update requirements to address #1149

Taken from `main` PR #1147 
Author: eltociear

.dev_scripts/test_regression_txt2img_dream_v1_4.sh

@@ -5,8 +5,7 @@ SAMPLES_DIR=${OUT_DIR}
 python scripts/dream.py \
     --from_file ${PROMPT_FILE} \
     --outdir ${OUT_DIR} \
-    --sampler plms \
-    --full_precision
+    --sampler plms
 
 # original output by CompVis/stable-diffusion
 IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"

.github/CODEOWNERS

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

.github/workflows/create-caches.yml

@@ -0,0 +1,97 @@
+name: Create Caches
+
+on: workflow_dispatch
+
+jobs:
+  os_matrix:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+        include:
+          - os: ubuntu-latest
+            environment-file: environment.yml
+            default-shell: bash -l {0}
+          - os: macos-latest
+            environment-file: environment-mac.yml
+            default-shell: bash -l {0}
+    name: Test invoke.py on ${{ matrix.os }} with conda
+    runs-on: ${{ matrix.os }}
+    defaults:
+      run:
+        shell: ${{ matrix.default-shell }}
+    steps:
+      - name: Checkout sources
+        uses: actions/checkout@v3
+
+      - name: setup miniconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-activate-base: false
+          auto-update-conda: false
+          miniconda-version: latest
+
+      - name: set environment
+        run: |
+          [[ "$GITHUB_REF" == 'refs/heads/main' ]] \
+            && echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV \
+            || echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
+          echo "CONDA_ROOT=$CONDA" >> $GITHUB_ENV
+          echo "CONDA_ENV_NAME=invokeai" >> $GITHUB_ENV
+
+      - name: Use Cached Stable Diffusion v1.4 Model
+        id: cache-sd-v1-4
+        uses: actions/cache@v3
+        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: |
+          [[ -d models/ldm/stable-diffusion-v1 ]] \
+            || mkdir -p models/ldm/stable-diffusion-v1
+          [[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
+            || curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
+
+      - name: Use cached Conda Environment
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
+        with:
+          path: ${{ env.CONDA_ROOT }}/envs/${{ env.CONDA_ENV_NAME }}
+          key: ${{ env.cache-name }}
+          restore-keys: ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Use cached Conda Packages
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
+        with:
+          path: ${{ env.CONDA_PKGS_DIR }}
+          key: ${{ env.cache-name }}
+          restore-keys: ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Activate Conda Env
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: ${{ matrix.environment-file }}
+
+      - name: Use Cached Huggingface and Torch models
+        id: cache-hugginface-torch
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-hugginface-torch
+        with:
+          path: ~/.cache
+          key: ${{ env.cache-name }}
+          restore-keys: |
+            ${{ env.cache-name }}-${{ hashFiles('scripts/preload_models.py') }}
+
+      - name: run preload_models.py
+        run: python scripts/preload_models.py

.github/workflows/mkdocs-material.yml

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

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

@@ -0,0 +1,126 @@
+name: Test Invoke with Conda
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+      - 'fix-gh-actions-fork'
+  pull_request:
+    branches:
+      - 'main'
+      - 'development'
+
+jobs:
+  os_matrix:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+        include:
+          - os: ubuntu-latest
+            environment-file: environment.yml
+            default-shell: bash -l {0}
+          - os: macos-latest
+            environment-file: environment-mac.yml
+            default-shell: bash -l {0}
+    name: Test invoke.py on ${{ matrix.os }} with conda
+    runs-on: ${{ matrix.os }}
+    defaults:
+      run:
+        shell: ${{ matrix.default-shell }}
+    steps:
+      - name: Checkout sources
+        uses: actions/checkout@v3
+
+      - name: setup miniconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-activate-base: false
+          auto-update-conda: false
+          miniconda-version: latest
+
+      - name: set test prompt to main branch validation
+        if: ${{ github.ref == 'refs/heads/main' }}
+        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV
+
+      - name: set test prompt to development branch validation
+        if: ${{ github.ref == 'refs/heads/development' }}
+        run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
+
+      - name: set test prompt to Pull Request validation
+        if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
+        run: echo "TEST_PROMPTS=tests/pr_prompt.txt" >> $GITHUB_ENV
+
+      - name: set conda environment name
+        run: echo "CONDA_ENV_NAME=invokeai" >> $GITHUB_ENV
+
+      - name: Use Cached Stable Diffusion v1.4 Model
+        id: cache-sd-v1-4
+        uses: actions/cache@v3
+        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: |
+          [[ -d models/ldm/stable-diffusion-v1 ]] \
+            || mkdir -p models/ldm/stable-diffusion-v1
+          [[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
+            || curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
+
+      - name: Use cached Conda Environment
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
+        with:
+          path: ${{ env.CONDA }}/envs/${{ env.CONDA_ENV_NAME }}
+          key: env-${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Use cached Conda Packages
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-conda-pkgs-${{ env.CONDA_ENV_NAME }}
+          conda-env-file: ${{ matrix.environment-file }}
+        with:
+          path: ${{ env.CONDA_PKGS_DIR }}
+          key: pkgs-${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
+
+      - name: Activate Conda Env
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: ${{ matrix.environment-file }}
+
+      - name: Use Cached Huggingface and Torch models
+        id: cache-hugginface-torch
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-hugginface-torch
+        with:
+          path: ~/.cache
+          key: ${{ env.cache-name }}
+          restore-keys: |
+            ${{ env.cache-name }}-${{ hashFiles('scripts/preload_models.py') }}
+
+      - name: run preload_models.py
+        run: python scripts/preload_models.py
+
+      - name: Run the tests
+        run: |
+          time python scripts/invoke.py \
+            --from_file ${{ env.TEST_PROMPTS }}
+
+      - name: export conda env
+        run: |
+          mkdir -p outputs/img-samples
+          conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}.yml
+
+      - name: Archive results
+        uses: actions/upload-artifact@v3
+        with:
+          name: results_${{ matrix.os }}
+          path: outputs/img-samples

.gitignore

@@ -1,6 +1,10 @@
 # ignore default image save location and model symbolic link
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
+**/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/
@@ -176,10 +180,22 @@ src
 **/__pycache__/
 outputs
 
-# Logs and associated folders 
+# 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

CHANGELOG.md

@@ -1,137 +0,0 @@
-# **Changelog**
-
-## v1.13 (in process)
-
-- Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
-- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling [Kevin Gibbons](https://github.com/bakkot)
-- WebUI supports incremental display of in-progress images during generation [Kevin Gibbons](https://github.com/bakkot)
-- Output directory can be specified on the dream> command line.
-- The grid was displaying duplicated images when not enough images to fill the final row [Muhammad Usama](https://github.com/SMUsamaShah)
-- Can specify --grid on dream.py command line as the default.
-- Miscellaneous internal bug and stability fixes.
-
----
-
-## v1.12 (28 August 2022)
-
-- Improved file handling, including ability to read prompts from standard input.
-  (kudos to [Yunsaki](https://github.com/yunsaki)
-- The web server is now integrated with the dream.py script. Invoke by adding --web to
-  the dream.py command arguments.
-- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
-  enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
-  VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
-  [Oceanswave](https://github.com/oceanswave) for their work on this.
-- You can now swap samplers on the dream> command line. [Blessedcoolant](https://github.com/blessedcoolant)
-
----
-
-## v1.11 (26 August 2022)
-
-- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
-- You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
-  Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
-- Variant generation support temporarily disabled pending more general solution.
-- Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
-  iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
-  for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
-  significantly.
-
----
-
-## v1.10 (25 August 2022)
-
-- A barebones but fully functional interactive web server for online generation of txt2img and img2img.
-
----
-
-## v1.09 (24 August 2022)
-
-- 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 (24 August 2022)
-
-- Escape single quotes on the dream> 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 (23 August 2022)
-
-- 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 (23 August 2022)
-
-- Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
-- Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
-
----
-
-## v1.05 (22 August 2022 - after the drop)
-
-- 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 dream> prompt to set and retrieve
-  the path of the output directory.
-
----
-
-## v1.04 (22 August 2022 - after the drop)
-
-- Updated README to reflect installation of the released weights.
-- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
-  tokenizer.
-
----
-
-## v1.03 (22 August 2022)
-
-- The original txt2img and img2img scripts from the CompViz repository have been moved into
-  a subfolder named "orig_scripts", to reduce confusion.
-
----
-
-## v1.02 (21 August 2022)
-
-- 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 (21 August 2022)
-
-- added k_lms sampling.
-  **Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
-- use half precision arithmetic by default, resulting in faster execution and lower memory requirements
-  Pass argument --full_precision to dream.py to get slower but more accurate image generation
-
----
-
-## Links
-
-- **[Read Me](readme.md)**

LICENSE

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

README-CompViz.md

@@ -1,210 +0,0 @@
-# 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
-                        (note that the seeds for each image in the batch will be unavailable)
-  --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}
-}
-
-```
-
-

