Some overrides to make it work

.gitattributes

@@ -2,4 +2,3 @@
 # Only affects text files and ignores other file types.
 # For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
 * text=auto
-docker/** text eol=lf

.github/CODEOWNERS

@@ -1,34 +1,34 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant @hipsterusername
+/.github/workflows/  @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein @blessedcoolant @hipsterusername @Millu
-/mkdocs.yml @lstein  @blessedcoolant @hipsterusername @Millu
+/docs/ @lstein @blessedcoolant @hipsterusername
+/mkdocs.yml @lstein  @blessedcoolant
 
 # nodes
-/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername
+/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising 
 
 # installation and configuration
-/pyproject.toml  @lstein @blessedcoolant @hipsterusername
-/docker/  @lstein @blessedcoolant @hipsterusername
-/scripts/ @ebr @lstein @hipsterusername
-/installer/ @lstein @ebr @hipsterusername
-/invokeai/assets @lstein @ebr @hipsterusername
-/invokeai/configs @lstein @hipsterusername
-/invokeai/version @lstein @blessedcoolant @hipsterusername
+/pyproject.toml  @lstein @blessedcoolant
+/docker/  @lstein @blessedcoolant
+/scripts/ @ebr @lstein
+/installer/ @lstein @ebr
+/invokeai/assets @lstein @ebr
+/invokeai/configs @lstein
+/invokeai/version @lstein @blessedcoolant
 
 # web ui
-/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
-/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
+/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp
+/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising
 
 # front ends
-/invokeai/frontend/CLI @lstein @hipsterusername
-/invokeai/frontend/install @lstein @ebr @hipsterusername 
-/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
-/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
-/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername
+/invokeai/frontend/CLI @lstein
+/invokeai/frontend/install @lstein @ebr  
+/invokeai/frontend/merge @lstein @blessedcoolant 
+/invokeai/frontend/training @lstein @blessedcoolant 
+/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp 
 
 

.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml

@@ -1,5 +1,5 @@
 name: Feature Request
-description: Contribute a idea or request a new feature
+description: Commit a idea or Request a new feature
 title: '[enhancement]: '
 labels: ['enhancement']
 # assignees:
@@ -9,14 +9,14 @@ body:
   - type: markdown
     attributes:
       value: |
-        Thanks for taking the time to fill out this feature request!
+        Thanks for taking the time to fill out this Feature request!
 
   - type: checkboxes
     attributes:
       label: Is there an existing issue for this?
       description: |
         Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
-        to see if a similar issue already exists for the feature you want to request
+        to see if a simmilar issue already exists for the feature you want to request
       options:
         - label: I have searched the existing issues
           required: true
@@ -34,9 +34,12 @@ body:
     id: whatisexpected
     attributes:
       label: What should this feature add?
-      description: Explain the functionality this feature should add. Feature requests should be for single features. Please create multiple requests if you want to request multiple features.
+      description: Please try to explain the functionality this feature should add
       placeholder: |
-        I'd like a button that creates an image of banana sushi every time I press it. Each image should be different. There should be a toggle next to the button that enables strawberry mode, in which the images are of strawberry sushi instead.
+        Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
+        Great benefits with automatic labeling, assigning and other functionalitys not available in that form
+        via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
+        https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
     validations:
       required: true
 
@@ -48,6 +51,6 @@ body:
 
   - type: textarea
     attributes:
-      label: Additional Content
+      label: Aditional Content
       description: Add any other context or screenshots about the feature request here.
-      placeholder: This is a mockup of the design how I imagine it <screenshot>
+      placeholder: This is a Mockup of the design how I imagine it <screenshot>

.github/pull_request_template.md

@@ -1,66 +0,0 @@
-## What type of PR is this? (check all applicable)
-
-- [ ] Refactor
-- [ ] Feature
-- [ ] Bug Fix
-- [ ] Optimization
-- [ ] Documentation Update
-- [ ] Community Node Submission
-
-
-## Have you discussed this change with the InvokeAI team?
-- [ ] Yes
-- [ ] No, because:
-
-      
-## Have you updated all relevant documentation?
-- [ ] Yes
-- [ ] No
-
-
-## Description
-
-
-## Related Tickets & Documents
-
-<!--
-For pull requests that relate or close an issue, please include them
-below. 
-
-For example having the text: "closes #1234" would connect the current pull
-request to issue 1234.  And when we merge the pull request, Github will
-automatically close the issue.
--->
-
-- Related Issue #
-- Closes #
-
-## QA Instructions, Screenshots, Recordings
-
-<!-- 
-Please provide steps on how to test changes, any hardware or 
-software specifications as well as any other pertinent information. 
--->
-
-## Merge Plan
-
-<!--
-A merge plan describes how this PR should be handled after it is approved.
-
-Example merge plans:
-- "This PR can be merged when approved"
-- "This must be squash-merged when approved"
-- "DO NOT MERGE - I will rebase and tidy commits before merging"
-- "#dev-chat on discord needs to be advised of this change when it is merged"
-
-A merge plan is particularly important for large PRs or PRs that touch the
-database in any way.
--->
-
-## Added/updated tests?
-
-- [ ] Yes
-- [ ] No : _please replace this line with details on why tests
-      have not been included_
-
-## [optional] Are there any post deployment tasks we need to perform?

.github/workflows/lint-frontend.yml

@@ -2,6 +2,8 @@ name: Lint frontend
 
 on:
   pull_request:
+    paths:
+      - 'invokeai/frontend/web/**'
     types:
       - 'ready_for_review'
       - 'opened'
@@ -9,6 +11,8 @@ on:
   push:
     branches:
       - 'main'
+    paths:
+      - 'invokeai/frontend/web/**'
   merge_group:
   workflow_dispatch:
 
@@ -21,23 +25,13 @@ jobs:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-22.04
     steps:
-      - name: Setup Node 20
-        uses: actions/setup-node@v4
+      - name: Setup Node 18
+        uses: actions/setup-node@v3
         with:
-          node-version: '20'
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Setup pnpm
-        uses: pnpm/action-setup@v2
-        with:
-          version: 8
-      - name: Install dependencies
-        run: 'pnpm install --prefer-frozen-lockfile'
-      - name: Typescript
-        run: 'pnpm run lint:tsc'
-      - name: Madge
-        run: 'pnpm run lint:madge'
-      - name: ESLint
-        run: 'pnpm run lint:eslint'
-      - name: Prettier
-        run: 'pnpm run lint:prettier'
+          node-version: '18'
+      - uses: actions/checkout@v3
+      - run: 'yarn install --frozen-lockfile'
+      - run: 'yarn run lint:tsc'
+      - run: 'yarn run lint:madge'
+      - run: 'yarn run lint:eslint'
+      - run: 'yarn run lint:prettier'

.github/workflows/pyflakes.yml

@@ -0,0 +1,20 @@
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+      - development
+      - 'release-candidate-*'
+
+jobs:
+  pyflakes:
+    name: runner / pyflakes
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: pyflakes
+        uses: reviewdog/action-pyflakes@v1
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          reporter: github-pr-review

.github/workflows/pypi-release.yml

@@ -15,38 +15,20 @@ jobs:
       TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
       TWINE_NON_INTERACTIVE: 1
     steps:
-      - name: Checkout sources
-        uses: actions/checkout@v4
+      - name: checkout sources
+        uses: actions/checkout@v3
 
-      - name: Setup Node 20
-        uses: actions/setup-node@v4
-        with:
-          node-version: '20'
-
-      - name: Setup pnpm
-        uses: pnpm/action-setup@v2
-        with:
-          version: 8
-
-      - name: Install pnpm dependencies
-        working-directory: invokeai/frontend/web
-        run: 'pnpm install --prefer-frozen-lockfile'
-
-      - name: Build frontend
-        working-directory: invokeai/frontend/web
-        run: 'pnpm build'
-
-      - name: Install python deps
+      - name: install deps
         run: pip install --upgrade build twine
 
-      - name: Build wheel package
+      - name: build package
         run: python3 -m build
 
-      - name: Check distribution
+      - name: check distribution
         run: twine check dist/*
 
-      - name: Check PyPI versions
-        if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
+      - name: check PyPI versions
+        if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3'
         run: |
           pip install --upgrade requests
           python -c "\
@@ -54,6 +36,6 @@ jobs:
           EXISTS=scripts.pypi_helper.local_on_pypi(); \
           print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
 
-      - name: Upload package
+      - name: upload package
         if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != ''
         run: twine upload dist/*

.github/workflows/style-checks.yml

@@ -1,12 +1,13 @@
-name: style checks
+name: Black  # TODO: add isort and flake8 later
 
 on:
-  pull_request:
+  pull_request: {}
   push:
-    branches: main
+    branches: master
+    tags: "*"
 
 jobs:
-  ruff:
+  test:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -18,7 +19,9 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install ruff
+          pip install --upgrade pip wheel
+          pip install .[test]
 
-      - run: ruff check --output-format=github .
-      - run: ruff format --check .
+      # - run: isort --check-only .
+      - run: black --check .
+      # - run: flake8

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

@@ -0,0 +1,50 @@
+name: Test invoke.py pip
+
+# This is a dummy stand-in for the actual tests
+# we don't need to run python tests on non-Python changes
+# But PRs require passing tests to be mergeable
+
+on:
+  pull_request:
+    paths:
+      - '**'
+      - '!pyproject.toml'
+      - '!invokeai/**'
+      - '!tests/**'
+      - 'invokeai/frontend/web/**'
+  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:
+          - '3.10'
+        pytorch:
+          - linux-cuda-11_7
+          - linux-rocm-5_2
+          - linux-cpu
+          - macos-default
+          - windows-cpu
+        include:
+          - pytorch: linux-cuda-11_7
+            os: ubuntu-22.04
+          - pytorch: linux-rocm-5_2
+            os: ubuntu-22.04
+          - pytorch: linux-cpu
+            os: ubuntu-22.04
+          - pytorch: macos-default
+            os: macOS-12
+          - pytorch: windows-cpu
+            os: windows-2022
+    name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: skip
+        run: echo "no build required"

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

@@ -3,7 +3,16 @@ on:
   push:
     branches:
       - 'main'
+    paths:
+      - 'pyproject.toml'
+      - 'invokeai/**'
+      - '!invokeai/frontend/web/**'
   pull_request:
+    paths:
+      - 'pyproject.toml'
+      - 'invokeai/**'
+      - 'tests/**'
+      - '!invokeai/frontend/web/**'
     types:
       - 'ready_for_review'
       - 'opened'
@@ -56,23 +65,10 @@ jobs:
         id: checkout-sources
         uses: actions/checkout@v3
 
-      - name: Check for changed python files
-        id: changed-files
-        uses: tj-actions/changed-files@v37
-        with:
-          files_yaml: |
-            python:
-              - 'pyproject.toml'
-              - 'invokeai/**'
-              - '!invokeai/frontend/web/**'
-              - 'tests/**'
-
       - name: set test prompt to main branch validation
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
 
       - name: setup python
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
@@ -80,7 +76,6 @@ jobs:
           cache-dependency-path: pyproject.toml
 
       - name: install invokeai
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         env:
           PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
         run: >
@@ -88,7 +83,6 @@ jobs:
           --editable=".[test]"
 
       - name: run pytest
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         id: run-pytest
         run: pytest
 

.gitignore

@@ -1,4 +1,22 @@
+# ignore default image save location and model symbolic link
 .idea/
+embeddings/
+outputs/
+models/ldm/stable-diffusion-v1/model.ckpt
+**/restoration/codeformer/weights
+
+# ignore user models config
+configs/models.user.yaml
+config/models.user.yml
+invokeai.init
+.version
+.last_model
+
+# ignore the Anaconda/Miniconda installer used while building Docker image
+anaconda.sh
+
+# ignore a directory which serves as a place for initial images
+inputs/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -16,7 +34,7 @@ __pycache__/
 .Python
 build/
 develop-eggs/
-dist/
+# dist/
 downloads/
 eggs/
 .eggs/
@@ -133,10 +151,12 @@ celerybeat.pid
 
 # Environments
 .env
-.venv*
+.venv
 env/
 venv/
 ENV/
+env.bak/
+venv.bak/
 
 # Spyder project settings
 .spyderproject
@@ -169,17 +189,44 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+src
 **/__pycache__/
+outputs
 
+# Logs and associated folders
+# created from generated embeddings.
+logs
+testtube
+checkpoints
 # If it's a Mac
 .DS_Store
 
+invokeai/frontend/yarn.lock
+invokeai/frontend/node_modules
+
 # Let the frontend manage its own gitignore
 !invokeai/frontend/web/*
 
 # Scratch folder
 .scratch/
 .vscode/
+gfpgan/
+models/ldm/stable-diffusion-v1/*.sha256
+
+
+# GFPGAN model files
+gfpgan/
+
+# config file (will be created by installer)
+configs/models.yaml
+
+# ignore initfile
+.invokeai
+
+# ignore environment.yml and requirements.txt
+# these are links to the real files in environments-and-requirements
+environment.yml
+requirements.txt
 
 # source installer files
 installer/*zip
@@ -187,4 +234,3 @@ installer/install.bat
 installer/install.sh
 installer/update.bat
 installer/update.sh
-installer/InvokeAI-Installer/

.pre-commit-config.yaml

@@ -8,17 +8,3 @@ repos:
     language: system
     entry: black
     types: [python]
-
-  - id: flake8
-    name: flake8
-    stages: [commit]
-    language: system
-    entry: flake8
-    types: [python]
-
-  - id: isort
-    name: isort
-    stages: [commit]
-    language: system
-    entry: isort
-    types: [python]

Makefile

@@ -1,52 +0,0 @@
-# simple Makefile with scripts that are otherwise hard to remember
-# to use, run from the repo root `make <command>`
-
-default: help
-
-help:
-	@echo Developer commands:
-	@echo
-	@echo "ruff           Run ruff, fixing any safely-fixable errors and formatting"
-	@echo "ruff-unsafe    Run ruff, fixing all fixable errors and formatting"
-	@echo "mypy           Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors"
-	@echo "mypy-all       Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
-	@echo "frontend-build Build the frontend in order to run on localhost:9090"
-	@echo "frontend-dev   Run the frontend in developer mode on localhost:5173"
-	@echo "installer-zip  Build the installer .zip file for the current version"
-	@echo "tag-release    Tag the GitHub repository with the current version (use at release time only!)"
-
-# Runs ruff, fixing any safely-fixable errors and formatting
-ruff:
-		ruff check . --fix
-		ruff format .
-
-# Runs ruff, fixing all errors it can fix and formatting
-ruff-unsafe:
-		ruff check . --fix --unsafe-fixes
-		ruff format .
-
-# Runs mypy, using the config in pyproject.toml
-mypy:
-		mypy scripts/invokeai-web.py
-
-# Runs mypy, ignoring the config in pyproject.toml but still ignoring missing (untyped) imports
-# (many files are ignored by the config, so this is useful for checking all files)
-mypy-all:
-		mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports
-
-# Build the frontend
-frontend-build:
-	cd invokeai/frontend/web && pnpm build
-
-# Run the frontend in dev mode
-frontend-dev:
-	cd invokeai/frontend/web && pnpm dev
-
-# Installer zip file
-installer-zip:
-	cd installer && ./create_installer.sh
-
-# Tag the release
-tag-release:
-	cd installer && ./tag_release.sh
-

README.md

@@ -43,16 +43,16 @@ Web Interface, interactive Command Line Interface, and also serves as
 the foundation for multiple commercial products.
 
 **Quick links**: [[How to
-  Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<a
+  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/issues">Bug Reports</a>]
+  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>] 
-   [<a
-  href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>] 
+  Ideas & Q&A</a>]
 
 <div align="center">
 
@@ -81,7 +81,7 @@ Table of Contents 📝
 ## Quick Start
 
 For full installation and upgrade instructions, please see:
-[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)
+[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
 
 If upgrading from version 2.3, please read [Migrating a 2.3 root
 directory to 3.0](#migrating-to-3) first.
@@ -123,10 +123,10 @@ and go to http://localhost:9090.
 
 ### Command-Line Installation (for developers and users familiar with Terminals)
 
-You must have Python 3.10 through 3.11 installed on your machine. Earlier or
+You must have Python 3.9 through 3.11 installed on your machine. Earlier or
 later versions are not supported.
-Node.js also needs to be installed along with `pnpm` (can be installed with
-the command `npm install -g pnpm` if needed)
+Node.js also needs to be installed along with yarn (can be installed with
+the command `npm install -g yarn` if needed)
 
 1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
 2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
@@ -161,7 +161,7 @@ the command `npm install -g pnpm` if needed)
     _For Windows/Linux with an NVIDIA GPU:_
 
     ```terminal
-    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
     ```
 
     _For Linux with an AMD GPU:_
@@ -175,7 +175,7 @@ the command `npm install -g pnpm` if needed)
     pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
     ``` 
 
-    _For Macintoshes, either Intel or M1/M2/M3:_
+    _For Macintoshes, either Intel or M1/M2:_
 
     ```sh
     pip install InvokeAI --use-pep517
@@ -184,9 +184,8 @@ the command `npm install -g pnpm` if needed)
 6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
 
     ```terminal
-    invokeai-configure --root .
+    invokeai-configure
     ```
-	Don't miss the dot at the end!
 
 7. Launch the web server (do it every time you run InvokeAI):
 
@@ -194,9 +193,15 @@ the command `npm install -g pnpm` if needed)
     invokeai-web
     ```
 
-8. Point your browser to http://localhost:9090 to bring up the web interface.
+8. Build Node.js assets
 
-9. Type `banana sushi` in the box on the top left and click `Invoke`.
+  ```terminal
+  cd invokeai/frontend/web/
+  yarn vite build
+  ```
+
+9. Point your browser to http://localhost:9090 to bring up the web interface.
+10. 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`.
@@ -306,30 +311,13 @@ InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
 You may now launch the WebUI in the usual way, by selecting option [1]
 from the launcher script
 
-#### Migrating Images
+#### Migration Caveats
 
 The migration script will migrate your invokeai settings and models,
 including textual inversion models, LoRAs and merges that you may have
 installed previously. However it does **not** migrate the generated
-images stored in your 2.3-format outputs directory. To do this, you 
-need to run an additional step:
-
-1. From a working InvokeAI 3.0 root directory, start the launcher and
-enter menu option [8] to open the "developer's console".
-
-2. At the developer's console command line, type the command:
-
-```bash
-invokeai-import-images
-```
-
-3. This will lead you through the process of confirming the desired
-   source and destination for the imported images. The images will
-   appear in the gallery board of your choice, and contain the
-   original prompt, model name, and other parameters used to generate
-   the image.
-   
-(Many kudos to **techjedi** for contributing this script.)
+images stored in your 2.3-format outputs directory. You will need to
+manually import selected images into the 3.0 gallery via drag-and-drop.
 
 ## Hardware Requirements
 
@@ -368,9 +356,9 @@ InvokeAI offers a locally hosted Web Server & React Frontend, with an industry l
 
 The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
 
-### *Workflows & Nodes*
+### *Node Architecture & Editor (Beta)*
 
-InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
+Invoke AI's backend is built on a graph-based execution architecture. This allows for customizable generation pipelines to be developed by professional users looking to create specific workflows to support their production use-cases, and will be extended in the future with additional capabilities.
 
 ### *Board & Gallery Management*
 
@@ -383,9 +371,8 @@ Invoke AI provides an organized gallery system for easily storing, accessing, an
 - *Upscaling Tools*
 - *Embedding Manager & Support*
 - *Model Manager & Support*
-- *Workflow creation & management*
 - *Node-Based Architecture*
-
+- *Node-Based Plug-&-Play UI (Beta)*
 
 ### Latest Changes
 
@@ -395,19 +382,21 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 
 ### Troubleshooting
 
-Please check out our **[Troubleshooting Guide](https://invoke-ai.github.io/InvokeAI/installation/010_INSTALL_AUTOMATED/#troubleshooting)** to get solutions for common installation
-problems and other issues. For more help, please join our [Discord][discord link]
+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
 
 Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
 cleanup, testing, or code reviews, is very much encouraged to do so.
 
-Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
+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, we have a new contributor checklist you can follow to get started contributing: 
-[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
+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**.
 
 We hope you enjoy using our software as much as we enjoy creating it,
 and we hope that some of those of you who are reading this will elect
@@ -423,7 +412,7 @@ their time, hard work and effort.
 
 ### Support
 
-For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
+For support, please use this repository's GitHub Issues tracking service, or join the Discord.
 
 Original portions of the software are Copyright (c) 2023 by respective contributors.
 

docker/.env.sample

@@ -1,15 +1,13 @@
 ## Make a copy of this file named `.env` and fill in the values below.
-## Any environment variables supported by InvokeAI can be specified here,
-## in addition to the examples below.
+## Any environment variables supported by InvokeAI can be specified here.
 
 # INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
 # Outputs will also be stored here by default.
 # This **must** be an absolute path.
 INVOKEAI_ROOT=
 
-# Get this value from your HuggingFace account settings page.
-# HUGGING_FACE_HUB_TOKEN=
+HUGGINGFACE_TOKEN=
 
-## optional variables specific to the docker setup.
-# GPU_DRIVER=cuda # or rocm
-# CONTAINER_UID=1000
+## optional variables specific to the docker setup
+# GPU_DRIVER=cuda
+# CONTAINER_UID=1000

docker/Dockerfile

@@ -2,7 +2,7 @@
 
 ## Builder stage
 
-FROM library/ubuntu:23.04 AS builder
+FROM library/ubuntu:22.04 AS builder
 
 ARG DEBIAN_FRONTEND=noninteractive
 RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
@@ -10,7 +10,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     --mount=type=cache,target=/var/lib/apt,sharing=locked \
     apt update && apt-get install -y \
         git \
-        python3-venv \
+        python3.10-venv \
         python3-pip \
         build-essential
 
@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.1.0
-ARG TORCHVISION_VERSION=0.16
+ARG TORCH_VERSION=2.0.1
+ARG TORCHVISION_VERSION=0.15.2
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,9 +35,9 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
     else \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \
     fi &&\
     pip install $extra_index_url_arg \
         torch==$TORCH_VERSION \
@@ -70,7 +70,7 @@ RUN --mount=type=cache,target=/usr/lib/node_modules \
 
 #### Runtime stage ---------------------------------------
 
-FROM library/ubuntu:23.04 AS runtime
+FROM library/ubuntu:22.04 AS runtime
 
 ARG DEBIAN_FRONTEND=noninteractive
 ENV PYTHONUNBUFFERED=1
@@ -85,7 +85,6 @@ RUN apt update && apt install -y --no-install-recommends \
         iotop \
         bzip2 \
         gosu \
-        magic-wormhole \
         libglib2.0-0 \
         libgl1-mesa-glx \
         python3-venv \
@@ -95,13 +94,15 @@ RUN apt update && apt install -y --no-install-recommends \
         libstdc++-10-dev &&\
     apt-get clean && apt-get autoclean
 
+# globally add magic-wormhole
+# for ease of transferring data to and from the container
+# when running in sandboxed cloud environments; e.g. Runpod etc.
+RUN pip install magic-wormhole
 
 ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 ENV INVOKEAI_ROOT=/invokeai
 ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
-ENV CONTAINER_UID=${CONTAINER_UID:-1000}
-ENV CONTAINER_GID=${CONTAINER_GID:-1000}
 
 # --link requires buldkit w/ dockerfile syntax 1.4
 COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
@@ -119,7 +120,9 @@ WORKDIR ${INVOKEAI_SRC}
 RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
 RUN python3 -c "from patchmatch import patch_match"
 
-RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
+# Create unprivileged user and make the local dir
+RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke
+RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]

docker/README.md

@@ -5,7 +5,7 @@ All commands are to be run from the `docker` directory: `cd docker`
 #### Linux
 
 1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
-2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
+2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04).
     - The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
 3. Ensure docker daemon is able to access the GPU.
     - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
@@ -20,6 +20,7 @@ This is done via Docker Desktop preferences
 
 ## Quickstart
 
+
 1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
     a. the desired location of the InvokeAI runtime directory, or
     b. an existing, v3.0.0 compatible runtime directory.
@@ -41,22 +42,20 @@ The Docker daemon on the system must be already set up to use the GPU. In case o
 
 Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
 
-You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
+You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
 
-Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended):
+Example (most values are optional):
 
-```bash
+```
 INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
 HUGGINGFACE_TOKEN=the_actual_token
 CONTAINER_UID=1000
 GPU_DRIVER=cuda
 ```
 
-Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
-
 ## Even Moar Customizing!
 
-See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
+See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
 
 ### Reconfigure the runtime directory
 
@@ -64,7 +63,7 @@ Can be used to download additional models from the supported model list
 
 In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
 
-```yaml
+```
 command:
   - invokeai-configure
   - --yes
@@ -72,7 +71,7 @@ command:
 
 Or install models:
 
-```yaml
+```
 command:
   - invokeai-model-install
-```
+```

docker/build.sh

@@ -5,7 +5,7 @@ build_args=""
 
 [[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
 
-echo "docker compose build args:"
+echo "docker-compose build args:"
 echo $build_args
 
-docker compose build $build_args
+docker-compose build $build_args

docker/docker-compose.yml

@@ -15,10 +15,6 @@ services:
             - driver: nvidia
               count: 1
               capabilities: [gpu]
-    # For AMD support, comment out the deploy section above and uncomment the devices section below:
-    #devices:
-    #  - /dev/kfd:/dev/kfd
-    #  - /dev/dri:/dev/dri
     build:
       context: ..
       dockerfile: docker/Dockerfile

docker/docker-entrypoint.sh

@@ -19,7 +19,7 @@ set -e -o pipefail
 # Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
 
 USER_ID=${CONTAINER_UID:-1000}
-USER=ubuntu
+USER=invoke
 usermod -u ${USER_ID} ${USER} 1>/dev/null
 
 configure() {
@@ -29,8 +29,8 @@ configure() {
         echo "To reconfigure InvokeAI, delete the above file."
         echo "======================================================================"
     else
-        mkdir -p "${INVOKEAI_ROOT}"
-        chown --recursive ${USER} "${INVOKEAI_ROOT}"
+        mkdir -p ${INVOKEAI_ROOT}
+        chown --recursive ${USER} ${INVOKEAI_ROOT}
         gosu ${USER} invokeai-configure --yes --default_only
     fi
 }
@@ -50,16 +50,16 @@ fi
 if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then
     apt-get update
     apt-get install -y openssh-server
-    pushd "$HOME"
+    pushd $HOME
     mkdir -p .ssh
-    echo "${PUBLIC_KEY}" > .ssh/authorized_keys
+    echo ${PUBLIC_KEY} > .ssh/authorized_keys
     chmod -R 700 .ssh
     popd
     service ssh start
 fi
 
 
-cd "${INVOKEAI_ROOT}"
+cd ${INVOKEAI_ROOT}
 
 # Run the CMD as the Container User (not root).
 exec gosu ${USER} "$@"

docker/run.sh

@@ -1,11 +1,8 @@
 #!/usr/bin/env bash
 set -e
 
-# This script is provided for backwards compatibility with the old docker setup.
-# it doesn't do much aside from wrapping the usual docker compose CLI.
-
 SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
 cd "$SCRIPTDIR" || exit 1
 
-docker compose up -d
-docker compose logs -f
+docker-compose up --build -d
+docker-compose logs -f

docs/CHANGELOG.md

@@ -488,7 +488,7 @@ sections describe what's new for InvokeAI.
 
 - A choice of installer scripts that automate installation and configuration.
   See
-  [Installation](installation/INSTALLATION.md).
+  [Installation](installation/index.md).
 - A streamlined manual installation process that works for both Conda and
   PIP-only installs. See
   [Manual Installation](installation/020_INSTALL_MANUAL.md).
@@ -657,7 +657,7 @@ sections describe what's new for InvokeAI.
 
 ## v1.13 <small>(3 September 2022)</small>
 
-- Support image variations (see [VARIATIONS](deprecated/VARIATIONS.md)
+- Support image variations (see [VARIATIONS](features/VARIATIONS.md)
   ([Kevin Gibbons](https://github.com/bakkot) and many contributors and
   reviewers)
 - Supports a Google Colab notebook for a standalone server running on Google

docs/contributing/CONTRIBUTING.md

@@ -1,41 +1,36 @@
-# Contributing
+# How to Contribute
 
+## Welcome to Invoke AI
 Invoke AI originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
 
 
-# Methods of Contributing to Invoke AI
+## Contributing to Invoke AI
 Anyone who wishes to contribute to InvokeAI, whether features, bug fixes, code cleanup, testing, code reviews, documentation or translation is very much encouraged to do so.
 
-## Development
-If you’d like to help with development, please see our [development guide](contribution_guides/development.md). 
+To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
 
-**New Contributors:** If you’re unfamiliar with contributing to open source projects, take a look at our [new contributor guide](contribution_guides/newContributorChecklist.md).
+### Areas of contribution: 
 
-## Nodes
-If you’d like to add a Node, please see our [nodes contribution guide](../nodes/contributingNodes.md).
+#### Development
+If you’d like to help with development, please see our [development guide](contribution_guides/development.md). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
 
-## Support and Triaging
-Helping support other users in [Discord](https://discord.gg/ZmtBAhwWhy) and on Github are valuable forms of contribution that we greatly appreciate. 
+#### Documentation
+If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documenation.md).
 
-We receive many issues and requests for help from users. We're limited in bandwidth relative to our the user base, so providing answers to questions or helping identify causes of issues is very helpful. By doing this, you enable us to spend time on the highest priority work. 
+#### Translation
+If you'd like to help with translation, please see our [translation guide](docs/contributing/.contribution_guides/translation.md).
 
-## Documentation
-If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documentation.md).
-
-## Translation
-If you'd like to help with translation, please see our [translation guide](contribution_guides/translation.md).
-
-## Tutorials 
+#### Tutorials 
 Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
 
 We hope you enjoy using our software as much as we enjoy creating it, and we hope that some of those of you who are reading this will elect to become part of our contributor community.
 
 
-# Contributors
+### Contributors
 
 This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
 
-# Code of Conduct
+### Code of Conduct
 
 The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
 
@@ -49,7 +44,8 @@ By making a contribution to this project, you certify that:
 This disclaimer is not a license and does not grant any rights or permissions. You must obtain necessary permissions and licenses, including from third parties, before contributing to this project.
 
 This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.
-# Support
+
+### Support
 
 For support, please use this repository's [GitHub Issues](https://github.com/invoke-ai/InvokeAI/issues), or join the [Discord](https://discord.gg/ZmtBAhwWhy).
 

docs/contributing/INVOCATIONS.md

@@ -1,6 +1,6 @@
-# Nodes
+# Invocations
 
-Features in InvokeAI are added in the form of modular nodes systems called
+Features in InvokeAI are added in the form of modular node-like systems called
 **Invocations**.
 
 An Invocation is simply a single operation that takes in some inputs and gives
@@ -9,34 +9,13 @@ complex functionality.
 
 ## Invocations Directory
 
-InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
+InvokeAI Invocations can be found in the `invokeai/app/invocations` directory.
 
-New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup. 
-
-Example `nodes`  subfolder structure:
-```py
-├── __init__.py # Invoke-managed custom node loader
-│
-├── cool_node
-│   ├── __init__.py # see example below
-│   └── cool_node.py
-│
-└── my_node_pack
-    ├── __init__.py # see example below
-    ├── tasty_node.py
-    ├── bodacious_node.py
-    ├── utils.py
-    └── extra_nodes
-        └── fancy_node.py
-```
-
-Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
- See the README in the nodes folder for more examples: 
-
-```py
-from .cool_node import CoolInvocation
-```
+You can add your new functionality to one of the existing Invocations in this
+directory or create a new file in this directory as per your needs.
 
+**Note:** _All Invocations must be inside this directory for InvokeAI to
+recognize them as valid Invocations._
 
 ## Creating A New Invocation
 
@@ -50,13 +29,12 @@ The first set of things we need to do when creating a new Invocation are -
 
 - Create a new class that derives from a predefined parent class called
   `BaseInvocation`.
+- The name of every Invocation must end with the word `Invocation` in order for
+  it to be recognized as an Invocation.
 - Every Invocation must have a `docstring` that describes what this Invocation
   does.
-- While not strictly required, we suggest every invocation class name ends in
-  "Invocation", eg "CropImageInvocation".
-- Every Invocation must use the `@invocation` decorator to provide its unique
-  invocation type. You may also provide its title, tags and category using the
-  decorator.
+- Every Invocation must have a unique `type` field defined which becomes its
+  indentifier.
 - Invocations are strictly typed. We make use of the native
   [typing](https://docs.python.org/3/library/typing.html) library and the
   installed [pydantic](https://pydantic-docs.helpmanual.io/) library for
@@ -65,11 +43,12 @@ The first set of things we need to do when creating a new Invocation are -
 So let us do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from typing import Literal
+from .baseinvocation import BaseInvocation
 
-@invocation('resize')
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
+    type: Literal['resize'] = 'resize'
 ```
 
 That's great.
@@ -83,10 +62,8 @@ our Invocation takes.
 
