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Merge branch 'ryan/remove-attention-map-saving' into ryan/regional-conditioning

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This commit is contained in:
Ryan Dick 2024-03-01 11:03:04 -05:00
commit ffc4ebb14c
740 changed files with 24428 additions and 31726 deletions
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33
.github/actions/install-frontend-deps/action.yml vendored Normal file
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name: Install frontend dependencies
description: Installs frontend dependencies with pnpm, with caching
runs:
using: 'composite'
steps:
- name: Setup Node 18
uses: actions/setup-node@v4
with:
node-version: '18'
- name: Setup pnpm
uses: pnpm/action-setup@v2
with:
version: 8
run_install: false
- name: Get pnpm store directory
shell: bash
run: |
echo "STORE_PATH=$(pnpm store path --silent)" >> $GITHUB_ENV
- uses: actions/cache@v3
name: Setup pnpm cache
with:
path: ${{ env.STORE_PATH }}
key: ${{ runner.os }}-pnpm-store-${{ hashFiles('**/pnpm-lock.yaml') }}
restore-keys: |
${{ runner.os }}-pnpm-store-
- name: Install frontend dependencies
run: pnpm install --prefer-frozen-lockfile
shell: bash
working-directory: invokeai/frontend/web

11
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name: Install python dependencies
description: Install python dependencies with pip, with caching
runs:
using: 'composite'
steps:
- name: Setup python
uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: pip
cache-dependency-path: pyproject.toml

2
.github/workflows/build-container.yml vendored
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@ -11,7 +11,7 @@ on:
- 'docker/docker-entrypoint.sh'
- 'workflows/build-container.yml'
tags:
- 'v*'
- 'v*.*.*'
workflow_dispatch:
permissions:

43
.github/workflows/check-frontend.yml vendored Normal file
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# This workflow runs the frontend code quality checks.
#
# It may be triggered via dispatch, or by another workflow.
name: 'Check: frontend'
on:
workflow_dispatch:
workflow_call:
defaults:
run:
working-directory: invokeai/frontend/web
jobs:
check-frontend:
runs-on: ubuntu-latest
timeout-minutes: 10 # expected run time: <2 min
steps:
- uses: actions/checkout@v4
- name: Set up frontend
uses: ./.github/actions/install-frontend-deps
- name: Run tsc check
run: 'pnpm run lint:tsc'
shell: bash
- name: Run dpdm check
run: 'pnpm run lint:dpdm'
shell: bash
- name: Run eslint check
run: 'pnpm run lint:eslint'
shell: bash
- name: Run prettier check
run: 'pnpm run lint:prettier'
shell: bash
- name: Run knip check
run: 'pnpm run lint:knip'
shell: bash

72
.github/workflows/check-pytest.yml vendored Normal file
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# This workflow runs pytest on the codebase in a matrix of platforms.
#
# It may be triggered via dispatch, or by another workflow.
name: 'Check: pytest'
on:
workflow_dispatch:
workflow_call:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
cancel-in-progress: true
jobs:
matrix:
strategy:
matrix:
python-version:
- '3.10'
pytorch:
- linux-cuda-11_7
- linux-rocm-5_2
- linux-cpu
- macos-default
- windows-cpu
include:
- pytorch: linux-cuda-11_7
os: ubuntu-22.04
github-env: $GITHUB_ENV
- pytorch: linux-rocm-5_2
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
github-env: $GITHUB_ENV
- pytorch: linux-cpu
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/cpu'
github-env: $GITHUB_ENV
- pytorch: macos-default
os: macOS-12
github-env: $GITHUB_ENV
- pytorch: windows-cpu
os: windows-2022
github-env: $env:GITHUB_ENV
name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
runs-on: ${{ matrix.os }}
timeout-minutes: 30 # expected run time: <10 min, depending on platform
env:
PIP_USE_PEP517: '1'
steps:
- uses: actions/checkout@v4
- name: set test prompt to main branch validation
run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
- name: setup python
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: pip
cache-dependency-path: pyproject.toml
- name: install invokeai
env:
PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
run: >
pip3 install
--editable=".[test]"
- name: run pytest
id: run-pytest
run: pytest

33
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# This workflow runs the python code quality checks.
#
# It may be triggered via dispatch, or by another workflow.
#
# TODO: Add mypy or pyright to the checks.
name: 'Check: python'
on:
workflow_dispatch:
workflow_call:
jobs:
check-backend:
runs-on: ubuntu-latest
timeout-minutes: 5 # expected run time: <1 min
steps:
- uses: actions/checkout@v4
- name: Install python dependencies
uses: ./.github/actions/install-python-deps
- name: Install ruff
run: pip install ruff
shell: bash
- name: Ruff check
run: ruff check --output-format=github .
shell: bash
- name: Ruff format
run: ruff format --check .
shell: bash

43
.github/workflows/lint-frontend.yml vendored
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@ -1,43 +0,0 @@
name: Lint frontend
on:
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
push:
branches:
- 'main'
merge_group:
workflow_dispatch:
defaults:
run:
working-directory: invokeai/frontend/web
jobs:
lint-frontend:
if: github.event.pull_request.draft == false
runs-on: ubuntu-22.04
steps:
- name: Setup Node 18
uses: actions/setup-node@v4
with:
node-version: '18'
- name: Checkout
uses: actions/checkout@v4
- name: Setup pnpm
uses: pnpm/action-setup@v2
with:
version: '8.12.1'
- name: Install dependencies
run: 'pnpm install --prefer-frozen-lockfile'
- name: Typescript
run: 'pnpm run lint:tsc'
- name: Madge
run: 'pnpm run lint:madge'
- name: ESLint
run: 'pnpm run lint:eslint'
- name: Prettier
run: 'pnpm run lint:prettier'

53
.github/workflows/mkdocs-material.yml vendored
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@ -1,51 +1,38 @@
name: mkdocs-material
# This is a mostly a copy-paste from https://github.com/squidfunk/mkdocs-material/blob/master/docs/publishing-your-site.md
name: mkdocs
on:
push:
branches:
- 'refs/heads/main'
- main
workflow_dispatch:
permissions:
contents: write
contents: write
jobs:
mkdocs-material:
deploy:
if: github.event.pull_request.draft == false
runs-on: ubuntu-latest
env:
REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
REPO_NAME: '${{ github.repository }}'
SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
steps:
- name: checkout sources
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: setup python
uses: actions/setup-python@v4
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: pip
cache-dependency-path: pyproject.toml
- name: install requirements
env:
PIP_USE_PEP517: 1
run: |
python -m \
pip install ".[docs]"
- name: confirm buildability
run: |
python -m \
mkdocs build \
--clean \
--verbose
- name: deploy to gh-pages
if: ${{ github.ref == 'refs/heads/main' }}
run: |
python -m \
mkdocs gh-deploy \
--clean \
--force
- run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
- uses: actions/cache@v4
with:
key: mkdocs-material-${{ env.cache_id }}
path: .cache
restore-keys: |
mkdocs-material-
- run: python -m pip install ".[docs]"
- run: mkdocs gh-deploy --force

39
.github/workflows/on-change-check-frontend.yml vendored Normal file
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# This workflow runs of `check-frontend.yml` on push or pull request.
#
# The actual checks are in a separate workflow to support simpler workflow
# composition without awkward or complicated conditionals.
name: 'On change: run check-frontend'
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
jobs:
check-changed-frontend-files:
if: github.event.pull_request.draft == false
runs-on: ubuntu-latest
outputs:
frontend_any_changed: ${{ steps.changed-files.outputs.frontend_any_changed }}
steps:
- uses: actions/checkout@v4
- name: Check for changed frontend files
id: changed-files
uses: tj-actions/changed-files@v41
with:
files_yaml: |
frontend:
- 'invokeai/frontend/web/**'
run-check-frontend:
needs: check-changed-frontend-files
if: ${{ needs.check-changed-frontend-files.outputs.frontend_any_changed == 'true' }}
uses: ./.github/workflows/check-frontend.yml

42
.github/workflows/on-change-check-python.yml vendored Normal file
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@ -0,0 +1,42 @@
# This workflow runs of `check-python.yml` on push or pull request.
#
# The actual checks are in a separate workflow to support simpler workflow
# composition without awkward or complicated conditionals.
name: 'On change: run check-python'
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
jobs:
check-changed-python-files:
if: github.event.pull_request.draft == false
runs-on: ubuntu-latest
outputs:
python_any_changed: ${{ steps.changed-files.outputs.python_any_changed }}
steps:
- uses: actions/checkout@v4
- name: Check for changed python files
id: changed-files
uses: tj-actions/changed-files@v41
with:
files_yaml: |
python:
- 'pyproject.toml'
- 'invokeai/**'
- '!invokeai/frontend/web/**'
- 'tests/**'
run-check-python:
needs: check-changed-python-files
if: ${{ needs.check-changed-python-files.outputs.python_any_changed == 'true' }}
uses: ./.github/workflows/check-python.yml

42
.github/workflows/on-change-pytest.yml vendored Normal file
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@ -0,0 +1,42 @@
# This workflow runs of `check-pytest.yml` on push or pull request.
#
# The actual checks are in a separate workflow to support simpler workflow
# composition without awkward or complicated conditionals.
name: 'On change: run pytest'
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
jobs:
check-changed-python-files:
if: github.event.pull_request.draft == false
runs-on: ubuntu-latest
outputs:
python_any_changed: ${{ steps.changed-files.outputs.python_any_changed }}
steps:
- uses: actions/checkout@v4
- name: Check for changed python files
id: changed-files
uses: tj-actions/changed-files@v41
with:
files_yaml: |
python:
- 'pyproject.toml'
- 'invokeai/**'
- '!invokeai/frontend/web/**'
- 'tests/**'
run-pytest:
needs: check-changed-python-files
if: ${{ needs.check-changed-python-files.outputs.python_any_changed == 'true' }}
uses: ./.github/workflows/check-pytest.yml

67
.github/workflows/pypi-release.yml vendored
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@ -1,67 +0,0 @@
name: PyPI Release
on:
workflow_dispatch:
inputs:
publish_package:
description: 'Publish build on PyPi? [true/false]'
required: true
default: 'false'
jobs:
build-and-release:
if: github.repository == 'invoke-ai/InvokeAI'
runs-on: ubuntu-22.04
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
TWINE_NON_INTERACTIVE: 1
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Setup Node 18
uses: actions/setup-node@v4
with:
node-version: '18'
- name: Setup pnpm
uses: pnpm/action-setup@v2
with:
version: '8.12.1'
- name: Install frontend dependencies
run: pnpm install --prefer-frozen-lockfile
working-directory: invokeai/frontend/web
- name: Build frontend
run: pnpm run build
working-directory: invokeai/frontend/web
- name: Install python dependencies
run: pip install --upgrade build twine
- name: Build python package
run: python3 -m build
- name: Upload build as workflow artifact
uses: actions/upload-artifact@v4
with:
name: dist
path: dist
- name: Check distribution
run: twine check dist/*
- name: Check PyPI versions
if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
run: |
pip install --upgrade requests
python -c "\
import scripts.pypi_helper; \
EXISTS=scripts.pypi_helper.local_on_pypi(); \
print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
- name: Publish build on PyPi
if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != '' && github.event.inputs.publish_package == 'true'
run: twine upload dist/*

103
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@ -0,0 +1,103 @@
name: Release
on:
push:
tags:
- 'v*'
workflow_dispatch:
inputs:
skip_code_checks:
description: 'Skip code checks'
required: true
default: true
type: boolean
jobs:
check-version:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: samuelcolvin/check-python-version@v4
id: check-python-version
with:
version_file_path: invokeai/version/invokeai_version.py
check-frontend:
if: github.event.inputs.skip_code_checks != 'true'
uses: ./.github/workflows/check-frontend.yml
check-python:
if: github.event.inputs.skip_code_checks != 'true'
uses: ./.github/workflows/check-python.yml
check-pytest:
if: github.event.inputs.skip_code_checks != 'true'
uses: ./.github/workflows/check-pytest.yml
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Install python dependencies
uses: ./.github/actions/install-python-deps
- name: Install pypa/build
run: pip install --upgrade build
- name: Setup frontend
uses: ./.github/actions/install-frontend-deps
- name: Run create_installer.sh
id: create_installer
run: ./create_installer.sh --skip_frontend_checks
working-directory: installer
- name: Upload python distribution artifact
uses: actions/upload-artifact@v4
with:
name: dist
path: ${{ steps.create_installer.outputs.DIST_PATH }}
- name: Upload installer artifact
uses: actions/upload-artifact@v4
with:
name: ${{ steps.create_installer.outputs.INSTALLER_FILENAME }}
path: ${{ steps.create_installer.outputs.INSTALLER_PATH }}
publish-testpypi:
runs-on: ubuntu-latest
needs: [check-version, check-frontend, check-python, check-pytest, build]
if: github.event_name != 'workflow_dispatch'
environment:
name: testpypi
url: https://test.pypi.org/p/invokeai
steps:
- name: Download distribution from build job
uses: actions/download-artifact@v4
with:
name: dist
path: dist/
- name: Publish distribution to TestPyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
repository-url: https://test.pypi.org/legacy/
publish-pypi:
runs-on: ubuntu-latest
needs: [check-version, check-frontend, check-python, check-pytest, build]
if: github.event_name != 'workflow_dispatch'
environment:
name: pypi
url: https://pypi.org/p/invokeai
steps:
- name: Download distribution from build job
uses: actions/download-artifact@v4
with:
name: dist
path: dist/
- name: Publish distribution to PyPI
uses: pypa/gh-action-pypi-publish@release/v1

24
.github/workflows/style-checks.yml vendored
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@ -1,24 +0,0 @@
name: style checks
on:
pull_request:
push:
branches: main
jobs:
ruff:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Setup Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install dependencies with pip
run: |
pip install ruff
- run: ruff check --output-format=github .
- run: ruff format --check .

129
.github/workflows/test-invoke-pip.yml vendored
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@ -1,129 +0,0 @@
name: Test invoke.py pip
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
cancel-in-progress: true
jobs:
matrix:
if: github.event.pull_request.draft == false
strategy:
matrix:
python-version:
# - '3.9'
- '3.10'
pytorch:
- linux-cuda-11_7
- linux-rocm-5_2
- linux-cpu
- macos-default
- windows-cpu
include:
- pytorch: linux-cuda-11_7
os: ubuntu-22.04
github-env: $GITHUB_ENV
- pytorch: linux-rocm-5_2
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
github-env: $GITHUB_ENV
- pytorch: linux-cpu
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/cpu'
github-env: $GITHUB_ENV
- pytorch: macos-default
os: macOS-12
github-env: $GITHUB_ENV
- pytorch: windows-cpu
os: windows-2022
github-env: $env:GITHUB_ENV
name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
runs-on: ${{ matrix.os }}
env:
PIP_USE_PEP517: '1'
steps:
- name: Checkout sources
id: checkout-sources
uses: actions/checkout@v3
- name: Check for changed python files
id: changed-files
uses: tj-actions/changed-files@v41
with:
files_yaml: |
python:
- 'pyproject.toml'
- 'invokeai/**'
- '!invokeai/frontend/web/**'
- 'tests/**'
- name: set test prompt to main branch validation
if: steps.changed-files.outputs.python_any_changed == 'true'
run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
- name: setup python
if: steps.changed-files.outputs.python_any_changed == 'true'
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: pip
cache-dependency-path: pyproject.toml
- name: install invokeai
if: steps.changed-files.outputs.python_any_changed == 'true'
env:
PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
run: >
pip3 install
--editable=".[test]"
- name: run pytest
if: steps.changed-files.outputs.python_any_changed == 'true'
id: run-pytest
run: pytest
# - name: run invokeai-configure
# env:
# HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
# run: >
# invokeai-configure
# --yes
# --default_only
# --full-precision
# # can't use fp16 weights without a GPU
# - name: run invokeai
# id: run-invokeai
# env:
# # Set offline mode to make sure configure preloaded successfully.
# HF_HUB_OFFLINE: 1
# HF_DATASETS_OFFLINE: 1
# TRANSFORMERS_OFFLINE: 1
# INVOKEAI_OUTDIR: ${{ github.workspace }}/results
# run: >
# invokeai
# --no-patchmatch
# --no-nsfw_checker
# --precision=float32
# --always_use_cpu
# --use_memory_db
# --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
# --from_file ${{ env.TEST_PROMPTS }}
# - name: Archive results
# env:
# INVOKEAI_OUTDIR: ${{ github.workspace }}/results
# uses: actions/upload-artifact@v3
# with:
# name: results
# path: ${{ env.INVOKEAI_OUTDIR }}

2
.prettierrc.yaml
View File

@ -7,7 +7,7 @@ embeddedLanguageFormatting: auto
overrides:
- files: '*.md'
options:
proseWrap: always
proseWrap: preserve
printWidth: 80
parser: markdown
cursorOffset: -1

39
Makefile
View File

@ -6,33 +6,44 @@ default: help
help:
@echo Developer commands:
@echo
@echo "ruff Run ruff, fixing any safely-fixable errors and formatting"
@echo "ruff-unsafe Run ruff, fixing all fixable errors and formatting"
@echo "mypy Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors"
@echo "mypy-all Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
@echo "frontend-build Build the frontend in order to run on localhost:9090"
@echo "frontend-dev Run the frontend in developer mode on localhost:5173"
@echo "installer-zip Build the installer .zip file for the current version"
@echo "tag-release Tag the GitHub repository with the current version (use at release time only!)"
@echo "ruff Run ruff, fixing any safely-fixable errors and formatting"
@echo "ruff-unsafe Run ruff, fixing all fixable errors and formatting"
@echo "mypy Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors"
@echo "mypy-all Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
@echo "test" Run the unit tests.
@echo "frontend-install" Install the pnpm modules needed for the front end
@echo "frontend-build Build the frontend in order to run on localhost:9090"
@echo "frontend-dev Run the frontend in developer mode on localhost:5173"
@echo "installer-zip Build the installer .zip file for the current version"
@echo "tag-release Tag the GitHub repository with the current version (use at release time only!)"
# Runs ruff, fixing any safely-fixable errors and formatting
ruff:
ruff check . --fix
ruff format .
ruff check . --fix
ruff format .
# Runs ruff, fixing all errors it can fix and formatting
ruff-unsafe:
ruff check . --fix --unsafe-fixes
ruff format .
ruff check . --fix --unsafe-fixes
ruff format .
# Runs mypy, using the config in pyproject.toml
mypy:
mypy scripts/invokeai-web.py
mypy scripts/invokeai-web.py
# Runs mypy, ignoring the config in pyproject.toml but still ignoring missing (untyped) imports
# (many files are ignored by the config, so this is useful for checking all files)
mypy-all:
mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports
mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports
# Run the unit tests
test:
pytest ./tests
# Install the pnpm modules needed for the front end
frontend-install:
rm -rf invokeai/frontend/web/node_modules
cd invokeai/frontend/web && pnpm install
# Build the frontend
frontend-build:

8
docker/Dockerfile
View File

@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
ARG TORCH_VERSION=2.1.0
ARG TORCHVISION_VERSION=0.16
ARG TORCH_VERSION=2.1.2
ARG TORCHVISION_VERSION=0.16.2
ARG GPU_DRIVER=cuda
ARG TARGETPLATFORM="linux/amd64"
# unused but available
@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
elif [ "$GPU_DRIVER" = "rocm" ]; then \
extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
else \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
fi &&\
@ -54,7 +54,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
pip install -e ".[xformers]"; \
else \
pip install -e "."; \
pip install $extra_index_url_arg -e "."; \
fi
# #### Build the Web UI ------------------------------------

2
docker/run.sh
View File

@ -21,7 +21,7 @@ run() {
printf "%s\n" "$build_args"
fi
docker compose build $build_args
docker compose build $build_args $service_name
unset build_args
printf "%s\n" "starting service $service_name"

139
docs/RELEASE.md Normal file
View File

@ -0,0 +1,139 @@
# Release Process
The app is published in twice, in different build formats.
- A [PyPI] distribution. This includes both a source distribution and built distribution (a wheel). Users install with `pip install invokeai`. The updater uses this build.
- An installer on the [InvokeAI Releases Page]. This is a zip file with install scripts and a wheel. This is only used for new installs.
## General Prep
Make a developer call-out for PRs to merge. Merge and test things out.
While the release workflow does not include end-to-end tests, it does pause before publishing so you can download and test the final build.
## Release Workflow
The `release.yml` workflow runs a number of jobs to handle code checks, tests, build and publish on PyPI.
It is triggered on **tag push**, when the tag matches `v*`. It doesn't matter if you've prepped a release branch like `release/v3.5.0` or are releasing from `main` - it works the same.
> Because commits are reference-counted, it is safe to create a release branch, tag it, let the workflow run, then delete the branch. So long as the tag exists, that commit will exist.
### Triggering the Workflow
Run `make tag-release` to tag the current commit and kick off the workflow.
The release may also be run [manually].
### Workflow Jobs and Process
The workflow consists of a number of concurrently-run jobs, and two final publish jobs.
The publish jobs run if the 5 concurrent jobs all succeed and if/when the publish jobs are approved.
#### `check-version` Job
This job checks that the git ref matches the app version. It matches the ref against the `__version__` variable in `invokeai/version/invokeai_version.py`.
When the workflow is triggered by tag push, the ref is the tag. If the workflow is run manually, the ref is the target selected from the **Use workflow from** dropdown.
This job uses [samuelcolvin/check-python-version].
> Any valid [version specifier] works, so long as the tag matches the version. The release workflow works exactly the same for `RC`, `post`, `dev`, etc.
#### Check and Test Jobs
This is our test suite.
- **`check-pytest`**: runs `pytest` on matrix of platforms
- **`check-python`**: runs `ruff` (format and lint)
- **`check-frontend`**: runs `prettier` (format), `eslint` (lint), `madge` (circular refs) and `tsc` (static type check)
> **TODO** We should add `mypy` or `pyright` to the **`check-python`** job.
> **TODO** We should add an end-to-end test job that generates an image.
#### `build` Job
This sets up both python and frontend dependencies and builds the python package. Internally, this runs `installer/create_installer.sh` and uploads two artifacts:
- **`dist`**: the python distribution, to be published on PyPI
- **`InvokeAI-installer-${VERSION}.zip`**: the installer to be included in the GitHub release
#### Sanity Check & Smoke Test
At this point, the release workflow pauses (the remaining jobs all require approval).
A maintainer should go to the **Summary** tab of the workflow, download the installer and test it. Ensure the app loads and generates.
> The same wheel file is bundled in the installer and in the `dist` artifact, which is uploaded to PyPI. You should end up with the exactly the same installation of the `invokeai` package from any of these methods.
#### PyPI Publish Jobs
The publish jobs will skip if any of the previous jobs skip or fail.
They use [GitHub environments], which are configured as [trusted publishers] on PyPI.
Both jobs require a maintainer to approve them from the workflow's **Summary** tab.
- Click the **Review deployments** button
- Select the environment (either `testpypi` or `pypi`)
- Click **Approve and deploy**
> **If the version already exists on PyPI, the publish jobs will fail.** PyPI only allows a given version to be published once - you cannot change it. If version published on PyPI has a problem, you'll need to "fail forward" by bumping the app version and publishing a followup release.
#### `publish-testpypi` Job
Publishes the distribution on the [Test PyPI] index, using the `testpypi` GitHub environment.
This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release.
If approved and successful, you could try out the test release like this:
```sh
# Create a new virtual environment
python -m venv ~/.test-invokeai-dist --prompt test-invokeai-dist
# Install the distribution from Test PyPI
pip install --index-url https://test.pypi.org/simple/ invokeai
# Run and test the app
invokeai-web
# Cleanup
deactivate
rm -rf ~/.test-invokeai-dist
```
#### `publish-pypi` Job
Publishes the distribution on the production PyPI index, using the `pypi` GitHub environment.
## Publish the GitHub Release with installer
Once the release is published to PyPI, it's time to publish the GitHub release.
1. [Draft a new release] on GitHub, choosing the tag that triggered the release.
2. Write the release notes, describing important changes. The **Generate release notes** button automatically inserts the changelog and new contributors, and you can copy/paste the intro from previous releases.
3. Upload the zip file created in **`build`** job into the Assets section of the release notes. You can also upload the zip into the body of the release notes, since it can be hard for users to find the Assets section.
4. Check the **Set as a pre-release** and **Create a discussion for this release** checkboxes at the bottom of the release page.
5. Publish the pre-release.
6. Announce the pre-release in Discord.
> **TODO** Workflows can create a GitHub release from a template and upload release assets. One popular action to handle this is [ncipollo/release-action]. A future enhancement to the release process could set this up.
## Manually Running the Release Workflow
The release workflow can be run manually. This is useful to get an installer build and test it out without needing to push a tag.
When run this way, you'll see **Skip code checks** checkbox. This allows the workflow to run without the time-consuming 3 code quality check jobs.
The publish jobs will skip if the workflow was run manually.
[InvokeAI Releases Page]: https://github.com/invoke-ai/InvokeAI/releases
[PyPI]: https://pypi.org/
[Draft a new release]: https://github.com/invoke-ai/InvokeAI/releases/new
[Test PyPI]: https://test.pypi.org/
[version specifier]: https://packaging.python.org/en/latest/specifications/version-specifiers/
[ncipollo/release-action]: https://github.com/ncipollo/release-action
[GitHub environments]: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment
[trusted publishers]: https://docs.pypi.org/trusted-publishers/
[samuelcolvin/check-python-version]: https://github.com/samuelcolvin/check-python-version
[manually]: #manually-running-the-release-workflow