README-Mac-MPS.md

@@ -1,322 +0,0 @@
-# macOS Instructions
-
-Requirements
-
-- macOS 12.3 Monterey or later
-- Python
-- Patience
-- Apple Silicon*
-
-*I haven't tested any of this on Intel Macs but I have read that one person got
-it to work, so Apple Silicon might not be requried.
-
-Things have moved really fast and so these instructions change often and are
-often out-of-date. One of the problems is that there are so many different ways to
-run this.
-
-We are trying to build a testing setup so that when we make changes it doesn't
-always break.
-
-How to (this hasn't been 100% tested yet):
-
-First get the weights checkpoint download started - it's big:
-
-1. Sign up at https://huggingface.co
-2. Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
-3. Accept the terms and click Access Repository: 
-4. Download [sd-v1-4.ckpt (4.27 GB)](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt) and note where you have saved it (probably the Downloads folder)
-
-While that is downloading, open Terminal and run the following commands one at a time.
-
-```
-# install brew (and Xcode command line tools):
-/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-
-# install python 3, git, cmake, protobuf:
-brew install cmake protobuf rust
-
-# install miniconda (M1 arm64 version):
-curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o Miniconda3-latest-MacOSX-arm64.sh
-/bin/bash Miniconda3-latest-MacOSX-arm64.sh
-
-# clone the repo
-git clone https://github.com/lstein/stable-diffusion.git
-cd stable-diffusion
-
-#
-# wait until the checkpoint file has downloaded, then proceed
-#
-
-# create symlink to checkpoint
-mkdir -p models/ldm/stable-diffusion-v1/
-
-PATH_TO_CKPT="$HOME/Downloads"  # or wherever you saved sd-v1-4.ckpt
-
-ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
-
-# install packages
-PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yaml
-conda activate ldm
-
-# only need to do this once
-python scripts/preload_models.py
-
-# run SD!
-python scripts/dream.py --full_precision  # half-precision requires autocast and won't work
-```
-
-The original scripts should work as well.
-
-```
-python scripts/orig_scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
-```
-
-Note, `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env create -f environment-mac.yaml`
-never finishing in some situations. So it isn't required but wont hurt.
-
-After you follow all the instructions and run dream.py you might get several
-errors. Here's the errors I've seen and found solutions for.
-
-### Is it slow?
-
-Be sure to specify 1 sample and 1 iteration.
-
-	python ./scripts/orig_scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
-
-### Doesn't work anymore?
-
-PyTorch nightly includes support for MPS. Because of this, this setup is
-inherently unstable. One morning I woke up and it no longer worked no matter
-what I did until I switched to miniforge. However, I have another Mac that works
-just fine with Anaconda. If you can't get it to work, please search a little
-first because many of the errors will get posted and solved. If you can't find
-a solution please [create an issue](https://github.com/lstein/stable-diffusion/issues).
-
-One debugging step is to update to the latest version of PyTorch nightly.
-
-	conda install pytorch torchvision torchaudio -c pytorch-nightly
-
-If `conda env create -f environment-mac.yaml` takes forever run this.
-
-	git clean -f
-
-And run this.
-
-	conda clean --yes --all
-
-Or you could reset Anaconda.
-
-	conda update --force-reinstall -y -n base -c defaults conda
-
-### "No module named cv2", torch, 'ldm', 'transformers', 'taming', etc.
-
-There are several causes of these errors.
-
-First, did you remember to `conda activate ldm`? If your terminal prompt
-begins with "(ldm)" then you activated it. If it begins with "(base)"
-or something else you haven't.
-
-Second, you might've run `./scripts/preload_models.py` or `./scripts/dream.py`
-instead of `python ./scripts/preload_models.py` or `python ./scripts/dream.py`.
-The cause of this error is long so it's below.
-
-Third, if it says you're missing taming you need to rebuild your virtual
-environment.
-
-	conda env remove -n ldm
-	conda env create -f environment-mac.yaml
-
-Fourth, If you have activated the ldm virtual environment and tried rebuilding
-it, maybe the problem could be that I have something installed that
-you don't and you'll just need to manually install it. Make sure you
-activate the virtual environment so it installs there instead of
-globally.
-
-	conda activate ldm
-	pip install *name*
-
-You might also need to install Rust (I mention this again below).
-
-### How many snakes are living in your computer?
-
-Here's the reason why you have to specify which python to use.
-There are several versions of python on macOS and the computer is
-picking the wrong one. More specifically, preload_models.py and dream.py says to
-find the first `python3` in the path environment variable. You can see which one
-it is picking with `which python3`. These are the mostly likely paths you'll see.
-
-	% which python3
-	/usr/bin/python3
-
-The above path is part of the OS. However, that path is a stub that asks you if
-you want to install Xcode. If you have Xcode installed already,
-/usr/bin/python3 will execute /Library/Developer/CommandLineTools/usr/bin/python3 or
-/Applications/Xcode.app/Contents/Developer/usr/bin/python3 (depending on which
-Xcode you've selected with `xcode-select`).
-
-	% which python3
-	/opt/homebrew/bin/python3
-
-If you installed python3 with Homebrew and you've modified your path to search
-for Homebrew binaries before system ones, you'll see the above path.
-
-	% which python
-	/opt/anaconda3/bin/python
-
-If you drop the "3" you get an entirely different python. Note: starting in
-macOS 12.3, /usr/bin/python no longer exists (it was python 2 anyway).
-
-If you have Anaconda installed, this is what you'll see. There is a
-/opt/anaconda3/bin/python3 also.
-
-	(ldm) % which python
-	/Users/name/miniforge3/envs/ldm/bin/python
-
-This is what you'll see if you have miniforge and you've correctly activated
-the ldm environment. This is the goal.
-
-It's all a mess and you should know [how to modify the path environment variable](https://support.apple.com/guide/terminal/use-environment-variables-apd382cc5fa-4f58-4449-b20a-41c53c006f8f/mac)
-if you want to fix it. Here's a brief hint of all the ways you can modify it
-(don't really have the time to explain it all here).
-
-- ~/.zshrc
-- ~/.bash_profile
-- ~/.bashrc
-- /etc/paths.d
-- /etc/path
-
-Which one you use will depend on what you have installed except putting a file
-in /etc/paths.d is what I prefer to do.
-
-### Debugging?
-
-Tired of waiting for your renders to finish before you can see if it
-works? Reduce the steps! The image quality will be horrible but at least you'll
-get quick feedback.
-
-	python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
-
-### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'...
-
-	python scripts/preload_models.py
-
-### "The operator [name] is not current implemented for the MPS device." (sic)
-
-Example error.
-
-```
-...
-NotImplementedError: The operator 'aten::_index_put_impl_' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on [https://github.com/pytorch/pytorch/issues/77764](https://github.com/pytorch/pytorch/issues/77764). As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
-```
-
-The lstein branch includes this fix in [environment-mac.yaml](https://github.com/lstein/stable-diffusion/blob/main/environment-mac.yaml).
-
-### "Could not build wheels for tokenizers"
-
-I have not seen this error because I had Rust installed on my computer before I started playing with Stable Diffusion. The fix is to install Rust.
-
-	curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
-
-### How come `--seed` doesn't work?
-
-First this:
-
-> Completely reproducible results are not guaranteed across PyTorch
-releases, individual commits, or different platforms. Furthermore,
-results may not be reproducible between CPU and GPU executions, even
-when using identical seeds.
-
-[PyTorch docs](https://pytorch.org/docs/stable/notes/randomness.html)
-
-Second, we might have a fix that at least gets a consistent seed sort of. We're
-still working on it.
-
-### libiomp5.dylib error?
-
-	OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.
-
-You are likely using an Intel package by mistake. Be sure to run conda with
-the environment variable `CONDA_SUBDIR=osx-arm64`, like so:
-
-`CONDA_SUBDIR=osx-arm64 conda install ...`
-
-This error happens with Anaconda on Macs when the Intel-only `mkl` is pulled in by
-a dependency. [nomkl](https://stackoverflow.com/questions/66224879/what-is-the-nomkl-python-package-used-for)
-is a metapackage designed to prevent this, by making it impossible to install
-`mkl`, but if your environment is already broken it may not work.
-
-Do *not* use `os.environ['KMP_DUPLICATE_LIB_OK']='True'` or equivalents as this
-masks the underlying issue of using Intel packages.
-
-### Not enough memory.
-
-This seems to be a common problem and is probably the underlying
-problem for a lot of symptoms (listed below). The fix is to lower your
-image size or to add `model.half()` right after the model is loaded. I
-should probably test it out. I've read that the reason this fixes
-problems is because it converts the model from 32-bit to 16-bit and
-that leaves more RAM for other things. I have no idea how that would
-affect the quality of the images though.
-
-See [this issue](https://github.com/CompVis/stable-diffusion/issues/71).
-
-### "Error: product of dimension sizes > 2**31'"
-
-This error happens with img2img, which I haven't played with too much
-yet. But I know it's because your image is too big or the resolution
-isn't a multiple of 32x32. Because the stable-diffusion model was
-trained on images that were 512 x 512, it's always best to use that
-output size (which is the default). However, if you're using that size
-and you get the above error, try 256 x 256 or 512 x 256 or something
-as the source image.
-
-BTW, 2**31-1 = [2,147,483,647](https://en.wikipedia.org/wiki/2,147,483,647#In_computing), which is also 32-bit signed [LONG_MAX](https://en.wikipedia.org/wiki/C_data_types) in C.
-
-### I just got Rickrolled! Do I have a virus?
-
-You don't have a virus. It's part of the project. Here's
-[Rick](https://github.com/lstein/stable-diffusion/blob/main/assets/rick.jpeg)
-and here's [the
-code](https://github.com/lstein/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
-that swaps him in. It's a NSFW filter, which IMO, doesn't work very
-good (and we call this "computer vision", sheesh).
-
-Actually, this could be happening because there's not enough RAM. You could try the `model.half()` suggestion or specify smaller output images.
-
-### My images come out black
-
-We might have this fixed, we are still testing.
-
-There's a [similar issue](https://github.com/CompVis/stable-diffusion/issues/69)
-on CUDA GPU's where the images come out green. Maybe it's the same issue?
-Someone in that issue says to use "--precision full", but this fork
-actually disables that flag. I don't know why, someone else provided
-that code and I don't know what it does. Maybe the `model.half()`
-suggestion above would fix this issue too. I should probably test it.
-
-### "view size is not compatible with input tensor's size and stride"
-
-```
-  File "/opt/anaconda3/envs/ldm/lib/python3.10/site-packages/torch/nn/functional.py", line 2511, in layer_norm
-    return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
-RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
-```
-
-Update to the latest version of lstein/stable-diffusion. We were
-patching pytorch but we found a file in stable-diffusion that we could
-change instead. This is a 32-bit vs 16-bit problem.
-
-### The processor must support the Intel bla bla bla
-
-What? Intel? On an Apple Silicon?
-
-	Intel MKL FATAL ERROR: This system does not meet the minimum requirements for use of the Intel(R) Math Kernel Library.
-	The processor must support the Intel(R) Supplemental Streaming SIMD Extensions 3 (Intel(R) SSSE3) instructions.
-	The processor must support the Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) instructions.
-	The processor must support the Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
-
-This is due to the Intel `mkl` package getting picked up when you try to install
-something that depends on it-- Rosetta can translate some Intel instructions but
-not the specialized ones here. To avoid this, make sure to use the environment
-variable `CONDA_SUBDIR=osx-arm64`, which restricts the Conda environment to only
-use ARM packages, and use `nomkl` as described above.

README.md

@@ -1,799 +1,204 @@
-<h1 align='center'><b>Stable Diffusion Dream Script</b></h1>
+<div align="center">
 
-<p align='center'>
-<img src="static/logo_temp.png"/>
-</p>
+# InvokeAI: A Stable Diffusion Toolkit
 
-<p align="center">
-    <img src="https://img.shields.io/github/last-commit/lstein/stable-diffusion?logo=Python&logoColor=green&style=for-the-badge" alt="last-commit"/>
-    <img src="https://img.shields.io/github/stars/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="stars"/>
-    <br>
-    <img src="https://img.shields.io/github/issues/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="issues"/>
-    <img src="https://img.shields.io/github/issues-pr/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="pull-requests"/>
-</p>
+_Formally known as lstein/stable-diffusion_
 
-This is a fork of CompVis/stable-diffusion, the wonderful open source
-text-to-image generator. This fork supports:
+![project logo](docs/assets/logo.png)
 
-1. An interactive command-line interface that accepts the same prompt
-   and switches as the Discord bot.
+[![discord badge]][discord link]
 
-2. A basic Web interface that allows you to run a local web server for
-   generating images in your browser.
+[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
 
-3. Support for img2img in which you provide a seed image to guide the
-      image creation. (inpainting & masking coming soon)
+[![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]
 
-4. A notebook for running the code on Google Colab.
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
 
-5. Upscaling and face fixing using the optional ESRGAN and GFPGAN
-   packages.
+[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>
 
-6. Weighted subprompts for prompt tuning.
+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.
 
-7. [Image variations](VARIATIONS.md) which allow you to systematically
-generate variations of an image you like and combine two or more
-images together to combine the best features of both.
+**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>]
 
-8. Textual inversion for customization of the prompt language and images.
+<div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
 
-8. ...and more!
 
-This fork is rapidly evolving, so use the Issues panel to report bugs
-and make feature requests, and check back periodically for
-improvements and bug fixes.
+_Note: This fork is rapidly evolving. Please use the
+[Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
+requests. Be sure to use the provided templates. They will help aid diagnose issues faster._
 
-# Table of Contents
+## Table of Contents
 
-1. [Major Features](#features)
-2. [Changelog](#latest-changes)
-3. [Installation](#installation)
-   1. [Linux](#linux)
-   1. [Windows](#windows)
-   1. [MacOS](README-Mac-MPS.md)
-4. [Troubleshooting](#troubleshooting)
-5. [Contributing](#contributing)
-6. [Support](#support)
+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)
 
-# Features
+### Installation
 
-## Interactive command-line interface similar to the Discord bot
+This fork is supported across multiple platforms. You can find individual installation instructions
+below.
 
-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.
+- #### [Linux](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_LINUX/)
 
-The script uses the readline library to allow for in-line editing,
-command history (up and down arrows), 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
+- #### [Windows](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_WINDOWS/)
 
-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.
+- #### [Macintosh](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_MAC/)
 
-```
-(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
-* Initializing, be patient...
-Loading model from models/ldm/text2img-large/model.ckpt
-(...more initialization messages...)
+### Hardware Requirements
 
-* Initialization done! Awaiting your command...
-dream> 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
+#### System
 
-dream> "there's a fly in my soup" -n6 -g
-    outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
-    seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
-dream> q
+You wil need one of the following:
 
-# this shows how to retrieve the prompt stored in the saved image's metadata
-(ldm) ~/stable-diffusion$ python3 ./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
-```
+- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
+- An Apple computer with an M1 chip.
 
-<p align='center'>
-<img src="static/dream-py-demo.png"/>
-</p>
+#### Memory
 
-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.
+- At least 12 GB Main Memory RAM.
 
