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Lincoln Stein 2023-07-15 19:47:50 -04:00
commit ccbfa5d862
324 changed files with 10448 additions and 7616 deletions
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26
.dockerignore
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@ -1,25 +1,9 @@
# use this file as a whitelist
*
!invokeai
!ldm
!pyproject.toml
!docker/docker-entrypoint.sh
!LICENSE
# ignore frontend/web but whitelist dist
invokeai/frontend/web/
!invokeai/frontend/web/dist/
# ignore invokeai/assets but whitelist invokeai/assets/web
invokeai/assets/
!invokeai/assets/web/
# Guard against pulling in any models that might exist in the directory tree
**/*.pt*
**/*.ckpt
# Byte-compiled / optimized / DLL files
**/__pycache__/
**/*.py[cod]
# Distribution / packaging
**/*.egg-info/
**/*.egg
**/node_modules
**/__pycache__
**/*.egg-info

4
.github/CODEOWNERS vendored
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@ -6,7 +6,7 @@
/mkdocs.yml @lstein @blessedcoolant
# nodes
/invokeai/app/ @Kyle0654 @blessedcoolant
/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising
# installation and configuration
/pyproject.toml @lstein @blessedcoolant
@ -22,7 +22,7 @@
/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
# generation, model management, postprocessing
/invokeai/backend @damian0815 @lstein @blessedcoolant @jpphoto @gregghelt2 @StAlKeR7779
/invokeai/backend @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising
# front ends
/invokeai/frontend/CLI @lstein

83
.github/workflows/build-container.yml vendored
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@ -3,17 +3,15 @@ on:
push:
branches:
- 'main'
- 'update/ci/docker/*'
- 'update/docker/*'
- 'dev/ci/docker/*'
- 'dev/docker/*'
paths:
- 'pyproject.toml'
- '.dockerignore'
- 'invokeai/**'
- 'docker/Dockerfile'
- 'docker/docker-entrypoint.sh'
- 'workflows/build-container.yml'
tags:
- 'v*.*.*'
- 'v*'
workflow_dispatch:
permissions:
@ -26,23 +24,27 @@ jobs:
strategy:
fail-fast: false
matrix:
flavor:
- rocm
- cuda
- cpu
include:
- flavor: rocm
pip-extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
- flavor: cuda
pip-extra-index-url: ''
- flavor: cpu
pip-extra-index-url: 'https://download.pytorch.org/whl/cpu'
gpu-driver:
- cuda
- cpu
- rocm
runs-on: ubuntu-latest
name: ${{ matrix.flavor }}
name: ${{ matrix.gpu-driver }}
env:
PLATFORMS: 'linux/amd64,linux/arm64'
DOCKERFILE: 'docker/Dockerfile'
# torch/arm64 does not support GPU currently, so arm64 builds
# would not be GPU-accelerated.
# re-enable arm64 if there is sufficient demand.
# PLATFORMS: 'linux/amd64,linux/arm64'
PLATFORMS: 'linux/amd64'
steps:
- name: Free up more disk space on the runner
# https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
run: |
sudo rm -rf /usr/share/dotnet
sudo rm -rf "$AGENT_TOOLSDIRECTORY"
sudo swapoff /mnt/swapfile
sudo rm -rf /mnt/swapfile
- name: Checkout
uses: actions/checkout@v3
@ -53,7 +55,7 @@ jobs:
github-token: ${{ secrets.GITHUB_TOKEN }}
images: |
ghcr.io/${{ github.repository }}
${{ vars.DOCKERHUB_REPOSITORY }}
${{ env.DOCKERHUB_REPOSITORY }}
tags: |
type=ref,event=branch
type=ref,event=tag
@ -62,8 +64,8 @@ jobs:
type=pep440,pattern={{major}}
type=sha,enable=true,prefix=sha-,format=short
flavor: |
latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}
suffix=-${{ matrix.flavor }},onlatest=false
latest=${{ matrix.gpu-driver == 'cuda' && github.ref == 'refs/heads/main' }}
suffix=-${{ matrix.gpu-driver }},onlatest=false
- name: Set up QEMU
uses: docker/setup-qemu-action@v2
@ -81,34 +83,33 @@ jobs:
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Login to Docker Hub
if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
# - name: Login to Docker Hub
# if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
# uses: docker/login-action@v2
# with:
# username: ${{ secrets.DOCKERHUB_USERNAME }}
# password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build container
id: docker_build
uses: docker/build-push-action@v4
with:
context: .
file: ${{ env.DOCKERFILE }}
file: docker/Dockerfile
platforms: ${{ env.PLATFORMS }}
push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
build-args: PIP_EXTRA_INDEX_URL=${{ matrix.pip-extra-index-url }}
cache-from: |
type=gha,scope=${{ github.ref_name }}-${{ matrix.flavor }}
type=gha,scope=main-${{ matrix.flavor }}
cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.flavor }}
type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
type=gha,scope=main-${{ matrix.gpu-driver }}
cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
- name: Docker Hub Description
if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
uses: peter-evans/dockerhub-description@v3
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
repository: ${{ vars.DOCKERHUB_REPOSITORY }}
short-description: ${{ github.event.repository.description }}
# - name: Docker Hub Description
# if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
# uses: peter-evans/dockerhub-description@v3
# with:
# username: ${{ secrets.DOCKERHUB_USERNAME }}
# password: ${{ secrets.DOCKERHUB_TOKEN }}
# repository: ${{ vars.DOCKERHUB_REPOSITORY }}
# short-description: ${{ github.event.repository.description }}

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docker/.env.sample Normal file
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@ -0,0 +1,13 @@
## Make a copy of this file named `.env` and fill in the values below.
## Any environment variables supported by InvokeAI can be specified here.
# 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.
# This **must** be an absolute path.
INVOKEAI_ROOT=
HUGGINGFACE_TOKEN=
## optional variables specific to the docker setup
# GPU_DRIVER=cuda
# CONTAINER_UID=1000

206
docker/Dockerfile
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@ -1,107 +1,129 @@
# syntax=docker/dockerfile:1
# syntax=docker/dockerfile:1.4
ARG PYTHON_VERSION=3.9
##################
## base image ##
##################
FROM --platform=${TARGETPLATFORM} python:${PYTHON_VERSION}-slim AS python-base
## Builder stage
LABEL org.opencontainers.image.authors="mauwii@outlook.de"
FROM library/ubuntu:22.04 AS builder
# Prepare apt for buildkit cache
RUN rm -f /etc/apt/apt.conf.d/docker-clean \
&& echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' >/etc/apt/apt.conf.d/keep-cache
ARG DEBIAN_FRONTEND=noninteractive
RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt update && apt-get install -y \
git \
python3.10-venv \
python3-pip \
build-essential
# Install dependencies
RUN \
--mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt-get update \
&& apt-get install -y \
--no-install-recommends \
libgl1-mesa-glx=20.3.* \
libglib2.0-0=2.66.* \
libopencv-dev=4.5.*
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
# Set working directory and env
ARG APPDIR=/usr/src
ARG APPNAME=InvokeAI
WORKDIR ${APPDIR}
ENV PATH ${APPDIR}/${APPNAME}/bin:$PATH
# Keeps Python from generating .pyc files in the container
ENV PYTHONDONTWRITEBYTECODE 1
# Turns off buffering for easier container logging
ENV PYTHONUNBUFFERED 1
# Don't fall back to legacy build system
ENV PIP_USE_PEP517=1
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
ARG TORCH_VERSION=2.0.1
ARG TORCHVISION_VERSION=0.15.2
ARG GPU_DRIVER=cuda
ARG TARGETPLATFORM="linux/amd64"
# unused but available
ARG BUILDPLATFORM
#######################
## build pyproject ##
#######################
FROM python-base AS pyproject-builder
WORKDIR ${INVOKEAI_SRC}
# Install build dependencies
RUN \
--mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt-get update \
&& apt-get install -y \
--no-install-recommends \
build-essential=12.9 \
gcc=4:10.2.* \
python3-dev=3.9.*
# Install pytorch before all other pip packages
# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
# x86_64/CUDA is default
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m venv ${VIRTUAL_ENV} &&\
if [ "$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/rocm5.4.2"; \
else \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \
fi &&\
pip install $extra_index_url_arg \
torch==$TORCH_VERSION \
torchvision==$TORCHVISION_VERSION
# Prepare pip for buildkit cache
ARG PIP_CACHE_DIR=/var/cache/buildkit/pip
ENV PIP_CACHE_DIR ${PIP_CACHE_DIR}
RUN mkdir -p ${PIP_CACHE_DIR}
# Install the local package.
# Editable mode helps use the same image for development:
# the local working copy can be bind-mounted into the image
# at path defined by ${INVOKEAI_SRC}
COPY invokeai ./invokeai
COPY pyproject.toml ./
RUN --mount=type=cache,target=/root/.cache/pip \
# xformers + triton fails to install on arm64
if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
pip install -e ".[xformers]"; \
else \
pip install -e "."; \
fi
# Create virtual environment
RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
python3 -m venv "${APPNAME}" \
--upgrade-deps
# #### Build the Web UI ------------------------------------
# Install requirements
COPY --link pyproject.toml .
COPY --link invokeai/version/invokeai_version.py invokeai/version/__init__.py invokeai/version/
ARG PIP_EXTRA_INDEX_URL
ENV PIP_EXTRA_INDEX_URL ${PIP_EXTRA_INDEX_URL}
RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
"${APPNAME}"/bin/pip install .
FROM node:18 AS web-builder
WORKDIR /build
COPY invokeai/frontend/web/ ./
RUN --mount=type=cache,target=/usr/lib/node_modules \
npm install --include dev
RUN --mount=type=cache,target=/usr/lib/node_modules \
yarn vite build
# Install pyproject.toml
COPY --link . .
RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
"${APPNAME}/bin/pip" install .
# Build patchmatch
#### Runtime stage ---------------------------------------
FROM library/ubuntu:22.04 AS runtime
ARG DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1
RUN apt update && apt install -y --no-install-recommends \
git \
curl \
vim \
tmux \
ncdu \
iotop \
bzip2 \
gosu \
libglib2.0-0 \
libgl1-mesa-glx \
python3-venv \
python3-pip \
build-essential \
libopencv-dev \
libstdc++-10-dev &&\
apt-get clean && apt-get autoclean
# globally add magic-wormhole
# for ease of transferring data to and from the container
# when running in sandboxed cloud environments; e.g. Runpod etc.
RUN pip install magic-wormhole
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
ENV INVOKEAI_ROOT=/invokeai
ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
# --link requires buldkit w/ dockerfile syntax 1.4
COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
# Link amdgpu.ids for ROCm builds
# contributed by https://github.com/Rubonnek
RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
WORKDIR ${INVOKEAI_SRC}
# build patchmatch
RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
RUN python3 -c "from patchmatch import patch_match"
#####################
## runtime image ##
#####################
FROM python-base AS runtime
# Create unprivileged user and make the local dir
RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke
RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT}
# Create a new user
ARG UNAME=appuser
RUN useradd \
--no-log-init \
-m \
-U \
"${UNAME}"
# Create volume directory
ARG VOLUME_DIR=/data
RUN mkdir -p "${VOLUME_DIR}" \
&& chown -hR "${UNAME}:${UNAME}" "${VOLUME_DIR}"
# Setup runtime environment
USER ${UNAME}:${UNAME}
COPY --chown=${UNAME}:${UNAME} --from=pyproject-builder ${APPDIR}/${APPNAME} ${APPNAME}
ENV INVOKEAI_ROOT ${VOLUME_DIR}
ENV TRANSFORMERS_CACHE ${VOLUME_DIR}/.cache
ENV INVOKE_MODEL_RECONFIGURE "--yes --default_only"
EXPOSE 9090
ENTRYPOINT [ "invokeai" ]
CMD [ "--web", "--host", "0.0.0.0", "--port", "9090" ]
VOLUME [ "${VOLUME_DIR}" ]
COPY docker/docker-entrypoint.sh ./
ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
CMD ["invokeai-web", "--host", "0.0.0.0"]

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docker/README.md Normal file
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# InvokeAI Containerized
All commands are to be run from the `docker` directory: `cd docker`
#### Linux
1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04).
- The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
3. Ensure docker daemon is able to access the GPU.
- You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
#### macOS
1. Ensure Docker has at least 16GB RAM
2. Enable VirtioFS for file sharing
3. Enable `docker compose` V2 support
This is done via Docker Desktop preferences
## Quickstart
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. `docker compose up`
The image will be built automatically if needed.
The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. The runtime directory will be populated with the base configs and models necessary to start generating.
### Use a GPU
- Linux is *recommended* for GPU support in Docker.
- WSL2 is *required* for Windows.
- only `x86_64` architecture is supported.
The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
## Customize
Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
Example (most values are optional):
```
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000
GPU_DRIVER=cuda
```
## Even Moar Customizing!
See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
### Reconfigure the runtime directory
Can be used to download additional models from the supported model list
In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
```
command:
- invokeai-configure
- --yes
```
Or install models:
```
command:
- invokeai-model-install
```

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docker/build.sh
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@ -1,51 +1,11 @@
#!/usr/bin/env bash
set -e
# If you want to build a specific flavor, set the CONTAINER_FLAVOR environment variable
# e.g. CONTAINER_FLAVOR=cpu ./build.sh
# Possible Values are:
# - cpu
# - cuda
# - rocm
# Don't forget to also set it when executing run.sh
# if it is not set, the script will try to detect the flavor by itself.
#
# Doc can be found here:
# https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
build_args=""
SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
cd "$SCRIPTDIR" || exit 1
[[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
source ./env.sh
echo "docker-compose build args:"
echo $build_args
DOCKERFILE=${INVOKE_DOCKERFILE:-./Dockerfile}
# print the settings
echo -e "You are using these values:\n"
echo -e "Dockerfile:\t\t${DOCKERFILE}"
echo -e "index-url:\t\t${PIP_EXTRA_INDEX_URL:-none}"
echo -e "Volumename:\t\t${VOLUMENAME}"
echo -e "Platform:\t\t${PLATFORM}"
echo -e "Container Registry:\t${CONTAINER_REGISTRY}"
echo -e "Container Repository:\t${CONTAINER_REPOSITORY}"
echo -e "Container Tag:\t\t${CONTAINER_TAG}"
echo -e "Container Flavor:\t${CONTAINER_FLAVOR}"
echo -e "Container Image:\t${CONTAINER_IMAGE}\n"
# Create docker volume
if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
echo -e "Volume already exists\n"
else
echo -n "creating docker volume "
docker volume create "${VOLUMENAME}"
fi
# Build Container
docker build \
--platform="${PLATFORM:-linux/amd64}" \
--tag="${CONTAINER_IMAGE:-invokeai}" \
${CONTAINER_FLAVOR:+--build-arg="CONTAINER_FLAVOR=${CONTAINER_FLAVOR}"} \
${PIP_EXTRA_INDEX_URL:+--build-arg="PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}"} \
${PIP_PACKAGE:+--build-arg="PIP_PACKAGE=${PIP_PACKAGE}"} \
--file="${DOCKERFILE}" \
..
docker-compose build $build_args

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docker/docker-compose.yml Normal file
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@ -0,0 +1,48 @@
# Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
version: '3.8'
services:
invokeai:
image: "local/invokeai:latest"
# edit below to run on a container runtime other than nvidia-container-runtime.
# not yet tested with rocm/AMD GPUs
# Comment out the "deploy" section to run on CPU only
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
build:
context: ..
dockerfile: docker/Dockerfile
# variables without a default will automatically inherit from the host environment
environment:
- INVOKEAI_ROOT
- HF_HOME
# Create a .env file in the same directory as this docker-compose.yml file
# and populate it with environment variables. See .env.sample
env_file:
- .env
ports:
- "${INVOKEAI_PORT:-9090}:9090"
volumes:
- ${INVOKEAI_ROOT:-~/invokeai}:${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}}
tty: true
stdin_open: true
# # Example of running alternative commands/scripts in the container
# command:
# - bash
# - -c
# - |
# invokeai-model-install --yes --default-only --config_file ${INVOKEAI_ROOT}/config_custom.yaml
# invokeai-nodes-web --host 0.0.0.0

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docker/docker-entrypoint.sh Executable file
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#!/bin/bash
set -e -o pipefail
### Container entrypoint
# Runs the CMD as defined by the Dockerfile or passed to `docker run`
# Can be used to configure the runtime dir
# Bypass by using ENTRYPOINT or `--entrypoint`
### Set INVOKEAI_ROOT pointing to a valid runtime directory
# Otherwise configure the runtime dir first.
### Configure the InvokeAI runtime directory (done by default)):
# docker run --rm -it <this image> --configure
# or skip with --no-configure
### Set the CONTAINER_UID envvar to match your user.
# Ensures files created in the container are owned by you:
# docker run --rm -it -v /some/path:/invokeai -e CONTAINER_UID=$(id -u) <this image>
# Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
USER_ID=${CONTAINER_UID:-1000}
USER=invoke
usermod -u ${USER_ID} ${USER} 1>/dev/null
configure() {
# Configure the runtime directory
if [[ -f ${INVOKEAI_ROOT}/invokeai.yaml ]]; then
echo "${INVOKEAI_ROOT}/invokeai.yaml exists. InvokeAI is already configured."
echo "To reconfigure InvokeAI, delete the above file."
echo "======================================================================"
else
mkdir -p ${INVOKEAI_ROOT}
chown --recursive ${USER} ${INVOKEAI_ROOT}
gosu ${USER} invokeai-configure --yes --default_only
fi
}
## Skip attempting to configure.
## Must be passed first, before any other args.
if [[ $1 != "--no-configure" ]]; then
configure
else
shift
fi
### Set the $PUBLIC_KEY env var to enable SSH access.
# We do not install openssh-server in the image by default to avoid bloat.
# but it is useful to have the full SSH server e.g. on Runpod.
# (use SCP to copy files to/from the image, etc)
if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then
apt-get update
apt-get install -y openssh-server
pushd $HOME
mkdir -p .ssh
echo ${PUBLIC_KEY} > .ssh/authorized_keys
chmod -R 700 .ssh
popd
service ssh start
fi
cd ${INVOKEAI_ROOT}
# Run the CMD as the Container User (not root).
exec gosu ${USER} "$@"

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

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@ -1,41 +1,8 @@
#!/usr/bin/env bash
set -e
# How to use: https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
cd "$SCRIPTDIR" || exit 1
source ./env.sh
# Create outputs directory if it does not exist
[[ -d ./outputs ]] || mkdir ./outputs
echo -e "You are using these values:\n"
echo -e "Volumename:\t${VOLUMENAME}"
echo -e "Invokeai_tag:\t${CONTAINER_IMAGE}"
echo -e "local Models:\t${MODELSPATH:-unset}\n"
docker run \
--interactive \
--tty \
--rm \
--platform="${PLATFORM}" \
--name="${REPOSITORY_NAME}" \
--hostname="${REPOSITORY_NAME}" \
--mount type=volume,volume-driver=local,source="${VOLUMENAME}",target=/data \
--mount type=bind,source="$(pwd)"/outputs/,target=/data/outputs/ \
${MODELSPATH:+--mount="type=bind,source=${MODELSPATH},target=/data/models"} \
${HUGGING_FACE_HUB_TOKEN:+--env="HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}"} \
--publish=9090:9090 \
--cap-add=sys_nice \
${GPU_FLAGS:+--gpus="${GPU_FLAGS}"} \
"${CONTAINER_IMAGE}" ${@:+$@}
echo -e "\nCleaning trash folder ..."
for f in outputs/.Trash*; do
if [ -e "$f" ]; then
rm -Rf "$f"
break
fi
done
docker-compose up --build -d
docker-compose logs -f

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# InvokeAI - A Stable Diffusion Toolkit
Stable Diffusion distribution by InvokeAI: https://github.com/invoke-ai
The Docker image tracks the `main` branch of the InvokeAI project, which means it includes the latest features, but may contain some bugs.
Your working directory is mounted under the `/workspace` path inside the pod. The models are in `/workspace/invokeai/models`, and outputs are in `/workspace/invokeai/outputs`.
> **Only the /workspace directory will persist between pod restarts!**
> **If you _terminate_ (not just _stop_) the pod, the /workspace will be lost.**
## Quickstart
1. Launch a pod from this template. **It will take about 5-10 minutes to run through the initial setup**. Be patient.
1. Wait for the application to load.
- TIP: you know it's ready when the CPU usage goes idle
- You can also check the logs for a line that says "_Point your browser at..._"
1. Open the Invoke AI web UI: click the `Connect` => `connect over HTTP` button.
1. Generate some art!
## Other things you can do
At any point you may edit the pod configuration and set an arbitrary Docker command. For example, you could run a command to downloads some models using `curl`, or fetch some images and place them into your outputs to continue a working session.
If you need to run *multiple commands*, define them in the Docker Command field like this:
`bash -c "cd ${INVOKEAI_ROOT}/outputs; wormhole receive 2-foo-bar; invoke.py --web --host 0.0.0.0"`
### Copying your data in and out of the pod
This image includes a couple of handy tools to help you get the data into the pod (such as your custom models or embeddings), and out of the pod (such as downloading your outputs). Here are your options for getting your data in and out of the pod:
- **SSH server**:
1. Make sure to create and set your Public Key in the RunPod settings (follow the official instructions)
1. Add an exposed port 22 (TCP) in the pod settings!
1. When your pod restarts, you will see a new entry in the `Connect` dialog. Use this SSH server to `scp` or `sftp` your files as necessary, or SSH into the pod using the fully fledged SSH server.
- [**Magic Wormhole**](https://magic-wormhole.readthedocs.io/en/latest/welcome.html):
1. On your computer, `pip install magic-wormhole` (see above instructions for details)
1. Connect to the command line **using the "light" SSH client** or the browser-based console. _Currently there's a bug where `wormhole` isn't available when connected to "full" SSH server, as described above_.
1. `wormhole send /workspace/invokeai/outputs` will send the entire `outputs` directory. You can also send individual files.
1. Once packaged, you will see a `wormhole receive <123-some-words>` command. Copy it
1. Paste this command into the terminal on your local machine to securely download the payload.
1. It works the same in reverse: you can `wormhole send` some models from your computer to the pod. Again, save your files somewhere in `/workspace` or they will be lost when the pod is stopped.
- **RunPod's Cloud Sync feature** may be used to sync the persistent volume to cloud storage. You could, for example, copy the entire `/workspace` to S3, add some custom models to it, and copy it back from S3 when launching new pod configurations. Follow the Cloud Sync instructions.
### Disable the NSFW checker
The NSFW checker is enabled by default. To disable it, edit the pod configuration and set the following command:
```
invoke --web --host 0.0.0.0 --no-nsfw_checker
```
---
Template ©2023 Eugene Brodsky [ebr](https://github.com/ebr)

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---
title: Configuration
---
# :material-tune-variant: InvokeAI Configuration
## Intro
InvokeAI has numerous runtime settings which can be used to adjust
many aspects of its operations, including the location of files and
directories, memory usage, and performance. These settings can be
viewed and customized in several ways:
1. By editing settings in the `invokeai.yaml` file.
2. By setting environment variables.
3. On the command-line, when InvokeAI is launched.
In addition, the most commonly changed settings are accessible
graphically via the `invokeai-configure` script.
### How the Configuration System Works
When InvokeAI is launched, the very first thing it needs to do is to
find its "root" directory, which contains its configuration files,
installed models, its database of images, and the folder(s) of
generated images themselves. In this document, the root directory will
be referred to as ROOT.
#### Finding the Root Directory
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.
2. Next it looks for the environment variable INVOKEAI_ROOT, and uses
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.
4. Finally, InvokeAI looks for a directory in the current user's home
directory named `invokeai`.
#### Reading the InvokeAI Configuration File
Once the root directory has been located, InvokeAI looks for a file
named `ROOT/invokeai.yaml`, and if present reads configuration values
from it. The top of this file looks like this:
```
InvokeAI:
Web Server:
host: localhost
port: 9090
allow_origins: []
allow_credentials: true
allow_methods:
- '*'
allow_headers:
- '*'
Features:
esrgan: true
internet_available: true
log_tokenization: false
nsfw_checker: false
patchmatch: true
restore: true
...
```
This lines in this file are used to establish default values for
Invoke's settings. In the above fragment, the Web Server's listening
port is set to 9090 by the `port` setting.
