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4
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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CODE_OF_CONDUCT.md → docs/CODE_OF_CONDUCT.md
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docs/contributing/contribution_guides/development.md
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@ -45,5 +45,5 @@ For backend related work, please reach out to **@blessedcoolant**, **@lstein**,
## **What does the Code of Conduct mean for me?**
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.

4
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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2
docs/features/VARIATIONS.md → docs/deprecated/VARIATIONS.md
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@ -126,6 +126,6 @@ amounts of image-to-image variation even when the seed is fixed and the
`-v` argument is very low. Others are more deterministic. Feel free to
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.

5
docs/features/CONCEPTS.md
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@ -28,8 +28,9 @@ by placing them in the designated directory for the compatible model type
### An Example
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; |
| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |

2
docs/features/CONFIGURATION.md
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@ -82,7 +82,7 @@ format of YAML files can be found
[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
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

6
docs/features/CONTROLNET.md
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@ -46,7 +46,7 @@ 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
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:
@ -145,8 +145,8 @@ Additionally, each ControlNet section can be expanded in order to manipulate set
#### 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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docs/features/MODEL_MERGING.md
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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

10
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

7
docs/index.md
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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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docs/installation/010_INSTALL_AUTOMATED.md
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@ -40,7 +40,7 @@ experimental versions later.
this, open up a command-line window ("Terminal" on Linux and
Macintosh, "Command" or "Powershell" on Windows) and type `python
--version`. If Python is installed, it will print out the version
number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
number. If it is version `3.10.*` or `3.11.*` you meet
requirements.
!!! warning "What to do if you have an unsupported version"
@ -48,7 +48,7 @@ experimental versions later.
Go to [Python Downloads](https://www.python.org/downloads/)
and download the appropriate installer package for your
platform. We recommend [Version
3.10.9](https://www.python.org/downloads/release/python-3109/),
3.10.12](https://www.python.org/downloads/release/python-3109/),
which has been extensively tested with InvokeAI.
_Please select your platform in the section below for platform-specific

4
docs/installation/020_INSTALL_MANUAL.md
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@ -32,7 +32,7 @@ gaming):
* **Python**
version 3.9 through 3.11
version 3.10 through 3.11
* **CUDA Tools**
@ -65,7 +65,7 @@ gaming):
To install InvokeAI with virtual environments and the PIP package
manager, please follow these steps:
1. Please make sure you are using Python 3.9 through 3.11. The rest of the install
1. Please make sure you are using Python 3.10 through 3.11. The rest of the install
procedure depends on this and will not work with other versions:
```bash

2
docs/installation/050_INSTALLING_MODELS.md
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@ -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

3
docs/installation/060_INSTALL_PATCHMATCH.md
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@ -59,8 +59,7 @@ Prior to installing PyPatchMatch, you need to take the following steps:
`from patchmatch import patch_match`: It should look like the following:
```py
Python 3.9.5 (default, Nov 23 2021, 15:27:38)
[GCC 9.3.0] on linux
Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from patchmatch import patch_match
Compiling and loading c extensions from "/home/lstein/Projects/InvokeAI/.invokeai-env/src/pypatchmatch/patchmatch".

4
docs/installation/deprecated_documentation/INSTALL_LINUX.md
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@ -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
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@ -150,7 +150,7 @@ will do our best to help.
To use an alternative model you may invoke the `!switch` command in
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
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@ -128,7 +128,7 @@ python scripts/invoke.py --web --max_load_models=3 \
```
These options are described in detail in the
[Command-Line Interface](../../features/CLI.md) documentation.
[Command-Line Interface](../../deprecated/CLI.md) documentation.
## Troubleshooting

4
docs/installation/deprecated_documentation/INSTALL_WINDOWS.md
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@ -75,7 +75,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
obtaining an access token for downloading. It will then download and install the
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

13
installer/install.bat.in
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@ -1,7 +1,7 @@
@echo off
setlocal EnableExtensions EnableDelayedExpansion
@rem This script requires the user to install Python 3.9 or higher. All other
@rem This script requires the user to install Python 3.10 or higher. All other
@rem requirements are downloaded as needed.
@rem change to the script's directory
@ -19,7 +19,7 @@ set INVOKEAI_VERSION=latest
set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
set PYTHON_URL=https://www.python.org/downloads/windows/
set MINIMUM_PYTHON_VERSION=3.9.0
set MINIMUM_PYTHON_VERSION=3.10.0
set PYTHON_URL=https://www.python.org/downloads/release/python-3109/
set err_msg=An error has occurred and the script could not continue.
@ -28,8 +28,7 @@ set err_msg=An error has occurred and the script could not continue.
echo This script will install InvokeAI and its dependencies.
echo.
echo BEFORE YOU START PLEASE MAKE SURE TO DO THE FOLLOWING
echo 1. Install python 3.9 or 3.10. Python version 3.11 and above are
echo not supported at the moment.
echo 1. Install python 3.10 or 3.11. Python version 3.9 is no longer supported.
echo 2. Double-click on the file WinLongPathsEnabled.reg in order to
echo enable long path support on your system.
echo 3. Install the Visual C++ core libraries.
@ -46,19 +45,19 @@ echo ***** Checking and Updating Python *****
call python --version >.tmp1 2>.tmp2
if %errorlevel% == 1 (
set err_msg=Please install Python 3.10. See %INSTRUCTIONS% for details.
set err_msg=Please install Python 3.10-11. See %INSTRUCTIONS% for details.
goto err_exit
)
for /f "tokens=2" %%i in (.tmp1) do set python_version=%%i
if "%python_version%" == "" (
set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.9 from %PYTHON_URL%
set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.12 from %PYTHON_URL%
goto err_exit
)
call :compareVersions %MINIMUM_PYTHON_VERSION% %python_version%
if %errorlevel% == 1 (
set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.9 from %PYTHON_URL%
set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.12 from %PYTHON_URL%
goto err_exit
)

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

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

8
installer/readme.txt
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@ -4,7 +4,7 @@ Project homepage: https://github.com/invoke-ai/InvokeAI
Preparations:
You will need to install Python 3.9 or higher for this installer
You will need to install Python 3.10 or higher for this installer
to work. Instructions are given here:
https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
@ -14,15 +14,15 @@ Preparations:
python --version
If all is well, it will print "Python 3.X.X", where the version number
is at least 3.9.*, and not higher than 3.11.*.
is at least 3.10.*, and not higher than 3.11.*.
If this works, check the version of the Python package manager, pip:
pip --version
You should get a message that indicates that the pip package
installer was derived from Python 3.9 or 3.10. For example:
"pip 22.3.1 from /usr/bin/pip (python 3.9)"
installer was derived from Python 3.10 or 3.11. For example:
"pip 22.0.1 from /usr/bin/pip (python 3.10)"
Long Paths on Windows:

40
installer/templates/invoke.bat.in
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@ -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

2
invokeai/app/api/routers/images.py
View File

@ -42,7 +42,7 @@ async def upload_image(
crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
) -> ImageDTO:
"""Uploads an image"""
if not file.content_type.startswith("image"):
if not file.content_type or not file.content_type.startswith("image"):
raise HTTPException(status_code=415, detail="Not an image")
contents = await file.read()

109
invokeai/app/api/routers/models.py
View File

@ -2,11 +2,11 @@
import pathlib
from typing import List, Literal, Optional, Union
from typing import Annotated, List, Literal, Optional, Union
from fastapi import Body, Path, Query, Response
from fastapi.routing import APIRouter
from pydantic import BaseModel, parse_obj_as
from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
from starlette.exceptions import HTTPException
from invokeai.backend import BaseModelType, ModelType
@ -23,8 +23,14 @@ from ..dependencies import ApiDependencies
models_router = APIRouter(prefix="/v1/models", tags=["models"])
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
update_models_response_adapter = TypeAdapter(UpdateModelResponse)
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
import_models_response_adapter = TypeAdapter(ImportModelResponse)
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
convert_models_response_adapter = TypeAdapter(ConvertModelResponse)
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
@ -32,6 +38,11 @@ ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
class ModelsList(BaseModel):
models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
model_config = ConfigDict(use_enum_values=True)
models_list_adapter = TypeAdapter(ModelsList)
@models_router.get(
"/",
@ -49,7 +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 = parse_obj_as(ModelsList, {"models": models_raw})
models = models_list_adapter.validate_python({"models": models_raw})
return models
@ -105,11 +116,14 @@ async def update_model(
info.path = new_info.get("path")
# replace empty string values with None/null to avoid phenomenon of vae: ''
info_dict = info.dict()
info_dict = info.model_dump()
info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
ApiDependencies.invoker.services.model_manager.update_model(
model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict
model_name=model_name,
base_model=base_model,
model_type=model_type,
model_attributes=info_dict,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@ -117,7 +131,7 @@ async def update_model(
base_model=base_model,
model_type=model_type,
)
model_response = parse_obj_as(UpdateModelResponse, model_raw)
model_response = update_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
@ -159,7 +173,8 @@ async def import_model(
try:
installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type)
items_to_import=items_to_import,
prediction_type_helper=lambda x: prediction_types.get(prediction_type),
)
info = installed_models.get(location)
@ -171,7 +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 parse_obj_as(ImportModelResponse, model_raw)
return import_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
@ -205,13 +220,18 @@ async def add_model(
try:
ApiDependencies.invoker.services.model_manager.add_model(
info.model_name, info.base_model, info.model_type, model_attributes=info.dict()
info.model_name,
info.base_model,
info.model_type,
model_attributes=info.model_dump(),
)
logger.info(f"Successfully added {info.model_name}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.model_name, base_model=info.base_model, model_type=info.model_type
model_name=info.model_name,
base_model=info.base_model,
model_type=info.model_type,
)
return parse_obj_as(ImportModelResponse, model_raw)
return import_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@ -223,7 +243,10 @@ async def add_model(
@models_router.delete(
"/{base_model}/{model_type}/{model_name}",
operation_id="del_model",
responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
responses={
204: {"description": "Model deleted successfully"},
404: {"description": "Model not found"},
},
status_code=204,
response_model=None,
)
@ -279,7 +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 = parse_obj_as(ConvertModelResponse, model_raw)
response = convert_models_response_adapter.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:
@ -302,7 +325,8 @@ async def search_for_models(
) -> List[pathlib.Path]:
if not search_path.is_dir():
raise HTTPException(
status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory"
status_code=404,
detail=f"The search path '{search_path}' does not exist or is not directory",
)
return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
@ -337,6 +361,26 @@ async def sync_to_config() -> bool:
return True
# There's some weird pydantic-fastapi behaviour that requires this to be a separate class
# TODO: After a few updates, see if it works inside the route operation handler?
class MergeModelsBody(BaseModel):
model_names: List[str] = Field(description="model name", min_length=2, max_length=3)
merged_model_name: Optional[str] = Field(description="Name of destination model")
alpha: Optional[float] = Field(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5)
interp: Optional[MergeInterpolationMethod] = Field(description="Interpolation method")
force: Optional[bool] = Field(
description="Force merging of models created with different versions of diffusers",
default=False,
)
merge_dest_directory: Optional[str] = Field(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
)
model_config = ConfigDict(protected_namespaces=())
@models_router.put(
"/merge/{base_model}",
operation_id="merge_models",
@ -349,31 +393,23 @@ async def sync_to_config() -> bool:
response_model=MergeModelResponse,
)
async def merge_models(
body: Annotated[MergeModelsBody, Body(description="Model configuration", embed=True)],
base_model: BaseModelType = Path(description="Base model"),
model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
merged_model_name: Optional[str] = Body(description="Name of destination model"),
alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
force: Optional[bool] = Body(
description="Force merging of models created with different versions of diffusers", default=False
),
merge_dest_directory: Optional[str] = Body(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
),
) -> MergeModelResponse:
"""Convert a checkpoint model into a diffusers model"""
logger = ApiDependencies.invoker.services.logger
try:
logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
logger.info(
f"Merging models: {body.model_names} into {body.merge_dest_directory or '<MODELS>'}/{body.merged_model_name}"
)
dest = pathlib.Path(body.merge_dest_directory) if body.merge_dest_directory else None
result = ApiDependencies.invoker.services.model_manager.merge_models(
model_names,
base_model,
merged_model_name=merged_model_name or "+".join(model_names),
alpha=alpha,
interp=interp,
force=force,
model_names=body.model_names,
base_model=base_model,
merged_model_name=body.merged_model_name or "+".join(body.model_names),
alpha=body.alpha,
interp=body.interp,
force=body.force,
merge_dest_directory=dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@ -381,9 +417,12 @@ async def merge_models(
base_model=base_model,
model_type=ModelType.Main,
)
response = parse_obj_as(ConvertModelResponse, model_raw)
response = convert_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException:
raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
raise HTTPException(
status_code=404,
detail=f"One or more of the models '{body.model_names}' not found",
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
return response

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",

456
invokeai/app/api/routers/sessions.py
View File

@ -1,57 +1,50 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from typing import Annotated, Optional, Union
from fastapi import Body, HTTPException, Path, Query, Response
from fastapi import HTTPException, Path
from fastapi.routing import APIRouter
from pydantic.fields import Field
from invokeai.app.services.shared.pagination import PaginatedResults
# Importing * is bad karma but needed here for node detection
from ...invocations import * # noqa: F401 F403
from ...invocations.baseinvocation import BaseInvocation
from ...services.shared.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
from ...services.shared.graph import GraphExecutionState
from ..dependencies import ApiDependencies
session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
@session_router.post(
"/",
operation_id="create_session",
responses={
200: {"model": GraphExecutionState},
400: {"description": "Invalid json"},
},
deprecated=True,
)
async def create_session(
queue_id: str = Query(default="", description="The id of the queue to associate the session with"),
graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"),
) -> GraphExecutionState:
"""Creates a new session, optionally initializing it with an invocation graph"""
session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph)
return session
# @session_router.post(
# "/",
# operation_id="create_session",
# responses={
# 200: {"model": GraphExecutionState},
# 400: {"description": "Invalid json"},
# },
# deprecated=True,
# )
# async def create_session(
# queue_id: str = Query(default="", description="The id of the queue to associate the session with"),
# graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"),
# ) -> GraphExecutionState:
# """Creates a new session, optionally initializing it with an invocation graph"""
# session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph)
# return session
@session_router.get(
"/",
operation_id="list_sessions",
responses={200: {"model": PaginatedResults[GraphExecutionState]}},
deprecated=True,
)
async def list_sessions(
page: int = Query(default=0, description="The page of results to get"),
per_page: int = Query(default=10, description="The number of results per page"),
query: str = Query(default="", description="The query string to search for"),
) -> PaginatedResults[GraphExecutionState]:
"""Gets a list of sessions, optionally searching"""
if query == "":
result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
else:
result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
return result
# @session_router.get(
# "/",
# operation_id="list_sessions",
# responses={200: {"model": PaginatedResults[GraphExecutionState]}},
# deprecated=True,
# )
# async def list_sessions(
# page: int = Query(default=0, description="The page of results to get"),
# per_page: int = Query(default=10, description="The number of results per page"),
# query: str = Query(default="", description="The query string to search for"),
# ) -> PaginatedResults[GraphExecutionState]:
# """Gets a list of sessions, optionally searching"""
# if query == "":
# result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
# else:
# result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
# return result
@session_router.get(
@ -61,7 +54,6 @@ async def list_sessions(
200: {"model": GraphExecutionState},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def get_session(
session_id: str = Path(description="The id of the session to get"),
@ -74,211 +66,211 @@ async def get_session(
return session
@session_router.post(
"/{session_id}/nodes",
operation_id="add_node",
responses={
200: {"model": str},
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def add_node(
session_id: str = Path(description="The id of the session"),
node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
description="The node to add"
),
) -> str:
"""Adds a node to the graph"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None:
raise HTTPException(status_code=404)
# @session_router.post(
# "/{session_id}/nodes",
# operation_id="add_node",
# responses={
# 200: {"model": str},
# 400: {"description": "Invalid node or link"},
# 404: {"description": "Session not found"},
# },
# deprecated=True,
# )
# async def add_node(
# session_id: str = Path(description="The id of the session"),
# node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
# description="The node to add"
# ),
# ) -> str:
# """Adds a node to the graph"""
# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
# if session is None:
# raise HTTPException(status_code=404)
try:
session.add_node(node)
ApiDependencies.invoker.services.graph_execution_manager.set(
session
) # TODO: can this be done automatically, or add node through an API?
