Configure the NSFW checker at install time with default on (#1624)

* configure the NSFW checker at install time with default on

1. Changes the --safety_checker argument to --nsfw_checker and
--no-nsfw_checker. The original argument is recognized for backward
compatibility.

2. The configure script asks users whether to enable the checker
(default yes). Also offers users ability to select default sampler and
number of generation steps.

3.Enables the pasting of the caution icon on blurred images when
InvokeAI is installed into the package directory.

4. Adds documentation for the NSFW checker, including caveats about
accuracy, memory requirements, and intermediate image dispaly.

* use better fitting icon

* NSFW defaults false for testing

* set default back to nsfw active

Co-authored-by: Matthias Wild <40327258+mauwii@users.noreply.github.com>
Co-authored-by: mauwii <Mauwii@outlook.de>

.dockerignore

@@ -1,26 +1,12 @@
 *
 !backend
+!configs
+!environments-and-requirements
 !frontend
-!binary_installer
+!installer
 !ldm
 !main.py
 !scripts
 !server
 !static
 !setup.py
-!docker-build
-!docs
-docker-build/Dockerfile
-
-# Guard against pulling in any models that might exist in the directory tree
-**/*.pt*
-
-# unignore configs, but only ignore the custom models.yaml, in case it exists
-!configs
-configs/models.yaml
-
-# unignore environment dirs/files, but ignore the environment.yml file or symlink in case it exists
-!environment*
-environment.yml
-
-**/__pycache__

.github/workflows/build-cloud-img.yml

@@ -1,87 +0,0 @@
-name: Build and push cloud image
-on:
-  workflow_dispatch:
-  push:
-    branches:
-    - main
-    tags:
-      - v*
-  # we will NOT push the image on pull requests, only test buildability.
-  pull_request:
-    branches:
-    - main
-
-permissions:
-  contents: read
-  packages: write
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}
-
-jobs:
-  docker:
-    strategy:
-      fail-fast: false
-      matrix:
-        arch:
-        - x86_64
-        # requires resolving a patchmatch issue
-        # - aarch64
-    runs-on: ubuntu-latest
-    name: ${{ matrix.arch }}
-    steps:
-    - name: Checkout
-      uses: actions/checkout@v3
-
-    - name: Set up QEMU
-      uses: docker/setup-qemu-action@v2
-      if: matrix.arch == 'aarch64'
-
-    - name: Docker meta
-      id: meta
-      uses: docker/metadata-action@v4
-      with:
-        images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-        # see https://github.com/docker/metadata-action
-        # will push the following tags:
-        # :edge
-        # :main (+ any other branches enabled in the workflow)
-        # :<tag>
-        # :1.2.3 (for semver tags)
-        # :1.2 (for semver tags)
-        # :<sha>
-        tags: |
-          type=edge,branch=main
-          type=ref,event=branch
-          type=ref,event=tag
-          type=semver,pattern={{version}}
-          type=semver,pattern={{major}}.{{minor}}
-          type=sha
-        # suffix image tags with architecture
-        flavor: |
-          latest=auto
-          suffix=-${{ matrix.arch }},latest=true
-
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v2
-
-    # do not login to container registry on PRs
-    - if: github.event_name != 'pull_request'
-      name: Docker login
-      uses: docker/login-action@v2
-      with:
-        registry: ghcr.io
-        username: ${{ github.actor }}
-        password: ${{ secrets.GITHUB_TOKEN }}
-
-    - name: Build and push cloud image
-      uses: docker/build-push-action@v3
-      with:
-        context: .
-        file: docker-build/Dockerfile.cloud
-        platforms: Linux/${{ matrix.arch }}
-        # do not push the image on PRs
-        push: ${{ github.event_name != 'pull_request' }}
-        tags: ${{ steps.meta.outputs.tags }}
-        labels: ${{ steps.meta.outputs.labels }}

.github/workflows/build-container.yml

@@ -5,12 +5,17 @@ on:
   push:
     branches:
       - 'main'
+      - 'development'
+      - 'update-dockerfile'
 
 jobs:
   docker:
     strategy:
       fail-fast: false
       matrix:
+        arch:
+          - x86_64
+          - aarch64
         pip-requirements:
           - requirements-lin-amd.txt
           - requirements-lin-cuda.txt
@@ -32,7 +37,7 @@ jobs:
         with:
           context: .
           file: docker-build/Dockerfile
-          platforms: linux/amd64,linux/arm64
+          platforms: Linux/${{ matrix.arch }}
           push: false
-          tags: ${{ env.dockertag }}:${{ matrix.pip-requirements }}
+          tags: ${{ env.dockertag }}:${{ matrix.pip-requirements }}-${{ matrix.arch }}
           build-args: pip_requirements=${{ matrix.pip-requirements }}

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

@@ -4,6 +4,7 @@ on:
     branches:
       - 'main'
       - 'development'
+      - 'fix-gh-actions-fork'
   pull_request:
     branches:
       - 'main'
@@ -19,28 +20,16 @@ jobs:
           - environment-lin-amd.yml
           - environment-lin-cuda.yml
           - environment-mac.yml
-          - environment-win-cuda.yml
         include:
           - environment-yaml: environment-lin-amd.yml
-            os: ubuntu-22.04
-            curl-command: curl
-            github-env: $GITHUB_ENV
+            os: ubuntu-latest
             default-shell: bash -l {0}
           - environment-yaml: environment-lin-cuda.yml
-            os: ubuntu-22.04
-            curl-command: curl
-            github-env: $GITHUB_ENV
+            os: ubuntu-latest
             default-shell: bash -l {0}
           - environment-yaml: environment-mac.yml
             os: macos-12
-            curl-command: curl
-            github-env: $GITHUB_ENV
             default-shell: bash -l {0}
-          - environment-yaml: environment-win-cuda.yml
-            os: windows-2022
-            curl-command: curl.exe
-            github-env: $env:GITHUB_ENV
-            default-shell: pwsh
           - stable-diffusion-model: stable-diffusion-1.5
             stable-diffusion-model-url: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
             stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1
@@ -83,15 +72,15 @@ jobs:
 
       - name: set test prompt to main branch validation
         if: ${{ github.ref == 'refs/heads/main' }}
-        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
+        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" >> ${{ matrix.github-env }}
+        run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
 
       - name: set test prompt to Pull Request validation
         if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
-        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
+        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
 
       - name: Use Cached Stable Diffusion Model
         id: cache-sd-model
@@ -107,20 +96,22 @@ jobs:
         if: ${{ steps.cache-sd-model.outputs.cache-hit != 'true' }}
         run: |
           mkdir -p "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}"
-          ${{ matrix.curl-command }} -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" -o "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}/${{ matrix.stable-diffusion-model-dl-name }}" -L ${{ matrix.stable-diffusion-model-url }}
+          curl \
+            -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
+            -o "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}/${{ matrix.stable-diffusion-model-dl-name }}" \
+            -L ${{ matrix.stable-diffusion-model-url }}
 
       - name: run configure_invokeai.py
         id: run-preload-models
         run: |
           python scripts/configure_invokeai.py --no-interactive --yes
 
-      - name: cat invokeai.init
+      - name: cat ~/.invokeai
         id: cat-invokeai
-        run: cat ${{ env.INVOKEAI_ROOT }}/invokeai.init
+        run: cat ~/.invokeai
 
       - name: Run the tests
         id: run-tests
-        if: matrix.os != 'windows-2022'
         run: |
           time python scripts/invoke.py \
             --no-patchmatch \
@@ -132,13 +123,11 @@ jobs:
 
       - name: export conda env
         id: export-conda-env
-        if: matrix.os != 'windows-2022'
         run: |
           mkdir -p outputs/img-samples
-          conda env export --name ${{ env.CONDA_ENV_NAME }} > ${{ env.INVOKEAI_ROOT }}/outputs/environment-${{ runner.os }}-${{ runner.arch }}.yml
+          conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}-${{ runner.arch }}.yml
 
       - name: Archive results
-        if: matrix.os != 'windows-2022'
         id: archive-results
         uses: actions/upload-artifact@v3
         with:

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

@@ -19,50 +19,35 @@ jobs:
           - requirements-lin-cuda.txt
           - requirements-lin-amd.txt
           - requirements-mac-mps-cpu.txt
-          - requirements-win-colab-cuda.txt
         python-version:
           # - '3.9'
           - '3.10'
         include:
           - requirements-file: requirements-lin-cuda.txt
-            os: ubuntu-22.04
-            curl-command: curl
-            github-env: $GITHUB_ENV
+            os: ubuntu-latest
+            default-shell: bash -l {0}
           - requirements-file: requirements-lin-amd.txt
-            os: ubuntu-22.04
-            curl-command: curl
-            github-env: $GITHUB_ENV
+            os: ubuntu-latest
+            default-shell: bash -l {0}
           - requirements-file: requirements-mac-mps-cpu.txt
             os: macOS-12
-            curl-command: curl
-            github-env: $GITHUB_ENV
-          - requirements-file: requirements-win-colab-cuda.txt
-            os: windows-2022
-            curl-command: curl.exe
-            github-env: $env:GITHUB_ENV
+            default-shell: bash -l {0}
           - stable-diffusion-model: stable-diffusion-1.5
             stable-diffusion-model-url: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
             stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1
             stable-diffusion-model-dl-name: v1-5-pruned-emaonly.ckpt
     name: ${{ matrix.requirements-file }} on ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
+    defaults:
+      run:
+        shell: ${{ matrix.default-shell }}
+    env:
+      INVOKEAI_ROOT: '${{ github.workspace }}/invokeai'
     steps:
       - name: Checkout sources
         id: checkout-sources
         uses: actions/checkout@v3
 
-      - name: set INVOKEAI_ROOT Windows
-        if: matrix.os == 'windows-2022'
-        run: |
-          echo "INVOKEAI_ROOT=${{ github.workspace }}\invokeai" >> ${{ matrix.github-env }}
-          echo "INVOKEAI_OUTDIR=${{ github.workspace }}\invokeai\outputs" >> ${{ matrix.github-env }}
-
-      - name: set INVOKEAI_ROOT others
-        if: matrix.os != 'windows-2022'
-        run: |
-          echo "INVOKEAI_ROOT=${{ github.workspace }}/invokeai" >> ${{ matrix.github-env }}
-          echo "INVOKEAI_OUTDIR=${{ github.workspace }}/invokeai/outputs" >> ${{ matrix.github-env }}
-
       - name: create models.yaml from example
         run: |
           mkdir -p ${{ env.INVOKEAI_ROOT }}/configs
@@ -70,15 +55,15 @@ jobs:
 
       - name: set test prompt to main branch validation
         if: ${{ github.ref == 'refs/heads/main' }}
-        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
+        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" >> ${{ matrix.github-env }}
+        run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
 
       - name: set test prompt to Pull Request validation
         if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
-        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
+        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
 
       - name: create requirements.txt
         run: cp 'environments-and-requirements/${{ matrix.requirements-file }}' '${{ matrix.requirements-file }}'
@@ -87,14 +72,14 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          # cache: 'pip'
-          # cache-dependency-path: ${{ matrix.requirements-file }}
+          cache: 'pip'
+          cache-dependency-path: ${{ matrix.requirements-file }}
 
       # - name: install dependencies
       #   run: ${{ env.pythonLocation }}/bin/pip install --upgrade pip setuptools wheel
 
       - name: install requirements
-        run: pip3 install -r '${{ matrix.requirements-file }}'
+        run: ${{ env.pythonLocation }}/bin/pip install -r '${{ matrix.requirements-file }}'
 
       - name: Use Cached Stable Diffusion Model
         id: cache-sd-model
@@ -110,20 +95,33 @@ jobs:
         if: ${{ steps.cache-sd-model.outputs.cache-hit != 'true' }}
         run: |
           mkdir -p "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}"
-          ${{ matrix.curl-command }} -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" -o "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}/${{ matrix.stable-diffusion-model-dl-name }}" -L ${{ matrix.stable-diffusion-model-url }}
+          curl \
+            -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
+            -o "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}/${{ matrix.stable-diffusion-model-dl-name }}" \
+            -L ${{ matrix.stable-diffusion-model-url }}
 
       - name: run configure_invokeai.py
         id: run-preload-models
-        run: python3 scripts/configure_invokeai.py --no-interactive --yes
+        run: |
+          ${{ env.pythonLocation }}/bin/python scripts/configure_invokeai.py --no-interactive --yes
+
+      - name: cat ~/.invokeai
+        id: cat-invokeai
+        run: cat ~/.invokeai
 
       - name: Run the tests
         id: run-tests
-        if: matrix.os != 'windows-2022'
-        run: python3 scripts/invoke.py --no-patchmatch --no-nsfw_checker --model ${{ matrix.stable-diffusion-model }} --from_file ${{ env.TEST_PROMPTS }} --root="${{ env.INVOKEAI_ROOT }}" --outdir="${{ env.INVOKEAI_OUTDIR }}"
+        run: |
+          time ${{ env.pythonLocation }}/bin/python scripts/invoke.py \
+            --no-patchmatch \
+            --no-nsfw_checker \
+            --model ${{ matrix.stable-diffusion-model }} \
+            --from_file ${{ env.TEST_PROMPTS }} \
+            --root="${{ env.INVOKEAI_ROOT }}" \
+            --outdir="${{ env.INVOKEAI_ROOT }}/outputs"
 
       - name: Archive results
         id: archive-results
-        if: matrix.os != 'windows-2022'
         uses: actions/upload-artifact@v3
         with:
           name: results_${{ matrix.requirements-file }}_${{ matrix.python-version }}

.gitignore

@@ -6,7 +6,6 @@ models/ldm/stable-diffusion-v1/model.ckpt
 # ignore user models config
 configs/models.user.yaml
 config/models.user.yml
-invokeai.init
 
 # ignore the Anaconda/Miniconda installer used while building Docker image
 anaconda.sh
@@ -223,11 +222,12 @@ environment.yml
 requirements.txt
 
 # source installer files
-installer/*zip
-installer/install.bat
-installer/install.sh
-installer/update.bat
-installer/update.sh
+source_installer/*zip
+source_installer/invokeAI
+install.bat
+install.sh
+update.bat
+update.sh
 
 # this may be present if the user created a venv
 invokeai

README.md

@@ -1,9 +1,11 @@
 <div align="center">
 
-![project logo](docs/assets/invoke_ai_banner.png)
-
 # InvokeAI: A Stable Diffusion Toolkit
 
+_Formerly known as lstein/stable-diffusion_
+
+![project logo](docs/assets/logo.png)
+
 [![discord badge]][discord link]
 
 [![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
@@ -36,33 +38,18 @@ 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, macOS and Linux machines, with
+generation process. It runs on Windows, Mac and Linux machines, with
 GPU cards with as little as 4 GB of RAM. It provides both a polished
 Web interface (see below), and an easy-to-use command-line interface.
 
-**Quick links**: [[How to Install](#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
-
-_Note: InvokeAI 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 us diagnose issues faster._
-
-# Getting Started with InvokeAI
-
-For full installation and upgrade instructions, please see:
-[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
-
-1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.2.3)
-2. Download the .zip file for your OS (Windows/macOS/Linux).
-3. Unzip the file. 
-4. If you are on Windows, double-click on the `install.bat` script. On macOS, open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press return. On Linux, run `install.sh`.
-5. Wait a while, until it is done. 
-6. The folder where you ran the installer from will now be filled with lots of files. If you are on Windows, double-click on the `invoke.bat` file. On macOS, open a Terminal window, drag `invoke.sh` from the folder into the Terminal, and press return. On Linux, run `invoke.sh`
-7. Press 2 to open the "browser-based UI", press enter/return, wait a minute or two for Stable Diffusion to start up, then open your browser and go to http://localhost:9090. 
-8. Type `banana sushi` in the box on the top left and click `Invoke`:
+**Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
 
 <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
+requests. Be sure to use the provided templates. They will help aid diagnose issues faster._
 
 ## Table of Contents
 
@@ -82,13 +69,10 @@ This fork is supported across Linux, Windows and Macintosh. Linux
 users can use either an Nvidia-based card (with CUDA support) or an
 AMD card (using the ROCm driver). For full installation and upgrade
 instructions, please see:
-[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_SOURCE/)
+[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
 
 ### Hardware Requirements
 
-InvokeAI is supported across Linux, Windows and macOS. Linux
-users can use either an Nvidia-based card (with CUDA support) or an
-AMD card (using the ROCm driver).
 #### System
 
 You wil need one of the following:
@@ -96,10 +80,6 @@ You wil need one of the following:
 - An NVIDIA-based graphics card with 4 GB or more VRAM memory.
 - An Apple computer with an M1 chip.
 
-We do not recommend the GTX 1650 or 1660 series video cards. They are
-unable to run in half-precision mode and do not have sufficient VRAM
-to render 512x512 images.
-
 #### Memory
 
 - At least 12 GB Main Memory RAM.
@@ -117,12 +97,11 @@ Similarly, specify full-precision mode on Apple M1 hardware.
 
 Precision is auto configured based on the device. If however you encounter
 errors like 'expected type Float but found Half' or 'not implemented for Half'
-you can try starting `invoke.py` with the `--precision=float32` flag to your initialization command
+you can try starting `invoke.py` with the `--precision=float32` flag:
 
 ```bash
 (invokeai) ~/InvokeAI$ python scripts/invoke.py --precision=float32
 ```
-Or by updating your InvokeAI configuration file with this argument.
 
 ### Features
 
@@ -151,7 +130,39 @@ Or by updating your InvokeAI configuration file with this argument.
 
 ### Latest Changes
 
-For our latest changes, view our [Release Notes](https://github.com/invoke-ai/InvokeAI/releases)
+- 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://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`.
+
+For older changelogs, please visit the **[CHANGELOG](https://invoke-ai.github.io/InvokeAI/CHANGELOG#v114-11-september-2022)**.
 
 ### Troubleshooting
 
@@ -161,9 +172,8 @@ problems and other issues.
 # Contributing
 
 Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
-cleanup, testing, or code reviews, is very much encouraged to do so. 
-
-To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board. 
+cleanup, testing, or code reviews, is very much encouraged to do so. To join, just raise your hand on the InvokeAI
+Discord server or discussion board. 
 
 If you are unfamiliar with how
 to contribute to GitHub projects, here is a

backend/invoke_ai_web_server.py

@@ -18,11 +18,9 @@ from PIL.Image import Image as ImageType
 from uuid import uuid4
 from threading import Event
 
-from ldm.generate import Generate
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.conditioning import get_tokens_for_prompt, get_prompt_structure
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
-from ldm.invoke.prompt_parser import split_weighted_subprompts, Blend
+from ldm.invoke.prompt_parser import split_weighted_subprompts
 from ldm.invoke.generator.inpaint import infill_methods
 
 from backend.modules.parameters import parameters_to_command
@@ -41,7 +39,7 @@ if not os.path.isabs(args.outdir):
 
 
 class InvokeAIWebServer:
-    def __init__(self, generate: Generate, gfpgan, codeformer, esrgan) -> None:
+    def __init__(self, generate, gfpgan, codeformer, esrgan) -> None:
         self.host = args.host
         self.port = args.port
 
@@ -245,16 +243,14 @@ class InvokeAIWebServer:
 
     def find_frontend(self):
         my_dir = os.path.dirname(__file__)
-        # LS: setup.py seems to put the frontend in different places on different systems, so
-        # this is fragile and needs to be replaced with a better way of finding the front end.
-        for candidate in (os.path.join(my_dir,'..','frontend','dist'),          # pip install -e .
-                          os.path.join(my_dir,'../../../../frontend','dist'),   # pip install . (Linux, Mac)
-                          os.path.join(my_dir,'../../../frontend','dist'),      # pip install . (Windows)
+        for candidate in (os.path.join(my_dir,'..','frontend','dist'),         # pip install -e .
+                          os.path.join(my_dir,'../../../../frontend','dist')   # pip install .
         ):
             if os.path.exists(candidate):
                 return candidate
         assert "Frontend files cannot be found. Cannot continue"
 
+                                        
     def setup_app(self):
         self.result_url = "outputs/"
         self.init_image_url = "outputs/init-images/"
@@ -779,10 +775,10 @@ class InvokeAIWebServer:
                 ).convert("RGBA")
 
                 """
-                The outpaint image and mask are pre-cropped by the UI, so the bounding box we pass
+                The outpaint image and mask are pre-cropped by the UI, so the bounding box we pass 
                 to the generator should be:
                     {
-                        "x": 0,
+                        "x": 0, 
                         "y": 0,
                         "width": original_bounding_box["width"],
                         "height": original_bounding_box["height"]
@@ -802,7 +798,7 @@ class InvokeAIWebServer:
                 )
 
                 """
-                Apply the mask to the init image, creating a "mask" image with
+                Apply the mask to the init image, creating a "mask" image with 
                 transparency where inpainting should occur. This is the kind of
                 mask that prompt2image() needs.
                 """
@@ -908,13 +904,16 @@ class InvokeAIWebServer:
                         },
                     )
 
-
                 if generation_parameters["progress_latents"]:
                     image = self.generate.sample_to_lowres_estimated_image(sample)
                     (width, height) = image.size
                     width *= 8
                     height *= 8
-                    img_base64 = image_to_dataURL(image)
+                    buffered = io.BytesIO()
+                    image.save(buffered, format="PNG")
+                    img_base64 = "data:image/png;base64," + base64.b64encode(
+                        buffered.getvalue()
+                    ).decode("UTF-8")
                     self.socketio.emit(
                         "intermediateResult",
                         {
@@ -932,7 +931,7 @@ class InvokeAIWebServer:
                 self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
-            def image_done(image, seed, first_seed, attention_maps_image=None):
+            def image_done(image, seed, first_seed):
                 if self.canceled.is_set():
                     raise CanceledException
 
@@ -1094,12 +1093,6 @@ class InvokeAIWebServer:
                 self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
-                parsed_prompt, _ = get_prompt_structure(generation_parameters["prompt"])
-                tokens = None if type(parsed_prompt) is Blend else \
-                    get_tokens_for_prompt(self.generate.model, parsed_prompt)
-                attention_maps_image_base64_url = None if attention_maps_image is None \
-                    else image_to_dataURL(attention_maps_image)
-
                 self.socketio.emit(
                     "generationResult",
                     {
@@ -1112,8 +1105,6 @@ class InvokeAIWebServer:
                         "height": height,
                         "boundingBox": original_bounding_box,
                         "generationMode": generation_parameters["generation_mode"],
-                        "attentionMaps": attention_maps_image_base64_url,
-                        "tokens": tokens,
                     },
                 )
                 eventlet.sleep(0)
@@ -1125,7 +1116,7 @@ class InvokeAIWebServer:
             self.generate.prompt2image(
                 **generation_parameters,
                 step_callback=image_progress,
-                image_callback=image_done
+                image_callback=image_done,
             )
 
         except KeyboardInterrupt:
@@ -1572,19 +1563,6 @@ def dataURL_to_image(dataURL: str) -> ImageType:
     )
     return image
 
-"""
-Converts an image into a base64 image dataURL.
-"""
-
-def image_to_dataURL(image: ImageType) -> str:
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    image_base64 = "data:image/png;base64," + base64.b64encode(
-        buffered.getvalue()
-    ).decode("UTF-8")
-    return image_base64
-
-
 
 """
 Converts a base64 image dataURL into bytes.

binary_installer/readme.txt

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

configs/models.yaml.example

@@ -25,5 +25,3 @@ inpainting-1.5:
   config: configs/stable-diffusion/v1-inpainting-inference.yaml
   vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
   description: RunwayML SD 1.5 model optimized for inpainting
-  width: 512
-  height: 512

configs/stable-diffusion/v1-inference.yaml

@@ -32,7 +32,7 @@ model:
         placeholder_strings: ["*"]
         initializer_words: ['sculpture']
         per_image_tokens: false
-        num_vectors_per_token: 1
+        num_vectors_per_token: 8
         progressive_words: False
 
     unet_config:

docker-build/Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.10-slim AS builder
+FROM ubuntu:22.10
 
 # use bash
 SHELL [ "/bin/bash", "-c" ]
@@ -7,42 +7,28 @@ SHELL [ "/bin/bash", "-c" ]
 RUN apt-get update \
   && apt-get install -y \
     --no-install-recommends \
-    gcc=4:10.2.* \
-    libgl1-mesa-glx=20.3.* \
-    libglib2.0-0=2.66.* \
-    python3-dev=3.9.* \
+    build-essential \
+    gcc \
+    git \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    pip \
+    python3 \
+    python3-dev \
   && apt-get clean \
   && rm -rf /var/lib/apt/lists/*
 
-# set workdir, PATH and copy sources
-WORKDIR /usr/src/app
-ENV PATH /usr/src/app/.venv/bin:$PATH
+# set workdir and copy sources
+WORKDIR /invokeai
 ARG PIP_REQUIREMENTS=requirements-lin-cuda.txt
 COPY . ./environments-and-requirements/${PIP_REQUIREMENTS} ./
 
-# install requirements
-RUN python3 -m venv .venv \
-  && pip install \
-    --no-cache-dir \
-    -r ${PIP_REQUIREMENTS}
-
-FROM python:3.10-slim AS runtime
-
-# Install necesarry packages
-RUN apt-get update \
-  && apt-get install -y \
-    --no-install-recommends \
-    libgl1-mesa-glx=20.3.* \
-    libglib2.0-0=2.66.* \
-  && apt-get clean \
-  && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /usr/src/app
-COPY --from=builder /usr/src/app .
+# install requirements and link outputs folder
+RUN pip install \
+  --no-cache-dir \
+  -r ${PIP_REQUIREMENTS}
 
 # set Environment, Entrypoint and default CMD
 ENV INVOKEAI_ROOT /data
-ENV PATH=/usr/src/app/.venv/bin:$PATH
-
-ENTRYPOINT [ "python3", "scripts/invoke.py" ]
+ENTRYPOINT [ "python3", "scripts/invoke.py", "--outdir=/data/outputs" ]
 CMD [ "--web", "--host=0.0.0.0" ]

docker-build/Dockerfile.cloud

@@ -1,86 +0,0 @@
-#######################
-#### Builder stage ####
-
-FROM library/ubuntu:22.04 AS builder
-
-ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt update && apt-get install -y \
-        git \
-        libglib2.0-0 \
-        libgl1-mesa-glx \
-        python3-venv \
-        python3-pip \
-        build-essential \
-        python3-opencv \
-        libopencv-dev
-
-# This is needed for patchmatch support
-RUN cd /usr/lib/x86_64-linux-gnu/pkgconfig/ &&\
-   ln -sf opencv4.pc opencv.pc
-
-ARG WORKDIR=/invokeai
-WORKDIR ${WORKDIR}
-
-ENV VIRTUAL_ENV=${WORKDIR}/.venv
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m venv ${VIRTUAL_ENV} &&\
-    pip install --extra-index-url https://download.pytorch.org/whl/cu116 \
-        torch==1.12.0+cu116 \
-        torchvision==0.13.0+cu116 &&\
-    pip install -e git+https://github.com/invoke-ai/PyPatchMatch@0.1.3#egg=pypatchmatch
-
-COPY . .
-RUN --mount=type=cache,target=/root/.cache/pip \
-    cp environments-and-requirements/requirements-lin-cuda.txt requirements.txt && \
-    pip install -r requirements.txt &&\
-    pip install -e .
-
-
-#######################
-#### Runtime stage ####
-
-FROM library/ubuntu:22.04 as runtime
-
-ARG DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt update && apt install -y --no-install-recommends \
-        git \
-        curl \
-        ncdu \
-        iotop \
-        bzip2 \
-        libglib2.0-0 \
-        libgl1-mesa-glx \
-        python3-venv \
-        python3-pip \
-        build-essential \
-        python3-opencv \
-        libopencv-dev &&\
-    apt-get clean && apt-get autoclean
-
-ARG WORKDIR=/invokeai
-WORKDIR ${WORKDIR}
-
-ENV INVOKEAI_ROOT=/mnt/invokeai
-ENV VIRTUAL_ENV=${WORKDIR}/.venv
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
-COPY --from=builder ${WORKDIR} ${WORKDIR}
-COPY --from=builder /usr/lib/x86_64-linux-gnu/pkgconfig /usr/lib/x86_64-linux-gnu/pkgconfig
-
-# build patchmatch
-RUN python -c "from patchmatch import patch_match"
-
-## workaround for non-existent initfile when runtime directory is mounted; see #1613
-RUN touch /root/.invokeai
-
-ENTRYPOINT ["bash"]
-
-CMD ["-c", "python3 scripts/invoke.py --web --host 0.0.0.0"]

docker-build/Makefile

@@ -1,44 +0,0 @@
-# Directory in the container where the INVOKEAI_ROOT (runtime dir) will be mounted
-INVOKEAI_ROOT=/mnt/invokeai
-# Host directory to contain the runtime dir. Will be mounted at INVOKEAI_ROOT path in the container
-HOST_MOUNT_PATH=${HOME}/invokeai
-
-IMAGE=local/invokeai:latest
-
-USER=$(shell id -u)
-GROUP=$(shell id -g)
-
-# All downloaded models, config, etc will end up in ${HOST_MOUNT_PATH} on the host.
-# This is consistent with the expected non-Docker behaviour.
-# Contents can be moved to a persistent storage and used to prime the cache on another host.
-
-build:
-	DOCKER_BUILDKIT=1 docker build -t local/invokeai:latest -f Dockerfile.cloud ..
-
-configure:
-	docker run --rm -it --runtime=nvidia --gpus=all \
-		-v ${HOST_MOUNT_PATH}:${INVOKEAI_ROOT} \
-		-e INVOKEAI_ROOT=${INVOKEAI_ROOT} \
-		${IMAGE} -c "python scripts/configure_invokeai.py"
-
-# Run the container with the runtime dir mounted and the web server exposed on port 9090
-web:
-	docker run --rm -it --runtime=nvidia --gpus=all \
-		-v ${HOST_MOUNT_PATH}:${INVOKEAI_ROOT} \
-		-e INVOKEAI_ROOT=${INVOKEAI_ROOT} \
-		-p 9090:9090 \
-		${IMAGE} -c "python scripts/invoke.py --web --host 0.0.0.0"
-
-# Run the cli with the runtime dir mounted
-cli:
-	docker run --rm -it --runtime=nvidia --gpus=all \
-		-v ${HOST_MOUNT_PATH}:${INVOKEAI_ROOT} \
-		-e INVOKEAI_ROOT=${INVOKEAI_ROOT} \
-		${IMAGE} -c "python scripts/invoke.py"
-
-# Run the container with the runtime dir mounted and open a bash shell
-shell:
-	docker run --rm -it --runtime=nvidia --gpus=all \
-		-v ${HOST_MOUNT_PATH}:${INVOKEAI_ROOT} ${IMAGE} --
-
-.PHONY: build configure web cli shell

docker-build/build.sh

@@ -12,13 +12,13 @@ pip_requirements=${PIP_REQUIREMENTS:-requirements-lin-cuda.txt}
 dockerfile=${INVOKE_DOCKERFILE:-docker-build/Dockerfile}
 
 # print the settings
-echo -e "You are using these values:\n"
-echo -e "Dockerfile:\t ${dockerfile}"
-echo -e "requirements:\t ${pip_requirements}"
-echo -e "volumename:\t ${volumename}"
-echo -e "arch:\t\t ${arch}"
-echo -e "platform:\t ${platform}"
-echo -e "invokeai_tag:\t ${invokeai_tag}\n"
+echo "You are using these values:"
+echo -e "Dockerfile:\t\t ${dockerfile}"
+echo -e "requirements:\t\t ${pip_requirements}"
+echo -e "volumename:\t\t ${volumename}"
+echo -e "arch:\t\t\t ${arch}"
+echo -e "platform:\t\t ${platform}"
+echo -e "invokeai_tag:\t\t ${invokeai_tag}\n"
 
 if [[ -n "$(docker volume ls -f name="${volumename}" -q)" ]]; then
   echo "Volume already exists"

docker-build/run.sh

@@ -3,10 +3,6 @@ set -e
 
 source ./docker-build/env.sh || echo "please run from repository root" || exit 1
 
-echo -e "You are using these values:\n"
-echo -e "volumename:\t ${volumename}"
-echo -e "invokeai_tag:\t ${invokeai_tag}\n"
-
 docker run \
   --interactive \
   --tty \

docs/CHANGELOG.md

@@ -171,12 +171,12 @@ title: Changelog
 - Integrate sd-v1-5 model into test matrix (easily expandable), remove
   unecesarry caches by @mauwii in
   https://github.com/invoke-ai/InvokeAI/pull/1293
-- add --no-interactive to configure_invokeai step by @mauwii in
+- add --no-interactive to preload_models step by @mauwii in
   https://github.com/invoke-ai/InvokeAI/pull/1302
 - 1-click installer and updater. Uses micromamba to install git and conda into a
   contained environment (if necessary) before running the normal installation
   script by @cmdr2 in https://github.com/invoke-ai/InvokeAI/pull/1253
-- configure_invokeai.py script downloads the weight files by @lstein in
+- preload_models.py script downloads the weight files by @lstein in
   https://github.com/invoke-ai/InvokeAI/pull/1290
 
 ## v2.0.1 <small>(13 October 2022)</small>

docs/features/CLI.md

@@ -130,34 +130,20 @@ file should contain the startup options as you would type them on the
 command line (`--steps=10 --grid`), one argument per line, or a
 mixture of both using any of the accepted command switch formats:
 
-!!! example "my unmodified initialization file"
+!!! example ""
 
-    ```bash title="~/.invokeai" linenums="1"
-    # InvokeAI initialization file
-    # This is the InvokeAI initialization file, which contains command-line default values.
-    # Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
-    # or renaming it and then running configure_invokeai.py again.
-
-    # The --root option below points to the folder in which InvokeAI stores its models, configs and outputs.
-    --root="/Users/mauwii/invokeai"
-
-    # the --outdir option controls the default location of image files.
-    --outdir="/Users/mauwii/invokeai/outputs"
-
-    # You may place other  frequently-used startup commands here, one or more per line.
-    # Examples:
-    # --web --host=0.0.0.0
-    # --steps=20
-    # -Ak_euler_a -C10.0
+    ```bash
+    --web
+    --steps=28
+    --grid
+    -f 0.6 -C 11.0 -A k_euler_a
     ```
 
-!!! note
-
-    The  initialization file only accepts the command line arguments.
-    There are additional arguments that you can provide on the `invoke>` command
-    line (such as `-n` or `--iterations`) that cannot be entered into this file.
-    Also be alert for empty blank lines at the end of the file, which will cause
-    an arguments error at startup time.
+Note that the initialization file only accepts the command line arguments.
+There are additional arguments that you can provide on the `invoke>` command
+line (such as `-n` or `--iterations`) that cannot be entered into this file.
+Also be alert for empty blank lines at the end of the file, which will cause
+an arguments error at startup time.
 
