change workflow to deploy from v2.3 branch

Fixes #3228, where the check to see if invokeai is running fails because
a process no longer exists.

.coveragerc

@@ -0,0 +1,6 @@
+[run]
+omit='.env/*'
+source='.'
+
+[report]
+show_missing = true

.dockerignore

@@ -3,21 +3,23 @@
 !invokeai
 !ldm
 !pyproject.toml
-!README.md
 
 # Guard against pulling in any models that might exist in the directory tree
 **/*.pt*
 **/*.ckpt
 
 # ignore frontend but whitelist dist
-invokeai/frontend/**
-!invokeai/frontend/dist
+invokeai/frontend/
+!invokeai/frontend/dist/
 
 # ignore invokeai/assets but whitelist invokeai/assets/web
-invokeai/assets
-!invokeai/assets/web
+invokeai/assets/
+!invokeai/assets/web/
 
-# ignore python cache
-**/__pycache__
+# Byte-compiled / optimized / DLL files
+**/__pycache__/
 **/*.py[cod]
-**/*.egg-info
+
+# Distribution / packaging
+*.egg-info/
+*.egg

.editorconfig

@@ -1,5 +1,8 @@
+root = true
+
 # All files
 [*]
+max_line_length = 80
 charset = utf-8
 end_of_line = lf
 indent_size = 2
@@ -10,3 +13,18 @@ trim_trailing_whitespace = true
 # Python
 [*.py]
 indent_size = 4
+max_line_length = 120
+
+# css
+[*.css]
+indent_size = 4
+
+# flake8
+[.flake8]
+indent_size = 4
+
+# Markdown MkDocs
+[docs/**/*.md]
+max_line_length = 80
+indent_size = 4
+indent_style = unset

.flake8

@@ -0,0 +1,37 @@
+[flake8]
+max-line-length = 120
+extend-ignore =
+    # See https://github.com/PyCQA/pycodestyle/issues/373
+    E203,
+    # use Bugbear's B950 instead
+    E501,
+    # from black repo https://github.com/psf/black/blob/main/.flake8
+    E266, W503, B907
+extend-select =
+    # Bugbear line length
+    B950
+extend-exclude =
+    scripts/orig_scripts/*
+    ldm/models/*
+    ldm/modules/*
+    ldm/data/*
+    ldm/generate.py
+    ldm/util.py
+    ldm/simplet2i.py
+per-file-ignores =
+    # B950 line too long
+    # W605 invalid escape sequence
+    # F841 assigned to but never used
+    # F401 imported but unused
+    tests/test_prompt_parser.py: B950, W605, F401
+    tests/test_textual_inversion.py: F841, B950
+    # B023 Function definition does not bind loop variable
+    scripts/legacy_api.py: F401, B950, B023, F841
+    ldm/invoke/__init__.py: F401
+    # B010 Do not call setattr with a constant attribute value
+    ldm/invoke/server_legacy.py: B010
+# =====================
+# flake-quote settings:
+# =====================
+# Set this to match black style:
+inline-quotes = double

.github/CODEOWNERS

@@ -1,18 +1,18 @@
 # continuous integration
-/.github/workflows/ @mauwii
+/.github/workflows/  @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein @mauwii @tildebyte
-mkdocs.yml @lstein @mauwii
+/docs/ @lstein  @blessedcoolant
+mkdocs.yml  @lstein @ebr
 
 # installation and configuration
-/pyproject.toml @mauwii @lstein @ebr
-/docker/ @mauwii
-/scripts/ @ebr @lstein
-/installer/ @ebr @lstein @tildebyte
+/pyproject.toml  @lstein @ebr
+/docker/ @lstein
+/scripts/ @ebr @lstein @blessedcoolant
+/installer/ @ebr @lstein 
 ldm/invoke/config @lstein @ebr
-invokeai/assets @lstein @ebr
-invokeai/configs @lstein @ebr
+invokeai/assets @lstein @blessedcoolant
+invokeai/configs @lstein @ebr  @blessedcoolant
 /ldm/invoke/_version.py @lstein @blessedcoolant
 
 # web ui
@@ -20,31 +20,42 @@ invokeai/configs @lstein @ebr
 /invokeai/backend @blessedcoolant @psychedelicious
 
 # generation and model management
-/ldm/*.py @lstein
-/ldm/generate.py @lstein @keturn
+/ldm/*.py @lstein @blessedcoolant
+/ldm/generate.py @lstein @gregghelt2
 /ldm/invoke/args.py @lstein @blessedcoolant
-/ldm/invoke/ckpt* @lstein
-/ldm/invoke/ckpt_generator @lstein
-/ldm/invoke/CLI.py @lstein
-/ldm/invoke/config @lstein @ebr @mauwii
-/ldm/invoke/generator @keturn @damian0815
-/ldm/invoke/globals.py @lstein @blessedcoolant
-/ldm/invoke/merge_diffusers.py @lstein
+/ldm/invoke/ckpt* @lstein @blessedcoolant
+/ldm/invoke/ckpt_generator @lstein @blessedcoolant
+/ldm/invoke/CLI.py @lstein @blessedcoolant
+/ldm/invoke/config @lstein @ebr  @blessedcoolant
+/ldm/invoke/generator @gregghelt2 @damian0815
+/ldm/invoke/globals.py @lstein @blessedcoolant 
+/ldm/invoke/merge_diffusers.py @lstein @blessedcoolant
 /ldm/invoke/model_manager.py @lstein @blessedcoolant
-/ldm/invoke/txt2mask.py @lstein
-/ldm/invoke/patchmatch.py @Kyle0654
+/ldm/invoke/txt2mask.py @lstein @blessedcoolant
+/ldm/invoke/patchmatch.py @Kyle0654 @lstein
 /ldm/invoke/restoration @lstein @blessedcoolant
 
 # attention, textual inversion, model configuration
-/ldm/models @damian0815 @keturn
-/ldm/modules @damian0815 @keturn
+/ldm/models @damian0815 @gregghelt2 @blessedcoolant
+/ldm/modules/textual_inversion_manager.py @lstein @blessedcoolant
+/ldm/modules/attention.py @damian0815 @gregghelt2
+/ldm/modules/diffusionmodules @damian0815 @gregghelt2
+/ldm/modules/distributions @damian0815 @gregghelt2
+/ldm/modules/ema.py  @damian0815 @gregghelt2
+/ldm/modules/embedding_manager.py @lstein
+/ldm/modules/encoders @damian0815 @gregghelt2
+/ldm/modules/image_degradation @damian0815 @gregghelt2
+/ldm/modules/losses  @damian0815 @gregghelt2
+/ldm/modules/x_transformer.py @damian0815 @gregghelt2
 
 # Nodes
-apps/ @Kyle0654
+apps/ @Kyle0654 @jpphoto
 
 # legacy REST API
-# is CapableWeb still engaged?
-/ldm/invoke/pngwriter.py @CapableWeb
-/ldm/invoke/server_legacy.py @CapableWeb
-/scripts/legacy_api.py @CapableWeb
-/tests/legacy_tests.sh @CapableWeb
+# these are dead code
+#/ldm/invoke/pngwriter.py @CapableWeb
+#/ldm/invoke/server_legacy.py @CapableWeb
+#/scripts/legacy_api.py @CapableWeb
+#/tests/legacy_tests.sh @CapableWeb
+
+

.github/workflows/build-container.yml

@@ -3,9 +3,19 @@ on:
   push:
     branches:
       - 'main'
-      - 'update/ci/*'
+      - 'update/ci/docker/*'
+      - 'update/docker/*'
+    paths:
+      - 'pyproject.toml'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
+      - 'docker/Dockerfile'
     tags:
       - 'v*.*.*'
+  workflow_dispatch:
+
 
 jobs:
   docker:
@@ -20,18 +30,15 @@ jobs:
         include:
           - flavor: amd
             pip-extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
-            dockerfile: docker/Dockerfile
-            platforms: linux/amd64,linux/arm64
           - flavor: cuda
             pip-extra-index-url: ''
-            dockerfile: docker/Dockerfile
-            platforms: linux/amd64,linux/arm64
           - flavor: cpu
             pip-extra-index-url: 'https://download.pytorch.org/whl/cpu'
-            dockerfile: docker/Dockerfile
-            platforms: linux/amd64,linux/arm64
     runs-on: ubuntu-latest
     name: ${{ matrix.flavor }}
+    env:
+      PLATFORMS: 'linux/amd64,linux/arm64'
+      DOCKERFILE: 'docker/Dockerfile'
     steps:
       - name: Checkout
         uses: actions/checkout@v3
@@ -41,7 +48,9 @@ jobs:
         uses: docker/metadata-action@v4
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
-          images: ghcr.io/${{ github.repository }}
+          images: |
+            ghcr.io/${{ github.repository }}
+            ${{ vars.DOCKERHUB_REPOSITORY }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
@@ -52,13 +61,14 @@ jobs:
           flavor: |
             latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}
             suffix=-${{ matrix.flavor }},onlatest=false
+
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
         with:
-          platforms: ${{ matrix.platforms }}
+          platforms: ${{ env.PLATFORMS }}
 
       - name: Login to GitHub Container Registry
         if: github.event_name != 'pull_request'
@@ -68,25 +78,34 @@ jobs:
           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
+        id: docker_build
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: ${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
-          push: ${{ github.event_name != 'pull_request' }}
+          file: ${{ env.DOCKERFILE }}
+          platforms: ${{ env.PLATFORMS }}
+          push: ${{ github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           build-args: PIP_EXTRA_INDEX_URL=${{ matrix.pip-extra-index-url }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
+          cache-from: |
+            type=gha,scope=${{ github.ref_name }}-${{ matrix.flavor }}
+            type=gha,scope=main-${{ matrix.flavor }}
+          cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.flavor }}
 
-      - name: Output image, digest and metadata to summary
-        run: |
-          {
-          echo imageid: "${{ steps.docker_build.outputs.imageid }}"
-          echo digest: "${{ steps.docker_build.outputs.digest }}"
-          echo labels: "${{ steps.meta.outputs.labels }}"
-          echo tags: "${{ steps.meta.outputs.tags }}"
-          echo version: "${{ steps.meta.outputs.version }}"
-          } >> "$GITHUB_STEP_SUMMARY"
+      - 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/mkdocs-material.yml

@@ -9,6 +9,10 @@ jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
+    env:
+      REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
+      REPO_NAME: '${{ github.repository }}'
+      SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
     steps:
       - name: checkout sources
         uses: actions/checkout@v3
@@ -19,11 +23,15 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: '3.10'
+          cache: pip
+          cache-dependency-path: pyproject.toml
 
       - name: install requirements
+        env:
+          PIP_USE_PEP517: 1
         run: |
           python -m \
-            pip install -r docs/requirements-mkdocs.txt
+            pip install ".[docs]"
 
       - name: confirm buildability
         run: |
@@ -33,7 +41,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/main' }}
+        if: ${{ github.ref == 'refs/heads/v2.3' }}
         run: |
           python -m \
             mkdocs gh-deploy \

.github/workflows/pypi-release.yml

@@ -28,7 +28,7 @@ jobs:
         run: twine check dist/*
 
       - name: check PyPI versions
-        if: github.ref == 'refs/heads/main'
+        if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3'
         run: |
           pip install --upgrade requests
           python -c "\

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

@@ -0,0 +1,67 @@
+name: Test invoke.py pip
+on:
+  pull_request:
+    paths-ignore:
+      - 'pyproject.toml'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
+  merge_group:
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  matrix:
+    if: github.event.pull_request.draft == false
+    strategy:
+      matrix:
+        python-version:
+          # - '3.9'
+          - '3.10'
+        pytorch:
+          # - linux-cuda-11_6
+          - linux-cuda-11_7
+          - linux-rocm-5_2
+          - linux-cpu
+          - macos-default
+          - windows-cpu
+          # - windows-cuda-11_6
+          # - windows-cuda-11_7
+        include:
+          # - pytorch: linux-cuda-11_6
+          #   os: ubuntu-22.04
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $GITHUB_ENV
+          - pytorch: linux-cuda-11_7
+            os: ubuntu-22.04
+            github-env: $GITHUB_ENV
+          - pytorch: linux-rocm-5_2
+            os: ubuntu-22.04
+            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
+            github-env: $GITHUB_ENV
+          - pytorch: linux-cpu
+            os: ubuntu-22.04
+            extra-index-url: 'https://download.pytorch.org/whl/cpu'
+            github-env: $GITHUB_ENV
+          - pytorch: macos-default
+            os: macOS-12
+            github-env: $GITHUB_ENV
+          - pytorch: windows-cpu
+            os: windows-2022
+            github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_6
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_7
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu117'
+          #   github-env: $env:GITHUB_ENV
+    name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}
+    steps:
+      - run: 'echo "No build required"'

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

@@ -3,11 +3,24 @@ on:
   push:
     branches:
       - 'main'
+    paths:
+      - 'pyproject.toml'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
   pull_request:
+    paths:
+      - 'pyproject.toml'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
     types:
       - 'ready_for_review'
       - 'opened'
       - 'synchronize'
+  merge_group:
   workflow_dispatch:
 
 concurrency:

.gitignore

@@ -1,4 +1,5 @@
 # ignore default image save location and model symbolic link
+.idea/
 embeddings/
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
@@ -67,6 +68,7 @@ htmlcov/
 .cache
 nosetests.xml
 coverage.xml
+cov.xml
 *.cover
 *.py,cover
 .hypothesis/
@@ -231,5 +233,3 @@ installer/install.sh
 installer/update.bat
 installer/update.sh
 
-# no longer stored in source directory
-models

.pre-commit-config.yaml

@@ -0,0 +1,41 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+  - repo: https://github.com/psf/black
+    rev: 23.1.0
+    hooks:
+      - id: black
+
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 6.0.0
+    hooks:
+      - id: flake8
+        additional_dependencies:
+          - flake8-black
+          - flake8-bugbear
+          - flake8-comprehensions
+          - flake8-simplify
+
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: 'v3.0.0-alpha.4'
+    hooks:
+      - id: prettier
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: check-added-large-files
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: check-merge-conflict
+      - id: check-symlinks
+      - id: check-toml
+      - id: end-of-file-fixer
+      - id: no-commit-to-branch
+        args: ['--branch', 'main']
+      - id: trailing-whitespace

.prettierignore

@@ -0,0 +1,14 @@
+invokeai/frontend/.husky
+invokeai/frontend/patches
+
+# Ignore artifacts:
+build
+coverage
+static
+invokeai/frontend/dist
+
+# Ignore all HTML files:
+*.html
+
+# Ignore deprecated docs
+docs/installation/deprecated_documentation

.prettierrc.yaml

@@ -1,9 +1,9 @@
-endOfLine: lf
-tabWidth: 2
-useTabs: false
-singleQuote: true
-quoteProps: as-needed
 embeddedLanguageFormatting: auto
+endOfLine: lf
+singleQuote: true
+semi: true
+trailingComma: es5
+useTabs: false
 overrides:
   - files: '*.md'
     options:
@@ -11,3 +11,9 @@ overrides:
       printWidth: 80
       parser: markdown
       cursorOffset: -1
+  - files: docs/**/*.md
+    options:
+      tabWidth: 4
+  - files: 'invokeai/frontend/public/locales/*.json'
+    options:
+      tabWidth: 4

.pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+DJANGO_SETTINGS_MODULE = webtas.settings
+; python_files = tests.py test_*.py *_tests.py
+
+addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml

README.md

@@ -1,6 +1,6 @@
 <div align="center">
 
-![project logo](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/invoke_ai_banner.png)
+![project logo](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/invoke_ai_banner.png)
 
 # InvokeAI: A Stable Diffusion Toolkit
 
@@ -10,10 +10,10 @@
 
 [![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]
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
 
 [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/workflows/test-invoke-conda.yml
+[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
@@ -28,12 +28,14 @@
 [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
+[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
+[translation status link]: https://hosted.weblate.org/engage/invokeai/
 
 </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](#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/">Code and Downloads</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>]
+**Quick links**: [[How to Install](https://invoke-ai.github.io/InvokeAI/#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/">Code and Downloads</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>]
 
 _Note: InvokeAI is rapidly evolving. Please use the
 [Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
@@ -41,38 +43,136 @@ requests. Be sure to use the provided templates. They will help us diagnose issu
 
 <div align="center">
 
-![canvas preview](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/canvas_preview.png)
+![canvas preview](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/canvas_preview.png)
 
 </div>
 
-# Getting Started with InvokeAI
+## Table of Contents
+
+1. [Quick Start](#getting-started-with-invokeai)
+2. [Installation](#detailed-installation-instructions)
+3. [Hardware Requirements](#hardware-requirements)
+4. [Features](#features)
+5. [Latest Changes](#latest-changes)
+6. [Troubleshooting](#troubleshooting)
+7. [Contributing](#contributing)
+8. [Contributors](#contributors)
+9. [Support](#support)
+10. [Further Reading](#further-reading)
+
+## Getting Started with InvokeAI
 
 For full installation and upgrade instructions, please see:
 [InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
 
+### 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. If you are on Windows, double-click on the `install.bat` script. On macOS, open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press return. On Linux, run `install.sh`.
-5. Wait a while, until it is done.
-6. The folder where you ran the installer from will now be filled with lots of files. If you are on Windows, 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`
-7. 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.
-8. Type `banana sushi` in the box on the top left and click `Invoke`
 
+4. If you are on Windows, double-click on the `install.bat` script. On
+macOS, open a Terminal window, drag the file `install.sh` from Finder
+into the Terminal, and press return. On Linux, run `install.sh`.
 
-## Table of Contents
+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.
 
-1. [Installation](#installation)
-2. [Hardware Requirements](#hardware-requirements)
-3. [Features](#features)
-4. [Latest Changes](#latest-changes)
-5. [Troubleshooting](#troubleshooting)
-6. [Contributing](#contributing)
-7. [Contributors](#contributors)
-8. [Support](#support)
-9. [Further Reading](#further-reading)
+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 generaiton models.
 
-## Installation
+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 users familiar with Terminals)
+
+You must have Python 3.9 or 3.10 installed on your machine. Earlier or later versions are
+not supported.
+
+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/cu117
+    ```
+
+    _For Linux with an AMD GPU:_
+
+    ```sh
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    ```
+
+    _For Macintoshes, either Intel or M1/M2:_
+
+    ```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
+    ```
+
+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
@@ -80,13 +180,13 @@ 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/)
 
-### Hardware Requirements
+## 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
+### System
 
 You will need one of the following:
 
@@ -98,11 +198,11 @@ 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
+### Memory
 
 - At least 12 GB Main Memory RAM.
 
-#### Disk
+### Disk
 
 - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
@@ -152,13 +252,15 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
 problems and other issues.
 
-# Contributing
+## 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.
 
 To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
 
+If you'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
+
 If you are unfamiliar with how
 to contribute to GitHub projects, here is a
 [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**.
@@ -175,6 +277,8 @@ 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.
 
+Thanks to [Weblate](https://weblate.org/) for generously providing translation services to this project.
+
 ### Support
 
 For support, please use this repository's GitHub Issues tracking service, or join the Discord.

binary_installer/install.bat.in

@@ -1,164 +0,0 @@
-@echo off
-
-@rem This script will install git (if not found on the PATH variable)
-@rem  using micromamba (an 8mb static-linked single-file binary, conda replacement).
-@rem For users who already have git, this step will be skipped.
-
-@rem Next, it'll download the project's source code.
-@rem Then it will download a self-contained, standalone Python and unpack it.
-@rem Finally, it'll create the Python virtual environment and preload the models.
-
-@rem This enables a user to install this project without manually installing git or Python
-
-@rem change to the script's directory
-PUSHD "%~dp0"
-
-set "no_cache_dir=--no-cache-dir"
-if "%1" == "use-cache" (
-    set "no_cache_dir="
-)
-
-echo ***** Installing InvokeAI.. *****
-@rem Config
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-@rem https://mamba.readthedocs.io/en/latest/installation.html
-set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
-set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
-set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
-set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
-
-set PACKAGES_TO_INSTALL=
-
-call git --version >.tmp1 2>.tmp2
-if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
-
-@rem Cleanup
-del /q .tmp1 .tmp2
-
-@rem (if necessary) install git into a contained environment
-if "%PACKAGES_TO_INSTALL%" NEQ "" (
-    @rem download micromamba
-    echo ***** Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to micromamba.exe *****
-
-    call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > micromamba.exe
-
-    @rem test the mamba binary
-    echo ***** Micromamba version:  *****
-    call micromamba.exe --version
-
-    @rem create the installer env
-    if not exist "%INSTALL_ENV_DIR%" (
-        call micromamba.exe create -y --prefix "%INSTALL_ENV_DIR%"
-    )
-
-    echo ***** Packages to install:%PACKAGES_TO_INSTALL% *****
-
-    call micromamba.exe install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
-
-    if not exist "%INSTALL_ENV_DIR%" (
-        echo ----- There was a problem while installing "%PACKAGES_TO_INSTALL%" using micromamba. Cannot continue. -----
-        pause
-        exit /b
-    )
-)
-
-del /q micromamba.exe
-
-@rem For 'git' only
-set PATH=%INSTALL_ENV_DIR%\Library\bin;%PATH%
-
-@rem Download/unpack/clean up InvokeAI release sourceball
-set err_msg=----- InvokeAI source download failed -----
-echo Trying to download "%RELEASE_URL%%RELEASE_SOURCEBALL%"
-curl -L %RELEASE_URL%%RELEASE_SOURCEBALL% --output InvokeAI.tgz
-if %errorlevel% neq 0 goto err_exit
-
-set err_msg=----- InvokeAI source unpack failed -----
-tar -zxf InvokeAI.tgz
-if %errorlevel% neq 0 goto err_exit
-
-del /q InvokeAI.tgz
-
-set err_msg=----- InvokeAI source copy failed -----
-cd InvokeAI-*
-xcopy . .. /e /h
-if %errorlevel% neq 0 goto err_exit
-cd ..
-
-@rem cleanup
-for /f %%i in ('dir /b InvokeAI-*') do rd /s /q %%i
-rd /s /q .dev_scripts .github docker-build tests
-del /q requirements.in requirements-mkdocs.txt shell.nix
-
-echo ***** Unpacked InvokeAI source *****
-
-@rem Download/unpack/clean up python-build-standalone
-set err_msg=----- Python download failed -----
-curl -L %PYTHON_BUILD_STANDALONE_URL%/%PYTHON_BUILD_STANDALONE% --output python.tgz
-if %errorlevel% neq 0 goto err_exit
-
-set err_msg=----- Python unpack failed -----
-tar -zxf python.tgz
-if %errorlevel% neq 0 goto err_exit
-
-del /q python.tgz
-
-echo ***** Unpacked python-build-standalone *****
-
-@rem create venv
-set err_msg=----- problem creating venv -----
-.\python\python -E -s -m venv .venv
-if %errorlevel% neq 0 goto err_exit
-call .venv\Scripts\activate.bat
-
-echo ***** Created Python virtual environment *****
-
-@rem Print venv's Python version
-set err_msg=----- problem calling venv's python -----
-echo We're running under
-.venv\Scripts\python --version
-if %errorlevel% neq 0 goto err_exit
-
-set err_msg=----- pip update failed -----
-.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location --upgrade pip wheel
-if %errorlevel% neq 0 goto err_exit
-
-echo ***** Updated pip and wheel *****
-
-set err_msg=----- requirements file copy failed -----
-copy binary_installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
-if %errorlevel% neq 0 goto err_exit
-
-set err_msg=----- main pip install failed -----
-.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -r requirements.txt
-if %errorlevel% neq 0 goto err_exit
-
-echo ***** Installed Python dependencies *****
-
-set err_msg=----- InvokeAI setup failed -----
-.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -e .
-if %errorlevel% neq 0 goto err_exit
-
-copy binary_installer\invoke.bat.in .\invoke.bat
-echo ***** Installed invoke launcher script ******
-
-@rem more cleanup
-rd /s /q binary_installer installer_files
-
-@rem preload the models
-call .venv\Scripts\python scripts\configure_invokeai.py
-set err_msg=----- model download clone failed -----
-if %errorlevel% neq 0 goto err_exit
-deactivate
-
-echo ***** Finished downloading models *****
-
-echo All done! Execute the file invoke.bat in this directory to start InvokeAI
-pause
-exit
-
-:err_exit
-    echo %err_msg%
-    pause
-    exit

