Merge branch 'development' into fix_generate.py

.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 \
+    --sampler plms
-    --full_precision
 
 # original output by CompVis/stable-diffusion
 IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"

.dockerignore

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

.github/CODEOWNERS

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

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -0,0 +1,102 @@
+name: 🐞 Bug Report
+
+description: File a bug report
+
+title: '[bug]: '
+
+labels: ['bug']
+
+# assignees:
+#   - moderator_bot
+#   - lstein
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this Bug Report!
+
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: |
+        Please use the [search function](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
+        irst to see if an issue already exists for the bug you encountered.
+      options:
+        - label: I have searched the existing issues
+          required: true
+
+  - type: markdown
+    attributes:
+      value: __Describe your environment__
+
+  - type: dropdown
+    id: os_dropdown
+    attributes:
+      label: OS
+      description: Which operating System did you use when the bug occured
+      multiple: false
+      options:
+        - 'Linux'
+        - 'Windows'
+        - 'macOS'
+    validations:
+      required: true
+
+  - type: dropdown
+    id: gpu_dropdown
+    attributes:
+      label: GPU
+      description: Which kind of Graphic-Adapter is your System using
+      multiple: false
+      options:
+        - 'cuda'
+        - 'amd'
+        - 'mps'
+        - 'cpu'
+    validations:
+      required: true
+
+  - type: input
+    id: vram
+    attributes:
+      label: VRAM
+      description: Size of the VRAM if known
+      placeholder: 8GB
+    validations:
+      required: false
+
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: |
+        Briefly describe what happened, what you expected to happen and how to reproduce this bug.
+      placeholder: When using the webinterface and right-clicking on button X instead of the popup-menu there error Y appears
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Screenshots
+      description: If applicable, add screenshots to help explain your problem
+      placeholder: this is what the result looked like <screenshot>
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here
+      placeholder: Only happens when there is full moon and Friday the 13th on Christmas Eve 🎅🏻
+    validations:
+      required: false
+
+  - type: input
+    id: contact
+    attributes:
+      label: Contact Details
+      description: __OPTIONAL__ How can we get in touch with you if we need more info (besides this issue)?
+      placeholder: ex. email@example.com, discordname, twitter, ...
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml

@@ -0,0 +1,56 @@
+name: Feature Request
+description: Commit a idea or Request a new feature
+title: '[enhancement]: '
+labels: ['enhancement']
+# assignees:
+#   - lstein
+#   - tildebyte
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this Feature request!
+
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: |
+        Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
+        to see if a simmilar issue already exists for the feature you want to request
+      options:
+        - label: I have searched the existing issues
+          required: true
+
+  - type: input
+    id: contact
+    attributes:
+      label: Contact Details
+      description: __OPTIONAL__ How could we get in touch with you if we need more info (besides this issue)?
+      placeholder: ex. email@example.com, discordname, twitter, ...
+    validations:
+      required: false
+
+  - type: textarea
+    id: whatisexpected
+    attributes:
+      label: What should this feature add?
+      description: Please try to explain the functionality this feature should add
+      placeholder: |
+        Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
+        Great benefits with automatic labeling, assigning and other functionalitys not available in that form
+        via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
+        https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Alternatives
+      description: Describe alternatives you've considered
+      placeholder: A clear and concise description of any alternative solutions or features you've considered.
+
+  - type: textarea
+    attributes:
+      label: Aditional Content
+      description: Add any other context or screenshots about the feature request here.
+      placeholder: This is a Mockup of the design how I imagine it <screenshot>

.github/ISSUE_TEMPLATE/bug_report.md

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

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,14 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Project-Documentation
+    url: https://invoke-ai.github.io/InvokeAI/
+    about: Should be your first place to go when looking for manuals/FAQs regarding our InvokeAI Toolkit
+  - name: Discord
+    url: https://discord.gg/ZmtBAhwWhy
+    about: Our Discord Community could maybe help you out via live-chat
+  - name: GitHub Community Support
+    url: https://github.com/orgs/community/discussions
+    about: Please ask and answer questions regarding the GitHub Platform here.
+  - name: GitHub Security Bug Bounty
+    url: https://bounty.github.com/
+    about: Please report security vulnerabilities of the GitHub Platform here.

