update changelogs

At step counts greater than ~75, the ksamplers start producing noisy
images when using the Karras noise schedule. This PR reverts to using
the model's own noise schedule, which eliminates the problem at the
cost of slowing convergence at lower step counts.

This PR also introduces a new CLI `--save_intermediates <n>' argument,
which will save every nth intermediate image into a subdirectory
named `intermediates/<image_prefix>'.

Addresses issue #1083.

.dev_scripts/diff_images.py

@@ -0,0 +1,32 @@
+import argparse
+
+import numpy as np
+from PIL import Image
+
+
+def read_image_int16(image_path):
+    image = Image.open(image_path)
+    return np.array(image).astype(np.int16)
+
+
+def calc_images_mean_L1(image1_path, image2_path):
+    image1 = read_image_int16(image1_path)
+    image2 = read_image_int16(image2_path)
+    assert image1.shape == image2.shape
+
+    mean_L1 = np.abs(image1 - image2).mean()
+    return mean_L1
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('image1_path')
+    parser.add_argument('image2_path')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    mean_L1 = calc_images_mean_L1(args.image1_path, args.image2_path)
+    print(mean_L1)

.dev_scripts/sample_command.txt

@@ -0,0 +1 @@
+"a photograph of an astronaut riding a horse" -s50 -S42

.dev_scripts/test_regression_txt2img_dream_v1_4.sh

@@ -0,0 +1,19 @@
+# generate an image
+PROMPT_FILE=".dev_scripts/sample_command.txt"
+OUT_DIR="outputs/img-samples/test_regression_txt2img_v1_4"
+SAMPLES_DIR=${OUT_DIR}
+python scripts/dream.py \
+    --from_file ${PROMPT_FILE} \
+    --outdir ${OUT_DIR} \
+    --sampler plms
+
+# original output by CompVis/stable-diffusion
+IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"
+# new output
+IMAGE2=`ls -A ${SAMPLES_DIR}/*.png | sort | tail -n 1`
+
+echo ""
+echo "comparing the following two images"
+echo "IMAGE1: ${IMAGE1}"
+echo "IMAGE2: ${IMAGE2}"
+python .dev_scripts/diff_images.py ${IMAGE1} ${IMAGE2}

.dev_scripts/test_regression_txt2img_v1_4.sh

@@ -0,0 +1,23 @@
+# generate an image
+PROMPT="a photograph of an astronaut riding a horse"
+OUT_DIR="outputs/txt2img-samples/test_regression_txt2img_v1_4"
+SAMPLES_DIR="outputs/txt2img-samples/test_regression_txt2img_v1_4/samples"
+python scripts/orig_scripts/txt2img.py \
+    --prompt "${PROMPT}" \
+    --outdir ${OUT_DIR} \
+    --plms \
+    --ddim_steps 50 \
+    --n_samples 1 \
+    --n_iter 1 \
+    --seed 42
+
+# original output by CompVis/stable-diffusion
+IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"
+# new output
+IMAGE2=`ls -A ${SAMPLES_DIR}/*.png | sort | tail -n 1`
+
+echo ""
+echo "comparing the following two images"
+echo "IMAGE1: ${IMAGE1}"
+echo "IMAGE2: ${IMAGE2}"
+python .dev_scripts/diff_images.py ${IMAGE1} ${IMAGE2}

.gitattributes

@@ -0,0 +1,4 @@
+# Auto normalizes line endings on commit so devs don't need to change local settings.
+# Only affects text files and ignores other file types. 
+# For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
+* text=auto

.github/CODEOWNERS

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

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,36 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe your environment**
+- GPU: [cuda/amd/mps/cpu]
+- VRAM: [if known]
+- CPU arch: [x86/arm]
+- OS: [Linux/Windows/macOS]
+- Python: [Anaconda/miniconda/miniforge/pyenv/other (explain)]
+- Branch: [if `git status` says anything other than "On branch main" paste it here]
+- Commit: [run `git show` and paste the line that starts with "Merge" here]
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/workflows/create-caches.yml

@@ -0,0 +1,70 @@
+name: Create Caches
+on:
+  workflow_dispatch
+jobs:
+  build:
+    strategy:
+      matrix:
+        os: [ ubuntu-latest, macos-12 ]
+    name: Create Caches on ${{ matrix.os }} conda
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: Set platform variables
+        id: vars
+        run: |
+            if [ "$RUNNER_OS" = "macOS" ]; then
+                echo "::set-output name=ENV_FILE::environment-mac.yml"
+                echo "::set-output name=PYTHON_BIN::/usr/local/miniconda/envs/ldm/bin/python"
+            elif [ "$RUNNER_OS" = "Linux" ]; then
+                echo "::set-output name=ENV_FILE::environment.yml"
+                echo "::set-output name=PYTHON_BIN::/usr/share/miniconda/envs/ldm/bin/python"
+            fi
+      - name: Checkout sources
+        uses: actions/checkout@v3
+      - name: Use Cached Stable Diffusion v1.4 Model
+        id: cache-sd-v1-4
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-sd-v1-4
+        with:
+          path: models/ldm/stable-diffusion-v1/model.ckpt
+          key: ${{ env.cache-name }}
+          restore-keys: |
+            ${{ env.cache-name }}
+      - name: Download Stable Diffusion v1.4 Model
+        if: ${{ steps.cache-sd-v1-4.outputs.cache-hit != 'true' }}
+        run: |
+          if [ ! -e models/ldm/stable-diffusion-v1 ]; then
+              mkdir -p models/ldm/stable-diffusion-v1
+          fi
+          if [ ! -e models/ldm/stable-diffusion-v1/model.ckpt ]; then
+            curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
+          fi
+      - name: Use Cached Dependencies
+        id: cache-conda-env-ldm
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-conda-env-ldm
+        with:
+          path: ~/.conda/envs/ldm
+          key: ${{ env.cache-name }}
+          restore-keys: |
+            ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(steps.vars.outputs.ENV_FILE) }}
+      - name: Install Dependencies
+        if: ${{ steps.cache-conda-env-ldm.outputs.cache-hit != 'true' }}
+        run: |
+          conda env create -f ${{ steps.vars.outputs.ENV_FILE }}
+      - name: Use Cached Huggingface and Torch models
+        id: cache-huggingface-torch
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-huggingface-torch
+        with:
+          path: ~/.cache
+          key: ${{ env.cache-name }}
+          restore-keys: |
+            ${{ env.cache-name }}-${{ hashFiles('scripts/preload_models.py') }}
+      - name: Download Huggingface and Torch models
+        if: ${{ steps.cache-huggingface-torch.outputs.cache-hit != 'true' }}
+        run: |
+          ${{ steps.vars.outputs.PYTHON_BIN }} scripts/preload_models.py

.github/workflows/mkdocs-flow.yml

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

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

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

.gitignore

@@ -0,0 +1,201 @@
+# ignore default image save location and model symbolic link
+outputs/
+models/ldm/stable-diffusion-v1/model.ckpt
+ldm/dream/restoration/codeformer/weights
+
+# ignore the Anaconda/Miniconda installer used while building Docker image
+anaconda.sh
+
+# ignore a directory which serves as a place for initial images
+inputs/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# emacs autosave and recovery files
+*~
+.#*
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+src
+**/__pycache__/
+outputs
+
+# Logs and associated folders 
+# created from generated embeddings.
+logs
+testtube
+checkpoints
+# If it's a Mac
+.DS_Store
+
+# Let the frontend manage its own gitignore
+!frontend/*
+
+# Scratch folder
+.scratch/
+.vscode/
+gfpgan/
+models/ldm/stable-diffusion-v1/model.sha256
+
+# GFPGAN model files
+gfpgan/

.prettierrc.yaml

@@ -0,0 +1,13 @@
+endOfLine: lf
+tabWidth: 2
+useTabs: false
+singleQuote: true
+quoteProps: as-needed
+embeddedLanguageFormatting: auto
+overrides:
+  - files: '*.md'
+    options:
+      proseWrap: always
+      printWidth: 80
+      parser: markdown
+      cursorOffset: -1

LICENSE

@@ -1,9 +1,27 @@
-All rights reserved by the authors.
-You must not distribute the weights provided to you directly or indirectly without explicit consent of the authors.
-You must not distribute harmful, offensive, dehumanizing content or otherwise harmful representations of people or their environments, cultures, religions, etc. produced with the model weights
-or other generated content described in the "Misuse and Malicious Use" section in the model card.
-The model weights are provided for research purposes only.
+MIT License
 
+Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
+
+This software is derived from a fork of the source code available from
+https://github.com/pesser/stable-diffusion and
+https://github.com/CompViz/stable-diffusion. They carry the following
+copyrights:
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+Please see individual source code files for copyright and authorship
+attributions.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
@@ -11,4 +29,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