-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.
+#### Disk
 
-## 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.
-
-You may also pass a -v<count> option to generate count variants on the original image. This is done by
-passing the first generated image back into img2img the requested number of times. It generates interesting
-variants.
-
-## GFPGAN and Real-ESRGAN Support
-
-The script also provides the ability to do face restoration and
-upscaling with the help of GFPGAN and Real-ESRGAN respectively.
-
-To use the ability, clone the **[GFPGAN
-repository](https://github.com/TencentARC/GFPGAN)** and follow their
-installation instructions. By default, we expect GFPGAN to be
-installed in a 'GFPGAN' sibling directory. Be sure that the `"ldm"`
-conda environment is active as you install GFPGAN.
-
-You can use the `--gfpgan_dir` argument with `dream.py` to set a
-custom path to your GFPGAN directory. _There are other GFPGAN related
-boot arguments if you wish to customize further._
-
-You can install **Real-ESRGAN** by typing the following command.
-
-```
-pip install realesrgan
-```
-
-**Note: Internet connection needed:**
-Users whose GPU machines are isolated from the Internet (e.g. on a
-University cluster) should be aware that the first time you run
-dream.py with GFPGAN and Real-ESRGAN turned on, it will try to
-download model files from the Internet. To rectify this, you may run
-`python3 scripts/preload_models.py` after you have installed GFPGAN
-and all its dependencies.
-
-**Usage**
-
-You will now have access to two new prompt arguments.
-
-**Upscaling**
-
-`-U : <upscaling_factor> <upscaling_strength>`
-
-The upscaling prompt argument takes two values. The first value is a
-scaling factor and should be set to either `2` or `4` only. This will
-either scale the image 2x or 4x respectively using different models.
-
-You can set the scaling stength between `0` and `1.0` to control
-intensity of the of the scaling. This is handy because AI upscalers
-generally tend to smooth out texture details. If you wish to retain
-some of those for natural looking results, we recommend using values
-between `0.5 to 0.8`.
-
-If you do not explicitly specify an upscaling_strength, it will
-default to 0.75.
-
-**Face Restoration**
-
-`-G : <gfpgan_strength>`
-
-This prompt argument controls the strength of the face restoration
-that is being applied. Similar to upscaling, values between `0.5 to 0.8` are recommended.
-
-You can use either one or both without any conflicts. In cases where
-you use both, the image will be first upscaled and then the face
-restoration process will be executed to ensure you get the highest
-quality facial features.
-
-`--save_orig`
-
-When you use either `-U` or `-G`, the final result you get is upscaled
-or face modified. If you want to save the original Stable Diffusion
-generation, you can use the `-save_orig` prompt argument to save the
-original unaffected version too.
-
-**Example Usage**
-
-```
-dream > superman dancing with a panda bear -U 2 0.6 -G 0.4
-```
-
-This also works with img2img:
-
-```
-dream> a man wearing a pineapple hat -I path/to/your/file.png -U 2 0.5 -G 0.6
-```
+- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
 **Note**
 
-GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid
-crashes and memory overloads during the Stable Diffusion process,
-these effects are applied after Stable Diffusion has completed its
-work.
+If you have a Nvidia 10xx series card (e.g. the 1080ti), please
+run the dream script in full-precision mode as shown below.
 
-In single image generations, you will see the output right away but
-when you are using multiple iterations, the images will first be
-generated and then upscaled and face restored after that process is
-complete. While the image generation is taking place, you will still
-be able to preview the base images.
+Similarly, specify full-precision mode on Apple M1 hardware.
 
-If you wish to stop during the image generation but want to upscale or
-face restore a particular generated image, pass it again with the same
-prompt and generated seed along with the `-U` and `-G` prompt
-arguments to perform those actions.
+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:
 
