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

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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)
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.
Node.js also needs to be installed along with yarn (can be installed with
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.
## 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.
# Outputs will also be stored here by default.
# This **must** be an absolute path.
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
# CONTAINER_UID=1000

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docker/Dockerfile
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@ -2,7 +2,7 @@
## Builder stage
FROM library/ubuntu:22.04 AS builder
FROM library/ubuntu:23.04 AS builder
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
@ -10,7 +10,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt update && apt-get install -y \
git \
python3.10-venv \
python3-venv \
python3-pip \
build-essential
@ -37,7 +37,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
elif [ "$GPU_DRIVER" = "rocm" ]; then \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
else \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
fi &&\
pip install $extra_index_url_arg \
torch==$TORCH_VERSION \
@ -70,7 +70,7 @@ RUN --mount=type=cache,target=/usr/lib/node_modules \
#### Runtime stage ---------------------------------------
FROM library/ubuntu:22.04 AS runtime
FROM library/ubuntu:23.04 AS runtime
ARG DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
@ -85,6 +85,7 @@ RUN apt update && apt install -y --no-install-recommends \
iotop \
bzip2 \
gosu \
magic-wormhole \
libglib2.0-0 \
libgl1-mesa-glx \
python3-venv \
@ -94,10 +95,6 @@ RUN apt update && apt install -y --no-install-recommends \
libstdc++-10-dev &&\
apt-get clean && apt-get autoclean
# globally add magic-wormhole
# for ease of transferring data to and from the container
# when running in sandboxed cloud environments; e.g. Runpod etc.
RUN pip install magic-wormhole
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
@ -120,9 +117,7 @@ WORKDIR ${INVOKEAI_SRC}
RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
RUN python3 -c "from patchmatch import patch_match"
# Create unprivileged user and make the local dir
RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke
RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT}
RUN mkdir -p ${INVOKEAI_ROOT} && chown -R 1000:1000 ${INVOKEAI_ROOT}
COPY docker/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
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.
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)
@ -20,7 +20,6 @@ This is done via Docker Desktop preferences
## Quickstart
1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
a. the desired location of the InvokeAI runtime directory, or
b. an existing, v3.0.0 compatible runtime directory.
@ -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.
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
HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000
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!
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
@ -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
```
```yaml
command:
- invokeai-configure
- --yes
@ -71,7 +72,7 @@ command:
Or install models:
```
```yaml
command:
- 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)
echo "docker-compose build args:"
echo "docker compose 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.
USER_ID=${CONTAINER_UID:-1000}
USER=invoke
USER=ubuntu
usermod -u ${USER_ID} ${USER} 1>/dev/null
configure() {

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docker/run.sh
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@ -1,8 +1,11 @@
#!/usr/bin/env bash
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]}")
cd "$SCRIPTDIR" || exit 1
docker-compose up --build -d
docker-compose logs -f
docker compose up --build -d
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.
See
[Installation](installation/index.md).
[Installation](installation/INSTALLATION.md).
- A streamlined manual installation process that works for both Conda and
PIP-only installs. See
[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>
- 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
reviewers)
- Supports a Google Colab notebook for a standalone server running on Google

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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?**
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 |
| `--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. |
| `--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). |
| `--with_variations <pattern>` | | `None` | Combine two or more variations. See [Variations](../features/VARIATIONS.md) for now to use this. |
| `--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](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 |
| `--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.) |

0
docs/other/TRANSLATION.md → docs/deprecated/TRANSLATION.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
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
image generation requests.

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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
Here are a few examples to illustrate how it works. All these images were
generated using the command-line client and the Stable Diffusion 1.5 model:
Here are a few examples to illustrate how it works. All these images
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; |
| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |

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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/).
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".
#### Reading Environment Variables

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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
outcome.
#### How it works
ControlNet works by analyzing an input image, pre-processing that
image to identify relevant information that can be interpreted by each
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
specific result.
#### Models
#### Installation
InvokeAI provides access to a series of ControlNet models that provide
different effects or styles in your generated images. Currently
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.
different effects or styles in your generated images.
***InvokeAI does not currently support checkpoint-format
ControlNets. These come in the form of a single file with the
extension `.safetensors`.***
To install ControlNet Models:
Diffuser-style ControlNet models are available at HuggingFace
(http://huggingface.co) and accessed via their repo IDs (identifiers
in the format "author/modelname"). The easiest way to install them is
1. The easiest way to install them is
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
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
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
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:
**Canny**:
@ -133,6 +127,30 @@ 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.
## 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.
**Note:** T2I-Adapter models and ControlNet models cannot currently be used together.
## IP-Adapter
@ -140,13 +158,13 @@ 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 + 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
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.
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.
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](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.
#### Using IP-Adapter

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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.
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
`invoke` command-line client and its `!optimize` command. You
must select at least two models to merge. The third can be left at
"None" if you desire.
"Convert" option in the Web-based Model Manager tab.
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
from 0 to 1. The higher the value, the more weight is given to the

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docs/features/UTILITIES.md
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@ -8,7 +8,7 @@ title: Command-line Utilities
InvokeAI comes with several scripts that are accessible via the
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
environment (typically, but not necessarily `invokeai/.venv`).
@ -34,7 +34,7 @@ invokeai-web --ram 7
## **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
console-based GUI. Alternatively, you can run it non-interactively
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**
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
zillion arguments, but a typical training session can be launched
with:
@ -68,7 +68,7 @@ in Windows).
## **invokeai-install**
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
non-interactively to list, add and remove models as shown by these
examples:
@ -148,7 +148,7 @@ launch the web server against it with `invokeai-web --root InvokeAI-New`.
## **invokeai-update**
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.
## **invokeai-metadata**

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docs/features/index.md
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@ -28,7 +28,7 @@ Learn how to install and use ControlNet models for fine control over
image output.
### * [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

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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!)
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*

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

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@ -4,30 +4,31 @@ title: Installing with 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
deployment to other environments (on-premises or cloud), follow these
instructions.
Container support for AMD GPUs has been reported to work by the community, but has not received
extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and
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?
They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
use a Docker volume to store the largest model files and image outputs as a
first step in decoupling storage and compute. Future enhancements can do this
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.
They provide a flexible, reliable way to build and deploy InvokeAI.
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
container. You'll also need to specify the InvokeAI requirements file that
matches the container's OS and the architecture it will run on.
The container is configured for CUDA by default, but can be built to support AMD GPUs
by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time.
Developers on Apple silicon (M1/M2): You
[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
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)
### Prerequisites
@ -58,222 +69,33 @@ a token and copy it, since you will need in for the next step.
### 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
in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
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:
At a minimum, you might want to set the `INVOKEAI_ROOT` environment variable
to point to the location where you wish to store your InvokeAI models, configuration, and outputs.
<figure markdown>
| 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 |
| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name |
| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored |
| `ARCH` | arch of the build machine | Can be changed if you want to build the image for another arch |
| `CONTAINER_REGISTRY` | ghcr.io | Name of the Container Registry to use for the full tag |
| `CONTAINER_REPOSITORY` | `$(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 |
| `INVOKEAI_ROOT` | `~/invokeai` | **Required** - the location of your InvokeAI root directory. It will be created if it does not exist.
| `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|
| `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.
</figure>
#### Build the Image
I provided a build script, which is located next to the Dockerfile in
`docker/build.sh`. It can be executed from repository root like this:
Use the standard `docker compose build` command from within the `docker` directory.
```bash
./docker/build.sh
```
The build Script not only builds the container, but also creates the docker
volume if not existing yet.
If using an AMD GPU:
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
#### Run the Container
After the build process is done, you can run the container via the provided
`docker/run.sh` script
Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
```bash
./docker/run.sh
```
When used without arguments, the container will start the webserver and provide
you the link to open it. But if you want to use some other parameters you can
also do so.
!!! example "run script example"
```bash
./docker/run.sh "banana sushi" -Ak_lms -S42 -s10
```
This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
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
```
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)

2
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View File

@ -84,7 +84,7 @@ InvokeAI root directory's `autoimport` folder.
### 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 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

4
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View File

@ -79,7 +79,7 @@ title: Manual Installation, Linux
and obtaining an access token for downloading. It will then download and
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.
7. Start generating images!
@ -112,7 +112,7 @@ title: Manual Installation, Linux
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
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`.
8. Subsequently, to relaunch the script, be sure to run "conda activate

2
docs/installation/deprecated_documentation/INSTALL_MAC.md
View File

@ -150,7 +150,7 @@ will do our best to help.
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
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`.
---

2
docs/installation/deprecated_documentation/INSTALL_SOURCE.md
View File

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

4
docs/installation/deprecated_documentation/INSTALL_WINDOWS.md
View File

@ -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
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.
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
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
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`.
9. Subsequently, to relaunch the script, first activate the Anaconda

2
docs/nodes/communityNodes.md
View File

@ -181,7 +181,7 @@ This includes 15 Nodes:
**Output Example:**
<img src="https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif" width="500" />
<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/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)
--------------------------------

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

@ -9,41 +9,37 @@ set INVOKEAI_ROOT=.
:start
echo Desired action:
echo 1. Generate images with the browser-based interface
echo 2. Explore InvokeAI nodes using a command-line interface
echo 3. Run textual inversion training
echo 4. Merge models (diffusers type only)
echo 5. Download and install models
echo 6. Change InvokeAI startup options
echo 7. Re-run the configure script to fix a broken install or to complete a major upgrade
echo 8. Open the developer console
echo 9. Update InvokeAI
echo 10. Run the InvokeAI image database maintenance script
echo 11. Command-line help
echo 2. Run textual inversion training
echo 3. Merge models (diffusers type only)
echo 4. Download and install models
echo 5. Change InvokeAI startup options
echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade
echo 7. Open the developer console
echo 8. Update InvokeAI
echo 9. Run the InvokeAI image database maintenance script
echo 10. Command-line help
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 /I "%choice%" == "1" (
echo Starting the InvokeAI browser-based UI..
python .venv\Scripts\invokeai-web.exe %*
) 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..
python .venv\Scripts\invokeai-ti.exe --gui
) ELSE IF /I "%choice%" == "4" (
) ELSE IF /I "%choice%" == "3" (
echo Starting model merging script..
python .venv\Scripts\invokeai-merge.exe --gui
) ELSE IF /I "%choice%" == "5" (
) ELSE IF /I "%choice%" == "4" (
echo Running invokeai-model-install...
python .venv\Scripts\invokeai-model-install.exe
) ELSE IF /I "%choice%" == "6" (
) ELSE IF /I "%choice%" == "5" (
echo Running invokeai-configure...