LICENSE-ModelWeights.txt

@@ -0,0 +1,294 @@
+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+CreativeML Open RAIL-M
+dated August 22, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and
+have the potential to transform the way artists, among other
+individuals, conceive and benefit from AI or ML technologies as a tool
+for content creation.
+
+Notwithstanding the current and potential benefits that these
+artifacts can bring to society at large, there are also concerns about
+potential misuses of them, either due to their technical limitations
+or ethical considerations.
+
+In short, this license strives for both the open and responsible
+downstream use of the accompanying model. When it comes to the open
+character, we took inspiration from open source permissive licenses
+regarding the grant of IP rights. Referring to the downstream
+responsible use, we added use-based restrictions not permitting the
+use of the Model in very specific scenarios, in order for the licensor
+to be able to enforce the license in case potential misuses of the
+Model may occur. At the same time, we strive to promote open and
+responsible research on generative models for art and content
+generation.
+
+Even though downstream derivative versions of the model could be
+released under different licensing terms, the latter will always have
+to include - at minimum - the same use-based restrictions as the ones
+in the original license (this license). We believe in the intersection
+between open and responsible AI development; thus, this License aims
+to strike a balance between both in order to enable responsible
+open-science in the field of AI.
+
+This License governs the use of the model (and its derivatives) and is
+informed by the model card associated with the model.
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+1. Definitions
+
+- "License" means the terms and conditions for use, reproduction, and
+  Distribution as defined in this document.
+
+- "Data" means a collection of information and/or content extracted
+  from the dataset used with the Model, including to train, pretrain,
+  or otherwise evaluate the Model. The Data is not licensed under this
+  License.
+
+- "Output" means the results of operating a Model as embodied in
+  informational content resulting therefrom.
+
+- "Model" means any accompanying machine-learning based assemblies
+  (including checkpoints), consisting of learnt weights, parameters
+  (including optimizer states), corresponding to the model
+  architecture as embodied in the Complementary Material, that have
+  been trained or tuned, in whole or in part on the Data, using the
+  Complementary Material.
+
+- "Derivatives of the Model" means all modifications to the Model,
+  works based on the Model, or any other model which is created or
+  initialized by transfer of patterns of the weights, parameters,
+  activations or output of the Model, to the other model, in order to
+  cause the other model to perform similarly to the Model, including -
+  but not limited to - distillation methods entailing the use of
+  intermediate data representations or methods based on the generation
+  of synthetic data by the Model for training the other model.
+
+- "Complementary Material" means the accompanying source code and
+  scripts used to define, run, load, benchmark or evaluate the Model,
+  and used to prepare data for training or evaluation, if any. This
+  includes any accompanying documentation, tutorials, examples, etc,
+  if any.
+
+- "Distribution" means any transmission, reproduction, publication or
+  other sharing of the Model or Derivatives of the Model to a third
+  party, including providing the Model as a hosted service made
+  available by electronic or other remote means - e.g. API-based or
+  web access.
+
+- "Licensor" means the copyright owner or entity authorized by the
+  copyright owner that is granting the License, including the persons
+  or entities that may have rights in the Model and/or distributing
+  the Model.
+
+- "You" (or "Your") means an individual or Legal Entity exercising
+  permissions granted by this License and/or making use of the Model
+  for whichever purpose and in any field of use, including usage of
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README.md

@@ -1,488 +1,205 @@
-# Stable Diffusion Dream Script
+<div align="center">
 
-This is a fork of CompVis/stable-diffusion, the wonderful open source
-text-to-image generator.
+# InvokeAI: A Stable Diffusion Toolkit
 
-The original has been modified in several ways:
+_Formally known as lstein/stable-diffusion_
 
-## Interactive command-line interface similar to the Discord bot
+![project logo](docs/assets/logo.png)
 
-The *dream.py* script, located in scripts/dream.py, 
-provides an interactive interface to image generation similar to
-the "dream mothership" bot that Stable AI provided on its Discord
-server. Unlike the txt2img.py and img2img.py scripts provided in the
-original CompViz/stable-diffusion source code repository, the
-time-consuming initialization of the AI model
-initialization only happens once. After that image generation 
-from the command-line interface is very fast.
+[![discord badge]][discord link]
 
-The script uses the readline library to allow for in-line editing,
-command history (up and down arrows), autocompletion, and more.
+[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
 
-The script is confirmed to work on Linux and Windows systems. It should
-work on MacOSX as well, but this is not confirmed. Note that this script
-runs from the command-line (CMD or Terminal window), and does not have a GUI.
+[![CI checks on main badge]][CI checks on main link] [![CI checks on dev badge]][CI checks on dev link] [![latest commit to dev badge]][latest commit to dev link]
 
-~~~~
-(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
-* Initializing, be patient...
-Loading model from models/ldm/text2img-large/model.ckpt
-LatentDiffusion: Running in eps-prediction mode
-DiffusionWrapper has 872.30 M params.
-making attention of type 'vanilla' with 512 in_channels
-Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
-making attention of type 'vanilla' with 512 in_channels
-Loading Bert tokenizer from "models/bert"
-setting sampler to plms
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
 
-* Initialization done! Awaiting your command...
-dream> ashley judd riding a camel -n2 -s150
-Outputs:
-   outputs/txt2img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
-   outputs/txt2img-samples/00010.png: "ashley judd riding a camel" -n2 -s150-S 1362479620
+[CI checks on dev badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
+[CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
+[CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
+[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
+[discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
+[discord link]: https://discord.gg/ZmtBAhwWhy
+[github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
+[github forks link]: https://useful-forks.github.io/?repo=invoke-ai%2FInvokeAI
+[github open issues badge]: https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
+[github open issues link]: https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
+[github open prs badge]: https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
+[github open prs link]: https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
+[github stars badge]: https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
+[github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
+[latest commit to dev badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
+[latest commit to dev link]: https://github.com/invoke-ai/InvokeAI/commits/development
+[latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
+[latest release link]: https://github.com/invoke-ai/InvokeAI/releases
+</div>
 
-dream> "there's a fly in my soup" -n6 -g
-    outputs/txt2img-samples/00041.png: "there's a fly in my soup" -n6 -g -S 2685670268
-    seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
-~~~~
+This is a fork of
+[CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion),
+the open source text-to-image generator. It provides a streamlined
+process with various new features and options to aid the image
+generation process. It runs on Windows, Mac and Linux machines, with
+GPU cards with as little as 4 GB of RAM. It provides both a polished
+Web interface (see below), and an easy-to-use command-line interface.
 
-The dream> prompt's arguments are pretty much
-identical to those used in the Discord bot, except you don't need to
-type "!dream" (it doesn't hurt if you do). A significant change is that creation of individual images 
-is now the default
-unless --grid (-g) is given. For backward compatibility, the -i switch is recognized.
-For command-line help type -h (or --help) at the dream> prompt.
+**Quick links**: [<a href="https://discord.gg/NwVCmKwY">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
 
-The script itself also recognizes a series of command-line switches that will change
-important global defaults, such as the directory for image outputs and the location
-of the model weight files.
-
-## Image-to-Image
-
-This script also provides an img2img feature that lets you seed your
-creations with a drawing or photo. This is a really cool feature that tells
-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:
-
-~~~~
-dream> "waterfall and rainbow" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
-~~~~
-
-The --init_img (-I) option gives the path to the seed picture. --strength (-f) controls how much
-the original will be modified, ranging from 0.0 (keep the original intact), to 1.0 (ignore the original
-completely). The default is 0.75, and ranges from 0.25-0.75 give interesting results.
-
-## Changes
-
-- v1.01 (21 August 2022)
-* added k_lms sampling **Please run "conda update -f environment.yaml" to load the k_lms dependencies**
-* use half precision arithmetic by default, resulting in faster execution and lower memory requirements
-Pass argument --full_precision to dream.py to get slower but more accurate image generation
+<div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
 
 
-## Installation
+_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._
 
-### Linux/Mac
+## Table of Contents
 
-1. You will need to install the following prerequisites if they are not already available. Use your
-operating system's preferred installer
-* Python (version 3.8 or higher)
-* git
+1. [Installation](#installation)
+2. [Hardware Requirements](#hardware-requirements)
+3. [Features](#features)
+4. [Latest Changes](#latest-changes)
+5. [Troubleshooting](#troubleshooting)
+6. [Contributing](#contributing)
+7. [Contributors](#contributors)
+8. [Support](#support)
+9. [Further Reading](#further-reading)
 
-2. Install the Python Anaconda environment manager using pip3.
-```
-~$ pip3 install anaconda
-```
-After installing anaconda, you should log out of your system and log back in. If the installation
-worked, your command prompt will be prefixed by the name of the current anaconda environment, "(base)".
+### Installation
 
-3. Copy the stable-diffusion source code from GitHub:
-```
-(base) ~$ git clone https://github.com/lstein/stable-diffusion.git
-```
-This will create stable-diffusion folder where you will follow the rest of the steps.
+This fork is supported across multiple platforms. You can find individual installation instructions
+below.
 
