illustration of two generate alternatives

.coveragerc

@@ -0,0 +1,6 @@
+[run]
+omit='.env/*'
+source='.'
+
+[report]
+show_missing = true

.dockerignore

@@ -4,22 +4,22 @@
 !ldm
 !pyproject.toml
 
-# ignore frontend/web but whitelist dist
-invokeai/frontend/web/
-!invokeai/frontend/web/dist/
+# Guard against pulling in any models that might exist in the directory tree
+**/*.pt*
+**/*.ckpt
+
+# ignore frontend but whitelist dist
+invokeai/frontend/
+!invokeai/frontend/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
-
 # Byte-compiled / optimized / DLL files
 **/__pycache__/
 **/*.py[cod]
 
 # Distribution / packaging
-**/*.egg-info/
-**/*.egg
+*.egg-info/
+*.egg

.git-blame-ignore-revs

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

.github/CODEOWNERS

@@ -3,32 +3,49 @@
 
 # documentation
 /docs/ @lstein @mauwii @tildebyte @blessedcoolant
-/mkdocs.yml @lstein @mauwii @blessedcoolant
-
-# nodes
-/invokeai/app/ @Kyle0654 @blessedcoolant
+mkdocs.yml @lstein @mauwii @blessedcoolant
 
 # installation and configuration
-/pyproject.toml @mauwii @lstein @blessedcoolant
+/pyproject.toml @mauwii @lstein @ebr @blessedcoolant
 /docker/ @mauwii @lstein @blessedcoolant
-/scripts/ @ebr @lstein
-/installer/ @lstein @ebr
-/invokeai/assets @lstein @ebr
-/invokeai/configs @lstein
-/invokeai/version @lstein @blessedcoolant
+/scripts/ @ebr @lstein @blessedcoolant
+/installer/ @ebr @lstein @tildebyte  @blessedcoolant
+ldm/invoke/config @lstein @ebr  @blessedcoolant
+invokeai/assets @lstein @ebr  @blessedcoolant
+invokeai/configs @lstein @ebr  @blessedcoolant
+/ldm/invoke/_version.py @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
+# generation and model management
+/ldm/*.py @lstein @blessedcoolant
+/ldm/generate.py @lstein @keturn @blessedcoolant
+/ldm/invoke/args.py @lstein @blessedcoolant
+/ldm/invoke/ckpt* @lstein @blessedcoolant
+/ldm/invoke/ckpt_generator @lstein @blessedcoolant
+/ldm/invoke/CLI.py @lstein @blessedcoolant
+/ldm/invoke/config @lstein @ebr @mauwii @blessedcoolant
+/ldm/invoke/generator @keturn @damian0815 @blessedcoolant
+/ldm/invoke/globals.py @lstein @blessedcoolant 
+/ldm/invoke/merge_diffusers.py @lstein @blessedcoolant
+/ldm/invoke/model_manager.py @lstein @blessedcoolant
+/ldm/invoke/txt2mask.py @lstein @blessedcoolant
+/ldm/invoke/patchmatch.py @Kyle0654 @blessedcoolant @lstein
+/ldm/invoke/restoration @lstein @blessedcoolant
 
-# 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
+# attention, textual inversion, model configuration
+/ldm/models @damian0815 @keturn @lstein @blessedcoolant
+/ldm/modules @damian0815 @keturn @lstein @blessedcoolant
 
+# Nodes
+apps/ @Kyle0654 @lstein @blessedcoolant
+
+# legacy REST API
+# is CapableWeb still engaged?
+/ldm/invoke/pngwriter.py @CapableWeb @lstein @blessedcoolant
+/ldm/invoke/server_legacy.py @CapableWeb @lstein @blessedcoolant
+/scripts/legacy_api.py @CapableWeb @lstein @blessedcoolant
+/tests/legacy_tests.sh @CapableWeb @lstein @blessedcoolant
 

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -65,16 +65,6 @@ 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/stale.yaml

@@ -1,19 +0,0 @@
-# Number of days of inactivity before an issue becomes stale
-daysUntilStale: 28
-# Number of days of inactivity before a stale issue is closed
-daysUntilClose: 14
-# Issues with these labels will never be considered stale
-exemptLabels:
-  - pinned
-  - security
-# Label to use when marking an issue as stale
-staleLabel: stale
-# Comment to post when marking an issue as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Please
-  update the ticket if this is still a problem on the latest release.
-# Comment to post when closing a stale issue. Set to `false` to disable
-closeComment: >
-  Due to inactivity, this issue has been automatically closed. If this is
-  still a problem on the latest release, please recreate the issue.

.github/workflows/build-container.yml

@@ -5,20 +5,17 @@ on:
       - 'main'
       - 'update/ci/docker/*'
       - 'update/docker/*'
-      - 'dev/ci/docker/*'
-      - 'dev/docker/*'
     paths:
       - 'pyproject.toml'
-      - '.dockerignore'
-      - 'invokeai/**'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
       - 'docker/Dockerfile'
     tags:
       - 'v*.*.*'
   workflow_dispatch:
 
-permissions:
-  contents: write
-  packages: write
 
 jobs:
   docker:
@@ -27,11 +24,11 @@ jobs:
       fail-fast: false
       matrix:
         flavor:
-          - rocm
+          - amd
           - cuda
           - cpu
         include:
-          - flavor: rocm
+          - flavor: amd
             pip-extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
           - flavor: cuda
             pip-extra-index-url: ''
@@ -57,9 +54,9 @@ jobs:
           tags: |
             type=ref,event=branch
             type=ref,event=tag
-            type=pep440,pattern={{version}}
-            type=pep440,pattern={{major}}.{{minor}}
-            type=pep440,pattern={{major}}
+            type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
+            type=semver,pattern={{major}}
             type=sha,enable=true,prefix=sha-,format=short
           flavor: |
             latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}
@@ -95,7 +92,7 @@ jobs:
           context: .
           file: ${{ env.DOCKERFILE }}
           platforms: ${{ env.PLATFORMS }}
-          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
+          push: ${{ github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           build-args: PIP_EXTRA_INDEX_URL=${{ matrix.pip-extra-index-url }}

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

@@ -1,27 +0,0 @@
-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

@@ -3,22 +3,14 @@ name: Lint frontend
 on:
   pull_request:
     paths:
-      - 'invokeai/frontend/web/**'
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
+      - 'invokeai/frontend/**'
   push:
-    branches:
-      - 'main'
     paths:
-      - 'invokeai/frontend/web/**'
-  merge_group:
-  workflow_dispatch:
+      - 'invokeai/frontend/**'
 
 defaults:
   run:
-    working-directory: invokeai/frontend/web
+    working-directory: invokeai/frontend
 
 jobs:
   lint-frontend:
@@ -31,7 +23,7 @@ jobs:
           node-version: '18'
       - uses: actions/checkout@v3
       - run: 'yarn install --frozen-lockfile'
-      - run: 'yarn run lint:tsc'
-      - run: 'yarn run lint:madge'
-      - run: 'yarn run lint:eslint'
-      - run: 'yarn run lint:prettier'
+      - run: 'yarn tsc'
+      - run: 'yarn run madge'
+      - run: 'yarn run lint --max-warnings=0'
+      - run: 'yarn run prettier --check'

.github/workflows/mkdocs-material.yml

@@ -5,9 +5,6 @@ on:
       - 'main'
       - 'development'
 
-permissions:
-    contents: write
-
 jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false

.github/workflows/pypi-release.yml

@@ -3,7 +3,7 @@ name: PyPI Release
 on:
   push:
     paths:
-      - 'invokeai/version/invokeai_version.py'
+      - 'ldm/invoke/_version.py'
   workflow_dispatch:
 
 jobs:

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

@@ -1,11 +1,12 @@
 name: Test invoke.py pip
 on:
   pull_request:
-    paths:
-      - '**'
-      - '!pyproject.toml'
-      - '!invokeai/**'
-      - 'invokeai/frontend/web/**'
+    paths-ignore:
+      - 'pyproject.toml'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
   merge_group:
   workflow_dispatch:
 

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

@@ -5,13 +5,17 @@ on:
       - 'main'
     paths:
       - 'pyproject.toml'
-      - 'invokeai/**'
-      - '!invokeai/frontend/web/**'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
   pull_request:
     paths:
       - 'pyproject.toml'
-      - 'invokeai/**'
-      - '!invokeai/frontend/web/**'
+      - 'ldm/**'
+      - 'invokeai/backend/**'
+      - 'invokeai/configs/**'
+      - 'invokeai/frontend/dist/**'
     types:
       - 'ready_for_review'
       - 'opened'
@@ -108,7 +112,7 @@ jobs:
       - 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}')"
+          "import os;from ldm.invoke.globals import Globals;OUTDIR=os.path.join(Globals.root,str('outputs'));print(f'INVOKEAI_OUTDIR={OUTDIR}')"
           >> ${{ matrix.github-env }}
 
       - name: run invokeai-configure

.gitignore

@@ -63,7 +63,6 @@ pip-delete-this-directory.txt
 htmlcov/
 .tox/
 .nox/
-.coveragerc
 .coverage
 .coverage.*
 .cache
@@ -74,7 +73,6 @@ cov.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
-.pytest.ini
 cover/
 junit/
 
@@ -200,7 +198,7 @@ checkpoints
 .DS_Store
 
 # Let the frontend manage its own gitignore
-!invokeai/frontend/web/*
+!invokeai/frontend/*
 
 # Scratch folder
 .scratch/
@@ -215,6 +213,11 @@ 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
 
@@ -229,3 +232,6 @@ installer/install.bat
 installer/install.sh
 installer/update.bat
 installer/update.sh
+
+# no longer stored in source directory
+models

.pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+DJANGO_SETTINGS_MODULE = webtas.settings
+; python_files = tests.py test_*.py *_tests.py
+
+addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml

README.md

@@ -84,7 +84,7 @@ installing lots of models.
 
 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 generation models.
+select a set of starting image generaiton models.
 
 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
@@ -139,13 +139,13 @@ not supported.
     _For Windows/Linux with an NVIDIA GPU:_
 
     ```terminal
-    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+    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
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
     _For Macintoshes, either Intel or M1/M2:_

coverage/.gitignore

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

docker/Dockerfile

@@ -4,15 +4,15 @@ ARG PYTHON_VERSION=3.9
 ##################
 ##  base image  ##
 ##################
-FROM --platform=${TARGETPLATFORM} python:${PYTHON_VERSION}-slim AS python-base
+FROM python:${PYTHON_VERSION}-slim AS python-base
 
 LABEL org.opencontainers.image.authors="mauwii@outlook.de"
 
-# Prepare apt for buildkit cache
+# prepare 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
+# Install necessary packages
 RUN \
   --mount=type=cache,target=/var/cache/apt,sharing=locked \
   --mount=type=cache,target=/var/lib/apt,sharing=locked \
@@ -23,7 +23,7 @@ RUN \
     libglib2.0-0=2.66.* \
     libopencv-dev=4.5.*
 
-# Set working directory and env
+# set working directory and env
 ARG APPDIR=/usr/src
 ARG APPNAME=InvokeAI
 WORKDIR ${APPDIR}
@@ -32,7 +32,7 @@ ENV PATH ${APPDIR}/${APPNAME}/bin:$PATH
 ENV PYTHONDONTWRITEBYTECODE 1
 # Turns off buffering for easier container logging
 ENV PYTHONUNBUFFERED 1
-# Don't fall back to legacy build system
+# don't fall back to legacy build system
 ENV PIP_USE_PEP517=1
 
 #######################
@@ -40,7 +40,7 @@ ENV PIP_USE_PEP517=1
 #######################
 FROM python-base AS pyproject-builder
 
-# Install build dependencies
+# Install dependencies
 RUN \
   --mount=type=cache,target=/var/cache/apt,sharing=locked \
   --mount=type=cache,target=/var/lib/apt,sharing=locked \
@@ -51,30 +51,26 @@ RUN \
     gcc=4:10.2.* \
     python3-dev=3.9.*
 
-# Prepare pip for buildkit cache
+# 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=${PIP_CACHE_DIR} \
+# create virtual environment
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
   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/
+# copy sources
+COPY --link . .
+
+# install pyproject.toml
 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} \
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
   "${APPNAME}/bin/pip" install .
 
-# Build patchmatch
+# build patchmatch
 RUN python3 -c "from patchmatch import patch_match"
 
 #####################
@@ -90,14 +86,14 @@ RUN useradd \
   -U \
   "${UNAME}"
 
-# Create volume directory
+# create volume directory
 ARG VOLUME_DIR=/data
 RUN mkdir -p "${VOLUME_DIR}" \
-  && chown -hR "${UNAME}:${UNAME}" "${VOLUME_DIR}"
+  && chown -R "${UNAME}" "${VOLUME_DIR}"
 
-# Setup runtime environment
-USER ${UNAME}:${UNAME}
-COPY --chown=${UNAME}:${UNAME} --from=pyproject-builder ${APPDIR}/${APPNAME} ${APPNAME}
+# setup runtime environment
+USER ${UNAME}
+COPY --chown=${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"

docker/build.sh

@@ -41,7 +41,7 @@ else
 fi
 
 # Build Container
-docker build \
+DOCKER_BUILDKIT=1 docker build \
     --platform="${PLATFORM:-linux/amd64}" \
     --tag="${CONTAINER_IMAGE:-invokeai}" \
     ${CONTAINER_FLAVOR:+--build-arg="CONTAINER_FLAVOR=${CONTAINER_FLAVOR}"} \

docker/env.sh

@@ -49,6 +49,3 @@ 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

@@ -21,10 +21,10 @@ docker run \
   --tty \
   --rm \
   --platform="${PLATFORM}" \
-  --name="${REPOSITORY_NAME}" \
-  --hostname="${REPOSITORY_NAME}" \
-  --mount type=volume,volume-driver=local,source="${VOLUMENAME}",target=/data \
-  --mount type=bind,source="$(pwd)"/outputs/,target=/data/outputs/ \
+  --name="${REPOSITORY_NAME,,}" \
+  --hostname="${REPOSITORY_NAME,,}" \
+  --mount=source="${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 \
@@ -32,7 +32,7 @@ docker run \
   ${GPU_FLAGS:+--gpus="${GPU_FLAGS}"} \
   "${CONTAINER_IMAGE}" ${@:+$@}
 
-echo -e "\nCleaning trash folder ..."
+# Remove Trash folder
 for f in outputs/.Trash*; do
   if [ -e "$f" ]; then
     rm -Rf "$f"

docs/contributing/INVOCATIONS.md

@@ -1,18 +1,10 @@
 # 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.
+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.
+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:
 
@@ -49,54 +41,34 @@ class UpscaleInvocation(BaseInvocation):
 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.
+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. |
 
-Inputs consist of three parts: a name, a type hint, and a `Field` with default,
-description, and validation information. For example:
+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.
 
-| 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. |
+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).
 
-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.
+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)
@@ -116,22 +88,13 @@ previous invocation by name and matching.
             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`.
 
-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).
 
-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.
+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"""
@@ -139,64 +102,4 @@ class ImageOutput(BaseInvocationOutput):
 
     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.
-
-## Schema Generation
-
-Invocation, output and related classes are used to generate an OpenAPI schema.
-
-### Required Properties
-
-The schema generation treat all properties with default values as optional. This
-makes sense internally, but when when using these classes via the generated
-schema, we end up with e.g. the `ImageOutput` class having its `image` property
-marked as optional.
-
-We know that this property will always be present, so the additional logic
-needed to always check if the property exists adds a lot of extraneous cruft.
-
-To fix this, we can leverage `pydantic`'s
-[schema customisation](https://docs.pydantic.dev/usage/schema/#schema-customization)
-to mark properties that we know will always be present as required.
-
-Here's that `ImageOutput` class, without the needed schema customisation:
-
-```python
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    type: Literal["image"] = "image"
-    image:      ImageField = Field(default=None, description="The output image")
-```
-
-The generated OpenAPI schema, and all clients/types generated from it, will have
-the `type` and `image` properties marked as optional, even though we know they
-will always have a value by the time we can interact with them via the API.
-
-Here's the same class, but with the schema customisation added:
-
-```python
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    type: Literal["image"] = "image"
-    image:      ImageField = Field(default=None, description="The output image")
-
-    class Config:
-        schema_extra = {
-            'required': [
-                'type',
-                'image',
-            ]
-        }
-```
-
-The resultant schema (and any API client or types generated from it) will now
-have see `type` as string literal `"image"` and `image` as an `ImageField`
-object.
-
-See this `pydantic` issue for discussion on this solution:
-<https://github.com/pydantic/pydantic/discussions/4577>
+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

@@ -1,83 +0,0 @@
-# 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/INPAINTING.md

@@ -168,15 +168,11 @@ used by Stable Diffusion 1.4 and 1.5.
 After installation, your `models.yaml` should contain an entry that looks like
 this one:
 
-```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
-```
+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/PROMPTS.md

@@ -268,7 +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 hotdog
+invoke> a fluffy cat eating a hotdot
 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

@@ -17,7 +17,7 @@ notebooks.
 
 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
+library](../installation/070_INSTALL_XFORMERS) to accelerate the
 training process further. During training, about ~8 GB is temporarily
 needed in order to store intermediate models, checkpoints and logs.
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -417,7 +417,7 @@ Then type the following commands:
 
 === "AMD System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
 ### Corrupted configuration file

docs/installation/020_INSTALL_MANUAL.md

@@ -148,13 +148,13 @@ manager, please follow these steps:
     === "CUDA (NVidia)"
 
         ```bash
-        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+        pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
         ```
 
     === "ROCm (AMD)"
 
         ```bash
-        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
         ```
 
     === "CPU (Intel Macs & non-GPU systems)"
@@ -315,7 +315,7 @@ installation protocol (important!)
 
     === "ROCm (AMD)"
         ```bash
-        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
         ```
 
     === "CPU (Intel Macs & non-GPU systems)"

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -110,7 +110,7 @@ recipes are available
 
 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
+https://download.pytorch.org/whl/rocm5.2` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 This will be done automatically for you if you use the installer

docs/installation/050_INSTALLING_MODELS.md

@@ -50,7 +50,7 @@ subset that are currently installed are found in
 |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-inpainting|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-inpainting |
+|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 |

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 successfully installing opencv, pypatchmatch will be built.
+The next time you start `invoke`, after sucesfully 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 following:
+   `from patchmatch import patch_match`: It should look like the follwing:
 
     ```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 you're ready to go!
+If you see no errors, then you're ready to go!

installer/create_installer.sh

@@ -11,10 +11,10 @@ if [[ -v "VIRTUAL_ENV" ]]; then
     exit -1
 fi
 
-VERSION=$(cd ..; python -c "from invokeai.version import __version__ as version; print(version)")
+VERSION=$(cd ..; python -c "from ldm.invoke import __version__ as version; print(version)")
 PATCH=""
 VERSION="v${VERSION}${PATCH}"
-LATEST_TAG="v3.0-latest"
+LATEST_TAG="v2.3-latest"
 
 echo Building installer for version $VERSION
 echo "Be certain that you're in the 'installer' directory before continuing."

installer/lib/installer.py

@@ -291,7 +291,7 @@ class InvokeAiInstance:
                     src = Path(__file__).parents[1].expanduser().resolve()
                     # if the above directory contains one of these files, we'll do a source install
                     next(src.glob("pyproject.toml"))
-                    next(src.glob("invokeai"))
+                    next(src.glob("ldm"))
                 except StopIteration:
                     print("Unable to find a wheel or perform a source install. Giving up.")
 
@@ -342,14 +342,14 @@ class InvokeAiInstance:
 
         introduction()
 
-        from invokeai.frontend.install import invokeai_configure
+        from ldm.invoke.config import invokeai_configure
 
         # NOTE: currently the config script does its own arg parsing! this means the command-line switches
         # from the installer will also automatically propagate down to the config script.
         # this may change in the future with config refactoring!
         succeeded = False
         try:
-            invokeai_configure()
+            invokeai_configure.main()
             succeeded = True
         except requests.exceptions.ConnectionError as e:
             print(f'\nA network error was encountered during configuration and download: {str(e)}')
@@ -456,7 +456,7 @@ def get_torch_source() -> (Union[str, None],str):
     optional_modules = None
     if OS == "Linux":
         if device == "rocm":
-            url = "https://download.pytorch.org/whl/rocm5.4.2"
+            url = "https://download.pytorch.org/whl/rocm5.2"
         elif device == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 

installer/templates/invoke.sh.in

@@ -24,9 +24,9 @@ if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
+while true
+do
 if [ "$0" != "bash" ]; then
-  while true
-  do
     echo "Do you want to generate images using the"
     echo "1. command-line interface"
     echo "2. browser-based UI"
@@ -67,29 +67,29 @@ if [ "$0" != "bash" ]; then
             ;;
         7)
             invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
-            ;;
-        8)
-            echo "Developer Console:"
+	    ;;
+	8)
+	    echo "Developer Console:"
             file_name=$(basename "${BASH_SOURCE[0]}")
             bash --init-file "$file_name"
             ;;
         9)
-            echo "Update:"
+	    echo "Update:"
             invokeai-update
             ;;
         10)
             invokeai --help
             ;;
-        [qQ])
+	[qQ])
             exit 0
             ;;
         *)
             echo "Invalid selection"
             exit;;
     esac
- done
 else # in developer console
     python --version
     echo "Press ^D to exit"
     export PS1="(InvokeAI) \u@\h \w> "
 fi
+done

invokeai/README

@@ -1,11 +1,3 @@
-Organization of the source tree:
-
-app -- Home of nodes invocations and services
-assets -- Images and other data files used by InvokeAI
-backend -- Non-user facing libraries, including the rendering
-	core.
-configs -- Configuration files used at install and run times
-frontend -- User-facing scripts, including the CLI and the WebUI
-version -- Current InvokeAI version string, stored
-	in version/invokeai_version.py
-	
+After version 2.3 is released, the ldm/invoke modules will be migrated to this location
+so that we have a proper invokeai distribution. Currently it is only being used for
+data files.

invokeai/app/api/dependencies.py

@@ -1,96 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import os
-from argparse import Namespace
-
-from invokeai.app.services.metadata import PngMetadataService, MetadataServiceBase
-
-from ..services.default_graphs import create_system_graphs
-
-from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-
-from ...backend import Globals
-from ..services.model_manager_initializer import get_model_manager
-from ..services.restoration_services import RestorationServices
-from ..services.graph import GraphExecutionState, LibraryGraph
-from ..services.image_storage import DiskImageStorage
-from ..services.invocation_queue import MemoryInvocationQueue
-from ..services.invocation_services import InvocationServices
-from ..services.invoker import Invoker
-from ..services.processor import DefaultInvocationProcessor
-from ..services.sqlite import SqliteItemStorage
-from .events import FastAPIEventService
-
-
-# TODO: is there a better way to achieve this?
-def check_internet() -> bool:
-    """
-    Return true if the internet is reachable.
-    It does this by pinging huggingface.co.
-    """
-    import urllib.request
-
-    host = "http://huggingface.co"
-    try:
-        urllib.request.urlopen(host, timeout=1)
-        return True
-    except:
-        return False
-
-
-class ApiDependencies:
-    """Contains and initializes all dependencies for the API"""
-
-    invoker: Invoker = None
-
-    @staticmethod
-    def initialize(config, event_handler_id: int):
-        Globals.try_patchmatch = config.patchmatch
-        Globals.always_use_cpu = config.always_use_cpu
-        Globals.internet_available = config.internet_available and check_internet()
-        Globals.disable_xformers = not config.xformers
-        Globals.ckpt_convert = config.ckpt_convert
-
-        # TODO: Use a logger
-        print(f">> Internet connectivity is {Globals.internet_available}")
-
-        events = FastAPIEventService(event_handler_id)
-
-        output_folder = os.path.abspath(
-            os.path.join(os.path.dirname(__file__), "../../../../outputs")
-        )
-
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents'))
-
-        metadata = PngMetadataService()
-
-        images = DiskImageStorage(f'{output_folder}/images', metadata_service=metadata)
-
-        # TODO: build a file/path manager?
-        db_location = os.path.join(output_folder, "invokeai.db")
-
-        services = InvocationServices(
-            model_manager=get_model_manager(config),
-            events=events,
-            latents=latents,
-            images=images,
-            metadata=metadata,
-            queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](
-                filename=db_location, table_name="graphs"
-            ),
-            graph_execution_manager=SqliteItemStorage[GraphExecutionState](
-                filename=db_location, table_name="graph_executions"
-            ),
-            processor=DefaultInvocationProcessor(),
-            restoration=RestorationServices(config),
-        )
-
-        create_system_graphs(services.graph_library)
-
-        ApiDependencies.invoker = Invoker(services)
-
-    @staticmethod
-    def shutdown():
-        if ApiDependencies.invoker:
-            ApiDependencies.invoker.stop()

invokeai/app/api/events.py

@@ -1,52 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import asyncio
-import threading
-from queue import Empty, Queue
-from typing import Any
-
-from fastapi_events.dispatcher import dispatch
-
-from ..services.events import EventServiceBase
-
-
-class FastAPIEventService(EventServiceBase):
-    event_handler_id: int
-    __queue: Queue
-    __stop_event: threading.Event
-
-    def __init__(self, event_handler_id: int) -> None:
-        self.event_handler_id = event_handler_id
-        self.__queue = Queue()
-        self.__stop_event = threading.Event()
-        asyncio.create_task(self.__dispatch_from_queue(stop_event=self.__stop_event))
-
-        super().__init__()
-
-    def stop(self, *args, **kwargs):
-        self.__stop_event.set()
-        self.__queue.put(None)
-
-    def dispatch(self, event_name: str, payload: Any) -> None:
-        self.__queue.put(dict(event_name=event_name, payload=payload))
-
-    async def __dispatch_from_queue(self, stop_event: threading.Event):
-        """Get events on from the queue and dispatch them, from the correct thread"""
-        while not stop_event.is_set():
-            try:
-                event = self.__queue.get(block=False)
-                if not event:  # Probably stopping
-                    continue
-
-                dispatch(
-                    event.get("event_name"),
-                    payload=event.get("payload"),
-                    middleware_id=self.event_handler_id,
-                )
-
-            except Empty:
-                await asyncio.sleep(0.001)
-                pass
-
-            except asyncio.CancelledError as e:
-                raise e  # Raise a proper error

invokeai/app/api/models/images.py

@@ -1,34 +0,0 @@
-from typing import Optional
-from pydantic import BaseModel, Field
-
-from invokeai.app.models.image import ImageType
-from invokeai.app.services.metadata import InvokeAIMetadata
-
-
-class ImageResponseMetadata(BaseModel):
-    """An image's metadata. Used only in HTTP responses."""
-
-    created: int = Field(description="The creation timestamp of the image")
-    width: int = Field(description="The width of the image in pixels")
-    height: int = Field(description="The height of the image in pixels")
-    invokeai: Optional[InvokeAIMetadata] = Field(
-        description="The image's InvokeAI-specific metadata"
-    )
-
-
-class ImageResponse(BaseModel):
-    """The response type for images"""
-
-    image_type: ImageType = Field(description="The type of the image")
-    image_name: str = Field(description="The name of the image")
-    image_url: str = Field(description="The url of the image")
-    thumbnail_url: str = Field(description="The url of the image's thumbnail")
-    metadata: ImageResponseMetadata = Field(description="The image's metadata")
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")

invokeai/app/api/routers/images.py

@@ -1,128 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-import io
-from datetime import datetime, timezone
-import json
-import os
-from typing import Any
-import uuid
-
-from fastapi import HTTPException, Path, Query, Request, UploadFile
-from fastapi.responses import FileResponse, Response
-from fastapi.routing import APIRouter
-from PIL import Image
-from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
-from invokeai.app.services.metadata import InvokeAIMetadata
-from invokeai.app.services.item_storage import PaginatedResults
-
-from ...services.image_storage import ImageType
-from ..dependencies import ApiDependencies
-
-images_router = APIRouter(prefix="/v1/images", tags=["images"])
-
-
-@images_router.get("/{image_type}/{image_name}", operation_id="get_image")
-async def get_image(
-    image_type: ImageType = Path(description="The type of image to get"),
-    image_name: str = Path(description="The name of the image to get"),
-) -> FileResponse | Response:
-    """Gets a result"""
-
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=image_type, image_name=image_name
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
-
-
-@images_router.get(
-    "/{image_type}/thumbnails/{image_name}", operation_id="get_thumbnail"
-)
-async def get_thumbnail(
-    image_type: ImageType = Path(description="The type of image to get"),
-    image_name: str = Path(description="The name of the image to get"),
-) -> FileResponse | Response:
-    """Gets a thumbnail"""
-
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=image_type, image_name=image_name, is_thumbnail=True
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
-
-
-@images_router.post(
-    "/uploads/",
-    operation_id="upload_image",
-    responses={
-        201: {
-            "description": "The image was uploaded successfully",
-            "model": ImageResponse,
-        },
-        415: {"description": "Image upload failed"},
-    },
-    status_code=201,
-)
-async def upload_image(
-    file: UploadFile, request: Request, response: Response
-) -> ImageResponse:
-    if not file.content_type.startswith("image"):
-        raise HTTPException(status_code=415, detail="Not an image")
-
-    contents = await file.read()
-
-    try:
-        img = Image.open(io.BytesIO(contents))
-    except:
-        # Error opening the image
-        raise HTTPException(status_code=415, detail="Failed to read image")
-
-    filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
-
-    (image_path, thumbnail_path, ctime) = ApiDependencies.invoker.services.images.save(
-        ImageType.UPLOAD, filename, img
-    )
-
-    invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
-
-    res = ImageResponse(
-        image_type=ImageType.UPLOAD,
-        image_name=filename,
-        image_url=f"api/v1/images/{ImageType.UPLOAD.value}/{filename}",
-        thumbnail_url=f"api/v1/images/{ImageType.UPLOAD.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
-        metadata=ImageResponseMetadata(
-            created=ctime,
-            width=img.width,
-            height=img.height,
-            invokeai=invokeai_metadata,
-        ),
-    )
-
-    response.status_code = 201
-    response.headers["Location"] = request.url_for(
-        "get_image", image_type=ImageType.UPLOAD.value, image_name=filename
-    )
-
-    return res
-
-
-@images_router.get(
-    "/",
-    operation_id="list_images",
-    responses={200: {"model": PaginatedResults[ImageResponse]}},
-)
-async def list_images(
-    image_type: ImageType = Query(
-        default=ImageType.RESULT, description="The type of images to get"
-    ),
-    page: int = Query(default=0, description="The page of images to get"),
-    per_page: int = Query(default=10, description="The number of images per page"),
-) -> PaginatedResults[ImageResponse]:
-    """Gets a list of images"""
-    result = ApiDependencies.invoker.services.images.list(image_type, page, per_page)
-    return result