return session.id
except NodeAlreadyExecutedError:
raise HTTPException(status_code=400)
except IndexError:
raise HTTPException(status_code=400)
# try:
# session.add_node(node)
# ApiDependencies.invoker.services.graph_execution_manager.set(
# session
# ) # TODO: can this be done automatically, or add node through an API?
# return session.id
# except NodeAlreadyExecutedError:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(status_code=400)
@session_router.put(
"/{session_id}/nodes/{node_path}",
operation_id="update_node",
responses={
200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def update_node(
session_id: str = Path(description="The id of the session"),
node_path: str = Path(description="The path to the node in the graph"),
node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
description="The new node"
),
) -> GraphExecutionState:
"""Updates a node in the graph and removes all linked edges"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None:
raise HTTPException(status_code=404)
# @session_router.put(
# "/{session_id}/nodes/{node_path}",
# operation_id="update_node",
# responses={
# 200: {"model": GraphExecutionState},
# 400: {"description": "Invalid node or link"},
# 404: {"description": "Session not found"},
# },
# deprecated=True,
# )
# async def update_node(
# session_id: str = Path(description="The id of the session"),
# node_path: str = Path(description="The path to the node in the graph"),
# node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
# description="The new node"
# ),
# ) -> GraphExecutionState:
# """Updates a node in the graph and removes all linked edges"""
# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
# if session is None:
# raise HTTPException(status_code=404)
try:
session.update_node(node_path, node)
ApiDependencies.invoker.services.graph_execution_manager.set(
session
) # TODO: can this be done automatically, or add node through an API?
return session
except NodeAlreadyExecutedError:
raise HTTPException(status_code=400)
except IndexError:
raise HTTPException(status_code=400)
# try:
# session.update_node(node_path, node)
# ApiDependencies.invoker.services.graph_execution_manager.set(
# session
# ) # TODO: can this be done automatically, or add node through an API?
# return session
# except NodeAlreadyExecutedError:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(status_code=400)
@session_router.delete(
"/{session_id}/nodes/{node_path}",
operation_id="delete_node",
responses={
200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def delete_node(
session_id: str = Path(description="The id of the session"),
node_path: str = Path(description="The path to the node to delete"),
) -> GraphExecutionState:
"""Deletes a node in the graph and removes all linked edges"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None:
raise HTTPException(status_code=404)
# @session_router.delete(
# "/{session_id}/nodes/{node_path}",
# operation_id="delete_node",
# responses={
# 200: {"model": GraphExecutionState},
# 400: {"description": "Invalid node or link"},
# 404: {"description": "Session not found"},
# },
# deprecated=True,
# )
# async def delete_node(
# session_id: str = Path(description="The id of the session"),
# node_path: str = Path(description="The path to the node to delete"),
# ) -> GraphExecutionState:
# """Deletes a node in the graph and removes all linked edges"""
# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
# if session is None:
# raise HTTPException(status_code=404)
try:
session.delete_node(node_path)
ApiDependencies.invoker.services.graph_execution_manager.set(
session
) # TODO: can this be done automatically, or add node through an API?
return session
except NodeAlreadyExecutedError:
raise HTTPException(status_code=400)
except IndexError:
raise HTTPException(status_code=400)
# try:
# session.delete_node(node_path)
# ApiDependencies.invoker.services.graph_execution_manager.set(
# session
# ) # TODO: can this be done automatically, or add node through an API?
# return session
# except NodeAlreadyExecutedError:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(status_code=400)
@session_router.post(
"/{session_id}/edges",
operation_id="add_edge",
responses={
200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def add_edge(
session_id: str = Path(description="The id of the session"),
edge: Edge = Body(description="The edge to add"),
) -> GraphExecutionState:
"""Adds an edge to the graph"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None:
raise HTTPException(status_code=404)
# @session_router.post(
# "/{session_id}/edges",
# operation_id="add_edge",
# responses={
# 200: {"model": GraphExecutionState},
# 400: {"description": "Invalid node or link"},
# 404: {"description": "Session not found"},
# },
# deprecated=True,
# )
# async def add_edge(
# session_id: str = Path(description="The id of the session"),
# edge: Edge = Body(description="The edge to add"),
# ) -> GraphExecutionState:
# """Adds an edge to the graph"""
# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
# if session is None:
# raise HTTPException(status_code=404)
try:
session.add_edge(edge)
ApiDependencies.invoker.services.graph_execution_manager.set(
session
) # TODO: can this be done automatically, or add node through an API?
return session
except NodeAlreadyExecutedError:
raise HTTPException(status_code=400)
except IndexError:
raise HTTPException(status_code=400)
# try:
# session.add_edge(edge)
# ApiDependencies.invoker.services.graph_execution_manager.set(
# session
# ) # TODO: can this be done automatically, or add node through an API?
# return session
# except NodeAlreadyExecutedError:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(status_code=400)
# TODO: the edge being in the path here is really ugly, find a better solution
@session_router.delete(
"/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}",
operation_id="delete_edge",
responses={
200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def delete_edge(
session_id: str = Path(description="The id of the session"),
from_node_id: str = Path(description="The id of the node the edge is coming from"),
from_field: str = Path(description="The field of the node the edge is coming from"),
to_node_id: str = Path(description="The id of the node the edge is going to"),
to_field: str = Path(description="The field of the node the edge is going to"),
) -> GraphExecutionState:
"""Deletes an edge from the graph"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None:
raise HTTPException(status_code=404)
# # TODO: the edge being in the path here is really ugly, find a better solution
# @session_router.delete(
# "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}",
# operation_id="delete_edge",
# responses={
# 200: {"model": GraphExecutionState},
# 400: {"description": "Invalid node or link"},
# 404: {"description": "Session not found"},
# },
# deprecated=True,
# )
# async def delete_edge(
# session_id: str = Path(description="The id of the session"),
# from_node_id: str = Path(description="The id of the node the edge is coming from"),
# from_field: str = Path(description="The field of the node the edge is coming from"),
# to_node_id: str = Path(description="The id of the node the edge is going to"),
# to_field: str = Path(description="The field of the node the edge is going to"),
# ) -> GraphExecutionState:
# """Deletes an edge from the graph"""
# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
# if session is None:
# raise HTTPException(status_code=404)
try:
edge = Edge(
source=EdgeConnection(node_id=from_node_id, field=from_field),
destination=EdgeConnection(node_id=to_node_id, field=to_field),
)
session.delete_edge(edge)
ApiDependencies.invoker.services.graph_execution_manager.set(
session
) # TODO: can this be done automatically, or add node through an API?
return session
except NodeAlreadyExecutedError:
raise HTTPException(status_code=400)
except IndexError:
raise HTTPException(status_code=400)
# try:
# edge = Edge(
# source=EdgeConnection(node_id=from_node_id, field=from_field),
# destination=EdgeConnection(node_id=to_node_id, field=to_field),
# )
# session.delete_edge(edge)
# ApiDependencies.invoker.services.graph_execution_manager.set(
# session
# ) # TODO: can this be done automatically, or add node through an API?
# return session
# except NodeAlreadyExecutedError:
# raise HTTPException(status_code=400)
# except IndexError:
# raise HTTPException(status_code=400)
@session_router.put(
"/{session_id}/invoke",
operation_id="invoke_session",
responses={
200: {"model": None},
202: {"description": "The invocation is queued"},
400: {"description": "The session has no invocations ready to invoke"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def invoke_session(
queue_id: str = Query(description="The id of the queue to associate the session with"),
session_id: str = Path(description="The id of the session to invoke"),
all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
) -> Response:
"""Invokes a session"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None:
raise HTTPException(status_code=404)
# @session_router.put(
# "/{session_id}/invoke",
# operation_id="invoke_session",
# responses={
# 200: {"model": None},
# 202: {"description": "The invocation is queued"},
# 400: {"description": "The session has no invocations ready to invoke"},
# 404: {"description": "Session not found"},
# },
# deprecated=True,
# )
# async def invoke_session(
# queue_id: str = Query(description="The id of the queue to associate the session with"),
# session_id: str = Path(description="The id of the session to invoke"),
# all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
# ) -> Response:
# """Invokes a session"""
# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
# if session is None:
# raise HTTPException(status_code=404)
if session.is_complete():
raise HTTPException(status_code=400)
# if session.is_complete():
# raise HTTPException(status_code=400)
ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all)
return Response(status_code=202)
# ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all)
# return Response(status_code=202)
@session_router.delete(
"/{session_id}/invoke",
operation_id="cancel_session_invoke",
responses={202: {"description": "The invocation is canceled"}},
deprecated=True,
)
async def cancel_session_invoke(
session_id: str = Path(description="The id of the session to cancel"),
) -> Response:
"""Invokes a session"""
ApiDependencies.invoker.cancel(session_id)
return Response(status_code=202)
# @session_router.delete(
# "/{session_id}/invoke",
# operation_id="cancel_session_invoke",
# responses={202: {"description": "The invocation is canceled"}},
# deprecated=True,
# )
# async def cancel_session_invoke(
# session_id: str = Path(description="The id of the session to cancel"),
# ) -> Response:
# """Invokes a session"""
# ApiDependencies.invoker.cancel(session_id)
# return Response(status_code=202)

3
invokeai/app/api/routers/utilities.py
View File

@ -1,4 +1,4 @@
from typing import Optional
from typing import Optional, Union
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
from fastapi import Body
@ -27,6 +27,7 @@ async def parse_dynamicprompts(
combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
) -> DynamicPromptsResponse:
"""Creates a batch process"""
generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator]
try:
error: Optional[str] = None
if combinatorial:

41
invokeai/app/api_app.py
View File

@ -22,7 +22,7 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.schema import schema
from pydantic.json_schema import models_json_schema
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
@ -51,7 +51,7 @@ mimetypes.add_type("text/css", ".css")
# Create the app
# TODO: create this all in a method so configuration/etc. can be passed in?
app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
# Add event handler
event_handler_id: int = id(app)
@ -63,10 +63,6 @@ app.add_middleware(
socket_io = SocketIO(app)
# Add startup event to load dependencies
@app.on_event("startup")
async def startup_event():
app.add_middleware(
CORSMiddleware,
allow_origins=app_config.allow_origins,
@ -75,6 +71,10 @@ async def startup_event():
allow_headers=app_config.allow_headers,
)
# Add startup event to load dependencies
@app.on_event("startup")
async def startup_event():
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
@ -85,11 +85,6 @@ async def shutdown_event():
# Include all routers
# TODO: REMOVE
# app.include_router(
# invocation.invocation_router,
# prefix = '/api')
app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api")
@ -117,6 +112,7 @@ def custom_openapi():
description="An API for invoking AI image operations",
version="1.0.0",
routes=app.routes,
separate_input_output_schemas=False, # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
)
# Add all outputs
@ -127,29 +123,32 @@ def custom_openapi():
output_type = signature(invoker.invoke).return_annotation
output_types.add(output_type)
output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
for schema_key, output_schema in output_schemas["definitions"].items():
output_schema["class"] = "output"
openapi_schema["components"]["schemas"][schema_key] = output_schema
output_schemas = models_json_schema(
models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
)
for schema_key, output_schema in output_schemas[1]["$defs"].items():
# TODO: note that we assume the schema_key here is the TYPE.__name__
# This could break in some cases, figure out a better way to do it
output_type_titles[schema_key] = output_schema["title"]
# Add Node Editor UI helper schemas
ui_config_schemas = schema([UIConfigBase, _InputField, _OutputField], ref_prefix="#/components/schemas/")
for schema_key, ui_config_schema in ui_config_schemas["definitions"].items():
ui_config_schemas = models_json_schema(
[(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
ref_template="#/components/schemas/{model}",
)
for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
openapi_schema["components"]["schemas"][schema_key] = ui_config_schema
# Add a reference to the output type to additionalProperties of the invoker schema
for invoker in all_invocations:
invoker_name = invoker.__name__
output_type = signature(invoker.invoke).return_annotation
output_type = signature(obj=invoker.invoke).return_annotation
output_type_title = output_type_titles[output_type.__name__]
invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
invoker_schema["output"] = outputs_ref
invoker_schema["class"] = "invocation"
openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output"
from invokeai.backend.model_management.models import get_model_config_enums
@ -172,7 +171,7 @@ def custom_openapi():
return app.openapi_schema
app.openapi = 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")

313
invokeai/app/cli/commands.py
View File

@ -1,313 +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.type_) == Literal:
allowed_values = get_args(field.type_)
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.field_info.description,
)
else:
command_parser.add_argument(
f"--{name}",
dest=name,
type=field.type_,
default=default,
help=field.field_info.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:
# type: Literal['your_command_name'] = 'your_command_name'
@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()

513
invokeai/app/invocations/baseinvocation.py
View File

@ -7,28 +7,16 @@ import re
from abc import ABC, abstractmethod
from enum import Enum
from inspect import signature
from typing import (
TYPE_CHECKING,
AbstractSet,
Any,
Callable,
ClassVar,
Literal,
Mapping,
Optional,
Type,
TypeVar,
Union,
get_args,
get_type_hints,
)
from types import UnionType
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
import semver
from pydantic import BaseModel, Field, validator
from pydantic.fields import ModelField, Undefined
from pydantic.typing import NoArgAnyCallable
from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
from pydantic.fields import _Unset
from pydantic_core import PydanticUndefined
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.util.misc import uuid_string
if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices
@ -211,6 +199,11 @@ class _InputField(BaseModel):
ui_choice_labels: Optional[dict[str, str]]
item_default: Optional[Any]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
class _OutputField(BaseModel):
"""
@ -224,34 +217,36 @@ class _OutputField(BaseModel):
ui_type: Optional[UIType]
ui_order: Optional[int]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
def get_type(klass: BaseModel) -> str:
"""Helper function to get an invocation or invocation output's type. This is the default value of the `type` field."""
return klass.model_fields["type"].default
def InputField(
*args: Any,
default: Any = Undefined,
default_factory: Optional[NoArgAnyCallable] = None,
alias: Optional[str] = None,
title: Optional[str] = None,
description: Optional[str] = None,
exclude: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None,
include: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None,
const: Optional[bool] = None,
gt: Optional[float] = None,
ge: Optional[float] = None,
lt: Optional[float] = None,
le: Optional[float] = None,
multiple_of: Optional[float] = None,
allow_inf_nan: Optional[bool] = None,
max_digits: Optional[int] = None,
decimal_places: Optional[int] = None,
min_items: Optional[int] = None,
max_items: Optional[int] = None,
unique_items: Optional[bool] = None,
min_length: Optional[int] = None,
max_length: Optional[int] = None,
allow_mutation: bool = True,
regex: Optional[str] = None,
discriminator: Optional[str] = None,
repr: bool = True,
# copied from pydantic's Field
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
input: Input = Input.Any,
ui_type: Optional[UIType] = None,
ui_component: Optional[UIComponent] = None,
@ -259,7 +254,6 @@ def InputField(
ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None,
item_default: Optional[Any] = None,
**kwargs: Any,
) -> Any:
"""
Creates an input field for an invocation.
@ -289,18 +283,26 @@ def InputField(
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
: param bool item_default: [None] Specifies the default item value, if this is a collection input. \
Ignored for non-collection fields..
Ignored for non-collection fields.
"""
return Field(
*args,
json_schema_extra_: dict[str, Any] = dict(
input=input,
ui_type=ui_type,
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
item_default=item_default,
ui_choice_labels=ui_choice_labels,
)
field_args = dict(
default=default,
default_factory=default_factory,
alias=alias,
title=title,
description=description,
exclude=exclude,
include=include,
const=const,
pattern=pattern,
strict=strict,
gt=gt,
ge=ge,
lt=lt,
@ -309,57 +311,92 @@ def InputField(
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
min_items=min_items,
max_items=max_items,
unique_items=unique_items,
min_length=min_length,
max_length=max_length,
allow_mutation=allow_mutation,
regex=regex,
discriminator=discriminator,
repr=repr,
input=input,
ui_type=ui_type,
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
item_default=item_default,
ui_choice_labels=ui_choice_labels,
**kwargs,
)
"""
Invocation definitions have their fields typed correctly for their `invoke()` functions.
