Compare commits
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bug-instal
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feat/diffu
Author | SHA1 | Date | |
---|---|---|---|
7487f96da2 |
8
.github/CODEOWNERS
vendored
@ -1,5 +1,5 @@
|
||||
# continuous integration
|
||||
/.github/workflows/ @lstein @blessedcoolant @hipsterusername @ebr
|
||||
/.github/workflows/ @lstein @blessedcoolant @hipsterusername
|
||||
|
||||
# documentation
|
||||
/docs/ @lstein @blessedcoolant @hipsterusername @Millu
|
||||
@ -10,7 +10,7 @@
|
||||
|
||||
# installation and configuration
|
||||
/pyproject.toml @lstein @blessedcoolant @hipsterusername
|
||||
/docker/ @lstein @blessedcoolant @hipsterusername @ebr
|
||||
/docker/ @lstein @blessedcoolant @hipsterusername
|
||||
/scripts/ @ebr @lstein @hipsterusername
|
||||
/installer/ @lstein @ebr @hipsterusername
|
||||
/invokeai/assets @lstein @ebr @hipsterusername
|
||||
@ -26,7 +26,9 @@
|
||||
|
||||
# front ends
|
||||
/invokeai/frontend/CLI @lstein @hipsterusername
|
||||
/invokeai/frontend/install @lstein @ebr @hipsterusername
|
||||
/invokeai/frontend/install @lstein @ebr @hipsterusername
|
||||
/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
|
||||
/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
|
||||
/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername
|
||||
|
||||
|
||||
|
98
.github/ISSUE_TEMPLATE/BUG_REPORT.yml
vendored
@ -6,6 +6,10 @@ title: '[bug]: '
|
||||
|
||||
labels: ['bug']
|
||||
|
||||
# assignees:
|
||||
# - moderator_bot
|
||||
# - lstein
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
@ -14,9 +18,10 @@ body:
|
||||
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Is there an existing issue for this problem?
|
||||
label: Is there an existing issue for this?
|
||||
description: |
|
||||
Please [search](https://github.com/invoke-ai/InvokeAI/issues) first to see if an issue already exists for the problem.
|
||||
Please use the [search function](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
|
||||
irst to see if an issue already exists for the bug you encountered.
|
||||
options:
|
||||
- label: I have searched the existing issues
|
||||
required: true
|
||||
@ -28,119 +33,80 @@ body:
|
||||
- type: dropdown
|
||||
id: os_dropdown
|
||||
attributes:
|
||||
label: Operating system
|
||||
description: Your computer's operating system.
|
||||
label: OS
|
||||
description: Which operating System did you use when the bug occured
|
||||
multiple: false
|
||||
options:
|
||||
- 'Linux'
|
||||
- 'Windows'
|
||||
- 'macOS'
|
||||
- 'other'
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: dropdown
|
||||
id: gpu_dropdown
|
||||
attributes:
|
||||
label: GPU vendor
|
||||
description: Your GPU's vendor.
|
||||
label: GPU
|
||||
description: Which kind of Graphic-Adapter is your System using
|
||||
multiple: false
|
||||
options:
|
||||
- 'Nvidia (CUDA)'
|
||||
- 'AMD (ROCm)'
|
||||
- 'Apple Silicon (MPS)'
|
||||
- 'None (CPU)'
|
||||
- 'cuda'
|
||||
- 'amd'
|
||||
- 'mps'
|
||||
- 'cpu'
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: input
|
||||
id: gpu_model
|
||||
attributes:
|
||||
label: GPU model
|
||||
description: Your GPU's model. If on Apple Silicon, this is your Mac's chip. Leave blank if on CPU.
|
||||
placeholder: ex. RTX 2080 Ti, Mac M1 Pro
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: input
|
||||
id: vram
|
||||
attributes:
|
||||
label: GPU VRAM
|
||||
description: Your GPU's VRAM. If on Apple Silicon, this is your Mac's unified memory. Leave blank if on CPU.
|
||||
label: VRAM
|
||||
description: Size of the VRAM if known
|
||||
placeholder: 8GB
|
||||
validations:
|
||||
required: false
|
||||
|
||||
|
||||
- type: input
|
||||
id: version-number
|
||||
attributes:
|
||||
label: Version number
|
||||
label: What version did you experience this issue on?
|
||||
description: |
|
||||
The version of Invoke you have installed. If it is not the latest version, please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
|
||||
placeholder: ex. 3.6.1
|
||||
Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
|
||||
placeholder: X.X.X
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: input
|
||||
id: browser-version
|
||||
attributes:
|
||||
label: Browser
|
||||
description: Your web browser and version.
|
||||
placeholder: ex. Firefox 123.0b3
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: python-deps
|
||||
attributes:
|
||||
label: Python dependencies
|
||||
description: |
|
||||
If the problem occurred during image generation, click the gear icon at the bottom left corner, click "About", click the copy button and then paste here.
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: textarea
|
||||
id: what-happened
|
||||
attributes:
|
||||
label: What happened
|
||||
label: What happened?
|
||||
description: |
|
||||
Describe what happened. Include any relevant error messages, stack traces and screenshots here.
|
||||
placeholder: I clicked button X and then Y happened.
|
||||
Briefly describe what happened, what you expected to happen and how to reproduce this bug.
|
||||
placeholder: When using the webinterface and right-clicking on button X instead of the popup-menu there error Y appears
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: what-you-expected
|
||||
attributes:
|
||||
label: What you expected to happen
|
||||
description: Describe what you expected to happen.
|
||||
placeholder: I expected Z to happen.
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: how-to-repro
|
||||
attributes:
|
||||
label: How to reproduce the problem
|
||||
description: List steps to reproduce the problem.
|
||||
placeholder: Start the app, generate an image with these settings, then click button X.
|
||||
label: Screenshots
|
||||
description: If applicable, add screenshots to help explain your problem
|
||||
placeholder: this is what the result looked like <screenshot>
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: textarea
|
||||
id: additional-context
|
||||
attributes:
|
||||
label: Additional context
|
||||
description: Any other context that might help us to understand the problem.
|
||||
description: Add any other context about the problem here
|
||||
placeholder: Only happens when there is full moon and Friday the 13th on Christmas Eve 🎅🏻
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: input
|
||||
id: discord-username
|
||||
id: contact
|
||||
attributes:
|
||||
label: Discord username
|
||||
description: If you are on the Invoke discord and would prefer to be contacted there, please provide your username.
|
||||
placeholder: supercoolusername123
|
||||
label: Contact Details
|
||||
description: __OPTIONAL__ How can we get in touch with you if we need more info (besides this issue)?
|
||||
placeholder: ex. email@example.com, discordname, twitter, ...
|
||||
validations:
|
||||
required: false
|
||||
|
33
.github/actions/install-frontend-deps/action.yml
vendored
@ -1,33 +0,0 @@
|
||||
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
|
||||
|
||||
- name: setup cache
|
||||
uses: actions/cache@v4
|
||||
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
|
59
.github/pr_labels.yml
vendored
@ -1,59 +0,0 @@
|
||||
root:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: '*'
|
||||
|
||||
python-deps:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: 'pyproject.toml'
|
||||
|
||||
python:
|
||||
- changed-files:
|
||||
- all-globs-to-any-file:
|
||||
- 'invokeai/**'
|
||||
- '!invokeai/frontend/web/**'
|
||||
|
||||
python-tests:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: 'tests/**'
|
||||
|
||||
ci-cd:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: .github/**
|
||||
|
||||
docker:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: docker/**
|
||||
|
||||
installer:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: installer/**
|
||||
|
||||
docs:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: docs/**
|
||||
|
||||
invocations:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: 'invokeai/app/invocations/**'
|
||||
|
||||
backend:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: 'invokeai/backend/**'
|
||||
|
||||
api:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: 'invokeai/app/api/**'
|
||||
|
||||
services:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: 'invokeai/app/services/**'
|
||||
|
||||
frontend-deps:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file:
|
||||
- '**/*/package.json'
|
||||
- '**/*/pnpm-lock.yaml'
|
||||
|
||||
frontend:
|
||||
- changed-files:
|
||||
- any-glob-to-any-file: 'invokeai/frontend/web/**'
|
7
.github/workflows/build-container.yml
vendored
@ -11,7 +11,7 @@ on:
|
||||
- 'docker/docker-entrypoint.sh'
|
||||
- 'workflows/build-container.yml'
|
||||
tags:
|
||||
- 'v*.*.*'
|
||||
- 'v*'
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
@ -40,14 +40,10 @@ jobs:
|
||||
- name: Free up more disk space on the runner
|
||||
# https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
|
||||
run: |
|
||||
echo "----- Free space before cleanup"
|
||||
df -h
|
||||
sudo rm -rf /usr/share/dotnet
|
||||
sudo rm -rf "$AGENT_TOOLSDIRECTORY"
|
||||
sudo swapoff /mnt/swapfile
|
||||
sudo rm -rf /mnt/swapfile
|
||||
echo "----- Free space after cleanup"
|
||||
df -h
|
||||
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v3
|
||||
@ -95,7 +91,6 @@ jobs:
|
||||
# password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
|
||||
- name: Build container
|
||||
timeout-minutes: 40
|
||||
id: docker_build
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
|
45
.github/workflows/build-installer.yml
vendored
@ -1,45 +0,0 @@
|
||||
# Builds and uploads the installer and python build artifacts.
|
||||
|
||||
name: build installer
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
workflow_call:
|
||||
|
||||
jobs:
|
||||
build-installer:
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 5 # expected run time: <2 min
|
||||
steps:
|
||||
- name: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: setup python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.10'
|
||||
cache: pip
|
||||
cache-dependency-path: pyproject.toml
|
||||
|
||||
- name: install pypa/build
|
||||
run: pip install --upgrade build
|
||||
|
||||
- name: setup frontend
|
||||
uses: ./.github/actions/install-frontend-deps
|
||||
|
||||
- name: create installer
|
||||
id: create_installer
|
||||
run: ./create_installer.sh
|
||||
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 }}
|
80
.github/workflows/frontend-checks.yml
vendored
@ -1,80 +0,0 @@
|
||||
# Runs frontend code quality checks.
|
||||
#
|
||||
# Checks for changes to frontend files before running the checks.
|
||||
# If always_run is true, always runs the checks.
|
||||
|
||||
name: 'frontend checks'
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
pull_request:
|
||||
types:
|
||||
- 'ready_for_review'
|
||||
- 'opened'
|
||||
- 'synchronize'
|
||||
merge_group:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the checks'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
workflow_call:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the checks'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: invokeai/frontend/web
|
||||
|
||||
jobs:
|
||||
frontend-checks:
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 10 # expected run time: <2 min
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: check for changed frontend files
|
||||
if: ${{ inputs.always_run != true }}
|
||||
id: changed-files
|
||||
uses: tj-actions/changed-files@v42
|
||||
with:
|
||||
files_yaml: |
|
||||
frontend:
|
||||
- 'invokeai/frontend/web/**'
|
||||
|
||||
- name: install dependencies
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
uses: ./.github/actions/install-frontend-deps
|
||||
|
||||
- name: tsc
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: 'pnpm lint:tsc'
|
||||
shell: bash
|
||||
|
||||
- name: dpdm
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: 'pnpm lint:dpdm'
|
||||
shell: bash
|
||||
|
||||
- name: eslint
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: 'pnpm lint:eslint'
|
||||
shell: bash
|
||||
|
||||
- name: prettier
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: 'pnpm lint:prettier'
|
||||
shell: bash
|
||||
|
||||
- name: knip
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: 'pnpm lint:knip'
|
||||
shell: bash
|
60
.github/workflows/frontend-tests.yml
vendored
@ -1,60 +0,0 @@
|
||||
# Runs frontend tests.
|
||||
#
|
||||
# Checks for changes to frontend files before running the tests.
|
||||
# If always_run is true, always runs the tests.
|
||||
|
||||
name: 'frontend tests'
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
pull_request:
|
||||
types:
|
||||
- 'ready_for_review'
|
||||
- 'opened'
|
||||
- 'synchronize'
|
||||
merge_group:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the tests'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
workflow_call:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the tests'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: invokeai/frontend/web
|
||||
|
||||
jobs:
|
||||
frontend-tests:
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 10 # expected run time: <2 min
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: check for changed frontend files
|
||||
if: ${{ inputs.always_run != true }}
|
||||
id: changed-files
|
||||
uses: tj-actions/changed-files@v42
|
||||
with:
|
||||
files_yaml: |
|
||||
frontend:
|
||||
- 'invokeai/frontend/web/**'
|
||||
|
||||
- name: install dependencies
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
uses: ./.github/actions/install-frontend-deps
|
||||
|
||||
- name: vitest
|
||||
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: 'pnpm test:no-watch'
|
||||
shell: bash
|
18
.github/workflows/label-pr.yml
vendored
@ -1,18 +0,0 @@
|
||||
name: 'label PRs'
|
||||
on:
|
||||
- pull_request_target
|
||||
|
||||
jobs:
|
||||
labeler:
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: write
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: label PRs
|
||||
uses: actions/labeler@v5
|
||||
with:
|
||||
configuration-path: .github/pr_labels.yml
|
43
.github/workflows/lint-frontend.yml
vendored
Normal file
@ -0,0 +1,43 @@
|
||||
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'
|
54
.github/workflows/mkdocs-material.yml
vendored
@ -1,49 +1,51 @@
|
||||
# This is a mostly a copy-paste from https://github.com/squidfunk/mkdocs-material/blob/master/docs/publishing-your-site.md
|
||||
|
||||
name: mkdocs
|
||||
|
||||
name: mkdocs-material
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
workflow_dispatch:
|
||||
- 'refs/heads/main'
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
contents: write
|
||||
|
||||
jobs:
|
||||
deploy:
|
||||
mkdocs-material:
|
||||
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://${{ github.repository_owner }}.github.io/InvokeAI'
|
||||
|
||||
steps:
|
||||
- name: checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: checkout sources
|
||||
uses: actions/checkout@v3
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: setup python
|
||||
uses: actions/setup-python@v5
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: '3.10'
|
||||
cache: pip
|
||||
cache-dependency-path: pyproject.toml
|
||||
|
||||
- name: set cache id
|
||||
run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
|
||||
- name: install requirements
|
||||
env:
|
||||
PIP_USE_PEP517: 1
|
||||
run: |
|
||||
python -m \
|
||||
pip install ".[docs]"
|
||||
|
||||
- name: use cache
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
key: mkdocs-material-${{ env.cache_id }}
|
||||
path: .cache
|
||||
restore-keys: |
|
||||
mkdocs-material-
|
||||
- name: confirm buildability
|
||||
run: |
|
||||
python -m \
|
||||
mkdocs build \
|
||||
--clean \
|
||||
--verbose
|
||||
|
||||
- name: install dependencies
|
||||
run: python -m pip install ".[docs]"
|
||||
|
||||
- name: build & deploy
|
||||
run: mkdocs gh-deploy --force
|
||||
- name: deploy to gh-pages
|
||||
if: ${{ github.ref == 'refs/heads/main' }}
|
||||
run: |
|
||||
python -m \
|
||||
mkdocs gh-deploy \
|
||||
--clean \
|
||||
--force
|
||||
|
67
.github/workflows/pypi-release.yml
vendored
Normal file
@ -0,0 +1,67 @@
|
||||
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/*
|
76
.github/workflows/python-checks.yml
vendored
@ -1,76 +0,0 @@
|
||||
# Runs python code quality checks.
|
||||
#
|
||||
# Checks for changes to python files before running the checks.
|
||||
# If always_run is true, always runs the checks.
|
||||
#
|
||||
# TODO: Add mypy or pyright to the checks.
|
||||
|
||||
name: 'python checks'
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
pull_request:
|
||||
types:
|
||||
- 'ready_for_review'
|
||||
- 'opened'
|
||||
- 'synchronize'
|
||||
merge_group:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the checks'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
workflow_call:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the checks'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
|
||||
jobs:
|
||||
python-checks:
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 5 # expected run time: <1 min
|
||||
steps:
|
||||
- name: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: check for changed python files
|
||||
if: ${{ inputs.always_run != true }}
|
||||
id: changed-files
|
||||
uses: tj-actions/changed-files@v42
|
||||
with:
|
||||
files_yaml: |
|
||||
python:
|
||||
- 'pyproject.toml'
|
||||
- 'invokeai/**'
|
||||
- '!invokeai/frontend/web/**'
|
||||
- 'tests/**'
|
||||
|
||||
- name: setup python
|
||||
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.10'
|
||||
cache: pip
|
||||
cache-dependency-path: pyproject.toml
|
||||
|
||||
- name: install ruff
|
||||
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: pip install ruff
|
||||
shell: bash
|
||||
|
||||
- name: ruff check
|
||||
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: ruff check --output-format=github .
|
||||
shell: bash
|
||||
|
||||
- name: ruff format
|
||||
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
|
||||
run: ruff format --check .
|
||||
shell: bash
|
107
.github/workflows/python-tests.yml
vendored
@ -1,107 +0,0 @@
|
||||
# Runs python tests on a matrix of python versions and platforms.
|
||||
#
|
||||
# Checks for changes to python files before running the tests.
|
||||
# If always_run is true, always runs the tests.
|
||||
|
||||
name: 'python tests'
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
- 'bug-install-job-running-multiple-times'
|
||||
pull_request:
|
||||
types:
|
||||
- 'ready_for_review'
|
||||
- 'opened'
|
||||
- 'synchronize'
|
||||
merge_group:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the tests'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
workflow_call:
|
||||
inputs:
|
||||
always_run:
|
||||
description: 'Always run the tests'
|
||||
required: true
|
||||
type: boolean
|
||||
default: true
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
matrix:
|
||||
strategy:
|
||||
matrix:
|
||||
python-version:
|
||||
- '3.10'
|
||||
- '3.11'
|
||||
platform:
|
||||
- linux-cuda-11_7
|
||||
- linux-rocm-5_2
|
||||
- linux-cpu
|
||||
- macos-default
|
||||
- windows-cpu
|
||||
include:
|
||||
- platform: linux-cuda-11_7
|
||||
os: ubuntu-22.04
|
||||
github-env: $GITHUB_ENV
|
||||
- platform: linux-rocm-5_2
|
||||
os: ubuntu-22.04
|
||||
extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
|
||||
github-env: $GITHUB_ENV
|
||||
- platform: linux-cpu
|
||||
os: ubuntu-22.04
|
||||
extra-index-url: 'https://download.pytorch.org/whl/cpu'
|
||||
github-env: $GITHUB_ENV
|
||||
- platform: macos-default
|
||||
os: macOS-12
|
||||
github-env: $GITHUB_ENV
|
||||
- platform: windows-cpu
|
||||
os: windows-2022
|
||||
github-env: $env:GITHUB_ENV
|
||||
name: 'py${{ matrix.python-version }}: ${{ matrix.platform }}'
|
||||
runs-on: ${{ matrix.os }}
|
||||
timeout-minutes: 15 # expected run time: 2-6 min, depending on platform
|
||||
env:
|
||||
PIP_USE_PEP517: '1'
|
||||
steps:
|
||||
- name: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: check for changed python files
|
||||
if: ${{ inputs.always_run != true }}
|
||||
id: changed-files
|
||||
uses: tj-actions/changed-files@v42
|
||||
with:
|
||||
files_yaml: |
|
||||
python:
|
||||
- 'pyproject.toml'
|
||||
- 'invokeai/**'
|
||||
- '!invokeai/frontend/web/**'
|
||||
- 'tests/**'
|
||||
|
||||
- name: setup python
|
||||
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
cache: pip
|
||||
cache-dependency-path: pyproject.toml
|
||||
|
||||
- name: install dependencies
|
||||
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == 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' || inputs.always_run == true }}
|
||||
run: pytest
|
108
.github/workflows/release.yml
vendored
@ -1,108 +0,0 @@
|
||||
# Main release workflow. Triggered on tag push or manual trigger.
|
||||
#
|
||||
# - Runs all code checks and tests
|
||||
# - Verifies the app version matches the tag version.
|
||||
# - Builds the installer and build, uploading them as artifacts.
|
||||
# - Publishes to TestPyPI and PyPI. Both are conditional on the previous steps passing and require a manual approval.
|
||||
#
|
||||
# See docs/RELEASE.md for more information on the release process.
|
||||
|
||||
name: release
|
||||
|
||||
on:
|
||||
push:
|
||||
tags:
|
||||
- 'v*'
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
check-version:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: check python version
|
||||
uses: samuelcolvin/check-python-version@v4
|
||||
id: check-python-version
|
||||
with:
|
||||
version_file_path: invokeai/version/invokeai_version.py
|
||||
|
||||
frontend-checks:
|
||||
uses: ./.github/workflows/frontend-checks.yml
|
||||
with:
|
||||
always_run: true
|
||||
|
||||
frontend-tests:
|
||||
uses: ./.github/workflows/frontend-tests.yml
|
||||
with:
|
||||
always_run: true
|
||||
|
||||
python-checks:
|
||||
uses: ./.github/workflows/python-checks.yml
|
||||
with:
|
||||
always_run: true
|
||||
|
||||
python-tests:
|
||||
uses: ./.github/workflows/python-tests.yml
|
||||
with:
|
||||
always_run: true
|
||||
|
||||
build:
|
||||
uses: ./.github/workflows/build-installer.yml
|
||||
|
||||
publish-testpypi:
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 5 # expected run time: <1 min
|
||||
needs:
|
||||
[
|
||||
check-version,
|
||||
frontend-checks,
|
||||
frontend-tests,
|
||||
python-checks,
|
||||
python-tests,
|
||||
build,
|
||||
]
|
||||
environment:
|
||||
name: testpypi
|
||||
url: https://test.pypi.org/p/invokeai
|
||||
permissions:
|
||||
id-token: write
|
||||
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
|
||||
timeout-minutes: 5 # expected run time: <1 min
|
||||
needs:
|
||||
[
|
||||
check-version,
|
||||
frontend-checks,
|
||||
frontend-tests,
|
||||
python-checks,
|
||||
python-tests,
|
||||
build,
|
||||
]
|
||||
environment:
|
||||
name: pypi
|
||||
url: https://pypi.org/p/invokeai
|
||||
permissions:
|
||||
id-token: write
|
||||
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
Normal file
@ -0,0 +1,24 @@
|
||||
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
Normal file
@ -0,0 +1,129 @@
|
||||
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@v37
|
||||
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 }}
|
@ -7,7 +7,7 @@ embeddedLanguageFormatting: auto
|
||||
overrides:
|
||||
- files: '*.md'
|
||||
options:
|
||||
proseWrap: preserve
|
||||
proseWrap: always
|
||||
printWidth: 80
|
||||
parser: markdown
|
||||
cursorOffset: -1
|
||||
|
48
Makefile
@ -6,50 +6,33 @@ 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 "test Run the unit tests."
|
||||
@echo "update-config-docstring Update the app's config docstring so mkdocs can autogenerate it correctly."
|
||||
@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 "frontend-typegen Generate types for the frontend from the OpenAPI schema"
|
||||
@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 "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
|
||||
|
||||
# Run the unit tests
|
||||
test:
|
||||
pytest ./tests
|
||||
|
||||
# Update config docstring
|
||||
update-config-docstring:
|
||||
python scripts/update_config_docstring.py
|
||||
|
||||
# Install the pnpm modules needed for the front end
|
||||
frontend-install:
|
||||
rm -rf invokeai/frontend/web/node_modules
|
||||
cd invokeai/frontend/web && pnpm install
|
||||
mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports
|
||||
|
||||
# Build the frontend
|
||||
frontend-build:
|
||||
@ -59,9 +42,6 @@ frontend-build:
|
||||
frontend-dev:
|
||||
cd invokeai/frontend/web && pnpm dev
|
||||
|
||||
frontend-typegen:
|
||||
cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen
|
||||
|
||||
# Installer zip file
|
||||
installer-zip:
|
||||
cd installer && ./create_installer.sh
|
||||
|
14
README.md
@ -1,10 +1,10 @@
|
||||
<div align="center">
|
||||
|
||||

|
||||

|
||||
|
||||
# Invoke - Professional Creative AI Tools for Visual Media
|
||||
## To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
|
||||
|
||||
# Invoke AI - Generative AI for Professional Creatives
|
||||
## Professional Creative Tools for Stable Diffusion, Custom-Trained Models, and more.
|
||||
To learn more about Invoke AI, get started instantly, or implement our Business solutions, visit [invoke.ai](https://invoke.ai)
|
||||
|
||||
|
||||
[![discord badge]][discord link]
|
||||
@ -56,9 +56,7 @@ the foundation for multiple commercial products.
|
||||
|
||||
<div align="center">
|
||||
|
||||
|
||||

|
||||
|
||||

|
||||
|
||||
</div>
|
||||
|
||||
@ -169,7 +167,7 @@ the command `npm install -g pnpm` if needed)
|
||||
_For Linux with an AMD GPU:_
|
||||
|
||||
```sh
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
```
|
||||
|
||||
_For non-GPU systems:_
|
||||
|
@ -2,13 +2,10 @@
|
||||
## Any environment variables supported by InvokeAI can be specified here,
|
||||
## in addition to the examples below.
|
||||
|
||||
# HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where InvokeAI will store data.
|
||||
# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
|
||||
# Outputs will also be stored here by default.
|
||||
# If relative, it will be relative to the docker directory in which the docker-compose.yml file is located
|
||||
#HOST_INVOKEAI_ROOT=../../invokeai-data
|
||||
|
||||
# INVOKEAI_ROOT is the path to the root of the InvokeAI repository within the container.
|
||||
# INVOKEAI_ROOT=~/invokeai
|
||||
# This **must** be an absolute path.
|
||||
INVOKEAI_ROOT=
|
||||
|
||||
# Get this value from your HuggingFace account settings page.
|
||||
# HUGGING_FACE_HUB_TOKEN=
|
||||
|
@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
|
||||
ENV VIRTUAL_ENV=/opt/venv/invokeai
|
||||
|
||||
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
|
||||
ARG TORCH_VERSION=2.1.2
|
||||
ARG TORCHVISION_VERSION=0.16.2
|
||||
ARG TORCH_VERSION=2.1.0
|
||||
ARG TORCHVISION_VERSION=0.16
|
||||
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 [ "$GPU_DRIVER" = "rocm" ]; then \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
|
||||
extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
|
||||
else \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
|
||||
fi &&\
|
||||
@ -54,12 +54,12 @@ RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
|
||||
pip install -e ".[xformers]"; \
|
||||
else \
|
||||
pip install $extra_index_url_arg -e "."; \
|
||||
pip install -e "."; \
|
||||
fi
|
||||
|
||||
# #### Build the Web UI ------------------------------------
|
||||
|
||||
FROM node:20-slim AS web-builder
|
||||
FROM node:18-slim AS web-builder
|
||||
ENV PNPM_HOME="/pnpm"
|
||||
ENV PATH="$PNPM_HOME:$PATH"
|
||||
RUN corepack enable
|
||||
@ -68,7 +68,7 @@ WORKDIR /build
|
||||
COPY invokeai/frontend/web/ ./
|
||||
RUN --mount=type=cache,target=/pnpm/store \
|
||||
pnpm install --frozen-lockfile
|
||||
RUN npx vite build
|
||||
RUN pnpm run build
|
||||
|
||||
#### Runtime stage ---------------------------------------
|
||||
|
||||
|
@ -28,7 +28,7 @@ This is done via Docker Desktop preferences
|
||||
|
||||
### Configure Invoke environment
|
||||
|
||||
1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
|
||||
1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
|
||||
a. the desired location of the InvokeAI runtime directory, or
|
||||
b. an existing, v3.0.0 compatible runtime directory.
