update requirements to address #1149

Taken from `main` PR #1147 
Author: eltociear

.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/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 +46,52 @@ 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
+          [[ -d models/ldm/stable-diffusion-v1 ]] \
+            || mkdir -p models/ldm/stable-diffusion-v1
+          [[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
+            || curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
+
+      - name: Use cached Conda Environment
         uses: actions/cache@v3
         env:
-          cache-name: cache-conda-env-ldm
+          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
         with:
-          path: ~/.conda/envs/ldm
+          path: ${{ env.CONDA_ROOT }}/envs/${{ env.CONDA_ENV_NAME }}
           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-huggingface-torch
+          restore-keys: ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Use cached Conda Packages
         uses: actions/cache@v3
         env:
-          cache-name: cache-huggingface-torch
+          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
+        with:
+          path: ${{ env.CONDA_PKGS_DIR }}
+          key: ${{ env.cache-name }}
+          restore-keys: ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Activate Conda Env
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: ${{ matrix.environment-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-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

@@ -1,28 +0,0 @@
-name: Deploy
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-jobs:
-  build:
-    name: Deploy docs to GitHub Pages
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-      - name: Build
-        uses: Tiryoh/actions-mkdocs@v0
-        with:
-          mkdocs_version: 'latest' # option
-          requirements: '/requirements-mkdocs.txt' # option
-          configfile: '/mkdocs.yml' # option
-      - name: Deploy
-        uses: peaceiris/actions-gh-pages@v3
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          publish_dir: ./site

.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-invoke-conda.yml

@@ -4,29 +4,55 @@ on:
     branches:
       - 'main'
       - 'development'
+      - 'fix-gh-actions-fork'
+  pull_request:
+    branches:
+      - 'main'
+      - 'development'
+
 jobs:
   os_matrix:
     strategy:
       matrix:
-        os: [ ubuntu-latest, macos-12 ]
+        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: ${{ matrix.default-shell }}
     steps:
-      - run: |
-          echo The PR was merged
-      - name: Set platform variables
-        id: vars
-        run: |
-          # Note, can't "activate" via github action; specifying the env's python has the same effect
-          if [ "$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: setup miniconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-activate-base: false
+          auto-update-conda: false
+          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/pr_prompt.txt" >> $GITHUB_ENV
+
+      - name: set conda environment name
+        run: echo "CONDA_ENV_NAME=invokeai" >> $GITHUB_ENV
+
       - name: Use Cached Stable Diffusion v1.4 Model
         id: cache-sd-v1-4
         uses: actions/cache@v3
@@ -35,31 +61,40 @@ 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
+          [[ -d models/ldm/stable-diffusion-v1 ]] \
+            || mkdir -p models/ldm/stable-diffusion-v1
+          [[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
+            || curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
+
+      - name: Use cached Conda Environment
         uses: actions/cache@v3
         env:
-          cache-name: cache-conda-env-ldm
+          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
         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 }}
+          path: ${{ env.CONDA }}/envs/${{ env.CONDA_ENV_NAME }}
+          key: env-${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Use cached Conda Packages
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-conda-pkgs-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
+        with:
+          path: ${{ env.CONDA_PKGS_DIR }}
+          key: pkgs-${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Activate Conda Env
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: ${{ matrix.environment-file }}
+
       - name: Use Cached Huggingface and Torch models
         id: cache-hugginface-torch
         uses: actions/cache@v3
@@ -70,28 +105,22 @@ jobs:
           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 preload_models.py
+        run: python scripts/preload_models.py
+
       - 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/invoke.py --from_file tests/preflight_prompts.txt
-          elif [[ ${{ github.ref }} == 'refs/heads/development' ]]; then
-            time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/invoke.py --from_file tests/dev_prompts.txt
-          fi
+          time python scripts/invoke.py \
+            --from_file ${{ env.TEST_PROMPTS }}
+
+      - name: export conda env
+        run: |
           mkdir -p outputs/img-samples
+          conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}.yml
+
       - name: Archive results
         uses: actions/upload-artifact@v3
         with:
-          name: results
+          name: results_${{ matrix.os }}
           path: outputs/img-samples

.gitignore

@@ -1,7 +1,7 @@
 # ignore default image save location and model symbolic link
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
-ldm/dream/restoration/codeformer/weights
+**/restoration/codeformer/weights
 
 # ignore the Anaconda/Miniconda installer used while building Docker image
 anaconda.sh
@@ -180,7 +180,7 @@ src
 **/__pycache__/
 outputs
 
-# Logs and associated folders 
+# Logs and associated folders
 # created from generated embeddings.
 logs
 testtube

LICENSE

@@ -1,17 +1,6 @@
 MIT License
 
-Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
-
-This software is derived from a fork of the source code available from
-https://github.com/pesser/stable-diffusion and
-https://github.com/CompViz/stable-diffusion. They carry the following
-copyrights:
-
-Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
-Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
-
-Please see individual source code files for copyright and authorship
-attributions.
+Copyright (c) 2022 InvokeAI Team
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

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**
+_Formally known as lstein/stable-diffusion_
 
 ![project logo](docs/assets/logo.png)
 
@@ -47,7 +40,12 @@ 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, and an easy-to-use command-line interface.
+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
@@ -70,11 +68,11 @@ requests. Be sure to use the provided templates. They will help aid diagnose iss
 This fork is supported across multiple platforms. You can find individual installation instructions
 below.
 
-- #### [Linux](docs/installation/INSTALL_LINUX.md)
+- #### [Linux](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_LINUX/)
 
-- #### [Windows](docs/installation/INSTALL_WINDOWS.md)
+- #### [Windows](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_WINDOWS/)
 
-- #### [Macintosh](docs/installation/INSTALL_MAC.md)
+- #### [Macintosh](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_MAC/)
 
 ### Hardware Requirements
 
@@ -91,7 +89,7 @@ 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**
 
@@ -105,97 +103,73 @@ errors like 'expected type Float but found Half' or 'not implemented for Half'
 you can try starting `invoke.py` with the `--precision=float32` flag:
 
 ```bash
-(ldm) ~/stable-diffusion$ python scripts/invoke.py --precision=float32
+(invokeai) ~/InvokeAI$ 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)
-- [Outpainting Support](docs/features/OUTPAINTING.md)
-- [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)
-- [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)
-- [Thresholding and Perlin Noise Initialization Options](/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options)
-- [Negative/Unconditioned Prompts](docs/features/PROMPTS.md#negative-and-unconditioned-prompts)
-- [Variations](docs/features/VARIATIONS.md)
-- [Personalizing Text-to-Image Generation](docs/features/TEXTUAL_INVERSION.md)
-- [Simplified API for text to image generation](docs/features/OTHER.md#simplified-api)
+- [Web Server](https://invoke-ai.github.io/InvokeAI/features/WEB/)
+- [Interactive Command Line Interface](https://invoke-ai.github.io/InvokeAI/features/CLI/)
+- [Image To Image](https://invoke-ai.github.io/InvokeAI/features/IMG2IMG/)
+- [Inpainting Support](https://invoke-ai.github.io/InvokeAI/features/INPAINTING/)
+- [Outpainting Support](https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/)
+- [Upscaling, face-restoration and outpainting](https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/)
+- [Reading Prompts From File](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#reading-prompts-from-a-file)
+- [Prompt Blending](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#prompt-blending)
+- [Thresholding and Perlin Noise Initialization Options](https://invoke-ai.github.io/InvokeAI/features/OTHER/#thresholding-and-perlin-noise-initialization-options)
+- [Negative/Unconditioned Prompts](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#negative-and-unconditioned-prompts)
+- [Variations](https://invoke-ai.github.io/InvokeAI/features/VARIATIONS/)
+- [Personalizing Text-to-Image Generation](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/)
+- [Simplified API for text to image generation](https://invoke-ai.github.io/InvokeAI/features/OTHER/#simplified-api)
 
 #### Other Features
 
-- [Creating Transparent Regions for Inpainting](docs/features/INPAINTING.md#creating-transparent-regions-for-inpainting)
-- [Preload Models](docs/features/OTHER.md#preload-models)
+- [Google Colab](https://invoke-ai.github.io/InvokeAI/features/OTHER/#google-colab)
+- [Seamless Tiling](https://invoke-ai.github.io/InvokeAI/features/OTHER/#seamless-tiling)
+- [Shortcut: Reusing Seeds](https://invoke-ai.github.io/InvokeAI/features/OTHER/#shortcuts-reusing-seeds)
+- [Preload Models](https://invoke-ai.github.io/InvokeAI/features/OTHER/#preload-models)
 
 ### Latest Changes
 
+- 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 (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>
+  - Support for <a href="https://invoke-ai.github.io/InvokeAI/features/INPAINTING/">inpainting</a> and <a href="https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/">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 <a href="https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#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>
+  - Support in both WebGUI and CLI for <a href="https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/">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 `--hires` option on `invoke>` line allows <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/#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>.
+  - Improved <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/">command-line completion behavior</a>.
     New commands added:
-       * List command-line history with `!history`
-       * Search command-line history with `!search`
-       * Clear history with `!clear`
+    - 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 invoke.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)**.
+For older changelogs, please visit the **[CHANGELOG](https://invoke-ai.github.io/InvokeAI/CHANGELOG#v114-11-september-2022)**.
 
 ### Troubleshooting
 
-Please check out our **[Q&A](docs/help/TROUBLESHOOT.md)** to get solutions for common installation
+Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
 problems and other issues.
 
 # Contributing
@@ -213,7 +187,7 @@ changes.
 ### Contributors
 
 This fork is a combined effort of various people from across the world.
-[Check out the list of all these amazing people](docs/other/CONTRIBUTORS.md). We thank them for
+[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for
 their time, hard work and effort.
 
 ### Support
@@ -227,4 +201,4 @@ Original portions of the software are Copyright (c) 2020
 ### Further Reading
 
 Please see the original README for more information on this software and underlying algorithm,
-located in the file [README-CompViz.md](docs/other/README-CompViz.md).
+located in the file [README-CompViz.md](https://invoke-ai.github.io/InvokeAI/other/README-CompViz/).

backend/invoke_ai_web_server.py

@@ -319,7 +319,7 @@ class InvokeAIWebServer:
                 elif postprocessing_parameters['type'] == 'gfpgan':
                     image = self.gfpgan.process(
                         image=image,
-                        strength=postprocessing_parameters['gfpgan_strength'],
+                        strength=postprocessing_parameters['facetool_strength'],
                         seed=seed,
                     )
                 else:
@@ -625,7 +625,7 @@ class InvokeAIWebServer:
                         seed=seed,
                     )
                     postprocessing = True
-                    all_parameters['gfpgan_strength'] = gfpgan_parameters[
+                    all_parameters['facetool_strength'] = gfpgan_parameters[
                         'strength'
                     ]
 
@@ -723,6 +723,7 @@ class InvokeAIWebServer:
                 'height',
                 'extra',
                 'seamless',
+                'hires_fix',
             ]
 
             rfc_dict = {}
@@ -735,12 +736,12 @@ class InvokeAIWebServer:
             postprocessing = []
 
             # 'postprocessing' is either null or an
-            if 'gfpgan_strength' in parameters:
+            if 'facetool_strength' in parameters:
 
                 postprocessing.append(
                     {
                         'type': 'gfpgan',
-                        'strength': float(parameters['gfpgan_strength']),
+                        'strength': float(parameters['facetool_strength']),
                     }
                 )
 
@@ -837,7 +838,7 @@ class InvokeAIWebServer:
             elif parameters['type'] == 'gfpgan':
                 postprocessing_metadata['type'] = 'gfpgan'
                 postprocessing_metadata['strength'] = parameters[
-                    'gfpgan_strength'
+                    'facetool_strength'
                 ]
             else:
                 raise TypeError(f"Invalid type: {parameters['type']}")

backend/modules/parameters.py

@@ -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

@@ -349,7 +349,7 @@ def handle_run_gfpgan_event(original_image, gfpgan_parameters):
     eventlet.sleep(0)
 
     image = gfpgan.process(
-        image=image, strength=gfpgan_parameters["gfpgan_strength"], seed=seed
+        image=image, strength=gfpgan_parameters["facetool_strength"], seed=seed
     )
 
     progress["currentStatus"] = "Saving image"
@@ -464,7 +464,7 @@ def parameters_to_post_processed_image_metadata(parameters, original_image_path,
         image["strength"] = parameters["upscale"][1]
     elif type == "gfpgan":
         image["type"] = "gfpgan"
-        image["strength"] = parameters["gfpgan_strength"]
+        image["strength"] = parameters["facetool_strength"]
     else:
         raise TypeError(f"Invalid type: {type}")
 
@@ -493,6 +493,7 @@ def parameters_to_generated_image_metadata(parameters):
         "height",
         "extra",
         "seamless",
+        "hires_fix",
     ]
 
     rfc_dict = {}
@@ -505,10 +506,10 @@ def parameters_to_generated_image_metadata(parameters):
     postprocessing = []
 
     # 'postprocessing' is either null or an
-    if "gfpgan_strength" in parameters:
+    if "facetool_strength" in parameters:
 
         postprocessing.append(
-            {"type": "gfpgan", "strength": float(parameters["gfpgan_strength"])}
+            {"type": "gfpgan", "strength": float(parameters["facetool_strength"])}
         )
 
     if "upscale" in parameters:
@@ -751,7 +752,7 @@ def generate_images(generation_parameters, esrgan_parameters, gfpgan_parameters)
                 image=image, strength=gfpgan_parameters["strength"], seed=seed
             )
             postprocessing = True
-            all_parameters["gfpgan_strength"] = gfpgan_parameters["strength"]
+            all_parameters["facetool_strength"] = gfpgan_parameters["strength"]
 
         progress["currentStatus"] = "Saving image"
         socketio.emit("progressUpdate", progress)

configs/models.yaml

@@ -9,10 +9,12 @@
 laion400m:
     config:  configs/latent-diffusion/txt2img-1p4B-eval.yaml
     weights: models/ldm/text2img-large/model.ckpt
+    description: Latent Diffusion LAION400M model
     width: 256
     height: 256
 stable-diffusion-1.4:
     config:  configs/stable-diffusion/v1-inference.yaml
     weights: models/ldm/stable-diffusion-v1/model.ckpt
+    description: Stable Diffusion inference model version 1.4
     width: 512
     height: 512

docs/CHANGELOG.md

@@ -1,18 +1,73 @@
-# **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 invoke> command line.
-- The grid was displaying duplicated images when not enough images to fill the final row [Muhammad Usama](https://github.com/SMUsamaShah)
+## 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 [inpainting](features/INPAINTING.md) and [outpainting](features/OUTPAINTING.md)
+- img2img runs on all k* samplers
+- Support for [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
+- Support for CodeFormer face reconstruction
+- Support for Textual Inversion on Macintoshes
+- Support in both WebGUI and CLI for [post-processing of previously-generated images](features/POSTPROCESS.md)
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+- New `--hires` option on `invoke>` line allows [larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img), at the cost of some performance.
+- New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see [Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
+- Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+- Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+- Improved [command-line completion behavior](features/CLI.md)
+    New commands added:
+  - List command-line history with `!history`
+  - Search command-line history with `!search`
+  - Clear history with `!clear`
+- Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+    configure. To switch away from auto use the new flag like `--precision=float32`.
+
+## v1.14 <small>(11 September 2022)</small>
+
+- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
+- Full support for Apple hardware with M1 or M2 chips.
+- Add "seamless mode" for circular tiling of image. Generates beautiful effects.
+    ([prixt](https://github.com/prixt)).
+- Inpainting support.
+- Improved web server GUI.
+- Lots of code and documentation cleanups.
+
+## v1.13 <small>(3 September 2022)</small>
+
+- Support image variations (see [VARIATIONS](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)
@@ -26,26 +81,26 @@
 
 ---
 
-## 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-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)
+  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). [
@@ -55,7 +110,7 @@
 
 ---
 
-## v1.08 (24 August 2022)
+## v1.08 <small>(24 August 2022)</small>
 
 - Escape single quotes on the invoke> command before trying to parse. This avoids
   parse errors.
@@ -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),
@@ -99,7 +154,7 @@
 
 ---
 
-## 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,
@@ -123,7 +178,7 @@
 
 ---
 
-## 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!!**
@@ -134,4 +189,4 @@
 
 ## Links
 
-- **[Read Me](../readme.md)**
+- **[Read Me](index.md)**

docs/features/CHANGELOG.md

@@ -1,143 +0,0 @@
----
-title: Changelog
----
-
-# :octicons-log-16: Changelog
-
-## v1.13
-
-- 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 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 invoke.py command line as the default.
-- Miscellaneous internal bug and stability fixes.
-
----
-
-## v1.12 <small>(28 August 2022)</small>
-
-- Improved file handling, including ability to read prompts from standard input.
-  (kudos to [Yunsaki](https://github.com/yunsaki)
-- The web server is now integrated with the invoke.py script. Invoke by adding
-  --web to the invoke.py command arguments.
-- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
-  enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
-  VRAM requirements are modestly reduced. Thanks to both
-  [Blessedcoolant](https://github.com/blessedcoolant) and
-  [Oceanswave](https://github.com/oceanswave) for their work on this.
-- You can now swap samplers on the invoke> command line.
-  [Blessedcoolant](https://github.com/blessedcoolant)
-
----
-
-## v1.11 <small>(26 August 2022)</small>
-
-- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module.
-  (kudos to [Oceanswave](https://github.com/Oceanswave))
-- You now can specify a seed of -1 to use the previous image's seed, -2 to use
-  the seed for the image generated before that, etc. Seed memory only extends
-  back to the previous command, but will work on all images generated with the
-  -n# switch.
-- Variant generation support temporarily disabled pending more general solution.
-- Created a feature branch named **yunsaki-morphing-invoke** which adds
-  experimental support for iteratively modifying the prompt and its parameters.
-  Please
-  see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86) for
-  a synopsis of how this works. Note that when this feature is eventually added
-  to the main branch, it will may be modified significantly.
-
----
-
-## v1.10 <small>(25 August 2022)</small>
-
-- A barebones but fully functional interactive web server for online generation
-  of txt2img and img2img.
-
----
-
-## v1.09 <small>(24 August 2022)</small>
-
-- A new -v option allows you to generate multiple variants of an initial image
-  in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave).
-- [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
-- Added ability to personalize text to image generation (kudos to
-  [Oceanswave](https://github.com/Oceanswave) and
-  [nicolai256](https://github.com/nicolai256))
-- Enabled all of the samplers from k_diffusion
-
----
-
-## v1.08 <small>(24 August 2022)</small>
-
-- Escape single quotes on the invoke> command before trying to parse. This avoids
-  parse errors.
-- Removed instruction to get Python3.8 as first step in Windows install.
-  Anaconda3 does it for you.
-- Added bounds checks for numeric arguments that could cause crashes.
-- Cleaned up the copyright and license agreement files.
-
----
-
-## v1.07 <small>(23 August 2022)</small>
-
-- Image filenames will now never fill gaps in the sequence, but will be assigned
-  the next higher name in the chosen directory. This ensures that the alphabetic
-  and chronological sort orders are the same.
-
----
-
-## v1.06 <small>(23 August 2022)</small>
-
-- Added weighted prompt support contributed by
-  [xraxra](https://github.com/xraxra)
-- Example of using weighted prompts to tweak a demonic figure contributed by
-  [bmaltais](https://github.com/bmaltais)
-
----
-
-## v1.05 <small>(22 August 2022 - after the drop)</small>
-
-- Filenames now use the following formats: 000010.95183149.png -- Two files
-  produced by the same command (e.g. -n2), 000010.26742632.png -- distinguished
-  by a different seed.
-    000011.455191342.01.png -- Two files produced by the same command using
-    000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
-    000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid
-  can be regenerated with the indicated key
-
-- It should no longer be possible for one image to overwrite another
-- You can use the "cd" and "pwd" commands at the invoke> prompt to set and
-  retrieve the path of the output directory.
-
-## v1.04 <small>(22 August 2022 - after the drop)</small>
-
-- Updated README to reflect installation of the released weights.
-- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
-  tokenizer.
-
-## v1.03 <small>(22 August 2022)</small>
-
-- The original txt2img and img2img scripts from the CompViz repository have been
-  moved into a subfolder named "orig_scripts", to reduce confusion.
-
-## v1.02 <small>(21 August 2022)</small>
-
-- A copy of the prompt and all of its switches and options is now stored in the
-  corresponding image in a tEXt metadata field named "Dream". You can read the
-  prompt using scripts/images2prompt.py, or an image editor that allows you to
-  explore the full metadata. **Please run "conda env update -f environment.yaml"
-  to load the k_lms dependencies!!**
-
-## v1.01 <small>(21 August 2022)</small>
-
-- 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 invoke.py to get
-  slower but more accurate image generation

docs/features/CLI.md

@@ -34,7 +34,7 @@ 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/invoke.py
+(invokeai) ~/stable-diffusion$ python3 ./scripts/invoke.py
 * Initializing, be patient...
 Loading model from models/ldm/text2img-large/model.ckpt
 (...more initialization messages...)
@@ -51,7 +51,7 @@ invoke> "there's a fly in my soup" -n6 -g
 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
@@ -60,7 +60,7 @@ invoke> q
 ![invoke-py-demo](../assets/dream-py-demo.png)
 
 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).
+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).
@@ -75,8 +75,7 @@ the location of the model weight files.
 
 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 +85,7 @@ 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)         |
 | `--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.                                                 |
@@ -101,42 +101,47 @@ overridden on a per-prompt basis (see [List of prompt arguments]
 | `--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 "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 |
+    </div>
 
-**A note on path names:** 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.
+!!! 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 invoke.py script initializes, it will present you with a
-**invoke>** 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).
+`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
 
-~~~~
-invoke> 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
+    ```
+
+    This will create the requested image with the dimensions 640 (width)
+    and 480 (height).
 
