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Merge branch 'main' into feat/nodes/freeu

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Kent Keirsey 2023-11-06 05:39:58 -08:00 committed by GitHub
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1
.gitattributes vendored
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@ -2,3 +2,4 @@
# Only affects text files and ignores other file types. # Only affects text files and ignores other file types.
# For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/ # For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
* text=auto * text=auto
docker/** text eol=lf

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.gitignore vendored
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@ -1,8 +1,5 @@
.idea/ .idea/
# ignore the Anaconda/Miniconda installer used while building Docker image
anaconda.sh
# Byte-compiled / optimized / DLL files # Byte-compiled / optimized / DLL files
__pycache__/ __pycache__/
*.py[cod] *.py[cod]
@ -136,12 +133,10 @@ celerybeat.pid
# Environments # Environments
.env .env
.venv .venv*
env/ env/
venv/ venv/
ENV/ ENV/
env.bak/
venv.bak/
# Spyder project settings # Spyder project settings
.spyderproject .spyderproject
@ -186,11 +181,6 @@ cython_debug/
.scratch/ .scratch/
.vscode/ .vscode/
# ignore environment.yml and requirements.txt
# these are links to the real files in environments-and-requirements
environment.yml
requirements.txt
# source installer files # source installer files
installer/*zip installer/*zip
installer/install.bat installer/install.bat

2
README.md
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@ -123,7 +123,7 @@ and go to http://localhost:9090.
### Command-Line Installation (for developers and users familiar with Terminals) ### Command-Line Installation (for developers and users familiar with Terminals)
You must have Python 3.9 through 3.11 installed on your machine. Earlier or You must have Python 3.10 through 3.11 installed on your machine. Earlier or
later versions are not supported. later versions are not supported.
Node.js also needs to be installed along with yarn (can be installed with Node.js also needs to be installed along with yarn (can be installed with
the command `npm install -g yarn` if needed) the command `npm install -g yarn` if needed)

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docker/.env.sample
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@ -1,13 +1,15 @@
## Make a copy of this file named `.env` and fill in the values below. ## Make a copy of this file named `.env` and fill in the values below.
## Any environment variables supported by InvokeAI can be specified here. ## Any environment variables supported by InvokeAI can be specified here,
## in addition to the examples below.
# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data. # INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
# Outputs will also be stored here by default. # Outputs will also be stored here by default.
# This **must** be an absolute path. # This **must** be an absolute path.
INVOKEAI_ROOT= INVOKEAI_ROOT=
HUGGINGFACE_TOKEN= # Get this value from your HuggingFace account settings page.
# HUGGING_FACE_HUB_TOKEN=
## optional variables specific to the docker setup ## optional variables specific to the docker setup.
# GPU_DRIVER=cuda # GPU_DRIVER=cuda
# CONTAINER_UID=1000 # CONTAINER_UID=1000

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docker/Dockerfile
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@ -2,7 +2,7 @@
## Builder stage ## Builder stage
FROM library/ubuntu:22.04 AS builder FROM library/ubuntu:23.04 AS builder
ARG DEBIAN_FRONTEND=noninteractive 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 rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
@ -10,7 +10,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \ --mount=type=cache,target=/var/lib/apt,sharing=locked \
apt update && apt-get install -y \ apt update && apt-get install -y \
git \ git \
python3.10-venv \ python3-venv \
python3-pip \ python3-pip \
build-essential build-essential
@ -37,7 +37,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
elif [ "$GPU_DRIVER" = "rocm" ]; then \ elif [ "$GPU_DRIVER" = "rocm" ]; then \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \ extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
else \ else \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \ extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
fi &&\ fi &&\
pip install $extra_index_url_arg \ pip install $extra_index_url_arg \
torch==$TORCH_VERSION \ torch==$TORCH_VERSION \
@ -70,7 +70,7 @@ RUN --mount=type=cache,target=/usr/lib/node_modules \
#### Runtime stage --------------------------------------- #### Runtime stage ---------------------------------------
FROM library/ubuntu:22.04 AS runtime FROM library/ubuntu:23.04 AS runtime
ARG DEBIAN_FRONTEND=noninteractive ARG DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1 ENV PYTHONUNBUFFERED=1
@ -85,6 +85,7 @@ RUN apt update && apt install -y --no-install-recommends \
iotop \ iotop \
bzip2 \ bzip2 \
gosu \ gosu \
magic-wormhole \
libglib2.0-0 \ libglib2.0-0 \
libgl1-mesa-glx \ libgl1-mesa-glx \
python3-venv \ python3-venv \
@ -94,10 +95,6 @@ RUN apt update && apt install -y --no-install-recommends \
libstdc++-10-dev &&\ libstdc++-10-dev &&\
apt-get clean && apt-get autoclean 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 INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai ENV VIRTUAL_ENV=/opt/venv/invokeai
@ -120,9 +117,7 @@ WORKDIR ${INVOKEAI_SRC}
RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
RUN python3 -c "from patchmatch import patch_match" RUN python3 -c "from patchmatch import patch_match"
# Create unprivileged user and make the local dir RUN mkdir -p ${INVOKEAI_ROOT} && chown -R 1000:1000 ${INVOKEAI_ROOT}
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}
COPY docker/docker-entrypoint.sh ./ COPY docker/docker-entrypoint.sh ./
ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"] ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]

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docker/README.md
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@ -5,7 +5,7 @@ All commands are to be run from the `docker` directory: `cd docker`
#### Linux #### Linux
1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`) 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). 2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
- The deprecated `docker-compose` (hyphenated) CLI continues to work for now. - The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
3. Ensure docker daemon is able to access the GPU. 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) - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
@ -20,7 +20,6 @@ This is done via Docker Desktop preferences
## Quickstart ## 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: 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 a. the desired location of the InvokeAI runtime directory, or
b. an existing, v3.0.0 compatible runtime directory. b. an existing, v3.0.0 compatible runtime directory.
@ -42,20 +41,22 @@ The Docker daemon on the system must be already set up to use the GPU. In case o
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. 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. 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): Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended):
``` ```bash
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
HUGGINGFACE_TOKEN=the_actual_token HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000 CONTAINER_UID=1000
GPU_DRIVER=cuda GPU_DRIVER=cuda
``` ```
Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
## Even Moar Customizing! ## 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. See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
### Reconfigure the runtime directory ### Reconfigure the runtime directory
@ -63,7 +64,7 @@ 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 In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
``` ```yaml
command: command:
- invokeai-configure - invokeai-configure
- --yes - --yes
@ -71,7 +72,7 @@ command:
Or install models: Or install models:
``` ```yaml
command: command:
- invokeai-model-install - invokeai-model-install
``` ```

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docker/build.sh
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@ -5,7 +5,7 @@ build_args=""
[[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env) [[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
echo "docker-compose build args:" echo "docker compose build args:"
echo $build_args echo $build_args
docker-compose build $build_args docker compose build $build_args

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docker/docker-entrypoint.sh
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@ -19,7 +19,7 @@ set -e -o pipefail
# Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS. # Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
USER_ID=${CONTAINER_UID:-1000} USER_ID=${CONTAINER_UID:-1000}
USER=invoke USER=ubuntu
usermod -u ${USER_ID} ${USER} 1>/dev/null usermod -u ${USER_ID} ${USER} 1>/dev/null
configure() { configure() {

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docker/run.sh
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@ -1,8 +1,11 @@
#!/usr/bin/env bash #!/usr/bin/env bash
set -e set -e
# This script is provided for backwards compatibility with the old docker setup.
# it doesn't do much aside from wrapping the usual docker compose CLI.
SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}") SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
cd "$SCRIPTDIR" || exit 1 cd "$SCRIPTDIR" || exit 1
docker-compose up --build -d docker compose up --build -d
docker-compose logs -f docker compose logs -f

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docs/CHANGELOG.md
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@ -488,7 +488,7 @@ sections describe what's new for InvokeAI.
- A choice of installer scripts that automate installation and configuration. - A choice of installer scripts that automate installation and configuration.
See See
[Installation](installation/index.md). [Installation](installation/INSTALLATION.md).
- A streamlined manual installation process that works for both Conda and - A streamlined manual installation process that works for both Conda and
PIP-only installs. See PIP-only installs. See
[Manual Installation](installation/020_INSTALL_MANUAL.md). [Manual Installation](installation/020_INSTALL_MANUAL.md).
@ -657,7 +657,7 @@ sections describe what's new for InvokeAI.
## v1.13 <small>(3 September 2022)</small> ## v1.13 <small>(3 September 2022)</small>
- Support image variations (see [VARIATIONS](features/VARIATIONS.md) - Support image variations (see [VARIATIONS](deprecated/VARIATIONS.md)
([Kevin Gibbons](https://github.com/bakkot) and many contributors and ([Kevin Gibbons](https://github.com/bakkot) and many contributors and
reviewers) reviewers)
- Supports a Google Colab notebook for a standalone server running on Google - Supports a Google Colab notebook for a standalone server running on Google

0
CODE_OF_CONDUCT.md → docs/CODE_OF_CONDUCT.md
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docs/contributing/contribution_guides/development.md
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@ -45,5 +45,5 @@ For backend related work, please reach out to **@blessedcoolant**, **@lstein**,
## **What does the Code of Conduct mean for me?** ## **What does the Code of Conduct mean for me?**
Our [Code of Conduct](CODE_OF_CONDUCT.md) means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code. Our [Code of Conduct](../../CODE_OF_CONDUCT.md) means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code.

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docs/deprecated/CLI.md
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@ -211,8 +211,8 @@ Here are the invoke> command that apply to txt2img:
| `--facetool <name>` | `-ft <name>` | `-ft gfpgan` | Select face restoration algorithm to use: gfpgan, codeformer | | `--facetool <name>` | `-ft <name>` | `-ft gfpgan` | Select face restoration algorithm to use: gfpgan, codeformer |
| `--codeformer_fidelity` | `-cf <float>` | `0.75` | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality | | `--codeformer_fidelity` | `-cf <float>` | `0.75` | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality |
| `--save_original` | `-save_orig` | `False` | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. | | `--save_original` | `-save_orig` | `False` | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
| `--variation <float>` | `-v<float>` | `0.0` | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](../features/VARIATIONS.md). | | `--variation <float>` | `-v<float>` | `0.0` | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](VARIATIONS.md). |
| `--with_variations <pattern>` | | `None` | Combine two or more variations. See [Variations](../features/VARIATIONS.md) for now to use this. | | `--with_variations <pattern>` | | `None` | Combine two or more variations. See [Variations](VARIATIONS.md) for now to use this. |
| `--save_intermediates <n>` | | `None` | Save the image from every nth step into an "intermediates" folder inside the output directory | | `--save_intermediates <n>` | | `None` | Save the image from every nth step into an "intermediates" folder inside the output directory |
| `--h_symmetry_time_pct <float>` | | `None` | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) | | `--h_symmetry_time_pct <float>` | | `None` | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) |
| `--v_symmetry_time_pct <float>` | | `None` | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) | | `--v_symmetry_time_pct <float>` | | `None` | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) |

0
docs/other/TRANSLATION.md → docs/deprecated/TRANSLATION.md
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docs/features/VARIATIONS.md → docs/deprecated/VARIATIONS.md
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@ -126,6 +126,6 @@ amounts of image-to-image variation even when the seed is fixed and the
`-v` argument is very low. Others are more deterministic. Feel free to `-v` argument is very low. Others are more deterministic. Feel free to
experiment until you find the combination that you like. experiment until you find the combination that you like.
Also be aware of the [Perlin Noise](OTHER.md#thresholding-and-perlin-noise-initialization-options) Also be aware of the [Perlin Noise](../features/OTHER.md#thresholding-and-perlin-noise-initialization-options)
feature, which provides another way of introducing variability into your feature, which provides another way of introducing variability into your
image generation requests. image generation requests.

5
docs/features/CONCEPTS.md
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@ -28,8 +28,9 @@ by placing them in the designated directory for the compatible model type
### An Example ### An Example
Here are a few examples to illustrate how it works. All these images were Here are a few examples to illustrate how it works. All these images
generated using the command-line client and the Stable Diffusion 1.5 model: were generated using the legacy command-line client and the Stable
Diffusion 1.5 model:
| Japanese gardener | Japanese gardener &lt;ghibli-face&gt; | Japanese gardener &lt;hoi4-leaders&gt; | Japanese gardener &lt;cartoona-animals&gt; | | Japanese gardener | Japanese gardener &lt;ghibli-face&gt; | Japanese gardener &lt;hoi4-leaders&gt; | Japanese gardener &lt;cartoona-animals&gt; |
| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: | | :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |

2
docs/features/CONFIGURATION.md
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@ -82,7 +82,7 @@ format of YAML files can be found
[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/). [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 You can fix a broken `invokeai.yaml` by deleting it and running the
configuration script again -- option [7] in the launcher, "Re-run the configuration script again -- option [6] in the launcher, "Re-run the
configure script". configure script".
#### Reading Environment Variables #### Reading Environment Variables

71
docs/features/CONTROLNET.md
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@ -17,9 +17,6 @@ image generation, providing you with a way to direct the network
towards generating images that better fit your desired style or towards generating images that better fit your desired style or
outcome. outcome.
#### How it works
ControlNet works by analyzing an input image, pre-processing that ControlNet works by analyzing an input image, pre-processing that
image to identify relevant information that can be interpreted by each image to identify relevant information that can be interpreted by each
specific ControlNet model, and then inserting that control information specific ControlNet model, and then inserting that control information
@ -27,35 +24,21 @@ into the generation process. This can be used to adjust the style,
composition, or other aspects of the image to better achieve a composition, or other aspects of the image to better achieve a
specific result. specific result.
#### Installation
#### Models
InvokeAI provides access to a series of ControlNet models that provide InvokeAI provides access to a series of ControlNet models that provide
different effects or styles in your generated images. Currently different effects or styles in your generated images.
InvokeAI only supports "diffuser" style ControlNet models. These are
folders that contain the files `config.json` and/or
`diffusion_pytorch_model.safetensors` and
`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
the name of the model.
***InvokeAI does not currently support checkpoint-format To install ControlNet Models:
ControlNets. These come in the form of a single file with the
extension `.safetensors`.***
Diffuser-style ControlNet models are available at HuggingFace 1. The easiest way to install them is
(http://huggingface.co) and accessed via their repo IDs (identifiers
in the format "author/modelname"). The easiest way to install them is
to use the InvokeAI model installer application. Use the to use the InvokeAI model installer application. Use the
`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate `invoke.sh`/`invoke.bat` launcher to select item [4] and then navigate
to the CONTROLNETS section. Select the models you wish to install and to the CONTROLNETS section. Select the models you wish to install and
press "APPLY CHANGES". You may also enter additional HuggingFace press "APPLY CHANGES". You may also enter additional HuggingFace
repo_ids in the "Additional models" textbox: repo_ids in the "Additional models" textbox.
2. Using the "Add Model" function of the model manager, enter the HuggingFace Repo ID of the ControlNet. The ID is in the format "author/repoName"
![Model Installer -
Controlnetl](../assets/installing-models/model-installer-controlnet.png){:width="640px"}
Command-line users can launch the model installer using the command
`invokeai-model-install`.
_Be aware that some ControlNet models require additional code _Be aware that some ControlNet models require additional code
functionality in order to work properly, so just installing a functionality in order to work properly, so just installing a
@ -63,6 +46,17 @@ third-party ControlNet model may not have the desired effect._ Please
read and follow the documentation for installing a third party model read and follow the documentation for installing a third party model
not currently included among InvokeAI's default list. not currently included among InvokeAI's default list.
Currently InvokeAI **only** supports 🤗 Diffusers-format ControlNet models. These are
folders that contain the files `config.json` and/or
`diffusion_pytorch_model.safetensors` and
`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
the name of the model.
🤗 Diffusers-format ControlNet models are available at HuggingFace
(http://huggingface.co) and accessed via their repo IDs (identifiers
in the format "author/modelname").
#### ControlNet Models
The models currently supported include: The models currently supported include:
**Canny**: **Canny**:
@ -133,6 +127,29 @@ Start/End - 0 represents the start of the generation, 1 represents the end. The
Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke. Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke.
## T2I-Adapter
[T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) is a tool similar to ControlNet that allows for control over the generation process by providing control information during the generation process. T2I-Adapter models tend to be smaller and more efficient than ControlNets.
##### Installation
To install T2I-Adapter Models:
1. The easiest way to install models is
to use the InvokeAI model installer application. Use the
`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
to the T2I-Adapters section. Select the models you wish to install and
press "APPLY CHANGES". You may also enter additional HuggingFace
repo_ids in the "Additional models" textbox.
2. Using the "Add Model" function of the model manager, enter the HuggingFace Repo ID of the T2I-Adapter. The ID is in the format "author/repoName"
#### Usage
Each T2I Adapter has two settings that are applied.
Weight - Strength of the model applied to the generation for the section, defined by start/end.
Start/End - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
Additionally, each section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process.
## IP-Adapter ## IP-Adapter
@ -140,13 +157,13 @@ Additionally, each ControlNet section can be expanded in order to manipulate set
![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.jpg) ![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.jpg)
![IP-Adapter + IMG2IMG](https://github.com/tencent-ailab/IP-Adapter/blob/main/assets/demo/image-to-image.jpg) ![IP-Adapter + IMG2IMG](https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/demo/image-to-image.jpg)
#### Installation #### Installation
There are several ways to install IP-Adapter models with an existing InvokeAI installation: There are several ways to install IP-Adapter models with an existing InvokeAI installation:
1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [5] to download models. 1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models. 2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.
3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. 3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder.
#### Using IP-Adapter #### Using IP-Adapter

7
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@ -16,9 +16,10 @@ Model Merging can be be done by navigating to the Model Manager and clicking the
display all the diffusers-style models that InvokeAI knows about. display all the diffusers-style models that InvokeAI knows about.
If you do not see the model you are looking for, then it is probably If you do not see the model you are looking for, then it is probably
a legacy checkpoint model and needs to be converted using the a legacy checkpoint model and needs to be converted using the
`invoke` command-line client and its `!optimize` command. You "Convert" option in the Web-based Model Manager tab.
must select at least two models to merge. The third can be left at
"None" if you desire. You must select at least two models to merge. The third can be left
at "None" if you desire.
* Alpha: This is the ratio to use when combining models. It ranges * Alpha: This is the ratio to use when combining models. It ranges
from 0 to 1. The higher the value, the more weight is given to the from 0 to 1. The higher the value, the more weight is given to the

10
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@ -8,7 +8,7 @@ title: Command-line Utilities
InvokeAI comes with several scripts that are accessible via the InvokeAI comes with several scripts that are accessible via the
command line. To access these commands, start the "developer's command line. To access these commands, start the "developer's
console" from the launcher (`invoke.bat` menu item [8]). Users who are console" from the launcher (`invoke.bat` menu item [7]). Users who are
familiar with Python can alternatively activate InvokeAI's virtual familiar with Python can alternatively activate InvokeAI's virtual
environment (typically, but not necessarily `invokeai/.venv`). environment (typically, but not necessarily `invokeai/.venv`).
@ -34,7 +34,7 @@ invokeai-web --ram 7
## **invokeai-merge** ## **invokeai-merge**
This is the model merge script, the same as launcher option [4]. Call This is the model merge script, the same as launcher option [3]. Call
it with the `--gui` command-line argument to start the interactive it with the `--gui` command-line argument to start the interactive
console-based GUI. Alternatively, you can run it non-interactively console-based GUI. Alternatively, you can run it non-interactively
using command-line arguments as illustrated in the example below which using command-line arguments as illustrated in the example below which
@ -48,7 +48,7 @@ invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffus
## **invokeai-ti** ## **invokeai-ti**
This is the textual inversion training script that is run by launcher This is the textual inversion training script that is run by launcher
option [3]. Call it with `--gui` to run the interactive console-based option [2]. Call it with `--gui` to run the interactive console-based
front end. It can also be run non-interactively. It has about a front end. It can also be run non-interactively. It has about a
zillion arguments, but a typical training session can be launched zillion arguments, but a typical training session can be launched
with: with:
@ -68,7 +68,7 @@ in Windows).
## **invokeai-install** ## **invokeai-install**
This is the console-based model install script that is run by launcher This is the console-based model install script that is run by launcher
option [5]. If called without arguments, it will launch the option [4]. If called without arguments, it will launch the
interactive console-based interface. It can also be used interactive console-based interface. It can also be used
non-interactively to list, add and remove models as shown by these non-interactively to list, add and remove models as shown by these
examples: examples:
@ -148,7 +148,7 @@ launch the web server against it with `invokeai-web --root InvokeAI-New`.
## **invokeai-update** ## **invokeai-update**
This is the interactive console-based script that is run by launcher This is the interactive console-based script that is run by launcher
menu item [9] to update to a new version of InvokeAI. It takes no menu item [8] to update to a new version of InvokeAI. It takes no
command-line arguments. command-line arguments.
## **invokeai-metadata** ## **invokeai-metadata**

2
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@ -28,7 +28,7 @@ Learn how to install and use ControlNet models for fine control over
image output. image output.
### * [Image-to-Image Guide](IMG2IMG.md) ### * [Image-to-Image Guide](IMG2IMG.md)
Use a seed image to build new creations in the CLI. Use a seed image to build new creations.
## Model Management ## Model Management

4
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@ -57,7 +57,9 @@ Prompts provide the models directions on what to generate. As a general rule of
Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see. (Like Stable Diffusion!) Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see. (Like Stable Diffusion!)
Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at ****. Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision! Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at https://models.invoke.ai
Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision!
- *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas* - *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas*

7
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@ -143,7 +143,6 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
<!-- seperator --> <!-- seperator -->
### Prompt Engineering ### Prompt Engineering
- [Prompt Syntax](features/PROMPTS.md) - [Prompt Syntax](features/PROMPTS.md)
- [Generating Variations](features/VARIATIONS.md)
### InvokeAI Configuration ### InvokeAI Configuration
- [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md) - [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
@ -166,10 +165,8 @@ still a work in progress, but coming soon.
### Command-Line Interface Retired ### Command-Line Interface Retired
The original "invokeai" command-line interface has been retired. The All "invokeai" command-line interfaces have been retired as of version
`invokeai` command will now launch a new command-line client that can 3.4.
be used by developers to create and test nodes. It is not intended to
be used for routine image generation or manipulation.
To launch the Web GUI from the command-line, use the command To launch the Web GUI from the command-line, use the command
`invokeai-web` rather than the traditional `invokeai --web`. `invokeai-web` rather than the traditional `invokeai --web`.

4
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@ -40,7 +40,7 @@ experimental versions later.
this, open up a command-line window ("Terminal" on Linux and this, open up a command-line window ("Terminal" on Linux and
Macintosh, "Command" or "Powershell" on Windows) and type `python Macintosh, "Command" or "Powershell" on Windows) and type `python
--version`. If Python is installed, it will print out the version --version`. If Python is installed, it will print out the version
number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet number. If it is version `3.10.*` or `3.11.*` you meet
requirements. requirements.
!!! warning "What to do if you have an unsupported version" !!! warning "What to do if you have an unsupported version"
@ -48,7 +48,7 @@ experimental versions later.
Go to [Python Downloads](https://www.python.org/downloads/) Go to [Python Downloads](https://www.python.org/downloads/)
and download the appropriate installer package for your and download the appropriate installer package for your
platform. We recommend [Version platform. We recommend [Version
3.10.9](https://www.python.org/downloads/release/python-3109/), 3.10.12](https://www.python.org/downloads/release/python-3109/),
which has been extensively tested with InvokeAI. which has been extensively tested with InvokeAI.
_Please select your platform in the section below for platform-specific _Please select your platform in the section below for platform-specific

4
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@ -32,7 +32,7 @@ gaming):
* **Python** * **Python**
version 3.9 through 3.11 version 3.10 through 3.11
* **CUDA Tools** * **CUDA Tools**
@ -65,7 +65,7 @@ gaming):
To install InvokeAI with virtual environments and the PIP package To install InvokeAI with virtual environments and the PIP package
manager, please follow these steps: manager, please follow these steps:
1. Please make sure you are using Python 3.9 through 3.11. The rest of the install 1. Please make sure you are using Python 3.10 through 3.11. The rest of the install
procedure depends on this and will not work with other versions: procedure depends on this and will not work with other versions:
```bash ```bash

