changing the theme on the upper right.
+
+There are also a series of icons to the left of the control panel (see
+highlighted area in the screenshot below) which select among a series of tabs
+for performing different types of operations.
+
+<figure markdown>
+![Invoke Web Server - Control Panel](../assets/invoke-web-server-2.png){:width="512px"}
+</figure>
+
+From top to bottom, these are:
+
+1. Text to Image - generate images from text
+2. Image to Image - from an uploaded starting image (drawing or photograph)
+   generate a new one, modified by the text prompt
+3. Unified Canvas - Interactively combine multiple images, extend them
+   with outpainting,and modify interior portions of the image with
+   inpainting, erase portions of a starting image and have the AI fill in
+   the erased region from a text prompt.
+4. Node Editor - (experimental) this panel allows you to create
+   pipelines of common operations and combine them into workflows.
+5. Model Manager - this panel allows you to import and configure new
+   models using URLs, local paths, or HuggingFace diffusers repo_ids.
+
+## Walkthrough
+
+The following walkthrough will exercise most (but not all) of the WebUI's
+feature set.
+
+### Text to Image
+
+1. Launch the WebUI using launcher option [1] and connect to it with
+   your browser by accessing `http://localhost:9090`. If the browser
+   and server are running on different machines on your LAN, add the
+   option `--host 0.0.0.0` to the `invoke.sh` launch command line and connect to
+   the machine hosting the web server using its IP address or domain
+   name.
+
+2. If all goes well, the WebUI should come up and you'll see a green dot
+   meaning `connected`  on the upper right.
+
+![Invoke Web Server - Control Panel](../assets/invoke-control-panel-1.png){ align=right width=300px }
+
+#### Basics
+
+1.  Generate an image by typing _bluebird_ into the large prompt field
+    on the upper left and then clicking on the Invoke button or pressing
+	the return button.
+	After a short wait, you'll see a large image of a bluebird in the
+    image panel, and a new thumbnail in the gallery on the right.
+
+    If you need more room on the screen, you can turn the gallery off
+    by typing the **g** hotkey. You can turn it back on later by clicking the
+    image icon that appears in the gallery's place. The list of hotkeys can
+	be found by clicking on the keyboard icon above the image gallery.
+
+2.  Generate a bunch of bluebird images by increasing the number of
+    requested images by adjusting the Images counter just below the Invoke
+    button. As each is generated, it will be added to the gallery. You can
+    switch the active image by clicking on the gallery thumbnails.
+	
+	If you'd like to watch the image generation progress, click the hourglass
+	icon above the main image area. As generation progresses, you'll see
+	increasingly detailed versions of the ultimate image.
+
+3.  Try playing with different settings, including changing the main
+    model, the image width and height, the Scheduler, the Steps and
+    the CFG scale.
+	
+	The _Model_ changes the main model. Thousands of custom models are
+	now available, which generate a variety of image styles and
+	subjects. While InvokeAI comes with a few starter models, it is
+	easy to import new models into the application. See [Installing
+	Models](../installation/050_INSTALLING_MODELS.md) for more details.
+
+    Image _Width_ and _Height_ do what you'd expect. However, be aware that
+    larger images consume more VRAM memory and take longer to generate.
+
+    The _Scheduler_ controls how the AI selects the image to display. Some
+    samplers are more "creative" than others and will produce a wider range of
+    variations (see next section). Some samplers run faster than others.
+
+    _Steps_ controls how many noising/denoising/sampling steps the AI will take.
+    The higher this value, the more refined the image will be, but the longer
+    the image will take to generate. A typical strategy is to generate images
+    with a low number of steps in order to select one to work on further, and
+    then regenerate it using a higher number of steps.
+
+    The _CFG Scale_ controls how hard the AI tries to match the generated image
+    to the input prompt. You can go as high or low as you like, but generally
+    values greater than 20 won't improve things much, and values lower than 5
+    will produce unexpected images. There are complex interactions between
+    _Steps_, _CFG Scale_ and the _Scheduler_, so experiment to find out what works
+    for you.
+	
+	The _Seed_ controls the series of values returned by InvokeAI's
+    random number generator. Each unique seed value will generate a different
+	image. To regenerate a previous image, simply use the original image's
+	seed value. A slider to the right of the _Seed_ field will change the
+	seed each time an image is generated.