 ### **Inputs**
 
-Every Invocation input must be defined using the `InputField` function. This is
-a wrapper around the pydantic `Field` function, which handles a few extra things
-and provides type hints. Like everything else, this should be strictly typed and
-defined.
+Every Invocation input is a pydantic `Field` and like everything else should be
+strictly typed and defined.
 
 So let us create these inputs for our Invocation. First up, the `image` input we
 need. Generally, we can use standard variable types in Python but InvokeAI
@@ -99,51 +76,55 @@ create your own custom field types later in this guide. For now, let's go ahead
 and use it.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from typing import Literal, Union
+from pydantic import Field
+
+from .baseinvocation import BaseInvocation
+from ..models.image import ImageField
 
-@invocation('resize')
 class ResizeInvocation(BaseInvocation):
+    '''Resizes an image'''
+    type: Literal['resize'] = 'resize'
 
     # Inputs
-    image: ImageField = InputField(description="The input image")
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
 ```
 
 Let us break down our input code.
 
 ```python
-image: ImageField = InputField(description="The input image")
+image: Union[ImageField, None] = Field(description="The input image", default=None)
 ```
 
-| Part      | Value                                       | Description                                                                     |
-| --------- | ------------------------------------------- | ------------------------------------------------------------------------------- |
-| Name      | `image`                                     | The variable that will hold our image                                           |
-| Type Hint | `ImageField`                                | The types for our field. Indicates that the image must be an `ImageField` type. |
-| Field     | `InputField(description="The input image")` | The image variable is an `InputField` which needs a description.                |
+| Part      | Value                                                | Description                                                                                        |
+| --------- | ---------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
+| Name      | `image`                                              | The variable that will hold our image                                                              |
+| Type Hint | `Union[ImageField, None]`                            | The types for our field. Indicates that the image can either be an `ImageField` type or `None`     |
+| Field     | `Field(description="The input image", default=None)` | The image variable is a field which needs a description and a default value that we set to `None`. |
 
 Great. Now let us create our other inputs for `width` and `height`
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from typing import Literal, Union
+from pydantic import Field
+
+from .baseinvocation import BaseInvocation
+from ..models.image import ImageField
 
-@invocation('resize')
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
+    type: Literal['resize'] = 'resize'
 
     # Inputs
-    image: ImageField = InputField(description="The input image")
-    width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
+    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
+    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
 ```
 
-As you might have noticed, we added two new arguments to the `InputField`
-definition for `width` and `height`, called `gt` and `le`. They stand for
-_greater than or equal to_ and _less than or equal to_.
-
-These impose contraints on those fields, and will raise an exception if the
-values do not meet the constraints. Field constraints are provided by
-**pydantic**, so anything you see in the **pydantic docs** will work.
+As you might have noticed, we added two new parameters to the field type for
+`width` and `height` called `gt` and `le`. These basically stand for _greater
+than or equal to_ and _less than or equal to_. There are various other param
+types for field that you can find on the **pydantic** documentation.
 
 **Note:** _Any time it is possible to define constraints for our field, we
 should do it so the frontend has more information on how to parse this field._
@@ -160,17 +141,20 @@ that are provided by it by InvokeAI.
 Let us create this function first.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
+from typing import Literal, Union
+from pydantic import Field
+
+from .baseinvocation import BaseInvocation, InvocationContext
+from ..models.image import ImageField
 
-@invocation('resize')
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
+    type: Literal['resize'] = 'resize'
 
     # Inputs
-    image: ImageField = InputField(description="The input image")
-    width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
+    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
+    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
 
     def invoke(self, context: InvocationContext):
         pass
@@ -189,18 +173,21 @@ all the necessary info related to image outputs. So let us use that.
 We will cover how to create your own output types later in this guide.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.image import ImageOutput
+from typing import Literal, Union
+from pydantic import Field
+
+from .baseinvocation import BaseInvocation, InvocationContext
+from ..models.image import ImageField
+from .image import ImageOutput
 
-@invocation('resize')
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
+    type: Literal['resize'] = 'resize'
 
     # Inputs
-    image: ImageField = InputField(description="The input image")
-    width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
+    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
+    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         pass
@@ -208,34 +195,39 @@ class ResizeInvocation(BaseInvocation):
 
 Perfect. Now that we have our Invocation setup, let us do what we want to do.
 
-- We will first load the image using one of the services provided by InvokeAI to
-  load the image.
+- We will first load the image. Generally we do this using the `PIL` library but
+  we can use one of the services provided by InvokeAI to load the image.
 - We will resize the image using `PIL` to our input data.
 - We will output this image in the format we set above.
 
 So let's do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.image import ImageOutput, ResourceOrigin, ImageCategory
+from typing import Literal, Union
+from pydantic import Field
+
+from .baseinvocation import BaseInvocation, InvocationContext
+from ..models.image import ImageField, ResourceOrigin, ImageCategory
+from .image import ImageOutput
 
-@invocation("resize")
 class ResizeInvocation(BaseInvocation):
-    """Resizes an image"""
+    '''Resizes an image'''
+    type: Literal['resize'] = 'resize'
 
-    image: ImageField = InputField(description="The input image")
-    width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
+    # Inputs
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
+    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
+    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the image using InvokeAI's predefined Image Service. Returns the PIL image.
-        image = context.services.images.get_pil_image(self.image.image_name)
+        # Load the image using InvokeAI's predefined Image Service.
+        image = context.services.images.get_pil_image(self.image.image_origin, self.image.image_name)
 
         # Resizing the image
+        # Because we used the above service, we already have a PIL image. So we can simply resize.
         resized_image = image.resize((self.width, self.height))
 
-        # Save the image using InvokeAI's predefined Image Service. Returns the prepared PIL image.
+        # Preparing the image for output using InvokeAI's predefined Image Service.
         output_image = context.services.images.create(
             image=resized_image,
             image_origin=ResourceOrigin.INTERNAL,
@@ -249,6 +241,7 @@ class ResizeInvocation(BaseInvocation):
         return ImageOutput(
             image=ImageField(
                 image_name=output_image.image_name,
+                image_origin=output_image.image_origin,
             ),
             width=output_image.width,
             height=output_image.height,
@@ -260,24 +253,6 @@ certain way that the images need to be dispatched in order to be stored and read
 correctly. In 99% of the cases when dealing with an image output, you can simply
 copy-paste the template above.
 
-### Customization
-
-We can use the `@invocation` decorator to provide some additional info to the
-UI, like a custom title, tags and category.
-
-We also encourage providing a version. This must be a
-[semver](https://semver.org/) version string ("$MAJOR.$MINOR.$PATCH"). The UI
-will let users know if their workflow is using a mismatched version of the node.
-
-```python
-@invocation("resize", title="My Resizer", tags=["resize", "image"], category="My Invocations", version="1.0.0")
-class ResizeInvocation(BaseInvocation):
-    """Resizes an image"""
-
-    image: ImageField = InputField(description="The input image")
-    ...
-```
-
 That's it. You made your own **Resize Invocation**.
 
 ## Result
@@ -295,57 +270,9 @@ new Invocation ready to be used.
 
 ![resize node editor](../assets/contributing/resize_node_editor.png)
 
-## Contributing Nodes
+# Advanced
 
-Once you've created a Node, the next step is to share it with the community! The
-best way to do this is to submit a Pull Request to add the Node to the
-[Community Nodes](nodes/communityNodes) list. If you're not sure how to do that,
-take a look a at our [contributing nodes overview](contributingNodes).
-
-## Advanced
-
-### Custom Output Types
-
-Like with custom inputs, sometimes you might find yourself needing custom
-outputs that InvokeAI does not provide. We can easily set one up.
-
-Now that you are familiar with Invocations and Inputs, let us use that knowledge
-to create an output that has an `image` field, a `color` field and a `string`
-field.
-
-- An invocation output is a class that derives from the parent class of
-  `BaseInvocationOutput`.
-- All invocation outputs must use the `@invocation_output` decorator to provide
-  their unique output type.
-- Output fields must use the provided `OutputField` function. This is very
-  similar to the `InputField` function described earlier - it's a wrapper around
-  `pydantic`'s `Field()`.
-- It is not mandatory but we recommend using names ending with `Output` for
-  output types.
-- It is not mandatory but we highly recommend adding a `docstring` to describe
-  what your output type is for.
-
-Now that we know the basic rules for creating a new output type, let us go ahead
-and make it.
-
-```python
-from .baseinvocation import BaseInvocationOutput, OutputField, invocation_output
-from .primitives import ImageField, ColorField
-
-@invocation_output('image_color_string_output')
-class ImageColorStringOutput(BaseInvocationOutput):
-    '''Base class for nodes that output a single image'''
-
-    image: ImageField = OutputField(description="The image")
-    color: ColorField = OutputField(description="The color")
-    text: str = OutputField(description="The string")
-```
-
-That's all there is to it.
-
-<!-- TODO: DANGER - we probably do not want people to create their own field types, because this requires a lot of work on the frontend to accomodate.
-
-### Custom Input Fields
+## Custom Input Fields
 
 Now that you know how to create your own Invocations, let us dive into slightly
 more advanced topics.
@@ -399,7 +326,173 @@ like this.
 color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
 ```
 
-### Custom Components For Frontend
+**Extra Config**
+
+All input fields also take an additional `Config` class that you can use to do
+various advanced things like setting required parameters and etc.
+
+Let us do that for our _ColorField_ and enforce all the values because we did
+not define any defaults for our fields.
+
+```python
+class ColorField(BaseModel):
+    '''A field that holds the rgba values of a color'''
+    r: int = Field(ge=0, le=255, description="The red channel")
+    g: int = Field(ge=0, le=255, description="The green channel")
+    b: int = Field(ge=0, le=255, description="The blue channel")
+    a: int = Field(ge=0, le=255, description="The alpha channel")
+
+    class Config:
+        schema_extra = {"required": ["r", "g", "b", "a"]}
+```
+
+Now it becomes mandatory for the user to supply all the values required by our
+input field.
+
+We will discuss the `Config` class in extra detail later in this guide and how
+you can use it to make your Invocations more robust.
+
+## Custom Output Types
+
+Like with custom inputs, sometimes you might find yourself needing custom
+outputs that InvokeAI does not provide. We can easily set one up.
+
+Now that you are familiar with Invocations and Inputs, let us use that knowledge
+to put together a custom output type for an Invocation that returns _width_,
+_height_ and _background_color_ that we need to create a blank image.
+
+- A custom output type is a class that derives from the parent class of
+  `BaseInvocationOutput`.
+- It is not mandatory but we recommend using names ending with `Output` for
+  output types. So we'll call our class `BlankImageOutput`
+- It is not mandatory but we highly recommend adding a `docstring` to describe
+  what your output type is for.
+- Like Invocations, each output type should have a `type` variable that is
+  **unique**
+
+Now that we know the basic rules for creating a new output type, let us go ahead
+and make it.
+
+```python
+from typing import Literal
+from pydantic import Field
+
+from .baseinvocation import BaseInvocationOutput
+
+class BlankImageOutput(BaseInvocationOutput):
+    '''Base output type for creating a blank image'''
+    type: Literal['blank_image_output'] = 'blank_image_output'
+
+    # Inputs
+    width: int = Field(description='Width of blank image')
+    height: int = Field(description='Height of blank image')
+    bg_color: ColorField = Field(description='Background color of blank image')
+
+    class Config:
+        schema_extra = {"required": ["type", "width", "height", "bg_color"]}
+```
+
+All set. We now have an output type that requires what we need to create a
+blank_image. And if you noticed it, we even used the `Config` class to ensure
+the fields are required.
+
+## Custom Configuration
+
+As you might have noticed when making inputs and outputs, we used a class called
+`Config` from _pydantic_ to further customize them. Because our inputs and
+outputs essentially inherit from _pydantic_'s `BaseModel` class, all
+[configuration options](https://docs.pydantic.dev/latest/usage/schema/#schema-customization)
+that are valid for _pydantic_ classes are also valid for our inputs and outputs.
+You can do the same for your Invocations too but InvokeAI makes our life a
+little bit easier on that end.
+
+InvokeAI provides a custom configuration class called `InvocationConfig`
+particularly for configuring Invocations. This is exactly the same as the raw
+`Config` class from _pydantic_ with some extra stuff on top to help faciliate
+parsing of the scheme in the frontend UI.
+
+At the current moment, tihs `InvocationConfig` class is further improved with
+the following features related the `ui`.
+
+| Config Option | Field Type                                                                                                    | Example                                                                                                               |
+| ------------- | ------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
+| type_hints    | `Dict[str, Literal["integer", "float", "boolean", "string", "enum", "image", "latents", "model", "control"]]` | `type_hint: "model"` provides type hints related to the model like displaying a list of available models              |
+| tags          | `List[str]`                                                                                                   | `tags: ['resize', 'image']` will classify your invocation under the tags of resize and image.                         |
+| title         | `str`                                                                                                         | `title: 'Resize Image` will rename your to this custom title rather than infer from the name of the Invocation class. |
+
+So let us update your `ResizeInvocation` with some extra configuration and see
+how that works.
+
+```python
+from typing import Literal, Union
+from pydantic import Field
+
+from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
+from ..models.image import ImageField, ResourceOrigin, ImageCategory
+from .image import ImageOutput
+
+class ResizeInvocation(BaseInvocation):
+    '''Resizes an image'''
+    type: Literal['resize'] = 'resize'
+
+    # Inputs
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
+    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
+    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
+
+    class Config(InvocationConfig):
+        schema_extra: {
+            ui: {
+                tags: ['resize', 'image'],
+                title: ['My Custom Resize']
+            }
+        }
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        # Load the image using InvokeAI's predefined Image Service.
+        image = context.services.images.get_pil_image(self.image.image_origin, self.image.image_name)
+
+        # Resizing the image
+        # Because we used the above service, we already have a PIL image. So we can simply resize.
+        resized_image = image.resize((self.width, self.height))
+
+        # Preparing the image for output using InvokeAI's predefined Image Service.
+        output_image = context.services.images.create(
+            image=resized_image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
+
+        # Returning the Image
+        return ImageOutput(
+            image=ImageField(
+                image_name=output_image.image_name,
+                image_origin=output_image.image_origin,
+            ),
+            width=output_image.width,
+            height=output_image.height,
+        )
+```
+
+We now customized our code to let the frontend know that our Invocation falls
+under `resize` and `image` categories. So when the user searches for these
+particular words, our Invocation will show up too.
+
+We also set a custom title for our Invocation. So instead of being called
+`Resize`, it will be called `My Custom Resize`.
+
+As simple as that.
+
+As time goes by, InvokeAI will further improve and add more customizability for
+Invocation configuration. We will have more documentation regarding this at a
+later time.
+
+# **[TODO]**
+
+## Custom Components For Frontend
 
 Every backend input type should have a corresponding frontend component so the
 UI knows what to render when you use a particular field type.
@@ -417,4 +510,281 @@ Let us create a new component for our custom color field we created above. When
 we use a color field, let us say we want the UI to display a color picker for
 the user to pick from rather than entering values. That is what we will build
 now.
--->
+
+---
+
+# OLD -- TO BE DELETED OR MOVED LATER
+
+---
+
+## 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."""
+
+    # fmt: off
+    type: Literal["upscale"] = "upscale"
+
+    # Inputs
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
+    strength: float                = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2, 4]           = Field(default=2, description="The upscale level")
+    # fmt: on
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["upscaling", "image"],
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(
+            self.image.image_origin, self.image.image_name
+        )
+        results = context.services.restoration.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_dto = context.services.images.create(
+            image=results[0][0],
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
+
+        return ImageOutput(
+            image=ImageField(
+                image_name=image_dto.image_name,
+                image_origin=image_dto.image_origin,
+            ),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
+```
+
+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.
+
+### Config
+
+```py
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["upscaling", "image"],
+            },
+        }
+```
+
+This is an optional configuration for the invocation. It inherits from
+pydantic's model `Config` class, and it used primarily to customize the
+autogenerated OpenAPI schema.
+
+The UI relies on the OpenAPI schema in two ways:
+
+- An API client & Typescript types are generated from it. This happens at build
+  time.
+- The node editor parses the schema into a template used by the UI to create the
+  node editor UI. This parsing happens at runtime.
+
+In this example, a `ui` key has been added to the `schema_extra` dict to provide
+some tags for the UI, to facilitate filtering nodes.
+
+See the Schema Generation section below for more information.
+
+### Invoke Function
+
+```py
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(
+            self.image.image_origin, self.image.image_name
+        )
+        results = context.services.restoration.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_dto = context.services.images.create(
+            image=results[0][0],
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
+
+        return ImageOutput(
+            image=ImageField(
+                image_name=image_dto.image_name,
+                image_origin=image_dto.image_origin,
+            ),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+```
+
+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"""
+
+    # fmt: off
+    type: Literal["image_output"] = "image_output"
+    image:      ImageField = Field(default=None, description="The output image")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
+    # fmt: on
+
+    class Config:
+        schema_extra = {"required": ["type", "image", "width", "height"]}
+```
+
+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.
+
+## Schema Generation
+
+Invocation, output and related classes are used to generate an OpenAPI schema.
+
+### Required Properties
+
+The schema generation treat all properties with default values as optional. This
+makes sense internally, but when when using these classes via the generated
+schema, we end up with e.g. the `ImageOutput` class having its `image` property
+marked as optional.
+
+We know that this property will always be present, so the additional logic
+needed to always check if the property exists adds a lot of extraneous cruft.
+
+To fix this, we can leverage `pydantic`'s
+[schema customisation](https://docs.pydantic.dev/usage/schema/#schema-customization)
+to mark properties that we know will always be present as required.
+
+Here's that `ImageOutput` class, without the needed schema customisation:
+
+```python
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    # fmt: off
+    type: Literal["image_output"] = "image_output"
+    image:      ImageField = Field(default=None, description="The output image")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
+    # fmt: on
+```
+
+The OpenAPI schema that results from this `ImageOutput` will have the `type`,
+`image`, `width` and `height` properties marked as optional, even though we know
+they will always have a value.
+
+```python
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    # fmt: off
+    type: Literal["image_output"] = "image_output"
+    image:      ImageField = Field(default=None, description="The output image")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
+    # fmt: on
+
+    # Add schema customization
+    class Config:
+        schema_extra = {"required": ["type", "image", "width", "height"]}
+```
+
+With the customization in place, the schema will now show these properties as
+required, obviating the need for extensive null checks in client code.
+
+See this `pydantic` issue for discussion on this solution:
+<https://github.com/pydantic/pydantic/discussions/4577>

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -35,34 +35,46 @@ access.
 
 ## Backend
 
-The backend is contained within the `./invokeai/backend` and `./invokeai/app` directories.
-To get started please install the development dependencies.
+The backend is contained within the `./invokeai/backend` folder structure. To
+get started however please install the development dependencies.
 
 From the root of the repository run the following command. Note the use of `"`.
 