89
docs/contributing/INVOCATIONS.md
View File

@ -9,11 +9,15 @@ complex functionality.
## Invocations Directory
InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These
can be used as examples to create your own nodes.
New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup.
New nodes should be added to a subfolder in `nodes` direction found at the root
level of the InvokeAI installation location. Nodes added to this folder will be
able to be used upon application startup.
Example `nodes` subfolder structure:
Example `nodes` subfolder structure:
```py
├── __init__.py # Invoke-managed custom node loader
│
@ -30,14 +34,14 @@ Example `nodes` subfolder structure:
└── fancy_node.py
```
Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
See the README in the nodes folder for more examples:
Each node folder must have an `__init__.py` file that imports its nodes. Only
nodes imported in the `__init__.py` file are loaded. See the README in the nodes
folder for more examples:
```py
from .cool_node import CoolInvocation
```
## Creating A New Invocation
In order to understand the process of creating a new Invocation, let us actually
@ -131,7 +135,6 @@ from invokeai.app.invocations.primitives import ImageField
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
# Inputs
image: ImageField = InputField(description="The input image")
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@ -167,7 +170,6 @@ from invokeai.app.invocations.primitives import ImageField
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
# Inputs
image: ImageField = InputField(description="The input image")
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@ -197,7 +199,6 @@ from invokeai.app.invocations.image import ImageOutput
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
# Inputs
image: ImageField = InputField(description="The input image")
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@ -229,30 +230,17 @@ class ResizeInvocation(BaseInvocation):
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
def invoke(self, context: InvocationContext) -> ImageOutput:
# Load the image using InvokeAI's predefined Image Service. Returns the PIL image.
image = context.services.images.get_pil_image(self.image.image_name)
# Load the input image as a PIL image
image = context.images.get_pil(self.image.image_name)
# Resizing the image
# Resize the image
resized_image = image.resize((self.width, self.height))
# Save the image using InvokeAI's predefined Image Service. Returns the prepared PIL image.
output_image = context.services.images.create(
image=resized_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
# Save the image
image_dto = context.images.save(image=resized_image)
# Returning the Image
return ImageOutput(
image=ImageField(
image_name=output_image.image_name,
),
width=output_image.width,
height=output_image.height,
)
# Return an ImageOutput
return ImageOutput.build(image_dto)
```
**Note:** Do not be overwhelmed by the `ImageOutput` process. InvokeAI has a
@ -343,27 +331,25 @@ class ImageColorStringOutput(BaseInvocationOutput):
That's all there is to it.
<!-- TODO: DANGER - we probably do not want people to create their own field types, because this requires a lot of work on the frontend to accomodate.
### Custom Input Fields
Now that you know how to create your own Invocations, let us dive into slightly
more advanced topics.
While creating your own Invocations, you might run into a scenario where the
existing input types in InvokeAI do not meet your requirements. In such cases,
you can create your own input types.
existing fields in InvokeAI do not meet your requirements. In such cases, you
can create your own fields.
Let us create one as an example. Let us say we want to create a color input
field that represents a color code. But before we start on that here are some
general good practices to keep in mind.
**Good Practices**
### Best Practices
- There is no naming convention for input fields but we highly recommend that
you name it something appropriate like `ColorField`.
- It is not mandatory but it is heavily recommended to add a relevant
`docstring` to describe your input field.
`docstring` to describe your field.
- Keep your field in the same file as the Invocation that it is made for or in
another file where it is relevant.
@ -378,10 +364,13 @@ class ColorField(BaseModel):
pass
```
Perfect. Now let us create our custom inputs for our field. This is exactly
similar how you created input fields for your Invocation. All the same rules
apply. Let us create four fields representing the _red(r)_, _blue(b)_,
_green(g)_ and _alpha(a)_ channel of the color.
Perfect. Now let us create the properties for our field. This is similar to how
you created input fields for your Invocation. All the same rules apply. Let us
create four fields representing the _red(r)_, _blue(b)_, _green(g)_ and
_alpha(a)_ channel of the color.
> Technically, the properties are _also_ called fields - but in this case, it
> refers to a `pydantic` field.
```python
class ColorField(BaseModel):
@ -396,25 +385,11 @@ That's it. We now have a new input field type that we can use in our Invocations
like this.
```python
color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
```
### Custom Components For Frontend
### Using the custom field
Every backend input type should have a corresponding frontend component so the
UI knows what to render when you use a particular field type.
When you start the UI, your custom field will be automatically recognized.
If you are using existing field types, we already have components for those. So
you don't have to worry about creating anything new. But this might not always
be the case. Sometimes you might want to create new field types and have the
frontend UI deal with it in a different way.
This is where we venture into the world of React and Javascript and create our
own new components for our Invocations. Do not fear the world of JS. It's
actually pretty straightforward.
Let us create a new component for our custom color field we created above. When
we use a color field, let us say we want the UI to display a color picker for
the user to pick from rather than entering values. That is what we will build
now.
-->
Custom fields only support connection inputs in the Workflow Editor.

386
docs/contributing/MODEL_MANAGER.md
View File

@ -28,7 +28,7 @@ model. These are the:
Hugging Face, as well as discriminating among model versions in
Civitai, but can be used for arbitrary content.
* _ModelLoadServiceBase_ (**CURRENTLY UNDER DEVELOPMENT - NOT IMPLEMENTED**)
* _ModelLoadServiceBase_
Responsible for loading a model from disk
into RAM and VRAM and getting it ready for inference.
@ -41,10 +41,10 @@ The four main services can be found in
* `invokeai/app/services/model_records/`
* `invokeai/app/services/model_install/`
* `invokeai/app/services/downloads/`
* `invokeai/app/services/model_loader/` (**under development**)
* `invokeai/app/services/model_load/`
Code related to the FastAPI web API can be found in
`invokeai/app/api/routers/model_records.py`.
`invokeai/app/api/routers/model_manager_v2.py`.
***
@ -84,10 +84,10 @@ diffusers model. When this happens, `original_hash` is unchanged, but
`ModelType`, `ModelFormat` and `BaseModelType` are string enums that
are defined in `invokeai.backend.model_manager.config`. They are also
imported by, and can be reexported from,
`invokeai.app.services.model_record_service`:
`invokeai.app.services.model_manager.model_records`:
```
from invokeai.app.services.model_record_service import ModelType, ModelFormat, BaseModelType
from invokeai.app.services.model_records import ModelType, ModelFormat, BaseModelType
```
The `path` field can be absolute or relative. If relative, it is taken
@ -123,7 +123,7 @@ taken to be the `models_dir` directory.
`variant` is an enumerated string class with values `normal`,
`inpaint` and `depth`. If needed, it can be imported if needed from
either `invokeai.app.services.model_record_service` or
either `invokeai.app.services.model_records` or
`invokeai.backend.model_manager.config`.
### ONNXSD2Config
@ -134,7 +134,7 @@ either `invokeai.app.services.model_record_service` or
| `upcast_attention` | bool | Model requires its attention module to be upcast |
The `SchedulerPredictionType` enum can be imported from either
`invokeai.app.services.model_record_service` or
`invokeai.app.services.model_records` or
`invokeai.backend.model_manager.config`.
### Other config classes
@ -157,15 +157,6 @@ indicates that the model is compatible with any of the base
models. This works OK for some models, such as the IP Adapter image
encoders, but is an all-or-nothing proposition.
Another issue is that the config class hierarchy is paralleled to some
extent by a `ModelBase` class hierarchy defined in
`invokeai.backend.model_manager.models.base` and its subclasses. These
are classes representing the models after they are loaded into RAM and
include runtime information such as load status and bytes used. Some
of the fields, including `name`, `model_type` and `base_model`, are
shared between `ModelConfigBase` and `ModelBase`, and this is a
potential source of confusion.
## Reading and Writing Model Configuration Records
The `ModelRecordService` provides the ability to retrieve model
@ -177,11 +168,11 @@ initialization and can be retrieved within an invocation from the
`InvocationContext` object:
```
store = context.services.model_record_store
store = context.services.model_manager.store
```
or from elsewhere in the code by accessing
`ApiDependencies.invoker.services.model_record_store`.
`ApiDependencies.invoker.services.model_manager.store`.
### Creating a `ModelRecordService`
@ -190,7 +181,7 @@ you can directly create either a `ModelRecordServiceSQL` or a
`ModelRecordServiceFile` object:
```
from invokeai.app.services.model_record_service import ModelRecordServiceSQL, ModelRecordServiceFile
from invokeai.app.services.model_records import ModelRecordServiceSQL, ModelRecordServiceFile
store = ModelRecordServiceSQL.from_connection(connection, lock)
store = ModelRecordServiceSQL.from_db_file('/path/to/sqlite_database.db')
@ -252,7 +243,7 @@ So a typical startup pattern would be:
```
import sqlite3
from invokeai.app.services.thread import lock
from invokeai.app.services.model_record_service import ModelRecordServiceBase
from invokeai.app.services.model_records import ModelRecordServiceBase
from invokeai.app.services.config import InvokeAIAppConfig
config = InvokeAIAppConfig.get_config()
@ -260,19 +251,6 @@ db_conn = sqlite3.connect(config.db_path.as_posix(), check_same_thread=False)
store = ModelRecordServiceBase.open(config, db_conn, lock)
```
_A note on simultaneous access to `invokeai.db`_: The current InvokeAI
service architecture for the image and graph databases is careful to
use a shared sqlite3 connection and a thread lock to ensure that two
threads don't attempt to access the database simultaneously. However,
the default `sqlite3` library used by Python reports using
**Serialized** mode, which allows multiple threads to access the
database simultaneously using multiple database connections (see
https://www.sqlite.org/threadsafe.html and
https://ricardoanderegg.com/posts/python-sqlite-thread-safety/). Therefore
it should be safe to allow the record service to open its own SQLite
database connection. Opening a model record service should then be as
simple as `ModelRecordServiceBase.open(config)`.
### Fetching a Model's Configuration from `ModelRecordServiceBase`
Configurations can be retrieved in several ways.
@ -468,6 +446,44 @@ required parameters:
Once initialized, the installer will provide the following methods:
#### install_job = installer.heuristic_import(source, [config], [access_token])
This is a simplified interface to the installer which takes a source
string, an optional model configuration dictionary and an optional
access token.
The `source` is a string that can be any of these forms
1. A path on the local filesystem (`C:\\users\\fred\\model.safetensors`)
2. A Url pointing to a single downloadable model file (`https://civitai.com/models/58390/detail-tweaker-lora-lora`)
3. A HuggingFace repo_id with any of the following formats:
- `model/name` -- entire model
- `model/name:fp32` -- entire model, using the fp32 variant
- `model/name:fp16:vae` -- vae submodel, using the fp16 variant
- `model/name::vae` -- vae submodel, using default precision
- `model/name:fp16:path/to/model.safetensors` -- an individual model file, fp16 variant
- `model/name::path/to/model.safetensors` -- an individual model file, default variant
Note that by specifying a relative path to the top of the HuggingFace
repo, you can download and install arbitrary models files.
The variant, if not provided, will be automatically filled in with
`fp32` if the user has requested full precision, and `fp16`
otherwise. If a variant that does not exist is requested, then the
method will install whatever HuggingFace returns as its default
revision.
`config` is an optional dict of values that will override the
autoprobed values for model type, base, scheduler prediction type, and
so forth. See [Model configuration and
probing](#Model-configuration-and-probing) for details.
`access_token` is an optional access token for accessing resources
that need authentication.
The method will return a `ModelInstallJob`. This object is discussed
at length in the following section.
#### install_job = installer.import_model()
The `import_model()` method is the core of the installer. The
@ -486,9 +502,10 @@ source2 = LocalModelSource(path='/opt/models/sushi_diffusers') # a local dif
source3 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5') # a repo_id
source4 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', subfolder='vae') # a subfolder within a repo_id
source5 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', variant='fp16') # a named variant of a HF model
source6 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', subfolder='OrangeMix/OrangeMix1.ckpt') # path to an individual model file
source6 = URLModelSource(url='https://civitai.com/api/download/models/63006') # model located at a URL
source7 = URLModelSource(url='https://civitai.com/api/download/models/63006', access_token='letmein') # with an access token
source7 = URLModelSource(url='https://civitai.com/api/download/models/63006') # model located at a URL
source8 = URLModelSource(url='https://civitai.com/api/download/models/63006', access_token='letmein') # with an access token
for source in [source1, source2, source3, source4, source5, source6, source7]:
install_job = installer.install_model(source)
@ -544,7 +561,6 @@ can be passed to `import_model()`.
attributes returned by the model prober. See the section below for
details.
#### LocalModelSource
This is used for a model that is located on a locally-accessible Posix
@ -737,7 +753,7 @@ and `cancelled`, as well as `in_terminal_state`. The last will return
True if the job is in the complete, errored or cancelled states.
#### Model confguration and probing
#### Model configuration and probing
The install service uses the `invokeai.backend.model_manager.probe`
module during import to determine the model's type, base type, and
@ -776,6 +792,14 @@ returns a list of completed jobs. The optional `timeout` argument will
return from the call if jobs aren't completed in the specified
time. An argument of 0 (the default) will block indefinitely.
#### jobs = installer.wait_for_job(job, [timeout])
Like `wait_for_installs()`, but block until a specific job has
completed or errored, and then return the job. The optional `timeout`
argument will return from the call if the job doesn't complete in the
specified time. An argument of 0 (the default) will block
indefinitely.
#### jobs = installer.list_jobs()
Return a list of all active and complete `ModelInstallJobs`.
@ -838,6 +862,31 @@ This method is similar to `unregister()`, but also unconditionally
deletes the corresponding model weights file(s), regardless of whether
they are inside or outside the InvokeAI models hierarchy.
#### path = installer.download_and_cache(remote_source, [access_token], [timeout])
This utility routine will download the model file located at source,
cache it, and return the path to the cached file. It does not attempt
to determine the model type, probe its configuration values, or
register it with the models database.
You may provide an access token if the remote source requires
authorization. The call will block indefinitely until the file is
completely downloaded, cancelled or raises an error of some sort. If
you provide a timeout (in seconds), the call will raise a
`TimeoutError` exception if the download hasn't completed in the
specified period.
You may use this mechanism to request any type of file, not just a
model. The file will be stored in a subdirectory of
`INVOKEAI_ROOT/models/.cache`. If the requested file is found in the
cache, its path will be returned without redownloading it.
Be aware that the models cache is cleared of infrequently-used files
and directories at regular intervals when the size of the cache
exceeds the value specified in Invoke's `convert_cache` configuration
variable.
#### List[str]=installer.scan_directory(scan_dir: Path, install: bool)
This method will recursively scan the directory indicated in
@ -1128,7 +1177,7 @@ job = queue.create_download_job(
event_handlers=[my_handler1, my_handler2], # if desired
start=True,
)
```
```
The `filename` argument forces the downloader to use the specified
name for the file rather than the name provided by the remote source,
@ -1171,6 +1220,13 @@ queue or was not created by this queue.
This method will block until all the active jobs in the queue have
reached a terminal state (completed, errored or cancelled).
#### queue.wait_for_job(job, [timeout])
This method will block until the indicated job has reached a terminal
state (completed, errored or cancelled). If the optional timeout is
provided, the call will block for at most timeout seconds, and raise a
TimeoutError otherwise.
#### jobs = queue.list_jobs()
This will return a list of all jobs, including ones that have not yet
@ -1449,9 +1505,9 @@ set of keys to the corresponding model config objects.
Find all model metadata records that have the given author and return
a set of keys to the corresponding model config objects.
# The remainder of this documentation is provisional, pending implementation of the Load service
***
## Let's get loaded, the lowdown on ModelLoadService
## The Lowdown on the ModelLoadService
The `ModelLoadService` is responsible for loading a named model into
memory so that it can be used for inference. Despite the fact that it
@ -1465,7 +1521,7 @@ create alternative instances if you wish.
### Creating a ModelLoadService object
The class is defined in
`invokeai.app.services.model_loader_service`. It is initialized with
`invokeai.app.services.model_load`. It is initialized with
an InvokeAIAppConfig object, from which it gets configuration
information such as the user's desired GPU and precision, and with a
previously-created `ModelRecordServiceBase` object, from which it
@ -1475,26 +1531,29 @@ Here is a typical initialization pattern:
```
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.model_record_service import ModelRecordServiceBase
from invokeai.app.services.model_loader_service import ModelLoadService
from invokeai.app.services.model_load import ModelLoadService, ModelLoaderRegistry
config = InvokeAIAppConfig.get_config()
store = ModelRecordServiceBase.open(config)
loader = ModelLoadService(config, store)
ram_cache = ModelCache(
max_cache_size=config.ram_cache_size, max_vram_cache_size=config.vram_cache_size, logger=logger
)
convert_cache = ModelConvertCache(
cache_path=config.models_convert_cache_path, max_size=config.convert_cache_size
)
loader = ModelLoadService(
app_config=config,
ram_cache=ram_cache,
convert_cache=convert_cache,
registry=ModelLoaderRegistry
)
```
Note that we are relying on the contents of the application
configuration to choose the implementation of
`ModelRecordServiceBase`.
### load_model(model_config, [submodel_type], [context]) -> LoadedModel
### get_model(key, [submodel_type], [context]) -> ModelInfo:
*** TO DO: change to get_model(key, context=None, **kwargs)
The `get_model()` method, like its similarly-named cousin in
`ModelRecordService`, receives the unique key that identifies the
The `load_model()` method takes an `AnyModelConfig` returned by
`ModelRecordService.get_model()` and returns the corresponding loaded
model. It loads the model into memory, gets the model ready for use,
and returns a `ModelInfo` object.
and returns a `LoadedModel` object.
The optional second argument, `subtype` is a `SubModelType` string
enum, such as "vae". It is mandatory when used with a main model, and
@ -1504,46 +1563,45 @@ The optional third argument, `context` can be provided by
an invocation to trigger model load event reporting. See below for
details.
The returned `ModelInfo` object shares some fields in common with
`ModelConfigBase`, but is otherwise a completely different beast:
The returned `LoadedModel` object contains a copy of the configuration
record returned by the model record `get_model()` method, as well as
the in-memory loaded model:
| **Field Name** | **Type** | **Description** |
| **Attribute Name** | **Type** | **Description** |
|----------------|-----------------|------------------|
| `key` | str | The model key derived from the ModelRecordService database |
| `name` | str | Name of this model |
| `base_model` | BaseModelType | Base model for this model |
| `type` | ModelType or SubModelType | Either the model type (non-main) or the submodel type (main models)|
| `location` | Path or str | Location of the model on the filesystem |
| `precision` | torch.dtype | The torch.precision to use for inference |
| `context` | ModelCache.ModelLocker | A context class used to lock the model in VRAM while in use |
| `config` | AnyModelConfig | A copy of the model's configuration record for retrieving base type, etc. |
| `model` | AnyModel | The instantiated model (details below) |
| `locker` | ModelLockerBase | A context manager that mediates the movement of the model into VRAM |
The types for `ModelInfo` and `SubModelType` can be imported from
`invokeai.app.services.model_loader_service`.
Because the loader can return multiple model types, it is typed to
return `AnyModel`, a Union `ModelMixin`, `torch.nn.Module`,
`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
models. The others are obvious.
To use the model, you use the `ModelInfo` as a context manager using
the following pattern:
`LoadedModel` acts as a context manager. The context loads the model
into the execution device (e.g. VRAM on CUDA systems), locks the model
in the execution device for the duration of the context, and returns
the model. Use it like this:
```
model_info = loader.get_model('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
with model_info as vae:
image = vae.decode(latents)[0]
```
The `vae` model will stay locked in the GPU during the period of time
it is in the context manager's scope.
`get_model_by_key()` may raise any of the following exceptions:
`get_model()` may raise any of the following exceptions:
- `UnknownModelException` -- key not in database
- `ModelNotFoundException` -- key in database but model not found at path
- `InvalidModelException` -- the model is guilty of a variety of sins
- `UnknownModelException` -- key not in database
- `ModelNotFoundException` -- key in database but model not found at path
- `NotImplementedException` -- the loader doesn't know how to load this type of model
** TO DO: ** Resolve discrepancy between ModelInfo.location and
ModelConfig.path.
### Emitting model loading events
When the `context` argument is passed to `get_model()`, it will
When the `context` argument is passed to `load_model_*()`, it will
retrieve the invocation event bus from the passed `InvocationContext`
object to emit events on the invocation bus. The two events are
"model_load_started" and "model_load_completed". Both carry the
@ -1556,10 +1614,174 @@ payload=dict(
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
model_key=model_key,
submodel=submodel,
submodel_type=submodel,
hash=model_info.hash,
location=str(model_info.location),
precision=str(model_info.precision),
)
```
### Adding Model Loaders
Model loaders are small classes that inherit from the `ModelLoader`
base class. They typically implement one method `_load_model()` whose
signature is:
```
def _load_model(
self,
model_path: Path,
model_variant: Optional[ModelRepoVariant] = None,
submodel_type: Optional[SubModelType] = None,
) -> AnyModel:
```
`_load_model()` will be passed the path to the model on disk, an
optional repository variant (used by the diffusers loaders to select,
e.g. the `fp16` variant, and an optional submodel_type for main and
onnx models.
To install a new loader, place it in
`invokeai/backend/model_manager/load/model_loaders`. Inherit from
`ModelLoader` and use the `@ModelLoaderRegistry.register()` decorator to
indicate what type of models the loader can handle.
Here is a complete example from `generic_diffusers.py`, which is able
to load several different diffusers types:
```
from pathlib import Path
from typing import Optional
from invokeai.backend.model_manager import (
AnyModel,
BaseModelType,
ModelFormat,
ModelRepoVariant,
ModelType,
SubModelType,
)
from .. import ModelLoader, ModelLoaderRegistry
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T2IAdapter, format=ModelFormat.Diffusers)
class GenericDiffusersLoader(ModelLoader):
"""Class to load simple diffusers models."""
def _load_model(
self,
model_path: Path,
model_variant: Optional[ModelRepoVariant] = None,
submodel_type: Optional[SubModelType] = None,
) -> AnyModel:
model_class = self._get_hf_load_class(model_path)
if submodel_type is not None:
raise Exception(f"There are no submodels in models of type {model_class}")
variant = model_variant.value if model_variant else None
result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant) # type: ignore
return result
```
Note that a loader can register itself to handle several different
model types. An exception will be raised if more than one loader tries
to register the same model type.
#### Conversion
Some models require conversion to diffusers format before they can be
loaded. These loaders should override two additional methods:
```
_needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool
_convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
```
The first method accepts the model configuration, the path to where
the unmodified model is currently installed, and a proposed
destination for the converted model. This method returns True if the
model needs to be converted. It typically does this by comparing the
last modification time of the original model file to the modification
time of the converted model. In some cases you will also want to check
the modification date of the configuration record, in the event that
the user has changed something like the scheduler prediction type that
will require the model to be re-converted. See `controlnet.py` for an
example of this logic.
The second method accepts the model configuration, the path to the
original model on disk, and the desired output path for the converted
model. It does whatever it needs to do to get the model into diffusers
format, and returns the Path of the resulting model. (The path should
ordinarily be the same as `output_path`.)
## The ModelManagerService object
For convenience, the API provides a `ModelManagerService` object which
gives a single point of access to the major model manager
services. This object is created at initialization time and can be
found in the global `ApiDependencies.invoker.services.model_manager`
object, or in `context.services.model_manager` from within an
invocation.
In the examples below, we have retrieved the manager using:
```
mm = ApiDependencies.invoker.services.model_manager
```
The following properties and methods will be available:
### mm.store
This retrieves the `ModelRecordService` associated with the
manager. Example:
```
configs = mm.store.get_model_by_attr(name='stable-diffusion-v1-5')
```
### mm.install
This retrieves the `ModelInstallService` associated with the manager.
Example:
```
job = mm.install.heuristic_import(`https://civitai.com/models/58390/detail-tweaker-lora-lora`)
```
### mm.load
This retrieves the `ModelLoaderService` associated with the manager. Example:
```
configs = mm.store.get_model_by_attr(name='stable-diffusion-v1-5')
assert len(configs) > 0
loaded_model = mm.load.load_model(configs[0])
```
The model manager also offers a few convenience shortcuts for loading
models:
### mm.load_model_by_config(model_config, [submodel], [context]) -> LoadedModel
Same as `mm.load.load_model()`.
### mm.load_model_by_attr(model_name, base_model, model_type, [submodel], [context]) -> LoadedModel
This accepts the combination of the model's name, type and base, which
it passes to the model record config store for retrieval. If a unique
model config is found, this method returns a `LoadedModel`. It can
raise the following exceptions:
```
UnknownModelException -- model with these attributes not known
NotImplementedException -- the loader doesn't know how to load this type of model
ValueError -- more than one model matches this combination of base/type/name
```
### mm.load_model_by_key(key, [submodel], [context]) -> LoadedModel
This method takes a model key, looks it up using the
`ModelRecordServiceBase` object in `mm.store`, and passes the returned
model configuration to `load_model_by_config()`. It may raise a
`NotImplementedException`.

45
docs/nodes/INVOCATION_API.md Normal file
View File

@ -0,0 +1,45 @@
# Invocation API
Each invocation's `invoke` method is provided a single arg - the Invocation
Context.
This object provides access to various methods, used to interact with the
application. Loading and saving images, logging messages, etc.
!!! warning ""
This API may shift slightly until the release of v4.0.0 as we work through a few final updates to the Model Manager.
```py
class MyInvocation(BaseInvocation):
...
def invoke(self, context: InvocationContext) -> ImageOutput:
image_pil = context.images.get_pil(image_name)
# Do something to the image
image_dto = context.images.save(image_pil)
# Log a message
context.logger.info(f"Did something cool, image saved!")
...
```
<!-- prettier-ignore-start -->
::: invokeai.app.services.shared.invocation_context.InvocationContext
options:
members: false
::: invokeai.app.services.shared.invocation_context.ImagesInterface
::: invokeai.app.services.shared.invocation_context.TensorsInterface
::: invokeai.app.services.shared.invocation_context.ConditioningInterface
::: invokeai.app.services.shared.invocation_context.ModelsInterface
::: invokeai.app.services.shared.invocation_context.LoggerInterface
::: invokeai.app.services.shared.invocation_context.ConfigInterface
::: invokeai.app.services.shared.invocation_context.UtilInterface
::: invokeai.app.services.shared.invocation_context.BoardsInterface
<!-- prettier-ignore-end -->

148
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View File

@ -0,0 +1,148 @@
# Invoke v4.0.0 Nodes API Migration guide
Invoke v4.0.0 is versioned as such due to breaking changes to the API utilized
by nodes, both core and custom.
## Motivation
Prior to v4.0.0, the `invokeai` python package has not be set up to be utilized
as a library. That is to say, it didn't have any explicitly public API, and node
authors had to work with the unstable internal application API.
v4.0.0 introduces a stable public API for nodes.
## Changes
There are two node-author-facing changes:
1. Import Paths
1. Invocation Context API
### Import Paths
All public objects are now exported from `invokeai.invocation_api`:
```py
# Old
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
InputField,
InvocationContext,
invocation,
)
from invokeai.app.invocations.primitives import ImageField
# New
from invokeai.invocation_api import (
BaseInvocation,
ImageField,
InputField,
InvocationContext,
invocation,
)
```
It's possible that we've missed some classes you need in your node. Please let
us know if that's the case.
### Invocation Context API
Most nodes utilize the Invocation Context, an object that is passed to the
`invoke` that provides access to data and services a node may need.
Until now, that object and the services it exposed were internal. Exposing them
to nodes means that changes to our internal implementation could break nodes.
The methods on the services are also often fairly complicated and allowed nodes
to footgun.
In v4.0.0, this object has been refactored to be much simpler.
See [INVOCATION_API](./INVOCATION_API.md) for full details of the API.
!!! warning ""
This API may shift slightly until the release of v4.0.0 as we work through a few final updates to the Model Manager.
#### Improved Service Methods
The biggest offender was the image save method:
```py
# Old
image_dto = context.services.images.create(
image=image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
# New
image_dto = context.images.save(image=image)
```
Other methods are simplified, or enhanced with additional functionality:
```py
# Old
image = context.services.images.get_pil_image(image_name)
# New
image = context.images.get_pil(image_name)
image_cmyk = context.images.get_pil(image_name, "CMYK")
```
We also had some typing issues around tensors:
```py
# Old
# `latents` typed as `torch.Tensor`, but could be `ConditioningFieldData`
latents = context.services.latents.get(self.latents.latents_name)
# `data` typed as `torch.Tenssor,` but could be `ConditioningFieldData`
context.services.latents.save(latents_name, data)
# New - separate methods for tensors and conditioning data w/ correct typing
# Also, the service generates the names
tensor_name = context.tensors.save(tensor)
tensor = context.tensors.load(tensor_name)
# For conditioning
cond_name = context.conditioning.save(cond_data)
cond_data = context.conditioning.load(cond_name)
```
#### Output Construction
Core Outputs have builder functions right on them - no need to manually
construct these objects, or use an extra utility:
```py
# Old
image_output = ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
latents_output = build_latents_output(latents_name=name, latents=latents, seed=None)
noise_output = NoiseOutput(
noise=LatentsField(latents_name=latents_name, seed=seed),
width=latents.size()[3] * 8,
height=latents.size()[2] * 8,
)
cond_output = ConditioningOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
# New
image_output = ImageOutput.build(image_dto)
latents_output = LatentsOutput.build(latents_name=name, latents=noise, seed=self.seed)
noise_output = NoiseOutput.build(latents_name=name, latents=noise, seed=self.seed)
cond_output = ConditioningOutput.build(conditioning_name)
```
You can still create the objects using constructors if you want, but we suggest
using the builder methods.

29
docs/nodes/communityNodes.md
View File

@ -32,6 +32,7 @@ To use a community workflow, download the the `.json` node graph file and load i
+ [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
+ [Image Picker](#image-picker)
+ [Image Resize Plus](#image-resize-plus)
+ [Latent Upscale](#latent-upscale)
+ [Load Video Frame](#load-video-frame)
+ [Make 3D](#make-3d)
+ [Mask Operations](#mask-operations)
@ -290,6 +291,13 @@ View:
</br><img src="https://raw.githubusercontent.com/VeyDlin/image-resize-plus-node/master/.readme/node.png" width="500" />
--------------------------------
### Latent Upscale
**Description:** This node uses a small (~2.4mb) model to upscale the latents used in a Stable Diffusion 1.5 or Stable Diffusion XL image generation, rather than the typical interpolation method, avoiding the traditional downsides of the latent upscale technique.
**Node Link:** [https://github.com/gogurtenjoyer/latent-upscale](https://github.com/gogurtenjoyer/latent-upscale)
--------------------------------
### Load Video Frame
@ -346,12 +354,21 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
**Description:** A set of nodes for Metadata. Collect Metadata from within an `iterate` node & extract metadata from an image.
- `Metadata Item Linked` - Allows collecting of metadata while within an iterate node with no need for a collect node or conversion to metadata node.
- `Metadata From Image` - Provides Metadata from an image.
- `Metadata To String` - Extracts a String value of a label from metadata.
- `Metadata To Integer` - Extracts an Integer value of a label from metadata.
- `Metadata To Float` - Extracts a Float value of a label from metadata.
- `Metadata To Scheduler` - Extracts a Scheduler value of a label from metadata.
- `Metadata Item Linked` - Allows collecting of metadata while within an iterate node with no need for a collect node or conversion to metadata node
- `Metadata From Image` - Provides Metadata from an image
- `Metadata To String` - Extracts a String value of a label from metadata
- `Metadata To Integer` - Extracts an Integer value of a label from metadata
- `Metadata To Float` - Extracts a Float value of a label from metadata
- `Metadata To Scheduler` - Extracts a Scheduler value of a label from metadata
- `Metadata To Bool` - Extracts Bool types from metadata
- `Metadata To Model` - Extracts model types from metadata
- `Metadata To SDXL Model` - Extracts SDXL model types from metadata
- `Metadata To LoRAs` - Extracts Loras from metadata.
- `Metadata To SDXL LoRAs` - Extracts SDXL Loras from metadata
- `Metadata To ControlNets` - Extracts ControNets from metadata
- `Metadata To IP-Adapters` - Extracts IP-Adapters from metadata
- `Metadata To T2I-Adapters` - Extracts T2I-Adapters from metadata
- `Denoise Latents + Metadata` - This is an inherited version of the existing `Denoise Latents` node but with a metadata input and output.
**Node Link:** https://github.com/skunkworxdark/metadata-linked-nodes

5
docs/requirements-mkdocs.txt
View File

@ -1,5 +0,0 @@
mkdocs
mkdocs-material>=8, <9
mkdocs-git-revision-date-localized-plugin
mkdocs-redirects==1.2.0

5
docs/stylesheets/extra.css
View File

@ -1,5 +0,0 @@
:root {
--md-primary-fg-color: #35A4DB;
--md-primary-fg-color--light: #35A4DB;
--md-primary-fg-color--dark: #35A4DB;
}

110
installer/create_installer.sh
View File

@ -2,22 +2,18 @@
set -e
BCYAN="\e[1;36m"
BYELLOW="\e[1;33m"
BGREEN="\e[1;32m"
BRED="\e[1;31m"
RED="\e[31m"
RESET="\e[0m"
function is_bin_in_path {
builtin type -P "$1" &>/dev/null
}
BCYAN="\033[1;36m"
BYELLOW="\033[1;33m"
BGREEN="\033[1;32m"
BRED="\033[1;31m"
RED="\033[31m"
RESET="\033[0m"
function git_show {
git show -s --format=oneline --abbrev-commit "$1" | cat
}
if [[ -v "VIRTUAL_ENV" ]]; then
if [[ ! -z "${VIRTUAL_ENV}" ]]; then
# we can't just call 'deactivate' because this function is not exported
# to the environment of this script from the bash process that runs the script
echo -e "${BRED}A virtual environment is activated. Please deactivate it before proceeding.${RESET}"
@ -26,31 +22,63 @@ fi
cd "$(dirname "$0")"
echo
echo -e "${BYELLOW}This script must be run from the installer directory!${RESET}"
echo "The current working directory is $(pwd)"
read -p "If that looks right, press any key to proceed, or CTRL-C to exit..."
echo
# Some machines only have `python3` in PATH, others have `python` - make an alias.
# We can use a function to approximate an alias within a non-interactive shell.
if ! is_bin_in_path python && is_bin_in_path python3; then
function python {
python3 "$@"
}
fi
VERSION=$(
cd ..
python -c "from invokeai.version import __version__ as version; print(version)"
python3 -c "from invokeai.version import __version__ as version; print(version)"
)
PATCH=""
VERSION="v${VERSION}${PATCH}"
VERSION="v${VERSION}"
if [[ ! -z ${CI} ]]; then
echo
echo -e "${BCYAN}CI environment detected${RESET}"
echo
else
echo
echo -e "${BYELLOW}This script must be run from the installer directory!${RESET}"
echo "The current working directory is $(pwd)"
read -p "If that looks right, press any key to proceed, or CTRL-C to exit..."
echo
fi
echo -e "${BGREEN}HEAD${RESET}:"
git_show HEAD
echo
# ---------------------- FRONTEND ----------------------
pushd ../invokeai/frontend/web >/dev/null
echo "Installing frontend dependencies..."
echo
pnpm i --frozen-lockfile
echo
if [[ ! -z ${CI} ]]; then
echo "Building frontend without checks..."
# In CI, we have already done the frontend checks and can just build
pnpm vite build
else
echo "Running checks and building frontend..."
# This runs all the frontend checks and builds
pnpm build
fi
echo
popd
# ---------------------- BACKEND ----------------------
echo
echo "Building wheel..."
echo
# install the 'build' package in the user site packages, if needed
# could be improved by using a temporary venv, but it's tiny and harmless
if [[ $(python3 -c 'from importlib.util import find_spec; print(find_spec("build") is None)') == "True" ]]; then
pip install --user build
fi
rm -rf ../build
python3 -m build --outdir dist/ ../.
# ----------------------
echo
@ -78,10 +106,28 @@ chmod a+x InvokeAI-Installer/install.sh
cp install.bat.in InvokeAI-Installer/install.bat
cp WinLongPathsEnabled.reg InvokeAI-Installer/
# Zip everything up
zip -r InvokeAI-installer-$VERSION.zip InvokeAI-Installer
FILENAME=InvokeAI-installer-$VERSION.zip
# clean up
rm -rf InvokeAI-Installer tmp dist ../invokeai/frontend/web/dist/
# Zip everything up
zip -r ${FILENAME} InvokeAI-Installer
echo
echo -e "${BGREEN}Built installer: ./${FILENAME}${RESET}"
echo -e "${BGREEN}Built PyPi distribution: ./dist${RESET}"
# clean up, but only if we are not in a github action
if [[ -z ${CI} ]]; then
echo
echo "Cleaning up intermediate build files..."
rm -rf InvokeAI-Installer tmp ../invokeai/frontend/web/dist/
fi
if [[ ! -z ${CI} ]]; then
echo
echo "Setting GitHub action outputs..."
echo "INSTALLER_FILENAME=${FILENAME}" >>$GITHUB_OUTPUT
echo "INSTALLER_PATH=installer/${FILENAME}" >>$GITHUB_OUTPUT
echo "DIST_PATH=installer/dist/" >>$GITHUB_OUTPUT
fi
exit 0

41
installer/tag_release.sh
View File

@ -2,12 +2,12 @@
set -e
BCYAN="\e[1;36m"
BYELLOW="\e[1;33m"
BGREEN="\e[1;32m"
BRED="\e[1;31m"
RED="\e[31m"
RESET="\e[0m"
BCYAN="\033[1;36m"
BYELLOW="\033[1;33m"
BGREEN="\033[1;32m"
BRED="\033[1;31m"
RED="\033[31m"
RESET="\033[0m"
function does_tag_exist {
git rev-parse --quiet --verify "refs/tags/$1" >/dev/null
@ -23,49 +23,40 @@ function git_show {
VERSION=$(
cd ..
python -c "from invokeai.version import __version__ as version; print(version)"
python3 -c "from invokeai.version import __version__ as version; print(version)"
)
PATCH=""
MAJOR_VERSION=$(echo $VERSION | sed 's/\..*$//')
VERSION="v${VERSION}${PATCH}"
LATEST_TAG="v${MAJOR_VERSION}-latest"
if does_tag_exist $VERSION; then
echo -e "${BCYAN}${VERSION}${RESET} already exists:"
git_show_ref tags/$VERSION
echo
fi
if does_tag_exist $LATEST_TAG; then
echo -e "${BCYAN}${LATEST_TAG}${RESET} already exists:"
git_show_ref tags/$LATEST_TAG
echo
fi
echo -e "${BGREEN}HEAD${RESET}:"
git_show
echo
echo -e -n "Create tags ${BCYAN}${VERSION}${RESET} and ${BCYAN}${LATEST_TAG}${RESET} @ ${BGREEN}HEAD${RESET}, ${RED}deleting existing tags on remote${RESET}? "
echo -e "${BGREEN}git remote -v${RESET}:"
git remote -v
echo
echo -e -n "Create tags ${BCYAN}${VERSION}${RESET} @ ${BGREEN}HEAD${RESET}, ${RED}deleting existing tags on origin remote${RESET}? "
read -e -p 'y/n [n]: ' input
RESPONSE=${input:='n'}
if [ "$RESPONSE" == 'y' ]; then
echo
echo -e "Deleting ${BCYAN}${VERSION}${RESET} tag on remote..."
git push --delete origin $VERSION
echo -e "Deleting ${BCYAN}${VERSION}${RESET} tag on origin remote..."
git push origin :refs/tags/$VERSION
echo -e "Tagging ${BGREEN}HEAD${RESET} with ${BCYAN}${VERSION}${RESET} locally..."
echo -e "Tagging ${BGREEN}HEAD${RESET} with ${BCYAN}${VERSION}${RESET} on locally..."
if ! git tag -fa $VERSION; then
echo "Existing/invalid tag"
exit -1
fi
echo -e "Deleting ${BCYAN}${LATEST_TAG}${RESET} tag on remote..."
git push --delete origin $LATEST_TAG
echo -e "Tagging ${BGREEN}HEAD${RESET} with ${BCYAN}${LATEST_TAG}${RESET} locally..."
git tag -fa $LATEST_TAG
echo -e "Pushing updated tags to remote..."
echo -e "Pushing updated tags to origin remote..."
git push origin --tags
fi
exit 0

0
invokeai/app/services/invocation_processor/__init__.py → invokeai/app/__init__.py
View File

51
invokeai/app/api/dependencies.py
View File

@ -2,9 +2,12 @@
from logging import Logger
from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
import torch
from invokeai.app.services.object_serializer.object_serializer_disk import ObjectSerializerDisk
from invokeai.app.services.object_serializer.object_serializer_forward_cache import ObjectSerializerForwardCache
from invokeai.app.services.shared.sqlite.sqlite_util import init_db
from invokeai.backend.model_manager.metadata import ModelMetadataStore
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
@ -12,26 +15,22 @@ from ..services.board_image_records.board_image_records_sqlite import SqliteBoar
from ..services.board_images.board_images_default import BoardImagesService
from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
from ..services.boards.boards_default import BoardService
from ..services.bulk_download.bulk_download_default import BulkDownloadService
from ..services.config import InvokeAIAppConfig
from ..services.download import DownloadQueueService
from ..services.image_files.image_files_disk import DiskImageFileStorage
from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
from ..services.images.images_default import ImageService
from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
from ..services.invocation_services import InvocationServices
from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
from ..services.invoker import Invoker
from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
from ..services.model_install import ModelInstallService
from ..services.model_manager.model_manager_default import ModelManagerService
from ..services.model_metadata import ModelMetadataStoreSQL
from ..services.model_records import ModelRecordServiceSQL
from ..services.names.names_default import SimpleNameService
from ..services.session_processor.session_processor_default import DefaultSessionProcessor
from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
from ..services.shared.graph import GraphExecutionState
from ..services.urls.urls_default import LocalUrlService
from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
from .events import FastAPIEventService
@ -68,6 +67,9 @@ class ApiDependencies:
logger.debug(f"Internet connectivity is {config.internet_available}")
output_folder = config.output_path
if output_folder is None:
raise ValueError("Output folder is not set")
image_files = DiskImageFileStorage(f"{output_folder}/images")
db = init_db(config=config, logger=logger, image_files=image_files)
@ -80,26 +82,26 @@ class ApiDependencies:
