Merge branch 'main' into dev/pytorch2

'hotdot' to 'hotdog'; the world's least important PR :)

.dockerignore

@@ -1,19 +1,25 @@
+# use this file as a whitelist
 *
-!backend
-!environments-and-requirements
-!frontend
+!invokeai
 !ldm
-!main.py
-!scripts
-!server
-!static
-!setup.py
+!pyproject.toml
+
+# ignore frontend/web but whitelist dist
+invokeai/frontend/web/
+!invokeai/frontend/web/dist/
+
+# ignore invokeai/assets but whitelist invokeai/assets/web
+invokeai/assets/
+!invokeai/assets/web/
 
 # Guard against pulling in any models that might exist in the directory tree
 **/*.pt*
+**/*.ckpt
 
-# unignore configs, but only ignore the custom models.yaml, in case it exists
-!configs
-configs/models.yaml
+# Byte-compiled / optimized / DLL files
+**/__pycache__/
+**/*.py[cod]
 
-**/__pycache__
+# Distribution / packaging
+**/*.egg-info/
+**/*.egg

.git-blame-ignore-revs

@@ -0,0 +1 @@
+b3dccfaeb636599c02effc377cdd8a87d658256c

.github/CODEOWNERS

@@ -1,7 +1,34 @@
-ldm/invoke/pngwriter.py @CapableWeb
-ldm/invoke/server_legacy.py @CapableWeb
-scripts/legacy_api.py @CapableWeb
-tests/legacy_tests.sh @CapableWeb
-installer/ @tildebyte
-.github/workflows/ @mauwii
-docker_build/ @mauwii
+# continuous integration
+/.github/workflows/ @mauwii @lstein @blessedcoolant
+
+# documentation
+/docs/ @lstein @mauwii @tildebyte @blessedcoolant
+/mkdocs.yml @lstein @mauwii @blessedcoolant
+
+# nodes
+/invokeai/app/ @Kyle0654 @blessedcoolant
+
+# installation and configuration
+/pyproject.toml @mauwii @lstein @blessedcoolant
+/docker/ @mauwii @lstein @blessedcoolant
+/scripts/ @ebr @lstein
+/installer/ @lstein @ebr
+/invokeai/assets @lstein @ebr
+/invokeai/configs @lstein
+/invokeai/version @lstein @blessedcoolant
+
+# web ui
+/invokeai/frontend @blessedcoolant @psychedelicious @lstein
+/invokeai/backend @blessedcoolant @psychedelicious @lstein
+
+# generation, model management, postprocessing
+/invokeai/backend @keturn @damian0815 @lstein @blessedcoolant @jpphoto
+
+# front ends
+/invokeai/frontend/CLI @lstein
+/invokeai/frontend/install @lstein @ebr @mauwii 
+/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/web @psychedelicious @blessedcoolant
+
+

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -65,6 +65,16 @@ body:
       placeholder: 8GB
     validations:
       required: false
+      
+  - type: input
+    id: version-number
+    attributes:
+      label: What version did you experience this issue on?
+      description: |
+        Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
+      placeholder: X.X.X
+    validations:
+      required: true
 
   - type: textarea
     id: what-happened

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

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

.github/workflows/build-container.yml

@@ -3,72 +3,112 @@ on:
   push:
     branches:
       - 'main'
+      - 'update/ci/docker/*'
+      - 'update/docker/*'
+      - 'dev/ci/docker/*'
+      - 'dev/docker/*'
+    paths:
+      - 'pyproject.toml'
+      - '.dockerignore'
+      - 'invokeai/**'
+      - 'docker/Dockerfile'
+    tags:
+      - 'v*.*.*'
+  workflow_dispatch:
+
+permissions:
+  contents: write
+  packages: write
 
 jobs:
   docker:
+    if: github.event.pull_request.draft == false
     strategy:
       fail-fast: false
       matrix:
-        registry:
-          - ghcr.io
         flavor:
-          - amd
+          - rocm
           - cuda
-          # - cloud
+          - cpu
         include:
-          - flavor: amd
-            pip-requirements: requirements-lin-amd.txt
-            dockerfile: docker-build/Dockerfile
-            platforms: linux/amd64,linux/arm64
+          - flavor: rocm
+            pip-extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
           - flavor: cuda
-            pip-requirements: requirements-lin-cuda.txt
-            dockerfile: docker-build/Dockerfile
-            platforms: linux/amd64,linux/arm64
-          # - flavor: cloud
-          #   pip-requirements: requirements-lin-cuda.txt
-          #   dockerfile: docker-build/Dockerfile.cloud
-          #   platforms: linux/amd64
+            pip-extra-index-url: ''
+          - flavor: cpu
+            pip-extra-index-url: 'https://download.pytorch.org/whl/cpu'
     runs-on: ubuntu-latest
     name: ${{ matrix.flavor }}
+    env:
+      PLATFORMS: 'linux/amd64,linux/arm64'
+      DOCKERFILE: 'docker/Dockerfile'
     steps:
       - name: Checkout
         uses: actions/checkout@v3
 
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
-
       - name: Docker meta
         id: meta
         uses: docker/metadata-action@v4
         with:
-          images: ${{ matrix.registry }}/${{ github.repository }}-${{ matrix.flavor }}
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          images: |
+            ghcr.io/${{ github.repository }}
+            ${{ vars.DOCKERHUB_REPOSITORY }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
-            type=semver,pattern={{version}}
-            type=semver,pattern={{major}}.{{minor}}
-            type=sha
+            type=pep440,pattern={{version}}
+            type=pep440,pattern={{major}}.{{minor}}
+            type=pep440,pattern={{major}}
+            type=sha,enable=true,prefix=sha-,format=short
           flavor: |
-            latest=true
+            latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}
+            suffix=-${{ matrix.flavor }},onlatest=false
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
+        with:
+          platforms: ${{ env.PLATFORMS }}
 
-      - if: github.event_name != 'pull_request'
-        name: Docker login
+      - name: Login to GitHub Container Registry
+        if: github.event_name != 'pull_request'
         uses: docker/login-action@v2
         with:
-          registry: ${{ matrix.registry }}
-          username: ${{ github.actor }}
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
+      - name: Login to Docker Hub
+        if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
       - name: Build container
-        uses: docker/build-push-action@v3
+        id: docker_build
+        uses: docker/build-push-action@v4
         with:
           context: .
-          file: ${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
-          push: ${{ github.event_name != 'pull_request' }}
+          file: ${{ env.DOCKERFILE }}
+          platforms: ${{ env.PLATFORMS }}
+          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
-          build-args: pip_requirements=${{ matrix.pip-requirements }}
+          build-args: PIP_EXTRA_INDEX_URL=${{ matrix.pip-extra-index-url }}
+          cache-from: |
+            type=gha,scope=${{ github.ref_name }}-${{ matrix.flavor }}
+            type=gha,scope=main-${{ matrix.flavor }}
+          cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.flavor }}
+
+      - name: Docker Hub Description
+        if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
+        uses: peter-evans/dockerhub-description@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+          repository: ${{ vars.DOCKERHUB_REPOSITORY }}
+          short-description: ${{ github.event.repository.description }}

.github/workflows/clean-caches.yml

@@ -0,0 +1,34 @@
+name: cleanup caches by a branch
+on:
+  pull_request:
+    types:
+      - closed
+  workflow_dispatch:
+
+jobs:
+  cleanup:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v3
+
+      - name: Cleanup
+        run: |
+          gh extension install actions/gh-actions-cache
+
+          REPO=${{ github.repository }}
+          BRANCH=${{ github.ref }}
+
+          echo "Fetching list of cache key"
+          cacheKeysForPR=$(gh actions-cache list -R $REPO -B $BRANCH | cut -f 1 )
+
+          ## Setting this to not fail the workflow while deleting cache keys.
+          set +e
+          echo "Deleting caches..."
+          for cacheKey in $cacheKeysForPR
+          do
+              gh actions-cache delete $cacheKey -R $REPO -B $BRANCH --confirm
+          done
+          echo "Done"
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/close-inactive-issues.yml

@@ -0,0 +1,27 @@
+name: Close inactive issues
+on:
+  schedule:
+    - cron: "00 6 * * *"
+
+env:
+  DAYS_BEFORE_ISSUE_STALE: 14
+  DAYS_BEFORE_ISSUE_CLOSE: 28
+
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v5
+        with:
+          days-before-issue-stale: ${{ env.DAYS_BEFORE_ISSUE_STALE }}
+          days-before-issue-close: ${{ env.DAYS_BEFORE_ISSUE_CLOSE }}
+          stale-issue-label: "Inactive Issue"
+          stale-issue-message: "There has been no activity in this issue for ${{ env.DAYS_BEFORE_ISSUE_STALE }} days. If this issue is still being experienced, please reply with an updated confirmation that the issue is still being experienced with the latest release."
+          close-issue-message: "Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue."
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          operations-per-run: 500

.github/workflows/lint-frontend.yml

@@ -3,17 +3,26 @@ name: Lint frontend
 on:
   pull_request:
     paths:
-      - 'frontend/**'
+      - 'invokeai/frontend/web/**'
+    types:
+      - 'ready_for_review'
+      - 'opened'
+      - 'synchronize'
   push:
+    branches:
+      - 'main'
     paths:
-      - 'frontend/**'
+      - 'invokeai/frontend/web/**'
+  merge_group:
+  workflow_dispatch:
 
 defaults:
   run:
-    working-directory: frontend
+    working-directory: invokeai/frontend/web
 
 jobs:
   lint-frontend:
+    if: github.event.pull_request.draft == false
     runs-on: ubuntu-22.04
     steps:
       - name: Setup Node 18
@@ -22,7 +31,7 @@ jobs:
           node-version: '18'
       - uses: actions/checkout@v3
       - run: 'yarn install --frozen-lockfile'
-      - run: 'yarn tsc'
-      - run: 'yarn run madge'
-      - run: 'yarn run lint --max-warnings=0'
-      - run: 'yarn run prettier --check'
+      - run: 'yarn run lint:tsc'
+      - run: 'yarn run lint:madge'
+      - run: 'yarn run lint:eslint'
+      - run: 'yarn run lint:prettier'

.github/workflows/mkdocs-material.yml

@@ -5,8 +5,12 @@ on:
       - 'main'
       - 'development'
 
+permissions:
+    contents: write
+
 jobs:
   mkdocs-material:
+    if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
     steps:
       - name: checkout sources

.github/workflows/pyflakes.yml

@@ -9,6 +9,7 @@ on:
 jobs:
   pyflakes:
     name: runner / pyflakes
+    if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2

.github/workflows/pypi-release.yml

@@ -0,0 +1,41 @@
+name: PyPI Release
+
+on:
+  push:
+    paths:
+      - 'invokeai/version/invokeai_version.py'
+  workflow_dispatch:
+
+jobs:
+  release:
+    if: github.repository == 'invoke-ai/InvokeAI'
+    runs-on: ubuntu-22.04
+    env:
+      TWINE_USERNAME: __token__
+      TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+      TWINE_NON_INTERACTIVE: 1
+    steps:
+      - name: checkout sources
+        uses: actions/checkout@v3
+
+      - name: install deps
+        run: pip install --upgrade build twine
+
+      - name: build package
+        run: python3 -m build
+
+      - name: check distribution
+        run: twine check dist/*
+
+      - name: check PyPI versions
+        if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3'
+        run: |
+          pip install --upgrade requests
+          python -c "\
+          import scripts.pypi_helper; \
+          EXISTS=scripts.pypi_helper.local_on_pypi(); \
+          print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
+
+      - name: upload package
+        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != ''
+        run: twine upload dist/*

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

@@ -0,0 +1,66 @@
+name: Test invoke.py pip
+on:
+  pull_request:
+    paths:
+      - '**'
+      - '!pyproject.toml'
+      - '!invokeai/**'
+      - 'invokeai/frontend/web/**'
+  merge_group:
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  matrix:
+    if: github.event.pull_request.draft == false
+    strategy:
+      matrix:
+        python-version:
+          # - '3.9'
+          - '3.10'
+        pytorch:
+          # - linux-cuda-11_6
+          - linux-cuda-11_7
+          - linux-rocm-5_2
+          - linux-cpu
+          - macos-default
+          - windows-cpu
+          # - windows-cuda-11_6
+          # - windows-cuda-11_7
+        include:
+          # - pytorch: linux-cuda-11_6
+          #   os: ubuntu-22.04
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $GITHUB_ENV
+          - pytorch: linux-cuda-11_7
+            os: ubuntu-22.04
+            github-env: $GITHUB_ENV
+          - pytorch: linux-rocm-5_2
+            os: ubuntu-22.04
+            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
+            github-env: $GITHUB_ENV
+          - pytorch: linux-cpu
+            os: ubuntu-22.04
+            extra-index-url: 'https://download.pytorch.org/whl/cpu'
+            github-env: $GITHUB_ENV
+          - pytorch: macos-default
+            os: macOS-12
+            github-env: $GITHUB_ENV
+          - pytorch: windows-cpu
+            os: windows-2022
+            github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_6
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_7
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu117'
+          #   github-env: $env:GITHUB_ENV
+    name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}
+    steps:
+      - run: 'echo "No build required"'

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

@@ -3,15 +3,25 @@ on:
   push:
     branches:
       - 'main'
+    paths:
+      - 'pyproject.toml'
+      - 'invokeai/**'
+      - '!invokeai/frontend/web/**'
   pull_request:
+    paths:
+      - 'pyproject.toml'
+      - 'invokeai/**'
+      - '!invokeai/frontend/web/**'
     types:
       - 'ready_for_review'
       - 'opened'
       - 'synchronize'
+  merge_group:
+  workflow_dispatch:
 
 concurrency:
-   group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-   cancel-in-progress: true
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
 
 jobs:
   matrix:
@@ -22,19 +32,19 @@ jobs:
           # - '3.9'
           - '3.10'
         pytorch:
-          - linux-cuda-11_6
+          # - linux-cuda-11_6
           - linux-cuda-11_7
           - linux-rocm-5_2
           - linux-cpu
           - macos-default
           - windows-cpu
-          - windows-cuda-11_6
-          - windows-cuda-11_7
+          # - windows-cuda-11_6
+          # - windows-cuda-11_7
         include:
-          - pytorch: linux-cuda-11_6
-            os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/cu116'
-            github-env: $GITHUB_ENV
+          # - pytorch: linux-cuda-11_6
+          #   os: ubuntu-22.04
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $GITHUB_ENV
           - pytorch: linux-cuda-11_7
             os: ubuntu-22.04
             github-env: $GITHUB_ENV
@@ -52,38 +62,23 @@ jobs:
           - pytorch: windows-cpu
             os: windows-2022
             github-env: $env:GITHUB_ENV
-          - pytorch: windows-cuda-11_6
-            os: windows-2022
-            extra-index-url: 'https://download.pytorch.org/whl/cu116'
-            github-env: $env:GITHUB_ENV
-          - pytorch: windows-cuda-11_7
-            os: windows-2022
-            extra-index-url: 'https://download.pytorch.org/whl/cu117'
-            github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_6
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_7
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu117'
+          #   github-env: $env:GITHUB_ENV
     name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
+    env:
+      PIP_USE_PEP517: '1'
     steps:
       - name: Checkout sources
         id: checkout-sources
         uses: actions/checkout@v3
 
-      - name: setup python
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Set Cache-Directory Windows
-        if: runner.os == 'Windows'
-        id: set-cache-dir-windows
-        run: |
-          echo "CACHE_DIR=$HOME\invokeai\models" >> ${{ matrix.github-env }}
-          echo "PIP_NO_CACHE_DIR=1" >> ${{ matrix.github-env }}
-
-      - name: Set Cache-Directory others
-        if: runner.os != 'Windows'
-        id: set-cache-dir-others
-        run: echo "CACHE_DIR=$HOME/invokeai/models" >> ${{ matrix.github-env }}
-
       - name: set test prompt to main branch validation
         if: ${{ github.ref == 'refs/heads/main' }}
         run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
@@ -92,49 +87,58 @@ jobs:
         if: ${{ github.ref != 'refs/heads/main' }}
         run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
 
+      - name: setup python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: pip
+          cache-dependency-path: pyproject.toml
+
       - name: install invokeai
-        run: pip3 install --use-pep517 -e .
         env:
           PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
+        run: >
+          pip3 install
+          --editable=".[test]"
 
-      - name: Use Cached models
-        id: cache-sd-model
-        uses: actions/cache@v3
-        env:
-          cache-name: huggingface-models
-        with:
-          path: ${{ env.CACHE_DIR }}
-          key: ${{ env.cache-name }}
-          enableCrossOsArchive: true
+      - name: run pytest
+        id: run-pytest
+        run: pytest
 
-      - name: run configure_invokeai
+      - name: set INVOKEAI_OUTDIR
+        run: >
+          python -c
+          "import os;from invokeai.backend.globals import Globals;OUTDIR=os.path.join(Globals.root,str('outputs'));print(f'INVOKEAI_OUTDIR={OUTDIR}')"
+          >> ${{ matrix.github-env }}
+
+      - name: run invokeai-configure
         id: run-preload-models
         env:
           HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
         run: >
-          configure_invokeai
+          invokeai-configure
           --yes
           --default_only
           --full-precision
         # can't use fp16 weights without a GPU
 
-      - name: Run the tests
-        if: runner.os != 'Windows'
-        id: run-tests
+      - name: run invokeai
+        id: run-invokeai
         env:
           # Set offline mode to make sure configure preloaded successfully.
           HF_HUB_OFFLINE: 1
           HF_DATASETS_OFFLINE: 1
           TRANSFORMERS_OFFLINE: 1
         run: >
-          invoke
+          invokeai
           --no-patchmatch
           --no-nsfw_checker
           --from_file ${{ env.TEST_PROMPTS }}
+          --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
 
       - name: Archive results
         id: archive-results
         uses: actions/upload-artifact@v3
         with:
-          name: results_${{ matrix.pytorch }}_${{ matrix.python-version }}
-          path: ${{ env.INVOKEAI_ROOT }}/outputs
+          name: results
+          path: ${{ env.INVOKEAI_OUTDIR }}

.gitignore

@@ -1,4 +1,5 @@
 # ignore default image save location and model symbolic link
+.idea/
 embeddings/
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
@@ -62,16 +63,20 @@ pip-delete-this-directory.txt
 htmlcov/
 .tox/
 .nox/
+.coveragerc
 .coverage
 .coverage.*
 .cache
 nosetests.xml
 coverage.xml
+cov.xml
 *.cover
 *.py,cover
 .hypothesis/
 .pytest_cache/
+.pytest.ini
 cover/
+junit/
 
 # Translations
 *.mo
@@ -195,7 +200,7 @@ checkpoints
 .DS_Store
 
 # Let the frontend manage its own gitignore
-!frontend/*
+!invokeai/frontend/web/*
 
 # Scratch folder
 .scratch/
@@ -210,11 +215,6 @@ gfpgan/
 # config file (will be created by installer)
 configs/models.yaml
 
-# weights (will be created by installer)
-models/ldm/stable-diffusion-v1/*.ckpt
-models/clipseg
-models/gfpgan
-
 # ignore initfile
 .invokeai
 
@@ -229,9 +229,3 @@ installer/install.bat
 installer/install.sh
 installer/update.bat
 installer/update.sh
-
-# this may be present if the user created a venv
-invokeai
-
-# no longer stored in source directory
-models

README.md

@@ -1,6 +1,6 @@
 <div align="center">
 
-![project logo](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/invoke_ai_banner.png)
+![project logo](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/invoke_ai_banner.png)
 
 # InvokeAI: A Stable Diffusion Toolkit
 
@@ -10,10 +10,10 @@
 
 [![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link]
 
-[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
 
 [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
+[CI checks on main link]:https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
 [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
@@ -28,12 +28,14 @@
 [latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
 [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
+[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
+[translation status link]: https://hosted.weblate.org/engage/invokeai/
 
 </div>
 
 InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.
 
-**Quick links**: [[How to Install](#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
+**Quick links**: [[How to Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
 
 _Note: InvokeAI is rapidly evolving. Please use the
 [Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
@@ -41,38 +43,136 @@ requests. Be sure to use the provided templates. They will help us diagnose issu
 
 <div align="center">
 
-![canvas preview](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/canvas_preview.png)
+![canvas preview](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/canvas_preview.png)
 
 </div>
 
-# Getting Started with InvokeAI
+## Table of Contents
+
+1. [Quick Start](#getting-started-with-invokeai)
+2. [Installation](#detailed-installation-instructions)
+3. [Hardware Requirements](#hardware-requirements)
+4. [Features](#features)
+5. [Latest Changes](#latest-changes)
+6. [Troubleshooting](#troubleshooting)
+7. [Contributing](#contributing)
+8. [Contributors](#contributors)
+9. [Support](#support)
+10. [Further Reading](#further-reading)
+
+## Getting Started with InvokeAI
 
 For full installation and upgrade instructions, please see:
 [InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
 
+### Automatic Installer (suggested for 1st time users)
+
 1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
+
 2. Download the .zip file for your OS (Windows/macOS/Linux).
+
 3. Unzip the file.
-4. If you are on Windows, double-click on the `install.bat` script. On macOS, open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press return. On Linux, run `install.sh`.
-5. Wait a while, until it is done.
-6. The folder where you ran the installer from will now be filled with lots of files. If you are on Windows, double-click on the `invoke.bat` file. On macOS, open a Terminal window, drag `invoke.sh` from the folder into the Terminal, and press return. On Linux, run `invoke.sh`
-7. Press 2 to open the "browser-based UI", press enter/return, wait a minute or two for Stable Diffusion to start up, then open your browser and go to http://localhost:9090.
-8. Type `banana sushi` in the box on the top left and click `Invoke`
 
+4. If you are on Windows, double-click on the `install.bat` script. On
+macOS, open a Terminal window, drag the file `install.sh` from Finder
+into the Terminal, and press return. On Linux, run `install.sh`.
 
-## Table of Contents
+5. You'll be asked to confirm the location of the folder in which
+to install InvokeAI and its image generation model files. Pick a
+location with at least 15 GB of free memory. More if you plan on
+installing lots of models.
 
-1. [Installation](#installation)
-2. [Hardware Requirements](#hardware-requirements)
-3. [Features](#features)
-4. [Latest Changes](#latest-changes)
-5. [Troubleshooting](#troubleshooting)
-6. [Contributing](#contributing)
-7. [Contributors](#contributors)
-8. [Support](#support)
-9. [Further Reading](#further-reading)
+6. Wait while the installer does its thing. After installing the software,
+the installer will launch a script that lets you configure InvokeAI and
+select a set of starting image generaiton models.
 
-## Installation
+7. Find the folder that InvokeAI was installed into (it is not the
+same as the unpacked zip file directory!) The default location of this
+folder (if you didn't change it in step 5) is `~/invokeai` on
+Linux/Mac systems, and `C:\Users\YourName\invokeai` on Windows. This directory will contain launcher scripts named `invoke.sh` and `invoke.bat`.
+
+8. On Windows systems, double-click on the `invoke.bat` file. On
+macOS, open a Terminal window, drag `invoke.sh` from the folder into
+the Terminal, and press return. On Linux, run `invoke.sh`
+
+9. Press 2 to open the "browser-based UI", press enter/return, wait a
+minute or two for Stable Diffusion to start up, then open your browser
+and go to http://localhost:9090.
+
+10. Type `banana sushi` in the box on the top left and click `Invoke`
+
+### Command-Line Installation (for users familiar with Terminals)
+
+You must have Python 3.9 or 3.10 installed on your machine. Earlier or later versions are
+not supported.
+
+1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
+2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
+
+    ```terminal
+    mkdir invokeai
+    ````
+
+3. Create a virtual environment named `.venv` inside this directory and activate it:
+
+    ```terminal
+    cd invokeai
+    python -m venv .venv --prompt InvokeAI
+    ```
+
+4. Activate the virtual environment (do it every time you run InvokeAI)
+
+    _For Linux/Mac users:_
+
+    ```sh
+    source .venv/bin/activate
+    ```
+
+    _For Windows users:_
+
+    ```ps
+    .venv\Scripts\activate
+    ```
+
+5. Install the InvokeAI module and its dependencies. Choose the command suited for your platform & GPU.
+
+    _For Windows/Linux with an NVIDIA GPU:_
+
+    ```terminal
+    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+    ```
+
+    _For Linux with an AMD GPU:_
+
+    ```sh
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    ```
+
+    _For Macintoshes, either Intel or M1/M2:_
+
+    ```sh
+    pip install InvokeAI --use-pep517
+    ```
+
+6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
+
+    ```terminal
+    invokeai-configure
+    ```
+
+7. Launch the web server (do it every time you run InvokeAI):
+
+    ```terminal
+    invokeai --web
+    ```
+
+8. Point your browser to http://localhost:9090 to bring up the web interface.
+9. Type `banana sushi` in the box on the top left and click `Invoke`.
+
+Be sure to activate the virtual environment each time before re-launching InvokeAI,
+using `source .venv/bin/activate` or `.venv\Scripts\activate`.
+
+### Detailed Installation Instructions
 
 This fork is supported across Linux, Windows and Macintosh. Linux
 users can use either an Nvidia-based card (with CUDA support) or an
@@ -80,28 +180,29 @@ AMD card (using the ROCm driver). For full installation and upgrade
 instructions, please see:
 [InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_SOURCE/)
 
-### Hardware Requirements
+## Hardware Requirements
 
 InvokeAI is supported across Linux, Windows and macOS. Linux
 users can use either an Nvidia-based card (with CUDA support) or an
 AMD card (using the ROCm driver).
 
-#### System
+### System
 
 You will need one of the following:
 
 - An NVIDIA-based graphics card with 4 GB or more VRAM memory.
 - An Apple computer with an M1 chip.
+- An AMD-based graphics card with 4GB or more VRAM memory. (Linux only)
 
 We do not recommend the GTX 1650 or 1660 series video cards. They are
 unable to run in half-precision mode and do not have sufficient VRAM
 to render 512x512 images.
 
-#### Memory
+### Memory
 
 - At least 12 GB Main Memory RAM.
 
-#### Disk
+### Disk
 
 - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
@@ -151,13 +252,15 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
 problems and other issues.
 
-# Contributing
+## Contributing
 
 Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
 cleanup, testing, or code reviews, is very much encouraged to do so.
 
 To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
 
+If you'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
+
 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. You can **make your pull request against the "main" branch**.
@@ -174,6 +277,8 @@ 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.
 
+Thanks to [Weblate](https://weblate.org/) for generously providing translation services to this project.
+
 ### Support
 
 For support, please use this repository's GitHub Issues tracking service, or join the Discord.

backend/modules/create_cmd_parser.py

@@ -1,55 +0,0 @@
-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/get_canvas_generation_mode.py

@@ -1,117 +0,0 @@
-from PIL import Image, ImageChops
-from PIL.Image import Image as ImageType
-from typing import Union, Literal
-
-# https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
-def check_for_any_transparency(img: Union[ImageType, str]) -> bool:
-    if type(img) is str:
-        img = Image.open(str)
-
-    if img.info.get("transparency", None) is not None:
-        return True
-    if img.mode == "P":
-        transparent = img.info.get("transparency", -1)
-        for _, index in img.getcolors():
-            if index == transparent:
-                return True
-    elif img.mode == "RGBA":
-        extrema = img.getextrema()
-        if extrema[3][0] < 255:
-            return True
-    return False
-
-
-def get_canvas_generation_mode(
-    init_img: Union[ImageType, str], init_mask: Union[ImageType, str]
-) -> Literal["txt2img", "outpainting", "inpainting", "img2img",]:
-    if type(init_img) is str:
-        init_img = Image.open(init_img)
-
-    if type(init_mask) is str:
-        init_mask = Image.open(init_mask)
-
-    init_img = init_img.convert("RGBA")
-
-    # Get alpha from init_img
-    init_img_alpha = init_img.split()[-1]
-    init_img_alpha_mask = init_img_alpha.convert("L")
-    init_img_has_transparency = check_for_any_transparency(init_img)
-
-    if init_img_has_transparency:
-        init_img_is_fully_transparent = (
-            True if init_img_alpha_mask.getbbox() is None else False
-        )
-
-    """
-    Mask images are white in areas where no change should be made, black where changes
-    should be made.
-    """
-
-    # Fit the mask to init_img's size and convert it to greyscale
-    init_mask = init_mask.resize(init_img.size).convert("L")
-
-    """
-    PIL.Image.getbbox() returns the bounding box of non-zero areas of the image, so we first
-    invert the mask image so that masked areas are white and other areas black == zero.
-    getbbox() now tells us if the are any masked areas.
-    """
-    init_mask_bbox = ImageChops.invert(init_mask).getbbox()
-    init_mask_exists = False if init_mask_bbox is None else True
-
-    if init_img_has_transparency:
-        if init_img_is_fully_transparent:
-            return "txt2img"
-        else:
-            return "outpainting"
-    else:
-        if init_mask_exists:
-            return "inpainting"
-        else:
-            return "img2img"
-
-
-def main():
-    # Testing
-    init_img_opaque = "test_images/init-img_opaque.png"
-    init_img_partial_transparency = "test_images/init-img_partial_transparency.png"
-    init_img_full_transparency = "test_images/init-img_full_transparency.png"
-    init_mask_no_mask = "test_images/init-mask_no_mask.png"
-    init_mask_has_mask = "test_images/init-mask_has_mask.png"
-
-    print(
-        "OPAQUE IMAGE, NO MASK, expect img2img, got ",
-        get_canvas_generation_mode(init_img_opaque, init_mask_no_mask),
-    )
-
-    print(
-        "IMAGE WITH TRANSPARENCY, NO MASK, expect outpainting, got ",
-        get_canvas_generation_mode(
-            init_img_partial_transparency, init_mask_no_mask
-        ),
-    )
-
-    print(
-        "FULLY TRANSPARENT IMAGE NO MASK, expect txt2img, got ",
-        get_canvas_generation_mode(init_img_full_transparency, init_mask_no_mask),
-    )
-
-    print(
-        "OPAQUE IMAGE, WITH MASK, expect inpainting, got ",
-        get_canvas_generation_mode(init_img_opaque, init_mask_has_mask),
-    )
-
-    print(
-        "IMAGE WITH TRANSPARENCY, WITH MASK, expect outpainting, got ",
-        get_canvas_generation_mode(
-            init_img_partial_transparency, init_mask_has_mask
-        ),
-    )
-
-    print(
-        "FULLY TRANSPARENT IMAGE WITH MASK, expect txt2img, got ",
-        get_canvas_generation_mode(init_img_full_transparency, init_mask_has_mask),
-    )
-
-
-if __name__ == "__main__":
-    main()

backend/modules/parameters.py

@@ -1,73 +0,0 @@
-from backend.modules.parse_seed_weights import parse_seed_weights
-import argparse
-
-SAMPLER_CHOICES = [
-    "ddim",
-    "k_dpm_2_a",
-    "k_dpm_2",
-    "k_dpmpp_2_a",
-    "k_dpmpp_2",
-    "k_euler_a",
-    "k_euler",
-    "k_heun",
-    "k_lms",
-    "plms",
-    # diffusers:
-    "pndm",
-]
-
-
-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)

binary_installer/install.bat.in

@@ -147,7 +147,7 @@ echo ***** Installed invoke launcher script ******
 rd /s /q binary_installer installer_files
 