|
||||
1. Execute `run.sh`
|
||||
|
@ -21,9 +21,7 @@ x-invokeai: &invokeai
|
||||
ports:
|
||||
- "${INVOKEAI_PORT:-9090}:9090"
|
||||
volumes:
|
||||
- type: bind
|
||||
source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
|
||||
target: ${INVOKEAI_ROOT:-/invokeai}
|
||||
- ${INVOKEAI_ROOT:-~/invokeai}:${INVOKEAI_ROOT:-/invokeai}
|
||||
- ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
|
||||
# - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
|
||||
# - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}
|
||||
|
@ -21,7 +21,7 @@ run() {
|
||||
printf "%s\n" "$build_args"
|
||||
fi
|
||||
|
||||
docker compose build $build_args $service_name
|
||||
docker compose build $build_args
|
||||
unset build_args
|
||||
|
||||
printf "%s\n" "starting service $service_name"
|
||||
|
142
docs/RELEASE.md
@ -1,142 +0,0 @@
|
||||
# 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 dispatched [manually].
|
||||
|
||||
### Workflow Jobs and Process
|
||||
|
||||
The workflow consists of a number of concurrently-run jobs, and two final publish jobs.
|
||||
|
||||
The publish jobs require manual approval and are only run if the other jobs succeed.
|
||||
|
||||
#### `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
|
||||
|
||||
- **`python-tests`**: runs `pytest` on matrix of platforms
|
||||
- **`python-checks`**: runs `ruff` (format and lint)
|
||||
- **`frontend-tests`**: runs `vitest`
|
||||
- **`frontend-checks`**: runs `prettier` (format), `eslint` (lint), `dpdm` (circular refs), `tsc` (static type check) and `knip` (unused imports)
|
||||
|
||||
> **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-installer` 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 as the remaining publish jobs 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 run if any of the previous jobs 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.
|
||||
|
||||
## Manual Build
|
||||
|
||||
The `build installer` workflow can be dispatched manually. This is useful to test the installer for a given branch or tag.
|
||||
|
||||
No checks are run, it just builds.
|
||||
|
||||
## Manual Release
|
||||
|
||||
The `release` workflow can be dispatched manually. You must dispatch the workflow from the right tag, else it will fail the version check.
|
||||
|
||||
This functionality is available as a fallback in case something goes wonky. Typically, releases should be triggered via tag push as described above.
|
||||
|
||||
[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]: #manual-release
|
Before Width: | Height: | Size: 46 KiB After Width: | Height: | Size: 297 KiB |
Before Width: | Height: | Size: 4.9 MiB After Width: | Height: | Size: 1.1 MiB |
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Before Width: | Height: | Size: 122 KiB After Width: | Height: | Size: 209 KiB |
Before Width: | Height: | Size: 95 KiB After Width: | Height: | Size: 114 KiB |
Before Width: | Height: | Size: 123 KiB After Width: | Height: | Size: 187 KiB |
Before Width: | Height: | Size: 107 KiB After Width: | Height: | Size: 112 KiB |
Before Width: | Height: | Size: 61 KiB After Width: | Height: | Size: 132 KiB |
Before Width: | Height: | Size: 119 KiB After Width: | Height: | Size: 167 KiB |
BIN
docs/assets/nodes/groupsrandseed.png
Normal file
After Width: | Height: | Size: 70 KiB |
Before Width: | Height: | Size: 60 KiB After Width: | Height: | Size: 59 KiB |
Before Width: | Height: | Size: 129 KiB |
@ -9,15 +9,11 @@ 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.
|
||||
|
||||
Example `nodes` subfolder structure:
|
||||
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:
|
||||
```py
|
||||
├── __init__.py # Invoke-managed custom node loader
|
||||
│
|
||||
@ -34,14 +30,14 @@ 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
|
||||
@ -135,6 +131,7 @@ 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")
|
||||
@ -170,6 +167,7 @@ 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")
|
||||
@ -199,6 +197,7 @@ 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")
|
||||
@ -230,17 +229,30 @@ 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 input image as a PIL image
|
||||
image = context.images.get_pil(self.image.image_name)
|
||||
# Load the image using InvokeAI's predefined Image Service. Returns the PIL image.
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
# Resize the image
|
||||
# Resizing the image
|
||||
resized_image = image.resize((self.width, self.height))
|
||||
|
||||
# Save the image
|
||||
image_dto = context.images.save(image=resized_image)
|
||||
# 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,
|
||||
)
|
||||
|
||||
# Return an ImageOutput
|
||||
return ImageOutput.build(image_dto)
|
||||
# Returning the Image
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=output_image.image_name,
|
||||
),
|
||||
width=output_image.width,
|
||||
height=output_image.height,
|
||||
)
|
||||
```
|
||||
|
||||
**Note:** Do not be overwhelmed by the `ImageOutput` process. InvokeAI has a
|
||||
@ -331,25 +343,27 @@ 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 fields in InvokeAI do not meet your requirements. In such cases, you
|
||||
can create your own fields.
|
||||
existing input types in InvokeAI do not meet your requirements. In such cases,
|
||||
you can create your own input types.
|
||||
|
||||
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.
|
||||
|
||||
### Best Practices
|
||||
**Good 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 field.
|
||||
`docstring` to describe your input field.
|
||||
- Keep your field in the same file as the Invocation that it is made for or in
|
||||
another file where it is relevant.
|
||||
|
||||
@ -364,13 +378,10 @@ class ColorField(BaseModel):
|
||||
pass
|
||||
```
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
```python
|
||||
class ColorField(BaseModel):
|
||||
@ -385,11 +396,25 @@ That's it. We now have a new input field type that we can use in our Invocations
|
||||
like this.
|
||||
|
||||
```python
|
||||
color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
|
||||
color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
|
||||
```
|
||||
|
||||
### Using the custom field
|
||||
### Custom Components For Frontend
|
||||
|
||||
When you start the UI, your custom field will be automatically recognized.
|
||||
Every backend input type should have a corresponding frontend component so the
|
||||
UI knows what to render when you use a particular field type.
|
||||
|
||||
Custom fields only support connection inputs in the Workflow Editor.
|
||||
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.
|
||||
-->
|
||||
|
@ -0,0 +1,76 @@
|
||||
# Contributing to the Frontend
|
||||
|
||||
# InvokeAI Web UI
|
||||
|
||||
- [InvokeAI Web UI](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#invokeai-web-ui)
|
||||
- [Stack](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#stack)
|
||||
- [Contributing](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#contributing)
|
||||
- [Dev Environment](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#dev-environment)
|
||||
- [Production builds](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#production-builds)
|
||||
|
||||
The UI is a fairly straightforward Typescript React app, with the Unified Canvas being more complex.
|
||||
|
||||
Code is located in `invokeai/frontend/web/` for review.
|
||||
|
||||
## Stack
|
||||
|
||||
State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
|
||||
|
||||
- `createAsyncThunk` for HTTP requests
|
||||
- `createEntityAdapter` for fetching images and models
|
||||
- `createListenerMiddleware` for workflows
|
||||
|
||||
The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
|
||||
|
||||
Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
|
||||
|
||||
[Chakra-UI](https://github.com/chakra-ui/chakra-ui) & [Mantine](https://github.com/mantinedev/mantine) for components and styling.
|
||||
|
||||
[Konva](https://github.com/konvajs/react-konva) for the canvas, but we are pushing the limits of what is feasible with it (and HTML canvas in general). We plan to rebuild it with [PixiJS](https://github.com/pixijs/pixijs) to take advantage of WebGL's improved raster handling.
|
||||
|
||||
[Vite](https://vitejs.dev/) for bundling.
|
||||
|
||||
Localisation is via [i18next](https://github.com/i18next/react-i18next), but translation happens on our [Weblate](https://hosted.weblate.org/engage/invokeai/) project. Only the English source strings should be changed on this repo.
|
||||
|
||||
## Contributing
|
||||
|
||||
Thanks for your interest in contributing to the InvokeAI Web UI!
|
||||
|
||||
We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](https://discord.gg/ZmtBAhwWhy) if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
|
||||
|
||||
### Dev Environment
|
||||
|
||||
**Setup**
|
||||
|
||||
1. Install [node](https://nodejs.org/en/download/). You can confirm node is installed with:
|
||||
```bash
|
||||
node --version
|
||||
```
|
||||
|
||||
2. Install [pnpm](https://pnpm.io/) and confirm it is installed by running this:
|
||||
```bash
|
||||
npm install --global pnpm
|
||||
pnpm --version
|
||||
```
|
||||
|
||||
From `invokeai/frontend/web/` run `pnpm install` to get everything set up.
|
||||
|
||||
Start everything in dev mode:
|
||||
1. Ensure your virtual environment is running
|
||||
2. Start the dev server: `pnpm dev`
|
||||
3. Start the InvokeAI Nodes backend: `python scripts/invokeai-web.py # run from the repo root`
|
||||
4. Point your browser to the dev server address e.g. [http://localhost:5173/](http://localhost:5173/)
|
||||
|
||||
### VSCode Remote Dev
|
||||
|
||||
We've noticed an intermittent issue with the VSCode Remote Dev port forwarding. If you use this feature of VSCode, you may intermittently click the Invoke button and then get nothing until the request times out. Suggest disabling the IDE's port forwarding feature and doing it manually via SSH:
|
||||
|
||||
`ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host`
|
||||
|
||||
### Production builds
|
||||
|
||||
For a number of technical and logistical reasons, we need to commit UI build artefacts to the repo.
|
||||
|
||||
If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
|
||||
|
||||
To build for production, run `pnpm build`.
|
@ -12,7 +12,7 @@ To get started, take a look at our [new contributors checklist](newContributorCh
|
||||
Once you're setup, for more information, you can review the documentation specific to your area of interest:
|
||||
|
||||
* #### [InvokeAI Architecure](../ARCHITECTURE.md)
|
||||
* #### [Frontend Documentation](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web)
|
||||
* #### [Frontend Documentation](./contributingToFrontend.md)
|
||||
* #### [Node Documentation](../INVOCATIONS.md)
|
||||
* #### [Local Development](../LOCAL_DEVELOPMENT.md)
|
||||
|
||||
|
@ -1,133 +0,0 @@
|
||||
# Invoke UI
|
||||
|
||||
Invoke's UI is made possible by many contributors and open-source libraries. Thank you!
|
||||
|
||||
## Dev environment
|
||||
|
||||
### Setup
|
||||
|
||||
1. Install [node] and [pnpm].
|
||||
1. Run `pnpm i` to install all packages.
|
||||
|
||||
#### Run in dev mode
|
||||
|
||||
1. From `invokeai/frontend/web/`, run `pnpm dev`.
|
||||
1. From repo root, run `python scripts/invokeai-web.py`.
|
||||
1. Point your browser to the dev server address, e.g. <http://localhost:5173/>
|
||||
|
||||
### Package scripts
|
||||
|
||||
- `dev`: run the frontend in dev mode, enabling hot reloading
|
||||
- `build`: run all checks (madge, eslint, prettier, tsc) and then build the frontend
|
||||
- `typegen`: generate types from the OpenAPI schema (see [Type generation])
|
||||
- `lint:dpdm`: check circular dependencies
|
||||
- `lint:eslint`: check code quality
|
||||
- `lint:prettier`: check code formatting
|
||||
- `lint:tsc`: check type issues
|
||||
- `lint:knip`: check for unused exports or objects (failures here are just suggestions, not hard fails)
|
||||
- `lint`: run all checks concurrently
|
||||
- `fix`: run `eslint` and `prettier`, fixing fixable issues
|
||||
|
||||
### Type generation
|
||||
|
||||
We use [openapi-typescript] to generate types from the app's OpenAPI schema.
|
||||
|
||||
The generated types are committed to the repo in [schema.ts].
|
||||
|
||||
```sh
|
||||
# from the repo root, start the server
|
||||
python scripts/invokeai-web.py
|
||||
# from invokeai/frontend/web/, run the script
|
||||
pnpm typegen
|
||||
```
|
||||
|
||||
### Localization
|
||||
|
||||
We use [i18next] for localization, but translation to languages other than English happens on our [Weblate] project.
|
||||
|
||||
Only the English source strings should be changed on this repo.
|
||||
|
||||
### VSCode
|
||||
|
||||
#### Example debugger config
|
||||
|
||||
```jsonc
|
||||
{
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
"type": "chrome",
|
||||
"request": "launch",
|
||||
"name": "Invoke UI",
|
||||
"url": "http://localhost:5173",
|
||||
"webRoot": "${workspaceFolder}/invokeai/frontend/web"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Remote dev
|
||||
|
||||
We've noticed an intermittent timeout issue with the VSCode remote dev port forwarding.
|
||||
|
||||
We suggest disabling the editor's port forwarding feature and doing it manually via SSH:
|
||||
|
||||
```sh
|
||||
ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host
|
||||
```
|
||||
|
||||
## Contributing Guidelines
|
||||
|
||||
Thanks for your interest in contributing to the Invoke Web UI!
|
||||
|
||||
Please follow these guidelines when contributing.
|
||||
|
||||
### Check in before investing your time
|
||||
|
||||
Please check in before you invest your time on anything besides a trivial fix, in case it conflicts with ongoing work or isn't aligned with the vision for the app.
|
||||
|
||||
If a feature request or issue doesn't already exist for the thing you want to work on, please create one.
|
||||
|
||||
Ping `@psychedelicious` on [discord] in the `#frontend-dev` channel or in the feature request / issue you want to work on - we're happy to chat.
|
||||
|
||||
### Code conventions
|
||||
|
||||
- This is a fairly complex app with a deep component tree. Please use memoization (`useCallback`, `useMemo`, `memo`) with enthusiasm.
|
||||
- If you need to add some global, ephemeral state, please use [nanostores] if possible.
|
||||
- Be careful with your redux selectors. If they need to be parameterized, consider creating them inside a `useMemo`.
|
||||
- Feel free to use `lodash` (via `lodash-es`) to make the intent of your code clear.
|
||||
- Please add comments describing the "why", not the "how" (unless it is really arcane).
|
||||
|
||||
### Commit format
|
||||
|
||||
Please use the [conventional commits] spec for the web UI, with a scope of "ui":
|
||||
|
||||
- `chore(ui): bump deps`
|
||||
- `chore(ui): lint`
|
||||
- `feat(ui): add some cool new feature`
|
||||
- `fix(ui): fix some bug`
|
||||
|
||||
### Submitting a PR
|
||||
|
||||
- Ensure your branch is tidy. Use an interactive rebase to clean up the commit history and reword the commit messages if they are not descriptive.
|
||||
- Run `pnpm lint`. Some issues are auto-fixable with `pnpm fix`.
|
||||
- Fill out the PR form when creating the PR.
|
||||
- It doesn't need to be super detailed, but a screenshot or video is nice if you changed something visually.
|
||||
- If a section isn't relevant, delete it. There are no UI tests at this time.
|
||||
|
||||
## Other docs
|
||||
|
||||
- [Workflows - Design and Implementation]
|
||||
- [State Management]
|
||||
|
||||
[node]: https://nodejs.org/en/download/
|
||||
[pnpm]: https://github.com/pnpm/pnpm
|
||||
[discord]: https://discord.gg/ZmtBAhwWhy
|
||||
[i18next]: https://github.com/i18next/react-i18next
|
||||
[Weblate]: https://hosted.weblate.org/engage/invokeai/
|
||||
[openapi-typescript]: https://github.com/drwpow/openapi-typescript
|
||||
[Type generation]: #type-generation
|
||||
[schema.ts]: https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/src/services/api/schema.ts
|
||||
[conventional commits]: https://www.conventionalcommits.org/en/v1.0.0/
|
||||
[Workflows - Design and Implementation]: ./WORKFLOWS.md
|
||||
[State Management]: ./STATE_MGMT.md
|
@ -1,38 +0,0 @@
|
||||
# State Management
|
||||
|
||||
The app makes heavy use of Redux Toolkit, its Query library, and `nanostores`.
|
||||
|
||||
## Redux
|
||||
|
||||
TODO
|
||||
|
||||
## `nanostores`
|
||||
|
||||
[nanostores] is a tiny state management library. It provides both imperative and declarative APIs.
|
||||
|
||||
### Example
|
||||
|
||||
```ts
|
||||
export const $myStringOption = atom<string | null>(null);
|
||||
|
||||
// Outside a component, or within a callback for performance-critical logic
|
||||
$myStringOption.get();
|
||||
$myStringOption.set('new value');
|
||||
|
||||
// Inside a component
|
||||
const myStringOption = useStore($myStringOption);
|
||||
```
|
||||
|
||||
### Where to put nanostores
|
||||
|
||||
- For global application state, export your stores from `invokeai/frontend/web/src/app/store/nanostores/`.
|
||||
- For feature state, create a file for the stores next to the redux slice definition (e.g. `invokeai/frontend/web/src/features/myFeature/myFeatureNanostores.ts`).
|
||||
- For hooks with global state, export the store from the same file the hook is in, or put it next to the hook.
|
||||
|
||||
### When to use nanostores
|
||||
|
||||
- For non-serializable data that needs to be available throughout the app, use `nanostores` instead of a global.
|
||||
- For ephemeral global state (i.e. state that does not need to be persisted), use `nanostores` instead of redux.
|
||||
- For performance-critical code and in callbacks, redux selectors can be problematic due to the declarative reactivity system. Consider refactoring to use `nanostores` if there's a **measurable** performance issue.
|
||||
|
||||
[nanostores]: https://github.com/nanostores/nanostores/
|
@ -1,53 +0,0 @@
|
||||
## :octicons-log-16: Important Changes Since Version 2.3
|
||||
|
||||
### Nodes
|
||||
|
||||
Behind the scenes, InvokeAI has been completely rewritten to support
|
||||
"nodes," small unitary operations that can be combined into graphs to
|
||||
form arbitrary workflows. For example, there is a prompt node that
|
||||
processes the prompt string and feeds it to a text2latent node that
|
||||
generates a latent image. The latents are then fed to a latent2image
|
||||
node that translates the latent image into a PNG.
|
||||
|
||||
The WebGUI has a node editor that allows you to graphically design and
|
||||
execute custom node graphs. The ability to save and load graphs is
|
||||
still a work in progress, but coming soon.
|
||||
|
||||
### Command-Line Interface Retired
|
||||
|
||||
All "invokeai" command-line interfaces have been retired as of version
|
||||
3.4.
|
||||
|
||||
To launch the Web GUI from the command-line, use the command
|
||||
`invokeai-web` rather than the traditional `invokeai --web`.
|
||||
|
||||
### ControlNet
|
||||
|
||||
This version of InvokeAI features ControlNet, a system that allows you
|
||||
to achieve exact poses for human and animal figures by providing a
|
||||
model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
|
||||
|
||||
### New Schedulers
|
||||
|
||||
The list of schedulers has been completely revamped and brought up to date:
|
||||
|
||||
| **Short Name** | **Scheduler** | **Notes** |
|
||||
|----------------|---------------------------------|-----------------------------|
|
||||
| **ddim** | DDIMScheduler | |
|
||||
| **ddpm** | DDPMScheduler | |
|
||||
| **deis** | DEISMultistepScheduler | |
|
||||
| **lms** | LMSDiscreteScheduler | |
|
||||
| **pndm** | PNDMScheduler | |
|
||||
| **heun** | HeunDiscreteScheduler | original noise schedule |
|
||||
| **heun_k** | HeunDiscreteScheduler | using karras noise schedule |
|
||||
| **euler** | EulerDiscreteScheduler | original noise schedule |
|
||||
| **euler_k** | EulerDiscreteScheduler | using karras noise schedule |
|
||||
| **kdpm_2** | KDPM2DiscreteScheduler | |
|
||||
| **kdpm_2_a** | KDPM2AncestralDiscreteScheduler | |
|
||||
| **dpmpp_2s** | DPMSolverSinglestepScheduler | |
|
||||
| **dpmpp_2m** | DPMSolverMultistepScheduler | original noise scnedule |
|
||||
| **dpmpp_2m_k** | DPMSolverMultistepScheduler | using karras noise schedule |
|
||||
| **unipc** | UniPCMultistepScheduler | CPU only |
|
||||
| **lcm** | LCMScheduler | |
|
||||
|
||||
Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
|
@ -31,18 +31,18 @@ be referred to as ROOT.
|
||||
To find its root directory, InvokeAI uses the following recipe:
|
||||
|
||||
1. It first looks for the argument `--root <path>` on the command line
|
||||
it was launched from, and uses the indicated path if present.
|
||||
it was launched from, and uses the indicated path if present.
|
||||
|
||||
2. Next it looks for the environment variable INVOKEAI_ROOT, and uses
|
||||
the directory path found there if present.
|
||||
the directory path found there if present.
|
||||
|
||||
3. If neither of these are present, then InvokeAI looks for the
|
||||
folder containing the `.venv` Python virtual environment directory for
|
||||
the currently active environment. This directory is checked for files
|
||||
expected inside the InvokeAI root before it is used.
|
||||
folder containing the `.venv` Python virtual environment directory for
|
||||
the currently active environment. This directory is checked for files
|
||||
expected inside the InvokeAI root before it is used.
|
||||
|
||||
4. Finally, InvokeAI looks for a directory in the current user's home
|
||||
directory named `invokeai`.
|
||||
directory named `invokeai`.
|
||||
|
||||
#### Reading the InvokeAI Configuration File
|
||||
|
||||
@ -149,75 +149,104 @@ usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS
|
||||
|
||||
## The Configuration Settings
|
||||
|
||||
The config is managed by the `InvokeAIAppConfig` class, which is a pydantic model. The below docs are autogenerated from the class.
|
||||
The configuration settings are divided into several distinct
|
||||
groups in `invokeia.yaml`:
|
||||
|
||||
When editing your `invokeai.yaml` file, you'll need to put settings under their appropriate group. The group for each setting is denoted in the table below.
|
||||
### Web Server
|
||||
|
||||
Following the table are additional explanations for certain settings.
|
||||
| Setting | Default Value | Description |
|
||||
|---------------------|---------------|----------------------------------------------------------------------------------------------------------------------------|
|
||||
| `host` | `localhost` | Name or IP address of the network interface that the web server will listen on |
|
||||
| `port` | `9090` | Network port number that the web server will listen on |
|
||||
| `allow_origins` | `[]` | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
|
||||
| `allow_credentials` | `true` | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
|
||||
| `allow_methods` | `*` | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API |
|
||||
| `allow_headers` | `*` | List of HTTP headers that the web server will accept when accessing the API |
|
||||
| `ssl_certfile` | null | Path to an SSL certificate file, used to enable HTTPS. |
|
||||
| `ssl_keyfile` | null | Path to an SSL keyfile, if the key is not included in the certificate file. |
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
::: invokeai.app.services.config.config_default.InvokeAIAppConfig
|
||||
options:
|
||||
heading_level: 3
|
||||
members: false
|
||||
<!-- prettier-ignore-end -->
|
||||
The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
|
||||
|
||||
### Model Marketplace API Keys
|
||||
### Features
|
||||
|
||||
Some model marketplaces require an API key to download models. You can provide a URL pattern and appropriate token in your `invokeai.yaml` file to provide that API key.
|
||||
These configuration settings allow you to enable and disable various InvokeAI features:
|
||||
|
||||
The pattern can be any valid regex (you may need to surround the pattern with quotes):
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `esrgan` | `true` | Activate the ESRGAN upscaling options|
|
||||
| `internet_available` | `true` | When a resource is not available locally, try to fetch it via the internet |
|
||||
| `log_tokenization` | `false` | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
|
||||
| `patchmatch` | `true` | Activate the "patchmatch" algorithm for improved inpainting |
|
||||
|
||||
```yaml
|
||||
InvokeAI:
|
||||
Model Install:
|
||||
remote_api_tokens:
|
||||
# Any URL containing `models.com` will automatically use `your_models_com_token`
|
||||
- url_regex: models.com
|
||||
token: your_models_com_token
|
||||
# Any URL matching this contrived regex will use `some_other_token`
|
||||
- url_regex: '^[a-z]{3}whatever.*\.com$'
|
||||
token: some_other_token
|
||||
```
|
||||
### Generation
|
||||
|
||||
The provided token will be added as a `Bearer` token to the network requests to download the model files. As far as we know, this works for all model marketplaces that require authorization.
|
||||
These options tune InvokeAI's memory and performance characteristics.
|
||||
|
||||
### Model Hashing
|
||||
| Setting | Default Value | Description |
|
||||
|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| `sequential_guidance` | `false` | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
|
||||
| `attention_type` | `auto` | Select the type of attention to use. One of `auto`,`normal`,`xformers`,`sliced`, or `torch-sdp` |
|
||||
| `attention_slice_size` | `auto` | When "sliced" attention is selected, set the slice size. One of `auto`, `balanced`, `max` or the integers 1-8|
|
||||
| `force_tiled_decode` | `false` | Force the VAE step to decode in tiles, reducing memory consumption at the cost of performance |
|
||||
|
||||
Models are hashed during installation, providing a stable identifier for models across all platforms. The default algorithm is `blake3`, with a multi-threaded implementation.