 ```zsh
-pip install ".[dev,test]"
+pip install ".[test]"
 ```
 
-These are optional groups of packages which are defined within the `pyproject.toml`
-and will be required for testing the changes you make to the code.
+This in an optional group of packages which is defined within the
+`pyproject.toml` and will be required for testing the changes you make the the
+code.
 
-### Tests
+### Running Tests
 
-See the [tests documentation](./TESTS.md) for information about running and writing tests.
-### Reloading Changes
+We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
+be found under the `./tests` folder and can be run with a single `pytest`
+command. Optionally, to review test coverage you can append `--cov`.
 
-Experimenting with changes to the Python source code is a drag if you have to re-start the server —
-and re-load those multi-gigabyte models —
-after every change.
+```zsh
+pytest --cov
+```
 
-For a faster development workflow, add the `--dev_reload` flag when starting the server.
-The server will watch for changes to all the Python files in the `invokeai` directory and apply those changes to the
-running server on the fly.
+Test outcomes and coverage will be reported in the terminal. In addition a more
+detailed report is created in both XML and HTML format in the `./coverage`
+folder. The HTML one in particular can help identify missing statements
+requiring tests to ensure coverage. This can be run by opening
+`./coverage/html/index.html`.
 
-This will allow you to avoid restarting the server (and reloading models) in most cases, but there are some caveats; see
-the [jurigged documentation](https://github.com/breuleux/jurigged#caveats) for details.
+For example.
 
+```zsh
+pytest --cov; open ./coverage/html/index.html
+```
+
+??? info "HTML coverage report output"
+
+    ![html-overview](../assets/contributing/html-overview.png)
+
+    ![html-detail](../assets/contributing/html-detail.png)
 
 ## Front End
 
@@ -142,23 +154,6 @@ and so you'll have access to the same python environment as the InvokeAI app.
 
 This is _super_ handy.
 
-#### Enabling Type-Checking with Pylance
-
-We use python's typing system in InvokeAI. PR reviews will include checking that types are present and correct. We don't enforce types with `mypy` at this time, but that is on the horizon.
-
-Using a code analysis tool to automatically type check your code (and types) is very important when writing with types. These tools provide immediate feedback in your editor when types are incorrect, and following their suggestions lead to fewer runtime bugs.
-
-Pylance, installed at the beginning of this guide, is the de-facto python LSP (language server protocol). It provides type checking in the editor (among many other features). Once installed, you do need to enable type checking manually:
-
-- Open a python file
-- Look along the status bar in VSCode for `{ } Python`
-- Click the `{ }`
-- Turn type checking on - basic is fine
-
-You'll now see red squiggly lines where type issues are detected. Hover your cursor over the indicated symbols to see what's wrong.
-
-In 99% of cases when the type checker says there is a problem, there really is a problem, and you should take some time to understand and resolve what it is pointing out.
-
 #### Debugging configs with `launch.json`
 
 Debugging configs are managed in a `launch.json` file. Like most VSCode configs,

docs/contributing/TESTS.md

@@ -1,89 +0,0 @@
-# InvokeAI Backend Tests
-
-We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
-
-## Fast vs. Slow
-All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
-
-'Fast' tests are run to validate every PR, and are fast enough that they can be run routinely during development.
-
-'Slow' tests are currently only run manually on an ad-hoc basis. In the future, they may be automated to run nightly. Most developers are only expected to run the 'slow' tests that directly relate to the feature(s) that they are working on.
-
-As a rule of thumb, tests should be marked as 'slow' if there is a chance that they take >1s (e.g. on a CPU-only machine with slow internet connection). Common examples of slow tests are tests that depend on downloading a model, or running model inference.
-
-## Running Tests
-
-Below are some common test commands:
-```bash
-# Run the fast tests. (This implicitly uses the configured default option: `-m "not slow"`.)
-pytest tests/
-
-# Equivalent command to run the fast tests.
-pytest tests/ -m "not slow"
-
-# Run the slow tests.
-pytest tests/ -m "slow"
-
-# Run the slow tests from a specific file.
-pytest tests/path/to/slow_test.py -m "slow"
-
-# Run all tests (fast and slow).
-pytest tests -m ""
-```
-
-## Test Organization
-
-All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
-
-TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
-
-## Tests that depend on models
-
-There are a few things to keep in mind when adding tests that depend on models.
-
-1. If a required model is not already present, it should automatically be downloaded as part of the test setup.
-2. If a model is already downloaded, it should not be re-downloaded unnecessarily.
-3. Take reasonable care to keep the total number of models required for the tests low. Whenever possible, re-use models that are already required for other tests. If you are adding a new model, consider including a comment to explain why it is required/unique.
-
-There are several utilities to help with model setup for tests. Here is a sample test that depends on a model:
-```python
-import pytest
-import torch
-
-from invokeai.backend.model_management.models.base import BaseModelType, ModelType
-from invokeai.backend.util.test_utils import install_and_load_model
-
-@pytest.mark.slow
-def test_model(model_installer, torch_device):
-    model_info = install_and_load_model(
-        model_installer=model_installer,
-        model_path_id_or_url="HF/dummy_model_id",
-        model_name="dummy_model",
-        base_model=BaseModelType.StableDiffusion1,
-        model_type=ModelType.Dummy,
-    )
-
-    dummy_input = build_dummy_input(torch_device)
-
-    with torch.no_grad(), model_info as model:
-        model.to(torch_device, dtype=torch.float32)
-        output = model(dummy_input)
-
-    # Validate output...
-
-```
-
-## Test Coverage
-
-To review test coverage, append `--cov` to your pytest command:
-```bash
-pytest tests/ --cov
-```
-
-Test outcomes and coverage will be reported in the terminal. In addition, a more detailed report is created in both XML and HTML format in the `./coverage` folder. The HTML output is particularly helpful in identifying untested statements where coverage should be improved. The HTML report can be viewed by opening `./coverage/html/index.html`.
-
-??? info "HTML coverage report output"
-
-    ![html-overview](../assets/contributing/html-overview.png)
-
-    ![html-detail](../assets/contributing/html-detail.png)

docs/contributing/contribution_guides/development.md

@@ -4,46 +4,88 @@
 
 If you are looking to help to with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
 
-
-## **Get Started**
-
-To get started, take a look at our [new contributors checklist](newContributorChecklist.md)
-
-Once you're setup, for more information, you can review the documentation specific to your area of interest:
+For more information, please review our area specific documentation:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](./contributingToFrontend.md)
+* #### [Frontend Documentation](development_guides/contributingToFrontend.md)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 
-
-
-If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md), [translation](translation.md) or helping support other users and triage issues as they're reported in GitHub.
+If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md) or [translation](translation.md).
 
 There are two paths to making a development contribution: 
 
 1. Choosing an open issue to address. Open issues can be found in the [Issues](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen) section of the InvokeAI repository. These are tagged by the issue type (bug, enhancement, etc.) along with the “good first issues” tag denoting if they are suitable for first time contributors.
-    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help. 
+    1. Additional items can be found on our roadmap <******************************link to roadmap>******************************. The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help. 
 2. Opening a new issue or feature to add. **Please make sure you have searched through existing issues before creating new ones.**
 
 *Regardless of what you choose, please post in the  [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord before you start development in order to confirm that the issue or feature is aligned with the current direction of the project. We value our contributors time and effort and want to ensure that no one’s time is being misspent.*
 
 ## Best Practices: 
 * Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
-* Comments! Commenting your code helps reviewers easily understand your contribution
+* Comments! Commenting your code helps reviwers easily understand your contribution
 * Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
 * Make all communications public. This ensure knowledge is shared with the whole community
 
+## **How do I make a contribution?**
+
+Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
+
+Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
+
+1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
+2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
+3. Clone the repository to your local machine using:
+
+```bash
+git clone https://github.com/your-GitHub-username/InvokeAI.git
+```
+
+If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
+
+4. Create a new branch for your fix using:
+
+```bash
+git checkout -b branch-name-here
+```
+
+5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
+6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
+
+```bash
+git add insert-paths-of-changed-files-here
+```
+
+7. Store the contents of the index with a descriptive message.
+
+```bash
+git commit -m "Insert a short message of the changes made here"
+```
+
+8. Push the changes to the remote repository using
+
+```markdown
+git push origin branch-name-here
+```
+
+9. Submit a pull request to the **main** branch of the InvokeAI repository.
+10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
+11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
+12. Wait for the pull request to be reviewed by other collaborators.
+13. Make changes to the pull request if the reviewer(s) recommend them.
+14. Celebrate your success after your pull request is merged!
+
+If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
+
 ## **Where can I go for help?**
 
 If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
 
-For frontend related work, **@psychedelicious** is the best person to reach out to. 
-
-For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@psychedelicious**.
+For frontend related work, **@pyschedelicious** is the best person to reach out to. 
 
+For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.
 
 ## **What does the Code of Conduct mean for me?**
 
-Our [Code of Conduct](../../CODE_OF_CONDUCT.md)  means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code.
+Our [Code of Conduct](CODE_OF_CONDUCT.md)  means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code.
 

docs/contributing/contribution_guides/documentation.md

@@ -10,4 +10,4 @@ When updating or creating documentation, please keep in mind InvokeAI is a tool
 
 ## Help & Questions
 
-Please ping @imic or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.
+Please ping @imic1 or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.

docs/contributing/contribution_guides/newContributorChecklist.md

@@ -1,68 +0,0 @@
-# New Contributor Guide
-
-If you're a new contributor to InvokeAI or Open Source Projects, this is the guide for you. 
-
-## New Contributor Checklist
-- [x] Set up your local development environment & fork of InvokAI by following [the steps outlined here](../../installation/020_INSTALL_MANUAL.md#developer-install)
-- [x] Set up your local tooling with [this guide](InvokeAI/contributing/LOCAL_DEVELOPMENT/#developing-invokeai-in-vscode). Feel free to skip this step if you already have tooling you're comfortable with. 
-- [x] Familiarize yourself with [Git](https://www.atlassian.com/git) & our project structure by reading through the [development documentation](development.md)
-- [x] Join the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord
-- [x] Choose an issue to work on! This can be achieved by asking in the #dev-chat channel, tackling a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) or finding an item on the [roadmap](https://github.com/orgs/invoke-ai/projects/7). If nothing in any of those places catches your eye, feel free to work on something of interest to you! 
-- [x] Make your first Pull Request with the guide below
-- [x] Happy development! Don't be afraid to ask for help - we're happy to help you contribute!
-
-
-## How do I make a contribution?
-
-Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
-
-Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
-
-1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
-2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
-3. Clone the repository to your local machine using:
-```bash
-git clone https://github.com/your-GitHub-username/InvokeAI.git
-```
-If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
-4. Create a new branch for your fix using:
-```bash
-git checkout -b branch-name-here
-```
-5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
-6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
-```bash
-git add -A
-```
-7. Store the contents of the index with a descriptive message.
-```bash
-git commit -m "Insert a short message of the changes made here"
-```
-8. Push the changes to the remote repository using
-```bash
-git push origin branch-name-here
-```
-9. Submit a pull request to the **main** branch of the InvokeAI repository. If you're not sure how to, [follow this guide](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)
-10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
-11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
-12. Wait for the pull request to be reviewed by other collaborators.
-13. Make changes to the pull request if the reviewer(s) recommend them.
-14. Celebrate your success after your pull request is merged!
-
-If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
-
-
-## Best Practices: 
-* Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
-* Comments! Commenting your code helps reviewers easily understand your contribution
-* Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
-* Make all communications public. This ensure knowledge is shared with the whole community
-
-
-## **Where can I go for help?**
-
-If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
-
-For frontend related work, **@pyschedelicious** is the best person to reach out to. 
-
-For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.

docs/deprecated/CLI.md

@@ -211,8 +211,8 @@ Here are the invoke> command that apply to txt2img:
 | `--facetool <name>`                       | `-ft <name>`                              | `-ft gfpgan`                             | Select face restoration algorithm to use: gfpgan, codeformer                                                                                                                                                                                     |
 | `--codeformer_fidelity`                   | `-cf <float>`                             | `0.75`                                   | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality                                                                                                     |
 | `--save_original`                         | `-save_orig`                              | `False`                                  | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced.                                                                                                                                             |
-| `--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.                                                                                                                                                           |
+| `--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](../features/VARIATIONS.md). |
+| `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](../features/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.)                                   |

docs/deprecated/VARIATIONS.md

@@ -1,131 +0,0 @@
----
-title: Variations
----
-
-# :material-tune-variant: Variations
-
-## Intro
-
-InvokeAI's support for variations enables you to do the following:
-
-1. Generate a series of systematic variations of an image, given a prompt. The
-   amount of variation from one image to the next can be controlled.
-
-2. Given two or more variations that you like, you can combine them in a
-   weighted fashion.
-
-!!! Information ""
-
-    This cheat sheet provides a quick guide for how this works in practice, using
-    variations to create the desired image of Xena, Warrior Princess.
-
-## Step 1 -- Find a base image that you like
-
-The prompt we will use throughout is:
-
-`#!bash "lucy lawless as xena, warrior princess, character portrait, high resolution."`
-
-This will be indicated as `#!bash "prompt"` in the examples below.
-
-First we let SD create a series of images in the usual way, in this case
-requesting six iterations.
-
-<figure markdown>
-![var1](../assets/variation_walkthru/000001.3357757885.png)
-<figcaption> Seed 3357757885 looks nice </figcaption>
-</figure>
-
----
-
-## Step 2 - Generating Variations
-
-Let's try to generate some variations on this image. We select the "*"
-symbol in the line of icons above the image in order to fix the prompt
-and seed. Then we open up the "Variations" section of the generation
-panel and use the slider to set the variation amount to 0.2. The
-higher this value, the more each generated image will differ from the
-previous one.
-
-Now we run the prompt a second time, requesting six iterations. You
-will see six images that are thematically related to each other. Try
-increasing and decreasing the variation amount and see what happens.
-
-### **Variation Sub Seeding**
-
-Note that the output for each image has a `-V` option giving the "variant
-subseed" for that image, consisting of a seed followed by the variation amount
-used to generate it.
-
-This gives us a series of closely-related variations, including the two shown
-here.
-
-<figure markdown>
-![var2](../assets/variation_walkthru/000002.3647897225.png)
-<figcaption>subseed 3647897225</figcaption>
-</figure>
-
-<figure markdown>
-![var3](../assets/variation_walkthru/000002.1614299449.png)
-<figcaption>subseed 1614299449</figcaption>
-</figure>
-
-I like the expression on Xena's face in the first one (subseed 3647897225), and
-the armor on her shoulder in the second one (subseed 1614299449). Can we combine
-them to get the best of both worlds?
-
-We combine the two variations using `-V` (`--with_variations`). Again, we must
-provide the seed for the originally-chosen image in order for this to work.
-
-```bash
-invoke> "prompt"  -S3357757885 -V3647897225,0.1,1614299449,0.1
-Outputs:
-./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
-```
-
-Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The
-resulting image is close, but not exactly what I wanted:
-
-<figure markdown>
-![var4](../assets/variation_walkthru/000003.1614299449.png)
-<figcaption> subseed 1614299449 </figcaption>
-</figure>
-
-We could either try combining the images with different weights, or we can
-generate more variations around the almost-but-not-quite image. We do the
-latter, using both the `-V` (combining) and `-v` (variation strength) options.
-Note that we use `-n6` to generate 6 variations:
-
-```bash
-invoke> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1 -v0.05 -n6
-Outputs:
-./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
-./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
-./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3747154981:0.05 -S3357757885
-./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2664260391:0.05 -S3357757885
-./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,1642517170:0.05 -S3357757885
-./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
-```
-
-This produces six images, all slight variations on the combination of the chosen
-two images. Here's the one I like best:
-
-<figure markdown>
-![var5](../assets/variation_walkthru/000004.3747154981.png)
-<figcaption> subseed 3747154981 </figcaption>
-</figure>
-
-As you can see, this is a very powerful tool, which when combined with subprompt
-weighting, gives you great control over the content and quality of your
-generated images.
-
-## Variations and Samplers
-
-The sampler you choose has a strong effect on variation strength. Some
-samplers, such as `k_euler_a` are very "creative" and produce significant
-amounts of image-to-image variation even when the seed is fixed and the
-`-v` argument is very low. Others are more deterministic. Feel free to
-experiment until you find the combination that you like.
-
-Also be aware of the [Perlin Noise](../features/OTHER.md#thresholding-and-perlin-noise-initialization-options)
-feature, which provides another way of introducing variability into your
-image generation requests.

docs/features/CONCEPTS.md

@@ -0,0 +1,88 @@
+---
+title: Textual Inversion Embeddings and LoRAs
+---
+
+# :material-library-shelves: Textual Inversions and LoRAs
+
+With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
+
+
+## Using Textual Inversion Files
+
+Textual inversion (TI) files are small models that customize the output of
+Stable Diffusion image generation. They can augment SD with specialized subjects
+and artistic styles. They are also known as "embeds" in the machine learning
+world.
+
+Each TI file introduces one or more vocabulary terms to the SD model. These are
+known in InvokeAI as "triggers." Triggers are denoted using angle brackets 
+as in "<trigger-phrase>". The two most common type of
+TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
+different TI training packages. InvokeAI supports both formats, but its
+[built-in TI training system](TRAINING.md) produces `.pt`.
+
+The [Hugging Face company](https://huggingface.co/sd-concepts-library) has
+amassed a large ligrary of >800 community-contributed TI files covering a
+broad range of subjects and styles. You can also install your own or others' TI files 
+by placing them in the designated directory for the compatible model type
+
+### An Example
+
+Here are a few examples to illustrate how it works. All these images were
+generated using the command-line client and the Stable Diffusion 1.5 model:
+
+|         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
+| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
+| ![](../assets/concepts/image1.png) |  ![](../assets/concepts/image2.png)   |   ![](../assets/concepts/image3.png)   |     ![](../assets/concepts/image4.png)     |
+
+You can also combine styles and concepts:
+
+<figure markdown>
+  | A portrait of &lt;alf&gt; in &lt;cartoona-animal&gt; style |
+  | :--------------------------------------------------------: |
+  | ![](../assets/concepts/image5.png)                         |
+</figure>
+
+
+## Installing your Own TI Files
+
+You may install any number of `.pt` and `.bin` files simply by copying them into
+the `embedding` directory of the corresponding InvokeAI models directory (usually `invokeai`
+in your home directory). For example, you can simply move a Stable Diffusion 1.5 embedding file to
+the `sd-1/embedding` folder. Be careful not to overwrite one file with another.
+For example, TI files generated by the Hugging Face toolkit share the named
+`learned_embedding.bin`. You can rename these, or use subdirectories to keep them distinct.
+
+At startup time, InvokeAI will scan the various `embedding` directories and load any TI
+files it finds there for compatible models. At startup you will see a message similar to this one:
+
+```bash
+>> Current embedding manager terms: <HOI4-Leader>, <princess-knight>
+```
+To use these when generating, simply type the `<` key in your prompt to open the Textual Inversion WebUI and 
+select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
+
+## Using LoRAs
+
+LoRA files are models that customize the output of Stable Diffusion
+image generation.  Larger than embeddings, but much smaller than full
+models, they augment SD with improved understanding of subjects and
+artistic styles.
+
+Unlike TI files, LoRAs do not introduce novel vocabulary into the
+model's known tokens. Instead, LoRAs augment the model's weights that
+are applied to generate imagery. LoRAs may be supplied with a
+"trigger" word that they have been explicitly trained on, or may
+simply apply their effect without being triggered.
+
+LoRAs are typically stored in .safetensors files, which are the most
+secure way to store and transmit these types of weights. You may
+install any number of `.safetensors` LoRA files simply by copying them
+into the `autoimport/lora` directory of the corresponding InvokeAI models
+directory (usually `invokeai` in your home directory).
+
+To use these when generating, open the LoRA menu item in the options
+panel, select the LoRAs you want to apply and ensure that they have
+the appropriate weight recommended by the model provider. Typically,
+most LoRAs perform best at a weight of .75-1.
+

docs/features/CONFIGURATION.md

@@ -82,7 +82,7 @@ format of YAML files can be found
 [here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
 
 You can fix a broken `invokeai.yaml` by deleting it and running the
-configuration script again -- option [6] in the launcher, "Re-run the
+configuration script again -- option [7] in the launcher, "Re-run the
 configure script".
 
 #### Reading Environment Variables
@@ -154,16 +154,14 @@ groups in `invokeia.yaml`:
 
 ### Web Server
 
-| Setting             | Default Value | Description                                                                                                                |
-|---------------------|---------------|----------------------------------------------------------------------------------------------------------------------------|
-| `host`              | `localhost`   | Name or IP address of the network interface that the web server will listen on                                             |
-| `port`              | `9090`        | Network port number that the web server will listen on                                                                     |
-| `allow_origins`     | `[]`          | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
-| `allow_credentials` | `true`        | Require credentials for a foreign host to access the InvokeAI API (don't change this)                                      |
-| `allow_methods`     | `*`           | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API                          |
-| `allow_headers`     | `*`           | List of HTTP headers that the web server will accept when accessing the API                                                |
-| `ssl_certfile`      | null          | Path to an SSL certificate file, used to enable HTTPS.                                                                     |
-| `ssl_keyfile`       | null          | Path to an SSL keyfile, if the key is not included in the certificate file.                                                |
+| Setting  | Default Value  |  Description |
+|----------|----------------|--------------|
+| `host`     | `localhost`      | Name or IP address of the network interface that the web server will listen on  |
+| `port`     | `9090`           | Network port number that the web server will listen on  |
+| `allow_origins`  | `[]`       | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
+| `allow_credentials | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
+| `allow_methods` | `*`         | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API  |
+| `allow_headers` | `*`         | List of HTTP headers that the web server will accept when accessing the API  |
 
 The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
 
@@ -177,27 +175,22 @@ These configuration settings allow you to enable and disable various InvokeAI fe
 | `internet_available` | `true`     | When a resource is not available locally, try to fetch it via the internet |
 | `log_tokenization` | `false`      | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
 | `patchmatch` | `true`     | Activate the "patchmatch" algorithm for improved inpainting |
+| `restore`    | `true`     | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
 
-### Generation
+### Memory/Performance
 
 These options tune InvokeAI's memory and performance characteristics.
 
-| Setting               | Default Value | Description                                                                                                                                                                                                                                                      |
-|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `sequential_guidance` | `false`       | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss                                                                                                                                          |
-| `attention_type`      | `auto`        | Select the type of attention to use. One of `auto`,`normal`,`xformers`,`sliced`, or `torch-sdp`                                                                                                                                                                  |
-| `attention_slice_size` | `auto`       | When "sliced" attention is selected, set the slice size. One of `auto`, `balanced`, `max` or the integers 1-8|
-| `force_tiled_decode`  | `false`       | Force the VAE step to decode in tiles, reducing memory consumption at the cost of performance |
-
-### Device
-
-These options configure the generation execution device.
-
-| Setting               | Default Value | Description                                                                                                                                                                                                                                                      |
-|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `device`           | `auto`        | Preferred execution device. One of `auto`, `cpu`, `cuda`, `cuda:1`, `mps`. `auto` will choose the device depending on the hardware platform and the installed torch capabilities. |
-| `precision`           | `auto`        | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
-
+| Setting  | Default Value  |  Description |
+|----------|----------------|--------------|
+| `always_use_cpu`     | `false`      | Use the CPU to generate images, even if a GPU is available |
+| `free_gpu_mem`       | `false`      | Aggressively free up GPU memory after each operation; this will allow you to run in low-VRAM environments with some performance penalties |
+| `max_cache_size`       | `6`      | Amount of CPU RAM (in GB) to reserve for caching models in memory; more cache allows you to keep models in memory and switch among them quickly |
+| `max_vram_cache_size`  | `2.75`   | Amount of GPU VRAM (in GB) to reserve for caching models in VRAM; more cache speeds up generation but reduces the size of the images that can be generated. This can be set to zero to maximize the amount of memory available for generation. |
+| `precision`       | `auto`      | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
+| `sequential_guidance`     | `false`      | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
+| `xformers_enabled`        | `true`      | If the x-formers memory-efficient attention module is installed, activate it for better memory usage and generation speed|
+| `tiled_decode`            | `false`     | If true, then during the VAE decoding phase the image will be decoded a section at a time, reducing memory consumption at the cost of a performance hit |
 
 ### Paths
 

docs/features/CONTROLNET.md

@@ -1,11 +1,13 @@
 ---
-title: Control Adapters
+title: ControlNet
 ---
 
-# :material-loupe: Control Adapters
+# :material-loupe: ControlNet
 
 ## ControlNet
 
+ControlNet
+
 ControlNet is a powerful set of features developed by the open-source
 community (notably, Stanford researcher
 [**@ilyasviel**](https://github.com/lllyasviel)) that allows you to
@@ -17,6 +19,9 @@ image generation, providing you with a way to direct the network
 towards generating images that better fit your desired style or
 outcome.
 
+
+### How it works
+
 ControlNet works by analyzing an input image, pre-processing that
 image to identify relevant information that can be interpreted by each
 specific ControlNet model, and then inserting that control information
@@ -24,21 +29,35 @@ into the generation process. This can be used to adjust the style,
 composition, or other aspects of the image to better achieve a
 specific result.
 
-#### Installation
+
+### Models
 
 InvokeAI provides access to a series of ControlNet models that provide
-different effects or styles in your generated images.
+different effects or styles in your generated images.  Currently
+InvokeAI only supports "diffuser" style ControlNet models. These are
+folders that contain the files `config.json` and/or
+`diffusion_pytorch_model.safetensors` and
+`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
+the name of the model.
 
-To install ControlNet Models:
+***InvokeAI does not currently support checkpoint-format
+ControlNets. These come in the form of a single file with the
+extension `.safetensors`.***
 
-1. The easiest way to install them is
+Diffuser-style ControlNet models are available at HuggingFace
+(http://huggingface.co) and accessed via their repo IDs (identifiers
+in the format "author/modelname"). The easiest way to install them is
 to use the InvokeAI model installer application. Use the
-`invoke.sh`/`invoke.bat` launcher to select item [4] and then navigate
+`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
 to the CONTROLNETS section. Select the models you wish to install and
 press "APPLY CHANGES". You may also enter additional HuggingFace
-repo_ids in the "Additional models" textbox. 
-2. Using the "Add Model" function of the  model manager, enter the HuggingFace Repo ID of the ControlNet. The ID is in the format "author/repoName"
+repo_ids in the "Additional models" textbox:
 
+![Model Installer -
+Controlnetl](../assets/installing-models/model-installer-controlnet.png){:width="640px"}
+
+Command-line users can launch the model installer using the command
+`invokeai-model-install`.
 
 _Be aware that some ControlNet models require additional code
 functionality in order to work properly, so just installing a
@@ -46,17 +65,6 @@ third-party ControlNet model may not have the desired effect._ Please
 read and follow the documentation for installing a third party model
 not currently included among InvokeAI's default list.
 
-Currently InvokeAI **only** supports 🤗 Diffusers-format ControlNet models. These are
-folders that contain the files `config.json` and/or
-`diffusion_pytorch_model.safetensors` and
-`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
-the name of the model.
-
-🤗 Diffusers-format ControlNet models are available at HuggingFace
-(http://huggingface.co) and accessed via their repo IDs (identifiers
-in the format "author/modelname").
-
-#### ControlNet Models
 The models currently supported include:
 
 **Canny**:
@@ -88,8 +96,6 @@ A model that generates normal maps from input images, allowing for more realisti
 **Image Segmentation**: 
 A model that divides input images into segments or regions, each of which corresponds to a different object or part of the image. (More details coming soon)
 
-**QR Code Monster**:
-A model that helps generate creative QR codes that still scan. Can also be used to create images with text, logos or shapes within them. 
 
 **Openpose**: 
 The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image. 
@@ -98,7 +104,7 @@ The OpenPose control model allows for the identification of the general pose of
 
 The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.
 
-**Tile**:
+**Tile (experimental)**:
 
 The Tile model fills out details in the image to match the image, rather than the prompt. The Tile Model is a versatile tool that offers a range of functionalities. Its primary capabilities can be boiled down to two main behaviors:
 
@@ -111,10 +117,12 @@ The Tile Model can be a powerful tool in your arsenal for enhancing image qualit
 
 With Pix2Pix, you can input an image into the controlnet, and then "instruct" the model to change it using your prompt. For example, you can say "Make it winter" to add more wintry elements to a scene.
 
+**Inpaint**: Coming Soon - Currently this model is available but not functional on the Canvas. An upcoming release will provide additional capabilities for using this model when inpainting.
+
 Each of these models can be adjusted and combined with other ControlNet models to achieve different results, giving you even more control over your image generation process.
 
 
-### Using ControlNet
+## Using ControlNet
 
 To use ControlNet, you can simply select the desired model and adjust both the ControlNet and Pre-processor settings to achieve the desired result. You can also use multiple ControlNet models at the same time, allowing you to achieve even more complex effects or styles in your generated images.
 
@@ -126,54 +134,3 @@ Weight - Strength of the Controlnet model applied to the generation for the sect
 Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
 
 Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke.
-
-## T2I-Adapter
-[T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) is a tool similar to ControlNet that allows for control over the generation process by providing control information during the generation process. T2I-Adapter models tend to be smaller and more efficient than ControlNets. 
-
-##### Installation
-To install T2I-Adapter Models:
-
-1. The easiest way to install models is
-to use the InvokeAI model installer application. Use the
-`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
-to the T2I-Adapters section. Select the models you wish to install and
-press "APPLY CHANGES". You may also enter additional HuggingFace
-repo_ids in the "Additional models" textbox. 
-2. Using the "Add Model" function of the  model manager, enter the HuggingFace Repo ID of the T2I-Adapter. The ID is in the format "author/repoName"
-
-#### Usage
-Each T2I Adapter has two settings that are applied.
-
-Weight - Strength of the model applied to the generation for the section, defined by start/end.
-
-Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
-
-Additionally, each  section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process.
-
-
-## IP-Adapter
-
-[IP-Adapter](https://ip-adapter.github.io) is a tooling that allows for image prompt capabilities with text-to-image diffusion models. IP-Adapter works by analyzing the given image prompt to extract features, then passing those features to the UNet along with any other conditioning provided. 
-
-![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.jpg)
-
-![IP-Adapter + IMG2IMG](https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/demo/image-to-image.jpg)
-
-#### Installation
-There are several ways to install IP-Adapter models with an existing InvokeAI installation:
-
-1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
-2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
-3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. 
-
-#### Using IP-Adapter
-
-IP-Adapter can be used by navigating to the *Control Adapters* options and enabling IP-Adapter. 
-
-IP-Adapter requires an image to be used as the Image Prompt. It can also be used in conjunction with text prompts, Image-to-Image, Inpainting, Outpainting, ControlNets and LoRAs.
-
-
-Each IP-Adapter has two settings that are applied to the IP-Adapter:
-
-* Weight - Strength of the IP-Adapter model applied to the generation for the section, defined by start/end
-* Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the IP-Adapter applied.

docs/features/LORAS.md

@@ -1,53 +0,0 @@
----
-title: LoRAs & LCM-LoRAs
----
-
-# :material-library-shelves: LoRAs & LCM-LoRAs
-
-With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
-
-## LoRAs
-
-Low-Rank Adaptation (LoRA) files are models that customize the output of Stable Diffusion
-image generation.  Larger than embeddings, but much smaller than full
-models, they augment SD with improved understanding of subjects and
-artistic styles.
-
-Unlike TI files, LoRAs do not introduce novel vocabulary into the
-model's known tokens. Instead, LoRAs augment the model's weights that
-are applied to generate imagery. LoRAs may be supplied with a
-"trigger" word that they have been explicitly trained on, or may
-simply apply their effect without being triggered.
-
-LoRAs are typically stored in .safetensors files, which are the most
-secure way to store and transmit these types of weights. You may
-install any number of `.safetensors` LoRA files simply by copying them
-into the `autoimport/lora` directory of the corresponding InvokeAI models
-directory (usually `invokeai` in your home directory).
-
-To use these when generating, open the LoRA menu item in the options
-panel, select the LoRAs you want to apply and ensure that they have
-the appropriate weight recommended by the model provider. Typically,
-most LoRAs perform best at a weight of .75-1.
-
-
-## LCM-LoRAs
-Latent Consistency Models (LCMs) allowed a reduced number of steps to be used to generate images with Stable Diffusion. These are created by distilling base models, creating models that only require a small number of steps to generate images. However, LCMs require that any fine-tune of a base model be distilled to be used as an LCM. 
-
-LCM-LoRAs are models that provide the benefit of LCMs but are able to be used as LoRAs and applied to any fine tune of a base model. LCM-LoRAs are created by training a small number of adapters, rather than distilling the entire fine-tuned base model. The resulting LoRA can be used the same way as a standard LoRA, but with a greatly reduced step count. This enables SDXL images to be generated up to 10x faster than without the use of LCM-LoRAs. 
-
-
-**Using LCM-LoRAs**
-
-LCM-LoRAs are natively supported in InvokeAI throughout the application. To get started, install any diffusers format LCM-LoRAs using the model manager and select it in the LoRA field.
-
-There are a number parameter differences when using LCM-LoRAs and standard generation: 
-
-- When using LCM-LoRAs, the LoRA strength should be lower than if using a standard LoRA, with 0.35 recommended as a starting point.  
-- The LCM scheduler should be used for generation
-- CFG-Scale should be reduced to ~1
-- Steps should be reduced in the range of 4-8
-
-Standard LoRAs can also be used alongside LCM-LoRAs, but will also require a lower strength, with 0.45 being recommended as a starting point. 
-
-More information can be found here: https://huggingface.co/blog/lcm_lora#fast-inference-with-sdxl-lcm-loras

docs/features/MODEL_MERGING.md

@@ -2,51 +2,17 @@
 title: Model Merging
 ---
 
-InvokeAI provides the ability to merge two or three diffusers-type models into a new merged model. The
-resulting model will combine characteristics of the original, and can
-be used to teach an old model new tricks.
+# :material-image-off: Model Merging
 
 ## How to Merge Models
 
-Model Merging can be be done by navigating to the Model Manager and clicking the "Merge Models" tab. From there, you can select the models and settings you want to use to merge th models. 
-
-## Settings
-
-* Model Selection: there are three multiple choice fields that
-  display all the diffusers-style models that InvokeAI knows about.
-  If you do not see the model you are looking for, then it is probably
-  a legacy checkpoint model and needs to be converted using the
-  "Convert" option in the Web-based Model Manager tab.
-  
-  You must select at least two models to merge. The third can be left
-  at "None" if you desire.
-
-* Alpha: This is the ratio to use when combining models. It ranges
-  from 0 to 1. The higher the value, the more weight is given to the
-  2d and (optionally) 3d models. So if you have two models named "A"
-  and "B", an alpha value of 0.25 will give you a merged model that is
-  25% A and 75% B.
-
-* Interpolation Method: This is the method used to combine
-  weights. The options are "weighted_sum" (the default), "sigmoid",
-  "inv_sigmoid" and "add_difference". Each produces slightly different
-  results. When three models are in use, only "add_difference" is
-  available.
-
-* Save Location: The location you want the merged model to be saved in. Default is in the InvokeAI root folder
-
-* Name for merged model: This is the name for the new model. Please
-  use InvokeAI conventions - only alphanumeric letters and the
-  characters ".+-".
-
-* Ignore Mismatches / Force: Not all models are compatible with each other. The merge
-  script will check for compatibility and refuse to merge ones that
-  are incompatible. Set this checkbox to try merging anyway.
-
-
+As of version 2.3, InvokeAI comes with a script that allows you to
+merge two or three diffusers-type models into a new merged model. The
+resulting model will combine characteristics of the original, and can
+be used to teach an old model new tricks.
 
 You may run the merge script by starting the invoke launcher
-(`invoke.sh` or `invoke.bat`) and choosing the option (4) for _merge
+(`invoke.sh` or `invoke.bat`) and choosing the option for _merge
 models_. This will launch a text-based interactive user interface that
 prompts you to select the models to merge, how to merge them, and the
 merged model name.
@@ -74,4 +40,34 @@ this to get back.
 If the merge runs successfully, it will create a new diffusers model
 under the selected name and register it with InvokeAI.
 
+## The Settings
+
+* Model Selection -- there are three multiple choice fields that
+  display all the diffusers-style models that InvokeAI knows about.
+  If you do not see the model you are looking for, then it is probably
+  a legacy checkpoint model and needs to be converted using the
+  `invoke` command-line client and its `!optimize` command. You
+  must select at least two models to merge. The third can be left at
+  "None" if you desire.
+
+* Alpha -- This is the ratio to use when combining models. It ranges
+  from 0 to 1. The higher the value, the more weight is given to the
+  2d and (optionally) 3d models. So if you have two models named "A"
+  and "B", an alpha value of 0.25 will give you a merged model that is
+  25% A and 75% B.
+
+* Interpolation Method -- This is the method used to combine
+  weights. The options are "weighted_sum" (the default), "sigmoid",
+  "inv_sigmoid" and "add_difference". Each produces slightly different
+  results. When three models are in use, only "add_difference" is
+  available. (TODO: cite a reference that describes what these
+  interpolation methods actually do and how to decide among them).
+
+* Force -- Not all models are compatible with each other. The merge
+  script will check for compatibility and refuse to merge ones that
+  are incompatible. Set this checkbox to try merging anyway.
+
+* Name for merged model - This is the name for the new model. Please
+  use InvokeAI conventions - only alphanumeric letters and the
+  characters ".+-".
 

docs/features/NODES.md

@@ -0,0 +1,208 @@
+# Nodes Editor (Experimental)
+
+🚨
+*The node editor is experimental. We've made it accessible because we use it to develop the application, but we have not addressed the many known rough edges. It's very easy to shoot yourself in the foot, and we cannot offer support for it until it sees full release (ETA v3.1). Everything is subject to change without warning.* 
+🚨
+
+The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. The node processing flow is usually done from left (inputs) to right (outputs), though linearity can become abstracted the more complex the node graph becomes. Nodes inputs and outputs are connected by dragging connectors from node to node.
+
+To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
+
+## Anatomy of a Node
+
+Individual nodes are made up of the following:
+
+- Inputs: Edge points on the left side of the node window where you connect outputs from other nodes.
+- Outputs: Edge points on the right side of the node window where you connect to inputs on other nodes.
+- Options: Various options which are either manually configured, or overridden by connecting an output from another node to the input.
+
+## Diffusion Overview
+
+Taking the time to understand the diffusion process will help you to understand how to set up your nodes in the nodes editor. 
+
+There are two main spaces Stable Diffusion works in: image space and latent space.
+
+Image space represents images in pixel form that you look at. Latent space represents compressed inputs. It’s in latent space that Stable Diffusion processes images. A VAE (Variational Auto Encoder) is responsible for compressing and encoding inputs into latent space, as well as decoding outputs back into image space.
+
+When you generate an image using text-to-image, multiple steps occur in latent space:
+1. Random noise is generated at the chosen height and width. The noise’s characteristics are dictated by the chosen (or not chosen) seed. This noise tensor is passed into latent space. We’ll call this noise A.
+1. Using a model’s U-Net, a noise predictor examines noise A, and the words tokenized by CLIP from your prompt (conditioning). It generates its own noise tensor to predict what the final image might look like in latent space. We’ll call this noise B.
+1. Noise B is subtracted from noise A in an attempt to create a final latent image indicative of the inputs. This step is repeated for the number of sampler steps chosen.
+1. The VAE decodes the final latent image from latent space into image space.
+
+image-to-image is a similar process, with only step 1 being different:
+1. The input image is decoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how much noise is added, 0 being none, and 1 being all-encompassing. We’ll call this noise A. The process is then the same as steps 2-4 in the text-to-image explanation above. 
+
+Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
+
+A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from the latent image across the sampler steps chosen (step 3 above). Less noise is usually subtracted at higher sampler steps. 
+
+## Node Types (Base Nodes)
+
+| Node <img width=160 align="right"> | Function                                                                              |
+| ---------------------------------- | --------------------------------------------------------------------------------------|
+| Add                                | Adds two numbers |
+| CannyImageProcessor                | Canny edge detection for ControlNet |
+| ClipSkip                           | Skip layers in clip text_encoder model |
+| Collect                            | Collects values into a collection |
+| Prompt (Compel)                    | Parse prompt using compel package to conditioning |
+| ContentShuffleImageProcessor       | Applies content shuffle processing to image |
+| ControlNet                         | Collects ControlNet info to pass to other nodes |
+| CvInpaint                          | Simple inpaint using opencv |
+| Divide                             | Divides two numbers |
+| DynamicPrompt                      | Parses a prompt using adieyal/dynamic prompt's random or combinatorial generator |
+| FloatLinearRange                   | Creates a range |
+| HedImageProcessor                  | Applies HED edge detection to image |
+| ImageBlur                          | Blurs an image |
+| ImageChannel                       | Gets a channel from an image |
+| ImageCollection                    | Load a collection of images and provide it as output |
+| ImageConvert                       | Converts an image to a different mode |
+| ImageCrop                          | Crops an image to a specified box. The box can be outside of the image. |
+| ImageInverseLerp                   | Inverse linear interpolation of all pixels of an image |
+| ImageLerp                          | Linear interpolation of all pixels of an image |
+| ImageMultiply                      | Multiplies two images together using `PIL.ImageChops.Multiply()` |
+| ImageNSFWBlurInvocation            | Detects and blurs images that may contain sexually explicit content |
+| ImagePaste                         | Pastes an image into another image |
+| ImageProcessor                     | Base class for invocations that reprocess images for ControlNet |
+| ImageResize                        | Resizes an image to specific dimensions |
+| ImageScale                         | Scales an image by a factor |
+| ImageToLatents                     | Scales latents by a given factor |
+| ImageWatermarkInvocation           | Adds an invisible watermark to images |
+| InfillColor                        | Infills transparent areas of an image with a solid color |
+| InfillPatchMatch                   | Infills transparent areas of an image using the PatchMatch algorithm |
+| InfillTile                         | Infills transparent areas of an image with tiles of the image |
+| Inpaint                            | Generates an image using inpaint |
+| Iterate                            | Iterates over a list of items |
+| LatentsToImage                     | Generates an image from latents |
+| LatentsToLatents                   | Generates latents using latents as base image |
+| LeresImageProcessor                | Applies leres processing to image |
+| LineartAnimeImageProcessor         | Applies line art anime processing to image |
+| LineartImageProcessor              | Applies line art processing to image |
+| LoadImage                          | Load an image and provide it as output |
+| Lora Loader                        | Apply selected lora to unet and text_encoder |
+| Model Loader                       | Loads a main model, outputting its submodels |
+| MaskFromAlpha                      | Extracts the alpha channel of an image as a mask |
+| MediapipeFaceProcessor             | Applies mediapipe face processing to image |
+| MidasDepthImageProcessor           | Applies Midas depth processing to image |
+| MlsdImageProcessor                 | Applied MLSD processing to image |
+| Multiply                           | Multiplies two numbers |
+| Noise                              | Generates latent noise |
+| NormalbaeImageProcessor            | Applies NormalBAE processing to image |
+| OpenposeImageProcessor             | Applies Openpose processing to image |
+| ParamFloat                         | A float parameter |
+| ParamInt                           | An integer parameter |
+| PidiImageProcessor                 | Applies PIDI processing to an image |
+| Progress Image                     | Displays the progress image in the Node Editor |
+| RandomInit                         | Outputs a single random integer |
+| RandomRange                        | Creates a collection of random numbers |
+| Range                              | Creates a range of numbers from start to stop with step |
+| RangeOfSize                        | Creates a range from start to start + size with step |
+| ResizeLatents                      | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8. |
+| RestoreFace                        | Restores faces in the image |
+| ScaleLatents                       | Scales latents by a given factor |
+| SegmentAnythingProcessor           | Applies segment anything processing to image |
+| ShowImage                          | Displays a provided image, and passes it forward in the pipeline |
+| StepParamEasing                    | Experimental per-step parameter for easing for denoising steps |
+| Subtract                           | Subtracts two numbers |
+| TextToLatents                      | Generates latents from conditionings |
+| TileResampleProcessor              | Bass class for invocations that preprocess images for ControlNet |
+| Upscale                            | Upscales an image |
+| VAE Loader                         | Loads a VAE model, outputting a VaeLoaderOutput |
+| ZoeDepthImageProcessor             | Applies Zoe depth processing to image |
+
+## Node Grouping Concepts
+
+There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
+
+### Noise
+
+As described, an initial noise tensor is necessary for the latent diffusion process. As a result, all non-image *ToLatents nodes require a noise node input.  
+
+![groupsnoise](../assets/nodes/groupsnoise.png)
+
+### Conditioning
+
+As described, conditioning is necessary for the latent diffusion process, whether empty or not. As a result, all non-image *ToLatents nodes require positive and negative conditioning inputs. Conditioning is reliant on a CLIP tokenizer provided by the Model Loader node.
+
+![groupsconditioning](../assets/nodes/groupsconditioning.png)
+
+### Image Space & VAE
+
+The ImageToLatents node doesn't require a noise node input, but requires a VAE input to convert the image from image space into latent space. In reverse, the LatentsToImage node requires a VAE input to convert from latent space back into image space.
+
+![groupsimgvae](../assets/nodes/groupsimgvae.png)
+
+### Defined & Random Seeds
+
+It is common to want to use both the same seed (for continuity) and random seeds (for variance). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
+
+![groupsrandseed](../assets/nodes/groupsrandseed.png)
+
+### Control
+
+Control means to guide the diffusion process to adhere to a defined input or structure. Control can be provided as input to non-image *ToLatents nodes from ControlNet nodes. ControlNet nodes usually require an image processor which converts an input image for use with ControlNet.
+
+![groupscontrol](../assets/nodes/groupscontrol.png)
+
+### LoRA
+
+The Lora Loader node lets you load a LoRA (say that ten times fast) and pass it as output to both the Prompt (Compel) and non-image *ToLatents nodes. A model's CLIP tokenizer is passed through the LoRA into Prompt (Compel), where it affects conditioning. A model's U-Net is also passed through the LoRA into a non-image *ToLatents node, where it affects noise prediction.
+
+![groupslora](../assets/nodes/groupslora.png)
+
+### Scaling
+
+Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results.
+
+![groupsallscale](../assets/nodes/groupsallscale.png)
+
+### Iteration + Multiple Images as Input
+
+Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and pass them out one at a time.
+
+![groupsiterate](../assets/nodes/groupsiterate.png)
+
+### Multiple Image Generation + Random Seeds
+
+Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. 
+
+To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
+
+![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png)
+
+## Examples
+
+With our knowledge of node grouping and the diffusion process, let’s break down some basic graphs in the nodes editor. Note that a node's options can be overridden by inputs from other nodes. These examples aren't strict rules to follow and only demonstrate some basic configurations.
+
+### Basic text-to-image Node Graph
+
+![nodest2i](../assets/nodes/nodest2i.png)
+
+- Model Loader: A necessity to generating images (as we’ve read above). We choose our model from the dropdown. It outputs a U-Net, CLIP tokenizer, and VAE.
+- Prompt (Compel): Another necessity. Two prompt nodes are created. One will output positive conditioning (what you want, ‘dog’), one will output negative (what you don’t want, ‘cat’). They both input the CLIP tokenizer that the Model Loader node outputs.
+- Noise: Consider this noise A from step one of the text-to-image explanation above. Choose a seed number, width, and height.
+- TextToLatents: This node takes many inputs for converting and processing text & noise from image space into latent space, hence the name TextTo**Latents**. In this setup, it inputs positive and negative conditioning from the prompt nodes for processing (step 2 above). It inputs noise from the noise node for processing (steps 2 & 3 above). Lastly, it inputs a U-Net from the Model Loader node for processing (step 2 above). It outputs latents for use in the next LatentsToImage node. Choose number of sampler steps, CFG scale, and scheduler.
+- LatentsToImage: This node takes in processed latents from the TextToLatents node, and the model’s VAE from the Model Loader node which is responsible for decoding latents back into the image space, hence the name LatentsTo**Image**. This node is the last stop, and once the image is decoded, it is saved to the gallery.
+
+### Basic image-to-image Node Graph
+
+![nodesi2i](../assets/nodes/nodesi2i.png)
+
+- Model Loader: Choose a model from the dropdown.
+- Prompt (Compel): Two prompt nodes. One positive (dog), one negative (dog). Same CLIP inputs from the Model Loader node as before.
+- ImageToLatents: Upload a source image directly in the node window, via drag'n'drop from the gallery, or passed in as input. The ImageToLatents node inputs the VAE from the Model Loader node to decode the chosen image from image space into latent space, hence the name ImageTo**Latents**. It outputs latents for use in the next LatentsToLatents node. It also outputs the source image's width and height for use in the next Noise node if the final image is to be the same dimensions as the source image.
+- Noise: A noise tensor is created with the width and height of the source image, and connected to the next LatentsToLatents node. Notice the width and height fields are overridden by the input from the ImageToLatents width and height outputs.
+- LatentsToLatents: The inputs and options are nearly identical to TextToLatents, except that LatentsToLatents also takes latents as an input. Considering our source image is already converted to latents in the last ImageToLatents node, and text + noise are no longer the only inputs to process, we use the LatentsToLatents node. 
+- LatentsToImage: Like previously, the LatentsToImage node will use the VAE from the Model Loader as input to decode the latents from LatentsToLatents into image space, and save it to the gallery.
+
+### Basic ControlNet Node Graph
+
+![nodescontrol](../assets/nodes/nodescontrol.png)
+
+- Model Loader
+- Prompt (Compel)
+- Noise: Width and height of the CannyImageProcessor ControlNet image is passed in to set the dimensions of the noise passed to TextToLatents.
+- CannyImageProcessor: The CannyImageProcessor node is used to process the source image being used as a ControlNet. Each ControlNet processor node applies control in different ways, and has some different options to configure. Width and height are passed to noise, as mentioned. The processed ControlNet image is output to the ControlNet node.
+- ControlNet: Select the type of control model. In this case, canny is chosen as the CannyImageProcessor was used to generate the ControlNet image. Configure the control node options, and pass the control output to TextToLatents.
+- TextToLatents: Similar to the basic text-to-image example, except ControlNet is passed to the control input edge point.
+- LatentsToImage

docs/features/POSTPROCESS.md

@@ -4,13 +4,35 @@ title: Postprocessing
 
 # :material-image-edit: Postprocessing
 
-This sections details the ability to improve faces and upscale images.
+## Intro
+
+This extension provides the ability to restore faces and upscale images.
 
 ## Face Fixing
 
-As of InvokeAI 3.0, the easiest way to improve faces created during image generation is through the Inpainting functionality of the Unified Canvas. Simply add the image containing the faces that you would like to improve to the canvas, mask the face to be improved and run the invocation. For best results, make sure to use an inpainting specific model; these are usually identified by the "-inpainting" term in the model name. 
+The default face restoration module is GFPGAN. The default upscale is
+Real-ESRGAN. For an alternative face restoration module, see
+[CodeFormer Support](#codeformer-support) below.
 
-## Upscaling
+As of version 1.14, environment.yaml will install the Real-ESRGAN package into
+the standard install location for python packages, and will put GFPGAN into a
+subdirectory of "src" in the InvokeAI directory. Upscaling with Real-ESRGAN
+should "just work" without further intervention. Simply indicate the desired scale on
+the popup in the Web GUI.
+
+**GFPGAN** requires a series of downloadable model files to work. These are
+loaded when you run `invokeai-configure`. If GFPAN is failing with an
+error, please run the following from the InvokeAI directory:
+
+```bash
+invokeai-configure
+```
+
+If you do not run this script in advance, the GFPGAN module will attempt to
+download the models files the first time you try to perform facial
+reconstruction.
+
+### Upscaling
 
 Open the upscaling dialog by clicking on the "expand" icon located
 above the image display area in the Web UI:
@@ -19,23 +41,82 @@ above the image display area in the Web UI:
 ![upscale1](../assets/features/upscale-dialog.png)
 </figure>
 
-The default upscaling option is Real-ESRGAN x2 Plus, which will scale your image by a factor of two. This means upscaling a 512x512 image will result in a new 1024x1024 image.
+There are three different upscaling parameters that you can
+adjust. The first is the scale itself, either 2x or 4x.
 
-Other options are the x4 upscalers, which will scale your image by a factor of 4. 
+The second is the "Denoising Strength." Higher values will smooth out
+the image and remove digital chatter, but may lose fine detail at
+higher values.
 
+Third, "Upscale Strength" allows you to adjust how the You can set the
+scaling stength between `0` and `1.0` to control the intensity of the
+scaling. AI upscalers generally tend to smooth out texture details. If
+you wish to retain some of those for natural looking results, we
+recommend using values between `0.5 to 0.8`.
+
+[This figure](../assets/features/upscaling-montage.png) illustrates
+the effects of denoising and strength. The original image was 512x512,
+4x scaled to 2048x2048. The "original" version on the upper left was
+scaled using simple pixel averaging. The remainder use the ESRGAN
+upscaling algorithm at different levels of denoising and strength.
+
+<figure markdown>
+![upscaling](../assets/features/upscaling-montage.png){ width=720 }
+</figure>
+
+Both denoising and strength default to 0.75.
+
+### Face Restoration
+
+InvokeAI offers alternative two face restoration algorithms,
+[GFPGAN](https://github.com/TencentARC/GFPGAN) and
+[CodeFormer](https://huggingface.co/spaces/sczhou/CodeFormer). These
+algorithms improve the appearance of faces, particularly eyes and
+mouths. Issues with faces are less common with the latest set of
+Stable Diffusion models than with the original 1.4 release, but the
+restoration algorithms can still make a noticeable improvement in
+certain cases. You can also apply restoration to old photographs you
+upload.
+
+To access face restoration, click the "smiley face" icon in the
+toolbar above the InvokeAI image panel. You will be presented with a
+dialog that offers a choice between the two algorithm and sliders that
+allow you to adjust their parameters. Alternatively, you may open the
+left-hand accordion panel labeled "Face Restoration" and have the
+restoration algorithm of your choice applied to generated images
+automatically.
+
+
+Like upscaling, there are a number of parameters that adjust the face
+restoration output. GFPGAN has a single parameter, `strength`, which
+controls how much the algorithm is allowed to adjust the
+image. CodeFormer has two parameters, `strength`, and `fidelity`,
+which together control the quality of the output image as described in
+the [CodeFormer project
+page](https://shangchenzhou.com/projects/CodeFormer/). Default values
+are 0.75 for both parameters, which achieves a reasonable balance
+between changing the image too much and not enough.
+
+[This figure](../assets/features/restoration-montage.png) illustrates
+the effects of adjusting GFPGAN and CodeFormer parameters.
+
+<figure markdown>
+![upscaling](../assets/features/restoration-montage.png){ width=720 }
+</figure>
 
 !!! note
 
-    Real-ESRGAN is memory intensive. In order to avoid crashes and memory overloads
+    GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads
     during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
     its work.
 
     In single image generations, you will see the output right away but when you are using multiple
-    iterations, the images will first be generated and then upscaled after that
+    iterations, the images will first be generated and then upscaled and face restored after that
     process is complete. While the image generation is taking place, you will still be able to preview
     the base images.
 
 ## How to disable
 
-If, for some reason, you do not wish to load the ESRGAN libraries,
-you can disable them on the invoke.py command line with the `--no_esrgan` options.
+If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries,
+you can disable them on the invoke.py command line with the `--no_restore` and
+`--no_esrgan` options, respectively.

docs/features/PROMPTS.md

@@ -4,6 +4,80 @@ title: Prompting-Features
 
 # :octicons-command-palette-24: Prompting-Features
 
+## **Negative and Unconditioned Prompts**
+
+Any words between a pair of square brackets will instruct Stable
+Diffusion to attempt to ban the concept from the generated image. The
+same effect is achieved by placing words in the "Negative Prompts"
+textbox in the Web UI.
+
+```text
+this is a test prompt [not really] to make you understand [cool] how this works.
+```
+
+In the above statement, the words 'not really cool` will be ignored by Stable
+Diffusion.
+
+Here's a prompt that depicts what it does.
+
+original prompt:
+
+`#!bash "A fantastical translucent pony made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve"`
+
+`#!bash parameters: steps=20, dimensions=512x768, CFG=7.5, Scheduler=k_euler_a, seed=1654590180`
+
+<figure markdown>
+
+![step1](../assets/negative_prompt_walkthru/step1.png)
+
+</figure>
+
+That image has a woman, so if we want the horse without a rider, we can
+influence the image not to have a woman by putting [woman] in the prompt, like
+this:
+
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]"`
+(same parameters as above)
+
+<figure markdown>
+
+![step2](../assets/negative_prompt_walkthru/step2.png)
+
+</figure>
+
+That's nice - but say we also don't want the image to be quite so blue. We can
+add "blue" to the list of negative prompts, so it's now [woman blue]:
+
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]"`
+(same parameters as above)
+
+<figure markdown>
+
+![step3](../assets/negative_prompt_walkthru/step3.png)
+
+</figure>
+
+Getting close - but there's no sense in having a saddle when our horse doesn't
+have a rider, so we'll add one more negative prompt: [woman blue saddle].
+
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]"`
+(same parameters as above)
+
+<figure markdown>
+
+![step4](../assets/negative_prompt_walkthru/step4.png)
+
+</figure>
+
+!!! notes "Notes about this feature:"
+
+    * The only requirement for words to be ignored is that they are in between a pair of square brackets.
+    * You can provide multiple words within the same bracket.
+    * You can provide multiple brackets with multiple words in different places of your prompt. That works just fine.
+    * To improve typical anatomy problems, you can add negative prompts like `[bad anatomy, extra legs, extra arms, extra fingers, poorly drawn hands, poorly drawn feet, disfigured, out of frame, tiling, bad art, deformed, mutated]`.
+
+---
+
 ## **Prompt Syntax Features**
 
 The InvokeAI prompting language has the following features:
@@ -28,6 +102,9 @@ The following syntax is recognised:
   `a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++`
   is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2),
   etc.)
+- attention also applies to `[unconditioning]` so
+  `a tall thin man picking apricots [(ladder)0.01]` will _very gently_ nudge SD
+  away from trying to draw the man on a ladder
 
 You can use this to increase or decrease the amount of something. Starting from
 this prompt of `a man picking apricots from a tree`, let's see what happens if
@@ -73,7 +150,7 @@ Or, alternatively, with more man:
 | ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
 | ![](../assets/prompt_syntax/mountain-man1.png) | ![](../assets/prompt_syntax/mountain-man2.png) | ![](../assets/prompt_syntax/mountain-man3.png) | ![](../assets/prompt_syntax/mountain-man4.png) |
 
-### Prompt Blending
+### Blending between prompts
 
 - `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
 - The existing prompt blending using `:<weight>` will continue to be supported -
@@ -91,24 +168,6 @@ Or, alternatively, with more man:
 See the section below on "Prompt Blending" for more information about how this
 works.
 
-### Prompt Conjunction  
-Join multiple clauses together to create a conjoined prompt. Each clause will be passed to CLIP separately. 
-
-For example, the prompt: 
-
-```bash
-"A mystical valley surround by towering granite cliffs, watercolor, warm"
-```
-
-Can be used with .and():
-```bash
-("A mystical valley", "surround by towering granite cliffs", "watercolor", "warm").and()
-```
-
-Each will give you different results - try them out and see what you prefer!
-
-
-
 ### Cross-Attention Control ('prompt2prompt')
 
 Sometimes an image you generate is almost right, and you just want to change one
@@ -120,7 +179,7 @@ Generate an image with a given prompt, record the seed of the image, and then
 use the `prompt2prompt` syntax to substitute words in the original prompt for
 words in a new prompt. This works for `img2img` as well.
 
-For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
+For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
   - `a cat playing with a ball in the forest`
   - `a dog playing with a ball in the forest`
 
@@ -131,7 +190,7 @@ For example, consider the prompt `a cat.swap(dog) playing with a ball in the for
 
       - For multiple word swaps, use parentheses: `a (fluffy cat).swap(barking dog) playing with a ball in the forest`.
       - To swap a comma, use quotes: `a ("fluffy, grey cat").swap("big, barking dog") playing with a ball in the forest`.
-- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to (bloc97's)[(https://github.com/bloc97/CrossAttentionControl)] `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
+- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to bloc97's `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
   intuitively understand. `t_start` and `t_end` are used to control on which steps cross-attention control should run. With the default values `t_start=0` and `t_end=1`, cross-attention control is active on every step of image generation. Other values can be used to turn cross-attention control off for part of the image generation process.
     - For example, if doing a diffusion with 10 steps for the prompt is `a cat.swap(dog, t_start=0.3, t_end=1.0) playing with a ball in the forest`, the first 3 steps will be run as `a cat playing with a ball in the forest`, while the last 7 steps will run as `a dog playing with a ball in the forest`, but the pixels that represent `dog` will be locked to the pixels that would have represented `cat` if the `cat` prompt had been used instead.
     - Conversely, for `a cat.swap(dog, t_start=0, t_end=0.7) playing with a ball in the forest`, the first 7 steps will run as `a dog playing with a ball in the forest` with the pixels that represent `dog` locked to the same pixels that would have represented `cat` if the `cat` prompt was being used instead. The final 3 steps will just run `a cat playing with a ball in the forest`.
@@ -142,7 +201,7 @@ Prompt2prompt `.swap()` is not compatible with xformers, which will be temporari
 The `prompt2prompt` code is based off
 [bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
 
-### Escaping parentheses and speech marks 
+### Escaping parantheses () and speech marks ""
 
 If the model you are using has parentheses () or speech marks "" as part of its
 syntax, you will need to "escape" these using a backslash, so that`(my_keyword)`
@@ -153,16 +212,23 @@ the parentheses as part of the prompt syntax and it will get confused.
 
 ## **Prompt Blending**
 
-You may blend together prompts to explore the AI's
+You may blend together different sections of the prompt to explore the AI's
 latent semantic space and generate interesting (and often surprising!)
 variations. The syntax is:
 