265
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@ -4,30 +4,31 @@ title: Installing with Docker
# :fontawesome-brands-docker: Docker # :fontawesome-brands-docker: Docker
!!! warning "For most users" !!! warning "macOS and AMD GPU Users"
We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md) We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md),
because Docker containers can not access the GPU on macOS.
!!! tip "For developers" !!! warning "AMD GPU Users"
For container-related development tasks or for enabling easy Container support for AMD GPUs has been reported to work by the community, but has not received
deployment to other environments (on-premises or cloud), follow these extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and
instructions. use the `build.sh` script to build the image for this to take effect at build time.
For general use, install locally to leverage your machine's GPU. !!! tip "Linux and Windows Users"
For optimal performance, configure your Docker daemon to access your machine's GPU.
Docker Desktop on Windows [includes GPU support](https://www.docker.com/blog/wsl-2-gpu-support-for-docker-desktop-on-nvidia-gpus/).
Linux users should install and configure the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
## Why containers? ## Why containers?
They provide a flexible, reliable way to build and deploy InvokeAI. You'll also They provide a flexible, reliable way to build and deploy InvokeAI.
use a Docker volume to store the largest model files and image outputs as a See [Processes](https://12factor.net/processes) under the Twelve-Factor App
first step in decoupling storage and compute. Future enhancements can do this methodology for details on why running applications in such a stateless fashion is important.
for other assets. See [Processes](https://12factor.net/processes) under the
Twelve-Factor App methodology for details on why running applications in such a
stateless fashion is important.
You can specify the target platform when building the image and running the The container is configured for CUDA by default, but can be built to support AMD GPUs
container. You'll also need to specify the InvokeAI requirements file that by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time.
matches the container's OS and the architecture it will run on.
Developers on Apple silicon (M1/M2): You Developers on Apple silicon (M1/M2): You
[can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224) [can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
@ -36,6 +37,16 @@ development purposes it's fine. Once you're done with development tasks on your
laptop you can build for the target platform and architecture and deploy to laptop you can build for the target platform and architecture and deploy to
another environment with NVIDIA GPUs on-premises or in the cloud. another environment with NVIDIA GPUs on-premises or in the cloud.
## TL;DR
This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options.
```bash
# docker compose commands should be run from the `docker` directory
cd docker
docker compose up
```
## Installation in a Linux container (desktop) ## Installation in a Linux container (desktop)
### Prerequisites ### Prerequisites
@ -58,222 +69,44 @@ a token and copy it, since you will need in for the next step.
### Setup ### Setup
Set the fork you want to use and other variables. Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
!!! tip Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
I preffer to save my env vars At a minimum, you might want to set the `INVOKEAI_ROOT` environment variable
in the repository root in a `.env` (or `.envrc`) file to automatically re-apply to point to the location where you wish to store your InvokeAI models, configuration, and outputs.
them when I come back.
The build- and run- scripts contain default values for almost everything,
besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
created in the last step.
Some Suggestions of variables you may want to change besides the Token:
<figure markdown> <figure markdown>
| Environment-Variable <img width="220" align="right"/> | Default value <img width="360" align="right"/> | Description | | Environment-Variable <img width="220" align="right"/> | Default value <img width="360" align="right"/> | Description |
| ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `HUGGING_FACE_HUB_TOKEN` | No default, but **required**! | This is the only **required** variable, without it you can't download the huggingface models | | `INVOKEAI_ROOT` | `~/invokeai` | **Required** - the location of your InvokeAI root directory. It will be created if it does not exist.
| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name | | `HUGGING_FACE_HUB_TOKEN` | | InvokeAI will work without it, but some of the integrations with HuggingFace (like downloading from models from private repositories) may not work|
| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored | | `GPU_DRIVER` | `cuda` | Optionally change this to `rocm` to build the image for AMD GPUs. NOTE: Use the `build.sh` script to build the image for this to take effect.
| `ARCH` | arch of the build machine | Can be changed if you want to build the image for another arch |
| `CONTAINER_REGISTRY` | ghcr.io | Name of the Container Registry to use for the full tag |
| `CONTAINER_REPOSITORY` | `$(whoami)/${REPOSITORY_NAME}` | Name of the Container Repository |
| `CONTAINER_FLAVOR` | `cuda` | The flavor of the image to built, available options are `cuda`, `rocm` and `cpu`. If you choose `rocm` or `cpu`, the extra-index-url will be selected automatically, unless you set one yourself. |
| `CONTAINER_TAG` | `${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}` | The Container Repository / Tag which will be used |
| `INVOKE_DOCKERFILE` | `Dockerfile` | The Dockerfile which should be built, handy for development |
| `PIP_EXTRA_INDEX_URL` | | If you want to use a custom pip-extra-index-url |
</figure> </figure>
#### Build the Image #### Build the Image
I provided a build script, which is located next to the Dockerfile in Use the standard `docker compose build` command from within the `docker` directory.
`docker/build.sh`. It can be executed from repository root like this:
```bash If using an AMD GPU:
./docker/build.sh a: set the `GPU_DRIVER=rocm` environment variable in `docker-compose.yml` and continue using `docker compose build` as usual, or
``` b: set `GPU_DRIVER=rocm` in the `.env` file and use the `build.sh` script, provided for convenience
The build Script not only builds the container, but also creates the docker
volume if not existing yet.
#### Run the Container #### Run the Container
After the build process is done, you can run the container via the provided Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
`docker/run.sh` script
```bash Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at [http://localhost:9090](http://localhost:9090)
./docker/run.sh
```
When used without arguments, the container will start the webserver and provide ## Troubleshooting / FAQ
you the link to open it. But if you want to use some other parameters you can
also do so.
!!! example "run script example" - Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
```bash and you may have cloned this repository before the issue was fixed. To solve this, please change
./docker/run.sh "banana sushi" -Ak_lms -S42 -s10 the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
``` (`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
Finally, you may delete `docker-entrypoint.sh` followed by `git pull; git checkout docker/docker-entrypoint.sh`
This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps. to reset the file to its most recent version.
For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)
Find out more about available CLI-Parameters at [features/CLI.md](../../features/CLI/#arguments)
---
## Running the container on your GPU
If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
the container with an extra environment variable to enable GPU usage and have
the process run much faster:
```bash
GPU_FLAGS=all ./docker/run.sh
```
This passes the `--gpus all` to docker and uses the GPU.
If you don't have a GPU (or your host is not yet setup to use it) you will see a
message like this:
`docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
You can use the full set of GPU combinations documented here:
https://docs.docker.com/config/containers/resource_constraints/#gpu
For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
choose a specific device identified by a UUID.
---
!!! warning "Deprecated"
From here on you will find the the previous Docker-Docs, which will still
provide some usefull informations.
## Usage (time to have fun)
### Startup
If you're on a **Linux container** the `invoke` script is **automatically
started** and the output dir set to the Docker volume you created earlier.
If you're **directly on macOS follow these startup instructions**. With the
Conda environment activated (`conda activate ldm`), run the interactive
interface that combines the functionality of the original scripts `txt2img` and
`img2img`: Use the more accurate but VRAM-intensive full precision math because
half-precision requires autocast and won't work. By default the images are saved
in `outputs/img-samples/`.
```Shell
python3 scripts/invoke.py --full_precision
```
You'll get the script's prompt. You can see available options or quit.
```Shell
invoke> -h
invoke> q
```
### Text to Image
For quick (but bad) image results test with 5 steps (default 50) and 1 sample
image. This will let you know that everything is set up correctly. Then increase
steps to 100 or more for good (but slower) results. The prompt can be in quotes
or not.
```Shell
invoke> The hulk fighting with sheldon cooper -s5 -n1
invoke> "woman closeup highly detailed" -s 150
# Reuse previous seed and apply face restoration
invoke> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.75
```
You'll need to experiment to see if face restoration is making it better or
worse for your specific prompt.
If you're on a container the output is set to the Docker volume. You can copy it
wherever you want. You can download it from the Docker Desktop app, Volumes,
my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
`docker cp` can't expand `*.png` so you'll need to specify the image file name.
On your host Mac (you can use the name of any container that mounted the
volume):
```Shell
docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
```
### Image to Image
You can also do text-guided image-to-image translation. For example, turning a
sketch into a detailed drawing.
`strength` is a value between 0.0 and 1.0 that controls the amount of noise that
is added to the input image. Values that approach 1.0 allow for lots of
variations but will also produce images that are not semantically consistent
with the input. 0.0 preserves image exactly, 1.0 replaces it completely.
Make sure your input image size dimensions are multiples of 64 e.g. 512x512.
Otherwise you'll get `Error: product of dimension sizes > 2**31'`. If you still
get the error
[try a different size](https://support.apple.com/guide/preview/resize-rotate-or-flip-an-image-prvw2015/mac#:~:text=image's%20file%20size-,In%20the%20Preview%20app%20on%20your%20Mac%2C%20open%20the%20file,is%20shown%20at%20the%20bottom.)
like 512x256.
If you're on a Docker container, copy your input image into the Docker volume
```Shell
docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
```
Try it out generating an image (or more). The `invoke` script needs absolute
paths to find the image so don't use `~`.
If you're on your Mac
```Shell
invoke> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75 --steps 100 -n4
```
If you're on a Linux container on your Mac
```Shell
invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75 --steps 50 -n1
```
### Web Interface
You can use the `invoke` script with a graphical web interface. Start the web
server with:
```Shell
python3 scripts/invoke.py --full_precision --web
```
If it's running on your Mac point your Mac web browser to
<http://127.0.0.1:9090>
Press Control-C at the command line to stop the web server.
### Notes
Some text you can add at the end of the prompt to make it very pretty:
```Shell
cinematic photo, highly detailed, cinematic lighting, ultra-detailed, ultrarealistic, photorealism, Octane Rendering, cyberpunk lights, Hyper Detail, 8K, HD, Unreal Engine, V-Ray, full hd, cyberpunk, abstract, 3d octane render + 4k UHD + immense detail + dramatic lighting + well lit + black, purple, blue, pink, cerulean, teal, metallic colours, + fine details, ultra photoreal, photographic, concept art, cinematic composition, rule of thirds, mysterious, eerie, photorealism, breathtaking detailed, painting art deco pattern, by hsiao, ron cheng, john james audubon, bizarre compositions, exquisite detail, extremely moody lighting, painted by greg rutkowski makoto shinkai takashi takeuchi studio ghibli, akihiko yoshida
```
The original scripts should work as well.
```Shell
python3 scripts/orig_scripts/txt2img.py --help
python3 scripts/orig_scripts/txt2img.py --ddim_steps 100 --n_iter 1 --n_samples 1 --plms --prompt "new born baby kitten. Hyper Detail, Octane Rendering, Unreal Engine, V-Ray"
python3 scripts/orig_scripts/txt2img.py --ddim_steps 5 --n_iter 1 --n_samples 1 --plms --prompt "ocean" # or --klms
```

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@ -84,7 +84,7 @@ InvokeAI root directory's `autoimport` folder.
### Installation via `invokeai-model-install` ### Installation via `invokeai-model-install`
From the `invoke` launcher, choose option [5] "Download and install From the `invoke` launcher, choose option [4] "Download and install
models." This will launch the same script that prompted you to select models." This will launch the same script that prompted you to select
models at install time. You can use this to add models that you models at install time. You can use this to add models that you
skipped the first time around. It is all right to specify a model that skipped the first time around. It is all right to specify a model that

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@ -59,8 +59,7 @@ Prior to installing PyPatchMatch, you need to take the following steps:
`from patchmatch import patch_match`: It should look like the following: `from patchmatch import patch_match`: It should look like the following:
```py ```py
Python 3.9.5 (default, Nov 23 2021, 15:27:38) Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information. Type "help", "copyright", "credits" or "license" for more information.
>>> from patchmatch import patch_match >>> from patchmatch import patch_match
Compiling and loading c extensions from "/home/lstein/Projects/InvokeAI/.invokeai-env/src/pypatchmatch/patchmatch". Compiling and loading c extensions from "/home/lstein/Projects/InvokeAI/.invokeai-env/src/pypatchmatch/patchmatch".

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@ -79,7 +79,7 @@ title: Manual Installation, Linux
and obtaining an access token for downloading. It will then download and and obtaining an access token for downloading. It will then download and
install the weights files for you. install the weights files for you.
Please look [here](../INSTALL_MANUAL.md) for a manual process for doing Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing
the same thing. the same thing.
7. Start generating images! 7. Start generating images!
@ -112,7 +112,7 @@ title: Manual Installation, Linux
To use an alternative model you may invoke the `!switch` command in To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line either the CLI or the Web UI. See [Command Line
Client](../../features/CLI.md#model-selection-and-importation). The Client](../../deprecated/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`. model names are defined in `configs/models.yaml`.
8. Subsequently, to relaunch the script, be sure to run "conda activate 8. Subsequently, to relaunch the script, be sure to run "conda activate

2
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@ -150,7 +150,7 @@ will do our best to help.
To use an alternative model you may invoke the `!switch` command in To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line either the CLI or the Web UI. See [Command Line
Client](../../features/CLI.md#model-selection-and-importation). The Client](../../deprecated/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`. model names are defined in `configs/models.yaml`.
--- ---

2
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@ -128,7 +128,7 @@ python scripts/invoke.py --web --max_load_models=3 \
``` ```
These options are described in detail in the These options are described in detail in the
[Command-Line Interface](../../features/CLI.md) documentation. [Command-Line Interface](../../deprecated/CLI.md) documentation.
## Troubleshooting ## Troubleshooting

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@ -75,7 +75,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
obtaining an access token for downloading. It will then download and install the obtaining an access token for downloading. It will then download and install the
weights files for you. weights files for you.
Please look [here](../INSTALL_MANUAL.md) for a manual process for doing the Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing the
same thing. same thing.
8. Start generating images! 8. Start generating images!
@ -108,7 +108,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
To use an alternative model you may invoke the `!switch` command in To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line either the CLI or the Web UI. See [Command Line
Client](../../features/CLI.md#model-selection-and-importation). The Client](../../deprecated/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`. model names are defined in `configs/models.yaml`.
9. Subsequently, to relaunch the script, first activate the Anaconda 9. Subsequently, to relaunch the script, first activate the Anaconda

24
docs/nodes/communityNodes.md
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@ -4,11 +4,16 @@ These are nodes that have been developed by the community, for the community. If
If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md). If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md).
To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. If you used the automated installation, this can be found inside the `.venv` folder. Along with the node, an example node graph should be provided to help you get started with the node. To use a node, add the node to the `nodes` folder found in your InvokeAI install location.
The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future.
If you'd prefer, you can also just download the `.py` file from the linked repository and add it to the `nodes` folder.
To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor. To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor.
- Community Nodes - Community Nodes
+ [Average Images](#average-images)
+ [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj) + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
+ [Film Grain](#film-grain) + [Film Grain](#film-grain)
+ [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes) + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
@ -33,6 +38,13 @@ To use a community workflow, download the the `.json` node graph file and load i
- [Help](#help) - [Help](#help)
--------------------------------
### Average Images
**Description:** This node takes in a collection of images of the same size and averages them as output. It converts everything to RGB mode first.
**Node Link:** https://github.com/JPPhoto/average-images-node
-------------------------------- --------------------------------
### Depth Map from Wavefront OBJ ### Depth Map from Wavefront OBJ
@ -177,12 +189,8 @@ This includes 15 Nodes:
**Node Link:** https://github.com/helix4u/load_video_frame **Node Link:** https://github.com/helix4u/load_video_frame
**Example Node Graph:** https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json
**Output Example:** **Output Example:**
<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/_git_assets/testmp4_embed_converted.gif" width="500" />
<img src="https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif" width="500" />
[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
-------------------------------- --------------------------------
### Make 3D ### Make 3D
@ -325,9 +333,9 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
**Description:** This node allows you to do super cool things with InvokeAI. **Description:** This node allows you to do super cool things with InvokeAI.
**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py **Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py
**Example Node Graph:** https://github.com/invoke-ai/InvokeAI/fake_node_graph.json **Example Workflow:** https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json
**Output Examples** **Output Examples**

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@ -4,7 +4,7 @@ To learn about the specifics of creating a new node, please visit our [Node crea
Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps: Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps:
- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node. - Make sure the node is contained in a new Python (.py) file. Preferably, the node is in a repo with a README detailing the nodes usage & examples to help others more easily use your node. Including the tag "invokeai-node" in your repository's README can also help other users find it more easily.
- Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list - Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
- Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node. - Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project. - A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.

12
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@ -2,13 +2,17 @@
We've curated some example workflows for you to get started with Workflows in InvokeAI We've curated some example workflows for you to get started with Workflows in InvokeAI
To use them, right click on your desired workflow, press "Download Linked File". You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images! To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord. If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
* [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json) * [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json) * [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_Text_to_Image.json)
* [SDXL (with Refiner) Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json) * [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json) * [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json)
* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json)
* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json.json)
* [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json) * [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
* [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json) * [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)
* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/QR_Code_Monster.json)

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@ -0,0 +1,985 @@
{
"name": "Multi ControlNet (Canny & Depth)",
"author": "Millu",
"description": "A sample workflow using canny & depth ControlNets to guide the generation process. ",
"version": "0.1.0",
"contact": "millun@invoke.ai",
"tags": "ControlNet, canny, depth",
"notes": "",
"exposedFields": [
{
"nodeId": "54486974-835b-4d81-8f82-05f9f32ce9e9",
"fieldName": "model"
},
{
"nodeId": "7ce68934-3419-42d4-ac70-82cfc9397306",
"fieldName": "prompt"
},
{
"nodeId": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
"fieldName": "prompt"
},
{
"nodeId": "c4b23e64-7986-40c4-9cad-46327b12e204",
"fieldName": "image"
},
{
"nodeId": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
"fieldName": "image"
}
],
"meta": {
"version": "1.0.0"
},
"nodes": [
{
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"type": "invocation",
"data": {
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"type": "image",
"inputs": {
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"name": "image",
"type": "ImageField",
"fieldKind": "input",
"label": "Depth Input Image"
}
},
"outputs": {
"image": {
"id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73",
"name": "image",
"type": "ImageField",
"fieldKind": "output"
},
"width": {
"id": "12f298fd-1d11-4cca-9426-01240f7ec7cf",
"name": "width",
"type": "integer",
"fieldKind": "output"
},
"height": {
"id": "c47dabcb-44e8-40c9-992d-81dca59f598e",
"name": "height",
"type": "integer",
"fieldKind": "output"
}
},
"label": "",
"isOpen": true,
"notes": "",
"embedWorkflow": false,
"isIntermediate": true,
"useCache": true,
"version": "1.0.0"
},
"width": 320,
"height": 225,
"position": {
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"y": 40.5529847930888
}
},
{
"id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
"type": "invocation",
"data": {
"id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
"type": "controlnet",
"inputs": {
"image": {
"id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
"name": "image",
"type": "ImageField",
"fieldKind": "input",
"label": ""
},
"control_model": {
"id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
"name": "control_model",
"type": "ControlNetModelField",
"fieldKind": "input",
"label": "",
"value": {
"model_name": "sd-controlnet-depth",
"base_model": "sd-1"
}
},
"control_weight": {
"id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f",
"name": "control_weight",
"type": "FloatPolymorphic",
"fieldKind": "input",
"label": "",
"value": 1
},
"begin_step_percent": {
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"name": "begin_step_percent",
"type": "float",
"fieldKind": "input",
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"value": 0
},
"end_step_percent": {
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"fieldKind": "input",
"label": "",
"value": 1
},
"control_mode": {
"id": "d2f14561-9443-4374-9270-e2f05007944e",
"name": "control_mode",
"type": "enum",
"fieldKind": "input",
"label": "",
"value": "balanced"
},
"resize_mode": {
"id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd",
"name": "resize_mode",
"type": "enum",
"fieldKind": "input",
"label": "",
"value": "just_resize"
}
},
"outputs": {
"control": {
"id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087",
"name": "control",
"type": "ControlField",
"fieldKind": "output"
}
},
"label": "",
"isOpen": true,
"notes": "",
"embedWorkflow": false,
"isIntermediate": true,
"useCache": true,
"version": "1.0.0"
},
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"y": -49.39005411272677
}
},
{
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"type": "invocation",
"data": {
"id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
"type": "compel",
"inputs": {
"prompt": {
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"name": "prompt",
"type": "string",
"fieldKind": "input",
"label": "Negative Prompt",
"value": ""
},
"clip": {
"id": "06d59e91-9cca-411d-bf05-86b099b3e8f7",
"name": "clip",
"type": "ClipField",
"fieldKind": "input",
"label": ""
}
},
"outputs": {
"conditioning": {
"id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
"name": "conditioning",
"type": "ConditioningField",
"fieldKind": "output"
}
},
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},
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}
},
{
"id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
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"inputs": {
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"name": "model",
"type": "MainModelField",
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"label": "",
"value": {
"model_name": "stable-diffusion-v1-5",
"base_model": "sd-1",
"model_type": "main"
}
}
},
"outputs": {
"unet": {
"id": "ee24fb16-da38-4c66-9fbc-e8f296ed40d2",
"name": "unet",
"type": "UNetField",
"fieldKind": "output"
},
"clip": {
"id": "f3fb0524-8803-41c1-86db-a61a13ee6a33",
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"type": "ClipField",
"fieldKind": "output"
},
"vae": {
"id": "5c4878a8-b40f-44ab-b146-1c1f42c860b3",
"name": "vae",
"type": "VaeField",
"fieldKind": "output"
}
},
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"isOpen": true,
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"embedWorkflow": false,
"isIntermediate": true,
"useCache": true,
"version": "1.0.0"
},
"width": 320,
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"position": {
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}
},
{
"id": "7ce68934-3419-42d4-ac70-82cfc9397306",
"type": "invocation",
"data": {
"id": "7ce68934-3419-42d4-ac70-82cfc9397306",
"type": "compel",
"inputs": {
"prompt": {
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"name": "prompt",
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"fieldKind": "input",
"label": "Positive Prompt",
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},
"clip": {
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"name": "clip",
"type": "ClipField",
"fieldKind": "input",
"label": ""
}
},
"outputs": {
"conditioning": {
"id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
"name": "conditioning",
"type": "ConditioningField",
"fieldKind": "output"
}
},
"label": "",
"isOpen": true,
"notes": "",
"embedWorkflow": false,
"isIntermediate": true,
"useCache": true,
"version": "1.0.0"
},
"width": 320,
"height": 261,
"position": {
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"y": -1201.659695420063
}
},
{
"id": "d204d184-f209-4fae-a0a1-d152800844e1",
"type": "invocation",
"data": {
"id": "d204d184-f209-4fae-a0a1-d152800844e1",
"type": "controlnet",
"inputs": {
"image": {
"id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
"name": "image",
"type": "ImageField",
"fieldKind": "input",
"label": ""
},
"control_model": {
"id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
"name": "control_model",
"type": "ControlNetModelField",
"fieldKind": "input",
"label": "",
"value": {
"model_name": "sd-controlnet-canny",
"base_model": "sd-1"
}
},
"control_weight": {
"id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f",
"name": "control_weight",
"type": "FloatPolymorphic",
"fieldKind": "input",
"label": "",
"value": 1
},
"begin_step_percent": {
"id": "c258a276-352a-416c-8358-152f11005c0c",
"name": "begin_step_percent",
"type": "float",
"fieldKind": "input",
"label": "",
"value": 0
},
"end_step_percent": {
"id": "43001125-0d70-4f87-8e79-da6603ad6c33",
"name": "end_step_percent",
"type": "float",
"fieldKind": "input",
"label": "",
"value": 1
},
"control_mode": {
"id": "d2f14561-9443-4374-9270-e2f05007944e",
"name": "control_mode",
"type": "enum",
"fieldKind": "input",
"label": "",
"value": "balanced"
},
"resize_mode": {
"id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd",
"name": "resize_mode",
"type": "enum",
"fieldKind": "input",
"label": "",
"value": "just_resize"
}
},
"outputs": {
"control": {
"id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087",
"name": "control",
"type": "ControlField",
"fieldKind": "output"
}
},
"label": "",
"isOpen": true,
"notes": "",
"embedWorkflow": false,
"isIntermediate": true,
"useCache": true,
"version": "1.0.0"
},
"width": 320,
"height": 508,
"position": {
"x": 4479.68542130465,
"y": -618.4221638099414
}
},
{
"id": "c4b23e64-7986-40c4-9cad-46327b12e204",
"type": "invocation",
"data": {
"id": "c4b23e64-7986-40c4-9cad-46327b12e204",
"type": "image",
"inputs": {
"image": {
"id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
"name": "image",
"type": "ImageField",
"fieldKind": "input",
"label": "Canny Input Image"
}
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719
docs/workflows/Prompt_from_File.json Normal file
View File

