fix broken url fetch in preload_models.py

- The installer will pull from the branch release-candidate-2-1 for the
  purposes of testing.
- This needs to be changed to "main" before release.

.dockerignore

@@ -0,0 +1,3 @@
+*
+!environment*.yml
+!docker-build

.github/CODEOWNERS

@@ -0,0 +1,4 @@
+ldm/invoke/pngwriter.py @CapableWeb
+ldm/invoke/server_legacy.py @CapableWeb
+scripts/legacy_api.py @CapableWeb
+tests/legacy_tests.sh @CapableWeb

.github/workflows/build-container.yml

@@ -0,0 +1,42 @@
+# Building the Image without pushing to confirm it is still buildable
+# confirum functionality would unfortunately need way more resources
+name: build container image
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+  pull_request:
+    branches:
+      - 'main'
+      - 'development'
+
+jobs:
+  docker:
+    runs-on: ubuntu-latest
+    steps:
+      - name: prepare docker-tag
+        env:
+          repository: ${{ github.repository }}
+        run: echo "dockertag=${repository,,}" >> $GITHUB_ENV
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Cache Docker layers
+        uses: actions/cache@v2
+        with:
+          path: /tmp/.buildx-cache
+          key: buildx-${{ hashFiles('docker-build/Dockerfile') }}
+      - name: Build container
+        uses: docker/build-push-action@v3
+        with:
+          context: .
+          file: docker-build/Dockerfile
+          platforms: linux/amd64
+          push: false
+          tags: ${{ env.dockertag }}:latest
+          cache-from: type=local,src=/tmp/.buildx-cache
+          cache-to: type=local,dest=/tmp/.buildx-cache

.github/workflows/create-caches.yml

@@ -1,26 +1,43 @@
 name: Create Caches
-on:
-  workflow_dispatch
+
+on: workflow_dispatch
+
 jobs:
-  build:
+  os_matrix:
     strategy:
       matrix:
-        os: [ ubuntu-latest, macos-12 ]
-    name: Create Caches on ${{ matrix.os }} conda
+        os: [ubuntu-latest, macos-latest]
+        include:
+          - os: ubuntu-latest
+            environment-file: environment.yml
+            default-shell: bash -l {0}
+          - os: macos-latest
+            environment-file: environment-mac.yml
+            default-shell: bash -l {0}
+    name: Test invoke.py on ${{ matrix.os }} with conda
     runs-on: ${{ matrix.os }}
+    defaults:
+      run:
+        shell: ${{ matrix.default-shell }}
     steps:
-      - name: Set platform variables
-        id: vars
-        run: |
-            if [ "$RUNNER_OS" = "macOS" ]; then
-                echo "::set-output name=ENV_FILE::environment-mac.yml"
-                echo "::set-output name=PYTHON_BIN::/usr/local/miniconda/envs/ldm/bin/python"
-            elif [ "$RUNNER_OS" = "Linux" ]; then
-                echo "::set-output name=ENV_FILE::environment.yml"
-                echo "::set-output name=PYTHON_BIN::/usr/share/miniconda/envs/ldm/bin/python"
-            fi
       - name: Checkout sources
         uses: actions/checkout@v3
+
+      - name: setup miniconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-activate-base: false
+          auto-update-conda: false
+          miniconda-version: latest
+
+      - name: set environment
+        run: |
+          [[ "$GITHUB_REF" == 'refs/heads/main' ]] \
+            && echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV \
+            || echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
+          echo "CONDA_ROOT=$CONDA" >> $GITHUB_ENV
+          echo "CONDA_ENV_NAME=invokeai" >> $GITHUB_ENV
+
       - name: Use Cached Stable Diffusion v1.4 Model
         id: cache-sd-v1-4
         uses: actions/cache@v3
@@ -29,42 +46,35 @@ jobs:
         with:
           path: models/ldm/stable-diffusion-v1/model.ckpt
           key: ${{ env.cache-name }}
-          restore-keys: |
-            ${{ env.cache-name }}
+          restore-keys: ${{ env.cache-name }}
+
       - name: Download Stable Diffusion v1.4 Model
         if: ${{ steps.cache-sd-v1-4.outputs.cache-hit != 'true' }}
         run: |
-          if [ ! -e models/ldm/stable-diffusion-v1 ]; then
-              mkdir -p models/ldm/stable-diffusion-v1
-          fi
-          if [ ! -e models/ldm/stable-diffusion-v1/model.ckpt ]; then
-            curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
-          fi
-      - name: Use Cached Dependencies
-        id: cache-conda-env-ldm
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-conda-env-ldm
+          [[ -d models/ldm/stable-diffusion-v1 ]] \
+            || mkdir -p models/ldm/stable-diffusion-v1
+          [[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
+            || curl \
+              -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
+              -o models/ldm/stable-diffusion-v1/model.ckpt \
+              -L https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
+
+      - name: Activate Conda Env
+        uses: conda-incubator/setup-miniconda@v2
         with:
-          path: ~/.conda/envs/ldm
-          key: ${{ env.cache-name }}
-          restore-keys: |
-            ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(steps.vars.outputs.ENV_FILE) }}
-      - name: Install Dependencies
-        if: ${{ steps.cache-conda-env-ldm.outputs.cache-hit != 'true' }}
-        run: |
-          conda env create -f ${{ steps.vars.outputs.ENV_FILE }}
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: ${{ matrix.environment-file }}
+
       - name: Use Cached Huggingface and Torch models
-        id: cache-huggingface-torch
+        id: cache-hugginface-torch
         uses: actions/cache@v3
         env:
-          cache-name: cache-huggingface-torch
+          cache-name: cache-hugginface-torch
         with:
           path: ~/.cache
           key: ${{ env.cache-name }}
           restore-keys: |
             ${{ env.cache-name }}-${{ hashFiles('scripts/preload_models.py') }}
-      - name: Download Huggingface and Torch models
-        if: ${{ steps.cache-huggingface-torch.outputs.cache-hit != 'true' }}
-        run: |
-          ${{ steps.vars.outputs.PYTHON_BIN }} scripts/preload_models.py
+
+      - name: run preload_models.py
+        run: python scripts/preload_models.py

.github/workflows/mkdocs-flow.yml

@@ -3,9 +3,9 @@ on:
   push:
     branches:
       - main
-  pull_request:
-    branches:
-      - main
+  # pull_request:
+  #   branches:
+  #     - main
 jobs:
   build:
     name: Deploy docs to GitHub Pages

.github/workflows/mkdocs-material.yml

@@ -0,0 +1,40 @@
+name: mkdocs-material
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+
+jobs:
+  mkdocs-material:
+    runs-on: ubuntu-latest
+    steps:
+      - name: checkout sources
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+
+      - name: setup python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: install requirements
+        run: |
+          python -m \
+            pip install -r requirements-mkdocs.txt
+
+      - name: confirm buildability
+        run: |
+          python -m \
+            mkdocs build \
+            --clean \
+            --verbose
+
+      - name: deploy to gh-pages
+        if: ${{ github.ref == 'refs/heads/main' }}
+        run: |
+          python -m \
+            mkdocs gh-deploy \
+            --clean \
+            --force

.github/workflows/test-dream-conda.yml

@@ -1,97 +0,0 @@
-name: Test Dream with Conda
-on:
-  push:
-    branches:
-      - 'main'
-      - 'development'
-jobs:
-  os_matrix:
-    strategy:
-      matrix:
-        os: [ ubuntu-latest, macos-12 ]
-    name: Test dream.py on ${{ matrix.os }} with conda
-    runs-on: ${{ matrix.os }}
-    steps:
-      - run: |
-          echo The PR was merged
-      - name: Set platform variables
-        id: vars
-        run: |
-          # Note, can't "activate" via github action; specifying the env's python has the same effect
-          if [ "$RUNNER_OS" = "macOS" ]; then
-              echo "::set-output name=ENV_FILE::environment-mac.yml"
-              echo "::set-output name=PYTHON_BIN::/usr/local/miniconda/envs/ldm/bin/python"
-          elif [ "$RUNNER_OS" = "Linux" ]; then
-              echo "::set-output name=ENV_FILE::environment.yml"
-              echo "::set-output name=PYTHON_BIN::/usr/share/miniconda/envs/ldm/bin/python"
-          fi
-      - name: Checkout sources
-        uses: actions/checkout@v3
-      - name: Use Cached Stable Diffusion v1.4 Model
-        id: cache-sd-v1-4
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-sd-v1-4
-        with:
-          path: models/ldm/stable-diffusion-v1/model.ckpt
-          key: ${{ env.cache-name }}
-          restore-keys: |
-            ${{ env.cache-name }}
-      - name: Download Stable Diffusion v1.4 Model
-        if: ${{ steps.cache-sd-v1-4.outputs.cache-hit != 'true' }}
-        run: |
-          if [ ! -e models/ldm/stable-diffusion-v1 ]; then
-              mkdir -p models/ldm/stable-diffusion-v1
-          fi
-          if [ ! -e models/ldm/stable-diffusion-v1/model.ckpt ]; then
-            curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
-          fi
-      - name: Use Cached Dependencies
-        id: cache-conda-env-ldm
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-conda-env-ldm
-        with:
-          path: ~/.conda/envs/ldm
-          key: ${{ env.cache-name }}
-          restore-keys: |
-            ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(steps.vars.outputs.ENV_FILE) }}
-      - name: Install Dependencies
-        if: ${{ steps.cache-conda-env-ldm.outputs.cache-hit != 'true' }}
-        run: |
-          conda env create -f ${{ steps.vars.outputs.ENV_FILE }}
-      - name: Use Cached Huggingface and Torch models
-        id: cache-hugginface-torch
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-hugginface-torch
-        with:
-          path: ~/.cache
-          key: ${{ env.cache-name }}
-          restore-keys: |
-            ${{ env.cache-name }}-${{ hashFiles('scripts/preload_models.py') }}
-      - name: Download Huggingface and Torch models
-        if: ${{ steps.cache-hugginface-torch.outputs.cache-hit != 'true' }}
-        run: |
-          ${{ steps.vars.outputs.PYTHON_BIN }} scripts/preload_models.py
-#       - name: Run tmate
-#         uses: mxschmitt/action-tmate@v3
-#         timeout-minutes: 30
-      - name: Run the tests
-        run: |
-          # Note, can't "activate" via github action; specifying the env's python has the same effect
-          if [ $(uname) = "Darwin" ]; then
-              export PYTORCH_ENABLE_MPS_FALLBACK=1
-          fi
-          # Utterly hacky, but I don't know how else to do this
-          if [[ ${{ github.ref }} == 'refs/heads/master' ]]; then
-            time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/preflight_prompts.txt
-          elif [[ ${{ github.ref }} == 'refs/heads/development' ]]; then
-            time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/dev_prompts.txt
-          fi
-          mkdir -p outputs/img-samples
-      - name: Archive results
-        uses: actions/upload-artifact@v3
-        with:
-          name: results
-          path: outputs/img-samples

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

@@ -0,0 +1,109 @@
+name: Test invoke.py
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+  pull_request:
+    branches:
+      - 'main'
+      - 'development'
+
+jobs:
+  matrix:
+    strategy:
+      fail-fast: false
+      matrix:
+        stable-diffusion-model:
+          - 'https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt'
+          - 'https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt'
+        os:
+          - ubuntu-latest
+          - macOS-12
+        include:
+          - os: ubuntu-latest
+            environment-file: environment.yml
+            default-shell: bash -l {0}
+          - os: macOS-12
+            environment-file: environment-mac.yml
+            default-shell: bash -l {0}
+          - stable-diffusion-model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
+            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/model.ckpt
+            stable-diffusion-model-switch: stable-diffusion-1.4
+          - stable-diffusion-model: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-switch: stable-diffusion-1.5
+    name: ${{ matrix.os }} with ${{ matrix.stable-diffusion-model-switch }}
+    runs-on: ${{ matrix.os }}
+    env:
+      CONDA_ENV_NAME: invokeai
+    defaults:
+      run:
+        shell: ${{ matrix.default-shell }}
+    steps:
+      - name: Checkout sources
+        id: checkout-sources
+        uses: actions/checkout@v3
+
+      - name: Use cached conda packages
+        id: use-cached-conda-packages
+        uses: actions/cache@v3
+        with:
+          path: ~/conda_pkgs_dir
+          key: conda-pkgs-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles(matrix.environment-file) }}
+
+      - name: Activate Conda Env
+        id: activate-conda-env
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: ${{ matrix.environment-file }}
+          miniconda-version: latest
+
+      - name: set test prompt to main branch validation
+        if: ${{ github.ref == 'refs/heads/main' }}
+        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV
+
+      - name: set test prompt to development branch validation
+        if: ${{ github.ref == 'refs/heads/development' }}
+        run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
+
+      - name: set test prompt to Pull Request validation
+        if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
+        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
+
+      - name: Download ${{ matrix.stable-diffusion-model-switch }}
+        id: download-stable-diffusion-model
+        run: |
+          [[ -d models/ldm/stable-diffusion-v1 ]] \
+            || mkdir -p models/ldm/stable-diffusion-v1
+          curl \
+            -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
+            -o ${{ matrix.stable-diffusion-model-dl-path }} \
+            -L ${{ matrix.stable-diffusion-model }}
+
+      - name: run preload_models.py
+        id: run-preload-models
+        run: |
+          python scripts/preload_models.py \
+            --no-interactive
+
+      - name: Run the tests
+        id: run-tests
+        run: |
+          time python scripts/invoke.py \
+            --model ${{ matrix.stable-diffusion-model-switch }} \
+            --from_file ${{ env.TEST_PROMPTS }}
+
+      - name: export conda env
+        id: export-conda-env
+        run: |
+          mkdir -p outputs/img-samples
+          conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}-${{ runner.arch }}.yml
+
+      - name: Archive results
+        id: archive-results
+        uses: actions/upload-artifact@v3
+        with:
+          name: results_${{ matrix.os }}_${{ matrix.stable-diffusion-model-switch }}
+          path: outputs/img-samples

.gitignore

@@ -1,7 +1,11 @@
 # ignore default image save location and model symbolic link
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
-ldm/dream/restoration/codeformer/weights
+ldm/invoke/restoration/codeformer/weights
+
+# ignore user models config
+configs/models.user.yaml
+config/models.user.yml
 
 # ignore the Anaconda/Miniconda installer used while building Docker image
 anaconda.sh
@@ -195,7 +199,13 @@ checkpoints
 .scratch/
 .vscode/
 gfpgan/
-models/ldm/stable-diffusion-v1/model.sha256
+models/ldm/stable-diffusion-v1/*.sha256
 
 # GFPGAN model files
 gfpgan/
+
+# config file (will be created by installer)
+configs/models.yaml
+
+# weights (will be created by installer)
+models/ldm/stable-diffusion-v1/*.ckpt

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
+Copyright (c) 2022 Lincoln Stein and InvokeAI Organization
 
 This software is derived from a fork of the source code available from
 https://github.com/pesser/stable-diffusion and

README.md

@@ -2,14 +2,7 @@
 
 # InvokeAI: A Stable Diffusion Toolkit
 
-_Note: This fork is rapidly evolving. Please use the
-[Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to
-report bugs and make feature requests. Be sure to use the provided
-templates. They will help aid diagnose issues faster._
-
-_This repository was formally known as lstein/stable-diffusion_
-
-# **Table of Contents**
+_Formerly known as lstein/stable-diffusion_
 
 ![project logo](docs/assets/logo.png)
 
@@ -24,7 +17,7 @@ _This repository was formally known as lstein/stable-diffusion_
 [CI checks on dev badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
 [CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
 [CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
-[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-dream-conda.yml
+[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
 [discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
 [discord link]: https://discord.gg/ZmtBAhwWhy
 [github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
@@ -41,10 +34,18 @@ _This repository was formally known as lstein/stable-diffusion_
 [latest release link]: https://github.com/invoke-ai/InvokeAI/releases
 </div>
 
-This is a fork of [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion), the open
-source text-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 or RAM.
+This is a fork of
+[CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion),
+the open source text-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, with
+GPU cards with as little as 4 GB of RAM. It provides both a polished
+Web interface (see below), and an easy-to-use command-line interface.
+
+**Quick links**: [<a href="https://discord.gg/NwVCmKwY">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
+
+<div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
+
 
 _Note: This fork is rapidly evolving. Please use the
 [Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
@@ -88,22 +89,28 @@ You wil need one of the following:
 
 #### Disk
 
-- At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
+- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
-#### Note
+**Note**
+
+If you have a Nvidia 10xx series card (e.g. the 1080ti), please
+run the dream script in full-precision mode as shown below.
+
+Similarly, specify full-precision mode on Apple M1 hardware.
 
 Precision is auto configured based on the device. If however you encounter
 errors like 'expected type Float but found Half' or 'not implemented for Half'
-you can try starting `dream.py` with the `--precision=float32` flag:
+you can try starting `invoke.py` with the `--precision=float32` flag:
 
 ```bash
-(ldm) ~/stable-diffusion$ python scripts/dream.py --precision=float32
+(ldm) ~/stable-diffusion$ python scripts/invoke.py --precision=float32
 ```
 
 ### Features
 
 #### Major Features
 
+- [Web Server](docs/features/WEB.md)
 - [Interactive Command Line Interface](docs/features/CLI.md)
 - [Image To Image](docs/features/IMG2IMG.md)
 - [Inpainting Support](docs/features/INPAINTING.md)
@@ -111,7 +118,6 @@ you can try starting `dream.py` with the `--precision=float32` flag:
 - [Upscaling, face-restoration and outpainting](docs/features/POSTPROCESS.md)
 - [Seamless Tiling](docs/features/OTHER.md#seamless-tiling)
 - [Google Colab](docs/features/OTHER.md#google-colab)
-- [Web Server](docs/features/WEB.md)
 - [Reading Prompts From File](docs/features/PROMPTS.md#reading-prompts-from-a-file)
 - [Shortcut: Reusing Seeds](docs/features/OTHER.md#shortcuts-reusing-seeds)
 - [Prompt Blending](docs/features/PROMPTS.md#prompt-blending)
@@ -128,39 +134,38 @@ you can try starting `dream.py` with the `--precision=float32` flag:
 
 ### Latest Changes
 
-- vNEXT (TODO 2022)
+- v2.0.1 (13 October 2022)
+  - fix noisy images at high step count when using k* samplers
+  - dream.py script now calls invoke.py module directly rather than
+    via a new python process (which could break the environment)
 
-  - Deprecated `--full_precision` / `-F`. Simply omit it and `dream.py` will auto
+- v2.0.0 (9 October 2022)
+
+  - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
+    for backward compatibility.
+  - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/INPAINTING.md">inpainting</a> and <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OUTPAINTING.md">outpainting</a>
+  - img2img runs on all k* samplers
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/PROMPTS.md#negative-and-unconditioned-prompts">negative prompts</a>
+  - Support for CodeFormer face reconstruction
+  - Support for Textual Inversion on Macintoshes
+  - Support in both WebGUI and CLI for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/POSTPROCESS.md">post-processing of previously-generated images</a>
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+  - New `--hires` option on `invoke>` line allows <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.md#this-is-an-example-of-txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
+  - New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
+  - Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+  - Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+  - Improved <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.md">command-line completion behavior</a>.
+    New commands added:
+       * List command-line history with `!history`
+       * Search command-line history with `!search`
+       * Clear history with `!clear`
+  - Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
     configure. To switch away from auto use the new flag like `--precision=float32`.
 
-- v1.14 (11 September 2022)
-
-  - Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
-  - Full support for Apple hardware with M1 or M2 chips.
-  - Add "seamless mode" for circular tiling of image. Generates beautiful effects.
-    ([prixt](https://github.com/prixt)).
-  - Inpainting support.
-  - Improved web server GUI.
-  - Lots of code and documentation cleanups.
-
-- v1.13 (3 September 2022
-
-  - Support image variations (see [VARIATIONS](docs/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 hardware
-    [Arturo Mendivil](https://github.com/artmen1516)
-  - WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
-    [Kevin Gibbons](https://github.com/bakkot)
-  - WebUI supports incremental display of in-progress images during generation
-    [Kevin Gibbons](https://github.com/bakkot)
-  - A new configuration file scheme that allows new models (including upcoming
-    stable-diffusion-v1.5) to be added without altering the code.
-    ([David Wager](https://github.com/maddavid12))
-  - Can specify --grid on dream.py command line as the default.
-  - Miscellaneous internal bug and stability fixes.
-  - Works on M1 Apple hardware.
-  - Multiple bug fixes.
-
 For older changelogs, please visit the **[CHANGELOG](docs/features/CHANGELOG.md)**.
 
 ### Troubleshooting

backend/modules/create_cmd_parser.py

@@ -1,6 +1,6 @@
 import argparse
 import os
-from ldm.dream.args import PRECISION_CHOICES
+from ldm.invoke.args import PRECISION_CHOICES
 
 
 def create_cmd_parser():

backend/modules/parameters.py

@@ -15,7 +15,7 @@ SAMPLER_CHOICES = [
 
 def parameters_to_command(params):
     """
-    Converts dict of parameters into a `dream.py` REPL command.
+    Converts dict of parameters into a `invoke.py` REPL command.
     """
 
     switches = list()
@@ -36,6 +36,8 @@ def parameters_to_command(params):
         switches.append(f'-A {params["sampler_name"]}')
     if "seamless" in params and params["seamless"] == True:
         switches.append(f"--seamless")
+    if "hires_fix" in params and params["hires_fix"] == True:
+        switches.append(f"--hires")
     if "init_img" in params and len(params["init_img"]) > 0:
         switches.append(f'-I {params["init_img"]}')
     if "init_mask" in params and len(params["init_mask"]) > 0:
@@ -46,8 +48,14 @@ def parameters_to_command(params):
         switches.append(f'-f {params["strength"]}')
         if "fit" in params and params["fit"] == True:
             switches.append(f"--fit")
-    if "gfpgan_strength" in params and params["gfpgan_strength"]:
+    if "facetool" in params:
+        switches.append(f'-ft {params["facetool"]}')
+    if "facetool_strength" in params and params["facetool_strength"]:
+        switches.append(f'-G {params["facetool_strength"]}')
+    elif "gfpgan_strength" in params and params["gfpgan_strength"]:
         switches.append(f'-G {params["gfpgan_strength"]}')
+    if "codeformer_fidelity" in params:
+        switches.append(f'-cf {params["codeformer_fidelity"]}')
     if "upscale" in params and params["upscale"]:
         switches.append(f'-U {params["upscale"][0]} {params["upscale"][1]}')
     if "variation_amount" in params and params["variation_amount"] > 0:

backend/server.py

@@ -1,821 +0,0 @@
-import mimetypes
-import transformers
-import json
-import os
-import traceback
-import eventlet
-import glob
-import shlex
-import math
-import shutil
-import sys
-
-sys.path.append(".")
-
-from argparse import ArgumentTypeError
-from modules.create_cmd_parser import create_cmd_parser
-
-parser = create_cmd_parser()
-opt = parser.parse_args()
-
-
-from flask_socketio import SocketIO
-from flask import Flask, send_from_directory, url_for, jsonify
-from pathlib import Path
-from PIL import Image
-from pytorch_lightning import logging
-from threading import Event
-from uuid import uuid4
-from send2trash import send2trash
-
-
-from ldm.generate import Generate
-from ldm.dream.restoration import Restoration
-from ldm.dream.pngwriter import PngWriter, retrieve_metadata
-from ldm.dream.args import APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.dream.conditioning import split_weighted_subprompts
-
-from modules.parameters import parameters_to_command
-
-
-"""
-USER CONFIG
-"""
-if opt.cors and "*" in opt.cors:
-    raise ArgumentTypeError('"*" is not an allowed CORS origin')
-
-
-output_dir = "outputs/"  # Base output directory for images
-host = opt.host  # Web & socket.io host
-port = opt.port  # Web & socket.io port
-verbose = opt.verbose  # enables copious socket.io logging
-precision = opt.precision
-free_gpu_mem = opt.free_gpu_mem
-embedding_path = opt.embedding_path
-additional_allowed_origins = (
-    opt.cors if opt.cors else []
-)  # additional CORS allowed origins
-model = "stable-diffusion-1.4"
-
-"""
-END USER CONFIG
-"""
-
-
-print("* Initializing, be patient...\n")
-
-
-"""
-SERVER SETUP
-"""
-
-
-# fix missing mimetypes on windows due to registry wonkiness
-mimetypes.add_type("application/javascript", ".js")
-mimetypes.add_type("text/css", ".css")
-
-app = Flask(__name__, static_url_path="", static_folder="../frontend/dist/")
-
-
-app.config["OUTPUTS_FOLDER"] = "../outputs"
-
-
-@app.route("/outputs/<path:filename>")
-def outputs(filename):
-    return send_from_directory(app.config["OUTPUTS_FOLDER"], filename)
-
-
-@app.route("/", defaults={"path": ""})
-def serve(path):
-    return send_from_directory(app.static_folder, "index.html")
-
-
-logger = True if verbose else False
-engineio_logger = True if verbose else False
-
-# default 1,000,000, needs to be higher for socketio to accept larger images
-max_http_buffer_size = 10000000
-
-cors_allowed_origins = [f"http://{host}:{port}"] + additional_allowed_origins
-
-socketio = SocketIO(
-    app,
-    logger=logger,
-    engineio_logger=engineio_logger,
-    max_http_buffer_size=max_http_buffer_size,
-    cors_allowed_origins=cors_allowed_origins,
-    ping_interval=(50, 50),
-    ping_timeout=60,
-)
-
-
-"""
-END SERVER SETUP
-"""
-
-
-"""
-APP SETUP
-"""
-
-
-class CanceledException(Exception):
-    pass
-
-
-try:
-    gfpgan, codeformer, esrgan = None, None, None
-    from ldm.dream.restoration.base import Restoration
-
-    restoration = Restoration()
-    gfpgan, codeformer = restoration.load_face_restore_models()
-    esrgan = restoration.load_esrgan()
-
-    # coreformer.process(self, image, strength, device, seed=None, fidelity=0.75)
-
-except (ModuleNotFoundError, ImportError):
-    print(traceback.format_exc(), file=sys.stderr)
-    print(">> You may need to install the ESRGAN and/or GFPGAN modules")
-
-canceled = Event()
-
-# reduce logging outputs to error
-transformers.logging.set_verbosity_error()
-logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
-
-# Initialize and load model
-generate = Generate(
-    model,
-    precision=precision,
-    embedding_path=embedding_path,
-)
-generate.free_gpu_mem = free_gpu_mem
-generate.load_model()
-
-
-# location for "finished" images
-result_path = os.path.join(output_dir, "img-samples/")
-
-# temporary path for intermediates
-intermediate_path = os.path.join(result_path, "intermediates/")
-
-# path for user-uploaded init images and masks
-init_image_path = os.path.join(result_path, "init-images/")
-mask_image_path = os.path.join(result_path, "mask-images/")
-
-# txt log
-log_path = os.path.join(result_path, "dream_log.txt")
-
-# make all output paths
-[
-    os.makedirs(path, exist_ok=True)
-    for path in [result_path, intermediate_path, init_image_path, mask_image_path]
-]
-
-
-"""
-END APP SETUP
-"""
-
-
-"""
-SOCKET.IO LISTENERS
-"""
-
-
-@socketio.on("requestSystemConfig")
-def handle_request_capabilities():
-    print(f">> System config requested")
-    config = get_system_config()
-    socketio.emit("systemConfig", config)
-
-
-@socketio.on("requestImages")
-def handle_request_images(page=1, offset=0, last_mtime=None):
-    chunk_size = 50
-
-    if last_mtime:
-        print(f">> Latest images requested")
-    else:
-        print(
-            f">> Page {page} of images requested (page size {chunk_size} offset {offset})"
-        )
-
-    paths = glob.glob(os.path.join(result_path, "*.png"))
-    sorted_paths = sorted(paths, key=lambda x: os.path.getmtime(x), reverse=True)
-
-    if last_mtime:
-        image_paths = filter(lambda x: os.path.getmtime(x) > last_mtime, sorted_paths)
-    else:
-
-        image_paths = sorted_paths[
-            slice(chunk_size * (page - 1) + offset, chunk_size * page + offset)
-        ]
-        page = page + 1
-
-    image_array = []
-
-    for path in image_paths:
-        metadata = retrieve_metadata(path)
-        image_array.append(
-            {
-                "url": path,
-                "mtime": os.path.getmtime(path),
-                "metadata": metadata["sd-metadata"],
-            }
-        )
-
-    socketio.emit(
-        "galleryImages",
-        {
-            "images": image_array,
-            "nextPage": page,
-            "offset": offset,
-            "onlyNewImages": True if last_mtime else False,
-        },
-    )
-
-
-@socketio.on("generateImage")
-def handle_generate_image_event(
-    generation_parameters, esrgan_parameters, gfpgan_parameters
-):
-    print(
-        f">> Image generation requested: {generation_parameters}\nESRGAN parameters: {esrgan_parameters}\nGFPGAN parameters: {gfpgan_parameters}"
-    )
-    generate_images(generation_parameters, esrgan_parameters, gfpgan_parameters)
-
-
-@socketio.on("runESRGAN")
-def handle_run_esrgan_event(original_image, esrgan_parameters):
-    print(
-        f'>> ESRGAN upscale requested for "{original_image["url"]}": {esrgan_parameters}'
-    )
-    progress = {
-        "currentStep": 1,
-        "totalSteps": 1,
-        "currentIteration": 1,
-        "totalIterations": 1,
-        "currentStatus": "Preparing",
-        "isProcessing": True,
-        "currentStatusHasSteps": False,
-    }
-
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = Image.open(original_image["url"])
-
-    seed = (
-        original_image["metadata"]["seed"]
-        if "seed" in original_image["metadata"]
-        else "unknown_seed"
-    )
-
-    progress["currentStatus"] = "Upscaling"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = esrgan.process(
-        image=image,
-        upsampler_scale=esrgan_parameters["upscale"][0],
-        strength=esrgan_parameters["upscale"][1],
-        seed=seed,
-    )
-
-    progress["currentStatus"] = "Saving image"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    esrgan_parameters["seed"] = seed
-    metadata = parameters_to_post_processed_image_metadata(
-        parameters=esrgan_parameters,
-        original_image_path=original_image["url"],
-        type="esrgan",
-    )
-    command = parameters_to_command(esrgan_parameters)
-
-    path = save_image(image, command, metadata, result_path, postprocessing="esrgan")
-
-    write_log_message(f'[Upscaled] "{original_image["url"]}" > "{path}": {command}')
-
-    progress["currentStatus"] = "Finished"
-    progress["currentStep"] = 0
-    progress["totalSteps"] = 0
-    progress["currentIteration"] = 0
-    progress["totalIterations"] = 0
-    progress["isProcessing"] = False
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    socketio.emit(
-        "esrganResult",
-        {
-            "url": os.path.relpath(path),
-            "mtime": os.path.getmtime(path),
-            "metadata": metadata,
-        },
-    )
-
-
-@socketio.on("runGFPGAN")
-def handle_run_gfpgan_event(original_image, gfpgan_parameters):
-    print(
-        f'>> GFPGAN face fix requested for "{original_image["url"]}": {gfpgan_parameters}'
-    )
-    progress = {
-        "currentStep": 1,
-        "totalSteps": 1,
-        "currentIteration": 1,
-        "totalIterations": 1,
-        "currentStatus": "Preparing",
-        "isProcessing": True,
-        "currentStatusHasSteps": False,
-    }
-
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = Image.open(original_image["url"])
-
-    seed = (
-        original_image["metadata"]["seed"]
-        if "seed" in original_image["metadata"]
-        else "unknown_seed"
-    )
-
-    progress["currentStatus"] = "Fixing faces"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = gfpgan.process(
-        image=image, strength=gfpgan_parameters["gfpgan_strength"], seed=seed
-    )
-
-    progress["currentStatus"] = "Saving image"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    gfpgan_parameters["seed"] = seed
-    metadata = parameters_to_post_processed_image_metadata(
-        parameters=gfpgan_parameters,
-        original_image_path=original_image["url"],
-        type="gfpgan",
-    )
-    command = parameters_to_command(gfpgan_parameters)
-
-    path = save_image(image, command, metadata, result_path, postprocessing="gfpgan")
-
-    write_log_message(f'[Fixed faces] "{original_image["url"]}" > "{path}": {command}')
-
-    progress["currentStatus"] = "Finished"
-    progress["currentStep"] = 0
-    progress["totalSteps"] = 0
-    progress["currentIteration"] = 0
-    progress["totalIterations"] = 0
-    progress["isProcessing"] = False
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    socketio.emit(
-        "gfpganResult",
-        {
-            "url": os.path.relpath(path),
-            "mtime": os.path.mtime(path),
-            "metadata": metadata,
-        },
-    )
-
-
-@socketio.on("cancel")
-def handle_cancel():
-    print(f">> Cancel processing requested")
-    canceled.set()
-    socketio.emit("processingCanceled")
-
-
-# TODO: I think this needs a safety mechanism.
-@socketio.on("deleteImage")
-def handle_delete_image(path, uuid):
-    print(f'>> Delete requested "{path}"')
-    send2trash(path)
-    socketio.emit("imageDeleted", {"url": path, "uuid": uuid})
-
-
-# TODO: I think this needs a safety mechanism.
-@socketio.on("uploadInitialImage")
-def handle_upload_initial_image(bytes, name):
-    print(f'>> Init image upload requested "{name}"')
-    uuid = uuid4().hex
-    split = os.path.splitext(name)
-    name = f"{split[0]}.{uuid}{split[1]}"
-    file_path = os.path.join(init_image_path, name)
-    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-    newFile = open(file_path, "wb")
-    newFile.write(bytes)
-    socketio.emit("initialImageUploaded", {"url": file_path, "uuid": ""})
-
-
-# TODO: I think this needs a safety mechanism.
-@socketio.on("uploadMaskImage")
-def handle_upload_mask_image(bytes, name):
-    print(f'>> Mask image upload requested "{name}"')
-    uuid = uuid4().hex
-    split = os.path.splitext(name)
-    name = f"{split[0]}.{uuid}{split[1]}"
-    file_path = os.path.join(mask_image_path, name)
-    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-    newFile = open(file_path, "wb")
-    newFile.write(bytes)
-    socketio.emit("maskImageUploaded", {"url": file_path, "uuid": ""})
-
-
-"""
-END SOCKET.IO LISTENERS
-"""
-
-
-"""
-ADDITIONAL FUNCTIONS
-"""
-
-
-def get_system_config():
-    return {
-        "model": "stable diffusion",
-        "model_id": model,
-        "model_hash": generate.model_hash,
-        "app_id": APP_ID,
-        "app_version": APP_VERSION,
-    }
-
-
-def parameters_to_post_processed_image_metadata(parameters, original_image_path, type):
-    # top-level metadata minus `image` or `images`
-    metadata = get_system_config()
-
-    orig_hash = calculate_init_img_hash(original_image_path)
-
-    image = {"orig_path": original_image_path, "orig_hash": orig_hash}
-
-    if type == "esrgan":
-        image["type"] = "esrgan"
-        image["scale"] = parameters["upscale"][0]
-        image["strength"] = parameters["upscale"][1]
-    elif type == "gfpgan":
-        image["type"] = "gfpgan"
-        image["strength"] = parameters["gfpgan_strength"]
-    else:
-        raise TypeError(f"Invalid type: {type}")
-
-    metadata["image"] = image
-    return metadata
-
-
-def parameters_to_generated_image_metadata(parameters):
-    # top-level metadata minus `image` or `images`
-
-    metadata = get_system_config()
-    # remove any image keys not mentioned in RFC #266
-    rfc266_img_fields = [
-        "type",
-        "postprocessing",
-        "sampler",
-        "prompt",
-        "seed",
-        "variations",
-        "steps",
-        "cfg_scale",
-        "threshold",
-        "perlin",
-        "step_number",
-        "width",
-        "height",
-        "extra",
-        "seamless",
-    ]
-
-    rfc_dict = {}
-
-    for item in parameters.items():
-        key, value = item
-        if key in rfc266_img_fields:
-            rfc_dict[key] = value
-
-    postprocessing = []
-
-    # 'postprocessing' is either null or an
-    if "gfpgan_strength" in parameters:
-
-        postprocessing.append(
-            {"type": "gfpgan", "strength": float(parameters["gfpgan_strength"])}
-        )
-
-    if "upscale" in parameters:
-        postprocessing.append(
-            {
-                "type": "esrgan",
-                "scale": int(parameters["upscale"][0]),
-                "strength": float(parameters["upscale"][1]),
-            }
-        )
-
-    rfc_dict["postprocessing"] = postprocessing if len(postprocessing) > 0 else None
-
-    # semantic drift
-    rfc_dict["sampler"] = parameters["sampler_name"]
-
-    # display weighted subprompts (liable to change)
-    subprompts = split_weighted_subprompts(parameters["prompt"])
-    subprompts = [{"prompt": x[0], "weight": x[1]} for x in subprompts]
-    rfc_dict["prompt"] = subprompts
-
-    # 'variations' should always exist and be an array, empty or consisting of {'seed': seed, 'weight': weight} pairs
-    variations = []
-
-    if "with_variations" in parameters:
-        variations = [
-            {"seed": x[0], "weight": x[1]} for x in parameters["with_variations"]
-        ]
-
-    rfc_dict["variations"] = variations
-
-    if "init_img" in parameters:
-        rfc_dict["type"] = "img2img"
-        rfc_dict["strength"] = parameters["strength"]
-        rfc_dict["fit"] = parameters["fit"]  # TODO: Noncompliant
-        rfc_dict["orig_hash"] = calculate_init_img_hash(parameters["init_img"])
-        rfc_dict["init_image_path"] = parameters["init_img"]  # TODO: Noncompliant
-        rfc_dict["sampler"] = "ddim"  # TODO: FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
-        if "init_mask" in parameters:
-            rfc_dict["mask_hash"] = calculate_init_img_hash(
-                parameters["init_mask"]
-            )  # TODO: Noncompliant
-            rfc_dict["mask_image_path"] = parameters["init_mask"]  # TODO: Noncompliant
-    else:
-        rfc_dict["type"] = "txt2img"
-
-    metadata["image"] = rfc_dict
-
-    return metadata
-
-
-def make_unique_init_image_filename(name):
-    uuid = uuid4().hex
-    split = os.path.splitext(name)
-    name = f"{split[0]}.{uuid}{split[1]}"
-    return name
-
-
-def write_log_message(message, log_path=log_path):
-    """Logs the filename and parameters used to generate or process that image to log file"""
-    message = f"{message}\n"
-    with open(log_path, "a", encoding="utf-8") as file:
-        file.writelines(message)
-
-
-def save_image(
-    image, command, metadata, output_dir, step_index=None, postprocessing=False
-):
-    pngwriter = PngWriter(output_dir)
-    prefix = pngwriter.unique_prefix()
-
-    seed = "unknown_seed"
-
-    if "image" in metadata:
-        if "seed" in metadata["image"]:
-            seed = metadata["image"]["seed"]
-
-    filename = f"{prefix}.{seed}"
-
-    if step_index:
-        filename += f".{step_index}"
-    if postprocessing:
-        filename += f".postprocessed"
-
-    filename += ".png"
-
-    path = pngwriter.save_image_and_prompt_to_png(
-        image=image, dream_prompt=command, metadata=metadata, name=filename
-    )
-
-    return path
-
-
-def calculate_real_steps(steps, strength, has_init_image):
-    return math.floor(strength * steps) if has_init_image else steps
-
-
-def generate_images(generation_parameters, esrgan_parameters, gfpgan_parameters):
-    canceled.clear()
-
-    step_index = 1
-    prior_variations = (
-        generation_parameters["with_variations"]
-        if "with_variations" in generation_parameters
-        else []
-    )
-    """
-    If a result image is used as an init image, and then deleted, we will want to be
-    able to use it as an init image in the future. Need to copy it.
-
-    If the init/mask image doesn't exist in the init_image_path/mask_image_path,
-    make a unique filename for it and copy it there.
-    """
-    if "init_img" in generation_parameters:
-        filename = os.path.basename(generation_parameters["init_img"])
-        if not os.path.exists(os.path.join(init_image_path, filename)):
-            unique_filename = make_unique_init_image_filename(filename)
-            new_path = os.path.join(init_image_path, unique_filename)
-            shutil.copy(generation_parameters["init_img"], new_path)
-            generation_parameters["init_img"] = new_path
-        if "init_mask" in generation_parameters:
-            filename = os.path.basename(generation_parameters["init_mask"])
-            if not os.path.exists(os.path.join(mask_image_path, filename)):
-                unique_filename = make_unique_init_image_filename(filename)
-                new_path = os.path.join(init_image_path, unique_filename)
-                shutil.copy(generation_parameters["init_img"], new_path)
-                generation_parameters["init_mask"] = new_path
-
-    totalSteps = calculate_real_steps(
-        steps=generation_parameters["steps"],
-        strength=generation_parameters["strength"]
-        if "strength" in generation_parameters
-        else None,
-        has_init_image="init_img" in generation_parameters,
-    )
-
-    progress = {
-        "currentStep": 1,
-        "totalSteps": totalSteps,
-        "currentIteration": 1,
-        "totalIterations": generation_parameters["iterations"],
-        "currentStatus": "Preparing",
-        "isProcessing": True,
-        "currentStatusHasSteps": False,
-    }
-
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    def image_progress(sample, step):
-        if canceled.is_set():
-            raise CanceledException
-
-        nonlocal step_index
-        nonlocal generation_parameters
-        nonlocal progress
-
-        progress["currentStep"] = step + 1
-        progress["currentStatus"] = "Generating"
-        progress["currentStatusHasSteps"] = True
-
-        if (
-            generation_parameters["progress_images"]
-            and step % 5 == 0
-            and step < generation_parameters["steps"] - 1
-        ):
-            image = generate.sample_to_image(sample)
-
-            metadata = parameters_to_generated_image_metadata(generation_parameters)
-            command = parameters_to_command(generation_parameters)
-            path = save_image(image, command, metadata, intermediate_path, step_index=step_index, postprocessing=False)
-
-            step_index += 1
-            socketio.emit(
-                "intermediateResult",
-                {
-                    "url": os.path.relpath(path),
-                    "mtime": os.path.getmtime(path),
-                    "metadata": metadata,
-                },
-            )
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-    def image_done(image, seed, first_seed):
-        nonlocal generation_parameters
-        nonlocal esrgan_parameters
-        nonlocal gfpgan_parameters
-        nonlocal progress
-
-        step_index = 1
-        nonlocal prior_variations
-
-        progress["currentStatus"] = "Generation complete"
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-        all_parameters = generation_parameters
-        postprocessing = False
-
-        if (
-            "variation_amount" in all_parameters
-            and all_parameters["variation_amount"] > 0
-        ):
-            first_seed = first_seed or seed
-            this_variation = [[seed, all_parameters["variation_amount"]]]
-            all_parameters["with_variations"] = prior_variations + this_variation
-            all_parameters["seed"] = first_seed
-        elif ("with_variations" in all_parameters):
-            all_parameters["seed"] = first_seed
-        else:
-            all_parameters["seed"] = seed
-
-        if esrgan_parameters:
-            progress["currentStatus"] = "Upscaling"
-            progress["currentStatusHasSteps"] = False
-            socketio.emit("progressUpdate", progress)
-            eventlet.sleep(0)
-
-            image = esrgan.process(
-                image=image,
-                upsampler_scale=esrgan_parameters["level"],
-                strength=esrgan_parameters["strength"],
-                seed=seed,
-            )
-
-            postprocessing = True
-            all_parameters["upscale"] = [
-                esrgan_parameters["level"],
-                esrgan_parameters["strength"],
-            ]
-
-        if gfpgan_parameters:
-            progress["currentStatus"] = "Fixing faces"
-            progress["currentStatusHasSteps"] = False
-            socketio.emit("progressUpdate", progress)
-            eventlet.sleep(0)
-
-            image = gfpgan.process(
-                image=image, strength=gfpgan_parameters["strength"], seed=seed
-            )
-            postprocessing = True
-            all_parameters["gfpgan_strength"] = gfpgan_parameters["strength"]
-
-        progress["currentStatus"] = "Saving image"
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-        metadata = parameters_to_generated_image_metadata(all_parameters)
-        command = parameters_to_command(all_parameters)
-
-        path = save_image(
-            image, command, metadata, result_path, postprocessing=postprocessing
-        )
-
-        print(f'>> Image generated: "{path}"')
-        write_log_message(f'[Generated] "{path}": {command}')
-
-        if progress["totalIterations"] > progress["currentIteration"]:
-            progress["currentStep"] = 1
-            progress["currentIteration"] += 1
-            progress["currentStatus"] = "Iteration finished"
-            progress["currentStatusHasSteps"] = False
-        else:
-            progress["currentStep"] = 0
-            progress["totalSteps"] = 0
-            progress["currentIteration"] = 0
-            progress["totalIterations"] = 0
-            progress["currentStatus"] = "Finished"
-            progress["isProcessing"] = False
-
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-        socketio.emit(
-            "generationResult",
-            {
-                "url": os.path.relpath(path),
-                "mtime": os.path.getmtime(path),
-                "metadata": metadata,
-            },
-        )
-        eventlet.sleep(0)
-
-    try:
-        generate.prompt2image(
-            **generation_parameters,
-            step_callback=image_progress,
-            image_callback=image_done,
-        )
-
-    except KeyboardInterrupt:
-        raise
-    except CanceledException:
-        pass
-    except Exception as e:
-        socketio.emit("error", {"message": (str(e))})
-        print("\n")
-        traceback.print_exc()
-        print("\n")
-
-
-"""
-END ADDITIONAL FUNCTIONS
-"""
-
-
-if __name__ == "__main__":
-    print(f">> Starting server at http://{host}:{port}")
-    socketio.run(app, host=host, port=port)

configs/autoencoder/autoencoder_kl_16x16x16.yaml

@@ -1,54 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 16
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 16
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16]
-      dropout: 0.0
-
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/autoencoder/autoencoder_kl_32x32x4.yaml

@@ -1,53 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 4
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 4
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [ ]
-      dropout: 0.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/autoencoder/autoencoder_kl_64x64x3.yaml

@@ -1,54 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 3
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 3
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [ ]
-      dropout: 0.0
-
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/autoencoder/autoencoder_kl_8x8x64.yaml

@@ -1,53 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 64
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 64
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16,8]
-      dropout: 0.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/latent-diffusion/celebahq-ldm-vq-4.yaml

@@ -1,86 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ckpt_path: models/first_stage_models/vq-f4/model.ckpt
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 48
-    num_workers: 5
-    wrap: false
-    train:
-      target: taming.data.faceshq.CelebAHQTrain
-      params:
-        size: 256
-    validation:
-      target: taming.data.faceshq.CelebAHQValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/cin-ldm-vq-f8.yaml

@@ -1,98 +0,0 @@
-model:
-  base_learning_rate: 1.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: class_label
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 256
-        attention_resolutions:
-        #note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 32 for f8
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 4
-        num_head_channels: 32
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 512
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 4
-        n_embed: 16384
-        ckpt_path: configs/first_stage_models/vq-f8/model.yaml
-        ddconfig:
-          double_z: false
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 32
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.ClassEmbedder
-      params:
-        embed_dim: 512
-        key: class_label
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 64
-    num_workers: 12
-    wrap: false
-    train:
-      target: ldm.data.imagenet.ImageNetTrain
-      params:
-        config:
-          size: 256
-    validation:
-      target: ldm.data.imagenet.ImageNetValidation
-      params:
-        config:
-          size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/cin256-v2.yaml

@@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 0.0001
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: class_label
-    image_size: 64
-    channels: 3
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss
-    use_ema: False
-    
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 192
-        attention_resolutions:
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 5
-        num_heads: 1
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 512
-    
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.ClassEmbedder
-      params:
-        n_classes: 1001
-        embed_dim: 512
-        key: class_label

configs/latent-diffusion/ffhq-ldm-vq-4.yaml

@@ -1,85 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 42
-    num_workers: 5
-    wrap: false
-    train:
-      target: taming.data.faceshq.FFHQTrain
-      params:
-        size: 256
-    validation:
-      target: taming.data.faceshq.FFHQValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml

@@ -1,85 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
-        embed_dim: 3
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 48
-    num_workers: 5
-    wrap: false
-    train:
-      target: ldm.data.lsun.LSUNBedroomsTrain
-      params:
-        size: 256
-    validation:
-      target: ldm.data.lsun.LSUNBedroomsValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml

@@ -1,91 +0,0 @@
-model:
-  base_learning_rate: 5.0e-5   # set to target_lr by starting main.py with '--scale_lr False'
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0155
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    loss_type: l1
-    first_stage_key: "image"
-    cond_stage_key: "image"
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: False
-    concat_mode: False
-    scale_by_std: True
-    monitor: 'val/loss_simple_ema'
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [10000]
-        cycle_lengths: [10000000000000]
-        f_start: [1.e-6]
-        f_max: [1.]
-        f_min: [ 1.]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 192
-        attention_resolutions: [ 1, 2, 4, 8 ]   # 32, 16, 8, 4
-        num_res_blocks: 2
-        channel_mult: [ 1,2,2,4,4 ]  # 32, 16, 8, 4, 2
-        num_heads: 8
-        use_scale_shift_norm: True
-        resblock_updown: True
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: "val/rec_loss"
-        ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
-        ddconfig:
-          double_z: True
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config: "__is_unconditional__"
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 96
-    num_workers: 5
-    wrap: False
-    train:
-      target: ldm.data.lsun.LSUNChurchesTrain
-      params:
-        size: 256
-    validation:
-      target: ldm.data.lsun.LSUNChurchesValidation
-      params:
-        size: 256
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/txt2img-1p4B-eval.yaml

@@ -1,71 +0,0 @@
-model:
-  base_learning_rate: 5.0e-05
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.012
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions:
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 4
-        - 4
-        num_heads: 8
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 1280
-        use_checkpoint: true
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.BERTEmbedder
-      params:
-        n_embed: 1280
-        n_layer: 32

configs/models.yaml

@@ -1,18 +1,36 @@
 # This file describes the alternative machine learning models
-#  available to the dream script.
+# available to InvokeAI script.
 #
 # To add a new model, follow the examples below. Each
 # model requires a model config file, a weights file,
 # and the width and height of the images it
 # was trained on.
-
-laion400m:
-    config:  configs/latent-diffusion/txt2img-1p4B-eval.yaml
-    weights: models/ldm/text2img-large/model.ckpt
-    width: 256
-    height: 256
 stable-diffusion-1.4:
-    config:  configs/stable-diffusion/v1-inference.yaml
-    weights: models/ldm/stable-diffusion-v1/model.ckpt
-    width: 512
-    height: 512
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  weights: ./models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
+  width: 512
+  height: 512
+stable-diffusion-1.5:
+  description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
+  weights: ./models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  default: true
+inpainting-1.5:
+  description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
+  weights: ./models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+  config: ./configs/stable-diffusion/v1-inpainting-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+waifu-diffusion-1.3:
+  description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)
+  weights: ./models/ldm/stable-diffusion-v1/model-epoch09-float32.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt

configs/retrieval-augmented-diffusion/768x768.yaml

@@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 0.0001
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.015
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: jpg
-    cond_stage_key: nix
-    image_size: 48
-    channels: 16
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_by_std: false
-    scale_factor: 0.22765929
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 48
-        in_channels: 16
-        out_channels: 16
-        model_channels: 448
-        attention_resolutions:
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        use_scale_shift_norm: false
-        resblock_updown: false
-        num_head_channels: 32
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: true
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        monitor: val/rec_loss
-        embed_dim: 16
-        ddconfig:
-          double_z: true
-          z_channels: 16
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config:
-      target: torch.nn.Identity

configs/stable-diffusion/v1-inference.yaml

@@ -32,7 +32,7 @@ model:
         placeholder_strings: ["*"]
         initializer_words: ['face', 'man', 'photo', 'africanmale']
         per_image_tokens: false
-        num_vectors_per_token: 6
+        num_vectors_per_token: 1
         progressive_words: False
 
     unet_config:
@@ -76,4 +76,4 @@ model:
           target: torch.nn.Identity
 
     cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

configs/stable-diffusion/v1-inpainting-inference.yaml

@@ -0,0 +1,79 @@
+model:
+  base_learning_rate: 7.5e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid   # important
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    finetune_keys: null
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    personalization_config:
+      target: ldm.modules.embedding_manager.EmbeddingManager
+      params:
+        placeholder_strings: ["*"]
+        initializer_words: ['face', 'man', 'photo', 'africanmale']
+        per_image_tokens: false
+        num_vectors_per_token: 1
+        progressive_words: False
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 9  # 4 data + 4 downscaled image + 1 mask
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

docker-build/Dockerfile

@@ -1,57 +1,74 @@
-FROM        debian 
+FROM ubuntu AS get_miniconda
 
-ARG         gsd
-ENV         GITHUB_STABLE_DIFFUSION $gsd
+SHELL ["/bin/bash", "-c"]
 
-ARG         rsd
-ENV         REQS $rsd
+# install wget
+RUN apt-get update \
+  && apt-get install -y \
+    wget \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
 
-ARG         cs
-ENV         CONDA_SUBDIR $cs
+# download and install miniconda
+ARG conda_version=py39_4.12.0-Linux-x86_64
+ARG conda_prefix=/opt/conda
+RUN wget --progress=dot:giga -O /miniconda.sh \
+  https://repo.anaconda.com/miniconda/Miniconda3-${conda_version}.sh \
+  && bash /miniconda.sh -b -p ${conda_prefix} \
+  && rm -f /miniconda.sh
 
-ENV         PIP_EXISTS_ACTION="w"
+FROM ubuntu AS invokeai
 
-# TODO: Optimize image size
+# use bash
+SHELL [ "/bin/bash", "-c" ]
 
-SHELL       ["/bin/bash", "-c"]
+# clean bashrc
+RUN echo "" > ~/.bashrc
 
-WORKDIR     /
-RUN         apt update && apt upgrade -y \
-            && apt install -y \
-            git \
-            libgl1-mesa-glx \
-            libglib2.0-0 \
-            pip \
-            python3 \
-            && git clone $GITHUB_STABLE_DIFFUSION
+# Install necesarry packages
+RUN apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    gcc \
+    git \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    pip \
+    python3 \
+    python3-dev \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
 
-# Install Anaconda or Miniconda
-COPY        anaconda.sh .
-RUN         bash anaconda.sh -b -u -p /anaconda && /anaconda/bin/conda init bash
+# clone repository and create symlinks
+ARG invokeai_git=https://github.com/invoke-ai/InvokeAI.git
+ARG project_name=invokeai
+RUN git clone ${invokeai_git} /${project_name} \
+  && mkdir /${project_name}/models/ldm/stable-diffusion-v1 \
+  && ln -s /data/models/sd-v1-4.ckpt /${project_name}/models/ldm/stable-diffusion-v1/model.ckpt \
+  && ln -s /data/outputs/ /${project_name}/outputs
 
-# SD 
-WORKDIR     /stable-diffusion 
-RUN         source ~/.bashrc \
-            && conda create -y --name ldm && conda activate ldm \
-            && conda config --env --set subdir $CONDA_SUBDIR \
-            && pip3 install -r $REQS \   
-            && pip3 install basicsr facexlib realesrgan \          
-            && mkdir models/ldm/stable-diffusion-v1 \
-            && ln -s "/data/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt 
+# set workdir
+WORKDIR /${project_name}
 
-# Face restoreation
-# by default expected in a sibling directory to stable-diffusion
-WORKDIR     /       
-RUN         git clone https://github.com/TencentARC/GFPGAN.git 
+# install conda env and preload models
+ARG conda_prefix=/opt/conda
+ARG conda_env_file=environment.yml
+COPY --from=get_miniconda ${conda_prefix} ${conda_prefix}
+RUN source ${conda_prefix}/etc/profile.d/conda.sh \
+  && conda init bash \
+  && source ~/.bashrc \
+  && conda env create \
+    --name ${project_name} \
+    --file ${conda_env_file} \
+  && rm -Rf ~/.cache \
+  && conda clean -afy \
+  && echo "conda activate ${project_name}" >> ~/.bashrc \
+  && ln -s /data/models/GFPGANv1.4.pth ./src/gfpgan/experiments/pretrained_models/GFPGANv1.4.pth \
+  && conda activate ${project_name} \
+  && python scripts/preload_models.py
 