invokeai/app/api/routers/models.py

@@ -1,251 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and 2023 Kent Keirsey (https://github.com/hipsterusername)
-
-import shutil
-import asyncio
-from typing import Annotated, Any, List, Literal, Optional, Union
-
-from fastapi.routing import APIRouter, HTTPException
-from pydantic import BaseModel, Field, parse_obj_as
-from pathlib import Path
-from ..dependencies import ApiDependencies
-from invokeai.backend.globals import Globals, global_converted_ckpts_dir
-from invokeai.backend.args import Args
-
-
-
-models_router = APIRouter(prefix="/v1/models", tags=["models"])
-
-
-class VaeRepo(BaseModel):
-    repo_id: str = Field(description="The repo ID to use for this VAE")
-    path: Optional[str] = Field(description="The path to the VAE")
-    subfolder: Optional[str] = Field(description="The subfolder to use for this VAE")
-
-class ModelInfo(BaseModel):
-    description: Optional[str] = Field(description="A description of the model")
-    
-class CkptModelInfo(ModelInfo):
-    format: Literal['ckpt'] = 'ckpt'
-
-    config: str = Field(description="The path to the model config")
-    weights: str = Field(description="The path to the model weights")
-    vae: str = Field(description="The path to the model VAE")
-    width: Optional[int] = Field(description="The width of the model")
-    height: Optional[int] = Field(description="The height of the model")
-
-class DiffusersModelInfo(ModelInfo):
-    format: Literal['diffusers'] = 'diffusers'
-
-    vae: Optional[VaeRepo] = Field(description="The VAE repo to use for this model")
-    repo_id: Optional[str] = Field(description="The repo ID to use for this model")
-    path: Optional[str] = Field(description="The path to the model")
-
-class CreateModelRequest(BaseModel):
-    name: str = Field(description="The name of the model")
-    info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
-
-class CreateModelResponse(BaseModel):
-    name: str = Field(description="The name of the new model")
-    info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
-    status: str = Field(description="The status of the API response")
-
-class ConversionRequest(BaseModel):
-    name: str = Field(description="The name of the new model")
-    info: CkptModelInfo = Field(description="The converted model info")
-    save_location: str = Field(description="The path to save the converted model weights")
-    
-
-class ConvertedModelResponse(BaseModel):
-    name: str = Field(description="The name of the new model")
-    info: DiffusersModelInfo = Field(description="The converted model info")
-
-class ModelsList(BaseModel):
-    models: dict[str, Annotated[Union[(CkptModelInfo,DiffusersModelInfo)], Field(discriminator="format")]]
-
-
-@models_router.get(
-    "/",
-    operation_id="list_models",
-    responses={200: {"model": ModelsList }},
-)
-async def list_models() -> ModelsList:
-    """Gets a list of models"""
-    models_raw = ApiDependencies.invoker.services.model_manager.list_models()
-    models = parse_obj_as(ModelsList, { "models": models_raw })
-    return models
-
-
-@models_router.post(
-    "/",
-    operation_id="update_model",
-    responses={200: {"status": "success"}},
-)
-async def update_model(
-    model_request: CreateModelRequest
-) -> CreateModelResponse:
-    """ Add Model """
-    model_request_info = model_request.info
-    info_dict = model_request_info.dict()
-    model_response = CreateModelResponse(name=model_request.name, info=model_request.info, status="success")
-
-    ApiDependencies.invoker.services.model_manager.add_model(
-        model_name=model_request.name,
-        model_attributes=info_dict,
-        clobber=True,
-    )
-
-    return model_response
-
-
-@models_router.delete(
-    "/{model_name}",
-    operation_id="del_model",
-    responses={
-        204: {
-        "description": "Model deleted successfully"
-        }, 
-        404: {
-        "description": "Model not found"
-        }
-    },
-)
-async def delete_model(model_name: str) -> None:
-    """Delete Model"""
-    model_names = ApiDependencies.invoker.services.model_manager.model_names()
-    model_exists = model_name in model_names
-
-    # check if model exists
-    print(f">> Checking for model {model_name}...")
-           
-    if model_exists:
-        print(f">> Deleting Model: {model_name}")
-        ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True)
-        print(f">> Model Deleted: {model_name}")
-        raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully")
-    
-    else:
-        print(f">> Model not found")
-        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
-    
-
-            # @socketio.on("convertToDiffusers")
-        # def convert_to_diffusers(model_to_convert: dict):
-        #     try:
-        #         if model_info := self.generate.model_manager.model_info(
-        #             model_name=model_to_convert["model_name"]
-        #         ):
-        #             if "weights" in model_info:
-        #                 ckpt_path = Path(model_info["weights"])
-        #                 original_config_file = Path(model_info["config"])
-        #                 model_name = model_to_convert["model_name"]
-        #                 model_description = model_info["description"]
-        #             else:
-        #                 self.socketio.emit(
-        #                     "error", {"message": "Model is not a valid checkpoint file"}
-        #                 )
-        #         else:
-        #             self.socketio.emit(
-        #                 "error", {"message": "Could not retrieve model info."}
-        #             )
-
-        #         if not ckpt_path.is_absolute():
-        #             ckpt_path = Path(Globals.root, ckpt_path)
-
-        #         if original_config_file and not original_config_file.is_absolute():
-        #             original_config_file = Path(Globals.root, original_config_file)
-
-        #         diffusers_path = Path(
-        #             ckpt_path.parent.absolute(), f"{model_name}_diffusers"
-        #         )
-
-        #         if model_to_convert["save_location"] == "root":
-        #             diffusers_path = Path(
-        #                 global_converted_ckpts_dir(), f"{model_name}_diffusers"
-        #             )
-
-        #         if (
-        #             model_to_convert["save_location"] == "custom"
-        #             and model_to_convert["custom_location"] is not None
-        #         ):
-        #             diffusers_path = Path(
-        #                 model_to_convert["custom_location"], f"{model_name}_diffusers"
-        #             )
-
-        #         if diffusers_path.exists():
-        #             shutil.rmtree(diffusers_path)
-
-        #         self.generate.model_manager.convert_and_import(
-        #             ckpt_path,
-        #             diffusers_path,
-        #             model_name=model_name,
-        #             model_description=model_description,
-        #             vae=None,
-        #             original_config_file=original_config_file,
-        #             commit_to_conf=opt.conf,
-        #         )
-
-        #         new_model_list = self.generate.model_manager.list_models()
-        #         socketio.emit(
-        #             "modelConverted",
-        #             {
-        #                 "new_model_name": model_name,
-        #                 "model_list": new_model_list,
-        #                 "update": True,
-        #             },
-        #         )
-        #         print(f">> Model Converted: {model_name}")
-        #     except Exception as e:
-        #         self.handle_exceptions(e)
-
-        # @socketio.on("mergeDiffusersModels")
-        # def merge_diffusers_models(model_merge_info: dict):
-        #     try:
-        #         models_to_merge = model_merge_info["models_to_merge"]
-        #         model_ids_or_paths = [
-        #             self.generate.model_manager.model_name_or_path(x)
-        #             for x in models_to_merge
-        #         ]
-        #         merged_pipe = merge_diffusion_models(
-        #             model_ids_or_paths,
-        #             model_merge_info["alpha"],
-        #             model_merge_info["interp"],
-        #             model_merge_info["force"],
-        #         )
-
-        #         dump_path = global_models_dir() / "merged_models"
-        #         if model_merge_info["model_merge_save_path"] is not None:
-        #             dump_path = Path(model_merge_info["model_merge_save_path"])
-
-        #         os.makedirs(dump_path, exist_ok=True)
-        #         dump_path = dump_path / model_merge_info["merged_model_name"]
-        #         merged_pipe.save_pretrained(dump_path, safe_serialization=1)
-
-        #         merged_model_config = dict(
-        #             model_name=model_merge_info["merged_model_name"],
-        #             description=f'Merge of models {", ".join(models_to_merge)}',
-        #             commit_to_conf=opt.conf,
-        #         )
-
-        #         if vae := self.generate.model_manager.config[models_to_merge[0]].get(
-        #             "vae", None
-        #         ):
-        #             print(f">> Using configured VAE assigned to {models_to_merge[0]}")
-        #             merged_model_config.update(vae=vae)
-
-        #         self.generate.model_manager.import_diffuser_model(
-        #             dump_path, **merged_model_config
-        #         )
-        #         new_model_list = self.generate.model_manager.list_models()
-
-        #         socketio.emit(
-        #             "modelsMerged",
-        #             {
-        #                 "merged_models": models_to_merge,
-        #                 "merged_model_name": model_merge_info["merged_model_name"],
-        #                 "model_list": new_model_list,
-        #                 "update": True,
-        #             },
-        #         )
-        #         print(f">> Models Merged: {models_to_merge}")
-        #         print(f">> New Model Added: {model_merge_info['merged_model_name']}")
-        #     except Exception as e:

invokeai/app/api/routers/sessions.py

@@ -1,287 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Annotated, List, Optional, Union
-
-from fastapi import Body, Path, Query
-from fastapi.responses import Response
-from fastapi.routing import APIRouter
-from pydantic.fields import Field
-
-from ...invocations import *
-from ...invocations.baseinvocation import BaseInvocation
-from ...services.graph import (
-    Edge,
-    EdgeConnection,
-    Graph,
-    GraphExecutionState,
-    NodeAlreadyExecutedError,
-)
-from ...services.item_storage import PaginatedResults
-from ..dependencies import ApiDependencies
-
-session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
-
-
-@session_router.post(
-    "/",
-    operation_id="create_session",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid json"},
-    },
-)
-async def create_session(
-    graph: Optional[Graph] = Body(
-        default=None, description="The graph to initialize the session with"
-    )
-) -> GraphExecutionState:
-    """Creates a new session, optionally initializing it with an invocation graph"""
-    session = ApiDependencies.invoker.create_execution_state(graph)
-    return session
-
-
-@session_router.get(
-    "/",
-    operation_id="list_sessions",
-    responses={200: {"model": PaginatedResults[GraphExecutionState]}},
-)
-async def list_sessions(
-    page: int = Query(default=0, description="The page of results to get"),
-    per_page: int = Query(default=10, description="The number of results per page"),
-    query: str = Query(default="", description="The query string to search for"),
-) -> PaginatedResults[GraphExecutionState]:
-    """Gets a list of sessions, optionally searching"""
-    if query == "":
-        result = ApiDependencies.invoker.services.graph_execution_manager.list(
-            page, per_page
-        )
-    else:
-        result = ApiDependencies.invoker.services.graph_execution_manager.search(
-            query, page, per_page
-        )
-    return result
-
-
-@session_router.get(
-    "/{session_id}",
-    operation_id="get_session",
-    responses={
-        200: {"model": GraphExecutionState},
-        404: {"description": "Session not found"},
-    },
-)
-async def get_session(
-    session_id: str = Path(description="The id of the session to get"),
-) -> GraphExecutionState:
-    """Gets a session"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        return Response(status_code=404)
-    else:
-        return session
-
-
-@session_router.post(
-    "/{session_id}/nodes",
-    operation_id="add_node",
-    responses={
-        200: {"model": str},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-)
-async def add_node(
-    session_id: str = Path(description="The id of the session"),
-    node: Annotated[
-        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
-    ] = Body(description="The node to add"),
-) -> str:
-    """Adds a node to the graph"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        return Response(status_code=404)
-
-    try:
-        session.add_node(node)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session.id
-    except NodeAlreadyExecutedError:
-        return Response(status_code=400)
-    except IndexError:
-        return Response(status_code=400)
-
-
-@session_router.put(
-    "/{session_id}/nodes/{node_path}",
-    operation_id="update_node",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-)
-async def update_node(
-    session_id: str = Path(description="The id of the session"),
-    node_path: str = Path(description="The path to the node in the graph"),
-    node: Annotated[
-        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
-    ] = Body(description="The new node"),
-) -> GraphExecutionState:
-    """Updates a node in the graph and removes all linked edges"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        return Response(status_code=404)
-
-    try:
-        session.update_node(node_path, node)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        return Response(status_code=400)
-    except IndexError:
-        return Response(status_code=400)
-
-
-@session_router.delete(
-    "/{session_id}/nodes/{node_path}",
-    operation_id="delete_node",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-)
-async def delete_node(
-    session_id: str = Path(description="The id of the session"),
-    node_path: str = Path(description="The path to the node to delete"),
-) -> GraphExecutionState:
-    """Deletes a node in the graph and removes all linked edges"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        return Response(status_code=404)
-
-    try:
-        session.delete_node(node_path)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        return Response(status_code=400)
-    except IndexError:
-        return Response(status_code=400)
-
-
-@session_router.post(
-    "/{session_id}/edges",
-    operation_id="add_edge",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-)
-async def add_edge(
-    session_id: str = Path(description="The id of the session"),
-    edge: Edge = Body(description="The edge to add"),
-) -> GraphExecutionState:
-    """Adds an edge to the graph"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        return Response(status_code=404)
-
-    try:
-        session.add_edge(edge)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        return Response(status_code=400)
-    except IndexError:
-        return Response(status_code=400)
-
-
-# TODO: the edge being in the path here is really ugly, find a better solution
-@session_router.delete(
-    "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}",
-    operation_id="delete_edge",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-)
-async def delete_edge(
-    session_id: str = Path(description="The id of the session"),
-    from_node_id: str = Path(description="The id of the node the edge is coming from"),
-    from_field: str = Path(description="The field of the node the edge is coming from"),
-    to_node_id: str = Path(description="The id of the node the edge is going to"),
-    to_field: str = Path(description="The field of the node the edge is going to"),
-) -> GraphExecutionState:
-    """Deletes an edge from the graph"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        return Response(status_code=404)
-
-    try:
-        edge = Edge(
-            source=EdgeConnection(node_id=from_node_id, field=from_field),
-            destination=EdgeConnection(node_id=to_node_id, field=to_field)
-        )
-        session.delete_edge(edge)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        return Response(status_code=400)
-    except IndexError:
-        return Response(status_code=400)
-
-
-@session_router.put(
-    "/{session_id}/invoke",
-    operation_id="invoke_session",
-    responses={
-        200: {"model": None},
-        202: {"description": "The invocation is queued"},
-        400: {"description": "The session has no invocations ready to invoke"},
-        404: {"description": "Session not found"},
-    },
-)
-async def invoke_session(
-    session_id: str = Path(description="The id of the session to invoke"),
-    all: bool = Query(
-        default=False, description="Whether or not to invoke all remaining invocations"
-    ),
-) -> None:
-    """Invokes a session"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        return Response(status_code=404)
-
-    if session.is_complete():
-        return Response(status_code=400)
-
-    ApiDependencies.invoker.invoke(session, invoke_all=all)
-    return Response(status_code=202)
-
-
-@session_router.delete(
-    "/{session_id}/invoke",
-    operation_id="cancel_session_invoke",
-    responses={
-        202: {"description": "The invocation is canceled"}
-    },
-)
-async def cancel_session_invoke(
-    session_id: str = Path(description="The id of the session to cancel"),
-) -> None:
-    """Invokes a session"""
-    ApiDependencies.invoker.cancel(session_id)
-    return Response(status_code=202)

invokeai/app/api/sockets.py

@@ -1,38 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from fastapi import FastAPI
-from fastapi_events.handlers.local import local_handler
-from fastapi_events.typing import Event
-from fastapi_socketio import SocketManager
-
-from ..services.events import EventServiceBase
-
-
-class SocketIO:
-    __sio: SocketManager
-
-    def __init__(self, app: FastAPI):
-        self.__sio = SocketManager(app=app)
-        self.__sio.on("subscribe", handler=self._handle_sub)
-        self.__sio.on("unsubscribe", handler=self._handle_unsub)
-
-        local_handler.register(
-            event_name=EventServiceBase.session_event, _func=self._handle_session_event
-        )
-
-    async def _handle_session_event(self, event: Event):
-        await self.__sio.emit(
-            event=event[1]["event"],
-            data=event[1]["data"],
-            room=event[1]["data"]["graph_execution_state_id"],
-        )
-
-    async def _handle_sub(self, sid, data, *args, **kwargs):
-        if "session" in data:
-            self.__sio.enter_room(sid, data["session"])
-
-        # @app.sio.on('unsubscribe')
-
-    async def _handle_unsub(self, sid, data, *args, **kwargs):
-        if "session" in data:
-            self.__sio.leave_room(sid, data["session"])

invokeai/app/api_app.py

@@ -1,160 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-import asyncio
-from inspect import signature
-
-import uvicorn
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
-from fastapi.openapi.utils import get_openapi
-from fastapi.staticfiles import StaticFiles
-from fastapi_events.handlers.local import local_handler
-from fastapi_events.middleware import EventHandlerASGIMiddleware
-from pydantic.schema import schema
-
-from ..backend import Args
-from .api.dependencies import ApiDependencies
-from .api.routers import images, sessions, models
-from .api.sockets import SocketIO
-from .invocations import *
-from .invocations.baseinvocation import BaseInvocation
-
-# Create the app
-# TODO: create this all in a method so configuration/etc. can be passed in?
-app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
-
-# Add event handler
-event_handler_id: int = id(app)
-app.add_middleware(
-    EventHandlerASGIMiddleware,
-    handlers=[
-        local_handler
-    ],  # TODO: consider doing this in services to support different configurations
-    middleware_id=event_handler_id,
-)
-
-# Add CORS
-# TODO: use configuration for this
-origins = []
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-socket_io = SocketIO(app)
-
-config = {}
-
-
-# Add startup event to load dependencies
-@app.on_event("startup")
-async def startup_event():
-    config = Args()
-    config.parse_args()
-
-    ApiDependencies.initialize(
-        config=config, event_handler_id=event_handler_id
-    )
-
-
-# Shut down threads
-@app.on_event("shutdown")
-async def shutdown_event():
-    ApiDependencies.shutdown()
-
-
-# Include all routers
-# TODO: REMOVE
-# app.include_router(
-#     invocation.invocation_router,
-#     prefix = '/api')
-
-app.include_router(sessions.session_router, prefix="/api")
-
-app.include_router(images.images_router, prefix="/api")
-
-app.include_router(models.models_router, prefix="/api")
-
-
-# Build a custom OpenAPI to include all outputs
-# TODO: can outputs be included on metadata of invocation schemas somehow?
-def custom_openapi():
-    if app.openapi_schema:
-        return app.openapi_schema
-    openapi_schema = get_openapi(
-        title=app.title,
-        description="An API for invoking AI image operations",
-        version="1.0.0",
-        routes=app.routes,
-    )
-
-    # Add all outputs
-    all_invocations = BaseInvocation.get_invocations()
-    output_types = set()
-    output_type_titles = dict()
-    for invoker in all_invocations:
-        output_type = signature(invoker.invoke).return_annotation
-        output_types.add(output_type)
-
-    output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
-    for schema_key, output_schema in output_schemas["definitions"].items():
-        openapi_schema["components"]["schemas"][schema_key] = output_schema
-
-        # TODO: note that we assume the schema_key here is the TYPE.__name__
-        # This could break in some cases, figure out a better way to do it
-        output_type_titles[schema_key] = output_schema["title"]
-
-    # Add a reference to the output type to additionalProperties of the invoker schema
-    for invoker in all_invocations:
-        invoker_name = invoker.__name__
-        output_type = signature(invoker.invoke).return_annotation
-        output_type_title = output_type_titles[output_type.__name__]
-        invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
-        outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
-
-        invoker_schema["output"] = outputs_ref
-
-    app.openapi_schema = openapi_schema
-    return app.openapi_schema
-
-
-app.openapi = custom_openapi
-
-# Override API doc favicons
-app.mount("/static", StaticFiles(directory="static/dream_web"), name="static")
-
-
-@app.get("/docs", include_in_schema=False)
-def overridden_swagger():
-    return get_swagger_ui_html(
-        openapi_url=app.openapi_url,
-        title=app.title,
-        swagger_favicon_url="/static/favicon.ico",
-    )
-
-
-@app.get("/redoc", include_in_schema=False)
-def overridden_redoc():
-    return get_redoc_html(
-        openapi_url=app.openapi_url,
-        title=app.title,
-        redoc_favicon_url="/static/favicon.ico",
-    )
-
-
-def invoke_api():
-    # Start our own event loop for eventing usage
-    # TODO: determine if there's a better way to do this
-    loop = asyncio.new_event_loop()
-    config = uvicorn.Config(app=app, host="0.0.0.0", port=9090, loop=loop)
-    # Use access_log to turn off logging
-
-    server = uvicorn.Server(config)
-    loop.run_until_complete(server.serve())
-
-
-if __name__ == "__main__":
-    invoke_api()

invokeai/app/cli/commands.py

@@ -1,286 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from abc import ABC, abstractmethod
-import argparse
-from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_type_hints
-from pydantic import BaseModel, Field
-import networkx as nx
-import matplotlib.pyplot as plt
-
-from ..invocations.baseinvocation import BaseInvocation
-from ..invocations.image import ImageField
-from ..services.graph import GraphExecutionState, LibraryGraph, GraphInvocation, Edge
-from ..services.invoker import Invoker
-
-
-def add_field_argument(command_parser, name: str, field, default_override = None):
-    default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
-    if get_origin(field.type_) == Literal:
-        allowed_values = get_args(field.type_)
-        allowed_types = set()
-        for val in allowed_values:
-            allowed_types.add(type(val))
-        allowed_types_list = list(allowed_types)
-        field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
-
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field_type,
-            default=default,
-            choices=allowed_values,
-            help=field.field_info.description,
-        )
-    else:
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field.type_,
-            default=default,
-            help=field.field_info.description,
-        )
-
-
-def add_parsers(
-    subparsers,
-    commands: list[type],
-    command_field: str = "type",
-    exclude_fields: list[str] = ["id", "type"],
-    add_arguments: Callable[[argparse.ArgumentParser], None]|None = None
-    ):
-    """Adds parsers for each command to the subparsers"""
-
-    # Create subparsers for each command
-    for command in commands:
-        hints = get_type_hints(command)
-        cmd_name = get_args(hints[command_field])[0]
-        command_parser = subparsers.add_parser(cmd_name, help=command.__doc__)
-
-        if add_arguments is not None:
-            add_arguments(command_parser)
-
-        # Convert all fields to arguments
-        fields = command.__fields__ # type: ignore
-        for name, field in fields.items():
-            if name in exclude_fields:
-                continue
-
-            add_field_argument(command_parser, name, field)
-
-
-def add_graph_parsers(
-    subparsers,
-    graphs: list[LibraryGraph],
-    add_arguments: Callable[[argparse.ArgumentParser], None]|None = None
-):
-    for graph in graphs:
-        command_parser = subparsers.add_parser(graph.name, help=graph.description)
-        
-        if add_arguments is not None:
-            add_arguments(command_parser)
-
-        # Add arguments for inputs
-        for exposed_input in graph.exposed_inputs:
-            node = graph.graph.get_node(exposed_input.node_path)
-            field = node.__fields__[exposed_input.field]
-            default_override = getattr(node, exposed_input.field)
-            add_field_argument(command_parser, exposed_input.alias, field, default_override)
-
-
-class CliContext:
-    invoker: Invoker
-    session: GraphExecutionState
-    parser: argparse.ArgumentParser
-    defaults: dict[str, Any]
-    graph_nodes: dict[str, str]
-    nodes_added: list[str]
-
-    def __init__(self, invoker: Invoker, session: GraphExecutionState, parser: argparse.ArgumentParser):
-        self.invoker = invoker
-        self.session = session
-        self.parser = parser
-        self.defaults = dict()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
-
-    def get_session(self):
-        self.session = self.invoker.services.graph_execution_manager.get(self.session.id)
-        return self.session
-
-    def reset(self):
-        self.session = self.invoker.create_execution_state()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
-        # Leave defaults unchanged
-
-    def add_node(self, node: BaseInvocation):
-        self.get_session()
-        self.session.graph.add_node(node)
-        self.nodes_added.append(node.id)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
-    def add_edge(self, edge: Edge):
-        self.get_session()
-        self.session.add_edge(edge)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
-
-class ExitCli(Exception):
-    """Exception to exit the CLI"""
-    pass
-
-
-class BaseCommand(ABC, BaseModel):
-    """A CLI command"""
-
-    # All commands must include a type name like this:
-    # type: Literal['your_command_name'] = 'your_command_name'
-
-    @classmethod
-    def get_all_subclasses(cls):
-        subclasses = []
-        toprocess = [cls]
-        while len(toprocess) > 0:
-            next = toprocess.pop(0)
-            next_subclasses = next.__subclasses__()
-            subclasses.extend(next_subclasses)
-            toprocess.extend(next_subclasses)
-        return subclasses
-
-    @classmethod
-    def get_commands(cls):
-        return tuple(BaseCommand.get_all_subclasses())
-
-    @classmethod
-    def get_commands_map(cls):
-        # Get the type strings out of the literals and into a dictionary
-        return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseCommand.get_all_subclasses()))
-
-    @abstractmethod
-    def run(self, context: CliContext) -> None:
-        """Run the command. Raise ExitCli to exit."""
-        pass
-
-
-class ExitCommand(BaseCommand):
-    """Exits the CLI"""
-    type: Literal['exit'] = 'exit'
-
-    def run(self, context: CliContext) -> None:
-        raise ExitCli()
-
-
-class HelpCommand(BaseCommand):
-    """Shows help"""
-    type: Literal['help'] = 'help'
-
-    def run(self, context: CliContext) -> None:
-        context.parser.print_help()
-
-
-def get_graph_execution_history(
-    graph_execution_state: GraphExecutionState,
-) -> Iterable[str]:
-    """Gets the history of fully-executed invocations for a graph execution"""
-    return (
-        n
-        for n in reversed(graph_execution_state.executed_history)
-        if n in graph_execution_state.graph.nodes
-    )
-
-
-def get_invocation_command(invocation) -> str:
-    fields = invocation.__fields__.items()
-    type_hints = get_type_hints(type(invocation))
-    command = [invocation.type]
-    for name, field in fields:
-        if name in ["id", "type"]:
-            continue
-
-        # TODO: add links
-
-        # Skip image fields when serializing command
-        type_hint = type_hints.get(name) or None
-        if type_hint is ImageField or ImageField in get_args(type_hint):
-            continue
-
-        field_value = getattr(invocation, name)
-        field_default = field.default
-        if field_value != field_default:
-            if type_hint is str or str in get_args(type_hint):
-                command.append(f'--{name} "{field_value}"')
-            else:
-                command.append(f"--{name} {field_value}")
-
-    return " ".join(command)
-
-
-class HistoryCommand(BaseCommand):
-    """Shows the invocation history"""
-    type: Literal['history'] = 'history'
-
-    # Inputs
-    # fmt: off
-    count: int = Field(default=5, gt=0, description="The number of history entries to show")
-    # fmt: on
-
-    def run(self, context: CliContext) -> None:
-        history = list(get_graph_execution_history(context.get_session()))
-        for i in range(min(self.count, len(history))):
-            entry_id = history[-1 - i]
-            entry = context.get_session().graph.get_node(entry_id)
-            print(f"{entry_id}: {get_invocation_command(entry)}")
-
-
-class SetDefaultCommand(BaseCommand):
-    """Sets a default value for a field"""
-    type: Literal['default'] = 'default'
-
-    # Inputs
-    # fmt: off
-    field: str = Field(description="The field to set the default for")
-    value: str = Field(description="The value to set the default to, or None to clear the default")
-    # fmt: on
-
-    def run(self, context: CliContext) -> None:
-        if self.value is None:
-            if self.field in context.defaults:
-                del context.defaults[self.field]
-        else:
-            context.defaults[self.field] = self.value
-
-
-class DrawGraphCommand(BaseCommand):
-    """Debugs a graph"""
-    type: Literal['draw_graph'] = 'draw_graph'
-
-    def run(self, context: CliContext) -> None:
-        session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
-        nxgraph = session.graph.nx_graph_flat()
-
-        # Draw the networkx graph
-        plt.figure(figsize=(20, 20))
-        pos = nx.spectral_layout(nxgraph)
-        nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
-        nx.draw_networkx_edges(nxgraph, pos, width=2)
-        nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
-        plt.axis("off")
-        plt.show()
-
-
-class DrawExecutionGraphCommand(BaseCommand):
-    """Debugs an execution graph"""
-    type: Literal['draw_xgraph'] = 'draw_xgraph'
-
-    def run(self, context: CliContext) -> None:
-        session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
-        nxgraph = session.execution_graph.nx_graph_flat()
-
-        # Draw the networkx graph
-        plt.figure(figsize=(20, 20))
-        pos = nx.spectral_layout(nxgraph)
-        nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
-        nx.draw_networkx_edges(nxgraph, pos, width=2)
-        nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
-        plt.axis("off")
-        plt.show()

invokeai/app/cli/completer.py

@@ -1,167 +0,0 @@
-"""
-Readline helper functions for cli_app.py
-You may import the global singleton `completer` to get access to the
-completer object.
-"""
-import atexit
-import readline
-import shlex
-
-from pathlib import Path
-from typing import List, Dict, Literal, get_args, get_type_hints, get_origin
-
-from ...backend import ModelManager, Globals
-from ..invocations.baseinvocation import BaseInvocation
-from .commands import BaseCommand
-
-# singleton object, class variable
-completer = None
-
-class Completer(object):
-    
-    def __init__(self, model_manager: ModelManager):
-        self.commands = self.get_commands()
-        self.matches = None
-        self.linebuffer = None
-        self.manager = model_manager
-        return
-
-    def complete(self, text, state):
-        """
-        Complete commands and switches fromm the node CLI command line.
-        Switches are determined in a context-specific manner.
-        """
-
-        buffer = readline.get_line_buffer()
-        if state == 0:
-            options = None
-            try:
-                current_command, current_switch = self.get_current_command(buffer)
-                options = self.get_command_options(current_command, current_switch)
-            except IndexError:
-                pass
-            options = options or list(self.parse_commands().keys())
-            
-            if not text:  # first time
-                self.matches = options
-            else:
-                self.matches = [s for s in options if s and s.startswith(text)]
-
-        try:
-            match = self.matches[state]
-        except IndexError:
-            match = None
-        return match
-
-    @classmethod
-    def get_commands(self)->List[object]:
-        """
-        Return a list of all the client commands and invocations.
-        """
-        return BaseCommand.get_commands() + BaseInvocation.get_invocations()
-
-    def get_current_command(self, buffer: str)->tuple[str, str]:
-        """
-        Parse the readline buffer to find the most recent command and its switch.
-        """
-        if len(buffer)==0:
-            return None, None
-        tokens = shlex.split(buffer)
-        command = None
-        switch = None
-        for t in tokens:
-            if t[0].isalpha():
-                if switch is None:
-                    command = t
-            else:
-                switch = t
-        # don't try to autocomplete switches that are already complete
-        if switch and buffer.endswith(' '):
-            switch=None
-        return command or '', switch or ''
-
-    def parse_commands(self)->Dict[str, List[str]]:
-        """
-        Return a dict in which the keys are the command name
-        and the values are the parameters the command takes.
-        """
-        result = dict()
-        for command in self.commands:
-            hints = get_type_hints(command)
-            name = get_args(hints['type'])[0]
-            result.update({name:hints})
-        return result
-
-    def get_command_options(self, command: str, switch: str)->List[str]:
-        """
-        Return all the parameters that can be passed to the command as
-        command-line switches. Returns None if the command is unrecognized.
-        """
-        parsed_commands = self.parse_commands()
-        if command not in parsed_commands:
-            return None
-        
-        # handle switches in the format "-foo=bar"
-        argument = None
-        if switch and '=' in switch:
-            switch, argument = switch.split('=')
-            
-        parameter = switch.strip('-')
-        if parameter in parsed_commands[command]:
-            if argument is None:
-                return self.get_parameter_options(parameter, parsed_commands[command][parameter])
-            else:
-                return [f"--{parameter}={x}" for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])]
-        else:
-            return [f"--{x}" for x in parsed_commands[command].keys()]
-
-    def get_parameter_options(self, parameter: str, typehint)->List[str]:
-        """
-        Given a parameter type (such as Literal), offers autocompletions.
-        """
-        if get_origin(typehint) == Literal:
-            return get_args(typehint)
-        if parameter == 'model':
-            return self.manager.model_names()
-        
-    def _pre_input_hook(self):
-        if self.linebuffer:
-            readline.insert_text(self.linebuffer)
-            readline.redisplay()
-            self.linebuffer = None
-    
-def set_autocompleter(model_manager: ModelManager) -> Completer:
-    global completer
-    
-    if completer:
-        return completer
-    
-    completer = Completer(model_manager)
-
-    readline.set_completer(completer.complete)
-    # pyreadline3 does not have a set_auto_history() method
-    try:
-        readline.set_auto_history(True)
-    except:
-        pass
-    readline.set_pre_input_hook(completer._pre_input_hook)
-    readline.set_completer_delims(" ")
-    readline.parse_and_bind("tab: complete")
-    readline.parse_and_bind("set print-completions-horizontally off")
-    readline.parse_and_bind("set page-completions on")
-    readline.parse_and_bind("set skip-completed-text on")
-    readline.parse_and_bind("set show-all-if-ambiguous on")
-
-    histfile = Path(Globals.root, ".invoke_history")
-    try:
-        readline.read_history_file(histfile)
-        readline.set_history_length(1000)
-    except FileNotFoundError:
-        pass
-    except OSError:  # file likely corrupted
-        newname = f"{histfile}.old"
-        print(
-            f"## Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
-        )
-        histfile.replace(Path(newname))
-    atexit.register(readline.write_history_file, histfile)