.github/ISSUE_TEMPLATE/feature_request.md

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

.github/workflows/build-container.yml

@@ -0,0 +1,48 @@
+# Building the Image without pushing to confirm it is still buildable
+# confirum functionality would unfortunately need way more resources
+name: build container image
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+
+jobs:
+  docker:
+    strategy:
+      fail-fast: false
+      matrix:
+        arch:
+          - x86_64
+          - aarch64
+        include:
+          - arch: x86_64
+            conda-env-file: environment.yml
+          - arch: aarch64
+            conda-env-file: environment-linux-aarch64.yml
+    runs-on: ubuntu-latest
+    name: ${{ matrix.arch }}
+    steps:
+      - name: prepare docker-tag
+        env:
+          repository: ${{ github.repository }}
+        run: echo "dockertag=${repository,,}" >> $GITHUB_ENV
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Build container
+        uses: docker/build-push-action@v3
+        with:
+          context: .
+          file: docker-build/Dockerfile
+          platforms: Linux/${{ matrix.arch }}
+          push: false
+          tags: ${{ env.dockertag }}:${{ matrix.arch }}
+          build-args: |
+            conda_env_file=${{ matrix.conda-env-file }}
+            conda_version=py39_4.12.0-Linux-${{ matrix.arch }}
+            invokeai_git=${{ github.repository }}
+            invokeai_branch=${{ github.ref_name }}

.github/workflows/create-caches.yml

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

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

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

@@ -0,0 +1,123 @@
+name: Test invoke.py
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+      - 'fix-gh-actions-fork'
+  pull_request:
+    branches:
+      - 'main'
+      - 'development'
+
+jobs:
+  matrix:
+    strategy:
+      fail-fast: false
+      matrix:
+        stable-diffusion-model:
+          # - 'https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt'
+          - 'https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt'
+        os:
+          - ubuntu-latest
+          - macOS-12
+        include:
+          - os: ubuntu-latest
+            environment-file: environment.yml
+            default-shell: bash -l {0}
+          - os: macOS-12
+            environment-file: environment-mac.yml
+            default-shell: bash -l {0}
+          # - stable-diffusion-model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
+          #   stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
+          #   stable-diffusion-model-switch: stable-diffusion-1.4
+          - stable-diffusion-model: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-switch: stable-diffusion-1.5
+    name: ${{ matrix.os }} with ${{ matrix.stable-diffusion-model-switch }}
+    runs-on: ${{ matrix.os }}
+    env:
+      CONDA_ENV_NAME: invokeai
+    defaults:
+      run:
+        shell: ${{ matrix.default-shell }}
+    steps:
+      - name: Checkout sources
+        id: checkout-sources
+        uses: actions/checkout@v3
+
+      - name: create models.yaml from example
+        run: cp configs/models.yaml.example configs/models.yaml
+
+      - name: Use cached conda packages
+        id: use-cached-conda-packages
+        uses: actions/cache@v3
+        with:
+          path: ~/conda_pkgs_dir
+          key: conda-pkgs-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles(matrix.environment-file) }}
+
+      - name: Activate Conda Env
+        id: activate-conda-env
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: ${{ matrix.environment-file }}
+          miniconda-version: latest
+
+      - name: set test prompt to main branch validation
+        if: ${{ github.ref == 'refs/heads/main' }}
+        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV
+
+      - name: set test prompt to development branch validation
+        if: ${{ github.ref == 'refs/heads/development' }}
+        run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
+
+      - name: set test prompt to Pull Request validation
+        if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
+        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
+
+      - name: Use Cached Stable Diffusion Model
+        id: cache-sd-model
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-${{ matrix.stable-diffusion-model-switch }}
+        with:
+          path: ${{ matrix.stable-diffusion-model-dl-path }}
+          key: ${{ env.cache-name }}
+
+      - name: Download ${{ matrix.stable-diffusion-model-switch }}
+        id: download-stable-diffusion-model
+        if: ${{ steps.cache-sd-model.outputs.cache-hit != 'true' }}
+        run: |
+          [[ -d models/ldm/stable-diffusion-v1 ]] \
+            || mkdir -p models/ldm/stable-diffusion-v1
+          curl \
+            -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
+            -o ${{ matrix.stable-diffusion-model-dl-path }} \
+            -L ${{ matrix.stable-diffusion-model }}
+
+      - name: run preload_models.py
+        id: run-preload-models
+        run: |
+          python scripts/preload_models.py \
+            --no-interactive
+
+      - name: Run the tests
+        id: run-tests
+        run: |
+          time python scripts/invoke.py \
+            --model ${{ matrix.stable-diffusion-model-switch }} \
+            --from_file ${{ env.TEST_PROMPTS }}
+
+      - name: export conda env
+        id: export-conda-env
+        run: |
+          mkdir -p outputs/img-samples
+          conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}-${{ runner.arch }}.yml
+
+      - name: Archive results
+        id: archive-results
+        uses: actions/upload-artifact@v3
+        with:
+          name: results_${{ matrix.os }}_${{ matrix.stable-diffusion-model-switch }}
+          path: outputs/img-samples