+
+![Invoke Web Server - Control Panel 2](../assets/control-panel-2.png){ align=right width=400px }
+
+4.  To regenerate a previously-generated image, select the image you
+    want and click the asterisk ("*") button at the top of the
+    image. This loads the text prompt and other original settings into
+    the control panel. If you then press _Invoke_ it will regenerate
+    the image exactly. You can also selectively modify the prompt or
+    other settings to tweak the image.
+
+    Alternatively, you may click on the "sprouting plant icon" to load
+    just the image's seed, and leave other settings unchanged or the
+    quote icon to load just the positive and negative prompts.
+
+5.  To regenerate a Stable Diffusion image that was generated by another SD
+    package, you need to know its text prompt and its _Seed_. Copy-paste the
+    prompt into the prompt box, unset the _Randomize Seed_ control in the
+    control panel, and copy-paste the desired _Seed_ into its text field. When
+    you Invoke, you will get something similar to the original image. It will
+    not be exact unless you also set the correct values for the original
+    sampler, CFG, steps and dimensions, but it will (usually) be close.
+	
+6.  To save an image, right click on it to bring up a menu that will
+	let you download the image, save it to a named image gallery, and
+	copy it to the clipboard, among other things.
+
+#### Upscaling
+
+![Invoke Web Server - Upscaling](../assets/upscaling.png){ align=right width=400px }
+
+"Upscaling" is the process of increasing the size of an image while
+    retaining the sharpness. InvokeAI uses an external library called
+    "ESRGAN" to do this. To invoke upscaling, simply select an image
+    and press the "expanding arrows" button above it. You can select
+    between 2X and 4X upscaling, and adjust the upscaling strength,
+    which has much the same meaning as in facial reconstruction. Try
+    running this on one of your previously-generated images.
+
+### Image to Image
+
+InvokeAI lets you take an existing image and use it as the basis for a new
+creation. You can use any sort of image, including a photograph, a scanned
+sketch, or a digital drawing, as long as it is in PNG or JPEG format.
+
+For this tutorial, we'll use the file named
+[Lincoln-and-Parrot-512.png](../assets/Lincoln-and-Parrot-512.png).
+
+1.  Click on the _Image to Image_ tab icon, which is the second icon
+    from the top on the left-hand side of the screen. This will bring
+    you to a screen similar to the one shown here:
+
+    ![Invoke Web Server - Image to Image Tab](../assets/invoke-web-server-6.png){ width="640px" }
+
+2.  Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or click
+    the blank area to get an upload dialog. The image will load into an area
+    marked _Initial Image_. (The WebUI will also load the most
+    recently-generated image from the gallery into a section on the left, but
+    this image will be replaced in the next step.)
+
+3.  Go to the prompt box and type _old sea captain with raven on shoulder_ and
+    press Invoke. A derived image will appear to the right of the original one:
+
+    ![Invoke Web Server - Image to Image example](../assets/invoke-web-server-7.png){:width="640px"}
+
+4.  Experiment with the different settings. The most influential one in Image to
+    Image is _Denoising Strength_ located about midway down the control
+    panel. By default it is set to 0.75, but can range from 0.0 to 0.99. The
+    higher the value, the more of the original image the AI will replace. A
+    value of 0 will leave the initial image completely unchanged, while 0.99
+    will replace it completely. However, the _Scheduler_ and _CFG Scale_ also
+    influence the final result. You can also generate variations in the same way
+    as described in Text to Image.
+
+5.  What if we only want to change certain part(s) of the image and
+    leave the rest intact? This is called Inpainting, and you can do
+    it in the [Unified Canvas](UNIFIED_CANVAS.md). The Unified Canvas
+    also allows you to extend borders of the image and fill in the
+    blank areas, a process called outpainting.
+
+6.  Would you like to modify a previously-generated image using the Image to
+    Image facility? Easy! While in the Image to Image panel, drag and drop any
+	image in the gallery into the Initial Image area, and it will be ready for
+	use. You can do the same thing with the main image display. Click on the
+	_Send to_ icon to get a menu of
+	commands and choose "Send to Image to Image".
+	
+	![Send To Icon](../assets/send-to-icon.png) 
+
+### Textual Inversion, LoRA and ControlNet
+
+InvokeAI supports several different types of model files that
+extending the capabilities of the main model by adding artistic
+styles, special effects, or subjects. By mixing and matching textual
+inversion, LoRA and ControlNet models, you can achieve many
+interesting and beautiful effects.
+
+We will give an example using a LoRA model named "Ink Scenery". This
+LoRA, which can be downloaded from Civitai (civitai.com), is
+specialized to paint landscapes that look like they were made with
+dripping india ink. To install this LoRA, we first download it and 
+put it into the `autoimport/lora` folder located inside the
+`invokeai` root directory. After restarting the web server, the
+LoRA will now become available for use.