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...
python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
) ELSE IF /I "%choice%" == "8" (
) ELSE IF /I "%choice%" == "7" (
echo Developer Console
echo Python command is:
where python
@ -55,13 +51,13 @@ IF /I "%choice%" == "1" (
echo *************************
echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
call cmd /k
) ELSE IF /I "%choice%" == "9" (
) ELSE IF /I "%choice%" == "8" (
echo Running 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...
python .venv\Scripts\invokeai-db-maintenance.exe
) ELSE IF /I "%choice%" == "11" (
) ELSE IF /I "%choice%" == "10" (
echo Displaying command line help...
python .venv\Scripts\invokeai-web.exe --help %*
pause

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

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

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

@ -2,6 +2,7 @@
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.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.sqlite import SqliteDatabase
from ..services.urls.urls_default import LocalUrlService
from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
from .events import FastAPIEventService
@ -90,6 +92,8 @@ class ApiDependencies:
session_processor = DefaultSessionProcessor()
session_queue = SqliteSessionQueue(db=db)
urls = LocalUrlService()
workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
workflow_records = SqliteWorkflowRecordsStorage(db=db)
services = InvocationServices(
board_image_records=board_image_records,
@ -114,6 +118,8 @@ class ApiDependencies:
session_processor=session_processor,
session_queue=session_queue,
urls=urls,
workflow_image_records=workflow_image_records,
workflow_records=workflow_records,
)
create_system_graphs(services.graph_library)

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

@ -1,13 +1,14 @@
import io
import traceback
from typing import Optional
from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
from fastapi.responses import FileResponse
from fastapi.routing import APIRouter
from PIL import Image
from pydantic import BaseModel, Field
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.images.images_common import ImageDTO, ImageUrlsDTO
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"):
raise HTTPException(status_code=415, detail="Not an image")
contents = await file.read()
metadata = None
workflow = None
contents = await file.read()
try:
pil_image = Image.open(io.BytesIO(contents))
if crop_visible:
bbox = pil_image.getbbox()
pil_image = pil_image.crop(bbox)
except Exception:
# Error opening the image
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
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:
image_dto = ApiDependencies.invoker.services.images.create(
image=pil_image,
@ -63,6 +85,8 @@ async def upload_image(
image_category=image_category,
session_id=session_id,
board_id=board_id,
metadata=metadata,
workflow=workflow,
is_intermediate=is_intermediate,
)
@ -71,6 +95,7 @@ async def upload_image(
return image_dto
except Exception:
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail="Failed to create image")
@ -87,7 +112,7 @@ async def delete_image(
pass
@images_router.post("/clear-intermediates", operation_id="clear_intermediates")
@images_router.delete("/intermediates", operation_id="clear_intermediates")
async def clear_intermediates() -> int:
"""Clears all intermediates"""
@ -99,6 +124,17 @@ async def clear_intermediates() -> int:
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(
"/i/{image_name}",
operation_id="update_image",
@ -135,11 +171,11 @@ async def get_image_dto(
@images_router.get(
"/i/{image_name}/metadata",
operation_id="get_image_metadata",
response_model=ImageMetadata,
response_model=Optional[MetadataField],
)
async def get_image_metadata(
image_name: str = Path(description="The name of image to get"),
) -> ImageMetadata:
) -> Optional[MetadataField]:
"""Gets an image's metadata"""
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"])
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
update_models_response_adapter = TypeAdapter(UpdateModelResponse)
UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
import_models_response_adapter = TypeAdapter(ImportModelResponse)
ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
convert_models_response_adapter = TypeAdapter(ConvertModelResponse)
ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
MergeModelResponse = 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)
models_list_adapter = TypeAdapter(ModelsList)
ModelsListValidator = TypeAdapter(ModelsList)
@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))
else:
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
@ -131,7 +131,7 @@ async def update_model(
base_model=base_model,
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:
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
@ -186,7 +186,7 @@ async def import_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
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:
logger.error(str(e))
@ -231,7 +231,7 @@ async def add_model(
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:
logger.error(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_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:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
except ValueError as e:
@ -417,7 +417,7 @@ async def merge_models(
base_model=base_model,
model_type=ModelType.Main,
)
response = convert_models_response_adapter.validate_python(model_raw)
response = ConvertModelResponseValidator.validate_python(model_raw)
except ModelNotFoundException:
raise HTTPException(
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,
ClearResult,
EnqueueBatchResult,
EnqueueGraphResult,
PruneResult,
SessionQueueItem,
SessionQueueItemDTO,
SessionQueueStatus,
)
from invokeai.app.services.shared.graph import Graph
from invokeai.app.services.shared.pagination import CursorPaginatedResults
from ..dependencies import ApiDependencies
@ -33,23 +31,6 @@ class SessionQueueAndProcessorStatus(BaseModel):
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(
"/{queue_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
# 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 pathlib import Path
import torch
import uvicorn
from fastapi import FastAPI
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.utils import get_openapi
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.json_schema import models_json_schema
from torch.backends.mps import is_available as is_mps_available
# for PyCharm:
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
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 .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 .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
if torch.backends.mps.is_available():
# noinspection PyUnresolvedReferences
if is_mps_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
logger = InvokeAILogger.get_logger(config=app_config)
# fix for windows mimetypes registry entries being borked
# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
mimetypes.add_type("application/javascript", ".js")
@ -71,16 +86,18 @@ app.add_middleware(
allow_headers=app_config.allow_headers,
)
app.add_middleware(GZipMiddleware, minimum_size=1000)
# Add startup event to load dependencies
@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)
# Shut down threads
@app.on_event("shutdown")
async def shutdown_event():
async def shutdown_event() -> None:
ApiDependencies.shutdown()
@ -88,23 +105,18 @@ async def shutdown_event():
app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api")
app.include_router(models.models_router, prefix="/api")
app.include_router(images.images_router, prefix="/api")
app.include_router(boards.boards_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(session_queue.session_queue_router, prefix="/api")
app.include_router(workflows.workflows_router, prefix="/api")
# Build a custom OpenAPI to include all outputs
# TODO: can outputs be included on metadata of invocation schemas somehow?
def custom_openapi():
def custom_openapi() -> dict[str, Any]:
if app.openapi_schema:
return app.openapi_schema
openapi_schema = get_openapi(
@ -159,7 +171,6 @@ def custom_openapi():
# print(f"Config with name {name} already defined")
continue
# "BaseModelType":{"title":"BaseModelType","description":"An enumeration.","enum":["sd-1","sd-2"],"type":"string"}
openapi_schema["components"]["schemas"][name] = dict(
title=name,
description="An enumeration.",
@ -173,34 +184,43 @@ def custom_openapi():
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)
def overridden_swagger():
def overridden_swagger() -> HTMLResponse:
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,
swagger_favicon_url="/static/favicon.ico",
swagger_favicon_url="/static/docs/favicon.ico",
)
@app.get("/redoc", include_in_schema=False)
def overridden_redoc():
def overridden_redoc() -> HTMLResponse:
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,
redoc_favicon_url="/static/favicon.ico",
redoc_favicon_url="/static/docs/favicon.ico",
)
# Must mount *after* the other routes else it borks em
app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui")
web_root_path = Path(list(web_dir.__path__)[0])
def invoke_api():
def find_port(port: int):
# Cannot add headers to StaticFiles, so we must serve index.html with a custom route
# Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
@app.get("/", include_in_schema=False, name="ui_root")
def get_index() -> FileResponse:
return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
# # Must mount *after* the other routes else it borks em
app.mount("/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"""
# Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
# https://github.com/WaylonWalker
@ -235,7 +255,7 @@ def invoke_api():
app=app,
host=app_config.host,
port=port,
loop=loop,
loop="asyncio",
log_level=app_config.log_level,
)
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__))
for f in os.listdir(dirname):
if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
__all__.append(f[:-3])
custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
custom_nodes_path.mkdir(parents=True, exist_ok=True)
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
import json
import inspect
import re
from abc import ABC, abstractmethod
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
import semver
from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
from pydantic.fields import _Unset
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
from pydantic.fields import FieldInfo, _Unset
from pydantic_core import PydanticUndefined
from invokeai.app.services.config.config_default import InvokeAIAppConfig
@ -26,6 +26,10 @@ class InvalidVersionError(ValueError):
pass
class InvalidFieldError(TypeError):
pass
class FieldDescriptions:
denoising_start = "When to start denoising, expressed a percentage of total steps"
denoising_end = "When to stop denoising, expressed a percentage of total steps"
@ -60,7 +64,12 @@ class FieldDescriptions:
denoised_latents = "Denoised latents tensor"
latents = "Latents tensor"
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"
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
fp32 = "Whether or not to use full float32 precision"
@ -167,8 +176,12 @@ class UIType(str, Enum):
Scheduler = "Scheduler"
WorkflowField = "WorkflowField"
IsIntermediate = "IsIntermediate"
MetadataField = "MetadataField"
BoardField = "BoardField"
Any = "Any"
MetadataItem = "MetadataItem"
MetadataItemCollection = "MetadataItemCollection"
MetadataItemPolymorphic = "MetadataItemPolymorphic"
MetadataDict = "MetadataDict"
# endregion
@ -294,6 +307,7 @@ def InputField(
ui_order=ui_order,
item_default=item_default,
ui_choice_labels=ui_choice_labels,
_field_kind="input",
)
field_args = dict(
@ -436,6 +450,7 @@ def OutputField(
ui_type=ui_type,
ui_hidden=ui_hidden,
ui_order=ui_order,
_field_kind="output",
),
)
@ -519,6 +534,7 @@ class BaseInvocationOutput(BaseModel):
schema["required"].extend(["type"])
model_config = ConfigDict(
protected_namespaces=(),
validate_assignment=True,
json_schema_serialization_defaults_required=True,
json_schema_extra=json_schema_extra,
@ -541,9 +557,6 @@ class MissingInputException(Exception):
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.
"""
@ -659,46 +672,93 @@ class BaseInvocation(ABC, BaseModel):
id: str = Field(
default_factory=uuid_string,
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,
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(
default=None,
description="The workflow to save with the image",
json_schema_extra=dict(ui_type=UIType.WorkflowField),
use_cache: bool = Field(
default=True, description="Whether or not to use the cache", json_schema_extra=dict(_field_kind="internal")
)
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]]
model_config = ConfigDict(
protected_namespaces=(),
validate_assignment=True,
json_schema_extra=json_schema_extra,
json_schema_serialization_defaults_required=True,
coerce_numbers_to_str=True,
)
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(
invocation_type: str,
title: Optional[str] = None,
@ -708,7 +768,7 @@ def invocation(
use_cache: Optional[bool] = True,
) -> 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 Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
@ -727,6 +787,8 @@ def invocation(
if invocation_type in BaseInvocation.get_invocation_types():
raise ValueError(f'Invocation type "{invocation_type}" already exists')
validate_fields(cls.model_fields, invocation_type)
# Add OpenAPI schema extras
uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
@ -757,8 +819,7 @@ def invocation(
invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = Field(
title="type",
default=invocation_type,
title="type", default=invocation_type, json_schema_extra=dict(_field_kind="internal")
)
docstring = cls.__doc__
@ -799,13 +860,12 @@ def invocation_output(
if output_type in BaseInvocationOutput.get_output_types():
raise ValueError(f'Invocation type "{output_type}" already exists')
validate_fields(cls.model_fields, output_type)
# Add the output type to the model.
output_type_annotation = Literal[output_type] # type: ignore
output_type_field = Field(
title="type",
default=output_type,
)
output_type_field = Field(title="type", default=output_type, json_schema_extra=dict(_field_kind="internal"))
docstring = cls.__doc__
cls = create_model(
@ -823,4 +883,37 @@ def invocation_output(
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")
)

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

@ -38,6 +38,8 @@ from .baseinvocation import (
InputField,
InvocationContext,
OutputField,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -127,12 +129,12 @@ class ControlNetInvocation(BaseInvocation):
# 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"""
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
return image
@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation):
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
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.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")
class CvInpaintInvocation(BaseInvocation):
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Simple inpaint using opencv."""