 ## List of prompt arguments
 
@@ -209,17 +195,15 @@ Here are the invoke> command that apply to txt2img:
 | `--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
+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 the nearest multiple
+of 64.
 
-    the width and height of the image must be multiples of 64. You can
-    provide different values, but they will be rounded down to the nearest multiple
-    of 64.
+### This is an example of img2img:
 
-!!! example "This is a example of img2img"
-
-    ```bash
-    invoke> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit
-    ```
+```
+invoke> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit
+```
 
 This will modify the indicated vacation photograph by making it more like the
 prompt. Results will vary greatly depending on what is in the image. We also ask
@@ -269,7 +253,7 @@ description of the part of the image to replace. For example, if you have an
 image of a breakfast plate with a bagel, toast and scrambled eggs, you can
 selectively mask the bagel and replace it with a piece of cake this way:
 
-```bash
+```
 invoke> a piece of cake -I /path/to/breakfast.png -tm bagel
 ```
 
@@ -281,7 +265,7 @@ are getting too much or too little masking you can adjust the threshold down (to
 get more mask), or up (to get less). In this example, by passing `-tm` a higher
 value, we are insisting on a more stringent classification.
 
-```bash
+```
 invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6
 ```
 
@@ -291,16 +275,16 @@ You can load and use hundreds of community-contributed Textual
 Inversion models just by typing the appropriate trigger phrase. Please
 see [Concepts Library](CONCEPTS.md) for more details.
 
-## Other Commands
+# Other Commands
 
 The CLI offers a number of commands that begin with "!".
 
-### Postprocessing images
+## Postprocessing images
 
 To postprocess a file using face restoration or upscaling, use the `!fix`
 command.
 
-#### `!fix`
+### `!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`
@@ -327,19 +311,19 @@ Some examples:
     [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
     ```
 
-#### `!mask`
+### !mask
 
 This command takes an image, a text prompt, and uses the `clipseg` algorithm to
 automatically generate a mask of the area that matches the text prompt. It is
 useful for debugging the text masking process prior to inpainting with the
 `--text_mask` argument. See [INPAINTING.md] for details.
 
-### Model selection and importation
+## 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`
+### !models
 
 This prints out a list of the models defined in `config/models.yaml'. The active
 model is bold-faced
@@ -352,7 +336,7 @@ laion400m                 not loaded  <no description>
 waifu-diffusion           not loaded  Waifu Diffusion v1.3
 </pre>
 
-#### `!switch <model>`
+### !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,
@@ -377,7 +361,7 @@ invoke> !switch waifu-diffusion
    | 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
+>> Max VRAM used to load the model: 2.17G 
 >> Current VRAM usage:2.17G
 >> Setting Sampler to k_lms
 
@@ -397,7 +381,7 @@ laion400m                 not loaded  <no description>
 waifu-diffusion               cached  Waifu Diffusion v1.3
 </pre>
 
-#### `!import_model <path/to/model/weights>`
+### !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
@@ -444,10 +428,10 @@ OK to import [n]? <b>y</b>
    | 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>
+invoke> 
 </pre>
 
-#### `!edit_model <name_of_model>`
+###!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
@@ -484,12 +468,12 @@ 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
+## History processing
 
 The CLI provides a series of convenient commands for reviewing previous actions,
 retrieving them, modifying them, and re-running them.
 
-#### `!history`
+### !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
@@ -501,22 +485,20 @@ 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:
 
-!!! 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
+```
 
-    ```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`
+### !fetch
 
 This command retrieves the generation parameters from a previously generated
 image and either loads them into the command line (Linux|Mac), or prints them
@@ -526,36 +508,33 @@ a folder with image png files, and wildcard \*.png to retrieve the dream command
 used to generate the images, and save them to a file commands.txt for further
 processing.
 
-!!! example "load the generation command for a single png file"
+This example loads the generation command for a single png file:
 
-    ```bash
-    invoke> !fetch 0000015.8929913.png
-    # the script returns the next line, ready for editing and running:
-    invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
-    ```
+```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
+```
 
-!!! example "fetch the generation commands from a batch of files and store them into `selected.txt`"
+This one fetches the generation commands from a batch of files and stores them
+into `selected.txt`:
 
-    ```bash
-    invoke> !fetch outputs\selected-imgs\*.png selected.txt
-    ```
+```bash
+invoke> !fetch outputs\selected-imgs\*.png selected.txt
+```
 
-#### `!replay`
+### !replay
 
 This command replays a text file generated by !fetch or created manually
 
-!!! example
+```
+invoke> !replay outputs\selected-imgs\selected.txt
+```
 
-    ```bash
-    invoke> !replay outputs\selected-imgs\selected.txt
-    ```
+Note that these commands may behave unexpectedly if given a PNG file that was
+not generated by InvokeAI.
 
-!!! note
-
-    These commands may behave unexpectedly if given a PNG file that was
-    not generated by InvokeAI.
-
-#### `!search <search string>`
+### !search <search string>
 
 This is similar to !history but it only returns lines that contain
 `search string`. For example:
@@ -565,7 +544,7 @@ 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!

docs/features/CONCEPTS.md

@@ -1,110 +1,130 @@
 ---
-title: Concepts Library
+title: The Hugging Face Concepts Library and Importing Textual Inversion files
 ---
 
-# :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files
+# :material-file-document: Concepts Library
 
 ## Using Textual Inversion Files
 
 Textual inversion (TI) files are small models that customize the output of
-Stable Diffusion image generation. They can augment SD with specialized subjects
-and artistic styles. They are also known as "embeds" in the machine learning
-world.
+Stable Diffusion image generation. They can augment SD with
+specialized subjects and artistic styles. They are also known as
+"embeds" in the machine learning world.
 
-Each TI file introduces one or more vocabulary terms to the SD model. These are
-known in InvokeAI as "triggers." Triggers are often, but not always, denoted
-using angle brackets as in "<trigger-phrase>". The two most common type of
-TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
-different TI training packages. InvokeAI supports both formats, but its
-[built-in TI training system](TEXTUAL_INVERSION.md) produces `.pt`.
+Each TI file introduces one or more vocabulary terms to the SD
+model. These are known in InvokeAI as "triggers." Triggers are often,
+but not always, denoted using angle brackets as in
+"<trigger-phrase>". The two most common type of TI files that you'll
+encounter are `.pt` and `.bin` files, which are produced by different
+TI training packages. InvokeAI supports both formats, but its [built-in
+TI training system](TEXTUAL_INVERSION.md) produces `.pt`.
 
-The [Hugging Face company](https://huggingface.co/sd-concepts-library) has
-amassed a large ligrary of >800 community-contributed TI files covering a
-broad range of subjects and styles. InvokeAI has built-in support for this
-library which downloads and merges TI files automatically upon request. You can
-also install your own or others' TI files by placing them in a designated
-directory.
+The [Hugging Face company](https://huggingface.co/sd-concepts-library)
+has amassed a large ligrary of >800 community-contributed TI files
+covering a broad range of subjects and styles. InvokeAI has built-in
+support for this library which downloads and merges TI files
+automatically upon request. You can also install your own or others'
+TI files by placing them in a designated directory.
 
 ### An Example
 
-Here are a few examples to illustrate how it works. All these images were
-generated using the command-line client and the Stable Diffusion 1.5 model:
+Here are a few examples to illustrate how it works. All these images
+were generated using the command-line client and the Stable Diffusion
+1.5 model:
 
-|         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
-| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
-| ![](../assets/concepts/image1.png) |  ![](../assets/concepts/image2.png)   |   ![](../assets/concepts/image3.png)   |     ![](../assets/concepts/image4.png)     |
+Japanese gardener
+<br>
+<img src="../assets/concepts/image1.png">
+
+Japanese gardener &lt;ghibli-face&gt;
+<br>
+<img src="../assets/concepts/image2.png">
+
+Japanese gardener &lt;hoi4-leaders&gt;
+<br>
+<img src="../assets/concepts/image3.png">
+
+Japanese gardener &lt;cartoona-animals&gt;
+<br>
+<img src="../assets/concepts/image4.png">
 
 You can also combine styles and concepts:
 
-<figure markdown>
-  ![](../assets/concepts/image5.png)
-  <figcaption>A portrait of &lt;alf&gt; in &lt;cartoona-animal&gt; style</figcaption>
-</figure>
+A portrait of &lt;alf&gt; in &lt;cartoona-animal&gt; style
+<br>
+<img src="../assets/concepts/image5.png">
+
 ## Using a Hugging Face Concept
 
-Hugging Face TI concepts are downloaded and installed automatically as you
-require them. This requires your machine to be connected to the Internet. To
-find out what each concept is for, you can browse the
-[Hugging Face concepts library](https://huggingface.co/sd-concepts-library) and
-look at examples of what each concept produces.
+Hugging Face TI concepts are downloaded and installed automatically as
+you require them. This requires your machine to be connected to the
+Internet. To find out what each concept is for, you can browse the
+[Hugging Face concepts
+library](https://huggingface.co/sd-concepts-library) and look at
+examples of what each concept produces.
 
-When you have an idea of a concept you wish to try, go to the command-line
-client (CLI) and type a "&lt;" character and the beginning of the Hugging Face
-concept name you wish to load. Press the Tab key, and the CLI will show you all
-matching concepts. You can also type "&lt;" and Tab to get a listing of all ~800
-concepts, but be prepared to scroll up to see them all! If there is more than
-one match you can continue to type and Tab until the concept is completed.
+When you have an idea of a concept you wish to try, go to the
+command-line client (CLI) and type a "&lt;" character and the beginning
+of the Hugging Face concept name you wish to load.  Press the Tab key,
+and the CLI will show you all matching concepts. You can also type "&lt;"
+and Tab to get a listing of all ~800 concepts, but be prepared to
+scroll up to see them all! If there is more than one match you can
+continue to type and Tab until the concept is completed.
 
-For example if you type "&lt;x" and Tab, you'll be prompted with the
-completions:
+For example if you type "&lt;x" and Tab, you'll be prompted with the completions:
 
 ```
-<xatu2>        <xatu>         <xbh>          <xi>           <xidiversity>  <xioboma>      <xuna>         <xyz>
+<xatu2>        <xatu>         <xbh>          <xi>           <xidiversity>  <xioboma>      <xuna>         <xyz>          
 ```
 
-Now type "id" and press Tab. It will be autocompleted to "&lt;xidiversity&gt;"
-because this is a unique match.
+Now type "id" and press Tab. It will be autocompleted to
+"&lt;xidiversity&gt;" because this is a unique match.
 
-Finish your prompt and generate as usual. You may include multiple concept terms
-in the prompt.
+Finish your prompt and generate as usual. You may include multiple
+concept terms in the prompt.
 
-If you have never used this concept before, you will see a message that the TI
-model is being downloaded and installed. After this, the concept will be saved
-locally (in the `models/sd-concepts-library` directory) for future use.
+If you have never used this concept before, you will see a message
+that the TI model is being downloaded and installed. After this, the
+concept will be saved locally (in the `models/sd-concepts-library`
+directory) for future use.
 
-Several steps happen during downloading and installation, including a scan of
-the file for malicious code. Should any errors occur, you will be warned and the
-concept will fail to load. Generation will then continue treating the trigger
-term as a normal string of characters (e.g. as literal "&lt;ghibli-face&gt;").
+Several steps happen during downloading and
+installation, including a scan of the file for malicious code. Should
+any errors occur, you will be warned and the concept will fail to
+load. Generation will then continue treating the trigger term as a
+normal string of characters (e.g. as literal "&lt;ghibli-face&gt;").
 
-Currently auto-installation of concepts is a feature only available on the
-command-line client. Support for the WebUI is a work in progress.
+Currently auto-installation of concepts is a feature only available on
+the command-line client. Support for the WebUI is a work in progress.
 
 ## Installing your Own TI Files
 
-You may install any number of `.pt` and `.bin` files simply by copying them into
-the `embeddings` directory of the InvokeAI runtime directory (usually `invokeai`
-in your home directory). You may create subdirectories in order to organize the
-files in any way you wish. Be careful not to overwrite one file with another.
-For example, TI files generated by the Hugging Face toolkit share the named
-`learned_embedding.bin`. You can use subdirectories to keep them distinct.
+You may install any number of `.pt` and `.bin` files simply by copying
+them into the `embeddings` directory of the InvokeAI runtime directory
+(usually `invokeai` in your home directory). You may create
+subdirectories in order to organize the files in any way you wish. Be
+careful not to overwrite one file with another. For example, TI files
+generated by the Hugging Face toolkit share the named
+`learned_embedding.bin`. You can use subdirectories to keep them
+distinct.
 
-At startup time, InvokeAI will scan the `embeddings` directory and load any TI
-files it finds there. At startup you will see a message similar to this one:
+At startup time, InvokeAI will scan the `embeddings` directory and
+load any TI files it finds there. At startup you will see a message
+similar to this one:
 
-```bash
+```
 >> Current embedding manager terms: *, <HOI4-Leader>, <princess-knight>
 ```
 
-Note the `*` trigger term. This is a placeholder term that many early TI
-tutorials taught people to use rather than a more descriptive term.
-Unfortunately, if you have multiple TI files that all use this term, only the
-first one loaded will be triggered by use of the term.
+Note the "*" trigger term. This is a placeholder term that many early
+TI tutorials taught people to use rather than a more descriptive
+term. Unfortunately, if you have multiple TI files that all use this
+term, only the first one loaded will be triggered by use of the term.
 
-To avoid this problem, you can use the `merge_embeddings.py` script to merge two
-or more TI files together. If it encounters a collision of terms, the script
-will prompt you to select new terms that do not collide. See
-[Textual Inversion](TEXTUAL_INVERSION.md) for details.
+To avoid this problem, you can use the `merge_embeddings.py` script to
+merge two or more TI files together. If it encounters a collision of
+terms, the script will prompt you to select new terms that do not
+collide. See [Textual Inversion](TEXTUAL_INVERSION.md) for details.
 
 ## Further Reading
 

docs/features/IMG2IMG.md

@@ -12,19 +12,21 @@ stable diffusion to build the prompt on top of the image you provide, preserving
 the original's basic shape and layout. To use it, provide the `--init_img`
 option as shown here:
 
-!!! example ""
+```commandline
+tree on a hill with a river, nature photograph, national geographic -I./test-pictures/tree-and-river-sketch.png -f 0.85
+```
 
-    ```commandline
-    tree on a hill with a river, nature photograph, national geographic -I./test-pictures/tree-and-river-sketch.png -f 0.85
-    ```
+This will take the original image shown here:
 
-    <figure markdown>
+<figure markdown>
+![original-image](https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png){ width=320 }
+</figure>
 
-    | original image | generated image |
-    | :------------: | :-------------: |
-    | ![original-image](https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png){ width=320 } | ![generated-image](https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png){ width=320 } |
+and generate a new image based on it as shown here:
 
-    </figure>
+<figure markdown>
+![generated-image](https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png){ width=320 }
+</figure>
 
 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
@@ -86,15 +88,13 @@ from a prompt. If the step count is 10, then the "latent space" (Stable
 Diffusion's internal representation of the image) for the prompt "fire" with
 seed `1592514025` develops something like this:
 
-!!! example ""
+```bash
+invoke> "fire" -s10 -W384 -H384 -S1592514025
+```
 
-    ```bash
-    invoke> "fire" -s10 -W384 -H384 -S1592514025
-    ```
-
-    <figure markdown>
-    ![latent steps](../assets/img2img/000019.steps.png){ width=720 }
-    </figure>
+<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,
@@ -109,23 +109,25 @@ into the sequence at the appropriate point, with just the right amount of noise.
 
 ### A concrete example
 
-!!! example "I want SD to draw a fire based on this hand-drawn image"
+I want SD to draw a fire based on this hand-drawn image:
 
-    ![drawing of a fireplace](../assets/img2img/fire-drawing.png){ align=left }
+<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:
+Let's only do 10 steps, to make it easier to see what's happening. If strength
+is `0.7`, this is what the internal steps the algorithm has to take will look
+like:
 
-    <figure markdown>
-    ![gravity32](../assets/img2img/000032.steps.gravity.png)
-    </figure>
+<figure markdown>
+![gravity32](../assets/img2img/000032.steps.gravity.png)
+</figure>
 
-    With strength `0.4`, the steps look more like this:
+With strength `0.4`, the steps look more like this:
 
-    <figure markdown>
-    ![gravity30](../assets/img2img/000030.steps.gravity.png)
-    </figure>
+<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`:

docs/features/OUTPAINTING.md

@@ -120,7 +120,7 @@ A number of caveats:
    (`--iterations`) argument.
 
 3. Your results will be _much_ better if you use the `inpaint-1.5` model
-   released by runwayML and installed by default by `scripts/configure_invokeai.py`.
+   released by runwayML and installed by default by `scripts/preload_models.py`.
    This model was trained specifically to harmoniously fill in image gaps. The
    standard model will work as well, but you may notice color discontinuities at
    the border.

docs/features/POSTPROCESS.md

@@ -28,17 +28,21 @@ should "just work" without further intervention. Simply pass the `--upscale`
 the popup in the Web GUI.
 
 **GFPGAN** requires a series of downloadable model files to work. These are
-loaded when you run `scripts/configure_invokeai.py`. If GFPAN is failing with an
+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/configure_invokeai.py
+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.
 
+## Usage
+
+You will now have access to two new prompt arguments.
+
 ### Upscaling
 
 `-U : <upscaling_factor> <upscaling_strength>`
@@ -106,7 +110,7 @@ This repo also allows you to perform face restoration using
 [CodeFormer](https://github.com/sczhou/CodeFormer).
 
 In order to setup CodeFormer to work, you need to download the models like with
-GFPGAN. You can do this either by running `configure_invokeai.py` or by manually
+GFPGAN. You can do this either by running `preload_models.py` or by manually
 downloading the
 [model file](https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth)
 and saving it to `ldm/invoke/restoration/codeformer/weights` folder.
@@ -115,7 +119,7 @@ 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.
 
-### CodeFormer Usage
+### Usage
 
 The following command will perform face restoration with CodeFormer instead of
 the default gfpgan.
@@ -156,7 +160,7 @@ 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.
 
-## How to disable
+### 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/index.md

@@ -1,5 +0,0 @@
----
-title: Overview
----
-
-Here you can find the documentation for different features.

docs/index.md

@@ -82,18 +82,9 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM.
 
 This fork is supported across Linux, Windows and Macintosh. Linux
 users can use either an Nvidia-based card (with CUDA support) or an
-AMD card (using the ROCm driver).
-
-First time users, please see [Automated
-Installer](installation/INSTALL_AUTOMATED.md) for a walkthrough of
-getting InvokeAI up and running on your system. For alternative
-installation and upgrade instructions, please see: [InvokeAI
-Installation Overview](installation/)
-
-Linux users who wish to make use of the PyPatchMatch inpainting
-functions will need to perform a bit of extra work to enable this
-module. Instructions can be found at [Installing
-PyPatchMatch](installation/INSTALL_PATCHMATCH.md).
+AMD card (using the ROCm driver). For full installation and upgrade
+instructions, please see:
+[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
 
 ## :fontawesome-solid-computer: Hardware Requirements
 
@@ -105,25 +96,22 @@ You wil need one of the following:
 - :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux only)
 - :fontawesome-brands-apple: An Apple computer with an M1 chip.
 
-We do **not recommend** the following video cards due to issues with
-their running in half-precision mode and having insufficient VRAM to
-render 512x512 images in full-precision mode:
-
-- NVIDIA 10xx series cards such as the 1080ti
-- GTX 1650 series cards
-- GTX 1660 series cards
-
 ### :fontawesome-solid-memory: Memory
 
 - At least 12 GB Main Memory RAM.
 
 ### :fontawesome-regular-hard-drive: Disk
 
-- At least 18 GB of free disk space for the machine learning model, Python, and
+- At least 12 GB of free disk space for the machine learning model, Python, and
   all its dependencies.
 
 !!! 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.
+
     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:
@@ -135,8 +123,7 @@ render 512x512 images in full-precision mode:
 
 - [The InvokeAI Web Interface](features/WEB.md)
     - [WebGUI hotkey reference guide](features/WEBUIHOTKEYS.md)
-    - [WebGUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
-<!-- seperator -->
+<!-- this link does not exist    - [WebGUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md) -->
 - [The Command Line Interace](features/CLI.md)
     - [Image2Image](features/IMG2IMG.md)
     - [Inpainting](features/INPAINTING.md)
@@ -149,7 +136,6 @@ render 512x512 images in full-precision mode:
 - [Prompt Engineering](features/PROMPTS.md)
 <!-- seperator -->
 - Miscellaneous
-    - [NSFW Checker](features/NSFW.md)
     - [Embiggen upscaling](features/EMBIGGEN.md)
     - [Other](features/OTHER.md)
 
@@ -174,7 +160,7 @@ render 512x512 images in full-precision mode:
 - You can now load
   [multiple models and switch among them quickly](https://docs.google.com/presentation/d/1WywGA1rny7bpFh7CLSdTr4nNpVKdlUeT0Bj0jCsILyU/edit?usp=sharing)
   without leaving the CLI.
-- The installation process (via `scripts/configure_invokeai.py`) now lets you select
+- The installation process (via `scripts/preload_models.py`) now lets you select
   among several popular
   [Stable Diffusion models](https://invoke-ai.github.io/InvokeAI/installation/INSTALLING_MODELS/)
   and downloads and installs them on your behalf. Among other models, this

docs/installation/BUILDING_BINARY_INSTALLERS.md

@@ -1,89 +0,0 @@
----
-title: build binary installers
----
-
-# :simple-buildkite: How to build "binary" installers (InvokeAI-mac/windows/linux_on_*.zip)
-
-## 1. Ensure `installers/requirements.in` is correct
-
-and up to date on the branch to be installed.
-
-## <a name="step-2"></a> 2. Run `pip-compile` on each platform.
-
-On each target platform, in the branch that is to be installed, and
-inside the InvokeAI git root folder, run the following commands:
-
-```commandline
-conda activate invokeai # or however you activate python
-pip install pip-tools
-pip-compile --allow-unsafe --generate-hashes --output-file=binary_installer/<reqsfile>.txt binary_installer/requirements.in
-```
-where `<reqsfile>.txt` is whichever of
-```commandline
-py3.10-darwin-arm64-mps-reqs.txt
-py3.10-darwin-x86_64-reqs.txt
-py3.10-linux-x86_64-cuda-reqs.txt
-py3.10-windows-x86_64-cuda-reqs.txt
-```
-matches the current OS and architecture.
-> There is no way to cross-compile these. They must be done on a system matching the target OS and arch.
-
-## <a name="step-3"></a> 3. Set github repository and branch
-
-Once all reqs files have been collected and committed **to the branch
-to be installed**, edit `binary_installer/install.sh.in` and `binary_installer/install.bat.in` so that `RELEASE_URL`
-and `RELEASE_SOURCEBALL` point to the github repo and branch that is
-to be installed.
-
-For example, to install `main` branch of `InvokeAI`, they should be
-set as follows:
-
-`install.sh.in`:
-```commandline
-RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
-```
-
-`install.bat.in`:
-```commandline
-set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
-```
-
-Or, to install `damians-cool-feature` branch of `damian0815`, set them
-as follows:
-
-`install.sh.in`:
-```commandline
-RELEASE_URL=https://github.com/damian0815/InvokeAI
-RELEASE_SOURCEBALL=/archive/refs/heads/damians-cool-feature.tar.gz
-```
-
-`install.bat.in`:
-```commandline
-set RELEASE_URL=https://github.com/damian0815/InvokeAI
-set RELEASE_SOURCEBALL=/archive/refs/heads/damians-cool-feature.tar.gz
-```
-
-The branch and repo specified here **must** contain the correct reqs
-files. The installer zip files **do not** contain requirements files,
-they are pulled from the specified branch during the installation
-process.
-
-## 4. Create zip files.
-
-cd into the `installers/` folder and run
-`./create_installers.sh`. This will create
-`InvokeAI-mac_on_<branch>.zip`,
-`InvokeAI-windows_on_<branch>.zip` and
-`InvokeAI-linux_on_<branch>.zip`. These files can be distributed to end users.
-
-These zips will continue to function as installers for all future
-pushes to those branches, as long as necessary changes to
-`requirements.in` are propagated in a timely manner to the
-`py3.10-*-reqs.txt` files using pip-compile as outlined in [step
-2](#step-2).
-
-To actually install, users should unzip the appropriate zip file into an empty
-folder and run `install.sh` on macOS/Linux or `install.bat` on
-Windows.

docs/installation/INSTALLING_MODELS.md

@@ -56,7 +56,7 @@ unofficial Stable Diffusion models and where they can be obtained.
 
 There are three ways to install weights files:
 
-1. During InvokeAI installation, the `configure_invokeai.py` script can download
+1. During InvokeAI installation, the `preload_models.py` script can download
    them for you.
 
 2. You can use the command-line interface (CLI) to import, configure and modify
@@ -65,13 +65,13 @@ There are three ways to install weights files:
 3. You can download the files manually and add the appropriate entries to
    `models.yaml`.
 
-### Installation via `configure_invokeai.py`
+### Installation via `preload_models.py`
 
-This is the most automatic way. Run `scripts/configure_invokeai.py` from the
+This is the most automatic way. Run `scripts/preload_models.py` from the
 console. It will ask you to select which models to download and lead you through
 the steps of setting up a Hugging Face account if you haven't done so already.
 
-To start, run `python scripts/configure_invokeai.py` from within the InvokeAI:
+To start, run `python scripts/preload_models.py` from within the InvokeAI:
 directory
 
 !!! example ""
@@ -162,12 +162,6 @@ the command-line client's `!import_model` command.
          Type a bit of the path name and hit ++tab++ in order to get a choice of
          possible completions.
 