@ -0,0 +1,719 @@
{
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758
docs/workflows/QR_Code_Monster.json Normal file
View File

@ -0,0 +1,758 @@
{
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432
docs/workflows/SDXL_Text_to_Image.json
View File

@ -26,10 +26,6 @@
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973
docs/workflows/SDXL_w_Refiner_Text_to_Image.json
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366
docs/workflows/Text_to_Image.json
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@ -18,10 +18,6 @@
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"inputs": {
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"id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
"name": "tiled",
"type": "boolean",
"fieldKind": "input",
"label": "",
"value": false
},
"fp32": {
"id": "b146d873-ffb9-4767-986a-5360504841a2",
"name": "fp32",
"type": "boolean",
"fieldKind": "input",
"label": "",
"value": false
},
"latents": {
"id": "65441abd-7713-4b00-9d8d-3771404002e8",
"name": "latents",
"type": "LatentsField",
"fieldKind": "input",
"label": ""
},
"vae": {
"id": "a478b833-6e13-4611-9a10-842c89603c74",
"name": "vae",
"type": "VaeField",
"fieldKind": "input",
"label": ""
}
},
"outputs": {
"image": {
"id": "c87ae925-f858-417a-8940-8708ba9b4b53",
"name": "image",
"type": "ImageField",
"fieldKind": "output"
},
"width": {
"id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c",
"name": "width",
"type": "integer",
"fieldKind": "output"
},
"height": {
"id": "23e41c00-a354-48e8-8f59-5875679c27ab",
"name": "height",
"type": "integer",
"fieldKind": "output"
}
},
"label": "",
"isOpen": true,
"notes": "",
"embedWorkflow": true,
"isIntermediate": false
},
"width": 320,
"height": 266,
"position": {
"x": 1800,
"y": 200
} }
}, },
{ {
"id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
"type": "invocation", "type": "invocation",
"data": { "data": {
"version": "1.0.0",
"id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
"type": "main_model_loader", "type": "main_model_loader",
"inputs": { "inputs": {
@ -261,23 +186,24 @@
} }
}, },
"label": "", "label": "",
"isOpen": false, "isOpen": true,
"notes": "", "notes": "",
"embedWorkflow": false, "embedWorkflow": false,
"isIntermediate": true "isIntermediate": true,
"useCache": true,
"version": "1.0.0"
}, },
"width": 320, "width": 320,
"height": 32, "height": 226,
"position": { "position": {
"x": 1000, "x": 163.04436745878343,
"y": 200 "y": 254.63156870373479
} }
}, },
{ {
"id": "7d8bf987-284f-413a-b2fd-d825445a5d6c", "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
"type": "invocation", "type": "invocation",
"data": { "data": {
"version": "1.0.0",
"id": "7d8bf987-284f-413a-b2fd-d825445a5d6c", "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
"type": "compel", "type": "compel",
"inputs": { "inputs": {
@ -309,20 +235,21 @@
"isOpen": true, "isOpen": true,
"notes": "", "notes": "",
"embedWorkflow": false, "embedWorkflow": false,
"isIntermediate": true "isIntermediate": true,
"useCache": true,
"version": "1.0.0"
}, },
"width": 320, "width": 320,
"height": 235, "height": 261,
"position": { "position": {
"x": 1000, "x": 595.7263915923627,
"y": -75 "y": 239.67783573351227
} }
}, },
{ {
"id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
"type": "invocation", "type": "invocation",
"data": { "data": {
"version": "1.0.0",
"id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
"type": "rand_int", "type": "rand_int",
"inputs": { "inputs": {
@ -352,51 +279,66 @@
} }
}, },
"label": "Random Seed", "label": "Random Seed",
"isOpen": false, "isOpen": true,
"notes": "", "notes": "",
"embedWorkflow": false, "embedWorkflow": false,
"isIntermediate": true "isIntermediate": true,
"useCache": false,
"version": "1.0.0"
}, },
"width": 320, "width": 320,
"height": 32, "height": 218,
"position": { "position": {
"x": 1000, "x": 541.094822888628,
"y": 275 "y": 694.5704476446829
} }
}, },
{ {
"id": "75899702-fa44-46d2-b2d5-3e17f234c3e7", "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
"type": "invocation", "type": "invocation",
"data": { "data": {
"version": "1.0.0", "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
"id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
"type": "denoise_latents", "type": "denoise_latents",
"inputs": { "inputs": {
"positive_conditioning": {
"id": "90b7f4f8-ada7-4028-8100-d2e54f192052",
"name": "positive_conditioning",
"type": "ConditioningField",
"fieldKind": "input",
"label": ""
},
"negative_conditioning": {
"id": "9393779e-796c-4f64-b740-902a1177bf53",
"name": "negative_conditioning",
"type": "ConditioningField",
"fieldKind": "input",
"label": ""
},
"noise": { "noise": {
"id": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b", "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
"name": "noise", "name": "noise",
"type": "LatentsField", "type": "LatentsField",
"fieldKind": "input", "fieldKind": "input",
"label": "" "label": ""
}, },
"steps": { "steps": {
"id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c", "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
"name": "steps", "name": "steps",
"type": "integer", "type": "integer",
"fieldKind": "input", "fieldKind": "input",
"label": "", "label": "",
"value": 36 "value": 10
}, },
"cfg_scale": { "cfg_scale": {
"id": "107267ce-4666-4cd7-94b3-7476b7973ae9", "id": "87dd04d3-870e-49e1-98bf-af003a810109",
"name": "cfg_scale", "name": "cfg_scale",
"type": "float", "type": "FloatPolymorphic",
"fieldKind": "input", "fieldKind": "input",
"label": "", "label": "",
"value": 7.5 "value": 7.5
}, },
"denoising_start": { "denoising_start": {
"id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1", "id": "f369d80f-4931-4740-9bcd-9f0620719fab",
"name": "denoising_start", "name": "denoising_start",
"type": "float", "type": "float",
"fieldKind": "input", "fieldKind": "input",
@ -404,7 +346,7 @@
"value": 0 "value": 0
}, },
"denoising_end": { "denoising_end": {
"id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f", "id": "747d10e5-6f02-445c-994c-0604d814de8c",
"name": "denoising_end", "name": "denoising_end",
"type": "float", "type": "float",
"fieldKind": "input", "fieldKind": "input",
@ -412,71 +354,71 @@
"value": 1 "value": 1
}, },
"scheduler": { "scheduler": {
"id": "53092874-a43b-4623-91a2-76e62fdb1f2e", "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
"name": "scheduler", "name": "scheduler",
"type": "Scheduler", "type": "Scheduler",
"fieldKind": "input", "fieldKind": "input",
"label": "", "label": "",
"value": "euler" "value": "euler"
}, },
"unet": {
"id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b",
"name": "unet",
"type": "UNetField",
"fieldKind": "input",
"label": ""
},
"control": { "control": {
"id": "7abe57cc-469d-437e-ad72-a18efa28215f", "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
"name": "control", "name": "control",
"type": "ControlField", "type": "ControlPolymorphic",
"fieldKind": "input",
"label": ""
},
"ip_adapter": {
"id": "1d6948f7-3a65-4a65-a20c-768b287251aa",
"name": "ip_adapter",
"type": "IPAdapterPolymorphic",
"fieldKind": "input",
"label": ""
},
"t2i_adapter": {
"id": "75e67b09-952f-4083-aaf4-6b804d690412",
"name": "t2i_adapter",
"type": "T2IAdapterPolymorphic",
"fieldKind": "input", "fieldKind": "input",
"label": "" "label": ""
}, },
"latents": { "latents": {
"id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129", "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
"name": "latents", "name": "latents",
"type": "LatentsField", "type": "LatentsField",
"fieldKind": "input", "fieldKind": "input",
"label": "" "label": ""
}, },
"denoise_mask": { "denoise_mask": {
"id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1", "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
"name": "denoise_mask", "name": "denoise_mask",
"type": "DenoiseMaskField", "type": "DenoiseMaskField",
"fieldKind": "input", "fieldKind": "input",
"label": "" "label": ""
},
"positive_conditioning": {
"id": "c7160303-8a23-4f15-9197-855d48802a7f",
"name": "positive_conditioning",
"type": "ConditioningField",
"fieldKind": "input",
"label": ""
},
"negative_conditioning": {
"id": "fd750efa-1dfc-4d0b-accb-828e905ba320",
"name": "negative_conditioning",
"type": "ConditioningField",
"fieldKind": "input",
"label": ""
},
"unet": {
"id": "af1f41ba-ce2a-4314-8d7f-494bb5800381",
"name": "unet",
"type": "UNetField",
"fieldKind": "input",
"label": ""
} }
}, },
"outputs": { "outputs": {
"latents": { "latents": {
"id": "8508d04d-f999-4a44-94d0-388ab1401d27", "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
"name": "latents", "name": "latents",
"type": "LatentsField", "type": "LatentsField",
"fieldKind": "output" "fieldKind": "output"
}, },
"width": { "width": {
"id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e", "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
"name": "width", "name": "width",
"type": "integer", "type": "integer",
"fieldKind": "output" "fieldKind": "output"
}, },
"height": { "height": {
"id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96", "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
"name": "height", "name": "height",
"type": "integer", "type": "integer",
"fieldKind": "output" "fieldKind": "output"
@ -486,13 +428,95 @@
"isOpen": true, "isOpen": true,
"notes": "", "notes": "",
"embedWorkflow": false, "embedWorkflow": false,
"isIntermediate": true "isIntermediate": true,
"useCache": true,
"version": "1.4.0"
}, },
"width": 320, "width": 320,
"height": 558, "height": 646,
"position": { "position": {
"x": 1400, "x": 1476.5794704734735,
"y": 200 "y": 256.80174342731783
}
},
{
"id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
"type": "invocation",
"data": {
"id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
"type": "l2i",
"inputs": {
"metadata": {
"id": "ab375f12-0042-4410-9182-29e30db82c85",
"name": "metadata",
"type": "MetadataField",
"fieldKind": "input",
"label": ""
},
"latents": {
"id": "3a7e7efd-bff5-47d7-9d48-615127afee78",
"name": "latents",
"type": "LatentsField",
"fieldKind": "input",
"label": ""
},
"vae": {
"id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b",
"name": "vae",
"type": "VaeField",
"fieldKind": "input",
"label": ""
},
"tiled": {
"id": "da52059a-0cee-4668-942f-519aa794d739",
"name": "tiled",
"type": "boolean",
"fieldKind": "input",
"label": "",
"value": false
},
"fp32": {
"id": "c4841df3-b24e-4140-be3b-ccd454c2522c",
"name": "fp32",
"type": "boolean",
"fieldKind": "input",
"label": "",
"value": false
}
},
"outputs": {
"image": {
"id": "72d667d0-cf85-459d-abf2-28bd8b823fe7",
"name": "image",
"type": "ImageField",
"fieldKind": "output"
},
"width": {
"id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc",
"name": "width",
"type": "integer",
"fieldKind": "output"
},
"height": {
"id": "230f359c-b4ea-436c-b372-332d7dcdca85",
"name": "height",
"type": "integer",
"fieldKind": "output"
}
},
"label": "",
"isOpen": true,
"notes": "",
"embedWorkflow": false,
"isIntermediate": false,
"useCache": true,
"version": "1.0.0"
},
"width": 320,
"height": 267,
"position": {
"x": 2037.9648469717395,
"y": 426.10844427600136
} }
} }
], ],
@ -522,52 +546,52 @@
"type": "default" "type": "default"
}, },
{ {
"source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", "source": "55705012-79b9-4aac-9f26-c0b10309785b",
"sourceHandle": "vae", "sourceHandle": "noise",
"target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
"targetHandle": "vae", "targetHandle": "noise",
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae", "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
"type": "default"
},
{
"source": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
"sourceHandle": "latents",
"target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
"targetHandle": "latents",
"id": "reactflow__edge-75899702-fa44-46d2-b2d5-3e17f234c3e7latents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
"type": "default" "type": "default"
}, },
{ {
"source": "7d8bf987-284f-413a-b2fd-d825445a5d6c", "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
"sourceHandle": "conditioning", "sourceHandle": "conditioning",
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
"targetHandle": "positive_conditioning", "targetHandle": "positive_conditioning",
"id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning", "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
"type": "default" "type": "default"
}, },
{ {
"source": "93dc02a4-d05b-48ed-b99c-c9b616af3402", "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
"sourceHandle": "conditioning", "sourceHandle": "conditioning",
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
"targetHandle": "negative_conditioning", "targetHandle": "negative_conditioning",
"id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning", "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
"type": "default" "type": "default"
}, },
{ {
"source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
"sourceHandle": "unet", "sourceHandle": "unet",
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
"targetHandle": "unet", "targetHandle": "unet",
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet", "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
"type": "default" "type": "default"
}, },
{ {
"source": "55705012-79b9-4aac-9f26-c0b10309785b", "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
"sourceHandle": "noise", "sourceHandle": "latents",
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
"targetHandle": "noise", "targetHandle": "latents",
"id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-75899702-fa44-46d2-b2d5-3e17f234c3e7noise", "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
"type": "default"
},
{
"source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
"sourceHandle": "vae",
"target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
"targetHandle": "vae",
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
"type": "default" "type": "default"
} }
] ]
} }

13
installer/install.bat.in
View File

@ -1,7 +1,7 @@
@echo off @echo off
setlocal EnableExtensions EnableDelayedExpansion setlocal EnableExtensions EnableDelayedExpansion
@rem This script requires the user to install Python 3.9 or higher. All other @rem This script requires the user to install Python 3.10 or higher. All other
@rem requirements are downloaded as needed. @rem requirements are downloaded as needed.
@rem change to the script's directory @rem change to the script's directory
@ -19,7 +19,7 @@ set INVOKEAI_VERSION=latest
set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/ set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
set PYTHON_URL=https://www.python.org/downloads/windows/ set PYTHON_URL=https://www.python.org/downloads/windows/
set MINIMUM_PYTHON_VERSION=3.9.0 set MINIMUM_PYTHON_VERSION=3.10.0
set PYTHON_URL=https://www.python.org/downloads/release/python-3109/ set PYTHON_URL=https://www.python.org/downloads/release/python-3109/
set err_msg=An error has occurred and the script could not continue. set err_msg=An error has occurred and the script could not continue.
@ -28,8 +28,7 @@ set err_msg=An error has occurred and the script could not continue.
echo This script will install InvokeAI and its dependencies. echo This script will install InvokeAI and its dependencies.
echo. echo.
echo BEFORE YOU START PLEASE MAKE SURE TO DO THE FOLLOWING echo BEFORE YOU START PLEASE MAKE SURE TO DO THE FOLLOWING
echo 1. Install python 3.9 or 3.10. Python version 3.11 and above are echo 1. Install python 3.10 or 3.11. Python version 3.9 is no longer supported.
echo not supported at the moment.
echo 2. Double-click on the file WinLongPathsEnabled.reg in order to echo 2. Double-click on the file WinLongPathsEnabled.reg in order to
echo enable long path support on your system. echo enable long path support on your system.
echo 3. Install the Visual C++ core libraries. echo 3. Install the Visual C++ core libraries.
@ -46,19 +45,19 @@ echo ***** Checking and Updating Python *****
call python --version >.tmp1 2>.tmp2 call python --version >.tmp1 2>.tmp2
if %errorlevel% == 1 ( if %errorlevel% == 1 (
set err_msg=Please install Python 3.10. See %INSTRUCTIONS% for details. set err_msg=Please install Python 3.10-11. See %INSTRUCTIONS% for details.
goto err_exit goto err_exit
) )
for /f "tokens=2" %%i in (.tmp1) do set python_version=%%i for /f "tokens=2" %%i in (.tmp1) do set python_version=%%i
if "%python_version%" == "" ( if "%python_version%" == "" (
set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.9 from %PYTHON_URL% set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.12 from %PYTHON_URL%
goto err_exit goto err_exit
) )
call :compareVersions %MINIMUM_PYTHON_VERSION% %python_version% call :compareVersions %MINIMUM_PYTHON_VERSION% %python_version%
if %errorlevel% == 1 ( if %errorlevel% == 1 (
set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.9 from %PYTHON_URL% set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.12 from %PYTHON_URL%
goto err_exit goto err_exit
) )

4
installer/install.sh.in
View File

@ -8,10 +8,10 @@ cd $scriptdir
function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; } function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
MINIMUM_PYTHON_VERSION=3.9.0 MINIMUM_PYTHON_VERSION=3.10.0
MAXIMUM_PYTHON_VERSION=3.11.100 MAXIMUM_PYTHON_VERSION=3.11.100
PYTHON="" PYTHON=""
for candidate in python3.11 python3.10 python3.9 python3 python ; do for candidate in python3.11 python3.10 python3 python ; do
if ppath=`which $candidate`; then if ppath=`which $candidate`; then
# when using `pyenv`, the executable for an inactive Python version will exist but will not be operational # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
# we check that this found executable can actually run # we check that this found executable can actually run

10
installer/lib/installer.py
View File

@ -13,7 +13,7 @@ from pathlib import Path
from tempfile import TemporaryDirectory from tempfile import TemporaryDirectory
from typing import Union from typing import Union
SUPPORTED_PYTHON = ">=3.9.0,<=3.11.100" SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100"
INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"] INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp" BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp"
@ -67,7 +67,6 @@ class Installer:
# Cleaning up temporary directories on Windows results in a race condition # Cleaning up temporary directories on Windows results in a race condition
# and a stack trace. # and a stack trace.
# `ignore_cleanup_errors` was only added in Python 3.10 # `ignore_cleanup_errors` was only added in Python 3.10
# users of Python 3.9 will see a gnarly stack trace on installer exit
if OS == "Windows" and int(platform.python_version_tuple()[1]) >= 10: if OS == "Windows" and int(platform.python_version_tuple()[1]) >= 10:
venv_dir = TemporaryDirectory(prefix=BOOTSTRAP_VENV_PREFIX, ignore_cleanup_errors=True) venv_dir = TemporaryDirectory(prefix=BOOTSTRAP_VENV_PREFIX, ignore_cleanup_errors=True)
else: else:
@ -139,13 +138,6 @@ class Installer:
except shutil.SameFileError: except shutil.SameFileError:
venv.create(venv_dir, with_pip=True, symlinks=True) venv.create(venv_dir, with_pip=True, symlinks=True)
# upgrade pip in Python 3.9 environments
if int(platform.python_version_tuple()[1]) == 9:
from plumbum import FG, local
pip = local[get_pip_from_venv(venv_dir)]
pip["install", "--upgrade", "pip"] & FG
return venv_dir return venv_dir
def install( def install(

8
installer/readme.txt
View File

@ -4,7 +4,7 @@ Project homepage: https://github.com/invoke-ai/InvokeAI
Preparations: Preparations:
You will need to install Python 3.9 or higher for this installer You will need to install Python 3.10 or higher for this installer
to work. Instructions are given here: to work. Instructions are given here:
https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/ https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
@ -14,15 +14,15 @@ Preparations:
python --version python --version
If all is well, it will print "Python 3.X.X", where the version number If all is well, it will print "Python 3.X.X", where the version number
is at least 3.9.*, and not higher than 3.11.*. is at least 3.10.*, and not higher than 3.11.*.
If this works, check the version of the Python package manager, pip: If this works, check the version of the Python package manager, pip:
pip --version pip --version
You should get a message that indicates that the pip package You should get a message that indicates that the pip package
installer was derived from Python 3.9 or 3.10. For example: installer was derived from Python 3.10 or 3.11. For example:
"pip 22.3.1 from /usr/bin/pip (python 3.9)" "pip 22.0.1 from /usr/bin/pip (python 3.10)"
Long Paths on Windows: Long Paths on Windows:

40
installer/templates/invoke.bat.in
View File

@ -9,41 +9,37 @@ set INVOKEAI_ROOT=.
:start :start
echo Desired action: echo Desired action:
echo 1. Generate images with the browser-based interface echo 1. Generate images with the browser-based interface
echo 2. Explore InvokeAI nodes using a command-line interface echo 2. Run textual inversion training
echo 3. Run textual inversion training echo 3. Merge models (diffusers type only)
echo 4. Merge models (diffusers type only) echo 4. Download and install models
echo 5. Download and install models echo 5. Change InvokeAI startup options
echo 6. Change InvokeAI startup options echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade
echo 7. Re-run the configure script to fix a broken install or to complete a major upgrade echo 7. Open the developer console
echo 8. Open the developer console echo 8. Update InvokeAI
echo 9. Update InvokeAI echo 9. Run the InvokeAI image database maintenance script
echo 10. Run the InvokeAI image database maintenance script echo 10. Command-line help
echo 11. Command-line help
echo Q - Quit echo Q - Quit
set /P choice="Please enter 1-11, Q: [1] " set /P choice="Please enter 1-10, Q: [1] "
if not defined choice set choice=1 if not defined choice set choice=1
IF /I "%choice%" == "1" ( IF /I "%choice%" == "1" (
echo Starting the InvokeAI browser-based UI.. echo Starting the InvokeAI browser-based UI..
python .venv\Scripts\invokeai-web.exe %* python .venv\Scripts\invokeai-web.exe %*
) ELSE IF /I "%choice%" == "2" ( ) ELSE IF /I "%choice%" == "2" (
echo Starting the InvokeAI command-line..
python .venv\Scripts\invokeai.exe %*
) ELSE IF /I "%choice%" == "3" (
echo Starting textual inversion training.. echo Starting textual inversion training..
python .venv\Scripts\invokeai-ti.exe --gui python .venv\Scripts\invokeai-ti.exe --gui
) ELSE IF /I "%choice%" == "4" ( ) ELSE IF /I "%choice%" == "3" (
echo Starting model merging script.. echo Starting model merging script..
python .venv\Scripts\invokeai-merge.exe --gui python .venv\Scripts\invokeai-merge.exe --gui
) ELSE IF /I "%choice%" == "5" ( ) ELSE IF /I "%choice%" == "4" (
echo Running invokeai-model-install... echo Running invokeai-model-install...
python .venv\Scripts\invokeai-model-install.exe python .venv\Scripts\invokeai-model-install.exe
) ELSE IF /I "%choice%" == "6" ( ) ELSE IF /I "%choice%" == "5" (
echo Running invokeai-configure... echo Running invokeai-configure...
python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
) ELSE IF /I "%choice%" == "7" ( ) ELSE IF /I "%choice%" == "6" (
echo Running invokeai-configure... echo Running invokeai-configure...
python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
) ELSE IF /I "%choice%" == "8" ( ) ELSE IF /I "%choice%" == "7" (
echo Developer Console echo Developer Console
echo Python command is: echo Python command is:
where python where python
@ -55,13 +51,13 @@ IF /I "%choice%" == "1" (
echo ************************* echo *************************
echo *** Type `exit` to quit this shell and deactivate the Python virtual environment *** echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
call cmd /k call cmd /k
) ELSE IF /I "%choice%" == "9" ( ) ELSE IF /I "%choice%" == "8" (
echo Running invokeai-update... echo Running invokeai-update...
python -m invokeai.frontend.install.invokeai_update python -m invokeai.frontend.install.invokeai_update
) ELSE IF /I "%choice%" == "10" ( ) ELSE IF /I "%choice%" == "9" (
echo Running the db maintenance script... echo Running the db maintenance script...
python .venv\Scripts\invokeai-db-maintenance.exe python .venv\Scripts\invokeai-db-maintenance.exe
) ELSE IF /I "%choice%" == "11" ( ) ELSE IF /I "%choice%" == "10" (
echo Displaying command line help... echo Displaying command line help...
python .venv\Scripts\invokeai-web.exe --help %* python .venv\Scripts\invokeai-web.exe --help %*
pause pause