You can edit this file with a text editor such as "Notepad" (do not
use Word or any other word processor). When editing, be careful to
maintain the indentation, and do not add extraneous text, as syntax
errors will prevent InvokeAI from launching. A basic guide to the
format of YAML files can be found
[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
You can fix a broken `invokeai.yaml` by deleting it and running the
configuration script again -- option [7] in the launcher, "Re-run the
configure script".
#### Reading Environment Variables
Next InvokeAI looks for defined environment variables in the format
`INVOKEAI_<setting_name>`, for example `INVOKEAI_port`. Environment
variable values take precedence over configuration file variables. On
a Macintosh system, for example, you could change the port that the
web server listens on by setting the environment variable this way:
```
export INVOKEAI_port=8000
invokeai-web
```
Please check out these
[Macintosh](https://phoenixnap.com/kb/set-environment-variable-mac)
and
[Windows](https://phoenixnap.com/kb/windows-set-environment-variable)
guides for setting temporary and permanent environment variables.
#### Reading the Command Line
Lastly, InvokeAI takes settings from the command line, which override
everything else. The command-line settings have the same name as the
corresponding configuration file settings, preceded by a `--`, for
example `--port 8000`.
If you are using the launcher (`invoke.sh` or `invoke.bat`) to launch
InvokeAI, then just pass the command-line arguments to the launcher:
```
invoke.bat --port 8000 --host 0.0.0.0
```
The arguments will be applied when you select the web server option
(and the other options as well).
If, on the other hand, you prefer to launch InvokeAI directly from the
command line, you would first activate the virtual environment (known
as the "developer's console" in the launcher), and run `invokeai-web`:
```
> C:\Users\Fred\invokeai\.venv\scripts\activate
(.venv) > invokeai-web --port 8000 --host 0.0.0.0
```
You can get a listing and brief instructions for each of the
command-line options by giving the `--help` argument:
```
(.venv) > invokeai-web --help
usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials]
[--allow_methods [ALLOW_METHODS ...]] [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan]
[--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
[--nsfw_checker | --no-nsfw_checker] [--patchmatch | --no-patchmatch] [--restore | --no-restore]
[--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_cache_size MAX_CACHE_SIZE]
[--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--precision {auto,float16,float32,autocast}]
[--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled]
[--tiled_decode | --no-tiled_decode] [--root ROOT] [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR]
[--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH] [--models_dir MODELS_DIR]
[--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
[--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]]
[--log_format {plain,color,syslog,legacy}] [--log_level {debug,info,warning,error,critical}]
...
```
## The Configuration Settings
The configuration settings are divided into several distinct
groups in `invokeia.yaml`:
### Web Server
| 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 |
The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
### Features
These configuration settings allow you to enable and disable various InvokeAI features:
| 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 |
| `nsfw_checker` | `true` | Activate the NSFW checker to blur out risque images |
| `patchmatch` | `true` | Activate the "patchmatch" algorithm for improved inpainting |
| `restore` | `true` | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
### Memory/Performance
These options tune InvokeAI's memory and performance characteristics.
| Setting | Default Value | Description |
|----------|----------------|--------------|
| `always_use_cpu` | `false` | Use the CPU to generate images, even if a GPU is available |
| `free_gpu_mem` | `false` | Aggressively free up GPU memory after each operation; this will allow you to run in low-VRAM environments with some performance penalties |
| `max_cache_size` | `6` | Amount of CPU RAM (in GB) to reserve for caching models in memory; more cache allows you to keep models in memory and switch among them quickly |
| `max_vram_cache_size` | `2.75` | Amount of GPU VRAM (in GB) to reserve for caching models in VRAM; more cache speeds up generation but reduces the size of the images that can be generated. This can be set to zero to maximize the amount of memory available for generation. |
| `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 |
| `sequential_guidance` | `false` | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
| `xformers_enabled` | `true` | If the x-formers memory-efficient attention module is installed, activate it for better memory usage and generation speed|
| `tiled_decode` | `false` | If true, then during the VAE decoding phase the image will be decoded a section at a time, reducing memory consumption at the cost of a performance hit |
### Paths
These options set the paths of various directories and files used by
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`.
| 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. 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:
```
log_handlers:
- console
- syslog=localhost
- file=/var/log/invokeai.log
```
* `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
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:
```
syslog=/dev/log` - log to the /dev/log device
syslog=localhost` - log to the network logger running on the local machine
syslog=localhost:512` - same as above, but using a non-standard port
syslog=fredserver,facility=LOG_USER,socktype=SOCK_DRAM`
- 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
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
POST method.
```
http=http://my.server/path/to/logger,method=POST
```
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.

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# Nodes Editor (Experimental Beta)
The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. The node processing flow is usually done from left (inputs) to right (outputs), though linearity can become abstracted the more complex the node graph becomes. Nodes inputs and outputs are connected by dragging connectors from node to node.
To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
## Anatomy of a Node
Individual nodes are made up of the following:
- Inputs: Edge points on the left side of the node window where you connect outputs from other nodes.
- Outputs: Edge points on the right side of the node window where you connect to inputs on other nodes.
- Options: Various options which are either manually configured, or overridden by connecting an output from another node to the input.
## Diffusion Overview
Taking the time to understand the diffusion process will help you to understand how to set up your nodes in the nodes editor.
There are two main spaces Stable Diffusion works in: image space and latent space.
Image space represents images in pixel form that you look at. Latent space represents compressed inputs. It’s in latent space that Stable Diffusion processes images. A VAE (Variational Auto Encoder) is responsible for compressing and encoding inputs into latent space, as well as decoding outputs back into image space.
When you generate an image using text-to-image, multiple steps occur in latent space:
1. Random noise is generated at the chosen height and width. The noise’s characteristics are dictated by the chosen (or not chosen) seed. This noise tensor is passed into latent space. We’ll call this noise A.
1. Using a model’s U-Net, a noise predictor examines noise A, and the words tokenized by CLIP from your prompt (conditioning). It generates its own noise tensor to predict what the final image might look like in latent space. We’ll call this noise B.
1. Noise B is subtracted from noise A in an attempt to create a final latent image indicative of the inputs. This step is repeated for the number of sampler steps chosen.
1. The VAE decodes the final latent image from latent space into image space.
image-to-image is a similar process, with only step 1 being different:
1. The input image is decoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how much noise is added, 0 being none, and 1 being all-encompassing. We’ll call this noise A. The process is then the same as steps 2-4 in the text-to-image explanation above.
Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from the latent image across the sampler steps chosen (step 3 above). Less noise is usually subtracted at higher sampler steps.
## Node Types (Base Nodes)
| Node <img width=160 align="right"> | Function |
| ---------------------------------- | --------------------------------------------------------------------------------------|
| Add | Adds two numbers |
| CannyImageProcessor | Canny edge detection for ControlNet |
| ClipSkip | Skip layers in clip text_encoder model |
| Collect | Collects values into a collection |
| Prompt (Compel) | Parse prompt using compel package to conditioning |
| ContentShuffleImageProcessor | Applies content shuffle processing to image |
| ControlNet | Collects ControlNet info to pass to other nodes |
| CvInpaint | Simple inpaint using opencv |
| Divide | Divides two numbers |
| DynamicPrompt | Parses a prompt using adieyal/dynamic prompt's random or combinatorial generator |
| FloatLinearRange | Creates a range |
| HedImageProcessor | Applies HED edge detection to image |
| ImageBlur | Blurs an image |
| ImageChannel | Gets a channel from an image |
| ImageCollection | Load a collection of images and provide it as output |
| ImageConvert | Converts an image to a different mode |
| ImageCrop | Crops an image to a specified box. The box can be outside of the image. |
| ImageInverseLerp | Inverse linear interpolation of all pixels of an image |
| ImageLerp | Linear interpolation of all pixels of an image |
| ImageMultiply | Multiplies two images together using `PIL.ImageChops.Multiply()` |
| ImagePaste | Pastes an image into another image |
| ImageProcessor | Base class for invocations that reprocess images for ControlNet |
| ImageResize | Resizes an image to specific dimensions |
| ImageScale | Scales an image by a factor |
| ImageToLatents | Scales latents by a given factor |
| InfillColor | Infills transparent areas of an image with a solid color |
| InfillPatchMatch | Infills transparent areas of an image using the PatchMatch algorithm |
| InfillTile | Infills transparent areas of an image with tiles of the image |
| Inpaint | Generates an image using inpaint |
| Iterate | Iterates over a list of items |
| LatentsToImage | Generates an image from latents |
| LatentsToLatents | Generates latents using latents as base image |
| LeresImageProcessor | Applies leres processing to image |
| LineartAnimeImageProcessor | Applies line art anime processing to image |
| LineartImageProcessor | Applies line art processing to image |
| LoadImage | Load an image and provide it as output |
| Lora Loader | Apply selected lora to unet and text_encoder |
| Model Loader | Loads a main model, outputting its submodels |
| MaskFromAlpha | Extracts the alpha channel of an image as a mask |
| MediapipeFaceProcessor | Applies mediapipe face processing to image |
| MidasDepthImageProcessor | Applies Midas depth processing to image |
| MlsdImageProcessor | Applied MLSD processing to image |
| Multiply | Multiplies two numbers |
| Noise | Generates latent noise |
| NormalbaeImageProcessor | Applies NormalBAE processing to image |
| OpenposeImageProcessor | Applies Openpose processing to image |
| ParamFloat | A float parameter |
| ParamInt | An integer parameter |
| PidiImageProcessor | Applies PIDI processing to an image |
| Progress Image | Displays the progress image in the Node Editor |
| RandomInit | Outputs a single random integer |
| RandomRange | Creates a collection of random numbers |
| Range | Creates a range of numbers from start to stop with step |
| RangeOfSize | Creates a range from start to start + size with step |
| ResizeLatents | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8. |
| RestoreFace | Restores faces in the image |
| ScaleLatents | Scales latents by a given factor |
| SegmentAnythingProcessor | Applies segment anything processing to image |
| ShowImage | Displays a provided image, and passes it forward in the pipeline |
| StepParamEasing | Experimental per-step parameter for easing for denoising steps |
| Subtract | Subtracts two numbers |
| TextToLatents | Generates latents from conditionings |
| TileResampleProcessor | Bass class for invocations that preprocess images for ControlNet |
| Upscale | Upscales an image |
| VAE Loader | Loads a VAE model, outputting a VaeLoaderOutput |
| ZoeDepthImageProcessor | Applies Zoe depth processing to image |
## Node Grouping 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).
### Noise
As described, an initial noise tensor is necessary for the latent diffusion process. As a result, all non-image *ToLatents nodes require a noise node input.
<img width="654" alt="groupsnoise" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/2e8d297e-ad55-4d27-bc93-c119dad2a2c5">
### Conditioning
As described, conditioning is necessary for the latent diffusion process, whether empty or not. As a result, all non-image *ToLatents nodes require positive and negative conditioning inputs. Conditioning is reliant on a CLIP tokenizer provided by the Model Loader node.
<img width="1024" alt="groupsconditioning" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/f8f7ad8a-8d9c-418e-b5ad-1437b774b27e">
### Image Space & VAE
The ImageToLatents node doesn't require a noise node input, but requires a VAE input to convert the image from image space into latent space. In reverse, the LatentsToImage node requires a VAE input to convert from latent space back into image space.
<img width="637" alt="groupsimgvae" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/dd99969c-e0a8-4f78-9b17-3ffe179cef9a">
### Defined & Random Seeds
It is common to want to use both the same seed (for continuity) and random seeds (for variance). 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.
<img width="922" alt="groupsrandseed" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/af55bc20-60f6-438e-aba5-3ec871443710">
### Control
Control means to guide the diffusion process to adhere to a defined input or structure. Control can be provided as input to non-image *ToLatents nodes from ControlNet nodes. ControlNet nodes usually require an image processor which converts an input image for use with ControlNet.
<img width="805" alt="groupscontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/cc9c5de7-23a7-46c8-bbad-1f3609d999a6">
### LoRA
The Lora Loader node lets you load a LoRA (say that ten times fast) and pass it as output to both the Prompt (Compel) and non-image *ToLatents nodes. A model's CLIP tokenizer is passed through the LoRA into Prompt (Compel), where it affects conditioning. A model's U-Net is also passed through the LoRA into a non-image *ToLatents node, where it affects noise prediction.
<img width="993" alt="groupslora" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/630962b0-d914-4505-b3ea-ccae9b0269da">
### Scaling
Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results.
<img width="644" alt="groupsallscale" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/99314f05-dd9f-4b6d-b378-31de55346a13">
### Iteration + Multiple Images as Input
Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and pass them out one at a time.
<img width="788" alt="groupsiterate" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/4af5ca27-82c9-4018-8c5b-024d3ee0a121">
### Multiple Image Generation + Random Seeds
Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection.
To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
<img width="1027" alt="groupsmultigenseeding" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/518d1b2b-fed1-416b-a052-ab06552521b3">
## Examples
With our knowledge of node grouping and the diffusion process, let’s break down some basic graphs in the nodes editor. Note that a node's options can be overridden by inputs from other nodes. These examples aren't strict rules to follow and only demonstrate some basic configurations.
### Basic text-to-image Node Graph
<img width="875" alt="nodest2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/17c67720-c376-4db8-94f0-5e00381a61ee">
- Model Loader: A necessity to generating images (as we’ve read above). We choose our model from the dropdown. It outputs a U-Net, CLIP tokenizer, and VAE.
- Prompt (Compel): Another necessity. Two prompt nodes are created. One will output positive conditioning (what you want, ‘dog’), one will output negative (what you don’t want, ‘cat’). They both input the CLIP tokenizer that the Model Loader node outputs.
- Noise: Consider this noise A from step one of the text-to-image explanation above. Choose a seed number, width, and height.
- TextToLatents: This node takes many inputs for converting and processing text & noise from image space into latent space, hence the name TextTo**Latents**. In this setup, it inputs positive and negative conditioning from the prompt nodes for processing (step 2 above). It inputs noise from the noise node for processing (steps 2 & 3 above). Lastly, it inputs a U-Net from the Model Loader node for processing (step 2 above). It outputs latents for use in the next LatentsToImage node. Choose number of sampler steps, CFG scale, and scheduler.
- LatentsToImage: This node takes in processed latents from the TextToLatents node, and the model’s VAE from the Model Loader node which is responsible for decoding latents back into the image space, hence the name LatentsTo**Image**. This node is the last stop, and once the image is decoded, it is saved to the gallery.
### Basic image-to-image Node Graph
<img width="998" alt="nodesi2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/3f2c95d5-cee7-4415-9b79-b46ee60a92fe">
- Model Loader: Choose a model from the dropdown.
- Prompt (Compel): Two prompt nodes. One positive (dog), one negative (dog). Same CLIP inputs from the Model Loader node as before.
- ImageToLatents: Upload a source image directly in the node window, via drag'n'drop from the gallery, or passed in as input. The ImageToLatents node inputs the VAE from the Model Loader node to decode the chosen image from image space into latent space, hence the name ImageTo**Latents**. It outputs latents for use in the next LatentsToLatents node. It also outputs the source image's width and height for use in the next Noise node if the final image is to be the same dimensions as the source image.
- Noise: A noise tensor is created with the width and height of the source image, and connected to the next LatentsToLatents node. Notice the width and height fields are overridden by the input from the ImageToLatents width and height outputs.
- LatentsToLatents: The inputs and options are nearly identical to TextToLatents, except that LatentsToLatents also takes latents as an input. Considering our source image is already converted to latents in the last ImageToLatents node, and text + noise are no longer the only inputs to process, we use the LatentsToLatents node.
- LatentsToImage: Like previously, the LatentsToImage node will use the VAE from the Model Loader as input to decode the latents from LatentsToLatents into image space, and save it to the gallery.
### Basic ControlNet Node Graph
<img width="703" alt="nodescontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/b02ded86-ceb4-44a2-9910-e19ad184d471">
- Model Loader
- Prompt (Compel)
- Noise: Width and height of the CannyImageProcessor ControlNet image is passed in to set the dimensions of the noise passed to TextToLatents.
- CannyImageProcessor: The CannyImageProcessor node is used to process the source image being used as a ControlNet. Each ControlNet processor node applies control in different ways, and has some different options to configure. Width and height are passed to noise, as mentioned. The processed ControlNet image is output to the ControlNet node.
- ControlNet: Select the type of control model. In this case, canny is chosen as the CannyImageProcessor was used to generate the ControlNet image. Configure the control node options, and pass the control output to TextToLatents.
- TextToLatents: Similar to the basic text-to-image example, except ControlNet is passed to the control input edge point.
- LatentsToImage

3
docs/index.md
View File

@ -153,6 +153,9 @@ This method is recommended for those familiar with running Docker containers
- [Prompt Syntax](features/PROMPTS.md)
- [Generating Variations](features/VARIATIONS.md)
### InvokeAI Configuration
- [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
## :octicons-log-16: Important Changes Since Version 2.3
### Nodes

1
installer/lib/installer.py
View File

@ -248,6 +248,7 @@ class InvokeAiInstance:
"install",
"--require-virtualenv",
"torch~=2.0.0",
"torchmetrics==0.11.4",
"torchvision>=0.14.1",
"--force-reinstall",
"--find-links" if find_links is not None else None,

4
installer/templates/invoke.bat.in
View File

@ -20,7 +20,7 @@ echo 9. Update InvokeAI
echo 10. Command-line help
echo Q - Quit
set /P choice="Please enter 1-10, Q: [2] "
if not defined choice set choice=2
if not defined choice set choice=1
IF /I "%choice%" == "1" (
echo Starting the InvokeAI browser-based UI..
python .venv\Scripts\invokeai-web.exe %*
@ -56,7 +56,7 @@ IF /I "%choice%" == "1" (
call cmd /k
) ELSE IF /I "%choice%" == "9" (
echo Running invokeai-update...
python .venv\Scripts\invokeai-update.exe %*
python -m invokeai.frontend.install.invokeai_update
) ELSE IF /I "%choice%" == "10" (
echo Displaying command line help...
python .venv\Scripts\invokeai.exe --help %*

2
installer/templates/invoke.sh.in
View File

@ -93,7 +93,7 @@ do_choice() {
9)
clear
printf "Update InvokeAI\n"
invokeai-update
python -m invokeai.frontend.install.invokeai_update
;;
10)
clear

7
invokeai/app/api/dependencies.py
View File

@ -13,10 +13,10 @@ from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.metadata import CoreMetadataService
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
from ..services.default_graphs import create_system_graphs
from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
@ -58,7 +58,8 @@ class ApiDependencies:
@staticmethod
def initialize(config, event_handler_id: int, logger: Logger = logger):
logger.info(f"Internet connectivity is {config.internet_available}")
logger.debug(f'InvokeAI version {__version__}')
logger.debug(f"Internet connectivity is {config.internet_available}")
events = FastAPIEventService(event_handler_id)
@ -73,7 +74,6 @@ class ApiDependencies:
)
urls = LocalUrlService()
metadata = CoreMetadataService()
image_record_storage = SqliteImageRecordStorage(db_location)
image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
names = SimpleNameService()
@ -109,7 +109,6 @@ class ApiDependencies:
board_image_record_storage=board_image_record_storage,
image_record_storage=image_record_storage,
image_file_storage=image_file_storage,
metadata=metadata,
url=urls,
logger=logger,
names=names,

30
invokeai/app/api/routers/app_info.py
View File

@ -1,18 +1,36 @@
from fastapi.routing import APIRouter
from pydantic import BaseModel
from pydantic import BaseModel, Field
from invokeai.backend.image_util.patchmatch import PatchMatch
from invokeai.version import __version__
app_router = APIRouter(prefix="/v1/app", tags=['app'])
app_router = APIRouter(prefix="/v1/app", tags=["app"])
class AppVersion(BaseModel):
"""App Version Response"""
version: str
version: str = Field(description="App version")
@app_router.get('/version', operation_id="app_version",
status_code=200,
response_model=AppVersion)
class AppConfig(BaseModel):
"""App Config Response"""
infill_methods: list[str] = Field(description="List of available infill methods")
@app_router.get(
"/version", operation_id="app_version", status_code=200, response_model=AppVersion
)
async def get_version() -> AppVersion:
return AppVersion(version=__version__)
@app_router.get(
"/config", operation_id="get_config", status_code=200, response_model=AppConfig
)
async def get_config() -> AppConfig:
infill_methods = ['tile']
if PatchMatch.patchmatch_available():
infill_methods.append('patchmatch')
return AppConfig(infill_methods=infill_methods)

63
invokeai/app/api/routers/images.py
View File

@ -1,25 +1,27 @@
import io
from typing import Optional
from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
from fastapi.routing import APIRouter
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 invokeai.app.models.image import (
ImageCategory,
ResourceOrigin,
)
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.services.image_record_storage import OffsetPaginatedResults
from invokeai.app.services.models.image_record import (
ImageDTO,
ImageRecordChanges,
ImageUrlsDTO,
)
from invokeai.app.services.item_storage import PaginatedResults
from invokeai.app.services.models.image_record import (ImageDTO,
ImageRecordChanges,
ImageUrlsDTO)
from ..dependencies import ApiDependencies
images_router = APIRouter(prefix="/v1/images", tags=["images"])
# images are immutable; set a high max-age
IMAGE_MAX_AGE = 31536000
@images_router.post(
"/",
@ -103,23 +105,38 @@ async def update_image(
@images_router.get(
"/{image_name}/metadata",
operation_id="get_image_metadata",
"/{image_name}",
operation_id="get_image_dto",
response_model=ImageDTO,
)
async def get_image_metadata(
async def get_image_dto(
image_name: str = Path(description="The name of image to get"),
) -> ImageDTO:
"""Gets an image's metadata"""
"""Gets an image's DTO"""
try:
return ApiDependencies.invoker.services.images.get_dto(image_name)
except Exception as e:
raise HTTPException(status_code=404)
@images_router.get(
"/{image_name}/metadata",
operation_id="get_image_metadata",
response_model=ImageMetadata,
)
async def get_image_metadata(
image_name: str = Path(description="The name of image to get"),
) -> ImageMetadata:
"""Gets an image's metadata"""
try:
return ApiDependencies.invoker.services.images.get_metadata(image_name)
except Exception as e:
raise HTTPException(status_code=404)
@images_router.get(
"/{image_name}",
"/{image_name}/full",
operation_id="get_image_full",
response_class=Response,
responses={
@ -141,12 +158,14 @@ async def get_image_full(
if not ApiDependencies.invoker.services.images.validate_path(path):
raise HTTPException(status_code=404)
return FileResponse(
response = FileResponse(
path,
media_type="image/png",
filename=image_name,
content_disposition_type="inline",
)
response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
return response
except Exception as e:
raise HTTPException(status_code=404)
@ -175,9 +194,11 @@ async def get_image_thumbnail(
if not ApiDependencies.invoker.services.images.validate_path(path):
raise HTTPException(status_code=404)
return FileResponse(
response = FileResponse(
path, media_type="image/webp", content_disposition_type="inline"
)
response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
return response
except Exception as e:
raise HTTPException(status_code=404)
@ -208,10 +229,10 @@ async def get_image_urls(
@images_router.get(
"/",
operation_id="list_images_with_metadata",
operation_id="list_image_dtos",
response_model=OffsetPaginatedResults[ImageDTO],
)
async def list_images_with_metadata(
async def list_image_dtos(
image_origin: Optional[ResourceOrigin] = Query(
default=None, description="The origin of images to list"
),
@ -227,7 +248,7 @@ async def list_images_with_metadata(
offset: int = Query(default=0, description="The page offset"),
limit: int = Query(default=10, description="The number of images per page"),
) -> OffsetPaginatedResults[ImageDTO]:
"""Gets a list of images"""
"""Gets a list of image DTOs"""
image_dtos = ApiDependencies.invoker.services.images.get_many(
offset,

170
invokeai/app/api/routers/models.py
View File

@ -1,6 +1,7 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2024 Lincoln Stein
# 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 Literal, List, Optional, Union
from fastapi import Body, Path, Query, Response
@ -22,6 +23,7 @@ UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
class ModelsList(BaseModel):
models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
@ -83,7 +85,7 @@ async def update_model(
return model_response
@models_router.post(
"/",
"/import",
operation_id="import_model",
responses= {
201: {"description" : "The model imported successfully"},
@ -99,7 +101,7 @@ async def import_model(
prediction_type: Optional[Literal['v_prediction','epsilon','sample']] = \
Body(description='Prediction type for SDv2 checkpoint files', default="v_prediction"),
) -> ImportModelResponse:
""" Add a model using its local path, repo_id, or remote URL """
""" Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically """
items_to_import = {location}
prediction_types = { x.value: x for x in SchedulerPredictionType }
@ -131,18 +133,100 @@ async def import_model(
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.dict()
)
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 parse_obj_as(ImportModelResponse, model_raw)
except KeyError 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.post(
"/rename/{base_model}/{model_type}/{model_name}",
operation_id="rename_model",
responses= {
201: {"description" : "The model was renamed successfully"},
404: {"description" : "The model could not be found"},
409: {"description" : "There is already a model corresponding to the new name"},
},
status_code=201,
response_model=ImportModelResponse
)
async def rename_model(
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="current model name"),
new_name: Optional[str] = Query(description="new model name", default=None),
new_base: Optional[BaseModelType] = Query(description="new model base", default=None),
) -> ImportModelResponse:
""" Rename a model"""
logger = ApiDependencies.invoker.services.logger
try:
result = ApiDependencies.invoker.services.model_manager.rename_model(
base_model = base_model,
model_type = model_type,
model_name = model_name,
new_name = new_name,
new_base = new_base,
)
logger.debug(result)
logger.info(f'Successfully renamed {model_name}=>{new_name}')
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=new_name or model_name,
base_model=new_base or base_model,
model_type=model_type
)
return parse_obj_as(ImportModelResponse, model_raw)
except KeyError 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"
}
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"),
@ -178,14 +262,17 @@ 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"""
"""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
model_type = model_type,
convert_dest_directory = dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(model_name,
base_model = base_model,
@ -196,6 +283,53 @@ async def convert_model(
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.get(
"/sync",
operation_id="sync_to_config",
responses={
201: { "description": "synchronization successful" },
},
status_code = 201,
response_model = None
)
async def sync_to_config(
)->None:
"""Call after making changes to models.yaml, autoimport directories or models directory to synchronize
in-memory data structures with disk data structures."""