board_records = SqliteBoardRecordStorage(db=db)
boards = BoardService()
events = FastAPIEventService(event_handler_id)
graph_execution_manager = ItemStorageMemory[GraphExecutionState]()
bulk_download = BulkDownloadService()
image_records = SqliteImageRecordStorage(db=db)
images = ImageService()
invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
model_manager = ModelManagerService(config, logger)
model_record_service = ModelRecordServiceSQL(db=db)
tensors = ObjectSerializerForwardCache(
ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
)
conditioning = ObjectSerializerForwardCache(
ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
)
download_queue_service = DownloadQueueService(event_bus=events)
metadata_store = ModelMetadataStore(db=db)
model_install_service = ModelInstallService(
app_config=config,
record_store=model_record_service,
model_metadata_service = ModelMetadataStoreSQL(db=db)
model_manager = ModelManagerService.build_model_manager(
app_config=configuration,
model_record_service=ModelRecordServiceSQL(db=db, metadata_store=model_metadata_service),
download_queue=download_queue_service,
metadata_store=metadata_store,
event_bus=events,
events=events,
)
names = SimpleNameService()
performance_statistics = InvocationStatsService()
processor = DefaultInvocationProcessor()
queue = MemoryInvocationQueue()
session_processor = DefaultSessionProcessor()
session_queue = SqliteSessionQueue(db=db)
urls = LocalUrlService()
@ -110,27 +112,24 @@ class ApiDependencies:
board_images=board_images,
board_records=board_records,
boards=boards,
bulk_download=bulk_download,
configuration=configuration,
events=events,
graph_execution_manager=graph_execution_manager,
image_files=image_files,
image_records=image_records,
images=images,
invocation_cache=invocation_cache,
latents=latents,
logger=logger,
model_manager=model_manager,
model_records=model_record_service,
download_queue=download_queue_service,
model_install=model_install_service,
names=names,
performance_statistics=performance_statistics,
processor=processor,
queue=queue,
session_processor=session_processor,
session_queue=session_queue,
urls=urls,
workflow_records=workflow_records,
tensors=tensors,
conditioning=conditioning,
)
ApiDependencies.invoker = Invoker(services)

8
invokeai/app/api/routers/download_queue.py
View File

@ -36,7 +36,7 @@ async def list_downloads() -> List[DownloadJob]:
400: {"description": "Bad request"},
},
)
async def prune_downloads():
async def prune_downloads() -> Response:
"""Prune completed and errored jobs."""
queue = ApiDependencies.invoker.services.download_queue
queue.prune_jobs()
@ -55,7 +55,7 @@ async def download(
) -> DownloadJob:
"""Download the source URL to the file or directory indicted in dest."""
queue = ApiDependencies.invoker.services.download_queue
return queue.download(source, dest, priority, access_token)
return queue.download(source, Path(dest), priority, access_token)
@download_queue_router.get(
@ -87,7 +87,7 @@ async def get_download_job(
)
async def cancel_download_job(
id: int = Path(description="ID of the download job to cancel."),
):
) -> Response:
"""Cancel a download job using its ID."""
try:
queue = ApiDependencies.invoker.services.download_queue
@ -105,7 +105,7 @@ async def cancel_download_job(
204: {"description": "Download jobs have been cancelled"},
},
)
async def cancel_all_download_jobs():
async def cancel_all_download_jobs() -> Response:
"""Cancel all download jobs."""
ApiDependencies.invoker.services.download_queue.cancel_all_jobs()
return Response(status_code=204)

67
invokeai/app/api/routers/images.py
View File

@ -2,13 +2,13 @@ import io
import traceback
from typing import Optional
from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request, Response, UploadFile
from fastapi.responses import FileResponse
from fastapi.routing import APIRouter
from PIL import Image
from pydantic import BaseModel, Field, ValidationError
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
@ -375,16 +375,67 @@ async def unstar_images_in_list(
class ImagesDownloaded(BaseModel):
response: Optional[str] = Field(
description="If defined, the message to display to the user when images begin downloading"
default=None, description="The message to display to the user when images begin downloading"
)
bulk_download_item_name: Optional[str] = Field(
default=None, description="The name of the bulk download item for which events will be emitted"
)
@images_router.post("/download", operation_id="download_images_from_list", response_model=ImagesDownloaded)
@images_router.post(
"/download", operation_id="download_images_from_list", response_model=ImagesDownloaded, status_code=202
)
async def download_images_from_list(
image_names: list[str] = Body(description="The list of names of images to download", embed=True),
background_tasks: BackgroundTasks,
image_names: Optional[list[str]] = Body(
default=None, description="The list of names of images to download", embed=True
),
board_id: Optional[str] = Body(
default=None, description="The board from which image should be downloaded from", embed=True
default=None, description="The board from which image should be downloaded", embed=True
),
) -> ImagesDownloaded:
# return ImagesDownloaded(response="Your images are downloading")
raise HTTPException(status_code=501, detail="Endpoint is not yet implemented")
if (image_names is None or len(image_names) == 0) and board_id is None:
raise HTTPException(status_code=400, detail="No images or board id specified.")
bulk_download_item_id: str = ApiDependencies.invoker.services.bulk_download.generate_item_id(board_id)
background_tasks.add_task(
ApiDependencies.invoker.services.bulk_download.handler,
image_names,
board_id,
bulk_download_item_id,
)
return ImagesDownloaded(bulk_download_item_name=bulk_download_item_id + ".zip")
@images_router.api_route(
"/download/{bulk_download_item_name}",
methods=["GET"],
operation_id="get_bulk_download_item",
response_class=Response,
responses={
200: {
"description": "Return the complete bulk download item",
"content": {"application/zip": {}},
},
404: {"description": "Image not found"},
},
)
async def get_bulk_download_item(
background_tasks: BackgroundTasks,
bulk_download_item_name: str = Path(description="The bulk_download_item_name of the bulk download item to get"),
) -> FileResponse:
"""Gets a bulk download zip file"""
try:
path = ApiDependencies.invoker.services.bulk_download.get_path(bulk_download_item_name)
response = FileResponse(
path,
media_type="application/zip",
filename=bulk_download_item_name,
content_disposition_type="inline",
)
response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
background_tasks.add_task(ApiDependencies.invoker.services.bulk_download.delete, bulk_download_item_name)
return response
except Exception:
raise HTTPException(status_code=404)

751
invokeai/app/api/routers/model_manager.py Normal file
View File

@ -0,0 +1,751 @@
# Copyright (c) 2023 Lincoln D. Stein
"""FastAPI route for model configuration records."""
import pathlib
import shutil
from hashlib import sha1
from random import randbytes
from typing import Any, Dict, List, Optional, Set
from fastapi import Body, Path, Query, Response
from fastapi.routing import APIRouter
from pydantic import BaseModel, ConfigDict, Field
from starlette.exceptions import HTTPException
from typing_extensions import Annotated
from invokeai.app.services.model_install import ModelInstallJob
from invokeai.app.services.model_records import (
DuplicateModelException,
InvalidModelException,
ModelRecordOrderBy,
ModelSummary,
UnknownModelException,
)
from invokeai.app.services.shared.pagination import PaginatedResults
from invokeai.backend.model_manager.config import (
AnyModelConfig,
BaseModelType,
MainCheckpointConfig,
ModelFormat,
ModelType,
SubModelType,
)
from invokeai.backend.model_manager.merge import MergeInterpolationMethod, ModelMerger
from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
from invokeai.backend.model_manager.search import ModelSearch
from ..dependencies import ApiDependencies
model_manager_router = APIRouter(prefix="/v2/models", tags=["model_manager"])
class ModelsList(BaseModel):
"""Return list of configs."""
models: List[AnyModelConfig]
model_config = ConfigDict(use_enum_values=True)
class ModelTagSet(BaseModel):
"""Return tags for a set of models."""
key: str
name: str
author: str
tags: Set[str]
##############################################################################
# These are example inputs and outputs that are used in places where Swagger
# is unable to generate a correct example.
##############################################################################
example_model_config = {
"path": "string",
"name": "string",
"base": "sd-1",
"type": "main",
"format": "checkpoint",
"config": "string",
"key": "string",
"original_hash": "string",
"current_hash": "string",
"description": "string",
"source": "string",
"last_modified": 0,
"vae": "string",
"variant": "normal",
"prediction_type": "epsilon",
"repo_variant": "fp16",
"upcast_attention": False,
"ztsnr_training": False,
}
example_model_input = {
"path": "/path/to/model",
"name": "model_name",
"base": "sd-1",
"type": "main",
"format": "checkpoint",
"config": "configs/stable-diffusion/v1-inference.yaml",
"description": "Model description",
"vae": None,
"variant": "normal",
}
example_model_metadata = {
"name": "ip_adapter_sd_image_encoder",
"author": "InvokeAI",
"tags": [
"transformers",
"safetensors",
"clip_vision_model",
"endpoints_compatible",
"region:us",
"has_space",
"license:apache-2.0",
],
"files": [
{
"url": "https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/README.md",
"path": "ip_adapter_sd_image_encoder/README.md",
"size": 628,
"sha256": None,
},
{
"url": "https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/config.json",
"path": "ip_adapter_sd_image_encoder/config.json",
"size": 560,
"sha256": None,
},
{
"url": "https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/model.safetensors",
"path": "ip_adapter_sd_image_encoder/model.safetensors",
"size": 2528373448,
"sha256": "6ca9667da1ca9e0b0f75e46bb030f7e011f44f86cbfb8d5a36590fcd7507b030",
},
],
"type": "huggingface",
"id": "InvokeAI/ip_adapter_sd_image_encoder",
"tag_dict": {"license": "apache-2.0"},
"last_modified": "2023-09-23T17:33:25Z",
}
##############################################################################
# ROUTES
##############################################################################
@model_manager_router.get(
"/",
operation_id="list_model_records",
)
async def list_model_records(
base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
model_name: Optional[str] = Query(default=None, description="Exact match on the name of the model"),
model_format: Optional[ModelFormat] = Query(
default=None, description="Exact match on the format of the model (e.g. 'diffusers')"
),
) -> ModelsList:
"""Get a list of models."""
record_store = ApiDependencies.invoker.services.model_manager.store
found_models: list[AnyModelConfig] = []
if base_models:
for base_model in base_models:
found_models.extend(
record_store.search_by_attr(
base_model=base_model, model_type=model_type, model_name=model_name, model_format=model_format
)
)
else:
found_models.extend(
record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
)
return ModelsList(models=found_models)
@model_manager_router.get(
"/get_by_attrs",
operation_id="get_model_records_by_attrs",
response_model=AnyModelConfig,
)
async def get_model_records_by_attrs(
name: str = Query(description="The name of the model"),
type: ModelType = Query(description="The type of the model"),
base: BaseModelType = Query(description="The base model of the model"),
) -> AnyModelConfig:
"""Gets a model by its attributes. The main use of this route is to provide backwards compatibility with the old
model manager, which identified models by a combination of name, base and type."""
configs = ApiDependencies.invoker.services.model_manager.store.search_by_attr(
base_model=base, model_type=type, model_name=name
)
if not configs:
raise HTTPException(status_code=404, detail="No model found with these attributes")
return configs[0]
@model_manager_router.get(
"/i/{key}",
operation_id="get_model_record",
responses={
200: {
"description": "The model configuration was retrieved successfully",
"content": {"application/json": {"example": example_model_config}},
},
400: {"description": "Bad request"},
404: {"description": "The model could not be found"},
},
)
async def get_model_record(
key: str = Path(description="Key of the model record to fetch."),
) -> AnyModelConfig:
"""Get a model record"""
record_store = ApiDependencies.invoker.services.model_manager.store
try:
config: AnyModelConfig = record_store.get_model(key)
return config
except UnknownModelException as e:
raise HTTPException(status_code=404, detail=str(e))
@model_manager_router.get("/summary", operation_id="list_model_summary")
async def list_model_summary(
page: int = Query(default=0, description="The page to get"),
per_page: int = Query(default=10, description="The number of models per page"),
order_by: ModelRecordOrderBy = Query(default=ModelRecordOrderBy.Default, description="The attribute to order by"),
) -> PaginatedResults[ModelSummary]:
"""Gets a page of model summary data."""
record_store = ApiDependencies.invoker.services.model_manager.store
results: PaginatedResults[ModelSummary] = record_store.list_models(page=page, per_page=per_page, order_by=order_by)
return results
@model_manager_router.get(
"/i/{key}/metadata",
operation_id="get_model_metadata",
responses={
200: {
"description": "The model metadata was retrieved successfully",
"content": {"application/json": {"example": example_model_metadata}},
},
400: {"description": "Bad request"},
},
)
async def get_model_metadata(
key: str = Path(description="Key of the model repo metadata to fetch."),
) -> Optional[AnyModelRepoMetadata]:
"""Get a model metadata object."""
record_store = ApiDependencies.invoker.services.model_manager.store
result: Optional[AnyModelRepoMetadata] = record_store.get_metadata(key)
return result
@model_manager_router.get(
"/tags",
operation_id="list_tags",
)
async def list_tags() -> Set[str]:
"""Get a unique set of all the model tags."""
record_store = ApiDependencies.invoker.services.model_manager.store
result: Set[str] = record_store.list_tags()
return result
class FoundModel(BaseModel):
path: str = Field(description="Path to the model")
is_installed: bool = Field(description="Whether or not the model is already installed")
@model_manager_router.get(
"/scan_folder",
operation_id="scan_for_models",
responses={
200: {"description": "Directory scanned successfully"},
400: {"description": "Invalid directory path"},
},
status_code=200,
response_model=List[FoundModel],
)
async def scan_for_models(
scan_path: str = Query(description="Directory path to search for models", default=None),
) -> List[FoundModel]:
path = pathlib.Path(scan_path)
if not scan_path or not path.is_dir():
raise HTTPException(
status_code=400,
detail=f"The search path '{scan_path}' does not exist or is not directory",
)
search = ModelSearch()
try:
found_model_paths = search.search(path)
models_path = ApiDependencies.invoker.services.configuration.models_path
# If the search path includes the main models directory, we need to exclude core models from the list.
# TODO(MM2): Core models should be handled by the model manager so we can determine if they are installed
# without needing to crawl the filesystem.
core_models_path = pathlib.Path(models_path, "core").resolve()
non_core_model_paths = [p for p in found_model_paths if not p.is_relative_to(core_models_path)]
installed_models = ApiDependencies.invoker.services.model_manager.store.search_by_attr()
resolved_installed_model_paths: list[str] = []
installed_model_sources: list[str] = []
# This call lists all installed models.
for model in installed_models:
path = pathlib.Path(model.path)
# If the model has a source, we need to add it to the list of installed sources.
if model.source:
installed_model_sources.append(model.source)
# If the path is not absolute, that means it is in the app models directory, and we need to join it with
# the models path before resolving.
if not path.is_absolute():
resolved_installed_model_paths.append(str(pathlib.Path(models_path, path).resolve()))
continue
resolved_installed_model_paths.append(str(path.resolve()))
scan_results: list[FoundModel] = []
# Check if the model is installed by comparing the resolved paths, appending to the scan result.
for p in non_core_model_paths:
path = str(p)
is_installed = path in resolved_installed_model_paths or path in installed_model_sources
found_model = FoundModel(path=path, is_installed=is_installed)
scan_results.append(found_model)
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"An error occurred while searching the directory: {e}",
)
return scan_results
@model_manager_router.get(
"/tags/search",
operation_id="search_by_metadata_tags",
)
async def search_by_metadata_tags(
tags: Set[str] = Query(default=None, description="Tags to search for"),
) -> ModelsList:
"""Get a list of models."""
record_store = ApiDependencies.invoker.services.model_manager.store
results = record_store.search_by_metadata_tag(tags)
return ModelsList(models=results)
@model_manager_router.patch(
"/i/{key}",
operation_id="update_model_record",
responses={
200: {
"description": "The model was updated successfully",
"content": {"application/json": {"example": example_model_config}},
},
400: {"description": "Bad request"},
404: {"description": "The model could not be found"},
409: {"description": "There is already a model corresponding to the new name"},
},
status_code=200,
)
async def update_model_record(
key: Annotated[str, Path(description="Unique key of model")],
info: Annotated[
AnyModelConfig, Body(description="Model config", discriminator="type", example=example_model_input)
],
) -> AnyModelConfig:
"""Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
logger = ApiDependencies.invoker.services.logger
record_store = ApiDependencies.invoker.services.model_manager.store
try:
model_response: AnyModelConfig = record_store.update_model(key, config=info)
logger.info(f"Updated model: {key}")
except UnknownModelException as e:
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
return model_response
@model_manager_router.delete(
"/i/{key}",
operation_id="del_model_record",
responses={
204: {"description": "Model deleted successfully"},
404: {"description": "Model not found"},
},
status_code=204,
)
async def del_model_record(
key: str = Path(description="Unique key of model to remove from model registry."),
) -> Response:
"""
Delete model record from database.
The configuration record will be removed. The corresponding weights files will be
deleted as well if they reside within the InvokeAI "models" directory.
"""
logger = ApiDependencies.invoker.services.logger
try:
installer = ApiDependencies.invoker.services.model_manager.install
installer.delete(key)
logger.info(f"Deleted model: {key}")
return Response(status_code=204)
except UnknownModelException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@model_manager_router.post(
"/i/",
operation_id="add_model_record",
responses={
201: {
"description": "The model added successfully",
"content": {"application/json": {"example": example_model_config}},
},
409: {"description": "There is already a model corresponding to this path or repo_id"},
415: {"description": "Unrecognized file/folder format"},
},
status_code=201,
)
async def add_model_record(
config: Annotated[
AnyModelConfig, Body(description="Model config", discriminator="type", example=example_model_input)
],
) -> AnyModelConfig:
"""Add a model using the configuration information appropriate for its type."""
logger = ApiDependencies.invoker.services.logger
record_store = ApiDependencies.invoker.services.model_manager.store
if config.key == "<NOKEY>":
config.key = sha1(randbytes(100)).hexdigest()
logger.info(f"Created model {config.key} for {config.name}")
try:
record_store.add_model(config.key, config)
except DuplicateModelException as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
except InvalidModelException as e:
logger.error(str(e))
raise HTTPException(status_code=415)
# now fetch it out
result: AnyModelConfig = record_store.get_model(config.key)
return result
@model_manager_router.post(
"/install",
operation_id="install_model",
responses={
201: {"description": "The model imported successfully"},
415: {"description": "Unrecognized file/folder format"},
424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
409: {"description": "There is already a model corresponding to this path or repo_id"},
},
status_code=201,
)
async def install_model(
source: str = Query(description="Model source to install, can be a local path, repo_id, or remote URL"),
# TODO(MM2): Can we type this?
config: Optional[Dict[str, Any]] = Body(
description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
default=None,
example={"name": "string", "description": "string"},
),
access_token: Optional[str] = None,
) -> ModelInstallJob:
"""Install a model using a string identifier.
`source` can be any of the following.
1. A path on the local filesystem ('C:\\users\\fred\\model.safetensors')
2. A Url pointing to a single downloadable model file
3. A HuggingFace repo_id with any of the following formats:
- model/name
- model/name:fp16:vae
- model/name::vae -- use default precision
- model/name:fp16:path/to/model.safetensors
- model/name::path/to/model.safetensors
`config` is an optional dict containing model configuration values that will override
the ones that are probed automatically.
`access_token` is an optional access token for use with Urls that require
authentication.
Models will be downloaded, probed, configured and installed in a
series of background threads. The return object has `status` attribute
that can be used to monitor progress.
See the documentation for `import_model_record` for more information on
interpreting the job information returned by this route.
"""
logger = ApiDependencies.invoker.services.logger
try:
installer = ApiDependencies.invoker.services.model_manager.install
result: ModelInstallJob = installer.heuristic_import(
source=source,
config=config,
access_token=access_token,
)
logger.info(f"Started installation of {source}")
except UnknownModelException as e:
logger.error(str(e))
raise HTTPException(status_code=424, detail=str(e))
except InvalidModelException as e:
logger.error(str(e))
raise HTTPException(status_code=415)
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
return result
@model_manager_router.get(
"/import",
operation_id="list_model_install_jobs",
)
async def list_model_install_jobs() -> List[ModelInstallJob]:
"""Return the list of model install jobs.
Install jobs have a numeric `id`, a `status`, and other fields that provide information on
the nature of the job and its progress. The `status` is one of:
* "waiting" -- Job is waiting in the queue to run
* "downloading" -- Model file(s) are downloading
* "running" -- Model has downloaded and the model probing and registration process is running
* "completed" -- Installation completed successfully
* "error" -- An error occurred. Details will be in the "error_type" and "error" fields.
* "cancelled" -- Job was cancelled before completion.
Once completed, information about the model such as its size, base
model, type, and metadata can be retrieved from the `config_out`
field. For multi-file models such as diffusers, information on individual files
can be retrieved from `download_parts`.
See the example and schema below for more information.
"""
jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_manager.install.list_jobs()
return jobs
@model_manager_router.get(
"/import/{id}",
operation_id="get_model_install_job",
responses={
200: {"description": "Success"},
404: {"description": "No such job"},
},
)
async def get_model_install_job(id: int = Path(description="Model install id")) -> ModelInstallJob:
"""
Return model install job corresponding to the given source. See the documentation for 'List Model Install Jobs'
for information on the format of the return value.
"""
try:
result: ModelInstallJob = ApiDependencies.invoker.services.model_manager.install.get_job_by_id(id)
return result
except ValueError as e:
raise HTTPException(status_code=404, detail=str(e))
@model_manager_router.delete(
"/import/{id}",
operation_id="cancel_model_install_job",
responses={
201: {"description": "The job was cancelled successfully"},
415: {"description": "No such job"},
},
status_code=201,
)
async def cancel_model_install_job(id: int = Path(description="Model install job ID")) -> None:
"""Cancel the model install job(s) corresponding to the given job ID."""
installer = ApiDependencies.invoker.services.model_manager.install
try:
job = installer.get_job_by_id(id)
except ValueError as e:
raise HTTPException(status_code=415, detail=str(e))
installer.cancel_job(job)
@model_manager_router.patch(
"/import",
operation_id="prune_model_install_jobs",
responses={
204: {"description": "All completed and errored jobs have been pruned"},
400: {"description": "Bad request"},
},
)
async def prune_model_install_jobs() -> Response:
"""Prune all completed and errored jobs from the install job list."""
ApiDependencies.invoker.services.model_manager.install.prune_jobs()
return Response(status_code=204)
@model_manager_router.patch(
"/sync",
operation_id="sync_models_to_config",
responses={
204: {"description": "Model config record database resynced with files on disk"},
400: {"description": "Bad request"},
},
)
async def sync_models_to_config() -> Response:
"""
Traverse the models and autoimport directories.
Model files without a corresponding
record in the database are added. Orphan records without a models file are deleted.
"""
ApiDependencies.invoker.services.model_manager.install.sync_to_config()
return Response(status_code=204)
@model_manager_router.put(
"/convert/{key}",
operation_id="convert_model",
responses={
200: {
"description": "Model converted successfully",
"content": {"application/json": {"example": example_model_config}},
},
400: {"description": "Bad request"},
404: {"description": "Model not found"},
409: {"description": "There is already a model registered at this location"},
},
)
async def convert_model(
key: str = Path(description="Unique key of the safetensors main model to convert to diffusers format."),
) -> AnyModelConfig:
"""
Permanently convert a model into diffusers format, replacing the safetensors version.
Note that during the conversion process the key and model hash will change.
The return value is the model configuration for the converted model.
"""
model_manager = ApiDependencies.invoker.services.model_manager
logger = ApiDependencies.invoker.services.logger
loader = ApiDependencies.invoker.services.model_manager.load
store = ApiDependencies.invoker.services.model_manager.store
installer = ApiDependencies.invoker.services.model_manager.install
try:
model_config = store.get_model(key)
except UnknownModelException as e:
logger.error(str(e))
raise HTTPException(status_code=424, detail=str(e))
if not isinstance(model_config, MainCheckpointConfig):
logger.error(f"The model with key {key} is not a main checkpoint model.")
raise HTTPException(400, f"The model with key {key} is not a main checkpoint model.")
# loading the model will convert it into a cached diffusers file
model_manager.load_model_by_config(model_config, submodel_type=SubModelType.Scheduler)
# Get the path of the converted model from the loader
cache_path = loader.convert_cache.cache_path(key)
assert cache_path.exists()
# temporarily rename the original safetensors file so that there is no naming conflict
original_name = model_config.name
model_config.name = f"{original_name}.DELETE"
store.update_model(key, config=model_config)
# install the diffusers
try:
new_key = installer.install_path(
cache_path,
config={
"name": original_name,
"description": model_config.description,
"original_hash": model_config.original_hash,
"source": model_config.source,
},
)
except DuplicateModelException as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
# get the original metadata
if orig_metadata := store.get_metadata(key):
store.metadata_store.add_metadata(new_key, orig_metadata)
# delete the original safetensors file
installer.delete(key)
# delete the cached version
shutil.rmtree(cache_path)
# return the config record for the new diffusers directory
new_config: AnyModelConfig = store.get_model(new_key)
return new_config
@model_manager_router.put(
"/merge",
operation_id="merge",
responses={
200: {
"description": "Model converted successfully",
"content": {"application/json": {"example": example_model_config}},
},
400: {"description": "Bad request"},
404: {"description": "Model not found"},
409: {"description": "There is already a model registered at this location"},
},
)
async def merge(
keys: List[str] = Body(description="Keys for two to three models to merge", min_length=2, max_length=3),
merged_model_name: Optional[str] = Body(description="Name of destination model", default=None),
alpha: float = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
force: bool = Body(
description="Force merging of models created with different versions of diffusers",
default=False,
),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method", default=None),
merge_dest_directory: Optional[str] = Body(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
),
) -> AnyModelConfig:
"""
Merge diffusers models. The process is controlled by a set parameters provided in the body of the request.
```
Argument Description [default]
-------- ----------------------
keys List of 2-3 model keys to merge together. All models must use the same base type.
merged_model_name Name for the merged model [Concat model names]
alpha Alpha value (0.0-1.0). Higher values give more weight to the second model [0.5]
force If true, force the merge even if the models were generated by different versions of the diffusers library [False]
interp Interpolation method. One of "weighted_sum", "sigmoid", "inv_sigmoid" or "add_difference" [weighted_sum]
merge_dest_directory Specify a directory to store the merged model in [models directory]
```
"""
logger = ApiDependencies.invoker.services.logger
try:
logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
installer = ApiDependencies.invoker.services.model_manager.install
merger = ModelMerger(installer)
model_names = [installer.record_store.get_model(x).name for x in keys]
response = merger.merge_diffusion_models_and_save(
model_keys=keys,
merged_model_name=merged_model_name or "+".join(model_names),
alpha=alpha,
interp=interp,
force=force,
merge_dest_directory=dest,
)
except UnknownModelException:
raise HTTPException(
status_code=404,
detail=f"One or more of the models '{keys}' not found",
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
return response

472
invokeai/app/api/routers/model_records.py
View File

@ -1,472 +0,0 @@
# Copyright (c) 2023 Lincoln D. Stein
"""FastAPI route for model configuration records."""
import pathlib
from hashlib import sha1
from random import randbytes
from typing import Any, Dict, List, Optional, Set
from fastapi import Body, Path, Query, Response
from fastapi.routing import APIRouter
from pydantic import BaseModel, ConfigDict
from starlette.exceptions import HTTPException
from typing_extensions import Annotated
from invokeai.app.services.model_install import ModelInstallJob, ModelSource
from invokeai.app.services.model_records import (
DuplicateModelException,
InvalidModelException,
ModelRecordOrderBy,
ModelSummary,
UnknownModelException,
)
from invokeai.app.services.shared.pagination import PaginatedResults
from invokeai.backend.model_manager.config import (
AnyModelConfig,
BaseModelType,
ModelFormat,
ModelType,
)
from invokeai.backend.model_manager.merge import MergeInterpolationMethod, ModelMerger
from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
from ..dependencies import ApiDependencies
model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager_v2_unstable"])
class ModelsList(BaseModel):
"""Return list of configs."""
models: List[AnyModelConfig]
model_config = ConfigDict(use_enum_values=True)
class ModelTagSet(BaseModel):
"""Return tags for a set of models."""
key: str
name: str
author: str
tags: Set[str]
@model_records_router.get(
"/",
operation_id="list_model_records",
)
async def list_model_records(
base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
model_name: Optional[str] = Query(default=None, description="Exact match on the name of the model"),
model_format: Optional[ModelFormat] = Query(
default=None, description="Exact match on the format of the model (e.g. 'diffusers')"
),
) -> ModelsList:
"""Get a list of models."""
record_store = ApiDependencies.invoker.services.model_records
found_models: list[AnyModelConfig] = []
if base_models:
for base_model in base_models:
found_models.extend(
record_store.search_by_attr(
base_model=base_model, model_type=model_type, model_name=model_name, model_format=model_format
)
)
else:
found_models.extend(
record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
)
return ModelsList(models=found_models)
@model_records_router.get(
"/i/{key}",
operation_id="get_model_record",
responses={
200: {"description": "Success"},
400: {"description": "Bad request"},
404: {"description": "The model could not be found"},
},
)
async def get_model_record(
key: str = Path(description="Key of the model record to fetch."),
) -> AnyModelConfig:
"""Get a model record"""
record_store = ApiDependencies.invoker.services.model_records
try:
return record_store.get_model(key)
except UnknownModelException as e:
raise HTTPException(status_code=404, detail=str(e))
@model_records_router.get("/meta", operation_id="list_model_summary")
async def list_model_summary(
page: int = Query(default=0, description="The page to get"),
per_page: int = Query(default=10, description="The number of models per page"),
order_by: ModelRecordOrderBy = Query(default=ModelRecordOrderBy.Default, description="The attribute to order by"),
) -> PaginatedResults[ModelSummary]:
"""Gets a page of model summary data."""
return ApiDependencies.invoker.services.model_records.list_models(page=page, per_page=per_page, order_by=order_by)
@model_records_router.get(
"/meta/i/{key}",
operation_id="get_model_metadata",
responses={
200: {"description": "Success"},
400: {"description": "Bad request"},
404: {"description": "No metadata available"},
},
)
async def get_model_metadata(
key: str = Path(description="Key of the model repo metadata to fetch."),
) -> Optional[AnyModelRepoMetadata]:
"""Get a model metadata object."""
record_store = ApiDependencies.invoker.services.model_records
result = record_store.get_metadata(key)
if not result:
raise HTTPException(status_code=404, detail="No metadata for a model with this key")
return result
@model_records_router.get(
"/tags",
operation_id="list_tags",
)
async def list_tags() -> Set[str]:
"""Get a unique set of all the model tags."""
record_store = ApiDependencies.invoker.services.model_records
return record_store.list_tags()
@model_records_router.get(
"/tags/search",
operation_id="search_by_metadata_tags",
)
async def search_by_metadata_tags(
tags: Set[str] = Query(default=None, description="Tags to search for"),
) -> ModelsList:
"""Get a list of models."""
record_store = ApiDependencies.invoker.services.model_records
results = record_store.search_by_metadata_tag(tags)
return ModelsList(models=results)
@model_records_router.patch(
"/i/{key}",
operation_id="update_model_record",
responses={
200: {"description": "The model was updated successfully"},
400: {"description": "Bad request"},
404: {"description": "The model could not be found"},
409: {"description": "There is already a model corresponding to the new name"},
},
status_code=200,
response_model=AnyModelConfig,
)
async def update_model_record(
key: Annotated[str, Path(description="Unique key of model")],
info: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
) -> AnyModelConfig:
"""Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
logger = ApiDependencies.invoker.services.logger
record_store = ApiDependencies.invoker.services.model_records
try:
model_response = record_store.update_model(key, config=info)
logger.info(f"Updated model: {key}")
except UnknownModelException as e:
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
return model_response
@model_records_router.delete(
"/i/{key}",
operation_id="del_model_record",
responses={
204: {"description": "Model deleted successfully"},
404: {"description": "Model not found"},
},
status_code=204,
)
async def del_model_record(
key: str = Path(description="Unique key of model to remove from model registry."),
) -> Response:
"""
Delete model record from database.
The configuration record will be removed. The corresponding weights files will be
deleted as well if they reside within the InvokeAI "models" directory.
"""
logger = ApiDependencies.invoker.services.logger
try:
installer = ApiDependencies.invoker.services.model_install
installer.delete(key)
logger.info(f"Deleted model: {key}")
return Response(status_code=204)
except UnknownModelException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@model_records_router.post(
"/i/",
operation_id="add_model_record",
responses={
201: {"description": "The model added successfully"},
409: {"description": "There is already a model corresponding to this path or repo_id"},
415: {"description": "Unrecognized file/folder format"},
},
status_code=201,
)
async def add_model_record(
config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
) -> AnyModelConfig:
"""Add a model using the configuration information appropriate for its type."""
logger = ApiDependencies.invoker.services.logger
record_store = ApiDependencies.invoker.services.model_records
if config.key == "<NOKEY>":
config.key = sha1(randbytes(100)).hexdigest()
logger.info(f"Created model {config.key} for {config.name}")
try:
record_store.add_model(config.key, config)
except DuplicateModelException as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
except InvalidModelException as e:
logger.error(str(e))
raise HTTPException(status_code=415)
# now fetch it out
return record_store.get_model(config.key)
@model_records_router.post(
"/import",
operation_id="import_model_record",
responses={
201: {"description": "The model imported successfully"},
415: {"description": "Unrecognized file/folder format"},
424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
409: {"description": "There is already a model corresponding to this path or repo_id"},
},
status_code=201,
)
async def import_model(
source: ModelSource,
config: Optional[Dict[str, Any]] = Body(
description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
default=None,
),
) -> ModelInstallJob:
"""Add a model using its local path, repo_id, or remote URL.
Models will be downloaded, probed, configured and installed in a
series of background threads. The return object has `status` attribute
that can be used to monitor progress.
The source object is a discriminated Union of LocalModelSource,
HFModelSource and URLModelSource. Set the "type" field to the
appropriate value:
* To install a local path using LocalModelSource, pass a source of form:
`{
"type": "local",
"path": "/path/to/model",
"inplace": false
}`
The "inplace" flag, if true, will register the model in place in its
current filesystem location. Otherwise, the model will be copied
into the InvokeAI models directory.
* To install a HuggingFace repo_id using HFModelSource, pass a source of form:
`{
"type": "hf",
"repo_id": "stabilityai/stable-diffusion-2.0",
"variant": "fp16",
"subfolder": "vae",
"access_token": "f5820a918aaf01"
}`
The `variant`, `subfolder` and `access_token` fields are optional.
* To install a remote model using an arbitrary URL, pass:
`{
"type": "url",
"url": "http://www.civitai.com/models/123456",
"access_token": "f5820a918aaf01"
}`
The `access_token` field is optonal
The model's configuration record will be probed and filled in
automatically. To override the default guesses, pass "metadata"
with a Dict containing the attributes you wish to override.
Installation occurs in the background. Either use list_model_install_jobs()
to poll for completion, or listen on the event bus for the following events:
"model_install_running"
"model_install_completed"
"model_install_error"
On successful completion, the event's payload will contain the field "key"
containing the installed ID of the model. On an error, the event's payload
will contain the fields "error_type" and "error" describing the nature of the
error and its traceback, respectively.
"""
logger = ApiDependencies.invoker.services.logger
try:
installer = ApiDependencies.invoker.services.model_install
result: ModelInstallJob = installer.import_model(
source=source,
config=config,
)
logger.info(f"Started installation of {source}")
except UnknownModelException as e:
logger.error(str(e))
raise HTTPException(status_code=424, detail=str(e))
except InvalidModelException as e:
logger.error(str(e))
raise HTTPException(status_code=415)
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
return result
@model_records_router.get(
"/import",
operation_id="list_model_install_jobs",
)
async def list_model_install_jobs() -> List[ModelInstallJob]:
"""Return list of model install jobs."""
jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_install.list_jobs()
return jobs
@model_records_router.get(
"/import/{id}",
operation_id="get_model_install_job",
responses={
200: {"description": "Success"},
404: {"description": "No such job"},
},
)
async def get_model_install_job(id: int = Path(description="Model install id")) -> ModelInstallJob:
"""Return model install job corresponding to the given source."""
try:
return ApiDependencies.invoker.services.model_install.get_job_by_id(id)
except ValueError as e:
raise HTTPException(status_code=404, detail=str(e))
@model_records_router.delete(
"/import/{id}",
operation_id="cancel_model_install_job",
responses={
201: {"description": "The job was cancelled successfully"},
415: {"description": "No such job"},
},
status_code=201,
)
async def cancel_model_install_job(id: int = Path(description="Model install job ID")) -> None:
"""Cancel the model install job(s) corresponding to the given job ID."""
installer = ApiDependencies.invoker.services.model_install
try:
job = installer.get_job_by_id(id)
except ValueError as e:
raise HTTPException(status_code=415, detail=str(e))
installer.cancel_job(job)
@model_records_router.patch(
"/import",
operation_id="prune_model_install_jobs",
responses={
204: {"description": "All completed and errored jobs have been pruned"},
400: {"description": "Bad request"},
},
)
async def prune_model_install_jobs() -> Response:
"""Prune all completed and errored jobs from the install job list."""
ApiDependencies.invoker.services.model_install.prune_jobs()
return Response(status_code=204)
@model_records_router.patch(
"/sync",
operation_id="sync_models_to_config",
responses={
204: {"description": "Model config record database resynced with files on disk"},
400: {"description": "Bad request"},
},
)
async def sync_models_to_config() -> Response:
"""
Traverse the models and autoimport directories.
Model files without a corresponding
record in the database are added. Orphan records without a models file are deleted.
"""
ApiDependencies.invoker.services.model_install.sync_to_config()
return Response(status_code=204)
@model_records_router.put(
"/merge",
operation_id="merge",
)
async def merge(
keys: List[str] = Body(description="Keys for two to three models to merge", min_length=2, max_length=3),
merged_model_name: Optional[str] = Body(description="Name of destination model", default=None),
alpha: float = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
force: bool = Body(
description="Force merging of models created with different versions of diffusers",
default=False,
),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method", default=None),
merge_dest_directory: Optional[str] = Body(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
),
) -> AnyModelConfig:
"""
Merge diffusers models.
keys: List of 2-3 model keys to merge together. All models must use the same base type.
merged_model_name: Name for the merged model [Concat model names]
alpha: Alpha value (0.0-1.0). Higher values give more weight to the second model [0.5]
force: If true, force the merge even if the models were generated by different versions of the diffusers library [False]
interp: Interpolation method. One of "weighted_sum", "sigmoid", "inv_sigmoid" or "add_difference" [weighted_sum]
merge_dest_directory: Specify a directory to store the merged model in [models directory]
"""
print(f"here i am, keys={keys}")
logger = ApiDependencies.invoker.services.logger
try:
logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
installer = ApiDependencies.invoker.services.model_install
merger = ModelMerger(installer)
model_names = [installer.record_store.get_model(x).name for x in keys]
response = merger.merge_diffusion_models_and_save(
model_keys=keys,
merged_model_name=merged_model_name or "+".join(model_names),
alpha=alpha,
interp=interp,
force=force,
merge_dest_directory=dest,
)
except UnknownModelException:
raise HTTPException(
status_code=404,
detail=f"One or more of the models '{keys}' not found",
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
return response

427
invokeai/app/api/routers/models.py
View File

@ -1,427 +0,0 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2023 Lincoln D. Stein
import pathlib
from typing import Annotated, List, Literal, Optional, Union
from fastapi import Body, Path, Query, Response
from fastapi.routing import APIRouter
from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
from starlette.exceptions import HTTPException
from invokeai.backend import BaseModelType, ModelType
from invokeai.backend.model_management import MergeInterpolationMethod
from invokeai.backend.model_management.models import (
OPENAPI_MODEL_CONFIGS,
InvalidModelException,
ModelNotFoundException,
SchedulerPredictionType,
)
from ..dependencies import ApiDependencies
models_router = APIRouter(prefix="/v1/models", tags=["models"])
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
class ModelsList(BaseModel):
models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
model_config = ConfigDict(use_enum_values=True)
ModelsListValidator = TypeAdapter(ModelsList)
@models_router.get(
"/",
operation_id="list_models",
responses={200: {"model": ModelsList}},
)
async def list_models(
base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
) -> ModelsList:
"""Gets a list of models"""
if base_models and len(base_models) > 0:
models_raw = []
for base_model in base_models:
models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
else:
models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
models = ModelsListValidator.validate_python({"models": models_raw})
return models