 @rem preload the models
-call .venv\Scripts\python scripts\configure_invokeai.py
+call .venv\Scripts\python ldm\invoke\config\invokeai_configure.py
 set err_msg=----- model download clone failed -----
 if %errorlevel% neq 0 goto err_exit
 deactivate

configs/INITIAL_MODELS.yaml

@@ -1,116 +0,0 @@
-stable-diffusion-2.1-768:
-   description: Stable Diffusion version 2.1 diffusers model, trained on 768x768 images (5.21 GB)
-   repo_id: stabilityai/stable-diffusion-2-1
-   format: diffusers
-   recommended: True
-stable-diffusion-2.1-base:
-   description: Stable Diffusion version 2.1 diffusers base model, trained on 512x512 images (5.21 GB)
-   repo_id: stabilityai/stable-diffusion-2-1-base
-   format: diffusers
-   recommended: False
-stable-diffusion-1.5:
-   description: Stable Diffusion version 1.5 weight file (4.27 GB)
-   repo_id: runwayml/stable-diffusion-v1-5
-   format: diffusers
-   recommended: True
-   default: True
-   vae:
-     repo_id: stabilityai/sd-vae-ft-mse
-stable-diffusion-1.4:
-   description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
-   repo_id: CompVis/stable-diffusion-v1-4
-   recommended: False
-   format: diffusers
-   vae:
-      repo_id: stabilityai/sd-vae-ft-mse
-   width: 512
-   height: 512
-inpainting-1.5:
-   description: RunwayML SD 1.5 model optimized for inpainting (ckpt version) (4.27 GB)
-   repo_id: runwayml/stable-diffusion-inpainting
-   config: v1-inpainting-inference.yaml
-   file: sd-v1-5-inpainting.ckpt
-   format: ckpt
-   vae:
-      repo_id: stabilityai/sd-vae-ft-mse-original
-      file: vae-ft-mse-840000-ema-pruned.ckpt
-   recommended: True
-   width: 512
-   height: 512
-waifu-diffusion-1.4:
-  description: Latest waifu diffusion 1.4 (diffusers version)
-  format: diffusers
-  repo_id: hakurei/waifu-diffusion
-  recommended: True
-waifu-diffusion-1.3:
-   description: Stable Diffusion 1.4 fine tuned on anime-styled images (ckpt version) (4.27 GB)
-   repo_id: hakurei/waifu-diffusion-v1-3
-   config: v1-inference.yaml
-   file: model-epoch09-float32.ckpt
-   format: ckpt
-   vae:
-      repo_id: stabilityai/sd-vae-ft-mse-original
-      file: vae-ft-mse-840000-ema-pruned.ckpt
-   recommended: False
-   width: 512
-   height: 512
-trinart-2.0:
-   description: An SD model finetuned with ~40,000 assorted high resolution manga/anime-style pictures (2.13 GB)
-   repo_id: naclbit/trinart_stable_diffusion_v2
-   format: diffusers
-   recommended: False
-   vae:
-     repo_id: stabilityai/sd-vae-ft-mse
-trinart_characters-2_0:
-   description: An SD model finetuned with 19.2M anime/manga style images (ckpt version) (4.27 GB)
-   repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
-   config: v1-inference.yaml
-   file: derrida_final.ckpt
-   format: ckpt
-   vae:
-      repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
-      file: autoencoder_fix_kl-f8-trinart_characters.ckpt
-   recommended: False
-   width: 512
-   height: 512
-anything-4.0:
-   description: High-quality, highly detailed anime style images with just a few prompts
-   format: diffusers
-   repo_id: andite/anything-v4.0
-   recommended: False
-papercut-1.0:
-   description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
-   repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
-   format: diffusers
-   vae:
-     repo_id: stabilityai/sd-vae-ft-mse
-   recommended: False
-voxel_art-1.0:
-   description: Stable Diffusion trained on voxel art (use "VoxelArt" in your prompts) (4.27 GB)
-   repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model
-   config: v1-inference.yaml
-   file: VoxelArt_v1.ckpt
-   format: ckpt
-   vae:
-     repo_id: stabilityai/sd-vae-ft-mse
-   recommended: False
-   width: 512
-   height: 512
-ft-mse-improved-autoencoder-840000:
-   description: StabilityAI improved autoencoder fine-tuned for human faces. Use with legacy .ckpt models ONLY (335 MB)
-   repo_id: stabilityai/sd-vae-ft-mse-original
-   format: ckpt
-   config: VAE/default
-   file: vae-ft-mse-840000-ema-pruned.ckpt
-   recommended: False
-   width: 512
-   height: 512
-trinart_vae:
-   description: Custom autoencoder for trinart_characters for legacy .ckpt models only (335 MB)
-   repo_id: naclbit/trinart_characters_19.2m_stable_diffusion_v1
-   config: VAE/trinart
-   format: ckpt
-   file: autoencoder_fix_kl-f8-trinart_characters.ckpt
-   recommended: False
-   width: 512
-   height: 512

configs/sd-concepts.txt

@@ -1,803 +0,0 @@
-sd-concepts-library/001glitch-core
-sd-concepts-library/2814-roth
-sd-concepts-library/3d-female-cyborgs
-sd-concepts-library/4tnght
-sd-concepts-library/80s-anime-ai
-sd-concepts-library/80s-anime-ai-being
-sd-concepts-library/852style-girl
-sd-concepts-library/8bit
-sd-concepts-library/8sconception
-sd-concepts-library/Aflac-duck
-sd-concepts-library/Akitsuki
-sd-concepts-library/Atako
-sd-concepts-library/Exodus-Styling
-sd-concepts-library/RINGAO
-sd-concepts-library/a-female-hero-from-the-legend-of-mir
-sd-concepts-library/a-hat-kid
-sd-concepts-library/a-tale-of-two-empires
-sd-concepts-library/aadhav-face
-sd-concepts-library/aavegotchi
-sd-concepts-library/abby-face
-sd-concepts-library/abstract-concepts
-sd-concepts-library/accurate-angel
-sd-concepts-library/agm-style-nao
-sd-concepts-library/aj-fosik
-sd-concepts-library/alberto-mielgo
-sd-concepts-library/alex-portugal
-sd-concepts-library/alex-thumbnail-object-2000-steps
-sd-concepts-library/aleyna-tilki
-sd-concepts-library/alf
-sd-concepts-library/alicebeta
-sd-concepts-library/alien-avatar
-sd-concepts-library/alisa
-sd-concepts-library/all-rings-albuns
-sd-concepts-library/altvent
-sd-concepts-library/altyn-helmet
-sd-concepts-library/amine
-sd-concepts-library/amogus
-sd-concepts-library/anders-zorn
-sd-concepts-library/angus-mcbride-style
-sd-concepts-library/animalve3-1500seq
-sd-concepts-library/anime-background-style
-sd-concepts-library/anime-background-style-v2
-sd-concepts-library/anime-boy
-sd-concepts-library/anime-girl
-sd-concepts-library/anyXtronXredshift
-sd-concepts-library/anya-forger
-sd-concepts-library/apex-wingman
-sd-concepts-library/apulian-rooster-v0-1
-sd-concepts-library/arcane-face
-sd-concepts-library/arcane-style-jv
-sd-concepts-library/arcimboldo-style
-sd-concepts-library/armando-reveron-style
-sd-concepts-library/armor-concept
-sd-concepts-library/arq-render
-sd-concepts-library/art-brut
-sd-concepts-library/arthur1
-sd-concepts-library/artist-yukiko-kanagai
-sd-concepts-library/arwijn
-sd-concepts-library/ashiok
-sd-concepts-library/at-wolf-boy-object
-sd-concepts-library/atm-ant
-sd-concepts-library/atm-ant-2
-sd-concepts-library/axe-tattoo
-sd-concepts-library/ayush-spider-spr
-sd-concepts-library/azura-from-vibrant-venture
-sd-concepts-library/ba-shiroko
-sd-concepts-library/babau
-sd-concepts-library/babs-bunny
-sd-concepts-library/babushork
-sd-concepts-library/backrooms
-sd-concepts-library/bad_Hub_Hugh
-sd-concepts-library/bada-club
-sd-concepts-library/baldi
-sd-concepts-library/baluchitherian
-sd-concepts-library/bamse
-sd-concepts-library/bamse-og-kylling
-sd-concepts-library/bee
-sd-concepts-library/beholder
-sd-concepts-library/beldam
-sd-concepts-library/belen
-sd-concepts-library/bella-goth
-sd-concepts-library/belle-delphine
-sd-concepts-library/bert-muppet
-sd-concepts-library/better-collage3
-sd-concepts-library/between2-mt-fade
-sd-concepts-library/birb-style
-sd-concepts-library/black-and-white-design
-sd-concepts-library/black-waifu
-sd-concepts-library/bloo
-sd-concepts-library/blue-haired-boy
-sd-concepts-library/blue-zombie
-sd-concepts-library/blue-zombiee
-sd-concepts-library/bluebey
-sd-concepts-library/bluebey-2
-sd-concepts-library/bobs-burgers
-sd-concepts-library/boissonnard
-sd-concepts-library/bonzi-monkey
-sd-concepts-library/borderlands
-sd-concepts-library/bored-ape-textual-inversion
-sd-concepts-library/boris-anderson
-sd-concepts-library/bozo-22
-sd-concepts-library/breakcore
-sd-concepts-library/brittney-williams-art
-sd-concepts-library/bruma
-sd-concepts-library/brunnya
-sd-concepts-library/buddha-statue
-sd-concepts-library/bullvbear
-sd-concepts-library/button-eyes
-sd-concepts-library/canadian-goose
-sd-concepts-library/canary-cap
-sd-concepts-library/cancer_style
-sd-concepts-library/captain-haddock
-sd-concepts-library/captainkirb
-sd-concepts-library/car-toy-rk
-sd-concepts-library/carasibana
-sd-concepts-library/carlitos-el-mago
-sd-concepts-library/carrascharacter
-sd-concepts-library/cartoona-animals
-sd-concepts-library/cat-toy
-sd-concepts-library/centaur
-sd-concepts-library/cgdonny1
-sd-concepts-library/cham
-sd-concepts-library/chandra-nalaar
-sd-concepts-library/char-con
-sd-concepts-library/character-pingu
-sd-concepts-library/cheburashka
-sd-concepts-library/chen-1
-sd-concepts-library/child-zombie
-sd-concepts-library/chillpill
-sd-concepts-library/chonkfrog
-sd-concepts-library/chop
-sd-concepts-library/christo-person
-sd-concepts-library/chuck-walton
-sd-concepts-library/chucky
-sd-concepts-library/chungus-poodl-pet
-sd-concepts-library/cindlop
-sd-concepts-library/collage-cutouts
-sd-concepts-library/collage14
-sd-concepts-library/collage3
-sd-concepts-library/collage3-hubcity
-sd-concepts-library/cologne
-sd-concepts-library/color-page
-sd-concepts-library/colossus
-sd-concepts-library/command-and-conquer-remastered-cameos
-sd-concepts-library/concept-art
-sd-concepts-library/conner-fawcett-style
-sd-concepts-library/conway-pirate
-sd-concepts-library/coop-himmelblau
-sd-concepts-library/coraline
-sd-concepts-library/cornell-box
-sd-concepts-library/cortana
-sd-concepts-library/covid-19-rapid-test
-sd-concepts-library/cow-uwu
-sd-concepts-library/cowboy
-sd-concepts-library/crazy-1
-sd-concepts-library/crazy-2
-sd-concepts-library/crb-portraits
-sd-concepts-library/crb-surrealz
-sd-concepts-library/crbart
-sd-concepts-library/crested-gecko
-sd-concepts-library/crinos-form-garou
-sd-concepts-library/cry-baby-style
-sd-concepts-library/crybaby-style-2-0
-sd-concepts-library/csgo-awp-object
-sd-concepts-library/csgo-awp-texture-map
-sd-concepts-library/cubex
-sd-concepts-library/cumbia-peruana
-sd-concepts-library/cute-bear
-sd-concepts-library/cute-cat
-sd-concepts-library/cute-game-style
-sd-concepts-library/cyberpunk-lucy
-sd-concepts-library/dabotap
-sd-concepts-library/dan-mumford
-sd-concepts-library/dan-seagrave-art-style
-sd-concepts-library/dark-penguin-pinguinanimations
-sd-concepts-library/darkpenguinanimatronic
-sd-concepts-library/darkplane
-sd-concepts-library/david-firth-artstyle
-sd-concepts-library/david-martinez-cyberpunk
-sd-concepts-library/david-martinez-edgerunners
-sd-concepts-library/david-moreno-architecture
-sd-concepts-library/daycare-attendant-sun-fnaf
-sd-concepts-library/ddattender
-sd-concepts-library/degods
-sd-concepts-library/degodsheavy
-sd-concepts-library/depthmap
-sd-concepts-library/depthmap-style
-sd-concepts-library/design
-sd-concepts-library/detectivedinosaur1
-sd-concepts-library/diaosu-toy
-sd-concepts-library/dicoo
-sd-concepts-library/dicoo2
-sd-concepts-library/dishonored-portrait-styles
-sd-concepts-library/disquieting-muses
-sd-concepts-library/ditko
-sd-concepts-library/dlooak
-sd-concepts-library/doc
-sd-concepts-library/doener-red-line-art
-sd-concepts-library/dog
-sd-concepts-library/dog-django
-sd-concepts-library/doge-pound
-sd-concepts-library/dong-ho
-sd-concepts-library/dong-ho2
-sd-concepts-library/doose-s-realistic-art-style
-sd-concepts-library/dq10-anrushia
-sd-concepts-library/dr-livesey
-sd-concepts-library/dr-strange
-sd-concepts-library/dragonborn
-sd-concepts-library/dreamcore
-sd-concepts-library/dreamy-painting
-sd-concepts-library/drive-scorpion-jacket
-sd-concepts-library/dsmuses
-sd-concepts-library/dtv-pkmn
-sd-concepts-library/dullboy-caricature
-sd-concepts-library/duranduran
-sd-concepts-library/durer-style
-sd-concepts-library/dyoudim-style
-sd-concepts-library/early-mishima-kurone
-sd-concepts-library/eastward
-sd-concepts-library/eddie
-sd-concepts-library/edgerunners-style
-sd-concepts-library/edgerunners-style-v2
-sd-concepts-library/el-salvador-style-style
-sd-concepts-library/elegant-flower
-sd-concepts-library/elspeth-tirel
-sd-concepts-library/eru-chitanda-casual
-sd-concepts-library/erwin-olaf-style
-sd-concepts-library/ettblackteapot
-sd-concepts-library/explosions-cat
-sd-concepts-library/eye-of-agamotto
-sd-concepts-library/f-22
-sd-concepts-library/facadeplace
-sd-concepts-library/fairy-tale-painting-style
-sd-concepts-library/fairytale
-sd-concepts-library/fang-yuan-001
-sd-concepts-library/faraon-love-shady
-sd-concepts-library/fasina
-sd-concepts-library/felps
-sd-concepts-library/female-kpop-singer
-sd-concepts-library/fergal-cat
-sd-concepts-library/filename-2
-sd-concepts-library/fileteado-porteno
-sd-concepts-library/final-fantasy-logo
-sd-concepts-library/fireworks-over-water
-sd-concepts-library/fish
-sd-concepts-library/flag-ussr
-sd-concepts-library/flatic
-sd-concepts-library/floral
-sd-concepts-library/fluid-acrylic-jellyfish-creatures-style-of-carl-ingram-art
-sd-concepts-library/fnf-boyfriend
-sd-concepts-library/fold-structure
-sd-concepts-library/fox-purple
-sd-concepts-library/fractal
-sd-concepts-library/fractal-flame
-sd-concepts-library/fractal-temple-style
-sd-concepts-library/frank-frazetta
-sd-concepts-library/franz-unterberger
-sd-concepts-library/freddy-fazbear
-sd-concepts-library/freefonix-style
-sd-concepts-library/furrpopasthetic
-sd-concepts-library/fursona
-sd-concepts-library/fzk
-sd-concepts-library/galaxy-explorer
-sd-concepts-library/ganyu-genshin-impact
-sd-concepts-library/garcon-the-cat
-sd-concepts-library/garfield-pizza-plush
-sd-concepts-library/garfield-pizza-plush-v2
-sd-concepts-library/gba-fe-class-cards
-sd-concepts-library/gba-pokemon-sprites
-sd-concepts-library/geggin
-sd-concepts-library/ggplot2
-sd-concepts-library/ghost-style
-sd-concepts-library/ghostproject-men
-sd-concepts-library/gibasachan-v0
-sd-concepts-library/gim
-sd-concepts-library/gio
-sd-concepts-library/giygas
-sd-concepts-library/glass-pipe
-sd-concepts-library/glass-prism-cube
-sd-concepts-library/glow-forest
-sd-concepts-library/goku
-sd-concepts-library/gram-tops
-sd-concepts-library/green-blue-shanshui
-sd-concepts-library/green-tent
-sd-concepts-library/grifter
-sd-concepts-library/grisstyle
-sd-concepts-library/grit-toy
-sd-concepts-library/gt-color-paint-2
-sd-concepts-library/gta5-artwork
-sd-concepts-library/guttestreker
-sd-concepts-library/gymnastics-leotard-v2
-sd-concepts-library/half-life-2-dog
-sd-concepts-library/handstand
-sd-concepts-library/hanfu-anime-style
-sd-concepts-library/happy-chaos
-sd-concepts-library/happy-person12345
-sd-concepts-library/happy-person12345-assets
-sd-concepts-library/harley-quinn
-sd-concepts-library/harmless-ai-1
-sd-concepts-library/harmless-ai-house-style-1
-sd-concepts-library/hd-emoji
-sd-concepts-library/heather
-sd-concepts-library/henjo-techno-show
-sd-concepts-library/herge-style
-sd-concepts-library/hiten-style-nao
-sd-concepts-library/hitokomoru-style-nao
-sd-concepts-library/hiyuki-chan
-sd-concepts-library/hk-bamboo
-sd-concepts-library/hk-betweenislands
-sd-concepts-library/hk-bicycle
-sd-concepts-library/hk-blackandwhite
-sd-concepts-library/hk-breakfast
-sd-concepts-library/hk-buses
-sd-concepts-library/hk-clouds
-sd-concepts-library/hk-goldbuddha
-sd-concepts-library/hk-goldenlantern
-sd-concepts-library/hk-hkisland
-sd-concepts-library/hk-leaves
-sd-concepts-library/hk-market
-sd-concepts-library/hk-oldcamera
-sd-concepts-library/hk-opencamera
-sd-concepts-library/hk-peach
-sd-concepts-library/hk-phonevax
-sd-concepts-library/hk-streetpeople
-sd-concepts-library/hk-vintage
-sd-concepts-library/hoi4
-sd-concepts-library/hoi4-leaders
-sd-concepts-library/homestuck-sprite
-sd-concepts-library/homestuck-troll
-sd-concepts-library/hours-sentry-fade
-sd-concepts-library/hours-style
-sd-concepts-library/hrgiger-drmacabre
-sd-concepts-library/huang-guang-jian
-sd-concepts-library/huatli
-sd-concepts-library/huayecai820-greyscale
-sd-concepts-library/hub-city
-sd-concepts-library/hubris-oshri
-sd-concepts-library/huckleberry
-sd-concepts-library/hydrasuit
-sd-concepts-library/i-love-chaos
-sd-concepts-library/ibere-thenorio
-sd-concepts-library/ic0n
-sd-concepts-library/ie-gravestone
-sd-concepts-library/ikea-fabler
-sd-concepts-library/illustration-style
-sd-concepts-library/ilo-kunst
-sd-concepts-library/ilya-shkipin
-sd-concepts-library/im-poppy
-sd-concepts-library/ina-art
-sd-concepts-library/indian-watercolor-portraits
-sd-concepts-library/indiana
-sd-concepts-library/ingmar-bergman
-sd-concepts-library/insidewhale
-sd-concepts-library/interchanges
-sd-concepts-library/inuyama-muneto-style-nao
-sd-concepts-library/irasutoya
-sd-concepts-library/iridescent-illustration-style
-sd-concepts-library/iridescent-photo-style
-sd-concepts-library/isabell-schulte-pv-pvii-3000steps
-sd-concepts-library/isabell-schulte-pviii-1-image-style
-sd-concepts-library/isabell-schulte-pviii-1024px-1500-steps-style
-sd-concepts-library/isabell-schulte-pviii-12tiles-3000steps-style
-sd-concepts-library/isabell-schulte-pviii-4-tiles-1-lr-3000-steps-style
-sd-concepts-library/isabell-schulte-pviii-4-tiles-3-lr-5000-steps-style
-sd-concepts-library/isabell-schulte-pviii-4tiles-500steps
-sd-concepts-library/isabell-schulte-pviii-4tiles-6000steps
-sd-concepts-library/isabell-schulte-pviii-style
-sd-concepts-library/isometric-tile-test
-sd-concepts-library/jacqueline-the-unicorn
-sd-concepts-library/james-web-space-telescope
-sd-concepts-library/jamie-hewlett-style
-sd-concepts-library/jamiels
-sd-concepts-library/jang-sung-rak-style
-sd-concepts-library/jetsetdreamcastcovers
-sd-concepts-library/jin-kisaragi
-sd-concepts-library/jinjoon-lee-they
-sd-concepts-library/jm-bergling-monogram
-sd-concepts-library/joe-mad
-sd-concepts-library/joe-whiteford-art-style
-sd-concepts-library/joemad
-sd-concepts-library/john-blanche
-sd-concepts-library/johnny-silverhand
-sd-concepts-library/jojo-bizzare-adventure-manga-lineart
-sd-concepts-library/jos-de-kat
-sd-concepts-library/junji-ito-artstyle
-sd-concepts-library/kaleido
-sd-concepts-library/kaneoya-sachiko
-sd-concepts-library/kanovt
-sd-concepts-library/kanv1
-sd-concepts-library/karan-gloomy
-sd-concepts-library/karl-s-lzx-1
-sd-concepts-library/kasumin
-sd-concepts-library/kawaii-colors
-sd-concepts-library/kawaii-girl-plus-object
-sd-concepts-library/kawaii-girl-plus-style
-sd-concepts-library/kawaii-girl-plus-style-v1-1
-sd-concepts-library/kay
-sd-concepts-library/kaya-ghost-assasin
-sd-concepts-library/ki
-sd-concepts-library/kinda-sus
-sd-concepts-library/kings-quest-agd
-sd-concepts-library/kiora
-sd-concepts-library/kira-sensei
-sd-concepts-library/kirby
-sd-concepts-library/klance
-sd-concepts-library/kodakvision500t
-sd-concepts-library/kogatan-shiny
-sd-concepts-library/kogecha
-sd-concepts-library/kojima-ayami
-sd-concepts-library/koko-dog
-sd-concepts-library/kuvshinov
-sd-concepts-library/kysa-v-style
-sd-concepts-library/laala-character
-sd-concepts-library/larrette
-sd-concepts-library/lavko
-sd-concepts-library/lazytown-stephanie
-sd-concepts-library/ldr
-sd-concepts-library/ldrs
-sd-concepts-library/led-toy
-sd-concepts-library/lego-astronaut
-sd-concepts-library/leica
-sd-concepts-library/leif-jones
-sd-concepts-library/lex
-sd-concepts-library/liliana
-sd-concepts-library/liliana-vess
-sd-concepts-library/liminal-spaces-2-0
-sd-concepts-library/liminalspaces
-sd-concepts-library/line-art
-sd-concepts-library/line-style
-sd-concepts-library/linnopoke
-sd-concepts-library/liquid-light
-sd-concepts-library/liqwid-aquafarmer
-sd-concepts-library/lizardman
-sd-concepts-library/loab-character
-sd-concepts-library/loab-style
-sd-concepts-library/lofa
-sd-concepts-library/logo-with-face-on-shield
-sd-concepts-library/lolo
-sd-concepts-library/looney-anime
-sd-concepts-library/lost-rapper
-sd-concepts-library/lphr-style
-sd-concepts-library/lucario
-sd-concepts-library/lucky-luke
-sd-concepts-library/lugal-ki-en
-sd-concepts-library/luinv2
-sd-concepts-library/lula-13
-sd-concepts-library/lumio
-sd-concepts-library/lxj-o4
-sd-concepts-library/m-geo
-sd-concepts-library/m-geoo
-sd-concepts-library/madhubani-art
-sd-concepts-library/mafalda-character
-sd-concepts-library/magic-pengel
-sd-concepts-library/malika-favre-art-style
-sd-concepts-library/manga-style
-sd-concepts-library/marbling-art
-sd-concepts-library/margo
-sd-concepts-library/marty
-sd-concepts-library/marty6
-sd-concepts-library/mass
-sd-concepts-library/masyanya
-sd-concepts-library/masyunya
-sd-concepts-library/mate
-sd-concepts-library/matthew-stone
-sd-concepts-library/mattvidpro
-sd-concepts-library/maurice-quentin-de-la-tour-style
-sd-concepts-library/maus
-sd-concepts-library/max-foley
-sd-concepts-library/mayor-richard-irvin
-sd-concepts-library/mechasoulall
-sd-concepts-library/medazzaland
-sd-concepts-library/memnarch-mtg
-sd-concepts-library/metagabe
-sd-concepts-library/meyoco
-sd-concepts-library/meze-audio-elite-headphones
-sd-concepts-library/midjourney-style
-sd-concepts-library/mikako-method
-sd-concepts-library/mikako-methodi2i
-sd-concepts-library/miko-3-robot
-sd-concepts-library/milady
-sd-concepts-library/mildemelwe-style
-sd-concepts-library/million-live-akane-15k
-sd-concepts-library/million-live-akane-3k
-sd-concepts-library/million-live-akane-shifuku-3k
-sd-concepts-library/million-live-spade-q-object-3k
-sd-concepts-library/million-live-spade-q-style-3k
-sd-concepts-library/minecraft-concept-art
-sd-concepts-library/mishima-kurone
-sd-concepts-library/mizkif
-sd-concepts-library/moeb-style
-sd-concepts-library/moebius
-sd-concepts-library/mokoko
-sd-concepts-library/mokoko-seed
-sd-concepts-library/monster-girl
-sd-concepts-library/monster-toy
-sd-concepts-library/monte-novo
-sd-concepts-library/moo-moo
-sd-concepts-library/morino-hon-style
-sd-concepts-library/moxxi
-sd-concepts-library/msg
-sd-concepts-library/mtg-card
-sd-concepts-library/mtl-longsky
-sd-concepts-library/mu-sadr
-sd-concepts-library/munch-leaks-style
-sd-concepts-library/museum-by-coop-himmelblau
-sd-concepts-library/muxoyara
-sd-concepts-library/my-hero-academia-style
-sd-concepts-library/my-mug
-sd-concepts-library/mycat
-sd-concepts-library/mystical-nature
-sd-concepts-library/naf
-sd-concepts-library/nahiri
-sd-concepts-library/namine-ritsu
-sd-concepts-library/naoki-saito
-sd-concepts-library/nard-style
-sd-concepts-library/naruto
-sd-concepts-library/natasha-johnston
-sd-concepts-library/nathan-wyatt
-sd-concepts-library/naval-portrait
-sd-concepts-library/nazuna
-sd-concepts-library/nebula
-sd-concepts-library/ned-flanders
-sd-concepts-library/neon-pastel
-sd-concepts-library/new-priests
-sd-concepts-library/nic-papercuts
-sd-concepts-library/nikodim
-sd-concepts-library/nissa-revane
-sd-concepts-library/nixeu
-sd-concepts-library/noggles
-sd-concepts-library/nomad
-sd-concepts-library/nouns-glasses
-sd-concepts-library/obama-based-on-xi
-sd-concepts-library/obama-self-2
-sd-concepts-library/og-mox-style
-sd-concepts-library/ohisashiburi-style
-sd-concepts-library/oleg-kuvaev
-sd-concepts-library/olli-olli
-sd-concepts-library/on-kawara
-sd-concepts-library/one-line-drawing
-sd-concepts-library/onepunchman
-sd-concepts-library/onzpo
-sd-concepts-library/orangejacket
-sd-concepts-library/ori
-sd-concepts-library/ori-toor
-sd-concepts-library/orientalist-art
-sd-concepts-library/osaka-jyo
-sd-concepts-library/osaka-jyo2
-sd-concepts-library/osrsmini2
-sd-concepts-library/osrstiny
-sd-concepts-library/other-mother
-sd-concepts-library/ouroboros
-sd-concepts-library/outfit-items
-sd-concepts-library/overprettified
-sd-concepts-library/owl-house
-sd-concepts-library/painted-by-silver-of-999
-sd-concepts-library/painted-by-silver-of-999-2
-sd-concepts-library/painted-student
-sd-concepts-library/painting
-sd-concepts-library/pantone-milk
-sd-concepts-library/paolo-bonolis
-sd-concepts-library/party-girl
-sd-concepts-library/pascalsibertin
-sd-concepts-library/pastelartstyle
-sd-concepts-library/paul-noir
-sd-concepts-library/pen-ink-portraits-bennorthen
-sd-concepts-library/phan
-sd-concepts-library/phan-s-collage
-sd-concepts-library/phc
-sd-concepts-library/phoenix-01
-sd-concepts-library/pineda-david
-sd-concepts-library/pink-beast-pastelae-style
-sd-concepts-library/pintu
-sd-concepts-library/pion-by-august-semionov
-sd-concepts-library/piotr-jablonski
-sd-concepts-library/pixel-mania
-sd-concepts-library/pixel-toy
-sd-concepts-library/pjablonski-style
-sd-concepts-library/plant-style
-sd-concepts-library/plen-ki-mun
-sd-concepts-library/pokemon-conquest-sprites
-sd-concepts-library/pool-test
-sd-concepts-library/poolrooms
-sd-concepts-library/poring-ragnarok-online
-sd-concepts-library/poutine-dish
-sd-concepts-library/princess-knight-art
-sd-concepts-library/progress-chip
-sd-concepts-library/puerquis-toy
-sd-concepts-library/purplefishli
-sd-concepts-library/pyramidheadcosplay
-sd-concepts-library/qpt-atrium
-sd-concepts-library/quiesel
-sd-concepts-library/r-crumb-style
-sd-concepts-library/rahkshi-bionicle
-sd-concepts-library/raichu
-sd-concepts-library/rail-scene
-sd-concepts-library/rail-scene-style
-sd-concepts-library/ralph-mcquarrie
-sd-concepts-library/ransom
-sd-concepts-library/rayne-weynolds
-sd-concepts-library/rcrumb-portraits-style
-sd-concepts-library/rd-chaos
-sd-concepts-library/rd-paintings
-sd-concepts-library/red-glasses
-sd-concepts-library/reeducation-camp
-sd-concepts-library/reksio-dog
-sd-concepts-library/rektguy
-sd-concepts-library/remert
-sd-concepts-library/renalla
-sd-concepts-library/repeat
-sd-concepts-library/retro-girl
-sd-concepts-library/retro-mecha-rangers
-sd-concepts-library/retropixelart-pinguin
-sd-concepts-library/rex-deno
-sd-concepts-library/rhizomuse-machine-bionic-sculpture
-sd-concepts-library/ricar
-sd-concepts-library/rickyart
-sd-concepts-library/rico-face
-sd-concepts-library/riker-doll
-sd-concepts-library/rikiart
-sd-concepts-library/rikiboy-art
-sd-concepts-library/rilakkuma
-sd-concepts-library/rishusei-style
-sd-concepts-library/rj-palmer
-sd-concepts-library/rl-pkmn-test
-sd-concepts-library/road-to-ruin
-sd-concepts-library/robertnava
-sd-concepts-library/roblox-avatar
-sd-concepts-library/roy-lichtenstein
-sd-concepts-library/ruan-jia
-sd-concepts-library/russian
-sd-concepts-library/s1m-naoto-ohshima
-sd-concepts-library/saheeli-rai
-sd-concepts-library/sakimi-style
-sd-concepts-library/salmonid
-sd-concepts-library/sam-yang
-sd-concepts-library/sanguo-guanyu
-sd-concepts-library/sas-style
-sd-concepts-library/scarlet-witch
-sd-concepts-library/schloss-mosigkau
-sd-concepts-library/scrap-style
-sd-concepts-library/scratch-project
-sd-concepts-library/sculptural-style
-sd-concepts-library/sd-concepts-library-uma-meme
-sd-concepts-library/seamless-ground
-sd-concepts-library/selezneva-alisa
-sd-concepts-library/sem-mac2n
-sd-concepts-library/senneca
-sd-concepts-library/seraphimmoonshadow-art
-sd-concepts-library/sewerslvt
-sd-concepts-library/she-hulk-law-art
-sd-concepts-library/she-mask
-sd-concepts-library/sherhook-painting
-sd-concepts-library/sherhook-painting-v2
-sd-concepts-library/shev-linocut
-sd-concepts-library/shigure-ui-style
-sd-concepts-library/shiny-polyman
-sd-concepts-library/shrunken-head
-sd-concepts-library/shu-doll
-sd-concepts-library/shvoren-style
-sd-concepts-library/sims-2-portrait
-sd-concepts-library/singsing
-sd-concepts-library/singsing-doll
-sd-concepts-library/sintez-ico
-sd-concepts-library/skyfalls
-sd-concepts-library/slm
-sd-concepts-library/smarties
-sd-concepts-library/smiling-friend-style
-sd-concepts-library/smooth-pencils
-sd-concepts-library/smurf-style
-sd-concepts-library/smw-map
-sd-concepts-library/society-finch
-sd-concepts-library/sorami-style
-sd-concepts-library/spider-gwen
-sd-concepts-library/spritual-monsters
-sd-concepts-library/stable-diffusion-conceptualizer
-sd-concepts-library/star-tours-posters
-sd-concepts-library/stardew-valley-pixel-art
-sd-concepts-library/starhavenmachinegods
-sd-concepts-library/sterling-archer
-sd-concepts-library/stretch-re1-robot
-sd-concepts-library/stuffed-penguin-toy
-sd-concepts-library/style-of-marc-allante
-sd-concepts-library/summie-style
-sd-concepts-library/sunfish
-sd-concepts-library/super-nintendo-cartridge
-sd-concepts-library/supitcha-mask
-sd-concepts-library/sushi-pixel
-sd-concepts-library/swamp-choe-2
-sd-concepts-library/t-skrang
-sd-concepts-library/takuji-kawano
-sd-concepts-library/tamiyo
-sd-concepts-library/tangles
-sd-concepts-library/tb303
-sd-concepts-library/tcirle
-sd-concepts-library/teelip-ir-landscape
-sd-concepts-library/teferi
-sd-concepts-library/tela-lenca
-sd-concepts-library/tela-lenca2
-sd-concepts-library/terraria-style
-sd-concepts-library/tesla-bot
-sd-concepts-library/test
-sd-concepts-library/test-epson
-sd-concepts-library/test2
-sd-concepts-library/testing
-sd-concepts-library/thalasin
-sd-concepts-library/thegeneral
-sd-concepts-library/thorneworks
-sd-concepts-library/threestooges
-sd-concepts-library/thunderdome-cover
-sd-concepts-library/thunderdome-covers
-sd-concepts-library/ti-junglepunk-v0
-sd-concepts-library/tili-concept
-sd-concepts-library/titan-robot
-sd-concepts-library/tnj
-sd-concepts-library/toho-pixel
-sd-concepts-library/tomcat
-sd-concepts-library/tonal1
-sd-concepts-library/tony-diterlizzi-s-planescape-art
-sd-concepts-library/towerplace
-sd-concepts-library/toy
-sd-concepts-library/toy-bonnie-plush
-sd-concepts-library/toyota-sera
-sd-concepts-library/transmutation-circles
-sd-concepts-library/trash-polka-artstyle
-sd-concepts-library/travis-bedel
-sd-concepts-library/trigger-studio
-sd-concepts-library/trust-support
-sd-concepts-library/trypophobia
-sd-concepts-library/ttte
-sd-concepts-library/tubby
-sd-concepts-library/tubby-cats
-sd-concepts-library/tudisco
-sd-concepts-library/turtlepics
-sd-concepts-library/type
-sd-concepts-library/ugly-sonic
-sd-concepts-library/uliana-kudinova
-sd-concepts-library/uma
-sd-concepts-library/uma-clean-object
-sd-concepts-library/uma-meme
-sd-concepts-library/uma-meme-style
-sd-concepts-library/uma-style-classic
-sd-concepts-library/unfinished-building
-sd-concepts-library/urivoldemort
-sd-concepts-library/uzumaki
-sd-concepts-library/valorantstyle
-sd-concepts-library/vb-mox
-sd-concepts-library/vcr-classique
-sd-concepts-library/venice
-sd-concepts-library/vespertine
-sd-concepts-library/victor-narm
-sd-concepts-library/vietstoneking
-sd-concepts-library/vivien-reid
-sd-concepts-library/vkuoo1
-sd-concepts-library/vraska
-sd-concepts-library/w3u
-sd-concepts-library/walter-wick-photography
-sd-concepts-library/warhammer-40k-drawing-style
-sd-concepts-library/waterfallshadow
-sd-concepts-library/wayne-reynolds-character
-sd-concepts-library/wedding
-sd-concepts-library/wedding-HandPainted
-sd-concepts-library/werebloops
-sd-concepts-library/wheatland
-sd-concepts-library/wheatland-arknight
-sd-concepts-library/wheelchair
-sd-concepts-library/wildkat
-sd-concepts-library/willy-hd
-sd-concepts-library/wire-angels
-sd-concepts-library/wish-artist-stile
-sd-concepts-library/wlop-style
-sd-concepts-library/wojak
-sd-concepts-library/wojaks-now
-sd-concepts-library/wojaks-now-now-now
-sd-concepts-library/xatu
-sd-concepts-library/xatu2
-sd-concepts-library/xbh
-sd-concepts-library/xi
-sd-concepts-library/xidiversity
-sd-concepts-library/xioboma
-sd-concepts-library/xuna
-sd-concepts-library/xyz
-sd-concepts-library/yb-anime
-sd-concepts-library/yerba-mate
-sd-concepts-library/yesdelete
-sd-concepts-library/yf21
-sd-concepts-library/yilanov2
-sd-concepts-library/yinit
-sd-concepts-library/yoji-shinkawa-style
-sd-concepts-library/yolandi-visser
-sd-concepts-library/yoshi
-sd-concepts-library/youpi2
-sd-concepts-library/youtooz-candy
-sd-concepts-library/yuji-himukai-style
-sd-concepts-library/zaney
-sd-concepts-library/zaneypixelz
-sd-concepts-library/zdenek-art
-sd-concepts-library/zero
-sd-concepts-library/zero-bottle
-sd-concepts-library/zero-suit-samus
-sd-concepts-library/zillertal-can
-sd-concepts-library/zizigooloo
-sd-concepts-library/zk
-sd-concepts-library/zoroark

configs/stable-diffusion/v1-finetune.yaml

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

configs/stable-diffusion/v1-finetune_style.yaml

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

configs/stable-diffusion/v1-inference.yaml

@@ -1,79 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  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: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 10000 ]
-        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: ['sculpture']
-        per_image_tokens: false
-        num_vectors_per_token: 1
-        progressive_words: False
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