This typing is often more specific than the actual invocation definition requires, because
fields may have values provided only by connections.
For example, consider an ResizeImageInvocation with an `image: ImageField` field.
`image` is required during the call to `invoke()`, but when the python class is instantiated,
the field may not be present. This is fine, because that image field will be provided by a
an ancestor node that outputs the image.
So we'd like to type that `image` field as `Optional[ImageField]`. If we do that, however, then
we need to handle a lot of extra logic in the `invoke()` function to check if the field has a
value or not. This is very tedious.
Ideally, the invocation definition would be able to specify that the field is required during
invocation, but optional during instantiation. So the field would be typed as `image: ImageField`,
but when calling the `invoke()` function, we raise an error if the field is not present.
To do this, we need to do a bit of fanagling to make the pydantic field optional, and then do
extra validation when calling `invoke()`.
There is some additional logic here to cleaning create the pydantic field via the wrapper.
"""
# Filter out field args not provided
provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
if (default is not PydanticUndefined) and (default_factory is not PydanticUndefined):
raise ValueError("Cannot specify both default and default_factory")
# because we are manually making fields optional, we need to store the original required bool for reference later
if default is PydanticUndefined and default_factory is PydanticUndefined:
json_schema_extra_.update(dict(orig_required=True))
else:
json_schema_extra_.update(dict(orig_required=False))
# make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
default_ = None if default is PydanticUndefined else default
provided_args.update(dict(default=default_))
if default is not PydanticUndefined:
# before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
json_schema_extra_.update(dict(default=default))
json_schema_extra_.update(dict(orig_default=default))
elif default is not PydanticUndefined and default_factory is PydanticUndefined:
default_ = default
provided_args.update(dict(default=default_))
json_schema_extra_.update(dict(orig_default=default_))
elif default_factory is not PydanticUndefined:
provided_args.update(dict(default_factory=default_factory))
# TODO: cannot serialize default_factory...
# json_schema_extra_.update(dict(orig_default_factory=default_factory))
return Field(
**provided_args,
json_schema_extra=json_schema_extra_,
)
def OutputField(
*args: Any,
default: Any = Undefined,
default_factory: Optional[NoArgAnyCallable] = None,
alias: Optional[str] = None,
title: Optional[str] = None,
description: Optional[str] = None,
exclude: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None,
include: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None,
const: Optional[bool] = None,
gt: Optional[float] = None,
ge: Optional[float] = None,
lt: Optional[float] = None,
le: Optional[float] = None,
multiple_of: Optional[float] = None,
allow_inf_nan: Optional[bool] = None,
max_digits: Optional[int] = None,
decimal_places: Optional[int] = None,
min_items: Optional[int] = None,
max_items: Optional[int] = None,
unique_items: Optional[bool] = None,
min_length: Optional[int] = None,
max_length: Optional[int] = None,
allow_mutation: bool = True,
regex: Optional[str] = None,
discriminator: Optional[str] = None,
repr: bool = True,
# copied from pydantic's Field
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
ui_type: Optional[UIType] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
**kwargs: Any,
) -> Any:
"""
Creates an output field for an invocation output.
@ -379,15 +416,12 @@ def OutputField(
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
*args,
default=default,
default_factory=default_factory,
alias=alias,
title=title,
description=description,
exclude=exclude,
include=include,
const=const,
pattern=pattern,
strict=strict,
gt=gt,
ge=ge,
lt=lt,
@ -396,19 +430,13 @@ def OutputField(
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
min_items=min_items,
max_items=max_items,
unique_items=unique_items,
min_length=min_length,
max_length=max_length,
allow_mutation=allow_mutation,
regex=regex,
discriminator=discriminator,
repr=repr,
json_schema_extra=dict(
ui_type=ui_type,
ui_hidden=ui_hidden,
ui_order=ui_order,
**kwargs,
),
)
@ -422,7 +450,13 @@ class UIConfigBase(BaseModel):
title: Optional[str] = Field(default=None, description="The node's display name")
category: Optional[str] = Field(default=None, description="The node's category")
version: Optional[str] = Field(
default=None, description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".'
default=None,
description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
)
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
@ -457,24 +491,39 @@ class BaseInvocationOutput(BaseModel):
All invocation outputs must use the `@invocation_output` decorator to provide their unique type.
"""
@classmethod
def get_all_subclasses_tuple(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 tuple(subclasses)
_output_classes: ClassVar[set[BaseInvocationOutput]] = set()
@classmethod
def register_output(cls, output: BaseInvocationOutput) -> None:
cls._output_classes.add(output)
@classmethod
def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
return cls._output_classes
@classmethod
def get_outputs_union(cls) -> UnionType:
outputs_union = Union[tuple(cls._output_classes)] # type: ignore [valid-type]
return outputs_union # type: ignore [return-value]
@classmethod
def get_output_types(cls) -> Iterable[str]:
return map(lambda i: get_type(i), BaseInvocationOutput.get_outputs())
class Config:
@staticmethod
def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
# Because we use a pydantic Literal field with default value for the invocation type,
# it will be typed as optional in the OpenAPI schema. Make it required manually.
if "required" not in schema or not isinstance(schema["required"], list):
schema["required"] = list()
schema["required"].extend(["type"])
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
json_schema_extra=json_schema_extra,
)
class RequiredConnectionException(Exception):
"""Raised when an field which requires a connection did not receive a value."""
@ -498,58 +547,54 @@ class BaseInvocation(ABC, BaseModel):
All invocations must use the `@invocation` decorator to provide their unique type.
"""
_invocation_classes: ClassVar[set[BaseInvocation]] = set()
@classmethod
def get_all_subclasses(cls):
def register_invocation(cls, invocation: BaseInvocation) -> None:
cls._invocation_classes.add(invocation)
@classmethod
def get_invocations_union(cls) -> UnionType:
invocations_union = Union[tuple(cls._invocation_classes)] # type: ignore [valid-type]
return invocations_union # type: ignore [return-value]
@classmethod
def get_invocations(cls) -> Iterable[BaseInvocation]:
app_config = InvokeAIAppConfig.get_config()
subclasses = []
toprocess = [cls]
while len(toprocess) > 0:
next = toprocess.pop(0)
next_subclasses = next.__subclasses__()
subclasses.extend(next_subclasses)
toprocess.extend(next_subclasses)
allowed_invocations = []
for sc in subclasses:
allowed_invocations: set[BaseInvocation] = set()
for sc in cls._invocation_classes:
invocation_type = get_type(sc)
is_in_allowlist = (
sc.__fields__.get("type").default in app_config.allow_nodes
if isinstance(app_config.allow_nodes, list)
else True
invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
)
is_in_denylist = (
sc.__fields__.get("type").default in app_config.deny_nodes
if isinstance(app_config.deny_nodes, list)
else False
invocation_type in app_config.deny_nodes if isinstance(app_config.deny_nodes, list) else False
)
if is_in_allowlist and not is_in_denylist:
allowed_invocations.append(sc)
allowed_invocations.add(sc)
return allowed_invocations
@classmethod
def get_invocations(cls):
return tuple(BaseInvocation.get_all_subclasses())
@classmethod
def get_invocations_map(cls):
def get_invocations_map(cls) -> dict[str, BaseInvocation]:
# 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),
BaseInvocation.get_all_subclasses(),
lambda i: (get_type(i), i),
BaseInvocation.get_invocations(),
)
)
@classmethod
def get_output_type(cls):
def get_invocation_types(cls) -> Iterable[str]:
return map(lambda i: get_type(i), BaseInvocation.get_invocations())
@classmethod
def get_output_type(cls) -> BaseInvocationOutput:
return signature(cls.invoke).return_annotation
class Config:
validate_assignment = True
validate_all = True
@staticmethod
def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
# Add the various UI-facing attributes to the schema. These are used to build the invocation templates.
uiconfig = getattr(model_class, "UIConfig", None)
if uiconfig and hasattr(uiconfig, "title"):
schema["title"] = uiconfig.title
@ -568,34 +613,25 @@ class BaseInvocation(ABC, BaseModel):
"""Invoke with provided context and return outputs."""
pass
def __init__(self, **data):
# nodes may have required fields, that can accept input from connections
# on instantiation of the model, we need to exclude these from validation
restore = dict()
try:
field_names = list(self.__fields__.keys())
for field_name in field_names:
# if the field is required and may get its value from a connection, exclude it from validation
field = self.__fields__[field_name]
_input = field.field_info.extra.get("input", None)
if _input in [Input.Connection, Input.Any] and field.required:
if field_name not in data:
restore[field_name] = self.__fields__.pop(field_name)
# instantiate the node, which will validate the data
super().__init__(**data)
finally:
# restore the removed fields
for field_name, field in restore.items():
self.__fields__[field_name] = field
def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
for field_name, field in self.__fields__.items():
_input = field.field_info.extra.get("input", None)
if field.required and not hasattr(self, field_name):
if _input == Input.Connection:
raise RequiredConnectionException(self.__fields__["type"].default, field_name)
elif _input == Input.Any:
raise MissingInputException(self.__fields__["type"].default, field_name)
for field_name, field in self.model_fields.items():
if not field.json_schema_extra or callable(field.json_schema_extra):
# something has gone terribly awry, we should always have this and it should be a dict
continue
# Here we handle the case where the field is optional in the pydantic class, but required
# in the `invoke()` method.
orig_default = field.json_schema_extra.get("orig_default", PydanticUndefined)
orig_required = field.json_schema_extra.get("orig_required", True)
input_ = field.json_schema_extra.get("input", None)
if orig_default is not PydanticUndefined and not hasattr(self, field_name):
setattr(self, field_name, orig_default)
if orig_required and orig_default is PydanticUndefined and getattr(self, field_name) is None:
if input_ == Input.Connection:
raise RequiredConnectionException(self.model_fields["type"].default, field_name)
elif input_ == Input.Any:
raise MissingInputException(self.model_fields["type"].default, field_name)
# skip node cache codepath if it's disabled
if context.services.configuration.node_cache_size == 0:
@ -618,23 +654,31 @@ class BaseInvocation(ABC, BaseModel):
return self.invoke(context)
def get_type(self) -> str:
return self.__fields__["type"].default
return self.model_fields["type"].default
id: str = Field(
description="The id of this instance of an invocation. Must be unique among all instances of invocations."
default_factory=uuid_string,
description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
)
is_intermediate: bool = InputField(
default=False, description="Whether or not this is an intermediate invocation.", ui_type=UIType.IsIntermediate
is_intermediate: Optional[bool] = Field(
default=False,
description="Whether or not this is an intermediate invocation.",
json_schema_extra=dict(ui_type=UIType.IsIntermediate),
)
workflow: Optional[str] = InputField(
workflow: Optional[str] = Field(
default=None,
description="The workflow to save with the image",
ui_type=UIType.WorkflowField,
json_schema_extra=dict(ui_type=UIType.WorkflowField),
)
use_cache: Optional[bool] = Field(
default=True,
description="Whether or not to use the cache",
)
use_cache: bool = InputField(default=True, description="Whether or not to use the cache")
@validator("workflow", pre=True)
@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:
@ -645,8 +689,14 @@ class BaseInvocation(ABC, BaseModel):
UIConfig: ClassVar[Type[UIConfigBase]]
model_config = ConfigDict(
validate_assignment=True,
json_schema_extra=json_schema_extra,
json_schema_serialization_defaults_required=True,
)
GenericBaseInvocation = TypeVar("GenericBaseInvocation", bound=BaseInvocation)
TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
def invocation(
@ -656,7 +706,7 @@ def invocation(
category: Optional[str] = None,
version: Optional[str] = None,
use_cache: Optional[bool] = True,
) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]:
) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]:
"""
Adds metadata to an invocation.
@ -668,12 +718,15 @@ def invocation(
:param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor.
"""
def wrapper(cls: Type[GenericBaseInvocation]) -> Type[GenericBaseInvocation]:
def wrapper(cls: Type[TBaseInvocation]) -> Type[TBaseInvocation]:
# Validate invocation types on creation of invocation classes
# TODO: ensure unique?
if re.compile(r"^\S+$").match(invocation_type) is None:
raise ValueError(f'"invocation_type" must consist of non-whitespace characters, got "{invocation_type}"')
if invocation_type in BaseInvocation.get_invocation_types():
raise ValueError(f'Invocation type "{invocation_type}" already exists')
# Add OpenAPI schema extras
uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
@ -691,59 +744,83 @@ def invocation(
raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
cls.UIConfig.version = version
if use_cache is not None:
cls.__fields__["use_cache"].default = use_cache
cls.model_fields["use_cache"].default = use_cache
# Add the invocation type to the model.
# You'd be tempted to just add the type field and rebuild the model, like this:
# cls.model_fields.update(type=FieldInfo.from_annotated_attribute(Literal[invocation_type], invocation_type))
# cls.model_rebuild() or cls.model_rebuild(force=True)
# Unfortunately, because the `GraphInvocation` uses a forward ref in its `graph` field's annotation, this does
# not work. Instead, we have to create a new class with the type field and patch the original class with it.
# Add the invocation type to the pydantic model of the invocation
invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = ModelField.infer(
name="type",
value=invocation_type,
annotation=invocation_type_annotation,
class_validators=None,
config=cls.__config__,
invocation_type_field = Field(
title="type",
default=invocation_type,
)
cls.__fields__.update({"type": invocation_type_field})
# to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html
if annotations := cls.__dict__.get("__annotations__", None):
annotations.update({"type": invocation_type_annotation})
docstring = cls.__doc__
cls = create_model(
cls.__qualname__,
__base__=cls,
__module__=cls.__module__,
type=(invocation_type_annotation, invocation_type_field),
)
cls.__doc__ = docstring
# TODO: how to type this correctly? it's typed as ModelMetaclass, a private class in pydantic
BaseInvocation.register_invocation(cls) # type: ignore
return cls
return wrapper
GenericBaseInvocationOutput = TypeVar("GenericBaseInvocationOutput", bound=BaseInvocationOutput)
TBaseInvocationOutput = TypeVar("TBaseInvocationOutput", bound=BaseInvocationOutput)
def invocation_output(
output_type: str,
) -> Callable[[Type[GenericBaseInvocationOutput]], Type[GenericBaseInvocationOutput]]:
) -> Callable[[Type[TBaseInvocationOutput]], Type[TBaseInvocationOutput]]:
"""
Adds metadata to an invocation output.
:param str output_type: The type of the invocation output. Must be unique among all invocation outputs.
"""
def wrapper(cls: Type[GenericBaseInvocationOutput]) -> Type[GenericBaseInvocationOutput]:
def wrapper(cls: Type[TBaseInvocationOutput]) -> Type[TBaseInvocationOutput]:
# Validate output types on creation of invocation output classes
# TODO: ensure unique?
if re.compile(r"^\S+$").match(output_type) is None:
raise ValueError(f'"output_type" must consist of non-whitespace characters, got "{output_type}"')
# Add the output type to the pydantic model of the invocation output
output_type_annotation = Literal[output_type] # type: ignore
output_type_field = ModelField.infer(
name="type",
value=output_type,
annotation=output_type_annotation,
class_validators=None,
config=cls.__config__,
)
cls.__fields__.update({"type": output_type_field})
if output_type in BaseInvocationOutput.get_output_types():
raise ValueError(f'Invocation type "{output_type}" already exists')
# to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html
if annotations := cls.__dict__.get("__annotations__", None):
annotations.update({"type": output_type_annotation})
# Add the output type to the model.
output_type_annotation = Literal[output_type] # type: ignore
output_type_field = Field(
title="type",
default=output_type,
)
docstring = cls.__doc__
cls = create_model(
cls.__qualname__,
__base__=cls,
__module__=cls.__module__,
type=(output_type_annotation, output_type_field),
)
cls.__doc__ = docstring
BaseInvocationOutput.register_output(cls) # type: ignore # TODO: how to type this correctly?
return cls
return wrapper
GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel)

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

@ -2,7 +2,7 @@
import numpy as np
from pydantic import validator
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput
from invokeai.app.util.misc import SEED_MAX, get_random_seed
@ -20,9 +20,9 @@ class RangeInvocation(BaseInvocation):
stop: int = InputField(default=10, description="The stop of the range")
step: int = InputField(default=1, description="The step of the range")
@validator("stop")
def stop_gt_start(cls, v, values):
if "start" in values and v <= values["start"]:
@field_validator("stop")
def stop_gt_start(cls, v: int, info: ValidationInfo):
if "start" in info.data and v <= info.data["start"]:
raise ValueError("stop must be greater than start")
return v

70
invokeai/app/invocations/compel.py
View File

@ -1,6 +1,6 @@
import re
from dataclasses import dataclass
from typing import List, Union
from typing import List, Optional, Union
import torch
from compel import Compel, ReturnedEmbeddingsType
@ -43,7 +43,13 @@ class ConditioningFieldData:
# PerpNeg = "perp_neg"
@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning", version="1.0.0")
@invocation(
"compel",
title="Prompt",
tags=["prompt", "compel"],
category="conditioning",
version="1.0.0",
)
class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning."""