61
installer/templates/invoke.sh.in
View File

@ -58,52 +58,47 @@ do_choice() {
invokeai-web $PARAMS invokeai-web $PARAMS
;; ;;
2) 2)
clear
printf "Explore InvokeAI nodes using a command-line interface\n"
invokeai $PARAMS
;;
3)
clear clear
printf "Textual inversion training\n" printf "Textual inversion training\n"
invokeai-ti --gui $PARAMS invokeai-ti --gui $PARAMS
;; ;;
4) 3)
clear clear
printf "Merge models (diffusers type only)\n" printf "Merge models (diffusers type only)\n"
invokeai-merge --gui $PARAMS invokeai-merge --gui $PARAMS
;; ;;
5) 4)
clear clear
printf "Download and install models\n" printf "Download and install models\n"
invokeai-model-install --root ${INVOKEAI_ROOT} invokeai-model-install --root ${INVOKEAI_ROOT}
;; ;;
6) 5)
clear clear
printf "Change InvokeAI startup options\n" printf "Change InvokeAI startup options\n"
invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
;; ;;
7) 6)
clear clear
printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n" printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n"
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights
;; ;;
8) 7)
clear clear
printf "Open the developer console\n" printf "Open the developer console\n"
file_name=$(basename "${BASH_SOURCE[0]}") file_name=$(basename "${BASH_SOURCE[0]}")
bash --init-file "$file_name" bash --init-file "$file_name"
;; ;;
9) 8)
clear clear
printf "Update InvokeAI\n" printf "Update InvokeAI\n"
python -m invokeai.frontend.install.invokeai_update python -m invokeai.frontend.install.invokeai_update
;; ;;
10) 9)
clear clear
printf "Running the db maintenance script\n" printf "Running the db maintenance script\n"
invokeai-db-maintenance --root ${INVOKEAI_ROOT} invokeai-db-maintenance --root ${INVOKEAI_ROOT}
;; ;;
11) 10)
clear clear
printf "Command-line help\n" printf "Command-line help\n"
invokeai-web --help invokeai-web --help
@ -121,16 +116,15 @@ do_choice() {
do_dialog() { do_dialog() {
options=( options=(
1 "Generate images with a browser-based interface" 1 "Generate images with a browser-based interface"
2 "Explore InvokeAI nodes using a command-line interface" 2 "Textual inversion training"
3 "Textual inversion training" 3 "Merge models (diffusers type only)"
4 "Merge models (diffusers type only)" 4 "Download and install models"
5 "Download and install models" 5 "Change InvokeAI startup options"
6 "Change InvokeAI startup options" 6 "Re-run the configure script to fix a broken install or to complete a major upgrade"
7 "Re-run the configure script to fix a broken install or to complete a major upgrade" 7 "Open the developer console"
8 "Open the developer console" 8 "Update InvokeAI"
9 "Update InvokeAI" 9 "Run the InvokeAI image database maintenance script"
10 "Run the InvokeAI image database maintenance script" 10 "Command-line help"
11 "Command-line help"
) )
choice=$(dialog --clear \ choice=$(dialog --clear \
@ -155,18 +149,17 @@ do_line_input() {
printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n" printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
printf "What would you like to do?\n" printf "What would you like to do?\n"
printf "1: Generate images using the browser-based interface\n" printf "1: Generate images using the browser-based interface\n"
printf "2: Explore InvokeAI nodes using the command-line interface\n" printf "2: Run textual inversion training\n"
printf "3: Run textual inversion training\n" printf "3: Merge models (diffusers type only)\n"
printf "4: Merge models (diffusers type only)\n" printf "4: Download and install models\n"
printf "5: Download and install models\n" printf "5: Change InvokeAI startup options\n"
printf "6: Change InvokeAI startup options\n" printf "6: Re-run the configure script to fix a broken install\n"
printf "7: Re-run the configure script to fix a broken install\n" printf "7: Open the developer console\n"
printf "8: Open the developer console\n" printf "8: Update InvokeAI\n"
printf "9: Update InvokeAI\n" printf "9: Run the InvokeAI image database maintenance script\n"
printf "10: Run the InvokeAI image database maintenance script\n" printf "10: Command-line help\n"
printf "11: Command-line help\n"
printf "Q: Quit\n\n" printf "Q: Quit\n\n"
read -p "Please enter 1-11, Q: [1] " yn read -p "Please enter 1-10, Q: [1] " yn
choice=${yn:='1'} choice=${yn:='1'}
do_choice $choice do_choice $choice
clear clear

6
invokeai/app/api/dependencies.py
View File

@ -2,6 +2,7 @@
from logging import Logger from logging import Logger
from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage
from invokeai.backend.util.logging import InvokeAILogger from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__ from invokeai.version.invokeai_version import __version__
@ -30,6 +31,7 @@ from ..services.shared.default_graphs import create_system_graphs
from ..services.shared.graph import GraphExecutionState, LibraryGraph from ..services.shared.graph import GraphExecutionState, LibraryGraph
from ..services.shared.sqlite import SqliteDatabase from ..services.shared.sqlite import SqliteDatabase
from ..services.urls.urls_default import LocalUrlService from ..services.urls.urls_default import LocalUrlService
from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
from .events import FastAPIEventService from .events import FastAPIEventService
@ -90,6 +92,8 @@ class ApiDependencies:
session_processor = DefaultSessionProcessor() session_processor = DefaultSessionProcessor()
session_queue = SqliteSessionQueue(db=db) session_queue = SqliteSessionQueue(db=db)
urls = LocalUrlService() urls = LocalUrlService()
workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
workflow_records = SqliteWorkflowRecordsStorage(db=db)
services = InvocationServices( services = InvocationServices(
board_image_records=board_image_records, board_image_records=board_image_records,
@ -114,6 +118,8 @@ class ApiDependencies:
session_processor=session_processor, session_processor=session_processor,
session_queue=session_queue, session_queue=session_queue,
urls=urls, urls=urls,
workflow_image_records=workflow_image_records,
workflow_records=workflow_records,
) )
create_system_graphs(services.graph_library) create_system_graphs(services.graph_library)

50
invokeai/app/api/routers/images.py
View File

@ -1,13 +1,14 @@
import io import io
import traceback
from typing import Optional from typing import Optional
from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
from fastapi.responses import FileResponse from fastapi.responses import FileResponse
from fastapi.routing import APIRouter from fastapi.routing import APIRouter
from PIL import Image from PIL import Image
from pydantic import BaseModel, Field from pydantic import BaseModel, Field, ValidationError
from invokeai.app.invocations.metadata import ImageMetadata from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults from invokeai.app.services.shared.pagination import OffsetPaginatedResults
@ -45,17 +46,38 @@ async def upload_image(
if not file.content_type or not file.content_type.startswith("image"): if not file.content_type or not file.content_type.startswith("image"):
raise HTTPException(status_code=415, detail="Not an image") raise HTTPException(status_code=415, detail="Not an image")
contents = await file.read() metadata = None
workflow = None
contents = await file.read()
try: try:
pil_image = Image.open(io.BytesIO(contents)) pil_image = Image.open(io.BytesIO(contents))
if crop_visible: if crop_visible:
bbox = pil_image.getbbox() bbox = pil_image.getbbox()
pil_image = pil_image.crop(bbox) pil_image = pil_image.crop(bbox)
except Exception: except Exception:
# Error opening the image ApiDependencies.invoker.services.logger.error(traceback.format_exc())
raise HTTPException(status_code=415, detail="Failed to read image") raise HTTPException(status_code=415, detail="Failed to read image")
# TODO: retain non-invokeai metadata on upload?
# attempt to parse metadata from image
metadata_raw = pil_image.info.get("invokeai_metadata", None)
if metadata_raw:
try:
metadata = MetadataFieldValidator.validate_json(metadata_raw)
except ValidationError:
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
pass
# attempt to parse workflow from image
workflow_raw = pil_image.info.get("invokeai_workflow", None)
if workflow_raw is not None:
try:
workflow = WorkflowFieldValidator.validate_json(workflow_raw)
except ValidationError:
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
pass
try: try:
image_dto = ApiDependencies.invoker.services.images.create( image_dto = ApiDependencies.invoker.services.images.create(
image=pil_image, image=pil_image,
@ -63,6 +85,8 @@ async def upload_image(
image_category=image_category, image_category=image_category,
session_id=session_id, session_id=session_id,
board_id=board_id, board_id=board_id,
metadata=metadata,
workflow=workflow,
is_intermediate=is_intermediate, is_intermediate=is_intermediate,
) )
@ -71,6 +95,7 @@ async def upload_image(
return image_dto return image_dto
except Exception: except Exception:
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail="Failed to create image") raise HTTPException(status_code=500, detail="Failed to create image")
@ -87,7 +112,7 @@ async def delete_image(
pass pass
@images_router.post("/clear-intermediates", operation_id="clear_intermediates") @images_router.delete("/intermediates", operation_id="clear_intermediates")
async def clear_intermediates() -> int: async def clear_intermediates() -> int:
"""Clears all intermediates""" """Clears all intermediates"""
@ -99,6 +124,17 @@ async def clear_intermediates() -> int:
pass pass
@images_router.get("/intermediates", operation_id="get_intermediates_count")
async def get_intermediates_count() -> int:
"""Gets the count of intermediate images"""
try:
return ApiDependencies.invoker.services.images.get_intermediates_count()
except Exception:
raise HTTPException(status_code=500, detail="Failed to get intermediates")
pass
@images_router.patch( @images_router.patch(
"/i/{image_name}", "/i/{image_name}",
operation_id="update_image", operation_id="update_image",
@ -135,11 +171,11 @@ async def get_image_dto(
@images_router.get( @images_router.get(
"/i/{image_name}/metadata", "/i/{image_name}/metadata",
operation_id="get_image_metadata", operation_id="get_image_metadata",
response_model=ImageMetadata, response_model=Optional[MetadataField],
) )
async def get_image_metadata( async def get_image_metadata(
image_name: str = Path(description="The name of image to get"), image_name: str = Path(description="The name of image to get"),
) -> ImageMetadata: ) -> Optional[MetadataField]:
"""Gets an image's metadata""" """Gets an image's metadata"""
try: try:

20
invokeai/app/api/routers/models.py
View File

@ -23,13 +23,13 @@ from ..dependencies import ApiDependencies
models_router = APIRouter(prefix="/v1/models", tags=["models"]) models_router = APIRouter(prefix="/v1/models", tags=["models"])
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
update_models_response_adapter = TypeAdapter(UpdateModelResponse) UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
import_models_response_adapter = TypeAdapter(ImportModelResponse) ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
convert_models_response_adapter = TypeAdapter(ConvertModelResponse) ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)] ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
@ -41,7 +41,7 @@ class ModelsList(BaseModel):
model_config = ConfigDict(use_enum_values=True) model_config = ConfigDict(use_enum_values=True)
models_list_adapter = TypeAdapter(ModelsList) ModelsListValidator = TypeAdapter(ModelsList)
@models_router.get( @models_router.get(
@ -60,7 +60,7 @@ async def list_models(
models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type)) models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
else: else:
models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type) models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
models = models_list_adapter.validate_python({"models": models_raw}) models = ModelsListValidator.validate_python({"models": models_raw})
return models return models
@ -131,7 +131,7 @@ async def update_model(
base_model=base_model, base_model=base_model,
model_type=model_type, model_type=model_type,
) )
model_response = update_models_response_adapter.validate_python(model_raw) model_response = UpdateModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=str(e)) raise HTTPException(status_code=404, detail=str(e))
except ValueError as e: except ValueError as e:
@ -186,7 +186,7 @@ async def import_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.name, base_model=info.base_model, model_type=info.model_type model_name=info.name, base_model=info.base_model, model_type=info.model_type
) )
return import_models_response_adapter.validate_python(model_raw) return ImportModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
logger.error(str(e)) logger.error(str(e))
@ -231,7 +231,7 @@ async def add_model(
base_model=info.base_model, base_model=info.base_model,
model_type=info.model_type, model_type=info.model_type,
) )
return import_models_response_adapter.validate_python(model_raw) return ImportModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
logger.error(str(e)) logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e)) raise HTTPException(status_code=404, detail=str(e))
@ -302,7 +302,7 @@ async def convert_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name, base_model=base_model, model_type=model_type model_name, base_model=base_model, model_type=model_type
) )
response = convert_models_response_adapter.validate_python(model_raw) response = ConvertModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}") raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
except ValueError as e: except ValueError as e:
@ -417,7 +417,7 @@ async def merge_models(
base_model=base_model, base_model=base_model,
model_type=ModelType.Main, model_type=ModelType.Main,
) )
response = convert_models_response_adapter.validate_python(model_raw) response = ConvertModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException: except ModelNotFoundException:
raise HTTPException( raise HTTPException(
status_code=404, status_code=404,

19
invokeai/app/api/routers/session_queue.py
View File

@ -12,13 +12,11 @@ from invokeai.app.services.session_queue.session_queue_common import (
CancelByBatchIDsResult, CancelByBatchIDsResult,
ClearResult, ClearResult,
EnqueueBatchResult, EnqueueBatchResult,
EnqueueGraphResult,
PruneResult, PruneResult,
SessionQueueItem, SessionQueueItem,
SessionQueueItemDTO, SessionQueueItemDTO,
SessionQueueStatus, SessionQueueStatus,
) )
from invokeai.app.services.shared.graph import Graph
from invokeai.app.services.shared.pagination import CursorPaginatedResults from invokeai.app.services.shared.pagination import CursorPaginatedResults
from ..dependencies import ApiDependencies from ..dependencies import ApiDependencies
@ -33,23 +31,6 @@ class SessionQueueAndProcessorStatus(BaseModel):
processor: SessionProcessorStatus processor: SessionProcessorStatus
@session_queue_router.post(
"/{queue_id}/enqueue_graph",
operation_id="enqueue_graph",
responses={
201: {"model": EnqueueGraphResult},
},
)
async def enqueue_graph(
queue_id: str = Path(description="The queue id to perform this operation on"),
graph: Graph = Body(description="The graph to enqueue"),
prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
) -> EnqueueGraphResult:
"""Enqueues a graph for single execution."""
return ApiDependencies.invoker.services.session_queue.enqueue_graph(queue_id=queue_id, graph=graph, prepend=prepend)
@session_queue_router.post( @session_queue_router.post(
"/{queue_id}/enqueue_batch", "/{queue_id}/enqueue_batch",
operation_id="enqueue_batch", operation_id="enqueue_batch",

20
invokeai/app/api/routers/workflows.py Normal file
View File

@ -0,0 +1,20 @@
from fastapi import APIRouter, Path
from invokeai.app.api.dependencies import ApiDependencies
from invokeai.app.invocations.baseinvocation import WorkflowField
workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
@workflows_router.get(
"/i/{workflow_id}",
operation_id="get_workflow",
responses={
200: {"model": WorkflowField},
},
)
async def get_workflow(
workflow_id: str = Path(description="The workflow to get"),
) -> WorkflowField:
"""Gets a workflow"""
return ApiDependencies.invoker.services.workflow_records.get(workflow_id)

78
invokeai/app/api_app.py
View File

@ -1,3 +1,7 @@
from typing import Any
from fastapi.responses import HTMLResponse
from .services.config import InvokeAIAppConfig from .services.config import InvokeAIAppConfig
# parse_args() must be called before any other imports. if it is not called first, consumers of the config # parse_args() must be called before any other imports. if it is not called first, consumers of the config
@ -13,17 +17,20 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from inspect import signature from inspect import signature
from pathlib import Path from pathlib import Path
import torch
import uvicorn import uvicorn
from fastapi import FastAPI from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
from fastapi.openapi.utils import get_openapi from fastapi.openapi.utils import get_openapi
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.json_schema import models_json_schema from pydantic.json_schema import models_json_schema
from torch.backends.mps import is_available as is_mps_available
# for PyCharm:
# noinspection PyUnresolvedReferences # noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import) import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
import invokeai.frontend.web as web_dir import invokeai.frontend.web as web_dir
@ -31,19 +38,27 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from ..backend.util.logging import InvokeAILogger from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies from .api.dependencies import ApiDependencies
from .api.routers import app_info, board_images, boards, images, models, session_queue, sessions, utilities from .api.routers import (
app_info,
board_images,
boards,
images,
models,
session_queue,
sessions,
utilities,
workflows,
)
from .api.sockets import SocketIO from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
if torch.backends.mps.is_available(): if is_mps_available():
# noinspection PyUnresolvedReferences
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import) import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
app_config = InvokeAIAppConfig.get_config() app_config = InvokeAIAppConfig.get_config()
app_config.parse_args() app_config.parse_args()
logger = InvokeAILogger.get_logger(config=app_config) logger = InvokeAILogger.get_logger(config=app_config)
# fix for windows mimetypes registry entries being borked # fix for windows mimetypes registry entries being borked
# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352 # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
mimetypes.add_type("application/javascript", ".js") mimetypes.add_type("application/javascript", ".js")
@ -71,16 +86,18 @@ app.add_middleware(
allow_headers=app_config.allow_headers, allow_headers=app_config.allow_headers,
) )
app.add_middleware(GZipMiddleware, minimum_size=1000)
# Add startup event to load dependencies # Add startup event to load dependencies
@app.on_event("startup") @app.on_event("startup")
async def startup_event(): async def startup_event() -> None:
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger) ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
# Shut down threads # Shut down threads
@app.on_event("shutdown") @app.on_event("shutdown")
async def shutdown_event(): async def shutdown_event() -> None:
ApiDependencies.shutdown() ApiDependencies.shutdown()
@ -88,23 +105,18 @@ async def shutdown_event():
app.include_router(sessions.session_router, prefix="/api") app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api") app.include_router(utilities.utilities_router, prefix="/api")
app.include_router(models.models_router, prefix="/api") app.include_router(models.models_router, prefix="/api")
app.include_router(images.images_router, prefix="/api") app.include_router(images.images_router, prefix="/api")
app.include_router(boards.boards_router, prefix="/api") app.include_router(boards.boards_router, prefix="/api")
app.include_router(board_images.board_images_router, prefix="/api") app.include_router(board_images.board_images_router, prefix="/api")
app.include_router(app_info.app_router, prefix="/api") app.include_router(app_info.app_router, prefix="/api")
app.include_router(session_queue.session_queue_router, prefix="/api") app.include_router(session_queue.session_queue_router, prefix="/api")
app.include_router(workflows.workflows_router, prefix="/api")
# Build a custom OpenAPI to include all outputs # Build a custom OpenAPI to include all outputs
# TODO: can outputs be included on metadata of invocation schemas somehow? # TODO: can outputs be included on metadata of invocation schemas somehow?
def custom_openapi(): def custom_openapi() -> dict[str, Any]:
if app.openapi_schema: if app.openapi_schema:
return app.openapi_schema return app.openapi_schema
openapi_schema = get_openapi( openapi_schema = get_openapi(
@ -159,7 +171,6 @@ def custom_openapi():
# print(f"Config with name {name} already defined") # print(f"Config with name {name} already defined")
continue continue
# "BaseModelType":{"title":"BaseModelType","description":"An enumeration.","enum":["sd-1","sd-2"],"type":"string"}
openapi_schema["components"]["schemas"][name] = dict( openapi_schema["components"]["schemas"][name] = dict(
title=name, title=name,
description="An enumeration.", description="An enumeration.",
@ -173,34 +184,43 @@ def custom_openapi():
app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid assignment app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid assignment
# Override API doc favicons
app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
@app.get("/docs", include_in_schema=False) @app.get("/docs", include_in_schema=False)
def overridden_swagger(): def overridden_swagger() -> HTMLResponse:
return get_swagger_ui_html( return get_swagger_ui_html(
openapi_url=app.openapi_url, openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
title=app.title, title=app.title,
swagger_favicon_url="/static/favicon.ico", swagger_favicon_url="/static/docs/favicon.ico",
) )
@app.get("/redoc", include_in_schema=False) @app.get("/redoc", include_in_schema=False)
def overridden_redoc(): def overridden_redoc() -> HTMLResponse:
return get_redoc_html( return get_redoc_html(
openapi_url=app.openapi_url, openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
title=app.title, title=app.title,
redoc_favicon_url="/static/favicon.ico", redoc_favicon_url="/static/docs/favicon.ico",
) )
# Must mount *after* the other routes else it borks em web_root_path = Path(list(web_dir.__path__)[0])
app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui")
def invoke_api(): # Cannot add headers to StaticFiles, so we must serve index.html with a custom route
def find_port(port: int): # Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
@app.get("/", include_in_schema=False, name="ui_root")
def get_index() -> FileResponse:
return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
# # Must mount *after* the other routes else it borks em
app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static") # docs favicon is in here
app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
def invoke_api() -> None:
def find_port(port: int) -> int:
"""Find a port not in use starting at given port""" """Find a port not in use starting at given port"""
# Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon! # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
# https://github.com/WaylonWalker # https://github.com/WaylonWalker
@ -235,7 +255,7 @@ def invoke_api():
app=app, app=app,
host=app_config.host, host=app_config.host,
port=port, port=port,
loop=loop, loop="asyncio",
log_level=app_config.log_level, log_level=app_config.log_level,
) )
server = uvicorn.Server(config) server = uvicorn.Server(config)