-    !!! tip "on Windows, you can drag model files onto the command-line"
-
-         Once you have typed in `!import_model `, you can drag the model `.ckpt` file
-         onto the command-line to insert the model path. This way, you don't need to
-         type it or copy/paste.
-
 4.  Follow the wizard's instructions to complete installation as shown in the
     example here:
 
@@ -244,7 +238,7 @@ arabian-nights-1.0:
 | arabian-nights-1.0 | This is the name of the model that you will refer to from within the CLI and the WebGUI when you need to load and use the model.                                                                                                                                                                                                                                                                                                                                                                                                  |
 | description        | Any description that you want to add to the model to remind you what it is.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 | weights            | Relative path to the .ckpt weights file for this model.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| config             | This is the confusingly-named configuration file for the model itself. Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens to need a custom configuration, in which case the place you downloaded it from will tell you what to use instead. For example, the runwayML custom inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`. This is already inclued in the InvokeAI distribution and is configured automatically for you by the `configure_invokeai.py` script. |
+| config             | This is the confusingly-named configuration file for the model itself. Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens to need a custom configuration, in which case the place you downloaded it from will tell you what to use instead. For example, the runwayML custom inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`. This is already inclued in the InvokeAI distribution and is configured automatically for you by the `preload_models.py` script. |
 | vae                | If you want to add a VAE file to the model, then enter its path here.                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
 | width, height      | This is the width and height of the images used to train the model. Currently they are always 512 and 512.                                                                                                                                                                                                                                                                                                                                                                                                                        |
 

docs/installation/INSTALL_AUTOMATED.md

@@ -1,315 +0,0 @@
----
-title: InvokeAI Automated Installation
----
-
-# InvokeAI Automated Installation
-
-## Introduction
-
-The automated installer is a shell script that attempts to automate every step
-needed to install and run InvokeAI on a stock computer running recent versions
-of Linux, MacOS or Windows. It will leave you with a version that runs a stable
-version of InvokeAI with the option to upgrade to experimental versions later.
-
-## Walk through
-
-1.  Make sure that your system meets the
-    [hardware requirements](../index.md#hardware-requirements) and has the
-    appropriate GPU drivers installed. In particular, if you are a Linux user
-    with an AMD GPU installed, you may need to install the
-    [ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
-
-    !!! info "Required Space"
-
-        Installation requires roughly 18G of free disk space to load the libraries and
-        recommended model weights files.
-
-2.  Check that your system has an up-to-date Python installed. To do this, open
-    up a command-line window ("Terminal" on Linux and Macintosh, "Command" or
-    "Powershell" on Windows) and type `python --version`. If Python is
-    installed, it will print out the version number. If it is version `3.9.1` or
-    higher, you meet requirements.
-
-    !!! warning "If you see an older version, or get a command not found error"
-
-        Go to [Python Downloads](https://www.python.org/downloads/) and
-        download the appropriate installer package for your platform. We recommend
-        [Version 3.10.9](https://www.python.org/downloads/release/python-3109/),
-        which has been extensively tested with InvokeAI.
-
-    !!! warning "At this time we do not recommend Python 3.11"
-
-    === "Windows users"
-
-        - During the Python configuration process,
-        Please look out for a checkbox to add Python to your PATH
-        and select it. If the install script complains that it can't
-        find python, then open the Python installer again and choose
-        "Modify" existing installation.
-
-        - There is a slight possibility that you will encountered
-        DLL load errors at the very end of the installation process. This is caused
-        by not having up to date Visual C++ redistributable libraries. If this
-        happens to you, you can install the C++ libraries from this site:
-        https://learn.microsoft.com/en-us/cpp/windows/deploying-native-desktop-applications-visual-cpp?view=msvc-170
-
-    === "Mac users"
-
-        - After installing Python, you may need to run the
-        following command from the Terminal in order to install the Web
-        certificates needed to download model data from https sites. If
-        you see lots of CERTIFICATE ERRORS during the last part of the
-        install, this is the problem, and you can fix it with this command:
-
-            `/Applications/Python\ 3.10/Install\ Certificates.command`
-
-        -  You may need to install the Xcode command line tools. These
-        are a set of tools that are needed to run certain applications in a
-        Terminal, including InvokeAI. This package is provided directly by Apple.
-
-              - To install, open a terminal window and run `xcode-select
-              --install`. You will get a macOS system popup guiding you through the
-              install. If you already have them installed, you will instead see some
-              output in the Terminal advising you that the tools are already installed.
-
-              - More information can be found here:
-                https://www.freecodecamp.org/news/install-xcode-command-line-tools/
-
-    === "Linux users"
-
-        - See [Installing Python in Ubuntu](#installing-python-in-ubuntu) for some
-        platform-specific tips.
-
-3.  The source installer is distributed in ZIP files. Go to the
-    [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest), and
-    look for a series of files named:
-
-    - [InvokeAI-installer-2.2.4-mac.zip](https://github.com/invoke-ai/InvokeAI/releases/latest/download/InvokeAI-installer-2.2.4-mac.zip)
-    - [InvokeAI-installer-2.2.4-windows.zip](https://github.com/invoke-ai/InvokeAI/releases/latest/download/InvokeAI-installer-2.2.4-windows.zip)
-    - [InvokeAI-installer-2.2.4-linux.zip](https://github.com/invoke-ai/InvokeAI/releases/latest/download/InvokeAI-installer-2.2.4-linux.zip)
-
-    Download the one that is appropriate for your operating system.
-
-4.  Unpack the zip file into a convenient directory. This will create a new
-    directory named "InvokeAI-Installer". This example shows how this would look
-    using the `unzip` command-line tool, but you may use any graphical or
-    command-line Zip extractor:
-
-    ```cmd
-    C:\Documents\Linco> unzip InvokeAI-installer-2.2.4-windows.zip
-    Archive:  C: \Linco\Downloads\InvokeAI-installer-2.2.4-windows.zip
-    creating: InvokeAI-Installer\
-    inflating: InvokeAI-Installer\install.bat
-    inflating: InvokeAI-Installer\readme.txt
-    ...
-    ```
-
-    After successful installation, you can delete the `InvokeAI-Installer`
-    directory.
-
-5.  **Windows only** Please double-click on the file WinLongPathsEnabled.reg and
-    accept the dialog box that asks you if you wish to modify your registry.
-    This activates long filename support on your system and will prevent
-    mysterious errors during installation.
-
-6.  If you are using a desktop GUI, double-click the installer file. It will be
-    named `install.bat` on Windows systems and `install.sh` on Linux and
-    Macintosh systems.
-
-    On Windows systems you will probably get an "Untrusted Publisher" warning.
-    Click on "More Info" and select "Run Anyway." You trust us, right?
-
-7.  Alternatively, from the command line, run the shell script or .bat file:
-
-    ```cmd
-    C:\Documents\Linco> cd InvokeAI-Installer
-    C:\Documents\Linco\invokeAI> install.bat
-    ```
-
-8.  The script will ask you to choose where to install InvokeAI. Select a
-    directory with at least 18G of free space for a full install. InvokeAI and
-    all its support files will be installed into a new directory named
-    `invokeai` located at the location you specify.
-
-    - The default is to install the `invokeai` directory in your home directory,
-      usually `C:\Users\YourName\invokeai` on Windows systems,
-      `/home/YourName/invokeai` on Linux systems, and `/Users/YourName/invokeai`
-      on Macintoshes, where "YourName" is your login name.
-
-    - The script uses tab autocompletion to suggest directory path completions.
-      Type part of the path (e.g. "C:\Users") and press ++tab++ repeatedly
-      to suggest completions.
-
-9.  Sit back and let the install script work. It will install the third-party
-    libraries needed by InvokeAI, then download the current InvokeAI release and
-    install it.
-
-    Be aware that some of the library download and install steps take a long
-    time. In particular, the `pytorch` package is quite large and often appears
-    to get "stuck" at 99.9%. Have patience and the installation step will
-    eventually resume. However, there are occasions when the library install
-    does legitimately get stuck. If you have been waiting for more than ten
-    minutes and nothing is happening, you can interrupt the script with ^C. You
-    may restart it and it will pick up where it left off.
-
-10. After installation completes, the installer will launch a script called
-    `configure_invokeai.py`, which will guide you through the first-time process
-    of selecting one or more Stable Diffusion model weights files, downloading
-    and configuring them. We provide a list of popular models that InvokeAI
-    performs well with. However, you can add more weight files later on using
-    the command-line client or the Web UI. See
-    [Installing Models](INSTALLING_MODELS.md) for details.
-
-    Note that the main Stable Diffusion weights file is protected by a license
-    agreement that you must agree to in order to use. The script will list the
-    steps you need to take to create an account on the official site that hosts
-    the weights files, accept the agreement, and provide an access token that
-    allows InvokeAI to legally download and install the weights files.
-
-    If you have already downloaded the weights file(s) for another Stable
-    Diffusion distribution, you may skip this step (by selecting "skip" when
-    prompted) and configure InvokeAI to use the previously-downloaded files. The
-    process for this is described in [Installing Models](INSTALLING_MODELS.md).
-
-11. The script will now exit and you'll be ready to generate some images. Look
-    for the directory `invokeai` installed in the location you chose at the
-    beginning of the install session. Look for a shell script named `invoke.sh`
-    (Linux/Mac) or `invoke.bat` (Windows). Launch the script by double-clicking
-    it or typing its name at the command-line:
-
-    ```cmd
-    C:\Documents\Linco> cd invokeai
-    C:\Documents\Linco\invokeAI> invoke.bat
-    ```
-
-    - The `invoke.bat` (`invoke.sh`) script will give you the choice of starting
-      (1) the command-line interface, or (2) the web GUI. If you start the
-      latter, you can load the user interface by pointing your browser at
-      http://localhost:9090.
-
-    - The script also offers you a third option labeled "open the developer
-      console". If you choose this option, you will be dropped into a
-      command-line interface in which you can run python commands directly,
-      access developer tools, and launch InvokeAI with customized options.
-
-12. You can launch InvokeAI with several different command-line arguments that
-    customize its behavior. For example, you can change the location of the
-    inage output directory, or select your favorite sampler. See the
-    [Command-Line Interface](../features/CLI.md) for a full list of the options.
-
-        - To set defaults that will take effect every time you launch InvokeAI,
-        use a text editor (e.g. Notepad) to exit the file
-        `invokeai\invokeai.init`. It contains a variety of examples that you can
-        follow to add and modify launch options.
-
-!!! warning "The `invokeai` directory contains the `invoke` application, its
-configuration files, the model weight files, and outputs of image generation.
-Once InvokeAI is installed, do not move or remove this directory."
-
-## Troubleshooting
-
-### _Package dependency conflicts_
-
-If you have previously installed InvokeAI or another Stable Diffusion package,
-the installer may occasionally pick up outdated libraries and either the
-installer or `invoke` will fail with complaints about library conflicts. You can
-address this by entering the `invokeai` directory and running `update.sh`, which
-will bring InvokeAI up to date with the latest libraries.
-
-### ldm from pypi
-
-!!! warning
-
-    Some users have tried to correct dependency problems by installing
-    the `ldm` package from PyPi.org. Unfortunately this is an unrelated package that
-    has nothing to do with the 'latent diffusion model' used by InvokeAI. Installing
-    ldm will make matters worse. If you've installed ldm, uninstall it with
-    `pip uninstall ldm`.
-
-### Corrupted configuration file
-
-Everything seems to install ok, but `invoke` complains of a corrupted
-configuration file and goes back into the configuration process (asking you to
-download models, etc), but this doesn't fix the problem.
-
-This issue is often caused by a misconfigured configuration directive in the
-`invokeai\invokeai.init` initialization file that contains startup settings. The
-easiest way to fix the problem is to move the file out of the way and re-run
-`configure_invokeai.py`. Enter the developer's console (option 3 of the launcher
-script) and run this command:
-
-```cmd
-configure_invokeai.py --root=.
-```
-
-Note the dot (.) after `--root`. It is part of the command.
-
-_If none of these maneuvers fixes the problem_ then please report the problem to
-the [InvokeAI Issues](https://github.com/invoke-ai/InvokeAI/issues) section, or
-visit our [Discord Server](https://discord.gg/ZmtBAhwWhy) for interactive
-assistance.
-
-### other problems
-
-If you run into problems during or after installation, the InvokeAI team is
-available to help you. Either create an
-[Issue](https://github.com/invoke-ai/InvokeAI/issues) at our GitHub site, or
-make a request for help on the "bugs-and-support" channel of our
-[Discord server](https://discord.gg/ZmtBAhwWhy). We are a 100% volunteer
-organization, but typically somebody will be available to help you within 24
-hours, and often much sooner.
-
-## Updating to newer versions
-
-This distribution is changing rapidly, and we add new features on a daily basis.
-To update to the latest released version (recommended), run the `update.sh`
-(Linux/Mac) or `update.bat` (Windows) scripts. This will fetch the latest
-release and re-run the `configure_invokeai` script to download any updated
-models files that may be needed. You can also use this to add additional models
-that you did not select at installation time.
-
-You can now close the developer console and run `invoke` as before. If you get
-complaints about missing models, then you may need to do the additional step of
-running `configure_invokeai.py`. This happens relatively infrequently. To do
-this, simply open up the developer's console again and type
-`python scripts/configure_invokeai.py`.
-
-You may also use the `update` script to install any selected version of
-InvokeAI. From https://github.com/invoke-ai/InvokeAI, navigate to the zip file
-link of the version you wish to install. You can find the zip links by going to
-the one of the release pages and looking for the **Assets** section at the
-bottom. Alternatively, you can browse "branches" and "tags" at the top of the
-big code directory on the InvokeAI welcome page. When you find the version you
-want to install, go to the green "<> Code" button at the top, and copy the
-"Download ZIP" link.
-
-Now run `update.sh` (or `update.bat`) with the URL of the desired InvokeAI
-version as its argument. For example, this will install the old 2.2.0 release.
-
-```cmd
-update.sh https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v2.2.0.zip
-```
-
-## Installing Python in Ubuntu
-
-For reasons that are not entirely clear, installing the correct version of
-Python can be a bit of a challenge on Ubuntu, Linux Mint, and other
-Ubuntu-derived distributions.
-
-In particular, Ubuntu version 20.04 LTS comes with an old version of Python,
-does not come with the PIP package manager installed, and to make matters worse,
-the `python` command points to Python2, not Python3.
-
-Here is the quick recipe for bringing your system up to date:
-
-```
-sudo apt update
-sudo apt install python3.9
-sudo apt install python3-pip
-cd /usr/bin
-sudo ln -sf python3.9 python3
-sudo ln -sf python3 python
-```
-
-You can still access older versions of Python by calling `python2`, `python3.8`,
-etc.

docs/installation/INSTALL_DOCKER.md

@@ -6,7 +6,7 @@ title: Docker
 
 !!! warning "For end users"
 
-    We highly recommend to Install InvokeAI locally using [these instructions](index.md)
+    We highly recommend to Install InvokeAI locally using [these instructions](index.md)"
 
 !!! tip "For developers"
 
@@ -16,10 +16,6 @@ title: Docker
 
     For general use, install locally to leverage your machine's GPU.
 
-!!! tip "For running on a cloud instance/service"
-
-    Check out the [Running InvokeAI in the cloud with Docker](#running-invokeai-in-the-cloud-with-docker) section below
-
 ## Why containers?
 
 They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
@@ -40,7 +36,7 @@ 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 in a Linux container (desktop)
+## Installation on a Linux container
 
 ### Prerequisites
 
@@ -121,91 +117,12 @@ also do so.
     ./docker-build/run.sh "banana sushi" -Ak_lms -S42 -s10
     ```
 
-    This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
+    This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps. 
 
     Find out more about available CLI-Parameters at [features/CLI.md](../../features/CLI/#arguments)
 
 ---
 
-## Running InvokeAI in the cloud with Docker
-
-We offer an optimized Ubuntu-based image that has been well-tested in cloud deployments. Note: it also works well locally on Linux x86_64 systems with an Nvidia GPU. It *may* also work on Windows under WSL2 and on Intel Mac (not tested).
-
-An advantage of this method is that it does not need any local setup or additional dependencies.
-
-See the `docker-build/Dockerfile.cloud` file to familizarize yourself with the image's content.
-
-### Prerequisites
-
-- a `docker` runtime
-- `make` (optional but helps for convenience)
-- Huggingface token to download models, or an existing InvokeAI runtime directory from a previous installation
-
- Neither local Python nor any dependencies are required. If you don't have `make` (part of `build-essentials` on Ubuntu), or do not wish to install it, the commands from the `docker-build/Makefile` are readily adaptable to be executed directly.
-
-### Building and running the image locally
-
-1. Clone this repo and `cd docker-build`
-1. `make build` - this will build the image. (This does *not* require a GPU-capable system).
-1. _(skip this step if you already have a complete InvokeAI runtime directory)_
-    - `make configure` (This does *not* require a GPU-capable system)
-    - this will create a local cache of models and configs (a.k.a the _runtime dir_)
-    - enter your Huggingface token when prompted
-1. `make web`
-1. Open the `http://localhost:9090` URL in your browser, and enjoy the banana sushi!
-
-To use InvokeAI on the cli, run `make cli`. To open a Bash shell in the container for arbitraty advanced use, `make shell`.
-
-#### Building and running without `make`
-
-(Feel free to adapt paths such as `${HOME}/invokeai` to your liking, and modify the CLI arguments as necessary).
-
-!!! example "Build the image and configure the runtime directory"
-    ```Shell
-    cd docker-build
-
-    DOCKER_BUILDKIT=1 docker build -t local/invokeai:latest -f Dockerfile.cloud ..
-
-    docker run --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/configure_invokeai.py"
-    ```
-
-!!! example "Run the web server"
-    ```Shell
-    docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai -p9090:9090 local/invokeai:latest
-    ```
-
-    Access the Web UI at http://localhost:9090
-
-!!! example "Run the InvokeAI interactive CLI"
-    ```
-    docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/invoke.py"
-    ```
-
-### Running the image in the cloud
-
-This image works anywhere you can run a container with a mounted Docker volume. You may either build this image on a cloud instance, or build and push it to your Docker registry. To manually run this on a cloud instance (such as AWS EC2, GCP or Azure VM):
-
-1. build this image either in the cloud (you'll need to pull the repo), or locally
-1. `docker tag` it as `your-registry/invokeai` and push to your registry (i.e. Dockerhub)
-1. `docker pull` it on your cloud instance
-1. configure the runtime directory as per above example, using `docker run ... configure_invokeai.py` script
-1. use either one of the `docker run` commands above, substituting the image name for your own image.
-
-To run this on Runpod, please refer to the following Runpod template: https://www.runpod.io/console/gpu-secure-cloud?template=vm19ukkycf (you need a Runpod subscription). When launching the template, feel free to set the image to pull your own build.
-
-The template's `README` provides ample detail, but at a high level, the process is as follows:
-
-1. create a pod using this Docker image
-1. ensure the pod has an `INVOKEAI_ROOT=<path_to_your_persistent_volume>` environment variable, and that it corresponds to the path to your pod's persistent volume mount
-1. Run the pod with `sleep infinity` as the Docker command
-1. Use Runpod basic SSH to connect to the pod, and run `python scripts/configure_invokeai.py` script
-1. Stop the pod, and change the Docker command to `python scripts/invoke.py --web --host 0.0.0.0`
-1. Run the pod again, connect to your pod on HTTP port 9090, and enjoy the banana sushi!
-
-Running on other cloud providers such as Vast.ai will likely work in a similar fashion.
-
----
-
 !!! warning "Deprecated"
 
     From here on you will find the the previous Docker-Docs, which will still
@@ -218,12 +135,12 @@ Running on other cloud providers such as Vast.ai will likely work in a similar f
 If you're on a **Linux container** the `invoke` script is **automatically
 started** and the output dir set to the Docker volume you created earlier.
 
-If you're **directly on macOS follow these startup instructions**.
+If you're **directly on macOS follow these startup instructions**.  
 With the Conda environment activated (`conda activate ldm`), run the interactive
 interface that combines the functionality of the original scripts `txt2img` and
-`img2img`:
+`img2img`:  
 Use the more accurate but VRAM-intensive full precision math because
-half-precision requires autocast and won't work.
+half-precision requires autocast and won't work.  
 By default the images are saved in `outputs/img-samples/`.
 
 ```Shell
@@ -240,8 +157,8 @@ invoke> q
 ### Text to Image
 
 For quick (but bad) image results test with 5 steps (default 50) and 1 sample
-image. This will let you know that everything is set up correctly.
-Then increase steps to 100 or more for good (but slower) results.
+image. This will let you know that everything is set up correctly.  
+Then increase steps to 100 or more for good (but slower) results.  
 The prompt can be in quotes or not.
 
 ```Shell
@@ -255,8 +172,8 @@ You'll need to experiment to see if face restoration is making it better or
 worse for your specific prompt.
 
 If you're on a container the output is set to the Docker volume. You can copy it
-wherever you want.
-You can download it from the Docker Desktop app, Volumes, my-vol, data.
+wherever you want.  
+You can download it from the Docker Desktop app, Volumes, my-vol, data.  
 Or you can copy it from your Mac terminal. Keep in mind `docker cp` can't expand
 `*.png` so you'll need to specify the image file name.
 

docs/installation/INSTALL_INVOKE.md

@@ -1,8 +1,8 @@
 ---
-title: InvokeAI Binary Installer
+title: InvokeAI Installer
 ---
 
-The InvokeAI binary installer is a shell script that will install InvokeAI onto a stock
+The InvokeAI installer is a shell script that will install InvokeAI onto a stock
 computer running recent versions of Linux, MacOSX or Windows. It will leave you
 with a version that runs a stable version of InvokeAI. When a new version of
 InvokeAI is released, you will download and reinstall the new version.
@@ -36,7 +36,7 @@ recommended model weights files.
 
 1. Download the
    [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest) of
-   InvokeAI's installer for your platform. Look for a file named `InvokeAI-binary-<your platform>.zip`
+   InvokeAI's installer for your platform
 
 2. Place the downloaded package someplace where you have plenty of HDD space,
    and have full permissions (i.e. `~/` on Lin/Mac; your home folder on Windows)

docs/installation/INSTALL_JUPYTER.md

@@ -2,10 +2,12 @@
 title: Running InvokeAI on Google Colab using a Jupyter Notebook
 ---
 
+# THIS NEEDS TO BE FLESHED OUT
+
 ## Introduction
 
 We have a [Jupyter
-notebook](https://github.com/invoke-ai/InvokeAI/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
+notebook](https://github.com/invoke-ai/InvokeAI/blob/main/notebooks/Stable-Diffusion-local-Windows.ipynb)
 with cell-by-cell installation steps. It will download the code in
 this repo as one of the steps, so instead of cloning this repo, simply
 download the notebook from the link above and load it up in VSCode
@@ -14,19 +16,12 @@ start running the cells one-by-one.
 
 !!! Note "you will need NVIDIA drivers, Python 3.10, and Git installed beforehand"
 
-## Running Online On Google Colabotary
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/invoke-ai/InvokeAI/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
+## Walkthrough
 
-## Running Locally (Cloning)
+## Updating to newer versions
 
-1. Install the Jupyter Notebook python library (one-time):
-pip install jupyter
+### Updating the stable version
 
-2. Clone the InvokeAI repository:
-git clone https://github.com/invoke-ai/InvokeAI.git
-cd invoke-ai
-3. Create a virtual environment using conda:
-conda create -n invoke jupyter
-4. Activate the environment and start the Jupyter notebook:
-conda activate invoke
-jupyter notebook
+### Updating to the development version
+
+## Troubleshooting

docs/installation/INSTALL_MANUAL.md

@@ -8,7 +8,7 @@ title: Manual Installation
 
 !!! warning "This is for advanced Users"
 
-    who are already experienced with using conda or pip
+    who are already expirienced with using conda or pip
 
 ## Introduction
 
@@ -155,10 +155,10 @@ command-line completion.
         process for this is described in [here](INSTALLING_MODELS.md).
 
     ```bash
-    python scripts/configure_invokeai.py
+    python scripts/preload_models.py
     ```
 
-    The script `configure_invokeai.py` will interactively guide you through the
+    The script `preload_models.py` will interactively guide you through the
     process of downloading and installing the weights files needed for InvokeAI.
     Note that the main Stable Diffusion weights file is protected by a license
     agreement that you have to agree to. The script will list the steps you need
@@ -220,7 +220,7 @@ greatest version, launch the Anaconda window, enter `InvokeAI` and type:
 ```bash
 git pull
 conda env update
-python scripts/configure_invokeai.py --no-interactive  #optional
+python scripts/preload_models.py --no-interactive  #optional
 ```
 
 This will bring your local copy into sync with the remote one. The last step may
@@ -359,7 +359,7 @@ brew install llvm
 
 If brew config has Clang installed, update to the latest llvm and try creating the environment again.
 
-#### `configure_invokeai.py` or `invoke.py` crashes at an early stage
+#### `preload_models.py` or `invoke.py` crashes at an early stage
 
 This is usually due to an incomplete or corrupted Conda install. Make sure you
 have linked to the correct environment file and run `conda update` again.

docs/installation/INSTALL_PATCHMATCH.md

@@ -1,115 +0,0 @@
----
-title: Installing PyPatchMatch
----
-
-# :octicons-paintbrush-16: Installing PyPatchMatch
-
-pypatchmatch is a Python module for inpainting images. It is not
-needed to run InvokeAI, but it greatly improves the quality of
-inpainting and outpainting and is recommended.
-
-Unfortunately, it is a C++ optimized module and installation
-can be somewhat challenging. This guide leads you through the steps.
-
-## Windows
-
-You're in luck! On Windows platforms PyPatchMatch will install
-automatically on Windows systems with no extra intervention.
-
-## Macintosh
-
-PyPatchMatch is not currently supported, but the team is working on
-it.
-
-## Linux
-
-Prior to installing PyPatchMatch, you need to take the following
-steps:
-
-### Debian Based Distros
-
-
-1. Install the `build-essential` tools:
-
-   ```
-   sudo apt update
-   sudo apt install build-essential
-   ```
-
-2. Install `opencv`:
-
-   ```
-   sudo apt install python3-opencv libopencv-dev
-   ```
-
-3. Fix the naming of the `opencv` package configuration file:
-
-   ```
-   cd /usr/lib/x86_64-linux-gnu/pkgconfig/
-   ln -sf opencv4.pc opencv.pc
-   ```
-
-4. Activate the environment you use for invokeai, either with
-`conda` or with a virtual environment.
-
-5. Do a "develop" install of pypatchmatch:
-
-   ```
-   pip install "git+https://github.com/invoke-ai/PyPatchMatch@0.1.3#egg=pypatchmatch"
-   ```
-
-6. Confirm that pypatchmatch is installed.
-   At the command-line prompt enter `python`, and
-   then at the `>>>` line type `from patchmatch import patch_match`:
-   It should look like the follwing:
-
-   ```
-   Python 3.9.5 (default, Nov 23 2021, 15:27:38) 
-   [GCC 9.3.0] on linux
-   Type "help", "copyright", "credits" or "license" for more information.
-   >>> from patchmatch import patch_match
-   Compiling and loading c extensions from "/home/lstein/Projects/InvokeAI/.invokeai-env/src/pypatchmatch/patchmatch".
-   rm -rf build/obj libpatchmatch.so
-   mkdir: created directory 'build/obj'
-   mkdir: created directory 'build/obj/csrc/'
-   [dep] csrc/masked_image.cpp ...
-   [dep] csrc/nnf.cpp ...
-   [dep] csrc/inpaint.cpp ...
-   [dep] csrc/pyinterface.cpp ...
-   [CC] csrc/pyinterface.cpp ...
-   [CC] csrc/inpaint.cpp ...
-   [CC] csrc/nnf.cpp ...
-   [CC] csrc/masked_image.cpp ...
-   [link] libpatchmatch.so ...
-   ```
-
-
-### Arch Based Distros
-
-1. Install the `base-devel` package:
-   ```
-   sudo pacman -Syu
-   sudo pacman -S --needed base-devel
-   ```
-
-2. Install `opencv`:
-   ```
-   sudo pacman -S opencv
-   ```
-   or for CUDA support
-   ```
-   sudo pacman -S opencv-cuda
-   ```
-
-3. Fix the naming of the `opencv` package configuration file:
-   ```
-   cd /usr/lib/pkgconfig/
-   ln -sf opencv4.pc opencv.pc
-   ```
-
-**Next, Follow Steps 4-6 from the Debian Section above**
-
-
-If you see no errors, then you're ready to go!
-
-

docs/installation/INSTALL_SOURCE.md

@@ -10,6 +10,7 @@ The source installer is a shell script that attempts to automate every step
 needed to install and run InvokeAI on a stock computer running recent versions
 of Linux, MacOS or Windows. It will leave you with a version that runs a stable
 version of InvokeAI with the option to upgrade to experimental versions later.
+It is not as foolproof as the [InvokeAI installer](INSTALL_INVOKE.md)
 
 Before you begin, make sure that you meet the
 [hardware requirements](index.md#Hardware_Requirements) and has the appropriate
@@ -29,9 +30,9 @@ off the process.
     [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest), and
     look for a series of files named:
 
-    - [invokeAI-src-installer-2.2.3-mac.zip](https://github.com/invoke-ai/InvokeAI/releases/latest/download/invokeAI-src-installer-2.2.3-mac.zip)
-    - [invokeAI-src-installer-2.2.3-windows.zip](https://github.com/invoke-ai/InvokeAI/releases/latest/download/invokeAI-src-installer-2.2.3-windows.zip)
-    - [invokeAI-src-installer-2.2.3-linux.zip](https://github.com/invoke-ai/InvokeAI/releases/latest/download/invokeAI-src-installer-2.2.3-linux.zip)
+    - invokeAI-src-installer-mac.zip
+    - invokeAI-src-installer-windows.zip
+    - invokeAI-src-installer-linux.zip
 
     Download the one that is appropriate for your operating system.
 
@@ -50,44 +51,23 @@ off the process.
     inflating: invokeAI\readme.txt
     ```
 
-3. If you are a macOS user, you may need to install the Xcode command line tools. 
-   These are a set of tools that are needed to run certain applications in a Terminal, 
-   including InvokeAI. This package is provided directly by Apple.
-   
-   To install, open a terminal window and run `xcode-select --install`. You will get
-   a macOS system popup guiding you through the install. If you already have them
-   installed, you will instead see some output in the Terminal advising you that the 
-   tools are already installed.
-   
-   More information can be found here:
-   https://www.freecodecamp.org/news/install-xcode-command-line-tools/
-
-4.  If you are using a desktop GUI, double-click the installer file. It will be
+3.  If you are using a desktop GUI, double-click the installer file. It will be
     named `install.bat` on Windows systems and `install.sh` on Linux and
     Macintosh systems.
 
-5.  Alternatively, from the command line, run the shell script or .bat file:
+4.  Alternatively, form the command line, run the shell script or .bat file:
 
     ```cmd
     C:\Documents\Linco> cd invokeAI
     C:\Documents\Linco\invokeAI> install.bat
     ```
 
-6.  Sit back and let the install script work. It will install various binary
+5.  Sit back and let the install script work. It will install various binary
     requirements including Conda, Git and Python, then download the current
     InvokeAI code and install it along with its dependencies.
 
-    Be aware that some of the library download and install steps take a long time.
-    In particular, the `pytorch` package is quite large and often appears to get
-    "stuck" at 99.9%. Similarly, the `pip installing requirements` step may
-    appear to hang. Have patience and the installation step will eventually
-    resume. However, there are occasions when the library install does
-    legitimately get stuck. If you have been waiting for more than ten minutes
-    and nothing is happening, you can interrupt the script with ^C. You may restart
-    it and it will pick up where it left off.
-
-7.  After installation completes, the installer will launch a script called
-    `configure_invokeai.py`, which will guide you through the first-time process of
+6.  After installation completes, the installer will launch a script called
+    `preload_models.py`, which will guide you through the first-time process of
     selecting one or more Stable Diffusion model weights files, downloading and
     configuring them.
 
@@ -102,7 +82,7 @@ off the process.
     prompted) and configure InvokeAI to use the previously-downloaded files. The
     process for this is described in [Installing Models](INSTALLING_MODELS.md).
 
-8.  The script will now exit and you'll be ready to generate some images. The
+7.  The script will now exit and you'll be ready to generate some images. The
     invokeAI directory will contain numerous files. Look for a shell script
     named `invoke.sh` (Linux/Mac) or `invoke.bat` (Windows). Launch the script
     by double-clicking it or typing its name at the command-line:
@@ -130,71 +110,6 @@ python scripts/invoke.py --web --max_load_models=3 \
 These options are described in detail in the
 [Command-Line Interface](../features/CLI.md) documentation.
 
-## Troubleshooting
-
-_Package dependency conflicts_ If you have previously installed
-InvokeAI or another Stable Diffusion package, the installer may
-occasionally pick up outdated libraries and either the installer or
-`invoke` will fail with complaints out library conflicts. There are
-two steps you can take to clear this problem. Both of these are done
-from within the "developer's console", which you can get to by
-launching `invoke.sh` (or `invoke.bat`) and selecting launch option
-#3:
-
-1. Remove the previous `invokeai` environment completely. From within
-   the developer's console, give the command `conda env remove -n
-   invokeai`. This will delete previous files installed by `invoke`.
-
-   Then exit from the developer's console and launch the script
-   `update.sh` (or `update.bat`). This will download the most recent
-   InvokeAI (including bug fixes) and reinstall the environment.
-   You should then be able to run `invoke.sh`/`invoke.bat`.
-
-2. If this doesn't work, you can try cleaning your system's conda
-   cache. This is slightly more extreme, but won't interfere with
-   any other python-based programs installed on your computer.
-   From the developer's console, run the command `conda clean -a`
-   and answer "yes" to all prompts.
-
-   After this is done, run `update.sh` and try again as before.
-
-_"Corrupted configuration file."__ Everything seems to install ok, but
-`invoke` complains of a corrupted configuration file and goes calls
-`configure_invokeai.py` to fix, but this doesn't fix the problem.
-
-This issue is often caused by a misconfigured configuration directive
-in the `.invokeai` initialization file that contains startup settings.
-This can be corrected by fixing the offending line.
-
-First find `.invokeai`. It is a small text file located in your home
-directory, `~/.invokeai` on Mac and Linux systems, and `C:\Users\*your
-name*\.invokeai` on Windows systems. Open it with a text editor
-(e.g. Notepad on Windows, TextEdit on Macs, or `nano` on Linux)
-and look for the lines starting with `--root` and `--outdir`.
-
-An example is here:
-
-```cmd
---root="/home/lstein/invokeai"
---outdir="/home/lstein/invokeai/outputs"
-```
-
-There should not be whitespace before or after the directory paths,
-and the paths should not end with slashes:
-
-```cmd
---root="/home/lstein/invokeai "     # wrong! no whitespace here
---root="/home\lstein\invokeai\"     # wrong! shouldn't end in a slash
-```
-
-Fix the problem with your text editor and save as a **plain text**
-file. This should clear the issue.
-
-_If none of these maneuvers fixes the problem_ then please report the
-problem to the [InvokeAI
-Issues](https://github.com/invoke-ai/InvokeAI/issues) section, or
-visit our [Discord Server](https://discord.gg/ZmtBAhwWhy) for interactive assistance.
-
 ## Updating to newer versions
 
 This section describes how to update InvokeAI to new versions of the software.
@@ -204,15 +119,31 @@ This section describes how to update InvokeAI to new versions of the software.
 This distribution is changing rapidly, and we add new features on a daily basis.
 To update to the latest released version (recommended), run the `update.sh`
 (Linux/Mac) or `update.bat` (Windows) scripts. This will fetch the latest
-release and re-run the `configure_invokeai` script to download any updated models
+release and re-run the `preload_models` script to download any updated models
 files that may be needed. You can also use this to add additional models that
 you did not select at installation time.
 
+### Updating to the development version
+
+There may be times that there is a feature in the `development` branch of
+InvokeAI that you'd like to take advantage of. Or perhaps there is a branch that
+corrects an annoying bug. To do this, you will use the developer's console.
+
+From within the invokeAI directory, run the command `invoke.sh` (Linux/Mac) or
+`invoke.bat` (Windows) and selection option (3) to open the developers console.
+Then run the following command to get the `development branch`:
+
+```bash
+git checkout development
+git pull
+conda env update
+```
+
 You can now close the developer console and run `invoke` as before. If you get
 complaints about missing models, then you may need to do the additional step of
-running `configure_invokeai.py`. This happens relatively infrequently. To do this,
+running `preload_models.py`. This happens relatively infrequently. To do this,
 simply open up the developer's console again and type
-`python scripts/configure_invokeai.py`.
+`python scripts/preload_models.py`.
 
 ## Troubleshooting
 

docs/installation/index.md

@@ -5,30 +5,58 @@ title: Overview
 We offer several ways to install InvokeAI, each one suited to your
 experience and preferences.
 
-1.  [Automated Installer](INSTALL_AUTOMATED.md)
+1. [InvokeAI installer](INSTALL_INVOKE.md)
 
-    This is a script that will install all of InvokeAI's essential
-    third party libraries and InvokeAI itself. It includes access to a
-    "developer console" which will help us debug problems with you and
-    give you to access experimental features.
+    This is a installer script that installs InvokeAI and all the
+    third party libraries it depends on. When a new version of
+    InvokeAI is released, you will download and reinstall the new
+    version.
 