invokeai/app/cli_app.py

@@ -1,386 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import argparse
-import os
-import re
-import shlex
-import time
-from typing import (
-    Union,
-    get_type_hints,
-)
-
-from pydantic import BaseModel
-from pydantic.fields import Field
-
-from invokeai.app.services.metadata import PngMetadataService
-
-from .services.default_graphs import create_system_graphs
-
-from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-
-from ..backend import Args
-from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers, get_graph_execution_history
-from .cli.completer import set_autocompleter
-from .invocations import *
-from .invocations.baseinvocation import BaseInvocation
-from .services.events import EventServiceBase
-from .services.model_manager_initializer import get_model_manager
-from .services.restoration_services import RestorationServices
-from .services.graph import Edge, EdgeConnection, ExposedNodeInput, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
-from .services.default_graphs import default_text_to_image_graph_id
-from .services.image_storage import DiskImageStorage
-from .services.invocation_queue import MemoryInvocationQueue
-from .services.invocation_services import InvocationServices
-from .services.invoker import Invoker
-from .services.processor import DefaultInvocationProcessor
-from .services.sqlite import SqliteItemStorage
-
-
-class CliCommand(BaseModel):
-    command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type")  # type: ignore
-
-
-class InvalidArgs(Exception):
-    pass
-
-
-def add_invocation_args(command_parser):
-    # Add linking capability
-    command_parser.add_argument(
-        "--link",
-        "-l",
-        action="append",
-        nargs=3,
-        help="A link in the format 'source_node source_field dest_field'. source_node can be relative to history (e.g. -1)",
-    )
-
-    command_parser.add_argument(
-        "--link_node",
-        "-ln",
-        action="append",
-        help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)",
-    )
-
-
-def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
-    # Create invocation parser
-    parser = argparse.ArgumentParser()
-
-    def exit(*args, **kwargs):
-        raise InvalidArgs
-
-    parser.exit = exit
-    subparsers = parser.add_subparsers(dest="type")
-
-    # Create subparsers for each invocation
-    invocations = BaseInvocation.get_all_subclasses()
-    add_parsers(subparsers, invocations, add_arguments=add_invocation_args)
-
-    # Create subparsers for each command
-    commands = BaseCommand.get_all_subclasses()
-    add_parsers(subparsers, commands, exclude_fields=["type"])
-
-    # Create subparsers for exposed CLI graphs
-    # TODO: add a way to identify these graphs
-    text_to_image = services.graph_library.get(default_text_to_image_graph_id)
-    add_graph_parsers(subparsers, [text_to_image], add_arguments=add_invocation_args)
-
-    return parser
-
-
-class NodeField():
-    alias: str
-    node_path: str
-    field: str
-    field_type: type
-
-    def __init__(self, alias: str, node_path: str, field: str, field_type: type):
-        self.alias = alias
-        self.node_path = node_path
-        self.field = field
-        self.field_type = field_type
-
-
-def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str,NodeField]:
-    return {k:NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
-
-
-def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_input.node_path))
-    return NodeField(alias=exposed_input.alias, node_path=f'{node_id}.{exposed_input.node_path}', field=exposed_input.field, field_type=get_type_hints(node_type)[exposed_input.field])
-
-
-def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_output.node_path))
-    node_output_type = node_type.get_output_type()
-    return NodeField(alias=exposed_output.alias, node_path=f'{node_id}.{exposed_output.node_path}', field=exposed_output.field, field_type=get_type_hints(node_output_type)[exposed_output.field])
-
-
-def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the inputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_input_field(graph, e.alias, invocation.id) for e in graph.exposed_inputs}
-
-
-def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the outputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type.get_output_type()), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
-
-
-def generate_matching_edges(
-    a: BaseInvocation, b: BaseInvocation, context: CliContext
-) -> list[Edge]:
-    """Generates all possible edges between two invocations"""
-    afields = get_node_outputs(a, context)
-    bfields = get_node_inputs(b, context)
-
-    matching_fields = set(afields.keys()).intersection(bfields.keys())
-
-    # Remove invalid fields
-    invalid_fields = set(["type", "id"])
-    matching_fields = matching_fields.difference(invalid_fields)
-
-    # Validate types
-    matching_fields = [f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)]
-
-    edges = [
-        Edge(
-            source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
-            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field)
-        )
-        for alias in matching_fields
-    ]
-    return edges
-
-
-class SessionError(Exception):
-    """Raised when a session error has occurred"""
-    pass
-
-
-def invoke_all(context: CliContext):
-    """Runs all invocations in the specified session"""
-    context.invoker.invoke(context.session, invoke_all=True)
-    while not context.get_session().is_complete():
-        # Wait some time
-        time.sleep(0.1)
-
-    # Print any errors
-    if context.session.has_error():
-        for n in context.session.errors:
-            print(
-                f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
-            )
-        
-        raise SessionError()
-
-
-def invoke_cli():
-    config = Args()
-    config.parse_args()
-    model_manager = get_model_manager(config)
-
-    # This initializes the autocompleter and returns it.
-    # Currently nothing is done with the returned Completer
-    # object, but the object can be used to change autocompletion
-    # behavior on the fly, if desired.
-    completer = set_autocompleter(model_manager)
-
-    events = EventServiceBase()
-
-    metadata = PngMetadataService()
-
-    output_folder = os.path.abspath(
-        os.path.join(os.path.dirname(__file__), "../../../outputs")
-    )
-
-    # TODO: build a file/path manager?
-    db_location = os.path.join(output_folder, "invokeai.db")
-
-    services = InvocationServices(
-        model_manager=model_manager,
-        events=events,
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
-        images=DiskImageStorage(f'{output_folder}/images', metadata_service=metadata),
-        metadata=metadata,
-        queue=MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=db_location, table_name="graphs"
-        ),
-        graph_execution_manager=SqliteItemStorage[GraphExecutionState](
-            filename=db_location, table_name="graph_executions"
-        ),
-        processor=DefaultInvocationProcessor(),
-        restoration=RestorationServices(config),
-    )
-
-    system_graphs = create_system_graphs(services.graph_library)
-    system_graph_names = set([g.name for g in system_graphs])
-
-    invoker = Invoker(services)
-    session: GraphExecutionState = invoker.create_execution_state()
-    parser = get_command_parser(services)
-
-    re_negid = re.compile('^-[0-9]+$')
-
-    # Uncomment to print out previous sessions at startup
-    # print(services.session_manager.list())
-
-    context = CliContext(invoker, session, parser)
-
-    while True:
-        try:
-            cmd_input = input("invoke> ")
-        except (KeyboardInterrupt, EOFError):
-            # Ctrl-c exits
-            break
-
-        try:
-            # Refresh the state of the session
-            #history = list(get_graph_execution_history(context.session))
-            history = list(reversed(context.nodes_added))
-
-            # Split the command for piping
-            cmds = cmd_input.split("|")
-            start_id = len(context.nodes_added)
-            current_id = start_id
-            new_invocations = list()
-            for cmd in cmds:
-                if cmd is None or cmd.strip() == "":
-                    raise InvalidArgs("Empty command")
-
-                # Parse args to create invocation
-                args = vars(context.parser.parse_args(shlex.split(cmd.strip())))
-
-                # Override defaults
-                for field_name, field_default in context.defaults.items():
-                    if field_name in args:
-                        args[field_name] = field_default
-
-                # Parse invocation
-                command: CliCommand = None # type:ignore
-                system_graph: LibraryGraph|None = None
-                if args['type'] in system_graph_names:
-                    system_graph = next(filter(lambda g: g.name == args['type'], system_graphs))
-                    invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
-                    for exposed_input in system_graph.exposed_inputs:
-                        if exposed_input.alias in args:
-                            node = invocation.graph.get_node(exposed_input.node_path)
-                            field = exposed_input.field
-                            setattr(node, field, args[exposed_input.alias])
-                    command = CliCommand(command = invocation)
-                    context.graph_nodes[invocation.id] = system_graph.id
-                else:
-                    args["id"] = current_id
-                    command = CliCommand(command=args)
-
-                if command is None:
-                    continue
-
-                # Run any CLI commands immediately
-                if isinstance(command.command, BaseCommand):
-                    # Invoke all current nodes to preserve operation order
-                    invoke_all(context)
-
-                    # Run the command
-                    command.command.run(context)
-                    continue
-
-                # TODO: handle linking with library graphs
-                # Pipe previous command output (if there was a previous command)
-                edges: list[Edge] = list()
-                if len(history) > 0 or current_id != start_id:
-                    from_id = (
-                        history[0] if current_id == start_id else str(current_id - 1)
-                    )
-                    from_node = (
-                        next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
-                        if current_id != start_id
-                        else context.session.graph.get_node(from_id)
-                    )
-                    matching_edges = generate_matching_edges(
-                        from_node, command.command, context
-                    )
-                    edges.extend(matching_edges)
-
-                # Parse provided links
-                if "link_node" in args and args["link_node"]:
-                    for link in args["link_node"]:
-                        node_id = link
-                        if re_negid.match(node_id):
-                            node_id = str(current_id + int(node_id))
-
-                        link_node = context.session.graph.get_node(node_id)
-                        matching_edges = generate_matching_edges(
-                            link_node, command.command, context
-                        )
-                        matching_destinations = [e.destination for e in matching_edges]
-                        edges = [e for e in edges if e.destination not in matching_destinations]
-                        edges.extend(matching_edges)
-
-                if "link" in args and args["link"]:
-                    for link in args["link"]:
-                        edges = [e for e in edges if e.destination.node_id != command.command.id or e.destination.field != link[2]]
-
-                        node_id = link[0]
-                        if re_negid.match(node_id):
-                            node_id = str(current_id + int(node_id))
-
-                        # TODO: handle missing input/output
-                        node_output = get_node_outputs(context.session.graph.get_node(node_id), context)[link[1]]
-                        node_input = get_node_inputs(command.command, context)[link[2]]
-
-                        edges.append(
-                            Edge(
-                                source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
-                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field)
-                            )
-                        )
-
-                new_invocations.append((command.command, edges))
-
-                current_id = current_id + 1
-
-                # Add the node to the session
-                context.add_node(command.command)
-                for edge in edges:
-                    print(edge)
-                    context.add_edge(edge)
-
-            # Execute all remaining nodes
-            invoke_all(context)
-
-        except InvalidArgs:
-            print('Invalid command, use "help" to list commands')
-            continue
-
-        except SessionError:
-            # Start a new session
-            print("Session error: creating a new session")
-            context.reset()
-
-        except ExitCli:
-            break
-
-        except SystemExit:
-            continue
-
-    invoker.stop()
-
-
-if __name__ == "__main__":
-    invoke_cli()

invokeai/app/invocations/__init__.py

@@ -1,12 +0,0 @@
-import os
-
-__all__ = []
-
-dirname = os.path.dirname(os.path.abspath(__file__))
-for f in os.listdir(dirname):
-    if (
-        f != "__init__.py"
-        and os.path.isfile("%s/%s" % (dirname, f))
-        and f[-3:] == ".py"
-    ):
-        __all__.append(f[:-3])

invokeai/app/invocations/baseinvocation.py

@@ -1,131 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from abc import ABC, abstractmethod
-from inspect import signature
-from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict
-
-from pydantic import BaseModel, Field
-
-from ..services.invocation_services import InvocationServices
-
-
-class InvocationContext:
-    services: InvocationServices
-    graph_execution_state_id: str
-
-    def __init__(self, services: InvocationServices, graph_execution_state_id: str):
-        self.services = services
-        self.graph_execution_state_id = graph_execution_state_id
-
-
-class BaseInvocationOutput(BaseModel):
-    """Base class for all invocation outputs"""
-
-    # All outputs must include a type name like this:
-    # type: Literal['your_output_name']
-
-    @classmethod
-    def get_all_subclasses_tuple(cls):
-        subclasses = []
-        toprocess = [cls]
-        while len(toprocess) > 0:
-            next = toprocess.pop(0)
-            next_subclasses = next.__subclasses__()
-            subclasses.extend(next_subclasses)
-            toprocess.extend(next_subclasses)
-        return tuple(subclasses)
-
-
-class BaseInvocation(ABC, BaseModel):
-    """A node to process inputs and produce outputs.
-    May use dependency injection in __init__ to receive providers.
-    """
-
-    # All invocations must include a type name like this:
-    # type: Literal['your_output_name']
-
-    @classmethod
-    def get_all_subclasses(cls):
-        subclasses = []
-        toprocess = [cls]
-        while len(toprocess) > 0:
-            next = toprocess.pop(0)
-            next_subclasses = next.__subclasses__()
-            subclasses.extend(next_subclasses)
-            toprocess.extend(next_subclasses)
-        return subclasses
-
-    @classmethod
-    def get_invocations(cls):
-        return tuple(BaseInvocation.get_all_subclasses())
-
-    @classmethod
-    def get_invocations_map(cls):
-        # Get the type strings out of the literals and into a dictionary
-        return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseInvocation.get_all_subclasses()))
-    
-    @classmethod
-    def get_output_type(cls):
-        return signature(cls.invoke).return_annotation
-
-    @abstractmethod
-    def invoke(self, context: InvocationContext) -> BaseInvocationOutput:
-        """Invoke with provided context and return outputs."""
-        pass
-    
-    #fmt: off
-    id: str = Field(description="The id of this node. Must be unique among all nodes.")
-    #fmt: on
-
-
-# TODO: figure out a better way to provide these hints
-# TODO: when we can upgrade to python 3.11, we can use the`NotRequired` type instead of `total=False`
-class UIConfig(TypedDict, total=False):
-    type_hints: Dict[
-        str,
-        Literal[
-            "integer",
-            "float",
-            "boolean",
-            "string",
-            "enum",
-            "image",
-            "latents",
-            "model",
-        ],
-    ]
-    tags: List[str]
-    title: str
-
-class CustomisedSchemaExtra(TypedDict):
-    ui: UIConfig
-
-
-class InvocationConfig(BaseModel.Config):
-    """Customizes pydantic's BaseModel.Config class for use by Invocations.
-
-    Provide `schema_extra` a `ui` dict to add hints for generated UIs.
-
-    `tags`
-    - A list of strings, used to categorise invocations.
-
-    `type_hints`
-    - A dict of field types which override the types in the invocation definition.
-    - Each key should be the name of one of the invocation's fields.
-    - Each value should be one of the valid types:
-      - `integer`, `float`, `boolean`, `string`, `enum`, `image`, `latents`, `model`
-
-    ```python
-    class Config(InvocationConfig):
-      schema_extra = {
-          "ui": {
-              "tags": ["stable-diffusion", "image"],
-              "type_hints": {
-                  "initial_image": "image",
-              },
-          },
-      }
-    ```
-    """
-
-    schema_extra: CustomisedSchemaExtra

invokeai/app/invocations/collections.py

@@ -1,64 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal, Optional
-
-import numpy as np
-import numpy.random
-from pydantic import Field
-
-from .baseinvocation import (
-    BaseInvocation,
-    InvocationConfig,
-    InvocationContext,
-    BaseInvocationOutput,
-)
-
-
-class IntCollectionOutput(BaseInvocationOutput):
-    """A collection of integers"""
-
-    type: Literal["int_collection"] = "int_collection"
-
-    # Outputs
-    collection: list[int] = Field(default=[], description="The int collection")
-
-
-class RangeInvocation(BaseInvocation):
-    """Creates a range"""
-
-    type: Literal["range"] = "range"
-
-    # Inputs
-    start: int = Field(default=0, description="The start of the range")
-    stop: int = Field(default=10, description="The stop of the range")
-    step: int = Field(default=1, description="The step of the range")
-
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        return IntCollectionOutput(
-            collection=list(range(self.start, self.stop, self.step))
-        )
-
-
-class RandomRangeInvocation(BaseInvocation):
-    """Creates a collection of random numbers"""
-
-    type: Literal["random_range"] = "random_range"
-
-    # Inputs
-    low: int = Field(default=0, description="The inclusive low value")
-    high: int = Field(
-        default=np.iinfo(np.int32).max, description="The exclusive high value"
-    )
-    size: int = Field(default=1, description="The number of values to generate")
-    seed: Optional[int] = Field(
-        ge=0,
-        le=np.iinfo(np.int32).max,
-        description="The seed for the RNG",
-        default_factory=lambda: numpy.random.randint(0, np.iinfo(np.int32).max),
-    )
-
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        rng = np.random.default_rng(self.seed)
-        return IntCollectionOutput(
-            collection=list(rng.integers(low=self.low, high=self.high, size=self.size))
-        )

invokeai/app/invocations/cv.py

@@ -1,69 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal
-
-import cv2 as cv
-import numpy
-from PIL import Image, ImageOps
-from pydantic import BaseModel, Field
-
-from invokeai.app.models.image import ImageField, ImageType
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
-
-
-class CvInvocationConfig(BaseModel):
-    """Helper class to provide all OpenCV invocations with additional config"""
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["cv", "image"],
-            },
-        }
-
-
-class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
-    """Simple inpaint using opencv."""
-    #fmt: off
-    type: Literal["cv_inpaint"] = "cv_inpaint"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to inpaint")
-    mask: ImageField = Field(default=None, description="The mask to use when inpainting")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-        mask = context.services.images.get(self.mask.image_type, self.mask.image_name)
-
-        # Convert to cv image/mask
-        # TODO: consider making these utility functions
-        cv_image = cv.cvtColor(numpy.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
-        cv_mask = numpy.array(ImageOps.invert(mask))
-
-        # Inpaint
-        cv_inpainted = cv.inpaint(cv_image, cv_mask, 3, cv.INPAINT_TELEA)
-
-        # Convert back to Pillow
-        # TODO: consider making a utility function
-        image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
-
-        image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, image_inpainted, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=image_inpainted,
-        )

invokeai/app/invocations/generate.py

@@ -1,281 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from functools import partial
-from typing import Literal, Optional, Union
-
-import numpy as np
-from torch import Tensor
-
-from pydantic import BaseModel, Field
-
-from invokeai.app.models.image import ImageField, ImageType
-from invokeai.app.invocations.util.choose_model import choose_model
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
-from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
-from ...backend.stable_diffusion import PipelineIntermediateState
-from ..util.step_callback import stable_diffusion_step_callback
-
-SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
-
-
-class SDImageInvocation(BaseModel):
-    """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["stable-diffusion", "image"],
-                "type_hints": {
-                    "model": "model",
-                },
-            },
-        }
-
-
-# Text to image
-class TextToImageInvocation(BaseInvocation, SDImageInvocation):
-    """Generates an image using text2img."""
-
-    type: Literal["txt2img"] = "txt2img"
-
-    # Inputs
-    # TODO: consider making prompt optional to enable providing prompt through a link
-    # fmt: off
-    prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
-    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
-    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
-    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
-    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
-    # fmt: on
-
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Handle invalid model parameter
-        model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        outputs = Txt2Img(model).generate(
-            prompt=self.prompt,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generate_output = next(outputs)
-
-        # Results are image and seed, unwrap for now and ignore the seed
-        # TODO: pre-seed?
-        # TODO: can this return multiple results? Should it?
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(
-            image_type, image_name, generate_output.image, metadata
-        )
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=generate_output.image,
-        )
-
-
-class ImageToImageInvocation(TextToImageInvocation):
-    """Generates an image using img2img."""
-
-    type: Literal["img2img"] = "img2img"
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image")
-    strength: float = Field(
-        default=0.75, gt=0, le=1, description="The strength of the original image"
-    )
-    fit: bool = Field(
-        default=True,
-        description="Whether or not the result should be fit to the aspect ratio of the input image",
-    )
-
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = (
-            None
-            if self.image is None
-            else context.services.images.get(
-                self.image.image_type, self.image.image_name
-            )
-        )
-        mask = None
-
-        # Handle invalid model parameter
-        model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        outputs = Img2Img(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            init_mask=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
-
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generator_output = next(outputs)
-
-        result_image = generator_output.image
-
-        # Results are image and seed, unwrap for now and ignore the seed
-        # TODO: pre-seed?
-        # TODO: can this return multiple results? Should it?
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, result_image, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=result_image,
-        )
-
-
-class InpaintInvocation(ImageToImageInvocation):
-    """Generates an image using inpaint."""
-
-    type: Literal["inpaint"] = "inpaint"
-
-    # Inputs
-    mask: Union[ImageField, None] = Field(description="The mask")
-    inpaint_replace: float = Field(
-        default=0.0,
-        ge=0.0,
-        le=1.0,
-        description="The amount by which to replace masked areas with latent noise",
-    )
-
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = (
-            None
-            if self.image is None
-            else context.services.images.get(
-                self.image.image_type, self.image.image_name
-            )
-        )
-        mask = (
-            None
-            if self.mask is None
-            else context.services.images.get(self.mask.image_type, self.mask.image_name)
-        )
-
-        # Handle invalid model parameter
-        model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        outputs = Inpaint(model).generate(
-            prompt=self.prompt,
-            init_img=image,
-            init_mask=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
-
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generator_output = next(outputs)
-
-        result_image = generator_output.image
-
-        # Results are image and seed, unwrap for now and ignore the seed
-        # TODO: pre-seed?
-        # TODO: can this return multiple results? Should it?
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, result_image, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=result_image,
-        )

invokeai/app/invocations/image.py

@@ -1,369 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal, Optional
-
-import numpy
-from PIL import Image, ImageFilter, ImageOps
-from pydantic import BaseModel, Field
-
-from ..models.image import ImageField, ImageType
-from .baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InvocationContext,
-    InvocationConfig,
-)
-
-
-class PILInvocationConfig(BaseModel):
-    """Helper class to provide all PIL invocations with additional config"""
-
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["PIL", "image"],
-            },
-        }
-
-
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    # fmt: off
-    type: Literal["image"] = "image"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:   Optional[int] = Field(default=None, description="The width of the image in pixels")
-    height:  Optional[int] = Field(default=None, description="The height of the image in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {
-            "required": ["type", "image", "width", "height", "mode"]
-        }
-
-
-def build_image_output(
-    image_type: ImageType, image_name: str, image: Image.Image
-) -> ImageOutput:
-    """Builds an ImageOutput and its ImageField"""
-    image_field = ImageField(
-        image_name=image_name,
-        image_type=image_type,
-    )
-    return ImageOutput(
-        image=image_field,
-        width=image.width,
-        height=image.height,
-        mode=image.mode,
-    )
-
-
-class MaskOutput(BaseInvocationOutput):
-    """Base class for invocations that output a mask"""
-
-    # fmt: off
-    type: Literal["mask"] = "mask"
-    mask:      ImageField = Field(default=None, description="The output mask")
-    # fmt: on
-
-    class Config:
-        schema_extra = {
-            "required": [
-                "type",
-                "mask",
-            ]
-        }
-
-
-class LoadImageInvocation(BaseInvocation):
-    """Load an image and provide it as output."""
-
-    # fmt: off
-    type: Literal["load_image"] = "load_image"
-
-    # Inputs
-    image_type: ImageType = Field(description="The type of the image")
-    image_name:       str = Field(description="The name of the image")
-    # fmt: on
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image_type, self.image_name)
-
-        return build_image_output(
-            image_type=self.image_type,
-            image_name=self.image_name,
-            image=image,
-        )
-
-
-class ShowImageInvocation(BaseInvocation):
-    """Displays a provided image, and passes it forward in the pipeline."""
-
-    type: Literal["show_image"] = "show_image"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to show")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-        if image:
-            image.show()
-
-        # TODO: how to handle failure?
-
-        return build_image_output(
-            image_type=self.image.image_type,
-            image_name=self.image.image_name,
-            image=image,
-        )
-
-
-class CropImageInvocation(BaseInvocation, PILInvocationConfig):
-    """Crops an image to a specified box. The box can be outside of the image."""
-
-    # fmt: off
-    type: Literal["crop"] = "crop"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to crop")
-    x:      int = Field(default=0, description="The left x coordinate of the crop rectangle")
-    y:      int = Field(default=0, description="The top y coordinate of the crop rectangle")
-    width:  int = Field(default=512, gt=0, description="The width of the crop rectangle")
-    height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-
-        image_crop = Image.new(
-            mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0)
-        )
-        image_crop.paste(image, (-self.x, -self.y))
-
-        image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, image_crop, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=image_crop,
-        )
-
-
-class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
-    """Pastes an image into another image."""
-
-    # fmt: off
-    type: Literal["paste"] = "paste"
-
-    # Inputs
-    base_image:     ImageField = Field(default=None, description="The base image")
-    image:          ImageField = Field(default=None, description="The image to paste")
-    mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
-    x:                     int = Field(default=0, description="The left x coordinate at which to paste the image")
-    y:                     int = Field(default=0, description="The top y coordinate at which to paste the image")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        base_image = context.services.images.get(
-            self.base_image.image_type, self.base_image.image_name
-        )
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-        mask = (
-            None
-            if self.mask is None
-            else ImageOps.invert(
-                context.services.images.get(self.mask.image_type, self.mask.image_name)
-            )
-        )
-        # TODO: probably shouldn't invert mask here... should user be required to do it?
-
-        min_x = min(0, self.x)
-        min_y = min(0, self.y)
-        max_x = max(base_image.width, image.width + self.x)
-        max_y = max(base_image.height, image.height + self.y)
-
-        new_image = Image.new(
-            mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0)
-        )
-        new_image.paste(base_image, (abs(min_x), abs(min_y)))
-        new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
-
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, new_image, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=new_image,
-        )
-
-
-class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
-    """Extracts the alpha channel of an image as a mask."""
-
-    # fmt: off
-    type: Literal["tomask"] = "tomask"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to create the mask from")
-    invert:      bool = Field(default=False, description="Whether or not to invert the mask")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> MaskOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-
-        image_mask = image.split()[-1]
-        if self.invert:
-            image_mask = ImageOps.invert(image_mask)
-
-        image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, image_mask, metadata)
-        return MaskOutput(mask=ImageField(image_type=image_type, image_name=image_name))
-
-
-class BlurInvocation(BaseInvocation, PILInvocationConfig):
-    """Blurs an image"""
-
-    # fmt: off
-    type: Literal["blur"] = "blur"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to blur")
-    radius:     float = Field(default=8.0, ge=0, description="The blur radius")
-    blur_type: Literal["gaussian", "box"] = Field(default="gaussian", description="The type of blur")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-
-        blur = (
-            ImageFilter.GaussianBlur(self.radius)
-            if self.blur_type == "gaussian"
-            else ImageFilter.BoxBlur(self.radius)
-        )
-        blur_image = image.filter(blur)
-
-        image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, blur_image, metadata)
-        return build_image_output(
-            image_type=image_type, image_name=image_name, image=blur_image
-        )
-
-
-class LerpInvocation(BaseInvocation, PILInvocationConfig):
-    """Linear interpolation of all pixels of an image"""
-
-    # fmt: off
-    type: Literal["lerp"] = "lerp"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to lerp")
-    min: int = Field(default=0, ge=0, le=255, description="The minimum output value")
-    max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-
-        image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
-        image_arr = image_arr * (self.max - self.min) + self.max
-
-        lerp_image = Image.fromarray(numpy.uint8(image_arr))
-
-        image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, lerp_image, metadata)
-        return build_image_output(
-            image_type=image_type, image_name=image_name, image=lerp_image
-        )
-
-
-class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
-    """Inverse linear interpolation of all pixels of an image"""
-
-    # fmt: off
-    type: Literal["ilerp"] = "ilerp"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to lerp")
-    min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
-    max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-
-        image_arr = numpy.asarray(image, dtype=numpy.float32)
-        image_arr = (
-            numpy.minimum(
-                numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1
-            )
-            * 255
-        )
-
-        ilerp_image = Image.fromarray(numpy.uint8(image_arr))
-
-        image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, ilerp_image, metadata)
-        return build_image_output(
-            image_type=image_type, image_name=image_name, image=ilerp_image
-        )

invokeai/app/invocations/latent.py

@@ -1,371 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-import random
-from typing import Literal, Optional
-from pydantic import BaseModel, Field
-import torch
-
-from invokeai.app.invocations.util.choose_model import choose_model
-
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
-
-from ...backend.model_management.model_manager import ModelManager
-from ...backend.util.devices import choose_torch_device, torch_dtype
-from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
-from ...backend.image_util.seamless import configure_model_padding
-from ...backend.prompting.conditioning import get_uc_and_c_and_ec
-from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-import numpy as np
-from ..services.image_storage import ImageType
-from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput, build_image_output
-from ...backend.stable_diffusion import PipelineIntermediateState
-from diffusers.schedulers import SchedulerMixin as Scheduler
-import diffusers
-from diffusers import DiffusionPipeline
-
-
-class LatentsField(BaseModel):
-    """A latents field used for passing latents between invocations"""
-
-    latents_name: Optional[str] = Field(default=None, description="The name of the latents")
-
-    class Config:
-        schema_extra = {"required": ["latents_name"]}
-
-class LatentsOutput(BaseInvocationOutput):
-    """Base class for invocations that output latents"""
-    #fmt: off
-    type: Literal["latent_output"] = "latent_output"
-    latents: LatentsField            = Field(default=None, description="The output latents")
-    #fmt: on
-
-class NoiseOutput(BaseInvocationOutput):
-    """Invocation noise output"""
-    #fmt: off
-    type: Literal["noise_output"] = "noise_output"
-    noise: LatentsField            = Field(default=None, description="The output noise")
-    #fmt: on
-
-
-# TODO: this seems like a hack
-scheduler_map = dict(
-    ddim=diffusers.DDIMScheduler,
-    dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-    k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-    k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_euler=diffusers.EulerDiscreteScheduler,
-    k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-    k_heun=diffusers.HeunDiscreteScheduler,
-    k_lms=diffusers.LMSDiscreteScheduler,
-    plms=diffusers.PNDMScheduler,
-)
-
-
-SAMPLER_NAME_VALUES = Literal[
-    tuple(list(scheduler_map.keys()))
-]
-
-
-def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-    scheduler_class = scheduler_map.get(scheduler_name,'ddim')
-    scheduler = scheduler_class.from_config(model.scheduler.config)
-    # hack copied over from generate.py
-    if not hasattr(scheduler, 'uses_inpainting_model'):
-        scheduler.uses_inpainting_model = lambda: False
-    return scheduler
-
-
-def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_channels:int=4, use_mps_noise:bool=False, downsampling_factor:int = 8):
-    # limit noise to only the diffusion image channels, not the mask channels
-    input_channels = min(latent_channels, 4)
-    use_device = "cpu" if (use_mps_noise or device.type == "mps") else device
-    generator = torch.Generator(device=use_device).manual_seed(seed)
-    x = torch.randn(
-        [
-            1,
-            input_channels,
-            height // downsampling_factor,
-            width //  downsampling_factor,
-        ],
-        dtype=torch_dtype(device),
-        device=use_device,
-        generator=generator,
-    ).to(device)
-    # if self.perlin > 0.0:
-    #     perlin_noise = self.get_perlin_noise(
-    #         width // self.downsampling_factor, height // self.downsampling_factor
-    #     )
-    #     x = (1 - self.perlin) * x + self.perlin * perlin_noise
-    return x
-
-
-def random_seed():
-    return random.randint(0, np.iinfo(np.uint32).max)
-
-
-class NoiseInvocation(BaseInvocation):
-    """Generates latent noise."""
-
-    type: Literal["noise"] = "noise"
-
-    # Inputs
-    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
-    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting noise", )
-    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting noise", )
-
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "noise"],
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> NoiseOutput:
-        device = torch.device(choose_torch_device())
-        noise = get_noise(self.width, self.height, device, self.seed)
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, noise)
-        return NoiseOutput(
-            noise=LatentsField(latents_name=name)
-        )
-
-
-# Text to image
-class TextToLatentsInvocation(BaseInvocation):
-    """Generates latents from a prompt."""
-
-    type: Literal["t2l"] = "t2l"
-
-    # Inputs
-    # TODO: consider making prompt optional to enable providing prompt through a link
-    # fmt: off
-    prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
-    noise: Optional[LatentsField] = Field(description="The noise to use")
-    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
-    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
-    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
-    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
-    # fmt: on
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "image"],
-                "type_hints": {
-                  "model": "model"
-                }
-            },
-        }
-
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline:
-        model_info = choose_model(model_manager, self.model)
-        model_name = model_info['model_name']
-        model_hash = model_info['hash']
-        model: StableDiffusionGeneratorPipeline = model_info['model']
-        model.scheduler = get_scheduler(
-            model=model,
-            scheduler_name=self.scheduler
-        )
-
-        if isinstance(model, DiffusionPipeline):
-            for component in [model.unet, model.vae]:
-                configure_model_padding(component,
-                                        self.seamless,
-                                        self.seamless_axes
-                                        )
-        else:
-            configure_model_padding(model,
-                                    self.seamless,
-                                    self.seamless_axes
-                                    )
-
-        return model
-
-
-    def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
-        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model)
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            self.cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=0.0,#threshold,
-                warmup=0.2,#warmup,
-                h_symmetry_time_pct=None,#h_symmetry_time_pct,
-                v_symmetry_time_pct=None#v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
-        return conditioning_data
-
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
-
-        model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
-
-        # TODO: Verify the noise is the right size
-
-        result_latents, result_attention_map_saver = model.latents_from_embeddings(
-            latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)),
-            noise=noise,
-            num_inference_steps=self.steps,
-            conditioning_data=conditioning_data,
-            callback=step_callback
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
-
-
-class LatentsToLatentsInvocation(TextToLatentsInvocation):
-    """Generates latents using latents as base image."""
-
-    type: Literal["l2l"] = "l2l"
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents"],
-                "type_hints": {
-                    "model": "model"
-                }
-            },
-        }
-
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.5, description="The strength of the latents to use")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
-        latent = context.services.latents.get(self.latents.latents_name)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
-
-        model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
-
-        # TODO: Verify the noise is the right size
-
-        initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
-            latent, device=model.device, dtype=latent.dtype
-        )
-
-        timesteps, _ = model.get_img2img_timesteps(
-            self.steps,
-            self.strength,
-            device=model.device,
-        )
-
-        result_latents, result_attention_map_saver = model.latents_from_embeddings(
-            latents=initial_latents,
-            timesteps=timesteps,
-            noise=noise,
-            num_inference_steps=self.steps,
-            conditioning_data=conditioning_data,
-            callback=step_callback
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
-
-
-# Latent to image
-class LatentsToImageInvocation(BaseInvocation):
-    """Generates an image from latents."""
-
-    type: Literal["l2i"] = "l2i"
-
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
-    model: str = Field(default="", description="The model to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "image"],
-                "type_hints": {
-                  "model": "model"
-                }
-            },
-        }
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        # TODO: this only really needs the vae
-        model_info = choose_model(context.services.model_manager, self.model)
-        model: StableDiffusionGeneratorPipeline = model_info['model']
-
-        with torch.inference_mode():
-            np_image = model.decode_latents(latents)
-            image = model.numpy_to_pil(np_image)[0]
-
-            image_type = ImageType.RESULT
-            image_name = context.services.images.create_name(
-                context.graph_execution_state_id, self.id
-            )
-
-            metadata = context.services.metadata.build_metadata(
-                session_id=context.graph_execution_state_id, node=self
-            )
-
-            context.services.images.save(image_type, image_name, image, metadata)
-            return build_image_output(
-                image_type=image_type,
-                image_name=image_name,
-                image=image
-            )