@ -61,17 +67,19 @@ class CompelInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(),
**self.clip.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(),
**self.clip.text_encoder.model_dump(),
context=context,
)
def _lora_loader():
for lora in self.clip.loras:
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}), context=context
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
@ -160,11 +168,11 @@ class SDXLPromptInvocationBase:
zero_on_empty: bool,
):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(),
**clip_field.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(),
**clip_field.text_encoder.model_dump(),
context=context,
)
@ -172,7 +180,11 @@ class SDXLPromptInvocationBase:
if prompt == "" and zero_on_empty:
cpu_text_encoder = text_encoder_info.context.model
c = torch.zeros(
(1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size),
(
1,
cpu_text_encoder.config.max_position_embeddings,
cpu_text_encoder.config.hidden_size,
),
dtype=text_encoder_info.context.cache.precision,
)
if get_pooled:
@ -186,7 +198,9 @@ class SDXLPromptInvocationBase:
def _lora_loader():
for lora in clip_field.loras:
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}), context=context
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
@ -273,8 +287,16 @@ class SDXLPromptInvocationBase:
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
prompt: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
style: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
prompt: str = InputField(
default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
)
style: str = InputField(
default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
)
original_width: int = InputField(default=1024, description="")
original_height: int = InputField(default=1024, description="")
crop_top: int = InputField(default=0, description="")
@ -310,7 +332,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
[
c1,
torch.zeros(
(c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]), device=c1.device, dtype=c1.dtype
(c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]),
device=c1.device,
dtype=c1.dtype,
),
],
dim=1,
@ -321,7 +345,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
[
c2,
torch.zeros(
(c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]), device=c2.device, dtype=c2.dtype
(c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]),
device=c2.device,
dtype=c2.dtype,
),
],
dim=1,
@ -359,7 +385,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
"""Parse prompt using compel package to conditioning."""
style: str = InputField(
default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea
default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
) # TODO: ?
original_width: int = InputField(default=1024, description="")
original_height: int = InputField(default=1024, description="")
@ -403,10 +431,16 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
class ClipSkipInvocationOutput(BaseInvocationOutput):
"""Clip skip node output"""
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning", version="1.0.0")
@invocation(
"clip_skip",
title="CLIP Skip",
tags=["clipskip", "clip", "skip"],
category="conditioning",
version="1.0.0",
)
class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""
@ -421,7 +455,9 @@ class ClipSkipInvocation(BaseInvocation):
def get_max_token_count(
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
tokenizer,
prompt: Union[FlattenedPrompt, Blend, Conjunction],
truncate_if_too_long=False,
) -> int:
if type(prompt) is Blend:
blend: Blend = prompt

24
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -2,7 +2,7 @@
# initial implementation by Gregg Helt, 2023
# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
from builtins import bool, float
from typing import Dict, List, Literal, Optional, Union
from typing import Dict, List, Literal, Union
import cv2
import numpy as np
@ -24,7 +24,7 @@ from controlnet_aux import (
)
from controlnet_aux.util import HWC3, ade_palette
from PIL import Image
from pydantic import BaseModel, Field, validator
from pydantic import BaseModel, ConfigDict, Field, field_validator
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
@ -57,6 +57,8 @@ class ControlNetModelField(BaseModel):
model_name: str = Field(description="Name of the ControlNet model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class ControlField(BaseModel):
image: ImageField = Field(description="The control image")
@ -71,7 +73,7 @@ class ControlField(BaseModel):
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@validator("control_weight")
@field_validator("control_weight")
def validate_control_weight(cls, v):
"""Validate that all control weights in the valid range"""
if isinstance(v, list):
@ -124,9 +126,7 @@ class ControlNetInvocation(BaseInvocation):
)
@invocation(
"image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0"
)
# This invocation exists for other invocations to subclass it - do not register with @invocation!
class ImageProcessorInvocation(BaseInvocation):
"""Base class for invocations that preprocess images for ControlNet"""
@ -393,9 +393,9 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
h: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
w: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
f: Optional[int] = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
def run_processor(self, image):
content_shuffle_processor = ContentShuffleDetector()
@ -575,14 +575,14 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
def run_processor(self, image: Image.Image):
image = image.convert("RGB")
image = np.array(image, dtype=np.uint8)
height, width = image.shape[:2]
np_image = np.array(image, dtype=np.uint8)
height, width = np_image.shape[:2]
width_tile_size = min(self.color_map_tile_size, width)
height_tile_size = min(self.color_map_tile_size, height)
color_map = cv2.resize(
image,
np_image,
(width // width_tile_size, height // height_tile_size),
interpolation=cv2.INTER_CUBIC,
)

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

@ -8,7 +8,7 @@ import numpy as np
from mediapipe.python.solutions.face_mesh import FaceMesh # type: ignore[import]
from PIL import Image, ImageDraw, ImageFilter, ImageFont, ImageOps
from PIL.Image import Image as ImageType
from pydantic import validator
from pydantic import field_validator
import invokeai.assets.fonts as font_assets
from invokeai.app.invocations.baseinvocation import (
@ -550,7 +550,7 @@ class FaceMaskInvocation(BaseInvocation):
)
invert_mask: bool = InputField(default=False, description="Toggle to invert the mask")
@validator("face_ids")
@field_validator("face_ids")
def validate_comma_separated_ints(cls, v) -> str:
comma_separated_ints_regex = re.compile(r"^\d*(,\d+)*$")
if comma_separated_ints_regex.match(v) is None:

154
invokeai/app/invocations/image.py
View File

@ -36,7 +36,13 @@ class ShowImageInvocation(BaseInvocation):
)
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
@invocation(
"blank_image",
title="Blank Image",
tags=["image"],
category="image",
version="1.0.0",
)
class BlankImageInvocation(BaseInvocation):
"""Creates a blank image and forwards it to the pipeline"""
@ -65,7 +71,13 @@ class BlankImageInvocation(BaseInvocation):
)
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
@invocation(
"img_crop",
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.0.0",
)
class ImageCropInvocation(BaseInvocation):
"""Crops an image to a specified box. The box can be outside of the image."""
@ -98,7 +110,13 @@ class ImageCropInvocation(BaseInvocation):
)
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
@invocation(
"img_paste",
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.0.1",
)
class ImagePasteInvocation(BaseInvocation):
"""Pastes an image into another image."""
@ -151,7 +169,13 @@ class ImagePasteInvocation(BaseInvocation):
)
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
@invocation(
"tomask",
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.0.0",
)
class MaskFromAlphaInvocation(BaseInvocation):
"""Extracts the alpha channel of an image as a mask."""
@ -182,7 +206,13 @@ class MaskFromAlphaInvocation(BaseInvocation):
)
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
@invocation(
"img_mul",
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.0.0",
)
class ImageMultiplyInvocation(BaseInvocation):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
@ -215,7 +245,13 @@ 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")
@invocation(
"img_chan",
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.0.0",
)
class ImageChannelInvocation(BaseInvocation):
"""Gets a channel from an image."""
@ -247,7 +283,13 @@ 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")
@invocation(
"img_conv",
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.0.0",
)
class ImageConvertInvocation(BaseInvocation):
"""Converts an image to a different mode."""
@ -276,7 +318,13 @@ class ImageConvertInvocation(BaseInvocation):
)
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
@invocation(
"img_blur",
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.0.0",
)
class ImageBlurInvocation(BaseInvocation):
"""Blurs an image"""
@ -330,7 +378,13 @@ PIL_RESAMPLING_MAP = {
}
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
@invocation(
"img_resize",
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.0.0",
)
class ImageResizeInvocation(BaseInvocation):
"""Resizes an image to specific dimensions"""
@ -359,7 +413,7 @@ class ImageResizeInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -370,7 +424,13 @@ class ImageResizeInvocation(BaseInvocation):
)
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
@invocation(
"img_scale",
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.0.0",
)
class ImageScaleInvocation(BaseInvocation):
"""Scales an image by a factor"""
@ -411,7 +471,13 @@ class ImageScaleInvocation(BaseInvocation):
)
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
@invocation(
"img_lerp",
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.0.0",
)
class ImageLerpInvocation(BaseInvocation):
"""Linear interpolation of all pixels of an image"""
@ -444,7 +510,13 @@ class ImageLerpInvocation(BaseInvocation):
)
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
@invocation(
"img_ilerp",
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.0.0",
)
class ImageInverseLerpInvocation(BaseInvocation):
"""Inverse linear interpolation of all pixels of an image"""
@ -456,7 +528,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
image = context.services.images.get_pil_image(self.image.image_name)
image_arr = numpy.asarray(image, dtype=numpy.float32)
image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255
image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255 # type: ignore [assignment]
ilerp_image = Image.fromarray(numpy.uint8(image_arr))
@ -477,7 +549,13 @@ class ImageInverseLerpInvocation(BaseInvocation):
)
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
@invocation(
"img_nsfw",
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.0.0",
)
class ImageNSFWBlurInvocation(BaseInvocation):
"""Add blur to NSFW-flagged images"""
@ -505,7 +583,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -515,7 +593,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
height=image_dto.height,
)
def _get_caution_img(self) -> Image:
def _get_caution_img(self) -> Image.Image:
import invokeai.app.assets.images as image_assets
caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
@ -523,7 +601,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
@invocation(
"img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image", version="1.0.0"
"img_watermark",
title="Add Invisible Watermark",
tags=["image", "watermark"],
category="image",
version="1.0.0",
)
class ImageWatermarkInvocation(BaseInvocation):
"""Add an invisible watermark to an image"""
@ -544,7 +626,7 @@ class ImageWatermarkInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -555,7 +637,13 @@ class ImageWatermarkInvocation(BaseInvocation):
)
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
@invocation(
"mask_edge",
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.0.0",
)
class MaskEdgeInvocation(BaseInvocation):
"""Applies an edge mask to an image"""
@ -601,7 +689,11 @@ class MaskEdgeInvocation(BaseInvocation):
@invocation(
"mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image", version="1.0.0"
"mask_combine",
title="Combine Masks",
tags=["image", "mask", "multiply"],
category="image",
version="1.0.0",
)
class MaskCombineInvocation(BaseInvocation):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
@ -632,7 +724,13 @@ class MaskCombineInvocation(BaseInvocation):
)
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
@invocation(
"color_correct",
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.0.0",
)
class ColorCorrectInvocation(BaseInvocation):
"""
Shifts the colors of a target image to match the reference image, optionally
@ -742,7 +840,13 @@ class ColorCorrectInvocation(BaseInvocation):
)
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
@invocation(
"img_hue_adjust",
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.0.0",
)
class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image."""
@ -980,7 +1084,7 @@ class SaveImageInvocation(BaseInvocation):
image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: CoreMetadata = InputField(
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
@ -997,7 +1101,7 @@ class SaveImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)

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

@ -2,7 +2,7 @@ import os
from builtins import float
from typing import List, Union
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
@ -25,11 +25,15 @@ class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class CLIPVisionModelField(BaseModel):
model_name: str = Field(description="Name of the CLIP Vision image encoder model")
base_model: BaseModelType = Field(description="Base model (usually 'Any')")
model_config = ConfigDict(protected_namespaces=())
class IPAdapterField(BaseModel):
image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")

143
invokeai/app/invocations/latent.py
View File

@ -19,7 +19,7 @@ from diffusers.models.attention_processor import (
)
from diffusers.schedulers import DPMSolverSDEScheduler
from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import validator
from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField
@ -84,12 +84,20 @@ class SchedulerOutput(BaseInvocationOutput):
scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents", version="1.0.0")
@invocation(
"scheduler",
title="Scheduler",
tags=["scheduler"],
category="latents",
version="1.0.0",
)
class SchedulerInvocation(BaseInvocation):
"""Selects a scheduler."""
scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler
default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
)
def invoke(self, context: InvocationContext) -> SchedulerOutput:
@ -97,7 +105,11 @@ class SchedulerInvocation(BaseInvocation):
@invocation(
"create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents", version="1.0.0"
"create_denoise_mask",
title="Create Denoise Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.0",
)
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
@ -106,7 +118,11 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32, ui_order=4)
fp32: bool = InputField(
default=DEFAULT_PRECISION == "float32",
description=FieldDescriptions.fp32,
ui_order=4,
)
def prep_mask_tensor(self, mask_image):
if mask_image.mode != "L":
@ -134,7 +150,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
if image is not None:
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
context=context,
)
@ -167,7 +183,7 @@ def get_scheduler(
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict(),
**scheduler_info.model_dump(),
context=context,
)
with orig_scheduler_info as orig_scheduler:
@ -209,34 +225,64 @@ class DenoiseLatentsInvocation(BaseInvocation):
negative_conditioning: ConditioningField = InputField(
description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
)
noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3)
noise: Optional[LatentsField] = InputField(
default=None,
description=FieldDescriptions.noise,
input=Input.Connection,
ui_order=3,
)
steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
cfg_scale: Union[float, List[float]] = InputField(
default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
)
denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
denoising_start: float = InputField(
default=0.0,
ge=0,
le=1,
description=FieldDescriptions.denoising_start,
)
denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler
default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
)
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
control: Union[ControlField, list[ControlField]] = InputField(
unet: UNetField = InputField(
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
ui_order=2,
)
control: Optional[Union[ControlField, list[ControlField]]] = InputField(
default=None,
input=Input.Connection,
ui_order=5,
)
ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
description=FieldDescriptions.ip_adapter,
title="IP-Adapter",
default=None,
input=Input.Connection,
ui_order=6,
)
t2i_adapter: Union[T2IAdapterField, list[T2IAdapterField]] = InputField(
description=FieldDescriptions.t2i_adapter, title="T2I-Adapter", default=None, input=Input.Connection, ui_order=7
t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]] = InputField(
description=FieldDescriptions.t2i_adapter,
title="T2I-Adapter",
default=None,
input=Input.Connection,
ui_order=7,
)
latents: Optional[LatentsField] = InputField(
default=None, description=FieldDescriptions.latents, input=Input.Connection
)
latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
denoise_mask: Optional[DenoiseMaskField] = InputField(
default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=8
default=None,
description=FieldDescriptions.mask,
input=Input.Connection,
ui_order=8,
)
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -259,7 +305,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
node=self.model_dump(),
source_node_id=source_node_id,
base_model=base_model,
)
@ -459,6 +505,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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)
)
@ -633,7 +680,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
# TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
# below. Investigate whether this is appropriate.
t2i_adapter_data = self.run_t2i_adapters(
context, self.t2i_adapter, latents.shape, do_classifier_free_guidance=True
context,
self.t2i_adapter,
latents.shape,
do_classifier_free_guidance=True,
)
# Get the source node id (we are invoking the prepared node)
@ -646,7 +696,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
**lora.model_dump(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
@ -654,7 +704,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
**self.unet.unet.model_dump(),
context=context,
)
with (
@ -705,7 +755,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
denoising_end=self.denoising_end,
)
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
(
result_latents,
result_attention_map_saver,
) = pipeline.latents_from_embeddings(
latents=latents,
timesteps=timesteps,
init_timestep=init_timestep,
@ -733,7 +786,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
@invocation(
"l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0"
"l2i",
title="Latents to Image",
tags=["latents", "image", "vae", "l2i"],
category="latents",
version="1.0.0",
)
class LatentsToImageInvocation(BaseInvocation):
"""Generates an image from latents."""