-2. [Manual Installation](INSTALL_MANUAL.md)
+    This installer is designed for people who want the system to "just
+    work", don't have an interest in tinkering with it, and do not
+    care about upgrading to unreleased experimental features.
+
+    **Important Caveats**
+    - This script does not support AMD GPUs. For Linux AMD support,
+    please use the manual or source code installer methods.
+    - This script has difficulty on some Macintosh machines
+    that have previously been used for Python development due to
+    conflicting development tools versions. Mac developers may wish
+    to try the source code installer or one of the manual methods instead.
+
+2. [Source code installer](INSTALL_SOURCE.md)
+
+    This is a script that will install InvokeAI and all its essential
+    third party libraries. In contrast to the previous installer, it
+    includes access to a "developer console" which will allow you to
+    access experimental features on the development branch.
+
+    This method is recommended for individuals who are wish to stay
+    on the cutting edge of InvokeAI development and are not afraid
+    of occasional breakage.
+
+3. [Manual Installation](INSTALL_MANUAL.md)
 
     In this method you will manually run the commands needed to install
     InvokeAI and its dependencies. We offer two recipes: one suited to
     those who prefer the `conda` tool, and one suited to those who prefer
-    `pip` and Python virtual environments. In our hands the pip install
-    is faster and more reliable, but your mileage may vary.
+    `pip` and Python virtual environments.
 
     This method is recommended for users who have previously used `conda`
     or `pip` in the past, developers, and anyone who wishes to remain on
     the cutting edge of future InvokeAI development and is willing to put
     up with occasional glitches and breakage.
 
-3. [Docker Installation](INSTALL_DOCKER.md)
+4. [Docker Installation](INSTALL_DOCKER.md)
 
     We also offer a method for creating Docker containers containing
     InvokeAI and its dependencies. This method is recommended for
     individuals with experience with Docker containers and understand
     the pluses and minuses of a container-based install.
 
+5. [Jupyter Notebooks Installation](INSTALL_JUPYTER.md)
+
+    This method is suitable for running InvokeAI on a Google Colab
+    account. It is recommended for individuals who have previously
+    worked on the Colab and are comfortable with the Jupyter notebook
+    environment.

docs/installation/older_docs_to_be_removed/INSTALL_LINUX.md

@@ -69,7 +69,7 @@ title: Manual Installation, Linux
     machine-learning models:
 
     ```bash
-    (invokeai) ~/InvokeAI$ python3 scripts/configure_invokeai.py
+    (invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
     ```
 
     !!! note
@@ -79,7 +79,7 @@ title: Manual Installation, Linux
         and obtaining an access token for downloading. It will then download and
         install the weights files for you.
 
-        Please look [here](../INSTALL_MANUAL.md) for a manual process for doing
+        Please look [here](INSTALLING_MODELS.md) for a manual process for doing
         the same thing.
 
 7.  Start generating images!
@@ -112,7 +112,7 @@ title: Manual Installation, Linux
         To use an alternative model you may invoke the `!switch` command in
         the CLI, or pass `--model <model_name>` during `invoke.py` launch for
         either the CLI or the Web UI. See [Command Line
-        Client](../../features/CLI.md#model-selection-and-importation). The
+        Client](../features/CLI.md#model-selection-and-importation). The
         model names are defined in `configs/models.yaml`.
 
 8. Subsequently, to relaunch the script, be sure to run "conda activate

docs/installation/older_docs_to_be_removed/INSTALL_MAC.md

@@ -111,7 +111,7 @@ will do our best to help.
 
 !!! todo "Download the model weight files"
 
-    The `configure_invokeai.py` script downloads and installs the model weight
+    The `preload_models.py` script downloads and installs the model weight
     files for you. It will lead you through the process of getting a Hugging Face
     account, accepting the Stable Diffusion model weight license agreement, and
     creating a download token:
@@ -119,7 +119,7 @@ will do our best to help.
     ```bash
     # This will take some time, depending on the speed of your internet connection
     # and will consume about 10GB of space
-    python scripts/configure_invokeai.py
+    python scripts/preload_models.py
     ```
 
 !!! todo "Run InvokeAI!"
@@ -150,7 +150,7 @@ will do our best to help.
     To use an alternative model you may invoke the `!switch` command in
     the CLI, or pass `--model <model_name>` during `invoke.py` launch for
     either the CLI or the Web UI. See [Command Line
-    Client](../../features/CLI.md#model-selection-and-importation). The
+    Client](../features/CLI.md#model-selection-and-importation). The
     model names are defined in `configs/models.yaml`.
 
 ---
@@ -220,8 +220,8 @@ There are several causes of these errors:
    with "(invokeai)" then you activated it. If it begins with "(base)" or
    something else you haven't.
 
-2. You might've run `./scripts/configure_invokeai.py` or `./scripts/invoke.py`
-   instead of `python ./scripts/configure_invokeai.py` or
+2. You might've run `./scripts/preload_models.py` or `./scripts/invoke.py`
+   instead of `python ./scripts/preload_models.py` or
    `python ./scripts/invoke.py`. The cause of this error is long so it's below.
 
     <!-- I could not find out where the error is, otherwise would have marked it as a footnote -->
@@ -359,7 +359,7 @@ python ./scripts/txt2img.py \
 ### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'
 
 ```bash
-python scripts/configure_invokeai.py
+python scripts/preload_models.py
 ```
 
 ---

docs/installation/older_docs_to_be_removed/INSTALL_WINDOWS.md

@@ -7,7 +7,7 @@ title: Manual Installation, Windows
 ## **Notebook install (semi-automated)**
 
 We have a
-[Jupyter notebook](https://github.com/invoke-ai/InvokeAI/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
+[Jupyter notebook](https://github.com/invoke-ai/InvokeAI/blob/main/notebooks/Stable-Diffusion-local-Windows.ipynb)
 with cell-by-cell installation steps. It will download the code in this repo as
 one of the steps, so instead of cloning this repo, simply download the notebook
 from the link above and load it up in VSCode (with the appropriate extensions
@@ -65,7 +65,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
 7. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
 
     ```bash
-    python scripts/configure_invokeai.py
+    python scripts/preload_models.py
     ```
 
     !!! note
@@ -75,7 +75,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
           obtaining an access token for downloading. It will then download and install the
           weights files for you.
 
-          Please look [here](../INSTALL_MANUAL.md) for a manual process for doing the
+          Please look [here](INSTALLING_MODELS.md) for a manual process for doing the
           same thing.
 
 8. Start generating images!
@@ -108,7 +108,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
         To use an alternative model you may invoke the `!switch` command in
         the CLI, or pass `--model <model_name>` during `invoke.py` launch for
         either the CLI or the Web UI. See [Command Line
-        Client](../../features/CLI.md#model-selection-and-importation). The
+        Client](../features/CLI.md#model-selection-and-importation). The
         model names are defined in `configs/models.yaml`.
 
 9. Subsequently, to relaunch the script, first activate the Anaconda

docs/other/CONTRIBUTORS.md

@@ -15,16 +15,16 @@ We thank them for all of their time and hard work.
 
 ## **Current core team**
 
-* @lstein (Lincoln Stein) - Co-maintainer
-* @blessedcoolant - Co-maintainer
-* @hipsterusername (Kent Keirsey) - Product Manager
-* @psychedelicious - Web Team Leader
-* @Kyle0654 (Kyle Schouviller) - Node Architect and General Backend Wizard
-* @damian0815 - Attention Systems and Gameplay Engineer
-* @mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
-* @Netsvetaev (Artur Netsvetaev) - UI/UX Developer
-* @tildebyte - general gadfly and resident (self-appointed) know-it-all
-* @keturn - Lead for Diffusers port
+* lstein (Lincoln Stein) - Co-maintainer
+* blessedcoolant - Co-maintainer
+* hipsterusername (Kent Keirsey) - Product Manager
+* psychedelicious - Web Team Leader
+* Kyle0654 (Kyle Schouviller) - Node Architect and General Backend Wizard
+* damian0815 - Attention Systems and Gameplay Engineer
+* mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
+* Netsvetaev (Artur Netsvetaev) - UI/UX Developer
+* tildebyte - general gadfly and resident (self-appointed) know-it-all
+* keturn - Lead for Diffusers port
 
 ## **Contributions by**
 

environments-and-requirements/environment-lin-aarch64.yml

@@ -42,5 +42,5 @@ dependencies:
       - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
       - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
       - git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.2#egg=gfpgan
-      - git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
+      - -e git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
       - -e .

environments-and-requirements/environment-lin-amd.yml

@@ -44,5 +44,5 @@ dependencies:
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
     - git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.2#egg=gfpgan
-    - git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
+    - -e git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
     - -e .

environments-and-requirements/environment-lin-cuda.yml

@@ -43,5 +43,5 @@ dependencies:
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
     - git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.2#egg=gfpgan
-    - git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
+    - -e git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
     - -e .

environments-and-requirements/environment-mac.yml

@@ -59,7 +59,7 @@ dependencies:
       - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
       - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
       - git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.2#egg=gfpgan
-      - git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
+      - -e git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
       - -e .
 variables:
   PYTORCH_ENABLE_MPS_FALLBACK: 1

environments-and-requirements/environment-win-cuda.yml

@@ -13,6 +13,7 @@ dependencies:
   - cudatoolkit=11.6
   - pip:
     - albumentations==0.4.3
+    - basicsr==1.4.1
     - dependency_injector==4.40.0
     - diffusers==0.6.0
     - einops==0.3.0
@@ -43,5 +44,5 @@ dependencies:
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
     - git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.1#egg=gfpgan
-    - git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
+    - -e git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch
     - -e .

environments-and-requirements/requirements-base.txt

@@ -1,16 +1,15 @@
 # pip will resolve the version which matches torch
 albumentations
 dependency_injector==4.40.0
-diffusers==0.10.*
+diffusers
 einops
 eventlet
 facexlib
 flask==2.1.3
 flask_cors==3.0.10
 flask_socketio==5.3.0
-flaskwebgui==1.0.3
+flaskwebgui==0.3.7
 getpass_asterisk
-gfpgan==1.3.8
 huggingface-hub
 imageio
 imageio-ffmpeg
@@ -18,7 +17,6 @@ kornia
 numpy
 omegaconf
 opencv-python
-picklescan
 pillow
 pip>=22
 pudb
@@ -33,8 +31,11 @@ taming-transformers-rom1504
 test-tube>=0.7.5
 torch-fidelity
 torchmetrics
-transformers==4.25.*
-https://github.com/Birch-san/k-diffusion/archive/refs/heads/mps.zip#egg=k-diffusion
-https://github.com/invoke-ai/PyPatchMatch/archive/refs/tags/0.1.4.zip#egg=pypatchmatch
-https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip#egg=clip
-https://github.com/invoke-ai/clipseg/archive/relaxed-python-requirement.zip#egg=clipseg
+transformers==4.21.*
+picklescan
+git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.1#egg=gfpgan ; platform_system == 'Windows'
+git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.2#egg=gfpgan ; platform_system != 'Windows'
+git+https://github.com/openai/CLIP.git@main#egg=clip
+git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
+git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
+git+https://github.com/invoke-ai/PyPatchMatch@0.1.4#egg=pypatchmatch

environments-and-requirements/requirements-lin-cuda.txt

@@ -1,5 +1,2 @@
---extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
 -r environments-and-requirements/requirements-base.txt
-torch
-torchvision
 -e .

environments-and-requirements/requirements-win-colab-cuda.txt

@@ -1,6 +1,7 @@
 -r environments-and-requirements/requirements-base.txt
 # Get hardware-appropriate torch/torchvision
 --extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
+basicsr==1.4.1
 torch==1.12.1
 torchvision==0.13.1
 -e .

frontend/dist/index.html

@@ -2,24 +2,17 @@
 <html lang="en">
 
 <head>
-  <script type="module" crossorigin src="./assets/polyfills.1ff60148.js"></script>
-
   <meta charset="UTF-8" />
   <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.d864890e.js"></script>
-  <link rel="stylesheet" href="./assets/index.81f1c71c.css">
-  <script type="module">try{import.meta.url;import("_").catch(()=>1);}catch(e){}window.__vite_is_modern_browser=true;</script>
-  <script type="module">!function(){if(window.__vite_is_modern_browser)return;console.warn("vite: loading legacy build because dynamic import or import.meta.url is unsupported, syntax error above should be ignored");var e=document.getElementById("vite-legacy-polyfill"),n=document.createElement("script");n.src=e.src,n.onload=function(){System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))},document.body.appendChild(n)}();</script>
+  <script type="module" crossorigin src="./assets/index.faf4c870.js"></script>
+  <link rel="stylesheet" href="./assets/index.c609c0c8.css">
 </head>
 
 <body>
   <div id="root"></div>
   
-  <script nomodule>!function(){var e=document,t=e.createElement("script");if(!("noModule"in t)&&"onbeforeload"in t){var n=!1;e.addEventListener("beforeload",(function(e){if(e.target===t)n=!0;else if(!e.target.hasAttribute("nomodule")||!n)return;e.preventDefault()}),!0),t.type="module",t.src=".",e.head.appendChild(t),t.remove()}}();</script>
-  <script nomodule crossorigin id="vite-legacy-polyfill" src="./assets/polyfills-legacy-dde3a68a.js"></script>
-  <script nomodule crossorigin id="vite-legacy-entry" data-src="./assets/index-legacy-8e84772c.js">System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))</script>
 </body>
 
 </html>

frontend/package.json

@@ -53,7 +53,6 @@
     "@types/react-transition-group": "^4.4.5",
     "@typescript-eslint/eslint-plugin": "^5.36.2",
     "@typescript-eslint/parser": "^5.36.2",
-    "@vitejs/plugin-legacy": "^3.0.1",
     "@vitejs/plugin-react": "^2.0.1",
     "eslint": "^8.23.0",
     "eslint-plugin-prettier": "^4.2.1",
@@ -61,7 +60,6 @@
     "patch-package": "^6.5.0",
     "postinstall-postinstall": "^2.1.0",
     "sass": "^1.55.0",
-    "terser": "^5.16.1",
     "tsc-watch": "^5.0.3",
     "typescript": "^4.6.4",
     "vite": "^3.0.7",

frontend/src/app/socketio/emitters.ts

@@ -42,6 +42,7 @@ const makeSocketIOEmitters = (
         options: optionsState,
         system: systemState,
         canvas: canvasState,
+        gallery: galleryState,
       } = state;
 
       const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
@@ -54,6 +55,13 @@ const makeSocketIOEmitters = (
 
       dispatch(generationRequested());
 
+      if (!['txt2img', 'img2img'].includes(generationMode)) {
+        if (!galleryState.currentImage?.url) return;
+
+        frontendToBackendParametersConfig.imageToProcessUrl =
+          galleryState.currentImage.url;
+      }
+
       const { generationParameters, esrganParameters, facetoolParameters } =
         frontendToBackendParameters(frontendToBackendParametersConfig);
 

frontend/src/common/util/parameterTranslation.ts

@@ -30,7 +30,13 @@ export const frontendToBackendParameters = (
 ): { [key: string]: any } => {
   const canvasBaseLayer = getCanvasBaseLayer();
 
-  const { generationMode, optionsState, canvasState, systemState } = config;
+  const {
+    generationMode,
+    optionsState,
+    canvasState,
+    systemState,
+    imageToProcessUrl,
+  } = config;
 
   const {
     cfgScale,
@@ -158,6 +164,7 @@ export const frontendToBackendParameters = (
 
     generationParameters.fit = false;
 
+    generationParameters.init_img = imageToProcessUrl;
     generationParameters.strength = img2imgStrength;
 
     generationParameters.invert_mask = shouldPreserveMaskedArea;

frontend/src/features/options/components/PromptInput/PromptInput.tsx

@@ -62,7 +62,7 @@ const PromptInput = () => {
         <Textarea
           id="prompt"
           name="prompt"
-          placeholder="Type prompt here. [negative tokens], (upweight)++, (downweight)--, swap and blend are available (see docs)"
+          placeholder="I'm dreaming of..."
           size={'lg'}
           value={prompt}
           onChange={handleChangePrompt}

frontend/src/styles/Mixins/_Responsive.scss

@@ -1,159 +0,0 @@
-@media (max-width: 600px) {
-    #root{
-        .app-content{
-            padding: 5px;
-            .site-header {
-                position: fixed;
-                display: flex;
-                height: 100px;
-                z-index: 1;
-                .site-header-left-side{
-                    position: absolute;
-                    display: flex;
-                    min-width: 145px;
-                    float: left;
-                    padding-left: 0;
-                }
-                .site-header-right-side{
-                    display: grid;
-                    grid-template-columns: 1fr 1fr 1fr 1fr 1fr 1fr;
-                    grid-template-rows: 25px 25px 25px;
-                    grid-template-areas: 'logoSpace logoSpace logoSpace sampler sampler sampler'
-                                        'status status status status status status'
-                                        'btn1 btn2 btn3 btn4 btn5 btn6';
-                    row-gap: 15px;
-                    .chakra-popover__popper{
-                        grid-area: logoSpace;
-                    }
-                    > :nth-child(1).chakra-text{
-                        grid-area: status;
-                        width: 100%;
-                        display: flex;
-                        justify-content: center;
-                    }
-                    > :nth-child(2){
-                        grid-area: sampler;
-                        display: flex;
-                        justify-content: center;
-                        align-items: center;
-                        select{
-                            width: 185px;
-                            margin-top: 10px;
-                        }
-                        .chakra-select__icon-wrapper{
-                            right:10px;
-                            svg{
-                                margin-top: 10px;
-                            }
-                        }
-                    }
-                    > :nth-child(3){
-                        grid-area: btn1;
-                    }
-                    > :nth-child(4){
-                        grid-area: btn2;
-                    }
-                    > :nth-child(6){
-                        grid-area: btn3;
-                    }
-                    > :nth-child(7){
-                        grid-area: btn4;
-                    }
-                    > :nth-child(8){
-                        grid-area: btn5;
-                    }
-                    > :nth-child(9){
-                        grid-area: btn6;
-                    }
-                    
-                }
-            }
-            .app-tabs{
-                position: fixed;
-                display: flex;
-                flex-direction: column;
-                row-gap: 15px;
-                max-width: 100%;
-                overflow: hidden;
-                margin-top: 120px;
-                .app-tabs-list{
-                    display: flex;
-                    justify-content: space-between;
-                }
-                .app-tabs-panels{
-                    overflow: hidden;
-                    overflow-y: scroll;
-                    .workarea-main{
-                        display: grid;
-                        grid-template-areas: 'workarea'
-                                            'options'
-                                            'gallery';
-                        row-gap: 15px;
-                        .options-panel-wrapper{
-                            grid-area: options;
-                            width: 100%;
-                            max-width: 100%;
-                            height: inherit;
-                            overflow: inherit;
-                            padding: 0 10px;
-                            .main-options-row{
-                                max-width: 100%;
-                            }
-                            .advanced-settings-item{
-                                max-width: 100%;
-                            }
-                        }
-                        .workarea-children-wrapper{
-                            grid-area: workarea; 
-                            .workarea-split-view{
-                                display: flex;
-                                flex-direction: column;
-                            }
-                            .current-image-options{
-                                column-gap: 3px;
-                            }
-                            .text-to-image-area{
-                                padding: 0;
-                            }
-                            .current-image-preview {
-                                height: 430px;
-                            }
-                            
-                            //image 2 image 
-                            .image-upload-button {
-                                row-gap: 10px;
-                                padding: 5px;
-                                svg {
-                                    width: 2rem;
-                                    height: 2rem;
-                                    margin-top: 10px;
-                                }
-                            }
-
-                            //Cavas Painting
-                            .inpainting-settings{
-                                display: flex;
-                                flex-wrap: wrap;
-                                row-gap: 10px;
-                            }
-                            .inpainting-canvas-area{
-                                .konvajs-content{
-                                    height: 400px !important;
-                                }
-                            }
-                        }
-                        .image-gallery-wrapper{
-                            grid-area: gallery;
-                            min-height: 400px;
-                            .image-gallery-popup{
-                                width: 100% !important;
-                                max-width: 100% !important;
-                            }
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
-

frontend/src/styles/Mixins/index.scss

@@ -1,4 +1,3 @@
 @forward './Shared';
 @forward './Buttons';
 @forward './Variables';
-@forward './Responsive';

frontend/vite.config.ts

@@ -2,20 +2,12 @@ import { defineConfig } from 'vite';
 import react from '@vitejs/plugin-react';
 import eslint from 'vite-plugin-eslint';
 import tsconfigPaths from 'vite-tsconfig-paths';
-import legacy from '@vitejs/plugin-legacy';
 
 // https://vitejs.dev/config/
 export default defineConfig(({ mode }) => {
   const common = {
     base: '',
-    plugins: [
-      react(),
-      eslint(),
-      tsconfigPaths(),
-      legacy({
-        modernPolyfills: ['es.array.find-last'],
-      }),
-    ],
+    plugins: [react(), eslint(), tsconfigPaths()],
     server: {
       // Proxy HTTP requests to the flask server
       proxy: {
@@ -43,11 +35,7 @@ export default defineConfig(({ mode }) => {
       },
     },
     build: {
-      /**
-       * We need to polyfill for Array.prototype.findLast(); the polyfill plugin above
-       * overrides any target specified here.
-       */
-      // target: 'esnext', 
+      target: 'esnext',
       chunkSizeWarningLimit: 1500, // we don't really care about chunk size
     },
   };

frontend/yarn.lock

@@ -213,11 +213,6 @@
   dependencies:
     regenerator-runtime "^0.13.10"
 
-"@babel/standalone@^7.20.6":
-  version "7.20.6"
-  resolved "https://registry.yarnpkg.com/@babel/standalone/-/standalone-7.20.6.tgz#7deb7ad244176414c3cbde020aad0607afdbe2fe"
-  integrity sha512-u5at/CbBLETf7kx2LOY4XdhseD79Y099WZKAOMXeT8qvd9OSR515my2UNBBLY4qIht/Qi9KySeQHQwQwxJN4Sw==
-
 "@babel/template@^7.18.10":
   version "7.18.10"
   resolved "https://registry.yarnpkg.com/@babel/template/-/template-7.18.10.tgz#6f9134835970d1dbf0835c0d100c9f38de0c5e71"
@@ -1209,7 +1204,7 @@
     "@jridgewell/set-array" "^1.0.0"
     "@jridgewell/sourcemap-codec" "^1.4.10"
 
-"@jridgewell/gen-mapping@^0.3.0", "@jridgewell/gen-mapping@^0.3.2":
+"@jridgewell/gen-mapping@^0.3.2":
   version "0.3.2"
   resolved "https://registry.yarnpkg.com/@jridgewell/gen-mapping/-/gen-mapping-0.3.2.tgz#c1aedc61e853f2bb9f5dfe6d4442d3b565b253b9"
   integrity sha512-mh65xKQAzI6iBcFzwv28KVWSmCkdRBWoOh+bYQGW3+6OZvbbN3TqMGo5hqYxQniRcH9F2VZIoJCm4pa3BPDK/A==
@@ -1228,15 +1223,7 @@
   resolved "https://registry.yarnpkg.com/@jridgewell/set-array/-/set-array-1.1.2.tgz#7c6cf998d6d20b914c0a55a91ae928ff25965e72"
   integrity sha512-xnkseuNADM0gt2bs+BvhO0p78Mk762YnZdsuzFV018NoG1Sj1SCQvpSqa7XUaTam5vAGasABV9qXASMKnFMwMw==
 
-"@jridgewell/source-map@^0.3.2":
-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/source-map/-/source-map-0.3.2.tgz#f45351aaed4527a298512ec72f81040c998580fb"
-  integrity sha512-m7O9o2uR8k2ObDysZYzdfhb08VuEml5oWGiosa1VdaPZ/A6QyPkAJuwN0Q1lhULOf6B7MtQmHENS743hWtCrgw==
-  dependencies:
-    "@jridgewell/gen-mapping" "^0.3.0"
-    "@jridgewell/trace-mapping" "^0.3.9"
-
-"@jridgewell/sourcemap-codec@1.4.14", "@jridgewell/sourcemap-codec@^1.4.10", "@jridgewell/sourcemap-codec@^1.4.13":
+"@jridgewell/sourcemap-codec@1.4.14", "@jridgewell/sourcemap-codec@^1.4.10":
   version "1.4.14"
   resolved "https://registry.yarnpkg.com/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.14.tgz#add4c98d341472a289190b424efbdb096991bb24"
   integrity sha512-XPSJHWmi394fuUuzDnGz1wiKqWfo1yXecHQMRf2l6hztTO+nPru658AyDngaBe7isIxEkRsPR3FZh+s7iVa4Uw==
@@ -1851,17 +1838,6 @@
     "@typescript-eslint/types" "5.44.0"
     eslint-visitor-keys "^3.3.0"
 
-"@vitejs/plugin-legacy@^3.0.1":
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/@vitejs/plugin-legacy/-/plugin-legacy-3.0.1.tgz#bccc0eaf15a64e1854313acebec879854e413deb"
-  integrity sha512-XCtEjxoR3rmy000ujYRBp5kggWqzHz9+F20/yIMUWOzbvu0+KW1e14Fvb8h7SpNn+bfjGW1RiAs1Vrgb7Js+iQ==
-  dependencies:
-    "@babel/standalone" "^7.20.6"
-    core-js "^3.26.1"
-    magic-string "^0.27.0"
-    regenerator-runtime "^0.13.11"
-    systemjs "^6.13.0"
-
 "@vitejs/plugin-react@^2.0.1":
   version "2.2.0"
   resolved "https://registry.yarnpkg.com/@vitejs/plugin-react/-/plugin-react-2.2.0.tgz#1b9f63b8b6bc3f56258d20cd19b33f5cc761ce6e"
@@ -1903,7 +1879,7 @@ acorn-jsx@^5.3.2:
   resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.3.2.tgz#7ed5bb55908b3b2f1bc55c6af1653bada7f07937"
   integrity sha512-rq9s+JNhf0IChjtDXxllJ7g41oZk5SlXtp0LHwyA5cejwn7vKmKp4pPri6YEePv2PU65sAsegbXtIinmDFDXgQ==
 
-acorn@^8.5.0, acorn@^8.8.0:
+acorn@^8.8.0:
   version "8.8.1"
   resolved "https://registry.yarnpkg.com/acorn/-/acorn-8.8.1.tgz#0a3f9cbecc4ec3bea6f0a80b66ae8dd2da250b73"
   integrity sha512-7zFpHzhnqYKrkYdUjF1HI1bzd0VygEGX8lFk4k5zVMqHEoES+P+7TKI+EvLO9WVMJ8eekdO0aDEK044xTXwPPA==
@@ -2026,11 +2002,6 @@ browserslist@^4.21.3:
     node-releases "^2.0.6"
     update-browserslist-db "^1.0.9"
 
-buffer-from@^1.0.0:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.2.tgz#2b146a6fd72e80b4f55d255f35ed59a3a9a41bd5"
-  integrity sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==
-
 callsites@^3.0.0:
   version "3.1.0"
   resolved "https://registry.yarnpkg.com/callsites/-/callsites-3.1.0.tgz#b3630abd8943432f54b3f0519238e33cd7df2f73"
@@ -2102,11 +2073,6 @@ color-name@~1.1.4:
   resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
   integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
 
-commander@^2.20.0:
-  version "2.20.3"
-  resolved "https://registry.yarnpkg.com/commander/-/commander-2.20.3.tgz#fd485e84c03eb4881c20722ba48035e8531aeb33"
-  integrity sha512-GpVkmM8vF2vQUkj2LvZmD35JxeJOLCwJ9cUkugyk2nuhbv3+mJvpLYYt+0+USMxE+oj+ey/lJEnhZw75x/OMcQ==
-
 commander@^4.0.0:
   version "4.1.1"
   resolved "https://registry.yarnpkg.com/commander/-/commander-4.1.1.tgz#9fd602bd936294e9e9ef46a3f4d6964044b18068"
@@ -2139,11 +2105,6 @@ copy-to-clipboard@3.3.1:
   dependencies:
     toggle-selection "^1.0.6"
 
-core-js@^3.26.1:
-  version "3.26.1"
-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-3.26.1.tgz#7a9816dabd9ee846c1c0fe0e8fcad68f3709134e"
-  integrity sha512-21491RRQVzUn0GGM9Z1Jrpr6PNPxPi+Za8OM9q4tksTSnlbXXGKK1nXNg/QvwFYettXvSX6zWKCtHHfjN4puyA==
-
 cors@~2.8.5:
   version "2.8.5"
   resolved "https://registry.yarnpkg.com/cors/-/cors-2.8.5.tgz#eac11da51592dd86b9f06f6e7ac293b3df875d29"
@@ -3091,13 +3052,6 @@ magic-string@^0.26.7:
   dependencies:
     sourcemap-codec "^1.4.8"
 
-magic-string@^0.27.0:
-  version "0.27.0"
-  resolved "https://registry.yarnpkg.com/magic-string/-/magic-string-0.27.0.tgz#e4a3413b4bab6d98d2becffd48b4a257effdbbf3"
-  integrity sha512-8UnnX2PeRAPZuN12svgR9j7M1uWMovg/CEnIwIG0LFkXSJJe4PdfUGiTGl8V9bsBHFUtfVINcSyYxd7q+kx9fA==
-  dependencies:
-    "@jridgewell/sourcemap-codec" "^1.4.13"
-
 map-stream@~0.1.0:
   version "0.1.0"
   resolved "https://registry.yarnpkg.com/map-stream/-/map-stream-0.1.0.tgz#e56aa94c4c8055a16404a0674b78f215f7c8e194"
@@ -3601,7 +3555,7 @@ redux@^4.2.0:
   dependencies:
     "@babel/runtime" "^7.9.2"
 
-regenerator-runtime@^0.13.10, regenerator-runtime@^0.13.11:
+regenerator-runtime@^0.13.10:
   version "0.13.11"
   resolved "https://registry.yarnpkg.com/regenerator-runtime/-/regenerator-runtime-0.13.11.tgz#f6dca3e7ceec20590d07ada785636a90cdca17f9"
   integrity sha512-kY1AZVr2Ra+t+piVaJ4gxaFaReZVH40AKNo7UCX6W+dEwBo/2oZJzqfuN1qLq1oL45o56cPaTXELwrTh8Fpggg==
@@ -3770,24 +3724,11 @@ socket.io@^4.5.2:
   resolved "https://registry.yarnpkg.com/source-map-js/-/source-map-js-1.0.2.tgz#adbc361d9c62df380125e7f161f71c826f1e490c"
   integrity sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==
 
-source-map-support@~0.5.20:
-  version "0.5.21"
-  resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.21.tgz#04fe7c7f9e1ed2d662233c28cb2b35b9f63f6e4f"
-  integrity sha512-uBHU3L3czsIyYXKX88fdrGovxdSCoTGDRZ6SYXtSRxLZUzHg5P/66Ht6uoUlHu9EZod+inXhKo3qQgwXUT/y1w==
-  dependencies:
-    buffer-from "^1.0.0"
-    source-map "^0.6.0"
-
 source-map@^0.5.7:
   version "0.5.7"
   resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.5.7.tgz#8a039d2d1021d22d1ea14c80d8ea468ba2ef3fcc"
   integrity sha512-LbrmJOMUSdEVxIKvdcJzQC+nQhe8FUZQTXQy6+I75skNgn3OoQ0DZA8YnFa7gp8tqtL3KPf1kmo0R5DoApeSGQ==
 
-source-map@^0.6.0:
-  version "0.6.1"
-  resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
-  integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
-
 sourcemap-codec@^1.4.8:
   version "1.4.8"
   resolved "https://registry.yarnpkg.com/sourcemap-codec/-/sourcemap-codec-1.4.8.tgz#ea804bd94857402e6992d05a38ef1ae35a9ab4c4"
@@ -3873,21 +3814,6 @@ supports-preserve-symlinks-flag@^1.0.0:
   resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
   integrity sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==
 