invokeai/app/invocations/math.py

@@ -1,75 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal
-
-from pydantic import BaseModel, Field
-
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-
-
-class MathInvocationConfig(BaseModel):
-    """Helper class to provide all math invocations with additional config"""
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["math"],
-            }
-        }
-
-
-class IntOutput(BaseInvocationOutput):
-    """An integer output"""
-    #fmt: off
-    type: Literal["int_output"] = "int_output"
-    a: int = Field(default=None, description="The output integer")
-    #fmt: on
-
-
-class AddInvocation(BaseInvocation, MathInvocationConfig):
-    """Adds two numbers"""
-    #fmt: off
-    type: Literal["add"] = "add"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a + self.b)
-
-
-class SubtractInvocation(BaseInvocation, MathInvocationConfig):
-    """Subtracts two numbers"""
-    #fmt: off
-    type: Literal["sub"] = "sub"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a - self.b)
-
-
-class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
-    """Multiplies two numbers"""
-    #fmt: off
-    type: Literal["mul"] = "mul"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a * self.b)
-
-
-class DivideInvocation(BaseInvocation, MathInvocationConfig):
-    """Divides two numbers"""
-    #fmt: off
-    type: Literal["div"] = "div"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=int(self.a / self.b))

invokeai/app/invocations/params.py

@@ -1,18 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal
-from pydantic import Field
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
-from .math import IntOutput
-
-# Pass-through parameter nodes - used by subgraphs
-
-class ParamIntInvocation(BaseInvocation):
-    """An integer parameter"""
-    #fmt: off
-    type: Literal["param_int"] = "param_int"
-    a: int = Field(default=0, description="The integer value")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a)

invokeai/app/invocations/prompt.py

@@ -1,22 +0,0 @@
-from typing import Literal
-
-from pydantic.fields import Field
-
-from .baseinvocation import BaseInvocationOutput
-
-
-class PromptOutput(BaseInvocationOutput):
-    """Base class for invocations that output a prompt"""
-    #fmt: off
-    type: Literal["prompt"] = "prompt"
-
-    prompt: str = Field(default=None, description="The output prompt")
-    #fmt: on
-
-    class Config:
-        schema_extra = {
-            'required': [
-                'type',
-                'prompt',
-            ]
-        }

invokeai/app/invocations/reconstruct.py

@@ -1,56 +0,0 @@
-from typing import Literal, Union
-
-from pydantic import Field
-
-from invokeai.app.models.image import ImageField, ImageType
-
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
-
-class RestoreFaceInvocation(BaseInvocation):
-    """Restores faces in an image."""
-    #fmt: off
-    type:  Literal["restore_face"] = "restore_face"
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image")
-    strength:                float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration"  )
-    #fmt: on
-    
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["restoration", "image"],
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-        results = context.services.restoration.upscale_and_reconstruct(
-            image_list=[[image, 0]],
-            upscale=None,
-            strength=self.strength,  # 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
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, results[0][0], metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=results[0][0]
-        )

invokeai/app/invocations/upscale.py

@@ -1,60 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal, Union
-
-from pydantic import Field
-
-from invokeai.app.models.image import ImageField, ImageType
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
-
-
-class UpscaleInvocation(BaseInvocation):
-    """Upscales an image."""
-    #fmt: off
-    type: Literal["upscale"] = "upscale"
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
-    level: Literal[2, 4] = Field(default=2, description="The upscale level")
-    #fmt: on
-
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["upscaling", "image"],
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(
-            self.image.image_type, self.image.image_name
-        )
-        results = context.services.restoration.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
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, results[0][0], metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=results[0][0]
-        )

invokeai/app/invocations/util/choose_model.py

@@ -1,14 +0,0 @@
-from invokeai.backend.model_management.model_manager import ModelManager
-
-
-def choose_model(model_manager: ModelManager, model_name: str):
-    """Returns the default model if the `model_name` not a valid model, else returns the selected model."""
-    if model_manager.valid_model(model_name):
-        model = model_manager.get_model(model_name)
-    else:
-        model = model_manager.get_model()
-        print(
-            f"* Warning: '{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead."
-        )
-
-    return model

invokeai/app/models/exceptions.py

@@ -1,3 +0,0 @@
-class CanceledException(Exception):
-    """Execution canceled by user."""
-    pass

invokeai/app/models/image.py

@@ -1,29 +0,0 @@
-from enum import Enum
-from typing import Optional
-from pydantic import BaseModel, Field
-
-
-class ImageType(str, Enum):
-    RESULT = "results"
-    INTERMEDIATE = "intermediates"
-    UPLOAD = "uploads"
-
-
-def is_image_type(obj):
-    try:
-        ImageType(obj)
-    except ValueError:
-        return False
-    return True
-
-
-class ImageField(BaseModel):
-    """An image field used for passing image objects between invocations"""
-
-    image_type: ImageType = Field(
-        default=ImageType.RESULT, description="The type of the image"
-    )
-    image_name: Optional[str] = Field(default=None, description="The name of the image")
-
-    class Config:
-        schema_extra = {"required": ["image_type", "image_name"]}

invokeai/app/services/default_graphs.py

@@ -1,56 +0,0 @@
-from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
-from ..invocations.params import ParamIntInvocation
-from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
-from .item_storage import ItemStorageABC
-
-
-default_text_to_image_graph_id = '539b2af5-2b4d-4d8c-8071-e54a3255fc74'
-
-
-def create_text_to_image() -> LibraryGraph:
-    return LibraryGraph(
-        id=default_text_to_image_graph_id,
-        name='t2i',
-        description='Converts text to an image',
-        graph=Graph(
-            nodes={
-                'width': ParamIntInvocation(id='width', a=512),
-                'height': ParamIntInvocation(id='height', a=512),
-                '3': NoiseInvocation(id='3'),
-                '4': TextToLatentsInvocation(id='4'),
-                '5': LatentsToImageInvocation(id='5')
-            },
-            edges=[
-                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
-                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='4', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='4', field='height')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='4', field='noise')),
-                Edge(source=EdgeConnection(node_id='4', field='latents'), destination=EdgeConnection(node_id='5', field='latents')),
-            ]
-        ),
-        exposed_inputs=[
-            ExposedNodeInput(node_path='4', field='prompt', alias='prompt'),
-            ExposedNodeInput(node_path='width', field='a', alias='width'),
-            ExposedNodeInput(node_path='height', field='a', alias='height')
-        ],
-        exposed_outputs=[
-            ExposedNodeOutput(node_path='5', field='image', alias='image')
-        ])
-
-
-def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
-    """Creates the default system graphs, or adds new versions if the old ones don't match"""
-
-    graphs: list[LibraryGraph] = list()
-
-    text_to_image = graph_library.get(default_text_to_image_graph_id)
-    
-    # TODO: Check if the graph is the same as the default one, and if not, update it
-    #if text_to_image is None:
-    text_to_image = create_text_to_image()
-    graph_library.set(text_to_image)
-
-    graphs.append(text_to_image)
-
-    return graphs

invokeai/app/services/events.py

@@ -1,103 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Any
-from invokeai.app.api.models.images import ProgressImage
-from invokeai.app.util.misc import get_timestamp
-
-
-class EventServiceBase:
-    session_event: str = "session_event"
-
-    """Basic event bus, to have an empty stand-in when not needed"""
-
-    def dispatch(self, event_name: str, payload: Any) -> None:
-        pass
-
-    def __emit_session_event(self, event_name: str, payload: dict) -> None:
-        payload["timestamp"] = get_timestamp()
-        self.dispatch(
-            event_name=EventServiceBase.session_event,
-            payload=dict(event=event_name, data=payload),
-        )
-
-    # Define events here for every event in the system.
-    # This will make them easier to integrate until we find a schema generator.
-    def emit_generator_progress(
-        self,
-        graph_execution_state_id: str,
-        node: dict,
-        source_node_id: str,
-        progress_image: ProgressImage | None,
-        step: int,
-        total_steps: int,
-    ) -> None:
-        """Emitted when there is generation progress"""
-        self.__emit_session_event(
-            event_name="generator_progress",
-            payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
-                progress_image=progress_image.dict() if progress_image is not None else None,
-                step=step,
-                total_steps=total_steps,
-            ),
-        )
-
-    def emit_invocation_complete(
-        self,
-        graph_execution_state_id: str,
-        result: dict,
-        node: dict,
-        source_node_id: str,
-    ) -> None:
-        """Emitted when an invocation has completed"""
-        self.__emit_session_event(
-            event_name="invocation_complete",
-            payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
-                result=result,
-            ),
-        )
-
-    def emit_invocation_error(
-        self,
-        graph_execution_state_id: str,
-        node: dict,
-        source_node_id: str,
-        error: str,
-    ) -> None:
-        """Emitted when an invocation has completed"""
-        self.__emit_session_event(
-            event_name="invocation_error",
-            payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
-                error=error,
-            ),
-        )
-
-    def emit_invocation_started(
-        self, graph_execution_state_id: str, node: dict, source_node_id: str
-    ) -> None:
-        """Emitted when an invocation has started"""
-        self.__emit_session_event(
-            event_name="invocation_started",
-            payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
-            ),
-        )
-
-    def emit_graph_execution_complete(self, graph_execution_state_id: str) -> None:
-        """Emitted when a session has completed all invocations"""
-        self.__emit_session_event(
-            event_name="graph_execution_state_complete",
-            payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-            ),
-        )

invokeai/app/services/image_storage.py

@@ -1,238 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import os
-from glob import glob
-from abc import ABC, abstractmethod
-from pathlib import Path
-from queue import Queue
-from typing import Dict, List, Tuple
-
-from PIL.Image import Image
-import PIL.Image as PILImage
-from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
-from invokeai.app.models.image import ImageType
-from invokeai.app.services.metadata import (
-    InvokeAIMetadata,
-    MetadataServiceBase,
-    build_invokeai_metadata_pnginfo,
-)
-from invokeai.app.services.item_storage import PaginatedResults
-from invokeai.app.util.misc import get_timestamp
-from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
-
-
-class ImageStorageBase(ABC):
-    """Responsible for storing and retrieving images."""
-
-    @abstractmethod
-    def get(self, image_type: ImageType, image_name: str) -> Image:
-        """Retrieves an image as PIL Image."""
-        pass
-
-    @abstractmethod
-    def list(
-        self, image_type: ImageType, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[ImageResponse]:
-        """Gets a paginated list of images."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def get_path(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        """Gets the path to an image or its thumbnail."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates an image path."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> Tuple[str, str, int]:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image path, thumbnail path, and created timestamp."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_type: ImageType, image_name: str) -> None:
-        """Deletes an image and its thumbnail (if one exists)."""
-        pass
-
-    def create_name(self, context_id: str, node_id: str) -> str:
-        """Creates a unique contextual image filename."""
-        return f"{context_id}_{node_id}_{str(get_timestamp())}.png"
-
-
-class DiskImageStorage(ImageStorageBase):
-    """Stores images on disk"""
-
-    __output_folder: str
-    __cache_ids: Queue  # TODO: this is an incredibly naive cache
-    __cache: Dict[str, Image]
-    __max_cache_size: int
-    __metadata_service: MetadataServiceBase
-
-    def __init__(self, output_folder: str, metadata_service: MetadataServiceBase):
-        self.__output_folder = output_folder
-        self.__cache = dict()
-        self.__cache_ids = Queue()
-        self.__max_cache_size = 10  # TODO: get this from config
-        self.__metadata_service = metadata_service
-
-        Path(output_folder).mkdir(parents=True, exist_ok=True)
-
-        # TODO: don't hard-code. get/save/delete should maybe take subpath?
-        for image_type in ImageType:
-            Path(os.path.join(output_folder, image_type)).mkdir(
-                parents=True, exist_ok=True
-            )
-            Path(os.path.join(output_folder, image_type, "thumbnails")).mkdir(
-                parents=True, exist_ok=True
-            )
-
-    def list(
-        self, image_type: ImageType, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[ImageResponse]:
-        dir_path = os.path.join(self.__output_folder, image_type)
-        image_paths = glob(f"{dir_path}/*.png")
-        count = len(image_paths)
-
-        sorted_image_paths = sorted(
-            glob(f"{dir_path}/*.png"), key=os.path.getctime, reverse=True
-        )
-
-        page_of_image_paths = sorted_image_paths[
-            page * per_page : (page + 1) * per_page
-        ]
-
-        page_of_images: List[ImageResponse] = []
-
-        for path in page_of_image_paths:
-            filename = os.path.basename(path)
-            img = PILImage.open(path)
-
-            invokeai_metadata = self.__metadata_service.get_metadata(img)
-
-            page_of_images.append(
-                ImageResponse(
-                    image_type=image_type.value,
-                    image_name=filename,
-                    # TODO: DiskImageStorage should not be building URLs...?
-                    image_url=f"api/v1/images/{image_type.value}/{filename}",
-                    thumbnail_url=f"api/v1/images/{image_type.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
-                    # TODO: Creation of this object should happen elsewhere (?), just making it fit here so it works
-                    metadata=ImageResponseMetadata(
-                        created=int(os.path.getctime(path)),
-                        width=img.width,
-                        height=img.height,
-                        invokeai=invokeai_metadata,
-                    ),
-                )
-            )
-
-        page_count_trunc = int(count / per_page)
-        page_count_mod = count % per_page
-        page_count = page_count_trunc if page_count_mod == 0 else page_count_trunc + 1
-
-        return PaginatedResults[ImageResponse](
-            items=page_of_images,
-            page=page,
-            pages=page_count,
-            per_page=per_page,
-            total=count,
-        )
-
-    def get(self, image_type: ImageType, image_name: str) -> Image:
-        image_path = self.get_path(image_type, image_name)
-        cache_item = self.__get_cache(image_path)
-        if cache_item:
-            return cache_item
-
-        image = PILImage.open(image_path)
-        self.__set_cache(image_path, image)
-        return image
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    def get_path(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
-
-        if is_thumbnail:
-            path = os.path.join(
-                self.__output_folder, image_type, "thumbnails", basename
-            )
-        else:
-            path = os.path.join(self.__output_folder, image_type, basename)
-
-        return path
-
-    def validate_path(self, path: str) -> bool:
-        try:
-            os.stat(path)
-            return True
-        except Exception:
-            return False
-
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> Tuple[str, str, int]:
-        image_path = self.get_path(image_type, image_name)
-
-        # TODO: Reading the image and then saving it strips the metadata...
-        if metadata:
-            pnginfo = build_invokeai_metadata_pnginfo(metadata=metadata)
-            image.save(image_path, "PNG", pnginfo=pnginfo)
-        else:
-            image.save(image_path) # this saved image has an empty info
-
-        thumbnail_name = get_thumbnail_name(image_name)
-        thumbnail_path = self.get_path(image_type, thumbnail_name, is_thumbnail=True)
-        thumbnail_image = make_thumbnail(image)
-        thumbnail_image.save(thumbnail_path)
-
-        self.__set_cache(image_path, image)
-        self.__set_cache(thumbnail_path, thumbnail_image)
-
-        return (image_path, thumbnail_path, int(os.path.getctime(image_path)))
-
-    def delete(self, image_type: ImageType, image_name: str) -> None:
-        image_path = self.get_path(image_type, image_name)
-        thumbnail_path = self.get_path(image_type, image_name, True)
-        if os.path.exists(image_path):
-            os.remove(image_path)
-
-        if image_path in self.__cache:
-            del self.__cache[image_path]
-
-        if os.path.exists(thumbnail_path):
-            os.remove(thumbnail_path)
-
-        if thumbnail_path in self.__cache:
-            del self.__cache[thumbnail_path]
-
-    def __get_cache(self, image_name: str) -> Image:
-        return None if image_name not in self.__cache else self.__cache[image_name]
-
-    def __set_cache(self, image_name: str, image: Image):
-        if not image_name in self.__cache:
-            self.__cache[image_name] = image
-            self.__cache_ids.put(
-                image_name
-            )  # TODO: this should refresh position for LRU cache
-            if len(self.__cache) > self.__max_cache_size:
-                cache_id = self.__cache_ids.get()
-                del self.__cache[cache_id]

invokeai/app/services/invocation_queue.py

@@ -1,68 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import time
-from abc import ABC, abstractmethod
-from queue import Queue
-
-from pydantic import BaseModel, Field
-
-
-class InvocationQueueItem(BaseModel):
-    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
-    invocation_id: str = Field(description="The ID of the node being invoked")
-    invoke_all: bool = Field(default=False)
-    timestamp: float = Field(default_factory=time.time)
-
-
-class InvocationQueueABC(ABC):
-    """Abstract base class for all invocation queues"""
-
-    @abstractmethod
-    def get(self) -> InvocationQueueItem:
-        pass
-
-    @abstractmethod
-    def put(self, item: InvocationQueueItem | None) -> None:
-        pass
-
-    @abstractmethod
-    def cancel(self, graph_execution_state_id: str) -> None:
-        pass
-
-    @abstractmethod
-    def is_canceled(self, graph_execution_state_id: str) -> bool:
-        pass
-
-
-class MemoryInvocationQueue(InvocationQueueABC):
-    __queue: Queue
-    __cancellations: dict[str, float]
-
-    def __init__(self):
-        self.__queue = Queue()
-        self.__cancellations = dict()
-
-    def get(self) -> InvocationQueueItem:
-        item = self.__queue.get()
-
-        while isinstance(item, InvocationQueueItem) \
-            and item.graph_execution_state_id in self.__cancellations \
-            and self.__cancellations[item.graph_execution_state_id] > item.timestamp:
-            item = self.__queue.get()
-
-        # Clear old items
-        for graph_execution_state_id in list(self.__cancellations.keys()):
-            if self.__cancellations[graph_execution_state_id] < item.timestamp:
-                del self.__cancellations[graph_execution_state_id]
-
-        return item
-
-    def put(self, item: InvocationQueueItem | None) -> None:
-        self.__queue.put(item)
-
-    def cancel(self, graph_execution_state_id: str) -> None:
-        if graph_execution_state_id not in self.__cancellations:
-            self.__cancellations[graph_execution_state_id] = time.time()
-
-    def is_canceled(self, graph_execution_state_id: str) -> bool:
-        return graph_execution_state_id in self.__cancellations

invokeai/app/services/invocation_services.py

@@ -1,50 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-from invokeai.app.services.metadata import MetadataServiceBase
-from invokeai.backend import ModelManager
-
-from .events import EventServiceBase
-from .latent_storage import LatentsStorageBase
-from .image_storage import ImageStorageBase
-from .restoration_services import RestorationServices
-from .invocation_queue import InvocationQueueABC
-from .item_storage import ItemStorageABC
-
-class InvocationServices:
-    """Services that can be used by invocations"""
-
-    events: EventServiceBase
-    latents: LatentsStorageBase
-    images: ImageStorageBase
-    metadata: MetadataServiceBase
-    queue: InvocationQueueABC
-    model_manager: ModelManager
-    restoration: RestorationServices
-
-    # NOTE: we must forward-declare any types that include invocations, since invocations can use services
-    graph_library: ItemStorageABC["LibraryGraph"]
-    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
-    processor: "InvocationProcessorABC"
-
-    def __init__(
-            self,
-            model_manager: ModelManager,
-            events: EventServiceBase,
-            latents: LatentsStorageBase,
-            images: ImageStorageBase,
-            metadata: MetadataServiceBase,
-            queue: InvocationQueueABC,
-            graph_library: ItemStorageABC["LibraryGraph"],
-            graph_execution_manager: ItemStorageABC["GraphExecutionState"],
-            processor: "InvocationProcessorABC",
-            restoration: RestorationServices,
-    ):
-        self.model_manager = model_manager
-        self.events = events
-        self.latents = latents
-        self.images = images
-        self.metadata = metadata
-        self.queue = queue
-        self.graph_library = graph_library
-        self.graph_execution_manager = graph_execution_manager
-        self.processor = processor
-        self.restoration = restoration

invokeai/app/services/invoker.py

@@ -1,90 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from abc import ABC
-from threading import Event, Thread
-
-from ..invocations.baseinvocation import InvocationContext
-from .graph import Graph, GraphExecutionState
-from .invocation_queue import InvocationQueueABC, InvocationQueueItem
-from .invocation_services import InvocationServices
-from .item_storage import ItemStorageABC
-
-
-class Invoker:
-    """The invoker, used to execute invocations"""
-
-    services: InvocationServices
-
-    def __init__(self, services: InvocationServices):
-        self.services = services
-        self._start()
-
-    def invoke(
-        self, graph_execution_state: GraphExecutionState, invoke_all: bool = False
-    ) -> str | None:
-        """Determines the next node to invoke and returns the id of the invoked node, or None if there are no nodes to execute"""
-
-        # Get the next invocation
-        invocation = graph_execution_state.next()
-        if not invocation:
-            return None
-
-        # Save the execution state
-        self.services.graph_execution_manager.set(graph_execution_state)
-
-        # Queue the invocation
-        self.services.queue.put(
-            InvocationQueueItem(
-                # session_id    = session.id,
-                graph_execution_state_id=graph_execution_state.id,
-                invocation_id=invocation.id,
-                invoke_all=invoke_all,
-            )
-        )
-
-        return invocation.id
-
-    def create_execution_state(self, graph: Graph | None = None) -> GraphExecutionState:
-        """Creates a new execution state for the given graph"""
-        new_state = GraphExecutionState(graph=Graph() if graph is None else graph)
-        self.services.graph_execution_manager.set(new_state)
-        return new_state
-    
-    def cancel(self, graph_execution_state_id: str) -> None:
-        """Cancels the given execution state"""
-        self.services.queue.cancel(graph_execution_state_id)
-
-    def __start_service(self, service) -> None:
-        # Call start() method on any services that have it
-        start_op = getattr(service, "start", None)
-        if callable(start_op):
-            start_op(self)
-
-    def __stop_service(self, service) -> None:
-        # Call stop() method on any services that have it
-        stop_op = getattr(service, "stop", None)
-        if callable(stop_op):
-            stop_op(self)
-
-    def _start(self) -> None:
-        """Starts the invoker. This is called automatically when the invoker is created."""
-        for service in vars(self.services):
-            self.__start_service(getattr(self.services, service))
-
-        for service in vars(self.services):
-            self.__start_service(getattr(self.services, service))
-
-    def stop(self) -> None:
-        """Stops the invoker. A new invoker will have to be created to execute further."""
-        # First stop all services
-        for service in vars(self.services):
-            self.__stop_service(getattr(self.services, service))
-
-        for service in vars(self.services):
-            self.__stop_service(getattr(self.services, service))
-
-        self.services.queue.put(None)
-
-
-class InvocationProcessorABC(ABC):
-    pass

invokeai/app/services/item_storage.py

@@ -1,62 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Callable, Generic, TypeVar
-
-from pydantic import BaseModel, Field
-from pydantic.generics import GenericModel
-
-T = TypeVar("T", bound=BaseModel)
-
-
-class PaginatedResults(GenericModel, Generic[T]):
-    """Paginated results"""
-    #fmt: off
-    items: list[T] = Field(description="Items")
-    page: int = Field(description="Current Page")
-    pages: int = Field(description="Total number of pages")
-    per_page: int = Field(description="Number of items per page")
-    total: int = Field(description="Total number of items in result")
-    #fmt: on
-
-class ItemStorageABC(ABC, Generic[T]):
-    _on_changed_callbacks: list[Callable[[T], None]]
-    _on_deleted_callbacks: list[Callable[[str], None]]
-
-    def __init__(self) -> None:
-        self._on_changed_callbacks = list()
-        self._on_deleted_callbacks = list()
-
-    """Base item storage class"""
-
-    @abstractmethod
-    def get(self, item_id: str) -> T:
-        pass
-
-    @abstractmethod
-    def set(self, item: T) -> None:
-        pass
-
-    @abstractmethod
-    def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
-        pass
-
-    @abstractmethod
-    def search(
-        self, query: str, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[T]:
-        pass
-
-    def on_changed(self, on_changed: Callable[[T], None]) -> None:
-        """Register a callback for when an item is changed"""
-        self._on_changed_callbacks.append(on_changed)
-
-    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
-        """Register a callback for when an item is deleted"""
-        self._on_deleted_callbacks.append(on_deleted)
-
-    def _on_changed(self, item: T) -> None:
-        for callback in self._on_changed_callbacks:
-            callback(item)
-
-    def _on_deleted(self, item_id: str) -> None:
-        for callback in self._on_deleted_callbacks:
-            callback(item_id)

invokeai/app/services/latent_storage.py

@@ -1,93 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-import os
-from abc import ABC, abstractmethod
-from pathlib import Path
-from queue import Queue
-from typing import Dict
-
-import torch
-
-class LatentsStorageBase(ABC):
-    """Responsible for storing and retrieving latents."""
-
-    @abstractmethod
-    def get(self, name: str) -> torch.Tensor:
-        pass
-
-    @abstractmethod
-    def set(self, name: str, data: torch.Tensor) -> None:
-        pass
-
-    @abstractmethod
-    def delete(self, name: str) -> None:
-        pass
-
-
-class ForwardCacheLatentsStorage(LatentsStorageBase):
-    """Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
-    
-    __cache: Dict[str, torch.Tensor]
-    __cache_ids: Queue
-    __max_cache_size: int
-    __underlying_storage: LatentsStorageBase
-
-    def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
-        self.__underlying_storage = underlying_storage
-        self.__cache = dict()
-        self.__cache_ids = Queue()
-        self.__max_cache_size = max_cache_size
-
-    def get(self, name: str) -> torch.Tensor:
-        cache_item = self.__get_cache(name)
-        if cache_item is not None:
-            return cache_item
-
-        latent = self.__underlying_storage.get(name)
-        self.__set_cache(name, latent)
-        return latent
-
-    def set(self, name: str, data: torch.Tensor) -> None:
-        self.__underlying_storage.set(name, data)
-        self.__set_cache(name, data)
-
-    def delete(self, name: str) -> None:
-        self.__underlying_storage.delete(name)
-        if name in self.__cache:
-            del self.__cache[name]
-
-    def __get_cache(self, name: str) -> torch.Tensor|None:
-        return None if name not in self.__cache else self.__cache[name]
-
-    def __set_cache(self, name: str, data: torch.Tensor):
-        if not name in self.__cache:
-            self.__cache[name] = data
-            self.__cache_ids.put(name)
-            if self.__cache_ids.qsize() > self.__max_cache_size:
-                self.__cache.pop(self.__cache_ids.get())
-
-
-class DiskLatentsStorage(LatentsStorageBase):
-    """Stores latents in a folder on disk without caching"""
-
-    __output_folder: str
-
-    def __init__(self, output_folder: str):
-        self.__output_folder = output_folder
-        Path(output_folder).mkdir(parents=True, exist_ok=True)
-
-    def get(self, name: str) -> torch.Tensor:
-        latent_path = self.get_path(name)
-        return torch.load(latent_path)
-
-    def set(self, name: str, data: torch.Tensor) -> None:
-        latent_path = self.get_path(name)
-        torch.save(data, latent_path)
-
-    def delete(self, name: str) -> None:
-        latent_path = self.get_path(name)
-        os.remove(latent_path)
-
-    def get_path(self, name: str) -> str:
-        return os.path.join(self.__output_folder, name)
-    

invokeai/app/services/metadata.py

@@ -1,96 +0,0 @@
-import json
-from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, TypedDict
-from PIL import Image, PngImagePlugin
-from pydantic import BaseModel
-
-from invokeai.app.models.image import ImageType, is_image_type
-
-
-class MetadataImageField(TypedDict):
-    """Pydantic-less ImageField, used for metadata parsing."""
-
-    image_type: ImageType
-    image_name: str
-
-
-class MetadataLatentsField(TypedDict):
-    """Pydantic-less LatentsField, used for metadata parsing."""
-
-    latents_name: str
-
-
-# TODO: This is a placeholder for `InvocationsUnion` pending resolution of circular imports
-NodeMetadata = Dict[
-    str, str | int | float | bool | MetadataImageField | MetadataLatentsField
-]
-
-
-class InvokeAIMetadata(TypedDict, total=False):
-    """InvokeAI-specific metadata format."""
-
-    session_id: Optional[str]
-    node: Optional[NodeMetadata]
-
-
-def build_invokeai_metadata_pnginfo(
-    metadata: InvokeAIMetadata | None,
-) -> PngImagePlugin.PngInfo:
-    """Builds a PngInfo object with key `"invokeai"` and value `metadata`"""
-    pnginfo = PngImagePlugin.PngInfo()
-
-    if metadata is not None:
-        pnginfo.add_text("invokeai", json.dumps(metadata))
-
-    return pnginfo
-
-
-class MetadataServiceBase(ABC):
-    @abstractmethod
-    def get_metadata(self, image: Image.Image) -> InvokeAIMetadata | None:
-        """Gets the InvokeAI metadata from a PIL Image, skipping invalid values"""
-        pass
-
-    @abstractmethod
-    def build_metadata(
-        self, session_id: str, node: BaseModel
-    ) -> InvokeAIMetadata | None:
-        """Builds an InvokeAIMetadata object"""
-        pass
-
-
-class PngMetadataService(MetadataServiceBase):
-    """Handles loading and building metadata for images."""
-
-    # TODO: Use `InvocationsUnion` to **validate** metadata as representing a fully-functioning node
-    def _load_metadata(self, image: Image.Image) -> dict | None:
-        """Loads a specific info entry from a PIL Image."""
-
-        try:
-            info = image.info.get("invokeai")
-
-            if type(info) is not str:
-                return None
-
-            loaded_metadata = json.loads(info)
-
-            if type(loaded_metadata) is not dict:
-                return None
-
-            if len(loaded_metadata.items()) == 0:
-                return None
-
-            return loaded_metadata
-        except:
-            return None
-
-    def get_metadata(self, image: Image.Image) -> dict | None:
-        """Retrieves an image's metadata as a dict"""
-        loaded_metadata = self._load_metadata(image)
-
-        return loaded_metadata
-
-    def build_metadata(self, session_id: str, node: BaseModel) -> InvokeAIMetadata:
-        metadata = InvokeAIMetadata(session_id=session_id, node=node.dict())
-
-        return metadata

invokeai/app/services/model_manager_initializer.py

@@ -1,120 +0,0 @@
-import os
-import sys
-import torch
-from argparse import Namespace
-from invokeai.backend import Args
-from omegaconf import OmegaConf
-from pathlib import Path
-
-import invokeai.version
-from ...backend import ModelManager
-from ...backend.util import choose_precision, choose_torch_device
-from ...backend import Globals
-
-# TODO: Replace with an abstract class base ModelManagerBase
-def get_model_manager(config: Args) -> ModelManager:
-    if not config.conf:
-        config_file = os.path.join(Globals.root, "configs", "models.yaml")
-        if not os.path.exists(config_file):
-            report_model_error(
-                config, FileNotFoundError(f"The file {config_file} could not be found.")
-            )
-
-    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
-
-    # these two lines prevent a horrible warning message from appearing
-    # when the frozen CLIP tokenizer is imported
-    import transformers  # type: ignore
-
-    transformers.logging.set_verbosity_error()
-    import diffusers
-
-    diffusers.logging.set_verbosity_error()
-
-    # normalize the config directory relative to root
-    if not os.path.isabs(config.conf):
-        config.conf = os.path.normpath(os.path.join(Globals.root, config.conf))
-
-    if config.embeddings:
-        if not os.path.isabs(config.embedding_path):
-            embedding_path = os.path.normpath(
-                os.path.join(Globals.root, config.embedding_path)
-            )
-        else:
-            embedding_path = config.embedding_path
-    else:
-        embedding_path = None
-
-    # migrate legacy models
-    ModelManager.migrate_models()
-
-    # creating the model manager
-    try:
-        device = torch.device(choose_torch_device())
-        precision = 'float16' if config.precision=='float16' \
-        else 'float32' if config.precision=='float32' \
-        else choose_precision(device)
-        
-        model_manager = ModelManager(
-            OmegaConf.load(config.conf),
-            precision=precision,
-            device_type=device,
-            max_loaded_models=config.max_loaded_models,
-            embedding_path = Path(embedding_path),
-        )
-    except (FileNotFoundError, TypeError, AssertionError) as e:
-        report_model_error(config, e)
-    except (IOError, KeyError) as e:
-        print(f"{e}. Aborting.")
-        sys.exit(-1)
-
-    # try to autoconvert new models
-    # autoimport new .ckpt files
-    if path := config.autoconvert:
-        model_manager.autoconvert_weights(
-            conf_path=config.conf,
-            weights_directory=path,
-        )
-
-    return model_manager
-
-def report_model_error(opt: Namespace, e: Exception):
-    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
-    print(
-        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
-    )
-    yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
-    if yes_to_all:
-        print(
-            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
-        )
-    else:
-        response = input(
-            "Do you want to run invokeai-configure script to select and/or reinstall models? [y] "
-        )
-        if response.startswith(("n", "N")):
-            return
-
-    print("invokeai-configure is launching....\n")
-
-    # Match arguments that were set on the CLI
-    # only the arguments accepted by the configuration script are parsed
-    root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
-    config = ["--config", opt.conf] if opt.conf is not None else []
-    previous_config = sys.argv
-    sys.argv = ["invokeai-configure"]
-    sys.argv.extend(root_dir)
-    sys.argv.extend(config.to_dict())
-    if yes_to_all is not None:
-        for arg in yes_to_all.split():
-            sys.argv.append(arg)
-
-    from invokeai.frontend.install import invokeai_configure
-
-    invokeai_configure()
-    # TODO: Figure out how to restart
-    # print('** InvokeAI will now restart')
-    # sys.argv = previous_args
-    # main() # would rather do a os.exec(), but doesn't exist?
-    # sys.exit(0)

invokeai/app/services/processor.py

@@ -1,130 +0,0 @@
-import traceback
-from threading import Event, Thread
-
-from ..invocations.baseinvocation import InvocationContext
-from .invocation_queue import InvocationQueueItem
-from .invoker import InvocationProcessorABC, Invoker
-from ..models.exceptions import CanceledException
-
-class DefaultInvocationProcessor(InvocationProcessorABC):
-    __invoker_thread: Thread
-    __stop_event: Event
-    __invoker: Invoker
-
-    def start(self, invoker) -> None:
-        self.__invoker = invoker
-        self.__stop_event = Event()
-        self.__invoker_thread = Thread(
-            name="invoker_processor",
-            target=self.__process,
-            kwargs=dict(stop_event=self.__stop_event),
-        )
-        self.__invoker_thread.daemon = (
-            True  # TODO: probably better to just not use threads?
-        )
-        self.__invoker_thread.start()
-
-    def stop(self, *args, **kwargs) -> None:
-        self.__stop_event.set()
-
-    def __process(self, stop_event: Event):
-        try:
-            while not stop_event.is_set():
-                queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
-                if not queue_item:  # Probably stopping
-                    continue
-
-                graph_execution_state = (
-                    self.__invoker.services.graph_execution_manager.get(
-                        queue_item.graph_execution_state_id
-                    )
-                )
-                invocation = graph_execution_state.execution_graph.get_node(
-                    queue_item.invocation_id
-                )
-
-                # get the source node id to provide to clients (the prepared node id is not as useful)
-                source_node_id = graph_execution_state.prepared_source_mapping[invocation.id]
-
-                # Send starting event
-                self.__invoker.services.events.emit_invocation_started(
-                    graph_execution_state_id=graph_execution_state.id,
-                    node=invocation.dict(),
-                    source_node_id=source_node_id
-                )
-
-                # Invoke
-                try:
-                    outputs = invocation.invoke(
-                        InvocationContext(
-                            services=self.__invoker.services,
-                            graph_execution_state_id=graph_execution_state.id,
-                        )
-                    )
-
-                    # Check queue to see if this is canceled, and skip if so
-                    if self.__invoker.services.queue.is_canceled(
-                        graph_execution_state.id
-                    ):
-                        continue
-
-                    # Save outputs and history
-                    graph_execution_state.complete(invocation.id, outputs)
-
-                    # Save the state changes
-                    self.__invoker.services.graph_execution_manager.set(
-                        graph_execution_state
-                    )
-
-                    # Send complete event
-                    self.__invoker.services.events.emit_invocation_complete(
-                        graph_execution_state_id=graph_execution_state.id,
-                        node=invocation.dict(),
-                        source_node_id=source_node_id,
-                        result=outputs.dict(),
-                    )
-
-                except KeyboardInterrupt:
-                    pass
-
-                except CanceledException:
-                    pass
-
-                except Exception as e:
-                    error = traceback.format_exc()
-
-                    # Save error
-                    graph_execution_state.set_node_error(invocation.id, error)
-
-                    # Save the state changes
-                    self.__invoker.services.graph_execution_manager.set(
-                        graph_execution_state
-                    )
-
-                    # Send error event
-                    self.__invoker.services.events.emit_invocation_error(
-                        graph_execution_state_id=graph_execution_state.id,
-                        node=invocation.dict(),
-                        source_node_id=source_node_id,
-                        error=error,
-                    )
-
-                    pass
-                
-                # Check queue to see if this is canceled, and skip if so
-                if self.__invoker.services.queue.is_canceled(
-                    graph_execution_state.id
-                ):
-                    continue
-
-                # Queue any further commands if invoking all
-                is_complete = graph_execution_state.is_complete()
-                if queue_item.invoke_all and not is_complete:
-                    self.__invoker.invoke(graph_execution_state, invoke_all=True)
-                elif is_complete:
-                    self.__invoker.services.events.emit_graph_execution_complete(
-                        graph_execution_state.id
-                    )
-
-        except KeyboardInterrupt:
-            ...  # Log something?