@models_router.patch(
"/{base_model}/{model_type}/{model_name}",
operation_id="update_model",
responses={
200: {"description": "The model was updated successfully"},
400: {"description": "Bad request"},
404: {"description": "The model could not be found"},
409: {"description": "There is already a model corresponding to the new name"},
},
status_code=200,
response_model=UpdateModelResponse,
)
async def update_model(
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
) -> UpdateModelResponse:
"""Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
logger = ApiDependencies.invoker.services.logger
try:
previous_info = ApiDependencies.invoker.services.model_manager.list_model(
model_name=model_name,
base_model=base_model,
model_type=model_type,
)
# rename operation requested
if info.model_name != model_name or info.base_model != base_model:
ApiDependencies.invoker.services.model_manager.rename_model(
base_model=base_model,
model_type=model_type,
model_name=model_name,
new_name=info.model_name,
new_base=info.base_model,
)
logger.info(f"Successfully renamed {base_model.value}/{model_name}=>{info.base_model}/{info.model_name}")
# update information to support an update of attributes
model_name = info.model_name
base_model = info.base_model
new_info = ApiDependencies.invoker.services.model_manager.list_model(
model_name=model_name,
base_model=base_model,
model_type=model_type,
)
if new_info.get("path") != previous_info.get(
"path"
): # model manager moved model path during rename - don't overwrite it
info.path = new_info.get("path")
# replace empty string values with None/null to avoid phenomenon of vae: ''
info_dict = info.model_dump()
info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
ApiDependencies.invoker.services.model_manager.update_model(
model_name=model_name,
base_model=base_model,
model_type=model_type,
model_attributes=info_dict,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=model_name,
base_model=base_model,
model_type=model_type,
)
model_response = UpdateModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
except Exception as e:
logger.error(str(e))
raise HTTPException(status_code=400, detail=str(e))
return model_response
@models_router.post(
"/import",
operation_id="import_model",
responses={
201: {"description": "The model imported successfully"},
404: {"description": "The model could not be found"},
415: {"description": "Unrecognized file/folder format"},
424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
409: {"description": "There is already a model corresponding to this path or repo_id"},
},
status_code=201,
response_model=ImportModelResponse,
)
async def import_model(
location: str = Body(description="A model path, repo_id or URL to import"),
prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
description="Prediction type for SDv2 checkpoints and rare SDv1 checkpoints",
default=None,
),
) -> ImportModelResponse:
"""Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
location = location.strip("\"' ")
items_to_import = {location}
prediction_types = {x.value: x for x in SchedulerPredictionType}
logger = ApiDependencies.invoker.services.logger
try:
installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
items_to_import=items_to_import,
prediction_type_helper=lambda x: prediction_types.get(prediction_type),
)
info = installed_models.get(location)
if not info:
logger.error("Import failed")
raise HTTPException(status_code=415)
logger.info(f"Successfully imported {location}, got {info}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.name, base_model=info.base_model, model_type=info.model_type
)
return ImportModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
except InvalidModelException as e:
logger.error(str(e))
raise HTTPException(status_code=415)
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
@models_router.post(
"/add",
operation_id="add_model",
responses={
201: {"description": "The model added successfully"},
404: {"description": "The model could not be found"},
424: {"description": "The model appeared to add successfully, but could not be found in the model manager"},
409: {"description": "There is already a model corresponding to this path or repo_id"},
},
status_code=201,
response_model=ImportModelResponse,
)
async def add_model(
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
) -> ImportModelResponse:
"""Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
logger = ApiDependencies.invoker.services.logger
try:
ApiDependencies.invoker.services.model_manager.add_model(
info.model_name,
info.base_model,
info.model_type,
model_attributes=info.model_dump(),
)
logger.info(f"Successfully added {info.model_name}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.model_name,
base_model=info.base_model,
model_type=info.model_type,
)
return ImportModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
@models_router.delete(
"/{base_model}/{model_type}/{model_name}",
operation_id="del_model",
responses={
204: {"description": "Model deleted successfully"},
404: {"description": "Model not found"},
},
status_code=204,
response_model=None,
)
async def delete_model(
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
) -> Response:
"""Delete Model"""
logger = ApiDependencies.invoker.services.logger
try:
ApiDependencies.invoker.services.model_manager.del_model(
model_name, base_model=base_model, model_type=model_type
)
logger.info(f"Deleted model: {model_name}")
return Response(status_code=204)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@models_router.put(
"/convert/{base_model}/{model_type}/{model_name}",
operation_id="convert_model",
responses={
200: {"description": "Model converted successfully"},
400: {"description": "Bad request"},
404: {"description": "Model not found"},
},
status_code=200,
response_model=ConvertModelResponse,
)
async def convert_model(
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
convert_dest_directory: Optional[str] = Query(
default=None, description="Save the converted model to the designated directory"
),
) -> ConvertModelResponse:
"""Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
logger = ApiDependencies.invoker.services.logger
try:
logger.info(f"Converting model: {model_name}")
dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
ApiDependencies.invoker.services.model_manager.convert_model(
model_name,
base_model=base_model,
model_type=model_type,
convert_dest_directory=dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name, base_model=base_model, model_type=model_type
)
response = ConvertModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
return response
@models_router.get(
"/search",
operation_id="search_for_models",
responses={
200: {"description": "Directory searched successfully"},
404: {"description": "Invalid directory path"},
},
status_code=200,
response_model=List[pathlib.Path],
)
async def search_for_models(
search_path: pathlib.Path = Query(description="Directory path to search for models"),
) -> List[pathlib.Path]:
if not search_path.is_dir():
raise HTTPException(
status_code=404,
detail=f"The search path '{search_path}' does not exist or is not directory",
)
return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
@models_router.get(
"/ckpt_confs",
operation_id="list_ckpt_configs",
responses={
200: {"description": "paths retrieved successfully"},
},
status_code=200,
response_model=List[pathlib.Path],
)
async def list_ckpt_configs() -> List[pathlib.Path]:
"""Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
@models_router.post(
"/sync",
operation_id="sync_to_config",
responses={
201: {"description": "synchronization successful"},
},
status_code=201,
response_model=bool,
)
async def sync_to_config() -> bool:
"""Call after making changes to models.yaml, autoimport directories or models directory to synchronize
in-memory data structures with disk data structures."""
ApiDependencies.invoker.services.model_manager.sync_to_config()
return True
# There's some weird pydantic-fastapi behaviour that requires this to be a separate class
# TODO: After a few updates, see if it works inside the route operation handler?
class MergeModelsBody(BaseModel):
model_names: List[str] = Field(description="model name", min_length=2, max_length=3)
merged_model_name: Optional[str] = Field(description="Name of destination model")
alpha: Optional[float] = Field(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5)
interp: Optional[MergeInterpolationMethod] = Field(description="Interpolation method")
force: Optional[bool] = Field(
description="Force merging of models created with different versions of diffusers",
default=False,
)
merge_dest_directory: Optional[str] = Field(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
)
model_config = ConfigDict(protected_namespaces=())
@models_router.put(
"/merge/{base_model}",
operation_id="merge_models",
responses={
200: {"description": "Model converted successfully"},
400: {"description": "Incompatible models"},
404: {"description": "One or more models not found"},
},
status_code=200,
response_model=MergeModelResponse,
)
async def merge_models(
body: Annotated[MergeModelsBody, Body(description="Model configuration", embed=True)],
base_model: BaseModelType = Path(description="Base model"),
) -> MergeModelResponse:
"""Convert a checkpoint model into a diffusers model"""
logger = ApiDependencies.invoker.services.logger
try:
logger.info(
f"Merging models: {body.model_names} into {body.merge_dest_directory or '<MODELS>'}/{body.merged_model_name}"
)
dest = pathlib.Path(body.merge_dest_directory) if body.merge_dest_directory else None
result = ApiDependencies.invoker.services.model_manager.merge_models(
model_names=body.model_names,
base_model=base_model,
merged_model_name=body.merged_model_name or "+".join(body.model_names),
alpha=body.alpha,
interp=body.interp,
force=body.force,
merge_dest_directory=dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
result.name,
base_model=base_model,
model_type=ModelType.Main,
)
response = ConvertModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException:
raise HTTPException(
status_code=404,
detail=f"One or more of the models '{body.model_names}' not found",
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
return response

276
invokeai/app/api/routers/sessions.py
View File

@ -1,276 +0,0 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from fastapi import HTTPException, Path
from fastapi.routing import APIRouter
from ...services.shared.graph import GraphExecutionState
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"},
# },
# deprecated=True,
# )
# async def create_session(
# queue_id: str = Query(default="", description="The id of the queue to associate the session with"),
# 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(queue_id=queue_id, graph=graph)
# return session
# @session_router.get(
# "/",
# operation_id="list_sessions",
# responses={200: {"model": PaginatedResults[GraphExecutionState]}},
# deprecated=True,
# )
# 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:
raise HTTPException(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"},
# },
# deprecated=True,
# )
# async def add_node(
# session_id: str = Path(description="The id of the session"),
# node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
# 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:
# raise HTTPException(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:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(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"},
# },
# deprecated=True,
# )
# 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")] = Body( # type: ignore
# 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:
# raise HTTPException(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:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(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"},
# },
# deprecated=True,
# )
# 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:
# raise HTTPException(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:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(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"},
# },
# deprecated=True,
# )
# 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:
# raise HTTPException(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:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(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"},
# },
# deprecated=True,
# )
# 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:
# raise HTTPException(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:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(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"},
# },
# deprecated=True,
# )
# async def invoke_session(
# queue_id: str = Query(description="The id of the queue to associate the session with"),
# 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"),
# ) -> Response:
# """Invokes a session"""
# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
# if session is None:
# raise HTTPException(status_code=404)
# if session.is_complete():
# raise HTTPException(status_code=400)
# ApiDependencies.invoker.invoke(queue_id, 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"}},
# deprecated=True,
# )
# async def cancel_session_invoke(
# session_id: str = Path(description="The id of the session to cancel"),
# ) -> Response:
# """Invokes a session"""
# ApiDependencies.invoker.cancel(session_id)
# return Response(status_code=202)

29
invokeai/app/api/sockets.py
View File

@ -12,16 +12,26 @@ class SocketIO:
__sio: AsyncServer
__app: ASGIApp
__sub_queue: str = "subscribe_queue"
__unsub_queue: str = "unsubscribe_queue"
__sub_bulk_download: str = "subscribe_bulk_download"
__unsub_bulk_download: str = "unsubscribe_bulk_download"
def __init__(self, app: FastAPI):
self.__sio = AsyncServer(async_mode="asgi", cors_allowed_origins="*")
self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="/ws/socket.io")
app.mount("/ws", self.__app)
self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)
self.__sio.on("unsubscribe_queue", handler=self._handle_unsub_queue)
self.__sio.on(self.__sub_queue, handler=self._handle_sub_queue)
self.__sio.on(self.__unsub_queue, handler=self._handle_unsub_queue)
local_handler.register(event_name=EventServiceBase.queue_event, _func=self._handle_queue_event)
local_handler.register(event_name=EventServiceBase.model_event, _func=self._handle_model_event)
self.__sio.on(self.__sub_bulk_download, handler=self._handle_sub_bulk_download)
self.__sio.on(self.__unsub_bulk_download, handler=self._handle_unsub_bulk_download)
local_handler.register(event_name=EventServiceBase.bulk_download_event, _func=self._handle_bulk_download_event)
async def _handle_queue_event(self, event: Event):
await self.__sio.emit(
event=event[1]["event"],
@ -39,3 +49,18 @@ class SocketIO:
async def _handle_model_event(self, event: Event) -> None:
await self.__sio.emit(event=event[1]["event"], data=event[1]["data"])
async def _handle_bulk_download_event(self, event: Event):
await self.__sio.emit(
event=event[1]["event"],
data=event[1]["data"],
room=event[1]["data"]["bulk_download_id"],
)
async def _handle_sub_bulk_download(self, sid, data, *args, **kwargs):
if "bulk_download_id" in data:
await self.__sio.enter_room(sid, data["bulk_download_id"])
async def _handle_unsub_bulk_download(self, sid, data, *args, **kwargs):
if "bulk_download_id" in data:
await self.__sio.leave_room(sid, data["bulk_download_id"])

74
invokeai/app/api_app.py
View File

@ -2,10 +2,12 @@
# which are imported/used before parse_args() is called will get the default config values instead of the
# values from the command line or config file.
import sys
from contextlib import asynccontextmanager
from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
from invokeai.version.invokeai_version import __version__
from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
from .services.config import InvokeAIAppConfig
app_config = InvokeAIAppConfig.get_config()
@ -47,18 +49,14 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
boards,
download_queue,
images,
model_records,
models,
model_manager,
session_queue,
sessions,
utilities,
workflows,
)
from .api.sockets import SocketIO
from .invocations.baseinvocation import (
BaseInvocation,
InputFieldJSONSchemaExtra,
OutputFieldJSONSchemaExtra,
UIConfigBase,
)
@ -74,9 +72,25 @@ logger = InvokeAILogger.get_logger(config=app_config)
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")
@asynccontextmanager
async def lifespan(app: FastAPI):
# Add startup event to load dependencies
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
yield
# Shut down threads
ApiDependencies.shutdown()
# Create the app
# TODO: create this all in a method so configuration/etc. can be passed in?
app = FastAPI(title="Invoke - Community Edition", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
app = FastAPI(
title="Invoke - Community Edition",
docs_url=None,
redoc_url=None,
separate_input_output_schemas=False,
lifespan=lifespan,
)
# Add event handler
event_handler_id: int = id(app)
@ -99,24 +113,9 @@ app.add_middleware(
app.add_middleware(GZipMiddleware, minimum_size=1000)
# Add startup event to load dependencies
@app.on_event("startup")
async def startup_event() -> None:
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
# Shut down threads
@app.on_event("shutdown")
async def shutdown_event() -> None:
ApiDependencies.shutdown()
# Include all routers
app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api")
app.include_router(models.models_router, prefix="/api")
app.include_router(model_records.model_records_router, prefix="/api")
app.include_router(model_manager.model_manager_router, prefix="/api")
app.include_router(download_queue.download_queue_router, prefix="/api")
app.include_router(images.images_router, prefix="/api")
app.include_router(boards.boards_router, prefix="/api")
@ -154,6 +153,8 @@ def custom_openapi() -> dict[str, Any]:
# 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"]
openapi_schema["components"]["schemas"][schema_key] = output_schema
openapi_schema["components"]["schemas"][schema_key]["class"] = "output"
# Add Node Editor UI helper schemas
ui_config_schemas = models_json_schema(
@ -176,23 +177,24 @@ def custom_openapi() -> dict[str, Any]:
outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
invoker_schema["output"] = outputs_ref
invoker_schema["class"] = "invocation"
openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output"
from invokeai.backend.model_management.models import get_model_config_enums
# This code no longer seems to be necessary?
# Leave it here just in case
#
# from invokeai.backend.model_manager import get_model_config_formats
# formats = get_model_config_formats()
# for model_config_name, enum_set in formats.items():
for model_config_format_enum in set(get_model_config_enums()):
name = model_config_format_enum.__qualname__
# if model_config_name in openapi_schema["components"]["schemas"]:
# # print(f"Config with name {name} already defined")
# continue
if name in openapi_schema["components"]["schemas"]:
# print(f"Config with name {name} already defined")
continue
openapi_schema["components"]["schemas"][name] = {
"title": name,
"description": "An enumeration.",
"type": "string",
"enum": [v.value for v in model_config_format_enum],
}
# openapi_schema["components"]["schemas"][model_config_name] = {
# "title": model_config_name,
# "description": "An enumeration.",
# "type": "string",
# "enum": [v.value for v in enum_set],
# }
app.openapi_schema = openapi_schema
return app.openapi_schema

522
invokeai/app/invocations/baseinvocation.py
View File

@ -8,17 +8,33 @@ import warnings
from abc import ABC, abstractmethod
from enum import Enum
from inspect import signature
from types import UnionType
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
from typing import (
TYPE_CHECKING,
Annotated,
Any,
Callable,
ClassVar,
Iterable,
Literal,
Optional,
Type,
TypeVar,
Union,
cast,
)
import semver
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
from pydantic.fields import FieldInfo, _Unset
from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, create_model
from pydantic.fields import FieldInfo
from pydantic_core import PydanticUndefined
from typing_extensions import TypeAliasType
from invokeai.app.invocations.fields import (
FieldKind,
Input,
)
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.metaenum import MetaEnum
from invokeai.app.util.misc import uuid_string
from invokeai.backend.util.logging import InvokeAILogger
@ -52,393 +68,6 @@ class Classification(str, Enum, metaclass=MetaEnum):
Prototype = "prototype"
class Input(str, Enum, metaclass=MetaEnum):
"""
The type of input a field accepts.
- `Input.Direct`: The field must have its value provided directly, when the invocation and field \
are instantiated.
- `Input.Connection`: The field must have its value provided by a connection.
- `Input.Any`: The field may have its value provided either directly or by a connection.
"""
Connection = "connection"
Direct = "direct"
Any = "any"
class FieldKind(str, Enum, metaclass=MetaEnum):
"""
The kind of field.
- `Input`: An input field on a node.
- `Output`: An output field on a node.
- `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
"metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
allowing "metadata" for that field.
- `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
but which are used to store information about the node. For example, the `id` and `type` fields are node
attributes.
The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
startup, and when generating the OpenAPI schema for the workflow editor.
"""
Input = "input"
Output = "output"
Internal = "internal"
NodeAttribute = "node_attribute"
class UIType(str, Enum, metaclass=MetaEnum):
"""
Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
- Model Fields
The most common node-author-facing use will be for model fields. Internally, there is no difference
between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
the field is an SDXL main model field.
- Any Field
We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
- Scheduler Field
Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
- Internal Fields
Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
should not be used by node authors.
- DEPRECATED Fields
These types are deprecated and should not be used by node authors. A warning will be logged if one is
used, and the type will be ignored. They are included here for backwards compatibility.
"""
# region Model Field Types
SDXLMainModel = "SDXLMainModelField"
SDXLRefinerModel = "SDXLRefinerModelField"
ONNXModel = "ONNXModelField"
VaeModel = "VAEModelField"
LoRAModel = "LoRAModelField"
ControlNetModel = "ControlNetModelField"
IPAdapterModel = "IPAdapterModelField"
# endregion
# region Misc Field Types
Scheduler = "SchedulerField"
Any = "AnyField"
# endregion
# region Internal Field Types
_Collection = "CollectionField"
_CollectionItem = "CollectionItemField"
# endregion
# region DEPRECATED
Boolean = "DEPRECATED_Boolean"
Color = "DEPRECATED_Color"
Conditioning = "DEPRECATED_Conditioning"
Control = "DEPRECATED_Control"
Float = "DEPRECATED_Float"
Image = "DEPRECATED_Image"
Integer = "DEPRECATED_Integer"
Latents = "DEPRECATED_Latents"
String = "DEPRECATED_String"
BooleanCollection = "DEPRECATED_BooleanCollection"
ColorCollection = "DEPRECATED_ColorCollection"
ConditioningCollection = "DEPRECATED_ConditioningCollection"
ControlCollection = "DEPRECATED_ControlCollection"
FloatCollection = "DEPRECATED_FloatCollection"
ImageCollection = "DEPRECATED_ImageCollection"
IntegerCollection = "DEPRECATED_IntegerCollection"
LatentsCollection = "DEPRECATED_LatentsCollection"
StringCollection = "DEPRECATED_StringCollection"
BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
StringPolymorphic = "DEPRECATED_StringPolymorphic"
MainModel = "DEPRECATED_MainModel"
UNet = "DEPRECATED_UNet"
Vae = "DEPRECATED_Vae"
CLIP = "DEPRECATED_CLIP"
Collection = "DEPRECATED_Collection"
CollectionItem = "DEPRECATED_CollectionItem"
Enum = "DEPRECATED_Enum"
WorkflowField = "DEPRECATED_WorkflowField"
IsIntermediate = "DEPRECATED_IsIntermediate"
BoardField = "DEPRECATED_BoardField"
MetadataItem = "DEPRECATED_MetadataItem"
MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
MetadataDict = "DEPRECATED_MetadataDict"
# endregion
class UIComponent(str, Enum, metaclass=MetaEnum):
"""
The type of UI component to use for a field, used to override the default components, which are
inferred from the field type.
"""
None_ = "none"
Textarea = "textarea"
Slider = "slider"
class InputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
and by the workflow editor during schema parsing and UI rendering.
"""
input: Input
orig_required: bool
field_kind: FieldKind
default: Optional[Any] = None
orig_default: Optional[Any] = None
ui_hidden: bool = False
ui_type: Optional[UIType] = None
ui_component: Optional[UIComponent] = None
ui_order: Optional[int] = None
ui_choice_labels: Optional[dict[str, str]] = None
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
class OutputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
during schema parsing and UI rendering.
"""
field_kind: FieldKind
ui_hidden: bool
ui_type: Optional[UIType]
ui_order: Optional[int]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
def InputField(
# copied from pydantic's Field
# TODO: Can we support default_factory?
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
input: Input = Input.Any,
ui_type: Optional[UIType] = None,
ui_component: Optional[UIComponent] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None,
) -> Any:
"""
Creates an input field for an invocation.
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
that adds a few extra parameters to support graph execution and the node editor UI.
:param Input input: [Input.Any] The kind of input this field requires. \
`Input.Direct` means a value must be provided on instantiation. \
`Input.Connection` means the value must be provided by a connection. \
`Input.Any` means either will do.
:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
In some situations, the field's type is not enough to infer the correct UI type. \
For example, model selection fields should render a dropdown UI component to select a model. \
Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
`MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
:param UIComponent ui_component: [None] Optionally specifies a specific component to use in the UI. \
The UI will always render a suitable component, but sometimes you want something different than the default. \
For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
For this case, you could provide `UIComponent.Textarea`.
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
:param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
"""
json_schema_extra_ = InputFieldJSONSchemaExtra(
input=input,
ui_type=ui_type,
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
ui_choice_labels=ui_choice_labels,
field_kind=FieldKind.Input,
orig_required=True,
)
"""
There is a conflict between the typing of invocation definitions and the typing of an invocation's
`invoke()` function.
On instantiation of a node, the invocation definition is used to create the python class. At this time,
any number of fields may be optional, because they may be provided by connections.
On calling of `invoke()`, however, those fields may be required.
For example, consider an ResizeImageInvocation with an `image: ImageField` field.
`image` is required during the call to `invoke()`, but when the python class is instantiated,
the field may not be present. This is fine, because that image field will be provided by a
connection from an ancestor node, which outputs an image.
This means we want to type the `image` field as optional for the node class definition, but required
for the `invoke()` function.
If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
`invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
any static type analysis tools will complain.
To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
but secretly make them optional in `InputField()`. We also store the original required bool and/or default
value. When we call `invoke()`, we use this stored information to do an additional check on the class.
"""
if default_factory is not _Unset and default_factory is not None:
default = default_factory()
logger.warn('"default_factory" is not supported, calling it now to set "default"')
# These are the args we may wish pass to the pydantic `Field()` function
field_args = {
"default": default,
"title": title,
"description": description,
"pattern": pattern,
"strict": strict,
"gt": gt,
"ge": ge,
"lt": lt,
"le": le,
"multiple_of": multiple_of,
"allow_inf_nan": allow_inf_nan,
"max_digits": max_digits,
"decimal_places": decimal_places,
"min_length": min_length,
"max_length": max_length,
}
# We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
# Because we are manually making fields optional, we need to store the original required bool for reference later
json_schema_extra_.orig_required = default is PydanticUndefined
# Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if input is Input.Any or input is Input.Connection:
default_ = None if default is PydanticUndefined else default
provided_args.update({"default": default_})
if default is not PydanticUndefined:
# Before invoking, we'll check for the original default value and set it on the field if the field has no value
json_schema_extra_.default = default
json_schema_extra_.orig_default = default
elif default is not PydanticUndefined:
default_ = default
provided_args.update({"default": default_})
json_schema_extra_.orig_default = default_
return Field(
**provided_args,
json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
)
def OutputField(
# copied from pydantic's Field
default: Any = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
ui_type: Optional[UIType] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
) -> Any:
"""
Creates an output field for an invocation output.
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
that adds a few extra parameters to support graph execution and the node editor UI.
:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
In some situations, the field's type is not enough to infer the correct UI type. \
For example, model selection fields should render a dropdown UI component to select a model. \
Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
`MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
default=default,
title=title,
description=description,
pattern=pattern,
strict=strict,
gt=gt,
ge=ge,
lt=lt,
le=le,
multiple_of=multiple_of,
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
min_length=min_length,
max_length=max_length,
json_schema_extra=OutputFieldJSONSchemaExtra(
ui_type=ui_type,
ui_hidden=ui_hidden,
ui_order=ui_order,
field_kind=FieldKind.Output,
).model_dump(exclude_none=True),
)
class UIConfigBase(BaseModel):
"""
Provides additional node configuration to the UI.
@ -460,33 +89,6 @@ class UIConfigBase(BaseModel):
)
class InvocationContext:
"""Initialized and provided to on execution of invocations."""
services: InvocationServices
graph_execution_state_id: str
queue_id: str
queue_item_id: int
queue_batch_id: str
workflow: Optional[WorkflowWithoutID]
def __init__(
self,
services: InvocationServices,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
workflow: Optional[WorkflowWithoutID],
):
self.services = services
self.graph_execution_state_id = graph_execution_state_id
self.queue_id = queue_id
self.queue_item_id = queue_item_id
self.queue_batch_id = queue_batch_id
self.workflow = workflow
class BaseInvocationOutput(BaseModel):
"""
Base class for all invocation outputs.
@ -495,6 +97,7 @@ class BaseInvocationOutput(BaseModel):
"""
_output_classes: ClassVar[set[BaseInvocationOutput]] = set()
_typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
@classmethod
def register_output(cls, output: BaseInvocationOutput) -> None:
@ -507,10 +110,14 @@ class BaseInvocationOutput(BaseModel):
return cls._output_classes
@classmethod
def get_outputs_union(cls) -> UnionType:
"""Gets a union of all invocation outputs."""
outputs_union = Union[tuple(cls._output_classes)] # type: ignore [valid-type]
return outputs_union # type: ignore [return-value]
def get_typeadapter(cls) -> TypeAdapter[Any]:
"""Gets a pydantc TypeAdapter for the union of all invocation output types."""
if not cls._typeadapter:
InvocationOutputsUnion = TypeAliasType(
"InvocationOutputsUnion", Annotated[Union[tuple(cls._output_classes)], Field(discriminator="type")]
)
cls._typeadapter = TypeAdapter(InvocationOutputsUnion)
return cls._typeadapter
@classmethod
def get_output_types(cls) -> Iterable[str]:
@ -559,6 +166,7 @@ class BaseInvocation(ABC, BaseModel):
"""
_invocation_classes: ClassVar[set[BaseInvocation]] = set()
_typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
@classmethod
def get_type(cls) -> str:
@ -571,10 +179,14 @@ class BaseInvocation(ABC, BaseModel):
cls._invocation_classes.add(invocation)
@classmethod
def get_invocations_union(cls) -> UnionType:
"""Gets a union of all invocation types."""
invocations_union = Union[tuple(cls._invocation_classes)] # type: ignore [valid-type]
return invocations_union # type: ignore [return-value]
def get_typeadapter(cls) -> TypeAdapter[Any]:
"""Gets a pydantc TypeAdapter for the union of all invocation types."""
if not cls._typeadapter:
InvocationsUnion = TypeAliasType(
"InvocationsUnion", Annotated[Union[tuple(cls._invocation_classes)], Field(discriminator="type")]
)
cls._typeadapter = TypeAdapter(InvocationsUnion)
return cls._typeadapter
@classmethod
def get_invocations(cls) -> Iterable[BaseInvocation]:
@ -632,7 +244,7 @@ class BaseInvocation(ABC, BaseModel):
"""Invoke with provided context and return outputs."""
pass
def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
def invoke_internal(self, context: InvocationContext, services: "InvocationServices") -> BaseInvocationOutput:
"""
Internal invoke method, calls `invoke()` after some prep.
Handles optional fields that are required to call `invoke()` and invocation cache.
@ -657,23 +269,23 @@ class BaseInvocation(ABC, BaseModel):
raise MissingInputException(self.model_fields["type"].default, field_name)
# skip node cache codepath if it's disabled
if context.services.configuration.node_cache_size == 0:
if services.configuration.node_cache_size == 0:
return self.invoke(context)
output: BaseInvocationOutput
if self.use_cache:
key = context.services.invocation_cache.create_key(self)
cached_value = context.services.invocation_cache.get(key)
key = services.invocation_cache.create_key(self)
cached_value = services.invocation_cache.get(key)
if cached_value is None:
context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
output = self.invoke(context)
context.services.invocation_cache.save(key, output)
services.invocation_cache.save(key, output)
return output
else:
context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
return cached_value
else:
context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
return self.invoke(context)
id: str = Field(
@ -714,9 +326,7 @@ RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
"workflow",
}
RESERVED_INPUT_FIELD_NAMES = {
"metadata",
}
RESERVED_INPUT_FIELD_NAMES = {"metadata", "board"}
RESERVED_OUTPUT_FIELD_NAMES = {"type"}
@ -926,37 +536,3 @@ def invocation_output(
return cls
return wrapper
class MetadataField(RootModel):
"""
Pydantic model for metadata with custom root of type dict[str, Any].
Metadata is stored without a strict schema.
"""
root: dict[str, Any] = Field(description="The metadata")
MetadataFieldValidator = TypeAdapter(MetadataField)
class WithMetadata(BaseModel):
metadata: Optional[MetadataField] = Field(
default=None,
description=FieldDescriptions.metadata,
json_schema_extra=InputFieldJSONSchemaExtra(
field_kind=FieldKind.Internal,
input=Input.Connection,
orig_required=False,
).model_dump(exclude_none=True),
)
class WithWorkflow:
workflow = None
def __init_subclass__(cls) -> None:
logger.warn(
f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
)
super().__init_subclass__()

4
invokeai/app/invocations/collections.py
View File

@ -5,9 +5,11 @@ import numpy as np
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import SEED_MAX
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, invocation
from .fields import InputField
@invocation(

216
invokeai/app/invocations/compel.py
View File

@ -1,40 +1,28 @@
from dataclasses import dataclass
from typing import List, Optional, Union
from typing import Iterator, List, Optional, Tuple, Union, cast
import torch
from compel import Compel, ReturnedEmbeddingsType
from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
from invokeai.app.invocations.primitives import ConditioningOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.ti_utils import generate_ti_list
from invokeai.backend.lora import LoRAModelRaw
from invokeai.backend.model_patcher import ModelPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
ConditioningFieldData,
ExtraConditioningInfo,
SDXLConditioningInfo,
)
from invokeai.backend.util.devices import torch_dtype
from ...backend.model_management.lora import ModelPatcher
from ...backend.model_management.models import ModelNotFoundException, ModelType
from ...backend.util.devices import torch_dtype
from ..util.ti_utils import extract_ti_triggers_from_prompt
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIComponent,
invocation,
invocation_output,
)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
from .model import ClipField
@dataclass
class ConditioningFieldData:
conditionings: List[BasicConditioningInfo]
# unconditioned: Optional[torch.Tensor]
# unconditioned: Optional[torch.Tensor]
# class ConditioningAlgo(str, Enum):
@ -48,7 +36,7 @@ class ConditioningFieldData:
title="Prompt",
tags=["prompt", "compel"],
category="conditioning",
version="1.0.0",
version="1.0.1",
)
class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning."""
@ -66,49 +54,27 @@ class CompelInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.model_dump(),
context=context,
)
tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
tokenizer_model = tokenizer_info.model
assert isinstance(tokenizer_model, CLIPTokenizer)
text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
text_encoder_model = text_encoder_info.model
assert isinstance(text_encoder_model, CLIPTextModel)
def _lora_loader():
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
for lora in self.clip.loras:
lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}), context=context
)
yield (lora_info.context.model, lora.weight)
lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
assert isinstance(lora_info.model, LoRAModelRaw)
yield (lora_info.model, lora.weight)
del lora_info
return
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
# loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
for trigger in extract_ti_triggers_from_prompt(self.prompt):
name = trigger[1:-1]
try:
ti_list.append(
(
name,
context.services.model_manager.get_model(
model_name=name,
base_model=self.clip.text_encoder.base_model,
model_type=ModelType.TextualInversion,
context=context,
).context.model,
)
)
except ModelNotFoundException:
# print(e)
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
with (
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
tokenizer,
ti_manager,
),
@ -116,8 +82,9 @@ class CompelInvocation(BaseInvocation):
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
ModelPatcher.apply_clip_skip(text_encoder_model, self.clip.skipped_layers),
):
assert isinstance(text_encoder, CLIPTextModel)
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,
@ -128,7 +95,7 @@ class CompelInvocation(BaseInvocation):
conjunction = Compel.parse_prompt_string(self.prompt)
if context.services.configuration.log_tokenization:
if context.config.get().log_tokenization:
log_tokenization_for_conjunction(conjunction, tokenizer)
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
@ -149,17 +116,14 @@ class CompelInvocation(BaseInvocation):
]
)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
context.services.latents.save(conditioning_name, conditioning_data)
conditioning_name = context.conditioning.save(conditioning_data)
return ConditioningOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
return ConditioningOutput.build(conditioning_name)
class SDXLPromptInvocationBase:
"""Prompt processor for SDXL models."""
def run_clip_compel(
self,
context: InvocationContext,
@ -168,26 +132,25 @@ class SDXLPromptInvocationBase:
get_pooled: bool,
lora_prefix: str,
zero_on_empty: bool,
):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.model_dump(),
context=context,
)
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
tokenizer_model = tokenizer_info.model
assert isinstance(tokenizer_model, CLIPTokenizer)
text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
text_encoder_model = text_encoder_info.model
assert isinstance(text_encoder_model, (CLIPTextModel, CLIPTextModelWithProjection))
# return zero on empty
if prompt == "" and zero_on_empty:
cpu_text_encoder = text_encoder_info.context.model
cpu_text_encoder = text_encoder_info.model
assert isinstance(cpu_text_encoder, torch.nn.Module)
c = torch.zeros(
(
1,
cpu_text_encoder.config.max_position_embeddings,
cpu_text_encoder.config.hidden_size,
),
dtype=text_encoder_info.context.cache.precision,
dtype=cpu_text_encoder.dtype,
)
if get_pooled:
c_pooled = torch.zeros(
@ -198,40 +161,21 @@ class SDXLPromptInvocationBase:
c_pooled = None
return c, c_pooled, None
def _lora_loader():
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
for lora in clip_field.loras:
lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}), context=context
)
yield (lora_info.context.model, lora.weight)
lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
lora_model = lora_info.model
assert isinstance(lora_model, LoRAModelRaw)
yield (lora_model, lora.weight)
del lora_info
return
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
# loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
for trigger in extract_ti_triggers_from_prompt(prompt):
name = trigger[1:-1]
try:
ti_list.append(
(
name,
context.services.model_manager.get_model(
model_name=name,
base_model=clip_field.text_encoder.base_model,
model_type=ModelType.TextualInversion,
context=context,
).context.model,
)
)
except ModelNotFoundException:
# print(e)
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
ti_list = generate_ti_list(prompt, text_encoder_info.config.base, context)
with (
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
tokenizer,
ti_manager,
),
@ -239,8 +183,10 @@ class SDXLPromptInvocationBase:
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
ModelPatcher.apply_clip_skip(text_encoder_model, clip_field.skipped_layers),
):
assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
text_encoder = cast(CLIPTextModel, text_encoder)
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,
@ -253,7 +199,7 @@ class SDXLPromptInvocationBase:
conjunction = Compel.parse_prompt_string(prompt)
if context.services.configuration.log_tokenization:
if context.config.get().log_tokenization:
# TODO: better logging for and syntax
log_tokenization_for_conjunction(conjunction, tokenizer)
@ -286,7 +232,7 @@ class SDXLPromptInvocationBase:
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
version="1.0.0",
version="1.0.1",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -357,6 +303,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
dim=1,
)
assert c2_pooled is not None
conditioning_data = ConditioningFieldData(
conditionings=[
SDXLConditioningInfo(
@ -368,14 +315,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
]
)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
context.services.latents.save(conditioning_name, conditioning_data)
conditioning_name = context.conditioning.save(conditioning_data)
return ConditioningOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
return ConditioningOutput.build(conditioning_name)
@invocation(
@ -383,7 +325,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
title="SDXL Refiner Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
version="1.0.0",
version="1.0.1",
)
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -410,6 +352,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
assert c2_pooled is not None
conditioning_data = ConditioningFieldData(
conditionings=[
SDXLConditioningInfo(
@ -421,14 +364,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
]
)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
context.services.latents.save(conditioning_name, conditioning_data)
conditioning_name = context.conditioning.save(conditioning_data)
return ConditioningOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
return ConditioningOutput.build(conditioning_name)
@invocation_output("clip_skip_output")
@ -449,7 +387,7 @@ class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers)
skipped_layers: int = InputField(default=0, ge=0, description=FieldDescriptions.skipped_layers)
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
self.clip.skipped_layers += self.skipped_layers
@ -459,9 +397,9 @@ class ClipSkipInvocation(BaseInvocation):
def get_max_token_count(
tokenizer,
tokenizer: CLIPTokenizer,
prompt: Union[FlattenedPrompt, Blend, Conjunction],
truncate_if_too_long=False,
truncate_if_too_long: bool = False,
) -> int:
if type(prompt) is Blend:
blend: Blend = prompt
@ -473,7 +411,9 @@ def get_max_token_count(
return len(get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long))
def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> List[str]:
def get_tokens_for_prompt_object(
tokenizer: CLIPTokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long: bool = True
) -> List[str]:
if type(parsed_prompt) is Blend:
raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
@ -486,24 +426,29 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
for x in parsed_prompt.children
]
text = " ".join(text_fragments)
tokens = tokenizer.tokenize(text)
tokens: List[str] = tokenizer.tokenize(text)
if truncate_if_too_long:
max_tokens_length = tokenizer.model_max_length - 2 # typically 75
tokens = tokens[0:max_tokens_length]
return tokens
def log_tokenization_for_conjunction(c: Conjunction, tokenizer, display_label_prefix=None):
def log_tokenization_for_conjunction(
c: Conjunction, tokenizer: CLIPTokenizer, display_label_prefix: Optional[str] = None
) -> None:
display_label_prefix = display_label_prefix or ""
for i, p in enumerate(c.prompts):
if len(c.prompts) > 1:
this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
else:
assert display_label_prefix is not None
this_display_label_prefix = display_label_prefix
log_tokenization_for_prompt_object(p, tokenizer, display_label_prefix=this_display_label_prefix)
def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
def log_tokenization_for_prompt_object(
p: Union[Blend, FlattenedPrompt], tokenizer: CLIPTokenizer, display_label_prefix: Optional[str] = None