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

@@ -1,79 +0,0 @@
-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: ['sculpture']
-        per_image_tokens: false
-        num_vectors_per_token: 8
-        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

@@ -1,110 +0,0 @@
-model:
-  base_learning_rate: 5.0e-03
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: true   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-    embedding_reg_weight: 0.0
-
-    personalization_config:
-      target: ldm.modules.embedding_manager.EmbeddingManager
-      params:
-        placeholder_strings: ["*"]
-        initializer_words: ['sculpture']
-        per_image_tokens: false
-        num_vectors_per_token: 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
-

configs/stable-diffusion/v2-inference-v.yaml

@@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    parameterization: "v"
-    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
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        use_fp16: True
-        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_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          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.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"

coverage/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

docker-build/Dockerfile

@@ -1,65 +0,0 @@
-FROM python:3.10-slim AS builder
-
-# use bash
-SHELL [ "/bin/bash", "-c" ]
-
-# Install necesarry packages
-RUN apt-get update \
-  && apt-get install -y \
-    --no-install-recommends \
-    gcc=4:10.2.* \
-    libgl1-mesa-glx=20.3.* \
-    libglib2.0-0=2.66.* \
-    python3-dev=3.9.* \
-  && apt-get clean \
-  && rm -rf /var/lib/apt/lists/*
-
-# set WORKDIR, PATH and copy sources
-ARG APPDIR=/usr/src/app
-WORKDIR ${APPDIR}
-ENV PATH ${APPDIR}/.venv/bin:$PATH
-ARG PIP_REQUIREMENTS=requirements-lin-cuda.txt
-COPY . ./environments-and-requirements/${PIP_REQUIREMENTS} ./
-
-# install requirements
-RUN python3 -m venv .venv \
-  && pip install \
-    --upgrade \
-    --no-cache-dir \
-    'wheel>=0.38.4' \
-  && pip install \
-    --no-cache-dir \
-    -r ${PIP_REQUIREMENTS}
-
-FROM python:3.10-slim AS runtime
-
-# setup environment
-ARG APPDIR=/usr/src/app
-WORKDIR ${APPDIR}
-COPY --from=builder ${APPDIR} .
-ENV \
-  PATH=${APPDIR}/.venv/bin:$PATH \
-  INVOKEAI_ROOT=/data \
-  INVOKE_MODEL_RECONFIGURE=--yes
-
-# Install necesarry packages
-RUN apt-get update \
-  && apt-get install -y \
-    --no-install-recommends \
-    build-essential=12.9 \
-    libgl1-mesa-glx=20.3.* \
-    libglib2.0-0=2.66.* \
-    libopencv-dev=4.5.* \
-  && ln -sf \
-    /usr/lib/"$(arch)"-linux-gnu/pkgconfig/opencv4.pc \
-    /usr/lib/"$(arch)"-linux-gnu/pkgconfig/opencv.pc \
-  && python3 -c "from patchmatch import patch_match" \
-  && apt-get remove -y \
-    --autoremove \
-    build-essential \
-  && apt-get autoclean \
-  && rm -rf /var/lib/apt/lists/*
-
-# set Entrypoint and default CMD
-ENTRYPOINT [ "python3", "scripts/invoke.py" ]
-CMD [ "--web", "--host=0.0.0.0" ]

docker-build/Dockerfile.cloud

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

docker-build/Makefile

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

docker-build/build.sh

@@ -1,35 +0,0 @@
-#!/usr/bin/env bash
-set -e
-
-# How to use: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_DOCKER/#setup
-
-source ./docker-build/env.sh \
-  || echo "please execute docker-build/build.sh from repository root" \
-  || exit 1
-
-PIP_REQUIREMENTS=${PIP_REQUIREMENTS:-requirements-lin-cuda.txt}
-DOCKERFILE=${INVOKE_DOCKERFILE:-docker-build/Dockerfile}
-
-# print the settings
-echo -e "You are using these values:\n"
-echo -e "Dockerfile:\t ${DOCKERFILE}"
-echo -e "Requirements:\t ${PIP_REQUIREMENTS}"
-echo -e "Volumename:\t ${VOLUMENAME}"
-echo -e "arch:\t\t ${ARCH}"
-echo -e "Platform:\t ${PLATFORM}"
-echo -e "Invokeai_tag:\t ${INVOKEAI_TAG}\n"
-
-if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
-  echo -e "Volume already exists\n"
-else
-  echo -n "createing docker volume "
-  docker volume create "${VOLUMENAME}"
-fi
-
-# Build Container
-docker build \
-  --platform="${PLATFORM}" \
-  --tag="${INVOKEAI_TAG}" \
-  --build-arg="PIP_REQUIREMENTS=${PIP_REQUIREMENTS}" \
-  --file="${DOCKERFILE}" \
-  .

docker-build/env.sh

@@ -1,10 +0,0 @@
-#!/usr/bin/env bash
-
-# Variables shared by build.sh and run.sh
-REPOSITORY_NAME=${REPOSITORY_NAME:-$(basename "$(git rev-parse --show-toplevel)")}
-VOLUMENAME=${VOLUMENAME:-${REPOSITORY_NAME,,}_data}
-ARCH=${ARCH:-$(uname -m)}
-PLATFORM=${PLATFORM:-Linux/${ARCH}}
-CONTAINER_FLAVOR=${CONTAINER_FLAVOR:-cuda}
-INVOKEAI_BRANCH=$(git branch --show)
-INVOKEAI_TAG=${REPOSITORY_NAME,,}-${CONTAINER_FLAVOR}:${INVOKEAI_TAG:-${INVOKEAI_BRANCH/\//-}}

docker-build/run.sh

@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-set -e
-
-# How to use: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_DOCKER/#run-the-container
-# IMPORTANT: You need to have a token on huggingface.co to be able to download the checkpoints!!!
-
-source ./docker-build/env.sh \
-  || echo "please run from repository root" \
-  || exit 1
-
-# check if HUGGINGFACE_TOKEN is available
-# You must have accepted the terms of use for required models
-HUGGINGFACE_TOKEN=${HUGGINGFACE_TOKEN:?Please set your token for Huggingface as HUGGINGFACE_TOKEN}
-
-echo -e "You are using these values:\n"
-echo -e "Volumename:\t ${VOLUMENAME}"
-echo -e "Invokeai_tag:\t ${INVOKEAI_TAG}\n"
-
-docker run \
-  --interactive \
-  --tty \
-  --rm \
-  --platform="$PLATFORM" \
-  --name="${REPOSITORY_NAME,,}" \
-  --hostname="${REPOSITORY_NAME,,}" \
-  --mount="source=$VOLUMENAME,target=/data" \
-  --env="HUGGINGFACE_TOKEN=${HUGGINGFACE_TOKEN}" \
-  --publish=9090:9090 \
-  --cap-add=sys_nice \
-  ${GPU_FLAGS:+--gpus=${GPU_FLAGS}} \
-  "$INVOKEAI_TAG" ${1:+$@}

docker/Dockerfile

@@ -0,0 +1,107 @@
+# syntax=docker/dockerfile:1
+
+ARG PYTHON_VERSION=3.9
+##################
+##  base image  ##
+##################
+FROM --platform=${TARGETPLATFORM} python:${PYTHON_VERSION}-slim AS python-base
+
+LABEL org.opencontainers.image.authors="mauwii@outlook.de"
+
+# Prepare apt for buildkit cache
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+  && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' >/etc/apt/apt.conf.d/keep-cache
+
+# Install dependencies
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    libgl1-mesa-glx=20.3.* \
+    libglib2.0-0=2.66.* \
+    libopencv-dev=4.5.*
+
+# Set working directory and env
+ARG APPDIR=/usr/src
+ARG APPNAME=InvokeAI
+WORKDIR ${APPDIR}
+ENV PATH ${APPDIR}/${APPNAME}/bin:$PATH
+# Keeps Python from generating .pyc files in the container
+ENV PYTHONDONTWRITEBYTECODE 1
+# Turns off buffering for easier container logging
+ENV PYTHONUNBUFFERED 1
+# Don't fall back to legacy build system
+ENV PIP_USE_PEP517=1
+
+#######################
+##  build pyproject  ##
+#######################
+FROM python-base AS pyproject-builder
+
+# Install build dependencies
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    build-essential=12.9 \
+    gcc=4:10.2.* \
+    python3-dev=3.9.*
+
+# Prepare pip for buildkit cache
+ARG PIP_CACHE_DIR=/var/cache/buildkit/pip
+ENV PIP_CACHE_DIR ${PIP_CACHE_DIR}
+RUN mkdir -p ${PIP_CACHE_DIR}
+
+# Create virtual environment
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+  python3 -m venv "${APPNAME}" \
+  --upgrade-deps
+
+# Install requirements
+COPY --link pyproject.toml .
+COPY --link invokeai/version/invokeai_version.py invokeai/version/__init__.py invokeai/version/
+ARG PIP_EXTRA_INDEX_URL
+ENV PIP_EXTRA_INDEX_URL ${PIP_EXTRA_INDEX_URL}
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+  "${APPNAME}"/bin/pip install .
+
+# Install pyproject.toml
+COPY --link . .
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+  "${APPNAME}/bin/pip" install .
+
+# Build patchmatch
+RUN python3 -c "from patchmatch import patch_match"
+
+#####################
+##  runtime image  ##
+#####################
+FROM python-base AS runtime
+
+# Create a new user
+ARG UNAME=appuser
+RUN useradd \
+  --no-log-init \
+  -m \
+  -U \
+  "${UNAME}"
+
+# Create volume directory
+ARG VOLUME_DIR=/data
+RUN mkdir -p "${VOLUME_DIR}" \
+  && chown -hR "${UNAME}:${UNAME}" "${VOLUME_DIR}"
+
+# Setup runtime environment
+USER ${UNAME}:${UNAME}
+COPY --chown=${UNAME}:${UNAME} --from=pyproject-builder ${APPDIR}/${APPNAME} ${APPNAME}
+ENV INVOKEAI_ROOT ${VOLUME_DIR}
+ENV TRANSFORMERS_CACHE ${VOLUME_DIR}/.cache
+ENV INVOKE_MODEL_RECONFIGURE "--yes --default_only"
+EXPOSE 9090
+ENTRYPOINT [ "invokeai" ]
+CMD [ "--web", "--host", "0.0.0.0", "--port", "9090" ]
+VOLUME [ "${VOLUME_DIR}" ]

docker/build.sh

@@ -0,0 +1,51 @@
+#!/usr/bin/env bash
+set -e
+
+# If you want to build a specific flavor, set the CONTAINER_FLAVOR environment variable
+#   e.g. CONTAINER_FLAVOR=cpu ./build.sh
+#   Possible Values are:
+#     - cpu
+#     - cuda
+#     - rocm
+#   Don't forget to also set it when executing run.sh
+#   if it is not set, the script will try to detect the flavor by itself.
+#
+# Doc can be found here:
+#   https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
+
+SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
+cd "$SCRIPTDIR" || exit 1
+
+source ./env.sh
+
+DOCKERFILE=${INVOKE_DOCKERFILE:-./Dockerfile}
+
+# print the settings
+echo -e "You are using these values:\n"
+echo -e "Dockerfile:\t\t${DOCKERFILE}"
+echo -e "index-url:\t\t${PIP_EXTRA_INDEX_URL:-none}"
+echo -e "Volumename:\t\t${VOLUMENAME}"
+echo -e "Platform:\t\t${PLATFORM}"
+echo -e "Container Registry:\t${CONTAINER_REGISTRY}"
+echo -e "Container Repository:\t${CONTAINER_REPOSITORY}"
+echo -e "Container Tag:\t\t${CONTAINER_TAG}"
+echo -e "Container Flavor:\t${CONTAINER_FLAVOR}"
+echo -e "Container Image:\t${CONTAINER_IMAGE}\n"
+
+# Create docker volume
+if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
+    echo -e "Volume already exists\n"
+else
+    echo -n "creating docker volume "
+    docker volume create "${VOLUMENAME}"
+fi
+
+# Build Container
+docker build \
+    --platform="${PLATFORM:-linux/amd64}" \
+    --tag="${CONTAINER_IMAGE:-invokeai}" \
+    ${CONTAINER_FLAVOR:+--build-arg="CONTAINER_FLAVOR=${CONTAINER_FLAVOR}"} \
+    ${PIP_EXTRA_INDEX_URL:+--build-arg="PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}"} \
+    ${PIP_PACKAGE:+--build-arg="PIP_PACKAGE=${PIP_PACKAGE}"} \
+    --file="${DOCKERFILE}" \
+    ..

docker/env.sh

@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+
+# This file is used to set environment variables for the build.sh and run.sh scripts.
+
+# Try to detect the container flavor if no PIP_EXTRA_INDEX_URL got specified
+if [[ -z "$PIP_EXTRA_INDEX_URL" ]]; then
+
+  # Activate virtual environment if not already activated and exists
+  if [[ -z $VIRTUAL_ENV ]]; then
+    [[ -e "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" ]] \
+      && source "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" \
+      && echo "Activated virtual environment: $VIRTUAL_ENV"
+  fi
+
+  # Decide which container flavor to build if not specified
+  if [[ -z "$CONTAINER_FLAVOR" ]] && python -c "import torch" &>/dev/null; then
+    # Check for CUDA and ROCm
+    CUDA_AVAILABLE=$(python -c "import torch;print(torch.cuda.is_available())")
+    ROCM_AVAILABLE=$(python -c "import torch;print(torch.version.hip is not None)")
+    if [[ "${CUDA_AVAILABLE}" == "True" ]]; then
+      CONTAINER_FLAVOR="cuda"
+    elif [[ "${ROCM_AVAILABLE}" == "True" ]]; then
+      CONTAINER_FLAVOR="rocm"
+    else
+      CONTAINER_FLAVOR="cpu"
+    fi
+  fi
+
+  # Set PIP_EXTRA_INDEX_URL based on container flavor
+  if [[ "$CONTAINER_FLAVOR" == "rocm" ]]; then
+    PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/rocm"
+  elif [[ "$CONTAINER_FLAVOR" == "cpu" ]]; then
+    PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
+  # elif [[ -z "$CONTAINER_FLAVOR" || "$CONTAINER_FLAVOR" == "cuda" ]]; then
+  #   PIP_PACKAGE=${PIP_PACKAGE-".[xformers]"}
+  fi
+fi
+
+# Variables shared by build.sh and run.sh
+REPOSITORY_NAME="${REPOSITORY_NAME-$(basename "$(git rev-parse --show-toplevel)")}"
+REPOSITORY_NAME="${REPOSITORY_NAME,,}"
+VOLUMENAME="${VOLUMENAME-"${REPOSITORY_NAME}_data"}"
+ARCH="${ARCH-$(uname -m)}"
+PLATFORM="${PLATFORM-linux/${ARCH}}"
+INVOKEAI_BRANCH="${INVOKEAI_BRANCH-$(git branch --show)}"
+CONTAINER_REGISTRY="${CONTAINER_REGISTRY-"ghcr.io"}"
+CONTAINER_REPOSITORY="${CONTAINER_REPOSITORY-"$(whoami)/${REPOSITORY_NAME}"}"
+CONTAINER_FLAVOR="${CONTAINER_FLAVOR-cuda}"
+CONTAINER_TAG="${CONTAINER_TAG-"${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}"}"
+CONTAINER_IMAGE="${CONTAINER_REGISTRY}/${CONTAINER_REPOSITORY}:${CONTAINER_TAG}"
+CONTAINER_IMAGE="${CONTAINER_IMAGE,,}"
+
+# enable docker buildkit
+export DOCKER_BUILDKIT=1

docker/run.sh

@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+set -e
+
+# How to use: https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
+
+SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
+cd "$SCRIPTDIR" || exit 1
+
+source ./env.sh
+
+# Create outputs directory if it does not exist
+[[ -d ./outputs ]] || mkdir ./outputs
+
+echo -e "You are using these values:\n"
+echo -e "Volumename:\t${VOLUMENAME}"
+echo -e "Invokeai_tag:\t${CONTAINER_IMAGE}"
+echo -e "local Models:\t${MODELSPATH:-unset}\n"
+
+docker run \
+  --interactive \
+  --tty \
+  --rm \
+  --platform="${PLATFORM}" \
+  --name="${REPOSITORY_NAME}" \
+  --hostname="${REPOSITORY_NAME}" \
+  --mount type=volume,volume-driver=local,source="${VOLUMENAME}",target=/data \
+  --mount type=bind,source="$(pwd)"/outputs/,target=/data/outputs/ \
+  ${MODELSPATH:+--mount="type=bind,source=${MODELSPATH},target=/data/models"} \
+  ${HUGGING_FACE_HUB_TOKEN:+--env="HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}"} \
+  --publish=9090:9090 \
+  --cap-add=sys_nice \
+  ${GPU_FLAGS:+--gpus="${GPU_FLAGS}"} \
+  "${CONTAINER_IMAGE}" ${@:+$@}
+
+echo -e "\nCleaning trash folder ..."
+for f in outputs/.Trash*; do
+  if [ -e "$f" ]; then
+    rm -Rf "$f"
+    break
+  fi
+done

docs/CHANGELOG.md

@@ -4,7 +4,7 @@ title: Changelog
 
 # :octicons-log-16: **Changelog**
 
-## v2.3.0 <small>(15 January 2023)</small>	
+## v2.3.0 <small>(15 January 2023)</small>
 
 **Transition to diffusers
 
@@ -44,7 +44,7 @@ introduces several changes you should know about.
   A configuration stanza for a diffuers model stored locally should
   look like this, with a `format` of `diffusers`, but a `path` field
   that points at the directory that contains `model_index.json`:
-  
+
   ```
   waifu-diffusion:
   description: Latest waifu diffusion 1.4
@@ -52,12 +52,17 @@ introduces several changes you should know about.
   path: models/diffusers/hakurei-haifu-diffusion-1.4
   ```
 
-2. The format of the models directory has changed to mimic the
-   HuggingFace cache directory. By default, diffusers models are
-   now automatically downloaded and retrieved from the directory
-   `ROOTDIR/models/diffusers`, while other models are stored in
-   the directory `ROOTDIR/models/hub`. This organization is the
-   same as that used by HuggingFace for its cache management.
+2. In order of precedence, InvokeAI will now use HF_HOME, then
+   XDG_CACHE_HOME, then finally default to `ROOTDIR/models` to
+   store HuggingFace diffusers models.
+
+   Consequently, the format of the models directory has changed to
+   mimic the HuggingFace cache directory. When HF_HOME and XDG_HOME
+   are not set, diffusers models are now automatically downloaded
+   and retrieved from the directory `ROOTDIR/models/diffusers`,
+   while other models are stored in the directory
+   `ROOTDIR/models/hub`. This organization is the same as that used
+   by HuggingFace for its cache management.
 
    This allows you to share diffusers and ckpt model files easily with
    other machine learning applications that use the HuggingFace
@@ -66,7 +71,13 @@ introduces several changes you should know about.
    cache models in. To tell InvokeAI to use the standard HuggingFace
    cache directory, you would set HF_HOME like this (Linux/Mac):
 
-   `export HF_HOME=~/.cache/hugging_face`
+   `export HF_HOME=~/.cache/huggingface`
+
+   Both HuggingFace and InvokeAI will fall back to the XDG_CACHE_HOME
+   environment variable if HF_HOME is not set; this path
+   takes precedence over `ROOTDIR/models` to allow for the same sharing
+   with other machine learning applications that use HuggingFace
+   libraries.
 
 3. If you upgrade to InvokeAI 2.3.* from an earlier version, there
    will be a one-time migration from the old models directory format
@@ -83,7 +94,7 @@ introduces several changes you should know about.
    !import_model /opt/sd-models/sd-1.4.ckpt
    !import_model https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/blob/main/PaperCut_v1.ckpt
    ```
-   
+
 **KNOWN BUGS (15 January 2023)
 
 1. On CUDA systems, the 768 pixel stable-diffusion-2.0 and
@@ -250,7 +261,7 @@ sections describe what's new for InvokeAI.
   [Installation](installation/index.md).
 - A streamlined manual installation process that works for both Conda and
   PIP-only installs. See
-  [Manual Installation](installation/INSTALL_MANUAL.md).
+  [Manual Installation](installation/020_INSTALL_MANUAL.md).
 - The ability to save frequently-used startup options (model to load, steps,
   sampler, etc) in a `.invokeai` file. See
   [Client](features/CLI.md)

docs/contributing/ARCHITECTURE.md

@@ -0,0 +1,93 @@
+# Invoke.AI Architecture
+
+```mermaid
+flowchart TB
+
+  subgraph apps[Applications]
+    webui[WebUI]
+    cli[CLI]
+
+  subgraph webapi[Web API]
+    api[HTTP API]
+    sio[Socket.IO]
+  end
+
+  end
+
+  subgraph invoke[Invoke]
+    direction LR
+    invoker
+    services
+    sessions
+    invocations
+  end
+
+  subgraph core[AI Core]
+    Generate
+  end
+
+  webui --> webapi
+  webapi --> invoke
+  cli --> invoke
+
+  invoker --> services & sessions
+  invocations --> services
+  sessions --> invocations
+
+  services --> core
+
+  %% Styles
+  classDef sg fill:#5028C8,font-weight:bold,stroke-width:2,color:#fff,stroke:#14141A
+  classDef default stroke-width:2px,stroke:#F6B314,color:#fff,fill:#14141A
+
+  class apps,webapi,invoke,core sg
+
+```
+
+## Applications
+
+Applications are built on top of the invoke framework. They should construct `invoker` and then interact through it. They should avoid interacting directly with core code in order to support a variety of configurations.
+
+### Web UI
+
+The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
+
+| Component | Description |
+| --- | --- |
+| api_app.py | Sets up the API app, annotates the OpenAPI spec with additional data, and runs the API |
+| dependencies | Creates all invoker services and the invoker, and provides them to the API |
+| events | An eventing system that could in the future be adapted to support horizontal scale-out |
+| sockets | The Socket.IO interface - handles listening to and emitting session events (events are defined in the events service module) |
+| routers | API definitions for different areas of API functionality |
+
+### CLI
+
+The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
+
+## Invoke
+
+The Invoke framework provides the interface to the underlying AI systems and is built with flexibility and extensibility in mind. There are four major concepts: invoker, sessions, invocations, and services.
+
+### Invoker
+
+The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
+- **invocation services**, which are used by invocations to interact with core functionality.
+- **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
+
+### Sessions
+
+Invocations and links between them form a graph, which is maintained in a session. Sessions can be queued for invocation, which will execute their graph (either the next ready invocation, or all invocations). Sessions also maintain execution history for the graph (including storage of any outputs). An invocation may be added to a session at any time, and there is capability to add and entire graph at once, as well as to automatically link new invocations to previous invocations. Invocations can not be deleted or modified once added.
+
+The session graph does not support looping. This is left as an application problem to prevent additional complexity in the graph.
+
+### Invocations
+
+Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
+
+### Services
+
+Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
+
+## AI Core
+
+The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).

docs/contributing/INVOCATIONS.md

@@ -0,0 +1,105 @@
+# Invocations
+
+Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.
+
+## Creating a new invocation
+
+To create a new invocation, either find the appropriate module file in `/ldm/invoke/app/invocations` to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of [typing](https://docs.python.org/3/library/typing.html) and [pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration into the CLI and API.
+
+An invocation looks like this:
+
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+
+    # Inputs
+    image: Union[ImageField,None] = Field(description="The input image")
+    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2,4]           = Field(default=2, description = "The upscale level")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
+        )
+
+        # Results are image and seed, unwrap for now
+        # TODO: can this return multiple results?
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+```
+
+Each portion is important to implement correctly.
+
+### Class definition and type
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+```
+All invocations must derive from `BaseInvocation`. They should have a docstring that declares what they do in a single, short line. They should also have a `type` with a type hint that's `Literal["command_name"]`, where `command_name` is what the user will type on the CLI or use in the API to create this invocation. The `command_name` must be unique. The `type` must be assigned to the value of the literal in the type hint.
+
+### Inputs
+```py
+    # Inputs
+    image: Union[ImageField,None] = Field(description="The input image")
+    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2,4]           = Field(default=2, description="The upscale level")
+```
+Inputs consist of three parts: a name, a type hint, and a `Field` with default, description, and validation information. For example:
+| Part | Value | Description |
+| ---- | ----- | ----------- |
+| Name | `strength` | This field is referred to as `strength` |
+| Type Hint | `float` | This field must be of type `float` |
+| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
+
+Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this field to be parsed with `None` as a value, which enables linking to previous invocations. All fields should either provide a default value or allow `None` as a value, so that they can be overwritten with a linked output from another invocation.
+
+The special type `ImageField` is also used here. All images are passed as `ImageField`, which protects them from pydantic validation errors (since images only ever come from links).
+
+Finally, note that for all linking, the `type` of the linked fields must match. If the `name` also matches, then the field can be **automatically linked** to a previous invocation by name and matching.
+
+### Invoke Function
+```py
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
+        )
+
+        # Results are image and seed, unwrap for now
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+```
+The `invoke` function is the last portion of an invocation. It is provided an `InvocationContext` which contains services to perform work as well as a `session_id` for use as needed. It should return a class with output values that derives from `BaseInvocationOutput`.
+
+Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).
+
+Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the `InvocationServices` class, and make sure it can be overridden.
+
+### Outputs
+```py
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+    type: Literal['image'] = 'image'
+
+    image: ImageField = Field(default=None, description="The output image")
+```
+Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -0,0 +1,83 @@
+# Local Development
+
+If you are looking to contribute you will need to have a local development
+environment. See the
+[Developer Install](../installation/020_INSTALL_MANUAL.md#developer-install) for
+full details.
+
+Broadly this involves cloning the repository, installing the pre-reqs, and
+InvokeAI (in editable form). Assuming this is working, choose your area of
+focus.
+
+## Documentation
+
+We use [mkdocs](https://www.mkdocs.org) for our documentation with the
+[material theme](https://squidfunk.github.io/mkdocs-material/). Documentation is
+written in markdown files under the `./docs` folder and then built into a static
+website for hosting with GitHub Pages at
+[invoke-ai.github.io/InvokeAI](https://invoke-ai.github.io/InvokeAI).
+
+To contribute to the documentation you'll need to install the dependencies. Note
+the use of `"`.
+
+```zsh
+pip install ".[docs]"
+```
+
+Now, to run the documentation locally with hot-reloading for changes made.
+
+```zsh
+mkdocs serve
+```
+
+You'll then be prompted to connect to `http://127.0.0.1:8080` in order to
+access.
+
+## Backend
+
+The backend is contained within the `./invokeai/backend` folder structure. To
+get started however please install the development dependencies.
+
+From the root of the repository run the following command. Note the use of `"`.
+
+```zsh
+pip install ".[test]"
+```
+
+This in an optional group of packages which is defined within the
+`pyproject.toml` and will be required for testing the changes you make the the
+code.
+
+### Running Tests
+
+We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
+be found under the `./tests` folder and can be run with a single `pytest`
+command. Optionally, to review test coverage you can append `--cov`.
+
+```zsh
+pytest --cov
+```
+
+Test outcomes and coverage will be reported in the terminal. In addition a more
+detailed report is created in both XML and HTML format in the `./coverage`
+folder. The HTML one in particular can help identify missing statements
+requiring tests to ensure coverage. This can be run by opening
+`./coverage/html/index.html`.
+
+For example.
+
+```zsh
+pytest --cov; open ./coverage/html/index.html
+```
+
+??? info "HTML coverage report output"
+
+    ![html-overview](../assets/contributing/html-overview.png)
+
+    ![html-detail](../assets/contributing/html-detail.png)
+
+## Front End
+
+<!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
+
+--8<-- "invokeai/frontend/web/README.md"

docs/features/CLI.md

@@ -6,38 +6,51 @@ title: Command-Line Interface
 
 ## **Interactive Command Line Interface**
 
-The `invoke.py` script, located in `scripts/`, provides an interactive interface
-to image generation similar to the "invoke mothership" bot that Stable AI
-provided on its Discord server.
+The InvokeAI command line interface (CLI) provides scriptable access
+to InvokeAI's features.Some advanced features are only available
+through the CLI, though they eventually find their way into the WebUI.
 
-Unlike the `txt2img.py` and `img2img.py` scripts provided in the original
-[CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) source
-code repository, the time-consuming initialization of the AI model
-initialization only happens once. After that image generation from the
-command-line interface is very fast.
+The CLI is accessible from the `invoke.sh`/`invoke.bat` launcher by
+selecting option (1). Alternatively, it can be launched directly from
+the command line by activating the InvokeAI environment and giving the
+command:
+
+```bash
+invokeai
+```
+
+After some startup messages, you will be presented with the `invoke> `
+prompt. Here you can type prompts to generate images and issue other
+commands to load and manipulate generative models. The CLI has a large
+number of command-line options that control its behavior. To get a
+concise summary of the options, call `invokeai` with the `--help` argument:
+
+```bash
+invokeai --help
+```
 
 The script uses the readline library to allow for in-line editing, command
 history (++up++ and ++down++), autocompletion, and more. To help keep track of
 which prompts generated which images, the script writes a log file of image
 names and prompts to the selected output directory.
 
-In addition, as of version 1.02, it also writes the prompt into the PNG file's
-metadata where it can be retrieved using `scripts/images2prompt.py`
-
-The script is confirmed to work on Linux, Windows and Mac systems.
-
-!!! note
-
-    This script runs from the command-line or can be used as a Web application. The Web GUI is
-    currently rudimentary, but a much better replacement is on its way.
+Here is a typical session
 
 ```bash
-(invokeai) ~/stable-diffusion$ python3 ./scripts/invoke.py
+PS1:C:\Users\fred> invokeai
 * Initializing, be patient...
-Loading model from models/ldm/text2img-large/model.ckpt
-(...more initialization messages...)
-
-* Initialization done! Awaiting your command...
+* Initializing, be patient...
+>> Initialization file /home/lstein/invokeai/invokeai.init found. Loading...
+>> Internet connectivity is True
+>> InvokeAI, version 2.3.0-rc5
+>> InvokeAI runtime directory is "/home/lstein/invokeai"
+>> GFPGAN Initialized
+>> CodeFormer Initialized
+>> ESRGAN Initialized
+>> Using device_type cuda
+>> xformers memory-efficient attention is available and enabled
+     (...more initialization messages...)
+* Initialization done! Awaiting your command (-h for help, 'q' to quit)
 invoke> ashley judd riding a camel -n2 -s150
 Outputs:
    outputs/img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
@@ -47,27 +60,15 @@ invoke> "there's a fly in my soup" -n6 -g
     outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
     seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
 invoke> q
-
-# this shows how to retrieve the prompt stored in the saved image's metadata
-(invokeai) ~/stable-diffusion$ python ./scripts/images2prompt.py outputs/img_samples/*.png
-00009.png: "ashley judd riding a camel" -s150 -S 416354203
-00010.png: "ashley judd riding a camel" -s150 -S 1362479620
-00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
 ```
 
 ![invoke-py-demo](../assets/dream-py-demo.png)
 
-The `invoke>` prompt's arguments are pretty much identical to those used in the
-Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you
-do). A significant change is that creation of individual images is now the
-default unless `--grid` (`-g`) is given. A full list is given in
-[List of prompt arguments](#list-of-prompt-arguments).
-
 ## Arguments
 
-The script itself also recognizes a series of command-line switches that will
-change important global defaults, such as the directory for image outputs and
-the location of the model weight files.
+The script recognizes a series of command-line switches that will
+change important global defaults, such as the directory for image
+outputs and the location of the model weight files.
 