return ApiDependencies.invoker.services.model_manager.sync_to_config()
@models_router.put(
"/merge/{base_model}",
@ -215,17 +349,21 @@ async def merge_models(
alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
force: Optional[bool] = Body(description="Force merging of models created with different versions of diffusers", default=False),
merge_dest_directory: Optional[str] = Body(description="Save the merged model to the designated directory (with 'merged_model_name' appended)", default=None)
) -> MergeModelResponse:
"""Convert a checkpoint model into a diffusers model"""
logger = ApiDependencies.invoker.services.logger
try:
logger.info(f"Merging models: {model_names}")
logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
result = ApiDependencies.invoker.services.model_manager.merge_models(model_names,
base_model,
merged_model_name or "+".join(model_names),
alpha,
interp,
force)
merged_model_name=merged_model_name or "+".join(model_names),
alpha=alpha,
interp=interp,
force=force,
merge_dest_directory = dest
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(result.name,
base_model = base_model,
model_type = ModelType.Main,

11
invokeai/app/api_app.py
View File

@ -1,5 +1,6 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
# Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import asyncio
import sys
from inspect import signature
import uvicorn
@ -20,6 +21,13 @@ from ..backend.util.logging import InvokeAILogger
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
logger = InvokeAILogger.getLogger(config=app_config)
from invokeai.version.invokeai_version import __version__
# we call this early so that the message appears before
# other invokeai initialization messages
if app_config.version:
print(f'InvokeAI version {__version__}')
sys.exit(0)
import invokeai.frontend.web as web_dir
import mimetypes
@ -28,6 +36,7 @@ from .api.dependencies import ApiDependencies
from .api.routers import sessions, models, images, boards, board_images, app_info
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation
import torch
if torch.backends.mps.is_available():

10
invokeai/app/cli_app.py
View File

@ -16,6 +16,12 @@ from invokeai.backend.util.logging import InvokeAILogger
config = InvokeAIAppConfig.get_config()
config.parse_args()
logger = InvokeAILogger().getLogger(config=config)
from invokeai.version.invokeai_version import __version__
# we call this early so that the message appears before other invokeai initialization messages
if config.version:
print(f'InvokeAI version {__version__}')
sys.exit(0)
from invokeai.app.services.board_image_record_storage import (
SqliteBoardImageRecordStorage,
@ -28,7 +34,6 @@ from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.metadata import CoreMetadataService
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
from .services.default_graphs import (default_text_to_image_graph_id,
@ -208,6 +213,7 @@ def invoke_all(context: CliContext):
raise SessionError()
def invoke_cli():
logger.info(f'InvokeAI version {__version__}')
# get the optional list of invocations to execute on the command line
parser = config.get_parser()
parser.add_argument('commands',nargs='*')
@ -237,7 +243,6 @@ def invoke_cli():
)
urls = LocalUrlService()
metadata = CoreMetadataService()
image_record_storage = SqliteImageRecordStorage(db_location)
image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
names = SimpleNameService()
@ -270,7 +275,6 @@ def invoke_cli():
board_image_record_storage=board_image_record_storage,
image_record_storage=image_record_storage,
image_file_storage=image_file_storage,
metadata=metadata,
url=urls,
logger=logger,
names=names,

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

@ -128,7 +128,7 @@ class CompelInvocation(BaseInvocation):
text_encoder=text_encoder,
textual_inversion_manager=ti_manager,
dtype_for_device_getter=torch_dtype,
truncate_long_prompts=True, # TODO:
truncate_long_prompts=False,
)
conjunction = Compel.parse_prompt_string(self.prompt)
@ -140,9 +140,6 @@ class CompelInvocation(BaseInvocation):
c, options = compel.build_conditioning_tensor_for_prompt_object(
prompt)
# TODO: long prompt support
# if not self.truncate_long_prompts:
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(
tokenizer, conjunction),

26
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -9,6 +9,7 @@ from typing import Literal, Optional, Union, List, Dict
from PIL import Image
from pydantic import BaseModel, Field, validator
from ...backend.model_management import BaseModelType, ModelType
from ..models.image import ImageField, ImageCategory, ResourceOrigin
from .baseinvocation import (
BaseInvocation,
@ -105,9 +106,15 @@ CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control
# CONTROLNET_RESIZE_VALUES = Literal[tuple(["just_resize", "crop_resize", "fill_resize"])]
class ControlNetModelField(BaseModel):
"""ControlNet model field"""
model_name: str = Field(description="Name of the ControlNet model")
base_model: BaseModelType = Field(description="Base model")
class ControlField(BaseModel):
image: ImageField = Field(default=None, description="The control image")
control_model: Optional[str] = Field(default=None, description="The ControlNet model to use")
control_model: Optional[ControlNetModelField] = Field(default=None, description="The ControlNet model to use")
# control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
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,
@ -118,15 +125,15 @@ class ControlField(BaseModel):
# resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@validator("control_weight")
def abs_le_one(cls, v):
"""validate that all abs(values) are <=1"""
def validate_control_weight(cls, v):
"""Validate that all control weights in the valid range"""
if isinstance(v, list):
for i in v:
if abs(i) > 1:
raise ValueError('all abs(control_weight) must be <= 1')
if i < -1 or i > 2:
raise ValueError('Control weights must be within -1 to 2 range')
else:
if abs(v) > 1:
raise ValueError('abs(control_weight) must be <= 1')
if v < -1 or v > 2:
raise ValueError('Control weights must be within -1 to 2 range')
return v
class Config:
schema_extra = {
@ -134,6 +141,7 @@ class ControlField(BaseModel):
"ui": {
"type_hints": {
"control_weight": "float",
"control_model": "controlnet_model",
# "control_weight": "number",
}
}
@ -154,10 +162,10 @@ class ControlNetInvocation(BaseInvocation):
type: Literal["controlnet"] = "controlnet"
# Inputs
image: ImageField = Field(default=None, description="The control image")
control_model: CONTROLNET_NAME_VALUES = Field(default="lllyasviel/sd-controlnet-canny",
control_model: ControlNetModelField = Field(default="lllyasviel/sd-controlnet-canny",
description="control model used")
control_weight: Union[float, List[float]] = Field(default=1.0, description="The weight given to the ControlNet")
begin_step_percent: float = Field(default=0, ge=0, le=1,
begin_step_percent: float = Field(default=0, ge=-1, le=2,
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)")

90
invokeai/app/invocations/generate.py
View File

@ -154,40 +154,42 @@ class InpaintInvocation(BaseInvocation):
@contextmanager
def load_model_old_way(self, context, scheduler):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}))
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
#unet = unet_info.context.model
#vae = vae_info.context.model
with vae_info as vae,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
with ExitStack() as stack:
loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
device = context.services.model_manager.mgr.cache.execution_device
dtype = context.services.model_manager.mgr.cache.precision
with vae_info as vae,\
unet_info as unet,\
ModelPatcher.apply_lora_unet(unet, loras):
pipeline = StableDiffusionGeneratorPipeline(
vae=vae,
text_encoder=None,
tokenizer=None,
unet=unet,
scheduler=scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
precision="float16" if dtype == torch.float16 else "float32",
execution_device=device,
)
device = context.services.model_manager.mgr.cache.execution_device
dtype = context.services.model_manager.mgr.cache.precision
pipeline = StableDiffusionGeneratorPipeline(
vae=vae,
text_encoder=None,
tokenizer=None,
unet=unet,
scheduler=scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
precision="float16" if dtype == torch.float16 else "float32",
execution_device=device,
)
yield OldModelInfo(
name=self.unet.unet.model_name,
hash="<NO-HASH>",
model=pipeline,
)
yield OldModelInfo(
name=self.unet.unet.model_name,
hash="<NO-HASH>",
model=pipeline,
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = (
@ -226,21 +228,21 @@ class InpaintInvocation(BaseInvocation):
), # Shorthand for passing all of the parameters above manually
)
# Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
# each time it is called. We only need the first one.
generator_output = next(outputs)
# Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
# each time it is called. We only need the first one.
generator_output = next(outputs)
image_dto = context.services.images.create(
image=generator_output.image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate,
)
image_dto = context.services.images.create(
image=generator_output.image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)

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

@ -5,6 +5,7 @@ from typing import Literal, Optional
import numpy
from PIL import Image, ImageFilter, ImageOps, ImageChops
from pydantic import BaseModel, Field
from typing import Union
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import (
@ -398,8 +399,8 @@ class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
# Inputs
image: Optional[ImageField] = Field(default=None, description="The image to resize")
width: int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
height: int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
width: Union[int, None] = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
height: Union[int, None] = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
resample_mode: PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
# fmt: on

114
invokeai/app/invocations/latent.py
View File

@ -1,5 +1,6 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from contextlib import ExitStack
from typing import List, Literal, Optional, Union
import einops
@ -9,9 +10,10 @@ from diffusers.image_processor import VaeImageProcessor
from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import BaseModel, Field, validator
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.model_management.models.base import ModelType
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.stable_diffusion.diffusers_pipeline import (
@ -21,6 +23,7 @@ from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import torch_dtype
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .compel import ConditioningField
@ -77,16 +80,21 @@ def get_scheduler(
scheduler_name: str,
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(
scheduler_name, SCHEDULER_MAP['ddim'])
scheduler_name, SCHEDULER_MAP['ddim']
)
orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict())
**scheduler_info.dict()
)
with orig_scheduler_info as orig_scheduler:
scheduler_config = orig_scheduler.config
if "_backup" in scheduler_config:
scheduler_config = scheduler_config["_backup"]
scheduler_config = {**scheduler_config, **
scheduler_extra_config, "_backup": scheduler_config}
scheduler_config = {
**scheduler_config,
**scheduler_extra_config,
"_backup": scheduler_config,
}
scheduler = scheduler_class.from_config(scheduler_config)
# hack copied over from generate.py
@ -143,8 +151,11 @@ class TextToLatentsInvocation(BaseInvocation):
# TODO: pass this an emitter method or something? or a session for dispatching?
def dispatch_progress(
self, context: InvocationContext, source_node_id: str,
intermediate_state: PipelineIntermediateState) -> None:
self,
context: InvocationContext,
source_node_id: str,
intermediate_state: PipelineIntermediateState,
) -> None:
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
@ -153,11 +164,16 @@ class TextToLatentsInvocation(BaseInvocation):
)
def get_conditioning_data(
self, context: InvocationContext, scheduler) -> ConditioningData:
self,
context: InvocationContext,
scheduler,
) -> ConditioningData:
c, extra_conditioning_info = context.services.latents.get(
self.positive_conditioning.conditioning_name)
self.positive_conditioning.conditioning_name
)
uc, _ = context.services.latents.get(
self.negative_conditioning.conditioning_name)
self.negative_conditioning.conditioning_name
)
conditioning_data = ConditioningData(
unconditioned_embeddings=uc,
@ -184,7 +200,10 @@ class TextToLatentsInvocation(BaseInvocation):
return conditioning_data
def create_pipeline(
self, unet, scheduler) -> StableDiffusionGeneratorPipeline:
self,
unet,
scheduler,
) -> StableDiffusionGeneratorPipeline:
# TODO:
# configure_model_padding(
# unet,
@ -219,6 +238,7 @@ class TextToLatentsInvocation(BaseInvocation):
model: StableDiffusionGeneratorPipeline,
control_input: List[ControlField],
latents_shape: List[int],
exit_stack: ExitStack,
do_classifier_free_guidance: bool = True,
) -> List[ControlNetData]:
@ -244,25 +264,19 @@ class TextToLatentsInvocation(BaseInvocation):
control_data = []
control_models = []
for control_info in control_list:
# handle control models
if ("," in control_info.control_model):
control_model_split = control_info.control_model.split(",")
control_name = control_model_split[0]
control_subfolder = control_model_split[1]
print("Using HF model subfolders")
print(" control_name: ", control_name)
print(" control_subfolder: ", control_subfolder)
control_model = ControlNetModel.from_pretrained(
control_name, subfolder=control_subfolder,
torch_dtype=model.unet.dtype).to(
model.device)
else:
control_model = ControlNetModel.from_pretrained(
control_info.control_model, torch_dtype=model.unet.dtype).to(model.device)
control_model = exit_stack.enter_context(
context.services.model_manager.get_model(
model_name=control_info.control_model.model_name,
model_type=ModelType.ControlNet,
base_model=control_info.control_model.base_model,
)
)
control_models.append(control_model)
control_image_field = control_info.image
input_image = context.services.images.get_pil_image(
control_image_field.image_name)
control_image_field.image_name
)
# self.image.image_type, self.image.image_name
# FIXME: still need to test with different widths, heights, devices, dtypes
# and add in batch_size, num_images_per_prompt?
@ -284,7 +298,8 @@ class TextToLatentsInvocation(BaseInvocation):
weight=control_info.control_weight,
begin_step_percent=control_info.begin_step_percent,
end_step_percent=control_info.end_step_percent,
control_mode=control_info.control_mode,)
control_mode=control_info.control_mode,
)
control_data.append(control_item)
# MultiControlNetModel has been refactored out, just need list[ControlNetData]
return control_data
@ -295,7 +310,8 @@ class TextToLatentsInvocation(BaseInvocation):
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(
context.graph_execution_state_id)
context.graph_execution_state_id
)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
@ -304,14 +320,17 @@ class TextToLatentsInvocation(BaseInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}))
**lora.dict(exclude={"weight"})
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict())
with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
**self.unet.unet.dict()
)
with ExitStack() as exit_stack,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
scheduler = get_scheduler(
@ -328,6 +347,7 @@ class TextToLatentsInvocation(BaseInvocation):
latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
# TODO: Verify the noise is the right size
@ -380,7 +400,8 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(
context.graph_execution_state_id)
context.graph_execution_state_id
)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
@ -389,14 +410,17 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}))
**lora.dict(exclude={"weight"})
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict())
with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
**self.unet.unet.dict()
)
with ExitStack() as exit_stack,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
scheduler = get_scheduler(
@ -413,11 +437,13 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
# TODO: Verify the noise is the right size
initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
latent, device=unet.device, dtype=latent.dtype)
latent, device=unet.device, dtype=latent.dtype
)
timesteps, _ = pipeline.get_img2img_timesteps(
self.steps,
@ -457,6 +483,7 @@ class LatentsToImageInvocation(BaseInvocation):
default=False,
description="Decode latents by overlaping tiles(less memory consumption)")
fp32: bool = Field(False, description="Decode in full precision")
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
# Schema customisation
class Config(InvocationConfig):
@ -526,7 +553,8 @@ class LatentsToImageInvocation(BaseInvocation):
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
)
return ImageOutput(
@ -548,9 +576,9 @@ class ResizeLatentsInvocation(BaseInvocation):
# Inputs
latents: Optional[LatentsField] = Field(
description="The latents to resize")
width: int = Field(
width: Union[int, None] = Field(default=512,
ge=64, multiple_of=8, description="The width to resize to (px)")
height: int = Field(
height: Union[int, None] = Field(default=512,
ge=64, multiple_of=8, description="The height to resize to (px)")
mode: LATENTS_INTERPOLATION_MODE = Field(
default="bilinear", description="The interpolation mode")
@ -564,7 +592,8 @@ class ResizeLatentsInvocation(BaseInvocation):
resized_latents = torch.nn.functional.interpolate(
latents, size=(self.height // 8, self.width // 8),
mode=self.mode, antialias=self.antialias
if self.mode in ["bilinear", "bicubic"] else False,)
if self.mode in ["bilinear", "bicubic"] else False,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
torch.cuda.empty_cache()
@ -598,7 +627,8 @@ class ScaleLatentsInvocation(BaseInvocation):
resized_latents = torch.nn.functional.interpolate(
latents, scale_factor=self.scale_factor, mode=self.mode,
antialias=self.antialias
if self.mode in ["bilinear", "bicubic"] else False,)
if self.mode in ["bilinear", "bicubic"] else False,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
torch.cuda.empty_cache()

124
invokeai/app/invocations/metadata.py Normal file
View File

@ -0,0 +1,124 @@
from typing import Literal, Optional, Union
from pydantic import BaseModel, Field
from invokeai.app.invocations.baseinvocation import (BaseInvocation,
BaseInvocationOutput,
InvocationContext)
from invokeai.app.invocations.controlnet_image_processors import ControlField
from invokeai.app.invocations.model import (LoRAModelField, MainModelField,
VAEModelField)
class LoRAMetadataField(BaseModel):
"""LoRA metadata for an image generated in InvokeAI."""
lora: LoRAModelField = Field(description="The LoRA model")
weight: float = Field(description="The weight of the LoRA model")
class CoreMetadata(BaseModel):
"""Core generation metadata for an image generated in InvokeAI."""
generation_mode: str = Field(description="The generation mode that output this image",)
positive_prompt: str = Field(description="The positive prompt parameter")
negative_prompt: str = Field(description="The negative prompt parameter")
width: int = Field(description="The width parameter")
height: int = Field(description="The height parameter")
seed: int = Field(description="The seed used for noise generation")
rand_device: str = Field(description="The device used for random number generation")
cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
steps: int = Field(description="The number of steps used for inference")
scheduler: str = Field(description="The scheduler used for inference")
clip_skip: int = Field(description="The number of skipped CLIP layers",)
model: MainModelField = Field(description="The main model used for inference")
controlnets: list[ControlField]= Field(description="The ControlNets used for inference")
loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
strength: Union[float, None] = Field(
default=None,
description="The strength used for latents-to-latents",
)
init_image: Union[str, None] = Field(
default=None, description="The name of the initial image"
)
vae: Union[VAEModelField, None] = Field(
default=None,
description="The VAE used for decoding, if the main model's default was not used",
)
class ImageMetadata(BaseModel):
"""An image's generation metadata"""
metadata: Optional[dict] = Field(
default=None,
description="The image's core metadata, if it was created in the Linear or Canvas UI",
)
graph: Optional[dict] = Field(
default=None, description="The graph that created the image"
)
class MetadataAccumulatorOutput(BaseInvocationOutput):
"""The output of the MetadataAccumulator node"""
type: Literal["metadata_accumulator_output"] = "metadata_accumulator_output"
metadata: CoreMetadata = Field(description="The core metadata for the image")
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""
type: Literal["metadata_accumulator"] = "metadata_accumulator"
generation_mode: str = Field(description="The generation mode that output this image",)
positive_prompt: str = Field(description="The positive prompt parameter")
negative_prompt: str = Field(description="The negative prompt parameter")
width: int = Field(description="The width parameter")
height: int = Field(description="The height parameter")
seed: int = Field(description="The seed used for noise generation")
rand_device: str = Field(description="The device used for random number generation")
cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
steps: int = Field(description="The number of steps used for inference")
scheduler: str = Field(description="The scheduler used for inference")
clip_skip: int = Field(description="The number of skipped CLIP layers",)
model: MainModelField = Field(description="The main model used for inference")
controlnets: list[ControlField]= Field(description="The ControlNets used for inference")
loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
strength: Union[float, None] = Field(
default=None,
description="The strength used for latents-to-latents",
)
init_image: Union[str, None] = Field(
default=None, description="The name of the initial image"
)
vae: Union[VAEModelField, None] = Field(
default=None,
description="The VAE used for decoding, if the main model's default was not used",
)
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
"""Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(
metadata=CoreMetadata(
generation_mode=self.generation_mode,
positive_prompt=self.positive_prompt,
negative_prompt=self.negative_prompt,
width=self.width,
height=self.height,
seed=self.seed,
rand_device=self.rand_device,
cfg_scale=self.cfg_scale,
steps=self.steps,
scheduler=self.scheduler,
model=self.model,
strength=self.strength,
init_image=self.init_image,
vae=self.vae,
controlnets=self.controlnets,
loras=self.loras,
clip_skip=self.clip_skip,
)
)

93
invokeai/app/models/metadata.py
View File

@ -1,93 +0,0 @@
from typing import Optional, Union, List
from pydantic import BaseModel, Extra, Field, StrictFloat, StrictInt, StrictStr
class ImageMetadata(BaseModel):
"""
Core generation metadata for an image/tensor generated in InvokeAI.
Also includes any metadata from the image's PNG tEXt chunks.
Generated by traversing the execution graph, collecting the parameters of the nearest ancestors
of a given node.
Full metadata may be accessed by querying for the session in the `graph_executions` table.
"""
class Config:
extra = Extra.allow
"""
This lets the ImageMetadata class accept arbitrary additional fields. The CoreMetadataService
won't add any fields that are not already defined, but other a different metadata service
implementation might.
"""
type: Optional[StrictStr] = Field(
default=None,
description="The type of the ancestor node of the image output node.",
)
"""The type of the ancestor node of the image output node."""
positive_conditioning: Optional[StrictStr] = Field(
default=None, description="The positive conditioning."
)
"""The positive conditioning"""
negative_conditioning: Optional[StrictStr] = Field(
default=None, description="The negative conditioning."
)
"""The negative conditioning"""
width: Optional[StrictInt] = Field(
default=None, description="Width of the image/latents in pixels."
)
"""Width of the image/latents in pixels"""
height: Optional[StrictInt] = Field(
default=None, description="Height of the image/latents in pixels."
)
"""Height of the image/latents in pixels"""
seed: Optional[StrictInt] = Field(
default=None, description="The seed used for noise generation."
)
"""The seed used for noise generation"""
# cfg_scale: Optional[StrictFloat] = Field(
# cfg_scale: Union[float, list[float]] = Field(
cfg_scale: Union[StrictFloat, List[StrictFloat]] = Field(
default=None, description="The classifier-free guidance scale."
)
"""The classifier-free guidance scale"""
steps: Optional[StrictInt] = Field(
default=None, description="The number of steps used for inference."
)
"""The number of steps used for inference"""
scheduler: Optional[StrictStr] = Field(
default=None, description="The scheduler used for inference."
)
"""The scheduler used for inference"""
model: Optional[StrictStr] = Field(
default=None, description="The model used for inference."
)
"""The model used for inference"""
strength: Optional[StrictFloat] = Field(
default=None,
description="The strength used for image-to-image/latents-to-latents.",
)
"""The strength used for image-to-image/latents-to-latents."""
latents: Optional[StrictStr] = Field(
default=None, description="The ID of the initial latents."
)
"""The ID of the initial latents"""
vae: Optional[StrictStr] = Field(
default=None, description="The VAE used for decoding."
)
"""The VAE used for decoding"""
unet: Optional[StrictStr] = Field(
default=None, description="The UNet used dor inference."
)
"""The UNet used dor inference"""
clip: Optional[StrictStr] = Field(
default=None, description="The CLIP Encoder used for conditioning."