 Here are the invoke> command that apply to txt2img:
 
-| Argument           |  Shortcut  |  Default            |  Description |
+| 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 |
@@ -147,6 +152,7 @@ Here are the invoke> command that apply to txt2img:
 | --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. |
 | --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 |
@@ -154,10 +160,13 @@ Here are the invoke> command that apply to txt2img:
 | --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
@@ -179,69 +188,262 @@ 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
 
-~~~~
-invoke> 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:
 
-| 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.|
 
+# Other Commands
 
-# Convenience commands
+The CLI offers a number of commands that begin with "!".
 
-In addition to the standard image generation arguments, there are a
-series of convenience commands that begin with !:
+## Postprocessing images
 
-## !fix
+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:
-~~~
-invoke> !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:
 
-~~~
-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
+    ```bash
+    invoke> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
+    ```
 
-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
-~~~
+!!! example ""
+
+    Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen:
+
+    ```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
+
+# Model selection and importation
+
+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.
+```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
+```
+
+Note that this command may behave unexpectedly if given a PNG file that
+was not generated by InvokeAI.
+
+### `!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`,
+where "NNN" is the history line number. For example:
+
+```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
+[18] beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6
+[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
+...
+invoke> !20
+invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+```
 
 ## !fetch
 
@@ -259,81 +461,56 @@ invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
 Note that this command may behave unexpectedly if given a PNG file that
 was not generated by InvokeAI.
 
-## !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,
-where "NNN" is the history line number. For example:
-
-~~~
-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
-[18] beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6
-[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
-...
-invoke> !20
-invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
-~~~
-
-## !search <search string>
+### !search <search string>
 
 This is similar to !history but it only returns lines that contain
 `search string`. For example:
 
-~~~
+```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 invoke.py with the "winpty" program and have the `pyreadline3`
+launch `invoke.py` with the `winpty` program and have the `pyreadline3`
 library installed:
 
-~~~
+```batch
 > winpty python scripts\invoke.py
-~~~
+```
 
 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
-invoke.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:
 
-~~~
+```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
-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
-```
+    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
-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
-```
+!!! 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:
 
-~~~~
+```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
@@ -15,13 +17,17 @@ tree on a hill with a river, nature photograph, national geographic -I./test-pic
 
 This will take the original image shown here:
 
+<figure markdown>
 <img src="https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png" width=350>
-                                                                                                               
+</figure>
+
 and generate a new image based on it as shown here:
 
+<figure markdown>
 <img src="https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png" width=350>
+</figure>
 
-The `--init_img (-I)` option gives the path to the seed picture. `--strength (-f)` controls how much
+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 `0.25-0.90` give interesting results. 
 Other relevant options include `-C` (classification free guidance scale), and `-s` (steps). Unlike `txt2img`, 
@@ -35,23 +41,23 @@ 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`
-
+<figure markdown>
 <img src="https://user-images.githubusercontent.com/111189/194135220-16b62181-b60c-4248-8989-4834a8fd7fbd.png" width=350>
+<caption markdown>photograph of a tree on a hill with a river</caption>
+</figure>
 
-(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.)
+!!! 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
 
-<<<<<<< HEAD
-=======
 **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
@@ -60,74 +66,67 @@ by width x height:
 tree on a hill with a river, national geographic -I./test-pictures/big-sketch.png -H512 -W512 --fit
 ~~~
 
->>>>>>> main
 ## 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
-<<<<<<< HEAD
-dream> "fire" -s10 -W384 -H384 -S1592514025
-=======
 invoke> "fire" -s10 -W384 -H384 -S1592514025
->>>>>>> main
 ```
 
+<figure markdown>
 ![latent steps](../assets/img2img/000019.steps.png)
+</figure>
 
-Put simply: starting from a frame of fuzz/static, SD finds details in each frame that it thinks look like "fire" and brings them a little bit more into focus, gradually scrubbing out the fuzz until a clear image remains. 
+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:
 
+<figure markdown>
 ![drawing of a fireplace](../assets/img2img/fire-drawing.png)
+</figure>
 
 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)
+<figure markdown>
+![gravity32](../assets/img2img/000032.steps.gravity.png)
+</figure>
 
 With strength `0.4`, the steps look more like this:
 
-![](../assets/img2img/000030.steps.gravity.png)
+<figure markdown>
+![gravity30](../assets/img2img/000030.steps.gravity.png)
+</figure>
 
 Notice how much more fuzzy the starting image is for strength `0.7` compared to `0.4`, and notice also how much longer the sequence is with `0.7`:
 
 |  | strength = 0.7 | strength = 0.4 |
 | -- | -- | -- |
 | initial image that SD sees | ![](../assets/img2img/000032.step-0.png) | ![](../assets/img2img/000030.step-0.png) |
-<<<<<<< HEAD
-| steps argument to `dream>` | `-S10` | `-S10` |
-=======
 | steps argument to `invoke>` | `-S10` | `-S10` |
->>>>>>> main
 | 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
-<<<<<<< HEAD
-dream> "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. 
-=======
 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 `invoke.py` and check your `outputs/img-samples/intermediates` folder while generating an image. 
->>>>>>> main
 
 ### Compensating for the reduced step count
 
@@ -136,44 +135,51 @@ 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
-<<<<<<< HEAD
-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
->>>>>>> main
 ```
 
-![](../assets/img2img/000035.1592514025.png)
+<figure markdown>
+![000035.1592514025](../assets/img2img/000035.1592514025.png)
+</figure>
 
-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
-<<<<<<< HEAD
-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
->>>>>>> main
 ```
 
-![](../assets/img2img/000046.1592514025.png)
+<figure markdown>
+![000046.1592514025](../assets/img2img/000046.1592514025.png)
+</figure>
 
 In both cases the image is nice and clean and "finished", but because at strength `0.7` Stable Diffusion has been give so much more freedom to improve on my badly-drawn flames, they've come out looking much better. You can really see the difference when looking at the latent steps. There's more noise on the first image with strength `0.7`:
 
-![](../assets/img2img/000046.steps.gravity.png)
+<figure markdown>
+![gravity46](../assets/img2img/000046.steps.gravity.png)
+</figure>
 
 than there is for strength `0.4`:
 
-![](../assets/img2img/000035.steps.gravity.png)
+<figure markdown>
+![gravity35](../assets/img2img/000035.steps.gravity.png)
+</figure>
 
-and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image. 
+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)
+<figure markdown>
+![gravity45](../assets/img2img/000045.1592514025.png)
+</figure>
 
 By comparing the latents we can sort of see that something got interpreted differently enough on the third or fourth step to lead to a rather different interpretation of the flames.
 
-![](../assets/img2img/000046.steps.gravity.png)
-![](../assets/img2img/000045.steps.gravity.png)
+<figure markdown>
+![gravity46](../assets/img2img/000046.steps.gravity.png)
+</figure>
 
-This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see 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. 
+<figure markdown>
+![gravity45](../assets/img2img/000045.steps.gravity.png)
+</figure>
+
+This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see [stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step.

docs/features/INPAINTING.md

@@ -6,21 +6,29 @@ 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 invoke> 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, `invoke.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
 invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent.png
@@ -28,6 +36,26 @@ invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent
 
 We are hoping to get rid of the need for this workaround in an upcoming release.
 
+### 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.
+
 ---
 
 ## Recipe for GIMP
@@ -36,7 +64,7 @@ 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 (^L) and select "Floating Selection"
 6. Set opacity to a value between 0% and 99%
@@ -44,33 +72,46 @@ We are hoping to get rid of the need for this workaround in an upcoming release.
 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)
+    <figure markdown>
+    ![step1](../assets/step1.png)
+    </figure>
 
 2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area you desire to inpaint.
 
-![step2](../assets/step2.png)
+    <figure markdown>
+    ![step2](../assets/step2.png)
+    </figure>
 
 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)
+    <figure markdown>
+    ![step4](../assets/step4.png)
+    </figure>
 
 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)
+    <figure markdown>
+    ![step5](../assets/step5.png)
+    </figure>
 
-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)
+    <figure markdown>
+    ![step6](../assets/step6.png)
+    </figure>
 
 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)
+    <figure markdown>
+   ![step7](../assets/step7.png)
+    </figure>
 
 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)
 
 ---
 
@@ -33,12 +31,12 @@ invoke> "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
@@ -58,7 +56,7 @@ 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 +69,30 @@ combination of integers and floating point numbers, and they do not need to add
 
 ---
 
-## Thresholding and Perlin Noise Initialization Options
+## **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 +124,7 @@ 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

@@ -25,14 +25,16 @@ implementations.
 
 Consider this image:
 
+<figure markdown>
 ![curly_woman](../assets/outpainting/curly.png)
+</figure>
 
 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!
 
-~~~~
+```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 +44,9 @@ specify any number of pixels to extend. You can also abbreviate
 
 The result looks like this:
 
+<figure markdown>
 ![curly_woman_outcrop](../assets/outpainting/curly-outcrop.png)
+</figure>
 
 The new image is actually slightly larger than the original (576x576,
 because 64 pixels were added to the top and right sides.)
@@ -66,33 +70,36 @@ 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:
 
-~~~
+```bash
 invoke> !fix images/curly.png --out_direction top 64
-~~~
+```
 
 (you can abbreviate `--out_direction` as `-D`.
 
 The result is shown here:
 
+<figure markdown>
 ![curly_woman_outpaint](../assets/outpainting/curly-outpaint.png)
+</figure>
 
 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
+- You can only specify one direction to extend at a time.
+- 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
+- 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
+- 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
+- For best results, try increasing the step number.
+- 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`.
+!!! tip
 
+    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
@@ -33,13 +34,13 @@ 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
 python scripts/preload_models.py
-~~~~
+```
 
 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. 
+reconstruction.
 
 Alternatively, if you have GFPGAN installed elsewhere, or if you are
 using an earlier version of this package which asked you to install
@@ -69,7 +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.
@@ -88,13 +89,13 @@ too.
 ### Example Usage
 
 ```bash
-invoke> 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
-invoke> 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
@@ -122,20 +123,20 @@ 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.
+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.
 
-### 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
@@ -161,7 +162,7 @@ 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:
 
-```
+```bash
 invoke> !fix ./outputs/img-samples/000044.2945021133.png -G 0.8 -U 2
 ```
 
@@ -169,7 +170,7 @@ 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 invoke.py command line with the `--no_restore` and

docs/features/PROMPTS.md

@@ -1,8 +1,8 @@
 ---
-title: Prompting Features
+title: Prompting-Features
 ---
 
-# :octicons-command-palette-24: Prompting Features
+# :octicons-command-palette-24: Prompting-Features
 
 ## **Reading Prompts from a File**
 
@@ -19,14 +19,15 @@ innovative packaging for a squid's dinner           -S137038382
 Then pass this file's name to `invoke.py` when you invoke it:
 
 ```bash
-(ldm) ~/stable-diffusion$ python3 scripts/invoke.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/invoke.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.
 ```
 
@@ -46,25 +47,33 @@ 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`
 
+<figure markdown>
 ![step1](../assets/negative_prompt_walkthru/step1.png)
+</figure>
 
 That image has a woman, so if we want the horse without a rider, we can influence the image not to have a woman by putting [woman] in the prompt, like this:
 
 `#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
+<figure markdown>
 ![step2](../assets/negative_prompt_walkthru/step2.png)
+</figure>
 
 That's nice - but say we also don't want the image to be quite so blue. We can add "blue" to the list of negative prompts, so it's now [woman blue]:
 
 `#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
+<figure markdown>
 ![step3](../assets/negative_prompt_walkthru/step3.png)
+</figure>
 
 Getting close - but there's no sense in having a saddle when our horse doesn't have a rider, so we'll add one more negative prompt: [woman blue saddle].
 
 `#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
+<figure markdown>
 ![step4](../assets/negative_prompt_walkthru/step4.png)
+</figure>
 
 !!! notes "Notes about this feature:"
 
@@ -101,44 +110,58 @@ 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.
 
+---
+
+<figure markdown>
 ### "blue sphere, red cube, hybrid"
+</figure>
 
 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>
+<figure markdown>
+![blue-sphere-red-cube-hyprid](../assets/prompt-blending/blue-sphere-red-cube-hybrid.png)
+</figure>
 
 It's interesting to see how the AI expressed the concept of "cube" as
 the four quadrants of the enclosing frame. If you look closely, there
 is depth there, so the enclosing frame is actually a cube.
 
+<figure markdown>
 ### "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)
+</figure>
 
 Now that's interesting. We get neither a blue sphere nor a red cube,
 but a red sphere embedded in a brick wall, which represents a melding
 of concepts within the AI's "latent space" of semantic
 representations. Where is Ludwig Wittgenstein when you need him?
 
+<figure markdown>
 ### "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)
+</figure>
 
 Definitely more blue-spherey. The cube is gone entirely, but it's
 really cool abstract art.
 
+<figure 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)
+</figure>
 
 Whoa...! I see blue and red, but no spheres or cubes. Is the word
 "hybrid" summoning up the concept of some sort of scifi creature?
 Let's find out.
 
+<figure 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)
+</figure>
 
 Indeed, removing the word "hybrid" produces an image that is more like
 what we'd expect.
@@ -146,4 +169,3 @@ 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
@@ -59,7 +59,8 @@ Once the model is trained, specify the trained .pt or .bin file when starting
 invoke using
 
 ```bash
-python3 ./scripts/invoke.py --embedding_path /path/to/embedding.pt
+python3 ./scripts/invoke.py \
+    --embedding_path /path/to/embedding.pt
 ```
 
 Then, to utilize your subject at the invoke prompt
@@ -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

@@ -25,10 +25,11 @@ 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:
@@ -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>
 
 ---
 
@@ -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
@@ -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
@@ -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

@@ -2,12 +2,14 @@
 title: InvokeAI Web Server
 ---
 
+# :material-web: InvokeAI Web Server
+
 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) ~/InvokeAI$ python3 scripts/invoke.py --web
+(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web
 ```
 
 You can then connect to the server by pointing your web browser at
@@ -17,7 +19,7 @@ either the IP address of the host you are running it on, or the
 wildcard `0.0.0.0`. For example:
 
 ```bash
-(ldm) ~/InvokeAI$ python3 scripts/invoke.py --web --host 0.0.0.0
+(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web --host 0.0.0.0
 ```
 
 # Quick guided walkthrough of the WebGUI's features
@@ -25,7 +27,7 @@ wildcard `0.0.0.0`. For example:
 While most of the WebGUI's features are intuitive, here is a guided
 walkthrough through its various components.
 
-<img src="../assets/invoke-web-server-1.png" width=640>
+![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:
@@ -53,7 +55,9 @@ 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.
 
-<img src="../assets/invoke-web-server-2.png" width=512>
+<figure markdown>
+![Invoke Web Server - Control Panel](../assets/invoke-web-server-2.png){:width="512px"}
+</figure>
 
 From top to bottom, these are:
 
@@ -86,51 +90,51 @@ using its IP address or domain name.
 
 #### Basics
 
-3. Generate an image by typing *strawberry sushi* into the large
+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.
+    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.
+    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.
 
-4. Generate a bunch of strawberry sushi images by increasing the
+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.
 
-5. Try playing with different settings, including image width and
+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.
+    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.
+    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.
+    *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.
+    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
@@ -138,8 +142,8 @@ 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.
+    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
@@ -152,21 +156,21 @@ steps and dimensions, but it will (usually) be close.
 
 #### Variations on a theme
 
-5. Let's try generating some variations. Select your favorite sushi
+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.
+    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.
 
-6. For even more variations, experiment with increasing the setting
+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.
@@ -179,7 +183,7 @@ 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).
   
-7. Invoke a prompt that generates a mangled face. A prompt that often
+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
@@ -188,15 +192,16 @@ 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)
 
-<img src="../assets/invoke-web-server-3.png">
-<img src="../assets/invoke-web-server-4.png">
+    ![Invoke Web Server - Original Image](../assets/invoke-web-server-3.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.
+    ![Invoke Web Server - Retouched Image](../assets/invoke-web-server-4.png)
 
-8. "Upscaling" is the process of increasing the size of an image while
+    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
@@ -204,7 +209,7 @@ 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.
 
-9. Finally, you can run facial reconstruction and/or upscaling
+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*.
@@ -222,28 +227,32 @@ 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.
 
-10. Click on the *Image to Image* tab icon, which is the second icon
+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:
 
-<img src="../assets/invoke-web-server-5.png">
+    <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:
+    This will bring you to a screen similar to the one shown here:
 
-<img src="../assets/invoke-web-server-6.png" width=640>
+    <figure markdown>
+    ![Invoke Web Server - Image to Image Tab](../assets/invoke-web-server-6.png){:width="640px"}
+    </figure>
 
-Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or
+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.)
 
-11. Go to the prompt box and type *old sea captain with raven on
+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:
 
-<img src="../assets/invoke-web-server-7.png" width=640>
+    ![Invoke Web Server - Image to Image example](../assets/invoke-web-server-7.png){:width="640px"}
 
-12. Experiment with the different settings. The most influential one
+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
@@ -253,7 +262,7 @@ 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.
 
-13. What if we only want to change certain part(s) of the image and
+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
@@ -261,18 +270,20 @@ 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:
+    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:
 
-<img src="../assets/invoke-web-server-8.png" width=640>
+<figure markdown>
+![Invoke Web Server - Inpainting](../assets/invoke-web-server-8.png){:width="640px"}
+</figure>
 
-14. Would you like to modify a previously-generated image using the
+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
@@ -282,7 +293,7 @@ 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.
 
-<img src="../assets/invoke-web-server-9.png" width=640>
+![Invoke Web Server - Use as Image Links](../assets/invoke-web-server-9.png){:width="640px"}
 
 ## Parting remarks
 
@@ -294,53 +305,54 @@ were not covered here.
 The WebGUI is only rapid development. Check back regularly for
 updates!
 
-# Reference
+## Reference
 
-## Additional Options
-  `--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.
+### 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
+### 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 
+#### 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. 
+#### 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](./UPSCALE.md)
+- [Upscaling](./POSTPROCESS.md#upscaling)
 - [Image to Image](./IMG2IMG.md)
 - [Inpainting](./INPAINTING.md)
 - [Other](./OTHER.md)
 
-### Invocation Gallery
+#### 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
+#### 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

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.
@@ -63,7 +65,7 @@ Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory a
 
 ### **QUESTION**
 
-`invoke.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)
 
@@ -29,8 +28,8 @@ template: main.html
 [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/htRgbc7e?icon=discord
-[discord link]: https://discord.com/invite/htRgbc7e
+[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/invoke-ai/InvokeAI?icon=github
@@ -46,16 +45,20 @@ template: main.html
 
 </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/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 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
 
@@ -81,55 +84,59 @@ 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
+!!! info
 
     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 `invoke.py` with the `--full_precision` flag:
+    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 
+    `invoke.py` with the `--precision=float32` flag:
 
     ```bash
-    (ldm) ~/stable-diffusion$ python scripts/invoke.py --full_precision
+    (invokeai) ~/InvokeAI$ python scripts/invoke.py --full_precision
     ```
+
 ## :octicons-log-16: Latest Changes
 
-### vNEXT <small>(TODO 2022)</small>
+### v2.0.1 <small>(13 October 2022)</small>
 
-  - 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`.
+- 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)
 
-### v1.14 <small>(11 September 2022)</small>
+### v2.0.0 <small>(9 October 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.
+- `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://invoke-ai.github.io/InvokeAI/features/INPAINTING/">inpainting</a> and <a href="https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/">outpainting</a>
+- img2img runs on all k* samplers
+- Support for <a href="https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#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://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/">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://invoke-ai.github.io/InvokeAI/features/CLI/#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://invoke-ai.github.io/InvokeAI/features/CLI/">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`.
 
-### v1.13 <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.
-
-For older changelogs, please visit the **[CHANGELOG](features/CHANGELOG.md)**.
+For older changelogs, please visit the **[CHANGELOG](CHANGELOG.md#v114-11-september-2022)**.
 
 ## :material-target: Troubleshooting
 

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
@@ -26,11 +32,11 @@ 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
+#### Get the data files
 
 Go to
 [Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original),
@@ -44,14 +50,14 @@ 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
+### Setup
 
 Set the fork you want to use and other variables.
 