-WORKDIR     /GFPGAN 
-RUN         pip3 install -r requirements.txt \
-            && python3 setup.py develop \
-            && ln -s "/data/GFPGANv1.4.pth" experiments/pretrained_models/GFPGANv1.4.pth
-
-WORKDIR     /stable-diffusion 
-RUN         python3 scripts/preload_models.py         
-
-WORKDIR     /
-COPY        entrypoint.sh .
-ENTRYPOINT ["/entrypoint.sh"]
+# Copy entrypoint and set env
+ENV CONDA_PREFIX=${conda_prefix}
+ENV PROJECT_NAME=${project_name}
+COPY docker-build/entrypoint.sh /
+ENTRYPOINT [ "/entrypoint.sh" ]

docker-build/build.sh

@@ -0,0 +1,81 @@
+#!/usr/bin/env bash
+set -e
+# IMPORTANT: You need to have a token on huggingface.co to be able to download the checkpoint!!!
+# configure values by using env when executing build.sh
+# f.e. env ARCH=aarch64 GITHUB_INVOKE_AI=https://github.com/yourname/yourfork.git ./build.sh
+
+source ./docker-build/env.sh || echo "please run from repository root" || exit 1
+
+invokeai_conda_version=${INVOKEAI_CONDA_VERSION:-py39_4.12.0-${platform/\//-}}
+invokeai_conda_prefix=${INVOKEAI_CONDA_PREFIX:-\/opt\/conda}
+invokeai_conda_env_file=${INVOKEAI_CONDA_ENV_FILE:-environment.yml}
+invokeai_git=${INVOKEAI_GIT:-https://github.com/invoke-ai/InvokeAI.git}
+huggingface_token=${HUGGINGFACE_TOKEN?}
+
+# print the settings
+echo "You are using these values:"
+echo -e "project_name:\t\t ${project_name}"
+echo -e "volumename:\t\t ${volumename}"
+echo -e "arch:\t\t\t ${arch}"
+echo -e "platform:\t\t ${platform}"
+echo -e "invokeai_conda_version:\t ${invokeai_conda_version}"
+echo -e "invokeai_conda_prefix:\t ${invokeai_conda_prefix}"
+echo -e "invokeai_conda_env_file: ${invokeai_conda_env_file}"
+echo -e "invokeai_git:\t\t ${invokeai_git}"
+echo -e "invokeai_tag:\t\t ${invokeai_tag}\n"
+
+_runAlpine() {
+  docker run \
+    --rm \
+    --interactive \
+    --tty \
+    --mount source="$volumename",target=/data \
+    --workdir /data \
+    alpine "$@"
+}
+
+_copyCheckpoints() {
+  echo "creating subfolders for models and outputs"
+  _runAlpine mkdir models
+  _runAlpine mkdir outputs
+  echo -n "downloading sd-v1-4.ckpt"
+  _runAlpine wget --header="Authorization: Bearer ${huggingface_token}" -O models/sd-v1-4.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
+  echo "done"
+  echo "downloading GFPGANv1.4.pth"
+  _runAlpine wget -O models/GFPGANv1.4.pth https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
+}
+
+_checkVolumeContent() {
+  _runAlpine ls -lhA /data/models
+}
+
+_getModelMd5s() {
+  _runAlpine  \
+    alpine sh -c "md5sum /data/models/*"
+}
+
+if [[ -n "$(docker volume ls -f name="${volumename}" -q)" ]]; then
+  echo "Volume already exists"
+  if [[ -z "$(_checkVolumeContent)" ]]; then
+    echo "looks empty, copying checkpoint"
+    _copyCheckpoints
+  fi
+  echo "Models in ${volumename}:"
+  _checkVolumeContent
+else
+  echo -n "createing docker volume "
+  docker volume create "${volumename}"
+  _copyCheckpoints
+fi
+
+# Build Container
+docker build \
+  --platform="${platform}" \
+  --tag "${invokeai_tag}" \
+  --build-arg project_name="${project_name}" \
+  --build-arg conda_version="${invokeai_conda_version}" \
+  --build-arg conda_prefix="${invokeai_conda_prefix}" \
+  --build-arg conda_env_file="${invokeai_conda_env_file}" \
+  --build-arg invokeai_git="${invokeai_git}" \
+  --file ./docker-build/Dockerfile \
+  .

docker-build/entrypoint.sh

@@ -1,10 +1,8 @@
 #!/bin/bash
+set -e
 
-cd /stable-diffusion
+source "${CONDA_PREFIX}/etc/profile.d/conda.sh"
+conda activate "${PROJECT_NAME}"
 
-if [ $# -eq 0 ]; then
-    python3 scripts/dream.py --full_precision -o /data
-    # bash
-else
-    python3 scripts/dream.py --full_precision -o /data "$@"
-fi
+python scripts/invoke.py \
+  ${@:---web --host=0.0.0.0}

docker-build/env.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+project_name=${PROJECT_NAME:-invokeai}
+volumename=${VOLUMENAME:-${project_name}_data}
+arch=${ARCH:-x86_64}
+platform=${PLATFORM:-Linux/${arch}}
+invokeai_tag=${INVOKEAI_TAG:-${project_name}-${arch}}
+
+export project_name
+export volumename
+export arch
+export platform
+export invokeai_tag

docker-build/run.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+set -e
+
+source ./docker-build/env.sh || echo "please run from repository root" || exit 1
+
+docker run \
+  --interactive \
+  --tty \
+  --rm \
+  --platform "$platform" \
+  --name "$project_name" \
+  --hostname "$project_name" \
+  --mount source="$volumename",target=/data \
+  --publish 9090:9090 \
+  "$invokeai_tag" ${1:+$@}

docs/CHANGELOG.md

@@ -1,51 +1,106 @@
-# **Changelog**
+---
+title: Changelog
+---
 
-## v1.13 (in process)
+# :octicons-log-16: **Changelog**
 
-- Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
-- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling [Kevin Gibbons](https://github.com/bakkot)
-- WebUI supports incremental display of in-progress images during generation [Kevin Gibbons](https://github.com/bakkot)
-- Output directory can be specified on the dream> command line.
-- The grid was displaying duplicated images when not enough images to fill the final row [Muhammad Usama](https://github.com/SMUsamaShah)
-- Can specify --grid on dream.py command line as the default.
-- Miscellaneous internal bug and stability fixes.
+## v2.0.1 (13 October 2022)
+
+  - fix noisy images at high step count when using k* samplers
+  - dream.py script now calls invoke.py module directly rather than
+    via a new python process (which could break the environment)
+
+## v2.0.0 <small>(9 October 2022)</small>
+
+  - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
+    for backward compatibility.
+  - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/INPAINTING.md">inpainting</a> and <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OUTPAINTING.md">outpainting</a>
+  - img2img runs on all k* samplers
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/PROMPTS.md#negative-and-unconditioned-prompts">negative prompts</a>
+  - Support for CodeFormer face reconstruction
+  - Support for Textual Inversion on Macintoshes
+  - Support in both WebGUI and CLI for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/POSTPROCESS.md">post-processing of previously-generated images</a>
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+  - New `--hires` option on `invoke>` line allows <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.m#this-is-an-example-of-txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
+  - New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
+  - Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+  - Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+  - Improved <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.m">command-line completion behavior</a>.
+    New commands added:
+       * List command-line history with `!history`
+       * Search command-line history with `!search`
+       * Clear history with `!clear`
+  - Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+    configure. To switch away from auto use the new flag like `--precision=float32`.
+
+## v1.14 <small>(11 September 2022)</small>
+
+  - Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
+  - Full support for Apple hardware with M1 or M2 chips.
+  - Add "seamless mode" for circular tiling of image. Generates beautiful effects.
+    ([prixt](https://github.com/prixt)).
+  - Inpainting support.
+  - Improved web server GUI.
+  - Lots of code and documentation cleanups.
+
+## v1.13 <small>(3 September 2022)</small>
+
+  - Support image variations (see [VARIATIONS](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 hardware
+    [Arturo Mendivil](https://github.com/artmen1516)
+  - WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
+    [Kevin Gibbons](https://github.com/bakkot)
+  - WebUI supports incremental display of in-progress images during generation
+    [Kevin Gibbons](https://github.com/bakkot)
+  - A new configuration file scheme that allows new models (including upcoming
+    stable-diffusion-v1.5) to be added without altering the code.
+    ([David Wager](https://github.com/maddavid12))
+  - Can specify --grid on invoke.py command line as the default.
+  - Miscellaneous internal bug and stability fixes.
+  - Works on M1 Apple hardware.
+  - Multiple bug fixes.
 
 ---
 
-## v1.12 (28 August 2022)
+## v1.12 <small>(28 August 2022)</small>
 
 - Improved file handling, including ability to read prompts from standard input.
   (kudos to [Yunsaki](https://github.com/yunsaki)
-- The web server is now integrated with the dream.py script. Invoke by adding --web to
-  the dream.py command arguments.
+- The web server is now integrated with the invoke.py script. Invoke by adding --web to
+  the invoke.py command arguments.
 - Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
   enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
   VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
   [Oceanswave](https://github.com/oceanswave) for their work on this.
-- You can now swap samplers on the dream> command line. [Blessedcoolant](https://github.com/blessedcoolant)
+- You can now swap samplers on the invoke> command line. [Blessedcoolant](https://github.com/blessedcoolant)
 
 ---
 
-## v1.11 (26 August 2022)
+## v1.11 <small>(26 August 2022)</small>
 
 - NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
 - You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
   Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
 - Variant generation support temporarily disabled pending more general solution.
-- Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
+- Created a feature branch named **yunsaki-morphing-invoke** which adds experimental support for
   iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
   for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
   significantly.
 
 ---
 
-## v1.10 (25 August 2022)
+## v1.10 <small>(25 August 2022)</small>
 
 - A barebones but fully functional interactive web server for online generation of txt2img and img2img.
 
 ---
 
-## v1.09 (24 August 2022)
+## v1.09 <small>(24 August 2022)</small>
 
 - A new -v option allows you to generate multiple variants of an initial image
   in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
@@ -55,9 +110,9 @@
 
 ---
 
-## v1.08 (24 August 2022)
+## v1.08 <small>(24 August 2022)</small>
 
-- Escape single quotes on the dream> command before trying to parse. This avoids
+- Escape single quotes on the invoke> command before trying to parse. This avoids
   parse errors.
 - Removed instruction to get Python3.8 as first step in Windows install.
   Anaconda3 does it for you.
@@ -66,7 +121,7 @@
 
 ---
 
-## v1.07 (23 August 2022)
+## v1.07 <small>(23 August 2022)</small>
 
 - Image filenames will now never fill gaps in the sequence, but will be assigned the
   next higher name in the chosen directory. This ensures that the alphabetic and chronological
@@ -74,14 +129,14 @@
 
 ---
 
-## v1.06 (23 August 2022)
+## v1.06 <small>(23 August 2022)</small>
 
 - Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
 - Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
 
 ---
 
-## v1.05 (22 August 2022 - after the drop)
+## v1.05 <small>(22 August 2022 - after the drop)</small>
 
 - Filenames now use the following formats:
   000010.95183149.png -- Two files produced by the same command (e.g. -n2),
@@ -94,12 +149,12 @@
   be regenerated with the indicated key
 
 - It should no longer be possible for one image to overwrite another
-- You can use the "cd" and "pwd" commands at the dream> prompt to set and retrieve
+- You can use the "cd" and "pwd" commands at the invoke> prompt to set and retrieve
   the path of the output directory.
 
 ---
 
-## v1.04 (22 August 2022 - after the drop)
+## v1.04 <small>(22 August 2022 - after the drop)</small>
 
 - Updated README to reflect installation of the released weights.
 - Suppressed very noisy and inconsequential warning when loading the frozen CLIP
@@ -107,14 +162,14 @@
 
 ---
 
-## v1.03 (22 August 2022)
+## v1.03 <small>(22 August 2022)</small>
 
 - The original txt2img and img2img scripts from the CompViz repository have been moved into
   a subfolder named "orig_scripts", to reduce confusion.
 
 ---
 
-## v1.02 (21 August 2022)
+## v1.02 <small>(21 August 2022)</small>
 
 - A copy of the prompt and all of its switches and options is now stored in the corresponding
   image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
@@ -123,15 +178,15 @@
 
 ---
 
-## v1.01 (21 August 2022)
+## v1.01 <small>(21 August 2022)</small>
 
 - added k_lms sampling.
   **Please run "conda env update" to load the k_lms dependencies!!**
 - use half precision arithmetic by default, resulting in faster execution and lower memory requirements
-  Pass argument --full_precision to dream.py to get slower but more accurate image generation
+  Pass argument --full_precision to invoke.py to get slower but more accurate image generation
 
 ---
 
 ## Links
 
-- **[Read Me](../readme.md)**
+- **[Read Me](index.md)**

docs/features/CHANGELOG.md

@@ -4,7 +4,7 @@ title: Changelog
 
 # :octicons-log-16: Changelog
 
-## v1.13 <small>(in process)</small>
+## v1.13
 
 - Supports a Google Colab notebook for a standalone server running on Google
   hardware [Arturo Mendivil](https://github.com/artmen1516)
@@ -12,10 +12,10 @@ title: Changelog
   [Kevin Gibbons](https://github.com/bakkot)
 - WebUI supports incremental display of in-progress images during generation
   [Kevin Gibbons](https://github.com/bakkot)
-- Output directory can be specified on the dream> command line.
+- Output directory can be specified on the invoke> command line.
 - The grid was displaying duplicated images when not enough images to fill the
   final row [Muhammad Usama](https://github.com/SMUsamaShah)
-- Can specify --grid on dream.py command line as the default.
+- Can specify --grid on invoke.py command line as the default.
 - Miscellaneous internal bug and stability fixes.
 
 ---
@@ -24,14 +24,14 @@ title: Changelog
 
 - Improved file handling, including ability to read prompts from standard input.
   (kudos to [Yunsaki](https://github.com/yunsaki)
-- The web server is now integrated with the dream.py script. Invoke by adding
-  --web to the dream.py command arguments.
+- The web server is now integrated with the invoke.py script. Invoke by adding
+  --web to the invoke.py command arguments.
 - Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
   enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
   VRAM requirements are modestly reduced. Thanks to both
   [Blessedcoolant](https://github.com/blessedcoolant) and
   [Oceanswave](https://github.com/oceanswave) for their work on this.
-- You can now swap samplers on the dream> command line.
+- You can now swap samplers on the invoke> command line.
   [Blessedcoolant](https://github.com/blessedcoolant)
 
 ---
@@ -45,7 +45,7 @@ title: Changelog
   back to the previous command, but will work on all images generated with the
   -n# switch.
 - Variant generation support temporarily disabled pending more general solution.
-- Created a feature branch named **yunsaki-morphing-dream** which adds
+- Created a feature branch named **yunsaki-morphing-invoke** which adds
   experimental support for iteratively modifying the prompt and its parameters.
   Please
   see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86) for
@@ -75,7 +75,7 @@ title: Changelog
 
 ## v1.08 <small>(24 August 2022)</small>
 
-- Escape single quotes on the dream> command before trying to parse. This avoids
+- Escape single quotes on the invoke> command before trying to parse. This avoids
   parse errors.
 - Removed instruction to get Python3.8 as first step in Windows install.
   Anaconda3 does it for you.
@@ -112,7 +112,7 @@ title: Changelog
   can be regenerated with the indicated key
 
 - It should no longer be possible for one image to overwrite another
-- You can use the "cd" and "pwd" commands at the dream> prompt to set and
+- You can use the "cd" and "pwd" commands at the invoke> prompt to set and
   retrieve the path of the output directory.
 
 ## v1.04 <small>(22 August 2022 - after the drop)</small>
@@ -139,5 +139,5 @@ title: Changelog
 - added k_lms sampling. **Please run "conda env update -f environment.yaml" to
   load the k_lms dependencies!!**
 - use half precision arithmetic by default, resulting in faster execution and
-  lower memory requirements Pass argument --full_precision to dream.py to get
+  lower memory requirements Pass argument --full_precision to invoke.py to get
   slower but more accurate image generation

docs/features/CLI.md

@@ -8,8 +8,8 @@ hide:
 
 ## **Interactive Command Line Interface**
 
-The `dream.py` script, located in `scripts/dream.py`, provides an interactive
-interface to image generation similar to the "dream mothership" bot that Stable
+The `invoke.py` script, located in `scripts/`, provides an interactive
+interface to image generation similar to the "invoke mothership" bot that Stable
 AI provided on its Discord server.
 
 Unlike the `txt2img.py` and `img2img.py` scripts provided in the original
@@ -34,33 +34,33 @@ The script is confirmed to work on Linux, Windows and Mac systems.
     currently rudimentary, but a much better replacement is on its way.
 
 ```bash
-(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
+(invokeai) ~/stable-diffusion$ python3 ./scripts/invoke.py
 * Initializing, be patient...
 Loading model from models/ldm/text2img-large/model.ckpt
 (...more initialization messages...)
 
 * Initialization done! Awaiting your command...
-dream> ashley judd riding a camel -n2 -s150
+invoke> ashley judd riding a camel -n2 -s150
 Outputs:
    outputs/img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
    outputs/img-samples/00010.png: "ashley judd riding a camel" -n2 -s150 -S 1362479620
 
-dream> "there's a fly in my soup" -n6 -g
+invoke> "there's a fly in my soup" -n6 -g
     outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
     seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
-dream> q
+invoke> q
 
 # this shows how to retrieve the prompt stored in the saved image's metadata
-(ldm) ~/stable-diffusion$ python ./scripts/images2prompt.py outputs/img_samples/*.png
+(invokeai) ~/stable-diffusion$ python ./scripts/images2prompt.py outputs/img_samples/*.png
 00009.png: "ashley judd riding a camel" -s150 -S 416354203
 00010.png: "ashley judd riding a camel" -s150 -S 1362479620
 00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
 ```
 
-![dream-py-demo](../assets/dream-py-demo.png)
+![invoke-py-demo](../assets/dream-py-demo.png)
 
-The `dream>` prompt's arguments are pretty much identical to those used in the
-Discord bot, except you don't need to type "!dream" (it doesn't hurt if you do).
+The `invoke>` prompt's arguments are pretty much identical to those used in the
+Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you do).
 A significant change is that creation of individual images is now the default
 unless `--grid` (`-g`) is given. A full list is given in
 [List of prompt arguments](#list-of-prompt-arguments).
@@ -73,10 +73,9 @@ the location of the model weight files.
 
 ### List of arguments recognized at the command line
 
-These command-line arguments can be passed to `dream.py` when you first run it
+These command-line arguments can be passed to `invoke.py` when you first run it
 from the Windows, Mac or Linux command line. Some set defaults that can be
-overridden on a per-prompt basis (see [List of prompt arguments]
-(#list-of-prompt-arguments). Others
+overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt-arguments). Others
 
 | Argument <img width="240" align="right"/> | Shortcut <img width="100" align="right"/> | Default <img width="320" align="right"/>       | Description                                                                                          |
 | ----------------------------------------- | ----------------------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
@@ -86,6 +85,8 @@ overridden on a per-prompt basis (see [List of prompt arguments]
 | `--from_file <path>`                      |                                           | `None`                                         | Read list of prompts from a file. Use `-` to read from standard input                                |
 | `--model <modelname>`                     |                                           | `stable-diffusion-1.4`                         | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" |
 | `--full_precision`                        | `-F`                                      | `False`                                        | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards.   |
+| `--png_compression <0-9>`                 | `-z<0-9>`                                 |  6                                             | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
+| `--safety-checker`                        |                                           |  False                                         | Activate safety checker for NSFW and other potentially disturbing imagery                            |
 | `--web`                                   |                                           | `False`                                        | Start in web server mode                                                                             |
 | `--host <ip addr>`                        |                                           | `localhost`                                    | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any.                |
 | `--port <port>`                           |                                           | `9090`                                         | Which port web server should listen for requests on.                                                 |
@@ -97,46 +98,52 @@ overridden on a per-prompt basis (see [List of prompt arguments]
 | `--embedding_path <path>`                 |                                           | `None`                                         | Path to pre-trained embedding manager checkpoints, for custom models                                 |
 | `--gfpgan_dir`                            |                                           | `src/gfpgan`                                   | Path to where GFPGAN is installed.                                                                   |
 | `--gfpgan_model_path`                     |                                           | `experiments/pretrained_models/GFPGANv1.4.pth` | Path to GFPGAN model file, relative to `--gfpgan_dir`.                                               |
-| `--device <device>`                       | `-d<device>`                              | `torch.cuda.current_device()`                  | Device to run SD on, e.g. "cuda:0"                                                                   |
 | `--free_gpu_mem`                          |                                           | `False`                                        | Free GPU memory after sampling, to allow image decoding and saving in low VRAM conditions            |
 | `--precision`                             |                                           | `auto`                                         | Set model precision, default is selected by device. Options: auto, float32, float16, autocast        |
 
-#### deprecated
+!!! warning deprecated
 
-These arguments are deprecated but still work:
+    These arguments are deprecated but still work:
 
+    <div align="center" markdown>
 
-| Argument           |  Shortcut  |  Default            |  Description |
-|--------------------|------------|---------------------|--------------|
-| --weights <path>   |            | None                | Pth to weights file; use `--model stable-diffusion-1.4` instead |
-| --laion400m        | -l         | False               | Use older LAION400m weights; use `--model=laion400m` instead |
+    | Argument           |  Shortcut  |  Default            |  Description |
+    |--------------------|------------|---------------------|--------------|
+    | `--weights <path>`   |            | `None`                | Pth to weights file; use `--model stable-diffusion-1.4` instead |
+    | `--laion400m`        | `-l`         | `False`               | Use older LAION400m weights; use `--model=laion400m` instead |
 
-**A note on path names:** On Windows systems, you may run into
-  problems when passing the dream script standard backslashed path
-  names because the Python interpreter treats "\" as an escape.
-  You can either double your slashes (ick): C:\\\\path\\\\to\\\\my\\\\file, or
-  use Linux/Mac style forward slashes (better): C:/path/to/my/file.
+    </div>
+
+!!! tip
+
+      On Windows systems, you may run into
+      problems when passing the invoke script standard backslashed path
+      names because the Python interpreter treats "\" as an escape.
+      You can either double your slashes (ick): `C:\\path\\to\\my\\file`, or
+      use Linux/Mac style forward slashes (better): `C:/path/to/my/file`.
 
 ## List of prompt arguments
 
-After the dream.py script initializes, it will present you with a
-**dream>** prompt. Here you can enter information to generate images
-from text (txt2img), to embellish an existing image or sketch
-(img2img), or to selectively alter chosen regions of the image
-(inpainting).
+After the invoke.py script initializes, it will present you with a
+`invoke>` prompt. Here you can enter information to generate images
+from text ([txt2img](#txt2img)), to embellish an existing image or sketch
+([img2img](#img2img)), or to selectively alter chosen regions of the image
+([inpainting](#inpainting)).
 
-### This is an example of txt2img:
+### txt2img
 
-~~~~
-dream> waterfall and rainbow -W640 -H480
-~~~~
+!!! example
 
-This will create the requested image with the dimensions 640 (width)
-and 480 (height).
+    ```bash
+    invoke> waterfall and rainbow -W640 -H480
+    ```
 
-Here are the dream> command that apply to txt2img:
+    This will create the requested image with the dimensions 640 (width)
+    and 480 (height).
 
-| Argument           |  Shortcut  |  Default            |  Description |
+Here are the invoke> command that apply to txt2img:
+
+| Argument <img width="680" align="right"/> | Shortcut <img width="420" align="right"/> | Default <img width="480" align="right"/> | Description |
 |--------------------|------------|---------------------|--------------|
 | "my prompt"        |            |                    | Text prompt to use. The quotation marks are optional. |
 | --width <int>      | -W<int>   | 512                 | Width of generated image |
@@ -146,18 +153,24 @@ Here are the dream> command that apply to txt2img:
 | --cfg_scale <float>| -C<float> | 7.5                 | How hard to try to match the prompt to the generated image; any number greater than 1.0 works, but the useful range is roughly 5.0 to 20.0 |
 | --seed <int>       | -S<int>   | None                | Set the random seed for the next series of images. This can be used to recreate an image generated previously.|
 | --sampler <sampler>| -A<sampler>| k_lms              | Sampler to use. Use -h to get list of available samplers. |
+| --karras_max <int> |           | 29                  | When using k_* samplers, set the maximum number of steps before shifting from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts) This value is sticky. [29] |
 | --hires_fix        |           |                     | Larger images often have duplication artefacts. This option suppresses duplicates by generating the image at low res, and then using img2img to increase the resolution |
+| --png_compression <0-9> | -z<0-9> |  6           | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
 | --grid             | -g        | False               | Turn on grid mode to return a single image combining all the images generated by this prompt |
 | --individual       | -i        | True                | Turn off grid mode (deprecated; leave off --grid instead) |
 | --outdir <path>    |  -o<path> | outputs/img_samples  | Temporarily change the location of these images |
 | --seamless         |           | False               | Activate seamless tiling for interesting effects |
+| --seamless_axes    |           | x,y                 | Specify which axes to use circular convolution on. |
 | --log_tokenization | -t        | False               | Display a color-coded list of the parsed tokens derived from the prompt |
 | --skip_normalization| -x       | False               | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts) |
 | --upscale <int> <float> | -U <int> <float> | -U 1 0.75| Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75. |
-| --gfpgan_strength <float>  | -G <float> | -G0        | Fix faces using the GFPGAN algorithm; argument indicates how hard the algorithm should try (0.0-1.0) |
+| --facetool_strength <float>  | -G <float> | -G0        | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0) |
+| --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> | -V<pattern>| None      | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |
+| --with_variations <pattern> |    | None              | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |
+| --save_intermediates <n>    |    | None              | Save the image from every nth step into an "intermediates" folder inside the output directory |
 
 Note that the width and height of the image must be multiples of
 64. You can provide different values, but they will be rounded down to
@@ -167,7 +180,7 @@ the nearest multiple of 64.
 ### This is an example of img2img:	
 
 ~~~~
-dream> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit
+invoke> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit
 ~~~~
 
 This will modify the indicated vacation photograph by making it more
@@ -179,100 +192,281 @@ photo and you may run out of memory if it is large.
 In addition to the command-line options recognized by txt2img, img2img
 accepts additional options:
 
-| Argument           |  Shortcut  |  Default            |  Description |
-|--------------------|------------|---------------------|--------------|
-| --init_img <path>  | -I<path>   | None                | Path to the initialization image |
-| --fit              | -F         | False               | Scale the image to fit into the specified -H and -W dimensions |
-| --strength <float> | -s<float>  | 0.75                | How hard to try to match the prompt to the initial image. Ranges from 0.0-0.99, with higher values replacing the initial image completely.|
+| Argument <img width="160" align="right"/> |  Shortcut   |  Default        |  Description |
+|----------------------|-------------|-----------------|--------------|
+| `--init_img <path>`  | `-I<path>`  | `None`          | Path to the initialization image |
+| `--fit`              | `-F`        | `False`         | Scale the image to fit into the specified -H and -W dimensions |
+| `--strength <float>` | `-s<float>` | `0.75`          | How hard to try to match the prompt to the initial image. Ranges from 0.0-0.99, with higher values replacing the initial image completely.|
 
-### This is an example of inpainting:
+### inpainting
 
-~~~~
-dream> waterfall and rainbow -I./vacation-photo.png -M./vacation-mask.png -W640 -H480 --fit
-~~~~
+!!! example
 
-This will do the same thing as img2img, but image alterations will
-only occur within transparent areas defined by the mask file specified
-by -M. You may also supply just a single initial image with the areas
-to overpaint made transparent, but you must be careful not to destroy
-the pixels underneath when you create the transparent areas. See
-[Inpainting](./INPAINTING.md) for details.
+    ```bash
+    invoke> waterfall and rainbow -I./vacation-photo.png -M./vacation-mask.png -W640 -H480 --fit
+    ```
+
+    This will do the same thing as img2img, but image alterations will
+    only occur within transparent areas defined by the mask file specified
+    by `-M`. You may also supply just a single initial image with the areas
+    to overpaint made transparent, but you must be careful not to destroy
+    the pixels underneath when you create the transparent areas. See
+    [Inpainting](./INPAINTING.md) for details.
 
 inpainting accepts all the arguments used for txt2img and img2img, as
-well as the --mask (-M) argument:
+well as the --mask (-M) and --text_mask (-tm) arguments:
 
-| Argument           |  Shortcut  |  Default            |  Description |
+| Argument <img width="100" align="right"/> |  Shortcut  |  Default            |  Description |
 |--------------------|------------|---------------------|--------------|
-| --init_mask <path> | -M<path>   | None                |Path to an image the same size as the initial_image, with areas for inpainting made transparent.|
+| `--init_mask <path>` | `-M<path>`   | `None`                |Path to an image the same size as the initial_image, with areas for inpainting made transparent.|
+| `--invert_mask   ` |                | False                 |If true, invert the mask so that transparent areas are opaque and vice versa.|
+| `--text_mask <prompt> [<float>]` | `-tm <prompt> [<float>]` | <none>  | Create a mask from a text prompt describing part of the image|
 
+The mask may either be an image with transparent areas, in which case
+the inpainting will occur in the transparent areas only, or a black
+and white image, in which case all black areas will be painted into.
 