) -> None:
display_label_prefix = display_label_prefix or ""
if type(p) is Blend:
blend: Blend = p
@ -543,7 +488,12 @@ def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokeniz
log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
def log_tokenization_for_text(
text: str,
tokenizer: CLIPTokenizer,
display_label: Optional[str] = None,
truncate_if_too_long: Optional[bool] = False,
) -> None:
"""shows how the prompt is tokenized
# usually tokens have '</w>' to indicate end-of-word,
# but for readability it has been replaced with ' '

7
invokeai/app/invocations/conditioning.py
View File

@ -2,11 +2,10 @@ import torch
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
InputField,
InvocationContext,
WithMetadata,
invocation,
)
from invokeai.app.invocations.fields import InputField, WithMetadata
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, MaskField, MaskOutput
@ -51,9 +50,7 @@ class RectangleMaskInvocation(BaseInvocation, WithMetadata):
:, self.y_top : self.y_top + self.rectangle_height, self.x_left : self.x_left + self.rectangle_width
] = True
mask_name = f"{context.graph_execution_state_id}__{self.id}_mask"
context.services.latents.save(mask_name, mask)
mask_name = context.tensors.save(mask)
return MaskOutput(
mask=MaskField(mask_name=mask_name),
width=self.width,

17
invokeai/app/invocations/constants.py Normal file
View File

@ -0,0 +1,17 @@
from typing import Literal
from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
LATENT_SCALE_FACTOR = 8
"""
HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
factor is hard-coded to a literal '8' rather than using this constant.
The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
"""
SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
"""A literal type representing the valid scheduler names."""
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
"""A literal type for PIL image modes supported by Invoke"""

97
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -23,27 +23,24 @@ from controlnet_aux import (
)
from controlnet_aux.util import HWC3, ade_palette
from PIL import Image
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
from pydantic import BaseModel, Field, field_validator, model_validator
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.invocations.fields import (
FieldDescriptions,
ImageField,
Input,
InputField,
OutputField,
WithBoard,
WithMetadata,
)
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
from ...backend.model_management import BaseModelType
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
WithMetadata,
invocation,
invocation_output,
)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
CONTROLNET_RESIZE_VALUES = Literal[
@ -57,10 +54,7 @@ CONTROLNET_RESIZE_VALUES = Literal[
class ControlNetModelField(BaseModel):
"""ControlNet model field"""
model_name: str = Field(description="Name of the ControlNet model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="Model config record key for the ControlNet model")
class ControlField(BaseModel):
@ -140,7 +134,7 @@ class ControlNetInvocation(BaseInvocation):
# This invocation exists for other invocations to subclass it - do not register with @invocation!
class ImageProcessorInvocation(BaseInvocation, WithMetadata):
class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Base class for invocations that preprocess images for ControlNet"""
image: ImageField = InputField(description="The image to process")
@ -149,23 +143,18 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
# superclass just passes through image without processing
return image
def load_image(self, context: InvocationContext) -> Image.Image:
# allows override for any special formatting specific to the preprocessor
return context.images.get_pil(self.image.image_name, "RGB")
def invoke(self, context: InvocationContext) -> ImageOutput:
raw_image = context.services.images.get_pil_image(self.image.image_name)
raw_image = self.load_image(context)
# image type should be PIL.PngImagePlugin.PngImageFile ?
processed_image = self.run_processor(raw_image)
# currently can't see processed image in node UI without a showImage node,
# so for now setting image_type to RESULT instead of INTERMEDIATE so will get saved in gallery
image_dto = context.services.images.create(
image=processed_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.CONTROL,
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=processed_image)
"""Builds an ImageOutput and its ImageField"""
processed_image_field = ImageField(image_name=image_dto.image_name)
@ -184,7 +173,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
title="Canny Processor",
tags=["controlnet", "canny"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class CannyImageProcessorInvocation(ImageProcessorInvocation):
"""Canny edge detection for ControlNet"""
@ -196,6 +185,10 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)"
)
def load_image(self, context: InvocationContext) -> Image.Image:
# Keep alpha channel for Canny processing to detect edges of transparent areas
return context.images.get_pil(self.image.image_name, "RGBA")
def run_processor(self, image):
canny_processor = CannyDetector()
processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
@ -207,7 +200,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
title="HED (softedge) Processor",
tags=["controlnet", "hed", "softedge"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class HedImageProcessorInvocation(ImageProcessorInvocation):
"""Applies HED edge detection to image"""
@ -236,7 +229,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Processor",
tags=["controlnet", "lineart"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class LineartImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art processing to image"""
@ -258,7 +251,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Anime Processor",
tags=["controlnet", "lineart", "anime"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art anime processing to image"""
@ -281,7 +274,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
title="Midas Depth Processor",
tags=["controlnet", "midas"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Midas depth processing to image"""
@ -308,7 +301,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Normal BAE Processor",
tags=["controlnet"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies NormalBae processing to image"""
@ -325,7 +318,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
@invocation(
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.1"
)
class MlsdImageProcessorInvocation(ImageProcessorInvocation):
"""Applies MLSD processing to image"""
@ -348,7 +341,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
@invocation(
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.1"
)
class PidiImageProcessorInvocation(ImageProcessorInvocation):
"""Applies PIDI processing to image"""
@ -375,7 +368,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
title="Content Shuffle Processor",
tags=["controlnet", "contentshuffle"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
"""Applies content shuffle processing to image"""
@ -405,7 +398,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
title="Zoe (Depth) Processor",
tags=["controlnet", "zoe", "depth"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Zoe depth processing to image"""
@ -421,7 +414,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Mediapipe Face Processor",
tags=["controlnet", "mediapipe", "face"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
"""Applies mediapipe face processing to image"""
@ -430,10 +423,6 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
def run_processor(self, image):
# MediaPipeFaceDetector throws an error if image has alpha channel
# so convert to RGB if needed
if image.mode == "RGBA":
image = image.convert("RGB")
mediapipe_face_processor = MediapipeFaceDetector()
processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
return processed_image
@ -444,7 +433,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
title="Leres (Depth) Processor",
tags=["controlnet", "leres", "depth"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class LeresImageProcessorInvocation(ImageProcessorInvocation):
"""Applies leres processing to image"""
@ -473,7 +462,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
title="Tile Resample Processor",
tags=["controlnet", "tile"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class TileResamplerProcessorInvocation(ImageProcessorInvocation):
"""Tile resampler processor"""
@ -513,7 +502,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
title="Segment Anything Processor",
tags=["controlnet", "segmentanything"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
"""Applies segment anything processing to image"""
@ -555,7 +544,7 @@ class SamDetectorReproducibleColors(SamDetector):
title="Color Map Processor",
tags=["controlnet"],
category="controlnet",
version="1.2.0",
version="1.2.1",
)
class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
"""Generates a color map from the provided image"""
@ -563,7 +552,6 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
def run_processor(self, image: Image.Image):
image = image.convert("RGB")
np_image = np.array(image, dtype=np.uint8)
height, width = np_image.shape[:2]
@ -603,9 +591,6 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
depth_anything_detector = DepthAnythingDetector()
depth_anything_detector.load_model(model_size=self.model_size)
if image.mode == "RGBA":
image = image.convert("RGB")
processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
return processed_image
@ -625,7 +610,7 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
draw_hands: bool = InputField(default=False)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
def run_processor(self, image):
def run_processor(self, image: Image.Image):
dw_openpose = DWOpenposeDetector()
processed_image = dw_openpose(
image,

32
invokeai/app/invocations/cv.py
View File

@ -5,22 +5,24 @@ import cv2 as cv
import numpy
from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.invocations.fields import ImageField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, WithBoard, WithMetadata
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
class CvInpaintInvocation(BaseInvocation, WithMetadata):
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.1")
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Simple inpaint using opencv."""
image: ImageField = InputField(description="The image to inpaint")
mask: ImageField = InputField(description="The mask to use when inpainting")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
mask = context.services.images.get_pil_image(self.mask.image_name)
image = context.images.get_pil(self.image.image_name)
mask = context.images.get_pil(self.mask.image_name)
# Convert to cv image/mask
# TODO: consider making these utility functions
@ -34,18 +36,6 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata):
# TODO: consider making a utility function
image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
image_dto = context.services.images.create(
image=image_inpainted,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=context.workflow,
)
image_dto = context.images.save(image=image_inpainted)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)

110
invokeai/app/invocations/facetools.py
View File

@ -13,15 +13,13 @@ from pydantic import field_validator
import invokeai.assets.fonts as font_assets
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
InputField,
InvocationContext,
OutputField,
WithMetadata,
invocation,
invocation_output,
)
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.invocations.fields import ImageField, InputField, OutputField, WithBoard, WithMetadata
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory
from invokeai.app.services.shared.invocation_context import InvocationContext
@invocation_output("face_mask_output")
@ -306,37 +304,37 @@ def extract_face(
# Adjust the crop boundaries to stay within the original image's dimensions
if x_min < 0:
context.services.logger.warning("FaceTools --> -X-axis padding reached image edge.")
context.logger.warning("FaceTools --> -X-axis padding reached image edge.")
x_max -= x_min
x_min = 0
elif x_max > mask.width:
context.services.logger.warning("FaceTools --> +X-axis padding reached image edge.")
context.logger.warning("FaceTools --> +X-axis padding reached image edge.")
x_min -= x_max - mask.width
x_max = mask.width
if y_min < 0:
context.services.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
context.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
y_max -= y_min
y_min = 0
elif y_max > mask.height:
context.services.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
context.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
y_min -= y_max - mask.height
y_max = mask.height
# Ensure the crop is square and adjust the boundaries if needed
if x_max - x_min != crop_size:
context.services.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
context.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
diff = crop_size - (x_max - x_min)
x_min -= diff // 2
x_max += diff - diff // 2
if y_max - y_min != crop_size:
context.services.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
context.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
diff = crop_size - (y_max - y_min)
y_min -= diff // 2
y_max += diff - diff // 2
context.services.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
context.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
# Crop the output image to the specified size with the center of the face mesh as the center.
mask = mask.crop((x_min, y_min, x_max, y_max))
@ -368,7 +366,7 @@ def get_faces_list(
# Generate the face box mask and get the center of the face.
if not should_chunk:
context.services.logger.info("FaceTools --> Attempting full image face detection.")
context.logger.info("FaceTools --> Attempting full image face detection.")
result = generate_face_box_mask(
context=context,
minimum_confidence=minimum_confidence,
@ -380,7 +378,7 @@ def get_faces_list(
draw_mesh=draw_mesh,
)
if should_chunk or len(result) == 0:
context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
context.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
width, height = image.size
image_chunks = []
x_offsets = []
@ -399,7 +397,7 @@ def get_faces_list(
x_offsets.append(x)
y_offsets.append(0)
fx += increment
context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}")
context.logger.info(f"FaceTools --> Chunk starting at x = {x}")
elif height > width:
# Portrait - slice the image vertically
fy = 0.0
@ -411,10 +409,10 @@ def get_faces_list(
x_offsets.append(0)
y_offsets.append(y)
fy += increment
context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
context.logger.info(f"FaceTools --> Chunk starting at y = {y}")
for idx in range(len(image_chunks)):
context.services.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
context.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
result = result + generate_face_box_mask(
context=context,
minimum_confidence=minimum_confidence,
@ -428,7 +426,7 @@ def get_faces_list(
if len(result) == 0:
# Give up
context.services.logger.warning(
context.logger.warning(
"FaceTools --> No face detected in chunked input image. Passing through original image."
)
@ -437,7 +435,7 @@ def get_faces_list(
return all_faces
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.1")
class FaceOffInvocation(BaseInvocation, WithMetadata):
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
@ -470,11 +468,11 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
)
if len(all_faces) == 0:
context.services.logger.warning("FaceOff --> No faces detected. Passing through original image.")
context.logger.warning("FaceOff --> No faces detected. Passing through original image.")
return None
if self.face_id > len(all_faces) - 1:
context.services.logger.warning(
context.logger.warning(
f"FaceOff --> Face ID {self.face_id} is outside of the number of faces detected ({len(all_faces)}). Passing through original image."
)
return None
@ -486,7 +484,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
return face_data
def invoke(self, context: InvocationContext) -> FaceOffOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
result = self.faceoff(context=context, image=image)
if result is None:
@ -500,24 +498,9 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
x = result["x_min"]
y = result["y_min"]
image_dto = context.services.images.create(
image=result_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=context.workflow,
)
image_dto = context.images.save(image=result_image)
mask_dto = context.services.images.create(
image=result_mask,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.MASK,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
mask_dto = context.images.save(image=result_mask, image_category=ImageCategory.MASK)
output = FaceOffOutput(
image=ImageField(image_name=image_dto.image_name),
@ -531,7 +514,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
return output
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.1")
class FaceMaskInvocation(BaseInvocation, WithMetadata):
"""Face mask creation using mediapipe face detection"""
@ -580,7 +563,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
if len(intersected_face_ids) == 0:
id_range_str = ",".join([str(id) for id in id_range])
context.services.logger.warning(
context.logger.warning(
f"Face IDs must be in range of detected faces - requested {self.face_ids}, detected {id_range_str}. Passing through original image."
)
return FaceMaskResult(
@ -616,27 +599,12 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
)
def invoke(self, context: InvocationContext) -> FaceMaskOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
result = self.facemask(context=context, image=image)
image_dto = context.services.images.create(
image=result["image"],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=context.workflow,
)
image_dto = context.images.save(image=result["image"])
mask_dto = context.services.images.create(
image=result["mask"],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.MASK,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
mask_dto = context.images.save(image=result["mask"], image_category=ImageCategory.MASK)
output = FaceMaskOutput(
image=ImageField(image_name=image_dto.image_name),
@ -649,9 +617,9 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
@invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.1"
)
class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
class FaceIdentifierInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
image: ImageField = InputField(description="Image to face detect")
@ -705,21 +673,9 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
return image
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
result_image = self.faceidentifier(context=context, image=image)
image_dto = context.services.images.create(
image=result_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=context.workflow,
)
image_dto = context.images.save(image=result_image)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)

576
invokeai/app/invocations/fields.py Normal file
View File

@ -0,0 +1,576 @@
from enum import Enum
from typing import Any, Callable, Optional, Tuple
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter
from pydantic.fields import _Unset
from pydantic_core import PydanticUndefined
from invokeai.app.util.metaenum import MetaEnum
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.get_logger()
class UIType(str, Enum, metaclass=MetaEnum):
"""
Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
- Model Fields
The most common node-author-facing use will be for model fields. Internally, there is no difference
between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
the field is an SDXL main model field.
- Any Field
We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
- Scheduler Field
Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
- Internal Fields
Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
should not be used by node authors.
- DEPRECATED Fields
These types are deprecated and should not be used by node authors. A warning will be logged if one is
used, and the type will be ignored. They are included here for backwards compatibility.
"""
# region Model Field Types
SDXLMainModel = "SDXLMainModelField"
SDXLRefinerModel = "SDXLRefinerModelField"
ONNXModel = "ONNXModelField"
VaeModel = "VAEModelField"
LoRAModel = "LoRAModelField"
ControlNetModel = "ControlNetModelField"
IPAdapterModel = "IPAdapterModelField"
# endregion
# region Misc Field Types
Scheduler = "SchedulerField"
Any = "AnyField"
# endregion
# region Internal Field Types
_Collection = "CollectionField"
_CollectionItem = "CollectionItemField"
# endregion
# region DEPRECATED
Boolean = "DEPRECATED_Boolean"
Color = "DEPRECATED_Color"
Conditioning = "DEPRECATED_Conditioning"
Control = "DEPRECATED_Control"
Float = "DEPRECATED_Float"
Image = "DEPRECATED_Image"
Integer = "DEPRECATED_Integer"
Latents = "DEPRECATED_Latents"
String = "DEPRECATED_String"
BooleanCollection = "DEPRECATED_BooleanCollection"
ColorCollection = "DEPRECATED_ColorCollection"
ConditioningCollection = "DEPRECATED_ConditioningCollection"
ControlCollection = "DEPRECATED_ControlCollection"
FloatCollection = "DEPRECATED_FloatCollection"
ImageCollection = "DEPRECATED_ImageCollection"
IntegerCollection = "DEPRECATED_IntegerCollection"
LatentsCollection = "DEPRECATED_LatentsCollection"
StringCollection = "DEPRECATED_StringCollection"
BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
StringPolymorphic = "DEPRECATED_StringPolymorphic"
MainModel = "DEPRECATED_MainModel"
UNet = "DEPRECATED_UNet"
Vae = "DEPRECATED_Vae"
CLIP = "DEPRECATED_CLIP"
Collection = "DEPRECATED_Collection"
CollectionItem = "DEPRECATED_CollectionItem"
Enum = "DEPRECATED_Enum"
WorkflowField = "DEPRECATED_WorkflowField"
IsIntermediate = "DEPRECATED_IsIntermediate"
BoardField = "DEPRECATED_BoardField"
MetadataItem = "DEPRECATED_MetadataItem"
MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
MetadataDict = "DEPRECATED_MetadataDict"
class UIComponent(str, Enum, metaclass=MetaEnum):
"""
The type of UI component to use for a field, used to override the default components, which are
inferred from the field type.
"""
None_ = "none"
Textarea = "textarea"
Slider = "slider"
class FieldDescriptions:
denoising_start = "When to start denoising, expressed a percentage of total steps"
denoising_end = "When to stop denoising, expressed a percentage of total steps"
cfg_scale = "Classifier-Free Guidance scale"
cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
scheduler = "Scheduler to use during inference"
positive_cond = "Positive conditioning tensor"
negative_cond = "Negative conditioning tensor"
noise = "Noise tensor"
clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
unet = "UNet (scheduler, LoRAs)"
vae = "VAE"
cond = "Conditioning tensor"
controlnet_model = "ControlNet model to load"
vae_model = "VAE model to load"
lora_model = "LoRA model to load"
main_model = "Main model (UNet, VAE, CLIP) to load"
sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
lora_weight = "The weight at which the LoRA is applied to each model"
compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
raw_prompt = "Raw prompt text (no parsing)"
sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
skipped_layers = "Number of layers to skip in text encoder"
seed = "Seed for random number generation"
steps = "Number of steps to run"
width = "Width of output (px)"
height = "Height of output (px)"
control = "ControlNet(s) to apply"
ip_adapter = "IP-Adapter to apply"
t2i_adapter = "T2I-Adapter(s) to apply"
denoised_latents = "Denoised latents tensor"
latents = "Latents tensor"
strength = "Strength of denoising (proportional to steps)"
metadata = "Optional metadata to be saved with the image"
metadata_collection = "Collection of Metadata"
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
metadata_item_label = "Label for this metadata item"
metadata_item_value = "The value for this metadata item (may be any type)"
workflow = "Optional workflow to be saved with the image"
interp_mode = "Interpolation mode"
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
fp32 = "Whether or not to use full float32 precision"
precision = "Precision to use"
tiled = "Processing using overlapping tiles (reduce memory consumption)"
detect_res = "Pixel resolution for detection"
image_res = "Pixel resolution for output image"
safe_mode = "Whether or not to use safe mode"
scribble_mode = "Whether or not to use scribble mode"
scale_factor = "The factor by which to scale"
blend_alpha = (
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
)
num_1 = "The first number"
num_2 = "The second number"
mask = "The mask to use for the operation"
board = "The board to save the image to"
image = "The image to process"
tile_size = "Tile size"
inclusive_low = "The inclusive low value"
exclusive_high = "The exclusive high value"
decimal_places = "The number of decimal places to round to"
freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
class ImageField(BaseModel):
"""An image primitive field"""
image_name: str = Field(description="The name of the image")
class BoardField(BaseModel):
"""A board primitive field"""
board_id: str = Field(description="The id of the board")
class MaskField(BaseModel):
"""A mask primitive field."""
mask_name: str = Field(description="The name of the mask.")
class DenoiseMaskField(BaseModel):
"""An inpaint mask field"""
mask_name: str = Field(description="The name of the mask image")
masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
gradient: bool = Field(default=False, description="Used for gradient inpainting")
class LatentsField(BaseModel):
"""A latents tensor primitive field"""
latents_name: str = Field(description="The name of the latents")
seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
class ColorField(BaseModel):
"""A color primitive field"""
r: int = Field(ge=0, le=255, description="The red component")
g: int = Field(ge=0, le=255, description="The green component")
b: int = Field(ge=0, le=255, description="The blue component")
a: int = Field(ge=0, le=255, description="The alpha component")
def tuple(self) -> Tuple[int, int, int, int]:
return (self.r, self.g, self.b, self.a)
class ConditioningField(BaseModel):
"""A conditioning tensor primitive value"""
conditioning_name: str = Field(description="The name of conditioning tensor")
mask: Optional[MaskField] = Field(
default=None,
description="The bool mask associated with this conditioning tensor. Excluded regions should be set to False, "
"included regions should be set to True.",
)
class MetadataField(RootModel):
"""
Pydantic model for metadata with custom root of type dict[str, Any].
Metadata is stored without a strict schema.
"""
root: dict[str, Any] = Field(description="The metadata")
MetadataFieldValidator = TypeAdapter(MetadataField)
class Input(str, Enum, metaclass=MetaEnum):
"""
The type of input a field accepts.
- `Input.Direct`: The field must have its value provided directly, when the invocation and field \
are instantiated.
- `Input.Connection`: The field must have its value provided by a connection.
- `Input.Any`: The field may have its value provided either directly or by a connection.
"""
Connection = "connection"
Direct = "direct"
Any = "any"
class FieldKind(str, Enum, metaclass=MetaEnum):
"""
The kind of field.
- `Input`: An input field on a node.
- `Output`: An output field on a node.
- `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
"metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
allowing "metadata" for that field.
- `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
but which are used to store information about the node. For example, the `id` and `type` fields are node
attributes.
The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
startup, and when generating the OpenAPI schema for the workflow editor.
"""
Input = "input"
Output = "output"
Internal = "internal"
NodeAttribute = "node_attribute"
class InputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
and by the workflow editor during schema parsing and UI rendering.
"""
input: Input
orig_required: bool
field_kind: FieldKind
default: Optional[Any] = None
orig_default: Optional[Any] = None
ui_hidden: bool = False
ui_type: Optional[UIType] = None
ui_component: Optional[UIComponent] = None
ui_order: Optional[int] = None
ui_choice_labels: Optional[dict[str, str]] = None
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
class WithMetadata(BaseModel):
"""
Inherit from this class if your node needs a metadata input field.
"""
metadata: Optional[MetadataField] = Field(
default=None,
description=FieldDescriptions.metadata,
json_schema_extra=InputFieldJSONSchemaExtra(
field_kind=FieldKind.Internal,
input=Input.Connection,
orig_required=False,
).model_dump(exclude_none=True),
)
class WithWorkflow:
workflow = None
def __init_subclass__(cls) -> None:
logger.warn(
f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
)
super().__init_subclass__()
class WithBoard(BaseModel):
"""
Inherit from this class if your node needs a board input field.
"""
board: Optional[BoardField] = Field(
default=None,
description=FieldDescriptions.board,
json_schema_extra=InputFieldJSONSchemaExtra(
field_kind=FieldKind.Internal,
input=Input.Direct,
orig_required=False,
).model_dump(exclude_none=True),
)
class OutputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
during schema parsing and UI rendering.
"""
field_kind: FieldKind
ui_hidden: bool
ui_type: Optional[UIType]
ui_order: Optional[int]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
def InputField(
# copied from pydantic's Field
# TODO: Can we support default_factory?
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
input: Input = Input.Any,
ui_type: Optional[UIType] = None,
ui_component: Optional[UIComponent] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None,
) -> Any:
"""
Creates an input field for an invocation.
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
that adds a few extra parameters to support graph execution and the node editor UI.
:param Input input: [Input.Any] The kind of input this field requires. \
`Input.Direct` means a value must be provided on instantiation. \
`Input.Connection` means the value must be provided by a connection. \
`Input.Any` means either will do.
:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
In some situations, the field's type is not enough to infer the correct UI type. \
For example, model selection fields should render a dropdown UI component to select a model. \
Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
`MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
:param UIComponent ui_component: [None] Optionally specifies a specific component to use in the UI. \
The UI will always render a suitable component, but sometimes you want something different than the default. \
For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
For this case, you could provide `UIComponent.Textarea`.
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
:param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
"""
json_schema_extra_ = InputFieldJSONSchemaExtra(
input=input,
ui_type=ui_type,
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
ui_choice_labels=ui_choice_labels,
field_kind=FieldKind.Input,
orig_required=True,
)
"""
There is a conflict between the typing of invocation definitions and the typing of an invocation's
`invoke()` function.
On instantiation of a node, the invocation definition is used to create the python class. At this time,
any number of fields may be optional, because they may be provided by connections.
On calling of `invoke()`, however, those fields may be required.
For example, consider an ResizeImageInvocation with an `image: ImageField` field.
`image` is required during the call to `invoke()`, but when the python class is instantiated,
the field may not be present. This is fine, because that image field will be provided by a
connection from an ancestor node, which outputs an image.
This means we want to type the `image` field as optional for the node class definition, but required
for the `invoke()` function.
If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
`invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
any static type analysis tools will complain.
To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
but secretly make them optional in `InputField()`. We also store the original required bool and/or default
value. When we call `invoke()`, we use this stored information to do an additional check on the class.
"""
if default_factory is not _Unset and default_factory is not None:
default = default_factory()
logger.warn('"default_factory" is not supported, calling it now to set "default"')
# These are the args we may wish pass to the pydantic `Field()` function
field_args = {
"default": default,
"title": title,
"description": description,
"pattern": pattern,
"strict": strict,
"gt": gt,
"ge": ge,
"lt": lt,
"le": le,
"multiple_of": multiple_of,
"allow_inf_nan": allow_inf_nan,
"max_digits": max_digits,
"decimal_places": decimal_places,
"min_length": min_length,
"max_length": max_length,
}
# We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
# Because we are manually making fields optional, we need to store the original required bool for reference later
json_schema_extra_.orig_required = default is PydanticUndefined
# Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if input is Input.Any or input is Input.Connection:
default_ = None if default is PydanticUndefined else default
provided_args.update({"default": default_})
if default is not PydanticUndefined:
# Before invoking, we'll check for the original default value and set it on the field if the field has no value
json_schema_extra_.default = default
json_schema_extra_.orig_default = default
elif default is not PydanticUndefined:
default_ = default
provided_args.update({"default": default_})
json_schema_extra_.orig_default = default_
return Field(
**provided_args,
json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
)
def OutputField(
# copied from pydantic's Field
default: Any = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
ui_type: Optional[UIType] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
) -> Any:
"""
Creates an output field for an invocation output.
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
that adds a few extra parameters to support graph execution and the node editor UI.
:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
In some situations, the field's type is not enough to infer the correct UI type. \
For example, model selection fields should render a dropdown UI component to select a model. \
Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
`MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
default=default,
title=title,
description=description,
pattern=pattern,
strict=strict,
gt=gt,
ge=ge,
lt=lt,
le=le,
multiple_of=multiple_of,
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
min_length=min_length,
max_length=max_length,
json_schema_extra=OutputFieldJSONSchemaExtra(
ui_type=ui_type,
ui_hidden=ui_hidden,
ui_order=ui_order,
field_kind=FieldKind.Output,
).model_dump(exclude_none=True),
)

623
invokeai/app/invocations/image.py
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123
invokeai/app/invocations/infill.py
View File

@ -6,14 +6,16 @@ from typing import Literal, Optional, get_args
import numpy as np
from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.invocations.fields import ColorField, ImageField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import SEED_MAX
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, WithBoard, WithMetadata
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
@ -118,8 +120,8 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
return si
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
class InfillColorInvocation(BaseInvocation, WithMetadata):
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image with a solid color"""
image: ImageField = InputField(description="The image to infill")
@ -129,33 +131,20 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
solid_bg = Image.new("RGBA", image.size, self.color.tuple())
infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
infilled.paste(image, (0, 0), image.split()[-1])
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=infilled)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
class InfillTileInvocation(BaseInvocation, WithMetadata):
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image with tiles of the image"""
image: ImageField = InputField(description="The image to infill")
@ -168,33 +157,20 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
infilled.paste(image, (0, 0), image.split()[-1])
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=infilled)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)
@invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1"
)
class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
image: ImageField = InputField(description="The image to infill")
@ -202,7 +178,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
image = context.images.get_pil(self.image.image_name).convert("RGBA")
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
@ -227,77 +203,38 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
infilled.paste(image, (0, 0), mask=image.split()[-1])
# image.paste(infilled, (0, 0), mask=image.split()[-1])
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=infilled)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
class LaMaInfillInvocation(BaseInvocation, WithMetadata):
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image using the LaMa model"""
image: ImageField = InputField(description="The image to infill")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
infilled = infill_lama(image.copy())
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=infilled)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
class CV2InfillInvocation(BaseInvocation, WithMetadata):
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
class CV2InfillInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image using OpenCV Inpainting"""
image: ImageField = InputField(description="The image to infill")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
infilled = infill_cv2(image.copy())
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=infilled)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)

55
invokeai/app/invocations/ip_adapter.py
View File

@ -1,38 +1,29 @@
import os
from builtins import float
from typing import List, Union
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
from pydantic import BaseModel, Field, field_validator, model_validator
from typing_extensions import Self
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.model_management.models.base import BaseModelType, ModelType
from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.model_manager.config import BaseModelType, ModelType
# LS: Consider moving these two classes into model.py
class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="Key to the IP-Adapter model")
class CLIPVisionModelField(BaseModel):
model_name: str = Field(description="Name of the CLIP Vision image encoder model")
base_model: BaseModelType = Field(description="Base model (usually 'Any')")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="Key to the CLIP Vision image encoder model")
class IPAdapterField(BaseModel):
@ -49,12 +40,12 @@ class IPAdapterField(BaseModel):
@field_validator("weight")
@classmethod
def validate_ip_adapter_weight(cls, v):
def validate_ip_adapter_weight(cls, v: float) -> float:
validate_weights(v)
return v
@model_validator(mode="after")
def validate_begin_end_step_percent(self):
def validate_begin_end_step_percent(self) -> Self:
validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
return self
@ -65,7 +56,7 @@ class IPAdapterOutput(BaseInvocationOutput):
ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.1")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.2")
class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes."""
@ -87,33 +78,25 @@ class IPAdapterInvocation(BaseInvocation):
@field_validator("weight")
@classmethod
def validate_ip_adapter_weight(cls, v):
def validate_ip_adapter_weight(cls, v: float) -> float:
validate_weights(v)
return v
@model_validator(mode="after")
def validate_begin_end_step_percent(self):
def validate_begin_end_step_percent(self) -> Self:
validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
return self
def invoke(self, context: InvocationContext) -> IPAdapterOutput:
# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
ip_adapter_info = context.services.model_manager.model_info(
self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
)
# HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
# directly, and 2) we are reading from disk every time this invocation is called without caching the result.
# A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
# is currently messy due to differences between how the model info is generated when installing a model from
# disk vs. downloading the model.
image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
)
ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
image_encoder_model_id = ip_adapter_info.image_encoder_model_id
image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
image_encoder_model = CLIPVisionModelField(
model_name=image_encoder_model_name,
base_model=BaseModelType.Any,
image_encoder_models = context.models.search_by_attrs(
name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
)
assert len(image_encoder_models) == 1
image_encoder_model = CLIPVisionModelField(key=image_encoder_models[0].key)
return IPAdapterOutput(
ip_adapter=IPAdapterField(
image=self.image,

437
invokeai/app/invocations/latent.py
View File

@ -3,14 +3,16 @@ import inspect
import math
from contextlib import ExitStack
from functools import singledispatchmethod
from typing import List, Literal, Optional, Union
from typing import Any, Iterator, List, Literal, Optional, Tuple, Union
import einops
import numpy as np
import numpy.typing as npt
import torch
import torchvision
import torchvision.transforms as T
from diffusers import AutoencoderKL, AutoencoderTiny
from diffusers.configuration_utils import ConfigMixin
from diffusers.image_processor import VaeImageProcessor
from diffusers.models.adapter import T2IAdapter
from diffusers.models.attention_processor import (
@ -19,28 +21,42 @@ from diffusers.models.attention_processor import (
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from diffusers.schedulers import DPMSolverSDEScheduler
from diffusers.schedulers import SchedulerMixin as Scheduler
from PIL import Image, ImageFilter
from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
from invokeai.app.invocations.fields import (
ConditioningField,
DenoiseMaskField,
FieldDescriptions,
ImageField,
Input,
InputField,
LatentsField,
OutputField,
UIType,
WithBoard,
WithMetadata,
)
from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.primitives import (
DenoiseMaskField,
DenoiseMaskOutput,
ImageField,
ImageOutput,
LatentsField,
LatentsOutput,
build_latents_output,
)
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
from invokeai.backend.model_management.models import ModelType, SilenceWarnings
from invokeai.backend.lora import LoRAModelRaw
from invokeai.backend.model_manager import BaseModelType, LoadedModel
from invokeai.backend.model_patcher import ModelPatcher
from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
IPAdapterConditioningInfo,
@ -49,11 +65,8 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
TextConditioningData,
TextConditioningRegions,
)
from invokeai.backend.util.silence_warnings import SilenceWarnings
from ...backend.model_management.lora import ModelPatcher
from ...backend.model_management.models import BaseModelType
from ...backend.model_management.seamless import set_seamless
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.stable_diffusion.diffusers_pipeline import (
ControlNetData,
IPAdapterData,
@ -66,16 +79,9 @@ from ...backend.util.devices import choose_precision, choose_torch_device
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
WithMetadata,
invocation,
invocation_output,
)
from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .model import ModelInfo, UNetField, VaeField
@ -84,18 +90,10 @@ if choose_torch_device() == torch.device("mps"):
DEFAULT_PRECISION = choose_precision(choose_torch_device())
SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
# HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
# be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
# factor is hard-coded to a literal '8' rather than using this constant.
# The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
LATENT_SCALE_FACTOR = 8
@invocation_output("scheduler_output")
class SchedulerOutput(BaseInvocationOutput):
scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
scheduler: SCHEDULER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
@invocation(
@ -108,7 +106,7 @@ class SchedulerOutput(BaseInvocationOutput):
class SchedulerInvocation(BaseInvocation):
"""Selects a scheduler."""
scheduler: SAMPLER_NAME_VALUES = InputField(
scheduler: SCHEDULER_NAME_VALUES = InputField(
default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
@ -123,7 +121,7 @@ class SchedulerInvocation(BaseInvocation):
title="Create Denoise Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.0",
version="1.0.1",
)
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
@ -138,10 +136,10 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
ui_order=4,
)
def prep_mask_tensor(self, mask_image):
def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
if mask_image.mode != "L":
mask_image = mask_image.convert("L")
mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
if mask_tensor.dim() == 3:
mask_tensor = mask_tensor.unsqueeze(0)
# if shape is not None:
@ -151,41 +149,90 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
if self.image is not None:
image = context.services.images.get_pil_image(self.image.image_name)
image = image_resized_to_grid_as_tensor(image.convert("RGB"))
if image.dim() == 3:
image = image.unsqueeze(0)
image = context.images.get_pil(self.image.image_name)
image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
if image_tensor.dim() == 3:
image_tensor = image_tensor.unsqueeze(0)
else:
image = None
image_tensor = None
mask = self.prep_mask_tensor(
context.services.images.get_pil_image(self.mask.image_name),
context.images.get_pil(self.mask.image_name),
)
if image is not None:
vae_info = context.services.model_manager.get_model(
**self.vae.vae.model_dump(),
context=context,
)
if image_tensor is not None:
vae_info = context.models.load(**self.vae.vae.model_dump())