-## Google Colab
-
-Stable Diffusion AI Notebook: <a href="https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/Stable_Diffusion_AI_Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> <br>
-Open and follow instructions to use an isolated environment running Dream.<br>
-
-Output example:
-![Colab Notebook](static/colab_notebook.png)
-
-## Barebones Web Server
-
-As of version 1.10, this distribution comes with a bare bones web
-server (see screenshot). To use it, run the _dream.py_ script by
-adding the **--web** option.
-
-```
-(ldm) ~/stable-diffusion$ python3 scripts/dream.py --web
+```bash
+(invokeai) ~/InvokeAI$ python scripts/invoke.py --precision=float32
 ```
 
-You can then connect to the server by pointing your web browser at
-http://localhost:9090, or to the network name or IP address of the server.
-
-Kudos to [Tesseract Cat](https://github.com/TesseractCat) for
-contributing this code, and to [dagf2101](https://github.com/dagf2101)
-for refining it.
-
-![Dream Web Server](static/dream_web_server.png)
-
-## Reading Prompts from a File
-
-You can automate dream.py by providing a text file with the prompts
-you want to run, one line per prompt. The text file must be composed
-with a text editor (e.g. Notepad) and not a word processor. Each line
-should look like what you would type at the dream> prompt:
-
-```
-a beautiful sunny day in the park, children playing -n4 -C10
-stormy weather on a mountain top, goats grazing     -s100
-innovative packaging for a squid's dinner           -S137038382
-```
-
-Then pass this file's name to dream.py when you invoke it:
-
-```
-(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file "path/to/prompts.txt"
-```
-
-You may read a series of prompts from standard input by providing a filename of "-":
-
-```
-(ldm) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/dream.py --from_file -
-```
-
-## Shortcut for reusing seeds from the previous command
-
-Since it is so common to reuse seeds while refining a prompt, there is
-now a shortcut as of version 1.11. Provide a **-S** (or **--seed**)
-switch of -1 to use the seed of the most recent image generated. If
-you produced multiple images with the **-n** switch, then you can go
-back further using -2, -3, etc. up to the first image generated by the
-previous command. Sorry, but you can't go back further than one
-command.
-
-Here's an example of using this to do a quick refinement. It also
-illustrates using the new **-G** switch to turn on upscaling and
-face enhancement (see previous section):
-
-```
-dream> a cute child playing hopscotch -G0.5
-[...]
-outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
-
-# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
-dream> a cute child playing hopscotch -G1.0 -s100 -S -1
-reusing previous seed 3498014304
-[...]
-outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
-```
-
-## Weighted Prompts
-
-You may weight different sections of the prompt to tell the sampler to attach different levels of
-priority to them, by adding :(number) to the end of the section you wish to up- or downweight.
-For example consider this prompt:
-
-```
-    tabby cat:0.25 white duck:0.75 hybrid
-```
-
-This will tell the sampler to invest 25% of its effort on the tabby
-cat aspect of the image and 75% on the white duck aspect
-(surprisingly, this example actually works). The prompt weights can
-use any combination of integers and floating point numbers, and they
-do not need to add up to 1.
-
-## Personalizing Text-to-Image Generation
-
-You may personalize the generated images to provide your own styles or objects by training a new LDM checkpoint
-and introducing a new vocabulary to the fixed model.
-
-To train, prepare a folder that contains images sized at 512x512 and execute the following:
-
-
-WINDOWS: As the default backend is not available on Windows, if you're using that platform, set the environment variable `PL_TORCH_DISTRIBUTED_BACKEND=gloo`
-    
-```
-(ldm) ~/stable-diffusion$ python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
-                                            -t \
-                                            --actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
-                                            -n my_cat \
-                                            --gpus 0, \
-                                            --data_root D:/textual-inversion/my_cat \
-                                            --init_word 'cat'
-```
-
-During the training process, files will be created in /logs/[project][time][project]/
-where you can see the process.
-
-conditioning\* contains the training prompts
-inputs, reconstruction the input images for the training epoch
-samples, samples scaled for a sample of the prompt and one with the init word provided
-
-On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
-
-Note: According to the associated paper, the optimal number of images
-is 3-5. Your model may not converge if you use more images than that.
-
-Training will run indefinately, but you may wish to stop it before the
-heat death of the universe, when you find a low loss epoch or around
-~5000 iterations.
-
-Once the model is trained, specify the trained .pt file when starting
-dream using
-
-```
-(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py --embedding_path /path/to/embedding.pt --full_precision
-```
-
-Then, to utilize your subject at the dream prompt
-
-```
-dream> "a photo of *"
-```
-
-this also works with image2image
-
-```
-dream> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
-```
-
-It's also possible to train multiple tokens (modify the placeholder string in configs/stable-diffusion/v1-finetune.yaml) and combine LDM checkpoints using:
-
-```
-(ldm) ~/stable-diffusion$ python3 ./scripts/merge_embeddings.py \
-                                            --manager_ckpts /path/to/first/embedding.pt /path/to/second/embedding.pt [...] \
-                                            --output_path /path/to/output/embedding.pt
-```
-
-Credit goes to @rinongal and the repository located at
-https://github.com/rinongal/textual_inversion Please see the
-repository and associated paper for details and limitations.
-
-# Latest Changes
-
-- v1.13 (in process)
-
-  - 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)
-  - Can specify --grid on dream.py command line as the default.
-  - Miscellaneous internal bug and stability fixes.
-  - Works on M1 Apple hardware.
-  - Multiple bug fixes.
-
-For older changelogs, please visit **[CHANGELOGS](CHANGELOG.md)**.
-
-# Installation
-
-There are separate installation walkthroughs for [Linux](#linux), [Windows](#windows) and [Macintosh](#Macintosh)
-
-## Linux
-
-1. You will need to install the following prerequisites if they are not already available. Use your
-   operating system's preferred installer
-
-- Python (version 3.8.5 recommended; higher may work)
-- git
-
-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)".
-
-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.
-
-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.
-
-6. Load a couple of small machine-learning models required by stable diffusion:
-
-```
-(ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
-```
-
-Note that this step is necessary because I modified the original
-just-in-time model loading scheme to allow the script to work on GPU
-machines that are not internet connected. See [Workaround for machines with limited internet connectivity](#workaround-for-machines-with-limited-internet-connectivity)
-
-7. Now you need to install the weights for the stable diffusion model.
-
-For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
-Use your credentials to log in, and then point your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
-You may be asked to sign a license agreement at this point.
-
-Click on "Files and versions" near the top of the page, and then click on the file named "sd-v1-4.ckpt". You'll be taken
-to a page that prompts you to click the "download" link. Save the file somewhere safe on your local machine.
-
-Now run the following commands from within the stable-diffusion directory. This will create a symbolic
-link from the stable-diffusion model.ckpt file, to the true location of the sd-v1-4.ckpt file.
-
-```
-(ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
-(ldm) ~/stable-diffusion$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
-```
-
-8. Start generating images!
-
-```
-# for the pre-release weights use the -l or --liaon400m switch
-(ldm) ~/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
-
-### Notebook install (semi-automated)
-
-We have a
-[Jupyter notebook](https://github.com/lstein/stable-diffusion/blob/main/Stable-Diffusion-local-Windows.ipynb)
-with cell-by-cell installation steps. It will download the code in this repo as
-one of the steps, so instead of cloning this repo, simply download the notebook
-from the link above and load it up in VSCode (with the
-appropriate extensions installed)/Jupyter/JupyterLab and start running the cells one-by-one.
-
-Note that you will need NVIDIA drivers, Python 3.10, and Git installed
-beforehand - simplified
-[step-by-step instructions](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)
-are available in the wiki (you'll only need steps 1, 2, & 3 ).
-
-### Manual installs
-
-#### pip
-
-See
-[Easy-peasy Windows install](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)
-in the wiki
-
-#### Conda
-
-1. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
-
-2. Install Git from here: https://git-scm.com/download/win
-
-3. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
-
-4. 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.
-
-5. 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
-```
-
-6. Run the following two commands:
-
-```
-conda env create -f environment.yaml    (step 6a)
-conda activate ldm                      (step 6b)
-```
-
-This will install all python requirements and activate the "ldm" environment which sets PATH and other environment variables properly.
-
-7. Run the command:
-
-```
-python scripts\preload_models.py
-```
-
-This installs several machine learning models that stable diffusion
-requires. (Note that this step is required. I created it because some people
-are using GPU systems that are behind a firewall and the models can't be
-downloaded just-in-time)
-
-8. Now you need to install the weights for the big stable diffusion model.
-
-For running with the released weights, you will first need to set up
-an acount with Hugging Face (https://huggingface.co). Use your
-credentials to log in, and then point your browser at
-https://huggingface.co/CompVis/stable-diffusion-v-1-4-original. You
-may be asked to sign a license agreement at this point.
-
-Click on "Files and versions" near the top of the page, and then click
-on the file named "sd-v1-4.ckpt". You'll be taken to a page that
-prompts you to click the "download" link. Now save the file somewhere
-safe on your local machine. The weight file is >4 GB in size, so
-downloading may take a while.
-
-Now run the following commands from **within the stable-diffusion
-directory** to copy the weights file to the right place:
-
-```
-mkdir -p models\ldm\stable-diffusion-v1
-copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
-```
-
-Please replace "C:\path\to\sd-v1.4.ckpt" with the correct path to wherever
-you stashed this file. If you prefer not to copy or move the .ckpt file,
-you may instead create a shortcut to it from within
-"models\ldm\stable-diffusion-v1\".
-
-9. Start generating images!
-
-```
-# for the pre-release weights
-python scripts\dream.py -l
-
-# for the post-release weights
-python scripts\dream.py
-```
-
-10. Subsequently, to relaunch the script, first activate the Anaconda
-command window (step 3), enter the stable-diffusion directory (step 5,
-"cd \path\to\stable-diffusion"), run "conda activate ldm" (step 6b),
-and then launch the dream script (step 9).
-
-**Note:** Tildebyte has written an alternative ["Easy peasy Windows
-install"](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)
-which uses the Windows Powershell and pew. If you are having trouble
-with Anaconda on Windows, give this a try (or try it first!)
-
-### 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.
-
-## Macintosh
-
-See [README-Mac-MPS](README-Mac-MPS.md) for instructions.
-
-# 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.txt2img("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. A set of example scripts is
-coming RSN.
-
-# 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
-```
-
-# Troubleshooting
-
-Here are a few common installation problems and their solutions. Often
-these are caused by incomplete installations or crashes during the
-install process.
-
-- PROBLEM: During "conda env create -f environment.yaml", conda
-  hangs indefinitely.
-
-- SOLUTION: Enter the stable-diffusion directory and completely
-  remove the "src" directory and all its contents. The safest way
-  to do this is to enter the stable-diffusion directory and
-  give the command "git clean -f". If this still doesn't fix
-  the problem, try "conda clean -all" and then restart at the
-  "conda env create" step.
-
----
-
-- PROBLEM: dream.py crashes with the complaint that it can't find
-  ldm.simplet2i.py. Or it complains that function is being passed
-  incorrect parameters.
-
-- SOLUTION: Reinstall the stable diffusion modules. Enter the
-  stable-diffusion directory and give the command "pip install -e ."
-
----
-
-- PROBLEM: dream.py dies, complaining of various missing modules, none
-  of which starts with "ldm".
-
-- SOLUTION: From within the stable-diffusion directory, run "conda env
-  update -f environment.yaml" This is also frequently the solution to
-  complaints about an unknown function in a module.
-
----
-
-- PROBLEM: There's a feature or bugfix in the Stable Diffusion GitHub
-  that you want to try out.
-
-- SOLUTION: If the fix/feature is on the "main" branch, enter the stable-diffusion
-  directory and do a "git pull". Usually this will be sufficient, but if
-  you start to see errors about missing or incorrect modules, use the
-  command "pip install -e ." and/or "conda env update -f environment.yaml"
-  (These commands won't break anything.)
-
-- If the feature/fix is on a branch (e.g. "foo-bugfix"), the recipe is similar, but
-  do a "git pull <name of branch>".
-
-- If the feature/fix is in a pull request that has not yet been made
-  part of the main branch or a feature/bugfix branch, then from the page
-  for the desired pull request, look for the line at the top that reads
-  "xxxx wants to merge xx commits into lstein:main from YYYYYY". Copy
-  the URL in YYYY. It should have the format
-  https://github.com/<name of contributor>/stable-diffusion/tree/<name
-  of branch>
-
-- Then **go to the directory above stable-diffusion**, and rename the
-  directory to "stable-diffusion.lstein", "stable-diffusion.old", or
-  whatever. You can then git clone the branch that contains the
-  pull request:
-
-```
-git clone https://github.com/<name of contributor>/stable-diffusion/tree/<name
-of branch>
-```
-
-You will need to go through the install procedure again, but it should
-be fast because all the dependencies are already loaded.
+### Features
+
+#### Major Features
+
+- [Web Server](https://invoke-ai.github.io/InvokeAI/features/WEB/)
+- [Interactive Command Line Interface](https://invoke-ai.github.io/InvokeAI/features/CLI/)
+- [Image To Image](https://invoke-ai.github.io/InvokeAI/features/IMG2IMG/)
+- [Inpainting Support](https://invoke-ai.github.io/InvokeAI/features/INPAINTING/)
+- [Outpainting Support](https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/)
+- [Upscaling, face-restoration and outpainting](https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/)
+- [Reading Prompts From File](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#reading-prompts-from-a-file)
+- [Prompt Blending](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#prompt-blending)
+- [Thresholding and Perlin Noise Initialization Options](https://invoke-ai.github.io/InvokeAI/features/OTHER/#thresholding-and-perlin-noise-initialization-options)
+- [Negative/Unconditioned Prompts](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#negative-and-unconditioned-prompts)
+- [Variations](https://invoke-ai.github.io/InvokeAI/features/VARIATIONS/)
+- [Personalizing Text-to-Image Generation](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/)
+- [Simplified API for text to image generation](https://invoke-ai.github.io/InvokeAI/features/OTHER/#simplified-api)
+
+#### Other Features
+
+- [Google Colab](https://invoke-ai.github.io/InvokeAI/features/OTHER/#google-colab)
+- [Seamless Tiling](https://invoke-ai.github.io/InvokeAI/features/OTHER/#seamless-tiling)
+- [Shortcut: Reusing Seeds](https://invoke-ai.github.io/InvokeAI/features/OTHER/#shortcuts-reusing-seeds)
+- [Preload Models](https://invoke-ai.github.io/InvokeAI/features/OTHER/#preload-models)
+
+### Latest Changes
+
+- v2.0.1 (13 October 2022)
+  - fix noisy images at high step count when using k* samplers
+  - dream.py script now calls invoke.py module directly rather than
+    via a new python process (which could break the environment)
+
+- v2.0.0 (9 October 2022)
+
+  - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
+    for backward compatibility.
+  - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+  - Support for <a href="https://invoke-ai.github.io/InvokeAI/features/INPAINTING/">inpainting</a> and <a href="https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/">outpainting</a>
+  - img2img runs on all k* samplers
+  - Support for <a href="https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#negative-and-unconditioned-prompts">negative prompts</a>
+  - Support for CodeFormer face reconstruction
+  - Support for Textual Inversion on Macintoshes
+  - Support in both WebGUI and CLI for <a href="https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/">post-processing of previously-generated images</a>
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+  - New `--hires` option on `invoke>` line allows <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/#txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
+  - New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
+  - Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+  - Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+  - Improved <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/">command-line completion behavior</a>.
+    New commands added:
+    - List command-line history with `!history`
+    - Search command-line history with `!search`
+    - Clear history with `!clear`
+  - Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+    configure. To switch away from auto use the new flag like `--precision=float32`.
+
+For older changelogs, please visit the **[CHANGELOG](https://invoke-ai.github.io/InvokeAI/CHANGELOG#v114-11-september-2022)**.
+
+### Troubleshooting
+
+Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
+problems and other issues.
 
 # Contributing
 
-Anyone who wishes to contribute to this project, whether
-documentation, features, bug fixes, code cleanup, testing, or code
-reviews, is very much encouraged to do so. 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).
+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.
+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.
 
-# Support
+### Contributors
 
-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.
+This fork is a combined effort of various people from across the world.
+[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for
+their time, hard work and effort.
 
-_Original Author:_ Lincoln D. Stein <lincoln.stein@gmail.com>
+### Support
 
-_Contributions by:_
-[Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison),
-[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan](https://github.com/Oceanswave),
-[nicolai256](https://github.com/nicolai256), [Benjamin Warner](https://github.com/warner-benjamin),
-[tildebyte](https://github.com/tildebyte),[yunsaki](https://github.com/yunsaki), [James Reynolds][https://github.com/magnusviri],
-[Tesseract Cat](https://github.com/TesseractCat), and many more!
+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.
 
-(If you have contributed and don't see your name on the list of
-contributors, please let lstein know about the omission, or make a
-pull request)
+Original portions of the software are Copyright (c) 2020
+[Lincoln D. Stein](https://github.com/lstein)
 
-Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
+### Further Reading
 
-# Further Reading
-
-Please see the original README for more information on this software
-and underlying algorithm, located in the file [README-CompViz.md](README-CompViz.md).
+Please see the original README for more information on this software and underlying algorithm,
+located in the file [README-CompViz.md](https://invoke-ai.github.io/InvokeAI/other/README-CompViz/).

VARIATIONS.md

@@ -1,113 +0,0 @@
-# Cheat Sheat for Generating Variations
-
-Release 1.13 of SD-Dream adds support for image variations. There are two things that you can do:
-
-1. Generate a series of systematic variations of an image, given a
-prompt. The amount of variation from one image to the next can be
-controlled.
-
-2. Given two or more variations that you like, you can combine them in
-a weighted fashion
-
-This cheat sheet provides a quick guide for how this works in
-practice, using variations to create the desired image of Xena,
-Warrior Princess.
-
-## Step 1 -- find a base image that you like
-
-The prompt we will use throughout is "lucy lawless as xena, warrior
-princess, character portrait, high resolution." This will be indicated
-as "prompt" in the examples below.
-
-First we let SD create a series of images in the usual way, in this case
-requesting six iterations:
-
-~~~
-dream> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
-...
-Outputs:
-./outputs/Xena/000001.1579445059.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1579445059
-./outputs/Xena/000001.1880768722.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1880768722
-./outputs/Xena/000001.332057179.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S332057179
-./outputs/Xena/000001.2224800325.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S2224800325
-./outputs/Xena/000001.465250761.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S465250761
-./outputs/Xena/000001.3357757885.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S3357757885
-~~~
-
-The one with seed 3357757885 looks nice:
-
-<img src="static/variation_walkthru/000001.3357757885.png"/>
-
-Let's try to generate some variations. Using the same seed, we pass
-the argument -v0.1 (or --variant_amount), which generates a series of
-variations each differing by a variation amount of 0.2. This number
-ranges from 0 to 1.0, with higher numbers being larger amounts of
-variation.
-
-~~~
-dream> "prompt" -n6 -S3357757885 -v0.2
-...
-Outputs:
-./outputs/Xena/000002.784039624.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 784039624:0.2 -S3357757885
-./outputs/Xena/000002.3647897225.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.2 -S3357757885
-./outputs/Xena/000002.917731034.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 917731034:0.2 -S3357757885
-./outputs/Xena/000002.4116285959.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 4116285959:0.2 -S3357757885
-./outputs/Xena/000002.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1614299449:0.2 -S3357757885
-./outputs/Xena/000002.1335553075.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1335553075:0.2 -S3357757885
-~~~
-
-Note that the output for each image has a -V option giving the
-"variant subseed" for that image, consisting of a seed followed by the
-variation amount used to generate it.
-
-This gives us a series of closely-related variations, including the
-two shown here.
-
-<img src="static/variation_walkthru/000002.3647897225.png">
-<img src="static/variation_walkthru/000002.1614299449.png">
-
-
-I like the expression on Xena's face in the first one (subseed
-3647897225), and the armor on her shoulder in the second one (subseed
-1614299449). Can we combine them to get the best of both worlds?
-
-We combine the two variations using -V (--with_variations). Again, we
-must provide the seed for the originally-chosen image in order for
-this to work.
-
-~~~
-dream> "prompt"  -S3357757885 -V3647897225,0.1;1614299449,0.1
-Outputs:
-./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
-~~~
-
-Here we are providing equal weights (0.1 and 0.1) for both the
-subseeds. The resulting image is close, but not exactly what I
-wanted:
-
-<img src="static/variation_walkthru/000003.1614299449.png">
-
-We could either try combining the images with different weights, or we
-can generate more variations around the almost-but-not-quite image. We
-do the latter, using both the -V (combining) and -v (variation
-strength) options. Note that we use -n6 to generate 6 variations:
-
-~~~~
-dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1 -v0.05 -n6
-Outputs:
-./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
-./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
-./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3747154981:0.05 -S3357757885
-./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2664260391:0.05 -S3357757885
-./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,1642517170:0.05 -S3357757885
-./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
-~~~~
-
-This produces six images, all slight variations on the combination of
-the chosen two images. Here's the one I like best:
-
-<img src="static/variation_walkthru/000004.3747154981.png">
-
-As you can see, this is a very powerful too, which when combined with
-subprompt weighting, gives you great control over the content and
-quality of your generated images.

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 +1,69 @@
+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 "hires_fix" in params and params["hires_fix"] == True:
+        switches.append(f"--hires")
+    if "init_img" in params and len(params["init_img"]) > 0:
+        switches.append(f'-I {params["init_img"]}')
+    if "init_mask" in params and len(params["init_mask"]) > 0:
+        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 "facetool" in params:
+        switches.append(f'-ft {params["facetool"]}')
+    if "facetool_strength" in params and params["facetool_strength"]:
+        switches.append(f'-G {params["facetool_strength"]}')
+    elif "gfpgan_strength" in params and params["gfpgan_strength"]:
+        switches.append(f'-G {params["gfpgan_strength"]}')
+    if "codeformer_fidelity" in params:
+        switches.append(f'-cf {params["codeformer_fidelity"]}')
+    if "upscale" in params and params["upscale"]:
+        switches.append(f'-U {params["upscale"][0]} {params["upscale"][1]}')
+    if "variation_amount" in params and params["variation_amount"] > 0:
+        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 +1,822 @@
+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["facetool_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["facetool_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",
+        "hires_fix",
+    ]
+
+    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 "facetool_strength" in parameters:
+
+        postprocessing.append(
+            {"type": "gfpgan", "strength": float(parameters["facetool_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["facetool_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

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

configs/stable-diffusion/v1-finetune.yaml

@@ -105,5 +105,6 @@ lightning:
 
   trainer:
     benchmark: True
-    max_steps: 4000
-    
+    max_steps: 4000000
+#    max_steps: 4000
+    

configs/stable-diffusion/v1-inference.yaml

@@ -30,7 +30,7 @@ model:
       target: ldm.modules.embedding_manager.EmbeddingManager
       params:
         placeholder_strings: ["*"]
-        initializer_words: ["sculpture"]
+        initializer_words: ['face', 'man', 'photo', 'africanmale']
         per_image_tokens: false
         num_vectors_per_token: 1
         progressive_words: False

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 [inpainting](features/INPAINTING.md) and [outpainting](features/OUTPAINTING.md)
+- img2img runs on all k* samplers
+- Support for [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
+- Support for CodeFormer face reconstruction
+- Support for Textual Inversion on Macintoshes
+- Support in both WebGUI and CLI for [post-processing of previously-generated images](features/POSTPROCESS.md)
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+- New `--hires` option on `invoke>` line allows [larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img), at the cost of some performance.
+- New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see [Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
+- Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+- Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+- Improved [command-line completion behavior](features/CLI.md)
+    New commands added:
+  - List command-line history with `!history`
+  - Search command-line history with `!search`
+  - Clear history with `!clear`
+- Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+    configure. To switch away from auto use the new flag like `--precision=float32`.
+
+## v1.14 <small>(11 September 2022)</small>
+
+- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
+- Full support for Apple hardware with M1 or M2 chips.
+- Add "seamless mode" for circular tiling of image. Generates beautiful effects.
+    ([prixt](https://github.com/prixt)).
+- Inpainting support.
+- Improved web server GUI.
+- Lots of code and documentation cleanups.
+
+## v1.13 <small>(3 September 2022)</small>
+
+- Support image variations (see [VARIATIONS](features/VARIATIONS.md)
+    ([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers)
+- Supports a Google Colab notebook for a standalone server running on Google hardware
+    [Arturo Mendivil](https://github.com/artmen1516)
+- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
+    [Kevin Gibbons](https://github.com/bakkot)
+- WebUI supports incremental display of in-progress images during generation
+    [Kevin Gibbons](https://github.com/bakkot)
+- A new configuration file scheme that allows new models (including upcoming
+    stable-diffusion-v1.5) to be added without altering the code.
+    ([David Wager](https://github.com/maddavid12))
+- Can specify --grid on invoke.py command line as the default.
+- Miscellaneous internal bug and stability fixes.
+- Works on M1 Apple hardware.
+- Multiple bug fixes.
+
+---
+
+## v1.12 <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/CLI.md

@@ -0,0 +1,521 @@
+---
+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.   |
+| `--png_compression <0-9>`                 | `-z<0-9>`                                 |  6                                             | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
+| `--web`                                   |                                           | `False`                                        | Start in web server mode                                                                             |
+| `--host <ip addr>`                        |                                           | `localhost`                                    | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any.                |
+| `--port <port>`                           |                                           | `9090`                                         | Which port web server should listen for requests on.                                                 |
+| `--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 "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 |
+| `--png_compression <0-9>` | `-z<0-9>` |  6           | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
+| --grid             | -g        | False               | Turn on grid mode to return a single image combining all the images generated by this prompt |
+| --individual       | -i        | True                | Turn off grid mode (deprecated; leave off --grid instead) |
+| --outdir <path>    |  -o<path> | outputs/img_samples  | Temporarily change the location of these images |
+| --seamless         |           | False               | Activate seamless tiling for interesting effects |
+| --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. |
+| --facetool_strength <float>  | -G <float> | -G0        | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0) |
+| --facetool <name> | -ft <name> | -ft gfpgan | Select face restoration algorithm to use: gfpgan, codeformer |
+| --codeformer_fidelity | -cf <float> | 0.75 | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality |
+| --save_original    | -save_orig| False               | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
+| --variation <float>  |-v<float>| 0.0                 | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with -S<seed> and -n<int> to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
+| --with_variations <pattern> |    | None              | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |
+| --save_intermediates <n>    |    | None              | Save the image from every nth step into an "intermediates" folder inside the output directory |
+
+Note that the width and height of the image must be multiples of
+64. You can provide different values, but they will be rounded down to
+the nearest multiple of 64.
+
+
+### This is an example of img2img:	
+
+~~~~
+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.|
+
+# Other Commands
+
+The CLI offers a number of commands that begin with "!".
+
+## Postprocessing images
+
+To postprocess a file using face restoration or upscaling, use the
+`!fix` command.
+
+### `!fix`
+
+This command runs a post-processor on a previously-generated image. It
+takes a PNG filename or path and applies your choice of the `-U`, `-G`, or
+`--embiggen` switches in order to fix faces or upscale. If you provide a
+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
+
+# Model selection and importation
+
+The CLI allows you to add new models on the fly, as well as to switch
+among them rapidly without leaving the script.
+
+## !models
+
+This prints out a list of the models defined in `config/models.yaml'.
+The active model is bold-faced
+
+Example:
+<pre>
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
+waifu-diffusion           not loaded  Waifu Diffusion v1.3
+</pre>
+
+## !switch <model>
+
+This quickly switches from one model to another without leaving the 
+CLI script. `invoke.py` uses a memory caching system; once a model
+has been loaded, switching back and forth is quick. The following
+example shows this in action. Note how the second column of the 
+`!models` table changes to `cached` after a model is first loaded,
+and that the long initialization step is not needed when loading
+a cached model.
+
+<pre>
+invoke> !models
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          cached  Stable Diffusion v1.4</b>
+waifu-diffusion               active  Waifu Diffusion v1.3
+
+invoke> !switch waifu-diffusion
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+   | LatentDiffusion: Running in eps-prediction mode
+   | DiffusionWrapper has 859.52 M params.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Using faster float16 precision
+>> Model loaded in 18.24s
+>> Max VRAM used to load the model: 2.17G 
+>> Current VRAM usage:2.17G
+>> Setting Sampler to k_lms
+
+invoke> !models
+laion400m                 not loaded  <no description>
+stable-diffusion-1.4          cached  Stable Diffusion v1.4
+<b>waifu-diffusion               active  Waifu Diffusion v1.3</b>
+
+invoke> !switch stable-diffusion-1.4
+>> Caching model waifu-diffusion in system RAM
+>> Retrieving model stable-diffusion-1.4 from system RAM cache
+>> Setting Sampler to k_lms
+
+invoke> !models
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
+waifu-diffusion               cached  Waifu Diffusion v1.3
+</pre>
+
+## !import_model <path/to/model/weights>
+
+This command imports a new model weights file into InvokeAI, makes it
+available for image generation within the script, and writes out the
+configuration for the model into `config/models.yaml` for use in 
+subsequent sessions.
+
+Provide `!import_model` with the path to a weights file ending in
+`.ckpt`.  If you type a partial path and press tab, the CLI will
+autocomplete. Although it will also autocomplete to `.vae` files,
+these are not currenty supported (but will be soon).
+
+When you hit return, the CLI will prompt you to fill in additional
+information about the model, including the short name you wish to use
+for it with the `!switch` command, a brief description of the model,
+the default image width and height to use with this model, and the
+model's configuration file. The latter three fields are automatically
+filled with reasonable defaults. In the example below, the bold-faced
+text shows what the user typed in with the exception of the width,
+height and configuration file paths, which were filled in
+automatically.
+
+Example:
+
+<pre>
+invoke> <b>!import_model models/ldm/stable-diffusion-v1/	model-epoch08-float16.ckpt</b>
+>> Model import in process. Please enter the values needed to configure this model:
+
+Name for this model: <b>waifu-diffusion</b>
+Description of this model: <b>Waifu Diffusion v1.3</b>
+Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
+Default image width: <b>512</b>
+Default image height: <b>512</b>
+>> New configuration:
+waifu-diffusion:
+  config: configs/stable-diffusion/v1-inference.yaml
+  description: Waifu Diffusion v1.3
+  height: 512
+  weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+  width: 512
+OK to import [n]? <b>y</b>
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+   | LatentDiffusion: Running in eps-prediction mode
+   | DiffusionWrapper has 859.52 M params.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Using faster float16 precision
+invoke> 
+</pre>
+
+##!edit_model <name_of_model>
+
+The `!edit_model` command can be used to modify a model that is
+already defined in `config/models.yaml`. Call it with the short
+name of the model you wish to modify, and it will allow you to
+modify the model's `description`, `weights` and other fields.
+
+Example:
+<pre>
+invoke> <b>!edit_model waifu-diffusion</b>
+>> Editing model waifu-diffusion from configuration file ./configs/models.yaml
+description: <b>Waifu diffusion v1.4beta</b>
+weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b>
+config: configs/stable-diffusion/v1-inference.yaml
+width: 512
+height: 512
+
+>> New configuration:
+waifu-diffusion:
+  config: configs/stable-diffusion/v1-inference.yaml
+  description: Waifu diffusion v1.4beta
+  weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
+  height: 512
+  width: 512
+
+OK to import [n]? y
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
+...
+</pre>
+=======
+    invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3
+    ...lots of text...
+    Outputs:
+    [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix "outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512 -H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25
+    ```
+# History processing
+
+The CLI provides a series of convenient commands for reviewing previous
+actions, retrieving them, modifying them, and re-running them.
+```bash
+invoke> !fetch 0000015.8929913.png
+# the script returns the next line, ready for editing and running:
+invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
+```
+
+Note that this command may behave unexpectedly if given a PNG file that
+was not generated by InvokeAI.
+
+### `!history`
+
+The invoke script keeps track of all the commands you issue during a
+session, allowing you to re-run them. On Mac and Linux systems, it
+also writes the command-line history out to disk, giving you access to
+the most recent 1000 commands issued.
+
+The `!history` command will return a numbered list of all the commands
+issued during the session (Windows), or the most recent 1000 commands
+(Mac|Linux). You can then repeat a command by using the command `!NNN`,
+where "NNN" is the history line number. For example:
+
+```bash
+invoke> !history
+...
+[14] happy woman sitting under tree wearing broad hat and flowing garment
+[15] beautiful woman sitting under tree wearing broad hat and flowing garment
+[18] beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6
+[20] watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+[21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+...
+invoke> !20
+invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
+```
+
+## !fetch
+
+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.
+
+~~~
+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.
+
+### !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.