-# Convenience commands
+`--text_mask` (short form `-tm`) is a way to generate a mask using a
+text description of the part of the image to replace. For example, if
+you have an image of a breakfast plate with a bagel, toast and
+scrambled eggs, you can selectively mask the bagel and replace it with
+a piece of cake this way:
 
-In addition to the standard image generation arguments, there are a
-series of convenience commands that begin with !:
+~~~
+invoke> a piece of cake -I /path/to/breakfast.png -tm bagel
+~~~
 
-## !fix
+The algorithm uses <a
+href="https://github.com/timojl/clipseg">clipseg</a> to classify
+different regions of the image. The classifier puts out a confidence
+score for each region it identifies. Generally regions that score
+above 0.5 are reliable, but if you are getting too much or too little
+masking you can adjust the threshold down (to get more mask), or up
+(to get less). In this example, by passing `-tm` a higher value, we
+are insisting on a more stringent classification.
+
+~~~
+invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6
+~~~
+
+# Other Commands
+
+The CLI offers a number of commands that begin with "!".
+
+## Postprocessing images
+
+To postprocess a file using face restoration or upscaling, use the
+`!fix` command.
+
+### `!fix`
 
 This command runs a post-processor on a previously-generated image. It
-takes a PNG filename or path and applies your choice of the -U, -G, or
---embiggen switches in order to fix faces or upscale. If you provide a
+takes a PNG filename or path and applies your choice of the `-U`, `-G`, or
+`--embiggen` switches in order to fix faces or upscale. If you provide a
 filename, the script will look for it in the current output
 directory. Otherwise you can provide a full or partial path to the
 desired file.
 
 Some examples:
 
-Upscale to 4X its original size and fix faces using codeformer:
-~~~
-dream> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
-~~~
+!!! example ""
 
-Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen:
+    Upscale to 4X its original size and fix faces using codeformer:
 
-~~~
-dream> !fix 0000045.4829112.png -G0.8 -ft gfpgan
->> fixing outputs/img-samples/0000045.4829112.png
->> retrieved seed 4829112 and prompt "boy enjoying a banana split"
->> GFPGAN - Restoring Faces for image seed:4829112
-Outputs:
-[1] outputs/img-samples/000017.4829112.gfpgan-00.png: !fix "outputs/img-samples/0000045.4829112.png" -s 50 -S  -W 512 -H 512 -C 7.5 -A k_lms -G 0.8
+    ```bash
+    invoke> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
+    ```
 
-dream> !fix 000017.4829112.gfpgan-00.png --embiggen 3
-...lots of text...
-Outputs:
-[2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix "outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512 -H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25
-~~~
+!!! example ""
 
-## !fetch
+    Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen:
 
-This command retrieves the generation parameters from a previously
-generated image and either loads them into the command line.  You may
-provide either the name of a file in the current output directory, or
-a full file path.
+    ```bash
+    invoke> !fix 0000045.4829112.png -G0.8 -ft gfpgan
+    >> fixing outputs/img-samples/0000045.4829112.png
+    >> retrieved seed 4829112 and prompt "boy enjoying a banana split"
+    >> GFPGAN - Restoring Faces for image seed:4829112
+    Outputs:
+    [1] outputs/img-samples/000017.4829112.gfpgan-00.png: !fix "outputs/img-samples/0000045.4829112.png" -s 50 -S  -W 512 -H 512 -C 7.5 -A k_lms -G 0.8
 
-~~~
-dream> !fetch 0000015.8929913.png
-# the script returns the next line, ready for editing and running:
-dream> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
-~~~
+### !mask
 
-Note that this command may behave unexpectedly if given a PNG file that
-was not generated by InvokeAI.
+This command takes an image, a text prompt, and uses the `clipseg`
+algorithm to automatically generate a mask of the area that matches
+the text prompt. It is useful for debugging the text masking process
+prior to inpainting with the `--text_mask` argument. See
+[INPAINTING.md] for details.
 
-## !history
+## Model selection and importation
 
-The dream script keeps track of all the commands you issue during a
+The CLI allows you to add new models on the fly, as well as to switch
+among them rapidly without leaving the script.
+
+### !models
+
+This prints out a list of the models defined in `config/models.yaml'.
+The active model is bold-faced
+
+Example:
+<pre>
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
+waifu-diffusion           not loaded  Waifu Diffusion v1.3
+</pre>
+
+### !switch <model>
+
+This quickly switches from one model to another without leaving the 
+CLI script. `invoke.py` uses a memory caching system; once a model
+has been loaded, switching back and forth is quick. The following
+example shows this in action. Note how the second column of the 
+`!models` table changes to `cached` after a model is first loaded,
+and that the long initialization step is not needed when loading
+a cached model.
+
+<pre>
+invoke> !models
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          cached  Stable Diffusion v1.4</b>
+waifu-diffusion               active  Waifu Diffusion v1.3
+
+invoke> !switch waifu-diffusion
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+   | LatentDiffusion: Running in eps-prediction mode
+   | DiffusionWrapper has 859.52 M params.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Using faster float16 precision
+>> Model loaded in 18.24s
+>> Max VRAM used to load the model: 2.17G 
+>> Current VRAM usage:2.17G
+>> Setting Sampler to k_lms
+
+invoke> !models
+laion400m                 not loaded  <no description>
+stable-diffusion-1.4          cached  Stable Diffusion v1.4
+<b>waifu-diffusion               active  Waifu Diffusion v1.3</b>
+
+invoke> !switch stable-diffusion-1.4
+>> Caching model waifu-diffusion in system RAM
+>> Retrieving model stable-diffusion-1.4 from system RAM cache
+>> Setting Sampler to k_lms
+
+invoke> !models
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
+waifu-diffusion               cached  Waifu Diffusion v1.3
+</pre>
+
+### !import_model <path/to/model/weights>
+
+This command imports a new model weights file into InvokeAI, makes it
+available for image generation within the script, and writes out the
+configuration for the model into `config/models.yaml` for use in 
+subsequent sessions.
+
+Provide `!import_model` with the path to a weights file ending in
+`.ckpt`.  If you type a partial path and press tab, the CLI will
+autocomplete. Although it will also autocomplete to `.vae` files,
+these are not currenty supported (but will be soon).
+
+When you hit return, the CLI will prompt you to fill in additional
+information about the model, including the short name you wish to use
+for it with the `!switch` command, a brief description of the model,
+the default image width and height to use with this model, and the
+model's configuration file. The latter three fields are automatically
+filled with reasonable defaults. In the example below, the bold-faced
+text shows what the user typed in with the exception of the width,
+height and configuration file paths, which were filled in
+automatically.
+
+Example:
+
+<pre>
+invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b>
+>> Model import in process. Please enter the values needed to configure this model:
+
+Name for this model: <b>waifu-diffusion</b>
+Description of this model: <b>Waifu Diffusion v1.3</b>
+Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
+Default image width: <b>512</b>
+Default image height: <b>512</b>
+>> New configuration:
+waifu-diffusion:
+  config: configs/stable-diffusion/v1-inference.yaml
+  description: Waifu Diffusion v1.3
+  height: 512
+  weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+  width: 512
+OK to import [n]? <b>y</b>
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+   | LatentDiffusion: Running in eps-prediction mode
+   | DiffusionWrapper has 859.52 M params.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Using faster float16 precision
+invoke> 
+</pre>
+
+###!edit_model <name_of_model>
+
+The `!edit_model` command can be used to modify a model that is
+already defined in `config/models.yaml`. Call it with the short
+name of the model you wish to modify, and it will allow you to
+modify the model's `description`, `weights` and other fields.
+
+Example:
+<pre>
+invoke> <b>!edit_model waifu-diffusion</b>
+>> Editing model waifu-diffusion from configuration file ./configs/models.yaml
+description: <b>Waifu diffusion v1.4beta</b>
+weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b>
+config: configs/stable-diffusion/v1-inference.yaml
+width: 512
+height: 512
+
+>> New configuration:
+waifu-diffusion:
+  config: configs/stable-diffusion/v1-inference.yaml
+  description: Waifu diffusion v1.4beta
+  weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
+  height: 512
+  width: 512
+
+OK to import [n]? y
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
+...
+</pre>
+=======
+    invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3
+    ...lots of text...
+    Outputs:
+    [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix "outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512 -H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25
+    ```
+## History processing
+
+The CLI provides a series of convenient commands for reviewing previous
+actions, retrieving them, modifying them, and re-running them.
+
+### !history
+
+The invoke script keeps track of all the commands you issue during a
 session, allowing you to re-run them. On Mac and Linux systems, it
 also writes the command-line history out to disk, giving you access to
 the most recent 1000 commands issued.
 
 The `!history` command will return a numbered list of all the commands
 issued during the session (Windows), or the most recent 1000 commands
-(Mac|Linux). You can then repeat a command by using the command !NNN,
+(Mac|Linux). You can then repeat a command by using the command `!NNN`,
 where "NNN" is the history line number. For example:
 
-~~~
-dream> !history
+```bash
+invoke> !history
 ...
 [14] happy woman sitting under tree wearing broad hat and flowing garment
 [15] beautiful woman sitting under tree wearing broad hat and flowing garment
@@ -280,61 +474,98 @@ dream> !history
 [20] watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
 [21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
 ...
-dream> !20
-dream> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+invoke> !20
+invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+```
+
+### !fetch
+
+This command retrieves the generation parameters from a previously
+generated image and either loads them into the command line
+(Linux|Mac), or prints them out in a comment for copy-and-paste
+(Windows). You may provide either the name of a file in the current
+output directory, or a full file path.  Specify path to a folder with
+image png files, and wildcard *.png to retrieve the dream command used
+to generate the images, and save them to a file commands.txt for
+further processing.
+
+This example loads the generation command for a single png file:
+
+```bash
+invoke> !fetch 0000015.8929913.png
+# the script returns the next line, ready for editing and running:
+invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
+```
+
+This one fetches the generation commands from a batch of files and
+stores them into `selected.txt`:
+
+```bash
+invoke> !fetch outputs\selected-imgs\*.png selected.txt
+```
+
+### !replay
+
+This command replays a text file generated by !fetch or created manually
+
+~~~
+invoke> !replay outputs\selected-imgs\selected.txt
 ~~~
 
-## !search <search string>
+Note that these commands may behave unexpectedly if given a PNG file that
+was not generated by InvokeAI.
+
+### !search <search string>
 
 This is similar to !history but it only returns lines that contain
 `search string`. For example:
 
-~~~
-dream> !search surreal
+```bash
+invoke> !search surreal
 [21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
-~~~
+```
 
-## !clear
+### `!clear`
 
 This clears the search history from memory and disk. Be advised that
 this operation is irreversible and does not issue any warnings!
 
-# Command-line editing and completion
+## Command-line editing and completion
 
 The command-line offers convenient history tracking, editing, and
 command completion.
 
-- To scroll through previous commands and potentially edit/reuse them, use the up and down cursor keys.
-- To edit the current command, use the left and right cursor keys to position the cursor, and then backspace, delete or insert characters.
-- To move to the very beginning of the command, type CTRL-A (or command-A on the Mac)
-- To move to the end of the command, type CTRL-E.
-- To cut a section of the command, position the cursor where you want to start cutting and type CTRL-K.
-- To paste a cut section back in, position the cursor where you want to paste, and type CTRL-Y
+- To scroll through previous commands and potentially edit/reuse them, use the ++up++ and ++down++ keys.
+- To edit the current command, use the ++left++ and ++right++ keys to position the cursor, and then ++backspace++, ++delete++ or insert characters.
+- To move to the very beginning of the command, type ++ctrl+a++ (or ++command+a++ on the Mac)
+- To move to the end of the command, type ++ctrl+e++.
+- To cut a section of the command, position the cursor where you want to start cutting and type ++ctrl+k++
+- To paste a cut section back in, position the cursor where you want to paste, and type ++ctrl+y++
 
 Windows users can get similar, but more limited, functionality if they
-launch dream.py with the "winpty" program and have the `pyreadline3`
+launch `invoke.py` with the `winpty` program and have the `pyreadline3`
 library installed:
 
-~~~
-> winpty python scripts\dream.py
-~~~
+```batch
+> winpty python scripts\invoke.py
+```
 
-On the Mac and Linux platforms, when you exit dream.py, the last 1000
+On the Mac and Linux platforms, when you exit invoke.py, the last 1000
 lines of your command-line history will be saved. When you restart
-dream.py, you can access the saved history using the up-arrow key.
+`invoke.py`, you can access the saved history using the ++up++ key.
 
 In addition, limited command-line completion is installed. In various
-contexts, you can start typing your command and press tab. A list of
+contexts, you can start typing your command and press ++tab++. A list of
 potential completions will be presented to you. You can then type a
-little more, hit tab again, and eventually autocomplete what you want.
+little more, hit ++tab++ again, and eventually autocomplete what you want.
 
 When specifying file paths using the one-letter shortcuts, the CLI
 will attempt to complete pathnames for you. This is most handy for the
--I (init image) and -M (init mask) paths. To initiate completion, start
-the path with a slash ("/") or "./". For example:
+`-I` (init image) and `-M` (init mask) paths. To initiate completion, start
+the path with a slash (`/`) or `./`. For example:
 
-~~~
-dream> zebra with a mustache -I./test-pictures<TAB>
+```bash
+invoke> zebra with a mustache -I./test-pictures<TAB>
 -I./test-pictures/Lincoln-and-Parrot.png  -I./test-pictures/zebra.jpg        -I./test-pictures/madonna.png
 -I./test-pictures/bad-sketch.png          -I./test-pictures/man_with_eagle/
 ```

docs/features/EMBIGGEN.md

@@ -43,7 +43,7 @@ it's similar to that, except it can work up to an arbitrarily large size
 has extra logic to re-run any number of the tile sub-sections of the image
 if for example a small part of a huge run got messed up.
 
-## Usage
+### Usage
 
 `-embiggen <scaling_factor> <esrgan_strength> <overlap_ratio OR overlap_pixels>`
 
@@ -100,26 +100,30 @@ Tiles are numbered starting with one, and left-to-right,
 top-to-bottom.  So, if you are generating a 3x3 tiled image, the
 middle row would be `4 5 6`.
 
-## Example Usage
+### Examples
 
-Running Embiggen with 512x512 tiles on an existing image, scaling up by a factor of 2.5x;
-and doing the same again (default ESRGAN strength is 0.75, default overlap between tiles is 0.25):
+!!! example ""
 
-```bash
-dream > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5
-dream > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5 0.75 0.25
-```
+    Running Embiggen with 512x512 tiles on an existing image, scaling up by a factor of 2.5x;
+    and doing the same again (default ESRGAN strength is 0.75, default overlap between tiles is 0.25):
 
-If your starting image was also 512x512 this should have taken 9 tiles.
+    ```bash
+    invoke > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5
+    invoke > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5 0.75 0.25
+    ```
 
-If there weren't enough clouds in the sky of that forest you just made
-(and that image is about 1280 pixels (512*2.5) wide A.K.A. three
-512x512 tiles with 0.25 overlaps wide) we can replace that top row of
-tiles:
+    If your starting image was also 512x512 this should have taken 9 tiles.
 
-```bash
-dream> a photo of puffy clouds over a forest at sunset -s 100 -W 512 -H 512 -I outputs/000002.seed.png -f 0.5 -embiggen_tiles 1 2 3
-```
+!!! example ""
+
+    If there weren't enough clouds in the sky of that forest you just made
+    (and that image is about 1280 pixels (512*2.5) wide A.K.A. three
+    512x512 tiles with 0.25 overlaps wide) we can replace that top row of
+    tiles:
+
+    ```bash
+    invoke> a photo of puffy clouds over a forest at sunset -s 100 -W 512 -H 512 -I outputs/000002.seed.png -f 0.5 -embiggen_tiles 1 2 3
+    ```
 
 ## Fixing Previously-Generated Images
 
@@ -128,27 +132,27 @@ look up the original prompt and provide an initial image. Just use the
 syntax `!fix path/to/file.png <embiggen>`. For example, you can rewrite the
 previous command to look like this:
 
-~~~~
-dream> !fix ./outputs/000002.seed.png -embiggen_tiles 1 2 3
-~~~~
+```bash
+invoke> !fix ./outputs/000002.seed.png -embiggen_tiles 1 2 3
+```
 
 A new file named `000002.seed.fixed.png` will be created in the output directory. Note that
 the `!fix` command does not replace the original file, unlike the behavior at generate time.
 You do not need to provide the prompt, and `!fix` automatically selects a good strength for
 embiggen-ing.
 
+!!! note
 
-**Note**
-Because the same prompt is used on all the tiled images, and the model
-doesn't have the context of anything outside the tile being run - it
-can end up creating repeated pattern (also called 'motifs') across all
-the tiles based on that prompt. The best way to combat this is
-lowering the `--strength` (`-f`) to stay more true to the init image,
-and increasing the number of steps so there is more compute-time to
-create the detail.  Anecdotally `--strength` 0.35-0.45 works pretty
-well on most things. It may also work great in some examples even with
-the `--strength` set high for patterns, landscapes, or subjects that
-are more abstract. Because this is (relatively) fast, you can also
-preserve the best parts from each.
+    Because the same prompt is used on all the tiled images, and the model
+    doesn't have the context of anything outside the tile being run - it
+    can end up creating repeated pattern (also called 'motifs') across all
+    the tiles based on that prompt. The best way to combat this is
+    lowering the `--strength` (`-f`) to stay more true to the init image,
+    and increasing the number of steps so there is more compute-time to
+    create the detail.  Anecdotally `--strength` 0.35-0.45 works pretty
+    well on most things. It may also work great in some examples even with
+    the `--strength` set high for patterns, landscapes, or subjects that
+    are more abstract. Because this is (relatively) fast, you can also
+    preserve the best parts from each.
 
 Author: [Travco](https://github.com/travco)

docs/features/IMG2IMG.md

@@ -2,7 +2,9 @@
 title: Image-to-Image
 ---
 
-# :material-image-multiple: **IMG2IMG**
+# :material-image-multiple: Image-to-Image
+
+## `img2img`
 
 This script also provides an `img2img` feature that lets you seed your creations with an initial
 drawing or photo. This is a really cool feature that tells stable diffusion to build the prompt on
@@ -10,77 +12,120 @@ top of the image you provide, preserving the original's basic shape and layout.
 the `--init_img` option as shown here:
 
 ```commandline
-dream> "waterfall and rainbow" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
+tree on a hill with a river, nature photograph, national geographic -I./test-pictures/tree-and-river-sketch.png -f 0.85
 ```
 
-The `--init_img (-I)` option gives the path to the seed picture. `--strength (-f)` controls how much
+This will take the original image shown here:
+
+<div align="center" markdown>
+<img src="https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png" width=350>
+</div>
+
+and generate a new image based on it as shown here:
+
+<div align="center" markdown>
+<img src="https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png" width=350>
+</div>
+
+The `--init_img` (`-I`) option gives the path to the seed picture. `--strength` (`-f`) controls how much
 the original will be modified, ranging from `0.0` (keep the original intact), to `1.0` (ignore the
-original completely). The default is `0.75`, and ranges from `0.25-0.75` give interesting results.
+original completely). The default is `0.75`, and ranges from `0.25-0.90` give interesting results. 
+Other relevant options include `-C` (classification free guidance scale), and `-s` (steps). Unlike `txt2img`, 
+adding steps will continuously change the resulting image and it will not converge.
 
 You may also pass a `-v<variation_amount>` option to generate `-n<iterations>` count variants on
 the original image. This is done by passing the first generated image
 back into img2img the requested number of times. It generates
 interesting variants.
 
+Note that the prompt makes a big difference. For example, this slight variation on the prompt produces
+a very different image:
+
+`photograph of a tree on a hill with a river`
+
+<div align="center" markdown>
+<img src="https://user-images.githubusercontent.com/111189/194135220-16b62181-b60c-4248-8989-4834a8fd7fbd.png" width=350>
+</div>
+
+!!! tip
+
+    When designing prompts, think about how the images scraped from the internet were captioned. Very few photographs will
+    be labeled "photograph" or "photorealistic." They will, however, be captioned with the publication, photographer, camera
+    model, or film settings.
+
 If the initial image contains transparent regions, then Stable Diffusion will only draw within the
-transparent regions, a process called "inpainting". However, for this to work correctly, the color
+transparent regions, a process called [`inpainting`](./INPAINTING.md#creating-transparent-regions-for-inpainting). However, for this to work correctly, the color
 information underneath the transparent needs to be preserved, not erased.
 
-More details can be found here:
-[Creating Transparent Images For Inpainting](./INPAINTING.md#creating-transparent-regions-for-inpainting)
+!!! warning
+
+**IMPORTANT ISSUE** `img2img` does not work properly on initial images smaller than 512x512. Please scale your
+image to at least 512x512 before using it. Larger images are not a problem, but may run out of VRAM on your
+GPU card. To fix this, use the --fit option, which downscales the initial image to fit within the box specified
+by width x height:
+~~~
+tree on a hill with a river, national geographic -I./test-pictures/big-sketch.png -H512 -W512 --fit
+~~~
 
 ## How does it actually work, though?
 
-The main difference between `img2img` and `prompt2img` is the starting point. While `prompt2img` always starts with pure 
-gaussian noise and progressively refines it over the requested number of steps, `img2img` skips some of these earlier steps 
-(how many it skips is indirectly controlled by the `--strength` parameter), and uses instead your initial image mixed with gaussian noise as the starting image. 
+The main difference between `img2img` and `prompt2img` is the starting point. While `prompt2img` always starts with pure
+gaussian noise and progressively refines it over the requested number of steps, `img2img` skips some of these earlier steps
+(how many it skips is indirectly controlled by the `--strength` parameter), and uses instead your initial image mixed with gaussian noise as the starting image.
 
 **Let's start** by thinking about vanilla `prompt2img`, just generating an image from a prompt. If the step count is 10, then the "latent space" (Stable Diffusion's internal representation of the image) for the prompt "fire" with seed `1592514025` develops something like this:
 
 ```commandline
-dream> "fire" -s10 -W384 -H384 -S1592514025
+invoke> "fire" -s10 -W384 -H384 -S1592514025
 ```
 
+<div align="center" markdown>
 ![latent steps](../assets/img2img/000019.steps.png)
+</div>
 
-Put simply: starting from a frame of fuzz/static, SD finds details in each frame that it thinks look like "fire" and brings them a little bit more into focus, gradually scrubbing out the fuzz until a clear image remains. 
+Put simply: starting from a frame of fuzz/static, SD finds details in each frame that it thinks look like "fire" and brings them a little bit more into focus, gradually scrubbing out the fuzz until a clear image remains.
 
-**When you use `img2img`** some of the earlier steps are cut, and instead an initial image of your choice is used. But because of how the maths behind Stable Diffusion works, this image needs to be mixed with just the right amount of noise (fuzz/static) for where it is being inserted. This is where the strength parameter comes in. Depending on the set strength, your image will be inserted into the sequence at the appropriate point, with just the right amount of noise. 
+**When you use `img2img`** some of the earlier steps are cut, and instead an initial image of your choice is used. But because of how the maths behind Stable Diffusion works, this image needs to be mixed with just the right amount of noise (fuzz/static) for where it is being inserted. This is where the strength parameter comes in. Depending on the set strength, your image will be inserted into the sequence at the appropriate point, with just the right amount of noise.
 
 ### A concrete example
 
-Say I want SD to draw a fire based on this hand-drawn image:
+I want SD to draw a fire based on this hand-drawn image:
 
+<div align="center" markdown>
 ![drawing of a fireplace](../assets/img2img/fire-drawing.png)
+</div>
 
 Let's only do 10 steps, to make it easier to see what's happening. If strength is `0.7`, this is what the internal steps the algorithm has to take will look like:
 
-![](../assets/img2img/000032.steps.gravity.png)
+<div align="center" markdown>
+![gravity32](../assets/img2img/000032.steps.gravity.png)
+</div>
 
 With strength `0.4`, the steps look more like this:
 
-![](../assets/img2img/000030.steps.gravity.png)
+<div align="center" markdown>
+![gravity30](../assets/img2img/000030.steps.gravity.png)
+</div>
 
 Notice how much more fuzzy the starting image is for strength `0.7` compared to `0.4`, and notice also how much longer the sequence is with `0.7`:
 
 |  | strength = 0.7 | strength = 0.4 |
 | -- | -- | -- |
 | initial image that SD sees | ![](../assets/img2img/000032.step-0.png) | ![](../assets/img2img/000030.step-0.png) |
-| steps argument to `dream>` | `-S10` | `-S10` |
+| steps argument to `invoke>` | `-S10` | `-S10` |
 | steps actually taken | 7 | 4 |
-| latent space at each step | ![](../assets/img2img/000032.steps.gravity.png) | ![](../assets/img2img/000030.steps.gravity.png) |
-| output | ![](../assets/img2img/000032.1592514025.png) | ![](../assets/img2img/000030.1592514025.png) |
+| latent space at each step | ![gravity32](../assets/img2img/000032.steps.gravity.png) | ![gravity30](../assets/img2img/000030.steps.gravity.png) |
+| output | ![000032.1592514025](../assets/img2img/000032.1592514025.png) | ![000030.1592514025](../assets/img2img/000030.1592514025.png) |
 
 Both of the outputs look kind of like what I was thinking of. With the strength higher, my input becomes more vague, *and* Stable Diffusion has more steps to refine its output. But it's not really making what I want, which is a picture of cheery open fire. With the strength lower, my input is more clear, *but* Stable Diffusion has less chance to refine itself, so the result ends up inheriting all the problems of my bad drawing.
 
-
-If you want to try this out yourself, all of these are using a seed of `1592514025` with a width/height of `384`, step count `10`, the default sampler (`k_lms`), and the single-word prompt `fire`:
+If you want to try this out yourself, all of these are using a seed of `1592514025` with a width/height of `384`, step count `10`, the default sampler (`k_lms`), and the single-word prompt `"fire"`:
 
 ```commandline
-dream> "fire" -s10 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png --strength 0.7
+invoke> "fire" -s10 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png --strength 0.7
 ```
 
-The code for rendering intermediates is on my (damian0815's) branch [document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) - run `dream.py` and check your `outputs/img-samples/intermediates` folder while generating an image. 
+The code for rendering intermediates is on my (damian0815's) branch [document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) - run `invoke.py` and check your `outputs/img-samples/intermediates` folder while generating an image. 
 
 ### Compensating for the reduced step count
 
@@ -89,36 +134,42 @@ After putting this guide together I was curious to see how the difference would
 Here's strength `0.4` (note step count `50`, which is `20 ÷ 0.4` to make sure SD does `20` steps from my image):
 
 ```commandline
-dream> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
+invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
 ```
 
-![](../assets/img2img/000035.1592514025.png)
+<div align="center" markdown>
+![000035.1592514025](../assets/img2img/000035.1592514025.png)
+</div>
 
-and strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to make sure SD does `20` steps from my image):
+and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to make sure SD does `20` steps from my image):
 
 ```commandline
-dream> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
+invoke> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
 ```
 
-![](../assets/img2img/000046.1592514025.png)
+<div align="center" markdown>
+![000046.1592514025](../assets/img2img/000046.1592514025.png)
+</div>
 
 In both cases the image is nice and clean and "finished", but because at strength `0.7` Stable Diffusion has been give so much more freedom to improve on my badly-drawn flames, they've come out looking much better. You can really see the difference when looking at the latent steps. There's more noise on the first image with strength `0.7`:
 
-![](../assets/img2img/000046.steps.gravity.png)
+![gravity46](../assets/img2img/000046.steps.gravity.png)
 
 than there is for strength `0.4`:
 
-![](../assets/img2img/000035.steps.gravity.png)
+![gravity35](../assets/img2img/000035.steps.gravity.png)
 
-and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image. 
+and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image.
 
 Unfortunately, it seems that `img2img` is very sensitive to the step count. Here's strength `0.7` with a step count of `29` (SD did 19 steps from my image):
 
-![](../assets/img2img/000045.1592514025.png)
+<div align="center" markdown>
+![gravity45](../assets/img2img/000045.1592514025.png)
+</div>
 
 By comparing the latents we can sort of see that something got interpreted differently enough on the third or fourth step to lead to a rather different interpretation of the flames.
 
-![](../assets/img2img/000046.steps.gravity.png)
-![](../assets/img2img/000045.steps.gravity.png)
+![gravity46](../assets/img2img/000046.steps.gravity.png)
+![gravity45](../assets/img2img/000045.steps.gravity.png)
 
-This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see https://huggingface.co/blog/stable_diffusion for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step. 
+This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see [stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step.

docs/features/INPAINTING.md

@@ -6,27 +6,234 @@ title: Inpainting
 
 ## **Creating Transparent Regions for Inpainting**
 
-Inpainting is really cool. To do it, you start with an initial image and use a photoeditor to make
-one or more regions transparent (i.e. they have a "hole" in them). You then provide the path to this
-image at the dream> command line using the `-I` switch. Stable Diffusion will only paint within the
-transparent region.
+Inpainting is really cool. To do it, you start with an initial image
+and use a photoeditor to make one or more regions transparent
+(i.e. they have a "hole" in them). You then provide the path to this
+image at the dream> command line using the `-I` switch. Stable
+Diffusion will only paint within the transparent region.
 