img_mask = tv_resize(mask, image.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
masked_image = image * torch.where(img_mask < 0.5, 0.0, 1.0)
img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
# TODO:
masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
masked_latents_name = f"{context.graph_execution_state_id}__{self.id}_masked_latents"
context.services.latents.save(masked_latents_name, masked_latents)
masked_latents_name = context.tensors.save(tensor=masked_latents)
else:
masked_latents_name = None
mask_name = f"{context.graph_execution_state_id}__{self.id}_mask"
context.services.latents.save(mask_name, mask)
mask_name = context.tensors.save(tensor=mask)
return DenoiseMaskOutput(
denoise_mask=DenoiseMaskField(
mask_name=mask_name,
masked_latents_name=masked_latents_name,
),
return DenoiseMaskOutput.build(
mask_name=mask_name,
masked_latents_name=masked_latents_name,
gradient=False,
)
@invocation(
"create_gradient_mask",
title="Create Gradient Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.0",
)
class CreateGradientMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
mask: ImageField = InputField(default=None, description="Image which will be masked", ui_order=1)
edge_radius: int = InputField(
default=16, ge=0, description="How far to blur/expand the edges of the mask", ui_order=2
)
coherence_mode: Literal["Gaussian Blur", "Box Blur", "Staged"] = InputField(default="Gaussian Blur", ui_order=3)
minimum_denoise: float = InputField(
default=0.0, ge=0, le=1, description="Minimum denoise level for the coherence region", ui_order=4
)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
mask_image = context.images.get_pil(self.mask.image_name, mode="L")
if self.coherence_mode == "Box Blur":
blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
else: # Gaussian Blur OR Staged
# Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
# redistribute blur so that the edges are 0 and blur out to 1
blur_tensor = (blur_tensor - 0.5) * 2
threshold = 1 - self.minimum_denoise
if self.coherence_mode == "Staged":
# wherever the blur_tensor is masked to any degree, convert it to threshold
blur_tensor = torch.where((blur_tensor < 1), threshold, blur_tensor)
else:
# wherever the blur_tensor is above threshold but less than 1, drop it to threshold
blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
# multiply original mask to force actually masked regions to 0
blur_tensor = mask_tensor * blur_tensor
mask_name = context.tensors.save(tensor=blur_tensor.unsqueeze(1))
return DenoiseMaskOutput.build(
mask_name=mask_name,
masked_latents_name=None,
gradient=True,
)
@ -196,10 +243,7 @@ def get_scheduler(
seed: int,
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.model_dump(),
context=context,
)
orig_scheduler_info = context.models.load(**scheduler_info.model_dump())
with orig_scheduler_info as orig_scheduler:
scheduler_config = orig_scheduler.config
@ -207,7 +251,7 @@ def get_scheduler(
scheduler_config = scheduler_config["_backup"]
scheduler_config = {
**scheduler_config,
**scheduler_extra_config,
**scheduler_extra_config, # FIXME
"_backup": scheduler_config,
}
@ -220,6 +264,7 @@ def get_scheduler(
# hack copied over from generate.py
if not hasattr(scheduler, "uses_inpainting_model"):
scheduler.uses_inpainting_model = lambda: False
assert isinstance(scheduler, Scheduler)
return scheduler
@ -228,7 +273,7 @@ def get_scheduler(
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
version="1.5.1",
version="1.5.2",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
@ -256,7 +301,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
description=FieldDescriptions.denoising_start,
)
denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
scheduler: SAMPLER_NAME_VALUES = InputField(
scheduler: SCHEDULER_NAME_VALUES = InputField(
default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
@ -303,7 +348,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
)
@field_validator("cfg_scale")
def ge_one(cls, v):
def ge_one(cls, v: Union[List[float], float]) -> Union[List[float], float]:
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
for i in v:
@ -346,12 +391,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]] = []
text_embeddings_masks: list[Optional[torch.Tensor]] = []
for cond in cond_list:
cond_data = context.services.latents.get(cond.conditioning_name)
cond_data = context.conditioning.load(cond.conditioning_name)
text_embeddings.append(cond_data.conditionings[0].to(device=device, dtype=dtype))
mask = cond.mask
if mask is not None:
mask = context.services.latents.get(mask.mask_name)
mask = context.tensors.load(mask.mask_name)
text_embeddings_masks.append(mask)
return text_embeddings, text_embeddings_masks
@ -494,13 +539,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
guidance_scale=self.cfg_scale,
guidance_rescale_multiplier=self.cfg_rescale_multiplier,
)
return conditioning_data
def create_pipeline(
self,
unet,
scheduler,
unet: UNet2DConditionModel,
scheduler: Scheduler,
) -> StableDiffusionGeneratorPipeline:
# TODO:
# configure_model_padding(
@ -511,10 +555,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
class FakeVae:
class FakeVaeConfig:
def __init__(self):
def __init__(self) -> None:
self.block_out_channels = [0]
def __init__(self):
def __init__(self) -> None:
self.config = FakeVae.FakeVaeConfig()
return StableDiffusionGeneratorPipeline(
@ -531,11 +575,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
def prep_control_data(
self,
context: InvocationContext,
control_input: Union[ControlField, List[ControlField]],
control_input: Optional[Union[ControlField, List[ControlField]]],
latents_shape: List[int],
exit_stack: ExitStack,
do_classifier_free_guidance: bool = True,
) -> List[ControlNetData]:
) -> Optional[List[ControlNetData]]:
# Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
control_height_resize = latents_shape[2] * LATENT_SCALE_FACTOR
control_width_resize = latents_shape[3] * LATENT_SCALE_FACTOR
@ -557,18 +601,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
# and if weight is None, populate with default 1.0?
controlnet_data = []
for control_info in control_list:
control_model = exit_stack.enter_context(
context.services.model_manager.get_model(
model_name=control_info.control_model.model_name,
model_type=ModelType.ControlNet,
base_model=control_info.control_model.base_model,
context=context,
)
)
control_model = exit_stack.enter_context(context.models.load(key=control_info.control_model.key))
# control_models.append(control_model)
control_image_field = control_info.image
input_image = context.services.images.get_pil_image(control_image_field.image_name)
input_image = context.images.get_pil(control_image_field.image_name)
# self.image.image_type, self.image.image_name
# FIXME: still need to test with different widths, heights, devices, dtypes
# and add in batch_size, num_images_per_prompt?
@ -624,27 +661,17 @@ class DenoiseLatentsInvocation(BaseInvocation):
ip_adapter_data_list = []
for single_ip_adapter in ip_adapter:
ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
context.services.model_manager.get_model(
model_name=single_ip_adapter.ip_adapter_model.model_name,
model_type=ModelType.IPAdapter,
base_model=single_ip_adapter.ip_adapter_model.base_model,
context=context,
)
context.models.load(key=single_ip_adapter.ip_adapter_model.key)
)
image_encoder_model_info = context.services.model_manager.get_model(
model_name=single_ip_adapter.image_encoder_model.model_name,
model_type=ModelType.CLIPVision,
base_model=single_ip_adapter.image_encoder_model.base_model,
context=context,
)
image_encoder_model_info = context.models.load(key=single_ip_adapter.image_encoder_model.key)
# `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
single_ipa_images = single_ip_adapter.image
if not isinstance(single_ipa_images, list):
single_ipa_images = [single_ipa_images]
single_ipa_image_fields = single_ip_adapter.image
if not isinstance(single_ipa_image_fields, list):
single_ipa_image_fields = [single_ipa_image_fields]
single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_image_fields]
# TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
@ -685,26 +712,20 @@ class DenoiseLatentsInvocation(BaseInvocation):
t2i_adapter_data = []
for t2i_adapter_field in t2i_adapter:
t2i_adapter_model_info = context.services.model_manager.get_model(
model_name=t2i_adapter_field.t2i_adapter_model.model_name,
model_type=ModelType.T2IAdapter,
base_model=t2i_adapter_field.t2i_adapter_model.base_model,
context=context,
)
image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
t2i_adapter_model_config = context.models.get_config(key=t2i_adapter_field.t2i_adapter_model.key)
t2i_adapter_loaded_model = context.models.load(key=t2i_adapter_field.t2i_adapter_model.key)
image = context.images.get_pil(t2i_adapter_field.image.image_name)
# The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
if t2i_adapter_model_config.base == BaseModelType.StableDiffusion1:
max_unet_downscale = 8
elif t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusionXL:
elif t2i_adapter_model_config.base == BaseModelType.StableDiffusionXL:
max_unet_downscale = 4
else:
raise ValueError(
f"Unexpected T2I-Adapter base model type: '{t2i_adapter_field.t2i_adapter_model.base_model}'."
)
raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
t2i_adapter_model: T2IAdapter
with t2i_adapter_model_info as t2i_adapter_model:
with t2i_adapter_loaded_model as t2i_adapter_model:
total_downscale_factor = t2i_adapter_model.total_downscale_factor
# Resize the T2I-Adapter input image.
@ -724,7 +745,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
do_classifier_free_guidance=False,
width=t2i_input_width,
height=t2i_input_height,
num_channels=t2i_adapter_model.config.in_channels,
num_channels=t2i_adapter_model.config["in_channels"], # mypy treats this as a FrozenDict
device=t2i_adapter_model.device,
dtype=t2i_adapter_model.dtype,
resize_mode=t2i_adapter_field.resize_mode,
@ -749,7 +770,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
# original idea by https://github.com/AmericanPresidentJimmyCarter
# TODO: research more for second order schedulers timesteps
def init_scheduler(self, scheduler, device, steps, denoising_start, denoising_end, seed: int):
def init_scheduler(
self,
scheduler: Union[Scheduler, ConfigMixin],
device: torch.device,
steps: int,
denoising_start: float,
denoising_end: float,
seed: int,
) -> Tuple[int, List[int], int]:
assert isinstance(scheduler, ConfigMixin)
if scheduler.config.get("cpu_only", False):
scheduler.set_timesteps(steps, device="cpu")
timesteps = scheduler.timesteps.to(device=device)
@ -761,11 +791,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
_timesteps = timesteps[:: scheduler.order]
# get start timestep index
t_start_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_start)))
t_start_val = int(round(scheduler.config["num_train_timesteps"] * (1 - denoising_start)))
t_start_idx = len(list(filter(lambda ts: ts >= t_start_val, _timesteps)))
# get end timestep index
t_end_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_end)))
t_end_val = int(round(scheduler.config["num_train_timesteps"] * (1 - denoising_end)))
t_end_idx = len(list(filter(lambda ts: ts >= t_end_val, _timesteps[t_start_idx:])))
# apply order to indexes
@ -786,18 +816,20 @@ class DenoiseLatentsInvocation(BaseInvocation):
return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
def prep_inpaint_mask(self, context, latents):
def prep_inpaint_mask(
self, context: InvocationContext, latents: torch.Tensor
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], bool]:
if self.denoise_mask is None:
return None, None
return None, None, False
mask = context.services.latents.get(self.denoise_mask.mask_name)
mask = context.tensors.load(self.denoise_mask.mask_name)
mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
if self.denoise_mask.masked_latents_name is not None:
masked_latents = context.services.latents.get(self.denoise_mask.masked_latents_name)
masked_latents = context.tensors.load(self.denoise_mask.masked_latents_name)
else:
masked_latents = None
return 1 - mask, masked_latents
return 1 - mask, masked_latents, self.denoise_mask.gradient
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
@ -805,11 +837,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
seed = None
noise = None
if self.noise is not None:
noise = context.services.latents.get(self.noise.latents_name)
noise = context.tensors.load(self.noise.latents_name)
seed = self.noise.seed
if self.latents is not None:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
if seed is None:
seed = self.latents.seed
@ -824,7 +856,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
if seed is None:
seed = 0
mask, masked_latents = self.prep_inpaint_mask(context, latents)
mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents)
# TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
# below. Investigate whether this is appropriate.
@ -835,35 +867,30 @@ class DenoiseLatentsInvocation(BaseInvocation):
do_classifier_free_guidance=True,
)
# 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]
# get the unet's config so that we can pass the base to dispatch_progress()
unet_config = context.models.get_config(self.unet.unet.key)
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
def step_callback(state: PipelineIntermediateState) -> None:
context.util.sd_step_callback(state, unet_config.base)
def _lora_loader():
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
yield (lora_info.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.model_dump(),
context=context,
)
unet_info = context.models.load(**self.unet.unet.model_dump())
assert isinstance(unet_info.model, UNet2DConditionModel)
with (
ExitStack() as exit_stack,
ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
set_seamless(unet_info.context.model, self.unet.seamless_axes),
ModelPatcher.apply_freeu(unet_info.model, self.unet.freeu_config),
set_seamless(unet_info.model, self.unet.seamless_axes), # FIXME
unet_info as unet,
# Apply the LoRA after unet has been moved to its target device for faster patching.
ModelPatcher.apply_lora_unet(unet, _lora_loader()),
):
assert isinstance(unet, UNet2DConditionModel)
latents = latents.to(device=unet.device, dtype=unet.dtype)
if noise is not None:
noise = noise.to(device=unet.device, dtype=unet.dtype)
@ -917,6 +944,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
seed=seed,
mask=mask,
masked_latents=masked_latents,
gradient_mask=gradient_mask,
num_inference_steps=num_inference_steps,
scheduler_step_kwargs=scheduler_step_kwargs,
conditioning_data=conditioning_data,
@ -932,9 +960,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
name = context.tensors.save(tensor=result_latents)
return LatentsOutput.build(latents_name=name, latents=result_latents, seed=seed)
@invocation(
@ -942,9 +969,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
title="Latents to Image",
tags=["latents", "image", "vae", "l2i"],
category="latents",
version="1.2.0",
version="1.2.1",
)
class LatentsToImageInvocation(BaseInvocation, WithMetadata):
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -960,14 +987,12 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.model_dump(),
context=context,
)
vae_info = context.models.load(**self.vae.vae.model_dump())
with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
assert isinstance(vae, torch.nn.Module)
latents = latents.to(vae.device)
if self.fp32:
vae.to(dtype=torch.float32)
@ -994,7 +1019,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
vae.to(dtype=torch.float16)
latents = latents.half()
if self.tiled or context.services.configuration.tiled_decode:
if self.tiled or context.config.get().tiled_decode:
vae.enable_tiling()
else:
vae.disable_tiling()
@ -1018,22 +1043,9 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
image_dto = context.services.images.create(
image=image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=image)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@ -1044,7 +1056,7 @@ LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic",
title="Resize Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
version="1.0.1",
)
class ResizeLatentsInvocation(BaseInvocation):
"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@ -1067,7 +1079,7 @@ class ResizeLatentsInvocation(BaseInvocation):
antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
# TODO:
device = choose_torch_device()
@ -1085,10 +1097,8 @@ class ResizeLatentsInvocation(BaseInvocation):
if device == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
context.services.latents.save(name, resized_latents)
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
name = context.tensors.save(tensor=resized_latents)
return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@invocation(
@ -1096,7 +1106,7 @@ class ResizeLatentsInvocation(BaseInvocation):
title="Scale Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
version="1.0.1",
)
class ScaleLatentsInvocation(BaseInvocation):
"""Scales latents by a given factor."""
@ -1110,7 +1120,7 @@ class ScaleLatentsInvocation(BaseInvocation):
antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
# TODO:
device = choose_torch_device()
@ -1129,10 +1139,8 @@ class ScaleLatentsInvocation(BaseInvocation):
if device == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
context.services.latents.save(name, resized_latents)
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
name = context.tensors.save(tensor=resized_latents)
return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@invocation(
@ -1140,7 +1148,7 @@ class ScaleLatentsInvocation(BaseInvocation):
title="Image to Latents",
tags=["latents", "image", "vae", "i2l"],
category="latents",
version="1.0.0",
version="1.0.1",
)
class ImageToLatentsInvocation(BaseInvocation):
"""Encodes an image into latents."""
@ -1156,8 +1164,9 @@ class ImageToLatentsInvocation(BaseInvocation):
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
@staticmethod
def vae_encode(vae_info, upcast, tiled, image_tensor):
def vae_encode(vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor) -> torch.Tensor:
with vae_info as vae:
assert isinstance(vae, torch.nn.Module)
orig_dtype = vae.dtype
if upcast:
vae.to(dtype=torch.float32)
@ -1201,12 +1210,9 @@ class ImageToLatentsInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.model_dump(),
context=context,
)
vae_info = context.models.load(**self.vae.vae.model_dump())
image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
if image_tensor.dim() == 3:
@ -1214,22 +1220,26 @@ class ImageToLatentsInvocation(BaseInvocation):
latents = self.vae_encode(vae_info, self.fp32, self.tiled, image_tensor)
name = f"{context.graph_execution_state_id}__{self.id}"
latents = latents.to("cpu")
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents, seed=None)
name = context.tensors.save(tensor=latents)
return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
@singledispatchmethod
@staticmethod
def _encode_to_tensor(vae: AutoencoderKL, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
assert isinstance(vae, torch.nn.Module)
image_tensor_dist = vae.encode(image_tensor).latent_dist
latents = image_tensor_dist.sample().to(dtype=vae.dtype) # FIXME: uses torch.randn. make reproducible!
latents: torch.Tensor = image_tensor_dist.sample().to(
dtype=vae.dtype
) # FIXME: uses torch.randn. make reproducible!
return latents
@_encode_to_tensor.register
@staticmethod
def _(vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
return vae.encode(image_tensor).latents
assert isinstance(vae, torch.nn.Module)
latents: torch.FloatTensor = vae.encode(image_tensor).latents
return latents
@invocation(
@ -1237,7 +1247,7 @@ class ImageToLatentsInvocation(BaseInvocation):
title="Blend Latents",
tags=["latents", "blend"],
category="latents",
version="1.0.0",
version="1.0.1",
)
class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size."""
@ -1253,8 +1263,8 @@ class BlendLatentsInvocation(BaseInvocation):
alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents_a = context.services.latents.get(self.latents_a.latents_name)
latents_b = context.services.latents.get(self.latents_b.latents_name)
latents_a = context.tensors.load(self.latents_a.latents_name)
latents_b = context.tensors.load(self.latents_b.latents_name)
if latents_a.shape != latents_b.shape:
raise Exception("Latents to blend must be the same size.")
@ -1262,7 +1272,12 @@ class BlendLatentsInvocation(BaseInvocation):
# TODO:
device = choose_torch_device()
def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
def slerp(
t: Union[float, npt.NDArray[Any]], # FIXME: maybe use np.float32 here?
v0: Union[torch.Tensor, npt.NDArray[Any]],
v1: Union[torch.Tensor, npt.NDArray[Any]],
DOT_THRESHOLD: float = 0.9995,
) -> Union[torch.Tensor, npt.NDArray[Any]]:
"""
Spherical linear interpolation
Args:
@ -1295,12 +1310,16 @@ class BlendLatentsInvocation(BaseInvocation):
v2 = s0 * v0 + s1 * v1
if inputs_are_torch:
v2 = torch.from_numpy(v2).to(device)
return v2
v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
return v2_torch
else:
assert isinstance(v2, np.ndarray)
return v2
# blend
blended_latents = slerp(self.alpha, latents_a, latents_b)
bl = slerp(self.alpha, latents_a, latents_b)
assert isinstance(bl, torch.Tensor)
blended_latents: torch.Tensor = bl # for type checking convenience
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
blended_latents = blended_latents.to("cpu")
@ -1308,10 +1327,8 @@ class BlendLatentsInvocation(BaseInvocation):
if device == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
context.services.latents.save(name, blended_latents)
return build_latents_output(latents_name=name, latents=blended_latents)
name = context.tensors.save(tensor=blended_latents)
return LatentsOutput.build(latents_name=name, latents=blended_latents)
# The Crop Latents node was copied from @skunkworxdark's implementation here:
@ -1321,7 +1338,7 @@ class BlendLatentsInvocation(BaseInvocation):
title="Crop Latents",
tags=["latents", "crop"],
category="latents",
version="1.0.0",
version="1.0.1",
)
# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
# Currently, if the class names conflict then 'GET /openapi.json' fails.
@ -1356,7 +1373,7 @@ class CropLatentsCoreInvocation(BaseInvocation):
)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
x1 = self.x // LATENT_SCALE_FACTOR
y1 = self.y // LATENT_SCALE_FACTOR
@ -1365,10 +1382,9 @@ class CropLatentsCoreInvocation(BaseInvocation):
cropped_latents = latents[..., y1:y2, x1:x2]
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, cropped_latents)
name = context.tensors.save(tensor=cropped_latents)
return build_latents_output(latents_name=name, latents=cropped_latents)
return LatentsOutput.build(latents_name=name, latents=cropped_latents)
@invocation_output("ideal_size_output")
@ -1396,15 +1412,16 @@ class IdealSizeInvocation(BaseInvocation):
description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in initial generation artifacts if too large)",
)
def trim_to_multiple_of(self, *args, multiple_of=LATENT_SCALE_FACTOR):
def trim_to_multiple_of(self, *args: int, multiple_of: int = LATENT_SCALE_FACTOR) -> Tuple[int, ...]:
return tuple((x - x % multiple_of) for x in args)
def invoke(self, context: InvocationContext) -> IdealSizeOutput:
unet_config = context.models.get_config(**self.unet.unet.model_dump())
aspect = self.width / self.height
dimension = 512
if self.unet.unet.base_model == BaseModelType.StableDiffusion2:
dimension: float = 512
if unet_config.base == BaseModelType.StableDiffusion2:
dimension = 768
elif self.unet.unet.base_model == BaseModelType.StableDiffusionXL:
elif unet_config.base == BaseModelType.StableDiffusionXL:
dimension = 1024
dimension = dimension * self.multiplier
min_dimension = math.floor(dimension * 0.5)

5
invokeai/app/invocations/math.py
View File

@ -5,10 +5,11 @@ from typing import Literal
import numpy as np
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.fields import FieldDescriptions, InputField
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, invocation
@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")

20
invokeai/app/invocations/metadata.py
View File

@ -5,20 +5,22 @@ from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
MetadataField,
OutputField,
UIType,
invocation,
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import ControlField
from invokeai.app.invocations.fields import (
FieldDescriptions,
ImageField,
InputField,
MetadataField,
OutputField,
UIType,
)
from invokeai.app.invocations.ip_adapter import IPAdapterModelField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.services.shared.invocation_context import InvocationContext
from ...version import __version__
@ -31,7 +33,7 @@ class MetadataItemField(BaseModel):
class LoRAMetadataField(BaseModel):
"""LoRA Metadata Field"""
lora: LoRAModelField = Field(description=FieldDescriptions.lora_model)
model: LoRAModelField = Field(description=FieldDescriptions.lora_model)
weight: float = Field(description=FieldDescriptions.lora_weight)
@ -112,7 +114,7 @@ GENERATION_MODES = Literal[
]
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.1")
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.1.1")
class CoreMetadataInvocation(BaseInvocation):
"""Collects core generation metadata into a MetadataField"""

211
invokeai/app/invocations/model.py
View File

@ -1,31 +1,24 @@
import copy
from typing import List, Optional
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.shared.models import FreeUConfig
from ...backend.model_management import BaseModelType, ModelType, SubModelType
from ...backend.model_manager import SubModelType
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
class ModelInfo(BaseModel):
model_name: str = Field(description="Info to load submodel")
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Info to load submodel")
submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="Key of model as returned by ModelRecordServiceBase.get_model()")
submodel_type: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
class LoraInfo(ModelInfo):
@ -55,7 +48,7 @@ class VaeField(BaseModel):
@invocation_output("unet_output")
class UNetOutput(BaseInvocationOutput):
"""Base class for invocations that output a UNet field"""
"""Base class for invocations that output a UNet field."""
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
@ -84,20 +77,13 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
class MainModelField(BaseModel):
"""Main model field"""
model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="Model key")
class LoRAModelField(BaseModel):
"""LoRA model field"""
model_name: str = Field(description="Name of the LoRA model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="LoRA model key")
@invocation(
@ -105,7 +91,7 @@ class LoRAModelField(BaseModel):
title="Main Model",
tags=["model"],
category="model",
version="1.0.0",
version="1.0.1",
)
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
@ -114,85 +100,40 @@ class MainModelLoaderInvocation(BaseInvocation):
# TODO: precision?
def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
base_model = self.model.base_model
model_name = self.model.model_name
model_type = ModelType.Main
key = self.model.key
# TODO: not found exceptions
if not context.services.model_manager.model_exists(
model_name=model_name,
base_model=base_model,
model_type=model_type,
):
raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
"""
if not context.services.model_manager.model_exists(
model_name=self.model_name,
model_type=SDModelType.Diffusers,
submodel=SDModelType.Tokenizer,
):
raise Exception(
f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
)
if not context.services.model_manager.model_exists(
model_name=self.model_name,
model_type=SDModelType.Diffusers,
submodel=SDModelType.TextEncoder,
):
raise Exception(
f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
)
if not context.services.model_manager.model_exists(
model_name=self.model_name,
model_type=SDModelType.Diffusers,
submodel=SDModelType.UNet,
):
raise Exception(
f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
)
"""
if not context.models.exists(key):
raise Exception(f"Unknown model {key}")
return ModelLoaderOutput(
unet=UNetField(
unet=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.UNet,
key=key,
submodel_type=SubModelType.UNet,
),
scheduler=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Scheduler,
key=key,
submodel_type=SubModelType.Scheduler,
),
loras=[],
),
clip=ClipField(
tokenizer=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Tokenizer,
key=key,
submodel_type=SubModelType.Tokenizer,
),
text_encoder=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.TextEncoder,
key=key,
submodel_type=SubModelType.TextEncoder,
),
loras=[],
skipped_layers=0,
),
vae=VaeField(
vae=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Vae,
key=key,
submodel_type=SubModelType.Vae,
),
),
)
@ -206,7 +147,7 @@ class LoraLoaderOutput(BaseInvocationOutput):
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
class LoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
@ -229,21 +170,16 @@ class LoraLoaderInvocation(BaseInvocation):
if self.lora is None:
raise Exception("No LoRA provided")
base_model = self.lora.base_model
lora_name = self.lora.model_name
lora_key = self.lora.key
if not context.services.model_manager.model_exists(
base_model=base_model,
model_name=lora_name,
model_type=ModelType.Lora,
):
raise Exception(f"Unkown lora name: {lora_name}!")
if not context.models.exists(lora_key):
raise Exception(f"Unkown lora: {lora_key}!")
if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
raise Exception(f'Lora "{lora_name}" already applied to unet')
if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
raise Exception(f'Lora "{lora_key}" already applied to unet')
if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
raise Exception(f'Lora "{lora_name}" already applied to clip')
if self.clip is not None and any(lora.key == lora_key for lora in self.clip.loras):
raise Exception(f'Lora "{lora_key}" already applied to clip')
output = LoraLoaderOutput()
@ -251,10 +187,8 @@ class LoraLoaderInvocation(BaseInvocation):
output.unet = copy.deepcopy(self.unet)
output.unet.loras.append(
LoraInfo(
base_model=base_model,
model_name=lora_name,
model_type=ModelType.Lora,
submodel=None,
key=lora_key,
submodel_type=None,
weight=self.weight,
)
)
@ -263,10 +197,8 @@ class LoraLoaderInvocation(BaseInvocation):
output.clip = copy.deepcopy(self.clip)
output.clip.loras.append(
LoraInfo(
base_model=base_model,
model_name=lora_name,
model_type=ModelType.Lora,
submodel=None,
key=lora_key,
submodel_type=None,
weight=self.weight,
)
)
@ -288,7 +220,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
title="SDXL LoRA",
tags=["lora", "model"],
category="model",
version="1.0.0",
version="1.0.1",
)
class SDXLLoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
@ -318,24 +250,19 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
if self.lora is None:
raise Exception("No LoRA provided")
base_model = self.lora.base_model
lora_name = self.lora.model_name
lora_key = self.lora.key
if not context.services.model_manager.model_exists(
base_model=base_model,
model_name=lora_name,
model_type=ModelType.Lora,
):
raise Exception(f"Unknown lora name: {lora_name}!")
if not context.models.exists(lora_key):
raise Exception(f"Unknown lora: {lora_key}!")
if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
raise Exception(f'Lora "{lora_name}" already applied to unet')
if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
raise Exception(f'Lora "{lora_key}" already applied to unet')
if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
raise Exception(f'Lora "{lora_name}" already applied to clip')
if self.clip is not None and any(lora.key == lora_key for lora in self.clip.loras):
raise Exception(f'Lora "{lora_key}" already applied to clip')
if self.clip2 is not None and any(lora.model_name == lora_name for lora in self.clip2.loras):
raise Exception(f'Lora "{lora_name}" already applied to clip2')
if self.clip2 is not None and any(lora.key == lora_key for lora in self.clip2.loras):
raise Exception(f'Lora "{lora_key}" already applied to clip2')
output = SDXLLoraLoaderOutput()
@ -343,10 +270,8 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
output.unet = copy.deepcopy(self.unet)
output.unet.loras.append(
LoraInfo(
base_model=base_model,
model_name=lora_name,
model_type=ModelType.Lora,
submodel=None,
key=lora_key,
submodel_type=None,
weight=self.weight,
)
)
@ -355,10 +280,8 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
output.clip = copy.deepcopy(self.clip)
output.clip.loras.append(
LoraInfo(
base_model=base_model,
model_name=lora_name,
model_type=ModelType.Lora,
submodel=None,
key=lora_key,
submodel_type=None,
weight=self.weight,
)
)
@ -367,10 +290,8 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
output.clip2 = copy.deepcopy(self.clip2)
output.clip2.loras.append(
LoraInfo(
base_model=base_model,
model_name=lora_name,
model_type=ModelType.Lora,
submodel=None,
key=lora_key,
submodel_type=None,
weight=self.weight,
)
)
@ -381,13 +302,10 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
class VAEModelField(BaseModel):
"""Vae model field"""
model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="Model's key")
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
class VaeLoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
@ -398,25 +316,12 @@ class VaeLoaderInvocation(BaseInvocation):
)
def invoke(self, context: InvocationContext) -> VAEOutput:
base_model = self.vae_model.base_model
model_name = self.vae_model.model_name
model_type = ModelType.Vae
key = self.vae_model.key
if not context.services.model_manager.model_exists(
base_model=base_model,
model_name=model_name,
model_type=model_type,
):
raise Exception(f"Unkown vae name: {model_name}!")
return VAEOutput(
vae=VaeField(
vae=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
)
)
)
if not context.models.exists(key):
raise Exception(f"Unkown vae: {key}!")
return VAEOutput(vae=VaeField(vae=ModelInfo(key=key)))
@invocation_output("seamless_output")

31
invokeai/app/invocations/noise.py
View File

@ -4,17 +4,15 @@
import torch
from pydantic import field_validator
from invokeai.app.invocations.latent import LatentsField
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import SEED_MAX
from ...backend.util.devices import choose_torch_device, torch_dtype
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
@ -69,13 +67,13 @@ class NoiseOutput(BaseInvocationOutput):
width: int = OutputField(description=FieldDescriptions.width)
height: int = OutputField(description=FieldDescriptions.height)
def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
return NoiseOutput(
noise=LatentsField(latents_name=latents_name, seed=seed),
width=latents.size()[3] * 8,
height=latents.size()[2] * 8,
)
@classmethod
def build(cls, latents_name: str, latents: torch.Tensor, seed: int) -> "NoiseOutput":
return cls(
noise=LatentsField(latents_name=latents_name, seed=seed),
width=latents.size()[3] * LATENT_SCALE_FACTOR,
height=latents.size()[2] * LATENT_SCALE_FACTOR,
)
@invocation(
@ -96,13 +94,13 @@ class NoiseInvocation(BaseInvocation):
)
width: int = InputField(
default=512,
multiple_of=8,
multiple_of=LATENT_SCALE_FACTOR,
gt=0,
description=FieldDescriptions.width,
)
height: int = InputField(
default=512,
multiple_of=8,
multiple_of=LATENT_SCALE_FACTOR,
gt=0,
description=FieldDescriptions.height,
)
@ -124,6 +122,5 @@ class NoiseInvocation(BaseInvocation):
seed=self.seed,
use_cpu=self.use_cpu,
)
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, noise)
return build_noise_output(latents_name=name, latents=noise, seed=self.seed)
name = context.tensors.save(tensor=noise)
return NoiseOutput.build(latents_name=name, latents=noise, seed=self.seed)

508
invokeai/app/invocations/onnx.py
View File

@ -1,508 +0,0 @@
# Copyright (c) 2023 Borisov Sergey (https://github.com/StAlKeR7779)
import inspect
# from contextlib import ExitStack
from typing import List, Literal, Union
import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend import BaseModelType, ModelType, SubModelType
from ...backend.model_management import ONNXModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.util import choose_torch_device
from ..util.ti_utils import extract_ti_triggers_from_prompt
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIComponent,
UIType,
WithMetadata,
invocation,
invocation_output,
)
from .controlnet_image_processors import ControlField
from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, build_latents_output, get_scheduler
from .model import ClipField, ModelInfo, UNetField, VaeField
ORT_TO_NP_TYPE = {
"tensor(bool)": np.bool_,
"tensor(int8)": np.int8,
"tensor(uint8)": np.uint8,
"tensor(int16)": np.int16,
"tensor(uint16)": np.uint16,
"tensor(int32)": np.int32,
"tensor(uint32)": np.uint32,
"tensor(int64)": np.int64,
"tensor(uint64)": np.uint64,
"tensor(float16)": np.float16,
"tensor(float)": np.float32,
"tensor(double)": np.float64,
}
PRECISION_VALUES = Literal[tuple(ORT_TO_NP_TYPE.keys())]
@invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning", version="1.0.0")
class ONNXPromptInvocation(BaseInvocation):
prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea)
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.model_dump(),
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.model_dump(),
)
with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder: # , ExitStack() as stack:
loras = [
(
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.clip.loras
]
ti_list = []
for trigger in extract_ti_triggers_from_prompt(self.prompt):
name = trigger[1:-1]
try:
ti_list.append(
(
name,
context.services.model_manager.get_model(
model_name=name,
base_model=self.clip.text_encoder.base_model,
model_type=ModelType.TextualInversion,
).context.model,
)
)
except Exception:
# print(e)
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
if loras or ti_list:
text_encoder.release_session()
with (
ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
):
text_encoder.create_session()
# copy from
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L153
text_inputs = tokenizer(
self.prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
"""
untruncated_ids = tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
if not np.array_equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
"""
prompt_embeds = text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
# TODO: hacky but works ;D maybe rename latents somehow?
context.services.latents.save(conditioning_name, (prompt_embeds, None))
return ConditioningOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
# Text to image
@invocation(
"t2l_onnx",
title="ONNX Text to Latents",
tags=["latents", "inference", "txt2img", "onnx"],
category="latents",
version="1.0.0",
)
class ONNXTextToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
positive_conditioning: ConditioningField = InputField(
description=FieldDescriptions.positive_cond,
input=Input.Connection,
)
negative_conditioning: ConditioningField = InputField(
description=FieldDescriptions.negative_cond,
input=Input.Connection,
)
noise: LatentsField = InputField(
description=FieldDescriptions.noise,
input=Input.Connection,
)
steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
cfg_scale: Union[float, List[float]] = InputField(
default=7.5,
ge=1,
description=FieldDescriptions.cfg_scale,
)
scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, input=Input.Direct, ui_type=UIType.Scheduler
)
precision: PRECISION_VALUES = InputField(default="tensor(float16)", description=FieldDescriptions.precision)
unet: UNetField = InputField(
description=FieldDescriptions.unet,
input=Input.Connection,
)
control: Union[ControlField, list[ControlField]] = InputField(
default=None,
description=FieldDescriptions.control,
)
# seamless: bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
for i in v:
if i < 1:
raise ValueError("cfg_scale must be greater than 1")
else:
if v < 1:
raise ValueError("cfg_scale must be greater than 1")
return v
# based on
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
def invoke(self, context: InvocationContext) -> LatentsOutput:
c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name)
uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
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]
if isinstance(c, torch.Tensor):
c = c.cpu().numpy()
if isinstance(uc, torch.Tensor):
uc = uc.cpu().numpy()
device = torch.device(choose_torch_device())
prompt_embeds = np.concatenate([uc, c])
latents = context.services.latents.get(self.noise.latents_name)
if isinstance(latents, torch.Tensor):
latents = latents.cpu().numpy()
# TODO: better execution device handling
latents = latents.astype(ORT_TO_NP_TYPE[self.precision])
# get the initial random noise unless the user supplied it
do_classifier_free_guidance = True
# latents_dtype = prompt_embeds.dtype
# latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
# if latents.shape != latents_shape:
# raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
seed=0, # TODO: refactor this node
)
def torch2numpy(latent: torch.Tensor):
return latent.cpu().numpy()
def numpy2torch(latent, device):
return torch.from_numpy(latent).to(device)
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.model_dump(),
source_node_id=source_node_id,
)
scheduler.set_timesteps(self.steps)
latents = latents * np.float64(scheduler.init_noise_sigma)
extra_step_kwargs = {}
if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
extra_step_kwargs.update(
eta=0.0,
)
unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump())
with unet_info as unet: # , ExitStack() as stack:
# loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
loras = [
(
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.unet.loras
]
if loras:
unet.release_session()
with ONNXModelPatcher.apply_lora_unet(unet, loras):
# TODO:
_, _, h, w = latents.shape
unet.create_session(h, w)
timestep_dtype = next(
(input.type for input in unet.session.get_inputs() if input.name == "timestep"), "tensor(float16)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
for i in tqdm(range(len(scheduler.timesteps))):
t = scheduler.timesteps[i]
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = scheduler.scale_model_input(numpy2torch(latent_model_input, device), t)
latent_model_input = latent_model_input.cpu().numpy()
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)
noise_pred = noise_pred[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
scheduler_output = scheduler.step(
numpy2torch(noise_pred, device), t, numpy2torch(latents, device), **extra_step_kwargs
)
latents = torch2numpy(scheduler_output.prev_sample)
state = PipelineIntermediateState(
run_id="test", step=i, timestep=timestep, latents=scheduler_output.prev_sample
)
dispatch_progress(self, context=context, source_node_id=source_node_id, intermediate_state=state)
# call the callback, if provided
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
torch.cuda.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=torch.from_numpy(latents))
# Latent to image
@invocation(
"l2i_onnx",
title="ONNX Latents to Image",
tags=["latents", "image", "vae", "onnx"],
category="image",
version="1.2.0",
)
class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
"""Generates an image from latents."""
latents: LatentsField = InputField(
description=FieldDescriptions.denoised_latents,
input=Input.Connection,
)
vae: VaeField = InputField(
description=FieldDescriptions.vae,
input=Input.Connection,
)
# tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
def invoke(self, context: InvocationContext) -> ImageOutput:
latents = context.services.latents.get(self.latents.latents_name)
if self.vae.vae.submodel != SubModelType.VaeDecoder:
raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
vae_info = context.services.model_manager.get_model(
**self.vae.vae.model_dump(),
)
# clear memory as vae decode can request a lot
torch.cuda.empty_cache()
with vae_info as vae:
vae.create_session()
# copied from
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L427
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate([vae(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])])
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
image = VaeImageProcessor.numpy_to_pil(image)[0]
torch.cuda.empty_cache()
image_dto = context.services.images.create(
image=image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
@invocation_output("model_loader_output_onnx")
class ONNXModelLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
unet: UNetField = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
vae_decoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Decoder")
vae_encoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Encoder")
class OnnxModelField(BaseModel):
"""Onnx model field"""
model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0")
class OnnxModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
model: OnnxModelField = InputField(
description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type=UIType.ONNXModel
)
def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
base_model = self.model.base_model
model_name = self.model.model_name
model_type = ModelType.ONNX
# TODO: not found exceptions
if not context.services.model_manager.model_exists(
model_name=model_name,
base_model=base_model,
model_type=model_type,
):
raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
"""
if not context.services.model_manager.model_exists(
model_name=self.model_name,
model_type=SDModelType.Diffusers,
submodel=SDModelType.Tokenizer,
):
raise Exception(
f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
)
if not context.services.model_manager.model_exists(
model_name=self.model_name,
model_type=SDModelType.Diffusers,
submodel=SDModelType.TextEncoder,
):
raise Exception(
f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
)
if not context.services.model_manager.model_exists(
model_name=self.model_name,
model_type=SDModelType.Diffusers,
submodel=SDModelType.UNet,
):
raise Exception(
f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
)
"""
return ONNXModelLoaderOutput(
unet=UNetField(
unet=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.UNet,
),
scheduler=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Scheduler,
),
loras=[],
),
clip=ClipField(
tokenizer=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Tokenizer,
),
text_encoder=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.TextEncoder,
),
loras=[],
skipped_layers=0,
),
vae_decoder=VaeField(
vae=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.VaeDecoder,
),
),
vae_encoder=VaeField(
vae=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.VaeEncoder,
),
),
)

42
invokeai/app/invocations/param_easing.py
View File

@ -40,8 +40,10 @@ from easing_functions import (
from matplotlib.ticker import MaxNLocator
from invokeai.app.invocations.primitives import FloatCollectionOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, invocation
from .fields import InputField