docs/features/EMBIGGEN.md

@@ -0,0 +1,158 @@
+---
+title: Embiggen
+---
+
+# :material-loupe: Embiggen
+
+**upscale your images on limited memory machines**
+
+GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid
+crashes and memory overloads during the Stable Diffusion process,
+these effects are applied after Stable Diffusion has completed its
+work.
+
+In single image generations, you will see the output right away but
+when you are using multiple iterations, the images will first be
+generated and then upscaled and face restored after that process is
+complete. While the image generation is taking place, you will still
+be able to preview the base images.
+
+If you wish to stop during the image generation but want to upscale or
+face restore a particular generated image, pass it again with the same
+prompt and generated seed along with the `-U` and `-G` prompt
+arguments to perform those actions.
+
+## Embiggen
+
+If you wanted to be able to do more (pixels) without running out of VRAM,
+or you want to upscale with details that couldn't possibly appear
+without the context of a prompt, this is the feature to try out.
+
+Embiggen automates the process of taking an init image, upscaling it,
+cutting it into smaller tiles that slightly overlap, running all the
+tiles through img2img to refine details with respect to the prompt,
+and "stitching" the tiles back together into a cohesive image.
+
+It automatically computes how many tiles are needed, and so it can be fed
+*ANY* size init image and perform Img2Img on it (though it will be run only
+one tile at a time, which can cause problems, see the Note at the end).
+
+If you're familiar with "GoBig" (ala [progrock-stable](https://github.com/lowfuel/progrock-stable))
+it's similar to that, except it can work up to an arbitrarily large size
+(instead of just 2x), with tile overlaps configurable as a ratio, and
+has extra logic to re-run any number of the tile sub-sections of the image
+if for example a small part of a huge run got messed up.
+
+### Usage
+
+`-embiggen <scaling_factor> <esrgan_strength> <overlap_ratio OR overlap_pixels>`
+
+Takes a scaling factor relative to the size of the `--init_img` (`-I`), followed by
+ESRGAN upscaling strength (0 - 1.0), followed by minimum amount of overlap
+between tiles as a decimal ratio (0 - 1.0) *OR* a number of pixels.
+
+The scaling factor is how much larger than the `--init_img` the output
+should be, and will multiply both x and y axis, so an image that is a
+scaling factor of 3.0 has 3*3= 9 times as many pixels, and will take
+(at least) 9 times as long (see overlap for why it might be
+longer). If the `--init_img` is already the right size `-embiggen 1`,
+and it can also be less than one if the init_img is too big.
+
+Esrgan_strength defaults to 0.75, and the overlap_ratio defaults to
+0.25, both are optional.
+
+Unlike Img2Img, the `--width` (`-W`) and `--height` (`-H`) arguments
+do not control the size of the image as a whole, but the size of the
+tiles used to Embiggen the image.
+
+ESRGAN is used to upscale the `--init_img` prior to cutting it into
+tiles/pieces to run through img2img and then stitch back
+together. Embiggen can be run without ESRGAN; just set the strength to
+zero (e.g. `-embiggen 1.75 0`). The output of Embiggen can also be
+upscaled after it's finished (`-U`).
+
+The overlap is the minimum that tiles will overlap with adjacent
+tiles, specified as either a ratio or a number of pixels. How much the
+tiles overlap determines the likelihood the tiling will be noticable,
+really small overlaps (e.g. a couple of pixels) may produce noticeable
+grid-like fuzzy distortions in the final stitched image. Though, as
+the overlapping space doesn't contribute to making the image bigger,
+and the larger the overlap the more tiles (and the more time) it will
+take to finish.
+
+Because the overlapping parts of tiles don't "contribute" to
+increasing size, every tile after the first in a row or column
+effectively only covers an extra `1 - overlap_ratio` on each axis. If
+the input/`--init_img` is same size as a tile, the ideal (for time)
+scaling factors with the default overlap (0.25) are 1.75, 2.5, 3.25,
+4.0 etc..
+
+`-embiggen_tiles <spaced list of tiles>`
+
+An advanced usage useful if you only want to alter parts of the image
+while running Embiggen. It takes a list of tiles by number to run and
+replace onto the initial image e.g. `1 3 5`. It's useful for either
+fixing problem spots from a previous Embiggen run, or selectively
+altering the prompt for sections of an image - for creative or
+coherency reasons.
+
+Tiles are numbered starting with one, and left-to-right,
+top-to-bottom.  So, if you are generating a 3x3 tiled image, the
+middle row would be `4 5 6`.
+
+### Examples
+
+!!! example ""
+
+    Running Embiggen with 512x512 tiles on an existing image, scaling up by a factor of 2.5x;
+    and doing the same again (default ESRGAN strength is 0.75, default overlap between tiles is 0.25):
+
+    ```bash
+    invoke > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5
+    invoke > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5 0.75 0.25
+    ```
+
+    If your starting image was also 512x512 this should have taken 9 tiles.
+
+!!! example ""
+
+    If there weren't enough clouds in the sky of that forest you just made
+    (and that image is about 1280 pixels (512*2.5) wide A.K.A. three
+    512x512 tiles with 0.25 overlaps wide) we can replace that top row of
+    tiles:
+
+    ```bash
+    invoke> a photo of puffy clouds over a forest at sunset -s 100 -W 512 -H 512 -I outputs/000002.seed.png -f 0.5 -embiggen_tiles 1 2 3
+    ```
+
+## Fixing Previously-Generated Images
+
+It is easy to apply embiggen to any previously-generated file without having to
+look up the original prompt and provide an initial image. Just use the
+syntax `!fix path/to/file.png <embiggen>`. For example, you can rewrite the
+previous command to look like this:
+
+```bash
+invoke> !fix ./outputs/000002.seed.png -embiggen_tiles 1 2 3
+```
+
+A new file named `000002.seed.fixed.png` will be created in the output directory. Note that
+the `!fix` command does not replace the original file, unlike the behavior at generate time.
+You do not need to provide the prompt, and `!fix` automatically selects a good strength for
+embiggen-ing.
+
+!!! note
+
+    Because the same prompt is used on all the tiled images, and the model
+    doesn't have the context of anything outside the tile being run - it
+    can end up creating repeated pattern (also called 'motifs') across all
+    the tiles based on that prompt. The best way to combat this is
+    lowering the `--strength` (`-f`) to stay more true to the init image,
+    and increasing the number of steps so there is more compute-time to
+    create the detail.  Anecdotally `--strength` 0.35-0.45 works pretty
+    well on most things. It may also work great in some examples even with
+    the `--strength` set high for patterns, landscapes, or subjects that
+    are more abstract. Because this is (relatively) fast, you can also
+    preserve the best parts from each.
+
+Author: [Travco](https://github.com/travco)

docs/features/IMG2IMG.md

@@ -0,0 +1,185 @@
+---
+title: Image-to-Image
+---
+
+# :material-image-multiple: Image-to-Image
+
+## `img2img`
+
+This script also provides an `img2img` feature that lets you seed your creations with an initial
+drawing or photo. This is a really cool feature that tells stable diffusion to build the prompt on
+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:
+
+```commandline
+tree on a hill with a river, nature photograph, national geographic -I./test-pictures/tree-and-river-sketch.png -f 0.85
+```
+
+This will take the original image shown here:
+
+<figure markdown>
+<img src="https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png" width=350>
+</figure>
+
+and generate a new image based on it as shown here:
+
+<figure markdown>
+<img src="https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png" width=350>
+</figure>
+
+The `--init_img` (`-I`) option gives the path to the seed picture. `--strength` (`-f`) controls how much
+the original will be modified, ranging from `0.0` (keep the original intact), to `1.0` (ignore the
+original completely). The default is `0.75`, and ranges from `0.25-0.90` give interesting results. 
+Other relevant options include `-C` (classification free guidance scale), and `-s` (steps). Unlike `txt2img`, 
+adding steps will continuously change the resulting image and it will not converge.
+
+You may also pass a `-v<variation_amount>` option to generate `-n<iterations>` count variants on
+the original image. This is done by passing the first generated image
+back into img2img the requested number of times. It generates
+interesting variants.
+
+Note that the prompt makes a big difference. For example, this slight variation on the prompt produces
+a very different image:
+
+<figure markdown>
+<img src="https://user-images.githubusercontent.com/111189/194135220-16b62181-b60c-4248-8989-4834a8fd7fbd.png" width=350>
+<caption markdown>photograph of a tree on a hill with a river</caption>
+</figure>
+
+!!! tip
+
+    When designing prompts, think about how the images scraped from the internet were captioned. Very few photographs will
+    be labeled "photograph" or "photorealistic." They will, however, be captioned with the publication, photographer, camera
+    model, or film settings.
+
+If the initial image contains transparent regions, then Stable Diffusion will only draw within the
+transparent regions, a process called [`inpainting`](./INPAINTING.md#creating-transparent-regions-for-inpainting). However, for this to work correctly, the color
+information underneath the transparent needs to be preserved, not erased.
+
+!!! warning
+
+**IMPORTANT ISSUE** `img2img` does not work properly on initial images smaller than 512x512. Please scale your
+image to at least 512x512 before using it. Larger images are not a problem, but may run out of VRAM on your
+GPU card. To fix this, use the --fit option, which downscales the initial image to fit within the box specified
+by width x height:
+~~~
+tree on a hill with a river, national geographic -I./test-pictures/big-sketch.png -H512 -W512 --fit
+~~~
+
+## How does it actually work, though?
+
+The main difference between `img2img` and `prompt2img` is the starting point. While `prompt2img` always starts with pure
+gaussian noise and progressively refines it over the requested number of steps, `img2img` skips some of these earlier steps
+(how many it skips is indirectly controlled by the `--strength` parameter), and uses instead your initial image mixed with gaussian noise as the starting image.
+
+**Let's start** by thinking about vanilla `prompt2img`, just generating an image from a prompt. If the step count is 10, then the "latent space" (Stable Diffusion's internal representation of the image) for the prompt "fire" with seed `1592514025` develops something like this:
+
+```commandline
+invoke> "fire" -s10 -W384 -H384 -S1592514025
+```
+
+<figure markdown>
+![latent steps](../assets/img2img/000019.steps.png)
+</figure>
+
+Put simply: starting from a frame of fuzz/static, SD finds details in each frame that it thinks look like "fire" and brings them a little bit more into focus, gradually scrubbing out the fuzz until a clear image remains.
+
+**When you use `img2img`** some of the earlier steps are cut, and instead an initial image of your choice is used. But because of how the maths behind Stable Diffusion works, this image needs to be mixed with just the right amount of noise (fuzz/static) for where it is being inserted. This is where the strength parameter comes in. Depending on the set strength, your image will be inserted into the sequence at the appropriate point, with just the right amount of noise.
+
+### A concrete example
+
+I want SD to draw a fire based on this hand-drawn image:
+
+<figure markdown>
+![drawing of a fireplace](../assets/img2img/fire-drawing.png)
+</figure>
+
+Let's only do 10 steps, to make it easier to see what's happening. If strength is `0.7`, this is what the internal steps the algorithm has to take will look like:
+
+<figure markdown>
+![gravity32](../assets/img2img/000032.steps.gravity.png)
+</figure>
+
+With strength `0.4`, the steps look more like this:
+
+<figure markdown>
+![gravity30](../assets/img2img/000030.steps.gravity.png)
+</figure>
+
+Notice how much more fuzzy the starting image is for strength `0.7` compared to `0.4`, and notice also how much longer the sequence is with `0.7`:
+
+|  | strength = 0.7 | strength = 0.4 |
+| -- | -- | -- |
+| initial image that SD sees | ![](../assets/img2img/000032.step-0.png) | ![](../assets/img2img/000030.step-0.png) |
+| steps argument to `invoke>` | `-S10` | `-S10` |
+| steps actually taken | 7 | 4 |
+| latent space at each step | ![gravity32](../assets/img2img/000032.steps.gravity.png) | ![gravity30](../assets/img2img/000030.steps.gravity.png) |
+| output | ![000032.1592514025](../assets/img2img/000032.1592514025.png) | ![000030.1592514025](../assets/img2img/000030.1592514025.png) |
+
+Both of the outputs look kind of like what I was thinking of. With the strength higher, my input becomes more vague, *and* Stable Diffusion has more steps to refine its output. But it's not really making what I want, which is a picture of cheery open fire. With the strength lower, my input is more clear, *but* Stable Diffusion has less chance to refine itself, so the result ends up inheriting all the problems of my bad drawing.
+
+If you want to try this out yourself, all of these are using a seed of `1592514025` with a width/height of `384`, step count `10`, the default sampler (`k_lms`), and the single-word prompt `"fire"`:
+
+If you want to try this out yourself, all of these are using a seed of `1592514025` with a width/height of `384`, step count `10`, the default sampler (`k_lms`), and the single-word prompt `fire`:
+
+```commandline
+invoke> "fire" -s10 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png --strength 0.7
+```
+
+The code for rendering intermediates is on my (damian0815's) branch [document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) - run `invoke.py` and check your `outputs/img-samples/intermediates` folder while generating an image. 
+
+### Compensating for the reduced step count
+
+After putting this guide together I was curious to see how the difference would be if I increased the step count to compensate, so that SD could have the same amount of steps to develop the image regardless of the strength. So I ran the generation again using the same seed, but this time adapting the step count to give each generation 20 steps.
+
+Here's strength `0.4` (note step count `50`, which is `20 ÷ 0.4` to make sure SD does `20` steps from my image):
+
+```commandline
+invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
+```
+
+<figure markdown>
+![000035.1592514025](../assets/img2img/000035.1592514025.png)
+</figure>
+
+and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to make sure SD does `20` steps from my image):
+
+```commandline
+invoke> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
+```
+
+<figure markdown>
+![000046.1592514025](../assets/img2img/000046.1592514025.png)
+</figure>
+
+In both cases the image is nice and clean and "finished", but because at strength `0.7` Stable Diffusion has been give so much more freedom to improve on my badly-drawn flames, they've come out looking much better. You can really see the difference when looking at the latent steps. There's more noise on the first image with strength `0.7`:
+
+<figure markdown>
+![gravity46](../assets/img2img/000046.steps.gravity.png)
+</figure>
+
+than there is for strength `0.4`:
+
+<figure markdown>
+![gravity35](../assets/img2img/000035.steps.gravity.png)
+</figure>
+
+and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image.
+
+Unfortunately, it seems that `img2img` is very sensitive to the step count. Here's strength `0.7` with a step count of `29` (SD did 19 steps from my image):
+
+<figure markdown>
+![gravity45](../assets/img2img/000045.1592514025.png)
+</figure>
+
+By comparing the latents we can sort of see that something got interpreted differently enough on the third or fourth step to lead to a rather different interpretation of the flames.
+
+<figure markdown>
+![gravity46](../assets/img2img/000046.steps.gravity.png)
+</figure>
+
+<figure markdown>
+![gravity45](../assets/img2img/000045.steps.gravity.png)
+</figure>
+
+This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see [stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step.