 ```bash
-("prompt #1", "prompt #2").blend(0.25, 0.75)
+blue sphere:0.25 red cube:0.75 hybrid
 ```
 
-This will tell the sampler to blend 25% of the concept of prompt #1 with 75%
-of the concept of prompt #2. It is recommended to keep the sum of the weights to around 1.0, but interesting things might happen if you go outside of this range.
+This will tell the sampler to blend 25% of the concept of a blue sphere with 75%
+of the concept of a red cube. The blend weights can use any combination of
+integers and floating point numbers, and they do not need to add up to 1.
+Everything to the left of the `:XX` up to the previous `:XX` is used for
+merging, so the overall effect is:
+
+```bash
+0.25 * "blue sphere" + 0.75 * "white duck" + hybrid
+```
 
 Because you are exploring the "mind" of the AI, the AI's way of mixing two
 concepts may not match yours, leading to surprising effects. To illustrate, here
@@ -170,14 +236,13 @@ are three images generated using various combinations of blend weights. As
 usual, unless you fix the seed, the prompts will give you different results each
 time you run them.
 
-Let's examine how this affects image generation results:
+<figure markdown>
 
+### "blue sphere, red cube, hybrid"
 
-```bash
-"blue sphere, red cube, hybrid"
-```
+</figure>
 
-This example doesn't use blending at all and represents the default way of mixing
+This example doesn't use melding at all and represents the default way of mixing
 concepts.
 
 <figure markdown>
@@ -186,47 +251,55 @@ concepts.
 
 </figure>
 
-It's interesting to see how the AI expressed the concept of "cube" within the sphere. If you look closely, there is depth there, so the enclosing frame is actually a cube.
+It's interesting to see how the AI expressed the concept of "cube" as the four
+quadrants of the enclosing frame. If you look closely, there is depth there, so
+the enclosing frame is actually a cube.
 
 <figure markdown>
 
-```bash
-("blue sphere", "red cube").blend(0.25, 0.75)
-```
+### "blue sphere:0.25 red cube:0.75 hybrid"
 
 ![blue-sphere-25-red-cube-75](../assets/prompt-blending/blue-sphere-0.25-red-cube-0.75-hybrid.png)
 
 </figure>
 
-Now that's interesting. We get an image with a resemblance of a red cube, with a hint of blue shadows which represents a melding of concepts within the AI's "latent space" of semantic representations. 
+Now that's interesting. We get neither a blue sphere nor a red cube, but a red
+sphere embedded in a brick wall, which represents a melding of concepts within
+the AI's "latent space" of semantic representations. Where is Ludwig
+Wittgenstein when you need him?
 
 <figure markdown>
 
-```bash
-("blue sphere", "red cube").blend(0.75, 0.25)
-```
+### "blue sphere:0.75 red cube:0.25 hybrid"
 
 ![blue-sphere-75-red-cube-25](../assets/prompt-blending/blue-sphere-0.75-red-cube-0.25-hybrid.png)
 
 </figure>
 
-Definitely more blue-spherey. 
+Definitely more blue-spherey. The cube is gone entirely, but it's really cool
+abstract art.
 
 <figure markdown>
 
-```bash
-("blue sphere", "red cube").blend(0.5, 0.5)
-```
-</figure>
+### "blue sphere:0.5 red cube:0.5 hybrid"
 
-<figure markdown>
 ![blue-sphere-5-red-cube-5-hybrid](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5-hybrid.png)
+
 </figure>
 
+Whoa...! I see blue and red, but no spheres or cubes. Is the word "hybrid"
+summoning up the concept of some sort of scifi creature? Let's find out.
 
-Whoa...! I see blue and red, and if I squint, spheres and cubes.
+<figure markdown>
 
+### "blue sphere:0.5 red cube:0.5"
 
+![blue-sphere-5-red-cube-5](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5.png)
+
+</figure>
+
+Indeed, removing the word "hybrid" produces an image that is more like what we'd
+expect.
 
 ## Dynamic Prompts
 
@@ -246,7 +319,7 @@ To create a Dynamic Prompt, follow these steps:
     Within the braces, separate each option using a vertical bar |.
     If you want to include multiple options from a single group, prefix with the desired number and $$.
 
-For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {style1|style2|style3}.
+For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {2$$style1|style2|style3}.
 ### How Dynamic Prompts Work
 
 Once a Dynamic Prompt is configured, the system generates an array of combinations using the options provided. Each group of options in curly braces is treated independently, with the system selecting one option from each group. For a prefixed set (e.g., 2$$), the system will select two distinct options.
@@ -273,36 +346,3 @@ Below are some useful strategies for creating Dynamic Prompts:
     Experiment with different quantities for the prefix. For example, 3$$ will select three distinct options.
     Be aware of coherence in your prompts. Although the system can generate all possible combinations, not all may semantically make sense. Therefore, carefully choose the options for each group.
     Always review and fine-tune the generated prompts as needed. While Dynamic Prompts can help you generate a multitude of combinations, the final polishing and refining remain in your hands.
-
-
-## SDXL Prompting
-
-Prompting with SDXL is slightly different than prompting with SD1.5 or SD2.1 models - SDXL expects a prompt _and_ a style. 
-
-
-### Prompting 
-<figure markdown>
-
-![SDXL prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt.png)
-
-</figure>
-
-In the prompt box, enter a positive or negative prompt as you normally would. 
-
-For the style box you can enter a style that you want the image to be generated in. You can use styles from this example list, or any other style you wish: anime, photographic, digital art, comic book, fantasy art, analog film, neon punk, isometric, low poly, origami, line art, cinematic, 3d model, pixel art, etc.
-
-
-### Concatenated Prompts
-
-
-InvokeAI also has the option to concatenate the prompt and style inputs, by pressing the "link" button in the Positive Prompt box. 
-
-This concatenates the prompt & style inputs, and passes the joined prompt and style to the SDXL model. 
-![SDXL concatenated prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt-concatenated.png)
-
-
-
-
-
-
-

docs/features/TEXTUAL_INVERSIONS.md

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

docs/features/TRAINING.md

@@ -43,22 +43,27 @@ into the directory
 
 InvokeAI 2.3 and higher comes with a text console-based training front
 end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
-start training tool selecting choice (3):
+start the front end by selecting choice (3):
 