@ -748,7 +805,7 @@ class LatentsToImageInvocation(BaseInvocation):
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
metadata: CoreMetadata = InputField(
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
@ -759,7 +816,7 @@ class LatentsToImageInvocation(BaseInvocation):
latents = context.services.latents.get(self.latents.latents_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
context=context,
)
@ -821,7 +878,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -835,7 +892,13 @@ class LatentsToImageInvocation(BaseInvocation):
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
@invocation(
"lresize",
title="Resize Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
)
class ResizeLatentsInvocation(BaseInvocation):
"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@ -881,7 +944,13 @@ class ResizeLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
@invocation(
"lscale",
title="Scale Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
)
class ScaleLatentsInvocation(BaseInvocation):
"""Scales latents by a given factor."""
@ -920,7 +989,11 @@ class ScaleLatentsInvocation(BaseInvocation):
@invocation(
"i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents", version="1.0.0"
"i2l",
title="Image to Latents",
tags=["latents", "image", "vae", "i2l"],
category="latents",
version="1.0.0",
)
class ImageToLatentsInvocation(BaseInvocation):
"""Encodes an image into latents."""
@ -984,7 +1057,7 @@ class ImageToLatentsInvocation(BaseInvocation):
image = context.services.images.get_pil_image(self.image.image_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
context=context,
)
@ -1012,7 +1085,13 @@ class ImageToLatentsInvocation(BaseInvocation):
return vae.encode(image_tensor).latents
@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
@invocation(
"lblend",
title="Blend Latents",
tags=["latents", "blend"],
category="latents",
version="1.0.0",
)
class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size."""

15
invokeai/app/invocations/math.py
View File

@ -3,7 +3,7 @@
from typing import Literal
import numpy as np
from pydantic import validator
from pydantic import field_validator
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
@ -72,7 +72,14 @@ class RandomIntInvocation(BaseInvocation):
return IntegerOutput(value=np.random.randint(self.low, self.high))
@invocation("rand_float", title="Random Float", tags=["math", "float", "random"], category="math", version="1.0.0")
@invocation(
"rand_float",
title="Random Float",
tags=["math", "float", "random"],
category="math",
version="1.0.1",
use_cache=False,
)
class RandomFloatInvocation(BaseInvocation):
"""Outputs a single random float"""
@ -178,7 +185,7 @@ class IntegerMathInvocation(BaseInvocation):
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
@validator("b")
@field_validator("b")
def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero")
@ -252,7 +259,7 @@ class FloatMathInvocation(BaseInvocation):
a: float = InputField(default=0, description=FieldDescriptions.num_1)
b: float = InputField(default=0, description=FieldDescriptions.num_2)
@validator("b")
@field_validator("b")
def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero")

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

@ -48,7 +48,7 @@ class CoreMetadata(BaseModelExcludeNull):
default=None,
description="The generation mode that output this image",
)
created_by: Optional[str] = Field(description="The name of the creator of the 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")
@ -223,4 +223,4 @@ class MetadataAccumulatorInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
"""Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict()))
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))

78
invokeai/app/invocations/model.py
View File

@ -1,7 +1,7 @@
import copy
from typing import List, Optional
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from ...backend.model_management import BaseModelType, ModelType, SubModelType
from .baseinvocation import (
@ -24,6 +24,8 @@ class ModelInfo(BaseModel):
model_type: ModelType = Field(description="Info to load submodel")
submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
model_config = ConfigDict(protected_namespaces=())
class LoraInfo(ModelInfo):
weight: float = Field(description="Lora's weight which to use when apply to model")
@ -65,6 +67,8 @@ class MainModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
class LoRAModelField(BaseModel):
"""LoRA model field"""
@ -72,8 +76,16 @@ class LoRAModelField(BaseModel):
model_name: str = Field(description="Name of the LoRA model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
@invocation("main_model_loader", title="Main Model", tags=["model"], category="model", version="1.0.0")
@invocation(
"main_model_loader",
title="Main Model",
tags=["model"],
category="model",
version="1.0.0",
)
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
@ -180,10 +192,16 @@ class LoraLoaderInvocation(BaseInvocation):
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
)
clip: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP"
default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP",
)
def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
@ -244,20 +262,35 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model", version="1.0.0")
@invocation(
"sdxl_lora_loader",
title="SDXL LoRA",
tags=["lora", "model"],
category="model",
version="1.0.0",
)
class SDXLLoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
)
clip: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1"
default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP 1",
)
clip2: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2"
default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP 2",
)
def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
@ -330,6 +363,8 @@ class VAEModelField(BaseModel):
model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
@invocation_output("vae_loader_output")
class VaeLoaderOutput(BaseInvocationOutput):
@ -343,7 +378,10 @@ class VaeLoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
vae_model: VAEModelField = InputField(
description=FieldDescriptions.vae_model, input=Input.Direct, ui_type=UIType.VaeModel, title="VAE"
description=FieldDescriptions.vae_model,
input=Input.Direct,
ui_type=UIType.VaeModel,
title="VAE",
)
def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
@ -372,19 +410,31 @@ class VaeLoaderInvocation(BaseInvocation):
class SeamlessModeOutput(BaseInvocationOutput):
"""Modified Seamless Model output"""
unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet")
vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model", version="1.0.0")
@invocation(
"seamless",
title="Seamless",
tags=["seamless", "model"],
category="model",
version="1.0.0",
)
class SeamlessModeInvocation(BaseInvocation):
"""Applies the seamless transformation to the Model UNet and VAE."""
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
)
vae: Optional[VaeField] = InputField(
default=None, description=FieldDescriptions.vae_model, input=Input.Connection, title="VAE"
default=None,
description=FieldDescriptions.vae_model,
input=Input.Connection,
title="VAE",
)
seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")

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

@ -2,7 +2,7 @@
import torch
from pydantic import validator
from pydantic import field_validator
from invokeai.app.invocations.latent import LatentsField
from invokeai.app.util.misc import SEED_MAX, get_random_seed
@ -65,7 +65,7 @@ Nodes
class NoiseOutput(BaseInvocationOutput):
"""Invocation noise output"""
noise: LatentsField = OutputField(default=None, description=FieldDescriptions.noise)
noise: LatentsField = OutputField(description=FieldDescriptions.noise)
width: int = OutputField(description=FieldDescriptions.width)
height: int = OutputField(description=FieldDescriptions.height)
@ -78,7 +78,13 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
)
@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents", version="1.0.0")
@invocation(
"noise",
title="Noise",
tags=["latents", "noise"],
category="latents",
version="1.0.0",
)
class NoiseInvocation(BaseInvocation):
"""Generates latent noise."""
@ -105,7 +111,7 @@ class NoiseInvocation(BaseInvocation):
description="Use CPU for noise generation (for reproducible results across platforms)",
)
@validator("seed", pre=True)
@field_validator("seed", mode="before")
def modulo_seed(cls, v):
"""Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
return v % (SEED_MAX + 1)

30
invokeai/app/invocations/onnx.py
View File

@ -9,7 +9,7 @@ from typing import List, Literal, Optional, Union
import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, Field, validator
from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm
from invokeai.app.invocations.metadata import CoreMetadata
@ -63,14 +63,17 @@ class ONNXPromptInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(),
**self.clip.tokenizer.model_dump(),
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(),
**self.clip.text_encoder.model_dump(),
)
with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder: # , ExitStack() as stack:
loras = [
(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
(
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.clip.loras
]
@ -175,14 +178,14 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
description=FieldDescriptions.unet,
input=Input.Connection,
)
control: Optional[Union[ControlField, list[ControlField]]] = InputField(
control: Union[ControlField, list[ControlField]] = InputField(
default=None,
description=FieldDescriptions.control,
)
# seamless: bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'")
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -241,7 +244,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
node=self.model_dump(),
source_node_id=source_node_id,
)
@ -254,12 +257,15 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
eta=0.0,
)
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump())
with unet_info as unet: # , ExitStack() as stack:
# loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
loras = [
(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
(
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.unet.loras
]
@ -346,7 +352,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
)
# clear memory as vae decode can request a lot
@ -375,7 +381,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -403,6 +409,8 @@ class OnnxModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0")
class OnnxModelLoaderInvocation(BaseInvocation):

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

@ -44,13 +44,22 @@ from invokeai.app.invocations.primitives import FloatCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@invocation("float_range", title="Float Range", tags=["math", "range"], category="math", version="1.0.0")
@invocation(
"float_range",
title="Float Range",
tags=["math", "range"],
category="math",
version="1.0.0",
)
class FloatLinearRangeInvocation(BaseInvocation):
"""Creates a range"""
start: float = InputField(default=5, description="The first value of the range")
stop: float = InputField(default=10, description="The last value of the range")
steps: int = InputField(default=30, description="number of values to interpolate over (including start and stop)")
steps: int = InputField(
default=30,
description="number of values to interpolate over (including start and stop)",
)
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
param_list = list(np.linspace(self.start, self.stop, self.steps))
@ -95,7 +104,13 @@ EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
# actually I think for now could just use CollectionOutput (which is list[Any]
@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step", version="1.0.0")
@invocation(
"step_param_easing",
title="Step Param Easing",
tags=["step", "easing"],
category="step",
version="1.0.0",
)
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""
@ -159,7 +174,9 @@ class StepParamEasingInvocation(BaseInvocation):
context.services.logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = num_easing_steps % 2 == 0 # even number of steps
easing_function = easing_class(
start=self.start_value, end=self.end_value, duration=base_easing_duration - 1
start=self.start_value,
end=self.end_value,
duration=base_easing_duration - 1,
)
base_easing_vals = list()
for step_index in range(base_easing_duration):
@ -199,7 +216,11 @@ class StepParamEasingInvocation(BaseInvocation):
#
else: # no mirroring (default)
easing_function = easing_class(start=self.start_value, end=self.end_value, duration=num_easing_steps - 1)
easing_function = easing_class(
start=self.start_value,
end=self.end_value,
duration=num_easing_steps - 1,
)
for step_index in range(num_easing_steps):
step_val = easing_function.ease(step_index)
easing_list.append(step_val)

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

@ -3,7 +3,7 @@ from typing import Optional, Union
import numpy as np
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
from pydantic import validator
from pydantic import field_validator
from invokeai.app.invocations.primitives import StringCollectionOutput
@ -21,7 +21,10 @@ from .baseinvocation import BaseInvocation, InputField, InvocationContext, UICom
class DynamicPromptInvocation(BaseInvocation):
"""Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
prompt: str = InputField(description="The prompt to parse with dynamicprompts", ui_component=UIComponent.Textarea)
prompt: str = InputField(
description="The prompt to parse with dynamicprompts",
ui_component=UIComponent.Textarea,
)
max_prompts: int = InputField(default=1, description="The number of prompts to generate")
combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
@ -36,21 +39,31 @@ class DynamicPromptInvocation(BaseInvocation):
return StringCollectionOutput(collection=prompts)
@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt", version="1.0.0")
@invocation(
"prompt_from_file",
title="Prompts from File",
tags=["prompt", "file"],
category="prompt",
version="1.0.0",
)
class PromptsFromFileInvocation(BaseInvocation):
"""Loads prompts from a text file"""
file_path: str = InputField(description="Path to prompt text file")
pre_prompt: Optional[str] = InputField(
default=None, description="String to prepend to each prompt", ui_component=UIComponent.Textarea
default=None,
description="String to prepend to each prompt",
ui_component=UIComponent.Textarea,
)
post_prompt: Optional[str] = InputField(
default=None, description="String to append to each prompt", ui_component=UIComponent.Textarea
default=None,
description="String to append to each prompt",
ui_component=UIComponent.Textarea,
)
start_line: int = InputField(default=1, ge=1, description="Line in the file to start start from")
max_prompts: int = InputField(default=1, ge=0, description="Max lines to read from file (0=all)")
@validator("file_path")
@field_validator("file_path")
def file_path_exists(cls, v):
if not exists(v):
raise ValueError(FileNotFoundError)
@ -79,6 +92,10 @@ class PromptsFromFileInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> StringCollectionOutput:
prompts = self.promptsFromFile(
self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts
self.file_path,
self.pre_prompt,
self.post_prompt,
self.start_line,
self.max_prompts,
)
return StringCollectionOutput(collection=prompts)

4
invokeai/app/invocations/t2i_adapter.py
View File

@ -1,6 +1,6 @@
from typing import Union
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
@ -23,6 +23,8 @@ class T2IAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the T2I-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class T2IAdapterField(BaseModel):
image: ImageField = Field(description="The T2I-Adapter image prompt.")

3
invokeai/app/invocations/upscale.py
View File

@ -7,6 +7,7 @@ import numpy as np
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from PIL import Image
from pydantic import ConfigDict
from realesrgan import RealESRGANer
from invokeai.app.invocations.primitives import ImageField, ImageOutput
@ -38,6 +39,8 @@ class ESRGANInvocation(BaseInvocation):
default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)"
)
model_config = ConfigDict(protected_namespaces=())
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
models_path = context.services.configuration.models_path

12
invokeai/app/services/board_records/board_records_common.py
View File

@ -1,7 +1,7 @@
from datetime import datetime
from typing import Optional, Union
from pydantic import BaseModel, Extra, Field
from pydantic import BaseModel, Field
from invokeai.app.util.misc import get_iso_timestamp
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
@ -18,9 +18,9 @@ class BoardRecord(BaseModelExcludeNull):
"""The created timestamp of the image."""
updated_at: Union[datetime, str] = Field(description="The updated timestamp of the board.")
"""The updated timestamp of the image."""
deleted_at: Union[datetime, str, None] = Field(description="The deleted timestamp of the board.")
deleted_at: Optional[Union[datetime, str]] = Field(default=None, description="The deleted timestamp of the board.")
"""The updated timestamp of the image."""
cover_image_name: Optional[str] = Field(description="The name of the cover image of the board.")
cover_image_name: Optional[str] = Field(default=None, description="The name of the cover image of the board.")
"""The name of the cover image of the board."""
@ -46,9 +46,9 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord:
)
class BoardChanges(BaseModel, extra=Extra.forbid):
board_name: Optional[str] = Field(description="The board's new name.")
cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.")
class BoardChanges(BaseModel, extra="forbid"):
board_name: Optional[str] = Field(default=None, description="The board's new name.")
cover_image_name: Optional[str] = Field(default=None, description="The name of the board's new cover image.")
class BoardRecordNotFoundException(Exception):

2
invokeai/app/services/boards/boards_common.py
View File

@ -17,7 +17,7 @@ class BoardDTO(BoardRecord):
def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
"""Converts a board record to a board DTO."""