-4. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
-```
-(base) ~$ cd stable-diffusion
-(base) ~/stable-diffusion$
-```
-5. Use anaconda to copy necessary python packages, create a new python environment named "ldm",
-and activate the environment. 
-```
-(base) ~/stable-diffusion$ conda env create -f environment.yaml
-(base) ~/stable-diffusion$ conda activate ldm
-(ldm) ~/stable-diffusion$
-```
-After these steps, your command prompt will be prefixed by "(ldm)" as shown above.
+- #### [Linux](docs/installation/INSTALL_LINUX.md)
 
-6. Load a couple of small machine-learning models required by stable diffusion:
-```
-(ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
+- #### [Windows](docs/installation/INSTALL_WINDOWS.md)
+
+- #### [Macintosh](docs/installation/INSTALL_MAC.md)
+
+### Hardware Requirements
+
+#### System
+
+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.
+
+#### Memory
+
+- At least 12 GB Main Memory RAM.
+
+#### Disk
+
+- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
+
+**Note**
+
+If you have a Nvidia 10xx series card (e.g. the 1080ti), please
+run the dream script in full-precision mode as shown below.
+
+Similarly, specify full-precision mode on Apple M1 hardware.
+
+Precision is auto configured based on the device. If however you encounter
+errors like 'expected type Float but found Half' or 'not implemented for Half'
+you can try starting `invoke.py` with the `--precision=float32` flag:
+
+```bash
+(ldm) ~/stable-diffusion$ python scripts/invoke.py --precision=float32
 ```
 
-7. Now you need to install the weights for the stable diffusion model.
-
-For testing prior to the release of the real weights, you can use an older weight file that produces low-quality images. Create a directory within stable-diffusion named "models/ldm/text2img.large", and use the wget URL downloader tool to copy the weight file into it:
-```
-(ldm) ~/stable-diffusion$ mkdir -p models/ldm/text2img-large
-(ldm) ~/stable-diffusion$ wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt
-```
-For testing with the released weighs, you will do something similar, but with a directory named "models/ldm/stable-diffusion-v1"
-```
-(ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
-(ldm) ~/stable-diffusion$ wget -O models/ldm/stable-diffusion-v1/model.ckpt <ENTER URL HERE>
-```
-These weight files are ~5 GB in size, so downloading may take a while.
-
-8. Start generating images!
-```
-# for the pre-release weights use the -l or --liaon400m switch
-(ldm) ~/stable-diffusion$ python3 scripts/dream.py -l
-
-# for the post-release weights do not use the switch
-(ldm) ~/stable-diffusion$ python3 scripts/dream.py
-
-# for additional configuration switches and arguments, use -h or --help
-(ldm) ~/stable-diffusion$ python3 scripts/dream.py -h
-```
-9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the "stable-diffusion" 
-directory, and then launch the dream script (step 8). If you forget to activate the ldm environment, the script will fail with multiple ModuleNotFound errors.
-
-### Updating to newer versions of the script
-
-This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
-```
-(ldm) ~/stable-diffusion$ git pull
-```
-This will bring your local copy into sync with the remote one.
-
-### Windows
-
-1. Install the most recent Python from here: https://www.python.org/downloads/windows/
-
-2. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
-
-3. Install Git from here: https://git-scm.com/download/win
-
-4. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
-
-5. Run the command:
-```
-git clone https://github.com/lstein/stable-diffusion.git
-```
-This will create stable-diffusion folder where you will follow the rest of the steps.
-
-6. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
-```
-cd stable-diffusion
-```
-
-7. Run the following two commands:
-```
-conda env create -f environment.yaml    (step 7a)
-conda activate ldm                      (step 7b)
-```
-This will install all python requirements and activate the "ldm" environment which sets PATH and other environment variables properly.
-
-8. Run the command:
-```
-python scripts\preload_models.py
-```
-This installs two machine learning models that stable diffusion requires.
-
-9. Now you need to install the weights for the big stable diffusion model.
-
-For testing prior to the release of the real weights, create a directory within stable-diffusion named "models\ldm\text2img.large".
-
-For testing with the released weights, create a directory within stable-diffusion named "models\ldm\stable-diffusion-v1".
-
-Then use a web browser to copy model.ckpt into the appropriate directory. For the text2img.large (pre-release) model, the weights are at https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt. Check back here later for the release URL.
-
-10. Start generating images!
-```
-# for the pre-release weights
-python scripts\dream.py -l
-
-# for the post-release weights
-python scripts\dream.py
-```
-11. Subsequently, to relaunch the script, first activate the Anaconda command window (step 4), run "conda activate ldm" (step 7b), and then launch the dream script (step 10).
-
-### Updating to newer versions of the script
-
-This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
-```
-git pull
-```
-This will bring your local copy into sync with the remote one.
-
-## Simplified API for text to image generation
-
-For programmers who wish to incorporate stable-diffusion into other
-products, this repository includes a simplified API for text to image generation, which
-lets you create images from a prompt in just three lines of code:
-
-~~~~
-from ldm.simplet2i import T2I
-model   = T2I()
-outputs = model.text2image("a unicorn in manhattan")
-~~~~
-
-Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...]
-Please see ldm/simplet2i.py for more information.
-
-
-## Workaround for machines with limited internet connectivity
-
-My development machine is a GPU node in a high-performance compute
-cluster which has no connection to the internet. During model
-initialization, stable-diffusion tries to download the Bert tokenizer
-and a file needed by the kornia library.  This obviously didn't work
-for me.
-
-To work around this, I have modified ldm/modules/encoders/modules.py
-to look for locally cached Bert files rather than attempting to
-download them. For this to work, you must run
-"scripts/preload_models.py" once from an internet-connected machine
-prior to running the code on an isolated one. This assumes that both
-machines share a common network-mounted filesystem with a common
-.cache directory.
-
-~~~~
-(ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py
-preloading bert tokenizer...
-Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
-Downloading: 100%|██████████████████████████████████| 226k/226k [00:00<00:00, 2.79MB/s]
-Downloading: 100%|██████████████████████████████████| 455k/455k [00:00<00:00, 4.36MB/s]
-Downloading: 100%|██████████████████████████████████| 570/570 [00:00<00:00, 477kB/s]
-...success
-preloading kornia requirements...
-Downloading: "https://github.com/DagnyT/hardnet/raw/master/pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth" to /u/lstein/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
-100%|███████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 101MB/s]
-...success
-~~~~
-
-If you don't need this change and want to download the files just in
-time, copy over the file ldm/modules/encoders/modules.py from the
-CompVis/stable-diffusion repository. Or you can run preload_models.py
-on the target machine.
-
-## Support
-
-For support,
-please use this repository's GitHub Issues tracking service. Feel free
-to send me an email if you use and like the script.
-
-*Author:* Lincoln D. Stein <lincoln.stein@gmail.com>
-
-# Original README from CompViz/stable-diffusion
-*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
-
-[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
-[Robin Rombach](https://github.com/rromb)\*,
-[Andreas Blattmann](https://github.com/ablattmann)\*,
-[Dominik Lorenz](https://github.com/qp-qp)\,
-[Patrick Esser](https://github.com/pesser),
-[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
-
-**CVPR '22 Oral**
-
-which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).
-
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0006.png)
-[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
-model.
-Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. 
-Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), 
-this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
-With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
-See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
-
-  
-## Requirements
-
-A suitable [conda](https://conda.io/) environment named `ldm` can be created
-and activated with:
-
-```
-conda env create -f environment.yaml
-conda activate ldm
-```
-
-You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
-
-```
-conda install pytorch torchvision -c pytorch
-pip install transformers==4.19.2
-pip install -e .
-```
-
-## Stable Diffusion v1
-
-Stable Diffusion v1 refers to a specific configuration of the model
-architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
-and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and 
-then finetuned on 512x512 images.
-
-*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
-in its training data. 
-Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).
-Research into the safe deployment of general text-to-image models is an ongoing effort. To prevent misuse and harm, we currently provide access to the checkpoints only for [academic research purposes upon request](https://stability.ai/academia-access-form).
-**This is an experiment in safe and community-driven publication of a capable and general text-to-image model. We are working on a public release with a more permissive license that also incorporates ethical considerations.***
-
-[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form) 
-
-### Weights
-
-We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
-which were trained as follows,
-
-- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
-  194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
-- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
-  515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
-filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
-- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
-steps show the relative improvements of the checkpoints:
-![sd evaluation results](assets/v1-variants-scores.jpg)
-
-
-
-### Text-to-Image with Stable Diffusion
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0005.png)
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0007.png)
-
-Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
-
-
-#### Sampling Script
-
-After [obtaining the weights](#weights), link them
-```
-mkdir -p models/ldm/stable-diffusion-v1/
-ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt 