|
||||
### Device
|
||||
|
||||
If your models are stored on a spinning hard drive, we suggest using `blake3_single`, the single-threaded implementation. The hashes are the same, but it's much faster on spinning disks.
|
||||
These options configure the generation execution device.
|
||||
|
||||
```yaml
|
||||
InvokeAI:
|
||||
Model Install:
|
||||
hashing_algorithm: blake3_single
|
||||
```
|
||||
| Setting | Default Value | Description |
|
||||
|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| `device` | `auto` | Preferred execution device. One of `auto`, `cpu`, `cuda`, `cuda:1`, `mps`. `auto` will choose the device depending on the hardware platform and the installed torch capabilities. |
|
||||
| `precision` | `auto` | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
|
||||
|
||||
Model hashing is a one-time operation, but it may take a couple minutes to hash a large model collection. You may opt out of model hashing entirely by setting the algorithm to `random`.
|
||||
|
||||
```yaml
|
||||
InvokeAI:
|
||||
Model Install:
|
||||
hashing_algorithm: random
|
||||
```
|
||||
|
||||
Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than `blake3`.
|
||||
|
||||
### Paths
|
||||
|
||||
These options set the paths of various directories and files used by
|
||||
InvokeAI. Relative paths are interpreted relative to the root directory, so
|
||||
if root is `/home/fred/invokeai` and the path is
|
||||
InvokeAI. Relative paths are interpreted relative to INVOKEAI_ROOT, so
|
||||
if INVOKEAI_ROOT is `/home/fred/invokeai` and the path is
|
||||
`autoimport/main`, then the corresponding directory will be located at
|
||||
`/home/fred/invokeai/autoimport/main`.
|
||||
|
||||
Note that the autoimport directory will be searched recursively,
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `autoimport_dir` | `autoimport/main` | At startup time, read and import any main model files found in this directory |
|
||||
| `lora_dir` | `autoimport/lora` | At startup time, read and import any LoRA/LyCORIS models found in this directory |
|
||||
| `embedding_dir` | `autoimport/embedding` | At startup time, read and import any textual inversion (embedding) models found in this directory |
|
||||
| `controlnet_dir` | `autoimport/controlnet` | At startup time, read and import any ControlNet models found in this directory |
|
||||
| `conf_path` | `configs/models.yaml` | Location of the `models.yaml` model configuration file |
|
||||
| `models_dir` | `models` | Location of the directory containing models installed by InvokeAI's model manager |
|
||||
| `legacy_conf_dir` | `configs/stable-diffusion` | Location of the directory containing the .yaml configuration files for legacy checkpoint models |
|
||||
| `db_dir` | `databases` | Location of the directory containing InvokeAI's image, schema and session database |
|
||||
| `outdir` | `outputs` | Location of the directory in which the gallery of generated and uploaded images will be stored |
|
||||
| `use_memory_db` | `false` | Keep database information in memory rather than on disk; this will not preserve image gallery information across restarts |
|
||||
|
||||
Note that the autoimport directories will be searched recursively,
|
||||
allowing you to organize the models into folders and subfolders in any
|
||||
way you wish.
|
||||
way you wish. In addition, while we have split up autoimport
|
||||
directories by the type of model they contain, this isn't
|
||||
necessary. You can combine different model types in the same folder
|
||||
and InvokeAI will figure out what they are. So you can easily use just
|
||||
one autoimport directory by commenting out the unneeded paths:
|
||||
|
||||
```
|
||||
Paths:
|
||||
autoimport_dir: autoimport
|
||||
# lora_dir: null
|
||||
# embedding_dir: null
|
||||
# controlnet_dir: null
|
||||
```
|
||||
|
||||
### Logging
|
||||
|
||||
These settings control the information, warning, and debugging
|
||||
messages printed to the console log while InvokeAI is running:
|
||||
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `log_handlers` | `console` | This controls where log messages are sent, and can be a list of one or more destinations. Values include `console`, `file`, `syslog` and `http`. These are described in more detail below |
|
||||
| `log_format` | `color` | This controls the formatting of the log messages. Values are `plain`, `color`, `legacy` and `syslog` |
|
||||
| `log_level` | `debug` | This filters messages according to the level of severity and can be one of `debug`, `info`, `warning`, `error` and `critical`. For example, setting to `warning` will display all messages at the warning level or higher, but won't display "debug" or "info" messages |
|
||||
|
||||
Several different log handler destinations are available, and multiple destinations are supported by providing a list:
|
||||
|
||||
```
|
||||
@ -227,9 +256,9 @@ Several different log handler destinations are available, and multiple destinati
|
||||
- file=/var/log/invokeai.log
|
||||
```
|
||||
|
||||
- `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
|
||||
* `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
|
||||
|
||||
- `syslog` is only available on Linux and Macintosh systems. It uses
|
||||
* `syslog` is only available on Linux and Macintosh systems. It uses
|
||||
the operating system's "syslog" facility to write log file entries
|
||||
locally or to a remote logging machine. `syslog` offers a variety
|
||||
of configuration options:
|
||||
@ -242,7 +271,7 @@ Several different log handler destinations are available, and multiple destinati
|
||||
- Log to LAN-connected server "fredserver" using the facility LOG_USER and datagram packets.
|
||||
```
|
||||
|
||||
- `http` can be used to log to a remote web server. The server must be
|
||||
* `http` can be used to log to a remote web server. The server must be
|
||||
properly configured to receive and act on log messages. The option
|
||||
accepts the URL to the web server, and a `method` argument
|
||||
indicating whether the message should be submitted using the GET or
|
||||
@ -254,7 +283,7 @@ Several different log handler destinations are available, and multiple destinati
|
||||
|
||||
The `log_format` option provides several alternative formats:
|
||||
|
||||
- `color` - default format providing time, date and a message, using text colors to distinguish different log severities
|
||||
- `plain` - same as above, but monochrome text only
|
||||
- `syslog` - the log level and error message only, allowing the syslog system to attach the time and date
|
||||
- `legacy` - a format similar to the one used by the legacy 2.3 InvokeAI releases.
|
||||
* `color` - default format providing time, date and a message, using text colors to distinguish different log severities
|
||||
* `plain` - same as above, but monochrome text only
|
||||
* `syslog` - the log level and error message only, allowing the syslog system to attach the time and date
|
||||
* `legacy` - a format similar to the one used by the legacy 2.3 InvokeAI releases.
|
||||
|
@ -94,8 +94,6 @@ A model that helps generate creative QR codes that still scan. Can also be used
|
||||
**Openpose**:
|
||||
The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image.
|
||||
|
||||
*Note:* The DWPose Processor has replaced the OpenPose processor in Invoke. Workflows and generations that relied on the OpenPose Processor will need to be updated to use the DWPose Processor instead.
|
||||
|
||||
**Mediapipe Face**:
|
||||
|
||||
The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.
|
||||
|
@ -1,35 +0,0 @@
|
||||
---
|
||||
title: Database
|
||||
---
|
||||
|
||||
# Invoke's SQLite Database
|
||||
|
||||
Invoke uses a SQLite database to store image, workflow, model, and execution data.
|
||||
|
||||
We take great care to ensure your data is safe, by utilizing transactions and a database migration system.
|
||||
|
||||
Even so, when testing an prerelease version of the app, we strongly suggest either backing up your database or using an in-memory database. This ensures any prelease hiccups or databases schema changes will not cause problems for your data.
|
||||
|
||||
## Database Backup
|
||||
|
||||
Backing up your database is very simple. Invoke's data is stored in an `$INVOKEAI_ROOT` directory - where your `invoke.sh`/`invoke.bat` and `invokeai.yaml` files live.
|
||||
|
||||
To back up your database, copy the `invokeai.db` file from `$INVOKEAI_ROOT/databases/invokeai.db` to somewhere safe.
|
||||
|
||||
If anything comes up during prelease testing, you can simply copy your backup back into `$INVOKEAI_ROOT/databases/`.
|
||||
|
||||
## In-Memory Database
|
||||
|
||||
SQLite can run on an in-memory database. Your existing database is untouched when this mode is enabled, but your existing data won't be accessible.
|
||||
|
||||
This is very useful for testing, as there is no chance of a database change modifying your "physical" database.
|
||||
|
||||
To run Invoke with a memory database, edit your `invokeai.yaml` file, and add `use_memory_db: true` to the `Paths:` stanza:
|
||||
|
||||
```yaml
|
||||
InvokeAI:
|
||||
Development:
|
||||
use_memory_db: true
|
||||
```
|
||||
|
||||
Delete this line (or set it to `false`) to use your main database.
|
@ -229,28 +229,29 @@ clarity on the intent and common use cases we expect for utilizing them.
|
||||
currently being rendered by your browser into a merged copy of the image. This
|
||||
lowers the resource requirements and should improve performance.
|
||||
|
||||
### Compositing / Seam Correction
|
||||
### Seam Correction
|
||||
|
||||
When doing Inpainting or Outpainting, Invoke needs to merge the pixels generated
|
||||
by Stable Diffusion into your existing image. This is achieved through compositing - the area around the the boundary between your image and the new generation is
|
||||
by Stable Diffusion into your existing image. To do this, the area around the
|
||||
`seam` at the boundary between your image and the new generation is
|
||||
automatically blended to produce a seamless output. In a fully automatic
|
||||
process, a mask is generated to cover the boundary, and then the area of the boundary is
|
||||
process, a mask is generated to cover the seam, and then the area of the seam is
|
||||
Inpainted.
|
||||
|
||||
Although the default options should work well most of the time, sometimes it can
|
||||
help to alter the parameters that control the Compositing. A larger blur and
|
||||
a blur setting have been noted as producing
|
||||
consistently strong results . Strength of 0.7 is best for reducing hard seams.
|
||||
|
||||
- **Mode** - What part of the image will have the the Compositing applied to it.
|
||||
- **Mask edge** will apply Compositing to the edge of the masked area
|
||||
- **Mask** will apply Compositing to the entire masked area
|
||||
- **Unmasked** will apply Compositing to the entire image
|
||||
- **Steps** - Number of generation steps that will occur during the Coherence Pass, similar to Denoising Steps. Higher step counts will generally have better results.
|
||||
- **Strength** - How much noise is added for the Coherence Pass, similar to Denoising Strength. A strength of 0 will result in an unchanged image, while a strength of 1 will result in an image with a completely new area as defined by the Mode setting.
|
||||
- **Blur** - Adjusts the pixel radius of the the mask. A larger blur radius will cause the mask to extend past the visibly masked area, while too small of a blur radius will result in a mask that is smaller than the visibly masked area.
|
||||
- **Blur Method** - The method of blur applied to the masked area.
|
||||
help to alter the parameters that control the seam Inpainting. A wider seam and
|
||||
a blur setting of about 1/3 of the seam have been noted as producing
|
||||
consistently strong results (e.g. 96 wide and 16 blur - adds up to 32 blur with
|
||||
both sides). Seam strength of 0.7 is best for reducing hard seams.
|
||||
|
||||
- **Seam Size** - The size of the seam masked area. Set higher to make a larger
|
||||
mask around the seam.
|
||||
- **Seam Blur** - The size of the blur that is applied on _each_ side of the
|
||||
masked area.
|
||||
- **Seam Strength** - The Image To Image Strength parameter used for the
|
||||
Inpainting generation that is applied to the seam area.
|
||||
- **Seam Steps** - The number of generation steps that should be used to Inpaint
|
||||
the seam.
|
||||
|
||||
### Infill & Scaling
|
||||
|
||||
|
Before Width: | Height: | Size: 4.2 KiB |
@ -18,7 +18,7 @@ title: Home
|
||||
width: 100%;
|
||||
max-width: 100%;
|
||||
height: 50px;
|
||||
background-color: #35A4DB;
|
||||
background-color: #448AFF;
|
||||
color: #fff;
|
||||
font-size: 16px;
|
||||
border: none;
|
||||
@ -43,7 +43,7 @@ title: Home
|
||||
<div align="center" markdown>
|
||||
|
||||
|
||||
[](https://github.com/invoke-ai/InvokeAI)
|
||||
[](https://github.com/invoke-ai/InvokeAI)
|
||||
|
||||
[![discord badge]][discord link]
|
||||
|
||||
@ -117,11 +117,6 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
|
||||
|
||||
## :octicons-gift-24: InvokeAI Features
|
||||
|
||||
### Installation
|
||||
- [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
|
||||
- [Manual Installation](installation/020_INSTALL_MANUAL.md)
|
||||
- [Docker Installation](installation/040_INSTALL_DOCKER.md)
|
||||
|
||||
### The InvokeAI Web Interface
|
||||
- [WebUI overview](features/WEB.md)
|
||||
- [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
|
||||
@ -150,6 +145,60 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
|
||||
- [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
|
||||
- [Database Maintenance and other Command Line Utilities](features/UTILITIES.md)
|
||||
|
||||
## :octicons-log-16: Important Changes Since Version 2.3
|
||||
|
||||
### Nodes
|
||||
|
||||
Behind the scenes, InvokeAI has been completely rewritten to support
|
||||
"nodes," small unitary operations that can be combined into graphs to
|
||||
form arbitrary workflows. For example, there is a prompt node that
|
||||
processes the prompt string and feeds it to a text2latent node that
|
||||
generates a latent image. The latents are then fed to a latent2image
|
||||
node that translates the latent image into a PNG.
|
||||
|
||||
The WebGUI has a node editor that allows you to graphically design and
|
||||
execute custom node graphs. The ability to save and load graphs is
|
||||
still a work in progress, but coming soon.
|
||||
|
||||
### Command-Line Interface Retired
|
||||
|
||||
All "invokeai" command-line interfaces have been retired as of version
|
||||
3.4.
|
||||
|
||||
To launch the Web GUI from the command-line, use the command
|
||||
`invokeai-web` rather than the traditional `invokeai --web`.
|
||||
|
||||
### ControlNet
|
||||
|
||||
This version of InvokeAI features ControlNet, a system that allows you
|
||||
to achieve exact poses for human and animal figures by providing a
|
||||
model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
|
||||
|
||||
### New Schedulers
|
||||
|
||||
The list of schedulers has been completely revamped and brought up to date:
|
||||
|
||||
| **Short Name** | **Scheduler** | **Notes** |
|
||||
|----------------|---------------------------------|-----------------------------|
|
||||
| **ddim** | DDIMScheduler | |
|
||||
| **ddpm** | DDPMScheduler | |
|
||||
| **deis** | DEISMultistepScheduler | |
|
||||
| **lms** | LMSDiscreteScheduler | |
|
||||
| **pndm** | PNDMScheduler | |
|
||||
| **heun** | HeunDiscreteScheduler | original noise schedule |
|
||||
| **heun_k** | HeunDiscreteScheduler | using karras noise schedule |
|
||||
| **euler** | EulerDiscreteScheduler | original noise schedule |
|
||||
| **euler_k** | EulerDiscreteScheduler | using karras noise schedule |
|
||||
| **kdpm_2** | KDPM2DiscreteScheduler | |
|
||||
| **kdpm_2_a** | KDPM2AncestralDiscreteScheduler | |
|
||||
| **dpmpp_2s** | DPMSolverSinglestepScheduler | |
|
||||
| **dpmpp_2m** | DPMSolverMultistepScheduler | original noise scnedule |
|
||||
| **dpmpp_2m_k** | DPMSolverMultistepScheduler | using karras noise schedule |
|
||||
| **unipc** | UniPCMultistepScheduler | CPU only |
|
||||
| **lcm** | LCMScheduler | |
|
||||
|
||||
Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
|
||||
|
||||
## :material-target: Troubleshooting
|
||||
|
||||
Please check out our **[:material-frequently-asked-questions:
|
||||
|
@ -477,7 +477,7 @@ Then type the following commands:
|
||||
|
||||
=== "AMD System"
|
||||
```bash
|
||||
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
||||
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
```
|
||||
|
||||
### Corrupted configuration file
|
||||
|
@ -154,7 +154,7 @@ manager, please follow these steps:
|
||||
=== "ROCm (AMD)"
|
||||
|
||||
```bash
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
```
|
||||
|
||||
=== "CPU (Intel Macs & non-GPU systems)"
|
||||
@ -230,13 +230,13 @@ manager, please follow these steps:
|
||||
=== "local Webserver"
|
||||
|
||||
```bash
|
||||
invokeai-web
|
||||
invokeai --web
|
||||
```
|
||||
|
||||
=== "Public Webserver"
|
||||
|
||||
```bash
|
||||
invokeai-web --host 0.0.0.0
|
||||
invokeai --web --host 0.0.0.0
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
@ -313,7 +313,7 @@ code for InvokeAI. For this to work, you will need to install the
|
||||
on your system, please see the [Git Installation
|
||||
Guide](https://github.com/git-guides/install-git)
|
||||
|
||||
You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/README.md).
|
||||
You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md).
|
||||
|
||||
If you have a "normal" installation, you should create a totally separate virtual environment for the git-based installation, else the two may interfere.
|
||||
|
||||
@ -345,7 +345,7 @@ installation protocol (important!)
|
||||
|
||||
=== "ROCm (AMD)"
|
||||
```bash
|
||||
pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
||||
pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
```
|
||||
|
||||
=== "CPU (Intel Macs & non-GPU systems)"
|
||||
@ -361,7 +361,7 @@ installation protocol (important!)
|
||||
Be sure to pass `-e` (for an editable install) and don't forget the
|
||||
dot ("."). It is part of the command.
|
||||
|
||||
5. Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/README.md) and do a production build of the UI as described.
|
||||
5. Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md) and do a production build of the UI as described.
|
||||
|
||||
6. You can now run `invokeai` and its related commands. The code will be
|
||||
read from the repository, so that you can edit the .py source files
|
||||
@ -402,4 +402,4 @@ environment variable INVOKEAI_ROOT to point to the installation directory.
|
||||
Note that if you run into problems with the Conda installation, the InvokeAI
|
||||
staff will **not** be able to help you out. Caveat Emptor!
|
||||
|
||||
[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
|
||||
[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
|
@ -134,7 +134,7 @@ recipes are available
|
||||
|
||||
When installing torch and torchvision manually with `pip`, remember to provide
|
||||
the argument `--extra-index-url
|
||||
https://download.pytorch.org/whl/rocm5.6` as described in the [Manual
|
||||
https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
|
||||
Installation Guide](020_INSTALL_MANUAL.md).
|
||||
|
||||
This will be done automatically for you if you use the installer
|
||||
|
@ -69,7 +69,7 @@ a token and copy it, since you will need in for the next step.
|
||||
|
||||
### Setup
|
||||
|
||||
Set up your environmnent variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
|
||||
Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
|
||||
|
||||
Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
|
||||
|
||||
|
@ -18,18 +18,13 @@ either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
|
||||
driver).
|
||||
|
||||
|
||||
## **[Automated Installer (Recommended)](010_INSTALL_AUTOMATED.md)**
|
||||
✅ This is the recommended installation method for first-time users.
|
||||
## **[Automated Installer](010_INSTALL_AUTOMATED.md)**
|
||||
✅ This is the recommended installation method for first-time users.
|
||||
|
||||
This is a script that will install all of InvokeAI's essential
|
||||
third party libraries and InvokeAI itself.
|
||||
|
||||
🖥️ **Download the latest installer .zip file here** : https://github.com/invoke-ai/InvokeAI/releases/latest
|
||||
|
||||
- *Look for the file labelled "InvokeAI-installer-v3.X.X.zip" at the bottom of the page*
|
||||
- If you experience issues, read through the full [installation instructions](010_INSTALL_AUTOMATED.md) to make sure you have met all of the installation requirements. If you need more help, join the [Discord](discord.gg/invoke-ai) or create an issue on [Github](https://github.com/invoke-ai/InvokeAI).
|
||||
|
||||
|
||||
third party libraries and InvokeAI itself. It includes access to a
|
||||
"developer console" which will help us debug problems with you and
|
||||
give you to access experimental features.
|
||||
|
||||
## **[Manual Installation](020_INSTALL_MANUAL.md)**
|
||||
This method is recommended for experienced users and developers.
|
||||
|
@ -1,63 +0,0 @@
|
||||
# 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!")
|
||||
...
|
||||
```
|
||||
|
||||
The full API is documented below.
|
||||
|
||||
## Invocation Mixins
|
||||
|
||||
Two important mixins are provided to facilitate working with metadata and gallery boards.
|
||||
|
||||
### `WithMetadata`
|
||||
|
||||
Inherit from this class (in addition to `BaseInvocation`) to add a `metadata` input to your node. When you do this, you can access the metadata dict from `self.metadata` in the `invoke()` function.
|
||||
|
||||
The dict will be populated via the node's input, and you can add any metadata you'd like to it. When you call `context.images.save()`, if the metadata dict has any data, it be automatically embedded in the image.
|
||||
|
||||
### `WithBoard`
|
||||
|
||||
Inherit from this class (in addition to `BaseInvocation`) to add a `board` input to your node. This renders as a drop-down to select a board. The user's selection will be accessible from `self.board` in the `invoke()` function.
|
||||
|
||||
When you call `context.images.save()`, if a board was selected, the image will added to that board as it is 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 -->
|
@ -6,17 +6,10 @@ If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](
|
||||
|
||||
## Features
|
||||
|
||||
### Workflow Library
|
||||
The Workflow Library enables you to save workflows to the Invoke database, allowing you to easily creating, modify and share workflows as needed.
|
||||
|
||||
A curated set of workflows are provided by default - these are designed to help explain important nodes' usage in the Workflow Editor.
|
||||
|
||||

|
||||
|
||||
### Linear View
|
||||
The Workflow Editor allows you to create a UI for your workflow, to make it easier to iterate on your generations.
|
||||
|
||||
To add an input to the Linear UI, right click on the **input label** and select "Add to Linear View".
|
||||
To add an input to the Linear UI, right click on the input label and select "Add to Linear View".
|
||||
|
||||
The Linear UI View will also be part of the saved workflow, allowing you share workflows and enable other to use them, regardless of complexity.
|
||||
|
||||
@ -37,7 +30,7 @@ Any node or input field can be renamed in the workflow editor. If the input fiel
|
||||
Nodes have a "Use Cache" option in their footer. This allows for performance improvements by using the previously cached values during the workflow processing.
|
||||
|
||||
|
||||
## Important Nodes & Concepts
|
||||
## Important Concepts
|
||||
|
||||
There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
|
||||
|
||||
@ -63,7 +56,7 @@ The ImageToLatents node takes in a pixel image and a VAE and outputs a latents.
|
||||
|
||||
It is common to want to use both the same seed (for continuity) and random seeds (for variety). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
|
||||
|
||||

|
||||

|
||||
|
||||
### ControlNet
|
||||
|
||||
|
@ -1,148 +0,0 @@
|
||||
# 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.
|
@ -14,7 +14,6 @@ To use a community workflow, download the the `.json` node graph file and load i
|
||||
|
||||
- Community Nodes
|
||||
+ [Adapters-Linked](#adapters-linked-nodes)
|
||||
+ [Autostereogram](#autostereogram-nodes)
|
||||
+ [Average Images](#average-images)
|
||||
+ [Clean Image Artifacts After Cut](#clean-image-artifacts-after-cut)
|
||||
+ [Close Color Mask](#close-color-mask)
|
||||
@ -26,24 +25,21 @@ To use a community workflow, download the the `.json` node graph file and load i
|
||||
+ [GPT2RandomPromptMaker](#gpt2randompromptmaker)
|
||||
+ [Grid to Gif](#grid-to-gif)
|
||||
+ [Halftone](#halftone)
|
||||
+ [Hand Refiner with MeshGraphormer](#hand-refiner-with-meshgraphormer)
|
||||
+ [Ideal Size](#ideal-size)
|
||||
+ [Image and Mask Composition Pack](#image-and-mask-composition-pack)
|
||||
+ [Image Dominant Color](#image-dominant-color)
|
||||
+ [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)
|
||||
+ [Match Histogram](#match-histogram)
|
||||
+ [Metadata-Linked](#metadata-linked-nodes)
|
||||
+ [Negative Image](#negative-image)
|
||||
+ [Nightmare Promptgen](#nightmare-promptgen)
|
||||
+ [Negative Image](#negative-image)
|
||||
+ [Oobabooga](#oobabooga)
|
||||
+ [Prompt Tools](#prompt-tools)
|
||||
+ [Remote Image](#remote-image)
|
||||
+ [BriaAI Background Remove](#briaai-remove-background)
|
||||
+ [Remove Background](#remove-background)
|
||||
+ [Retroize](#retroize)
|
||||
+ [Size Stepper Nodes](#size-stepper-nodes)
|
||||
@ -70,17 +66,6 @@ Note: These are inherited from the core nodes so any update to the core nodes sh
|
||||
|
||||
**Node Link:** https://github.com/skunkworxdark/adapters-linked-nodes
|
||||
|
||||
--------------------------------
|
||||
### Autostereogram Nodes
|
||||
|
||||
**Description:** Generate autostereogram images from a depth map. This is not a very practically useful node but more a 90s nostalgic indulgence as I used to love these images as a kid.
|
||||
|
||||
**Node Link:** https://github.com/skunkworxdark/autostereogram_nodes
|
||||
|
||||
**Example Usage:**
|
||||
</br>
|
||||
<img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider-depth.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-dots.png" width="200" /> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-pattern.png" width="200" />
|
||||
|
||||
--------------------------------
|
||||
### Average Images
|
||||
|
||||
@ -211,18 +196,13 @@ CMYK Halftone Output:
|
||||
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
|
||||
|
||||
--------------------------------
|
||||
### Ideal Size
|
||||
|
||||
### Hand Refiner with MeshGraphormer
|
||||
**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
|
||||
|
||||
**Description**: Hand Refiner takes in your image and automatically generates a fixed depth map for the hands along with a mask of the hands region that will conveniently allow you to use them along with ControlNet to fix the wonky hands generated by Stable Diffusion
|
||||
|
||||
**Node Link:** https://github.com/blessedcoolant/invoke_meshgraphormer
|
||||
|
||||
**View**
|
||||
<img src="https://raw.githubusercontent.com/blessedcoolant/invoke_meshgraphormer/main/assets/preview.jpg" />
|
||||
**Node Link:** https://github.com/JPPhoto/ideal-size-node
|
||||
|
||||
--------------------------------
|
||||
|
||||
### Image and Mask Composition Pack
|
||||
|
||||
**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
|
||||
@ -291,13 +271,6 @@ 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
|
||||
|
||||
@ -354,21 +327,12 @@ 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 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.