return BoardDTO(
**board_record.dict(exclude={"cover_image_name"}),
**board_record.model_dump(exclude={"cover_image_name"}),
cover_image_name=cover_image_name,
image_count=image_count,
)

47
invokeai/app/services/config/config_base.py
View File

@ -18,7 +18,7 @@ from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
from omegaconf import DictConfig, ListConfig, OmegaConf
from pydantic import BaseSettings
from pydantic_settings import BaseSettings, SettingsConfigDict
from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str
@ -32,12 +32,14 @@ class InvokeAISettings(BaseSettings):
initconf: ClassVar[Optional[DictConfig]] = None
argparse_groups: ClassVar[Dict] = {}
model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
def parse_args(self, argv: Optional[list] = sys.argv[1:]):
parser = self.get_parser()
opt, unknown_opts = parser.parse_known_args(argv)
if len(unknown_opts) > 0:
print("Unknown args:", unknown_opts)
for name in self.__fields__:
for name in self.model_fields:
if name not in self._excluded():
value = getattr(opt, name)
if isinstance(value, ListConfig):
@ -54,10 +56,12 @@ class InvokeAISettings(BaseSettings):
cls = self.__class__
type = get_args(get_type_hints(cls)["type"])[0]
field_dict = dict({type: dict()})
for name, field in self.__fields__.items():
for name, field in self.model_fields.items():
if name in cls._excluded_from_yaml():
continue
category = field.field_info.extra.get("category") or "Uncategorized"
category = (
field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
)
value = getattr(self, name)
if category not in field_dict[type]:
field_dict[type][category] = dict()
@ -73,7 +77,7 @@ class InvokeAISettings(BaseSettings):
else:
settings_stanza = "Uncategorized"
env_prefix = getattr(cls.Config, "env_prefix", None)
env_prefix = getattr(cls.model_config, "env_prefix", None)
env_prefix = env_prefix if env_prefix is not None else settings_stanza.upper()
initconf = (
@ -89,14 +93,18 @@ class InvokeAISettings(BaseSettings):
for key, value in os.environ.items():
upcase_environ[key.upper()] = value
fields = cls.__fields__
fields = cls.model_fields
cls.argparse_groups = {}
for name, field in fields.items():
if name not in cls._excluded():
current_default = field.default
category = field.field_info.extra.get("category", "Uncategorized")
category = (
field.json_schema_extra.get("category", "Uncategorized")
if field.json_schema_extra
else "Uncategorized"
)
env_name = env_prefix + "_" + name
if category in initconf and name in initconf.get(category):
field.default = initconf.get(category).get(name)
@ -146,11 +154,6 @@ class InvokeAISettings(BaseSettings):
"tiled_decode",
]
class Config:
env_file_encoding = "utf-8"
arbitrary_types_allowed = True
case_sensitive = True
@classmethod
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
field_type = get_type_hints(cls).get(name)
@ -161,7 +164,7 @@ class InvokeAISettings(BaseSettings):
if field.default_factory is None
else field.default_factory()
)
if category := field.field_info.extra.get("category"):
if category := (field.json_schema_extra.get("category", None) if field.json_schema_extra else None):
if category not in cls.argparse_groups:
cls.argparse_groups[category] = command_parser.add_argument_group(category)
argparse_group = cls.argparse_groups[category]
@ -169,7 +172,7 @@ class InvokeAISettings(BaseSettings):
argparse_group = command_parser
if get_origin(field_type) == Literal:
allowed_values = get_args(field.type_)
allowed_values = get_args(field.annotation)
allowed_types = set()
for val in allowed_values:
allowed_types.add(type(val))
@ -182,7 +185,7 @@ class InvokeAISettings(BaseSettings):
type=field_type,
default=default,
choices=allowed_values,
help=field.field_info.description,
help=field.description,
)
elif get_origin(field_type) == Union:
@ -191,7 +194,7 @@ class InvokeAISettings(BaseSettings):
dest=name,
type=int_or_float_or_str,
default=default,
help=field.field_info.description,
help=field.description,
)
elif get_origin(field_type) == list:
@ -199,17 +202,17 @@ class InvokeAISettings(BaseSettings):
f"--{name}",
dest=name,
nargs="*",
type=field.type_,
type=field.annotation,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
help=field.description,
)
else:
argparse_group.add_argument(
f"--{name}",
dest=name,
type=field.type_,
type=field.annotation,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
help=field.description,
)

138
invokeai/app/services/config/config_default.py
View File

@ -144,8 +144,8 @@ which is set to the desired top-level name. For example, to create a
class InvokeBatch(InvokeAISettings):
type: Literal["InvokeBatch"] = "InvokeBatch"
node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources')
cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources')
node_count : int = Field(default=1, description="Number of nodes to run on", json_schema_extra=dict(category='Resources'))
cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", json_schema_extra=dict(category='Resources'))
This will now read and write from the "InvokeBatch" section of the
config file, look for environment variables named INVOKEBATCH_*, and
@ -175,7 +175,8 @@ from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
from omegaconf import DictConfig, OmegaConf
from pydantic import Field, parse_obj_as
from pydantic import Field, TypeAdapter
from pydantic_settings import SettingsConfigDict
from .config_base import InvokeAISettings
@ -185,6 +186,21 @@ LEGACY_INIT_FILE = Path("invokeai.init")
DEFAULT_MAX_VRAM = 0.5
class Categories(object):
WebServer = dict(category="Web Server")
Features = dict(category="Features")
Paths = dict(category="Paths")
Logging = dict(category="Logging")
Development = dict(category="Development")
Other = dict(category="Other")
ModelCache = dict(category="Model Cache")
Device = dict(category="Device")
Generation = dict(category="Generation")
Queue = dict(category="Queue")
Nodes = dict(category="Nodes")
MemoryPerformance = dict(category="Memory/Performance")
class InvokeAIAppConfig(InvokeAISettings):
"""
Generate images using Stable Diffusion. Use "invokeai" to launch
@ -201,86 +217,88 @@ class InvokeAIAppConfig(InvokeAISettings):
type: Literal["InvokeAI"] = "InvokeAI"
# WEB
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
port : int = Field(default=9090, description="Port to bind to", category='Web Server')
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
allow_credentials : bool = Field(default=True, description="Allow CORS credentials", category='Web Server')
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
host : str = Field(default="127.0.0.1", description="IP address to bind to", json_schema_extra=Categories.WebServer)
port : int = Field(default=9090, description="Port to bind to", json_schema_extra=Categories.WebServer)
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", json_schema_extra=Categories.WebServer)
allow_credentials : bool = Field(default=True, description="Allow CORS credentials", json_schema_extra=Categories.WebServer)
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", json_schema_extra=Categories.WebServer)
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", json_schema_extra=Categories.WebServer)
# FEATURES
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", json_schema_extra=Categories.Features)
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", json_schema_extra=Categories.Features)
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", json_schema_extra=Categories.Features)
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", json_schema_extra=Categories.Features)
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', json_schema_extra=Categories.Features)
# PATHS
root : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
autoimport_dir : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
lora_dir : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
embedding_dir : Path = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths')
controlnet_dir : Path = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', category='Paths')
conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
models_dir : Path = Field(default='models', description='Path to the models directory', category='Paths')
legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
db_dir : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths')
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
root : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
autoimport_dir : Optional[Path] = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
lora_dir : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
embedding_dir : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
controlnet_dir : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
conf_path : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
models_dir : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
legacy_conf_dir : Optional[Path] = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', 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
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', json_schema_extra=Categories.Logging)
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging")
log_sql : bool = Field(default=False, description="Log SQL queries", category="Logging")
log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', json_schema_extra=Categories.Logging)
log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", json_schema_extra=Categories.Logging)
log_sql : bool = Field(default=False, description="Log SQL queries", json_schema_extra=Categories.Logging)
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", category="Development")
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", json_schema_extra=Categories.Development)
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
version : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
# CACHE
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", category="Model Cache", )
vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", category="Model Cache", )
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", )
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
# DEVICE
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", category="Device", )
precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", category="Device", )
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
# GENERATION
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", )
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", category="Generation", )
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", category="Generation", )
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", json_schema_extra=Categories.Generation)
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", json_schema_extra=Categories.Generation)
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', json_schema_extra=Categories.Generation)
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Generation)
png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
# QUEUE
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", )
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", json_schema_extra=Categories.Queue)
# NODES
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes")
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", category="Nodes")
node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", category="Nodes", )
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", json_schema_extra=Categories.Nodes)
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes)
node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", json_schema_extra=Categories.Nodes)
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance)
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.MemoryPerformance)
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.MemoryPerformance)
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.MemoryPerformance)
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
# fmt: on
class Config:
validate_assignment = True
env_prefix = "INVOKEAI"
model_config = SettingsConfigDict(validate_assignment=True, env_prefix="INVOKEAI")
def parse_args(self, argv: Optional[list[str]] = None, conf: Optional[DictConfig] = None, clobber=False):
def parse_args(
self,
argv: Optional[list[str]] = None,
conf: Optional[DictConfig] = None,
clobber=False,
):
"""
Update settings with contents of init file, environment, and
command-line settings.
@ -308,7 +326,11 @@ class InvokeAIAppConfig(InvokeAISettings):
if self.singleton_init and not clobber:
hints = get_type_hints(self.__class__)
for k in self.singleton_init:
setattr(self, k, parse_obj_as(hints[k], self.singleton_init[k]))
setattr(
self,
k,
TypeAdapter(hints[k]).validate_python(self.singleton_init[k]),
)
@classmethod
def get_config(cls, **kwargs) -> InvokeAIAppConfig:

8
invokeai/app/services/events/events_base.py
View File

@ -2,7 +2,6 @@
from typing import Any, Optional
from invokeai.app.invocations.model import ModelInfo
from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
from invokeai.app.services.session_queue.session_queue_common import (
BatchStatus,
@ -11,6 +10,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
SessionQueueStatus,
)
from invokeai.app.util.misc import get_timestamp
from invokeai.backend.model_management.model_manager import ModelInfo
from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
@ -55,7 +55,7 @@ class EventServiceBase:
graph_execution_state_id=graph_execution_state_id,
node_id=node.get("id"),
source_node_id=source_node_id,
progress_image=progress_image.dict() if progress_image is not None else None,
progress_image=progress_image.model_dump() if progress_image is not None else None,
step=step,
order=order,
total_steps=total_steps,
@ -291,8 +291,8 @@ class EventServiceBase:
started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
),
batch_status=batch_status.dict(),
queue_status=queue_status.dict(),
batch_status=batch_status.model_dump(),
queue_status=queue_status.model_dump(),
),
)

3
invokeai/app/services/image_files/image_files_base.py
View File

@ -1,4 +1,5 @@
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Optional
from PIL.Image import Image as PILImageType
@ -13,7 +14,7 @@ class ImageFileStorageBase(ABC):
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
def get_path(self, image_name: str, thumbnail: bool = False) -> Path:
"""Gets the internal path to an image or thumbnail."""
pass

14
invokeai/app/services/image_records/image_records_base.py
View File

@ -34,8 +34,8 @@ class ImageRecordStorageBase(ABC):
@abstractmethod
def get_many(
self,
offset: Optional[int] = None,
limit: Optional[int] = None,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
@ -69,11 +69,11 @@ class ImageRecordStorageBase(ABC):
image_category: ImageCategory,
width: int,
height: int,
session_id: Optional[str],
node_id: Optional[str],
metadata: Optional[dict],
is_intermediate: bool = False,
starred: bool = False,
is_intermediate: Optional[bool] = False,
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
) -> datetime:
"""Saves an image record."""
pass

8
invokeai/app/services/image_records/image_records_common.py
View File

@ -3,7 +3,7 @@ import datetime
from enum import Enum
from typing import Optional, Union
from pydantic import Extra, Field, StrictBool, StrictStr
from pydantic import Field, StrictBool, StrictStr
from invokeai.app.util.metaenum import MetaEnum
from invokeai.app.util.misc import get_iso_timestamp
@ -129,7 +129,9 @@ class ImageRecord(BaseModelExcludeNull):
"""The created timestamp of the image."""
updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the image.")
"""The updated timestamp of the image."""
deleted_at: Union[datetime.datetime, str, None] = Field(description="The deleted timestamp of the image.")
deleted_at: Optional[Union[datetime.datetime, str]] = Field(
default=None, description="The deleted timestamp of the image."
)
"""The deleted timestamp of the image."""
is_intermediate: bool = Field(description="Whether this is an intermediate image.")
"""Whether this is an intermediate image."""
@ -147,7 +149,7 @@ class ImageRecord(BaseModelExcludeNull):
"""Whether this image is starred."""
class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid):
class ImageRecordChanges(BaseModelExcludeNull, extra="allow"):
"""A set of changes to apply to an image record.
Only limited changes are valid:

20
invokeai/app/services/image_records/image_records_sqlite.py
View File

@ -2,7 +2,7 @@ import json
import sqlite3
import threading
from datetime import datetime
from typing import Optional, cast
from typing import Optional, Union, cast
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
@ -117,7 +117,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
"""
)
def get(self, image_name: str) -> Optional[ImageRecord]:
def get(self, image_name: str) -> ImageRecord:
try:
self._lock.acquire()
@ -223,8 +223,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
def get_many(
self,
offset: Optional[int] = None,
limit: Optional[int] = None,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
@ -249,7 +249,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
"""
query_conditions = ""
query_params = []
query_params: list[Union[int, str, bool]] = []
if image_origin is not None:
query_conditions += """--sql
@ -387,13 +387,13 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
image_name: str,
image_origin: ResourceOrigin,
image_category: ImageCategory,
session_id: Optional[str],
width: int,
height: int,
node_id: Optional[str],
metadata: Optional[dict],
is_intermediate: bool = False,
starred: bool = False,
is_intermediate: Optional[bool] = False,
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
) -> datetime:
try:
metadata_json = None if metadata is None else json.dumps(metadata)

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

@ -49,7 +49,7 @@ class ImageServiceABC(ABC):
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: bool = False,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:

6
invokeai/app/services/images/images_common.py
View File

@ -20,7 +20,9 @@ class ImageUrlsDTO(BaseModelExcludeNull):
class ImageDTO(ImageRecord, ImageUrlsDTO):
"""Deserialized image record, enriched for the frontend."""
board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.")
board_id: Optional[str] = Field(
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
@ -34,7 +36,7 @@ def image_record_to_dto(
) -> ImageDTO:
"""Converts an image record to an image DTO."""
return ImageDTO(
**image_record.dict(),
**image_record.model_dump(),
image_url=image_url,
thumbnail_url=thumbnail_url,
board_id=board_id,

6
invokeai/app/services/images/images_default.py
View File

@ -41,7 +41,7 @@ class ImageService(ImageServiceABC):
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: bool = False,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
@ -146,7 +146,7 @@ class ImageService(ImageServiceABC):
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
def get_metadata(self, image_name: str) -> ImageMetadata:
try:
image_record = self.__invoker.services.image_records.get(image_name)
metadata = self.__invoker.services.image_records.get_metadata(image_name)
@ -174,7 +174,7 @@ class ImageService(ImageServiceABC):
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self.__invoker.services.image_files.get_path(image_name, thumbnail)
return str(self.__invoker.services.image_files.get_path(image_name, thumbnail))
except Exception as e:
self.__invoker.services.logger.error("Problem getting image path")
raise e

9
invokeai/app/services/invocation_cache/invocation_cache_memory.py
View File

@ -58,7 +58,12 @@ class MemoryInvocationCache(InvocationCacheBase):
# If the cache is full, we need to remove the least used
number_to_delete = len(self._cache) + 1 - self._max_cache_size
self._delete_oldest_access(number_to_delete)
self._cache[key] = CachedItem(invocation_output, invocation_output.json())
self._cache[key] = CachedItem(
invocation_output,
invocation_output.model_dump_json(
warnings=False, exclude_defaults=True, exclude_unset=True, include={"type"}
),
)
def _delete_oldest_access(self, number_to_delete: int) -> None:
number_to_delete = min(number_to_delete, len(self._cache))
@ -85,7 +90,7 @@ class MemoryInvocationCache(InvocationCacheBase):
@staticmethod
def create_key(invocation: BaseInvocation) -> int:
return hash(invocation.json(exclude={"id"}))
return hash(invocation.model_dump_json(exclude={"id"}, warnings=False))
def disable(self) -> None:
with self._lock:

10
invokeai/app/services/invocation_processor/invocation_processor_default.py
View File

@ -89,7 +89,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
)
@ -127,9 +127,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
result=outputs.dict(),
result=outputs.model_dump(),
)
self.__invoker.services.performance_statistics.log_stats()
@ -157,7 +157,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
error_type=e.__class__.__name__,
error=error,
@ -187,7 +187,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
error_type=e.__class__.__name__,
error=traceback.format_exc(),

2
invokeai/app/services/invocation_stats/invocation_stats_default.py
View File

@ -72,7 +72,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
)
self.collector.update_invocation_stats(
graph_id=self.graph_id,
invocation_type=self.invocation.type, # type: ignore - `type` is not on the `BaseInvocation` model, but *is* on all invocations
invocation_type=self.invocation.type, # type: ignore # `type` is not on the `BaseInvocation` model, but *is* on all invocations
time_used=time.time() - self.start_time,
vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0,
)

17
invokeai/app/services/item_storage/item_storage_sqlite.py
View File

@ -2,7 +2,7 @@ import sqlite3
import threading
from typing import Generic, Optional, TypeVar, get_args
from pydantic import BaseModel, parse_raw_as
from pydantic import BaseModel, TypeAdapter
from invokeai.app.services.shared.pagination import PaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
@ -18,6 +18,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
_cursor: sqlite3.Cursor
_id_field: str
_lock: threading.RLock
_adapter: Optional[TypeAdapter[T]]
def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
super().__init__()
@ -27,6 +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._create_table()
@ -45,16 +47,21 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._lock.release()
def _parse_item(self, item: str) -> T:
# __orig_class__ is technically an implementation detail of the typing module, not a supported API
item_type = get_args(self.__orig_class__)[0] # type: ignore
return parse_raw_as(item_type, item)
if self._adapter 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)
def set(self, item: T):
try:
self._lock.acquire()
self._cursor.execute(
f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
(item.json(),),
(item.model_dump_json(warnings=False, exclude_none=True),),
)
self._conn.commit()
finally:

2
invokeai/app/services/model_manager/model_manager_base.py
View File

@ -231,7 +231,7 @@ class ModelManagerServiceBase(ABC):
def merge_models(
self,
model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge"
default=None, min_length=2, max_length=3, description="List of model names to merge"
),
base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged"

2
invokeai/app/services/model_manager/model_manager_default.py
View File

@ -327,7 +327,7 @@ class ModelManagerService(ModelManagerServiceBase):
def merge_models(
self,
model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge"
default=None, min_length=2, max_length=3, description="List of model names to merge"
),
base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged"

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."""