binary_installer/install.sh.in

@@ -1,235 +0,0 @@
-#!/usr/bin/env bash
-
-# ensure we're in the correct folder in case user's CWD is somewhere else
-scriptdir=$(dirname "$0")
-cd "$scriptdir"
-
-set -euo pipefail
-IFS=$'\n\t'
-
-function _err_exit {
-    if test "$1" -ne 0
-    then
-        echo -e "Error code $1; Error caught was '$2'"
-        read -p "Press any key to exit..."
-        exit
-    fi
-}
-
-# This script will install git (if not found on the PATH variable)
-#  using micromamba (an 8mb static-linked single-file binary, conda replacement).
-# For users who already have git, this step will be skipped.
-
-# Next, it'll download the project's source code.
-# Then it will download a self-contained, standalone Python and unpack it.
-# Finally, it'll create the Python virtual environment and preload the models.
-
-# This enables a user to install this project without manually installing git or Python
-
-echo -e "\n***** Installing InvokeAI into $(pwd)... *****\n"
-
-export no_cache_dir="--no-cache-dir"
-if [ $# -ge 1 ]; then
-    if [ "$1" = "use-cache" ]; then
-        export no_cache_dir=""
-    fi
-fi
-
-
-OS_NAME=$(uname -s)
-case "${OS_NAME}" in
-    Linux*)     OS_NAME="linux";;
-    Darwin*)    OS_NAME="darwin";;
-    *)          echo -e "\n----- Unknown OS: $OS_NAME! This script runs only on Linux or macOS -----\n" && exit
-esac
-
-OS_ARCH=$(uname -m)
-case "${OS_ARCH}" in
-    x86_64*)    ;;
-    arm64*)     ;;
-    *)          echo -e "\n----- Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64 -----\n" && exit
-esac
-
-# https://mamba.readthedocs.io/en/latest/installation.html
-MAMBA_OS_NAME=$OS_NAME
-MAMBA_ARCH=$OS_ARCH
-if [ "$OS_NAME" == "darwin" ]; then
-    MAMBA_OS_NAME="osx"
-fi
-
-if [ "$OS_ARCH" == "linux" ]; then
-    MAMBA_ARCH="aarch64"
-fi
-
-if [ "$OS_ARCH" == "x86_64" ]; then
-    MAMBA_ARCH="64"
-fi
-
-PY_ARCH=$OS_ARCH
-if [ "$OS_ARCH" == "arm64" ]; then
-    PY_ARCH="aarch64"
-fi
-
-# Compute device ('cd' segment of reqs files) detect goes here
-# This needs a ton of work
-# Suggestions:
-#   - lspci
-#   - check $PATH for nvidia-smi, gtt CUDA/GPU version from output
-#   - Surely there's a similar utility for AMD?
-CD="cuda"
-if [ "$OS_NAME" == "darwin" ] && [ "$OS_ARCH" == "arm64" ]; then
-    CD="mps"
-fi
-
-# config
-INSTALL_ENV_DIR="$(pwd)/installer_files/env"
-MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${MAMBA_OS_NAME}-${MAMBA_ARCH}/latest"
-RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
-PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
-if [ "$OS_NAME" == "darwin" ]; then
-    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-apple-darwin-install_only.tar.gz
-elif [ "$OS_NAME" == "linux" ]; then
-    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-unknown-linux-gnu-install_only.tar.gz
-fi
-echo "INSTALLING $RELEASE_SOURCEBALL FROM $RELEASE_URL"
-
-PACKAGES_TO_INSTALL=""
-
-if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
-
-# (if necessary) install git and conda into a contained environment
-if [ "$PACKAGES_TO_INSTALL" != "" ]; then
-    # download micromamba
-    echo -e "\n***** Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to micromamba *****\n"
-
-    curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvjO bin/micromamba > micromamba
-
-    chmod u+x ./micromamba
-
-    # test the mamba binary
-    echo -e "\n***** Micromamba version: *****\n"
-    ./micromamba --version
-
-    # create the installer env
-    if [ ! -e "$INSTALL_ENV_DIR" ]; then
-        ./micromamba create -y --prefix "$INSTALL_ENV_DIR"
-    fi
-
-    echo -e "\n***** Packages to install:$PACKAGES_TO_INSTALL *****\n"
-
-    ./micromamba install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge "$PACKAGES_TO_INSTALL"
-
-    if [ ! -e "$INSTALL_ENV_DIR" ]; then
-        echo -e "\n----- There was a problem while initializing micromamba. Cannot continue. -----\n"
-        exit
-    fi
-fi
-
-rm -f micromamba.exe
-
-export PATH="$INSTALL_ENV_DIR/bin:$PATH"
-
-# Download/unpack/clean up InvokeAI release sourceball
-_err_msg="\n----- InvokeAI source download failed -----\n"
-curl -L $RELEASE_URL/$RELEASE_SOURCEBALL --output InvokeAI.tgz
-_err_exit $? _err_msg
-_err_msg="\n----- InvokeAI source unpack failed -----\n"
-tar -zxf InvokeAI.tgz
-_err_exit $? _err_msg
-
-rm -f InvokeAI.tgz
-
-_err_msg="\n----- InvokeAI source copy failed -----\n"
-cd InvokeAI-*
-cp -r . ..
-_err_exit $? _err_msg
-cd ..
-
-# cleanup
-rm -rf InvokeAI-*/
-rm -rf .dev_scripts/ .github/ docker-build/ tests/ requirements.in requirements-mkdocs.txt shell.nix
-
-echo -e "\n***** Unpacked InvokeAI source *****\n"
-
-# Download/unpack/clean up python-build-standalone
-_err_msg="\n----- Python download failed -----\n"
-curl -L $PYTHON_BUILD_STANDALONE_URL/$PYTHON_BUILD_STANDALONE --output python.tgz
-_err_exit $? _err_msg
-_err_msg="\n----- Python unpack failed -----\n"
-tar -zxf python.tgz
-_err_exit $? _err_msg
-
-rm -f python.tgz
-
-echo -e "\n***** Unpacked python-build-standalone *****\n"
-
-# create venv
-_err_msg="\n----- problem creating venv -----\n"
-
-if [ "$OS_NAME" == "darwin" ]; then
-    # patch sysconfig so that extensions can build properly
-    # adapted from https://github.com/cashapp/hermit-packages/commit/fcba384663892f4d9cfb35e8639ff7a28166ee43
-    PYTHON_INSTALL_DIR="$(pwd)/python"
-    SYSCONFIG="$(echo python/lib/python*/_sysconfigdata_*.py)"
-    TMPFILE="$(mktemp)"
-    chmod +w "${SYSCONFIG}"
-    cp "${SYSCONFIG}" "${TMPFILE}"
-    sed "s,'/install,'${PYTHON_INSTALL_DIR},g" "${TMPFILE}" > "${SYSCONFIG}"
-    rm -f "${TMPFILE}"
-fi
-
-./python/bin/python3 -E -s -m venv .venv
-_err_exit $? _err_msg
-source .venv/bin/activate
-
-echo -e "\n***** Created Python virtual environment *****\n"
-
-# Print venv's Python version
-_err_msg="\n----- problem calling venv's python -----\n"
-echo -e "We're running under"
-.venv/bin/python3 --version
-_err_exit $? _err_msg
-
-_err_msg="\n----- pip update failed -----\n"
-.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location --upgrade pip
-_err_exit $? _err_msg
-
-echo -e "\n***** Updated pip *****\n"
-
-_err_msg="\n----- requirements file copy failed -----\n"
-cp binary_installer/py3.10-${OS_NAME}-"${OS_ARCH}"-${CD}-reqs.txt requirements.txt
-_err_exit $? _err_msg
-
-_err_msg="\n----- main pip install failed -----\n"
-.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -r requirements.txt
-_err_exit $? _err_msg
-
-echo -e "\n***** Installed Python dependencies *****\n"
-
-_err_msg="\n----- InvokeAI setup failed -----\n"
-.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -e .
-_err_exit $? _err_msg
-
-echo -e "\n***** Installed InvokeAI *****\n"
-
-cp binary_installer/invoke.sh.in ./invoke.sh
-chmod a+rx ./invoke.sh
-echo -e "\n***** Installed invoke launcher script ******\n"
-
-# more cleanup
-rm -rf binary_installer/ installer_files/
-
-# preload the models
-.venv/bin/python3 scripts/configure_invokeai.py
-_err_msg="\n----- model download clone failed -----\n"
-_err_exit $? _err_msg
-deactivate
-
-echo -e "\n***** Finished downloading models *****\n"
-
-echo "All done! Run the command"
-echo "  $scriptdir/invoke.sh"
-echo "to start InvokeAI."
-read -p "Press any key to exit..."
-exit

binary_installer/invoke.bat.in

@@ -1,36 +0,0 @@
-@echo off
-
-PUSHD "%~dp0"
-call .venv\Scripts\activate.bat
-
-echo Do you want to generate images using the
-echo 1. command-line
-echo 2. browser-based UI
-echo OR
-echo 3. open the developer console
-set /p choice="Please enter 1, 2 or 3: "
-if /i "%choice%" == "1" (
-    echo Starting the InvokeAI command-line.
-    .venv\Scripts\python scripts\invoke.py %*
-) else if /i "%choice%" == "2" (
-    echo Starting the InvokeAI browser-based UI.
-    .venv\Scripts\python scripts\invoke.py --web %*
-) else if /i "%choice%" == "3" (
-    echo Developer Console
-    echo Python command is:
-    where python
-    echo Python version is:
-    python --version
-    echo *************************
-    echo You are now in the system shell, with the local InvokeAI Python virtual environment activated,
-    echo so that you can troubleshoot this InvokeAI installation as necessary.
-    echo *************************
-    echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
-    call cmd /k
-) else (
-    echo Invalid selection
-    pause
-    exit /b
-)
-
-deactivate

binary_installer/invoke.sh.in

@@ -1,46 +0,0 @@
-#!/usr/bin/env sh
-
-set -eu
-
-. .venv/bin/activate
-
-# set required env var for torch on mac MPS
- if [ "$(uname -s)" == "Darwin" ]; then
-     export PYTORCH_ENABLE_MPS_FALLBACK=1
- fi
-
-echo "Do you want to generate images using the"
-echo "1. command-line"
-echo "2. browser-based UI"
-echo "OR"
-echo "3. open the developer console"
-echo "Please enter 1, 2, or 3:"
-read choice
-
-case $choice in
-    1)
-        printf "\nStarting the InvokeAI command-line..\n";
-        .venv/bin/python scripts/invoke.py $*;
-    ;;
-    2)
-        printf "\nStarting the InvokeAI browser-based UI..\n";
-        .venv/bin/python scripts/invoke.py --web $*;
-    ;;
-    3)
-        printf "\nDeveloper Console:\n";
-        printf "Python command is:\n\t";
-        which python;
-        printf "Python version is:\n\t";
-        python --version;
-        echo "*************************"
-        echo "You are now in your user shell ($SHELL) with the local InvokeAI Python virtual environment activated,";
-        echo "so that you can troubleshoot this InvokeAI installation as necessary.";
-        printf "*************************\n"
-        echo "*** Type \`exit\` to quit this shell and deactivate the Python virtual environment *** ";
-        /usr/bin/env "$SHELL";
-    ;;
-    *)
-        echo "Invalid selection";
-        exit
-    ;;
-esac

binary_installer/readme.txt

@@ -1,17 +0,0 @@
-InvokeAI
-
-Project homepage: https://github.com/invoke-ai/InvokeAI
-
-Installation on Windows:
-    NOTE: You might need to enable Windows Long Paths. If you're not sure,
-    then you almost certainly need to. Simply double-click the 'WinLongPathsEnabled.reg'
-    file. Note that you will need to have admin privileges in order to
-    do this.
-
-    Please double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
-
-Installation on Linux and Mac:
-    Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
-
-After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh'
-file (on Linux/Mac) to start InvokeAI.

binary_installer/requirements.in

@@ -1,33 +0,0 @@
---prefer-binary
---extra-index-url https://download.pytorch.org/whl/torch_stable.html
---extra-index-url https://download.pytorch.org/whl/cu116
---trusted-host https://download.pytorch.org
-accelerate~=0.15
-albumentations
-diffusers[torch]~=0.11
-einops
-eventlet
-flask_cors
-flask_socketio
-flaskwebgui==1.0.3
-getpass_asterisk
-imageio-ffmpeg
-pyreadline3
-realesrgan
-send2trash
-streamlit
-taming-transformers-rom1504
-test-tube
-torch-fidelity
-torch==1.12.1 ; platform_system == 'Darwin'
-torch==1.12.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
-torchvision==0.13.1 ; platform_system == 'Darwin'
-torchvision==0.13.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
-transformers
-picklescan
-https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip
-https://github.com/invoke-ai/clipseg/archive/1f754751c85d7d4255fa681f4491ff5711c1c288.zip
-https://github.com/invoke-ai/GFPGAN/archive/3f5d2397361199bc4a91c08bb7d80f04d7805615.zip ; platform_system=='Windows'
-https://github.com/invoke-ai/GFPGAN/archive/c796277a1cf77954e5fc0b288d7062d162894248.zip ; platform_system=='Linux' or platform_system=='Darwin'
-https://github.com/Birch-san/k-diffusion/archive/363386981fee88620709cf8f6f2eea167bd6cd74.zip
-https://github.com/invoke-ai/PyPatchMatch/archive/129863937a8ab37f6bbcec327c994c0f932abdbc.zip

docker/Dockerfile

@@ -1,57 +1,63 @@
 # syntax=docker/dockerfile:1
+
 ARG PYTHON_VERSION=3.9
 ##################
 ##  base image  ##
 ##################
 FROM python:${PYTHON_VERSION}-slim AS python-base
 
-# prepare for buildkit cache
-RUN rm -f /etc/apt/apt.conf.d/docker-clean
+LABEL org.opencontainers.image.authors="mauwii@outlook.de"
 
-# Install necesarry packages
+# prepare for buildkit cache
+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
+
+# Install necessary packages
 RUN \
   --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
   apt-get update \
-  && apt-get install \
-    -yqq \
+  && apt-get install -y \
     --no-install-recommends \
     libgl1-mesa-glx=20.3.* \
     libglib2.0-0=2.66.* \
-    libopencv-dev=4.5.* \
-  && rm -rf /var/lib/apt/lists/*
+    libopencv-dev=4.5.*
 
-# set working directory and path
+# set working directory and env
 ARG APPDIR=/usr/src
 ARG APPNAME=InvokeAI
 WORKDIR ${APPDIR}
-ENV PATH=${APPDIR}/${APPNAME}/bin:$PATH
+ENV PATH ${APPDIR}/${APPNAME}/bin:$PATH
+# Keeps Python from generating .pyc files in the container
+ENV PYTHONDONTWRITEBYTECODE 1
+# Turns off buffering for easier container logging
+ENV PYTHONUNBUFFERED 1
+# don't fall back to legacy build system
+ENV PIP_USE_PEP517=1
 
 #######################
 ##  build pyproject  ##
 #######################
 FROM python-base AS pyproject-builder
-ENV PIP_USE_PEP517=1
 
-# prepare for buildkit cache
+# Install dependencies
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    build-essential=12.9 \
+    gcc=4:10.2.* \
+    python3-dev=3.9.*
+
+# prepare pip for buildkit cache
 ARG PIP_CACHE_DIR=/var/cache/buildkit/pip
 ENV PIP_CACHE_DIR ${PIP_CACHE_DIR}
 RUN mkdir -p ${PIP_CACHE_DIR}
 
-# Install dependencies
-RUN \
-  --mount=type=cache,target=${PIP_CACHE_DIR} \
-  --mount=type=cache,target=/var/cache/apt,sharing=locked \
-  apt-get update \
-  && apt-get install \
-    -yqq \
-    --no-install-recommends \
-    build-essential=12.9 \
-    gcc=4:10.2.* \
-    python3-dev=3.9.* \
-  && rm -rf /var/lib/apt/lists/*
-
 # create virtual environment
-RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
   python3 -m venv "${APPNAME}" \
   --upgrade-deps
 
@@ -61,9 +67,8 @@ COPY --link . .
 # install pyproject.toml
 ARG PIP_EXTRA_INDEX_URL
 ENV PIP_EXTRA_INDEX_URL ${PIP_EXTRA_INDEX_URL}
-ARG PIP_PACKAGE=.
-RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
-  "${APPDIR}/${APPNAME}/bin/pip" install ${PIP_PACKAGE}
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
+  "${APPNAME}/bin/pip" install .
 
 # build patchmatch
 RUN python3 -c "from patchmatch import patch_match"
@@ -73,14 +78,26 @@ RUN python3 -c "from patchmatch import patch_match"
 #####################
 FROM python-base AS runtime
 
-# setup environment
-COPY --from=pyproject-builder --link ${APPDIR}/${APPNAME} ${APPDIR}/${APPNAME}
-ENV INVOKEAI_ROOT=/data
-ENV INVOKE_MODEL_RECONFIGURE="--yes --default_only"
+# Create a new user
+ARG UNAME=appuser
+RUN useradd \
+  --no-log-init \
+  -m \
+  -U \
+  "${UNAME}"
 
-# set Entrypoint and default CMD
+# create volume directory
+ARG VOLUME_DIR=/data
+RUN mkdir -p "${VOLUME_DIR}" \
+  && chown -R "${UNAME}" "${VOLUME_DIR}"
+
+# setup runtime environment
+USER ${UNAME}
+COPY --chown=${UNAME} --from=pyproject-builder ${APPDIR}/${APPNAME} ${APPNAME}
+ENV INVOKEAI_ROOT ${VOLUME_DIR}
+ENV TRANSFORMERS_CACHE ${VOLUME_DIR}/.cache
+ENV INVOKE_MODEL_RECONFIGURE "--yes --default_only"
+EXPOSE 9090
 ENTRYPOINT [ "invokeai" ]
-CMD [ "--web", "--host=0.0.0.0" ]
-VOLUME [ "/data" ]
-
-LABEL org.opencontainers.image.authors="mauwii@outlook.de"
+CMD [ "--web", "--host", "0.0.0.0", "--port", "9090" ]
+VOLUME [ "${VOLUME_DIR}" ]

docker/build.sh

@@ -1,19 +1,24 @@
 #!/usr/bin/env bash
 set -e
 
-# How to use: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_DOCKER/#setup
-# Some possible pip extra-index urls (cuda 11.7 is available without extra url):
-#   CUDA 11.6:  https://download.pytorch.org/whl/cu116
-#   ROCm 5.2:   https://download.pytorch.org/whl/rocm5.2
-#   CPU:        https://download.pytorch.org/whl/cpu
-#   as found on https://pytorch.org/get-started/locally/
+# If you want to build a specific flavor, set the CONTAINER_FLAVOR environment variable
+#   e.g. CONTAINER_FLAVOR=cpu ./build.sh
+#   Possible Values are:
+#     - cpu
+#     - cuda
+#     - rocm
+#   Don't forget to also set it when executing run.sh
+#   if it is not set, the script will try to detect the flavor by itself.
+#
+# Doc can be found here:
+#   https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
 
-SCRIPTDIR=$(dirname "$0")
+SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
 cd "$SCRIPTDIR" || exit 1
 
 source ./env.sh
 
-DOCKERFILE=${INVOKE_DOCKERFILE:-Dockerfile}
+DOCKERFILE=${INVOKE_DOCKERFILE:-./Dockerfile}
 
 # print the settings
 echo -e "You are using these values:\n"
@@ -21,23 +26,25 @@ echo -e "Dockerfile:\t\t${DOCKERFILE}"
 echo -e "index-url:\t\t${PIP_EXTRA_INDEX_URL:-none}"
 echo -e "Volumename:\t\t${VOLUMENAME}"
 echo -e "Platform:\t\t${PLATFORM}"
-echo -e "Registry:\t\t${CONTAINER_REGISTRY}"
-echo -e "Repository:\t\t${CONTAINER_REPOSITORY}"
+echo -e "Container Registry:\t${CONTAINER_REGISTRY}"
+echo -e "Container Repository:\t${CONTAINER_REPOSITORY}"
 echo -e "Container Tag:\t\t${CONTAINER_TAG}"
+echo -e "Container Flavor:\t${CONTAINER_FLAVOR}"
 echo -e "Container Image:\t${CONTAINER_IMAGE}\n"
 
 # Create docker volume
 if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
     echo -e "Volume already exists\n"
 else
-    echo -n "createing docker volume "
+    echo -n "creating docker volume "
     docker volume create "${VOLUMENAME}"
 fi
 
 # Build Container
 DOCKER_BUILDKIT=1 docker build \
-    --platform="${PLATFORM}" \
-    --tag="${CONTAINER_IMAGE}" \
+    --platform="${PLATFORM:-linux/amd64}" \
+    --tag="${CONTAINER_IMAGE:-invokeai}" \
+    ${CONTAINER_FLAVOR:+--build-arg="CONTAINER_FLAVOR=${CONTAINER_FLAVOR}"} \
     ${PIP_EXTRA_INDEX_URL:+--build-arg="PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}"} \
     ${PIP_PACKAGE:+--build-arg="PIP_PACKAGE=${PIP_PACKAGE}"} \
     --file="${DOCKERFILE}" \

docker/env.sh

@@ -1,19 +1,31 @@
 #!/usr/bin/env bash
 
+# This file is used to set environment variables for the build.sh and run.sh scripts.
+
+# Try to detect the container flavor if no PIP_EXTRA_INDEX_URL got specified
 if [[ -z "$PIP_EXTRA_INDEX_URL" ]]; then
+
+  # Activate virtual environment if not already activated and exists
+  if [[ -z $VIRTUAL_ENV ]]; then
+    [[ -e "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" ]] \
+      && source "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" \
+      && echo "Activated virtual environment: $VIRTUAL_ENV"
+  fi
+
   # Decide which container flavor to build if not specified
   if [[ -z "$CONTAINER_FLAVOR" ]] && python -c "import torch" &>/dev/null; then
     # Check for CUDA and ROCm
     CUDA_AVAILABLE=$(python -c "import torch;print(torch.cuda.is_available())")
     ROCM_AVAILABLE=$(python -c "import torch;print(torch.version.hip is not None)")
-    if [[ "$(uname -s)" != "Darwin" && "${CUDA_AVAILABLE}" == "True" ]]; then
+    if [[ "${CUDA_AVAILABLE}" == "True" ]]; then
       CONTAINER_FLAVOR="cuda"
-    elif [[ "$(uname -s)" != "Darwin" && "${ROCM_AVAILABLE}" == "True" ]]; then
+    elif [[ "${ROCM_AVAILABLE}" == "True" ]]; then
       CONTAINER_FLAVOR="rocm"
     else
       CONTAINER_FLAVOR="cpu"
     fi
   fi
+
   # Set PIP_EXTRA_INDEX_URL based on container flavor
   if [[ "$CONTAINER_FLAVOR" == "rocm" ]]; then
     PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/rocm"
@@ -26,9 +38,10 @@ fi
 
 # Variables shared by build.sh and run.sh
 REPOSITORY_NAME="${REPOSITORY_NAME-$(basename "$(git rev-parse --show-toplevel)")}"
-VOLUMENAME="${VOLUMENAME-"${REPOSITORY_NAME,,}_data"}"
+REPOSITORY_NAME="${REPOSITORY_NAME,,}"
+VOLUMENAME="${VOLUMENAME-"${REPOSITORY_NAME}_data"}"
 ARCH="${ARCH-$(uname -m)}"
-PLATFORM="${PLATFORM-Linux/${ARCH}}"
+PLATFORM="${PLATFORM-linux/${ARCH}}"
 INVOKEAI_BRANCH="${INVOKEAI_BRANCH-$(git branch --show)}"
 CONTAINER_REGISTRY="${CONTAINER_REGISTRY-"ghcr.io"}"
 CONTAINER_REPOSITORY="${CONTAINER_REPOSITORY-"$(whoami)/${REPOSITORY_NAME}"}"

docker/run.sh

@@ -1,14 +1,16 @@
 #!/usr/bin/env bash
 set -e
 
-# How to use: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_DOCKER/#run-the-container
-# IMPORTANT: You need to have a token on huggingface.co to be able to download the checkpoints!!!
+# How to use: https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
 
-SCRIPTDIR=$(dirname "$0")
+SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
 cd "$SCRIPTDIR" || exit 1
 
 source ./env.sh
 
+# Create outputs directory if it does not exist
+[[ -d ./outputs ]] || mkdir ./outputs
+
 echo -e "You are using these values:\n"
 echo -e "Volumename:\t${VOLUMENAME}"
 echo -e "Invokeai_tag:\t${CONTAINER_IMAGE}"
@@ -22,10 +24,18 @@ docker run \
   --name="${REPOSITORY_NAME,,}" \
   --hostname="${REPOSITORY_NAME,,}" \
   --mount=source="${VOLUMENAME}",target=/data \
-  ${MODELSPATH:+-u "$(id -u):$(id -g)"} \
+  --mount type=bind,source="$(pwd)"/outputs,target=/data/outputs \
   ${MODELSPATH:+--mount="type=bind,source=${MODELSPATH},target=/data/models"} \
   ${HUGGING_FACE_HUB_TOKEN:+--env="HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}"} \
   --publish=9090:9090 \
   --cap-add=sys_nice \
   ${GPU_FLAGS:+--gpus="${GPU_FLAGS}"} \
-  "${CONTAINER_IMAGE}" ${1:+$@}
+  "${CONTAINER_IMAGE}" ${@:+$@}
+
+# Remove Trash folder
+for f in outputs/.Trash*; do
+  if [ -e "$f" ]; then
+    rm -Rf "$f"
+    break
+  fi
+done

docs/.markdownlint.jsonc

@@ -0,0 +1,5 @@
+{
+  "MD046": false,
+  "MD007": false,
+  "MD030": false
+}

docs/contributing/ARCHITECTURE.md

@@ -0,0 +1,93 @@
+# Invoke.AI Architecture
+
+```mermaid
+flowchart TB
+
+  subgraph apps[Applications]
+    webui[WebUI]
+    cli[CLI]
+
+  subgraph webapi[Web API]
+    api[HTTP API]
+    sio[Socket.IO]
+  end
+
+  end
+
+  subgraph invoke[Invoke]
+    direction LR
+    invoker
+    services
+    sessions
+    invocations
+  end
+
+  subgraph core[AI Core]
+    Generate
+  end
+
+  webui --> webapi
+  webapi --> invoke
+  cli --> invoke
+
+  invoker --> services & sessions
+  invocations --> services
+  sessions --> invocations
+
+  services --> core
+
+  %% Styles
+  classDef sg fill:#5028C8,font-weight:bold,stroke-width:2,color:#fff,stroke:#14141A
+  classDef default stroke-width:2px,stroke:#F6B314,color:#fff,fill:#14141A
+
+  class apps,webapi,invoke,core sg
+
+```
+
+## Applications
+
+Applications are built on top of the invoke framework. They should construct `invoker` and then interact through it. They should avoid interacting directly with core code in order to support a variety of configurations.
+
+### 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 `/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 |
+| --- | --- |
+| api_app.py | Sets up the API app, annotates the OpenAPI spec with additional data, and runs the API |
+| dependencies | Creates all invoker services and the invoker, and provides them to the API |
+| events | An eventing system that could in the future be adapted to support horizontal scale-out |
+| sockets | The Socket.IO interface - handles listening to and emitting session events (events are defined in the events service module) |
+| routers | API definitions for different areas of API functionality |
+
+### 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
+
+The Invoke framework provides the interface to the underlying AI systems and is built with flexibility and extensibility in mind. There are four major concepts: invoker, sessions, invocations, and services.
+
+### Invoker
+
+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.
+
+### Sessions
+
+Invocations and links between them form a graph, which is maintained in a session. Sessions can be queued for invocation, which will execute their graph (either the next ready invocation, or all invocations). Sessions also maintain execution history for the graph (including storage of any outputs). An invocation may be added to a session at any time, and there is capability to add and entire graph at once, as well as to automatically link new invocations to previous invocations. Invocations can not be deleted or modified once added.
+
+The session graph does not support looping. This is left as an application problem to prevent additional complexity in the graph.
+
+### 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 `/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 `/ldm/invoke/app/`).

docs/contributing/INVOCATIONS.md

@@ -0,0 +1,105 @@
+# Invocations
+
+Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.
+
+## Creating a new invocation
+
+To create a new invocation, either find the appropriate module file in `/ldm/invoke/app/invocations` to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of [typing](https://docs.python.org/3/library/typing.html) and [pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration into the CLI and API.
+
+An invocation looks like this:
+
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+
+    # Inputs
+    image: Union[ImageField,None] = Field(description="The input image")
+    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2,4]           = Field(default=2, description = "The upscale level")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
+        )
+
+        # Results are image and seed, unwrap for now
+        # TODO: can this return multiple results?
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+```
+
+Each portion is important to implement correctly.
+
+### Class definition and type
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+```
+All invocations must derive from `BaseInvocation`. They should have a docstring that declares what they do in a single, short line. They should also have a `type` with a type hint that's `Literal["command_name"]`, where `command_name` is what the user will type on the CLI or use in the API to create this invocation. The `command_name` must be unique. The `type` must be assigned to the value of the literal in the type hint.
+
+### Inputs
+```py
+    # Inputs
+    image: Union[ImageField,None] = Field(description="The input image")
+    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2,4]           = Field(default=2, description="The upscale level")
+```
+Inputs consist of three parts: a name, a type hint, and a `Field` with default, description, and validation information. For example:
+| Part | Value | Description |
+| ---- | ----- | ----------- |
+| Name | `strength` | This field is referred to as `strength` |
+| Type Hint | `float` | This field must be of type `float` |
+| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
+
+Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this field to be parsed with `None` as a value, which enables linking to previous invocations. All fields should either provide a default value or allow `None` as a value, so that they can be overwritten with a linked output from another invocation.
+
+The special type `ImageField` is also used here. All images are passed as `ImageField`, which protects them from pydantic validation errors (since images only ever come from links).
+
+Finally, note that for all linking, the `type` of the linked fields must match. If the `name` also matches, then the field can be **automatically linked** to a previous invocation by name and matching.
+
+### Invoke Function
+```py
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
+        )
+
+        # Results are image and seed, unwrap for now
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+```
+The `invoke` function is the last portion of an invocation. It is provided an `InvocationContext` which contains services to perform work as well as a `session_id` for use as needed. It should return a class with output values that derives from `BaseInvocationOutput`.
+
+Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).
+
+Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the `InvocationServices` class, and make sure it can be overridden.
+
+### Outputs
+```py
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+    type: Literal['image'] = 'image'
+
+    image: ImageField = Field(default=None, description="The output image")
+```
+Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.

docs/features/CLI.md

@@ -214,6 +214,8 @@ Here are the invoke> command that apply to txt2img:
 | `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
 | `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this.                                                                                                                                                           |
 | `--save_intermediates <n>`                |                                           | `None`                                   | Save the image from every nth step into an "intermediates" folder inside the output directory                                                                                                                                                    |
+| `--h_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
+| `--v_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
 
 !!! note
 

docs/features/CONCEPTS.md

@@ -1,5 +1,5 @@
 ---
-title: Concepts Library
+title: Styles and Subjects
 ---
 
 # :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files
@@ -25,10 +25,14 @@ library which downloads and merges TI files automatically upon request. You can
 also install your own or others' TI files by placing them in a designated
 directory.
 