 ### List of arguments recognized at the command line
 
@@ -82,10 +83,14 @@ overridden on a per-prompt basis (see
 | `--outdir <path>`                         | `-o<path>`                                | `outputs/img_samples`                          | Location for generated images.                                                                       |
 | `--prompt_as_dir`                         | `-p`                                      | `False`                                        | Name output directories using the prompt text.                                                       |
 | `--from_file <path>`                      |                                           | `None`                                         | Read list of prompts from a file. Use `-` to read from standard input                                |
-| `--model <modelname>`                     |                                           | `stable-diffusion-1.4`                         | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" |
-| `--full_precision`                        | `-F`                                      | `False`                                        | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards.   |
+| `--model <modelname>`                     |                                           | `stable-diffusion-1.5`                         | Loads the initial model specified in configs/models.yaml. |
+| `--ckpt_convert `           |                                                         | `False`                                        | If provided both .ckpt and .safetensors files will be auto-converted into diffusers format in memory |
+| `--autoconvert <path>`                    |                          | `None`                                        | On startup, scan the indicated directory for new .ckpt/.safetensor files and automatically convert and import them |
+| `--precision`                             |                                           | `fp16`                                         | Provide `fp32` for full precision mode, `fp16` for half-precision. `fp32` needed for Macintoshes and some NVidia cards. |
 | `--png_compression <0-9>`                 | `-z<0-9>`                                 | `6`                                            | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
 | `--safety-checker`                        |                                           | `False`                                        | Activate safety checker for NSFW and other potentially disturbing imagery                            |
+| `--patchmatch`, `--no-patchmatch`                 |                                   | `--patchmatch`                                        | Load/Don't load the PatchMatch inpainting extension    |
+| `--xformers`, `--no-xformers`                 |                                   | `--xformers`                                        | Load/Don't load the Xformers memory-efficient attention module (CUDA only)    |
 | `--web`                                   |                                           | `False`                                        | Start in web server mode                                                                             |
 | `--host <ip addr>`                        |                                           | `localhost`                                    | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any.                |
 | `--port <port>`                           |                                           | `9090`                                         | Which port web server should listen for requests on.                                                 |
@@ -109,6 +114,7 @@ overridden on a per-prompt basis (see
 
     | Argument           |  Shortcut  |  Default            |  Description |
     |--------------------|------------|---------------------|--------------|
+    | `--full_precision`  |             | `False`                | Same as `--precision=fp32`|
     | `--weights <path>`   |            | `None`                | Path to weights file; use `--model stable-diffusion-1.4` instead |
     | `--laion400m`        | `-l`         | `False`               | Use older LAION400m weights; use `--model=laion400m` instead |
 
@@ -136,7 +142,7 @@ mixture of both using any of the accepted command switch formats:
     # InvokeAI initialization file
     # This is the InvokeAI initialization file, which contains command-line default values.
     # Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
-    # or renaming it and then running configure_invokeai.py again.
+    # or renaming it and then running invokeai-configure again.
 
     # The --root option below points to the folder in which InvokeAI stores its models, configs and outputs.
     --root="/Users/mauwii/invokeai"
@@ -208,6 +214,8 @@ Here are the invoke> command that apply to txt2img:
 | `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
 | `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this.                                                                                                                                                           |
 | `--save_intermediates <n>`                |                                           | `None`                                   | Save the image from every nth step into an "intermediates" folder inside the output directory                                                                                                                                                    |
+| `--h_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
+| `--v_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
 
 !!! note
 
@@ -336,8 +344,10 @@ useful for debugging the text masking process prior to inpainting with the
 
 ### Model selection and importation
 
-The CLI allows you to add new models on the fly, as well as to switch among them
-rapidly without leaving the script.
+The CLI allows you to add new models on the fly, as well as to switch
+among them rapidly without leaving the script. There are several
+different model formats, each described in the [Model Installation
+Guide](../installation/050_INSTALLING_MODELS.md).
 
 #### `!models`
 
@@ -347,9 +357,9 @@ model is bold-faced
 Example:
 
 <pre>
-laion400m                 not loaded  <no description>
-<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
-waifu-diffusion           not loaded  Waifu Diffusion v1.3
+inpainting-1.5            not loaded  Stable Diffusion inpainting model
+<b>stable-diffusion-1.5          active  Stable Diffusion v1.5</b>
+waifu-diffusion           not loaded  Waifu Diffusion v1.4
 </pre>
 
 #### `!switch <model>`
@@ -361,43 +371,30 @@ Note how the second column of the `!models` table changes to `cached` after a
 model is first loaded, and that the long initialization step is not needed when
 loading a cached model.
 
-<pre>
-invoke> !models
-laion400m                 not loaded  <no description>
-<b>stable-diffusion-1.4          cached  Stable Diffusion v1.4</b>
-waifu-diffusion               active  Waifu Diffusion v1.3
+#### `!import_model <hugging_face_repo_ID>`
 
-invoke> !switch waifu-diffusion
->> Caching model stable-diffusion-1.4 in system RAM
->> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
-   | LatentDiffusion: Running in eps-prediction mode
-   | DiffusionWrapper has 859.52 M params.
-   | Making attention of type 'vanilla' with 512 in_channels
-   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
-   | Making attention of type 'vanilla' with 512 in_channels
-   | Using faster float16 precision
->> Model loaded in 18.24s
->> Max VRAM used to load the model: 2.17G
->> Current VRAM usage:2.17G
->> Setting Sampler to k_lms
+This imports and installs a `diffusers`-style model that is stored on
+the [HuggingFace Web Site](https://huggingface.co). You can look up
+any [Stable Diffusion diffusers
+model](https://huggingface.co/models?library=diffusers) and install it
+with a command like the following:
 
-invoke> !models
-laion400m                 not loaded  <no description>
-stable-diffusion-1.4          cached  Stable Diffusion v1.4
-<b>waifu-diffusion               active  Waifu Diffusion v1.3</b>
+```bash
+!import_model prompthero/openjourney
+```
 
-invoke> !switch stable-diffusion-1.4
->> Caching model waifu-diffusion in system RAM
->> Retrieving model stable-diffusion-1.4 from system RAM cache
->> Setting Sampler to k_lms
+#### `!import_model <path/to/diffusers/directory>`
 
-invoke> !models
-laion400m                 not loaded  <no description>
-<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
-waifu-diffusion               cached  Waifu Diffusion v1.3
-</pre>
+If you have a copy of a `diffusers`-style model saved to disk, you can
+import it by passing the path to model's top-level directory.
 
-#### `!import_model <path/to/model/weights>`
+#### `!import_model <url>`
+
+For a `.ckpt` or `.safetensors` file, if you have a direct download
+URL for the file, you can provide it to `!import_model` and the file
+will be downloaded and installed for you.
+
+#### `!import_model <path/to/model/weights.ckpt>`
 
 This command imports a new model weights file into InvokeAI, makes it available
 for image generation within the script, and writes out the configuration for the
@@ -417,35 +414,12 @@ below, the bold-faced text shows what the user typed in with the exception of
 the width, height and configuration file paths, which were filled in
 automatically.
 
-Example:
+#### `!import_model <path/to/directory_of_models>`
 
-<pre>
-invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b>
->> Model import in process. Please enter the values needed to configure this model:
-
-Name for this model: <b>waifu-diffusion</b>
-Description of this model: <b>Waifu Diffusion v1.3</b>
-Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
-Default image width: <b>512</b>
-Default image height: <b>512</b>
->> New configuration:
-waifu-diffusion:
-  config: configs/stable-diffusion/v1-inference.yaml
-  description: Waifu Diffusion v1.3
-  height: 512
-  weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
-  width: 512
-OK to import [n]? <b>y</b>
->> Caching model stable-diffusion-1.4 in system RAM
->> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
-   | LatentDiffusion: Running in eps-prediction mode
-   | DiffusionWrapper has 859.52 M params.
-   | Making attention of type 'vanilla' with 512 in_channels
-   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
-   | Making attention of type 'vanilla' with 512 in_channels
-   | Using faster float16 precision
-invoke>
-</pre>
+If you provide the path of a directory that contains one or more
+`.ckpt` or `.safetensors` files, the CLI will scan the directory and
+interactively offer to import the models it finds there. Also see the
+`--autoconvert` command-line option.
 
 #### `!edit_model <name_of_model>`
 
@@ -479,11 +453,6 @@ OK to import [n]? y
 ...
 </pre>
 
-======= invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3 ...lots of
-text... Outputs: [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix
-"outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512
--H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25 ```
-
 ### History processing
 
 The CLI provides a series of convenient commands for reviewing previous actions,

docs/features/CONCEPTS.md

@@ -51,7 +51,7 @@ You can also combine styles and concepts:
     If you used an installer to install InvokeAI, you may have already set a HuggingFace token.
     If you skipped this step, you can:
 
-    - run the InvokeAI configuration script again (if you used a manual installer): `scripts/configure_invokeai.py`
+    - run the InvokeAI configuration script again (if you used a manual installer): `invokeai-configure`
     - set one of the `HUGGINGFACE_TOKEN` or `HUGGING_FACE_HUB_TOKEN` environment variables to contain your token
 
     Finally, if you already used any HuggingFace library on your computer, you might already have a token

docs/features/IMG2IMG.md

@@ -4,13 +4,24 @@ title: Image-to-Image
 
 # :material-image-multiple: Image-to-Image
 
-## `img2img`
+Both the Web and command-line interfaces provide an "img2img" feature
+that lets you seed your creations with an initial drawing or
+photo. This is a really cool feature that tells stable diffusion to
+build the prompt on top of the image you provide, preserving the
+original's basic shape and layout.
 
-This script also provides an `img2img` feature that lets you seed your creations
-with an initial drawing or photo. This is a really cool feature that tells
-stable diffusion to build the prompt on top of the image you provide, preserving
-the original's basic shape and layout. To use it, provide the `--init_img`
-option as shown here:
+See the [WebUI Guide](WEB.md) for a walkthrough of the img2img feature
+in the InvokeAI web server. This document describes how to use img2img
+in the command-line tool.
+
+## Basic Usage
+
+Launch the command-line client by launching `invoke.sh`/`invoke.bat`
+and choosing option (1). Alternative, activate the InvokeAI
+environment and issue the command `invokeai`.
+
+Once the `invoke> ` prompt appears, you can start an img2img render by
+pointing to a seed file with the `-I` option as shown here:
 
 !!! example ""
 

docs/features/INPAINTING.md

@@ -168,11 +168,15 @@ used by Stable Diffusion 1.4 and 1.5.
 After installation, your `models.yaml` should contain an entry that looks like
 this one:
 
-inpainting-1.5: weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
-description: SD inpainting v1.5 config:
-configs/stable-diffusion/v1-inpainting-inference.yaml vae:
-models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt width: 512
-height: 512
+```yml
+inpainting-1.5:
+  weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+  description: SD inpainting v1.5
+  config: configs/stable-diffusion/v1-inpainting-inference.yaml
+  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  width: 512
+  height: 512
+```
 
 As shown in the example, you may include a VAE fine-tuning weights file as well.
 This is strongly recommended.

docs/features/MODEL_MERGING.md

@@ -18,10 +18,9 @@ prompts you to select the models to merge, how to merge them, and the
 merged model name.
 
 Alternatively you may activate InvokeAI's virtual environment from the
-command line, and call the script via `merge_models_fe.py` (the "fe"
-stands for "front end"). There is also a version that accepts
-command-line arguments, which you can run with the command
-`merge_models.py`.
+command line, and call the script via `merge_models --gui` to open up
+a version that has a nice graphical front end. To get the commandline-
+only version, omit `--gui`.
 
 The user interface for the text-based interactive script is
 straightforward. It shows you a series of setting fields. Use control-N (^N)
@@ -47,7 +46,7 @@ under the selected name and register it with InvokeAI.
   display all the diffusers-style models that InvokeAI knows about.
   If you do not see the model you are looking for, then it is probably
   a legacy checkpoint model and needs to be converted using the
-  `invoke.py` command-line client and its `!optimize` command. You
+  `invoke` command-line client and its `!optimize` command. You
   must select at least two models to merge. The third can be left at
   "None" if you desire.
 

docs/features/OUTPAINTING.md

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

docs/features/POSTPROCESS.md

@@ -28,11 +28,11 @@ should "just work" without further intervention. Simply pass the `--upscale`
 the popup in the Web GUI.
 
 **GFPGAN** requires a series of downloadable model files to work. These are
-loaded when you run `scripts/configure_invokeai.py`. If GFPAN is failing with an
+loaded when you run `invokeai-configure`. If GFPAN is failing with an
 error, please run the following from the InvokeAI directory:
 
 ```bash
-python scripts/configure_invokeai.py
+invokeai-configure
 ```
 
 If you do not run this script in advance, the GFPGAN module will attempt to
@@ -106,7 +106,7 @@ This repo also allows you to perform face restoration using
 [CodeFormer](https://github.com/sczhou/CodeFormer).
 
 In order to setup CodeFormer to work, you need to download the models like with
-GFPGAN. You can do this either by running `configure_invokeai.py` or by manually
+GFPGAN. You can do this either by running `invokeai-configure` or by manually
 downloading the
 [model file](https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth)
 and saving it to `ldm/invoke/restoration/codeformer/weights` folder.

docs/features/PROMPTS.md

@@ -40,7 +40,7 @@ for adj in adjectives:
             print(f'a {adj} day -A{samp} -C{cg}')
 ```
 
-It's output looks like this (abbreviated):
+Its output looks like this (abbreviated):
 
 ```bash
 a sunny day -Aklms -C7.5
@@ -239,28 +239,24 @@ Generate an image with a given prompt, record the seed of the image, and then
 use the `prompt2prompt` syntax to substitute words in the original prompt for
 words in a new prompt. This works for `img2img` as well.
 
-- `a ("fluffy cat").swap("smiling dog") eating a hotdog`.
-    - quotes optional: `a (fluffy cat).swap(smiling dog) eating a hotdog`.
-    - for single word substitutions parentheses are also optional:
-      `a cat.swap(dog) eating a hotdog`.
-- Supports options `s_start`, `s_end`, `t_start`, `t_end` (each 0-1) loosely
-  corresponding to bloc97's `prompt_edit_spatial_start/_end` and
-  `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
-  intuitively understand.
-    - Example usage:`a (cat).swap(dog, s_end=0.3) eating a hotdog` - the `s_end`
-      argument means that the "spatial" (self-attention) edit will stop having any
-      effect after 30% (=0.3) of the steps have been done, leaving Stable
-      Diffusion with 70% of the steps where it is free to decide for itself how to
-      reshape the cat-form into a dog form.
-    - The numbers represent a percentage through the step sequence where the edits
-      should happen. 0 means the start (noisy starting image), 1 is the end (final
-      image).
-        - For img2img, the step sequence does not start at 0 but instead at
-          (1-strength) - so if strength is 0.7, s_start and s_end must both be
-          greater than 0.3 (1-0.7) to have any effect.
-- Convenience option `shape_freedom` (0-1) to specify how much "freedom" Stable
-  Diffusion should have to change the shape of the subject being swapped.
-    - `a (cat).swap(dog, shape_freedom=0.5) eating a hotdog`.
+For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
+  - `a cat playing with a ball in the forest`
+  - `a dog playing with a ball in the forest`
+
+| `a cat playing with a ball in the forest` | `a dog playing with a ball in the forest` |
+| --- | --- |
+| img | img |
+
+
+      - For multiple word swaps, use parentheses: `a (fluffy cat).swap(barking dog) playing with a ball in the forest`.
+      - To swap a comma, use quotes: `a ("fluffy, grey cat").swap("big, barking dog") playing with a ball in the forest`.
+- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to bloc97's `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
+  intuitively understand. `t_start` and `t_end` are used to control on which steps cross-attention control should run. With the default values `t_start=0` and `t_end=1`, cross-attention control is active on every step of image generation. Other values can be used to turn cross-attention control off for part of the image generation process.
+    - For example, if doing a diffusion with 10 steps for the prompt is `a cat.swap(dog, t_start=0.3, t_end=1.0) playing with a ball in the forest`, the first 3 steps will be run as `a cat playing with a ball in the forest`, while the last 7 steps will run as `a dog playing with a ball in the forest`, but the pixels that represent `dog` will be locked to the pixels that would have represented `cat` if the `cat` prompt had been used instead.
+    - Conversely, for `a cat.swap(dog, t_start=0, t_end=0.7) playing with a ball in the forest`, the first 7 steps will run as `a dog playing with a ball in the forest` with the pixels that represent `dog` locked to the same pixels that would have represented `cat` if the `cat` prompt was being used instead. The final 3 steps will just run `a cat playing with a ball in the forest`.
+    > For img2img, the step sequence does not start at 0 but instead at `(1.0-strength)` - so if the img2img `strength` is `0.7`, `t_start` and `t_end` must both be greater than `0.3` (`1.0-0.7`) to have any effect.
+
+Prompt2prompt `.swap()` is not compatible with xformers, which will be temporarily disabled when doing a `.swap()` - so you should expect to use more VRAM and run slower that with xformers enabled.
 
 The `prompt2prompt` code is based off
 [bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
@@ -272,7 +268,7 @@ model is so good at inpainting, a good substitute is to use the `clipseg` text
 masking option:
 
 ```bash
-invoke> a fluffy cat eating a hotdot
+invoke> a fluffy cat eating a hotdog
 Outputs:
 [1010] outputs/000025.2182095108.png: a fluffy cat eating a hotdog
 invoke> a smiling dog eating a hotdog -I 000025.2182095108.png -tm cat

docs/features/TEXTUAL_INVERSION.md

@@ -10,83 +10,278 @@ You may personalize the generated images to provide your own styles or objects
 by training a new LDM checkpoint and introducing a new vocabulary to the fixed
 model as a (.pt) embeddings file. Alternatively, you may use or train
 HuggingFace Concepts embeddings files (.bin) from
-<https://huggingface.co/sd-concepts-library> and its associated notebooks.
+<https://huggingface.co/sd-concepts-library> and its associated
+notebooks.
 
-## **Training**
+## **Hardware and Software Requirements**
 
-To train, prepare a folder that contains images sized at 512x512 and execute the
-following:
+You will need a GPU to perform training in a reasonable length of
+time, and at least 12 GB of VRAM. We recommend using the [`xformers`
+library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
+training process further. During training, about ~8 GB is temporarily
+needed in order to store intermediate models, checkpoints and logs.
 
-### WINDOWS
+## **Preparing for Training**
 
-As the default backend is not available on Windows, if you're using that
-platform, set the environment variable `PL_TORCH_DISTRIBUTED_BACKEND` to `gloo`
+To train, prepare a folder that contains 3-5 images that illustrate
+the object or concept. It is good to provide a variety of examples or
+poses to avoid overtraining the system. Format these images as PNG
+(preferred) or JPG. You do not need to resize or crop the images in
+advance, but for more control you may wish to do so.
 
-```bash
-python3 ./main.py -t \
-    --base ./configs/stable-diffusion/v1-finetune.yaml \
-    --actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
-    -n my_cat \
-    --gpus 0 \
-    --data_root D:/textual-inversion/my_cat \
-    --init_word 'cat'
+Place the training images in a directory on the machine InvokeAI runs
+on. We recommend placing them in a subdirectory of the
+`text-inversion-training-data` folder located in the InvokeAI root
+directory, ordinarily `~/invokeai` (Linux/Mac), or
+`C:\Users\your_name\invokeai` (Windows). For example, to create an
+embedding for the "psychedelic" style, you'd place the training images
+into the directory
+`~invokeai/text-inversion-training-data/psychedelic`.
+
+## **Launching Training Using the Console Front End**
+
+InvokeAI 2.3 and higher comes with a text console-based training front
+end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
+start the front end by selecting choice (3):
+
+```sh
+Do you want to generate images using the
+1. command-line
+2. browser-based UI
+3. textual inversion training
+4. open the developer console
+Please enter 1, 2, 3, or 4: [1] 3
 ```
 
-During the training process, files will be created in
-`/logs/[project][time][project]/` where you can see the process.
+From the command line, with the InvokeAI virtual environment active,
+you can launch the front end with the command `invokeai-ti --gui`.
 
-Conditioning contains the training prompts inputs, reconstruction the input
-images for the training epoch samples, samples scaled for a sample of the prompt
-and one with the init word provided.
+This will launch a text-based front end that will look like this:
 
-On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
+<figure markdown>
+![ti-frontend](../assets/textual-inversion/ti-frontend.png)
+</figure>
 
-!!! note
+The interface is keyboard-based. Move from field to field using
+control-N (^N) to move to the next field and control-P (^P) to the
+previous one. <Tab> and <shift-TAB> work as well. Once a field is
+active, use the cursor keys. In a checkbox group, use the up and down
+cursor keys to move from choice to choice, and <space> to select a
+choice. In a scrollbar, use the left and right cursor keys to increase
+and decrease the value of the scroll. In textfields, type the desired
+values.
 
-    According to the associated paper, the optimal number of
-    images is 3-5. Your model may not converge if you use more images than
-    that.
+The number of parameters may look intimidating, but in most cases the
+predefined defaults work fine. The red circled fields in the above
+illustration are the ones you will adjust most frequently.
 
-Training will run indefinitely, but you may wish to stop it (with ctrl-c) before
-the heat death of the universe, when you find a low loss epoch or around ~5000
-iterations. Note that you can set a fixed limit on the number of training steps
-by decreasing the "max_steps" option in
-configs/stable_diffusion/v1-finetune.yaml (currently set to 4000000)
+### Model Name
 
-## **Run the Model**
+This will list all the diffusers models that are currently
+installed. Select the one you wish to use as the basis for your
+embedding. Be aware that if you use a SD-1.X-based model for your
+training, you will only be able to use this embedding with other
+SD-1.X-based models. Similarly, if you train on SD-2.X, you will only
+be able to use the embeddings with models based on SD-2.X.
 
-Once the model is trained, specify the trained .pt or .bin file when starting
-invoke using
+### Trigger Term
 
-```bash
-python3 ./scripts/invoke.py \
-    --embedding_path /path/to/embedding.pt
+This is the prompt term you will use to trigger the embedding. Type a
+single word or phrase you wish to use as the trigger, example
+"psychedelic" (without angle brackets). Within InvokeAI, you will then
+be able to activate the trigger using the syntax `<psychedelic>`.
+
+### Initializer
+
+This is a single character that is used internally during the training
+process as a placeholder for the trigger term. It defaults to "*" and
+can usually be left alone.
+
+### Resume from last saved checkpoint
+
+As training proceeds, textual inversion will write a series of
+intermediate files that can be used to resume training from where it
+was left off in the case of an interruption. This checkbox will be
+automatically selected if you provide a previously used trigger term
+and at least one checkpoint file is found on disk.
+
+Note that as of 20 January 2023, resume does not seem to be working
+properly due to an issue with the upstream code.
+
+### Data Training Directory
+
+This is the location of the images to be used for training. When you
+select a trigger term like "my-trigger", the frontend will prepopulate
+this field with `~/invokeai/text-inversion-training-data/my-trigger`,
+but you can change the path to wherever you want.
+
+### Output Destination Directory
+
+This is the location of the logs, checkpoint files, and embedding
+files created during training. When you select a trigger term like
+"my-trigger", the frontend will prepopulate this field with
+`~/invokeai/text-inversion-output/my-trigger`, but you can change the
+path to wherever you want.
+
+### Image resolution
+
+The images in the training directory will be automatically scaled to
+the value you use here. For best results, you will want to use the
+same default resolution of the underlying model (512 pixels for
+SD-1.5, 768 for the larger version of SD-2.1).
+
+### Center crop images
+
+If this is selected, your images will be center cropped to make them
+square before resizing them to the desired resolution. Center cropping
+can indiscriminately cut off the top of subjects' heads for portrait
+aspect images, so if you have images like this, you may wish to use a
+photoeditor to manually crop them to a square aspect ratio.
+
+### Mixed precision
+
+Select the floating point precision for the embedding. "no" will
+result in a full 32-bit precision, "fp16" will provide 16-bit
+precision, and "bf16" will provide mixed precision (only available
+when XFormers is used).
+
+### Max training steps
+
+How many steps the training will take before the model converges. Most
+training sets will converge with 2000-3000 steps.
+
+### Batch size
+
+This adjusts how many training images are processed simultaneously in
+each step. Higher values will cause the training process to run more
+quickly, but use more memory. The default size will run with GPUs with
+as little as 12 GB.
+
+### Learning rate
+
+The rate at which the system adjusts its internal weights during
+training. Higher values risk overtraining (getting the same image each
+time), and lower values will take more steps to train a good
+model. The default of 0.0005 is conservative; you may wish to increase
+it to 0.005 to speed up training.
+
+### Scale learning rate by number of GPUs, steps and batch size
+
+If this is selected (the default) the system will adjust the provided
+learning rate to improve performance.
+
+### Use xformers acceleration
+
+This will activate XFormers memory-efficient attention. You need to
+have XFormers installed for this to have an effect.
+
+### Learning rate scheduler
+
+This adjusts how the learning rate changes over the course of
+training. The default "constant" means to use a constant learning rate
+for the entire training session. The other values scale the learning
+rate according to various formulas.
+
+Only "constant" is supported by the XFormers library.
+
+### Gradient accumulation steps
+
+This is a parameter that allows you to use bigger batch sizes than
+your GPU's VRAM would ordinarily accommodate, at the cost of some
+performance.
+
+### Warmup steps
+
+If "constant_with_warmup" is selected in the learning rate scheduler,
+then this provides the number of warmup steps. Warmup steps have a
+very low learning rate, and are one way of preventing early
+overtraining.
+
+## The training run
+
+Start the training run by advancing to the OK button (bottom right)
+and pressing <enter>. A series of progress messages will be displayed
+as the training process proceeds. This may take an hour or two,
+depending on settings and the speed of your system. Various log and
+checkpoint files will be written into the output directory (ordinarily
+`~/invokeai/text-inversion-output/my-model/`)
+
+At the end of successful training, the system will copy the file
+`learned_embeds.bin` into the InvokeAI root directory's `embeddings`
+directory, using a subdirectory named after the trigger token. For
+example, if the trigger token was `psychedelic`, then look for the
+embeddings file in
+`~/invokeai/embeddings/psychedelic/learned_embeds.bin`
+
+You may now launch InvokeAI and try out a prompt that uses the trigger
+term. For example `a plate of banana sushi in <psychedelic> style`.
+
+## **Training with the Command-Line Script**
+
+Training can also be done using a traditional command-line script. It
+can be launched from within the "developer's console", or from the
+command line after activating InvokeAI's virtual environment.
+
+It accepts a large number of arguments, which can be summarized by
+passing the `--help` argument:
+
+```sh
+invokeai-ti --help
 ```
 
-Then, to utilize your subject at the invoke prompt
-
-```bash
-invoke> "a photo of *"
+Typical usage is shown here:
+```sh
+invokeai-ti \
+       --model=stable-diffusion-1.5 \
+       --resolution=512 \
+       --learnable_property=style \
+       --initializer_token='*' \
+       --placeholder_token='<psychedelic>' \
+       --train_data_dir=/home/lstein/invokeai/training-data/psychedelic \
+       --output_dir=/home/lstein/invokeai/text-inversion-training/psychedelic \
+       --scale_lr \
+       --train_batch_size=8 \
+       --gradient_accumulation_steps=4 \
+       --max_train_steps=3000 \
+       --learning_rate=0.0005 \
+       --resume_from_checkpoint=latest \
+       --lr_scheduler=constant \
+       --mixed_precision=fp16 \
+       --only_save_embeds
 ```
 
-This also works with image2image
+## Using Embeddings
 
-```bash
-invoke> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
-```
+After training completes, the resultant embeddings will be saved into your `$INVOKEAI_ROOT/embeddings/<trigger word>/learned_embeds.bin`.
 
-For .pt files it's also possible to train multiple tokens (modify the
-placeholder string in `configs/stable-diffusion/v1-finetune.yaml`) and combine
-LDM checkpoints using:
+These will be automatically loaded when you start InvokeAI.
 
-```bash
-python3 ./scripts/merge_embeddings.py \
-    --manager_ckpts /path/to/first/embedding.pt \
-    [</path/to/second/embedding.pt>,[...]] \
-    --output_path /path/to/output/embedding.pt
-```
+Add the trigger word, surrounded by angle brackets, to use that embedding. For example, if your trigger word was `terence`, use `<terence>` in prompts. This is the same syntax used by the HuggingFace concepts library.
 
-Credit goes to rinongal and the repository
+**Note:** `.pt` embeddings do not require the angle brackets.
 
-Please see [the repository](https://github.com/rinongal/textual_inversion) and
-associated paper for details and limitations.
+## Troubleshooting
+
+### `Cannot load embedding for <trigger>. It was trained on a model with token dimension 1024, but the current model has token dimension 768`
+
+Messages like this indicate you trained the embedding on a different base model than the currently selected one.
+
+For example, in the error above, the training was done on SD2.1 (768x768) but it was used on SD1.5 (512x512).
+
+## Reading
+
+For more information on textual inversion, please see the following
+resources:
+
+* The [textual inversion repository](https://github.com/rinongal/textual_inversion) and
+  associated paper for details and limitations.
+* [HuggingFace's textual inversion training
+  page](https://huggingface.co/docs/diffusers/training/text_inversion)
+* [HuggingFace example script
+  documentation](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)
+  (Note that this script is similar to, but not identical, to
+  `textual_inversion`, but produces embed files that are completely compatible.
+
+---
+
+copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/features/WEB.md

@@ -5,11 +5,14 @@ title: InvokeAI Web Server
 # :material-web: InvokeAI Web Server
 
 As of version 2.0.0, this distribution comes with a full-featured web server
-(see screenshot). To use it, run the `invoke.py` script by adding the `--web`
-option:
+(see screenshot).
+
+To use it, launch the `invoke.sh`/`invoke.bat` script and select
+option (2). Alternatively, with the InvokeAI environment active, run
+the `invokeai` script by adding the `--web` option:
 
 ```bash
-(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web
+invokeai --web
 ```
 
 You can then connect to the server by pointing your web browser at
@@ -19,17 +22,23 @@ address of the host you are running it on, or the wildcard `0.0.0.0`. For
 example:
 
 ```bash
-(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web --host 0.0.0.0
+invoke.sh --host 0.0.0.0
 ```
 
-## Quick guided walkthrough of the WebGUI's features
+or
 
-While most of the WebGUI's features are intuitive, here is a guided walkthrough
+```bash
+invokeai --web --host 0.0.0.0
+```
+
+## Quick guided walkthrough of the WebUI's features
+
+While most of the WebUI's features are intuitive, here is a guided walkthrough
 through its various components.
 
 ![Invoke Web Server - Major Components](../assets/invoke-web-server-1.png){:width="640px"}
 
-The screenshot above shows the Text to Image tab of the WebGUI. There are three
+The screenshot above shows the Text to Image tab of the WebUI. There are three
 main sections:
 
 1. A **control panel** on the left, which contains various settings for text to
@@ -63,12 +72,14 @@ From top to bottom, these are:
 1. Text to Image - generate images from text
 2. Image to Image - from an uploaded starting image (drawing or photograph)
    generate a new one, modified by the text prompt
-3. Inpainting (pending) - Interactively erase portions of a starting image and
-   have the AI fill in the erased region from a text prompt.
-4. Outpainting (pending) - Interactively add blank space to the borders of a
-   starting image and fill in the background from a text prompt.
-5. Postprocessing (pending) - Interactively postprocess generated images using a
-   variety of filters.
+3. Unified Canvas - Interactively combine multiple images, extend them
+   with outpainting,and modify interior portions of the image with
+   inpainting, erase portions of a starting image and have the AI fill in
+   the erased region from a text prompt.
+4. Workflow Management (not yet implemented) - this panel will allow you to create
+   pipelines of common operations and combine them into workflows.
+5. Training (not yet implemented) - this panel will provide an interface to [textual
+   inversion training](TEXTUAL_INVERSION.md) and fine tuning.
 
 The inpainting, outpainting and postprocessing tabs are currently in
 development. However, limited versions of their features can already be accessed
@@ -76,18 +87,18 @@ through the Text to Image and Image to Image tabs.
 
 ## Walkthrough
 
-The following walkthrough will exercise most (but not all) of the WebGUI's
+The following walkthrough will exercise most (but not all) of the WebUI's
 feature set.
 
 ### Text to Image
 
-1. Launch the WebGUI using `python scripts/invoke.py --web` and connect to it
+1. Launch the WebUI using `python scripts/invoke.py --web` and connect to it
    with your browser by accessing `http://localhost:9090`. If the browser and
    server are running on different machines on your LAN, add the option
    `--host 0.0.0.0` to the launch command line and connect to the machine
    hosting the web server using its IP address or domain name.
 
-2. If all goes well, the WebGUI should come up and you'll see a green
+2. If all goes well, the WebUI should come up and you'll see a green
    `connected` message on the upper right.
 
 #### Basics
@@ -234,7 +245,7 @@ walkthrough.
 
 2.  Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or click
     the blank area to get an upload dialog. The image will load into an area
-    marked _Initial Image_. (The WebGUI will also load the most
+    marked _Initial Image_. (The WebUI will also load the most
     recently-generated image from the gallery into a section on the left, but
     this image will be replaced in the next step.)
 
@@ -284,13 +295,17 @@ initial image" icons are located.
 
 ![Invoke Web Server - Use as Image Links](../assets/invoke-web-server-9.png){:width="640px"}
 
+### Unified Canvas
+
+See the [Unified Canvas Guide](UNIFIED_CANVAS.md)
+
 ## Parting remarks
 
 This concludes the walkthrough, but there are several more features that you can
 explore. Please check out the [Command Line Interface](CLI.md) documentation for
 further explanation of the advanced features that were not covered here.
 
-The WebGUI is only rapid development. Check back regularly for updates!
+The WebUI is only rapid development. Check back regularly for updates!
 