@@ -132,9 +138,9 @@ docker run -it \
 $TAG_STABLE_DIFFUSION
 ```
 
-# Usage (time to have fun)
+## Usage (time to have fun)
 
-## Startup
+### Startup
 
 If you're on a **Linux container** the `invoke` script is **automatically
 started** and the output dir set to the Docker volume you created earlier.
@@ -158,7 +164,7 @@ 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.  
@@ -188,7 +194,7 @@ 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.
@@ -225,7 +231,7 @@ If you're on a Linux container on your Mac
 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 `invoke` script with a graphical web interface. Start the web
 server with:
@@ -238,7 +244,7 @@ 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

@@ -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
 
@@ -69,7 +67,7 @@ This will create InvokeAI folder where you will follow the rest of the steps.
 
       - 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.)
+      - 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
@@ -79,34 +77,33 @@ This will create InvokeAI folder where you will follow the rest of the steps.
       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
-```
+    ```bash
+    (invokeai) ~/InvokeAI$ mkdir -p models/ldm/stable-diffusion-v1
+    (invokeai) ~/InvokeAI$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
+    ```
 
 8. Start generating images!
 
-```
-# for the pre-release weights use the -l or --liaon400m switch
-(ldm) ~/InvokeAI$ python3 scripts/invoke.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/invoke.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/invoke.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 invoke 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

@@ -2,6 +2,8 @@
 title: macOS
 ---
 
+# :fontawesome-brands-apple: macOS
+
 Invoke AI runs quite well on M1 Macs and we have a number of M1 users
 in the community.
 
@@ -26,98 +28,128 @@ First you need to download a large checkpoint file.
 
 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).
 
-Do not just copy and paste the whole thing into your terminal!
+!!! todo "Homebrew"
 
-```bash
-# Install brew (and Xcode command line tools):
-/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+    If you have no brew installation yet (otherwise skip):
 
-# Now there are two options to get the Python (miniconda) environment up and running:
-# 1. Alongside pyenv
-# 2. Standalone
-#
-# If you don't know what we are talking about, choose 2.
-#
-# If you are familiar with python environments, you'll know there are other options
-# for setting up the environment - you are on your own if you go one of those routes.
+    ```bash title="install brew (and Xcode command line tools)"
+    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+    ```
 
-##### BEGIN TWO DIFFERENT OPTIONS #####
+!!! todo "Conda Installation"
 
-### BEGIN OPTION 1: Installing alongside pyenv ###
-brew install pyenv-virtualenv # you might have this from before, no problem
-pyenv install anaconda3-2022.05
-pyenv virtualenv anaconda3-2022.05
-eval "$(pyenv init -)"
-pyenv activate anaconda3-2022.05
-### END OPTION 1 ###
+    Now there are two different ways to set up the Python (miniconda) environment:
 
-### BEGIN OPTION 2: Installing standalone ###
-# Install cmake, protobuf, and rust:
-brew install cmake protobuf rust
+       1. Standalone
+       2. with pyenv
 
-# BEGIN ARCHITECTURE-DEPENDENT STEP #
-# For M1: install miniconda (M1 arm64 version):
-curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o Miniconda3-latest-MacOSX-arm64.sh
-/bin/bash Miniconda3-latest-MacOSX-arm64.sh
+    If you don't know what we are talking about, choose Standalone. If you are familiar with python environments, choose "with pyenv"
 
-# For Intel: install miniconda (Intel x86-64 version):
-curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh
-/bin/bash Miniconda3-latest-MacOSX-x86_64.sh
-# END ARCHITECTURE-DEPENDENT STEP #
+    === "Standalone"
 
-### END OPTION 2 ###
+        ```bash title="Install cmake, protobuf, and rust"
+        brew install cmake protobuf rust
+        ```
 
-##### END TWO DIFFERENT OPTIONS #####
+        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:
 
-# Clone the Invoke AI repo
-git clone https://github.com/invoke-ai/InvokeAI.git
-cd InvokeAI
+        === "M1 arm64"
 
-### WAIT FOR THE CHECKPOINT FILE TO DOWNLOAD, THEN PROCEED ###
+            ```bash title="Install miniconda for M1 arm64"
+            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh \
+              -o Miniconda3-latest-MacOSX-arm64.sh
+            /bin/bash Miniconda3-latest-MacOSX-arm64.sh
+            ```
 
-# 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, and if you need to delete Invoke AI, you won't have to download it again.
+        === "Intel x86_64"
 
-# Make the directory in the repo for the symlink
-mkdir -p models/ldm/stable-diffusion-v1/
+            ```bash title="Install miniconda for Intel"
+            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh \
+              -o Miniconda3-latest-MacOSX-x86_64.sh
+            /bin/bash Miniconda3-latest-MacOSX-x86_64.sh
+            ```
 
-# This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
-PATH_TO_CKPT="$HOME/Downloads"
+    === "with pyenv"
 
-# Create a link to the checkpoint
-ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
+        ```bash
+        brew install pyenv-virtualenv
+        pyenv install anaconda3-2022.05
+        pyenv virtualenv anaconda3-2022.05
+        eval "$(pyenv init -)"
+        pyenv activate anaconda3-2022.05
+        ```
 
-# BEGIN ARCHITECTURE-DEPENDENT STEP #
-# For M1: Create the environment & install packages
-PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
+!!! todo "Clone the Invoke AI repo"
 
-# For Intel: Create the environment & install packages
-PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
+```bash 
+    git clone https://github.com/invoke-ai/InvokeAI.git
+    cd InvokeAI
+    ```
 
-# END ARCHITECTURE-DEPENDENT STEP #
+!!! todo "Wait until the checkpoint-file download finished, then proceed"
 
-# Activate the environment (you need to do this every time you want to run SD)
-conda activate ldm
+    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.
 
-# This will download some bits and pieces and make take a while
-python scripts/preload_models.py
+    ```{.bash .annotate}
+    # Make the directory in the repo for the symlink
+    mkdir -p models/ldm/stable-diffusion-v1/
 
-# Run SD!
-python scripts/dream.py
-```
-# or run the web interface!
-python scripts/invoke.py --web
+    # This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
+    PATH_TO_CKPT="$HOME/Downloads" # (1)!
 
-# The original scripts should work as well.
-python scripts/orig_scripts/txt2img.py \
-  --prompt "a photograph of an astronaut riding a horse" \
-  --plms
-```
+    # Create a link to the checkpoint
+    ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
+    ```
 
-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.
+    1. replace `$HOME/Downloads` with the Location where you actually stored the Checkppoint (`sd-v1-4.ckpt`)
+
+!!! todo "Create the environment & install packages"
+
+    === "M1 Mac"
+
+        ```bash
+        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
+        ```
+
+    === "Intel x86_64 Mac"
+
+        ```bash
+        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
+        ```
+
+    ```bash
+    # Activate the environment (you need to do this every time you want to run SD)
+    conda activate invokeai
+
+    # This will download some bits and pieces and make take a while
+    (invokeai) python scripts/preload_models.py
+
+    # Run SD!
+    (invokeai) python scripts/dream.py
+
+    # or run the web interface!
+    (invokeai) python scripts/invoke.py --web
+
+    # 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
@@ -154,10 +186,9 @@ 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
@@ -169,22 +200,22 @@ 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/invoke.py`
@@ -198,17 +229,17 @@ 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>
     ```
 
@@ -266,12 +297,12 @@ 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:
 
@@ -451,7 +482,7 @@ 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.
 ```
@@ -487,7 +518,7 @@ Generating:   0%|                                                              |
                                                                                                     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

@@ -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 +83,44 @@ 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.
+      1. For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
+      2. Use your credentials to log in, and then point your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
+      3. You may be asked to sign a license agreement at this point.
+      4. 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.
+      5. The weight file is >4 GB in size, so
+        downloading may take a while.
 
-Now run the following commands from **within the InvokeAI directory** to copy the weights file to the right place:
+    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
-```
+    ```batch
+    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\`.
+    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\`.
 
 9. Start generating images!
 
-    ```bash
-    # for the pre-release weights
+    ```batch title="for the pre-release weights"
     python scripts\invoke.py -l
+    ```
 
-    # for the post-release weights
+    ```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 ldm` (step 6b), and then launch the invoke script (step 9).
+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

environment-mac.yml

@@ -1,57 +1,55 @@
-name: ldm
+name: invokeai
 channels:
   - pytorch
   - conda-forge
 dependencies:
-  - python==3.9.13
-  - pip==22.2.2
+  - python>=3.9, <3.10
+  - pip>=22.2
 
   # pytorch left unpinned
-  - pytorch==1.12.1
-  - torchvision==0.13.1
-
+  - pytorch
+  - torchvision
   # I suggest to keep the other deps sorted for convenience.
   # To determine what the latest versions should be, run:
   #
   # ```shell
-  # sed -E 's/ldm/ldm-updated/;20,99s/- ([^=]+)==.+/- \1/' environment-mac.yml > environment-mac-updated.yml
-  # CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac-updated.yml && conda list -n ldm-updated | awk ' {print "  - " $1 "==" $2;} '
+  # sed -E 's/invokeai/invokeai-updated/;20,99s/- ([^=]+)==.+/- \1/' environment-mac.yml > environment-mac-updated.yml
+  # CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac-updated.yml && conda list -n invokeai-updated | awk ' {print "  - " $1 "==" $2;} '
   # ```
-  - albumentations==1.2.1
-  - coloredlogs==15.0.1
-  - einops==0.4.1
-  - grpcio==1.46.4
-  - humanfriendly==10.0
-  - imageio==2.21.2
-  - imageio-ffmpeg==0.4.7
-  - imgaug==0.4.0
-  - kornia==0.6.7
-  - mpmath==1.2.1
-  - nomkl=1.0
-  - numpy==1.23.2
-  - omegaconf==2.1.1
-  - openh264==2.3.0
-  - onnx==1.12.0
-  - onnxruntime==1.12.1
-  - pudb==2022.1
-  - pytorch-lightning==1.7.5
-  - scipy==1.9.1
-  - streamlit==1.12.2
-  - sympy==1.10.1
-  - tensorboard==2.10.0
-  - torchmetrics==0.9.3
+  - albumentations
+  - coloredlogs
+  - einops
+  - grpcio
+  - humanfriendly
+  - imageio
+  - imageio-ffmpeg
+  - imgaug
+  - kornia
+  - mpmath
+  - nomkl
+  - numpy
+  - omegaconf
+  - openh264
+  - onnx
+  - onnxruntime
+  - pudb
+  - pytorch-lightning
+  - scipy
+  - streamlit
+  - sympy
+  - tensorboard
+  - torchmetrics
   - pip:
       - flask==2.1.3
       - flask_socketio==5.3.0
       - flask_cors==3.0.10
       - dependency_injector==4.40.0
       - eventlet==0.33.1
-      - opencv-python==4.6.0
-      - protobuf==3.19.5
+      - protobuf==3.19.6
       - realesrgan==0.2.5.0
       - send2trash==1.8.0
       - test-tube==0.7.5
-      - transformers==4.21.2
+      - transformers==4.21.3
       - torch-fidelity==0.3.0
       - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
       - -e git+https://github.com/openai/CLIP.git@main#egg=clip

environment.yml

@@ -1,21 +1,21 @@
-name: ldm
+name: invokeai
 channels:
   - pytorch
   - defaults
 dependencies:
   - python>=3.9
-  - pip=20.3
-  - cudatoolkit=11.3
-  - pytorch=1.11.0
-  - torchvision=0.12.0
-  - numpy=1.19.2
+  - pip>=22.2
+  - cudatoolkit
+  - pytorch
+  - torchvision
+  - numpy
   - pip:
     - albumentations==0.4.3
     - opencv-python==4.5.5.64
     - pudb==2019.2
     - imageio==2.9.0
     - imageio-ffmpeg==0.4.2
-    - pytorch-lightning==1.4.2
+    - pytorch-lightning==1.7.7
     - omegaconf==2.1.1
     - realesrgan==0.2.5.0
     - test-tube>=0.7.5
@@ -25,8 +25,8 @@ dependencies:
     - einops==0.3.0
     - pyreadline3
     - torch-fidelity==0.3.0
-    - transformers==4.19.2
-    - torchmetrics==0.6.0
+    - transformers==4.21.3
+    - torchmetrics==0.7.0
     - flask==2.1.3
     - flask_socketio==5.3.0
     - flask_cors==3.0.10

frontend/dist/index.html

@@ -6,7 +6,7 @@
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>InvokeAI - A Stable Diffusion Toolkit</title>
   <link rel="shortcut icon" type="icon" href="/assets/favicon.0d253ced.ico" />
-  <script type="module" crossorigin src="/assets/index.989a0ca2.js"></script>
+  <script type="module" crossorigin src="/assets/index.ea68b5f5.js"></script>
   <link rel="stylesheet" href="/assets/index.58175ea1.css">
 </head>
 

frontend/src/app/constants.ts

@@ -50,6 +50,7 @@ export const PARAMETERS: { [key: string]: string } = {
   maskPath: 'Initial Image Mask',
   shouldFitToWidthHeight: 'Fit Initial Image',
   seamless: 'Seamless Tiling',
+  hiresFix: 'High Resolution Optimizations',
 };
 
 export const NUMPY_RAND_MIN = 0;

frontend/src/app/features.ts

@@ -14,10 +14,13 @@ export enum Feature {
   FACE_CORRECTION,
   IMAGE_TO_IMAGE,
 }
-
+/** For each tooltip in the UI, the below feature definitions & props will pull relevant information into the tooltip.
+ * 
+ * To-do: href & GuideImages are placeholders, and are not currently utilized, but will be updated (along with the tooltip UI) as feature and UI development and we get a better idea on where things "forever homes" will be . 
+ */
 export const FEATURES: Record<Feature, FeatureHelpInfo> = {
   [Feature.PROMPT]: {
-    text: 'This field will take all prompt text, including both content and stylistic terms. CLI Commands will not work in the prompt.',
+    text: 'This field will take all prompt text, including both content and stylistic terms. While weights can be included in the prompt, standard CLI Commands/parameters will not work.',
     href: 'link/to/docs/feature3.html',
     guideImage: 'asset/path.gif',
   },
@@ -27,17 +30,16 @@ export const FEATURES: Record<Feature, FeatureHelpInfo> = {
     guideImage: 'asset/path.gif',
   },
   [Feature.OTHER]: {
-    text: 'Additional Options',
-    href: 'link/to/docs/feature3.html',
+    text: 'These options will enable alternative processing modes for Invoke. Seamless tiling will work to generate repeating patterns in the output. High Resolution Optimization performs a two-step generation cycle, and should be used at higher resolutions when you desire a more coherent image/composition. ',    href: 'link/to/docs/feature3.html',
     guideImage: 'asset/path.gif',
   },
   [Feature.SEED]: {
-    text: 'Seed values provide an initial set of noise which guide the denoising process.',
+    text: 'Seed values provide an initial set of noise which guide the denoising process, and can be randomized or populated with a seed from a previous invocation. The Threshold feature can be used to mitigate undesirable outcomes at higher CFG values (try between 0-10), and Perlin can be used to add Perlin noise into the denoising process - Both serve to add variation to your outputs. ',
     href: 'link/to/docs/feature3.html',
     guideImage: 'asset/path.gif',
   },
   [Feature.VARIATIONS]: {
-    text: 'Try a variation with an amount of between 0 and 1 to change the output image for the set seed.',
+    text: 'Try a variation with an amount of between 0 and 1 to change the output image for the set seed - Interesting variations on the seed are found between 0.1 and 0.3.',
     href: 'link/to/docs/feature3.html',
     guideImage: 'asset/path.gif',
   },
@@ -47,8 +49,8 @@ export const FEATURES: Record<Feature, FeatureHelpInfo> = {
     guideImage: 'asset/path.gif',
   },
   [Feature.FACE_CORRECTION]: {
-    text: 'Using GFPGAN or CodeFormer, Face Correction will attempt to identify faces in outputs, and correct any defects/abnormalities. Higher values will apply a stronger corrective pressure on outputs.',
-    href: 'link/to/docs/feature2.html',
+    text: 'Using GFPGAN, Face Correction will attempt to identify faces in outputs, and correct any defects/abnormalities. Higher values will apply a stronger corrective pressure on outputs, resulting in more appealing faces (with less respect for accuracy of the original subject).',
+    href: 'link/to/docs/feature3.html',
     guideImage: 'asset/path.gif',
   },
   [Feature.IMAGE_TO_IMAGE]: {

frontend/src/app/invokeai.d.ts

@@ -55,6 +55,7 @@ export declare type CommonGeneratedImageMetadata = {
   width: number;
   height: number;
   seamless: boolean;
+  hires_fix: boolean;
   extra: null | Record<string, never>; // Pending development of RFC #266
 };
 

frontend/src/app/socketio/emitters.ts

@@ -76,7 +76,7 @@ const makeSocketIOEmitters = (
       const { gfpganStrength } = getState().options;
 
       const gfpganParameters = {
-        gfpgan_strength: gfpganStrength,
+        facetool_strength: gfpganStrength,
       };
       socketio.emit('runPostprocessing', imageToProcess, {
         type: 'gfpgan',

frontend/src/common/util/parameterTranslation.ts

@@ -29,6 +29,7 @@ export const frontendToBackendParameters = (
     sampler,
     seed,
     seamless,
+    hiresFix,
     shouldUseInitImage,
     img2imgStrength,
     initialImagePath,
@@ -59,6 +60,7 @@ export const frontendToBackendParameters = (
     sampler_name: sampler,
     seed,
     seamless,
+    hires_fix: hiresFix,
     progress_images: shouldDisplayInProgress,
   };
 
@@ -123,10 +125,11 @@ export const backendToFrontendParameters = (parameters: {
     sampler_name,
     seed,
     seamless,
+    hires_fix,
     progress_images,
     variation_amount,
     with_variations,
-    gfpgan_strength,
+    facetool_strength,
     upscale,
     init_img,
     init_mask,
@@ -151,9 +154,9 @@ export const backendToFrontendParameters = (parameters: {
     }
   }
 
-  if (gfpgan_strength > 0) {
+  if (facetool_strength > 0) {
     options.shouldRunGFPGAN = true;
-    options.gfpganStrength = gfpgan_strength;
+    options.gfpganStrength = facetool_strength;
   }
 
   if (upscale) {
@@ -185,6 +188,7 @@ export const backendToFrontendParameters = (parameters: {
     options.sampler = sampler_name;
     options.seed = seed;
     options.seamless = seamless;
+    options.hiresFix = hires_fix;
   }
 
   return options;

frontend/src/features/gallery/ImageMetaDataViewer/ImageMetadataViewer.tsx

@@ -16,11 +16,13 @@ import {
   setCfgScale,
   setGfpganStrength,
   setHeight,
+  setHiresFix,
   setImg2imgStrength,
   setInitialImagePath,
   setMaskPath,
   setPrompt,
   setSampler,
+  setSeamless,
   setSeed,
   setSeedWeights,
   setShouldFitToWidthHeight,
@@ -116,6 +118,7 @@ const ImageMetadataViewer = memo(
       steps,
       cfg_scale,
       seamless,
+      hires_fix,
       width,
       height,
       strength,
@@ -214,7 +217,14 @@ const ImageMetadataViewer = memo(
                 <MetadataItem
                   label="Seamless"
                   value={seamless}
-                  onClick={() => dispatch(setWidth(seamless))}
+                  onClick={() => dispatch(setSeamless(seamless))}
+                />
+              )}
+              {hires_fix && (
+                <MetadataItem
+                  label="High Resolution Optimization"
+                  value={hires_fix}
+                  onClick={() => dispatch(setHiresFix(hires_fix))}
                 />
               )}
               {width && (

frontend/src/features/options/HiresOptions.tsx

@@ -0,0 +1,32 @@
+import { Flex } from '@chakra-ui/react';
+import { RootState } from '../../app/store';
+import { useAppDispatch, useAppSelector } from '../../app/store';
+import { setHiresFix } from './optionsSlice';
+import { ChangeEvent } from 'react';
+import IAISwitch from '../../common/components/IAISwitch';
+
+/**
+ * Image output options. Includes width, height, seamless tiling.
+ */
+const HiresOptions = () => {
+  const dispatch = useAppDispatch();
+
+  const hiresFix = useAppSelector((state: RootState) => state.options.hiresFix);
+
+  const handleChangeHiresFix = (e: ChangeEvent<HTMLInputElement>) =>
+    dispatch(setHiresFix(e.target.checked));
+
+
+  return (
+    <Flex gap={2} direction={'column'}>
+      <IAISwitch
+        label="High Res Optimization"
+        fontSize={'md'}
+        isChecked={hiresFix}
+        onChange={handleChangeHiresFix}
+      />
+    </Flex>
+  );
+};
+
+export default HiresOptions;

frontend/src/features/options/OutputOptions.tsx

@@ -1,29 +1,14 @@
 import { Flex } from '@chakra-ui/react';
-import { RootState } from '../../app/store';
-import { useAppDispatch, useAppSelector } from '../../app/store';
-import { setSeamless } from './optionsSlice';
-import { ChangeEvent } from 'react';
-import IAISwitch from '../../common/components/IAISwitch';
 