82
invokeai/app/services/session_queue/session_queue_common.py
View File

@ -3,8 +3,8 @@ import json
from itertools import chain, product
from typing import Generator, Iterable, Literal, NamedTuple, Optional, TypeAlias, Union, cast
from pydantic import BaseModel, Field, StrictStr, parse_raw_as, root_validator, validator
from pydantic.json import pydantic_encoder
from pydantic import BaseModel, ConfigDict, Field, StrictStr, TypeAdapter, field_validator, model_validator
from pydantic_core import to_jsonable_python
from invokeai.app.invocations.baseinvocation import BaseInvocation
from invokeai.app.services.shared.graph import Graph, GraphExecutionState, NodeNotFoundError
@ -17,7 +17,7 @@ class BatchZippedLengthError(ValueError):
"""Raise when a batch has items of different lengths."""
class BatchItemsTypeError(TypeError):
class BatchItemsTypeError(ValueError): # this cannot be a TypeError in pydantic v2
"""Raise when a batch has items of different types."""
@ -70,7 +70,7 @@ class Batch(BaseModel):
default=1, ge=1, description="Int stating how many times to iterate through all possible batch indices"
)
@validator("data")
@field_validator("data")
def validate_lengths(cls, v: Optional[BatchDataCollection]):
if v is None:
return v
@ -81,7 +81,7 @@ class Batch(BaseModel):
raise BatchZippedLengthError("Zipped batch items must all have the same length")
return v
@validator("data")
@field_validator("data")
def validate_types(cls, v: Optional[BatchDataCollection]):
if v is None:
return v
@ -94,7 +94,7 @@ class Batch(BaseModel):
raise BatchItemsTypeError("All items in a batch must have the same type")
return v
@validator("data")
@field_validator("data")
def validate_unique_field_mappings(cls, v: Optional[BatchDataCollection]):
if v is None:
return v
@ -107,34 +107,35 @@ class Batch(BaseModel):
paths.add(pair)
return v
@root_validator(skip_on_failure=True)
@model_validator(mode="after")
def validate_batch_nodes_and_edges(cls, values):
batch_data_collection = cast(Optional[BatchDataCollection], values["data"])
batch_data_collection = cast(Optional[BatchDataCollection], values.data)
if batch_data_collection is None:
return values
graph = cast(Graph, values["graph"])
graph = cast(Graph, values.graph)
for batch_data_list in batch_data_collection:
for batch_data in batch_data_list:
try:
node = cast(BaseInvocation, graph.get_node(batch_data.node_path))
except NodeNotFoundError:
raise NodeNotFoundError(f"Node {batch_data.node_path} not found in graph")
if batch_data.field_name not in node.__fields__:
if batch_data.field_name not in node.model_fields:
raise NodeNotFoundError(f"Field {batch_data.field_name} not found in node {batch_data.node_path}")
return values
@validator("graph")
@field_validator("graph")
def validate_graph(cls, v: Graph):
v.validate_self()
return v
class Config:
schema_extra = {
"required": [
model_config = ConfigDict(
json_schema_extra=dict(
required=[
"graph",
"runs",
]
}
)
)
# endregion Batch
@ -146,15 +147,21 @@ DEFAULT_QUEUE_ID = "default"
QUEUE_ITEM_STATUS = Literal["pending", "in_progress", "completed", "failed", "canceled"]
adapter_NodeFieldValue = 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 parse_raw_as(list[NodeFieldValue], field_values_raw) if field_values_raw is not None else None
return adapter_NodeFieldValue.validate_json(field_values_raw) if field_values_raw is not None else None
adapter_GraphExecutionState = TypeAdapter(GraphExecutionState)
def get_session(queue_item_dict: dict) -> GraphExecutionState:
session_raw = queue_item_dict.get("session", "{}")
return parse_raw_as(GraphExecutionState, session_raw)
session = adapter_GraphExecutionState.validate_json(session_raw, strict=False)
return session
class SessionQueueItemWithoutGraph(BaseModel):
@ -178,14 +185,14 @@ class SessionQueueItemWithoutGraph(BaseModel):
)
@classmethod
def from_dict(cls, queue_item_dict: dict) -> "SessionQueueItemDTO":
def queue_item_dto_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItemDTO":
# must parse these manually
queue_item_dict["field_values"] = get_field_values(queue_item_dict)
return SessionQueueItemDTO(**queue_item_dict)
class Config:
schema_extra = {
"required": [
model_config = ConfigDict(
json_schema_extra=dict(
required=[
"item_id",
"status",
"batch_id",
@ -196,7 +203,8 @@ class SessionQueueItemWithoutGraph(BaseModel):
"created_at",
"updated_at",
]
}
)
)
class SessionQueueItemDTO(SessionQueueItemWithoutGraph):
@ -207,15 +215,15 @@ class SessionQueueItem(SessionQueueItemWithoutGraph):
session: GraphExecutionState = Field(description="The fully-populated session to be executed")
@classmethod
def from_dict(cls, queue_item_dict: dict) -> "SessionQueueItem":
def queue_item_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItem":
# must parse these manually
queue_item_dict["field_values"] = get_field_values(queue_item_dict)
queue_item_dict["session"] = get_session(queue_item_dict)
return SessionQueueItem(**queue_item_dict)
class Config:
schema_extra = {
"required": [
model_config = ConfigDict(
json_schema_extra=dict(
required=[
"item_id",
"status",
"batch_id",
@ -227,7 +235,8 @@ class SessionQueueItem(SessionQueueItemWithoutGraph):
"created_at",
"updated_at",
]
}
)
)
# endregion Queue Items
@ -267,14 +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"""
@ -321,7 +322,7 @@ def populate_graph(graph: Graph, node_field_values: Iterable[NodeFieldValue]) ->
"""
Populates the given graph with the given batch data items.
"""
graph_clone = graph.copy(deep=True)
graph_clone = graph.model_copy(deep=True)
for item in node_field_values:
node = graph_clone.get_node(item.node_path)
if node is None:
@ -354,7 +355,7 @@ def create_session_nfv_tuples(
for item in batch_datum.items
]
node_field_values_to_zip.append(node_field_values)
data.append(list(zip(*node_field_values_to_zip)))
data.append(list(zip(*node_field_values_to_zip))) # type: ignore [arg-type]
# create generator to yield session,nfv tuples
count = 0
@ -409,11 +410,11 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
values_to_insert.append(
SessionQueueValueToInsert(
queue_id, # queue_id
session.json(), # session (json)
session.model_dump_json(warnings=False, exclude_none=True), # session (json)
session.id, # session_id
batch.batch_id, # batch_id
# must use pydantic_encoder bc field_values is a list of models
json.dumps(field_values, default=pydantic_encoder) if field_values else None, # field_values (json)
json.dumps(field_values, default=to_jsonable_python) if field_values else None, # field_values (json)
priority, # priority
)
)
@ -421,3 +422,6 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
# endregion Util
Batch.model_rebuild(force=True)
SessionQueueItem.model_rebuild(force=True)

40
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.dict(),
queue_item=SessionQueueItemDTO.from_dict(dict(result)),
)
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
try:
self.__lock.acquire()
@ -351,7 +323,7 @@ class SqliteSessionQueue(SessionQueueBase):
self.__lock.release()
if result is None:
return None
queue_item = SessionQueueItem.from_dict(dict(result))
queue_item = SessionQueueItem.queue_item_from_dict(dict(result))
queue_item = self._set_queue_item_status(item_id=queue_item.item_id, status="in_progress")
return queue_item
@ -380,7 +352,7 @@ class SqliteSessionQueue(SessionQueueBase):
self.__lock.release()
if result is None:
return None
return SessionQueueItem.from_dict(dict(result))
return SessionQueueItem.queue_item_from_dict(dict(result))
def get_current(self, queue_id: str) -> Optional[SessionQueueItem]:
try:
@ -404,7 +376,7 @@ class SqliteSessionQueue(SessionQueueBase):
self.__lock.release()
if result is None:
return None
return SessionQueueItem.from_dict(dict(result))
return SessionQueueItem.queue_item_from_dict(dict(result))
def _set_queue_item_status(
self, item_id: int, status: QUEUE_ITEM_STATUS, error: Optional[str] = None
@ -564,7 +536,7 @@ class SqliteSessionQueue(SessionQueueBase):
queue_item = self.get_queue_item(item_id)
if queue_item.status not in ["canceled", "failed", "completed"]:
status = "failed" if error is not None else "canceled"
queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error)
queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error) # type: ignore [arg-type] # mypy seems to not narrow the Literals here
self.__invoker.services.queue.cancel(queue_item.session_id)
self.__invoker.services.events.emit_session_canceled(
queue_item_id=queue_item.item_id,
@ -699,7 +671,7 @@ class SqliteSessionQueue(SessionQueueBase):
self.__lock.release()
if result is None:
raise SessionQueueItemNotFoundError(f"No queue item with id {item_id}")
return SessionQueueItem.from_dict(dict(result))
return SessionQueueItem.queue_item_from_dict(dict(result))
def list_queue_items(
self,
@ -751,7 +723,7 @@ class SqliteSessionQueue(SessionQueueBase):
params.append(limit + 1)
self.__cursor.execute(query, params)
results = cast(list[sqlite3.Row], self.__cursor.fetchall())
items = [SessionQueueItemDTO.from_dict(dict(result)) for result in results]
items = [SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)) for result in results]
has_more = False
if len(items) > limit:
# remove the extra item

6
invokeai/app/services/shared/default_graphs.py
View File

@ -80,10 +80,10 @@ def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[Li
# TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
graphs: list[LibraryGraph] = list()
# text_to_image = graph_library.get(default_text_to_image_graph_id)
text_to_image = graph_library.get(default_text_to_image_graph_id)
# # TODO: Check if the graph is the same as the default one, and if not, update it
# #if text_to_image is None:
# TODO: Check if the graph is the same as the default one, and if not, update it
# if text_to_image is None:
text_to_image = create_text_to_image()
graph_library.set(text_to_image)

70
invokeai/app/services/shared/graph.py
View File

@ -5,7 +5,7 @@ import itertools
from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
import networkx as nx
from pydantic import BaseModel, root_validator, validator
from pydantic import BaseModel, ConfigDict, field_validator, model_validator
from pydantic.fields import Field
# Importing * is bad karma but needed here for node detection
@ -235,7 +235,8 @@ class CollectInvocationOutput(BaseInvocationOutput):
class CollectInvocation(BaseInvocation):
"""Collects values into a collection"""
item: Any = InputField(
item: Optional[Any] = InputField(
default=None,
description="The item to collect (all inputs must be of the same type)",
ui_type=UIType.CollectionItem,
title="Collection Item",
@ -250,8 +251,8 @@ class CollectInvocation(BaseInvocation):
return CollectInvocationOutput(collection=copy.copy(self.collection))
InvocationsUnion = Union[BaseInvocation.get_invocations()] # type: ignore
InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()] # type: ignore
InvocationsUnion: Any = BaseInvocation.get_invocations_union()
InvocationOutputsUnion: Any = BaseInvocationOutput.get_outputs_union()
class Graph(BaseModel):
@ -378,13 +379,13 @@ class Graph(BaseModel):
raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph")
# output fields are not on the node object directly, they are on the output type
if edge.source.field not in source_node.get_output_type().__fields__:
if edge.source.field not in source_node.get_output_type().model_fields:
raise NodeFieldNotFoundError(
f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
)
# input fields are on the node
if edge.destination.field not in destination_node.__fields__:
if edge.destination.field not in destination_node.model_fields:
raise NodeFieldNotFoundError(
f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
)
@ -395,24 +396,24 @@ class Graph(BaseModel):
raise CyclicalGraphError("Graph contains cycles")
# Validate all edge connections are valid
for e in self.edges:
for edge in self.edges:
if not are_connections_compatible(
self.get_node(e.source.node_id),
e.source.field,
self.get_node(e.destination.node_id),
e.destination.field,
self.get_node(edge.source.node_id),
edge.source.field,
self.get_node(edge.destination.node_id),
edge.destination.field,
):
raise InvalidEdgeError(
f"Invalid edge from {e.source.node_id}.{e.source.field} to {e.destination.node_id}.{e.destination.field}"
f"Invalid edge from {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate all iterators & collectors
# TODO: may need to validate all iterators & collectors in subgraphs so edge connections in parent graphs will be available
for n in self.nodes.values():
if isinstance(n, IterateInvocation) and not self._is_iterator_connection_valid(n.id):
raise InvalidEdgeError(f"Invalid iterator node {n.id}")
if isinstance(n, CollectInvocation) and not self._is_collector_connection_valid(n.id):
raise InvalidEdgeError(f"Invalid collector node {n.id}")
for node in self.nodes.values():
if isinstance(node, IterateInvocation) and not self._is_iterator_connection_valid(node.id):
raise InvalidEdgeError(f"Invalid iterator node {node.id}")
if isinstance(node, CollectInvocation) and not self._is_collector_connection_valid(node.id):
raise InvalidEdgeError(f"Invalid collector node {node.id}")
return None
@ -594,7 +595,7 @@ class Graph(BaseModel):
def _get_input_edges_and_graphs(
self, node_path: str, prefix: Optional[str] = None
) -> list[tuple["Graph", str, Edge]]:
) -> list[tuple["Graph", Union[str, None], Edge]]:
"""Gets all input edges for a node along with the graph they are in and the graph's path"""
edges = list()
@ -636,7 +637,7 @@ class Graph(BaseModel):
def _get_output_edges_and_graphs(
self, node_path: str, prefix: Optional[str] = None
) -> list[tuple["Graph", str, Edge]]:
) -> list[tuple["Graph", Union[str, None], Edge]]:
"""Gets all output edges for a node along with the graph they are in and the graph's path"""
edges = list()
@ -817,15 +818,15 @@ class GraphExecutionState(BaseModel):
default_factory=dict,
)
@validator("graph")
@field_validator("graph")
def graph_is_valid(cls, v: Graph):
"""Validates that the graph is valid"""
v.validate_self()
return v
class Config:
schema_extra = {
"required": [
model_config = ConfigDict(
json_schema_extra=dict(
required=[
"id",
"graph",
"execution_graph",
@ -836,7 +837,8 @@ class GraphExecutionState(BaseModel):
"prepared_source_mapping",
"source_prepared_mapping",
]
}
)
)
def next(self) -> Optional[BaseInvocation]:
"""Gets the next node ready to execute."""