-```
-and sample with
-```
-python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms 
-```
-
-By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, 
-and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
-
-```commandline
-usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS]
-                  [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --prompt [PROMPT]     the prompt to render
-  --outdir [OUTDIR]     dir to write results to
-  --skip_grid           do not save a grid, only individual samples. Helpful when evaluating lots of samples
-  --skip_save           do not save individual samples. For speed measurements.
-  --ddim_steps DDIM_STEPS
-                        number of ddim sampling steps
-  --plms                use plms sampling
-  --laion400m           uses the LAION400M model
-  --fixed_code          if enabled, uses the same starting code across samples
-  --ddim_eta DDIM_ETA   ddim eta (eta=0.0 corresponds to deterministic sampling
-  --n_iter N_ITER       sample this often
-  --H H                 image height, in pixel space
-  --W W                 image width, in pixel space
-  --C C                 latent channels
-  --f F                 downsampling factor
-  --n_samples N_SAMPLES
-                        how many samples to produce for each given prompt. A.k.a. batch size
-  --n_rows N_ROWS       rows in the grid (default: n_samples)
-  --scale SCALE         unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
-  --from-file FROM_FILE
-                        if specified, load prompts from this file
-  --config CONFIG       path to config which constructs model
-  --ckpt CKPT           path to checkpoint of model
-  --seed SEED           the seed (for reproducible sampling)
-  --precision {full,autocast}
-                        evaluate at this precision
-
-```
-Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints. 
-For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
-non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
-which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
-
-
-#### Diffusers Integration
-
-Another way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
-```py
-# make sure you're logged in with `huggingface-cli login`
-from torch import autocast
-from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
-
-pipe = StableDiffusionPipeline.from_pretrained(
-	"CompVis/stable-diffusion-v1-3-diffusers", 
-	use_auth_token=True
-)
-
-prompt = "a photo of an astronaut riding a horse on mars"
-with autocast("cuda"):
-    image = pipe(prompt)["sample"][0]  
-    
-image.save("astronaut_rides_horse.png")
-```
-
-
-
-### Image Modification with Stable Diffusion
-
-By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different 
-tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script, 
-we provide a script to perform image modification with Stable Diffusion.  
-
-The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
-```
-python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8
-```
-Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
-Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.
-
-**Input**
-
-![sketch-in](assets/stable-samples/img2img/sketch-mountains-input.jpg)
-
-**Outputs**
-
-![out3](assets/stable-samples/img2img/mountains-3.png)
-![out2](assets/stable-samples/img2img/mountains-2.png)
-
-This procedure can, for example, also be used to upscale samples from the base model.
-
-
-## Comments 
-
-- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
-and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). 
-Thanks for open-sourcing!
-
-- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). 
-
-
-## BibTeX
-
-```
-@misc{rombach2021highresolution,
-      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
-      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
-      year={2021},
-      eprint={2112.10752},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-
-```
-
-
+### Features
+
+#### Major Features
+
+- [Web Server](docs/features/WEB.md)
+- [Interactive Command Line Interface](docs/features/CLI.md)
+- [Image To Image](docs/features/IMG2IMG.md)
+- [Inpainting Support](docs/features/INPAINTING.md)
+- [Outpainting Support](docs/features/OUTPAINTING.md)
+- [Upscaling, face-restoration and outpainting](docs/features/POSTPROCESS.md)
+- [Seamless Tiling](docs/features/OTHER.md#seamless-tiling)
+- [Google Colab](docs/features/OTHER.md#google-colab)
+- [Reading Prompts From File](docs/features/PROMPTS.md#reading-prompts-from-a-file)
+- [Shortcut: Reusing Seeds](docs/features/OTHER.md#shortcuts-reusing-seeds)
+- [Prompt Blending](docs/features/PROMPTS.md#prompt-blending)
+- [Thresholding and Perlin Noise Initialization Options](/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options)
+- [Negative/Unconditioned Prompts](docs/features/PROMPTS.md#negative-and-unconditioned-prompts)
+- [Variations](docs/features/VARIATIONS.md)
+- [Personalizing Text-to-Image Generation](docs/features/TEXTUAL_INVERSION.md)
+- [Simplified API for text to image generation](docs/features/OTHER.md#simplified-api)
+
+#### Other Features
+
+- [Creating Transparent Regions for Inpainting](docs/features/INPAINTING.md#creating-transparent-regions-for-inpainting)
+- [Preload Models](docs/features/OTHER.md#preload-models)
+
+### Latest Changes
+
+- v2.0.1 (13 October 2022)
+  - fix noisy images at high step count when using k* samplers
+  - dream.py script now calls invoke.py module directly rather than
+    via a new python process (which could break the environment)
+
+- v2.0.0 (9 October 2022)
+
+  - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
+    for backward compatibility.
+  - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/INPAINTING.md">inpainting</a> and <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OUTPAINTING.md">outpainting</a>
+  - img2img runs on all k* samplers
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/PROMPTS.md#negative-and-unconditioned-prompts">negative prompts</a>
+  - Support for CodeFormer face reconstruction
+  - Support for Textual Inversion on Macintoshes
+  - Support in both WebGUI and CLI for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/POSTPROCESS.md">post-processing of previously-generated images</a>
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+  - New `--hires` option on `invoke>` line allows <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.md#this-is-an-example-of-txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
+  - New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
+  - Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+  - Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+  - Improved <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.md">command-line completion behavior</a>.
+    New commands added:
+       * List command-line history with `!history`
+       * Search command-line history with `!search`
+       * Clear history with `!clear`
+  - Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+    configure. To switch away from auto use the new flag like `--precision=float32`.
+
+For older changelogs, please visit the **[CHANGELOG](docs/features/CHANGELOG.md)**.
+
+### Troubleshooting
+
+Please check out our **[Q&A](docs/help/TROUBLESHOOT.md)** to get solutions for common installation
+problems and other issues.
+
+# Contributing
+
+Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
+cleanup, testing, or code reviews, is very much encouraged to do so. If you are unfamiliar with how
+to contribute to GitHub projects, here is a
+[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github).
+
+A full set of contribution guidelines, along with templates, are in progress, but for now the most
+important thing is to **make your pull request against the "development" branch**, and not against
+"main". This will help keep public breakage to a minimum and will allow you to propose more radical
+changes.
+
+### Contributors
+
+This fork is a combined effort of various people from across the world.
+[Check out the list of all these amazing people](docs/other/CONTRIBUTORS.md). We thank them for
+their time, hard work and effort.
+
+### Support
+
+For support, please use this repository's GitHub Issues tracking service. Feel free to send me an
+email if you use and like the script.
+
+Original portions of the software are Copyright (c) 2020
+[Lincoln D. Stein](https://github.com/lstein)
+
+### Further Reading
+
+Please see the original README for more information on this software and underlying algorithm,
+located in the file [README-CompViz.md](docs/other/README-CompViz.md).

backend/modules/create_cmd_parser.py

@@ -0,0 +1,55 @@
+import argparse
+import os
+from ldm.invoke.args import PRECISION_CHOICES
+
+
+def create_cmd_parser():
+    parser = argparse.ArgumentParser(description="InvokeAI web UI")
+    parser.add_argument(
+        "--host",
+        type=str,
+        help="The host to serve on",
+        default="localhost",
+    )
+    parser.add_argument("--port", type=int, help="The port to serve on", default=9090)
+    parser.add_argument(
+        "--cors",
+        nargs="*",
+        type=str,
+        help="Additional allowed origins, comma-separated",
+    )
+    parser.add_argument(
+        "--embedding_path",
+        type=str,
+        help="Path to a pre-trained embedding manager checkpoint - can only be set on command line",
+    )
+    # TODO: Can't get flask to serve images from any dir (saving to the dir does work when specified)
+    # parser.add_argument(
+    #     "--output_dir",
+    #     default="outputs/",
+    #     type=str,
+    #     help="Directory for output images",
+    # )
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="store_true",
+        help="Enables verbose logging",
+    )
+    parser.add_argument(
+        "--precision",
+        dest="precision",
+        type=str,
+        choices=PRECISION_CHOICES,
+        metavar="PRECISION",
+        help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
+        default="auto",
+    )
+    parser.add_argument(
+        '--free_gpu_mem',
+        dest='free_gpu_mem',
+        action='store_true',
+        help='Force free gpu memory before final decoding',
+    )
+
+    return parser

backend/modules/parameters.py

@@ -0,0 +1,61 @@
+from backend.modules.parse_seed_weights import parse_seed_weights
+import argparse
+
+SAMPLER_CHOICES = [
+    "ddim",
+    "k_dpm_2_a",
+    "k_dpm_2",
+    "k_euler_a",
+    "k_euler",
+    "k_heun",
+    "k_lms",
+    "plms",
+]
+
+
+def parameters_to_command(params):
+    """
+    Converts dict of parameters into a `invoke.py` REPL command.
+    """
+
+    switches = list()
+
+    if "prompt" in params:
+        switches.append(f'"{params["prompt"]}"')
+    if "steps" in params:
+        switches.append(f'-s {params["steps"]}')
+    if "seed" in params:
+        switches.append(f'-S {params["seed"]}')
+    if "width" in params:
+        switches.append(f'-W {params["width"]}')
+    if "height" in params:
+        switches.append(f'-H {params["height"]}')
+    if "cfg_scale" in params:
+        switches.append(f'-C {params["cfg_scale"]}')
+    if "sampler_name" in params:
+        switches.append(f'-A {params["sampler_name"]}')
+    if "seamless" in params and params["seamless"] == True:
+        switches.append(f"--seamless")
+    if "init_img" in params and len(params["init_img"]) > 0:
+        switches.append(f'-I {params["init_img"]}')
+    if "init_mask" in params and len(params["init_mask"]) > 0:
+        switches.append(f'-M {params["init_mask"]}')
+    if "init_color" in params and len(params["init_color"]) > 0:
+        switches.append(f'--init_color {params["init_color"]}')
+    if "strength" in params and "init_img" in params:
+        switches.append(f'-f {params["strength"]}')
+        if "fit" in params and params["fit"] == True:
+            switches.append(f"--fit")
+    if "gfpgan_strength" in params and params["gfpgan_strength"]:
+        switches.append(f'-G {params["gfpgan_strength"]}')
+    if "upscale" in params and params["upscale"]:
+        switches.append(f'-U {params["upscale"][0]} {params["upscale"][1]}')
+    if "variation_amount" in params and params["variation_amount"] > 0:
+        switches.append(f'-v {params["variation_amount"]}')
+        if "with_variations" in params:
+            seed_weight_pairs = ",".join(
+                f"{seed}:{weight}" for seed, weight in params["with_variations"]
+            )
+            switches.append(f"-V {seed_weight_pairs}")
+
+    return " ".join(switches)

backend/modules/parse_seed_weights.py

@@ -0,0 +1,47 @@
+def parse_seed_weights(seed_weights):
+    """
+    Accepts seed weights as string in "12345:0.1,23456:0.2,3456:0.3" format
+    Validates them
+    If valid: returns as [[12345, 0.1], [23456, 0.2], [3456, 0.3]]
+    If invalid: returns False
+    """
+
+    # Must be a string
+    if not isinstance(seed_weights, str):
+        return False
+    # String must not be empty
+    if len(seed_weights) == 0:
+        return False
+
+    pairs = []
+
+    for pair in seed_weights.split(","):
+        split_values = pair.split(":")
+
+        # Seed and weight are required
+        if len(split_values) != 2:
+            return False
+
+        if len(split_values[0]) == 0 or len(split_values[1]) == 1:
+            return False
+
+        # Try casting the seed to int and weight to float
+        try:
+            seed = int(split_values[0])
+            weight = float(split_values[1])
+        except ValueError:
+            return False
+
+        # Seed must be 0 or above
+        if not seed >= 0:
+            return False
+
+        # Weight must be between 0 and 1
+        if not (weight >= 0 and weight <= 1):
+            return False
+
+        # This pair is valid
+        pairs.append([seed, weight])
+
+    # All pairs are valid
+    return pairs

backend/server.py

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

configs/models.yaml

@@ -0,0 +1,18 @@
+# This file describes the alternative machine learning models
+#  available to the dream script.
+#
+# To add a new model, follow the examples below. Each
+# model requires a model config file, a weights file,
+# and the width and height of the images it
+# was trained on.
+
+laion400m:
+    config:  configs/latent-diffusion/txt2img-1p4B-eval.yaml
+    weights: models/ldm/text2img-large/model.ckpt
+    width: 256
+    height: 256
+stable-diffusion-1.4:
+    config:  configs/stable-diffusion/v1-inference.yaml
+    weights: models/ldm/stable-diffusion-v1/model.ckpt
+    width: 512
+    height: 512

configs/stable-diffusion/v1-finetune.yaml

@@ -0,0 +1,110 @@
+model:
+  base_learning_rate: 5.0e-03
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: image
+    cond_stage_key: caption
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: true   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+    embedding_reg_weight: 0.0
+
+    personalization_config:
+      target: ldm.modules.embedding_manager.EmbeddingManager
+      params:
+        placeholder_strings: ["*"]
+        initializer_words: ["sculpture"]
+        per_image_tokens: false
+        num_vectors_per_token: 1
+        progressive_words: False
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 1
+    num_workers: 2
+    wrap: false
+    train:
+      target: ldm.data.personalized.PersonalizedBase
+      params:
+        size: 512
+        set: train
+        per_image_tokens: false
+        repeats: 100
+    validation:
+      target: ldm.data.personalized.PersonalizedBase
+      params:
+        size: 512
+        set: val
+        per_image_tokens: false
+        repeats: 10
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 500
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 500
+        max_images: 8
+        increase_log_steps: False
+
+  trainer:
+    benchmark: True
+    max_steps: 4000000
+#    max_steps: 4000
+    

configs/stable-diffusion/v1-finetune_style.yaml

@@ -0,0 +1,103 @@
+model:
+  base_learning_rate: 5.0e-03
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: image
+    cond_stage_key: caption
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: true   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+    embedding_reg_weight: 0.0
+
+    personalization_config:
+      target: ldm.modules.embedding_manager.EmbeddingManager
+      params:
+        placeholder_strings: ["*"]
+        initializer_words: ["painting"]
+        per_image_tokens: false
+        num_vectors_per_token: 1
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 2
+    num_workers: 16
+    wrap: false
+    train:
+      target: ldm.data.personalized_style.PersonalizedBase
+      params:
+        size: 512
+        set: train
+        per_image_tokens: false
+        repeats: 100
+    validation:
+      target: ldm.data.personalized_style.PersonalizedBase
+      params:
+        size: 512
+        set: val
+        per_image_tokens: false
+        repeats: 10
+
+lightning:
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 500
+        max_images: 8
+        increase_log_steps: False
+
+  trainer:
+    benchmark: True

configs/stable-diffusion/v1-inference.yaml

@@ -26,6 +26,15 @@ model:
         f_max: [ 1. ]
         f_min: [ 1. ]
 
+    personalization_config:
+      target: ldm.modules.embedding_manager.EmbeddingManager
+      params:
+        placeholder_strings: ["*"]
+        initializer_words: ['face', 'man', 'photo', 'africanmale']
+        per_image_tokens: false
+        num_vectors_per_token: 1
+        progressive_words: False
+
     unet_config:
       target: ldm.modules.diffusionmodules.openaimodel.UNetModel
       params:

configs/stable-diffusion/v1-m1-finetune.yaml

@@ -0,0 +1,110 @@
+model:
+  base_learning_rate: 5.0e-03
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: image
+    cond_stage_key: caption
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: true   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+    embedding_reg_weight: 0.0
+
+    personalization_config:
+      target: ldm.modules.embedding_manager.EmbeddingManager
+      params:
+        placeholder_strings: ["*"]
+        initializer_words: ['face', 'man', 'photo', 'africanmale']
+        per_image_tokens: false
+        num_vectors_per_token: 6
+        progressive_words: False
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 1
+    num_workers: 2
+    wrap: false
+    train:
+      target: ldm.data.personalized.PersonalizedBase
+      params:
+        size: 512
+        set: train
+        per_image_tokens: false
+        repeats: 100
+    validation:
+      target: ldm.data.personalized.PersonalizedBase
+      params:
+        size: 512
+        set: val
+        per_image_tokens: false
+        repeats: 10
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 500
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 500
+        max_images: 5
+        increase_log_steps: False
+
+  trainer:
+    benchmark: False
+    max_steps: 6200
+#    max_steps: 4000
+    

docker-build/Dockerfile

@@ -0,0 +1,57 @@
+FROM        debian 
+
+ARG         gsd
+ENV         GITHUB_STABLE_DIFFUSION $gsd
+
+ARG         rsd
+ENV         REQS $rsd
+
+ARG         cs
+ENV         CONDA_SUBDIR $cs
+
+ENV         PIP_EXISTS_ACTION="w"
+
+# TODO: Optimize image size
+
+SHELL       ["/bin/bash", "-c"]
+
+WORKDIR     /
+RUN         apt update && apt upgrade -y \
+            && apt install -y \
+            git \
+            libgl1-mesa-glx \
+            libglib2.0-0 \
+            pip \
+            python3 \
+            && git clone $GITHUB_STABLE_DIFFUSION
+
+# Install Anaconda or Miniconda
+COPY        anaconda.sh .
+RUN         bash anaconda.sh -b -u -p /anaconda && /anaconda/bin/conda init bash
+
+# SD 
+WORKDIR     /stable-diffusion 
+RUN         source ~/.bashrc \
+            && conda create -y --name ldm && conda activate ldm \
+            && conda config --env --set subdir $CONDA_SUBDIR \
+            && pip3 install -r $REQS \   
+            && pip3 install basicsr facexlib realesrgan \          
+            && mkdir models/ldm/stable-diffusion-v1 \
+            && ln -s "/data/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt 
+
+# Face restoreation
+# by default expected in a sibling directory to stable-diffusion
+WORKDIR     /       
+RUN         git clone https://github.com/TencentARC/GFPGAN.git 
+
+WORKDIR     /GFPGAN 
+RUN         pip3 install -r requirements.txt \
+            && python3 setup.py develop \
+            && ln -s "/data/GFPGANv1.4.pth" experiments/pretrained_models/GFPGANv1.4.pth
+
+WORKDIR     /stable-diffusion 
+RUN         python3 scripts/preload_models.py         
+
+WORKDIR     /
+COPY        entrypoint.sh .
+ENTRYPOINT ["/entrypoint.sh"]

docker-build/entrypoint.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cd /stable-diffusion
+
+if [ $# -eq 0 ]; then
+    python3 scripts/dream.py --full_precision -o /data
+    # bash
+else
+    python3 scripts/dream.py --full_precision -o /data "$@"
+fi

docs/CHANGELOG.md

@@ -0,0 +1,192 @@
+---
+title: Changelog
+---
+
+# :octicons-log-16: **Changelog**
+
+## v2.0.1 (13 October 2022)
+
+  - fix noisy images at high step count when using k* samplers
+  - dream.py script now calls invoke.py module directly rather than
+    via a new python process (which could break the environment)
+
+## v2.0.0 <small>(9 October 2022)</small>
+
+  - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
+    for backward compatibility.
+  - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/INPAINTING.md">inpainting</a> and <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OUTPAINTING.md">outpainting</a>
+  - img2img runs on all k* samplers
+  - Support for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/PROMPTS.md#negative-and-unconditioned-prompts">negative prompts</a>
+  - Support for CodeFormer face reconstruction
+  - Support for Textual Inversion on Macintoshes