-/**
- * Image output options. Includes width, height, seamless tiling.
- */
+import HiresOptions from './HiresOptions';
+import SeamlessOptions from './SeamlessOptions';
+
 const OutputOptions = () => {
-  const dispatch = useAppDispatch();
-
-  const seamless = useAppSelector((state: RootState) => state.options.seamless);
-
-  const handleChangeSeamless = (e: ChangeEvent<HTMLInputElement>) =>
-    dispatch(setSeamless(e.target.checked));
 
   return (
     <Flex gap={2} direction={'column'}>
-      <IAISwitch
-        label="Seamless tiling"
-        fontSize={'md'}
-        isChecked={seamless}
-        onChange={handleChangeSeamless}
-      />
+      <SeamlessOptions />
+      <HiresOptions />
     </Flex>
   );
 };

frontend/src/features/options/SeamlessOptions.tsx

@@ -0,0 +1,28 @@
+import { Flex } from '@chakra-ui/react';
+import { RootState } from '../../app/store';
+import { useAppDispatch, useAppSelector } from '../../app/store';
+import { setSeamless } from './optionsSlice';
+import { ChangeEvent } from 'react';
+import IAISwitch from '../../common/components/IAISwitch';
+
+const SeamlessOptions = () => {
+  const dispatch = useAppDispatch();
+
+  const seamless = useAppSelector((state: RootState) => state.options.seamless);
+
+  const handleChangeSeamless = (e: ChangeEvent<HTMLInputElement>) =>
+    dispatch(setSeamless(e.target.checked));
+
+  return (
+    <Flex gap={2} direction={'column'}>
+      <IAISwitch
+        label="Seamless tiling"
+        fontSize={'md'}
+        isChecked={seamless}
+        onChange={handleChangeSeamless}
+      />
+    </Flex>
+  );
+};
+
+export default SeamlessOptions;

frontend/src/features/options/optionsSlice.ts

@@ -25,6 +25,7 @@ export interface OptionsState {
   initialImagePath: string | null;
   maskPath: string;
   seamless: boolean;
+  hiresFix: boolean;
   shouldFitToWidthHeight: boolean;
   shouldGenerateVariations: boolean;
   variationAmount: number;
@@ -50,6 +51,7 @@ const initialOptionsState: OptionsState = {
   perlin: 0,
   seed: 0,
   seamless: false,
+  hiresFix: false,
   shouldUseInitImage: false,
   img2imgStrength: 0.75,
   initialImagePath: null,
@@ -138,6 +140,9 @@ export const optionsSlice = createSlice({
     setSeamless: (state, action: PayloadAction<boolean>) => {
       state.seamless = action.payload;
     },
+    setHiresFix: (state, action: PayloadAction<boolean>) => {
+      state.hiresFix = action.payload;
+    },
     setShouldFitToWidthHeight: (state, action: PayloadAction<boolean>) => {
       state.shouldFitToWidthHeight = action.payload;
     },
@@ -180,6 +185,7 @@ export const optionsSlice = createSlice({
         threshold,
         perlin,
         seamless,
+        hires_fix,
         width,
         height,
         strength,
@@ -256,6 +262,7 @@ export const optionsSlice = createSlice({
       if (perlin) state.perlin = perlin;
       if (typeof perlin === 'undefined') state.perlin = 0;      
       if (typeof seamless === 'boolean') state.seamless = seamless;
+      if (typeof hires_fix === 'boolean') state.hiresFix = hires_fix;
       if (width) state.width = width;
       if (height) state.height = height;
     },
@@ -301,6 +308,7 @@ export const {
   setSampler,
   setSeed,
   setSeamless,
+  setHiresFix,
   setImg2imgStrength,
   setGfpganStrength,
   setUpscalingLevel,

ldm/generate.py

@@ -33,6 +33,43 @@ from ldm.invoke.args import metadata_from_png
 from ldm.invoke.image_util import InitImageResizer
 from ldm.invoke.devices import choose_torch_device, choose_precision
 from ldm.invoke.conditioning import get_uc_and_c
+from ldm.invoke.model_cache import ModelCache
+
+def fix_func(orig):
+    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        def new_func(*args, **kw):
+            device = kw.get("device", "mps")
+            kw["device"]="cpu"
+            return orig(*args, **kw).to(device)
+        return new_func
+    return orig
+
+torch.rand = fix_func(torch.rand)
+torch.rand_like = fix_func(torch.rand_like)
+torch.randn = fix_func(torch.randn)
+torch.randn_like = fix_func(torch.randn_like)
+torch.randint = fix_func(torch.randint)
+torch.randint_like = fix_func(torch.randint_like)
+torch.bernoulli = fix_func(torch.bernoulli)
+torch.multinomial = fix_func(torch.multinomial)
+
+def fix_func(orig):
+    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        def new_func(*args, **kw):
+            device = kw.get("device", "mps")
+            kw["device"]="cpu"
+            return orig(*args, **kw).to(device)
+        return new_func
+    return orig
+
+torch.rand = fix_func(torch.rand)
+torch.rand_like = fix_func(torch.rand_like)
+torch.randn = fix_func(torch.randn)
+torch.randn_like = fix_func(torch.randn_like)
+torch.randint = fix_func(torch.randint)
+torch.randint_like = fix_func(torch.randint_like)
+torch.bernoulli = fix_func(torch.bernoulli)
+torch.multinomial = fix_func(torch.multinomial)
 
 """Simplified text to image API for stable diffusion/latent diffusion
 
@@ -95,7 +132,7 @@ gr = Generate(
 
           # these are deprecated - use conf and model instead
           weights     = path to model weights ('models/ldm/stable-diffusion-v1/model.ckpt')
-          config      = path to model configuraiton ('configs/stable-diffusion/v1-inference.yaml')
+          config      = path to model configuration ('configs/stable-diffusion/v1-inference.yaml')
           )
 
 """
@@ -123,12 +160,11 @@ class Generate:
             esrgan=None,
             free_gpu_mem=False,
     ):
-        models              = OmegaConf.load(conf)
-        mconfig             = models[model]
-        self.weights        = mconfig.weights if weights is None else weights
-        self.config         = mconfig.config  if config  is None else config
-        self.height         = mconfig.height
-        self.width          = mconfig.width
+        mconfig             = OmegaConf.load(conf)
+        self.model_name     = model
+        self.height         = None
+        self.width          = None
+        self.model_cache    = None
         self.iterations     = 1
         self.steps          = 50
         self.cfg_scale      = 7.5
@@ -137,8 +173,10 @@ class Generate:
         self.precision      = precision
         self.strength       = 0.75
         self.seamless       = False
+        self.hires_fix      = False
         self.embedding_path = embedding_path
         self.model          = None     # empty for now
+        self.model_hash     = None
         self.sampler        = None
         self.device         = None
         self.session_peakmem = None
@@ -149,11 +187,13 @@ class Generate:
         self.codeformer = codeformer
         self.esrgan = esrgan
         self.free_gpu_mem = free_gpu_mem
+        self.size_matters = True  # used to warn once about large image sizes and VRAM
 
         # Note that in previous versions, there was an option to pass the
         # device to Generate(). However the device was then ignored, so
         # it wasn't actually doing anything. This logic could be reinstated.
         device_type = choose_torch_device()
+        print(f'>> Using device_type {device_type}')
         self.device = torch.device(device_type)
         if full_precision:
             if self.precision != 'auto':
@@ -164,6 +204,9 @@ class Generate:
         if self.precision == 'auto':
             self.precision = choose_precision(self.device)
 
+        # model caching system for fast switching
+        self.model_cache = ModelCache(mconfig,self.device,self.precision)
+
         # for VRAM usage statistics
         self.session_peakmem = torch.cuda.max_memory_allocated() if self._has_cuda else None
         transformers.logging.set_verbosity_error()
@@ -231,10 +274,12 @@ class Generate:
             embiggen_tiles =    None,
             # these are specific to GFPGAN/ESRGAN
             facetool         = None,
-            gfpgan_strength  = 0,
+            facetool_strength  = 0,
             codeformer_fidelity = None,
             save_original    = False,
             upscale          = None,
+            # this is specific to inpainting and causes more extreme inpainting
+            inpaint_replace  = 0.0,
             # Set this True to handle KeyboardInterrupt internally
             catch_interrupts = False,
             hires_fix        = False,
@@ -251,9 +296,10 @@ class Generate:
            height                          // height of image, in multiples of 64 (512)
            cfg_scale                       // how strongly the prompt influences the image (7.5) (must be >1)
            seamless                        // whether the generated image should tile
+           hires_fix                        // whether the Hires Fix should be applied during generation
            init_img                        // path to an initial image
            strength                        // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
-           gfpgan_strength                 // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely
+           facetool_strength               // strength for GFPGAN/CodeFormer. 0.0 preserves image exactly, 1.0 replaces it completely
            ddim_eta                        // image randomness (eta=0.0 means the same seed always produces the same image)
            step_callback                   // a function or method that will be called each step
            image_callback                  // a function or method that will be called each time an image is generated
@@ -284,6 +330,7 @@ class Generate:
         width = width or self.width
         height = height or self.height
         seamless = seamless or self.seamless
+        hires_fix = hires_fix or self.hires_fix
         cfg_scale = cfg_scale or self.cfg_scale
         ddim_eta = ddim_eta or self.ddim_eta
         iterations = iterations or self.iterations
@@ -294,7 +341,12 @@ class Generate:
         with_variations = [] if with_variations is None else with_variations
 
         # will instantiate the model or return it from cache
-        model = self.load_model()
+        model = self.set_model(self.model_name)
+
+        # self.width and self.height are set by set_model()
+        # to the width and height of the image training set
+        width = width or self.width
+        height = height or self.height
         
         for m in model.modules():
             if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
@@ -326,6 +378,7 @@ class Generate:
                 f'variation weights must be in [0.0, 1.0]: got {[weight for _, weight in with_variations]}'
 
         width, height, _ = self._resolution_check(width, height, log=True)
+        assert inpaint_replace >=0.0 and inpaint_replace <= 1.0,'inpaint_replace must be between 0.0 and 1.0'
 
         if sampler_name and (sampler_name != self.sampler_name):
             self.sampler_name = sampler_name
@@ -353,6 +406,8 @@ class Generate:
                 height,
                 fit=fit,
             )
+
+            # TODO: Hacky selection of operation to perform. Needs to be refactored.
             if (init_image is not None) and (mask_image is not None):
                 generator = self._make_inpaint()
             elif (embiggen != None or embiggen_tiles != None):
@@ -367,6 +422,7 @@ class Generate:
             generator.set_variation(
                 self.seed, variation_amount, with_variations
             )
+
             results = generator.generate(
                 prompt,
                 iterations=iterations,
@@ -388,6 +444,7 @@ class Generate:
                 perlin=perlin,
                 embiggen=embiggen,
                 embiggen_tiles=embiggen_tiles,
+                inpaint_replace=inpaint_replace,
             )
 
             if init_color:
@@ -395,11 +452,11 @@ class Generate:
                                     reference_image_path = init_color,
                                     image_callback       = image_callback)
 
-            if upscale is not None or gfpgan_strength > 0:
+            if upscale is not None or facetool_strength > 0:
                 self.upscale_and_reconstruct(results,
                                              upscale        = upscale,
                                              facetool       = facetool,
-                                             strength       = gfpgan_strength,
+                                             strength       = facetool_strength,
                                              codeformer_fidelity = codeformer_fidelity,
                                              save_original  = save_original,
                                              image_callback = image_callback)
@@ -442,7 +499,7 @@ class Generate:
             self,
             image_path,
             tool                = 'gfpgan',  # one of 'upscale', 'gfpgan', 'codeformer', 'outpaint', or 'embiggen'
-            gfpgan_strength     = 0.0,
+            facetool_strength   = 0.0,
             codeformer_fidelity = 0.75,
             upscale             = None,
             out_direction       = None,
@@ -489,11 +546,11 @@ class Generate:
                 facetool = 'codeformer'
             elif tool == 'upscale':
                 facetool = 'gfpgan'   # but won't be run
-                gfpgan_strength = 0
+                facetool_strength = 0
             return self.upscale_and_reconstruct(
                 [[image,seed]],
                 facetool = facetool,
-                strength = gfpgan_strength,
+                strength = facetool_strength,
                 codeformer_fidelity = codeformer_fidelity,
                 save_original = save_original,
                 upscale = upscale,
@@ -584,8 +641,9 @@ class Generate:
             # this returns a torch tensor
             init_mask = self._create_init_mask(image, width, height, fit=fit)
             
-        if (image.width * image.height) > (self.width * self.height):
+        if (image.width * image.height) > (self.width * self.height) and self.size_matters:
             print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
+            self.size_matters = False
 
         init_image   = self._create_init_image(image,width,height,fit=fit)                   # this returns a torch tensor
 
@@ -635,29 +693,40 @@ class Generate:
         return self.generators['inpaint']
 
     def load_model(self):
-        """Load and initialize the model from configuration variables passed at object creation time"""
-        if self.model is None:
-            seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
-            try:
-                model = self._load_model_from_config(self.config, self.weights)
-                if self.embedding_path is not None:
-                    model.embedding_manager.load(
-                        self.embedding_path, self.precision == 'float32' or self.precision == 'autocast'
-                    )
-                self.model = model.to(self.device)
-                # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
-                self.model.cond_stage_model.device = self.device
-            except AttributeError as e:
-                print(f'>> Error loading model. {str(e)}', file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                raise SystemExit from e
+        '''
+        preload model identified in self.model_name
+        '''
+        self.set_model(self.model_name)
 
-            self._set_sampler()
+    def set_model(self,model_name):
+        """ 
+        Given the name of a model defined in models.yaml, will load and initialize it
+        and return the model object. Previously-used models will be cached.
+        """
+        if self.model_name == model_name and self.model is not None:
+            return self.model
 
-            for m in self.model.modules():
-                if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                    m._orig_padding_mode = m.padding_mode
+        model_data = self.model_cache.get_model(model_name)
+        if model_data is None or len(model_data) == 0:
+            print(f'** Model switch failed **')
+            return self.model
 
+        self.model = model_data['model']
+        self.width = model_data['width']
+        self.height= model_data['height']
+        self.model_hash = model_data['hash']
+
+        # uncache generators so they pick up new models
+        self.generators = {}
+        
+        seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
+        if self.embedding_path is not None:
+            model.embedding_manager.load(
+                self.embedding_path, self.precision == 'float32' or self.precision == 'autocast'
+            )
+
+        self._set_sampler()
+        self.model_name = model_name
         return self.model
 
     def correct_colors(self,
@@ -761,53 +830,6 @@ class Generate:
 
         print(msg)
 
-    # Be warned: config is the path to the model config file, not the invoke conf file!
-    # Also note that we can get config and weights from self, so why do we need to
-    # pass them as args?
-    def _load_model_from_config(self, config, weights):
-        print(f'>> Loading model from {weights}')
-
-        # for usage statistics
-        device_type = choose_torch_device()
-        if device_type == 'cuda':
-            torch.cuda.reset_peak_memory_stats()
-        tic = time.time()
-
-        # this does the work
-        c     = OmegaConf.load(config)
-        with open(weights,'rb') as f:
-            weight_bytes = f.read()
-        self.model_hash  = self._cached_sha256(weights,weight_bytes)
-        pl_sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
-        del weight_bytes
-        sd    = pl_sd['state_dict']
-        model = instantiate_from_config(c.model)
-        m, u  = model.load_state_dict(sd, strict=False)
-
-        if self.precision == 'float16':
-            print('>> Using faster float16 precision')
-            model.to(torch.float16)
-        else:
-            print('>> Using more accurate float32 precision')
-
-        model.to(self.device)
-        model.eval()
-
-        # usage statistics
-        toc = time.time()
-        print(
-            f'>> Model loaded in', '%4.2fs' % (toc - tic)
-        )
-        if self._has_cuda():
-            print(
-                '>> Max VRAM used to load the model:',
-                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
-                '\n>> Current VRAM usage:'
-                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
-            )
-
-        return model
-
     def _load_img(self, img, width, height)->Image:
         if isinstance(img, Image.Image):
             image = img
@@ -951,26 +973,6 @@ class Generate:
     def _has_cuda(self):
         return self.device.type == 'cuda'
 
-    def _cached_sha256(self,path,data):
-        dirname    = os.path.dirname(path)
-        basename   = os.path.basename(path)
-        base, _    = os.path.splitext(basename)
-        hashpath   = os.path.join(dirname,base+'.sha256')
-        if os.path.exists(hashpath) and os.path.getmtime(path) <= os.path.getmtime(hashpath):
-            with open(hashpath) as f:
-                hash = f.read()
-            return hash
-        print(f'>> Calculating sha256 hash of weights file')
-        tic = time.time()
-        sha = hashlib.sha256()
-        sha.update(data)
-        hash = sha.hexdigest()
-        toc = time.time()
-        print(f'>> sha256 = {hash}','(%4.2fs)' % (toc - tic))
-        with open(hashpath,'w') as f:
-            f.write(hash)
-        return hash
-
     def write_intermediate_images(self,modulus,path):
         counter = -1
         if not os.path.exists(path):

ldm/invoke/args.py

@@ -239,12 +239,17 @@ class Args(object):
                 switches.append(f'--init_color {a["init_color"]}')
             if a['strength'] and a['strength']>0:
                 switches.append(f'-f {a["strength"]}')
+            if a['inpaint_replace']:
+                switches.append(f'--inpaint_replace')
         else:
             switches.append(f'-A {a["sampler_name"]}')
 
-        # gfpgan-specific parameters
-        if a['gfpgan_strength']:
-            switches.append(f'-G {a["gfpgan_strength"]}')
+        # facetool-specific parameters, only print if running facetool
+        if a['facetool_strength']:
+            switches.append(f'-G {a["facetool_strength"]}')
+            switches.append(f'-ft {a["facetool"]}')
+            if a["facetool"] == "codeformer":
+                switches.append(f'-cf {a["codeformer_fidelity"]}')
 
         if a['outcrop']:
             switches.append(f'-c {" ".join([str(u) for u in a["outcrop"]])}')
@@ -262,11 +267,12 @@ class Args(object):
         # outpainting parameters
         if a['out_direction']:
             switches.append(f'-D {" ".join([str(u) for u in a["out_direction"]])}')
+
         # LS: slight semantic drift which needs addressing in the future:
         # 1. Variations come out of the stored metadata as a packed string with the keyword "variations"
         # 2. However, they come out of the CLI (and probably web) with the keyword "with_variations" and
         #    in broken-out form. Variation (1) should be changed to comply with (2)
-        if a['with_variations']:
+        if a['with_variations'] and len(a['with_variations'])>0:
             formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in (a["with_variations"]))
             switches.append(f'-V {formatted_variations}')
         if 'variations' in a and len(a['variations'])>0:
@@ -372,6 +378,14 @@ class Args(object):
             default='stable-diffusion-1.4',
             help='Indicates which diffusion model to load. (currently "stable-diffusion-1.4" (default) or "laion400m")',
         )
+        model_group.add_argument(
+            '--png_compression','-z',
+            type=int,
+            default=6,
+            choices=range(0,9),
+            dest='png_compression',
+            help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
+        )
         model_group.add_argument(
             '--sampler',
             '-A',
@@ -636,6 +650,21 @@ class Args(object):
             dest='hires_fix',
             help='Create hires image using img2img to prevent duplicated objects'
         )
+        render_group.add_argument(
+            '--save_intermediates',
+            type=int,
+            default=0,
+            dest='save_intermediates',
+            help='Save every nth intermediate image into an "intermediates" directory within the output directory'
+        )
+        render_group.add_argument(
+            '--png_compression','-z',
+            type=int,
+            default=6,
+            choices=range(0,10),
+            dest='png_compression',
+            help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
+        )
         img2img_group.add_argument(
             '-I',
             '--init_img',
@@ -683,6 +712,13 @@ class Args(object):
             metavar=('direction','pixels'),
             help='Outcrop the image with one or more direction/pixel pairs: -c top 64 bottom 128 left 64 right 64',
         )
+        img2img_group.add_argument(
+            '-r',
+            '--inpaint_replace',
+            type=float,
+            default=0.0,
+            help='when inpainting, adjust how aggressively to replace the part of the picture under the mask, from 0.0 (a gentle merge) to 1.0 (replace entirely)',
+        )
         postprocessing_group.add_argument(
             '-ft',
             '--facetool',
@@ -692,6 +728,7 @@ class Args(object):
         )
         postprocessing_group.add_argument(
             '-G',
+            '--facetool_strength',
             '--gfpgan_strength',
             type=float,
             help='The strength at which to apply the face restoration to the result.',
@@ -788,7 +825,8 @@ def metadata_dumps(opt,
 
     # 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','strength']
+                         'cfg_scale','threshold','perlin','step_number','width','height','extra','strength',
+                         'init_img','init_mask']
 
     rfc_dict ={}
 
@@ -809,11 +847,15 @@ def metadata_dumps(opt,
     # 'variations' should always exist and be an array, empty or consisting of {'seed': seed, 'weight': weight} pairs
     rfc_dict['variations'] = [{'seed':x[0],'weight':x[1]} for x in opt.with_variations] if opt.with_variations else []
 