 ```sh
-1 "Generate images with a browser-based interface"
-2 "Explore InvokeAI nodes using a command-line interface"
-3 "Textual inversion training"
-4 "Merge models (diffusers type only)"
-5 "Download and install models"
-6 "Change InvokeAI startup options"
-7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
-8 "Open the developer console"
-9 "Update InvokeAI"
+Do you want to generate images using the
+1: Browser-based UI
+2: Command-line interface
+3: Run 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
+10: Command-line help
+Q: Quit
+
+Please enter 1-10, Q: [1] 
 ```
 
-Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
-you can then launch the front end with the command `invokeai-ti --gui`.
+From the command line, with the InvokeAI virtual environment active,
+you can launch the front end with the command `invokeai-ti --gui`.
 
 This will launch a text-based front end that will look like this:
 

docs/features/UTILITIES.md

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

docs/features/VARIATIONS.md

@@ -0,0 +1,131 @@
+---
+title: Variations
+---
+
+# :material-tune-variant: Variations
+
+## Intro
+
+InvokeAI's support for variations enables you to do the following:
+
+1. Generate a series of systematic variations of an image, given a prompt. The
+   amount of variation from one image to the next can be controlled.
+
+2. Given two or more variations that you like, you can combine them in a
+   weighted fashion.
+
+!!! Information ""
+
+    This cheat sheet provides a quick guide for how this works in practice, using
+    variations to create the desired image of Xena, Warrior Princess.
+
+## Step 1 -- Find a base image that you like
+
+The prompt we will use throughout is:
+
+`#!bash "lucy lawless as xena, warrior princess, character portrait, high resolution."`
+
+This will be indicated as `#!bash "prompt"` in the examples below.
+
+First we let SD create a series of images in the usual way, in this case
+requesting six iterations.
+
+<figure markdown>
+![var1](../assets/variation_walkthru/000001.3357757885.png)
+<figcaption> Seed 3357757885 looks nice </figcaption>
+</figure>
+
+---
+
+## Step 2 - Generating Variations
+
+Let's try to generate some variations on this image. We select the "*"
+symbol in the line of icons above the image in order to fix the prompt
+and seed. Then we open up the "Variations" section of the generation
+panel and use the slider to set the variation amount to 0.2. The
+higher this value, the more each generated image will differ from the
+previous one.
+
+Now we run the prompt a second time, requesting six iterations. You
+will see six images that are thematically related to each other. Try
+increasing and decreasing the variation amount and see what happens.
+
+### **Variation Sub Seeding**
+
+Note that the output for each image has a `-V` option giving the "variant
+subseed" for that image, consisting of a seed followed by the variation amount
+used to generate it.
+
+This gives us a series of closely-related variations, including the two shown
+here.
+
+<figure markdown>
+![var2](../assets/variation_walkthru/000002.3647897225.png)
+<figcaption>subseed 3647897225</figcaption>
+</figure>
+
+<figure markdown>
+![var3](../assets/variation_walkthru/000002.1614299449.png)
+<figcaption>subseed 1614299449</figcaption>
+</figure>
+
+I like the expression on Xena's face in the first one (subseed 3647897225), and
+the armor on her shoulder in the second one (subseed 1614299449). Can we combine
+them to get the best of both worlds?
+
+We combine the two variations using `-V` (`--with_variations`). Again, we must
+provide the seed for the originally-chosen image in order for this to work.
+
+```bash
+invoke> "prompt"  -S3357757885 -V3647897225,0.1,1614299449,0.1
+Outputs:
+./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
+```
+
+Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The
+resulting image is close, but not exactly what I wanted:
+
+<figure markdown>
+![var4](../assets/variation_walkthru/000003.1614299449.png)
+<figcaption> subseed 1614299449 </figcaption>
+</figure>
+
+We could either try combining the images with different weights, or we can
+generate more variations around the almost-but-not-quite image. We do the
+latter, using both the `-V` (combining) and `-v` (variation strength) options.
+Note that we use `-n6` to generate 6 variations:
+
+```bash
+invoke> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1 -v0.05 -n6
+Outputs:
+./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
+./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
+./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3747154981:0.05 -S3357757885
+./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2664260391:0.05 -S3357757885
+./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,1642517170:0.05 -S3357757885
+./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
+```
+
+This produces six images, all slight variations on the combination of the chosen
+two images. Here's the one I like best:
+
+<figure markdown>
+![var5](../assets/variation_walkthru/000004.3747154981.png)
+<figcaption> subseed 3747154981 </figcaption>
+</figure>
+
+As you can see, this is a very powerful tool, which when combined with subprompt
+weighting, gives you great control over the content and quality of your
+generated images.
+
+## Variations and Samplers
+
+The sampler you choose has a strong effect on variation strength. Some
+samplers, such as `k_euler_a` are very "creative" and produce significant
+amounts of image-to-image variation even when the seed is fixed and the
+`-v` argument is very low. Others are more deterministic. Feel free to
+experiment until you find the combination that you like.
+
+Also be aware of the [Perlin Noise](OTHER.md#thresholding-and-perlin-noise-initialization-options)
+feature, which provides another way of introducing variability into your
+image generation requests.

docs/features/index.md

@@ -4,9 +4,6 @@ title: Overview
 
 Here you can find the documentation for InvokeAI's various features.
 
-## The [Getting Started Guide](../help/gettingStartedWithAI) 
-A getting started guide for those new to AI image generation. 
-
 ## The Basics
 ### * The [Web User Interface](WEB.md)
 Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
@@ -20,7 +17,7 @@ a single convenient digital artist-optimized user interface.
 ### * [Prompt Engineering](PROMPTS.md)
 Get the images you want with the InvokeAI  prompt engineering language.
 
-### * The [LoRA, LyCORIS, LCM-LoRA Models](CONCEPTS.md)
+### * The [LoRA, LyCORIS and Textual Inversion Models](CONCEPTS.md)
 Add custom subjects and styles using a variety of fine-tuned models.
 
 ### * [ControlNet](CONTROLNET.md)
@@ -28,7 +25,11 @@ Learn how to install and use ControlNet models for fine control over
 image output.
 
 ### * [Image-to-Image Guide](IMG2IMG.md)
-Use a seed image to build new creations.
+Use a seed image to build new creations in 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
 
@@ -40,20 +41,17 @@ guide also covers optimizing models to load quickly.
 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_INVERSIONS.md)
+### * [Textual Inversion](TRAINING.md)
 Personalize models by adding your own style or subjects.
 
 ## Other Features
 
-### * [The NSFW Checker](WATERMARK+NSFW.md)
+### * [The NSFW Checker](NSFW.md)
 Prevent InvokeAI from displaying unwanted racy images.
 
 ### * [Controlling Logging](LOGGING.md)
 Control how InvokeAI logs status messages.
 
-### * [Command-line Utilities](UTILITIES.md)
-A list of the command-line utilities available with InvokeAI.
-
 <!-- OUT OF DATE
 ### * [Miscellaneous](OTHER.md)
 Run InvokeAI on Google Colab, generate images with repeating patterns,

docs/help/FAQ.md

@@ -1,43 +0,0 @@
-# FAQs
-
-**Where do I get started? How can I install Invoke?**
-
-- You can download the latest installers [here](https://github.com/invoke-ai/InvokeAI/releases) - Note that any releases marked as *pre-release* are in a beta state. You may experience some issues, but we appreciate your help testing those! For stable/reliable installations, please install the **[Latest Release](https://github.com/invoke-ai/InvokeAI/releases/latest)**
-
-**How can I download models? Can I use models I already have downloaded?**
-
-- Models can be downloaded through the model manager, or through option [4] in the invoke.bat/invoke.sh launcher script. To download a model through the Model Manager, use the HuggingFace Repo ID by pressing the “Copy” button next to the repository name. Alternatively, to download a model from CivitAi, use the download link in the Model Manager.
-- Models that are already downloaded can be used by creating a symlink to the model location in the `autoimport` folder or by using the Model Manger’s “Scan for Models” function.
-
-**My images are taking a long time to generate. How can I speed up generation?** 
-
-- A common solution is to reduce the size of your RAM & VRAM cache to 0.25. This ensures your system has enough memory to generate images.
-- Additionally, check the [hardware requirements](https://invoke-ai.github.io/InvokeAI/#hardware-requirements) to ensure that your system is capable of generating images.
-- Lastly, double check your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup. 
-
-**I’ve installed Python on Windows but the installer says it can’t find it?**
-
-- Then ensure that you checked  **'Add python.exe to PATH'** when installing Python. This can be found at the bottom of the Python Installer window. If you already have Python installed, this can be done with the modify / repair feature of the installer.
-
-**I’ve installed everything successfully but I still get an error about Triton when starting Invoke?**
-
-- This can be safely ignored. InvokeAI doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
-
-**I updated to 3.4.0 and now xFormers can’t load C++/CUDA?**
-
-- An issue occurred with your PyTorch update. Follow these steps to fix :
-    1. Launch your invoke.bat / invoke.sh and select the option to open the developer console
-    2. Run:`pip install ".[xformers]" --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121`
-    - If you run into an error with `typing_extensions`, re-open the developer console and run:  `pip install -U typing-extensions`
-
-**It says my pip is out of date - is that why my install isn't working?**
-- An out of date won't cause an installation to fail. The cause of the error can likely be found above the message that says pip is out of date.
-- If you saw that warning but the install went well, don't worry about it (but you can update pip afterwards if you'd like).   
-
-**How can I generate the exact same that I found on the internet?**
-Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
-
-
-**Where can I get more help?** 
-
-- Create an issue on [GitHub](https://github.com/invoke-ai/InvokeAI/issues) or post in the [#help channel](https://discord.com/channels/1020123559063990373/1149510134058471514) of the InvokeAI Discord

docs/help/diffusion.md

@@ -1,27 +0,0 @@
-Taking the time to understand the diffusion process will help you to understand how to more effectively use InvokeAI.
-
-There are two main ways Stable Diffusion works - with images, and latents.
-
-Image space represents images in pixel form that you look at. Latent space represents compressed inputs. It’s in latent space that Stable Diffusion processes images. A VAE (Variational Auto Encoder) is responsible for compressing and encoding inputs into latent space, as well as decoding outputs back into image space.
-
-To fully understand the diffusion process, we need to understand a few more terms: UNet, CLIP, and conditioning.
-
-A U-Net is a model trained on a large number of latent images with with known amounts of random noise added.  This means that the U-Net can be given a slightly noisy image and it will predict the pattern of noise needed to subtract from the image in order to recover the original. 
-
-CLIP is a model that tokenizes and encodes text into conditioning. This conditioning guides the model during the denoising steps to produce a new image. 
-
-The U-Net and CLIP work together during the image generation process at each denoising step, with the U-Net removing noise in such a way that the result is similar to images in the U-Net’s training set, while CLIP guides the U-Net towards creating images that are most similar to the prompt.
-
-
-When you generate an image using text-to-image, multiple steps occur in latent space:
-1. Random noise is generated at the chosen height and width. The noise’s characteristics are dictated by  seed. This noise tensor is passed into latent space. We’ll call this noise A.
-2. Using a model’s U-Net, a noise predictor examines noise A, and the words tokenized by CLIP from your prompt (conditioning). It generates its own noise tensor to predict what the final image might look like in latent space. We’ll call this noise B.
-3. Noise B is subtracted from noise A in an attempt to create a latent image consistent with the prompt. This step is repeated for the number of sampler steps chosen.
-4. The VAE decodes the final latent image from latent space into image space.
-
-Image-to-image is a similar process, with only step 1 being different:
-1. The input image is encoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how may noise steps are added, and the amount of noise added at each step. A Denoising Strength of 0 means there are 0 steps and no noise added, resulting in an unchanged image, while a Denoising Strength of 1 results in the image being completely replaced with noise and a full set of denoising steps are performance. The process is then the same as steps 2-4 in the text-to-image process. 
-
-Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
-
-A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from the latent image across the sampler steps chosen (step 3 above). Less noise is usually subtracted at higher sampler steps. 

docs/help/gettingStartedWithAI.md

@@ -1,97 +0,0 @@
-# Getting Started with AI Image Generation
-
-New to image generation with AI? You’re in the right place! 
-
-This is a high level walkthrough of some of the concepts and terms you’ll see as you start using InvokeAI. Please note, this is not an exhaustive guide and may be out of date due to the rapidly changing nature of the space. 
-
-## Using InvokeAI
-
-### **Prompt Crafting**
-
-- Prompts are the basis of using InvokeAI, providing the models directions on what to generate. As a general rule of thumb, the more detailed your prompt is, the better your result will be.
-
- *To get started, here’s an easy template to use for structuring your prompts:*
-
-- Subject, Style, Quality, Aesthetic
-    - **Subject:** What your image will be about. E.g. “a futuristic city with trains”,  “penguins floating on icebergs”, “friends sharing beers”
-    - **Style:** The style or medium in which your image will be in. E.g. “photograph”, “pencil sketch”, “oil paints”, or “pop art”, “cubism”, “abstract”
-    - **Quality:** A particular aspect or trait that you would like to see emphasized in your image. E.g. "award-winning", "featured in {relevant set of high quality works}", "professionally acclaimed". Many people often use "masterpiece".
-    - **Aesthetics:** The visual impact and design of the artwork. This can be colors, mood, lighting, setting, etc.
-- There are two prompt boxes: *Positive Prompt* & *Negative Prompt*.
-    - A **Positive** Prompt includes words you want the model to reference when creating an image.
-    - Negative Prompt is for anything you want the model to eliminate when creating an image. It doesn’t always interpret things exactly the way you would, but helps control the generation process. Always try to include a few terms - you can typically use lower quality image terms like “blurry” or “distorted” with good success.
-- Some examples prompts you can try on your own:
-    - A detailed oil painting of a tranquil forest at sunset with vibrant+ colors and soft, golden light filtering through the trees
-    - friends sharing beers in a busy city, realistic colored pencil sketch, twilight, masterpiece, bright, lively
-
-### Generation Workflows
-
-- Invoke offers a number of different workflows for interacting with models to produce images. Each is extremely powerful on its own, but together provide you an unparalleled way of producing high quality creative outputs that align with your vision.
-    - **Text to Image:** The text to image tab focuses on the key workflow of using a prompt to generate a new image. It includes other features that help control the generation process as well.
-    - **Image to Image:** With image to image, you provide an image as a reference (called the “initial image”), which provides more guidance around color and structure to the AI as it generates a new image. This is provided alongside the same features as Text to Image.
-    - **Unified Canvas:** The Unified Canvas is an advanced AI-first image editing tool that is easy to use, but hard to master. Drag an image onto the canvas from your gallery in order to regenerate certain elements, edit content or colors (known as inpainting), or extend the image with an exceptional degree of consistency and clarity (called outpainting).
-
-### Improving Image Quality
-
-- Fine tuning your prompt - the more specific you are, the closer the image will turn out to what is in your head!  Adding more details in the Positive Prompt or Negative Prompt can help add / remove pieces of your image to improve it - You can also use advanced techniques like upweighting and downweighting to control the influence of certain words. [Learn more here](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#prompt-syntax-features).
-    - **Tip: If you’re seeing poor results, try adding the things you don’t like about the image to your negative prompt may help. E.g. distorted, low quality, unrealistic, etc.**
-- Explore different models - Other models can produce different results due to the data they’ve been trained on. Each model has specific language and settings it works best with; a model’s documentation is your friend here.  Play around with some and see what works best for you!
-- Increasing Steps - The number of steps used controls how much time the model is given to produce an image, and depends on the “Scheduler” used. The schedule controls how each step is processed by the model. More steps tends to mean better results, but will take longer - We recommend at least 30 steps for most
-- Tweak and Iterate - Remember, it’s best to change one thing at a time so you know what is working and what isn't. Sometimes you just need to try a new image, and other times using a new prompt might be the ticket. For testing, consider turning off the “random” Seed - Using the same seed with the same settings will produce the same image, which makes it the perfect way to learn exactly what your changes are doing.
-- Explore Advanced Settings - InvokeAI has a full suite of tools available to allow you complete control over your image creation process - Check out our [docs if you want to learn more](https://invoke-ai.github.io/InvokeAI/features/).
-
-
-## Terms & Concepts
-
-If you're interested in learning more, check out [this presentation](https://docs.google.com/presentation/d/1IO78i8oEXFTZ5peuHHYkVF-Y3e2M6iM5tCnc-YBfcCM/edit?usp=sharing) from one of our maintainers (@lstein). 
-
-### Stable Diffusion
-
-Stable Diffusion is deep learning, text-to-image model that is the foundation of the capabilities found in InvokeAI. Since the release of Stable Diffusion, there have been many subsequent models created based on Stable Diffusion that are designed to generate specific types of images. 
-
-### Prompts
-
-Prompts provide the models directions on what to generate. As a general rule of thumb, the more detailed your prompt is, the better your result will be.
-
-### Models
-
-Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see. (Like Stable Diffusion!)
-
-Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at https://models.invoke.ai 
-
-Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision!
-
-- *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas*
-
-### Scheduler
-
-Schedulers guide the process of removing noise (de-noising) from data. They determine:
-
-1. The number of steps to take to remove the noise.
-2. Whether the steps are random (stochastic) or predictable (deterministic).
-3. The specific method (algorithm) used for de-noising.
-
-Experimenting with different schedulers is recommended as each will produce different outputs!
-
-### Steps
-
-The number of de-noising steps each generation through. 
-
-Schedulers can be intricate and there's often a balance to strike between how quickly they can de-noise data and how well they can do it. It's typically advised to experiment with different schedulers to see which one gives the best results. There has been a lot written on the internet about different schedulers, as well as exploring what the right level of "steps" are for each. You can save generation time by reducing the number of steps used, but you'll want to make sure that you are satisfied with the quality of images produced!
-
-### Low-Rank Adaptations / LoRAs
-
-Low-Rank Adaptations (LoRAs) are like a smaller, more focused version of models, intended to focus on training a better understanding of how a specific character, style, or concept looks.
-
-### Textual Inversion Embeddings
-
-Textual Inversion Embeddings, like LoRAs, assist with more easily prompting for certain characters, styles, or concepts. However, embeddings are trained to update the relationship between a specific word (known as the “trigger”) and the intended output. 
-
-### ControlNet
-
-ControlNets are neural network models that are able to extract key features from an existing image and use these features to guide the output of the image generation model. 
-
-### VAE
-
-Variational auto-encoder (VAE) is a encode/decode model that translates the "latents" image produced during the image generation procees to the large pixel images that we see. 
-

docs/index.md

@@ -11,35 +11,6 @@ title: Home
   ```
 -->
 
-<!-- CSS styling -->
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.1/css/fontawesome.min.css">
-<style>
-    .button {
-      width: 100%;
-      max-width: 100%;
-      height: 50px;
-      background-color: #448AFF;
-      color: #fff;
-      font-size: 16px;
-      border: none;
-      cursor: pointer;
-      border-radius: 0.2rem;
-    }
-
-    .button-container {
-      display: grid;
-      grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
-      gap: 20px;
-      justify-content: center;
-    }
-
-    .button:hover {
-      background-color: #526CFE;
-    }
-</style>
-
-
-
 <div align="center" markdown>
 
 
@@ -51,9 +22,9 @@ title: Home
 [![github stars badge]][github stars link]
 [![github forks badge]][github forks link]
 
-<!-- [![CI checks on main badge]][ci checks on main link]
+[![CI checks on main badge]][ci checks on main link]
 [![CI checks on dev badge]][ci checks on dev link]
-[![latest commit to dev badge]][latest commit to dev link] -->
+<!-- [![latest commit to dev badge]][latest commit to dev link] -->
 
 [![github open issues badge]][github open issues link]
 [![github open prs badge]][github open prs link]
@@ -99,20 +70,61 @@ image-to-image generator. It provides a streamlined process with various new
 features and options to aid the image generation process. It runs on Windows,
 Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 
+**Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</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>]
+
 <div align="center"><img src="assets/invoke-web-server-1.png" width=640></div>
 
-## :octicons-link-24: Quick Links
+!!! note
 
-<div class="button-container">
-    <a href="installation/INSTALLATION"> <button class="button">Installation</button> </a>
-    <a href="features/"> <button class="button">Features</button> </a>
-    <a href="help/gettingStartedWithAI/"> <button class="button">Getting Started</button> </a>
-    <a href="help/FAQ/"> <button class="button">FAQ</button> </a>
-    <a href="contributing/CONTRIBUTING/"> <button class="button">Contributing</button> </a>
-    <a href="https://github.com/invoke-ai/InvokeAI/"> <button class="button">Code and Downloads</button> </a>
-    <a href="https://github.com/invoke-ai/InvokeAI/issues"> <button class="button">Bug Reports </button> </a>
-    <a href="https://discord.gg/ZmtBAhwWhy"> <button class="button"> Join the Discord Server!</button> </a>
-</div>
+    This fork is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
+
+## :octicons-package-dependencies-24: Installation
+
+This fork is supported across Linux, Windows and Macintosh. Linux users can use
+either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
+driver).
+
+### [Installation Getting Started Guide](installation)
+#### **[Automated Installer](installation/010_INSTALL_AUTOMATED.md)**
+✅ This is the recommended installation method for first-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)
+
+## :fontawesome-solid-computer: Hardware Requirements
+
+### :octicons-cpu-24: System
+
+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.
+
+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
+
+### :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.
 
 
 ## :octicons-gift-24: InvokeAI Features
@@ -140,10 +152,10 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 <!-- seperator -->
 ### Prompt Engineering
 - [Prompt Syntax](features/PROMPTS.md)
+- [Generating Variations](features/VARIATIONS.md)
 
 ### InvokeAI Configuration
 - [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
-- [Database Maintenance and other Command Line Utilities](features/UTILITIES.md)
 
 ## :octicons-log-16: Important Changes Since Version 2.3
 
@@ -162,8 +174,10 @@ still a work in progress, but coming soon.
 
 ### Command-Line Interface Retired
 
-All "invokeai" command-line interfaces have been retired as of version
-3.4.
+The original "invokeai" command-line interface has been retired. The
+`invokeai` command will now launch a new command-line client that can
+be used by developers to create and test nodes. It is not intended to
+be used for routine image generation or manipulation.
 
 To launch the Web GUI from the command-line, use the command
 `invokeai-web` rather than the traditional `invokeai --web`.
@@ -195,7 +209,6 @@ The list of schedulers has been completely revamped and brought up to date:
 | **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
 | **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
 | **unipc**      | UniPCMultistepScheduler         | CPU only                    |
-| **lcm**        | LCMScheduler                    |                             |
 
 Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
 
@@ -217,7 +230,7 @@ encouraged to do so.
 
 ## :octicons-person-24: Contributors
 
-This software is a combined effort of various people from across the world.
+This fork is a combined effort of various people from across the world.
 [Check out the list of all these amazing people](other/CONTRIBUTORS.md). We
 thank them for their time, hard work and effort.
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -40,7 +40,7 @@ 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 `3.10.*` or `3.11.*` you meet
+    number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
     requirements.
 
     !!! warning "What to do if you have an unsupported version"
@@ -48,7 +48,7 @@ experimental versions later.
         Go to [Python Downloads](https://www.python.org/downloads/)
         and download the appropriate installer package for your
         platform. We recommend [Version
-        3.10.12](https://www.python.org/downloads/release/python-3109/),
+        3.10.9](https://www.python.org/downloads/release/python-3109/),
         which has been extensively tested with InvokeAI.
 
     _Please select your platform in the section below for platform-specific
@@ -179,7 +179,7 @@ experimental versions later.
     you will have the choice of CUDA (NVidia cards), ROCm (AMD cards),
     or CPU (no graphics acceleration). On Windows, you'll have the
     choice of CUDA vs CPU, and on Macs you'll be offered CPU only. When
-    you select CPU on M1/M2/M3 Macintoshes, you will get MPS-based
+    you select CPU on M1 or M2 Macintoshes, you will get MPS-based
     graphics acceleration without installing additional drivers. If you
     are unsure what GPU you are using, you can ask the installer to
     guess.
@@ -264,7 +264,7 @@ experimental versions later.
 	  you can create several levels of subfolders and drop your models into
       whichever ones you want.
 	
-    - ***LICENSE***     
+    - ***Autoimport FolderLICENSE***     
 
     At the bottom of the screen you will see a checkbox for accepting
     the CreativeML Responsible AI Licenses. You need to accept the license
@@ -372,71 +372,8 @@ experimental versions later.
         Once InvokeAI is installed, do not move or remove this directory."
 
 
-<a name="troubleshooting"></a>
 ## Troubleshooting
 
-### _OSErrors on Windows while installing dependencies_
-
-During a zip file installation or an online update, installation stops
-with an error like this:
-
-![broken-dependency-screenshot](../assets/troubleshooting/broken-dependency.png){:width="800px"}
-
-This seems to happen particularly often with the `pydantic` and
-`numpy` packages. The most reliable solution requires several manual
-steps to complete installation.
-
-Open up a Powershell window and navigate to the `invokeai` directory
-created by the installer. Then give the following series of commands:
-
-```cmd
-rm .\.venv -r -force
-python -mvenv .venv
-.\.venv\Scripts\activate
-pip install invokeai
-invokeai-configure --yes --root .
-```
-
-If you see anything marked as an error during this process please stop
-and seek help on the Discord [installation support
-channel](https://discord.com/channels/1020123559063990373/1041391462190956654). A
-few warning messages are OK.
-
-If you are updating from a previous version, this should restore your
-system to a working state. If you are installing from scratch, there
-is one additional command to give:
-
-```cmd
-wget -O invoke.bat https://raw.githubusercontent.com/invoke-ai/InvokeAI/main/installer/templates/invoke.bat.in
-```
-
-This will create the `invoke.bat` script needed to launch InvokeAI and
-its related programs.
-
-
-### _Stable Diffusion XL Generation Fails after Trying to Load unet_
-
-InvokeAI is working in other respects, but when trying to generate
-images with Stable Diffusion XL you get a "Server Error". The text log
-in the launch window contains this log line above several more lines of
-error messages:
-
-```INFO --> Loading model:D:\LONG\PATH\TO\MODEL, type sdxl:main:unet```
-
-This failure mode occurs when there is a network glitch during
-downloading the very large SDXL model.
-
-To address this, first go to the Web Model Manager and delete the
-Stable-Diffusion-XL-base-1.X model. Then navigate to HuggingFace and
-manually download the .safetensors version of the model. The 1.0
-version is located at
-https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main
-and the file is named `sd_xl_base_1.0.safetensors`.
-
-Save this file to disk and then reenter the Model Manager. Navigate to
-Import Models->Add Model, then type (or drag-and-drop) the path to the
-.safetensors file. Press "Add Model".
-
 ### _Package dependency conflicts_
 
 If you have previously installed InvokeAI or another Stable Diffusion
@@ -471,7 +408,7 @@ Then type the following commands:
 
 === "NVIDIA System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117
     pip install xformers
     ```
 

docs/installation/020_INSTALL_MANUAL.md

@@ -8,9 +8,9 @@ title: Installing Manually
 
 </figure>
 
-!!! warning "This is for Advanced Users"
+!!! warning "This is for advanced Users"
 
-    **Python experience is mandatory**
+    **python experience is mandatory**
 
 ## Introduction
 
@@ -32,7 +32,7 @@ gaming):
 
 * **Python**
 
-    version 3.10 through 3.11
+    version 3.9 through 3.11
 
 * **CUDA Tools**
 
@@ -65,7 +65,7 @@ gaming):
 To install InvokeAI with virtual environments and the PIP package
 manager, please follow these steps:
 
-1.  Please make sure you are using Python 3.10 through 3.11. The rest of the install
+1.  Please make sure you are using Python 3.9 through 3.11. The rest of the install
     procedure depends on this and will not work with other versions:
 
     ```bash
@@ -148,7 +148,7 @@ manager, please follow these steps:
     === "CUDA (NVidia)"
 
         ```bash
-        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
         ```
 
     === "ROCm (AMD)"
@@ -192,10 +192,8 @@ manager, please follow these steps:
     your outputs.
 
     ```terminal
-    invokeai-configure --root .
+    invokeai-configure
     ```
-	
-	Don't miss the dot at the end of the command!
 
     The script `invokeai-configure` will interactively guide you through the
     process of downloading and installing the weights files needed for InvokeAI.
@@ -227,6 +225,12 @@ manager, please follow these steps:
 
         !!! warning "Make sure that the virtual environment is activated, which should create `(.venv)` in front of your prompt!"
 