invokeai/app/services/restoration_services.py

@@ -1,109 +0,0 @@
-import sys
-import traceback
-import torch
-from ...backend.restoration import Restoration
-from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
-
-# This should be a real base class for postprocessing functions,
-# but right now we just instantiate the existing gfpgan, esrgan
-# and codeformer functions.
-class RestorationServices:
-    '''Face restoration and upscaling'''
-    
-    def __init__(self,args):
-        try:
-            gfpgan, codeformer, esrgan = None, None, None
-            if args.restore or args.esrgan:
-                restoration = Restoration()
-                if args.restore:
-                    gfpgan, codeformer = restoration.load_face_restore_models(
-                        args.gfpgan_model_path
-                    )
-                else:
-                    print(">> Face restoration disabled")
-                    if args.esrgan:
-                        esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
-                    else:
-                        print(">> Upscaling disabled")
-            else:
-                print(">> Face restoration and upscaling disabled")
-        except (ModuleNotFoundError, ImportError):
-            print(traceback.format_exc(), file=sys.stderr)
-            print(">> You may need to install the ESRGAN and/or GFPGAN modules")
-        self.device = torch.device(choose_torch_device())
-        self.gfpgan = gfpgan
-        self.codeformer = codeformer
-        self.esrgan = esrgan
-
-    # note that this one method does gfpgan and codepath reconstruction, as well as
-    # esrgan upscaling
-    # TO DO: refactor into separate methods
-    def upscale_and_reconstruct(
-        self,
-        image_list,
-        facetool="gfpgan",
-        upscale=None,
-        upscale_denoise_str=0.75,
-        strength=0.0,
-        codeformer_fidelity=0.75,
-        save_original=False,
-        image_callback=None,
-        prefix=None,
-    ):
-        results = []
-        for r in image_list:
-            image, seed = r
-            try:
-                if strength > 0:
-                    if self.gfpgan is not None or self.codeformer is not None:
-                        if facetool == "gfpgan":
-                            if self.gfpgan is None:
-                                print(
-                                    ">> GFPGAN not found. Face restoration is disabled."
-                                )
-                            else:
-                                image = self.gfpgan.process(image, strength, seed)
-                        if facetool == "codeformer":
-                            if self.codeformer is None:
-                                print(
-                                    ">> CodeFormer not found. Face restoration is disabled."
-                                )
-                            else:
-                                cf_device = (
-                                    CPU_DEVICE if self.device == MPS_DEVICE else self.device
-                                )
-                                image = self.codeformer.process(
-                                    image=image,
-                                    strength=strength,
-                                    device=cf_device,
-                                    seed=seed,
-                                    fidelity=codeformer_fidelity,
-                                )
-                    else:
-                        print(">> Face Restoration is disabled.")
-                if upscale is not None:
-                    if self.esrgan is not None:
-                        if len(upscale) < 2:
-                            upscale.append(0.75)
-                        image = self.esrgan.process(
-                            image,
-                            upscale[1],
-                            seed,
-                            int(upscale[0]),
-                            denoise_str=upscale_denoise_str,
-                        )
-                    else:
-                        print(">> ESRGAN is disabled. Image not upscaled.")
-            except Exception as e:
-                print(
-                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
-                )
-
-            if image_callback is not None:
-                image_callback(image, seed, upscaled=True, use_prefix=prefix)
-            else:
-                r[0] = image
-
-            results.append([image, seed])
-
-        return results

invokeai/app/services/sqlite.py

@@ -1,140 +0,0 @@
-import sqlite3
-from threading import Lock
-from typing import Generic, TypeVar, Union, get_args
-
-from pydantic import BaseModel, parse_raw_as
-
-from .item_storage import ItemStorageABC, PaginatedResults
-
-T = TypeVar("T", bound=BaseModel)
-
-sqlite_memory = ":memory:"
-
-
-class SqliteItemStorage(ItemStorageABC, Generic[T]):
-    _filename: str
-    _table_name: str
-    _conn: sqlite3.Connection
-    _cursor: sqlite3.Cursor
-    _id_field: str
-    _lock: Lock
-
-    def __init__(self, filename: str, table_name: str, id_field: str = "id"):
-        super().__init__()
-
-        self._filename = filename
-        self._table_name = table_name
-        self._id_field = id_field  # TODO: validate that T has this field
-        self._lock = Lock()
-
-        self._conn = sqlite3.connect(
-            self._filename, check_same_thread=False
-        )  # TODO: figure out a better threading solution
-        self._cursor = self._conn.cursor()
-
-        self._create_table()
-
-    def _create_table(self):
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""CREATE TABLE IF NOT EXISTS {self._table_name} (
-                item TEXT,
-                id TEXT GENERATED ALWAYS AS (json_extract(item, '$.{self._id_field}')) VIRTUAL NOT NULL);"""
-            )
-            self._cursor.execute(
-                f"""CREATE UNIQUE INDEX IF NOT EXISTS {self._table_name}_id ON {self._table_name}(id);"""
-            )
-        finally:
-            self._lock.release()
-
-    def _parse_item(self, item: str) -> T:
-        item_type = get_args(self.__orig_class__)[0]
-        return parse_raw_as(item_type, item)
-
-    def set(self, item: T):
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
-                (item.json(),),
-            )
-            self._conn.commit()
-        finally:
-            self._lock.release()
-        self._on_changed(item)
-
-    def get(self, id: str) -> Union[T, None]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
-            )
-            result = self._cursor.fetchone()
-        finally:
-            self._lock.release()
-
-        if not result:
-            return None
-
-        return self._parse_item(result[0])
-
-    def delete(self, id: str):
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),)
-            )
-            self._conn.commit()
-        finally:
-            self._lock.release()
-        self._on_deleted(id)
-
-    def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} LIMIT ? OFFSET ?;""",
-                (per_page, page * per_page),
-            )
-            result = self._cursor.fetchall()
-
-            items = list(map(lambda r: self._parse_item(r[0]), result))
-
-            self._cursor.execute(f"""SELECT count(*) FROM {self._table_name};""")
-            count = self._cursor.fetchone()[0]
-        finally:
-            self._lock.release()
-
-        pageCount = int(count / per_page) + 1
-
-        return PaginatedResults[T](
-            items=items, page=page, pages=pageCount, per_page=per_page, total=count
-        )
-
-    def search(
-        self, query: str, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[T]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} WHERE item LIKE ? LIMIT ? OFFSET ?;""",
-                (f"%{query}%", per_page, page * per_page),
-            )
-            result = self._cursor.fetchall()
-
-            items = list(map(lambda r: self._parse_item(r[0]), result))
-
-            self._cursor.execute(
-                f"""SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;""",
-                (f"%{query}%",),
-            )
-            count = self._cursor.fetchone()[0]
-        finally:
-            self._lock.release()
-
-        pageCount = int(count / per_page) + 1
-
-        return PaginatedResults[T](
-            items=items, page=page, pages=pageCount, per_page=per_page, total=count
-        )

invokeai/app/util/misc.py

@@ -1,5 +0,0 @@
-import datetime
-
-
-def get_timestamp():
-    return int(datetime.datetime.now(datetime.timezone.utc).timestamp())

invokeai/app/util/step_callback.py

@@ -1,55 +0,0 @@
-from invokeai.app.api.models.images import ProgressImage
-from invokeai.app.models.exceptions import CanceledException
-from ..invocations.baseinvocation import InvocationContext
-from ...backend.util.util import image_to_dataURL
-from ...backend.generator.base import Generator
-from ...backend.stable_diffusion import PipelineIntermediateState
-
-
-def stable_diffusion_step_callback(
-    context: InvocationContext,
-    intermediate_state: PipelineIntermediateState,
-    node: dict,
-    source_node_id: str,
-):
-    if context.services.queue.is_canceled(context.graph_execution_state_id):
-        raise CanceledException
-
-    # Some schedulers report not only the noisy latents at the current timestep,
-    # but also their estimate so far of what the de-noised latents will be. Use
-    # that estimate if it is available.
-    if intermediate_state.predicted_original is not None:
-        sample = intermediate_state.predicted_original
-    else:
-        sample = intermediate_state.latents
-
-    # TODO: This does not seem to be needed any more?
-    # # txt2img provides a Tensor in the step_callback
-    # # img2img provides a PipelineIntermediateState
-    # if isinstance(sample, PipelineIntermediateState):
-    #     # this was an img2img
-    #     print('img2img')
-    #     latents = sample.latents
-    #     step = sample.step
-    # else:
-    #     print('txt2img')
-    #     latents = sample
-    #     step = intermediate_state.step
-
-    # TODO: only output a preview image when requested
-    image = Generator.sample_to_lowres_estimated_image(sample)
-
-    (width, height) = image.size
-    width *= 8
-    height *= 8
-
-    dataURL = image_to_dataURL(image, image_format="JPEG")
-
-    context.services.events.emit_generator_progress(
-        graph_execution_state_id=context.graph_execution_state_id,
-        node=node,
-        source_node_id=source_node_id,
-        progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
-        step=intermediate_state.step,
-        total_steps=node["steps"],
-    )

invokeai/app/util/thumbnails.py

@@ -1,15 +0,0 @@
-import os
-from PIL import Image
-
-
-def get_thumbnail_name(image_name: str) -> str:
-    """Formats given an image name, returns the appropriate thumbnail image name"""
-    thumbnail_name = os.path.splitext(image_name)[0] + ".webp"
-    return thumbnail_name
-
-
-def make_thumbnail(image: Image.Image, size: int = 256) -> Image.Image:
-    """Makes a thumbnail from a PIL Image"""
-    thumbnail = image.copy()
-    thumbnail.thumbnail(size=(size, size))
-    return thumbnail

invokeai/backend/__init__.py

@@ -1,16 +1,5 @@
-"""
+'''
 Initialization file for invokeai.backend
-"""
-from .generate import Generate
-from .generator import (
-    InvokeAIGeneratorBasicParams,
-    InvokeAIGenerator,
-    InvokeAIGeneratorOutput,
-    Txt2Img,
-    Img2Img,
-    Inpaint
-)
-from .model_management import ModelManager
-from .safety_checker import SafetyChecker
-from .args import Args
-from .globals import Globals
+'''
+from .invoke_ai_web_server import InvokeAIWebServer
+

invokeai/backend/config/invokeai_configure.py

@@ -1,867 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
-# Before running stable-diffusion on an internet-isolated machine,
-# run this script from one with internet connectivity. The
-# two machines must share a common .cache directory.
-#
-# Coauthor: Kevin Turner http://github.com/keturn
-#
-import sys
-print("Loading Python libraries...\n",file=sys.stderr)
-
-import argparse
-import io
-import os
-import re
-import shutil
-import traceback
-import warnings
-from argparse import Namespace
-from pathlib import Path
-from shutil import get_terminal_size
-from urllib import request
-
-import npyscreen
-import torch
-import transformers
-from diffusers import AutoencoderKL
-from huggingface_hub import HfFolder
-from huggingface_hub import login as hf_hub_login
-from omegaconf import OmegaConf
-from tqdm import tqdm
-from transformers import (
-    AutoProcessor,
-    CLIPSegForImageSegmentation,
-    CLIPTextModel,
-    CLIPTokenizer,
-)
-
-import invokeai.configs as configs
-
-from ...frontend.install.model_install import addModelsForm, process_and_execute
-from ...frontend.install.widgets import (
-    CenteredButtonPress,
-    IntTitleSlider,
-    set_min_terminal_size,
-)
-from ..args import PRECISION_CHOICES, Args
-from ..globals import Globals, global_cache_dir, global_config_dir, global_config_file
-from .model_install_backend import (
-    default_dataset,
-    download_from_hf,
-    hf_download_with_resume,
-    recommended_datasets,
-)
-
-warnings.filterwarnings("ignore")
-
-transformers.logging.set_verbosity_error()
-
-# --------------------------globals-----------------------
-Model_dir = "models"
-Weights_dir = "ldm/stable-diffusion-v1/"
-
-# the initial "configs" dir is now bundled in the `invokeai.configs` package
-Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
-
-Default_config_file = Path(global_config_dir()) / "models.yaml"
-SD_Configs = Path(global_config_dir()) / "stable-diffusion"
-
-Datasets = OmegaConf.load(Dataset_path)
-
-# minimum size for the UI
-MIN_COLS = 135
-MIN_LINES = 45
-
-INIT_FILE_PREAMBLE = """# 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 invokeai-configure again.
-# Place  frequently-used startup commands here, one or more per line.
-# Examples:
-# --outdir=D:\data\images
-# --no-nsfw_checker
-# --web --host=0.0.0.0
-# --steps=20
-# -Ak_euler_a -C10.0
-"""
-
-
-# --------------------------------------------
-def postscript(errors: None):
-    if not any(errors):
-        message = f"""
-** INVOKEAI INSTALLATION SUCCESSFUL **
-If you installed manually from source or with 'pip install': activate the virtual environment
-then run one of the following commands to start InvokeAI.
-
-Web UI:
-   invokeai --web # (connect to http://localhost:9090)
-   invokeai --web --host 0.0.0.0 # (connect to http://your-lan-ip:9090 from another computer on the local network)
-
-Command-line interface:
-   invokeai
-
-If you installed using an installation script, run:
-  {Globals.root}/invoke.{"bat" if sys.platform == "win32" else "sh"}
-
-Add the '--help' argument to see all of the command-line switches available for use.
-"""
-
-    else:
-        message = "\n** There were errors during installation. It is possible some of the models were not fully downloaded.\n"
-        for err in errors:
-            message += f"\t - {err}\n"
-        message += "Please check the logs above and correct any issues."
-
-    print(message)
-
-
-# ---------------------------------------------
-def yes_or_no(prompt: str, default_yes=True):
-    default = "y" if default_yes else "n"
-    response = input(f"{prompt} [{default}] ") or default
-    if default_yes:
-        return response[0] not in ("n", "N")
-    else:
-        return response[0] in ("y", "Y")
-
-
-# ---------------------------------------------
-def HfLogin(access_token) -> str:
-    """
-    Helper for logging in to Huggingface
-    The stdout capture is needed to hide the irrelevant "git credential helper" warning
-    """
-
-    capture = io.StringIO()
-    sys.stdout = capture
-    try:
-        hf_hub_login(token=access_token, add_to_git_credential=False)
-        sys.stdout = sys.__stdout__
-    except Exception as exc:
-        sys.stdout = sys.__stdout__
-        print(exc)
-        raise exc
-
-
-# -------------------------------------
-class ProgressBar:
-    def __init__(self, model_name="file"):
-        self.pbar = None
-        self.name = model_name
-
-    def __call__(self, block_num, block_size, total_size):
-        if not self.pbar:
-            self.pbar = tqdm(
-                desc=self.name,
-                initial=0,
-                unit="iB",
-                unit_scale=True,
-                unit_divisor=1000,
-                total=total_size,
-            )
-        self.pbar.update(block_size)
-
-
-# ---------------------------------------------
-def download_with_progress_bar(model_url: str, model_dest: str, label: str = "the"):
-    try:
-        print(f"Installing {label} model file {model_url}...", end="", file=sys.stderr)
-        if not os.path.exists(model_dest):
-            os.makedirs(os.path.dirname(model_dest), exist_ok=True)
-            request.urlretrieve(
-                model_url, model_dest, ProgressBar(os.path.basename(model_dest))
-            )
-            print("...downloaded successfully", file=sys.stderr)
-        else:
-            print("...exists", file=sys.stderr)
-    except Exception:
-        print("...download failed", file=sys.stderr)
-        print(f"Error downloading {label} model", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-
-# ---------------------------------------------
-# this will preload the Bert tokenizer fles
-def download_bert():
-    print("Installing bert tokenizer...", file=sys.stderr)
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", category=DeprecationWarning)
-        from transformers import BertTokenizerFast
-
-        download_from_hf(BertTokenizerFast, "bert-base-uncased")
-
-
-# ---------------------------------------------
-def download_sd1_clip():
-    print("Installing SD1 clip model...", file=sys.stderr)
-    version = "openai/clip-vit-large-patch14"
-    download_from_hf(CLIPTokenizer, version)
-    download_from_hf(CLIPTextModel, version)
-
-
-# ---------------------------------------------
-def download_sd2_clip():
-    version = "stabilityai/stable-diffusion-2"
-    print("Installing SD2 clip model...", file=sys.stderr)
-    download_from_hf(CLIPTokenizer, version, subfolder="tokenizer")
-    download_from_hf(CLIPTextModel, version, subfolder="text_encoder")
-
-
-# ---------------------------------------------
-def download_realesrgan():
-    print("Installing models from RealESRGAN...", file=sys.stderr)
-    model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth"
-    wdn_model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth"
-
-    model_dest = os.path.join(
-        Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
-    )
-
-    wdn_model_dest = os.path.join(
-        Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
-    )
-
-    download_with_progress_bar(model_url, model_dest, "RealESRGAN")
-    download_with_progress_bar(wdn_model_url, wdn_model_dest, "RealESRGANwdn")
-
-
-def download_gfpgan():
-    print("Installing GFPGAN models...", file=sys.stderr)
-    for model in (
-        [
-            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",
-            "./models/gfpgan/GFPGANv1.4.pth",
-        ],
-        [
-            "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",
-            "./models/gfpgan/weights/detection_Resnet50_Final.pth",
-        ],
-        [
-            "https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",
-            "./models/gfpgan/weights/parsing_parsenet.pth",
-        ],
-    ):
-        model_url, model_dest = model[0], os.path.join(Globals.root, model[1])
-        download_with_progress_bar(model_url, model_dest, "GFPGAN weights")
-
-
-# ---------------------------------------------
-def download_codeformer():
-    print("Installing CodeFormer model file...", file=sys.stderr)
-    model_url = (
-        "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
-    )
-    model_dest = os.path.join(Globals.root, "models/codeformer/codeformer.pth")
-    download_with_progress_bar(model_url, model_dest, "CodeFormer")
-
-
-# ---------------------------------------------
-def download_clipseg():
-    print("Installing clipseg model for text-based masking...", file=sys.stderr)
-    CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
-    try:
-        download_from_hf(AutoProcessor, CLIPSEG_MODEL)
-        download_from_hf(CLIPSegForImageSegmentation, CLIPSEG_MODEL)
-    except Exception:
-        print("Error installing clipseg model:")
-        print(traceback.format_exc())
-
-
-# -------------------------------------
-def download_safety_checker():
-    print("Installing model for NSFW content detection...", file=sys.stderr)
-    try:
-        from diffusers.pipelines.stable_diffusion.safety_checker import (
-            StableDiffusionSafetyChecker,
-        )
-        from transformers import AutoFeatureExtractor
-    except ModuleNotFoundError:
-        print("Error installing NSFW checker model:")
-        print(traceback.format_exc())
-        return
-    safety_model_id = "CompVis/stable-diffusion-safety-checker"
-    print("AutoFeatureExtractor...", file=sys.stderr)
-    download_from_hf(AutoFeatureExtractor, safety_model_id)
-    print("StableDiffusionSafetyChecker...", file=sys.stderr)
-    download_from_hf(StableDiffusionSafetyChecker, safety_model_id)
-
-
-# -------------------------------------
-def download_vaes():
-    print("Installing stabilityai VAE...", file=sys.stderr)
-    try:
-        # first the diffusers version
-        repo_id = "stabilityai/sd-vae-ft-mse"
-        args = dict(
-            cache_dir=global_cache_dir("hub"),
-        )
-        if not AutoencoderKL.from_pretrained(repo_id, **args):
-            raise Exception(f"download of {repo_id} failed")
-
-        repo_id = "stabilityai/sd-vae-ft-mse-original"
-        model_name = "vae-ft-mse-840000-ema-pruned.ckpt"
-        # next the legacy checkpoint version
-        if not hf_download_with_resume(
-            repo_id=repo_id,
-            model_name=model_name,
-            model_dir=str(Globals.root / Model_dir / Weights_dir),
-        ):
-            raise Exception(f"download of {model_name} failed")
-    except Exception as e:
-        print(f"Error downloading StabilityAI standard VAE: {str(e)}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-
-# -------------------------------------
-def get_root(root: str = None) -> str:
-    if root:
-        return root
-    elif os.environ.get("INVOKEAI_ROOT"):
-        return os.environ.get("INVOKEAI_ROOT")
-    else:
-        return Globals.root
-
-
-# -------------------------------------
-class editOptsForm(npyscreen.FormMultiPage):
-    # for responsive resizing - disabled
-    # FIX_MINIMUM_SIZE_WHEN_CREATED = False
-
-    def create(self):
-        program_opts = self.parentApp.program_opts
-        old_opts = self.parentApp.invokeai_opts
-        first_time = not (Globals.root / Globals.initfile).exists()
-        access_token = HfFolder.get_token()
-        window_width, window_height = get_terminal_size()
-        for i in [
-            "Configure startup settings. You can come back and change these later.",
-            "Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.",
-            "Use cursor arrows to make a checkbox selection, and space to toggle.",
-        ]:
-            self.add_widget_intelligent(
-                npyscreen.FixedText,
-                value=i,
-                editable=False,
-                color="CONTROL",
-            )
-
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="== BASIC OPTIONS ==",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Select an output directory for images:",
-            editable=False,
-            color="CONTROL",
-        )
-        self.outdir = self.add_widget_intelligent(
-            npyscreen.TitleFilename,
-            name="(<tab> autocompletes, ctrl-N advances):",
-            value=old_opts.outdir or str(default_output_dir()),
-            select_dir=True,
-            must_exist=False,
-            use_two_lines=False,
-            labelColor="GOOD",
-            begin_entry_at=40,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Activate the NSFW checker to blur images showing potential sexual imagery:",
-            editable=False,
-            color="CONTROL",
-        )
-        self.safety_checker = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="NSFW checker",
-            value=old_opts.safety_checker,
-            relx=5,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        for i in [
-            "If you have an account at HuggingFace you may paste your access token here",
-            'to allow InvokeAI to download styles & subjects from the "Concept Library".',
-            "See https://huggingface.co/settings/tokens",
-        ]:
-            self.add_widget_intelligent(
-                npyscreen.FixedText,
-                value=i,
-                editable=False,
-                color="CONTROL",
-            )
-
-        self.hf_token = self.add_widget_intelligent(
-            npyscreen.TitlePassword,
-            name="Access Token (ctrl-shift-V pastes):",
-            value=access_token,
-            begin_entry_at=42,
-            use_two_lines=False,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="== ADVANCED OPTIONS ==",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="GPU Management",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
-        self.free_gpu_mem = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Free GPU memory after each generation",
-            value=old_opts.free_gpu_mem,
-            relx=5,
-            scroll_exit=True,
-        )
-        self.xformers = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Enable xformers support if available",
-            value=old_opts.xformers,
-            relx=5,
-            scroll_exit=True,
-        )
-        self.ckpt_convert = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Load legacy checkpoint models into memory as diffusers models",
-            value=old_opts.ckpt_convert,
-            relx=5,
-            scroll_exit=True,
-        )
-        self.always_use_cpu = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Force CPU to be used on GPU systems",
-            value=old_opts.always_use_cpu,
-            relx=5,
-            scroll_exit=True,
-        )
-        precision = old_opts.precision or (
-            "float32" if program_opts.full_precision else "auto"
-        )
-        self.precision = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="Precision",
-            values=PRECISION_CHOICES,
-            value=PRECISION_CHOICES.index(precision),
-            begin_entry_at=3,
-            max_height=len(PRECISION_CHOICES) + 1,
-            scroll_exit=True,
-        )
-        self.max_loaded_models = self.add_widget_intelligent(
-            IntTitleSlider,
-            name="Number of models to cache in CPU memory (each will use 2-4 GB!)",
-            value=old_opts.max_loaded_models,
-            out_of=10,
-            lowest=1,
-            begin_entry_at=4,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Directory containing embedding/textual inversion files:",
-            editable=False,
-            color="CONTROL",
-        )
-        self.embedding_path = self.add_widget_intelligent(
-            npyscreen.TitleFilename,
-            name="(<tab> autocompletes, ctrl-N advances):",
-            value=str(default_embedding_dir()),
-            select_dir=True,
-            must_exist=False,
-            use_two_lines=False,
-            labelColor="GOOD",
-            begin_entry_at=40,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="== LICENSE ==",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
-        for i in [
-            "BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ",
-            "AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT",
-            "https://huggingface.co/spaces/CompVis/stable-diffusion-license",
-        ]:
-            self.add_widget_intelligent(
-                npyscreen.FixedText,
-                value=i,
-                editable=False,
-                color="CONTROL",
-            )
-        self.license_acceptance = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="I accept the CreativeML Responsible AI License",
-            value=not first_time,
-            relx=2,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        label = (
-            "DONE"
-            if program_opts.skip_sd_weights or program_opts.default_only
-            else "NEXT"
-        )
-        self.ok_button = self.add_widget_intelligent(
-            CenteredButtonPress,
-            name=label,
-            relx=(window_width - len(label)) // 2,
-            rely=-3,
-            when_pressed_function=self.on_ok,
-        )
-
-    def on_ok(self):
-        options = self.marshall_arguments()
-        if self.validate_field_values(options):
-            self.parentApp.new_opts = options
-            if hasattr(self.parentApp, "model_select"):
-                self.parentApp.setNextForm("MODELS")
-            else:
-                self.parentApp.setNextForm(None)
-            self.editing = False
-        else:
-            self.editing = True
-
-    def validate_field_values(self, opt: Namespace) -> bool:
-        bad_fields = []
-        if not opt.license_acceptance:
-            bad_fields.append(
-                "Please accept the license terms before proceeding to model downloads"
-            )
-        if not Path(opt.outdir).parent.exists():
-            bad_fields.append(
-                f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
-            )
-        if not Path(opt.embedding_path).parent.exists():
-            bad_fields.append(
-                f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_path).parent)} is an existing directory."
-            )
-        if len(bad_fields) > 0:
-            message = "The following problems were detected and must be corrected:\n"
-            for problem in bad_fields:
-                message += f"* {problem}\n"
-            npyscreen.notify_confirm(message)
-            return False
-        else:
-            return True
-
-    def marshall_arguments(self):
-        new_opts = Namespace()
-
-        for attr in [
-            "outdir",
-            "safety_checker",
-            "free_gpu_mem",
-            "max_loaded_models",
-            "xformers",
-            "always_use_cpu",
-            "embedding_path",
-            "ckpt_convert",
-        ]:
-            setattr(new_opts, attr, getattr(self, attr).value)
-
-        new_opts.hf_token = self.hf_token.value
-        new_opts.license_acceptance = self.license_acceptance.value
-        new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
-
-        return new_opts
-
-
-class EditOptApplication(npyscreen.NPSAppManaged):
-    def __init__(self, program_opts: Namespace, invokeai_opts: Namespace):
-        super().__init__()
-        self.program_opts = program_opts
-        self.invokeai_opts = invokeai_opts
-        self.user_cancelled = False
-        self.user_selections = default_user_selections(program_opts)
-
-    def onStart(self):
-        npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
-        self.options = self.addForm(
-            "MAIN",
-            editOptsForm,
-            name="InvokeAI Startup Options",
-        )
-        if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
-            self.model_select = self.addForm(
-                "MODELS",
-                addModelsForm,
-                name="Install Stable Diffusion Models",
-                multipage=True,
-            )
-
-    def new_opts(self):
-        return self.options.marshall_arguments()
-
-
-def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Namespace:
-    editApp = EditOptApplication(program_opts, invokeai_opts)
-    editApp.run()
-    return editApp.new_opts()
-
-
-def default_startup_options(init_file: Path) -> Namespace:
-    opts = Args().parse_args([])
-    outdir = Path(opts.outdir)
-    if not outdir.is_absolute():
-        opts.outdir = str(Globals.root / opts.outdir)
-    if not init_file.exists():
-        opts.safety_checker = True
-    return opts
-
-
-def default_user_selections(program_opts: Namespace) -> Namespace:
-    return Namespace(
-        starter_models=default_dataset()
-        if program_opts.default_only
-        else recommended_datasets()
-        if program_opts.yes_to_all
-        else dict(),
-        purge_deleted_models=False,
-        scan_directory=None,
-        autoscan_on_startup=None,
-        import_model_paths=None,
-        convert_to_diffusers=None,
-    )
-
-
-# -------------------------------------
-def initialize_rootdir(root: str, yes_to_all: bool = False):
-    print("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
-
-    for name in (
-        "models",
-        "configs",
-        "embeddings",
-        "text-inversion-output",
-        "text-inversion-training-data",
-    ):
-        os.makedirs(os.path.join(root, name), exist_ok=True)
-
-    configs_src = Path(configs.__path__[0])
-    configs_dest = Path(root) / "configs"
-    if not os.path.samefile(configs_src, configs_dest):
-        shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
-
-
-# -------------------------------------
-def run_console_ui(
-    program_opts: Namespace, initfile: Path = None
-) -> (Namespace, Namespace):
-    # parse_args() will read from init file if present
-    invokeai_opts = default_startup_options(initfile)
-
-    set_min_terminal_size(MIN_COLS, MIN_LINES)
-    editApp = EditOptApplication(program_opts, invokeai_opts)
-    editApp.run()
-    if editApp.user_cancelled:
-        return (None, None)
-    else:
-        return (editApp.new_opts, editApp.user_selections)
-
-
-# -------------------------------------
-def write_opts(opts: Namespace, init_file: Path):
-    """
-    Update the invokeai.init file with values from opts Namespace
-    """
-    # touch file if it doesn't exist
-    if not init_file.exists():
-        with open(init_file, "w") as f:
-            f.write(INIT_FILE_PREAMBLE)
-
-    # We want to write in the changed arguments without clobbering
-    # any other initialization values the user has entered. There is
-    # no good way to do this because of the one-way nature of
-    # argparse: i.e. --outdir could be --outdir, --out, or -o
-    # initfile needs to be replaced with a fully structured format
-    # such as yaml; this is a hack that will work much of the time
-    args_to_skip = re.compile(
-        "^--?(o|out|no-xformer|xformer|no-ckpt|ckpt|free|no-nsfw|nsfw|prec|max_load|embed|always|ckpt|free_gpu)"
-    )
-    # fix windows paths
-    opts.outdir = opts.outdir.replace("\\", "/")
-    opts.embedding_path = opts.embedding_path.replace("\\", "/")
-    new_file = f"{init_file}.new"
-    try:
-        lines = [x.strip() for x in open(init_file, "r").readlines()]
-        with open(new_file, "w") as out_file:
-            for line in lines:
-                if len(line) > 0 and not args_to_skip.match(line):
-                    out_file.write(line + "\n")
-            out_file.write(
-                f"""
---outdir={opts.outdir}
---embedding_path={opts.embedding_path}
---precision={opts.precision}
---max_loaded_models={int(opts.max_loaded_models)}
---{'no-' if not opts.safety_checker else ''}nsfw_checker
---{'no-' if not opts.xformers else ''}xformers
---{'no-' if not opts.ckpt_convert else ''}ckpt_convert
-{'--free_gpu_mem' if opts.free_gpu_mem else ''}
-{'--always_use_cpu' if opts.always_use_cpu else ''}
-"""
-            )
-    except OSError as e:
-        print(f"** An error occurred while writing the init file: {str(e)}")
-
-    os.replace(new_file, init_file)
-
-    if opts.hf_token:
-        HfLogin(opts.hf_token)
-
-
-# -------------------------------------
-def default_output_dir() -> Path:
-    return Globals.root / "outputs"
-
-
-# -------------------------------------
-def default_embedding_dir() -> Path:
-    return Globals.root / "embeddings"
-
-
-# -------------------------------------
-def write_default_options(program_opts: Namespace, initfile: Path):
-    opt = default_startup_options(initfile)
-    opt.hf_token = HfFolder.get_token()
-    write_opts(opt, initfile)
-
-
-# -------------------------------------
-def main():
-    parser = argparse.ArgumentParser(description="InvokeAI model downloader")
-    parser.add_argument(
-        "--skip-sd-weights",
-        dest="skip_sd_weights",
-        action=argparse.BooleanOptionalAction,
-        default=False,
-        help="skip downloading the large Stable Diffusion weight files",
-    )
-    parser.add_argument(
-        "--skip-support-models",
-        dest="skip_support_models",
-        action=argparse.BooleanOptionalAction,
-        default=False,
-        help="skip downloading the support models",
-    )
-    parser.add_argument(
-        "--full-precision",
-        dest="full_precision",
-        action=argparse.BooleanOptionalAction,
-        type=bool,
-        default=False,
-        help="use 32-bit weights instead of faster 16-bit weights",
-    )
-    parser.add_argument(
-        "--yes",
-        "-y",
-        dest="yes_to_all",
-        action="store_true",
-        help='answer "yes" to all prompts',
-    )
-    parser.add_argument(
-        "--default_only",
-        action="store_true",
-        help="when --yes specified, only install the default model",
-    )
-    parser.add_argument(
-        "--config_file",
-        "-c",
-        dest="config_file",
-        type=str,
-        default=None,
-        help="path to configuration file to create",
-    )
-    parser.add_argument(
-        "--root_dir",
-        dest="root",
-        type=str,
-        default=None,
-        help="path to root of install directory",
-    )
-    opt = parser.parse_args()
-
-    # setting a global here
-    Globals.root = Path(os.path.expanduser(get_root(opt.root) or ""))
-
-    errors = set()
-
-    try:
-        models_to_download = default_user_selections(opt)
-
-        # We check for to see if the runtime directory is correctly initialized.
-        init_file = Path(Globals.root, Globals.initfile)
-        if not init_file.exists() or not global_config_file().exists():
-            initialize_rootdir(Globals.root, opt.yes_to_all)
-
-        if opt.yes_to_all:
-            write_default_options(opt, init_file)
-            init_options = Namespace(
-                precision="float32" if opt.full_precision else "float16"
-            )
-        else:
-            init_options, models_to_download = run_console_ui(opt, init_file)
-            if init_options:
-                write_opts(init_options, init_file)
-            else:
-                print(
-                    '\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n'
-                )
-                sys.exit(0)
-
-        if opt.skip_support_models:
-            print("\n** SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST **")
-        else:
-            print("\n** DOWNLOADING SUPPORT MODELS **")
-            download_bert()
-            download_sd1_clip()
-            download_sd2_clip()
-            download_realesrgan()
-            download_gfpgan()
-            download_codeformer()
-            download_clipseg()
-            download_safety_checker()
-            download_vaes()
-
-        if opt.skip_sd_weights:
-            print("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
-        elif models_to_download:
-            print("\n** DOWNLOADING DIFFUSION WEIGHTS **")
-            process_and_execute(opt, models_to_download)
-
-        postscript(errors=errors)
-    except KeyboardInterrupt:
-        print("\nGoodbye! Come back soon.")
-
-
-# -------------------------------------
-if __name__ == "__main__":
-    main()