+You may also be interested in using [LoRA Models](LORAS.md) to
+generate images with specialized styles and subjects.
+
 ### An Example
 
-Here are a few examples to illustrate how it works. All these images were
-generated using the command-line client and the Stable Diffusion 1.5 model:
+Here are a few examples to illustrate how Textual Inversion works. All
+these images were generated using the command-line client and the
+Stable Diffusion 1.5 model:
 
 |         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
 | :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
@@ -109,21 +113,50 @@ For example, TI files generated by the Hugging Face toolkit share the named
 `learned_embedding.bin`. You can use subdirectories to keep them distinct.
 
 At startup time, InvokeAI will scan the `embeddings` directory and load any TI
-files it finds there. At startup you will see a message similar to this one:
+files it finds there. At startup you will see messages similar to these:
 
 ```bash
->> Current embedding manager terms: *, <HOI4-Leader>, <princess-knight>
+>> Loading embeddings from /data/lstein/invokeai-2.3/embeddings
+   | Loading v1 embedding file: style-hamunaptra
+   | Loading v4 embedding file: embeddings/learned_embeds-steps-500.bin
+   | Loading v2 embedding file: lfa
+   | Loading v3 embedding file: easynegative
+   | Loading v1 embedding file: rem_rezero
+   | Loading v2 embedding file: midj-strong
+   | Loading v4 embedding file: anime-background-style-v2/learned_embeds.bin
+   | Loading v4 embedding file: kamon-style/learned_embeds.bin
+   ** Notice: kamon-style/learned_embeds.bin was trained on a model with an incompatible token dimension: 768 vs 1024.
+>> Textual inversion triggers: <anime-background-style-v2>, <easynegative>, <lfa>, <midj-strong>, <milo>, Rem3-2600, Style-Hamunaptra
 ```
 
-Note the `*` trigger term. This is a placeholder term that many early TI
-tutorials taught people to use rather than a more descriptive term.
-Unfortunately, if you have multiple TI files that all use this term, only the
-first one loaded will be triggered by use of the term.
+Textual Inversion embeddings trained on version 1.X stable diffusion
+models are incompatible with version 2.X models and vice-versa.
 
-To avoid this problem, you can use the `merge_embeddings.py` script to merge two
-or more TI files together. If it encounters a collision of terms, the script
-will prompt you to select new terms that do not collide. See
-[Textual Inversion](TEXTUAL_INVERSION.md) for details.
+After the embeddings load, InvokeAI will print out a list of all the
+recognized trigger terms. To trigger the term, include it in the
+prompt exactly as written, including angle brackets if any and
+respecting the capitalization.
+
+There are at least four different embedding file formats, and each uses
+a different convention for the trigger terms. In some cases, the
+trigger term is specified in the file contents and may or may not be
+surrounded by angle brackets. In the example above, `Rem3-2600`,
+`Style-Hamunaptra`, and `<midj-strong>` were specified this way and
+there is no easy way to change the term.
+
+In other cases the trigger term is not contained within the embedding
+file. In this case, InvokeAI constructs a trigger term consisting of
+the base name of the file (without the file extension) surrounded by
+angle brackets. In the example above `<easynegative`> is such a file
+(the filename was `easynegative.safetensors`). In such cases, you can
+change the trigger term simply by renaming the file.
+
+## Training your own Textual Inversion models
+
+InvokeAI provides a script that lets you train your own Textual
+Inversion embeddings using a small number (about a half-dozen) images
+of your desired style or subject. Please see [Textual
+Inversion](TEXTUAL_INVERSION.md) for details.
 
 ## Further Reading
 

docs/features/LORAS.md

@@ -0,0 +1,100 @@
+---
+title: Low-Rank Adaptation (LoRA) Models
+---
+
+# :material-library-shelves: Using Low-Rank Adaptation (LoRA) Models
+
+## Introduction
+
+LoRA is a technique for fine-tuning Stable Diffusion models using much
+less time and memory than traditional training techniques. The
+resulting model files are much smaller than full model files, and can
+be used to generate specialized styles and subjects.
+
+LoRAs are built on top of Stable Diffusion v1.x or 2.x checkpoint or
+diffusers models. To load a LoRA, you include its name in the text
+prompt using a simple syntax described below. While you will generally
+get the best results when you use the same model the LoRA was trained
+on, they will work to a greater or lesser extent with other models.
+The major caveat is that a LoRA built on top of a SD v1.x model cannot
+be used with a v2.x model, and vice-versa. If you try, you will get an
+error! You may refer to multiple LoRAs in your prompt.
+
+When you apply a LoRA in a prompt you can specify a weight. The higher
+the weight, the more influence it will have on the image. Useful
+ranges for weights are usually in the 0.0 to 1.0 range (with ranges
+between 0.5 and 1.0 being most typical). However you can specify a
+higher weight if you wish. Like models, each LoRA has a slightly
+different useful weight range and will interact with other generation
+parameters such as the CFG, step count and sampler. The author of the
+LoRA will often provide guidance on the best settings, but feel free
+to experiment. Be aware that it often helps to reduce the CFG value
+when using LoRAs.
+
+## Installing LoRAs
+
+This is very easy! Download a LoRA model file from your favorite site
+(e.g. [CIVITAI](https://civitai.com) and place it in the `loras`
+folder in the InvokeAI root directory (usually `~invokeai/loras` on
+Linux/Macintosh machines, and `C:\Users\your-name\invokeai/loras` on
+Windows systems). If the `loras` folder does not already exist, just
+create it. The vast majority of LoRA models use the Kohya file format,
+which is a type of `.safetensors` file.
+
+You may change where InvokeAI looks for the `loras` folder by passing the
+`--lora_directory` option to the `invoke.sh`/`invoke.bat` launcher, or
+by placing the option in `invokeai.init`. For example:
+
+```
+invoke.sh --lora_directory=C:\Users\your-name\SDModels\lora
+```
+
+## Using a LoRA in your prompt
+
+To activate a LoRA use the syntax `withLora(my-lora-name,weight)`
+somewhere in the text of the prompt. The position doesn't matter; use
+whatever is most comfortable for you.
+
+For example, if you have a LoRA named `parchment_people.safetensors`
+in your `loras` directory, you can load it with a weight of 0.9 with a
+prompt like this one:
+
+```
+family sitting at dinner table withLora(parchment_people,0.9)
+```
+
+Add additional `withLora()` phrases to load more LoRAs.
+
+You may omit the weight entirely to default to a weight of 1.0:
+
+```
+family sitting at dinner table withLora(parchment_people)
+```
+
+If you watch the console as your prompt executes, you will see
+messages relating to the loading and execution of the LoRA. If things
+don't work as expected, note down the console messages and report them
+on the InvokeAI Issues pages or Discord channel.
+
+That's pretty much all you need to know!
+
+## Training Kohya Models
+
+InvokeAI cannot currently train LoRA models, but it can load and use
+existing LoRA ones to generate images. While there are several LoRA
+model file formats, the predominant one is ["Kohya"
+format](https://github.com/kohya-ss/sd-scripts), written by [Kohya
+S.](https://github.com/kohya-ss). InvokeAI provides support for this
+format. For creating your own Kohya models, we recommend the Windows
+GUI written by former InvokeAI-team member
+[bmaltais](https://github.com/bmaltais), which can be found at
+[kohya_ss](https://github.com/bmaltais/kohya_ss).
+
+We can also recommend the [HuggingFace DreamBooth Training
+UI](https://huggingface.co/spaces/lora-library/LoRA-DreamBooth-Training-UI),
+a paid service that supports both Textual Inversion and LoRA training.
+
+You may also be interested in [Textual
+Inversion](TEXTUAL_INVERSION.md) training, which is supported by
+InvokeAI as a text console and command-line tool.
+

docs/features/PROMPTS.md

@@ -40,7 +40,7 @@ for adj in adjectives:
             print(f'a {adj} day -A{samp} -C{cg}')
 ```
 
-It's output looks like this (abbreviated):
+Its output looks like this (abbreviated):
 
 ```bash
 a sunny day -Aklms -C7.5

docs/features/TEXTUAL_INVERSION.md

@@ -154,8 +154,11 @@ training sets will converge with 2000-3000 steps.
 
 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.
+quickly, but use more memory. The default size is selected based on
+whether you have the `xformers` memory-efficient attention library
+installed. If `xformers` is available, the batch size will be 8,
+otherwise 3.  These values were chosen to allow training to run with
+GPUs with as little as 12 GB VRAM.
 
 ### Learning rate
 
@@ -172,8 +175,10 @@ 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.
+This will activate XFormers memory-efficient attention, which will
+reduce memory requirements by half or more and allow you to select a
+higher batch size. You need to have XFormers installed for this to
+have an effect.
 