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,
InvocationContext,
OutputField,
WithMetadata,
WithWorkflow,
invocation,
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")
class FaceOffInvocation(BaseInvocation):
class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Bound, extract, and mask a face from an image using MediaPipe 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")
class FaceMaskInvocation(BaseInvocation):
class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Face mask creation using mediapipe face detection"""
image: ImageField = InputField(description="Image to face detect")
@ -650,7 +652,7 @@ class FaceMaskInvocation(BaseInvocation):
@invocation(
"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."""
image: ImageField = InputField(description="Image to face detect")

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

@ -7,13 +7,21 @@ import cv2
import numpy
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.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
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")
@ -36,14 +44,8 @@ class ShowImageInvocation(BaseInvocation):
)
@invocation(
"blank_image",
title="Blank Image",
tags=["image"],
category="image",
version="1.0.0",
)
class BlankImageInvocation(BaseInvocation):
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Creates a blank image and forwards it to the pipeline"""
width: int = InputField(default=512, description="The width of the image")
@ -61,6 +63,7 @@ class BlankImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -71,14 +74,8 @@ class BlankImageInvocation(BaseInvocation):
)
@invocation(
"img_crop",
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.0.0",
)
class ImageCropInvocation(BaseInvocation):
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Crops an image to a specified box. The box can be outside of the image."""
image: ImageField = InputField(description="The image to crop")
@ -100,6 +97,7 @@ class ImageCropInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -110,14 +108,8 @@ class ImageCropInvocation(BaseInvocation):
)
@invocation(
"img_paste",
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.0.1",
)
class ImagePasteInvocation(BaseInvocation):
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Pastes an image into another image."""
base_image: ImageField = InputField(description="The base image")
@ -159,6 +151,7 @@ class ImagePasteInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -169,14 +162,8 @@ class ImagePasteInvocation(BaseInvocation):
)
@invocation(
"tomask",
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.0.0",
)
class MaskFromAlphaInvocation(BaseInvocation):
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Extracts the alpha channel of an image as a mask."""
image: ImageField = InputField(description="The image to create the mask from")
@ -196,6 +183,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -206,14 +194,8 @@ class MaskFromAlphaInvocation(BaseInvocation):
)
@invocation(
"img_mul",
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.0.0",
)
class ImageMultiplyInvocation(BaseInvocation):
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
image1: ImageField = InputField(description="The first image to multiply")
@ -232,6 +214,7 @@ class ImageMultiplyInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -245,14 +228,8 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation(
"img_chan",
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.0.0",
)
class ImageChannelInvocation(BaseInvocation):
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Gets a channel from an image."""
image: ImageField = InputField(description="The image to get the channel from")
@ -270,6 +247,7 @@ class ImageChannelInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -283,14 +261,8 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@invocation(
"img_conv",
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.0.0",
)
class ImageConvertInvocation(BaseInvocation):
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Converts an image to a different mode."""
image: ImageField = InputField(description="The image to convert")
@ -308,6 +280,7 @@ class ImageConvertInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -318,14 +291,8 @@ class ImageConvertInvocation(BaseInvocation):
)
@invocation(
"img_blur",
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.0.0",
)
class ImageBlurInvocation(BaseInvocation):
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Blurs an image"""
image: ImageField = InputField(description="The image to blur")
@ -348,6 +315,7 @@ class ImageBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -378,23 +346,14 @@ PIL_RESAMPLING_MAP = {
}
@invocation(
"img_resize",
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.0.0",
)
class ImageResizeInvocation(BaseInvocation):
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Resizes an image to specific dimensions"""
image: ImageField = InputField(description="The image to resize")
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)")
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:
image = context.services.images.get_pil_image(self.image.image_name)
@ -413,7 +372,7 @@ class ImageResizeInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -424,14 +383,8 @@ class ImageResizeInvocation(BaseInvocation):
)
@invocation(
"img_scale",
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.0.0",
)
class ImageScaleInvocation(BaseInvocation):
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Scales an image by a factor"""
image: ImageField = InputField(description="The image to scale")
@ -461,6 +414,7 @@ class ImageScaleInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -471,14 +425,8 @@ class ImageScaleInvocation(BaseInvocation):
)
@invocation(
"img_lerp",
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.0.0",
)
class ImageLerpInvocation(BaseInvocation):
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp")
@ -500,6 +448,7 @@ class ImageLerpInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -510,14 +459,8 @@ class ImageLerpInvocation(BaseInvocation):
)
@invocation(
"img_ilerp",
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.0.0",
)
class ImageInverseLerpInvocation(BaseInvocation):
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Inverse linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp")
@ -539,6 +482,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -549,20 +493,11 @@ class ImageInverseLerpInvocation(BaseInvocation):
)
@invocation(
"img_nsfw",
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.0.0",
)
class ImageNSFWBlurInvocation(BaseInvocation):
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add blur to NSFW-flagged images"""
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:
image = context.services.images.get_pil_image(self.image.image_name)
@ -583,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -607,14 +542,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ImageWatermarkInvocation(BaseInvocation):
class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add an invisible watermark to an image"""
image: ImageField = InputField(description="The image to check")
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:
image = context.services.images.get_pil_image(self.image.image_name)
@ -626,7 +558,7 @@ class ImageWatermarkInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -637,14 +569,8 @@ class ImageWatermarkInvocation(BaseInvocation):
)
@invocation(
"mask_edge",
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.0.0",
)
class MaskEdgeInvocation(BaseInvocation):
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Applies an edge mask to an image"""
image: ImageField = InputField(description="The image to apply the mask to")
@ -678,6 +604,7 @@ class MaskEdgeInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -695,7 +622,7 @@ class MaskEdgeInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class MaskCombineInvocation(BaseInvocation):
class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
mask1: ImageField = InputField(description="The first mask to combine")
@ -714,6 +641,7 @@ class MaskCombineInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -724,14 +652,8 @@ class MaskCombineInvocation(BaseInvocation):
)
@invocation(
"color_correct",
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.0.0",
)
class ColorCorrectInvocation(BaseInvocation):
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""
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.
@ -830,6 +752,7 @@ class ColorCorrectInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -840,14 +763,8 @@ class ColorCorrectInvocation(BaseInvocation):
)
@invocation(
"img_hue_adjust",
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.0.0",
)
class ImageHueAdjustmentInvocation(BaseInvocation):
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Adjusts the Hue of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -875,6 +792,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow,
)
@ -950,7 +868,7 @@ CHANNEL_FORMATS = {
category="image",
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."""
image: ImageField = InputField(description="The image to adjust")
@ -984,6 +902,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow,
)
@ -1020,7 +939,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ImageChannelMultiplyInvocation(BaseInvocation):
class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Scale a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -1060,6 +979,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
workflow=self.workflow,
metadata=self.metadata,
)
return ImageOutput(
@ -1079,16 +999,11 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
version="1.0.1",
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."""
image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -1101,7 +1016,7 @@ class SaveImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
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.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
@ -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")
class InfillColorInvocation(BaseInvocation):
class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with a solid color"""
image: ImageField = InputField(description="The image to infill")
@ -143,6 +143,7 @@ class InfillColorInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
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")
class InfillTileInvocation(BaseInvocation):
class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with tiles of the image"""
image: ImageField = InputField(description="The image to infill")
@ -179,6 +180,7 @@ class InfillTileInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -192,7 +194,7 @@ class InfillTileInvocation(BaseInvocation):
@invocation(
"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"""
image: ImageField = InputField(description="The image to infill")
@ -232,6 +234,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
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")
class LaMaInfillInvocation(BaseInvocation):
class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the LaMa model"""
image: ImageField = InputField(description="The image to infill")
@ -260,6 +263,8 @@ class LaMaInfillInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
return ImageOutput(
@ -269,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation):
)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class CV2InfillInvocation(BaseInvocation):
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using OpenCV Inpainting"""
image: ImageField = InputField(description="The image to infill")
@ -287,6 +292,8 @@ class CV2InfillInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
return ImageOutput(

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

@ -36,7 +36,7 @@ class CLIPVisionModelField(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.")
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")
@ -55,12 +55,12 @@ class IPAdapterOutput(BaseInvocationOutput):
ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes."""
# Inputs
image: 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(
description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
)

29
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 invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import (
DenoiseMaskField,
DenoiseMaskOutput,
@ -64,6 +63,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -214,7 +215,7 @@ def get_scheduler(
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
version="1.3.0",
version="1.4.0",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
@ -491,16 +492,21 @@ class DenoiseLatentsInvocation(BaseInvocation):
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
# 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:
# Get image embeddings from CLIP and ImageProjModel.
(
image_prompt_embeds,
uncond_image_prompt_embeds,
) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
single_ipa_images, image_encoder_model
)
conditioning_data.ip_adapter_conditioning.append(
IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
)
@ -787,7 +793,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
category="latents",
version="1.0.0",
)
class LatentsToImageInvocation(BaseInvocation):
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -800,11 +806,6 @@ class LatentsToImageInvocation(BaseInvocation):
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
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()
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -873,7 +874,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

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

@ -3,7 +3,7 @@
from typing import Literal
import numpy as np
from pydantic import field_validator
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
@ -186,12 +186,12 @@ class IntegerMathInvocation(BaseInvocation):
b: int = InputField(default=0, description=FieldDescriptions.num_2)
@field_validator("b")
def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0:
def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
if info.data["operation"] == "DIV" and v == 0:
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")
elif values["operation"] == "EXP" and v < 0:
elif info.data["operation"] == "EXP" and v < 0:
raise ValueError("Result of exponentiation is not an integer")
return v
@ -260,12 +260,12 @@ class FloatMathInvocation(BaseInvocation):
b: float = InputField(default=0, description=FieldDescriptions.num_2)
@field_validator("b")
def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0:
def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
if info.data["operation"] == "DIV" and v == 0:
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")
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")
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 (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
InputField,
InvocationContext,
MetadataField,
OutputField,
UIType,
invocation,
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.primitives import ImageField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
from ...version import __version__
class LoRAMetadataField(BaseModelExcludeNull):
"""LoRA metadata for an image generated in InvokeAI."""
lora: LoRAModelField = Field(description="The LoRA model")
weight: float = Field(description="The weight of the LoRA model")
class MetadataItemField(BaseModel):
label: str = Field(description=FieldDescriptions.metadata_item_label)
value: Any = Field(description=FieldDescriptions.metadata_item_value)
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.")
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
weight: float = Field(description="The weight of 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)"
ip_adapter_model: IPAdapterModelField = Field(
description="The IP-Adapter model.",
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
weight: Union[float, list[float]] = Field(
description="The weight given to the IP-Adapter",
)
begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
@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):
"""Core generation metadata for an image generated in InvokeAI."""