+  - Support in both WebGUI and CLI for <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/POSTPROCESS.md">post-processing of previously-generated images</a>
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+  - New `--hires` option on `invoke>` line allows <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.m#this-is-an-example-of-txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
+  - New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
+  - Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+  - Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+  - Improved <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/CLI.m">command-line completion behavior</a>.
+    New commands added:
+       * List command-line history with `!history`
+       * Search command-line history with `!search`
+       * Clear history with `!clear`
+  - Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+    configure. To switch away from auto use the new flag like `--precision=float32`.
+
+## v1.14 <small>(11 September 2022)</small>
+
+  - Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
+  - Full support for Apple hardware with M1 or M2 chips.
+  - Add "seamless mode" for circular tiling of image. Generates beautiful effects.
+    ([prixt](https://github.com/prixt)).
+  - Inpainting support.
+  - Improved web server GUI.
+  - Lots of code and documentation cleanups.
+
+## v1.13 <small>(3 September 2022)</small>
+
+  - Support image variations (see [VARIATIONS](features/VARIATIONS.md)
+    ([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers)
+  - Supports a Google Colab notebook for a standalone server running on Google hardware
+    [Arturo Mendivil](https://github.com/artmen1516)
+  - WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
+    [Kevin Gibbons](https://github.com/bakkot)
+  - WebUI supports incremental display of in-progress images during generation
+    [Kevin Gibbons](https://github.com/bakkot)
+  - A new configuration file scheme that allows new models (including upcoming
+    stable-diffusion-v1.5) to be added without altering the code.
+    ([David Wager](https://github.com/maddavid12))
+  - Can specify --grid on invoke.py command line as the default.
+  - Miscellaneous internal bug and stability fixes.
+  - Works on M1 Apple hardware.
+  - Multiple bug fixes.
+
+---
+
+## v1.12 <small>(28 August 2022)</small>
+
+- Improved file handling, including ability to read prompts from standard input.
+  (kudos to [Yunsaki](https://github.com/yunsaki)
+- The web server is now integrated with the invoke.py script. Invoke by adding --web to
+  the invoke.py command arguments.
+- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
+  enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
+  VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
+  [Oceanswave](https://github.com/oceanswave) for their work on this.
+- You can now swap samplers on the invoke> command line. [Blessedcoolant](https://github.com/blessedcoolant)
+
+---
+
+## v1.11 <small>(26 August 2022)</small>
+
+- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
+- You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
+  Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
+- Variant generation support temporarily disabled pending more general solution.
+- Created a feature branch named **yunsaki-morphing-invoke** which adds experimental support for
+  iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
+  for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
+  significantly.
+
+---
+
+## v1.10 <small>(25 August 2022)</small>
+
+- A barebones but fully functional interactive web server for online generation of txt2img and img2img.
+
+---
+
+## v1.09 <small>(24 August 2022)</small>
+
+- A new -v option allows you to generate multiple variants of an initial image
+  in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
+  See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
+- Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
+- Enabled all of the samplers from k_diffusion
+
+---
+
+## v1.08 <small>(24 August 2022)</small>
+
+- Escape single quotes on the invoke> command before trying to parse. This avoids
+  parse errors.
+- Removed instruction to get Python3.8 as first step in Windows install.
+  Anaconda3 does it for you.
+- Added bounds checks for numeric arguments that could cause crashes.
+- Cleaned up the copyright and license agreement files.
+
+---
+
+## v1.07 <small>(23 August 2022)</small>
+
+- Image filenames will now never fill gaps in the sequence, but will be assigned the
+  next higher name in the chosen directory. This ensures that the alphabetic and chronological
+  sort orders are the same.
+
+---
+
+## v1.06 <small>(23 August 2022)</small>
+
+- Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
+- Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
+
+---
+
+## v1.05 <small>(22 August 2022 - after the drop)</small>
+
+- Filenames now use the following formats:
+  000010.95183149.png -- Two files produced by the same command (e.g. -n2),
+  000010.26742632.png -- distinguished by a different seed.
+
+  000011.455191342.01.png -- Two files produced by the same command using
+  000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
+
+  000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid can
+  be regenerated with the indicated key
+
+- It should no longer be possible for one image to overwrite another
+- You can use the "cd" and "pwd" commands at the invoke> prompt to set and retrieve
+  the path of the output directory.
+
+---
+
+## v1.04 <small>(22 August 2022 - after the drop)</small>
+
+- Updated README to reflect installation of the released weights.
+- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
+  tokenizer.
+
+---
+
+## v1.03 <small>(22 August 2022)</small>
+
+- The original txt2img and img2img scripts from the CompViz repository have been moved into
+  a subfolder named "orig_scripts", to reduce confusion.
+
+---
+
+## v1.02 <small>(21 August 2022)</small>
+
+- A copy of the prompt and all of its switches and options is now stored in the corresponding
+  image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
+  or an image editor that allows you to explore the full metadata.
+  **Please run "conda env update" to load the k_lms dependencies!!**
+
+---
+
+## v1.01 <small>(21 August 2022)</small>
+
+- added k_lms sampling.
+  **Please run "conda env update" to load the k_lms dependencies!!**
+- use half precision arithmetic by default, resulting in faster execution and lower memory requirements
+  Pass argument --full_precision to invoke.py to get slower but more accurate image generation
+
+---
+
+## Links
+
+- **[Read Me](index.md)**

docs/features/CHANGELOG.md

@@ -0,0 +1,143 @@
+---
+title: Changelog
+---
+
+# :octicons-log-16: Changelog
+
+## v1.13
+
+- Supports a Google Colab notebook for a standalone server running on Google
+  hardware [Arturo Mendivil](https://github.com/artmen1516)
+- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
+  [Kevin Gibbons](https://github.com/bakkot)
+- WebUI supports incremental display of in-progress images during generation
+  [Kevin Gibbons](https://github.com/bakkot)
+- Output directory can be specified on the invoke> command line.
+- The grid was displaying duplicated images when not enough images to fill the
+  final row [Muhammad Usama](https://github.com/SMUsamaShah)
+- Can specify --grid on invoke.py command line as the default.
+- Miscellaneous internal bug and stability fixes.
+
+---
+
+## v1.12 <small>(28 August 2022)</small>
+
+- Improved file handling, including ability to read prompts from standard input.
+  (kudos to [Yunsaki](https://github.com/yunsaki)
+- The web server is now integrated with the invoke.py script. Invoke by adding
+  --web to the invoke.py command arguments.
+- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
+  enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
+  VRAM requirements are modestly reduced. Thanks to both
+  [Blessedcoolant](https://github.com/blessedcoolant) and
+  [Oceanswave](https://github.com/oceanswave) for their work on this.
+- You can now swap samplers on the invoke> command line.
+  [Blessedcoolant](https://github.com/blessedcoolant)
+
+---
+
+## v1.11 <small>(26 August 2022)</small>
+
+- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module.
+  (kudos to [Oceanswave](https://github.com/Oceanswave))
+- You now can specify a seed of -1 to use the previous image's seed, -2 to use
+  the seed for the image generated before that, etc. Seed memory only extends
+  back to the previous command, but will work on all images generated with the
+  -n# switch.
+- Variant generation support temporarily disabled pending more general solution.
+- Created a feature branch named **yunsaki-morphing-invoke** which adds
+  experimental support for iteratively modifying the prompt and its parameters.
+  Please
+  see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86) for
+  a synopsis of how this works. Note that when this feature is eventually added
+  to the main branch, it will may be modified significantly.
+
+---
+
+## v1.10 <small>(25 August 2022)</small>
+
+- A barebones but fully functional interactive web server for online generation
+  of txt2img and img2img.
+
+---
+
+## v1.09 <small>(24 August 2022)</small>
+
+- A new -v option allows you to generate multiple variants of an initial image
+  in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave).
+- [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
+- Added ability to personalize text to image generation (kudos to
+  [Oceanswave](https://github.com/Oceanswave) and
+  [nicolai256](https://github.com/nicolai256))
+- Enabled all of the samplers from k_diffusion
+
+---
+
+## v1.08 <small>(24 August 2022)</small>
+
+- Escape single quotes on the invoke> command before trying to parse. This avoids
+  parse errors.
+- Removed instruction to get Python3.8 as first step in Windows install.
+  Anaconda3 does it for you.
+- Added bounds checks for numeric arguments that could cause crashes.
+- Cleaned up the copyright and license agreement files.
+
+---
+
+## v1.07 <small>(23 August 2022)</small>
+
+- Image filenames will now never fill gaps in the sequence, but will be assigned
+  the next higher name in the chosen directory. This ensures that the alphabetic
+  and chronological sort orders are the same.
+
+---
+
+## v1.06 <small>(23 August 2022)</small>
+
+- Added weighted prompt support contributed by
+  [xraxra](https://github.com/xraxra)
+- Example of using weighted prompts to tweak a demonic figure contributed by
+  [bmaltais](https://github.com/bmaltais)
+
+---
+
+## v1.05 <small>(22 August 2022 - after the drop)</small>
+
+- Filenames now use the following formats: 000010.95183149.png -- Two files
+  produced by the same command (e.g. -n2), 000010.26742632.png -- distinguished
+  by a different seed.
+    000011.455191342.01.png -- Two files produced by the same command using