@invocation(
@ -109,7 +111,7 @@ EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
title="Step Param Easing",
tags=["step", "easing"],
category="step",
version="1.0.0",
version="1.0.1",
)
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""
@ -148,19 +150,19 @@ class StepParamEasingInvocation(BaseInvocation):
postlist = list(num_poststeps * [self.post_end_value])
if log_diagnostics:
context.services.logger.debug("start_step: " + str(start_step))
context.services.logger.debug("end_step: " + str(end_step))
context.services.logger.debug("num_easing_steps: " + str(num_easing_steps))
context.services.logger.debug("num_presteps: " + str(num_presteps))
context.services.logger.debug("num_poststeps: " + str(num_poststeps))
context.services.logger.debug("prelist size: " + str(len(prelist)))
context.services.logger.debug("postlist size: " + str(len(postlist)))
context.services.logger.debug("prelist: " + str(prelist))
context.services.logger.debug("postlist: " + str(postlist))
context.logger.debug("start_step: " + str(start_step))
context.logger.debug("end_step: " + str(end_step))
context.logger.debug("num_easing_steps: " + str(num_easing_steps))
context.logger.debug("num_presteps: " + str(num_presteps))
context.logger.debug("num_poststeps: " + str(num_poststeps))
context.logger.debug("prelist size: " + str(len(prelist)))
context.logger.debug("postlist size: " + str(len(postlist)))
context.logger.debug("prelist: " + str(prelist))
context.logger.debug("postlist: " + str(postlist))
easing_class = EASING_FUNCTIONS_MAP[self.easing]
if log_diagnostics:
context.services.logger.debug("easing class: " + str(easing_class))
context.logger.debug("easing class: " + str(easing_class))
easing_list = []
if self.mirror: # "expected" mirroring
# if number of steps is even, squeeze duration down to (number_of_steps)/2
@ -171,7 +173,7 @@ class StepParamEasingInvocation(BaseInvocation):
base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
if log_diagnostics:
context.services.logger.debug("base easing duration: " + str(base_easing_duration))
context.logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = num_easing_steps % 2 == 0 # even number of steps
easing_function = easing_class(
start=self.start_value,
@ -183,14 +185,14 @@ class StepParamEasingInvocation(BaseInvocation):
easing_val = easing_function.ease(step_index)
base_easing_vals.append(easing_val)
if log_diagnostics:
context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
if even_num_steps:
mirror_easing_vals = list(reversed(base_easing_vals))
else:
mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
if log_diagnostics:
context.services.logger.debug("base easing vals: " + str(base_easing_vals))
context.services.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
context.logger.debug("base easing vals: " + str(base_easing_vals))
context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
easing_list = base_easing_vals + mirror_easing_vals
# FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
@ -225,12 +227,12 @@ class StepParamEasingInvocation(BaseInvocation):
step_val = easing_function.ease(step_index)
easing_list.append(step_val)
if log_diagnostics:
context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
if log_diagnostics:
context.services.logger.debug("prelist size: " + str(len(prelist)))
context.services.logger.debug("easing_list size: " + str(len(easing_list)))
context.services.logger.debug("postlist size: " + str(len(postlist)))
context.logger.debug("prelist size: " + str(len(prelist)))
context.logger.debug("easing_list size: " + str(len(easing_list)))
context.logger.debug("postlist size: " + str(len(postlist)))
param_list = prelist + easing_list + postlist

136
invokeai/app/invocations/primitives.py
View File

@ -1,20 +1,29 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from typing import Optional, Tuple
from typing import Optional
import torch
from pydantic import BaseModel, Field
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
from invokeai.app.invocations.fields import (
ColorField,
ConditioningField,
DenoiseMaskField,
FieldDescriptions,
ImageField,
Input,
InputField,
LatentsField,
MaskField,
OutputField,
UIComponent,
)
from invokeai.app.services.images.images_common import ImageDTO
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIComponent,
invocation,
invocation_output,
)
@ -221,24 +230,6 @@ class StringCollectionInvocation(BaseInvocation):
# region Image
class ImageField(BaseModel):
"""An image primitive field"""
image_name: str = Field(description="The name of the image")
class BoardField(BaseModel):
"""A board primitive field"""
board_id: str = Field(description="The id of the board")
class MaskField(BaseModel):
"""A mask primitive field."""
mask_name: str = Field(description="The name of the mask.")
@invocation_output("mask_output")
class MaskOutput(BaseInvocationOutput):
"""A torch mask tensor.
@ -259,6 +250,14 @@ class ImageOutput(BaseInvocationOutput):
width: int = OutputField(description="The width of the image in pixels")
height: int = OutputField(description="The height of the image in pixels")
@classmethod
def build(cls, image_dto: ImageDTO) -> "ImageOutput":
return cls(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
@invocation_output("image_collection_output")
class ImageCollectionOutput(BaseInvocationOutput):
@ -269,16 +268,14 @@ class ImageCollectionOutput(BaseInvocationOutput):
)
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
class ImageInvocation(
BaseInvocation,
):
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.1")
class ImageInvocation(BaseInvocation):
"""An image primitive value"""
image: ImageField = InputField(description="The image to load")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.images.get_pil(self.image.image_name)
return ImageOutput(
image=ImageField(image_name=self.image.image_name),
@ -308,42 +305,44 @@ class ImageCollectionInvocation(BaseInvocation):
# region DenoiseMask
class DenoiseMaskField(BaseModel):
"""An inpaint mask field"""
mask_name: str = Field(description="The name of the mask image")
masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
@invocation_output("denoise_mask_output")
class DenoiseMaskOutput(BaseInvocationOutput):
"""Base class for nodes that output a single image"""
denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
@classmethod
def build(
cls, mask_name: str, masked_latents_name: Optional[str] = None, gradient: bool = False
) -> "DenoiseMaskOutput":
return cls(
denoise_mask=DenoiseMaskField(
mask_name=mask_name, masked_latents_name=masked_latents_name, gradient=gradient
),
)
# endregion
# region Latents
class LatentsField(BaseModel):
"""A latents tensor primitive field"""
latents_name: str = Field(description="The name of the latents")
seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
@invocation_output("latents_output")
class LatentsOutput(BaseInvocationOutput):
"""Base class for nodes that output a single latents tensor"""
latents: LatentsField = OutputField(
description=FieldDescriptions.latents,
)
latents: LatentsField = OutputField(description=FieldDescriptions.latents)
width: int = OutputField(description=FieldDescriptions.width)
height: int = OutputField(description=FieldDescriptions.height)
@classmethod
def build(cls, latents_name: str, latents: torch.Tensor, seed: Optional[int] = None) -> "LatentsOutput":
return cls(
latents=LatentsField(latents_name=latents_name, seed=seed),
width=latents.size()[3] * LATENT_SCALE_FACTOR,
height=latents.size()[2] * LATENT_SCALE_FACTOR,
)
@invocation_output("latents_collection_output")
class LatentsCollectionOutput(BaseInvocationOutput):
@ -355,7 +354,7 @@ class LatentsCollectionOutput(BaseInvocationOutput):
@invocation(
"latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.0"
"latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.1"
)
class LatentsInvocation(BaseInvocation):
"""A latents tensor primitive value"""
@ -363,9 +362,9 @@ class LatentsInvocation(BaseInvocation):
latents: LatentsField = InputField(description="The latents tensor", input=Input.Connection)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
return build_latents_output(self.latents.latents_name, latents)
return LatentsOutput.build(self.latents.latents_name, latents)
@invocation(
@ -386,31 +385,11 @@ class LatentsCollectionInvocation(BaseInvocation):
return LatentsCollectionOutput(collection=self.collection)
def build_latents_output(latents_name: str, latents: torch.Tensor, seed: Optional[int] = None):
return LatentsOutput(
latents=LatentsField(latents_name=latents_name, seed=seed),
width=latents.size()[3] * 8,
height=latents.size()[2] * 8,
)
# endregion
# region Color
class ColorField(BaseModel):
"""A color primitive field"""
r: int = Field(ge=0, le=255, description="The red component")
g: int = Field(ge=0, le=255, description="The green component")
b: int = Field(ge=0, le=255, description="The blue component")
a: int = Field(ge=0, le=255, description="The alpha component")
def tuple(self) -> Tuple[int, int, int, int]:
return (self.r, self.g, self.b, self.a)
@invocation_output("color_output")
class ColorOutput(BaseInvocationOutput):
"""Base class for nodes that output a single color"""
@ -442,23 +421,16 @@ class ColorInvocation(BaseInvocation):
# region Conditioning
class ConditioningField(BaseModel):
"""A conditioning tensor primitive value"""
conditioning_name: str = Field(description="The name of conditioning tensor")
mask: Optional[MaskField] = Field(
default=None,
description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
"included regions should be set to 1.",
)
@invocation_output("conditioning_output")
class ConditioningOutput(BaseInvocationOutput):
"""Base class for nodes that output a single conditioning tensor"""
conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
@classmethod
def build(cls, conditioning_name: str) -> "ConditioningOutput":
return cls(conditioning=ConditioningField(conditioning_name=conditioning_name))
@invocation_output("conditioning_collection_output")
class ConditioningCollectionOutput(BaseInvocationOutput):

4
invokeai/app/invocations/prompt.py
View File

@ -6,8 +6,10 @@ from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPrompt
from pydantic import field_validator
from invokeai.app.invocations.primitives import StringCollectionOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, UIComponent
@invocation(

110
invokeai/app/invocations/sdxl.py
View File

@ -1,14 +1,10 @@
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.model_manager import SubModelType
from ...backend.model_management import ModelType, SubModelType
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
@ -34,7 +30,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.0")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1")
class SDXLModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl base model, outputting its submodels."""
@ -44,72 +40,52 @@ class SDXLModelLoaderInvocation(BaseInvocation):
# TODO: precision?
def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
base_model = self.model.base_model
model_name = self.model.model_name
model_type = ModelType.Main
model_key = self.model.key
# TODO: not found exceptions
if not context.services.model_manager.model_exists(
model_name=model_name,
base_model=base_model,
model_type=model_type,
):
raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
if not context.models.exists(model_key):
raise Exception(f"Unknown model: {model_key}")
return SDXLModelLoaderOutput(
unet=UNetField(
unet=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.UNet,
key=model_key,
submodel_type=SubModelType.UNet,
),
scheduler=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Scheduler,
key=model_key,
submodel_type=SubModelType.Scheduler,
),
loras=[],
),
clip=ClipField(
tokenizer=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Tokenizer,
key=model_key,
submodel_type=SubModelType.Tokenizer,
),
text_encoder=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.TextEncoder,
key=model_key,
submodel_type=SubModelType.TextEncoder,
),
loras=[],
skipped_layers=0,
),
clip2=ClipField(
tokenizer=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Tokenizer2,
key=model_key,
submodel_type=SubModelType.Tokenizer2,
),
text_encoder=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.TextEncoder2,
key=model_key,
submodel_type=SubModelType.TextEncoder2,
),
loras=[],
skipped_layers=0,
),
vae=VaeField(
vae=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Vae,
key=model_key,
submodel_type=SubModelType.Vae,
),
),
)
@ -120,7 +96,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
title="SDXL Refiner Model",
tags=["model", "sdxl", "refiner"],
category="model",
version="1.0.0",
version="1.0.1",
)
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl refiner model, outputting its submodels."""
@ -133,56 +109,40 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
# TODO: precision?
def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput:
base_model = self.model.base_model
model_name = self.model.model_name
model_type = ModelType.Main
model_key = self.model.key
# TODO: not found exceptions
if not context.services.model_manager.model_exists(
model_name=model_name,
base_model=base_model,
model_type=model_type,
):
raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
if not context.models.exists(model_key):
raise Exception(f"Unknown model: {model_key}")
return SDXLRefinerModelLoaderOutput(
unet=UNetField(
unet=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.UNet,
key=model_key,
submodel_type=SubModelType.UNet,
),
scheduler=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Scheduler,
key=model_key,
submodel_type=SubModelType.Scheduler,
),
loras=[],
),
clip2=ClipField(
tokenizer=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Tokenizer2,
key=model_key,
submodel_type=SubModelType.Tokenizer2,
),
text_encoder=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.TextEncoder2,
key=model_key,
submodel_type=SubModelType.TextEncoder2,
),
loras=[],
skipped_layers=0,
),
vae=VaeField(
vae=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Vae,
key=model_key,
submodel_type=SubModelType.Vae,
),
),
)

7
invokeai/app/invocations/strings.py
View File

@ -2,16 +2,15 @@
import re
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
OutputField,
UIComponent,
invocation,
invocation_output,
)
from .fields import InputField, OutputField, UIComponent
from .primitives import StringOutput

16
invokeai/app/invocations/t2i_adapter.py
View File

@ -1,29 +1,21 @@
from typing import Union
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
from pydantic import BaseModel, Field, field_validator, model_validator
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.model_management.models.base import BaseModelType
from invokeai.app.services.shared.invocation_context import InvocationContext
class T2IAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the T2I-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
key: str = Field(description="Model record key for the T2I-Adapter model")
class T2IAdapterField(BaseModel):

31
invokeai/app/invocations/tiles.py
View File

@ -8,16 +8,12 @@ from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Classification,
Input,
InputField,
InvocationContext,
OutputField,
WithMetadata,
invocation,
invocation_output,
)
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.invocations.fields import ImageField, Input, InputField, OutputField, WithBoard, WithMetadata
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.tiles.tiles import (
calc_tiles_even_split,
calc_tiles_min_overlap,
@ -236,7 +232,7 @@ BLEND_MODES = Literal["Linear", "Seam"]
version="1.1.0",
classification=Classification.Beta,
)
class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Merge multiple tile images into a single image."""
# Inputs
@ -268,7 +264,7 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
# existed in memory at an earlier point in the graph.
tile_np_images: list[np.ndarray] = []
for image in images:
pil_image = context.services.images.get_pil_image(image.image_name)
pil_image = context.images.get_pil(image.image_name)
pil_image = pil_image.convert("RGB")
tile_np_images.append(np.array(pil_image))
@ -291,18 +287,5 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
# Convert into a PIL image and save
pil_image = Image.fromarray(np_image)
image_dto = context.services.images.create(
image=pil_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
image_dto = context.images.save(image=pil_image)
return ImageOutput.build(image_dto)

35
invokeai/app/invocations/upscale.py
View File

@ -8,13 +8,15 @@ import torch
from PIL import Image
from pydantic import ConfigDict
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.invocations.fields import ImageField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, WithBoard, WithMetadata
# TODO: Populate this from disk?
# TODO: Use model manager to load?
@ -29,8 +31,8 @@ if choose_torch_device() == torch.device("mps"):
from torch import mps
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
class ESRGANInvocation(BaseInvocation, WithMetadata):
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.1")
class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Upscales an image using RealESRGAN."""
image: ImageField = InputField(description="The input image")
@ -42,8 +44,8 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
model_config = ConfigDict(protected_namespaces=())
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
models_path = context.services.configuration.models_path
image = context.images.get_pil(self.image.image_name)
models_path = context.config.get().models_path
rrdbnet_model = None
netscale = None
@ -87,7 +89,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
netscale = 2
else:
msg = f"Invalid RealESRGAN model: {self.model_name}"
context.services.logger.error(msg)
context.logger.error(msg)
raise ValueError(msg)
esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
@ -110,19 +112,6 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
image_dto = context.services.images.create(
image=pil_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=context.workflow,
)
image_dto = context.images.save(image=pil_image)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput.build(image_dto)

0
invokeai/app/services/invocation_queue/__init__.py → invokeai/app/services/bulk_download/__init__.py
View File

44
invokeai/app/services/bulk_download/bulk_download_base.py Normal file
View File

@ -0,0 +1,44 @@
from abc import ABC, abstractmethod
from typing import Optional
class BulkDownloadBase(ABC):
"""Responsible for creating a zip file containing the images specified by the given image names or board id."""
@abstractmethod
def handler(
self, image_names: Optional[list[str]], board_id: Optional[str], bulk_download_item_id: Optional[str]
) -> None:
"""
Create a zip file containing the images specified by the given image names or board id.
:param image_names: A list of image names to include in the zip file.
:param board_id: The ID of the board. If provided, all images associated with the board will be included in the zip file.
:param bulk_download_item_id: The bulk_download_item_id that will be used to retrieve the bulk download item when it is prepared, if none is provided a uuid will be generated.
"""
@abstractmethod
def get_path(self, bulk_download_item_name: str) -> str:
"""
Get the path to the bulk download file.
:param bulk_download_item_name: The name of the bulk download item.
:return: The path to the bulk download file.
"""
@abstractmethod
def generate_item_id(self, board_id: Optional[str]) -> str:
"""
Generate an item ID for a bulk download item.
:param board_id: The ID of the board whose name is to be included in the item id.
:return: The generated item ID.
"""
@abstractmethod
def delete(self, bulk_download_item_name: str) -> None:
"""
Delete the bulk download file.
:param bulk_download_item_name: The name of the bulk download item.
"""

25
invokeai/app/services/bulk_download/bulk_download_common.py Normal file
View File

@ -0,0 +1,25 @@
DEFAULT_BULK_DOWNLOAD_ID = "default"
class BulkDownloadException(Exception):
"""Exception raised when a bulk download fails."""
def __init__(self, message="Bulk download failed"):
super().__init__(message)
self.message = message
class BulkDownloadTargetException(BulkDownloadException):
"""Exception raised when a bulk download target is not found."""
def __init__(self, message="The bulk download target was not found"):
super().__init__(message)
self.message = message
class BulkDownloadParametersException(BulkDownloadException):
"""Exception raised when a bulk download parameter is invalid."""
def __init__(self, message="No image names or board ID provided"):
super().__init__(message)
self.message = message

157
invokeai/app/services/bulk_download/bulk_download_default.py Normal file
View File

@ -0,0 +1,157 @@
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Optional, Union
from zipfile import ZipFile
from invokeai.app.services.board_records.board_records_common import BoardRecordNotFoundException
from invokeai.app.services.bulk_download.bulk_download_common import (
DEFAULT_BULK_DOWNLOAD_ID,
BulkDownloadException,
BulkDownloadParametersException,
BulkDownloadTargetException,
)
from invokeai.app.services.image_records.image_records_common import ImageRecordNotFoundException
from invokeai.app.services.images.images_common import ImageDTO
from invokeai.app.services.invoker import Invoker
from invokeai.app.util.misc import uuid_string
from .bulk_download_base import BulkDownloadBase
class BulkDownloadService(BulkDownloadBase):
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
def __init__(self):
self._temp_directory = TemporaryDirectory()
self._bulk_downloads_folder = Path(self._temp_directory.name) / "bulk_downloads"
self._bulk_downloads_folder.mkdir(parents=True, exist_ok=True)
def handler(
self, image_names: Optional[list[str]], board_id: Optional[str], bulk_download_item_id: Optional[str]
) -> None:
bulk_download_id: str = DEFAULT_BULK_DOWNLOAD_ID
bulk_download_item_id = bulk_download_item_id or uuid_string()
bulk_download_item_name = bulk_download_item_id + ".zip"
self._signal_job_started(bulk_download_id, bulk_download_item_id, bulk_download_item_name)
try:
image_dtos: list[ImageDTO] = []
if board_id:
image_dtos = self._board_handler(board_id)
elif image_names:
image_dtos = self._image_handler(image_names)
else:
raise BulkDownloadParametersException()
bulk_download_item_name: str = self._create_zip_file(image_dtos, bulk_download_item_id)
self._signal_job_completed(bulk_download_id, bulk_download_item_id, bulk_download_item_name)
except (
ImageRecordNotFoundException,
BoardRecordNotFoundException,
BulkDownloadException,
BulkDownloadParametersException,
) as e:
self._signal_job_failed(bulk_download_id, bulk_download_item_id, bulk_download_item_name, e)
except Exception as e:
self._signal_job_failed(bulk_download_id, bulk_download_item_id, bulk_download_item_name, e)
self._invoker.services.logger.error("Problem bulk downloading images.")
raise e
def _image_handler(self, image_names: list[str]) -> list[ImageDTO]:
return [self._invoker.services.images.get_dto(image_name) for image_name in image_names]
def _board_handler(self, board_id: str) -> list[ImageDTO]:
image_names = self._invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
return self._image_handler(image_names)
def generate_item_id(self, board_id: Optional[str]) -> str:
return uuid_string() if board_id is None else self._get_clean_board_name(board_id) + "_" + uuid_string()
def _get_clean_board_name(self, board_id: str) -> str:
if board_id == "none":
return "Uncategorized"
return self._clean_string_to_path_safe(self._invoker.services.board_records.get(board_id).board_name)
def _create_zip_file(self, image_dtos: list[ImageDTO], bulk_download_item_id: str) -> str:
"""
Create a zip file containing the images specified by the given image names or board id.
If download with the same bulk_download_id already exists, it will be overwritten.
:return: The name of the zip file.
"""
zip_file_name = bulk_download_item_id + ".zip"
zip_file_path = self._bulk_downloads_folder / (zip_file_name)
with ZipFile(zip_file_path, "w") as zip_file:
for image_dto in image_dtos:
image_zip_path = Path(image_dto.image_category.value) / image_dto.image_name
image_disk_path = self._invoker.services.images.get_path(image_dto.image_name)
zip_file.write(image_disk_path, arcname=image_zip_path)
return str(zip_file_name)
# from https://stackoverflow.com/questions/7406102/create-sane-safe-filename-from-any-unsafe-string
def _clean_string_to_path_safe(self, s: str) -> str:
"""Clean a string to be path safe."""
return "".join([c for c in s if c.isalpha() or c.isdigit() or c == " " or c == "_" or c == "-"]).rstrip()
def _signal_job_started(
self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
) -> None:
"""Signal that a bulk download job has started."""
if self._invoker:
assert bulk_download_id is not None
self._invoker.services.events.emit_bulk_download_started(
bulk_download_id=bulk_download_id,
bulk_download_item_id=bulk_download_item_id,
bulk_download_item_name=bulk_download_item_name,
)
def _signal_job_completed(
self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
) -> None:
"""Signal that a bulk download job has completed."""
if self._invoker:
assert bulk_download_id is not None
assert bulk_download_item_name is not None
self._invoker.services.events.emit_bulk_download_completed(
bulk_download_id=bulk_download_id,
bulk_download_item_id=bulk_download_item_id,
bulk_download_item_name=bulk_download_item_name,
)
def _signal_job_failed(
self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str, exception: Exception
) -> None:
"""Signal that a bulk download job has failed."""
if self._invoker:
assert bulk_download_id is not None
assert exception is not None
self._invoker.services.events.emit_bulk_download_failed(
bulk_download_id=bulk_download_id,
bulk_download_item_id=bulk_download_item_id,
bulk_download_item_name=bulk_download_item_name,
error=str(exception),
)
def stop(self, *args, **kwargs):
self._temp_directory.cleanup()
def delete(self, bulk_download_item_name: str) -> None:
path = self.get_path(bulk_download_item_name)
Path(path).unlink()
def get_path(self, bulk_download_item_name: str) -> str:
path = str(self._bulk_downloads_folder / bulk_download_item_name)
if not self._is_valid_path(path):
raise BulkDownloadTargetException()
return path
def _is_valid_path(self, path: Union[str, Path]) -> bool:
"""Validates the path given for a bulk download."""
path = path if isinstance(path, Path) else Path(path)
return path.exists()

12
invokeai/app/services/config/config_base.py
View File

@ -27,11 +27,11 @@ class InvokeAISettings(BaseSettings):
"""Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
initconf: ClassVar[Optional[DictConfig]] = None
argparse_groups: ClassVar[Dict] = {}
argparse_groups: ClassVar[Dict[str, Any]] = {}
model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
def parse_args(self, argv: Optional[list] = sys.argv[1:]):
def parse_args(self, argv: Optional[List[str]] = sys.argv[1:]) -> None:
"""Call to parse command-line arguments."""
parser = self.get_parser()
opt, unknown_opts = parser.parse_known_args(argv)
@ -68,7 +68,7 @@ class InvokeAISettings(BaseSettings):
return OmegaConf.to_yaml(conf)
@classmethod
def add_parser_arguments(cls, parser):
def add_parser_arguments(cls, parser: ArgumentParser) -> None:
"""Dynamically create arguments for a settings parser."""
if "type" in get_type_hints(cls):
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
@ -117,7 +117,8 @@ class InvokeAISettings(BaseSettings):
"""Return the category of a setting."""
hints = get_type_hints(cls)
if command_field in hints:
return get_args(hints[command_field])[0]
result: str = get_args(hints[command_field])[0]
return result
else:
return "Uncategorized"
@ -155,10 +156,11 @@ class InvokeAISettings(BaseSettings):
"lora_dir",
"embedding_dir",
"controlnet_dir",
"conf_path",
]
@classmethod
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
def add_field_argument(cls, command_parser, name: str, field, default_override=None) -> None:
"""Add the argparse arguments for a setting parser."""
field_type = get_type_hints(cls).get(name)
default = (

2
invokeai/app/services/config/config_common.py
View File

@ -21,7 +21,7 @@ class PagingArgumentParser(argparse.ArgumentParser):
It also supports reading defaults from an init file.
"""
def print_help(self, file=None):
def print_help(self, file=None) -> None:
text = self.format_help()
pydoc.pager(text)

37
invokeai/app/services/config/config_default.py
View File

@ -30,7 +30,6 @@ InvokeAI:
lora_dir: null
embedding_dir: null
controlnet_dir: null
conf_path: configs/models.yaml
models_dir: models
legacy_conf_dir: configs/stable-diffusion
db_dir: databases
@ -123,7 +122,6 @@ a Path object:
root_path - path to InvokeAI root
output_path - path to default outputs directory
model_conf_path - path to models.yaml
conf - alias for the above
embedding_path - path to the embeddings directory
lora_path - path to the LoRA directory
@ -163,17 +161,17 @@ two configs are kept in separate sections of the config file:
InvokeAI:
Paths:
root: /home/lstein/invokeai-main
conf_path: configs/models.yaml
legacy_conf_dir: configs/stable-diffusion
outdir: outputs
...
"""
from __future__ import annotations
import os
from pathlib import Path
from typing import Any, ClassVar, Dict, List, Literal, Optional, Union
from typing import Any, ClassVar, Dict, List, Literal, Optional
from omegaconf import DictConfig, OmegaConf
from pydantic import Field
@ -185,7 +183,9 @@ from .config_base import InvokeAISettings
INIT_FILE = Path("invokeai.yaml")
DB_FILE = Path("invokeai.db")
LEGACY_INIT_FILE = Path("invokeai.init")
DEFAULT_MAX_VRAM = 0.5
DEFAULT_RAM_CACHE = 10.0
DEFAULT_VRAM_CACHE = 0.25
DEFAULT_CONVERT_CACHE = 20.0
class Categories(object):
@ -235,8 +235,8 @@ class InvokeAIAppConfig(InvokeAISettings):
# PATHS
root : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
autoimport_dir : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
conf_path : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
models_dir : Path = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
convert_cache_dir : Path = Field(default=Path('models/.cache'), description='Path to the converted models cache directory', json_schema_extra=Categories.Paths)
legacy_conf_dir : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
db_dir : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : Path = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
@ -260,8 +260,10 @@ class InvokeAIAppConfig(InvokeAISettings):
version : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
# CACHE
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
ram : float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
convert_cache : float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
@ -296,6 +298,7 @@ class InvokeAIAppConfig(InvokeAISettings):
lora_dir : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
embedding_dir : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
controlnet_dir : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
conf_path : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
# this is not referred to in the source code and can be removed entirely
#free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
@ -404,6 +407,11 @@ class InvokeAIAppConfig(InvokeAISettings):
"""Path to the models directory."""
return self._resolve(self.models_dir)
@property
def models_convert_cache_path(self) -> Path:
"""Path to the converted cache models directory."""
return self._resolve(self.convert_cache_dir)
@property
def custom_nodes_path(self) -> Path:
"""Path to the custom nodes directory."""
@ -433,15 +441,20 @@ class InvokeAIAppConfig(InvokeAISettings):
return True
@property
def ram_cache_size(self) -> Union[Literal["auto"], float]:
"""Return the ram cache size using the legacy or modern setting."""
def ram_cache_size(self) -> float:
"""Return the ram cache size using the legacy or modern setting (GB)."""
return self.max_cache_size or self.ram
@property
def vram_cache_size(self) -> Union[Literal["auto"], float]:
"""Return the vram cache size using the legacy or modern setting."""
def vram_cache_size(self) -> float:
"""Return the vram cache size using the legacy or modern setting (GB)."""
return self.max_vram_cache_size or self.vram
@property
def convert_cache_size(self) -> float:
"""Return the convert cache size on disk (GB)."""
return self.convert_cache
@property
def use_cpu(self) -> bool:
"""Return true if the device is set to CPU or the always_use_cpu flag is set."""

1
invokeai/app/services/download/__init__.py
View File

@ -1,4 +1,5 @@
"""Init file for download queue."""
from .download_base import DownloadJob, DownloadJobStatus, DownloadQueueServiceBase, UnknownJobIDException
from .download_default import DownloadQueueService, TqdmProgress

13
invokeai/app/services/download/download_base.py
View File

@ -260,3 +260,16 @@ class DownloadQueueServiceBase(ABC):
def join(self) -> None:
"""Wait until all jobs are off the queue."""
pass
@abstractmethod
def wait_for_job(self, job: DownloadJob, timeout: int = 0) -> DownloadJob:
"""Wait until the indicated download job has reached a terminal state.
This will block until the indicated install job has completed,
been cancelled, or errored out.
:param job: The job to wait on.
:param timeout: Wait up to indicated number of seconds. Raise a TimeoutError if
the job hasn't completed within the indicated time.
"""
pass

26
invokeai/app/services/download/download_default.py
View File

@ -4,10 +4,11 @@
import os
import re
import threading
import time
import traceback
from pathlib import Path
from queue import Empty, PriorityQueue
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, Set
import requests
from pydantic.networks import AnyHttpUrl
@ -48,11 +49,12 @@ class DownloadQueueService(DownloadQueueServiceBase):
:param max_parallel_dl: Number of simultaneous downloads allowed [5].
:param requests_session: Optional requests.sessions.Session object, for unit tests.
"""
self._jobs = {}
self._jobs: Dict[int, DownloadJob] = {}
self._next_job_id = 0
self._queue = PriorityQueue()
self._queue: PriorityQueue[DownloadJob] = PriorityQueue()
self._stop_event = threading.Event()
self._worker_pool = set()
self._job_completed_event = threading.Event()
self._worker_pool: Set[threading.Thread] = set()
self._lock = threading.Lock()
self._logger = InvokeAILogger.get_logger("DownloadQueueService")
self._event_bus = event_bus
@ -188,6 +190,16 @@ class DownloadQueueService(DownloadQueueServiceBase):
if not job.in_terminal_state:
self.cancel_job(job)
def wait_for_job(self, job: DownloadJob, timeout: int = 0) -> DownloadJob:
"""Block until the indicated job has reached terminal state, or when timeout limit reached."""
start = time.time()
while not job.in_terminal_state:
if self._job_completed_event.wait(timeout=0.25): # in case we miss an event
self._job_completed_event.clear()
if timeout > 0 and time.time() - start > timeout:
raise TimeoutError("Timeout exceeded")
return job
def _start_workers(self, max_workers: int) -> None:
"""Start the requested number of worker threads."""
self._stop_event.clear()
@ -212,7 +224,6 @@ class DownloadQueueService(DownloadQueueServiceBase):
job.job_started = get_iso_timestamp()
self._do_download(job)
self._signal_job_complete(job)
except (OSError, HTTPError) as excp:
job.error_type = excp.__class__.__name__ + f"({str(excp)})"
job.error = traceback.format_exc()
@ -223,6 +234,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
finally:
job.job_ended = get_iso_timestamp()
self._job_completed_event.set() # signal a change to terminal state
self._queue.task_done()
self._logger.debug(f"Download queue worker thread {threading.current_thread().name} exiting.")
@ -407,11 +419,11 @@ class DownloadQueueService(DownloadQueueServiceBase):
# Example on_progress event handler to display a TQDM status bar
# Activate with:
# download_service.download('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().job_update
# download_service.download(DownloadJob('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().update))
class TqdmProgress(object):
"""TQDM-based progress bar object to use in on_progress handlers."""
_bars: Dict[int, tqdm] # the tqdm object
_bars: Dict[int, tqdm] # type: ignore
_last: Dict[int, int] # last bytes downloaded
def __init__(self) -> None: # noqa D107

145
invokeai/app/services/events/events_base.py
View File

@ -3,7 +3,7 @@
from typing import Any, Dict, List, Optional, Union
from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
from invokeai.app.services.session_processor.session_processor_common import ProgressImage
from invokeai.app.services.session_queue.session_queue_common import (
BatchStatus,
EnqueueBatchResult,
@ -11,12 +11,12 @@ from invokeai.app.services.session_queue.session_queue_common import (
SessionQueueStatus,
)
from invokeai.app.util.misc import get_timestamp
from invokeai.backend.model_management.model_manager import ModelInfo
from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
from invokeai.backend.model_manager import AnyModelConfig
class EventServiceBase:
queue_event: str = "queue_event"
bulk_download_event: str = "bulk_download_event"
download_event: str = "download_event"
model_event: str = "model_event"
@ -25,6 +25,14 @@ class EventServiceBase:
def dispatch(self, event_name: str, payload: Any) -> None:
pass
def _emit_bulk_download_event(self, event_name: str, payload: dict) -> None:
"""Bulk download events are emitted to a room with queue_id as the room name"""
payload["timestamp"] = get_timestamp()
self.dispatch(
event_name=EventServiceBase.bulk_download_event,
payload={"event": event_name, "data": payload},
)
def __emit_queue_event(self, event_name: str, payload: dict) -> None:
"""Queue events are emitted to a room with queue_id as the room name"""
payload["timestamp"] = get_timestamp()
@ -55,7 +63,7 @@ class EventServiceBase:
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
node: dict,
node_id: str,
source_node_id: str,
progress_image: Optional[ProgressImage],
step: int,
@ -70,7 +78,7 @@ class EventServiceBase:
"queue_item_id": queue_item_id,
"queue_batch_id": queue_batch_id,
"graph_execution_state_id": graph_execution_state_id,
"node_id": node.get("id"),
"node_id": node_id,
"source_node_id": source_node_id,
"progress_image": progress_image.model_dump() if progress_image is not None else None,
"step": step,
@ -171,10 +179,7 @@ class EventServiceBase:
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
submodel: SubModelType,
model_config: AnyModelConfig,
) -> None:
"""Emitted when a model is requested"""
self.__emit_queue_event(
@ -184,10 +189,7 @@ class EventServiceBase:
"queue_item_id": queue_item_id,
"queue_batch_id": queue_batch_id,
"graph_execution_state_id": graph_execution_state_id,
"model_name": model_name,
"base_model": base_model,
"model_type": model_type,
"submodel": submodel,
"model_config": model_config.model_dump(),
},
)
@ -197,11 +199,7 @@ class EventServiceBase:
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
submodel: SubModelType,
model_info: ModelInfo,
model_config: AnyModelConfig,
) -> None:
"""Emitted when a model is correctly loaded (returns model info)"""
self.__emit_queue_event(
@ -211,59 +209,7 @@ class EventServiceBase:
"queue_item_id": queue_item_id,
"queue_batch_id": queue_batch_id,
"graph_execution_state_id": graph_execution_state_id,
"model_name": model_name,
"base_model": base_model,
"model_type": model_type,
"submodel": submodel,
"hash": model_info.hash,
"location": str(model_info.location),
"precision": str(model_info.precision),
},
)
def emit_session_retrieval_error(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
error_type: str,
error: str,
) -> None:
"""Emitted when session retrieval fails"""
self.__emit_queue_event(
event_name="session_retrieval_error",
payload={
"queue_id": queue_id,
"queue_item_id": queue_item_id,
"queue_batch_id": queue_batch_id,
"graph_execution_state_id": graph_execution_state_id,
"error_type": error_type,
"error": error,
},
)
def emit_invocation_retrieval_error(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
node_id: str,
error_type: str,
error: str,
) -> None:
"""Emitted when invocation retrieval fails"""
self.__emit_queue_event(
event_name="invocation_retrieval_error",
payload={
"queue_id": queue_id,
"queue_item_id": queue_item_id,
"queue_batch_id": queue_batch_id,
"graph_execution_state_id": graph_execution_state_id,
"node_id": node_id,
"error_type": error_type,
"error": error,
"model_config": model_config.model_dump(),
},
)
@ -411,6 +357,7 @@ class EventServiceBase:
bytes: int,
total_bytes: int,
parts: List[Dict[str, Union[str, int]]],
id: int,
) -> None:
"""
Emit at intervals while the install job is in progress (remote models only).
@ -430,6 +377,7 @@ class EventServiceBase:
"bytes": bytes,
"total_bytes": total_bytes,
"parts": parts,
"id": id,
},
)
@ -444,7 +392,7 @@ class EventServiceBase:
payload={"source": source},
)
def emit_model_install_completed(self, source: str, key: str, total_bytes: Optional[int] = None) -> None:
def emit_model_install_completed(self, source: str, key: str, id: int, total_bytes: Optional[int] = None) -> None:
"""
Emit when an install job is completed successfully.
@ -454,11 +402,7 @@ class EventServiceBase:
"""
self.__emit_model_event(
event_name="model_install_completed",
payload={
"source": source,
"total_bytes": total_bytes,
"key": key,
},
payload={"source": source, "total_bytes": total_bytes, "key": key, "id": id},
)
def emit_model_install_cancelled(self, source: str) -> None:
@ -472,12 +416,7 @@ class EventServiceBase:
payload={"source": source},
)
def emit_model_install_error(
self,
source: str,
error_type: str,
error: str,
) -> None:
def emit_model_install_error(self, source: str, error_type: str, error: str, id: int) -> None:
"""
Emit when an install job encounters an exception.
@ -487,9 +426,45 @@ class EventServiceBase:
"""
self.__emit_model_event(
event_name="model_install_error",
payload={"source": source, "error_type": error_type, "error": error, "id": id},
)
def emit_bulk_download_started(
self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
) -> None:
"""Emitted when a bulk download starts"""
self._emit_bulk_download_event(
event_name="bulk_download_started",
payload={
"source": source,
"error_type": error_type,
"bulk_download_id": bulk_download_id,
"bulk_download_item_id": bulk_download_item_id,
"bulk_download_item_name": bulk_download_item_name,
},
)
def emit_bulk_download_completed(
self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str
) -> None:
"""Emitted when a bulk download completes"""
self._emit_bulk_download_event(
event_name="bulk_download_completed",
payload={
"bulk_download_id": bulk_download_id,
"bulk_download_item_id": bulk_download_item_id,
"bulk_download_item_name": bulk_download_item_name,
},
)
def emit_bulk_download_failed(
self, bulk_download_id: str, bulk_download_item_id: str, bulk_download_item_name: str, error: str
) -> None:
"""Emitted when a bulk download fails"""
self._emit_bulk_download_event(
event_name="bulk_download_failed",
payload={
"bulk_download_id": bulk_download_id,
"bulk_download_item_id": bulk_download_item_id,
"bulk_download_item_name": bulk_download_item_name,
"error": error,
},
)

2
invokeai/app/services/image_files/image_files_base.py
View File

@ -4,7 +4,7 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID

2
invokeai/app/services/image_files/image_files_disk.py
View File

@ -7,7 +7,7 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType
from send2trash import send2trash
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail

2
invokeai/app/services/image_records/image_records_base.py
View File

@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
from datetime import datetime
from typing import Optional
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin

2
invokeai/app/services/image_records/image_records_sqlite.py
View File

@ -3,7 +3,7 @@ import threading
from datetime import datetime
from typing import Optional, Union, cast
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase

2
invokeai/app/services/images/images_base.py
View File

@ -3,7 +3,7 @@ from typing import Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.image_records.image_records_common import (
ImageCategory,
ImageRecord,

4
invokeai/app/services/images/images_default.py
View File

@ -2,7 +2,7 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
@ -154,7 +154,7 @@ class ImageService(ImageServiceABC):
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image DTO")
self.__invoker.services.logger.error("Problem getting image metadata")
raise e
def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:

3
invokeai/app/services/invocation_cache/invocation_cache_memory.py
View File

@ -37,7 +37,8 @@ class MemoryInvocationCache(InvocationCacheBase):
if self._max_cache_size == 0:
return
self._invoker.services.images.on_deleted(self._delete_by_match)
self._invoker.services.latents.on_deleted(self._delete_by_match)
self._invoker.services.tensors.on_deleted(self._delete_by_match)
self._invoker.services.conditioning.on_deleted(self._delete_by_match)
def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
with self._lock:

5
invokeai/app/services/invocation_processor/invocation_processor_base.py
View File

@ -1,5 +0,0 @@
from abc import ABC
class InvocationProcessorABC(ABC): # noqa: B024
pass

15
invokeai/app/services/invocation_processor/invocation_processor_common.py
View File

@ -1,15 +0,0 @@
from pydantic import BaseModel, Field
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")
class CanceledException(Exception):
"""Execution canceled by user."""
pass

237
invokeai/app/services/invocation_processor/invocation_processor_default.py
View File

@ -1,237 +0,0 @@
import time
import traceback
from contextlib import suppress
from threading import BoundedSemaphore, Event, Thread
from typing import Optional
import invokeai.backend.util.logging as logger
from invokeai.app.invocations.baseinvocation import InvocationContext
from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
from invokeai.app.services.invocation_stats.invocation_stats_common import (
GESStatsNotFoundError,
)
from invokeai.app.util.profiler import Profiler
from ..invoker import Invoker
from .invocation_processor_base import InvocationProcessorABC
from .invocation_processor_common import CanceledException
class DefaultInvocationProcessor(InvocationProcessorABC):
__invoker_thread: Thread
__stop_event: Event
__invoker: Invoker
__threadLimit: BoundedSemaphore
def start(self, invoker: Invoker) -> None:
# if we do want multithreading at some point, we could make this configurable
self.__threadLimit = BoundedSemaphore(1)
self.__invoker = invoker
self.__stop_event = Event()
self.__invoker_thread = Thread(
name="invoker_processor",
target=self.__process,
kwargs={"stop_event": self.__stop_event},