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")
# add app version
data.update({"app_version": __version__})
return MetadataOutput(metadata=MetadataField.model_validate(data))
class ImageMetadata(BaseModelExcludeNull):
"""An image's generation metadata"""
@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
class MergeMetadataInvocation(BaseInvocation):
"""Merged a collection of MetadataDict into a single MetadataDict."""
metadata: Optional[dict] = Field(
default=None,
description="The image's core metadata, if it was created in the Linear or Canvas UI",
)
graph: Optional[dict] = Field(default=None, description="The graph that created the image")
collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
def invoke(self, context: InvocationContext) -> MetadataOutput:
data = {}
for item in self.collection:
data.update(item.model_dump())
return MetadataOutput(metadata=MetadataField.model_validate(data))
@invocation_output("metadata_accumulator_output")
class MetadataAccumulatorOutput(BaseInvocationOutput):
"""The output of the MetadataAccumulator node"""
metadata: CoreMetadata = OutputField(description="The core metadata for the image")
GENERATION_MODES = Literal[
"txt2img", "img2img", "inpaint", "outpaint", "sdxl_txt2img", "sdxl_img2img", "sdxl_inpaint", "sdxl_outpaint"
]
@invocation(
"metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
)
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
class CoreMetadataInvocation(BaseInvocation):
"""Collects core generation metadata into a MetadataField"""
generation_mode: Optional[str] = InputField(
generation_mode: Optional[GENERATION_MODES] = InputField(
default=None,
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")
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")
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(
default=None,
description="The number of skipped CLIP layers",
@ -220,7 +213,13 @@ class MetadataAccumulatorInvocation(BaseInvocation):
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"""
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
# from contextlib import ExitStack
from typing import List, Literal, Optional, Union
from typing import List, Literal, Union
import numpy as np
import torch
@ -12,7 +12,6 @@ from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm
from invokeai.app.invocations.metadata import CoreMetadata
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.util.step_callback import stable_diffusion_step_callback
@ -31,6 +30,8 @@ from .baseinvocation import (
OutputField,
UIComponent,
UIType,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -327,7 +328,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ONNXLatentsToImageInvocation(BaseInvocation):
class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -338,11 +339,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
description=FieldDescriptions.vae,
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)")
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -381,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

4
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")
class ImageInvocation(BaseInvocation):
class ImageInvocation(
BaseInvocation,
):
"""An image primitive value"""
image: ImageField = InputField(description="The image to load")

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.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: 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")
class ESRGANInvocation(BaseInvocation):
class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Upscales an image using RealESRGAN."""
image: ImageField = InputField(description="The input image")
@ -123,6 +123,7 @@ class ESRGANInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)

8
invokeai/app/services/config/config_default.py
View File

@ -243,6 +243,7 @@ class InvokeAIAppConfig(InvokeAISettings):
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)
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)
# LOGGING
@ -410,6 +411,13 @@ class InvokeAIAppConfig(InvokeAISettings):
"""
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
@property
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 invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files."""
@ -30,8 +32,8 @@ class ImageFileStorageBase(ABC):
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256,
) -> None:
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""

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
import json
from pathlib import Path
from queue import Queue
from typing import Dict, Optional, Union
@ -8,6 +7,7 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType
from send2trash import send2trash
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.invoker import Invoker
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
@ -55,8 +55,8 @@ class DiskImageFileStorage(ImageFileStorageBase):
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256,
) -> None:
try:
@ -65,20 +65,10 @@ class DiskImageFileStorage(ImageFileStorageBase):
pnginfo = PngImagePlugin.PngInfo()
if metadata is not None or workflow is not None:
if metadata is not None:
pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
if workflow is not None:
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)
if metadata is not None:
pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
if workflow is not None:
pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
image.save(
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 typing import Optional
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
@ -18,7 +19,7 @@ class ImageRecordStorageBase(ABC):
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata'."""
pass
@ -61,6 +62,11 @@ class ImageRecordStorageBase(ABC):
"""Deletes all intermediate image records, returning a list of deleted image names."""
pass
@abstractmethod
def get_intermediates_count(self) -> int:
"""Gets a count of all intermediate images."""
pass
@abstractmethod
def save(
self,
@ -73,7 +79,7 @@ class ImageRecordStorageBase(ABC):
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
metadata: Optional[MetadataField] = None,
) -> datetime:
"""Saves an image record."""
pass

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

@ -1,9 +1,9 @@
import json
import sqlite3
import threading
from datetime import datetime
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.sqlite import SqliteDatabase
@ -141,22 +141,26 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
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:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT images.metadata FROM images
SELECT metadata FROM images
WHERE image_name = ?;
""",
(image_name,),
)
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
if not result or not result[0]:
return None
return json.loads(result[0])
if not result:
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:
self._conn.rollback()
raise ImageRecordNotFoundException from e
@ -297,11 +301,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
images_query += query_conditions + query_pagination + ";"
# Add all the parameters
images_params = query_params.copy()
if limit is not None:
images_params.append(limit)
if offset is not None:
images_params.append(offset)
# Add the pagination parameters
images_params.extend([limit, offset])
# Build the list of images, deserializing each row
self._cursor.execute(images_query, images_params)
@ -357,6 +358,24 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
finally:
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]:
try:
self._lock.acquire()
@ -393,10 +412,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
metadata: Optional[MetadataField] = None,
) -> datetime:
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._cursor.execute(
"""--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 invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
from invokeai.app.services.image_records.image_records_common import (
ImageCategory,
ImageRecord,
@ -50,8 +50,8 @@ class ImageServiceABC(ABC):
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@ -81,7 +81,7 @@ class ImageServiceABC(ABC):
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata."""
pass
@ -123,6 +123,11 @@ class ImageServiceABC(ABC):
"""Deletes all intermediate images."""
pass
@abstractmethod
def get_intermediates_count(self) -> int:
"""Gets the number of intermediate images."""
pass
@abstractmethod
def delete_images_on_board(self, board_id: str):
"""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."
)
"""The id of the board the image belongs to, if one exists."""
pass
workflow_id: Optional[str] = Field(
default=None,
description="The workflow that generated this image.",
)
"""The workflow that generated this image."""
def image_record_to_dto(
@ -33,6 +36,7 @@ def image_record_to_dto(
image_url: str,
thumbnail_url: str,
board_id: Optional[str],
workflow_id: Optional[str],
) -> ImageDTO:
"""Converts an image record to an image DTO."""
return ImageDTO(
@ -40,4 +44,5 @@ def image_record_to_dto(
image_url=image_url,
thumbnail_url=thumbnail_url,
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 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.shared.pagination import OffsetPaginatedResults
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
from ..image_files.image_files_common import (
ImageFileDeleteException,
@ -42,8 +41,8 @@ class ImageService(ImageServiceABC):
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
@ -56,6 +55,12 @@ class ImageService(ImageServiceABC):
(width, height) = image.size
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
self.__invoker.services.image_records.save(
# Non-nullable fields
@ -73,6 +78,8 @@ class ImageService(ImageServiceABC):
)
if board_id is not None:
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(
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_dto = image_record_to_dto(
image_record,
self.__invoker.services.urls.get_image_url(image_name),
self.__invoker.services.urls.get_image_url(image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(image_name),
image_record=image_record,
image_url=self.__invoker.services.urls.get_image_url(image_name),
thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
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
@ -146,25 +154,22 @@ class ImageService(ImageServiceABC):
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> ImageMetadata:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try:
image_record = self.__invoker.services.image_records.get(image_name)
metadata = self.__invoker.services.image_records.get_metadata(image_name)
return 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:
return ImageMetadata(metadata=metadata)
session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
def get_workflow(self, image_name: str) -> Optional[WorkflowField]:
try:
workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name)
if workflow_id is None:
return None
return self.__invoker.services.workflow_records.get(workflow_id)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
@ -215,10 +220,11 @@ class ImageService(ImageServiceABC):
image_dtos = list(
map(
lambda r: image_record_to_dto(
r,
self.__invoker.services.urls.get_image_url(r.image_name),
self.__invoker.services.urls.get_image_url(r.image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
image_record=r,
image_url=self.__invoker.services.urls.get_image_url(r.image_name),
thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
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,
)
@ -284,3 +290,10 @@ class ImageService(ImageServiceABC):
except Exception as e:
self.__invoker.services.logger.error("Problem deleting image records and files")
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 .shared.graph import GraphExecutionState, LibraryGraph
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:
@ -55,6 +57,8 @@ class InvocationServices:
invocation_cache: "InvocationCacheBase"
names: "NameServiceBase"
urls: "UrlServiceBase"
workflow_image_records: "WorkflowImageRecordsStorageBase"
workflow_records: "WorkflowRecordsStorageBase"
def __init__(
self,
@ -80,6 +84,8 @@ class InvocationServices:
invocation_cache: "InvocationCacheBase",
names: "NameServiceBase",
urls: "UrlServiceBase",
workflow_image_records: "WorkflowImageRecordsStorageBase",
workflow_records: "WorkflowRecordsStorageBase",
):
self.board_images = board_images
self.board_image_records = board_image_records
@ -103,3 +109,5 @@ class InvocationServices:
self.invocation_cache = invocation_cache
self.names = names
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
_id_field: str
_lock: threading.RLock
_adapter: Optional[TypeAdapter[T]]
_validator: Optional[TypeAdapter[T]]
def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
super().__init__()
@ -28,7 +28,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._table_name = table_name
self._id_field = id_field # TODO: validate that T has this field
self._cursor = self._conn.cursor()
self._adapter: Optional[TypeAdapter[T]] = None
self._validator: Optional[TypeAdapter[T]] = None
self._create_table()
@ -47,14 +47,14 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._lock.release()
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 can create it when it is first needed instead.
__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]
return self._adapter.validate_json(item)
self._validator = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
return self._validator.validate_json(item)
def set(self, item: T):
try:

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

@ -0,0 +1 @@
from .model_manager_default import ModelManagerService

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,
ClearResult,
EnqueueBatchResult,
EnqueueGraphResult,
IsEmptyResult,
IsFullResult,
PruneResult,
@ -17,7 +16,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
SessionQueueItemDTO,
SessionQueueStatus,
)
from invokeai.app.services.shared.graph import Graph
from invokeai.app.services.shared.pagination import CursorPaginatedResults
@ -29,11 +27,6 @@ class SessionQueueBase(ABC):
"""Dequeues the next session queue item."""
pass
@abstractmethod
def enqueue_graph(self, queue_id: str, graph: Graph, prepend: bool) -> EnqueueGraphResult:
"""Enqueues a single graph for execution."""
pass
@abstractmethod
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
"""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"]
adapter_NodeFieldValue = TypeAdapter(list[NodeFieldValue])
NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
def get_field_values(queue_item_dict: dict) -> Optional[list[NodeFieldValue]]:
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:
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
@ -276,14 +276,6 @@ class EnqueueBatchResult(BaseModel):
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):
"""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,
ClearResult,
EnqueueBatchResult,
EnqueueGraphResult,
IsEmptyResult,
IsFullResult,
PruneResult,
@ -28,7 +27,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
calc_session_count,
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.sqlite import SqliteDatabase
@ -255,32 +253,6 @@ class SqliteSessionQueue(SessionQueueBase):
)
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:
try:
self.__lock.acquire()

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

@ -193,7 +193,7 @@ class GraphInvocation(BaseInvocation):
"""Execute a graph"""
# 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:
"""Invoke with provided services and return outputs."""