|
||||
- `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.
|
||||
|
||||
**Node Link:** https://github.com/skunkworxdark/metadata-linked-nodes
|
||||
|
||||
@ -382,13 +346,6 @@ Node Link: https://github.com/VeyDlin/negative-image-node
|
||||
View:
|
||||
</br><img src="https://raw.githubusercontent.com/VeyDlin/negative-image-node/master/.readme/node.png" width="500" />
|
||||
|
||||
--------------------------------
|
||||
### Nightmare Promptgen
|
||||
|
||||
**Description:** Nightmare Prompt Generator - Uses a local text generation model to create unique imaginative (but usually nightmarish) prompts for InvokeAI. By default, it allows you to choose from some gpt-neo models I finetuned on over 2500 of my own InvokeAI prompts in Compel format, but you're able to add your own, as well. Offers support for replacing any troublesome words with a random choice from list you can also define.
|
||||
|
||||
**Node Link:** [https://github.com/gogurtenjoyer/nightmare-promptgen](https://github.com/gogurtenjoyer/nightmare-promptgen)
|
||||
|
||||
--------------------------------
|
||||
### Oobabooga
|
||||
|
||||
@ -452,17 +409,6 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
|
||||
|
||||
**Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
|
||||
|
||||
--------------------------------
|
||||
|
||||
### BriaAI Remove Background
|
||||
|
||||
**Description**: Implements one click background removal with BriaAI's new version 1.4 model which seems to be be producing better results than any other previous background removal tool.
|
||||
|
||||
**Node Link:** https://github.com/blessedcoolant/invoke_bria_rmbg
|
||||
|
||||
**View**
|
||||
<img src="https://raw.githubusercontent.com/blessedcoolant/invoke_bria_rmbg/main/assets/preview.jpg" />
|
||||
|
||||
--------------------------------
|
||||
### Remove Background
|
||||
|
||||
|
@ -19,8 +19,6 @@ their descriptions.
|
||||
| Conditioning Primitive | A conditioning tensor primitive value |
|
||||
| Content Shuffle Processor | Applies content shuffle processing to image |
|
||||
| ControlNet | Collects ControlNet info to pass to other nodes |
|
||||
| Create Denoise Mask | Converts a greyscale or transparency image into a mask for denoising. |
|
||||
| Create Gradient Mask | Creates a mask for Gradient ("soft", "differential") inpainting that gradually expands during denoising. Improves edge coherence. |
|
||||
| Denoise Latents | Denoises noisy latents to decodable images |
|
||||
| Divide Integers | Divides two numbers |
|
||||
| Dynamic Prompt | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator |
|
||||
@ -38,7 +36,6 @@ their descriptions.
|
||||
| Integer Math | Perform basic math operations on two integers |
|
||||
| Convert Image Mode | Converts an image to a different mode. |
|
||||
| Crop Image | Crops an image to a specified box. The box can be outside of the image. |
|
||||
| Ideal Size | Calculates an ideal image size for latents for a first pass of a multi-pass upscaling to avoid duplication and other artifacts |
|
||||
| Image Hue Adjustment | Adjusts the Hue of an image. |
|
||||
| Inverse Lerp Image | Inverse linear interpolation of all pixels of an image |
|
||||
| Image Primitive | An image primitive value |
|
||||
@ -83,7 +80,7 @@ their descriptions.
|
||||
| ONNX Text to Latents | Generates latents from conditionings. |
|
||||
| ONNX Model Loader | Loads a main model, outputting its submodels. |
|
||||
| OpenCV Inpaint | Simple inpaint using opencv. |
|
||||
| DW Openpose Processor | Applies Openpose processing to image |
|
||||
| Openpose Processor | Applies Openpose processing to image |
|
||||
| PIDI Processor | Applies PIDI processing to image |
|
||||
| Prompts from File | Loads prompts from a text file |
|
||||
| Random Integer | Outputs a single random integer. |
|
||||
|
@ -1,6 +1,6 @@
|
||||
# Example Workflows
|
||||
|
||||
We've curated some example workflows for you to get started with Workflows in InvokeAI! These can also be found in the Workflow Library, located in the Workflow Editor of Invoke.
|
||||
We've curated some example workflows for you to get started with Workflows in InvokeAI
|
||||
|
||||
To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
|
||||
|
||||
|
@ -13,69 +13,46 @@ We thank them for all of their time and hard work.
|
||||
|
||||
- [Lincoln D. Stein](mailto:lincoln.stein@gmail.com)
|
||||
|
||||
## **Current Core Team**
|
||||
## **Current core team**
|
||||
|
||||
* @lstein (Lincoln Stein) - Co-maintainer
|
||||
* @blessedcoolant - Co-maintainer
|
||||
* @hipsterusername (Kent Keirsey) - Co-maintainer, CEO, Positive Vibes
|
||||
* @psychedelicious (Spencer Mabrito) - Web Team Leader
|
||||
* @chainchompa (Jennifer Player) - Web Development & Chain-Chomping
|
||||
* @josh is toast (Josh Corbett) - Web Development
|
||||
* @cheerio (Mary Rogers) - Lead Engineer & Web App Development
|
||||
* @Kyle0654 (Kyle Schouviller) - Node Architect and General Backend Wizard
|
||||
* @damian0815 - Attention Systems and Compel Maintainer
|
||||
* @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
|
||||
* @sunija - Standalone version
|
||||
* @genomancer (Gregg Helt) - Controlnet support
|
||||
* @StAlKeR7779 (Sergey Borisov) - Torch stack, ONNX, model management, optimization
|
||||
* @cheerio (Mary Rogers) - Lead Engineer & Web App Development
|
||||
* @brandon (Brandon Rising) - Platform, Infrastructure, Backend Systems
|
||||
* @ryanjdick (Ryan Dick) - Machine Learning & Training
|
||||
* @JPPhoto - Core image generation nodes
|
||||
* @dunkeroni - Image generation backend
|
||||
* @SkunkWorxDark - Image generation backend
|
||||
* @millu (Millun Atluri) - Community Manager, Documentation, Node-wrangler
|
||||
* @chainchompa (Jennifer Player) - Web Development & Chain-Chomping
|
||||
* @keturn (Kevin Turner) - Diffusers
|
||||
* @millu (Millun Atluri) - Community Wizard, Documentation, Node-wrangler,
|
||||
* @glimmerleaf (Devon Hopkins) - Community Wizard
|
||||
* @gogurt enjoyer - Discord moderator and end user support
|
||||
* @whosawhatsis - Discord moderator and end user support
|
||||
* @dwinrger - Discord moderator and end user support
|
||||
* @526christian - Discord moderator and end user support
|
||||
* @harvester62 - Discord moderator and end user support
|
||||
|
||||
|
||||
## **Honored Team Alumni**
|
||||
|
||||
* @StAlKeR7779 (Sergey Borisov) - Torch stack, ONNX, model management, optimization
|
||||
* @damian0815 - Attention Systems and Compel Maintainer
|
||||
* @netsvetaev (Artur) - Localization support
|
||||
* @Kyle0654 (Kyle Schouviller) - Node Architect and General Backend Wizard
|
||||
* @tildebyte - Installation and configuration
|
||||
* @mauwii (Matthias Wilde) - Installation, release, continuous integration
|
||||
|
||||
|
||||
## **Full List of Contributors by Commit Name**
|
||||
|
||||
- 이승석
|
||||
- AbdBarho
|
||||
- ablattmann
|
||||
- AdamOStark
|
||||
- Adam Rice
|
||||
- Airton Silva
|
||||
- Aldo Hoeben
|
||||
- Alexander Eichhorn
|
||||
- Alexandre D. Roberge
|
||||
- Alexandre Macabies
|
||||
- Alfie John
|
||||
- Andreas Rozek
|
||||
- Andre LaBranche
|
||||
- Andy Bearman
|
||||
- Andy Luhrs
|
||||
- Andy Pilate
|
||||
- Anonymous
|
||||
- Anthony Monthe
|
||||
- Any-Winter-4079
|
||||
- apolinario
|
||||
- Ar7ific1al
|
||||
- ArDiouscuros
|
||||
- Armando C. Santisbon
|
||||
- Arnold Cordewiner
|
||||
- Arthur Holstvoogd
|
||||
- artmen1516
|
||||
- Artur
|
||||
@ -87,16 +64,13 @@ We thank them for all of their time and hard work.
|
||||
- blhook
|
||||
- BlueAmulet
|
||||
- Bouncyknighter
|
||||
- Brandon
|
||||
- Brandon Rising
|
||||
- Brent Ozar
|
||||
- Brian Racer
|
||||
- bsilvereagle
|
||||
- c67e708d
|
||||
- camenduru
|
||||
- CapableWeb
|
||||
- Carson Katri
|
||||
- chainchompa
|
||||
- Chloe
|
||||
- Chris Dawson
|
||||
- Chris Hayes
|
||||
@ -112,45 +86,30 @@ We thank them for all of their time and hard work.
|
||||
- cpacker
|
||||
- Cragin Godley
|
||||
- creachec
|
||||
- CrypticWit
|
||||
- d8ahazard
|
||||
- damian
|
||||
- damian0815
|
||||
- Damian at mba
|
||||
- Damian Stewart
|
||||
- Daniel Manzke
|
||||
- Danny Beer
|
||||
- Dan Sully
|
||||
- Darren Ringer
|
||||
- David Burnett
|
||||
- David Ford
|
||||
- David Regla
|
||||
- David Sisco
|
||||
- David Wager
|
||||
- Daya Adianto
|
||||
- db3000
|
||||
- DekitaRPG
|
||||
- Denis Olshin
|
||||
- Dennis
|
||||
- dependabot[bot]
|
||||
- Dmitry Parnas
|
||||
- Dobrynia100
|
||||
- Dominic Letz
|
||||
- DrGunnarMallon
|
||||
- Drun555
|
||||
- dunkeroni
|
||||
- Edward Johan
|
||||
- elliotsayes
|
||||
- Elrik
|
||||
- ElrikUnderlake
|
||||
- Eric Khun
|
||||
- Eric Wolf
|
||||
- Eugene
|
||||
- Eugene Brodsky
|
||||
- ExperimentalCyborg
|
||||
- Fabian Bahl
|
||||
- Fabio 'MrWHO' Torchetti
|
||||
- Fattire
|
||||
- fattire
|
||||
- Felipe Nogueira
|
||||
- Félix Sanz
|
||||
@ -159,12 +118,8 @@ We thank them for all of their time and hard work.
|
||||
- gabrielrotbart
|
||||
- gallegonovato
|
||||
- Gérald LONLAS
|
||||
- Gille
|
||||
- GitHub Actions Bot
|
||||
- glibesyck
|
||||
- gogurtenjoyer
|
||||
- Gohsuke Shimada
|
||||
- greatwolf
|
||||
- greentext2
|
||||
- Gregg Helt
|
||||
- H4rk
|
||||
@ -176,7 +131,6 @@ We thank them for all of their time and hard work.
|
||||
- Hosted Weblate
|
||||
- Iman Karim
|
||||
- ismail ihsan bülbül
|
||||
- ItzAttila
|
||||
- Ivan Efimov
|
||||
- jakehl
|
||||
- Jakub Kolčář
|
||||
@ -187,7 +141,6 @@ We thank them for all of their time and hard work.
|
||||
- Jason Toffaletti
|
||||
- Jaulustus
|
||||
- Jeff Mahoney
|
||||
- Jennifer Player
|
||||
- jeremy
|
||||
- Jeremy Clark
|
||||
- JigenD
|
||||
@ -195,26 +148,19 @@ We thank them for all of their time and hard work.
|
||||
- Johan Roxendal
|
||||
- Johnathon Selstad
|
||||
- Jonathan
|
||||
- Jordan Hewitt
|
||||
- Joseph Dries III
|
||||
- Josh Corbett
|
||||
- JPPhoto
|
||||
- jspraul
|
||||
- junzi
|
||||
- Justin Wong
|
||||
- Juuso V
|
||||
- Kaspar Emanuel
|
||||
- Katsuyuki-Karasawa
|
||||
- Keerigan45
|
||||
- Kent Keirsey
|
||||
- Kevin Brack
|
||||
- Kevin Coakley
|
||||
- Kevin Gibbons
|
||||
- Kevin Schaul
|
||||
- Kevin Turner
|
||||
- Kieran Klaassen
|
||||
- krummrey
|
||||
- Kyle
|
||||
- Kyle Lacy
|
||||
- Kyle Schouviller
|
||||
- Lawrence Norton
|
||||
@ -225,15 +171,10 @@ We thank them for all of their time and hard work.
|
||||
- Lynne Whitehorn
|
||||
- majick
|
||||
- Marco Labarile
|
||||
- Marta Nahorniuk
|
||||
- Martin Kristiansen
|
||||
- Mary Hipp
|
||||
- maryhipp
|
||||
- Mary Hipp Rogers
|
||||
- mastercaster
|
||||
- mastercaster9000
|
||||
- Matthias Wild
|
||||
- mauwii
|
||||
- michaelk71
|
||||
- mickr777
|
||||
- Mihai
|
||||
@ -241,15 +182,11 @@ We thank them for all of their time and hard work.
|
||||
- Mikhail Tishin
|
||||
- Millun Atluri
|
||||
- Minjune Song
|
||||
- Mitchell Allain
|
||||
- mitien
|
||||
- mofuzz
|
||||
- Muhammad Usama
|
||||
- Name
|
||||
- _nderscore
|
||||
- Neil Wang
|
||||
- nekowaiz
|
||||
- nemuruibai
|
||||
- Netzer R
|
||||
- Nicholas Koh
|
||||
- Nicholas Körfer
|
||||
@ -260,11 +197,9 @@ We thank them for all of their time and hard work.
|
||||
- ofirkris
|
||||
- Olivier Louvignes
|
||||
- owenvincent
|
||||
- pand4z31
|
||||
- Patrick Esser
|
||||
- Patrick Tien
|
||||
- Patrick von Platen
|
||||
- Paul Curry
|
||||
- Paul Sajna
|
||||
- pejotr
|
||||
- Peter Baylies
|
||||
@ -272,7 +207,6 @@ We thank them for all of their time and hard work.
|
||||
- plucked
|
||||
- prixt
|
||||
- psychedelicious
|
||||
- psychedelicious@windows
|
||||
- Rainer Bernhardt
|
||||
- Riccardo Giovanetti
|
||||
- Rich Jones
|
||||
@ -281,22 +215,16 @@ We thank them for all of their time and hard work.
|
||||
- Robert Bolender
|
||||
- Robin Rombach
|
||||
- Rohan Barar
|
||||
- Rohinish
|
||||
- rpagliuca
|
||||
- rromb
|
||||
- Rupesh Sreeraman
|
||||
- Ryan
|
||||
- Ryan Cao
|
||||
- Ryan Dick
|
||||
- Saifeddine
|
||||
- Saifeddine ALOUI
|
||||
- Sam
|
||||
- SammCheese
|
||||
- Sam McLeod
|
||||
- Sammy
|
||||
- sammyf
|
||||
- Samuel Husso
|
||||
- Saurav Maheshkar
|
||||
- Scott Lahteine
|
||||
- Sean McLellan
|
||||
- Sebastian Aigner
|
||||
@ -304,21 +232,16 @@ We thank them for all of their time and hard work.
|
||||
- Sergey Krashevich
|
||||
- Shapor Naghibzadeh
|
||||
- Shawn Zhong
|
||||
- Simona Liliac
|
||||
- Simon Vans-Colina
|
||||
- skunkworxdark
|
||||
- slashtechno
|
||||
- SoheilRezaei
|
||||
- Song, Pengcheng
|
||||
- spezialspezial
|
||||
- ssantos
|
||||
- StAlKeR7779
|
||||
- Stefan Tobler
|
||||
- Stephan Koglin-Fischer
|
||||
- SteveCaruso
|
||||
- Steve Martinelli
|
||||
- Steven Frank
|
||||
- Surisen
|
||||
- System X - Files
|
||||
- Taylor Kems
|
||||
- techicode
|
||||
@ -337,34 +260,26 @@ We thank them for all of their time and hard work.
|
||||
- tyler
|
||||
- unknown
|
||||
- user1
|
||||
- vedant-3010
|
||||
- Vedant Madane
|
||||
- veprogames
|
||||
- wa.code
|
||||
- wfng92
|
||||
- whjms
|
||||
- whosawhatsis
|
||||
- Will
|
||||
- William Becher
|
||||
- William Chong
|
||||
- Wilson E. Alvarez
|
||||
- woweenie
|
||||
- Wubbbi
|
||||
- xra
|
||||
- Yeung Yiu Hung
|
||||
- ymgenesis
|
||||
- Yorzaren
|
||||
- Yosuke Shinya
|
||||
- yun saki
|
||||
- ZachNagengast
|
||||
- Zadagu
|
||||
- zeptofine
|
||||
- Zerdoumi
|
||||
- Васянатор
|
||||
- 冯不游
|
||||
- 唐澤 克幸
|
||||
|
||||
## **Original CompVis (Stable Diffusion) Authors**
|
||||
## **Original CompVis Authors**
|
||||
|
||||
- [Robin Rombach](https://github.com/rromb)
|
||||
- [Patrick von Platen](https://github.com/patrickvonplaten)
|
||||
|
5
docs/requirements-mkdocs.txt
Normal file
@ -0,0 +1,5 @@
|
||||
mkdocs
|
||||
mkdocs-material>=8, <9
|
||||
mkdocs-git-revision-date-localized-plugin
|
||||
mkdocs-redirects==1.2.0
|
||||
|
@ -2,65 +2,65 @@
|
||||
|
||||
set -e
|
||||
|
||||
BCYAN="\033[1;36m"
|
||||
BYELLOW="\033[1;33m"
|
||||
BGREEN="\033[1;32m"
|
||||
BRED="\033[1;31m"
|
||||
RED="\033[31m"
|
||||
RESET="\033[0m"
|
||||
BCYAN="\e[1;36m"
|
||||
BYELLOW="\e[1;33m"
|
||||
BGREEN="\e[1;32m"
|
||||
BRED="\e[1;31m"
|
||||
RED="\e[31m"
|
||||
RESET="\e[0m"
|
||||
|
||||
function git_show {
|
||||
git show -s --format=oneline --abbrev-commit "$1" | cat
|
||||
function is_bin_in_path {
|
||||
builtin type -P "$1" &>/dev/null
|
||||
}
|
||||
|
||||
if [[ ! -z "${VIRTUAL_ENV}" ]]; then
|
||||
function git_show {
|
||||
git show -s --format='%h %s' $1
|
||||
}
|
||||
|
||||
cd "$(dirname "$0")"
|
||||
|
||||
echo -e "${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
|
||||
|
||||
if [[ -v "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}"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
cd "$(dirname "$0")"
|
||||
|
||||
VERSION=$(
|
||||
cd ..
|
||||
python3 -c "from invokeai.version import __version__ as version; print(version)"
|
||||
python -c "from invokeai.version import __version__ as version; print(version)"
|
||||
)
|
||||
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 $(pwd)"
|
||||
read -p "If that looks right, press any key to proceed, or CTRL-C to exit..."
|
||||
echo
|
||||
fi
|
||||
PATCH=""
|
||||
VERSION="v${VERSION}${PATCH}"
|
||||
|
||||
echo -e "${BGREEN}HEAD${RESET}:"
|
||||
git_show HEAD
|
||||
git_show
|
||||
echo
|
||||
|
||||
# ---------------------- FRONTEND ----------------------
|
||||
|
||||
pushd ../invokeai/frontend/web >/dev/null
|
||||
echo
|
||||
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 "Building frontend..."
|
||||
echo
|
||||
pnpm build
|
||||
popd
|
||||
|
||||
# ---------------------- BACKEND ----------------------
|
||||
@ -71,13 +71,13 @@ 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
|
||||
if [[ $(python -c 'from importlib.util import find_spec; print(find_spec("build") is None)') == "True" ]]; then
|
||||
pip install --user build
|
||||
fi
|
||||
|
||||
rm -rf ../build
|
||||
|
||||
python3 -m build --outdir dist/ ../.
|
||||
python -m build --wheel --outdir dist/ ../.
|
||||
|
||||
# ----------------------
|
||||
|
||||
@ -97,37 +97,22 @@ done
|
||||
mkdir InvokeAI-Installer/lib
|
||||
cp lib/*.py InvokeAI-Installer/lib
|
||||
|
||||
# Move the wheel
|
||||
mv dist/*.whl InvokeAI-Installer/lib/
|
||||
|
||||
# Install scripts
|
||||
# Mac/Linux
|
||||
cp install.sh.in InvokeAI-Installer/install.sh
|
||||
chmod a+x InvokeAI-Installer/install.sh
|
||||
|
||||
# Windows
|
||||
cp install.bat.in InvokeAI-Installer/install.bat
|
||||
perl -p -e "s/^set INVOKEAI_VERSION=.*/set INVOKEAI_VERSION=$VERSION/" install.bat.in >InvokeAI-Installer/install.bat
|
||||
cp WinLongPathsEnabled.reg InvokeAI-Installer/
|
||||
|
||||
FILENAME=InvokeAI-installer-$VERSION.zip
|
||||
|
||||
# Zip everything up
|
||||
zip -r ${FILENAME} InvokeAI-Installer
|
||||
zip -r InvokeAI-installer-$VERSION.zip 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
|
||||
# clean up
|
||||
rm -rf InvokeAI-Installer tmp dist ../invokeai/frontend/web/dist/
|
||||
|
||||
exit 0
|
||||
|
@ -15,6 +15,7 @@ if "%1" == "use-cache" (
|
||||
@rem Config
|
||||
@rem The version in the next line is replaced by an up to date release number
|
||||
@rem when create_installer.sh is run. Change the release number there.
|
||||
set INVOKEAI_VERSION=latest
|
||||
set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
|
||||
set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
|
||||
set PYTHON_URL=https://www.python.org/downloads/windows/
|
||||
|
@ -11,7 +11,7 @@ import sys
|
||||
import venv
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from typing import Optional, Tuple
|
||||
from typing import Union
|
||||
|
||||
SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100"
|
||||
INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
|
||||
@ -21,20 +21,40 @@ OS = platform.uname().system
|
||||
ARCH = platform.uname().machine
|
||||
VERSION = "latest"
|
||||
|
||||
### Feature flags
|
||||
# Install the virtualenv into the runtime dir
|
||||
FF_VENV_IN_RUNTIME = True
|
||||
|
||||
# Install the wheel packaged with the installer
|
||||
FF_USE_LOCAL_WHEEL = True
|
||||
|
||||
|
||||
class Installer:
|
||||
"""
|
||||
Deploys an InvokeAI installation into a given path
|
||||
"""
|
||||
|
||||
reqs: list[str] = INSTALLER_REQS
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.reqs = INSTALLER_REQS
|
||||
self.preflight()
|
||||
if os.getenv("VIRTUAL_ENV") is not None:
|
||||
print("A virtual environment is already activated. Please 'deactivate' before installation.")
|
||||
sys.exit(-1)
|
||||
self.bootstrap()
|
||||
self.available_releases = get_github_releases()
|
||||
|
||||
def preflight(self) -> None:
|
||||
"""
|
||||
Preflight checks
|
||||
"""
|
||||
|
||||
# TODO
|
||||
# verify python version
|
||||
# on macOS verify XCode tools are present
|
||||
# verify libmesa, libglx on linux
|
||||
# check that the system arch is not i386 (?)
|
||||
# check that the system has a GPU, and the type of GPU
|
||||
|
||||
pass
|
||||
|
||||
def mktemp_venv(self) -> TemporaryDirectory:
|
||||
"""
|
||||
@ -58,9 +78,12 @@ class Installer:
|
||||
|
||||
return venv_dir
|
||||
|
||||
def bootstrap(self, verbose: bool = False) -> TemporaryDirectory | None:
|
||||
def bootstrap(self, verbose: bool = False) -> TemporaryDirectory:
|
||||
"""
|
||||
Bootstrap the installer venv with packages required at install time
|
||||
|
||||
:return: path to the virtual environment directory that was bootstrapped
|
||||
:rtype: TemporaryDirectory
|
||||
"""
|
||||
|
||||
print("Initializing the installer. This may take a minute - please wait...")
|
||||
@ -72,27 +95,39 @@ class Installer:
|
||||
cmd.extend(self.reqs)
|
||||
|
||||
try:
|
||||
# upgrade pip to the latest version to avoid a confusing message
|
||||
res = upgrade_pip(Path(venv_dir.name))
|
||||
if verbose:
|
||||
print(res)
|
||||
|
||||
# run the install prerequisites installation
|
||||
res = subprocess.check_output(cmd).decode()
|
||||
|
||||
if verbose:
|
||||
print(res)
|
||||
|
||||
return venv_dir
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(e)
|
||||
|
||||
def app_venv(self, venv_parent) -> Path:
|
||||
def app_venv(self, path: str = None):
|
||||
"""
|
||||
Create a virtualenv for the InvokeAI installation
|
||||
"""
|
||||
|
||||
venv_dir = venv_parent / ".venv"
|
||||
# explicit venv location
|
||||
# currently unused in normal operation
|
||||
# useful for testing or special cases
|
||||
if path is not None:
|
||||
venv_dir = Path(path)
|
||||
|
||||
# experimental / testing
|
||||
elif not FF_VENV_IN_RUNTIME:
|
||||
if OS == "Windows":
|
||||
venv_dir_parent = os.getenv("APPDATA", "~/AppData/Roaming")
|
||||
elif OS == "Darwin":
|
||||
# there is no environment variable on macOS to find this
|
||||
# TODO: confirm this is working as expected
|
||||
venv_dir_parent = "~/Library/Application Support"
|
||||
elif OS == "Linux":
|
||||
venv_dir_parent = os.getenv("XDG_DATA_DIR", "~/.local/share")
|
||||
venv_dir = Path(venv_dir_parent).expanduser().resolve() / f"InvokeAI/{VERSION}/venv"
|
||||
|
||||
# stable / current
|
||||
else:
|
||||
venv_dir = self.dest / ".venv"
|
||||
|
||||
# Prefer to copy python executables
|
||||
# so that updates to system python don't break InvokeAI
|
||||
@ -106,7 +141,7 @@ class Installer:
|
||||
return venv_dir
|
||||
|
||||
def install(
|
||||
self, version=None, root: str = "~/invokeai", yes_to_all=False, find_links: Optional[Path] = None
|
||||
self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
|
||||
) -> None:
|
||||
"""
|
||||
Install the InvokeAI application into the given runtime path
|
||||
@ -123,20 +158,15 @@ class Installer:
|
||||
|
||||
import messages
|
||||
|
||||
messages.welcome(self.available_releases)
|
||||
messages.welcome()
|
||||
|
||||
version = messages.choose_version(self.available_releases)
|
||||
|
||||
auto_dest = Path(os.environ.get("INVOKEAI_ROOT", root)).expanduser().resolve()
|
||||
destination = auto_dest if yes_to_all else messages.dest_path(root)
|
||||
if destination is None:
|
||||
print("Could not find or create the destination directory. Installation cancelled.")