 ## Reference
 

docs/features/index.md

@@ -2,4 +2,62 @@
 title: Overview
 ---
 
-Here you can find the documentation for different features.
+Here you can find the documentation for InvokeAI's various features.
+
+## The Basics
+### * The [Web User Interface](WEB.md)
+Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
+
+### * The [Unified Canvas](UNIFIED_CANVAS.md)
+Build complex scenes by combine and modifying multiple images in a stepwise
+fashion. This feature combines img2img, inpainting and outpainting in
+a single convenient digital artist-optimized user interface.
+
+### * The [Command Line Interface (CLI)](CLI.md)
+Scriptable access to InvokeAI's features.
+
+## Image Generation
+### * [Prompt Engineering](PROMPTS.md)
+Get the images you want with the InvokeAI  prompt engineering language.
+
+## * [Post-Processing](POSTPROCESS.md)
+Restore mangled faces and make images larger with upscaling. Also see the [Embiggen Upscaling Guide](EMBIGGEN.md).
+
+## * The [Concepts Library](CONCEPTS.md)
+Add custom subjects and styles using HuggingFace's repository of embeddings.
+
+### * [Image-to-Image Guide for the CLI](IMG2IMG.md)
+Use a seed image to build new creations in the CLI.
+
+### * [Inpainting Guide for the CLI](INPAINTING.md)
+Selectively erase and replace portions of an existing image in the CLI.
+
+### * [Outpainting Guide for the CLI](OUTPAINTING.md)
+Extend the borders of the image with an "outcrop" function within the CLI.
+
+### * [Generating Variations](VARIATIONS.md)
+Have an image you like and want to generate many more like it? Variations
+are the ticket.
+
+## Model Management
+
+## * [Model Installation](../installation/050_INSTALLING_MODELS.md)
+Learn how to import third-party models and switch among them. This
+guide also covers optimizing models to load quickly.
+
+## * [Merging Models](MODEL_MERGING.md)
+Teach an old model new tricks. Merge 2-3 models together to create a
+new model that combines characteristics of the originals.
+
+## * [Textual Inversion](TEXTUAL_INVERSION.md)
+Personalize models by adding your own style or subjects.
+
+# Other Features
+
+## * [The NSFW Checker](NSFW.md)
+Prevent InvokeAI from displaying unwanted racy images.
+
+## * [Miscellaneous](OTHER.md)
+Run InvokeAI on Google Colab, generate images with repeating patterns,
+batch process a file of prompts, increase the "creativity" of image
+generation by adding initial noise, and more!

docs/index.html

@@ -1,19 +0,0 @@
-<!-- HTML for static distribution bundle build -->
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8">
-    <title>Swagger UI</title>
-    <link rel="stylesheet" type="text/css" href="swagger-ui/swagger-ui.css" />
-    <link rel="stylesheet" type="text/css" href="swagger-ui/index.css" />
-    <link rel="icon" type="image/png" href="swagger-ui/favicon-32x32.png" sizes="32x32" />
-    <link rel="icon" type="image/png" href="swagger-ui/favicon-16x16.png" sizes="16x16" />
-  </head>
-
-  <body>
-    <div id="swagger-ui"></div>
-    <script src="swagger-ui/swagger-ui-bundle.js" charset="UTF-8"> </script>
-    <script src="swagger-ui/swagger-ui-standalone-preset.js" charset="UTF-8"> </script>
-    <script src="swagger-ui/swagger-initializer.js" charset="UTF-8"> </script>
-  </body>
-</html>

docs/index.md

@@ -81,28 +81,6 @@ Q&A</a>]
 
     This fork is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
 
-## :octicons-package-dependencies-24: Installation
-
-This fork is supported across Linux, Windows and Macintosh. Linux users can use
-either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
-driver).
-
-First time users, please see
-[Automated Installer](installation/INSTALL_AUTOMATED.md) for a walkthrough of
-getting InvokeAI up and running on your system. For alternative installation and
-upgrade instructions, please see:
-[InvokeAI Installation Overview](installation/)
-
-Users who wish to make use of the **PyPatchMatch** inpainting functions
-will need to perform a bit of extra work to enable this
-module. Instructions can be found at [Installing
-PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md).
-
-If you have an NVIDIA card, you can benefit from the significant
-memory savings and performance benefits provided by Facebook Lab's
-**xFormers** module. Instructions for Linux and Windows users can be found
-at [Installing xFormers](installation/070_INSTALL_XFORMERS.md).
-
 ## :fontawesome-solid-computer: Hardware Requirements
 
 ### :octicons-cpu-24: System
@@ -122,141 +100,146 @@ images in full-precision mode:
 - GTX 1650 series cards
 - GTX 1660 series cards
 
-### :fontawesome-solid-memory: Memory
+### :fontawesome-solid-memory: Memory and Disk
 
 - At least 12 GB Main Memory RAM.
-
-### :fontawesome-regular-hard-drive: Disk
-
 - At least 18 GB of free disk space for the machine learning model, Python, and
   all its dependencies.
 
-!!! info
+## :octicons-package-dependencies-24: Installation
 
-    Precision is auto configured based on the device. If however you encounter errors like
-    `expected type Float but found Half` or `not implemented for Half` you can try starting
-    `invoke.py` with the `--precision=float32` flag:
+This fork is supported across Linux, Windows and Macintosh. Linux users can use
+either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
+driver).
 
-    ```bash
-    (invokeai) ~/InvokeAI$ python scripts/invoke.py --full_precision
-    ```
+### [Installation Getting Started Guide](installation)
+#### [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
+This method is recommended for 1st time users
+#### [Manual Installation](installation/020_INSTALL_MANUAL.md)
+This method is recommended for experienced users and developers
+#### [Docker Installation](installation/040_INSTALL_DOCKER.md)
+This method is recommended for those familiar with running Docker containers
+### Other Installation Guides
+  - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
+  - [XFormers](installation/070_INSTALL_XFORMERS.md)
+  - [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
+  - [Installing New Models](installation/050_INSTALLING_MODELS.md)
 
 ## :octicons-gift-24: InvokeAI Features
 
-- [The InvokeAI Web Interface](features/WEB.md) -
-[WebGUI hotkey reference guide](features/WEBUIHOTKEYS.md) -
-[WebGUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
-<!-- seperator -->
-- [The Command Line Interace](features/CLI.md) -
-[Image2Image](features/IMG2IMG.md) - [Inpainting](features/INPAINTING.md) -
-[Outpainting](features/OUTPAINTING.md) -
-[Adding custom styles and subjects](features/CONCEPTS.md) -
-[Upscaling and Face Reconstruction](features/POSTPROCESS.md)
-<!-- seperator -->
-- [Generating Variations](features/VARIATIONS.md)
-<!-- seperator -->
-- [Prompt Engineering](features/PROMPTS.md)
-<!-- seperator -->
+### The InvokeAI Web Interface
+- [WebUI overview](features/WEB.md)
+- [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
+- [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
+<!-- separator -->
+### The InvokeAI Command Line Interface
+- [Command Line Interace Reference Guide](features/CLI.md)
+<!-- separator -->
+### Image Management
+- [Image2Image](features/IMG2IMG.md)
+- [Inpainting](features/INPAINTING.md)
+- [Outpainting](features/OUTPAINTING.md)
+- [Adding custom styles and subjects](features/CONCEPTS.md)
+- [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
+- [Embiggen upscaling](features/EMBIGGEN.md)
+- [Other Features](features/OTHER.md)
+
+<!-- separator -->
+### Model Management
+- [Installing](installation/050_INSTALLING_MODELS.md)
 - [Model Merging](features/MODEL_MERGING.md)
+- [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
+- [Textual Inversion](features/TEXTUAL_INVERSION.md)
+- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
 <!-- seperator -->
-- Miscellaneous
-  - [NSFW Checker](features/NSFW.md)
-  - [Embiggen upscaling](features/EMBIGGEN.md)
-  - [Other](features/OTHER.md)
+### Prompt Engineering
+- [Prompt Syntax](features/PROMPTS.md)
+- [Generating Variations](features/VARIATIONS.md)
 
 ## :octicons-log-16: Latest Changes
 
-### v2.2.4 <small>(11 December 2022)</small>
+### v2.3.0 <small>(9 February 2023)</small>
 
-#### the `invokeai` directory
+#### Migration to Stable Diffusion `diffusers` models
 
-Previously there were two directories to worry about, the directory that
-contained the InvokeAI source code and the launcher scripts, and the `invokeai`
-directory that contained the models files, embeddings, configuration and
-outputs. With the 2.2.4 release, this dual system is done away with, and
-everything, including the `invoke.bat` and `invoke.sh` launcher scripts, now
-live in a directory named `invokeai`. By default this directory is located in
-your home directory (e.g. `\Users\yourname` on Windows), but you can select
-where it goes at install time.
+Previous versions of InvokeAI supported the original model file format introduced with Stable Diffusion 1.4. In the original format, known variously as "checkpoint", or "legacy" format, there is a single large weights file ending with `.ckpt` or `.safetensors`. Though this format has served the community well, it has a number of disadvantages, including file size, slow loading times, and a variety of non-standard variants that require special-case code to handle. In addition, because checkpoint files are actually a bundle of multiple machine learning sub-models, it is hard to swap different sub-models in and out, or to share common sub-models. A new format, introduced by the StabilityAI company in collaboration with HuggingFace, is called `diffusers` and consists of a directory of individual models. The most immediate benefit of `diffusers` is that they load from disk very quickly. A longer term benefit is that in the near future `diffusers` models will be able to share common sub-models, dramatically reducing disk space when you have multiple fine-tune models derived from the same base.
 
-After installation, you can delete the install directory (the one that the zip
-file creates when it unpacks). Do **not** delete or move the `invokeai`
-directory!
+When you perform a new install of version 2.3.0, you will be offered the option to install the `diffusers` versions of a number of popular SD models, including Stable Diffusion versions 1.5 and 2.1 (including the 768x768 pixel version of 2.1). These will act and work just like the checkpoint versions. Do not be concerned if you already have a lot of ".ckpt" or ".safetensors" models on disk! InvokeAI 2.3.0 can still load these and generate images from them without any extra intervention on your part.
 
-##### Initialization file `invokeai/invokeai.init`
+To take advantage of the optimized loading times of `diffusers` models, InvokeAI offers options to convert legacy checkpoint models into optimized `diffusers` models. If you use the `invokeai` command line interface, the relevant commands are:
 
-You can place frequently-used startup options in this file, such as the default
-number of steps or your preferred sampler. To keep everything in one place, this
-file has now been moved into the `invokeai` directory and is named
-`invokeai.init`.
+* `!convert_model` -- Take the path to a local checkpoint file or a URL that is pointing to one, convert it into a `diffusers` model, and import it into InvokeAI's models registry file.
+* `!optimize_model` -- If you already have a checkpoint model in your InvokeAI models file, this command will accept its short name and convert it into a like-named `diffusers` model, optionally deleting the original checkpoint file.
+* `!import_model` -- Take the local path of either a checkpoint file or a `diffusers` model directory and import it into InvokeAI's registry file. You may also provide the ID of any diffusers model that has been published on the [HuggingFace models repository](https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads) and it will be downloaded and installed automatically.
 
-#### To update from Version 2.2.3
+The WebGUI offers similar functionality for model management.
 
-The easiest route is to download and unpack one of the 2.2.4 installer files.
-When it asks you for the location of the `invokeai` runtime directory, respond
-with the path to the directory that contains your 2.2.3 `invokeai`. That is, if
-`invokeai` lives at `C:\Users\fred\invokeai`, then answer with `C:\Users\fred`
-and answer "Y" when asked if you want to reuse the directory.
+For advanced users, new command-line options provide additional functionality. Launching `invokeai` with the argument `--autoconvert <path to directory>` takes the path to a directory of checkpoint files, automatically converts them into `diffusers` models and imports them. Each time the script is launched, the directory will be scanned for new checkpoint files to be loaded. Alternatively, the `--ckpt_convert` argument will cause any checkpoint or safetensors model that is already registered with InvokeAI to be converted into a `diffusers` model on the fly, allowing you to take advantage of future diffusers-only features without explicitly converting the model and saving it to disk.
 
-The `update.sh` (`update.bat`) script that came with the 2.2.3 source installer
-does not know about the new directory layout and won't be fully functional.
+Please see [INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/) for more information on model management in both the command-line and Web interfaces.
 
-#### To update to 2.2.5 (and beyond) there's now an update path.
+#### Support for the `XFormers` Memory-Efficient Crossattention Package
 
-As they become available, you can update to more recent versions of InvokeAI
-using an `update.sh` (`update.bat`) script located in the `invokeai` directory.
-Running it without any arguments will install the most recent version of
-InvokeAI. Alternatively, you can get set releases by running the `update.sh`
-script with an argument in the command shell. This syntax accepts the path to
-the desired release's zip file, which you can find by clicking on the green
-"Code" button on this repository's home page.
+On CUDA (Nvidia) systems, version 2.3.0 supports the `XFormers` library. Once installed,  the`xformers` package dramatically reduces the memory footprint of loaded Stable Diffusion models files and modestly increases image generation speed. `xformers` will be installed and activated automatically if you specify a CUDA system at install time.
 
-#### Other 2.2.4 Improvements
+The caveat with using `xformers` is that it introduces slightly non-deterministic behavior, and images generated using the same seed and other settings will be subtly different between invocations. Generally the changes are unnoticeable unless you rapidly shift back and forth between images, but to disable `xformers` and restore fully deterministic behavior, you may launch InvokeAI using the `--no-xformers` option. This is most conveniently done by opening the file `invokeai/invokeai.init` with a text editor, and adding the line `--no-xformers` at the bottom.
 
-- Fix InvokeAI GUI initialization by @addianto in #1687
-- fix link in documentation by @lstein in #1728
-- Fix broken link by @ShawnZhong in #1736
-- Remove reference to binary installer by @lstein in #1731
-- documentation fixes for 2.2.3 by @lstein in #1740
-- Modify installer links to point closer to the source installer by @ebr in
-  #1745
-- add documentation warning about 1650/60 cards by @lstein in #1753
-- Fix Linux source URL in installation docs by @andybearman in #1756
-- Make install instructions discoverable in readme by @damian0815 in #1752
-- typo fix by @ofirkris in #1755
-- Non-interactive model download (support HUGGINGFACE_TOKEN) by @ebr in #1578
-- fix(srcinstall): shell installer - cp scripts instead of linking by @tildebyte
-  in #1765
-- stability and usage improvements to binary & source installers by @lstein in
-  #1760
-- fix off-by-one bug in cross-attention-control by @damian0815 in #1774
-- Eventually update APP_VERSION to 2.2.3 by @spezialspezial in #1768
-- invoke script cds to its location before running by @lstein in #1805
-- Make PaperCut and VoxelArt models load again by @lstein in #1730
-- Fix --embedding_directory / --embedding_path not working by @blessedcoolant in
-  #1817
-- Clean up readme by @hipsterusername in #1820
-- Optimized Docker build with support for external working directory by @ebr in
-  #1544
-- disable pushing the cloud container by @mauwii in #1831
-- Fix docker push github action and expand with additional metadata by @ebr in
-  #1837
-- Fix Broken Link To Notebook by @VedantMadane in #1821
-- Account for flat models by @spezialspezial in #1766
-- Update invoke.bat.in isolate environment variables by @lynnewu in #1833
-- Arch Linux Specific PatchMatch Instructions & fixing conda install on linux by
-  @SammCheese in #1848
-- Make force free GPU memory work in img2img by @addianto in #1844
-- New installer by @lstein
+#### A Negative Prompt Box in the WebUI
 
+There is now a separate text input box for negative prompts in the WebUI. This is convenient for stashing frequently-used negative prompts ("mangled limbs, bad anatomy"). The `[negative prompt]` syntax continues to work in the main prompt box as well.
+
+To see exactly how your prompts are being parsed, launch `invokeai` with the `--log_tokenization` option. The console window will then display the tokenization process for both positive and negative prompts.
+
+#### Model Merging
+
+Version 2.3.0 offers an intuitive user interface for merging up to three Stable Diffusion models using an intuitive user interface. Model merging allows you to mix the behavior of models to achieve very interesting effects. To use this, each of the models must already be imported into InvokeAI and saved in `diffusers` format, then launch the merger using a new menu item in the InvokeAI launcher script (`invoke.sh`, `invoke.bat`) or directly from the command line with `invokeai-merge --gui`. You will be prompted to select the models to merge, the proportions in which to mix them, and the mixing algorithm. The script will create a new merged `diffusers` model and import it into InvokeAI for your use.
+
+See [MODEL MERGING](https://invoke-ai.github.io/InvokeAI/features/MODEL_MERGING/) for more details.
+
+#### Textual Inversion Training
+
+Textual Inversion (TI) is a technique for training a Stable Diffusion model to emit a particular subject or style when triggered by a keyword phrase. You can perform TI training by placing a small number of images of the subject or style in a directory, and choosing a distinctive trigger phrase, such as "pointillist-style". After successful training, The subject or style will be activated by including `<pointillist-style>` in your prompt.
+
+Previous versions of InvokeAI were able to perform TI, but it required using a command-line script with dozens of obscure command-line arguments. Version 2.3.0 features an intuitive TI frontend that will build a TI model on top of any `diffusers` model. To access training you can launch from a new item in the launcher script or from the command line using `invokeai-ti --gui`.
+
+See [TEXTUAL INVERSION](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/) for further details.
+
+#### A New Installer Experience
+
+The InvokeAI installer has been upgraded in order to provide a smoother and hopefully more glitch-free experience. In addition, InvokeAI is now packaged as a PyPi project, allowing developers and power-users to install InvokeAI with the command `pip install InvokeAI  --use-pep517`. Please see [Installation](#installation) for details.
+
+Developers should be aware that the `pip` installation procedure has been simplified and that the `conda` method is no longer supported at all. Accordingly, the `environments_and_requirements` directory has been deleted from the repository.
+
+#### Command-line name changes
+
+All of InvokeAI's functionality, including the WebUI, command-line interface, textual inversion training and model merging, can all be accessed from the `invoke.sh` and `invoke.bat` launcher scripts. The menu of options has been expanded to add the new functionality. For the convenience of developers and power users, we have normalized the names of the InvokeAI command-line scripts:
+
+* `invokeai` -- Command-line client
+* `invokeai --web` -- Web GUI
+* `invokeai-merge --gui` -- Model merging script with graphical front end
+* `invokeai-ti --gui` -- Textual inversion script with graphical front end
+* `invokeai-configure` -- Configuration tool for initializing the `invokeai` directory and selecting popular starter models.
+
+For backward compatibility, the old command names are also recognized, including `invoke.py` and `configure-invokeai.py`. However, these are deprecated and will eventually be removed.
+
+Developers should be aware that the locations of the script's source code has been moved. The new locations are:
+* `invokeai` =>  `ldm/invoke/CLI.py`
+* `invokeai-configure` => `ldm/invoke/config/configure_invokeai.py`
+* `invokeai-ti`=> `ldm/invoke/training/textual_inversion.py`
+* `invokeai-merge` => `ldm/invoke/merge_diffusers`
+
+Developers are strongly encouraged to perform an "editable" install of InvokeAI using `pip install -e .  --use-pep517` in the Git repository, and then to call the scripts using their 2.3.0 names, rather than executing the scripts directly. Developers should also be aware that the several important data files have been relocated into a new directory named `invokeai`. This includes the WebGUI's `frontend` and `backend` directories, and the `INITIAL_MODELS.yaml` files used by the installer to select starter models. Eventually all InvokeAI modules will be in subdirectories of `invokeai`.
+
+Please see [2.3.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.3.0) for further details.
 For older changelogs, please visit the
 **[CHANGELOG](CHANGELOG/#v223-2-december-2022)**.
 
 ## :material-target: Troubleshooting
 
-Please check out our
-**[:material-frequently-asked-questions: Q&A](help/TROUBLESHOOT.md)** to get
-solutions for common installation problems and other issues.
+Please check out our **[:material-frequently-asked-questions:
+Troubleshooting
+Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)** to
+get solutions for common installation problems and other issues.
 
 ## :octicons-repo-push-24: Contributing
 
@@ -282,8 +265,8 @@ thank them for their time, hard work and effort.
 For support, please use this repository's GitHub Issues tracking service. Feel
 free to send me an email if you use and like the script.
 
-Original portions of the software are Copyright (c) 2020
-[Lincoln D. Stein](https://github.com/lstein)
+Original portions of the software are Copyright (c) 2022-23
+by [The InvokeAI Team](https://github.com/invoke-ai).
 
 ## :octicons-book-24: Further Reading
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -6,57 +6,106 @@ title: Installing with the Automated Installer
 
 ## Introduction
 
-The automated installer is a shell script that attempts to automate every step
-needed to install and run InvokeAI on a stock computer running recent versions
-of Linux, MacOS or Windows. It will leave you with a version that runs a stable
-version of InvokeAI with the option to upgrade to experimental versions later.
+The automated installer is a Python script that automates the steps
+needed to install and run InvokeAI on a stock computer running recent
+versions of Linux, MacOS or Windows. It will leave you with a version
+that runs a stable version of InvokeAI with the option to upgrade to
+experimental versions later.
 
 ## Walk through
 
-1.  Make sure that your system meets the
-    [hardware requirements](../index.md#hardware-requirements) and has the
-    appropriate GPU drivers installed. In particular, if you are a Linux user
-    with an AMD GPU installed, you may need to install the
-    [ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
+1.  <a name="hardware_requirements">**Hardware Requirements**: </a>Make sure that your system meets the [hardware
+    requirements](../index.md#hardware-requirements) and has the
+    appropriate GPU drivers installed. For a system with an NVIDIA
+    card installed, you will need to install the CUDA driver, while
+    AMD-based cards require the ROCm driver. In most cases, if you've
+    already used the system for gaming or other graphics-intensive
+    tasks, the appropriate drivers will already be installed. If
+    unsure, check the [GPU Driver Guide](030_INSTALL_CUDA_AND_ROCM.md)
 
     !!! info "Required Space"
 
-        Installation requires roughly 18G of free disk space to load the libraries and
-        recommended model weights files.
+        Installation requires roughly 18G of free disk space to load
+        the libraries and recommended model weights files.
 
-        Regardless of your destination disk, your *system drive* (`C:\` on Windows, `/` on macOS/Linux) requires at least 6GB of free disk space to download and cache python dependencies. NOTE for Linux users: if your temporary directory is mounted as a `tmpfs`, ensure it has sufficient space.
+        Regardless of your destination disk, your *system drive*
+        (`C:\` on Windows, `/` on macOS/Linux) requires at least 6GB
+        of free disk space to download and cache python
+        dependencies.
 
-2.  Check that your system has an up-to-date Python installed. To do this, open
-    up a command-line window ("Terminal" on Linux and Macintosh, "Command" or
-    "Powershell" on Windows) and type `python --version`. If Python is
-    installed, it will print out the version number. If it is version `3.9.1` or
-    higher, you meet requirements.
+	NOTE for Linux users: if your temporary directory is mounted
+        as a `tmpfs`, ensure it has sufficient space.
 
-    !!! warning "If you see an older version, or get a command not found error"
+2.  <a name="software_requirements">**Software Requirements**: </a>Check that your system has an up-to-date Python installed. To do
+    this, open up a command-line window ("Terminal" on Linux and
+    Macintosh, "Command" or "Powershell" on Windows) and type `python
+    --version`. If Python is installed, it will print out the version
+    number. If it is version `3.9.*` or `3.10.*`, you meet
+    requirements. We do not recommend using Python 3.11 or higher,
+    as not all the libraries that InvokeAI depends on work properly
+    with this version.
 
-        Go to [Python Downloads](https://www.python.org/downloads/) and
-        download the appropriate installer package for your platform. We recommend
-        [Version 3.10.9](https://www.python.org/downloads/release/python-3109/),
+    !!! warning "What to do if you have an unsupported version"
+
+        Go to [Python Downloads](https://www.python.org/downloads/)
+        and download the appropriate installer package for your
+        platform. We recommend [Version
+        3.10.9](https://www.python.org/downloads/release/python-3109/),
         which has been extensively tested with InvokeAI.
 
-    !!! warning "At this time we do not recommend Python 3.11"
-
     _Please select your platform in the section below for platform-specific
     setup requirements._
 
-    === "Windows users"
+    === "Windows"
+        During the Python configuration process, look out for a
+        checkbox to add Python to your PATH and select it. If the
+        install script complains that it can't find python, then open
+        the Python installer again and choose "Modify" existing
+        installation.
 
-        - During the Python configuration process,
-        look out for a checkbox to add Python to your PATH
-        and select it. If the install script complains that it can't
-        find python, then open the Python installer again and choose
-        "Modify" existing installation.
+        Installation requires an up to date version of the Microsoft
+        Visual C libraries. Please install the 2015-2022 libraries
+        available here:
+        https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
 
-        - Installation requires an up to date version of the Microsoft Visual C libraries. Please install the 2015-2022 libraries available here: https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
+        Please double-click on the file `WinLongPathsEnabled.reg` and
+        accept the dialog box that asks you if you wish to modify your registry.
+        This activates long filename support on your system and will prevent
+        mysterious errors during installation.
 
-    === "Mac users"
+    === "Linux"
+         To install an appropriate version of Python on Ubuntu 22.04
+         and higher, run the following:
 
-        - After installing Python, you may need to run the
+         ```
+         sudo apt update
+         sudo apt install -y python3 python3-pip python3-venv
+         sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
+         ```
+
+         On Ubuntu 20.04, the process is slightly different:
+
+         ```
+         sudo apt update
+         sudo apt install -y software-properties-common
+         sudo add-apt-repository -y ppa:deadsnakes/ppa
+         sudo apt install python3.10 python3-pip python3.10-venv
+         sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
+         ```
+
+         Both `python` and `python3` commands are now pointing at
+         Python3.10. You can still access older versions of Python by
+         calling `python2`, `python3.8`, etc.
+
+         Linux systems require a couple of additional graphics
+         libraries to be installed for proper functioning of
+         `python3-opencv`. Please run the following:
+
+         `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
+
+    === "Mac"
+
+        After installing Python, you may need to run the
         following command from the Terminal in order to install the Web
         certificates needed to download model data from https sites. If
         you see lots of CERTIFICATE ERRORS during the last part of the
@@ -64,110 +113,81 @@ version of InvokeAI with the option to upgrade to experimental versions later.
 
             `/Applications/Python\ 3.10/Install\ Certificates.command`
 
-        -  You may need to install the Xcode command line tools. These
+        You may need to install the Xcode command line tools. These
         are a set of tools that are needed to run certain applications in a
-        Terminal, including InvokeAI. This package is provided directly by Apple.
+        Terminal, including InvokeAI. This package is provided
+        directly by Apple. To install, open a terminal window and run `xcode-select --install`. You will get a macOS system popup guiding you through the
+        install. If you already have them installed, you will instead see some
+        output in the Terminal advising you that the tools are already installed. More information can be found at [FreeCode Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
 
-              - To install, open a terminal window and run `xcode-select
-              --install`. You will get a macOS system popup guiding you through the
-              install. If you already have them installed, you will instead see some
-              output in the Terminal advising you that the tools are already installed.
+3.  **Download the Installer**: The InvokeAI installer is distributed as a ZIP files. Go to the
+    [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest),
+    and look for a file named:
 
-              - More information can be found here:
-                https://www.freecodecamp.org/news/install-xcode-command-line-tools/
+    - InvokeAI-installer-v2.X.X.zip
 
-    === "Linux users"
+    where "2.X.X" is the latest released version. The file is located
+    at the very bottom of the release page, under **Assets**.
 
-        For reasons that are not entirely clear, installing the correct version of Python can be a bit of a challenge on Ubuntu, Linux Mint, Pop!_OS, and other Debian-derived distributions.
+4.  **Unpack the installer**: Unpack the zip file into a convenient directory. This will create a new
+    directory named "InvokeAI-Installer". When unpacked, the directory
+    will look like this:
 
-        On Ubuntu 22.04 and higher, run the following:
+    <figure markdown>
+    ![zipfile-screenshot](../assets/installer-walkthrough/unpacked-zipfile.png)
+    </figure>
 
-        ```
-        sudo apt update
-        sudo apt install -y python3 python3-pip python3-venv
-        sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
-        ```
+5.  **Launch the installer script from the desktop**: If you are using a desktop GUI, double-click the installer file
+    appropriate for your platform. It will be named `install.bat` on
+    Windows systems and `install.sh` on Linux and Macintosh
+    systems. Be aware that your system's file browser may suppress the
+    display of the file extension.
 
-        On Ubuntu 20.04, the process is slightly different:
+    On Windows systems if you get an "Untrusted Publisher" warning.
+    Click on "More Info" and then select "Run Anyway." You trust us, right?
 
-        ```
-        sudo apt update
-        sudo apt install -y software-properties-common
-        sudo add-apt-repository -y ppa:deadsnakes/ppa
-        sudo apt install python3.10 python3-pip python3.10-venv
-        sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
-        ```
-
-        Both `python` and `python3` commands are now pointing at Python3.10. You can still access older versions of Python by calling `python2`, `python3.8`, etc.
-
-        Linux systems require a couple of additional graphics libraries to be installed for proper functioning of `python3-opencv`. Please run the following:
-
-        `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
-
-3.  The source installer is distributed in ZIP files. Go to the
-    [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest), and
-    look for a series of files named:
-
-    - InvokeAI-installer-2.X.X-mac.zip
-    - InvokeAI-installer-2.X.X-windows.zip
-    - InvokeAI-installer-2.X.X-linux.zip
-
-    (Where 2.X.X is the current release number).
-    Download the one that is appropriate for your operating system.
-
-4.  Unpack the zip file into a convenient directory. This will create a new
-    directory named "InvokeAI-Installer". This example shows how this would look
-    using the `unzip` command-line tool, but you may use any graphical or
-    command-line Zip extractor:
-
-    ```cmd
-    C:\Documents\Linco> unzip InvokeAI-installer-2.X.X-windows.zip
-    Archive:  C: \Linco\Downloads\InvokeAI-installer-2.X.X-windows.zip
-    creating: InvokeAI-Installer\
-    inflating: InvokeAI-Installer\install.bat
-    inflating: InvokeAI-Installer\readme.txt
-    ...
-    ```
-
-    After successful installation, you can delete the `InvokeAI-Installer`
-    directory.
-
-5.  **Windows only** Please double-click on the file WinLongPathsEnabled.reg and
-    accept the dialog box that asks you if you wish to modify your registry.
-    This activates long filename support on your system and will prevent
-    mysterious errors during installation.
-
-6.  If you are using a desktop GUI, double-click the installer file. It will be
-    named `install.bat` on Windows systems and `install.sh` on Linux and
-    Macintosh systems.
-
-    On Windows systems you will probably get an "Untrusted Publisher" warning.
-    Click on "More Info" and select "Run Anyway." You trust us, right?
-
-7.  Alternatively, from the command line, run the shell script or .bat file:
+6.  **[Alternative] Launch the installer script from the command line**: Alternatively, from the command line, run the shell script or .bat file:
 
     ```cmd
     C:\Documents\Linco> cd InvokeAI-Installer
-    C:\Documents\Linco\invokeAI> install.bat
+    C:\Documents\Linco\invokeAI> .\install.bat
     ```
 
-8.  The script will ask you to choose where to install InvokeAI. Select a
+7.  **Select the location to install InvokeAI**: The script will ask you to choose where to install InvokeAI. Select a
     directory with at least 18G of free space for a full install. InvokeAI and
     all its support files will be installed into a new directory named
     `invokeai` located at the location you specify.
 
+    <figure markdown>
+    ![confirm-install-directory-screenshot](../assets/installer-walkthrough/confirm-directory.png)
+    </figure>
+
     - The default is to install the `invokeai` directory in your home directory,
       usually `C:\Users\YourName\invokeai` on Windows systems,
       `/home/YourName/invokeai` on Linux systems, and `/Users/YourName/invokeai`
       on Macintoshes, where "YourName" is your login name.
 
+    -If you have previously installed InvokeAI, you will be asked to
+     confirm whether you want to reinstall into this directory.  You
+     may choose to reinstall, in which case your version will be upgraded,
+     or choose a different directory.
+
     - The script uses tab autocompletion to suggest directory path completions.
       Type part of the path (e.g. "C:\Users") and press ++tab++ repeatedly
       to suggest completions.
 
-9.  Sit back and let the install script work. It will install the third-party
-    libraries needed by InvokeAI, then download the current InvokeAI release and
-    install it.
+8.  **Select your GPU**: The installer will autodetect your platform and will request you to
+    confirm the type of GPU your graphics card has. On Linux systems,
+    you will have the choice of CUDA (NVidia cards), ROCm (AMD cards),
+    or CPU (no graphics acceleration). On Windows, you'll have the
+    choice of CUDA vs CPU, and on Macs you'll be offered CPU only. When
+    you select CPU on M1 or M2 Macintoshes, you will get MPS-based
+    graphics acceleration without installing additional drivers. If you
+    are unsure what GPU you are using, you can ask the installer to
+    guess.
+
+9.  **Watch it go!**: Sit back and let the install script work. It will install the third-party
+    libraries needed by InvokeAI and the application itself.
 
     Be aware that some of the library download and install steps take a long
     time. In particular, the `pytorch` package is quite large and often appears
@@ -177,26 +197,141 @@ version of InvokeAI with the option to upgrade to experimental versions later.
     minutes and nothing is happening, you can interrupt the script with ^C. You
     may restart it and it will pick up where it left off.
 
-10. After installation completes, the installer will launch a script called
-    `configure_invokeai.py`, which will guide you through the first-time process
-    of selecting one or more Stable Diffusion model weights files, downloading
-    and configuring them. We provide a list of popular models that InvokeAI
-    performs well with. However, you can add more weight files later on using
-    the command-line client or the Web UI. See
-    [Installing Models](050_INSTALLING_MODELS.md) for details.
+    <figure markdown>
+    ![initial-settings-screenshot](../assets/installer-walkthrough/settings-form.png)
+    </figure>
 
-    Note that the main Stable Diffusion weights file is protected by a license
-    agreement that you must agree to in order to use. The script will list the
-    steps you need to take to create an account on the official site that hosts
-    the weights files, accept the agreement, and provide an access token that
-    allows InvokeAI to legally download and install the weights files.
+10. **Post-install Configuration**: After installation completes, the
+    installer will launch the configuration form, which will guide you
+    through the first-time process of adjusting some of InvokeAI's
+    startup settings. To move around this form use ctrl-N for
+    &lt;N&gt;ext and ctrl-P for &lt;P&gt;revious, or use &lt;tab&gt;
+    and shift-&lt;tab&gt; to move forward and back. Once you are in a
+    multi-checkbox field use the up and down cursor keys to select the
+    item you want, and &lt;space&gt; to toggle it on and off.  Within
+    a directory field, pressing &lt;tab&gt; will provide autocomplete
+    options.
 