)
"""The CLIP Encoder used for conditioning"""
extra: Optional[StrictStr] = Field(
default=None,
description="Uploaded image metadata, extracted from the PNG tEXt chunk.",
)
"""Uploaded image metadata, extracted from the PNG tEXt chunk."""

19
invokeai/app/services/config.py
View File

@ -23,7 +23,8 @@ InvokeAI:
xformers_enabled: false
sequential_guidance: false
precision: float16
max_loaded_models: 4
max_cache_size: 6
max_vram_cache_size: 2.7
always_use_cpu: false
free_gpu_mem: false
Features:
@ -168,7 +169,7 @@ from argparse import ArgumentParser
from omegaconf import OmegaConf, DictConfig
from pathlib import Path
from pydantic import BaseSettings, Field, parse_obj_as
from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
INIT_FILE = Path('invokeai.yaml')
MODEL_CORE = Path('models/core')
@ -199,7 +200,7 @@ class InvokeAISettings(BaseSettings):
type = get_args(get_type_hints(cls)['type'])[0]
field_dict = dict({type:dict()})
for name,field in self.__fields__.items():
if name in cls._excluded():
if name in cls._excluded_from_yaml():
continue
category = field.field_info.extra.get("category") or "Uncategorized"
value = getattr(self,name)
@ -270,7 +271,13 @@ class InvokeAISettings(BaseSettings):
@classmethod
def _excluded(self)->List[str]:
# combination of deprecated parameters and internal ones that shouldn't be exposed
return ['type','initconf']
@classmethod
def _excluded_from_yaml(self)->List[str]:
# combination of deprecated parameters and internal ones that shouldn't be exposed
return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model']
class Config:
env_file_encoding = 'utf-8'
@ -363,8 +370,10 @@ setting environment variables INVOKEAI_<setting>.
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
max_loaded_models : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='Memory/Performance')
max_loaded_models : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='DEPRECATED')
max_cache_size : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
gpu_mem_reserved : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED')
precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
@ -389,6 +398,8 @@ setting environment variables INVOKEAI_<setting>.
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
log_format : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
log_level : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="debug", description="Emit logging messages at this level or higher", category="Logging")
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
#fmt: on
def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):

21
invokeai/app/services/image_file_storage.py
View File

@ -1,14 +1,14 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import json
from abc import ABC, abstractmethod
from pathlib import Path
from queue import Queue
from typing import Dict, Optional, Union
from PIL.Image import Image as PILImageType
from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType
from send2trash import send2trash
from invokeai.app.models.metadata import ImageMetadata
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
@ -59,7 +59,8 @@ class ImageFileStorageBase(ABC):
self,
image: PILImageType,
image_name: str,
metadata: Optional[ImageMetadata] = None,
metadata: Optional[dict] = None,
graph: Optional[dict] = None,
thumbnail_size: int = 256,
) -> None:
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
@ -110,20 +111,22 @@ class DiskImageFileStorage(ImageFileStorageBase):
self,
image: PILImageType,
image_name: str,
metadata: Optional[ImageMetadata] = None,
metadata: Optional[dict] = None,
graph: Optional[dict] = None,
thumbnail_size: int = 256,
) -> None:
try:
self.__validate_storage_folders()
image_path = self.get_path(image_name)
pnginfo = PngImagePlugin.PngInfo()
if metadata is not None:
pnginfo = PngImagePlugin.PngInfo()
pnginfo.add_text("invokeai", metadata.json())
image.save(image_path, "PNG", pnginfo=pnginfo)
else:
image.save(image_path, "PNG")
pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
if graph is not None:
pnginfo.add_text("invokeai_graph", json.dumps(graph))
image.save(image_path, "PNG", pnginfo=pnginfo)
thumbnail_name = get_thumbnail_name(image_name)
thumbnail_path = self.get_path(thumbnail_name, thumbnail=True)
thumbnail_image = make_thumbnail(image, thumbnail_size)

67
invokeai/app/services/image_record_storage.py
View File

@ -1,3 +1,4 @@
import json
import sqlite3
import threading
from abc import ABC, abstractmethod
@ -8,7 +9,6 @@ from pydantic import BaseModel, Field
from pydantic.generics import GenericModel
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.models.metadata import ImageMetadata
from invokeai.app.services.models.image_record import (
ImageRecord, ImageRecordChanges, deserialize_image_record)
@ -48,6 +48,28 @@ class ImageRecordDeleteException(Exception):
super().__init__(message)
IMAGE_DTO_COLS = ", ".join(
list(
map(
lambda c: "images." + c,
[
"image_name",
"image_origin",
"image_category",
"width",
"height",
"session_id",
"node_id",
"is_intermediate",
"created_at",
"updated_at",
"deleted_at",
],
)
)
)
class ImageRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the image record store."""
@ -58,6 +80,11 @@ class ImageRecordStorageBase(ABC):
"""Gets an image record."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
"""Gets an image's metadata'."""
pass
@abstractmethod
def update(
self,
@ -102,7 +129,7 @@ class ImageRecordStorageBase(ABC):
height: int,
session_id: Optional[str],
node_id: Optional[str],
metadata: Optional[ImageMetadata],
metadata: Optional[dict],
is_intermediate: bool = False,
) -> datetime:
"""Saves an image record."""
@ -206,7 +233,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
self._cursor.execute(
f"""--sql
SELECT * FROM images
SELECT {IMAGE_DTO_COLS} FROM images
WHERE image_name = ?;
""",
(image_name,),
@ -224,6 +251,28 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
return deserialize_image_record(dict(result))
def get_metadata(self, image_name: str) -> Optional[dict]:
try:
self._lock.acquire()
self._cursor.execute(
f"""--sql
SELECT images.metadata FROM images
WHERE image_name = ?;
""",
(image_name,),
)
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
if not result or not result[0]:
return None
return json.loads(result[0])
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordNotFoundException from e
finally:
self._lock.release()
def update(
self,
image_name: str,
@ -291,8 +340,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
WHERE 1=1
"""
images_query = """--sql
SELECT images.*
images_query = f"""--sql
SELECT {IMAGE_DTO_COLS}
FROM images
LEFT JOIN board_images ON board_images.image_name = images.image_name
WHERE 1=1
@ -410,12 +459,12 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
width: int,
height: int,
node_id: Optional[str],
metadata: Optional[ImageMetadata],
metadata: Optional[dict],
is_intermediate: bool = False,
) -> datetime:
try:
metadata_json = (
None if metadata is None else metadata.json(exclude_none=True)
None if metadata is None else json.dumps(metadata)
)
self._lock.acquire()
self._cursor.execute(
@ -465,9 +514,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
finally:
self._lock.release()
def get_most_recent_image_for_board(
self, board_id: str
) -> Optional[ImageRecord]:
def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
try:
self._lock.acquire()
self._cursor.execute(

116
invokeai/app/services/images.py
View File

@ -1,39 +1,30 @@
import json
from abc import ABC, abstractmethod
from logging import Logger
from typing import Optional, TYPE_CHECKING, Union
from typing import TYPE_CHECKING, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.models.image import (
ImageCategory,
ResourceOrigin,
InvalidImageCategoryException,
InvalidOriginException,
)
from invokeai.app.models.metadata import ImageMetadata
from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
from invokeai.app.services.image_record_storage import (
ImageRecordDeleteException,
ImageRecordNotFoundException,
ImageRecordSaveException,
ImageRecordStorageBase,
OffsetPaginatedResults,
)
from invokeai.app.services.models.image_record import (
ImageRecord,
ImageDTO,
ImageRecordChanges,
image_record_to_dto,
)
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.models.image import (ImageCategory,
InvalidImageCategoryException,
InvalidOriginException, ResourceOrigin)
from invokeai.app.services.board_image_record_storage import \
BoardImageRecordStorageBase
from invokeai.app.services.graph import Graph
from invokeai.app.services.image_file_storage import (
ImageFileDeleteException,
ImageFileNotFoundException,
ImageFileSaveException,
ImageFileStorageBase,
)
from invokeai.app.services.item_storage import ItemStorageABC, PaginatedResults
from invokeai.app.services.metadata import MetadataServiceBase
ImageFileDeleteException, ImageFileNotFoundException,
ImageFileSaveException, ImageFileStorageBase)
from invokeai.app.services.image_record_storage import (
ImageRecordDeleteException, ImageRecordNotFoundException,
ImageRecordSaveException, ImageRecordStorageBase, OffsetPaginatedResults)
from invokeai.app.services.item_storage import ItemStorageABC
from invokeai.app.services.models.image_record import (ImageDTO, ImageRecord,
ImageRecordChanges,
image_record_to_dto)
from invokeai.app.services.resource_name import NameServiceBase
from invokeai.app.services.urls import UrlServiceBase
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
if TYPE_CHECKING:
from invokeai.app.services.graph import GraphExecutionState
@ -51,6 +42,7 @@ class ImageServiceABC(ABC):
node_id: Optional[str] = None,
session_id: Optional[str] = None,
is_intermediate: bool = False,
metadata: Optional[dict] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@ -79,6 +71,11 @@ class ImageServiceABC(ABC):
"""Gets an image DTO."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
"""Gets an image's metadata."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's path."""
@ -124,7 +121,6 @@ class ImageServiceDependencies:
image_records: ImageRecordStorageBase
image_files: ImageFileStorageBase
board_image_records: BoardImageRecordStorageBase
metadata: MetadataServiceBase
urls: UrlServiceBase
logger: Logger
names: NameServiceBase
@ -135,7 +131,6 @@ class ImageServiceDependencies:
image_record_storage: ImageRecordStorageBase,
image_file_storage: ImageFileStorageBase,
board_image_record_storage: BoardImageRecordStorageBase,
metadata: MetadataServiceBase,
url: UrlServiceBase,
logger: Logger,
names: NameServiceBase,
@ -144,7 +139,6 @@ class ImageServiceDependencies:
self.image_records = image_record_storage
self.image_files = image_file_storage
self.board_image_records = board_image_record_storage
self.metadata = metadata
self.urls = url
self.logger = logger
self.names = names
@ -165,6 +159,7 @@ class ImageService(ImageServiceABC):
node_id: Optional[str] = None,
session_id: Optional[str] = None,
is_intermediate: bool = False,
metadata: Optional[dict] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
@ -174,7 +169,16 @@ class ImageService(ImageServiceABC):
image_name = self._services.names.create_image_name()
metadata = self._get_metadata(session_id, node_id)
graph = None
if session_id is not None:
session_raw = self._services.graph_execution_manager.get_raw(session_id)
if session_raw is not None:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self._services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
(width, height) = image.size
@ -191,14 +195,12 @@ class ImageService(ImageServiceABC):
is_intermediate=is_intermediate,
# Nullable fields
node_id=node_id,
session_id=session_id,
metadata=metadata,
session_id=session_id,
)
self._services.image_files.save(
image_name=image_name,
image=image,
metadata=metadata,
image_name=image_name, image=image, metadata=metadata, graph=graph
)
image_dto = self.get_dto(image_name)
@ -268,6 +270,34 @@ class ImageService(ImageServiceABC):
self._services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
try:
image_record = self._services.image_records.get(image_name)
if not image_record.session_id:
return ImageMetadata()
session_raw = self._services.graph_execution_manager.get_raw(
image_record.session_id
)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self._services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
metadata = self._services.image_records.get_metadata(image_name)
return ImageMetadata(graph=graph, metadata=metadata)
except ImageRecordNotFoundException:
self._services.logger.error("Image record not found")
raise
except Exception as e:
self._services.logger.error("Problem getting image DTO")
raise e
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self._services.image_files.get_path(image_name, thumbnail)
@ -367,15 +397,3 @@ class ImageService(ImageServiceABC):
except Exception as e:
self._services.logger.error("Problem deleting image records and files")
raise e
def _get_metadata(
self, session_id: Optional[str] = None, node_id: Optional[str] = None
) -> Optional[ImageMetadata]:
"""Get the metadata for a node."""
metadata = None
if node_id is not None and session_id is not None:
session = self._services.graph_execution_manager.get(session_id)
metadata = self._services.metadata.create_image_metadata(session, node_id)
return metadata

10
invokeai/app/services/item_storage.py
View File

@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
from typing import Callable, Generic, TypeVar
from typing import Callable, Generic, Optional, TypeVar
from pydantic import BaseModel, Field
from pydantic.generics import GenericModel
@ -29,14 +29,22 @@ class ItemStorageABC(ABC, Generic[T]):
@abstractmethod
def get(self, item_id: str) -> T:
"""Gets the item, parsing it into a Pydantic model"""
pass
@abstractmethod
def get_raw(self, item_id: str) -> Optional[str]:
"""Gets the raw item as a string, skipping Pydantic parsing"""
pass
@abstractmethod
def set(self, item: T) -> None:
"""Sets the item"""
pass
@abstractmethod
def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
"""Gets a paginated list of items"""
pass
@abstractmethod

142
invokeai/app/services/metadata.py
View File

@ -1,142 +0,0 @@
from abc import ABC, abstractmethod
from typing import Any, Optional
import networkx as nx
from invokeai.app.models.metadata import ImageMetadata
from invokeai.app.services.graph import Graph, GraphExecutionState
class MetadataServiceBase(ABC):
"""Handles building metadata for nodes, images, and outputs."""
@abstractmethod
def create_image_metadata(
self, session: GraphExecutionState, node_id: str
) -> ImageMetadata:
"""Builds an ImageMetadata object for a node."""
pass
class CoreMetadataService(MetadataServiceBase):
_ANCESTOR_TYPES = ["t2l", "l2l"]
"""The ancestor types that contain the core metadata"""
_ANCESTOR_PARAMS = ["type", "steps", "model", "cfg_scale", "scheduler", "strength"]
"""The core metadata parameters in the ancestor types"""
_NOISE_FIELDS = ["seed", "width", "height"]
"""The core metadata parameters in the noise node"""
def create_image_metadata(
self, session: GraphExecutionState, node_id: str
) -> ImageMetadata:
metadata = self._build_metadata_from_graph(session, node_id)
return metadata
def _find_nearest_ancestor(self, G: nx.DiGraph, node_id: str) -> Optional[str]:
"""
Finds the id of the nearest ancestor (of a valid type) of a given node.
Parameters:
G (nx.DiGraph): The execution graph, converted in to a networkx DiGraph. Its nodes must
have the same data as the execution graph.
node_id (str): The ID of the node.
Returns:
str | None: The ID of the nearest ancestor, or None if there are no valid ancestors.
"""
# Retrieve the node from the graph
node = G.nodes[node_id]
# If the node type is one of the core metadata node types, return its id
if node.get("type") in self._ANCESTOR_TYPES:
return node.get("id")
# Else, look for the ancestor in the predecessor nodes
for predecessor in G.predecessors(node_id):
result = self._find_nearest_ancestor(G, predecessor)
if result:
return result
# If there are no valid ancestors, return None
return None
def _get_additional_metadata(
self, graph: Graph, node_id: str
) -> Optional[dict[str, Any]]:
"""
Returns additional metadata for a given node.
Parameters:
graph (Graph): The execution graph.
node_id (str): The ID of the node.
Returns:
dict[str, Any] | None: A dictionary of additional metadata.
"""
metadata = {}
# Iterate over all edges in the graph
for edge in graph.edges:
dest_node_id = edge.destination.node_id
dest_field = edge.destination.field
source_node_dict = graph.nodes[edge.source.node_id].dict()
# If the destination node ID matches the given node ID, gather necessary metadata
if dest_node_id == node_id:
# Prompt
if dest_field == "positive_conditioning":
metadata["positive_conditioning"] = source_node_dict.get("prompt")
# Negative prompt
if dest_field == "negative_conditioning":
metadata["negative_conditioning"] = source_node_dict.get("prompt")
# Seed, width and height
if dest_field == "noise":
for field in self._NOISE_FIELDS:
metadata[field] = source_node_dict.get(field)
return metadata
def _build_metadata_from_graph(
self, session: GraphExecutionState, node_id: str
) -> ImageMetadata:
"""
Builds an ImageMetadata object for a node.
Parameters:
session (GraphExecutionState): The session.
node_id (str): The ID of the node.
Returns:
ImageMetadata: The metadata for the node.
"""
# We need to do all the traversal on the execution graph
graph = session.execution_graph
# Find the nearest `t2l`/`l2l` ancestor of the given node
ancestor_id = self._find_nearest_ancestor(graph.nx_graph_with_data(), node_id)
# If no ancestor was found, return an empty ImageMetadata object
if ancestor_id is None:
return ImageMetadata()
ancestor_node = graph.get_node(ancestor_id)
# Grab all the core metadata from the ancestor node
ancestor_metadata = {
param: val
for param, val in ancestor_node.dict().items()
if param in self._ANCESTOR_PARAMS
}
# Get this image's prompts and noise parameters
addl_metadata = self._get_additional_metadata(graph, ancestor_id)
# If additional metadata was found, add it to the main metadata
if addl_metadata is not None:
ancestor_metadata.update(addl_metadata)
return ImageMetadata(**ancestor_metadata)

104
invokeai/app/services/model_manager_service.py
View File

@ -19,7 +19,7 @@ from invokeai.backend.model_management import (
ModelMerger,
MergeInterpolationMethod,
)
from invokeai.backend.model_management.model_search import FindModels
import torch
from invokeai.app.models.exceptions import CanceledException
@ -167,6 +167,27 @@ class ModelManagerServiceBase(ABC):
"""
pass
@abstractmethod
def rename_model(self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str,
):
"""
Rename the indicated model.
"""
pass
@abstractmethod
def list_checkpoint_configs(
self
)->List[Path]:
"""
List the checkpoint config paths from ROOT/configs/stable-diffusion.
"""
pass
@abstractmethod
def convert_model(
self,
@ -220,6 +241,7 @@ class ModelManagerServiceBase(ABC):
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = None
) -> AddModelResult:
"""
Merge two to three diffusrs pipeline models and save as a new model.
@ -228,9 +250,26 @@ class ModelManagerServiceBase(ABC):
:param merged_model_name: Name of destination merged model
:param alpha: Alpha strength to apply to 2d and 3d model
:param interp: Interpolation method. None (default)
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
"""
pass
@abstractmethod
def search_for_models(self, directory: Path)->List[Path]:
"""
Return list of all models found in the designated directory.
"""
pass
@abstractmethod
def sync_to_config(self):
"""
Re-read models.yaml, rescan the models directory, and reimport models
in the autoimport directories. Call after making changes outside the
model manager API.
"""
pass
@abstractmethod
def commit(self, conf_file: Optional[Path] = None) -> None:
"""
@ -258,9 +297,7 @@ class ModelManagerService(ModelManagerServiceBase):
config_file = config.model_conf_path
else:
config_file = config.root_dir / "configs/models.yaml"
if not config_file.exists():
raise IOError(f"The file {config_file} could not be found.")
logger.debug(f'config file={config_file}')
device = torch.device(choose_torch_device())
@ -433,16 +470,18 @@ class ModelManagerService(ModelManagerServiceBase):
"""
Delete the named model from configuration. If delete_files is true,
then the underlying weight file or diffusers directory will be deleted
as well. Call commit() to write to disk.
as well.
"""
self.logger.debug(f'delete model {model_name}')
self.mgr.del_model(model_name, base_model, model_type)
self.mgr.commit()
def convert_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main,ModelType.Vae],
convert_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),
) -> AddModelResult:
"""
Convert a checkpoint file into a diffusers folder, deleting the cached
@ -451,13 +490,14 @@ class ModelManagerService(ModelManagerServiceBase):
:param model_name: Name of the model to convert
:param base_model: Base model type
:param model_type: Type of model ['vae' or 'main']
:param convert_dest_directory: Save the converted model to the designated directory (`models/etc/etc` by default)
This will raise a ValueError unless the model is not a checkpoint. It will
also raise a ValueError in the event that there is a similarly-named diffusers
directory already in place.
"""
self.logger.debug(f'convert model {model_name}')
return self.mgr.convert_model(model_name, base_model, model_type)
return self.mgr.convert_model(model_name, base_model, model_type, convert_dest_directory)
def commit(self, conf_file: Optional[Path]=None):
"""
@ -538,6 +578,7 @@ class ModelManagerService(ModelManagerServiceBase):
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),
) -> AddModelResult:
"""
Merge two to three diffusrs pipeline models and save as a new model.
@ -546,6 +587,7 @@ class ModelManagerService(ModelManagerServiceBase):
:param merged_model_name: Name of destination merged model
:param alpha: Alpha strength to apply to 2d and 3d model
:param interp: Interpolation method. None (default)
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
"""
merger = ModelMerger(self.mgr)
try:
@ -556,7 +598,55 @@ class ModelManagerService(ModelManagerServiceBase):
alpha = alpha,
interp = interp,
force = force,
merge_dest_directory=merge_dest_directory,
)
except AssertionError as e:
raise ValueError(e)
return result
def search_for_models(self, directory: Path)->List[Path]:
"""
Return list of all models found in the designated directory.
"""
search = FindModels(directory,self.logger)
return search.list_models()
def sync_to_config(self):
"""
Re-read models.yaml, rescan the models directory, and reimport models
in the autoimport directories. Call after making changes outside the
model manager API.
"""
return self.mgr.sync_to_config()
def list_checkpoint_configs(self)->List[Path]:
"""
List the checkpoint config paths from ROOT/configs/stable-diffusion.
"""
config = self.mgr.app_config
conf_path = config.legacy_conf_path
root_path = config.root_path
return [(conf_path / x).relative_to(root_path) for x in conf_path.glob('**/*.yaml')]
def rename_model(self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
):
"""
Rename the indicated model. Can provide a new name and/or a new base.
:param model_name: Current name of the model
:param base_model: Current base of the model
:param model_type: Model type (can't be changed)
:param new_name: New name for the model
:param new_base: New base for the model
"""
self.mgr.rename_model(base_model = base_model,
model_type = model_type,
model_name = model_name,
new_name = new_name,
new_base = new_base,
)

19
invokeai/app/services/models/image_record.py
View File

@ -1,13 +1,14 @@
import datetime
from typing import Optional, Union
from pydantic import BaseModel, Extra, Field, StrictBool, StrictStr
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.models.metadata import ImageMetadata
from invokeai.app.util.misc import get_iso_timestamp
class ImageRecord(BaseModel):
"""Deserialized image record."""
"""Deserialized image record without metadata."""
image_name: str = Field(description="The unique name of the image.")
"""The unique name of the image."""
@ -43,11 +44,6 @@ class ImageRecord(BaseModel):
description="The node ID that generated this image, if it is a generated image.",
)
"""The node ID that generated this image, if it is a generated image."""
metadata: Optional[ImageMetadata] = Field(
default=None,
description="A limited subset of the image's generation metadata. Retrieve the image's session for full metadata.",
)
"""A limited subset of the image's generation metadata. Retrieve the image's session for full metadata."""
class ImageRecordChanges(BaseModel, extra=Extra.forbid):
@ -112,6 +108,7 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
# Retrieve all the values, setting "reasonable" defaults if they are not present.
# TODO: do we really need to handle default values here? ideally the data is the correct shape...
image_name = image_dict.get("image_name", "unknown")
image_origin = ResourceOrigin(
image_dict.get("image_origin", ResourceOrigin.INTERNAL.value)
@ -128,13 +125,6 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
deleted_at = image_dict.get("deleted_at", get_iso_timestamp())
is_intermediate = image_dict.get("is_intermediate", False)
raw_metadata = image_dict.get("metadata")
if raw_metadata is not None:
metadata = ImageMetadata.parse_raw(raw_metadata)
else:
metadata = None
return ImageRecord(
image_name=image_name,
image_origin=image_origin,
@ -143,7 +133,6 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
height=height,
session_id=session_id,
node_id=node_id,
metadata=metadata,
created_at=created_at,
updated_at=updated_at,
deleted_at=deleted_at,

1
invokeai/app/services/processor.py
View File

@ -104,6 +104,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
except Exception as e:
error = traceback.format_exc()
logger.error(error)
# Save error
graph_execution_state.set_node_error(invocation.id, error)

17
invokeai/app/services/sqlite.py
View File

@ -1,6 +1,6 @@
import sqlite3
from threading import Lock
from typing import Generic, TypeVar, Optional, Union, get_args
from typing import Generic, Optional, TypeVar, get_args
from pydantic import BaseModel, parse_raw_as
@ -78,6 +78,21 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
return self._parse_item(result[0])
def get_raw(self, id: str) -> Optional[str]:
try:
self._lock.acquire()
self._cursor.execute(
f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
)
result = self._cursor.fetchone()
finally:
self._lock.release()
if not result:
return None
return result[0]
def delete(self, id: str):
try:
self._lock.acquire()

2
invokeai/app/services/urls.py
View File

@ -22,4 +22,4 @@ class LocalUrlService(UrlServiceBase):
if thumbnail:
return f"{self._base_url}/images/{image_basename}/thumbnail"
return f"{self._base_url}/images/{image_basename}"
return f"{self._base_url}/images/{image_basename}/full"

55
invokeai/app/util/metadata.py Normal file
View File

@ -0,0 +1,55 @@
import json
from typing import Optional
from pydantic import ValidationError
from invokeai.app.services.graph import Edge
def get_metadata_graph_from_raw_session(session_raw: str) -> Optional[dict]:
"""
Parses raw session string, returning a dict of the graph.