-There's a catch. In the current implementation, you have to prepare the initial image correctly so
-that the underlying colors are preserved under the transparent area. Many imaging editing
-applications will by default erase the color information under the transparent pixels and replace
-them with white or black, which will lead to suboptimal inpainting. You also must take care to
-export the PNG file in such a way that the color information is preserved.
+There's a catch. In the current implementation, you have to prepare
+the initial image correctly so that the underlying colors are
+preserved under the transparent area. Many imaging editing
+applications will by default erase the color information under the
+transparent pixels and replace them with white or black, which will
+lead to suboptimal inpainting. It often helps to apply incomplete
+transparency, such as any value between 1 and 99%
 
-If your photoeditor is erasing the underlying color information, `dream.py` will give you a big fat
-warning. If you can't find a way to coax your photoeditor to retain color values under transparent
-areas, then you can combine the `-I` and `-M` switches to provide both the original unedited image
-and the masked (partially transparent) image:
+You also must take care to export the PNG file in such a way that the
+color information is preserved. There is often an option in the export
+dialog that lets you specify this.
+
+If your photoeditor is erasing the underlying color information,
+`dream.py` will give you a big fat warning. If you can't find a way to
+coax your photoeditor to retain color values under transparent areas,
+then you can combine the `-I` and `-M` switches to provide both the
+original unedited image and the masked (partially transparent) image:
 
 ```bash
-dream> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent.png
+invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent.png
 ```
 
-We are hoping to get rid of the need for this workaround in an upcoming release.
+## **Masking using Text**
+
+You can also create a mask using a text prompt to select the part of
+the image you want to alter, using the <a
+href="https://github.com/timojl/clipseg">clipseg</a> algorithm. This
+works on any image, not just ones generated by InvokeAI.
+
+The `--text_mask` (short form `-tm`) option takes two arguments. The
+first argument is a text description of the part of the image you wish
+to mask (paint over). If the text description contains a space, you must
+surround it with quotation marks. The optional second argument is the
+minimum threshold for the mask classifier's confidence score, described
+in more detail below.
+
+To see how this works in practice, here's an image of a still life
+painting that I got off the web.
+
+<img src="../assets/still-life-scaled.jpg">
+
+You can selectively mask out the
+orange and replace it with a baseball in this way:
+
+~~~
+invoke> a baseball -I /path/to/still_life.png -tm orange
+~~~
+
+<img src="../assets/still-life-inpainted.png">
+
+The clipseg classifier produces a confidence score for each region it
+identifies. Generally regions that score above 0.5 are reliable, but
+if you are getting too much or too little masking you can adjust the
+threshold down (to get more mask), or up (to get less). In this
+example, by passing `-tm` a higher value, we are insisting on a tigher
+mask. However, if you make it too high, the orange may not be picked
+up at all!
+
+~~~
+invoke> a baseball -I /path/to/breakfast.png -tm orange 0.6
+~~~
+
+The `!mask` command may be useful for debugging problems with the
+text2mask feature. The syntax is `!mask /path/to/image.png -tm <text>
+<threshold>`
+
+It will generate three files:
+
+- The image with the selected area highlighted.
+  - it will be named XXXXX.<imagename>.<prompt>.selected.png
+- The image with the un-selected area highlighted.
+  - it will be named XXXXX.<imagename>.<prompt>.deselected.png
+- The image with the selected area converted into a black and white
+  image according to the threshold level
+  - it will be named XXXXX.<imagename>.<prompt>.masked.png
+
+The `.masked.png` file can then be directly passed to the `invoke>`
+prompt in the CLI via the `-M` argument. Do not attempt this with
+the `selected.png` or `deselected.png` files, as they contain some
+transparency throughout the image and will not produce the desired
+results.
+
+Here is an example of how `!mask` works:
+
+```
+invoke> !mask ./test-pictures/curly.png -tm hair 0.5
+>> generating masks from ./test-pictures/curly.png
+>> Initializing clipseg model for text to mask inference
+Outputs:
+[941.1] outputs/img-samples/000019.curly.hair.deselected.png: !mask ./test-pictures/curly.png -tm hair 0.5
+[941.2] outputs/img-samples/000019.curly.hair.selected.png: !mask ./test-pictures/curly.png -tm hair 0.5
+[941.3] outputs/img-samples/000019.curly.hair.masked.png: !mask ./test-pictures/curly.png -tm hair 0.5
+```
+
+**Original image "curly.png"**
+<img src="../assets/outpainting/curly.png">
+
+**000019.curly.hair.selected.png**
+<img src="../assets/inpainting/000019.curly.hair.selected.png">
+
+**000019.curly.hair.deselected.png**
+<img src="../assets/inpainting/000019.curly.hair.deselected.png">
+
+**000019.curly.hair.masked.png**
+<img src="../assets/inpainting/000019.curly.hair.masked.png">
+
+It looks like we selected the hair pretty well at the 0.5 threshold
+(which is the default, so we didn't actually have to specify it), so
+let's have some fun:
+
+```
+invoke> medusa with cobras -I ./test-pictures/curly.png -M 000019.curly.hair.masked.png -C20
+>> loaded input image of size 512x512 from ./test-pictures/curly.png
+...
+Outputs:
+[946] outputs/img-samples/000024.801380492.png: "medusa with cobras" -s 50 -S 801380492 -W 512 -H 512 -C 20.0 -I ./test-pictures/curly.png -A k_lms -f 0.75
+```
+
+<img src="../assets/inpainting/000024.801380492.png">
+
+You can also skip the `!mask` creation step and just select the masked
+
+region directly:
+```
+invoke> medusa with cobras -I ./test-pictures/curly.png -tm hair -C20
+```
+
+## Using the RunwayML inpainting model
+
+The [RunwayML Inpainting Model
+v1.5](https://huggingface.co/runwayml/stable-diffusion-inpainting) is
+a specialized version of [Stable Diffusion
+v1.5](https://huggingface.co/spaces/runwayml/stable-diffusion-v1-5)
+that contains extra channels specifically designed to enhance
+inpainting and outpainting. While it can do regular `txt2img` and
+`img2img`, it really shines when filling in missing regions. It has an
+almost uncanny ability to blend the new regions with existing ones in
+a semantically coherent way.
+
+To install the inpainting model, follow the
+[instructions](INSTALLING-MODELS.md) for installing a new model. You
+may use either the CLI (`invoke.py` script) or directly edit the
+`configs/models.yaml` configuration file to do this. The main thing to
+watch out for is that the the model `config` option must be set up to
+use `v1-inpainting-inference.yaml` rather than the `v1-inference.yaml`
+file that is used by Stable Diffusion 1.4 and 1.5.
+
+After installation, your `models.yaml` should contain an entry that
+looks like this one:
+
+ inpainting-1.5:
+   weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+   description: SD inpainting v1.5
+   config: configs/stable-diffusion/v1-inpainting-inference.yaml
+   vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+   width: 512
+   height: 512
+
+As shown in the example, you may include a VAE fine-tuning weights
+file as well. This is strongly recommended.
+
+To use the custom inpainting model, launch `invoke.py` with the
+argument `--model inpainting-1.5` or alternatively from within the
+script use the `!switch inpainting-1.5` command to load and switch to
+the inpainting model.
+
+You can now do inpainting and outpainting exactly as described above,
+but there will (likely) be a noticeable improvement in
+coherence. Txt2img and Img2img will work as well.
+
+There are a few caveats to be aware of:
+
+1. The inpainting model is larger than the standard model, and will
+   use nearly 4 GB of GPU VRAM. This makes it unlikely to run on
+   a 4 GB graphics card.
+
+2. When operating in Img2img mode, the inpainting model is much less
+   steerable than the standard model. It is great for making small
+   changes, such as changing the pattern of a fabric, or slightly
+   changing a subject's expression or hair, but the model will
+   resist making the dramatic alterations that the standard
+   model lets you do.
+
+3. While the `--hires` option works fine with the inpainting model,
+   some special features, such as `--embiggen` are disabled.
+
+4. Prompt weighting (`banana++ sushi`) and merging work well with
+   the inpainting model, but prompt swapping (a ("fluffy cat").swap("smiling dog") eating a hotdog`)
+   will not have any effect due to the way the model is set up.
+   You may use text masking (with `-tm thing-to-mask`) as an
+   effective replacement.
+
+5. The model tends to oversharpen image if you use high step or CFG
+   values. If you need to do large steps, use the standard model.
+
+6. The `--strength` (`-f`) option has no effect on the inpainting
+   model due to its fundamental differences with the standard
+   model. It will always take the full number of steps you specify.
+
+## Troubleshooting
+
+Here are some troubleshooting tips for inpainting and outpainting.
+
+## Inpainting is not changing the masked region enough!
+
+One of the things to understand about how inpainting works is that it
+is equivalent to running img2img on just the masked (transparent)
+area. img2img builds on top of the existing image data, and therefore
+will attempt to preserve colors, shapes and textures to the best of
+its ability. Unfortunately this means that if you want to make a
+dramatic change in the inpainted region, for example replacing a red
+wall with a blue one, the algorithm will fight you.
+
+You have a couple of options. The first is to increase the values of
+the requested steps (`-sXXX`), strength (`-f0.XX`), and/or
+condition-free guidance (`-CXX.X`). If this is not working for you, a
+more extreme step is to provide the `--inpaint_replace 0.X` (`-r0.X`)
+option. This value ranges from 0.0 to 1.0. The higher it is the less
+attention the algorithm will pay to the data underneath the masked
+region. At high values this will enable you to replace colored regions
+entirely, but beware that the masked region mayl not blend in with the
+surrounding unmasked regions as well.
 
 ---
 
@@ -36,41 +243,42 @@ We are hoping to get rid of the need for this workaround in an upcoming release.
 
 1. Open image in GIMP.
 2. Layer->Transparency->Add Alpha Channel
-3. Use lasoo tool to select region to mask
+3. Use lasso tool to select region to mask
 4. Choose Select -> Float to create a floating selection
-5. Open the Layers toolbar (++ctrl+l++) and select "Floating Selection"
-6. Set opacity to 0%
+5. Open the Layers toolbar (^L) and select "Floating Selection"
+6. Set opacity to a value between 0% and 99%
 7. Export as PNG
 8. In the export dialogue, Make sure the "Save colour values from
    transparent pixels" checkbox is selected.
 
+---
 
 ## Recipe for Adobe Photoshop
 
 1. Open image in Photoshop
 
-![step1](../assets/step1.png)
+    <div align="center" markdown>![step1](../assets/step1.png)</div>
 
 2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area you desire to inpaint.
 
-![step2](../assets/step2.png)
+    <div align="center" markdown>![step2](../assets/step2.png)</div>
 
 3. Because we'll be applying a mask over the area we want to preserve, you should now select the inverse by using the ++shift+ctrl+i++ shortcut, or right clicking and using the "Select Inverse" option.
 
-4. You'll now create a mask by selecting the image layer, and Masking the selection. Make sure that you don't delete any of the undrlying image, or your inpainting results will be dramatically impacted.
+4. You'll now create a mask by selecting the image layer, and Masking the selection. Make sure that you don't delete any of the underlying image, or your inpainting results will be dramatically impacted.
 
-![step4](../assets/step4.png)
+    <div align="center" markdown>![step4](../assets/step4.png)</div>
 
 5. Make sure to hide any background layers that are present. You should see the mask applied to your image layer, and the image on your canvas should display the checkered background.
 
-![step5](../assets/step5.png)
+    <div align="center" markdown>![step5](../assets/step5.png)</div>
 
-6. Save the image as a transparent PNG by using the "Save a Copy" option in the File menu, or using the Alt + Ctrl + S keyboard shortcut
+6. Save the image as a transparent PNG by using `File`-->`Save a Copy` from the menu bar, or by using the keyboard shortcut ++alt+ctrl+s++
 
-![step6](../assets/step6.png)
+    <div align="center" markdown>![step6](../assets/step6.png)</div>
 
-7. After following the inpainting instructions above (either through the CLI or the Web UI), marvel at your newfound ability to selectively dream. Lookin' good!
+7. After following the inpainting instructions above (either through the CLI or the Web UI), marvel at your newfound ability to selectively invoke. Lookin' good!
 
-![step7](../assets/step7.png)
+    <div align="center" markdown>![step7](../assets/step7.png)</div>
 
 8. In the export dialogue, Make sure the "Save colour values from transparent pixels" checkbox is selected.  

docs/features/OTHER.md

@@ -6,15 +6,13 @@ title: Others
 
 ## **Google Colab**
 
-Stable Diffusion AI Notebook: <a
-href="https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb"
-target="_parent">
-<img
-src="https://colab.research.google.com/assets/colab-badge.svg"
-alt="Open In Colab"/></a> <br> Open and follow instructions to use an isolated environment running
-Dream.<br>
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg){ align="right" }](https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
 
-Output Example: ![Colab Notebook](../assets/colab_notebook.png)
+Open and follow instructions to use an isolated environment running Dream.
+
+Output Example:
+
+![Colab Notebook](../assets/colab_notebook.png)
 
 ---
 
@@ -22,10 +20,16 @@ Output Example: ![Colab Notebook](../assets/colab_notebook.png)
 
 The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the
 `--seamless` option when starting the script which will result in all generated images to tile, or
-for each `dream>` prompt as shown here:
+for each `invoke>` prompt as shown here:
 
 ```python
-dream> "pond garden with lotus by claude monet" --seamless -s100 -n4
+invoke> "pond garden with lotus by claude monet" --seamless -s100 -n4
+```
+
+By default this will tile on both the X and Y axes. However, you can also specify specific axes to tile on with `--seamless_axes`.
+Possible values are `x`, `y`, and `x,y`:
+```python
+invoke> "pond garden with lotus by claude monet" --seamless --seamless_axes=x -s100 -n4
 ```
 
 ---
@@ -33,21 +37,21 @@ dream> "pond garden with lotus by claude monet" --seamless -s100 -n4
 ## **Shortcuts: Reusing Seeds**
 
 Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version
-1.11. Provide a `**-S**` (or `**--seed**`) switch of `-1` to use the seed of the most recent image
-generated. If you produced multiple images with the `**-n**` switch, then you can go back further
-using -2, -3, etc. up to the first image generated by the previous command. Sorry, but you can't go
+1.11. Provide a `-S` (or `--seed`) switch of `-1` to use the seed of the most recent image
+generated. If you produced multiple images with the `-n` switch, then you can go back further
+using `-2`, `-3`, etc. up to the first image generated by the previous command. Sorry, but you can't go
 back further than one command.
 
-Here's an example of using this to do a quick refinement. It also illustrates using the new `**-G**`
+Here's an example of using this to do a quick refinement. It also illustrates using the new `-G`
 switch to turn on upscaling and face enhancement (see previous section):
 
 ```bash
-dream> a cute child playing hopscotch -G0.5
+invoke> a cute child playing hopscotch -G0.5
 [...]
 outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
 
 # I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
-dream> a cute child playing hopscotch -G1.0 -s100 -S -1
+invoke> a cute child playing hopscotch -G1.0 -s100 -S -1
 reusing previous seed 3498014304
 [...]
 outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
@@ -58,7 +62,7 @@ outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.
 ## **Weighted Prompts**
 
 You may weight different sections of the prompt to tell the sampler to attach different levels of
-priority to them, by adding `:(number)` to the end of the section you wish to up- or downweight. For
+priority to them, by adding `:<percent>` to the end of the section you wish to up- or downweight. For
 example consider this prompt:
 
 ```bash
@@ -71,24 +75,47 @@ combination of integers and floating point numbers, and they do not need to add
 
 ---
 
-## Thresholding and Perlin Noise Initialization Options
+## **Filename Format**
+
+The argument `--fnformat` allows to specify the filename of the
+ image. Supported wildcards are all arguments what can be set such as
+ `perlin`, `seed`, `threshold`, `height`, `width`, `gfpgan_strength`,
+ `sampler_name`, `steps`, `model`, `upscale`, `prompt`, `cfg_scale`,
+ `prefix`.
+
+The following prompt
+```bash
+dream> a red car --steps 25 -C 9.8 --perlin 0.1 --fnformat {prompt}_steps.{steps}_cfg.{cfg_scale}_perlin.{perlin}.png
+```
+
+generates a file with the name: `outputs/img-samples/a red car_steps.25_cfg.9.8_perlin.0.1.png`
+
+---
+
+## **Thresholding and Perlin Noise Initialization Options**
 
 Two new options are the thresholding (`--threshold`) and the perlin noise initialization (`--perlin`) options. Thresholding limits the range of the latent values during optimization, which helps combat oversaturation with higher CFG scale values. Perlin noise initialization starts with a percentage (a value ranging from 0 to 1) of perlin noise mixed into the initial noise. Both features allow for more variations and options in the course of generating images.
 
-For better intuition into what these options do in practice, [here is a graphic demonstrating them both](static/truncation_comparison.jpg) in use. In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from
+For better intuition into what these options do in practice:
+
+![here is a graphic demonstrating them both](../assets/truncation_comparison.jpg)
+
+In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from
 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are fixed, so the initial prompt is as follows (no thresholding or perlin noise):
 
-```
-    a portrait of a beautiful young lady -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0
+```bash
+invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0
 ```
 
 Here's an example of another prompt used when setting the threshold to 5 and perlin noise to 0.2:
 
-```
-    a portrait of a beautiful young lady -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2
+```bash
+invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2
 ```
 
-Note: currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization.
+!!! note
+
+    currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization.
 
 ---
 
@@ -120,7 +147,7 @@ internet. In the following runs, it will load up the cached versions of the requ
 `.cache` directory of the system.
 
 ```bash
-(ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py
+(invokeai) ~/stable-diffusion$ python3 ./scripts/preload_models.py
 preloading bert tokenizer...
 Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
 Downloading: 100%|██████████████████████████████████| 226k/226k [00:00<00:00, 2.79MB/s]

docs/features/OUTPAINTING.md

@@ -15,24 +15,65 @@ InvokeAI supports two versions of outpainting, one called "outpaint"
 and the other "outcrop." They work slightly differently and each has
 its advantages and drawbacks.
 
+### Outpainting
+
+Outpainting is the same as inpainting, except that the painting occurs
+in the regions outside of the original image. To outpaint using the
+`invoke.py` command line script, prepare an image in which the borders
+to be extended are pure black. Add an alpha channel (if there isn't one
+already), and make the borders completely transparent and the interior
+completely opaque. If you wish to modify the interior as well, you may
+create transparent holes in the transparency layer, which `img2img` will
+paint into as usual.
+
+Pass the image as the argument to the `-I` switch as you would for
+regular inpainting:
+
+ invoke> a stream by a river -I /path/to/transparent_img.png
+
+You'll likely be delighted by the results.
+
+### Tips
+
+1. Do not try to expand the image too much at once. Generally it is best
+   to expand the margins in 64-pixel increments. 128 pixels often works,
+   but your mileage may vary depending on the nature of the image you are
+   trying to outpaint into.
+
+2. There are a series of switches that can be used to adjust how the
+   inpainting algorithm operates. In particular, you can use these to
+   minimize the seam that sometimes appears between the original image
+   and the extended part. These switches are:
+
+  --seam_size SEAM_SIZE    Size of the mask around the seam between original and outpainted image (0)
+  --seam_blur SEAM_BLUR    The amount to blur the seam inwards (0)
+  --seam_strength STRENGTH The img2img strength to use when filling the seam (0.7)
+  --seam_steps SEAM_STEPS  The number of steps to use to fill the seam. (10)
+  --tile_size TILE_SIZE    The tile size to use for filling outpaint areas (32)
+
 ### Outcrop
 
-The `outcrop` extension allows you to extend the image in 64 pixel
-increments in any dimension. You can apply the module to any image
-previously-generated by InvokeAI. Note that it will **not** work with
-arbitrary photographs or Stable Diffusion images created by other
-implementations.
+The `outcrop` extension gives you a convenient `!fix` postprocessing
+command that allows you to extend a previously-generated image in 64
+pixel increments in any direction. You can apply the module to any
+image previously-generated by InvokeAI. Note that it works with
+arbitrary PNG photographs, but not currently with JPG or other
+formats. Outcropping is particularly effective when combined with the
+[runwayML custom inpainting
+model](INPAINTING.md#using-the-runwayml-inpainting-model).
 
 Consider this image:
 
+<div align="center" markdown>
 ![curly_woman](../assets/outpainting/curly.png)
+</div>
 
 Pretty nice, but it's annoying that the top of her head is cut
 off. She's also a bit off center. Let's fix that!
 
-~~~~
-dream> !fix images/curly.png --outcrop top 64 right 64
-~~~~
+```bash
+invoke> !fix images/curly.png --outcrop top 64 right 64
+```
 
 This is saying to apply the `outcrop` extension by extending the top
 of the image by 64 pixels, and the right of the image by the same
@@ -42,7 +83,9 @@ specify any number of pixels to extend. You can also abbreviate
 
 The result looks like this:
 
+<div align="center" markdown>
 ![curly_woman_outcrop](../assets/outpainting/curly-outcrop.png)
+</div>
 
 The new image is actually slightly larger than the original (576x576,
 because 64 pixels were added to the top and right sides.)
@@ -60,39 +103,3 @@ you'll get a slightly different result. You can run it repeatedly
 until you get an image you like. Unfortunately `!fix` does not
 currently respect the `-n` (`--iterations`) argument.
 
-## Outpaint
-
-The `outpaint` extension does the same thing, but with subtle
-differences. Starting with the same image, here is how we would add an
-additional 64 pixels to the top of the image:
-
-~~~
-dream> !fix images/curly.png --out_direction top 64
-~~~
-
-(you can abbreviate ``--out_direction` as `-D`.
-
-The result is shown here:
-
-![curly_woman_outpaint](../assets/outpainting/curly-outpaint.png)
-
-Although the effect is similar, there are significant differences from
-outcropping:
-
-1. You can only specify one direction to extend at a time.
-2. The image is **not** resized. Instead, the image is shifted by the specified
-number of pixels. If you look carefully, you'll see that less of the lady's
-torso is visible in the image.
-3. Because the image dimensions remain the same, there's no rounding
-to multiples of 64.
-4. Attempting to outpaint larger areas will frequently give rise to ugly
-   ghosting effects.
-5. For best results, try increasing the step number.
-6. If you don't specify a pixel value in -D, it will default to half
-   of the whole image, which is likely not what you want.
-
-Neither `outpaint` nor `outcrop` are perfect, but we continue to tune
-and improve them. If one doesn't work, try the other. You may also
-wish to experiment with other `img2img` arguments, such as `-C`, `-f`
-and `-s`.
-

docs/features/POSTPROCESS.md

@@ -1,8 +1,9 @@
-
 ---
 title: Postprocessing
 ---
 
+# :material-image-edit: Postprocessing
+
 ## Intro
 
 This extension provides the ability to restore faces and upscale
@@ -20,39 +21,33 @@ The default face restoration module is GFPGAN. The default upscale is
 Real-ESRGAN. For an alternative face restoration module, see [CodeFormer
 Support] below.
 
-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. (The reason for this is
-that the standard GFPGAN distribution has a minor bug that adversely affects
-image color.) Upscaling with Real-ESRGAN should "just work" without further
-intervention. Simply pass the --upscale (-U) option on the dream> command line,
-or indicate the desired scale on the popup in the Web GUI.
+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 pass the --upscale (-U) option on the
+invoke> command line, or indicate the desired scale on the popup in
+the Web GUI.
 
-For **GFPGAN** to work, there is one additional step needed. You will need to
-download and copy the GFPGAN
-[models file](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth)
-into **src/gfpgan/experiments/pretrained_models**. On Mac and Linux systems,
-here's how you'd do it using **wget**:
+**GFPGAN** requires a series of downloadable model files to
+work. These are loaded when you run `scripts/preload_models.py`. If
+GFPAN is failing with an error, please run the following from the
+InvokeAI directory:
 
 ```bash
-wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P src/gfpgan/experiments/pretrained_models/
+python scripts/preload_models.py
 ```
 
-Make sure that you're in the InvokeAI directory when you do this.
+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.
 
-Alternatively, if you have GFPGAN installed elsewhere, or if you are using an
-earlier version of this package which asked you to install GFPGAN in a sibling
-directory, you may use the `--gfpgan_dir` argument with `dream.py` to set a
-custom path to your GFPGAN directory. _There are other GFPGAN related boot
-arguments if you wish to customize further._
-
-!!! warning "Internet connection needed"
-
-    Users whose GPU machines are isolated from the Internet (e.g.
-    on a University cluster) should be aware that the first time you run dream.py with GFPGAN and
-    Real-ESRGAN turned on, it will try to download model files from the Internet. To rectify this, you
-    may run `python3 scripts/preload_models.py` after you have installed GFPGAN and all its
-    dependencies.
+Alternatively, if you have GFPGAN installed elsewhere, or if you are
+using an earlier version of this package which asked you to install
+GFPGAN in a sibling directory, you may use the `--gfpgan_dir` argument
+with `invoke.py` to set a custom path to your GFPGAN directory. _There
+are other GFPGAN related boot arguments if you wish to customize
+further._
 
 ## Usage
 
@@ -75,7 +70,7 @@ If you do not explicitly specify an upscaling_strength, it will default to 0.75.
 
 ### Face Restoration
 
-`-G : <gfpgan_strength>`
+`-G : <facetool_strength>`
 
 This prompt argument controls the strength of the face restoration that is being
 applied. Similar to upscaling, values between `0.5 to 0.8` are recommended.
@@ -94,13 +89,13 @@ too.
 ### Example Usage
 
 ```bash
-dream> superman dancing with a panda bear -U 2 0.6 -G 0.4
+invoke> "superman dancing with a panda bear" -U 2 0.6 -G 0.4
 ```
 
 This also works with img2img:
 
 ```bash
-dream> a man wearing a pineapple hat -I path/to/your/file.png -U 2 0.5 -G 0.6
+invoke> "a man wearing a pineapple hat" -I path/to/your/file.png -U 2 0.5 -G 0.6
 ```
 
 !!! note
@@ -124,24 +119,24 @@ actions.
 This repo also allows you to perform face restoration using
 [CodeFormer](https://github.com/sczhou/CodeFormer).
 
-In order to setup CodeFormer to work, you need to download the models like with
-GFPGAN. You can do this either by running `preload_models.py` or by manually
-downloading the
-[model file](https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth)
-and saving it to `ldm/restoration/codeformer/weights` folder.
+In order to setup CodeFormer to work, you need to download the models
+like with GFPGAN. You can do this either by running
+`preload_models.py` or by manually downloading the [model
+file](https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth)
+and saving it to `ldm/invoke/restoration/codeformer/weights` folder.
 
-You can use `-ft` prompt argument to swap between CodeFormer and the default
-GFPGAN. The above mentioned `-G` prompt argument will allow you to control the
-strength of the restoration effect.
+You can use `-ft` prompt argument to swap between CodeFormer and the
+default GFPGAN. The above mentioned `-G` prompt argument will allow
+you to control the strength of the restoration effect.
 
-### Usage:
+### Usage
 
 The following command will perform face restoration with CodeFormer instead of
 the default gfpgan.
 
 `<prompt> -G 0.8 -ft codeformer`
 
-### Other Options:
+### Other Options
 
 - `-cf` - cf or CodeFormer Fidelity takes values between `0` and `1`. 0 produces
   high quality results but low accuracy and 1 produces lower quality results but
@@ -167,16 +162,16 @@ previously-generated file. Just use the syntax `!fix path/to/file.png
 2X for a file named `./outputs/img-samples/000044.2945021133.png`,
 just run:
 
-```
-dream> !fix ./outputs/img-samples/000044.2945021133.png -G 0.8 -U 2
+```bash
+invoke> !fix ./outputs/img-samples/000044.2945021133.png -G 0.8 -U 2
 ```
 
 A new file named `000044.2945021133.fixed.png` will be created in the output
 directory. Note that the `!fix` command does not replace the original file,
 unlike the behavior at generate time.
 
-### Disabling:
+### Disabling
 
 If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries,
-you can disable them on the dream.py command line with the `--no_restore` and
+you can disable them on the invoke.py command line with the `--no_restore` and
 `--no_upscale` options, respectively.

docs/features/PROMPTS.md

@@ -1,14 +1,14 @@
 ---
-title: Prompting Features
+title: Prompting-Features
 ---
 
-# :octicons-command-palette-24: Prompting Features
+# :octicons-command-palette-24: Prompting-Features
 
 ## **Reading Prompts from a File**
 
-You can automate `dream.py` by providing a text file with the prompts you want to run, one line per
+You can automate `invoke.py` by providing a text file with the prompts you want to run, one line per
 prompt. The text file must be composed with a text editor (e.g. Notepad) and not a word processor.
-Each line should look like what you would type at the dream> prompt:
+Each line should look like what you would type at the invoke> prompt:
 
 ```bash
 a beautiful sunny day in the park, children playing -n4 -C10
@@ -16,17 +16,18 @@ stormy weather on a mountain top, goats grazing     -s100
 innovative packaging for a squid's dinner           -S137038382
 ```
 
-Then pass this file's name to `dream.py` when you invoke it:
+Then pass this file's name to `invoke.py` when you invoke it:
 
 ```bash
-(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file "path/to/prompts.txt"
+(invokeai) ~/stable-diffusion$ python3 scripts/invoke.py --from_file "path/to/prompts.txt"
 ```
 
 You may read a series of prompts from standard input by providing a filename of `-`:
 
 ```bash
-(ldm) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/dream.py --from_file -
+(invokeai) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/invoke.py --from_file -
 ```
+
 ---
 
 ## **Negative and Unconditioned Prompts**
@@ -34,7 +35,7 @@ You may read a series of prompts from standard input by providing a filename of
 Any words between a pair of square brackets will instruct Stable
 Diffusion to attempt to ban the concept from the generated image.
 