+    # if variations are present then we need to replace 'seed' with 'orig_seed'
+    if hasattr(opt,'first_seed'):
+        rfc_dict['seed'] = opt.first_seed
+
     if opt.init_img:
-        rfc_dict['type']           = 'img2img'
-        rfc_dict['strength_steps'] = rfc_dict.pop('strength')
-        rfc_dict['orig_hash']      = calculate_init_img_hash(opt.init_img)
-        rfc_dict['sampler']        = 'ddim'  # TODO: FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
+        rfc_dict['type']            = 'img2img'
+        rfc_dict['strength_steps']  = rfc_dict.pop('strength')
+        rfc_dict['orig_hash']       = calculate_init_img_hash(opt.init_img)
+        rfc_dict['inpaint_replace'] = opt.inpaint_replace
     else:
         rfc_dict['type']  = 'txt2img'
         rfc_dict.pop('strength')

ldm/invoke/generator/base.py

@@ -5,6 +5,7 @@ including img2img, txt2img, and inpaint
 import torch
 import numpy as  np
 import random
+import os
 from tqdm import tqdm, trange
 from PIL               import Image
 from einops import rearrange, repeat
@@ -168,3 +169,14 @@ class Generator():
 
         return v2
 
+    # this is a handy routine for debugging use. Given a generated sample,
+    # convert it into a PNG image and store it at the indicated path
+    def save_sample(self, sample, filepath):
+        image = self.sample_to_image(sample)
+        dirname = os.path.dirname(filepath) or '.'
+        if not os.path.exists(dirname):
+            print(f'** creating directory {dirname}')
+            os.makedirs(dirname, exist_ok=True)
+        image.save(filepath,'PNG')
+
+        

ldm/invoke/generator/inpaint.py

@@ -18,7 +18,7 @@ class Inpaint(Img2Img):
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
                        conditioning,init_image,mask_image,strength,
-                       step_callback=None,**kwargs):
+                       step_callback=None,inpaint_replace=False,**kwargs):
         """
         Returns a function returning an image derived from the prompt and
         the initial image + mask.  Return value depends on the seed at
@@ -58,6 +58,14 @@ class Inpaint(Img2Img):
                 noise=x_T
             )
 
+            # to replace masked area with latent noise, weighted by inpaint_replace strength
+            if inpaint_replace > 0.0:
+                print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
+                l_noise = self.get_noise(kwargs['width'],kwargs['height'])
+                inverted_mask = 1.0-mask_image  # there will be 1s where the mask is
+                masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
+                z_enc   = z_enc * mask_image + masked_region
+
             # decode it
             samples = sampler.decode(
                 z_enc,

ldm/invoke/model_cache.py

@@ -0,0 +1,281 @@
+'''
+Manage a cache of Stable Diffusion model files for fast switching. 
+They are moved between GPU and CPU as necessary. If CPU memory falls
+below a preset minimum, the least recently used model will be
+cleared and loaded from disk when next needed.
+'''
+
+import torch
+import os
+import io
+import time
+import gc
+import hashlib
+import psutil
+import transformers
+from sys import getrefcount
+from omegaconf import OmegaConf
+from omegaconf.errors import ConfigAttributeError
+from ldm.util import instantiate_from_config
+
+GIGS=2**30
+AVG_MODEL_SIZE=2.1*GIGS
+DEFAULT_MIN_AVAIL=2*GIGS
+
+class ModelCache(object):
+    def __init__(self, config:OmegaConf, device_type:str, precision:str, min_avail_mem=DEFAULT_MIN_AVAIL):
+        '''
+        Initialize with the path to the models.yaml config file,
+        the torch device type, and precision. The optional
+        min_avail_mem argument specifies how much unused system
+        (CPU) memory to preserve. The cache of models in RAM will
+        grow until this value is approached. Default is 2G.
+        '''
+        # prevent nasty-looking CLIP log message
+        transformers.logging.set_verbosity_error()
+        self.config = config
+        self.precision = precision
+        self.device = torch.device(device_type)
+        self.min_avail_mem = min_avail_mem
+        self.models = {}
+        self.stack = []  # this is an LRU FIFO
+        self.current_model = None
+
+    def get_model(self, model_name:str):
+        '''
+        Given a model named identified in models.yaml, return
+        the model object. If in RAM will load into GPU VRAM.
+        If on disk, will load from there.
+        '''
+        if model_name not in self.config:
+            print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
+            return None
+
+        if self.current_model != model_name:
+            self.unload_model(self.current_model)
+        
+        if model_name in self.models:
+            requested_model = self.models[model_name]['model']
+            print(f'>> Retrieving model {model_name} from system RAM cache')
+            self.models[model_name]['model'] = self._model_from_cpu(requested_model)
+            width = self.models[model_name]['width']
+            height = self.models[model_name]['height']
+            hash = self.models[model_name]['hash']
+        else:
+            self._check_memory()
+            try:
+                requested_model, width, height, hash = self._load_model(model_name)
+                self.models[model_name] = {}
+                self.models[model_name]['model'] = requested_model
+                self.models[model_name]['width'] = width
+                self.models[model_name]['height'] = height
+                self.models[model_name]['hash'] = hash
+            except Exception as e:
+                print(f'** model {model_name} could not be loaded: {str(e)}')
+                print(f'** restoring {self.current_model}')
+                return self.get_model(self.current_model)
+        
+        self.current_model = model_name
+        self._push_newest_model(model_name)
+        return {
+            'model':requested_model,
+            'width':width,
+            'height':height,
+            'hash': hash
+        }
+
+    def list_models(self) -> dict:
+        '''
+        Return a dict of models in the format:
+        { model_name1: {'status': ('active'|'cached'|'not loaded'),
+                        'description': description,
+                       },
+          model_name2: { etc }
+        '''
+        result = {}
+        for name in self.config:
+            try:
+                description = self.config[name].description
+            except ConfigAttributeError:
+                description = '<no description>'
+            if self.current_model == name:
+                status = 'active'
+            elif name in self.models:
+                status = 'cached'
+            else:
+                status = 'not loaded'
+            result[name]={}
+            result[name]['status']=status
+            result[name]['description']=description
+        return result
+    
+    def print_models(self):
+        '''
+        Print a table of models, their descriptions, and load status
+        '''
+        models = self.list_models()
+        for name in models:
+            line = f'{name:25s} {models[name]["status"]:>10s}  {models[name]["description"]}'
+            if models[name]['status'] == 'active':
+                print(f'\033[1m{line}\033[0m')
+            else:
+                print(line)
+
+    def add_model(self, model_name:str, model_attributes:dict, clobber=False) ->str:
+        '''
+        Update the named model with a dictionary of attributes. Will fail with an
+        assertion error if the name already exists. Pass clobber=True to overwrite.
+        On a successful update, the config will be changed in memory and a YAML
+        string will be returned.
+        '''
+        omega = self.config
+        # check that all the required fields are present
+        for field in ('description','weights','height','width','config'):
+            assert field in model_attributes, f'required field {field} is missing'
+
+        assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
+        config = omega[model_name] if model_name in omega else {}
+        for field in model_attributes:
+            config[field] = model_attributes[field]
+
+        omega[model_name] = config
+        return OmegaConf.to_yaml(omega)
+    
+    def _check_memory(self):
+        avail_memory = psutil.virtual_memory()[1]
+        if AVG_MODEL_SIZE + self.min_avail_mem > avail_memory:
+            least_recent_model = self._pop_oldest_model()
+            if least_recent_model is not None:
+                del self.models[least_recent_model]
+                gc.collect()
+
+        
+    def _load_model(self, model_name:str):
+        """Load and initialize the model from configuration variables passed at object creation time"""
+        if model_name not in self.config:
+            print(f'"{model_name}" is not a known model name. Please check your models.yaml file')
+            return None
+
+        mconfig = self.config[model_name]
+        config = mconfig.config
+        weights = mconfig.weights
+        width = mconfig.width
+        height = mconfig.height
+
+        print(f'>> Loading {model_name} from {weights}')
+
+        # for usage statistics
+        if self._has_cuda():
+            torch.cuda.reset_peak_memory_stats()
+            torch.cuda.empty_cache()
+
+        tic = time.time()
+
+        # this does the work
+        c     = OmegaConf.load(config)
+        with open(weights,'rb') as f:
+            weight_bytes = f.read()
+        model_hash  = self._cached_sha256(weights,weight_bytes)
+        pl_sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
+        del weight_bytes
+        sd    = pl_sd['state_dict']
+        model = instantiate_from_config(c.model)
+        m, u  = model.load_state_dict(sd, strict=False)
+
+        if self.precision == 'float16':
+            print('   | Using faster float16 precision')
+            model.to(torch.float16)
+        else:
+            print('   | Using more accurate float32 precision')
+
+        model.to(self.device)
+        # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
+        model.cond_stage_model.device = self.device
+        model.eval()
+
+        for m in model.modules():
+            if isinstance(m, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
+                m._orig_padding_mode = m.padding_mode
+
+        # usage statistics
+        toc = time.time()
+        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
+        if self._has_cuda():
+            print(
+                '>> Max VRAM used to load the model:',
+                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
+                '\n>> Current VRAM usage:'
+                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
+            )
+        return model, width, height, model_hash
+        
+    def unload_model(self, model_name:str):
+        if model_name not in self.models:
+            return
+        print(f'>> Caching model {model_name} in system RAM')
+        model = self.models[model_name]['model']
+        self.models[model_name]['model'] = self._model_to_cpu(model)
+        gc.collect()
+        if self._has_cuda():
+            torch.cuda.empty_cache()
+
+    def _model_to_cpu(self,model):
+        if self.device != 'cpu':
+            model.cond_stage_model.device = 'cpu'
+            model.first_stage_model.to('cpu')
+            model.cond_stage_model.to('cpu') 
+            model.model.to('cpu')
+            return model.to('cpu')
+        else:
+            return model
+
+    def _model_from_cpu(self,model):
+        if self.device != 'cpu':
+            model.to(self.device)
+            model.first_stage_model.to(self.device)
+            model.cond_stage_model.to(self.device)
+            model.cond_stage_model.device = self.device
+        return model
+
+    def _pop_oldest_model(self):
+        '''
+        Remove the first element of the FIFO, which ought
+        to be the least recently accessed model. Do not
+        pop the last one, because it is in active use!
+        '''
+        if len(self.stack) > 1:
+            return self.stack.pop(0)
+
+    def _push_newest_model(self,model_name:str):
+        '''
+        Maintain a simple FIFO. First element is always the
+        least recent, and last element is always the most recent.
+        '''
+        try:
+            self.stack.remove(model_name)
+        except ValueError:
+            pass
+        self.stack.append(model_name)
+        
+    def _has_cuda(self):
+        return self.device.type == 'cuda'
+
+    def _cached_sha256(self,path,data):
+        dirname    = os.path.dirname(path)
+        basename   = os.path.basename(path)
+        base, _    = os.path.splitext(basename)
+        hashpath   = os.path.join(dirname,base+'.sha256')
+        if os.path.exists(hashpath) and os.path.getmtime(path) <= os.path.getmtime(hashpath):
+            with open(hashpath) as f:
+                hash = f.read()
+            return hash
+        print(f'>> Calculating sha256 hash of weights file')
+        tic = time.time()
+        sha = hashlib.sha256()
+        sha.update(data)
+        hash = sha.hexdigest()
+        toc = time.time()
+        print(f'>> sha256 = {hash}','(%4.2fs)' % (toc - tic))
+        with open(hashpath,'w') as f:
+            f.write(hash)
+        return hash

ldm/invoke/pngwriter.py

@@ -33,13 +33,13 @@ class PngWriter:
 
     # saves image named _image_ to outdir/name, writing metadata from prompt
     # returns full path of output
-    def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None):
+    def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None, compress_level=6):
         path = os.path.join(self.outdir, name)
         info = PngImagePlugin.PngInfo()
         info.add_text('Dream', dream_prompt)
         if metadata:
           info.add_text('sd-metadata', json.dumps(metadata))
-        image.save(path, 'PNG', pnginfo=info)
+        image.save(path, 'PNG', pnginfo=info, compress_level=compress_level)
         return path
 
     def retrieve_metadata(self,img_basename):
@@ -66,3 +66,43 @@ def write_metadata(img_path:str, meta:dict):
     info = PngImagePlugin.PngInfo()
     info.add_text('sd-metadata', json.dumps(meta))
     im.save(img_path,'PNG',pnginfo=info)
+
+class PromptFormatter:
+    def __init__(self, t2i, opt):
+        self.t2i = t2i
+        self.opt = opt
+
+    # note: the t2i object should provide all these values.
+    # there should be no need to or against opt values
+    def normalize_prompt(self):
+        """Normalize the prompt and switches"""
+        t2i = self.t2i
+        opt = self.opt
+
+        switches = list()
+        switches.append(f'"{opt.prompt}"')
+        switches.append(f'-s{opt.steps        or t2i.steps}')
+        switches.append(f'-W{opt.width        or t2i.width}')
+        switches.append(f'-H{opt.height       or t2i.height}')
+        switches.append(f'-C{opt.cfg_scale    or t2i.cfg_scale}')
+        switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
+# to do: put model name into the t2i object
+#        switches.append(f'--model{t2i.model_name}')
+        if opt.seamless or t2i.seamless:
+            switches.append(f'--seamless')
+        if opt.init_img:
+            switches.append(f'-I{opt.init_img}')
+        if opt.fit:
+            switches.append(f'--fit')
+        if opt.strength and opt.init_img is not None:
+            switches.append(f'-f{opt.strength or t2i.strength}')
+        if opt.gfpgan_strength:
+            switches.append(f'-G{opt.gfpgan_strength}')
+        if opt.upscale:
+            switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
+        if opt.variation_amount > 0:
+            switches.append(f'-v{opt.variation_amount}')
+        if opt.with_variations:
+            formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in opt.with_variations)
+            switches.append(f'-V{formatted_variations}')
+        return ' '.join(switches)

ldm/invoke/readline.py

@@ -21,6 +21,8 @@ except (ImportError,ModuleNotFoundError):
     readline_available = False
 
 IMG_EXTENSIONS     = ('.png','.jpg','.jpeg','.PNG','.JPG','.JPEG','.gif','.GIF')
+WEIGHT_EXTENSIONS  = ('.ckpt','.bae')
+CONFIG_EXTENSIONS  = ('.yaml','.yml')
 COMMANDS = (
     '--steps','-s',
     '--seed','-S',
@@ -31,6 +33,7 @@ COMMANDS = (
     '--perlin',
     '--grid','-g',
     '--individual','-i',
+    '--save_intermediates',
     '--init_img','-I',
     '--init_mask','-M',
     '--init_color',
@@ -41,13 +44,25 @@ COMMANDS = (
     '--embedding_path',
     '--device',
     '--grid','-g',
-    '--gfpgan_strength','-G',
+    '--facetool','-ft',
+    '--facetool_strength','-G',
+    '--codeformer_fidelity','-cf',
     '--upscale','-U',
     '-save_orig','--save_original',
     '--skip_normalize','-x',
     '--log_tokenization','-t',
     '--hires_fix',
+    '--inpaint_replace','-r',
+    '--png_compression','-z',
     '!fix','!fetch','!history','!search','!clear',
+    '!models','!switch','!import_model','!edit_model'
+    )
+MODEL_COMMANDS = (
+    '!switch',
+    '!edit_model',
+    )
+WEIGHT_COMMANDS = (
+    '!import_model',
     )
 IMG_PATH_COMMANDS = (
     '--outdir[=\s]',
@@ -60,16 +75,19 @@ IMG_FILE_COMMANDS=(
     '--init_color[=\s]',
     '--embedding_path[=\s]',
     )
-path_regexp = '('+'|'.join(IMG_PATH_COMMANDS+IMG_FILE_COMMANDS) + ')\s*\S*$'
+path_regexp   = '('+'|'.join(IMG_PATH_COMMANDS+IMG_FILE_COMMANDS) + ')\s*\S*$'
+weight_regexp = '('+'|'.join(WEIGHT_COMMANDS) + ')\s*\S*$'
 
 class Completer(object):
-    def __init__(self, options):
+    def __init__(self, options, models=[]):
         self.options     = sorted(options)
+        self.models      = sorted(models)
         self.seeds       = set()
         self.matches     = list()
         self.default_dir = None
         self.linebuffer  = None
         self.auto_history_active = True
+        self.extensions = None
         return
 
     def complete(self, text, state):
@@ -80,7 +98,13 @@ class Completer(object):
         buffer = readline.get_line_buffer()
 
         if state == 0:
-            if re.search(path_regexp,buffer):
+
+            # extensions defined, so go directly into path completion mode
+            if self.extensions is not None:
+                self.matches = self._path_completions(text, state, self.extensions)
+                
+            # looking for an image file
+            elif re.search(path_regexp,buffer):
                 do_shortcut = re.search('^'+'|'.join(IMG_FILE_COMMANDS),buffer)
                 self.matches = self._path_completions(text, state, IMG_EXTENSIONS,shortcut_ok=do_shortcut)
 
@@ -88,6 +112,13 @@ class Completer(object):
             elif re.search('(-S\s*|--seed[=\s])\d*$',buffer): 
                 self.matches= self._seed_completions(text,state)
 
+            # looking for a model
+            elif re.match('^'+'|'.join(MODEL_COMMANDS),buffer):
+                self.matches= self._model_completions(text, state)
+
+            elif re.search(weight_regexp,buffer):
+                self.matches = self._path_completions(text, state, WEIGHT_EXTENSIONS)
+
             # This is the first time for this text, so build a match list.
             elif text:
                 self.matches = [
@@ -104,6 +135,13 @@ class Completer(object):
             response = None
         return response
 
+    def complete_extensions(self, extensions:list):
+        '''
+        If called with a list of extensions, will force completer
+        to do file path completions.
+        '''
+        self.extensions=extensions
+
     def add_history(self,line):
         '''
         Pass thru to readline
@@ -188,6 +226,21 @@ class Completer(object):
         matches.sort()
         return matches
 
+    def _model_completions(self, text, state):
+        m = re.search('(!switch\s+)(\w*)',text)
+        if m:
+            switch  = m.groups()[0]
+            partial = m.groups()[1]
+        else:
+            switch  = ''
+            partial = text
+        matches = list()
+        for s in self.models:
+            if s.startswith(partial):
+                matches.append(switch+s)
+        matches.sort()
+        return matches
+
     def _pre_input_hook(self):
         if self.linebuffer:
             readline.insert_text(self.linebuffer)
@@ -266,9 +319,9 @@ class DummyCompleter(Completer):
     def set_line(self,line):
         print(f'# {line}')
 
-def get_completer(opt:Args)->Completer:
+def get_completer(opt:Args, models=[])->Completer:
     if readline_available:
-        completer = Completer(COMMANDS)
+        completer = Completer(COMMANDS,models)
 
         readline.set_completer(
             completer.complete

ldm/invoke/server.py

@@ -31,12 +31,13 @@ def build_opt(post_data, seed, gfpgan_model_exists):
     setattr(opt, 'embiggen', None)
     setattr(opt, 'embiggen_tiles', None)
 
-    setattr(opt, 'gfpgan_strength', float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0)
+    setattr(opt, 'facetool_strength', float(post_data['facetool_strength']) if gfpgan_model_exists else 0)
     setattr(opt, 'upscale', [int(post_data['upscale_level']), float(post_data['upscale_strength'])] if post_data['upscale_level'] != '' else None)
     setattr(opt, 'progress_images', 'progress_images' in post_data)
     setattr(opt, 'seed', None if int(post_data['seed']) == -1 else int(post_data['seed']))
     setattr(opt, 'threshold', float(post_data['threshold']))
     setattr(opt, 'perlin', float(post_data['perlin']))
+    setattr(opt, 'hires_fix', 'hires_fix' in post_data)
     setattr(opt, 'variation_amount', float(post_data['variation_amount']) if int(post_data['seed']) != -1 else 0)
     setattr(opt, 'with_variations', [])
     setattr(opt, 'embiggen', None)
@@ -196,7 +197,7 @@ class DreamServer(BaseHTTPRequestHandler):
                 ) + '\n',"utf-8"))
 