@ -910,7 +912,7 @@ class GraphExecutionState(BaseModel):
input_collection = getattr(input_collection_prepared_node_output, input_collection_edge.source.field)
self_iteration_count = len(input_collection)
new_nodes = list()
new_nodes: list[str] = list()
if self_iteration_count == 0:
# TODO: should this raise a warning? It might just happen if an empty collection is input, and should be valid.
return new_nodes
@ -920,7 +922,7 @@ class GraphExecutionState(BaseModel):
# Create new edges for this iteration
# For collect nodes, this may contain multiple inputs to the same field
new_edges = list()
new_edges: list[Edge] = list()
for edge in input_edges:
for input_node_id in (n[1] for n in iteration_node_map if n[0] == edge.source.node_id):
new_edge = Edge(
@ -1179,18 +1181,18 @@ class LibraryGraph(BaseModel):
description="The outputs exposed by this graph", default_factory=list
)
@validator("exposed_inputs", "exposed_outputs")
def validate_exposed_aliases(cls, v):
@field_validator("exposed_inputs", "exposed_outputs")
def validate_exposed_aliases(cls, v: list[Union[ExposedNodeInput, ExposedNodeOutput]]):
if len(v) != len(set(i.alias for i in v)):
raise ValueError("Duplicate exposed alias")
return v
@root_validator
@model_validator(mode="after")
def validate_exposed_nodes(cls, values):
graph = values["graph"]
graph = values.graph
# Validate exposed inputs
for exposed_input in values["exposed_inputs"]:
for exposed_input in values.exposed_inputs:
if not graph.has_node(exposed_input.node_path):
raise ValueError(f"Exposed input node {exposed_input.node_path} does not exist")
node = graph.get_node(exposed_input.node_path)
@ -1200,7 +1202,7 @@ class LibraryGraph(BaseModel):
)
# Validate exposed outputs
for exposed_output in values["exposed_outputs"]:
for exposed_output in values.exposed_outputs:
if not graph.has_node(exposed_output.node_path):
raise ValueError(f"Exposed output node {exposed_output.node_path} does not exist")
node = graph.get_node(exposed_output.node_path)
@ -1212,4 +1214,6 @@ class LibraryGraph(BaseModel):
return values
GraphInvocation.update_forward_refs()
GraphInvocation.model_rebuild(force=True)
Graph.model_rebuild(force=True)
GraphExecutionState.model_rebuild(force=True)

7
invokeai/app/services/shared/pagination.py
View File

@ -1,12 +1,11 @@
from typing import Generic, TypeVar
from pydantic import BaseModel, Field
from pydantic.generics import GenericModel
GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel)
class CursorPaginatedResults(GenericModel, Generic[GenericBaseModel]):
class CursorPaginatedResults(BaseModel, Generic[GenericBaseModel]):
"""
Cursor-paginated results
Generic must be a Pydantic model
@ -17,7 +16,7 @@ class CursorPaginatedResults(GenericModel, Generic[GenericBaseModel]):
items: list[GenericBaseModel] = Field(..., description="Items")
class OffsetPaginatedResults(GenericModel, Generic[GenericBaseModel]):
class OffsetPaginatedResults(BaseModel, Generic[GenericBaseModel]):
"""
Offset-paginated results
Generic must be a Pydantic model
@ -29,7 +28,7 @@ class OffsetPaginatedResults(GenericModel, Generic[GenericBaseModel]):
items: list[GenericBaseModel] = Field(description="Items")
class PaginatedResults(GenericModel, Generic[GenericBaseModel]):
class PaginatedResults(BaseModel, Generic[GenericBaseModel]):
"""
Paginated results
Generic must be a Pydantic model

2
invokeai/app/util/controlnet_utils.py
View File

@ -265,7 +265,7 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device:
def prepare_control_image(
image: Image,
image: Image.Image,
width: int,
height: int,
num_channels: int = 3,

6
invokeai/app/util/misc.py
View File

@ -1,4 +1,5 @@
import datetime
import typing
import uuid
import numpy as np
@ -27,3 +28,8 @@ def get_random_seed():
def uuid_string():
res = uuid.uuid4()
return str(res)
def is_optional(value: typing.Any):
"""Checks if a value is typed as Optional. Note that Optional is sugar for Union[x, None]."""
return typing.get_origin(value) is typing.Union and type(None) in typing.get_args(value)

4
invokeai/app/util/model_exclude_null.py
View File

@ -13,11 +13,11 @@ From https://github.com/tiangolo/fastapi/discussions/8882#discussioncomment-5154
class BaseModelExcludeNull(BaseModel):
def dict(self, *args, **kwargs) -> dict[str, Any]:
def model_dump(self, *args, **kwargs) -> dict[str, Any]:
"""
Override the default dict method to exclude None values in the response
"""
kwargs.pop("exclude_none", None)
return super().dict(*args, exclude_none=True, **kwargs)
return super().model_dump(*args, exclude_none=True, **kwargs)
pass

0
invokeai/app/cli/__init__.py → invokeai/assets/__init__.py
View File

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]

14
invokeai/backend/image_util/txt2mask.py
View File

@ -41,18 +41,18 @@ config = InvokeAIAppConfig.get_config()
class SegmentedGrayscale(object):
def __init__(self, image: Image, heatmap: torch.Tensor):
def __init__(self, image: Image.Image, heatmap: torch.Tensor):
self.heatmap = heatmap
self.image = image
def to_grayscale(self, invert: bool = False) -> Image:
def to_grayscale(self, invert: bool = False) -> Image.Image:
return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
def to_mask(self, threshold: float = 0.5) -> Image:
def to_mask(self, threshold: float = 0.5) -> Image.Image:
discrete_heatmap = self.heatmap.lt(threshold).int()
return self._rescale(Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L"))
def to_transparent(self, invert: bool = False) -> Image:
def to_transparent(self, invert: bool = False) -> Image.Image:
transparent_image = self.image.copy()
# For img2img, we want the selected regions to be transparent,
# but to_grayscale() returns the opposite. Thus invert.
@ -61,7 +61,7 @@ class SegmentedGrayscale(object):
return transparent_image
# unscales and uncrops the 352x352 heatmap so that it matches the image again
def _rescale(self, heatmap: Image) -> Image:
def _rescale(self, heatmap: Image.Image) -> Image.Image:
size = self.image.width if (self.image.width > self.image.height) else self.image.height
resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS)
return resized_image.crop((0, 0, self.image.width, self.image.height))
@ -82,7 +82,7 @@ class Txt2Mask(object):
self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL, cache_dir=config.cache_dir)
@torch.no_grad()
def segment(self, image, prompt: str) -> SegmentedGrayscale:
def segment(self, image: Image.Image, prompt: str) -> SegmentedGrayscale:
"""
Given a prompt string such as "a bagel", tries to identify the object in the
provided image and returns a SegmentedGrayscale object in which the brighter
@ -99,7 +99,7 @@ class Txt2Mask(object):
heatmap = torch.sigmoid(outputs.logits)
return SegmentedGrayscale(image, heatmap)
def _scale_and_crop(self, image: Image) -> Image:
def _scale_and_crop(self, image: Image.Image) -> Image.Image:
scaled_image = Image.new("RGB", (CLIPSEG_SIZE, CLIPSEG_SIZE))
if image.width > image.height: # width is constraint
scale = CLIPSEG_SIZE / image.width

2
invokeai/backend/image_util/util.py
View File

@ -9,7 +9,7 @@ class InitImageResizer:
def __init__(self, Image):
self.image = Image
def resize(self, width=None, height=None) -> Image:
def resize(self, width=None, height=None) -> Image.Image:
"""
Return a copy of the image resized to fit within
a box width x height. The aspect ratio is

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

@ -793,7 +793,11 @@ def migrate_init_file(legacy_format: Path):
old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
new = InvokeAIAppConfig.get_config()
fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
fields = [
x
for x, y in InvokeAIAppConfig.model_fields.items()
if (y.json_schema_extra.get("category", None) if y.json_schema_extra else None) != "DEPRECATED"
]
for attr in fields:
if hasattr(old, attr):
try:

16
invokeai/backend/model_management/model_manager.py
View File

@ -236,13 +236,13 @@ import types
from dataclasses import dataclass
from pathlib import Path
from shutil import move, rmtree
from typing import Callable, Dict, List, Literal, Optional, Set, Tuple, Union
from typing import Callable, Dict, List, Literal, Optional, Set, Tuple, Union, cast
import torch
import yaml
from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
@ -294,6 +294,8 @@ class AddModelResult(BaseModel):
base_model: BaseModelType = Field(description="The base model")
config: ModelConfigBase = Field(description="The configuration of the model")
model_config = ConfigDict(protected_namespaces=())
MAX_CACHE_SIZE = 6.0 # GB
@ -576,7 +578,7 @@ class ModelManager(object):
"""
model_key = self.create_key(model_name, base_model, model_type)
if model_key in self.models:
return self.models[model_key].dict(exclude_defaults=True)
return self.models[model_key].model_dump(exclude_defaults=True)
else:
return None # TODO: None or empty dict on not found
@ -632,7 +634,7 @@ class ModelManager(object):
continue
model_dict = dict(
**model_config.dict(exclude_defaults=True),
**model_config.model_dump(exclude_defaults=True),
# OpenAPIModelInfoBase
model_name=cur_model_name,
base_model=cur_base_model,
@ -900,14 +902,16 @@ class ModelManager(object):
Write current configuration out to the indicated file.
"""
data_to_save = dict()
data_to_save["__metadata__"] = self.config_meta.dict()
data_to_save["__metadata__"] = self.config_meta.model_dump()
for model_key, model_config in self.models.items():
model_name, base_model, model_type = self.parse_key(model_key)
model_class = self._get_implementation(base_model, model_type)
if model_class.save_to_config:
# TODO: or exclude_unset better fits here?
data_to_save[model_key] = model_config.dict(exclude_defaults=True, exclude={"error"})
data_to_save[model_key] = cast(BaseModel, model_config).model_dump(
exclude_defaults=True, exclude={"error"}, mode="json"
)
# alias for config file
data_to_save[model_key]["format"] = data_to_save[model_key].pop("model_format")

16
invokeai/backend/model_management/models/__init__.py
View File

@ -2,7 +2,7 @@ import inspect
from enum import Enum
from typing import Literal, get_origin
from pydantic import BaseModel
from pydantic import BaseModel, ConfigDict, create_model
from .base import ( # noqa: F401
BaseModelType,
@ -106,6 +106,8 @@ class OpenAPIModelInfoBase(BaseModel):
base_model: BaseModelType
model_type: ModelType
model_config = ConfigDict(protected_namespaces=())
for base_model, models in MODEL_CLASSES.items():
for model_type, model_class in models.items():
@ -121,17 +123,11 @@ for base_model, models in MODEL_CLASSES.items():
if openapi_cfg_name in vars():
continue
api_wrapper = type(
api_wrapper = create_model(
openapi_cfg_name,
(cfg, OpenAPIModelInfoBase),
dict(
__annotations__=dict(
model_type=Literal[model_type.value],
),
),
__base__=(cfg, OpenAPIModelInfoBase),
model_type=(Literal[model_type], model_type), # type: ignore
)
# globals()[openapi_cfg_name] = api_wrapper
vars()[openapi_cfg_name] = api_wrapper
OPENAPI_MODEL_CONFIGS.append(api_wrapper)

13
invokeai/backend/model_management/models/base.py
View File

@ -19,7 +19,7 @@ from diffusers import logging as diffusers_logging
from onnx import numpy_helper
from onnxruntime import InferenceSession, SessionOptions, get_available_providers
from picklescan.scanner import scan_file_path
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from transformers import logging as transformers_logging
@ -86,14 +86,21 @@ class ModelError(str, Enum):
NotFound = "not_found"
def model_config_json_schema_extra(schema: dict[str, Any]) -> None:
if "required" not in schema:
schema["required"] = []
schema["required"].append("model_type")
class ModelConfigBase(BaseModel):
path: str # or Path
description: Optional[str] = Field(None)
model_format: Optional[str] = Field(None)
error: Optional[ModelError] = Field(None)
class Config:
use_enum_values = True
model_config = ConfigDict(
use_enum_values=True, protected_namespaces=(), json_schema_extra=model_config_json_schema_extra
)
class EmptyConfigLoader(ConfigMixin):

6
invokeai/backend/model_management/models/ip_adapter.py
View File

@ -58,14 +58,16 @@ class IPAdapterModel(ModelBase):
def get_model(
self,
torch_dtype: Optional[torch.dtype],
torch_dtype: torch.dtype,
child_type: Optional[SubModelType] = None,
) -> typing.Union[IPAdapter, IPAdapterPlus]:
if child_type is not None:
raise ValueError("There are no child models in an IP-Adapter model.")
model = build_ip_adapter(
ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype
ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"),
device=torch.device("cpu"),
dtype=torch_dtype,
)
self.model_size = model.calc_size()

8
invokeai/backend/model_management/seamless.py
View File

@ -96,7 +96,7 @@ def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axe
finally:
for module, orig_conv_forward in to_restore:
module._conv_forward = orig_conv_forward
if hasattr(m, "asymmetric_padding_mode"):
del m.asymmetric_padding_mode
if hasattr(m, "asymmetric_padding"):
del m.asymmetric_padding
if hasattr(module, "asymmetric_padding_mode"):
del module.asymmetric_padding_mode
if hasattr(module, "asymmetric_padding"):
del module.asymmetric_padding

10
invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py
View File

@ -1,7 +1,8 @@
import math
from typing import Optional
import PIL
import torch
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from torchvision.transforms.functional import resize as tv_resize
@ -11,7 +12,7 @@ class AttentionMapSaver:
self.token_ids = token_ids
self.latents_shape = latents_shape
# self.collated_maps = #torch.zeros([len(token_ids), latents_shape[0], latents_shape[1]])
self.collated_maps = {}
self.collated_maps: dict[str, torch.Tensor] = {}
def clear_maps(self):
self.collated_maps = {}
@ -38,9 +39,10 @@ class AttentionMapSaver:
def write_maps_to_disk(self, path: str):
pil_image = self.get_stacked_maps_image()
if pil_image is not None:
pil_image.save(path, "PNG")
def get_stacked_maps_image(self) -> PIL.Image:
def get_stacked_maps_image(self) -> Optional[Image.Image]:
"""
Scale all collected attention maps to the same size, blend them together and return as an image.
:return: An image containing a vertical stack of blended attention maps, one for each requested token.
@ -95,4 +97,4 @@ class AttentionMapSaver:
return None
merged_bytes = merged.mul(0xFF).byte()
return PIL.Image.fromarray(merged_bytes.numpy(), mode="L")
return Image.fromarray(merged_bytes.numpy(), mode="L")

6
invokeai/frontend/web/public/locales/zh_CN.json
View File

@ -1101,16 +1101,16 @@
"contentShuffle": "Content Shuffle",
"f": "F",
"h": "H",
"controlnet": "$t(controlnet.controlAdapter) #{{number}} ($t(common.controlNet))",
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"control": "Control (普通控制)",
"coarse": "Coarse",
"depthMidas": "Depth (Midas)",
"w": "W",
"ip_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.ipAdapter))",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"mediapipeFace": "Mediapipe Face",
"mlsd": "M-LSD",
"lineart": "Lineart",
"t2i_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.t2iAdapter))",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"megaControl": "Mega Control (超级控制)",
"depthZoe": "Depth (Zoe)",
"colorMap": "Color",

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());

38
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({
@ -37,7 +37,11 @@ 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 = {
const enqueueBatchArg: BatchConfig = {
prepend: true,
batch: {
graph: {
nodes: {
[ca.processorNode.id]: {
...ca.processorNode,
@ -63,15 +67,16 @@ export const addControlNetImageProcessedListener = () => {
},
},
],
},
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();
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) {

4
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts
View File

@ -151,7 +151,9 @@ export const addRequestedSingleImageDeletionListener = () => {
if (wasImageDeleted) {
dispatch(
api.util.invalidateTags([{ type: 'Board', id: imageDTO.board_id }])
api.util.invalidateTags([
{ type: 'Board', id: imageDTO.board_id ?? 'none' },
])
);
}
},

26
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({
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/common/components/IAIMantineMultiSelect.tsx
View File

@ -6,7 +6,7 @@ import { useMantineMultiSelectStyles } from 'mantine-theme/hooks/useMantineMulti
import { KeyboardEvent, RefObject, memo, useCallback } from 'react';
type IAIMultiSelectProps = Omit<MultiSelectProps, 'label'> & {
tooltip?: string;
tooltip?: string | null;
inputRef?: RefObject<HTMLInputElement>;
label?: string;
};

2
invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx
View File

@ -12,7 +12,7 @@ export type IAISelectDataType = {
};
type IAISelectProps = Omit<SelectProps, 'label'> & {
tooltip?: string;
tooltip?: string | null;
label?: string;
inputRef?: RefObject<HTMLInputElement>;
};

2
invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx
View File

@ -10,7 +10,7 @@ export type IAISelectDataType = {
};
export type IAISelectProps = Omit<SelectProps, 'label'> & {
tooltip?: string;
tooltip?: string | null;
inputRef?: RefObject<HTMLInputElement>;
label?: string;
};

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