docs/features/INPAINTING.md

@@ -0,0 +1,117 @@
+---
+title: Inpainting
+---
+
+# :octicons-paintbrush-16: Inpainting
+
+## **Creating Transparent Regions for Inpainting**
+
+Inpainting is really cool. To do it, you start with an initial image
+and use a photoeditor to make one or more regions transparent
+(i.e. they have a "hole" in them). You then provide the path to this
+image at the dream> command line using the `-I` switch. Stable
+Diffusion will only paint within the transparent region.
+
+There's a catch. In the current implementation, you have to prepare
+the initial image correctly so that the underlying colors are
+preserved under the transparent area. Many imaging editing
+applications will by default erase the color information under the
+transparent pixels and replace them with white or black, which will
+lead to suboptimal inpainting. It often helps to apply incomplete
+transparency, such as any value between 1 and 99%
+
+You also must take care to export the PNG file in such a way that the
+color information is preserved. There is often an option in the export
+dialog that lets you specify this.
+
+If your photoeditor is erasing the underlying color information,
+`dream.py` will give you a big fat warning. If you can't find a way to
+coax your photoeditor to retain color values under transparent areas,
+then you can combine the `-I` and `-M` switches to provide both the
+original unedited image and the masked (partially transparent) image:
+
+```bash
+invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent.png
+```
+
+We are hoping to get rid of the need for this workaround in an upcoming release.
+
+### Inpainting is not changing the masked region enough!
+
+One of the things to understand about how inpainting works is that it
+is equivalent to running img2img on just the masked (transparent)
+area. img2img builds on top of the existing image data, and therefore
+will attempt to preserve colors, shapes and textures to the best of
+its ability. Unfortunately this means that if you want to make a
+dramatic change in the inpainted region, for example replacing a red
+wall with a blue one, the algorithm will fight you.
+
+You have a couple of options. The first is to increase the values of
+the requested steps (`-sXXX`), strength (`-f0.XX`), and/or
+condition-free guidance (`-CXX.X`). If this is not working for you, a
+more extreme step is to provide the `--inpaint_replace 0.X` (`-r0.X`)
+option. This value ranges from 0.0 to 1.0. The higher it is the less
+attention the algorithm will pay to the data underneath the masked
+region. At high values this will enable you to replace colored regions
+entirely, but beware that the masked region mayl not blend in with the
+surrounding unmasked regions as well.
+
+---
+
+## Recipe for GIMP
+
+[GIMP](https://www.gimp.org/) is a popular Linux photoediting tool.
+
+1. Open image in GIMP.
+2. Layer->Transparency->Add Alpha Channel
+3. Use lasso tool to select region to mask
+4. Choose Select -> Float to create a floating selection
+5. Open the Layers toolbar (^L) and select "Floating Selection"
+6. Set opacity to a value between 0% and 99%
+7. Export as PNG
+8. In the export dialogue, Make sure the "Save colour values from
+   transparent pixels" checkbox is selected.
+
+---
+
+## Recipe for Adobe Photoshop
+
+1. Open image in Photoshop
+
+    <figure markdown>
+    ![step1](../assets/step1.png)
+    </figure>
+
+2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area you desire to inpaint.
+
+    <figure markdown>
+    ![step2](../assets/step2.png)
+    </figure>
+
+3. Because we'll be applying a mask over the area we want to preserve, you should now select the inverse by using the ++shift+ctrl+i++ shortcut, or right clicking and using the "Select Inverse" option.
+
+4. You'll now create a mask by selecting the image layer, and Masking the selection. Make sure that you don't delete any of the underlying image, or your inpainting results will be dramatically impacted.
+
+    <figure markdown>
+    ![step4](../assets/step4.png)
+    </figure>
+
+5. Make sure to hide any background layers that are present. You should see the mask applied to your image layer, and the image on your canvas should display the checkered background.
+
+    <figure markdown>
+    ![step5](../assets/step5.png)
+    </figure>
+
+6. Save the image as a transparent PNG by using `File`-->`Save a Copy` from the menu bar, or by using the keyboard shortcut ++alt+ctrl+s++
+
+    <figure markdown>
+    ![step6](../assets/step6.png)
+    </figure>
+
+7. After following the inpainting instructions above (either through the CLI or the Web UI), marvel at your newfound ability to selectively invoke. Lookin' good!
+
+    <figure markdown>
+   ![step7](../assets/step7.png)
+    </figure>
+
+8. In the export dialogue, Make sure the "Save colour values from transparent pixels" checkbox is selected.  

docs/features/OTHER.md

@@ -0,0 +1,138 @@
+---
+title: Others
+---
+
+# :fontawesome-regular-share-from-square: Others
+
+## **Google Colab**
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg){ align="right" }](https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
+
+Open and follow instructions to use an isolated environment running Dream.
+
+Output Example:
+
+![Colab Notebook](../assets/colab_notebook.png)
+
+---
+
+## **Seamless Tiling**
+
+The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the
+`--seamless` option when starting the script which will result in all generated images to tile, or
+for each `invoke>` prompt as shown here:
+
+```python
+invoke> "pond garden with lotus by claude monet" --seamless -s100 -n4
+```
+
+---
+
+## **Shortcuts: Reusing Seeds**
+
+Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version
+1.11. Provide a `-S` (or `--seed`) switch of `-1` to use the seed of the most recent image
+generated. If you produced multiple images with the `-n` switch, then you can go back further
+using `-2`, `-3`, etc. up to the first image generated by the previous command. Sorry, but you can't go
+back further than one command.
+
+Here's an example of using this to do a quick refinement. It also illustrates using the new `-G`
+switch to turn on upscaling and face enhancement (see previous section):
+
+```bash
+invoke> a cute child playing hopscotch -G0.5
+[...]
+outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
+
+# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
+invoke> a cute child playing hopscotch -G1.0 -s100 -S -1
+reusing previous seed 3498014304
+[...]
+outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
+```
+
+---
+
+## **Weighted Prompts**
+
+You may weight different sections of the prompt to tell the sampler to attach different levels of
+priority to them, by adding `:<percent>` to the end of the section you wish to up- or downweight. For
+example consider this prompt:
+
+```bash
+tabby cat:0.25 white duck:0.75 hybrid
+```
+
+This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75%
+on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
+combination of integers and floating point numbers, and they do not need to add up to 1.
+
+---
+
+## **Thresholding and Perlin Noise Initialization Options**
+
+Two new options are the thresholding (`--threshold`) and the perlin noise initialization (`--perlin`) options. Thresholding limits the range of the latent values during optimization, which helps combat oversaturation with higher CFG scale values. Perlin noise initialization starts with a percentage (a value ranging from 0 to 1) of perlin noise mixed into the initial noise. Both features allow for more variations and options in the course of generating images.
+
+For better intuition into what these options do in practice:
+
+![here is a graphic demonstrating them both](../assets/truncation_comparison.jpg)
+
+In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from
+0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are fixed, so the initial prompt is as follows (no thresholding or perlin noise):
+
+```bash
+invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0
+```
+
+Here's an example of another prompt used when setting the threshold to 5 and perlin noise to 0.2:
+
+```bash
+invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2
+```
+
+!!! note
+
+    currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization.
+
+---
+
+## **Simplified API**
+
+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:
+
+```bash
+from ldm.generate import Generate
+g       = Generate()
+outputs = g.txt2img("a unicorn in manhattan")
+```
+
+Outputs is a list of lists in the format [filename1,seed1],[filename2,seed2]...].
+
+Please see ldm/generate.py for more information. A set of example scripts is coming RSN.
+
+---
+
+## **Preload Models**
+
+In situations where you have limited internet connectivity or are blocked behind a firewall, you can
+use the preload script to preload the required files for Stable Diffusion to run.
+
+The preload script `scripts/preload_models.py` needs to be run once at least while connected to the
+internet. In the following runs, it will load up the cached versions of the required files from the
+`.cache` directory of the system.
+
+```bash
+(invokeai) ~/stable-diffusion$ python3 ./scripts/preload_models.py
+preloading bert tokenizer...
+Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
+Downloading: 100%|██████████████████████████████████| 226k/226k [00:00<00:00, 2.79MB/s]
+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
+```

docs/features/OUTPAINTING.md

@@ -0,0 +1,105 @@
+---
+title: Outpainting
+---
+
+# :octicons-paintbrush-16: Outpainting
+
+## Outpainting and outcropping
+
+Outpainting is a process by which the AI generates parts of the image
+that are outside its original frame. It can be used to fix up images
+in which the subject is off center, or when some detail (often the top
+of someone's head!) is cut off.
+
+InvokeAI supports two versions of outpainting, one called "outpaint"
+and the other "outcrop." They work slightly differently and each has
+its advantages and drawbacks.
+
+### Outcrop
+
+The `outcrop` extension allows you to extend the image in 64 pixel
+increments in any dimension. You can apply the module to any image
+previously-generated by InvokeAI. Note that it will **not** work with
+arbitrary photographs or Stable Diffusion images created by other
+implementations.
+
+Consider this image:
+
+<figure markdown>
+![curly_woman](../assets/outpainting/curly.png)
+</figure>
+
+Pretty nice, but it's annoying that the top of her head is cut
+off. She's also a bit off center. Let's fix that!
+
+```bash
+invoke> !fix images/curly.png --outcrop top 64 right 64
+```
+
+This is saying to apply the `outcrop` extension by extending the top
+of the image by 64 pixels, and the right of the image by the same
+amount. You can use any combination of top|left|right|bottom, and
+specify any number of pixels to extend. You can also abbreviate
+`--outcrop` to `-c`.
+
+The result looks like this:
+
+<figure markdown>
+![curly_woman_outcrop](../assets/outpainting/curly-outcrop.png)
+</figure>
+
+The new image is actually slightly larger than the original (576x576,
+because 64 pixels were added to the top and right sides.)
+
+A number of caveats:
+
+1. Although you can specify any pixel values, they will be rounded up
+to the nearest multiple of 64. Smaller values are better. Larger
+extensions are more likely to generate artefacts. However, if you wish
+you can run the !fix command repeatedly to cautiously expand the
+image.
+
+2. The extension is stochastic, meaning that each time you run it
+you'll get a slightly different result. You can run it repeatedly
+until you get an image you like. Unfortunately `!fix` does not
+currently respect the `-n` (`--iterations`) argument.
+
+## Outpaint
+
+The `outpaint` extension does the same thing, but with subtle
+differences. Starting with the same image, here is how we would add an
+additional 64 pixels to the top of the image:
+
+```bash
+invoke> !fix images/curly.png --out_direction top 64
+```
+
+(you can abbreviate `--out_direction` as `-D`.
+
+The result is shown here:
+
+<figure markdown>
+![curly_woman_outpaint](../assets/outpainting/curly-outpaint.png)
+</figure>
+
+Although the effect is similar, there are significant differences from
+outcropping:
+
+- You can only specify one direction to extend at a time.
+- The image is **not** resized. Instead, the image is shifted by the specified
+number of pixels. If you look carefully, you'll see that less of the lady's
+torso is visible in the image.
+- Because the image dimensions remain the same, there's no rounding
+to multiples of 64.
+- Attempting to outpaint larger areas will frequently give rise to ugly
+   ghosting effects.
+- For best results, try increasing the step number.
+- If you don't specify a pixel value in `-D`, it will default to half
+   of the whole image, which is likely not what you want.
+
+!!! tip
+
+    Neither `outpaint` nor `outcrop` are perfect, but we continue to tune
+    and improve them. If one doesn't work, try the other. You may also
+    wish to experiment with other `img2img` arguments, such as `-C`, `-f`
+    and `-s`.