312
invokeai/app/cli/commands.py
View File

@ -1,312 +0,0 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
import argparse
from abc import ABC, abstractmethod
from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
import matplotlib.pyplot as plt
import networkx as nx
from pydantic import BaseModel, Field
import invokeai.backend.util.logging as logger
from ..invocations.baseinvocation import BaseInvocation
from ..invocations.image import ImageField
from ..services.graph import Edge, GraphExecutionState, LibraryGraph
from ..services.invoker import Invoker
def add_field_argument(command_parser, name: str, field, default_override=None):
default = (
default_override
if default_override is not None
else field.default
if field.default_factory is None
else field.default_factory()
)
if get_origin(field.annotation) == Literal:
allowed_values = get_args(field.annotation)
allowed_types = set()
for val in allowed_values:
allowed_types.add(type(val))
allowed_types_list = list(allowed_types)
field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list] # type: ignore
command_parser.add_argument(
f"--{name}",
dest=name,
type=field_type,
default=default,
choices=allowed_values,
help=field.description,
)
else:
command_parser.add_argument(
f"--{name}",
dest=name,
type=field.annotation,
default=default,
help=field.description,
)
def add_parsers(
subparsers,
commands: list[type],
command_field: str = "type",
exclude_fields: list[str] = ["id", "type"],
add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None,
):
"""Adds parsers for each command to the subparsers"""
# Create subparsers for each command
for command in commands:
hints = get_type_hints(command)
cmd_name = get_args(hints[command_field])[0]
command_parser = subparsers.add_parser(cmd_name, help=command.__doc__)
if add_arguments is not None:
add_arguments(command_parser)
# Convert all fields to arguments
fields = command.__fields__ # type: ignore
for name, field in fields.items():
if name in exclude_fields:
continue
add_field_argument(command_parser, name, field)
def add_graph_parsers(
subparsers, graphs: list[LibraryGraph], add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
):
for graph in graphs:
command_parser = subparsers.add_parser(graph.name, help=graph.description)
if add_arguments is not None:
add_arguments(command_parser)
# Add arguments for inputs
for exposed_input in graph.exposed_inputs:
node = graph.graph.get_node(exposed_input.node_path)
field = node.__fields__[exposed_input.field]
default_override = getattr(node, exposed_input.field)
add_field_argument(command_parser, exposed_input.alias, field, default_override)
class CliContext:
invoker: Invoker
session: GraphExecutionState
parser: argparse.ArgumentParser
defaults: dict[str, Any]
graph_nodes: dict[str, str]
nodes_added: list[str]
def __init__(self, invoker: Invoker, session: GraphExecutionState, parser: argparse.ArgumentParser):
self.invoker = invoker
self.session = session
self.parser = parser
self.defaults = dict()
self.graph_nodes = dict()
self.nodes_added = list()
def get_session(self):
self.session = self.invoker.services.graph_execution_manager.get(self.session.id)
return self.session
def reset(self):
self.session = self.invoker.create_execution_state()
self.graph_nodes = dict()
self.nodes_added = list()
# Leave defaults unchanged
def add_node(self, node: BaseInvocation):
self.get_session()
self.session.graph.add_node(node)
self.nodes_added.append(node.id)
self.invoker.services.graph_execution_manager.set(self.session)
def add_edge(self, edge: Edge):
self.get_session()
self.session.add_edge(edge)
self.invoker.services.graph_execution_manager.set(self.session)
class ExitCli(Exception):
"""Exception to exit the CLI"""
pass
class BaseCommand(ABC, BaseModel):
"""A CLI command"""
# All commands must include a type name like this:
@classmethod
def get_all_subclasses(cls):
subclasses = []
toprocess = [cls]
while len(toprocess) > 0:
next = toprocess.pop(0)
next_subclasses = next.__subclasses__()
subclasses.extend(next_subclasses)
toprocess.extend(next_subclasses)
return subclasses
@classmethod
def get_commands(cls):
return tuple(BaseCommand.get_all_subclasses())
@classmethod
def get_commands_map(cls):
# Get the type strings out of the literals and into a dictionary
return dict(map(lambda t: (get_args(get_type_hints(t)["type"])[0], t), BaseCommand.get_all_subclasses()))
@abstractmethod
def run(self, context: CliContext) -> None:
"""Run the command. Raise ExitCli to exit."""
pass
class ExitCommand(BaseCommand):
"""Exits the CLI"""
type: Literal["exit"] = "exit"
def run(self, context: CliContext) -> None:
raise ExitCli()
class HelpCommand(BaseCommand):
"""Shows help"""
type: Literal["help"] = "help"
def run(self, context: CliContext) -> None:
context.parser.print_help()
def get_graph_execution_history(
graph_execution_state: GraphExecutionState,
) -> Iterable[str]:
"""Gets the history of fully-executed invocations for a graph execution"""
return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
def get_invocation_command(invocation) -> str:
fields = invocation.__fields__.items()
type_hints = get_type_hints(type(invocation))
command = [invocation.type]
for name, field in fields:
if name in ["id", "type"]:
continue
# TODO: add links
# Skip image fields when serializing command
type_hint = type_hints.get(name) or None
if type_hint is ImageField or ImageField in get_args(type_hint):
continue
field_value = getattr(invocation, name)
field_default = field.default
if field_value != field_default:
if type_hint is str or str in get_args(type_hint):
command.append(f'--{name} "{field_value}"')
else:
command.append(f"--{name} {field_value}")
return " ".join(command)
class HistoryCommand(BaseCommand):
"""Shows the invocation history"""
type: Literal["history"] = "history"
# Inputs
# fmt: off
count: int = Field(default=5, gt=0, description="The number of history entries to show")
# fmt: on
def run(self, context: CliContext) -> None:
history = list(get_graph_execution_history(context.get_session()))
for i in range(min(self.count, len(history))):
entry_id = history[-1 - i]
entry = context.get_session().graph.get_node(entry_id)
logger.info(f"{entry_id}: {get_invocation_command(entry)}")
class SetDefaultCommand(BaseCommand):
"""Sets a default value for a field"""
type: Literal["default"] = "default"
# Inputs
# fmt: off
field: str = Field(description="The field to set the default for")
value: str = Field(description="The value to set the default to, or None to clear the default")
# fmt: on
def run(self, context: CliContext) -> None:
if self.value is None:
if self.field in context.defaults:
del context.defaults[self.field]
else:
context.defaults[self.field] = self.value
class DrawGraphCommand(BaseCommand):
"""Debugs a graph"""
type: Literal["draw_graph"] = "draw_graph"
def run(self, context: CliContext) -> None:
session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
nxgraph = session.graph.nx_graph_flat()
# Draw the networkx graph
plt.figure(figsize=(20, 20))
pos = nx.spectral_layout(nxgraph)
nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
nx.draw_networkx_edges(nxgraph, pos, width=2)
nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
plt.axis("off")
plt.show()
class DrawExecutionGraphCommand(BaseCommand):
"""Debugs an execution graph"""
type: Literal["draw_xgraph"] = "draw_xgraph"
def run(self, context: CliContext) -> None:
session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
nxgraph = session.execution_graph.nx_graph_flat()
# Draw the networkx graph
plt.figure(figsize=(20, 20))
pos = nx.spectral_layout(nxgraph)
nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
nx.draw_networkx_edges(nxgraph, pos, width=2)
nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
plt.axis("off")
plt.show()
class SortedHelpFormatter(argparse.HelpFormatter):
def _iter_indented_subactions(self, action):
try:
get_subactions = action._get_subactions
except AttributeError:
pass
else:
self._indent()
if isinstance(action, argparse._SubParsersAction):
for subaction in sorted(get_subactions(), key=lambda x: x.dest):
yield subaction
else:
for subaction in get_subactions():
yield subaction
self._dedent()

171
invokeai/app/cli/completer.py
View File

@ -1,171 +0,0 @@
"""
Readline helper functions for cli_app.py
You may import the global singleton `completer` to get access to the
completer object.
"""
import atexit
import readline
import shlex
from pathlib import Path
from typing import Dict, List, Literal, get_args, get_origin, get_type_hints
import invokeai.backend.util.logging as logger
from ...backend import ModelManager
from ..invocations.baseinvocation import BaseInvocation
from ..services.invocation_services import InvocationServices
from .commands import BaseCommand
# singleton object, class variable
completer = None
class Completer(object):
def __init__(self, model_manager: ModelManager):
self.commands = self.get_commands()
self.matches = None
self.linebuffer = None
self.manager = model_manager
return
def complete(self, text, state):
"""
Complete commands and switches fromm the node CLI command line.
Switches are determined in a context-specific manner.
"""
buffer = readline.get_line_buffer()
if state == 0:
options = None
try:
current_command, current_switch = self.get_current_command(buffer)
options = self.get_command_options(current_command, current_switch)
except IndexError:
pass
options = options or list(self.parse_commands().keys())
if not text: # first time
self.matches = options
else:
self.matches = [s for s in options if s and s.startswith(text)]
try:
match = self.matches[state]
except IndexError:
match = None
return match
@classmethod
def get_commands(self) -> List[object]:
"""
Return a list of all the client commands and invocations.
"""
return BaseCommand.get_commands() + BaseInvocation.get_invocations()
def get_current_command(self, buffer: str) -> tuple[str, str]:
"""
Parse the readline buffer to find the most recent command and its switch.
"""
if len(buffer) == 0:
return None, None
tokens = shlex.split(buffer)
command = None
switch = None
for t in tokens:
if t[0].isalpha():
if switch is None:
command = t
else:
switch = t
# don't try to autocomplete switches that are already complete
if switch and buffer.endswith(" "):
switch = None
return command or "", switch or ""
def parse_commands(self) -> Dict[str, List[str]]:
"""
Return a dict in which the keys are the command name
and the values are the parameters the command takes.
"""
result = dict()
for command in self.commands:
hints = get_type_hints(command)
name = get_args(hints["type"])[0]
result.update({name: hints})
return result
def get_command_options(self, command: str, switch: str) -> List[str]:
"""
Return all the parameters that can be passed to the command as
command-line switches. Returns None if the command is unrecognized.
"""
parsed_commands = self.parse_commands()
if command not in parsed_commands:
return None
# handle switches in the format "-foo=bar"
argument = None
if switch and "=" in switch:
switch, argument = switch.split("=")
parameter = switch.strip("-")
if parameter in parsed_commands[command]:
if argument is None:
return self.get_parameter_options(parameter, parsed_commands[command][parameter])
else:
return [
f"--{parameter}={x}"
for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])
]
else:
return [f"--{x}" for x in parsed_commands[command].keys()]
def get_parameter_options(self, parameter: str, typehint) -> List[str]:
"""
Given a parameter type (such as Literal), offers autocompletions.
"""
if get_origin(typehint) == Literal:
return get_args(typehint)
if parameter == "model":
return self.manager.model_names()
def _pre_input_hook(self):
if self.linebuffer:
readline.insert_text(self.linebuffer)
readline.redisplay()
self.linebuffer = None
def set_autocompleter(services: InvocationServices) -> Completer:
global completer
if completer:
return completer
completer = Completer(services.model_manager)
readline.set_completer(completer.complete)
try:
readline.set_auto_history(True)
except AttributeError:
# pyreadline3 does not have a set_auto_history() method
pass
readline.set_pre_input_hook(completer._pre_input_hook)
readline.set_completer_delims(" ")
readline.parse_and_bind("tab: complete")
readline.parse_and_bind("set print-completions-horizontally off")
readline.parse_and_bind("set page-completions on")
readline.parse_and_bind("set skip-completed-text on")
readline.parse_and_bind("set show-all-if-ambiguous on")
histfile = Path(services.configuration.root_dir / ".invoke_history")
try:
readline.read_history_file(histfile)
readline.set_history_length(1000)
except FileNotFoundError:
pass
except OSError: # file likely corrupted
newname = f"{histfile}.old"
logger.error(f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}")
histfile.replace(Path(newname))
atexit.register(readline.write_history_file, histfile)

484
invokeai/app/cli_app.py
View File

@ -1,484 +0,0 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from .services.config import InvokeAIAppConfig
# parse_args() must be called before any other imports. if it is not called first, consumers of the config
# which are imported/used before parse_args() is called will get the default config values instead of the
# values from the command line or config file.
if True: # hack to make flake8 happy with imports coming after setting up the config
import argparse
import re
import shlex
import sqlite3
import sys
import time
from typing import Optional, Union, get_type_hints
import torch
from pydantic import BaseModel, ValidationError
from pydantic.fields import Field
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
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.invocation_stats import InvocationStatsService
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 .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
from .cli.completer import set_autocompleter
from .invocations.baseinvocation import BaseInvocation
from .services.default_graphs import create_system_graphs, default_text_to_image_graph_id
from .services.events import EventServiceBase
from .services.graph import (
Edge,
EdgeConnection,
GraphExecutionState,
GraphInvocation,
LibraryGraph,
are_connection_types_compatible,
)
from .services.image_file_storage import DiskImageFileStorage
from .services.invocation_queue import MemoryInvocationQueue
from .services.invocation_services import InvocationServices
from .services.invoker import Invoker
from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
from .services.model_manager_service import ModelManagerService
from .services.processor import DefaultInvocationProcessor
from .services.sqlite import SqliteItemStorage
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
config = InvokeAIAppConfig.get_config()
config.parse_args()
logger = InvokeAILogger().get_logger(config=config)
class CliCommand(BaseModel):
command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type") # type: ignore
class InvalidArgs(Exception):
pass
def add_invocation_args(command_parser):
# Add linking capability
command_parser.add_argument(
"--link",
"-l",
action="append",
nargs=3,
help="A link in the format 'source_node source_field dest_field'. source_node can be relative to history (e.g. -1)",
)
command_parser.add_argument(
"--link_node",
"-ln",
action="append",
help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)",
)
def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
# Create invocation parser
parser = argparse.ArgumentParser(formatter_class=SortedHelpFormatter)
def exit(*args, **kwargs):
raise InvalidArgs
parser.exit = exit
subparsers = parser.add_subparsers(dest="type")
# Create subparsers for each invocation
invocations = BaseInvocation.get_all_subclasses()
add_parsers(subparsers, invocations, add_arguments=add_invocation_args)
# Create subparsers for each command
commands = BaseCommand.get_all_subclasses()
add_parsers(subparsers, commands, exclude_fields=["type"])
# Create subparsers for exposed CLI graphs
# TODO: add a way to identify these graphs
text_to_image = services.graph_library.get(default_text_to_image_graph_id)
add_graph_parsers(subparsers, [text_to_image], add_arguments=add_invocation_args)
return parser
class NodeField:
alias: str
node_path: str
field: str
field_type: type
def __init__(self, alias: str, node_path: str, field: str, field_type: type):
self.alias = alias
self.node_path = node_path
self.field = field
self.field_type = field_type
def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str, NodeField]:
return {k: NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
"""Gets the node field for the specified field alias"""
exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
node_type = type(graph.graph.get_node(exposed_input.node_path))
return NodeField(
alias=exposed_input.alias,
node_path=f"{node_id}.{exposed_input.node_path}",
field=exposed_input.field,
field_type=get_type_hints(node_type)[exposed_input.field],
)
def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
"""Gets the node field for the specified field alias"""
exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
node_type = type(graph.graph.get_node(exposed_output.node_path))
node_output_type = node_type.get_output_type()
return NodeField(
alias=exposed_output.alias,
node_path=f"{node_id}.{exposed_output.node_path}",
field=exposed_output.field,
field_type=get_type_hints(node_output_type)[exposed_output.field],
)
def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
"""Gets the inputs for the specified invocation from the context"""
node_type = type(invocation)
if node_type is not GraphInvocation:
return fields_from_type_hints(get_type_hints(node_type), invocation.id)
else:
graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
return {e.alias: get_node_input_field(graph, e.alias, invocation.id) for e in graph.exposed_inputs}
def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
"""Gets the outputs for the specified invocation from the context"""
node_type = type(invocation)
if node_type is not GraphInvocation:
return fields_from_type_hints(get_type_hints(node_type.get_output_type()), invocation.id)
else:
graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliContext) -> list[Edge]:
"""Generates all possible edges between two invocations"""
afields = get_node_outputs(a, context)
bfields = get_node_inputs(b, context)
matching_fields = set(afields.keys()).intersection(bfields.keys())
# Remove invalid fields
invalid_fields = set(["type", "id"])
matching_fields = matching_fields.difference(invalid_fields)
# Validate types
matching_fields = [
f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)
]
edges = [
Edge(
source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field),
)
for alias in matching_fields
]
return edges
class SessionError(Exception):
"""Raised when a session error has occurred"""
pass
def invoke_all(context: CliContext):
"""Runs all invocations in the specified session"""
context.invoker.invoke(context.session, invoke_all=True)
while not context.get_session().is_complete():
# Wait some time
time.sleep(0.1)
# Print any errors
if context.session.has_error():
for n in context.session.errors:
context.invoker.services.logger.error(
f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
)
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="*")
invocation_commands = parser.parse_args().commands
# get the optional file to read commands from.
# Simplest is to use it for STDIN
if infile := config.from_file:
sys.stdin = open(infile, "r")
model_manager = ModelManagerService(config, logger)
events = EventServiceBase()
output_folder = config.output_path
# TODO: build a file/path manager?
if config.use_memory_db:
db_location = ":memory:"
else:
db_location = config.db_path
db_location.parent.mkdir(parents=True, exist_ok=True)
db_conn = sqlite3.connect(db_location, check_same_thread=False) # TODO: figure out a better threading solution
logger.info(f'InvokeAI database location is "{db_location}"')
graph_execution_manager = SqliteItemStorage[GraphExecutionState](conn=db_conn, table_name="graph_executions")
urls = LocalUrlService()
image_record_storage = SqliteImageRecordStorage(conn=db_conn)
image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
names = SimpleNameService()
board_record_storage = SqliteBoardRecordStorage(conn=db_conn)
board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn)
boards = BoardService(
services=BoardServiceDependencies(
board_image_record_storage=board_image_record_storage,
board_record_storage=board_record_storage,
image_record_storage=image_record_storage,
url=urls,
logger=logger,
)
)
board_images = BoardImagesService(
services=BoardImagesServiceDependencies(
board_image_record_storage=board_image_record_storage,
board_record_storage=board_record_storage,
image_record_storage=image_record_storage,
url=urls,
logger=logger,
)
)
images = ImageService(
services=ImageServiceDependencies(
board_image_record_storage=board_image_record_storage,
image_record_storage=image_record_storage,
image_file_storage=image_file_storage,
url=urls,
logger=logger,
names=names,
graph_execution_manager=graph_execution_manager,
)
)
services = InvocationServices(
model_manager=model_manager,
events=events,
latents=ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")),
images=images,
boards=boards,
board_images=board_images,
queue=MemoryInvocationQueue(),
graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, table_name="graphs"),
graph_execution_manager=graph_execution_manager,
processor=DefaultInvocationProcessor(),
performance_statistics=InvocationStatsService(graph_execution_manager),
logger=logger,
configuration=config,
invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
)
system_graphs = create_system_graphs(services.graph_library)
system_graph_names = set([g.name for g in system_graphs])
set_autocompleter(services)
invoker = Invoker(services)
session: GraphExecutionState = invoker.create_execution_state()
parser = get_command_parser(services)
re_negid = re.compile("^-[0-9]+$")
# Uncomment to print out previous sessions at startup
# print(services.session_manager.list())
context = CliContext(invoker, session, parser)
set_autocompleter(services)
command_line_args_exist = len(invocation_commands) > 0
done = False
while not done:
try:
if command_line_args_exist:
cmd_input = invocation_commands.pop(0)
done = len(invocation_commands) == 0
else:
cmd_input = input("invoke> ")
except (KeyboardInterrupt, EOFError):
# Ctrl-c exits
break
try:
# Refresh the state of the session
# history = list(get_graph_execution_history(context.session))
history = list(reversed(context.nodes_added))
# Split the command for piping
cmds = cmd_input.split("|")
start_id = len(context.nodes_added)
current_id = start_id
new_invocations = list()
for cmd in cmds:
if cmd is None or cmd.strip() == "":
raise InvalidArgs("Empty command")
# Parse args to create invocation
args = vars(context.parser.parse_args(shlex.split(cmd.strip())))
# Override defaults
for field_name, field_default in context.defaults.items():
if field_name in args:
args[field_name] = field_default
# Parse invocation
command: CliCommand = None # type:ignore
system_graph: Optional[LibraryGraph] = None
if args["type"] in system_graph_names:
system_graph = next(filter(lambda g: g.name == args["type"], system_graphs))
invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
for exposed_input in system_graph.exposed_inputs:
if exposed_input.alias in args:
node = invocation.graph.get_node(exposed_input.node_path)
field = exposed_input.field
setattr(node, field, args[exposed_input.alias])
command = CliCommand(command=invocation)
context.graph_nodes[invocation.id] = system_graph.id
else:
args["id"] = current_id
command = CliCommand(command=args)
if command is None:
continue
# Run any CLI commands immediately
if isinstance(command.command, BaseCommand):
# Invoke all current nodes to preserve operation order
invoke_all(context)
# Run the command
command.command.run(context)
continue
# TODO: handle linking with library graphs
# Pipe previous command output (if there was a previous command)
edges: list[Edge] = list()
if len(history) > 0 or current_id != start_id:
from_id = history[0] if current_id == start_id else str(current_id - 1)
from_node = (
next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
if current_id != start_id
else context.session.graph.get_node(from_id)
)
matching_edges = generate_matching_edges(from_node, command.command, context)
edges.extend(matching_edges)
# Parse provided links
if "link_node" in args and args["link_node"]:
for link in args["link_node"]:
node_id = link
if re_negid.match(node_id):
node_id = str(current_id + int(node_id))
link_node = context.session.graph.get_node(node_id)
matching_edges = generate_matching_edges(link_node, command.command, context)
matching_destinations = [e.destination for e in matching_edges]
edges = [e for e in edges if e.destination not in matching_destinations]
edges.extend(matching_edges)
if "link" in args and args["link"]:
for link in args["link"]:
edges = [
e
for e in edges
if e.destination.node_id != command.command.id or e.destination.field != link[2]
]
node_id = link[0]
if re_negid.match(node_id):
node_id = str(current_id + int(node_id))
# TODO: handle missing input/output
node_output = get_node_outputs(context.session.graph.get_node(node_id), context)[link[1]]
node_input = get_node_inputs(command.command, context)[link[2]]
edges.append(
Edge(
source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field),
)
)
new_invocations.append((command.command, edges))
current_id = current_id + 1
# Add the node to the session
context.add_node(command.command)
for edge in edges:
print(edge)
context.add_edge(edge)
# Execute all remaining nodes
invoke_all(context)
except InvalidArgs:
invoker.services.logger.warning('Invalid command, use "help" to list commands')
continue
except ValidationError:
invoker.services.logger.warning('Invalid command arguments, run "<command> --help" for summary')
except SessionError:
# Start a new session
invoker.services.logger.warning("Session error: creating a new session")
context.reset()
except ExitCli:
break
except SystemExit:
continue
invoker.stop()
if __name__ == "__main__":
if config.version:
print(f"InvokeAI version {__version__}")
else:
invoke_cli()

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

@ -1,8 +1,28 @@
import os import shutil
import sys
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
__all__ = [] from invokeai.app.services.config.config_default import InvokeAIAppConfig
dirname = os.path.dirname(os.path.abspath(__file__)) custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
for f in os.listdir(dirname): custom_nodes_path.mkdir(parents=True, exist_ok=True)
if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
__all__.append(f[:-3]) custom_nodes_init_path = str(custom_nodes_path / "__init__.py")
custom_nodes_readme_path = str(custom_nodes_path / "README.md")
# copy our custom nodes __init__.py to the custom nodes directory
shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path)
shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path)
# Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
spec = spec_from_file_location("custom_nodes", custom_nodes_init_path)
if spec is None or spec.loader is None:
raise RuntimeError(f"Could not load custom nodes from {custom_nodes_init_path}")
module = module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
# add core nodes to __all__
python_files = filter(lambda f: not f.name.startswith("_"), Path(__file__).parent.glob("*.py"))
__all__ = list(f.stem for f in python_files) # type: ignore