+
+To see this LoRA at work, we'll first generate an image without it
+using the standard `stable-diffusion-v1-5` model. Choose this
+model and enter the prompt "mountains, ink". Here is a typical
+generated image, a mountain range rendered in ink and watercolor
+wash:
+
+![Ink Scenery without LoRA](../assets/lora-example-0.png){ width=512px }
+
+Now let's install and activate the Ink Scenery LoRA. Go to
+https://civitai.com/models/78605/ink-scenery-or and download the LoRA
+model file to `invokeai/autoimport/lora` and restart the web
+server. (Alternatively, you can use [InvokeAI's Web Model
+Manager](../installation/050_INSTALLING_MODELS.md) to download and
+install the LoRA directly by typing its URL into the _Import
+Models_->_Location_ field).
+
+Scroll down the control panel until you get to the LoRA accordion
+section, and open it:
+
+![LoRA Section](../assets/lora-example-1.png){ width=512px }
+
+Click the popup menu and select "Ink scenery". (If it isn't there, then
+the model wasn't installed to the right place, or perhaps you forgot
+to restart the web server.) The LoRA section will change to look like this:
+
+![LoRA Section Loaded](../assets/lora-example-2.png){ width=512px }
+
+Note that there is now a slider control for _Ink scenery_. The slider
+controls how much influence the LoRA model will have on the generated
+image.
+
+Run the "mountains, ink" prompt again and observe the change in style:
+
+![Ink Scenery](../assets/lora-example-3.png){ width=512px }
+
+Try adjusting the weight slider for larger and smaller weights and
+generate the image after each adjustment. The higher the weight, the
+more influence the LoRA will have.
+
+To remove the LoRA completely, just click on its trash can icon.
+
+Multiple LoRAs can be added simultaneously and combined with textual
+inversions and ControlNet models. Please see [Textual Inversions and
+LoRAs](CONCEPTS.md) and [Using ControlNet](CONTROLNET.md) for details.
+
+## Summary
+
+This walkthrough just skims the surface of the many things InvokeAI
+can do. Please see [Features](index.md) for more detailed reference
+guides.
+
+## Acknowledgements
+
+A huge shout-out to the core team working to make the Web GUI a reality,
+including [psychedelicious](https://github.com/psychedelicious),
+[Kyle0654](https://github.com/Kyle0654) and
+[blessedcoolant](https://github.com/blessedcoolant).
+[hipsterusername](https://github.com/hipsterusername) was the team's unofficial
+cheerleader and added tooltips/docs.

docs/features/WEBUIHOTKEYS.md

@@ -0,0 +1,75 @@
+---
+title: WebUI Hotkey List
+---
+
+# :material-keyboard: **WebUI Hotkey List**
+
+## App Hotkeys
+
+| Setting         | Hotkey             |
+| --------------- | ------------------ |
+| ++ctrl+enter++  | Invoke             |
+| ++shift+x++     | Cancel             |
+| ++alt+a++       | Focus Prompt       |
+| ++o++           | Toggle Options     |
+| ++shift+o++     | Pin Options        |
+| ++z++           | Toggle Viewer      |
+| ++g++           | Toggle Gallery     |
+| ++f++           | Maximize Workspace |
+| ++1++ - ++5++   | Change Tabs        |
+| ++"`"++         | Toggle Console     |
+
+## General Hotkeys
+
+| Setting        | Hotkey                 |
+| -------------- | ---------------------- |
+| ++p++          | Set Prompt             |
+| ++s++          | Set Seed               |
+| ++a++          | Set Parameters         |
+| ++shift+r++    | Restore Faces          |
+| ++shift+u++    | Upscale                |
+| ++i++          | Show Info              |
+| ++shift+i++    | Send To Image To Image |
+| ++del++        | Delete Image           |
+| ++esc++        | Close Panels           |
+
+## Gallery Hotkeys
+
+| Setting               | Hotkey                      |
+| ----------------------| --------------------------- |
+| ++arrow-left++        | Previous Image              |
+| ++arrow-right++       | Next Image                  |
+| ++shift+g++           | Toggle Gallery Pin          |
+| ++shift+arrow-up++    | Increase Gallery Image Size |
+| ++shift+arrow-down++  | Decrease Gallery Image Size |
+
+## Unified Canvas Hotkeys
+
+| Setting                           | Hotkey                 |
+| --------------------------------- | ---------------------- |
+| ++b++                             | Select Brush           |
+| ++e++                             | Select Eraser          |
+| ++bracket-left++                  | Decrease Brush Size    |
+| ++bracket-right++                 | Increase Brush Size    |
+| ++shift+bracket-left++            | Decrease Brush Opacity |
+| ++shift+bracket-right++           | Increase Brush Opacity |
+| ++v++                             | Move Tool              |