.gitignore

@@ -1,6 +1,14 @@
 # ignore default image save location and model symbolic link
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
+**/restoration/codeformer/weights
+
+# ignore user models config
+configs/models.user.yaml
+config/models.user.yml
+
+# ignore the Anaconda/Miniconda installer used while building Docker image
+anaconda.sh
 
 # ignore a directory which serves as a place for initial images
 inputs/
@@ -77,9 +85,6 @@ db.sqlite3-journal
 instance/
 .webassets-cache
 
-# WebUI temp files:
-img2img-tmp.png
-
 # Scrapy stuff:
 .scrapy
 
@@ -186,3 +191,21 @@ 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/*.sha256
+
+# GFPGAN model files
+gfpgan/
+
+# config file (will be created by installer)
+configs/models.yaml
+
+# weights (will be created by installer)
+models/ldm/stable-diffusion-v1/*.ckpt

.prettierrc.yaml

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

LICENSE

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

README.md

@@ -1,64 +1,95 @@
-<h1 align='center'><b>InvokeAI: A Stable Diffusion Toolkit</b></h1>
+<div align="center">
 
-<p align='center'>
+# InvokeAI: A Stable Diffusion Toolkit
-<img src="docs/assets/logo.png"/>
-</p>
 
-<p align="center">
+_Formerly known as lstein/stable-diffusion_
-    <img src="https://img.shields.io/github/last-commit/invoke-ai/InvokeAI?logo=Python&logoColor=green&style=for-the-badge" alt="last-commit"/>
+
-    <img src="https://img.shields.io/github/stars/invoke-ai/InvokeAI?logo=GitHub&style=for-the-badge" alt="stars"/>
+![project logo](docs/assets/logo.png)
-    <br>
+
-    <img src="https://img.shields.io/github/issues/invoke-ai/InvokeAI?logo=GitHub&style=for-the-badge" alt="issues"/>
+[![discord badge]][discord link]
-    <img src="https://img.shields.io/github/issues-pr/invoke-ai/InvokeAI?logo=GitHub&style=for-the-badge" alt="pull-requests"/>
+
-</p>
+[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
+
+[![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]
+
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
+
+[CI checks on dev badge]: https://flat.badgen.net/github/checks/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>
 
 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,
+generation process. It runs on Windows, Mac and Linux machines, with
-and runs on GPU cards with as little as 4 GB or RAM.
+GPU cards with as little as 4 GB of RAM. It provides both a polished
+Web interface (see below), and an easy-to-use command-line interface.
+
+**Quick links**: [<a href="https://discord.gg/NwVCmKwY">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
+
+<div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
+
 
 _Note: This fork is rapidly evolving. Please use the
-[Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to
+[Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
-report bugs and make feature requests. Be sure to use the provided
+requests. Be sure to use the provided templates. They will help aid diagnose issues faster._
-templates. They will help aid diagnose issues faster._
 
-_This repository was formally known as lstein/stable-diffusion_
+## Table of Contents
-
-# **Table of Contents**
 
 1. [Installation](#installation)
-2. [Major Features](#features)
+2. [Hardware Requirements](#hardware-requirements)
-3. [Changelog](#latest-changes)
+3. [Features](#features)
-4. [Troubleshooting](#troubleshooting)
+4. [Latest Changes](#latest-changes)
-5. [Contributing](#contributing)
+5. [Troubleshooting](#troubleshooting)
-6. [Support](#support)
+6. [Contributing](#contributing)
+7. [Contributors](#contributors)
+8. [Support](#support)
+9. [Further Reading](#further-reading)
 
-# Installation
+### Installation
 
-This fork is supported across multiple platforms. You can find individual installation instructions below.
+This fork is supported across multiple platforms. You can find individual installation instructions
+below.
 