169
invokeai/app/invocations/baseinvocation.py
View File

@ -2,7 +2,7 @@
from __future__ import annotations from __future__ import annotations
import json import inspect
import re import re
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from enum import Enum from enum import Enum
@ -11,8 +11,8 @@ from types import UnionType
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
import semver import semver
from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
from pydantic.fields import _Unset from pydantic.fields import FieldInfo, _Unset
from pydantic_core import PydanticUndefined from pydantic_core import PydanticUndefined
from invokeai.app.services.config.config_default import InvokeAIAppConfig from invokeai.app.services.config.config_default import InvokeAIAppConfig
@ -26,6 +26,10 @@ class InvalidVersionError(ValueError):
pass pass
class InvalidFieldError(TypeError):
pass
class FieldDescriptions: class FieldDescriptions:
denoising_start = "When to start denoising, expressed a percentage of total steps" denoising_start = "When to start denoising, expressed a percentage of total steps"
denoising_end = "When to stop denoising, expressed a percentage of total steps" denoising_end = "When to stop denoising, expressed a percentage of total steps"
@ -60,7 +64,12 @@ class FieldDescriptions:
denoised_latents = "Denoised latents tensor" denoised_latents = "Denoised latents tensor"
latents = "Latents tensor" latents = "Latents tensor"
strength = "Strength of denoising (proportional to steps)" strength = "Strength of denoising (proportional to steps)"
core_metadata = "Optional core metadata to be written to image" metadata = "Optional metadata to be saved with the image"
metadata_collection = "Collection of Metadata"
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
metadata_item_label = "Label for this metadata item"
metadata_item_value = "The value for this metadata item (may be any type)"
workflow = "Optional workflow to be saved with the image"
interp_mode = "Interpolation mode" interp_mode = "Interpolation mode"
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)" torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
fp32 = "Whether or not to use full float32 precision" fp32 = "Whether or not to use full float32 precision"
@ -171,8 +180,12 @@ class UIType(str, Enum):
Scheduler = "Scheduler" Scheduler = "Scheduler"
WorkflowField = "WorkflowField" WorkflowField = "WorkflowField"
IsIntermediate = "IsIntermediate" IsIntermediate = "IsIntermediate"
MetadataField = "MetadataField"
BoardField = "BoardField" BoardField = "BoardField"
Any = "Any"
MetadataItem = "MetadataItem"
MetadataItemCollection = "MetadataItemCollection"
MetadataItemPolymorphic = "MetadataItemPolymorphic"
MetadataDict = "MetadataDict"
# endregion # endregion
@ -298,6 +311,7 @@ def InputField(
ui_order=ui_order, ui_order=ui_order,
item_default=item_default, item_default=item_default,
ui_choice_labels=ui_choice_labels, ui_choice_labels=ui_choice_labels,
_field_kind="input",
) )
field_args = dict( field_args = dict(
@ -440,6 +454,7 @@ def OutputField(
ui_type=ui_type, ui_type=ui_type,
ui_hidden=ui_hidden, ui_hidden=ui_hidden,
ui_order=ui_order, ui_order=ui_order,
_field_kind="output",
), ),
) )
@ -523,6 +538,7 @@ class BaseInvocationOutput(BaseModel):
schema["required"].extend(["type"]) schema["required"].extend(["type"])
model_config = ConfigDict( model_config = ConfigDict(
protected_namespaces=(),
validate_assignment=True, validate_assignment=True,
json_schema_serialization_defaults_required=True, json_schema_serialization_defaults_required=True,
json_schema_extra=json_schema_extra, json_schema_extra=json_schema_extra,
@ -545,9 +561,6 @@ class MissingInputException(Exception):
class BaseInvocation(ABC, BaseModel): class BaseInvocation(ABC, BaseModel):
""" """
A node to process inputs and produce outputs.
May use dependency injection in __init__ to receive providers.
All invocations must use the `@invocation` decorator to provide their unique type. All invocations must use the `@invocation` decorator to provide their unique type.
""" """
@ -663,46 +676,93 @@ class BaseInvocation(ABC, BaseModel):
id: str = Field( id: str = Field(
default_factory=uuid_string, default_factory=uuid_string,
description="The id of this instance of an invocation. Must be unique among all instances of invocations.", description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
json_schema_extra=dict(_field_kind="internal"),
) )
is_intermediate: Optional[bool] = Field( is_intermediate: bool = Field(
default=False, default=False,
description="Whether or not this is an intermediate invocation.", description="Whether or not this is an intermediate invocation.",
json_schema_extra=dict(ui_type=UIType.IsIntermediate), json_schema_extra=dict(ui_type=UIType.IsIntermediate, _field_kind="internal"),
) )
workflow: Optional[str] = Field( use_cache: bool = Field(
default=None, default=True, description="Whether or not to use the cache", json_schema_extra=dict(_field_kind="internal")
description="The workflow to save with the image",
json_schema_extra=dict(ui_type=UIType.WorkflowField),
) )
use_cache: Optional[bool] = Field(
default=True,
description="Whether or not to use the cache",
)
@field_validator("workflow", mode="before")
@classmethod
def validate_workflow_is_json(cls, v):
"""We don't have a workflow schema in the backend, so we just check that it's valid JSON"""
if v is None:
return None
try:
json.loads(v)
except json.decoder.JSONDecodeError:
raise ValueError("Workflow must be valid JSON")
return v
UIConfig: ClassVar[Type[UIConfigBase]] UIConfig: ClassVar[Type[UIConfigBase]]
model_config = ConfigDict( model_config = ConfigDict(
protected_namespaces=(),
validate_assignment=True, validate_assignment=True,
json_schema_extra=json_schema_extra, json_schema_extra=json_schema_extra,
json_schema_serialization_defaults_required=True, json_schema_serialization_defaults_required=True,
coerce_numbers_to_str=True,
) )
TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation) TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
RESERVED_INPUT_FIELD_NAMES = {
"id",
"is_intermediate",
"use_cache",
"type",
"workflow",
"metadata",
}
RESERVED_OUTPUT_FIELD_NAMES = {"type"}
class _Model(BaseModel):
pass
# Get all pydantic model attrs, methods, etc
RESERVED_PYDANTIC_FIELD_NAMES = set(map(lambda m: m[0], inspect.getmembers(_Model())))
def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
"""
Validates the fields of an invocation or invocation output:
- must not override any pydantic reserved fields
- must be created via `InputField`, `OutputField`, or be an internal field defined in this file
"""
for name, field in model_fields.items():
if name in RESERVED_PYDANTIC_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)')
field_kind = (
# _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
field.json_schema_extra.get("_field_kind", None)
if field.json_schema_extra
else None
)
# must have a field_kind
if field_kind is None or field_kind not in {"input", "output", "internal"}:
raise InvalidFieldError(
f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)'
)
if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)')
if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)')
# internal fields *must* be in the reserved list
if (
field_kind == "internal"
and name not in RESERVED_INPUT_FIELD_NAMES
and name not in RESERVED_OUTPUT_FIELD_NAMES
):
raise InvalidFieldError(
f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
)
return None
def invocation( def invocation(
invocation_type: str, invocation_type: str,
title: Optional[str] = None, title: Optional[str] = None,
@ -712,7 +772,7 @@ def invocation(
use_cache: Optional[bool] = True, use_cache: Optional[bool] = True,
) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]: ) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]:
""" """
Adds metadata to an invocation. Registers an invocation.
:param str invocation_type: The type of the invocation. Must be unique among all invocations. :param str invocation_type: The type of the invocation. Must be unique among all invocations.
:param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None. :param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
@ -731,6 +791,8 @@ def invocation(
if invocation_type in BaseInvocation.get_invocation_types(): if invocation_type in BaseInvocation.get_invocation_types():
raise ValueError(f'Invocation type "{invocation_type}" already exists') raise ValueError(f'Invocation type "{invocation_type}" already exists')
validate_fields(cls.model_fields, invocation_type)
# Add OpenAPI schema extras # Add OpenAPI schema extras
uiconf_name = cls.__qualname__ + ".UIConfig" uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name: if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
@ -761,8 +823,7 @@ def invocation(
invocation_type_annotation = Literal[invocation_type] # type: ignore invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = Field( invocation_type_field = Field(
title="type", title="type", default=invocation_type, json_schema_extra=dict(_field_kind="internal")
default=invocation_type,
) )
docstring = cls.__doc__ docstring = cls.__doc__
@ -803,13 +864,12 @@ def invocation_output(
if output_type in BaseInvocationOutput.get_output_types(): if output_type in BaseInvocationOutput.get_output_types():
raise ValueError(f'Invocation type "{output_type}" already exists') raise ValueError(f'Invocation type "{output_type}" already exists')
validate_fields(cls.model_fields, output_type)
# Add the output type to the model. # Add the output type to the model.
output_type_annotation = Literal[output_type] # type: ignore output_type_annotation = Literal[output_type] # type: ignore
output_type_field = Field( output_type_field = Field(title="type", default=output_type, json_schema_extra=dict(_field_kind="internal"))
title="type",
default=output_type,
)
docstring = cls.__doc__ docstring = cls.__doc__
cls = create_model( cls = create_model(
@ -827,4 +887,37 @@ def invocation_output(
return wrapper return wrapper
GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel) class WorkflowField(RootModel):
"""
Pydantic model for workflows with custom root of type dict[str, Any].
Workflows are stored without a strict schema.
"""
root: dict[str, Any] = Field(description="The workflow")
WorkflowFieldValidator = TypeAdapter(WorkflowField)
class WithWorkflow(BaseModel):
workflow: Optional[WorkflowField] = Field(
default=None, description=FieldDescriptions.workflow, json_schema_extra=dict(_field_kind="internal")
)
class MetadataField(RootModel):
"""
Pydantic model for metadata with custom root of type dict[str, Any].
Metadata is stored without a strict schema.
"""
root: dict[str, Any] = Field(description="The metadata")
MetadataFieldValidator = TypeAdapter(MetadataField)
class WithMetadata(BaseModel):
metadata: Optional[MetadataField] = Field(
default=None, description=FieldDescriptions.metadata, json_schema_extra=dict(_field_kind="internal")
)

6
invokeai/app/invocations/compel.py
View File

@ -108,13 +108,14 @@ class CompelInvocation(BaseInvocation):
print(f'Warn: trigger: "{trigger}" not found') print(f'Warn: trigger: "{trigger}" not found')
with ( with (
ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as ( ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer, tokenizer,
ti_manager, ti_manager,
), ),
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers), ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
text_encoder_info as text_encoder, text_encoder_info as text_encoder,
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
): ):
compel = Compel( compel = Compel(
tokenizer=tokenizer, tokenizer=tokenizer,
@ -229,13 +230,14 @@ class SDXLPromptInvocationBase:
print(f'Warn: trigger: "{trigger}" not found') print(f'Warn: trigger: "{trigger}" not found')
with ( with (
ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as ( ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer, tokenizer,
ti_manager, ti_manager,
), ),
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers), ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
text_encoder_info as text_encoder, text_encoder_info as text_encoder,
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
): ):
compel = Compel( compel = Compel(
tokenizer=tokenizer, tokenizer=tokenizer,

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

@ -38,6 +38,8 @@ from .baseinvocation import (
InputField, InputField,
InvocationContext, InvocationContext,
OutputField, OutputField,
WithMetadata,
WithWorkflow,
invocation, invocation,
invocation_output, invocation_output,
) )
@ -127,12 +129,12 @@ class ControlNetInvocation(BaseInvocation):
# This invocation exists for other invocations to subclass it - do not register with @invocation! # This invocation exists for other invocations to subclass it - do not register with @invocation!
class ImageProcessorInvocation(BaseInvocation): class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Base class for invocations that preprocess images for ControlNet""" """Base class for invocations that preprocess images for ControlNet"""
image: ImageField = InputField(description="The image to process") image: ImageField = InputField(description="The image to process")
def run_processor(self, image): def run_processor(self, image: Image.Image) -> Image.Image:
# superclass just passes through image without processing # superclass just passes through image without processing
return image return image
@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation):
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
node_id=self.id, node_id=self.id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )

51
invokeai/app/invocations/custom_nodes/README.md Normal file
View File

@ -0,0 +1,51 @@
# Custom Nodes / Node Packs
Copy your node packs to this directory.
When nodes are added or changed, you must restart the app to see the changes.
## Directory Structure
For a node pack to be loaded, it must be placed in a directory alongside this
file. Here's an example structure:
```py
.
├── __init__.py # Invoke-managed custom node loader
│
├── cool_node
│ ├── __init__.py # see example below
│ └── cool_node.py
│
└── my_node_pack
├── __init__.py # see example below
├── tasty_node.py
├── bodacious_node.py
├── utils.py
└── extra_nodes
└── fancy_node.py
```
## Node Pack `__init__.py`
Each node pack must have an `__init__.py` file that imports its nodes.
The structure of each node or node pack is otherwise not important.
Here are examples, based on the example directory structure.
### `cool_node/__init__.py`
```py
from .cool_node import CoolInvocation
```
### `my_node_pack/__init__.py`
```py
from .tasty_node import TastyInvocation
from .bodacious_node import BodaciousInvocation
from .extra_nodes.fancy_node import FancyInvocation
```
Only nodes imported in the `__init__.py` file are loaded.

51
invokeai/app/invocations/custom_nodes/init.py Normal file
View File

@ -0,0 +1,51 @@
"""
Invoke-managed custom node loader. See README.md for more information.
"""
import sys
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.get_logger()
loaded_count = 0
for d in Path(__file__).parent.iterdir():
# skip files
if not d.is_dir():
continue
# skip hidden directories
if d.name.startswith("_") or d.name.startswith("."):
continue
# skip directories without an `__init__.py`
init = d / "__init__.py"
if not init.exists():
continue
module_name = init.parent.stem
# skip if already imported
if module_name in globals():
continue
# we have a legit module to import
spec = spec_from_file_location(module_name, init.absolute())
if spec is None or spec.loader is None:
logger.warn(f"Could not load {init}")
continue
module = module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
loaded_count += 1
del init, module_name
logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}")

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

@ -8,11 +8,11 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0") @invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
class CvInpaintInvocation(BaseInvocation): class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Simple inpaint using opencv.""" """Simple inpaint using opencv."""
image: ImageField = InputField(description="The image to inpaint") image: ImageField = InputField(description="The image to inpaint")

8
invokeai/app/invocations/facetools.py
View File

@ -16,6 +16,8 @@ from invokeai.app.invocations.baseinvocation import (
InputField, InputField,
InvocationContext, InvocationContext,
OutputField, OutputField,
WithMetadata,
WithWorkflow,
invocation, invocation,
invocation_output, invocation_output,
) )
@ -437,7 +439,7 @@ def get_faces_list(
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2") @invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
class FaceOffInvocation(BaseInvocation): class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Bound, extract, and mask a face from an image using MediaPipe detection""" """Bound, extract, and mask a face from an image using MediaPipe detection"""
image: ImageField = InputField(description="Image for face detection") image: ImageField = InputField(description="Image for face detection")
@ -531,7 +533,7 @@ class FaceOffInvocation(BaseInvocation):
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2") @invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
class FaceMaskInvocation(BaseInvocation): class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Face mask creation using mediapipe face detection""" """Face mask creation using mediapipe face detection"""
image: ImageField = InputField(description="Image to face detect") image: ImageField = InputField(description="Image to face detect")
@ -650,7 +652,7 @@ class FaceMaskInvocation(BaseInvocation):
@invocation( @invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2" "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
) )
class FaceIdentifierInvocation(BaseInvocation): class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools.""" """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
image: ImageField = InputField(description="Image to face detect") image: ImageField = InputField(description="Image to face detect")

223
invokeai/app/invocations/image.py
View File

@ -7,13 +7,21 @@ import cv2
import numpy import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker from invokeai.backend.image_util.safety_checker import SafetyChecker
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation from .baseinvocation import (
BaseInvocation,
FieldDescriptions,
Input,
InputField,
InvocationContext,
WithMetadata,
WithWorkflow,
invocation,
)
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0") @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@ -36,14 +44,8 @@ class ShowImageInvocation(BaseInvocation):
) )
@invocation( @invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
"blank_image", class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
title="Blank Image",
tags=["image"],
category="image",
version="1.0.0",
)
class BlankImageInvocation(BaseInvocation):
"""Creates a blank image and forwards it to the pipeline""" """Creates a blank image and forwards it to the pipeline"""
width: int = InputField(default=512, description="The width of the image") width: int = InputField(default=512, description="The width of the image")
@ -61,6 +63,7 @@ class BlankImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -71,14 +74,8 @@ class BlankImageInvocation(BaseInvocation):
) )
@invocation( @invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
"img_crop", class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.0.0",
)
class ImageCropInvocation(BaseInvocation):
"""Crops an image to a specified box. The box can be outside of the image.""" """Crops an image to a specified box. The box can be outside of the image."""
image: ImageField = InputField(description="The image to crop") image: ImageField = InputField(description="The image to crop")
@ -100,6 +97,7 @@ class ImageCropInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -110,14 +108,8 @@ class ImageCropInvocation(BaseInvocation):
) )
@invocation( @invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
"img_paste", class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.0.1",
)
class ImagePasteInvocation(BaseInvocation):
"""Pastes an image into another image.""" """Pastes an image into another image."""
base_image: ImageField = InputField(description="The base image") base_image: ImageField = InputField(description="The base image")
@ -159,6 +151,7 @@ class ImagePasteInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -169,14 +162,8 @@ class ImagePasteInvocation(BaseInvocation):
) )
@invocation( @invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
"tomask", class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.0.0",
)
class MaskFromAlphaInvocation(BaseInvocation):
"""Extracts the alpha channel of an image as a mask.""" """Extracts the alpha channel of an image as a mask."""
image: ImageField = InputField(description="The image to create the mask from") image: ImageField = InputField(description="The image to create the mask from")
@ -196,6 +183,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -206,14 +194,8 @@ class MaskFromAlphaInvocation(BaseInvocation):
) )
@invocation( @invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
"img_mul", class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.0.0",
)
class ImageMultiplyInvocation(BaseInvocation):
"""Multiplies two images together using `PIL.ImageChops.multiply()`.""" """Multiplies two images together using `PIL.ImageChops.multiply()`."""
image1: ImageField = InputField(description="The first image to multiply") image1: ImageField = InputField(description="The first image to multiply")
@ -232,6 +214,7 @@ class ImageMultiplyInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -245,14 +228,8 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"] IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation( @invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
"img_chan", class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.0.0",
)
class ImageChannelInvocation(BaseInvocation):
"""Gets a channel from an image.""" """Gets a channel from an image."""
image: ImageField = InputField(description="The image to get the channel from") image: ImageField = InputField(description="The image to get the channel from")
@ -270,6 +247,7 @@ class ImageChannelInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -283,14 +261,8 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"] IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@invocation( @invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
"img_conv", class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.0.0",
)
class ImageConvertInvocation(BaseInvocation):
"""Converts an image to a different mode.""" """Converts an image to a different mode."""
image: ImageField = InputField(description="The image to convert") image: ImageField = InputField(description="The image to convert")
@ -308,6 +280,7 @@ class ImageConvertInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -318,14 +291,8 @@ class ImageConvertInvocation(BaseInvocation):
) )
@invocation( @invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
"img_blur", class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.0.0",
)
class ImageBlurInvocation(BaseInvocation):
"""Blurs an image""" """Blurs an image"""
image: ImageField = InputField(description="The image to blur") image: ImageField = InputField(description="The image to blur")
@ -348,6 +315,7 @@ class ImageBlurInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -378,23 +346,14 @@ PIL_RESAMPLING_MAP = {
} }
@invocation( @invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
"img_resize", class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.0.0",
)
class ImageResizeInvocation(BaseInvocation):
"""Resizes an image to specific dimensions""" """Resizes an image to specific dimensions"""
image: ImageField = InputField(description="The image to resize") image: ImageField = InputField(description="The image to resize")
width: int = InputField(default=512, gt=0, description="The width to resize to (px)") width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
height: int = InputField(default=512, gt=0, description="The height to resize to (px)") height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode") resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
@ -413,7 +372,7 @@ class ImageResizeInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None, metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -424,14 +383,8 @@ class ImageResizeInvocation(BaseInvocation):
) )
@invocation( @invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
"img_scale", class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.0.0",
)
class ImageScaleInvocation(BaseInvocation):
"""Scales an image by a factor""" """Scales an image by a factor"""
image: ImageField = InputField(description="The image to scale") image: ImageField = InputField(description="The image to scale")
@ -461,6 +414,7 @@ class ImageScaleInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -471,14 +425,8 @@ class ImageScaleInvocation(BaseInvocation):
) )
@invocation( @invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
"img_lerp", class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.0.0",
)
class ImageLerpInvocation(BaseInvocation):
"""Linear interpolation of all pixels of an image""" """Linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp") image: ImageField = InputField(description="The image to lerp")
@ -500,6 +448,7 @@ class ImageLerpInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -510,14 +459,8 @@ class ImageLerpInvocation(BaseInvocation):
) )
@invocation( @invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
"img_ilerp", class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.0.0",
)
class ImageInverseLerpInvocation(BaseInvocation):
"""Inverse linear interpolation of all pixels of an image""" """Inverse linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp") image: ImageField = InputField(description="The image to lerp")
@ -539,6 +482,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -549,20 +493,11 @@ class ImageInverseLerpInvocation(BaseInvocation):
) )
@invocation( @invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
"img_nsfw", class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.0.0",
)
class ImageNSFWBlurInvocation(BaseInvocation):
"""Add blur to NSFW-flagged images""" """Add blur to NSFW-flagged images"""
image: ImageField = InputField(description="The image to check") image: ImageField = InputField(description="The image to check")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
@ -583,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None, metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -607,14 +542,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
category="image", category="image",
version="1.0.0", version="1.0.0",
) )
class ImageWatermarkInvocation(BaseInvocation): class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add an invisible watermark to an image""" """Add an invisible watermark to an image"""
image: ImageField = InputField(description="The image to check") image: ImageField = InputField(description="The image to check")
text: str = InputField(default="InvokeAI", description="Watermark text") text: str = InputField(default="InvokeAI", description="Watermark text")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
@ -626,7 +558,7 @@ class ImageWatermarkInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None, metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -637,14 +569,8 @@ class ImageWatermarkInvocation(BaseInvocation):
) )
@invocation( @invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
"mask_edge", class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.0.0",
)
class MaskEdgeInvocation(BaseInvocation):
"""Applies an edge mask to an image""" """Applies an edge mask to an image"""
image: ImageField = InputField(description="The image to apply the mask to") image: ImageField = InputField(description="The image to apply the mask to")
@ -678,6 +604,7 @@ class MaskEdgeInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -695,7 +622,7 @@ class MaskEdgeInvocation(BaseInvocation):
category="image", category="image",
version="1.0.0", version="1.0.0",
) )
class MaskCombineInvocation(BaseInvocation): class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.""" """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
mask1: ImageField = InputField(description="The first mask to combine") mask1: ImageField = InputField(description="The first mask to combine")
@ -714,6 +641,7 @@ class MaskCombineInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -724,14 +652,8 @@ class MaskCombineInvocation(BaseInvocation):
) )
@invocation( @invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
"color_correct", class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.0.0",
)
class ColorCorrectInvocation(BaseInvocation):
""" """
Shifts the colors of a target image to match the reference image, optionally Shifts the colors of a target image to match the reference image, optionally
using a mask to only color-correct certain regions of the target image. using a mask to only color-correct certain regions of the target image.
@ -830,6 +752,7 @@ class ColorCorrectInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -840,14 +763,8 @@ class ColorCorrectInvocation(BaseInvocation):
) )
@invocation( @invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
"img_hue_adjust", class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.0.0",
)
class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image.""" """Adjusts the Hue of an image."""
image: ImageField = InputField(description="The image to adjust") image: ImageField = InputField(description="The image to adjust")
@ -875,6 +792,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -950,7 +868,7 @@ CHANNEL_FORMATS = {
category="image", category="image",
version="1.0.0", version="1.0.0",
) )
class ImageChannelOffsetInvocation(BaseInvocation): class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Add or subtract a value from a specific color channel of an image.""" """Add or subtract a value from a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust") image: ImageField = InputField(description="The image to adjust")
@ -984,6 +902,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -1020,7 +939,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
category="image", category="image",
version="1.0.0", version="1.0.0",
) )
class ImageChannelMultiplyInvocation(BaseInvocation): class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Scale a specific color channel of an image.""" """Scale a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust") image: ImageField = InputField(description="The image to adjust")
@ -1060,6 +979,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
workflow=self.workflow, workflow=self.workflow,
metadata=self.metadata,
) )
return ImageOutput( return ImageOutput(
@ -1079,16 +999,11 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
version="1.0.1", version="1.0.1",
use_cache=False, use_cache=False,
) )
class SaveImageInvocation(BaseInvocation): class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Saves an image. Unlike an image primitive, this invocation stores a copy of the image.""" """Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
image: ImageField = InputField(description=FieldDescriptions.image) image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct) board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
def invoke(self, context: InvocationContext) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
@ -1101,7 +1016,7 @@ class SaveImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None, metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )

21
invokeai/app/invocations/infill.py
View File

@ -13,7 +13,7 @@ from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch from invokeai.backend.image_util.patchmatch import PatchMatch
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
@ -119,7 +119,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") @invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillColorInvocation(BaseInvocation): class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with a solid color""" """Infills transparent areas of an image with a solid color"""
image: ImageField = InputField(description="The image to infill") image: ImageField = InputField(description="The image to infill")
@ -143,6 +143,7 @@ class InfillColorInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -154,7 +155,7 @@ class InfillColorInvocation(BaseInvocation):
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") @invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillTileInvocation(BaseInvocation): class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with tiles of the image""" """Infills transparent areas of an image with tiles of the image"""
image: ImageField = InputField(description="The image to infill") image: ImageField = InputField(description="The image to infill")
@ -179,6 +180,7 @@ class InfillTileInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -192,7 +194,7 @@ class InfillTileInvocation(BaseInvocation):
@invocation( @invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0" "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
) )
class InfillPatchMatchInvocation(BaseInvocation): class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the PatchMatch algorithm""" """Infills transparent areas of an image using the PatchMatch algorithm"""
image: ImageField = InputField(description="The image to infill") image: ImageField = InputField(description="The image to infill")
@ -232,6 +234,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )
@ -243,7 +246,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") @invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class LaMaInfillInvocation(BaseInvocation): class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the LaMa model""" """Infills transparent areas of an image using the LaMa model"""
image: ImageField = InputField(description="The image to infill") image: ImageField = InputField(description="The image to infill")
@ -260,6 +263,8 @@ class LaMaInfillInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
) )
return ImageOutput( return ImageOutput(
@ -269,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation):
) )
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") @invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
class CV2InfillInvocation(BaseInvocation): class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using OpenCV Inpainting""" """Infills transparent areas of an image using OpenCV Inpainting"""
image: ImageField = InputField(description="The image to infill") image: ImageField = InputField(description="The image to infill")
@ -287,6 +292,8 @@ class CV2InfillInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
) )
return ImageOutput( return ImageOutput(

8
invokeai/app/invocations/ip_adapter.py
View File

@ -36,7 +36,7 @@ class CLIPVisionModelField(BaseModel):
class IPAdapterField(BaseModel): class IPAdapterField(BaseModel):
image: ImageField = Field(description="The IP-Adapter image prompt.") image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.") ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.") image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet") weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
@ -55,19 +55,19 @@ class IPAdapterOutput(BaseInvocationOutput):
ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter") ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0") @invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
class IPAdapterInvocation(BaseInvocation): class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes.""" """Collects IP-Adapter info to pass to other nodes."""
# Inputs # Inputs
image: ImageField = InputField(description="The IP-Adapter image prompt.") image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
ip_adapter_model: IPAdapterModelField = InputField( ip_adapter_model: IPAdapterModelField = InputField(
description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1 description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
) )
# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float) # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
weight: Union[float, List[float]] = InputField( weight: Union[float, List[float]] = InputField(
default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight" default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
) )
begin_step_percent: float = InputField( begin_step_percent: float = InputField(

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

@ -23,7 +23,6 @@ from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ( from invokeai.app.invocations.primitives import (
DenoiseMaskField, DenoiseMaskField,
DenoiseMaskOutput, DenoiseMaskOutput,
@ -64,6 +63,8 @@ from .baseinvocation import (
InvocationContext, InvocationContext,
OutputField, OutputField,
UIType, UIType,
WithMetadata,
WithWorkflow,
invocation, invocation,
invocation_output, invocation_output,
) )
@ -214,7 +215,7 @@ def get_scheduler(
title="Denoise Latents", title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"], tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents", category="latents",
version="1.3.0", version="1.4.0",
) )
class DenoiseLatentsInvocation(BaseInvocation): class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images""" """Denoises noisy latents to decodable images"""
@ -491,16 +492,21 @@ class DenoiseLatentsInvocation(BaseInvocation):
context=context, context=context,
) )
input_image = context.services.images.get_pil_image(single_ip_adapter.image.image_name) # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
single_ipa_images = single_ip_adapter.image
if not isinstance(single_ipa_images, list):
single_ipa_images = [single_ipa_images]
single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
# TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments. # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
with image_encoder_model_info as image_encoder_model: with image_encoder_model_info as image_encoder_model:
# Get image embeddings from CLIP and ImageProjModel. # Get image embeddings from CLIP and ImageProjModel.
( image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
image_prompt_embeds, single_ipa_images, image_encoder_model
uncond_image_prompt_embeds, )
) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
conditioning_data.ip_adapter_conditioning.append( conditioning_data.ip_adapter_conditioning.append(
IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds) IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
) )
@ -708,6 +714,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config), ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
set_seamless(unet_info.context.model, self.unet.seamless_axes), set_seamless(unet_info.context.model, self.unet.seamless_axes),
unet_info as unet, unet_info as unet,
# Apply the LoRA after unet has been moved to its target device for faster patching.
ModelPatcher.apply_lora_unet(unet, _lora_loader()),
): ):
latents = latents.to(device=unet.device, dtype=unet.dtype) latents = latents.to(device=unet.device, dtype=unet.dtype)
if noise is not None: if noise is not None:
@ -788,7 +796,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
category="latents", category="latents",
version="1.0.0", version="1.0.0",
) )
class LatentsToImageInvocation(BaseInvocation): class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents.""" """Generates an image from latents."""
latents: LatentsField = InputField( latents: LatentsField = InputField(
@ -801,11 +809,6 @@ class LatentsToImageInvocation(BaseInvocation):
) )
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled) tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32) fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
@torch.no_grad() @torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
@ -874,7 +877,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None, metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )

18
invokeai/app/invocations/math.py
View File

@ -3,7 +3,7 @@
from typing import Literal from typing import Literal
import numpy as np import numpy as np
from pydantic import field_validator from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
@ -186,12 +186,12 @@ class IntegerMathInvocation(BaseInvocation):
b: int = InputField(default=0, description=FieldDescriptions.num_2) b: int = InputField(default=0, description=FieldDescriptions.num_2)
@field_validator("b") @field_validator("b")
def no_unrepresentable_results(cls, v, values): def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
if values["operation"] == "DIV" and v == 0: if info.data["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero") raise ValueError("Cannot divide by zero")
elif values["operation"] == "MOD" and v == 0: elif info.data["operation"] == "MOD" and v == 0:
raise ValueError("Cannot divide by zero") raise ValueError("Cannot divide by zero")
elif values["operation"] == "EXP" and v < 0: elif info.data["operation"] == "EXP" and v < 0:
raise ValueError("Result of exponentiation is not an integer") raise ValueError("Result of exponentiation is not an integer")
return v return v
@ -260,12 +260,12 @@ class FloatMathInvocation(BaseInvocation):
b: float = InputField(default=0, description=FieldDescriptions.num_2) b: float = InputField(default=0, description=FieldDescriptions.num_2)
@field_validator("b") @field_validator("b")
def no_unrepresentable_results(cls, v, values): def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
if values["operation"] == "DIV" and v == 0: if info.data["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero") raise ValueError("Cannot divide by zero")
elif values["operation"] == "EXP" and values["a"] == 0 and v < 0: elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
raise ValueError("Cannot raise zero to a negative power") raise ValueError("Cannot raise zero to a negative power")
elif values["operation"] == "EXP" and type(values["a"] ** v) is complex: elif info.data["operation"] == "EXP" and type(info.data["a"] ** v) is complex:
raise ValueError("Root operation resulted in a complex number") raise ValueError("Root operation resulted in a complex number")
return v return v

189
invokeai/app/invocations/metadata.py
View File

@ -1,13 +1,16 @@
from typing import Optional from typing import Any, Literal, Optional, Union
from pydantic import Field from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import ( from invokeai.app.invocations.baseinvocation import (
BaseInvocation, BaseInvocation,
BaseInvocationOutput, BaseInvocationOutput,
FieldDescriptions,
InputField, InputField,
InvocationContext, InvocationContext,
MetadataField,
OutputField, OutputField,
UIType,
invocation, invocation,
invocation_output, invocation_output,
) )
@ -16,116 +19,104 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
from invokeai.app.invocations.primitives import ImageField from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
from ...version import __version__ from ...version import __version__
class LoRAMetadataField(BaseModelExcludeNull): class MetadataItemField(BaseModel):
"""LoRA metadata for an image generated in InvokeAI.""" label: str = Field(description=FieldDescriptions.metadata_item_label)
value: Any = Field(description=FieldDescriptions.metadata_item_value)
lora: LoRAModelField = Field(description="The LoRA model")
weight: float = Field(description="The weight of the LoRA model")
class IPAdapterMetadataField(BaseModelExcludeNull): class LoRAMetadataField(BaseModel):
"""LoRA Metadata Field"""
lora: LoRAModelField = Field(description=FieldDescriptions.lora_model)
weight: float = Field(description=FieldDescriptions.lora_weight)
class IPAdapterMetadataField(BaseModel):
"""IP Adapter Field, minus the CLIP Vision Encoder model"""
image: ImageField = Field(description="The IP-Adapter image prompt.") image: ImageField = Field(description="The IP-Adapter image prompt.")
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.") ip_adapter_model: IPAdapterModelField = Field(
weight: float = Field(description="The weight of the IP-Adapter model") description="The IP-Adapter model.",
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
) )
end_step_percent: float = Field( weight: Union[float, list[float]] = Field(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)" description="The weight given to the IP-Adapter",
)
begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
@invocation_output("metadata_item_output")
class MetadataItemOutput(BaseInvocationOutput):
"""Metadata Item Output"""
item: MetadataItemField = OutputField(description="Metadata Item")
@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0")
class MetadataItemInvocation(BaseInvocation):
"""Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
label: str = InputField(description=FieldDescriptions.metadata_item_label)
value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
def invoke(self, context: InvocationContext) -> MetadataItemOutput:
return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
@invocation_output("metadata_output")
class MetadataOutput(BaseInvocationOutput):
metadata: MetadataField = OutputField(description="Metadata Dict")
@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0")
class MetadataInvocation(BaseInvocation):
"""Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
items: Union[list[MetadataItemField], MetadataItemField] = InputField(
description=FieldDescriptions.metadata_item_polymorphic
) )
def invoke(self, context: InvocationContext) -> MetadataOutput:
if isinstance(self.items, MetadataItemField):
# single metadata item
data = {self.items.label: self.items.value}
else:
# collection of metadata items
data = {item.label: item.value for item in self.items}
class CoreMetadata(BaseModelExcludeNull): # add app version
"""Core generation metadata for an image generated in InvokeAI.""" data.update({"app_version": __version__})
return MetadataOutput(metadata=MetadataField.model_validate(data))
app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
generation_mode: Optional[str] = Field(
default=None,
description="The generation mode that output this image",
)
created_by: Optional[str] = Field(default=None, description="The name of the creator of the image")
positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
width: Optional[int] = Field(default=None, description="The width parameter")
height: Optional[int] = Field(default=None, description="The height parameter")
seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
clip_skip: Optional[int] = Field(
default=None,
description="The number of skipped CLIP layers",
)
model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
default=None, description="The IP Adapters used for inference"
)
t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
vae: Optional[VAEModelField] = Field(
default=None,
description="The VAE used for decoding, if the main model's default was not used",
)
# Latents-to-Latents
strength: Optional[float] = Field(
default=None,
description="The strength used for latents-to-latents",
)
init_image: Optional[str] = Field(default=None, description="The name of the initial image")
# SDXL
positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter")
negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter")
# SDXL Refiner
refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used")
refiner_cfg_scale: Optional[float] = Field(
default=None,
description="The classifier-free guidance scale parameter used for the refiner",
)
refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner")
refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
refiner_positive_aesthetic_score: Optional[float] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_negative_aesthetic_score: Optional[float] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
class ImageMetadata(BaseModelExcludeNull): @invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
"""An image's generation metadata""" class MergeMetadataInvocation(BaseInvocation):
"""Merged a collection of MetadataDict into a single MetadataDict."""
metadata: Optional[dict] = Field( collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
default=None,
description="The image's core metadata, if it was created in the Linear or Canvas UI", def invoke(self, context: InvocationContext) -> MetadataOutput:
) data = {}
graph: Optional[dict] = Field(default=None, description="The graph that created the image") for item in self.collection:
data.update(item.model_dump())
return MetadataOutput(metadata=MetadataField.model_validate(data))
@invocation_output("metadata_accumulator_output") GENERATION_MODES = Literal[
class MetadataAccumulatorOutput(BaseInvocationOutput): "txt2img", "img2img", "inpaint", "outpaint", "sdxl_txt2img", "sdxl_img2img", "sdxl_inpaint", "sdxl_outpaint"
"""The output of the MetadataAccumulator node""" ]
metadata: CoreMetadata = OutputField(description="The core metadata for the image")
@invocation( @invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
"metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0" class CoreMetadataInvocation(BaseInvocation):
) """Collects core generation metadata into a MetadataField"""
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""
generation_mode: Optional[str] = InputField( generation_mode: Optional[GENERATION_MODES] = InputField(
default=None, default=None,
description="The generation mode that output this image", description="The generation mode that output this image",
) )
@ -138,6 +129,8 @@ class MetadataAccumulatorInvocation(BaseInvocation):
cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter") cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = InputField(default=None, description="The number of steps used for inference") steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference") scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
seamless_y: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the Y axis")
clip_skip: Optional[int] = InputField( clip_skip: Optional[int] = InputField(
default=None, default=None,
description="The number of skipped CLIP layers", description="The number of skipped CLIP layers",
@ -220,7 +213,13 @@ class MetadataAccumulatorInvocation(BaseInvocation):
description="The start value used for refiner denoising", description="The start value used for refiner denoising",
) )
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput: def invoke(self, context: InvocationContext) -> MetadataOutput:
"""Collects and outputs a CoreMetadata object""" """Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump())) return MetadataOutput(
metadata=MetadataField.model_validate(
self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
)
)
model_config = ConfigDict(extra="allow")

14
invokeai/app/invocations/onnx.py
View File

@ -4,7 +4,7 @@ import inspect
import re import re
# from contextlib import ExitStack # from contextlib import ExitStack
from typing import List, Literal, Optional, Union from typing import List, Literal, Union
import numpy as np import numpy as np
import torch import torch
@ -12,7 +12,6 @@ from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, ConfigDict, Field, field_validator from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm from tqdm import tqdm
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.step_callback import stable_diffusion_step_callback from invokeai.app.util.step_callback import stable_diffusion_step_callback
@ -31,6 +30,8 @@ from .baseinvocation import (
OutputField, OutputField,
UIComponent, UIComponent,
UIType, UIType,
WithMetadata,
WithWorkflow,
invocation, invocation,
invocation_output, invocation_output,
) )
@ -327,7 +328,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
category="image", category="image",
version="1.0.0", version="1.0.0",
) )
class ONNXLatentsToImageInvocation(BaseInvocation): class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents.""" """Generates an image from latents."""
latents: LatentsField = InputField( latents: LatentsField = InputField(
@ -338,11 +339,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
description=FieldDescriptions.vae, description=FieldDescriptions.vae,
input=Input.Connection, input=Input.Connection,
) )
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
# tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)") # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
def invoke(self, context: InvocationContext) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
@ -381,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None, metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )

6
invokeai/app/invocations/primitives.py
View File

@ -251,7 +251,9 @@ class ImageCollectionOutput(BaseInvocationOutput):
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0") @invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
class ImageInvocation(BaseInvocation): class ImageInvocation(
BaseInvocation,
):
"""An image primitive value""" """An image primitive value"""
image: ImageField = InputField(description="The image to load") image: ImageField = InputField(description="The image to load")
@ -291,7 +293,7 @@ class DenoiseMaskField(BaseModel):
"""An inpaint mask field""" """An inpaint mask field"""
mask_name: str = Field(description="The name of the mask image") mask_name: str = Field(description="The name of the mask image")
masked_latents_name: Optional[str] = Field(description="The name of the masked image latents") masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
@invocation_output("denoise_mask_output") @invocation_output("denoise_mask_output")

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

@ -14,7 +14,7 @@ from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.util.devices import choose_torch_device from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
# TODO: Populate this from disk? # TODO: Populate this from disk?
# TODO: Use model manager to load? # TODO: Use model manager to load?
@ -30,7 +30,7 @@ if choose_torch_device() == torch.device("mps"):
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0") @invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
class ESRGANInvocation(BaseInvocation): class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Upscales an image using RealESRGAN.""" """Upscales an image using RealESRGAN."""
image: ImageField = InputField(description="The input image") image: ImageField = InputField(description="The input image")
@ -123,6 +123,7 @@ class ESRGANInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow, workflow=self.workflow,
) )

10
invokeai/app/services/config/config_default.py
View File

@ -45,6 +45,7 @@ InvokeAI:
ram: 13.5 ram: 13.5
vram: 0.25 vram: 0.25
lazy_offload: true lazy_offload: true
log_memory_usage: false
Device: Device:
device: auto device: auto
precision: auto precision: auto
@ -243,6 +244,7 @@ class InvokeAIAppConfig(InvokeAISettings):
db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths) db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths) outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths) use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
custom_nodes_dir : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths)
from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths) from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
# LOGGING # LOGGING
@ -260,6 +262,7 @@ class InvokeAIAppConfig(InvokeAISettings):
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, ) ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, ) vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, ) lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
# DEVICE # DEVICE
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device) device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
@ -410,6 +413,13 @@ class InvokeAIAppConfig(InvokeAISettings):
""" """
return self._resolve(self.models_dir) return self._resolve(self.models_dir)
@property
def custom_nodes_path(self) -> Path:
"""
Path to the custom nodes directory
"""
return self._resolve(self.custom_nodes_dir)
# the following methods support legacy calls leftover from the Globals era # the following methods support legacy calls leftover from the Globals era
@property @property
def full_precision(self) -> bool: def full_precision(self) -> bool:

6
invokeai/app/services/image_files/image_files_base.py
View File

@ -4,6 +4,8 @@ from typing import Optional
from PIL.Image import Image as PILImageType from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
class ImageFileStorageBase(ABC): class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files.""" """Low-level service responsible for storing and retrieving image files."""
@ -30,8 +32,8 @@ class ImageFileStorageBase(ABC):
self, self,
image: PILImageType, image: PILImageType,
image_name: str, image_name: str,
metadata: Optional[dict] = None, metadata: Optional[MetadataField] = None,
workflow: Optional[str] = None, workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256, thumbnail_size: int = 256,
) -> None: ) -> None:
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp.""" """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""

24
invokeai/app/services/image_files/image_files_disk.py
View File

@ -1,5 +1,4 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import json
from pathlib import Path from pathlib import Path
from queue import Queue from queue import Queue
from typing import Dict, Optional, Union from typing import Dict, Optional, Union
@ -8,6 +7,7 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType from PIL.Image import Image as PILImageType
from send2trash import send2trash from send2trash import send2trash
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.invoker import Invoker from invokeai.app.services.invoker import Invoker
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
@ -55,8 +55,8 @@ class DiskImageFileStorage(ImageFileStorageBase):
self, self,
image: PILImageType, image: PILImageType,
image_name: str, image_name: str,
metadata: Optional[dict] = None, metadata: Optional[MetadataField] = None,
workflow: Optional[str] = None, workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256, thumbnail_size: int = 256,
) -> None: ) -> None:
try: try:
@ -65,20 +65,10 @@ class DiskImageFileStorage(ImageFileStorageBase):
pnginfo = PngImagePlugin.PngInfo() pnginfo = PngImagePlugin.PngInfo()
if metadata is not None or workflow is not None: if metadata is not None:
if metadata is not None: pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
pnginfo.add_text("invokeai_metadata", json.dumps(metadata)) if workflow is not None:
if workflow is not None: pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
pnginfo.add_text("invokeai_workflow", workflow)
else:
# For uploaded images, we want to retain metadata. PIL strips it on save; manually add it back
# TODO: retain non-invokeai metadata on save...
original_metadata = image.info.get("invokeai_metadata", None)
if original_metadata is not None:
pnginfo.add_text("invokeai_metadata", original_metadata)
original_workflow = image.info.get("invokeai_workflow", None)
if original_workflow is not None:
pnginfo.add_text("invokeai_workflow", original_workflow)
image.save( image.save(
image_path, image_path,

10
invokeai/app/services/image_records/image_records_base.py
View File

@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
from datetime import datetime from datetime import datetime
from typing import Optional from typing import Optional
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.shared.pagination import OffsetPaginatedResults from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
@ -18,7 +19,7 @@ class ImageRecordStorageBase(ABC):
pass pass
@abstractmethod @abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]: def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata'.""" """Gets an image's metadata'."""
pass pass
@ -61,6 +62,11 @@ class ImageRecordStorageBase(ABC):
"""Deletes all intermediate image records, returning a list of deleted image names.""" """Deletes all intermediate image records, returning a list of deleted image names."""
pass pass
@abstractmethod
def get_intermediates_count(self) -> int:
"""Gets a count of all intermediate images."""
pass
@abstractmethod @abstractmethod
def save( def save(
self, self,
@ -73,7 +79,7 @@ class ImageRecordStorageBase(ABC):
starred: Optional[bool] = False, starred: Optional[bool] = False,
session_id: Optional[str] = None, session_id: Optional[str] = None,
node_id: Optional[str] = None, node_id: Optional[str] = None,
metadata: Optional[dict] = None, metadata: Optional[MetadataField] = None,
) -> datetime: ) -> datetime:
"""Saves an image record.""" """Saves an image record."""
pass pass

45
invokeai/app/services/image_records/image_records_sqlite.py
View File

@ -1,9 +1,9 @@
import json
import sqlite3 import sqlite3
import threading import threading
from datetime import datetime from datetime import datetime
from typing import Optional, Union, cast from typing import Optional, Union, cast
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
from invokeai.app.services.shared.pagination import OffsetPaginatedResults from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase from invokeai.app.services.shared.sqlite import SqliteDatabase
@ -141,22 +141,26 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
return deserialize_image_record(dict(result)) return deserialize_image_record(dict(result))
def get_metadata(self, image_name: str) -> Optional[dict]: def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try: try:
self._lock.acquire() self._lock.acquire()
self._cursor.execute( self._cursor.execute(
"""--sql """--sql
SELECT images.metadata FROM images SELECT metadata FROM images
WHERE image_name = ?; WHERE image_name = ?;
""", """,
(image_name,), (image_name,),
) )
result = cast(Optional[sqlite3.Row], self._cursor.fetchone()) result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
if not result or not result[0]:
return None if not result:
return json.loads(result[0]) raise ImageRecordNotFoundException
as_dict = dict(result)
metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
return MetadataFieldValidator.validate_json(metadata_raw) if metadata_raw is not None else None
except sqlite3.Error as e: except sqlite3.Error as e:
self._conn.rollback() self._conn.rollback()
raise ImageRecordNotFoundException from e raise ImageRecordNotFoundException from e
@ -297,11 +301,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
images_query += query_conditions + query_pagination + ";" images_query += query_conditions + query_pagination + ";"
# Add all the parameters # Add all the parameters
images_params = query_params.copy() images_params = query_params.copy()
# Add the pagination parameters
if limit is not None: images_params.extend([limit, offset])
images_params.append(limit)
if offset is not None:
images_params.append(offset)
# Build the list of images, deserializing each row # Build the list of images, deserializing each row
self._cursor.execute(images_query, images_params) self._cursor.execute(images_query, images_params)
@ -357,6 +358,24 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
finally: finally:
self._lock.release() self._lock.release()
def get_intermediates_count(self) -> int:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT COUNT(*) FROM images
WHERE is_intermediate = TRUE;
"""
)
count = cast(int, self._cursor.fetchone()[0])
self._conn.commit()
return count
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordDeleteException from e
finally:
self._lock.release()
def delete_intermediates(self) -> list[str]: def delete_intermediates(self) -> list[str]:
try: try:
self._lock.acquire() self._lock.acquire()
@ -393,10 +412,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
starred: Optional[bool] = False, starred: Optional[bool] = False,
session_id: Optional[str] = None, session_id: Optional[str] = None,
node_id: Optional[str] = None, node_id: Optional[str] = None,
metadata: Optional[dict] = None, metadata: Optional[MetadataField] = None,
) -> datetime: ) -> datetime:
try: try:
metadata_json = None if metadata is None else json.dumps(metadata) metadata_json = metadata.model_dump_json() if metadata is not None else None
self._lock.acquire() self._lock.acquire()
self._cursor.execute( self._cursor.execute(
"""--sql """--sql

13
invokeai/app/services/images/images_base.py
View File

@ -3,7 +3,7 @@ from typing import Callable, Optional
from PIL.Image import Image as PILImageType from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.image_records.image_records_common import ( from invokeai.app.services.image_records.image_records_common import (
ImageCategory, ImageCategory,
ImageRecord, ImageRecord,
@ -50,8 +50,8 @@ class ImageServiceABC(ABC):
session_id: Optional[str] = None, session_id: Optional[str] = None,
board_id: Optional[str] = None, board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False, is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None, metadata: Optional[MetadataField] = None,
workflow: Optional[str] = None, workflow: Optional[WorkflowField] = None,
) -> ImageDTO: ) -> ImageDTO:
"""Creates an image, storing the file and its metadata.""" """Creates an image, storing the file and its metadata."""
pass pass
@ -81,7 +81,7 @@ class ImageServiceABC(ABC):
pass pass
@abstractmethod @abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata: def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata.""" """Gets an image's metadata."""
pass pass
@ -123,6 +123,11 @@ class ImageServiceABC(ABC):
"""Deletes all intermediate images.""" """Deletes all intermediate images."""
pass pass
@abstractmethod
def get_intermediates_count(self) -> int:
"""Gets the number of intermediate images."""
pass
@abstractmethod @abstractmethod
def delete_images_on_board(self, board_id: str): def delete_images_on_board(self, board_id: str):
"""Deletes all images on a board.""" """Deletes all images on a board."""