+| ++shift+f++                       | Fill Bounding Box      |
+| ++del++ / ++backspace++           | Erase Bounding Box     |
+| ++c++                             | Select Color Picker    |
+| ++n++                             | Toggle Snap            |
+| ++"Hold Space"++                  | Quick Toggle Move      |
+| ++q++                             | Toggle Layer           |
+| ++shift+c++                       | Clear Mask             |
+| ++h++                             | Hide Mask              |
+| ++shift+h++                       | Show/Hide Bounding Box |
+| ++shift+m++                       | Merge Visible          |
+| ++shift+s++                       | Save To Gallery        |
+| ++ctrl+c++                        | Copy To Clipboard      |
+| ++shift+d++                       | Download Image         |
+| ++ctrl+z++                        | Undo                   |
+| ++ctrl+y++ / ++ctrl+shift+z++     | Redo                   |
+| ++r++                             | Reset View             |
+| ++arrow-left++                    | Previous Staging Image |
+| ++arrow-right++                   | Next Staging Image     |
+| ++enter++                         | Accept Staging Image   |

docs/features/canvas_projects.md

@@ -1,56 +0,0 @@
----
-title: Canvas Projects
----
-
-# :material-folder-zip: Canvas Projects
-
-## Save and Restore Your Canvas Work
-
-Canvas Projects let you save your entire canvas setup to a file and load it back later. This is useful when you want to:
-
-- **Switch between tasks** without losing your current canvas arrangement
-- **Back up complex setups** with multiple layers, masks, and reference images
-- **Share canvas layouts** with others or transfer them between machines
-- **Recover from deleted images** — all images are embedded in the project file
-
-## What Gets Saved
-
-A canvas project file (`.invk`) captures everything about your current canvas session:
-
-- **All layers** — raster layers, control layers, inpaint masks, regional guidance
-- **All drawn content** — brush strokes, pasted images, eraser marks
-- **Reference images** — global IP-Adapter / FLUX Redux images with crop settings
-- **Regional guidance** — per-region prompts and reference images
-- **Bounding box** — position, size, aspect ratio, and scale settings
-- **All generation parameters** — prompts, seed, steps, CFG scale, guidance, scheduler, model, VAE, dimensions, img2img strength, infill settings, canvas coherence, refiner settings, FLUX/Z-Image specific parameters, and more
-- **LoRAs** — all added LoRA models with their weights and enabled/disabled state
-
-## How to Save a Project
-
-You can save from two places:
-
-1. **Toolbar** — Click the **Archive icon** in the canvas toolbar, then select **Save Canvas Project**
-2. **Context menu** — Right-click the canvas, open the **Project** submenu, then select **Save Canvas Project**
-
-A dialog will ask you to enter a **project name**. This name is used as the filename (e.g., entering "My Portrait" saves as `My Portrait.invk`) and is stored inside the project file.
-
-## How to Load a Project
-
-1. **Toolbar** — Click the **Archive icon**, then select **Load Canvas Project**
-2. **Context menu** — Right-click the canvas, open the **Project** submenu, then select **Load Canvas Project**
-
-A file dialog will open. Select your `.invk` file. You will see a confirmation dialog warning that loading will replace your current canvas. Click **Load** to proceed.
-
-### What Happens on Load
-
-- Your current canvas is **completely replaced** — all existing layers, masks, reference images, and parameters are overwritten
-- Images that are already present on your InvokeAI server are reused automatically (no duplicate uploads)
-- Images that were deleted from the server are re-uploaded from the project file
-- If the saved model is not installed on your system, the model identifier is still restored — you will need to select an available model manually
-
-## Good to Know
-
-- **No undo** — Loading a project replaces your canvas entirely. There is no way to undo this action, so save your current project first if you want to keep it.
-- **Image deduplication** — When loading, images already on your server are not re-uploaded. Only missing images are uploaded from the project file.
-- **File size** — The `.invk` file size depends on the number and resolution of images in your canvas. A project with many high-resolution layers can be large.
-- **Model availability** — The project saves which model was selected, but does not include the model itself. If the model is not installed when you load the project, you will need to select a different one.