|
||||
sys.exit(0)
|
||||
default_path = os.environ.get("INVOKEAI_ROOT") or Path(root).expanduser().resolve()
|
||||
self.dest = default_path if yes_to_all else messages.dest_path(root)
|
||||
|
||||
# create the venv for the app
|
||||
self.venv = self.app_venv(venv_parent=destination)
|
||||
self.venv = self.app_venv()
|
||||
|
||||
self.instance = InvokeAiInstance(runtime=destination, venv=self.venv, version=version)
|
||||
self.instance = InvokeAiInstance(runtime=self.dest, venv=self.venv, version=version)
|
||||
|
||||
# install dependencies and the InvokeAI application
|
||||
(extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
|
||||
@ -160,7 +190,7 @@ class InvokeAiInstance:
|
||||
A single runtime directory *may* be shared by multiple virtual environments, though this isn't currently tested or supported.
|
||||
"""
|
||||
|
||||
def __init__(self, runtime: Path, venv: Path, version: str = "stable") -> None:
|
||||
def __init__(self, runtime: Path, venv: Path, version: str) -> None:
|
||||
self.runtime = runtime
|
||||
self.venv = venv
|
||||
self.pip = get_pip_from_venv(venv)
|
||||
@ -169,7 +199,6 @@ class InvokeAiInstance:
|
||||
set_sys_path(venv)
|
||||
os.environ["INVOKEAI_ROOT"] = str(self.runtime.expanduser().resolve())
|
||||
os.environ["VIRTUAL_ENV"] = str(self.venv.expanduser().resolve())
|
||||
upgrade_pip(venv)
|
||||
|
||||
def get(self) -> tuple[Path, Path]:
|
||||
"""
|
||||
@ -183,7 +212,54 @@ class InvokeAiInstance:
|
||||
|
||||
def install(self, extra_index_url=None, optional_modules=None, find_links=None):
|
||||
"""
|
||||
Install the package from PyPi.
|
||||
Install this instance, including dependencies and the app itself
|
||||
|
||||
:param extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
|
||||
:type extra_index_url: str
|
||||
"""
|
||||
|
||||
import messages
|
||||
|
||||
# install torch first to ensure the correct version gets installed.
|
||||
# works with either source or wheel install with negligible impact on installation times.
|
||||
messages.simple_banner("Installing PyTorch :fire:")
|
||||
self.install_torch(extra_index_url, find_links)
|
||||
|
||||
messages.simple_banner("Installing the InvokeAI Application :art:")
|
||||
self.install_app(extra_index_url, optional_modules, find_links)
|
||||
|
||||
def install_torch(self, extra_index_url=None, find_links=None):
|
||||
"""
|
||||
Install PyTorch
|
||||
"""
|
||||
|
||||
from plumbum import FG, local
|
||||
|
||||
pip = local[self.pip]
|
||||
|
||||
(
|
||||
pip[
|
||||
"install",
|
||||
"--require-virtualenv",
|
||||
"numpy~=1.24.0", # choose versions that won't be uninstalled during phase 2
|
||||
"urllib3~=1.26.0",
|
||||
"requests~=2.28.0",
|
||||
"torch==2.1.2",
|
||||
"torchmetrics==0.11.4",
|
||||
"torchvision>=0.16.2",
|
||||
"--force-reinstall",
|
||||
"--find-links" if find_links is not None else None,
|
||||
find_links,
|
||||
"--extra-index-url" if extra_index_url is not None else None,
|
||||
extra_index_url,
|
||||
]
|
||||
& FG
|
||||
)
|
||||
|
||||
def install_app(self, extra_index_url=None, optional_modules=None, find_links=None):
|
||||
"""
|
||||
Install the application with pip.
|
||||
Supports installation from PyPi or from a local source directory.
|
||||
|
||||
:param extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
|
||||
:type extra_index_url: str
|
||||
@ -195,52 +271,53 @@ class InvokeAiInstance:
|
||||
:type find_links: Path
|
||||
"""
|
||||
|
||||
import messages
|
||||
|
||||
# not currently used, but may be useful for "install most recent version" option
|
||||
if self.version == "prerelease":
|
||||
## this only applies to pypi installs; TODO actually use this
|
||||
if self.version == "pre":
|
||||
version = None
|
||||
pre_flag = "--pre"
|
||||
elif self.version == "stable":
|
||||
version = None
|
||||
pre_flag = None
|
||||
pre = "--pre"
|
||||
else:
|
||||
version = self.version
|
||||
pre_flag = None
|
||||
pre = None
|
||||
|
||||
src = "invokeai"
|
||||
if optional_modules:
|
||||
src += optional_modules
|
||||
if version:
|
||||
src += f"=={version}"
|
||||
## TODO: only local wheel will be installed as of now; support for --version arg is TODO
|
||||
if FF_USE_LOCAL_WHEEL:
|
||||
# if no wheel, try to do a source install before giving up
|
||||
try:
|
||||
src = str(next(Path(__file__).parent.glob("InvokeAI-*.whl")))
|
||||
except StopIteration:
|
||||
try:
|
||||
src = Path(__file__).parents[1].expanduser().resolve()
|
||||
# if the above directory contains one of these files, we'll do a source install
|
||||
next(src.glob("pyproject.toml"))
|
||||
next(src.glob("invokeai"))
|
||||
except StopIteration:
|
||||
print("Unable to find a wheel or perform a source install. Giving up.")
|
||||
|
||||
messages.simple_banner("Installing the InvokeAI Application :art:")
|
||||
elif version == "source":
|
||||
# this makes an assumption about the location of the installer package in the source tree
|
||||
src = Path(__file__).parents[1].expanduser().resolve()
|
||||
else:
|
||||
# will install from PyPi
|
||||
src = f"invokeai=={version}" if version is not None else "invokeai"
|
||||
|
||||
from plumbum import FG, ProcessExecutionError, local # type: ignore
|
||||
from plumbum import FG, local
|
||||
|
||||
pip = local[self.pip]
|
||||
|
||||
pipeline = pip[
|
||||
"install",
|
||||
"--require-virtualenv",
|
||||
"--force-reinstall",
|
||||
"--use-pep517",
|
||||
str(src),
|
||||
"--find-links" if find_links is not None else None,
|
||||
find_links,
|
||||
"--extra-index-url" if extra_index_url is not None else None,
|
||||
extra_index_url,
|
||||
pre_flag,
|
||||
]
|
||||
|
||||
try:
|
||||
_ = pipeline & FG
|
||||
except ProcessExecutionError as e:
|
||||
print(f"Error: {e}")
|
||||
print(
|
||||
"Could not install InvokeAI. Please try downloading the latest version of the installer and install again."
|
||||
)
|
||||
sys.exit(1)
|
||||
(
|
||||
pip[
|
||||
"install",
|
||||
"--require-virtualenv",
|
||||
"--use-pep517",
|
||||
str(src) + (optional_modules if optional_modules else ""),
|
||||
"--find-links" if find_links is not None else None,
|
||||
find_links,
|
||||
"--extra-index-url" if extra_index_url is not None else None,
|
||||
extra_index_url,
|
||||
pre,
|
||||
]
|
||||
& FG
|
||||
)
|
||||
|
||||
def configure(self):
|
||||
"""
|
||||
@ -296,6 +373,7 @@ class InvokeAiInstance:
|
||||
|
||||
ext = "bat" if OS == "Windows" else "sh"
|
||||
|
||||
# scripts = ['invoke', 'update']
|
||||
scripts = ["invoke"]
|
||||
|
||||
for script in scripts:
|
||||
@ -330,23 +408,6 @@ def get_pip_from_venv(venv_path: Path) -> str:
|
||||
return str(venv_path.expanduser().resolve() / pip)
|
||||
|
||||
|
||||
def upgrade_pip(venv_path: Path) -> str | None:
|
||||
"""
|
||||
Upgrade the pip executable in the given virtual environment
|
||||
"""
|
||||
|
||||
python = "Scripts\\python.exe" if OS == "Windows" else "bin/python"
|
||||
python = str(venv_path.expanduser().resolve() / python)
|
||||
|
||||
try:
|
||||
result = subprocess.check_output([python, "-m", "pip", "install", "--upgrade", "pip"]).decode()
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(e)
|
||||
result = None
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def set_sys_path(venv_path: Path) -> None:
|
||||
"""
|
||||
Given a path to a virtual environment, set the sys.path, in a cross-platform fashion,
|
||||
@ -370,43 +431,7 @@ def set_sys_path(venv_path: Path) -> None:
|
||||
sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
|
||||
|
||||
|
||||
def get_github_releases() -> tuple[list, list] | None:
|
||||
"""
|
||||
Query Github for published (pre-)release versions.
|
||||
Return a tuple where the first element is a list of stable releases and the second element is a list of pre-releases.
|
||||
Return None if the query fails for any reason.
|
||||
"""
|
||||
|
||||
import requests
|
||||
|
||||
## get latest releases using github api
|
||||
url = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
|
||||
releases, pre_releases = [], []
|
||||
try:
|
||||
res = requests.get(url)
|
||||
res.raise_for_status()
|
||||
tag_info = res.json()
|
||||
for tag in tag_info:
|
||||
if not tag["prerelease"]:
|
||||
releases.append(tag["tag_name"].lstrip("v"))
|
||||
else:
|
||||
pre_releases.append(tag["tag_name"].lstrip("v"))
|
||||
except requests.HTTPError as e:
|
||||
print(f"Error: {e}")
|
||||
print("Could not fetch version information from GitHub. Please check your network connection and try again.")
|
||||
return
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
print("An unexpected error occurred while trying to fetch version information from GitHub. Please try again.")
|
||||
return
|
||||
|
||||
releases.sort(reverse=True)
|
||||
pre_releases.sort(reverse=True)
|
||||
|
||||
return releases, pre_releases
|
||||
|
||||
|
||||
def get_torch_source() -> Tuple[str | None, str | None]:
|
||||
def get_torch_source() -> (Union[str, None], str):
|
||||
"""
|
||||
Determine the extra index URL for pip to use for torch installation.
|
||||
This depends on the OS and the graphics accelerator in use.
|
||||
@ -421,26 +446,25 @@ def get_torch_source() -> Tuple[str | None, str | None]:
|
||||
:rtype: list
|
||||
"""
|
||||
|
||||
from messages import select_gpu
|
||||
from messages import graphical_accelerator
|
||||
|
||||
# device can be one of: "cuda", "rocm", "cpu", "cuda_and_dml, autodetect"
|
||||
device = select_gpu()
|
||||
# device can be one of: "cuda", "rocm", "cpu", "idk"
|
||||
device = graphical_accelerator()
|
||||
|
||||
url = None
|
||||
optional_modules = "[onnx]"
|
||||
if OS == "Linux":
|
||||
if device.value == "rocm":
|
||||
url = "https://download.pytorch.org/whl/rocm5.6"
|
||||
elif device.value == "cpu":
|
||||
if device == "rocm":
|
||||
url = "https://download.pytorch.org/whl/rocm5.4.2"
|
||||
elif device == "cpu":
|
||||
url = "https://download.pytorch.org/whl/cpu"
|
||||
|
||||
elif OS == "Windows":
|
||||
if device.value == "cuda":
|
||||
url = "https://download.pytorch.org/whl/cu121"
|
||||
optional_modules = "[xformers,onnx-cuda]"
|
||||
if device.value == "cuda_and_dml":
|
||||
url = "https://download.pytorch.org/whl/cu121"
|
||||
optional_modules = "[xformers,onnx-directml]"
|
||||
if device == "cuda":
|
||||
url = "https://download.pytorch.org/whl/cu121"
|
||||
optional_modules = "[xformers,onnx-cuda]"
|
||||
if device == "cuda_and_dml":
|
||||
url = "https://download.pytorch.org/whl/cu121"
|
||||
optional_modules = "[xformers,onnx-directml]"
|
||||
|
||||
# in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
|
||||
|
||||
|
@ -5,11 +5,10 @@ Installer user interaction
|
||||
|
||||
import os
|
||||
import platform
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
|
||||
from prompt_toolkit import HTML, prompt
|
||||
from prompt_toolkit.completion import FuzzyWordCompleter, PathCompleter
|
||||
from prompt_toolkit.completion import PathCompleter
|
||||
from prompt_toolkit.validation import Validator
|
||||
from rich import box, print
|
||||
from rich.console import Console, Group, group
|
||||
@ -36,26 +35,16 @@ else:
|
||||
console = Console(style=Style(color="grey74", bgcolor="grey19"))
|
||||
|
||||
|
||||
def welcome(available_releases: tuple | None = None) -> None:
|
||||
def welcome():
|
||||
@group()
|
||||
def text():
|
||||
if (platform_specific := _platform_specific_help()) is not None:
|
||||
if (platform_specific := _platform_specific_help()) != "":
|
||||
yield platform_specific
|
||||
yield ""
|
||||
yield Text.from_markup(
|
||||
"Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
|
||||
justify="center",
|
||||
)
|
||||
if available_releases is not None:
|
||||
latest_stable = available_releases[0][0]
|
||||
last_pre = available_releases[1][0]
|
||||
yield ""
|
||||
yield Text.from_markup(
|
||||
f"[red3]🠶[/] Latest stable release (recommended): [b bright_white]{latest_stable}", justify="center"
|
||||
)
|
||||
yield Text.from_markup(
|
||||
f"[red3]🠶[/] Last published pre-release version: [b bright_white]{last_pre}", justify="center"
|
||||
)
|
||||
|
||||
console.rule()
|
||||
print(
|
||||
@ -72,30 +61,19 @@ def welcome(available_releases: tuple | None = None) -> None:
|
||||
console.line()
|
||||
|
||||
|
||||
def choose_version(available_releases: tuple | None = None) -> str:
|
||||
"""
|
||||
Prompt the user to choose an Invoke version to install
|
||||
"""
|
||||
|
||||
# short circuit if we couldn't get a version list
|
||||
# still try to install the latest stable version
|
||||
if available_releases is None:
|
||||
return "stable"
|
||||
|
||||
console.print(":grey_question: [orange3]Please choose an Invoke version to install.")
|
||||
|
||||
choices = available_releases[0] + available_releases[1]
|
||||
|
||||
response = prompt(
|
||||
message=f" <Enter> to install the recommended release ({choices[0]}). <Tab> or type to pick a version: ",
|
||||
complete_while_typing=True,
|
||||
completer=FuzzyWordCompleter(choices),
|
||||
)
|
||||
console.print(f" Version {choices[0] if response == '' else response} will be installed.")
|
||||
|
||||
def confirm_install(dest: Path) -> bool:
|
||||
if dest.exists():
|
||||
print(f":exclamation: Directory {dest} already exists :exclamation:")
|
||||
dest_confirmed = Confirm.ask(
|
||||
":stop_sign: (re)install in this location?",
|
||||
default=False,
|
||||
)
|
||||
else:
|
||||
print(f"InvokeAI will be installed in {dest}")
|
||||
dest_confirmed = Confirm.ask("Use this location?", default=True)
|
||||
console.line()
|
||||
|
||||
return "stable" if response == "" else response
|
||||
return dest_confirmed
|
||||
|
||||
|
||||
def user_wants_auto_configuration() -> bool:
|
||||
@ -131,23 +109,7 @@ def user_wants_auto_configuration() -> bool:
|
||||
return choice.lower().startswith("a")
|
||||
|
||||
|
||||
def confirm_install(dest: Path) -> bool:
|
||||
if dest.exists():
|
||||
print(f":stop_sign: Directory {dest} already exists!")
|
||||
print(" Is this location correct?")
|
||||
default = False
|
||||
else:
|
||||
print(f":file_folder: InvokeAI will be installed in {dest}")
|
||||
default = True
|
||||
|
||||
dest_confirmed = Confirm.ask(" Please confirm:", default=default)
|
||||
|
||||
console.line()
|
||||
|
||||
return dest_confirmed
|
||||
|
||||
|
||||
def dest_path(dest=None) -> Path | None:
|
||||
def dest_path(dest=None) -> Path:
|
||||
"""
|
||||
Prompt the user for the destination path and create the path
|
||||
|
||||
@ -162,21 +124,25 @@ def dest_path(dest=None) -> Path | None:
|
||||
else:
|
||||
dest = Path.cwd().expanduser().resolve()
|
||||
prev_dest = init_path = dest
|
||||
dest_confirmed = False
|
||||
|
||||
dest_confirmed = confirm_install(dest)
|
||||
|
||||
while not dest_confirmed:
|
||||
browse_start = (dest or Path.cwd()).expanduser().resolve()
|
||||
# if the given destination already exists, the starting point for browsing is its parent directory.
|
||||
# the user may have made a typo, or otherwise wants to place the root dir next to an existing one.
|
||||
# if the destination dir does NOT exist, then the user must have changed their mind about the selection.
|
||||
# since we can't read their mind, start browsing at Path.cwd().
|
||||
browse_start = (prev_dest.parent if prev_dest.exists() else Path.cwd()).expanduser().resolve()
|
||||
|
||||
path_completer = PathCompleter(
|
||||
only_directories=True,
|
||||
expanduser=True,
|
||||
get_paths=lambda: [str(browse_start)], # noqa: B023
|
||||
get_paths=lambda: [browse_start], # noqa: B023
|
||||
# get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
|
||||
)
|
||||
|
||||
console.line()
|
||||
|
||||
console.print(f":grey_question: [orange3]Please select the install destination:[/] \\[{browse_start}]: ")
|
||||
console.print(f"[orange3]Please select the destination directory for the installation:[/] \\[{browse_start}]: ")
|
||||
selected = prompt(
|
||||
">>> ",
|
||||
complete_in_thread=True,
|
||||
@ -189,7 +155,6 @@ def dest_path(dest=None) -> Path | None:
|
||||
)
|
||||
prev_dest = dest
|
||||
dest = Path(selected)
|
||||
|
||||
console.line()
|
||||
|
||||
dest_confirmed = confirm_install(dest.expanduser().resolve())
|
||||
@ -217,45 +182,41 @@ def dest_path(dest=None) -> Path | None:
|
||||
console.rule("Goodbye!")
|
||||
|
||||
|
||||
class GpuType(Enum):
|
||||
CUDA = "cuda"
|
||||
CUDA_AND_DML = "cuda_and_dml"
|
||||
ROCM = "rocm"
|
||||
CPU = "cpu"
|
||||
AUTODETECT = "autodetect"
|
||||
|
||||
|
||||
def select_gpu() -> GpuType:
|
||||
def graphical_accelerator():
|
||||
"""
|
||||
Prompt the user to select the GPU driver
|
||||
Prompt the user to select the graphical accelerator in their system
|
||||
This does not validate user's choices (yet), but only offers choices
|
||||
valid for the platform.
|
||||
CUDA is the fallback.
|
||||
We may be able to detect the GPU driver by shelling out to `modprobe` or `lspci`,
|
||||
but this is not yet supported or reliable. Also, some users may have exotic preferences.
|
||||
"""
|
||||
|
||||
if ARCH == "arm64" and OS != "Darwin":
|
||||
print(f"Only CPU acceleration is available on {ARCH} architecture. Proceeding with that.")
|
||||
return GpuType.CPU
|
||||
return "cpu"
|
||||
|
||||
nvidia = (
|
||||
"an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
|
||||
GpuType.CUDA,
|
||||
"cuda",
|
||||
)
|
||||
nvidia_with_dml = (
|
||||
"an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX) -- ALPHA",
|
||||
GpuType.CUDA_AND_DML,
|
||||
"cuda_and_dml",
|
||||
)
|
||||
amd = (
|
||||
"an [gold1 b]AMD[/] GPU (using ROCm™)",
|
||||
GpuType.ROCM,
|
||||
"rocm",
|
||||
)
|
||||
cpu = (
|
||||
"Do not install any GPU support, use CPU for generation (slow)",
|
||||
GpuType.CPU,
|
||||
"no compatible GPU, or specifically prefer to use the CPU",
|
||||
"cpu",
|
||||
)
|
||||
autodetect = (
|
||||
idk = (
|
||||
"I'm not sure what to choose",
|
||||
GpuType.AUTODETECT,
|
||||
"idk",
|
||||
)
|
||||
|
||||
options = []
|
||||
if OS == "Windows":
|
||||
options = [nvidia, nvidia_with_dml, cpu]
|
||||
if OS == "Linux":
|
||||
@ -269,7 +230,7 @@ def select_gpu() -> GpuType:
|
||||
return options[0][1]
|
||||
|
||||
# "I don't know" is always added the last option
|
||||
options.append(autodetect) # type: ignore
|
||||
options.append(idk)
|
||||
|
||||
options = {str(i): opt for i, opt in enumerate(options, 1)}
|
||||
|
||||
@ -304,9 +265,9 @@ def select_gpu() -> GpuType:
|
||||
),
|
||||
)
|
||||
|
||||
if options[choice][1] is GpuType.AUTODETECT:
|
||||
if options[choice][1] == "idk":
|
||||
console.print(
|
||||
"No problem. We will install CUDA support first :crossed_fingers: If Invoke does not detect a GPU, please re-run the installer and select one of the other GPU types."
|
||||
"No problem. We will try to install a version that [i]should[/i] be compatible. :crossed_fingers:"
|
||||
)
|
||||
|
||||
return options[choice][1]
|
||||
@ -330,7 +291,7 @@ def windows_long_paths_registry() -> None:
|
||||
"""
|
||||
|
||||
with open(str(Path(__file__).parent / "WinLongPathsEnabled.reg"), "r", encoding="utf-16le") as code:
|
||||
syntax = Syntax(code.read(), line_numbers=True, lexer="regedit")
|
||||
syntax = Syntax(code.read(), line_numbers=True)
|
||||
|
||||
console.print(
|
||||
Panel(
|
||||
@ -340,7 +301,7 @@ def windows_long_paths_registry() -> None:
|
||||
"We will now apply a registry fix to enable long paths on Windows. InvokeAI needs this to function correctly. We are asking your permission to modify the Windows Registry on your behalf.",
|
||||
"",
|
||||
"This is the change that will be applied:",
|
||||
str(syntax),
|
||||
syntax,
|
||||
]
|
||||
)
|
||||
),
|
||||
@ -379,7 +340,7 @@ def introduction() -> None:
|
||||
console.line(2)
|
||||
|
||||
|
||||
def _platform_specific_help() -> Text | None:
|
||||
def _platform_specific_help() -> str:
|
||||
if OS == "Darwin":
|
||||
text = Text.from_markup(
|
||||
"""[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
|
||||
@ -393,5 +354,5 @@ def _platform_specific_help() -> Text | None:
|
||||
[deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
|
||||
)
|
||||
else:
|
||||
return
|
||||
text = ""
|
||||
return text
|
||||
|
@ -2,12 +2,12 @@
|
||||
|
||||
set -e
|
||||
|
||||
BCYAN="\033[1;36m"
|
||||
BYELLOW="\033[1;33m"
|
||||
BGREEN="\033[1;32m"
|
||||
BRED="\033[1;31m"
|
||||
RED="\033[31m"
|
||||
RESET="\033[0m"
|
||||
BCYAN="\e[1;36m"
|
||||
BYELLOW="\e[1;33m"
|
||||
BGREEN="\e[1;32m"
|
||||
BRED="\e[1;31m"
|
||||
RED="\e[31m"
|
||||
RESET="\e[0m"
|
||||
|
||||
function does_tag_exist {
|
||||
git rev-parse --quiet --verify "refs/tags/$1" >/dev/null
|
||||
@ -23,40 +23,49 @@ function git_show {
|
||||
|
||||
VERSION=$(
|
||||
cd ..
|
||||
python3 -c "from invokeai.version import __version__ as version; print(version)"
|
||||
python -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${RESET}:"
|
||||
git_show
|
||||
echo
|
||||
|
||||
echo -e "${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}? "
|
||||
echo -e -n "Create tags ${BCYAN}${VERSION}${RESET} and ${BCYAN}${LATEST_TAG}${RESET} @ ${BGREEN}HEAD${RESET}, ${RED}deleting existing tags on remote${RESET}? "
|
||||
read -e -p 'y/n [n]: ' input
|
||||
RESPONSE=${input:='n'}
|
||||
if [ "$RESPONSE" == 'y' ]; then
|
||||
echo
|
||||
echo -e "Deleting ${BCYAN}${VERSION}${RESET} tag on origin remote..."
|
||||
git push origin :refs/tags/$VERSION
|
||||
echo -e "Deleting ${BCYAN}${VERSION}${RESET} tag on remote..."
|
||||
git push --delete origin $VERSION
|
||||
|
||||
echo -e "Tagging ${BGREEN}HEAD${RESET} with ${BCYAN}${VERSION}${RESET} on locally..."
|
||||
echo -e "Tagging ${BGREEN}HEAD${RESET} with ${BCYAN}${VERSION}${RESET} locally..."
|
||||
if ! git tag -fa $VERSION; then
|
||||
echo "Existing/invalid tag"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
echo -e "Pushing updated tags to origin remote..."
|
||||
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..."