-- ## [Linux](docs/installation/INSTALL_LINUX.md)
+- #### [Linux](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_LINUX/)
-- ## [Windows](docs/installation/INSTALL_WINDOWS.md)
-- ## [Macintosh](docs/installation/INSTALL_MAC.md)
 
-## **Hardware Requirements**
+- #### [Windows](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_WINDOWS/)
 
-**System**
+- #### [Macintosh](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_MAC/)
+
+### Hardware Requirements
+
+#### System
 
 You wil need one of the following:
 
 - An NVIDIA-based graphics card with 4 GB or more VRAM memory.
 - An Apple computer with an M1 chip.
 
-**Memory**
+#### Memory
 
 - At least 12 GB Main Memory RAM.
 
-**Disk**
+#### Disk
 
-- At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
+- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
 **Note**
 
@@ -67,99 +98,107 @@ run the dream script in full-precision mode as shown below.
 
 Similarly, specify full-precision mode on Apple M1 hardware.
 
-To run in full-precision mode, start `dream.py` with the
+Precision is auto configured based on the device. If however you encounter
-`--full_precision` flag:
+errors like 'expected type Float but found Half' or 'not implemented for Half'
+you can try starting `invoke.py` with the `--precision=float32` flag:
 
-```
+```bash
-(ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
+(invokeai) ~/InvokeAI$ python scripts/invoke.py --precision=float32
 ```
 
-# Features
+### Features
 
-## **Major Features**
+#### Major Features
 
-- ## [Interactive Command Line Interface](docs/features/CLI.md)
+- [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)
 
-- ## [Image To Image](docs/features/IMG2IMG.md)
+#### Other Features
 
-- ## [Inpainting Support](docs/features/INPAINTING.md)
+- [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)
 
-- ## [GFPGAN and Real-ESRGAN Support](docs/features/UPSCALE.md)
+### Latest Changes
 
-- ## [Seamless Tiling](docs/features/OTHER.md#seamless-tiling)
+- 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)
 
-- ## [Google Colab](docs/features/OTHER.md#google-colab)
+- v2.0.0 (9 October 2022)
 
-- ## [Web Server](docs/features/WEB.md)
+  - `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`.
 
-- ## [Reading Prompts From File](docs/features/OTHER.md#reading-prompts-from-a-file)
+For older changelogs, please visit the **[CHANGELOG](https://invoke-ai.github.io/InvokeAI/CHANGELOG#v114-11-september-2022)**.
 
-- ## [Shortcut: Reusing Seeds](docs/features/OTHER.md#shortcuts-reusing-seeds)
+### Troubleshooting
 
-- ## [Weighted Prompts](docs/features/OTHER.md#weighted-prompts)
+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.
-- ## [Variations](docs/features/VARIATIONS.md)
-
-- ## [Personalizing Text-to-Image Generation](docs/features/TEXTUAL_INVERSION.md)
-
-- ## [Simplified API for text to image generation](docs/features/OTHER.md#simplified-api)
-
-## **Other Features**
-
-- ### [Creating Transparent Regions for Inpainting](docs/features/INPAINTING.md#creating-transparent-regions-for-inpainting)
-
-- ### [Preload Models](docs/features/OTHER.md#preload-models)
-
-# Latest Changes
-
-- v1.14 (11 September 2022)
-
-  - Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
-  - Full support for Apple hardware with M1 or M2 chips.
-  - Add "seamless mode" for circular tiling of image. Generates beautiful effects. ([prixt](https://github.com/prixt)).
-  - Inpainting support.
-  - Improved web server GUI.
-  - Lots of code and documentation cleanups.
-
-- v1.13 (3 September 2022
-
-  - Support image variations (see [VARIATIONS](docs/features/VARIATIONS.md) ([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers)
-  - Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
-  - WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling [Kevin Gibbons](https://github.com/bakkot)
-  - WebUI supports incremental display of in-progress images during generation [Kevin Gibbons](https://github.com/bakkot)
-  - A new configuration file scheme that allows new models (including upcoming stable-diffusion-v1.5)
-    to be added without altering the code. ([David Wager](https://github.com/maddavid12))
-  - Can specify --grid on dream.py command line as the default.
-  - Miscellaneous internal bug and stability fixes.
-  - Works on M1 Apple hardware.
-  - Multiple bug fixes.
-
-For older changelogs, please visit **[CHANGELOGS](docs/CHANGELOG.md)**.
-
-# Troubleshooting
-
-Please check out our **[Q&A](docs/help/TROUBLESHOOT.md)** to get solutions for common installation problems and other issues.
 