@ -439,6 +439,14 @@ class Graph(BaseModel):
except Exception as 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):
"""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}"
)
# Validate if collector output type matches input type (if this edge results in both being set)
if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
# Validate that we are not connecting collector to iterator (currently unsupported)
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):
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}"
@ -725,16 +744,15 @@ class Graph(BaseModel):
# Get the input root type
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
if not all(is_list_or_contains_list(f) for f in output_fields):
return False
# 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 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.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
def invisible_watermark_available(self) -> bool:
def invisible_watermark_available(cls) -> bool:
return config.invisible_watermark
@classmethod
def add_watermark(self, image: Image, watermark_text: str) -> Image:
if not self.invisible_watermark_available():
def add_watermark(cls, image: Image.Image, watermark_text: str) -> Image.Image:
if not cls.invisible_watermark_available():
return image
logger.debug(f'Applying invisible watermark "{watermark_text}"')
bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)

26
invokeai/backend/image_util/safety_checker.py
View File

@ -26,8 +26,8 @@ class SafetyChecker:
tried_load: bool = False
@classmethod
def _load_safety_checker(self):
if self.tried_load:
def _load_safety_checker(cls):
if cls.tried_load:
return
if config.nsfw_checker:
@ -35,31 +35,31 @@ class SafetyChecker:
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
self.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
cls.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
logger.info("NSFW checker initialized")
except Exception as e:
logger.warning(f"Could not load NSFW checker: {str(e)}")
else:
logger.info("NSFW checker loading disabled")
self.tried_load = True
cls.tried_load = True
@classmethod
def safety_checker_available(self) -> bool:
self._load_safety_checker()
return self.safety_checker is not None
def safety_checker_available(cls) -> bool:
cls._load_safety_checker()
return cls.safety_checker is not None
@classmethod
def has_nsfw_concept(self, image: Image) -> bool:
if not self.safety_checker_available():
def has_nsfw_concept(cls, image: Image.Image) -> bool:
if not cls.safety_checker_available():
return False
device = choose_torch_device()
features = self.feature_extractor([image], return_tensors="pt")
features = cls.feature_extractor([image], return_tensors="pt")
features.to(device)
self.safety_checker.to(device)
cls.safety_checker.to(device)
x_image = np.array(image).astype(np.float32) / 255.0
x_image = x_image[None].transpose(0, 3, 1, 2)
with SilenceWarnings():
checked_image, has_nsfw_concept = self.safety_checker(images=x_image, clip_input=features.pixel_values)
checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values)
return has_nsfw_concept[0]

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

@ -460,6 +460,12 @@ class ModelInstall(object):
possible_conf = path.with_suffix(".yaml")
if possible_conf.exists():
legacy_conf = str(self.relative_to_root(possible_conf))
else:
legacy_conf = Path(
self.config.root_path,
"configs/controlnet",
("cldm_v15.yaml" if info.base_type == BaseModelType("sd-1") else "cldm_v21.yaml"),
)
if legacy_conf:
attributes.update(dict(config=str(legacy_conf)))

21
invokeai/backend/ip_adapter/attention_processor.py
View File

@ -67,6 +67,12 @@ class IPAttnProcessor2_0(torch.nn.Module):
temb=None,
ip_adapter_image_prompt_embeds=None,
):
"""Apply IP-Adapter attention.
Args:
ip_adapter_image_prompt_embeds (torch.Tensor): The image prompt embeddings.
Shape: (batch_size, num_ip_images, seq_len, ip_embedding_len).
"""
residual = hidden_states
if attn.spatial_norm is not None:
@ -127,26 +133,35 @@ class IPAttnProcessor2_0(torch.nn.Module):
for ipa_embed, ipa_weights, scale in zip(ip_adapter_image_prompt_embeds, self._weights, self._scales):
# The batch dimensions should match.
assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
# The channel dimensions should match.
assert ipa_embed.shape[2] == encoder_hidden_states.shape[2]
# The token_len dimensions should match.
assert ipa_embed.shape[-1] == encoder_hidden_states.shape[-1]
ip_hidden_states = ipa_embed
# Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
ip_key = ipa_weights.to_k_ip(ip_hidden_states)
ip_value = ipa_weights.to_v_ip(ip_hidden_states)
# Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# The output of sdpa has shape: (batch, num_heads, seq_len, head_dim)
# Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
ip_hidden_states = F.scaled_dot_product_attention(
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
# Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
ip_hidden_states = ip_hidden_states.to(query.dtype)
# Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
hidden_states = hidden_states + scale * ip_hidden_states
# linear proj

2
invokeai/backend/model_management/model_manager.py
View File

@ -1011,6 +1011,8 @@ class ModelManager(object):
self.logger.warning(f"Not a valid model: {model_path}. {e}")
except NotImplementedError as e:
self.logger.warning(e)
except Exception as e:
self.logger.warning(f"Error loading model {model_path}. {e}")
imported_models = self.scan_autoimport_directory()
if (new_models_found or imported_models) and self.config_path:

3
invokeai/backend/model_management/models/controlnet.py
View File

@ -132,13 +132,14 @@ def _convert_controlnet_ckpt_and_cache(
model_path: str,
output_path: str,
base_model: BaseModelType,
model_config: ControlNetModel.CheckpointConfig,
model_config: str,
) -> str:
"""
Convert the controlnet from checkpoint format to diffusers format,
cache it to disk, and return Path to converted
file. If already on disk then just returns Path.
"""
print(f"DEBUG: controlnet config = {model_config}")
app_config = InvokeAIAppConfig.get_config()
weights = app_config.root_path / model_path
output_path = Path(output_path)

4
invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
View File

@ -55,11 +55,11 @@ class PostprocessingSettings:
class IPAdapterConditioningInfo:
cond_image_prompt_embeds: torch.Tensor
"""IP-Adapter image encoder conditioning embeddings.
Shape: (batch_size, num_tokens, encoding_dim).
Shape: (num_images, num_tokens, encoding_dim).
"""
uncond_image_prompt_embeds: torch.Tensor
"""IP-Adapter image encoding embeddings to use for unconditional generation.
Shape: (batch_size, num_tokens, encoding_dim).
Shape: (num_images, num_tokens, encoding_dim).
"""

11
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -345,9 +345,12 @@ class InvokeAIDiffuserComponent:
cross_attention_kwargs = None
if conditioning_data.ip_adapter_conditioning is not None:
# Note that we 'stack' to produce tensors of shape (batch_size, num_ip_images, seq_len, token_len).
cross_attention_kwargs = {
"ip_adapter_image_prompt_embeds": [
torch.cat([ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds])
torch.stack(
[ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds]
)
for ipa_conditioning in conditioning_data.ip_adapter_conditioning
]
}
@ -415,9 +418,10 @@ class InvokeAIDiffuserComponent:
# Run unconditional UNet denoising.
cross_attention_kwargs = None
if conditioning_data.ip_adapter_conditioning is not None:
# Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len).
cross_attention_kwargs = {
"ip_adapter_image_prompt_embeds": [
ipa_conditioning.uncond_image_prompt_embeds
torch.unsqueeze(ipa_conditioning.uncond_image_prompt_embeds, dim=0)
for ipa_conditioning in conditioning_data.ip_adapter_conditioning
]
}
@ -444,9 +448,10 @@ class InvokeAIDiffuserComponent:
# Run conditional UNet denoising.
cross_attention_kwargs = None
if conditioning_data.ip_adapter_conditioning is not None:
# Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len).
cross_attention_kwargs = {
"ip_adapter_image_prompt_embeds": [
ipa_conditioning.cond_image_prompt_embeds
torch.unsqueeze(ipa_conditioning.cond_image_prompt_embeds, dim=0)
for ipa_conditioning in conditioning_data.ip_adapter_conditioning
]
}

2
invokeai/backend/training/textual_inversion_training.py
View File

@ -41,7 +41,7 @@ from transformers import CLIPTextModel, CLIPTokenizer
# invokeai stuff
from invokeai.app.services.config import InvokeAIAppConfig, PagingArgumentParser
from invokeai.app.services.model_manager_service import ModelManagerService
from invokeai.app.services.model_manager import ModelManagerService
from invokeai.backend.model_management.models import SubModelType
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):

79
invokeai/configs/controlnet/cldm_v15.yaml Normal file
View File

@ -0,0 +1,79 @@
model:
target: cldm.cldm.ControlLDM
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
control_key: "hint"
image_size: 64
channels: 4
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
only_mid_control: False
control_stage_config:
target: cldm.cldm.ControlNet
params:
image_size: 32 # unused
in_channels: 4
hint_channels: 3
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
unet_config:
target: cldm.cldm.ControlledUnetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

85
invokeai/configs/controlnet/cldm_v21.yaml Normal file
View File

@ -0,0 +1,85 @@
model:
target: cldm.cldm.ControlLDM
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
control_key: "hint"
image_size: 64
channels: 4
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
only_mid_control: False
control_stage_config:
target: cldm.cldm.ControlNet
params:
use_checkpoint: True
image_size: 32 # unused
in_channels: 4
hint_channels: 3
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
unet_config:
target: cldm.cldm.ControlledUnetModel
params:
use_checkpoint: True
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"

6
invokeai/frontend/merge/merge_diffusers.py
View File

@ -131,6 +131,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
values=[
"Models Built on SD-1.x",
"Models Built on SD-2.x",
"Models Built on SDXL",
],
value=[self.current_base],
columns=4,
@ -309,7 +310,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
else:
return True
def get_model_names(self, base_model: Optional[BaseModelType] = None) -> List[str]:
def get_model_names(self, base_model: BaseModelType = BaseModelType.StableDiffusion1) -> List[str]:
model_names = [
info["model_name"]
for info in self.model_manager.list_models(model_type=ModelType.Main, base_model=base_model)
@ -318,7 +319,8 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
return sorted(model_names)
def _populate_models(self, value=None):
base_model = tuple(BaseModelType)[value[0]]
bases = ["sd-1", "sd-2", "sdxl"]
base_model = BaseModelType(bases[value[0]])
self.model_names = self.get_model_names(base_model)
models_plus_none = self.model_names.copy()

114
invokeai/frontend/web/public/locales/nl.json
View File

@ -106,9 +106,9 @@
"allImagesLoaded": "Alle afbeeldingen geladen",
"loadMore": "Laad meer",
"noImagesInGallery": "Geen afbeeldingen om te tonen",
"deleteImage": "Wis afbeelding",
"deleteImageBin": "Gewiste afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
"deleteImagePermanent": "Gewiste afbeeldingen kunnen niet worden hersteld.",
"deleteImage": "Verwijder afbeelding",
"deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
"deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.",
"assets": "Eigen onderdelen",
"images": "Afbeeldingen",
"autoAssignBoardOnClick": "Ken automatisch bord toe bij klikken",
@ -386,11 +386,11 @@
"deleteModel": "Verwijder model",
"deleteConfig": "Verwijder configuratie",
"deleteMsg1": "Weet je zeker dat je dit model wilt verwijderen uit InvokeAI?",
"deleteMsg2": "Hiermee ZAL het model van schijf worden verwijderd als het zich bevindt in de InvokeAI-beginmap. Als je het model vanaf een eigen locatie gebruikt, dan ZAL het model NIET van schijf worden verwijderd.",
"deleteMsg2": "Hiermee ZAL het model van schijf worden verwijderd als het zich bevindt in de beginmap van InvokeAI. Als je het model vanaf een eigen locatie gebruikt, dan ZAL het model NIET van schijf worden verwijderd.",
"formMessageDiffusersVAELocationDesc": "Indien niet opgegeven, dan zal InvokeAI kijken naar het VAE-bestand in de hierboven gegeven modellocatie.",
"repoIDValidationMsg": "Online repository van je model",
"formMessageDiffusersModelLocation": "Locatie Diffusers-model",
"convertToDiffusersHelpText3": "Je checkpoint-bestand op schijf ZAL worden verwijderd als het zich in de InvokeAI root map bevindt. Het zal NIET worden verwijderd als het zich in een andere locatie bevindt.",
"convertToDiffusersHelpText3": "Je checkpoint-bestand op de schijf ZAL worden verwijderd als het zich in de beginmap van InvokeAI bevindt. Het ZAL NIET worden verwijderd als het zich in een andere locatie bevindt.",
"convertToDiffusersHelpText6": "Wil je dit model omzetten?",
"allModels": "Alle modellen",
"checkpointModels": "Checkpoints",
@ -458,11 +458,11 @@
"noCustomLocationProvided": "Geen Aangepaste Locatie Opgegeven",
"syncModels": "Synchroniseer Modellen",
"modelsSynced": "Modellen Gesynchroniseerd",
"modelSyncFailed": "Synchronisatie Modellen Gefaald",
"modelSyncFailed": "Synchronisatie modellen mislukt",
"modelDeleteFailed": "Model kon niet verwijderd worden",
"convertingModelBegin": "Model aan het converteren. Even geduld.",
"importModels": "Importeer Modellen",
"syncModelsDesc": "Als je modellen niet meer synchroon zijn met de backend, kan je ze met deze optie verversen. Dit wordt typisch gebruikt in het geval je het models.yaml bestand met de hand bewerkt of als je modellen aan de InvokeAI root map toevoegt nadat de applicatie gestart werd.",
"syncModelsDesc": "Als je modellen niet meer synchroon zijn met de backend, kan je ze met deze optie vernieuwen. Dit wordt meestal gebruikt in het geval je het bestand models.yaml met de hand bewerkt of als je modellen aan de beginmap van InvokeAI toevoegt nadat de applicatie gestart is.",
"loraModels": "LoRA's",
"onnxModels": "Onnx",
"oliveModels": "Olives",
@ -615,14 +615,14 @@
"resetWebUI": "Herstel web-UI",
"resetWebUIDesc1": "Herstel web-UI herstelt alleen de lokale afbeeldingscache en de onthouden instellingen van je browser. Het verwijdert geen afbeeldingen van schijf.",
"resetWebUIDesc2": "Als afbeeldingen niet getoond worden in de galerij of iets anders werkt niet, probeer dan eerst deze herstelfunctie voordat je een fout aanmeldt op GitHub.",
"resetComplete": "Webgebruikersinterface is hersteld.",
"resetComplete": "Webinterface is hersteld.",
"useSlidersForAll": "Gebruik schuifbalken voor alle opties",
"consoleLogLevel": "Logboekniveau",
"consoleLogLevel": "Niveau logboek",
"shouldLogToConsole": "Schrijf logboek naar console",
"developer": "Ontwikkelaar",
"general": "Algemeen",
"showProgressInViewer": "Toon voortgangsafbeeldingen in viewer",
"generation": "Generatie",
"generation": "Genereren",
"ui": "Gebruikersinterface",
"antialiasProgressImages": "Voer anti-aliasing uit op voortgangsafbeeldingen",
"showAdvancedOptions": "Toon uitgebreide opties",
@ -631,16 +631,16 @@
"beta": "Bèta",
"experimental": "Experimenteel",
"alternateCanvasLayout": "Omwisselen Canvas Layout",
"enableNodesEditor": "Knopen Editor Inschakelen",
"autoChangeDimensions": "Werk bij wijziging afmetingen bij naar modelstandaard",
"enableNodesEditor": "Schakel Knooppunteditor in",
"autoChangeDimensions": "Werk B/H bij naar modelstandaard bij wijziging",
"clearIntermediates": "Wis tussentijdse afbeeldingen",
"clearIntermediatesDesc3": "Je galerijafbeeldingen zullen niet worden verwijderd.",
"clearIntermediatesWithCount_one": "Wis {{count}} tussentijdse afbeelding",
"clearIntermediatesWithCount_other": "Wis {{count}} tussentijdse afbeeldingen",
"clearIntermediatesDesc2": "Tussentijdse afbeeldingen zijn nevenproducten bij een generatie, die afwijken van de uitvoerafbeeldingen in de galerij. Het wissen van tussentijdse afbeeldingen zal schijfruimte vrijmaken.",
"clearIntermediatesDesc2": "Tussentijdse afbeeldingen zijn nevenproducten bij het genereren. Deze wijken af van de uitvoerafbeeldingen in de galerij. Als je tussentijdse afbeeldingen wist, wordt schijfruimte vrijgemaakt.",
"intermediatesCleared_one": "{{count}} tussentijdse afbeelding gewist",
"intermediatesCleared_other": "{{count}} tussentijdse afbeeldingen gewist",
"clearIntermediatesDesc1": "Het wissen van tussentijdse onderdelen zet de staat van je canvas en ControlNet terug.",
"clearIntermediatesDesc1": "Als je tussentijdse afbeeldingen wist, dan wordt de staat hersteld van je canvas en van ControlNet.",
"intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen"
},
"toast": {
@ -881,7 +881,7 @@
"conditioningCollectionDescription": "Conditionering kan worden doorgegeven tussen knooppunten.",
"colorPolymorphic": "Polymorfisme kleur",
"colorCodeEdgesHelp": "Kleurgecodeerde randen op basis van hun verbonden velden",
"collectionDescription": "Beschrijving",
"collectionDescription": "TODO",
"float": "Zwevende-kommagetal",
"workflowContact": "Contactpersoon",
"skippingReservedFieldType": "Overslaan van gereserveerd veldsoort",
@ -898,7 +898,7 @@
"sourceNode": "Bronknooppunt",
"nodeOpacity": "Dekking knooppunt",
"pickOne": "Kies er een",
"collectionItemDescription": "Beschrijving",
"collectionItemDescription": "TODO",
"integerDescription": "Gehele getallen zijn getallen zonder een decimaalteken.",
"outputField": "Uitvoerveld",
"unableToLoadWorkflow": "Kan werkstroom niet valideren",
@ -944,7 +944,7 @@
"inputNode": "Invoerknooppunt",
"enumDescription": "Enumeraties zijn waarden die uit een aantal opties moeten worden gekozen.",
"unkownInvocation": "Onbekende aanroepsoort",
"loRAModelFieldDescription": "Beschrijving",
"loRAModelFieldDescription": "TODO",
"imageField": "Afbeelding",
"skippedReservedOutput": "Overgeslagen gereserveerd uitvoerveld",
"animatedEdgesHelp": "Animeer gekozen randen en randen verbonden met de gekozen knooppunten",
@ -953,7 +953,7 @@
"unknownTemplate": "Onbekend sjabloon",
"noWorkflow": "Geen werkstroom",
"removeLinearView": "Verwijder uit lineaire weergave",
"colorCollectionDescription": "Beschrijving",
"colorCollectionDescription": "TODO",
"integerCollectionDescription": "Een verzameling gehele getallen.",
"colorPolymorphicDescription": "Een verzameling kleuren.",
"sDXLMainModelField": "SDXL-model",
@ -1028,7 +1028,7 @@
"loadingNodes": "Bezig met laden van knooppunten...",
"snapToGridHelp": "Lijn knooppunten uit op raster bij verplaatsing",
"workflowSettings": "Instellingen werkstroomeditor",
"mainModelFieldDescription": "Beschrijving",
"mainModelFieldDescription": "TODO",
"sDXLRefinerModelField": "Verfijningsmodel",
"loRAModelField": "LoRA",
"unableToParseEdge": "Kan rand niet inlezen",
@ -1039,13 +1039,13 @@
"controlnet": {
"amult": "a_mult",
"resize": "Schaal",
"showAdvanced": "Toon uitgebreid",
"showAdvanced": "Toon uitgebreide opties",
"contentShuffleDescription": "Verschuift het materiaal in de afbeelding",
"bgth": "bg_th",
"addT2IAdapter": "Voeg $t(common.t2iAdapter) toe",
"pidi": "PIDI",
"importImageFromCanvas": "Importeer afbeelding uit canvas",
"lineartDescription": "Zet afbeelding om naar lineart",
"lineartDescription": "Zet afbeelding om naar line-art",
"normalBae": "Normale BAE",
"importMaskFromCanvas": "Importeer masker uit canvas",
"hed": "HED",
@ -1053,7 +1053,7 @@
"contentShuffle": "Verschuif materiaal",
"controlNetEnabledT2IDisabled": "$t(common.controlNet) ingeschakeld, $t(common.t2iAdapter)s uitgeschakeld",
"ipAdapterModel": "Adaptermodel",
"resetControlImage": "Zet controle-afbeelding terug",
"resetControlImage": "Herstel controle-afbeelding",
"beginEndStepPercent": "Percentage begin-/eindstap",
"mlsdDescription": "Minimalistische herkenning lijnsegmenten",
"duplicate": "Maak kopie",
@ -1061,8 +1061,8 @@
"f": "F",
"h": "H",
"prompt": "Prompt",
"depthMidasDescription": "Generatie van diepteblad via Midas",
"controlnet": "$t(controlnet.controlAdapter) #{{number}} ($t(common.controlNet))",
"depthMidasDescription": "Genereer diepteblad via Midas",
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"openPoseDescription": "Menselijke pose-benadering via Openpose",
"control": "Controle",
"resizeMode": "Modus schaling",
@ -1080,7 +1080,7 @@
"enableControlnet": "Schakel ControlNet in",
"detectResolution": "Herken resolutie",
"controlNetT2IMutexDesc": "Gelijktijdig gebruik van $t(common.controlNet) en $t(common.t2iAdapter) wordt op dit moment niet ondersteund.",
"ip_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.ipAdapter))",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"pidiDescription": "PIDI-afbeeldingsverwerking",
"mediapipeFace": "Mediapipe - Gezicht",
"mlsd": "M-LSD",
@ -1088,10 +1088,10 @@
"fill": "Vul",
"cannyDescription": "Herkenning Canny-rand",
"addIPAdapter": "Voeg $t(common.ipAdapter) toe",
"lineart": "Lineart",
"lineart": "Line-art",
"colorMapDescription": "Genereert een kleurenblad van de afbeelding",
"lineartAnimeDescription": "Lineartverwerking in anime-stijl",
"t2i_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.t2iAdapter))",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"minConfidence": "Min. vertrouwensniveau",
"imageResolution": "Resolutie afbeelding",
"megaControl": "Zeer veel controle",
@ -1110,15 +1110,15 @@
"controlAdapter_other": "Control-adapters",
"safe": "Veilig",
"colorMapTileSize": "Grootte tegel",
"lineartAnime": "Lineart-anime",
"lineartAnime": "Line-art voor anime",
"ipAdapterImageFallback": "Geen IP-adapterafbeelding gekozen",
"mediapipeFaceDescription": "Gezichtsherkenning met Mediapipe",
"canny": "Canny",
"depthZoeDescription": "Generatie van diepteblad via Zoe",
"depthZoeDescription": "Genereer diepteblad via Zoe",
"hedDescription": "Herkenning van holistisch-geneste randen",
"setControlImageDimensions": "Stel afmetingen controle-afbeelding in op B/H",
"scribble": "Krabbel",
"resetIPAdapterImage": "Zet IP-adapterafbeelding terug",
"resetIPAdapterImage": "Herstel IP-adapterafbeelding",
"handAndFace": "Hand en gezicht",
"enableIPAdapter": "Schakel IP-adapter in",
"maxFaces": "Max. gezichten"
@ -1132,7 +1132,7 @@
"label": "Gedrag seedwaarde"
},
"enableDynamicPrompts": "Schakel dynamische prompts in",
"combinatorial": "Combinatorische generatie",
"combinatorial": "Combinatorisch genereren",
"maxPrompts": "Max. prompts",
"promptsWithCount_one": "{{count}} prompt",
"promptsWithCount_other": "{{count}} prompts",
@ -1141,7 +1141,7 @@
"popovers": {
"noiseUseCPU": {
"paragraphs": [
"Bestuurt of ruis wordt gegenereerd op de CPU of de GPU.",
"Bepaalt of ruis wordt gegenereerd op de CPU of de GPU.",
"Met CPU-ruis ingeschakeld zal een bepaalde seedwaarde dezelfde afbeelding opleveren op welke machine dan ook.",
"Er is geen prestatieverschil bij het inschakelen van CPU-ruis."