 ### Learning rate scheduler
 
@@ -250,6 +255,67 @@ invokeai-ti \
        --only_save_embeds
 ```
 
+## Using Distributed Training
+
+If you have multiple GPUs on one machine, or a cluster of GPU-enabled
+machines, you can activate distributed training. See the [HuggingFace
+Accelerate pages](https://huggingface.co/docs/accelerate/index) for
+full information, but the basic recipe is:
+
+1. Enter the InvokeAI developer's console command line by selecting
+option [8] from the `invoke.sh`/`invoke.bat` script.
+
+2. Configurate Accelerate using `accelerate config`:
+```sh
+accelerate config
+```
+This will guide you through the configuration process, including
+specifying how many machines you will run training on and the number
+of GPUs pe rmachine.
+
+You only need to do this once.
+
+3. Launch training from the command line using `accelerate launch`. Be sure
+that your current working directory is the InvokeAI root directory (usually
+named `invokeai` in your home directory):
+
+```sh
+accelerate launch .venv/bin/invokeai-ti \
+       --model=stable-diffusion-1.5 \
+       --resolution=512 \
+       --learnable_property=object \
+       --initializer_token='*' \
+       --placeholder_token='<shraddha>' \
+       --train_data_dir=/home/lstein/invokeai/text-inversion-training-data/shraddha \
+       --output_dir=/home/lstein/invokeai/text-inversion-training/shraddha \
+       --scale_lr \
+       --train_batch_size=10 \
+       --gradient_accumulation_steps=4 \
+       --max_train_steps=2000 \
+       --learning_rate=0.0005 \
+       --lr_scheduler=constant \
+       --mixed_precision=fp16 \
+       --only_save_embeds
+```
+
+## Using Embeddings
+
+After training completes, the resultant embeddings will be saved into your `$INVOKEAI_ROOT/embeddings/<trigger word>/learned_embeds.bin`.
+
+These will be automatically loaded when you start InvokeAI.
+
+Add the trigger word, surrounded by angle brackets, to use that embedding. For example, if your trigger word was `terence`, use `<terence>` in prompts. This is the same syntax used by the HuggingFace concepts library.
+
+**Note:** `.pt` embeddings do not require the angle brackets.
+
+## 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

docs/features/index.md

@@ -2,62 +2,84 @@
 title: Overview
 ---
 
-Here you can find the documentation for InvokeAI's various features.
+-   The Basics
 
-## The Basics
-### * The [Web User Interface](WEB.md)
-Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
+    -   The [Web User Interface](WEB.md)
 
-### * The [Unified Canvas](UNIFIED_CANVAS.md)
-Build complex scenes by combine and modifying multiple images in a stepwise
-fashion. This feature combines img2img, inpainting and outpainting in
-a single convenient digital artist-optimized user interface.
+        Guide to the Web interface. Also see the
+        [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
 
-### * The [Command Line Interface (CLI)](CLI.md)
-Scriptable access to InvokeAI's features.
+    -   The [Unified Canvas](UNIFIED_CANVAS.md)
 
-## Image Generation
-### * [Prompt Engineering](PROMPTS.md)
-Get the images you want with the InvokeAI  prompt engineering language.
+        Build complex scenes by combine and modifying multiple images in a
+        stepwise fashion. This feature combines img2img, inpainting and
+        outpainting in a single convenient digital artist-optimized user
+        interface.
 
-## * [Post-Processing](POSTPROCESS.md)
-Restore mangled faces and make images larger with upscaling. Also see the [Embiggen Upscaling Guide](EMBIGGEN.md).
+    -   The [Command Line Interface (CLI)](CLI.md)
 
-## * The [Concepts Library](CONCEPTS.md)
-Add custom subjects and styles using HuggingFace's repository of embeddings.
+        Scriptable access to InvokeAI's features.
 
-### * [Image-to-Image Guide for the CLI](IMG2IMG.md)
-Use a seed image to build new creations in the CLI.
+    -   [Visual Manual for InvokeAI](https://docs.google.com/presentation/d/e/2PACX-1vSE90aC7bVVg0d9KXVMhy-Wve-wModgPFp7AGVTOCgf4xE03SnV24mjdwldolfCr59D_35oheHe4Cow/pub?start=false&loop=true&delayms=60000) (contributed by Statcomm)
 
-### * [Inpainting Guide for the CLI](INPAINTING.md)
-Selectively erase and replace portions of an existing image in the CLI.
+-   Image Generation
 
-### * [Outpainting Guide for the CLI](OUTPAINTING.md)
-Extend the borders of the image with an "outcrop" function within the CLI.
+    -   [Prompt Engineering](PROMPTS.md)
 
-### * [Generating Variations](VARIATIONS.md)
-Have an image you like and want to generate many more like it? Variations
-are the ticket.
+        Get the images you want with the InvokeAI prompt engineering language.
 
-## Model Management
+    -   [Post-Processing](POSTPROCESS.md)
 
-## * [Model Installation](../installation/050_INSTALLING_MODELS.md)
-Learn how to import third-party models and switch among them. This
-guide also covers optimizing models to load quickly.
+        Restore mangled faces and make images larger with upscaling. Also see
+        the [Embiggen Upscaling Guide](EMBIGGEN.md).
 
-## * [Merging Models](MODEL_MERGING.md)
-Teach an old model new tricks. Merge 2-3 models together to create a
-new model that combines characteristics of the originals.
+    -   The [Concepts Library](CONCEPTS.md)
 
-## * [Textual Inversion](TEXTUAL_INVERSION.md)
-Personalize models by adding your own style or subjects.
+        Add custom subjects and styles using HuggingFace's repository of
+        embeddings.
 
-# Other Features
+    -   [Image-to-Image Guide for the CLI](IMG2IMG.md)
 
-## * [The NSFW Checker](NSFW.md)
-Prevent InvokeAI from displaying unwanted racy images.
+        Use a seed image to build new creations in the CLI.
 
-## * [Miscellaneous](OTHER.md)
-Run InvokeAI on Google Colab, generate images with repeating patterns,
-batch process a file of prompts, increase the "creativity" of image
-generation by adding initial noise, and more!
+    -   [Inpainting Guide for the CLI](INPAINTING.md)
+
+        Selectively erase and replace portions of an existing image in the CLI.
+
+    -   [Outpainting Guide for the CLI](OUTPAINTING.md)
+
+        Extend the borders of the image with an "outcrop" function within the
+        CLI.
+
+    -   [Generating Variations](VARIATIONS.md)
+
+        Have an image you like and want to generate many more like it?
+        Variations are the ticket.
+
+-   Model Management
+
+    -   [Model Installation](../installation/050_INSTALLING_MODELS.md)
+
+        Learn how to import third-party models and switch among them. This guide
+        also covers optimizing models to load quickly.
+
+    -   [Merging Models](MODEL_MERGING.md)
+
+        Teach an old model new tricks. Merge 2-3 models together to create a new
+        model that combines characteristics of the originals.
+
+    -   [Textual Inversion](TEXTUAL_INVERSION.md)
+
+        Personalize models by adding your own style or subjects.
+
+-   Other Features
+
+    -   [The NSFW Checker](NSFW.md)
+
+        Prevent InvokeAI from displaying unwanted racy images.
+
+    -   [Miscellaneous](OTHER.md)
+
+        Run InvokeAI on Google Colab, generate images with repeating patterns,
+        batch process a file of prompts, increase the "creativity" of image
+        generation by adding initial noise, and more!

docs/help/IDE-Settings/index.md

@@ -0,0 +1,4 @@
+# :octicons-file-code-16: IDE-Settings
+
+Here we will share settings for IDEs used by our developers, maybe you can find
+something interestening which will help to boost your development efficency 🔥

docs/help/IDE-Settings/vs-code.md

@@ -0,0 +1,250 @@
+---
+title: Visual Studio Code
+---
+
+# :material-microsoft-visual-studio-code:Visual Studio Code
+
+The Workspace Settings are stored in the project (repository) root and get
+higher priorized than your user settings.
+
+This helps to have different settings for different projects, while the user
+settings get used as a default value if no workspace settings are provided.
+
+## tasks.json
+
+First we will create a task configuration which will create a virtual
+environment and update the deps (pip, setuptools and wheel).
+
+Into this venv we will then install the pyproject.toml in editable mode with
+dev, docs and test dependencies.
+
+```json title=".vscode/tasks.json"
+{
+    // See https://go.microsoft.com/fwlink/?LinkId=733558
+    // for the documentation about the tasks.json format
+    "version": "2.0.0",
+    "tasks": [
+        {
+            "label": "Create virtual environment",
+            "detail": "Create .venv and upgrade pip, setuptools and wheel",
+            "command": "python3",
+            "args": [
+                "-m",
+                "venv",
+                ".venv",
+                "--prompt",
+                "InvokeAI",
+                "--upgrade-deps"
+            ],
+            "runOptions": {
+                "instanceLimit": 1,
+                "reevaluateOnRerun": true
+            },
+            "group": {
+                "kind": "build"
+            },
+            "presentation": {
+                "echo": true,
+                "reveal": "always",
+                "focus": false,
+                "panel": "shared",
+                "showReuseMessage": true,
+                "clear": false
+            }
+        },
+        {
+            "label": "build InvokeAI",
+            "detail": "Build pyproject.toml with extras dev, docs and test",
+            "command": "${workspaceFolder}/.venv/bin/python3",
+            "args": [
+                "-m",
+                "pip",
+                "install",
+                "--use-pep517",
+                "--editable",
+                ".[dev,docs,test]"
+            ],
+            "dependsOn": "Create virtual environment",
+            "dependsOrder": "sequence",
+            "group": {
+                "kind": "build",
+                "isDefault": true
+            },
+            "presentation": {
+                "echo": true,
+                "reveal": "always",
+                "focus": false,
+                "panel": "shared",
+                "showReuseMessage": true,
+                "clear": false
+            }
+        }
+    ]
+}
+```
+
+The fastest way to build InvokeAI now is ++cmd+shift+b++
+
+## launch.json
+
+This file is used to define debugger configurations, so that you can one-click
+launch and monitor the application, set halt points to inspect specific states,
+...
+
+```json title=".vscode/launch.json"
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "invokeai web",
+            "type": "python",
+            "request": "launch",
+            "program": ".venv/bin/invokeai",
+            "justMyCode": true
+        },
+        {
+            "name": "invokeai cli",
+            "type": "python",
+            "request": "launch",
+            "program": ".venv/bin/invokeai",
+            "justMyCode": true
+        },
+        {
+            "name": "mkdocs serve",
+            "type": "python",
+            "request": "launch",
+            "program": ".venv/bin/mkdocs",
+            "args": ["serve"],
+            "justMyCode": true
+        }
+    ]
+}
+```
+
+Then you only need to hit ++f5++ and the fun begins :nerd: (It is asumed that
+you have created a virtual environment via the [tasks](#tasksjson) from the
+previous step.)
+
+## extensions.json
+
+A list of recommended vscode-extensions to make your life easier:
+
+```json title=".vscode/extensions.json"
+{
+    "recommendations": [
+        "editorconfig.editorconfig",
+        "github.vscode-pull-request-github",
+        "ms-python.black-formatter",
+        "ms-python.flake8",
+        "ms-python.isort",
+        "ms-python.python",
+        "ms-python.vscode-pylance",
+        "redhat.vscode-yaml",
+        "tamasfe.even-better-toml",
+        "eamodio.gitlens",
+        "foxundermoon.shell-format",
+        "timonwong.shellcheck",
+        "esbenp.prettier-vscode",
+        "davidanson.vscode-markdownlint",
+        "yzhang.markdown-all-in-one",
+        "bierner.github-markdown-preview",
+        "ms-azuretools.vscode-docker",
+        "mads-hartmann.bash-ide-vscode"
+    ]
+}
+```
+
+## settings.json
+
+With bellow settings your files already get formated when you save them (only
+your modifications if available), which will help you to not run into trouble
+with the pre-commit hooks. If the hooks fail, they will prevent you from
+commiting, but most hooks directly add a fixed version, so that you just need to
+stage and commit them:
+
+```json title=".vscode/settings.json"
+{
+    "[json]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.quickSuggestions": {
+            "comments": false,
+            "strings": true,
+            "other": true
+        },
+        "editor.suggest.insertMode": "replace",
+        "gitlens.codeLens.scopes": ["document"]
+    },
+    "[jsonc]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.formatOnSave": true,
+        "editor.formatOnSaveMode": "modificationsIfAvailable"
+    },
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnSave": true,
+        "editor.formatOnSaveMode": "file"
+    },
+    "[toml]": {
+        "editor.defaultFormatter": "tamasfe.even-better-toml",
+        "editor.formatOnSave": true,
+        "editor.formatOnSaveMode": "modificationsIfAvailable"
+    },
+    "[yaml]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.formatOnSave": true,
+        "editor.formatOnSaveMode": "modificationsIfAvailable"
+    },
+    "[markdown]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.rulers": [80],
+        "editor.unicodeHighlight.ambiguousCharacters": false,
+        "editor.unicodeHighlight.invisibleCharacters": false,
+        "diffEditor.ignoreTrimWhitespace": false,
+        "editor.wordWrap": "on",
+        "editor.quickSuggestions": {
+            "comments": "off",
+            "strings": "off",
+            "other": "off"
+        },
+        "editor.formatOnSave": true,
+        "editor.formatOnSaveMode": "modificationsIfAvailable"
+    },
+    "[shellscript]": {
+        "editor.defaultFormatter": "foxundermoon.shell-format"
+    },
+    "[ignore]": {
+        "editor.defaultFormatter": "foxundermoon.shell-format"
+    },
+    "editor.rulers": [88],
+    "evenBetterToml.formatter.alignEntries": false,
+    "evenBetterToml.formatter.allowedBlankLines": 1,
+    "evenBetterToml.formatter.arrayAutoExpand": true,
+    "evenBetterToml.formatter.arrayTrailingComma": true,
+    "evenBetterToml.formatter.arrayAutoCollapse": true,
+    "evenBetterToml.formatter.columnWidth": 88,
+    "evenBetterToml.formatter.compactArrays": true,
+    "evenBetterToml.formatter.compactInlineTables": true,
+    "evenBetterToml.formatter.indentEntries": false,
+    "evenBetterToml.formatter.inlineTableExpand": true,
+    "evenBetterToml.formatter.reorderArrays": true,
+    "evenBetterToml.formatter.reorderKeys": true,
+    "evenBetterToml.formatter.compactEntries": false,
+    "evenBetterToml.schema.enabled": true,
+    "python.analysis.typeCheckingMode": "basic",
+    "python.formatting.provider": "black",
+    "python.languageServer": "Pylance",
+    "python.linting.enabled": true,
+    "python.linting.flake8Enabled": true,
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true,
+    "python.testing.pytestArgs": [
+        "tests",
+        "--cov=ldm",
+        "--cov-branch",
+        "--cov-report=term:skip-covered"
+    ],
+    "yaml.schemas": {
+        "https://json.schemastore.org/prettierrc.json": "${workspaceFolder}/.prettierrc.yaml"
+    }
+}
+```

docs/help/contributing/010_PULL_REQUEST.md

@@ -0,0 +1,135 @@
+---
+title: Pull-Request
+---
+
+# :octicons-git-pull-request-16: Pull-Request
+
+## pre-requirements
+
+To follow the steps in this tutorial you will need:
+
+-   [GitHub](https://github.com) account
+-   [git](https://git-scm.com/downloads) source controll
+-   Text / Code Editor (personally I preffer
+    [Visual Studio Code](https://code.visualstudio.com/Download))
+-   Terminal:
+    -   If you are on Linux/MacOS you can use bash or zsh
+    -   for Windows Users the commands are written for PowerShell
+
+## Fork Repository
+
+The first step to be done if you want to contribute to InvokeAI, is to fork the
+rpeository.
+
+Since you are already reading this doc, the easiest way to do so is by clicking
+[here](https://github.com/invoke-ai/InvokeAI/fork). You could also open
+[InvokeAI](https://github.com/invoke-ai/InvoekAI) and click on the "Fork" Button
+in the top right.
+
+## Clone your fork
+
+After you forked the Repository, you should clone it to your dev machine:
+
+=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
+
+    ``` sh
+    git clone https://github.com/<github username>/InvokeAI \
+    && cd InvokeAI
+    ```
+
+=== ":fontawesome-brands-windows:Windows"
+
+    ``` powershell
+    git clone https://github.com/<github username>/InvokeAI `
+    && cd InvokeAI
+    ```
+
+## Install in Editable Mode
+
+To install InvokeAI in editable mode, (as always) we recommend to create and
+activate a venv first. Afterwards you can install the InvokeAI Package,
+including dev and docs extras in editable mode, follwed by the installation of
+the pre-commit hook:
+
+=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
+
+    ``` sh
+    python -m venv .venv \
+      --prompt InvokeAI \
+      --upgrade-deps \
+    && source .venv/bin/activate \
+    && pip install \
+      --upgrade-deps \
+      --use-pep517 \
+      --editable=".[dev,docs]" \
+    && pre-commit install
+    ```
+
+=== ":fontawesome-brands-windows:Windows"
+
+    ``` powershell
+    python -m venv .venv `
+      --prompt InvokeAI `
+      --upgrade-deps `
+    && .venv/scripts/activate.ps1 `
+    && pip install `
+      --upgrade `
+      --use-pep517 `
+      --editable=".[dev,docs]" `
+    && pre-commit install
+    ```
+
+## Create a branch
+
+Make sure you are on main branch, from there create your feature branch:
+
+=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
+
+    ``` sh
+    git checkout main \
+    && git pull \
+    && git checkout -B <branch name>
+    ```
+
+=== ":fontawesome-brands-windows:Windows"
+
+    ``` powershell
+    git checkout main `
+    && git pull `
+    && git checkout -B <branch name>
+    ```
+
+## Commit your changes
+
+When you are done with adding / updating content, you need to commit those
+changes to your repository before you can actually open an PR:
+
+```{ .sh .annotate }
+git add <files you have changed> # (1)!
+git commit -m "A commit message which describes your change"
+git push
+```
+
+1. Replace this with a space seperated list of the files you changed, like:
+   `README.md foo.sh bar.json baz`
+
+## Create a Pull Request
+
+After pushing your changes, you are ready to create a Pull Request. just head
+over to your fork on [GitHub](https://github.com), which should already show you
+a message that there have been recent changes on your feature branch and a green
+button which you could use to create the PR.
+
+The default target for your PRs would be the main branch of
+[invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)
+
+Another way would be to create it in VS-Code or via the GitHub CLI (or even via
+the GitHub CLI in a VS-Code Terminal Window 🤭):
+
+```sh
+gh pr create
+```
+
+The CLI will inform you if there are still unpushed commits on your branch. It
+will also prompt you for things like the the Title and the Body (Description) if
+you did not already pass them as arguments.

docs/help/contributing/020_ISSUES.md

@@ -0,0 +1,26 @@
+---
+title: Issues
+---
+
+# :octicons-issue-opened-16: Issues
+
+## :fontawesome-solid-bug: Report a bug
+
+If you stumbled over a bug while using InvokeAI, we would apreciate it a lot if
+you
+[open a issue](https://github.com/invoke-ai/InvokeAI/issues/new?assignees=&labels=bug&template=BUG_REPORT.yml&title=%5Bbug%5D%3A+)
+to inform us about the details so that our developers can look into it.
+
+If you also know how to fix the bug, take a look [here](010_PULL_REQUEST.md) to
+find out how to create a Pull Request.
+
+## Request a feature
+
+If you have a idea for a new feature on your mind which you would like to see in
+InvokeAI, there is a
+[feature request](https://github.com/invoke-ai/InvokeAI/issues/new?assignees=&labels=bug&template=BUG_REPORT.yml&title=%5Bbug%5D%3A+)
+available in the issues section of the repository.
+
+If you are just curious which features already got requested you can find the
+overview of open requests
+[here](https://github.com/invoke-ai/InvokeAI/labels/enhancement)

docs/help/contributing/030_DOCS.md

@@ -0,0 +1,32 @@
+---
+title: docs
+---
+
+# :simple-readthedocs: MkDocs-Material
+
+If you want to contribute to the docs, there is a easy way to verify the results
+of your changes before commiting them.
+
+Just follow the steps in the [Pull-Requests](010_PULL_REQUEST.md) docs, there we
+already
+[create a venv and install the docs extras](010_PULL_REQUEST.md#install-in-editable-mode).
+When installed it's as simple as:
+
+```sh
+mkdocs serve
+```
+
+This will build the docs locally and serve them on your local host, even
+auto-refresh is included, so you can just update a doc, save it and tab to the
+browser, without the needs of restarting the `mkdocs serve`.
+
+More information about the "mkdocs flavored markdown syntax" can be found
+[here](https://squidfunk.github.io/mkdocs-material/reference/).
+
+## :material-microsoft-visual-studio-code:VS-Code
+
+We also provide a
+[launch configuration for VS-Code](../IDE-Settings/vs-code.md#launchjson) which
+includes a `mkdocs serve` entrypoint as well. You also don't have to worry about
+the formatting since this is automated via prettier, but this is of course not
+limited to VS-Code.

docs/help/contributing/090_NODE_TRANSFORMATION.md

@@ -0,0 +1,76 @@
+# Tranformation to nodes
+
+## Current state
+
+```mermaid
+flowchart TD
+    web[WebUI];
+    cli[CLI];
+    web --> |img2img| generate(generate);
+    web --> |txt2img| generate(generate);
+    cli --> |txt2img| generate(generate);
+    cli --> |img2img| generate(generate);
+    generate --> model_manager;
+    generate --> generators;
+    generate --> ti_manager[TI Manager];
+    generate --> etc;
+```
+
+## Transitional Architecture
+
+### first step
+
+```mermaid
+flowchart TD
+    web[WebUI];
+    cli[CLI];
+    web --> |img2img| img2img_node(Img2img node);
+    web --> |txt2img| generate(generate);
+    img2img_node --> model_manager;
+    img2img_node --> generators;
+    cli --> |txt2img| generate;
+    cli --> |img2img| generate;
+    generate --> model_manager;
+    generate --> generators;
+    generate --> ti_manager[TI Manager];
+    generate --> etc;
+```
+
+### second step
+
+```mermaid
+flowchart TD
+    web[WebUI];
+    cli[CLI];
+    web --> |img2img| img2img_node(img2img node);
+    img2img_node --> model_manager;
+    img2img_node --> generators;
+    web --> |txt2img| txt2img_node(txt2img node);
+    cli --> |txt2img| txt2img_node;
+    cli --> |img2img| generate(generate);
+    generate --> model_manager;
+    generate --> generators;
+    generate --> ti_manager[TI Manager];
+    generate --> etc;
+    txt2img_node --> model_manager;
+    txt2img_node --> generators;
+    txt2img_node --> ti_manager[TI Manager];
+```
+
+## Final Architecture
+
+```mermaid
+flowchart TD
+    web[WebUI];
+    cli[CLI];
+    web --> |img2img|img2img_node(img2img node);
+    cli --> |img2img|img2img_node;
+    web --> |txt2img|txt2img_node(txt2img node);
+    cli --> |txt2img|txt2img_node;
+    img2img_node --> model_manager;
+    txt2img_node --> model_manager;
+    img2img_node --> generators;
+    txt2img_node --> generators;
+    img2img_node --> ti_manager[TI Manager];
+    txt2img_node --> ti_manager[TI Manager];
+```

docs/help/contributing/index.md

@@ -0,0 +1,16 @@
+---
+title: Contributing
+---
+
+# :fontawesome-solid-code-commit: Contributing
+
+There are different ways how you can contribute to
+[InvokeAI](https://github.com/invoke-ai/InvokeAI), like Translations, opening
+Issues for Bugs or ideas how to improve.
+
+This Section of the docs will explain some of the different ways of how you can
+contribute to make it easier for newcommers as well as advanced users :nerd:
+
+If you want to contribute code, but you do not have an exact idea yet, take a
+look at the currently open
+[:fontawesome-solid-bug: Bug Reports](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)

docs/help/index.md

@@ -0,0 +1,12 @@
+# :material-help:Help
+
+If you are looking for help with the installation of InvokeAI, please take a
+look into the [Installation](../installation/index.md) section of the docs.
+
+Here you will find help to topics like
+
+-   how to contribute
+-   configuration recommendation for IDEs
+
+If you have an Idea about what's missing and aren't scared from contributing,
+just take a look at [DOCS](./contributing/030_DOCS.md) to find out how to do so.

docs/index.html

@@ -1,19 +0,0 @@
-<!-- HTML for static distribution bundle build -->
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8">
-    <title>Swagger UI</title>
-    <link rel="stylesheet" type="text/css" href="swagger-ui/swagger-ui.css" />
-    <link rel="stylesheet" type="text/css" href="swagger-ui/index.css" />
-    <link rel="icon" type="image/png" href="swagger-ui/favicon-32x32.png" sizes="32x32" />
-    <link rel="icon" type="image/png" href="swagger-ui/favicon-16x16.png" sizes="16x16" />
-  </head>
-
-  <body>
-    <div id="swagger-ui"></div>
-    <script src="swagger-ui/swagger-ui-bundle.js" charset="UTF-8"> </script>
-    <script src="swagger-ui/swagger-ui-standalone-preset.js" charset="UTF-8"> </script>
-    <script src="swagger-ui/swagger-initializer.js" charset="UTF-8"> </script>
-  </body>
-</html>

docs/index.md

@@ -2,6 +2,8 @@
 title: Home
 ---
 
+# :octicons-home-16: Home
+
 <!--
   The Docs you find here (/docs/*) are built and deployed via mkdocs. If you want to run a local version to verify your changes, it's as simple as::
 
@@ -29,36 +31,36 @@ title: Home
 [![github open prs badge]][github open prs link]
 
 [ci checks on dev badge]:
-  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
+    https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
 [ci checks on dev link]:
-  https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
+    https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
 [ci checks on main badge]:
-  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
+    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/workflows/test-invoke-conda.yml
+    https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
 [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
+    https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
 [github forks link]:
-  https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
+    https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
 [github open issues badge]:
-  https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
+    https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
 [github open issues link]:
-  https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
+    https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
 [github open prs badge]:
-  https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
+    https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
 [github open prs link]:
-  https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
+    https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
 [github stars badge]:
-  https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
+    https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
 [github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
 [latest commit to dev badge]:
-  https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
+    https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
 [latest commit to dev link]:
-  https://github.com/invoke-ai/InvokeAI/commits/development
+    https://github.com/invoke-ai/InvokeAI/commits/development
 [latest release badge]:
-  https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
+    https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
 [latest release link]: https://github.com/invoke-ai/InvokeAI/releases
 
 </div>
@@ -87,24 +89,24 @@ Q&A</a>]
 
 You wil need one of the following:
 
-- :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
-- :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
-  only)
-- :fontawesome-brands-apple: An Apple computer with an M1 chip.
+-   :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
+-   :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
+    only)
+-   :fontawesome-brands-apple: An Apple computer with an M1 chip.
 
 We do **not recommend** the following video cards due to issues with their
 running in half-precision mode and having insufficient VRAM to render 512x512
 images in full-precision mode:
 
-- NVIDIA 10xx series cards such as the 1080ti
-- GTX 1650 series cards
-- GTX 1660 series cards
+-   NVIDIA 10xx series cards such as the 1080ti
+-   GTX 1650 series cards
+-   GTX 1660 series cards
 
 ### :fontawesome-solid-memory: Memory and Disk
 
-- At least 12 GB Main Memory RAM.
-- At least 18 GB of free disk space for the machine learning model, Python, and
-  all its dependencies.
+-   At least 12 GB Main Memory RAM.
+-   At least 18 GB of free disk space for the machine learning model, Python,
+    and all its dependencies.
 
 ## :octicons-package-dependencies-24: Installation
 
@@ -113,133 +115,407 @@ either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
 driver).
 
 ### [Installation Getting Started Guide](installation)
+
 #### [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
+
 This method is recommended for 1st time users
+
 #### [Manual Installation](installation/020_INSTALL_MANUAL.md)
+
 This method is recommended for experienced users and developers
+
 #### [Docker Installation](installation/040_INSTALL_DOCKER.md)
+
 This method is recommended for those familiar with running Docker containers
+
 ### Other Installation Guides
-  - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
-  - [XFormers](installation/070_INSTALL_XFORMERS.md)
-  - [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
-  - [Installing New Models](installation/050_INSTALLING_MODELS.md)
+
+-   [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
+-   [XFormers](installation/070_INSTALL_XFORMERS.md)
+-   [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
+-   [Installing New Models](installation/050_INSTALLING_MODELS.md)
 
 ## :octicons-gift-24: InvokeAI Features
 
 ### The InvokeAI Web Interface
-- [WebUI overview](features/WEB.md)
-- [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
-- [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
-<!-- separator -->
-### The InvokeAI Command Line Interface
-- [Command Line Interace Reference Guide](features/CLI.md)
-<!-- separator -->
-### Image Management
-- [Image2Image](features/IMG2IMG.md)
-- [Inpainting](features/INPAINTING.md)
-- [Outpainting](features/OUTPAINTING.md)
-- [Adding custom styles and subjects](features/CONCEPTS.md)
-- [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
-- [Embiggen upscaling](features/EMBIGGEN.md)
-- [Other Features](features/OTHER.md)
+
+-   [WebUI overview](features/WEB.md)
+-   [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
+-   [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
+-   [Visual Manual for InvokeAI v2.3.1](https://docs.google.com/presentation/d/e/2PACX-1vSE90aC7bVVg0d9KXVMhy-Wve-wModgPFp7AGVTOCgf4xE03SnV24mjdwldolfCr59D_35oheHe4Cow/pub?start=false&loop=true&delayms=60000) (contributed by Statcomm)
 
 <!-- separator -->
+
+<!-- separator -->
+
+### The InvokeAI Command Line Interface
+
+-   [Command Line Interace Reference Guide](features/CLI.md)
+<!-- separator -->
+
+### Image Management
+
+-   [Image2Image](features/IMG2IMG.md)
+-   [Inpainting](features/INPAINTING.md)
+-   [Outpainting](features/OUTPAINTING.md)
+-   [Adding custom styles and subjects](features/CONCEPTS.md)
+-   [Using LoRA models](features/LORAS.md)
+-   [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
+-   [Embiggen upscaling](features/EMBIGGEN.md)
+-   [Other Features](features/OTHER.md)
+
+<!-- separator -->
+
 ### Model Management
-- [Installing](installation/050_INSTALLING_MODELS.md)
-- [Model Merging](features/MODEL_MERGING.md)
-- [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
-- [Textual Inversion](features/TEXTUAL_INVERSION.md)
-- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
+
+-   [Installing](installation/050_INSTALLING_MODELS.md)
+-   [Model Merging](features/MODEL_MERGING.md)
+-   [Adding custom styles and subjects via embeddings](features/CONCEPTS.md)
+-   [Textual Inversion](features/TEXTUAL_INVERSION.md)
+-   [Not Safe for Work (NSFW) Checker](features/NSFW.md)
 <!-- seperator -->
+
 ### Prompt Engineering
-- [Prompt Syntax](features/PROMPTS.md)
-- [Generating Variations](features/VARIATIONS.md)
+
+-   [Prompt Syntax](features/PROMPTS.md)
+-   [Generating Variations](features/VARIATIONS.md)
 
 ## :octicons-log-16: Latest Changes
 
+### v2.3.3 <small>(29 March 2023)</small>
+
+#### Bug Fixes
+1. 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.
+2. Textual inversion will select an appropriate batchsize based on whether `xformers` is active, and will default to `xformers` enabled if the library is detected.
+3. The batch script log file names have been fixed to be compatible with Windows.
+4. Occasional corruption of the `.next_prefix` file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
+5. An infinite loop when opening the developer's console from within the `invoke.sh` script has been corrected.
+
+#### Enhancements
+1. It is now possible to load and run several community-contributed SD-2.0 based models, including the infamous "Illuminati" model.
+2. The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI `embeddings` directory.
+3. If no `--model` is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
+4. 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`).
+5. 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
+```
+
+### v2.3.2 <small>(13 March 2023)</small>
+
+#### Bugfixes
+
+Since version 2.3.1 the following bugs have been fixed:
+
+1. Black images appearing for potential NSFW images when generating with legacy checkpoint models and both `--no-nsfw_checker` and `--ckpt_convert` turned on.
+2. 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.
+3. The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
+4. 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.
+5. Crashes that occurred during model merging.
+6. Restore previous naming of Stable Diffusion base and 768 models.
+7. 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
+
+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.
+
+### v2.3.1 <small>(26 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:
+
+1. ***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.
+
+![image](https://user-images.githubusercontent.com/111189/220638091-918492cc-0719-4194-b033-3741e8289b30.png)
+
+2. **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`.
+
+![image](https://user-images.githubusercontent.com/111189/220660363-22ff3a2e-8082-410e-a818-d2b3a0529bac.png)
+
+3. **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](https://invoke-ai.github.io/InvokeAI/installation/050_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:
+
+![image](https://user-images.githubusercontent.com/111189/220644777-3d3a90ca-f9e2-4e6d-93da-cbdd66bf12f3.png)
+
+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.
+
+![image](https://user-images.githubusercontent.com/111189/220650124-30a77137-d9cd-406e-a87d-d8283f99a4b3.png)
+
+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).
+
+![image](https://user-images.githubusercontent.com/111189/220658687-47fd0f2c-7069-4d95-aec9-7196fceb360d.png)
+
+#### 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_:
+
+![image](https://user-images.githubusercontent.com/111189/220646958-b7eca95e-dc39-4cd2-b277-63eac98ed446.png)
+
+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:
+
+![image](https://user-images.githubusercontent.com/111189/220647560-4a9265a1-6926-44f9-9d08-e1ef2ce61ff8.png)
+
+#### 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](https://hosted.weblate.org/engage/invokeai/), 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](https://github.com/invoke-ai/InvokeAI/blob/v2.3.1/docs/other/TRANSLATION.md) for more information on how to contribute.
+
+#### Numerous internal bugfixes and performance issues
+
+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](#full-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                                           |
+
+
 ### v2.3.0 <small>(9 February 2023)</small>
 
 #### Migration to Stable Diffusion `diffusers` models
 
-Previous versions of InvokeAI supported the original model file format introduced with Stable Diffusion 1.4. In the original format, known variously as "checkpoint", or "legacy" format, there is a single large weights file ending with `.ckpt` or `.safetensors`. Though this format has served the community well, it has a number of disadvantages, including file size, slow loading times, and a variety of non-standard variants that require special-case code to handle. In addition, because checkpoint files are actually a bundle of multiple machine learning sub-models, it is hard to swap different sub-models in and out, or to share common sub-models. A new format, introduced by the StabilityAI company in collaboration with HuggingFace, is called `diffusers` and consists of a directory of individual models. The most immediate benefit of `diffusers` is that they load from disk very quickly. A longer term benefit is that in the near future `diffusers` models will be able to share common sub-models, dramatically reducing disk space when you have multiple fine-tune models derived from the same base.