 # Contributing
 
-Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code cleanup, testing, or code reviews, is very much encouraged to do so. If you are unfamiliar with
+Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
-how to contribute to GitHub projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github).
+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.
 
-## **Contributors**
+### Contributors
 
-This fork is a combined effort of various people from across the world. [Check out the list of all these amazing people](docs/CONTRIBUTORS.md). We thank them for their time, hard work and effort.
+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.
 
-# Support
+### Support
 
-For support,
+For support, please use this repository's GitHub Issues tracking service. Feel free to send me an
-please use this repository's GitHub Issues tracking service. Feel free
+email if you use and like the script.
-to send me an email if you use and like the script.
 
-Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
+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
+Please see the original README for more information on this software and underlying algorithm,
-and underlying algorithm, located in the file [README-CompViz.md](docs/README-CompViz.md).
+located in the file [README-CompViz.md](https://invoke-ai.github.io/InvokeAI/other/README-CompViz/).

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

configs/autoencoder/autoencoder_kl_16x16x16.yaml

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

configs/autoencoder/autoencoder_kl_32x32x4.yaml

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

configs/autoencoder/autoencoder_kl_64x64x3.yaml

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

configs/autoencoder/autoencoder_kl_8x8x64.yaml

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

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

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

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

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

configs/latent-diffusion/cin256-v2.yaml

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

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

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

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

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

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

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

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

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

configs/models.yaml

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

configs/models.yaml.example

@@ -0,0 +1,27 @@
+# This file describes the alternative machine learning models
+# available to InvokeAI script.
+#
+# To add a new model, follow the examples below. Each
+# model requires a model config file, a weights file,
+# and the width and height of the images it
+# was trained on.
+stable-diffusion-1.5:
+  description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
+  weights: ./models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  default: true
+stable-diffusion-1.4:
+  description: Stable Diffusion inference model version 1.4
+  config: configs/stable-diffusion/v1-inference.yaml
+  weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
+  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  width: 512
+  height: 512
+inpainting-1.5:
+  weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+  config: configs/stable-diffusion/v1-inpainting-inference.yaml
+  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  description: RunwayML SD 1.5 model optimized for inpainting

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

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

configs/stable-diffusion/v1-inference.yaml

@@ -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
@@ -76,4 +76,4 @@ model:
           target: torch.nn.Identity
 
     cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

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

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

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 +1,75 @@
+FROM ubuntu AS get_miniconda
+
+SHELL ["/bin/bash", "-c"]
+
+# install wget
+RUN apt-get update \
+  && apt-get install -y \
+    wget \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
+
+# download and install miniconda
+ARG conda_version=py39_4.12.0-Linux-x86_64
+ARG conda_prefix=/opt/conda