+        === "CLI"
+
+            ```bash
+            invokeai
+            ```
+
         === "local Webserver"
 
             ```bash
@@ -239,12 +243,6 @@ manager, please follow these steps:
             invokeai --web --host 0.0.0.0
             ```
 
-        === "CLI"
-
-            ```bash
-            invokeai
-            ```
-
         If you choose the run the web interface, point your browser at
         http://localhost:9090 in order to load the GUI.
 
@@ -256,10 +254,6 @@ manager, please follow these steps:
         *highly recommended** if your virtual environment is located outside of
         your runtime directory.
 
-    !!! tip
-
-        On linux, it is recommended to run invokeai with the following env var: `MALLOC_MMAP_THRESHOLD_=1048576`. For example: `MALLOC_MMAP_THRESHOLD_=1048576 invokeai --web`. This helps to prevent memory fragmentation that can lead to memory accumulation over time. This env var is set automatically when running via `invoke.sh`.
-
 10.  Render away!
 
     Browse the [features](../features/index.md) section to learn about all the
@@ -291,20 +285,7 @@ manager, please follow these steps:
     Leave off the `--gui` option to run the script using command-line arguments. Pass the `--help` argument
     to get usage instructions.
 
-## Developer Install
-
-!!! warning
-
-    InvokeAI uses a SQLite database. By running on `main`, you accept responsibility for your database. This
-    means making regular backups (especially before pulling) and/or fixing it yourself in the event that a
-    PR introduces a schema change.
-    
-    If you don't need persistent backend storage, you can use an ephemeral in-memory database by setting
-    `use_memory_db: true` under `Path:` in your `invokeai.yaml` file.
-
-    If this is untenable, you should run the application via the official installer or a manual install of the
-    python package from pypi. These releases will not break your database.
-
+### Developer Install
 
 If you have an interest in how InvokeAI works, or you would like to
 add features or bugfixes, you are encouraged to install the source
@@ -313,34 +294,23 @@ code for InvokeAI. For this to work, you will need to install the
 on your system, please see the [Git Installation
 Guide](https://github.com/git-guides/install-git)
 
-You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md).
-
-If you have a "normal" installation, you should create a totally separate virtual environment for the git-based installation, else the two may interfere.
-
-> **Why do I need the frontend toolchain**?
->
-> The InvokeAI project uses trunk-based development. That means our `main` branch is the development branch, and releases are tags on that branch. Because development is very active, we don't keep an updated build of the UI in `main` - we only build it for production releases.
->
-> That means that between releases, to have a functioning application when running directly from the repo, you will need to run the UI in dev mode or build it regularly (any time the UI code changes).
-
-1. Create a fork of the InvokeAI repository through the GitHub UI or [this link](https://github.com/invoke-ai/InvokeAI/fork) 
-2. From the command line, run this command:
+1. From the command line, run this command:
    ```bash
-   git clone https://github.com/<your_github_username>/InvokeAI.git
+   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 your fork of the InvokeAI repository.
+    full source code from the InvokeAI repository.
 
-3. Activate the InvokeAI virtual environment as per step (4) of the manual
+2. Activate the InvokeAI virtual environment as per step (4) of the manual
 installation protocol (important!)
 
-4. Enter the InvokeAI repository directory and run one of these
+3. Enter the InvokeAI repository directory and run one of these
    commands, based on your GPU:
 
     === "CUDA (NVidia)"
         ```bash
-        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
         ```
 
     === "ROCm (AMD)"
@@ -361,15 +331,11 @@ installation protocol (important!)
     Be sure to pass `-e` (for an editable install) and don't forget the
     dot ("."). It is part of the command.
 
-5.  Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md) and do a production build of the UI as described.
-
-6.  You can now run `invokeai` and its related commands. The code will be
+    You can now run `invokeai` and its related commands. The code will be
     read from the repository, so that you can edit the .py source files
     and watch the code's behavior change.
 
-    When you pull in new changes to the repo, be sure to re-build the UI.
-
-7.  If you wish to contribute to the InvokeAI project, you are
+4.  If you wish to contribute to the InvokeAI project, you are
     encouraged to establish a GitHub account and "fork"
     https://github.com/invoke-ai/InvokeAI into your own copy of the
     repository. You can then use GitHub functions to create and submit
@@ -388,7 +354,7 @@ 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/cu121
+pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
 invokeai-configure --root ~/invokeai
 invokeai --root ~/invokeai --web
 ```
@@ -401,5 +367,3 @@ environment variable INVOKEAI_ROOT to point to the installation directory.
 
 Note that if you run into problems with the Conda installation, the InvokeAI
 staff will **not** be able to help you out. Caveat Emptor!
-
-[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -34,11 +34,11 @@ directly from NVIDIA. **Do not try to install Ubuntu's
 nvidia-cuda-toolkit package. It is out of date and will cause
 conflicts among the NVIDIA driver and binaries.**
 
-Go to [CUDA Toolkit
-Downloads](https://developer.nvidia.com/cuda-downloads), and use the
-target selection wizard to choose your operating system, hardware
-platform, and preferred installation method (e.g. "local" versus
-"network").
+Go to [CUDA Toolkit 11.7
+Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive),
+and use the target selection wizard to choose your operating system,
+hardware platform, and preferred installation method (e.g. "local"
+versus "network").
 
 This will provide you with a downloadable install file or, depending
 on your choices, a recipe for downloading and running a install shell
@@ -57,35 +57,11 @@ familiar with containerization technologies such as Docker.
 For downloads and instructions, visit the [NVIDIA CUDA Container
 Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
 
-### cuDNN Installation for 40/30 Series Optimization* (Optional)
-
-1. Find the InvokeAI folder
-2. Click on .venv folder - e.g., YourInvokeFolderHere\\.venv
-3. Click on Lib folder - e.g., YourInvokeFolderHere\\.venv\Lib
-4. Click on site-packages folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages
-5. Click on Torch directory - e.g., YourInvokeFolderHere\InvokeAI\\.venv\Lib\site-packages\torch
-6. Click on the lib folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib
-7. Copy everything inside the folder and save it elsewhere as a backup.
-8. Go to __https://developer.nvidia.com/cudnn__
-9. Login or create an Account. 
-10. Choose the newer version of cuDNN. **Note:** 
-There are two versions, 11.x or 12.x for the differents architectures(Turing,Maxwell Etc...) of GPUs.
-You can find which version you should download from [this link](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html).
-13. Download the latest version and extract it from the download location
-14. Find the bin folder E\cudnn-windows-x86_64-__Whatever Version__\bin
-15. Copy and paste the .dll files into YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib **Make sure to copy, and not move the files**
-16. If prompted, replace any existing files 
-
-**Notes:** 
-* If no change is seen or any issues are encountered, follow the same steps as above and paste the torch/lib backup folder you made earlier and replace it. If you didn't make a backup, you can also uninstall and reinstall torch through the command line to repair this folder. 
-* This optimization is  intended for the newer version of graphics card (40/30 series) but results have been seen with older graphics card.
-
-
 ### Torch Installation
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/cu121` as described in the [Manual
+https://download.pytorch.org/whl/cu117` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 ## :simple-amd: ROCm

docs/installation/040_INSTALL_DOCKER.md

@@ -4,49 +4,38 @@ title: Installing with Docker
 
 # :fontawesome-brands-docker: Docker
 
-!!! warning "macOS and AMD GPU Users"
+!!! warning "For end users"
 
-    We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md),
-    because Docker containers can not access the GPU on macOS.
+    We highly recommend to Install InvokeAI locally using [these instructions](index.md)
 
-!!! warning "AMD GPU Users"
+!!! tip "For developers"
 
-    Container support for AMD GPUs has been reported to work by the community, but has not received
-    extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and
-    use the `build.sh` script to build the image for this to take effect at build time.
+    For container-related development tasks or for enabling easy
+    deployment to other environments (on-premises or cloud), follow these
+    instructions.
 
-!!! tip "Linux and Windows Users"
-
-    For optimal performance, configure your Docker daemon to access your machine's GPU.
-    Docker Desktop on Windows [includes GPU support](https://www.docker.com/blog/wsl-2-gpu-support-for-docker-desktop-on-nvidia-gpus/).
-    Linux users should install and configure the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
+    For general use, install locally to leverage your machine's GPU.
 
 ## Why containers?
 
-They provide a flexible, reliable way to build and deploy InvokeAI.
-See [Processes](https://12factor.net/processes) under the Twelve-Factor App
-methodology for details on why running applications in such a stateless fashion is important.
+They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
+use a Docker volume to store the largest model files and image outputs as a
+first step in decoupling storage and compute. Future enhancements can do this
+for other assets. See [Processes](https://12factor.net/processes) under the
+Twelve-Factor App methodology for details on why running applications in such a
+stateless fashion is important.
 
-The container is configured for CUDA by default, but can be built to support AMD GPUs
-by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time.
+You can specify the target platform when building the image and running the
+container. You'll also need to specify the InvokeAI requirements file that
+matches the container's OS and the architecture it will run on.
 
-Developers on Apple silicon (M1/M2/M3): You
+Developers on Apple silicon (M1/M2): You
 [can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
 and performance is reduced compared with running it directly on macOS but for
 development purposes it's fine. Once you're done with development tasks on your
 laptop you can build for the target platform and architecture and deploy to
 another environment with NVIDIA GPUs on-premises or in the cloud.
 
-## TL;DR
-
-This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options.
-
-    ```bash
-    # docker compose commands should be run from the `docker` directory
-    cd docker
-    docker compose up
-    ```
-
 ## Installation in a Linux container (desktop)
 
 ### Prerequisites
@@ -69,44 +58,222 @@ a token and copy it, since you will need in for the next step.
 
 ### Setup
 
-Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
+Set the fork you want to use and other variables.
 
-Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
+!!! tip
 
-At a minimum, you might want to set the `INVOKEAI_ROOT` environment variable
-to point to the location where you wish to store your InvokeAI models, configuration, and outputs.
+    I preffer to save my env vars
+    in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
+    them when I come back.
+
+The build- and run- scripts contain default values for almost everything,
+besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
+created in the last step.
+
+Some Suggestions of variables you may want to change besides the Token:
 
 <figure markdown>
 
 | Environment-Variable <img width="220" align="right"/> | Default value <img width="360" align="right"/> | Description                                                                                                                                                                                       |
 | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `INVOKEAI_ROOT`                                       | `~/invokeai`                                   | **Required** - the location of your InvokeAI root directory. It will be created if it does not exist.
-| `HUGGING_FACE_HUB_TOKEN`                              |                                                | InvokeAI will work without it, but some of the integrations with HuggingFace (like downloading from models from private repositories) may not work|
-| `GPU_DRIVER`                                          | `cuda`                                         | Optionally change this to `rocm` to build the image for AMD GPUs. NOTE: Use the `build.sh` script to build the image for this to take effect.
+| `HUGGING_FACE_HUB_TOKEN`                              | No default, but **required**!                  | This is the only **required** variable, without it you can't download the huggingface models                                                                                                      |
+| `REPOSITORY_NAME`                                     | The Basename of the Repo folder                | This name will used as the container repository/image name                                                                                                                                        |
+| `VOLUMENAME`                                          | `${REPOSITORY_NAME,,}_data`                    | Name of the Docker Volume where model files will be stored                                                                                                                                        |
+| `ARCH`                                                | arch of the build machine                      | Can be changed if you want to build the image for another arch                                                                                                                                    |
+| `CONTAINER_REGISTRY`                                  | ghcr.io                                        | Name of the Container Registry to use for the full tag                                                                                                                                            |
+| `CONTAINER_REPOSITORY`                                | `$(whoami)/${REPOSITORY_NAME}`                 | Name of the Container Repository                                                                                                                                                                  |
+| `CONTAINER_FLAVOR`                                    | `cuda`                                         | The flavor of the image to built, available options are `cuda`, `rocm` and `cpu`. If you choose `rocm` or `cpu`, the extra-index-url will be selected automatically, unless you set one yourself. |
+| `CONTAINER_TAG`                                       | `${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}`   | The Container Repository / Tag which will be used                                                                                                                                                 |
+| `INVOKE_DOCKERFILE`                                   | `Dockerfile`                                   | The Dockerfile which should be built, handy for development                                                                                                                                       |
+| `PIP_EXTRA_INDEX_URL`                                 |                                                | If you want to use a custom pip-extra-index-url                                                                                                                                                   |
 
 </figure>
 
 #### Build the Image
 
-Use the standard `docker compose build` command from within the `docker` directory.
+I provided a build script, which is located next to the Dockerfile in
+`docker/build.sh`. It can be executed from repository root like this:
 
-If using an AMD GPU:
-    a: set the `GPU_DRIVER=rocm` environment variable in `docker-compose.yml` and continue using `docker compose build` as usual, or
-    b: set `GPU_DRIVER=rocm` in the `.env` file and use the `build.sh` script, provided for convenience
+```bash
+./docker/build.sh
+```
+
+The build Script not only builds the container, but also creates the docker
+volume if not existing yet.
 
 #### Run the Container
 
-Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
+After the build process is done, you can run the container via the provided
+`docker/run.sh` script
 
-Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at [http://localhost:9090](http://localhost:9090)
+```bash
+./docker/run.sh
+```
 
-## Troubleshooting / FAQ
+When used without arguments, the container will start the webserver and provide
+you the link to open it. But if you want to use some other parameters you can
+also do so.
 
-- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
-- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
-    and you may have cloned this repository before the issue was fixed. To solve this, please change
-    the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
-    (`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
-    Finally, you may delete `docker-entrypoint.sh` followed by  `git pull; git checkout docker/docker-entrypoint.sh`
-    to reset the file to its most recent version.
-    For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)
+!!! example "run script example"
+
+    ```bash
+    ./docker/run.sh "banana sushi" -Ak_lms -S42 -s10
+    ```
+
+    This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
+
+    Find out more about available CLI-Parameters at [features/CLI.md](../../features/CLI/#arguments)
+
+---
+
+## Running the container on your GPU
+
+If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
+the container with an extra environment variable to enable GPU usage and have
+the process run much faster:
+
+```bash
+GPU_FLAGS=all ./docker/run.sh
+```
+
+This passes the `--gpus all` to docker and uses the GPU.
+
+If you don't have a GPU (or your host is not yet setup to use it) you will see a
+message like this:
+
+`docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
+
+You can use the full set of GPU combinations documented here:
+
+https://docs.docker.com/config/containers/resource_constraints/#gpu
+
+For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
+choose a specific device identified by a UUID.
+
+---
+
+!!! warning "Deprecated"
+
+    From here on you will find the the previous Docker-Docs, which will still
+    provide some usefull informations.
+
+## Usage (time to have fun)
+
+### Startup
+
+If you're on a **Linux container** the `invoke` script is **automatically
+started** and the output dir set to the Docker volume you created earlier.
+
+If you're **directly on macOS follow these startup instructions**. With the
+Conda environment activated (`conda activate ldm`), run the interactive
+interface that combines the functionality of the original scripts `txt2img` and
+`img2img`: Use the more accurate but VRAM-intensive full precision math because
+half-precision requires autocast and won't work. By default the images are saved
+in `outputs/img-samples/`.
+
+```Shell
+python3 scripts/invoke.py --full_precision
+```
+
+You'll get the script's prompt. You can see available options or quit.
+
+```Shell
+invoke> -h
+invoke> q
+```
+
+### Text to Image
+
+For quick (but bad) image results test with 5 steps (default 50) and 1 sample
+image. This will let you know that everything is set up correctly. Then increase
+steps to 100 or more for good (but slower) results. The prompt can be in quotes
+or not.
+
+```Shell
+invoke> The hulk fighting with sheldon cooper -s5 -n1
+invoke> "woman closeup highly detailed"  -s 150
+# Reuse previous seed and apply face restoration
+invoke> "woman closeup highly detailed"  --steps 150 --seed -1 -G 0.75
+```
+
+You'll need to experiment to see if face restoration is making it better or
+worse for your specific prompt.
+
+If you're on a container the output is set to the Docker volume. You can copy it
+wherever you want. You can download it from the Docker Desktop app, Volumes,
+my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
+`docker cp` can't expand `*.png` so you'll need to specify the image file name.
+
+On your host Mac (you can use the name of any container that mounted the
+volume):
+
+```Shell
+docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
+```
+
+### Image to Image
+
+You can also do text-guided image-to-image translation. For example, turning a
+sketch into a detailed drawing.
+
+`strength` is a value between 0.0 and 1.0 that controls the amount of noise that
+is added to the input image. Values that approach 1.0 allow for lots of
+variations but will also produce images that are not semantically consistent
+with the input. 0.0 preserves image exactly, 1.0 replaces it completely.
+
+Make sure your input image size dimensions are multiples of 64 e.g. 512x512.
+Otherwise you'll get `Error: product of dimension sizes > 2**31'`. If you still
+get the error
+[try a different size](https://support.apple.com/guide/preview/resize-rotate-or-flip-an-image-prvw2015/mac#:~:text=image's%20file%20size-,In%20the%20Preview%20app%20on%20your%20Mac%2C%20open%20the%20file,is%20shown%20at%20the%20bottom.)
+like 512x256.
+
+If you're on a Docker container, copy your input image into the Docker volume
+
+```Shell
+docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
+```
+
+Try it out generating an image (or more). The `invoke` script needs absolute
+paths to find the image so don't use `~`.
+
+If you're on your Mac
+
+```Shell
+invoke> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75  --steps 100 -n4
+```
+
+If you're on a Linux container on your Mac
+
+```Shell
+invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75  --steps 50 -n1
+```
+
+### Web Interface
+
+You can use the `invoke` script with a graphical web interface. Start the web
+server with:
+
+```Shell
+python3 scripts/invoke.py --full_precision --web
+```
+
+If it's running on your Mac point your Mac web browser to
+<http://127.0.0.1:9090>
+
+Press Control-C at the command line to stop the web server.
+
+### Notes
+
+Some text you can add at the end of the prompt to make it very pretty:
+
+```Shell
+cinematic photo, highly detailed, cinematic lighting, ultra-detailed, ultrarealistic, photorealism, Octane Rendering, cyberpunk lights, Hyper Detail, 8K, HD, Unreal Engine, V-Ray, full hd, cyberpunk, abstract, 3d octane render + 4k UHD + immense detail + dramatic lighting + well lit + black, purple, blue, pink, cerulean, teal, metallic colours, + fine details, ultra photoreal, photographic, concept art, cinematic composition, rule of thirds, mysterious, eerie, photorealism, breathtaking detailed, painting art deco pattern, by hsiao, ron cheng, john james audubon, bizarre compositions, exquisite detail, extremely moody lighting, painted by greg rutkowski makoto shinkai takashi takeuchi studio ghibli, akihiko yoshida
+```
+
+The original scripts should work as well.
+
+```Shell
+python3 scripts/orig_scripts/txt2img.py --help
+python3 scripts/orig_scripts/txt2img.py --ddim_steps 100 --n_iter 1 --n_samples 1  --plms --prompt "new born baby kitten. Hyper Detail, Octane Rendering, Unreal Engine, V-Ray"
+python3 scripts/orig_scripts/txt2img.py --ddim_steps 5   --n_iter 1 --n_samples 1  --plms --prompt "ocean" # or --klms
+```

docs/installation/050_INSTALLING_MODELS.md

@@ -84,7 +84,7 @@ InvokeAI root directory's `autoimport` folder.
 
 ### Installation via `invokeai-model-install`
 
-From the `invoke` launcher, choose option [4] "Download and install
+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
@@ -124,7 +124,7 @@ installation. Examples:
 invokeai-model-install --list controlnet
 
 # (install the model at the indicated URL)
-invokeai-model-install --add https://civitai.com/api/download/models/128713
+invokeai-model-install --add http://civitai.com/2860
 
 # (delete the named model)
 invokeai-model-install --delete sd-1/main/analog-diffusion
@@ -170,17 +170,4 @@ elsewhere on disk and they will be autoimported. You can also create
 subfolders and organize them as you wish.
 
 The location of the autoimport directories are controlled by settings
-in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
-
-### Installing models that live in HuggingFace subfolders
-
-On rare occasions you may need to install a diffusers-style model that
-lives in a subfolder of a HuggingFace repo id. In this event, simply
-add ":_subfolder-name_" to the end of the repo id. For example, if the
-repo id is "monster-labs/control_v1p_sd15_qrcode_monster" and the model
-you wish to fetch lives in a subfolder named "v2", then the repo id to
-pass to the various model installers should be 
-
-```
-monster-labs/control_v1p_sd15_qrcode_monster:v2
-```
+in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).

docs/installation/060_INSTALL_PATCHMATCH.md

@@ -59,7 +59,8 @@ Prior to installing PyPatchMatch, you need to take the following steps:
    `from patchmatch import patch_match`: It should look like the following:
 
     ```py
-    Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux
+    Python 3.9.5 (default, Nov 23 2021, 15:27:38)
+    [GCC 9.3.0] on linux
     Type "help", "copyright", "credits" or "license" for more information.
     >>> from patchmatch import patch_match
     Compiling and loading c extensions from "/home/lstein/Projects/InvokeAI/.invokeai-env/src/pypatchmatch/patchmatch".

docs/installation/070_INSTALL_XFORMERS.md

@@ -28,21 +28,18 @@ command line, then just be sure to activate it's virtual environment.
 Then run the following three commands:
 
 ```sh
-pip install xformers~=0.0.22
-pip install triton    # WON'T WORK ON WINDOWS
+pip install xformers==0.0.16rc425
+pip install triton
 python -m xformers.info output
 ```
 
 The first command installs `xformers`, the second installs the
 `triton` training accelerator, and the third prints out the `xformers`
-installation status. On Windows, please omit the `triton` package,
-which is not available on that platform.
-
-If all goes well, you'll see a report like the
+installation status. If all goes well, you'll see a report like the
 following:
 
 ```sh
-xFormers 0.0.22
+xFormers 0.0.16rc425
 memory_efficient_attention.cutlassF:               available
 memory_efficient_attention.cutlassB:               available
 memory_efficient_attention.flshattF:               available
@@ -51,28 +48,22 @@ memory_efficient_attention.smallkF:                available
 memory_efficient_attention.smallkB:                available
 memory_efficient_attention.tritonflashattF:        available
 memory_efficient_attention.tritonflashattB:        available
-indexing.scaled_index_addF:                        available
-indexing.scaled_index_addB:                        available
-indexing.index_select:                             available
-swiglu.dual_gemm_silu:                             available
-swiglu.gemm_fused_operand_sum:                     available
 swiglu.fused.p.cpp:                                available
 is_triton_available:                               True
 is_functorch_available:                            False
-pytorch.version:                                   2.1.0+cu121
+pytorch.version:                                   1.13.1+cu117
 pytorch.cuda:                                      available
-gpu.compute_capability:                            8.9
-gpu.name:                                          NVIDIA GeForce RTX 4070
+gpu.compute_capability:                            8.6
+gpu.name:                                          NVIDIA RTX A2000 12GB
 build.info:                                        available
-build.cuda_version:                                1108
-build.python_version:                              3.10.11
-build.torch_version:                               2.1.0+cu121
+build.cuda_version:                                1107
+build.python_version:                              3.10.9
+build.torch_version:                               1.13.1+cu117
 build.env.TORCH_CUDA_ARCH_LIST:                    5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6
 build.env.XFORMERS_BUILD_TYPE:                     Release
 build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS:        None
 build.env.NVCC_FLAGS:                              None
-build.env.XFORMERS_PACKAGE_FROM:                   wheel-v0.0.20
-build.nvcc_version:                                11.8.89
+build.env.XFORMERS_PACKAGE_FROM:                   wheel-v0.0.16rc425
 source.privacy:                                    open source
 ```
 
@@ -92,24 +83,35 @@ installed from source. These instructions were written for a system
 running Ubuntu 22.04, but other Linux distributions should be able to
 adapt this recipe.
 
-#### 1. Install CUDA Toolkit 12.1
+#### 1. Install CUDA Toolkit 11.7
 
 You will need the CUDA developer's toolkit in order to compile and
 install xFormers. **Do not try to install Ubuntu's nvidia-cuda-toolkit
 package.** It is out of date and will cause conflicts among the NVIDIA
 driver and binaries. Instead install the CUDA Toolkit package provided
-by NVIDIA itself. Go to [CUDA Toolkit 12.1
-Downloads](https://developer.nvidia.com/cuda-12-1-0-download-archive)
+by NVIDIA itself. Go to [CUDA Toolkit 11.7
+Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive)
 and use the target selection wizard to choose your platform and Linux
 distribution. Select an installer type of "runfile (local)" at the
 last step.
 
 This will provide you with a recipe for downloading and running a
-install shell script that will install the toolkit and drivers.
+install shell script that will install the toolkit and drivers. For
+example, the install script recipe for Ubuntu 22.04 running on a
+x86_64 system is:
 
-#### 2. Confirm/Install pyTorch 2.1.0 with CUDA 12.1 support
+```
+wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
+sudo sh cuda_11.7.0_515.43.04_linux.run
+```
 
-If you are using InvokeAI 3.0.2 or higher, these will already be
+Rather than cut-and-paste this example, We recommend that you walk
+through the toolkit wizard in order to get the most up to date
+installer for your system.
+
+#### 2. Confirm/Install pyTorch 1.13 with CUDA 11.7 support
+
+If you are using InvokeAI 2.3 or higher, these will already be
 installed. If not, you can check whether you have the needed libraries
 using a quick command. Activate the invokeai virtual environment,
 either by entering the "developer's console", or manually with a
@@ -122,7 +124,7 @@ Then run the command:
 python -c 'exec("import torch\nprint(torch.__version__)")'
 ```
 
-If it prints __2.1.0+cu121__ you're good. If not, you can install the
+If it prints __1.13.1+cu117__ you're good. If not, you can install the
 most up to date libraries with this command:
 
 ```sh

docs/installation/INSTALLATION.md

@@ -1,83 +0,0 @@
-# Overview
-
-We offer several ways to install InvokeAI, each one suited to your
-experience and preferences. We suggest that everyone start by
-reviewing the
-[hardware](010_INSTALL_AUTOMATED.md#hardware_requirements) and
-[software](010_INSTALL_AUTOMATED.md#software_requirements)
-requirements, as they are the same across each install method. Then
-pick the install method most suitable to your level of experience and
-needs.
-
-See the [troubleshooting
-section](010_INSTALL_AUTOMATED.md#troubleshooting) of the automated
-install guide for frequently-encountered installation issues.
-
-This fork is supported across Linux, Windows and Macintosh. Linux users can use
-either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
-driver).
-
-
-## **[Automated Installer](010_INSTALL_AUTOMATED.md)**
-✅ This is the recommended installation method for first-time users. 
-
-  This is a script that will install all of InvokeAI's essential
-  third party libraries and InvokeAI itself. It includes access to a
-  "developer console" which will help us debug problems with you and
-  give you to access experimental features.
-
-## **[Manual Installation](020_INSTALL_MANUAL.md)**
-This method is recommended for experienced users and developers.
-
-  In this method you will manually run the commands needed to install
-  InvokeAI and its dependencies. We offer two recipes: one suited to
-  those who prefer the `conda` tool, and one suited to those who prefer
-  `pip` and Python virtual environments. In our hands the pip install
-  is faster and more reliable, but your mileage may vary.
-  Note that the conda installation method is currently deprecated and
-  will not be supported at some point in the future.
-
-## **[Docker Installation](040_INSTALL_DOCKER.md)**
-This method is recommended for those familiar with running Docker containers.
-
-We offer a method for creating Docker containers containing InvokeAI and its dependencies. This method is recommended for individuals with experience with Docker containers and understand the pluses and minuses of a container-based install.
-
-## Other Installation Guides
-  - [PyPatchMatch](060_INSTALL_PATCHMATCH.md)
-  - [XFormers](070_INSTALL_XFORMERS.md)
-  - [CUDA and ROCm Drivers](030_INSTALL_CUDA_AND_ROCM.md)
-  - [Installing New Models](050_INSTALLING_MODELS.md)
-
-## :fontawesome-solid-computer: Hardware Requirements
-
-### :octicons-cpu-24: System
-
-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.
-
-** SDXL 1.0 Requirements*
-To use SDXL, user must have one of the following: 
-- :simple-nvidia: An NVIDIA-based graphics card with 8 GB or more VRAM memory.
-- :simple-amd: An AMD-based graphics card with 16 GB or more VRAM memory (Linux
-  only)
-- :fontawesome-brands-apple: An Apple computer with an M1 chip.
-
-
-### :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.
-
-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
-

docs/installation/deprecated_documentation/INSTALL_LINUX.md

@@ -79,7 +79,7 @@ title: Manual Installation, Linux
         and obtaining an access token for downloading. It will then download and
         install the weights files for you.
 
-        Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing
+        Please look [here](../INSTALL_MANUAL.md) for a manual process for doing
         the same thing.
 
 7.  Start generating images!
@@ -112,7 +112,7 @@ title: Manual Installation, Linux
         To use an alternative model you may invoke the `!switch` command in
         the CLI, or pass `--model <model_name>` during `invoke.py` launch for
         either the CLI or the Web UI. See [Command Line
-        Client](../../deprecated/CLI.md#model-selection-and-importation). The
+        Client](../../features/CLI.md#model-selection-and-importation). The
         model names are defined in `configs/models.yaml`.
 
 8. Subsequently, to relaunch the script, be sure to run "conda activate

docs/installation/deprecated_documentation/INSTALL_MAC.md

@@ -150,7 +150,7 @@ will do our best to help.
     To use an alternative model you may invoke the `!switch` command in
     the CLI, or pass `--model <model_name>` during `invoke.py` launch for
     either the CLI or the Web UI. See [Command Line
-    Client](../../deprecated/CLI.md#model-selection-and-importation). The
+    Client](../../features/CLI.md#model-selection-and-importation). The
     model names are defined in `configs/models.yaml`.
 
 ---

docs/installation/deprecated_documentation/INSTALL_SOURCE.md

@@ -128,7 +128,7 @@ python scripts/invoke.py --web --max_load_models=3 \
 ```
 
 These options are described in detail in the
-[Command-Line Interface](../../deprecated/CLI.md) documentation.
+[Command-Line Interface](../../features/CLI.md) documentation.
 