-```bash
+```text
 this is a test prompt [not really] to make you understand [cool] how this works.
 ```
 
@@ -44,27 +45,35 @@ Here's a prompt that depicts what it does.
 
 original prompt:
 
-`#!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" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
+`#!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" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
+<div align="center" markdown>
 ![step1](../assets/negative_prompt_walkthru/step1.png)
+</div>
 
 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]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
+<div align="center" markdown>
 ![step2](../assets/negative_prompt_walkthru/step2.png)
+</div>
 
 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]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
+<div align="center" markdown>
 ![step3](../assets/negative_prompt_walkthru/step3.png)
+</div>
 
 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]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
+<div align="center" markdown>
 ![step4](../assets/negative_prompt_walkthru/step4.png)
+</div>
 
 !!! notes "Notes about this feature:"
 
@@ -75,6 +84,109 @@ Getting close - but there's no sense in having a saddle when our horse doesn't h
 
 ---
 
+## **Prompt Syntax Features**
+
+The InvokeAI prompting language has the following features:
+
+### Attention weighting 
+Append a word or phrase with `-` or `+`, or a weight between `0` and `2` (`1`=default), to decrease or increase "attention" (= a mix of per-token CFG weighting multiplier and, for `-`, a weighted blend with the prompt without the term).
+
+The following syntax is recognised:
+  * single words without parentheses: `a tall thin man picking apricots+`
+  * single or multiple words with parentheses: `a tall thin man picking (apricots)+` `a tall thin man picking (apricots)-` `a tall thin man (picking apricots)+` `a tall thin man (picking apricots)-` 
+  * more effect with more symbols `a tall thin man (picking apricots)++`
+  * nesting `a tall thin man (picking apricots+)++` (`apricots` effectively gets `+++`)
+  * all of the above with explicit numbers `a tall thin man picking (apricots)1.1` `a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++` is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2), etc.)
+  * 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 we increase and decrease how much attention we want Stable Diffusion to pay to the word `apricots`:
+
+![an AI generated image of a man picking apricots from a tree](../assets/prompt_syntax/apricots-0.png)
+
+Using `-` to reduce apricot-ness:
+
+| `a man picking apricots- from a tree` | `a man picking apricots-- from a tree` | `a man picking apricots--- from a tree` |
+| -- | -- | -- |
+| ![an AI generated image of a man picking apricots from a tree, with smaller apricots](../assets/prompt_syntax/apricots--1.png) | ![an AI generated image of a man picking apricots from a tree, with even smaller and fewer apricots](../assets/prompt_syntax/apricots--2.png) | ![an AI generated image of a man picking apricots from a tree, with very few very small apricots](../assets/prompt_syntax/apricots--3.png) |
+
+Using `+` to increase apricot-ness:
+
+| `a man picking apricots+ from a tree` | `a man picking apricots++ from a tree` | `a man picking apricots+++ from a tree` | `a man picking apricots++++ from a tree` | `a man picking apricots+++++ from a tree` |
+| -- | -- | -- | -- | -- |
+| ![an AI generated image of a man picking apricots from a tree, with larger, more vibrant apricots](../assets/prompt_syntax/apricots-1.png) | ![an AI generated image of a man picking apricots from a tree with even larger, even more vibrant apricots](../assets/prompt_syntax/apricots-2.png) | ![an AI generated image of a man picking apricots from a tree, but the man has been replaced by a pile of apricots](../assets/prompt_syntax/apricots-3.png) | ![an AI generated image of a man picking apricots from a tree, but the man has been replaced by a mound of giant melting-looking apricots](../assets/prompt_syntax/apricots-4.png) | ![an AI generated image of a man picking apricots from a tree, but the man and the leaves and parts of the ground have all been replaced by giant melting-looking apricots](../assets/prompt_syntax/apricots-5.png) |
+
+You can also change the balance between different parts of a prompt. For example, below is a `mountain man`:
+
+![an AI generated image of a mountain man](../assets/prompt_syntax/mountain-man.png)
+
+And here he is with more mountain:
+
+| `mountain+ man` | `mountain++ man` | `mountain+++ man` | 
+| -- | -- | -- |
+| ![](../assets/prompt_syntax/mountain1-man.png) | ![](../assets/prompt_syntax/mountain2-man.png) | ![](../assets/prompt_syntax/mountain3-man.png) |
+
+Or, alternatively, with more man:
+
+| `mountain man+` | `mountain man++` | `mountain man+++` | `mountain man++++` | 
+| -- | -- | -- | -- |
+| ![](../assets/prompt_syntax/mountain-man1.png) | ![](../assets/prompt_syntax/mountain-man2.png) | ![](../assets/prompt_syntax/mountain-man3.png) | ![](../assets/prompt_syntax/mountain-man4.png) |
+
+### 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 - `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)` is equivalent to `a tall thin man picking apricots:1 a tall thin man picking pears:1` in the old syntax.
+* Attention weights can be nested inside blends.
+* Non-normalized blends are supported by passing `no_normalize` as an additional argument to the blend weights, eg `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,-1,no_normalize)`. very fun to explore local maxima in the feature space, but also easy to produce garbage output.
+
+See the section below on "Prompt Blending" for more information about how this works.
+
+### Cross-Attention Control ('prompt2prompt')
+
+Sometimes an image you generate is almost right, and you just want to
+change one detail without affecting the rest. You could use a photo editor and inpainting
+to overpaint the area, but that's a pain. Here's where `prompt2prompt`
+comes in handy.
+
+Generate an image with a given prompt, record the seed of the image,
+and then use the `prompt2prompt` syntax to substitute words in the
+original prompt for words in a new prompt. This works for `img2img` as well.
+
+* `a ("fluffy cat").swap("smiling dog") eating a hotdog`.
+  * quotes optional: `a (fluffy cat).swap(smiling dog) eating a hotdog`.
+  * for single word substitutions parentheses are also optional: `a cat.swap(dog) eating a hotdog`.
+* Supports options `s_start`, `s_end`, `t_start`, `t_end` (each 0-1) loosely corresponding to bloc97's `prompt_edit_spatial_start/_end` and `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to intuitively understand.
+  * Example usage:`a (cat).swap(dog, s_end=0.3) eating a hotdog` - the `s_end` argument means that the "spatial" (self-attention) edit will stop having any effect after 30% (=0.3) of the steps have been done, leaving Stable Diffusion with 70% of the steps where it is free to decide for itself how to reshape the cat-form into a dog form. 
+  * The numbers represent a percentage through the step sequence where the edits should happen. 0 means the start (noisy starting image), 1 is the end (final image).
+    * For img2img, the step sequence does not start at 0 but instead at (1-strength) - so if strength is 0.7, s_start and s_end must both be greater than 0.3 (1-0.7) to have any effect. 
+* Convenience option `shape_freedom` (0-1) to specify how much "freedom" Stable Diffusion should have to change the shape of the subject being swapped. 
+  * `a (cat).swap(dog, shape_freedom=0.5) eating a hotdog`.
+
+
+
+The `prompt2prompt` code is based off [bloc97's
+colab](https://github.com/bloc97/CrossAttentionControl).
+
+Note that `prompt2prompt` is not currently working with the runwayML
+inpainting model, and may never work due to the way this model is set
+up. If you attempt to use `prompt2prompt` you will get the original
+image back. However, since this model is so good at inpainting, a
+good substitute is to use the `clipseg` text masking option:
+
+```
+invoke> a fluffy cat eating a hotdot
+Outputs:
+[1010] outputs/000025.2182095108.png: a fluffy cat eating a hotdog
+invoke> a smiling dog eating a hotdog -I 000025.2182095108.png -tm cat
+```
+
+### 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)` becomes `\(my_keyword\)`. Otherwise, the prompt
+parser will attempt to interpret the parentheses as part of the prompt
+syntax and it will get confused.
+
 ## **Prompt Blending**
 
 You may blend together different sections of the prompt to explore the
@@ -101,44 +213,58 @@ illustrate, here are three images generated using various combinations
 of blend weights. As usual, unless you fix the seed, the prompts will give you
 different results each time you run them.
 
+---
+
+<div align="center" markdown>
 ### "blue sphere, red cube, hybrid"
+</div>
 
 This example doesn't use melding at all and represents the default way
 of mixing concepts.
 
-<img src="../assets/prompt-blending/blue-sphere-red-cube-hybrid.png" width=256>
+<div align="center" markdown>
+![blue-sphere-red-cube-hyprid](../assets/prompt-blending/blue-sphere-red-cube-hybrid.png)
+</div>
 
 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.
 
+<div align="center" markdown>
 ### "blue sphere:0.25 red cube:0.75 hybrid"
 
-<img src="../assets/prompt-blending/blue-sphere:0.25-red-cube:0.75-hybrid.png" width=256>
+![blue-sphere-25-red-cube-75](../assets/prompt-blending/blue-sphere-0.25-red-cube-0.75-hybrid.png)
+</div>
 
 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?
 
+<div align="center" markdown>
 ### "blue sphere:0.75 red cube:0.25 hybrid"
 
-<img src="../assets/prompt-blending/blue-sphere:0.75-red-cube:0.25-hybrid.png" width=256>
+![blue-sphere-75-red-cube-25](../assets/prompt-blending/blue-sphere-0.75-red-cube-0.25-hybrid.png)
+</div>
 
 Definitely more blue-spherey. The cube is gone entirely, but it's
 really cool abstract art.
 
+<div align="center" markdown>
 ### "blue sphere:0.5 red cube:0.5 hybrid"
 
-<img src="../assets/prompt-blending/blue-sphere:0.5-red-cube:0.5-hybrid.png" width=256>
+![blue-sphere-5-red-cube-5-hybrid](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5-hybrid.png)
+</div>
 
 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.
 
+<div align="center" markdown>
 ### "blue sphere:0.5 red cube:0.5"
 
-<img src="../assets/prompt-blending/blue-sphere:0.5-red-cube:0.5.png" width=256>
+![blue-sphere-5-red-cube-5](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5.png)
+</div>
 
 Indeed, removing the word "hybrid" produces an image that is more like
 what we'd expect.
@@ -146,4 +272,3 @@ what we'd expect.
 In conclusion, prompt blending is great for exploring creative space,
 but can be difficult to direct. A forthcoming release of InvokeAI will
 feature more deterministic prompt weighting.
-

docs/features/TEXTUAL_INVERSION.md

@@ -1,8 +1,8 @@
 ---
-title: TEXTUAL_INVERSION
+title: Textual-Inversion
 ---
 
-# :material-file-document-plus-outline: TEXTUAL_INVERSION
+# :material-file-document: Textual Inversion
 
 ## **Personalizing Text-to-Image Generation**
 
@@ -23,13 +23,13 @@ As the default backend is not available on Windows, if you're using that
 platform, set the environment variable `PL_TORCH_DISTRIBUTED_BACKEND` to `gloo`
 
 ```bash
-python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
-                  --actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
-                  -t \
-                  -n my_cat \
-                  --gpus 0 \
-                  --data_root D:/textual-inversion/my_cat \
-                  --init_word 'cat'
+python3 ./main.py -t \
+    --base ./configs/stable-diffusion/v1-finetune.yaml \
+    --actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
+    -n my_cat \
+    --gpus 0 \
+    --data_root D:/textual-inversion/my_cat \
+    --init_word 'cat'
 ```
 
 During the training process, files will be created in
@@ -56,22 +56,23 @@ configs/stable_diffusion/v1-finetune.yaml (currently set to 4000000)
 ## **Run the Model**
 
 Once the model is trained, specify the trained .pt or .bin file when starting
-dream using
+invoke using
 
 ```bash
-python3 ./scripts/dream.py --embedding_path /path/to/embedding.pt
+python3 ./scripts/invoke.py \
+    --embedding_path /path/to/embedding.pt
 ```
 
-Then, to utilize your subject at the dream prompt
+Then, to utilize your subject at the invoke prompt
 
 ```bash
-dream> "a photo of *"
+invoke> "a photo of *"
 ```
 
 This also works with image2image
 
 ```bash
-dream> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
+invoke> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
 ```
 
 For .pt files it's also possible to train multiple tokens (modify the
@@ -80,9 +81,9 @@ LDM checkpoints using:
 
 ```bash
 python3 ./scripts/merge_embeddings.py \
-        --manager_ckpts /path/to/first/embedding.pt \
-        [</path/to/second/embedding.pt>,[...]] \
-        --output_path /path/to/output/embedding.pt
+    --manager_ckpts /path/to/first/embedding.pt \
+    [</path/to/second/embedding.pt>,[...]] \
+    --output_path /path/to/output/embedding.pt
 ```
 
 Credit goes to rinongal and the repository

docs/features/VARIATIONS.md

@@ -25,16 +25,17 @@ 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
-`lucy lawless as xena, warrior princess, character portrait, high resolution.`
+The prompt we will use throughout is:
 
-This will be indicated as `prompt` in the examples below.
+`#!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:
 
 ```bash
-dream> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
+invoke> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
 ...
 Outputs:
 ./outputs/Xena/000001.1579445059.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1579445059
@@ -45,7 +46,10 @@ Outputs:
 ./outputs/Xena/000001.3357757885.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S3357757885
 ```
 
+<figure markdown>
 ![var1](../assets/variation_walkthru/000001.3357757885.png)
+<figcaption> Seed 3357757885 looks nice </figcaption>
+</figure>
 
 ---
 
@@ -57,7 +61,7 @@ differing by a variation amount of 0.2. This number ranges from `0` to `1.0`,
 with higher numbers being larger amounts of variation.
 
 ```bash
-dream> "prompt" -n6 -S3357757885 -v0.2
+invoke> "prompt" -n6 -S3357757885 -v0.2
 ...
 Outputs:
 ./outputs/Xena/000002.784039624.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 784039624:0.2 -S3357757885
@@ -77,9 +81,15 @@ 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
@@ -89,7 +99,7 @@ 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
-dream> "prompt"  -S3357757885 -V3647897225,0.1,1614299449,0.1
+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
 ```
@@ -97,7 +107,10 @@ Outputs:
 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
@@ -105,7 +118,7 @@ latter, using both the `-V` (combining) and `-v` (variation strength) options.
 Note that we use `-n6` to generate 6 variations:
 
 ```bash
-dream> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1 -v0.05 -n6
+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
@@ -118,8 +131,23 @@ Outputs:
 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/WEB.md

@@ -1,21 +1,369 @@
 ---
-title: Barebones Web Server
+title: InvokeAI Web Server
 ---
 
-# :material-web: Barebones Web Server
+# :material-web: InvokeAI Web Server
 
-As of version 1.10, this distribution comes with a bare bones web server (see
-screenshot). To use it, run the `dream.py` script by adding the `--web`
-option.
+As of version 2.0.0, this distribution comes with a full-featured web
+server (see screenshot). To use it, run the `invoke.py` script by
+adding the `--web` option:
 
 ```bash
-(ldm) ~/stable-diffusion$ python3 scripts/dream.py --web
+(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web
 ```
 
 You can then connect to the server by pointing your web browser at
-http://localhost:9090, or to the network name or IP address of the server.
+http://localhost:9090. To reach the server from a different machine on
+your LAN, you may launch the web server with the `--host` argument and
+either the IP address of the host you are running it on, or the
+wildcard `0.0.0.0`. For example:
 
-Kudos to [Tesseract Cat](https://github.com/TesseractCat) for contributing this
-code, and to [dagf2101](https://github.com/dagf2101) for refining it.
+```bash
+(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web --host 0.0.0.0
+```
 
-![Dream Web Server](../assets/dream_web_server.png)
+# Quick guided walkthrough of the WebGUI's features
+
+While most of the WebGUI's features are intuitive, here is a guided
+walkthrough through its various components.
+
+![Invoke Web Server - Major Components](../assets/invoke-web-server-1.png){:width="640px"}
+
+The screenshot above shows the Text to Image tab of the WebGUI. There
+are three main sections:
+
+1. A **control panel** on the left, which contains various settings
+for text to image generation. The most important part is the text
+field (currently showing `strawberry sushi`) for entering the text
+prompt, and the camera icon directly underneath that will render the
+image. We'll call this the *Invoke* button from now on.
+
+2. The **current image** section in the middle, which shows a large
+format version of the image you are currently working on. A series of
+buttons at the top ("image to image", "Use All", "Use Seed", etc) lets
+you modify the image in various ways.
+
+3. A **gallery* section on the left that contains a history of the
+images you have generated. These images are read and written to the
+directory specified at launch time in `--outdir`.
+
+In addition to these three elements, there are a series of icons for
+changing global settings, reporting bugs, and changing the theme on
+the upper right.
+
+There are also a series of icons to the left of the control panel (see
+highlighted area in the screenshot below) which select among a series
+of tabs for performing different types of operations.
+
+<figure markdown>
+![Invoke Web Server - Control Panel](../assets/invoke-web-server-2.png){:width="512px"}
+</figure>
+
+From top to bottom, these are:
+
+1. Text to Image  - generate images from text
+2. Image to Image - from an uploaded starting image (drawing or photograph) generate a new one, modified by the text prompt
+3. Inpainting (pending) - Interactively erase portions of a starting image and have the AI fill in the erased region from a text prompt.
+4. Outpainting (pending) - Interactively add blank space to the borders of a starting image and fill in the background from a text prompt.
+5. Postprocessing (pending) - Interactively postprocess generated images using a variety of filters.
+
+The inpainting, outpainting and postprocessing tabs are currently in
+development. However, limited versions of their features can already
+be accessed through the Text to Image and Image to Image tabs.
+
+## Walkthrough
+
+The following walkthrough will exercise most (but not all) of the
+WebGUI's feature set.
+
+### Text to Image
+
+1. Launch the WebGUI using `python scripts/invoke.py --web` and
+connect to it with your browser by accessing
+`http://localhost:9090`. If the browser and server are running on
+different machines on your LAN, add the option `--host 0.0.0.0` to the
+launch command line and connect to the machine hosting the web server
+using its IP address or domain name.
+
+2. If all goes well, the WebGUI should come up and you'll see a green
+`connected` message on the upper right.
+
+#### Basics
+
+1. Generate an image by typing *strawberry sushi* into the large
+prompt field on the upper left and then clicking on the Invoke button
+(the one with the Camera icon). After a short wait, you'll see a large
+image of sushi in the image panel, and a new thumbnail in the gallery
+on the right.
+
+    If you need more room on the screen, you can turn the gallery  off
+    by clicking on the **x** to the right of "Your Invocations". You can
+    turn it back on later by clicking the image icon that appears in the
+    gallery's place.
+
+    The images are written into the directory indicated by the `--outdir`
+    option provided at script launch time. By default, this is
+    `outputs/img-samples` under the InvokeAI directory.
+
+2. Generate a bunch of strawberry sushi images by increasing the
+number of requested images by adjusting the Images counter just below
+the Camera button. As each is generated, it will be added to the
+gallery. You can switch the active image by clicking on the gallery
+thumbnails.
+
+3. Try playing with different settings, including image width and
+height, the Sampler, the Steps and the CFG scale.
+
+    Image *Width* and *Height* do what you'd expect. However, be aware that
+    larger images consume more VRAM memory and take longer to generate.
+
+    The *Sampler* controls how the AI selects the image to display. Some
+    samplers are more "creative" than others and will produce a wider
+    range of variations (see next section). Some samplers run faster than
+    others.
+
+    *Steps* controls how many noising/denoising/sampling steps the AI will
+    take. The higher this value, the more refined the image will be, but
+    the longer the image will take to generate. A typical strategy is to
+    generate images with a low number of steps in order to select one to
+    work on further, and then regenerate it using a higher number of
+    steps.
+
+    The *CFG Scale* controls how hard the AI tries to match the generated
+    image to the input prompt. You can go as high or low as you like, but
+    generally values greater than 20 won't improve things much, and values
+    lower than 5 will produce unexpected images. There are complex
+    interactions between *Steps*, *CFG Scale* and the *Sampler*, so
+    experiment to find out what works for you.
+
+6. To regenerate a previously-generated image, select the image you
+want and click *Use All*. This loads the text prompt and other
+original settings into the control panel. If you then press *Invoke*
+it will regenerate the image exactly. You can also selectively modify
+the prompt or other settings to tweak the image.
+
+    Alternatively, you may click on *Use Seed* to load just the image's
+    seed, and leave other settings unchanged.
+
+7. To regenerate a Stable Diffusion image that was generated by
+another SD package, you need to know its text prompt and its
+*Seed*. Copy-paste the prompt into the prompt box, unset the
+*Randomize Seed* control in the control panel, and copy-paste the
+desired *Seed* into its text field. When you Invoke, you will get
+something similar to the original image. It will not be exact unless
+you also set the correct values for the original sampler, CFG,
+steps and dimensions, but it will (usually) be close.
+
+#### Variations on a theme
+
+1. Let's try generating some variations. Select your favorite sushi
+image from the gallery to load it. Then select "Use All" from the list
+of buttons above. This will load up all the settings used to generate
+this image, including its unique seed.
+
+    Go down to the Variations section of the Control Panel and set the
+    button to On. Set Variation Amount to 0.2 to generate a modest
+    number of variations on the image, and also set the Image counter to
+    `4`. Press the `invoke` button. This will generate a series of related
+    images. To obtain smaller variations, just lower the Variation
+    Amount. You may also experiment with changing the Sampler. Some
+    samplers generate more variability than others. *k_euler_a* is
+    particularly creative, while *ddim* is pretty conservative.
+
+2. For even more variations, experiment with increasing the setting
+for *Perlin*. This adds a bit of noise to the image generation
+process. Note that values of Perlin noise greater than 0.15 produce
+poor images for several of the samplers.
+
+#### Facial reconstruction and upscaling
+
+Stable Diffusion frequently produces mangled faces, particularly when
+there are multiple figures in the same scene. Stable Diffusion has
+particular issues with generating reallistic eyes. InvokeAI provides
+the ability to reconstruct faces using either the GFPGAN or CodeFormer
+libraries. For more information see [POSTPROCESS](POSTPROCESS.md).
+  
+1. Invoke a prompt that generates a mangled face. A prompt that often
+gives this is "portrait of a lawyer, 3/4 shot" (this is not intended
+as a slur against lawyers!) Once you have an image that needs some
+touching up, load it into the Image panel, and press the button with
+the face icon (highlighted in the first screenshot below). A dialog
+box will appear. Leave *Strength* at 0.8 and press *Restore Faces". If
+all goes well, the eyes and other aspects of the face will be improved
+(see the second screenshot)
+
+    ![Invoke Web Server - Original Image](../assets/invoke-web-server-3.png)
+
+    ![Invoke Web Server - Retouched Image](../assets/invoke-web-server-4.png)
+
+    The facial reconstruction *Strength* field adjusts how aggressively
+    the face library will try to alter the face. It can be as high as 1.0,
+    but be aware that this often softens the face airbrush style, losing
+    some details. The default 0.8 is usually sufficient.
+
+2. "Upscaling" is the process of increasing the size of an image while
+retaining the sharpness. InvokeAI uses an external library called
+"ESRGAN" to do this. To invoke upscaling, simply select an image and
+press the *HD* button above it. You can select between 2X and 4X
+upscaling, and adjust the upscaling strength, which has much the same
+meaning as in facial reconstruction. Try running this on one of your
+previously-generated images.
+
+3. Finally, you can run facial reconstruction and/or upscaling
+automatically after each Invocation. Go to the Advanced Options
+section of the Control Panel and turn on *Restore Face* and/or
+*Upscale*.
+
+### Image to Image
+
+InvokeAI lets you take an existing image and use it as the basis for a
+new creation. You can use any sort of image, including a photograph, a
+scanned sketch, or a digital drawing, as long as it is in PNG or JPEG
+format.
+
+For this tutorial, we'll use files named
+[Lincoln-and-Parrot-512.png](../assets/Lincoln-and-Parrot-512.png),
+and
+[Lincoln-and-Parrot-512-transparent.png](../assets/Lincoln-and-Parrot-512-transparent.png).
+Download these images to your local machine now to continue with the walkthrough.
+
+1. Click on the *Image to Image* tab icon, which is the second icon
+from the top on the left-hand side of the screen:
+
+    <figure markdown>
+    ![Invoke Web Server - Image to Image Icon](../assets/invoke-web-server-5.png)
+    </figure>
+
+    This will bring you to a screen similar to the one shown here:
+
+    <figure markdown>
+    ![Invoke Web Server - Image to Image Tab](../assets/invoke-web-server-6.png){:width="640px"}
+    </figure>
+
+2. Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or
+click the blank area to get an upload dialog. The image will load into
+an area marked *Initial Image*. (The WebGUI will also load the most
+recently-generated image from the gallery into a section on the left,
+but this image will be replaced in the next step.)
+
+3. Go to the prompt box and type *old sea captain with raven on
+shoulder* and press Invoke. A derived image will appear to the right
+of the original one:
+
+    ![Invoke Web Server - Image to Image example](../assets/invoke-web-server-7.png){:width="640px"}
+
+4. Experiment with the different settings. The most influential one
+in Image to Image is *Image to Image Strength* located about midway
+down the control panel. By default it is set to 0.75, but can range
+from 0.0 to 0.99. The higher the value, the more of the original image
+the AI will replace. A value of 0 will leave the initial image
+completely unchanged, while 0.99 will replace it completely. However,
+the Sampler and CFG Scale also influence the final result. You can
+also generate variations in the same way as described in Text to
+Image.
+
+5. What if we only want to change certain part(s) of the image and
+leave the rest intact? This is called Inpainting, and a future version
+of the InvokeAI web server will provide an interactive painting canvas
+on which you can directly draw the areas you wish to Inpaint into. For
+now, you can achieve this effect by using an external photoeditor tool
+to make one or more regions of the image transparent as described in
+[INPAINTING.md] and uploading that.
+
+    The file
+    [Lincoln-and-Parrot-512-transparent.png](../assets/Lincoln-and-Parrot-512-transparent.png)
+    is a version of the earlier image in which the area around the parrot
+    has been replaced with transparency. Click on the "x" in the upper
+    right of the Initial Image and upload the transparent version. Using
+    the same prompt "old sea captain with raven on shoulder" try Invoking
+    an image. This time, only the parrot will be replaced, leaving the
+    rest of the original image intact:
+
+<figure markdown>
+![Invoke Web Server - Inpainting](../assets/invoke-web-server-8.png){:width="640px"}
+</figure>
+
+6. Would you like to modify a previously-generated image using the
+Image to Image facility? Easy! While in the Image to Image panel,
+hover over any of the gallery images to see a little menu of icons pop
+up.  Click the picture icon to instantly send the selected image to
+Image to Image as the initial image.
+
+You can do the same from the Text to Image tab by clicking on the
+picture icon above the central image panel. The screenshot below
+shows where the "use as initial image" icons are located.
+
+![Invoke Web Server - Use as Image Links](../assets/invoke-web-server-9.png){:width="640px"}
+
+## Parting remarks
+
+This concludes the walkthrough, but there are several more features that you
+can explore. Please check out the [Command Line Interface](CLI.md)
+documentation for further explanation of the advanced features that
+were not covered here.
+
+The WebGUI is only rapid development. Check back regularly for
+updates!
+
+## Reference
+
+### Additional Options
+
+ parameter <img width=160 align="right"> | effect
+-- | --
+`--web_develop` | Starts the web server in development mode.
+`--web_verbose` | Enables verbose logging
+`--cors [CORS ...]` | Additional allowed origins, comma-separated
+`--host HOST` | Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.
+`--port PORT` | Web server: Port to listen on
+`--gui` | Start InvokeAI GUI - This is the "desktop mode" version of the web app. It uses Flask to create a desktop app experience of the webserver.
+
+### Web Specific Features
+
+The web experience offers an incredibly easy-to-use experience for interacting with the InvokeAI toolkit. 
+For detailed guidance on individual features, see the Feature-specific help documents available in this directory.
+Note that the latest functionality available in the CLI may not always be available in the Web interface.
+
+#### Dark Mode & Light Mode
+
+The InvokeAI interface is available in a nano-carbon black & purple Dark Mode, and a "burn your eyes out Nosferatu" Light Mode. These can be toggled by clicking the Sun/Moon icons at the top right of the interface. 
+
+![InvokeAI Web Server - Dark Mode](../assets/invoke_web_dark.png)
+
+![InvokeAI Web Server - Light Mode](../assets/invoke_web_light.png)
+
+#### Invocation Toolbar
+
+The left side of the InvokeAI interface is available for customizing the prompt and the settings used for invoking your new image. Typing your prompt into the open text field and clicking the Invoke button will produce the image based on the settings configured in the toolbar.
+
+See below for additional documentation related to each feature:
+
+- [Core Prompt Settings](./CLI.md)
+- [Variations](./VARIATIONS.md)
+- [Upscaling](./POSTPROCESS.md#upscaling)
+- [Image to Image](./IMG2IMG.md)
+- [Inpainting](./INPAINTING.md)
+- [Other](./OTHER.md)
+
+#### Invocation Gallery
+
+The currently selected --outdir (or the default outputs folder) will display all previously generated files on load. As new invocations are generated, these will be dynamically added to the gallery, and can be previewed by selecting them. Each image also has a simple set of actions (e.g., Delete, Use Seed, Use All Parameters, etc.) that can be accessed by hovering over the image.
+
+#### Image Workspace
+
+When an image from the Invocation Gallery is selected, or is generated, the image will be displayed within the center of the interface. A quickbar of common image interactions are displayed along the top of the image, including:
+
+- Use image in the `Image to Image` workflow
+- Initialize Face Restoration on the selected file
+- Initialize Upscaling on the selected file
+- View File metadata and details
+- Delete the file
+
+## Acknowledgements
+
+A huge shout-out to the core team working to make this vision a
+reality, including
+[psychedelicious](https://github.com/psychedelicious),
+[Kyle0654](https://github.com/Kyle0654) and
+[blessedcoolant](https://github.com/blessedcoolant). [hipsterusername](https://github.com/hipsterusername)
+was the team's unofficial cheerleader and added tooltips/docs.