             # control state of the "postprocessing..." message
-            upscaling_requested = opt.upscale or opt.gfpgan_strength > 0
+            upscaling_requested = opt.upscale or opt.facetool_strength > 0
             nonlocal images_generated # NB: Is this bad python style? It is typical usage in a perl closure.
             nonlocal images_upscaled  # NB: Is this bad python style? It is typical usage in a perl closure.
             if upscaled:

ldm/invoke/server_legacy.py

@@ -0,0 +1,246 @@
+import argparse
+import json
+import base64
+import mimetypes
+import os
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from ldm.invoke.pngwriter import PngWriter, PromptFormatter
+from threading import Event
+
+def build_opt(post_data, seed, gfpgan_model_exists):
+    opt = argparse.Namespace()
+    setattr(opt, 'prompt', post_data['prompt'])
+    setattr(opt, 'init_img', post_data['initimg'])
+    setattr(opt, 'strength', float(post_data['strength']))
+    setattr(opt, 'iterations', int(post_data['iterations']))
+    setattr(opt, 'steps', int(post_data['steps']))
+    setattr(opt, 'width', int(post_data['width']))
+    setattr(opt, 'height', int(post_data['height']))
+    setattr(opt, 'seamless', 'seamless' in post_data)
+    setattr(opt, 'fit', 'fit' in post_data)
+    setattr(opt, 'mask', 'mask' in post_data)
+    setattr(opt, 'invert_mask', 'invert_mask' in post_data)
+    setattr(opt, 'cfg_scale', float(post_data['cfg_scale']))
+    setattr(opt, 'sampler_name', post_data['sampler_name'])
+    setattr(opt, 'gfpgan_strength', float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0)
+    setattr(opt, 'upscale', [int(post_data['upscale_level']), float(post_data['upscale_strength'])] if post_data['upscale_level'] != '' else None)
+    setattr(opt, 'progress_images', 'progress_images' in post_data)
+    setattr(opt, 'seed', None if int(post_data['seed']) == -1 else int(post_data['seed']))
+    setattr(opt, 'variation_amount', float(post_data['variation_amount']) if int(post_data['seed']) != -1 else 0)
+    setattr(opt, 'with_variations', [])
+
+    broken = False
+    if int(post_data['seed']) != -1 and post_data['with_variations'] != '':
+        for part in post_data['with_variations'].split(','):
+            seed_and_weight = part.split(':')
+            if len(seed_and_weight) != 2:
+                print(f'could not parse with_variation part "{part}"')
+                broken = True
+                break
+            try:
+                seed = int(seed_and_weight[0])
+                weight = float(seed_and_weight[1])
+            except ValueError:
+                print(f'could not parse with_variation part "{part}"')
+                broken = True
+                break
+            opt.with_variations.append([seed, weight])
+    
+    if broken:
+        raise CanceledException
+
+    if len(opt.with_variations) == 0:
+        opt.with_variations = None
+
+    return opt
+
+class CanceledException(Exception):
+    pass
+
+class DreamServer(BaseHTTPRequestHandler):
+    model = None
+    outdir = None
+    canceled = Event()
+
+    def do_GET(self):
+        if self.path == "/":
+            self.send_response(200)
+            self.send_header("Content-type", "text/html")
+            self.end_headers()
+            with open("./static/dream_web/index.html", "rb") as content:
+                self.wfile.write(content.read())
+        elif self.path == "/config.js":
+            # unfortunately this import can't be at the top level, since that would cause a circular import
+            from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists
+            self.send_response(200)
+            self.send_header("Content-type", "application/javascript")
+            self.end_headers()
+            config = {
+                'gfpgan_model_exists': gfpgan_model_exists
+            }
+            self.wfile.write(bytes("let config = " + json.dumps(config) + ";\n", "utf-8"))
+        elif self.path == "/run_log.json":
+            self.send_response(200)
+            self.send_header("Content-type", "application/json")
+            self.end_headers()
+            output = []
+            
+            log_file = os.path.join(self.outdir, "dream_web_log.txt")
+            if os.path.exists(log_file):
+                with open(log_file, "r") as log:
+                    for line in log:
+                        url, config = line.split(": {", maxsplit=1)
+                        config = json.loads("{" + config)
+                        config["url"] = url.lstrip(".")
+                        if os.path.exists(url):
+                            output.append(config)
+
+            self.wfile.write(bytes(json.dumps({"run_log": output}), "utf-8"))
+        elif self.path == "/cancel":
+            self.canceled.set()
+            self.send_response(200)
+            self.send_header("Content-type", "application/json")
+            self.end_headers()
+            self.wfile.write(bytes('{}', 'utf8'))
+        else:
+            path = "." + self.path
+            cwd = os.path.realpath(os.getcwd())
+            is_in_cwd = os.path.commonprefix((os.path.realpath(path), cwd)) == cwd
+            if not (is_in_cwd and os.path.exists(path)):
+                self.send_response(404)
+                return
+            mime_type = mimetypes.guess_type(path)[0]
+            if mime_type is not None:
+                self.send_response(200)
+                self.send_header("Content-type", mime_type)
+                self.end_headers()
+                with open("." + self.path, "rb") as content:
+                    self.wfile.write(content.read())
+            else:
+                self.send_response(404)
+
+    def do_POST(self):
+        self.send_response(200)
+        self.send_header("Content-type", "application/json")
+        self.end_headers()
+
+        # unfortunately this import can't be at the top level, since that would cause a circular import
+        # TODO temporarily commented out, import fails for some reason
+        # from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists
+        gfpgan_model_exists = False
+
+        content_length = int(self.headers['Content-Length'])
+        post_data = json.loads(self.rfile.read(content_length))
+        opt = build_opt(post_data, self.model.seed, gfpgan_model_exists)
+
+        self.canceled.clear()
+        print(f">> Request to generate with prompt: {opt.prompt}")
+        # In order to handle upscaled images, the PngWriter needs to maintain state
+        # across images generated by each call to prompt2img(), so we define it in
+        # the outer scope of image_done()
+        config = post_data.copy() # Shallow copy
+        config['initimg'] = config.pop('initimg_name', '')
+
+        images_generated = 0    # helps keep track of when upscaling is started
+        images_upscaled = 0     # helps keep track of when upscaling is completed
+        pngwriter = PngWriter(self.outdir)
+
+        prefix = pngwriter.unique_prefix()
+        # if upscaling is requested, then this will be called twice, once when
+        # the images are first generated, and then again when after upscaling
+        # is complete. The upscaling replaces the original file, so the second
+        # entry should not be inserted into the image list.
+        def image_done(image, seed, upscaled=False, first_seed=-1, use_prefix=None):
+            print(f'First seed: {first_seed}')
+            name = f'{prefix}.{seed}.png'
+            iter_opt = argparse.Namespace(**vars(opt)) # copy
+            if opt.variation_amount > 0:
+                this_variation = [[seed, opt.variation_amount]]
+                if opt.with_variations is None:
+                    iter_opt.with_variations = this_variation
+                else:
+                    iter_opt.with_variations = opt.with_variations + this_variation
+                iter_opt.variation_amount = 0
+            elif opt.with_variations is None:
+                iter_opt.seed = seed
+            normalized_prompt = PromptFormatter(self.model, iter_opt).normalize_prompt()
+            path = pngwriter.save_image_and_prompt_to_png(image, f'{normalized_prompt} -S{iter_opt.seed}', name)
+
+            if int(config['seed']) == -1:
+                config['seed'] = seed
+            # Append post_data to log, but only once!
+            if not upscaled:
+                with open(os.path.join(self.outdir, "dream_web_log.txt"), "a") as log:
+                    log.write(f"{path}: {json.dumps(config)}\n")
+
+                self.wfile.write(bytes(json.dumps(
+                    {'event': 'result', 'url': path, 'seed': seed, 'config': config}
+                ) + '\n',"utf-8"))
+
+            # control state of the "postprocessing..." message
+            upscaling_requested = opt.upscale or opt.gfpgan_strength > 0
+            nonlocal images_generated # NB: Is this bad python style? It is typical usage in a perl closure.
+            nonlocal images_upscaled  # NB: Is this bad python style? It is typical usage in a perl closure.
+            if upscaled:
+                images_upscaled += 1
+            else:
+                images_generated += 1
+            if upscaling_requested:
+                action = None
+                if images_generated >= opt.iterations:
+                    if images_upscaled < opt.iterations:
+                        action = 'upscaling-started'
+                    else:
+                        action = 'upscaling-done'
+                if action:
+                    x = images_upscaled + 1
+                    self.wfile.write(bytes(json.dumps(
+                        {'event': action, 'processed_file_cnt': f'{x}/{opt.iterations}'}
+                    ) + '\n',"utf-8"))
+
+        step_writer = PngWriter(os.path.join(self.outdir, "intermediates"))
+        step_index = 1
+        def image_progress(sample, step):
+            if self.canceled.is_set():
+                self.wfile.write(bytes(json.dumps({'event':'canceled'}) + '\n', 'utf-8'))
+                raise CanceledException
+            path = None
+            # since rendering images is moderately expensive, only render every 5th image
+            # and don't bother with the last one, since it'll render anyway
+            nonlocal step_index
+            if opt.progress_images and step % 5 == 0 and step < opt.steps - 1:
+                image = self.model.sample_to_image(sample)
+                name = f'{prefix}.{opt.seed}.{step_index}.png'
+                metadata = f'{opt.prompt} -S{opt.seed} [intermediate]'
+                path = step_writer.save_image_and_prompt_to_png(image, metadata, name)
+                step_index += 1
+            self.wfile.write(bytes(json.dumps(
+                {'event': 'step', 'step': step + 1, 'url': path}
+            ) + '\n',"utf-8"))
+
+        try:
+            if opt.init_img is None:
+                # Run txt2img
+                self.model.prompt2image(**vars(opt), step_callback=image_progress, image_callback=image_done)
+            else:
+                # Decode initimg as base64 to temp file
+                with open("./img2img-tmp.png", "wb") as f:
+                    initimg = opt.init_img.split(",")[1] # Ignore mime type
+                    f.write(base64.b64decode(initimg))
+                opt1 = argparse.Namespace(**vars(opt))
+                opt1.init_img = "./img2img-tmp.png"
+
+                try:
+                    # Run img2img
+                    self.model.prompt2image(**vars(opt1), step_callback=image_progress, image_callback=image_done)
+                finally:
+                    # Remove the temp file
+                    os.remove("./img2img-tmp.png")
+        except CanceledException:
+            print(f"Canceled.")
+            return
+
+
+class ThreadingDreamServer(ThreadingHTTPServer):
+    def __init__(self, server_address):
+        super(ThreadingDreamServer, self).__init__(server_address, DreamServer)

ldm/models/diffusion/ddpm.py

@@ -106,7 +106,7 @@ class DDPM(pl.LightningModule):
         ], 'currently only supporting "eps" and "x0"'
         self.parameterization = parameterization
         print(
-            f'{self.__class__.__name__}: Running in {self.parameterization}-prediction mode'
+            f'   | {self.__class__.__name__}: Running in {self.parameterization}-prediction mode'
         )
         self.cond_stage_model = None
         self.clip_denoised = clip_denoised
@@ -1353,7 +1353,7 @@ class LatentDiffusion(DDPM):
                 num_downs = self.first_stage_model.encoder.num_resolutions - 1
                 rescale_latent = 2 ** (num_downs)
 
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # get top left positions of patches as conforming for the bbbox tokenizer, therefore we
                 # need to rescale the tl patch coordinates to be in between (0,1)
                 tl_patch_coordinates = [
                     (

ldm/models/diffusion/ksampler.py

@@ -98,7 +98,8 @@ class KSampler(Sampler):
             rho=7.,
             device=self.device,
         )
-        self.sigmas = self.karras_sigmas
+        self.sigmas = self.model_sigmas
+        #self.sigmas = self.karras_sigmas
         
     # ALERT: We are completely overriding the sample() method in the base class, which
     # means that inpainting will not work. To get this to work we need to be able to

ldm/models/diffusion/sampler.py

@@ -140,7 +140,7 @@ class Sampler(object):
         conditioning=None,
         callback=None,
         normals_sequence=None,
-        img_callback=None,
+        img_callback=None,   # TODO: this is very confusing because it is called "step_callback" elsewhere. Change.
         quantize_x0=False,
         eta=0.0,
         mask=None,

ldm/modules/diffusionmodules/model.py

@@ -49,9 +49,15 @@ class Upsample(nn.Module):
                                         padding=1)
 
     def forward(self, x):
+        cpu_m1_cond = True if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available() and \
+                              x.size()[0] * x.size()[1] * x.size()[2] * x.size()[3] % 2**27 == 0 else False
+        if cpu_m1_cond:
+            x = x.to('cpu')  # send to cpu
         x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
         if self.with_conv:
             x = self.conv(x)
+        if cpu_m1_cond:
+            x = x.to('mps')  # return to mps
         return x
 
 
@@ -117,6 +123,14 @@ class ResnetBlock(nn.Module):
                                                     padding=0)
 
     def forward(self, x, temb):
+        if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            x_size = x.size()
+            if (x_size[0] * x_size[1] * x_size[2] * x_size[3]) % 2**29 == 0:
+                self.to('cpu')
+                x = x.to('cpu')
+            else:
+                self.to('mps')
+                x = x.to('mps')
         h = self.norm1(x)
         h = silu(h)
         h = self.conv1(h)
@@ -245,7 +259,7 @@ class AttnBlock(nn.Module):
 
 def make_attn(in_channels, attn_type="vanilla"):
     assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    print(f"   | Making attention of type '{attn_type}' with {in_channels} in_channels")
     if attn_type == "vanilla":
         return AttnBlock(in_channels)
     elif attn_type == "none":
@@ -521,7 +535,7 @@ class Decoder(nn.Module):
         block_in = ch*ch_mult[self.num_resolutions-1]
         curr_res = resolution // 2**(self.num_resolutions-1)
         self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
+        print("   | Working with z of shape {} = {} dimensions.".format(
             self.z_shape, np.prod(self.z_shape)))
 
         # z to block_in

ldm/modules/diffusionmodules/util.py

@@ -64,7 +64,8 @@ def make_ddim_timesteps(
 ):
     if ddim_discr_method == 'uniform':
         c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+        # ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+        ddim_timesteps = (np.arange(0, num_ddim_timesteps) * c).astype(int)
     elif ddim_discr_method == 'quad':
         ddim_timesteps = (
             (
@@ -81,8 +82,8 @@ def make_ddim_timesteps(
 
     # assert ddim_timesteps.shape[0] == num_ddim_timesteps
     # add one to get the final alpha values right (the ones from first scale to data during sampling)
-#    steps_out = ddim_timesteps + 1
-    steps_out = ddim_timesteps
+    steps_out = ddim_timesteps + 1
+    # steps_out = ddim_timesteps
 
     if verbose:
         print(f'Selected timesteps for ddim sampler: {steps_out}')

ldm/util.py

@@ -75,7 +75,7 @@ def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
         print(
-            f'{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'
+            f'   | {model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'
         )
     return total_params
 

mkdocs.yml

@@ -1,12 +1,12 @@
 # General
-site_name: Dream Script Docs
-site_url: https://lstein.github.io/stable-diffusion/
+site_name: Stable Diffusion Toolkit Docs
+site_url: https://invoke-ai.github.io/InvokeAI
 site_author: mauwii
-dev_addr: "127.0.0.1:8080"
+dev_addr: '127.0.0.1:8080'
 
 # Repository
-repo_name: lstein/stable-diffusion
-repo_url: https://github.com/lstein/stable-diffusion
+repo_name: 'invoke-ai/InvokeAI'
+repo_url: 'https://github.com/invoke-ai/InvokeAI'
 edit_uri: edit/main/docs/
 
 # Copyright
@@ -26,6 +26,7 @@ theme:
         name: Switch to dark mode
     - media: '(prefers-color-scheme: dark)'
       scheme: slate
+      primary: blue
       toggle:
         icon: material/lightbulb-outline
         name: Switch to light mode
@@ -55,8 +56,8 @@ markdown_extensions:
   - pymdownx.keys
   - pymdownx.magiclink:
       repo_url_shorthand: true
-      user: 'lstein'
-      repo: 'stable-diffusion'
+      user: 'invoke-ai'
+      repo: 'InvokeAI'
   - pymdownx.mark
   - pymdownx.smartsymbols
   - pymdownx.superfences:

notebooks/Stable-Diffusion-local-Windows.ipynb

@@ -52,14 +52,14 @@
    "outputs": [],
    "source": [
     "%%cmd\n",
-    "pew new --python 3.10 -r requirements-lin-win-colab-CUDA.txt --dont-activate invoke-ai"
+    "pew new --python 3.10 -r requirements-lin-win-colab-CUDA.txt --dont-activate invokeai"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Switch the notebook kernel to the new 'invoke-ai' environment!\n",
+    "# Switch the notebook kernel to the new 'invokeai' environment!\n",
     "\n",
     "## VSCode: restart VSCode and come back to this cell\n",
     "\n",
@@ -67,7 +67,7 @@
     "1. Type \"Select Interpreter\" and select \"Jupyter: Select Interpreter to Start Jupyter Server\"\n",
     "1. VSCode will say that it needs to install packages. Click the \"Install\" button.\n",
     "1. Once the install is finished, do 1 & 2 again\n",
-    "1. Pick 'invoke-ai'\n",
+    "1. Pick 'invokeai'\n",
     "1. Run the following cell"
    ]
   },
@@ -88,7 +88,7 @@
     "## Jupyter/JupyterLab\n",
     "\n",
     "1. Run the cell below\n",
-    "1. Click on the toolbar where it says \"(ipyknel)\" ↗️. You should get a pop-up asking you to \"Select Kernel\". Pick 'invoke-ai' from the drop-down.\n"
+    "1. Click on the toolbar where it says \"(ipyknel)\" ↗️. You should get a pop-up asking you to \"Select Kernel\". Pick 'invokeai' from the drop-down.\n"
    ]
   },
   {
@@ -106,9 +106,9 @@
    "source": [
     "# DO NOT RUN THIS CELL IF YOU ARE USING VSCODE!!\n",
     "%%cmd\n",
-    "pew workon invoke-ai\n",
+    "pew workon invokeai\n",
     "pip3 install ipykernel\n",
-    "python -m ipykernel install --name=invoke-ai"
+    "python -m ipykernel install --name=invokeai"
    ]
   },
   {
@@ -183,7 +183,7 @@
     "Now:\n",
     "\n",
     "1. `cd` to wherever the 'InvokeAI' directory is\n",
-    "1. Run `pew workon invoke-ai`\n",
+    "1. Run `pew workon invokeai`\n",
     "1. Run `winpty python scripts\\dream.py`"
    ]
   },

notebooks/Stable_Diffusion_AI_Notebook.ipynb

@@ -6,7 +6,7 @@
         "id": "ycYWcsEKc6w7"
       },
       "source": [
-        "# Stable Diffusion AI Notebook (Release 1.14)\n",
+        "# Stable Diffusion AI Notebook (Release 2.0.0)\n",
         "\n",
         "<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
         "#### Instructions:\n",
@@ -58,8 +58,8 @@
         "from os.path import exists\n",
         "\n",
         "!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
-        "%cd /content/stable-diffusion/\n",
-        "!git checkout --quiet tags/release-1.14.1"
+        "%cd /content/InvokeAI/\n",
+        "!git checkout --quiet tags/v2.0.0"
       ]
     },
     {
@@ -79,6 +79,7 @@
         "!pip install colab-xterm\n",
         "!pip install -r requirements-lin-win-colab-CUDA.txt\n",
         "!pip install clean-fid torchtext\n",
+        "!pip install transformers\n",
         "gc.collect()"
       ]
     },
@@ -106,7 +107,7 @@
       "source": [
         "#@title 5. Load small ML models required\n",
         "import gc\n",
-        "%cd /content/stable-diffusion/\n",
+        "%cd /content/InvokeAI/\n",
         "!python scripts/preload_models.py\n",
         "gc.collect()"
       ]
@@ -171,18 +172,18 @@
         "import os \n",
         "\n",
         "# Folder creation if it doesn't exist\n",
-        "if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1\"):\n",
+        "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
         "     print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
         "else:\n",
-        "    %mkdir /content/stable-diffusion/models/ldm/stable-diffusion-v1\n",
+        "    %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
         "    print(\"✅ Dir stable-diffusion-v1 created\")\n",
         "\n",
         "# Symbolic link if it doesn't exist\n",
-        "if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
+        "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
         "    print(\"❗ Symlink already created\")\n",
         "else: \n",
         "    src = model_path\n",
-        "    dst = '/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt'\n",
+        "    dst = '/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt'\n",
         "    os.symlink(src, dst) \n",
         "    print(\"✅ Symbolic link created successfully\")"
       ]
@@ -207,7 +208,7 @@
       "source": [
         "#@title 9. Run Terminal and Execute Dream bot\n",
         "#@markdown <font color=\"blue\">Steps:</font> <br>\n",
-        "#@markdown 1. Execute command `python scripts/dream.py` to run dream bot.<br>\n",
+        "#@markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
         "#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
         "#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
         "#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
@@ -233,7 +234,7 @@
         "%matplotlib inline\n",
         "\n",
         "images = []\n",
-        "for img_path in sorted(glob.glob('/content/stable-diffusion/outputs/img-samples/*.png'), reverse=True):\n",
+        "for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
         "    images.append(mpimg.imread(img_path))\n",
         "\n",
         "images = images[:15] \n",

requirements-linux-arm64.txt

@@ -12,12 +12,12 @@ pillow==9.2.0
 pudb==2019.2
 torch==1.12.1
 torchvision==0.13.0
-pytorch-lightning==1.4.2
+pytorch-lightning==1.7.7
 streamlit==1.12.0
 test-tube>=0.7.5
 torch-fidelity==0.3.0
 torchmetrics==0.6.0
-transformers==4.19.2
+transformers==4.21.3
 -e git+https://github.com/openai/CLIP.git@main#egg=clip
 -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
 -e git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion

requirements-mac-MPS-CPU.txt

@@ -1,6 +1,6 @@
 -r requirements.txt
 
-protobuf==3.19.4
+protobuf==3.19.6
 torch
 torchvision
 -e .