|
||||
git push origin --tags
|
||||
fi
|
||||
exit 0
|
||||
|
@ -15,7 +15,7 @@ echo 4. Download and install models
|
||||
echo 5. Change InvokeAI startup options
|
||||
echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade
|
||||
echo 7. Open the developer console
|
||||
echo 8. Update InvokeAI (DEPRECATED - please use the installer)
|
||||
echo 8. Update InvokeAI
|
||||
echo 9. Run the InvokeAI image database maintenance script
|
||||
echo 10. Command-line help
|
||||
echo Q - Quit
|
||||
@ -52,10 +52,8 @@ IF /I "%choice%" == "1" (
|
||||
echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
|
||||
call cmd /k
|
||||
) ELSE IF /I "%choice%" == "8" (
|
||||
echo UPDATING FROM WITHIN THE APP IS BEING DEPRECATED.
|
||||
echo Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.
|
||||
timeout 4
|
||||
python -m invokeai.frontend.install.invokeai_update
|
||||
echo Running invokeai-update...
|
||||
python -m invokeai.frontend.install.invokeai_update
|
||||
) ELSE IF /I "%choice%" == "9" (
|
||||
echo Running the db maintenance script...
|
||||
python .venv\Scripts\invokeai-db-maintenance.exe
|
||||
@ -79,3 +77,4 @@ pause
|
||||
|
||||
:ending
|
||||
exit /b
|
||||
|
||||
|
@ -90,9 +90,7 @@ do_choice() {
|
||||
;;
|
||||
8)
|
||||
clear
|
||||
printf "UPDATING FROM WITHIN THE APP IS BEING DEPRECATED\n"
|
||||
printf "Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.\n"
|
||||
sleep 4
|
||||
printf "Update InvokeAI\n"
|
||||
python -m invokeai.frontend.install.invokeai_update
|
||||
;;
|
||||
9)
|
||||
@ -124,7 +122,7 @@ do_dialog() {
|
||||
5 "Change InvokeAI startup options"
|
||||
6 "Re-run the configure script to fix a broken install or to complete a major upgrade"
|
||||
7 "Open the developer console"
|
||||
8 "Update InvokeAI (DEPRECATED - please use the installer)"
|
||||
8 "Update InvokeAI"
|
||||
9 "Run the InvokeAI image database maintenance script"
|
||||
10 "Command-line help"
|
||||
)
|
||||
|
72
installer/templates/update.bat.in
Normal file
@ -0,0 +1,72 @@
|
||||
@echo off
|
||||
setlocal EnableExtensions EnableDelayedExpansion
|
||||
|
||||
PUSHD "%~dp0"
|
||||
|
||||
set INVOKE_AI_VERSION=latest
|
||||
set arg=%1
|
||||
if "%arg%" neq "" (
|
||||
if "%arg:~0,2%" equ "/?" (
|
||||
echo Usage: update.bat ^<release name or branch^>
|
||||
echo Updates InvokeAI to use the indicated version of the code base.
|
||||
echo Find the version or branch for the release you want, and pass it as the argument.
|
||||
echo For example '.\update.bat v2.2.5' for release 2.2.5.
|
||||
echo '.\update.bat main' for the latest development version
|
||||
echo.
|
||||
echo If no argument provided then will install the most recent release, equivalent to
|
||||
echo '.\update.bat latest'
|
||||
exit /b
|
||||
) else (
|
||||
set INVOKE_AI_VERSION=%arg%
|
||||
)
|
||||
)
|
||||
|
||||
set INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/!INVOKE_AI_VERSION!.zip"
|
||||
set INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/!INVOKE_AI_VERSION!/environments-and-requirements/requirements-base.txt
|
||||
set INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
|
||||
|
||||
call curl -I "%INVOKE_AI_DEP%" -fs >.tmp.out
|
||||
if %errorlevel% neq 0 (
|
||||
echo '!INVOKE_AI_VERSION!' is not a known branch name or tag. Please check the version and try again.
|
||||
echo "Press any key to continue"
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
del .tmp.out
|
||||
|
||||
echo This script will update InvokeAI and all its dependencies to !INVOKE_AI_SRC!.
|
||||
echo If you do not want to do this, press control-C now!
|
||||
pause
|
||||
|
||||
call curl -L "%INVOKE_AI_DEP%" > environments-and-requirements/requirements-base.txt
|
||||
call curl -L "%INVOKE_AI_MODELS%" > configs/INITIAL_MODELS.yaml
|
||||
|
||||
|
||||
call .venv\Scripts\activate.bat
|
||||
call .venv\Scripts\python -mpip install -r requirements.txt
|
||||
if %errorlevel% neq 0 (
|
||||
echo Installation of requirements failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
call .venv\Scripts\python -mpip install !INVOKE_AI_SRC!
|
||||
if %errorlevel% neq 0 (
|
||||
echo Installation of InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
@rem call .venv\Scripts\invokeai-configure --root=.
|
||||
|
||||
@rem if %errorlevel% neq 0 (
|
||||
@rem echo Configuration InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
|
||||
@rem pause
|
||||
@rem exit /b
|
||||
@rem )
|
||||
|
||||
echo InvokeAI has been updated to '%INVOKE_AI_VERSION%'
|
||||
|
||||
echo "Press any key to continue"
|
||||
pause
|
||||
endlocal
|
58
installer/templates/update.sh.in
Normal file
@ -0,0 +1,58 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -eu
|
||||
|
||||
if [ $# -ge 1 ] && [ "${1:0:2}" == "-h" ]; then
|
||||
echo "Usage: update.sh <release>"
|
||||
echo "Updates InvokeAI to use the indicated version of the code base."
|
||||
echo "Find the version or branch for the release you want, and pass it as the argument."
|
||||
echo "For example: update.sh v2.2.5 for release 2.2.5."
|
||||
echo " update.sh main for the current development version."
|
||||
echo ""
|
||||
echo "If no argument provided then will install the version tagged with 'latest', equivalent to"
|
||||
echo "update.sh latest"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
INVOKE_AI_VERSION=${1:-latest}
|
||||
|
||||
INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/$INVOKE_AI_VERSION.zip"
|
||||
INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/environments-and-requirements/requirements-base.txt
|
||||
INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
|
||||
|
||||
# ensure we're in the correct folder in case user's CWD is somewhere else
|
||||
scriptdir=$(dirname "$0")
|
||||
cd "$scriptdir"
|
||||
|
||||
function _err_exit {
|
||||
if test "$1" -ne 0
|
||||
then
|
||||
echo "Something went wrong while installing InvokeAI and/or its requirements."
|
||||
echo "Update cannot continue. Please report this error to https://github.com/invoke-ai/InvokeAI/issues"
|
||||
echo -e "Error code $1; Error caught was '$2'"
|
||||
read -p "Press any key to exit..."
|
||||
exit
|
||||
fi
|
||||
}
|
||||
|
||||
if ! curl -I "$INVOKE_AI_DEP" -fs >/dev/null; then
|
||||
echo \'$INVOKE_AI_VERSION\' is not a known branch name or tag. Please check the version and try again.
|
||||
exit
|
||||
fi
|
||||
|
||||
echo This script will update InvokeAI and all its dependencies to version \'$INVOKE_AI_VERSION\'.
|
||||
echo If you do not want to do this, press control-C now!
|
||||
read -p "Press any key to continue, or CTRL-C to exit..."
|
||||
|
||||
curl -L "$INVOKE_AI_DEP" > environments-and-requirements/requirements-base.txt
|
||||
curl -L "$INVOKE_AI_MODELS" > configs/INITIAL_MODELS.yaml
|
||||
|
||||
. .venv/bin/activate
|
||||
|
||||
./.venv/bin/python -mpip install -r requirements.txt
|
||||
_err_exit $? "The pip program failed to install InvokeAI's requirements."
|
||||
|
||||
./.venv/bin/python -mpip install $INVOKE_AI_SRC
|
||||
_err_exit $? "The pip program failed to install InvokeAI."
|
||||
|
||||
echo InvokeAI updated to \'$INVOKE_AI_VERSION\'
|
@ -2,12 +2,7 @@
|
||||
|
||||
from logging import Logger
|
||||
|
||||
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.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.version.invokeai_version import __version__
|
||||
|
||||
@ -15,22 +10,27 @@ 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.model_images.model_images_default import ModelImageFileStorageDisk
|
||||
from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
|
||||
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_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
|
||||
@ -61,19 +61,14 @@ class ApiDependencies:
|
||||
invoker: Invoker
|
||||
|
||||
@staticmethod
|
||||
def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger) -> None:
|
||||
def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
|
||||
logger.info(f"InvokeAI version {__version__}")
|
||||
logger.info(f"Root directory = {str(config.root_path)}")
|
||||
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")
|
||||
|
||||
model_images_folder = config.models_path
|
||||
|
||||
db = init_db(config=config, logger=logger, image_files=image_files)
|
||||
|
||||
configuration = config
|
||||
@ -84,26 +79,21 @@ class ApiDependencies:
|
||||
board_records = SqliteBoardRecordStorage(db=db)
|
||||
boards = BoardService()
|
||||
events = FastAPIEventService(event_handler_id)
|
||||
bulk_download = BulkDownloadService()
|
||||
graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
|
||||
image_records = SqliteImageRecordStorage(db=db)
|
||||
images = ImageService()
|
||||
invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
|
||||
tensors = ObjectSerializerForwardCache(
|
||||
ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
|
||||
)
|
||||
conditioning = ObjectSerializerForwardCache(
|
||||
ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
|
||||
)
|
||||
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
|
||||
model_manager = ModelManagerService(config, logger)
|
||||
model_record_service = ModelRecordServiceSQL(db=db)
|
||||
download_queue_service = DownloadQueueService(event_bus=events)
|
||||
model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")
|
||||
model_manager = ModelManagerService.build_model_manager(
|
||||
app_config=configuration,
|
||||
model_record_service=ModelRecordServiceSQL(db=db),
|
||||
download_queue=download_queue_service,
|
||||
events=events,
|
||||
model_install_service = ModelInstallService(
|
||||
app_config=config, record_store=model_record_service, event_bus=events
|
||||
)
|
||||
names = SimpleNameService()
|
||||
performance_statistics = InvocationStatsService()
|
||||
processor = DefaultInvocationProcessor()
|
||||
queue = MemoryInvocationQueue()
|
||||
session_processor = DefaultSessionProcessor()
|
||||
session_queue = SqliteSessionQueue(db=db)
|
||||
urls = LocalUrlService()
|
||||
@ -114,31 +104,33 @@ 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_images=model_images_service,
|
||||
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)
|
||||
db.clean()
|
||||
|
||||
@staticmethod
|
||||
def shutdown() -> None:
|
||||
def shutdown():
|
||||
if ApiDependencies.invoker:
|
||||
ApiDependencies.invoker.stop()
|
||||
|
@ -1,28 +0,0 @@
|
||||
from typing import Any
|
||||
|
||||
from starlette.responses import Response
|
||||
from starlette.staticfiles import StaticFiles
|
||||
|
||||
|
||||
class NoCacheStaticFiles(StaticFiles):
|
||||
"""
|
||||
This class is used to override the default caching behavior of starlette for static files,
|
||||
ensuring we *never* cache static files. It modifies the file response headers to strictly
|
||||
never cache the files.
|
||||
|
||||
Static files include the javascript bundles, fonts, locales, and some images. Generated
|
||||
images are not included, as they are served by a router.
|
||||
"""
|
||||
|
||||
def __init__(self, *args: Any, **kwargs: Any):
|
||||
self.cachecontrol = "max-age=0, no-cache, no-store, , must-revalidate"
|
||||
self.pragma = "no-cache"
|
||||
self.expires = "0"
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def file_response(self, *args: Any, **kwargs: Any) -> Response:
|
||||
resp = super().file_response(*args, **kwargs)
|
||||
resp.headers.setdefault("Cache-Control", self.cachecontrol)
|
||||
resp.headers.setdefault("Pragma", self.pragma)
|
||||
resp.headers.setdefault("Expires", self.expires)
|
||||
return resp
|
@ -36,7 +36,7 @@ async def list_downloads() -> List[DownloadJob]:
|
||||
400: {"description": "Bad request"},
|
||||
},
|
||||
)
|
||||
async def prune_downloads() -> Response:
|
||||
async def prune_downloads():
|
||||
"""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, Path(dest), priority, access_token)
|
||||
return queue.download(source, 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() -> Response:
|
||||
async def cancel_all_download_jobs():
|
||||
"""Cancel all download jobs."""
|
||||
ApiDependencies.invoker.services.download_queue.cancel_all_jobs()
|
||||
return Response(status_code=204)
|
||||
|
@ -2,13 +2,13 @@ import io
|
||||
import traceback
|
||||
from typing import Optional
|
||||
|
||||
from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request, Response, UploadFile
|
||||
from fastapi import 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.fields import MetadataField, MetadataFieldValidator
|
||||
from invokeai.app.invocations.baseinvocation 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
|
||||
@ -375,67 +375,16 @@ async def unstar_images_in_list(
|
||||
|
||||
class ImagesDownloaded(BaseModel):
|
||||
response: Optional[str] = Field(
|
||||
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"
|
||||
description="If defined, the message to display to the user when images begin downloading"
|
||||
)
|
||||
|
||||
|
||||
@images_router.post(
|
||||
"/download", operation_id="download_images_from_list", response_model=ImagesDownloaded, status_code=202
|
||||
)
|
||||
@images_router.post("/download", operation_id="download_images_from_list", response_model=ImagesDownloaded)
|
||||
async def download_images_from_list(
|
||||
background_tasks: BackgroundTasks,
|
||||
image_names: Optional[list[str]] = Body(
|
||||
default=None, description="The list of names of images to download", embed=True
|
||||
),
|
||||
image_names: list[str] = Body(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", embed=True
|
||||
default=None, description="The board from which image should be downloaded from", embed=True
|
||||
),
|
||||
) -> ImagesDownloaded:
|
||||
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)
|
||||
# return ImagesDownloaded(response="Your images are downloading")
|
||||
raise HTTPException(status_code=501, detail="Endpoint is not yet implemented")
|
||||
|
@ -1,782 +0,0 @@
|
||||
# Copyright (c) 2023 Lincoln D. Stein
|
||||
"""FastAPI route for model configuration records."""
|
||||
|
||||
import io
|
||||
import pathlib
|
||||
import shutil
|
||||
import traceback
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from fastapi import Body, Path, Query, Response, UploadFile
|
||||
from fastapi.responses import FileResponse
|
||||
from fastapi.routing import APIRouter
|
||||
from PIL import Image
|
||||
from pydantic import AnyHttpUrl, 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 (
|
||||
InvalidModelException,
|
||||
UnknownModelException,
|
||||
)
|
||||
from invokeai.app.services.model_records.model_records_base import DuplicateModelException, ModelRecordChanges
|
||||
from invokeai.backend.model_manager.config import (
|
||||
AnyModelConfig,
|
||||
BaseModelType,
|
||||
MainCheckpointConfig,
|
||||
ModelFormat,
|
||||
ModelType,
|
||||
SubModelType,
|
||||
)
|
||||
from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
|
||||
from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
|
||||
from invokeai.backend.model_manager.search import ModelSearch
|
||||
|
||||
from ..dependencies import ApiDependencies
|
||||
|
||||
model_manager_router = APIRouter(prefix="/v2/models", tags=["model_manager"])
|
||||
|
||||
# images are immutable; set a high max-age
|
||||
IMAGE_MAX_AGE = 31536000
|
||||
|
||||
|
||||
class ModelsList(BaseModel):
|
||||
"""Return list of configs."""
|
||||
|
||||
models: List[AnyModelConfig]
|
||||
|
||||
model_config = ConfigDict(use_enum_values=True)
|
||||
|
||||
|
||||
##############################################################################
|
||||
# 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_path": "string",
|
||||
"key": "string",
|
||||
"hash": "string",
|
||||
"description": "string",
|
||||
"source": "string",
|
||||
"converted_at": 0,
|
||||
"variant": "normal",
|
||||
"prediction_type": "epsilon",
|
||||
"repo_variant": "fp16",
|
||||
"upcast_attention": False,
|
||||
}
|
||||
|
||||
example_model_input = {
|
||||
"path": "/path/to/model",
|
||||
"name": "model_name",
|
||||
"base": "sd-1",
|
||||
"type": "main",
|
||||
"format": "checkpoint",
|
||||
"config_path": "configs/stable-diffusion/v1-inference.yaml",
|
||||
"description": "Model description",
|
||||
"vae": None,
|
||||
"variant": "normal",
|
||||
}
|
||||
|
||||
##############################################################################
|
||||
# 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)
|
||||
)
|
||||
for model in found_models:
|
||||
cover_image = ApiDependencies.invoker.services.model_images.get_url(model.key)
|
||||
model.cover_image = cover_image
|
||||
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)
|
||||
cover_image = ApiDependencies.invoker.services.model_images.get_url(key)
|
||||
config.cover_image = cover_image
|
||||
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
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
class HuggingFaceModels(BaseModel):
|
||||
urls: List[AnyHttpUrl] | None = Field(description="URLs for all checkpoint format models in the metadata")
|
||||
is_diffusers: bool = Field(description="Whether the metadata is for a Diffusers format model")
|
||||
|
||||
|
||||
@model_manager_router.get(
|
||||
"/hugging_face",
|
||||
operation_id="get_hugging_face_models",
|
||||
responses={
|
||||
200: {"description": "Hugging Face repo scanned successfully"},
|
||||
400: {"description": "Invalid hugging face repo"},
|
||||
},
|
||||
status_code=200,
|
||||
response_model=HuggingFaceModels,
|
||||
)
|
||||
async def get_hugging_face_models(
|
||||
hugging_face_repo: str = Query(description="Hugging face repo to search for models", default=None),
|
||||
) -> HuggingFaceModels:
|
||||
try:
|
||||
metadata = HuggingFaceMetadataFetch().from_id(hugging_face_repo)
|
||||
except UnknownMetadataException:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="No HuggingFace repository found",
|
||||
)
|
||||
|
||||
assert isinstance(metadata, ModelMetadataWithFiles)
|
||||
|
||||
return HuggingFaceModels(
|
||||
urls=metadata.ckpt_urls,
|
||||
is_diffusers=metadata.is_diffusers,
|
||||
)
|
||||
|
||||
|
||||
@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")],
|
||||
changes: Annotated[ModelRecordChanges, Body(description="Model config", example=example_model_input)],
|
||||
) -> AnyModelConfig:
|
||||
"""Update a model's config."""
|
||||
logger = ApiDependencies.invoker.services.logger
|
||||
record_store = ApiDependencies.invoker.services.model_manager.store
|
||||
try:
|
||||
model_response: AnyModelConfig = record_store.update_model(key, changes=changes)
|
||||
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.get(
|
||||
"/i/{key}/image",
|
||||
operation_id="get_model_image",
|
||||
responses={
|
||||
200: {
|
||||
"description": "The model image was fetched successfully",
|
||||
},
|
||||
400: {"description": "Bad request"},
|
||||
404: {"description": "The model image could not be found"},
|
||||
},
|
||||
status_code=200,
|
||||
)
|
||||
async def get_model_image(
|
||||
key: str = Path(description="The name of model image file to get"),
|
||||
) -> FileResponse:
|
||||
"""Gets an image file that previews the model"""
|
||||
|
||||
try:
|
||||
path = ApiDependencies.invoker.services.model_images.get_path(key)
|
||||
|
||||
response = FileResponse(
|
||||
path,
|
||||
media_type="image/png",
|
||||
filename=key + ".png",
|
||||
content_disposition_type="inline",
|
||||
)
|
||||
response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
|
||||
return response
|
||||
except Exception:
|
||||
raise HTTPException(status_code=404)
|
||||
|
||||
|
||||
@model_manager_router.patch(
|
||||
"/i/{key}/image",
|
||||
operation_id="update_model_image",
|
||||
responses={
|
||||
200: {
|
||||
"description": "The model image was updated successfully",
|
||||
},
|
||||
400: {"description": "Bad request"},
|
||||
},
|
||||
status_code=200,
|
||||
)
|
||||
async def update_model_image(
|
||||
key: Annotated[str, Path(description="Unique key of model")],
|
||||
image: UploadFile,
|
||||
) -> None:
|
||||
if not image.content_type or not image.content_type.startswith("image"):
|
||||
raise HTTPException(status_code=415, detail="Not an image")
|
||||
|
||||
contents = await image.read()
|
||||
try:
|
||||
pil_image = Image.open(io.BytesIO(contents))
|
||||
|
||||
except Exception:
|
||||
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=415, detail="Failed to read image")
|
||||
|
||||
logger = ApiDependencies.invoker.services.logger
|
||||
model_images = ApiDependencies.invoker.services.model_images
|
||||
try:
|
||||
model_images.save(pil_image, key)
|
||||
logger.info(f"Updated image for model: {key}")
|
||||
except ValueError as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=409, detail=str(e))
|
||||
return
|
||||
|
||||
|
||||
@model_manager_router.delete(
|
||||
"/i/{key}",
|
||||
operation_id="delete_model",
|
||||
responses={
|
||||
204: {"description": "Model deleted successfully"},
|
||||
404: {"description": "Model not found"},
|
||||
},
|
||||
status_code=204,
|
||||
)
|
||||
async def delete_model(
|
||||
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.delete(
|
||||
"/i/{key}/image",
|
||||
operation_id="delete_model_image",
|
||||
responses={
|
||||
204: {"description": "Model image deleted successfully"},
|
||||
404: {"description": "Model image not found"},
|
||||
},
|
||||
status_code=204,
|
||||
)
|
||||
async def delete_model_image(
|
||||
key: str = Path(description="Unique key of model image to remove from model_images directory."),
|
||||
) -> None:
|
||||
logger = ApiDependencies.invoker.services.logger
|
||||
model_images = ApiDependencies.invoker.services.model_images
|
||||
try:
|
||||
model_images.delete(key)
|
||||
logger.info(f"Deleted model image: {key}")
|
||||
return
|
||||
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
|
||||
# try:
|
||||
# record_store.add_model(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"),
|
||||
inplace: Optional[bool] = Query(description="Whether or not to install a local model in place", default=False),
|
||||
# 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,
|
||||
inplace=bool(inplace),
|
||||
)
|
||||
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(
|
||||
"/install",
|
||||
operation_id="list_model_installs",
|
||||
)
|
||||
async def list_model_installs() -> 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 and type 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(
|
||||
"/install/{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(
|
||||
"/install/{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.delete(
|
||||
"/install",
|
||||
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.load_model(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"
|
||||
changes = ModelRecordChanges(name=model_config.name)
|
||||
store.update_model(key, changes=changes)
|
||||
|
||||
# install the diffusers
|
||||
try:
|
||||
new_key = installer.install_path(
|
||||
cache_path,
|
||||
config={
|
||||
"name": original_name,
|
||||
"description": model_config.description,
|
||||
"hash": model_config.hash,
|
||||
"source": model_config.source,
|
||||
},
|
||||
)
|
||||
except DuplicateModelException as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=409, detail=str(e))
|
||||
|
||||
# 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
|
322
invokeai/app/api/routers/model_records.py
Normal file
@ -0,0 +1,322 @@
|
||||
# Copyright (c) 2023 Lincoln D. Stein
|
||||
"""FastAPI route for model configuration records."""
|
||||
|
||||
|
||||
from hashlib import sha1
|
||||
from random import randbytes
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
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,
|
||||
UnknownModelException,
|
||||
)
|
||||
from invokeai.backend.model_manager.config import (
|
||||
AnyModelConfig,
|
||||
BaseModelType,
|
||||
ModelType,
|
||||
)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
@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[str] = 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.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_started"
|
||||
"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.
|
||||
|
||||
If the optional 'source' argument is provided, then the list will be filtered
|
||||
for partial string matches against the install source.