docs/features/WEBUIHOTKEYS.md

@@ -0,0 +1,58 @@
+# **WebUI Hotkey List**
+
+## General
+
+| Setting      | Hotkey                 |
+| ------------ | ---------------------- |
+| a            | Set All Parameters     |
+| s            | Set Seed               |
+| u            | Upscale                |
+| r            | Restoration            |
+| i            | Show Metadata          |
+| Ddl          | Delete Image           |
+| alt + a      | Focus prompt input     |
+| shift + i    | Send To Image to Image |
+| ctrl + enter | Start processing       |
+| shift + x    | cancel Processing      |
+| shift + d    | Toggle Dark Mode       |
+| `            | Toggle console         |
+
+## Tabs
+
+| Setting | Hotkey                    |
+| ------- | ------------------------- |
+| 1       | Go to Text To Image Tab   |
+| 2       | Go to Image to Image Tab  |
+| 3       | Go to Inpainting Tab      |
+| 4       | Go to Outpainting Tab     |
+| 5       | Go to Nodes Tab           |
+| 6       | Go to Post Processing Tab |
+
+## Gallery
+
+| Setting      | Hotkey                          |
+| ------------ | ------------------------------- |
+| g            | Toggle Gallery                  |
+| left arrow   | Go to previous image in gallery |
+| right arrow  | Go to next image in gallery     |
+| shift + p    | Pin gallery                     |
+| shift + up   | Increase gallery image size     |
+| shift + down | Decrease gallery image size     |
+| shift + r    | Reset image gallery size        |
+
+## Inpainting
+
+| Setting                    | Hotkey                |
+| -------------------------- | --------------------- |
+| [                          | Decrease brush size   |
+| ]                          | Increase brush size   |
+| alt + [                    | Decrease mask opacity |
+| alt + ]                    | Increase mask opacity |
+| b                          | Select brush          |
+| e                          | Select eraser         |
+| ctrl + z                   | Undo brush stroke     |
+| ctrl + shift + z, ctrl + y | Redo brush stroke     |
+| h                          | Hide mask             |
+| shift + m                  | Invert mask           |
+| shift + c                  | Clear mask            |
+| shift + j                  | Expand canvas         |

docs/help/SAMPLER_CONVERGENCE.md

@@ -1,8 +1,8 @@
 ---
-title: SAMPLER CONVERGENCE
+title: Sampler Convergence
 ---
 
-## *Sampler Convergence*
+# :material-palette-advanced: *Sampler Convergence*
 
 As features keep increasing, making the right choices for your needs can become increasingly difficult. What sampler to use? And for how many steps? Do you change the CFG value? Do you use prompt weighting? Do you allow variations?
 
@@ -14,12 +14,14 @@ In this document, we will talk about sampler convergence.
 
 Looking for a short version? Here's a TL;DR in 3 tables.
 
-| Remember  |
-|:---|
-| Results converge as steps (`-s`) are increased (except for `K_DPM_2_A` and `K_EULER_A`). Often at ≥ `-s100`, but may require ≥ `-s700`).  |
-| Producing a batch of candidate images at low (`-s8` to `-s30`) step counts can save you hours of computation.  |
-| `K_HEUN` and `K_DPM_2`  converge in less steps (but are slower).  |
-| `K_DPM_2_A` and `K_EULER_A` incorporate a lot of creativity/variability. |
+!!! note "Remember"
+
+    - Results converge as steps (`-s`) are increased (except for `K_DPM_2_A` and `K_EULER_A`). Often at ≥ `-s100`, but may require ≥ `-s700`).
+    - Producing a batch of candidate images at low (`-s8` to `-s30`) step counts can save you hours of computation.
+    - `K_HEUN` and `K_DPM_2`  converge in less steps (but are slower).
+    - `K_DPM_2_A` and `K_EULER_A` incorporate a lot of creativity/variability.
+
+<div align="center" markdown>
 
 | Sampler   | (3 sample avg) it/s (M1 Max 64GB, 512x512)  |
 |---|---|
@@ -32,10 +34,13 @@ Looking for a short version? Here's a TL;DR in 3 tables.
 |  `K_DPM_2_A` | 0.95 (slower)  |
 |  `K_EULER_A` | 1.86  |
 
-| Suggestions  |
-|:---|
-| For most use cases, `K_LMS`, `K_HEUN` and `K_DPM_2` are the best choices (the latter 2 run 0.5x as quick, but tend to converge 2x as quick as `K_LMS`). At very low steps (≤ `-s8`), `K_HEUN` and `K_DPM_2` are not recommended. Use `K_LMS` instead.|
-| For variability, use `K_EULER_A` (runs 2x as quick as `K_DPM_2_A`).  |
+</div>
+
+!!! tip "suggestions"
+
+    For most use cases, `K_LMS`, `K_HEUN` and `K_DPM_2` are the best choices (the latter 2 run 0.5x as quick, but tend to converge 2x as quick as `K_LMS`). At very low steps (≤ `-s8`), `K_HEUN` and `K_DPM_2` are not recommended. Use `K_LMS` instead.
+    
+    For variability, use `K_EULER_A` (runs 2x as quick as `K_DPM_2_A`).
 
 ---
 
@@ -60,15 +65,15 @@ This realization is very useful because it means you don't need to create a batc
 You can produce the same 100 images at `-s10` to `-s30` using a K-sampler (since they converge faster), get a rough idea of the final result, choose your 2 or 3 favorite ones, and then run `-s100` on those images to polish some details.
 The latter technique is 3-8x as quick.
 
-Example:
+!!! example
 
-At 60s per 100 steps.
+    At 60s per 100 steps.
 
-(Option A) 60s * 100 images = 6000s (100 images at `-s100`, manually picking 3 favorites)
+    A) 60s * 100 images = 6000s (100 images at `-s100`, manually picking 3 favorites)
 
-(Option B) 6s * 100 images + 60s * 3 images = 780s (100 images at `-s10`, manually picking 3 favorites, and running those 3 at `-s100` to polish details)
+    B) 6s *100 images + 60s* 3 images = 780s (100 images at `-s10`, manually picking 3 favorites, and running those 3 at `-s100` to polish details)
 
-The result is 1 hour and 40 minutes (Option A) vs 13 minutes (Option B).
+    The result is __1 hour and 40 minutes__ for Variant A, vs __13 minutes__ for Variant B.
 
 ### *Topic convergance*
 
@@ -110,9 +115,12 @@ Note also the point of convergence may not be the most desirable state (e.g. I p
 
 Once we understand the concept of sampler convergence, we must look into the performance of each sampler in terms of steps (iterations) per second, as not all samplers run at the same speed.
 
-On my M1 Max with 64GB of RAM, for a 512x512 image:
-| Sampler   | (3 sample average) it/s |
-|---|---|
+<div align="center" markdown>
+
+On my M1 Max with 64GB of RAM, for a 512x512 image
+
+| Sampler | (3 sample average) it/s |
+| :--- | :--- |
 |  `DDIM` | 1.89  |
 |  `PLMS` | 1.86  |
 |  `K_EULER` | 1.86  |
@@ -122,11 +130,13 @@ On my M1 Max with 64GB of RAM, for a 512x512 image:
 |  `K_DPM_2_A` | 0.95 (slower)  |
 |  `K_EULER_A` | 1.86  |
 
+</div>
+
 Combining our results with the steps per second of each sampler, three choices come out on top: `K_LMS`, `K_HEUN` and `K_DPM_2` (where the latter two run 0.5x as quick but tend to converge 2x as quick as `K_LMS`). For creativity and a lot of variation between iterations, `K_EULER_A` can be a good choice (which runs 2x as quick as `K_DPM_2_A`).
 
 Additionally, image generation at very low steps (≤ `-s8`) is not recommended for `K_HEUN` and `K_DPM_2`. Use `K_LMS` instead.
 
-<img width="397" alt="192044949-67d5d441-a0d5-4d5a-be30-5dda4fc28a00-min" src="https://user-images.githubusercontent.com/50542132/192046823-2714cb29-bbf3-4eb1-9213-e27a0963905c.png">
+![K-compare](https://user-images.githubusercontent.com/50542132/192046823-2714cb29-bbf3-4eb1-9213-e27a0963905c.png){ width=600}
 
 ### *Three key points*
 

docs/help/TROUBLESHOOT.md

@@ -1,5 +1,7 @@
 ---
 title: F.A.Q.
+hide:
+  - toc
 ---
 
 # :material-frequently-asked-questions: F.A.Q.
@@ -51,7 +53,7 @@ rm ${PIP_LOG}
 
 ### **QUESTION**
 
-`dream.py` crashes with the complaint that it can't find `ldm.simplet2i.py`. Or it complains that
+`invoke.py` crashes with the complaint that it can't find `ldm.simplet2i.py`. Or it complains that
 function is being passed incorrect parameters.
 
 ### **SOLUTION**
@@ -63,7 +65,7 @@ Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory a
 
 ### **QUESTION**
 
-`dream.py` dies, complaining of various missing modules, none of which starts with `ldm``.
+`invoke.py` dies, complaining of various missing modules, none of which starts with `ldm`.
 
 ### **SOLUTION**
 
@@ -87,9 +89,7 @@ Usually this will be sufficient, but if you start to see errors about
 missing or incorrect modules, use the command `pip install -e .`
 and/or `conda env update` (These commands won't break anything.)
 
-`pip install -e .` and/or
-
-`conda env update -f environment.yaml`
+`pip install -e .` and/or `conda env update -f environment.yaml`
 
 (These commands won't break anything.)
 

docs/index.md

@@ -1,6 +1,5 @@
 ---
 title: Home
-template: main.html
 ---
 
 <!--
@@ -13,7 +12,7 @@ template: main.html
 -->
 <div align="center" markdown>
 
-# :material-script-text-outline: Stable Diffusion Dream Script
+# ^^**InvokeAI: A Stable Diffusion Toolkit**^^ :tools: <br> <small>Formerly known as lstein/stable-diffusion</small>
 
 ![project logo](assets/logo.png)
 
@@ -25,37 +24,41 @@ template: main.html
 
 [![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
 
-[CI checks on dev badge]: https://flat.badgen.net/github/checks/lstein/stable-diffusion/development?label=CI%20status%20on%20dev&cache=900&icon=github
-[CI checks on dev link]: https://github.com/lstein/stable-diffusion/actions?query=branch%3Adevelopment
-[CI checks on main badge]: https://flat.badgen.net/github/checks/lstein/stable-diffusion/main?label=CI%20status%20on%20main&cache=900&icon=github
-[CI checks on main link]: https://github.com/lstein/stable-diffusion/actions/workflows/test-dream-conda.yml
-[discord badge]: https://flat.badgen.net/discord/members/htRgbc7e?icon=discord
-[discord link]: https://discord.com/invite/htRgbc7e
-[github forks badge]: https://flat.badgen.net/github/forks/lstein/stable-diffusion?icon=github
+[CI checks on dev badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
+[CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
+[CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
+[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
+[discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
+[discord link]: https://discord.gg/ZmtBAhwWhy
+[github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
 [github forks link]: https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
-[github open issues badge]: https://flat.badgen.net/github/open-issues/lstein/stable-diffusion?icon=github
-[github open issues link]: https://github.com/lstein/stable-diffusion/issues?q=is%3Aissue+is%3Aopen
-[github open prs badge]: https://flat.badgen.net/github/open-prs/lstein/stable-diffusion?icon=github
-[github open prs link]: https://github.com/lstein/stable-diffusion/pulls?q=is%3Apr+is%3Aopen
-[github stars badge]: https://flat.badgen.net/github/stars/lstein/stable-diffusion?icon=github
-[github stars link]: https://github.com/lstein/stable-diffusion/stargazers
-[latest commit to dev badge]: https://flat.badgen.net/github/last-commit/lstein/stable-diffusion/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
-[latest commit to dev link]: https://github.com/lstein/stable-diffusion/commits/development
-[latest release badge]: https://flat.badgen.net/github/release/lstein/stable-diffusion/development?icon=github
-[latest release link]: https://github.com/lstein/stable-diffusion/releases
+[github open issues badge]: https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
+[github open issues link]: https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
+[github open prs badge]: https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
+[github open prs link]: https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
+[github stars badge]: https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
+[github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
+[latest commit to dev badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
+[latest commit to dev link]: https://github.com/invoke-ai/InvokeAI/commits/development
+[latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
+[latest release link]: https://github.com/invoke-ai/InvokeAI/releases
 
 </div>
 
-This is a fork of [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion), the open
-source text-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 or RAM.
+<a href="https://github.com/invoke-ai/InvokeAI">InvokeAI</a> is an
+implementation of Stable Diffusion, the open source text-to-image and
+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 or RAM.
+
+**Quick links**: [<a href="https://discord.gg/NwVCmKwY">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>
 
 !!! note
 
-    This fork is rapidly evolving. Please use the
-    [Issues](https://github.com/lstein/stable-diffusion/issues) tab to report bugs and make feature
-    requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
+    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
 
@@ -81,25 +84,46 @@ You wil need one of the following:
 
 ### :fontawesome-regular-hard-drive: Disk
 
-- At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
+- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
 !!! note
 
-    If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
+    If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the invoke script in
     full-precision mode as shown below.
 
     Similarly, specify full-precision mode on Apple M1 hardware.
 
-    To run in full-precision mode, start `dream.py` with the `--full_precision` flag:
+    To run in full-precision mode, start `invoke.py` with the `--full_precision` flag:
 
     ```bash
-    (ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
+    (invokeai) ~/InvokeAI$ python scripts/invoke.py --full_precision
     ```
 ## :octicons-log-16: Latest Changes
 
-### vNEXT <small>(TODO 2022)</small>
-
-  - Deprecated `--full_precision` / `-F`. Simply omit it and `dream.py` will auto
+### v2.0.0 <small>(9 October 2022)</small>
+- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
+    for backward compatibility.
+- Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+- Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/INPAINTING.md">inpainting</a> and <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OUTPAINTING.md">outpainting</a>
+- img2img runs on all k* samplers
+- Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/PROMPTS.md#negative-and-unconditioned-prompts">negative prompts</a>
+- Support for CodeFormer face reconstruction
+- Support for Textual Inversion on Macintoshes
+- Support in both WebGUI and CLI for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/POSTPROCESS.md">post-processing of previously-generated images</a>
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+- New `--hires` option on `invoke>` line allows <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.m#this-is-an-example-of-txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
+- New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
+- Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+- Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+- Improved <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.m">command-line completion behavior</a>.
+    New commands added:
+       * List command-line history with `!history`
+       * Search command-line history with `!search`
+       * Clear history with `!clear`
+- Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
     configure. To switch away from auto use the new flag like `--precision=float32`.
 
 ### v1.14 <small>(11 September 2022)</small>
@@ -124,7 +148,7 @@ You wil need one of the following:
   [Kevin Gibbons](https://github.com/bakkot)
 - A new configuration file scheme that allows new models (including upcoming stable-diffusion-v1.5)
   to be added without altering the code. ([David Wager](https://github.com/maddavid12))
-- Can specify --grid on dream.py command line as the default.
+- Can specify --grid on invoke.py command line as the default.
 - Miscellaneous internal bug and stability fixes.
 - Works on M1 Apple hardware.
 - Multiple bug fixes.

docs/installation/INSTALLING_MODELS.md

@@ -0,0 +1,267 @@
+---
+title: Installing Models
+---
+
+# :octicons-paintbrush-16: Installing Models
+
+## Model Weight Files
+
+The model weight files ('*.ckpt') are the Stable Diffusion "secret
+sauce". They are the product of training the AI on millions of
+captioned images gathered from multiple sources.
+
+Originally there was only a single Stable Diffusion weights file,
+which many people named `model.ckpt`. Now there are dozens or more
+that have been "fine tuned" to provide particulary styles, genres, or
+other features. InvokeAI allows you to install and run multiple model
+weight files and switch between them quickly in the command-line and
+web interfaces.
+
+This manual will guide you through installing and configuring model
+weight files.
+
+## Base Models
+
+InvokeAI comes with support for a good initial set of models listed in
+the model configuration file `configs/models.yaml`. They are:
+
+| Model                   | Weight File                   |   Description                    | DOWNLOAD FROM            |
+| ----------------------  | ----------------------------- |--------------------------------- | ----------------|
+| stable-diffusion-1.5    | v1-5-pruned-emaonly.ckpt      | Most recent version of base Stable Diffusion model| https://huggingface.co/runwayml/stable-diffusion-v1-5 |
+| stable-diffusion-1.4    | sd-v1-4.ckpt                  | Previous version of base Stable Diffusion model | https://huggingface.co/CompVis/stable-diffusion-v-1-4-original |
+| inpainting-1.5          | sd-v1-5-inpainting.ckpt       | Stable Diffusion 1.5 model specialized for inpainting | https://huggingface.co/runwayml/stable-diffusion-inpainting |
+| waifu-diffusion-1.3     | model-epoch09-float32.ckpt    | Stable Diffusion 1.4 trained to produce anime images | https://huggingface.co/hakurei/waifu-diffusion-v1-3 |
+| <all models>            | vae-ft-mse-840000-ema-pruned.ckpt   | A fine-tune file add-on file that improves face generation | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/ |
+
+
+Note that these files are covered by an "Ethical AI" license which
+forbids certain uses. You will need to create an account on the
+Hugging Face website and accept the license terms before you can
+access the files.
+
+The predefined configuration file for InvokeAI (located at
+`configs/models.yaml`) provides entries for each of these weights
+files. `stable-diffusion-1.5` is the default model used, and we
+strongly recommend that you install this weights file if nothing else.
+
+## Community-Contributed Models
+
+There are too many to list here and more are being contributed every
+day. Hugging Face maintains a [fast-growing
+repository](https://huggingface.co/sd-concepts-library) of fine-tune
+(".bin") models that can be imported into InvokeAI by passing the
+`--embedding_path` option to the `invoke.py` command.
+
+[This page](https://rentry.org/sdmodels) hosts a large list of
+official and unofficial Stable Diffusion models and where they can be
+obtained.
+
+## Installation
+
+There are three ways to install weights files:
+
+1. During InvokeAI installation, the `preload_models.py` script can
+download them for you.
+
+2. You can use the command-line interface (CLI) to import, configure
+and modify new models files.
+
+3. You can download the files manually and add the appropriate entries
+to `models.yaml`.
+
+### Installation via `preload_models.py`
+
+This is the most automatic way. Run `scripts/preload_models.py` from
+the console.  It will ask you to select which models to download and
+lead you through the steps of setting up a Hugging Face account if you
+haven't done so already.
+
+To start, from within the InvokeAI directory run the command `python
+scripts/preload_models.py` (Linux/MacOS) or `python
+scripts\preload_models.py` (Windows):
+
+```
+Loading Python libraries...
+
+** INTRODUCTION **
+Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
+and other large models that are needed for text to image generation. At any point you may interrupt
+this program and resume later.
+
+** WEIGHT SELECTION **
+Would you like to download the Stable Diffusion model weights now? [y] 
+
+Choose the weight file(s) you wish to download. Before downloading you 
+will be given the option to view and change your selections.
+
+[1] stable-diffusion-1.5:
+    The newest Stable Diffusion version 1.5 weight file (4.27 GB) (recommended)
+    Download? [y] 
+[2] inpainting-1.5:
+    RunwayML SD 1.5 model optimized for inpainting (4.27 GB) (recommended)
+    Download? [y] 
+[3] stable-diffusion-1.4:
+    The original Stable Diffusion version 1.4 weight file (4.27 GB) 
+    Download? [n] n
+[4] waifu-diffusion-1.3:
+    Stable Diffusion 1.4 fine tuned on anime-styled images (4.27) 
+    Download? [n] y
+[5] ft-mse-improved-autoencoder-840000:
+    StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB) (recommended)
+    Download? [y] y
+The following weight files will be downloaded:
+   [1] stable-diffusion-1.5*
+   [2] inpainting-1.5
+   [4] waifu-diffusion-1.3
+   [5] ft-mse-improved-autoencoder-840000
+*default
+Ok to download? [y] 
+** LICENSE AGREEMENT FOR WEIGHT FILES **
+
+1. To download the Stable Diffusion weight files you need to read and accept the
+   CreativeML Responsible AI license. If you have not already done so, please 
+   create an account using the "Sign Up" button:
+
+   https://huggingface.co 
+
+   You will need to verify your email address as part of the HuggingFace
+   registration process.
+
+2. After creating the account, login under your account and accept
+   the license terms located here:
+
+   https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
+
+Press <enter> when you are ready to continue:
+...
+```
+
+When the script is complete, you will find the downloaded weights
+files in `models/ldm/stable-diffusion-v1` and a matching configuration
+file in `configs/models.yaml`.
+
+You can run the script again to add any models you didn't select the
+first time. Note that as a safety measure the script will _never_
+remove a previously-installed weights file. You will have to do this
+manually.
+
+### Installation via the CLI
+
+You can install a new model, including any of the community-supported
+ones, via the command-line client's `!import_model` command.
+
+1. First download the desired model weights file and place it under `models/ldm/stable-diffusion-v1/`.
+   You may rename the weights file to something more memorable if you wish. Record the path of the
+   weights file (e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`)
+
+2. Launch the `invoke.py` CLI with `python scripts/invoke.py`.
+
+3. At the `invoke>` command-line, enter the command `!import_model <path to model>`.
+   For example:
+
+   `invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+
+   (Hint - the CLI supports file path autocompletion. Type a bit of the path
+   name and hit <tab> in order to get a choice of possible completions.)
+
+4. Follow the wizard's instructions to complete installation as shown in the example
+   here:
+
+```
+invoke> <b>!import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt</b>
+>> Model import in process. Please enter the values needed to configure this model:
+
+Name for this model: <b>arabian-nights</b>
+Description of this model: <b>Arabian Nights Fine Tune v1.0</b>
+Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
+Default image width: <b>512</b>
+Default image height: <b>512</b>
+>> New configuration:
+arabian-nights:
+  config: configs/stable-diffusion/v1-inference.yaml
+  description: Arabian Nights Fine Tune v1.0
+  height: 512
+  weights: models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+  width: 512
+OK to import [n]? <b>y</b>
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+   | LatentDiffusion: Running in eps-prediction mode
+   | DiffusionWrapper has 859.52 M params.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Using faster float16 precision
+
+```
+
+If you've previously installed the fine-tune VAE file `vae-ft-mse-840000-ema-pruned.ckpt`,
+the wizard will also ask you if you want to add this VAE to the model.
+
+The appropriate entry for this model will be added to `configs/models.yaml` and it will
+be available to use in the CLI immediately.
+
+The CLI has additional commands for switching among, viewing, editing,
+deleting the available models. These are described in [Command Line
+Client](../features/CLI.md#model-selection-and-importation), but the two most
+frequently-used are `!models` and `!switch <name of model>`. The first
+prints a table of models that InvokeAI knows about and their load
+status. The second will load the requested model and lets you switch
+back and forth quickly among loaded models.
+
+### Manually editing of `configs/models.yaml`
+
+If you are comfortable with a text editor then you may simply edit
+`models.yaml` directly.
+
+First you need to download the desired .ckpt file and place it in
+`models/ldm/stable-diffusion-v1` as descirbed in step #1 in the
+previous section. Record the path to the weights file,
+e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+
+Then using a **text** editor (e.g. the Windows Notepad application),
+open the file `configs/models.yaml`, and add a new stanza that follows
+this model:
+
+```
+arabian-nights-1.0:
+  description: A great fine-tune in Arabian Nights style
+  weights: ./models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  default: false
+```
+
+* arabian-nights-1.0
+  - This is the name of the model that you will refer to from within the
+  CLI and the WebGUI when you need to load and use the model.
+
+* description
+  - Any description that you want to add to the model to remind you what
+    it is.
+
+* weights
+  - Relative path to the .ckpt weights file for this model.
+
+* config
+  - This is the confusingly-named configuration file for the model itself.
+  Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens
+  to need a custom configuration, in which case the place you downloaded it
+  from will tell you what to use instead. For example, the runwayML custom
+  inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`.
+  This is already inclued in the InvokeAI distribution and is configured automatically
+  for you by the `preload_models.py` script.
+
+* vae
+  - If you want to add a VAE file to the model, then enter its path here.
+
+* width, height
+  - This is the width and height of the images used to train the model.
+  Currently they are always 512 and 512.
+
+Save the `models.yaml` and relaunch InvokeAI. The new model should now be
+available for your use.
+
+

docs/installation/INSTALL_DOCKER.md

@@ -1,4 +1,10 @@
-# Before you begin
+---
+title: Docker
+---
+
+# :fontawesome-brands-docker: Docker
+
+## Before you begin
 
 - For end users: Install Stable Diffusion locally using the instructions for
   your OS.
@@ -6,7 +12,7 @@
   deployment to other environments (on-premises or cloud), follow these
   instructions. For general use, install locally to leverage your machine's GPU.
 
-# Why containers?
+## Why containers?
 
 They provide a flexible, reliable way to build and deploy Stable Diffusion.
 You'll also use a Docker volume to store the largest model files and image
@@ -26,117 +32,78 @@ 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.
 
-# Installation on a Linux container
+## Installation on a Linux container
 
-## Prerequisites
+### Prerequisites
 
-### Get the data files
-
-Go to
-[Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original),
-and click "Access repository" to Download the model file `sd-v1-4.ckpt` (~4 GB)
-to `~/Downloads`. You'll need to create an account but it's quick and free.
-
-Also download the face restoration model.
-
-```Shell
-cd ~/Downloads
-wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
-```
-
-### Install [Docker](https://github.com/santisbon/guides#docker)
+#### Install [Docker](https://github.com/santisbon/guides#docker)
 
 On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
 CPUs and Memory to avoid this
 [Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
 increase Swap and Disk image size too.
 
-## Setup
+#### Get a Huggingface-Token
+
+Go to [Hugging Face](https://huggingface.co/settings/tokens), create a token and
+temporary place it somewhere like a open texteditor window (but dont save it!,
+only keep it open, we need it in the next step)
+
+### Setup
 
 Set the fork you want to use and other variables.
 
-```Shell
-TAG_STABLE_DIFFUSION="santisbon/stable-diffusion"
-PLATFORM="linux/arm64"
-GITHUB_STABLE_DIFFUSION="-b orig-gfpgan https://github.com/santisbon/stable-diffusion.git"
-REQS_STABLE_DIFFUSION="requirements-linux-arm64.txt"
-CONDA_SUBDIR="osx-arm64"
+!!! tip
 
-echo $TAG_STABLE_DIFFUSION
-echo $PLATFORM
-echo $GITHUB_STABLE_DIFFUSION
-echo $REQS_STABLE_DIFFUSION
-echo $CONDA_SUBDIR
+    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:
+
+| Environment-Variable                                                | Description                                                              |
+| ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
+| `HUGGINGFACE_TOKEN="hg_aewirhghlawrgkjbarug2"`                      | This is the only required variable, without you can't get the checkpoint |
+| `ARCH=aarch64`                                                      | if you are using a ARM based CPU                                         |
+| `INVOKEAI_TAG=yourname/invokeai:latest`                             | the Container Repository / Tag which will be used                        |
+| `INVOKEAI_CONDA_ENV_FILE=environment-linux-aarch64.yml`             | since environment.yml wouldn't work with aarch                           |
+| `INVOKEAI_GIT="-b branchname https://github.com/username/reponame"` | if you want to use your own fork                                         |
+
+#### Build the Image
+
+I provided a build script, which is located in `docker-build/build.sh` but still
+needs to be executed from the Repository root.
+
+```bash
+docker-build/build.sh
 ```
 
-Create a Docker volume for the downloaded model files.
+The build Script not only builds the container, but also creates the docker
+volume if not existing yet, or if empty it will just download the models. When
+it is done you can run the container via the run script
 
-```Shell
-docker volume create my-vol
+```bash
+docker-build/run.sh
 ```
 
-Copy the data files to the Docker volume using a lightweight Linux container.
-We'll need the models at run time. You just need to create the container with
-the mountpoint; no need to run this dummy container.
+When used without arguments, the container will start the website and provide
+you the link to open it. But if you want to use some other parameters you can
+also do so.
 