invokeai/backend/config/model_install_backend.py

@@ -1,463 +0,0 @@
-"""
-Utility (backend) functions used by model_install.py
-"""
-import os
-import re
-import shutil
-import sys
-import warnings
-from pathlib import Path
-from tempfile import TemporaryFile
-from typing import List
-
-import requests
-from diffusers import AutoencoderKL
-from huggingface_hub import hf_hub_url
-from omegaconf import OmegaConf
-from omegaconf.dictconfig import DictConfig
-from tqdm import tqdm
-
-import invokeai.configs as configs
-
-from ..globals import Globals, global_cache_dir, global_config_dir
-from ..model_management import ModelManager
-from ..stable_diffusion import StableDiffusionGeneratorPipeline
-
-warnings.filterwarnings("ignore")
-
-# --------------------------globals-----------------------
-Model_dir = "models"
-Weights_dir = "ldm/stable-diffusion-v1/"
-
-# the initial "configs" dir is now bundled in the `invokeai.configs` package
-Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
-
-# initial models omegaconf
-Datasets = None
-
-Config_preamble = """
-# This file describes the alternative machine learning models
-# available to InvokeAI script.
-#
-# To add a new model, follow the examples below. Each
-# model requires a model config file, a weights file,
-# and the width and height of the images it
-# was trained on.
-"""
-
-
-def default_config_file():
-    return Path(global_config_dir()) / "models.yaml"
-
-
-def sd_configs():
-    return Path(global_config_dir()) / "stable-diffusion"
-
-
-def initial_models():
-    global Datasets
-    if Datasets:
-        return Datasets
-    return (Datasets := OmegaConf.load(Dataset_path))
-
-
-def install_requested_models(
-    install_initial_models: List[str] = None,
-    remove_models: List[str] = None,
-    scan_directory: Path = None,
-    external_models: List[str] = None,
-    scan_at_startup: bool = False,
-    precision: str = "float16",
-    purge_deleted: bool = False,
-    config_file_path: Path = None,
-):
-    """
-    Entry point for installing/deleting starter models, or installing external models.
-    """
-    config_file_path = config_file_path or default_config_file()
-    if not config_file_path.exists():
-        open(config_file_path, "w")
-
-    model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
-
-    if remove_models and len(remove_models) > 0:
-        print("== DELETING UNCHECKED STARTER MODELS ==")
-        for model in remove_models:
-            print(f"{model}...")
-            model_manager.del_model(model, delete_files=purge_deleted)
-        model_manager.commit(config_file_path)
-
-    if install_initial_models and len(install_initial_models) > 0:
-        print("== INSTALLING SELECTED STARTER MODELS ==")
-        successfully_downloaded = download_weight_datasets(
-            models=install_initial_models,
-            access_token=None,
-            precision=precision,
-        )  # FIX: for historical reasons, we don't use model manager here
-        update_config_file(successfully_downloaded, config_file_path)
-        if len(successfully_downloaded) < len(install_initial_models):
-            print("** Some of the model downloads were not successful")
-
-    # due to above, we have to reload the model manager because conf file
-    # was changed behind its back
-    model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
-
-    external_models = external_models or list()
-    if scan_directory:
-        external_models.append(str(scan_directory))
-
-    if len(external_models) > 0:
-        print("== INSTALLING EXTERNAL MODELS ==")
-        for path_url_or_repo in external_models:
-            try:
-                model_manager.heuristic_import(
-                    path_url_or_repo,
-                    commit_to_conf=config_file_path,
-                )
-            except KeyboardInterrupt:
-                sys.exit(-1)
-            except Exception:
-                pass
-
-    if scan_at_startup and scan_directory.is_dir():
-        argument = "--autoconvert"
-        initfile = Path(Globals.root, Globals.initfile)
-        replacement = Path(Globals.root, f"{Globals.initfile}.new")
-        directory = str(scan_directory).replace("\\", "/")
-        with open(initfile, "r") as input:
-            with open(replacement, "w") as output:
-                while line := input.readline():
-                    if not line.startswith(argument):
-                        output.writelines([line])
-                output.writelines([f"{argument} {directory}"])
-        os.replace(replacement, initfile)
-
-
-# -------------------------------------
-def yes_or_no(prompt: str, default_yes=True):
-    default = "y" if default_yes else "n"
-    response = input(f"{prompt} [{default}] ") or default
-    if default_yes:
-        return response[0] not in ("n", "N")
-    else:
-        return response[0] in ("y", "Y")
-
-
-# -------------------------------------
-def get_root(root: str = None) -> str:
-    if root:
-        return root
-    elif os.environ.get("INVOKEAI_ROOT"):
-        return os.environ.get("INVOKEAI_ROOT")
-    else:
-        return Globals.root
-
-
-# ---------------------------------------------
-def recommended_datasets() -> dict:
-    datasets = dict()
-    for ds in initial_models().keys():
-        if initial_models()[ds].get("recommended", False):
-            datasets[ds] = True
-    return datasets
-
-
-# ---------------------------------------------
-def default_dataset() -> dict:
-    datasets = dict()
-    for ds in initial_models().keys():
-        if initial_models()[ds].get("default", False):
-            datasets[ds] = True
-    return datasets
-
-
-# ---------------------------------------------
-def all_datasets() -> dict:
-    datasets = dict()
-    for ds in initial_models().keys():
-        datasets[ds] = True
-    return datasets
-
-
-# ---------------------------------------------
-# look for legacy model.ckpt in models directory and offer to
-# normalize its name
-def migrate_models_ckpt():
-    model_path = os.path.join(Globals.root, Model_dir, Weights_dir)
-    if not os.path.exists(os.path.join(model_path, "model.ckpt")):
-        return
-    new_name = initial_models()["stable-diffusion-1.4"]["file"]
-    print(
-        'The Stable Diffusion v4.1 "model.ckpt" is already installed. The name will be changed to {new_name} to avoid confusion.'
-    )
-    print(f"model.ckpt => {new_name}")
-    os.replace(
-        os.path.join(model_path, "model.ckpt"), os.path.join(model_path, new_name)
-    )
-
-
-# ---------------------------------------------
-def download_weight_datasets(
-    models: List[str], access_token: str, precision: str = "float32"
-):
-    migrate_models_ckpt()
-    successful = dict()
-    for mod in models:
-        print(f"Downloading {mod}:")
-        successful[mod] = _download_repo_or_file(
-            initial_models()[mod], access_token, precision=precision
-        )
-    return successful
-
-
-def _download_repo_or_file(
-    mconfig: DictConfig, access_token: str, precision: str = "float32"
-) -> Path:
-    path = None
-    if mconfig["format"] == "ckpt":
-        path = _download_ckpt_weights(mconfig, access_token)
-    else:
-        path = _download_diffusion_weights(mconfig, access_token, precision=precision)
-        if "vae" in mconfig and "repo_id" in mconfig["vae"]:
-            _download_diffusion_weights(
-                mconfig["vae"], access_token, precision=precision
-            )
-    return path
-
-
-def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
-    repo_id = mconfig["repo_id"]
-    filename = mconfig["file"]
-    cache_dir = os.path.join(Globals.root, Model_dir, Weights_dir)
-    return hf_download_with_resume(
-        repo_id=repo_id,
-        model_dir=cache_dir,
-        model_name=filename,
-        access_token=access_token,
-    )
-
-
-# ---------------------------------------------
-def download_from_hf(
-    model_class: object, model_name: str, cache_subdir: Path = Path("hub"), **kwargs
-):
-    path = global_cache_dir(cache_subdir)
-    model = model_class.from_pretrained(
-        model_name,
-        cache_dir=path,
-        resume_download=True,
-        **kwargs,
-    )
-    model_name = "--".join(("models", *model_name.split("/")))
-    return path / model_name if model else None
-
-
-def _download_diffusion_weights(
-    mconfig: DictConfig, access_token: str, precision: str = "float32"
-):
-    repo_id = mconfig["repo_id"]
-    model_class = (
-        StableDiffusionGeneratorPipeline
-        if mconfig.get("format", None) == "diffusers"
-        else AutoencoderKL
-    )
-    extra_arg_list = [{"revision": "fp16"}, {}] if precision == "float16" else [{}]
-    path = None
-    for extra_args in extra_arg_list:
-        try:
-            path = download_from_hf(
-                model_class,
-                repo_id,
-                safety_checker=None,
-                **extra_args,
-            )
-        except OSError as e:
-            if str(e).startswith("fp16 is not a valid"):
-                pass
-            else:
-                print(f"An unexpected error occurred while downloading the model: {e})")
-        if path:
-            break
-    return path
-
-
-# ---------------------------------------------
-def hf_download_with_resume(
-    repo_id: str, model_dir: str, model_name: str, access_token: str = None
-) -> Path:
-    model_dest = Path(os.path.join(model_dir, model_name))
-    os.makedirs(model_dir, exist_ok=True)
-
-    url = hf_hub_url(repo_id, model_name)
-
-    header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
-    open_mode = "wb"
-    exist_size = 0
-
-    if os.path.exists(model_dest):
-        exist_size = os.path.getsize(model_dest)
-        header["Range"] = f"bytes={exist_size}-"
-        open_mode = "ab"
-
-    resp = requests.get(url, headers=header, stream=True)
-    total = int(resp.headers.get("content-length", 0))
-
-    if (
-        resp.status_code == 416
-    ):  # "range not satisfiable", which means nothing to return
-        print(f"* {model_name}: complete file found. Skipping.")
-        return model_dest
-    elif resp.status_code != 200:
-        print(f"** An error occurred during downloading {model_name}: {resp.reason}")
-    elif exist_size > 0:
-        print(f"* {model_name}: partial file found. Resuming...")
-    else:
-        print(f"* {model_name}: Downloading...")
-
-    try:
-        if total < 2000:
-            print(f"*** ERROR DOWNLOADING {model_name}: {resp.text}")
-            return None
-
-        with open(model_dest, open_mode) as file, tqdm(
-            desc=model_name,
-            initial=exist_size,
-            total=total + exist_size,
-            unit="iB",
-            unit_scale=True,
-            unit_divisor=1000,
-        ) as bar:
-            for data in resp.iter_content(chunk_size=1024):
-                size = file.write(data)
-                bar.update(size)
-    except Exception as e:
-        print(f"An error occurred while downloading {model_name}: {str(e)}")
-        return None
-    return model_dest
-
-
-# ---------------------------------------------
-def update_config_file(successfully_downloaded: dict, config_file: Path):
-    config_file = (
-        Path(config_file) if config_file is not None else default_config_file()
-    )
-
-    # In some cases (incomplete setup, etc), the default configs directory might be missing.
-    # Create it if it doesn't exist.
-    # this check is ignored if opt.config_file is specified - user is assumed to know what they
-    # are doing if they are passing a custom config file from elsewhere.
-    if config_file is default_config_file() and not config_file.parent.exists():
-        configs_src = Dataset_path.parent
-        configs_dest = default_config_file().parent
-        shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
-
-    yaml = new_config_file_contents(successfully_downloaded, config_file)
-
-    try:
-        backup = None
-        if os.path.exists(config_file):
-            print(
-                f"** {config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
-            )
-            backup = config_file.with_suffix(".yaml.orig")
-            ## Ugh. Windows is unable to overwrite an existing backup file, raises a WinError 183
-            if sys.platform == "win32" and backup.is_file():
-                backup.unlink()
-            config_file.rename(backup)
-
-        with TemporaryFile() as tmp:
-            tmp.write(Config_preamble.encode())
-            tmp.write(yaml.encode())
-
-            with open(str(config_file.expanduser().resolve()), "wb") as new_config:
-                tmp.seek(0)
-                new_config.write(tmp.read())
-
-    except Exception as e:
-        print(f"**Error creating config file {config_file}: {str(e)} **")
-        if backup is not None:
-            print("restoring previous config file")
-            ## workaround, for WinError 183, see above
-            if sys.platform == "win32" and config_file.is_file():
-                config_file.unlink()
-            backup.rename(config_file)
-        return
-
-    print(f"Successfully created new configuration file {config_file}")
-
-
-# ---------------------------------------------
-def new_config_file_contents(
-    successfully_downloaded: dict,
-    config_file: Path,
-) -> str:
-    if config_file.exists():
-        conf = OmegaConf.load(str(config_file.expanduser().resolve()))
-    else:
-        conf = OmegaConf.create()
-
-    default_selected = None
-    for model in successfully_downloaded:
-        # a bit hacky - what we are doing here is seeing whether a checkpoint
-        # version of the model was previously defined, and whether the current
-        # model is a diffusers (indicated with a path)
-        if conf.get(model) and Path(successfully_downloaded[model]).is_dir():
-            delete_weights(model, conf[model])
-
-        stanza = {}
-        mod = initial_models()[model]
-        stanza["description"] = mod["description"]
-        stanza["repo_id"] = mod["repo_id"]
-        stanza["format"] = mod["format"]
-        # diffusers don't need width and height (probably .ckpt doesn't either)
-        # so we no longer require these in INITIAL_MODELS.yaml
-        if "width" in mod:
-            stanza["width"] = mod["width"]
-        if "height" in mod:
-            stanza["height"] = mod["height"]
-        if "file" in mod:
-            stanza["weights"] = os.path.relpath(
-                successfully_downloaded[model], start=Globals.root
-            )
-            stanza["config"] = os.path.normpath(
-                os.path.join(sd_configs(), mod["config"])
-            )
-        if "vae" in mod:
-            if "file" in mod["vae"]:
-                stanza["vae"] = os.path.normpath(
-                    os.path.join(Model_dir, Weights_dir, mod["vae"]["file"])
-                )
-            else:
-                stanza["vae"] = mod["vae"]
-        if mod.get("default", False):
-            stanza["default"] = True
-            default_selected = True
-
-        conf[model] = stanza
-
-    # if no default model was chosen, then we select the first
-    # one in the list
-    if not default_selected:
-        conf[list(successfully_downloaded.keys())[0]]["default"] = True
-
-    return OmegaConf.to_yaml(conf)
-
-
-# ---------------------------------------------
-def delete_weights(model_name: str, conf_stanza: dict):
-    if not (weights := conf_stanza.get("weights")):
-        return
-    if re.match("/VAE/", conf_stanza.get("config")):
-        return
-
-    print(
-        f"\n** The checkpoint version of {model_name} is superseded by the diffusers version. Deleting the original file {weights}?"
-    )
-
-    weights = Path(weights)
-    if not weights.is_absolute():
-        weights = Path(Globals.root) / weights
-        try:
-            weights.unlink()
-        except OSError as e:
-            print(str(e))

invokeai/backend/generator/__init__.py

@@ -1,13 +0,0 @@
-"""
-Initialization file for the invokeai.generator package
-"""
-from .base import (
-    InvokeAIGenerator,
-    InvokeAIGeneratorBasicParams,
-    InvokeAIGeneratorOutput,
-    Txt2Img,
-    Img2Img,
-    Inpaint,
-    Generator,
-)
-from .inpaint import infill_methods

invokeai/backend/generator/base.py

@@ -1,649 +0,0 @@
-"""
-Base class for invokeai.backend.generator.*
-including img2img, txt2img, and inpaint
-"""
-from __future__ import annotations
-
-import itertools
-import dataclasses
-import diffusers
-import os
-import random
-import traceback
-from abc import ABCMeta
-from argparse import Namespace
-from contextlib import nullcontext
-
-import cv2
-import numpy as np
-import torch
-from PIL import Image, ImageChops, ImageFilter
-from accelerate.utils import set_seed
-from diffusers import DiffusionPipeline
-from tqdm import trange
-from typing import Callable, List, Iterator, Optional, Type
-from dataclasses import dataclass, field
-from diffusers.schedulers import SchedulerMixin as Scheduler
-
-from ..image_util import configure_model_padding
-from ..util.util import rand_perlin_2d
-from ..safety_checker import SafetyChecker
-from ..prompting.conditioning import get_uc_and_c_and_ec
-from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
-
-downsampling = 8
-
-@dataclass
-class InvokeAIGeneratorBasicParams:
-    seed: Optional[int]=None
-    width: int=512
-    height: int=512
-    cfg_scale: float=7.5
-    steps: int=20
-    ddim_eta: float=0.0
-    scheduler: str='ddim'
-    precision: str='float16'
-    perlin: float=0.0
-    threshold: float=0.0
-    seamless: bool=False
-    seamless_axes: List[str]=field(default_factory=lambda: ['x', 'y'])
-    h_symmetry_time_pct: Optional[float]=None
-    v_symmetry_time_pct: Optional[float]=None
-    variation_amount: float = 0.0
-    with_variations: list=field(default_factory=list)
-    safety_checker: Optional[SafetyChecker]=None
-
-@dataclass
-class InvokeAIGeneratorOutput:
-    '''
-    InvokeAIGeneratorOutput is a dataclass that contains the outputs of a generation
-    operation, including the image, its seed, the model name used to generate the image
-    and the model hash, as well as all the generate() parameters that went into
-    generating the image (in .params, also available as attributes)
-    '''
-    image: Image.Image
-    seed: int
-    model_hash: str
-    attention_maps_images: List[Image.Image]
-    params: Namespace
-
-# we are interposing a wrapper around the original Generator classes so that
-# old code that calls Generate will continue to work.
-class InvokeAIGenerator(metaclass=ABCMeta):
-    scheduler_map = dict(
-        ddim=diffusers.DDIMScheduler,
-        dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-        k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-        k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_euler=diffusers.EulerDiscreteScheduler,
-        k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-        k_heun=diffusers.HeunDiscreteScheduler,
-        k_lms=diffusers.LMSDiscreteScheduler,
-        plms=diffusers.PNDMScheduler,
-    )
-
-    def __init__(self,
-                 model_info: dict,
-                 params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
-                 ):
-        self.model_info=model_info
-        self.params=params
-
-    def generate(self,
-                 prompt: str='',
-                 callback: Optional[Callable]=None,
-                 step_callback: Optional[Callable]=None,
-                 iterations: int=1,
-                 **keyword_args,
-                 )->Iterator[InvokeAIGeneratorOutput]:
-        '''
-        Return an iterator across the indicated number of generations.
-        Each time the iterator is called it will return an InvokeAIGeneratorOutput
-        object. Use like this:
-
-           outputs = txt2img.generate(prompt='banana sushi', iterations=5)
-           for result in outputs:
-               print(result.image, result.seed)
-
-        In the typical case of wanting to get just a single image, iterations
-        defaults to 1 and do:
-
-           output = next(txt2img.generate(prompt='banana sushi')
-
-        Pass None to get an infinite iterator.
-
-           outputs = txt2img.generate(prompt='banana sushi', iterations=None)
-           for o in outputs:
-               print(o.image, o.seed)
-
-        '''
-        generator_args = dataclasses.asdict(self.params)
-        generator_args.update(keyword_args)
-
-        model_info = self.model_info
-        model_name = model_info['model_name']
-        model:StableDiffusionGeneratorPipeline = model_info['model']
-        model_hash = model_info['hash']
-        scheduler: Scheduler = self.get_scheduler(
-            model=model,
-            scheduler_name=generator_args.get('scheduler')
-        )
-        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
-        gen_class = self._generator_class()
-        generator = gen_class(model, self.params.precision)
-        if self.params.variation_amount > 0:
-            generator.set_variation(generator_args.get('seed'),
-                                    generator_args.get('variation_amount'),
-                                    generator_args.get('with_variations')
-                                    )
-
-        if isinstance(model, DiffusionPipeline):
-            for component in [model.unet, model.vae]:
-                configure_model_padding(component,
-                                        generator_args.get('seamless',False),
-                                        generator_args.get('seamless_axes')
-                                        )
-        else:
-            configure_model_padding(model,
-                                    generator_args.get('seamless',False),
-                                    generator_args.get('seamless_axes')
-                                    )
-
-        iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1)
-        for i in iteration_count:
-            results = generator.generate(prompt,
-                                         conditioning=(uc, c, extra_conditioning_info),
-                                         step_callback=step_callback,
-                                         sampler=scheduler,
-                                         **generator_args,
-                                         )
-            output = InvokeAIGeneratorOutput(
-                image=results[0][0],
-                seed=results[0][1],
-                attention_maps_images=results[0][2],
-                model_hash = model_hash,
-                params=Namespace(model_name=model_name,**generator_args),
-            )
-            if callback:
-                callback(output)
-            yield output
-
-    @classmethod
-    def schedulers(self)->List[str]:
-        '''
-        Return list of all the schedulers that we currently handle.
-        '''
-        return list(self.scheduler_map.keys())
-
-    def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
-        return generator_class(model, self.params.precision)
-
-    def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-        scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
-        scheduler = scheduler_class.from_config(model.scheduler.config)
-        # hack copied over from generate.py
-        if not hasattr(scheduler, 'uses_inpainting_model'):
-            scheduler.uses_inpainting_model = lambda: False
-        return scheduler
-
-    @classmethod
-    def _generator_class(cls)->Type[Generator]:
-        '''
-        In derived classes return the name of the generator to apply.
-        If you don't override will return the name of the derived
-        class, which nicely parallels the generator class names.
-        '''
-        return Generator
-
-# ------------------------------------
-class Txt2Img(InvokeAIGenerator):
-    @classmethod
-    def _generator_class(cls):
-        from .txt2img import Txt2Img
-        return Txt2Img
-
-# ------------------------------------
-class Img2Img(InvokeAIGenerator):
-    def generate(self,
-               init_image: Image.Image | torch.FloatTensor,
-               strength: float=0.75,
-               **keyword_args
-               )->Iterator[InvokeAIGeneratorOutput]:
-        return super().generate(init_image=init_image,
-                                strength=strength,
-                                **keyword_args
-                                )
-    @classmethod
-    def _generator_class(cls):
-        from .img2img import Img2Img
-        return Img2Img
-
-# ------------------------------------
-# Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
-class Inpaint(Img2Img):
-    def generate(self,
-                 mask_image: Image.Image | torch.FloatTensor,
-                 # Seam settings - when 0, doesn't fill seam
-                 seam_size: int = 0,
-                 seam_blur: int = 0,
-                 seam_strength: float = 0.7,
-                 seam_steps: int = 10,
-                 tile_size: int = 32,
-                 inpaint_replace=False,
-                 infill_method=None,
-                 inpaint_width=None,
-                 inpaint_height=None,
-                 inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
-                 **keyword_args
-                 )->Iterator[InvokeAIGeneratorOutput]:
-        return super().generate(
-            mask_image=mask_image,
-            seam_size=seam_size,
-            seam_blur=seam_blur,
-            seam_strength=seam_strength,
-            seam_steps=seam_steps,
-            tile_size=tile_size,
-            inpaint_replace=inpaint_replace,
-            infill_method=infill_method,
-            inpaint_width=inpaint_width,
-            inpaint_height=inpaint_height,
-            inpaint_fill=inpaint_fill,
-            **keyword_args
-        )
-    @classmethod
-    def _generator_class(cls):
-        from .inpaint import Inpaint
-        return Inpaint
-
-# ------------------------------------
-class Embiggen(Txt2Img):
-    def generate(
-            self,
-            embiggen: list=None,
-            embiggen_tiles: list = None,
-            strength: float=0.75,
-            **kwargs)->Iterator[InvokeAIGeneratorOutput]:
-        return super().generate(embiggen=embiggen,
-                                embiggen_tiles=embiggen_tiles,
-                                strength=strength,
-                                **kwargs)
-
-    @classmethod
-    def _generator_class(cls):
-        from .embiggen import Embiggen
-        return Embiggen
-
-
-class Generator:
-    downsampling_factor: int
-    latent_channels: int
-    precision: str
-    model: DiffusionPipeline
-
-    def __init__(self, model: DiffusionPipeline, precision: str):
-        self.model = model
-        self.precision = precision
-        self.seed = None
-        self.latent_channels = model.channels
-        self.downsampling_factor = downsampling  # BUG: should come from model or config
-        self.safety_checker = None
-        self.perlin = 0.0
-        self.threshold = 0
-        self.variation_amount = 0
-        self.with_variations = []
-        self.use_mps_noise = False
-        self.free_gpu_mem = None
-
-    # this is going to be overridden in img2img.py, txt2img.py and inpaint.py
-    def get_make_image(self, prompt, **kwargs):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        """
-        raise NotImplementedError(
-            "image_iterator() must be implemented in a descendent class"
-        )
-
-    def set_variation(self, seed, variation_amount, with_variations):
-        self.seed = seed
-        self.variation_amount = variation_amount
-        self.with_variations = with_variations
-
-    def generate(
-        self,
-        prompt,
-        width,
-        height,
-        sampler,
-        init_image=None,
-        iterations=1,
-        seed=None,
-        image_callback=None,
-        step_callback=None,
-        threshold=0.0,
-        perlin=0.0,
-        h_symmetry_time_pct=None,
-        v_symmetry_time_pct=None,
-        safety_checker: SafetyChecker=None,
-        free_gpu_mem: bool = False,
-        **kwargs,
-    ):
-        scope = nullcontext
-        self.safety_checker = safety_checker
-        self.free_gpu_mem = free_gpu_mem
-        attention_maps_images = []
-        attention_maps_callback = lambda saver: attention_maps_images.append(
-            saver.get_stacked_maps_image()
-        )
-        make_image = self.get_make_image(
-            prompt,
-            sampler=sampler,
-            init_image=init_image,
-            width=width,
-            height=height,
-            step_callback=step_callback,
-            threshold=threshold,
-            perlin=perlin,
-            h_symmetry_time_pct=h_symmetry_time_pct,
-            v_symmetry_time_pct=v_symmetry_time_pct,
-            attention_maps_callback=attention_maps_callback,
-            **kwargs,
-        )
-        results = []
-        seed = seed if seed is not None and seed >= 0 else self.new_seed()
-        first_seed = seed
-        seed, initial_noise = self.generate_initial_noise(seed, width, height)
-
-        # There used to be an additional self.model.ema_scope() here, but it breaks
-        # the inpaint-1.5 model. Not sure what it did.... ?
-        with scope(self.model.device.type):
-            for n in trange(iterations, desc="Generating"):
-                x_T = None
-                if self.variation_amount > 0:
-                    set_seed(seed)
-                    target_noise = self.get_noise(width, height)
-                    x_T = self.slerp(self.variation_amount, initial_noise, target_noise)
-                elif initial_noise is not None:
-                    # i.e. we specified particular variations
-                    x_T = initial_noise
-                else:
-                    set_seed(seed)
-                    try:
-                        x_T = self.get_noise(width, height)
-                    except:
-                        print("** An error occurred while getting initial noise **")
-                        print(traceback.format_exc())
-
-                # Pass on the seed in case a layer beneath us needs to generate noise on its own.
-                image = make_image(x_T, seed)
-
-                if self.safety_checker is not None:
-                    image = self.safety_checker.check(image)
-
-                results.append([image, seed, attention_maps_images])
-
-                if image_callback is not None:
-                    attention_maps_image = (
-                        None
-                        if len(attention_maps_images) == 0
-                        else attention_maps_images[-1]
-                    )
-                    image_callback(
-                        image,
-                        seed,
-                        first_seed=first_seed,
-                        attention_maps_image=attention_maps_image,
-                    )
-
-                seed = self.new_seed()
-
-                # Free up memory from the last generation.
-                clear_cuda_cache = (
-                    kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
-                )
-                if clear_cuda_cache is not None:
-                    clear_cuda_cache()
-
-        return results
-
-    def sample_to_image(self, samples) -> Image.Image:
-        """
-        Given samples returned from a sampler, converts
-        it into a PIL Image
-        """
-        with torch.inference_mode():
-            image = self.model.decode_latents(samples)
-        return self.model.numpy_to_pil(image)[0]
-
-    def repaste_and_color_correct(
-        self,
-        result: Image.Image,
-        init_image: Image.Image,
-        init_mask: Image.Image,
-        mask_blur_radius: int = 8,
-    ) -> Image.Image:
-        if init_image is None or init_mask is None:
-            return result
-
-        # Get the original alpha channel of the mask if there is one.
-        # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
-        pil_init_mask = (
-            init_mask.getchannel("A")
-            if init_mask.mode == "RGBA"
-            else init_mask.convert("L")
-        )
-        pil_init_image = init_image.convert(
-            "RGBA"
-        )  # Add an alpha channel if one doesn't exist
-
-        # Build an image with only visible pixels from source to use as reference for color-matching.
-        init_rgb_pixels = np.asarray(init_image.convert("RGB"), dtype=np.uint8)
-        init_a_pixels = np.asarray(pil_init_image.getchannel("A"), dtype=np.uint8)
-        init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8)
-
-        # Get numpy version of result
-        np_image = np.asarray(result, dtype=np.uint8)
-
-        # Mask and calculate mean and standard deviation
-        mask_pixels = init_a_pixels * init_mask_pixels > 0
-        np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :]
-        np_image_masked = np_image[mask_pixels, :]
-
-        if np_init_rgb_pixels_masked.size > 0:
-            init_means = np_init_rgb_pixels_masked.mean(axis=0)
-            init_std = np_init_rgb_pixels_masked.std(axis=0)
-            gen_means = np_image_masked.mean(axis=0)
-            gen_std = np_image_masked.std(axis=0)
-
-            # Color correct
-            np_matched_result = np_image.copy()
-            np_matched_result[:, :, :] = (
-                (
-                    (
-                        (
-                            np_matched_result[:, :, :].astype(np.float32)
-                            - gen_means[None, None, :]
-                        )
-                        / gen_std[None, None, :]
-                    )
-                    * init_std[None, None, :]
-                    + init_means[None, None, :]
-                )
-                .clip(0, 255)
-                .astype(np.uint8)
-            )
-            matched_result = Image.fromarray(np_matched_result, mode="RGB")
-        else:
-            matched_result = Image.fromarray(np_image, mode="RGB")
-
-        # Blur the mask out (into init image) by specified amount
-        if mask_blur_radius > 0:
-            nm = np.asarray(pil_init_mask, dtype=np.uint8)
-            nmd = cv2.erode(
-                nm,
-                kernel=np.ones((3, 3), dtype=np.uint8),
-                iterations=int(mask_blur_radius / 2),
-            )
-            pmd = Image.fromarray(nmd, mode="L")
-            blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
-        else:
-            blurred_init_mask = pil_init_mask
-
-        multiplied_blurred_init_mask = ImageChops.multiply(
-            blurred_init_mask, self.pil_image.split()[-1]
-        )
-
-        # Paste original on color-corrected generation (using blurred mask)
-        matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
-        return matched_result
-
-    @staticmethod
-    def sample_to_lowres_estimated_image(samples):
-        # origingally adapted from code by @erucipe and @keturn here:
-        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
-
-        # these updated numbers for v1.5 are from @torridgristle
-        v1_5_latent_rgb_factors = torch.tensor(
-            [
-                #    R        G        B
-                [0.3444, 0.1385, 0.0670],  # L1
-                [0.1247, 0.4027, 0.1494],  # L2
-                [-0.3192, 0.2513, 0.2103],  # L3
-                [-0.1307, -0.1874, -0.7445],  # L4
-            ],
-            dtype=samples.dtype,
-            device=samples.device,
-        )
-
-        latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
-        latents_ubyte = (
-            ((latent_image + 1) / 2)
-            .clamp(0, 1)  # change scale from -1..1 to 0..1
-            .mul(0xFF)  # to 0..255
-            .byte()
-        ).cpu()
-
-        return Image.fromarray(latents_ubyte.numpy())
-
-    def generate_initial_noise(self, seed, width, height):
-        initial_noise = None
-        if self.variation_amount > 0 or len(self.with_variations) > 0:
-            # use fixed initial noise plus random noise per iteration
-            set_seed(seed)
-            initial_noise = self.get_noise(width, height)
-            for v_seed, v_weight in self.with_variations:
-                seed = v_seed
-                set_seed(seed)
-                next_noise = self.get_noise(width, height)
-                initial_noise = self.slerp(v_weight, initial_noise, next_noise)
-            if self.variation_amount > 0:
-                random.seed()  # reset RNG to an actually random state, so we can get a random seed for variations
-                seed = random.randrange(0, np.iinfo(np.uint32).max)
-        return (seed, initial_noise)
-
-    def get_perlin_noise(self, width, height):
-        fixdevice = "cpu" if (self.model.device.type == "mps") else self.model.device
-        # limit noise to only the diffusion image channels, not the mask channels
-        input_channels = min(self.latent_channels, 4)
-        # round up to the nearest block of 8
-        temp_width = int((width + 7) / 8) * 8
-        temp_height = int((height + 7) / 8) * 8
-        noise = torch.stack(
-            [
-                rand_perlin_2d(
-                    (temp_height, temp_width), (8, 8), device=self.model.device
-                ).to(fixdevice)
-                for _ in range(input_channels)
-            ],
-            dim=0,
-        ).to(self.model.device)
-        return noise[0:4, 0:height, 0:width]
-
-    def new_seed(self):
-        self.seed = random.randrange(0, np.iinfo(np.uint32).max)
-        return self.seed
-
-    def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
-        """
-        Spherical linear interpolation
-        Args:
-            t (float/np.ndarray): Float value between 0.0 and 1.0
-            v0 (np.ndarray): Starting vector
-            v1 (np.ndarray): Final vector
-            DOT_THRESHOLD (float): Threshold for considering the two vectors as
-                                colineal. Not recommended to alter this.
-        Returns:
-            v2 (np.ndarray): Interpolation vector between v0 and v1
-        """
-        inputs_are_torch = False
-        if not isinstance(v0, np.ndarray):
-            inputs_are_torch = True
-            v0 = v0.detach().cpu().numpy()
-        if not isinstance(v1, np.ndarray):
-            inputs_are_torch = True
-            v1 = v1.detach().cpu().numpy()
-
-        dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
-        if np.abs(dot) > DOT_THRESHOLD:
-            v2 = (1 - t) * v0 + t * v1
-        else:
-            theta_0 = np.arccos(dot)
-            sin_theta_0 = np.sin(theta_0)
-            theta_t = theta_0 * t
-            sin_theta_t = np.sin(theta_t)
-            s0 = np.sin(theta_0 - theta_t) / sin_theta_0
-            s1 = sin_theta_t / sin_theta_0
-            v2 = s0 * v0 + s1 * v1
-
-        if inputs_are_torch:
-            v2 = torch.from_numpy(v2).to(self.model.device)
-
-        return v2
-
-    # this is a handy routine for debugging use. Given a generated sample,
-    # convert it into a PNG image and store it at the indicated path
-    def save_sample(self, sample, filepath):
-        image = self.sample_to_image(sample)
-        dirname = os.path.dirname(filepath) or "."
-        if not os.path.exists(dirname):
-            print(f"** creating directory {dirname}")
-            os.makedirs(dirname, exist_ok=True)
-        image.save(filepath, "PNG")
-
-    def torch_dtype(self) -> torch.dtype:
-        return torch.float16 if self.precision == "float16" else torch.float32
-
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self, width, height):
-        device = self.model.device
-        # limit noise to only the diffusion image channels, not the mask channels
-        input_channels = min(self.latent_channels, 4)
-        if self.use_mps_noise or device.type == "mps":
-            x = torch.randn(
-                [
-                    1,
-                    input_channels,
-                    height // self.downsampling_factor,
-                    width // self.downsampling_factor,
-                ],
-                dtype=self.torch_dtype(),
-                device="cpu",
-            ).to(device)
-        else:
-            x = torch.randn(
-                [
-                    1,
-                    input_channels,
-                    height // self.downsampling_factor,
-                    width // self.downsampling_factor,
-                ],
-                dtype=self.torch_dtype(),
-                device=device,
-            )
-        if self.perlin > 0.0:
-            perlin_noise = self.get_perlin_noise(
-                width // self.downsampling_factor, height // self.downsampling_factor
-            )
-            x = (1 - self.perlin) * x + self.perlin * perlin_noise
-        return x