+RUN wget --progress=dot:giga -O /miniconda.sh \
+  https://repo.anaconda.com/miniconda/Miniconda3-${conda_version}.sh \
+  && bash /miniconda.sh -b -p ${conda_prefix} \
+  && rm -f /miniconda.sh
+
+FROM ubuntu AS invokeai
+
+# use bash
+SHELL [ "/bin/bash", "-c" ]
+
+# clean bashrc
+RUN echo "" > ~/.bashrc
+
+# Install necesarry packages
+RUN apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    gcc \
+    git \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    pip \
+    python3 \
+    python3-dev \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
+
+# clone repository, create models.yaml and create symlinks
+ARG invokeai_git=invoke-ai/InvokeAI
+ARG invokeai_branch=main
+ARG project_name=invokeai
+RUN git clone -b ${invokeai_branch} https://github.com/${invokeai_git}.git /${project_name} \
+  && cp /${project_name}/configs/models.yaml.example /${project_name}/configs/models.yaml \
+  && ln -s /data/models/v1-5-pruned-emaonly.ckpt /${project_name}/models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt \
+  && ln -s /data/outputs/ /${project_name}/outputs
+
+# set workdir
+WORKDIR /${project_name}
+
+# install conda env and preload models
+ARG conda_prefix=/opt/conda
+ARG conda_env_file=environment.yml
+COPY --from=get_miniconda ${conda_prefix} ${conda_prefix}
+RUN source ${conda_prefix}/etc/profile.d/conda.sh \
+  && conda init bash \
+  && source ~/.bashrc \
+  && conda env create \
+    --name ${project_name} \
+    --file ${conda_env_file} \
+  && rm -Rf ~/.cache \
+  && conda clean -afy \
+  && echo "conda activate ${project_name}" >> ~/.bashrc \
+  && conda activate ${project_name} \
+  && python scripts/preload_models.py \
+    --no-interactive
+
+# Copy entrypoint and set env
+ENV CONDA_PREFIX=${conda_prefix}
+ENV PROJECT_NAME=${project_name}
+COPY docker-build/entrypoint.sh /
+ENTRYPOINT [ "/entrypoint.sh" ]

docker-build/build.sh

@@ -0,0 +1,84 @@
+#!/usr/bin/env bash
+set -e
+# IMPORTANT: You need to have a token on huggingface.co to be able to download the checkpoint!!!
+# configure values by using env when executing build.sh
+# f.e. env ARCH=aarch64 GITHUB_INVOKE_AI=https://github.com/yourname/yourfork.git ./build.sh
+
+source ./docker-build/env.sh || echo "please run from repository root" || exit 1
+
+invokeai_conda_version=${INVOKEAI_CONDA_VERSION:-py39_4.12.0-${platform/\//-}}
+invokeai_conda_prefix=${INVOKEAI_CONDA_PREFIX:-\/opt\/conda}
+invokeai_conda_env_file=${INVOKEAI_CONDA_ENV_FILE:-environment.yml}
+invokeai_git=${INVOKEAI_GIT:-invoke-ai/InvokeAI}
+invokeai_branch=${INVOKEAI_BRANCH:-main}
+huggingface_token=${HUGGINGFACE_TOKEN?}
+
+# print the settings
+echo "You are using these values:"
+echo -e "project_name:\t\t ${project_name}"
+echo -e "volumename:\t\t ${volumename}"
+echo -e "arch:\t\t\t ${arch}"
+echo -e "platform:\t\t ${platform}"
+echo -e "invokeai_conda_version:\t ${invokeai_conda_version}"
+echo -e "invokeai_conda_prefix:\t ${invokeai_conda_prefix}"
+echo -e "invokeai_conda_env_file: ${invokeai_conda_env_file}"
+echo -e "invokeai_git:\t\t ${invokeai_git}"
+echo -e "invokeai_tag:\t\t ${invokeai_tag}\n"
+
+_runAlpine() {
+  docker run \
+    --rm \
+    --interactive \
+    --tty \
+    --mount source="$volumename",target=/data \
+    --workdir /data \
+    alpine "$@"
+}
+
+_copyCheckpoints() {
+  echo "creating subfolders for models and outputs"
+  _runAlpine mkdir models
+  _runAlpine mkdir outputs
+  echo "downloading v1-5-pruned-emaonly.ckpt"
+  _runAlpine wget \
+    --header="Authorization: Bearer ${huggingface_token}" \
+    -O models/v1-5-pruned-emaonly.ckpt \
+    https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
+  echo "done"
+}
+
+_checkVolumeContent() {
+  _runAlpine ls -lhA /data/models
+}
+
+_getModelMd5s() {
+  _runAlpine  \
+    alpine sh -c "md5sum /data/models/*.ckpt"
+}
+
+if [[ -n "$(docker volume ls -f name="${volumename}" -q)" ]]; then
+  echo "Volume already exists"
+  if [[ -z "$(_checkVolumeContent)" ]]; then
+    echo "looks empty, copying checkpoint"
+    _copyCheckpoints
+  fi
+  echo "Models in ${volumename}:"
+  _checkVolumeContent
+else
+  echo -n "createing docker volume "
+  docker volume create "${volumename}"
+  _copyCheckpoints
+fi
+
+# Build Container
+docker build \
+  --platform="${platform}" \
+  --tag "${invokeai_tag}" \
+  --build-arg project_name="${project_name}" \
+  --build-arg conda_version="${invokeai_conda_version}" \
+  --build-arg conda_prefix="${invokeai_conda_prefix}" \
+  --build-arg conda_env_file="${invokeai_conda_env_file}" \
+  --build-arg invokeai_git="${invokeai_git}" \