-systemjs@^6.13.0:
-  version "6.13.0"
-  resolved "https://registry.yarnpkg.com/systemjs/-/systemjs-6.13.0.tgz#7b28e74b44352e1650e8652499f42de724c3fc7f"
-  integrity sha512-P3cgh2bpaPvAO2NE3uRp/n6hmk4xPX4DQf+UzTlCAycssKdqhp6hjw+ENWe+aUS7TogKRFtptMosTSFeC6R55g==
-
-terser@^5.16.1:
-  version "5.16.1"
-  resolved "https://registry.yarnpkg.com/terser/-/terser-5.16.1.tgz#5af3bc3d0f24241c7fb2024199d5c461a1075880"
-  integrity sha512-xvQfyfA1ayT0qdK47zskQgRZeWLoOQ8JQ6mIgRGVNwZKdQMU+5FkCBjmv4QjcrTzyZquRw2FVtlJSRUmMKQslw==
-  dependencies:
-    "@jridgewell/source-map" "^0.3.2"
-    acorn "^8.5.0"
-    commander "^2.20.0"
-    source-map-support "~0.5.20"
-
 text-table@^0.2.0:
   version "0.2.0"
   resolved "https://registry.yarnpkg.com/text-table/-/text-table-0.2.0.tgz#7f5ee823ae805207c00af2df4a84ec3fcfa570b4"

installer/create_installer.sh

@@ -1,48 +0,0 @@
-#!/bin/bash
-
-cd "$(dirname "$0")"
-
-VERSION=$(grep ^VERSION ../setup.py | awk '{ print $3 }' | sed "s/'//g" )
-
-echo "Be certain that you're in the 'installer' directory before continuing."
-read -p "Press any key to continue, or CTRL-C to exit..."
-
-echo Building installer zip fles for InvokeAI v$VERSION
-
-# get rid of any old ones
-rm *.zip
-
-rm -rf InvokeAI-Installer
-mkdir InvokeAI-Installer
-
-cp -pr ../environments-and-requirements templates readme.txt InvokeAI-Installer/
-mkdir InvokeAI-Installer/templates/rootdir
-
-cp -pr ../configs InvokeAI-Installer/templates/rootdir/
-
-mkdir InvokeAI-Installer/templates/rootdir/{outputs,embeddings,models}
-
-cp install.sh.in InvokeAI-Installer/install.sh
-chmod a+rx InvokeAI-Installer/install.sh
-
-zip -r InvokeAI-installer-$VERSION-linux.zip InvokeAI-Installer
-zip -r InvokeAI-installer-$VERSION-mac.zip InvokeAI-Installer
-
-# now do the windows installer
-rm InvokeAI-Installer/install.sh
-cp install.bat.in InvokeAI-Installer/install.bat
-cp WinLongPathsEnabled.reg InvokeAI-Installer/
-
-# this gets rid of the "-e ." at the end of the windows requirements file
-# because it is easier to do it now than in the .bat install script
-egrep -v '^-e .' InvokeAI-Installer/environments-and-requirements/requirements-win-colab-cuda.txt >requirements.txt
-mv requirements.txt InvokeAI-Installer/environments-and-requirements/requirements-win-colab-cuda.txt
-zip -r InvokeAI-installer-$VERSION-windows.zip InvokeAI-Installer
-
-# clean up
-rm -rf InvokeAI-Installer
-
-
-exit 0
-
-

installer/create_installers.sh

@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+IFS=$'\n\t'
+
+echo "Be certain that you're in the 'installer' directory before continuing."
+read -p "Press any key to continue, or CTRL-C to exit..."
+
+# make the installer zip for linux and mac
+rm -rf InvokeAI
+mkdir -p InvokeAI
+cp install.sh InvokeAI
+cp readme.txt InvokeAI
+
+zip -r InvokeAI-linux.zip InvokeAI
+zip -r InvokeAI-mac.zip InvokeAI
+
+# make the installer zip for windows
+rm -rf InvokeAI
+mkdir -p InvokeAI
+cp install.bat InvokeAI
+cp readme.txt InvokeAI
+cp WinLongPathsEnabled.reg InvokeAI
+
+zip -r InvokeAI-windows.zip InvokeAI
+
+rm -rf InvokeAI
+
+echo "The installer zips are ready for distribution."

installer/install.bat

@@ -10,21 +10,21 @@
 
 @rem This enables a user to install this project without manually installing git or Python
 
-@rem change to the script's directory
-PUSHD "%~dp0"
-
 set "no_cache_dir=--no-cache-dir"
 if "%1" == "use-cache" (
     set "no_cache_dir="
 )
 
 echo ***** Installing InvokeAI.. *****
+echo "USING development BRANCH. REMEMBER TO CHANGE TO main BEFORE RELEASE"
 @rem Config
 set INSTALL_ENV_DIR=%cd%\installer_files\env
 @rem https://mamba.readthedocs.io/en/latest/installation.html
 set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
 set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+#set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+# RELEASE_SOURCEBALL=/archive/refs/heads/test-installer.tar.gz
+RELEASE_SOURCEBALL=/archive/refs/heads/development.tar.gz
 set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
 set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
 
@@ -127,7 +127,7 @@ if %errorlevel% neq 0 goto err_exit
 echo ***** Updated pip and wheel *****
 
 set err_msg=----- requirements file copy failed -----
-copy binary_installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
+copy installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
 if %errorlevel% neq 0 goto err_exit
 
 set err_msg=----- main pip install failed -----
@@ -140,11 +140,11 @@ set err_msg=----- InvokeAI setup failed -----
 .venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -e .
 if %errorlevel% neq 0 goto err_exit
 
-copy binary_installer\invoke.bat.in .\invoke.bat
+copy installer\invoke.bat .\invoke.bat
 echo ***** Installed invoke launcher script ******
 
 @rem more cleanup
-rd /s /q binary_installer installer_files
+rd /s /q installer installer_files
 
 @rem preload the models
 call .venv\Scripts\python scripts\configure_invokeai.py

installer/install.bat.in

@@ -1,215 +0,0 @@
-@echo off
-setlocal EnableExtensions EnableDelayedExpansion
-
-@rem This script requires the user to install Python 3.9 or higher. All other
-@rem requirements are downloaded as needed.
-
-@rem change to the script's directory
-PUSHD "%~dp0"
-
-set "no_cache_dir=--no-cache-dir"
-if "%1" == "use-cache" (
-    set "no_cache_dir="
-)
-
-@rem Config
-@rem this should be changed to the tagged release!
-set INVOKE_AI_SRC=https://github.com/invoke-ai/InvokeAI/archive/main.zip
-@rem set INVOKE_AI_SRC=https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v2.2.4.zip
-set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
-set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
-set PYTHON_URL=https://www.python.org/downloads/windows/
-set MINIMUM_PYTHON_VERSION=3.9.0
-set PYTHON_URL=https://www.python.org/downloads/release/python-3109/
-
-
-set err_msg=An error has occurred and the script could not continue.
-
-@rem --------------------------- Intro -------------------------------
-echo This script will install InvokeAI and its dependencies. Before you start,
-echo please make sure to do the following:
-echo 1. Install python 3.9 or higher.
-echo 2. Double-click on the file WinLongPathsEnabled.reg in order to
-echo    enable long path support on your system.
-echo 3. Some users have found they need to install the Visual C++ core
-echo    libraries or else they experience DLL loading problems at the end of the install.
-echo    Visual C++ is very likely already installed on your system, but if you get DLL
-echo    issues, please download and install the libraries by going to:
-echo    https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
-echo.
-echo See %INSTRUCTIONS% for more details.
-echo. 
-pause
-
-@rem ---------------------------- check Python version ---------------
-echo ***** Checking and Updating Python *****
-
-call python --version >.tmp1 2>.tmp2
-if %errorlevel% == 1 (
-   set err_msg=Please install Python 3.9 or higher. See %INSTRUCTIONS% for details.
-   goto err_exit
-)
-
-for /f "tokens=2" %%i in (.tmp1) do set python_version=%%i
-if "%python_version%" == "" (
-   set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.9 from %PYTHON_URL%
-   goto err_exit
-)
-
-call :compareVersions %MINIMUM_PYTHON_VERSION% %python_version%
-if %errorlevel% == 1 (
-   set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.9 from %PYTHON_URL%
-   goto err_exit
-)
-
-@rem Cleanup
-del /q .tmp1 .tmp2
-
-echo Updating PIP...
-call python -m pip install --no-warn-script-location -q --upgrade pip
-
-@rem --------------------- Get the requirements file ------------
-echo.
-echo Setting up requirements file for your system.
-copy /y environments-and-requirements\requirements-win-colab-cuda.txt .\requirements.txt
-
-@rem --------------------- Get the root directory for installation ------------
-set rootdir=""
-set response=""
-set selection=""
-:pick_rootdir
-	if %rootdir% neq "" goto :done
-	set /p selection=Select the path to install InvokeAI's directory into [%UserProfile%]: 
-	if %selection% == "" set selection=%UserProfile%
-	set dest=%selection%\invokeai
-	if exist %dest% (
-	   set response=y
-	   set /p response=The directory %dest% exists. Do you wish to resume install from a previous attempt? [Y/n]: 
-	   if !response! == "" set response=y
-	   if /I !response! == y (set rootdir=%dest%) else (goto :pick_rootdir)
-	) else (
-	   set rootdir=!dest!
-	)
-	set response=y
-	set /p response="You have chosen to install InvokeAI into %rootdir%. OK? [Y/n]: "
-	if !response! == "" set response=y
-	if /I !response! neq y set rootdir=""
-goto :pick_rootdir
-:done
-
-@rem ---------------------- Initialize the runtime directory ---------------------
-echo.
-echo *** Creating Runtime Directory %rootdir% ***
-if not exist %rootdir% mkdir %rootdir%
-@rem for unknown reasons the mkdir works but returns an error code
-if not exist %rootdir% (
-	set err_msg=Could not create the directory %rootdir%. Please check the directory's permissions and try again.
-	goto :err_exit
-)
-echo Successful.
-
-@rem --------------------------- Create and populate .venv ---------------------------
-echo.
-echo ** Creating Virtual Environment for InvokeAI **
-call python -mvenv %rootdir%\.venv
-if %errorlevel% neq 0 (
-   set err_msg=Could not create virtual environment %rootdir%\.venv. Please check the directory's permissions and try again.
-   goto :err_exit
-)
-echo Successful.
-
-echo.
-echo *** Installing InvokeAI Requirements ***
-call %rootdir%\.venv\Scripts\activate.bat
-copy environments-and-requirements\requirements-win-colab-cuda.txt .\requirements.txt
-call python -mpip install -r requirements.txt
-if %errorlevel% neq 0 (
-   set err_msg=Installation of requirements failed. See above for errors and check %TROUBLESHOOTING% for potential solutions.
-   goto :err_exit
-)
-echo Installation successful.
-
-echo.
-echo *** Installing InvokeAI Modules and Executables ***
-call python -mpip install %INVOKE_AI_SRC%
-if %errorlevel% neq 0 (
-   set err_msg=Installation of InvokeAI failed. See above for errors and check %TROUBLESHOOTING% for potential solutions.
-   goto :err_exit
-)
-echo Installation successful.
-
-@rem --------------------------- Set up the root directory ---------------------------
-xcopy /E /Y .\templates\rootdir %rootdir%
-PUSHD "%rootdir%"
-call .venv\Scripts\python .venv\Scripts\configure_invokeai.py --root="%rootdir%"
-if %errorlevel% neq 0 (
-   set err_msg=Configuration failed. See above for error messages and check %TROUBLESHOOTING% for potential solutions.
-   goto :err_exit
-)
-POPD
-copy .\templates\invoke.bat.in %rootdir%\invoke.bat
-copy .\templates\update.bat.in %rootdir%\update.bat
-
-@rem so that update.bat works
-mkdir %rootdir%\environments-and-requirements
-xcopy /I /Y .\environments-and-requirements %rootdir%\environments-and-requirements
-copy .\requirements.txt %rootdir%\requirements.txt
-
-
-echo.
-echo ***** Finished configuration *****
-echo All done. Execute the file %rootdir%\invoke.bat to start InvokeAI.
-pause
-deactivate
-exit
-
-@rem ------------------------ Subroutines ---------------
-@rem routine to do comparison of semantic version numbers
-@rem found at https://stackoverflow.com/questions/15807762/compare-version-numbers-in-batch-file
-:compareVersions
-::
-:: Compares two version numbers and returns the result in the ERRORLEVEL
-::
-:: Returns 1 if version1 > version2
-::         0 if version1 = version2
-::        -1 if version1 < version2
-::
-:: The nodes must be delimited by . or , or -
-::
-:: Nodes are normally strictly numeric, without a 0 prefix. A letter suffix
-:: is treated as a separate node
-::
-setlocal enableDelayedExpansion
-set "v1=%~1"
-set "v2=%~2"
-call :divideLetters v1
-call :divideLetters v2
-:loop
-call :parseNode "%v1%" n1 v1
-call :parseNode "%v2%" n2 v2
-if %n1% gtr %n2% exit /b 1
-if %n1% lss %n2% exit /b -1
-if not defined v1 if not defined v2 exit /b 0
-if not defined v1 exit /b -1
-if not defined v2 exit /b 1
-goto :loop
-
-
-:parseNode  version  nodeVar  remainderVar
-for /f "tokens=1* delims=.,-" %%A in ("%~1") do (
-  set "%~2=%%A"
-  set "%~3=%%B"
-)
-exit /b
-
-
-:divideLetters  versionVar
-for %%C in (a b c d e f g h i j k l m n o p q r s t u v w x y z) do set "%~1=!%~1:%%C=.%%C!"
-exit /b
-
-:err_exit
-echo %err_msg%
-echo The installer will exit now.
-pause
-exit /b
-

installer/install.sh

@@ -1,9 +1,5 @@
 #!/usr/bin/env bash
 
-# ensure we're in the correct folder in case user's CWD is somewhere else
-scriptdir=$(dirname "$0")
-cd "$scriptdir"
-
 set -euo pipefail
 IFS=$'\n\t'
 
@@ -26,8 +22,6 @@ function _err_exit {
 
 # This enables a user to install this project without manually installing git or Python
 
-echo -e "\n***** Installing InvokeAI into $(pwd)... *****\n"
-
 export no_cache_dir="--no-cache-dir"
 if [ $# -ge 1 ]; then
     if [ "$1" = "use-cache" ]; then
@@ -35,6 +29,10 @@ if [ $# -ge 1 ]; then
     fi
 fi
 
+echo "$no_cache_dir"
+
+echo -e "\n***** Installing InvokeAI... *****\n"
+
 
 OS_NAME=$(uname -s)
 case "${OS_NAME}" in
@@ -82,17 +80,19 @@ if [ "$OS_NAME" == "darwin" ] && [ "$OS_ARCH" == "arm64" ]; then
 fi
 
 # config
+echo "USING development BRANCH. REMEMBER TO CHANGE TO main BEFORE RELEASE"
 INSTALL_ENV_DIR="$(pwd)/installer_files/env"
 MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${MAMBA_OS_NAME}-${MAMBA_ARCH}/latest"
 RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+# RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+# RELEASE_SOURCEBALL=/archive/refs/heads/test-installer.tar.gz
+RELEASE_SOURCEBALL=/archive/refs/heads/development.tar.gz
 PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
 if [ "$OS_NAME" == "darwin" ]; then
     PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-apple-darwin-install_only.tar.gz
 elif [ "$OS_NAME" == "linux" ]; then
     PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-unknown-linux-gnu-install_only.tar.gz
 fi
-echo "INSTALLING $RELEASE_SOURCEBALL FROM $RELEASE_URL"
 
 PACKAGES_TO_INSTALL=""
 
@@ -192,33 +192,32 @@ echo -e "We're running under"
 _err_exit $? _err_msg
 
 _err_msg="\n----- pip update failed -----\n"
-.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location --upgrade pip
+.venv/bin/python3 -m pip install "$no_cache_dir" --no-warn-script-location --upgrade pip wheel
 _err_exit $? _err_msg
 
-echo -e "\n***** Updated pip *****\n"
+echo -e "\n***** Updated pip and wheel *****\n"
 
 _err_msg="\n----- requirements file copy failed -----\n"
-cp binary_installer/py3.10-${OS_NAME}-"${OS_ARCH}"-${CD}-reqs.txt requirements.txt
+cp installer/py3.10-${OS_NAME}-"${OS_ARCH}"-${CD}-reqs.txt requirements.txt
 _err_exit $? _err_msg
 
 _err_msg="\n----- main pip install failed -----\n"
-.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -r requirements.txt
+.venv/bin/python3 -m pip install "$no_cache_dir" --no-warn-script-location -r requirements.txt
 _err_exit $? _err_msg
 
 echo -e "\n***** Installed Python dependencies *****\n"
 
 _err_msg="\n----- InvokeAI setup failed -----\n"
-.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -e .
+.venv/bin/python3 -m pip install "$no_cache_dir" --no-warn-script-location -e .
 _err_exit $? _err_msg
 
 echo -e "\n***** Installed InvokeAI *****\n"
 
-cp binary_installer/invoke.sh.in ./invoke.sh
-chmod a+rx ./invoke.sh
+cp installer/invoke.sh .
 echo -e "\n***** Installed invoke launcher script ******\n"
 
 # more cleanup
-rm -rf binary_installer/ installer_files/
+rm -rf installer/ installer_files/
 
 # preload the models
 .venv/bin/python3 scripts/configure_invokeai.py
@@ -228,8 +227,6 @@ deactivate
 
 echo -e "\n***** Finished downloading models *****\n"
 
-echo "All done! Run the command"
-echo "  $scriptdir/invoke.sh"
-echo "to start InvokeAI."
+echo "All done! Run the command './invoke.sh' to start InvokeAI."
 read -p "Press any key to exit..."
 exit

installer/install.sh.in

@@ -1,217 +0,0 @@
-#!/usr/bin/env bash
-
-# ensure we're in the correct folder in case user's CWD is somewhere else
-scriptdir=$(dirname "$0")
-cd "$scriptdir"
-
-# make sure we are not already in a venv
-# (don't need to check status)
-deactivate >/dev/null 2>&1
-
-# this should be changed to the tagged release!
-INVOKE_AI_SRC=https://github.com/invoke-ai/InvokeAI/archive/main.zip
-# INVOKE_AI_SRC=https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v2.2.4.zip
-INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
-TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
-MINIMUM_PYTHON_VERSION=3.9.0
-
-set -euo pipefail
-IFS=$'\n\t'
-
-function _err_exit {
-    if test "$1" -ne 0
-    then
-        echo -e "Error code $1; Error caught was '$2'"
-	if [ "$OS_NAME" == "osx" ]; then
-	    echo "Something went wrong while installing InvokeAI and/or its requirements."
-	    echo "You may need to use the Xcode command line tools to proceed. See step number 3 of"
-	    echo "https://invoke-ai.github.io/InvokeAI/INSTALL_SOURCE#walk_through for"
-	    echo "installation instructions and then run this script again."
-	else
-	    echo "Something went wrong while installing InvokeAI and/or its requirements."
-	    echo "See https://invoke-ai.github.io/InvokeAI/INSTALL_SOURCE#troubleshooting for troubleshooting"
-	    echo "tips, or visit https://invoke-ai.github.io/InvokeAI/#installation for alternative"
-	    echo "installation methods"
-	fi
-        read -p "Press any key to exit..."
-        exit
-    fi
-}
-
-function readinput() {
-  local CLEAN_ARGS=""
-  while [[ $# -gt 0 ]]; do
-    local i="$1"
-    case "$i" in
-      "-i")
-        if read -i "default" 2>/dev/null <<< "test"; then 
-          CLEAN_ARGS="$CLEAN_ARGS -i \"$2\""
-        fi
-        shift
-        shift
-        ;;
-      "-p")
-        CLEAN_ARGS="$CLEAN_ARGS -p \"$2\""
-        shift
-        shift
-        ;;
-      *)
-        CLEAN_ARGS="$CLEAN_ARGS $1"
-        shift
-        ;;
-    esac
-  done
-  eval read $CLEAN_ARGS
-}
-
-
-function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
-
-echo "InvokeAI simple installer..."
-echo ""
-echo "Some of the installation steps take a long time to run. Please be patient."
-echo "If the script appears to hang for more than 10 minutes, please interrupt with control-C and retry."
-read -n 1 -s -r -p "<Press any key to start the install>"
-echo ""
-
-OS_NAME=$(uname -s)
-case "${OS_NAME}" in
-    Linux*)     OS_NAME="linux";;
-    Darwin*)    OS_NAME="osx";;
-    *)          echo "Unknown OS: $OS_NAME! This script runs only on Linux or Mac" && exit
-esac
-
-OS_ARCH=$(uname -m)
-case "${OS_ARCH}" in
-    x86_64*)    OS_ARCH="64";;
-    arm64*)     OS_ARCH="arm64";;
-    *)          echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit
-esac
-
-echo "Installing for $OS_NAME-$OS_ARCH"
-# confirm that python is installed and is up to date
-
-PYTHON=""
-for candidate in python3.10 python3.9 python3 python python3.11 ; do
-    if ppath=`which $candidate`; then
-	python_version=$($ppath -V | awk '{ print $2 }')    
-	if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
-	    PYTHON=$ppath
-	    echo Python $python_version found at $PYTHON
-	    break
-	fi
-    fi
-done
-
-if [ -z "$PYTHON" ]; then
-    echo "A suitable Python interpreter could not be found"
-    echo "Please install Python 3.9 or higher before running this script. See instructions at $INSTRUCTIONS for help."
-    read -p "Press any key to exit"
-    exit -1
-fi
-
-if [ "$OS_NAME" == "osx" ]; then
-    xcode_path=$(xcode-select --print-path)
-    _err_exit $? "xcode_path command not found"
-    export CPPFLAGS="-I$xcode_path/Library/Frameworks/Python3.framework/Versions/Current/Headers"
-    echo "Will compile wheels with CPPFLAGS=$CPPFLAGS"
-fi
-
-ROOTDIR=""
-while [ "$ROOTDIR" == "" ]
-do
-    echo
-    readinput -e -p "Select your preferred location for the 'invokeai' directory [$HOME]: " -i $HOME input
-    ROOTDIR=${input:=$HOME}/invokeai
-    read -e -p "InvokeAI will be installed into $ROOTDIR. OK? [y]: " input
-    RESPONSE=${input:='y'}
-    if [ "$RESPONSE" == 'y' ]; then
-	if [ -e $ROOTDIR ]; then
-	    echo
-	    read -e -p "Directory $ROOTDIR already exists. Do you want to resume an interrupted install? [y]: " input
-            RESPONSE=${input:='y'}
-	    if [ "$RESPONSE" != 'y' ]; then
-	        ROOTDIR=""
-	    fi	       
-	else
-	    mkdir -p $ROOTDIR
-	    if [ $? -ne 0 ]; then
-		echo "Could not create $ROOTDIR. Try again with a different install location."
-		ROOTDIR=""
-	    fi
-	fi
-    else
-	ROOTDIR=""
-    fi
-done
-
-#--------------------------------------------------------------------------------
-echo
-echo "** Creating Virtual Environment for InvokeAI **"
-
-$PYTHON -mpip install --upgrade pip
-$PYTHON -mvenv $ROOTDIR/.venv
-_err_exit $? "Python failed to create virtual environment $ROOTDIR/.venv. Please see $TROUBLESHOOTING for help."
-
-#--------------------------------------------------------------------------------
-echo
-echo "** Activating Virtual Environment for InvokeAI **"
-
-source $ROOTDIR/.venv/bin/activate
-_err_exit $? "Failed to activate virtual evironment $ROOTDIR/.venv. Please see $TROUBLESHOOTING for help."
-
-PYTHON=$ROOTDIR/.venv/bin/python
-
-#--------------------------------------------------------------------------------
-echo
-echo "*** Installing InvokeAI Dependencies ***"
-
-if [ "$OS_NAME" == "osx" ]; then
-    echo "macOS detected. Installing MPS and CPU support."
-    egrep -v '^-e .' environments-and-requirements/requirements-mac-mps-cpu.txt >requirements.txt
-else
-    if (lsmod | grep amdgpu) &>/dev/null ; then
-	echo "Linux system with AMD GPU driver detected. Installing ROCm and CPU support"
-	egrep -v '^-e .' environments-and-requirements/requirements-lin-amd.txt >requirements.txt
-    else
-	echo "Linux system detected. Installing CUDA and CPU support."
-	egrep -v '^-e .' environments-and-requirements/requirements-lin-cuda.txt >requirements.txt
-    fi
-fi
-
-$PYTHON -mpip install -r requirements.txt
-_err_exit $? "Failed to install InvokeAI's dependencies."
-
-#--------------------------------------------------------------------------------
-echo
-echo "*** Installing InvokeAI Modules and Executables ***"
-$PYTHON -mpip install $INVOKE_AI_SRC
-_err_exit $? "Installation of InvokeAI failed."
-
-#--------------------------------------------------------------------------------
-echo " *** Setting Up Root Directory $ROOTDIR *** "
-cp -pr templates/rootdir/* $ROOTDIR/
-cp templates/invoke.sh.in $ROOTDIR/invoke.sh
-chmod a+rx $ROOTDIR/invoke.sh
-cp templates/update.sh.in $ROOTDIR/update.sh
-chmod a+rx $ROOTDIR/update.sh
-
-# This allows the updater to work!
-cp -pr environments-and-requirements requirements.txt $ROOTDIR/
-
-#--------------------------------------------------------------------------------
-echo
-echo "*** Confguring InvokeAI ***"
-pushd $ROOTDIR
-./.venv/bin/configure_invokeai.py --root=$ROOTDIR
-_err_exit $? "Initial configuration failed. Please see above error messages and $TROUBLESHOOTING for help."
-
-#--------------------------------------------------------------------------------
-popd
-cp templates/invoke.sh.in $ROOTDIR/invoke.sh
-chmod a+rx $ROOTDIR/invoke.sh
-
-cp templates/update.sh.in $ROOTDIR/update.sh
-chmod a+rx $ROOTDIR/update.sh
-
-echo "You may now run InvokeAI by entering the directory $ROOTDIR and running invoke.sh"

installer/invoke.bat

@@ -1,6 +1,5 @@
 @echo off
 
-PUSHD "%~dp0"
 call .venv\Scripts\activate.bat
 
 echo Do you want to generate images using the
@@ -11,10 +10,10 @@ echo 3. open the developer console
 set /p choice="Please enter 1, 2 or 3: "
 if /i "%choice%" == "1" (
     echo Starting the InvokeAI command-line.
-    .venv\Scripts\python scripts\invoke.py %*
+    .venv\Scripts\python scripts\invoke.py
 ) else if /i "%choice%" == "2" (
     echo Starting the InvokeAI browser-based UI.
-    .venv\Scripts\python scripts\invoke.py --web %*
+    .venv\Scripts\python scripts\invoke.py --web
 ) else if /i "%choice%" == "3" (
     echo Developer Console
     echo Python command is:

installer/invoke.sh

@@ -4,11 +4,6 @@ set -eu
 
 . .venv/bin/activate
 
-# set required env var for torch on mac MPS
- if [ "$(uname -s)" == "Darwin" ]; then
-     export PYTORCH_ENABLE_MPS_FALLBACK=1
- fi
-
 echo "Do you want to generate images using the"
 echo "1. command-line"
 echo "2. browser-based UI"
@@ -20,11 +15,11 @@ read choice
 case $choice in
     1)
         printf "\nStarting the InvokeAI command-line..\n";
-        .venv/bin/python scripts/invoke.py $*;
+        .venv/bin/python scripts/invoke.py;
     ;;
     2)
         printf "\nStarting the InvokeAI browser-based UI..\n";
-        .venv/bin/python scripts/invoke.py --web $*;
+        .venv/bin/python scripts/invoke.py --web;
     ;;
     3)
         printf "\nDeveloper Console:\n";

installer/py3.10-darwin-x86_64-cpu-reqs.txt

@@ -4,7 +4,7 @@
 #
 #    pip-compile --allow-unsafe --generate-hashes --output-file=installer/py3.10-darwin-x86_64-cpu-reqs.txt installer/requirements.in
 #
---extra-index-url https://download.pytorch.org/whl/cu116
+--extra-index-url https://download.pytorch.org/whl/torch_stable.html
 --trusted-host https
 