 ## Troubleshooting
 

docs/installation/deprecated_documentation/INSTALL_WINDOWS.md

@@ -75,7 +75,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
           obtaining an access token for downloading. It will then download and install the
           weights files for you.
 
-          Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing the
+          Please look [here](../INSTALL_MANUAL.md) for a manual process for doing the
           same thing.
 
 8. Start generating images!
@@ -108,7 +108,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
         To use an alternative model you may invoke the `!switch` command in
         the CLI, or pass `--model <model_name>` during `invoke.py` launch for
         either the CLI or the Web UI. See [Command Line
-        Client](../../deprecated/CLI.md#model-selection-and-importation). The
+        Client](../../features/CLI.md#model-selection-and-importation). The
         model names are defined in `configs/models.yaml`.
 
 9. Subsequently, to relaunch the script, first activate the Anaconda

docs/installation/index.md

@@ -0,0 +1,57 @@
+---
+title: Overview
+---
+
+We offer several ways to install InvokeAI, each one suited to your
+experience and preferences. We suggest that everyone start by
+reviewing the
+[hardware](010_INSTALL_AUTOMATED.md#hardware_requirements) and
+[software](010_INSTALL_AUTOMATED.md#software_requirements)
+requirements, as they are the same across each install method. Then
+pick the install method most suitable to your level of experience and
+needs.
+
+See the [troubleshooting
+section](010_INSTALL_AUTOMATED.md#troubleshooting) of the automated
+install guide for frequently-encountered installation issues.
+
+## Installation options
+
+1. [Automated Installer](010_INSTALL_AUTOMATED.md)
+
+    This is a script that will install all of InvokeAI's essential
+    third party libraries and InvokeAI itself. It includes access to a
+    "developer console" which will help us debug problems with you and
+    give you to access experimental features.
+
+
+    ✅ This is the recommended option for first time users. 
+
+2. [Manual Installation](020_INSTALL_MANUAL.md)
+
+    In this method you will manually run the commands needed to install
+    InvokeAI and its dependencies. We offer two recipes: one suited to
+    those who prefer the `conda` tool, and one suited to those who prefer
+    `pip` and Python virtual environments. In our hands the pip install
+    is faster and more reliable, but your mileage may vary.
+    Note that the conda installation method is currently deprecated and
+    will not be supported at some point in the future.
+
+    This method is recommended for users who have previously used `conda`
+    or `pip` in the past, developers, and anyone who wishes to remain on
+    the cutting edge of future InvokeAI development and is willing to put
+    up with occasional glitches and breakage.
+
+3. [Docker Installation](040_INSTALL_DOCKER.md)
+
+    We also offer a method for creating Docker containers containing
+    InvokeAI and its dependencies. This method is recommended for
+    individuals with experience with Docker containers and understand
+    the pluses and minuses of a container-based install.
+
+## Quick Guides
+
+* [Installing CUDA and ROCm Drivers](./030_INSTALL_CUDA_AND_ROCM.md)
+* [Installing XFormers](./070_INSTALL_XFORMERS.md)
+* [Installing PyPatchMatch](./060_INSTALL_PATCHMATCH.md)
+* [Installing New Models](./050_INSTALLING_MODELS.md)

docs/javascripts/init_kapa_widget.js

@@ -1,10 +0,0 @@
-document.addEventListener("DOMContentLoaded", function () {
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docs/javascripts/tablesort.js

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docs/nodes/NODES.md

@@ -1,90 +0,0 @@
-# Using the Workflow Editor
-
-The workflow editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. Nodes take in inputs on the left side of the node, and return an output on the right side of the node. A node graph is composed of multiple nodes that are connected together to create a workflow. Nodes' inputs and outputs are connected by dragging connectors from node to node. Inputs and outputs are color coded for ease of use.
-
-If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](../help/diffusion.md) Understanding how diffusion works will enable you to more easily use the Workflow Editor and build workflows to suit your needs.
-
-## Features
-
-### Linear View
-The Workflow Editor allows you to create a UI for your workflow, to make it easier to iterate on your generations. 
-
-To add an input to the Linear UI, right click on the input label and select "Add to Linear View".
-
-The Linear UI View will also be part of the saved workflow, allowing you share workflows and enable other to use them, regardless of complexity. 
-
-![linearview](../assets/nodes/linearview.png)
-
-### Renaming Fields and Nodes
-Any node or input field can be renamed in the workflow editor. If the input field you have renamed has been added to the Linear View, the changed name will be reflected in the Linear View and the node. 
-
-### Managing Nodes
-
-* Ctrl+C to copy a node
-* Ctrl+V to paste a node
-* Backspace/Delete to delete a node
-* Shift+Click to drag and select multiple nodes 
-
-### Node Caching 
-
-Nodes have a "Use Cache" option in their footer. This allows for performance improvements by using the previously cached values during the workflow processing. 
-
-
-## Important Concepts 
-
-There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
-
-### Noise
-
-An initial noise tensor is necessary for the latent diffusion process. As a result, the Denoising node requires a noise node input.  
-
-![groupsnoise](../assets/nodes/groupsnoise.png)
-
-### Text Prompt Conditioning
-
-Conditioning is necessary for the latent diffusion process, whether empty or not. As a result, the Denoising node requires positive and negative conditioning inputs. Conditioning is reliant on a CLIP text encoder provided by the Model Loader node.
-
-![groupsconditioning](../assets/nodes/groupsconditioning.png)
-
-### Image to Latents & VAE
-
-The ImageToLatents node takes in a pixel image and a VAE and outputs a latents. The LatentsToImage node does the opposite, taking in a latents and a VAE and outpus a pixel image. 
-
-![groupsimgvae](../assets/nodes/groupsimgvae.png)
-
-### Defined & Random Seeds
-
-It is common to want to use both the same seed (for continuity) and random seeds (for variety). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
-
-![groupsrandseed](../assets/nodes/groupsrandseed.png)
-
-### ControlNet
-
-The ControlNet node outputs a Control, which can be provided as input to a Denoise Latents node. Depending on the type of ControlNet desired, ControlNet nodes usually require an image processor node, such as a Canny Processor or Depth Processor, which prepares an input image for use with ControlNet.
-
-![groupscontrol](../assets/nodes/groupscontrol.png)
-
-### LoRA
-
-The Lora Loader node lets you load a LoRA and pass it as output.A LoRA provides fine-tunes to the UNet and text encoder weights that augment the base model’s image and text vocabularies.
-
-![groupslora](../assets/nodes/groupslora.png)
-
-### Scaling
-
-Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. Upscaling is the process of enlarging an image and adding more detail. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results.
-
-![groupsallscale](../assets/nodes/groupsallscale.png)
-
-### Iteration + Multiple Images as Input
-
-Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and use them in an image generation workflow.
-
-![groupsiterate](../assets/nodes/groupsiterate.png)
-
-### Batch / Multiple Image Generation + Random Seeds
-
-Batch or multiple image generation in the workflow editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate, meaning this example will generate 4 images. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. This noise can then be fed to the Denoise Latents node for it to iterate through the denoising process with the different seeds provided.
-
-![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png)
-

docs/nodes/comfyToInvoke.md

@@ -1,80 +0,0 @@
-# ComfyUI to InvokeAI
-
-If you're coming to InvokeAI from ComfyUI, welcome! You'll find things are similar but different - the good news is that you already know how things should work, and it's just a matter of wiring them up! 
-
-Some things to note: 
-
-- InvokeAI's nodes tend to be more granular than default nodes in Comfy. This means each node in Invoke will do a specific task and you might need to use multiple nodes to achieve the same result. The added granularity improves the control you have have over your workflows. 
-- InvokeAI's backend and ComfyUI's backend are very different which means Comfy workflows are not able to be imported into InvokeAI. However, we have created a [list of popular workflows](exampleWorkflows.md) for you to get started with Nodes in InvokeAI!
-
-## Node Equivalents:
-
-| Comfy UI Category | ComfyUI Node | Invoke Equivalent      |
-|:---------------------------------- |:---------------------------------- | :----------------------------------|
-| Sampling |KSampler |Denoise Latents|
-| Sampling |Ksampler Advanced|Denoise Latents |
-| Loaders |Load Checkpoint | Main Model Loader _or_ SDXL Main Model Loader|
-| Loaders |Load VAE | VAE Loader |
-| Loaders |Load Lora | LoRA Loader _or_ SDXL Lora Loader|
-| Loaders |Load ControlNet Model | ControlNet|
-| Loaders |Load ControlNet Model (diff) | ControlNet|
-| Loaders |Load Style Model | Reference Only ControlNet will be coming in a future version of InvokeAI|
-| Loaders |unCLIPCheckpointLoader | N/A |
-| Loaders |GLIGENLoader | N/A |
-| Loaders |Hypernetwork Loader | N/A |
-| Loaders |Load Upscale Model | Occurs within "Upscale (RealESRGAN)"|
-|Conditioning |CLIP Text Encode (Prompt) | Compel (Prompt) or SDXL Compel (Prompt) |
-|Conditioning |CLIP Set Last Layer | CLIP Skip|
-|Conditioning |Conditioning (Average) | Use the .blend() feature of prompts |
-|Conditioning |Conditioning (Combine) | N/A |
-|Conditioning |Conditioning (Concat) | See the Prompt Tools Community Node|
-|Conditioning |Conditioning (Set Area) | N/A |
-|Conditioning |Conditioning (Set Mask) | Mask Edge |
-|Conditioning |CLIP Vision Encode | N/A |
-|Conditioning |unCLIPConditioning | N/A |
-|Conditioning |Apply ControlNet | ControlNet |
-|Conditioning |Apply ControlNet (Advanced) | ControlNet |
-|Latent |VAE Decode | Latents to Image|
-|Latent |VAE Encode | Image to Latents |
-|Latent |Empty Latent Image | Noise |
-|Latent |Upscale Latent |Resize Latents |
-|Latent |Upscale Latent By |Scale Latents |
-|Latent |Latent Composite | Blend Latents |
-|Latent |LatentCompositeMasked | N/A |
-|Image |Save Image | Image |
-|Image |Preview Image |Current |
-|Image |Load Image | Image|
-|Image |Empty Image| Blank Image |
-|Image |Invert Image | Invert Lerp Image |
-|Image |Batch Images | Link "Image" nodes into an "Image Collection" node |
-|Image |Pad Image for Outpainting | Outpainting is easily accomplished in the Unified Canvas |
-|Image |ImageCompositeMasked | Paste Image |
-|Image | Upscale Image | Resize Image |
-|Image | Upscale Image By | Upscale Image |
-|Image | Upscale Image (using Model) | Upscale Image |
-|Image | ImageBlur | Blur Image |
-|Image | ImageQuantize | N/A |
-|Image | ImageSharpen | N/A |
-|Image | Canny | Canny Processor |
-|Mask |Load Image (as Mask) | Image |
-|Mask |Convert Mask to Image | Image|
-|Mask |Convert Image to Mask | Image |
-|Mask |SolidMask | N/A |
-|Mask |InvertMask |Invert Lerp Image |
-|Mask |CropMask | Crop Image |
-|Mask |MaskComposite | Combine Mask |
-|Mask |FeatherMask | Blur Image |
-|Advanced | Load CLIP | Main Model Loader _or_ SDXL Main Model Loader|
-|Advanced | UNETLoader | Main Model Loader _or_ SDXL Main Model Loader|
-|Advanced | DualCLIPLoader | Main Model Loader _or_ SDXL Main Model Loader|
-|Advanced | Load Checkpoint | Main Model Loader _or_ SDXL Main Model Loader |
-|Advanced | ConditioningZeroOut | N/A |
-|Advanced | ConditioningSetTimestepRange | N/A |
-|Advanced | CLIPTextEncodeSDXLRefiner | Compel (Prompt) or SDXL Compel (Prompt) |
-|Advanced | CLIPTextEncodeSDXL |Compel (Prompt) or SDXL Compel (Prompt) |
-|Advanced | ModelMergeSimple | Model Merging is available in the Model Manager |
-|Advanced | ModelMergeBlocks | Model Merging is available in the Model Manager|
-|Advanced | CheckpointSave | Model saving is available in the Model Manager|
-|Advanced | CLIPMergeSimple | N/A |
-
-

docs/nodes/communityNodes.md

@@ -2,521 +2,51 @@
 
 These are nodes that have been developed by the community, for the community. If you're not sure what a node is, you can learn more about nodes [here](overview.md).
 
-If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md).
+If you'd like to submit a node for the community, please refer to the [node creation overview](./overview.md#contributing-nodes).
 
-To use a node, add the node to the `nodes` folder found in your InvokeAI install location. 
+To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations/` folder in your Invoke AI install location. Along with the node, an example node graph should be provided to help you get started with the node. 
 
-The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future. 
+To use a community node graph, download the the `.json` node graph file and load it into Invoke AI via the **Load Nodes** button on the Node Editor. 
 
-If you'd prefer, you can also just download the whole node folder from the linked repository and add it to the `nodes` folder. 
+## Disclaimer
 
-To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor. 
+The nodes linked below have been developed and contributed by members of the Invoke AI community. While we strive to ensure the quality and safety of these contributions, we do not guarantee the reliability or security of the nodes. If you have issues or concerns with any of the nodes below, please raise it on GitHub or in the Discord.
 
-- Community Nodes
-    + [Average Images](#average-images)
-    + [Clean Image Artifacts After Cut](#clean-image-artifacts-after-cut)
-    + [Close Color Mask](#close-color-mask) 
-    + [Clothing Mask](#clothing-mask)
-    + [Contrast Limited Adaptive Histogram Equalization](#contrast-limited-adaptive-histogram-equalization)
-    + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
-    + [Film Grain](#film-grain)
-    + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
-    + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
-    + [Grid to Gif](#grid-to-gif)
-    + [Halftone](#halftone)
-    + [Ideal Size](#ideal-size)
-    + [Image and Mask Composition Pack](#image-and-mask-composition-pack)
-    + [Image Dominant Color](#image-dominant-color)
-    + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
-    + [Image Picker](#image-picker)
-    + [Image Resize Plus](#image-resize-plus)
-    + [Load Video Frame](#load-video-frame)
-    + [Make 3D](#make-3d)
-    + [Mask Operations](#mask-operations)    
-    + [Match Histogram](#match-histogram)
-    + [Negative Image](#negative-image)    
-    + [Oobabooga](#oobabooga)
-    + [Prompt Tools](#prompt-tools)
-    + [Remote Image](#remote-image)
-    + [Remove Background](#remove-background)    
-    + [Retroize](#retroize)
-    + [Size Stepper Nodes](#size-stepper-nodes)
-    + [Simple Skin Detection](#simple-skin-detection)
-    + [Text font to Image](#text-font-to-image)
-    + [Thresholding](#thresholding)
-    + [Unsharp Mask](#unsharp-mask)
-    + [XY Image to Grid and Images to Grids nodes](#xy-image-to-grid-and-images-to-grids-nodes)
-- [Example Node Template](#example-node-template)
-- [Disclaimer](#disclaimer)
-- [Help](#help)
+## List of Nodes
 
+### Face Mask
 
---------------------------------
-### Average Images
+**Description:** This node autodetects a face in the image using MediaPipe and masks it by making it transparent. Via outpainting you can swap faces with other faces, or invert the mask and swap things around the face with other things. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control. The node also outputs an all-white mask in the same dimensions as the input image. This is needed by the inpaint node (and unified canvas) for outpainting.
 
-**Description:** This node takes in a collection of images of the same size and averages them as output. It converts everything to RGB mode first.
+**Node Link:** https://github.com/ymgenesis/InvokeAI/blob/facemaskmediapipe/invokeai/app/invocations/facemask.py
 
-**Node Link:** https://github.com/JPPhoto/average-images-node
-
---------------------------------
-### Clean Image Artifacts After Cut
-
-Description: Removes residual artifacts after an image is separated from its background.
-
-Node Link: https://github.com/VeyDlin/clean-artifact-after-cut-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/clean-artifact-after-cut-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Close Color Mask
-
-Description: Generates a mask for images based on a closely matching color, useful for color-based selections.
-
-Node Link: https://github.com/VeyDlin/close-color-mask-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/close-color-mask-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Clothing Mask
-
-Description: Employs a U2NET neural network trained for the segmentation of clothing items in images.
-
-Node Link: https://github.com/VeyDlin/clothing-mask-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/clothing-mask-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Contrast Limited Adaptive Histogram Equalization
-
-Description: Enhances local image contrast using adaptive histogram equalization with contrast limiting.
-
-Node Link: https://github.com/VeyDlin/clahe-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/clahe-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Depth Map from Wavefront OBJ
-
-**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation.
-
-To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations.
-
-**Node Link:** https://github.com/dwringer/depth-from-obj-node
-
-**Example Usage:**
-</br><img src="https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg" width="500" />
-
---------------------------------
-### Film Grain
-
-**Description:** This node adds a film grain effect to the input image based on the weights, seeds, and blur radii parameters. It works with RGB input images only.
-
-**Node Link:** https://github.com/JPPhoto/film-grain-node
-
---------------------------------
-### Generative Grammar-Based Prompt Nodes
-
-**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no nonterminal terms remain in the string.
-
-This includes 3 Nodes:
-- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output)
-- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading
-- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts.
-
-**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
-
-**Example Usage:**
-</br><img src="https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg" width="500" />
-
---------------------------------
-### GPT2RandomPromptMaker
-
-**Description:** A node for InvokeAI utilizes the GPT-2 language model to generate random prompts based on a provided seed and context.
-
-**Node Link:** https://github.com/mickr777/GPT2RandomPromptMaker
+**Example Node Graph:** https://www.mediafire.com/file/gohn5sb1bfp8use/21-July_2023-FaceMask.json/file
 
 **Output Examples** 
 
-Generated Prompt: An enchanted weapon will be usable by any character regardless of their alignment.
-
-<img src="https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c" width="200" />
+![2e3168cb-af6a-475d-bfac-c7b7fd67b4c2](https://github.com/ymgenesis/InvokeAI/assets/25252829/a5ad7d44-2ada-4b3c-a56e-a21f8244a1ac)
+![2_annotated](https://github.com/ymgenesis/InvokeAI/assets/25252829/53416c8a-a23b-4d76-bb6d-3cfd776e0096)
+![2fe2150c-fd08-4e26-8c36-f0610bf441bb](https://github.com/ymgenesis/InvokeAI/assets/25252829/b0f7ecfe-f093-4147-a904-b9f131b41dc9)
+![831b6b98-4f0f-4360-93c8-69a9c1338cbe](https://github.com/ymgenesis/InvokeAI/assets/25252829/fc7b0622-e361-4155-8a76-082894d084f0)
 
 --------------------------------
-### Grid to Gif
+### Super Cool Node Template
 
-**Description:** One node that turns a grid image into an image collection, one node that turns an image collection into a gif.
+**Description:** This node allows you to do super cool things with InvokeAI.
 
-**Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py
+**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
 
-**Example Node Graph:**  https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json
+**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
 
 **Output Examples** 
 
-<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/input.png" width="300" />
-<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/output.gif" width="300" />
+![Invoke AI](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png)
 
---------------------------------
-### Halftone
-
-**Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing.
-
-**Node Link:** https://github.com/JPPhoto/halftone-node
-
-**Example**
-
-Input:
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4" width="300" />
-
-Halftone Output:
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f" width="300" />
-
-CMYK Halftone Output:
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
-
---------------------------------
 ### Ideal Size
 
 **Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
 
 **Node Link:** https://github.com/JPPhoto/ideal-size-node
 
---------------------------------
-### Image and Mask Composition Pack
-
-**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
-
-This includes 15 Nodes:
-
-- *Adjust Image Hue Plus* - Rotate the hue of an image in one of several different color spaces.
-- *Blend Latents/Noise (Masked)* - Use a mask to blend part of one latents tensor [including Noise outputs] into another. Can be used to "renoise" sections during a multi-stage [masked] denoising process.
-- *Enhance Image* - Boost or reduce color saturation, contrast, brightness, sharpness, or invert colors of any image at any stage with this simple wrapper for pillow [PIL]'s ImageEnhance module.
-- *Equivalent Achromatic Lightness* - Calculates image lightness accounting for Helmholtz-Kohlrausch effect based on a method described by High, Green, and Nussbaum (2023).
-- *Text to Mask (Clipseg)* - Input a prompt and an image to generate a mask representing areas of the image matched by the prompt.
-- *Text to Mask Advanced (Clipseg)* - Output up to four prompt masks combined with logical "and", logical "or", or as separate channels of an RGBA image.
-- *Image Layer Blend* - Perform a layered blend of two images using alpha compositing. Opacity of top layer is selectable, with optional mask and several different blend modes/color spaces.
-- *Image Compositor* - Take a subject from an image with a flat backdrop and layer it on another image using a chroma key or flood select background removal.
-- *Image Dilate or Erode* - Dilate or expand a mask (or any image!). This is equivalent to an expand/contract operation.
-- *Image Value Thresholds* - Clip an image to pure black/white beyond specified thresholds.
-- *Offset Latents* - Offset a latents tensor in the vertical and/or horizontal dimensions, wrapping it around.
-- *Offset Image* - Offset an image in the vertical and/or horizontal dimensions, wrapping it around.
-- *Rotate/Flip Image* - Rotate an image in degrees clockwise/counterclockwise about its center, optionally resizing the image boundaries to fit, or flipping it about the vertical and/or horizontal axes.
-- *Shadows/Highlights/Midtones* - Extract three masks (with adjustable hard or soft thresholds) representing shadows, midtones, and highlights regions of an image.
-- *Text Mask (simple 2D)* - create and position a white on black (or black on white) line of text using any font locally available to Invoke.
-
-**Node Link:** https://github.com/dwringer/composition-nodes
-  
-</br><img src="https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg" width="500" />
-
---------------------------------
-### Image Dominant Color
-
-Description: Identifies and extracts the dominant color from an image using k-means clustering.
-
-Node Link: https://github.com/VeyDlin/image-dominant-color-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/image-dominant-color-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Image to Character Art Image Nodes
-
-**Description:** Group of nodes to convert an input image into ascii/unicode art Image
-
-**Node Link:** https://github.com/mickr777/imagetoasciiimage
-
-**Output Examples**
-
-<img src="https://user-images.githubusercontent.com/115216705/271817646-8e061fcc-9a2c-4fa9-bcc7-c0f7b01e9056.png" width="300" /><img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/3c4990eb-2f42-46b9-90f9-0088b939dc6a" width="300" /></br>
-<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/fee7f800-a4a8-41e2-a66b-c66e4343307e" width="300" />
-<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/1d9c1003-a45f-45c2-aac7-46470bb89330" width="300" />
-
---------------------------------
-
-### Image Picker
-
-**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose.
-
-**Node Link:** https://github.com/JPPhoto/image-picker-node
-
---------------------------------
-### Image Resize Plus
-
-Description: Provides various image resizing options such as fill, stretch, fit, center, and crop.
-
-Node Link: https://github.com/VeyDlin/image-resize-plus-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/image-resize-plus-node/master/.readme/node.png" width="500" />
-
-
---------------------------------
-### Load Video Frame
-
-**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs.
-
-**Node Link:** https://github.com/helix4u/load_video_frame
-
-**Output Example:** 
-<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/_git_assets/testmp4_embed_converted.gif" width="500" />
-
---------------------------------
-### Make 3D
-
-**Description:** Create compelling 3D stereo images from 2D originals.
-
-**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d)
-
-**Example Node Graph:**  https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false
-
-**Output Examples** 
-
-<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png" width="300" />
-<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png" width="300" />
-
---------------------------------
-### Mask Operations
-
-Description: Offers logical operations (OR, SUB, AND) for combining and manipulating image masks.
-
-Node Link: https://github.com/VeyDlin/mask-operations-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/mask-operations-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Match Histogram
-
-**Description:** An InvokeAI node to match a histogram from one image to another.  This is a bit like the `color correct` node in the main InvokeAI but this works in the YCbCr colourspace and can handle images of different sizes. Also does not require a mask input.
-- Option to only transfer luminance channel.
-- Option to save output as grayscale
-
-A good use case for this node is to normalize the colors of an image that has been through the tiled scaling workflow of my XYGrid Nodes. 
-
-See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
-
-**Node Link:** https://github.com/skunkworxdark/match_histogram
-
-**Output Examples** 
-
-<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
-
---------------------------------
-### Negative Image
-
-Description: Creates a negative version of an image, effective for visual effects and mask inversion.
-
-Node Link: https://github.com/VeyDlin/negative-image-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/negative-image-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Oobabooga
-
-**Description:** asks a local LLM running in Oobabooga's Text-Generation-Webui to write a prompt based on the user input.
-
-**Link:** https://github.com/sammyf/oobabooga-node
-
-**Example:**
-
-"describe a new mystical  creature in its natural environment"
-
-*can return*
-
-"The mystical creature I am describing to you is called the "Glimmerwing". It is a majestic, iridescent being that inhabits the depths of the most enchanted forests and glimmering lakes. Its body is covered in shimmering scales that reflect every color of the rainbow, and it has delicate, translucent wings that sparkle like diamonds in the sunlight. The Glimmerwing's home is a crystal-clear lake, surrounded by towering trees with leaves that shimmer like jewels. In this serene environment, the Glimmerwing spends its days swimming gracefully through the water, chasing schools of glittering fish and playing with the gentle ripples of the lake's surface.
-As the sun sets, the Glimmerwing perches on a branch of one of the trees, spreading its wings to catch the last rays of light. The creature's scales glow softly, casting a rainbow of colors across the forest floor. The Glimmerwing sings a haunting melody, its voice echoing through the stillness of the night air. Its song is said to have the power to heal the sick and bring peace to troubled souls. Those who are lucky enough to hear the Glimmerwing's song are forever changed by its beauty and grace."
-
-<img src="https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed" width="300" />
-
-**Requirement**
-
-a Text-Generation-Webui instance (might work remotely too, but I never tried it) and obviously InvokeAI 3.x
-
-**Note**
-
-This node works best with SDXL models, especially as the style can be described independently of the LLM's output.
-
---------------------------------
-### Prompt Tools 
-
-**Description:** A set of InvokeAI nodes that add general prompt (string) manipulation tools.  Designed to accompany the `Prompts From File` node and other prompt generation nodes.
-
-1. `Prompt To File` - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
-2. `PTFields Collect` - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
-3. `PTFields Expand` - Takes Json string and converts it to individual generation parameters. This can be fed from the Prompts to file node.
-4. `Prompt Strength` - Formats prompt with strength like the weighted format of compel 
-5. `Prompt Strength Combine` - Combines weighted prompts for .and()/.blend()
-6. `CSV To Index String` - Gets a string from a CSV by index. Includes a Random index option
-
-The following Nodes are now included in v3.2 of Invoke and are nolonger in this set of tools.<br>
-- `Prompt Join` -> `String Join`
-- `Prompt Join Three` -> `String Join Three`
-- `Prompt Replace` -> `String Replace`
-- `Prompt Split Neg` -> `String Split Neg`
-
-
-See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
-
-**Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
-
-**Workflow Examples** 
-
-<img src="https://github.com/skunkworxdark/prompt-tools/blob/main/images/CSVToIndexStringNode.png" width="300" />
-
---------------------------------
-### Remote Image
-
-**Description:** This is a pack of nodes to interoperate with other services, be they public websites or bespoke local servers. The pack consists of these nodes:
-
-- *Load Remote Image* - Lets you load remote images such as a realtime webcam image, an image of the day, or dynamically created images.
-- *Post Image to Remote Server* - Lets you upload an image to a remote server using an HTTP POST request, eg for storage, display or further processing.
-
-**Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
-
---------------------------------
-### Remove Background
-
-Description: An integration of the rembg package to remove backgrounds from images using multiple U2NET models.
-
-Node Link: https://github.com/VeyDlin/remove-background-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/remove-background-node/master/.readme/node.png" width="500" />
-
---------------------------------
-### Retroize
-
-**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images.
-
-**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
-
-**Retroize Output Examples**
-
-<img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
-
---------------------------------
-### Simple Skin Detection
-
-Description: Detects skin in images based on predefined color thresholds.
-
-Node Link: https://github.com/VeyDlin/simple-skin-detection-node
-
-View:
-</br><img src="https://raw.githubusercontent.com/VeyDlin/simple-skin-detection-node/master/.readme/node.png" width="500" />
-
-
---------------------------------
-### Size Stepper Nodes
-
-**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes.
-
-A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection.
-
-**Node Link:** https://github.com/dwringer/size-stepper-nodes
-
-**Example Usage:**
-</br><img src="https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg" width="500" />
-
---------------------------------
-### Text font to Image
-
-**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line
-
-**Node Link:** https://github.com/mickr777/textfontimage
-
-**Output Examples**
-
-<img src="https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36" width="300" />
-
-Results after using the depth controlnet
-
-<img src="https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a" width="300" />
-<img src="https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc" width="300" />
-<img src="https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89" width="300" />
-
---------------------------------
-### Thresholding
-
-**Description:** This node generates masks for highlights, midtones, and shadows given an input image. You can optionally specify a blur for the lookup table used in making those masks from the source image.
-
-**Node Link:** https://github.com/JPPhoto/thresholding-node
-
-**Examples**
-
-Input:
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632" width="300" />
-
-Highlights/Midtones/Shadows:
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/727021c1-36ff-4ec8-90c8-105e00de986d" width="300" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0b721bfc-f051-404e-b905-2f16b824ddfe" width="300" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/04c1297f-1c88-42b6-a7df-dd090b976286" width="300" />
-
-Highlights/Midtones/Shadows (with LUT blur enabled):
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/19aa718a-70c1-4668-8169-d68f4bd13771" width="300" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" width="300" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" width="300" />
-
---------------------------------
-### Unsharp Mask
-
-**Description:** Applies an unsharp mask filter to an image, preserving its alpha channel in the process.
-
-**Node Link:** https://github.com/JPPhoto/unsharp-mask-node
-
---------------------------------
-### XY Image to Grid and Images to Grids nodes
-
-**Description:** These nodes add the following to InvokeAI:
-- Generate grids of images from multiple input images
-- Create XY grid images with labels from parameters
-- Split images into overlapping tiles for processing (for super-resolution workflows)
-- Recombine image tiles into a single output image blending the seams 
-
-The nodes include:
-1. `Images To Grids` - Combine multiple images into a grid of images
-2. `XYImage To Grid` - Take X & Y params and creates a labeled image grid.
-3. `XYImage Tiles` - Super-resolution (embiggen) style tiled resizing
-4. `Image Tot XYImages` - Takes an image and cuts it up into a number of columns and rows.
-5. Multiple supporting nodes - Helper nodes for data wrangling and building `XYImage` collections
-
-See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
-
-**Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
-
-**Output Examples** 
-
-<img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
-
-
---------------------------------
-### Example Node Template
-
-**Description:** This node allows you to do super cool things with InvokeAI.
-
-**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py
-
-**Example Workflow:**  https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json
-
-**Output Examples** 
-
-</br><img src="https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png" width="500" />
-
-
-## Disclaimer
-
-The nodes linked have been developed and contributed by members of the Invoke AI community. While we strive to ensure the quality and safety of these contributions, we do not guarantee the reliability or security of the nodes. If you have issues or concerns with any of the nodes below, please raise it on GitHub or in the Discord.
-
-
 ## Help
 If you run into any issues with a node, please post in the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy). 
-