],
@ -1149,7 +1149,7 @@
},
"paramScheduler": {
"paragraphs": [
"De planner bepaalt hoe per keer ruis wordt toegevoegd aan een afbeelding of hoe een monster wordt bijgewerkt op basis van de uitvoer van een model."
"De planner bepaalt hoe ruis per iteratie wordt toegevoegd aan een afbeelding of hoe een monster wordt bijgewerkt op basis van de uitvoer van een model."
],
"heading": "Planner"
},
@ -1220,8 +1220,8 @@
},
"paramSeed": {
"paragraphs": [
"Bestuurt de startruis die gebruikt wordt bij het genereren.",
"Schakel \"Willekeurige seedwaarde\" uit om identieke resultaten te krijgen met dezelfde generatie-instellingen."
"Bepaalt de startruis die gebruikt wordt bij het genereren.",
"Schakel \"Willekeurige seedwaarde\" uit om identieke resultaten te krijgen met dezelfde genereer-instellingen."
],
"heading": "Seedwaarde"
},
@ -1240,7 +1240,7 @@
},
"dynamicPromptsSeedBehaviour": {
"paragraphs": [
"Bestuurt hoe de seedwaarde wordt gebruikt bij het genereren van prompts.",
"Bepaalt hoe de seedwaarde wordt gebruikt bij het genereren van prompts.",
"Per iteratie zal een unieke seedwaarde worden gebruikt voor elke iteratie. Gebruik dit om de promptvariaties binnen een enkele seedwaarde te verkennen.",
"Bijvoorbeeld: als je vijf prompts heb, dan zal voor elke afbeelding dezelfde seedwaarde gebruikt worden.",
"De optie Per afbeelding zal een unieke seedwaarde voor elke afbeelding gebruiken. Dit biedt meer variatie."
@ -1259,7 +1259,7 @@
"heading": "Model",
"paragraphs": [
"Model gebruikt voor de ontruisingsstappen.",
"Verschillende modellen zijn meestal getraind zich te specialiseren in het maken van bepaalde esthetische resultaten en materiaal."
"Verschillende modellen zijn meestal getraind om zich te specialiseren in het maken van bepaalde esthetische resultaten en materiaal."
]
},
"compositingCoherencePass": {
@ -1271,7 +1271,7 @@
"paramDenoisingStrength": {
"paragraphs": [
"Hoeveel ruis wordt toegevoegd aan de invoerafbeelding.",
"0 geeft een identieke afbeelding, waarbij 1 een volledig nieuwe afbeelding geeft."
"0 levert een identieke afbeelding op, waarbij 1 een volledig nieuwe afbeelding oplevert."
],
"heading": "Ontruisingssterkte"
},
@ -1284,7 +1284,7 @@
},
"paramNegativeConditioning": {
"paragraphs": [
"Het generatieproces voorkomt de gegeven begrippen in de negatieve prompt. Gebruik dit om bepaalde zaken of voorwerpen uit te sluiten van de uitvoerafbeelding.",
"Het genereerproces voorkomt de gegeven begrippen in de negatieve prompt. Gebruik dit om bepaalde zaken of voorwerpen uit te sluiten van de uitvoerafbeelding.",
"Ondersteunt Compel-syntax en -embeddingen."
],
"heading": "Negatieve prompt"
@ -1316,13 +1316,13 @@
"controlNet": {
"heading": "ControlNet",
"paragraphs": [
"ControlNets biedt begeleiding aan het generatieproces, waarbij hulp wordt geboden bij het maken van afbeelding met aangestuurde compositie, structuur of stijl, afhankelijk van het gekozen model."
"ControlNets begeleidt het genereerproces, waarbij geholpen wordt bij het maken van afbeeldingen met aangestuurde compositie, structuur of stijl, afhankelijk van het gekozen model."
]
},
"paramCFGScale": {
"heading": "CFG-schaal",
"paragraphs": [
"Bestuurt hoeveel je prompt invloed heeft op het generatieproces."
"Bepaalt hoeveel je prompt invloed heeft op het genereerproces."
]
},
"controlNetControlMode": {
@ -1335,7 +1335,7 @@
"heading": "Stappen",
"paragraphs": [
"Het aantal uit te voeren stappen tijdens elke generatie.",
"Hogere stappenaantallen geven meestal betere afbeeldingen ten koste van een grotere benodigde generatietijd."
"Een hoger aantal stappen geven meestal betere afbeeldingen, ten koste van een hogere benodigde tijd om te genereren."
]
},
"paramPositiveConditioning": {
@ -1356,7 +1356,7 @@
"seamless": "Naadloos",
"positivePrompt": "Positieve prompt",
"negativePrompt": "Negatieve prompt",
"generationMode": "Generatiemodus",
"generationMode": "Genereermodus",
"Threshold": "Drempelwaarde ruis",
"metadata": "Metagegevens",
"strength": "Sterkte Afbeelding naar afbeelding",
@ -1382,13 +1382,13 @@
},
"queue": {
"status": "Status",
"pruneSucceeded": "{{item_count}} voltooide onderdelen uit wachtrij gesnoeid",
"pruneSucceeded": "{{item_count}} voltooide onderdelen uit wachtrij opgeruimd",
"cancelTooltip": "Annuleer huidig onderdeel",
"queueEmpty": "Wachtrij leeg",
"pauseSucceeded": "Verwerker onderbroken",
"in_progress": "Bezig",
"queueFront": "Voeg toe aan voorkant van wachtrij",
"notReady": "Kan niet in wachtrij plaatsen",
"queueFront": "Voeg vooraan toe in wachtrij",
"notReady": "Fout bij plaatsen in wachtrij",
"batchFailedToQueue": "Fout bij reeks in wachtrij plaatsen",
"completed": "Voltooid",
"queueBack": "Voeg toe aan wachtrij",
@ -1402,22 +1402,22 @@
"front": "begin",
"clearSucceeded": "Wachtrij gewist",
"pause": "Onderbreek",
"pruneTooltip": "Snoei {{item_count}} voltooide onderdelen",
"pruneTooltip": "Ruim {{item_count}} voltooide onderdelen op",
"cancelSucceeded": "Onderdeel geannuleerd",
"batchQueuedDesc_one": "Voeg {{count}} sessie toe aan het {{direction}} van de wachtrij",
"batchQueuedDesc_other": "Voeg {{count}} sessies toe aan het {{direction}} van de wachtrij",
"graphQueued": "Graaf in wachtrij geplaatst",
"queue": "Wachtrij",
"batch": "Reeks",
"clearQueueAlertDialog": "Als je de wachtrij onmiddellijk wist, dan worden alle onderdelen die bezig zijn geannuleerd en wordt de gehele wachtrij gewist.",
"clearQueueAlertDialog": "Als je de wachtrij onmiddellijk wist, dan worden alle onderdelen die bezig zijn geannuleerd en wordt de wachtrij volledig gewist.",
"pending": "Wachtend",
"completedIn": "Voltooid na",
"resumeFailed": "Fout bij hervatten verwerker",
"clear": "Wis",
"prune": "Snoei",
"prune": "Ruim op",
"total": "Totaal",
"canceled": "Geannuleerd",
"pruneFailed": "Fout bij snoeien van wachtrij",
"pruneFailed": "Fout bij opruimen van wachtrij",
"cancelBatchSucceeded": "Reeks geannuleerd",
"clearTooltip": "Annuleer en wis alle onderdelen",
"current": "Huidig",
@ -1431,7 +1431,7 @@
"session": "Sessie",
"queueTotal": "Totaal {{total}}",
"resumeSucceeded": "Verwerker hervat",
"enqueueing": "Toevoegen van reeks aan wachtrij",
"enqueueing": "Bezig met toevoegen van reeks aan wachtrij",
"resumeTooltip": "Hervat verwerker",
"queueMaxExceeded": "Max. aantal van {{max_queue_size}} overschreden, {{skip}} worden overgeslagen",
"resume": "Hervat",
@ -1441,18 +1441,18 @@
"graphFailedToQueue": "Fout bij toevoegen graaf aan wachtrij"
},
"sdxl": {
"refinerStart": "Startwaarde verfijner",
"refinerStart": "Startwaarde verfijning",
"selectAModel": "Kies een model",
"scheduler": "Planner",
"cfgScale": "CFG-schaal",
"negStylePrompt": "Negatieve-stijlprompt",
"noModelsAvailable": "Geen modellen beschikbaar",
"refiner": "Verfijner",
"negAestheticScore": "Negatieve aantrekkelijkheidsscore",
"useRefiner": "Gebruik verfijner",
"refiner": "Verfijning",
"negAestheticScore": "Negatieve esthetische score",
"useRefiner": "Gebruik verfijning",
"denoisingStrength": "Sterkte ontruising",
"refinermodel": "Verfijnermodel",
"posAestheticScore": "Positieve aantrekkelijkheidsscore",
"refinermodel": "Verfijningsmodel",
"posAestheticScore": "Positieve esthetische score",
"concatPromptStyle": "Plak prompt- en stijltekst aan elkaar",
"loading": "Bezig met laden...",
"steps": "Stappen",

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

@ -1,15 +1,9 @@
import { isAnyOf } from '@reduxjs/toolkit';
import { queueApi } from 'services/api/endpoints/queue';
import { startAppListening } from '..';
const matcher = isAnyOf(
queueApi.endpoints.enqueueBatch.matchFulfilled,
queueApi.endpoints.enqueueGraph.matchFulfilled
);
export const addAnyEnqueuedListener = () => {
startAppListening({
matcher,
matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
effect: async (_, { dispatch, getState }) => {
const { data } = queueApi.endpoints.getQueueStatus.select()(getState());

84
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts
View File

@ -1,22 +1,22 @@
import { logger } from 'app/logging/logger';
import { parseify } from 'common/util/serialize';
import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
import {
pendingControlImagesCleared,
controlAdapterImageChanged,
selectControlAdapterById,
controlAdapterProcessedImageChanged,
pendingControlImagesCleared,
selectControlAdapterById,
} from 'features/controlAdapters/store/controlAdaptersSlice';
import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
import { SAVE_IMAGE } from 'features/nodes/util/graphBuilders/constants';
import { addToast } from 'features/system/store/systemSlice';
import { t } from 'i18next';
import { imagesApi } from 'services/api/endpoints/images';
import { queueApi } from 'services/api/endpoints/queue';
import { isImageOutput } from 'services/api/guards';
import { Graph, ImageDTO } from 'services/api/types';
import { BatchConfig, ImageDTO } from 'services/api/types';
import { socketInvocationComplete } from 'services/events/actions';
import { startAppListening } from '..';
import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
export const addControlNetImageProcessedListener = () => {
startAppListening({
@ -37,41 +37,46 @@ export const addControlNetImageProcessedListener = () => {
// ControlNet one-off procressing graph is just the processor node, no edges.