|
||||
"""
|
||||
jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_install.list_jobs()
|
||||
return jobs
|
||||
|
||||
|
||||
@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)
|
427
invokeai/app/api/routers/models.py
Normal file
@ -0,0 +1,427 @@
|
||||
# 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
Normal file
@ -0,0 +1,276 @@
|
||||
# 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)
|
@ -23,11 +23,10 @@ class DynamicPromptsResponse(BaseModel):
|
||||
)
|
||||
async def parse_dynamicprompts(
|
||||
prompt: str = Body(description="The prompt to parse with dynamicprompts"),
|
||||
max_prompts: int = Body(ge=1, le=10000, default=1000, description="The max number of prompts to generate"),
|
||||
max_prompts: int = Body(default=1000, description="The max number of prompts to generate"),
|
||||
combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
|
||||
) -> DynamicPromptsResponse:
|
||||
"""Creates a batch process"""
|
||||
max_prompts = min(max_prompts, 10000)
|
||||
generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator]
|
||||
try:
|
||||
error: Optional[str] = None
|
||||
|
@ -12,26 +12,16 @@ 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")
|
||||
self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
|
||||
app.mount("/ws", self.__app)
|
||||
|
||||
self.__sio.on(self.__sub_queue, handler=self._handle_sub_queue)
|
||||
self.__sio.on(self.__unsub_queue, handler=self._handle_unsub_queue)
|
||||
self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)
|
||||
self.__sio.on("unsubscribe_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"],
|
||||
@ -49,18 +39,3 @@ 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"])
|
||||
|
@ -3,8 +3,6 @@
|
||||
# values from the command line or config file.
|
||||
import sys
|
||||
|
||||
from invokeai.app.invocations.model import ModelIdentifierField
|
||||
from invokeai.app.services.session_processor.session_processor_common import ProgressImage
|
||||
from invokeai.version.invokeai_version import __version__
|
||||
|
||||
from .services.config import InvokeAIAppConfig
|
||||
@ -19,7 +17,6 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
|
||||
import asyncio
|
||||
import mimetypes
|
||||
import socket
|
||||
from contextlib import asynccontextmanager
|
||||
from inspect import signature
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
@ -30,7 +27,8 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
|
||||
from fastapi.openapi.utils import get_openapi
|
||||
from fastapi.responses import HTMLResponse
|
||||
from fastapi.responses import FileResponse, HTMLResponse
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from fastapi_events.handlers.local import local_handler
|
||||
from fastapi_events.middleware import EventHandlerASGIMiddleware
|
||||
from pydantic.json_schema import models_json_schema
|
||||
@ -40,7 +38,6 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
|
||||
# noinspection PyUnresolvedReferences
|
||||
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
|
||||
import invokeai.frontend.web as web_dir
|
||||
from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
|
||||
|
||||
from ..backend.util.logging import InvokeAILogger
|
||||
from .api.dependencies import ApiDependencies
|
||||
@ -50,17 +47,20 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
|
||||
boards,
|
||||
download_queue,
|
||||
images,
|
||||
model_manager,
|
||||
model_records,
|
||||
models,
|
||||
session_queue,
|
||||
sessions,
|
||||
utilities,
|
||||
workflows,
|
||||
)
|
||||
from .api.sockets import SocketIO
|
||||
from .invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
InputFieldJSONSchemaExtra,
|
||||
OutputFieldJSONSchemaExtra,
|
||||
UIConfigBase,
|
||||
)
|
||||
from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
|
||||
|
||||
if is_mps_available():
|
||||
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
|
||||
@ -74,25 +74,9 @@ 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,
|
||||
lifespan=lifespan,
|
||||
)
|
||||
app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
|
||||
|
||||
# Add event handler
|
||||
event_handler_id: int = id(app)
|
||||
@ -115,9 +99,24 @@ 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(model_manager.model_manager_router, prefix="/api")
|
||||
app.include_router(models.models_router, prefix="/api")
|
||||
app.include_router(model_records.model_records_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")
|
||||
@ -155,22 +154,18 @@ 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"
|
||||
|
||||
# Some models don't end up in the schemas as standalone definitions
|
||||
additional_schemas = models_json_schema(
|
||||
# Add Node Editor UI helper schemas
|
||||
ui_config_schemas = models_json_schema(
|
||||
[
|
||||
(UIConfigBase, "serialization"),
|
||||
(InputFieldJSONSchemaExtra, "serialization"),
|
||||
(OutputFieldJSONSchemaExtra, "serialization"),
|
||||
(ModelIdentifierField, "serialization"),
|
||||
(ProgressImage, "serialization"),
|
||||
],
|
||||
ref_template="#/components/schemas/{model}",
|
||||
)
|
||||
for schema_key, schema_json in additional_schemas[1]["$defs"].items():
|
||||
openapi_schema["components"]["schemas"][schema_key] = schema_json
|
||||
for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
|
||||
openapi_schema["components"]["schemas"][schema_key] = ui_config_schema
|
||||
|
||||
# Add a reference to the output type to additionalProperties of the invoker schema
|
||||
for invoker in all_invocations:
|
||||
@ -181,24 +176,23 @@ 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"
|
||||
|
||||
# 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():
|
||||
from invokeai.backend.model_management.models import get_model_config_enums
|
||||
|
||||
# if model_config_name in openapi_schema["components"]["schemas"]:
|
||||
# # print(f"Config with name {name} already defined")
|
||||
# continue
|
||||
for model_config_format_enum in set(get_model_config_enums()):
|
||||
name = model_config_format_enum.__qualname__
|
||||
|
||||
# openapi_schema["components"]["schemas"][model_config_name] = {
|
||||
# "title": model_config_name,
|
||||
# "description": "An enumeration.",
|
||||
# "type": "string",
|
||||
# "enum": [v.value for v in enum_set],
|
||||
# }
|
||||
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],
|
||||
}
|
||||
|
||||
app.openapi_schema = openapi_schema
|
||||
return app.openapi_schema
|
||||
@ -211,8 +205,8 @@ app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid a
|
||||
def overridden_swagger() -> HTMLResponse:
|
||||
return get_swagger_ui_html(
|
||||
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
|
||||
title=f"{app.title} - Swagger UI",
|
||||
swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
|
||||
title=app.title,
|
||||
swagger_favicon_url="/static/docs/favicon.ico",
|
||||
)
|
||||
|
||||
|
||||
@ -220,20 +214,26 @@ def overridden_swagger() -> HTMLResponse:
|
||||
def overridden_redoc() -> HTMLResponse:
|
||||
return get_redoc_html(
|
||||
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
|
||||
title=f"{app.title} - Redoc",
|
||||
redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
|
||||
title=app.title,
|
||||
redoc_favicon_url="/static/docs/favicon.ico",
|
||||
)
|
||||
|
||||
|
||||
web_root_path = Path(list(web_dir.__path__)[0])
|
||||
|
||||
try:
|
||||
app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
|
||||
except RuntimeError:
|
||||
logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
|
||||
app.mount(
|
||||
"/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
|
||||
) # docs favicon is in here
|
||||
# Only serve the UI if we it has a build
|
||||
if (web_root_path / "dist").exists():
|
||||
# Cannot add headers to StaticFiles, so we must serve index.html with a custom route
|
||||
# Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
|
||||
@app.get("/", include_in_schema=False, name="ui_root")
|
||||
def get_index() -> FileResponse:
|
||||
return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
|
||||
|
||||
# # Must mount *after* the other routes else it borks em
|
||||
app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
|
||||
app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
|
||||
|
||||
app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static") # docs favicon is in here
|
||||
|
||||
|
||||
def invoke_api() -> None:
|
||||
|
@ -8,33 +8,17 @@ import warnings
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from inspect import signature
|
||||
from typing import (
|
||||
TYPE_CHECKING,
|
||||
Annotated,
|
||||
Any,
|
||||
Callable,
|
||||
ClassVar,
|
||||
Iterable,
|
||||
Literal,
|
||||
Optional,
|
||||
Type,
|
||||
TypeVar,
|
||||
Union,
|
||||
cast,
|
||||
)
|
||||
from types import UnionType
|
||||
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
|
||||
|
||||
import semver
|
||||
from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, create_model
|
||||
from pydantic.fields import FieldInfo
|
||||
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
|
||||
from pydantic.fields import FieldInfo, _Unset
|
||||
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.shared.invocation_context import InvocationContext
|
||||
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.app.util.metaenum import MetaEnum
|
||||
from invokeai.app.util.misc import uuid_string
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
@ -68,6 +52,393 @@ 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.
|
||||
@ -89,6 +460,33 @@ 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.
|
||||
@ -97,7 +495,6 @@ class BaseInvocationOutput(BaseModel):
|
||||
"""
|
||||
|
||||
_output_classes: ClassVar[set[BaseInvocationOutput]] = set()
|
||||
_typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
|
||||
|
||||
@classmethod
|
||||
def register_output(cls, output: BaseInvocationOutput) -> None:
|
||||
@ -110,14 +507,10 @@ class BaseInvocationOutput(BaseModel):
|
||||
return cls._output_classes
|
||||
|
||||
@classmethod
|
||||
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
|
||||
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]
|
||||
|
||||
@classmethod
|
||||
def get_output_types(cls) -> Iterable[str]:
|
||||
@ -166,7 +559,6 @@ class BaseInvocation(ABC, BaseModel):
|
||||
"""
|
||||
|
||||
_invocation_classes: ClassVar[set[BaseInvocation]] = set()
|
||||
_typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
|
||||
|
||||
@classmethod
|
||||
def get_type(cls) -> str:
|
||||
@ -179,14 +571,10 @@ class BaseInvocation(ABC, BaseModel):
|
||||
cls._invocation_classes.add(invocation)
|
||||
|
||||
@classmethod
|
||||
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
|
||||
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]
|
||||
|
||||
@classmethod
|
||||
def get_invocations(cls) -> Iterable[BaseInvocation]:
|
||||
@ -244,7 +632,7 @@ class BaseInvocation(ABC, BaseModel):
|
||||
"""Invoke with provided context and return outputs."""
|
||||
pass
|
||||
|
||||
def invoke_internal(self, context: InvocationContext, services: "InvocationServices") -> BaseInvocationOutput:
|
||||
def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
|
||||
"""
|
||||
Internal invoke method, calls `invoke()` after some prep.
|
||||
Handles optional fields that are required to call `invoke()` and invocation cache.
|
||||
@ -269,23 +657,23 @@ class BaseInvocation(ABC, BaseModel):
|
||||
raise MissingInputException(self.model_fields["type"].default, field_name)
|
||||
|
||||
# skip node cache codepath if it's disabled
|
||||
if services.configuration.node_cache_size == 0:
|
||||
if context.services.configuration.node_cache_size == 0:
|
||||
return self.invoke(context)
|
||||
|
||||
output: BaseInvocationOutput
|
||||
if self.use_cache:
|
||||
key = services.invocation_cache.create_key(self)
|
||||
cached_value = services.invocation_cache.get(key)
|
||||
key = context.services.invocation_cache.create_key(self)
|
||||
cached_value = context.services.invocation_cache.get(key)
|
||||
if cached_value is None:
|
||||
services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
|
||||
context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
|
||||
output = self.invoke(context)
|
||||
services.invocation_cache.save(key, output)
|
||||
context.services.invocation_cache.save(key, output)
|
||||
return output
|
||||
else:
|
||||
services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
|
||||
context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
|
||||
return cached_value
|
||||
else:
|
||||
services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
|
||||
context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
|
||||
return self.invoke(context)
|
||||
|
||||
id: str = Field(
|
||||
@ -326,7 +714,9 @@ RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
|
||||
"workflow",
|
||||
}
|
||||
|
||||
RESERVED_INPUT_FIELD_NAMES = {"metadata", "board"}
|
||||
RESERVED_INPUT_FIELD_NAMES = {
|
||||
"metadata",
|
||||
}
|
||||
|
||||
RESERVED_OUTPUT_FIELD_NAMES = {"type"}
|
||||
|
||||
@ -536,3 +926,37 @@ 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__()
|
||||
|
@ -5,11 +5,9 @@ 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, invocation
|
||||
from .fields import InputField
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
|
||||
|
||||
|
||||
@invocation(
|
||||
|
@ -1,28 +1,40 @@
|
||||
from typing import Iterator, List, Optional, Tuple, Union, cast
|
||||
from dataclasses import dataclass
|
||||
from typing import List, Optional, Union
|
||||
|
||||
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.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.app.invocations.primitives import ConditioningField, ConditioningOutput
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
|
||||
BasicConditioningInfo,
|
||||
ConditioningFieldData,
|
||||
ExtraConditioningInfo,
|
||||
SDXLConditioningInfo,
|
||||
)
|
||||
from invokeai.backend.util.devices import torch_dtype
|
||||
|
||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
|
||||
from .model import CLIPField
|
||||
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 .model import ClipField
|
||||
|
||||
# unconditioned: Optional[torch.Tensor]
|
||||
|
||||
@dataclass
|
||||
class ConditioningFieldData:
|
||||
conditionings: List[BasicConditioningInfo]
|
||||
# unconditioned: Optional[torch.Tensor]
|
||||
|
||||
|
||||
# class ConditioningAlgo(str, Enum):
|
||||
@ -36,7 +48,7 @@ from .model import CLIPField
|
||||
title="Prompt",
|
||||
tags=["prompt", "compel"],
|
||||
category="conditioning",
|
||||
version="1.0.1",
|
||||
version="1.0.0",
|
||||
)
|
||||
class CompelInvocation(BaseInvocation):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
@ -46,7 +58,7 @@ class CompelInvocation(BaseInvocation):
|
||||
description=FieldDescriptions.compel_prompt,
|
||||
ui_component=UIComponent.Textarea,
|
||||
)
|
||||
clip: CLIPField = InputField(
|
||||
clip: ClipField = InputField(
|
||||
title="CLIP",
|
||||
description=FieldDescriptions.clip,
|
||||
input=Input.Connection,
|
||||
@ -54,27 +66,49 @@ class CompelInvocation(BaseInvocation):
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> ConditioningOutput:
|
||||
tokenizer_info = context.models.load(self.clip.tokenizer)
|
||||
tokenizer_model = tokenizer_info.model
|
||||
assert isinstance(tokenizer_model, CLIPTokenizer)
|
||||
text_encoder_info = context.models.load(self.clip.text_encoder)
|
||||
text_encoder_model = text_encoder_info.model
|
||||
assert isinstance(text_encoder_model, CLIPTextModel)
|
||||
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,
|
||||
)
|
||||
|
||||
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
|
||||
def _lora_loader():
|
||||
for lora in self.clip.loras:
|
||||
lora_info = context.models.load(lora.lora)
|
||||
assert isinstance(lora_info.model, LoRAModelRaw)
|
||||
yield (lora_info.model, lora.weight)
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.model_dump(exclude={"weight"}), context=context
|
||||
)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
# loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
|
||||
ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
|
||||
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')
|
||||
|
||||
with (
|
||||
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
),
|
||||
@ -82,9 +116,8 @@ 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_model, self.clip.skipped_layers),
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
|
||||
):
|
||||
assert isinstance(text_encoder, CLIPTextModel)
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=text_encoder,
|
||||
@ -95,7 +128,7 @@ class CompelInvocation(BaseInvocation):
|
||||
|
||||
conjunction = Compel.parse_prompt_string(self.prompt)
|
||||
|
||||
if context.config.get().log_tokenization:
|
||||
if context.services.configuration.log_tokenization:
|
||||
log_tokenization_for_conjunction(conjunction, tokenizer)
|
||||
|
||||
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
|
||||
@ -116,41 +149,45 @@ class CompelInvocation(BaseInvocation):
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = context.conditioning.save(conditioning_data)
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return ConditioningOutput.build(conditioning_name)
|
||||
return ConditioningOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class SDXLPromptInvocationBase:
|
||||
"""Prompt processor for SDXL models."""
|
||||
|
||||
def run_clip_compel(
|
||||
self,
|
||||
context: InvocationContext,
|
||||
clip_field: CLIPField,
|
||||
clip_field: ClipField,
|
||||
prompt: str,
|
||||
get_pooled: bool,
|
||||
lora_prefix: str,
|
||||
zero_on_empty: bool,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
|
||||
tokenizer_info = context.models.load(clip_field.tokenizer)
|
||||
tokenizer_model = tokenizer_info.model
|
||||
assert isinstance(tokenizer_model, CLIPTokenizer)
|
||||
text_encoder_info = context.models.load(clip_field.text_encoder)
|
||||
text_encoder_model = text_encoder_info.model
|
||||
assert isinstance(text_encoder_model, (CLIPTextModel, CLIPTextModelWithProjection))
|
||||
):
|
||||
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,
|
||||
)
|
||||
|
||||
# return zero on empty
|
||||
if prompt == "" and zero_on_empty:
|
||||
cpu_text_encoder = text_encoder_info.model
|
||||
assert isinstance(cpu_text_encoder, torch.nn.Module)
|
||||
cpu_text_encoder = text_encoder_info.context.model
|
||||
c = torch.zeros(
|
||||
(
|
||||
1,
|
||||
cpu_text_encoder.config.max_position_embeddings,
|
||||
cpu_text_encoder.config.hidden_size,
|
||||
),
|
||||
dtype=cpu_text_encoder.dtype,
|
||||
dtype=text_encoder_info.context.cache.precision,
|
||||
)
|
||||
if get_pooled:
|
||||
c_pooled = torch.zeros(
|
||||
@ -161,21 +198,40 @@ class SDXLPromptInvocationBase:
|
||||
c_pooled = None
|
||||
return c, c_pooled, None
|
||||
|
||||
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
|
||||
def _lora_loader():
|
||||
for lora in clip_field.loras:
|
||||
lora_info = context.models.load(lora.lora)
|
||||
lora_model = lora_info.model
|
||||
assert isinstance(lora_model, LoRAModelRaw)
|
||||
yield (lora_model, lora.weight)
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.model_dump(exclude={"weight"}), context=context
|
||||
)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
# loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
|
||||
ti_list = generate_ti_list(prompt, text_encoder_info.config.base, context)
|
||||
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')
|
||||
|
||||
with (
|
||||
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
),
|
||||
@ -183,10 +239,8 @@ 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_model, clip_field.skipped_layers),
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.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,
|
||||
@ -199,7 +253,7 @@ class SDXLPromptInvocationBase:
|
||||
|
||||
conjunction = Compel.parse_prompt_string(prompt)
|
||||
|
||||
if context.config.get().log_tokenization:
|
||||
if context.services.configuration.log_tokenization:
|
||||
# TODO: better logging for and syntax
|
||||
log_tokenization_for_conjunction(conjunction, tokenizer)
|
||||
|
||||
@ -232,7 +286,7 @@ class SDXLPromptInvocationBase:
|
||||
title="SDXL Prompt",
|
||||
tags=["sdxl", "compel", "prompt"],
|
||||
category="conditioning",
|
||||
version="1.0.1",
|
||||
version="1.0.0",
|
||||
)
|
||||
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
@ -253,8 +307,8 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
crop_left: int = InputField(default=0, description="")
|
||||
target_width: int = InputField(default=1024, description="")
|
||||
target_height: int = InputField(default=1024, description="")
|
||||
clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
|
||||
clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
|
||||
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
|
||||
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> ConditioningOutput:
|
||||
@ -303,7 +357,6 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
dim=1,
|
||||
)
|
||||
|
||||
assert c2_pooled is not None
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
@ -315,9 +368,14 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = context.conditioning.save(conditioning_data)
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return ConditioningOutput.build(conditioning_name)
|
||||
return ConditioningOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
@ -325,7 +383,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
title="SDXL Refiner Prompt",
|
||||
tags=["sdxl", "compel", "prompt"],
|
||||
category="conditioning",
|
||||
version="1.0.1",
|
||||
version="1.0.0",
|
||||
)
|
||||
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
@ -340,7 +398,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
|
||||
crop_top: int = InputField(default=0, description="")
|
||||
crop_left: int = InputField(default=0, description="")
|
||||
aesthetic_score: float = InputField(default=6.0, description=FieldDescriptions.sdxl_aesthetic)
|
||||
clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
|
||||
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> ConditioningOutput:
|
||||
@ -352,7 +410,6 @@ 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(
|
||||
@ -364,16 +421,21 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = context.conditioning.save(conditioning_data)
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return ConditioningOutput.build(conditioning_name)
|
||||
return ConditioningOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@invocation_output("clip_skip_output")
|
||||
class CLIPSkipInvocationOutput(BaseInvocationOutput):
|
||||
"""CLIP skip node output"""
|
||||
class ClipSkipInvocationOutput(BaseInvocationOutput):
|
||||
"""Clip skip node output"""
|
||||
|
||||
clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
|
||||
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
|
||||
|
||||
|
||||
@invocation(
|
||||
@ -383,23 +445,23 @@ class CLIPSkipInvocationOutput(BaseInvocationOutput):
|
||||
category="conditioning",
|
||||
version="1.0.0",
|
||||
)
|
||||
class CLIPSkipInvocation(BaseInvocation):
|
||||
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, ge=0, description=FieldDescriptions.skipped_layers)
|
||||
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
|
||||
skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> CLIPSkipInvocationOutput:
|
||||
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
|
||||
self.clip.skipped_layers += self.skipped_layers
|
||||
return CLIPSkipInvocationOutput(
|
||||
return ClipSkipInvocationOutput(
|
||||
clip=self.clip,
|
||||
)
|
||||
|
||||
|
||||
def get_max_token_count(
|
||||
tokenizer: CLIPTokenizer,
|
||||
tokenizer,
|
||||
prompt: Union[FlattenedPrompt, Blend, Conjunction],
|
||||
truncate_if_too_long: bool = False,
|
||||
truncate_if_too_long=False,
|
||||
) -> int:
|
||||
if type(prompt) is Blend:
|
||||
blend: Blend = prompt
|
||||
@ -411,9 +473,7 @@ 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: CLIPTokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long: bool = True
|
||||
) -> List[str]:
|
||||
def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=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")
|
||||
|
||||
@ -426,29 +486,24 @@ def get_tokens_for_prompt_object(
|
||||
for x in parsed_prompt.children
|
||||
]
|
||||
text = " ".join(text_fragments)
|
||||
tokens: List[str] = tokenizer.tokenize(text)
|
||||
tokens = 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: CLIPTokenizer, display_label_prefix: Optional[str] = None
|
||||
) -> None:
|
||||
def log_tokenization_for_conjunction(c: Conjunction, tokenizer, display_label_prefix=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: CLIPTokenizer, display_label_prefix: Optional[str] = None
|
||||
) -> None:
|
||||
def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
|
||||
display_label_prefix = display_label_prefix or ""
|
||||
if type(p) is Blend:
|
||||
blend: Blend = p
|
||||
@ -488,12 +543,7 @@ def log_tokenization_for_prompt_object(
|
||||
log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
|
||||
|
||||
|
||||
def log_tokenization_for_text(
|
||||
text: str,
|
||||
tokenizer: CLIPTokenizer,
|
||||
display_label: Optional[str] = None,
|
||||
truncate_if_too_long: Optional[bool] = False,
|
||||
) -> None:
|
||||
def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
|
||||
"""shows how the prompt is tokenized
|
||||
# usually tokens have '</w>' to indicate end-of-word,
|
||||
# but for readability it has been replaced with ' '
|
||||
|
@ -1,17 +0,0 @@
|
||||
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"""
|
@ -17,32 +17,31 @@ from controlnet_aux import (
|
||||
MidasDetector,
|
||||
MLSDdetector,
|
||||
NormalBaeDetector,
|
||||
OpenposeDetector,
|
||||
PidiNetDetector,
|
||||
SamDetector,
|
||||
ZoeDetector,
|
||||
)
|
||||
from controlnet_aux.util import HWC3, ade_palette
|
||||
from PIL import Image
|
||||
from pydantic import BaseModel, Field, field_validator, model_validator
|
||||
from pydantic import BaseModel, ConfigDict, Field, field_validator
|
||||
|
||||
from invokeai.app.invocations.fields import (
|
||||
FieldDescriptions,
|
||||
ImageField,
|
||||
from invokeai.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
from ...backend.model_management import BaseModelType
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
OutputField,
|
||||
UIType,
|
||||
WithBoard,
|
||||
WithMetadata,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
from invokeai.app.invocations.model import ModelIdentifierField
|
||||
from invokeai.app.invocations.primitives import ImageOutput
|
||||
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
|
||||
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 .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
|
||||
|
||||
CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
|
||||
CONTROLNET_RESIZE_VALUES = Literal[
|
||||
@ -53,9 +52,18 @@ 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=())
|
||||
|
||||
|
||||
class ControlField(BaseModel):
|
||||
image: ImageField = Field(description="The control image")
|
||||
control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
|
||||
control_model: ControlNetModelField = Field(description="The ControlNet model to use")
|
||||
control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
|
||||
begin_step_percent: float = Field(
|
||||
default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
|
||||
@ -67,16 +75,17 @@ class ControlField(BaseModel):
|
||||
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
|
||||
|
||||
@field_validator("control_weight")
|
||||
@classmethod
|
||||
def validate_control_weight(cls, v):
|
||||
validate_weights(v)
|
||||
"""Validate that all control weights in the valid range"""
|
||||
if isinstance(v, list):
|
||||
for i in v:
|
||||
if i < -1 or i > 2:
|
||||
raise ValueError("Control weights must be within -1 to 2 range")
|
||||
else:
|
||||
if v < -1 or v > 2:
|
||||
raise ValueError("Control weights must be within -1 to 2 range")
|
||||
return v
|
||||
|
||||
@model_validator(mode="after")
|
||||
def validate_begin_end_step_percent(self):
|
||||
validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
|
||||
return self
|
||||
|
||||
|
||||
@invocation_output("control_output")
|
||||
class ControlOutput(BaseInvocationOutput):
|
||||
@ -86,19 +95,17 @@ class ControlOutput(BaseInvocationOutput):
|
||||
control: ControlField = OutputField(description=FieldDescriptions.control)
|
||||
|
||||
|
||||
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
|
||||
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
|
||||
class ControlNetInvocation(BaseInvocation):
|
||||
"""Collects ControlNet info to pass to other nodes"""
|
||||
|
||||
image: ImageField = InputField(description="The control image")
|
||||
control_model: ModelIdentifierField = InputField(
|
||||
description=FieldDescriptions.controlnet_model, input=Input.Direct, ui_type=UIType.ControlNetModel
|
||||
)
|
||||
control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
|
||||
control_weight: Union[float, List[float]] = InputField(
|
||||
default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
|
||||
default=1.0, description="The weight given to the ControlNet"
|
||||
)
|
||||
begin_step_percent: float = InputField(
|
||||
default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
|
||||
default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
|
||||
)
|
||||
end_step_percent: float = InputField(
|
||||
default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
|
||||
@ -106,17 +113,6 @@ class ControlNetInvocation(BaseInvocation):
|
||||
control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
|
||||
resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
|
||||
|
||||
@field_validator("control_weight")
|
||||
@classmethod
|
||||
def validate_control_weight(cls, v):
|
||||
validate_weights(v)
|
||||
return v
|
||||
|
||||
@model_validator(mode="after")
|
||||
def validate_begin_end_step_percent(self) -> "ControlNetInvocation":
|
||||
validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
|
||||
return self
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ControlOutput:
|
||||
return ControlOutput(
|
||||
control=ControlField(
|
||||
@ -132,7 +128,7 @@ class ControlNetInvocation(BaseInvocation):