+Previous versions of InvokeAI supported the original model file format
+introduced with Stable Diffusion 1.4. In the original format, known variously as
+"checkpoint", or "legacy" format, there is a single large weights file ending
+with `.ckpt` or `.safetensors`. Though this format has served the community
+well, it has a number of disadvantages, including file size, slow loading times,
+and a variety of non-standard variants that require special-case code to handle.
+In addition, because checkpoint files are actually a bundle of multiple machine
+learning sub-models, it is hard to swap different sub-models in and out, or to
+share common sub-models. A new format, introduced by the StabilityAI company in
+collaboration with HuggingFace, is called `diffusers` and consists of a
+directory of individual models. The most immediate benefit of `diffusers` is
+that they load from disk very quickly. A longer term benefit is that in the near
+future `diffusers` models will be able to share common sub-models, dramatically
+reducing disk space when you have multiple fine-tune models derived from the
+same base.
 
-When you perform a new install of version 2.3.0, you will be offered the option to install the `diffusers` versions of a number of popular SD models, including Stable Diffusion versions 1.5 and 2.1 (including the 768x768 pixel version of 2.1). These will act and work just like the checkpoint versions. Do not be concerned if you already have a lot of ".ckpt" or ".safetensors" models on disk! InvokeAI 2.3.0 can still load these and generate images from them without any extra intervention on your part.
+When you perform a new install of version 2.3.0, you will be offered the option
+to install the `diffusers` versions of a number of popular SD models, including
+Stable Diffusion versions 1.5 and 2.1 (including the 768x768 pixel version of
+2.1). These will act and work just like the checkpoint versions. Do not be
+concerned if you already have a lot of ".ckpt" or ".safetensors" models on disk!
+InvokeAI 2.3.0 can still load these and generate images from them without any
+extra intervention on your part.
 
-To take advantage of the optimized loading times of `diffusers` models, InvokeAI offers options to convert legacy checkpoint models into optimized `diffusers` models. If you use the `invokeai` command line interface, the relevant commands are:
+To take advantage of the optimized loading times of `diffusers` models, InvokeAI
+offers options to convert legacy checkpoint models into optimized `diffusers`
+models. If you use the `invokeai` command line interface, the relevant commands
+are:
 
-* `!convert_model` -- Take the path to a local checkpoint file or a URL that is pointing to one, convert it into a `diffusers` model, and import it into InvokeAI's models registry file.
-* `!optimize_model` -- If you already have a checkpoint model in your InvokeAI models file, this command will accept its short name and convert it into a like-named `diffusers` model, optionally deleting the original checkpoint file.
-* `!import_model` -- Take the local path of either a checkpoint file or a `diffusers` model directory and import it into InvokeAI's registry file. You may also provide the ID of any diffusers model that has been published on the [HuggingFace models repository](https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads) and it will be downloaded and installed automatically.
+-   `!convert_model` -- Take the path to a local checkpoint file or a URL that
+    is pointing to one, convert it into a `diffusers` model, and import it into
+    InvokeAI's models registry file.
+-   `!optimize_model` -- If you already have a checkpoint model in your InvokeAI
+    models file, this command will accept its short name and convert it into a
+    like-named `diffusers` model, optionally deleting the original checkpoint
+    file.
+-   `!import_model` -- Take the local path of either a checkpoint file or a
+    `diffusers` model directory and import it into InvokeAI's registry file. You
+    may also provide the ID of any diffusers model that has been published on
+    the
+    [HuggingFace models repository](https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads)
+    and it will be downloaded and installed automatically.
 
 The WebGUI offers similar functionality for model management.
 
-For advanced users, new command-line options provide additional functionality. Launching `invokeai` with the argument `--autoconvert <path to directory>` takes the path to a directory of checkpoint files, automatically converts them into `diffusers` models and imports them. Each time the script is launched, the directory will be scanned for new checkpoint files to be loaded. Alternatively, the `--ckpt_convert` argument will cause any checkpoint or safetensors model that is already registered with InvokeAI to be converted into a `diffusers` model on the fly, allowing you to take advantage of future diffusers-only features without explicitly converting the model and saving it to disk.
+For advanced users, new command-line options provide additional functionality.
+Launching `invokeai` with the argument `--autoconvert <path to directory>` takes
+the path to a directory of checkpoint files, automatically converts them into
+`diffusers` models and imports them. Each time the script is launched, the
+directory will be scanned for new checkpoint files to be loaded. Alternatively,
+the `--ckpt_convert` argument will cause any checkpoint or safetensors model
+that is already registered with InvokeAI to be converted into a `diffusers`
+model on the fly, allowing you to take advantage of future diffusers-only
+features without explicitly converting the model and saving it to disk.
 
-Please see [INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/) for more information on model management in both the command-line and Web interfaces.
+Please see
+[INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/)
+for more information on model management in both the command-line and Web
+interfaces.
 
 #### Support for the `XFormers` Memory-Efficient Crossattention Package
 
-On CUDA (Nvidia) systems, version 2.3.0 supports the `XFormers` library. Once installed,  the`xformers` package dramatically reduces the memory footprint of loaded Stable Diffusion models files and modestly increases image generation speed. `xformers` will be installed and activated automatically if you specify a CUDA system at install time.
+On CUDA (Nvidia) systems, version 2.3.0 supports the `XFormers` library. Once
+installed, the`xformers` package dramatically reduces the memory footprint of
+loaded Stable Diffusion models files and modestly increases image generation
+speed. `xformers` will be installed and activated automatically if you specify a
+CUDA system at install time.
 
-The caveat with using `xformers` is that it introduces slightly non-deterministic behavior, and images generated using the same seed and other settings will be subtly different between invocations. Generally the changes are unnoticeable unless you rapidly shift back and forth between images, but to disable `xformers` and restore fully deterministic behavior, you may launch InvokeAI using the `--no-xformers` option. This is most conveniently done by opening the file `invokeai/invokeai.init` with a text editor, and adding the line `--no-xformers` at the bottom.
+The caveat with using `xformers` is that it introduces slightly
+non-deterministic behavior, and images generated using the same seed and other
+settings will be subtly different between invocations. Generally the changes are
+unnoticeable unless you rapidly shift back and forth between images, but to
+disable `xformers` and restore fully deterministic behavior, you may launch
+InvokeAI using the `--no-xformers` option. This is most conveniently done by
+opening the file `invokeai/invokeai.init` with a text editor, and adding the
+line `--no-xformers` at the bottom.
 
 #### A Negative Prompt Box in the WebUI
 
-There is now a separate text input box for negative prompts in the WebUI. This is convenient for stashing frequently-used negative prompts ("mangled limbs, bad anatomy"). The `[negative prompt]` syntax continues to work in the main prompt box as well.
+There is now a separate text input box for negative prompts in the WebUI. This
+is convenient for stashing frequently-used negative prompts ("mangled limbs, bad
+anatomy"). The `[negative prompt]` syntax continues to work in the main prompt
+box as well.
 
-To see exactly how your prompts are being parsed, launch `invokeai` with the `--log_tokenization` option. The console window will then display the tokenization process for both positive and negative prompts.
+To see exactly how your prompts are being parsed, launch `invokeai` with the
+`--log_tokenization` option. The console window will then display the
+tokenization process for both positive and negative prompts.
 
 #### Model Merging
 
-Version 2.3.0 offers an intuitive user interface for merging up to three Stable Diffusion models using an intuitive user interface. Model merging allows you to mix the behavior of models to achieve very interesting effects. To use this, each of the models must already be imported into InvokeAI and saved in `diffusers` format, then launch the merger using a new menu item in the InvokeAI launcher script (`invoke.sh`, `invoke.bat`) or directly from the command line with `invokeai-merge --gui`. You will be prompted to select the models to merge, the proportions in which to mix them, and the mixing algorithm. The script will create a new merged `diffusers` model and import it into InvokeAI for your use.
+Version 2.3.0 offers an intuitive user interface for merging up to three Stable
+Diffusion models using an intuitive user interface. Model merging allows you to
+mix the behavior of models to achieve very interesting effects. To use this,
+each of the models must already be imported into InvokeAI and saved in
+`diffusers` format, then launch the merger using a new menu item in the InvokeAI
+launcher script (`invoke.sh`, `invoke.bat`) or directly from the command line
+with `invokeai-merge --gui`. You will be prompted to select the models to merge,
+the proportions in which to mix them, and the mixing algorithm. The script will
+create a new merged `diffusers` model and import it into InvokeAI for your use.
 
-See [MODEL MERGING](https://invoke-ai.github.io/InvokeAI/features/MODEL_MERGING/) for more details.
+See
+[MODEL MERGING](https://invoke-ai.github.io/InvokeAI/features/MODEL_MERGING/)
+for more details.
 
 #### Textual Inversion Training
 
-Textual Inversion (TI) is a technique for training a Stable Diffusion model to emit a particular subject or style when triggered by a keyword phrase. You can perform TI training by placing a small number of images of the subject or style in a directory, and choosing a distinctive trigger phrase, such as "pointillist-style". After successful training, The subject or style will be activated by including `<pointillist-style>` in your prompt.
+Textual Inversion (TI) is a technique for training a Stable Diffusion model to
+emit a particular subject or style when triggered by a keyword phrase. You can
+perform TI training by placing a small number of images of the subject or style
+in a directory, and choosing a distinctive trigger phrase, such as
+"pointillist-style". After successful training, The subject or style will be
+activated by including `<pointillist-style>` in your prompt.
 
-Previous versions of InvokeAI were able to perform TI, but it required using a command-line script with dozens of obscure command-line arguments. Version 2.3.0 features an intuitive TI frontend that will build a TI model on top of any `diffusers` model. To access training you can launch from a new item in the launcher script or from the command line using `invokeai-ti --gui`.
+Previous versions of InvokeAI were able to perform TI, but it required using a
+command-line script with dozens of obscure command-line arguments. Version 2.3.0
+features an intuitive TI frontend that will build a TI model on top of any
+`diffusers` model. To access training you can launch from a new item in the
+launcher script or from the command line using `invokeai-ti --gui`.
 
-See [TEXTUAL INVERSION](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/) for further details.
+See
+[TEXTUAL INVERSION](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/)
+for further details.
 
 #### A New Installer Experience
 
-The InvokeAI installer has been upgraded in order to provide a smoother and hopefully more glitch-free experience. In addition, InvokeAI is now packaged as a PyPi project, allowing developers and power-users to install InvokeAI with the command `pip install InvokeAI  --use-pep517`. Please see [Installation](#installation) for details.
+The InvokeAI installer has been upgraded in order to provide a smoother and
+hopefully more glitch-free experience. In addition, InvokeAI is now packaged as
+a PyPi project, allowing developers and power-users to install InvokeAI with the
+command `pip install InvokeAI  --use-pep517`. Please see
+[Installation](#installation) for details.
 
-Developers should be aware that the `pip` installation procedure has been simplified and that the `conda` method is no longer supported at all. Accordingly, the `environments_and_requirements` directory has been deleted from the repository.
+Developers should be aware that the `pip` installation procedure has been
+simplified and that the `conda` method is no longer supported at all.
+Accordingly, the `environments_and_requirements` directory has been deleted from
+the repository.
 
 #### Command-line name changes
 
-All of InvokeAI's functionality, including the WebUI, command-line interface, textual inversion training and model merging, can all be accessed from the `invoke.sh` and `invoke.bat` launcher scripts. The menu of options has been expanded to add the new functionality. For the convenience of developers and power users, we have normalized the names of the InvokeAI command-line scripts:
+All of InvokeAI's functionality, including the WebUI, command-line interface,
+textual inversion training and model merging, can all be accessed from the
+`invoke.sh` and `invoke.bat` launcher scripts. The menu of options has been
+expanded to add the new functionality. For the convenience of developers and
+power users, we have normalized the names of the InvokeAI command-line scripts:
 
-* `invokeai` -- Command-line client
-* `invokeai --web` -- Web GUI
-* `invokeai-merge --gui` -- Model merging script with graphical front end
-* `invokeai-ti --gui` -- Textual inversion script with graphical front end
-* `invokeai-configure` -- Configuration tool for initializing the `invokeai` directory and selecting popular starter models.
+-   `invokeai` -- Command-line client
+-   `invokeai --web` -- Web GUI
+-   `invokeai-merge --gui` -- Model merging script with graphical front end
+-   `invokeai-ti --gui` -- Textual inversion script with graphical front end
+-   `invokeai-configure` -- Configuration tool for initializing the `invokeai`
+    directory and selecting popular starter models.
 
-For backward compatibility, the old command names are also recognized, including `invoke.py` and `configure-invokeai.py`. However, these are deprecated and will eventually be removed.
+For backward compatibility, the old command names are also recognized, including
+`invoke.py` and `configure-invokeai.py`. However, these are deprecated and will
+eventually be removed.
 
-Developers should be aware that the locations of the script's source code has been moved. The new locations are:
-* `invokeai` =>  `ldm/invoke/CLI.py`
-* `invokeai-configure` => `ldm/invoke/config/configure_invokeai.py`
-* `invokeai-ti`=> `ldm/invoke/training/textual_inversion.py`
-* `invokeai-merge` => `ldm/invoke/merge_diffusers`
+Developers should be aware that the locations of the script's source code has
+been moved. The new locations are:
 
-Developers are strongly encouraged to perform an "editable" install of InvokeAI using `pip install -e .  --use-pep517` in the Git repository, and then to call the scripts using their 2.3.0 names, rather than executing the scripts directly. Developers should also be aware that the several important data files have been relocated into a new directory named `invokeai`. This includes the WebGUI's `frontend` and `backend` directories, and the `INITIAL_MODELS.yaml` files used by the installer to select starter models. Eventually all InvokeAI modules will be in subdirectories of `invokeai`.
+-   `invokeai` => `ldm/invoke/CLI.py`
+-   `invokeai-configure` => `ldm/invoke/config/configure_invokeai.py`
+-   `invokeai-ti`=> `ldm/invoke/training/textual_inversion.py`
+-   `invokeai-merge` => `ldm/invoke/merge_diffusers`
 
-Please see [2.3.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.3.0) for further details.
-For older changelogs, please visit the
+Developers are strongly encouraged to perform an "editable" install of InvokeAI
+using `pip install -e .  --use-pep517` in the Git repository, and then to call
+the scripts using their 2.3.0 names, rather than executing the scripts directly.
+Developers should also be aware that the several important data files have been
+relocated into a new directory named `invokeai`. This includes the WebGUI's
+`frontend` and `backend` directories, and the `INITIAL_MODELS.yaml` files used
+by the installer to select starter models. Eventually all InvokeAI modules will
+be in subdirectories of `invokeai`.
+
+Please see
+[2.3.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.3.0)
+for further details. For older changelogs, please visit the
 **[CHANGELOG](CHANGELOG/#v223-2-december-2022)**.
 
 ## :material-target: Troubleshooting
 
-Please check out our **[:material-frequently-asked-questions:
-Troubleshooting
-Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)** to
-get solutions for common installation problems and other issues.
+Please check out our
+**[:material-frequently-asked-questions: Troubleshooting Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)**
+to get solutions for common installation problems and other issues.
 
 ## :octicons-repo-push-24: Contributing
 
@@ -265,8 +541,8 @@ thank them for their time, hard work and effort.
 For support, please use this repository's GitHub Issues tracking service. Feel
 free to send me an email if you use and like the script.
 
-Original portions of the software are Copyright (c) 2022-23
-by [The InvokeAI Team](https://github.com/invoke-ai).
+Original portions of the software are Copyright (c) 2022-23 by
+[The InvokeAI Team](https://github.com/invoke-ai).
 
 ## :octicons-book-24: Further Reading
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -40,9 +40,10 @@ experimental versions later.
     this, open up a command-line window ("Terminal" on Linux and
     Macintosh, "Command" or "Powershell" on Windows) and type `python
     --version`. If Python is installed, it will print out the version
-    number. If it is version `3.9.1` or `3.10.x`, you meet
-    requirements.
-
+    number. If it is version `3.9.*` or `3.10.*`, you meet
+    requirements. We do not recommend using Python 3.11 or higher,
+    as not all the libraries that InvokeAI depends on work properly
+    with this version.
 
     !!! warning "What to do if you have an unsupported version"
 
@@ -50,8 +51,7 @@ experimental versions later.
         and download the appropriate installer package for your
         platform. We recommend [Version
         3.10.9](https://www.python.org/downloads/release/python-3109/),
-        which has been extensively tested with InvokeAI. At this time
-        we do not recommend Python 3.11.
+        which has been extensively tested with InvokeAI.
 
     _Please select your platform in the section below for platform-specific
     setup requirements._
@@ -150,7 +150,7 @@ experimental versions later.
 
     ```cmd
     C:\Documents\Linco> cd InvokeAI-Installer
-    C:\Documents\Linco\invokeAI> install.bat
+    C:\Documents\Linco\invokeAI> .\install.bat
     ```
 
 7.  **Select the location to install InvokeAI**: The script will ask you to choose where to install InvokeAI. Select a
@@ -167,6 +167,11 @@ experimental versions later.
       `/home/YourName/invokeai` on Linux systems, and `/Users/YourName/invokeai`
       on Macintoshes, where "YourName" is your login name.
 
+    -If you have previously installed InvokeAI, you will be asked to
+     confirm whether you want to reinstall into this directory.  You
+     may choose to reinstall, in which case your version will be upgraded,
+     or choose a different directory.
+
     - The script uses tab autocompletion to suggest directory path completions.
       Type part of the path (e.g. "C:\Users") and press ++tab++ repeatedly
       to suggest completions.
@@ -181,11 +186,6 @@ experimental versions later.
     are unsure what GPU you are using, you can ask the installer to
     guess.
 
-    <figure markdown>
-    ![choose-gpu-screenshot](../assets/installer-walkthrough/choose-gpu.png)
-    </figure>
-
-
 9.  **Watch it go!**: Sit back and let the install script work. It will install the third-party
     libraries needed by InvokeAI and the application itself.
 
@@ -197,25 +197,141 @@ experimental versions later.
     minutes and nothing is happening, you can interrupt the script with ^C. You
     may restart it and it will pick up where it left off.
 
-10. **Post-install Configuration**: After installation completes, the installer will launch the
-    configuration script, which will guide you through the first-time
-    process of selecting one or more Stable Diffusion model weights
-    files, downloading and configuring them. We provide a list of
-    popular models that InvokeAI performs well with. However, you can
-    add more weight files later on using the command-line client or
-    the Web UI. See [Installing Models](050_INSTALLING_MODELS.md) for
-    details.
-
     <figure markdown>
-    ![downloading-models-screenshot](../assets/installer-walkthrough/downloading-models.png)
+    ![initial-settings-screenshot](../assets/installer-walkthrough/settings-form.png)
     </figure>
 
-    If you have already downloaded the weights file(s) for another Stable
-    Diffusion distribution, you may skip this step (by selecting "skip" when
-    prompted) and configure InvokeAI to use the previously-downloaded files. The
-    process for this is described in [Installing Models](050_INSTALLING_MODELS.md).
+10. **Post-install Configuration**: After installation completes, the
+    installer will launch the configuration form, which will guide you
+    through the first-time process of adjusting some of InvokeAI's
+    startup settings. To move around this form use ctrl-N for
+    &lt;N&gt;ext and ctrl-P for &lt;P&gt;revious, or use &lt;tab&gt;
+    and shift-&lt;tab&gt; to move forward and back. Once you are in a
+    multi-checkbox field use the up and down cursor keys to select the
+    item you want, and &lt;space&gt; to toggle it on and off.  Within
+    a directory field, pressing &lt;tab&gt; will provide autocomplete
+    options.
 
-11. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
+    Generally the defaults are fine, and you can come back to this screen at
+    any time to tweak your system. Here are the options you can adjust:
+
+    - ***Output directory for images***
+      This is the path to a directory in which InvokeAI will store all its
+      generated images.
+
+    - ***NSFW checker***
+      If checked, InvokeAI will test images for potential sexual content
+      and blur them out if found. Note that the NSFW checker consumes
+      an additional 0.6 GB of VRAM on top of the 2-3 GB of VRAM used
+      by most image models. If you have a low VRAM GPU (4-6 GB), you
+      can reduce out of memory errors by disabling the checker.
+
+    - ***HuggingFace Access Token***
+      InvokeAI has the ability to download embedded styles and subjects
+      from the HuggingFace Concept Library on-demand. However, some of
+      the concept library files are password protected. To make download
+      smoother, you can set up an account at huggingface.co, obtain an
+      access token, and paste it into this field. Note that you paste
+      to this screen using ctrl-shift-V
+
+    - ***Free GPU memory after each generation***
+        This is useful for low-memory machines and helps minimize the
+	amount of GPU VRAM used by InvokeAI.
+
+    - ***Enable xformers support if available***
+        If the xformers library was successfully installed, this will activate
+	it to reduce memory consumption and increase rendering speed noticeably.
+	Note that xformers has the side effect of generating slightly different
+	images even when presented with the same seed and other settings.
+
+    - ***Force CPU to be used on GPU systems***
+        This will use the (slow) CPU rather than the accelerated GPU. This
+	can be used to generate images on systems that don't have a compatible
+	GPU.
+
+    - ***Precision***
+        This controls whether to use float32 or float16 arithmetic.
+	float16 uses less memory but is also slightly less accurate.
+	Ordinarily the right arithmetic is picked automatically ("auto"),
+	but you may have to use float32 to get images on certain systems
+	and graphics cards. The "autocast" option is deprecated and
+	shouldn't be used unless you are asked to by a member of the team.
+
+    - ***Number of models to cache in CPU memory***
+        This allows you to keep models in memory and switch rapidly among
+	them rather than having them load from disk each time. This slider
+	controls how many models to keep loaded at once. Each
+	model will use 2-4 GB of RAM, so use this cautiously
+
+    - ***Directory containing embedding/textual inversion files***
+        This is the directory in which you can place custom embedding
+	files (.pt or .bin). During startup, this directory will be
+	scanned and InvokeAI will print out the text terms that
+	are available to trigger the embeddings.
+
+    At the bottom of the screen you will see a checkbox for accepting
+    the CreativeML Responsible AI License. You need to accept the license
+    in order to download Stable Diffusion models from the next screen.
+
+    _You can come back to the startup options form_ as many times as you like.
+    From the `invoke.sh` or `invoke.bat` launcher, select option (6) to relaunch
+    this script. On the command line, it is named `invokeai-configure`.
+
+11. **Downloading Models**: After you press `[NEXT]` on the screen, you will be taken
+    to another screen that prompts you to download a series of starter models. The ones
+    we recommend are preselected for you, but you are encouraged to use the checkboxes to
+    pick and choose.
+    You will probably wish to download `autoencoder-840000` for use with models that
+    were trained with an older version of the Stability VAE.
+
+    <figure markdown>
+    ![select-models-screenshot](../assets/installer-walkthrough/installing-models.png)
+    </figure>
+    
+    Below the preselected list of starter models is a large text field which you can use
+    to specify a series of models to import. You can specify models in a variety of formats,
+    each separated by a space or newline. The formats accepted are:
+
+    - The path to a .ckpt or .safetensors file. On most systems, you can drag a file from
+      the file browser to the textfield to automatically paste the path. Be sure to remove
+      extraneous quotation marks and other things that come along for the ride.
+
+    - The path to a directory containing a combination of `.ckpt` and `.safetensors` files.
+      The directory will be scanned from top to bottom (including subfolders) and any
+      file that can be imported will be.
+
+    - A URL pointing to a `.ckpt` or `.safetensors` file. You can cut
+      and paste directly from a web page, or simply drag the link from the web page
+      or navigation bar. (You can also use ctrl-shift-V to paste into this field)
+      The file will be downloaded and installed.
+      
+    - The HuggingFace repository ID (repo_id) for a `diffusers` model. These IDs have
+       the format _author_name/model_name_, as in `andite/anything-v4.0`
+      
+    - The path to a local directory containing a `diffusers`
+      model. These directories always have the file `model_index.json`
+      at their top level.
+
+    _Select a directory for models to import_ You may select a local
+    directory for autoimporting at startup time. If you select this
+    option, the directory you choose will be scanned for new
+    .ckpt/.safetensors files each time InvokeAI starts up, and any new
+    files will be automatically imported and made available for your
+    use.
+
+    _Convert imported models into diffusers_ When legacy checkpoint
+    files are imported, you may select to use them unmodified (the
+    default) or to convert them into `diffusers` models. The latter
+    load much faster and have slightly better rendering performance,
+    but not all checkpoint files can be converted. Note that Stable Diffusion
+    Version 2.X files are **only** supported in `diffusers` format and will
+    be converted regardless.
+     
+     _You can come back to the model install form_ as many times as you like.
+     From the `invoke.sh` or `invoke.bat` launcher, select option (5) to relaunch
+     this script. On the command line, it is named `invokeai-model-install`.
+
+12. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
     for the directory `invokeai` installed in the location you chose at the
     beginning of the install session. Look for a shell script named `invoke.sh`
     (Linux/Mac) or `invoke.bat` (Windows). Launch the script by double-clicking
@@ -301,7 +417,7 @@ Then type the following commands:
 
 === "AMD System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.2
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
     ```
 
 ### Corrupted configuration file
@@ -327,6 +443,52 @@ the [InvokeAI Issues](https://github.com/invoke-ai/InvokeAI/issues) section, or
 visit our [Discord Server](https://discord.gg/ZmtBAhwWhy) for interactive
 assistance.
 
+### Out of Memory Issues
+
+The models are large, VRAM is expensive, and you may find yourself
+faced with Out of Memory errors when generating images. Here are some
+tips to reduce the problem:
+
+* **4 GB of VRAM**
+
+This should be adequate for 512x512 pixel images using Stable Diffusion 1.5
+and derived models, provided that you **disable** the NSFW checker. To
+disable the filter, do one of the following:
+
+   * Select option (6) "_change InvokeAI startup options_" from the
+     launcher. This will bring up the console-based startup settings
+     dialogue and allow you to unselect the "NSFW Checker" option.
+   * Start the startup settings dialogue directly by running
+     `invokeai-configure --skip-sd-weights --skip-support-models`
+     from the command line.
+   * Find the `invokeai.init` initialization file in the InvokeAI root
+     directory, open it in a text editor, and change `--nsfw_checker`
+     to `--no-nsfw_checker`
+
+If you are on a CUDA system, you can realize significant memory
+savings by activating the `xformers` library as described above. The
+downside is `xformers` introduces non-deterministic behavior, such
+that images generated with exactly the same prompt and settings will
+be slightly different from each other. See above for more information.
+
+* **6 GB of VRAM**
+
+This is a border case. Using the SD 1.5 series you should be able to
+generate images up to 640x640 with the NSFW checker enabled, and up to
+1024x1024 with it disabled and `xformers` activated. 
+
+If you run into persistent memory issues there are a series of
+environment variables that you can set before launching InvokeAI that
+alter how the PyTorch machine learning library manages memory.  See
+https://pytorch.org/docs/stable/notes/cuda.html#memory-management for
+a list of these tweaks.
+
+* **12 GB of VRAM**
+
+This should be sufficient to generate larger images up to about
+1280x1280. If you wish to push further, consider activating
+`xformers`.
+
 ### Other Problems
 
 If you run into problems during or after installation, the InvokeAI team is
@@ -348,25 +510,11 @@ version (recommended), follow these steps:
 1. Start the `invoke.sh`/`invoke.bat` launch script from within the
    `invokeai` root directory.
 
-2. Choose menu item (6) "Developer's Console". This will launch a new
-   command line.
-
-3. Type the following command:
-
-   ```bash
-   pip install InvokeAI --upgrade
-   ```
-4.  Watch the installation run. Once it is complete, you may exit the
-    command line by typing `exit`, and then start InvokeAI from the
-    launch script as per usual.
-
-
-Alternatively, if you wish to get the most recent unreleased
-development version, perform the same steps to enter the developer's
-console, and then type:
-
-   ```bash
-   pip install https://github.com/invoke-ai/InvokeAI/archive/refs/heads/main.zip
-   ```
+2. Choose menu item (10) "Update InvokeAI".
 
+3. This will launch a menu that gives you the option of:
 
+   1. Updating to the latest official release;
+   2. Updating to the bleeding-edge development version; or
+   3. Manually entering the tag or branch name of a version of
+      InvokeAI you wish to try out.

docs/installation/020_INSTALL_MANUAL.md

@@ -30,25 +30,35 @@ Installation](010_INSTALL_AUTOMATED.md), and in many cases will
 already be installed (if, for example, you have used your system for
 gaming):
 
-* **Python** version 3.9 or 3.10 (3.11 is not recommended).
+* **Python**
 
-* **CUDA Tools** For those with _NVidia GPUs_, you will need to
-  install the [CUDA toolkit and optionally the XFormers library](070_INSTALL_XFORMERS.md).
+    version 3.9 or 3.10 (3.11 is not recommended).
 
-*  **ROCm Tools** For _Linux users with AMD GPUs_, you will need
-   to install the [ROCm toolkit](./030_INSTALL_CUDA_AND_ROCM.md). Note that
-   InvokeAI does not support AMD GPUs on Windows systems due to
-   lack of a Windows ROCm library.
+* **CUDA Tools**
 
-*  **Visual C++ Libraries** _Windows users_ must install the free
-   [Visual C++ libraries from Microsoft](https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170)
+    For those with _NVidia GPUs_, you will need to
+    install the [CUDA toolkit and optionally the XFormers library](070_INSTALL_XFORMERS.md).
 
-* **The Xcode command line tools** for _Macintosh users_. Instructions are
-  available at [Free Code Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
+* **ROCm Tools**
 
-* _Macintosh users_ may also need to run the `Install Certificates` command
-  if model downloads give lots of certificate errors. Run:
-  `/Applications/Python\ 3.10/Install\ Certificates.command`
+    For _Linux users with AMD GPUs_, you will need
+    to install the [ROCm toolkit](./030_INSTALL_CUDA_AND_ROCM.md). Note that
+    InvokeAI does not support AMD GPUs on Windows systems due to
+    lack of a Windows ROCm library.
+
+* **Visual C++ Libraries**
+
+    _Windows users_ must install the free
+    [Visual C++ libraries from Microsoft](https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170)
+
+* **The Xcode command line tools**
+
+    for _Macintosh users_. Instructions are available at
+    [Free Code Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
+
+    * _Macintosh users_ may also need to run the `Install Certificates` command
+      if model downloads give lots of certificate errors. Run:
+      `/Applications/Python\ 3.10/Install\ Certificates.command`
 
 ### Installation Walkthrough
 
@@ -75,7 +85,7 @@ manager, please follow these steps:
     === "Linux/Mac"
 
         ```bash
-        export INVOKEAI_ROOT="~/invokeai"
+        export INVOKEAI_ROOT=~/invokeai
         mkdir $INVOKEAI_ROOT
         ```
 
@@ -99,35 +109,30 @@ manager, please follow these steps:
    Windows environment variable using the Advanced System Settings dialogue.
    Refer to your operating system documentation for details.
 
-
-    === "Linux/Mac"
-        ```bash
-        cd $INVOKEAI_ROOT
-        python -m venv create .venv
-        ```
-
-    === "Windows"
-         ```bash
-         cd $INVOKEAI_ROOT
-     	 python -m venv create .venv
-     	 ```
+    ```terminal
+    cd $INVOKEAI_ROOT
+    python -m venv .venv --prompt InvokeAI
+    ```
 
 4.  Activate the new environment:
 
     === "Linux/Mac"
-    	```bash
+
+        ```bash
         source .venv/bin/activate
-    	```
+        ```
 
     === "Windows"
-    	```bash
-        .venv\script\activate
-    	```
-	If you get a permissions error at this point, run the command
-	`Set-ExecutionPolicy -ExecutionPolicy Unrestricted -Scope CurrentUser`
-	and try `activate` again.
 
-    The command-line prompt should change to to show `(.venv)` at the
+        ```ps
+        .venv\Scripts\activate
+        ```
+
+        If you get a permissions error at this point, run this command and try again
+
+        `Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser`
+
+    The command-line prompt should change to to show `(InvokeAI)` at the
     beginning of the prompt. Note that all the following steps should be
     run while inside the INVOKEAI_ROOT directory
 
@@ -137,40 +142,47 @@ manager, please follow these steps:
     python -m pip install --upgrade pip
     ```
 
-6.  Install the InvokeAI Package. The `--extra-index-url` option is used to select among CUDA, ROCm and CPU/MPS drivers as shown below:
+6. Install the InvokeAI Package. The `--extra-index-url` option is used to select among
+   CUDA, ROCm and CPU/MPS drivers as shown below:
 
     === "CUDA (NVidia)"
+
         ```bash
         pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
         ```
 