docs/nodes/contributingNodes.md

@@ -1,27 +0,0 @@
-# Contributing Nodes
-
-To learn about the specifics of creating a new node, please visit our [Node creation documentation](../contributing/INVOCATIONS.md). 
-
-Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps: 
-
-- Make sure the node is contained in a new Python (.py) file. Preferably, the node is in a repo with a README detailing the nodes usage & examples to help others more easily use your node. Including the tag "invokeai-node" in your repository's README can also help other users find it more easily. 
-- Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
-    - Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
-- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.
-
-### Community Node Template
-
-```markdown
---------------------------------
-### Super Cool Node Template
-
-**Description:** This node allows you to do super cool things with InvokeAI.
-
-**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
-
-**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
-
-**Output Examples** 
-
-![InvokeAI](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png)
-```

docs/nodes/defaultNodes.md

@@ -1,106 +0,0 @@
-# List of Default Nodes
-
-The table below contains a list of the default nodes shipped with InvokeAI and
-their descriptions.
-
-| Node <img width=160 align="right">                            | Function                                                                                                                                             |
-| :------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Add Integers                                                  | Adds two numbers                                                                                                                                     |
-| Boolean Primitive Collection                                  | A collection of boolean primitive values                                                                                                             |
-| Boolean Primitive                                             | A boolean primitive value                                                                                                                            |
-| Canny Processor                                               | Canny edge detection for ControlNet                                                                                                                  |
-| CenterPadCrop                                                 | Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image.                                          |
-| CLIP Skip                                                     | Skip layers in clip text_encoder model.                                                                                                              |
-| Collect                                                       | Collects values into a collection                                                                                                                    |
-| Color Correct                                                 | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image. |
-| Color Primitive                                               | A color primitive value                                                                                                                              |
-| Compel Prompt                                                 | Parse prompt using compel package to conditioning.                                                                                                   |
-| Conditioning Primitive Collection                             | A collection of conditioning tensor primitive values                                                                                                 |
-| Conditioning Primitive                                        | A conditioning tensor primitive value                                                                                                                |
-| Content Shuffle Processor                                     | Applies content shuffle processing to image                                                                                                          |
-| ControlNet                                                    | Collects ControlNet info to pass to other nodes                                                                                                      |
-| Denoise Latents                                               | Denoises noisy latents to decodable images                                                                                                           |
-| Divide Integers                                               | Divides two numbers                                                                                                                                  |
-| Dynamic Prompt                                                | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator                                                                      |
-| [FaceMask](./detailedNodes/faceTools.md#facemask)             | Generates masks for faces in an image to use with Inpainting                                                                                         |
-| [FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier) | Identifies and labels faces in an image                                                                                                              |
-| [FaceOff](./detailedNodes/faceTools.md#faceoff)               | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting                                                             |
-| Float Math                                                    | Perform basic math operations on two floats                                                                                                          |
-| Float Primitive Collection                                    | A collection of float primitive values                                                                                                               |
-| Float Primitive                                               | A float primitive value                                                                                                                              |
-| Float Range                                                   | Creates a range                                                                                                                                      |
-| HED (softedge) Processor                                      | Applies HED edge detection to image                                                                                                                  |
-| Blur Image                                                    | Blurs an image                                                                                                                                       |
-| Extract Image Channel                                         | Gets a channel from an image.                                                                                                                        |
-| Image Primitive Collection                                    | A collection of image primitive values                                                                                                               |
-| Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
-| Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
-| Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
-| Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
-| Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
-| Image Primitive                                               | An image primitive value                                                                                                                             |
-| Lerp Image                                                    | Linear interpolation of all pixels of an image                                                                                                       |
-| Offset Image Channel                                          | Add to or subtract from an image color channel by a uniform value.                                                                                   |
-| Multiply Image Channel                                        | Multiply or Invert an image color channel by a scalar value.                                                                                         |
-| Multiply Images                                               | Multiplies two images together using `PIL.ImageChops.multiply()`.                                                                                    |
-| Blur NSFW Image                                               | Add blur to NSFW-flagged images                                                                                                                      |
-| Paste Image                                                   | Pastes an image into another image.                                                                                                                  |
-| ImageProcessor                                                | Base class for invocations that preprocess images for ControlNet                                                                                     |
-| Resize Image                                                  | Resizes an image to specific dimensions                                                                                                              |
-| Round Float                                                   | Rounds a float to a specified number of decimal places                                                                                               |
-| Float to Integer                                              | Converts a float to an integer. Optionally rounds to an even multiple of a input number.                                                             |
-| Scale Image                                                   | Scales an image by a factor                                                                                                                          |
-| Image to Latents                                              | Encodes an image into latents.                                                                                                                       |
-| Add Invisible Watermark                                       | Add an invisible watermark to an image                                                                                                               |
-| Solid Color Infill                                            | Infills transparent areas of an image with a solid color                                                                                             |
-| PatchMatch Infill                                             | Infills transparent areas of an image using the PatchMatch algorithm                                                                                 |
-| Tile Infill                                                   | Infills transparent areas of an image with tiles of the image                                                                                        |
-| Integer Primitive Collection                                  | A collection of integer primitive values                                                                                                             |
-| Integer Primitive                                             | An integer primitive value                                                                                                                           |
-| Iterate                                                       | Iterates over a list of items                                                                                                                        |
-| Latents Primitive Collection                                  | A collection of latents tensor primitive values                                                                                                      |
-| Latents Primitive                                             | A latents tensor primitive value                                                                                                                     |
-| Latents to Image                                              | Generates an image from latents.                                                                                                                     |
-| Leres (Depth) Processor                                       | Applies leres processing to image                                                                                                                    |
-| Lineart Anime Processor                                       | Applies line art anime processing to image                                                                                                           |
-| Lineart Processor                                             | Applies line art processing to image                                                                                                                 |
-| LoRA Loader                                                   | Apply selected lora to unet and text_encoder.                                                                                                        |
-| Main Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
-| Combine Mask                                                  | Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.                                                                    |
-| Mask Edge                                                     | Applies an edge mask to an image                                                                                                                     |
-| Mask from Alpha                                               | Extracts the alpha channel of an image as a mask.                                                                                                    |
-| Mediapipe Face Processor                                      | Applies mediapipe face processing to image                                                                                                           |
-| Midas (Depth) Processor                                       | Applies Midas depth processing to image                                                                                                              |
-| MLSD Processor                                                | Applies MLSD processing to image                                                                                                                     |
-| Multiply Integers                                             | Multiplies two numbers                                                                                                                               |
-| Noise                                                         | Generates latent noise.                                                                                                                              |
-| Normal BAE Processor                                          | Applies NormalBae processing to image                                                                                                                |
-| ONNX Latents to Image                                         | Generates an image from latents.                                                                                                                     |
-| ONNX Prompt (Raw)                                             | A node to process inputs and produce outputs. May use dependency injection in **init** to receive providers.                                         |
-| ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
-| ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
-| OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |
-| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
-| PIDI Processor                                                | Applies PIDI processing to image                                                                                                                     |
-| Prompts from File                                             | Loads prompts from a text file                                                                                                                       |
-| Random Integer                                                | Outputs a single random integer.                                                                                                                     |
-| Random Range                                                  | Creates a collection of random numbers                                                                                                               |
-| Integer Range                                                 | Creates a range of numbers from start to stop with step                                                                                              |
-| Integer Range of Size                                         | Creates a range from start to start + size with step                                                                                                 |
-| Resize Latents                                                | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.                                                    |
-| SDXL Compel Prompt                                            | Parse prompt using compel package to conditioning.                                                                                                   |
-| SDXL LoRA Loader                                              | Apply selected lora to unet and text_encoder.                                                                                                        |
-| SDXL Main Model Loader                                        | Loads an sdxl base model, outputting its submodels.                                                                                                  |
-| SDXL Refiner Compel Prompt                                    | Parse prompt using compel package to conditioning.                                                                                                   |
-| SDXL Refiner Model Loader                                     | Loads an sdxl refiner model, outputting its submodels.                                                                                               |
-| Scale Latents                                                 | Scales latents by a given factor.                                                                                                                    |
-| Segment Anything Processor                                    | Applies segment anything processing to image                                                                                                         |
-| Show Image                                                    | Displays a provided image, and passes it forward in the pipeline.                                                                                    |
-| Step Param Easing                                             | Experimental per-step parameter easing for denoising steps                                                                                           |
-| String Primitive Collection                                   | A collection of string primitive values                                                                                                              |
-| String Primitive                                              | A string primitive value                                                                                                                             |
-| Subtract Integers                                             | Subtracts two numbers                                                                                                                                |
-| Tile Resample Processor                                       | Tile resampler processor                                                                                                                             |
-| Upscale (RealESRGAN)                                          | Upscales an image using RealESRGAN.                                                                                                                  |
-| VAE Loader                                                    | Loads a VAE model, outputting a VaeLoaderOutput                                                                                                      |
-| Zoe (Depth) Processor                                         | Applies Zoe depth processing to image                                                                                                                |

docs/nodes/detailedNodes/faceTools.md

@@ -1,154 +0,0 @@
-# Face Nodes
-
-## FaceOff
-
-FaceOff mimics a user finding a face in an image and resizing the bounding box
-around the head in Canvas.
-
-Enter a face ID (found with FaceIdentifier) to choose which face to mask.
-
-Just as you would add more context inside the bounding box by making it larger
-in Canvas, the node gives you a padding input (in pixels) which will
-simultaneously add more context, and increase the resolution of the bounding box
-so the face remains the same size inside it.
-
-The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
-threshold a detected face must reach for it to be processed. Lowering this value
-may help if detection is failing. If the detected masks are imperfect and stray
-too far outside/inside of faces, the node gives you X & Y offsets to shrink/grow
-the masks by a multiplier.
-
-FaceOff will output the face in a bounded image, taking the face off of the
-original image for input into any node that accepts image inputs. The node also
-outputs a face mask with the dimensions of the bounded image. The X & Y outputs
-are for connecting to the X & Y inputs of the Paste Image node, which will place
-the bounded image back on the original image using these coordinates.
-
-###### Inputs/Outputs
-
-| Input              | Description                                                                                                                                                                              |
-| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Image              | Image for face detection                                                                                                                                                                 |
-| Face ID            | The face ID to process, numbered from 0. Multiple faces not supported. Find a face's ID with FaceIdentifier node.                                                                        |
-| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
-| X Offset           | X-axis offset of the mask                                                                                                                                                                |
-| Y Offset           | Y-axis offset of the mask                                                                                                                                                                |
-| Padding            | All-axis padding around the mask in pixels                                                                                                                                               |
-| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
-
-| Output        | Description                                      |
-| ------------- | ------------------------------------------------ |
-| Bounded Image | Original image bound, cropped, and resized       |
-| Width         | The width of the bounded image in pixels         |
-| Height        | The height of the bounded image in pixels        |
-| Mask          | The output mask                                  |
-| X             | The x coordinate of the bounding box's left side |
-| Y             | The y coordinate of the bounding box's top side  |
-
-## FaceMask
-
-FaceMask mimics a user drawing masks on faces in an image in Canvas.
-
-The "Face IDs" input allows the user to select specific faces to be masked.
-Leave empty to detect and mask all faces, or a comma-separated list for a
-specific combination of faces (ex: `1,2,4`). A single integer will detect and
-mask that specific face. Find face IDs with the FaceIdentifier node.
-
-The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
-threshold a detected face must reach for it to be processed. Lowering this value
-may help if detection is failing.
-
-If the detected masks are imperfect and stray too far outside/inside of faces,
-the node gives you X & Y offsets to shrink/grow the masks by a multiplier. All
-masks shrink/grow together by the X & Y offset values.
-
-By default, masks are created to change faces. When masks are inverted, they
-change surrounding areas, protecting faces.
-
-###### Inputs/Outputs
-
-| Input              | Description                                                                                                                                                                              |
-| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Image              | Image for face detection                                                                                                                                                                 |
-| Face IDs           | Comma-separated list of face ids to mask eg '0,2,7'. Numbered from 0. Leave empty to mask all. Find face IDs with FaceIdentifier node.                                                   |
-| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
-| X Offset           | X-axis offset of the mask                                                                                                                                                                |
-| Y Offset           | Y-axis offset of the mask                                                                                                                                                                |
-| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
-| Invert Mask        | Toggle to invert the face mask                                                                                                                                                           |
-
-| Output | Description                       |
-| ------ | --------------------------------- |
-| Image  | The original image                |
-| Width  | The width of the image in pixels  |
-| Height | The height of the image in pixels |
-| Mask   | The output face mask              |
-
-## FaceIdentifier
-
-FaceIdentifier outputs an image with detected face IDs printed in white numbers
-onto each face.
-
-Face IDs can then be used in FaceMask and FaceOff to selectively mask all, a
-specific combination, or single faces.
-
-The FaceIdentifier output image is generated for user reference, and isn't meant
-to be passed on to other image-processing nodes.
-
-The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
-threshold a detected face must reach for it to be processed. Lowering this value
-may help if detection is failing. If an image is changed in the slightest, run
-it through FaceIdentifier again to get updated FaceIDs.
-
-###### Inputs/Outputs
-
-| Input              | Description                                                                                                                                                                              |
-| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Image              | Image for face detection                                                                                                                                                                 |
-| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
-| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
-
-| Output | Description                                                                                      |
-| ------ | ------------------------------------------------------------------------------------------------ |
-| Image  | The original image with small face ID numbers printed in white onto each face for user reference |
-| Width  | The width of the original image in pixels                                                        |
-| Height | The height of the original image in pixels                                                       |
-
-## Tips
-
-- If not all target faces are being detected, activate Chunk to bypass full
-  image face detection and greatly improve detection success.
-- Final results will vary between full-image detection and chunking for faces
-  that are detectable by both due to the nature of the process. Try either to
-  your taste.
-- Be sure Minimum Confidence is set the same when using FaceIdentifier with
-  FaceOff/FaceMask.
-- For FaceOff, use the color correction node before faceplace to correct edges
-  being noticeable in the final image (see example screenshot).
-- Non-inpainting models may struggle to paint/generate correctly around faces.
-- If your face won't change the way you want it to no matter what you change,
-  consider that the change you're trying to make is too much at that resolution.
-  For example, if an image is only 512x768 total, the face might only be 128x128
-  or 256x256, much smaller than the 512x512 your SD1.5 model was probably
-  trained on. Try increasing the resolution of the image by upscaling or
-  resizing, add padding to increase the bounding box's resolution, or use an
-  image where the face takes up more pixels.
-- If the resulting face seems out of place pasted back on the original image
-  (ie. too large, not proportional), add more padding on the FaceOff node to
-  give inpainting more context. Context and good prompting are important to
-  keeping things proportional.
-- If you find the mask is too big/small and going too far outside/inside the
-  area you want to affect, adjust the x & y offsets to shrink/grow the mask area
-- Use a higher denoise start value to resemble aspects of the original face or
-  surroundings. Denoise start = 0 & denoise end = 1 will make something new,
-  while denoise start = 0.50 & denoise end = 1 will be 50% old and 50% new.
-- mediapipe isn't good at detecting faces with lots of face paint, hair covering
-  the face, etc. Anything that obstructs the face will likely result in no faces
-  being detected.
-- If you find your face isn't being detected, try lowering the minimum
-  confidence value from 0.5. This could result in false positives, however
-  (random areas being detected as faces and masked).
-- After altering an image and wanting to process a different face in the newly
-  altered image, run the altered image through FaceIdentifier again to see the
-  new Face IDs. MediaPipe will most likely detect faces in a different order
-  after an image has been changed in the slightest.

docs/nodes/exampleWorkflows.md

@@ -1,18 +0,0 @@
-# Example Workflows
-
-We've curated some example workflows for you to get started with Workflows in InvokeAI
-
-To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
-
-If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
-
-* [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
-* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
-* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
-* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
-* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json)
-* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Prompt_from_File.json)
-* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json)
-* [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
-* [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)
-* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/QR_Code_Monster.json)

docs/nodes/overview.md

@@ -1,26 +1,42 @@
 # Nodes
 
 ## What are Nodes?
-An Node is simply a single operation that takes in inputs and returns
-out outputs. Multiple nodes can be linked together to create more
+An Node is simply a single operation that takes in some inputs and gives
+out some outputs. We can then chain multiple nodes together to create more
 complex functionality. All InvokeAI features are added through nodes.
 
-### Anatomy of a Node
+This means nodes can be used to easily extend the image generation capabilities of InvokeAI, and allow you build workflows to suit your needs. 
 
-Individual nodes are made up of the following:
-
-- Inputs: Edge points on the left side of the node window where you connect outputs from other nodes.
-- Outputs: Edge points on the right side of the node window where you connect to inputs on other nodes.
-- Options: Various options which are either manually configured, or overridden by connecting an output from another node to the input.
+You can read more about nodes and the node editor [here](../features/NODES.md). 
 
 
-With nodes, you can can easily extend the image generation capabilities of InvokeAI, and allow you build workflows that suit your needs. 
-
-You can read more about nodes and the node editor [here](../nodes/NODES.md). 
-
-To get started with nodes, take a look at some of our examples for [common workflows](../nodes/exampleWorkflows.md)
-
-## Downloading New Nodes
-To download a new node, visit our list of [Community Nodes](../nodes/communityNodes.md). These are nodes that have been created by the community, for the community. 
+## Downloading Nodes
+To download a new node, visit our list of [Community Nodes](communityNodes.md). These are nodes that have been created by the community, for the community. 
 
 
+## Contributing Nodes
+
+To learn about creating a new node, please visit our [Node creation documenation](../contributing/INVOCATIONS.md). 
+
+Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps: 
+* Make sure the node is contained in a new Python (.py) file 
+* Submit a pull request with a link to your node in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
+    * Make sure you are following the template below and have provided all relevant details about the node and what it does.
+* A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you might be asked for permission to include it in the core project.
+
+### Community Node Template
+
+```markdown
+--------------------------------
+### Super Cool Node Template
+
+**Description:** This node allows you to do super cool things with InvokeAI.
+
+**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
+
+**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
+
+**Output Examples** 
+
+![InvokeAI](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png)
+```

docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json

@@ -1,964 +0,0 @@
-{
-  "name": "ESRGAN img2img upscale w_ Canny ControlNet",
-  "author": "InvokeAI",
-  "description": "Sample workflow for using Upscaling with ControlNet with SD1.5",
-  "version": "1.0.1",
-  "contact": "invoke@invoke.ai",
-  "tags": "tiled, upscale controlnet, default",
-  "notes": "",
-  "exposedFields": [
-    {
-      "nodeId": "d8ace142-c05f-4f1d-8982-88dc7473958d",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "771bdf6a-0813-4099-a5d8-921a138754d4",
-      "fieldName": "image"
-    },
-    {
-      "nodeId": "63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16b",
-      "fieldName": "prompt"
-    }
-  ],
-  "meta": {
-    "version": "1.0.0"
-  },
-  "nodes": [
-    {
-      "id": "e8bf67fe-67de-4227-87eb-79e86afdfc74",
-      "type": "invocation",
-      "data": {
-        "id": "e8bf67fe-67de-4227-87eb-79e86afdfc74",
-        "type": "compel",
-        "inputs": {
-          "prompt": {
-            "id": "5f762fae-d791-42d9-8ab5-2b830c33ff20",
-            "name": "prompt",
-            "type": "string",
-            "fieldKind": "input",
-            "label": "",
-            "value": ""
-          },
-          "clip": {
-            "id": "8ac95f40-317d-4513-bbba-b99effd3b438",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
-          "conditioning": {
-            "id": "46c65b2b-c0b5-40c2-b183-74e9451c6d56",
-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 261,
-      "position": {
-        "x": 1261.0015571435993,
-        "y": 1513.9276360694537
-      }
-    },
-    {
-      "id": "d8ace142-c05f-4f1d-8982-88dc7473958d",
-      "type": "invocation",
-      "data": {
-        "id": "d8ace142-c05f-4f1d-8982-88dc7473958d",
-        "type": "main_model_loader",
-        "inputs": {
-          "model": {
-            "id": "b35ae88a-f2d2-43f6-958c-8c624391250f",
-            "name": "model",
-            "type": "MainModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "stable-diffusion-v1-5",
-              "base_model": "sd-1",
-              "model_type": "main"
-            }
-          }
-        },
-        "outputs": {
-          "unet": {
-            "id": "02f243cb-c6e2-42c5-8be9-ef0519d54383",
-            "name": "unet",
-            "type": "UNetField",
-            "fieldKind": "output"
-          },
-          "clip": {
-            "id": "7762ed13-5b28-40f4-85f1-710942ceb92a",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "output"
-          },
-          "vae": {
-            "id": "69566153-1918-417d-a3bb-32e9e857ef6b",
-            "name": "vae",
-            "type": "VaeField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 226,
-      "position": {
-        "x": 433.44132965778,
-        "y": 1419.9552496403696
-      }
-    },
-    {
-      "id": "771bdf6a-0813-4099-a5d8-921a138754d4",
-      "type": "invocation",
-      "data": {
-        "id": "771bdf6a-0813-4099-a5d8-921a138754d4",
-        "type": "image",
-        "inputs": {
-          "image": {
-            "id": "0f6d68a2-38bd-4f65-a112-0a256c7a2678",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "76f6f9b6-755b-4373-93fa-6a779998d2c8",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "6858e46b-707c-444f-beda-9b5f4aecfdf8",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "421bdc6e-ecd1-4935-9665-d38ab8314f79",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 225,
-      "position": {
-        "x": 11.612243766002848,
-        "y": 1989.909405085168
-      }
-    },
-    {
-      "id": "f7564dd2-9539-47f2-ac13-190804461f4e",
-      "type": "invocation",
-      "data": {
-        "id": "f7564dd2-9539-47f2-ac13-190804461f4e",
-        "type": "esrgan",
-        "inputs": {
-          "metadata": {
-            "id": "8fa0c7eb-5bd3-4575-98e7-72285c532504",
-            "name": "metadata",
-            "type": "MetadataField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "image": {
-            "id": "3c949799-a504-41c9-b342-cff4b8146c48",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "model_name": {
-            "id": "77cb4750-53d6-4c2c-bb5c-145981acbf17",
-            "name": "model_name",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "RealESRGAN_x4plus.pth"
-          },
-          "tile_size": {
-            "id": "7787b3ad-46ee-4248-995f-bc740e1f988b",
-            "name": "tile_size",
-            "type": "integer",
-            "fieldKind": "input",
-            "label": "",
-            "value": 400
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "37e6308e-e926-4e07-b0db-4e8601f495d0",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "c194d84a-fac7-4856-b646-d08477a5ad2b",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "b2a6206c-a9c8-4271-a055-0b93a7f7d505",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.1.0"
-      },
-      "width": 320,
-      "height": 339,
-      "position": {
-        "x": 436.07457889056195,
-        "y": 1967.3109314112623
-      }
-    },
-    {
-      "id": "1d887701-df21-4966-ae6e-a7d82307d7bd",
-      "type": "invocation",
-      "data": {
-        "id": "1d887701-df21-4966-ae6e-a7d82307d7bd",
-        "type": "canny_image_processor",
-        "inputs": {
-          "metadata": {
-            "id": "52c877c8-25d9-4949-8518-f536fcdd152d",
-            "name": "metadata",
-            "type": "MetadataField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "image": {
-            "id": "e0af11fe-4f95-4193-a599-cf40b6a963f5",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "low_threshold": {
-            "id": "ab775f7b-f556-4298-a9d6-2274f3a6c77c",
-            "name": "low_threshold",
-            "type": "integer",
-            "fieldKind": "input",
-            "label": "",
-            "value": 100
-          },
-          "high_threshold": {
-            "id": "9e58b615-06e4-417f-b0d8-63f1574cd174",
-            "name": "high_threshold",
-            "type": "integer",
-            "fieldKind": "input",
-            "label": "",
-            "value": 200
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "61feb8bf-95c9-4634-87e2-887fc43edbdf",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "9e203e41-73f7-4cfa-bdca-5040e5e60c55",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "ec7d99dc-0d82-4495-a759-6423808bff1c",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 339,
-      "position": {
-        "x": 1221.7155516160597,
-        "y": 1971.0099052871012
-      }
-    },
-    {
-      "id": "ca1d020c-89a8-4958-880a-016d28775cfa",
-      "type": "invocation",
-      "data": {
-        "id": "ca1d020c-89a8-4958-880a-016d28775cfa",
-        "type": "controlnet",
-        "inputs": {
-          "image": {
-            "id": "2973c126-e301-4595-a7dc-d6e1729ccdbf",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "control_model": {
-            "id": "4bb4d987-8491-4839-b41b-6e2f546fe2d0",
-            "name": "control_model",
-            "type": "ControlNetModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "sd-controlnet-canny",
-              "base_model": "sd-1"
-            }
-          },
-          "control_weight": {
-            "id": "a3cf387a-b58f-4058-858f-6a918efac609",
-            "name": "control_weight",
-            "type": "FloatPolymorphic",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "begin_step_percent": {
-            "id": "e0614f69-8a58-408b-9238-d3a44a4db4e0",
-            "name": "begin_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 0
-          },
-          "end_step_percent": {
-            "id": "ac683539-b6ed-4166-9294-2040e3ede206",
-            "name": "end_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "control_mode": {
-            "id": "f00b21de-cbd7-4901-8efc-e7134a2dc4c8",
-            "name": "control_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "balanced"
-          },
-          "resize_mode": {
-            "id": "cafb60ee-3959-4d57-a06c-13b83be6ea4f",
-            "name": "resize_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "just_resize"
-          }
-        },
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docs/workflows/Multi_ControlNet_Canny_and_Depth.json

@@ -1,985 +0,0 @@
-{
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docs/workflows/Prompt_from_File.json

@@ -1,719 +0,0 @@
-{
-  "name": "Prompt from File",
-  "author": "InvokeAI",
-  "description": "Sample workflow using prompt from file capabilities of InvokeAI ",
-  "version": "0.1.0",
-  "contact": "millun@invoke.ai",
-  "tags": "text2image, prompt from file, default",
-  "notes": "",
-  "exposedFields": [
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-      "nodeId": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
-      "fieldName": "model"
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-    {
-      "nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-      "fieldName": "file_path"
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-      "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
-      "type": "invocation",
-      "data": {
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-            "type": "ClipField",
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-            "label": ""
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-        "outputs": {
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-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
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-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
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-      "data": {
-        "id": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-        "type": "prompt_from_file",
-        "inputs": {
-          "file_path": {
-            "id": "37e37684-4f30-4ec8-beae-b333e550f904",
-            "name": "file_path",
-            "type": "string",
-            "fieldKind": "input",
-            "label": "Prompts File Path",
-            "value": ""
-          },
-          "pre_prompt": {
-            "id": "7de02feb-819a-4992-bad3-72a30920ddea",
-            "name": "pre_prompt",
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-            "name": "post_prompt",
-            "type": "string",
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-            "id": "efee9a48-05ab-4829-8429-becfa64a0782",
-            "name": "start_line",
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-            "label": "",
-            "value": 1
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-          "max_prompts": {
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-            "name": "max_prompts",
-            "type": "integer",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
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-        "outputs": {
-          "collection": {
-            "id": "77d5d7f1-9877-4ab1-9a8c-33e9ffa9abf3",
-            "name": "collection",
-            "type": "StringCollection",
-            "fieldKind": "output"
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-        "label": "Prompts from File",
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-            "type": "Collection",
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docs/workflows/QR_Code_Monster.json

@@ -1,758 +0,0 @@
-{
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