)
self.__invoker_thread.daemon = True # TODO: make async and do not use threads
self.__invoker_thread.start()
def stop(self, *args, **kwargs) -> None:
self.__stop_event.set()
def __process(self, stop_event: Event):
try:
self.__threadLimit.acquire()
queue_item: Optional[InvocationQueueItem] = None
profiler = (
Profiler(
logger=self.__invoker.services.logger,
output_dir=self.__invoker.services.configuration.profiles_path,
prefix=self.__invoker.services.configuration.profile_prefix,
)
if self.__invoker.services.configuration.profile_graphs
else None
)
def stats_cleanup(graph_execution_state_id: str) -> None:
if profiler:
profile_path = profiler.stop()
stats_path = profile_path.with_suffix(".json")
self.__invoker.services.performance_statistics.dump_stats(
graph_execution_state_id=graph_execution_state_id, output_path=stats_path
)
with suppress(GESStatsNotFoundError):
self.__invoker.services.performance_statistics.log_stats(graph_execution_state_id)
self.__invoker.services.performance_statistics.reset_stats(graph_execution_state_id)
while not stop_event.is_set():
try:
queue_item = self.__invoker.services.queue.get()
except Exception as e:
self.__invoker.services.logger.error("Exception while getting from queue:\n%s" % e)
if not queue_item: # Probably stopping
# do not hammer the queue
time.sleep(0.5)
continue
if profiler and profiler.profile_id != queue_item.graph_execution_state_id:
profiler.start(profile_id=queue_item.graph_execution_state_id)
try:
graph_execution_state = self.__invoker.services.graph_execution_manager.get(
queue_item.graph_execution_state_id
)
except Exception as e:
self.__invoker.services.logger.error("Exception while retrieving session:\n%s" % e)
self.__invoker.services.events.emit_session_retrieval_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=queue_item.graph_execution_state_id,
error_type=e.__class__.__name__,
error=traceback.format_exc(),
)
continue
try:
invocation = graph_execution_state.execution_graph.get_node(queue_item.invocation_id)
except Exception as e:
self.__invoker.services.logger.error("Exception while retrieving invocation:\n%s" % e)
self.__invoker.services.events.emit_invocation_retrieval_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=queue_item.graph_execution_state_id,
node_id=queue_item.invocation_id,
error_type=e.__class__.__name__,
error=traceback.format_exc(),
)
continue
# 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(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.model_dump(),
source_node_id=source_node_id,
)
# Invoke
try:
graph_id = graph_execution_state.id
with self.__invoker.services.performance_statistics.collect_stats(invocation, graph_id):
# use the internal invoke_internal(), which wraps the node's invoke() method,
# which handles a few things:
# - nodes that require a value, but get it only from a connection
# - referencing the invocation cache instead of executing the node
outputs = invocation.invoke_internal(
InvocationContext(
services=self.__invoker.services,
graph_execution_state_id=graph_execution_state.id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
queue_batch_id=queue_item.session_queue_batch_id,
workflow=queue_item.workflow,
)
)
# 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(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.model_dump(),
source_node_id=source_node_id,
result=outputs.model_dump(),
)
except KeyboardInterrupt:
pass
except CanceledException:
stats_cleanup(graph_execution_state.id)
pass
except Exception as e:
error = traceback.format_exc()
logger.error(error)
# 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)
self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
# Send error event
self.__invoker.services.events.emit_invocation_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.model_dump(),
source_node_id=source_node_id,
error_type=e.__class__.__name__,
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:
try:
self.__invoker.invoke(
session_queue_batch_id=queue_item.session_queue_batch_id,
session_queue_item_id=queue_item.session_queue_item_id,
session_queue_id=queue_item.session_queue_id,
graph_execution_state=graph_execution_state,
workflow=queue_item.workflow,
invoke_all=True,
)
except Exception as e:
self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
self.__invoker.services.events.emit_invocation_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.model_dump(),
source_node_id=source_node_id,
error_type=e.__class__.__name__,
error=traceback.format_exc(),
)
elif is_complete:
self.__invoker.services.events.emit_graph_execution_complete(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
)
stats_cleanup(graph_execution_state.id)
except KeyboardInterrupt:
pass # Log something? KeyboardInterrupt is probably not going to be seen by the processor
finally:
self.__threadLimit.release()

26
invokeai/app/services/invocation_queue/invocation_queue_base.py
View File

@ -1,26 +0,0 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from abc import ABC, abstractmethod
from typing import Optional
from .invocation_queue_common import InvocationQueueItem
class InvocationQueueABC(ABC):
"""Abstract base class for all invocation queues"""
@abstractmethod
def get(self) -> InvocationQueueItem:
pass
@abstractmethod
def put(self, item: Optional[InvocationQueueItem]) -> 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

23
invokeai/app/services/invocation_queue/invocation_queue_common.py
View File

@ -1,23 +0,0 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import time
from typing import Optional
from pydantic import BaseModel, Field
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
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")
session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
session_queue_item_id: int = Field(
description="The ID of session queue item from which this invocation queue item came"
)
session_queue_batch_id: str = Field(
description="The ID of the session batch from which this invocation queue item came"
)
workflow: Optional[WorkflowWithoutID] = Field(description="The workflow associated with this queue item")
invoke_all: bool = Field(default=False)
timestamp: float = Field(default_factory=time.time)

44
invokeai/app/services/invocation_queue/invocation_queue_memory.py
View File

@ -1,44 +0,0 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import time
from queue import Queue
from typing import Optional
from .invocation_queue_base import InvocationQueueABC
from .invocation_queue_common import InvocationQueueItem
class MemoryInvocationQueue(InvocationQueueABC):
__queue: Queue
__cancellations: dict[str, float]
def __init__(self):
self.__queue = Queue()
self.__cancellations = {}
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: Optional[InvocationQueueItem]) -> 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

59
invokeai/app/services/invocation_services.py
View File

@ -3,13 +3,20 @@ from __future__ import annotations
from typing import TYPE_CHECKING
from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
if TYPE_CHECKING:
from logging import Logger
import torch
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
from .board_image_records.board_image_records_base import BoardImageRecordStorageBase
from .board_images.board_images_base import BoardImagesServiceABC
from .board_records.board_records_base import BoardRecordStorageBase
from .boards.boards_base import BoardServiceABC
from .bulk_download.bulk_download_base import BulkDownloadBase
from .config import InvokeAIAppConfig
from .download import DownloadQueueServiceBase
from .events.events_base import EventServiceBase
@ -17,18 +24,11 @@ if TYPE_CHECKING:
from .image_records.image_records_base import ImageRecordStorageBase
from .images.images_base import ImageServiceABC
from .invocation_cache.invocation_cache_base import InvocationCacheBase
from .invocation_processor.invocation_processor_base import InvocationProcessorABC
from .invocation_queue.invocation_queue_base import InvocationQueueABC
from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
from .item_storage.item_storage_base import ItemStorageABC
from .latents_storage.latents_storage_base import LatentsStorageBase
from .model_install import ModelInstallServiceBase
from .model_manager.model_manager_base import ModelManagerServiceBase
from .model_records import ModelRecordServiceBase
from .names.names_base import NameServiceBase
from .session_processor.session_processor_base import SessionProcessorBase
from .session_queue.session_queue_base import SessionQueueBase
from .shared.graph import GraphExecutionState
from .urls.urls_base import UrlServiceBase
from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
@ -36,83 +36,50 @@ if TYPE_CHECKING:
class InvocationServices:
"""Services that can be used by invocations"""
# TODO: Just forward-declared everything due to circular dependencies. Fix structure.
board_images: "BoardImagesServiceABC"
board_image_record_storage: "BoardImageRecordStorageBase"
boards: "BoardServiceABC"
board_records: "BoardRecordStorageBase"
configuration: "InvokeAIAppConfig"
events: "EventServiceBase"
graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
images: "ImageServiceABC"
image_records: "ImageRecordStorageBase"
image_files: "ImageFileStorageBase"
latents: "LatentsStorageBase"
logger: "Logger"
model_manager: "ModelManagerServiceBase"
model_records: "ModelRecordServiceBase"
download_queue: "DownloadQueueServiceBase"
model_install: "ModelInstallServiceBase"
processor: "InvocationProcessorABC"
performance_statistics: "InvocationStatsServiceBase"
queue: "InvocationQueueABC"
session_queue: "SessionQueueBase"
session_processor: "SessionProcessorBase"
invocation_cache: "InvocationCacheBase"
names: "NameServiceBase"
urls: "UrlServiceBase"
workflow_records: "WorkflowRecordsStorageBase"
def __init__(
self,
board_images: "BoardImagesServiceABC",
board_image_records: "BoardImageRecordStorageBase",
boards: "BoardServiceABC",
board_records: "BoardRecordStorageBase",
bulk_download: "BulkDownloadBase",
configuration: "InvokeAIAppConfig",
events: "EventServiceBase",
graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
images: "ImageServiceABC",
image_files: "ImageFileStorageBase",
image_records: "ImageRecordStorageBase",
latents: "LatentsStorageBase",
logger: "Logger",
model_manager: "ModelManagerServiceBase",
model_records: "ModelRecordServiceBase",
download_queue: "DownloadQueueServiceBase",
model_install: "ModelInstallServiceBase",
processor: "InvocationProcessorABC",
performance_statistics: "InvocationStatsServiceBase",
queue: "InvocationQueueABC",
session_queue: "SessionQueueBase",
session_processor: "SessionProcessorBase",
invocation_cache: "InvocationCacheBase",
names: "NameServiceBase",
urls: "UrlServiceBase",
workflow_records: "WorkflowRecordsStorageBase",
tensors: "ObjectSerializerBase[torch.Tensor]",
conditioning: "ObjectSerializerBase[ConditioningFieldData]",
):
self.board_images = board_images
self.board_image_records = board_image_records
self.boards = boards
self.board_records = board_records
self.bulk_download = bulk_download
self.configuration = configuration
self.events = events
self.graph_execution_manager = graph_execution_manager
self.images = images
self.image_files = image_files
self.image_records = image_records
self.latents = latents
self.logger = logger
self.model_manager = model_manager
self.model_records = model_records
self.download_queue = download_queue
self.model_install = model_install
self.processor = processor
self.performance_statistics = performance_statistics
self.queue = queue
self.session_queue = session_queue
self.session_processor = session_processor
self.invocation_cache = invocation_cache
self.names = names
self.urls = urls
self.workflow_records = workflow_records
self.tensors = tensors
self.conditioning = conditioning

19
invokeai/app/services/invocation_stats/invocation_stats_base.py
View File

@ -3,7 +3,7 @@
Usage:
statistics = InvocationStatsService(graph_execution_manager)
statistics = InvocationStatsService()
with statistics.collect_stats(invocation, graph_execution_state.id):
... execute graphs...
statistics.log_stats()
@ -29,8 +29,8 @@ writes to the system log is stored in InvocationServices.performance_statistics.
"""
from abc import ABC, abstractmethod
from contextlib import AbstractContextManager
from pathlib import Path
from typing import ContextManager
from invokeai.app.invocations.baseinvocation import BaseInvocation
from invokeai.app.services.invocation_stats.invocation_stats_common import InvocationStatsSummary
@ -40,18 +40,17 @@ class InvocationStatsServiceBase(ABC):
"Abstract base class for recording node memory/time performance statistics"
@abstractmethod
def __init__(self):
def __init__(self) -> None:
"""
Initialize the InvocationStatsService and reset counters to zero
"""
pass
@abstractmethod
def collect_stats(
self,
invocation: BaseInvocation,
graph_execution_state_id: str,
) -> AbstractContextManager:
) -> ContextManager[None]:
"""
Return a context object that will capture the statistics on the execution
of invocaation. Use with: to place around the part of the code that executes the invocation.
@ -61,16 +60,12 @@ class InvocationStatsServiceBase(ABC):
pass
@abstractmethod
def reset_stats(self, graph_execution_state_id: str):
"""
Reset all statistics for the indicated graph.
:param graph_execution_state_id: The id of the session whose stats to reset.
:raises GESStatsNotFoundError: if the graph isn't tracked in the stats.
"""
def reset_stats(self):
"""Reset all stored statistics."""
pass
@abstractmethod
def log_stats(self, graph_execution_state_id: str):
def log_stats(self, graph_execution_state_id: str) -> None:
"""
Write out the accumulated statistics to the log or somewhere else.
:param graph_execution_state_id: The id of the session whose stats to log.

56
invokeai/app/services/invocation_stats/invocation_stats_default.py
View File

@ -2,6 +2,7 @@ import json
import time
from contextlib import contextmanager
from pathlib import Path
from typing import Generator
import psutil
import torch
@ -9,8 +10,7 @@ import torch
import invokeai.backend.util.logging as logger
from invokeai.app.invocations.baseinvocation import BaseInvocation
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.item_storage.item_storage_common import ItemNotFoundError
from invokeai.backend.model_management.model_cache import CacheStats
from invokeai.backend.model_manager.load.model_cache import CacheStats
from .invocation_stats_base import InvocationStatsServiceBase
from .invocation_stats_common import (
@ -41,22 +41,23 @@ class InvocationStatsService(InvocationStatsServiceBase):
self._invoker = invoker
@contextmanager
def collect_stats(self, invocation: BaseInvocation, graph_execution_state_id: str):
def collect_stats(self, invocation: BaseInvocation, graph_execution_state_id: str) -> Generator[None, None, None]:
# This is to handle case of the model manager not being initialized, which happens
# during some tests.
services = self._invoker.services
if not self._stats.get(graph_execution_state_id):
# First time we're seeing this graph_execution_state_id.
self._stats[graph_execution_state_id] = GraphExecutionStats()
self._cache_stats[graph_execution_state_id] = CacheStats()
# Prune stale stats. There should be none since we're starting a new graph, but just in case.
self._prune_stale_stats()
# Record state before the invocation.
start_time = time.time()
start_ram = psutil.Process().memory_info().rss
if torch.cuda.is_available():
torch.cuda.reset_peak_memory_stats()
if self._invoker.services.model_manager:
self._invoker.services.model_manager.collect_cache_stats(self._cache_stats[graph_execution_state_id])
assert services.model_manager.load is not None
services.model_manager.load.ram_cache.stats = self._cache_stats[graph_execution_state_id]
try:
# Let the invocation run.
@ -73,42 +74,9 @@ class InvocationStatsService(InvocationStatsServiceBase):
)
self._stats[graph_execution_state_id].add_node_execution_stats(node_stats)
def _prune_stale_stats(self):
"""Check all graphs being tracked and prune any that have completed/errored.
This shouldn't be necessary, but we don't have totally robust upstream handling of graph completions/errors, so
for now we call this function periodically to prevent them from accumulating.
"""
to_prune: list[str] = []
for graph_execution_state_id in self._stats:
try:
graph_execution_state = self._invoker.services.graph_execution_manager.get(graph_execution_state_id)
except ItemNotFoundError:
# TODO(ryand): What would cause this? Should this exception just be allowed to propagate?
logger.warning(f"Failed to get graph state for {graph_execution_state_id}.")
continue
if not graph_execution_state.is_complete():
# The graph is still running, don't prune it.
continue
to_prune.append(graph_execution_state_id)
for graph_execution_state_id in to_prune:
del self._stats[graph_execution_state_id]
del self._cache_stats[graph_execution_state_id]
if len(to_prune) > 0:
logger.info(f"Pruned stale graph stats for {to_prune}.")
def reset_stats(self, graph_execution_state_id: str):
try:
del self._stats[graph_execution_state_id]
del self._cache_stats[graph_execution_state_id]
except KeyError as e:
raise GESStatsNotFoundError(
f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}."
) from e
def reset_stats(self):
self._stats = {}
self._cache_stats = {}
def get_stats(self, graph_execution_state_id: str) -> InvocationStatsSummary:
graph_stats_summary = self._get_graph_summary(graph_execution_state_id)

52
invokeai/app/services/invoker.py
View File

@ -1,12 +1,7 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from typing import Optional
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
from .invocation_queue.invocation_queue_common import InvocationQueueItem
from .invocation_services import InvocationServices
from .shared.graph import Graph, GraphExecutionState
class Invoker:
@ -18,51 +13,6 @@ class Invoker:
self.services = services
self._start()
def invoke(
self,
session_queue_id: str,
session_queue_item_id: int,
session_queue_batch_id: str,
graph_execution_state: GraphExecutionState,
workflow: Optional[WorkflowWithoutID] = None,
invoke_all: bool = False,
) -> Optional[str]:
"""Determines the next node to invoke and enqueues it, preparing if needed.
Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""
# 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_queue_id=session_queue_id,
session_queue_item_id=session_queue_item_id,
session_queue_batch_id=session_queue_batch_id,
graph_execution_state_id=graph_execution_state.id,
invocation_id=invocation.id,
workflow=workflow,
invoke_all=invoke_all,
)
)
return invocation.id
def create_execution_state(self, graph: Optional[Graph] = 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)
@ -85,5 +35,3 @@ class Invoker:
# First stop all services
for service in vars(self.services):
self.__stop_service(getattr(self.services, service))
self.services.queue.put(None)

2
invokeai/app/services/item_storage/item_storage_base.py
View File

@ -30,7 +30,7 @@ class ItemStorageABC(ABC, Generic[T]):
@abstractmethod
def set(self, item: T) -> None:
"""
Sets the item. The id will be extracted based on id_field.
Sets the item.
:param item: the item to set
"""
pass

45
invokeai/app/services/latents_storage/latents_storage_base.py
View File

@ -1,45 +0,0 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from abc import ABC, abstractmethod
from typing import Callable
import torch
class LatentsStorageBase(ABC):
"""Responsible for storing and retrieving latents."""
_on_changed_callbacks: list[Callable[[torch.Tensor], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = []
self._on_deleted_callbacks = []
@abstractmethod
def get(self, name: str) -> torch.Tensor:
pass
@abstractmethod
def save(self, name: str, data: torch.Tensor) -> None:
pass
@abstractmethod
def delete(self, name: str) -> None:
pass
def on_changed(self, on_changed: Callable[[torch.Tensor], 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: torch.Tensor) -> 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)

58
invokeai/app/services/latents_storage/latents_storage_disk.py
View File

@ -1,58 +0,0 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from pathlib import Path
from typing import Union
import torch
from invokeai.app.services.invoker import Invoker
from .latents_storage_base import LatentsStorageBase
class DiskLatentsStorage(LatentsStorageBase):
"""Stores latents in a folder on disk without caching"""
__output_folder: Path
def __init__(self, output_folder: Union[str, Path]):
self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
self.__output_folder.mkdir(parents=True, exist_ok=True)
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
self._delete_all_latents()
def get(self, name: str) -> torch.Tensor:
latent_path = self.get_path(name)
return torch.load(latent_path)
def save(self, name: str, data: torch.Tensor) -> None:
self.__output_folder.mkdir(parents=True, exist_ok=True)
latent_path = self.get_path(name)
torch.save(data, latent_path)
def delete(self, name: str) -> None:
latent_path = self.get_path(name)
latent_path.unlink()
def get_path(self, name: str) -> Path:
return self.__output_folder / name
def _delete_all_latents(self) -> None:
"""
Deletes all latents from disk.
Must be called after we have access to `self._invoker` (e.g. in `start()`).
"""
deleted_latents_count = 0
freed_space = 0
for latents_file in Path(self.__output_folder).glob("*"):
if latents_file.is_file():
freed_space += latents_file.stat().st_size
deleted_latents_count += 1
latents_file.unlink()
if deleted_latents_count > 0:
freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
self._invoker.services.logger.info(
f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
)

68
invokeai/app/services/latents_storage/latents_storage_forward_cache.py
View File

@ -1,68 +0,0 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from queue import Queue
from typing import Dict, Optional
import torch
from invokeai.app.services.invoker import Invoker
from .latents_storage_base import LatentsStorageBase
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):
super().__init__()
self.__underlying_storage = underlying_storage
self.__cache = {}
self.__cache_ids = Queue()
self.__max_cache_size = max_cache_size
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
start_op = getattr(self.__underlying_storage, "start", None)
if callable(start_op):
start_op(invoker)
def stop(self, invoker: Invoker) -> None:
self._invoker = invoker
stop_op = getattr(self.__underlying_storage, "stop", None)
if callable(stop_op):
stop_op(invoker)
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 save(self, name: str, data: torch.Tensor) -> None:
self.__underlying_storage.save(name, data)
self.__set_cache(name, data)
self._on_changed(data)
def delete(self, name: str) -> None:
self.__underlying_storage.delete(name)
if name in self.__cache:
del self.__cache[name]
self._on_deleted(name)
def __get_cache(self, name: str) -> Optional[torch.Tensor]:
return None if name not in self.__cache else self.__cache[name]
def __set_cache(self, name: str, data: torch.Tensor):
if name not 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())

102
invokeai/app/services/model_install/model_install_base.py
View File

@ -14,11 +14,13 @@ from typing_extensions import Annotated
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
from invokeai.app.services.events import EventServiceBase
from invokeai.app.services.events.events_base import EventServiceBase
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.model_records import ModelRecordServiceBase
from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
from ..model_metadata import ModelMetadataStoreBase
class InstallStatus(str, Enum):
@ -26,6 +28,7 @@ class InstallStatus(str, Enum):
WAITING = "waiting" # waiting to be dequeued
DOWNLOADING = "downloading" # downloading of model files in process
DOWNLOADS_DONE = "downloads_done" # downloading done, waiting to run
RUNNING = "running" # being processed
COMPLETED = "completed" # finished running
ERROR = "error" # terminated with an error message
@ -127,8 +130,8 @@ class HFModelSource(StringLikeSource):
def __str__(self) -> str:
"""Return string version of repoid when string rep needed."""
base: str = self.repo_id
base += f":{self.variant or ''}"
base += f":{self.subfolder}" if self.subfolder else ""
base += f" ({self.variant})" if self.variant else ""
return base
@ -154,6 +157,7 @@ class ModelInstallJob(BaseModel):
id: int = Field(description="Unique ID for this job")
status: InstallStatus = Field(default=InstallStatus.WAITING, description="Current status of install process")
error_reason: Optional[str] = Field(default=None, description="Information about why the job failed")
config_in: Dict[str, Any] = Field(
default_factory=dict, description="Configuration information (e.g. 'description') to apply to model."
)
@ -175,6 +179,12 @@ class ModelInstallJob(BaseModel):
download_parts: Set[DownloadJob] = Field(
default_factory=set, description="Download jobs contributing to this install"
)
error: Optional[str] = Field(
default=None, description="On an error condition, this field will contain the text of the exception"
)
error_traceback: Optional[str] = Field(
default=None, description="On an error condition, this field will contain the exception traceback"
)
# internal flags and transitory settings
_install_tmpdir: Optional[Path] = PrivateAttr(default=None)
_exception: Optional[Exception] = PrivateAttr(default=None)
@ -182,7 +192,10 @@ class ModelInstallJob(BaseModel):
def set_error(self, e: Exception) -> None:
"""Record the error and traceback from an exception."""
self._exception = e
self.error = str(e)
self.error_traceback = self._format_error(e)
self.status = InstallStatus.ERROR
self.error_reason = self._exception.__class__.__name__ if self._exception else None
def cancel(self) -> None:
"""Call to cancel the job."""
@ -193,10 +206,9 @@ class ModelInstallJob(BaseModel):
"""Class name of the exception that led to status==ERROR."""
return self._exception.__class__.__name__ if self._exception else None
@property
def error(self) -> Optional[str]:
def _format_error(self, exception: Exception) -> str:
"""Error traceback."""
return "".join(traceback.format_exception(self._exception)) if self._exception else None
return "".join(traceback.format_exception(exception))
@property
def cancelled(self) -> bool:
@ -218,6 +230,11 @@ class ModelInstallJob(BaseModel):
"""Return true if job is downloading."""
return self.status == InstallStatus.DOWNLOADING
@property
def downloads_done(self) -> bool:
"""Return true if job's downloads ae done."""
return self.status == InstallStatus.DOWNLOADS_DONE
@property
def running(self) -> bool:
"""Return true if job is running."""
@ -243,7 +260,7 @@ class ModelInstallServiceBase(ABC):
app_config: InvokeAIAppConfig,
record_store: ModelRecordServiceBase,
download_queue: DownloadQueueServiceBase,
metadata_store: ModelMetadataStore,
metadata_store: ModelMetadataStoreBase,
event_bus: Optional["EventServiceBase"] = None,
):
"""
@ -324,6 +341,43 @@ class ModelInstallServiceBase(ABC):
:returns id: The string ID of the registered model.
"""
@abstractmethod
def heuristic_import(
self,
source: str,
config: Optional[Dict[str, Any]] = None,
access_token: Optional[str] = None,
) -> ModelInstallJob:
r"""Install the indicated model using heuristics to interpret user intentions.
:param source: String source
:param config: Optional dict. Any fields in this dict
will override corresponding autoassigned probe fields in the
model's config record as described in `import_model()`.
:param access_token: Optional access token for remote sources.
The source can be:
1. A local file path in posix() format (`/foo/bar` or `C:\foo\bar`)
2. An http or https URL (`https://foo.bar/foo`)
3. A HuggingFace repo_id (`foo/bar`, `foo/bar:fp16`, `foo/bar:fp16:vae`)
We extend the HuggingFace repo_id syntax to include the variant and the
subfolder or path. The following are acceptable alternatives:
stabilityai/stable-diffusion-v4
stabilityai/stable-diffusion-v4:fp16
stabilityai/stable-diffusion-v4:fp16:vae
stabilityai/stable-diffusion-v4::/checkpoints/sd4.safetensors
stabilityai/stable-diffusion-v4:onnx:vae
Because a local file path can look like a huggingface repo_id, the logic
first checks whether the path exists on disk, and if not, it is treated as
a parseable huggingface repo.
The previous support for recursing into a local folder and loading all model-like files
has been removed.
"""
pass
@abstractmethod
def import_model(
self,
@ -385,6 +439,18 @@ class ModelInstallServiceBase(ABC):
def cancel_job(self, job: ModelInstallJob) -> None:
"""Cancel the indicated job."""
@abstractmethod
def wait_for_job(self, job: ModelInstallJob, timeout: int = 0) -> ModelInstallJob:
"""Wait for the indicated job to reach a terminal state.
This will block until the indicated install job has completed,
been cancelled, or errored out.
:param job: The job to wait on.
:param timeout: Wait up to indicated number of seconds. Raise a TimeoutError if
the job hasn't completed within the indicated time.
"""
@abstractmethod
def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:
"""
@ -394,7 +460,8 @@ class ModelInstallServiceBase(ABC):
completed, been cancelled, or errored out.
:param timeout: Wait up to indicated number of seconds. Raise an Exception('timeout') if
installs do not complete within the indicated time.
installs do not complete within the indicated time. A timeout of zero (the default)
will block indefinitely until the installs complete.
"""
@abstractmethod
@ -410,3 +477,22 @@ class ModelInstallServiceBase(ABC):
@abstractmethod
def sync_to_config(self) -> None:
"""Synchronize models on disk to those in the model record database."""
@abstractmethod
def download_and_cache(self, source: Union[str, AnyHttpUrl], access_token: Optional[str] = None) -> Path:
"""
Download the model file located at source to the models cache and return its Path.
:param source: A Url or a string that can be converted into one.
:param access_token: Optional access token to access restricted resources.
The model file will be downloaded into the system-wide model cache
(`models/.cache`) if it isn't already there. Note that the model cache
is periodically cleared of infrequently-used entries when the model
converter runs.
Note that this doesn't automaticallly install or register the model, but is
intended for use by nodes that need access to models that aren't directly
supported by InvokeAI. The downloading process takes advantage of the download queue
to avoid interrupting other operations.
"""

171
invokeai/app/services/model_install/model_install_default.py
View File

@ -17,10 +17,10 @@ from pydantic.networks import AnyHttpUrl
from requests import Session
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase, TqdmProgress
from invokeai.app.services.events.events_base import EventServiceBase
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase, ModelRecordServiceSQL
from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase
from invokeai.backend.model_manager.config import (
AnyModelConfig,
BaseModelType,
@ -28,12 +28,10 @@ from invokeai.backend.model_manager.config import (
ModelRepoVariant,
ModelType,
)
from invokeai.backend.model_manager.hash import FastModelHash
from invokeai.backend.model_manager.metadata import (
AnyModelRepoMetadata,
CivitaiMetadataFetch,
HuggingFaceMetadataFetch,
ModelMetadataStore,
ModelMetadataWithFiles,
RemoteModelFile,
)
@ -50,6 +48,7 @@ from .model_install_base import (
ModelInstallJob,
ModelInstallServiceBase,
ModelSource,
StringLikeSource,
URLModelSource,
)
@ -64,7 +63,6 @@ class ModelInstallService(ModelInstallServiceBase):
app_config: InvokeAIAppConfig,
record_store: ModelRecordServiceBase,
download_queue: DownloadQueueServiceBase,
metadata_store: Optional[ModelMetadataStore] = None,
event_bus: Optional[EventServiceBase] = None,
session: Optional[Session] = None,
):
@ -86,19 +84,13 @@ class ModelInstallService(ModelInstallServiceBase):
self._lock = threading.Lock()
self._stop_event = threading.Event()
self._downloads_changed_event = threading.Event()
self._install_completed_event = threading.Event()
self._download_queue = download_queue
self._download_cache: Dict[AnyHttpUrl, ModelInstallJob] = {}
self._running = False
self._session = session
self._next_job_id = 0
# There may not necessarily be a metadata store initialized
# so we create one and initialize it with the same sql database
# used by the record store service.
if metadata_store:
self._metadata_store = metadata_store
else:
assert isinstance(record_store, ModelRecordServiceSQL)
self._metadata_store = ModelMetadataStore(record_store.db)
self._metadata_store = record_store.metadata_store # for convenience
@property
def app_config(self) -> InvokeAIAppConfig: # noqa D102
@ -145,7 +137,7 @@ class ModelInstallService(ModelInstallServiceBase):
) -> str: # noqa D102
model_path = Path(model_path)
config = config or {}
if config.get("source") is None:
if not config.get("source"):
config["source"] = model_path.resolve().as_posix()
return self._register(model_path, config)
@ -156,20 +148,24 @@ class ModelInstallService(ModelInstallServiceBase):
) -> str: # noqa D102
model_path = Path(model_path)
config = config or {}
if config.get("source") is None:
if not config.get("source"):
config["source"] = model_path.resolve().as_posix()
config["key"] = config.get("key", self._create_key())
info: AnyModelConfig = self._probe_model(Path(model_path), config)
old_hash = info.original_hash
dest_path = self.app_config.models_path / info.base.value / info.type.value / model_path.name
if preferred_name := config.get("name"):
preferred_name = Path(preferred_name).with_suffix(model_path.suffix)
dest_path = (
self.app_config.models_path / info.base.value / info.type.value / (preferred_name or model_path.name)
)
try:
new_path = self._copy_model(model_path, dest_path)
except FileExistsError as excp:
raise DuplicateModelException(
f"A model named {model_path.name} is already installed at {dest_path.as_posix()}"
) from excp
new_hash = FastModelHash.hash(new_path)
assert new_hash == old_hash, f"{model_path}: Model hash changed during installation, possibly corrupted."
return self._register(
new_path,
@ -177,7 +173,40 @@ class ModelInstallService(ModelInstallServiceBase):
info,
)
def heuristic_import(
self,
source: str,
config: Optional[Dict[str, Any]] = None,
access_token: Optional[str] = None,
) -> ModelInstallJob:
variants = "|".join(ModelRepoVariant.__members__.values())
hf_repoid_re = f"^([^/:]+/[^/:]+)(?::({variants})?(?::/?([^:]+))?)?$"
source_obj: Optional[StringLikeSource] = None
if Path(source).exists(): # A local file or directory
source_obj = LocalModelSource(path=Path(source))
elif match := re.match(hf_repoid_re, source):
source_obj = HFModelSource(
repo_id=match.group(1),
variant=match.group(2) if match.group(2) else None, # pass None rather than ''
subfolder=Path(match.group(3)) if match.group(3) else None,
access_token=access_token,
)
elif re.match(r"^https?://[^/]+", source):
source_obj = URLModelSource(
url=AnyHttpUrl(source),
access_token=access_token,
)
else:
raise ValueError(f"Unsupported model source: '{source}'")
return self.import_model(source_obj, config)
def import_model(self, source: ModelSource, config: Optional[Dict[str, Any]] = None) -> ModelInstallJob: # noqa D102
similar_jobs = [x for x in self.list_jobs() if x.source == source and not x.in_terminal_state]
if similar_jobs:
self._logger.warning(f"There is already an active install job for {source}. Not enqueuing.")
return similar_jobs[0]
if isinstance(source, LocalModelSource):
install_job = self._import_local_model(source, config)
self._install_queue.put(install_job) # synchronously install
@ -207,14 +236,25 @@ class ModelInstallService(ModelInstallServiceBase):
assert isinstance(jobs[0], ModelInstallJob)
return jobs[0]
def wait_for_job(self, job: ModelInstallJob, timeout: int = 0) -> ModelInstallJob:
"""Block until the indicated job has reached terminal state, or when timeout limit reached."""
start = time.time()
while not job.in_terminal_state:
if self._install_completed_event.wait(timeout=5): # in case we miss an event
self._install_completed_event.clear()
if timeout > 0 and time.time() - start > timeout:
raise TimeoutError("Timeout exceeded")
return job
# TODO: Better name? Maybe wait_for_jobs()? Maybe too easily confused with above
def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]: # noqa D102
"""Block until all installation jobs are done."""
start = time.time()
while len(self._download_cache) > 0:
if self._downloads_changed_event.wait(timeout=5): # in case we miss an event
if self._downloads_changed_event.wait(timeout=0.25): # in case we miss an event
self._downloads_changed_event.clear()
if timeout > 0 and time.time() - start > timeout:
raise Exception("Timeout exceeded")
raise TimeoutError("Timeout exceeded")
self._install_queue.join()
return self._install_jobs
@ -239,9 +279,9 @@ class ModelInstallService(ModelInstallServiceBase):
self._logger.info("Model installer (re)initialized")
def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]: # noqa D102
self._cached_model_paths = {Path(x.path) for x in self.record_store.all_models()}
self._cached_model_paths = {Path(x.path).absolute() for x in self.record_store.all_models()}
callback = self._scan_install if install else self._scan_register
search = ModelSearch(on_model_found=callback)
search = ModelSearch(on_model_found=callback, config=self._app_config)
self._models_installed.clear()
search.search(scan_dir)
return list(self._models_installed)
@ -268,6 +308,38 @@ class ModelInstallService(ModelInstallServiceBase):
path.unlink()
self.unregister(key)
def download_and_cache(
self,
source: Union[str, AnyHttpUrl],
access_token: Optional[str] = None,
timeout: int = 0,
) -> Path:
"""Download the model file located at source to the models cache and return its Path."""
model_hash = sha256(str(source).encode("utf-8")).hexdigest()[0:32]
model_path = self._app_config.models_convert_cache_path / model_hash
# We expect the cache directory to contain one and only one downloaded file.
# We don't know the file's name in advance, as it is set by the download
# content-disposition header.
if model_path.exists():
contents = [x for x in model_path.iterdir() if x.is_file()]
if len(contents) > 0:
return contents[0]
model_path.mkdir(parents=True, exist_ok=True)
job = self._download_queue.download(
source=AnyHttpUrl(str(source)),
dest=model_path,
access_token=access_token,
on_progress=TqdmProgress().update,
)
self._download_queue.wait_for_job(job, timeout)
if job.complete:
assert job.download_path is not None
return job.download_path
else:
raise Exception(job.error)
# --------------------------------------------------------------------------------------------
# Internal functions that manage the installer threads
# --------------------------------------------------------------------------------------------
@ -295,11 +367,12 @@ class ModelInstallService(ModelInstallServiceBase):
self._signal_job_errored(job)
elif (
job.waiting or job.downloading
job.waiting or job.downloads_done
): # local jobs will be in waiting state, remote jobs will be downloading state
job.total_bytes = self._stat_size(job.local_path)
job.bytes = job.total_bytes
self._signal_job_running(job)
job.config_in["source"] = str(job.source)
if job.inplace:
key = self.register_path(job.local_path, job.config_in)
else:
@ -330,6 +403,7 @@ class ModelInstallService(ModelInstallServiceBase):
# if this is an install of a remote file, then clean up the temporary directory
if job._install_tmpdir is not None:
rmtree(job._install_tmpdir)
self._install_completed_event.set()
self._install_queue.task_done()
self._logger.info("Install thread exiting")
@ -371,7 +445,7 @@ class ModelInstallService(ModelInstallServiceBase):
installed.update(self.scan_directory(models_dir))
self._logger.info(f"{len(installed)} new models registered; {len(defunct_models)} unregistered")
def _sync_model_path(self, key: str, ignore_hash_change: bool = False) -> AnyModelConfig:
def _sync_model_path(self, key: str) -> AnyModelConfig:
"""
Move model into the location indicated by its basetype, type and name.
@ -392,14 +466,7 @@ class ModelInstallService(ModelInstallServiceBase):
new_path = models_dir / model.base.value / model.type.value / model.name
self._logger.info(f"Moving {model.name} to {new_path}.")
new_path = self._move_model(old_path, new_path)
new_hash = FastModelHash.hash(new_path)
model.path = new_path.relative_to(models_dir).as_posix()
if model.current_hash != new_hash:
assert (
ignore_hash_change
), f"{model.name}: Model hash changed during installation, model is possibly corrupted"
model.current_hash = new_hash
self._logger.info(f"Model has new hash {model.current_hash}, but will continue to be identified by {key}")
self.record_store.update_model(key, model)
return model
@ -465,8 +532,10 @@ class ModelInstallService(ModelInstallServiceBase):
def _register(
self, model_path: Path, config: Optional[Dict[str, Any]] = None, info: Optional[AnyModelConfig] = None
) -> str:
# Note that we may be passed a pre-populated AnyModelConfig object,
# in which case the key field should have been populated by the caller (e.g. in `install_path`).
config["key"] = config.get("key", self._create_key())
info = info or ModelProbe.probe(model_path, config)
key = self._create_key()
model_path = model_path.absolute()
if model_path.is_relative_to(self.app_config.models_path):
@ -479,8 +548,8 @@ class ModelInstallService(ModelInstallServiceBase):
# make config relative to our root
legacy_conf = (self.app_config.root_dir / self.app_config.legacy_conf_dir / info.config).resolve()
info.config = legacy_conf.relative_to(self.app_config.root_dir).as_posix()
self.record_store.add_model(key, info)
return key
self.record_store.add_model(info.key, info)
return info.key
def _next_id(self) -> int:
with self._lock:
@ -489,10 +558,10 @@ class ModelInstallService(ModelInstallServiceBase):
return id
@staticmethod
def _guess_variant() -> ModelRepoVariant:
def _guess_variant() -> Optional[ModelRepoVariant]:
"""Guess the best HuggingFace variant type to download."""
precision = choose_precision(choose_torch_device())
return ModelRepoVariant.FP16 if precision == "float16" else ModelRepoVariant.DEFAULT
return ModelRepoVariant.FP16 if precision == "float16" else None
def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
return ModelInstallJob(
@ -517,7 +586,7 @@ class ModelInstallService(ModelInstallServiceBase):
if not source.access_token:
self._logger.info("No HuggingFace access token present; some models may not be downloadable.")
metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id)
metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id, source.variant)
assert isinstance(metadata, ModelMetadataWithFiles)
remote_files = metadata.download_urls(
variant=source.variant or self._guess_variant(),
@ -565,6 +634,8 @@ class ModelInstallService(ModelInstallServiceBase):
# TODO: Replace with tempfile.tmpdir() when multithreading is cleaned up.
# Currently the tmpdir isn't automatically removed at exit because it is
# being held in a daemon thread.
if len(remote_files) == 0:
raise ValueError(f"{source}: No downloadable files found")
tmpdir = Path(
mkdtemp(
dir=self._app_config.models_path,
@ -580,6 +651,16 @@ class ModelInstallService(ModelInstallServiceBase):
bytes=0,
total_bytes=0,
)
# In the event that there is a subfolder specified in the source,
# we need to remove it from the destination path in order to avoid
# creating unwanted subfolders
if hasattr(source, "subfolder") and source.subfolder:
root = Path(remote_files[0].path.parts[0])
subfolder = root / source.subfolder
else:
root = Path(".")
subfolder = Path(".")
# we remember the path up to the top of the tmpdir so that it may be
# removed safely at the end of the install process.
install_job._install_tmpdir = tmpdir
@ -589,7 +670,7 @@ class ModelInstallService(ModelInstallServiceBase):
self._logger.debug(f"remote_files={remote_files}")
for model_file in remote_files:
url = model_file.url
path = model_file.path
path = root / model_file.path.relative_to(subfolder)
self._logger.info(f"Downloading {url} => {path}")
install_job.total_bytes += model_file.size
assert hasattr(source, "access_token")
@ -652,13 +733,14 @@ class ModelInstallService(ModelInstallServiceBase):
self._signal_job_downloading(install_job)
def _download_complete_callback(self, download_job: DownloadJob) -> None:
self._logger.info(f"{download_job.source}: model download complete")
with self._lock:
install_job = self._download_cache[download_job.source]
self._download_cache.pop(download_job.source, None)
# are there any more active jobs left in this task?
if all(x.complete for x in install_job.download_parts):
# now enqueue job for actual installation into the models directory
if install_job.downloading and all(x.complete for x in install_job.download_parts):
install_job.status = InstallStatus.DOWNLOADS_DONE
self._install_queue.put(install_job)
# Let other threads know that the number of downloads has changed
@ -684,7 +766,7 @@ class ModelInstallService(ModelInstallServiceBase):
if not install_job:
return
self._downloads_changed_event.set()
self._logger.warning(f"Download {download_job.source} cancelled.")
self._logger.warning(f"{download_job.source}: model download cancelled")
# if install job has already registered an error, then do not replace its status with cancelled
if not install_job.errored:
install_job.cancel()
@ -731,6 +813,7 @@ class ModelInstallService(ModelInstallServiceBase):
parts=parts,
bytes=job.bytes,
total_bytes=job.total_bytes,
id=job.id,
)
def _signal_job_completed(self, job: ModelInstallJob) -> None:
@ -743,7 +826,7 @@ class ModelInstallService(ModelInstallServiceBase):
assert job.local_path is not None
assert job.config_out is not None
key = job.config_out.key
self._event_bus.emit_model_install_completed(str(job.source), key)
self._event_bus.emit_model_install_completed(str(job.source), key, id=job.id)
def _signal_job_errored(self, job: ModelInstallJob) -> None:
self._logger.info(f"{job.source}: model installation encountered an exception: {job.error_type}\n{job.error}")
@ -752,7 +835,7 @@ class ModelInstallService(ModelInstallServiceBase):
error = job.error
assert error_type is not None
assert error is not None
self._event_bus.emit_model_install_error(str(job.source), error_type, error)
self._event_bus.emit_model_install_error(str(job.source), error_type, error, id=job.id)
def _signal_job_cancelled(self, job: ModelInstallJob) -> None:
self._logger.info(f"{job.source}: model installation was cancelled")

6
invokeai/app/services/model_load/__init__.py Normal file
View File

@ -0,0 +1,6 @@
"""Initialization file for model load service module."""
from .model_load_base import ModelLoadServiceBase
from .model_load_default import ModelLoadService
__all__ = ["ModelLoadServiceBase", "ModelLoadService"]

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