-    If you have already downloaded the weights file(s) for another Stable
-    Diffusion distribution, you may skip this step (by selecting "skip" when
-    prompted) and configure InvokeAI to use the previously-downloaded files. The
-    process for this is described in [Installing Models](050_INSTALLING_MODELS.md).
+    Generally the defaults are fine, and you can come back to this screen at
+    any time to tweak your system. Here are the options you can adjust:
 
-11. The script will now exit and you'll be ready to generate some images. Look
+    - ***Output directory for images***
+      This is the path to a directory in which InvokeAI will store all its
+      generated images.
+
+    - ***NSFW checker***
+      If checked, InvokeAI will test images for potential sexual content
+      and blur them out if found. Note that the NSFW checker consumes
+      an additional 0.6 GB of VRAM on top of the 2-3 GB of VRAM used
+      by most image models. If you have a low VRAM GPU (4-6 GB), you
+      can reduce out of memory errors by disabling the checker.
+
+    - ***HuggingFace Access Token***
+      InvokeAI has the ability to download embedded styles and subjects
+      from the HuggingFace Concept Library on-demand. However, some of
+      the concept library files are password protected. To make download
+      smoother, you can set up an account at huggingface.co, obtain an
+      access token, and paste it into this field. Note that you paste
+      to this screen using ctrl-shift-V
+
+    - ***Free GPU memory after each generation***
+        This is useful for low-memory machines and helps minimize the
+	amount of GPU VRAM used by InvokeAI.
+
+    - ***Enable xformers support if available***
+        If the xformers library was successfully installed, this will activate
+	it to reduce memory consumption and increase rendering speed noticeably.
+	Note that xformers has the side effect of generating slightly different
+	images even when presented with the same seed and other settings.
+
+    - ***Force CPU to be used on GPU systems***
+        This will use the (slow) CPU rather than the accelerated GPU. This
+	can be used to generate images on systems that don't have a compatible
+	GPU.
+
+    - ***Precision***
+        This controls whether to use float32 or float16 arithmetic.
+	float16 uses less memory but is also slightly less accurate.
+	Ordinarily the right arithmetic is picked automatically ("auto"),
+	but you may have to use float32 to get images on certain systems
+	and graphics cards. The "autocast" option is deprecated and
+	shouldn't be used unless you are asked to by a member of the team.
+
+    - ***Number of models to cache in CPU memory***
+        This allows you to keep models in memory and switch rapidly among
+	them rather than having them load from disk each time. This slider
+	controls how many models to keep loaded at once. Each
+	model will use 2-4 GB of RAM, so use this cautiously
+
+    - ***Directory containing embedding/textual inversion files***
+        This is the directory in which you can place custom embedding
+	files (.pt or .bin). During startup, this directory will be
+	scanned and InvokeAI will print out the text terms that
+	are available to trigger the embeddings.
+
+    At the bottom of the screen you will see a checkbox for accepting
+    the CreativeML Responsible AI License. You need to accept the license
+    in order to download Stable Diffusion models from the next screen.
+
+    _You can come back to the startup options form_ as many times as you like.
+    From the `invoke.sh` or `invoke.bat` launcher, select option (6) to relaunch
+    this script. On the command line, it is named `invokeai-configure`.
+
+11. **Downloading Models**: After you press `[NEXT]` on the screen, you will be taken
+    to another screen that prompts you to download a series of starter models. The ones
+    we recommend are preselected for you, but you are encouraged to use the checkboxes to
+    pick and choose.
+    You will probably wish to download `autoencoder-840000` for use with models that
+    were trained with an older version of the Stability VAE.
+
+    <figure markdown>
+    ![select-models-screenshot](../assets/installer-walkthrough/installing-models.png)
+    </figure>
+    
+    Below the preselected list of starter models is a large text field which you can use
+    to specify a series of models to import. You can specify models in a variety of formats,
+    each separated by a space or newline. The formats accepted are:
+
+    - The path to a .ckpt or .safetensors file. On most systems, you can drag a file from
+      the file browser to the textfield to automatically paste the path. Be sure to remove
+      extraneous quotation marks and other things that come along for the ride.
+
+    - The path to a directory containing a combination of `.ckpt` and `.safetensors` files.
+      The directory will be scanned from top to bottom (including subfolders) and any
+      file that can be imported will be.
+
+    - A URL pointing to a `.ckpt` or `.safetensors` file. You can cut
+      and paste directly from a web page, or simply drag the link from the web page
+      or navigation bar. (You can also use ctrl-shift-V to paste into this field)
+      The file will be downloaded and installed.
+      
+    - The HuggingFace repository ID (repo_id) for a `diffusers` model. These IDs have
+       the format _author_name/model_name_, as in `andite/anything-v4.0`
+      
+    - The path to a local directory containing a `diffusers`
+      model. These directories always have the file `model_index.json`
+      at their top level.
+
+    _Select a directory for models to import_ You may select a local
+    directory for autoimporting at startup time. If you select this
+    option, the directory you choose will be scanned for new
+    .ckpt/.safetensors files each time InvokeAI starts up, and any new
+    files will be automatically imported and made available for your
+    use.
+
+    _Convert imported models into diffusers_ When legacy checkpoint
+    files are imported, you may select to use them unmodified (the
+    default) or to convert them into `diffusers` models. The latter
+    load much faster and have slightly better rendering performance,
+    but not all checkpoint files can be converted. Note that Stable Diffusion
+    Version 2.X files are **only** supported in `diffusers` format and will
+    be converted regardless.
+     
+     _You can come back to the model install form_ as many times as you like.
+     From the `invoke.sh` or `invoke.bat` launcher, select option (5) to relaunch
+     this script. On the command line, it is named `invokeai-model-install`.
+
+12. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
     for the directory `invokeai` installed in the location you chose at the
     beginning of the install session. Look for a shell script named `invoke.sh`
     (Linux/Mac) or `invoke.bat` (Windows). Launch the script by double-clicking
@@ -207,64 +342,98 @@ version of InvokeAI with the option to upgrade to experimental versions later.
     C:\Documents\Linco\invokeAI> invoke.bat
     ```
 
-    - The `invoke.bat` (`invoke.sh`) script will give you the choice of starting
-      (1) the command-line interface, or (2) the web GUI. If you start the
-      latter, you can load the user interface by pointing your browser at
-      http://localhost:9090.
+    - The `invoke.bat` (`invoke.sh`) script will give you the choice
+      of starting (1) the command-line interface, (2) the web GUI, (3)
+      textual inversion training, and (4) model merging.
 
-    - The script also offers you a third option labeled "open the developer
-      console". If you choose this option, you will be dropped into a
-      command-line interface in which you can run python commands directly,
-      access developer tools, and launch InvokeAI with customized options.
+    - By default, the script will launch the web interface. When you
+      do this, you'll see a series of startup messages ending with
+      instructions to point your browser at
+      http://localhost:9090. Click on this link to open up a browser
+      and start exploring InvokeAI's features.
 
-12. You can launch InvokeAI with several different command-line arguments that
+12. **InvokeAI Options**: You can launch InvokeAI with several different command-line arguments that
     customize its behavior. For example, you can change the location of the
     image output directory, or select your favorite sampler. See the
     [Command-Line Interface](../features/CLI.md) for a full list of the options.
 
-        - To set defaults that will take effect every time you launch InvokeAI,
-        use a text editor (e.g. Notepad) to exit the file
-        `invokeai\invokeai.init`. It contains a variety of examples that you can
-        follow to add and modify launch options.
+    - To set defaults that will take effect every time you launch InvokeAI,
+      use a text editor (e.g. Notepad) to exit the file
+      `invokeai\invokeai.init`. It contains a variety of examples that you can
+      follow to add and modify launch options.
+
+    - The launcher script also offers you an option labeled "open the developer
+      console". If you choose this option, you will be dropped into a
+      command-line interface in which you can run python commands directly,
+      access developer tools, and launch InvokeAI with customized options.
+
+
+    !!! warning "Do not move or remove the `invokeai` directory"
+      
+        The `invokeai` directory contains the `invokeai` application, its
+        configuration files, the model weight files, and outputs of image generation.
+        Once InvokeAI is installed, do not move or remove this directory."
 
-!!! warning "The `invokeai` directory contains the `invoke` application, its
-configuration files, the model weight files, and outputs of image generation.
-Once InvokeAI is installed, do not move or remove this directory."
 
 ## Troubleshooting
 
 ### _Package dependency conflicts_
 
-If you have previously installed InvokeAI or another Stable Diffusion package,
-the installer may occasionally pick up outdated libraries and either the
-installer or `invoke` will fail with complaints about library conflicts. You can
-address this by entering the `invokeai` directory and running `update.sh`, which
-will bring InvokeAI up to date with the latest libraries.
+If you have previously installed InvokeAI or another Stable Diffusion
+package, the installer may occasionally pick up outdated libraries and
+either the installer or `invoke` will fail with complaints about
+library conflicts. In this case, run the `invoke.sh`/`invoke.bat`
+command and enter the Developer's Console by picking option (5). This
+will take you to a command-line prompt.
 
-### ldm from pypi
+Then give this command:
 
-!!! warning
+`pip install InvokeAI --force-reinstall`
 
-    Some users have tried to correct dependency problems by installing
-    the `ldm` package from PyPi.org. Unfortunately this is an unrelated package that
-    has nothing to do with the 'latent diffusion model' used by InvokeAI. Installing
-    ldm will make matters worse. If you've installed ldm, uninstall it with
-    `pip uninstall ldm`.
+This should fix the issues.
+
+### InvokeAI runs extremely slowly on Linux or Windows systems
+
+The most frequent cause of this problem is when the installation
+process installed the CPU-only version of the torch machine-learning
+library, rather than a version that takes advantage of GPU
+acceleration. To confirm this issue, look at the InvokeAI startup
+messages. If you see a message saying ">> Using device CPU", then
+this is what happened.
+
+To fix this problem, first determine whether you have an NVidia or an
+AMD GPU. The former uses the CUDA driver, and the latter uses ROCm
+(only available on Linux). Then run the `invoke.sh`/`invoke.bat`
+command and enter the Developer's Console by picking option (5). This
+will take you to a command-line prompt.
+
+Then type the following commands:
+
+=== "NVIDIA System"
+    ```bash
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117
+    pip install xformers
+    ```
+
+=== "AMD System"
+    ```bash
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    ```
 
 ### Corrupted configuration file
 
-Everything seems to install ok, but `invoke` complains of a corrupted
+Everything seems to install ok, but `invokeai` complains of a corrupted
 configuration file and goes back into the configuration process (asking you to
 download models, etc), but this doesn't fix the problem.
 
 This issue is often caused by a misconfigured configuration directive in the
 `invokeai\invokeai.init` initialization file that contains startup settings. The
 easiest way to fix the problem is to move the file out of the way and re-run
-`configure_invokeai.py`. Enter the developer's console (option 3 of the launcher
+`invokeai-configure`. Enter the developer's console (option 3 of the launcher
 script) and run this command:
 
 ```cmd
-configure_invokeai.py --root=.
+invokeai-configure --root=.
 ```
 
 Note the dot (.) after `--root`. It is part of the command.
@@ -274,7 +443,53 @@ the [InvokeAI Issues](https://github.com/invoke-ai/InvokeAI/issues) section, or
 visit our [Discord Server](https://discord.gg/ZmtBAhwWhy) for interactive
 assistance.
 
-### other problems
+### Out of Memory Issues
+
+The models are large, VRAM is expensive, and you may find yourself
+faced with Out of Memory errors when generating images. Here are some
+tips to reduce the problem:
+
+* **4 GB of VRAM**
+
+This should be adequate for 512x512 pixel images using Stable Diffusion 1.5
+and derived models, provided that you **disable** the NSFW checker. To
+disable the filter, do one of the following:
+
+   * Select option (6) "_change InvokeAI startup options_" from the
+     launcher. This will bring up the console-based startup settings
+     dialogue and allow you to unselect the "NSFW Checker" option.
+   * Start the startup settings dialogue directly by running
+     `invokeai-configure --skip-sd-weights --skip-support-models`
+     from the command line.
+   * Find the `invokeai.init` initialization file in the InvokeAI root
+     directory, open it in a text editor, and change `--nsfw_checker`
+     to `--no-nsfw_checker`
+
+If you are on a CUDA system, you can realize significant memory
+savings by activating the `xformers` library as described above. The
+downside is `xformers` introduces non-deterministic behavior, such
+that images generated with exactly the same prompt and settings will
+be slightly different from each other. See above for more information.
+
+* **6 GB of VRAM**
+
+This is a border case. Using the SD 1.5 series you should be able to
+generate images up to 640x640 with the NSFW checker enabled, and up to
+1024x1024 with it disabled and `xformers` activated. 
+
+If you run into persistent memory issues there are a series of
+environment variables that you can set before launching InvokeAI that
+alter how the PyTorch machine learning library manages memory.  See
+https://pytorch.org/docs/stable/notes/cuda.html#memory-management for
+a list of these tweaks.
+
+* **12 GB of VRAM**
+
+This should be sufficient to generate larger images up to about
+1280x1280. If you wish to push further, consider activating
+`xformers`.
+
+### Other Problems
 
 If you run into problems during or after installation, the InvokeAI team is
 available to help you. Either create an
@@ -286,36 +501,20 @@ hours, and often much sooner.
 
 ## Updating to newer versions
 
-This distribution is changing rapidly, and we add new features on a daily basis.
-To update to the latest released version (recommended), run the `update.sh`
-(Linux/Mac) or `update.bat` (Windows) scripts. This will fetch the latest
-release and re-run the `configure_invokeai` script to download any updated
-models files that may be needed. You can also use this to add additional models
-that you did not select at installation time.
+This distribution is changing rapidly, and we add new features
+regularly. Releases are announced at
+http://github.com/invoke-ai/InvokeAI/releases, and at
+https://pypi.org/project/InvokeAI/ To update to the latest released
+version (recommended), follow these steps:
 
-You can now close the developer console and run `invoke` as before. If you get
-complaints about missing models, then you may need to do the additional step of
-running `configure_invokeai.py`. This happens relatively infrequently. To do
-this, simply open up the developer's console again and type
-`python scripts/configure_invokeai.py`.
+1. Start the `invoke.sh`/`invoke.bat` launch script from within the
+   `invokeai` root directory.
 
-You may also use the `update` script to install any selected version of
-InvokeAI. From https://github.com/invoke-ai/InvokeAI, navigate to the zip file
-link of the version you wish to install. You can find the zip links by going to
-the one of the release pages and looking for the **Assets** section at the
-bottom. Alternatively, you can browse "branches" and "tags" at the top of the
-big code directory on the InvokeAI welcome page. When you find the version you
-want to install, go to the green "&lt;&gt; Code" button at the top, and copy the
-"Download ZIP" link.
+2. Choose menu item (10) "Update InvokeAI".
 
-Now run `update.sh` (or `update.bat`) with the version number of the desired InvokeAI
-version as its argument. For example, this will install the old 2.2.0 release.
+3. This will launch a menu that gives you the option of:
 
-```cmd
-update.sh v2.2.0
-```
-
-You can get the list of version numbers by going to the [releases
-page](https://github.com/invoke-ai/InvokeAI/releases) or by browsing
-the (Tags)[https://github.com/invoke-ai/InvokeAI/tags] list from the
-Code section of the main github page.
+   1. Updating to the latest official release;
+   2. Updating to the bleeding-edge development version; or
+   3. Manually entering the tag or branch name of a version of
+      InvokeAI you wish to try out.

docs/installation/020_INSTALL_MANUAL.md

@@ -3,360 +3,199 @@ title: Installing Manually
 ---
 
 <figure markdown>
+
 # :fontawesome-brands-linux: Linux | :fontawesome-brands-apple: macOS | :fontawesome-brands-windows: Windows
+
 </figure>
 
 !!! warning "This is for advanced Users"
 
-    who are already experienced with using conda or pip
+    **python experience is mandatory**
 
 ## Introduction
 
-You have two choices for manual installation.
-The [first one](#pip-Install) uses basic Python virtual environment (`venv`)
-command and `pip` package manager.
-The [second one](#Conda-method) uses Anaconda3 package manager (`conda`).
-Both methods require you to enter commands on the terminal, also known as the
-"console".
-
-Note that the `conda` installation method is currently deprecated and will
-not be supported at some point in the future.
+!!! tip "Conda"
+    As of InvokeAI v2.3.0 installation using the `conda` package manager is no longer being supported. It will likely still work, but we are not testing this installation method.
 
 On Windows systems, you are encouraged to install and use the
 [PowerShell](https://learn.microsoft.com/en-us/powershell/scripting/install/installing-powershell-on-windows?view=powershell-7.3),
 which provides compatibility with Linux and Mac shells and nice
 features such as command-line completion.
 
-## pip Install
+### Prerequisites
+
+Before you start, make sure you have the following preqrequisites
+installed.  These are described in more detail in [Automated
+Installation](010_INSTALL_AUTOMATED.md), and in many cases will
+already be installed (if, for example, you have used your system for
+gaming):
+
+* **Python**
+
+    version 3.9 or 3.10 (3.11 is not recommended).
+
+* **CUDA Tools**
+
+    For those with _NVidia GPUs_, you will need to
+    install the [CUDA toolkit and optionally the XFormers library](070_INSTALL_XFORMERS.md).
+
+* **ROCm Tools**
+
+    For _Linux users with AMD GPUs_, you will need
+    to install the [ROCm toolkit](./030_INSTALL_CUDA_AND_ROCM.md). Note that
+    InvokeAI does not support AMD GPUs on Windows systems due to
+    lack of a Windows ROCm library.
+
+* **Visual C++ Libraries**
+
+    _Windows users_ must install the free
+    [Visual C++ libraries from Microsoft](https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170)
+
+* **The Xcode command line tools**
+
+    for _Macintosh users_. Instructions are available at
+    [Free Code Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
+
+    * _Macintosh users_ may also need to run the `Install Certificates` command
+      if model downloads give lots of certificate errors. Run:
+      `/Applications/Python\ 3.10/Install\ Certificates.command`
+
+### Installation Walkthrough
 
 To install InvokeAI with virtual environments and the PIP package
 manager, please follow these steps:
 
-1.  Make sure you are using Python 3.9 or 3.10. The rest of the install
-    procedure depends on this:
+1.  Please make sure you are using Python 3.9 or 3.10. The rest of the install
+    procedure depends on this and will not work with other versions:
 
     ```bash
     python -V
     ```
 
-2.  Clone the [InvokeAI](https://github.com/invoke-ai/InvokeAI) source code from
-    GitHub:
+2.  Create a directory to contain your InvokeAI library, configuration
+    files, and models. This is known as the "runtime" or "root"
+    directory, and often lives in your home directory under the name `invokeai`.
 
-    ```bash
-    git clone https://github.com/invoke-ai/InvokeAI.git
+    Please keep in mind the disk space requirements - you will need at
+    least 20GB for the models and the virtual environment.  From now
+    on we will refer to this directory as `INVOKEAI_ROOT`. For convenience,
+    the steps below create a shell variable of that name which contains the
+    path to `HOME/invokeai`.
+
+    === "Linux/Mac"
+
+        ```bash
+        export INVOKEAI_ROOT=~/invokeai
+        mkdir $INVOKEAI_ROOT
+        ```
+
+    === "Windows (Powershell)"
+
+        ```bash
+        Set-Variable -Name INVOKEAI_ROOT -Value $Home/invokeai
+        mkdir $INVOKEAI_ROOT
+        ```
+
+3. Enter the root (invokeai) directory and create a virtual Python
+   environment within it named `.venv`. If the command `python`
+   doesn't work, try `python3`. Note that while you may create the
+   virtual environment anywhere in the file system, we recommend that
+   you create it within the root directory as shown here. This makes
+   it possible for the InvokeAI applications to find the model data
+   and configuration. If you do not choose to install the virtual
+   environment inside the root directory, then you **must** set the
+   `INVOKEAI_ROOT` environment variable in your shell environment, for
+   example, by editing `~/.bashrc` or `~/.zshrc` files, or setting the
+   Windows environment variable using the Advanced System Settings dialogue.
+   Refer to your operating system documentation for details.
+
+    ```terminal
+    cd $INVOKEAI_ROOT
+    python -m venv .venv --prompt InvokeAI
     ```
 
-    This will create InvokeAI folder where you will follow the rest of the
-    steps.
+4.  Activate the new environment:
 
-3.  From within the InvokeAI top-level directory, create and activate a virtual
-    environment named `invokeai`:
+    === "Linux/Mac"
+
+        ```bash
+        source .venv/bin/activate
+        ```
+
+    === "Windows"
+
+        ```ps
+        .venv\Scripts\activate
+        ```
+
+        If you get a permissions error at this point, run this command and try again
+
+        `Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser`
+
+    The command-line prompt should change to to show `(InvokeAI)` at the
+    beginning of the prompt. Note that all the following steps should be
+    run while inside the INVOKEAI_ROOT directory
+
+5.  Make sure that pip is installed in your virtual environment and up to date:
 
     ```bash
-    python -m venv invokeai
-    source invokeai/bin/activate
-    ```
-
-4.  Make sure that pip is installed in your virtual environment an up to date:
-
-    ```bash
-    python -m ensurepip --upgrade
     python -m pip install --upgrade pip
     ```
 
-5.  Pick the correct `requirements*.txt` file for your hardware and operating
-    system.
+6. Install the InvokeAI Package. The `--extra-index-url` option is used to select among
+   CUDA, ROCm and CPU/MPS drivers as shown below:
 
-    We have created a series of environment files suited for different operating
-    systems and GPU hardware. They are located in the
-    `environments-and-requirements` directory:
+    === "CUDA (NVidia)"
 
-    <figure markdown>
+        ```bash
+        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+        ```
 
-    |              filename               |                               OS                                |
-    | :---------------------------------: | :-------------------------------------------------------------: |
-    |      requirements-lin-amd.txt       |                  Linux with an AMD (ROCm) GPU                   |
-    |     requirements-lin-arm64.txt      |                 Linux running on arm64 systems                  |
-    |      requirements-lin-cuda.txt      |                 Linux with an NVIDIA (CUDA) GPU                 |
-    |    requirements-mac-mps-cpu.txt     |                Macintoshes with MPS acceleration                |
-    | requirements-lin-win-colab-cuda.txt | Windows with an NVIDA (CUDA) GPU<br>(supports Google Colab too) |
+    === "ROCm (AMD)"
 
-    </figure>
+        ```bash
+        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+        ```
 
-    Select the appropriate requirements file, and make a link to it from
-    `requirements.txt` in the top-level InvokeAI directory. The command to do
-    this from the top-level directory is:
+    === "CPU (Intel Macs & non-GPU systems)"
 
-    !!! example ""
+        ```bash
+        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
+        ```
 
-        === "Macintosh and Linux"
+    === "MPS (M1 and M2 Macs)"
 
-            !!! info "Replace `xxx` and `yyy` with the appropriate OS and GPU codes."
+        ```bash
+        pip install InvokeAI --use-pep517
+        ```
 
-            ```bash
-            ln -sf environments-and-requirements/requirements-xxx-yyy.txt requirements.txt
-            ```
+7.  Deactivate and reactivate your runtime directory so that the invokeai-specific commands
+    become available in the environment
 
-        === "Windows"
+    === "Linux/Macintosh"
 
-            !!! info "on Windows, admin privileges are required to make links, so we use the copy command instead"
+        ```bash
+        deactivate && source .venv/bin/activate
+        ```
 
-            ```cmd
-            copy environments-and-requirements\requirements-lin-win-colab-cuda.txt requirements.txt
-            ```
+    === "Windows"
 
-        !!! warning
-
-            Please do not link or copy `environments-and-requirements/requirements-base.txt`.
-            This is a base requirements file that does not have the platform-specific
-            libraries. Also, be sure to link or copy the platform-specific file to
-            a top-level file named `requirements.txt` as shown here. Running pip on
-            a requirements file in a subdirectory will not work as expected.
-
-    When this is done, confirm that a file named `requirements.txt` has been
-    created in the InvokeAI root directory and that it points to the correct
-    file in `environments-and-requirements`.
-
-6.  Run PIP
-
-    ```bash
-    pip --python invokeai install --use-pep517 .
-    ```
-
-7.  Set up the runtime directory
-
-    In this step you will initialize a runtime directory that will
-    contain the models, model config files, directory for textual
-    inversion embeddings, and your outputs. This keeps the runtime
-    directory separate from the source code and aids in updating.
-
-    You may pick any location for this directory using the `--root_dir`
-    option (abbreviated --root). If you don't pass this option, it will
-    default to `invokeai` in your home directory.
-
-    ```bash
-    configure_invokeai --root_dir ~/Programs/invokeai
-    ```
-
-    The script `configure_invokeai.py` will interactively guide you through the
-    process of downloading and installing the weights files needed for InvokeAI.
-    Note that the main Stable Diffusion weights file is protected by a license
-    agreement that you have to agree to. The script will list the steps you need
-    to take to create an account on the site that hosts the weights files,
-    accept the agreement, and provide an access token that allows InvokeAI to
-    legally download and install the weights files.
-
-    If you get an error message about a module not being installed, check that
-    the `invokeai` environment is active and if not, repeat step 5.
-
-    Note that `configure_invokeai.py` and `invoke.py` should be installed
-    under your virtual environment directory and the system should find them
-    on the PATH. If this isn't working on your system, you can call the
-    scripts directory using `python scripts/configure_invokeai.py` and
-    `python scripts/invoke.py`.
-
-    !!! tip
-
-        If you have already downloaded the weights file(s) for another Stable
-        Diffusion distribution, you may skip this step (by selecting "skip" when
-        prompted) and configure InvokeAI to use the previously-downloaded files. The
-        process for this is described in [here](050_INSTALLING_MODELS.md).
-
-8.  Run the command-line- or the web- interface:
-
-    Activate the environment (with `source invokeai/bin/activate`), and then
-    run the script `invoke.py`. If you selected a non-default location
-    for the runtime directory, please specify the path with the `--root_dir`
-    option (abbreviated below as `--root`):
-
-    !!! example ""
-
-        !!! warning "Make sure that the virtual environment is activated, which should create `(invokeai)` in front of your prompt!"
-
-        === "CLI"
-
-            ```bash
-            invoke.py --root ~/Programs/invokeai
-            ```
-
-        === "local Webserver"
-
-            ```bash
-            invoke.py --web --root ~/Programs/invokeai
-            ```
-
-        === "Public Webserver"
-
-            ```bash
-            invoke.py --web --host 0.0.0.0 --root ~/Programs/invokeai
-            ```
-
-        If you choose the run the web interface, point your browser at
-        http://localhost:9090 in order to load the GUI.
-
-    !!! tip
-
-        You can permanently set the location of the runtime directory by setting the environment variable INVOKEAI_ROOT to the path of the directory.
-
-9.  Render away!
-
-    Browse the [features](../features/CLI.md) section to learn about all the things you
-    can do with InvokeAI.
-
-    Note that some GPUs are slow to warm up. In particular, when using an AMD
-    card with the ROCm driver, you may have to wait for over a minute the first
-    time you try to generate an image. Fortunately, after the warm-up period
-    rendering will be fast.
-
-10. Subsequently, to relaunch the script, be sure to enter `InvokeAI` directory,
-    activate the virtual environment, and then launch `invoke.py` script.
-    If you forget to activate the virtual environment,
-    the script will fail with multiple `ModuleNotFound` errors.
-
-    !!! tip
-
-        Do not move the source code repository after installation. The virtual environment directory has absolute paths in it that get confused if the directory is moved.
-
----
-
-### Conda method
-
-1.  Check that your system meets the
-    [hardware requirements](index.md#Hardware_Requirements) and has the
-    appropriate GPU drivers installed. In particular, if you are a Linux user
-    with an AMD GPU installed, you may need to install the
-    [ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
-
-    InvokeAI does not yet support Windows machines with AMD GPUs due to the lack
-    of ROCm driver support on this platform.
-
-    To confirm that the appropriate drivers are installed, run `nvidia-smi` on
-    NVIDIA/CUDA systems, and `rocm-smi` on AMD systems. These should return
-    information about the installed video card.
-
-    Macintosh users with MPS acceleration, or anybody with a CPU-only system,
-    can skip this step.
-
-2.  You will need to install Anaconda3 and Git if they are not already
-    available. Use your operating system's preferred package manager, or
-    download the installers manually. You can find them here:
-
-    - [Anaconda3](https://www.anaconda.com/)
-    - [git](https://git-scm.com/downloads)
-
-3.  Clone the [InvokeAI](https://github.com/invoke-ai/InvokeAI) source code from
-    GitHub:
-
-    ```bash
-    git clone https://github.com/invoke-ai/InvokeAI.git
-    ```
-
-    This will create InvokeAI folder where you will follow the rest of the
-    steps.
-
-4.  Enter the newly-created InvokeAI folder:
-
-    ```bash
-    cd InvokeAI
-    ```
-
-    From this step forward make sure that you are working in the InvokeAI
-    directory!
-
-5.  Select the appropriate environment file:
-
-    We have created a series of environment files suited for different operating
-    systems and GPU hardware. They are located in the
-    `environments-and-requirements` directory:
-
-    <figure markdown>
-
-    |         filename         |               OS               |
-    | :----------------------: | :----------------------------: |
-    | environment-lin-amd.yml  |  Linux with an AMD (ROCm) GPU  |
-    | environment-lin-cuda.yml | Linux with an NVIDIA CUDA GPU  |
-    | environment-mac.yml    |   Macintosh            |
-    | environment-win-cuda.yml | Windows with an NVIDA CUDA GPU |
-
-    </figure>
-
-    Choose the appropriate environment file for your system and link or copy it
-    to `environment.yml` in InvokeAI's top-level directory. To do so, run
-    following command from the repository-root:
-
-    !!! Example ""
-
-        === "Macintosh and Linux"
-
-            !!! todo "Replace `xxx` and `yyy` with the appropriate OS and GPU codes as seen in the table above"
-
-            ```bash
-            ln -sf environments-and-requirements/environment-xxx-yyy.yml environment.yml
-            ```
-
-            When this is done, confirm that a file `environment.yml` has been linked in
-            the InvokeAI root directory and that it points to the correct file in the
-            `environments-and-requirements`.
-
-            ```bash
-            ls -la
-            ```
-
-        === "Windows"
-
-            !!! todo " Since it requires admin privileges to create links, we will use the copy command to create your `environment.yml`"
-
-            ```cmd
-            copy environments-and-requirements\environment-win-cuda.yml environment.yml
-            ```
-
-            Afterwards verify that the file `environment.yml` has been created, either via the
-            explorer or by using the command `dir` from the terminal
-
-            ```cmd
-            dir
-            ```
-
-    !!! warning "Do not try to run conda on directly on the subdirectory environments file. This won't work. Instead, copy or link it to the top-level directory as shown."
-
-6.  Create the conda environment:
-
-    ```bash
-    conda env update
-    ```
-
-    This will create a new environment named `invokeai` and install all InvokeAI
-    dependencies into it. If something goes wrong you should take a look at
-    [troubleshooting](#troubleshooting).
-
-7.  Activate the `invokeai` environment:
-
-    In order to use the newly created environment you will first need to
-    activate it
-
-    ```bash
-    conda activate invokeai
-    ```
-
-    Your command-line prompt should change to indicate that `invokeai` is active
-    by prepending `(invokeai)`.
+        ```ps
+        deactivate
+        .venv\Scripts\activate
+        ```
 
 8.  Set up the runtime directory
 
-    In this step you will initialize a runtime directory that will
-    contain the models, model config files, directory for textual
-    inversion embeddings, and your outputs. This keeps the runtime
-    directory separate from the source code and aids in updating.
+    In this step you will initialize your runtime directory with the downloaded
+    models, model config files, directory for textual inversion embeddings, and
+    your outputs.
 
-    You may pick any location for this directory using the `--root_dir`
-    option (abbreviated --root). If you don't pass this option, it will
-    default to `invokeai` in your home directory.
-
-    ```bash
-    python scripts/configure_invokeai.py --root_dir ~/Programs/invokeai
+    ```terminal
+    invokeai-configure
     ```
 
-    The script `configure_invokeai.py` will interactively guide you through the
+    The script `invokeai-configure` will interactively guide you through the
     process of downloading and installing the weights files needed for InvokeAI.
     Note that the main Stable Diffusion weights file is protected by a license
     agreement that you have to agree to. The script will list the steps you need
@@ -367,46 +206,41 @@ manager, please follow these steps:
     If you get an error message about a module not being installed, check that
     the `invokeai` environment is active and if not, repeat step 5.
 
-    Note that `configure_invokeai.py` and `invoke.py` should be
-    installed under your conda directory and the system should find
-    them automatically on the PATH. If this isn't working on your
-    system, you can call the scripts directory using `python
-    scripts/configure_invoke.py` and `python scripts/invoke.py`.
-
     !!! tip
 
         If you have already downloaded the weights file(s) for another Stable
         Diffusion distribution, you may skip this step (by selecting "skip" when
         prompted) and configure InvokeAI to use the previously-downloaded files. The
-        process for this is described in [here](050_INSTALLING_MODELS.md).
+        process for this is described in [Installing Models](050_INSTALLING_MODELS.md).
 
 9.  Run the command-line- or the web- interface:
 
-    Activate the environment (with `source invokeai/bin/activate`), and then
-    run the script `invoke.py`. If you selected a non-default location
-    for the runtime directory, please specify the path with the `--root_dir`
-    option (abbreviated below as `--root`):
+    From within INVOKEAI_ROOT, activate the environment
+    (with `source .venv/bin/activate` or `.venv\scripts\activate), and then run
+    the script `invokeai`. If the virtual environment you selected is NOT inside
+    INVOKEAI_ROOT, then you must specify the path to the root directory by adding
+    `--root_dir \path\to\invokeai` to the commands below:
 
     !!! example ""
 
-        !!! warning "Make sure that the conda environment is activated, which should create `(invokeai)` in front of your prompt!"
+        !!! warning "Make sure that the virtual environment is activated, which should create `(.venv)` in front of your prompt!"
 
         === "CLI"
 
             ```bash
-            invoke.py --root ~/Programs/invokeai
+            invokeai
             ```
 
         === "local Webserver"
 
             ```bash
-            invoke.py --web --root ~/Programs/invokeai
+            invokeai --web
             ```
 
         === "Public Webserver"
 
             ```bash
-            invoke.py --web --host 0.0.0.0 --root ~/Programs/invokeai
+            invokeai --web --host 0.0.0.0
             ```
 
         If you choose the run the web interface, point your browser at
@@ -414,175 +248,122 @@ manager, please follow these steps:
 
     !!! tip
 
-        You can permanently set the location of the runtime directory by setting the environment variable INVOKEAI_ROOT to the path of your choice.
+        You can permanently set the location of the runtime directory
+        by setting the environment variable `INVOKEAI_ROOT` to the
+        path of the directory. As mentioned previously, this is
+        *highly recommended** if your virtual environment is located outside of
+        your runtime directory.
 
-10. Render away!
+10.  Render away!
 
-    Browse the [features](../features/CLI.md) section to learn about all the things you
-    can do with InvokeAI.
+    Browse the [features](../features/CLI.md) section to learn about all the
+    things you can do with InvokeAI.
 
-    Note that some GPUs are slow to warm up. In particular, when using an AMD
-    card with the ROCm driver, you may have to wait for over a minute the first
-    time you try to generate an image. Fortunately, after the warm up period
-    rendering will be fast.
 
-11. Subsequently, to relaunch the script, be sure to run "conda activate
-    invokeai", enter the `InvokeAI` directory, and then launch the invoke
-    script. If you forget to activate the 'invokeai' environment, the script
-    will fail with multiple `ModuleNotFound` errors.
+11.  Subsequently, to relaunch the script, activate the virtual environment, and
+    then launch `invokeai` command. If you forget to activate the virtual
+    environment you will most likeley receive a `command not found` error.
 
-## Creating an "install" version of InvokeAI
+    !!! warning
 
-If you wish you can install InvokeAI and all its dependencies in the
-runtime directory. This allows you to delete the source code
-repository and eliminates the need to provide `--root_dir` at startup
-time. Note that this method only works with the PIP method.
+        Do not move the runtime directory after installation. The virtual environment will get confused if the directory is moved.
 
-1. Follow the instructions for the PIP install, but in step #2 put the
-   virtual environment into the runtime directory. For example, assuming the
-   runtime directory lives in `~/Programs/invokeai`, you'd run:
+12. Other scripts
 
+    The [Textual Inversion](../features/TEXTUAL_INVERSION.md) script can be launched with the command:
+
+    ```bash
+    invokeai-ti --gui
+    ```
+
+    Similarly, the [Model Merging](../features/MODEL_MERGING.md) script can be launched with the command:
+
+    ```bash
+    invokeai-merge --gui
+    ```
+
+    Leave off the `--gui` option to run the script using command-line arguments. Pass the `--help` argument
+    to get usage instructions.
+
+### Developer Install
+
+If you have an interest in how InvokeAI works, or you would like to
+add features or bugfixes, you are encouraged to install the source
+code for InvokeAI. For this to work, you will need to install the
+`git` source code management program. If it is not already installed
+on your system, please see the [Git Installation
+Guide](https://github.com/git-guides/install-git)
+
+1. From the command line, run this command:
    ```bash
-   python -menv ~/Programs/invokeai
+   git clone https://github.com/invoke-ai/InvokeAI.git
    ```
 
-2. Now follow steps 3 to 5 in the PIP recipe, ending with the `pip install`
-   step.
+    This will create a directory named `InvokeAI` and populate it with the
+    full source code from the InvokeAI repository.
 
-3. Run one additional step while you are in the source code repository
-   directory `pip install --use-pep517 .` (note the dot at the end).
+2. Activate the InvokeAI virtual environment as per step (4) of the manual
+installation protocol (important!)
 
-4. That's all! Now, whenever you activate the virtual environment,
-   `invoke.py` will know where to look for the runtime directory without
-   needing a `--root_dir` argument. In addition, you can now move or
-   delete the source code repository entirely.
+3. Enter the InvokeAI repository directory and run one of these
+   commands, based on your GPU:
 
-   (Don't move the runtime directory!)
+    === "CUDA (NVidia)"
+        ```bash
+        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+        ```
 