docs/features/POSTPROCESS.md

@@ -0,0 +1,177 @@
+---
+title: Postprocessing
+---
+
+# :material-image-edit: Postprocessing
+
+## Intro
+
+This extension provides the ability to restore faces and upscale
+images.
+
+Face restoration and upscaling can be applied at the time you generate
+the images, or at any later time against a previously-generated PNG
+file, using the [!fix](#fixing-previously-generated-images)
+command. [Outpainting and outcropping](OUTPAINTING.md) can only be
+applied after the fact.
+
+## Face Fixing
+
+The default face restoration module is GFPGAN. The default upscale is
+Real-ESRGAN. For an alternative face restoration module, see [CodeFormer
+Support] below.
+
+As of version 1.14, environment.yaml will install the Real-ESRGAN
+package into the standard install location for python packages, and
+will put GFPGAN into a subdirectory of "src" in the InvokeAI
+directory. Upscaling with Real-ESRGAN should "just work" without
+further intervention. Simply pass the --upscale (-U) option on the
+invoke> command line, or indicate the desired scale on the popup in
+the Web GUI.
+
+**GFPGAN** requires a series of downloadable model files to
+work. These are loaded when you run `scripts/preload_models.py`. If
+GFPAN is failing with an error, please run the following from the
+InvokeAI directory:
+
+```bash
+python scripts/preload_models.py
+```
+
+If you do not run this script in advance, the GFPGAN module will attempt
+to download the models files the first time you try to perform facial
+reconstruction.
+
+Alternatively, if you have GFPGAN installed elsewhere, or if you are
+using an earlier version of this package which asked you to install
+GFPGAN in a sibling directory, you may use the `--gfpgan_dir` argument
+with `invoke.py` to set a custom path to your GFPGAN directory. _There
+are other GFPGAN related boot arguments if you wish to customize
+further._
+
+## Usage
+
+You will now have access to two new prompt arguments.
+
+### Upscaling
+
+`-U : <upscaling_factor> <upscaling_strength>`
+
+The upscaling prompt argument takes two values. The first value is a scaling
+factor and should be set to either `2` or `4` only. This will either scale the
+image 2x or 4x respectively using different models.
+
+You can set the scaling stength between `0` and `1.0` to control intensity of
+the of the scaling. This is handy because AI upscalers generally tend to smooth
+out texture details. If you wish to retain some of those for natural looking
+results, we recommend using values between `0.5 to 0.8`.
+
+If you do not explicitly specify an upscaling_strength, it will default to 0.75.
+
+### Face Restoration
+
+`-G : <facetool_strength>`
+
+This prompt argument controls the strength of the face restoration that is being
+applied. Similar to upscaling, values between `0.5 to 0.8` are recommended.
+
+You can use either one or both without any conflicts. In cases where you use
+both, the image will be first upscaled and then the face restoration process
+will be executed to ensure you get the highest quality facial features.
+
+`--save_orig`
+
+When you use either `-U` or `-G`, the final result you get is upscaled or face
+modified. If you want to save the original Stable Diffusion generation, you can
+use the `-save_orig` prompt argument to save the original unaffected version
+too.
+
+### Example Usage
+
+```bash
+invoke> "superman dancing with a panda bear" -U 2 0.6 -G 0.4
+```
+
+This also works with img2img:
+
+```bash
+invoke> "a man wearing a pineapple hat" -I path/to/your/file.png -U 2 0.5 -G 0.6
+```
+
+!!! note
+
+    GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads
+    during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
+    its work.
+
+    In single image generations, you will see the output right away but when you are using multiple
+    iterations, the images will first be generated and then upscaled and face restored after that
+    process is complete. While the image generation is taking place, you will still be able to preview
+    the base images.
+
+If you wish to stop during the image generation but want to upscale or face
+restore a particular generated image, pass it again with the same prompt and
+generated seed along with the `-U` and `-G` prompt arguments to perform those
+actions.
+
+## CodeFormer Support
+
+This repo also allows you to perform face restoration using
+[CodeFormer](https://github.com/sczhou/CodeFormer).
+
+In order to setup CodeFormer to work, you need to download the models
+like with GFPGAN. You can do this either by running
+`preload_models.py` or by manually downloading the [model
+file](https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth)
+and saving it to `ldm/invoke/restoration/codeformer/weights` folder.
+
+You can use `-ft` prompt argument to swap between CodeFormer and the
+default GFPGAN. The above mentioned `-G` prompt argument will allow
+you to control the strength of the restoration effect.
+
+### Usage
+
+The following command will perform face restoration with CodeFormer instead of
+the default gfpgan.
+
+`<prompt> -G 0.8 -ft codeformer`
+
+### Other Options
+
+- `-cf` - cf or CodeFormer Fidelity takes values between `0` and `1`. 0 produces
+  high quality results but low accuracy and 1 produces lower quality results but
+  higher accuacy to your original face.
+
+The following command will perform face restoration with CodeFormer. CodeFormer
+will output a result that is closely matching to the input face.
+
+`<prompt> -G 1.0 -ft codeformer -cf 0.9`
+
+The following command will perform face restoration with CodeFormer. CodeFormer
+will output a result that is the best restoration possible. This may deviate
+slightly from the original face. This is an excellent option to use in
+situations when there is very little facial data to work with.
+
+`<prompt> -G 1.0 -ft codeformer -cf 0.1`
+
+## Fixing Previously-Generated Images
+
+It is easy to apply face restoration and/or upscaling to any
+previously-generated file. Just use the syntax `!fix path/to/file.png
+<options>`. For example, to apply GFPGAN at strength 0.8 and upscale
+2X for a file named `./outputs/img-samples/000044.2945021133.png`,
+just run:
+
+```bash
+invoke> !fix ./outputs/img-samples/000044.2945021133.png -G 0.8 -U 2
+```
+
+A new file named `000044.2945021133.fixed.png` will be created in the output
+directory. Note that the `!fix` command does not replace the original file,
+unlike the behavior at generate time.
+
+### Disabling
+
+If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries,
+you can disable them on the invoke.py command line with the `--no_restore` and
+`--no_upscale` options, respectively.

docs/features/PROMPTS.md

@@ -0,0 +1,171 @@
+---
+title: Prompting-Features
+---
+
+# :octicons-command-palette-24: Prompting-Features
+
+## **Reading Prompts from a File**
+
+You can automate `invoke.py` by providing a text file with the prompts you want to run, one line per
+prompt. The text file must be composed with a text editor (e.g. Notepad) and not a word processor.
+Each line should look like what you would type at the invoke> prompt:
+
+```bash
+a beautiful sunny day in the park, children playing -n4 -C10
+stormy weather on a mountain top, goats grazing     -s100
+innovative packaging for a squid's dinner           -S137038382
+```
+
+Then pass this file's name to `invoke.py` when you invoke it:
+
+```bash
+(invokeai) ~/stable-diffusion$ python3 scripts/invoke.py --from_file "path/to/prompts.txt"
+```
+
+You may read a series of prompts from standard input by providing a filename of `-`:
+
+```bash
+(invokeai) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/invoke.py --from_file -
+```
+
+---
+
+## **Negative and Unconditioned Prompts**
+
+Any words between a pair of square brackets will instruct Stable
+Diffusion to attempt to ban the concept from the generated image.
+
+```text
+this is a test prompt [not really] to make you understand [cool] how this works.
+```
+
+In the above statement, the words 'not really cool` will be ignored by Stable Diffusion.
+
+Here's a prompt that depicts what it does.
+
+original prompt:
+
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
+
+<figure markdown>
+![step1](../assets/negative_prompt_walkthru/step1.png)
+</figure>
+
+That image has a woman, so if we want the horse without a rider, we can influence the image not to have a woman by putting [woman] in the prompt, like this:
+
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
+
+<figure markdown>
+![step2](../assets/negative_prompt_walkthru/step2.png)
+</figure>
+
+That's nice - but say we also don't want the image to be quite so blue. We can add "blue" to the list of negative prompts, so it's now [woman blue]:
+
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
+
+<figure markdown>
+![step3](../assets/negative_prompt_walkthru/step3.png)
+</figure>
+
+Getting close - but there's no sense in having a saddle when our horse doesn't have a rider, so we'll add one more negative prompt: [woman blue saddle].
+
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
+
+<figure markdown>
+![step4](../assets/negative_prompt_walkthru/step4.png)
+</figure>
+
+!!! notes "Notes about this feature:"
+
+    * The only requirement for words to be ignored is that they are in between a pair of square brackets.
+    * You can provide multiple words within the same bracket.
+    * You can provide multiple brackets with multiple words in different places of your prompt. That works just fine.
+    * To improve typical anatomy problems, you can add negative prompts like `[bad anatomy, extra legs, extra arms, extra fingers, poorly drawn hands, poorly drawn feet, disfigured, out of frame, tiling, bad art, deformed, mutated]`.
+
+---
+
+## **Prompt Blending**
+
+You may blend together different sections of the prompt to explore the
+AI's latent semantic space and generate interesting (and often
+surprising!) variations. The syntax is:
+
+```bash
+blue sphere:0.25 red cube:0.75 hybrid
+```
+
+This will tell the sampler to blend 25% of the concept of a blue
+sphere with 75% of the concept of a red cube. The blend weights can
+use any combination of integers and floating point numbers, and they
+do not need to add up to 1. Everything to the left of the `:XX` up to
+the previous `:XX` is used for merging, so the overall effect is:
+
+```bash
+0.25 * "blue sphere" + 0.75 * "white duck" + hybrid
+```
+
+Because you are exploring the "mind" of the AI, the AI's way of mixing
+two concepts may not match yours, leading to surprising effects. To
+illustrate, here are three images generated using various combinations
+of blend weights. As usual, unless you fix the seed, the prompts will give you
+different results each time you run them.
+
+---
+
+<figure markdown>
+### "blue sphere, red cube, hybrid"
+</figure>
+
+This example doesn't use melding at all and represents the default way
+of mixing concepts.
+
+<figure markdown>
+![blue-sphere-red-cube-hyprid](../assets/prompt-blending/blue-sphere-red-cube-hybrid.png)
+</figure>
+
+It's interesting to see how the AI expressed the concept of "cube" as
+the four quadrants of the enclosing frame. If you look closely, there
+is depth there, so the enclosing frame is actually a cube.
+
+<figure markdown>
+### "blue sphere:0.25 red cube:0.75 hybrid"
+
+![blue-sphere-25-red-cube-75](../assets/prompt-blending/blue-sphere-0.25-red-cube-0.75-hybrid.png)
+</figure>
+
+Now that's interesting. We get neither a blue sphere nor a red cube,
+but a red sphere embedded in a brick wall, which represents a melding
+of concepts within the AI's "latent space" of semantic
+representations. Where is Ludwig Wittgenstein when you need him?
+
+<figure markdown>
+### "blue sphere:0.75 red cube:0.25 hybrid"
+
+![blue-sphere-75-red-cube-25](../assets/prompt-blending/blue-sphere-0.75-red-cube-0.25-hybrid.png)
+</figure>
+
+Definitely more blue-spherey. The cube is gone entirely, but it's
+really cool abstract art.
+
+<figure markdown>
+### "blue sphere:0.5 red cube:0.5 hybrid"
+
+![blue-sphere-5-red-cube-5-hybrid](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5-hybrid.png)
+</figure>
+
+Whoa...! I see blue and red, but no spheres or cubes. Is the word
+"hybrid" summoning up the concept of some sort of scifi creature?
+Let's find out.
+
+<figure markdown>
+### "blue sphere:0.5 red cube:0.5"
+
+![blue-sphere-5-red-cube-5](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5.png)
+</figure>
+
+Indeed, removing the word "hybrid" produces an image that is more like
+what we'd expect.
+
+In conclusion, prompt blending is great for exploring creative space,
+but can be difficult to direct. A forthcoming release of InvokeAI will
+feature more deterministic prompt weighting.