 absl-py==1.3.0 \
@@ -987,6 +987,7 @@ numpy==1.23.4 \
     #   pandas
     #   pyarrow
     #   pydeck
+    #   pypatchmatch
     #   pytorch-lightning
     #   pywavelets
     #   qudida
@@ -1159,6 +1160,7 @@ pillow==9.3.0 \
     #   imageio
     #   k-diffusion
     #   matplotlib
+    #   pypatchmatch
     #   realesrgan
     #   scikit-image
     #   streamlit
@@ -1294,6 +1296,9 @@ pyparsing==3.0.9 \
     # via
     #   matplotlib
     #   packaging
+pypatchmatch @ https://github.com/invoke-ai/PyPatchMatch/archive/129863937a8ab37f6bbcec327c994c0f932abdbc.zip \
+    --hash=sha256:4ad6ec95379e7d122d494ff76633cc7cf9b71330d5efda147fceba81e3dc6cd2
+    # via -r installer/requirements.in
 pyreadline3==3.4.1 \
     --hash=sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae \
     --hash=sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb
@@ -1826,27 +1831,27 @@ toolz==0.12.0 \
     --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
     --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
     # via altair
-torch==1.12.0 ; platform_system == "Darwin" \
-    --hash=sha256:0399746f83b4541bcb5b219a18dbe8cade760aba1c660d2748a38c6dc338ebc7 \
-    --hash=sha256:0986685f2ec8b7c4d3593e8cfe96be85d462943f1a8f54112fc48d4d9fbbe903 \
-    --hash=sha256:13c7cca6b2ea3704d775444f02af53c5f072d145247e17b8cd7813ac57869f03 \
-    --hash=sha256:201abf43a99bb4980cc827dd4b38ac28f35e4dddac7832718be3d5479cafd2c1 \
-    --hash=sha256:2143d5fe192fd908b70b494349de5b1ac02854a8a902bd5f47d13d85b410e430 \
-    --hash=sha256:2568f011dddeb5990d8698cc375d237f14568ffa8489854e3b94113b4b6b7c8b \
-    --hash=sha256:3322d33a06e440d715bb214334bd41314c94632d9a2f07d22006bf21da3a2be4 \
-    --hash=sha256:349ea3ba0c0e789e0507876c023181f13b35307aebc2e771efd0e045b8e03e84 \
-    --hash=sha256:44a3804e9bb189574f5d02ccc2dc6e32e26a81b3e095463b7067b786048c6072 \
-    --hash=sha256:5ed69d5af232c5c3287d44cef998880dadcc9721cd020e9ae02f42e56b79c2e4 \
-    --hash=sha256:60d06ee2abfa85f10582d205404d52889d69bcbb71f7e211cfc37e3957ac19ca \
-    --hash=sha256:63341f96840a223f277e498d2737b39da30d9f57c7a1ef88857b920096317739 \
-    --hash=sha256:72207b8733523388c49d43ffcc4416d1d8cd64c40f7826332e714605ace9b1d2 \
-    --hash=sha256:7ddb167827170c4e3ff6a27157414a00b9fef93dea175da04caf92a0619b7aee \
-    --hash=sha256:844f1db41173b53fe40c44b3e04fcca23a6ce00ac328b7099f2800e611766845 \
-    --hash=sha256:a1325c9c28823af497cbf443369bddac9ac59f67f1e600f8ab9b754958e55b76 \
-    --hash=sha256:abbdc5483359b9495dc76e3bd7911ccd2ddc57706c117f8316832e31590af871 \
-    --hash=sha256:c0313438bc36448ffd209f5fb4e5f325b3af158cdf61c8829b8ddaf128c57816 \
-    --hash=sha256:e3e8348edca3e3cee5a67a2b452b85c57712efe1cc3ffdb87c128b3dde54534e \
-    --hash=sha256:fb47291596677570246d723ee6abbcbac07eeba89d8f83de31e3954f21f44879
+torch==1.12.1 ; platform_system == "Darwin" \
+    --hash=sha256:03e31c37711db2cd201e02de5826de875529e45a55631d317aadce2f1ed45aa8 \
+    --hash=sha256:0b44601ec56f7dd44ad8afc00846051162ef9c26a8579dda0a02194327f2d55e \
+    --hash=sha256:42e115dab26f60c29e298559dbec88444175528b729ae994ec4c65d56fe267dd \
+    --hash=sha256:42f639501928caabb9d1d55ddd17f07cd694de146686c24489ab8c615c2871f2 \
+    --hash=sha256:4e1b9c14cf13fd2ab8d769529050629a0e68a6fc5cb8e84b4a3cc1dd8c4fe541 \
+    --hash=sha256:68104e4715a55c4bb29a85c6a8d57d820e0757da363be1ba680fa8cc5be17b52 \
+    --hash=sha256:69fe2cae7c39ccadd65a123793d30e0db881f1c1927945519c5c17323131437e \
+    --hash=sha256:6cf6f54b43c0c30335428195589bd00e764a6d27f3b9ba637aaa8c11aaf93073 \
+    --hash=sha256:743784ccea0dc8f2a3fe6a536bec8c4763bd82c1352f314937cb4008d4805de1 \
+    --hash=sha256:8a34a2fbbaa07c921e1b203f59d3d6e00ed379f2b384445773bd14e328a5b6c8 \
+    --hash=sha256:976c3f997cea38ee91a0dd3c3a42322785414748d1761ef926b789dfa97c6134 \
+    --hash=sha256:9b356aea223772cd754edb4d9ecf2a025909b8615a7668ac7d5130f86e7ec421 \
+    --hash=sha256:9c038662db894a23e49e385df13d47b2a777ffd56d9bcd5b832593fab0a7e286 \
+    --hash=sha256:a8320ba9ad87e80ca5a6a016e46ada4d1ba0c54626e135d99b2129a4541c509d \
+    --hash=sha256:b5dbcca369800ce99ba7ae6dee3466607a66958afca3b740690d88168752abcf \
+    --hash=sha256:bfec2843daa654f04fda23ba823af03e7b6f7650a873cdb726752d0e3718dada \
+    --hash=sha256:cd26d8c5640c3a28c526d41ccdca14cf1cbca0d0f2e14e8263a7ac17194ab1d2 \
+    --hash=sha256:e9c8f4a311ac29fc7e8e955cfb7733deb5dbe1bdaabf5d4af2765695824b7e0d \
+    --hash=sha256:f00c721f489089dc6364a01fd84906348fe02243d0af737f944fddb36003400d \
+    --hash=sha256:f3b52a634e62821e747e872084ab32fbcb01b7fa7dbb7471b6218279f02a178a
     # via
     #   -r installer/requirements.in
     #   accelerate
@@ -1877,26 +1882,26 @@ torchmetrics==0.10.2 \
     --hash=sha256:43757d82266969906fc74b6e80766fcb2a0d52d6c3d09e3b7c98cf3b733fd20c \
     --hash=sha256:daa29d96bff5cff04d80eec5b9f5076993d6ac9c2d2163e88b6b31f8d38f7c25
     # via pytorch-lightning
-torchvision==0.13.0 ; platform_system == "Darwin" \
-    --hash=sha256:01e9e7b2e7724e66561e8d98f900985d80191e977c5c0b3f33ed31800ba0210c \
-    --hash=sha256:0e28740bd5695076f7c449af650fc474d6566722d446461c2ceebf9c9599b37f \
-    --hash=sha256:1b703701f0b99f307ad925b1abda2b3d5bdbf30643ff02102b6aeeb8840ae278 \
-    --hash=sha256:1e2049f1207631d42d743205f663f1d2235796565be3f18b0339d479626faf30 \
-    --hash=sha256:253eb0c67bf88cef4a79ec69058c3e94f9fde28b9e3699ad1afc0b3ed50f8075 \
-    --hash=sha256:42d95ab197d090efc5669fec02fbc603d05c859e50ca2c60180d1a113aa9b3e2 \
-    --hash=sha256:5c31e9b3004142dbfdf32adc4cf2d4fd709b820833e9786f839ae3a91ff65ef0 \
-    --hash=sha256:61d5093a50b7923a4e5bf9e0271001c29e01abec2348b7dd93370a0a9d15836c \
-    --hash=sha256:667cac55afb13cda7d362466e7eba3119e529b210e55507d231bead09aca5e1f \
-    --hash=sha256:6c4c35428c758adc485ff8f239b5ed68c1b6c26efa261a52e431cab0f7f22aec \
-    --hash=sha256:83a4d9d50787d1e886c94486b63b15978391f6cf1892fce6a93132c09b14e128 \
-    --hash=sha256:a20662c11dc14fd4eff102ceb946a7ee80b9f98303bb52435cc903f2c4c1fe10 \
-    --hash=sha256:acb72a40e5dc0cd454d28514dbdd589a5057afd9bb5c785b87a54718b999bfa1 \
-    --hash=sha256:ad458146aca15f652f9b0c227bebd5403602c7341f15f68f20ec119fa8e8f4a5 \
-    --hash=sha256:ada295dbfe55017b02acfab960a997387f5addbadd28ee5e575e24f692992ce4 \
-    --hash=sha256:b620a43df4131ad09f5761c415a016a9ea95aaf8ec8c91d030fb59bad591094a \
-    --hash=sha256:b7a2c9aebc7ef265777fe7e82577364288d98cf6b8cf0a63bb2621df78a7af1a \
-    --hash=sha256:c2278a189663087bb8e65915062aa7a25b8f8e5a3cfaa5879fe277e23e4bbf40 \
-    --hash=sha256:df16abf31e7a5fce8db1f781bf1e4f20c8bc730c7c3f657e946cc5820c04e465
+torchvision==0.13.1 ; platform_system == "Darwin" \
+    --hash=sha256:0298bae3b09ac361866088434008d82b99d6458fe8888c8df90720ef4b347d44 \
+    --hash=sha256:08f592ea61836ebeceb5c97f4d7a813b9d7dc651bbf7ce4401563ccfae6a21fc \
+    --hash=sha256:099874088df104d54d8008f2a28539ca0117b512daed8bf3c2bbfa2b7ccb187a \
+    --hash=sha256:0e77706cc90462653620e336bb90daf03d7bf1b88c3a9a3037df8d111823a56e \
+    --hash=sha256:19286a733c69dcbd417b86793df807bd227db5786ed787c17297741a9b0d0fc7 \
+    --hash=sha256:3567fb3def829229ec217c1e38f08c5128ff7fb65854cac17ebac358ff7aa309 \
+    --hash=sha256:4d8bf321c4380854ef04613935fdd415dce29d1088a7ff99e06e113f0efe9203 \
+    --hash=sha256:5e631241bee3661de64f83616656224af2e3512eb2580da7c08e08b8c965a8ac \
+    --hash=sha256:7552e80fa222252b8b217a951c85e172a710ea4cad0ae0c06fbb67addece7871 \
+    --hash=sha256:7cb789ceefe6dcd0dc8eeda37bfc45efb7cf34770eac9533861d51ca508eb5b3 \
+    --hash=sha256:83e9e2457f23110fd53b0177e1bc621518d6ea2108f570e853b768ce36b7c679 \
+    --hash=sha256:87c137f343197769a51333076e66bfcd576301d2cd8614b06657187c71b06c4f \
+    --hash=sha256:899eec0b9f3b99b96d6f85b9aa58c002db41c672437677b553015b9135b3be7e \
+    --hash=sha256:8e4d02e4d8a203e0c09c10dfb478214c224d080d31efc0dbf36d9c4051f7f3c6 \
+    --hash=sha256:b167934a5943242da7b1e59318f911d2d253feeca0d13ad5d832b58eed943401 \
+    --hash=sha256:c5ed609c8bc88c575226400b2232e0309094477c82af38952e0373edef0003fd \
+    --hash=sha256:e9a563894f9fa40692e24d1aa58c3ef040450017cfed3598ff9637f404f3fe3b \
+    --hash=sha256:ef5fe3ec1848123cd0ec74c07658192b3147dcd38e507308c790d5943e87b88c \
+    --hash=sha256:f230a1a40ed70d51e463ce43df243ec520902f8725de2502e485efc5eea9d864
     # via
     #   -r installer/requirements.in
     #   basicsr

installer/py3.10-linux-x86_64-cuda-reqs.txt

@@ -1,10 +1,9 @@
 #
-# This file is autogenerated by pip-compile with Python 3.9
-# by the following command:
+# This file is autogenerated by pip-compile with python 3.9
+# To update, run:
 #
-#    pip-compile --allow-unsafe --generate-hashes --output-file=binary_installer/py3.10-linux-x86_64-cuda-reqs.txt binary_installer/requirements.in
+#    pip-compile --allow-unsafe --generate-hashes --output-file=installer/py3.10-linux-x86_64-cuda-reqs.txt installer/requirements.in
 #
---extra-index-url https://download.pytorch.org/whl/torch_stable.html
 --extra-index-url https://download.pytorch.org/whl/cu116
 --trusted-host https
 
@@ -18,7 +17,7 @@ accelerate==0.14.0 \
     --hash=sha256:31c5bcc40564ef849b5bc1c4424a43ccaf9e26413b7df89c2e36bf81f070fd44 \
     --hash=sha256:b15d562c0889d0cf441b01faa025dfc29b163d061b6cc7d489c2c83b0a55ffab
     # via
-    #   -r binary_installer/requirements.in
+    #   -r installer/requirements.in
     #   k-diffusion
 addict==2.4.0 \
     --hash=sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc \
@@ -120,7 +119,7 @@ aiosignal==1.2.0 \
 albumentations==1.3.0 \
     --hash=sha256:294165d87d03bc8323e484927f0a5c1a3c64b0e7b9c32a979582a6c93c363bdf \
     --hash=sha256:be1af36832c8893314f2a5550e8ac19801e04770734c1b70fa3c996b41f37bed
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 altair==4.2.0 \
     --hash=sha256:0c724848ae53410c13fa28be2b3b9a9dcb7b5caa1a70f7f217bd663bb419935a \
     --hash=sha256:d87d9372e63b48cd96b2a6415f0cf9457f50162ab79dc7a31cd7e024dd840026
@@ -151,10 +150,6 @@ blinker==1.5 \
     --hash=sha256:1eb563df6fdbc39eeddc177d953203f99f097e9bf0e2b8f9f3cf18b6ca425e36 \
     --hash=sha256:923e5e2f69c155f2cc42dafbbd70e16e3fde24d2d4aa2ab72fbe386238892462
     # via streamlit
-boltons==21.0.0 \
-    --hash=sha256:65e70a79a731a7fe6e98592ecfb5ccf2115873d01dbc576079874629e5c90f13 \
-    --hash=sha256:b9bb7b58b2b420bbe11a6025fdef6d3e5edc9f76a42fb467afe7ca212ef9948b
-    # via torchsde
 cachetools==5.2.0 \
     --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
     --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
@@ -188,11 +183,11 @@ click==8.1.3 \
 clip @ https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip \
     --hash=sha256:b5842c25da441d6c581b53a5c60e0c2127ebafe0f746f8e15561a006c6c3be6a
     # via
-    #   -r binary_installer/requirements.in
+    #   -r installer/requirements.in
     #   clipseg
 clipseg @ https://github.com/invoke-ai/clipseg/archive/1f754751c85d7d4255fa681f4491ff5711c1c288.zip \
     --hash=sha256:14f43ed42f90be3fe57f06de483cb8be0f67f87a6f62a011339d45a39f4b4189
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 commonmark==0.9.1 \
     --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
     --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
@@ -279,7 +274,7 @@ decorator==5.1.1 \
 diffusers==0.7.2 \
     --hash=sha256:4a5f8b3a5fbd936bba7d459611cb35ec62875030367be32b232f9e19543e25a9 \
     --hash=sha256:fb814ffd150cc6f470380b8c6a521181a77beb2f44134d2aad2e4cd8aa2ced0e
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 dnspython==2.2.1 \
     --hash=sha256:0f7569a4a6ff151958b64304071d370daa3243d15941a7beedf0c9fe5105603e \
     --hash=sha256:a851e51367fb93e9e1361732c1d60dab63eff98712e503ea7d92e6eccb109b4f
@@ -299,7 +294,7 @@ entrypoints==0.4 \
 eventlet==0.33.1 \
     --hash=sha256:a085922698e5029f820cf311a648ac324d73cec0e4792877609d978a4b5bbf31 \
     --hash=sha256:afbe17f06a58491e9aebd7a4a03e70b0b63fd4cf76d8307bae07f280479b1515
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 facexlib==0.2.5 \
     --hash=sha256:31e20cc4ed5d63562d380e4564bae14ac0d5d1899a079bad87621e13564567e4 \
     --hash=sha256:cc7ceb56c5424319c47223cf75eef6828c34c66082707c6eb35b95d39779f02d
@@ -325,15 +320,15 @@ flask==2.2.2 \
 flask-cors==3.0.10 \
     --hash=sha256:74efc975af1194fc7891ff5cd85b0f7478be4f7f59fe158102e91abb72bb4438 \
     --hash=sha256:b60839393f3b84a0f3746f6cdca56c1ad7426aa738b70d6c61375857823181de
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 flask-socketio==5.3.1 \
     --hash=sha256:fd0ed0fc1341671d92d5f5b2f5503916deb7aa7e2940e6636cfa2c087c828bf9 \
     --hash=sha256:ff0c721f20bff1e2cfba77948727a8db48f187e89a72fe50c34478ce6efb3353
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 flaskwebgui==0.3.7 \
     --hash=sha256:4a69955308eaa8bb256ba04a994dc8f58a48dcd6f9599694ab1bcd9f43d88a5d \
     --hash=sha256:535974ce2672dcc74787c254de24cceed4101be75d96952dae82014dd57f061e
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 fonttools==4.38.0 \
     --hash=sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1 \
     --hash=sha256:820466f43c8be8c3009aef8b87e785014133508f0de64ec469e4efb643ae54fb
@@ -417,11 +412,11 @@ future==0.18.2 \
 getpass-asterisk==1.0.1 \
     --hash=sha256:20d45cafda0066d761961e0919728526baf7bb5151fbf48a7d5ea4034127d857 \
     --hash=sha256:7cc357a924cf62fa4e15b73cb4e5e30685c9084e464ffdc3fd9000a2b54ea9e9
-    # via -r binary_installer/requirements.in
-gfpgan @ https://github.com/invoke-ai/GFPGAN/archive/c796277a1cf77954e5fc0b288d7062d162894248.zip ; platform_system == "Linux" or platform_system == "Darwin" \
-    --hash=sha256:4155907b8b7db3686324554df7007eedd245cdf8656c21da9d9a3f44bef2fcaa
+    # via -r installer/requirements.in
+gfpgan @ https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip \
+    --hash=sha256:79e6d71c8f1df7c7ccb0ac6b9a2ccb615ad5cde818c8b6f285a8711c05aebf85
     # via
-    #   -r binary_installer/requirements.in
+    #   -r installer/requirements.in
     #   realesrgan
 gitdb==4.0.9 \
     --hash=sha256:8033ad4e853066ba6ca92050b9df2f89301b8fc8bf7e9324d412a63f8bf1a8fd \
@@ -582,7 +577,7 @@ imageio-ffmpeg==0.4.7 \
     --hash=sha256:7a08838f97f363e37ca41821b864fd3fdc99ab1fe2421040c78eb5f56a9e723e \
     --hash=sha256:8e724d12dfe83e2a6eb39619e820243ca96c81c47c2648e66e05f7ee24e14312 \
     --hash=sha256:fc60686ef03c2d0f842901b206223c30051a6a120384458761390104470846fd
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 importlib-metadata==5.0.0 \
     --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab \
     --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43
@@ -615,9 +610,9 @@ jsonschema==4.17.0 \
     # via
     #   altair
     #   jsonmerge
-k-diffusion @ https://github.com/Birch-san/k-diffusion/archive/363386981fee88620709cf8f6f2eea167bd6cd74.zip \
-    --hash=sha256:8eac5cdc08736e6d61908a1b2948f2b2f62691b01dc1aab978bddb3451af0d66
-    # via -r binary_installer/requirements.in
+k-diffusion @ https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip \
+    --hash=sha256:c3f2c84036aa98c3abf4552fafab04df5ca472aa639982795e05bb1db43ce5e4
+    # via -r installer/requirements.in
 kiwisolver==1.4.4 \
     --hash=sha256:02f79693ec433cb4b5f51694e8477ae83b3205768a6fb48ffba60549080e295b \
     --hash=sha256:03baab2d6b4a54ddbb43bba1a3a2d1627e82d205c5cf8f4c924dc49284b87166 \
@@ -1010,7 +1005,6 @@ numpy==1.23.4 \
     #   tifffile
     #   torch-fidelity
     #   torchmetrics
-    #   torchsde
     #   torchvision
     #   transformers
 oauthlib==3.2.2 \
@@ -1097,7 +1091,7 @@ pathtools==0.1.2 \
 picklescan==0.0.5 \
     --hash=sha256:368cf1b9a075bc1b6460ad82b694f260532b836c82f99d13846cd36e1bbe7f9a \
     --hash=sha256:57153eca04d5df5009f2cdd595aef261b8a6f27e03046a1c84f672aa6869c592
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 pillow==9.3.0 \
     --hash=sha256:03150abd92771742d4a8cd6f2fa6246d847dcd2e332a18d0c15cc75bf6703040 \
     --hash=sha256:073adb2ae23431d3b9bcbcff3fe698b62ed47211d0716b067385538a1b0f28b8 \
@@ -1306,11 +1300,11 @@ pyparsing==3.0.9 \
     #   packaging
 pypatchmatch @ https://github.com/invoke-ai/PyPatchMatch/archive/129863937a8ab37f6bbcec327c994c0f932abdbc.zip \
     --hash=sha256:4ad6ec95379e7d122d494ff76633cc7cf9b71330d5efda147fceba81e3dc6cd2
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 pyreadline3==3.4.1 \
     --hash=sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae \
     --hash=sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 pyrsistent==0.19.2 \
     --hash=sha256:055ab45d5911d7cae397dc418808d8802fb95262751872c841c170b0dbf51eed \
     --hash=sha256:111156137b2e71f3a9936baf27cb322e8024dac3dc54ec7fb9f0bcf3249e68bb \
@@ -1447,7 +1441,7 @@ qudida==0.0.4 \
 realesrgan==0.3.0 \
     --hash=sha256:0d36da96ab9f447071606e91f502ccdfb08f80cc82ee4f8caf720c7745ccec7e \
     --hash=sha256:59336c16c30dd5130eff350dd27424acb9b7281d18a6810130e265606c9a6088
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 regex==2022.10.31 \
     --hash=sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad \
     --hash=sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4 \
@@ -1662,7 +1656,6 @@ scipy==1.9.3 \
     #   scikit-learn
     #   torch-fidelity
     #   torchdiffeq
-    #   torchsde
 semver==2.13.0 \
     --hash=sha256:ced8b23dceb22134307c1b8abfa523da14198793d9787ac838e70e29e77458d4 \
     --hash=sha256:fa0fe2722ee1c3f57eac478820c3a5ae2f624af8264cbdf9000c980ff7f75e3f
@@ -1670,7 +1663,7 @@ semver==2.13.0 \
 send2trash==1.8.0 \
     --hash=sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d \
     --hash=sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 sentry-sdk==1.10.1 \
     --hash=sha256:06c0fa9ccfdc80d7e3b5d2021978d6eb9351fa49db9b5847cf4d1f2a473414ad \
     --hash=sha256:105faf7bd7b7fa25653404619ee261527266b14103fe1389e0ce077bd23a9691
@@ -1761,11 +1754,11 @@ smmap==5.0.0 \
 streamlit==1.14.0 \
     --hash=sha256:62556d873567e1b3427bcd118a57ee6946619f363bd6bba38df2d1f8225ecba0 \
     --hash=sha256:e078b8143d150ba721bdb9194218e311c5fe1d6d4156473a2dea6cc848a6c9fc
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 taming-transformers-rom1504==0.0.6 \
     --hash=sha256:051b5804c58caa247bcd51d17ddb525b4d5f892a29d42dc460f40e3e9e34e5d8 \
     --hash=sha256:73fe5fc1108accee4236ee6976e0987ab236afad0af06cb9f037641a908d2c32
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 tb-nightly==2.11.0a20221106 \
     --hash=sha256:8940457ee42db92f01da8bcdbbea1a476735eda559dde5976f5728919960af4a
     # via
@@ -1790,7 +1783,7 @@ tensorboard-plugin-wit==1.8.1 \
     #   tensorboard
 test-tube==0.7.5 \
     --hash=sha256:1379c33eb8cde3e9b36610f87da0f16c2e06496b1cfebac473df4e7be2faa124
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 threadpoolctl==3.1.0 \
     --hash=sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b \
     --hash=sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380
@@ -1850,7 +1843,7 @@ torch==1.12.0+cu116 ; platform_system == "Linux" or platform_system == "Windows"
     --hash=sha256:aa43d7b54b86f723f17c5c44df1078c59a6149fc4d42fbef08aafab9d61451c9 \
     --hash=sha256:f772be831447dd01ebd26cbedf619e668d1b269d69bf6b4ff46b1378362bff26
     # via
-    #   -r binary_installer/requirements.in
+    #   -r installer/requirements.in
     #   accelerate
     #   basicsr
     #   clean-fid
@@ -1866,12 +1859,11 @@ torch==1.12.0+cu116 ; platform_system == "Linux" or platform_system == "Windows"
     #   torch-fidelity
     #   torchdiffeq
     #   torchmetrics
-    #   torchsde
     #   torchvision
 torch-fidelity==0.3.0 \
     --hash=sha256:3d3e33db98919759cc4f3f24cb27e1e74bdc7c905d90a780630e4e1c18492b66 \
     --hash=sha256:d01284825595feb7dc3eae3dc9a0d8ced02be764813a3483f109bc142b52a1d3
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 torchdiffeq==0.2.3 \
     --hash=sha256:b5b01ec1294a2d8d5f77e567bf17c5de1237c0573cb94deefa88326f0e18c338 \
     --hash=sha256:fe75f434b9090ac0c27702e02bed21472b0f87035be6581f51edc5d4013ea31a
@@ -1880,10 +1872,6 @@ torchmetrics==0.10.2 \
     --hash=sha256:43757d82266969906fc74b6e80766fcb2a0d52d6c3d09e3b7c98cf3b733fd20c \
     --hash=sha256:daa29d96bff5cff04d80eec5b9f5076993d6ac9c2d2163e88b6b31f8d38f7c25
     # via pytorch-lightning
-torchsde==0.2.5 \
-    --hash=sha256:222be9e15610d37a4b5a71cfa0c442178f9fd9ca02f6522a3e11c370b3d0906b \
-    --hash=sha256:4c34373a94a357bdf60bbfee00c850f3563d634491555820b900c9a4f7eff300
-    # via k-diffusion
 torchvision==0.13.0+cu116 ; platform_system == "Linux" or platform_system == "Windows" \
     --hash=sha256:1696feadf1921c8fa1549bad774221293298288ebedaa14e44bc3e57e964a369 \
     --hash=sha256:572544b108eaf12638f3dca0f496a453c4b8d8256bcc8333d5355df641c0380c \
@@ -1894,7 +1882,7 @@ torchvision==0.13.0+cu116 ; platform_system == "Linux" or platform_system == "Wi
     --hash=sha256:cb6bf0117b8f4b601baeae54e8a6bb5c4942b054835ba997f438ddcb7adcfb90 \
     --hash=sha256:d1a3c124645e3460b3e50b54eb89a2575a5036bfa618f15dc4f5d635c716069d
     # via
-    #   -r binary_installer/requirements.in
+    #   -r installer/requirements.in
     #   basicsr
     #   clean-fid
     #   clip
@@ -1933,13 +1921,10 @@ tqdm==4.64.1 \
     #   taming-transformers-rom1504
     #   torch-fidelity
     #   transformers
-trampoline==0.1.2 \
-    --hash=sha256:36cc9a4ff9811843d177fc0e0740efbd7da39eadfe6e50c9e2937cbc06d899d9
-    # via torchsde
 transformers==4.24.0 \
     --hash=sha256:486f353a8e594002e48be0e2aba723d96eda839e63bfe274702a4b5eda85559b \
     --hash=sha256:b7ab50039ef9bf817eff14ab974f306fd20a72350bdc9df3a858fd009419322e
-    # via -r binary_installer/requirements.in
+    # via -r installer/requirements.in
 typing-extensions==4.4.0 \
     --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
     --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e

installer/py3.10-windows-x86_64-cuda-reqs.txt

@@ -4,7 +4,6 @@
 #
 #    pip-compile --allow-unsafe --generate-hashes --output-file=installer/py3.10-windows-x86_64-cuda-reqs.txt installer/requirements.in
 #
---extra-index-url https://download.pytorch.org/whl/torch_stable.html
 --extra-index-url https://download.pytorch.org/whl/cu116
 --trusted-host https
 
@@ -151,10 +150,6 @@ blinker==1.5 \
     --hash=sha256:1eb563df6fdbc39eeddc177d953203f99f097e9bf0e2b8f9f3cf18b6ca425e36 \
     --hash=sha256:923e5e2f69c155f2cc42dafbbd70e16e3fde24d2d4aa2ab72fbe386238892462
     # via streamlit
-boltons==21.0.0 \
-    --hash=sha256:65e70a79a731a7fe6e98592ecfb5ccf2115873d01dbc576079874629e5c90f13 \
-    --hash=sha256:b9bb7b58b2b420bbe11a6025fdef6d3e5edc9f76a42fb467afe7ca212ef9948b
-    # via torchsde
 cachetools==5.2.0 \
     --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
     --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
@@ -619,8 +614,8 @@ jsonschema==4.17.0 \
     # via
     #   altair
     #   jsonmerge
-k-diffusion @ https://github.com/Birch-san/k-diffusion/archive/363386981fee88620709cf8f6f2eea167bd6cd74.zip \
-    --hash=sha256:8eac5cdc08736e6d61908a1b2948f2b2f62691b01dc1aab978bddb3451af0d66
+k-diffusion @ https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip \
+    --hash=sha256:c3f2c84036aa98c3abf4552fafab04df5ca472aa639982795e05bb1db43ce5e4
     # via -r installer/requirements.in
 kiwisolver==1.4.4 \
     --hash=sha256:02f79693ec433cb4b5f51694e8477ae83b3205768a6fb48ffba60549080e295b \
@@ -1014,7 +1009,6 @@ numpy==1.23.4 \
     #   tifffile
     #   torch-fidelity
     #   torchmetrics
-    #   torchsde
     #   torchvision
     #   transformers
 oauthlib==3.2.2 \
@@ -1666,7 +1660,6 @@ scipy==1.9.3 \
     #   scikit-learn
     #   torch-fidelity
     #   torchdiffeq
-    #   torchsde
 semver==2.13.0 \
     --hash=sha256:ced8b23dceb22134307c1b8abfa523da14198793d9787ac838e70e29e77458d4 \
     --hash=sha256:fa0fe2722ee1c3f57eac478820c3a5ae2f624af8264cbdf9000c980ff7f75e3f
@@ -1870,7 +1863,6 @@ torch==1.12.0+cu116 ; platform_system == "Linux" or platform_system == "Windows"
     #   torch-fidelity
     #   torchdiffeq
     #   torchmetrics
-    #   torchsde
     #   torchvision
 torch-fidelity==0.3.0 \
     --hash=sha256:3d3e33db98919759cc4f3f24cb27e1e74bdc7c905d90a780630e4e1c18492b66 \
@@ -1884,10 +1876,6 @@ torchmetrics==0.10.2 \
     --hash=sha256:43757d82266969906fc74b6e80766fcb2a0d52d6c3d09e3b7c98cf3b733fd20c \
     --hash=sha256:daa29d96bff5cff04d80eec5b9f5076993d6ac9c2d2163e88b6b31f8d38f7c25
     # via pytorch-lightning
-torchsde==0.2.5 \
-    --hash=sha256:222be9e15610d37a4b5a71cfa0c442178f9fd9ca02f6522a3e11c370b3d0906b \
-    --hash=sha256:4c34373a94a357bdf60bbfee00c850f3563d634491555820b900c9a4f7eff300
-    # via k-diffusion
 torchvision==0.13.0+cu116 ; platform_system == "Linux" or platform_system == "Windows" \
     --hash=sha256:1696feadf1921c8fa1549bad774221293298288ebedaa14e44bc3e57e964a369 \
     --hash=sha256:572544b108eaf12638f3dca0f496a453c4b8d8256bcc8333d5355df641c0380c \
@@ -1937,9 +1925,6 @@ tqdm==4.64.1 \
     #   taming-transformers-rom1504
     #   torch-fidelity
     #   transformers
-trampoline==0.1.2 \
-    --hash=sha256:36cc9a4ff9811843d177fc0e0740efbd7da39eadfe6e50c9e2937cbc06d899d9
-    # via torchsde
 transformers==4.24.0 \
     --hash=sha256:486f353a8e594002e48be0e2aba723d96eda839e63bfe274702a4b5eda85559b \
     --hash=sha256:b7ab50039ef9bf817eff14ab974f306fd20a72350bdc9df3a858fd009419322e

installer/readme.txt

@@ -2,51 +2,16 @@ InvokeAI
 
 Project homepage: https://github.com/invoke-ai/InvokeAI
 
-Preparations:
+Installation on Windows:
+    NOTE: You might need to enable Windows Long Paths. If you're not sure,
+    then you almost certainly need to. Simply double-click the 'WinLongPathsEnabled.reg'
+    file. Note that you will need to have admin privileges in order to
+    do this.
 