-## Updating to newer versions of the script
+    === "ROCm (AMD)"
+        ```bash
+        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+        ```
 
-This distribution is changing rapidly. If you used the `git clone` method
-(step 5) to download the InvokeAI directory, then to update to the latest and
-greatest version, launch the Anaconda window, enter `InvokeAI` and type:
+    === "CPU (Intel Macs & non-GPU systems)"
+        ```bash
+        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
+        ```
 
-```bash
-git pull
-conda env update
-python scripts/configure_invokeai.py --skip-sd-weights #optional
+    === "MPS (M1 and M2 Macs)"
+        ```bash
+        pip install -e . --use-pep517
+        ```
+
+    Be sure to pass `-e` (for an editable install) and don't forget the
+    dot ("."). It is part of the command.
+
+    You can now run `invokeai` and its related commands. The code will be
+    read from the repository, so that you can edit the .py source files
+    and watch the code's behavior change.
+
+4.  If you wish to contribute to the InvokeAI project, you are
+    encouraged to establish a GitHub account and "fork"
+    https://github.com/invoke-ai/InvokeAI into your own copy of the
+    repository. You can then use GitHub functions to create and submit
+    pull requests to contribute improvements to the project.
+
+    Please see [Contributing](../index.md#contributing) for hints
+    on getting started.
+
+### Unsupported Conda Install
+
+Congratulations, you found the "secret" Conda installation
+instructions. If you really **really** want to use Conda with InvokeAI
+you can do so using this unsupported recipe:
+
+```
+mkdir ~/invokeai
+conda create -n invokeai python=3.10
+conda activate invokeai
+pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+invokeai-configure --root ~/invokeai
+invokeai --root ~/invokeai --web
 ```
 
-This will bring your local copy into sync with the remote one. The last step may
-be needed to take advantage of new features or released models. The
-`--skip-sd-weights` flag will prevent the script from prompting you to download
-the big Stable Diffusion weights files.
+The `pip install` command shown in this recipe is for Linux/Windows
+systems with an NVIDIA GPU. See step (6) above for the command to use
+with other platforms/GPU combinations. If you don't wish to pass the
+`--root` argument to `invokeai` with each launch, you may set the
+environment variable INVOKEAI_ROOT to point to the installation directory.
 
-## Troubleshooting
-
-Here are some common issues and their suggested solutions.
-
-### Conda
-
-#### Conda fails before completing `conda update`
-
-The usual source of these errors is a package incompatibility. While we have
-tried to minimize these, over time packages get updated and sometimes introduce
-incompatibilities.
-
-We suggest that you search
-[Issues](https://github.com/invoke-ai/InvokeAI/issues) or the "bugs-and-support"
-channel of the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy).
-
-You may also try to install the broken packages manually using PIP. To do this,
-activate the `invokeai` environment, and run `pip install` with the name and
-version of the package that is causing the incompatibility. For example:
-
-```bash
-pip install test-tube==0.7.5
-```
-
-You can keep doing this until all requirements are satisfied and the `invoke.py`
-script runs without errors. Please report to
-[Issues](https://github.com/invoke-ai/InvokeAI/issues) what you were able to do
-to work around the problem so that others can benefit from your investigation.
-
-### Create Conda Environment fails on MacOS
-
-If conda create environment fails with lmdb error, this is most likely caused by Clang.
-Run brew config to see which Clang is installed on your Mac. If Clang isn't installed, that's causing the error.
-Start by installing additional XCode command line tools, followed by brew install llvm.
-
-```bash
-xcode-select --install
-brew install llvm
-```
-
-If brew config has Clang installed, update to the latest llvm and try creating the environment again.
-
-#### `configure_invokeai.py` or `invoke.py` crashes at an early stage
-
-This is usually due to an incomplete or corrupted Conda install. Make sure you
-have linked to the correct environment file and run `conda update` again.
-
-If the problem persists, a more extreme measure is to clear Conda's caches and
-remove the `invokeai` environment:
-
-```bash
-conda deactivate
-conda env remove -n invokeai
-conda clean -a
-conda update
-```
-
-This removes all cached library files, including ones that may have been
-corrupted somehow. (This is not supposed to happen, but does anyway).
-
-#### `invoke.py` crashes at a later stage
-
-If the CLI or web site had been working ok, but something unexpected happens
-later on during the session, you've encountered a code bug that is probably
-unrelated to an install issue. Please search
-[Issues](https://github.com/invoke-ai/InvokeAI/issues), file a bug report, or
-ask for help on [Discord](https://discord.gg/ZmtBAhwWhy)
-
-#### My renders are running very slowly
-
-You may have installed the wrong torch (machine learning) package, and the
-system is running on CPU rather than the GPU. To check, look at the log messages
-that appear when `invoke.py` is first starting up. One of the earlier lines
-should say `Using device type cuda`. On AMD systems, it will also say "cuda",
-and on Macintoshes, it should say "mps". If instead the message says it is
-running on "cpu", then you may need to install the correct torch library.
-
-You may be able to fix this by installing a different torch library. Here are
-the magic incantations for Conda and PIP.
-
-!!! todo "For CUDA systems"
-
-    - conda
-
-      ```bash
-      conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
-      ```
-
-    - pip
-
-      ```bash
-      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
-      ```
-
-!!! todo "For AMD systems"
-
-    - conda
-
-      ```bash
-      conda activate invokeai
-      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
-      ```
-
-    - pip
-
-      ```bash
-      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
-      ```
-
-More information and troubleshooting tips can be found at https://pytorch.org.
+Note that if you run into problems with the Conda installation, the InvokeAI
+staff will **not** be able to help you out. Caveat Emptor!

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -0,0 +1,125 @@
+---
+title: NVIDIA Cuda / AMD ROCm
+---
+
+<figure markdown>
+
+# :simple-nvidia: CUDA | :simple-amd: ROCm
+
+</figure>
+
+In order for InvokeAI to run at full speed, you will need a graphics
+card with a supported GPU. InvokeAI supports NVidia cards via the CUDA
+driver on Windows and Linux, and AMD cards via the ROCm driver on Linux.
+
+## :simple-nvidia: CUDA
+
+### Linux and Windows Install
+
+If you have used your system for other graphics-intensive tasks, such
+as gaming, you may very well already have the CUDA drivers
+installed. To confirm, open up a command-line window and type:
+
+```
+nvidia-smi
+```
+
+If this command produces a status report on the GPU(s) installed on
+your system, CUDA is installed and you have no more work to do. If
+instead you get "command not found", or similar, then the driver will
+need to be installed.
+
+We strongly recommend that you install the CUDA Toolkit package
+directly from NVIDIA. **Do not try to install Ubuntu's
+nvidia-cuda-toolkit package. It is out of date and will cause
+conflicts among the NVIDIA driver and binaries.**
+
+Go to [CUDA Toolkit 11.7
+Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive),
+and use the target selection wizard to choose your operating system,
+hardware platform, and preferred installation method (e.g. "local"
+versus "network").
+
+This will provide you with a downloadable install file or, depending
+on your choices, a recipe for downloading and running a install shell
+script. Be sure to read and follow the full installation instructions.
+
+After an install that seems successful, you can confirm by again
+running `nvidia-smi` from the command line.
+
+### Linux Install with a Runtime Container
+
+On Linux systems, an alternative to installing CUDA Toolkit directly on
+your system is to run an NVIDIA software container that has the CUDA
+libraries already in place. This is recommended if you are already 
+familiar with containerization technologies such as Docker.
+
+For downloads and instructions, visit the [NVIDIA CUDA Container
+Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
+
+### Torch Installation
+
+When installing torch and torchvision manually with `pip`, remember to provide
+the argument `--extra-index-url
+https://download.pytorch.org/whl/cu117` as described in the [Manual
+Installation Guide](020_INSTALL_MANUAL.md).
+
+## :simple-amd: ROCm
+
+### Linux Install
+
+AMD GPUs are only supported on Linux platforms due to the lack of a
+Windows ROCm driver at the current time. Also be aware that support
+for newer AMD GPUs is spotty. Your mileage may vary.
+
+It is possible that the ROCm driver is already installed on your
+machine. To test, open up a terminal window and issue the following
+command:
+
+```
+rocm-smi
+```
+
+If you get a table labeled "ROCm System Management Interface" the
+driver is installed and you are done. If you get "command not found,"
+then the driver needs to be installed.
+
+Go to AMD's [ROCm Downloads
+Guide](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation_new.html#installation-methods)
+and scroll to the _Installation Methods_ section. Find the subsection
+for the install method for your preferred Linux distribution, and
+issue the commands given in the recipe.
+
+Annoyingly, the official AMD site does not have a recipe for the most
+recent version of Ubuntu, 22.04. However, this [community-contributed
+recipe](https://novaspirit.github.io/amdgpu-rocm-ubu22/) is reported
+to work well.
+
+After installation, please run `rocm-smi` a second time to confirm
+that the driver is present and the GPU is recognized. You may need to
+do a reboot in order to load the driver.
+
+### Linux Install with a ROCm-docker Container
+
+If you are comfortable with the Docker containerization system, then
+you can build a ROCm docker file. The source code and installation
+recipes are available
+[Here](https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/quick-start.md)
+
+### Torch Installation
+
+When installing torch and torchvision manually with `pip`, remember to provide
+the argument `--extra-index-url
+https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
+Installation Guide](020_INSTALL_MANUAL.md).
+
+This will be done automatically for you if you use the installer
+script.
+
+Be aware that the torch machine learning library does not seamlessly
+interoperate with all AMD GPUs and you may experience garbled images,
+black images, or long startup delays before rendering commences. Most
+of these issues can be solved by Googling for workarounds. If you have
+a problem and find a solution, please post an
+[Issue](https://github.com/invoke-ai/InvokeAI/issues) so that other
+users benefit and we can update this document.

docs/installation/040_INSTALL_DOCKER.md

@@ -16,10 +16,6 @@ title: Installing with Docker
 
     For general use, install locally to leverage your machine's GPU.
 
-!!! tip "For running on a cloud instance/service"
-
-    Check out the [Running InvokeAI in the cloud with Docker](#running-invokeai-in-the-cloud-with-docker) section below
-
 ## Why containers?
 
 They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
@@ -78,38 +74,40 @@ Some Suggestions of variables you may want to change besides the Token:
 
 <figure markdown>
 
-| Environment-Variable | Default value                   | Description                                                                                  |
-| -------------------- | -----------------------------   | -------------------------------------------------------------------------------------------- |
-| `HUGGINGFACE_TOKEN`  | No default, but **required**!   | This is the only **required** variable, without it you can't download the huggingface models |
-| `REPOSITORY_NAME`    | The Basename of the Repo folder | This name will used as the container repository/image name                                   |
-| `VOLUMENAME`         | `${REPOSITORY_NAME,,}_data`     | Name of the Docker Volume where model files will be stored                                   |
-| `ARCH`               | arch of the build machine       | can be changed if you want to build the image for another arch                               |
-| `INVOKEAI_TAG`       | latest                          | the Container Repository / Tag which will be used                                            |
-| `PIP_REQUIREMENTS`   | `requirements-lin-cuda.txt`     | the requirements file to use (from `environments-and-requirements`)                          |
-| `CONTAINER_FLAVOR`   | cuda                            | the flavor of the image, which can be changed if you build f.e. with amd requirements file.  |
-| `INVOKE_DOCKERFILE`  | `docker-build/Dockerfile`       | the Dockerfile which should be built, handy for development                                  |
+| Environment-Variable <img width="220" align="right"/> | Default value <img width="360" align="right"/> | Description                                                                                                                                                                                       |
+| ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `HUGGING_FACE_HUB_TOKEN`                              | No default, but **required**!                  | This is the only **required** variable, without it you can't download the huggingface models                                                                                                      |
+| `REPOSITORY_NAME`                                     | The Basename of the Repo folder                | This name will used as the container repository/image name                                                                                                                                        |
+| `VOLUMENAME`                                          | `${REPOSITORY_NAME,,}_data`                    | Name of the Docker Volume where model files will be stored                                                                                                                                        |
+| `ARCH`                                                | arch of the build machine                      | Can be changed if you want to build the image for another arch                                                                                                                                    |
+| `CONTAINER_REGISTRY`                                  | ghcr.io                                        | Name of the Container Registry to use for the full tag                                                                                                                                            |
+| `CONTAINER_REPOSITORY`                                | `$(whoami)/${REPOSITORY_NAME}`                 | Name of the Container Repository                                                                                                                                                                  |
+| `CONTAINER_FLAVOR`                                    | `cuda`                                         | The flavor of the image to built, available options are `cuda`, `rocm` and `cpu`. If you choose `rocm` or `cpu`, the extra-index-url will be selected automatically, unless you set one yourself. |
+| `CONTAINER_TAG`                                       | `${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}`   | The Container Repository / Tag which will be used                                                                                                                                                 |
+| `INVOKE_DOCKERFILE`                                   | `Dockerfile`                                   | The Dockerfile which should be built, handy for development                                                                                                                                       |
+| `PIP_EXTRA_INDEX_URL`                                 |                                                | If you want to use a custom pip-extra-index-url                                                                                                                                                   |
 
 </figure>
 
 #### Build the Image
 
-I provided a build script, which is located in `docker-build/build.sh` but still
-needs to be executed from the Repository root.
+I provided a build script, which is located next to the Dockerfile in
+`docker/build.sh`. It can be executed from repository root like this:
 
 ```bash
-./docker-build/build.sh
+./docker/build.sh
 ```
 
 The build Script not only builds the container, but also creates the docker
-volume if not existing yet, or if empty it will just download the models.
+volume if not existing yet.
 
 #### Run the Container
 
 After the build process is done, you can run the container via the provided
-`docker-build/run.sh` script
+`docker/run.sh` script
 
 ```bash
-./docker-build/run.sh
+./docker/run.sh
 ```
 
 When used without arguments, the container will start the webserver and provide
@@ -119,7 +117,7 @@ also do so.
 !!! example "run script example"
 
     ```bash
-    ./docker-build/run.sh "banana sushi" -Ak_lms -S42 -s10
+    ./docker/run.sh "banana sushi" -Ak_lms -S42 -s10
     ```
 
     This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
@@ -130,16 +128,18 @@ also do so.
 
 ## Running the container on your GPU
 
-If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running the container with an extra
-environment variable to enable GPU usage and have the process run much faster:
+If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
+the container with an extra environment variable to enable GPU usage and have
+the process run much faster:
 
 ```bash
-GPU_FLAGS=all ./docker-build/run.sh
+GPU_FLAGS=all ./docker/run.sh
 ```
 
 This passes the `--gpus all` to docker and uses the GPU.
 
-If you don't have a GPU (or your host is not yet setup to use it) you will see a message like this:
+If you don't have a GPU (or your host is not yet setup to use it) you will see a
+message like this:
 
 `docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
 
@@ -147,84 +147,8 @@ You can use the full set of GPU combinations documented here:
 
 https://docs.docker.com/config/containers/resource_constraints/#gpu
 
-For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to choose a specific device identified by a UUID.
-
-## Running InvokeAI in the cloud with Docker
-
-We offer an optimized Ubuntu-based image that has been well-tested in cloud deployments. Note: it also works well locally on Linux x86_64 systems with an Nvidia GPU. It *may* also work on Windows under WSL2 and on Intel Mac (not tested).
-
-An advantage of this method is that it does not need any local setup or additional dependencies.
-
-See the `docker-build/Dockerfile.cloud` file to familizarize yourself with the image's content.
-
-### Prerequisites
-
-- a `docker` runtime
-- `make` (optional but helps for convenience)
-- Huggingface token to download models, or an existing InvokeAI runtime directory from a previous installation
-
- Neither local Python nor any dependencies are required. If you don't have `make` (part of `build-essentials` on Ubuntu), or do not wish to install it, the commands from the `docker-build/Makefile` are readily adaptable to be executed directly.
-
-### Building and running the image locally
-
-1. Clone this repo and `cd docker-build`
-1. `make build` - this will build the image. (This does *not* require a GPU-capable system).
-1. _(skip this step if you already have a complete InvokeAI runtime directory)_
-    - `make configure` (This does *not* require a GPU-capable system)
-    - this will create a local cache of models and configs (a.k.a the _runtime dir_)
-    - enter your Huggingface token when prompted
-1. `make web`
-1. Open the `http://localhost:9090` URL in your browser, and enjoy the banana sushi!
-
-To use InvokeAI on the cli, run `make cli`. To open a Bash shell in the container for arbitraty advanced use, `make shell`.
-
-#### Building and running without `make`
-
-(Feel free to adapt paths such as `${HOME}/invokeai` to your liking, and modify the CLI arguments as necessary).
-
-!!! example "Build the image and configure the runtime directory"
-    ```Shell
-    cd docker-build
-
-    DOCKER_BUILDKIT=1 docker build -t local/invokeai:latest -f Dockerfile.cloud ..
-
-    docker run --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/configure_invokeai.py"
-    ```
-
-!!! example "Run the web server"
-    ```Shell
-    docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai -p9090:9090 local/invokeai:latest
-    ```
-
-    Access the Web UI at http://localhost:9090
-
-!!! example "Run the InvokeAI interactive CLI"
-    ```
-    docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/invoke.py"
-    ```
-
-### Running the image in the cloud
-
-This image works anywhere you can run a container with a mounted Docker volume. You may either build this image on a cloud instance, or build and push it to your Docker registry. To manually run this on a cloud instance (such as AWS EC2, GCP or Azure VM):
-
-1. build this image either in the cloud (you'll need to pull the repo), or locally
-1. `docker tag` it as `your-registry/invokeai` and push to your registry (i.e. Dockerhub)
-1. `docker pull` it on your cloud instance
-1. configure the runtime directory as per above example, using `docker run ... configure_invokeai.py` script
-1. use either one of the `docker run` commands above, substituting the image name for your own image.
-
-To run this on Runpod, please refer to the following Runpod template: https://www.runpod.io/console/gpu-secure-cloud?template=vm19ukkycf (you need a Runpod subscription). When launching the template, feel free to set the image to pull your own build.
-
-The template's `README` provides ample detail, but at a high level, the process is as follows:
-
-1. create a pod using this Docker image
-1. ensure the pod has an `INVOKEAI_ROOT=<path_to_your_persistent_volume>` environment variable, and that it corresponds to the path to your pod's persistent volume mount
-1. Run the pod with `sleep infinity` as the Docker command
-1. Use Runpod basic SSH to connect to the pod, and run `python scripts/configure_invokeai.py` script
-1. Stop the pod, and change the Docker command to `python scripts/invoke.py --web --host 0.0.0.0`
-1. Run the pod again, connect to your pod on HTTP port 9090, and enjoy the banana sushi!
-
-Running on other cloud providers such as Vast.ai will likely work in a similar fashion.
+For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
+choose a specific device identified by a UUID.
 
 ---
 
@@ -240,13 +164,12 @@ Running on other cloud providers such as Vast.ai will likely work in a similar f
 If you're on a **Linux container** the `invoke` script is **automatically
 started** and the output dir set to the Docker volume you created earlier.
 
-If you're **directly on macOS follow these startup instructions**.
-With the Conda environment activated (`conda activate ldm`), run the interactive
+If you're **directly on macOS follow these startup instructions**. With the
+Conda environment activated (`conda activate ldm`), run the interactive
 interface that combines the functionality of the original scripts `txt2img` and
-`img2img`:
-Use the more accurate but VRAM-intensive full precision math because
-half-precision requires autocast and won't work.
-By default the images are saved in `outputs/img-samples/`.
+`img2img`: Use the more accurate but VRAM-intensive full precision math because
+half-precision requires autocast and won't work. By default the images are saved
+in `outputs/img-samples/`.
 
 ```Shell
 python3 scripts/invoke.py --full_precision
@@ -262,9 +185,9 @@ invoke> q
 ### Text to Image
 
 For quick (but bad) image results test with 5 steps (default 50) and 1 sample
-image. This will let you know that everything is set up correctly.
-Then increase steps to 100 or more for good (but slower) results.
-The prompt can be in quotes or not.
+image. This will let you know that everything is set up correctly. Then increase
+steps to 100 or more for good (but slower) results. The prompt can be in quotes
+or not.
 
 ```Shell
 invoke> The hulk fighting with sheldon cooper -s5 -n1
@@ -277,10 +200,9 @@ You'll need to experiment to see if face restoration is making it better or
 worse for your specific prompt.
 
 If you're on a container the output is set to the Docker volume. You can copy it
-wherever you want.
-You can download it from the Docker Desktop app, Volumes, my-vol, data.
-Or you can copy it from your Mac terminal. Keep in mind `docker cp` can't expand
-`*.png` so you'll need to specify the image file name.
+wherever you want. You can download it from the Docker Desktop app, Volumes,
+my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
+`docker cp` can't expand `*.png` so you'll need to specify the image file name.
 
 On your host Mac (you can use the name of any container that mounted the
 volume):

docs/installation/050_INSTALLING_MODELS.md

@@ -4,249 +4,392 @@ title: Installing Models
 
 # :octicons-paintbrush-16: Installing Models
 
-## Model Weight Files
+## Checkpoint and Diffusers Models
 
-The model weight files ('\*.ckpt') are the Stable Diffusion "secret sauce". They
-are the product of training the AI on millions of captioned images gathered from
-multiple sources.
+The model checkpoint files ('\*.ckpt') are the Stable Diffusion
+"secret sauce". They are the product of training the AI on millions of
+captioned images gathered from multiple sources.
 
-Originally there was only a single Stable Diffusion weights file, which many
-people named `model.ckpt`. Now there are dozens or more that have been "fine
-tuned" to provide particulary styles, genres, or other features. InvokeAI allows
-you to install and run multiple model weight files and switch between them
-quickly in the command-line and web interfaces.
+Originally there was only a single Stable Diffusion weights file,
+which many people named `model.ckpt`. Now there are dozens or more
+that have been fine tuned to provide particulary styles, genres, or
+other features. In addition, there are several new formats that
+improve on the original checkpoint format: a `.safetensors` format
+which prevents malware from masquerading as a model, and `diffusers`
+models, the most recent innovation.
 
-This manual will guide you through installing and configuring model weight
-files.
+InvokeAI supports all three formats but strongly prefers the
+`diffusers` format. These are distributed as directories containing
+multiple subfolders, each of which contains a different aspect of the
+model. The advantage of this is that the models load from disk really
+fast. Another advantage is that `diffusers` models are supported by a
+large and active set of open source developers working at and with
+HuggingFace organization, and improvements in both rendering quality
+and performance are being made at a rapid pace. Among other features
+is the ability to download and install a `diffusers` model just by
+providing its HuggingFace repository ID.
+
+While InvokeAI will continue to support `.ckpt` and `.safetensors`
+models for the near future, these are deprecated and support will
+likely be withdrawn at some point in the not-too-distant future.
+
+This manual will guide you through installing and configuring model
+weight files and converting legacy `.ckpt` and `.safetensors` files
+into performant `diffusers` models.
 
 ## Base Models
 
-InvokeAI comes with support for a good initial set of models listed in the model
-configuration file `configs/models.yaml`. They are:
+InvokeAI comes with support for a good set of starter models. You'll
+find them listed in the master models file
+`configs/INITIAL_MODELS.yaml` in the InvokeAI root directory. The
+subset that are currently installed are found in
+`configs/models.yaml`. As of v2.3.1, the list of starter models is:
 
-| Model                | Weight File                       | Description                                                | DOWNLOAD FROM                                                  |
-| -------------------- | --------------------------------- | ---------------------------------------------------------- | -------------------------------------------------------------- |
-| stable-diffusion-1.5 | v1-5-pruned-emaonly.ckpt          | Most recent version of base Stable Diffusion model         | https://huggingface.co/runwayml/stable-diffusion-v1-5          |
-| stable-diffusion-1.4 | sd-v1-4.ckpt                      | Previous version of base Stable Diffusion model            | https://huggingface.co/CompVis/stable-diffusion-v-1-4-original |
-| inpainting-1.5       | sd-v1-5-inpainting.ckpt           | Stable Diffusion 1.5 model specialized for inpainting      | https://huggingface.co/runwayml/stable-diffusion-inpainting    |
-| waifu-diffusion-1.3  | model-epoch09-float32.ckpt        | Stable Diffusion 1.4 trained to produce anime images       | https://huggingface.co/hakurei/waifu-diffusion-v1-3            |
-| `<all models>`       | vae-ft-mse-840000-ema-pruned.ckpt | A fine-tune file add-on file that improves face generation | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/     |
+|Model Name | HuggingFace Repo ID | Description | URL |
+|---------- | ---------- | ----------- | --- |
+|stable-diffusion-1.5|runwayml/stable-diffusion-v1-5|Stable Diffusion version 1.5 diffusers model (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-v1-5 |
+|sd-inpainting-1.5|runwayml/stable-diffusion-inpainting|RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-inpainting |
+|stable-diffusion-2.1|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
+|sd-inpainting-2.0|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
+|analog-diffusion-1.0|wavymulder/Analog-Diffusion|An SD-1.5 model trained on diverse analog photographs (2.13 GB)|https://huggingface.co/wavymulder/Analog-Diffusion |
+|deliberate-1.0|XpucT/Deliberate|Versatile model that produces detailed images up to 768px (4.27 GB)|https://huggingface.co/XpucT/Deliberate |
+|d&d-diffusion-1.0|0xJustin/Dungeons-and-Diffusion|Dungeons & Dragons characters (2.13 GB)|https://huggingface.co/0xJustin/Dungeons-and-Diffusion |
+|dreamlike-photoreal-2.0|dreamlike-art/dreamlike-photoreal-2.0|A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)|https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0 |
+|inkpunk-1.0|Envvi/Inkpunk-Diffusion|Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)|https://huggingface.co/Envvi/Inkpunk-Diffusion |
+|openjourney-4.0|prompthero/openjourney|An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)|https://huggingface.co/prompthero/openjourney |
+|portrait-plus-1.0|wavymulder/portraitplus|An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)|https://huggingface.co/wavymulder/portraitplus |
+|seek-art-mega-1.0|coreco/seek.art_MEGA|A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)|https://huggingface.co/coreco/seek.art_MEGA |
+|trinart-2.0|naclbit/trinart_stable_diffusion_v2|An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)|https://huggingface.co/naclbit/trinart_stable_diffusion_v2 |
+|waifu-diffusion-1.4|hakurei/waifu-diffusion|An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)|https://huggingface.co/hakurei/waifu-diffusion |
 
-Note that these files are covered by an "Ethical AI" license which forbids
-certain uses. You will need to create an account on the Hugging Face website and
-accept the license terms before you can access the files.
-
-The predefined configuration file for InvokeAI (located at
-`configs/models.yaml`) provides entries for each of these weights files.
-`stable-diffusion-1.5` is the default model used, and we strongly recommend that
-you install this weights file if nothing else.
+Note that these files are covered by an "Ethical AI" license which
+forbids certain uses. When you initially download them, you are asked
+to accept the license terms. In addition, some of these models carry
+additional license terms that limit their use in commercial
+applications or on public servers. Be sure to familiarize yourself
+with the model terms by visiting the URLs in the table above.
 
 ## Community-Contributed Models
 
-There are too many to list here and more are being contributed every day.
-Hugging Face maintains a
-[fast-growing repository](https://huggingface.co/sd-concepts-library) of
-fine-tune (".bin") models that can be imported into InvokeAI by passing the
-`--embedding_path` option to the `invoke.py` command.
+There are too many to list here and more are being contributed every
+day.  [HuggingFace](https://huggingface.co/models?library=diffusers)
+is a great resource for diffusers models, and is also the home of a
+[fast-growing repository](https://huggingface.co/sd-concepts-library)
+of embedding (".bin") models that add subjects and/or styles to your
+images. The latter are automatically installed on the fly when you
+include the text `<concept-name>` in your prompt. See [Concepts
+Library](../features/CONCEPTS.md) for more information.
 
-[This page](https://rentry.org/sdmodels) hosts a large list of official and
-unofficial Stable Diffusion models and where they can be obtained.
+Another popular site for community-contributed models is
+[CIVITAI](https://civitai.com). This extensive site currently supports
+only `.safetensors` and `.ckpt` models, but they can be easily loaded
+into InvokeAI and/or converted into optimized `diffusers` models. Be
+aware that CIVITAI hosts many models that generate NSFW content.
+
+!!! note
+
+    InvokeAI 2.3.x does not support directly importing and
+    running Stable Diffusion version 2 checkpoint models. You may instead
+    convert them into `diffusers` models using the conversion methods
+    described below.
 
 ## Installation
 
-There are three ways to install weights files:
+There are multiple ways to install and manage models:
 
-1. During InvokeAI installation, the `configure_invokeai.py` script can download
-   them for you.
+1. The `invokeai-configure` script which will download and install them for you.
 
-2. You can use the command-line interface (CLI) to import, configure and modify
-   new models files.
+2. The command-line tool (CLI) has commands that allows you to import, configure and modify
+   models files.
 
-3. You can download the files manually and add the appropriate entries to
-   `models.yaml`.
+3. The web interface (WebUI) has a GUI for importing and managing
+   models.
 
-### Installation via `configure_invokeai.py`
+### Installation via `invokeai-configure`
 
-This is the most automatic way. Run `scripts/configure_invokeai.py` from the
-console. It will ask you to select which models to download and lead you through
-the steps of setting up a Hugging Face account if you haven't done so already.
-
-To start, run `python scripts/configure_invokeai.py` from within the InvokeAI:
-directory
-
-!!! example ""
-
-    ```text
-    Loading Python libraries...
-
-    ** INTRODUCTION **
-    Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
-    and other large models that are needed for text to image generation. At any point you may interrupt
-    this program and resume later.
-
-    ** WEIGHT SELECTION **
-    Would you like to download the Stable Diffusion model weights now? [y]
-
-    Choose the weight file(s) you wish to download. Before downloading you
-    will be given the option to view and change your selections.
-
-    [1] stable-diffusion-1.5:
-        The newest Stable Diffusion version 1.5 weight file (4.27 GB) (recommended)
-        Download? [y]
-    [2] inpainting-1.5:
-        RunwayML SD 1.5 model optimized for inpainting (4.27 GB) (recommended)
-        Download? [y]
-    [3] stable-diffusion-1.4:
-        The original Stable Diffusion version 1.4 weight file (4.27 GB)
-        Download? [n] n
-    [4] waifu-diffusion-1.3:
-        Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
-        Download? [n] y
-    [5] ft-mse-improved-autoencoder-840000:
-        StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB) (recommended)
-        Download? [y] y
-    The following weight files will be downloaded:
-      [1] stable-diffusion-1.5*
-      [2] inpainting-1.5
-      [4] waifu-diffusion-1.3
-      [5] ft-mse-improved-autoencoder-840000
-    *default
-    Ok to download? [y]
-    ** LICENSE AGREEMENT FOR WEIGHT FILES **
-
-    1. To download the Stable Diffusion weight files you need to read and accept the
-      CreativeML Responsible AI license. If you have not already done so, please
-      create an account using the "Sign Up" button:
-
-      https://huggingface.co
-
-      You will need to verify your email address as part of the HuggingFace
-      registration process.
-
-    2. After creating the account, login under your account and accept
-      the license terms located here:
-
-      https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
-
-    Press <enter> when you are ready to continue:
-    ...
-    ```
-
-When the script is complete, you will find the downloaded weights files in
-`models/ldm/stable-diffusion-v1` and a matching configuration file in
-`configs/models.yaml`.
-
-You can run the script again to add any models you didn't select the first time.
-Note that as a safety measure the script will _never_ remove a
-previously-installed weights file. You will have to do this manually.
+From the `invoke` launcher, choose option (6) "re-run the configure
+script to download new models." This will launch the same script that
+prompted you to select models at install time. You can use this to add
+models that you skipped the first time around. It is all right to
+specify a model that was previously downloaded; the script will just
+confirm that the files are complete.
 