     === "ROCm (AMD)"
+
         ```bash
         pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
         ```
 
     === "CPU (Intel Macs & non-GPU systems)"
+
         ```bash
         pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
         ```
 
     === "MPS (M1 and M2 Macs)"
+
         ```bash
-        pip install InvokeAI --use-pep517  --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install InvokeAI --use-pep517
         ```
 
 7.  Deactivate and reactivate your runtime directory so that the invokeai-specific commands
     become available in the environment
 
     === "Linux/Macintosh"
+
         ```bash
         deactivate && source .venv/bin/activate
         ```
 
     === "Windows"
-        ```bash
+
+        ```ps
         deactivate
-	.venv\Scripts\activate
+        .venv\Scripts\activate
         ```
 
 8.  Set up the runtime directory
@@ -179,7 +191,7 @@ manager, please follow these steps:
     models, model config files, directory for textual inversion embeddings, and
     your outputs.
 
-    ```bash
+    ```terminal
     invokeai-configure
     ```
 
@@ -283,13 +295,12 @@ on your system, please see the [Git Installation
 Guide](https://github.com/git-guides/install-git)
 
 1. From the command line, run this command:
-
    ```bash
    git clone https://github.com/invoke-ai/InvokeAI.git
    ```
 
-This will create a directory named `InvokeAI` and populate it with the
-full source code from the InvokeAI repository.
+    This will create a directory named `InvokeAI` and populate it with the
+    full source code from the InvokeAI repository.
 
 2. Activate the InvokeAI virtual environment as per step (4) of the manual
 installation protocol (important!)
@@ -314,7 +325,7 @@ installation protocol (important!)
 
     === "MPS (M1 and M2 Macs)"
         ```bash
-        pip install -e . --use-pep517  --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -e . --use-pep517
         ```
 
     Be sure to pass `-e` (for an editable install) and don't forget the
@@ -330,5 +341,29 @@ installation protocol (important!)
     repository. You can then use GitHub functions to create and submit
     pull requests to contribute improvements to the project.
 
-    Please see [Contributing](/index.md#Contributing) for hints
+    Please see [Contributing](../index.md#contributing) for hints
     on getting started.
+
+### Unsupported Conda Install
+
+Congratulations, you found the "secret" Conda installation
+instructions. If you really **really** want to use Conda with InvokeAI
+you can do so using this unsupported recipe:
+
+```
+mkdir ~/invokeai
+conda create -n invokeai python=3.10
+conda activate invokeai
+pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+invokeai-configure --root ~/invokeai
+invokeai --root ~/invokeai --web
+```
+
+The `pip install` command shown in this recipe is for Linux/Windows
+systems with an NVIDIA GPU. See step (6) above for the command to use
+with other platforms/GPU combinations. If you don't wish to pass the
+`--root` argument to `invokeai` with each launch, you may set the
+environment variable INVOKEAI_ROOT to point to the installation directory.
+
+Note that if you run into problems with the Conda installation, the InvokeAI
+staff will **not** be able to help you out. Caveat Emptor!

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -77,7 +77,7 @@ machine. To test, open up a terminal window and issue the following
 command:
 