-  You will need to install Python 3.9 or higher for this installer
-  to work. Instructions are given here:
-  https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
+    Please double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
 
-  Before you start the installer, please open up your system's command
-  line window (Terminal or Command) and type the commands:
+Installation on Linux and Mac:
+    Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
 
-     python --version
-
-  If all is well, it will print "Python 3.X.X", where the version number
-  is at least 3.9.1
-
-  If this works, check the version of the Python package manager, pip:
-
-     pip --version
-
-  You should get a message that indicates that the pip package
-  installer was derived from Python 3.9 or 3.10. For example:
-  "pip 22.3.1 from /usr/bin/pip (python 3.9)"
-
-Long Paths on Windows:
-
-  If you are on Windows, you will need to enable Windows Long Paths to
-  run InvokeAI successfully. If you're not sure what this is, you
-  almost certainly need to do this.
-
-  Simply double-click the "WinLongPathsEnabled.reg" file located in
-  this directory, and approve the Windows warnings. Note that you will
-  need to have admin privileges in order to do this.
-
-Launching the installer:
-
-  Windows: double-click the 'install.bat' file (while keeping it inside
-  the InvokeAI-Installer folder).
-
-  Linux and Mac: Please open the terminal application and run
-  './install.sh' (while keeping it inside the InvokeAI-Installer
-  folder).
-
-The installer will create a directory named "invokeai" in the folder
-of your choice. This directory contains everything you need to run
-invokeai. Once InvokeAI is up and running, you may delete the
-InvokeAI-Installer folder at your convenience.
-
-For more information, please see
-https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
+After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh'
+file (on Linux/Mac) to start InvokeAI.

installer/requirements.in

@@ -1,6 +1,5 @@
 --prefer-binary
 --extra-index-url https://download.pytorch.org/whl/torch_stable.html
---extra-index-url https://download.pytorch.org/whl/cu116
 --trusted-host https://download.pytorch.org
 accelerate~=0.14
 albumentations
@@ -8,7 +7,7 @@ diffusers
 eventlet
 flask_cors
 flask_socketio
-flaskwebgui==1.0.3
+flaskwebgui
 getpass_asterisk
 imageio-ffmpeg
 pyreadline3
@@ -26,7 +25,6 @@ transformers
 picklescan
 https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip
 https://github.com/invoke-ai/clipseg/archive/1f754751c85d7d4255fa681f4491ff5711c1c288.zip
-https://github.com/invoke-ai/GFPGAN/archive/3f5d2397361199bc4a91c08bb7d80f04d7805615.zip ; platform_system=='Windows'
-https://github.com/invoke-ai/GFPGAN/archive/c796277a1cf77954e5fc0b288d7062d162894248.zip ; platform_system=='Linux' or platform_system=='Darwin'
-https://github.com/Birch-san/k-diffusion/archive/363386981fee88620709cf8f6f2eea167bd6cd74.zip
+https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip
+https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip
 https://github.com/invoke-ai/PyPatchMatch/archive/129863937a8ab37f6bbcec327c994c0f932abdbc.zip

installer/templates/invoke.bat.in

@@ -1,37 +0,0 @@
-@echo off
-
-PUSHD "%~dp0"
-setlocal
-
-call .venv\Scripts\activate.bat
-set INVOKEAI_ROOT=.
-
-echo Do you want to generate images using the
-echo 1. command-line
-echo 2. browser-based UI
-echo 3. open the developer console
-set /P restore="Please enter 1, 2 or 3: "
-IF /I "%restore%" == "1" (
-    echo Starting the InvokeAI command-line..
-    python .venv\Scripts\invoke.py %*
-) ELSE IF /I "%restore%" == "2" (
-    echo Starting the InvokeAI browser-based UI..
-    python .venv\Scripts\invoke.py --web %*
-) ELSE IF /I "%restore%" == "3" (
-    echo Developer Console
-    echo Python command is:
-    where python
-    echo Python version is:
-    python --version
-    echo *************************
-    echo You are now in the system shell, with the local InvokeAI Python virtual environment activated,
-    echo so that you can troubleshoot this InvokeAI installation as necessary.
-    echo *************************
-    echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
-    call cmd /k
-) ELSE (
-    echo Invalid selection
-    pause
-    exit /b
-)
-endlocal

installer/templates/update.bat.in

@@ -1,52 +0,0 @@
-@echo off
-setlocal EnableExtensions EnableDelayedExpansion
-
-PUSHD "%~dp0"
-
-set INVOKE_AI_SRC=https://github.com/invoke-ai/InvokeAI/archive/main.zip
-set arg=%1
-if "%arg%" neq "" (
-   if "%arg:~0,4%" neq "http" (
-       echo Usage: update.bat ^<release URL^>.zip
-       echo Updates InvokeAI to use the indicated version of the code base.
-       echo Find the zip file for the release you want, and pass it as the argument.
-       echo For example update.sh https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v2.2.4.zip
-    echo.
-    echo If no argument provided then will install the most recent development version, equivalent to
-    echo update.bat https://github.com/invoke-ai/InvokeAI/archive/main.zip
-    exit /b
-   ) else (
-     set INVOKE_AI_SRC=%arg%
-   )
-)
-
-
-call .venv\Scripts\activate.bat
-
-echo This script will update InvokeAI and all its dependencies to !INVOKE_AI_SRC!.
-echo If you do not want to do this, press control-C now!
-pause
-
-call pip install -r requirements.txt
-if %errorlevel% neq 0 (
-   echo Installation of requirements failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
-   exit /b
-)
-
-call pip install !INVOKE_AI_SRC!
-if %errorlevel% neq 0 (
-   echo Installation of InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
-   exit /b
-)
-
-call .venv\Scripts\python .venv\Scripts\configure_invokeai.py --root="%rootdir%"
-
-if %errorlevel% neq 0 (
-   echo Configuration InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
-   exit /b
-)
-
-echo "Press any key to continue"
-pause
-endlocal
-

installer/templates/update.sh.in

@@ -1,52 +0,0 @@
-#!/bin/bash
-
-set -eu
-
-if [ $# -ge 1 ] && [ "${1:0:4}" != "http" ]; then
-    echo "Usage: update.sh <release URL>.zip"
-    echo "Updates InvokeAI to use the indicated version of the code base."
-    echo "Find the zip file for the release you want, and pass it as the argument."
-    echo "For example update.sh https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v2.2.3.zip"
-    echo ""
-    echo "If no argument provided then will install the most recent development version, equivalent to"
-    echo "update.sh https://github.com/invoke-ai/InvokeAI/archive/main.zip"
-    exit -1
-fi
-    
-INVOKE_AI_SRC=${1:-https://github.com/invoke-ai/InvokeAI/archive/main.zip}
-
-# ensure we're in the correct folder in case user's CWD is somewhere else
-scriptdir=$(dirname "$0")
-cd "$scriptdir"
-
-function _err_exit {
-    if test "$1" -ne 0
-    then
-	echo "Something went wrong while installing InvokeAI and/or its requirements."
-	echo "Update cannot continue. Please report this error to https://github.com/invoke-ai/InvokeAI/issues"
-        echo -e "Error code $1; Error caught was '$2'"
-        read -p "Press any key to exit..."
-        exit
-    fi
-}
-
-echo This script will update InvokeAI and all its dependencies from $INVOKE_AI_SRC.
-echo If you do not want to do this, press control-C now!
-read -p "Press any key to continue, or CTRL-C to exit..."
-
-. .venv/bin/activate
-
-pip install -r requirements.txt
-_err_exit $? "The pip program failed to install InvokeAI's requirements."
-
-pip install $INVOKE_AI_SRC
-_err_exit $? "The pip program failed to install InvokeAI."
-
-python .venv/bin/configure_invoke.py
-_err_exit $? "The configure script failed to run successfully."
-
-
-
-
-
-

ldm/generate.py

@@ -20,8 +20,6 @@ import cv2
 import skimage
 
 from omegaconf import OmegaConf
-
-import ldm.invoke.conditioning
 from ldm.invoke.generator.base import downsampling
 from PIL import Image, ImageOps
 from torch import nn
@@ -42,7 +40,7 @@ from ldm.invoke.model_cache import ModelCache
 from ldm.invoke.seamless import configure_model_padding
 from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
 from ldm.invoke.concepts_lib import Concepts
-
+    
 def fix_func(orig):
     if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
         def new_func(*args, **kw):
@@ -131,6 +129,7 @@ gr = Generate(
 
 """
 
+
 class Generate:
     """Generate class
     Stores default values for multiple configuration items
@@ -236,7 +235,7 @@ class Generate:
             except Exception:
                 print('** An error was encountered while installing the safety checker:')
                 print(traceback.format_exc())
-
+                
     def prompt2png(self, prompt, outdir, **kwargs):
         """
         Takes a prompt and an output directory, writes out the requested number
@@ -330,7 +329,7 @@ class Generate:
             infill_method = infill_methods[0], # The infill method to use
             force_outpaint: bool = False,
             enable_image_debugging = False,
-
+            
             **args,
     ):   # eat up additional cruft
         """
@@ -373,7 +372,7 @@ class Generate:
             def process_image(image,seed):
                 image.save(f{'images/seed.png'})
 
-        The code used to save images to a directory can be found in ldm/invoke/pngwriter.py.
+        The code used to save images to a directory can be found in ldm/invoke/pngwriter.py. 
         It contains code to create the requested output directory, select a unique informative
         name for each image, and write the prompt into the PNG metadata.
         """
@@ -453,15 +452,10 @@ class Generate:
         init_image = None
         mask_image = None
 
-
-        if self.free_gpu_mem and self.model.cond_stage_model.device != self.model.device:
-            self.model.cond_stage_model.device = self.model.device
-            self.model.cond_stage_model.to(self.model.device)
-
         try:
             uc, c, extra_conditioning_info = get_uc_and_c_and_ec(
                 prompt, model =self.model,
-                skip_normalize_legacy_blend=skip_normalize,
+                skip_normalize=skip_normalize,
                 log_tokens    =self.log_tokenization
             )
 
@@ -595,7 +589,7 @@ class Generate:
         seed = opt.seed or args.seed
         if seed is None or seed < 0:
             seed = random.randrange(0, np.iinfo(np.uint32).max)
-
+        
         prompt = opt.prompt or args.prompt or ''
         print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}')
 
@@ -613,8 +607,8 @@ class Generate:
         # todo: cross-attention control
         uc, c, extra_conditioning_info = get_uc_and_c_and_ec(
             prompt, model =self.model,
-            skip_normalize_legacy_blend=opt.skip_normalize,
-            log_tokens    =ldm.invoke.conditioning.log_tokenization
+            skip_normalize=opt.skip_normalize,
+            log_tokens    =opt.log_tokenization
         )
 
         if tool in ('gfpgan','codeformer','upscale'):
@@ -647,7 +641,7 @@ class Generate:
 
             opt.seed = seed
             opt.prompt = prompt
-
+            
             if len(extend_instructions) > 0:
                 restorer = Outcrop(image,self,)
                 return restorer.process (
@@ -689,7 +683,7 @@ class Generate:
                 image_callback = callback,
                 prefix         = prefix
             )
-
+                
         elif tool is None:
             print(f'* please provide at least one postprocessing option, such as -G or -U')
             return None
@@ -712,13 +706,13 @@ class Generate:
 
         if embiggen is not None:
             return self._make_embiggen()
-
+            
         if inpainting_model_in_use:
             return self._make_omnibus()
 
         if ((init_image is not None) and (mask_image is not None)) or force_outpaint:
             return self._make_inpaint()
-
+        
         if init_image is not None:
             return self._make_img2img()
 
@@ -749,7 +743,7 @@ class Generate:
         if self._has_transparency(image):
             self._transparency_check_and_warning(image, mask, force_outpaint)
             init_mask = self._create_init_mask(image, width, height, fit=fit)
-
+            
         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
@@ -763,9 +757,9 @@ class Generate:
         elif text_mask:
             init_mask = self._txt2mask(image, text_mask, width, height, fit=fit)
 
-        if init_mask and invert_mask:
+        if invert_mask:
             init_mask = ImageOps.invert(init_mask)
-
+            
         return init_image,init_mask
 
     # lots o' repeated code here! Turn into a make_func()
@@ -824,7 +818,7 @@ class Generate:
         self.set_model(self.model_name)
 
     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.
         """
@@ -836,7 +830,7 @@ class Generate:
         if not cache.valid_model(model_name):
             print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
             return self.model
-
+        
         cache.print_vram_usage()
 
         # have to get rid of all references to model in order
@@ -845,7 +839,7 @@ class Generate:
         self.sampler = None
         self.generators = {}
         gc.collect()
-
+        
         model_data = cache.get_model(model_name)
         if model_data is None:  # restore previous
             model_data = cache.get_model(self.model_name)
@@ -858,7 +852,7 @@ class Generate:
 
         # 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:
             self.model.embedding_manager.load(
@@ -907,7 +901,7 @@ class Generate:
                                 image_callback = None,
                                 prefix = None,
     ):
-
+            
         for r in image_list:
             image, seed = r
             try:
@@ -917,7 +911,7 @@ class Generate:
                             if self.gfpgan is None:
                                 print('>> GFPGAN not found. Face restoration is disabled.')
                             else:
-                              image = self.gfpgan.process(image, strength, seed)
+                              image = self.gfpgan.process(image, strength, seed)                              
                         if facetool == 'codeformer':
                             if self.codeformer is None:
                                 print('>> CodeFormer not found. Face restoration is disabled.')

ldm/invoke/CLI.py

@@ -8,7 +8,6 @@ import time
 import traceback
 import yaml
 
-from ldm.generate import Generate
 from ldm.invoke.globals import Globals
 from ldm.invoke.prompt_parser import PromptParser
 from ldm.invoke.readline import get_completer, Completer
@@ -28,7 +27,7 @@ def main():
     """Initialize command-line parsers and the diffusion model"""
     global infile
     print('* Initializing, be patient...')
-
+    
     opt  = Args()
     args = opt.parse_args()
     if not args:
@@ -46,8 +45,9 @@ def main():
             args.max_loaded_models = 1
 
     # alert - setting globals here
+    Globals.root = os.path.expanduser(args.root_dir or os.environ.get('INVOKEAI_ROOT') or os.path.abspath('.'))
     Globals.try_patchmatch = args.patchmatch
-
+    
     print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # loading here to avoid long delays on startup
@@ -68,8 +68,6 @@ def main():
     if opt.embeddings:
         if not os.path.isabs(opt.embedding_path):
             embedding_path = os.path.normpath(os.path.join(Globals.root,opt.embedding_path))
-        else:
-            embedding_path = opt.embedding_path
     else:
         embedding_path = None
 
@@ -281,7 +279,7 @@ def main_loop(gen, opt):
             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, prompt_in=None, attention_maps_image=None):
+            def image_writer(image, seed, upscaled=False, first_seed=None, use_prefix=None):
                 # note the seed is the seed of the current image
                 # the first_seed is the original seed that noise is added to
                 # when the -v switch is used to generate variations
@@ -310,7 +308,7 @@ def main_loop(gen, opt):
                     if use_prefix is not None:
                         prefix = use_prefix
                     postprocessed = upscaled if upscaled else operation=='postprocess'
-                    opt.prompt = gen.concept_lib().replace_triggers_with_concepts(opt.prompt or prompt_in)  # to avoid the problem of non-unique concept triggers
+                    opt.prompt = gen.concept_lib().replace_triggers_with_concepts(opt.prompt)  # to avoid the problem of non-unique concept triggers
                     filename, formatted_dream_prompt = prepare_image_metadata(
                         opt,
                         prefix,
@@ -341,8 +339,8 @@ def main_loop(gen, opt):
                             filename,
                             tool,
                             formatted_dream_prompt,
-                        )
-
+                        )                           
+                        
                     if (not postprocessed) or opt.save_original:
                         # only append to results if we didn't overwrite an earlier output
                         results.append([path, formatted_dream_prompt])
@@ -432,7 +430,7 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         add_embedding_terms(gen, completer)
         completer.add_history(command)
         operation = None
-
+        
     elif command.startswith('!models'):
         gen.model_cache.print_models()
         completer.add_history(command)
@@ -533,7 +531,7 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
 
     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: ')
@@ -564,7 +562,7 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
                 print('** Please enter a valid integer between 64 and 2048')
 
     make_default = input('Make this the default model? [n] ') in ('y','Y')
-
+    
     if write_config_file(opt.conf, gen, model_name, new_config, make_default=make_default):
         completer.add_model(model_name)
 
@@ -577,14 +575,14 @@ def del_config(model_name:str, gen, opt, completer):
     gen.model_cache.commit(opt.conf)
     print(f'** {model_name} deleted')
     completer.del_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]
@@ -597,10 +595,10 @@ def edit_config(model_name:str, gen, opt, completer):
     make_default = input('Make this the default model? [n] ') in ('y','Y')
     completer.complete_extensions(None)
     write_config_file(opt.conf, gen, model_name, new_config, clobber=True, make_default=make_default)
-
+    
 def write_config_file(conf_path, gen, model_name, new_config, clobber=False, make_default=False):
     current_model = gen.model_name
-
+    
     op = 'modify' if clobber else 'import'
     print('\n>> New configuration:')
     if make_default:
@@ -623,7 +621,7 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False, mak
         gen.model_cache.set_default_model(model_name)
 
     gen.model_cache.commit(conf_path)
-
+    
     do_switch = input(f'Keep model loaded? [y]')
     if len(do_switch)==0 or do_switch[0] in ('y','Y'):
         pass
@@ -653,7 +651,7 @@ def do_postprocess (gen, opt, callback):
         opt.prompt = opt.new_prompt
     else:
         opt.prompt = None
-
+        
     if os.path.dirname(file_path) == '': #basename given
         file_path = os.path.join(opt.outdir,file_path)
 
@@ -718,7 +716,7 @@ def add_postprocessing_to_metadata(opt,original_file,new_file,tool,command):
     )
     meta['image']['postprocessing'] = pp
     write_metadata(new_file,meta)
-
+    
 def prepare_image_metadata(
         opt,
         prefix,
@@ -789,28 +787,28 @@ def get_next_command(infile=None) -> str:  # command string
             print(f'#{command}')
     return command
 
-def invoke_ai_web_server_loop(gen: Generate, gfpgan, codeformer, esrgan):
+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__), '..'))
     )
-
+    
     invoke_ai_web_server = InvokeAIWebServer(generate=gen, gfpgan=gfpgan, codeformer=codeformer, esrgan=esrgan)
 
     try:
         invoke_ai_web_server.run()
     except KeyboardInterrupt:
         pass
-
+    
 def add_embedding_terms(gen,completer):
     '''
     Called after setting the model, updates the autocompleter with
     any terms loaded by the embedding manager.
     '''
     completer.add_embedding_terms(gen.model.embedding_manager.list_terms())
-
+    
 def split_variations(variations_string) -> list:
     # shotgun parsing, woo
     parts = []
@@ -867,7 +865,7 @@ def make_step_callback(gen, opt, prefix):
             image = gen.sample_to_image(img)
             image.save(filename,'PNG')
     return callback
-
+    
 def retrieve_dream_command(opt,command,completer):
     '''
     Given a full or partial path to a previously-generated image file,
@@ -875,7 +873,7 @@ def retrieve_dream_command(opt,command,completer):
     and pop it into the readline buffer (linux, Mac), or print out a comment
     for cut-and-paste (windows)
 
-    Given a wildcard path to a folder with image png files,
+    Given a wildcard path to a folder with image png files, 
     will retrieve and format the dream command used to generate the images,
     and save them to a file commands.txt for further processing
     '''
@@ -911,7 +909,7 @@ def write_commands(opt, file_path:str, outfilepath:str):
     except ValueError:
         print(f'## "{basename}": unacceptable pattern')
         return
-
+ 
     commands = []
     cmd = None
     for path in paths:
@@ -940,7 +938,7 @@ def emergency_model_reconfigure():
     print('   After reconfiguration is done, please relaunch invoke.py.                      ')
     print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
     print('configure_invokeai is launching....\n')
-
+    
     sys.argv = ['configure_invokeai','--interactive']
     import configure_invokeai
     configure_invokeai.main()

ldm/invoke/__init__.py

@@ -1 +0,0 @@
-__version__='2.2.4'

ldm/invoke/args.py

@@ -119,7 +119,7 @@ PRECISION_CHOICES = [
 
 # is there a way to pick this up during git commits?
 APP_ID      = 'invoke-ai/InvokeAI'
-APP_VERSION = 'v2.2.4'
+APP_VERSION = 'v2.2.0'
 
 class ArgFormatter(argparse.RawTextHelpFormatter):
         # use defined argument order to display usage
@@ -172,20 +172,14 @@ class Args(object):
         '''Parse the shell switches and store.'''
         try:
             sysargs = sys.argv[1:]
-            # pre-parse to get the root directory; ignore the rest
-            switches = self._arg_parser.parse_args(sysargs)
-            Globals.root = os.path.abspath(switches.root_dir or Globals.root)
-
-            # now use root directory to find the init file
-            initfile = os.path.expanduser(os.path.join(Globals.root,Globals.initfile))
-            legacyinit = os.path.expanduser('~/.invokeai')
+            initfile = os.path.expanduser(Globals.initfile)
             if os.path.exists(initfile):
                 print(f'>> Initialization file {initfile} found. Loading...')
                 sysargs.insert(0,f'@{initfile}')
-            elif os.path.exists(legacyinit):
-                print(f'>> WARNING: Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init.')
-                sysargs.insert(0,f'@{legacyinit}')
-
+            else:
+                from ldm.invoke.CLI import emergency_model_reconfigure
+                emergency_model_reconfigure()
+                sys.exit(-1)
             self._arg_switches = self._arg_parser.parse_args(sysargs)
             return self._arg_switches
         except Exception as e:
@@ -417,7 +411,7 @@ class Args(object):
         model_group.add_argument(
             '--root_dir',
             default=None,
-            help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai.',
+            help='Path to directory containing "models", "outputs" and "configs". If not present will try to read from ~/.invokeai and then from environment variable INVOKEAI_ROOT. Defaults to the current directory as a last resort.',
         )
         model_group.add_argument(
             '--config',

ldm/invoke/concepts_lib.py

@@ -36,7 +36,7 @@ class Concepts(object):
             models = self.hf_api.list_models(filter=ModelFilter(model_name='sd-concepts-library/'))
             self.concept_list = [a.id.split('/')[1] for a in models]
         except Exception as e:
-            print(f' ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}.')
+            print(' ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}.')
             print(' ** You may load .bin and .pt file(s) manually using the --embedding_directory argument.')
         return self.concept_list
 

ldm/invoke/conditioning.py

@@ -7,46 +7,20 @@ get_uc_and_c_and_ec()           get the conditioned and unconditioned latent, an
 
 '''
 import re
+from difflib import SequenceMatcher
 from typing import Union
 
 import torch
 
 from .prompt_parser import PromptParser, Blend, FlattenedPrompt, \
-    CrossAttentionControlledFragment, CrossAttentionControlSubstitute, Fragment
+    CrossAttentionControlledFragment, CrossAttentionControlSubstitute, Fragment, log_tokenization
 from ..models.diffusion import cross_attention_control
 from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ..modules.encoders.modules import WeightedFrozenCLIPEmbedder
 
 
-def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False):
-    prompt, negative_prompt = get_prompt_structure(prompt_string,
-                                                   skip_normalize_legacy_blend=skip_normalize_legacy_blend)
-    conditioning = _get_conditioning_for_prompt(prompt, negative_prompt, model, log_tokens)
+def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_normalize=False):
 
-    return conditioning
-
-
-def get_prompt_structure(prompt_string, skip_normalize_legacy_blend: bool = False) -> (
-Union[FlattenedPrompt, Blend], FlattenedPrompt):
-    """
-    parse the passed-in prompt string and return tuple (positive_prompt, negative_prompt)
-    """
-    prompt, negative_prompt = _parse_prompt_string(prompt_string,
-                                                   skip_normalize_legacy_blend=skip_normalize_legacy_blend)
-    return prompt, negative_prompt
-
-
-def get_tokens_for_prompt(model, parsed_prompt: FlattenedPrompt) -> [str]:
-    text_fragments = [x.text if type(x) is Fragment else
-                      (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else
-                       str(x))
-                      for x in parsed_prompt.children]
-    text = " ".join(text_fragments)
-    tokens = model.cond_stage_model.tokenizer.tokenize(text)
-    return tokens
-
-
-def _parse_prompt_string(prompt_string_uncleaned, skip_normalize_legacy_blend=False) -> Union[FlattenedPrompt, Blend]:
     # Extract Unconditioned Words From Prompt
     unconditioned_words = ''
     unconditional_regex = r'\[(.*?)\]'
@@ -65,7 +39,7 @@ def _parse_prompt_string(prompt_string_uncleaned, skip_normalize_legacy_blend=Fa
     pp = PromptParser()
 
     parsed_prompt: Union[FlattenedPrompt, Blend] = None
-    legacy_blend: Blend = pp.parse_legacy_blend(prompt_string_cleaned, skip_normalize_legacy_blend)
+    legacy_blend: Blend = pp.parse_legacy_blend(prompt_string_cleaned)
     if legacy_blend is not None:
         parsed_prompt = legacy_blend
     else:
@@ -73,150 +47,118 @@ def _parse_prompt_string(prompt_string_uncleaned, skip_normalize_legacy_blend=Fa
         parsed_prompt = pp.parse_conjunction(prompt_string_cleaned).prompts[0]
 
     parsed_negative_prompt: FlattenedPrompt = pp.parse_conjunction(unconditioned_words).prompts[0]
-    return parsed_prompt, parsed_negative_prompt
-
-
-def _get_conditioning_for_prompt(parsed_prompt: Union[Blend, FlattenedPrompt], parsed_negative_prompt: FlattenedPrompt,
-                                 model, log_tokens=False) \
-    -> tuple[torch.Tensor, torch.Tensor, InvokeAIDiffuserComponent.ExtraConditioningInfo]:
-    """
-    Process prompt structure and tokens, and return (conditioning, unconditioning, extra_conditioning_info)
-    """
-
     if log_tokens:
         print(f">> Parsed prompt to {parsed_prompt}")
         print(f">> Parsed negative prompt to {parsed_negative_prompt}")
 
     conditioning = None
-    cac_args: cross_attention_control.Arguments = None
+    cac_args:cross_attention_control.Arguments = None
 
     if type(parsed_prompt) is Blend:
-        conditioning = _get_conditioning_for_blend(model, parsed_prompt, log_tokens)
-    elif type(parsed_prompt) is FlattenedPrompt:
-        if parsed_prompt.wants_cross_attention_control:
-            conditioning, cac_args = _get_conditioning_for_cross_attention_control(model, parsed_prompt, log_tokens)
-
-        else:
-            conditioning, _ = _get_embeddings_and_tokens_for_prompt(model,
-                                                                    parsed_prompt,
-                                                                    log_tokens=log_tokens,
-                                                                    log_display_label="(prompt)")
+        blend: Blend = parsed_prompt
+        embeddings_to_blend = None
+        for i,flattened_prompt in enumerate(blend.prompts):
+            this_embedding, _ = build_embeddings_and_tokens_for_flattened_prompt(model,
+                                                                                 flattened_prompt,
+                                                                                 log_tokens=log_tokens,
+                                                                                 log_display_label=f"(blend part {i+1}, weight={blend.weights[i]})" )
+            embeddings_to_blend = this_embedding if embeddings_to_blend is None else torch.cat(
+                (embeddings_to_blend, this_embedding))
+        conditioning = WeightedFrozenCLIPEmbedder.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
+                                                                                blend.weights,
+                                                                                normalize=blend.normalize_weights)
     else:
-        raise ValueError(f"parsed_prompt is '{type(parsed_prompt)}' which is not a supported prompt type")
+        flattened_prompt: FlattenedPrompt = parsed_prompt
+        wants_cross_attention_control = type(flattened_prompt) is not Blend \
+                                        and any([issubclass(type(x), CrossAttentionControlledFragment) for x in flattened_prompt.children])
+        if wants_cross_attention_control:
+            original_prompt = FlattenedPrompt()
+            edited_prompt = FlattenedPrompt()
+            # for name, a0, a1, b0, b1 in edit_opcodes: only name == 'equal' is currently parsed
+            original_token_count = 0
+            edited_token_count = 0
+            edit_opcodes = []
+            edit_options = []
+            for fragment in flattened_prompt.children:
+                if type(fragment) is CrossAttentionControlSubstitute:
+                    original_prompt.append(fragment.original)
+                    edited_prompt.append(fragment.edited)
 
-    unconditioning, _ = _get_embeddings_and_tokens_for_prompt(model,
-                                                              parsed_negative_prompt,
-                                                              log_tokens=log_tokens,
-                                                              log_display_label="(unconditioning)")
+                    to_replace_token_count = get_tokens_length(model, fragment.original)
+                    replacement_token_count = get_tokens_length(model, fragment.edited)
+                    edit_opcodes.append(('replace',
+                                        original_token_count, original_token_count + to_replace_token_count,
+                                        edited_token_count, edited_token_count + replacement_token_count
+                                        ))
+                    original_token_count += to_replace_token_count
+                    edited_token_count += replacement_token_count
+                    edit_options.append(fragment.options)
+                #elif type(fragment) is CrossAttentionControlAppend:
+                #    edited_prompt.append(fragment.fragment)
+                else:
+                    # regular fragment
+                    original_prompt.append(fragment)
+                    edited_prompt.append(fragment)
+
+                    count = get_tokens_length(model, [fragment])
+                    edit_opcodes.append(('equal', original_token_count, original_token_count+count, edited_token_count, edited_token_count+count))
+                    edit_options.append(None)
+                    original_token_count += count
+                    edited_token_count += count
+            original_embeddings, original_tokens = build_embeddings_and_tokens_for_flattened_prompt(model,
+                                                                                                    original_prompt,
+                                                                                                    log_tokens=log_tokens,
+                                                                                                    log_display_label="(.swap originals)")
+            # naïvely building a single edited_embeddings like this disregards the effects of changing the absolute location of
+            # subsequent tokens when there is >1 edit and earlier edits change the total token count.
+            # eg "a cat.swap(smiling dog, s_start=0.5) eating a hotdog.swap(pizza)" - when the 'pizza' edit is active but the
+            # 'cat' edit is not, the 'pizza' feature vector will nevertheless be affected by the introduction of the extra
+            # token 'smiling' in the inactive 'cat' edit.
+            # todo: build multiple edited_embeddings, one for each edit, and pass just the edited fragments through to the CrossAttentionControl functions
+            edited_embeddings, edited_tokens = build_embeddings_and_tokens_for_flattened_prompt(model,
+                                                                                                edited_prompt,
+                                                                                                log_tokens=log_tokens,
+                                                                                                log_display_label="(.swap replacements)")
+
+            conditioning = original_embeddings
+            edited_conditioning = edited_embeddings
+            #print('>> got edit_opcodes', edit_opcodes, 'options', edit_options)
+            cac_args = cross_attention_control.Arguments(
+                edited_conditioning = edited_conditioning,
+                edit_opcodes = edit_opcodes,
+                edit_options = edit_options
+            )
+        else:
+            conditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model,
+                                                                               flattened_prompt,
+                                                                               log_tokens=log_tokens,
+                                                                               log_display_label="(prompt)")
+
+    unconditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model,
+                                                                         parsed_negative_prompt,
+                                                                         log_tokens=log_tokens,
+                                                                         log_display_label="(unconditioning)")
     if isinstance(conditioning, dict):
         # hybrid conditioning is in play
-        unconditioning, conditioning = _flatten_hybrid_conditioning(unconditioning, conditioning)
+        unconditioning, conditioning = flatten_hybrid_conditioning(unconditioning, conditioning)
         if cac_args is not None:
-            print(
-                ">> Hybrid conditioning cannot currently be combined with cross attention control. Cross attention control will be ignored.")
+            print(">> Hybrid conditioning cannot currently be combined with cross attention control. Cross attention control will be ignored.")
             cac_args = None
 
-    eos_token_index = 1
-    if type(parsed_prompt) is not Blend:
-        tokens = get_tokens_for_prompt(model, parsed_prompt)
-        eos_token_index = len(tokens)+1
     return (
         unconditioning, conditioning, InvokeAIDiffuserComponent.ExtraConditioningInfo(
-            tokens_count_including_eos_bos=eos_token_index + 1,
             cross_attention_control_args=cac_args
         )
     )
 
 
-def _get_conditioning_for_cross_attention_control(model, prompt: FlattenedPrompt, log_tokens: bool = True):
-    original_prompt = FlattenedPrompt()
-    edited_prompt = FlattenedPrompt()
-    # for name, a0, a1, b0, b1 in edit_opcodes: only name == 'equal' is currently parsed
-    original_token_count = 0
-    edited_token_count = 0
-    edit_options = []
-    edit_opcodes = []
-    # beginning of sequence
-    edit_opcodes.append(
-        ('equal', original_token_count, original_token_count + 1, edited_token_count, edited_token_count + 1))
-    edit_options.append(None)
-    original_token_count += 1
-    edited_token_count += 1
-    for fragment in prompt.children:
-        if type(fragment) is CrossAttentionControlSubstitute:
-            original_prompt.append(fragment.original)
-            edited_prompt.append(fragment.edited)
+def build_token_edit_opcodes(original_tokens, edited_tokens):
+    original_tokens = original_tokens.cpu().numpy()[0]
+    edited_tokens = edited_tokens.cpu().numpy()[0]
 
-            to_replace_token_count = _get_tokens_length(model, fragment.original)
-            replacement_token_count = _get_tokens_length(model, fragment.edited)
-            edit_opcodes.append(('replace',
-                                 original_token_count, original_token_count + to_replace_token_count,
-                                 edited_token_count, edited_token_count + replacement_token_count
-                                 ))
-            original_token_count += to_replace_token_count
-            edited_token_count += replacement_token_count
-            edit_options.append(fragment.options)
-        # elif type(fragment) is CrossAttentionControlAppend:
-        #    edited_prompt.append(fragment.fragment)
-        else:
-            # regular fragment
-            original_prompt.append(fragment)
-            edited_prompt.append(fragment)
+    return SequenceMatcher(None, original_tokens, edited_tokens).get_opcodes()
 
-            count = _get_tokens_length(model, [fragment])
-            edit_opcodes.append(('equal', original_token_count, original_token_count + count, edited_token_count,
-                                 edited_token_count + count))
-            edit_options.append(None)
-            original_token_count += count
-            edited_token_count += count
-    # end of sequence
-    edit_opcodes.append(
-        ('equal', original_token_count, original_token_count + 1, edited_token_count, edited_token_count + 1))
-    edit_options.append(None)
-    original_token_count += 1
-    edited_token_count += 1
-    original_embeddings, original_tokens = _get_embeddings_and_tokens_for_prompt(model,
-                                                                                 original_prompt,
-                                                                                 log_tokens=log_tokens,
-                                                                                 log_display_label="(.swap originals)")
-    # naïvely building a single edited_embeddings like this disregards the effects of changing the absolute location of
-    # subsequent tokens when there is >1 edit and earlier edits change the total token count.
-    # eg "a cat.swap(smiling dog, s_start=0.5) eating a hotdog.swap(pizza)" - when the 'pizza' edit is active but the
-    # 'cat' edit is not, the 'pizza' feature vector will nevertheless be affected by the introduction of the extra
-    # token 'smiling' in the inactive 'cat' edit.
-    # todo: build multiple edited_embeddings, one for each edit, and pass just the edited fragments through to the CrossAttentionControl functions
-    edited_embeddings, edited_tokens = _get_embeddings_and_tokens_for_prompt(model,
-                                                                             edited_prompt,
-                                                                             log_tokens=log_tokens,
-                                                                             log_display_label="(.swap replacements)")
-    conditioning = original_embeddings
-    edited_conditioning = edited_embeddings
-    # print('>> got edit_opcodes', edit_opcodes, 'options', edit_options)
-    cac_args = cross_attention_control.Arguments(
-        edited_conditioning=edited_conditioning,
-        edit_opcodes=edit_opcodes,
-        edit_options=edit_options
-    )
-    return conditioning, cac_args
-
-
-def _get_conditioning_for_blend(model, blend: Blend, log_tokens: bool = False):
-    embeddings_to_blend = None
-    for i, flattened_prompt in enumerate(blend.prompts):
-        this_embedding, _ = _get_embeddings_and_tokens_for_prompt(model,
-                                                                  flattened_prompt,
-                                                                  log_tokens=log_tokens,
-                                                                  log_display_label=f"(blend part {i + 1}, weight={blend.weights[i]})")
-        embeddings_to_blend = this_embedding if embeddings_to_blend is None else torch.cat(
-            (embeddings_to_blend, this_embedding))
-    conditioning = WeightedFrozenCLIPEmbedder.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
-                                                                      blend.weights,
-                                                                      normalize=blend.normalize_weights)
-    return conditioning
-
-
-def _get_embeddings_and_tokens_for_prompt(model, flattened_prompt: FlattenedPrompt, log_tokens: bool = False,
-                                          log_display_label: str = None):
+def build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt: FlattenedPrompt, log_tokens: bool=False, log_display_label: str=None):
     if type(flattened_prompt) is not FlattenedPrompt:
         raise Exception(f"embeddings can only be made from FlattenedPrompts, got {type(flattened_prompt)} instead")
     fragments = [x.text for x in flattened_prompt.children]
@@ -228,14 +170,12 @@ def _get_embeddings_and_tokens_for_prompt(model, flattened_prompt: FlattenedProm
 
     return embeddings, tokens
 
-
-def _get_tokens_length(model, fragments: list[Fragment]):
+def get_tokens_length(model, fragments: list[Fragment]):
     fragment_texts = [x.text for x in fragments]
     tokens = model.cond_stage_model.get_tokens(fragment_texts, include_start_and_end_markers=False)
     return sum([len(x) for x in tokens])
 