|
||||
|
||||
|
||||
# This invocation exists for other invocations to subclass it - do not register with @invocation!
|
||||
class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
class ImageProcessorInvocation(BaseInvocation, WithMetadata):
|
||||
"""Base class for invocations that preprocess images for ControlNet"""
|
||||
|
||||
image: ImageField = InputField(description="The image to process")
|
||||
@ -141,18 +137,23 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
# 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 = self.load_image(context)
|
||||
raw_image = context.services.images.get_pil_image(self.image.image_name)
|
||||
# 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.images.save(image=processed_image)
|
||||
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,
|
||||
)
|
||||
|
||||
"""Builds an ImageOutput and its ImageField"""
|
||||
processed_image_field = ImageField(image_name=image_dto.image_name)
|
||||
@ -171,7 +172,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
title="Canny Processor",
|
||||
tags=["controlnet", "canny"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class CannyImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Canny edge detection for ControlNet"""
|
||||
@ -183,10 +184,6 @@ 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)
|
||||
@ -198,7 +195,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="HED (softedge) Processor",
|
||||
tags=["controlnet", "hed", "softedge"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class HedImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies HED edge detection to image"""
|
||||
@ -227,7 +224,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Lineart Processor",
|
||||
tags=["controlnet", "lineart"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class LineartImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies line art processing to image"""
|
||||
@ -249,7 +246,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Lineart Anime Processor",
|
||||
tags=["controlnet", "lineart", "anime"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies line art anime processing to image"""
|
||||
@ -267,12 +264,37 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
|
||||
return processed_image
|
||||
|
||||
|
||||
@invocation(
|
||||
"openpose_image_processor",
|
||||
title="Openpose Processor",
|
||||
tags=["controlnet", "openpose", "pose"],
|
||||
category="controlnet",
|
||||
version="1.2.0",
|
||||
)
|
||||
class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies Openpose processing to image"""
|
||||
|
||||
hand_and_face: bool = InputField(default=False, description="Whether to use hands and face mode")
|
||||
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
|
||||
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
|
||||
|
||||
def run_processor(self, image):
|
||||
openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = openpose_processor(
|
||||
image,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution,
|
||||
hand_and_face=self.hand_and_face,
|
||||
)
|
||||
return processed_image
|
||||
|
||||
|
||||
@invocation(
|
||||
"midas_depth_image_processor",
|
||||
title="Midas Depth Processor",
|
||||
tags=["controlnet", "midas"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies Midas depth processing to image"""
|
||||
@ -299,7 +321,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Normal BAE Processor",
|
||||
tags=["controlnet"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies NormalBae processing to image"""
|
||||
@ -316,7 +338,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
|
||||
|
||||
|
||||
@invocation(
|
||||
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.1"
|
||||
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
|
||||
)
|
||||
class MlsdImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies MLSD processing to image"""
|
||||
@ -339,7 +361,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
|
||||
|
||||
|
||||
@invocation(
|
||||
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.1"
|
||||
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
|
||||
)
|
||||
class PidiImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies PIDI processing to image"""
|
||||
@ -366,7 +388,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Content Shuffle Processor",
|
||||
tags=["controlnet", "contentshuffle"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies content shuffle processing to image"""
|
||||
@ -396,7 +418,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Zoe (Depth) Processor",
|
||||
tags=["controlnet", "zoe", "depth"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies Zoe depth processing to image"""
|
||||
@ -412,7 +434,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Mediapipe Face Processor",
|
||||
tags=["controlnet", "mediapipe", "face"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies mediapipe face processing to image"""
|
||||
@ -421,6 +443,10 @@ 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
|
||||
@ -431,7 +457,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Leres (Depth) Processor",
|
||||
tags=["controlnet", "leres", "depth"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class LeresImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies leres processing to image"""
|
||||
@ -460,7 +486,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Tile Resample Processor",
|
||||
tags=["controlnet", "tile"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class TileResamplerProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Tile resampler processor"""
|
||||
@ -500,7 +526,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
|
||||
title="Segment Anything Processor",
|
||||
tags=["controlnet", "segmentanything"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Applies segment anything processing to image"""
|
||||
@ -542,7 +568,7 @@ class SamDetectorReproducibleColors(SamDetector):
|
||||
title="Color Map Processor",
|
||||
tags=["controlnet"],
|
||||
category="controlnet",
|
||||
version="1.2.1",
|
||||
version="1.2.0",
|
||||
)
|
||||
class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Generates a color map from the provided image"""
|
||||
@ -550,6 +576,7 @@ 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]
|
||||
|
||||
@ -564,56 +591,3 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
|
||||
color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
|
||||
color_map = Image.fromarray(color_map)
|
||||
return color_map
|
||||
|
||||
|
||||
DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
|
||||
|
||||
|
||||
@invocation(
|
||||
"depth_anything_image_processor",
|
||||
title="Depth Anything Processor",
|
||||
tags=["controlnet", "depth", "depth anything"],
|
||||
category="controlnet",
|
||||
version="1.0.1",
|
||||
)
|
||||
class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Generates a depth map based on the Depth Anything algorithm"""
|
||||
|
||||
model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
|
||||
default="small", description="The size of the depth model to use"
|
||||
)
|
||||
resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
|
||||
|
||||
def run_processor(self, image: Image.Image):
|
||||
depth_anything_detector = DepthAnythingDetector()
|
||||
depth_anything_detector.load_model(model_size=self.model_size)
|
||||
|
||||
processed_image = depth_anything_detector(image=image, resolution=self.resolution)
|
||||
return processed_image
|
||||
|
||||
|
||||
@invocation(
|
||||
"dw_openpose_image_processor",
|
||||
title="DW Openpose Image Processor",
|
||||
tags=["controlnet", "dwpose", "openpose"],
|
||||
category="controlnet",
|
||||
version="1.0.0",
|
||||
)
|
||||
class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
|
||||
"""Generates an openpose pose from an image using DWPose"""
|
||||
|
||||
draw_body: bool = InputField(default=True)
|
||||
draw_face: bool = InputField(default=False)
|
||||
draw_hands: bool = InputField(default=False)
|
||||
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
|
||||
|
||||
def run_processor(self, image: Image.Image):
|
||||
dw_openpose = DWOpenposeDetector()
|
||||
processed_image = dw_openpose(
|
||||
image,
|
||||
draw_face=self.draw_face,
|
||||
draw_hands=self.draw_hands,
|
||||
draw_body=self.draw_body,
|
||||
resolution=self.image_resolution,
|
||||
)
|
||||
return processed_image
|
||||
|
@ -5,24 +5,22 @@ import cv2 as cv
|
||||
import numpy
|
||||
from PIL import Image, ImageOps
|
||||
|
||||
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.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
|
||||
from .baseinvocation import BaseInvocation, invocation
|
||||
from .fields import InputField, WithBoard, WithMetadata
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
|
||||
|
||||
|
||||
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.1")
|
||||
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
|
||||
class CvInpaintInvocation(BaseInvocation, WithMetadata):
|
||||
"""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.images.get_pil(self.image.image_name)
|
||||
mask = context.images.get_pil(self.mask.image_name)
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
mask = context.services.images.get_pil_image(self.mask.image_name)
|
||||
|
||||
# Convert to cv image/mask
|
||||
# TODO: consider making these utility functions
|
||||
@ -36,6 +34,18 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
# TODO: consider making a utility function
|
||||
image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
|
||||
|
||||
image_dto = context.images.save(image=image_inpainted)
|
||||
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,
|
||||
)
|
||||
|
||||
return ImageOutput.build(image_dto)
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
@ -13,13 +13,15 @@ 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.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
|
||||
from invokeai.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
|
||||
|
||||
@invocation_output("face_mask_output")
|
||||
@ -304,37 +306,37 @@ def extract_face(
|
||||
|
||||
# Adjust the crop boundaries to stay within the original image's dimensions
|
||||
if x_min < 0:
|
||||
context.logger.warning("FaceTools --> -X-axis padding reached image edge.")
|
||||
context.services.logger.warning("FaceTools --> -X-axis padding reached image edge.")
|
||||
x_max -= x_min
|
||||
x_min = 0
|
||||
elif x_max > mask.width:
|
||||
context.logger.warning("FaceTools --> +X-axis padding reached image edge.")
|
||||
context.services.logger.warning("FaceTools --> +X-axis padding reached image edge.")
|
||||
x_min -= x_max - mask.width
|
||||
x_max = mask.width
|
||||
|
||||
if y_min < 0:
|
||||
context.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
|
||||
context.services.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
|
||||
y_max -= y_min
|
||||
y_min = 0
|
||||
elif y_max > mask.height:
|
||||
context.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
|
||||
context.services.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.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
|
||||
context.services.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.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
|
||||
context.services.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.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
|
||||
context.services.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))
|
||||
@ -366,7 +368,7 @@ def get_faces_list(
|
||||
|
||||
# Generate the face box mask and get the center of the face.
|
||||
if not should_chunk:
|
||||
context.logger.info("FaceTools --> Attempting full image face detection.")
|
||||
context.services.logger.info("FaceTools --> Attempting full image face detection.")
|
||||
result = generate_face_box_mask(
|
||||
context=context,
|
||||
minimum_confidence=minimum_confidence,
|
||||
@ -378,7 +380,7 @@ def get_faces_list(
|
||||
draw_mesh=draw_mesh,
|
||||
)
|
||||
if should_chunk or len(result) == 0:
|
||||
context.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
|
||||
context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
|
||||
width, height = image.size
|
||||
image_chunks = []
|
||||
x_offsets = []
|
||||
@ -397,7 +399,7 @@ def get_faces_list(
|
||||
x_offsets.append(x)
|
||||
y_offsets.append(0)
|
||||
fx += increment
|
||||
context.logger.info(f"FaceTools --> Chunk starting at x = {x}")
|
||||
context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}")
|
||||
elif height > width:
|
||||
# Portrait - slice the image vertically
|
||||
fy = 0.0
|
||||
@ -409,10 +411,10 @@ def get_faces_list(
|
||||
x_offsets.append(0)
|
||||
y_offsets.append(y)
|
||||
fy += increment
|
||||
context.logger.info(f"FaceTools --> Chunk starting at y = {y}")
|
||||
context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
|
||||
|
||||
for idx in range(len(image_chunks)):
|
||||
context.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
|
||||
context.services.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
|
||||
result = result + generate_face_box_mask(
|
||||
context=context,
|
||||
minimum_confidence=minimum_confidence,
|
||||
@ -426,7 +428,7 @@ def get_faces_list(
|
||||
|
||||
if len(result) == 0:
|
||||
# Give up
|
||||
context.logger.warning(
|
||||
context.services.logger.warning(
|
||||
"FaceTools --> No face detected in chunked input image. Passing through original image."
|
||||
)
|
||||
|
||||
@ -435,7 +437,7 @@ def get_faces_list(
|
||||
return all_faces
|
||||
|
||||
|
||||
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.1")
|
||||
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
|
||||
class FaceOffInvocation(BaseInvocation, WithMetadata):
|
||||
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
|
||||
|
||||
@ -468,11 +470,11 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
|
||||
)
|
||||
|
||||
if len(all_faces) == 0:
|
||||
context.logger.warning("FaceOff --> No faces detected. Passing through original image.")
|
||||
context.services.logger.warning("FaceOff --> No faces detected. Passing through original image.")
|
||||
return None
|
||||
|
||||
if self.face_id > len(all_faces) - 1:
|
||||
context.logger.warning(
|
||||
context.services.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
|
||||
@ -484,7 +486,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
|
||||
return face_data
|
||||
|
||||
def invoke(self, context: InvocationContext) -> FaceOffOutput:
|
||||
image = context.images.get_pil(self.image.image_name)
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
result = self.faceoff(context=context, image=image)
|
||||
|
||||
if result is None:
|
||||
@ -498,9 +500,24 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
|
||||
x = result["x_min"]
|
||||
y = result["y_min"]
|
||||
|
||||
image_dto = context.images.save(image=result_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,
|
||||
)
|
||||
|
||||
mask_dto = context.images.save(image=result_mask, image_category=ImageCategory.MASK)
|
||||
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,
|
||||
)
|
||||
|
||||
output = FaceOffOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
@ -514,7 +531,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
|
||||
return output
|
||||
|
||||
|
||||
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.1")
|
||||
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
|
||||
class FaceMaskInvocation(BaseInvocation, WithMetadata):
|
||||
"""Face mask creation using mediapipe face detection"""
|
||||
|
||||
@ -563,7 +580,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
|
||||
|
||||
if len(intersected_face_ids) == 0:
|
||||
id_range_str = ",".join([str(id) for id in id_range])
|
||||
context.logger.warning(
|
||||
context.services.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(
|
||||
@ -599,12 +616,27 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> FaceMaskOutput:
|
||||
image = context.images.get_pil(self.image.image_name)
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
result = self.facemask(context=context, image=image)
|
||||
|
||||
image_dto = context.images.save(image=result["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,
|
||||
)
|
||||
|
||||
mask_dto = context.images.save(image=result["mask"], image_category=ImageCategory.MASK)
|
||||
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,
|
||||
)
|
||||
|
||||
output = FaceMaskOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
@ -617,9 +649,9 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
|
||||
|
||||
|
||||
@invocation(
|
||||
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.1"
|
||||
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
|
||||
)
|
||||
class FaceIdentifierInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
|
||||
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
|
||||
|
||||
image: ImageField = InputField(description="Image to face detect")
|
||||
@ -673,9 +705,21 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
return image
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.images.get_pil(self.image.image_name)
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
result_image = self.faceidentifier(context=context, image=image)
|
||||
|
||||
image_dto = context.images.save(image=result_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,
|
||||
)
|
||||
|
||||
return ImageOutput.build(image_dto)
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
@ -1,567 +0,0 @@
|
||||
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
|
||||
MainModel = "MainModelField"
|
||||
SDXLMainModel = "SDXLMainModelField"
|
||||
SDXLRefinerModel = "SDXLRefinerModelField"
|
||||
ONNXModel = "ONNXModelField"
|
||||
VAEModel = "VAEModelField"
|
||||
LoRAModel = "LoRAModelField"
|
||||
ControlNetModel = "ControlNetModelField"
|
||||
IPAdapterModel = "IPAdapterModelField"
|
||||
T2IAdapterModel = "T2IAdapterModelField"
|
||||
# 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"
|
||||
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 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")
|
||||
# endregion
|
||||
|
||||
|
||||
class MetadataField(RootModel[dict[str, Any]]):
|
||||
"""
|
||||
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),
|
||||
)
|
@ -6,16 +6,14 @@ from typing import Literal, Optional, get_args
|
||||
import numpy as np
|
||||
from PIL import Image, ImageOps
|
||||
|
||||
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.invocations.primitives import ColorField, ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
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, invocation
|
||||
from .fields import InputField, WithBoard, WithMetadata
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
|
||||
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
|
||||
|
||||
|
||||
@ -120,8 +118,8 @@ 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="1.2.1")
|
||||
class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
|
||||
class InfillColorInvocation(BaseInvocation, WithMetadata):
|
||||
"""Infills transparent areas of an image with a solid color"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -131,20 +129,33 @@ class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.images.get_pil(self.image.image_name)
|
||||
image = context.services.images.get_pil_image(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.images.save(image=infilled)
|
||||
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,
|
||||
)
|
||||
|
||||
return ImageOutput.build(image_dto)
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
|
||||
class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
|
||||
class InfillTileInvocation(BaseInvocation, WithMetadata):
|
||||
"""Infills transparent areas of an image with tiles of the image"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -157,20 +168,33 @@ class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.images.get_pil(self.image.image_name)
|
||||
image = context.services.images.get_pil_image(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.images.save(image=infilled)
|
||||
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,
|
||||
)
|
||||
|
||||
return ImageOutput.build(image_dto)
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1"
|
||||
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
|
||||
)
|
||||
class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
|
||||
"""Infills transparent areas of an image using the PatchMatch algorithm"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -178,7 +202,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.images.get_pil(self.image.image_name).convert("RGBA")
|
||||
image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
|
||||
|
||||
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
|
||||
|
||||
@ -203,38 +227,77 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
infilled.paste(image, (0, 0), mask=image.split()[-1])
|
||||
# image.paste(infilled, (0, 0), mask=image.split()[-1])
|
||||
|
||||
image_dto = context.images.save(image=infilled)
|
||||
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,
|
||||
)
|
||||
|
||||
return ImageOutput.build(image_dto)
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
|
||||
class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
|
||||
class LaMaInfillInvocation(BaseInvocation, WithMetadata):
|
||||
"""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.images.get_pil(self.image.image_name)
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
infilled = infill_lama(image.copy())
|
||||
|
||||
image_dto = context.images.save(image=infilled)
|
||||
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,
|
||||
)
|
||||
|
||||
return ImageOutput.build(image_dto)
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
|
||||
class CV2InfillInvocation(BaseInvocation, WithMetadata, WithBoard):
|
||||
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
|
||||
class CV2InfillInvocation(BaseInvocation, WithMetadata):
|
||||
"""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.images.get_pil(self.image.image_name)
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
infilled = infill_cv2(image.copy())
|
||||
|
||||
image_dto = context.images.save(image=infilled)
|
||||
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,
|
||||
)
|
||||
|
||||
return ImageOutput.build(image_dto)
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
@ -1,28 +1,45 @@
|
||||
import os
|
||||
from builtins import float
|
||||
from typing import List, Union
|
||||
|
||||
from pydantic import BaseModel, Field, field_validator, model_validator
|
||||
from typing_extensions import Self
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
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, UIType
|
||||
from invokeai.app.invocations.model import ModelIdentifierField
|
||||
from invokeai.app.invocations.primitives import ImageField
|
||||
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
|
||||
from invokeai.app.services.shared.invocation_context import InvocationContext
|
||||
from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, IPAdapterConfig, ModelType
|
||||
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
|
||||
|
||||
|
||||
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=())
|
||||
|
||||
|
||||
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=())
|
||||
|
||||
|
||||
class IPAdapterField(BaseModel):
|
||||
image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
|
||||
ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model to use.")
|
||||
image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
|
||||
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
|
||||
image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
|
||||
weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
|
||||
# weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
|
||||
begin_step_percent: float = Field(
|
||||
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
|
||||
)
|
||||
@ -30,17 +47,6 @@ class IPAdapterField(BaseModel):
|
||||
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
|
||||
)
|
||||
|
||||
@field_validator("weight")
|
||||
@classmethod
|
||||
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) -> Self:
|
||||
validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
|
||||
return self
|
||||
|
||||
|
||||
@invocation_output("ip_adapter_output")
|
||||
class IPAdapterOutput(BaseInvocationOutput):
|
||||
@ -48,73 +54,53 @@ 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="1.1.2")
|
||||
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
|
||||
class IPAdapterInvocation(BaseInvocation):
|
||||
"""Collects IP-Adapter info to pass to other nodes."""
|
||||
|
||||
# Inputs
|
||||
image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
|
||||
ip_adapter_model: ModelIdentifierField = InputField(
|
||||
description="The IP-Adapter model.",
|
||||
title="IP-Adapter Model",
|
||||
input=Input.Direct,
|
||||
ui_order=-1,
|
||||
ui_type=UIType.IPAdapterModel,
|
||||
ip_adapter_model: IPAdapterModelField = InputField(
|
||||
description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
|
||||
)
|
||||
|
||||
# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
|
||||
weight: Union[float, List[float]] = InputField(
|
||||
default=1, description="The weight given to the IP-Adapter", title="Weight"
|
||||
default=1, ge=-1, description="The weight given to the IP-Adapter", title="Weight"
|
||||
)
|
||||
|
||||
begin_step_percent: float = InputField(
|
||||
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
|
||||
default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
|
||||
)
|
||||
end_step_percent: float = InputField(
|
||||
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
|
||||
)
|
||||
|
||||
@field_validator("weight")
|
||||
@classmethod
|
||||
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) -> 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.models.get_config(self.ip_adapter_model.key)
|
||||
assert isinstance(ip_adapter_info, IPAdapterConfig)
|
||||
image_encoder_model_id = ip_adapter_info.image_encoder_model_id
|
||||
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"])
|
||||
)
|
||||
image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
|
||||
image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
|
||||
image_encoder_model = CLIPVisionModelField(
|
||||
model_name=image_encoder_model_name,
|
||||
base_model=BaseModelType.Any,
|
||||
)
|
||||
return IPAdapterOutput(
|
||||
ip_adapter=IPAdapterField(
|
||||
image=self.image,
|
||||
ip_adapter_model=self.ip_adapter_model,
|
||||
image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
|
||||
image_encoder_model=image_encoder_model,
|
||||
weight=self.weight,
|
||||
begin_step_percent=self.begin_step_percent,
|
||||
end_step_percent=self.end_step_percent,
|
||||
),
|
||||
)
|
||||
|
||||
def _get_image_encoder(self, context: InvocationContext, image_encoder_model_name: str) -> AnyModelConfig:
|
||||
found = False
|
||||
while not found:
|
||||
image_encoder_models = context.models.search_by_attrs(
|
||||
name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
|
||||
)
|
||||
found = len(image_encoder_models) > 0
|
||||
if not found:
|
||||
context.logger.warning(
|
||||
f"The image encoder required by this IP Adapter ({image_encoder_model_name}) is not installed."
|
||||
)
|
||||
context.logger.warning("Downloading and installing now. This may take a while.")
|
||||
installer = context._services.model_manager.install
|
||||
job = installer.heuristic_import(f"InvokeAI/{image_encoder_model_name}")
|
||||
installer.wait_for_job(job, timeout=600) # wait up to 10 minutes - then raise a TimeoutException
|
||||
assert len(image_encoder_models) == 1
|
||||
return image_encoder_models[0]
|
||||
|
@ -5,11 +5,10 @@ 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.services.shared.invocation_context import InvocationContext
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
from .baseinvocation import BaseInvocation, invocation
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
|
||||
|
||||
|
||||
@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
|
||||
|
@ -5,23 +5,20 @@ from pydantic import BaseModel, ConfigDict, Field
|
||||
from invokeai.app.invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
from invokeai.app.invocations.controlnet_image_processors import (
|
||||
CONTROLNET_MODE_VALUES,
|
||||
CONTROLNET_RESIZE_VALUES,
|
||||
)
|
||||
from invokeai.app.invocations.fields import (
|
||||
FieldDescriptions,
|
||||
ImageField,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
MetadataField,
|
||||
OutputField,
|
||||
UIType,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
from invokeai.app.invocations.model import ModelIdentifierField
|
||||
from invokeai.app.services.shared.invocation_context import InvocationContext
|
||||
from invokeai.app.invocations.controlnet_image_processors import ControlField
|
||||
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 ...version import __version__
|
||||
|
||||
@ -34,7 +31,7 @@ class MetadataItemField(BaseModel):
|
||||
class LoRAMetadataField(BaseModel):
|
||||
"""LoRA Metadata Field"""
|
||||
|
||||
model: ModelIdentifierField = Field(description=FieldDescriptions.lora_model)
|
||||
lora: LoRAModelField = Field(description=FieldDescriptions.lora_model)
|
||||
weight: float = Field(description=FieldDescriptions.lora_weight)
|
||||
|
||||
|
||||
@ -42,41 +39,16 @@ class IPAdapterMetadataField(BaseModel):
|
||||
"""IP Adapter Field, minus the CLIP Vision Encoder model"""
|
||||
|
||||
image: ImageField = Field(description="The IP-Adapter image prompt.")
|
||||
ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
|
||||
weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
|
||||
ip_adapter_model: IPAdapterModelField = Field(
|
||||
description="The IP-Adapter model.",
|
||||
)
|
||||
weight: Union[float, list[float]] = Field(
|
||||
description="The weight given to the IP-Adapter",
|
||||
)
|
||||
begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
|
||||
end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
|
||||
|
||||
|
||||
class T2IAdapterMetadataField(BaseModel):
|
||||
image: ImageField = Field(description="The control image.")
|
||||
processed_image: Optional[ImageField] = Field(default=None, description="The control image, after processing.")
|
||||
t2i_adapter_model: ModelIdentifierField = Field(description="The T2I-Adapter model to use.")
|
||||
weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
|
||||
begin_step_percent: float = Field(
|
||||
default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
|
||||
)
|
||||
end_step_percent: float = Field(
|
||||
default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
|
||||
)
|
||||
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
|
||||
|
||||
|
||||
class ControlNetMetadataField(BaseModel):
|
||||
image: ImageField = Field(description="The control image")
|
||||
processed_image: Optional[ImageField] = Field(default=None, description="The control image, after processing.")
|
||||
control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
|
||||
control_weight: Union[float, list[float]] = Field(default=1, description="The weight given to the ControlNet")
|
||||
begin_step_percent: float = Field(
|
||||
default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
|
||||
)
|
||||
end_step_percent: float = Field(
|
||||
default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
|
||||
)
|
||||
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
|
||||
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
|
||||
|
||||
|
||||
@invocation_output("metadata_item_output")
|
||||
class MetadataItemOutput(BaseInvocationOutput):
|
||||
"""Metadata Item Output"""
|
||||
@ -140,7 +112,7 @@ GENERATION_MODES = Literal[
|
||||
]
|
||||
|
||||
|
||||
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.1.1")
|
||||
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.1")
|
||||
class CoreMetadataInvocation(BaseInvocation):
|
||||
"""Collects core generation metadata into a MetadataField"""
|
||||
|
||||
@ -166,14 +138,14 @@ class CoreMetadataInvocation(BaseInvocation):
|
||||
default=None,
|
||||
description="The number of skipped CLIP layers",
|
||||
)
|
||||
model: Optional[ModelIdentifierField] = InputField(default=None, description="The main model used for inference")
|
||||
controlnets: Optional[list[ControlNetMetadataField]] = InputField(
|
||||
model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference")
|
||||
controlnets: Optional[list[ControlField]] = InputField(
|
||||
default=None, description="The ControlNets used for inference"
|
||||
)
|
||||
ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
|
||||
default=None, description="The IP Adapters used for inference"
|
||||
)
|
||||
t2iAdapters: Optional[list[T2IAdapterMetadataField]] = InputField(
|
||||
t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
|
||||
default=None, description="The IP Adapters used for inference"
|
||||
)
|
||||
loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
|
||||
@ -185,7 +157,7 @@ class CoreMetadataInvocation(BaseInvocation):
|
||||
default=None,
|
||||
description="The name of the initial image",
|
||||
)
|
||||
vae: Optional[ModelIdentifierField] = InputField(
|
||||
vae: Optional[VAEModelField] = InputField(
|
||||
default=None,
|
||||
description="The VAE used for decoding, if the main model's default was not used",
|
||||
)
|
||||
@ -216,7 +188,7 @@ class CoreMetadataInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
# SDXL Refiner
|
||||
refiner_model: Optional[ModelIdentifierField] = InputField(
|
||||
refiner_model: Optional[MainModelField] = InputField(
|
||||
default=None,
|
||||
description="The SDXL Refiner model used",
|
||||
)
|
||||
@ -248,9 +220,10 @@ class CoreMetadataInvocation(BaseInvocation):
|
||||
def invoke(self, context: InvocationContext) -> MetadataOutput:
|
||||
"""Collects and outputs a CoreMetadata object"""
|
||||
|
||||
as_dict = self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
|
||||
as_dict["app_version"] = __version__
|
||||
|
||||
return MetadataOutput(metadata=MetadataField.model_validate(as_dict))
|
||||
return MetadataOutput(
|
||||
metadata=MetadataField.model_validate(
|
||||
self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
|
||||
)
|
||||
)
|
||||
|
||||
model_config = ConfigDict(extra="allow")
|
||||
|