Only the general graph shape is validated; none of the fields are validated.
Any `metadata_accumulator` nodes and edges are removed.
Any validation failure will return None.
"""
graph = json.loads(session_raw).get("graph", None)
# sanity check make sure the graph is at least reasonably shaped
if (
type(graph) is not dict
or "nodes" not in graph
or type(graph["nodes"]) is not dict
or "edges" not in graph
or type(graph["edges"]) is not list
):
# something has gone terribly awry, return an empty dict
return None
try:
# delete the `metadata_accumulator` node
del graph["nodes"]["metadata_accumulator"]
except KeyError:
# no accumulator node, all good
pass
# delete any edges to or from it
for i, edge in enumerate(graph["edges"]):
try:
# try to parse the edge
Edge(**edge)
except ValidationError:
# something has gone terribly awry, return an empty dict
return None
if (
edge["source"]["node_id"] == "metadata_accumulator"
or edge["destination"]["node_id"] == "metadata_accumulator"
):
del graph["edges"][i]
return graph

9
invokeai/backend/install/migrate_to_3.py
View File

@ -593,9 +593,12 @@ script, which will perform a full upgrade in place."""
config = InvokeAIAppConfig.get_config()
config.parse_args(['--root',str(dest_root)])
# TODO: revisit
# assert (dest_root / 'models').is_dir(), f"{dest_root} does not contain a 'models' subdirectory"
# assert (dest_root / 'invokeai.yaml').exists(), f"{dest_root} does not contain an InvokeAI init file."
# TODO: revisit - don't rely on invokeai.yaml to exist yet!
dest_is_setup = (dest_root / 'models/core').exists() and (dest_root / 'databases').exists()
if not dest_is_setup:
import invokeai.frontend.install.invokeai_configure
from invokeai.backend.install.invokeai_configure import initialize_rootdir
initialize_rootdir(dest_root, True)
do_migrate(src_root,dest_root)

6
invokeai/backend/install/model_install_backend.py
View File

@ -71,8 +71,6 @@ class ModelInstallList:
class InstallSelections():
install_models: List[str]= field(default_factory=list)
remove_models: List[str]=field(default_factory=list)
# scan_directory: Path = None
# autoscan_on_startup: bool=False
@dataclass
class ModelLoadInfo():
@ -121,8 +119,8 @@ class ModelInstall(object):
installed_models = self.mgr.list_models()
for md in installed_models:
base = md['base_model']
model_type = md['type']
name = md['name']
model_type = md['model_type']
name = md['model_name']
key = ModelManager.create_key(name, base, model_type)
if key in model_dict:
model_dict[key].installed = True

35
invokeai/backend/model_management/model_cache.py
View File

@ -36,6 +36,9 @@ from .models import BaseModelType, ModelType, SubModelType, ModelBase
# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
DEFAULT_MAX_CACHE_SIZE = 6.0
# amount of GPU memory to hold in reserve for use by generations (GB)
DEFAULT_MAX_VRAM_CACHE_SIZE= 2.75
# actual size of a gig
GIG = 1073741824
@ -82,6 +85,7 @@ class ModelCache(object):
def __init__(
self,
max_cache_size: float=DEFAULT_MAX_CACHE_SIZE,
max_vram_cache_size: float=DEFAULT_MAX_VRAM_CACHE_SIZE,
execution_device: torch.device=torch.device('cuda'),
storage_device: torch.device=torch.device('cpu'),
precision: torch.dtype=torch.float16,
@ -99,12 +103,11 @@ class ModelCache(object):
:param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
:param sha_chunksize: Chunksize to use when calculating sha256 model hash
'''
#max_cache_size = 9999
self.model_infos: Dict[str, ModelBase] = dict()
self.lazy_offloading = lazy_offloading
#self.sequential_offload: bool=sequential_offload
self.precision: torch.dtype=precision
self.max_cache_size: int=max_cache_size
self.max_cache_size: float=max_cache_size
self.max_vram_cache_size: float=max_vram_cache_size
self.execution_device: torch.device=execution_device
self.storage_device: torch.device=storage_device
self.sha_chunksize=sha_chunksize
@ -201,14 +204,22 @@ class ModelCache(object):
self._cache_stack.remove(key)
self._cache_stack.append(key)
return self.ModelLocker(self, key, cache_entry.model, gpu_load)
return self.ModelLocker(self, key, cache_entry.model, gpu_load, cache_entry.size)
class ModelLocker(object):
def __init__(self, cache, key, model, gpu_load):
def __init__(self, cache, key, model, gpu_load, size_needed):
'''
:param cache: The model_cache object
:param key: The key of the model to lock in GPU
:param model: The model to lock
:param gpu_load: True if load into gpu
:param size_needed: Size of the model to load
'''
self.gpu_load = gpu_load
self.cache = cache
self.key = key
self.model = model
self.size_needed = size_needed
self.cache_entry = self.cache._cached_models[self.key]
def __enter__(self) -> Any:
@ -222,7 +233,7 @@ class ModelCache(object):
try:
if self.cache.lazy_offloading:
self.cache._offload_unlocked_models()
self.cache._offload_unlocked_models(self.size_needed)
if self.model.device != self.cache.execution_device:
self.cache.logger.debug(f'Moving {self.key} into {self.cache.execution_device}')
@ -337,14 +348,20 @@ class ModelCache(object):
self.logger.debug(f"After unloading: cached_models={len(self._cached_models)}")
def _offload_unlocked_models(self):
for model_key, cache_entry in self._cached_models.items():
def _offload_unlocked_models(self, size_needed: int=0):
reserved = self.max_vram_cache_size * GIG
vram_in_use = torch.cuda.memory_allocated()
self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
for model_key, cache_entry in sorted(self._cached_models.items(), key=lambda x:x[1].size):
if vram_in_use <= reserved:
break
if not cache_entry.locked and cache_entry.loaded:
self.logger.debug(f'Offloading {model_key} from {self.execution_device} into {self.storage_device}')
with VRAMUsage() as mem:
cache_entry.model.to(self.storage_device)
self.logger.debug(f'GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB')
vram_in_use += mem.vram_used # note vram_used is negative
self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
def _local_model_hash(self, model_path: Union[str, Path]) -> str:
sha = hashlib.sha256()

208
invokeai/backend/model_management/model_manager.py
View File

@ -231,6 +231,7 @@ from __future__ import annotations
import os
import hashlib
import textwrap
import yaml
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, List, Tuple, Union, Dict, Set, Callable, types
@ -246,11 +247,12 @@ import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.util import CUDA_DEVICE, Chdir
from .model_cache import ModelCache, ModelLocker
from .model_search import ModelSearch
from .models import (
BaseModelType, ModelType, SubModelType,
ModelError, SchedulerPredictionType, MODEL_CLASSES,
ModelConfigBase, ModelNotFoundException,
)
ModelConfigBase, ModelNotFoundException, InvalidModelException,
)
# We are only starting to number the config file with release 3.
# The config file version doesn't have to start at release version, but it will help
@ -274,10 +276,6 @@ class ModelInfo():
def __exit__(self,*args, **kwargs):
self.context.__exit__(*args, **kwargs)
class InvalidModelError(Exception):
"Raised when an invalid model is requested"
pass
class AddModelResult(BaseModel):
name: str = Field(description="The name of the model after installation")
model_type: ModelType = Field(description="The type of model")
@ -314,6 +312,9 @@ class ModelManager(object):
self.config_path = None
if isinstance(config, (str, Path)):
self.config_path = Path(config)
if not self.config_path.exists():
logger.warning(f'The file {self.config_path} was not found. Initializing a new file')
self.initialize_model_config(self.config_path)
config = OmegaConf.load(self.config_path)
elif not isinstance(config, DictConfig):
@ -322,9 +323,31 @@ class ModelManager(object):
self.config_meta = ConfigMeta(**config.pop("__metadata__"))
# TODO: metadata not found
# TODO: version check
self.app_config = InvokeAIAppConfig.get_config()
self.logger = logger
self.cache = ModelCache(
max_cache_size=max_cache_size,
max_vram_cache_size = self.app_config.max_vram_cache_size,
execution_device = device_type,
precision = precision,
sequential_offload = sequential_offload,
logger = logger,
)
self._read_models(config)
def _read_models(self, config: Optional[DictConfig] = None):
if not config:
if self.config_path:
config = OmegaConf.load(self.config_path)
else:
return
self.models = dict()
for model_key, model_config in config.items():
if model_key.startswith('_'):
continue
model_name, base_model, model_type = self.parse_key(model_key)
model_class = MODEL_CLASSES[base_model][model_type]
# alias for config file
@ -332,20 +355,20 @@ class ModelManager(object):
self.models[model_key] = model_class.create_config(**model_config)
# check config version number and update on disk/RAM if necessary
self.app_config = InvokeAIAppConfig.get_config()
self.logger = logger
self.cache = ModelCache(
max_cache_size=max_cache_size,
execution_device = device_type,
precision = precision,
sequential_offload = sequential_offload,
logger = logger,
)
self.cache_keys = dict()
# add controlnet, lora and textual_inversion models from disk
self.scan_models_directory()
def sync_to_config(self):
"""
Call this when `models.yaml` has been changed externally.
This will reinitialize internal data structures
"""
# Reread models directory; note that this will reinitialize the cache,
# causing otherwise unreferenced models to be removed from memory
self._read_models()
def model_exists(
self,
model_name: str,
@ -386,6 +409,16 @@ class ModelManager(object):
def _get_model_cache_path(self, model_path):
return self.app_config.models_path / ".cache" / hashlib.md5(str(model_path).encode()).hexdigest()
@classmethod
def initialize_model_config(cls, config_path: Path):
"""Create empty config file"""
with open(config_path,'w') as yaml_file:
yaml_file.write(yaml.dump({'__metadata__':
{'version':'3.0.0'}
}
)
)
def get_model(
self,
model_name: str,
@ -516,7 +549,10 @@ class ModelManager(object):
model_keys = [self.create_key(model_name, base_model, model_type)] if model_name else sorted(self.models, key=str.casefold)
models = []
for model_key in model_keys:
model_config = self.models[model_key]
model_config = self.models.get(model_key)
if not model_config:
self.logger.error(f'Unknown model {model_name}')
raise KeyError(f'Unknown model {model_name}')
cur_model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
if base_model is not None and cur_base_model != base_model:
@ -527,9 +563,9 @@ class ModelManager(object):
model_dict = dict(
**model_config.dict(exclude_defaults=True),
# OpenAPIModelInfoBase
name=cur_model_name,
model_name=cur_model_name,
base_model=cur_base_model,
type=cur_model_type,
model_type=cur_model_type,
)
models.append(model_dict)
@ -578,6 +614,7 @@ class ModelManager(object):
rmtree(str(model_path))
else:
model_path.unlink()
self.commit()
# LS: tested
def add_model(
@ -634,11 +671,61 @@ class ModelManager(object):
config = model_config,
)
def rename_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
):
'''
Rename or rebase a model.
'''
if new_name is None and new_base is None:
self.logger.error("rename_model() called with neither a new_name nor a new_base. {model_name} unchanged.")
return
model_key = self.create_key(model_name, base_model, model_type)
model_cfg = self.models.get(model_key, None)
if not model_cfg:
raise KeyError(f"Unknown model: {model_key}")
old_path = self.app_config.root_path / model_cfg.path
new_name = new_name or model_name
new_base = new_base or base_model
new_key = self.create_key(new_name, new_base, model_type)
if new_key in self.models:
raise ValueError(f'Attempt to overwrite existing model definition "{new_key}"')
# if this is a model file/directory that we manage ourselves, we need to move it
if old_path.is_relative_to(self.app_config.models_path):
new_path = self.app_config.root_path / 'models' / new_base.value / model_type.value / new_name
move(old_path, new_path)
model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
# clean up caches
old_model_cache = self._get_model_cache_path(old_path)
if old_model_cache.exists():
if old_model_cache.is_dir():
rmtree(str(old_model_cache))
else:
old_model_cache.unlink()
cache_ids = self.cache_keys.pop(model_key, [])
for cache_id in cache_ids:
self.cache.uncache_model(cache_id)
self.models.pop(model_key, None) # delete
self.models[new_key] = model_cfg
self.commit()
def convert_model (
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main,ModelType.Vae],
dest_directory: Optional[Path]=None,
) -> AddModelResult:
'''
Convert a checkpoint file into a diffusers folder, deleting the cached
@ -665,14 +752,14 @@ class ModelManager(object):
)
checkpoint_path = self.app_config.root_path / info["path"]
old_diffusers_path = self.app_config.models_path / model.location
new_diffusers_path = self.app_config.models_path / base_model.value / model_type.value / model_name
new_diffusers_path = (dest_directory or self.app_config.models_path / base_model.value / model_type.value) / model_name
if new_diffusers_path.exists():
raise ValueError(f"A diffusers model already exists at {new_diffusers_path}")
try:
move(old_diffusers_path,new_diffusers_path)
info["model_format"] = "diffusers"
info["path"] = str(new_diffusers_path.relative_to(self.app_config.root_path))
info["path"] = str(new_diffusers_path) if dest_directory else str(new_diffusers_path.relative_to(self.app_config.root_path))
info.pop('config')
result = self.add_model(model_name, base_model, model_type,
@ -802,6 +889,8 @@ class ModelManager(object):
model_config: ModelConfigBase = model_class.probe_config(str(model_path))
self.models[model_key] = model_config
new_models_found = True
except InvalidModelException:
self.logger.warning(f"Not a valid model: {model_path}")
except NotImplementedError as e:
self.logger.warning(e)
@ -810,6 +899,7 @@ class ModelManager(object):
if (new_models_found or imported_models) and self.config_path:
self.commit()
def autoimport(self)->Dict[str, AddModelResult]:
'''
Scan the autoimport directory (if defined) and import new models, delete defunct models.
@ -817,57 +907,42 @@ class ModelManager(object):
# avoid circular import
from invokeai.backend.install.model_install_backend import ModelInstall
from invokeai.frontend.install.model_install import ask_user_for_prediction_type
class ScanAndImport(ModelSearch):
def __init__(self, directories, logger, ignore: Set[Path], installer: ModelInstall):
super().__init__(directories, logger)
self.installer = installer
self.ignore = ignore
def on_search_started(self):
self.new_models_found = dict()
def on_model_found(self, model: Path):
if model not in self.ignore:
self.new_models_found.update(self.installer.heuristic_import(model))
def on_search_completed(self):
self.logger.info(f'Scanned {self._items_scanned} files and directories, imported {len(self.new_models_found)} models')
def models_found(self):
return self.new_models_found
installer = ModelInstall(config = self.app_config,
model_manager = self,
prediction_type_helper = ask_user_for_prediction_type,
)
scanned_dirs = set()
config = self.app_config
known_paths = {(self.app_config.root_path / x['path']) for x in self.list_models()}
for autodir in [config.autoimport_dir,
config.lora_dir,
config.embedding_dir,
config.controlnet_dir]:
if autodir is None:
continue
self.logger.info(f'Scanning {autodir} for models to import')
installed = dict()
autodir = self.app_config.root_path / autodir
if not autodir.exists():
continue
items_scanned = 0
new_models_found = dict()
for root, dirs, files in os.walk(autodir):
items_scanned += len(dirs) + len(files)
for d in dirs:
path = Path(root) / d
if path in known_paths or path.parent in scanned_dirs:
scanned_dirs.add(path)
continue
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
new_models_found.update(installer.heuristic_import(path))
scanned_dirs.add(path)
for f in files:
path = Path(root) / f
if path in known_paths or path.parent in scanned_dirs:
continue
if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
import_result = installer.heuristic_import(path)
new_models_found.update(import_result)
self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
installed.update(new_models_found)
return installed
known_paths = {config.root_path / x['path'] for x in self.list_models()}
directories = {config.root_path / x for x in [config.autoimport_dir,
config.lora_dir,
config.embedding_dir,
config.controlnet_dir]
}
scanner = ScanAndImport(directories, self.logger, ignore=known_paths, installer=installer)
scanner.search()
return scanner.models_found()
def heuristic_import(self,
items_to_import: Set[str],
@ -905,3 +980,4 @@ class ModelManager(object):
successfully_installed.update(installed)
self.commit()
return successfully_installed

7
invokeai/backend/model_management/model_merge.py
View File

@ -11,7 +11,7 @@ from enum import Enum
from pathlib import Path
from diffusers import DiffusionPipeline
from diffusers import logging as dlogging
from typing import List, Union
from typing import List, Union, Optional
import invokeai.backend.util.logging as logger
@ -74,6 +74,7 @@ class ModelMerger(object):
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
force: bool = False,
merge_dest_directory: Optional[Path] = None,
**kwargs,
) -> AddModelResult:
"""
@ -85,7 +86,7 @@ class ModelMerger(object):
:param interp: The interpolation method to use for the merging. Supports "weighted_average", "sigmoid", "inv_sigmoid", "add_difference" and None.
Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_difference" is supported. Add_difference is A+(B-C).
:param force: Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
**kwargs - the default DiffusionPipeline.get_config_dict kwargs:
cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
"""
@ -111,7 +112,7 @@ class ModelMerger(object):
merged_pipe = self.merge_diffusion_models(
model_paths, alpha, merge_method, force, **kwargs
)
dump_path = config.models_path / base_model.value / ModelType.Main.value
dump_path = Path(merge_dest_directory) if merge_dest_directory else config.models_path / base_model.value / ModelType.Main.value
dump_path.mkdir(parents=True, exist_ok=True)
dump_path = dump_path / merged_model_name

12
invokeai/backend/model_management/model_probe.py
View File

@ -61,7 +61,7 @@ class ModelProbe(object):
elif isinstance(model,(dict,ModelMixin,ConfigMixin)):
return cls.probe(model_path=None, model=model, prediction_type_helper=prediction_type_helper)
else:
raise Exception("model parameter {model} is neither a Path, nor a model")
raise ValueError("model parameter {model} is neither a Path, nor a model")
@classmethod
def probe(cls,
@ -240,7 +240,7 @@ class CheckpointProbeBase(ProbeBase):
elif in_channels == 4:
return ModelVariantType.Normal
else:
raise Exception("Cannot determine variant type")
raise ValueError(f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}")
class PipelineCheckpointProbe(CheckpointProbeBase):
def get_base_type(self)->BaseModelType:
@ -254,7 +254,7 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
# TODO: Verify that this is correct! Need an XL checkpoint file for this.
if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
return BaseModelType.StableDiffusionXL
raise Exception("Cannot determine base type")
raise ValueError("Cannot determine base type")
def get_scheduler_prediction_type(self)->SchedulerPredictionType:
type = self.get_base_type()
@ -335,7 +335,7 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
return BaseModelType.StableDiffusion2
elif self.checkpoint_path and self.helper:
return self.helper(self.checkpoint_path)
raise Exception("Unable to determine base type for {self.checkpoint_path}")
raise ValueError("Unable to determine base type for {self.checkpoint_path}")
########################################################
# classes for probing folders
@ -428,7 +428,7 @@ class ControlNetFolderProbe(FolderProbeBase):
def get_base_type(self)->BaseModelType:
config_file = self.folder_path / 'config.json'
if not config_file.exists():
raise Exception(f"Cannot determine base type for {self.folder_path}")
raise ValueError(f"Cannot determine base type for {self.folder_path}")
with open(config_file,'r') as file:
config = json.load(file)
# no obvious way to distinguish between sd2-base and sd2-768
@ -445,7 +445,7 @@ class LoRAFolderProbe(FolderProbeBase):
model_file = base_file
break
if not model_file:
raise Exception('Unknown LoRA format encountered')
raise ValueError('Unknown LoRA format encountered')
return LoRACheckpointProbe(model_file,None).get_base_type()
############## register probe classes ######

103
invokeai/backend/model_management/model_search.py Normal file
View File

@ -0,0 +1,103 @@
# Copyright 2023, Lincoln D. Stein and the InvokeAI Team
"""
Abstract base class for recursive directory search for models.
"""
import os
from abc import ABC, abstractmethod
from typing import List, Set, types
from pathlib import Path
import invokeai.backend.util.logging as logger
class ModelSearch(ABC):
def __init__(self, directories: List[Path], logger: types.ModuleType=logger):
"""
Initialize a recursive model directory search.
:param directories: List of directory Paths to recurse through
:param logger: Logger to use
"""
self.directories = directories
self.logger = logger
self._items_scanned = 0
self._models_found = 0
self._scanned_dirs = set()
self._scanned_paths = set()
self._pruned_paths = set()
@abstractmethod
def on_search_started(self):
"""
Called before the scan starts.
"""
pass
@abstractmethod
def on_model_found(self, model: Path):
"""
Process a found model. Raise an exception if something goes wrong.
:param model: Model to process - could be a directory or checkpoint.