-```Shell
-cd ~/Downloads # or wherever you saved the files
+!!! warning "Deprecated"
 
-docker create --platform $PLATFORM --name dummy --mount source=my-vol,target=/data alpine
+    From here on it is the rest of the previous Docker-Docs, which will still
+    provide usefull informations for one or the other.
 
-docker cp sd-v1-4.ckpt dummy:/data
-docker cp GFPGANv1.4.pth dummy:/data
-```
+## Usage (time to have fun)
 
-Get the repo and download the Miniconda installer (we'll need it at build time).
-Replace the URL with the version matching your container OS and the architecture
-it will run on.
+### Startup
 
-```Shell
-cd ~
-git clone $GITHUB_STABLE_DIFFUSION
-
-cd stable-diffusion/docker-build
-chmod +x entrypoint.sh
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh && chmod +x anaconda.sh
-```
-
-Build the Docker image. Give it any tag `-t` that you want.  
-Choose the Linux container's host platform: x86-64/Intel is `amd64`. Apple
-silicon is `arm64`. If deploying the container to the cloud to leverage powerful
-GPU instances you'll be on amd64 hardware but if you're just trying this out
-locally on Apple silicon choose arm64.  
-The application uses libraries that need to match the host environment so use
-the appropriate requirements file.  
-Tip: Check that your shell session has the env variables set above.
-
-```Shell
-docker build -t $TAG_STABLE_DIFFUSION \
---platform $PLATFORM \
---build-arg gsd=$GITHUB_STABLE_DIFFUSION \
---build-arg rsd=$REQS_STABLE_DIFFUSION \
---build-arg cs=$CONDA_SUBDIR \
-.
-```
-
-Run a container using your built image.  
-Tip: Make sure you've created and populated the Docker volume (above).
-
-```Shell
-docker run -it \
---rm \
---platform $PLATFORM \
---name stable-diffusion \
---hostname stable-diffusion \
---mount source=my-vol,target=/data \
-$TAG_STABLE_DIFFUSION
-```
-
-# Usage (time to have fun)
-
-## Startup
-
-If you're on a **Linux container** the `dream` script is **automatically
+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**.  
@@ -148,17 +115,17 @@ half-precision requires autocast and won't work.
 By default the images are saved in `outputs/img-samples/`.
 
 ```Shell
-python3 scripts/dream.py --full_precision
+python3 scripts/invoke.py --full_precision
 ```
 
 You'll get the script's prompt. You can see available options or quit.
 
 ```Shell
-dream> -h
-dream> q
+invoke> -h
+invoke> q
 ```
 
-## Text to Image
+### 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.  
@@ -166,10 +133,10 @@ Then increase steps to 100 or more for good (but slower) results.
 The prompt can be in quotes or not.
 
 ```Shell
-dream> The hulk fighting with sheldon cooper -s5 -n1
-dream> "woman closeup highly detailed"  -s 150
+invoke> The hulk fighting with sheldon cooper -s5 -n1
+invoke> "woman closeup highly detailed"  -s 150
 # Reuse previous seed and apply face restoration
-dream> "woman closeup highly detailed"  --steps 150 --seed -1 -G 0.75
+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
@@ -188,7 +155,7 @@ volume):
 docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
 ```
 
-## Image to Image
+### Image to Image
 
 You can also do text-guided image-to-image translation. For example, turning a
 sketch into a detailed drawing.
@@ -210,35 +177,36 @@ If you're on a Docker container, copy your input image into the Docker volume
 docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
 ```
 
-Try it out generating an image (or more). The `dream` script needs absolute
+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
-dream> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75  --steps 100 -n4
+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
-dream> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75  --steps 50 -n1
+invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75  --steps 50 -n1
 ```
 
-## Web Interface
+### Web Interface
 
-You can use the `dream` script with a graphical web interface. Start the web
+You can use the `invoke` script with a graphical web interface. Start the web
 server with:
 
 ```Shell
-python3 scripts/dream.py --full_precision --web
+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
+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
+### Notes
 
 Some text you can add at the end of the prompt to make it very pretty:
 

docs/installation/INSTALL_LINUX.md

@@ -1,5 +1,5 @@
 ---
-title: Linux
+title: Manual Installation, Linux
 ---
 
 # :fontawesome-brands-linux: Linux
@@ -26,38 +26,36 @@ title: Linux
 
 3. Copy the InvokeAI source code from GitHub:
 
-```
-(base) ~$ git clone https://github.com/invoke-ai/InvokeAI.git
-```
+    ```bash
+    (base) ~$ git clone https://github.com/invoke-ai/InvokeAI.git
+    ```
 
-This will create InvokeAI folder where you will follow the rest of the steps.
+    This will create InvokeAI folder where you will follow the rest of the steps.
 
 4. Enter the newly-created InvokeAI folder. From this step forward make sure that you are working in the InvokeAI directory!
 
-```
-(base) ~$ cd InvokeAI
-(base) ~/InvokeAI$
-```
+    ```bash
+    (base) ~$ cd InvokeAI
+    (base) ~/InvokeAI$
+    ```
 
 5. Use anaconda to copy necessary python packages, create a new python
-   environment named `ldm` and activate the environment.
+   environment named `invokeai` and activate the environment.
 
+    ```bash
+    (base) ~/InvokeAI$ conda env create
+    (base) ~/InvokeAI$ conda activate invokeai
+    (invokeai) ~/InvokeAI$
+    ```
 
-```
-(base) ~/InvokeAI$ conda env create
-(base) ~/InvokeAI$ conda activate ldm
-(ldm) ~/InvokeAI$
-```
-
-    After these steps, your command prompt will be prefixed by `(ldm)` as shown
+    After these steps, your command prompt will be prefixed by `(invokeai)` as shown
     above.
 
 6. Load a couple of small machine-learning models required by stable diffusion:
 
-
-```
-(ldm) ~/InvokeAI$ python3 scripts/preload_models.py
-```
+    ```bash
+    (invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
+    ```
 
     !!! note
 
@@ -65,48 +63,39 @@ This will create InvokeAI folder where you will follow the rest of the steps.
         model loading scheme to allow the script to work on GPU machines that are not
         internet connected. See [Preload Models](../features/OTHER.md#preload-models)
 
-7. Now you need to install the weights for the stable diffusion model.
+7. Install the weights for the stable diffusion model.
 
-      - For running with the released weights, you will first need to set up an acount
-        with [Hugging Face](https://huggingface.co).
-      - Use your credentials to log in, and then point your browser [here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.)
-      - You may be asked to sign a license agreement at this point.
-      - Click on "Files and versions" near the top of the page, and then click on the
-        file named "sd-v1-4.ckpt". You'll be taken to a page that prompts you to click
-        the "download" link. Save the file somewhere safe on your local machine.
+- Sign up at https://huggingface.co
+- Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
+- Accept the terms and click Access Repository
+- Download [v1-5-pruned-emaonly.ckpt (4.27 GB)](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt)
+and move it into this directory under `models/ldm/stable_diffusion_v1/v1-5-pruned-emaonly.ckpt`
 
-      Now run the following commands from within the stable-diffusion directory.
-      This will create a symbolic link from the stable-diffusion model.ckpt file, to
-      the true location of the `sd-v1-4.ckpt` file.
-
-
-```
-(ldm) ~/InvokeAI$ mkdir -p models/ldm/stable-diffusion-v1
-(ldm) ~/InvokeAI$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
-```
+There are many other models that you can use. Please see [../features/INSTALLING_MODELS.md]
+for details.
 
 8. Start generating images!
 
-```
-# for the pre-release weights use the -l or --liaon400m switch
-(ldm) ~/InvokeAI$ python3 scripts/dream.py -l
+    ```bash
+    # for the pre-release weights use the -l or --liaon400m switch
+    (invokeai) ~/InvokeAI$ python3 scripts/invoke.py -l
 
-# for the post-release weights do not use the switch
-(ldm) ~/InvokeAI$ python3 scripts/dream.py
+    # for the post-release weights do not use the switch
+    (invokeai) ~/InvokeAI$ python3 scripts/invoke.py
 
-# for additional configuration switches and arguments, use -h or --help
-(ldm) ~/InvokeAI$ python3 scripts/dream.py -h
-```
+    # for additional configuration switches and arguments, use -h or --help
+    (invokeai) ~/InvokeAI$ python3 scripts/invoke.py -h
+    ```
 
-9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the `InvokeAI` directory, and then launch the dream script (step 8). If you forget to activate the ldm environment, the script will fail with multiple `ModuleNotFound` errors.
+9. Subsequently, to relaunch the script, be sure to run "conda activate invokeai" (step 5, second command), enter the `InvokeAI` directory, and then launch the invoke script (step 8). If you forget to activate the 'invokeai' environment, the script will fail with multiple `ModuleNotFound` errors.
 
 ## Updating to newer versions of the script
 
-
 This distribution is changing rapidly. If you used the `git clone` method (step 5) to download the InvokeAI directory, then to update to the latest and greatest version, launch the Anaconda window, enter `InvokeAI` and type:
 
-```
-(ldm) ~/InvokeAI$ git pull
+```bash
+(invokeai) ~/InvokeAI$ git pull
+(invokeai) ~/InvokeAI$ conda env update -f environment.yml
 ```
 
 This will bring your local copy into sync with the remote one.

docs/installation/INSTALL_MAC.md

@@ -1,69 +1,72 @@
 ---
-title: macOS
+title: Manual Installation, macOS
 ---
 
 # :fontawesome-brands-apple: macOS
 
+Invoke AI runs quite well on M1 Macs and we have a number of M1 users
+in the community.
+
+While the repo does run on Intel Macs, we only have a couple
+reports. If you have an Intel Mac and run into issues, please create
+an issue on Github and we will do our best to help.
+
 ## Requirements
 
 - macOS 12.3 Monterey or later
-- Python
-- Patience
-- Apple Silicon or Intel Mac
+- About 10GB of storage (and 10GB of data if your internet connection has data caps)
+- Any M1 Macs or an Intel Macs with 4GB+ of VRAM (ideally more)
 
-Things have moved really fast and so these instructions change often which makes
-them outdated pretty fast. One of the problems is that there are so many
-different ways to run this.
+## Installation
 
-We are trying to build a testing setup so that when we make changes it doesn't
-always break.
+First you need to download a large checkpoint file.
 
-## How to
+1. Sign up at https://huggingface.co
+2. Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
+3. Accept the terms and click Access Repository
+4. Download [v1-5-pruned-emaonly.ckpt (4.27 GB)](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt)
+and move it into this directory under `models/ldm/stable_diffusion_v1/v1-5-pruned-emaonly.ckpt`
 
-(this hasn't been 100% tested yet)
+There are many other models that you can try. Please see [../features/INSTALLING_MODELS.md]
+for details.
 
-First get the weights checkpoint download started since it's big and will take
-some time:
-
-1. Sign up at [huggingface.co](https://huggingface.co)
-2. Go to the
-   [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
-3. Accept the terms and click Access Repository:
-4. Download
-   [sd-v1-4.ckpt (4.27 GB)](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt)
-   and note where you have saved it (probably the Downloads folder)
-
-While that is downloading, open a Terminal and run the following commands:
+While that is downloading, open Terminal and run the following
+commands one at a time, reading the comments and taking care to run
+the appropriate command for your Mac's architecture (Intel or M1).
 
 !!! todo "Homebrew"
 
-    === "no brew installation yet"
+    If you have no brew installation yet (otherwise skip):
 
-        ```bash title="install brew (and Xcode command line tools)"
-        /bin/bash -c \
-          "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-        ```
-
-    === "brew is already installed"
-    
-        Only if you installed protobuf in a previous version of this tutorial, otherwise skip
-
-        `#!bash brew uninstall protobuf`
+    ```bash title="install brew (and Xcode command line tools)"
+    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+    ```
 
 !!! todo "Conda Installation"
 
     Now there are two different ways to set up the Python (miniconda) environment:
-    1. Standalone
-    2. with pyenv
-    If you don't know what we are talking about, choose Standalone
+
+       1. Standalone
+       2. with pyenv
+
+    If you don't know what we are talking about, choose Standalone. If you are familiar with python environments, choose "with pyenv"
 
     === "Standalone"
 
-        ```bash
-        # install cmake and rust:
-        brew install cmake rust
+        ```bash title="Install cmake, protobuf, and rust"
+        brew install cmake protobuf rust
         ```
 
+        Then clone the InvokeAI repository:
+        
+        ```bash title="Clone the InvokeAI repository:
+         # Clone the Invoke AI repo
+         git clone https://github.com/invoke-ai/InvokeAI.git
+         cd InvokeAI
+         ```
+  
+      Choose the appropriate architecture for your system and install miniconda:
+
         === "M1 arm64"
 
             ```bash title="Install miniconda for M1 arm64"
@@ -82,80 +85,82 @@ While that is downloading, open a Terminal and run the following commands:
 
     === "with pyenv"
 
-        ```{.bash .annotate}
-        brew install rust pyenv-virtualenv # (1)!
+        ```bash
+        brew install pyenv-virtualenv
         pyenv install anaconda3-2022.05
         pyenv virtualenv anaconda3-2022.05
         eval "$(pyenv init -)"
         pyenv activate anaconda3-2022.05
         ```
-        
-        1. You might already have this installed, if that is the case just continue.
 
-```{.bash .annotate title="local repo setup"}
-# clone the repo
-git clone https://github.com/invoke-ai/InvokeAI.git
+!!! todo "Clone the Invoke AI repo"
 
-cd InvokeAI
+```bash 
+    git clone https://github.com/invoke-ai/InvokeAI.git
+    cd InvokeAI
+    ```
 
-# wait until the checkpoint file has downloaded, then proceed
+!!! todo "Wait until the checkpoint-file download finished, then proceed"
 
-# create symlink to checkpoint
-mkdir -p models/ldm/stable-diffusion-v1/
+    We will leave the big checkpoint wherever you stashed it for long-term storage,
+    and make a link to it from the repo's folder. This allows you to use it for
+    other repos, or if you need to delete Invoke AI, you won't have to download it again.
 
-PATH_TO_CKPT="$HOME/Downloads" # (1)!
+    ```{.bash .annotate}
+    # Make the directory in the repo for the symlink
+    mkdir -p models/ldm/stable-diffusion-v1/
 
-ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" \
-  models/ldm/stable-diffusion-v1/model.ckpt
-```
+    # This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
+    PATH_TO_CKPT="$HOME/Downloads" # (1)!
 
-1. or wherever you saved sd-v1-4.ckpt
+    # Create a link to the checkpoint
+    ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
+    ```
 
-!!! todo "create Conda Environment"
+    1. replace `$HOME/Downloads` with the Location where you actually stored the Checkppoint (`sd-v1-4.ckpt`)
 
-    === "M1 arm64"
+!!! todo "Create the environment & install packages"
+
+    === "M1 Mac"
 
         ```bash
-        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 \
-          conda env create \
-          -f environment-mac.yml \
-          && conda activate ldm
+        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
         ```
 
-
-    === "Intel x86_64"
+    === "Intel x86_64 Mac"
 
         ```bash
-        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 \
-          conda env create \
-          -f environment-mac.yml \
-          && conda activate ldm
+        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
         ```
 
-```{.bash .annotate title="preload models and run script"}
-# only need to do this once
-python scripts/preload_models.py
+    ```bash
+    # Activate the environment (you need to do this every time you want to run SD)
+    conda activate invokeai
 
-# now you can run SD in CLI mode
-python scripts/dream.py --full_precision  # (1)!
+    # This will download some bits and pieces and make take a while
+    (invokeai) python scripts/preload_models.py
 
-# or run the web interface!
-python scripts/dream.py --web
+    # Run SD!
+    (invokeai) python scripts/dream.py
 
-# The original scripts should work as well.
-python scripts/orig_scripts/txt2img.py \
-  --prompt "a photograph of an astronaut riding a horse" \
-  --plms
-```
+    # or run the web interface!
+    (invokeai) python scripts/invoke.py --web
 
-Note, `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
-create -f environment-mac.yml` never finishing in some situations. So
-it isn't required but wont hurt.
+    # The original scripts should work as well.
+    (invokeai) python scripts/orig_scripts/txt2img.py \
+        --prompt "a photograph of an astronaut riding a horse" \
+        --plms
+    ```
+    !!! info
+
+        `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
+        create -f environment-mac.yml` never finishing in some situations. So
+        it isn't required but wont hurt.
 ---
 
 ## Common problems
 
-After you followed all the instructions and try to run dream.py, you might
+After you followed all the instructions and try to run invoke.py, you might
 get several errors. Here's the errors I've seen and found solutions for.
 
 ### Is it slow?
@@ -172,13 +177,13 @@ python ./scripts/orig_scripts/txt2img.py \
 
 ### Doesn't work anymore?
 
-PyTorch nightly includes support for MPS. Because of this, this setup is
-inherently unstable. One morning I woke up and it no longer worked no matter
-what I did until I switched to miniforge. However, I have another Mac that works
-just fine with Anaconda. If you can't get it to work, please search a little
-first because many of the errors will get posted and solved. If you can't find a
-solution please
-[create an issue](https://github.com/invoke-ai/InvokeAI/issues).
+PyTorch nightly includes support for MPS. Because of this, this setup
+is inherently unstable. One morning I woke up and it no longer worked
+no matter what I did until I switched to miniforge. However, I have
+another Mac that works just fine with Anaconda. If you can't get it to
+work, please search a little first because many of the errors will get
+posted and solved. If you can't find a solution please [create an
+issue](https://github.com/invoke-ai/InvokeAI/issues).
 
 One debugging step is to update to the latest version of PyTorch nightly.
 
@@ -187,10 +192,9 @@ conda install \
   pytorch \
   torchvision \
   -c pytorch-nightly \
-  -n ldm
+  -n invokeai
 ```
 
-
 If it takes forever to run `conda env create -f environment-mac.yml`, try this:
 
 ```bash
@@ -202,27 +206,27 @@ conda clean \
 
 Or you could try to completley reset Anaconda:
 
-    ```bash
-    conda update \
-      --force-reinstall \
-      -y \
-      -n base \
-     -c defaults conda
+```bash
+conda update \
+  --force-reinstall \
+  -y \
+  -n base \
+  -c defaults conda
 ```
 
 ---
 
-### "No module named cv2", torch, 'ldm', 'transformers', 'taming', etc
+### "No module named cv2", torch, 'invokeai', 'transformers', 'taming', etc
 
 There are several causes of these errors:
 
-1. Did you remember to `conda activate ldm`? If your terminal prompt begins with
-   "(ldm)" then you activated it. If it begins with "(base)" or something else
+1. Did you remember to `conda activate invokeai`? If your terminal prompt begins with
+   "(invokeai)" then you activated it. If it begins with "(base)" or something else
    you haven't.
 
-2. You might've run `./scripts/preload_models.py` or `./scripts/dream.py`
+2. You might've run `./scripts/preload_models.py` or `./scripts/invoke.py`
    instead of `python ./scripts/preload_models.py` or
-   `python ./scripts/dream.py`. The cause of this error is long so it's below.
+   `python ./scripts/invoke.py`. The cause of this error is long so it's below.
 
     <!-- I could not find out where the error is, otherwise would have marked it as a footnote -->
 
@@ -231,17 +235,17 @@ There are several causes of these errors:
 
     ```bash
     conda deactivate
-    conda env remove -n ldm
+    conda env remove -n invokeai
     conda env create -f environment-mac.yml
     ```
     
-4. If you have activated the ldm virtual environment and tried rebuilding it,
+4. If you have activated the invokeai virtual environment and tried rebuilding it,
    maybe the problem could be that I have something installed that you don't and
    you'll just need to manually install it. Make sure you activate the virtual
    environment so it installs there instead of globally.
 
     ```bash
-    conda activate ldm
+    conda activate invokeai
     pip install <package name>
     ```
 
@@ -299,12 +303,12 @@ should actually be the _same python_, which you can verify by comparing the
 output of `python3 -V` and `python -V`.
 
 ```bash
-(ldm) % which python
-/Users/name/miniforge3/envs/ldm/bin/python
+(invokeai) % which python
+/Users/name/miniforge3/envs/invokeai/bin/python
 ```
 
 The above is what you'll see if you have miniforge and correctly activated the
-ldm environment, while usingd the standalone setup instructions above.
+invokeai environment, while usingd the standalone setup instructions above.
 
 If you otherwise installed via pyenv, you will get this result:
 
@@ -378,7 +382,7 @@ python scripts/preload_models.py
     WARNING: this will be slower than running natively on MPS.
     ```
 
-This fork already includes a fix for this in
+The InvokeAI version includes this fix in
 [environment-mac.yml](https://github.com/invoke-ai/InvokeAI/blob/main/environment-mac.yml).
 
 ### "Could not build wheels for tokenizers"
@@ -463,13 +467,10 @@ C.
 
 You don't have a virus. It's part of the project. Here's
 [Rick](https://github.com/invoke-ai/InvokeAI/blob/main/assets/rick.jpeg)
-and here's
-[the code](https://github.com/invoke-ai/InvokeAI/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
-that swaps him in. It's a NSFW filter, which IMO, doesn't work very good (and we
-call this "computer vision", sheesh).
-
-Actually, this could be happening because there's not enough RAM. You could try
-the `model.half()` suggestion or specify smaller output images.
+and here's [the
+code](https://github.com/invoke-ai/InvokeAI/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
+that swaps him in. It's a NSFW filter, which IMO, doesn't work very
+good (and we call this "computer vision", sheesh).
 
 ---
 
@@ -487,16 +488,14 @@ this issue too. I should probably test it.
 ### "view size is not compatible with input tensor's size and stride"
 
 ```bash
-File "/opt/anaconda3/envs/ldm/lib/python3.10/site-packages/torch/nn/functional.py", line 2511, in layer_norm
+File "/opt/anaconda3/envs/invokeai/lib/python3.10/site-packages/torch/nn/functional.py", line 2511, in layer_norm
 return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
 RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
 ```
 
-Update to the latest version of invoke-ai/InvokeAI. We were patching
-pytorch but we found a file in stable-diffusion that we could change instead.
-This is a 32-bit vs 16-bit problem.
-
----
+Update to the latest version of invoke-ai/InvokeAI. We were
+patching pytorch but we found a file in stable-diffusion that we could
+change instead. This is a 32-bit vs 16-bit problem.
 
 ### The processor must support the Intel bla bla bla
 
@@ -519,13 +518,13 @@ use ARM packages, and use `nomkl` as described above.
 May appear when just starting to generate, e.g.:
 
 ```bash
-dream> clouds
+invoke> clouds
 Generating:   0%|                                                              | 0/1 [00:00<?, ?it/s]/Users/[...]/dev/stable-diffusion/ldm/modules/embedding_manager.py:152: UserWarning: The operator 'aten::nonzero' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1662016319283/work/aten/src/ATen/mps/MPSFallback.mm:11.)
   placeholder_idx = torch.where(
                                                                                                     loc("mps_add"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/20d6c351-ee94-11ec-bcaf-7247572f23b4/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":219:0)): error: input types 'tensor<2x1280xf32>' and 'tensor<*xf16>' are not broadcast compatible
 LLVM ERROR: Failed to infer result type(s).
 Abort trap: 6
-/Users/[...]/opt/anaconda3/envs/ldm/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
+/Users/[...]/opt/anaconda3/envs/invokeai/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
   warnings.warn('resource_tracker: There appear to be %d '
 ```
 

docs/installation/INSTALL_WINDOWS.md

@@ -1,5 +1,5 @@
 ---
-title: Windows
+title: Manual Installation, Windows
 ---
 
 # :fontawesome-brands-windows: Windows
@@ -39,7 +39,7 @@ in the wiki
 
 4. Run the command:
 
-    ```bash
+    ```batch
     git clone https://github.com/invoke-ai/InvokeAI.git
     ```
 
@@ -48,17 +48,21 @@ in the wiki
 
 5. Enter the newly-created InvokeAI folder. From this step forward make sure that you are working in the InvokeAI directory!
 
-```
-cd InvokeAI
-```
+    ```batch
+    cd InvokeAI
+    ```
 
 6. Run the following two commands:
 
-```
-conda env create       (step 6a)
-conda activate ldm     (step 6b)
-```
-    This will install all python requirements and activate the "ldm" environment
+    ```batch title="step 6a"
+    conda env create
+    ```
+
+    ```batch title="step 6b"
+    conda activate invokeai
+    ```
+
+    This will install all python requirements and activate the "invokeai" environment
     which sets PATH and other environment variables properly.
 
     Note that the long form of the first command is `conda env create -f environment.yml`. If the
@@ -67,7 +71,7 @@ conda activate ldm     (step 6b)
 
 7. Run the command:
 
-    ```bash
+    ```batch
     python scripts\preload_models.py
     ```
 
@@ -79,45 +83,35 @@ conda activate ldm     (step 6b)
 
 8. Now you need to install the weights for the big stable diffusion model.
 
-- For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
-- Use your credentials to log in, and then point your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
-- You may be asked to sign a license agreement at this point.
-- Click on "Files and versions" near the top of the page, and then click on the file named `sd-v1-4.ckpt`. You'll be taken to a page that
-  prompts you to click the "download" link. Now save the file somewhere safe on your local machine.
-- The weight file is >4 GB in size, so
-  downloading may take a while.
+   - Sign up at https://huggingface.co
+   - Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
+   - Accept the terms and click Access Repository
+   - Download [v1-5-pruned-emaonly.ckpt (4.27 GB)](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt)
+     and move it into this directory under `models/ldm/stable_diffusion_v1/v1-5-pruned-emaonly.ckpt`
 
-Now run the following commands from **within the InvokeAI directory** to copy the weights file to the right place:
-
-```
-mkdir -p models\ldm\stable-diffusion-v1
-copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
-```
-
-Please replace `C:\path\to\sd-v1.4.ckpt` with the correct path to wherever you stashed this file. If you prefer not to copy or move the .ckpt file,
-you may instead create a shortcut to it from within `models\ldm\stable-diffusion-v1\`.
+   There are many other models that you can use. Please see [../features/INSTALLING_MODELS.md]
+   for details.
 
 9. Start generating images!
 
-    ```bash
-    # for the pre-release weights
-    python scripts\dream.py -l
-
-    # for the post-release weights
-    python scripts\dream.py
+    ```batch title="for the pre-release weights"
+    python scripts\invoke.py -l
     ```
 
-10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3),enter the InvokeAI directory (step 5, `cd \path\to\InvokeAI`), run `conda activate ldm` (step 6b), and then launch the dream script (step 9).
+    ```batch title="for the post-release weights"
+    python scripts\invoke.py
+    ```
+
+10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3),enter the InvokeAI directory (step 5, `cd \path\to\InvokeAI`), run `conda activate invokeai` (step 6b), and then launch the invoke script (step 9).
+
+!!! tip "Tildebyte has written an alternative"
 
-    **Note:** Tildebyte has written an alternative
     ["Easy peasy Windows install"](https://github.com/invoke-ai/InvokeAI/wiki/Easy-peasy-Windows-install)
     which uses the Windows Powershell and pew. If you are having trouble with
     Anaconda on Windows, give this a try (or try it first!)
 
 ---
 
-This distribution is changing rapidly. If you used the `git clone` method (step 5) to download the InvokeAI directory, then to update to the latest and greatest version, launch the Anaconda window, enter `InvokeAI`, and type:
-
 This distribution is changing rapidly. If you used the `git clone` method
 (step 5) to download the stable-diffusion directory, then to update to the
 latest and greatest version, launch the Anaconda window, enter

docs/other/CONTRIBUTORS.md

@@ -58,6 +58,7 @@ We thank them for all of their time and hard work.
 - [rabidcopy](https://github.com/rabidcopy)
 - [Dominic Letz](https://github.com/dominicletz)
 - [Dmitry T.](https://github.com/ArDiouscuros)
+- [Kent Keirsey](https://github.com/hipsterusername)
 
 ## **Original CompVis Authors:**