9
invokeai/app/services/images/images_common.py
View File

@ -24,8 +24,11 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
default=None, description="The id of the board the image belongs to, if one exists." default=None, description="The id of the board the image belongs to, if one exists."
) )
"""The id of the board the image belongs to, if one exists.""" """The id of the board the image belongs to, if one exists."""
workflow_id: Optional[str] = Field(
pass default=None,
description="The workflow that generated this image.",
)
"""The workflow that generated this image."""
def image_record_to_dto( def image_record_to_dto(
@ -33,6 +36,7 @@ def image_record_to_dto(
image_url: str, image_url: str,
thumbnail_url: str, thumbnail_url: str,
board_id: Optional[str], board_id: Optional[str],
workflow_id: Optional[str],
) -> ImageDTO: ) -> ImageDTO:
"""Converts an image record to an image DTO.""" """Converts an image record to an image DTO."""
return ImageDTO( return ImageDTO(
@ -40,4 +44,5 @@ def image_record_to_dto(
image_url=image_url, image_url=image_url,
thumbnail_url=thumbnail_url, thumbnail_url=thumbnail_url,
board_id=board_id, board_id=board_id,
workflow_id=workflow_id,
) )

71
invokeai/app/services/images/images_default.py
View File

@ -2,10 +2,9 @@ from typing import Optional
from PIL.Image import Image as PILImageType from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.invoker import Invoker from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
from ..image_files.image_files_common import ( from ..image_files.image_files_common import (
ImageFileDeleteException, ImageFileDeleteException,
@ -42,8 +41,8 @@ class ImageService(ImageServiceABC):
session_id: Optional[str] = None, session_id: Optional[str] = None,
board_id: Optional[str] = None, board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False, is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None, metadata: Optional[MetadataField] = None,
workflow: Optional[str] = None, workflow: Optional[WorkflowField] = None,
) -> ImageDTO: ) -> ImageDTO:
if image_origin not in ResourceOrigin: if image_origin not in ResourceOrigin:
raise InvalidOriginException raise InvalidOriginException
@ -56,6 +55,12 @@ class ImageService(ImageServiceABC):
(width, height) = image.size (width, height) = image.size
try: try:
if workflow is not None:
created_workflow = self.__invoker.services.workflow_records.create(workflow)
workflow_id = created_workflow.model_dump()["id"]
else:
workflow_id = None
# TODO: Consider using a transaction here to ensure consistency between storage and database # TODO: Consider using a transaction here to ensure consistency between storage and database
self.__invoker.services.image_records.save( self.__invoker.services.image_records.save(
# Non-nullable fields # Non-nullable fields
@ -73,6 +78,8 @@ class ImageService(ImageServiceABC):
) )
if board_id is not None: if board_id is not None:
self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name) self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
if workflow_id is not None:
self.__invoker.services.workflow_image_records.create(workflow_id=workflow_id, image_name=image_name)
self.__invoker.services.image_files.save( self.__invoker.services.image_files.save(
image_name=image_name, image=image, metadata=metadata, workflow=workflow image_name=image_name, image=image, metadata=metadata, workflow=workflow
) )
@ -132,10 +139,11 @@ class ImageService(ImageServiceABC):
image_record = self.__invoker.services.image_records.get(image_name) image_record = self.__invoker.services.image_records.get(image_name)
image_dto = image_record_to_dto( image_dto = image_record_to_dto(
image_record, image_record=image_record,
self.__invoker.services.urls.get_image_url(image_name), image_url=self.__invoker.services.urls.get_image_url(image_name),
self.__invoker.services.urls.get_image_url(image_name, True), thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(image_name), board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name),
) )
return image_dto return image_dto
@ -146,25 +154,22 @@ class ImageService(ImageServiceABC):
self.__invoker.services.logger.error("Problem getting image DTO") self.__invoker.services.logger.error("Problem getting image DTO")
raise e raise e
def get_metadata(self, image_name: str) -> ImageMetadata: def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try: try:
image_record = self.__invoker.services.image_records.get(image_name) return self.__invoker.services.image_records.get_metadata(image_name)
metadata = self.__invoker.services.image_records.get_metadata(image_name) except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
if not image_record.session_id: def get_workflow(self, image_name: str) -> Optional[WorkflowField]:
return ImageMetadata(metadata=metadata) try:
workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name)
session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id) if workflow_id is None:
graph = None return None
return self.__invoker.services.workflow_records.get(workflow_id)
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
except ImageRecordNotFoundException: except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found") self.__invoker.services.logger.error("Image record not found")
raise raise
@ -215,10 +220,11 @@ class ImageService(ImageServiceABC):
image_dtos = list( image_dtos = list(
map( map(
lambda r: image_record_to_dto( lambda r: image_record_to_dto(
r, image_record=r,
self.__invoker.services.urls.get_image_url(r.image_name), image_url=self.__invoker.services.urls.get_image_url(r.image_name),
self.__invoker.services.urls.get_image_url(r.image_name, True), thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(r.image_name), board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
), ),
results.items, results.items,
) )
@ -284,3 +290,10 @@ class ImageService(ImageServiceABC):
except Exception as e: except Exception as e:
self.__invoker.services.logger.error("Problem deleting image records and files") self.__invoker.services.logger.error("Problem deleting image records and files")
raise e raise e
def get_intermediates_count(self) -> int:
try:
return self.__invoker.services.image_records.get_intermediates_count()
except Exception as e:
self.__invoker.services.logger.error("Problem getting intermediates count")
raise e

8
invokeai/app/services/invocation_services.py
View File

@ -27,6 +27,8 @@ if TYPE_CHECKING:
from .session_queue.session_queue_base import SessionQueueBase from .session_queue.session_queue_base import SessionQueueBase
from .shared.graph import GraphExecutionState, LibraryGraph from .shared.graph import GraphExecutionState, LibraryGraph
from .urls.urls_base import UrlServiceBase from .urls.urls_base import UrlServiceBase
from .workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
class InvocationServices: class InvocationServices:
@ -55,6 +57,8 @@ class InvocationServices:
invocation_cache: "InvocationCacheBase" invocation_cache: "InvocationCacheBase"
names: "NameServiceBase" names: "NameServiceBase"
urls: "UrlServiceBase" urls: "UrlServiceBase"
workflow_image_records: "WorkflowImageRecordsStorageBase"
workflow_records: "WorkflowRecordsStorageBase"
def __init__( def __init__(
self, self,
@ -80,6 +84,8 @@ class InvocationServices:
invocation_cache: "InvocationCacheBase", invocation_cache: "InvocationCacheBase",
names: "NameServiceBase", names: "NameServiceBase",
urls: "UrlServiceBase", urls: "UrlServiceBase",
workflow_image_records: "WorkflowImageRecordsStorageBase",
workflow_records: "WorkflowRecordsStorageBase",
): ):
self.board_images = board_images self.board_images = board_images
self.board_image_records = board_image_records self.board_image_records = board_image_records
@ -103,3 +109,5 @@ class InvocationServices:
self.invocation_cache = invocation_cache self.invocation_cache = invocation_cache
self.names = names self.names = names
self.urls = urls self.urls = urls
self.workflow_image_records = workflow_image_records
self.workflow_records = workflow_records

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

@ -18,7 +18,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
_cursor: sqlite3.Cursor _cursor: sqlite3.Cursor
_id_field: str _id_field: str
_lock: threading.RLock _lock: threading.RLock
_adapter: Optional[TypeAdapter[T]] _validator: Optional[TypeAdapter[T]]
def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"): def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
super().__init__() super().__init__()
@ -28,7 +28,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._table_name = table_name self._table_name = table_name
self._id_field = id_field # TODO: validate that T has this field self._id_field = id_field # TODO: validate that T has this field
self._cursor = self._conn.cursor() self._cursor = self._conn.cursor()
self._adapter: Optional[TypeAdapter[T]] = None self._validator: Optional[TypeAdapter[T]] = None
self._create_table() self._create_table()
@ -47,14 +47,14 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._lock.release() self._lock.release()
def _parse_item(self, item: str) -> T: def _parse_item(self, item: str) -> T:
if self._adapter is None: if self._validator is None:
""" """
We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so
we can create it when it is first needed instead. we can create it when it is first needed instead.
__orig_class__ is technically an implementation detail of the typing module, not a supported API __orig_class__ is technically an implementation detail of the typing module, not a supported API
""" """
self._adapter = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined] self._validator = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
return self._adapter.validate_json(item) return self._validator.validate_json(item)
def set(self, item: T): def set(self, item: T):
try: try:

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

@ -0,0 +1 @@
from .model_manager_default import ModelManagerService # noqa F401

7
invokeai/app/services/session_queue/session_queue_base.py
View File

@ -9,7 +9,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
CancelByQueueIDResult, CancelByQueueIDResult,
ClearResult, ClearResult,
EnqueueBatchResult, EnqueueBatchResult,
EnqueueGraphResult,
IsEmptyResult, IsEmptyResult,
IsFullResult, IsFullResult,
PruneResult, PruneResult,
@ -17,7 +16,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
SessionQueueItemDTO, SessionQueueItemDTO,
SessionQueueStatus, SessionQueueStatus,
) )
from invokeai.app.services.shared.graph import Graph
from invokeai.app.services.shared.pagination import CursorPaginatedResults from invokeai.app.services.shared.pagination import CursorPaginatedResults
@ -29,11 +27,6 @@ class SessionQueueBase(ABC):
"""Dequeues the next session queue item.""" """Dequeues the next session queue item."""
pass pass
@abstractmethod
def enqueue_graph(self, queue_id: str, graph: Graph, prepend: bool) -> EnqueueGraphResult:
"""Enqueues a single graph for execution."""
pass
@abstractmethod @abstractmethod
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult: def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
"""Enqueues all permutations of a batch for execution.""" """Enqueues all permutations of a batch for execution."""

16
invokeai/app/services/session_queue/session_queue_common.py
View File

@ -147,20 +147,20 @@ DEFAULT_QUEUE_ID = "default"
QUEUE_ITEM_STATUS = Literal["pending", "in_progress", "completed", "failed", "canceled"] QUEUE_ITEM_STATUS = Literal["pending", "in_progress", "completed", "failed", "canceled"]
adapter_NodeFieldValue = TypeAdapter(list[NodeFieldValue]) NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
def get_field_values(queue_item_dict: dict) -> Optional[list[NodeFieldValue]]: def get_field_values(queue_item_dict: dict) -> Optional[list[NodeFieldValue]]:
field_values_raw = queue_item_dict.get("field_values", None) field_values_raw = queue_item_dict.get("field_values", None)
return adapter_NodeFieldValue.validate_json(field_values_raw) if field_values_raw is not None else None return NodeFieldValueValidator.validate_json(field_values_raw) if field_values_raw is not None else None
adapter_GraphExecutionState = TypeAdapter(GraphExecutionState) GraphExecutionStateValidator = TypeAdapter(GraphExecutionState)
def get_session(queue_item_dict: dict) -> GraphExecutionState: def get_session(queue_item_dict: dict) -> GraphExecutionState:
session_raw = queue_item_dict.get("session", "{}") session_raw = queue_item_dict.get("session", "{}")
session = adapter_GraphExecutionState.validate_json(session_raw, strict=False) session = GraphExecutionStateValidator.validate_json(session_raw, strict=False)
return session return session
@ -276,14 +276,6 @@ class EnqueueBatchResult(BaseModel):
priority: int = Field(description="The priority of the enqueued batch") priority: int = Field(description="The priority of the enqueued batch")
class EnqueueGraphResult(BaseModel):
enqueued: int = Field(description="The total number of queue items enqueued")
requested: int = Field(description="The total number of queue items requested to be enqueued")
batch: Batch = Field(description="The batch that was enqueued")
priority: int = Field(description="The priority of the enqueued batch")
queue_item: SessionQueueItemDTO = Field(description="The queue item that was enqueued")
class ClearResult(BaseModel): class ClearResult(BaseModel):
"""Result of clearing the session queue""" """Result of clearing the session queue"""

28
invokeai/app/services/session_queue/session_queue_sqlite.py
View File

@ -17,7 +17,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
CancelByQueueIDResult, CancelByQueueIDResult,
ClearResult, ClearResult,
EnqueueBatchResult, EnqueueBatchResult,
EnqueueGraphResult,
IsEmptyResult, IsEmptyResult,
IsFullResult, IsFullResult,
PruneResult, PruneResult,
@ -28,7 +27,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
calc_session_count, calc_session_count,
prepare_values_to_insert, prepare_values_to_insert,
) )
from invokeai.app.services.shared.graph import Graph
from invokeai.app.services.shared.pagination import CursorPaginatedResults from invokeai.app.services.shared.pagination import CursorPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase from invokeai.app.services.shared.sqlite import SqliteDatabase
@ -255,32 +253,6 @@ class SqliteSessionQueue(SessionQueueBase):
) )
return cast(Union[int, None], self.__cursor.fetchone()[0]) or 0 return cast(Union[int, None], self.__cursor.fetchone()[0]) or 0
def enqueue_graph(self, queue_id: str, graph: Graph, prepend: bool) -> EnqueueGraphResult:
enqueue_result = self.enqueue_batch(queue_id=queue_id, batch=Batch(graph=graph), prepend=prepend)
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT *
FROM session_queue
WHERE queue_id = ?
AND batch_id = ?
""",
(queue_id, enqueue_result.batch.batch_id),
)
result = cast(Union[sqlite3.Row, None], self.__cursor.fetchone())
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
if result is None:
raise SessionQueueItemNotFoundError(f"No queue item with batch id {enqueue_result.batch.batch_id}")
return EnqueueGraphResult(
**enqueue_result.model_dump(),
queue_item=SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)),
)
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult: def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
try: try:
self.__lock.acquire() self.__lock.acquire()

34
invokeai/app/services/shared/graph.py
View File

@ -193,7 +193,7 @@ class GraphInvocation(BaseInvocation):
"""Execute a graph""" """Execute a graph"""
# TODO: figure out how to create a default here # TODO: figure out how to create a default here
graph: "Graph" = Field(description="The graph to run", default=None) graph: "Graph" = InputField(description="The graph to run", default=None)
def invoke(self, context: InvocationContext) -> GraphInvocationOutput: def invoke(self, context: InvocationContext) -> GraphInvocationOutput:
"""Invoke with provided services and return outputs.""" """Invoke with provided services and return outputs."""
@ -439,6 +439,14 @@ class Graph(BaseModel):
except Exception as e: except Exception as e:
raise UnknownGraphValidationError(f"Problem validating graph {e}") from e raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
def _is_destination_field_Any(self, edge: Edge) -> bool:
"""Checks if the destination field for an edge is of type typing.Any"""
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any
def _is_destination_field_list_of_Any(self, edge: Edge) -> bool:
"""Checks if the destination field for an edge is of type typing.Any"""
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
def _validate_edge(self, edge: Edge): def _validate_edge(self, edge: Edge):
"""Validates that a new edge doesn't create a cycle in the graph""" """Validates that a new edge doesn't create a cycle in the graph"""
@ -491,8 +499,19 @@ class Graph(BaseModel):
f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}" f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
) )
# Validate if collector output type matches input type (if this edge results in both being set) # Validate that we are not connecting collector to iterator (currently unsupported)
if isinstance(from_node, CollectInvocation) and edge.source.field == "collection": if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation):
raise InvalidEdgeError(
f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any]
if (
isinstance(from_node, CollectInvocation)
and edge.source.field == "collection"
and not self._is_destination_field_list_of_Any(edge)
and not self._is_destination_field_Any(edge)
):
if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination): if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
raise InvalidEdgeError( raise InvalidEdgeError(
f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}" f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
@ -725,16 +744,15 @@ class Graph(BaseModel):
# Get the input root type # Get the input root type
input_root_type = next(t[0] for t in type_degrees if t[1] == 0) # type: ignore input_root_type = next(t[0] for t in type_degrees if t[1] == 0) # type: ignore
# Verify that all outputs are lists
# if not all((get_origin(f) == list for f in output_fields)):
# return False
# Verify that all outputs are lists # Verify that all outputs are lists
if not all(is_list_or_contains_list(f) for f in output_fields): if not all(is_list_or_contains_list(f) for f in output_fields):
return False return False
# Verify that all outputs match the input type (are a base class or the same class) # Verify that all outputs match the input type (are a base class or the same class)
if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)): if not all(
is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0])
for f in output_fields
):
return False return False
return True return True

0
invokeai/app/cli/__init__.py → invokeai/app/services/workflow_image_records/__init__.py
View File

23
invokeai/app/services/workflow_image_records/workflow_image_records_base.py Normal file
View File

@ -0,0 +1,23 @@
from abc import ABC, abstractmethod
from typing import Optional
class WorkflowImageRecordsStorageBase(ABC):
"""Abstract base class for the one-to-many workflow-image relationship record storage."""
@abstractmethod
def create(
self,
workflow_id: str,
image_name: str,
) -> None:
"""Creates a workflow-image record."""
pass
@abstractmethod
def get_workflow_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's workflow id, if it has one."""
pass

122
invokeai/app/services/workflow_image_records/workflow_image_records_sqlite.py Normal file
View File

@ -0,0 +1,122 @@
import sqlite3
import threading
from typing import Optional, cast
from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.services.workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
class SqliteWorkflowImageRecordsStorage(WorkflowImageRecordsStorageBase):
"""SQLite implementation of WorkflowImageRecordsStorageBase."""
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.RLock
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
try:
self._lock.acquire()
self._create_tables()
self._conn.commit()
finally:
self._lock.release()
def _create_tables(self) -> None:
# Create the `workflow_images` junction table.
self._cursor.execute(
"""--sql
CREATE TABLE IF NOT EXISTS workflow_images (
workflow_id TEXT NOT NULL,
image_name TEXT NOT NULL,
created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-- updated via trigger
updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-- Soft delete, currently unused
deleted_at DATETIME,
-- enforce one-to-many relationship between workflows and images using PK
-- (we can extend this to many-to-many later)
PRIMARY KEY (image_name),
FOREIGN KEY (workflow_id) REFERENCES workflows (workflow_id) ON DELETE CASCADE,
FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE
);
"""
)
# Add index for workflow id
self._cursor.execute(
"""--sql
CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id ON workflow_images (workflow_id);
"""
)
# Add index for workflow id, sorted by created_at
self._cursor.execute(
"""--sql
CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id_created_at ON workflow_images (workflow_id, created_at);
"""
)
# Add trigger for `updated_at`.
self._cursor.execute(
"""--sql
CREATE TRIGGER IF NOT EXISTS tg_workflow_images_updated_at
AFTER UPDATE
ON workflow_images FOR EACH ROW
BEGIN
UPDATE workflow_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
WHERE workflow_id = old.workflow_id AND image_name = old.image_name;
END;
"""
)
def create(
self,
workflow_id: str,
image_name: str,
) -> None:
"""Creates a workflow-image record."""
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
INSERT INTO workflow_images (workflow_id, image_name)
VALUES (?, ?);
""",
(workflow_id, image_name),
)
self._conn.commit()
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
def get_workflow_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's workflow id, if it has one."""
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT workflow_id
FROM workflow_images
WHERE image_name = ?;
""",
(image_name,),
)
result = self._cursor.fetchone()
if result is None:
return None
return cast(str, result[0])
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()

0
invokeai/app/services/workflow_records/__init__.py Normal file
View File

17
invokeai/app/services/workflow_records/workflow_records_base.py Normal file
View File

@ -0,0 +1,17 @@
from abc import ABC, abstractmethod
from invokeai.app.invocations.baseinvocation import WorkflowField
class WorkflowRecordsStorageBase(ABC):
"""Base class for workflow storage services."""
@abstractmethod
def get(self, workflow_id: str) -> WorkflowField:
"""Get workflow by id."""
pass
@abstractmethod
def create(self, workflow: WorkflowField) -> WorkflowField:
"""Creates a workflow."""
pass

2
invokeai/app/services/workflow_records/workflow_records_common.py Normal file
View File

@ -0,0 +1,2 @@
class WorkflowNotFoundError(Exception):
"""Raised when a workflow is not found"""

102
invokeai/app/services/workflow_records/workflow_records_sqlite.py Normal file
View File

@ -0,0 +1,102 @@
import sqlite3
import threading
from invokeai.app.invocations.baseinvocation import WorkflowField, WorkflowFieldValidator
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowNotFoundError
from invokeai.app.util.misc import uuid_string
class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
_invoker: Invoker
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.RLock
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
self._create_tables()
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
def get(self, workflow_id: str) -> WorkflowField:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT workflow
FROM workflows
WHERE workflow_id = ?;
""",
(workflow_id,),
)
row = self._cursor.fetchone()
if row is None:
raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
return WorkflowFieldValidator.validate_json(row[0])
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
def create(self, workflow: WorkflowField) -> WorkflowField:
try:
# workflows do not have ids until they are saved
workflow_id = uuid_string()
workflow.root["id"] = workflow_id
self._lock.acquire()
self._cursor.execute(
"""--sql
INSERT INTO workflows(workflow)
VALUES (?);
""",
(workflow.model_dump_json(),),
)
self._conn.commit()
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return self.get(workflow_id)
def _create_tables(self) -> None:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
CREATE TABLE IF NOT EXISTS workflows (
workflow TEXT NOT NULL,
workflow_id TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.id')) VIRTUAL NOT NULL UNIQUE, -- gets implicit index
created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')) -- updated via trigger
);
"""
)
self._cursor.execute(
"""--sql
CREATE TRIGGER IF NOT EXISTS tg_workflows_updated_at
AFTER UPDATE
ON workflows FOR EACH ROW
BEGIN
UPDATE workflows
SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
WHERE workflow_id = old.workflow_id;
END;
"""
)
self._conn.commit()
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()

6
invokeai/backend/image_util/invisible_watermark.py
View File

@ -20,12 +20,12 @@ class InvisibleWatermark:
""" """
@classmethod @classmethod
def invisible_watermark_available(self) -> bool: def invisible_watermark_available(cls) -> bool:
return config.invisible_watermark return config.invisible_watermark
@classmethod @classmethod
def add_watermark(self, image: Image, watermark_text: str) -> Image: def add_watermark(cls, image: Image.Image, watermark_text: str) -> Image.Image:
if not self.invisible_watermark_available(): if not cls.invisible_watermark_available():
return image return image
logger.debug(f'Applying invisible watermark "{watermark_text}"') logger.debug(f'Applying invisible watermark "{watermark_text}"')
bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR) bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)

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