+  --build-arg invokeai_branch="${invokeai_branch}" \
+  --file ./docker-build/Dockerfile \
+  .

docker-build/entrypoint.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+set -e
+
+source "${CONDA_PREFIX}/etc/profile.d/conda.sh"
+conda activate "${PROJECT_NAME}"
+
+python scripts/invoke.py \
+  ${@:---web --host=0.0.0.0}

docker-build/env.sh

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

docker-build/run.sh

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

docs/CHANGELOG.md

@@ -1,51 +1,106 @@
-# **Changelog**
+---
+title: Changelog
+---
 
-## v1.13 (in process)
+# :octicons-log-16: **Changelog**
 
-- Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
+## v2.0.1 (13 October 2022)
-- 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)
+- fix noisy images at high step count when using k* samplers
-- Output directory can be specified on the dream> command line.
+- dream.py script now calls invoke.py module directly rather than
-- The grid was displaying duplicated images when not enough images to fill the final row [Muhammad Usama](https://github.com/SMUsamaShah)
+    via a new python process (which could break the environment)
-- Can specify --grid on dream.py command line as the default.
+
+## v2.0.0 <small>(9 October 2022)</small>
+
+- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
+    for backward compatibility.
+- Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+- Support for [inpainting](features/INPAINTING.md) and [outpainting](features/OUTPAINTING.md)
+- img2img runs on all k* samplers
+- Support for [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
+- Support for CodeFormer face reconstruction
+- Support for Textual Inversion on Macintoshes
+- Support in both WebGUI and CLI for [post-processing of previously-generated images](features/POSTPROCESS.md)
+    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
+    and "embiggen" upscaling. See the `!fix` command.
+- New `--hires` option on `invoke>` line allows [larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img), at the cost of some performance.
+- New `--perlin` and `--threshold` options allow you to add and control variation
+    during image generation (see [Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
+- Extensive metadata now written into PNG files, allowing reliable regeneration of images
+    and tweaking of previous settings.
+- Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
+- Improved [command-line completion behavior](features/CLI.md)
+    New commands added:
+  - List command-line history with `!history`
+  - Search command-line history with `!search`
+  - Clear history with `!clear`
+- Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
+    configure. To switch away from auto use the new flag like `--precision=float32`.
+
+## v1.14 <small>(11 September 2022)</small>
+
+- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
+- Full support for Apple hardware with M1 or M2 chips.
+- Add "seamless mode" for circular tiling of image. Generates beautiful effects.
+    ([prixt](https://github.com/prixt)).
+- Inpainting support.
+- Improved web server GUI.
+- Lots of code and documentation cleanups.
+
+## v1.13 <small>(3 September 2022)</small>
+
+- Support image variations (see [VARIATIONS](features/VARIATIONS.md)
+    ([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers)
+- Supports a Google Colab notebook for a standalone server running on Google hardware
+    [Arturo Mendivil](https://github.com/artmen1516)
+- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
+    [Kevin Gibbons](https://github.com/bakkot)
+- WebUI supports incremental display of in-progress images during generation
+    [Kevin Gibbons](https://github.com/bakkot)
+- A new configuration file scheme that allows new models (including upcoming
+    stable-diffusion-v1.5) to be added without altering the code.
+    ([David Wager](https://github.com/maddavid12))
+- Can specify --grid on invoke.py command line as the default.
 - Miscellaneous internal bug and stability fixes.
+- Works on M1 Apple hardware.
+- Multiple bug fixes.
 
 ---
 
-## v1.12 (28 August 2022)
+## v1.12 <small>(28 August 2022)</small>
 
 - Improved file handling, including ability to read prompts from standard input.
   (kudos to [Yunsaki](https://github.com/yunsaki)
-- The web server is now integrated with the dream.py script. Invoke by adding --web to
+- The web server is now integrated with the invoke.py script. Invoke by adding --web to
-  the dream.py command arguments.
+  the invoke.py command arguments.
 - Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
   enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
   VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
   [Oceanswave](https://github.com/oceanswave) for their work on this.
-- You can now swap samplers on the dream> command line. [Blessedcoolant](https://github.com/blessedcoolant)
+- You can now swap samplers on the invoke> command line. [Blessedcoolant](https://github.com/blessedcoolant)
 
 ---
 
-## v1.11 (26 August 2022)
+## v1.11 <small>(26 August 2022)</small>
 
 - NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
 - You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
   Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
 - Variant generation support temporarily disabled pending more general solution.
-- Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
+- Created a feature branch named **yunsaki-morphing-invoke** which adds experimental support for
-  iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
+  iteratively modifying the prompt and its parameters. Please see[Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
   for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
   significantly.
 
 ---
 
-## v1.10 (25 August 2022)
+## v1.10 <small>(25 August 2022)</small>
 
 - A barebones but fully functional interactive web server for online generation of txt2img and img2img.
 
 ---
 
-## v1.09 (24 August 2022)
+## v1.09 <small>(24 August 2022)</small>
 
 - A new -v option allows you to generate multiple variants of an initial image
   in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
@@ -55,9 +110,9 @@
 
 ---
 
-## v1.08 (24 August 2022)
+## v1.08 <small>(24 August 2022)</small>
 
-- Escape single quotes on the dream> command before trying to parse. This avoids
+- Escape single quotes on the invoke> command before trying to parse. This avoids
   parse errors.
 - Removed instruction to get Python3.8 as first step in Windows install.
   Anaconda3 does it for you.
@@ -66,7 +121,7 @@
 
 ---
 
-## v1.07 (23 August 2022)
+## v1.07 <small>(23 August 2022)</small>
 
 - Image filenames will now never fill gaps in the sequence, but will be assigned the
   next higher name in the chosen directory. This ensures that the alphabetic and chronological
@@ -74,14 +129,14 @@
 
 ---
 
-## v1.06 (23 August 2022)
+## v1.06 <small>(23 August 2022)</small>
 
 - Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
 - Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
 
 ---
 
-## v1.05 (22 August 2022 - after the drop)
+## v1.05 <small>(22 August 2022 - after the drop)</small>
 
 - Filenames now use the following formats:
   000010.95183149.png -- Two files produced by the same command (e.g. -n2),
@@ -94,12 +149,12 @@
   be regenerated with the indicated key
 
 - It should no longer be possible for one image to overwrite another
-- You can use the "cd" and "pwd" commands at the dream> prompt to set and retrieve
+- You can use the "cd" and "pwd" commands at the invoke> prompt to set and retrieve
   the path of the output directory.
 
 ---
 
-## v1.04 (22 August 2022 - after the drop)
+## v1.04 <small>(22 August 2022 - after the drop)</small>
 
 - Updated README to reflect installation of the released weights.
 - Suppressed very noisy and inconsequential warning when loading the frozen CLIP
@@ -107,31 +162,31 @@
 
 ---
 
-## v1.03 (22 August 2022)
+## v1.03 <small>(22 August 2022)</small>
 
 - The original txt2img and img2img scripts from the CompViz repository have been moved into
   a subfolder named "orig_scripts", to reduce confusion.
 
 ---
 
-## v1.02 (21 August 2022)
+## v1.02 <small>(21 August 2022)</small>
 
 - A copy of the prompt and all of its switches and options is now stored in the corresponding
   image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
   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!!**
+  **Please run "conda env update" to load the k_lms dependencies!!**
 
 ---
 
-## v1.01 (21 August 2022)
+## v1.01 <small>(21 August 2022)</small>
 
 - added k_lms sampling.
-  **Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
+  **Please run "conda env update" to load the k_lms dependencies!!**
 - use half precision arithmetic by default, resulting in faster execution and lower memory requirements
-  Pass argument --full_precision to dream.py to get slower but more accurate image generation
+  Pass argument --full_precision to invoke.py to get slower but more accurate image generation
 
 ---
 
 ## Links
 
-- **[Read Me](../readme.md)**
+- **[Read Me](index.md)**