scripts/dream.py

@@ -1,12 +1,11 @@
 #!/usr/bin/env python3
 # Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
 
-import sys
-import os.path
-
-script_path = sys.argv[0]
-script_args = sys.argv[1:]
-script_dir,script_name = os.path.split(script_path)
-script_dest = os.path.join(script_dir,'invoke.py')
-os.execlp('python3','python3',script_dest,*script_args)
+import warnings
+import invoke
 
+if __name__ == '__main__':
+    warnings.warn("dream.py is being deprecated, please run invoke.py for the "
+                  "new UI/API or legacy_api.py for the old API",
+                  DeprecationWarning)
+    invoke.main()

scripts/invoke.py

@@ -9,6 +9,7 @@ import copy
 import warnings
 import time
 import traceback
+import yaml
 sys.path.append('.')    # corrects a weird problem on Macs
 from ldm.invoke.readline import get_completer
 from ldm.invoke.args import Args, metadata_dumps, metadata_from_png, dream_cmd_from_png
@@ -16,8 +17,6 @@ from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
 from ldm.invoke.image_util import make_grid
 from ldm.invoke.log import write_log
 from omegaconf import OmegaConf
-from backend.invoke_ai_web_server import InvokeAIWebServer
-
 
 def main():
     """Initialize command-line parsers and the diffusion model"""
@@ -33,7 +32,7 @@ def main():
         print('--weights argument has been deprecated. Please edit ./configs/models.yaml, and select the weights using --model instead.')
         sys.exit(-1)
 
-    print('* Initializing, be patient...\n')
+    print('* Initializing, be patient...')
     from ldm.generate import Generate
 
     # these two lines prevent a horrible warning message from appearing
@@ -42,45 +41,7 @@ def main():
     transformers.logging.set_verbosity_error()
 
     # Loading Face Restoration and ESRGAN Modules
-    try:
-        gfpgan, codeformer, esrgan = None, None, None
-        if opt.restore or opt.esrgan:
-            from ldm.invoke.restoration import Restoration
-            restoration = Restoration()
-            if opt.restore:
-                gfpgan, codeformer = restoration.load_face_restore_models(opt.gfpgan_dir, opt.gfpgan_model_path)
-            else:
-                print('>> Face restoration disabled')
-            if opt.esrgan:
-                esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
-            else:
-                print('>> Upscaling disabled')
-        else:
-            print('>> Face restoration and upscaling disabled')
-    except (ModuleNotFoundError, ImportError):
-        print(traceback.format_exc(), file=sys.stderr)
-        print('>> You may need to install the ESRGAN and/or GFPGAN modules')
-
-    # creating a simple text2image object with a handful of
-    # defaults passed on the command line.
-    # additional parameters will be added (or overriden) during
-    # the user input loop
-    try:
-        gen = Generate(
-            conf           = opt.conf,
-            model          = opt.model,
-            sampler_name   = opt.sampler_name,
-            embedding_path = opt.embedding_path,
-            full_precision = opt.full_precision,
-            precision      = opt.precision,
-            gfpgan=gfpgan,
-            codeformer=codeformer,
-            esrgan=esrgan,
-            free_gpu_mem=opt.free_gpu_mem,
-            )
-    except (FileNotFoundError, IOError, KeyError) as e:
-        print(f'{e}. Aborting.')
-        sys.exit(-1)
+    gfpgan,codeformer,esrgan = load_face_restoration(opt)
 
     # make sure the output directory exists
     if not os.path.exists(opt.outdir):
@@ -100,6 +61,24 @@ def main():
             print(f'{e}. Aborting.')
             sys.exit(-1)
 
+    # creating a Generate object:
+    try:
+        gen = Generate(
+            conf           = opt.conf,
+            model          = opt.model,
+            sampler_name   = opt.sampler_name,
+            embedding_path = opt.embedding_path,
+            full_precision = opt.full_precision,
+            precision      = opt.precision,
+            gfpgan=gfpgan,
+            codeformer=codeformer,
+            esrgan=esrgan,
+            free_gpu_mem=opt.free_gpu_mem,
+            )
+    except (FileNotFoundError, IOError, KeyError) as e:
+        print(f'{e}. Aborting.')
+        sys.exit(-1)
+
     if opt.seamless:
         print(">> changed to seamless tiling mode")
 
@@ -116,7 +95,10 @@ def main():
             "\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
         )
 
-    main_loop(gen, opt, infile)
+    try:
+        main_loop(gen, opt, infile)
+    except KeyboardInterrupt:
+        print("\ngoodbye!")
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
 def main_loop(gen, opt, infile):
@@ -124,12 +106,13 @@ def main_loop(gen, opt, infile):
     done = False
     path_filter = re.compile(r'[<>:"/\\|?*]')
     last_results = list()
-    model_config = OmegaConf.load(opt.conf)[opt.model]
+    model_config = OmegaConf.load(opt.conf)
 
     # The readline completer reads history from the .dream_history file located in the
     # output directory specified at the time of script launch. We do not currently support
     # changing the history file midstream when the output directory is changed.
-    completer   = get_completer(opt)
+    completer   = get_completer(opt, models=list(model_config.keys()))
+    completer.set_default_dir(opt.outdir)
     output_cntr = completer.get_current_history_length()+1
 
     # os.pathconf is not available on Windows
@@ -141,11 +124,9 @@ def main_loop(gen, opt, infile):
         name_max = 255
 
     while not done:
-        operation = 'generate'   # default operation, alternative is 'postprocess'
 
-        if completer:
-            completer.set_default_dir(opt.outdir)
-            
+        operation = 'generate'
+
         try:
             command = get_next_command(infile)
         except EOFError:
@@ -164,41 +145,10 @@ def main_loop(gen, opt, infile):
             break
 
         if command.startswith('!'):
-            subcommand = command[1:]
+            command, operation = do_command(command, gen, opt, completer)
 
-            if subcommand.startswith('dream'):   # in case a stored prompt still contains the !dream command
-                command = command.replace('!dream ','',1)
-
-            elif subcommand.startswith('fix'):
-                command = command.replace('!fix ','',1)
-                operation = 'postprocess'
-
-            elif subcommand.startswith('fetch'):
-                file_path = command.replace('!fetch ','',1)
-                retrieve_dream_command(opt,file_path,completer)
-                continue
-
-            elif subcommand.startswith('history'):
-                completer.show_history()
-                continue
-
-            elif subcommand.startswith('search'):
-                search_str = command.replace('!search ','',1)
-                completer.show_history(search_str)
-                continue
-
-            elif subcommand.startswith('clear'):
-                completer.clear_history()
-                continue
-
-            elif re.match('^(\d+)',subcommand):
-                command_no = re.match('^(\d+)',subcommand).groups()[0]
-                command    = completer.get_line(int(command_no))
-                completer.set_line(command)
-                continue
-                
-            else:  # not a recognized subcommand, so give the --help text
-                command = '-h'
+        if operation is None:
+            continue
 
         if opt.parse_cmd(command) is None:
             continue
@@ -218,9 +168,9 @@ def main_loop(gen, opt, infile):
 
         # width and height are set by model if not specified
         if not opt.width:
-            opt.width = model_config.width
+            opt.width = gen.width
         if not opt.height:
-            opt.height = model_config.height
+            opt.height = gen.height
 
         # retrieve previous value of init image if requested
         if opt.init_img is not None and re.match('^-\\d+$', opt.init_img):
@@ -289,6 +239,7 @@ def main_loop(gen, opt, infile):
             grid_images      = dict()  # seed -> Image, only used if `opt.grid`
             prior_variations = opt.with_variations or []
             prefix = file_writer.unique_prefix()
+            step_callback = make_step_callback(gen, opt, prefix) if opt.save_intermediates > 0 else None
 
             def image_writer(image, seed, upscaled=False, first_seed=None, use_prefix=None):
                 # note the seed is the seed of the current image
@@ -322,6 +273,7 @@ def main_loop(gen, opt, infile):
                             model_hash = gen.model_hash,
                         ),
                         name      = filename,
+                        compress_level = opt.png_compression,
                     )
 
                     # update rfc metadata
@@ -350,6 +302,7 @@ def main_loop(gen, opt, infile):
                 opt.last_operation='generate'
                 gen.prompt2image(
                     image_callback=image_writer,
+                    step_callback=step_callback,
                     catch_interrupts=catch_ctrl_c,
                     **vars(opt)
                 )
@@ -392,13 +345,162 @@ def main_loop(gen, opt, infile):
 
     print('goodbye!')
 
+def do_command(command:str, gen, opt:Args, completer) -> tuple:
+    operation = 'generate'   # default operation, alternative is 'postprocess'
+
+    if command.startswith('!dream'):   # in case a stored prompt still contains the !dream command
+        command = command.replace('!dream ','',1)
+
+    elif command.startswith('!fix'):
+        command = command.replace('!fix ','',1)
+        operation = 'postprocess'
+
+    elif command.startswith('!switch'):
+        model_name = command.replace('!switch ','',1)
+        gen.set_model(model_name)
+        completer.add_history(command)
+        operation = None
+        
+    elif command.startswith('!models'):
+        gen.model_cache.print_models()
+        operation = None
+
+    elif command.startswith('!import'):
+        path = shlex.split(command)
+        if len(path) < 2:
+            print('** please provide a path to a .ckpt or .vae model file')
+        elif not os.path.exists(path[1]):
+            print(f'** {path[1]}: file not found')
+        else:
+            add_weights_to_config(path[1], gen, opt, completer)
+        completer.add_history(command)
+        operation = None
+
+    elif command.startswith('!edit'):
+        path = shlex.split(command)
+        if len(path) < 2:
+            print('** please provide the name of a model')
+        else:
+            edit_config(path[1], gen, opt, completer)
+        completer.add_history(command)
+        operation = None
+
+    elif command.startswith('!fetch'):
+        file_path = command.replace('!fetch ','',1)
+        retrieve_dream_command(opt,file_path,completer)
+        operation = None
+
+    elif command.startswith('!history'):
+        completer.show_history()
+        operation = None
+
+    elif command.startswith('!search'):
+        search_str = command.replace('!search ','',1)
+        completer.show_history(search_str)
+        operation = None
+
+    elif command.startswith('!clear'):
+        completer.clear_history()
+        operation = None
+
+    elif re.match('^!(\d+)',command):
+        command_no = re.match('^!(\d+)',command).groups()[0]
+        command    = completer.get_line(int(command_no))
+        completer.set_line(command)
+        operation = None
+
+    else:  # not a recognized command, so give the --help text
+        command = '-h'
+    return command, operation
+
+def add_weights_to_config(model_path:str, gen, opt, completer):
+    print(f'>> Model import in process. Please enter the values needed to configure this model:')
+    print()
+
+    new_config = {}
+    new_config['weights'] = model_path
+
+    done = False
+    while not done:
+        model_name = input('Short name for this model: ')
+        if not re.match('^[\w._-]+$',model_name):
+            print('** model name must contain only words, digits and the characters [._-] **')
+        else:
+            done = True
+    new_config['description'] = input('Description of this model: ')
+
+    completer.complete_extensions(('.yaml','.yml'))
+    completer.linebuffer = 'configs/stable-diffusion/v1-inference.yaml'
+    
+    done = False
+    while not done:
+        new_config['config'] = input('Configuration file for this model: ')
+        done = os.path.exists(new_config['config'])
+
+    completer.complete_extensions(None)
+
+    for field in ('width','height'):
+        done = False
+        while not done:
+            try:
+                completer.linebuffer = '512'
+                value = int(input(f'Default image {field}: '))
+                assert value >= 64 and value <= 2048
+                new_config[field] = value
+                done = True
+            except:
+                print('** Please enter a valid integer between 64 and 2048')
+
+    if write_config_file(opt.conf, gen, model_name, new_config):
+        gen.set_model(model_name)
+
+def edit_config(model_name:str, gen, opt, completer):
+    config = gen.model_cache.config
+    
+    if model_name not in config:
+        print(f'** Unknown model {model_name}')
+        return
+    
+    print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
+
+    conf = config[model_name]
+    new_config = {}
+    completer.complete_extensions(('.yaml','.yml','.ckpt','.vae'))
+    for field in ('description', 'weights', 'config', 'width','height'):
+        completer.linebuffer = str(conf[field]) if field in conf else ''
+        new_value = input(f'{field}: ')
+        new_config[field] = int(new_value) if field in ('width','height') else new_value
+    completer.complete_extensions(None)
+
+    if write_config_file(opt.conf, gen, model_name, new_config, clobber=True):
+        gen.set_model(model_name)
+    
+def write_config_file(conf_path, gen, model_name, new_config, clobber=False):
+    op = 'modify' if clobber else 'import'
+    print('\n>> New configuration:')
+    print(yaml.dump({model_name:new_config}))
+    if input(f'OK to {op} [n]? ') not in ('y','Y'):
+        return False
+
+    try:
+        yaml_str = gen.model_cache.add_model(model_name, new_config, clobber)
+    except AssertionError as e:
+        print(f'** configuration failed: {str(e)}')
+        return False
+    
+    tmpfile = os.path.join(os.path.dirname(conf_path),'new_config.tmp')
+    with open(tmpfile, 'w') as outfile:
+        outfile.write(yaml_str)
+    os.rename(tmpfile,conf_path)
+    return True
+
 def do_postprocess (gen, opt, callback):
     file_path = opt.prompt     # treat the prompt as the file pathname
     if os.path.dirname(file_path) == '': #basename given
         file_path = os.path.join(opt.outdir,file_path)
 
     tool=None
-    if opt.gfpgan_strength > 0:
+    if opt.facetool_strength > 0:
         tool = opt.facetool
     elif opt.embiggen:
         tool = 'embiggen'
@@ -414,7 +516,7 @@ def do_postprocess (gen, opt, callback):
         gen.apply_postprocessor(
             image_path      = file_path,
             tool            = tool,
-            gfpgan_strength = opt.gfpgan_strength,
+            facetool_strength = opt.facetool_strength,
             codeformer_fidelity = opt.codeformer_fidelity,
             save_original       = opt.save_original,
             upscale             = opt.upscale,
@@ -509,6 +611,7 @@ def get_next_command(infile=None) -> str:  # command string
 
 def invoke_ai_web_server_loop(gen, gfpgan, codeformer, esrgan):
     print('\n* --web was specified, starting web server...')
+    from backend.invoke_ai_web_server import InvokeAIWebServer
     # Change working directory to the stable-diffusion directory
     os.chdir(
         os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
@@ -547,6 +650,38 @@ def split_variations(variations_string) -> list:
     else:
         return parts
 
+def load_face_restoration(opt):
+    try:
+        gfpgan, codeformer, esrgan = None, None, None
+        if opt.restore or opt.esrgan:
+            from ldm.invoke.restoration import Restoration
+            restoration = Restoration()
+            if opt.restore:
+                gfpgan, codeformer = restoration.load_face_restore_models(opt.gfpgan_dir, opt.gfpgan_model_path)
+            else:
+                print('>> Face restoration disabled')
+            if opt.esrgan:
+                esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
+            else:
+                print('>> Upscaling disabled')
+        else:
+            print('>> Face restoration and upscaling disabled')
+    except (ModuleNotFoundError, ImportError):
+        print(traceback.format_exc(), file=sys.stderr)
+        print('>> You may need to install the ESRGAN and/or GFPGAN modules')
+    return gfpgan,codeformer,esrgan
+    
+def make_step_callback(gen, opt, prefix):
+    destination = os.path.join(opt.outdir,'intermediates',prefix)
+    os.makedirs(destination,exist_ok=True)
+    print(f'>> Intermediate images will be written into {destination}')
+    def callback(img, step):
+        if step % opt.save_intermediates == 0 or step == opt.steps-1:
+            filename = os.path.join(destination,f'{step:04}.png')
+            image = gen.sample_to_image(img)
+            image.save(filename,'PNG')
+    return callback
+    
 def retrieve_dream_command(opt,file_path,completer):
     '''
     Given a full or partial path to a previously-generated image file,