// Also we need to grab the image.
const graph: Graph = {
nodes: {
[ca.processorNode.id]: {
...ca.processorNode,
is_intermediate: true,
image: { image_name: ca.controlImage },
},
[SAVE_IMAGE]: {
id: SAVE_IMAGE,
type: 'save_image',
is_intermediate: true,
use_cache: false,
const enqueueBatchArg: BatchConfig = {
prepend: true,
batch: {
graph: {
nodes: {
[ca.processorNode.id]: {
...ca.processorNode,
is_intermediate: true,
image: { image_name: ca.controlImage },
},
[SAVE_IMAGE]: {
id: SAVE_IMAGE,
type: 'save_image',
is_intermediate: true,
use_cache: false,
},
},
edges: [
{
source: {
node_id: ca.processorNode.id,
field: 'image',
},
destination: {
node_id: SAVE_IMAGE,
field: 'image',
},
},
],
},
runs: 1,
},
edges: [
{
source: {
node_id: ca.processorNode.id,
field: 'image',
},
destination: {
node_id: SAVE_IMAGE,
field: 'image',
},
},
],
};
try {
const req = dispatch(
queueApi.endpoints.enqueueGraph.initiate(
{ graph, prepend: true },
{
fixedCacheKey: 'enqueueGraph',
}
)
queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
fixedCacheKey: 'enqueueBatch',
})
);
const enqueueResult = await req.unwrap();
req.reset();
@ -83,8 +88,8 @@ export const addControlNetImageProcessedListener = () => {
const [invocationCompleteAction] = await take(
(action): action is ReturnType<typeof socketInvocationComplete> =>
socketInvocationComplete.match(action) &&
action.payload.data.graph_execution_state_id ===
enqueueResult.queue_item.session_id &&
action.payload.data.queue_batch_id ===
enqueueResult.batch.batch_id &&
action.payload.data.source_node_id === SAVE_IMAGE
);
@ -116,7 +121,10 @@ export const addControlNetImageProcessedListener = () => {
);
}
} catch (error) {
log.error({ graph: parseify(graph) }, t('queue.graphFailedToQueue'));
log.error(
{ enqueueBatchArg: parseify(enqueueBatchArg) },
t('queue.graphFailedToQueue')
);
// handle usage-related errors
if (error instanceof Object) {

32
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/upscaleRequested.ts
View File

@ -6,7 +6,7 @@ import { addToast } from 'features/system/store/systemSlice';
import { t } from 'i18next';
import { queueApi } from 'services/api/endpoints/queue';
import { startAppListening } from '..';
import { ImageDTO } from 'services/api/types';
import { BatchConfig, ImageDTO } from 'services/api/types';
import { createIsAllowedToUpscaleSelector } from 'features/parameters/hooks/useIsAllowedToUpscale';
export const upscaleRequested = createAction<{ imageDTO: ImageDTO }>(
@ -44,20 +44,23 @@ export const addUpscaleRequestedListener = () => {
const { esrganModelName } = state.postprocessing;
const { autoAddBoardId } = state.gallery;
const graph = buildAdHocUpscaleGraph({
image_name,
esrganModelName,
autoAddBoardId,
});
const enqueueBatchArg: BatchConfig = {
prepend: true,
batch: {
graph: buildAdHocUpscaleGraph({
image_name,
esrganModelName,
autoAddBoardId,
}),
runs: 1,
},
};
try {
const req = dispatch(
queueApi.endpoints.enqueueGraph.initiate(
{ graph, prepend: true },
{
fixedCacheKey: 'enqueueGraph',
}
)
queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
fixedCacheKey: 'enqueueBatch',
})
);
const enqueueResult = await req.unwrap();
@ -67,7 +70,10 @@ export const addUpscaleRequestedListener = () => {
t('queue.graphQueued')
);
} catch (error) {
log.error({ graph: parseify(graph) }, t('queue.graphFailedToQueue'));
log.error(
{ enqueueBatchArg: parseify(enqueueBatchArg) },
t('queue.graphFailedToQueue')
);
// handle usage-related errors
if (error instanceof Object) {

2
invokeai/frontend/web/src/app/types/invokeai.ts
View File

@ -59,6 +59,8 @@ export type AppConfig = {
nodesAllowlist: string[] | undefined;
nodesDenylist: string[] | undefined;
maxUpscalePixels?: number;
metadataFetchDebounce?: number;
workflowFetchDebounce?: number;
sd: {
defaultModel?: string;
disabledControlNetModels: string[];

7
invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts
View File

@ -37,7 +37,12 @@ const useColorPicker = () => {
1
).data;
if (!(a && r && g && b)) {
if (
r === undefined ||
g === undefined ||
b === undefined ||
a === undefined
) {
return;
}

2
invokeai/frontend/web/src/features/controlAdapters/store/constants.ts
View File

@ -246,7 +246,7 @@ export const CONTROLNET_MODEL_DEFAULT_PROCESSORS: {
mlsd: 'mlsd_image_processor',
depth: 'midas_depth_image_processor',
bae: 'normalbae_image_processor',
sketch: 'lineart_image_processor',
sketch: 'pidi_image_processor',
scribble: 'lineart_image_processor',
lineart: 'lineart_image_processor',
lineart_anime: 'lineart_anime_image_processor',

39
invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx
View File

@ -27,7 +27,7 @@ import {
setShouldShowImageDetails,
setShouldShowProgressInViewer,
} from 'features/ui/store/uiSlice';
import { memo, useCallback, useMemo } from 'react';
import { memo, useCallback } from 'react';
import { useHotkeys } from 'react-hotkeys-hook';
import { useTranslation } from 'react-i18next';
import {
@ -38,10 +38,9 @@ import {
FaSeedling,
} from 'react-icons/fa';
import { FaCircleNodes, FaEllipsis } from 'react-icons/fa6';
import {
useGetImageDTOQuery,
useGetImageMetadataFromFileQuery,
} from 'services/api/endpoints/images';
import { useGetImageDTOQuery } from 'services/api/endpoints/images';
import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow';
import { menuListMotionProps } from 'theme/components/menu';
import { sentImageToImg2Img } from '../../store/actions';
import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems';
@ -89,7 +88,6 @@ const CurrentImageButtons = () => {
shouldShowImageDetails,
lastSelectedImage,
shouldShowProgressInViewer,
shouldFetchMetadataFromApi,
} = useAppSelector(currentImageButtonsSelector);
const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
@ -104,23 +102,12 @@ const CurrentImageButtons = () => {
lastSelectedImage?.image_name ?? skipToken
);
const getMetadataArg = useMemo(() => {
if (lastSelectedImage) {
return { image: lastSelectedImage, shouldFetchMetadataFromApi };
} else {
return skipToken;
}
}, [lastSelectedImage, shouldFetchMetadataFromApi]);
const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata(
lastSelectedImage?.image_name
);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
getMetadataArg,
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
const { workflow, isLoading: isLoadingWorkflow } = useDebouncedWorkflow(
lastSelectedImage?.workflow_id
);
const handleLoadWorkflow = useCallback(() => {
@ -257,7 +244,7 @@ const CurrentImageButtons = () => {
<ButtonGroup isAttached={true} isDisabled={shouldDisableToolbarButtons}>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingWorkflow}
icon={<FaCircleNodes />}
tooltip={`${t('nodes.loadWorkflow')} (W)`}
aria-label={`${t('nodes.loadWorkflow')} (W)`}
@ -265,7 +252,7 @@ const CurrentImageButtons = () => {
onClick={handleLoadWorkflow}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaQuoteRight />}
tooltip={`${t('parameters.usePrompt')} (P)`}
aria-label={`${t('parameters.usePrompt')} (P)`}
@ -273,7 +260,7 @@ const CurrentImageButtons = () => {
onClick={handleUsePrompt}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaSeedling />}
tooltip={`${t('parameters.useSeed')} (S)`}
aria-label={`${t('parameters.useSeed')} (S)`}
@ -281,7 +268,7 @@ const CurrentImageButtons = () => {
onClick={handleUseSeed}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaAsterisk />}
tooltip={`${t('parameters.useAll')} (A)`}
aria-label={`${t('parameters.useAll')} (A)`}

37
invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
View File

@ -2,7 +2,7 @@ import { Flex, MenuItem, Spinner } from '@chakra-ui/react';
import { useStore } from '@nanostores/react';
import { useAppToaster } from 'app/components/Toaster';
import { $customStarUI } from 'app/store/nanostores/customStarUI';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { useAppDispatch } from 'app/store/storeHooks';
import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
import {
imagesToChangeSelected,
@ -32,12 +32,12 @@ import {
import { FaCircleNodes } from 'react-icons/fa6';
import { MdStar, MdStarBorder } from 'react-icons/md';
import {
useGetImageMetadataFromFileQuery,
useStarImagesMutation,
useUnstarImagesMutation,
} from 'services/api/endpoints/images';
import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow';
import { ImageDTO } from 'services/api/types';
import { configSelector } from '../../../system/store/configSelectors';
import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
type SingleSelectionMenuItemsProps = {
@ -53,18 +53,13 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
const toaster = useAppToaster();
const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const customStarUi = useStore($customStarUI);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
{ image: imageDTO, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata(
imageDTO?.image_name
);
const { workflow, isLoading: isLoadingWorkflow } = useDebouncedWorkflow(
imageDTO?.workflow_id
);
const [starImages] = useStarImagesMutation();
@ -181,17 +176,17 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
{t('parameters.downloadImage')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaCircleNodes />}
icon={isLoadingWorkflow ? <SpinnerIcon /> : <FaCircleNodes />}
onClickCapture={handleLoadWorkflow}
isDisabled={isLoading || !workflow}
isDisabled={isLoadingWorkflow || !workflow}
>
{t('nodes.loadWorkflow')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaQuoteRight />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaQuoteRight />}
onClickCapture={handleRecallPrompt}
isDisabled={
isLoading ||
isLoadingMetadata ||
(metadata?.positive_prompt === undefined &&
metadata?.negative_prompt === undefined)
}
@ -199,16 +194,16 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
{t('parameters.usePrompt')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaSeedling />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaSeedling />}
onClickCapture={handleRecallSeed}
isDisabled={isLoading || metadata?.seed === undefined}
isDisabled={isLoadingMetadata || metadata?.seed === undefined}
>
{t('parameters.useSeed')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaAsterisk />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaAsterisk />}
onClickCapture={handleUseAllParameters}
isDisabled={isLoading || !metadata}
isDisabled={isLoadingMetadata || !metadata}
>
{t('parameters.useAll')}
</MenuItem>

22
invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx
View File

@ -10,15 +10,14 @@ import {
Text,
} from '@chakra-ui/react';
import { IAINoContentFallback } from 'common/components/IAIImageFallback';
import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent';
import { memo } from 'react';
import { useGetImageMetadataFromFileQuery } from 'services/api/endpoints/images';
import { useTranslation } from 'react-i18next';
import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow';
import { ImageDTO } from 'services/api/types';
import DataViewer from './DataViewer';
import ImageMetadataActions from './ImageMetadataActions';
import { useAppSelector } from '../../../../app/store/storeHooks';
import { configSelector } from '../../../system/store/configSelectors';
import { useTranslation } from 'react-i18next';
import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent';
type ImageMetadataViewerProps = {
image: ImageDTO;
@ -32,17 +31,8 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
// });
const { t } = useTranslation();
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const { metadata, workflow } = useGetImageMetadataFromFileQuery(
{ image, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
);
const { metadata } = useDebouncedMetadata(image.image_name);
const { workflow } = useDebouncedWorkflow(image.workflow_id);
return (
<Flex

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