"""
pass
@abstractmethod
def on_search_completed(self):
"""
Perform some activity when the scan is completed. May use instance
variables, items_scanned and models_found
"""
pass
def search(self):
self.on_search_started()
for dir in self.directories:
self.walk_directory(dir)
self.on_search_completed()
def walk_directory(self, path: Path):
for root, dirs, files in os.walk(path):
if str(Path(root).name).startswith('.'):
self._pruned_paths.add(root)
if any([Path(root).is_relative_to(x) for x in self._pruned_paths]):
continue
self._items_scanned += len(dirs) + len(files)
for d in dirs:
path = Path(root) / d
if path in self._scanned_paths or path.parent in self._scanned_dirs:
self._scanned_dirs.add(path)
continue
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
try:
self.on_model_found(path)
self._models_found += 1
self._scanned_dirs.add(path)
except Exception as e:
self.logger.warning(str(e))
for f in files:
path = Path(root) / f
if path.parent in self._scanned_dirs:
continue
if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
try:
self.on_model_found(path)
self._models_found += 1
except Exception as e:
self.logger.warning(str(e))
class FindModels(ModelSearch):
def on_search_started(self):
self.models_found: Set[Path] = set()
def on_model_found(self,model: Path):
self.models_found.add(model)
def on_search_completed(self):
pass
def list_models(self) -> List[Path]:
self.search()
return self.models_found

12
invokeai/backend/model_management/models/__init__.py
View File

@ -2,7 +2,7 @@ import inspect
from enum import Enum
from pydantic import BaseModel
from typing import Literal, get_origin
from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException
from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException, InvalidModelException
from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
from .sdxl import StableDiffusionXLModel
from .vae import VaeModel
@ -54,9 +54,9 @@ MODEL_CONFIGS = list()
OPENAPI_MODEL_CONFIGS = list()
class OpenAPIModelInfoBase(BaseModel):
name: str
model_name: str
base_model: BaseModelType
type: ModelType
model_type: ModelType
for base_model, models in MODEL_CLASSES.items():
@ -65,7 +65,9 @@ for base_model, models in MODEL_CLASSES.items():
model_configs.discard(None)
MODEL_CONFIGS.extend(model_configs)
for cfg in model_configs:
# LS: sort to get the checkpoint configs first, which makes
# for a better template in the Swagger docs
for cfg in sorted(model_configs, key=lambda x: str(x)):
model_name, cfg_name = cfg.__qualname__.split('.')[-2:]
openapi_cfg_name = model_name + cfg_name
if openapi_cfg_name in vars():
@ -73,7 +75,7 @@ for base_model, models in MODEL_CLASSES.items():
api_wrapper = type(openapi_cfg_name, (cfg, OpenAPIModelInfoBase), dict(
__annotations__ = dict(
type=Literal[model_type.value],
model_type=Literal[model_type.value],
),
))

4
invokeai/backend/model_management/models/base.py
View File

@ -15,6 +15,9 @@ from contextlib import suppress
from pydantic import BaseModel, Field
from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
class InvalidModelException(Exception):
pass
class ModelNotFoundException(Exception):
pass
@ -60,7 +63,6 @@ class ModelConfigBase(BaseModel):
path: str # or Path
description: Optional[str] = Field(None)
model_format: Optional[str] = Field(None)
# do not save to config
error: Optional[ModelError] = Field(None)
class Config:

37
invokeai/backend/model_management/models/controlnet.py
View File

@ -1,8 +1,7 @@
import os
import torch
from enum import Enum
from pathlib import Path
from typing import Optional, Union, Literal
from typing import Optional
from .base import (
ModelBase,
ModelConfigBase,
@ -13,6 +12,8 @@ from .base import (
calc_model_size_by_fs,
calc_model_size_by_data,
classproperty,
InvalidModelException,
ModelNotFoundException,
)
class ControlNetModelFormat(str, Enum):
@ -59,10 +60,20 @@ class ControlNetModel(ModelBase):
if child_type is not None:
raise Exception("There is no child models in controlnet model")
model = self.model_class.from_pretrained(
self.model_path,
torch_dtype=torch_dtype,
)
model = None
for variant in ['fp16',None]:
try:
model = self.model_class.from_pretrained(
self.model_path,
torch_dtype=torch_dtype,
variant=variant,
)
break
except:
pass
if not model:
raise ModelNotFoundException()
# calc more accurate size
self.model_size = calc_model_size_by_data(model)
return model
@ -73,10 +84,18 @@ class ControlNetModel(ModelBase):
@classmethod
def detect_format(cls, path: str):
if not os.path.exists(path):
raise ModelNotFoundException()
if os.path.isdir(path):
return ControlNetModelFormat.Diffusers
else:
return ControlNetModelFormat.Checkpoint
if os.path.exists(os.path.join(path, "config.json")):
return ControlNetModelFormat.Diffusers
if os.path.isfile(path):
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "pth"]]):
return ControlNetModelFormat.Checkpoint
raise InvalidModelException(f"Not a valid model: {path}")
@classmethod
def convert_if_required(

15
invokeai/backend/model_management/models/lora.py
View File

@ -9,6 +9,7 @@ from .base import (
ModelType,
SubModelType,
classproperty,
InvalidModelException,
)
# TODO: naming
from ..lora import LoRAModel as LoRAModelRaw
@ -56,10 +57,18 @@ class LoRAModel(ModelBase):
@classmethod
def detect_format(cls, path: str):
if not os.path.exists(path):
raise ModelNotFoundException()
if os.path.isdir(path):
return LoRAModelFormat.Diffusers
else:
return LoRAModelFormat.LyCORIS
if os.path.exists(os.path.join(path, "pytorch_lora_weights.bin")):
return LoRAModelFormat.Diffusers
if os.path.isfile(path):
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
return LoRAModelFormat.LyCORIS
raise InvalidModelException(f"Not a valid model: {path}")
@classmethod
def convert_if_required(

32
invokeai/backend/model_management/models/stable_diffusion.py
View File

@ -13,6 +13,7 @@ from .base import (
SilenceWarnings,
read_checkpoint_meta,
classproperty,
InvalidModelException,
)
from invokeai.app.services.config import InvokeAIAppConfig
from omegaconf import OmegaConf
@ -33,8 +34,7 @@ class StableDiffusion1Model(DiffusersModel):
vae: Optional[str] = Field(None)
config: str
variant: ModelVariantType
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
assert base_model == BaseModelType.StableDiffusion1
assert model_type == ModelType.Main
@ -95,10 +95,18 @@ class StableDiffusion1Model(DiffusersModel):
@classmethod
def detect_format(cls, model_path: str):
if not os.path.exists(model_path):
raise ModelNotFoundException()
if os.path.isdir(model_path):
return StableDiffusion1ModelFormat.Diffusers
else:
return StableDiffusion1ModelFormat.Checkpoint
if os.path.exists(os.path.join(model_path, "model_index.json")):
return StableDiffusion1ModelFormat.Diffusers
if os.path.isfile(model_path):
if any([model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
return StableDiffusion1ModelFormat.Checkpoint
raise InvalidModelException(f"Not a valid model: {model_path}")
@classmethod
def convert_if_required(
@ -197,10 +205,18 @@ class StableDiffusion2Model(DiffusersModel):
@classmethod
def detect_format(cls, model_path: str):
if not os.path.exists(model_path):
raise ModelNotFoundException()
if os.path.isdir(model_path):
return StableDiffusion2ModelFormat.Diffusers
else:
return StableDiffusion2ModelFormat.Checkpoint
if os.path.exists(os.path.join(model_path, "model_index.json")):
return StableDiffusion2ModelFormat.Diffusers
if os.path.isfile(model_path):
if any([model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
return StableDiffusion2ModelFormat.Checkpoint
raise InvalidModelException(f"Not a valid model: {model_path}")
@classmethod
def convert_if_required(

14
invokeai/backend/model_management/models/textual_inversion.py
View File

@ -9,6 +9,7 @@ from .base import (
SubModelType,
classproperty,
ModelNotFoundException,
InvalidModelException,
)
# TODO: naming
from ..lora import TextualInversionModel as TextualInversionModelRaw
@ -59,7 +60,18 @@ class TextualInversionModel(ModelBase):
@classmethod
def detect_format(cls, path: str):
return None
if not os.path.exists(path):
raise ModelNotFoundException()
if os.path.isdir(path):
if os.path.exists(os.path.join(path, "learned_embeds.bin")):
return None # diffusers-ti
if os.path.isfile(path):
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
return None
raise InvalidModelException(f"Not a valid model: {path}")
@classmethod
def convert_if_required(

15
invokeai/backend/model_management/models/vae.py
View File

@ -15,6 +15,7 @@ from .base import (
calc_model_size_by_fs,
calc_model_size_by_data,
classproperty,
InvalidModelException,
)
from invokeai.app.services.config import InvokeAIAppConfig
from diffusers.utils import is_safetensors_available
@ -75,10 +76,18 @@ class VaeModel(ModelBase):
@classmethod
def detect_format(cls, path: str):
if not os.path.exists(path):
raise ModelNotFoundException()
if os.path.isdir(path):
return VaeModelFormat.Diffusers
else:
return VaeModelFormat.Checkpoint
if os.path.exists(os.path.join(path, "config.json")):
return VaeModelFormat.Diffusers
if os.path.isfile(path):
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
return VaeModelFormat.Checkpoint
raise InvalidModelException(f"Not a valid model: {path}")
@classmethod
def convert_if_required(

4
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -127,7 +127,7 @@ class AddsMaskGuidance:
def _t_for_field(self, field_name: str, t):
if field_name == "pred_original_sample":
return torch.zeros_like(t, dtype=t.dtype) # it represents t=0
return self.scheduler.timesteps[-1]
return t
def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
@ -631,7 +631,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
control_latent_input = torch.cat([unet_latent_input] * 2)
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
encoder_hidden_states = torch.cat([conditioning_data.unconditioned_embeddings])
encoder_hidden_states = conditioning_data.text_embeddings
else:
encoder_hidden_states = torch.cat([conditioning_data.unconditioned_embeddings,
conditioning_data.text_embeddings])

36
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -241,11 +241,45 @@ class InvokeAIDiffuserComponent:
def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
# fast batched path
def _pad_conditioning(cond, target_len, encoder_attention_mask):
conditioning_attention_mask = torch.ones((cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype)
if cond.shape[1] < max_len:
conditioning_attention_mask = torch.cat([
conditioning_attention_mask,
torch.zeros((cond.shape[0], max_len - cond.shape[1]), device=cond.device, dtype=cond.dtype),
], dim=1)
cond = torch.cat([
cond,
torch.zeros((cond.shape[0], max_len - cond.shape[1], cond.shape[2]), device=cond.device, dtype=cond.dtype),
], dim=1)
if encoder_attention_mask is None:
encoder_attention_mask = conditioning_attention_mask
else:
encoder_attention_mask = torch.cat([
encoder_attention_mask,
conditioning_attention_mask,
])
return cond, encoder_attention_mask
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
encoder_attention_mask = None
if unconditioning.shape[1] != conditioning.shape[1]:
max_len = max(unconditioning.shape[1], conditioning.shape[1])
unconditioning, encoder_attention_mask = _pad_conditioning(unconditioning, max_len, encoder_attention_mask)
conditioning, encoder_attention_mask = _pad_conditioning(conditioning, max_len, encoder_attention_mask)
both_conditionings = torch.cat([unconditioning, conditioning])
both_results = self.model_forward_callback(
x_twice, sigma_twice, both_conditionings, **kwargs,
x_twice, sigma_twice, both_conditionings,
encoder_attention_mask=encoder_attention_mask,
**kwargs,
)
unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
return unconditioned_next_x, conditioned_next_x

2
invokeai/frontend/install/model_install.py
View File

@ -773,7 +773,7 @@ def main():
config.parse_args(invoke_args)
logger = InvokeAILogger().getLogger(config=config)
if not (config.root_dir / config.conf_path.parent).exists():
if not (config.conf_path / 'models.yaml').exists():
logger.info(
"Your InvokeAI root directory is not set up. Calling invokeai-configure."
)

1
invokeai/frontend/web/.eslintrc.js
View File

@ -36,6 +36,7 @@ module.exports = {
],
'prettier/prettier': ['error', { endOfLine: 'auto' }],
'@typescript-eslint/ban-ts-comment': 'warn',
'@typescript-eslint/no-explicit-any': 'warn',
'@typescript-eslint/no-empty-interface': [
'error',
{

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invokeai/frontend/web/dist/index.html vendored
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@ -12,7 +12,7 @@
margin: 0;
}
</style>
<script type="module" crossorigin src="./assets/index-15b43c6c.js"></script>
<script type="module" crossorigin src="./assets/index-8888b06f.js"></script>
</head>
<body dir="ltr">

33
invokeai/frontend/web/dist/locales/en.json vendored
View File

@ -53,7 +53,7 @@
"linear": "Linear",
"nodes": "Node Editor",
"batch": "Batch Manager",
"modelmanager": "Model Manager",
"modelManager": "Model Manager",
"postprocessing": "Post Processing",
"nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
"postProcessing": "Post Processing",
@ -102,7 +102,8 @@
"openInNewTab": "Open in New Tab",
"dontAskMeAgain": "Don't ask me again",
"areYouSure": "Are you sure?",
"imagePrompt": "Image Prompt"
"imagePrompt": "Image Prompt",
"clearNodes": "Are you sure you want to clear all nodes?"
},
"gallery": {
"generations": "Generations",
@ -118,7 +119,7 @@
"pinGallery": "Pin Gallery",
"allImagesLoaded": "All Images Loaded",
"loadMore": "Load More",
"noImagesInGallery": "No Images In Gallery",
"noImagesInGallery": "No Images to Display",
"deleteImage": "Delete Image",
"deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
"deleteImagePermanent": "Deleted images cannot be restored.",
@ -342,6 +343,7 @@
"safetensorModels": "SafeTensors",
"modelAdded": "Model Added",
"modelUpdated": "Model Updated",
"modelUpdateFailed": "Model Update Failed",
"modelEntryDeleted": "Model Entry Deleted",
"cannotUseSpaces": "Cannot Use Spaces",
"addNew": "Add New",
@ -396,8 +398,8 @@
"delete": "Delete",
"deleteModel": "Delete Model",
"deleteConfig": "Delete Config",
"deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
"deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
"deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
"deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
"formMessageDiffusersModelLocation": "Diffusers Model Location",
"formMessageDiffusersModelLocationDesc": "Please enter at least one.",
"formMessageDiffusersVAELocation": "VAE Location",
@ -408,7 +410,7 @@
"convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
"convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
"convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
"convertToDiffusersHelpText6": "Do you wish to convert this model?",
"convertToDiffusersSaveLocation": "Save Location",
"v1": "v1",
@ -419,12 +421,14 @@
"pathToCustomConfig": "Path To Custom Config",
"statusConverting": "Converting",
"modelConverted": "Model Converted",
"modelConversionFailed": "Model Conversion Failed",
"sameFolder": "Same folder",
"invokeRoot": "InvokeAI folder",
"custom": "Custom",
"customSaveLocation": "Custom Save Location",
"merge": "Merge",
"modelsMerged": "Models Merged",
"modelsMergeFailed": "Model Merge Failed",
"mergeModels": "Merge Models",
"modelOne": "Model 1",
"modelTwo": "Model 2",
@ -445,7 +449,8 @@
"weightedSum": "Weighted Sum",
"none": "none",
"addDifference": "Add Difference",
"pickModelType": "Pick Model Type"
"pickModelType": "Pick Model Type",
"selectModel": "Select Model"
},
"parameters": {
"general": "General",
@ -528,7 +533,7 @@
"hidePreview": "Hide Preview",
"showPreview": "Show Preview",
"controlNetControlMode": "Control Mode",
"clipSkip": "Clip Skip",
"clipSkip": "CLIP Skip",
"aspectRatio": "Ratio"
},
"settings": {
@ -593,7 +598,11 @@
"metadataLoadFailed": "Failed to load metadata",
"initialImageSet": "Initial Image Set",
"initialImageNotSet": "Initial Image Not Set",
"initialImageNotSetDesc": "Could not load initial image"
"initialImageNotSetDesc": "Could not load initial image",
"nodesSaved": "Nodes Saved",
"nodesLoaded": "Nodes Loaded",
"nodesLoadedFailed": "Failed To Load Nodes",
"nodesCleared": "Nodes Cleared"
},
"tooltip": {
"feature": {
@ -674,5 +683,11 @@
"showProgressImages": "Show Progress Images",
"hideProgressImages": "Hide Progress Images",
"swapSizes": "Swap Sizes"
},
"nodes": {
"reloadSchema": "Reload Schema",
"saveNodes": "Save Nodes",
"loadNodes": "Load Nodes",
"clearNodes": "Clear Nodes"
}
}

1
invokeai/frontend/web/package.json
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@ -108,6 +108,7 @@
"roarr": "^7.15.0",
"serialize-error": "^11.0.0",
"socket.io-client": "^4.7.0",
"use-debounce": "^9.0.4",
"use-image": "^1.1.1",
"uuid": "^9.0.0",
"zod": "^3.21.4"

33
invokeai/frontend/web/public/locales/en.json
View File

@ -53,7 +53,7 @@
"linear": "Linear",
"nodes": "Node Editor",
"batch": "Batch Manager",
"modelmanager": "Model Manager",
"modelManager": "Model Manager",
"postprocessing": "Post Processing",
"nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
"postProcessing": "Post Processing",
@ -102,7 +102,8 @@
"openInNewTab": "Open in New Tab",
"dontAskMeAgain": "Don't ask me again",
"areYouSure": "Are you sure?",
"imagePrompt": "Image Prompt"
"imagePrompt": "Image Prompt",
"clearNodes": "Are you sure you want to clear all nodes?"
},
"gallery": {
"generations": "Generations",
@ -118,7 +119,7 @@
"pinGallery": "Pin Gallery",
"allImagesLoaded": "All Images Loaded",
"loadMore": "Load More",
"noImagesInGallery": "No Images In Gallery",
"noImagesInGallery": "No Images to Display",
"deleteImage": "Delete Image",
"deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
"deleteImagePermanent": "Deleted images cannot be restored.",
@ -342,6 +343,7 @@
"safetensorModels": "SafeTensors",
"modelAdded": "Model Added",
"modelUpdated": "Model Updated",
"modelUpdateFailed": "Model Update Failed",
"modelEntryDeleted": "Model Entry Deleted",
"cannotUseSpaces": "Cannot Use Spaces",
"addNew": "Add New",
@ -396,8 +398,8 @@
"delete": "Delete",
"deleteModel": "Delete Model",
"deleteConfig": "Delete Config",
"deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
"deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
"deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
"deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
"formMessageDiffusersModelLocation": "Diffusers Model Location",
"formMessageDiffusersModelLocationDesc": "Please enter at least one.",
"formMessageDiffusersVAELocation": "VAE Location",
@ -408,7 +410,7 @@
"convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
"convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
"convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
"convertToDiffusersHelpText6": "Do you wish to convert this model?",
"convertToDiffusersSaveLocation": "Save Location",
"v1": "v1",
@ -419,12 +421,14 @@
"pathToCustomConfig": "Path To Custom Config",
"statusConverting": "Converting",
"modelConverted": "Model Converted",
"modelConversionFailed": "Model Conversion Failed",
"sameFolder": "Same folder",
"invokeRoot": "InvokeAI folder",
"custom": "Custom",
"customSaveLocation": "Custom Save Location",
"merge": "Merge",
"modelsMerged": "Models Merged",
"modelsMergeFailed": "Model Merge Failed",
"mergeModels": "Merge Models",
"modelOne": "Model 1",
"modelTwo": "Model 2",
@ -445,7 +449,8 @@
"weightedSum": "Weighted Sum",
"none": "none",
"addDifference": "Add Difference",
"pickModelType": "Pick Model Type"
"pickModelType": "Pick Model Type",
"selectModel": "Select Model"
},
"parameters": {
"general": "General",
@ -528,7 +533,7 @@
"hidePreview": "Hide Preview",
"showPreview": "Show Preview",
"controlNetControlMode": "Control Mode",
"clipSkip": "Clip Skip",
"clipSkip": "CLIP Skip",
"aspectRatio": "Ratio"
},
"settings": {
@ -593,7 +598,11 @@
"metadataLoadFailed": "Failed to load metadata",
"initialImageSet": "Initial Image Set",
"initialImageNotSet": "Initial Image Not Set",
"initialImageNotSetDesc": "Could not load initial image"
"initialImageNotSetDesc": "Could not load initial image",
"nodesSaved": "Nodes Saved",
"nodesLoaded": "Nodes Loaded",
"nodesLoadedFailed": "Failed To Load Nodes",
"nodesCleared": "Nodes Cleared"
},
"tooltip": {
"feature": {
@ -674,5 +683,11 @@
"showProgressImages": "Show Progress Images",
"hideProgressImages": "Hide Progress Images",
"swapSizes": "Swap Sizes"
},
"nodes": {
"reloadSchema": "Reload Schema",
"saveNodes": "Save Nodes",
"loadNodes": "Load Nodes",
"clearNodes": "Clear Nodes"
}
}

5
invokeai/frontend/web/src/app/components/App.tsx
View File

@ -6,9 +6,7 @@ import { PartialAppConfig } from 'app/types/invokeai';
import ImageUploader from 'common/components/ImageUploader';
import GalleryDrawer from 'features/gallery/components/GalleryPanel';
import DeleteImageModal from 'features/imageDeletion/components/DeleteImageModal';
import Lightbox from 'features/lightbox/components/Lightbox';
import SiteHeader from 'features/system/components/SiteHeader';
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
import { configChanged } from 'features/system/store/configSlice';
import { languageSelector } from 'features/system/store/systemSelectors';
import FloatingGalleryButton from 'features/ui/components/FloatingGalleryButton';
@ -34,8 +32,6 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
const log = useLogger();
const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
const dispatch = useAppDispatch();
useEffect(() => {
@ -54,7 +50,6 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
return (
<>
<Grid w="100vw" h="100vh" position="relative" overflow="hidden">
{isLightboxEnabled && <Lightbox />}
<ImageUploader>
<Grid
sx={{

44
invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx
View File

@ -1,8 +1,4 @@
import { Box, ChakraProps, Flex, Heading, Image } from '@chakra-ui/react';
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import { memo } from 'react';
import { TypesafeDraggableData } from './typesafeDnd';
@ -32,24 +28,7 @@ const STYLES: ChakraProps['sx'] = {
},
};
const selector = createSelector(
stateSelector,
(state) => {
const gallerySelectionCount = state.gallery.selection.length;
const batchSelectionCount = state.batch.selection.length;
return {
gallerySelectionCount,
batchSelectionCount,
};
},
defaultSelectorOptions
);
const DragPreview = (props: OverlayDragImageProps) => {
const { gallerySelectionCount, batchSelectionCount } =
useAppSelector(selector);
if (!props.dragData) {
return;
}
@ -82,7 +61,7 @@ const DragPreview = (props: OverlayDragImageProps) => {
);
}
if (props.dragData.payloadType === 'BATCH_SELECTION') {
if (props.dragData.payloadType === 'IMAGE_NAMES') {
return (
<Flex
sx={{
@ -95,26 +74,7 @@ const DragPreview = (props: OverlayDragImageProps) => {
...STYLES,
}}
>
<Heading>{batchSelectionCount}</Heading>
<Heading size="sm">Images</Heading>
</Flex>
);
}
if (props.dragData.payloadType === 'GALLERY_SELECTION') {
return (
<Flex
sx={{
cursor: 'none',
userSelect: 'none',
position: 'relative',
alignItems: 'center',
justifyContent: 'center',
flexDir: 'column',
...STYLES,
}}
>
<Heading>{gallerySelectionCount}</Heading>
<Heading>{props.dragData.payload.image_names.length}</Heading>
<Heading size="sm">Images</Heading>
</Flex>
);

14
invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx
View File

@ -6,18 +6,18 @@ import {
useSensor,
useSensors,
} from '@dnd-kit/core';
import { snapCenterToCursor } from '@dnd-kit/modifiers';
import { dndDropped } from 'app/store/middleware/listenerMiddleware/listeners/imageDropped';
import { useAppDispatch } from 'app/store/storeHooks';
import { AnimatePresence, motion } from 'framer-motion';
import { PropsWithChildren, memo, useCallback, useState } from 'react';
import DragPreview from './DragPreview';
import { snapCenterToCursor } from '@dnd-kit/modifiers';
import { AnimatePresence, motion } from 'framer-motion';
import {
DndContext,
DragEndEvent,
DragStartEvent,
TypesafeDraggableData,
} from './typesafeDnd';
import { useAppDispatch } from 'app/store/storeHooks';
import { imageDropped } from 'app/store/middleware/listenerMiddleware/listeners/imageDropped';
type ImageDndContextProps = PropsWithChildren;
@ -42,18 +42,18 @@ const ImageDndContext = (props: ImageDndContextProps) => {
if (!activeData || !overData) {
return;
}
dispatch(imageDropped({ overData, activeData }));
dispatch(dndDropped({ overData, activeData }));
setActiveDragData(null);
},
[dispatch]
);
const mouseSensor = useSensor(MouseSensor, {
activationConstraint: { delay: 150, tolerance: 5 },
activationConstraint: { distance: 10 },
});
const touchSensor = useSensor(TouchSensor, {
activationConstraint: { delay: 150, tolerance: 5 },
activationConstraint: { distance: 10 },
});
// TODO: Use KeyboardSensor - needs composition of multiple collisionDetection algos

20
invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx
View File

@ -77,18 +77,14 @@ export type ImageDraggableData = BaseDragData & {
payload: { imageDTO: ImageDTO };
};
export type GallerySelectionDraggableData = BaseDragData & {
payloadType: 'GALLERY_SELECTION';
};
export type BatchSelectionDraggableData = BaseDragData & {
payloadType: 'BATCH_SELECTION';
export type ImageNamesDraggableData = BaseDragData & {
payloadType: 'IMAGE_NAMES';
payload: { image_names: string[] };
};
export type TypesafeDraggableData =
| ImageDraggableData
| GallerySelectionDraggableData
| BatchSelectionDraggableData;
| ImageNamesDraggableData;
interface UseDroppableTypesafeArguments
extends Omit<UseDroppableArguments, 'data'> {
@ -159,13 +155,11 @@ export const isValidDrop = (
case 'SET_NODES_IMAGE':
return payloadType === 'IMAGE_DTO';
case 'SET_MULTI_NODES_IMAGE':
return payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION';
return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
case 'ADD_TO_BATCH':
return payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION';
return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
case 'MOVE_BOARD':
return (
payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION' || 'BATCH_SELECTION'
);
return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
default:
return false;
}

6
invokeai/frontend/web/src/app/components/ThemeLocaleProvider.tsx
View File

@ -9,9 +9,9 @@ import { theme as invokeAITheme } from 'theme/theme';
import '@fontsource-variable/inter';
import { MantineProvider } from '@mantine/core';
import { mantineTheme } from 'mantine-theme/theme';
import 'overlayscrollbars/overlayscrollbars.css';
import 'theme/css/overlayscrollbars.css';
import { useMantineTheme } from 'mantine-theme/theme';
type ThemeLocaleProviderProps = {
children: ReactNode;
@ -35,8 +35,10 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
document.body.dir = direction;
}, [direction]);
const mantineTheme = useMantineTheme();
return (
<MantineProvider withGlobalStyles theme={mantineTheme}>
<MantineProvider theme={mantineTheme}>
<ChakraProvider theme={theme} colorModeManager={manager}>
{children}
</ChakraProvider>

5
invokeai/frontend/web/src/app/constants.ts
View File

@ -1,6 +1,7 @@
import { SchedulerParam } from 'features/parameters/store/parameterZodSchemas';
// zod needs the array to be `as const` to infer the type correctly
import { SchedulerParam } from 'features/parameters/types/parameterSchemas';
// this is the source of the `SchedulerParam` type, which is generated by zod
export const SCHEDULER_NAMES_AS_CONST = [
'euler',

67
invokeai/frontend/web/src/app/socketio/actions.ts
View File

@ -1,67 +0,0 @@
// import { createAction } from '@reduxjs/toolkit';
// import * as InvokeAI from 'app/types/invokeai';
// import { GalleryCategory } from 'features/gallery/store/gallerySlice';
// import { InvokeTabName } from 'features/ui/store/tabMap';
// /**
// * We can't use redux-toolkit's createSlice() to make these actions,
// * because they have no associated reducer. They only exist to dispatch
// * requests to the server via socketio. These actions will be handled
// * by the middleware.