 ```
-rocm-smi
+rocminfo
 ```
 
 If you get a table labeled "ROCm System Management Interface" the
@@ -95,9 +95,17 @@ recent version of Ubuntu, 22.04. However, this [community-contributed
 recipe](https://novaspirit.github.io/amdgpu-rocm-ubu22/) is reported
 to work well.
 
-After installation, please run `rocm-smi` a second time to confirm
+After installation, please run `rocminfo` a second time to confirm
 that the driver is present and the GPU is recognized. You may need to
-do a reboot in order to load the driver.
+do a reboot in order to load the driver. In addition, if you see
+errors relating to your username not being a member of the `render`
+group, you may fix this by adding yourself to this group with the command:
+
+```
+sudo usermod -a -G render myUserName
+```
+
+(Thanks to @EgoringKosmos for the usermod recipe.)
 
 ### Linux Install with a ROCm-docker Container
 
@@ -110,7 +118,7 @@ recipes are available
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/rocm5.2` as described in the [Manual
+https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 This will be done automatically for you if you use the installer

docs/installation/050_INSTALLING_MODELS.md

@@ -11,7 +11,7 @@ The model checkpoint files ('\*.ckpt') are the Stable Diffusion
 captioned images gathered from multiple sources.
 
 Originally there was only a single Stable Diffusion weights file,
-which many people named `model.ckpt`. Now there are dozens or more
+which many people named `model.ckpt`. Now there are hundreds
 that have been fine tuned to provide particulary styles, genres, or
 other features. In addition, there are several new formats that
 improve on the original checkpoint format: a `.safetensors` format
@@ -29,9 +29,10 @@ and performance are being made at a rapid pace. Among other features
 is the ability to download and install a `diffusers` model just by
 providing its HuggingFace repository ID.
 
-While InvokeAI will continue to support `.ckpt` and `.safetensors`
+While InvokeAI will continue to support legacy `.ckpt` and `.safetensors`
 models for the near future, these are deprecated and support will
-likely be withdrawn at some point in the not-too-distant future.
+be withdrawn in version 3.0, after which all legacy models will be
+converted into diffusers at the time they are loaded.
 
 This manual will guide you through installing and configuring model
 weight files and converting legacy `.ckpt` and `.safetensors` files
@@ -43,25 +44,31 @@ InvokeAI comes with support for a good set of starter models. You'll
 find them listed in the master models file
 `configs/INITIAL_MODELS.yaml` in the InvokeAI root directory. The
 subset that are currently installed are found in
-`configs/models.yaml`. The current list is:
+`configs/models.yaml`. As of v2.3.1, the list of starter models is:
 
-| Model                | HuggingFace Repo ID               | Description                                                | URL
-| -------------------- | --------------------------------- | ---------------------------------------------------------- | -------------------------------------------------------------- |
-| stable-diffusion-1.5 | runwayml/stable-diffusion-v1-5    | Most recent version of base Stable Diffusion model         | https://huggingface.co/runwayml/stable-diffusion-v1-5          |
-| stable-diffusion-1.4 | runwayml/stable-diffusion-v1-4    | Previous version of base Stable Diffusion model         | https://huggingface.co/runwayml/stable-diffusion-v1-4          |
-| inpainting-1.5       | runwayml/stable-diffusion-inpainting  | Stable diffusion 1.5 optimized for inpainting          | https://huggingface.co/runwayml/stable-diffusion-inpainting    |
-| stable-diffusion-2.1-base |stabilityai/stable-diffusion-2-1-base | Stable Diffusion version 2.1 trained on 512 pixel images | https://huggingface.co/stabilityai/stable-diffusion-2-1-base |
-| stable-diffusion-2.1-768 |stabilityai/stable-diffusion-2-1 | Stable Diffusion version 2.1 trained on 768 pixel images | https://huggingface.co/stabilityai/stable-diffusion-2-1        |
-| dreamlike-diffusion-1.0  | dreamlike-art/dreamlike-diffusion-1.0  | An SD 1.5 model finetuned on high quality art    | https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0   |
-| dreamlike-photoreal-2.0  | dreamlike-art/dreamlike-photoreal-2.0  | A photorealistic model trained on 768 pixel images| https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0   |
-| openjourney-4.0      | prompthero/openjourney              | An SD 1.5 model finetuned on Midjourney images prompt with "mdjrny-v4 style"   | https://huggingface.co/prompthero/openjourney  |
-| nitro-diffusion-1.0  | nitrosocke/Nitro-Diffusion          | An SD 1.5 model finetuned on three styles, prompt with "archer style", "arcane style" or "modern disney style" | https://huggingface.co/nitrosocke/Nitro-Diffusion|
-| trinart-2.0          | naclbit/trinart_stable_diffusion_v2 | An SD 1.5 model finetuned with ~40,000 assorted high resolution manga/anime-style pictures | https://huggingface.co/naclbit/trinart_stable_diffusion_v2|
-| trinart-characters-2_0 | naclbit/trinart_derrida_characters_v2_stable_diffusion | An SD 1.5 model finetuned with 19.2M manga/anime-style pictures | https://huggingface.co/naclbit/trinart_derrida_characters_v2_stable_diffusion|
+|Model Name | HuggingFace Repo ID | Description | URL |
+|---------- | ---------- | ----------- | --- |
+|stable-diffusion-1.5|runwayml/stable-diffusion-v1-5|Stable Diffusion version 1.5 diffusers model (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-v1-5 |
+|sd-inpainting-1.5|runwayml/stable-diffusion-inpainting|RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-inpainting |
+|stable-diffusion-2.1|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
+|sd-inpainting-2.0|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
+|analog-diffusion-1.0|wavymulder/Analog-Diffusion|An SD-1.5 model trained on diverse analog photographs (2.13 GB)|https://huggingface.co/wavymulder/Analog-Diffusion |
+|deliberate-1.0|XpucT/Deliberate|Versatile model that produces detailed images up to 768px (4.27 GB)|https://huggingface.co/XpucT/Deliberate |
+|d&d-diffusion-1.0|0xJustin/Dungeons-and-Diffusion|Dungeons & Dragons characters (2.13 GB)|https://huggingface.co/0xJustin/Dungeons-and-Diffusion |
+|dreamlike-photoreal-2.0|dreamlike-art/dreamlike-photoreal-2.0|A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)|https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0 |
+|inkpunk-1.0|Envvi/Inkpunk-Diffusion|Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)|https://huggingface.co/Envvi/Inkpunk-Diffusion |
+|openjourney-4.0|prompthero/openjourney|An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)|https://huggingface.co/prompthero/openjourney |
+|portrait-plus-1.0|wavymulder/portraitplus|An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)|https://huggingface.co/wavymulder/portraitplus |
+|seek-art-mega-1.0|coreco/seek.art_MEGA|A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)|https://huggingface.co/coreco/seek.art_MEGA |
+|trinart-2.0|naclbit/trinart_stable_diffusion_v2|An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)|https://huggingface.co/naclbit/trinart_stable_diffusion_v2 |
+|waifu-diffusion-1.4|hakurei/waifu-diffusion|An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)|https://huggingface.co/hakurei/waifu-diffusion |
 
-Note that these files are covered by an "Ethical AI" license which forbids
-certain uses. When you initially download them, you are asked to
-accept the license terms.
+Note that these files are covered by an "Ethical AI" license which
+forbids certain uses. When you initially download them, you are asked
+to accept the license terms. In addition, some of these models carry
+additional license terms that limit their use in commercial
+applications or on public servers. Be sure to familiarize yourself
+with the model terms by visiting the URLs in the table above.
 
 ## Community-Contributed Models
 
@@ -80,33 +87,70 @@ only `.safetensors` and `.ckpt` models, but they can be easily loaded
 into InvokeAI and/or converted into optimized `diffusers` models. Be
 aware that CIVITAI hosts many models that generate NSFW content.
 
+!!! note
+
+    InvokeAI 2.3.x does not support directly importing and
+    running Stable Diffusion version 2 checkpoint models. If you
+    try to import them, they will be automatically
+    converted into `diffusers` models on the fly. This adds about 20s
+    to loading time. To avoid this overhead, you are encouraged to
+    use one of the conversion methods described below to convert them
+    permanently.
+
 ## Installation
 
 There are multiple ways to install and manage models:
 
-1. The `invokeai-configure` script which will download and install them for you.
+1. The `invokeai-model-install` script which will download and install them for you.
 
 2. The command-line tool (CLI) has commands that allows you to import, configure and modify
    models files.
 
 3. The web interface (WebUI) has a GUI for importing and managing
-models.
+   models.
 
-### Installation via `invokeai-configure`
+### Installation via `invokeai-model-install`
 
-From the `invoke` launcher, choose option (6) "re-run the configure
-script to download new models." This will launch the same script that
-prompted you to select models at install time. You can use this to add
-models that you skipped the first time around. It is all right to
-specify a model that was previously downloaded; the script will just
-confirm that the files are complete.
+From the `invoke` launcher, choose option (5) "Download and install
+models."  This will launch the same script that prompted you to select
+models at install time. You can use this to add models that you
+skipped the first time around. It is all right to specify a model that
+was previously downloaded; the script will just confirm that the files
+are complete.
+
+This script allows you to load 3d party models. Look for a large text
+entry box labeled "IMPORT LOCAL AND REMOTE MODELS." In this box, you
+can cut and paste one or more of any of the following:
+
+1. A URL that points to a downloadable .ckpt or .safetensors file.
+2. A file path pointing to a .ckpt or .safetensors  file.
+3. A diffusers model repo_id (from HuggingFace) in the format
+   "owner/repo_name".
+4. A directory path pointing to a diffusers model directory.
+5. A directory path pointing to a directory containing a bunch of
+   .ckpt and .safetensors files. All will be imported.
+
+You can enter multiple items into the textbox, each one on a separate
+line. You can paste into the textbox using ctrl-shift-V or by dragging
+and dropping a file/directory from the desktop into the box.
+
+The script also lets you designate a directory that will be scanned
+for new model files each time InvokeAI starts up. These models will be
+added automatically.
+
+Lastly, the script gives you a checkbox option to convert legacy models
+into diffusers, or to run the legacy model directly. If you choose to
+convert, the original .ckpt/.safetensors file will **not** be deleted,
+but a new diffusers directory will be created, using twice your disk
+space. However, the diffusers version will load faster, and will be
+compatible with InvokeAI 3.0.
 
 ### Installation via the CLI
 
 You can install a new model, including any of the community-supported ones, via
 the command-line client's `!import_model` command.
 
-#### Installing `.ckpt` and `.safetensors` models
+#### Installing individual `.ckpt` and `.safetensors` models
 
 If the model is already downloaded to your local disk, use
 `!import_model /path/to/file.ckpt` to load it. For example:
@@ -131,15 +175,36 @@ invoke> !import_model https://example.org/sd_models/martians.safetensors
 For this to work, the URL must not be password-protected. Otherwise
 you will receive a 404 error.
 
-When you import a legacy model, the CLI will ask you a few questions
-about the model, including what size image it was trained on (usually
-512x512), what name and description you wish to use for it, what
-configuration file to use for it (usually the default
-`v1-inference.yaml`), whether you'd like to make this model the
-default at startup time, and whether you would like to install a
-custom VAE (variable autoencoder) file for the model. For recent
-models, the answer to the VAE question is usually "no," but it won't
-hurt to answer "yes".
+When you import a legacy model, the CLI will try to figure out what
+type of model it is and select the correct load configuration file.
+However, one thing it can't do is to distinguish between Stable
+Diffusion 2.x models trained on 512x512 vs 768x768 images. In this
+case, the CLI will pop up a menu of choices, asking you to select
+which type of model it is. Please consult the model documentation to
+identify the correct answer, as loading with the wrong configuration
+will lead to black images. You can correct the model type after the
+fact using the `!edit_model` command.
+
+After importing, the model will load. If this is successful, you will
+be asked if you want to keep the model loaded in memory to start
+generating immediately. You'll also be asked if you wish to make this
+the default model on startup. You can change this later using
+`!edit_model`.
+
+#### Importing a batch of `.ckpt` and `.safetensors` models from a directory
+
+You may also point `!import_model` to a directory containing a set of
+`.ckpt` or `.safetensors` files. They will be imported _en masse_.
+
+!!! example
+
+    ```console
+    invoke> !import_model C:/Users/fred/Downloads/civitai_models/
+    ```
+
+You will be given the option to import all models found in the
+directory, or select which ones to import. If there are subfolders
+within the directory, they will be searched for models to import.
 
 #### Installing `diffusers` models
 
@@ -173,6 +238,118 @@ description for the model, whether to make this the default model that
 is loaded at InvokeAI startup time, and whether to replace its
 VAE. Generally the answer to the latter question is "no".
 
+### Installation via the WebUI
+
+To access the WebUI Model Manager, click on the button that looks like
+a cube in the upper right side of the browser screen. This will bring
+up a dialogue that lists the models you have already installed, and
+allows you to load, delete or edit them:
+
+<figure markdown>
+
+![model-manager](../assets/installing-models/webui-models-1.png)
+
+</figure>
+
+To add a new model, click on **+ Add New** and select to either a
+checkpoint/safetensors model, or a diffusers model:
+
+<figure markdown>
+
+![model-manager-add-new](../assets/installing-models/webui-models-2.png)
+
+</figure>
+
+In this example, we chose **Add Diffusers**. As shown in the figure
+below, a new dialogue prompts you to enter the name to use for the
+model, its description, and either the location of the `diffusers`
+model on disk, or its Repo ID on the HuggingFace web site. If you
+choose to enter a path to disk, the system will autocomplete for you
+as you type:
+
+<figure markdown>
+
+![model-manager-add-diffusers](../assets/installing-models/webui-models-3.png)
+
+</figure>
+
+Press **Add Model** at the bottom of the dialogue (scrolled out of
+site in the figure), and the model will be downloaded, imported, and
+registered in `models.yaml`.
+
+The **Add Checkpoint/Safetensor Model** option is similar, except that
+in this case you can choose to scan an entire folder for
+checkpoint/safetensors files to import. Simply type in the path of the
+directory and press the "Search" icon. This will display the
+`.ckpt` and `.safetensors` found inside the directory and its
+subfolders, and allow you to choose which ones to import:
+
+<figure markdown>
+
+![model-manager-add-checkpoint](../assets/installing-models/webui-models-4.png)
+
+</figure>
+
+## Model Management Startup Options
+
+The `invoke` launcher and the `invokeai` script accept a series of
+command-line arguments that modify InvokeAI's behavior when loading
+models. These can be provided on the command line, or added to the
+InvokeAI root directory's `invokeai.init` initialization file.
+
+The arguments are:
+
+* `--model <model name>` -- Start up with the indicated model loaded
+* `--ckpt_convert` -- When a checkpoint/safetensors model is loaded, convert it into a `diffusers` model in memory. This does not permanently save the converted model to disk.
+* `--autoconvert <path/to/directory>` -- Scan the indicated directory path for new checkpoint/safetensors files, convert them into `diffusers` models, and import them into InvokeAI.
+
+Here is an example of providing an argument on the command line using
+the `invoke.sh` launch script:
+
+```bash
+invoke.sh --autoconvert /home/fred/stable-diffusion-checkpoints
+```
+
+And here is what the same argument looks like in `invokeai.init`:
+
+```bash
+--outdir="/home/fred/invokeai/outputs
+--no-nsfw_checker
+--autoconvert /home/fred/stable-diffusion-checkpoints
+```
+
+### Specifying a configuration file for legacy checkpoints
+
+Some checkpoint files come with instructions to use a specific .yaml
+configuration file. For InvokeAI load this file correctly, please put
+the config file in the same directory as the corresponding `.ckpt` or
+`.safetensors` file and make sure the file has the same basename as
+the model file. Here is an example:
+
+```bash
+wonderful-model-v2.ckpt
+wonderful-model-v2.yaml
+```
+
+This is not needed for `diffusers` models, which come with their own
+pre-packaged configuration.
+
+### Specifying a custom VAE file for legacy checkpoints
+
+To associate a custom VAE with a legacy file, place the VAE file in
+the same directory as the corresponding `.ckpt` or
+`.safetensors` file and make sure the file has the same basename as
+the model file. Use the suffix `.vae.pt` for VAE checkpoint files, and
+`.vae.safetensors` for VAE safetensors files. There is no requirement
+that both the model and the VAE follow the same format.
+
+Example:
+
+```bash
+wonderful-model-v2.pt
+wonderful-model-v2.vae.safetensors
+```
+
 ### Converting legacy models into `diffusers`
 
 The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
@@ -206,6 +383,11 @@ The model will be converted into `diffusers` format and replace the
 previously installed version. You will again be offered the
 opportunity to delete the original `.ckpt` or `.safetensors` file.
 
+Alternatively you can use the WebUI's model manager to handle diffusers
+optimization. Select the legacy model you wish to convert, and then
+look for a button labeled "Convert to Diffusers" in the upper right of
+the window.
+
 ### Related CLI Commands
 
 There are a whole series of additional model management commands in
@@ -219,7 +401,6 @@ Interface](../features/CLI.md). These include:
 
 ### Manually editing `configs/models.yaml`
 
-
 If you are comfortable with a text editor then you may simply edit `models.yaml`
 directly.
 
@@ -276,75 +457,3 @@ this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
 After you save the modified `models.yaml` file relaunch
 `invokeai`. The new model will now be available for your use.
 
-### Installation via the WebUI
-
-To access the WebUI Model Manager, click on the button that looks like
-a cute in the upper right side of the browser screen. This will bring
-up a dialogue that lists the models you have already installed, and
-allows you to load, delete or edit them:
-
-<figure markdown>
-![model-manager](../assets/installing-models/webui-models-1.png)
-</figure>
-
-To add a new model, click on **+ Add New** and select to either a
-checkpoint/safetensors model, or a diffusers model:
-
-<figure markdown>
-![model-manager-add-new](../assets/installing-models/webui-models-2.png)
-</figure>
-
-In this example, we chose **Add Diffusers**. As shown in the figure
-below, a new dialogue prompts you to enter the name to use for the
-model, its description, and either the location of the `diffusers`
-model on disk, or its Repo ID on the HuggingFace web site. If you
-choose to enter a path to disk, the system will autocomplete for you
-as you type:
-
-<figure markdown>
-![model-manager-add-diffusers](../assets/installing-models/webui-models-3.png)
-</figure>
-
-Press **Add Model** at the bottom of the dialogue (scrolled out of
-site in the figure), and the model will be downloaded, imported, and
-registered in `models.yaml`.
-
-The **Add Checkpoint/Safetensor Model** option is similar, except that
-in this case you can choose to scan an entire folder for
-checkpoint/safetensors files to import. Simply type in the path of the
-directory and press the "Search" icon. This will display the
-`.ckpt` and `.safetensors` found inside the directory and its
-subfolders, and allow you to choose which ones to import:
-
-<figure markdown>
-![model-manager-add-checkpoint](../assets/installing-models/webui-models-4.png)
-</figure>
-
-## Model Management Startup Options
-
-The `invoke` launcher and the `invokeai` script accept a series of
-command-line arguments that modify InvokeAI's behavior when loading
-models. These can be provided on the command line, or added to the
-InvokeAI root directory's `invokeai.init` initialization file.
-
-The arguments are:
-
-* `--model <model name>` -- Start up with the indicated model loaded
-* `--ckpt_convert` -- When a checkpoint/safetensors model is loaded, convert it into a `diffusers` model in memory. This does not permanently save the converted model to disk.
-* `--autoconvert <path/to/directory>` -- Scan the indicated directory path for new checkpoint/safetensors files, convert them into `diffusers` models, and import them into InvokeAI.
-
-Here is an example of providing an argument on the command line using
-the `invoke.sh` launch script:
-
-```bash
-invoke.sh --autoconvert /home/fred/stable-diffusion-checkpoints
-```
-
-And here is what the same argument looks like in `invokeai.init`:
-
-```
---outdir="/home/fred/invokeai/outputs
---no-nsfw_checker
---autoconvert /home/fred/stable-diffusion-checkpoints
-```
-

docs/openapi3_0.yaml

@@ -1,73 +0,0 @@
-openapi: 3.0.3
-info:
-  title: Stable Diffusion
-  description: |-
-    TODO: Description Here
-
-    Some useful links:
-    - [Stable Diffusion Dream Server](https://github.com/lstein/stable-diffusion)
-
-  license:
-    name: MIT License
-    url: https://github.com/lstein/stable-diffusion/blob/main/LICENSE
-  version: 1.0.0
-servers:
-  - url: http://localhost:9090/api
-tags:
-  - name: images
-    description: Retrieve and manage generated images
-paths:
-  /images/{imageId}:
-    get:
-      tags:
-        - images
-      summary: Get image by ID
-      description: Returns a single image
-      operationId: getImageById
-      parameters:
-        - name: imageId
-          in: path
-          description: ID of image to return
-          required: true
-          schema:
-            type: string
-      responses:
-        '200':
-          description: successful operation
-          content:
-            image/png:
-              schema:
-                type: string
-                format: binary
-        '404':
-          description: Image not found
-  /intermediates/{intermediateId}/{step}:
-    get:
-      tags:
-        - images
-      summary: Get intermediate image by ID
-      description: Returns a single intermediate image
-      operationId: getIntermediateById
-      parameters:
-        - name: intermediateId
-          in: path
-          description: ID of intermediate to return
-          required: true
-          schema:
-            type: string
-        - name: step
-          in: path
-          description: The generation step of the intermediate
-          required: true
-          schema:
-            type: string
-      responses:
-        '200':
-          description: successful operation
-          content:
-            image/png:
-              schema:
-                type: string
-                format: binary
-        '404':
-          description: Intermediate not found

docs/other/CONTRIBUTORS.md

@@ -23,14 +23,16 @@ We thank them for all of their time and hard work.
 * @damian0815 - Attention Systems and Gameplay Engineer
 * @mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
 * @Netsvetaev (Artur Netsvetaev) - UI/UX Developer
-* @tildebyte - General gadfly and resident (self-appointed) know-it-all
 * @keturn - Lead for Diffusers port
 * @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
 * @jpphoto (Jonathan Pollack) - Inference and rendering engine optimization
 * @genomancer (Gregg Helt) - Model training and merging
+* @gogurtenjoyer - User support and testing
+* @whosawwhatsis - User support and testing
 
 ## **Contributions by**
 
+- [tildebyte](https://github.com/tildebyte)
 - [Sean McLellan](https://github.com/Oceanswave)
 - [Kevin Gibbons](https://github.com/bakkot)
 - [Tesseract Cat](https://github.com/TesseractCat)
@@ -78,6 +80,7 @@ We thank them for all of their time and hard work.
 - [psychedelicious](https://github.com/psychedelicious)
 - [damian0815](https://github.com/damian0815)
 - [Eugene Brodsky](https://github.com/ebr)
+- [Statcomm](https://github.com/statcomm)
 
 ## **Original CompVis Authors**
 

docs/other/TRANSLATION.md

@@ -0,0 +1,19 @@
+# Translation
+
+InvokeAI uses [Weblate](https://weblate.org) for translation. Weblate is a FOSS project providing a scalable translation service. Weblate automates the tedious parts of managing translation of a growing project, and the service is generously provided at no cost to FOSS projects like InvokeAI.
+
+## Contributing
+
+If you'd like to contribute by adding or updating a translation, please visit our [Weblate project](https://hosted.weblate.org/engage/invokeai/). You'll need to sign in with your GitHub account (a number of other accounts are supported, including Google).
+
+Once signed in, select a language and then the Web UI component. From here you can Browse and Translate strings from English to your chosen language. Zen mode offers a simpler translation experience.
+
+Your changes will be attributed to you in the automated PR process; you don't need to do anything else.
+
+## Help & Questions
+
+Please check Weblate's [documentation](https://docs.weblate.org/en/latest/index.html) or ping @psychedelicious or @blessedcoolant on Discord if you have any questions.
+
+## Thanks
+
+Thanks to the InvokeAI community for their efforts to translate the project!

docs/requirements-mkdocs.txt

@@ -1,5 +0,0 @@
-mkdocs
-mkdocs-material>=8, <9
-mkdocs-git-revision-date-localized-plugin
-mkdocs-redirects==1.2.0
-

docs/swagger-ui/index.css

@@ -1,16 +0,0 @@
-html {
-    box-sizing: border-box;
-    overflow: -moz-scrollbars-vertical;
-    overflow-y: scroll;
-}
-
-*,
-*:before,
-*:after {
-    box-sizing: inherit;
-}
-
-body {
-    margin: 0;
-    background: #fafafa;
-}

docs/swagger-ui/oauth2-redirect.html

@@ -1,79 +0,0 @@
-<!doctype html>
-<html lang="en-US">
-<head>
-    <title>Swagger UI: OAuth2 Redirect</title>
-</head>
-<body>
-<script>
-    'use strict';
-    function run () {
-        var oauth2 = window.opener.swaggerUIRedirectOauth2;
-        var sentState = oauth2.state;
-        var redirectUrl = oauth2.redirectUrl;
-        var isValid, qp, arr;
-
-        if (/code|token|error/.test(window.location.hash)) {
-            qp = window.location.hash.substring(1).replace('?', '&');
-        } else {
-            qp = location.search.substring(1);
-        }
-
-        arr = qp.split("&");
-        arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
-        qp = qp ? JSON.parse('{' + arr.join() + '}',
-                function (key, value) {
-                    return key === "" ? value : decodeURIComponent(value);
-                }
-        ) : {};
-
-        isValid = qp.state === sentState;
-
-        if ((
-          oauth2.auth.schema.get("flow") === "accessCode" ||
-          oauth2.auth.schema.get("flow") === "authorizationCode" ||
-          oauth2.auth.schema.get("flow") === "authorization_code"
-        ) && !oauth2.auth.code) {
-            if (!isValid) {
-                oauth2.errCb({
-                    authId: oauth2.auth.name,
-                    source: "auth",
-                    level: "warning",
-                    message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
-                });
-            }
-
-            if (qp.code) {
-                delete oauth2.state;
-                oauth2.auth.code = qp.code;
-                oauth2.callback({auth: oauth2.auth, redirectUrl: redirectUrl});
-            } else {
-                let oauthErrorMsg;
-                if (qp.error) {
-                    oauthErrorMsg = "["+qp.error+"]: " +
-                        (qp.error_description ? qp.error_description+ ". " : "no accessCode received from the server. ") +
-                        (qp.error_uri ? "More info: "+qp.error_uri : "");
-                }
-
-                oauth2.errCb({
-                    authId: oauth2.auth.name,
-                    source: "auth",
-                    level: "error",
-                    message: oauthErrorMsg || "[Authorization failed]: no accessCode received from the server."
-                });
-            }
-        } else {
-            oauth2.callback({auth: oauth2.auth, token: qp, isValid: isValid, redirectUrl: redirectUrl});
-        }
-        window.close();
-    }
-
-    if (document.readyState !== 'loading') {
-        run();
-    } else {
-        document.addEventListener('DOMContentLoaded', function () {
-            run();
-        });
-    }
-</script>
-</body>
-</html>

docs/swagger-ui/swagger-initializer.js

@@ -1,20 +0,0 @@
-window.onload = function() {
-  //<editor-fold desc="Changeable Configuration Block">
-
-  // the following lines will be replaced by docker/configurator, when it runs in a docker-container
-  window.ui = SwaggerUIBundle({
-    url: "openapi3_0.yaml",
-    dom_id: '#swagger-ui',
-    deepLinking: true,
-    presets: [
-      SwaggerUIBundle.presets.apis,
-      SwaggerUIStandalonePreset
-    ],
-    plugins: [
-      SwaggerUIBundle.plugins.DownloadUrl
-    ],
-    layout: "StandaloneLayout"
-  });
-
-  //</editor-fold>
-};

installer/create_installer.sh

@@ -20,10 +20,9 @@ echo Building installer for version $VERSION
 echo "Be certain that you're in the 'installer' directory before continuing."
 read -p "Press any key to continue, or CTRL-C to exit..."
 
-read -e -p "Commit and tag this repo with '${VERSION}' and '${LATEST_TAG}'? [n]: " input
+read -e -p "Tag this repo with '${VERSION}' and '${LATEST_TAG}'? [n]: " input
 RESPONSE=${input:='n'}
 if [ "$RESPONSE" == 'y' ]; then
-    git commit -a
 
     if ! git tag $VERSION ; then
 	    echo "Existing/invalid tag"
@@ -32,6 +31,8 @@ if [ "$RESPONSE" == 'y' ]; then
 
     git push origin :refs/tags/$LATEST_TAG
     git tag -fa $LATEST_TAG
+
+    echo "remember to push --tags!"
 fi
 
 # ----------------------

installer/install.bat.in

@@ -67,6 +67,8 @@ del /q .tmp1 .tmp2
 @rem -------------- Install and Configure ---------------
 
 call python .\lib\main.py
+pause
+exit /b
 
 @rem ------------------------ Subroutines ---------------
 @rem routine to do comparison of semantic version numbers

installer/install.sh.in

@@ -9,13 +9,16 @@ cd $scriptdir
 function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
 
 MINIMUM_PYTHON_VERSION=3.9.0
+MAXIMUM_PYTHON_VERSION=3.11.0
 PYTHON=""
-for candidate in python3.10 python3.9 python3 python python3.11 ; do
+for candidate in python3.10 python3.9 python3 python ; do
     if ppath=`which $candidate`; then
         python_version=$($ppath -V | awk '{ print $2 }')
         if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
-            PYTHON=$ppath
-            break
+	    if [ $(version $python_version) -lt $(version "$MAXIMUM_PYTHON_VERSION") ]; then
+		PYTHON=$ppath
+		break
+	    fi
         fi
     fi
 done
@@ -28,3 +31,4 @@ if [ -z "$PYTHON" ]; then
 fi
 
 exec $PYTHON ./lib/main.py ${@}
+read -p "Press any key to exit"

installer/lib/installer.py

@@ -144,8 +144,8 @@ class Installer:
 
             from plumbum import FG, local
 
-            pip = local[get_pip_from_venv(venv_dir)]
-            pip[ "install", "--upgrade", "pip"] & FG
+            python = local[get_python_from_venv(venv_dir)]
+            python[ "-m", "pip", "install", "--upgrade", "pip"] & FG
 
         return venv_dir
 
@@ -241,14 +241,18 @@ class InvokeAiInstance:
 
         from plumbum import FG, local
 
+        # Note that we're installing pinned versions of torch and
+        # torchvision here, which *should* correspond to what is
+        # in pyproject.toml. This is to prevent torch 2.0 from
+        # being installed and immediately uninstalled and replaced with 1.13
         pip = local[self.pip]
 
         (
             pip[
                 "install",
                 "--require-virtualenv",
-                "torch",
-                "torchvision",
+                "torch~=1.13.1",
+                "torchvision~=0.14.1",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,
@@ -336,7 +340,8 @@ class InvokeAiInstance:
             elif el in ['-y','--yes','--yes-to-all']:
                 new_argv.append(el)
         sys.argv = new_argv
-
+        
+        import requests  # to catch download exceptions
         from messages import introduction
 
         introduction()
@@ -346,7 +351,21 @@ class InvokeAiInstance:
         # NOTE: currently the config script does its own arg parsing! this means the command-line switches
         # from the installer will also automatically propagate down to the config script.
         # this may change in the future with config refactoring!
-        invokeai_configure.main()
+        succeeded = False
+        try:
+            invokeai_configure.main()
+            succeeded = True
+        except requests.exceptions.ConnectionError as e:
+            print(f'\nA network error was encountered during configuration and download: {str(e)}')
+        except OSError as e:
+            print(f'\nAn OS error was encountered during configuration and download: {str(e)}')
+        except Exception as e:
+            print(f'\nA problem was encountered during the configuration and download steps: {str(e)}')
+        finally:
+            if not succeeded:
+                print('To try again, find the "invokeai" directory, run the script "invoke.sh" or "invoke.bat"')
+                print('and choose option 7 to fix a broken install, optionally followed by option 5 to install models.')
+                print('Alternatively you can relaunch the installer.')
 
     def install_user_scripts(self):
         """
@@ -364,6 +383,9 @@ class InvokeAiInstance:
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
 
+        if OS == "Linux":
+            shutil.copy(Path(__file__).parents[1] / "templates" / "dialogrc", self.runtime / '.dialogrc')
+
     def update(self):
         pass
 
@@ -390,6 +412,22 @@ def get_pip_from_venv(venv_path: Path) -> str:
     return str(venv_path.expanduser().resolve() / pip)
 
 
+def get_python_from_venv(venv_path: Path) -> str:
+    """
+    Given a path to a virtual environment, get the absolute path to the `python` executable
+    in a cross-platform fashion. Does not validate that the python executable
+    actually exists in the virtualenv.
+
+    :param venv_path: Path to the virtual environment
+    :type venv_path: Path
+    :return: Absolute path to the python executable
+    :rtype: str
+    """
+
+    python = "Scripts\python.exe" if OS == "Windows" else "bin/python"
+    return str(venv_path.expanduser().resolve() / python)
+
+
 def set_sys_path(venv_path: Path) -> None:
     """
     Given a path to a virtual environment, set the sys.path, in a cross-platform fashion,

installer/templates/dialogrc

@@ -0,0 +1,27 @@
+# Screen 
+use_shadow = OFF
+use_colors = ON
+screen_color = (BLACK, BLACK, ON)
+
+# Box 
+dialog_color = (YELLOW, BLACK , ON) 
+title_color = (YELLOW, BLACK, ON) 
+border_color = (YELLOW, BLACK, OFF) 
+border2_color = (YELLOW, BLACK, OFF) 
+
+# Button 
+button_active_color = (RED, BLACK, OFF) 
+button_inactive_color = (YELLOW, BLACK, OFF) 
+button_label_active_color = (YELLOW,BLACK,ON) 
+button_label_inactive_color = (YELLOW,BLACK,ON)
+ 
+# Menu box 
+menubox_color = (BLACK, BLACK, ON) 
+menubox_border_color = (YELLOW, BLACK, OFF) 
+menubox_border2_color = (YELLOW, BLACK, OFF) 
+
+# Menu window 
+item_color = (YELLOW, BLACK, OFF) 
+item_selected_color = (BLACK, YELLOW, OFF) 
+tag_key_color = (YELLOW, BLACK, OFF) 
+tag_key_selected_color = (BLACK, YELLOW, OFF)

installer/templates/invoke.bat.in

@@ -6,15 +6,20 @@ setlocal
 call .venv\Scripts\activate.bat
 set INVOKEAI_ROOT=.
 
+:start
 echo Do you want to generate images using the
-echo 1. command-line
+echo 1. command-line interface
 echo 2. browser-based UI
 echo 3. run textual inversion training
 echo 4. merge models (diffusers type only)
-echo 5. re-run the configure script to download new models
-echo 6. open the developer console
-echo 7. command-line help
-set /P restore="Please enter 1, 2, 3, 4, 5, 6 or 7: [2] "
+echo 5. download and install models
+echo 6. change InvokeAI startup options
+echo 7. re-run the configure script to fix a broken install
+echo 8. open the developer console
+echo 9. update InvokeAI
+echo 10. command-line help
+echo Q - quit
+set /P restore="Please enter 1-10, Q: [2] "
 if not defined restore set restore=2
 IF /I "%restore%" == "1" (
     echo Starting the InvokeAI command-line..
@@ -24,14 +29,20 @@ IF /I "%restore%" == "1" (
     python .venv\Scripts\invokeai.exe --web %*
 ) ELSE IF /I "%restore%" == "3" (
     echo Starting textual inversion training..
-    python .venv\Scripts\invokeai-ti.exe --gui %*
+    python .venv\Scripts\invokeai-ti.exe --gui
 ) ELSE IF /I "%restore%" == "4" (
     echo Starting model merging script..
-    python .venv\Scripts\invokeai-merge.exe --gui %*
+    python .venv\Scripts\invokeai-merge.exe --gui
 ) ELSE IF /I "%restore%" == "5" (
-    echo Running invokeai-configure...
-    python .venv\Scripts\invokeai-configure.exe %*
+    echo Running invokeai-model-install...
+    python .venv\Scripts\invokeai-model-install.exe
 ) ELSE IF /I "%restore%" == "6" (
+    echo Running invokeai-configure...
+    python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
+) ELSE IF /I "%restore%" == "7" (
+    echo Running invokeai-configure...
+    python .venv\Scripts\invokeai-configure.exe --yes --default_only
+) ELSE IF /I "%restore%" == "8" (
     echo Developer Console
     echo Python command is:
     where python
@@ -43,14 +54,27 @@ IF /I "%restore%" == "1" (
     echo *************************
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
-) ELSE IF /I "%restore%" == "7" (
+) ELSE IF /I "%restore%" == "9" (
+   echo Running invokeai-update...
+   python .venv\Scripts\invokeai-update.exe %*
+) ELSE IF /I "%restore%" == "10" (
     echo Displaying command line help...
     python .venv\Scripts\invokeai.exe --help %*
     pause
     exit /b
+) ELSE IF /I "%restore%" == "q" (
+    echo Goodbye!
+    goto ending
 ) ELSE (
     echo Invalid selection
     pause
     exit /b
 )
+goto start
+
 endlocal
+pause
+
+:ending
+exit /b
+

installer/templates/invoke.sh.in

@@ -1,5 +1,10 @@
 #!/bin/bash
 
+# MIT License
+
+# Coauthored by Lincoln Stein, Eugene Brodsky and Joshua Kimsey
+# Copyright 2023, The InvokeAI Development Team
+
 ####
 # This launch script assumes that:
 # 1. it is located in the runtime directory,
@@ -11,65 +16,168 @@
 
 set -eu
 
-# ensure we're in the correct folder in case user's CWD is somewhere else
+# Ensure we're in the correct folder in case user's CWD is somewhere else
 scriptdir=$(dirname "$0")
 cd "$scriptdir"
 
 . .venv/bin/activate
 
 export INVOKEAI_ROOT="$scriptdir"
+PARAMS=$@
 
-# set required env var for torch on mac MPS
+# Check to see if dialog is installed (it seems to be fairly standard, but good to check regardless) and if the user has passed the --no-tui argument to disable the dialog TUI
+tui=true
+if command -v dialog &>/dev/null; then
+    # This must use $@ to properly loop through the arguments passed by the user
+    for arg in "$@"; do
+        if [ "$arg" == "--no-tui" ]; then
+            tui=false
+            # Remove the --no-tui argument to avoid errors later on when passing arguments to InvokeAI
+            PARAMS=$(echo "$PARAMS" | sed 's/--no-tui//')
+            break
+        fi
+    done
+else
+    tui=false
+fi
+
+# Set required env var for torch on mac MPS
 if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
-if [ "$0" != "bash" ]; then
-    echo "Do you want to generate images using the"
-    echo "1. command-line"
-    echo "2. browser-based UI"
-    echo "3. run textual inversion training"
-    echo "4. merge models (diffusers type only)"
-    echo "5. open the developer console"
-    echo "6. re-run the configure script to download new models"
-    echo "7. command-line help "
-    echo ""
-    read -p "Please enter 1, 2, 3, 4, 5, 6 or 7: [2] " yn
-    choice=${yn:='2'}
-    case $choice in
-        1)
-            echo "Starting the InvokeAI command-line..."
-            exec invokeai $@
-            ;;
-        2)
-            echo "Starting the InvokeAI browser-based UI..."
-            exec invokeai --web $@
-            ;;
-        3)
-            echo "Starting Textual Inversion:"
-            exec invokeai-ti --gui $@
-            ;;
-        4)
-            echo "Merging Models:"
-            exec invokeai-merge --gui $@
-            ;;
-        5)
-            echo "Developer Console:"
-            file_name=$(basename "${BASH_SOURCE[0]}")
-            bash --init-file "$file_name"
-            ;;
-        6)
-            exec invokeai-configure --root ${INVOKEAI_ROOT}
-            ;;
-        7)
-            exec invokeai --help
-            ;;
-        *)
-            echo "Invalid selection"
-            exit;;
+# Primary function for the case statement to determine user input
+do_choice() {
+    case $1 in
+    1)
+        clear
+        printf "Generate images with a browser-based interface\n"
+        invokeai --web $PARAMS
+        ;;
+    2)
+        clear
+        printf "Generate images using a command-line interface\n"
+        invokeai $PARAMS
+        ;;
+    3)
+        clear
+        printf "Textual inversion training\n"
+        invokeai-ti --gui $PARAMS
+        ;;
+    4)
+        clear
+        printf "Merge models (diffusers type only)\n"
+        invokeai-merge --gui $PARAMS
+        ;;
+    5)
+        clear
+        printf "Download and install models\n"