+    000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
+    000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid
+  can be regenerated with the indicated key
+
+- It should no longer be possible for one image to overwrite another
+- You can use the "cd" and "pwd" commands at the invoke> prompt to set and
+  retrieve the path of the output directory.
+
+## v1.04 <small>(22 August 2022 - after the drop)</small>
+
+- Updated README to reflect installation of the released weights.
+- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
+  tokenizer.
+
+## v1.03 <small>(22 August 2022)</small>
+
+- The original txt2img and img2img scripts from the CompViz repository have been
+  moved into a subfolder named "orig_scripts", to reduce confusion.
+
+## v1.02 <small>(21 August 2022)</small>
+
+- A copy of the prompt and all of its switches and options is now stored in the
+  corresponding image in a tEXt metadata field named "Dream". You can read the
+  prompt using scripts/images2prompt.py, or an image editor that allows you to
+  explore the full metadata. **Please run "conda env update -f environment.yaml"
+  to load the k_lms dependencies!!**
+
+## v1.01 <small>(21 August 2022)</small>
+
+- added k_lms sampling. **Please run "conda env update -f environment.yaml" to
+  load the k_lms dependencies!!**
+- use half precision arithmetic by default, resulting in faster execution and
+  lower memory requirements Pass argument --full_precision to invoke.py to get
+  slower but more accurate image generation

docs/features/CLI.md

@@ -0,0 +1,359 @@
+---
+title: CLI
+hide:
+  - toc
+---
+
+# :material-bash: CLI
+
+## **Interactive Command Line Interface**
+
+The `invoke.py` script, located in `scripts/dream.py`, provides an interactive
+interface to image generation similar to the "invoke mothership" bot that Stable
+AI provided on its Discord server.
+
+Unlike the `txt2img.py` and `img2img.py` scripts provided in the original
+[CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) source
+code repository, the time-consuming initialization of the AI model
+initialization only happens once. After that image generation from the
+command-line interface is very fast.
+
+The script uses the readline library to allow for in-line editing, command
+history (++up++ and ++down++), autocompletion, and more. To help keep track of
+which prompts generated which images, the script writes a log file of image
+names and prompts to the selected output directory.
+
+In addition, as of version 1.02, it also writes the prompt into the PNG file's
+metadata where it can be retrieved using `scripts/images2prompt.py`
+
+The script is confirmed to work on Linux, Windows and Mac systems.
+
+!!! note
+
+    This script runs from the command-line or can be used as a Web application. The Web GUI is
+    currently rudimentary, but a much better replacement is on its way.
+
+```bash
+(invokeai) ~/stable-diffusion$ python3 ./scripts/invoke.py
+* Initializing, be patient...
+Loading model from models/ldm/text2img-large/model.ckpt
+(...more initialization messages...)
+
+* Initialization done! Awaiting your command...
+invoke> ashley judd riding a camel -n2 -s150
+Outputs:
+   outputs/img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
+   outputs/img-samples/00010.png: "ashley judd riding a camel" -n2 -s150 -S 1362479620
+
+invoke> "there's a fly in my soup" -n6 -g
+    outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
+    seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
+invoke> q
+
+# this shows how to retrieve the prompt stored in the saved image's metadata
+(invokeai) ~/stable-diffusion$ python ./scripts/images2prompt.py outputs/img_samples/*.png
+00009.png: "ashley judd riding a camel" -s150 -S 416354203
+00010.png: "ashley judd riding a camel" -s150 -S 1362479620
+00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
+```
+
+![invoke-py-demo](../assets/dream-py-demo.png)
+
+The `invoke>` prompt's arguments are pretty much identical to those used in the
+Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you do).
+A significant change is that creation of individual images is now the default
+unless `--grid` (`-g`) is given. A full list is given in
+[List of prompt arguments](#list-of-prompt-arguments).
+
+## Arguments
+
+The script itself also recognizes a series of command-line switches that will
+change important global defaults, such as the directory for image outputs and
+the location of the model weight files.
+
+### List of arguments recognized at the command line
+
+These command-line arguments can be passed to `invoke.py` when you first run it
+from the Windows, Mac or Linux command line. Some set defaults that can be
+overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt-arguments). Others
+
+| Argument <img width="240" align="right"/> | Shortcut <img width="100" align="right"/> | Default <img width="320" align="right"/>       | Description                                                                                          |
+| ----------------------------------------- | ----------------------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| `--help`                                  | `-h`                                      |                                                | Print a concise help message.                                                                        |
+| `--outdir <path>`                         | `-o<path>`                                | `outputs/img_samples`                          | Location for generated images.                                                                       |
+| `--prompt_as_dir`                         | `-p`                                      | `False`                                        | Name output directories using the prompt text.                                                       |
+| `--from_file <path>`                      |                                           | `None`                                         | Read list of prompts from a file. Use `-` to read from standard input                                |
+| `--model <modelname>`                     |                                           | `stable-diffusion-1.4`                         | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" |
+| `--full_precision`                        | `-F`                                      | `False`                                        | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards.   |
+| `--web`                                   |                                           | `False`                                        | Start in web server mode                                                                             |
+| `--host <ip addr>`                        |                                           | `localhost`                                    | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any.                |
+| `--port <port>`                           |                                           | `9090`                                         | Which port web server should listen for requests on.                                                 |
+| `--config <path>`                         |                                           | `configs/models.yaml`                          | Configuration file for models and their weights.                                                     |
+| `--iterations <int>`                      | `-n<int>`                                 | `1`                                            | How many images to generate per prompt.                                                              |
+| `--grid`                                  | `-g`                                      | `False`                                        | Save all image series as a grid rather than individually.                                            |
+| `--sampler <sampler>`                     | `-A<sampler>`                             | `k_lms`                                        | Sampler to use. Use `-h` to get list of available samplers.                                          |
+| `--seamless`                              |                                           | `False`                                        | Create interesting effects by tiling elements of the image.                                          |
+| `--embedding_path <path>`                 |                                           | `None`                                         | Path to pre-trained embedding manager checkpoints, for custom models                                 |
+| `--gfpgan_dir`                            |                                           | `src/gfpgan`                                   | Path to where GFPGAN is installed.                                                                   |
+| `--gfpgan_model_path`                     |                                           | `experiments/pretrained_models/GFPGANv1.4.pth` | Path to GFPGAN model file, relative to `--gfpgan_dir`.                                               |
+| `--device <device>`                       | `-d<device>`                              | `torch.cuda.current_device()`                  | Device to run SD on, e.g. "cuda:0"                                                                   |
+| `--free_gpu_mem`                          |                                           | `False`                                        | Free GPU memory after sampling, to allow image decoding and saving in low VRAM conditions            |
+| `--precision`                             |                                           | `auto`                                         | Set model precision, default is selected by device. Options: auto, float32, float16, autocast        |
+
+!!! warning deprecated
+
+    These arguments are deprecated but still work:
+
+    <div align="center" markdown>
+
+    | Argument           |  Shortcut  |  Default            |  Description |
+    |--------------------|------------|---------------------|--------------|
+    | `--weights <path>`   |            | `None`                | Pth to weights file; use `--model stable-diffusion-1.4` instead |
+    | `--laion400m`        | `-l`         | `False`               | Use older LAION400m weights; use `--model=laion400m` instead |
+
+    </div>
+
+!!! tip
+
+      On Windows systems, you may run into
+      problems when passing the invoke script standard backslashed path
+      names because the Python interpreter treats "\" as an escape.
+      You can either double your slashes (ick): `C:\\path\\to\\my\\file`, or
+      use Linux/Mac style forward slashes (better): `C:/path/to/my/file`.
+
+## List of prompt arguments
+
+After the invoke.py script initializes, it will present you with a
+`invoke>` prompt. Here you can enter information to generate images