// */
// export const generateImage = createAction<InvokeTabName>(
// 'socketio/generateImage'
// );
// export const runESRGAN = createAction<InvokeAI._Image>('socketio/runESRGAN');
// export const runFacetool = createAction<InvokeAI._Image>(
// 'socketio/runFacetool'
// );
// export const deleteImage = createAction<InvokeAI._Image>(
// 'socketio/deleteImage'
// );
// export const requestImages = createAction<GalleryCategory>(
// 'socketio/requestImages'
// );
// export const requestNewImages = createAction<GalleryCategory>(
// 'socketio/requestNewImages'
// );
// export const cancelProcessing = createAction<undefined>(
// 'socketio/cancelProcessing'
// );
// export const requestSystemConfig = createAction<undefined>(
// 'socketio/requestSystemConfig'
// );
// export const searchForModels = createAction<string>('socketio/searchForModels');
// export const addNewModel = createAction<
// InvokeAI.InvokeModelConfigProps | InvokeAI.InvokeDiffusersModelConfigProps
// >('socketio/addNewModel');
// export const deleteModel = createAction<string>('socketio/deleteModel');
// export const convertToDiffusers =
// createAction<InvokeAI.InvokeModelConversionProps>(
// 'socketio/convertToDiffusers'
// );
// export const mergeDiffusersModels =
// createAction<InvokeAI.InvokeModelMergingProps>(
// 'socketio/mergeDiffusersModels'
// );
// export const requestModelChange = createAction<string>(
// 'socketio/requestModelChange'
// );
// export const saveStagingAreaImageToGallery = createAction<string>(
// 'socketio/saveStagingAreaImageToGallery'
// );
// export const emptyTempFolder = createAction<undefined>(
// 'socketio/requestEmptyTempFolder'
// );
export default {};

209
invokeai/frontend/web/src/app/socketio/emitters.ts
View File

@ -1,209 +0,0 @@
import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
import * as InvokeAI from 'app/types/invokeai';
import type { RootState } from 'app/store/store';
import {
frontendToBackendParameters,
FrontendToBackendParametersConfig,
} from 'common/util/parameterTranslation';
import dateFormat from 'dateformat';
import {
GalleryCategory,
GalleryState,
removeImage,
} from 'features/gallery/store/gallerySlice';
import {
generationRequested,
modelChangeRequested,
modelConvertRequested,
modelMergingRequested,
setIsProcessing,
} from 'features/system/store/systemSlice';
import { InvokeTabName } from 'features/ui/store/tabMap';
import { Socket } from 'socket.io-client';
/**
* Returns an object containing all functions which use `socketio.emit()`.
* i.e. those which make server requests.
*/
const makeSocketIOEmitters = (
store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
socketio: Socket
) => {
// We need to dispatch actions to redux and get pieces of state from the store.
const { dispatch, getState } = store;
return {
emitGenerateImage: (generationMode: InvokeTabName) => {
dispatch(setIsProcessing(true));
const state: RootState = getState();
const {
generation: generationState,
postprocessing: postprocessingState,
system: systemState,
canvas: canvasState,
} = state;
const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
{
generationMode,
generationState,
postprocessingState,
canvasState,
systemState,
};
dispatch(generationRequested());
const { generationParameters, esrganParameters, facetoolParameters } =
frontendToBackendParameters(frontendToBackendParametersConfig);
socketio.emit(
'generateImage',
generationParameters,
esrganParameters,
facetoolParameters
);
// we need to truncate the init_mask base64 else it takes up the whole log
// TODO: handle maintaining masks for reproducibility in future
if (generationParameters.init_mask) {
generationParameters.init_mask = generationParameters.init_mask
.substr(0, 64)
.concat('...');
}
if (generationParameters.init_img) {
generationParameters.init_img = generationParameters.init_img
.substr(0, 64)
.concat('...');
}
dispatch(
addLogEntry({
timestamp: dateFormat(new Date(), 'isoDateTime'),
message: `Image generation requested: ${JSON.stringify({
...generationParameters,
...esrganParameters,
...facetoolParameters,
})}`,
})
);
},
emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
dispatch(setIsProcessing(true));
const {
postprocessing: {
upscalingLevel,
upscalingDenoising,
upscalingStrength,
},
} = getState();
const esrganParameters = {
upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
};
socketio.emit('runPostprocessing', imageToProcess, {
type: 'esrgan',
...esrganParameters,
});
dispatch(
addLogEntry({
timestamp: dateFormat(new Date(), 'isoDateTime'),
message: `ESRGAN upscale requested: ${JSON.stringify({
file: imageToProcess.url,
...esrganParameters,
})}`,
})
);
},
emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
dispatch(setIsProcessing(true));
const {
postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
} = getState();
const facetoolParameters: Record<string, unknown> = {
facetool_strength: facetoolStrength,
};
if (facetoolType === 'codeformer') {
facetoolParameters.codeformer_fidelity = codeformerFidelity;
}
socketio.emit('runPostprocessing', imageToProcess, {
type: facetoolType,
...facetoolParameters,
});
dispatch(
addLogEntry({
timestamp: dateFormat(new Date(), 'isoDateTime'),
message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
{
file: imageToProcess.url,
...facetoolParameters,
}
)}`,
})
);
},
emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
const { url, uuid, category, thumbnail } = imageToDelete;
dispatch(removeImage(imageToDelete));
socketio.emit('deleteImage', url, thumbnail, uuid, category);
},
emitRequestImages: (category: GalleryCategory) => {
const gallery: GalleryState = getState().gallery;
const { earliest_mtime } = gallery.categories[category];
socketio.emit('requestImages', category, earliest_mtime);
},
emitRequestNewImages: (category: GalleryCategory) => {
const gallery: GalleryState = getState().gallery;
const { latest_mtime } = gallery.categories[category];
socketio.emit('requestLatestImages', category, latest_mtime);
},
emitCancelProcessing: () => {
socketio.emit('cancel');
},
emitRequestSystemConfig: () => {
socketio.emit('requestSystemConfig');
},
emitSearchForModels: (modelFolder: string) => {
socketio.emit('searchForModels', modelFolder);
},
emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
socketio.emit('addNewModel', modelConfig);
},
emitDeleteModel: (modelName: string) => {
socketio.emit('deleteModel', modelName);
},
emitConvertToDiffusers: (
modelToConvert: InvokeAI.InvokeModelConversionProps
) => {
dispatch(modelConvertRequested());
socketio.emit('convertToDiffusers', modelToConvert);
},
emitMergeDiffusersModels: (
modelMergeInfo: InvokeAI.InvokeModelMergingProps
) => {
dispatch(modelMergingRequested());
socketio.emit('mergeDiffusersModels', modelMergeInfo);
},
emitRequestModelChange: (modelName: string) => {
dispatch(modelChangeRequested());
socketio.emit('requestModelChange', modelName);
},
emitSaveStagingAreaImageToGallery: (url: string) => {
socketio.emit('requestSaveStagingAreaImageToGallery', url);
},
emitRequestEmptyTempFolder: () => {
socketio.emit('requestEmptyTempFolder');
},
};
};
export default makeSocketIOEmitters;
export default {};

502
invokeai/frontend/web/src/app/socketio/listeners.ts
View File

@ -1,502 +0,0 @@
// import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
// import dateFormat from 'dateformat';
// import i18n from 'i18n';
// import { v4 as uuidv4 } from 'uuid';
// import * as InvokeAI from 'app/types/invokeai';
// import {
// addToast,
// errorOccurred,
// processingCanceled,
// setCurrentStatus,
// setFoundModels,
// setIsCancelable,
// setIsConnected,
// setIsProcessing,
// setModelList,
// setSearchFolder,
// setSystemConfig,
// setSystemStatus,
// } from 'features/system/store/systemSlice';
// import {
// addGalleryImages,
// addImage,
// clearIntermediateImage,
// GalleryState,
// removeImage,
// setIntermediateImage,
// } from 'features/gallery/store/gallerySlice';
// import type { RootState } from 'app/store/store';
// import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
// import {
// clearInitialImage,
// initialImageSelected,
// setInfillMethod,
// // setInitialImage,
// setMaskPath,
// } from 'features/parameters/store/generationSlice';
// import { tabMap } from 'features/ui/store/tabMap';
// import {
// requestImages,
// requestNewImages,
// requestSystemConfig,
// } from './actions';
// /**
// * Returns an object containing listener callbacks for socketio events.
// * TODO: This file is large, but simple. Should it be split up further?
// */
// const makeSocketIOListeners = (
// store: MiddlewareAPI<Dispatch<AnyAction>, RootState>
// ) => {
// const { dispatch, getState } = store;
// return {
// /**
// * Callback to run when we receive a 'connect' event.
// */
// onConnect: () => {
// try {
// dispatch(setIsConnected(true));
// dispatch(setCurrentStatus(i18n.t('common.statusConnected')));
// dispatch(requestSystemConfig());
// const gallery: GalleryState = getState().gallery;
// if (gallery.categories.result.latest_mtime) {
// dispatch(requestNewImages('result'));
// } else {
// dispatch(requestImages('result'));
// }
// if (gallery.categories.user.latest_mtime) {
// dispatch(requestNewImages('user'));
// } else {
// dispatch(requestImages('user'));
// }
// } catch (e) {
// console.error(e);
// }
// },
// /**
// * Callback to run when we receive a 'disconnect' event.
// */
// onDisconnect: () => {
// try {
// dispatch(setIsConnected(false));
// dispatch(setCurrentStatus(i18n.t('common.statusDisconnected')));
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Disconnected from server`,
// level: 'warning',
// })
// );
// } catch (e) {
// console.error(e);
// }
// },
// /**
// * Callback to run when we receive a 'generationResult' event.
// */
// onGenerationResult: (data: InvokeAI.ImageResultResponse) => {
// try {
// const state = getState();
// const { activeTab } = state.ui;
// const { shouldLoopback } = state.postprocessing;
// const { boundingBox: _, generationMode, ...rest } = data;
// const newImage = {
// uuid: uuidv4(),
// ...rest,
// };
// if (['txt2img', 'img2img'].includes(generationMode)) {
// dispatch(
// addImage({
// category: 'result',
// image: { ...newImage, category: 'result' },
// })
// );
// }
// if (generationMode === 'unifiedCanvas' && data.boundingBox) {
// const { boundingBox } = data;
// dispatch(
// addImageToStagingArea({
// image: { ...newImage, category: 'temp' },
// boundingBox,
// })
// );
// if (state.canvas.shouldAutoSave) {
// dispatch(
// addImage({
// image: { ...newImage, category: 'result' },
// category: 'result',
// })
// );
// }
// }
// // TODO: fix
// // if (shouldLoopback) {
// // const activeTabName = tabMap[activeTab];
// // switch (activeTabName) {
// // case 'img2img': {
// // dispatch(initialImageSelected(newImage.uuid));
// // // dispatch(setInitialImage(newImage));
// // break;
// // }
// // }
// // }
// dispatch(clearIntermediateImage());
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Image generated: ${data.url}`,
// })
// );
// } catch (e) {
// console.error(e);
// }
// },
// /**
// * Callback to run when we receive a 'intermediateResult' event.
// */
// onIntermediateResult: (data: InvokeAI.ImageResultResponse) => {
// try {
// dispatch(
// setIntermediateImage({
// uuid: uuidv4(),
// ...data,
// category: 'result',
// })
// );
// if (!data.isBase64) {
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Intermediate image generated: ${data.url}`,
// })
// );
// }
// } catch (e) {
// console.error(e);
// }
// },
// /**
// * Callback to run when we receive an 'esrganResult' event.
// */
// onPostprocessingResult: (data: InvokeAI.ImageResultResponse) => {
// try {
// dispatch(
// addImage({
// category: 'result',
// image: {
// uuid: uuidv4(),
// ...data,
// category: 'result',
// },
// })
// );
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Postprocessed: ${data.url}`,
// })
// );
// } catch (e) {
// console.error(e);
// }
// },
// /**
// * Callback to run when we receive a 'progressUpdate' event.
// * TODO: Add additional progress phases
// */
// onProgressUpdate: (data: InvokeAI.SystemStatus) => {
// try {
// dispatch(setIsProcessing(true));
// dispatch(setSystemStatus(data));
// } catch (e) {
// console.error(e);
// }
// },
// /**
// * Callback to run when we receive a 'progressUpdate' event.
// */
// onError: (data: InvokeAI.ErrorResponse) => {
// const { message, additionalData } = data;
// if (additionalData) {
// // TODO: handle more data than short message
// }
// try {
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Server error: ${message}`,
// level: 'error',
// })
// );
// dispatch(errorOccurred());
// dispatch(clearIntermediateImage());
// } catch (e) {
// console.error(e);
// }
// },
// /**
// * Callback to run when we receive a 'galleryImages' event.
// */
// onGalleryImages: (data: InvokeAI.GalleryImagesResponse) => {
// const { images, areMoreImagesAvailable, category } = data;
// /**
// * the logic here ideally would be in the reducer but we have a side effect:
// * generating a uuid. so the logic needs to be here, outside redux.
// */
// // Generate a UUID for each image
// const preparedImages = images.map((image): InvokeAI._Image => {
// return {
// uuid: uuidv4(),
// ...image,
// };
// });
// dispatch(
// addGalleryImages({
// images: preparedImages,
// areMoreImagesAvailable,
// category,
// })
// );
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Loaded ${images.length} images`,
// })
// );
// },
// /**
// * Callback to run when we receive a 'processingCanceled' event.
// */
// onProcessingCanceled: () => {
// dispatch(processingCanceled());
// const { intermediateImage } = getState().gallery;
// if (intermediateImage) {
// if (!intermediateImage.isBase64) {
// dispatch(
// addImage({
// category: 'result',
// image: intermediateImage,
// })
// );
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Intermediate image saved: ${intermediateImage.url}`,
// })
// );
// }
// dispatch(clearIntermediateImage());
// }
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Processing canceled`,
// level: 'warning',
// })
// );
// },
// /**
// * Callback to run when we receive a 'imageDeleted' event.
// */
// onImageDeleted: (data: InvokeAI.ImageDeletedResponse) => {
// const { url } = data;
// // remove image from gallery
// dispatch(removeImage(data));
// // remove references to image in options
// const {
// generation: { initialImage, maskPath },
// } = getState();
// if (
// initialImage === url ||
// (initialImage as InvokeAI._Image)?.url === url
// ) {
// dispatch(clearInitialImage());
// }
// if (maskPath === url) {
// dispatch(setMaskPath(''));
// }
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Image deleted: ${url}`,
// })
// );
// },
// onSystemConfig: (data: InvokeAI.SystemConfig) => {
// dispatch(setSystemConfig(data));
// if (!data.infill_methods.includes('patchmatch')) {
// dispatch(setInfillMethod(data.infill_methods[0]));
// }
// },
// onFoundModels: (data: InvokeAI.FoundModelResponse) => {
// const { search_folder, found_models } = data;
// dispatch(setSearchFolder(search_folder));
// dispatch(setFoundModels(found_models));
// },
// onNewModelAdded: (data: InvokeAI.ModelAddedResponse) => {
// const { new_model_name, model_list, update } = data;
// dispatch(setModelList(model_list));
// dispatch(setIsProcessing(false));
// dispatch(setCurrentStatus(i18n.t('modelManager.modelAdded')));
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Model Added: ${new_model_name}`,
// level: 'info',
// })
// );
// dispatch(
// addToast({
// title: !update
// ? `${i18n.t('modelManager.modelAdded')}: ${new_model_name}`
// : `${i18n.t('modelManager.modelUpdated')}: ${new_model_name}`,
// status: 'success',
// duration: 2500,
// isClosable: true,
// })
// );
// },
// onModelDeleted: (data: InvokeAI.ModelDeletedResponse) => {
// const { deleted_model_name, model_list } = data;
// dispatch(setModelList(model_list));
// dispatch(setIsProcessing(false));
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `${i18n.t(
// 'modelManager.modelAdded'
// )}: ${deleted_model_name}`,
// level: 'info',
// })
// );
// dispatch(
// addToast({
// title: `${i18n.t(
// 'modelManager.modelEntryDeleted'
// )}: ${deleted_model_name}`,
// status: 'success',
// duration: 2500,
// isClosable: true,
// })
// );
// },
// onModelConverted: (data: InvokeAI.ModelConvertedResponse) => {
// const { converted_model_name, model_list } = data;
// dispatch(setModelList(model_list));
// dispatch(setCurrentStatus(i18n.t('common.statusModelConverted')));
// dispatch(setIsProcessing(false));
// dispatch(setIsCancelable(true));
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Model converted: ${converted_model_name}`,
// level: 'info',
// })
// );
// dispatch(
// addToast({
// title: `${i18n.t(
// 'modelManager.modelConverted'
// )}: ${converted_model_name}`,
// status: 'success',
// duration: 2500,
// isClosable: true,
// })
// );
// },
// onModelsMerged: (data: InvokeAI.ModelsMergedResponse) => {
// const { merged_models, merged_model_name, model_list } = data;
// dispatch(setModelList(model_list));
// dispatch(setCurrentStatus(i18n.t('common.statusMergedModels')));
// dispatch(setIsProcessing(false));
// dispatch(setIsCancelable(true));
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Models merged: ${merged_models}`,
// level: 'info',
// })
// );
// dispatch(
// addToast({
// title: `${i18n.t('modelManager.modelsMerged')}: ${merged_model_name}`,
// status: 'success',
// duration: 2500,
// isClosable: true,
// })
// );
// },
// onModelChanged: (data: InvokeAI.ModelChangeResponse) => {
// const { model_name, model_list } = data;
// dispatch(setModelList(model_list));
// dispatch(setCurrentStatus(i18n.t('common.statusModelChanged')));
// dispatch(setIsProcessing(false));
// dispatch(setIsCancelable(true));
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Model changed: ${model_name}`,
// level: 'info',
// })
// );
// },
// onModelChangeFailed: (data: InvokeAI.ModelChangeResponse) => {
// const { model_name, model_list } = data;
// dispatch(setModelList(model_list));
// dispatch(setIsProcessing(false));
// dispatch(setIsCancelable(true));
// dispatch(errorOccurred());
// dispatch(
// addLogEntry({
// timestamp: dateFormat(new Date(), 'isoDateTime'),
// message: `Model change failed: ${model_name}`,
// level: 'error',
// })
// );
// },
// onTempFolderEmptied: () => {
// dispatch(
// addToast({
// title: i18n.t('toast.tempFoldersEmptied'),
// status: 'success',
// duration: 2500,
// isClosable: true,
// })
// );
// },
// };
// };
// export default makeSocketIOListeners;
export default {};

248
invokeai/frontend/web/src/app/socketio/middleware.ts
View File

@ -1,248 +0,0 @@
// import { Middleware } from '@reduxjs/toolkit';
// import { io } from 'socket.io-client';
// import makeSocketIOEmitters from './emitters';
// import makeSocketIOListeners from './listeners';
// import * as InvokeAI from 'app/types/invokeai';
// /**
// * Creates a socketio middleware to handle communication with server.
// *
// * Special `socketio/actionName` actions are created in actions.ts and
// * exported for use by the application, which treats them like any old
// * action, using `dispatch` to dispatch them.
// *
// * These actions are intercepted here, where `socketio.emit()` calls are
// * made on their behalf - see `emitters.ts`. The emitter functions
// * are the outbound communication to the server.
// *
// * Listeners are also established here - see `listeners.ts`. The listener
// * functions receive communication from the server and usually dispatch
// * some new action to handle whatever data was sent from the server.
// */
// export const socketioMiddleware = () => {
// const { origin } = new URL(window.location.href);
// const socketio = io(origin, {
// timeout: 60000,
// path: `${window.location.pathname}socket.io`,
// });
// socketio.disconnect();
// let areListenersSet = false;
// const middleware: Middleware = (store) => (next) => (action) => {
// const {
// onConnect,
// onDisconnect,
// onError,
// onPostprocessingResult,
// onGenerationResult,
// onIntermediateResult,
// onProgressUpdate,
// onGalleryImages,
// onProcessingCanceled,
// onImageDeleted,
// onSystemConfig,
// onModelChanged,
// onFoundModels,
// onNewModelAdded,
// onModelDeleted,
// onModelConverted,
// onModelsMerged,
// onModelChangeFailed,
// onTempFolderEmptied,
// } = makeSocketIOListeners(store);
// const {
// emitGenerateImage,
// emitRunESRGAN,
// emitRunFacetool,
// emitDeleteImage,
// emitRequestImages,
// emitRequestNewImages,
// emitCancelProcessing,
// emitRequestSystemConfig,
// emitSearchForModels,
// emitAddNewModel,
// emitDeleteModel,
// emitConvertToDiffusers,
// emitMergeDiffusersModels,
// emitRequestModelChange,
// emitSaveStagingAreaImageToGallery,
// emitRequestEmptyTempFolder,
// } = makeSocketIOEmitters(store, socketio);
// /**
// * If this is the first time the middleware has been called (e.g. during store setup),
// * initialize all our socket.io listeners.
// */
// if (!areListenersSet) {
// socketio.on('connect', () => onConnect());
// socketio.on('disconnect', () => onDisconnect());
// socketio.on('error', (data: InvokeAI.ErrorResponse) => onError(data));
// socketio.on('generationResult', (data: InvokeAI.ImageResultResponse) =>
// onGenerationResult(data)
// );
// socketio.on(
// 'postprocessingResult',
// (data: InvokeAI.ImageResultResponse) => onPostprocessingResult(data)
// );
// socketio.on('intermediateResult', (data: InvokeAI.ImageResultResponse) =>
// onIntermediateResult(data)
// );
// socketio.on('progressUpdate', (data: InvokeAI.SystemStatus) =>
// onProgressUpdate(data)
// );
// socketio.on('galleryImages', (data: InvokeAI.GalleryImagesResponse) =>
// onGalleryImages(data)
// );
// socketio.on('processingCanceled', () => {
// onProcessingCanceled();
// });
// socketio.on('imageDeleted', (data: InvokeAI.ImageDeletedResponse) => {
// onImageDeleted(data);
// });
// socketio.on('systemConfig', (data: InvokeAI.SystemConfig) => {
// onSystemConfig(data);
// });
// socketio.on('foundModels', (data: InvokeAI.FoundModelResponse) => {
// onFoundModels(data);
// });
// socketio.on('newModelAdded', (data: InvokeAI.ModelAddedResponse) => {
// onNewModelAdded(data);
// });
// socketio.on('modelDeleted', (data: InvokeAI.ModelDeletedResponse) => {
// onModelDeleted(data);
// });
// socketio.on('modelConverted', (data: InvokeAI.ModelConvertedResponse) => {
// onModelConverted(data);
// });
// socketio.on('modelsMerged', (data: InvokeAI.ModelsMergedResponse) => {
// onModelsMerged(data);
// });
// socketio.on('modelChanged', (data: InvokeAI.ModelChangeResponse) => {
// onModelChanged(data);
// });
// socketio.on('modelChangeFailed', (data: InvokeAI.ModelChangeResponse) => {
// onModelChangeFailed(data);
// });
// socketio.on('tempFolderEmptied', () => {
// onTempFolderEmptied();
// });
// areListenersSet = true;
// }
// /**
// * Handle redux actions caught by middleware.