invokeai/backend/generator/embiggen.py

@@ -1,558 +0,0 @@
-"""
-invokeai.backend.generator.embiggen descends from .generator
-and generates with .generator.img2img
-"""
-
-import numpy as np
-import torch
-from PIL import Image
-from tqdm import trange
-
-from .base import Generator
-from .img2img import Img2Img
-
-
-class Embiggen(Generator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None
-
-    # Replace generate because Embiggen doesn't need/use most of what it does normallly
-    def generate(
-        self,
-        prompt,
-        iterations=1,
-        seed=None,
-        image_callback=None,
-        step_callback=None,
-        **kwargs,
-    ):
-        make_image = self.get_make_image(prompt, step_callback=step_callback, **kwargs)
-        results = []
-        seed = seed if seed else self.new_seed()
-
-        # Noise will be generated by the Img2Img generator when called
-        for _ in trange(iterations, desc="Generating"):
-            # make_image will call Img2Img which will do the equivalent of get_noise itself
-            image = make_image()
-            results.append([image, seed])
-            if image_callback is not None:
-                image_callback(image, seed, prompt_in=prompt)
-            seed = self.new_seed()
-        return results
-
-    @torch.no_grad()
-    def get_make_image(
-        self,
-        prompt,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        init_img,
-        strength,
-        width,
-        height,
-        embiggen,
-        embiggen_tiles,
-        step_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and multi-stage twice-baked potato layering over the img2img on the initial image
-        Return value depends on the seed at the time you call it
-        """
-        assert (
-            not sampler.uses_inpainting_model()
-        ), "--embiggen is not supported by inpainting models"
-
-        # Construct embiggen arg array, and sanity check arguments
-        if embiggen == None:  # embiggen can also be called with just embiggen_tiles
-            embiggen = [1.0]  # If not specified, assume no scaling
-        elif embiggen[0] < 0:
-            embiggen[0] = 1.0
-            print(
-                ">> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
-            )
-        if len(embiggen) < 2:
-            embiggen.append(0.75)
-        elif embiggen[1] > 1.0 or embiggen[1] < 0:
-            embiggen[1] = 0.75
-            print(
-                ">> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
-            )
-        if len(embiggen) < 3:
-            embiggen.append(0.25)
-        elif embiggen[2] < 0:
-            embiggen[2] = 0.25
-            print(
-                ">> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
-            )
-
-        # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
-        # and then sort them, because... people.
-        if embiggen_tiles:
-            embiggen_tiles = list(map(lambda n: n - 1, embiggen_tiles))
-            embiggen_tiles.sort()
-
-        if strength >= 0.5:
-            print(
-                f"* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
-            )
-
-        # Prep img2img generator, since we wrap over it
-        gen_img2img = Img2Img(self.model, self.precision)
-
-        # Open original init image (not a tensor) to manipulate
-        initsuperimage = Image.open(init_img)
-
-        with Image.open(init_img) as img:
-            initsuperimage = img.convert("RGB")
-
-        # Size of the target super init image in pixels
-        initsuperwidth, initsuperheight = initsuperimage.size
-
-        # Increase by scaling factor if not already resized, using ESRGAN as able
-        if embiggen[0] != 1.0:
-            initsuperwidth = round(initsuperwidth * embiggen[0])
-            initsuperheight = round(initsuperheight * embiggen[0])
-            if embiggen[1] > 0:  # No point in ESRGAN upscaling if strength is set zero
-                from ..restoration.realesrgan import ESRGAN
-
-                esrgan = ESRGAN()
-                print(
-                    f">> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
-                )
-                if embiggen[0] > 2:
-                    initsuperimage = esrgan.process(
-                        initsuperimage,
-                        embiggen[1],  # upscale strength
-                        self.seed,
-                        4,  # upscale scale
-                    )
-                else:
-                    initsuperimage = esrgan.process(
-                        initsuperimage,
-                        embiggen[1],  # upscale strength
-                        self.seed,
-                        2,  # upscale scale
-                    )
-            # We could keep recursively re-running ESRGAN for a requested embiggen[0] larger than 4x
-            #   but from personal experiance it doesn't greatly improve anything after 4x
-            # Resize to target scaling factor resolution
-            initsuperimage = initsuperimage.resize(
-                (initsuperwidth, initsuperheight), Image.Resampling.LANCZOS
-            )
-
-        # Use width and height as tile widths and height
-        # Determine buffer size in pixels
-        if embiggen[2] < 1:
-            if embiggen[2] < 0:
-                embiggen[2] = 0
-            overlap_size_x = round(embiggen[2] * width)
-            overlap_size_y = round(embiggen[2] * height)
-        else:
-            overlap_size_x = round(embiggen[2])
-            overlap_size_y = round(embiggen[2])
-
-        # With overall image width and height known, determine how many tiles we need
-        def ceildiv(a, b):
-            return -1 * (-a // b)
-
-        # X and Y needs to be determined independantly (we may have savings on one based on the buffer pixel count)
-        # (initsuperwidth - width) is the area remaining to the right that we need to layers tiles to fill
-        # (width - overlap_size_x) is how much new we can fill with a single tile
-        emb_tiles_x = 1
-        emb_tiles_y = 1
-        if (initsuperwidth - width) > 0:
-            emb_tiles_x = ceildiv(initsuperwidth - width, width - overlap_size_x) + 1
-        if (initsuperheight - height) > 0:
-            emb_tiles_y = ceildiv(initsuperheight - height, height - overlap_size_y) + 1
-        # Sanity
-        assert (
-            emb_tiles_x > 1 or emb_tiles_y > 1
-        ), f"ERROR: Based on the requested dimensions of {initsuperwidth}x{initsuperheight} and tiles of {width}x{height} you don't need to Embiggen! Check your arguments."
-
-        # Prep alpha layers --------------
-        # https://stackoverflow.com/questions/69321734/how-to-create-different-transparency-like-gradient-with-python-pil
-        # agradientL is Left-side transparent
-        agradientL = (
-            Image.linear_gradient("L").rotate(90).resize((overlap_size_x, height))
-        )
-        # agradientT is Top-side transparent
-        agradientT = Image.linear_gradient("L").resize((width, overlap_size_y))
-        # radial corner is the left-top corner, made full circle then cut to just the left-top quadrant
-        agradientC = Image.new("L", (256, 256))
-        for y in range(256):
-            for x in range(256):
-                # Find distance to lower right corner (numpy takes arrays)
-                distanceToLR = np.sqrt([(255 - x) ** 2 + (255 - y) ** 2])[0]
-                # Clamp values to max 255
-                if distanceToLR > 255:
-                    distanceToLR = 255
-                # Place the pixel as invert of distance
-                agradientC.putpixel((x, y), round(255 - distanceToLR))
-
-        # Create alternative asymmetric diagonal corner to use on "tailing" intersections to prevent hard edges
-        # Fits for a left-fading gradient on the bottom side and full opacity on the right side.
-        agradientAsymC = Image.new("L", (256, 256))
-        for y in range(256):
-            for x in range(256):
-                value = round(max(0, x - (255 - y)) * (255 / max(1, y)))
-                # Clamp values
-                value = max(0, value)
-                value = min(255, value)
-                agradientAsymC.putpixel((x, y), value)
-
-        # Create alpha layers default fully white
-        alphaLayerL = Image.new("L", (width, height), 255)
-        alphaLayerT = Image.new("L", (width, height), 255)
-        alphaLayerLTC = Image.new("L", (width, height), 255)
-        # Paste gradients into alpha layers
-        alphaLayerL.paste(agradientL, (0, 0))
-        alphaLayerT.paste(agradientT, (0, 0))
-        alphaLayerLTC.paste(agradientL, (0, 0))
-        alphaLayerLTC.paste(agradientT, (0, 0))
-        alphaLayerLTC.paste(agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0))
-        # make masks with an asymmetric upper-right corner so when the curved transparent corner of the next tile
-        # to its right is placed it doesn't reveal a hard trailing semi-transparent edge in the overlapping space
-        alphaLayerTaC = alphaLayerT.copy()
-        alphaLayerTaC.paste(
-            agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-            (width - overlap_size_x, 0),
-        )
-        alphaLayerLTaC = alphaLayerLTC.copy()
-        alphaLayerLTaC.paste(
-            agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-            (width - overlap_size_x, 0),
-        )
-
-        if embiggen_tiles:
-            # Individual unconnected sides
-            alphaLayerR = Image.new("L", (width, height), 255)
-            alphaLayerR.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerB = Image.new("L", (width, height), 255)
-            alphaLayerB.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerTB = Image.new("L", (width, height), 255)
-            alphaLayerTB.paste(agradientT, (0, 0))
-            alphaLayerTB.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerLR = Image.new("L", (width, height), 255)
-            alphaLayerLR.paste(agradientL, (0, 0))
-            alphaLayerLR.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-
-            # Sides and corner Layers
-            alphaLayerRBC = Image.new("L", (width, height), 255)
-            alphaLayerRBC.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerRBC.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerRBC.paste(
-                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, height - overlap_size_y),
-            )
-            alphaLayerLBC = Image.new("L", (width, height), 255)
-            alphaLayerLBC.paste(agradientL, (0, 0))
-            alphaLayerLBC.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerLBC.paste(
-                agradientC.rotate(90).resize((overlap_size_x, overlap_size_y)),
-                (0, height - overlap_size_y),
-            )
-            alphaLayerRTC = Image.new("L", (width, height), 255)
-            alphaLayerRTC.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerRTC.paste(agradientT, (0, 0))
-            alphaLayerRTC.paste(
-                agradientC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, 0),
-            )
-
-            # All but X layers
-            alphaLayerABT = Image.new("L", (width, height), 255)
-            alphaLayerABT.paste(alphaLayerLBC, (0, 0))
-            alphaLayerABT.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerABT.paste(
-                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, height - overlap_size_y),
-            )
-            alphaLayerABL = Image.new("L", (width, height), 255)
-            alphaLayerABL.paste(alphaLayerRTC, (0, 0))
-            alphaLayerABL.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerABL.paste(
-                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, height - overlap_size_y),
-            )
-            alphaLayerABR = Image.new("L", (width, height), 255)
-            alphaLayerABR.paste(alphaLayerLBC, (0, 0))
-            alphaLayerABR.paste(agradientT, (0, 0))
-            alphaLayerABR.paste(
-                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
-            )
-            alphaLayerABB = Image.new("L", (width, height), 255)
-            alphaLayerABB.paste(alphaLayerRTC, (0, 0))
-            alphaLayerABB.paste(agradientL, (0, 0))
-            alphaLayerABB.paste(
-                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
-            )
-
-            # All-around layer
-            alphaLayerAA = Image.new("L", (width, height), 255)
-            alphaLayerAA.paste(alphaLayerABT, (0, 0))
-            alphaLayerAA.paste(agradientT, (0, 0))
-            alphaLayerAA.paste(
-                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
-            )
-            alphaLayerAA.paste(
-                agradientC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, 0),
-            )
-
-        # Clean up temporary gradients
-        del agradientL
-        del agradientT
-        del agradientC
-
-        def make_image():
-            # Make main tiles -------------------------------------------------
-            if embiggen_tiles:
-                print(f">> Making {len(embiggen_tiles)} Embiggen tiles...")
-            else:
-                print(
-                    f">> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
-                )
-
-            emb_tile_store = []
-            # Although we could use the same seed for every tile for determinism, at higher strengths this may
-            # produce duplicated structures for each tile and make the tiling effect more obvious
-            # instead track and iterate a local seed we pass to Img2Img
-            seed = self.seed
-            seedintlimit = (
-                np.iinfo(np.uint32).max - 1
-            )  # only retreive this one from numpy
-
-            for tile in range(emb_tiles_x * emb_tiles_y):
-                # Don't iterate on first tile
-                if tile != 0:
-                    if seed < seedintlimit:
-                        seed += 1
-                    else:
-                        seed = 0
-
-                # Determine if this is a re-run and replace
-                if embiggen_tiles and not tile in embiggen_tiles:
-                    continue
-                # Get row and column entries
-                emb_row_i = tile // emb_tiles_x
-                emb_column_i = tile % emb_tiles_x
-                # Determine bounds to cut up the init image
-                # Determine upper-left point
-                if emb_column_i + 1 == emb_tiles_x:
-                    left = initsuperwidth - width
-                else:
-                    left = round(emb_column_i * (width - overlap_size_x))
-                if emb_row_i + 1 == emb_tiles_y:
-                    top = initsuperheight - height
-                else:
-                    top = round(emb_row_i * (height - overlap_size_y))
-                right = left + width
-                bottom = top + height
-
-                # Cropped image of above dimension (does not modify the original)
-                newinitimage = initsuperimage.crop((left, top, right, bottom))
-                # DEBUG:
-                # newinitimagepath = init_img[0:-4] + f'_emb_Ti{tile}.png'
-                # newinitimage.save(newinitimagepath)
-
-                if embiggen_tiles:
-                    print(
-                        f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
-                    )
-                else:
-                    print(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
-
-                # create a torch tensor from an Image
-                newinitimage = np.array(newinitimage).astype(np.float32) / 255.0
-                newinitimage = newinitimage[None].transpose(0, 3, 1, 2)
-                newinitimage = torch.from_numpy(newinitimage)
-                newinitimage = 2.0 * newinitimage - 1.0
-                newinitimage = newinitimage.to(self.model.device)
-                clear_cuda_cache = (
-                    kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
-                )
-
-                tile_results = gen_img2img.generate(
-                    prompt,
-                    iterations=1,
-                    seed=seed,
-                    sampler=sampler,
-                    steps=steps,
-                    cfg_scale=cfg_scale,
-                    conditioning=conditioning,
-                    ddim_eta=ddim_eta,
-                    image_callback=None,  # called only after the final image is generated
-                    step_callback=step_callback,  # called after each intermediate image is generated
-                    width=width,
-                    height=height,
-                    init_image=newinitimage,  # notice that init_image is different from init_img
-                    mask_image=None,
-                    strength=strength,
-                    clear_cuda_cache=clear_cuda_cache,
-                )
-
-                emb_tile_store.append(tile_results[0][0])
-                # DEBUG (but, also has other uses), worth saving if you want tiles without a transparency overlap to manually composite
-                # emb_tile_store[-1].save(init_img[0:-4] + f'_emb_To{tile}.png')
-                del newinitimage
-
-            # Sanity check we have them all
-            if len(emb_tile_store) == (emb_tiles_x * emb_tiles_y) or (
-                embiggen_tiles != [] and len(emb_tile_store) == len(embiggen_tiles)
-            ):
-                outputsuperimage = Image.new("RGBA", (initsuperwidth, initsuperheight))
-                if embiggen_tiles:
-                    outputsuperimage.alpha_composite(
-                        initsuperimage.convert("RGBA"), (0, 0)
-                    )
-                for tile in range(emb_tiles_x * emb_tiles_y):
-                    if embiggen_tiles:
-                        if tile in embiggen_tiles:
-                            intileimage = emb_tile_store.pop(0)
-                        else:
-                            continue
-                    else:
-                        intileimage = emb_tile_store[tile]
-                    intileimage = intileimage.convert("RGBA")
-                    # Get row and column entries
-                    emb_row_i = tile // emb_tiles_x
-                    emb_column_i = tile % emb_tiles_x
-                    if emb_row_i == 0 and emb_column_i == 0 and not embiggen_tiles:
-                        left = 0
-                        top = 0
-                    else:
-                        # Determine upper-left point
-                        if emb_column_i + 1 == emb_tiles_x:
-                            left = initsuperwidth - width
-                        else:
-                            left = round(emb_column_i * (width - overlap_size_x))
-                        if emb_row_i + 1 == emb_tiles_y:
-                            top = initsuperheight - height
-                        else:
-                            top = round(emb_row_i * (height - overlap_size_y))
-                        # Handle gradients for various conditions
-                        # Handle emb_rerun case
-                        if embiggen_tiles:
-                            # top of image
-                            if emb_row_i == 0:
-                                if emb_column_i == 0:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) not in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerB)
-                                        # Otherwise do nothing on this tile
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerR)
-                                    else:
-                                        intileimage.putalpha(alphaLayerRBC)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    if (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down
-                                        intileimage.putalpha(alphaLayerL)
-                                    else:
-                                        intileimage.putalpha(alphaLayerLBC)
-                                else:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerL)
-                                        else:
-                                            intileimage.putalpha(alphaLayerLBC)
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerLR)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABT)
-                            # bottom of image
-                            elif emb_row_i == emb_tiles_y - 1:
-                                if emb_column_i == 0:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        intileimage.putalpha(alphaLayerTaC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerRTC)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    # No tiles to look ahead to
-                                    intileimage.putalpha(alphaLayerLTC)
-                                else:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        intileimage.putalpha(alphaLayerLTaC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABB)
-                            # vertical middle of image
-                            else:
-                                if emb_column_i == 0:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerTaC)
-                                        else:
-                                            intileimage.putalpha(alphaLayerTB)
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerRTC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABL)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    if (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down
-                                        intileimage.putalpha(alphaLayerLTC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABR)
-                                else:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerLTaC)
-                                        else:
-                                            intileimage.putalpha(alphaLayerABR)
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerABB)
-                                    else:
-                                        intileimage.putalpha(alphaLayerAA)
-                        # Handle normal tiling case (much simpler - since we tile left to right, top to bottom)
-                        else:
-                            if emb_row_i == 0 and emb_column_i >= 1:
-                                intileimage.putalpha(alphaLayerL)
-                            elif emb_row_i >= 1 and emb_column_i == 0:
-                                if (
-                                    emb_column_i + 1 == emb_tiles_x
-                                ):  # If we don't have anything that can be placed to the right
-                                    intileimage.putalpha(alphaLayerT)
-                                else:
-                                    intileimage.putalpha(alphaLayerTaC)
-                            else:
-                                if (
-                                    emb_column_i + 1 == emb_tiles_x
-                                ):  # If we don't have anything that can be placed to the right
-                                    intileimage.putalpha(alphaLayerLTC)
-                                else:
-                                    intileimage.putalpha(alphaLayerLTaC)
-                    # Layer tile onto final image
-                    outputsuperimage.alpha_composite(intileimage, (left, top))
-            else:
-                print(
-                    "Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
-                )
-
-            # after internal loops and patching up return Embiggen image
-            return outputsuperimage
-
-        # end of function declaration
-        return make_image

invokeai/backend/generator/img2img.py

@@ -1,101 +0,0 @@
-"""
-invokeai.backend.generator.img2img descends from .generator
-"""
-from typing import Optional
-
-import torch
-from accelerate.utils import set_seed
-from diffusers import logging
-
-from ..stable_diffusion import (
-    ConditioningData,
-    PostprocessingSettings,
-    StableDiffusionGeneratorPipeline,
-)
-from .base import Generator
-
-
-class Img2Img(Generator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None  # by get_noise()
-
-    def get_make_image(
-        self,
-        prompt,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        init_image,
-        strength,
-        step_callback=None,
-        threshold=0.0,
-        warmup=0.2,
-        perlin=0.0,
-        h_symmetry_time_pct=None,
-        v_symmetry_time_pct=None,
-        attention_maps_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it.
-        """
-        self.perlin = perlin
-
-        # noinspection PyTypeChecker
-        pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
-
-        uc, c, extra_conditioning_info = conditioning
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=threshold,
-                warmup=warmup,
-                h_symmetry_time_pct=h_symmetry_time_pct,
-                v_symmetry_time_pct=v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
-
-        def make_image(x_T: torch.Tensor, seed: int):
-            # FIXME: use x_T for initial seeded noise
-            # We're not at the moment because the pipeline automatically resizes init_image if
-            # necessary, which the x_T input might not match.
-            # In the meantime, reset the seed prior to generating pipeline output so we at least get the same result.
-            logging.set_verbosity_error()  # quench safety check warnings
-            pipeline_output = pipeline.img2img_from_embeddings(
-                init_image,
-                strength,
-                steps,
-                conditioning_data,
-                noise_func=self.get_noise_like,
-                callback=step_callback,
-                seed=seed,
-            )
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
-                attention_maps_callback(pipeline_output.attention_map_saver)
-            return pipeline.numpy_to_pil(pipeline_output.images)[0]
-
-        return make_image
-
-    def get_noise_like(self, like: torch.Tensor):
-        device = like.device
-        if device.type == "mps":
-            x = torch.randn_like(like, device="cpu").to(device)
-        else:
-            x = torch.randn_like(like, device=device)
-        if self.perlin > 0.0:
-            shape = like.shape
-            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(
-                shape[3], shape[2]
-            )
-        return x