+from text ([txt2img](#txt2img)), to embellish an existing image or sketch
+([img2img](#img2img)), or to selectively alter chosen regions of the image
+([inpainting](#inpainting)).
+
+### txt2img
+
+!!! example
+
+    ```bash
+    invoke> waterfall and rainbow -W640 -H480
+    ```
+
+    This will create the requested image with the dimensions 640 (width)
+    and 480 (height).
+
+Here are the invoke> command that apply to txt2img:
+
+| Argument <img width="680" align="right"/> | Shortcut <img width="420" align="right"/> | Default <img width="480" align="right"/> | Description |
+|--------------------|------------|---------------------|--------------|
+| `"my prompt"`        |            |                    | Text prompt to use. The quotation marks are optional. |
+| `--width <int>`      | `-W<int>`   | `512`                 | Width of generated image |
+| `--height <int>`     | `-H<int>`   | `512`                 | Height of generated image |
+| `--iterations <int>` | `-n<int>`   | `1`                   | How many images to generate from this prompt |
+| `--steps <int>`      | `-s<int>`   | `50`                  | How many steps of refinement to apply |
+| `--cfg_scale <float>`| `-C<float>` | `7.5`                 | How hard to try to match the prompt to the generated image; any number greater than 1.0 works, but the useful range is roughly 5.0 to 20.0 |
+| `--seed <int>`       | `-S<int>`   | `None`                | Set the random seed for the next series of images. This can be used to recreate an image generated previously.|
+| `--sampler <sampler>`| `-A<sampler>`| `k_lms`              | Sampler to use. Use -h to get list of available samplers. |
+| `--hires_fix`        |           |                     | Larger images often have duplication artefacts. This option suppresses duplicates by generating the image at low res, and then using img2img to increase the resolution |
+| `--grid`             | `-g`        | `False`               | Turn on grid mode to return a single image combining all the images generated by this prompt |
+| `--individual`       | `-i`        | `True`                | Turn off grid mode (deprecated; leave off `--grid` instead) |
+| `--outdir <path>`    |  `-o<path>` | `outputs/img_samples`  | Temporarily change the location of these images |
+| `--seamless`         |           | `False`               | Activate seamless tiling for interesting effects |
+| `--log_tokenization` | `-t`        | `False`               | Display a color-coded list of the parsed tokens derived from the prompt |
+| `--skip_normalization`| `-x`       | `False`               | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts) |
+| `--upscale <int> <float>` | `-U <int> <float>` | `-U 1 0.75`| Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75. |
+| `--gfpgan_strength <float>`  | `-G <float>` | `-G0`        | Fix faces using the GFPGAN algorithm; argument indicates how hard the algorithm should try (0.0-1.0) |
+| `--save_original`    | `-save_orig`| `False`               | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
+| `--variation <float>`  |`-v<float>`| `0.0`                 | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
+| `--with_variations <pattern>` | `-V<pattern>`| `None`      | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |
+
+!!! note
+
+    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.
+
+### img2img
+
+!!! example
+
+    ```bash
+    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 to `--fit` the image into a box no bigger than
+    640x480. Otherwise the image size will be identical to the provided
+    photo and you may run out of memory if it is large.
+
+In addition to the command-line options recognized by txt2img, img2img
+accepts additional options:
+
+| Argument <img width="160" align="right"/> |  Shortcut   |  Default        |  Description |
+|----------------------|-------------|-----------------|--------------|
+| `--init_img <path>`  | `-I<path>`  | `None`          | Path to the initialization image |
+| `--fit`              | `-F`        | `False`         | Scale the image to fit into the specified -H and -W dimensions |
+| `--strength <float>` | `-s<float>` | `0.75`          | How hard to try to match the prompt to the initial image. Ranges from 0.0-0.99, with higher values replacing the initial image completely.|
+
+### inpainting
+
+!!! example
+
+    ```bash
+    invoke> waterfall and rainbow -I./vacation-photo.png -M./vacation-mask.png -W640 -H480 --fit
+    ```
+
+    This will do the same thing as img2img, but image alterations will
+    only occur within transparent areas defined by the mask file specified
+    by `-M`. You may also supply just a single initial image with the areas
+    to overpaint made transparent, but you must be careful not to destroy
+    the pixels underneath when you create the transparent areas. See
+    [Inpainting](./INPAINTING.md) for details.
+
+inpainting accepts all the arguments used for txt2img and img2img, as
+well as the --mask (-M) argument:
+
+| Argument <img width="100" align="right"/> |  Shortcut  |  Default            |  Description |
+|--------------------|------------|---------------------|--------------|
+| `--init_mask <path>` | `-M<path>`   | `None`                |Path to an image the same size as the initial_image, with areas for inpainting made transparent.|
+
+## Convenience commands
+
+In addition to the standard image generation arguments, there are a
+series of convenience commands that begin with !:
+
+### `!fix`
+
+This command runs a post-processor on a previously-generated image. It
+takes a PNG filename or path and applies your choice of the `-U`, `-G`, or
+`--embiggen` switches in order to fix faces or upscale. If you provide a
+filename, the script will look for it in the current output
+directory. Otherwise you can provide a full or partial path to the
+desired file.
+
+Some examples:
+
+!!! example ""
+
+    Upscale to 4X its original size and fix faces using codeformer:
+
+    ```bash
+    invoke> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
+    ```
+
+!!! example ""
+
+    Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen:
+
+    ```bash
+    invoke> !fix 0000045.4829112.png -G0.8 -ft gfpgan
+    >> fixing outputs/img-samples/0000045.4829112.png
+    >> retrieved seed 4829112 and prompt "boy enjoying a banana split"
+    >> GFPGAN - Restoring Faces for image seed:4829112
+    Outputs:
+    [1] outputs/img-samples/000017.4829112.gfpgan-00.png: !fix "outputs/img-samples/0000045.4829112.png" -s 50 -S  -W 512 -H 512 -C 7.5 -A k_lms -G 0.8
+
+    invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3
+    ...lots of text...
+    Outputs:
+    [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix "outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512 -H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25
+    ```
+
+### `!fetch`
+
+This command retrieves the generation parameters from a previously
+generated image and either loads them into the command line.  You may
+provide either the name of a file in the current output directory, or
+a full file path.
+
+```bash
+invoke> !fetch 0000015.8929913.png
+# the script returns the next line, ready for editing and running:
+invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
+```
+
+Note that this command may behave unexpectedly if given a PNG file that
+was not generated by InvokeAI.
+
+### `!history`
+
+The invoke script keeps track of all the commands you issue during a
+session, allowing you to re-run them. On Mac and Linux systems, it
+also writes the command-line history out to disk, giving you access to
+the most recent 1000 commands issued.
+
+The `!history` command will return a numbered list of all the commands
+issued during the session (Windows), or the most recent 1000 commands
+(Mac|Linux). You can then repeat a command by using the command `!NNN`,
+where "NNN" is the history line number. For example:
+
+```bash
+invoke> !history
+...
+[14] happy woman sitting under tree wearing broad hat and flowing garment
+[15] beautiful woman sitting under tree wearing broad hat and flowing garment
+[18] beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6
+[20] watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+[21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+...
+invoke> !20
+invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+```
+
+### `!search <search string>`
+
+This is similar to !history but it only returns lines that contain
+`search string`. For example:
+
+```bash
+invoke> !search surreal
+[21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+```
+
+### `!clear`
+
+This clears the search history from memory and disk. Be advised that
+this operation is irreversible and does not issue any warnings!
+
+## Command-line editing and completion
+
+The command-line offers convenient history tracking, editing, and
+command completion.
+
+- To scroll through previous commands and potentially edit/reuse them, use the ++up++ and ++down++ keys.
+- To edit the current command, use the ++left++ and ++right++ keys to position the cursor, and then ++backspace++, ++delete++ or insert characters.
+- To move to the very beginning of the command, type ++ctrl+a++ (or ++command+a++ on the Mac)
+- To move to the end of the command, type ++ctrl+e++.
+- To cut a section of the command, position the cursor where you want to start cutting and type ++ctrl+k++
+- To paste a cut section back in, position the cursor where you want to paste, and type ++ctrl+y++
+
+Windows users can get similar, but more limited, functionality if they
+launch `invoke.py` with the `winpty` program and have the `pyreadline3`
+library installed:
+
+```batch
+> winpty python scripts\invoke.py
+```
+
+On the Mac and Linux platforms, when you exit invoke.py, the last 1000
+lines of your command-line history will be saved. When you restart
+`invoke.py`, you can access the saved history using the ++up++ key.
+
+In addition, limited command-line completion is installed. In various
+contexts, you can start typing your command and press ++tab++. A list of
+potential completions will be presented to you. You can then type a
+little more, hit ++tab++ again, and eventually autocomplete what you want.
+
+When specifying file paths using the one-letter shortcuts, the CLI
+will attempt to complete pathnames for you. This is most handy for the
+`-I` (init image) and `-M` (init mask) paths. To initiate completion, start
+the path with a slash (`/`) or `./`. For example:
+
+```bash
+invoke> zebra with a mustache -I./test-pictures<TAB>
+-I./test-pictures/Lincoln-and-Parrot.png  -I./test-pictures/zebra.jpg        -I./test-pictures/madonna.png
+-I./test-pictures/bad-sketch.png          -I./test-pictures/man_with_eagle/
+```
+
+You can then type ++z++, hit ++tab++ again, and it will autofill to `zebra.jpg`.
+
+More text completion features (such as autocompleting seeds) are on their way.