// */
// switch (action.type) {
// case 'socketio/generateImage': {
// emitGenerateImage(action.payload);
// break;
// }
// case 'socketio/runESRGAN': {
// emitRunESRGAN(action.payload);
// break;
// }
// case 'socketio/runFacetool': {
// emitRunFacetool(action.payload);
// break;
// }
// case 'socketio/deleteImage': {
// emitDeleteImage(action.payload);
// break;
// }
// case 'socketio/requestImages': {
// emitRequestImages(action.payload);
// break;
// }
// case 'socketio/requestNewImages': {
// emitRequestNewImages(action.payload);
// break;
// }
// case 'socketio/cancelProcessing': {
// emitCancelProcessing();
// break;
// }
// case 'socketio/requestSystemConfig': {
// emitRequestSystemConfig();
// break;
// }
// case 'socketio/searchForModels': {
// emitSearchForModels(action.payload);
// break;
// }
// case 'socketio/addNewModel': {
// emitAddNewModel(action.payload);
// break;
// }
// case 'socketio/deleteModel': {
// emitDeleteModel(action.payload);
// break;
// }
// case 'socketio/convertToDiffusers': {
// emitConvertToDiffusers(action.payload);
// break;
// }
// case 'socketio/mergeDiffusersModels': {
// emitMergeDiffusersModels(action.payload);
// break;
// }
// case 'socketio/requestModelChange': {
// emitRequestModelChange(action.payload);
// break;
// }
// case 'socketio/saveStagingAreaImageToGallery': {
// emitSaveStagingAreaImageToGallery(action.payload);
// break;
// }
// case 'socketio/requestEmptyTempFolder': {
// emitRequestEmptyTempFolder();
// break;
// }
// }
// next(action);
// };
// return middleware;
// };
export default {};

2
invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts
View File

@ -1,7 +1,6 @@
import { canvasPersistDenylist } from 'features/canvas/store/canvasPersistDenylist';
import { controlNetDenylist } from 'features/controlNet/store/controlNetDenylist';
import { galleryPersistDenylist } from 'features/gallery/store/galleryPersistDenylist';
import { lightboxPersistDenylist } from 'features/lightbox/store/lightboxPersistDenylist';
import { nodesPersistDenylist } from 'features/nodes/store/nodesPersistDenylist';
import { generationPersistDenylist } from 'features/parameters/store/generationPersistDenylist';
import { postprocessingPersistDenylist } from 'features/parameters/store/postprocessingPersistDenylist';
@ -16,7 +15,6 @@ const serializationDenylist: {
canvas: canvasPersistDenylist,
gallery: galleryPersistDenylist,
generation: generationPersistDenylist,
lightbox: lightboxPersistDenylist,
nodes: nodesPersistDenylist,
postprocessing: postprocessingPersistDenylist,
system: systemPersistDenylist,

2
invokeai/frontend/web/src/app/store/enhancers/reduxRemember/unserialize.ts
View File

@ -1,7 +1,6 @@
import { initialCanvasState } from 'features/canvas/store/canvasSlice';
import { initialControlNetState } from 'features/controlNet/store/controlNetSlice';
import { initialGalleryState } from 'features/gallery/store/gallerySlice';
import { initialLightboxState } from 'features/lightbox/store/lightboxSlice';
import { initialNodesState } from 'features/nodes/store/nodesSlice';
import { initialGenerationState } from 'features/parameters/store/generationSlice';
import { initialPostprocessingState } from 'features/parameters/store/postprocessingSlice';
@ -18,7 +17,6 @@ const initialStates: {
canvas: initialCanvasState,
gallery: initialGalleryState,
generation: initialGenerationState,
lightbox: initialLightboxState,
nodes: initialNodesState,
postprocessing: initialPostprocessingState,
system: initialSystemState,

8
invokeai/frontend/web/src/app/store/middleware/devtools/actionsDenylist.ts
View File

@ -1,4 +1,8 @@
/**
* This is a list of actions that should be excluded in the Redux DevTools.
*/
export const actionsDenylist = [
// very spammy canvas actions
'canvas/setCursorPosition',
'canvas/setStageCoordinates',
'canvas/setStageScale',
@ -7,7 +11,11 @@ export const actionsDenylist = [
'canvas/setBoundingBoxDimensions',
'canvas/setIsDrawing',
'canvas/addPointToCurrentLine',
// bazillions during generation
'socket/socketGeneratorProgress',
'socket/appSocketGeneratorProgress',
// every time user presses shift
'hotkeys/shiftKeyPressed',
// this happens after every state change
'@@REMEMBER_PERSISTED',
];

8
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts
View File

@ -8,6 +8,7 @@ import {
import type { AppDispatch, RootState } from '../../store';
import { addCommitStagingAreaImageListener } from './listeners/addCommitStagingAreaImageListener';
import { addAppConfigReceivedListener } from './listeners/appConfigReceived';
import { addAppStartedListener } from './listeners/appStarted';
import { addBoardIdSelectedListener } from './listeners/boardIdSelected';
import { addRequestedBoardImageDeletionListener } from './listeners/boardImagesDeleted';
@ -51,12 +52,12 @@ import {
} from './listeners/imageUrlsReceived';
import { addInitialImageSelectedListener } from './listeners/initialImageSelected';
import { addModelSelectedListener } from './listeners/modelSelected';
import { addModelsLoadedListener } from './listeners/modelsLoaded';
import { addReceivedOpenAPISchemaListener } from './listeners/receivedOpenAPISchema';
import {
addReceivedPageOfImagesFulfilledListener,
addReceivedPageOfImagesRejectedListener,
} from './listeners/receivedPageOfImages';
import { addSelectionAddedToBatchListener } from './listeners/selectionAddedToBatch';
import {
addSessionCanceledFulfilledListener,
addSessionCanceledPendingListener,
@ -213,9 +214,6 @@ addBoardIdSelectedListener();
// Node schemas
addReceivedOpenAPISchemaListener();
// Batches
addSelectionAddedToBatchListener();
// DND
addImageDroppedListener();
@ -224,3 +222,5 @@ addModelSelectedListener();
// app startup
addAppStartedListener();
addModelsLoadedListener();
addAppConfigReceivedListener();

17
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts Normal file
View File

@ -0,0 +1,17 @@
import { setInfillMethod } from 'features/parameters/store/generationSlice';
import { appInfoApi } from 'services/api/endpoints/appInfo';
import { startAppListening } from '..';
export const addAppConfigReceivedListener = () => {
startAppListening({
matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled,
effect: async (action, { getState, dispatch }) => {
const { infill_methods } = action.payload;
const infillMethod = getState().generation.infillMethod;
if (!infill_methods.includes(infillMethod)) {
dispatch(setInfillMethod(infill_methods[0]));
}
},
});
};

6
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appStarted.ts
View File

@ -1,5 +1,7 @@
import { createAction } from '@reduxjs/toolkit';
import {
ASSETS_CATEGORIES,
IMAGE_CATEGORIES,
INITIAL_IMAGE_LIMIT,
isLoadingChanged,
} from 'features/gallery/store/gallerySlice';
@ -20,7 +22,7 @@ export const addAppStartedListener = () => {
// fill up the gallery tab with images
await dispatch(
receivedPageOfImages({
categories: ['general'],
categories: IMAGE_CATEGORIES,
is_intermediate: false,
offset: 0,
limit: INITIAL_IMAGE_LIMIT,
@ -30,7 +32,7 @@ export const addAppStartedListener = () => {
// fill up the assets tab with images
await dispatch(
receivedPageOfImages({
categories: ['control', 'mask', 'user', 'other'],
categories: ASSETS_CATEGORIES,
is_intermediate: false,
offset: 0,
limit: INITIAL_IMAGE_LIMIT,

79
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardIdSelected.ts
View File

@ -1,15 +1,18 @@
import { log } from 'app/logging/useLogger';
import { startAppListening } from '..';
import { selectFilteredImages } from 'features/gallery/store/gallerySelectors';
import {
ASSETS_CATEGORIES,
IMAGE_CATEGORIES,
boardIdSelected,
imageSelected,
selectImagesAll,
boardIdSelected,
} from 'features/gallery/store/gallerySlice';
import { boardsApi } from 'services/api/endpoints/boards';
import {
IMAGES_PER_PAGE,
receivedPageOfImages,
} from 'services/api/thunks/image';
import { boardsApi } from 'services/api/endpoints/boards';
import { startAppListening } from '..';
const moduleLog = log.child({ namespace: 'boards' });
@ -24,19 +27,24 @@ export const addBoardIdSelectedListener = () => {
const state = getState();
const allImages = selectImagesAll(state);
if (!board_id) {
// a board was unselected
dispatch(imageSelected(allImages[0]?.image_name));
if (board_id === 'all') {
// Selected all images
dispatch(imageSelected(allImages[0]?.image_name ?? null));
return;
}
const { categories } = state.gallery;
if (board_id === 'batch') {
// Selected the batch
dispatch(imageSelected(state.gallery.batchImageNames[0] ?? null));
return;
}
const filteredImages = allImages.filter((i) => {
const isInCategory = categories.includes(i.image_category);
const isInSelectedBoard = board_id ? i.board_id === board_id : true;
return isInCategory && isInSelectedBoard;
});
const filteredImages = selectFilteredImages(state);
const categories =
state.gallery.galleryView === 'images'
? IMAGE_CATEGORIES
: ASSETS_CATEGORIES;
// get the board from the cache
const { data: boards } =
@ -45,7 +53,7 @@ export const addBoardIdSelectedListener = () => {
if (!board) {
// can't find the board in cache...
dispatch(imageSelected(allImages[0]?.image_name));
dispatch(boardIdSelected('all'));
return;
}
@ -63,48 +71,3 @@ export const addBoardIdSelectedListener = () => {
},
});
};
export const addBoardIdSelected_changeSelectedImage_listener = () => {
startAppListening({
actionCreator: boardIdSelected,
effect: (action, { getState, dispatch }) => {
const board_id = action.payload;
const state = getState();
// we need to check if we need to fetch more images
if (!board_id) {
// a board was unselected - we don't need to do anything
return;
}
const { categories } = state.gallery;
const filteredImages = selectImagesAll(state).filter((i) => {
const isInCategory = categories.includes(i.image_category);
const isInSelectedBoard = board_id ? i.board_id === board_id : true;
return isInCategory && isInSelectedBoard;
});
// get the board from the cache
const { data: boards } =
boardsApi.endpoints.listAllBoards.select()(state);
const board = boards?.find((b) => b.board_id === board_id);
if (!board) {
// can't find the board in cache...
return;
}
// if we haven't loaded one full page of images from this board, load more
if (
filteredImages.length < board.image_count &&
filteredImages.length < IMAGES_PER_PAGE
) {
dispatch(
receivedPageOfImages({ categories, board_id, is_intermediate: false })
);
}
},
});
};

16
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess.ts
View File

@ -13,7 +13,11 @@ import { RootState } from 'app/store/store';
const moduleLog = log.child({ namespace: 'controlNet' });
const predicate: AnyListenerPredicate<RootState> = (action, state) => {
const predicate: AnyListenerPredicate<RootState> = (
action,
state,
prevState
) => {
const isActionMatched =
controlNetProcessorParamsChanged.match(action) ||
controlNetModelChanged.match(action) ||
@ -25,6 +29,16 @@ const predicate: AnyListenerPredicate<RootState> = (action, state) => {
return false;
}
if (controlNetAutoConfigToggled.match(action)) {
// do not process if the user just disabled auto-config
if (
prevState.controlNet.controlNets[action.payload.controlNetId]
.shouldAutoConfig === true
) {
return false;
}
}
const { controlImage, processorType, shouldAutoConfig } =
state.controlNet.controlNets[action.payload.controlNetId];

20
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts
View File

@ -1,13 +1,13 @@
import { startAppListening } from '..';
import { imageMetadataReceived } from 'services/api/thunks/image';
import { log } from 'app/logging/useLogger';
import { controlNetImageProcessed } from 'features/controlNet/store/actions';
import { Graph } from 'services/api/types';
import { sessionCreated } from 'services/api/thunks/session';
import { sessionReadyToInvoke } from 'features/system/store/actions';
import { socketInvocationComplete } from 'services/events/actions';
import { isImageOutput } from 'services/api/guards';
import { controlNetProcessedImageChanged } from 'features/controlNet/store/controlNetSlice';
import { sessionReadyToInvoke } from 'features/system/store/actions';
import { isImageOutput } from 'services/api/guards';
import { imageDTOReceived } from 'services/api/thunks/image';
import { sessionCreated } from 'services/api/thunks/session';
import { Graph } from 'services/api/types';
import { socketInvocationComplete } from 'services/events/actions';
import { startAppListening } from '..';
const moduleLog = log.child({ namespace: 'controlNet' });
@ -63,10 +63,8 @@ export const addControlNetImageProcessedListener = () => {
// Wait for the ImageDTO to be received
const [imageMetadataReceivedAction] = await take(
(
action
): action is ReturnType<typeof imageMetadataReceived.fulfilled> =>
imageMetadataReceived.fulfilled.match(action) &&
(action): action is ReturnType<typeof imageDTOReceived.fulfilled> =>
imageDTOReceived.fulfilled.match(action) &&
action.payload.image_name === image_name
);
const processedControlImage = imageMetadataReceivedAction.payload;

9
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageAddedToBoard.ts
View File

@ -1,7 +1,6 @@
import { log } from 'app/logging/useLogger';
import { startAppListening } from '..';
import { imageMetadataReceived } from 'services/api/thunks/image';
import { boardImagesApi } from 'services/api/endpoints/boardImages';
import { startAppListening } from '..';
const moduleLog = log.child({ namespace: 'boards' });
@ -15,12 +14,6 @@ export const addImageAddedToBoardFulfilledListener = () => {
{ data: { board_id, image_name } },
'Image added to board'
);
dispatch(
imageMetadataReceived({
image_name,
})
);
},
});
};

23
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts
View File

@ -1,10 +1,10 @@
import { log } from 'app/logging/useLogger';
import { resetCanvas } from 'features/canvas/store/canvasSlice';
import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
import { selectNextImageToSelect } from 'features/gallery/store/gallerySelectors';
import {
imageRemoved,
imageSelected,
selectFilteredImages,
} from 'features/gallery/store/gallerySlice';
import {
imageDeletionConfirmed,
@ -12,7 +12,6 @@ import {
} from 'features/imageDeletion/store/imageDeletionSlice';
import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
import { clearInitialImage } from 'features/parameters/store/generationSlice';
import { clamp } from 'lodash-es';
import { api } from 'services/api';
import { imageDeleted } from 'services/api/thunks/image';
import { startAppListening } from '..';
@ -37,26 +36,10 @@ export const addRequestedImageDeletionListener = () => {
state.gallery.selection[state.gallery.selection.length - 1];
if (lastSelectedImage === image_name) {
const filteredImages = selectFilteredImages(state);
const ids = filteredImages.map((i) => i.image_name);
const deletedImageIndex = ids.findIndex(
(result) => result.toString() === image_name
);
const filteredIds = ids.filter((id) => id.toString() !== image_name);
const newSelectedImageIndex = clamp(
deletedImageIndex,
0,
filteredIds.length - 1
);
const newSelectedImageId = filteredIds[newSelectedImageIndex];
const newSelectedImageId = selectNextImageToSelect(state, image_name);
if (newSelectedImageId) {
dispatch(imageSelected(newSelectedImageId as string));
dispatch(imageSelected(newSelectedImageId));
} else {
dispatch(imageSelected(null));
}

168
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts
View File

@ -4,13 +4,12 @@ import {
TypesafeDroppableData,
} from 'app/components/ImageDnd/typesafeDnd';
import { log } from 'app/logging/useLogger';
import {
imageAddedToBatch,
imagesAddedToBatch,
} from 'features/batch/store/batchSlice';
import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
import { imageSelected } from 'features/gallery/store/gallerySlice';
import {
imageSelected,
imagesAddedToBatch,
} from 'features/gallery/store/gallerySlice';
import {
fieldValueChanged,
imageCollectionFieldValueChanged,
@ -21,57 +20,66 @@ import { startAppListening } from '../';
const moduleLog = log.child({ namespace: 'dnd' });
export const imageDropped = createAction<{
export const dndDropped = createAction<{
overData: TypesafeDroppableData;
activeData: TypesafeDraggableData;
}>('dnd/imageDropped');
}>('dnd/dndDropped');
export const addImageDroppedListener = () => {
startAppListening({
actionCreator: imageDropped,
effect: (action, { dispatch, getState }) => {
actionCreator: dndDropped,
effect: async (action, { dispatch, getState, take }) => {
const { activeData, overData } = action.payload;
const { actionType } = overData;
const state = getState();
moduleLog.debug(
{ data: { activeData, overData } },
'Image or selection dropped'
);
// set current image
if (
actionType === 'SET_CURRENT_IMAGE' &&
overData.actionType === 'SET_CURRENT_IMAGE' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO
) {
dispatch(imageSelected(activeData.payload.imageDTO.image_name));
return;
}
// set initial image
if (
actionType === 'SET_INITIAL_IMAGE' &&
overData.actionType === 'SET_INITIAL_IMAGE' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO
) {
dispatch(initialImageChanged(activeData.payload.imageDTO));
return;
}
// add image to batch
if (
actionType === 'ADD_TO_BATCH' &&
overData.actionType === 'ADD_TO_BATCH' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO
) {
dispatch(imageAddedToBatch(activeData.payload.imageDTO.image_name));
dispatch(imagesAddedToBatch([activeData.payload.imageDTO.image_name]));
return;
}
// add multiple images to batch
if (
actionType === 'ADD_TO_BATCH' &&
activeData.payloadType === 'GALLERY_SELECTION'
overData.actionType === 'ADD_TO_BATCH' &&
activeData.payloadType === 'IMAGE_NAMES'
) {
dispatch(imagesAddedToBatch(state.gallery.selection));
dispatch(imagesAddedToBatch(activeData.payload.image_names));
return;
}
// set control image
if (
actionType === 'SET_CONTROLNET_IMAGE' &&
overData.actionType === 'SET_CONTROLNET_IMAGE' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO
) {
@ -82,20 +90,22 @@ export const addImageDroppedListener = () => {
controlNetId,
})
);
return;
}
// set canvas image
if (
actionType === 'SET_CANVAS_INITIAL_IMAGE' &&
overData.actionType === 'SET_CANVAS_INITIAL_IMAGE' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO
) {
dispatch(setInitialCanvasImage(activeData.payload.imageDTO));
return;
}
// set nodes image
if (
actionType === 'SET_NODES_IMAGE' &&
overData.actionType === 'SET_NODES_IMAGE' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO
) {
@ -107,11 +117,12 @@ export const addImageDroppedListener = () => {
value: activeData.payload.imageDTO,
})
);
return;
}
// set multiple nodes images (single image handler)
if (
actionType === 'SET_MULTI_NODES_IMAGE' &&
overData.actionType === 'SET_MULTI_NODES_IMAGE' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO
) {
@ -123,43 +134,30 @@ export const addImageDroppedListener = () => {
value: [activeData.payload.imageDTO],
})
);
return;
}
// set multiple nodes images (multiple images handler)
if (
actionType === 'SET_MULTI_NODES_IMAGE' &&
activeData.payloadType === 'GALLERY_SELECTION'
overData.actionType === 'SET_MULTI_NODES_IMAGE' &&
activeData.payloadType === 'IMAGE_NAMES'
) {
const { fieldName, nodeId } = overData.context;
dispatch(
imageCollectionFieldValueChanged({
nodeId,
fieldName,
value: state.gallery.selection.map((image_name) => ({
value: activeData.payload.image_names.map((image_name) => ({
image_name,
})),
})
);
return;
}
// remove image from board
// TODO: remove board_id from `removeImageFromBoard()` endpoint
// TODO: handle multiple images
// if (
// actionType === 'MOVE_BOARD' &&
// activeData.payloadType === 'IMAGE_DTO' &&
// activeData.payload.imageDTO &&
// overData.boardId !== null
// ) {
// const { image_name } = activeData.payload.imageDTO;
// dispatch(
// boardImagesApi.endpoints.removeImageFromBoard.initiate({ image_name })
// );
// }
// add image to board
if (
actionType === 'MOVE_BOARD' &&
overData.actionType === 'MOVE_BOARD' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO &&
overData.context.boardId
@ -172,17 +170,89 @@ export const addImageDroppedListener = () => {
board_id: boardId,
})
);
return;
}
// add multiple images to board
// TODO: add endpoint
// if (
// actionType === 'ADD_TO_BATCH' &&
// activeData.payloadType === 'IMAGE_NAMES' &&
// activeData.payload.imageDTONames
// ) {
// dispatch(boardImagesApi.endpoints.addImagesToBoard.intiate({}));
// }
// remove image from board
if (
overData.actionType === 'MOVE_BOARD' &&
activeData.payloadType === 'IMAGE_DTO' &&
activeData.payload.imageDTO &&
overData.context.boardId === null
) {
const { image_name, board_id } = activeData.payload.imageDTO;
if (board_id) {
dispatch(
boardImagesApi.endpoints.removeImageFromBoard.initiate({
image_name,
board_id,
})
);
}
return;
}
// add gallery selection to board
if (
overData.actionType === 'MOVE_BOARD' &&
activeData.payloadType === 'IMAGE_NAMES' &&
overData.context.boardId
) {
console.log('adding gallery selection to board');
const board_id = overData.context.boardId;
dispatch(
boardImagesApi.endpoints.addManyBoardImages.initiate({
board_id,
image_names: activeData.payload.image_names,
})
);
return;
}
// remove gallery selection from board
if (
overData.actionType === 'MOVE_BOARD' &&
activeData.payloadType === 'IMAGE_NAMES' &&
overData.context.boardId === null
) {
console.log('removing gallery selection to board');
dispatch(
boardImagesApi.endpoints.deleteManyBoardImages.initiate({
image_names: activeData.payload.image_names,
})
);
return;
}
// add batch selection to board
if (
overData.actionType === 'MOVE_BOARD' &&
activeData.payloadType === 'IMAGE_NAMES' &&
overData.context.boardId
) {
const board_id = overData.context.boardId;
dispatch(
boardImagesApi.endpoints.addManyBoardImages.initiate({
board_id,
image_names: activeData.payload.image_names,
})
);
return;
}
// remove batch selection from board
if (
overData.actionType === 'MOVE_BOARD' &&
activeData.payloadType === 'IMAGE_NAMES' &&
overData.context.boardId === null
) {
dispatch(
boardImagesApi.endpoints.deleteManyBoardImages.initiate({
image_names: activeData.payload.image_names,
})
);
return;
}
},
});
};

8
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageMetadataReceived.ts
View File

@ -1,13 +1,13 @@
import { log } from 'app/logging/useLogger';
import { startAppListening } from '..';
import { imageMetadataReceived, imageUpdated } from 'services/api/thunks/image';
import { imageUpserted } from 'features/gallery/store/gallerySlice';
import { imageDTOReceived, imageUpdated } from 'services/api/thunks/image';
import { startAppListening } from '..';
const moduleLog = log.child({ namespace: 'image' });
export const addImageMetadataReceivedFulfilledListener = () => {
startAppListening({
actionCreator: imageMetadataReceived.fulfilled,
actionCreator: imageDTOReceived.fulfilled,
effect: (action, { getState, dispatch }) => {
const image = action.payload;
@ -40,7 +40,7 @@ export const addImageMetadataReceivedFulfilledListener = () => {
export const addImageMetadataReceivedRejectedListener = () => {
startAppListening({
actionCreator: imageMetadataReceived.rejected,
actionCreator: imageDTOReceived.rejected,
effect: (action, { getState, dispatch }) => {
moduleLog.debug(
{ data: { image: action.meta.arg } },

9
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageRemovedFromBoard.ts
View File

@ -1,7 +1,6 @@
import { log } from 'app/logging/useLogger';
import { startAppListening } from '..';
import { imageMetadataReceived } from 'services/api/thunks/image';
import { boardImagesApi } from 'services/api/endpoints/boardImages';
import { startAppListening } from '..';
const moduleLog = log.child({ namespace: 'boards' });
@ -15,12 +14,6 @@ export const addImageRemovedFromBoardFulfilledListener = () => {
{ data: { board_id, image_name } },
'Image added to board'
);
dispatch(
imageMetadataReceived({
image_name,
})
);
},
});
};

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