-
-def _flatten_hybrid_conditioning(uncond, cond):
+def flatten_hybrid_conditioning(uncond, cond):
     '''
     This handles the choice between a conditional conditioning
     that is a tensor (used by cross attention) vs one that has additional
@@ -254,29 +194,4 @@ def _flatten_hybrid_conditioning(uncond, cond):
             cond_flattened[k] = torch.cat([uncond[k], cond[k]])
     return uncond, cond_flattened
 
-
-def log_tokenization(text, model, display_label=None):
-    """ shows how the prompt is tokenized
-    # usually tokens have '</w>' to indicate end-of-word,
-    # but for readability it has been replaced with ' '
-    """
-
-    tokens = model.cond_stage_model.tokenizer.tokenize(text)
-    tokenized = ""
-    discarded = ""
-    usedTokens = 0
-    totalTokens = len(tokens)
-    for i in range(0, totalTokens):
-        token = tokens[i].replace('</w>', ' ')
-        # alternate color
-        s = (usedTokens % 6) + 1
-        if i < model.cond_stage_model.max_length:
-            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
-            usedTokens += 1
-        else:  # over max token length
-            discarded = discarded + f"\x1b[0;3{s};40m{token}"
-    print(f"\n>> Tokens {display_label or ''} ({usedTokens}):\n{tokenized}\x1b[0m")
-    if discarded != "":
-        print(
-            f">> Tokens Discarded ({totalTokens - usedTokens}):\n{discarded}\x1b[0m"
-        )
+            

ldm/invoke/generator/base.py

@@ -14,7 +14,6 @@ import cv2 as cv
 from einops import rearrange, repeat
 from pytorch_lightning import seed_everything
 from ldm.invoke.devices import choose_autocast
-from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
 from ldm.util import rand_perlin_2d
 
 downsampling = 8
@@ -52,12 +51,9 @@ class Generator():
     def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
                  image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
                  safety_checker:dict=None,
-                 attention_maps_callback = None,
                  **kwargs):
         scope = choose_autocast(self.precision)
         self.safety_checker = safety_checker
-        attention_maps_images = []
-        attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
         make_image = self.get_make_image(
             prompt,
             sampler = sampler,
@@ -67,7 +63,6 @@ class Generator():
             step_callback = step_callback,
             threshold     = threshold,
             perlin        = perlin,
-            attention_maps_callback = attention_maps_callback,
             **kwargs
         )
         results             = []
@@ -103,13 +98,12 @@ class Generator():
                 results.append([image, seed])
 
                 if image_callback is not None:
-                    attention_maps_image = None if len(attention_maps_images)==0 else attention_maps_images[-1]
-                    image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_image)
+                    image_callback(image, seed, first_seed=first_seed)
 
                 seed = self.new_seed()
 
         return results
-
+    
     def sample_to_image(self,samples)->Image.Image:
         """
         Given samples returned from a sampler, converts
@@ -172,12 +166,12 @@ class Generator():
             blurred_init_mask = pil_init_mask
 
         multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
-
+        
         # Paste original on color-corrected generation (using blurred mask)
         matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
         return matched_result
 
-
+        
 
     def sample_to_lowres_estimated_image(self,samples):
         # origingally adapted from code by @erucipe and @keturn here:
@@ -225,11 +219,11 @@ class Generator():
         (txt2img) or from the latent image (img2img, inpaint)
         """
         raise NotImplementedError("get_noise() must be implemented in a descendent class")
-
+    
     def get_perlin_noise(self,width,height):
         fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
         return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
-
+    
     def new_seed(self):
         self.seed = random.randrange(0, np.iinfo(np.uint32).max)
         return self.seed
@@ -331,4 +325,4 @@ class Generator():
             os.makedirs(dirname, exist_ok=True)
         image.save(filepath,'PNG')
 
-
+        

ldm/invoke/generator/embiggen.py

@@ -38,7 +38,7 @@ class Embiggen(Generator):
                 image = make_image()
                 results.append([image, seed])
                 if image_callback is not None:
-                    image_callback(image, seed, prompt_in=prompt)
+                    image_callback(image, seed)
                 seed = self.new_seed() 
         return results
 

ldm/invoke/generator/img2img.py

@@ -48,10 +48,6 @@ class Img2Img(Generator):
                 torch.tensor([t_enc]).to(self.model.device),
                 noise=x_T
             )
-
-            if self.free_gpu_mem and self.model.model.device != self.model.device:
-                self.model.model.to(self.model.device)
-
             # decode it
             samples = sampler.decode(
                 z_enc,
@@ -65,9 +61,6 @@ class Img2Img(Generator):
                 all_timesteps_count = steps
             )
 
-            if self.free_gpu_mem:
-                self.model.model.to("cpu")
-
             return self.sample_to_image(samples)
 
         return make_image
@@ -94,4 +87,4 @@ class Img2Img(Generator):
         image = torch.from_numpy(image)
         if normalize:
             image = 2.0 * image - 1.0
-        return image.to(self.model.device)
+        return image.to(self.model.device)    

ldm/invoke/generator/inpaint.py

@@ -27,7 +27,7 @@ if Globals.try_patchmatch:
         print('>> Patchmatch initialized')
         infill_methods.append('patchmatch')
     else:
-        print('>> Patchmatch not loaded (nonfatal)')
+        print('>> Patchmatch not loaded, please see https://github.com/invoke-ai/InvokeAI/blob/patchmatch-install-docs/docs/installation/INSTALL_PATCHMATCH.md')
 else:
     print('>> Patchmatch loading disabled')
 

ldm/invoke/generator/txt2img.py

@@ -6,7 +6,6 @@ import torch
 import numpy as  np
 from ldm.invoke.generator.base import Generator
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-import gc
 
 
 class Txt2Img(Generator):
@@ -15,9 +14,7 @@ class Txt2Img(Generator):
 
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,
-                       attention_maps_callback=None,
-                       **kwargs):
+                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
@@ -36,7 +33,7 @@ class Txt2Img(Generator):
 
             if self.free_gpu_mem and self.model.model.device != self.model.device:
                 self.model.model.to(self.model.device)
-
+                                
             sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
 
             samples, _ = sampler.sample(
@@ -52,15 +49,10 @@ class Txt2Img(Generator):
                 eta                          = ddim_eta,
                 img_callback                 = step_callback,
                 threshold                    = threshold,
-                attention_maps_callback      = attention_maps_callback,
             )
 
             if self.free_gpu_mem:
-                self.model.model.to('cpu')
-                self.model.cond_stage_model.device = 'cpu'
-                self.model.cond_stage_model.to('cpu')
-                gc.collect()
-                torch.cuda.empty_cache()
+                self.model.model.to("cpu")
 
             return self.sample_to_image(samples)
 

ldm/invoke/generator/txt2img2img.py

@@ -100,11 +100,7 @@ class Txt2Img2Img(Generator):
             )
 
             if self.free_gpu_mem:
-                self.model.model.to('cpu')
-                self.model.cond_stage_model.device = 'cpu'
-                self.model.cond_stage_model.to('cpu')
-                gc.collect()
-                torch.cuda.empty_cache()
+                self.model.model.to("cpu")
 
             return self.sample_to_image(samples)
 
@@ -146,7 +142,7 @@ class Txt2Img2Img(Generator):
                 **kwargs
                 )
             return result[0][0]
-
+            
         if sampler.uses_inpainting_model():
             return inpaint_make_image
         else:

ldm/invoke/globals.py

@@ -5,9 +5,7 @@ otherwise have to be passed through long and complex call chains.
 It defines a Namespace object named "Globals" that contains
 the attributes:
 
-  - root           - the root directory under which "models" and "outputs" can be found
-  - initfile       - path to the initialization file
-  - try_patchmatch - option to globally disable loading of 'patchmatch' module
+  - root    - the root directory under which "models" and "outputs" can be found
 '''
 
 import os
@@ -16,10 +14,10 @@ from argparse import Namespace
 Globals = Namespace()
 
 # This is usually overwritten by the command line and/or environment variables
-Globals.root = os.path.abspath(os.environ.get('INVOKEAI_ROOT') or os.path.expanduser('~/invokeai'))
+Globals.root = '.'
 
 # Where to look for the initialization file
-Globals.initfile = 'invokeai.init'
+Globals.initfile = os.path.expanduser('~/.invokeai')
 
 # Awkward workaround to disable attempted loading of pypatchmatch
 # which is causing CI tests to error out.

ldm/invoke/model_cache.py

@@ -227,9 +227,7 @@ class ModelCache(object):
         model_hash  = self._cached_sha256(weights,weight_bytes)
         sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
         del weight_bytes
-        # merged models from auto11 merge board are flat for some reason
-        if 'state_dict' in sd:
-            sd = sd['state_dict']
+        sd = sd['state_dict']
         model = instantiate_from_config(omega_config.model)
         model.load_state_dict(sd, strict=False)
 

ldm/invoke/pngwriter.py

@@ -57,13 +57,8 @@ def retrieve_metadata(img_path):
     metadata stored there, as a dict
     '''
     im = Image.open(img_path)
-    if hasattr(im, 'text'):
-        md = im.text.get('sd-metadata', '{}')
-        dream_prompt = im.text.get('Dream', '')
-    else:
-        # When trying to retrieve metadata from images without a 'text' payload, such as JPG images.
-        md = '{}'
-        dream_prompt = ''
+    md = im.text.get('sd-metadata', '{}')
+    dream_prompt = im.text.get('Dream', '')
     return {'sd-metadata': json.loads(md), 'Dream': dream_prompt}
 
 def write_metadata(img_path:str, meta:dict):

ldm/invoke/prompt_parser.py

@@ -3,7 +3,7 @@ from typing import Union, Optional
 import re
 import pyparsing as pp
 '''
-This module parses prompt strings and produces tree-like structures that can be used generate and control the conditioning tensors.
+This module parses prompt strings and produces tree-like structures that can be used generate and control the conditioning tensors. 
 weighted subprompts.
 
 Useful class exports:
@@ -69,12 +69,6 @@ class FlattenedPrompt():
         return len(self.children) == 0 or \
                (len(self.children) == 1 and len(self.children[0].text) == 0)
 
-    @property
-    def wants_cross_attention_control(self):
-        return any(
-            [issubclass(type(x), CrossAttentionControlledFragment) for x in self.children]
-        )
-
     def __repr__(self):
         return f"FlattenedPrompt:{self.children}"
     def __eq__(self, other):
@@ -246,12 +240,6 @@ class Blend():
         self.weights = weights
         self.normalize_weights = normalize_weights
 
-    @property
-    def wants_cross_attention_control(self):
-        # blends cannot cross-attention control
-        return False
-
-
     def __repr__(self):
         return f"Blend:{self.prompts} | weights {' ' if self.normalize_weights else '(non-normalized) '}{self.weights}"
     def __eq__(self, other):
@@ -289,8 +277,8 @@ class PromptParser():
 
         return self.flatten(root[0])
 
-    def parse_legacy_blend(self, text: str, skip_normalize: bool) -> Optional[Blend]:
-        weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
+    def parse_legacy_blend(self, text: str) -> Optional[Blend]:
+        weighted_subprompts = split_weighted_subprompts(text, skip_normalize=False)
         if len(weighted_subprompts) <= 1:
             return None
         strings = [x[0] for x in weighted_subprompts]
@@ -299,7 +287,7 @@ class PromptParser():
         parsed_conjunctions = [self.parse_conjunction(x) for x in strings]
         flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
 
-        return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)
+        return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=True)
 
 
     def flatten(self, root: Conjunction, verbose = False) -> Conjunction:
@@ -653,3 +641,27 @@ def split_weighted_subprompts(text, skip_normalize=False)->list:
         return [(x[0], equal_weight) for x in parsed_prompts]
     return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
 
+
+# shows how the prompt is tokenized
+# usually tokens have '</w>' to indicate end-of-word,
+# but for readability it has been replaced with ' '
+def log_tokenization(text, model, display_label=None):
+    tokens    = model.cond_stage_model.tokenizer.tokenize(text)
+    tokenized = ""
+    discarded = ""
+    usedTokens = 0
+    totalTokens = len(tokens)
+    for i in range(0, totalTokens):
+        token = tokens[i].replace('</w>', ' ')
+        # alternate color
+        s = (usedTokens % 6) + 1
+        if i < model.cond_stage_model.max_length:
+            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
+            usedTokens += 1
+        else:  # over max token length
+            discarded = discarded + f"\x1b[0;3{s};40m{token}"
+    print(f"\n>> Tokens {display_label or ''} ({usedTokens}):\n{tokenized}\x1b[0m")
+    if discarded != "":
+        print(
+            f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
+        )

ldm/invoke/readline.py

@@ -53,6 +53,7 @@ COMMANDS = (
     '--codeformer_fidelity','-cf',
     '--upscale','-U',
     '-save_orig','--save_original',
+    '--skip_normalize','-x',
     '--log_tokenization','-t',
     '--hires_fix',
     '--inpaint_replace','-r',
@@ -100,8 +101,7 @@ class Completer(object):
         self.linebuffer  = None
         self.auto_history_active = True
         self.extensions = None
-        self.concepts = None
-        self.embedding_terms = set()
+        self.concepts = Concepts().list_concepts()
         return
 
     def complete(self, text, state):
@@ -116,19 +116,19 @@ class Completer(object):
             # 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)
 
             # looking for a seed
-            elif re.search('(-S\s*|--seed[=\s])\d*$',buffer):
+            elif re.search('(-S\s*|--seed[=\s])\d*$',buffer): 
                 self.matches= self._seed_completions(text,state)
 
-            elif re.search('<[\w-]*$',buffer):
+            elif re.search('<[\w-]*$',buffer): 
                 self.matches= self._concept_completions(text,state)
-
+            
             # looking for a model
             elif re.match('^'+'|'.join(MODEL_COMMANDS),buffer):
                 self.matches= self._model_completions(text, state)
@@ -226,7 +226,7 @@ class Completer(object):
         if h_len < 1:
             print('<empty history>')
             return
-
+        
         for i in range(0,h_len):
             line = self.get_history_item(i+1)
             if match and match not in line:
@@ -270,21 +270,16 @@ class Completer(object):
         return matches
 
     def add_embedding_terms(self, terms:list[str]):
-        self.embedding_terms = set(terms)
-        if self.concepts:
-            self.embedding_terms.update(self.concepts)
+        self.concepts = Concepts().list_concepts()
+        self.concepts.extend(terms)
 
     def _concept_completions(self, text, state):
-        if self.concepts is None:
-            self.concepts = set(Concepts().list_concepts())
-            self.embedding_terms.update(self.concepts)
-
         partial = text[1:]  # this removes the leading '<'
         if len(partial) == 0:
-            return list(self.embedding_terms)  # whole dump - think if user wants this!
+            return self.concepts  # whole dump - think if user wants this!
 
         matches = list()
-        for concept in self.embedding_terms:
+        for concept in self.concepts:
             if concept.startswith(partial):
                 matches.append(f'<{concept}>')
         matches.sort()
@@ -366,7 +361,7 @@ class DummyCompleter(Completer):
     def __init__(self,options):
         super().__init__(options)
         self.history = list()
-
+        
     def add_history(self,line):
         self.history.append(line)
 
@@ -421,11 +416,7 @@ def get_completer(opt:Args, models=[])->Completer:
         readline.parse_and_bind('set skip-completed-text on')
         readline.parse_and_bind('set show-all-if-ambiguous on')
 
-        outdir = os.path.expanduser(opt.outdir)
-        if os.path.isabs(outdir):
-            histfile = os.path.join(outdir,'.invoke_history')
-        else:
-            histfile = os.path.join(Globals.root, outdir, '.invoke_history')
+        histfile = os.path.join(os.path.expanduser(opt.outdir), '.invoke_history')
         try:
             readline.read_history_file(histfile)
             readline.set_history_length(1000)

ldm/models/diffusion/cross_attention_control.py

@@ -1,14 +1,12 @@
 import enum
-import math
-from typing import Optional, Callable
+from typing import Optional
 
-import psutil
 import torch
-from torch import nn
 
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
 
+
 class Arguments:
     def __init__(self, edited_conditioning: torch.Tensor, edit_opcodes: list[tuple], edit_options: dict):
         """
@@ -65,13 +63,9 @@ class Context:
         self.clear_requests(cleanup=True)
 
     def register_cross_attention_modules(self, model):
-        for name,module in get_cross_attention_modules(model, CrossAttentionType.SELF):
-            if name in self.self_cross_attention_module_identifiers:
-                assert False, f"name {name} cannot appear more than once"
+        for name,module in get_attention_modules(model, CrossAttentionType.SELF):
             self.self_cross_attention_module_identifiers.append(name)
-        for name,module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
-            if name in self.tokens_cross_attention_module_identifiers:
-                assert False, f"name {name} cannot appear more than once"
+        for name,module in get_attention_modules(model, CrossAttentionType.TOKENS):
             self.tokens_cross_attention_module_identifiers.append(name)
 
     def request_save_attention_maps(self, cross_attention_type: CrossAttentionType):
@@ -172,135 +166,6 @@ class Context:
                 map_dict[offset] = slice.to('cpu')
 
 
-
-class InvokeAICrossAttentionMixin:
-    """
-    Enable InvokeAI-flavoured CrossAttention calculation, which does aggressive low-memory slicing and calls
-    through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
-    and dymamic slicing strategy selection.
-    """
-    def __init__(self):
-        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-        self.attention_slice_wrangler = None
-        self.slicing_strategy_getter = None
-        self.attention_slice_calculated_callback = None
-
-    def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
-        '''
-        Set custom attention calculator to be called when attention is calculated
-        :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
-        which returns either the suggested_attention_slice or an adjusted equivalent.
-            `module` is the current CrossAttention module for which the callback is being invoked.
-            `suggested_attention_slice` is the default-calculated attention slice
-            `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
-                If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
-
-        Pass None to use the default attention calculation.
-        :return:
-        '''
-        self.attention_slice_wrangler = wrangler
-
-    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
-        self.slicing_strategy_getter = getter
-
-    def set_attention_slice_calculated_callback(self, callback: Optional[Callable[[torch.Tensor], None]]):
-        self.attention_slice_calculated_callback = callback
-
-    def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
-        # calculate attention scores
-        #attention_scores = torch.einsum('b i d, b j d -> b i j', q, k)
-        attention_scores = torch.baddbmm(
-            torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
-            query,
-            key.transpose(-1, -2),
-            beta=0,
-            alpha=self.scale,
-        )
-
-        # calculate attention slice by taking the best scores for each latent pixel
-        default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
-        attention_slice_wrangler = self.attention_slice_wrangler
-        if attention_slice_wrangler is not None:
-            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
-        else:
-            attention_slice = default_attention_slice
-
-        if self.attention_slice_calculated_callback is not None:
-            self.attention_slice_calculated_callback(attention_slice, dim, offset, slice_size)
-
-        hidden_states = torch.bmm(attention_slice, value)
-        return hidden_states
-
-    def einsum_op_slice_dim0(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[0], slice_size):
-            end = i + slice_size
-            r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_slice_dim1(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
-            r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_mps_v1(self, q, k, v):
-        if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
-            return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-    def einsum_op_mps_v2(self, q, k, v):
-        if self.mem_total_gb > 8 and q.shape[1] <= 4096:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            return self.einsum_op_slice_dim0(q, k, v, 1)
-
-    def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
-        size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
-        if size_mb <= max_tensor_mb:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
-        if div <= q.shape[0]:
-            return self.einsum_op_slice_dim0(q, k, v, q.shape[0] // div)
-        return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
-
-    def einsum_op_cuda(self, q, k, v):
-        # check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
-        slicing_strategy_getter = self.slicing_strategy_getter
-        if slicing_strategy_getter is not None:
-            (dim, slice_size) = slicing_strategy_getter(self)
-            if dim is not None:
-                # print("using saved slicing strategy with dim", dim, "slice size", slice_size)
-                if dim == 0:
-                    return self.einsum_op_slice_dim0(q, k, v, slice_size)
-                elif dim == 1:
-                    return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-        # fallback for when there is no saved strategy, or saved strategy does not slice
-        mem_free_total = get_mem_free_total(q.device)
-        # Divide factor of safety as there's copying and fragmentation
-        return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
-
-
-    def get_invokeai_attention_mem_efficient(self, q, k, v):
-        if q.device.type == 'cuda':
-            #print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
-            return self.einsum_op_cuda(q, k, v)
-
-        if q.device.type == 'mps' or q.device.type == 'cpu':
-            if self.mem_total_gb >= 32:
-                return self.einsum_op_mps_v1(q, k, v)
-            return self.einsum_op_mps_v2(q, k, v)
-
-        # Smaller slices are faster due to L2/L3/SLC caches.
-        # Tested on i7 with 8MB L3 cache.
-        return self.einsum_op_tensor_mem(q, k, v, 32)
-
-
-
 def remove_cross_attention_control(model):
     remove_attention_function(model)
 
@@ -322,7 +187,7 @@ def setup_cross_attention_control(model, context: Context):
     # mask=1 means use base prompt attention, mask=0 means use edited prompt attention
     mask = torch.zeros(max_length)
     indices_target = torch.arange(max_length, dtype=torch.long)
-    indices = torch.arange(max_length, dtype=torch.long)
+    indices = torch.zeros(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
             if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
@@ -336,23 +201,10 @@ def setup_cross_attention_control(model, context: Context):
     inject_attention_function(model, context)
 
 
-def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
-    cross_attention_class: type = InvokeAICrossAttentionMixin
-    # cross_attention_class: type = InvokeAIDiffusersCrossAttention
+def get_attention_modules(model, which: CrossAttentionType):
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
-    attention_module_tuples = [(name,module) for name, module in model.named_modules() if
-                isinstance(module, cross_attention_class) and which_attn in name]
-    cross_attention_modules_in_model_count = len(attention_module_tuples)
-    expected_count = 16
-    if cross_attention_modules_in_model_count != expected_count:
-        # non-fatal error but .swap() won't work.
-        print(f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model " +
-              f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed " +
-              f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, " +
-              f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows " +
-              f"what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not " +
-              f"work properly until it is fixed.")
-    return attention_module_tuples
+    return [(name,module) for name, module in model.named_modules() if
+                  type(module).__name__ == "CrossAttention" and which_attn in name]
 
 
 def inject_attention_function(unet, context: Context):
@@ -392,52 +244,19 @@ def inject_attention_function(unet, context: Context):
 
         return attention_slice
 
-    cross_attention_modules = get_cross_attention_modules(unet, CrossAttentionType.TOKENS) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
-    for identifier, module in cross_attention_modules:
-        module.identifier = identifier
-        try:
+    for name, module in unet.named_modules():
+        module_name = type(module).__name__
+        if module_name == "CrossAttention":
+            module.identifier = name
             module.set_attention_slice_wrangler(attention_slice_wrangler)
-            module.set_slicing_strategy_getter(
-                lambda module: context.get_slicing_strategy(identifier)
-            )
-        except AttributeError as e:
-            if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")  # TODO
-            else:
-                raise
+            module.set_slicing_strategy_getter(lambda module, module_identifier=name: \
+                                                   context.get_slicing_strategy(module_identifier))
 
 
 def remove_attention_function(unet):
-    cross_attention_modules = get_cross_attention_modules(unet, CrossAttentionType.TOKENS) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
-    for identifier, module in cross_attention_modules:
-        try:
-            # clear wrangler callback
+    # clear wrangler callback
+    for name, module in unet.named_modules():
+        module_name = type(module).__name__
+        if module_name == "CrossAttention":
             module.set_attention_slice_wrangler(None)
             module.set_slicing_strategy_getter(None)
-        except AttributeError as e:
-            if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")
-            else:
-                raise
-
-
-def is_attribute_error_about(error: AttributeError, attribute: str):
-    if hasattr(error, 'name'):  # Python 3.10
-        return error.name == attribute
-    else:  # Python 3.9
-        return attribute in str(error)
-
-
-
-def get_mem_free_total(device):
-    #only on cuda
-    if not torch.cuda.is_available():
-        return None
-    stats = torch.cuda.memory_stats(device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
-    mem_free_cuda, _ = torch.cuda.mem_get_info(device)
-    mem_free_torch = mem_reserved - mem_active
-    mem_free_total = mem_free_cuda + mem_free_torch
-    return mem_free_total
-

ldm/models/diffusion/cross_attention_map_saving.py

@@ -1,95 +0,0 @@
-import math
-
-import PIL
-import torch
-from torchvision.transforms.functional import resize as tv_resize, InterpolationMode
-
-from ldm.models.diffusion.cross_attention_control import get_cross_attention_modules, CrossAttentionType
-
-
-class AttentionMapSaver():
-
-    def __init__(self, token_ids: range, latents_shape: torch.Size):
-        self.token_ids = token_ids
-        self.latents_shape = latents_shape
-        #self.collated_maps = #torch.zeros([len(token_ids), latents_shape[0], latents_shape[1]])
-        self.collated_maps = {}
-
-    def clear_maps(self):
-        self.collated_maps = {}
-
-    def add_attention_maps(self, maps: torch.Tensor, key: str):
-        """
-        Accumulate the given attention maps and store by summing with existing maps at the passed-in key (if any).
-        :param maps: Attention maps to store. Expected shape [A, (H*W), N] where A is attention heads count, H and W are the map size (fixed per-key) and N is the number of tokens (typically 77).
-        :param key: Storage key. If a map already exists for this key it will be summed with the incoming data. In this case the maps sizes (H and W) should match.
-        :return: None
-        """
-        key_and_size = f'{key}_{maps.shape[1]}'
-
-        # extract desired tokens
-        maps = maps[:, :, self.token_ids]
-
-        # merge attention heads to a single map per token
-        maps = torch.sum(maps, 0)
-
-        # store
-        if key_and_size not in self.collated_maps:
-            self.collated_maps[key_and_size] = torch.zeros_like(maps, device='cpu')
-        self.collated_maps[key_and_size] += maps.cpu()
-
-    def write_maps_to_disk(self, path: str):
-        pil_image = self.get_stacked_maps_image()
-        pil_image.save(path, 'PNG')
-
-    def get_stacked_maps_image(self) -> PIL.Image:
-        """
-        Scale all collected attention maps to the same size, blend them together and return as an image.
-        :return: An image containing a vertical stack of blended attention maps, one for each requested token.
-        """
-        num_tokens = len(self.token_ids)
-        if num_tokens == 0:
-            return None
-
-        latents_height = self.latents_shape[0]
-        latents_width = self.latents_shape[1]
-
-        merged = None
-
-        for key, maps in self.collated_maps.items():
-
-            # maps has shape [(H*W), N] for N tokens
-            # but we want [N, H, W]
-            this_scale_factor = math.sqrt(maps.shape[0] / (latents_width * latents_height))
-            this_maps_height = int(float(latents_height) * this_scale_factor)
-            this_maps_width = int(float(latents_width) * this_scale_factor)
-            # and we need to do some dimension juggling
-            maps = torch.reshape(torch.swapdims(maps, 0, 1), [num_tokens, this_maps_height, this_maps_width])
-
-            # scale to output size if necessary
-            if this_scale_factor != 1:
-                maps = tv_resize(maps, [latents_height, latents_width], InterpolationMode.BICUBIC)
-
-            # normalize
-            maps_min = torch.min(maps)
-            maps_range = torch.max(maps) - maps_min
-            #print(f"map {key} size {[this_maps_width, this_maps_height]} range {[maps_min, maps_min + maps_range]}")
-            maps_normalized = (maps - maps_min) / maps_range
-            # expand to (-0.1, 1.1) and clamp
-            maps_normalized_expanded = maps_normalized * 1.1 - 0.05
-            maps_normalized_expanded_clamped = torch.clamp(maps_normalized_expanded, 0, 1)
-
-            # merge together, producing a vertical stack
-            maps_stacked = torch.reshape(maps_normalized_expanded_clamped, [num_tokens * latents_height, latents_width])
-
-            if merged is None:
-                merged = maps_stacked
-            else:
-                # screen blend
-                merged = 1 - (1 - maps_stacked)*(1 - merged)
-
-        if merged is None:
-            return None
-
-        merged_bytes = merged.mul(0xff).byte()
-        return PIL.Image.fromarray(merged_bytes.numpy(), mode='L')

ldm/models/diffusion/ksampler.py

@@ -4,7 +4,6 @@ import k_diffusion as K
 import torch
 from torch import nn
 
-from .cross_attention_map_saving import AttentionMapSaver
 from .sampler import Sampler
 from .shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
@@ -37,7 +36,6 @@ class CFGDenoiser(nn.Module):
         self.invokeai_diffuser = InvokeAIDiffuserComponent(model,
                                                            model_forward_callback=lambda x, sigma, cond: self.inner_model(x, sigma, cond=cond))
 
-
     def prepare_to_sample(self, t_enc, **kwargs):
 
         extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
@@ -108,12 +106,12 @@ class KSampler(Sampler):
         else:
             print(f'>> Ksampler using karras noise schedule (steps < {self.karras_max})')
             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
     # modify the inner loop of k_heun, k_lms, etc, as is done in an ugly way
     # in the lstein/k-diffusion branch.
-
+    
     @torch.no_grad()
     def decode(
             self,
@@ -147,7 +145,7 @@ class KSampler(Sampler):
     @torch.no_grad()
     def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
         return x0
-
+    
     # Most of these arguments are ignored and are only present for compatibility with
     # other samples
     @torch.no_grad()
@@ -160,7 +158,6 @@ class KSampler(Sampler):
         callback=None,
         normals_sequence=None,
         img_callback=None,
-        attention_maps_callback=None,
         quantize_x0=False,
         eta=0.0,
         mask=None,
@@ -174,7 +171,7 @@ class KSampler(Sampler):
         log_every_t=100,
         unconditional_guidance_scale=1.0,
         unconditional_conditioning=None,
-        extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
+        extra_conditioning_info=None,
         threshold = 0,
         perlin = 0,
         # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
@@ -207,15 +204,6 @@ class KSampler(Sampler):
 
         model_wrap_cfg = CFGDenoiser(self.model, threshold=threshold, warmup=max(0.8*S,S-10))
         model_wrap_cfg.prepare_to_sample(S, extra_conditioning_info=extra_conditioning_info)
-
-        # setup attention maps saving. checks for None are because there are multiple code paths to get here.
-        attention_maps_saver = None
-        if attention_maps_callback is not None and extra_conditioning_info is not None:
-            eos_token_index = extra_conditioning_info.tokens_count_including_eos_bos - 1
-            attention_map_token_ids = range(1, eos_token_index)
-            attention_maps_saver = AttentionMapSaver(token_ids = attention_map_token_ids, latents_shape=x.shape[-2:])
-            model_wrap_cfg.invokeai_diffuser.setup_attention_map_saving(attention_maps_saver)
-
         extra_args = {
             'cond': conditioning,
             'uncond': unconditional_conditioning,
@@ -229,8 +217,6 @@ class KSampler(Sampler):
             ),
             None,
         )
-        if attention_maps_saver is not None:
-            attention_maps_callback(attention_maps_saver)
         return sampling_result
 
     # this code will support inpainting if and when ksampler API modified or
@@ -262,7 +248,7 @@ class KSampler(Sampler):
         # terrible, confusing names here
         steps = self.ddim_num_steps
         t_enc = self.t_enc
-
+        
         # sigmas is a full steps in length, but t_enc might
         # be less. We start in the middle of the sigma array
         # and work our way to the end after t_enc steps.
@@ -294,7 +280,7 @@ class KSampler(Sampler):
             return x_T + x
         else:
             return x
-
+        
     def prepare_to_sample(self,t_enc,**kwargs):
         self.t_enc      = t_enc
         self.model_wrap = None