 ### Installation via the CLI
 
 You can install a new model, including any of the community-supported ones, via
 the command-line client's `!import_model` command.
 
-1.  First download the desired model weights file and place it under
-    `models/ldm/stable-diffusion-v1/`. You may rename the weights file to
-    something more memorable if you wish. Record the path of the weights file
-    (e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`)
+#### Installing individual `.ckpt` and `.safetensors` models
 
-2.  Launch the `invoke.py` CLI with `python scripts/invoke.py`.
+If the model is already downloaded to your local disk, use
+`!import_model /path/to/file.ckpt` to load it. For example:
 
-3.  At the `invoke>` command-line, enter the command
-    `!import_model <path to model>`. For example:
+```bash
+invoke> !import_model C:/Users/fred/Downloads/martians.safetensors
+```
 
-    `invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+!!! tip "Forward Slashes"
+    On Windows systems, use forward slashes rather than backslashes
+    in your file paths.
+    If you do use backslashes,
+    you must double them like this:
+    `C:\\Users\\fred\\Downloads\\martians.safetensors`
 
-    !!! tip "the CLI supports file path autocompletion"
+Alternatively you can directly import the file using its URL:
 
+```bash
+invoke> !import_model https://example.org/sd_models/martians.safetensors
+```
+
+For this to work, the URL must not be password-protected. Otherwise
+you will receive a 404 error.
+
+When you import a legacy model, the CLI will first ask you what type
+of model this is. You can indicate whether it is a model based on
+Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
+or a 1.x inpainting model. Be careful to indicate the correct model
+type, or it will not load correctly. You can correct the model type
+after the fact using the `!edit_model` command.
+
+The system will then ask you a few other questions about the model,
+including what size image it was trained on (usually 512x512), what
+name and description you wish to use for it, and whether you would
+like to install a custom VAE (variable autoencoder) file for the
+model. For recent models, the answer to the VAE question is usually
+"no," but it won't hurt to answer "yes".
+
+After importing, the model will load. If this is successful, you will
+be asked if you want to keep the model loaded in memory to start
+generating immediately. You'll also be asked if you wish to make this
+the default model on startup. You can change this later using
+`!edit_model`.
+
+#### Importing a batch of `.ckpt` and `.safetensors` models from a directory
+
+You may also point `!import_model` to a directory containing a set of
+`.ckpt` or `.safetensors` files. They will be imported _en masse_.
+
+!!! example
+
+    ```console
+    invoke> !import_model C:/Users/fred/Downloads/civitai_models/
+    ```
+
+You will be given the option to import all models found in the
+directory, or select which ones to import. If there are subfolders
+within the directory, they will be searched for models to import.
+
+#### Installing `diffusers` models
+
+You can install a `diffusers` model from the HuggingFace site using
+`!import_model` and the HuggingFace repo_id for the model:
+
+```bash
+invoke> !import_model andite/anything-v4.0
+```
+
+Alternatively, you can download the model to disk and import it from
+there. The model may be distributed as a ZIP file, or as a Git
+repository:
+
+```bash
+invoke> !import_model C:/Users/fred/Downloads/andite--anything-v4.0
+```
+
+!!! tip "The CLI supports file path autocompletion"
          Type a bit of the path name and hit ++tab++ in order to get a choice of
          possible completions.
 
-    !!! tip "on Windows, you can drag model files onto the command-line"
+!!! tip "On Windows, you can drag model files onto the command-line"
+         Once you have typed in `!import_model `, you can drag the
+         model  file or directory onto the command-line to insert the model path. This way, you don't need to
+         type it or copy/paste. However, you will need to reverse or
+         double backslashes as noted above.
 
-         Once you have typed in `!import_model `, you can drag the model `.ckpt` file
-         onto the command-line to insert the model path. This way, you don't need to
-         type it or copy/paste.
+Before installing, the CLI will ask you for a short name and
+description for the model, whether to make this the default model that
+is loaded at InvokeAI startup time, and whether to replace its
+VAE. Generally the answer to the latter question is "no".
 
-4.  Follow the wizard's instructions to complete installation as shown in the
-    example here:
+### Converting legacy models into `diffusers`
 
-    !!! example ""
+The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
+models file into `diffusers` and install it.This will enable the model
+to load and run faster without loss of image quality.
 
-        ```text
-        invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
-        >> Model import in process. Please enter the values needed to configure this model:
+The usage is identical to `!import_model`. You may point the command
+to either a downloaded model file on disk, or to a (non-password
+protected) URL:
 
-        Name for this model: arabian-nights
-        Description of this model: Arabian Nights Fine Tune v1.0
-        Configuration file for this model: configs/stable-diffusion/v1-inference.yaml
-        Default image width: 512
-        Default image height: 512
-        >> New configuration:
-        arabian-nights:
-          config: configs/stable-diffusion/v1-inference.yaml
-          description: Arabian Nights Fine Tune v1.0
-          height: 512
-          weights: models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
-          width: 512
-        OK to import [n]? y
-        >> Caching model stable-diffusion-1.4 in system RAM
-        >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
-          | LatentDiffusion: Running in eps-prediction mode
-          | DiffusionWrapper has 859.52 M params.
-          | Making attention of type 'vanilla' with 512 in_channels
-          | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
-          | Making attention of type 'vanilla' with 512 in_channels
-          | Using faster float16 precision
-        ```
+```bash
+invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
+```
 
-If you've previously installed the fine-tune VAE file
-`vae-ft-mse-840000-ema-pruned.ckpt`, the wizard will also ask you if you want to
-add this VAE to the model.
+After a successful conversion, the CLI will offer you the option of
+deleting the original `.ckpt` or `.safetensors` file.
 
-The appropriate entry for this model will be added to `configs/models.yaml` and
-it will be available to use in the CLI immediately.
+### Optimizing a previously-installed model
 
-The CLI has additional commands for switching among, viewing, editing, deleting
-the available models. These are described in
-[Command Line Client](../features/CLI.md#model-selection-and-importation), but
-the two most frequently-used are `!models` and `!switch <name of model>`. The
-first prints a table of models that InvokeAI knows about and their load status.
-The second will load the requested model and lets you switch back and forth
-quickly among loaded models.
+Lastly, if you have previously installed a `.ckpt` or `.safetensors`
+file and wish to convert it into a `diffusers` model, you can do this
+without re-downloading and converting the original file using the
+`!optimize_model` command. Simply pass the short name of an existing
+installed model:
+
+```bash
+invoke> !optimize_model martians-v1.0
+```
+
+The model will be converted into `diffusers` format and replace the
+previously installed version. You will again be offered the
+opportunity to delete the original `.ckpt` or `.safetensors` file.
+
+### Related CLI Commands
+
+There are a whole series of additional model management commands in
+the CLI that you can read about in [Command-Line
+Interface](../features/CLI.md). These include:
+
+* `!models` - List all installed models
+* `!switch <model name>` - Switch to the indicated model
+* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
+* `!del_model <model name>` - Delete the indicated model
+
+### Manually editing `configs/models.yaml`
 
-### Manually editing of `configs/models.yaml`
 
 If you are comfortable with a text editor then you may simply edit `models.yaml`
 directly.
 
-First you need to download the desired .ckpt file and place it in
-`models/ldm/stable-diffusion-v1` as descirbed in step #1 in the previous
-section. Record the path to the weights file, e.g.
-`models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+You will need to download the desired `.ckpt/.safetensors` file and
+place it somewhere on your machine's filesystem. Alternatively, for a
+`diffusers` model, record the repo_id or download the whole model
+directory. Then using a **text** editor (e.g. the Windows Notepad
+application), open the file `configs/models.yaml`, and add a new
+stanza that follows this model:
 
-Then using a **text** editor (e.g. the Windows Notepad application), open the
-file `configs/models.yaml`, and add a new stanza that follows this model:
+#### A legacy model
+
+A legacy `.ckpt` or `.safetensors` entry will look like this:
 
 ```yaml
 arabian-nights-1.0:
   description: A great fine-tune in Arabian Nights style
-  weights: ./models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+  weights: ./path/to/arabian-nights-1.0.ckpt
   config: ./configs/stable-diffusion/v1-inference.yaml
+  format: ckpt
   width: 512
   height: 512
-  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
   default: false
 ```
 
-| name               | description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| :----------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| arabian-nights-1.0 | This is the name of the model that you will refer to from within the CLI and the WebGUI when you need to load and use the model.                                                                                                                                                                                                                                                                                                                                                                                                  |
-| description        | Any description that you want to add to the model to remind you what it is.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| weights            | Relative path to the .ckpt weights file for this model.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| config             | This is the confusingly-named configuration file for the model itself. Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens to need a custom configuration, in which case the place you downloaded it from will tell you what to use instead. For example, the runwayML custom inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`. This is already inclued in the InvokeAI distribution and is configured automatically for you by the `configure_invokeai.py` script. |
-| vae                | If you want to add a VAE file to the model, then enter its path here.                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| width, height      | This is the width and height of the images used to train the model. Currently they are always 512 and 512.                                                                                                                                                                                                                                                                                                                                                                                                                        |
+Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
 
-Save the `models.yaml` and relaunch InvokeAI. The new model should now be
-available for your use.
+#### A diffusers model
+
+A stanza for a `diffusers` model will look like this for a HuggingFace
+model with a repository ID:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  repo_id: captahab/arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+And for a downloaded directory:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  path: /path/to/captahab-arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+There is additional syntax for indicating an external VAE to use with
+this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
+
+After you save the modified `models.yaml` file relaunch
+`invokeai`. The new model will now be available for your use.
+
+### Installation via the WebUI
+
+To access the WebUI Model Manager, click on the button that looks like
+a cube in the upper right side of the browser screen. This will bring
+up a dialogue that lists the models you have already installed, and
+allows you to load, delete or edit them:
+
+<figure markdown>
+
+![model-manager](../assets/installing-models/webui-models-1.png)
+
+</figure>
+
+To add a new model, click on **+ Add New** and select to either a
+checkpoint/safetensors model, or a diffusers model:
+
+<figure markdown>
+
+![model-manager-add-new](../assets/installing-models/webui-models-2.png)
+
+</figure>
+
+In this example, we chose **Add Diffusers**. As shown in the figure
+below, a new dialogue prompts you to enter the name to use for the
+model, its description, and either the location of the `diffusers`
+model on disk, or its Repo ID on the HuggingFace web site. If you
+choose to enter a path to disk, the system will autocomplete for you
+as you type:
+
+<figure markdown>
+
+![model-manager-add-diffusers](../assets/installing-models/webui-models-3.png)
+
+</figure>
+
+Press **Add Model** at the bottom of the dialogue (scrolled out of
+site in the figure), and the model will be downloaded, imported, and
+registered in `models.yaml`.
+
+The **Add Checkpoint/Safetensor Model** option is similar, except that
+in this case you can choose to scan an entire folder for
+checkpoint/safetensors files to import. Simply type in the path of the
+directory and press the "Search" icon. This will display the
+`.ckpt` and `.safetensors` found inside the directory and its
+subfolders, and allow you to choose which ones to import:
+
+<figure markdown>
+
+![model-manager-add-checkpoint](../assets/installing-models/webui-models-4.png)
+
+</figure>
+
+## Model Management Startup Options
+
+The `invoke` launcher and the `invokeai` script accept a series of
+command-line arguments that modify InvokeAI's behavior when loading
+models. These can be provided on the command line, or added to the
+InvokeAI root directory's `invokeai.init` initialization file.
+
+The arguments are:
+
+* `--model <model name>` -- Start up with the indicated model loaded
+* `--ckpt_convert` -- When a checkpoint/safetensors model is loaded, convert it into a `diffusers` model in memory. This does not permanently save the converted model to disk.
+* `--autoconvert <path/to/directory>` -- Scan the indicated directory path for new checkpoint/safetensors files, convert them into `diffusers` models, and import them into InvokeAI.
+
+Here is an example of providing an argument on the command line using
+the `invoke.sh` launch script:
+
+```bash
+invoke.sh --autoconvert /home/fred/stable-diffusion-checkpoints
+```
+
+And here is what the same argument looks like in `invokeai.init`:
+
+```bash
+--outdir="/home/fred/invokeai/outputs
+--no-nsfw_checker
+--autoconvert /home/fred/stable-diffusion-checkpoints
+```

docs/installation/060_INSTALL_PATCHMATCH.md

@@ -24,7 +24,7 @@ You need to have opencv installed so that pypatchmatch can be built:
 brew install opencv
 ```
 
-The next time you start `invoke`, after sucesfully installing opencv, pypatchmatch will be built.
+The next time you start `invoke`, after successfully installing opencv, pypatchmatch will be built.
 
 ## Linux
 
@@ -56,7 +56,7 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 5. Confirm that pypatchmatch is installed. At the command-line prompt enter
    `python`, and then at the `>>>` line type
-   `from patchmatch import patch_match`: It should look like the follwing:
+   `from patchmatch import patch_match`: It should look like the following:
 
     ```py
     Python 3.9.5 (default, Nov 23 2021, 15:27:38)
@@ -108,4 +108,4 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 [**Next, Follow Steps 4-6 from the Debian Section above**](#linux)
 
-If you see no errors, then you're ready to go!
+If you see no errors you're ready to go!

docs/installation/INSTALL_AUTOMATED.md

@@ -1 +0,0 @@
-010_INSTALL_AUTOMATED.md

docs/installation/INSTALL_MANUAL.md

@@ -1,374 +0,0 @@
----
-title: Manual Installation
----
-
-<figure markdown>
-# :fontawesome-brands-linux: Linux | :fontawesome-brands-apple: macOS | :fontawesome-brands-windows: Windows
-</figure>
-
-!!! warning "This is for advanced Users"
-
-    who are already experienced with using conda or pip
-
-## Introduction
-
-You have two choices for manual installation, the [first one](#Conda_method)
-based on the Anaconda3 package manager (`conda`), and
-[a second one](#PIP_method) which uses basic Python virtual environment (`venv`)
-commands and the PIP package manager. Both methods require you to enter commands
-on the terminal, also known as the "console".
-
-On Windows systems you are encouraged to install and use the
-[Powershell](https://learn.microsoft.com/en-us/powershell/scripting/install/installing-powershell-on-windows?view=powershell-7.3),
-which provides compatibility with Linux and Mac shells and nice features such as
-command-line completion.
-
-### Conda method
-
-1.  Check that your system meets the
-    [hardware requirements](index.md#Hardware_Requirements) and has the
-    appropriate GPU drivers installed. In particular, if you are a Linux user
-    with an AMD GPU installed, you may need to install the
-    [ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
-
-    InvokeAI does not yet support Windows machines with AMD GPUs due to the lack
-    of ROCm driver support on this platform.
-
-    To confirm that the appropriate drivers are installed, run `nvidia-smi` on
-    NVIDIA/CUDA systems, and `rocm-smi` on AMD systems. These should return
-    information about the installed video card.
-
-    Macintosh users with MPS acceleration, or anybody with a CPU-only system,
-    can skip this step.
-
-2.  You will need to install Anaconda3 and Git if they are not already
-    available. Use your operating system's preferred package manager, or
-    download the installers manually. You can find them here:
-
-    - [Anaconda3](https://www.anaconda.com/)
-    - [git](https://git-scm.com/downloads)
-
-3.  Clone the [InvokeAI](https://github.com/invoke-ai/InvokeAI) source code from
-    GitHub:
-
-    ```bash
-    git clone https://github.com/invoke-ai/InvokeAI.git
-    ```
-
-    This will create InvokeAI folder where you will follow the rest of the
-    steps.
-
-4.  Enter the newly-created InvokeAI folder:
-
-    ```bash
-    cd InvokeAI
-    ```
-
-    From this step forward make sure that you are working in the InvokeAI
-    directory!
-
-5.  Select the appropriate environment file:
-
-    We have created a series of environment files suited for different operating
-    systems and GPU hardware. They are located in the
-    `environments-and-requirements` directory:
-
-    <figure markdown>
-
-    |         filename         |               OS               |
-    | :----------------------: | :----------------------------: |
-    | environment-lin-amd.yml  |  Linux with an AMD (ROCm) GPU  |
-    | environment-lin-cuda.yml | Linux with an NVIDIA CUDA GPU  |
-    | environment-mac.yml    |   Macintosh            |
-    | environment-win-cuda.yml | Windows with an NVIDA CUDA GPU |
-
-    </figure>
-
-    Choose the appropriate environment file for your system and link or copy it
-    to `environment.yml` in InvokeAI's top-level directory. To do so, run
-    following command from the repository-root:
-
-    !!! Example ""
-
-        === "Macintosh and Linux"
-
-            !!! todo "Replace `xxx` and `yyy` with the appropriate OS and GPU codes as seen in the table above"
-
-            ```bash
-            ln -sf environments-and-requirements/environment-xxx-yyy.yml environment.yml
-            ```
-
-            When this is done, confirm that a file `environment.yml` has been linked in
-            the InvokeAI root directory and that it points to the correct file in the
-            `environments-and-requirements`.
-
-            ```bash
-            ls -la
-            ```
-
-        === "Windows"
-
-            !!! todo " Since it requires admin privileges to create links, we will use the copy command to create your `environment.yml`"
-
-            ```cmd
-            copy environments-and-requirements\environment-win-cuda.yml environment.yml
-            ```
-
-            Afterwards verify that the file `environment.yml` has been created, either via the
-            explorer or by using the command `dir` from the terminal
-
-            ```cmd
-            dir
-            ```
-
-    !!! warning "Do not try to run conda on directly on the subdirectory environments file. This won't work. Instead, copy or link it to the top-level directory as shown."
-
-6.  Create the conda environment:
-
-    ```bash
-    conda env update
-    ```
-
-    This will create a new environment named `invokeai` and install all InvokeAI
-    dependencies into it. If something goes wrong you should take a look at
-    [troubleshooting](#troubleshooting).
-
-7.  Activate the `invokeai` environment:
-
-    In order to use the newly created environment you will first need to
-    activate it
-
-    ```bash
-    conda activate invokeai
-    ```
-
-    Your command-line prompt should change to indicate that `invokeai` is active
-    by prepending `(invokeai)`.
-
-8.  Pre-Load the model weights files:
-
-    !!! tip
-
-        If you have already downloaded the weights file(s) for another Stable
-        Diffusion distribution, you may skip this step (by selecting "skip" when
-        prompted) and configure InvokeAI to use the previously-downloaded files. The
-        process for this is described in [here](050_INSTALLING_MODELS.md).
-
-    ```bash
-    python scripts/configure_invokeai.py
-    ```
-
-    The script `configure_invokeai.py` will interactively guide you through the
-    process of downloading and installing the weights files needed for InvokeAI.
-    Note that the main Stable Diffusion weights file is protected by a license
-    agreement that you have to agree to. The script will list the steps you need
-    to take to create an account on the site that hosts the weights files,
-    accept the agreement, and provide an access token that allows InvokeAI to
-    legally download and install the weights files.
-
-    If you get an error message about a module not being installed, check that
-    the `invokeai` environment is active and if not, repeat step 5.
-
-9.  Run the command-line- or the web- interface:
-
-    !!! example ""
-
-        !!! warning "Make sure that the conda environment is activated, which should create `(invokeai)` in front of your prompt!"
-
-        === "CLI"
-
-            ```bash
-            python scripts/invoke.py
-            ```
-
-        === "local Webserver"
-
-            ```bash
-            python scripts/invoke.py --web
-            ```
-
-        === "Public Webserver"
-
-            ```bash
-            python scripts/invoke.py --web --host 0.0.0.0
-            ```
-
-        If you choose the run the web interface, point your browser at
-        http://localhost:9090 in order to load the GUI.
-
-10. Render away!
-
-    Browse the [features](../features/CLI.md) section to learn about all the things you
-    can do with InvokeAI.
-
-    Note that some GPUs are slow to warm up. In particular, when using an AMD
-    card with the ROCm driver, you may have to wait for over a minute the first
-    time you try to generate an image. Fortunately, after the warm up period
-    rendering will be fast.
-
-11. Subsequently, to relaunch the script, be sure to run "conda activate
-    invokeai", enter the `InvokeAI` directory, and then launch the invoke
-    script. If you forget to activate the 'invokeai' environment, the script
-    will fail with multiple `ModuleNotFound` errors.
-
-## Updating to newer versions of the script
-
-This distribution is changing rapidly. If you used the `git clone` method
-(step 5) to download the InvokeAI directory, then to update to the latest and
-greatest version, launch the Anaconda window, enter `InvokeAI` and type:
-
-```bash
-git pull
-conda env update
-python scripts/configure_invokeai.py --no-interactive  #optional
-```
-
-This will bring your local copy into sync with the remote one. The last step may
-be needed to take advantage of new features or released models. The
-`--no-interactive` flag will prevent the script from prompting you to download
-the big Stable Diffusion weights files.
-
-## pip Install
-
-To install InvokeAI with only the PIP package manager, please follow these
-steps:
-
-1.  Make sure you are using Python 3.9 or higher. The rest of the install
-    procedure depends on this:
-
-    ```bash
-    python -V
-    ```
-
-2.  Install the `virtualenv` tool if you don't have it already:
-
-    ```bash
-    pip install virtualenv
-    ```
-
-3.  From within the InvokeAI top-level directory, create and activate a virtual
-    environment named `invokeai`:
-
-    ```bash
-    virtualenv invokeai
-    source invokeai/bin/activate
-    ```
-
-4.  Run PIP
-
-    ```bash
-    pip --python invokeai install --use-pep517 .
-    ```
-
----
-
-## Troubleshooting
-
-Here are some common issues and their suggested solutions.
-
-### Conda
-
-#### Conda fails before completing `conda update`
-
-The usual source of these errors is a package incompatibility. While we have
-tried to minimize these, over time packages get updated and sometimes introduce
-incompatibilities.
-
-We suggest that you search
-[Issues](https://github.com/invoke-ai/InvokeAI/issues) or the "bugs-and-support"
-channel of the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy).
-
-You may also try to install the broken packages manually using PIP. To do this,
-activate the `invokeai` environment, and run `pip install` with the name and
-version of the package that is causing the incompatibility. For example:
-
-```bash
-pip install test-tube==0.7.5
-```
-
-You can keep doing this until all requirements are satisfied and the `invoke.py`
-script runs without errors. Please report to
-[Issues](https://github.com/invoke-ai/InvokeAI/issues) what you were able to do
-to work around the problem so that others can benefit from your investigation.
-
-### Create Conda Environment fails on MacOS
-
-If conda create environment fails with lmdb error, this is most likely caused by Clang.
-Run brew config to see which Clang is installed on your Mac. If Clang isn't installed, that's causing the error.
-Start by installing additional XCode command line tools, followed by brew install llvm.
-
-```bash
-xcode-select --install
-brew install llvm
-```
-
-If brew config has Clang installed, update to the latest llvm and try creating the environment again.
-
-#### `configure_invokeai.py` or `invoke.py` crashes at an early stage
-
-This is usually due to an incomplete or corrupted Conda install. Make sure you
-have linked to the correct environment file and run `conda update` again.
-
-If the problem persists, a more extreme measure is to clear Conda's caches and
-remove the `invokeai` environment:
-
-```bash
-conda deactivate
-conda env remove -n invokeai
-conda clean -a
-conda update
-```
-
-This removes all cached library files, including ones that may have been
-corrupted somehow. (This is not supposed to happen, but does anyway).
-
-#### `invoke.py` crashes at a later stage
-
-If the CLI or web site had been working ok, but something unexpected happens
-later on during the session, you've encountered a code bug that is probably
-unrelated to an install issue. Please search
-[Issues](https://github.com/invoke-ai/InvokeAI/issues), file a bug report, or
-ask for help on [Discord](https://discord.gg/ZmtBAhwWhy)
-
-#### My renders are running very slowly
-
-You may have installed the wrong torch (machine learning) package, and the
-system is running on CPU rather than the GPU. To check, look at the log messages
-that appear when `invoke.py` is first starting up. One of the earlier lines
-should say `Using device type cuda`. On AMD systems, it will also say "cuda",
-and on Macintoshes, it should say "mps". If instead the message says it is
-running on "cpu", then you may need to install the correct torch library.
-
-You may be able to fix this by installing a different torch library. Here are
-the magic incantations for Conda and PIP.
-
-!!! todo "For CUDA systems"
-
-    - conda
-
-      ```bash
-      conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
-      ```
-
-    - pip
-
-      ```bash
-      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
-      ```
-
-!!! todo "For AMD systems"
-
-    - conda
-
-      ```bash
-      conda activate invokeai
-      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
-      ```
-
-    - pip
-
-      ```bash
-      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
-      ```
-
-More information and troubleshooting tips can be found at https://pytorch.org.

docs/installation/index.md

@@ -3,7 +3,19 @@ title: Overview
 ---
 
 We offer several ways to install InvokeAI, each one suited to your
-experience and preferences.
+experience and preferences. We suggest that everyone start by
+reviewing the
+[hardware](010_INSTALL_AUTOMATED.md#hardware_requirements) and
+[software](010_INSTALL_AUTOMATED.md#software_requirements)
+requirements, as they are the same across each install method. Then
+pick the install method most suitable to your level of experience and
+needs.
+
+See the [troubleshooting
+section](010_INSTALL_AUTOMATED.md#troubleshooting) of the automated
+install guide for frequently-encountered installation issues.
+
+## Main Application
 
 1. [Automated Installer](010_INSTALL_AUTOMATED.md)
 
@@ -18,8 +30,8 @@ experience and preferences.
     InvokeAI and its dependencies. We offer two recipes: one suited to
     those who prefer the `conda` tool, and one suited to those who prefer
     `pip` and Python virtual environments. In our hands the pip install
-    is faster and more reliable, but your mileage may vary. 
-    Note that the conda installation method is currently deprecated and 
+    is faster and more reliable, but your mileage may vary.
+    Note that the conda installation method is currently deprecated and
     will not be supported at some point in the future.
 
     This method is recommended for users who have previously used `conda`
@@ -33,3 +45,10 @@ experience and preferences.
     InvokeAI and its dependencies. This method is recommended for
     individuals with experience with Docker containers and understand
     the pluses and minuses of a container-based install.
+
+## Quick Guides
+
+* [Installing CUDA and ROCm Drivers](./030_INSTALL_CUDA_AND_ROCM.md)
+* [Installing XFormers](./070_INSTALL_XFORMERS.md)
+* [Installing PyPatchMatch](./060_INSTALL_PATCHMATCH.md)
+* [Installing New Models](./050_INSTALLING_MODELS.md)

docs/openapi3_0.yaml

@@ -1,73 +0,0 @@
-openapi: 3.0.3
-info:
-  title: Stable Diffusion
-  description: |-
-    TODO: Description Here
-
-    Some useful links:
-    - [Stable Diffusion Dream Server](https://github.com/lstein/stable-diffusion)
-
-  license:
-    name: MIT License
-    url: https://github.com/lstein/stable-diffusion/blob/main/LICENSE
-  version: 1.0.0
-servers:
-  - url: http://localhost:9090/api
-tags:
-  - name: images
-    description: Retrieve and manage generated images
-paths:
-  /images/{imageId}:
-    get:
-      tags:
-        - images
-      summary: Get image by ID
-      description: Returns a single image
-      operationId: getImageById
-      parameters:
-        - name: imageId
-          in: path
-          description: ID of image to return
-          required: true
-          schema:
-            type: string
-      responses:
-        '200':
-          description: successful operation
-          content:
-            image/png:
-              schema:
-                type: string
-                format: binary
-        '404':
-          description: Image not found
-  /intermediates/{intermediateId}/{step}:
-    get:
-      tags:
-        - images
-      summary: Get intermediate image by ID
-      description: Returns a single intermediate image
-      operationId: getIntermediateById
-      parameters:
-        - name: intermediateId
-          in: path
-          description: ID of intermediate to return
-          required: true
-          schema:
-            type: string
-        - name: step
-          in: path
-          description: The generation step of the intermediate
-          required: true
-          schema:
-            type: string
-      responses:
-        '200':
-          description: successful operation
-          content:
-            image/png:
-              schema:
-                type: string
-                format: binary
-        '404':
-          description: Intermediate not found

docs/other/CONTRIBUTORS.md

@@ -23,9 +23,11 @@ We thank them for all of their time and hard work.
 * @damian0815 - Attention Systems and Gameplay Engineer
 * @mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
 * @Netsvetaev (Artur Netsvetaev) - UI/UX Developer
-* @tildebyte - general gadfly and resident (self-appointed) know-it-all
+* @tildebyte - General gadfly and resident (self-appointed) know-it-all
 * @keturn - Lead for Diffusers port
 * @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
+* @jpphoto (Jonathan Pollack) - Inference and rendering engine optimization
+* @genomancer (Gregg Helt) - Model training and merging
 
 ## **Contributions by**
 

docs/other/TRANSLATION.md

@@ -0,0 +1,19 @@
+# Translation
+
+InvokeAI uses [Weblate](https://weblate.org) for translation. Weblate is a FOSS project providing a scalable translation service. Weblate automates the tedious parts of managing translation of a growing project, and the service is generously provided at no cost to FOSS projects like InvokeAI.
+
+## Contributing
+
+If you'd like to contribute by adding or updating a translation, please visit our [Weblate project](https://hosted.weblate.org/engage/invokeai/). You'll need to sign in with your GitHub account (a number of other accounts are supported, including Google).
+
+Once signed in, select a language and then the Web UI component. From here you can Browse and Translate strings from English to your chosen language. Zen mode offers a simpler translation experience.
+
+Your changes will be attributed to you in the automated PR process; you don't need to do anything else.
+
+## Help & Questions
+
+Please check Weblate's [documentation](https://docs.weblate.org/en/latest/index.html) or ping @psychedelicious or @blessedcoolant on Discord if you have any questions.
+
+## Thanks
+
+Thanks to the InvokeAI community for their efforts to translate the project!

docs/swagger-ui/index.css

@@ -1,16 +0,0 @@
-html {
-    box-sizing: border-box;
-    overflow: -moz-scrollbars-vertical;
-    overflow-y: scroll;
-}
-
-*,
-*:before,
-*:after {
-    box-sizing: inherit;
-}
-
-body {
-    margin: 0;
-    background: #fafafa;
-}

docs/swagger-ui/oauth2-redirect.html

@@ -1,79 +0,0 @@
-<!doctype html>
-<html lang="en-US">
-<head>
-    <title>Swagger UI: OAuth2 Redirect</title>
-</head>
-<body>
-<script>
-    'use strict';
-    function run () {
-        var oauth2 = window.opener.swaggerUIRedirectOauth2;
-        var sentState = oauth2.state;
-        var redirectUrl = oauth2.redirectUrl;
-        var isValid, qp, arr;
-
-        if (/code|token|error/.test(window.location.hash)) {
-            qp = window.location.hash.substring(1).replace('?', '&');
-        } else {
-            qp = location.search.substring(1);
-        }
-
-        arr = qp.split("&");
-        arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
-        qp = qp ? JSON.parse('{' + arr.join() + '}',
-                function (key, value) {
-                    return key === "" ? value : decodeURIComponent(value);
-                }
-        ) : {};
-
-        isValid = qp.state === sentState;
-
-        if ((
-          oauth2.auth.schema.get("flow") === "accessCode" ||
-          oauth2.auth.schema.get("flow") === "authorizationCode" ||
-          oauth2.auth.schema.get("flow") === "authorization_code"
-        ) && !oauth2.auth.code) {
-            if (!isValid) {
-                oauth2.errCb({
-                    authId: oauth2.auth.name,
-                    source: "auth",
-                    level: "warning",
-                    message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
-                });
-            }
-
-            if (qp.code) {
-                delete oauth2.state;
-                oauth2.auth.code = qp.code;
-                oauth2.callback({auth: oauth2.auth, redirectUrl: redirectUrl});
-            } else {
-                let oauthErrorMsg;
-                if (qp.error) {
-                    oauthErrorMsg = "["+qp.error+"]: " +
-                        (qp.error_description ? qp.error_description+ ". " : "no accessCode received from the server. ") +
-                        (qp.error_uri ? "More info: "+qp.error_uri : "");
-                }
-
-                oauth2.errCb({
-                    authId: oauth2.auth.name,
-                    source: "auth",
-                    level: "error",
-                    message: oauthErrorMsg || "[Authorization failed]: no accessCode received from the server."
-                });
-            }
-        } else {
-            oauth2.callback({auth: oauth2.auth, token: qp, isValid: isValid, redirectUrl: redirectUrl});
-        }
-        window.close();
-    }
-
-    if (document.readyState !== 'loading') {
-        run();
-    } else {
-        document.addEventListener('DOMContentLoaded', function () {
-            run();
-        });
-    }
-</script>
-</body>
-</html>

docs/swagger-ui/swagger-initializer.js

@@ -1,20 +0,0 @@
-window.onload = function() {
-  //<editor-fold desc="Changeable Configuration Block">
-
-  // the following lines will be replaced by docker/configurator, when it runs in a docker-container
-  window.ui = SwaggerUIBundle({
-    url: "openapi3_0.yaml",
-    dom_id: '#swagger-ui',
-    deepLinking: true,
-    presets: [
-      SwaggerUIBundle.presets.apis,
-      SwaggerUIStandalonePreset
-    ],
-    plugins: [
-      SwaggerUIBundle.plugins.DownloadUrl
-    ],
-    layout: "StandaloneLayout"
-  });
-
-  //</editor-fold>
-};