+        invokeai-model-install --root ${INVOKEAI_ROOT}
+        ;;
+    6)
+        clear
+        printf "Change InvokeAI startup options\n"
+        invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
+        ;;
+    7)
+        clear
+        printf "Re-run the configure script to fix a broken install\n"
+        invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
+        ;;
+    8)
+        clear
+        printf "Open the developer console\n"
+        file_name=$(basename "${BASH_SOURCE[0]}")
+        bash --init-file "$file_name"
+        ;;
+    9)
+        clear
+        printf "Update InvokeAI\n"
+        invokeai-update
+        ;;
+    10)
+        clear
+        printf "Command-line help\n"
+        invokeai --help
+        ;;
+    "HELP 1")
+        clear
+        printf "Command-line help\n"
+        invokeai --help
+        ;;
+    *)
+        clear
+        printf "Exiting...\n"
+        exit
+        ;;
     esac
+    clear
+}
+
+# Dialog-based TUI for launcing Invoke functions
+do_dialog() {
+    options=(
+        1 "Generate images with a browser-based interface"
+        2 "Generate images 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"
+        8 "Open the developer console"
+        9 "Update InvokeAI")
+
+    choice=$(dialog --clear \
+        --backtitle "\Zb\Zu\Z3InvokeAI" \
+        --colors \
+        --title "What would you like to run?" \
+        --ok-label "Run" \
+        --cancel-label "Exit" \
+        --help-button \
+        --help-label "CLI Help" \
+        --menu "Select an option:" \
+        0 0 0 \
+        "${options[@]}" \
+        2>&1 >/dev/tty) || clear
+    do_choice "$choice"
+    clear
+}
+
+# Command-line interface for launching Invoke functions
+do_line_input() {
+    clear
+    printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
+    printf "Do you want to generate images using the\n"
+    printf "1: Browser-based UI\n"
+    printf "2: Command-line interface\n"
+    printf "3: Run textual inversion training\n"
+    printf "4: Merge models (diffusers type only)\n"
+    printf "5: Download and install models\n"
+    printf "6: Change InvokeAI startup options\n"
+    printf "7: Re-run the configure script to fix a broken install\n"
+    printf "8: Open the developer console\n"
+    printf "9: Update InvokeAI\n"
+    printf "10: Command-line help\n"
+    printf "Q: Quit\n\n"
+    read -p "Please enter 1-10, Q: [1] " yn
+    choice=${yn:='1'}
+    do_choice $choice
+    clear
+}
+
+# Main IF statement for launching Invoke with either the TUI or CLI, and for checking if the user is in the developer console
+if [ "$0" != "bash" ]; then
+    while true; do
+        if $tui; then
+            # .dialogrc must be located in the same directory as the invoke.sh script
+            export DIALOGRC="./.dialogrc"
+            do_dialog
+        else
+            do_line_input
+        fi
+    done
 else # in developer console
     python --version
-    echo "Press ^D to exit"
+    printf "Press ^D to exit\n"
     export PS1="(InvokeAI) \u@\h \w> "
 fi

invokeai/backend/invoke_ai_web_server.py

@@ -25,12 +25,23 @@ from invokeai.backend.modules.parameters import parameters_to_command
 import invokeai.frontend.dist as frontend
 from ldm.generate import Generate
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.conditioning import get_tokens_for_prompt, get_prompt_structure
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.invoke.conditioning import (
+    get_tokens_for_prompt_object,
+    get_prompt_structure,
+    split_weighted_subprompts,
+)
 from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
 from ldm.invoke.generator.inpaint import infill_methods
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import (
+    Globals,
+    global_converted_ckpts_dir,
+    global_models_dir,
+)
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
-from ldm.invoke.prompt_parser import split_weighted_subprompts, Blend
+from compel.prompt_parser import Blend
+from ldm.invoke.merge_diffusers import merge_diffusion_models
+from ldm.modules.lora_manager import LoraManager
 
 # Loading Arguments
 opt = Args()
@@ -43,7 +54,8 @@ if not os.path.isabs(args.outdir):
 
 # normalize the config directory relative to root
 if not os.path.isabs(opt.conf):
-    opt.conf = os.path.normpath(os.path.join(Globals.root,opt.conf))
+    opt.conf = os.path.normpath(os.path.join(Globals.root, opt.conf))
+
 
 class InvokeAIWebServer:
     def __init__(self, generate: Generate, gfpgan, codeformer, esrgan) -> None:
@@ -203,11 +215,7 @@ class InvokeAIWebServer:
                 return make_response(response, 200)
 
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
                 return make_response("Error uploading file", 500)
 
         self.load_socketio_listeners(self.socketio)
@@ -223,7 +231,7 @@ class InvokeAIWebServer:
                     server="flask_socketio",
                     width=1600,
                     height=1000,
-                    port=self.port
+                    port=self.port,
                 ).run()
             except KeyboardInterrupt:
                 import sys
@@ -290,41 +298,41 @@ class InvokeAIWebServer:
     def load_socketio_listeners(self, socketio):
         @socketio.on("requestSystemConfig")
         def handle_request_capabilities():
-            print(f">> System config requested")
+            print(">> System config requested")
             config = self.get_system_config()
             config["model_list"] = self.generate.model_manager.list_models()
             config["infill_methods"] = infill_methods()
             socketio.emit("systemConfig", config)
 
-        @socketio.on('searchForModels')
+        @socketio.on("searchForModels")
         def handle_search_models(search_folder: str):
             try:
                 if not search_folder:
                     socketio.emit(
-                    "foundModels",
-                    {'search_folder': None, 'found_models': None},
-                )
+                        "foundModels",
+                        {"search_folder": None, "found_models": None},
+                    )
                 else:
-                    search_folder, found_models = self.generate.model_manager.search_models(search_folder)
+                    (
+                        search_folder,
+                        found_models,
+                    ) = self.generate.model_manager.search_models(search_folder)
                     socketio.emit(
                         "foundModels",
-                        {'search_folder': search_folder, 'found_models': found_models},
+                        {"search_folder": search_folder, "found_models": found_models},
                     )
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
+                self.handle_exceptions(e)
                 print("\n")
 
         @socketio.on("addNewModel")
         def handle_add_model(new_model_config: dict):
             try:
-                model_name = new_model_config['name']
-                del new_model_config['name']
+                model_name = new_model_config["name"]
+                del new_model_config["name"]
                 model_attributes = new_model_config
-                if len(model_attributes['vae']) == 0:
-                    del model_attributes['vae']
+                if len(model_attributes["vae"]) == 0:
+                    del model_attributes["vae"]
                 update = False
                 current_model_list = self.generate.model_manager.list_models()
                 if model_name in current_model_list:
@@ -333,22 +341,24 @@ class InvokeAIWebServer:
                 print(f">> Adding New Model: {model_name}")
 
                 self.generate.model_manager.add_model(
-                    model_name=model_name, model_attributes=model_attributes, clobber=True)
+                    model_name=model_name,
+                    model_attributes=model_attributes,
+                    clobber=True,
+                )
                 self.generate.model_manager.commit(opt.conf)
 
                 new_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "newModelAdded",
-                    {"new_model_name": model_name,
-                     "model_list": new_model_list, 'update': update},
+                    {
+                        "new_model_name": model_name,
+                        "model_list": new_model_list,
+                        "update": update,
+                    },
                 )
                 print(f">> New Model Added: {model_name}")
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("deleteModel")
         def handle_delete_model(model_name: str):
@@ -359,16 +369,14 @@ class InvokeAIWebServer:
                 updated_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "modelDeleted",
-                    {"deleted_model_name": model_name,
-                     "model_list": updated_model_list},
+                    {
+                        "deleted_model_name": model_name,
+                        "model_list": updated_model_list,
+                    },
                 )
                 print(f">> Model Deleted: {model_name}")
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("requestModelChange")
         def handle_set_model(model_name: str):
@@ -387,11 +395,154 @@ class InvokeAIWebServer:
                         {"model_name": model_name, "model_list": model_list},
                     )
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
+                self.handle_exceptions(e)
 
-                traceback.print_exc()
-                print("\n")
+        @socketio.on("convertToDiffusers")
+        def convert_to_diffusers(model_to_convert: dict):
+            try:
+                if model_info := self.generate.model_manager.model_info(
+                    model_name=model_to_convert["model_name"]
+                ):
+                    if "weights" in model_info:
+                        ckpt_path = Path(model_info["weights"])
+                        original_config_file = Path(model_info["config"])
+                        model_name = model_to_convert["model_name"]
+                        model_description = model_info["description"]
+                    else:
+                        self.socketio.emit(
+                            "error", {"message": "Model is not a valid checkpoint file"}
+                        )
+                else:
+                    self.socketio.emit(
+                        "error", {"message": "Could not retrieve model info."}
+                    )
+
+                if not ckpt_path.is_absolute():
+                    ckpt_path = Path(Globals.root, ckpt_path)
+
+                if original_config_file and not original_config_file.is_absolute():
+                    original_config_file = Path(Globals.root, original_config_file)
+
+                diffusers_path = Path(
+                    ckpt_path.parent.absolute(), f"{model_name}_diffusers"
+                )
+
+                if model_to_convert["save_location"] == "root":
+                    diffusers_path = Path(
+                        global_converted_ckpts_dir(), f"{model_name}_diffusers"
+                    )
+
+                if (
+                    model_to_convert["save_location"] == "custom"
+                    and model_to_convert["custom_location"] is not None
+                ):
+                    diffusers_path = Path(
+                        model_to_convert["custom_location"], f"{model_name}_diffusers"
+                    )
+
+                if diffusers_path.exists():
+                    shutil.rmtree(diffusers_path)
+
+                self.generate.model_manager.convert_and_import(
+                    ckpt_path,
+                    diffusers_path,
+                    model_name=model_name,
+                    model_description=model_description,
+                    vae=None,
+                    original_config_file=original_config_file,
+                    commit_to_conf=opt.conf,
+                )
+
+                new_model_list = self.generate.model_manager.list_models()
+                socketio.emit(
+                    "modelConverted",
+                    {
+                        "new_model_name": model_name,
+                        "model_list": new_model_list,
+                        "update": True,
+                    },
+                )
+                print(f">> Model Converted: {model_name}")
+            except Exception as e:
+                self.handle_exceptions(e)
+
+        @socketio.on("mergeDiffusersModels")
+        def merge_diffusers_models(model_merge_info: dict):
+            try:
+                models_to_merge = model_merge_info["models_to_merge"]
+                model_ids_or_paths = [
+                    self.generate.model_manager.model_name_or_path(x)
+                    for x in models_to_merge
+                ]
+                merged_pipe = merge_diffusion_models(
+                    model_ids_or_paths,
+                    model_merge_info["alpha"],
+                    model_merge_info["interp"],
+                    model_merge_info["force"],
+                )
+
+                dump_path = global_models_dir() / "merged_models"
+                if model_merge_info["model_merge_save_path"] is not None:
+                    dump_path = Path(model_merge_info["model_merge_save_path"])
+
+                os.makedirs(dump_path, exist_ok=True)
+                dump_path = dump_path / model_merge_info["merged_model_name"]
+                merged_pipe.save_pretrained(dump_path, safe_serialization=1)
+
+                merged_model_config = dict(
+                    model_name=model_merge_info["merged_model_name"],
+                    description=f'Merge of models {", ".join(models_to_merge)}',
+                    commit_to_conf=opt.conf,
+                )
+
+                if vae := self.generate.model_manager.config[models_to_merge[0]].get(
+                    "vae", None
+                ):
+                    print(f">> Using configured VAE assigned to {models_to_merge[0]}")
+                    merged_model_config.update(vae=vae)
+
+                self.generate.model_manager.import_diffuser_model(
+                    dump_path, **merged_model_config
+                )
+                new_model_list = self.generate.model_manager.list_models()
+
+                socketio.emit(
+                    "modelsMerged",
+                    {
+                        "merged_models": models_to_merge,
+                        "merged_model_name": model_merge_info["merged_model_name"],
+                        "model_list": new_model_list,
+                        "update": True,
+                    },
+                )
+                print(f">> Models Merged: {models_to_merge}")
+                print(f">> New Model Added: {model_merge_info['merged_model_name']}")
+            except Exception as e:
+                self.handle_exceptions(e)
+
+        @socketio.on("getLoraModels")
+        def get_lora_models():
+            try:
+                model = self.generate.model
+                lora_mgr = LoraManager(model)
+                loras = lora_mgr.list_compatible_loras()
+                found_loras = []
+                for lora in sorted(loras, key=str.casefold):
+                    found_loras.append({"name":lora,"location":str(loras[lora])})
+                socketio.emit("foundLoras", found_loras)
+            except Exception as e:
+                self.handle_exceptions(e)
+
+        @socketio.on("getTextualInversionTriggers")
+        def get_ti_triggers():
+            try:
+                local_triggers = self.generate.model.textual_inversion_manager.get_all_trigger_strings()
+                locals = [{'name': x} for x in sorted(local_triggers, key=str.casefold)]
+                concepts = HuggingFaceConceptsLibrary().list_concepts(minimum_likes=5)
+                concepts = [{'name': f'<{x}>'} for x in sorted(concepts, key=str.casefold) if f'<{x}>' not in local_triggers]
+                socketio.emit("foundTextualInversionTriggers", {'local_triggers': locals, 'huggingface_concepts': concepts})
+            except Exception as e:
+                self.handle_exceptions(e)
 
         @socketio.on("requestEmptyTempFolder")
         def empty_temp_folder():
@@ -406,16 +557,14 @@ class InvokeAIWebServer:
                         )
                         os.remove(thumbnail_path)
                     except Exception as e:
-                        socketio.emit("error", {"message": f"Unable to delete {f}: {str(e)}"})
+                        socketio.emit(
+                            "error", {"message": f"Unable to delete {f}: {str(e)}"}
+                        )
                         pass
 
                 socketio.emit("tempFolderEmptied")
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("requestSaveStagingAreaImageToGallery")
         def save_temp_image_to_gallery(url):
@@ -455,11 +604,7 @@ class InvokeAIWebServer:
                 )
 
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("requestLatestImages")
         def handle_request_latest_images(category, latest_mtime):
@@ -515,7 +660,9 @@ class InvokeAIWebServer:
                             }
                         )
                     except Exception as e:
-                        socketio.emit("error", {"message": f"Unable to load {path}: {str(e)}"})
+                        socketio.emit(
+                            "error", {"message": f"Unable to load {path}: {str(e)}"}
+                        )
                         pass
 
                 socketio.emit(
@@ -523,11 +670,7 @@ class InvokeAIWebServer:
                     {"images": image_array, "category": category},
                 )
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("requestImages")
         def handle_request_images(category, earliest_mtime=None):
@@ -588,7 +731,9 @@ class InvokeAIWebServer:
                         )
                     except Exception as e:
                         print(f">> Unable to load {path}")
-                        socketio.emit("error", {"message": f"Unable to load {path}: {str(e)}"})
+                        socketio.emit(
+                            "error", {"message": f"Unable to load {path}: {str(e)}"}
+                        )
                         pass
 
                 socketio.emit(
@@ -600,11 +745,7 @@ class InvokeAIWebServer:
                     },
                 )
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("generateImage")
         def handle_generate_image_event(
@@ -626,9 +767,9 @@ class InvokeAIWebServer:
                         printable_parameters["init_mask"][:64] + "..."
                     )
 
-                print(f'\n>> Image Generation Parameters:\n\n{printable_parameters}\n')
-                print(f'>> ESRGAN Parameters: {esrgan_parameters}')
-                print(f'>> Facetool Parameters: {facetool_parameters}')
+                print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n")
+                print(f">> ESRGAN Parameters: {esrgan_parameters}")
+                print(f">> Facetool Parameters: {facetool_parameters}")
 
                 self.generate_images(
                     generation_parameters,
@@ -636,11 +777,7 @@ class InvokeAIWebServer:
                     facetool_parameters,
                 )
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("runPostprocessing")
         def handle_run_postprocessing(original_image, postprocessing_parameters):
@@ -662,16 +799,16 @@ class InvokeAIWebServer:
 
                 try:
                     seed = original_image["metadata"]["image"]["seed"]
-                except (KeyError) as e:
+                except KeyError:
                     seed = "unknown_seed"
                     pass
 
                 if postprocessing_parameters["type"] == "esrgan":
-                    progress.set_current_status("common:statusUpscalingESRGAN")
+                    progress.set_current_status("common.statusUpscalingESRGAN")
                 elif postprocessing_parameters["type"] == "gfpgan":
-                    progress.set_current_status("common:statusRestoringFacesGFPGAN")
+                    progress.set_current_status("common.statusRestoringFacesGFPGAN")
                 elif postprocessing_parameters["type"] == "codeformer":
-                    progress.set_current_status("common:statusRestoringFacesCodeFormer")
+                    progress.set_current_status("common.statusRestoringFacesCodeFormer")
 
                 socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
@@ -680,7 +817,8 @@ class InvokeAIWebServer:
                     image = self.esrgan.process(
                         image=image,
                         upsampler_scale=postprocessing_parameters["upscale"][0],
-                        strength=postprocessing_parameters["upscale"][1],
+                        denoise_str=postprocessing_parameters["upscale"][1],
+                        strength=postprocessing_parameters["upscale"][2],
                         seed=seed,
                     )
                 elif postprocessing_parameters["type"] == "gfpgan":
@@ -704,7 +842,7 @@ class InvokeAIWebServer:
                         f'{postprocessing_parameters["type"]} is not a valid postprocessing type'
                     )
 
-                progress.set_current_status("common:statusSavingImage")
+                progress.set_current_status("common.statusSavingImage")
                 socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
@@ -751,15 +889,11 @@ class InvokeAIWebServer:
                     },
                 )
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
         @socketio.on("cancel")
         def handle_cancel():
-            print(f">> Cancel processing requested")
+            print(">> Cancel processing requested")
             self.canceled.set()
 
         # TODO: I think this needs a safety mechanism.
@@ -780,11 +914,7 @@ class InvokeAIWebServer:
                     {"url": url, "uuid": uuid, "category": category},
                 )
             except Exception as e:
-                self.socketio.emit("error", {"message": (str(e))})
-                print("\n")
-
-                traceback.print_exc()
-                print("\n")
+                self.handle_exceptions(e)
 
     # App Functions
     def get_system_config(self):
@@ -841,9 +971,6 @@ class InvokeAIWebServer:
                 So we need to convert each into a PIL Image.
                 """
 
-                truncated_outpaint_image_b64 = generation_parameters["init_img"][:64]
-                truncated_outpaint_mask_b64 = generation_parameters["init_mask"][:64]
-
                 init_img_url = generation_parameters["init_img"]
 
                 original_bounding_box = generation_parameters["bounding_box"].copy()
@@ -923,7 +1050,9 @@ class InvokeAIWebServer:
             elif generation_parameters["generation_mode"] == "img2img":
                 init_img_url = generation_parameters["init_img"]
                 init_img_path = self.get_image_path_from_url(init_img_url)
-                generation_parameters["init_img"] = Image.open(init_img_path).convert('RGB')
+                generation_parameters["init_img"] = Image.open(init_img_path).convert(
+                    "RGB"
+                )
 
             def image_progress(sample, step):
                 if self.canceled.is_set():
@@ -934,10 +1063,10 @@ class InvokeAIWebServer:
                 nonlocal progress
 
                 generation_messages = {
-                    "txt2img": "common:statusGeneratingTextToImage",
-                    "img2img": "common:statusGeneratingImageToImage",
-                    "inpainting": "common:statusGeneratingInpainting",
-                    "outpainting": "common:statusGeneratingOutpainting",
+                    "txt2img": "common.statusGeneratingTextToImage",
+                    "img2img": "common.statusGeneratingImageToImage",
+                    "inpainting": "common.statusGeneratingInpainting",
+                    "outpainting": "common.statusGeneratingOutpainting",
                 }
 
                 progress.set_current_step(step + 1)
@@ -982,7 +1111,6 @@ class InvokeAIWebServer:
                         },
                     )
 
-
                 if generation_parameters["progress_latents"]:
                     image = self.generate.sample_to_lowres_estimated_image(sample)
                     (width, height) = image.size
@@ -1015,7 +1143,6 @@ class InvokeAIWebServer:
                 nonlocal facetool_parameters
                 nonlocal progress
 
-                step_index = 1
                 nonlocal prior_variations
 
                 """
@@ -1029,7 +1156,7 @@ class InvokeAIWebServer:
                         **generation_parameters["bounding_box"],
                     )
 
-                progress.set_current_status("common:statusGenerationComplete")
+                progress.set_current_status("common.statusGenerationComplete")
 
                 self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
@@ -1056,7 +1183,7 @@ class InvokeAIWebServer:
                     raise CanceledException
 
                 if esrgan_parameters:
-                    progress.set_current_status("common:statusUpscaling")
+                    progress.set_current_status("common.statusUpscaling")
                     progress.set_current_status_has_steps(False)
                     self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                     eventlet.sleep(0)
@@ -1064,6 +1191,7 @@ class InvokeAIWebServer:
                     image = self.esrgan.process(
                         image=image,
                         upsampler_scale=esrgan_parameters["level"],
+                        denoise_str=esrgan_parameters["denoise_str"],
                         strength=esrgan_parameters["strength"],
                         seed=seed,
                     )
@@ -1071,6 +1199,7 @@ class InvokeAIWebServer:
                     postprocessing = True
                     all_parameters["upscale"] = [
                         esrgan_parameters["level"],
+                        esrgan_parameters["denoise_str"],
                         esrgan_parameters["strength"],
                     ]
 
@@ -1079,9 +1208,11 @@ class InvokeAIWebServer:
 
                 if facetool_parameters:
                     if facetool_parameters["type"] == "gfpgan":
-                        progress.set_current_status("common:statusRestoringFacesGFPGAN")
+                        progress.set_current_status("common.statusRestoringFacesGFPGAN")
                     elif facetool_parameters["type"] == "codeformer":
-                        progress.set_current_status("common:statusRestoringFacesCodeFormer")
+                        progress.set_current_status(
+                            "common.statusRestoringFacesCodeFormer"
+                        )
 
                     progress.set_current_status_has_steps(False)
                     self.socketio.emit("progressUpdate", progress.to_formatted_dict())
@@ -1113,7 +1244,7 @@ class InvokeAIWebServer:
                     ]
                     all_parameters["facetool_type"] = facetool_parameters["type"]
 
-                progress.set_current_status("common:statusSavingImage")
+                progress.set_current_status("common.statusSavingImage")
                 self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
@@ -1125,7 +1256,8 @@ class InvokeAIWebServer:
                 )
 
                 if "init_mask" in all_parameters:
-                    all_parameters["init_mask"] = ""  # TODO: store the mask in metadata
+                    # TODO: store the mask in metadata
+                    all_parameters["init_mask"] = ""
 
                 if generation_parameters["generation_mode"] == "unifiedCanvas":
                     all_parameters["bounding_box"] = original_bounding_box
@@ -1160,7 +1292,7 @@ class InvokeAIWebServer:
 
                 if progress.total_iterations > progress.current_iteration:
                     progress.set_current_step(1)
-                    progress.set_current_status("common:statusIterationComplete")
+                    progress.set_current_status("common.statusIterationComplete")
                     progress.set_current_status_has_steps(False)
                 else:
                     progress.mark_complete()
@@ -1169,10 +1301,18 @@ class InvokeAIWebServer:
                 eventlet.sleep(0)
 
                 parsed_prompt, _ = get_prompt_structure(generation_parameters["prompt"])
-                tokens = None if type(parsed_prompt) is Blend else \
-                    get_tokens_for_prompt(self.generate.model, parsed_prompt)
-                attention_maps_image_base64_url = None if attention_maps_image is None \
+                tokens = (
+                    None
+                    if type(parsed_prompt) is Blend
+                    else get_tokens_for_prompt_object(
+                        self.generate.model.tokenizer, parsed_prompt
+                    )
+                )
+                attention_maps_image_base64_url = (
+                    None
+                    if attention_maps_image is None
                     else image_to_dataURL(attention_maps_image)
+                )
 
                 self.socketio.emit(
                     "generationResult",
@@ -1204,7 +1344,7 @@ class InvokeAIWebServer:
             self.generate.prompt2image(
                 **generation_parameters,
                 step_callback=diffusers_step_callback_adapter,
-                image_callback=image_done
+                image_callback=image_done,
             )
 
         except KeyboardInterrupt:
@@ -1221,11 +1361,7 @@ class InvokeAIWebServer:
             # Clear the CUDA cache on an exception
             self.empty_cuda_cache()
             print(e)
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def empty_cuda_cache(self):
         if self.generate.device.type == "cuda":
@@ -1287,7 +1423,8 @@ class InvokeAIWebServer:
                     {
                         "type": "esrgan",
                         "scale": int(parameters["upscale"][0]),
-                        "strength": float(parameters["upscale"][1]),
+                        "denoise_str": int(parameters["upscale"][1]),
+                        "strength": float(parameters["upscale"][2]),
                     }
                 )
 
@@ -1298,13 +1435,6 @@ class InvokeAIWebServer:
             # semantic drift
             rfc_dict["sampler"] = parameters["sampler_name"]
 
-            # display weighted subprompts (liable to change)
-            subprompts = split_weighted_subprompts(
-                parameters["prompt"], skip_normalize=True
-            )
-            subprompts = [{"prompt": x[0], "weight": x[1]} for x in subprompts]
-            rfc_dict["prompt"] = subprompts
-
             # 'variations' should always exist and be an array, empty or consisting of {'seed': seed, 'weight': weight} pairs
             variations = []
 
@@ -1331,11 +1461,7 @@ class InvokeAIWebServer:
             return metadata
 
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def parameters_to_post_processed_image_metadata(
         self, parameters, original_image_path
@@ -1361,7 +1487,8 @@ class InvokeAIWebServer:
             if parameters["type"] == "esrgan":
                 postprocessing_metadata["type"] = "esrgan"
                 postprocessing_metadata["scale"] = parameters["upscale"][0]
-                postprocessing_metadata["strength"] = parameters["upscale"][1]
+                postprocessing_metadata["denoise_str"] = parameters["upscale"][1]
+                postprocessing_metadata["strength"] = parameters["upscale"][2]
             elif parameters["type"] == "gfpgan":
                 postprocessing_metadata["type"] = "gfpgan"
                 postprocessing_metadata["strength"] = parameters["facetool_strength"]
@@ -1385,11 +1512,7 @@ class InvokeAIWebServer:
             return current_metadata
 
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def save_result_image(
         self,
@@ -1419,7 +1542,7 @@ class InvokeAIWebServer:
             if step_index:
                 filename += f".{step_index}"
             if postprocessing:
-                filename += f".postprocessed"
+                filename += ".postprocessed"
 
             filename += ".png"
 
@@ -1433,11 +1556,7 @@ class InvokeAIWebServer:
             return os.path.abspath(path)
 
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def make_unique_init_image_filename(self, name):
         try:
@@ -1446,11 +1565,7 @@ class InvokeAIWebServer:
             name = f"{split[0]}.{uuid}{split[1]}"
             return name
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def calculate_real_steps(self, steps, strength, has_init_image):
         import math
@@ -1465,11 +1580,7 @@ class InvokeAIWebServer:
                 file.writelines(message)
 
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def get_image_path_from_url(self, url):
         """Given a url to an image used by the client, returns the absolute file path to that image"""
@@ -1499,11 +1610,7 @@ class InvokeAIWebServer:
                     os.path.join(self.result_path, os.path.basename(url))
                 )
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def get_url_from_image_path(self, path):
         """Given an absolute file path to an image, returns the URL that the client can use to load the image"""
@@ -1521,11 +1628,7 @@ class InvokeAIWebServer:
             else:
                 return os.path.join(self.result_url, os.path.basename(path))
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
-
-            traceback.print_exc()
-            print("\n")
+            self.handle_exceptions(e)
 
     def save_file_unique_uuid_name(self, bytes, name, path):
         try:
@@ -1544,11 +1647,13 @@ class InvokeAIWebServer:
 
             return file_path
         except Exception as e:
-            self.socketio.emit("error", {"message": (str(e))})
-            print("\n")
+            self.handle_exceptions(e)
 
-            traceback.print_exc()
-            print("\n")
+    def handle_exceptions(self, exception, emit_key: str = "error"):
+        self.socketio.emit(emit_key, {"message": (str(exception))})
+        print("\n")
+        traceback.print_exc()
+        print("\n")
 
 
 class Progress:
@@ -1569,7 +1674,7 @@ class Progress:
         self.total_iterations = (
             generation_parameters["iterations"] if generation_parameters else 1
         )
-        self.current_status = "common:statusPreparing"
+        self.current_status = "common.statusPreparing"
         self.is_processing = True
         self.current_status_has_steps = False
         self.has_error = False
@@ -1599,7 +1704,7 @@ class Progress:
         self.has_error = has_error
 
     def mark_complete(self):
-        self.current_status = "common:statusProcessingComplete"
+        self.current_status = "common.statusProcessingComplete"
         self.current_step = 0
         self.total_steps = 0
         self.current_iteration = 0
@@ -1661,10 +1766,12 @@ def dataURL_to_image(dataURL: str) -> ImageType:
     )
     return image
 
+
 """
 Converts an image into a base64 image dataURL.
 """
 
+
 def image_to_dataURL(image: ImageType) -> str:
     buffered = io.BytesIO()
     image.save(buffered, format="PNG")
@@ -1674,7 +1781,6 @@ def image_to_dataURL(image: ImageType) -> str:
     return image_base64
 
 
-
 """
 Converts a base64 image dataURL into bytes.
 The dataURL is split on the first commma.

invokeai/configs/INITIAL_MODELS.yaml

@@ -6,83 +6,84 @@ stable-diffusion-1.5:
      repo_id: stabilityai/sd-vae-ft-mse
    recommended: True
    default: True
-inpainting-1.5:
+sd-inpainting-1.5:
   description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
   repo_id: runwayml/stable-diffusion-inpainting
   format: diffusers
   vae:
     repo_id: stabilityai/sd-vae-ft-mse
   recommended: True
-dreamlike-diffusion-1.0:
-  description: An SD 1.5 model fine tuned on high quality art by dreamlike.art, diffusers version (2.13 BG)
-  format: diffusers
-  repo_id: dreamlike-art/dreamlike-diffusion-1.0
-  vae:
-     repo_id: stabilityai/sd-vae-ft-mse
-  recommended: True
-dreamlike-photoreal-2.0:
-  description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
-  format: diffusers
-  repo_id: dreamlike-art/dreamlike-photoreal-2.0
-  recommended: False
 stable-diffusion-2.1-768:
    description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
    repo_id: stabilityai/stable-diffusion-2-1
    format: diffusers
    recommended: True
 stable-diffusion-2.1-base:
-   description: Stable Diffusion version 2.1 diffusers base model, trained on 512 pixel images (5.21 GB)
+   description: Stable Diffusion version 2.1 diffusers model, trained on 512 pixel images (5.21 GB)
    repo_id: stabilityai/stable-diffusion-2-1-base
    format: diffusers
    recommended: False
+sd-inpainting-2.0:
+   description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
+   repo_id: stabilityai/stable-diffusion-2-inpainting
+   format: diffusers
+   recommended: False
+analog-diffusion-1.0:
+   description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
+   repo_id: wavymulder/Analog-Diffusion
+   format: diffusers
+   recommended: false
+deliberate-1.0:
+   description: Versatile model that produces detailed images up to 768px (4.27 GB)
+   format: diffusers
+   repo_id: XpucT/Deliberate
+   recommended: False
+d&d-diffusion-1.0:
+   description: Dungeons & Dragons characters (2.13 GB)
+   format: diffusers
+   repo_id: 0xJustin/Dungeons-and-Diffusion
+   recommended: False
+dreamlike-photoreal-2.0:
+   description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
+   format: diffusers
+   repo_id: dreamlike-art/dreamlike-photoreal-2.0
+   recommended: False
+inkpunk-1.0:
+   description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
+   format: diffusers
+   repo_id: Envvi/Inkpunk-Diffusion
+   recommended: False
 openjourney-4.0:
-  description: An SD 1.5 model fine tuned on Midjourney images by PromptHero - include "mdjrny-v4 style" in your prompts (2.13 GB)
-  format: diffusers
-  repo_id: prompthero/openjourney
-  vae:
+   description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
+   format: diffusers
+   repo_id: prompthero/openjourney
+   vae:
      repo_id: stabilityai/sd-vae-ft-mse
-  recommended: False
-nitro-diffusion-1.0:
-   description: A SD 1.5 model trained on three artstyles - prompt with "archer style", "arcane style" and/or "modern disney style" (2.13 GB)
-   repo_id: nitrosocke/Nitro-Diffusion
+   recommended: False
+portrait-plus-1.0:
+   description: An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)
+   format: diffusers
+   repo_id: wavymulder/portraitplus
+   recommended: False
+seek-art-mega-1.0:
+   description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
+   repo_id: coreco/seek.art_MEGA
    format: diffusers
    vae:
      repo_id: stabilityai/sd-vae-ft-mse
    recommended: False
 trinart-2.0:
-   description: An SD model finetuned with ~40,000 assorted high resolution manga/anime-style pictures, diffusers version (2.13 GB)
+   description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
    repo_id: naclbit/trinart_stable_diffusion_v2
    format: diffusers
    vae:
      repo_id: stabilityai/sd-vae-ft-mse
    recommended: False
-trinart-characters-2_0:
-   description: An SD model finetuned with 19.2M anime/manga style images (ckpt version) (4.27 GB)
-   repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
-   config: v1-inference.yaml
-   file: derrida_final.ckpt
-   format: ckpt
+waifu-diffusion-1.4:
+   description: An SD-2.1 model trained on 5.4M anime/manga-style images (4.27 GB)
+   revision: main
+   repo_id: hakurei/waifu-diffusion
+   format: diffusers
    vae:
-      repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
-      file: autoencoder_fix_kl-f8-trinart_characters.ckpt
-   width: 512
-   height: 512
-   recommended: False
-ft-mse-improved-autoencoder-840000:
-   description: StabilityAI improved autoencoder fine-tuned for human faces. Improves legacy .ckpt models (335 MB)
-   repo_id: stabilityai/sd-vae-ft-mse-original
-   format: ckpt
-   config: VAE/default
-   file: vae-ft-mse-840000-ema-pruned.ckpt
-   width: 512
-   height: 512
-   recommended: True
-trinart_vae:
-   description: Custom autoencoder for trinart_characters for legacy .ckpt models only (335 MB)
-   repo_id: naclbit/trinart_characters_19.2m_stable_diffusion_v1
-   config: VAE/trinart
-   format: ckpt
-   file: autoencoder_fix_kl-f8-trinart_characters.ckpt
-   width: 512
-   height: 512
+     repo_id: stabilityai/sd-vae-ft-mse
    recommended: False

invokeai/configs/stable-diffusion/v2-inference.yaml

@@ -0,0 +1,67 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

invokeai/frontend/.eslintignore

@@ -2,4 +2,4 @@ dist/
 .husky/
 node_modules/
 patches/
-public/
+stats.html

invokeai/frontend/.gitignore

@@ -25,4 +25,13 @@ dist-ssr
 *.sw?
 
 # build stats
-stats.html
+stats.html
+
+# Yarn - https://yarnpkg.com/getting-started/qa#which-files-should-be-gitignored
+.pnp.*
+.yarn/*
+!.yarn/patches
+!.yarn/plugins
+!.yarn/releases
+!.yarn/sdks
+!.yarn/versions

invokeai/frontend/.husky/pre-commit

@@ -1,4 +1,4 @@
 #!/usr/bin/env sh
 . "$(dirname -- "$0")/_/husky.sh"
 
-cd invokeai/frontend/ && npx run lint
+cd invokeai/frontend/ && npm run lint-staged

invokeai/frontend/.prettierignore

@@ -2,4 +2,4 @@ dist/
 .husky/
 node_modules/
 patches/
-public/
+stats.html

invokeai/frontend/.prettierrc.js

@@ -1,6 +1,15 @@
 module.exports = {
   trailingComma: 'es5',
   tabWidth: 2,
+  endOfLine: 'auto',
   semi: true,
   singleQuote: true,
+  overrides: [
+    {
+      files: ['public/locales/*.json'],
+      options: {
+        tabWidth: 4,
+      },
+    },
+  ],
 };

invokeai/frontend/.yarnrc

@@ -0,0 +1,5 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+
+yarn-path ".yarn/releases/yarn-1.22.19.cjs"

invokeai/frontend/.yarnrc.yml

@@ -0,0 +1 @@
+yarnPath: .yarn/releases/yarn-1.22.19.cjs

invokeai/frontend/README.md

@@ -7,7 +7,7 @@ The UI is in `invokeai/frontend`.
 Install [node](https://nodejs.org/en/download/) (includes npm) and
 [yarn](https://yarnpkg.com/getting-started/install).
 
-From `invokeai/frontend/` run `yarn install` to get everything set up.
+From `invokeai/frontend/` run `yarn install --immutable` to get everything set up.
 
 ## Dev