invokeai/backend/generator/inpaint.py

@@ -1,411 +0,0 @@
-"""
-invokeai.backend.generator.inpaint descends from .generator
-"""
-from __future__ import annotations
-
-import math
-
-import cv2
-import numpy as np
-import PIL
-import torch
-from PIL import Image, ImageChops, ImageFilter, ImageOps
-
-from ..image_util import PatchMatch, debug_image
-from ..stable_diffusion.diffusers_pipeline import (
-    ConditioningData,
-    StableDiffusionGeneratorPipeline,
-    image_resized_to_grid_as_tensor,
-)
-from .img2img import Img2Img
-
-
-def infill_methods() -> list[str]:
-    methods = [
-        "tile",
-        "solid",
-    ]
-    if PatchMatch.patchmatch_available():
-        methods.insert(0, "patchmatch")
-    return methods
-
-
-class Inpaint(Img2Img):
-    def __init__(self, model, precision):
-        self.inpaint_height = 0
-        self.inpaint_width = 0
-        self.enable_image_debugging = False
-        self.init_latent = None
-        self.pil_image = None
-        self.pil_mask = None
-        self.mask_blur_radius = 0
-        self.infill_method = None
-        super().__init__(model, precision)
-
-    # Outpaint support code
-    def get_tile_images(self, image: np.ndarray, width=8, height=8):
-        _nrows, _ncols, depth = image.shape
-        _strides = image.strides
-
-        nrows, _m = divmod(_nrows, height)
-        ncols, _n = divmod(_ncols, width)
-        if _m != 0 or _n != 0:
-            return None
-
-        return np.lib.stride_tricks.as_strided(
-            np.ravel(image),
-            shape=(nrows, ncols, height, width, depth),
-            strides=(height * _strides[0], width * _strides[1], *_strides),
-            writeable=False,
-        )
-
-    def infill_patchmatch(self, im: Image.Image) -> Image:
-        if im.mode != "RGBA":
-            return im
-
-        # Skip patchmatch if patchmatch isn't available
-        if not PatchMatch.patchmatch_available():
-            return im
-
-        # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
-        im_patched_np = PatchMatch.inpaint(
-            im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
-        )
-        im_patched = Image.fromarray(im_patched_np, mode="RGB")
-        return im_patched
-
-    def tile_fill_missing(
-        self, im: Image.Image, tile_size: int = 16, seed: int = None
-    ) -> Image:
-        # Only fill if there's an alpha layer
-        if im.mode != "RGBA":
-            return im
-
-        a = np.asarray(im, dtype=np.uint8)
-
-        tile_size = (tile_size, tile_size)
-
-        # Get the image as tiles of a specified size
-        tiles = self.get_tile_images(a, *tile_size).copy()
-
-        # Get the mask as tiles
-        tiles_mask = tiles[:, :, :, :, 3]
-
-        # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
-        tmask_shape = tiles_mask.shape
-        tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
-        n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
-        tiles_mask = tiles_mask > 0
-        tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1)
-
-        # Get RGB tiles in single array and filter by the mask
-        tshape = tiles.shape
-        tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:]))
-        filtered_tiles = tiles_all[tiles_mask]
-
-        if len(filtered_tiles) == 0:
-            return im
-
-        # Find all invalid tiles and replace with a random valid tile
-        replace_count = (tiles_mask == False).sum()
-        rng = np.random.default_rng(seed=seed)
-        tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
-            rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
-        ]
-
-        # Convert back to an image
-        tiles_all = tiles_all.reshape(tshape)
-        tiles_all = tiles_all.swapaxes(1, 2)
-        st = tiles_all.reshape(
-            (
-                math.prod(tiles_all.shape[0:2]),
-                math.prod(tiles_all.shape[2:4]),
-                tiles_all.shape[4],
-            )
-        )
-        si = Image.fromarray(st, mode="RGBA")
-
-        return si
-
-    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
-        npimg = np.asarray(mask, dtype=np.uint8)
-
-        # Detect any partially transparent regions
-        npgradient = np.uint8(
-            255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0))
-        )
-
-        # Detect hard edges
-        npedge = cv2.Canny(npimg, threshold1=100, threshold2=200)
-
-        # Combine
-        npmask = npgradient + npedge
-
-        # Expand
-        npmask = cv2.dilate(
-            npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2)
-        )
-
-        new_mask = Image.fromarray(npmask)
-
-        if edge_blur > 0:
-            new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur))
-
-        return ImageOps.invert(new_mask)
-
-    def seam_paint(
-        self,
-        im: Image.Image,
-        seam_size: int,
-        seam_blur: int,
-        prompt,
-        seed,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        strength,
-        noise,
-        infill_method,
-        step_callback,
-    ) -> Image.Image:
-        hard_mask = self.pil_image.split()[-1].copy()
-        mask = self.mask_edge(hard_mask, seam_size, seam_blur)
-
-        make_image = self.get_make_image(
-            prompt,
-            sampler,
-            steps,
-            cfg_scale,
-            ddim_eta,
-            conditioning,
-            init_image=im.copy().convert("RGBA"),
-            mask_image=mask,
-            strength=strength,
-            mask_blur_radius=0,
-            seam_size=0,
-            step_callback=step_callback,
-            inpaint_width=im.width,
-            inpaint_height=im.height,
-            infill_method=infill_method,
-        )
-
-        seam_noise = self.get_noise(im.width, im.height)
-
-        result = make_image(seam_noise, seed)
-
-        return result
-
-    @torch.no_grad()
-    def get_make_image(
-        self,
-        prompt,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        init_image: PIL.Image.Image | torch.FloatTensor,
-        mask_image: PIL.Image.Image | torch.FloatTensor,
-        strength: float,
-        mask_blur_radius: int = 8,
-        # Seam settings - when 0, doesn't fill seam
-        seam_size: int = 0,
-        seam_blur: int = 0,
-        seam_strength: float = 0.7,
-        seam_steps: int = 10,
-        tile_size: int = 32,
-        step_callback=None,
-        inpaint_replace=False,
-        enable_image_debugging=False,
-        infill_method=None,
-        inpaint_width=None,
-        inpaint_height=None,
-        inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
-        attention_maps_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and
-        the initial image + mask.  Return value depends on the seed at
-        the time you call it.  kwargs are 'init_latent' and 'strength'
-        """
-
-        self.enable_image_debugging = enable_image_debugging
-        infill_method = infill_method or infill_methods()[0]
-        self.infill_method = infill_method
-
-        self.inpaint_width = inpaint_width
-        self.inpaint_height = inpaint_height
-
-        if isinstance(init_image, PIL.Image.Image):
-            self.pil_image = init_image.copy()
-
-            # Do infill
-            if infill_method == "patchmatch" and PatchMatch.patchmatch_available():
-                init_filled = self.infill_patchmatch(self.pil_image.copy())
-            elif infill_method == "tile":
-                init_filled = self.tile_fill_missing(
-                    self.pil_image.copy(), seed=self.seed, tile_size=tile_size
-                )
-            elif infill_method == "solid":
-                solid_bg = PIL.Image.new("RGBA", init_image.size, inpaint_fill)
-                init_filled = PIL.Image.alpha_composite(solid_bg, init_image)
-            else:
-                raise ValueError(
-                    f"Non-supported infill type {infill_method}", infill_method
-                )
-            init_filled.paste(init_image, (0, 0), init_image.split()[-1])
-
-            # Resize if requested for inpainting
-            if inpaint_width and inpaint_height:
-                init_filled = init_filled.resize((inpaint_width, inpaint_height))
-
-            debug_image(
-                init_filled, "init_filled", debug_status=self.enable_image_debugging
-            )
-
-            # Create init tensor
-            init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
-
-        if isinstance(mask_image, PIL.Image.Image):
-            self.pil_mask = mask_image.copy()
-            debug_image(
-                mask_image,
-                "mask_image BEFORE multiply with pil_image",
-                debug_status=self.enable_image_debugging,
-            )
-
-            init_alpha = self.pil_image.getchannel("A")
-            if mask_image.mode != "L":
-                # FIXME: why do we get passed an RGB image here? We can only use single-channel.
-                mask_image = mask_image.convert("L")
-            mask_image = ImageChops.multiply(mask_image, init_alpha)
-            self.pil_mask = mask_image
-
-            # Resize if requested for inpainting
-            if inpaint_width and inpaint_height:
-                mask_image = mask_image.resize((inpaint_width, inpaint_height))
-
-            debug_image(
-                mask_image,
-                "mask_image AFTER multiply with pil_image",
-                debug_status=self.enable_image_debugging,
-            )
-            mask: torch.FloatTensor = image_resized_to_grid_as_tensor(
-                mask_image, normalize=False
-            )
-        else:
-            mask: torch.FloatTensor = mask_image
-
-        self.mask_blur_radius = mask_blur_radius
-
-        # noinspection PyTypeChecker
-        pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
-
-        # todo: support cross-attention control
-        uc, c, _ = conditioning
-        conditioning_data = ConditioningData(
-            uc, c, cfg_scale
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
-
-        def make_image(x_T: torch.Tensor, seed: int):
-            pipeline_output = pipeline.inpaint_from_embeddings(
-                init_image=init_image,
-                mask=1 - mask,  # expects white means "paint here."
-                strength=strength,
-                num_inference_steps=steps,
-                conditioning_data=conditioning_data,
-                noise_func=self.get_noise_like,
-                callback=step_callback,
-                seed=seed,
-            )
-
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
-                attention_maps_callback(pipeline_output.attention_map_saver)
-
-            result = self.postprocess_size_and_mask(
-                pipeline.numpy_to_pil(pipeline_output.images)[0]
-            )
-
-            # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
-            if seam_size > 0:
-                old_image = self.pil_image or init_image
-                old_mask = self.pil_mask or mask_image
-
-                result = self.seam_paint(
-                    result,
-                    seam_size,
-                    seam_blur,
-                    prompt,
-                    seed,
-                    sampler,
-                    seam_steps,
-                    cfg_scale,
-                    ddim_eta,
-                    conditioning,
-                    seam_strength,
-                    x_T,
-                    infill_method,
-                    step_callback,
-                )
-
-                # Restore original settings
-                self.get_make_image(
-                    prompt,
-                    sampler,
-                    steps,
-                    cfg_scale,
-                    ddim_eta,
-                    conditioning,
-                    old_image,
-                    old_mask,
-                    strength,
-                    mask_blur_radius,
-                    seam_size,
-                    seam_blur,
-                    seam_strength,
-                    seam_steps,
-                    tile_size,
-                    step_callback,
-                    inpaint_replace,
-                    enable_image_debugging,
-                    inpaint_width=inpaint_width,
-                    inpaint_height=inpaint_height,
-                    infill_method=infill_method,
-                    **kwargs,
-                )
-
-            return result
-
-        return make_image
-
-    def sample_to_image(self, samples) -> Image.Image:
-        gen_result = super().sample_to_image(samples).convert("RGB")
-        return self.postprocess_size_and_mask(gen_result)
-
-    def postprocess_size_and_mask(self, gen_result: Image.Image) -> Image.Image:
-        debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
-
-        # Resize if necessary
-        if self.inpaint_width and self.inpaint_height:
-            gen_result = gen_result.resize(self.pil_image.size)
-
-        if self.pil_image is None or self.pil_mask is None:
-            return gen_result
-
-        corrected_result = self.repaste_and_color_correct(
-            gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius
-        )
-        debug_image(
-            corrected_result,
-            "corrected_result",
-            debug_status=self.enable_image_debugging,
-        )
-
-        return corrected_result

invokeai/backend/generator/txt2img.py

@@ -1,81 +0,0 @@
-"""
-invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
-"""
-import PIL.Image
-import torch
-
-from ..stable_diffusion import (
-    ConditioningData,
-    PostprocessingSettings,
-    StableDiffusionGeneratorPipeline,
-)
-from .base import Generator
-
-
-class Txt2Img(Generator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-
-    @torch.no_grad()
-    def get_make_image(
-        self,
-        prompt,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        width,
-        height,
-        step_callback=None,
-        threshold=0.0,
-        warmup=0.2,
-        perlin=0.0,
-        h_symmetry_time_pct=None,
-        v_symmetry_time_pct=None,
-        attention_maps_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        kwargs are 'width' and 'height'
-        """
-        self.perlin = perlin
-
-        # noinspection PyTypeChecker
-        pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
-
-        uc, c, extra_conditioning_info = conditioning
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=threshold,
-                warmup=warmup,
-                h_symmetry_time_pct=h_symmetry_time_pct,
-                v_symmetry_time_pct=v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
-
-        def make_image(x_T: torch.Tensor, _: int) -> PIL.Image.Image:
-            pipeline_output = pipeline.image_from_embeddings(
-                latents=torch.zeros_like(x_T, dtype=self.torch_dtype()),
-                noise=x_T,
-                num_inference_steps=steps,
-                conditioning_data=conditioning_data,
-                callback=step_callback,
-            )
-
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
-                attention_maps_callback(pipeline_output.attention_map_saver)
-
-            return pipeline.numpy_to_pil(pipeline_output.images)[0]
-
-        return make_image

invokeai/backend/generator/txt2img2img.py

@@ -1,207 +0,0 @@
-"""
-invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
-"""
-
-import math
-from typing import Callable, Optional
-
-import torch
-from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
-
-from ..stable_diffusion import PostprocessingSettings
-from .base import Generator
-from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ..stable_diffusion.diffusers_pipeline import ConditioningData
-from ..stable_diffusion.diffusers_pipeline import trim_to_multiple_of
-
-class Txt2Img2Img(Generator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None  # for get_noise()
-
-    def get_make_image(
-        self,
-        prompt: str,
-        sampler,
-        steps: int,
-        cfg_scale: float,
-        ddim_eta,
-        conditioning,
-        width: int,
-        height: int,
-        strength: float,
-        step_callback: Optional[Callable] = None,
-        threshold=0.0,
-        warmup=0.2,
-        perlin=0.0,
-        h_symmetry_time_pct=None,
-        v_symmetry_time_pct=None,
-        attention_maps_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        kwargs are 'width' and 'height'
-        """
-        self.perlin = perlin
-
-        # noinspection PyTypeChecker
-        pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
-
-        uc, c, extra_conditioning_info = conditioning
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=threshold,
-                warmup=0.2,
-                h_symmetry_time_pct=h_symmetry_time_pct,
-                v_symmetry_time_pct=v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
-
-        def make_image(x_T: torch.Tensor, _: int):
-            first_pass_latent_output, _ = pipeline.latents_from_embeddings(
-                latents=torch.zeros_like(x_T),
-                num_inference_steps=steps,
-                conditioning_data=conditioning_data,
-                noise=x_T,
-                callback=step_callback,
-            )
-
-            # Get our initial generation width and height directly from the latent output so
-            # the message below is accurate.
-            init_width = first_pass_latent_output.size()[3] * self.downsampling_factor
-            init_height = first_pass_latent_output.size()[2] * self.downsampling_factor
-            print(
-                f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
-            )
-
-            # resizing
-            resized_latents = torch.nn.functional.interpolate(
-                first_pass_latent_output,
-                size=(
-                    height // self.downsampling_factor,
-                    width // self.downsampling_factor,
-                ),
-                mode="bilinear",
-            )
-
-            # Free up memory from the last generation.
-            clear_cuda_cache = kwargs["clear_cuda_cache"] or None
-            if clear_cuda_cache is not None:
-                clear_cuda_cache()
-
-            second_pass_noise = self.get_noise_like(
-                resized_latents, override_perlin=True
-            )
-
-            # Clear symmetry for the second pass
-            from dataclasses import replace
-
-            new_postprocessing_settings = replace(
-                conditioning_data.postprocessing_settings, h_symmetry_time_pct=None
-            )
-            new_postprocessing_settings = replace(
-                new_postprocessing_settings, v_symmetry_time_pct=None
-            )
-            new_conditioning_data = replace(
-                conditioning_data, postprocessing_settings=new_postprocessing_settings
-            )
-
-            verbosity = get_verbosity()
-            set_verbosity_error()
-            pipeline_output = pipeline.img2img_from_latents_and_embeddings(
-                resized_latents,
-                num_inference_steps=steps,
-                conditioning_data=new_conditioning_data,
-                strength=strength,
-                noise=second_pass_noise,
-                callback=step_callback,
-            )
-            set_verbosity(verbosity)
-
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
-                attention_maps_callback(pipeline_output.attention_map_saver)
-
-            return pipeline.numpy_to_pil(pipeline_output.images)[0]
-
-        # FIXME: do we really need something entirely different for the inpainting model?
-
-        # in the case of the inpainting model being loaded, the trick of
-        # providing an interpolated latent doesn't work, so we transiently
-        # create a 512x512 PIL image, upscale it, and run the inpainting
-        # over it in img2img mode. Because the inpaing model is so conservative
-        # it doesn't change the image (much)
-
-        return make_image
-
-    def get_noise_like(self, like: torch.Tensor, override_perlin: bool = False):
-        device = like.device
-        if device.type == "mps":
-            x = torch.randn_like(like, device="cpu", dtype=self.torch_dtype()).to(
-                device
-            )
-        else:
-            x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
-        if self.perlin > 0.0 and override_perlin == False:
-            shape = like.shape
-            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(
-                shape[3], shape[2]
-            )
-        return x
-
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self, width, height, scale=True):
-        # print(f"Get noise: {width}x{height}")
-        if scale:
-            # Scale the input width and height for the initial generation
-            # Make their area equivalent to the model's resolution area (e.g. 512*512 = 262144),
-            # while keeping the minimum dimension at least 0.5 * resolution (e.g. 512*0.5 = 256)
-
-            aspect = width / height
-            dimension = self.model.unet.config.sample_size * self.model.vae_scale_factor
-            min_dimension = math.floor(dimension * 0.5)
-            model_area = (
-                dimension * dimension
-            )  # hardcoded for now since all models are trained on square images
-
-            if aspect > 1.0:
-                init_height = max(min_dimension, math.sqrt(model_area / aspect))
-                init_width = init_height * aspect
-            else:
-                init_width = max(min_dimension, math.sqrt(model_area * aspect))
-                init_height = init_width / aspect
-
-            scaled_width, scaled_height = trim_to_multiple_of(
-                math.floor(init_width), math.floor(init_height)
-            )
-
-        else:
-            scaled_width = width
-            scaled_height = height
-
-        device = self.model.device
-        channels = self.latent_channels
-        if channels == 9:
-            channels = 4  # we don't really want noise for all the mask channels
-        shape = (
-            1,
-            channels,
-            scaled_height // self.downsampling_factor,
-            scaled_width // self.downsampling_factor,
-        )
-        if self.use_mps_noise or device.type == "mps":
-            tensor = torch.empty(size=shape, device="cpu")
-            tensor = self.get_noise_like(like=tensor).to(device)
-        else:
-            tensor = torch.empty(size=shape, device=device)
-            tensor = self.get_noise_like(like=tensor)
-        return tensor

invokeai/backend/globals.py

@@ -1,122 +0,0 @@
-"""
-invokeai.backend.globals defines a small number of global variables that would
-otherwise have to be passed through long and complex call chains.
-
-It defines a Namespace object named "Globals" that contains
-the attributes:
-
-  - root           - the root directory under which "models" and "outputs" can be found
-  - initfile       - path to the initialization file
-  - try_patchmatch - option to globally disable loading of 'patchmatch' module
-  - always_use_cpu - force use of CPU even if GPU is available
-"""
-
-import os
-import os.path as osp
-from argparse import Namespace
-from pathlib import Path
-from typing import Union
-
-Globals = Namespace()
-
-# Where to look for the initialization file and other key components
-Globals.initfile = "invokeai.init"
-Globals.models_file = "models.yaml"
-Globals.models_dir = "models"
-Globals.config_dir = "configs"
-Globals.autoscan_dir = "weights"
-Globals.converted_ckpts_dir = "converted_ckpts"
-
-# Set the default root directory. This can be overwritten by explicitly
-# passing the `--root <directory>` argument on the command line.
-# logic is:
-# 1) use INVOKEAI_ROOT environment variable (no check for this being a valid directory)
-# 2) use VIRTUAL_ENV environment variable, with a check for initfile being there
-# 3) use ~/invokeai
-
-if os.environ.get("INVOKEAI_ROOT"):
-    Globals.root = osp.abspath(os.environ.get("INVOKEAI_ROOT"))
-elif (
-    os.environ.get("VIRTUAL_ENV")
-    and Path(os.environ.get("VIRTUAL_ENV"), "..", Globals.initfile).exists()
-):
-    Globals.root = osp.abspath(osp.join(os.environ.get("VIRTUAL_ENV"), ".."))
-else:
-    Globals.root = osp.abspath(osp.expanduser("~/invokeai"))
-
-# Try loading patchmatch
-Globals.try_patchmatch = True
-
-# Use CPU even if GPU is available (main use case is for debugging MPS issues)
-Globals.always_use_cpu = False
-
-# Whether the internet is reachable for dynamic downloads
-# The CLI will test connectivity at startup time.
-Globals.internet_available = True
-
-# Whether to disable xformers
-Globals.disable_xformers = False
-
-# Low-memory tradeoff for guidance calculations.
-Globals.sequential_guidance = False
-
-# whether we are forcing full precision
-Globals.full_precision = False
-
-# whether we should convert ckpt files into diffusers models on the fly
-Globals.ckpt_convert = True
-
-# logging tokenization everywhere
-Globals.log_tokenization = False
-
-
-def global_config_file() -> Path:
-    return Path(Globals.root, Globals.config_dir, Globals.models_file)
-
-
-def global_config_dir() -> Path:
-    return Path(Globals.root, Globals.config_dir)
-
-
-def global_models_dir() -> Path:
-    return Path(Globals.root, Globals.models_dir)
-
-
-def global_autoscan_dir() -> Path:
-    return Path(Globals.root, Globals.autoscan_dir)
-
-
-def global_converted_ckpts_dir() -> Path:
-    return Path(global_models_dir(), Globals.converted_ckpts_dir)
-
-
-def global_set_root(root_dir: Union[str, Path]):
-    Globals.root = root_dir
-
-
-def global_cache_dir(subdir: Union[str, Path] = "") -> Path:
-    """
-    Returns Path to the model cache directory. If a subdirectory
-    is provided, it will be appended to the end of the path, allowing
-    for Hugging Face-style conventions. Currently, Hugging Face has
-    moved all models into the "hub" subfolder, so for any pretrained
-    HF model, use:
-         global_cache_dir('hub')
-
-    The legacy location for transformers used to be global_cache_dir('transformers')
-    and global_cache_dir('diffusers') for diffusers.
-    """
-    home: str = os.getenv("HF_HOME")
-
-    if home is None:
-        home = os.getenv("XDG_CACHE_HOME")
-
-        if home is not None:
-            # Set `home` to $XDG_CACHE_HOME/huggingface, which is the default location mentioned in Hugging Face Hub Client Library.
-            # See: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
-            home += os.sep + "huggingface"
-
-    if home is not None:
-        return Path(home, subdir)
-    else:
-        return Path(Globals.root, "models", subdir)

invokeai/backend/image_util/__init__.py

@@ -1,24 +0,0 @@
-"""
-Initialization file for invokeai.backend.image_util methods.
-"""
-from .patchmatch import PatchMatch
-from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata
-from .seamless import configure_model_padding
-from .txt2mask import Txt2Mask
-from .util import InitImageResizer, make_grid
-
-
-def debug_image(
-    debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False
-):
-    if not debug_status:
-        return
-
-    image_copy = debug_image.copy().convert("RGBA")
-    ImageDraw.Draw(image_copy).text((5, 5), debug_text, (255, 0, 0))
-
-    if debug_show:
-        image_copy.show()
-
-    if debug_result:
-        return image_copy

invokeai/backend/image_util/patchmatch.py

@@ -1,47 +0,0 @@
-"""
-This module defines a singleton object, "patchmatch" that
-wraps the actual patchmatch object. It respects the global
-"try_patchmatch" attribute, so that patchmatch loading can
-be suppressed or deferred
-"""
-import numpy as np
-
-from invokeai.backend.globals import Globals
-
-
-class PatchMatch:
-    """
-    Thin class wrapper around the patchmatch function.
-    """
-
-    patch_match = None
-    tried_load: bool = False
-
-    def __init__(self):
-        super().__init__()
-
-    @classmethod
-    def _load_patch_match(self):
-        if self.tried_load:
-            return
-        if Globals.try_patchmatch:
-            from patchmatch import patch_match as pm
-
-            if pm.patchmatch_available:
-                print(">> Patchmatch initialized")
-            else:
-                print(">> Patchmatch not loaded (nonfatal)")
-            self.patch_match = pm
-        else:
-            print(">> Patchmatch loading disabled")
-        self.tried_load = True
-
-    @classmethod
-    def patchmatch_available(self) -> bool:
-        self._load_patch_match()
-        return self.patch_match and self.patch_match.patchmatch_available
-
-    @classmethod
-    def inpaint(self, *args, **kwargs) -> np.ndarray:
-        if self.patchmatch_available():
-            return self.patch_match.inpaint(*args, **kwargs)

invokeai/backend/image_util/pngwriter.py

@@ -1,121 +0,0 @@
-"""
-Two helper classes for dealing with PNG images and their path names.
-PngWriter -- Converts Images generated by T2I into PNGs, finds
-             appropriate names for them, and writes prompt metadata
-             into the PNG.
-
-Exports function retrieve_metadata(path)
-"""
-import json
-import os
-import re
-
-from PIL import Image, PngImagePlugin
-
-# -------------------image generation utils-----
-
-
-class PngWriter:
-    def __init__(self, outdir):
-        self.outdir = outdir
-        os.makedirs(outdir, exist_ok=True)
-
-    # gives the next unique prefix in outdir
-    def unique_prefix(self):
-        # sort reverse alphabetically until we find max+1
-        dirlist = sorted(os.listdir(self.outdir), reverse=True)
-        # find the first filename that matches our pattern or return 000000.0.png
-        existing_name = next(
-            (f for f in dirlist if re.match("^(\d+)\..*\.png", f)),
-            "0000000.0.png",
-        )
-        basecount = int(existing_name.split(".", 1)[0]) + 1
-        return f"{basecount:06}"
-
-    # saves image named _image_ to outdir/name, writing metadata from prompt
-    # returns full path of output
-    def save_image_and_prompt_to_png(
-        self, image, dream_prompt, name, metadata=None, compress_level=6
-    ):
-        path = os.path.join(self.outdir, name)
-        info = PngImagePlugin.PngInfo()
-        info.add_text("Dream", dream_prompt)
-        if metadata:
-            info.add_text("sd-metadata", json.dumps(metadata))
-        image.save(path, "PNG", pnginfo=info, compress_level=compress_level)
-        return path
-
-    def retrieve_metadata(self, img_basename):
-        """
-        Given a PNG filename stored in outdir, returns the "sd-metadata"
-        metadata stored there, as a dict
-        """
-        path = os.path.join(self.outdir, img_basename)
-        all_metadata = retrieve_metadata(path)
-        return all_metadata["sd-metadata"]
-
-
-def retrieve_metadata(img_path):
-    """
-    Given a path to a PNG image, returns the "sd-metadata"
-    metadata stored there, as a dict
-    """
-    im = Image.open(img_path)
-    if hasattr(im, "text"):
-        md = im.text.get("sd-metadata", "{}")
-        dream_prompt = im.text.get("Dream", "")
-    else:
-        # When trying to retrieve metadata from images without a 'text' payload, such as JPG images.
-        md = "{}"
-        dream_prompt = ""
-    return {"sd-metadata": json.loads(md), "Dream": dream_prompt}
-
-
-def write_metadata(img_path: str, meta: dict):
-    im = Image.open(img_path)
-    info = PngImagePlugin.PngInfo()
-    info.add_text("sd-metadata", json.dumps(meta))
-    im.save(img_path, "PNG", pnginfo=info)
-
-
-class PromptFormatter:
-    def __init__(self, t2i, opt):
-        self.t2i = t2i
-        self.opt = opt
-
-    # note: the t2i object should provide all these values.
-    # there should be no need to or against opt values
-    def normalize_prompt(self):
-        """Normalize the prompt and switches"""
-        t2i = self.t2i
-        opt = self.opt
-
-        switches = list()
-        switches.append(f'"{opt.prompt}"')
-        switches.append(f"-s{opt.steps        or t2i.steps}")
-        switches.append(f"-W{opt.width        or t2i.width}")
-        switches.append(f"-H{opt.height       or t2i.height}")
-        switches.append(f"-C{opt.cfg_scale    or t2i.cfg_scale}")
-        switches.append(f"-A{opt.sampler_name or t2i.sampler_name}")
-        # to do: put model name into the t2i object
-        #        switches.append(f'--model{t2i.model_name}')
-        if opt.seamless or t2i.seamless:
-            switches.append(f"--seamless")
-        if opt.init_img:
-            switches.append(f"-I{opt.init_img}")
-        if opt.fit:
-            switches.append(f"--fit")
-        if opt.strength and opt.init_img is not None:
-            switches.append(f"-f{opt.strength or t2i.strength}")
-        if opt.gfpgan_strength:
-            switches.append(f"-G{opt.gfpgan_strength}")
-        if opt.upscale:
-            switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
-        if opt.variation_amount > 0:
-            switches.append(f"-v{opt.variation_amount}")
-        if opt.with_variations:
-            formatted_variations = ",".join(
-                f"{seed}:{weight}" for seed, weight in opt.with_variations
-            )
-            switches.append(f"-V{formatted_variations}")
-        return " ".join(switches)

invokeai/backend/image_util/seamless.py

@@ -1,59 +0,0 @@
-import torch.nn as nn
-
-
-def _conv_forward_asymmetric(self, input, weight, bias):
-    """
-    Patch for Conv2d._conv_forward that supports asymmetric padding
-    """
-    working = nn.functional.pad(
-        input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"]
-    )
-    working = nn.functional.pad(
-        working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"]
-    )
-    return nn.functional.conv2d(
-        working,
-        weight,
-        bias,
-        self.stride,
-        nn.modules.utils._pair(0),
-        self.dilation,
-        self.groups,
-    )
-
-
-def configure_model_padding(model, seamless, seamless_axes):
-    """
-    Modifies the 2D convolution layers to use a circular padding mode based on the `seamless` and `seamless_axes` options.
-    """
-    # TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
-    for m in model.modules():
-        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-            if seamless:
-                m.asymmetric_padding_mode = {}
-                m.asymmetric_padding = {}
-                m.asymmetric_padding_mode["x"] = (
-                    "circular" if ("x" in seamless_axes) else "constant"
-                )
-                m.asymmetric_padding["x"] = (
-                    m._reversed_padding_repeated_twice[0],
-                    m._reversed_padding_repeated_twice[1],
-                    0,
-                    0,
-                )
-                m.asymmetric_padding_mode["y"] = (
-                    "circular" if ("y" in seamless_axes) else "constant"
-                )
-                m.asymmetric_padding["y"] = (
-                    0,
-                    0,
-                    m._reversed_padding_repeated_twice[2],
-                    m._reversed_padding_repeated_twice[3],
-                )
-                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
-            else:
-                m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
-                if hasattr(m, "asymmetric_padding_mode"):
-                    del m.asymmetric_padding_mode
-                if hasattr(m, "asymmetric_padding"):
-                    del m.asymmetric_padding

invokeai/backend/image_util/txt2mask.py

@@ -1,142 +0,0 @@
-"""Makes available the Txt2Mask class, which assists in the automatic
-assignment of masks via text prompt using clipseg.
-
-Here is typical usage:
-
-    from invokeai.backend.image_util.txt2mask import Txt2Mask, SegmentedGrayscale
-    from PIL import Image
-
-    txt2mask = Txt2Mask(self.device)
-    segmented = txt2mask.segment(Image.open('/path/to/img.png'),'a bagel')
-
-    # this will return a grayscale Image of the segmented data
-    grayscale = segmented.to_grayscale()
-
-    # this will return a semi-transparent image in which the
-    # selected object(s) are opaque and the rest is at various
-    # levels of transparency
-    transparent = segmented.to_transparent()
-
-    # this will return a masked image suitable for use in inpainting:
-    mask = segmented.to_mask(threshold=0.5)
-
-The threshold used in the call to to_mask() selects pixels for use in
-the mask that exceed the indicated confidence threshold. Values range
-from 0.0 to 1.0. The higher the threshold, the more confident the
-algorithm is. In limited testing, I have found that values around 0.5
-work fine.
-"""
-
-import numpy as np
-import torch
-from PIL import Image, ImageOps
-from torchvision import transforms
-from transformers import AutoProcessor, CLIPSegForImageSegmentation
-
-from invokeai.backend.globals import global_cache_dir
-
-CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
-CLIPSEG_SIZE = 352
-
-
-class SegmentedGrayscale(object):
-    def __init__(self, image: Image, heatmap: torch.Tensor):
-        self.heatmap = heatmap
-        self.image = image
-
-    def to_grayscale(self, invert: bool = False) -> Image:
-        return self._rescale(
-            Image.fromarray(
-                np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)
-            )
-        )
-
-    def to_mask(self, threshold: float = 0.5) -> Image:
-        discrete_heatmap = self.heatmap.lt(threshold).int()
-        return self._rescale(
-            Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L")
-        )
-
-    def to_transparent(self, invert: bool = False) -> Image:
-        transparent_image = self.image.copy()
-        # For img2img, we want the selected regions to be transparent,
-        # but to_grayscale() returns the opposite. Thus invert.
-        gs = self.to_grayscale(not invert)
-        transparent_image.putalpha(gs)
-        return transparent_image
-
-    # unscales and uncrops the 352x352 heatmap so that it matches the image again
-    def _rescale(self, heatmap: Image) -> Image:
-        size = (
-            self.image.width
-            if (self.image.width > self.image.height)
-            else self.image.height
-        )
-        resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS)
-        return resized_image.crop((0, 0, self.image.width, self.image.height))
-
-
-class Txt2Mask(object):
-    """
-    Create new Txt2Mask object. The optional device argument can be one of
-    'cuda', 'mps' or 'cpu'.
-    """
-
-    def __init__(self, device="cpu", refined=False):
-        print(">> Initializing clipseg model for text to mask inference")
-
-        # BUG: we are not doing anything with the device option at this time
-        self.device = device
-        self.processor = AutoProcessor.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
-        )
-        self.model = CLIPSegForImageSegmentation.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
-        )
-
-    @torch.no_grad()
-    def segment(self, image, prompt: str) -> SegmentedGrayscale:
-        """
-        Given a prompt string such as "a bagel", tries to identify the object in the
-        provided image and returns a SegmentedGrayscale object in which the brighter
-        pixels indicate where the object is inferred to be.
-        """
-        transform = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize(
-                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-                ),
-                transforms.Resize(
-                    (CLIPSEG_SIZE, CLIPSEG_SIZE)
-                ),  # must be multiple of 64...
-            ]
-        )
-
-        if type(image) is str:
-            image = Image.open(image).convert("RGB")
-
-        image = ImageOps.exif_transpose(image)
-        img = self._scale_and_crop(image)
-
-        inputs = self.processor(
-            text=[prompt], images=[img], padding=True, return_tensors="pt"
-        )
-        outputs = self.model(**inputs)
-        heatmap = torch.sigmoid(outputs.logits)
-        return SegmentedGrayscale(image, heatmap)
-
-    def _scale_and_crop(self, image: Image) -> Image:
-        scaled_image = Image.new("RGB", (CLIPSEG_SIZE, CLIPSEG_SIZE))
-        if image.width > image.height:  # width is constraint
-            scale = CLIPSEG_SIZE / image.width
-        else:
-            scale = CLIPSEG_SIZE / image.height
-        scaled_image.paste(
-            image.resize(
-                (int(scale * image.width), int(scale * image.height)),
-                resample=Image.Resampling.LANCZOS,
-            ),
-            box=(0, 0),
-        )
-        return scaled_image

invokeai/backend/image_util/util.py

@@ -1,71 +0,0 @@
-from math import ceil, floor, sqrt
-
-from PIL import Image
-
-
-class InitImageResizer:
-    """Simple class to create resized copies of an Image while preserving the aspect ratio."""
-
-    def __init__(self, Image):
-        self.image = Image
-
-    def resize(self, width=None, height=None) -> Image:
-        """
-        Return a copy of the image resized to fit within
-        a box width x height. The aspect ratio is
-        maintained. If neither width nor height are provided,
-        then returns a copy of the original image. If one or the other is
-        provided, then the other will be calculated from the
-        aspect ratio.
-
-        Everything is floored to the nearest multiple of 64 so
-        that it can be passed to img2img()
-        """
-        im = self.image
-
-        ar = im.width / float(im.height)
-
-        # Infer missing values from aspect ratio
-        if not (width or height):  # both missing
-            width = im.width
-            height = im.height
-        elif not height:  # height missing
-            height = int(width / ar)
-        elif not width:  # width missing
-            width = int(height * ar)
-
-        w_scale = width / im.width
-        h_scale = height / im.height
-        scale = min(w_scale, h_scale)
-        (rw, rh) = (int(scale * im.width), int(scale * im.height))
-
-        # round everything to multiples of 64
-        width, height, rw, rh = map(lambda x: x - x % 64, (width, height, rw, rh))
-
-        # no resize necessary, but return a copy
-        if im.width == width and im.height == height:
-            return im.copy()
-
-        # otherwise resize the original image so that it fits inside the bounding box
-        resized_image = self.image.resize((rw, rh), resample=Image.Resampling.LANCZOS)
-        return resized_image
-
-
-def make_grid(image_list, rows=None, cols=None):
-    image_cnt = len(image_list)
-    if None in (rows, cols):
-        rows = floor(sqrt(image_cnt))  # try to make it square
-        cols = ceil(image_cnt / rows)
-    width = image_list[0].width
-    height = image_list[0].height
-
-    grid_img = Image.new("RGB", (width * cols, height * rows))
-    i = 0
-    for r in range(0, rows):
-        for c in range(0, cols):
-            if i >= len(image_list):
-                break
-            grid_img.paste(image_list[i], (c * width, r * height))
-            i = i + 1
-
-    return grid_img