scripts/legacy_api.py

@@ -0,0 +1,685 @@
+#!/usr/bin/env python3
+# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
+
+import argparse
+import shlex
+import os
+import re
+import sys
+import copy
+import warnings
+import time
+import ldm.invoke.readline
+from ldm.invoke.pngwriter import PngWriter, PromptFormatter
+from ldm.invoke.server_legacy import DreamServer, ThreadingDreamServer
+from ldm.invoke.image_util import make_grid
+from omegaconf import OmegaConf
+
+# Placeholder to be replaced with proper class that tracks the
+# outputs and associates with the prompt that generated them.
+# Just want to get the formatting look right for now.
+output_cntr = 0
+
+
+def main():
+    """Initialize command-line parsers and the diffusion model"""
+    arg_parser = create_argv_parser()
+    opt = arg_parser.parse_args()
+
+    if opt.laion400m:
+        print('--laion400m flag has been deprecated. Please use --model laion400m instead.')
+        sys.exit(-1)
+    if opt.weights != 'model':
+        print('--weights argument has been deprecated. Please configure ./configs/models.yaml, and call it using --model instead.')
+        sys.exit(-1)
+
+    try:
+        models = OmegaConf.load(opt.config)
+        width = models[opt.model].width
+        height = models[opt.model].height
+        config = models[opt.model].config
+        weights = models[opt.model].weights
+    except (FileNotFoundError, IOError, KeyError) as e:
+        print(f'{e}. Aborting.')
+        sys.exit(-1)
+
+    print('* Initializing, be patient...\n')
+    sys.path.append('.')
+    from pytorch_lightning import logging
+    from ldm.generate import Generate
+
+    # these two lines prevent a horrible warning message from appearing
+    # when the frozen CLIP tokenizer is imported
+    import transformers
+
+    transformers.logging.set_verbosity_error()
+
+    # creating a simple text2image object with a handful of
+    # defaults passed on the command line.
+    # additional parameters will be added (or overriden) during
+    # the user input loop
+    t2i = Generate(
+        # width=width,
+        # height=height,
+        sampler_name=opt.sampler_name,
+        weights=weights,
+        full_precision=opt.full_precision,
+        config=config,
+        # grid=opt.grid,
+        # this is solely for recreating the prompt
+        # seamless=opt.seamless,
+        embedding_path=opt.embedding_path,
+        # device_type=opt.device,
+        # ignore_ctrl_c=opt.infile is None,
+    )
+
+    # make sure the output directory exists
+    if not os.path.exists(opt.outdir):
+        os.makedirs(opt.outdir)
+
+    # gets rid of annoying messages about random seed
+    logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
+
+    # load the infile as a list of lines
+    infile = None
+    if opt.infile:
+        try:
+            if os.path.isfile(opt.infile):
+                infile = open(opt.infile, 'r', encoding='utf-8')
+            elif opt.infile == '-':  # stdin
+                infile = sys.stdin
+            else:
+                raise FileNotFoundError(f'{opt.infile} not found.')
+        except (FileNotFoundError, IOError) as e:
+            print(f'{e}. Aborting.')
+            sys.exit(-1)
+
+    if opt.seamless:
+        print(">> changed to seamless tiling mode")
+
+    # preload the model
+    t2i.load_model()
+
+    if not infile:
+        print(
+            "\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
+        )
+
+    cmd_parser = create_cmd_parser()
+    if opt.web:
+        dream_server_loop(t2i, opt.host, opt.port, opt.outdir)
+    else:
+        main_loop(t2i, opt.outdir, opt.prompt_as_dir, cmd_parser, infile)
+
+
+def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
+    """prompt/read/execute loop"""
+    done = False
+    path_filter = re.compile(r'[<>:"/\\|?*]')
+    last_results = list()
+
+    # os.pathconf is not available on Windows
+    if hasattr(os, 'pathconf'):
+        path_max = os.pathconf(outdir, 'PC_PATH_MAX')
+        name_max = os.pathconf(outdir, 'PC_NAME_MAX')
+    else:
+        path_max = 260
+        name_max = 255
+
+    while not done:
+        try:
+            command = get_next_command(infile)
+        except EOFError:
+            done = True
+            continue
+        except KeyboardInterrupt:
+            done = True
+            continue
+
+        # skip empty lines
+        if not command.strip():
+            continue
+
+        if command.startswith(('#', '//')):
+            continue
+
+        # before splitting, escape single quotes so as not to mess
+        # up the parser
+        command = command.replace("'", "\\'")
+
+        try:
+            elements = shlex.split(command)
+        except ValueError as e:
+            print(str(e))
+            continue
+
+        if elements[0] == 'q':
+            done = True
+            break
+
+        if elements[0].startswith(
+            '!dream'
+        ):   # in case a stored prompt still contains the !dream command
+            elements.pop(0)
+
+        # rearrange the arguments to mimic how it works in the Dream bot.
+        switches = ['']
+        switches_started = False
+
+        for el in elements:
+            if el[0] == '-' and not switches_started:
+                switches_started = True
+            if switches_started:
+                switches.append(el)
+            else:
+                switches[0] += el
+                switches[0] += ' '
+        switches[0] = switches[0][: len(switches[0]) - 1]
+
+        try:
+            opt = parser.parse_args(switches)
+        except SystemExit:
+            parser.print_help()
+            continue
+        if len(opt.prompt) == 0:
+            print('Try again with a prompt!')
+            continue
+        # retrieve previous value!
+        if opt.init_img is not None and re.match('^-\\d+$', opt.init_img):
+            try:
+                opt.init_img = last_results[int(opt.init_img)][0]
+                print(f'>> Reusing previous image {opt.init_img}')
+            except IndexError:
+                print(
+                    f'>> No previous initial image at position {opt.init_img} found')
+                opt.init_img = None
+                continue
+
+        if opt.seed is not None and opt.seed < 0:   # retrieve previous value!
+            try:
+                opt.seed = last_results[opt.seed][1]
+                print(f'>> Reusing previous seed {opt.seed}')
+            except IndexError:
+                print(f'>> No previous seed at position {opt.seed} found')
+                opt.seed = None
+                continue
+
+        do_grid = opt.grid or t2i.grid
+
+        if opt.with_variations is not None:
+            # shotgun parsing, woo
+            parts = []
+            broken = False  # python doesn't have labeled loops...
+            for part in opt.with_variations.split(','):
+                seed_and_weight = part.split(':')
+                if len(seed_and_weight) != 2:
+                    print(f'could not parse with_variation part "{part}"')
+                    broken = True
+                    break
+                try:
+                    seed = int(seed_and_weight[0])
+                    weight = float(seed_and_weight[1])
+                except ValueError:
+                    print(f'could not parse with_variation part "{part}"')
+                    broken = True
+                    break
+                parts.append([seed, weight])
+            if broken:
+                continue
+            if len(parts) > 0:
+                opt.with_variations = parts
+            else:
+                opt.with_variations = None
+
+        if opt.outdir:
+            if not os.path.exists(opt.outdir):
+                os.makedirs(opt.outdir)
+            current_outdir = opt.outdir
+        elif prompt_as_dir:
+            # sanitize the prompt to a valid folder name
+            subdir = path_filter.sub('_', opt.prompt)[:name_max].rstrip(' .')
+
+            # truncate path to maximum allowed length
+            # 27 is the length of '######.##########.##.png', plus two separators and a NUL
+            subdir = subdir[:(path_max - 27 - len(os.path.abspath(outdir)))]
+            current_outdir = os.path.join(outdir, subdir)
+
+            print('Writing files to directory: "' + current_outdir + '"')
+
+            # make sure the output directory exists
+            if not os.path.exists(current_outdir):
+                os.makedirs(current_outdir)
+        else:
+            current_outdir = outdir
+
+        # Here is where the images are actually generated!
+        last_results = []
+        try:
+            file_writer = PngWriter(current_outdir)
+            prefix = file_writer.unique_prefix()
+            results = []  # list of filename, prompt pairs
+            grid_images = dict()  # seed -> Image, only used if `do_grid`
+
+            def image_writer(image, seed, upscaled=False):
+                path = None
+                if do_grid:
+                    grid_images[seed] = image
+                else:
+                    if upscaled and opt.save_original:
+                        filename = f'{prefix}.{seed}.postprocessed.png'
+                    else:
+                        filename = f'{prefix}.{seed}.png'
+                    if opt.variation_amount > 0:
+                        iter_opt = argparse.Namespace(**vars(opt))  # copy
+                        this_variation = [[seed, opt.variation_amount]]
+                        if opt.with_variations is None:
+                            iter_opt.with_variations = this_variation
+                        else:
+                            iter_opt.with_variations = opt.with_variations + this_variation
+                        iter_opt.variation_amount = 0
+                        normalized_prompt = PromptFormatter(
+                            t2i, iter_opt).normalize_prompt()
+                        metadata_prompt = f'{normalized_prompt} -S{iter_opt.seed}'
+                    elif opt.with_variations is not None:
+                        normalized_prompt = PromptFormatter(
+                            t2i, opt).normalize_prompt()
+                        # use the original seed - the per-iteration value is the last variation-seed
+                        metadata_prompt = f'{normalized_prompt} -S{opt.seed}'
+                    else:
+                        normalized_prompt = PromptFormatter(
+                            t2i, opt).normalize_prompt()
+                        metadata_prompt = f'{normalized_prompt} -S{seed}'
+                    path = file_writer.save_image_and_prompt_to_png(
+                        image, metadata_prompt, filename)
+                    if (not upscaled) or opt.save_original:
+                        # only append to results if we didn't overwrite an earlier output
+                        results.append([path, metadata_prompt])
+                last_results.append([path, seed])
+
+            t2i.prompt2image(image_callback=image_writer, **vars(opt))
+
+            if do_grid and len(grid_images) > 0:
+                grid_img   = make_grid(list(grid_images.values()))
+                grid_seeds = list(grid_images.keys())
+                first_seed = last_results[0][1]
+                filename = f'{prefix}.{first_seed}.png'
+                # TODO better metadata for grid images
+                normalized_prompt = PromptFormatter(
+                    t2i, opt).normalize_prompt()
+                metadata_prompt = f'{normalized_prompt} -S{first_seed} --grid -n{len(grid_images)} # {grid_seeds}'
+                path = file_writer.save_image_and_prompt_to_png(
+                    grid_img, metadata_prompt, filename
+                )
+                results = [[path, metadata_prompt]]
+
+        except AssertionError as e:
+            print(e)
+            continue
+
+        except OSError as e:
+            print(e)
+            continue
+
+        print('Outputs:')
+        log_path = os.path.join(current_outdir, 'dream_log.txt')
+        write_log_message(results, log_path)
+        print()
+
+    print('goodbye!')
+
+
+def get_next_command(infile=None) -> str:  # command string
+    if infile is None:
+        command = input('dream> ')
+    else:
+        command = infile.readline()
+        if not command:
+            raise EOFError
+        else:
+            command = command.strip()
+        print(f'#{command}')
+    return command
+
+
+def dream_server_loop(t2i, host, port, outdir):
+    print('\n* --web was specified, starting web server...')
+    # Change working directory to the stable-diffusion directory
+    os.chdir(
+        os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+    )
+
+    # Start server
+    DreamServer.model = t2i
+    DreamServer.outdir = outdir
+    dream_server = ThreadingDreamServer((host, port))
+    print(">> Started Stable Diffusion dream server!")
+    if host == '0.0.0.0':
+        print(
+            f"Point your browser at http://localhost:{port} or use the host's DNS name or IP address.")
+    else:
+        print(">> Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address.")
+        print(f">> Point your browser at http://{host}:{port}.")
+
+    try:
+        dream_server.serve_forever()
+    except KeyboardInterrupt:
+        pass
+
+    dream_server.server_close()
+
+
+def write_log_message(results, log_path):
+    """logs the name of the output image, prompt, and prompt args to the terminal and log file"""
+    global output_cntr
+    log_lines = [f'{path}: {prompt}\n' for path, prompt in results]
+    for l in log_lines:
+        output_cntr += 1
+        print(f'[{output_cntr}] {l}',end='')
+
+
+    with open(log_path, 'a', encoding='utf-8') as file:
+        file.writelines(log_lines)
+
+
+SAMPLER_CHOICES = [
+    'ddim',
+    'k_dpm_2_a',
+    'k_dpm_2',
+    'k_euler_a',
+    'k_euler',
+    'k_heun',
+    'k_lms',
+    'plms',
+]
+
+
+def create_argv_parser():
+    parser = argparse.ArgumentParser(
+        description="""Generate images using Stable Diffusion.
+        Use --web to launch the web interface. 
+        Use --from_file to load prompts from a file path or standard input ("-").
+        Otherwise you will be dropped into an interactive command prompt (type -h for help.)
+        Other command-line arguments are defaults that can usually be overridden
+        prompt the command prompt.
+"""
+    )
+    parser.add_argument(
+        '--laion400m',
+        '--latent_diffusion',
+        '-l',
+        dest='laion400m',
+        action='store_true',
+        help='Fallback to the latent diffusion (laion400m) weights and config',
+    )
+    parser.add_argument(
+        '--from_file',
+        dest='infile',
+        type=str,
+        help='If specified, load prompts from this file',
+    )
+    parser.add_argument(
+        '-n',
+        '--iterations',
+        type=int,
+        default=1,
+        help='Number of images to generate',
+    )
+    parser.add_argument(
+        '-F',
+        '--full_precision',
+        dest='full_precision',
+        action='store_true',
+        help='Use more memory-intensive full precision math for calculations',
+    )
+    parser.add_argument(
+        '-g',
+        '--grid',
+        action='store_true',
+        help='Generate a grid instead of individual images',
+    )
+    parser.add_argument(
+        '-A',
+        '-m',
+        '--sampler',
+        dest='sampler_name',
+        choices=SAMPLER_CHOICES,
+        metavar='SAMPLER_NAME',
+        default='k_lms',
+        help=f'Set the initial sampler. Default: k_lms. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+    )
+    parser.add_argument(
+        '--outdir',
+        '-o',
+        type=str,
+        default='outputs/img-samples',
+        help='Directory to save generated images and a log of prompts and seeds. Default: outputs/img-samples',
+    )
+    parser.add_argument(
+        '--seamless',
+        action='store_true',
+        help='Change the model to seamless tiling (circular) mode',
+    )
+    parser.add_argument(
+        '--embedding_path',
+        type=str,
+        help='Path to a pre-trained embedding manager checkpoint - can only be set on command line',
+    )
+    parser.add_argument(
+        '--prompt_as_dir',
+        '-p',
+        action='store_true',
+        help='Place images in subdirectories named after the prompt.',
+    )
+    # GFPGAN related args
+    parser.add_argument(
+        '--gfpgan_bg_upsampler',
+        type=str,
+        default='realesrgan',
+        help='Background upsampler. Default: realesrgan. Options: realesrgan, none.',
+
+    )
+    parser.add_argument(
+        '--gfpgan_bg_tile',
+        type=int,
+        default=400,
+        help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
+    )
+    parser.add_argument(
+        '--gfpgan_model_path',
+        type=str,
+        default='experiments/pretrained_models/GFPGANv1.3.pth',
+        help='Indicates the path to the GFPGAN model, relative to --gfpgan_dir.',
+    )
+    parser.add_argument(
+        '--gfpgan_dir',
+        type=str,
+        default='./src/gfpgan',
+        help='Indicates the directory containing the GFPGAN code.',
+    )
+    parser.add_argument(
+        '--web',
+        dest='web',
+        action='store_true',
+        help='Start in web server mode.',
+    )
+    parser.add_argument(
+        '--host',
+        type=str,
+        default='127.0.0.1',
+        help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
+    )
+    parser.add_argument(
+        '--port',
+        type=int,
+        default='9090',
+        help='Web server: Port to listen on'
+    )
+    parser.add_argument(
+        '--weights',
+        default='model',
+        help='Indicates the Stable Diffusion model to use.',
+    )
+    parser.add_argument(
+        '--device',
+        '-d',
+        type=str,
+        default='cuda',
+        help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if available"
+    )
+    parser.add_argument(
+        '--model',
+        default='stable-diffusion-1.4',
+        help='Indicates which diffusion model to load. (currently "stable-diffusion-1.4" (default) or "laion400m")',
+    )
+    parser.add_argument(
+        '--config',
+        default='configs/models.yaml',
+        help='Path to configuration file for alternate models.',
+    )
+    return parser
+
+
+def create_cmd_parser():
+    parser = argparse.ArgumentParser(
+        description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
+    )
+    parser.add_argument('prompt')
+    parser.add_argument('-s', '--steps', type=int, help='Number of steps')
+    parser.add_argument(
+        '-S',
+        '--seed',
+        type=int,
+        help='Image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
+    )
+    parser.add_argument(
+        '-n',
+        '--iterations',
+        type=int,
+        default=1,
+        help='Number of samplings to perform (slower, but will provide seeds for individual images)',
+    )
+    parser.add_argument(
+        '-W', '--width', type=int, help='Image width, multiple of 64'
+    )
+    parser.add_argument(
+        '-H', '--height', type=int, help='Image height, multiple of 64'
+    )
+    parser.add_argument(
+        '-C',
+        '--cfg_scale',
+        default=7.5,
+        type=float,
+        help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
+    )
+    parser.add_argument(
+        '-g', '--grid', action='store_true', help='generate a grid'
+    )
+    parser.add_argument(
+        '--outdir',
+        '-o',
+        type=str,
+        default=None,
+        help='Directory to save generated images and a log of prompts and seeds',
+    )
+    parser.add_argument(
+        '--seamless',
+        action='store_true',
+        help='Change the model to seamless tiling (circular) mode',
+    )
+    parser.add_argument(
+        '-i',
+        '--individual',
+        action='store_true',
+        help='Generate individual files (default)',
+    )
+    parser.add_argument(
+        '-I',
+        '--init_img',
+        type=str,
+        help='Path to input image for img2img mode (supersedes width and height)',
+    )
+    parser.add_argument(
+        '-M',
+        '--init_mask',
+        type=str,
+        help='Path to input mask for inpainting mode (supersedes width and height)',
+    )
+    parser.add_argument(
+        '-T',
+        '-fit',
+        '--fit',
+        action='store_true',
+        help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
+    )
+    parser.add_argument(
+        '-f',
+        '--strength',
+        default=0.75,
+        type=float,
+        help='Strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
+    )
+    parser.add_argument(
+        '-G',
+        '--gfpgan_strength',
+        default=0,
+        type=float,
+        help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
+    )
+    parser.add_argument(
+        '-U',
+        '--upscale',
+        nargs='+',
+        default=None,
+        type=float,
+        help='Scale factor (2, 4) for upscaling followed by upscaling strength (0-1.0). If strength not specified, defaults to 0.75'
+    )
+    parser.add_argument(
+        '-save_orig',
+        '--save_original',
+        action='store_true',
+        help='Save original. Use it when upscaling to save both versions.',
+    )
+    # variants is going to be superseded by a generalized "prompt-morph" function
+    #    parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
+    parser.add_argument(
+        '-x',
+        '--skip_normalize',
+        action='store_true',
+        help='Skip subprompt weight normalization',
+    )
+    parser.add_argument(
+        '-A',
+        '-m',
+        '--sampler',
+        dest='sampler_name',
+        default=None,
+        type=str,
+        choices=SAMPLER_CHOICES,
+        metavar='SAMPLER_NAME',
+        help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+    )
+    parser.add_argument(
+        '-t',
+        '--log_tokenization',
+        action='store_true',
+        help='shows how the prompt is split into tokens'
+    )
+    parser.add_argument(
+        '-v',
+        '--variation_amount',
+        default=0.0,
+        type=float,
+        help='If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different.'
+    )
+    parser.add_argument(
+        '-V',
+        '--with_variations',
+        default=None,
+        type=str,
+        help='list of variations to apply, in the format `seed:weight,seed:weight,...'
+    )
+    return parser
+
+
+if __name__ == '__main__':
+    main()

server/models.py

@@ -35,13 +35,14 @@ class DreamBase():
   perlin: float = 0.0
   sampler_name: string = 'klms'
   seamless: bool = False
+  hires_fix: bool = False
   model: str = None # The model to use (currently unused)
   embeddings = None # The embeddings to use (currently unused)
   progress_images: bool = False
 
   # GFPGAN
   enable_gfpgan: bool
-  gfpgan_strength: float = 0
+  facetool_strength: float = 0
 
   # Upscale
   enable_upscale: bool
@@ -91,12 +92,13 @@ class DreamBase():
       # model: str = None # The model to use (currently unused)
       # embeddings = None # The embeddings to use (currently unused)
       self.seamless = 'seamless' in j
+      self.hires_fix = 'hires_fix' in j
       self.progress_images = 'progress_images' in j
 
     # GFPGAN
     self.enable_gfpgan = 'enable_gfpgan' in j and bool(j.get('enable_gfpgan'))
     if self.enable_gfpgan:
-      self.gfpgan_strength = float(j.get('gfpgan_strength'))
+      self.facetool_strength = float(j.get('facetool_strength'))
 
     # Upscale
     self.enable_upscale = 'enable_upscale' in j and bool(j.get('enable_upscale'))

server/services.py

@@ -334,11 +334,11 @@ class GeneratorService:
       # TODO: Support no generation (just upscaling/gfpgan)
 
       upscale = None if not jobRequest.enable_upscale else jobRequest.upscale
-      gfpgan_strength = 0 if not jobRequest.enable_gfpgan else jobRequest.gfpgan_strength
+      facetool_strength = 0 if not jobRequest.enable_gfpgan else jobRequest.facetool_strength
 
       if not jobRequest.enable_generate:
         # If not generating, check if we're upscaling or running gfpgan
-        if not upscale and not gfpgan_strength:
+        if not upscale and not facetool_strength:
           # Invalid settings (TODO: Add message to help user)
           raise CanceledException()
 
@@ -347,7 +347,7 @@ class GeneratorService:
         self.__model.upscale_and_reconstruct(
           image_list = [[image,0]],
           upscale = upscale,
-          strength = gfpgan_strength,
+          strength = facetool_strength,
           save_original = False,
           image_callback = lambda image, seed, upscaled=False: self.__on_image_result(jobRequest, image, seed, upscaled))
 
@@ -371,10 +371,11 @@ class GeneratorService:
           steps            = jobRequest.steps,
           variation_amount = jobRequest.variation_amount,
           with_variations  = jobRequest.with_variations,
-          gfpgan_strength  = gfpgan_strength,
+          facetool_strength = facetool_strength,
           upscale          = upscale,
           sampler_name     = jobRequest.sampler_name,
           seamless         = jobRequest.seamless,
+          hires_fix        = jobRequest.hires_fix,
           embiggen         = jobRequest.embiggen,
           embiggen_tiles   = jobRequest.embiggen_tiles,
           step_callback    = lambda sample, step: self.__on_progress(jobRequest, sample, step),

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='invoke-ai',
-    version='2.0.0',
+    version='2.0.2',
     description='',
     packages=find_packages(),
     install_requires=[

static/dream_web/index.html

@@ -144,8 +144,8 @@
             <input type="checkbox" name="enable_gfpgan" id="enable_gfpgan">
             <label for="enable_gfpgan">Enable gfpgan</label>
           </legend>
-          <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength:</label>
-          <input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
+          <label title="Strength of the gfpgan (face fixing) algorithm." for="facetool_strength">GPFGAN Strength:</label>
+          <input value="0.8" min="0" max="1" type="number" id="facetool_strength" name="facetool_strength" step="0.05">
         </fieldset>
         <fieldset id="upscale">
           <legend>

static/legacy_web/index.html

@@ -100,8 +100,8 @@
 	</fieldset>
         <fieldset id="gfpgan">
 	  <div class="section-header">Post-processing options</div>
-          <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
-          <input value="0.0" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.1">
+          <label title="Strength of the gfpgan (face fixing) algorithm." for="facetool_strength">GPFGAN Strength (0 to disable):</label>
+          <input value="0.0" min="0" max="1" type="number" id="facetool_strength" name="facetool_strength" step="0.1">
           <label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
           <select id="upscale_level" name="upscale_level" value="">
             <option value="" selected>None</option>

tests/legacy_tests.sh

@@ -0,0 +1,46 @@
+#! /usr/bin/env bash
+
+# This file contains bunch of compatibility tests that ensures
+# that the API interface of `scripts/legacy-api.py` remains stable
+
+set -e
+
+OUTDIR=$(mktemp -d)
+
+echo "Using directory $OUTDIR"
+
+# Start API
+python -u scripts/legacy_api.py --web --host=localhost --port=3333 --outdir=$OUTDIR &> $OUTDIR/sd.log &
+APP_PID=$!
+
+echo "Wait for server to startup"
+
+tail -f -n0 $OUTDIR/sd.log | grep -qe "Point your browser at"
+
+echo "Started, continuing"
+
+if [ $? == 1 ]; then
+    echo "Search terminated without finding the pattern"
+fi
+
+# Generate image
+RESULT=$(curl -v -X POST -d '{"index":0,"variation_amount":0,"with_variations":"","steps":25,"width":512,"seed":"1337","prompt":"A cat wearing a hat","strength":0.5,"initimg":null,"cfg_scale":2,"iterations":1,"upscale_level":0,"upscale_strength":0,"sampler_name":"k_euler","height":512}' localhost:3333 | grep result)
+
+# Test 01 - Image contents
+FILENAME=$(echo $RESULT | jq -r .url)
+
+ACTUAL_CHECKSUM=$(sha256sum $FILENAME)
+EXPECTED_CHECKSUM="a77799226a4dfc62a1674498e575c775da042959a4b90b13e26f666c302f079f"
+
+if [ "$ACTUAL_CHECKSUM" != "$EXPECTED_CHECKSUM" ]; then
+    echo "Expected hash $EXPECTED_CHECKSUM but got hash $ACTUAL_CHECKSUM"
+    kill $APP_PID
+    # rm -r $OUTDIR
+    exit 33
+fi
+
+# Assert output
+
+# Cleanup
+kill $APP_PID
+# rm -r $OUTDIR

tests/pr_prompt.txt

@@ -0,0 +1 @@
+banana sushi -Ak_lms -S42 -s10